Node List¶
Full API documentation: nodes

class
mdp.nodes.
PCANode
¶ Filter the input data through the most significatives of its principal components.
Internal variables of interest
self.avg
 Mean of the input data (available after training).
self.v
 Transposed of the projection matrix (available after training).
self.d
 Variance corresponding to the PCA components (eigenvalues of the covariance matrix).
self.explained_variance
 When output_dim has been specified as a fraction of the total variance, this is the fraction of the total variance that is actually explained.
More information about Principal Component Analysis, a.k.a. discrete KarhunenLoeve transform can be found among others in I.T. Jolliffe, Principal Component Analysis, SpringerVerlag (1986).
Full API documentation: PCANode

class
mdp.nodes.
WhiteningNode
¶ Whiten the input data by filtering it through the most significatives of its principal components. All output signals have zero mean, unit variance and are decorrelated.
Internal variables of interest
self.avg
 Mean of the input data (available after training).
self.v
 Transpose of the projection matrix (available after training).
self.d
 Variance corresponding to the PCA components (eigenvalues of the covariance matrix).
self.explained_variance
 When output_dim has been specified as a fraction of the total variance, this is the fraction of the total variance that is actually explained.
Full API documentation: WhiteningNode

class
mdp.nodes.
NIPALSNode
¶ Perform Principal Component Analysis using the NIPALS algorithm. This algorithm is particularyl useful if you have more variable than observations, or in general when the number of variables is huge and calculating a full covariance matrix may be unfeasable. It’s also more efficient of the standard PCANode if you expect the number of significant principal components to be a small. In this case setting output_dim to be a certain fraction of the total variance, say 90%, may be of some help.
Internal variables of interest
self.avg
 Mean of the input data (available after training).
self.d
 Variance corresponding to the PCA components.
self.v
 Transposed of the projection matrix (available after training).
self.explained_variance
 When output_dim has been specified as a fraction of the total variance, this is the fraction of the total variance that is actually explained.
Reference for NIPALS (Nonlinear Iterative Partial Least Squares): Wold, H. Nonlinear estimation by iterative least squares procedures. in David, F. (Editor), Research Papers in Statistics, Wiley, New York, pp 411444 (1966).
More information about Principal Component Analysis, a.k.a. discrete KarhunenLoeve transform can be found among others in I.T. Jolliffe, Principal Component Analysis, SpringerVerlag (1986).
Original code contributed by: Michael Schmuker, Susanne Lezius, and Farzad Farkhooi (2008).
Full API documentation: NIPALSNode

class
mdp.nodes.
FastICANode
¶ Perform Independent Component Analysis using the FastICA algorithm. Note that FastICA is a batchalgorithm. This means that it needs all input data before it can start and compute the ICs. The algorithm is here given as a Node for convenience, but it actually accumulates all inputs it receives. Remember that to avoid running out of memory when you have many components and many time samples.
FastICA does not support the telescope mode (the convergence criterium is not robust in telescope mode).
Reference: Aapo Hyvarinen (1999). Fast and Robust FixedPoint Algorithms for Independent Component Analysis IEEE Transactions on Neural Networks, 10(3):626634.
Internal variables of interest
self.white
 The whitening node used for preprocessing.
self.filters
 The ICA filters matrix (this is the transposed of the projection matrix after whitening).
self.convergence
 The value of the convergence threshold.
History:
 1.4.1998 created for Matlab by Jarmo Hurri, Hugo Gavert, Jaakko Sarela, and Aapo Hyvarinen
 7.3.2003 modified for Python by Thomas Wendler
 3.6.2004 rewritten and adapted for scipy and MDP by MDP’s authors
 25.5.2005 now independent from scipy. Requires Numeric or numarray
 26.6.2006 converted to numpy
 14.9.2007 updated to Matlab version 2.5
 26.6.2012 added ability to run two stages of optimization [PK]
Full API documentation: FastICANode

class
mdp.nodes.
CuBICANode
¶ Perform Independent Component Analysis using the CuBICA algorithm. Note that CuBICA is a batchalgorithm, which means that it needs all input data before it can start and compute the ICs. The algorithm is here given as a Node for convenience, but it actually accumulates all inputs it receives. Remember that to avoid running out of memory when you have many components and many time samples.
As an alternative to this batch mode you might consider the telescope mode (see the docs of the
__init__
method).Reference: Blaschke, T. and Wiskott, L. (2003). CuBICA: Independent Component Analysis by Simultaneous Third and FourthOrder Cumulant Diagonalization. IEEE Transactions on Signal Processing, 52(5), pp. 12501256.
Internal variables of interest
self.white
 The whitening node used for preprocessing.
self.filters
 The ICA filters matrix (this is the transposed of the projection matrix after whitening).
self.convergence
 The value of the convergence threshold.
Full API documentation: CuBICANode

class
mdp.nodes.
TDSEPNode
¶ Perform Independent Component Analysis using the TDSEP algorithm. Note that TDSEP, as implemented in this Node, is an online algorithm, i.e. it is suited to be trained on huge data sets, provided that the training is done sending small chunks of data for each time.
Reference: Ziehe, Andreas and Muller, KlausRobert (1998). TDSEP an efficient algorithm for blind separation using time structure. in Niklasson, L, Boden, M, and Ziemke, T (Editors), Proc. 8th Int. Conf. Artificial Neural Networks (ICANN 1998).
Internal variables of interest
self.white
 The whitening node used for preprocessing.
self.filters
 The ICA filters matrix (this is the transposed of the projection matrix after whitening).
self.convergence
 The value of the convergence threshold.
Full API documentation: TDSEPNode

class
mdp.nodes.
JADENode
¶ Perform Independent Component Analysis using the JADE algorithm. Note that JADE is a batchalgorithm. This means that it needs all input data before it can start and compute the ICs. The algorithm is here given as a Node for convenience, but it actually accumulates all inputs it receives. Remember that to avoid running out of memory when you have many components and many time samples.
JADE does not support the telescope mode.
Main references:
 Cardoso, JeanFrancois and Souloumiac, Antoine (1993). Blind beamforming for non Gaussian signals. Radar and Signal Processing, IEE Proceedings F, 140(6): 362370.
 Cardoso, JeanFrancois (1999). Highorder contrasts for independent component analysis. Neural Computation, 11(1): 157192.
Original code contributed by: Gabriel Beckers (2008).
History:
 May 2005 version 1.8 for MATLAB released by JeanFrancois Cardoso
 Dec 2007 MATLAB version 1.8 ported to Python/NumPy by Gabriel Beckers
 Feb 15 2008 Python/NumPy version adapted for MDP by Gabriel Beckers
Full API documentation: JADENode

class
mdp.nodes.
SFANode
¶ Extract the slowly varying components from the input data. More information about Slow Feature Analysis can be found in Wiskott, L. and Sejnowski, T.J., Slow Feature Analysis: Unsupervised Learning of Invariances, Neural Computation, 14(4):715770 (2002).
Instance variables of interest
self.avg
 Mean of the input data (available after training)
self.sf
 Matrix of the SFA filters (available after training)
self.d
 Delta values corresponding to the SFA components (generalized
eigenvalues). [See the docs of the
get_eta_values
method for more information]
Special arguments for constructor
include_last_sample
If
False
the train method discards the last sample in every chunk during training when calculating the covariance matrix. The last sample is in this case only used for calculating the covariance matrix of the derivatives. The switch should be set toFalse
if you plan to train with several small chunks. For example we can split a sequence (index is time):x_1 x_2 x_3 x_4
in smaller parts like this:
x_1 x_2 x_2 x_3 x_3 x_4
The SFANode will see 3 derivatives for the temporal covariance matrix, and the first 3 points for the spatial covariance matrix. Of course you will need to use a generator that connects the small chunks (the last sample needs to be sent again in the next chunk). If
include_last_sample
was True, depending on the generator you use, you would either get:x_1 x_2 x_2 x_3 x_3 x_4
in which case the last sample of every chunk would be used twice when calculating the covariance matrix, or:
x_1 x_2 x_3 x_4
in which case you loose the derivative between
x_3
andx_2
.If you plan to train with a single big chunk leave
include_last_sample
to the default value, i.e.True
.You can even change this behaviour during training. Just set the corresponding switch in the train method.
Full API documentation: SFANode

class
mdp.nodes.
SFA2Node
¶ Get an input signal, expand it in the space of inhomogeneous polynomials of degree 2 and extract its slowly varying components. The
get_quadratic_form
method returns the inputoutput function of one of the learned unit as aQuadraticForm
object. See the documentation ofmdp.utils.QuadraticForm
for additional information.More information about Slow Feature Analysis can be found in Wiskott, L. and Sejnowski, T.J., Slow Feature Analysis: Unsupervised Learning of Invariances, Neural Computation, 14(4):715770 (2002).
Full API documentation: SFA2Node

class
mdp.nodes.
ISFANode
¶ Perform Independent Slow Feature Analysis on the input data.
Internal variables of interest
self.RP
 The global rotationpermutation matrix. This is the filter applied on input_data to get output_data
self.RPC
 The complete global rotationpermutation matrix. This is a matrix of dimension input_dim x input_dim (the ‘outer space’ is retained)
self.covs
A mdp.utils.MultipleCovarianceMatrices instance containing the current timedelayed covariance matrices of the input_data. After convergence the uppermost
output_dim
xoutput_dim
submatrices should be almost diagonal.self.covs[n1]
is the covariance matrix relative to then
th timelagNote: they are not cleared after convergence. If you need to free some memory, you can safely delete them with:
>>> del self.covs
self.initial_contrast
 A dictionary with the starting contrast and the SFA and ICA parts of it.
self.final_contrast
 Like the above but after convergence.
Note: If you intend to use this node for large datasets please have a look at the
stop_training
method documentation for speeding things up.References: Blaschke, T. , Zito, T., and Wiskott, L. (2007). Independent Slow Feature Analysis and Nonlinear Blind Source Separation. Neural Computation 19(4):9941021 (2007) http://itb.biologie.huberlin.de/~wiskott/Publications/BlasZitoWisk2007ISFANeurComp.pdf
Full API documentation: ISFANode

class
mdp.nodes.
XSFANode
¶ Perform Nonlinear Blind Source Separation using Slow Feature Analysis.
This node is designed to iteratively extract statistically independent sources from (in principle) arbitrary invertible nonlinear mixtures. The method relies on temporal correlations in the sources and consists of a combination of nonlinear SFA and a projection algorithm. More details can be found in the reference given below (once it’s published).
The node has multiple training phases. The number of training phases depends on the number of sources that must be extracted. The recommended way of training this node is through a container flow:
>>> flow = mdp.Flow([XSFANode()]) >>> flow.train(x)
doing so will automatically train all training phases. The argument
x
to theFlow.train
method can be an array or a list of iterables (see the section about Iterators in the MDP tutorial for more info).If the number of training samples is large, you may run into memory problems: use data iterators and chunk training to reduce memory usage.
If you need to debug training and/or execution of this node, the suggested approach is to use the capabilities of BiMDP. For example:
>>> flow = mdp.Flow([XSFANode()]) >>> tr_filename = bimdp.show_training(flow=flow, data_iterators=x) >>> ex_filename, out = bimdp.show_execution(flow, x=x)
this will run training and execution with bimdp inspection. Snapshots of the internal flow state for each training phase and execution step will be opened in a web brower and presented as a slideshow.
References: Sprekeler, H., Zito, T., and Wiskott, L. (2009). An Extension of Slow Feature Analysis for Nonlinear Blind Source Separation. Journal of Machine Learning Research. http://cogprints.org/7056/1/SprekelerZitoWiskottCogprints2010.pdf
Full API documentation: XSFANode

class
mdp.nodes.
FDANode
¶ Perform a (generalized) Fisher Discriminant Analysis of its input. It is a supervised node that implements FDA using a generalized eigenvalue approach.
FDANode has two training phases and is supervised so make sure to pay attention to the following points when you train it:
 call the
train
method with two arguments: the input data and the labels (see the doc string of thetrain
method for details).  if you are training the node by hand, call the
train
method twice.  if you are training the node using a flow (recommended), the
only argument to
Flow.train
must be a list of(data_point, label)
tuples or an iterator returning lists of such tuples, not a generator. TheFlow.train
function can be called just once as usual, since it takes care of rewinding the iterator to perform the second training step.
More information on Fisher Discriminant Analysis can be found for example in C. Bishop, Neural Networks for Pattern Recognition, Oxford Press, pp. 105112.
Internal variables of interest
self.avg
 Mean of the input data (available after training)
self.v
 Transposed of the projection matrix, so that
output = dot(inputself.avg, self.v)
(available after training).
Full API documentation: FDANode
 call the

class
mdp.nodes.
FANode
¶ Perform Factor Analysis.
The current implementation should be most efficient for long data sets: the sufficient statistics are collected in the training phase, and all EMcycles are performed at its end.
The
execute
method returns the Maximum A Posteriori estimate of the latent variables. Thegenerate_input
method generates observations from the prior distribution.Internal variables of interest
self.mu
 Mean of the input data (available after training)
self.A
 Generating weights (available after training)
self.E_y_mtx
 Weights for Maximum A Posteriori inference
self.sigma
 Vector of estimated variance of the noise for all input components
More information about Factor Analysis can be found in Max Welling’s classnotes: http://www.ics.uci.edu/~welling/classnotes/classnotes.html , in the chapter ‘Linear Models’.
Full API documentation: FANode

class
mdp.nodes.
RBMNode
¶ Restricted Boltzmann Machine node. An RBM is an undirected probabilistic network with binary variables. The graph is bipartite into observed (visible) and hidden (latent) variables.
By default, the
execute
method returns the probability of one of the hiden variables being equal to 1 given the input.Use the
sample_v
method to sample from the observed variables given a setting of the hidden variables, andsample_h
to do the opposite. Theenergy
method can be used to compute the energy of a given setting of all variables.The network is trained by Contrastive Divergence, as described in Hinton, G. E. (2002). Training products of experts by minimizing contrastive divergence. Neural Computation, 14(8):17111800
Internal variables of interest
self.w
 Generative weights between hidden and observed variables
self.bv
 bias vector of the observed variables
self.bh
 bias vector of the hidden variables
For more information on RBMs, see Geoffrey E. Hinton (2007) Boltzmann machine. Scholarpedia, 2(5):1668
Full API documentation: RBMNode

class
mdp.nodes.
RBMWithLabelsNode
¶ Restricted Boltzmann Machine with softmax labels. An RBM is an undirected probabilistic network with binary variables. In this case, the node is partitioned into a set of observed (visible) variables, a set of hidden (latent) variables, and a set of label variables (also observed), only one of which is active at any time. The node is able to learn associations between the visible variables and the labels.
By default, the
execute
method returns the probability of one of the hiden variables being equal to 1 given the input.Use the
sample_v
method to sample from the observed variables (visible and labels) given a setting of the hidden variables, andsample_h
to do the opposite. Theenergy
method can be used to compute the energy of a given setting of all variables.The network is trained by Contrastive Divergence, as described in Hinton, G. E. (2002). Training products of experts by minimizing contrastive divergence. Neural Computation, 14(8):17111800
Internal variables of interest:
self.w
 Generative weights between hidden and observed variables
self.bv
 bias vector of the observed variables
self.bh
 bias vector of the hidden variables
For more information on RBMs with labels, see
 Geoffrey E. Hinton (2007) Boltzmann machine. Scholarpedia, 2(5):1668.
 Hinton, G. E, Osindero, S., and Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18:15271554.
Full API documentation: RBMWithLabelsNode

class
mdp.nodes.
GrowingNeuralGasNode
¶ Learn the topological structure of the input data by building a corresponding graph approximation.
The algorithm expands on the original Neural Gas algorithm (see mdp.nodes NeuralGasNode) in that the algorithm adds new nodes are added to the graph as more data becomes available. Im this way, if the growth rate is appropriate, one can avoid overfitting or underfitting the data.
More information about the Growing Neural Gas algorithm can be found in B. Fritzke, A Growing Neural Gas Network Learns Topologies, in G. Tesauro, D. S. Touretzky, and T. K. Leen (editors), Advances in Neural Information Processing Systems 7, pages 625632. MIT Press, Cambridge MA, 1995.
Attributes and methods of interest
 graph – The corresponding mdp.graph.Graph object
Full API documentation: GrowingNeuralGasNode

class
mdp.nodes.
LLENode
¶ Perform a Locally Linear Embedding analysis on the data.
Internal variables of interest
self.training_projection
 The LLE projection of the training data (defined when training finishes).
self.desired_variance
 variance limit used to compute intrinsic dimensionality.
Based on the algorithm outlined in An Introduction to Locally Linear Embedding by L. Saul and S. Roweis, using improvements suggested in Locally Linear Embedding for Classification by D. deRidder and R.P.W. Duin.
References: Roweis, S. and Saul, L., Nonlinear dimensionality reduction by locally linear embedding, Science 290 (5500), pp. 23232326, 2000.
Original code contributed by: Jake VanderPlas, University of Washington,
Full API documentation: LLENode

class
mdp.nodes.
HLLENode
¶ Perform a Hessian Locally Linear Embedding analysis on the data.
Internal variables of interest
self.training_projection
 the HLLE projection of the training data (defined when training finishes)
self.desired_variance
 variance limit used to compute intrinsic dimensionality.
Implementation based on algorithm outlined in Donoho, D. L., and Grimes, C., Hessian Eigenmaps: new locally linear embedding techniques for highdimensional data, Proceedings of the National Academy of Sciences 100(10): 55915596, 2003.
Original code contributed by: Jake Vanderplas, University of Washington
Full API documentation: HLLENode

class
mdp.nodes.
LinearRegressionNode
¶ Compute leastsquare, multivariate linear regression on the input data, i.e., learn coefficients
b_j
so that:y_i = b_0 + b_1 x_1 + ... b_N x_N ,
for
i = 1 ... M
, minimizes the square error given the trainingx
‘s andy
‘s.This is a supervised learning node, and requires input data
x
and target datay
to be supplied during training (seetrain
docstring).Internal variables of interest
self.beta
 The coefficients of the linear regression
Full API documentation: LinearRegressionNode

class
mdp.nodes.
QuadraticExpansionNode
¶ Perform expansion in the space formed by all linear and quadratic monomials.
QuadraticExpansionNode()
is equivalent to aPolynomialExpansionNode(2)
Full API documentation: QuadraticExpansionNode

class
mdp.nodes.
PolynomialExpansionNode
¶ Perform expansion in a polynomial space.
Full API documentation: PolynomialExpansionNode

class
mdp.nodes.
RBFExpansionNode
¶ Expand input space with Gaussian Radial Basis Functions (RBFs).
The input data is filtered through a set of unnormalized Gaussian filters, i.e.:
y_j = exp(0.5/s_j * x  c_j^2)
for isotropic RBFs, or more in general:
y_j = exp(0.5 * (xc_j)^T S^1 (xc_j))
for anisotropic RBFs.
Full API documentation: RBFExpansionNode

class
mdp.nodes.
GeneralExpansionNode
¶ Expands the input samples by applying to them one or more functions provided.
The functions to be applied are specified by a list [f_0, ..., f_k], where f_i, for 0 <= i <= k, denotes a particular function. The input data given to these functions is a twodimensional array and the output is another twodimensional array. The dimensionality of the output should depend only on the dimensionality of the input. Given a twodimensional input array x, the output of the node is then [f_0(x), ..., f_k(x)], that is, the concatenation of each one of the computed arrays f_i(x).
This node has been designed to facilitate nonlinear, fixed but arbitrary transformations of the data samples within MDP flows.
Example:
>>> import mdp >>> from mdp import numx >>> def identity(x): return x >>> def u3(x): return numx.absolute(x)**3 #A simple nonlinear transformation >>> def norm2(x): #Computes the norm of each sample returning an Nx1 array >>> return ((x**2).sum(axis=1)**0.5).reshape((1,1)) >>> x = numx.array([[2., 2.], [0.2, 0.3], [0.6, 1.2]]) >>> gen = mdp.nodes.GeneralExpansionNode(funcs=[identity, u3, norm2]) >>> print(gen.execute(x)) >>> [[2. 2. 8. 8. 2.82842712] >>> [ 0.2 0.3 0.008 0.027 0.36055513] >>> [ 0.6 1.2 0.216 1.728 1.34164079]]
Original code contributed by Alberto Escalante.
Full API documentation: GeneralExpansionNode

class
mdp.nodes.
GrowingNeuralGasExpansionNode
¶ Perform a trainable radial basis expansion, where the centers and sizes of the basis functions are learned through a growing neural gas.
 positions of RBFs
 position of the nodes of the neural gas
 sizes of the RBFs
 mean distance to the neighbouring nodes.
Important: Adjust the maximum number of nodes to control the dimension of the expansion.
More information on this expansion type can be found in: B. Fritzke. Growing cell structuresa selforganizing network for unsupervised and supervised learning. Neural Networks 7, p. 1441–1460 (1994).
Full API documentation: GrowingNeuralGasExpansionNode

class
mdp.nodes.
NeuralGasNode
¶ Learn the topological structure of the input data by building a corresponding graph approximation (original Neural Gas algorithm).
The Neural Gas algorithm was originally published in Martinetz, T. and Schulten, K.: A “NeuralGas” Network Learns Topologies. In Kohonen, T., Maekisara, K., Simula, O., and Kangas, J. (eds.), Artificial Neural Networks. Elsevier, NorthHolland., 1991.
Attributes and methods of interest
 graph – The corresponding mdp.graph.Graph object
 max_epochs  maximum number of epochs until which to train.
Full API documentation: NeuralGasNode

class
mdp.nodes.
SignumClassifier
¶ This classifier node classifies as
1
if the sum of the data points is positive and as1
if the data point is negativeFull API documentation: SignumClassifier

class
mdp.nodes.
PerceptronClassifier
¶ A simple perceptron with input_dim input nodes.
Full API documentation: PerceptronClassifier

class
mdp.nodes.
SimpleMarkovClassifier
¶ A simple version of a Markov classifier. It can be trained on a vector of tuples the label being the next element in the testing data.
Full API documentation: SimpleMarkovClassifier

class
mdp.nodes.
DiscreteHopfieldClassifier
¶ Node for simulating a simple discrete Hopfield model
Full API documentation: DiscreteHopfieldClassifier

class
mdp.nodes.
KMeansClassifier
¶ Employs KMeans Clustering for a given number of centroids.
Full API documentation: KMeansClassifier

class
mdp.nodes.
NormalizeNode
¶ Make input signal meanfree and unit variance
Full API documentation: NormalizeNode

class
mdp.nodes.
GaussianClassifier
¶ Perform a supervised Gaussian classification.
Given a set of labelled data, the node fits a gaussian distribution to each class.
Full API documentation: GaussianClassifier

class
mdp.nodes.
NearestMeanClassifier
¶ NearestMean classifier.
Full API documentation: NearestMeanClassifier

class
mdp.nodes.
KNNClassifier
¶ KNearestNeighbour Classifier.
Full API documentation: KNNClassifier

class
mdp.nodes.
EtaComputerNode
¶ Compute the eta values of the normalized training data.
The delta value of a signal is a measure of its temporal variation, and is defined as the mean of the derivative squared, i.e.
delta(x) = mean(dx/dt(t)^2)
.delta(x)
is zero ifx
is a constant signal, and increases if the temporal variation of the signal is bigger.The eta value is a more intuitive measure of temporal variation, defined as:
eta(x) = T/(2*pi) * sqrt(delta(x))
If
x
is a signal of lengthT
which consists of a sine function that accomplishes exactlyN
oscillations, theneta(x)=N
.EtaComputerNode
normalizes the training data to have unit variance, such that it is possible to compare the temporal variation of two signals independently from their scaling.Reference: Wiskott, L. and Sejnowski, T.J. (2002). Slow Feature Analysis: Unsupervised Learning of Invariances, Neural Computation, 14(4):715770.
Important: if a data chunk is tlen data points long, this node is going to consider only the first tlen1 points together with their derivatives. This means in particular that the variance of the signal is not computed on all data points. This behavior is compatible with that of
SFANode
.This is an analysis node, i.e. the data is analyzed during training and the results are stored internally. Use the method
get_eta
to access them.Full API documentation: EtaComputerNode

class
mdp.nodes.
HitParadeNode
¶ Collect the first
n
local maxima and minima of the training signal which are separated by a minimum gapd
.This is an analysis node, i.e. the data is analyzed during training and the results are stored internally. Use the
get_maxima
andget_minima
methods to access them.Full API documentation: HitParadeNode

class
mdp.nodes.
NoiseNode
¶ Inject multiplicative or additive noise into the input data.
Original code contributed by Mathias Franzius.
Full API documentation: NoiseNode

class
mdp.nodes.
NormalNoiseNode
¶ Special version of
NoiseNode
for Gaussian additive noise.Unlike
NoiseNode
it does not store a noise function reference but simply usesnumx_rand.normal
.Full API documentation: NormalNoiseNode

class
mdp.nodes.
TimeFramesNode
¶ Copy delayed version of the input signal on the space dimensions.
For example, for
time_frames=3
andgap=2
:[ X(1) Y(1) [ X(1) Y(1) X(3) Y(3) X(5) Y(5) X(2) Y(2) X(2) Y(2) X(4) Y(4) X(6) Y(6) X(3) Y(3) > X(3) Y(3) X(5) Y(5) X(7) Y(7) X(4) Y(4) X(4) Y(4) X(6) Y(6) X(8) Y(8) X(5) Y(5) ... ... ... ... ... ... ] X(6) Y(6) X(7) Y(7) X(8) Y(8) ... ... ]
It is not always possible to invert this transformation (the transformation is not surjective. However, the
pseudo_inverse
method does the correct thing when it is indeed possible.Full API documentation: TimeFramesNode

class
mdp.nodes.
TimeDelayNode
¶ Copy delayed version of the input signal on the space dimensions.
For example, for
time_frames=3
andgap=2
:[ X(1) Y(1) [ X(1) Y(1) 0 0 0 0 X(2) Y(2) X(2) Y(2) 0 0 0 0 X(3) Y(3) > X(3) Y(3) X(1) Y(1) 0 0 X(4) Y(4) X(4) Y(4) X(2) Y(2) 0 0 X(5) Y(5) X(5) Y(5) X(3) Y(3) X(1) Y(1) X(6) Y(6) ... ... ... ... ... ... ] X(7) Y(7) X(8) Y(8) ... ... ]
This node provides similar functionality as the
TimeFramesNode
, only that it performs a time embedding into the past rather than into the future.See
TimeDelaySlidingWindowNode
for a sliding window delay node for application in a nonbatch manner.Original code contributed by Sebastian Hoefer. Dec 31, 2010
Full API documentation: TimeDelayNode

class
mdp.nodes.
TimeDelaySlidingWindowNode
¶ TimeDelaySlidingWindowNode
is an alternative toTimeDelayNode
which should be used for online learning/execution. Whereas theTimeDelayNode
works in a batch manner, for online application a sliding window is necessary which yields only one row per call.Applied to the same data the collection of all returned rows of the
TimeDelaySlidingWindowNode
is equivalent to the result of theTimeDelayNode
.Original code contributed by Sebastian Hoefer. Dec 31, 2010
Full API documentation: TimeDelaySlidingWindowNode

class
mdp.nodes.
CutoffNode
¶ Node to cut off values at specified bounds.
Works similar to
numpy.clip
, but also works when only a lower or upper bound is specified.Full API documentation: CutoffNode

class
mdp.nodes.
AdaptiveCutoffNode
¶ Node which uses the data history during training to learn cutoff values.
As opposed to the simple
CutoffNode
, a different cutoff value is learned for each data coordinate. For example if an upper cutoff fraction of 0.05 is specified, then the upper cutoff bound is set so that the upper 5% of the training data would have been clipped (in each dimension). The cutoff bounds are then applied during execution. This node also works as aHistogramNode
, so the histogram data is stored.When
stop_training
is called the cutoff values for each coordinate are calculated based on the collected histogram data.Full API documentation: AdaptiveCutoffNode

class
mdp.nodes.
HistogramNode
¶ Node which stores a history of the data during its training phase.
The data history is stored in
self.data_hist
and can also be deleted to free memory. Alternatively it can be automatically pickled to disk.Note that data is only stored during training.
Full API documentation: HistogramNode

class
mdp.nodes.
IdentityNode
¶ Execute returns the input data and the node is not trainable.
This node can be instantiated and is for example useful in complex network layouts.
Full API documentation: IdentityNode

class
mdp.nodes.
Convolution2DNode
¶ Convolve input data with filter banks.
The
filters
argument specifies a set of 2D filters that are convolved with the input data during execution. Convolution can be selected to be executed by linear filtering of the data, or in the frequency domain using a Discrete Fourier Transform.Input data can be given as 3D data, each row being a 2D array to be convolved with the filters, or as 2D data, in which case the
input_shape
argument must be specified.This node depends on
scipy
.Full API documentation: Convolution2DNode

class
mdp.nodes.
LibSVMClassifier
¶ The
LibSVMClassifier
class acts as a wrapper around the LibSVM library for support vector machines.Information to the parameters can be found on http://www.csie.ntu.edu.tw/~cjlin/libsvm/
The class provides access to change kernel and svm type with a text string.
Additionally
self.parameter
is exposed which allows to change all other svm parameters directly.This node depends on
libsvm
.Full API documentation: LibSVMClassifier

class
mdp.nodes.
SGDRegressorScikitsLearnNode
¶ Linear model fitted by minimizing a regularized empirical loss with SGD
This node has been automatically generated by wrapping the
sklearn.linear_model.stochastic_gradient.SGDRegressor
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.SGD stands for Stochastic Gradient Descent: the gradient of the loss is estimated each sample at a time and the model is updated along the way with a decreasing strength schedule (aka learning rate).
The regularizer is a penalty added to the loss function that shrinks model parameters towards the zero vector using either the squared euclidean norm L2 or the absolute norm L1 or a combination of both (Elastic Net). If the parameter update crosses the 0.0 value because of the regularizer, the update is truncated to 0.0 to allow for learning sparse models and achieve online feature selection.
This implementation works with data represented as dense numpy arrays of floating point values for the features.
Read more in the User Guide.
Parameters
 loss : str, ‘squared_loss’, ‘huber’, ‘epsilon_insensitive’, or ‘squared_epsilon_insensitive’
 The loss function to be used. Defaults to ‘squared_loss’ which refers to the ordinary least squares fit. ‘huber’ modifies ‘squared_loss’ to focus less on getting outliers correct by switching from squared to linear loss past a distance of epsilon. ‘epsilon_insensitive’ ignores errors less than epsilon and is linear past that; this is the loss function used in SVR. ‘squared_epsilon_insensitive’ is the same but becomes squared loss past a tolerance of epsilon.
 penalty : str, ‘none’, ‘l2’, ‘l1’, or ‘elasticnet’
 The penalty (aka regularization term) to be used. Defaults to ‘l2’ which is the standard regularizer for linear SVM models. ‘l1’ and ‘elasticnet’ might bring sparsity to the model (feature selection) not achievable with ‘l2’.
 alpha : float
 Constant that multiplies the regularization term. Defaults to 0.0001 Also used to compute learning_rate when set to ‘optimal’.
 l1_ratio : float
 The Elastic Net mixing parameter, with 0 <= l1_ratio <= 1. l1_ratio=0 corresponds to L2 penalty, l1_ratio=1 to L1. Defaults to 0.15.
 fit_intercept : bool
 Whether the intercept should be estimated or not. If False, the data is assumed to be already centered. Defaults to True.
 n_iter : int, optional
 The number of passes over the training data (aka epochs). The number of iterations is set to 1 if using partial_fit. Defaults to 5.
 shuffle : bool, optional
 Whether or not the training data should be shuffled after each epoch. Defaults to True.
 random_state : int seed, RandomState instance, or None (default)
 The seed of the pseudo random number generator to use when shuffling the data.
 verbose : integer, optional
 The verbosity level.
 epsilon : float
 Epsilon in the epsiloninsensitive loss functions; only if loss is ‘huber’, ‘epsilon_insensitive’, or ‘squared_epsilon_insensitive’. For ‘huber’, determines the threshold at which it becomes less important to get the prediction exactly right. For epsiloninsensitive, any differences between the current prediction and the correct label are ignored if they are less than this threshold.
 learning_rate : string, optional
The learning rate:
 constant: eta = eta0
 optimal: eta = 1.0/(alpha * t)
 invscaling: eta = eta0 / pow(t, power_t) [default]
 eta0 : double, optional
 The initial learning rate [default 0.01].
 power_t : double, optional
 The exponent for inverse scaling learning rate [default 0.25].
 warm_start : bool, optional
 When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution.
 average : bool or int, optional
 When set to True, computes the averaged SGD weights and stores the
result in the
coef_
attribute. If set to an int greater than 1, averaging will begin once the total number of samples seen reaches average. Soaverage=10 will
begin averaging after seeing 10 samples.
Attributes
coef_
: array, shape (n_features,) Weights assigned to the features.
intercept_
: array, shape (1,) The intercept term.
average_coef_
: array, shape (n_features,) Averaged weights assigned to the features.
average_intercept_
: array, shape (1,) The averaged intercept term.
Examples
>>> import numpy as np >>> from sklearn import linear_model >>> n_samples, n_features = 10, 5 >>> np.random.seed(0) >>> y = np.random.randn(n_samples) >>> X = np.random.randn(n_samples, n_features) >>> clf = linear_model.SGDRegressor() >>> clf.fit(X, y) ... SGDRegressor(alpha=0.0001, average=False, epsilon=0.1, eta0=0.01, fit_intercept=True, l1_ratio=0.15, learning_rate='invscaling', loss='squared_loss', n_iter=5, penalty='l2', power_t=0.25, random_state=None, shuffle=True, verbose=0, warm_start=False)
See also
Ridge, ElasticNet, Lasso, SVR
Full API documentation: SGDRegressorScikitsLearnNode

class
mdp.nodes.
PatchExtractorScikitsLearnNode
¶ Extracts patches from a collection of images
This node has been automatically generated by wrapping the
sklearn.feature_extraction.image.PatchExtractor
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Read more in the User Guide.
Parameters
 patch_size : tuple of ints (patch_height, patch_width)
 the dimensions of one patch
 max_patches : integer or float, optional default is None
 The maximum number of patches per image to extract. If max_patches is a float in (0, 1), it is taken to mean a proportion of the total number of patches.
 random_state : int or RandomState
 Pseudo number generator state used for random sampling.
Full API documentation: PatchExtractorScikitsLearnNode

class
mdp.nodes.
TheilSenRegressorScikitsLearnNode
¶ TheilSen Estimator: robust multivariate regression model.
This node has been automatically generated by wrapping the
sklearn.linear_model.theil_sen.TheilSenRegressor
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.The algorithm calculates least square solutions on subsets with size n_subsamples of the samples in X. Any value of n_subsamples between the number of features and samples leads to an estimator with a compromise between robustness and efficiency. Since the number of least square solutions is “n_samples choose n_subsamples”, it can be extremely large and can therefore be limited with max_subpopulation. If this limit is reached, the subsets are chosen randomly. In a final step, the spatial median (or L1 median) is calculated of all least square solutions.
Read more in the User Guide.
Parameters
 fit_intercept : boolean, optional, default True
 Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations.
 copy_X : boolean, optional, default True
 If True, X will be copied; else, it may be overwritten.
 max_subpopulation : int, optional, default 1e4
 Instead of computing with a set of cardinality ‘n choose k’, where n is the number of samples and k is the number of subsamples (at least number of features), consider only a stochastic subpopulation of a given maximal size if ‘n choose k’ is larger than max_subpopulation. For other than small problem sizes this parameter will determine memory usage and runtime if n_subsamples is not changed.
 n_subsamples : int, optional, default None
 Number of samples to calculate the parameters. This is at least the number of features (plus 1 if fit_intercept=True) and the number of samples as a maximum. A lower number leads to a higher breakdown point and a low efficiency while a high number leads to a low breakdown point and a high efficiency. If None, take the minimum number of subsamples leading to maximal robustness. If n_subsamples is set to n_samples, TheilSen is identical to least squares.
 max_iter : int, optional, default 300
 Maximum number of iterations for the calculation of spatial median.
 tol : float, optional, default 1.e3
 Tolerance when calculating spatial median.
 random_state : RandomState or an int seed, optional, default None
 A random number generator instance to define the state of the random permutations generator.
 n_jobs : integer, optional, default 1
 Number of CPUs to use during the cross validation. If
1
, use all the CPUs.  verbose : boolean, optional, default False
 Verbose mode when fitting the model.
Attributes
coef_
: array, shape = (n_features) Coefficients of the regression model (median of distribution).
intercept_
: float Estimated intercept of regression model.
breakdown_
: float Approximated breakdown point.
n_iter_
: int Number of iterations needed for the spatial median.
n_subpopulation_
: int Number of combinations taken into account from ‘n choose k’, where n is the number of samples and k is the number of subsamples.
References
 TheilSen Estimators in a Multiple Linear Regression Model, 2009 Xin Dang, Hanxiang Peng, Xueqin Wang and Heping Zhang http://www.math.iupui.edu/~hpeng/MTSE_0908.pdf
Full API documentation: TheilSenRegressorScikitsLearnNode

class
mdp.nodes.
SparseRandomProjectionScikitsLearnNode
¶ Reduce dimensionality through sparse random projection
This node has been automatically generated by wrapping the
sklearn.random_projection.SparseRandomProjection
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Sparse random matrix is an alternative to dense random projection matrix that guarantees similar embedding quality while being much more memory efficient and allowing faster computation of the projected data.
If we note s = 1 / density the components of the random matrix are drawn from:
 sqrt(s) / sqrt(n_components) with probability 1 / 2s
 0 with probability 1  1 / s
 +sqrt(s) / sqrt(n_components) with probability 1 / 2s
Read more in the User Guide.
Parameters
 n_components : int or ‘auto’, optional (default = ‘auto’)
Dimensionality of the target projection space.
n_components can be automatically adjusted according to the number of samples in the dataset and the bound given by the JohnsonLindenstrauss lemma. In that case the quality of the embedding is controlled by the
eps
parameter.It should be noted that JohnsonLindenstrauss lemma can yield very conservative estimated of the required number of components as it makes no assumption on the structure of the dataset.
 density : float in range ]0, 1], optional (default=’auto’)
Ratio of nonzero component in the random projection matrix.
If density = ‘auto’, the value is set to the minimum density as recommended by Ping Li et al.: 1 / sqrt(n_features).
Use density = 1 / 3.0 if you want to reproduce the results from Achlioptas, 2001.
 eps : strictly positive float, optional, (default=0.1)
Parameter to control the quality of the embedding according to the JohnsonLindenstrauss lemma when n_components is set to ‘auto’.
Smaller values lead to better embedding and higher number of dimensions (n_components) in the target projection space.
 dense_output : boolean, optional (default=False)
If True, ensure that the output of the random projection is a dense numpy array even if the input and random projection matrix are both sparse. In practice, if the number of components is small the number of zero components in the projected data will be very small and it will be more CPU and memory efficient to use a dense representation.
If False, the projected data uses a sparse representation if the input is sparse.
 random_state : integer, RandomState instance or None (default=None)
 Control the pseudo random number generator used to generate the matrix at fit time.
Attributes
n_component_
: int Concrete number of components computed when n_components=”auto”.
components_
: CSR matrix with shape [n_components, n_features] Random matrix used for the projection.
density_
: float in range 0.0  1.0 Concrete density computed from when density = “auto”.
See Also
GaussianRandomProjection
References
[1] Ping Li, T. Hastie and K. W. Church, 2006, “Very Sparse Random Projections”. http://www.stanford.edu/~hastie/Papers/Ping/KDD06_rp.pdf [2] D. Achlioptas, 2001, “Databasefriendly random projections”, http://www.cs.ucsc.edu/~optas/papers/jl.pdf Full API documentation: SparseRandomProjectionScikitsLearnNode

class
mdp.nodes.
LinearModelCVScikitsLearnNode
¶ This node has been automatically generated by wrapping the
sklearn.linear_model.coordinate_descent.LinearModelCV
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Full API documentation: LinearModelCVScikitsLearnNode

class
mdp.nodes.
DictionaryLearningScikitsLearnNode
¶ Dictionary learning
This node has been automatically generated by wrapping the
sklearn.decomposition.dict_learning.DictionaryLearning
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Finds a dictionary (a set of atoms) that can best be used to represent data using a sparse code.
Solves the optimization problem:
(U^*,V^*) = argmin 0.5  Y  U V _2^2 + alpha *  U _1 (U,V) with  V_k _2 = 1 for all 0 <= k < n_components
Read more in the User Guide.
Parameters
 n_components : int,
 number of dictionary elements to extract
 alpha : float,
 sparsity controlling parameter
 max_iter : int,
 maximum number of iterations to perform
 tol : float,
 tolerance for numerical error
 fit_algorithm : {‘lars’, ‘cd’}
lars: uses the least angle regression method to solve the lasso problem (linear_model.lars_path) cd: uses the coordinate descent method to compute the Lasso solution (linear_model.Lasso). Lars will be faster if the estimated components are sparse.
New in version 0.17: cd coordinate descent method to improve speed.
 transform_algorithm : {‘lasso_lars’, ‘lasso_cd’, ‘lars’, ‘omp’, ‘threshold’}
Algorithm used to transform the data lars: uses the least angle regression method (linear_model.lars_path) lasso_lars: uses Lars to compute the Lasso solution lasso_cd: uses the coordinate descent method to compute the Lasso solution (linear_model.Lasso). lasso_lars will be faster if the estimated components are sparse. omp: uses orthogonal matching pursuit to estimate the sparse solution threshold: squashes to zero all coefficients less than alpha from the projection
dictionary * X'
New in version 0.17: lasso_cd coordinate descent method to improve speed.
 transform_n_nonzero_coefs : int,
0.1 * n_features
by default  Number of nonzero coefficients to target in each column of the solution. This is only used by algorithm=’lars’ and algorithm=’omp’ and is overridden by alpha in the omp case.
 transform_alpha : float, 1. by default
 If algorithm=’lasso_lars’ or algorithm=’lasso_cd’, alpha is the penalty applied to the L1 norm. If algorithm=’threshold’, alpha is the absolute value of the threshold below which coefficients will be squashed to zero. If algorithm=’omp’, alpha is the tolerance parameter: the value of the reconstruction error targeted. In this case, it overrides n_nonzero_coefs.
 split_sign : bool, False by default
 Whether to split the sparse feature vector into the concatenation of its negative part and its positive part. This can improve the performance of downstream classifiers.
 n_jobs : int,
 number of parallel jobs to run
 code_init : array of shape (n_samples, n_components),
 initial value for the code, for warm restart
 dict_init : array of shape (n_components, n_features),
 initial values for the dictionary, for warm restart
verbose :
 degree of verbosity of the printed output
 random_state : int or RandomState
 Pseudo number generator state used for random sampling.
Attributes
components_
: array, [n_components, n_features] dictionary atoms extracted from the data
error_
: array vector of errors at each iteration
n_iter_
: int Number of iterations run.
Notes
References:
J. Mairal, F. Bach, J. Ponce, G. Sapiro, 2009: Online dictionary learning for sparse coding (http://www.di.ens.fr/sierra/pdfs/icml09.pdf)
See also
SparseCoder MiniBatchDictionaryLearning SparsePCA MiniBatchSparsePCA
Full API documentation: DictionaryLearningScikitsLearnNode

class
mdp.nodes.
MinMaxScalerScikitsLearnNode
¶ Transforms features by scaling each feature to a given range.
This node has been automatically generated by wrapping the
sklearn.preprocessing.data.MinMaxScaler
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.This estimator scales and translates each feature individually such that it is in the given range on the training set, i.e. between zero and one.
The transformation is given by:
X_std = (X  X.min(axis=0)) / (X.max(axis=0)  X.min(axis=0)) X_scaled = X_std * (max  min) + min
where min, max = feature_range.
This transformation is often used as an alternative to zero mean, unit variance scaling.
Read more in the User Guide.
Parameters
 feature_range: tuple (min, max), default=(0, 1)
 Desired range of transformed data.
 copy : boolean, optional, default True
 Set to False to perform inplace row normalization and avoid a copy (if the input is already a numpy array).
Attributes
min_
: ndarray, shape (n_features,) Per feature adjustment for minimum.
scale_
: ndarray, shape (n_features,)Per feature relative scaling of the data.
New in version 0.17: scale_ attribute.
data_min_
: ndarray, shape (n_features,)Per feature minimum seen in the data
New in version 0.17: data_min_ instead of deprecated data_min.
data_max_
: ndarray, shape (n_features,)Per feature maximum seen in the data
New in version 0.17: data_max_ instead of deprecated data_max.
data_range_
: ndarray, shape (n_features,)Per feature range
(data_max_  data_min_)
seen in the dataNew in version 0.17: data_range_ instead of deprecated data_range.
Full API documentation: MinMaxScalerScikitsLearnNode

class
mdp.nodes.
ElasticNetCVScikitsLearnNode
¶ Elastic Net model with iterative fitting along a regularization path
This node has been automatically generated by wrapping the
sklearn.linear_model.coordinate_descent.ElasticNetCV
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.The best model is selected by crossvalidation.
Read more in the User Guide.
Parameters
 l1_ratio : float or array of floats, optional
 float between 0 and 1 passed to ElasticNet (scaling between
l1 and l2 penalties). For
l1_ratio = 0
the penalty is an L2 penalty. Forl1_ratio = 1
it is an L1 penalty. For0 < l1_ratio < 1
, the penalty is a combination of L1 and L2 This parameter can be a list, in which case the different values are tested by crossvalidation and the one giving the best prediction score is used. Note that a good choice of list of values for l1_ratio is often to put more values close to 1 (i.e. Lasso) and less close to 0 (i.e. Ridge), as in[.1, .5, .7, .9, .95, .99, 1]
 eps : float, optional
 Length of the path.
eps=1e3
means thatalpha_min / alpha_max = 1e3
.  n_alphas : int, optional
 Number of alphas along the regularization path, used for each l1_ratio.
 alphas : numpy array, optional
 List of alphas where to compute the models. If None alphas are set automatically
 precompute : True  False  ‘auto’  arraylike
 Whether to use a precomputed Gram matrix to speed up
calculations. If set to
'auto'
let us decide. The Gram matrix can also be passed as argument.  max_iter : int, optional
 The maximum number of iterations
 tol : float, optional
 The tolerance for the optimization: if the updates are
smaller than
tol
, the optimization code checks the dual gap for optimality and continues until it is smaller thantol
.  cv : int, crossvalidation generator or an iterable, optional
Determines the crossvalidation splitting strategy. Possible inputs for cv are:
 None, to use the default 3fold crossvalidation,
 integer, to specify the number of folds.
 An object to be used as a crossvalidation generator.
 An iterable yielding train/test splits.
For integer/None inputs,
KFold
is used.Refer User Guide for the various crossvalidation strategies that can be used here.
 verbose : bool or integer
 Amount of verbosity.
 n_jobs : integer, optional
 Number of CPUs to use during the cross validation. If
1
, use all the CPUs.  positive : bool, optional
 When set to
True
, forces the coefficients to be positive.  selection : str, default ‘cyclic’
 If set to ‘random’, a random coefficient is updated every iteration rather than looping over features sequentially by default. This (setting to ‘random’) often leads to significantly faster convergence especially when tol is higher than 1e4.
 random_state : int, RandomState instance, or None (default)
 The seed of the pseudo random number generator that selects a random feature to update. Useful only when selection is set to ‘random’.
 fit_intercept : boolean
 whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered).
 normalize : boolean, optional, default False
 If
True
, the regressors X will be normalized before regression.  copy_X : boolean, optional, default True
 If
True
, X will be copied; else, it may be overwritten.
Attributes
alpha_
: float The amount of penalization chosen by cross validation
l1_ratio_
: float The compromise between l1 and l2 penalization chosen by cross validation
coef_
: array, shape (n_features,)  (n_targets, n_features) Parameter vector (w in the cost function formula),
intercept_
: float  array, shape (n_targets, n_features) Independent term in the decision function.
mse_path_
: array, shape (n_l1_ratio, n_alpha, n_folds) Mean square error for the test set on each fold, varying l1_ratio and alpha.
alphas_
: numpy array, shape (n_alphas,) or (n_l1_ratio, n_alphas) The grid of alphas used for fitting, for each l1_ratio.
n_iter_
: int number of iterations run by the coordinate descent solver to reach the specified tolerance for the optimal alpha.
Notes
See examples/linear_model/lasso_path_with_crossvalidation.py for an example.
To avoid unnecessary memory duplication the X argument of the fit method should be directly passed as a Fortrancontiguous numpy array.
The parameter l1_ratio corresponds to alpha in the glmnet R package while alpha corresponds to the lambda parameter in glmnet. More specifically, the optimization objective is:
1 / (2 * n_samples) * y  Xw^2_2 + + alpha * l1_ratio * w_1 + 0.5 * alpha * (1  l1_ratio) * w^2_2
If you are interested in controlling the L1 and L2 penalty separately, keep in mind that this is equivalent to:
a * L1 + b * L2
for:
alpha = a + b and l1_ratio = a / (a + b).
See also
enet_path ElasticNet
Full API documentation: ElasticNetCVScikitsLearnNode

class
mdp.nodes.
RBFSamplerScikitsLearnNode
¶ Approximates feature map of an RBF kernel by Monte Carlo approximation of its Fourier transform.
This node has been automatically generated by wrapping the
sklearn.kernel_approximation.RBFSampler
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.It implements a variant of Random Kitchen Sinks.[1]
Read more in the User Guide.
Parameters
 gamma : float
 Parameter of RBF kernel: exp(gamma * x^2)
 n_components : int
 Number of Monte Carlo samples per original feature. Equals the dimensionality of the computed feature space.
 random_state : {int, RandomState}, optional
 If int, random_state is the seed used by the random number generator; if RandomState instance, random_state is the random number generator.
Notes
See “Random Features for LargeScale Kernel Machines” by A. Rahimi and Benjamin Recht.
[1] “Weighted Sums of Random Kitchen Sinks: Replacing minimization with randomization in learning” by A. Rahimi and Benjamin Recht. (http://www.eecs.berkeley.edu/~brecht/papers/08.rah.rec.nips.pdf)
Full API documentation: RBFSamplerScikitsLearnNode

class
mdp.nodes.
OrthogonalMatchingPursuitCVScikitsLearnNode
¶ Crossvalidated Orthogonal Matching Pursuit model (OMP)
This node has been automatically generated by wrapping the
sklearn.linear_model.omp.OrthogonalMatchingPursuitCV
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Parameters
 copy : bool, optional
 Whether the design matrix X must be copied by the algorithm. A false value is only helpful if X is already Fortranordered, otherwise a copy is made anyway.
 fit_intercept : boolean, optional
 whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered).
 normalize : boolean, optional
 If False, the regressors X are assumed to be already normalized.
 max_iter : integer, optional
 Maximum numbers of iterations to perform, therefore maximum features
to include. 10% of
n_features
but at least 5 if available.  cv : int, crossvalidation generator or an iterable, optional
Determines the crossvalidation splitting strategy. Possible inputs for cv are:
 None, to use the default 3fold crossvalidation,
 integer, to specify the number of folds.
 An object to be used as a crossvalidation generator.
 An iterable yielding train/test splits.
For integer/None inputs,
KFold
is used.Refer User Guide for the various crossvalidation strategies that can be used here.
 n_jobs : integer, optional
 Number of CPUs to use during the cross validation. If
1
, use all the CPUs  verbose : boolean or integer, optional
 Sets the verbosity amount
Read more in the User Guide.
Attributes
intercept_
: float or array, shape (n_targets,) Independent term in decision function.
coef_
: array, shape (n_features,) or (n_features, n_targets) Parameter vector (w in the problem formulation).
n_nonzero_coefs_
: int Estimated number of nonzero coefficients giving the best mean squared error over the crossvalidation folds.
n_iter_
: int or arraylike Number of active features across every target for the model refit with the best hyperparameters got by crossvalidating across all folds.
See also
orthogonal_mp orthogonal_mp_gram lars_path Lars LassoLars OrthogonalMatchingPursuit LarsCV LassoLarsCV decomposition.sparse_encode
Full API documentation: OrthogonalMatchingPursuitCVScikitsLearnNode

class
mdp.nodes.
SkewedChi2SamplerScikitsLearnNode
¶ Approximates feature map of the “skewed chisquared” kernel by Monte Carlo approximation of its Fourier transform.
This node has been automatically generated by wrapping the
sklearn.kernel_approximation.SkewedChi2Sampler
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Read more in the User Guide.
Parameters
 skewedness : float
 “skewedness” parameter of the kernel. Needs to be crossvalidated.
 n_components : int
 number of Monte Carlo samples per original feature. Equals the dimensionality of the computed feature space.
 random_state : {int, RandomState}, optional
 If int, random_state is the seed used by the random number generator; if RandomState instance, random_state is the random number generator.
References
See “Random Fourier Approximations for Skewed Multiplicative Histogram Kernels” by Fuxin Li, Catalin Ionescu and Cristian Sminchisescu.
See also
 AdditiveChi2Sampler : A different approach for approximating an additive
 variant of the chi squared kernel.
sklearn.metrics.pairwise.chi2_kernel : The exact chi squared kernel.
Full API documentation: SkewedChi2SamplerScikitsLearnNode

class
mdp.nodes.
RandomTreesEmbeddingScikitsLearnNode
¶ An ensemble of totally random trees.
This node has been automatically generated by wrapping the
sklearn.ensemble.forest.RandomTreesEmbedding
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.An unsupervised transformation of a dataset to a highdimensional sparse representation. A datapoint is coded according to which leaf of each tree it is sorted into. Using a onehot encoding of the leaves, this leads to a binary coding with as many ones as there are trees in the forest.
The dimensionality of the resulting representation is
n_out <= n_estimators * max_leaf_nodes
. Ifmax_leaf_nodes == None
, the number of leaf nodes is at mostn_estimators * 2 ** max_depth
.Read more in the User Guide.
Parameters
 n_estimators : int
 Number of trees in the forest.
 max_depth : int
 The maximum depth of each tree. If None, then nodes are expanded until
all leaves are pure or until all leaves contain less than
min_samples_split samples.
Ignored if
max_leaf_nodes
is not None.  min_samples_split : integer, optional (default=2)
 The minimum number of samples required to split an internal node.
 min_samples_leaf : integer, optional (default=1)
 The minimum number of samples in newly created leaves. A split is
discarded if after the split, one of the leaves would contain less then
min_samples_leaf
samples.  min_weight_fraction_leaf : float, optional (default=0.)
 The minimum weighted fraction of the input samples required to be at a leaf node.
 max_leaf_nodes : int or None, optional (default=None)
 Grow trees with
max_leaf_nodes
in bestfirst fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes. If not None thenmax_depth
will be ignored.  sparse_output : bool, optional (default=True)
 Whether or not to return a sparse CSR matrix, as default behavior, or to return a dense array compatible with dense pipeline operators.
 n_jobs : integer, optional (default=1)
 The number of jobs to run in parallel for both fit and predict. If 1, then the number of jobs is set to the number of cores.
 random_state : int, RandomState instance or None, optional (default=None)
 If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.
 verbose : int, optional (default=0)
 Controls the verbosity of the tree building process.
 warm_start : bool, optional (default=False)
 When set to
True
, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new forest.
Attributes
estimators_
: list of DecisionTreeClassifier The collection of fitted subestimators.
References
[1] P. Geurts, D. Ernst., and L. Wehenkel, “Extremely randomized trees”, Machine Learning, 63(1), 342, 2006. [2] Moosmann, F. and Triggs, B. and Jurie, F. “Fast discriminative visual codebooks using randomized clustering forests” NIPS 2007 Full API documentation: RandomTreesEmbeddingScikitsLearnNode

class
mdp.nodes.
PerceptronScikitsLearnNode
¶ Perceptron
This node has been automatically generated by wrapping the
sklearn.linear_model.perceptron.Perceptron
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Read more in the User Guide.
Parameters
 penalty : None, ‘l2’ or ‘l1’ or ‘elasticnet’
 The penalty (aka regularization term) to be used. Defaults to None.
 alpha : float
 Constant that multiplies the regularization term if regularization is used. Defaults to 0.0001
 fit_intercept : bool
 Whether the intercept should be estimated or not. If False, the data is assumed to be already centered. Defaults to True.
 n_iter : int, optional
 The number of passes over the training data (aka epochs). Defaults to 5.
 shuffle : bool, optional, default True
 Whether or not the training data should be shuffled after each epoch.
 random_state : int seed, RandomState instance, or None (default)
 The seed of the pseudo random number generator to use when shuffling the data.
 verbose : integer, optional
 The verbosity level
 n_jobs : integer, optional
 The number of CPUs to use to do the OVA (One Versus All, for multiclass problems) computation. 1 means ‘all CPUs’. Defaults to 1.
 eta0 : double
 Constant by which the updates are multiplied. Defaults to 1.
 class_weight : dict, {class_label: weight} or “balanced” or None, optional
Preset for the class_weight fit parameter.
Weights associated with classes. If not given, all classes are supposed to have weight one.
The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as
n_samples / (n_classes * np.bincount(y))
 warm_start : bool, optional
 When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution.
Attributes
coef_
: array, shape = [1, n_features] if n_classes == 2 else [n_classes, n_features] Weights assigned to the features.
intercept_
: array, shape = [1] if n_classes == 2 else [n_classes] Constants in decision function.
Notes
Perceptron and SGDClassifier share the same underlying implementation. In fact, Perceptron() is equivalent to SGDClassifier(loss=”perceptron”, eta0=1, learning_rate=”constant”, penalty=None).
See also
SGDClassifier
References
http://en.wikipedia.org/wiki/Perceptron and references therein.
Full API documentation: PerceptronScikitsLearnNode

class
mdp.nodes.
RidgeClassifierScikitsLearnNode
¶ Classifier using Ridge regression.
This node has been automatically generated by wrapping the
sklearn.linear_model.ridge.RidgeClassifier
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Read more in the User Guide.
Parameters
 alpha : float
 Small positive values of alpha improve the conditioning of the problem
and reduce the variance of the estimates. Alpha corresponds to
C^1
in other linear models such as LogisticRegression or LinearSVC.  class_weight : dict or ‘balanced’, optional
Weights associated with classes in the form
{class_label: weight}
. If not given, all classes are supposed to have weight one.The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as
n_samples / (n_classes * np.bincount(y))
 copy_X : boolean, optional, default True
 If True, X will be copied; else, it may be overwritten.
 fit_intercept : boolean
 Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered).
 max_iter : int, optional
 Maximum number of iterations for conjugate gradient solver. The default value is determined by scipy.sparse.linalg.
 normalize : boolean, optional, default False
 If True, the regressors X will be normalized before regression.
 solver : {‘auto’, ‘svd’, ‘cholesky’, ‘lsqr’, ‘sparse_cg’, ‘sag’}
Solver to use in the computational routines:
‘auto’ chooses the solver automatically based on the type of data.
‘svd’ uses a Singular Value Decomposition of X to compute the Ridge coefficients. More stable for singular matrices than ‘cholesky’.
‘cholesky’ uses the standard scipy.linalg.solve function to obtain a closedform solution.
‘sparse_cg’ uses the conjugate gradient solver as found in scipy.sparse.linalg.cg. As an iterative algorithm, this solver is more appropriate than ‘cholesky’ for largescale data (possibility to set tol and max_iter).
‘lsqr’ uses the dedicated regularized leastsquares routine scipy.sparse.linalg.lsqr. It is the fatest but may not be available in old scipy versions. It also uses an iterative procedure.
‘sag’ uses a Stochastic Average Gradient descent. It also uses an iterative procedure, and is faster than other solvers when both n_samples and n_features are large.
New in version 0.17: Stochastic Average Gradient descent solver.
 tol : float
 Precision of the solution.
 random_state : int seed, RandomState instance, or None (default)
 The seed of the pseudo random number generator to use when shuffling the data. Used in ‘sag’ solver.
Attributes
coef_
: array, shape (n_features,) or (n_classes, n_features) Weight vector(s).
intercept_
: float  array, shape = (n_targets,) Independent term in decision function. Set to 0.0 if
fit_intercept = False
. n_iter_
: array or None, shape (n_targets,) Actual number of iterations for each target. Available only for sag and lsqr solvers. Other solvers will return None.
See also
Ridge, RidgeClassifierCV
Notes
For multiclass classification, n_class classifiers are trained in a oneversusall approach. Concretely, this is implemented by taking advantage of the multivariate response support in Ridge.
Full API documentation: RidgeClassifierScikitsLearnNode

class
mdp.nodes.
LinearSVRScikitsLearnNode
¶ Linear Support Vector Regression.
This node has been automatically generated by wrapping the
sklearn.svm.classes.LinearSVR
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Similar to SVR with parameter kernel=’linear’, but implemented in terms of liblinear rather than libsvm, so it has more flexibility in the choice of penalties and loss functions and should scale better to large numbers of samples.
This class supports both dense and sparse input.
Read more in the User Guide.
Parameters
 C : float, optional (default=1.0)
 Penalty parameter C of the error term. The penalty is a squared l2 penalty. The bigger this parameter, the less regularization is used.
 loss : string, ‘epsilon_insensitive’ or ‘squared_epsilon_insensitive’ (default=’epsilon_insensitive’)
 Specifies the loss function. ‘l1’ is the epsiloninsensitive loss (standard SVR) while ‘l2’ is the squared epsiloninsensitive loss.
 epsilon : float, optional (default=0.1)
 Epsilon parameter in the epsiloninsensitive loss function. Note
that the value of this parameter depends on the scale of the target
variable y. If unsure, set
epsilon=0
.  dual : bool, (default=True)
 Select the algorithm to either solve the dual or primal optimization problem. Prefer dual=False when n_samples > n_features.
 tol : float, optional (default=1e4)
 Tolerance for stopping criteria.
 fit_intercept : boolean, optional (default=True)
 Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (i.e. data is expected to be already centered).
 intercept_scaling : float, optional (default=1)
 When self.fit_intercept is True, instance vector x becomes [x, self.intercept_scaling], i.e. a “synthetic” feature with constant value equals to intercept_scaling is appended to the instance vector. The intercept becomes intercept_scaling * synthetic feature weight Note! the synthetic feature weight is subject to l1/l2 regularization as all other features. To lessen the effect of regularization on synthetic feature weight (and therefore on the intercept) intercept_scaling has to be increased.
 verbose : int, (default=0)
 Enable verbose output. Note that this setting takes advantage of a perprocess runtime setting in liblinear that, if enabled, may not work properly in a multithreaded context.
 random_state : int seed, RandomState instance, or None (default=None)
 The seed of the pseudo random number generator to use when shuffling the data.
 max_iter : int, (default=1000)
 The maximum number of iterations to be run.
Attributes
coef_
: array, shape = [n_features] if n_classes == 2 else [n_classes, n_features]Weights assigned to the features (coefficients in the primal problem). This is only available in the case of a linear kernel.
coef_ is a readonly property derived from raw_coef_ that follows the internal memory layout of liblinear.
intercept_
: array, shape = [1] if n_classes == 2 else [n_classes] Constants in decision function.
See also
 LinearSVC
 Implementation of Support Vector Machine classifier using the same library as this class (liblinear).
 SVR
Implementation of Support Vector Machine regression using libsvm:
 the kernel can be nonlinear but its SMO algorithm does not
 scale to large number of samples as LinearSVC does.
 sklearn.linear_model.SGDRegressor
 SGDRegressor can optimize the same cost function as LinearSVR by adjusting the penalty and loss parameters. In addition it requires less memory, allows incremental (online) learning, and implements various loss functions and regularization regimes.
Full API documentation: LinearSVRScikitsLearnNode

class
mdp.nodes.
QuadraticDiscriminantAnalysisScikitsLearnNode
¶ Quadratic Discriminant Analysis
This node has been automatically generated by wrapping the
sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.A classifier with a quadratic decision boundary, generated by fitting class conditional densities to the data and using Bayes’ rule.
The model fits a Gaussian density to each class.
New in version 0.17: QuadraticDiscriminantAnalysis
Changed in version 0.17: Deprecated
qda.QDA
have been moved to QuadraticDiscriminantAnalysis.Parameters
 priors : array, optional, shape = [n_classes]
 Priors on classes
 reg_param : float, optional
 Regularizes the covariance estimate as
(1reg_param)*Sigma + reg_param*np.eye(n_features)
Attributes
covariances_
: list of arraylike, shape = [n_features, n_features] Covariance matrices of each class.
means_
: arraylike, shape = [n_classes, n_features] Class means.
priors_
: arraylike, shape = [n_classes] Class priors (sum to 1).
rotations_
: list of arrays For each class k an array of shape [n_features, n_k], with
n_k = min(n_features, number of elements in class k)
It is the rotation of the Gaussian distribution, i.e. its principal axis. scalings_
: list of arrays For each class k an array of shape [n_k]. It contains the scaling of the Gaussian distributions along its principal axes, i.e. the variance in the rotated coordinate system.
 store_covariances : boolean
If True the covariance matrices are computed and stored in the self.covariances_ attribute.
New in version 0.17.
 tol : float, optional, default 1.0e4
Threshold used for rank estimation.
New in version 0.17.
Examples
>>> from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis >>> import numpy as np >>> X = np.array([[1, 1], [2, 1], [3, 2], [1, 1], [2, 1], [3, 2]]) >>> y = np.array([1, 1, 1, 2, 2, 2]) >>> clf = QuadraticDiscriminantAnalysis() >>> clf.fit(X, y) ... QuadraticDiscriminantAnalysis(priors=None, reg_param=0.0, store_covariances=False, tol=0.0001) >>> print(clf.predict([[0.8, 1]])) [1]
See also
 sklearn.discriminant_analysis.LinearDiscriminantAnalysis: Linear
 Discriminant Analysis
Full API documentation: QuadraticDiscriminantAnalysisScikitsLearnNode

class
mdp.nodes.
KNeighborsClassifierScikitsLearnNode
¶ Classifier implementing the knearest neighbors vote.
This node has been automatically generated by wrapping the
sklearn.neighbors.classification.KNeighborsClassifier
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Read more in the User Guide.
Parameters
 n_neighbors : int, optional (default = 5)
 Number of neighbors to use by default for
k_neighbors()
queries.  weights : str or callable
weight function used in prediction. Possible values:
 ‘uniform’ : uniform weights. All points in each neighborhood are weighted equally.
 ‘distance’ : weight points by the inverse of their distance. in this case, closer neighbors of a query point will have a greater influence than neighbors which are further away.
 [callable] : a userdefined function which accepts an array of distances, and returns an array of the same shape containing the weights.
Uniform weights are used by default.
 algorithm : {‘auto’, ‘ball_tree’, ‘kd_tree’, ‘brute’}, optional
Algorithm used to compute the nearest neighbors:
 ‘ball_tree’ will use
BallTree
 ‘kd_tree’ will use
KDTree
 ‘brute’ will use a bruteforce search.
 ‘auto’ will attempt to decide the most appropriate algorithm
based on the values passed to
fit()
method.
Note: fitting on sparse input will override the setting of this parameter, using brute force.
 ‘ball_tree’ will use
 leaf_size : int, optional (default = 30)
 Leaf size passed to BallTree or KDTree. This can affect the speed of the construction and query, as well as the memory required to store the tree. The optimal value depends on the nature of the problem.
 metric : string or DistanceMetric object (default = ‘minkowski’)
 the distance metric to use for the tree. The default metric is minkowski, and with p=2 is equivalent to the standard Euclidean metric. See the documentation of the DistanceMetric class for a list of available metrics.
 p : integer, optional (default = 2)
 Power parameter for the Minkowski metric. When p = 1, this is equivalent to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.
 metric_params : dict, optional (default = None)
 Additional keyword arguments for the metric function.
 n_jobs : int, optional (default = 1)
 The number of parallel jobs to run for neighbors search.
If
1
, then the number of jobs is set to the number of CPU cores. Doesn’t affectfit()
method.
Examples
>>> X = [[0], [1], [2], [3]] >>> y = [0, 0, 1, 1] >>> from sklearn.neighbors import KNeighborsClassifier >>> neigh = KNeighborsClassifier(n_neighbors=3) >>> neigh.fit(X, y) KNeighborsClassifier(...) >>> print(neigh.predict([[1.1]])) [0] >>> print(neigh.predict_proba([[0.9]])) [[ 0.66666667 0.33333333]]
See also
RadiusNeighborsClassifier KNeighborsRegressor RadiusNeighborsRegressor NearestNeighbors
Notes
See Nearest Neighbors in the online documentation for a discussion of the choice of
algorithm
andleaf_size
.Warning
Regarding the Nearest Neighbors algorithms, if it is found that two neighbors, neighbor k+1 and k, have identical distances but but different labels, the results will depend on the ordering of the training data.
http://en.wikipedia.org/wiki/Knearest_neighbor_algorithm
Full API documentation: KNeighborsClassifierScikitsLearnNode

class
mdp.nodes.
NearestCentroidScikitsLearnNode
¶ Nearest centroid classifier.
This node has been automatically generated by wrapping the
sklearn.neighbors.nearest_centroid.NearestCentroid
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Each class is represented by its centroid, with test samples classified to the class with the nearest centroid.
Read more in the User Guide.
Parameters
 metric: string, or callable
 The metric to use when calculating distance between instances in a feature array. If metric is a string or callable, it must be one of the options allowed by metrics.pairwise.pairwise_distances for its metric parameter. The centroids for the samples corresponding to each class is the point from which the sum of the distances (according to the metric) of all samples that belong to that particular class are minimized. If the “manhattan” metric is provided, this centroid is the median and for all other metrics, the centroid is now set to be the mean.
 shrink_threshold : float, optional (default = None)
 Threshold for shrinking centroids to remove features.
Attributes
centroids_
: arraylike, shape = [n_classes, n_features] Centroid of each class
Examples
>>> from sklearn.neighbors.nearest_centroid import NearestCentroid >>> import numpy as np >>> X = np.array([[1, 1], [2, 1], [3, 2], [1, 1], [2, 1], [3, 2]]) >>> y = np.array([1, 1, 1, 2, 2, 2]) >>> clf = NearestCentroid() >>> clf.fit(X, y) NearestCentroid(metric='euclidean', shrink_threshold=None) >>> print(clf.predict([[0.8, 1]])) [1]
See also
sklearn.neighbors.KNeighborsClassifier: nearest neighbors classifier
Notes
When used for text classification with tfidf vectors, this classifier is also known as the Rocchio classifier.
References
Tibshirani, R., Hastie, T., Narasimhan, B., & Chu, G. (2002). Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proceedings of the National Academy of Sciences of the United States of America, 99(10), 65676572. The National Academy of Sciences.
Full API documentation: NearestCentroidScikitsLearnNode

class
mdp.nodes.
ExtraTreeRegressorScikitsLearnNode
¶ An extremely randomized tree regressor.
This node has been automatically generated by wrapping the
sklearn.tree.tree.ExtraTreeRegressor
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Extratrees differ from classic decision trees in the way they are built. When looking for the best split to separate the samples of a node into two groups, random splits are drawn for each of the max_features randomly selected features and the best split among those is chosen. When max_features is set 1, this amounts to building a totally random decision tree.
Warning: Extratrees should only be used within ensemble methods.
Read more in the User Guide.
See also
ExtraTreeClassifier, ExtraTreesClassifier, ExtraTreesRegressor
References
[1] P. Geurts, D. Ernst., and L. Wehenkel, “Extremely randomized trees”, Machine Learning, 63(1), 342, 2006. Full API documentation: ExtraTreeRegressorScikitsLearnNode

class
mdp.nodes.
ExtraTreesClassifierScikitsLearnNode
¶ An extratrees classifier.
This node has been automatically generated by wrapping the
sklearn.ensemble.forest.ExtraTreesClassifier
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.This class implements a meta estimator that fits a number of randomized decision trees (a.k.a. extratrees) on various subsamples of the dataset and use averaging to improve the predictive accuracy and control overfitting.
Read more in the User Guide.
Parameters
 n_estimators : integer, optional (default=10)
 The number of trees in the forest.
 criterion : string, optional (default=”gini”)
 The function to measure the quality of a split. Supported criteria are “gini” for the Gini impurity and “entropy” for the information gain. Note: this parameter is treespecific.
 max_features : int, float, string or None, optional (default=”auto”)
The number of features to consider when looking for the best split:
 If int, then consider max_features features at each split.
 If float, then max_features is a percentage and int(max_features * n_features) features are considered at each split.
 If “auto”, then max_features=sqrt(n_features).
 If “sqrt”, then max_features=sqrt(n_features).
 If “log2”, then max_features=log2(n_features).
 If None, then max_features=n_features.
Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than
max_features
features. Note: this parameter is treespecific. max_depth : integer or None, optional (default=None)
 The maximum depth of the tree. If None, then nodes are expanded until
all leaves are pure or until all leaves contain less than
min_samples_split samples.
Ignored if
max_leaf_nodes
is not None. Note: this parameter is treespecific.  min_samples_split : integer, optional (default=2)
 The minimum number of samples required to split an internal node. Note: this parameter is treespecific.
 min_samples_leaf : integer, optional (default=1)
 The minimum number of samples in newly created leaves. A split is
discarded if after the split, one of the leaves would contain less then
min_samples_leaf
samples. Note: this parameter is treespecific.  min_weight_fraction_leaf : float, optional (default=0.)
 The minimum weighted fraction of the input samples required to be at a leaf node. Note: this parameter is treespecific.
 max_leaf_nodes : int or None, optional (default=None)
 Grow trees with
max_leaf_nodes
in bestfirst fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes. If not None thenmax_depth
will be ignored. Note: this parameter is treespecific.  bootstrap : boolean, optional (default=False)
 Whether bootstrap samples are used when building trees.
 oob_score : bool
 Whether to use outofbag samples to estimate the generalization error.
 n_jobs : integer, optional (default=1)
 The number of jobs to run in parallel for both fit and predict. If 1, then the number of jobs is set to the number of cores.
 random_state : int, RandomState instance or None, optional (default=None)
 If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.
 verbose : int, optional (default=0)
 Controls the verbosity of the tree building process.
 warm_start : bool, optional (default=False)
 When set to
True
, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new forest.
class_weight : dict, list of dicts, “balanced”, “balanced_subsample” or None, optional
Weights associated with classes in the form
{class_label: weight}
. If not given, all classes are supposed to have weight one. For multioutput problems, a list of dicts can be provided in the same order as the columns of y.The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as
n_samples / (n_classes * np.bincount(y))
The “balanced_subsample” mode is the same as “balanced” except that weights are computed based on the bootstrap sample for every tree grown.
For multioutput, the weights of each column of y will be multiplied.
Note that these weights will be multiplied with sample_weight (passed through the fit method) if sample_weight is specified.
Attributes
estimators_
: list of DecisionTreeClassifier The collection of fitted subestimators.
classes_
: array of shape = [n_classes] or a list of such arrays The classes labels (single output problem), or a list of arrays of class labels (multioutput problem).
n_classes_
: int or list The number of classes (single output problem), or a list containing the number of classes for each output (multioutput problem).
feature_importances_
: array of shape = [n_features] The feature importances (the higher, the more important the feature).
n_features_
: int The number of features when
fit
is performed. n_outputs_
: int The number of outputs when
fit
is performed. oob_score_
: float Score of the training dataset obtained using an outofbag estimate.
oob_decision_function_
: array of shape = [n_samples, n_classes] Decision function computed with outofbag estimate on the training set. If n_estimators is small it might be possible that a data point was never left out during the bootstrap. In this case, oob_decision_function_ might contain NaN.
References
[1] P. Geurts, D. Ernst., and L. Wehenkel, “Extremely randomized trees”, Machine Learning, 63(1), 342, 2006. See also
sklearn.tree.ExtraTreeClassifier : Base classifier for this ensemble. RandomForestClassifier : Ensemble Classifier based on trees with optimal
splits.Full API documentation: ExtraTreesClassifierScikitsLearnNode

class
mdp.nodes.
GridSearchCVScikitsLearnNode
¶ Exhaustive search over specified parameter values for an estimator.
This node has been automatically generated by wrapping the
sklearn.grid_search.GridSearchCV
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Important members are fit, predict.
GridSearchCV implements a “fit” and a “score” method. It also implements “predict”, “predict_proba”, “decision_function”, “transform” and “inverse_transform” if they are implemented in the estimator used.
The parameters of the estimator used to apply these methods are optimized by crossvalidated gridsearch over a parameter grid.
Read more in the User Guide.
Parameters
 estimator : estimator object.
 A object of that type is instantiated for each grid point.
This is assumed to implement the scikitlearn estimator interface.
Either estimator needs to provide a
score
function, orscoring
must be passed.  param_grid : dict or list of dictionaries
 Dictionary with parameters names (string) as keys and lists of parameter settings to try as values, or a list of such dictionaries, in which case the grids spanned by each dictionary in the list are explored. This enables searching over any sequence of parameter settings.
 scoring : string, callable or None, default=None
 A string (see model evaluation documentation) or
a scorer callable object / function with signature
scorer(estimator, X, y)
. IfNone
, thescore
method of the estimator is used.  fit_params : dict, optional
 Parameters to pass to the fit method.
 n_jobs : int, default=1
Number of jobs to run in parallel.
Changed in version 0.17: Upgraded to joblib 0.9.3.
 pre_dispatch : int, or string, optional
Controls the number of jobs that get dispatched during parallel execution. Reducing this number can be useful to avoid an explosion of memory consumption when more jobs get dispatched than CPUs can process. This parameter can be:
 None, in which case all the jobs are immediately created and spawned. Use this for lightweight and fastrunning jobs, to avoid delays due to ondemand spawning of the jobs
 An int, giving the exact number of total jobs that are spawned
 A string, giving an expression as a function of n_jobs, as in ‘2*n_jobs’
 iid : boolean, default=True
 If True, the data is assumed to be identically distributed across the folds, and the loss minimized is the total loss per sample, and not the mean loss across the folds.
 cv : int, crossvalidation generator or an iterable, optional
Determines the crossvalidation splitting strategy. Possible inputs for cv are:
 None, to use the default 3fold crossvalidation,
 integer, to specify the number of folds.
 An object to be used as a crossvalidation generator.
 An iterable yielding train/test splits.
For integer/None inputs, if
y
is binary or multiclass,StratifiedKFold
used. If the estimator is a classifier or ify
is neither binary nor multiclass,KFold
is used.Refer User Guide for the various crossvalidation strategies that can be used here.
 refit : boolean, default=True
 Refit the best estimator with the entire dataset. If “False”, it is impossible to make predictions using this GridSearchCV instance after fitting.
 verbose : integer
 Controls the verbosity: the higher, the more messages.
 error_score : ‘raise’ (default) or numeric
 Value to assign to the score if an error occurs in estimator fitting. If set to ‘raise’, the error is raised. If a numeric value is given, FitFailedWarning is raised. This parameter does not affect the refit step, which will always raise the error.
Examples
>>> from sklearn import svm, grid_search, datasets >>> iris = datasets.load_iris() >>> parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]} >>> svr = svm.SVC() >>> clf = grid_search.GridSearchCV(svr, parameters) >>> clf.fit(iris.data, iris.target) ... GridSearchCV(cv=None, error_score=..., estimator=SVC(C=1.0, cache_size=..., class_weight=..., coef0=..., decision_function_shape=None, degree=..., gamma=..., kernel='rbf', max_iter=1, probability=False, random_state=None, shrinking=True, tol=..., verbose=False), fit_params={}, iid=..., n_jobs=1, param_grid=..., pre_dispatch=..., refit=..., scoring=..., verbose=...)
Attributes
grid_scores_
: list of named tuplesContains scores for all parameter combinations in param_grid. Each entry corresponds to one parameter setting. Each named tuple has the attributes:
parameters
, a dict of parameter settingsmean_validation_score
, the mean score over the crossvalidation foldscv_validation_scores
, the list of scores for each fold
best_estimator_
: estimator Estimator that was chosen by the search, i.e. estimator which gave highest score (or smallest loss if specified) on the left out data. Not available if refit=False.
best_score_
: float Score of best_estimator on the left out data.
best_params_
: dict Parameter setting that gave the best results on the hold out data.
scorer_
: function Scorer function used on the held out data to choose the best parameters for the model.
Notes
The parameters selected are those that maximize the score of the left out data, unless an explicit score is passed in which case it is used instead.
If n_jobs was set to a value higher than one, the data is copied for each point in the grid (and not n_jobs times). This is done for efficiency reasons if individual jobs take very little time, but may raise errors if the dataset is large and not enough memory is available. A workaround in this case is to set pre_dispatch. Then, the memory is copied only pre_dispatch many times. A reasonable value for pre_dispatch is 2 * n_jobs.
See Also
ParameterGrid
: generates all the combinations of a an hyperparameter grid.
sklearn.cross_validation.train_test_split()
: utility function to split the data into a development set usable
 for fitting a GridSearchCV instance and an evaluation set for
 its final evaluation.
sklearn.metrics.make_scorer()
: Make a scorer from a performance metric or loss function.
Full API documentation: GridSearchCVScikitsLearnNode

class
mdp.nodes.
LassoCVScikitsLearnNode
¶ Lasso linear model with iterative fitting along a regularization path
This node has been automatically generated by wrapping the
sklearn.linear_model.coordinate_descent.LassoCV
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.The best model is selected by crossvalidation.
The optimization objective for Lasso is:
(1 / (2 * n_samples)) * y  Xw^2_2 + alpha * w_1
Read more in the User Guide.
Parameters
 eps : float, optional
 Length of the path.
eps=1e3
means thatalpha_min / alpha_max = 1e3
.  n_alphas : int, optional
 Number of alphas along the regularization path
 alphas : numpy array, optional
 List of alphas where to compute the models.
If
None
alphas are set automatically  precompute : True  False  ‘auto’  arraylike
 Whether to use a precomputed Gram matrix to speed up
calculations. If set to
'auto'
let us decide. The Gram matrix can also be passed as argument.  max_iter : int, optional
 The maximum number of iterations
 tol : float, optional
 The tolerance for the optimization: if the updates are
smaller than
tol
, the optimization code checks the dual gap for optimality and continues until it is smaller thantol
.  cv : int, crossvalidation generator or an iterable, optional
Determines the crossvalidation splitting strategy. Possible inputs for cv are:
 None, to use the default 3fold crossvalidation,
 integer, to specify the number of folds.
 An object to be used as a crossvalidation generator.
 An iterable yielding train/test splits.
For integer/None inputs,
KFold
is used.Refer User Guide for the various crossvalidation strategies that can be used here.
 verbose : bool or integer
 Amount of verbosity.
 n_jobs : integer, optional
 Number of CPUs to use during the cross validation. If
1
, use all the CPUs.  positive : bool, optional
 If positive, restrict regression coefficients to be positive
 selection : str, default ‘cyclic’
 If set to ‘random’, a random coefficient is updated every iteration rather than looping over features sequentially by default. This (setting to ‘random’) often leads to significantly faster convergence especially when tol is higher than 1e4.
 random_state : int, RandomState instance, or None (default)
 The seed of the pseudo random number generator that selects a random feature to update. Useful only when selection is set to ‘random’.
 fit_intercept : boolean, default True
 whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered).
 normalize : boolean, optional, default False
 If
True
, the regressors X will be normalized before regression.  copy_X : boolean, optional, default True
 If
True
, X will be copied; else, it may be overwritten.
Attributes
alpha_
: float The amount of penalization chosen by cross validation
coef_
: array, shape (n_features,)  (n_targets, n_features) parameter vector (w in the cost function formula)
intercept_
: float  array, shape (n_targets,) independent term in decision function.
mse_path_
: array, shape (n_alphas, n_folds) mean square error for the test set on each fold, varying alpha
alphas_
: numpy array, shape (n_alphas,) The grid of alphas used for fitting
dual_gap_
: ndarray, shape () The dual gap at the end of the optimization for the optimal alpha
(
alpha_
). n_iter_
: int number of iterations run by the coordinate descent solver to reach the specified tolerance for the optimal alpha.
Notes
See examples/linear_model/lasso_path_with_crossvalidation.py for an example.
To avoid unnecessary memory duplication the X argument of the fit method should be directly passed as a Fortrancontiguous numpy array.
See also
lars_path lasso_path LassoLars Lasso LassoLarsCV
Full API documentation: LassoCVScikitsLearnNode

class
mdp.nodes.
OneClassSVMScikitsLearnNode
¶ Unsupervised Outlier Detection.
This node has been automatically generated by wrapping the
sklearn.svm.classes.OneClassSVM
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Estimate the support of a highdimensional distribution.
The implementation is based on libsvm.
Read more in the User Guide.
Parameters
 kernel : string, optional (default=’rbf’)
 Specifies the kernel type to be used in the algorithm. It must be one of ‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’ or a callable. If none is given, ‘rbf’ will be used. If a callable is given it is used to precompute the kernel matrix.
 nu : float, optional
 An upper bound on the fraction of training errors and a lower bound of the fraction of support vectors. Should be in the interval (0, 1]. By default 0.5 will be taken.
 degree : int, optional (default=3)
 Degree of the polynomial kernel function (‘poly’). Ignored by all other kernels.
 gamma : float, optional (default=’auto’)
 Kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’. If gamma is ‘auto’ then 1/n_features will be used instead.
 coef0 : float, optional (default=0.0)
 Independent term in kernel function. It is only significant in ‘poly’ and ‘sigmoid’.
 tol : float, optional
 Tolerance for stopping criterion.
 shrinking : boolean, optional
 Whether to use the shrinking heuristic.
 cache_size : float, optional
 Specify the size of the kernel cache (in MB).
 verbose : bool, default: False
 Enable verbose output. Note that this setting takes advantage of a perprocess runtime setting in libsvm that, if enabled, may not work properly in a multithreaded context.
 max_iter : int, optional (default=1)
 Hard limit on iterations within solver, or 1 for no limit.
 random_state : int seed, RandomState instance, or None (default)
 The seed of the pseudo random number generator to use when shuffling the data for probability estimation.
Attributes
support_
: arraylike, shape = [n_SV] Indices of support vectors.
support_vectors_
: arraylike, shape = [nSV, n_features] Support vectors.
dual_coef_
: array, shape = [n_classes1, n_SV] Coefficients of the support vectors in the decision function.
coef_
: array, shape = [n_classes1, n_features]Weights assigned to the features (coefficients in the primal problem). This is only available in the case of a linear kernel.
coef_ is readonly property derived from dual_coef_ and support_vectors_
intercept_
: array, shape = [n_classes1] Constants in decision function.
Full API documentation: OneClassSVMScikitsLearnNode

class
mdp.nodes.
RidgeCVScikitsLearnNode
¶ Ridge regression with builtin crossvalidation.
This node has been automatically generated by wrapping the
sklearn.linear_model.ridge.RidgeCV
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.By default, it performs Generalized CrossValidation, which is a form of efficient LeaveOneOut crossvalidation.
Read more in the User Guide.
Parameters
 alphas : numpy array of shape [n_alphas]
 Array of alpha values to try.
Small positive values of alpha improve the conditioning of the
problem and reduce the variance of the estimates.
Alpha corresponds to
C^1
in other linear models such as LogisticRegression or LinearSVC.  fit_intercept : boolean
 Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered).
 normalize : boolean, optional, default False
 If True, the regressors X will be normalized before regression.
 scoring : string, callable or None, optional, default: None
 A string (see model evaluation documentation) or
a scorer callable object / function with signature
scorer(estimator, X, y)
.  cv : int, crossvalidation generator or an iterable, optional
Determines the crossvalidation splitting strategy. Possible inputs for cv are:
 None, to use the efficient LeaveOneOut crossvalidation
 integer, to specify the number of folds.
 An object to be used as a crossvalidation generator.
 An iterable yielding train/test splits.
For integer/None inputs, if
y
is binary or multiclass,StratifiedKFold
used, else,KFold
is used.Refer User Guide for the various crossvalidation strategies that can be used here.
 gcv_mode : {None, ‘auto’, ‘svd’, eigen’}, optional
Flag indicating which strategy to use when performing Generalized CrossValidation. Options are:
'auto' : use svd if n_samples > n_features or when X is a sparse matrix, otherwise use eigen 'svd' : force computation via singular value decomposition of X (does not work for sparse matrices) 'eigen' : force computation via eigendecomposition of X^T X
The ‘auto’ mode is the default and is intended to pick the cheaper option of the two depending upon the shape and format of the training data.
 store_cv_values : boolean, default=False
 Flag indicating if the crossvalidation values corresponding to each alpha should be stored in the cv_values_ attribute (see below). This flag is only compatible with cv=None (i.e. using Generalized CrossValidation).
Attributes
cv_values_
: array, shape = [n_samples, n_alphas] or shape = [n_samples, n_targets, n_alphas], optional Crossvalidation values for each alpha (if store_cv_values=True and cv=None). After fit() has been called, this attribute will contain the mean squared errors (by default) or the values of the {loss,score}_func function (if provided in the constructor).
coef_
: array, shape = [n_features] or [n_targets, n_features] Weight vector(s).
intercept_
: float  array, shape = (n_targets,) Independent term in decision function. Set to 0.0 if
fit_intercept = False
. alpha_
: float Estimated regularization parameter.
See also
Ridge: Ridge regression RidgeClassifier: Ridge classifier RidgeClassifierCV: Ridge classifier with builtin cross validation
Full API documentation: RidgeCVScikitsLearnNode

class
mdp.nodes.
LinearDiscriminantAnalysisScikitsLearnNode
¶ Linear Discriminant Analysis
This node has been automatically generated by wrapping the
sklearn.discriminant_analysis.LinearDiscriminantAnalysis
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.A classifier with a linear decision boundary, generated by fitting class conditional densities to the data and using Bayes’ rule.
The model fits a Gaussian density to each class, assuming that all classes share the same covariance matrix.
The fitted model can also be used to reduce the dimensionality of the input by projecting it to the most discriminative directions.
New in version 0.17: LinearDiscriminantAnalysis.
Changed in version 0.17: Deprecated
lda.LDA
have been moved to LinearDiscriminantAnalysis.Parameters
 solver : string, optional
Solver to use, possible values:
 ‘svd’: Singular value decomposition (default). Does not compute the
 covariance matrix, therefore this solver is recommended for
 data with a large number of features.
 ‘lsqr’: Least squares solution, can be combined with shrinkage.
 ‘eigen’: Eigenvalue decomposition, can be combined with shrinkage.
 shrinkage : string or float, optional
Shrinkage parameter, possible values:
 None: no shrinkage (default).
 ‘auto’: automatic shrinkage using the LedoitWolf lemma.
 float between 0 and 1: fixed shrinkage parameter.
Note that shrinkage works only with ‘lsqr’ and ‘eigen’ solvers.
 priors : array, optional, shape (n_classes,)
 Class priors.
 n_components : int, optional
 Number of components (< n_classes  1) for dimensionality reduction.
 store_covariance : bool, optional
Additionally compute class covariance matrix (default False).
New in version 0.17.
 tol : float, optional
Threshold used for rank estimation in SVD solver.
New in version 0.17.
Attributes
coef_
: array, shape (n_features,) or (n_classes, n_features) Weight vector(s).
intercept_
: array, shape (n_features,) Intercept term.
covariance_
: arraylike, shape (n_features, n_features) Covariance matrix (shared by all classes).
explained_variance_ratio_
: array, shape (n_components,) Percentage of variance explained by each of the selected components.
If
n_components
is not set then all components are stored and the sum of explained variances is equal to 1.0. Only available when eigen solver is used. means_
: arraylike, shape (n_classes, n_features) Class means.
priors_
: arraylike, shape (n_classes,) Class priors (sum to 1).
scalings_
: arraylike, shape (rank, n_classes  1) Scaling of the features in the space spanned by the class centroids.
xbar_
: arraylike, shape (n_features,) Overall mean.
classes_
: arraylike, shape (n_classes,) Unique class labels.
See also
 sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis: Quadratic
 Discriminant Analysis
Notes
The default solver is ‘svd’. It can perform both classification and transform, and it does not rely on the calculation of the covariance matrix. This can be an advantage in situations where the number of features is large. However, the ‘svd’ solver cannot be used with shrinkage.
The ‘lsqr’ solver is an efficient algorithm that only works for classification. It supports shrinkage.
The ‘eigen’ solver is based on the optimization of the between class scatter to within class scatter ratio. It can be used for both classification and transform, and it supports shrinkage. However, the ‘eigen’ solver needs to compute the covariance matrix, so it might not be suitable for situations with a high number of features.
Examples
>>> import numpy as np >>> from sklearn.discriminant_analysis import LinearDiscriminantAnalysis >>> X = np.array([[1, 1], [2, 1], [3, 2], [1, 1], [2, 1], [3, 2]]) >>> y = np.array([1, 1, 1, 2, 2, 2]) >>> clf = LinearDiscriminantAnalysis() >>> clf.fit(X, y) LinearDiscriminantAnalysis(n_components=None, priors=None, shrinkage=None, solver='svd', store_covariance=False, tol=0.0001) >>> print(clf.predict([[0.8, 1]])) [1]
Full API documentation: LinearDiscriminantAnalysisScikitsLearnNode

class
mdp.nodes.
PriorProbabilityEstimatorScikitsLearnNode
¶ An estimator predicting the probability of each
This node has been automatically generated by wrapping the
sklearn.ensemble.gradient_boosting.PriorProbabilityEstimator
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Full API documentation: PriorProbabilityEstimatorScikitsLearnNode

class
mdp.nodes.
ARDRegressionScikitsLearnNode
¶ Bayesian ARD regression.
This node has been automatically generated by wrapping the
sklearn.linear_model.bayes.ARDRegression
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Fit the weights of a regression model, using an ARD prior. The weights of the regression model are assumed to be in Gaussian distributions. Also estimate the parameters lambda (precisions of the distributions of the weights) and alpha (precision of the distribution of the noise). The estimation is done by an iterative procedures (Evidence Maximization)
Read more in the User Guide.
Parameters
 n_iter : int, optional
 Maximum number of iterations. Default is 300
 tol : float, optional
 Stop the algorithm if w has converged. Default is 1.e3.
 alpha_1 : float, optional
 Hyperparameter : shape parameter for the Gamma distribution prior over the alpha parameter. Default is 1.e6.
 alpha_2 : float, optional
 Hyperparameter : inverse scale parameter (rate parameter) for the Gamma distribution prior over the alpha parameter. Default is 1.e6.
 lambda_1 : float, optional
 Hyperparameter : shape parameter for the Gamma distribution prior over the lambda parameter. Default is 1.e6.
 lambda_2 : float, optional
 Hyperparameter : inverse scale parameter (rate parameter) for the Gamma distribution prior over the lambda parameter. Default is 1.e6.
 compute_score : boolean, optional
 If True, compute the objective function at each step of the model. Default is False.
 threshold_lambda : float, optional
 threshold for removing (pruning) weights with high precision from the computation. Default is 1.e+4.
 fit_intercept : boolean, optional
 whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered). Default is True.
 normalize : boolean, optional, default False
 If True, the regressors X will be normalized before regression.
 copy_X : boolean, optional, default True.
 If True, X will be copied; else, it may be overwritten.
 verbose : boolean, optional, default False
 Verbose mode when fitting the model.
Attributes
coef_
: array, shape = (n_features) Coefficients of the regression model (mean of distribution)
alpha_
: float estimated precision of the noise.
lambda_
: array, shape = (n_features) estimated precisions of the weights.
sigma_
: array, shape = (n_features, n_features) estimated variancecovariance matrix of the weights
scores_
: float if computed, value of the objective function (to be maximized)
Examples
>>> from sklearn import linear_model >>> clf = linear_model.ARDRegression() >>> clf.fit([[0,0], [1, 1], [2, 2]], [0, 1, 2]) ... ARDRegression(alpha_1=1e06, alpha_2=1e06, compute_score=False, copy_X=True, fit_intercept=True, lambda_1=1e06, lambda_2=1e06, n_iter=300, normalize=False, threshold_lambda=10000.0, tol=0.001, verbose=False) >>> clf.predict([[1, 1]]) array([ 1.])
Notes
See examples/linear_model/plot_ard.py for an example.
Full API documentation: ARDRegressionScikitsLearnNode

class
mdp.nodes.
ImputerScikitsLearnNode
¶ Imputation transformer for completing missing values.
This node has been automatically generated by wrapping the
sklearn.preprocessing.imputation.Imputer
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Read more in the User Guide.
Parameters
 missing_values : integer or “NaN”, optional (default=”NaN”)
 The placeholder for the missing values. All occurrences of missing_values will be imputed. For missing values encoded as np.nan, use the string value “NaN”.
 strategy : string, optional (default=”mean”)
The imputation strategy.
 If “mean”, then replace missing values using the mean along the axis.
 If “median”, then replace missing values using the median along the axis.
 If “most_frequent”, then replace missing using the most frequent value along the axis.
 axis : integer, optional (default=0)
The axis along which to impute.
 If axis=0, then impute along columns.
 If axis=1, then impute along rows.
 verbose : integer, optional (default=0)
 Controls the verbosity of the imputer.
 copy : boolean, optional (default=True)
If True, a copy of X will be created. If False, imputation will be done inplace whenever possible. Note that, in the following cases, a new copy will always be made, even if copy=False:
 If X is not an array of floating values;
 If X is sparse and missing_values=0;
 If axis=0 and X is encoded as a CSR matrix;
 If axis=1 and X is encoded as a CSC matrix.
Attributes
statistics_
: array of shape (n_features,) The imputation fill value for each feature if axis == 0.
Notes
 When
axis=0
, columns which only contained missing values at fit are discarded upon transform.  When
axis=1
, an exception is raised if there are rows for which it is not possible to fill in the missing values (e.g., because they only contain missing values).
Full API documentation: ImputerScikitsLearnNode

class
mdp.nodes.
VarianceThresholdScikitsLearnNode
¶ Feature selector that removes all lowvariance features.
This node has been automatically generated by wrapping the
sklearn.feature_selection.variance_threshold.VarianceThreshold
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.This feature selection algorithm looks only at the features (X), not the desired outputs (y), and can thus be used for unsupervised learning.
Read more in the User Guide.
Parameters
 threshold : float, optional
 Features with a trainingset variance lower than this threshold will be removed. The default is to keep all features with nonzero variance, i.e. remove the features that have the same value in all samples.
Attributes
variances_
: array, shape (n_features,) Variances of individual features.
Examples
The following dataset has integer features, two of which are the same in every sample. These are removed with the default setting for threshold:
>>> X = [[0, 2, 0, 3], [0, 1, 4, 3], [0, 1, 1, 3]] >>> selector = VarianceThreshold() >>> selector.fit_transform(X) array([[2, 0], [1, 4], [1, 1]])
Full API documentation: VarianceThresholdScikitsLearnNode

class
mdp.nodes.
GradientBoostingRegressorScikitsLearnNode
¶ Gradient Boosting for regression.
This node has been automatically generated by wrapping the
sklearn.ensemble.gradient_boosting.GradientBoostingRegressor
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.GB builds an additive model in a forward stagewise fashion; it allows for the optimization of arbitrary differentiable loss functions. In each stage a regression tree is fit on the negative gradient of the given loss function.
Read more in the User Guide.
Parameters
 loss : {‘ls’, ‘lad’, ‘huber’, ‘quantile’}, optional (default=’ls’)
 loss function to be optimized. ‘ls’ refers to least squares regression. ‘lad’ (least absolute deviation) is a highly robust loss function solely based on order information of the input variables. ‘huber’ is a combination of the two. ‘quantile’ allows quantile regression (use alpha to specify the quantile).
 learning_rate : float, optional (default=0.1)
 learning rate shrinks the contribution of each tree by learning_rate. There is a tradeoff between learning_rate and n_estimators.
 n_estimators : int (default=100)
 The number of boosting stages to perform. Gradient boosting is fairly robust to overfitting so a large number usually results in better performance.
 max_depth : integer, optional (default=3)
 maximum depth of the individual regression estimators. The maximum
depth limits the number of nodes in the tree. Tune this parameter
for best performance; the best value depends on the interaction
of the input variables.
Ignored if
max_leaf_nodes
is not None.  min_samples_split : integer, optional (default=2)
 The minimum number of samples required to split an internal node.
 min_samples_leaf : integer, optional (default=1)
 The minimum number of samples required to be at a leaf node.
 min_weight_fraction_leaf : float, optional (default=0.)
 The minimum weighted fraction of the input samples required to be at a leaf node.
 subsample : float, optional (default=1.0)
 The fraction of samples to be used for fitting the individual base learners. If smaller than 1.0 this results in Stochastic Gradient Boosting. subsample interacts with the parameter n_estimators. Choosing subsample < 1.0 leads to a reduction of variance and an increase in bias.
 max_features : int, float, string or None, optional (default=None)
The number of features to consider when looking for the best split:
 If int, then consider max_features features at each split.
 If float, then max_features is a percentage and
 int(max_features * n_features) features are considered at each
 split.
 If “auto”, then max_features=n_features.
 If “sqrt”, then max_features=sqrt(n_features).
 If “log2”, then max_features=log2(n_features).
 If None, then max_features=n_features.
Choosing max_features < n_features leads to a reduction of variance and an increase in bias.
Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than
max_features
features. max_leaf_nodes : int or None, optional (default=None)
 Grow trees with
max_leaf_nodes
in bestfirst fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes.  alpha : float (default=0.9)
 The alphaquantile of the huber loss function and the quantile
loss function. Only if
loss='huber'
orloss='quantile'
.  init : BaseEstimator, None, optional (default=None)
 An estimator object that is used to compute the initial
predictions.
init
has to providefit
andpredict
. If None it usesloss.init_estimator
.  verbose : int, default: 0
 Enable verbose output. If 1 then it prints progress and performance once in a while (the more trees the lower the frequency). If greater than 1 then it prints progress and performance for every tree.
 warm_start : bool, default: False
 When set to
True
, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just erase the previous solution.  random_state : int, RandomState instance or None, optional (default=None)
 If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.
 presort : bool or ‘auto’, optional (default=’auto’)
Whether to presort the data to speed up the finding of best splits in fitting. Auto mode by default will use presorting on dense data and default to normal sorting on sparse data. Setting presort to true on sparse data will raise an error.
New in version 0.17: optional parameter presort.
Attributes
feature_importances_
: array, shape = [n_features] The feature importances (the higher, the more important the feature).
oob_improvement_
: array, shape = [n_estimators] The improvement in loss (= deviance) on the outofbag samples
relative to the previous iteration.
oob_improvement_[0]
is the improvement in loss of the first stage over theinit
estimator. train_score_
: array, shape = [n_estimators] The ith score
train_score_[i]
is the deviance (= loss) of the model at iterationi
on the inbag sample. Ifsubsample == 1
this is the deviance on the training data. loss_
: LossFunction The concrete
LossFunction
object.  init : BaseEstimator
 The estimator that provides the initial predictions.
Set via the
init
argument orloss.init_estimator
. estimators_
: ndarray of DecisionTreeRegressor, shape = [n_estimators, 1] The collection of fitted subestimators.
See also
DecisionTreeRegressor, RandomForestRegressor
References
J. Friedman, Greedy Function Approximation: A Gradient Boosting Machine, The Annals of Statistics, Vol. 29, No. 5, 2001.
 Friedman, Stochastic Gradient Boosting, 1999
T. Hastie, R. Tibshirani and J. Friedman. Elements of Statistical Learning Ed. 2, Springer, 2009.
Full API documentation: GradientBoostingRegressorScikitsLearnNode

class
mdp.nodes.
OrthogonalMatchingPursuitScikitsLearnNode
¶ Orthogonal Matching Pursuit model (OMP)
This node has been automatically generated by wrapping the
sklearn.linear_model.omp.OrthogonalMatchingPursuit
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Parameters
 n_nonzero_coefs : int, optional
 Desired number of nonzero entries in the solution. If None (by default) this value is set to 10% of n_features.
 tol : float, optional
 Maximum norm of the residual. If not None, overrides n_nonzero_coefs.
 fit_intercept : boolean, optional
 whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered).
 normalize : boolean, optional
 If False, the regressors X are assumed to be already normalized.
 precompute : {True, False, ‘auto’}, default ‘auto’
 Whether to use a precomputed Gram and Xy matrix to speed up calculations. Improves performance when n_targets or n_samples is very large. Note that if you already have such matrices, you can pass them directly to the fit method.
Read more in the User Guide.
Attributes
coef_
: array, shape (n_features,) or (n_features, n_targets) parameter vector (w in the formula)
intercept_
: float or array, shape (n_targets,) independent term in decision function.
n_iter_
: int or arraylike Number of active features across every target.
Notes
Orthogonal matching pursuit was introduced in G. Mallat, Z. Zhang, Matching pursuits with timefrequency dictionaries, IEEE Transactions on Signal Processing, Vol. 41, No. 12. (December 1993), pp. 33973415. (http://blanche.polytechnique.fr/~mallat/papiers/MallatPursuit93.pdf)
This implementation is based on Rubinstein, R., Zibulevsky, M. and Elad, M., Efficient Implementation of the KSVD Algorithm using Batch Orthogonal Matching Pursuit Technical Report  CS Technion, April 2008. http://www.cs.technion.ac.il/~ronrubin/Publications/KSVDOMPv2.pdf
See also
orthogonal_mp orthogonal_mp_gram lars_path Lars LassoLars decomposition.sparse_encode
Full API documentation: OrthogonalMatchingPursuitScikitsLearnNode

class
mdp.nodes.
PLSCanonicalScikitsLearnNode
¶ PLSCanonical implements the 2 blocks canonical PLS of the original Wold algorithm [Tenenhaus 1998] p.204, referred as PLSC2A in [Wegelin 2000].
This node has been automatically generated by wrapping the
sklearn.cross_decomposition.pls_.PLSCanonical
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.This class inherits from PLS with mode=”A” and deflation_mode=”canonical”, norm_y_weights=True and algorithm=”nipals”, but svd should provide similar results up to numerical errors.
Read more in the User Guide.
Parameters
scale : boolean, scale data? (default True)
 algorithm : string, “nipals” or “svd”
 The algorithm used to estimate the weights. It will be called n_components times, i.e. once for each iteration of the outer loop.
 max_iter : an integer, (default 500)
 the maximum number of iterations of the NIPALS inner loop (used only if algorithm=”nipals”)
 tol : nonnegative real, default 1e06
 the tolerance used in the iterative algorithm
 copy : boolean, default True
 Whether the deflation should be done on a copy. Let the default value to True unless you don’t care about side effect
n_components : int, number of components to keep. (default 2).
Attributes
x_weights_
: array, shape = [p, n_components] X block weights vectors.
y_weights_
: array, shape = [q, n_components] Y block weights vectors.
x_loadings_
: array, shape = [p, n_components] X block loadings vectors.
y_loadings_
: array, shape = [q, n_components] Y block loadings vectors.
x_scores_
: array, shape = [n_samples, n_components] X scores.
y_scores_
: array, shape = [n_samples, n_components] Y scores.
x_rotations_
: array, shape = [p, n_components] X block to latents rotations.
y_rotations_
: array, shape = [q, n_components] Y block to latents rotations.
n_iter_
: arraylike Number of iterations of the NIPALS inner loop for each component. Not useful if the algorithm provided is “svd”.
Notes
Matrices:
T: ``x_scores_`` U: ``y_scores_`` W: ``x_weights_`` C: ``y_weights_`` P: ``x_loadings_`` Q: ``y_loadings__``
Are computed such that:
X = T P.T + Err and Y = U Q.T + Err T[:, k] = Xk W[:, k] for k in range(n_components) U[:, k] = Yk C[:, k] for k in range(n_components) ``x_rotations_`` = W (P.T W)^(1) ``y_rotations_`` = C (Q.T C)^(1)
where Xk and Yk are residual matrices at iteration k.
Slides explaining PLS <http://www.eigenvector.com/Docs/Wise_pls_properties.pdf>
For each component k, find weights u, v that optimize:
max corr(Xk u, Yk v) * std(Xk u) std(Yk u), such that ``u = v = 1``
Note that it maximizes both the correlations between the scores and the intrablock variances.
The residual matrix of X (Xk+1) block is obtained by the deflation on the current X score: x_score.
The residual matrix of Y (Yk+1) block is obtained by deflation on the current Y score. This performs a canonical symmetric version of the PLS regression. But slightly different than the CCA. This is mostly used for modeling.
This implementation provides the same results that the “plspm” package provided in the R language (Rproject), using the function plsca(X, Y). Results are equal or collinear with the function
pls(..., mode = "canonical")
of the “mixOmics” package. The difference relies in the fact that mixOmics implementation does not exactly implement the Wold algorithm since it does not normalize y_weights to one.Examples
>>> from sklearn.cross_decomposition import PLSCanonical >>> X = [[0., 0., 1.], [1.,0.,0.], [2.,2.,2.], [2.,5.,4.]] >>> Y = [[0.1, 0.2], [0.9, 1.1], [6.2, 5.9], [11.9, 12.3]] >>> plsca = PLSCanonical(n_components=2) >>> plsca.fit(X, Y) ... PLSCanonical(algorithm='nipals', copy=True, max_iter=500, n_components=2, scale=True, tol=1e06) >>> X_c, Y_c = plsca.transform(X, Y)
References
Jacob A. Wegelin. A survey of Partial Least Squares (PLS) methods, with emphasis on the twoblock case. Technical Report 371, Department of Statistics, University of Washington, Seattle, 2000.
Tenenhaus, M. (1998). La regression PLS: theorie et pratique. Paris:
Editions Technic.
See also
CCA PLSSVD
Full API documentation: PLSCanonicalScikitsLearnNode

class
mdp.nodes.
FeatureAgglomerationScikitsLearnNode
¶ Agglomerate features.
This node has been automatically generated by wrapping the
sklearn.cluster.hierarchical.FeatureAgglomeration
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Similar to AgglomerativeClustering, but recursively merges features instead of samples.
Read more in the User Guide.
Parameters
 n_clusters : int, default 2
 The number of clusters to find.
 connectivity : arraylike or callable, optional
 Connectivity matrix. Defines for each feature the neighboring features following a given structure of the data. This can be a connectivity matrix itself or a callable that transforms the data into a connectivity matrix, such as derived from kneighbors_graph. Default is None, i.e, the hierarchical clustering algorithm is unstructured.
 affinity : string or callable, default “euclidean”
 Metric used to compute the linkage. Can be “euclidean”, “l1”, “l2”, “manhattan”, “cosine”, or ‘precomputed’. If linkage is “ward”, only “euclidean” is accepted.
 memory : Instance of joblib.Memory or string, optional
 Used to cache the output of the computation of the tree. By default, no caching is done. If a string is given, it is the path to the caching directory.
 n_components : int (optional)
 Number of connected components. If None the number of connected components is estimated from the connectivity matrix. NOTE: This parameter is now directly determined from the connectivity matrix and will be removed in 0.18
 compute_full_tree : bool or ‘auto’, optional, default “auto”
 Stop early the construction of the tree at n_clusters. This is useful to decrease computation time if the number of clusters is not small compared to the number of features. This option is useful only when specifying a connectivity matrix. Note also that when varying the number of clusters and using caching, it may be advantageous to compute the full tree.
 linkage : {“ward”, “complete”, “average”}, optional, default “ward”
Which linkage criterion to use. The linkage criterion determines which distance to use between sets of features. The algorithm will merge the pairs of cluster that minimize this criterion.
 ward minimizes the variance of the clusters being merged.
 average uses the average of the distances of each feature of the two sets.
 complete or maximum linkage uses the maximum distances between all features of the two sets.
 pooling_func : callable, default np.mean
 This combines the values of agglomerated features into a single value, and should accept an array of shape [M, N] and the keyword argument axis=1, and reduce it to an array of size [M].
Attributes
labels_
: arraylike, (n_features,) cluster labels for each feature.
n_leaves_
: int Number of leaves in the hierarchical tree.
n_components_
: int The estimated number of connected components in the graph.
children_
: arraylike, shape (n_nodes1, 2) The children of each nonleaf node. Values less than n_features correspond to leaves of the tree which are the original samples. A node i greater than or equal to n_features is a nonleaf node and has children children_[i  n_features]. Alternatively at the ith iteration, children[i][0] and children[i][1] are merged to form node n_features + i
Full API documentation: FeatureAgglomerationScikitsLearnNode

class
mdp.nodes.
SelectPercentileScikitsLearnNode
¶ Select features according to a percentile of the highest scores.
This node has been automatically generated by wrapping the
sklearn.feature_selection.univariate_selection.SelectPercentile
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Read more in the User Guide.
Parameters
 score_func : callable
 Function taking two arrays X and y, and returning a pair of arrays (scores, pvalues).
 percentile : int, optional, default=10
 Percent of features to keep.
Attributes
scores_
: arraylike, shape=(n_features,) Scores of features.
pvalues_
: arraylike, shape=(n_features,) pvalues of feature scores.
Notes
Ties between features with equal scores will be broken in an unspecified way.
See also
f_classif: ANOVA Fvalue between labe/feature for classification tasks. chi2: Chisquared stats of nonnegative features for classification tasks. f_regression: Fvalue between label/feature for regression tasks. SelectKBest: Select features based on the k highest scores. SelectFpr: Select features based on a false positive rate test. SelectFdr: Select features based on an estimated false discovery rate. SelectFwe: Select features based on familywise error rate. GenericUnivariateSelect: Univariate feature selector with configurable mode.
Full API documentation: SelectPercentileScikitsLearnNode

class
mdp.nodes.
RandomForestRegressorScikitsLearnNode
¶ A random forest regressor.
This node has been automatically generated by wrapping the
sklearn.ensemble.forest.RandomForestRegressor
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.A random forest is a meta estimator that fits a number of classifying decision trees on various subsamples of the dataset and use averaging to improve the predictive accuracy and control overfitting. The subsample size is always the same as the original input sample size but the samples are drawn with replacement if bootstrap=True (default).
Read more in the User Guide.
Parameters
 n_estimators : integer, optional (default=10)
 The number of trees in the forest.
 criterion : string, optional (default=”mse”)
 The function to measure the quality of a split. The only supported criterion is “mse” for the mean squared error. Note: this parameter is treespecific.
 max_features : int, float, string or None, optional (default=”auto”)
The number of features to consider when looking for the best split:
 If int, then consider max_features features at each split.
 If float, then max_features is a percentage and int(max_features * n_features) features are considered at each split.
 If “auto”, then max_features=n_features.
 If “sqrt”, then max_features=sqrt(n_features).
 If “log2”, then max_features=log2(n_features).
 If None, then max_features=n_features.
Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than
max_features
features. Note: this parameter is treespecific. max_depth : integer or None, optional (default=None)
 The maximum depth of the tree. If None, then nodes are expanded until
all leaves are pure or until all leaves contain less than
min_samples_split samples.
Ignored if
max_leaf_nodes
is not None. Note: this parameter is treespecific.  min_samples_split : integer, optional (default=2)
 The minimum number of samples required to split an internal node. Note: this parameter is treespecific.
 min_samples_leaf : integer, optional (default=1)
 The minimum number of samples in newly created leaves. A split is
discarded if after the split, one of the leaves would contain less then
min_samples_leaf
samples. Note: this parameter is treespecific.  min_weight_fraction_leaf : float, optional (default=0.)
 The minimum weighted fraction of the input samples required to be at a leaf node. Note: this parameter is treespecific.
 max_leaf_nodes : int or None, optional (default=None)
 Grow trees with
max_leaf_nodes
in bestfirst fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes. If not None thenmax_depth
will be ignored. Note: this parameter is treespecific.  bootstrap : boolean, optional (default=True)
 Whether bootstrap samples are used when building trees.
 oob_score : bool
 whether to use outofbag samples to estimate the generalization error.
 n_jobs : integer, optional (default=1)
 The number of jobs to run in parallel for both fit and predict. If 1, then the number of jobs is set to the number of cores.
 random_state : int, RandomState instance or None, optional (default=None)
 If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.
 verbose : int, optional (default=0)
 Controls the verbosity of the tree building process.
 warm_start : bool, optional (default=False)
 When set to
True
, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new forest.
Attributes
estimators_
: list of DecisionTreeRegressor The collection of fitted subestimators.
feature_importances_
: array of shape = [n_features] The feature importances (the higher, the more important the feature).
n_features_
: int The number of features when
fit
is performed. n_outputs_
: int The number of outputs when
fit
is performed. oob_score_
: float Score of the training dataset obtained using an outofbag estimate.
oob_prediction_
: array of shape = [n_samples] Prediction computed with outofbag estimate on the training set.
References
[1]  Breiman, “Random Forests”, Machine Learning, 45(1), 532, 2001.
See also
DecisionTreeRegressor, ExtraTreesRegressor
Full API documentation: RandomForestRegressorScikitsLearnNode

class
mdp.nodes.
MultiTaskLassoCVScikitsLearnNode
¶ Multitask L1/L2 Lasso with builtin crossvalidation.
This node has been automatically generated by wrapping the
sklearn.linear_model.coordinate_descent.MultiTaskLassoCV
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.The optimization objective for MultiTaskLasso is:
(1 / (2 * n_samples)) * Y  XW^Fro_2 + alpha * W_21
Where:
W_21 = \sum_i \sqrt{\sum_j w_{ij}^2}
i.e. the sum of norm of each row.
Read more in the User Guide.
Parameters
 eps : float, optional
 Length of the path.
eps=1e3
means thatalpha_min / alpha_max = 1e3
.  alphas : arraylike, optional
 List of alphas where to compute the models. If not provided, set automaticlly.
 n_alphas : int, optional
 Number of alphas along the regularization path
 fit_intercept : boolean
 whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered).
 normalize : boolean, optional, default False
 If
True
, the regressors X will be normalized before regression.  copy_X : boolean, optional, default True
 If
True
, X will be copied; else, it may be overwritten.  max_iter : int, optional
 The maximum number of iterations.
 tol : float, optional
 The tolerance for the optimization: if the updates are
smaller than
tol
, the optimization code checks the dual gap for optimality and continues until it is smaller thantol
.  cv : int, crossvalidation generator or an iterable, optional
Determines the crossvalidation splitting strategy. Possible inputs for cv are:
 None, to use the default 3fold crossvalidation,
 integer, to specify the number of folds.
 An object to be used as a crossvalidation generator.
 An iterable yielding train/test splits.
For integer/None inputs,
KFold
is used.Refer User Guide for the various crossvalidation strategies that can be used here.
 verbose : bool or integer
 Amount of verbosity.
 n_jobs : integer, optional
 Number of CPUs to use during the cross validation. If
1
, use all the CPUs. Note that this is used only if multiple values for l1_ratio are given.  selection : str, default ‘cyclic’
 If set to ‘random’, a random coefficient is updated every iteration rather than looping over features sequentially by default. This (setting to ‘random’) often leads to significantly faster convergence especially when tol is higher than 1e4.
 random_state : int, RandomState instance, or None (default)
 The seed of the pseudo random number generator that selects a random feature to update. Useful only when selection is set to ‘random’.
Attributes
intercept_
: array, shape (n_tasks,) Independent term in decision function.
coef_
: array, shape (n_tasks, n_features) Parameter vector (W in the cost function formula).
alpha_
: float The amount of penalization chosen by cross validation
mse_path_
: array, shape (n_alphas, n_folds) mean square error for the test set on each fold, varying alpha
alphas_
: numpy array, shape (n_alphas,) The grid of alphas used for fitting.
n_iter_
: int number of iterations run by the coordinate descent solver to reach the specified tolerance for the optimal alpha.
See also
MultiTaskElasticNet ElasticNetCV MultiTaskElasticNetCV
Notes
The algorithm used to fit the model is coordinate descent.
To avoid unnecessary memory duplication the X argument of the fit method should be directly passed as a Fortrancontiguous numpy array.
Full API documentation: MultiTaskLassoCVScikitsLearnNode

class
mdp.nodes.
GaussianNBScikitsLearnNode
¶ Gaussian Naive Bayes (GaussianNB)
This node has been automatically generated by wrapping the
sklearn.naive_bayes.GaussianNB
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Can perform online updates to model parameters via partial_fit method. For details on algorithm used to update feature means and variance online, see Stanford CS tech report STANCS79773 by Chan, Golub, and LeVeque:
Read more in the User Guide.
Attributes
class_prior_
: array, shape (n_classes,) probability of each class.
class_count_
: array, shape (n_classes,) number of training samples observed in each class.
theta_
: array, shape (n_classes, n_features) mean of each feature per class
sigma_
: array, shape (n_classes, n_features) variance of each feature per class
Examples
>>> import numpy as np >>> X = np.array([[1, 1], [2, 1], [3, 2], [1, 1], [2, 1], [3, 2]]) >>> Y = np.array([1, 1, 1, 2, 2, 2]) >>> from sklearn.naive_bayes import GaussianNB >>> clf = GaussianNB() >>> clf.fit(X, Y) GaussianNB() >>> print(clf.predict([[0.8, 1]])) [1] >>> clf_pf = GaussianNB() >>> clf_pf.partial_fit(X, Y, np.unique(Y)) GaussianNB() >>> print(clf_pf.predict([[0.8, 1]])) [1]
Full API documentation: GaussianNBScikitsLearnNode

class
mdp.nodes.
LabelSpreadingScikitsLearnNode
¶ LabelSpreading model for semisupervised learning
This node has been automatically generated by wrapping the
sklearn.semi_supervised.label_propagation.LabelSpreading
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.This model is similar to the basic Label Propgation algorithm, but uses affinity matrix based on the normalized graph Laplacian and soft clamping across the labels.
Read more in the User Guide.
Parameters
 kernel : {‘knn’, ‘rbf’}
 String identifier for kernel function to use. Only ‘rbf’ and ‘knn’ kernels are currently supported.
 gamma : float
 parameter for rbf kernel
 n_neighbors : integer > 0
 parameter for knn kernel
 alpha : float
 clamping factor
 max_iter : float
 maximum number of iterations allowed
 tol : float
 Convergence tolerance: threshold to consider the system at steady state
Attributes
X_
: array, shape = [n_samples, n_features] Input array.
classes_
: array, shape = [n_classes] The distinct labels used in classifying instances.
label_distributions_
: array, shape = [n_samples, n_classes] Categorical distribution for each item.
transduction_
: array, shape = [n_samples] Label assigned to each item via the transduction.
n_iter_
: int Number of iterations run.
Examples
>>> from sklearn import datasets >>> from sklearn.semi_supervised import LabelSpreading >>> label_prop_model = LabelSpreading() >>> iris = datasets.load_iris() >>> random_unlabeled_points = np.where(np.random.random_integers(0, 1, ... size=len(iris.target))) >>> labels = np.copy(iris.target) >>> labels[random_unlabeled_points] = 1 >>> label_prop_model.fit(iris.data, labels) ... LabelSpreading(...)
References
Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal, Jason Weston, Bernhard Schoelkopf. Learning with local and global consistency (2004) http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.115.3219
See Also
LabelPropagation : Unregularized graph based semisupervised learning
Full API documentation: LabelSpreadingScikitsLearnNode

class
mdp.nodes.
OutputCodeClassifierScikitsLearnNode
¶ (ErrorCorrecting) OutputCode multiclass strategy
This node has been automatically generated by wrapping the
sklearn.multiclass.OutputCodeClassifier
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Outputcode based strategies consist in representing each class with a binary code (an array of 0s and 1s). At fitting time, one binary classifier per bit in the code book is fitted. At prediction time, the classifiers are used to project new points in the class space and the class closest to the points is chosen. The main advantage of these strategies is that the number of classifiers used can be controlled by the user, either for compressing the model (0 < code_size < 1) or for making the model more robust to errors (code_size > 1). See the documentation for more details.
Read more in the User Guide.
Parameters
 estimator : estimator object
 An estimator object implementing fit and one of decision_function or predict_proba.
 code_size : float
 Percentage of the number of classes to be used to create the code book. A number between 0 and 1 will require fewer classifiers than onevstherest. A number greater than 1 will require more classifiers than onevstherest.
 random_state : numpy.RandomState, optional
 The generator used to initialize the codebook. Defaults to numpy.random.
 n_jobs : int, optional, default: 1
 The number of jobs to use for the computation. If 1 all CPUs are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. For n_jobs below 1, (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = 2, all CPUs but one are used.
Attributes
estimators_
: list of int(n_classes * code_size) estimators Estimators used for predictions.
classes_
: numpy array of shape [n_classes] Array containing labels.
code_book_
: numpy array of shape [n_classes, code_size] Binary array containing the code of each class.
References
[1] “Solving multiclass learning problems via errorcorrecting output codes”, Dietterich T., Bakiri G., Journal of Artificial Intelligence Research 2, 1995. [2] “The error coding method and PICTs”, James G., Hastie T., Journal of Computational and Graphical statistics 7, 1998. [3] “The Elements of Statistical Learning”, Hastie T., Tibshirani R., Friedman J., page 606 (secondedition) 2008. Full API documentation: OutputCodeClassifierScikitsLearnNode

class
mdp.nodes.
NMFScikitsLearnNode
¶ NonNegative Matrix Factorization (NMF)
This node has been automatically generated by wrapping the
sklearn.decomposition.nmf.NMF
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Find two nonnegative matrices (W, H) whose product approximates the non negative matrix X. This factorization can be used for example for dimensionality reduction, source separation or topic extraction.
The objective function is:
0.5 * X  WH_Fro^2 + alpha * l1_ratio * vec(W)_1 + alpha * l1_ratio * vec(H)_1 + 0.5 * alpha * (1  l1_ratio) * W_Fro^2 + 0.5 * alpha * (1  l1_ratio) * H_Fro^2
Where:
A_Fro^2 = \sum_{i,j} A_{ij}^2 (Frobenius norm) vec(A)_1 = \sum_{i,j} abs(A_{ij}) (Elementwise L1 norm)
The objective function is minimized with an alternating minimization of W and H.
Read more in the User Guide.
Parameters
 n_components : int or None
 Number of components, if n_components is not set all features are kept.
 init : ‘random’  ‘nndsvd’  ‘nndsvda’  ‘nndsvdar’  ‘custom’
Method used to initialize the procedure. Default: ‘nndsvdar’ if n_components < n_features, otherwise random. Valid options:
‘random’: nonnegative random matrices, scaled with:
 sqrt(X.mean() / n_components)
 ‘nndsvd’: Nonnegative Double Singular Value Decomposition (NNDSVD)
initialization (better for sparseness)
 ‘nndsvda’: NNDSVD with zeros filled with the average of X
(better when sparsity is not desired)
 ‘nndsvdar’: NNDSVD with zeros filled with small random values
(generally faster, less accurate alternative to NNDSVDa for when sparsity is not desired)
‘custom’: use custom matrices W and H
 solver : ‘pg’  ‘cd’
Numerical solver to use:
 ‘pg’ is a Projected Gradient solver (deprecated).
 ‘cd’ is a Coordinate Descent solver (recommended).
New in version 0.17: Coordinate Descent solver.
Changed in version 0.17: Deprecated Projected Gradient solver.
 tol : double, default: 1e4
 Tolerance value used in stopping conditions.
 max_iter : integer, default: 200
 Number of iterations to compute.
 random_state : integer seed, RandomState instance, or None (default)
 Random number generator seed control.
 alpha : double, default: 0.
Constant that multiplies the regularization terms. Set it to zero to have no regularization.
New in version 0.17: alpha used in the Coordinate Descent solver.
 l1_ratio : double, default: 0.
The regularization mixing parameter, with 0 <= l1_ratio <= 1. For l1_ratio = 0 the penalty is an elementwise L2 penalty (aka Frobenius Norm). For l1_ratio = 1 it is an elementwise L1 penalty. For 0 < l1_ratio < 1, the penalty is a combination of L1 and L2.
New in version 0.17: Regularization parameter l1_ratio used in the Coordinate Descent solver.
 shuffle : boolean, default: False
If true, randomize the order of coordinates in the CD solver.
New in version 0.17: shuffle parameter used in the Coordinate Descent solver.
 nls_max_iter : integer, default: 2000
Number of iterations in NLS subproblem. Used only in the deprecated ‘pg’ solver.
Changed in version 0.17: Deprecated Projected Gradient solver. Use Coordinate Descent solver instead.
 sparseness : ‘data’  ‘components’  None, default: None
Where to enforce sparsity in the model. Used only in the deprecated ‘pg’ solver.
Changed in version 0.17: Deprecated Projected Gradient solver. Use Coordinate Descent solver instead.
 beta : double, default: 1
Degree of sparseness, if sparseness is not None. Larger values mean more sparseness. Used only in the deprecated ‘pg’ solver.
Changed in version 0.17: Deprecated Projected Gradient solver. Use Coordinate Descent solver instead.
 eta : double, default: 0.1
Degree of correctness to maintain, if sparsity is not None. Smaller values mean larger error. Used only in the deprecated ‘pg’ solver.
Changed in version 0.17: Deprecated Projected Gradient solver. Use Coordinate Descent solver instead.
Attributes
components_
: array, [n_components, n_features] Nonnegative components of the data.
reconstruction_err_
: number Frobenius norm of the matrix difference between
the training data and the reconstructed data from
the fit produced by the model.
 X  WH _2
n_iter_
: int Actual number of iterations.
Examples
>>> import numpy as np >>> X = np.array([[1,1], [2, 1], [3, 1.2], [4, 1], [5, 0.8], [6, 1]]) >>> from sklearn.decomposition import NMF >>> model = NMF(n_components=2, init='random', random_state=0) >>> model.fit(X) NMF(alpha=0.0, beta=1, eta=0.1, init='random', l1_ratio=0.0, max_iter=200, n_components=2, nls_max_iter=2000, random_state=0, shuffle=False, solver='cd', sparseness=None, tol=0.0001, verbose=0)
>>> model.components_ array([[ 2.09783018, 0.30560234], [ 2.13443044, 2.13171694]]) >>> model.reconstruction_err_ 0.00115993...
References
C.J. Lin. Projected gradient methods for nonnegative matrix factorization. Neural Computation, 19(2007), 27562779. http://www.csie.ntu.edu.tw/~cjlin/nmf/
Cichocki, Andrzej, and P. H. A. N. AnhHuy. “Fast local algorithms for large scale nonnegative matrix and tensor factorizations.” IEICE transactions on fundamentals of electronics, communications and computer sciences 92.3: 708721, 2009.
Full API documentation: NMFScikitsLearnNode

class
mdp.nodes.
ScaledLogOddsEstimatorScikitsLearnNode
¶ This node has been automatically generated by wrapping the
sklearn.ensemble.gradient_boosting.ScaledLogOddsEstimator
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Full API documentation: ScaledLogOddsEstimatorScikitsLearnNode

class
mdp.nodes.
MaxAbsScalerScikitsLearnNode
¶ Scale each feature by its maximum absolute value.
This node has been automatically generated by wrapping the
sklearn.preprocessing.data.MaxAbsScaler
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.This estimator scales and translates each feature individually such that the maximal absolute value of each feature in the training set will be 1.0. It does not shift/center the data, and thus does not destroy any sparsity.
This scaler can also be applied to sparse CSR or CSC matrices.
New in version 0.17.
Parameters
 copy : boolean, optional, default is True
 Set to False to perform inplace scaling and avoid a copy (if the input is already a numpy array).
Attributes
scale_
: ndarray, shape (n_features,)Per feature relative scaling of the data.
New in version 0.17: scale_ attribute.
max_abs_
: ndarray, shape (n_features,) Per feature maximum absolute value.
n_samples_seen_
: int The number of samples processed by the estimator. Will be reset on
new calls to fit, but increments across
partial_fit
calls.
Full API documentation: MaxAbsScalerScikitsLearnNode

class
mdp.nodes.
HashingVectorizerScikitsLearnNode
¶ Convert a collection of text documents to a matrix of token occurrences
This node has been automatically generated by wrapping the
sklearn.feature_extraction.text.HashingVectorizer
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.It turns a collection of text documents into a scipy.sparse matrix holding token occurrence counts (or binary occurrence information), possibly normalized as token frequencies if norm=’l1’ or projected on the euclidean unit sphere if norm=’l2’.
This text vectorizer implementation uses the hashing trick to find the token string name to feature integer index mapping.
This strategy has several advantages:
 it is very low memory scalable to large datasets as there is no need to store a vocabulary dictionary in memory
 it is fast to pickle and unpickle as it holds no state besides the constructor parameters
 it can be used in a streaming (partial fit) or parallel pipeline as there is no state computed during fit.
There are also a couple of cons (vs using a CountVectorizer with an inmemory vocabulary):
 there is no way to compute the inverse transform (from feature indices to string feature names) which can be a problem when trying to introspect which features are most important to a model.
 there can be collisions: distinct tokens can be mapped to the same feature index. However in practice this is rarely an issue if n_features is large enough (e.g. 2 ** 18 for text classification problems).
 no IDF weighting as this would render the transformer stateful.
The hash function employed is the signed 32bit version of Murmurhash3.
Read more in the User Guide.
Parameters
 input : string {‘filename’, ‘file’, ‘content’}
If ‘filename’, the sequence passed as an argument to fit is expected to be a list of filenames that need reading to fetch the raw content to analyze.
If ‘file’, the sequence items must have a ‘read’ method (filelike object) that is called to fetch the bytes in memory.
Otherwise the input is expected to be the sequence strings or bytes items are expected to be analyzed directly.
 encoding : string, default=’utf8’
 If bytes or files are given to analyze, this encoding is used to decode.
 decode_error : {‘strict’, ‘ignore’, ‘replace’}
 Instruction on what to do if a byte sequence is given to analyze that contains characters not of the given encoding. By default, it is ‘strict’, meaning that a UnicodeDecodeError will be raised. Other values are ‘ignore’ and ‘replace’.
 strip_accents : {‘ascii’, ‘unicode’, None}
 Remove accents during the preprocessing step. ‘ascii’ is a fast method that only works on characters that have an direct ASCII mapping. ‘unicode’ is a slightly slower method that works on any characters. None (default) does nothing.
 analyzer : string, {‘word’, ‘char’, ‘char_wb’} or callable
Whether the feature should be made of word or character ngrams. Option ‘char_wb’ creates character ngrams only from text inside word boundaries.
If a callable is passed it is used to extract the sequence of features out of the raw, unprocessed input.
 preprocessor : callable or None (default)
 Override the preprocessing (string transformation) stage while preserving the tokenizing and ngrams generation steps.
 tokenizer : callable or None (default)
 Override the string tokenization step while preserving the
preprocessing and ngrams generation steps.
Only applies if
analyzer == 'word'
.  ngram_range : tuple (min_n, max_n), default=(1, 1)
 The lower and upper boundary of the range of nvalues for different ngrams to be extracted. All values of n such that min_n <= n <= max_n will be used.
 stop_words : string {‘english’}, list, or None (default)
If ‘english’, a builtin stop word list for English is used.
If a list, that list is assumed to contain stop words, all of which will be removed from the resulting tokens. Only applies if
analyzer == 'word'
. lowercase : boolean, default=True
 Convert all characters to lowercase before tokenizing.
 token_pattern : string
 Regular expression denoting what constitutes a “token”, only used
if
analyzer == 'word'
. The default regexp selects tokens of 2 or more alphanumeric characters (punctuation is completely ignored and always treated as a token separator).  n_features : integer, default=(2 ** 20)
 The number of features (columns) in the output matrices. Small numbers of features are likely to cause hash collisions, but large numbers will cause larger coefficient dimensions in linear learners.
 norm : ‘l1’, ‘l2’ or None, optional
 Norm used to normalize term vectors. None for no normalization.
 binary: boolean, default=False.
 If True, all non zero counts are set to 1. This is useful for discrete probabilistic models that model binary events rather than integer counts.
 dtype: type, optional
 Type of the matrix returned by fit_transform() or transform().
 non_negative : boolean, default=False
 Whether output matrices should contain nonnegative values only; effectively calls abs on the matrix prior to returning it. When True, output values can be interpreted as frequencies. When False, output values will have expected value zero.
See also
CountVectorizer, TfidfVectorizer
Full API documentation: HashingVectorizerScikitsLearnNode

class
mdp.nodes.
LogisticRegressionCVScikitsLearnNode
¶ Logistic Regression CV (aka logit, MaxEnt) classifier.
This node has been automatically generated by wrapping the
sklearn.linear_model.logistic.LogisticRegressionCV
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.This class implements logistic regression using liblinear, newtoncg, sag of lbfgs optimizer. The newtoncg, sag and lbfgs solvers support only L2 regularization with primal formulation. The liblinear solver supports both L1 and L2 regularization, with a dual formulation only for the L2 penalty.
For the grid of Cs values (that are set by default to be ten values in a logarithmic scale between 1e4 and 1e4), the best hyperparameter is selected by the crossvalidator StratifiedKFold, but it can be changed using the cv parameter. In the case of newtoncg and lbfgs solvers, we warm start along the path i.e guess the initial coefficients of the present fit to be the coefficients got after convergence in the previous fit, so it is supposed to be faster for highdimensional dense data.
For a multiclass problem, the hyperparameters for each class are computed using the best scores got by doing a onevsrest in parallel across all folds and classes. Hence this is not the true multinomial loss.
Read more in the User Guide.
Parameters
 Cs : list of floats  int
 Each of the values in Cs describes the inverse of regularization strength. If Cs is as an int, then a grid of Cs values are chosen in a logarithmic scale between 1e4 and 1e4. Like in support vector machines, smaller values specify stronger regularization.
 fit_intercept : bool, default: True
 Specifies if a constant (a.k.a. bias or intercept) should be added to the decision function.
 class_weight : dict or ‘balanced’, optional
Weights associated with classes in the form
{class_label: weight}
. If not given, all classes are supposed to have weight one.The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as
n_samples / (n_classes * np.bincount(y))
Note that these weights will be multiplied with sample_weight (passed through the fit method) if sample_weight is specified.
New in version 0.17: class_weight == ‘balanced’
 cv : integer or crossvalidation generator
 The default crossvalidation generator used is Stratified KFolds.
If an integer is provided, then it is the number of folds used.
See the module
sklearn.cross_validation
module for the list of possible crossvalidation objects.  penalty : str, ‘l1’ or ‘l2’
 Used to specify the norm used in the penalization. The newtoncg and lbfgs solvers support only l2 penalties.
 dual : bool
 Dual or primal formulation. Dual formulation is only implemented for l2 penalty with liblinear solver. Prefer dual=False when n_samples > n_features.
 scoring : callabale
 Scoring function to use as crossvalidation criteria. For a list of
scoring functions that can be used, look at
sklearn.metrics
. The default scoring option used is accuracy_score.  solver : {‘newtoncg’, ‘lbfgs’, ‘liblinear’, ‘sag’}
Algorithm to use in the optimization problem.
 For small datasets, ‘liblinear’ is a good choice, whereas ‘sag’ is
faster for large ones.
 For multiclass problems, only ‘newtoncg’ and ‘lbfgs’ handle
multinomial loss; ‘sag’ and ‘liblinear’ are limited to oneversusrest schemes.
‘newtoncg’, ‘lbfgs’ and ‘sag’ only handle L2 penalty.
 ‘liblinear’ might be slower in LogisticRegressionCV because it does
not handle warmstarting.
 tol : float, optional
 Tolerance for stopping criteria.
 max_iter : int, optional
 Maximum number of iterations of the optimization algorithm.
 n_jobs : int, optional
 Number of CPU cores used during the crossvalidation loop. If given a value of 1, all cores are used.
 verbose : int
 For the ‘liblinear’, ‘sag’ and ‘lbfgs’ solvers set verbose to any positive number for verbosity.
 refit : bool
 If set to True, the scores are averaged across all folds, and the coefs and the C that corresponds to the best score is taken, and a final refit is done using these parameters. Otherwise the coefs, intercepts and C that correspond to the best scores across folds are averaged.
 multi_class : str, {‘ovr’, ‘multinomial’}
 Multiclass option can be either ‘ovr’ or ‘multinomial’. If the option chosen is ‘ovr’, then a binary problem is fit for each label. Else the loss minimised is the multinomial loss fit across the entire probability distribution. Works only for ‘lbfgs’ and ‘newtoncg’ solvers.
 intercept_scaling : float, default 1.
 Useful only if solver is liblinear. This parameter is useful only when the solver ‘liblinear’ is used and self.fit_intercept is set to True. In this case, x becomes [x, self.intercept_scaling], i.e. a “synthetic” feature with constant value equals to intercept_scaling is appended to the instance vector. The intercept becomes intercept_scaling * synthetic feature weight Note! the synthetic feature weight is subject to l1/l2 regularization as all other features. To lessen the effect of regularization on synthetic feature weight (and therefore on the intercept) intercept_scaling has to be increased.
 random_state : int seed, RandomState instance, or None (default)
 The seed of the pseudo random number generator to use when shuffling the data.
Attributes
coef_
: array, shape (1, n_features) or (n_classes, n_features)Coefficient of the features in the decision function.
coef_ is of shape (1, n_features) when the given problem is binary. coef_ is readonly property derived from raw_coef_ that follows the internal memory layout of liblinear.
intercept_
: array, shape (1,) or (n_classes,) Intercept (a.k.a. bias) added to the decision function. It is available only when parameter intercept is set to True and is of shape(1,) when the problem is binary.
Cs_
: array Array of C i.e. inverse of regularization parameter values used for crossvalidation.
coefs_paths_
: array, shape(n_folds, len(Cs_), n_features)
or(n_folds, len(Cs_), n_features + 1)
 dict with classes as the keys, and the path of coefficients obtained
during crossvalidating across each fold and then across each Cs
after doing an OvR for the corresponding class as values.
If the ‘multi_class’ option is set to ‘multinomial’, then
the coefs_paths are the coefficients corresponding to each class.
Each dict value has shape
(n_folds, len(Cs_), n_features)
or(n_folds, len(Cs_), n_features + 1)
depending on whether the intercept is fit or not. scores_
: dict dict with classes as the keys, and the values as the grid of scores obtained during crossvalidating each fold, after doing an OvR for the corresponding class. If the ‘multi_class’ option given is ‘multinomial’ then the same scores are repeated across all classes, since this is the multinomial class. Each dict value has shape (n_folds, len(Cs))
C_
: array, shape (n_classes,) or (n_classes  1,) Array of C that maps to the best scores across every class. If refit is set to False, then for each class, the best C is the average of the C’s that correspond to the best scores for each fold.
n_iter_
: array, shape (n_classes, n_folds, n_cs) or (1, n_folds, n_cs) Actual number of iterations for all classes, folds and Cs. In the binary or multinomial cases, the first dimension is equal to 1.
See also
LogisticRegression
Full API documentation: LogisticRegressionCVScikitsLearnNode

class
mdp.nodes.
ZeroEstimatorScikitsLearnNode
¶ This node has been automatically generated by wrapping the
sklearn.ensemble.gradient_boosting.ZeroEstimator
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Full API documentation: ZeroEstimatorScikitsLearnNode

class
mdp.nodes.
SVCScikitsLearnNode
¶ CSupport Vector Classification.
This node has been automatically generated by wrapping the
sklearn.svm.classes.SVC
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.The implementation is based on libsvm. The fit time complexity is more than quadratic with the number of samples which makes it hard to scale to dataset with more than a couple of 10000 samples.
The multiclass support is handled according to a onevsone scheme.
For details on the precise mathematical formulation of the provided kernel functions and how gamma, coef0 and degree affect each other, see the corresponding section in the narrative documentation:
svm_kernels.
Read more in the User Guide.
Parameters
 C : float, optional (default=1.0)
 Penalty parameter C of the error term.
 kernel : string, optional (default=’rbf’)
 Specifies the kernel type to be used in the algorithm.
It must be one of ‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’ or
a callable.
If none is given, ‘rbf’ will be used. If a callable is given it is
used to precompute the kernel matrix from data matrices; that matrix
should be an array of shape
(n_samples, n_samples)
.  degree : int, optional (default=3)
 Degree of the polynomial kernel function (‘poly’). Ignored by all other kernels.
 gamma : float, optional (default=’auto’)
 Kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’. If gamma is ‘auto’ then 1/n_features will be used instead.
 coef0 : float, optional (default=0.0)
 Independent term in kernel function. It is only significant in ‘poly’ and ‘sigmoid’.
 probability : boolean, optional (default=False)
 Whether to enable probability estimates. This must be enabled prior to calling fit, and will slow down that method.
 shrinking : boolean, optional (default=True)
 Whether to use the shrinking heuristic.
 tol : float, optional (default=1e3)
 Tolerance for stopping criterion.
 cache_size : float, optional
 Specify the size of the kernel cache (in MB).
 class_weight : {dict, ‘balanced’}, optional
 Set the parameter C of class i to class_weight[i]*C for
SVC. If not given, all classes are supposed to have
weight one.
The “balanced” mode uses the values of y to automatically adjust
weights inversely proportional to class frequencies in the input data
as
n_samples / (n_classes * np.bincount(y))
 verbose : bool, default: False
 Enable verbose output. Note that this setting takes advantage of a perprocess runtime setting in libsvm that, if enabled, may not work properly in a multithreaded context.
 max_iter : int, optional (default=1)
 Hard limit on iterations within solver, or 1 for no limit.
 decision_function_shape : ‘ovo’, ‘ovr’ or None, default=None
Whether to return a onevsrest (‘ovr’) ecision function of shape (n_samples, n_classes) as all other classifiers, or the original onevsone (‘ovo’) decision function of libsvm which has shape (n_samples, n_classes * (n_classes  1) / 2). The default of None will currently behave as ‘ovo’ for backward compatibility and raise a deprecation warning, but will change ‘ovr’ in 0.18.
New in version 0.17: decision_function_shape=’ovr’ is recommended.
Changed in version 0.17: Deprecated decision_function_shape=’ovo’ and None.
 random_state : int seed, RandomState instance, or None (default)
 The seed of the pseudo random number generator to use when shuffling the data for probability estimation.
Attributes
support_
: arraylike, shape = [n_SV] Indices of support vectors.
support_vectors_
: arraylike, shape = [n_SV, n_features] Support vectors.
n_support_
: arraylike, dtype=int32, shape = [n_class] Number of support vectors for each class.
dual_coef_
: array, shape = [n_class1, n_SV] Coefficients of the support vector in the decision function. For multiclass, coefficient for all 1vs1 classifiers. The layout of the coefficients in the multiclass case is somewhat nontrivial. See the section about multiclass classification in the SVM section of the User Guide for details.
coef_
: array, shape = [n_class1, n_features]Weights assigned to the features (coefficients in the primal problem). This is only available in the case of a linear kernel.
coef_ is a readonly property derived from dual_coef_ and support_vectors_.
intercept_
: array, shape = [n_class * (n_class1) / 2] Constants in decision function.
Examples
>>> import numpy as np >>> X = np.array([[1, 1], [2, 1], [1, 1], [2, 1]]) >>> y = np.array([1, 1, 2, 2]) >>> from sklearn.svm import SVC >>> clf = SVC() >>> clf.fit(X, y) SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, decision_function_shape=None, degree=3, gamma='auto', kernel='rbf', max_iter=1, probability=False, random_state=None, shrinking=True, tol=0.001, verbose=False) >>> print(clf.predict([[0.8, 1]])) [1]
See also
 SVR
 Support Vector Machine for Regression implemented using libsvm.
 LinearSVC
 Scalable Linear Support Vector Machine for classification implemented using liblinear. Check the See also section of LinearSVC for more comparison element.
Full API documentation: SVCScikitsLearnNode

class
mdp.nodes.
IsotonicRegressionScikitsLearnNode
¶ Isotonic regression model.
This node has been automatically generated by wrapping the
sklearn.isotonic.IsotonicRegression
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.The isotonic regression optimization problem is defined by:
min sum w_i (y[i]  y_[i]) ** 2 subject to y_[i] <= y_[j] whenever X[i] <= X[j] and min(y_) = y_min, max(y_) = y_max
where:
y[i]
are inputs (real numbers)
y_[i]
are fitted
X
specifies the order.
 If
X
is nondecreasing theny_
is nondecreasing. w[i]
are optional strictly positive weights (default to 1.0)
Read more in the User Guide.
Parameters
 y_min : optional, default: None
 If not None, set the lowest value of the fit to y_min.
 y_max : optional, default: None
 If not None, set the highest value of the fit to y_max.
 increasing : boolean or string, optional, default: True
If boolean, whether or not to fit the isotonic regression with y increasing or decreasing.
The string value “auto” determines whether y should increase or decrease based on the Spearman correlation estimate’s sign.
 out_of_bounds : string, optional, default: “nan”
 The
out_of_bounds
parameter handles how xvalues outside of the training domain are handled. When set to “nan”, predicted yvalues will be NaN. When set to “clip”, predicted yvalues will be set to the value corresponding to the nearest train interval endpoint. When set to “raise”, allowinterp1d
to throw ValueError.
Attributes
X_
: ndarray (n_samples, ) A copy of the input X.
y_
: ndarray (n_samples, ) Isotonic fit of y.
X_min_
: float Minimum value of input array X_ for left bound.
X_max_
: float Maximum value of input array X_ for right bound.
f_
: function The stepwise interpolating function that covers the domain X_.
Notes
Ties are broken using the secondary method from Leeuw, 1977.
References
Isotonic Median Regression: A Linear Programming Approach Nilotpal Chakravarti Mathematics of Operations Research Vol. 14, No. 2 (May, 1989), pp. 303308
Isotone Optimization in R : PoolAdjacentViolators Algorithm (PAVA) and Active Set Methods Leeuw, Hornik, Mair Journal of Statistical Software 2009
Correctness of Kruskal’s algorithms for monotone regression with ties Leeuw, Psychometrica, 1977
Full API documentation: IsotonicRegressionScikitsLearnNode

class
mdp.nodes.
VBGMMScikitsLearnNode
¶ Variational Inference for the Gaussian Mixture Model
This node has been automatically generated by wrapping the
sklearn.mixture.dpgmm.VBGMM
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Variational inference for a Gaussian mixture model probability distribution. This class allows for easy and efficient inference of an approximate posterior distribution over the parameters of a Gaussian mixture model with a fixed number of components.
Initialization is with normallydistributed means and identity covariance, for proper convergence.
Read more in the User Guide.
Parameters
 n_components: int, default 1
 Number of mixture components.
 covariance_type: string, default ‘diag’
 String describing the type of covariance parameters to use. Must be one of ‘spherical’, ‘tied’, ‘diag’, ‘full’.
 alpha: float, default 1
 Real number representing the concentration parameter of the dirichlet distribution. Intuitively, the higher the value of alpha the more likely the variational mixture of Gaussians model will use all components it can.
 tol : float, default 1e3
 Convergence threshold.
 n_iter : int, default 10
 Maximum number of iterations to perform before convergence.
 params : string, default ‘wmc’
 Controls which parameters are updated in the training process. Can contain any combination of ‘w’ for weights, ‘m’ for means, and ‘c’ for covars.
 init_params : string, default ‘wmc’
 Controls which parameters are updated in the initialization process. Can contain any combination of ‘w’ for weights, ‘m’ for means, and ‘c’ for covars. Defaults to ‘wmc’.
 verbose : int, default 0
 Controls output verbosity.
Attributes
 covariance_type : string
 String describing the type of covariance parameters used by the DPGMM. Must be one of ‘spherical’, ‘tied’, ‘diag’, ‘full’.
 n_features : int
 Dimensionality of the Gaussians.
 n_components : int (readonly)
 Number of mixture components.
weights_
: array, shape (n_components,) Mixing weights for each mixture component.
means_
: array, shape (n_components, n_features) Mean parameters for each mixture component.
precs_
: arrayPrecision (inverse covariance) parameters for each mixture component. The shape depends on covariance_type:
(`n_components`, 'n_features') if 'spherical', (`n_features`, `n_features`) if 'tied', (`n_components`, `n_features`) if 'diag', (`n_components`, `n_features`, `n_features`) if 'full'
converged_
: bool True when convergence was reached in fit(), False otherwise.
See Also
GMM : Finite Gaussian mixture model fit with EM DPGMM : Infinite Gaussian mixture model, using the dirichlet
process, fit with a variational algorithmFull API documentation: VBGMMScikitsLearnNode

class
mdp.nodes.
DictVectorizerScikitsLearnNode
¶ Transforms lists of featurevalue mappings to vectors.
This node has been automatically generated by wrapping the
sklearn.feature_extraction.dict_vectorizer.DictVectorizer
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.This transformer turns lists of mappings (dictlike objects) of feature names to feature values into Numpy arrays or scipy.sparse matrices for use with scikitlearn estimators.
When feature values are strings, this transformer will do a binary onehot (aka oneofK) coding: one booleanvalued feature is constructed for each of the possible string values that the feature can take on. For instance, a feature “f” that can take on the values “ham” and “spam” will become two features in the output, one signifying “f=ham”, the other “f=spam”.
Features that do not occur in a sample (mapping) will have a zero value in the resulting array/matrix.
Read more in the User Guide.
Parameters
 dtype : callable, optional
 The type of feature values. Passed to Numpy array/scipy.sparse matrix constructors as the dtype argument.
 separator: string, optional
 Separator string used when constructing new features for onehot coding.
 sparse: boolean, optional.
 Whether transform should produce scipy.sparse matrices. True by default.
 sort: boolean, optional.
 Whether
feature_names_
andvocabulary_
should be sorted when fitting. True by default.
Attributes
vocabulary_
: dict A dictionary mapping feature names to feature indices.
feature_names_
: list A list of length n_features containing the feature names (e.g., “f=ham” and “f=spam”).
Examples
>>> from sklearn.feature_extraction import DictVectorizer >>> v = DictVectorizer(sparse=False) >>> D = [{'foo': 1, 'bar': 2}, {'foo': 3, 'baz': 1}] >>> X = v.fit_transform(D) >>> X array([[ 2., 0., 1.], [ 0., 1., 3.]]) >>> v.inverse_transform(X) == [{'bar': 2.0, 'foo': 1.0}, {'baz': 1.0, 'foo': 3.0}] True >>> v.transform({'foo': 4, 'unseen_feature': 3}) array([[ 0., 0., 4.]])
See also
FeatureHasher : performs vectorization using only a hash function. sklearn.preprocessing.OneHotEncoder : handles nominal/categorical features
encoded as columns of integers.Full API documentation: DictVectorizerScikitsLearnNode

class
mdp.nodes.
LinearSVCScikitsLearnNode
¶ Linear Support Vector Classification.
This node has been automatically generated by wrapping the
sklearn.svm.classes.LinearSVC
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Similar to SVC with parameter kernel=’linear’, but implemented in terms of liblinear rather than libsvm, so it has more flexibility in the choice of penalties and loss functions and should scale better to large numbers of samples.
This class supports both dense and sparse input and the multiclass support is handled according to a onevstherest scheme.
Read more in the User Guide.
Parameters
 C : float, optional (default=1.0)
 Penalty parameter C of the error term.
 loss : string, ‘hinge’ or ‘squared_hinge’ (default=’squared_hinge’)
 Specifies the loss function. ‘hinge’ is the standard SVM loss (used e.g. by the SVC class) while ‘squared_hinge’ is the square of the hinge loss.
 penalty : string, ‘l1’ or ‘l2’ (default=’l2’)
 Specifies the norm used in the penalization. The ‘l2’
penalty is the standard used in SVC. The ‘l1’ leads to
coef_
vectors that are sparse.  dual : bool, (default=True)
 Select the algorithm to either solve the dual or primal optimization problem. Prefer dual=False when n_samples > n_features.
 tol : float, optional (default=1e4)
 Tolerance for stopping criteria.
 multi_class: string, ‘ovr’ or ‘crammer_singer’ (default=’ovr’)
 Determines the multiclass strategy if y contains more than
two classes.
"ovr"
trains n_classes onevsrest classifiers, while"crammer_singer"
optimizes a joint objective over all classes. While crammer_singer is interesting from a theoretical perspective as it is consistent, it is seldom used in practice as it rarely leads to better accuracy and is more expensive to compute. If"crammer_singer"
is chosen, the options loss, penalty and dual will be ignored.  fit_intercept : boolean, optional (default=True)
 Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (i.e. data is expected to be already centered).
 intercept_scaling : float, optional (default=1)
 When self.fit_intercept is True, instance vector x becomes
[x, self.intercept_scaling]
, i.e. a “synthetic” feature with constant value equals to intercept_scaling is appended to the instance vector. The intercept becomes intercept_scaling * synthetic feature weight Note! the synthetic feature weight is subject to l1/l2 regularization as all other features. To lessen the effect of regularization on synthetic feature weight (and therefore on the intercept) intercept_scaling has to be increased.  class_weight : {dict, ‘balanced’}, optional
 Set the parameter C of class i to
class_weight[i]*C
for SVC. If not given, all classes are supposed to have weight one. The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data asn_samples / (n_classes * np.bincount(y))
 verbose : int, (default=0)
 Enable verbose output. Note that this setting takes advantage of a perprocess runtime setting in liblinear that, if enabled, may not work properly in a multithreaded context.
 random_state : int seed, RandomState instance, or None (default=None)
 The seed of the pseudo random number generator to use when shuffling the data.
 max_iter : int, (default=1000)
 The maximum number of iterations to be run.
Attributes
coef_
: array, shape = [n_features] if n_classes == 2 else [n_classes, n_features]Weights assigned to the features (coefficients in the primal problem). This is only available in the case of a linear kernel.
coef_
is a readonly property derived fromraw_coef_
that follows the internal memory layout of liblinear.intercept_
: array, shape = [1] if n_classes == 2 else [n_classes] Constants in decision function.
Notes
The underlying C implementation uses a random number generator to select features when fitting the model. It is thus not uncommon to have slightly different results for the same input data. If that happens, try with a smaller
tol
parameter.The underlying implementation, liblinear, uses a sparse internal representation for the data that will incur a memory copy.
Predict output may not match that of standalone liblinear in certain cases. See differences from liblinear in the narrative documentation.
References
LIBLINEAR: A Library for Large Linear Classification
See also
 SVC
Implementation of Support Vector Machine classifier using libsvm:
 the kernel can be nonlinear but its SMO algorithm does not
 scale to large number of samples as LinearSVC does.
Furthermore SVC multiclass mode is implemented using one vs one scheme while LinearSVC uses one vs the rest. It is possible to implement one vs the rest with SVC by using the
sklearn.multiclass.OneVsRestClassifier
wrapper.Finally SVC can fit dense data without memory copy if the input is Ccontiguous. Sparse data will still incur memory copy though.
 sklearn.linear_model.SGDClassifier
 SGDClassifier can optimize the same cost function as LinearSVC by adjusting the penalty and loss parameters. In addition it requires less memory, allows incremental (online) learning, and implements various loss functions and regularization regimes.
Full API documentation: LinearSVCScikitsLearnNode

class
mdp.nodes.
RandomizedLassoScikitsLearnNode
¶ Randomized Lasso.
This node has been automatically generated by wrapping the
sklearn.linear_model.randomized_l1.RandomizedLasso
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Randomized Lasso works by resampling the train data and computing a Lasso on each resampling. In short, the features selected more often are good features. It is also known as stability selection.
Read more in the User Guide.
Parameters
 alpha : float, ‘aic’, or ‘bic’, optional
 The regularization parameter alpha parameter in the Lasso. Warning: this is not the alpha parameter in the stability selection article which is scaling.
 scaling : float, optional
 The alpha parameter in the stability selection article used to randomly scale the features. Should be between 0 and 1.
 sample_fraction : float, optional
 The fraction of samples to be used in each randomized design. Should be between 0 and 1. If 1, all samples are used.
 n_resampling : int, optional
 Number of randomized models.
 selection_threshold: float, optional
 The score above which features should be selected.
 fit_intercept : boolean, optional
 whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered).
 verbose : boolean or integer, optional
 Sets the verbosity amount
 normalize : boolean, optional, default True
 If True, the regressors X will be normalized before regression.
 precompute : True  False  ‘auto’
 Whether to use a precomputed Gram matrix to speed up calculations. If set to ‘auto’ let us decide. The Gram matrix can also be passed as argument.
 max_iter : integer, optional
 Maximum number of iterations to perform in the Lars algorithm.
 eps : float, optional
 The machineprecision regularization in the computation of the Cholesky diagonal factors. Increase this for very illconditioned systems. Unlike the ‘tol’ parameter in some iterative optimizationbased algorithms, this parameter does not control the tolerance of the optimization.
 n_jobs : integer, optional
 Number of CPUs to use during the resampling. If ‘1’, use all the CPUs
 random_state : int, RandomState instance or None, optional (default=None)
 If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.
 pre_dispatch : int, or string, optional
Controls the number of jobs that get dispatched during parallel execution. Reducing this number can be useful to avoid an explosion of memory consumption when more jobs get dispatched than CPUs can process. This parameter can be:
 None, in which case all the jobs are immediately created and spawned. Use this for lightweight and fastrunning jobs, to avoid delays due to ondemand spawning of the jobs
 An int, giving the exact number of total jobs that are spawned
 A string, giving an expression as a function of n_jobs, as in ‘2*n_jobs’
 memory : Instance of joblib.Memory or string
 Used for internal caching. By default, no caching is done. If a string is given, it is the path to the caching directory.
Attributes
scores_
: array, shape = [n_features] Feature scores between 0 and 1.
all_scores_
: array, shape = [n_features, n_reg_parameter] Feature scores between 0 and 1 for all values of the regularization parameter. The reference article suggests
scores_
is the max ofall_scores_
.
Examples
>>> from sklearn.linear_model import RandomizedLasso >>> randomized_lasso = RandomizedLasso()
Notes
See examples/linear_model/plot_sparse_recovery.py for an example.
References
Stability selection Nicolai Meinshausen, Peter Buhlmann Journal of the Royal Statistical Society: Series B Volume 72, Issue 4, pages 417473, September 2010 DOI: 10.1111/j.14679868.2010.00740.x
See also
RandomizedLogisticRegression, LogisticRegression
Full API documentation: RandomizedLassoScikitsLearnNode

class
mdp.nodes.
FastICAScikitsLearnNode
¶ FastICA: a fast algorithm for Independent Component Analysis.
This node has been automatically generated by wrapping the
sklearn.decomposition.fastica_.FastICA
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Read more in the User Guide.
Parameters
 n_components : int, optional
 Number of components to use. If none is passed, all are used.
 algorithm : {‘parallel’, ‘deflation’}
 Apply parallel or deflational algorithm for FastICA.
 whiten : boolean, optional
 If whiten is false, the data is already considered to be whitened, and no whitening is performed.
 fun : string or function, optional. Default: ‘logcosh’
The functional form of the G function used in the approximation to negentropy. Could be either ‘logcosh’, ‘exp’, or ‘cube’. You can also provide your own function. It should return a tuple containing the value of the function, and of its derivative, in the point. Example:
def my_g(x):
 return x ** 3, 3 * x ** 2
 fun_args : dictionary, optional
 Arguments to send to the functional form. If empty and if fun=’logcosh’, fun_args will take value {‘alpha’ : 1.0}.
 max_iter : int, optional
 Maximum number of iterations during fit.
 tol : float, optional
 Tolerance on update at each iteration.
 w_init : None of an (n_components, n_components) ndarray
 The mixing matrix to be used to initialize the algorithm.
 random_state : int or RandomState
 Pseudo number generator state used for random sampling.
Attributes
components_
: 2D array, shape (n_components, n_features) The unmixing matrix.
mixing_
: array, shape (n_features, n_components) The mixing matrix.
n_iter_
: int If the algorithm is “deflation”, n_iter is the maximum number of iterations run across all components. Else they are just the number of iterations taken to converge.
Notes
Implementation based on `A. Hyvarinen and E. Oja, Independent Component Analysis:
Algorithms and Applications, Neural Networks, 13(45), 2000, pp. 411430`
Full API documentation: FastICAScikitsLearnNode

class
mdp.nodes.
KernelRidgeScikitsLearnNode
¶ Kernel ridge regression.
This node has been automatically generated by wrapping the
sklearn.kernel_ridge.KernelRidge
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Kernel ridge regression (KRR) combines ridge regression (linear least squares with l2norm regularization) with the kernel trick. It thus learns a linear function in the space induced by the respective kernel and the data. For nonlinear kernels, this corresponds to a nonlinear function in the original space.
The form of the model learned by KRR is identical to support vector regression (SVR). However, different loss functions are used: KRR uses squared error loss while support vector regression uses epsiloninsensitive loss, both combined with l2 regularization. In contrast to SVR, fitting a KRR model can be done in closedform and is typically faster for mediumsized datasets. On the other hand, the learned model is nonsparse and thus slower than SVR, which learns a sparse model for epsilon > 0, at predictiontime.
This estimator has builtin support for multivariate regression (i.e., when y is a 2darray of shape [n_samples, n_targets]).
Read more in the User Guide.
Parameters
 alpha : {float, arraylike}, shape = [n_targets]
 Small positive values of alpha improve the conditioning of the problem
and reduce the variance of the estimates. Alpha corresponds to
(2*C)^1
in other linear models such as LogisticRegression or LinearSVC. If an array is passed, penalties are assumed to be specific to the targets. Hence they must correspond in number.  kernel : string or callable, default=”linear”
 Kernel mapping used internally. A callable should accept two arguments and the keyword arguments passed to this object as kernel_params, and should return a floating point number.
 gamma : float, default=None
 Gamma parameter for the RBF, laplacian, polynomial, exponential chi2 and sigmoid kernels. Interpretation of the default value is left to the kernel; see the documentation for sklearn.metrics.pairwise. Ignored by other kernels.
 degree : float, default=3
 Degree of the polynomial kernel. Ignored by other kernels.
 coef0 : float, default=1
 Zero coefficient for polynomial and sigmoid kernels. Ignored by other kernels.
 kernel_params : mapping of string to any, optional
 Additional parameters (keyword arguments) for kernel function passed as callable object.
Attributes
dual_coef_
: array, shape = [n_features] or [n_targets, n_features] Weight vector(s) in kernel space
X_fit_
: {arraylike, sparse matrix}, shape = [n_samples, n_features] Training data, which is also required for prediction
References
 Kevin P. Murphy “Machine Learning: A Probabilistic Perspective”, The MIT Press chapter 14.4.3, pp. 492493
See also
 Ridge
 Linear ridge regression.
 SVR
 Support Vector Regression implemented using libsvm.
Examples
>>> from sklearn.kernel_ridge import KernelRidge >>> import numpy as np >>> n_samples, n_features = 10, 5 >>> rng = np.random.RandomState(0) >>> y = rng.randn(n_samples) >>> X = rng.randn(n_samples, n_features) >>> clf = KernelRidge(alpha=1.0) >>> clf.fit(X, y) KernelRidge(alpha=1.0, coef0=1, degree=3, gamma=None, kernel='linear', kernel_params=None)
Full API documentation: KernelRidgeScikitsLearnNode

class
mdp.nodes.
MultinomialNBScikitsLearnNode
¶ Naive Bayes classifier for multinomial models
This node has been automatically generated by wrapping the
sklearn.naive_bayes.MultinomialNB
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.The multinomial Naive Bayes classifier is suitable for classification with discrete features (e.g., word counts for text classification). The multinomial distribution normally requires integer feature counts. However, in practice, fractional counts such as tfidf may also work.
Read more in the User Guide.
Parameters
 alpha : float, optional (default=1.0)
 Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing).
 fit_prior : boolean
 Whether to learn class prior probabilities or not. If false, a uniform prior will be used.
 class_prior : arraylike, size (n_classes,)
 Prior probabilities of the classes. If specified the priors are not adjusted according to the data.
Attributes
class_log_prior_
: array, shape (n_classes, ) Smoothed empirical log probability for each class.
intercept_
: property Mirrors
class_log_prior_
for interpreting MultinomialNB as a linear model. feature_log_prob_
: array, shape (n_classes, n_features) Empirical log probability of features
given a class,
P(x_iy)
. coef_
: property Mirrors
feature_log_prob_
for interpreting MultinomialNB as a linear model. class_count_
: array, shape (n_classes,) Number of samples encountered for each class during fitting. This value is weighted by the sample weight when provided.
feature_count_
: array, shape (n_classes, n_features) Number of samples encountered for each (class, feature) during fitting. This value is weighted by the sample weight when provided.
Examples
>>> import numpy as np >>> X = np.random.randint(5, size=(6, 100)) >>> y = np.array([1, 2, 3, 4, 5, 6]) >>> from sklearn.naive_bayes import MultinomialNB >>> clf = MultinomialNB() >>> clf.fit(X, y) MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True) >>> print(clf.predict(X[2:3])) [3]
Notes
For the rationale behind the names coef_ and intercept_, i.e. naive Bayes as a linear classifier, see J. Rennie et al. (2003), Tackling the poor assumptions of naive Bayes text classifiers, ICML.
References
C.D. Manning, P. Raghavan and H. Schuetze (2008). Introduction to Information Retrieval. Cambridge University Press, pp. 234265. http://nlp.stanford.edu/IRbook/html/htmledition/naivebayestextclassification1.html
Full API documentation: MultinomialNBScikitsLearnNode

class
mdp.nodes.
ForestRegressorScikitsLearnNode
¶ Base class for forest of treesbased regressors.
This node has been automatically generated by wrapping the
sklearn.ensemble.forest.ForestRegressor
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Warning: This class should not be used directly. Use derived classes instead.
Full API documentation: ForestRegressorScikitsLearnNode

class
mdp.nodes.
LocallyLinearEmbeddingScikitsLearnNode
¶ Locally Linear Embedding
This node has been automatically generated by wrapping the
sklearn.manifold.locally_linear.LocallyLinearEmbedding
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Read more in the User Guide.
Parameters
 n_neighbors : integer
 number of neighbors to consider for each point.
 n_components : integer
 number of coordinates for the manifold
 reg : float
 regularization constant, multiplies the trace of the local covariance matrix of the distances.
 eigen_solver : string, {‘auto’, ‘arpack’, ‘dense’}
auto : algorithm will attempt to choose the best method for input data
 arpack : use arnoldi iteration in shiftinvert mode.
 For this method, M may be a dense matrix, sparse matrix, or general linear operator. Warning: ARPACK can be unstable for some problems. It is best to try several random seeds in order to check results.
 dense : use standard dense matrix operations for the eigenvalue
 decomposition. For this method, M must be an array or matrix type. This method should be avoided for large problems.
 tol : float, optional
 Tolerance for ‘arpack’ method Not used if eigen_solver==’dense’.
 max_iter : integer
 maximum number of iterations for the arpack solver. Not used if eigen_solver==’dense’.
 method : string (‘standard’, ‘hessian’, ‘modified’ or ‘ltsa’)
 standard : use the standard locally linear embedding algorithm. see
 reference [1]
 hessian : use the Hessian eigenmap method. This method requires
n_neighbors > n_components * (1 + (n_components + 1) / 2
see reference [2] modified : use the modified locally linear embedding algorithm.
 see reference [3]
 ltsa : use local tangent space alignment algorithm
 see reference [4]
 hessian_tol : float, optional
 Tolerance for Hessian eigenmapping method.
Only used if
method == 'hessian'
 modified_tol : float, optional
 Tolerance for modified LLE method.
Only used if
method == 'modified'
 neighbors_algorithm : string [‘auto’’brute’’kd_tree’’ball_tree’]
 algorithm to use for nearest neighbors search, passed to neighbors.NearestNeighbors instance
 random_state: numpy.RandomState or int, optional
 The generator or seed used to determine the starting vector for arpack iterations. Defaults to numpy.random.
Attributes
embedding_vectors_
: arraylike, shape [n_components, n_samples] Stores the embedding vectors
reconstruction_error_
: float Reconstruction error associated with embedding_vectors_
nbrs_
: NearestNeighbors object Stores nearest neighbors instance, including BallTree or KDtree if applicable.
References
[1] Roweis, S. & Saul, L. Nonlinear dimensionality reduction by locally linear embedding. Science 290:2323 (2000). [2] Donoho, D. & Grimes, C. Hessian eigenmaps: Locally linear embedding techniques for highdimensional data. Proc Natl Acad Sci U S A. 100:5591 (2003). [3] Zhang, Z. & Wang, J. MLLE: Modified Locally Linear Embedding Using Multiple Weights. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.70.382 [4] Zhang, Z. & Zha, H. Principal manifolds and nonlinear dimensionality reduction via tangent space alignment. Journal of Shanghai Univ. 8:406 (2004) Full API documentation: LocallyLinearEmbeddingScikitsLearnNode

class
mdp.nodes.
AdaBoostClassifierScikitsLearnNode
¶ An AdaBoost classifier.
This node has been automatically generated by wrapping the
sklearn.ensemble.weight_boosting.AdaBoostClassifier
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.An AdaBoost [1] classifier is a metaestimator that begins by fitting a classifier on the original dataset and then fits additional copies of the classifier on the same dataset but where the weights of incorrectly classified instances are adjusted such that subsequent classifiers focus more on difficult cases.
This class implements the algorithm known as AdaBoostSAMME [2].
Read more in the User Guide.
Parameters
 base_estimator : object, optional (default=DecisionTreeClassifier)
 The base estimator from which the boosted ensemble is built. Support for sample weighting is required, as well as proper classes_ and n_classes_ attributes.
 n_estimators : integer, optional (default=50)
 The maximum number of estimators at which boosting is terminated. In case of perfect fit, the learning procedure is stopped early.
 learning_rate : float, optional (default=1.)
 Learning rate shrinks the contribution of each classifier by
learning_rate
. There is a tradeoff betweenlearning_rate
andn_estimators
.  algorithm : {‘SAMME’, ‘SAMME.R’}, optional (default=’SAMME.R’)
 If ‘SAMME.R’ then use the SAMME.R real boosting algorithm.
base_estimator
must support calculation of class probabilities. If ‘SAMME’ then use the SAMME discrete boosting algorithm. The SAMME.R algorithm typically converges faster than SAMME, achieving a lower test error with fewer boosting iterations.  random_state : int, RandomState instance or None, optional (default=None)
 If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.
Attributes
estimators_
: list of classifiers The collection of fitted subestimators.
classes_
: array of shape = [n_classes] The classes labels.
n_classes_
: int The number of classes.
estimator_weights_
: array of floats Weights for each estimator in the boosted ensemble.
estimator_errors_
: array of floats Classification error for each estimator in the boosted ensemble.
feature_importances_
: array of shape = [n_features] The feature importances if supported by the
base_estimator
.
See also
AdaBoostRegressor, GradientBoostingClassifier, DecisionTreeClassifier
References
[1] Y. Freund, R. Schapire, “A DecisionTheoretic Generalization of onLine Learning and an Application to Boosting”, 1995. [2]  Zhu, H. Zou, S. Rosset, T. Hastie, “Multiclass AdaBoost”, 2009.
Full API documentation: AdaBoostClassifierScikitsLearnNode

class
mdp.nodes.
LarsCVScikitsLearnNode
¶ Crossvalidated Least Angle Regression model
This node has been automatically generated by wrapping the
sklearn.linear_model.least_angle.LarsCV
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Read more in the User Guide.
Parameters
 fit_intercept : boolean
 whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered).
 positive : boolean (default=False)
 Restrict coefficients to be >= 0. Be aware that you might want to remove fit_intercept which is set True by default.
 verbose : boolean or integer, optional
 Sets the verbosity amount
 normalize : boolean, optional, default False
 If True, the regressors X will be normalized before regression.
 copy_X : boolean, optional, default True
 If
True
, X will be copied; else, it may be overwritten.  precompute : True  False  ‘auto’  arraylike
 Whether to use a precomputed Gram matrix to speed up
calculations. If set to
'auto'
let us decide. The Gram matrix can also be passed as argument.  max_iter: integer, optional
 Maximum number of iterations to perform.
 cv : int, crossvalidation generator or an iterable, optional
Determines the crossvalidation splitting strategy. Possible inputs for cv are:
 None, to use the default 3fold crossvalidation,
 integer, to specify the number of folds.
 An object to be used as a crossvalidation generator.
 An iterable yielding train/test splits.
For integer/None inputs,
KFold
is used.Refer User Guide for the various crossvalidation strategies that can be used here.
 max_n_alphas : integer, optional
 The maximum number of points on the path used to compute the residuals in the crossvalidation
 n_jobs : integer, optional
 Number of CPUs to use during the cross validation. If
1
, use all the CPUs  eps : float, optional
 The machineprecision regularization in the computation of the Cholesky diagonal factors. Increase this for very illconditioned systems.
Attributes
coef_
: array, shape (n_features,) parameter vector (w in the formulation formula)
intercept_
: float independent term in decision function
coef_path_
: array, shape (n_features, n_alphas) the varying values of the coefficients along the path
alpha_
: float the estimated regularization parameter alpha
alphas_
: array, shape (n_alphas,) the different values of alpha along the path
cv_alphas_
: array, shape (n_cv_alphas,) all the values of alpha along the path for the different folds
cv_mse_path_
: array, shape (n_folds, n_cv_alphas) the mean square error on leftout for each fold along the path
(alpha values given by
cv_alphas
) n_iter_
: arraylike or int the number of iterations run by Lars with the optimal alpha.
See also
lars_path, LassoLars, LassoLarsCV
Full API documentation: LarsCVScikitsLearnNode

class
mdp.nodes.
PolynomialFeaturesScikitsLearnNode
¶ Generate polynomial and interaction features.
This node has been automatically generated by wrapping the
sklearn.preprocessing.data.PolynomialFeatures
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Generate a new feature matrix consisting of all polynomial combinations of the features with degree less than or equal to the specified degree. For example, if an input sample is two dimensional and of the form [a, b], the degree2 polynomial features are [1, a, b, a^2, ab, b^2].
Parameters
 degree : integer
 The degree of the polynomial features. Default = 2.
 interaction_only : boolean, default = False
 If true, only interaction features are produced: features that are
products of at most
degree
distinct input features (so notx[1] ** 2
,x[0] * x[2] ** 3
, etc.).  include_bias : boolean
 If True (default), then include a bias column, the feature in which all polynomial powers are zero (i.e. a column of ones  acts as an intercept term in a linear model).
Examples
>>> X = np.arange(6).reshape(3, 2) >>> X array([[0, 1], [2, 3], [4, 5]]) >>> poly = PolynomialFeatures(2) >>> poly.fit_transform(X) array([[ 1., 0., 1., 0., 0., 1.], [ 1., 2., 3., 4., 6., 9.], [ 1., 4., 5., 16., 20., 25.]]) >>> poly = PolynomialFeatures(interaction_only=True) >>> poly.fit_transform(X) array([[ 1., 0., 1., 0.], [ 1., 2., 3., 6.], [ 1., 4., 5., 20.]])
Attributes
powers_
: array, shape (n_input_features, n_output_features) powers_[i, j] is the exponent of the jth input in the ith output.
n_input_features_
: int The total number of input features.
n_output_features_
: int The total number of polynomial output features. The number of output features is computed by iterating over all suitably sized combinations of input features.
Notes
Be aware that the number of features in the output array scales polynomially in the number of features of the input array, and exponentially in the degree. High degrees can cause overfitting.
See examples/linear_model/plot_polynomial_interpolation.py
Full API documentation: PolynomialFeaturesScikitsLearnNode

class
mdp.nodes.
AdditiveChi2SamplerScikitsLearnNode
¶ Approximate feature map for additive chi2 kernel.
This node has been automatically generated by wrapping the
sklearn.kernel_approximation.AdditiveChi2Sampler
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Uses sampling the fourier transform of the kernel characteristic at regular intervals.
Since the kernel that is to be approximated is additive, the components of the input vectors can be treated separately. Each entry in the original space is transformed into 2*sample_steps+1 features, where sample_steps is a parameter of the method. Typical values of sample_steps include 1, 2 and 3.
Optimal choices for the sampling interval for certain data ranges can be computed (see the reference). The default values should be reasonable.
Read more in the User Guide.
Parameters
 sample_steps : int, optional
 Gives the number of (complex) sampling points.
 sample_interval : float, optional
 Sampling interval. Must be specified when sample_steps not in {1,2,3}.
Notes
This estimator approximates a slightly different version of the additive chi squared kernel then
metric.additive_chi2
computes.See also
 SkewedChi2Sampler : A Fourierapproximation to a nonadditive variant of
 the chi squared kernel.
sklearn.metrics.pairwise.chi2_kernel : The exact chi squared kernel.
 sklearn.metrics.pairwise.additive_chi2_kernel : The exact additive chi
 squared kernel.
References
See “Efficient additive kernels via explicit feature maps” A. Vedaldi and A. Zisserman, Pattern Analysis and Machine Intelligence, 2011
Full API documentation: AdditiveChi2SamplerScikitsLearnNode

class
mdp.nodes.
QuantileEstimatorScikitsLearnNode
¶ This node has been automatically generated by wrapping the
sklearn.ensemble.gradient_boosting.QuantileEstimator
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Full API documentation: QuantileEstimatorScikitsLearnNode

class
mdp.nodes.
BirchScikitsLearnNode
¶ Implements the Birch clustering algorithm.
This node has been automatically generated by wrapping the
sklearn.cluster.birch.Birch
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Every new sample is inserted into the root of the Clustering Feature Tree. It is then clubbed together with the subcluster that has the centroid closest to the new sample. This is done recursively till it ends up at the subcluster of the leaf of the tree has the closest centroid.
Read more in the User Guide.
Parameters
 threshold : float, default 0.5
 The radius of the subcluster obtained by merging a new sample and the closest subcluster should be lesser than the threshold. Otherwise a new subcluster is started.
 branching_factor : int, default 50
 Maximum number of CF subclusters in each node. If a new samples enters such that the number of subclusters exceed the branching_factor then the node has to be split. The corresponding parent also has to be split and if the number of subclusters in the parent is greater than the branching factor, then it has to be split recursively.
 n_clusters : int, instance of sklearn.cluster model, default None
 Number of clusters after the final clustering step, which treats the subclusters from the leaves as new samples. By default, this final clustering step is not performed and the subclusters are returned as they are. If a model is provided, the model is fit treating the subclusters as new samples and the initial data is mapped to the label of the closest subcluster. If an int is provided, the model fit is AgglomerativeClustering with n_clusters set to the int.
 compute_labels : bool, default True
 Whether or not to compute labels for each fit.
 copy : bool, default True
 Whether or not to make a copy of the given data. If set to False, the initial data will be overwritten.
Attributes
root_
: _CFNode Root of the CFTree.
dummy_leaf_
: _CFNode Start pointer to all the leaves.
subcluster_centers_
: ndarray, Centroids of all subclusters read directly from the leaves.
subcluster_labels_
: ndarray, Labels assigned to the centroids of the subclusters after they are clustered globally.
labels_
: ndarray, shape (n_samples,) Array of labels assigned to the input data. if partial_fit is used instead of fit, they are assigned to the last batch of data.
Examples
>>> from sklearn.cluster import Birch >>> X = [[0, 1], [0.3, 1], [0.3, 1], [0, 1], [0.3, 1], [0.3, 1]] >>> brc = Birch(branching_factor=50, n_clusters=None, threshold=0.5, ... compute_labels=True) >>> brc.fit(X) Birch(branching_factor=50, compute_labels=True, copy=True, n_clusters=None, threshold=0.5) >>> brc.predict(X) array([0, 0, 0, 1, 1, 1])
References
 Tian Zhang, Raghu Ramakrishnan, Maron Livny BIRCH: An efficient data clustering method for large databases. http://www.cs.sfu.ca/CourseCentral/459/han/papers/zhang96.pdf
 Roberto Perdisci JBirch  Java implementation of BIRCH clustering algorithm https://code.google.com/p/jbirch/
Full API documentation: BirchScikitsLearnNode

class
mdp.nodes.
CountVectorizerScikitsLearnNode
¶ Convert a collection of text documents to a matrix of token counts
This node has been automatically generated by wrapping the
sklearn.feature_extraction.text.CountVectorizer
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.This implementation produces a sparse representation of the counts using scipy.sparse.coo_matrix.
If you do not provide an apriori dictionary and you do not use an analyzer that does some kind of feature selection then the number of features will be equal to the vocabulary size found by analyzing the data.
Read more in the User Guide.
Parameters
 input : string {‘filename’, ‘file’, ‘content’}
If ‘filename’, the sequence passed as an argument to fit is expected to be a list of filenames that need reading to fetch the raw content to analyze.
If ‘file’, the sequence items must have a ‘read’ method (filelike object) that is called to fetch the bytes in memory.
Otherwise the input is expected to be the sequence strings or bytes items are expected to be analyzed directly.
 encoding : string, ‘utf8’ by default.
 If bytes or files are given to analyze, this encoding is used to decode.
 decode_error : {‘strict’, ‘ignore’, ‘replace’}
 Instruction on what to do if a byte sequence is given to analyze that contains characters not of the given encoding. By default, it is ‘strict’, meaning that a UnicodeDecodeError will be raised. Other values are ‘ignore’ and ‘replace’.
 strip_accents : {‘ascii’, ‘unicode’, None}
 Remove accents during the preprocessing step. ‘ascii’ is a fast method that only works on characters that have an direct ASCII mapping. ‘unicode’ is a slightly slower method that works on any characters. None (default) does nothing.
 analyzer : string, {‘word’, ‘char’, ‘char_wb’} or callable
Whether the feature should be made of word or character ngrams. Option ‘char_wb’ creates character ngrams only from text inside word boundaries.
If a callable is passed it is used to extract the sequence of features out of the raw, unprocessed input. Only applies if
analyzer == 'word'
. preprocessor : callable or None (default)
 Override the preprocessing (string transformation) stage while preserving the tokenizing and ngrams generation steps.
 tokenizer : callable or None (default)
 Override the string tokenization step while preserving the
preprocessing and ngrams generation steps.
Only applies if
analyzer == 'word'
.  ngram_range : tuple (min_n, max_n)
 The lower and upper boundary of the range of nvalues for different ngrams to be extracted. All values of n such that min_n <= n <= max_n will be used.
 stop_words : string {‘english’}, list, or None (default)
If ‘english’, a builtin stop word list for English is used.
If a list, that list is assumed to contain stop words, all of which will be removed from the resulting tokens. Only applies if
analyzer == 'word'
.If None, no stop words will be used. max_df can be set to a value in the range [0.7, 1.0) to automatically detect and filter stop words based on intra corpus document frequency of terms.
 lowercase : boolean, True by default
 Convert all characters to lowercase before tokenizing.
 token_pattern : string
 Regular expression denoting what constitutes a “token”, only used
if
analyzer == 'word'
. The default regexp select tokens of 2 or more alphanumeric characters (punctuation is completely ignored and always treated as a token separator).  max_df : float in range [0.0, 1.0] or int, default=1.0
 When building the vocabulary ignore terms that have a document frequency strictly higher than the given threshold (corpusspecific stop words). If float, the parameter represents a proportion of documents, integer absolute counts. This parameter is ignored if vocabulary is not None.
 min_df : float in range [0.0, 1.0] or int, default=1
 When building the vocabulary ignore terms that have a document frequency strictly lower than the given threshold. This value is also called cutoff in the literature. If float, the parameter represents a proportion of documents, integer absolute counts. This parameter is ignored if vocabulary is not None.
 max_features : int or None, default=None
If not None, build a vocabulary that only consider the top max_features ordered by term frequency across the corpus.
This parameter is ignored if vocabulary is not None.
 vocabulary : Mapping or iterable, optional
 Either a Mapping (e.g., a dict) where keys are terms and values are indices in the feature matrix, or an iterable over terms. If not given, a vocabulary is determined from the input documents. Indices in the mapping should not be repeated and should not have any gap between 0 and the largest index.
 binary : boolean, default=False
 If True, all non zero counts are set to 1. This is useful for discrete probabilistic models that model binary events rather than integer counts.
 dtype : type, optional
 Type of the matrix returned by fit_transform() or transform().
Attributes
vocabulary_
: dict A mapping of terms to feature indices.
stop_words_
: setTerms that were ignored because they either:
 occurred in too many documents (max_df)
 occurred in too few documents (min_df)
 were cut off by feature selection (max_features).
This is only available if no vocabulary was given.
See also
HashingVectorizer, TfidfVectorizer
Notes
The
stop_words_
attribute can get large and increase the model size when pickling. This attribute is provided only for introspection and can be safely removed using delattr or set to None before pickling.Full API documentation: CountVectorizerScikitsLearnNode

class
mdp.nodes.
ExtraTreesRegressorScikitsLearnNode
¶ An extratrees regressor.
This node has been automatically generated by wrapping the
sklearn.ensemble.forest.ExtraTreesRegressor
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.This class implements a meta estimator that fits a number of randomized decision trees (a.k.a. extratrees) on various subsamples of the dataset and use averaging to improve the predictive accuracy and control overfitting.
Read more in the User Guide.
Parameters
 n_estimators : integer, optional (default=10)
 The number of trees in the forest.
 criterion : string, optional (default=”mse”)
 The function to measure the quality of a split. The only supported criterion is “mse” for the mean squared error. Note: this parameter is treespecific.
 max_features : int, float, string or None, optional (default=”auto”)
The number of features to consider when looking for the best split:
 If int, then consider max_features features at each split.
 If float, then max_features is a percentage and int(max_features * n_features) features are considered at each split.
 If “auto”, then max_features=n_features.
 If “sqrt”, then max_features=sqrt(n_features).
 If “log2”, then max_features=log2(n_features).
 If None, then max_features=n_features.
Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than
max_features
features. Note: this parameter is treespecific. max_depth : integer or None, optional (default=None)
 The maximum depth of the tree. If None, then nodes are expanded until
all leaves are pure or until all leaves contain less than
min_samples_split samples.
Ignored if
max_leaf_nodes
is not None. Note: this parameter is treespecific.  min_samples_split : integer, optional (default=2)
 The minimum number of samples required to split an internal node. Note: this parameter is treespecific.
 min_samples_leaf : integer, optional (default=1)
 The minimum number of samples in newly created leaves. A split is
discarded if after the split, one of the leaves would contain less then
min_samples_leaf
samples. Note: this parameter is treespecific.  min_weight_fraction_leaf : float, optional (default=0.)
 The minimum weighted fraction of the input samples required to be at a leaf node. Note: this parameter is treespecific.
 max_leaf_nodes : int or None, optional (default=None)
 Grow trees with
max_leaf_nodes
in bestfirst fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes. If not None thenmax_depth
will be ignored. Note: this parameter is treespecific.  bootstrap : boolean, optional (default=False)
 Whether bootstrap samples are used when building trees. Note: this parameter is treespecific.
 oob_score : bool
 Whether to use outofbag samples to estimate the generalization error.
 n_jobs : integer, optional (default=1)
 The number of jobs to run in parallel for both fit and predict. If 1, then the number of jobs is set to the number of cores.
 random_state : int, RandomState instance or None, optional (default=None)
 If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.
 verbose : int, optional (default=0)
 Controls the verbosity of the tree building process.
 warm_start : bool, optional (default=False)
 When set to
True
, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new forest.
Attributes
estimators_
: list of DecisionTreeRegressor The collection of fitted subestimators.
feature_importances_
: array of shape = [n_features] The feature importances (the higher, the more important the feature).
n_features_
: int The number of features.
n_outputs_
: int The number of outputs.
oob_score_
: float Score of the training dataset obtained using an outofbag estimate.
oob_prediction_
: array of shape = [n_samples] Prediction computed with outofbag estimate on the training set.
References
[1] P. Geurts, D. Ernst., and L. Wehenkel, “Extremely randomized trees”, Machine Learning, 63(1), 342, 2006. See also
sklearn.tree.ExtraTreeRegressor: Base estimator for this ensemble. RandomForestRegressor: Ensemble regressor using trees with optimal splits.
Full API documentation: ExtraTreesRegressorScikitsLearnNode

class
mdp.nodes.
LabelPropagationScikitsLearnNode
¶ Label Propagation classifier
This node has been automatically generated by wrapping the
sklearn.semi_supervised.label_propagation.LabelPropagation
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Read more in the User Guide.
Parameters
 kernel : {‘knn’, ‘rbf’}
 String identifier for kernel function to use. Only ‘rbf’ and ‘knn’ kernels are currently supported..
 gamma : float
 Parameter for rbf kernel
 n_neighbors : integer > 0
 Parameter for knn kernel
 alpha : float
 Clamping factor
 max_iter : float
 Change maximum number of iterations allowed
 tol : float
 Convergence tolerance: threshold to consider the system at steady state
Attributes
X_
: array, shape = [n_samples, n_features] Input array.
classes_
: array, shape = [n_classes] The distinct labels used in classifying instances.
label_distributions_
: array, shape = [n_samples, n_classes] Categorical distribution for each item.
transduction_
: array, shape = [n_samples] Label assigned to each item via the transduction.
n_iter_
: int Number of iterations run.
Examples
>>> from sklearn import datasets >>> from sklearn.semi_supervised import LabelPropagation >>> label_prop_model = LabelPropagation() >>> iris = datasets.load_iris() >>> random_unlabeled_points = np.where(np.random.random_integers(0, 1, ... size=len(iris.target))) >>> labels = np.copy(iris.target) >>> labels[random_unlabeled_points] = 1 >>> label_prop_model.fit(iris.data, labels) ... LabelPropagation(...)
References
Xiaojin Zhu and Zoubin Ghahramani. Learning from labeled and unlabeled data with label propagation. Technical Report CMUCALD02107, Carnegie Mellon University, 2002 http://pages.cs.wisc.edu/~jerryzhu/pub/CMUCALD02107.pdf
See Also
LabelSpreading : Alternate label propagation strategy more robust to noise
Full API documentation: LabelPropagationScikitsLearnNode

class
mdp.nodes.
GaussianProcessScikitsLearnNode
¶ The Gaussian Process model class.
This node has been automatically generated by wrapping the
sklearn.gaussian_process.gaussian_process.GaussianProcess
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Read more in the User Guide.
Parameters
 regr : string or callable, optional
A regression function returning an array of outputs of the linear regression functional basis. The number of observations n_samples should be greater than the size p of this basis. Default assumes a simple constant regression trend. Available builtin regression models are:
'constant', 'linear', 'quadratic'
 corr : string or callable, optional
A stationary autocorrelation function returning the autocorrelation between two points x and x’. Default assumes a squaredexponential autocorrelation model. Builtin correlation models are:
'absolute_exponential', 'squared_exponential', 'generalized_exponential', 'cubic', 'linear'
 beta0 : double array_like, optional
 The regression weight vector to perform Ordinary Kriging (OK). Default assumes Universal Kriging (UK) so that the vector beta of regression weights is estimated using the maximum likelihood principle.
 storage_mode : string, optional
 A string specifying whether the Cholesky decomposition of the correlation matrix should be stored in the class (storage_mode = ‘full’) or not (storage_mode = ‘light’). Default assumes storage_mode = ‘full’, so that the Cholesky decomposition of the correlation matrix is stored. This might be a useful parameter when one is not interested in the MSE and only plan to estimate the BLUP, for which the correlation matrix is not required.
 verbose : boolean, optional
 A boolean specifying the verbose level. Default is verbose = False.
 theta0 : double array_like, optional
 An array with shape (n_features, ) or (1, ). The parameters in the autocorrelation model. If thetaL and thetaU are also specified, theta0 is considered as the starting point for the maximum likelihood estimation of the best set of parameters. Default assumes isotropic autocorrelation model with theta0 = 1e1.
 thetaL : double array_like, optional
 An array with shape matching theta0’s. Lower bound on the autocorrelation parameters for maximum likelihood estimation. Default is None, so that it skips maximum likelihood estimation and it uses theta0.
 thetaU : double array_like, optional
 An array with shape matching theta0’s. Upper bound on the autocorrelation parameters for maximum likelihood estimation. Default is None, so that it skips maximum likelihood estimation and it uses theta0.
 normalize : boolean, optional
 Input X and observations y are centered and reduced wrt means and standard deviations estimated from the n_samples observations provided. Default is normalize = True so that data is normalized to ease maximum likelihood estimation.
 nugget : double or ndarray, optional
 Introduce a nugget effect to allow smooth predictions from noisy data. If nugget is an ndarray, it must be the same length as the number of data points used for the fit. The nugget is added to the diagonal of the assumed training covariance; in this way it acts as a Tikhonov regularization in the problem. In the special case of the squared exponential correlation function, the nugget mathematically represents the variance of the input values. Default assumes a nugget close to machine precision for the sake of robustness (nugget = 10. * MACHINE_EPSILON).
 optimizer : string, optional
A string specifying the optimization algorithm to be used. Default uses ‘fmin_cobyla’ algorithm from scipy.optimize. Available optimizers are:
'fmin_cobyla', 'Welch'
‘Welch’ optimizer is dued to Welch et al., see reference [WBSWM1992]. It consists in iterating over several onedimensional optimizations instead of running one single multidimensional optimization.
 random_start : int, optional
 The number of times the Maximum Likelihood Estimation should be performed from a random starting point. The first MLE always uses the specified starting point (theta0), the next starting points are picked at random according to an exponential distribution (loguniform on [thetaL, thetaU]). Default does not use random starting point (random_start = 1).
 random_state: integer or numpy.RandomState, optional
 The generator used to shuffle the sequence of coordinates of theta in the Welch optimizer. If an integer is given, it fixes the seed. Defaults to the global numpy random number generator.
Attributes
theta_
: array Specified theta OR the best set of autocorrelation parameters (the sought maximizer of the reduced likelihood function).
reduced_likelihood_function_value_
: array The optimal reduced likelihood function value.
Examples
>>> import numpy as np >>> from sklearn.gaussian_process import GaussianProcess >>> X = np.array([[1., 3., 5., 6., 7., 8.]]).T >>> y = (X * np.sin(X)).ravel() >>> gp = GaussianProcess(theta0=0.1, thetaL=.001, thetaU=1.) >>> gp.fit(X, y) GaussianProcess(beta0=None... ...
Notes
The presentation implementation is based on a translation of the DACE Matlab toolbox, see reference [NLNS2002].
References
[NLNS2002] H.B. Nielsen, S.N. Lophaven, H. B. Nielsen and J. Sondergaard. DACE  A MATLAB Kriging Toolbox. (2002) http://www2.imm.dtu.dk/~hbn/dace/dace.pdf [WBSWM1992] W.J. Welch, R.J. Buck, J. Sacks, H.P. Wynn, T.J. Mitchell, and M.D. Morris (1992). Screening, predicting, and computer experiments. Technometrics, 34(1) 15–25. http://www.jstor.org/pss/1269548 Full API documentation: GaussianProcessScikitsLearnNode

class
mdp.nodes.
MeanEstimatorScikitsLearnNode
¶ This node has been automatically generated by wrapping the
sklearn.ensemble.gradient_boosting.MeanEstimator
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Full API documentation: MeanEstimatorScikitsLearnNode

class
mdp.nodes.
SelectFromModelScikitsLearnNode
¶ Metatransformer for selecting features based on importance weights.
This node has been automatically generated by wrapping the
sklearn.feature_selection.from_model.SelectFromModel
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.New in version 0.17.
Parameters
 estimator : object
 The base estimator from which the transformer is built.
This can be both a fitted (if
prefit
is set to True) or a nonfitted estimator.  threshold : string, float, optional default None
 The threshold value to use for feature selection. Features whose
importance is greater or equal are kept while the others are
discarded. If “median” (resp. “mean”), then the
threshold
value is the median (resp. the mean) of the feature importances. A scaling factor (e.g., “1.25*mean”) may also be used. If None and if the estimator has a parameter penalty set to l1, either explicitly or implicity (e.g, Lasso), the threshold is used is 1e5. Otherwise, “mean” is used by default.  prefit : bool, default False
 Whether a prefit model is expected to be passed into the constructor
directly or not. If True,
transform
must be called directly and SelectFromModel cannot be used withcross_val_score
,GridSearchCV
and similar utilities that clone the estimator. Otherwise train the model usingfit
and thentransform
to do feature selection.
Attributes
 estimator_: an estimator
 The base estimator from which the transformer is built.
This is stored only when a nonfitted estimator is passed to the
SelectFromModel
, i.e when prefit is False.  threshold_: float
 The threshold value used for feature selection.
Full API documentation: SelectFromModelScikitsLearnNode

class
mdp.nodes.
RadiusNeighborsRegressorScikitsLearnNode
¶ Regression based on neighbors within a fixed radius.
This node has been automatically generated by wrapping the
sklearn.neighbors.regression.RadiusNeighborsRegressor
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.The target is predicted by local interpolation of the targets associated of the nearest neighbors in the training set.
Read more in the User Guide.
Parameters
 radius : float, optional (default = 1.0)
 Range of parameter space to use by default for :meth`radius_neighbors` queries.
 weights : str or callable
weight function used in prediction. Possible values:
 ‘uniform’ : uniform weights. All points in each neighborhood are weighted equally.
 ‘distance’ : weight points by the inverse of their distance. in this case, closer neighbors of a query point will have a greater influence than neighbors which are further away.
 [callable] : a userdefined function which accepts an array of distances, and returns an array of the same shape containing the weights.
Uniform weights are used by default.
 algorithm : {‘auto’, ‘ball_tree’, ‘kd_tree’, ‘brute’}, optional
Algorithm used to compute the nearest neighbors:
 ‘ball_tree’ will use
BallTree
 ‘kd_tree’ will use
KDtree
 ‘brute’ will use a bruteforce search.
 ‘auto’ will attempt to decide the most appropriate algorithm
based on the values passed to
fit()
method.
Note: fitting on sparse input will override the setting of this parameter, using brute force.
 ‘ball_tree’ will use
 leaf_size : int, optional (default = 30)
 Leaf size passed to BallTree or KDTree. This can affect the speed of the construction and query, as well as the memory required to store the tree. The optimal value depends on the nature of the problem.
 metric : string or DistanceMetric object (default=’minkowski’)
 the distance metric to use for the tree. The default metric is minkowski, and with p=2 is equivalent to the standard Euclidean metric. See the documentation of the DistanceMetric class for a list of available metrics.
 p : integer, optional (default = 2)
 Power parameter for the Minkowski metric. When p = 1, this is equivalent to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.
 metric_params : dict, optional (default = None)
 Additional keyword arguments for the metric function.
Examples
>>> X = [[0], [1], [2], [3]] >>> y = [0, 0, 1, 1] >>> from sklearn.neighbors import RadiusNeighborsRegressor >>> neigh = RadiusNeighborsRegressor(radius=1.0) >>> neigh.fit(X, y) RadiusNeighborsRegressor(...) >>> print(neigh.predict([[1.5]])) [ 0.5]
See also
NearestNeighbors KNeighborsRegressor KNeighborsClassifier RadiusNeighborsClassifier
Notes
See Nearest Neighbors in the online documentation for a discussion of the choice of
algorithm
andleaf_size
.http://en.wikipedia.org/wiki/Knearest_neighbor_algorithm
Full API documentation: RadiusNeighborsRegressorScikitsLearnNode

class
mdp.nodes.
PLSSVDScikitsLearnNode
¶ Partial Least Square SVD
This node has been automatically generated by wrapping the
sklearn.cross_decomposition.pls_.PLSSVD
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Simply perform a svd on the crosscovariance matrix: X’Y There are no iterative deflation here.
Read more in the User Guide.
Parameters
 n_components : int, default 2
 Number of components to keep.
 scale : boolean, default True
 Whether to scale X and Y.
 copy : boolean, default True
 Whether to copy X and Y, or perform inplace computations.
Attributes
x_weights_
: array, [p, n_components] X block weights vectors.
y_weights_
: array, [q, n_components] Y block weights vectors.
x_scores_
: array, [n_samples, n_components] X scores.
y_scores_
: array, [n_samples, n_components] Y scores.
See also
PLSCanonical CCA
Full API documentation: PLSSVDScikitsLearnNode

class
mdp.nodes.
GaussianRandomProjectionScikitsLearnNode
¶ Reduce dimensionality through Gaussian random projection
This node has been automatically generated by wrapping the
sklearn.random_projection.GaussianRandomProjection
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.The components of the random matrix are drawn from N(0, 1 / n_components).
Read more in the User Guide.
Parameters
 n_components : int or ‘auto’, optional (default = ‘auto’)
Dimensionality of the target projection space.
n_components can be automatically adjusted according to the number of samples in the dataset and the bound given by the JohnsonLindenstrauss lemma. In that case the quality of the embedding is controlled by the
eps
parameter.It should be noted that JohnsonLindenstrauss lemma can yield very conservative estimated of the required number of components as it makes no assumption on the structure of the dataset.
 eps : strictly positive float, optional (default=0.1)
Parameter to control the quality of the embedding according to the JohnsonLindenstrauss lemma when n_components is set to ‘auto’.
Smaller values lead to better embedding and higher number of dimensions (n_components) in the target projection space.
 random_state : integer, RandomState instance or None (default=None)
 Control the pseudo random number generator used to generate the matrix at fit time.
Attributes
n_component_
: int Concrete number of components computed when n_components=”auto”.
components_
: numpy array of shape [n_components, n_features] Random matrix used for the projection.
See Also
SparseRandomProjection
Full API documentation: GaussianRandomProjectionScikitsLearnNode

class
mdp.nodes.
OneHotEncoderScikitsLearnNode
¶ Encode categorical integer features using a onehot aka oneofK scheme.
This node has been automatically generated by wrapping the
sklearn.preprocessing.data.OneHotEncoder
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.The input to this transformer should be a matrix of integers, denoting the values taken on by categorical (discrete) features. The output will be a sparse matrix where each column corresponds to one possible value of one feature. It is assumed that input features take on values in the range [0, n_values).
This encoding is needed for feeding categorical data to many scikitlearn estimators, notably linear models and SVMs with the standard kernels.
Read more in the User Guide.
Parameters
 n_values : ‘auto’, int or array of ints
Number of values per feature.
 ‘auto’ : determine value range from training data.
 int : maximum value for all features.
 array : maximum value per feature.
 categorical_features: “all” or array of indices or mask
Specify what features are treated as categorical.
 ‘all’ (default): All features are treated as categorical.
 array of indices: Array of categorical feature indices.
 mask: Array of length n_features and with dtype=bool.
Noncategorical features are always stacked to the right of the matrix.
 dtype : number type, default=np.float
 Desired dtype of output.
 sparse : boolean, default=True
 Will return sparse matrix if set True else will return an array.
 handle_unknown : str, ‘error’ or ‘ignore’
 Whether to raise an error or ignore if a unknown categorical feature is present during transform.
Attributes
active_features_
: array Indices for active features, meaning values that actually occur
in the training set. Only available when n_values is
'auto'
. feature_indices_
: array of shape (n_features,) Indices to feature ranges.
Feature
i
in the original data is mapped to features fromfeature_indices_[i]
tofeature_indices_[i+1]
(and then potentially masked by active_features_ afterwards) n_values_
: array of shape (n_features,) Maximum number of values per feature.
Examples
Given a dataset with three features and two samples, we let the encoder find the maximum value per feature and transform the data to a binary onehot encoding.
>>> from sklearn.preprocessing import OneHotEncoder >>> enc = OneHotEncoder() >>> enc.fit([[0, 0, 3], [1, 1, 0], [0, 2, 1], [1, 0, 2]]) OneHotEncoder(categorical_features='all', dtype=<... 'float'>, handle_unknown='error', n_values='auto', sparse=True) >>> enc.n_values_ array([2, 3, 4]) >>> enc.feature_indices_ array([0, 2, 5, 9]) >>> enc.transform([[0, 1, 1]]).toarray() array([[ 1., 0., 0., 1., 0., 0., 1., 0., 0.]])
See also
 sklearn.feature_extraction.DictVectorizer : performs a onehot encoding of
 dictionary items (also handles stringvalued features).
 sklearn.feature_extraction.FeatureHasher : performs an approximate onehot
 encoding of dictionary items or strings.
Full API documentation: OneHotEncoderScikitsLearnNode

class
mdp.nodes.
KNeighborsRegressorScikitsLearnNode
¶ Regression based on knearest neighbors.
This node has been automatically generated by wrapping the
sklearn.neighbors.regression.KNeighborsRegressor
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.The target is predicted by local interpolation of the targets associated of the nearest neighbors in the training set.
Read more in the User Guide.
Parameters
 n_neighbors : int, optional (default = 5)
 Number of neighbors to use by default for
k_neighbors()
queries.  weights : str or callable
weight function used in prediction. Possible values:
 ‘uniform’ : uniform weights. All points in each neighborhood are weighted equally.
 ‘distance’ : weight points by the inverse of their distance. in this case, closer neighbors of a query point will have a greater influence than neighbors which are further away.
 [callable] : a userdefined function which accepts an array of distances, and returns an array of the same shape containing the weights.
Uniform weights are used by default.
 algorithm : {‘auto’, ‘ball_tree’, ‘kd_tree’, ‘brute’}, optional
Algorithm used to compute the nearest neighbors:
 ‘ball_tree’ will use
BallTree
 ‘kd_tree’ will use
KDtree
 ‘brute’ will use a bruteforce search.
 ‘auto’ will attempt to decide the most appropriate algorithm
based on the values passed to
fit()
method.
Note: fitting on sparse input will override the setting of this parameter, using brute force.
 ‘ball_tree’ will use
 leaf_size : int, optional (default = 30)
 Leaf size passed to BallTree or KDTree. This can affect the speed of the construction and query, as well as the memory required to store the tree. The optimal value depends on the nature of the problem.
 metric : string or DistanceMetric object (default=’minkowski’)
 the distance metric to use for the tree. The default metric is minkowski, and with p=2 is equivalent to the standard Euclidean metric. See the documentation of the DistanceMetric class for a list of available metrics.
 p : integer, optional (default = 2)
 Power parameter for the Minkowski metric. When p = 1, this is equivalent to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.
 metric_params : dict, optional (default = None)
 Additional keyword arguments for the metric function.
 n_jobs : int, optional (default = 1)
 The number of parallel jobs to run for neighbors search.
If
1
, then the number of jobs is set to the number of CPU cores. Doesn’t affectfit()
method.
Examples
>>> X = [[0], [1], [2], [3]] >>> y = [0, 0, 1, 1] >>> from sklearn.neighbors import KNeighborsRegressor >>> neigh = KNeighborsRegressor(n_neighbors=2) >>> neigh.fit(X, y) KNeighborsRegressor(...) >>> print(neigh.predict([[1.5]])) [ 0.5]
See also
NearestNeighbors RadiusNeighborsRegressor KNeighborsClassifier RadiusNeighborsClassifier
Notes
See Nearest Neighbors in the online documentation for a discussion of the choice of
algorithm
andleaf_size
.Warning
Regarding the Nearest Neighbors algorithms, if it is found that two neighbors, neighbor k+1 and k, have identical distances but but different labels, the results will depend on the ordering of the training data.
http://en.wikipedia.org/wiki/Knearest_neighbor_algorithm
Full API documentation: KNeighborsRegressorScikitsLearnNode

class
mdp.nodes.
KMeansScikitsLearnNode
¶ KMeans clustering
This node has been automatically generated by wrapping the
sklearn.cluster.k_means_.KMeans
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Read more in the User Guide.
Parameters
 n_clusters : int, optional, default: 8
 The number of clusters to form as well as the number of centroids to generate.
 max_iter : int, default: 300
 Maximum number of iterations of the kmeans algorithm for a single run.
 n_init : int, default: 10
 Number of time the kmeans algorithm will be run with different centroid seeds. The final results will be the best output of n_init consecutive runs in terms of inertia.
 init : {‘kmeans++’, ‘random’ or an ndarray}
Method for initialization, defaults to ‘kmeans++’:
‘kmeans++’ : selects initial cluster centers for kmean clustering in a smart way to speed up convergence. See section Notes in k_init for more details.
‘random’: choose k observations (rows) at random from data for the initial centroids.
If an ndarray is passed, it should be of shape (n_clusters, n_features) and gives the initial centers.
 precompute_distances : {‘auto’, True, False}
Precompute distances (faster but takes more memory).
‘auto’ : do not precompute distances if n_samples * n_clusters > 12 million. This corresponds to about 100MB overhead per job using double precision.
True : always precompute distances
False : never precompute distances
 tol : float, default: 1e4
 Relative tolerance with regards to inertia to declare convergence
 n_jobs : int
The number of jobs to use for the computation. This works by computing each of the n_init runs in parallel.
If 1 all CPUs are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. For n_jobs below 1, (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = 2, all CPUs but one are used.
 random_state : integer or numpy.RandomState, optional
 The generator used to initialize the centers. If an integer is given, it fixes the seed. Defaults to the global numpy random number generator.
 verbose : int, default 0
 Verbosity mode.
 copy_x : boolean, default True
 When precomputing distances it is more numerically accurate to center the data first. If copy_x is True, then the original data is not modified. If False, the original data is modified, and put back before the function returns, but small numerical differences may be introduced by subtracting and then adding the data mean.
Attributes
cluster_centers_
: array, [n_clusters, n_features] Coordinates of cluster centers
labels_
: Labels of each point
inertia_
: float Sum of distances of samples to their closest cluster center.
Notes
The kmeans problem is solved using Lloyd’s algorithm.
The average complexity is given by O(k n T), were n is the number of samples and T is the number of iteration.
The worst case complexity is given by O(n^(k+2/p)) with n = n_samples, p = n_features. (D. Arthur and S. Vassilvitskii, ‘How slow is the kmeans method?’ SoCG2006)
In practice, the kmeans algorithm is very fast (one of the fastest clustering algorithms available), but it falls in local minima. That’s why it can be useful to restart it several times.
See also
MiniBatchKMeans:
 Alternative online implementation that does incremental updates
 of the centers positions using minibatches.
 For large scale learning (say n_samples > 10k) MiniBatchKMeans is
 probably much faster to than the default batch implementation.
Full API documentation: KMeansScikitsLearnNode

class
mdp.nodes.
LatentDirichletAllocationScikitsLearnNode
¶ Latent Dirichlet Allocation with online variational Bayes algorithm
This node has been automatically generated by wrapping the
sklearn.decomposition.online_lda.LatentDirichletAllocation
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.New in version 0.17.
Parameters
 n_topics : int, optional (default=10)
 Number of topics.
 doc_topic_prior : float, optional (default=None)
 Prior of document topic distribution theta. If the value is None, defaults to 1 / n_topics. In the literature, this is called alpha.
 topic_word_prior : float, optional (default=None)
 Prior of topic word distribution beta. If the value is None, defaults to 1 / n_topics. In the literature, this is called eta.
 learning_method : ‘batch’  ‘online’, default=’online’
Method used to update _component. Only used in fit method. In general, if the data size is large, the online update will be much faster than the batch update. Valid options:
'batch': Batch variational Bayes method. Use all training data in each EM update. Old `components_` will be overwritten in each iteration. 'online': Online variational Bayes method. In each EM update, use minibatch of training data to update the ``components_`` variable incrementally. The learning rate is controlled by the ``learning_decay`` and the ``learning_offset`` parameters.
 learning_decay : float, optional (default=0.7)
 It is a parameter that control learning rate in the online learning
method. The value should be set between (0.5, 1.0] to guarantee
asymptotic convergence. When the value is 0.0 and batch_size is
n_samples
, the update method is same as batch learning. In the literature, this is called kappa.  learning_offset : float, optional (default=10.)
 A (positive) parameter that downweights early iterations in online learning. It should be greater than 1.0. In the literature, this is called tau_0.
 max_iter : integer, optional (default=10)
 The maximum number of iterations.
 total_samples : int, optional (default=1e6)
 Total number of documents. Only used in the partial_fit method.
 batch_size : int, optional (default=128)
 Number of documents to use in each EM iteration. Only used in online learning.
 evaluate_every : int optional (default=0)
 How often to evaluate perplexity. Only used in fit method. set it to 0 or and negative number to not evalute perplexity in training at all. Evaluating perplexity can help you check convergence in training process, but it will also increase total training time. Evaluating perplexity in every iteration might increase training time up to twofold.
 perp_tol : float, optional (default=1e1)
 Perplexity tolerance in batch learning. Only used when
evaluate_every
is greater than 0.  mean_change_tol : float, optional (default=1e3)
 Stopping tolerance for updating document topic distribution in Estep.
 max_doc_update_iter : int (default=100)
 Max number of iterations for updating document topic distribution in the Estep.
 n_jobs : int, optional (default=1)
 The number of jobs to use in the Estep. If 1, all CPUs are used. For
n_jobs
below 1, (n_cpus + 1 + n_jobs) are used.  verbose : int, optional (default=0)
 Verbosity level.
 random_state : int or RandomState instance or None, optional (default=None)
 Pseudorandom number generator seed control.
Attributes
components_
: array, [n_topics, n_features] Topic word distribution.
components_[i, j]
represents word j in topic i. In the literature, this is called lambda. n_batch_iter_
: int Number of iterations of the EM step.
n_iter_
: int Number of passes over the dataset.
References
 [1] “Online Learning for Latent Dirichlet Allocation”, Matthew D. Hoffman,
 David M. Blei, Francis Bach, 2010
 [2] “Stochastic Variational Inference”, Matthew D. Hoffman, David M. Blei,
 Chong Wang, John Paisley, 2013
[3] Matthew D. Hoffman’s onlineldavb code. Link:
Full API documentation: LatentDirichletAllocationScikitsLearnNode

class
mdp.nodes.
PassiveAggressiveRegressorScikitsLearnNode
¶ Passive Aggressive Regressor
This node has been automatically generated by wrapping the
sklearn.linear_model.passive_aggressive.PassiveAggressiveRegressor
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Read more in the User Guide.
Parameters
 C : float
 Maximum step size (regularization). Defaults to 1.0.
 epsilon : float
 If the difference between the current prediction and the correct label is below this threshold, the model is not updated.
 fit_intercept : bool
 Whether the intercept should be estimated or not. If False, the data is assumed to be already centered. Defaults to True.
 n_iter : int, optional
 The number of passes over the training data (aka epochs). Defaults to 5.
 shuffle : bool, default=True
 Whether or not the training data should be shuffled after each epoch.
 random_state : int seed, RandomState instance, or None (default)
 The seed of the pseudo random number generator to use when shuffling the data.
 verbose : integer, optional
 The verbosity level
 loss : string, optional
The loss function to be used:
 epsilon_insensitive: equivalent to PAI in the reference paper.
 squared_epsilon_insensitive: equivalent to PAII in the reference
 paper.
 warm_start : bool, optional
 When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution.
Attributes
coef_
: array, shape = [1, n_features] if n_classes == 2 else [n_classes, n_features] Weights assigned to the features.
intercept_
: array, shape = [1] if n_classes == 2 else [n_classes] Constants in decision function.
See also
SGDRegressor
References
Online PassiveAggressive Algorithms <http://jmlr.csail.mit.edu/papers/volume7/crammer06a/crammer06a.pdf> K. Crammer, O. Dekel, J. Keshat, S. ShalevShwartz, Y. Singer  JMLR (2006)
Full API documentation: PassiveAggressiveRegressorScikitsLearnNode

class
mdp.nodes.
RandomForestClassifierScikitsLearnNode
¶ A random forest classifier.
This node has been automatically generated by wrapping the
sklearn.ensemble.forest.RandomForestClassifier
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.A random forest is a meta estimator that fits a number of decision tree classifiers on various subsamples of the dataset and use averaging to improve the predictive accuracy and control overfitting. The subsample size is always the same as the original input sample size but the samples are drawn with replacement if bootstrap=True (default).
Read more in the User Guide.
Parameters
 n_estimators : integer, optional (default=10)
 The number of trees in the forest.
 criterion : string, optional (default=”gini”)
 The function to measure the quality of a split. Supported criteria are “gini” for the Gini impurity and “entropy” for the information gain. Note: this parameter is treespecific.
 max_features : int, float, string or None, optional (default=”auto”)
The number of features to consider when looking for the best split:
 If int, then consider max_features features at each split.
 If float, then max_features is a percentage and int(max_features * n_features) features are considered at each split.
 If “auto”, then max_features=sqrt(n_features).
 If “sqrt”, then max_features=sqrt(n_features) (same as “auto”).
 If “log2”, then max_features=log2(n_features).
 If None, then max_features=n_features.
Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than
max_features
features. Note: this parameter is treespecific. max_depth : integer or None, optional (default=None)
 The maximum depth of the tree. If None, then nodes are expanded until
all leaves are pure or until all leaves contain less than
min_samples_split samples.
Ignored if
max_leaf_nodes
is not None. Note: this parameter is treespecific.  min_samples_split : integer, optional (default=2)
 The minimum number of samples required to split an internal node. Note: this parameter is treespecific.
 min_samples_leaf : integer, optional (default=1)
 The minimum number of samples in newly created leaves. A split is
discarded if after the split, one of the leaves would contain less then
min_samples_leaf
samples. Note: this parameter is treespecific.  min_weight_fraction_leaf : float, optional (default=0.)
 The minimum weighted fraction of the input samples required to be at a leaf node. Note: this parameter is treespecific.
 max_leaf_nodes : int or None, optional (default=None)
 Grow trees with
max_leaf_nodes
in bestfirst fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes. If not None thenmax_depth
will be ignored. Note: this parameter is treespecific.  bootstrap : boolean, optional (default=True)
 Whether bootstrap samples are used when building trees.
 oob_score : bool
 Whether to use outofbag samples to estimate the generalization error.
 n_jobs : integer, optional (default=1)
 The number of jobs to run in parallel for both fit and predict. If 1, then the number of jobs is set to the number of cores.
 random_state : int, RandomState instance or None, optional (default=None)
 If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.
 verbose : int, optional (default=0)
 Controls the verbosity of the tree building process.
 warm_start : bool, optional (default=False)
 When set to
True
, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new forest.
class_weight : dict, list of dicts, “balanced”, “balanced_subsample” or None, optional
Weights associated with classes in the form
{class_label: weight}
. If not given, all classes are supposed to have weight one. For multioutput problems, a list of dicts can be provided in the same order as the columns of y.The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as
n_samples / (n_classes * np.bincount(y))
The “balanced_subsample” mode is the same as “balanced” except that weights are computed based on the bootstrap sample for every tree grown.
For multioutput, the weights of each column of y will be multiplied.
Note that these weights will be multiplied with sample_weight (passed through the fit method) if sample_weight is specified.
Attributes
estimators_
: list of DecisionTreeClassifier The collection of fitted subestimators.
classes_
: array of shape = [n_classes] or a list of such arrays The classes labels (single output problem), or a list of arrays of class labels (multioutput problem).
n_classes_
: int or list The number of classes (single output problem), or a list containing the number of classes for each output (multioutput problem).
n_features_
: int The number of features when
fit
is performed. n_outputs_
: int The number of outputs when
fit
is performed. feature_importances_
: array of shape = [n_features] The feature importances (the higher, the more important the feature).
oob_score_
: float Score of the training dataset obtained using an outofbag estimate.
oob_decision_function_
: array of shape = [n_samples, n_classes] Decision function computed with outofbag estimate on the training set. If n_estimators is small it might be possible that a data point was never left out during the bootstrap. In this case, oob_decision_function_ might contain NaN.
References
[1]  Breiman, “Random Forests”, Machine Learning, 45(1), 532, 2001.
See also
DecisionTreeClassifier, ExtraTreesClassifier
Full API documentation: RandomForestClassifierScikitsLearnNode

class
mdp.nodes.
LabelEncoderScikitsLearnNode
¶ Encode labels with value between 0 and n_classes1.
This node has been automatically generated by wrapping the
sklearn.preprocessing.label.LabelEncoder
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Read more in the User Guide.
Attributes
classes_
: array of shape (n_class,) Holds the label for each class.
Examples
LabelEncoder can be used to normalize labels.
>>> from sklearn import preprocessing >>> le = preprocessing.LabelEncoder() >>> le.fit([1, 2, 2, 6]) LabelEncoder() >>> le.classes_ array([1, 2, 6]) >>> le.transform([1, 1, 2, 6]) array([0, 0, 1, 2]...) >>> le.inverse_transform([0, 0, 1, 2]) array([1, 1, 2, 6])
It can also be used to transform nonnumerical labels (as long as they are hashable and comparable) to numerical labels.
>>> le = preprocessing.LabelEncoder() >>> le.fit(["paris", "paris", "tokyo", "amsterdam"]) LabelEncoder() >>> list(le.classes_) ['amsterdam', 'paris', 'tokyo'] >>> le.transform(["tokyo", "tokyo", "paris"]) array([2, 2, 1]...) >>> list(le.inverse_transform([2, 2, 1])) ['tokyo', 'tokyo', 'paris']
Full API documentation: LabelEncoderScikitsLearnNode

class
mdp.nodes.
RidgeScikitsLearnNode
¶ Linear least squares with l2 regularization.
This node has been automatically generated by wrapping the
sklearn.linear_model.ridge.Ridge
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.This model solves a regression model where the loss function is the linear least squares function and regularization is given by the l2norm. Also known as Ridge Regression or Tikhonov regularization. This estimator has builtin support for multivariate regression (i.e., when y is a 2darray of shape [n_samples, n_targets]).
Read more in the User Guide.
Parameters
 alpha : {float, arraylike}, shape (n_targets)
 Small positive values of alpha improve the conditioning of the problem
and reduce the variance of the estimates. Alpha corresponds to
C^1
in other linear models such as LogisticRegression or LinearSVC. If an array is passed, penalties are assumed to be specific to the targets. Hence they must correspond in number.  copy_X : boolean, optional, default True
 If True, X will be copied; else, it may be overwritten.
 fit_intercept : boolean
 Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered).
 max_iter : int, optional
 Maximum number of iterations for conjugate gradient solver. For ‘sparse_cg’ and ‘lsqr’ solvers, the default value is determined by scipy.sparse.linalg. For ‘sag’ solver, the default value is 1000.
 normalize : boolean, optional, default False
 If True, the regressors X will be normalized before regression.
 solver : {‘auto’, ‘svd’, ‘cholesky’, ‘lsqr’, ‘sparse_cg’, ‘sag’}
Solver to use in the computational routines:
 ‘auto’ chooses the solver automatically based on the type of data.
 ‘svd’ uses a Singular Value Decomposition of X to compute the Ridge coefficients. More stable for singular matrices than ‘cholesky’.
 ‘cholesky’ uses the standard scipy.linalg.solve function to obtain a closedform solution.
 ‘sparse_cg’ uses the conjugate gradient solver as found in scipy.sparse.linalg.cg. As an iterative algorithm, this solver is more appropriate than ‘cholesky’ for largescale data (possibility to set tol and max_iter).
 ‘lsqr’ uses the dedicated regularized leastsquares routine scipy.sparse.linalg.lsqr. It is the fatest but may not be available in old scipy versions. It also uses an iterative procedure.
 ‘sag’ uses a Stochastic Average Gradient descent. It also uses an iterative procedure, and is often faster than other solvers when both n_samples and n_features are large. Note that ‘sag’ fast convergence is only guaranteed on features with approximately the same scale. You can preprocess the data with a scaler from sklearn.preprocessing.
All last four solvers support both dense and sparse data. However, only ‘sag’ supports sparse input when fit_intercept is True.
New in version 0.17: Stochastic Average Gradient descent solver.
 tol : float
 Precision of the solution.
 random_state : int seed, RandomState instance, or None (default)
The seed of the pseudo random number generator to use when shuffling the data. Used in ‘sag’ solver.
New in version 0.17: random_state to support Stochastic Average Gradient.
Attributes
coef_
: array, shape (n_features,) or (n_targets, n_features) Weight vector(s).
intercept_
: float  array, shape = (n_targets,) Independent term in decision function. Set to 0.0 if
fit_intercept = False
. n_iter_
: array or None, shape (n_targets,) Actual number of iterations for each target. Available only for sag and lsqr solvers. Other solvers will return None.
See also
RidgeClassifier, RidgeCV, KernelRidge
Examples
>>> from sklearn.linear_model import Ridge >>> import numpy as np >>> n_samples, n_features = 10, 5 >>> np.random.seed(0) >>> y = np.random.randn(n_samples) >>> X = np.random.randn(n_samples, n_features) >>> clf = Ridge(alpha=1.0) >>> clf.fit(X, y) Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None, normalize=False, random_state=None, solver='auto', tol=0.001)
Full API documentation: RidgeScikitsLearnNode

class
mdp.nodes.
ElasticNetScikitsLearnNode
¶ Linear regression with combined L1 and L2 priors as regularizer.
This node has been automatically generated by wrapping the
sklearn.linear_model.coordinate_descent.ElasticNet
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Minimizes the objective function:
1 / (2 * n_samples) * y  Xw^2_2 + + alpha * l1_ratio * w_1 + 0.5 * alpha * (1  l1_ratio) * w^2_2
If you are interested in controlling the L1 and L2 penalty separately, keep in mind that this is equivalent to:
a * L1 + b * L2
where:
alpha = a + b and l1_ratio = a / (a + b)
The parameter l1_ratio corresponds to alpha in the glmnet R package while alpha corresponds to the lambda parameter in glmnet. Specifically, l1_ratio = 1 is the lasso penalty. Currently, l1_ratio <= 0.01 is not reliable, unless you supply your own sequence of alpha.
Read more in the User Guide.
Parameters
 alpha : float
 Constant that multiplies the penalty terms. Defaults to 1.0
See the notes for the exact mathematical meaning of this
parameter.
alpha = 0
is equivalent to an ordinary least square, solved by theLinearRegression
object. For numerical reasons, usingalpha = 0
with the Lasso object is not advised and you should prefer the LinearRegression object.  l1_ratio : float
 The ElasticNet mixing parameter, with
0 <= l1_ratio <= 1
. Forl1_ratio = 0
the penalty is an L2 penalty.For l1_ratio = 1
it is an L1 penalty. For0 < l1_ratio < 1
, the penalty is a combination of L1 and L2.  fit_intercept : bool
 Whether the intercept should be estimated or not. If
False
, the data is assumed to be already centered.  normalize : boolean, optional, default False
 If
True
, the regressors X will be normalized before regression.  precompute : True  False  ‘auto’  arraylike
 Whether to use a precomputed Gram matrix to speed up
calculations. If set to
'auto'
let us decide. The Gram matrix can also be passed as argument. For sparse input this option is alwaysTrue
to preserve sparsity. WARNING : The'auto'
option is deprecated and will be removed in 0.18.  max_iter : int, optional
 The maximum number of iterations
 copy_X : boolean, optional, default True
 If
True
, X will be copied; else, it may be overwritten.  tol : float, optional
 The tolerance for the optimization: if the updates are
smaller than
tol
, the optimization code checks the dual gap for optimality and continues until it is smaller thantol
.  warm_start : bool, optional
 When set to
True
, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution.  positive : bool, optional
 When set to
True
, forces the coefficients to be positive.  selection : str, default ‘cyclic’
 If set to ‘random’, a random coefficient is updated every iteration rather than looping over features sequentially by default. This (setting to ‘random’) often leads to significantly faster convergence especially when tol is higher than 1e4.
 random_state : int, RandomState instance, or None (default)
 The seed of the pseudo random number generator that selects a random feature to update. Useful only when selection is set to ‘random’.
Attributes
coef_
: array, shape (n_features,)  (n_targets, n_features) parameter vector (w in the cost function formula)
sparse_coef_
: scipy.sparse matrix, shape (n_features, 1)  (n_targets, n_features)sparse_coef_
is a readonly property derived fromcoef_
intercept_
: float  array, shape (n_targets,) independent term in decision function.
n_iter_
: arraylike, shape (n_targets,) number of iterations run by the coordinate descent solver to reach the specified tolerance.
Notes
To avoid unnecessary memory duplication the X argument of the fit method should be directly passed as a Fortrancontiguous numpy array.
See also
SGDRegressor: implements elastic net regression with incremental training. SGDClassifier: implements logistic regression with elastic net penalty
(SGDClassifier(loss="log", penalty="elasticnet")
).Full API documentation: ElasticNetScikitsLearnNode

class
mdp.nodes.
IsomapScikitsLearnNode
¶ Isomap Embedding
This node has been automatically generated by wrapping the
sklearn.manifold.isomap.Isomap
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Nonlinear dimensionality reduction through Isometric Mapping
Read more in the User Guide.
Parameters
 n_neighbors : integer
 number of neighbors to consider for each point.
 n_components : integer
 number of coordinates for the manifold
 eigen_solver : [‘auto’’arpack’’dense’]
‘auto’ : Attempt to choose the most efficient solver for the given problem.
‘arpack’ : Use Arnoldi decomposition to find the eigenvalues and eigenvectors.
‘dense’ : Use a direct solver (i.e. LAPACK) for the eigenvalue decomposition.
 tol : float
 Convergence tolerance passed to arpack or lobpcg. not used if eigen_solver == ‘dense’.
 max_iter : integer
 Maximum number of iterations for the arpack solver. not used if eigen_solver == ‘dense’.
 path_method : string [‘auto’’FW’’D’]
Method to use in finding shortest path.
‘auto’ : attempt to choose the best algorithm automatically.
‘FW’ : FloydWarshall algorithm.
‘D’ : Dijkstra’s algorithm.
 neighbors_algorithm : string [‘auto’’brute’’kd_tree’’ball_tree’]
 Algorithm to use for nearest neighbors search, passed to neighbors.NearestNeighbors instance.
Attributes
embedding_
: arraylike, shape (n_samples, n_components) Stores the embedding vectors.
kernel_pca_
: object KernelPCA object used to implement the embedding.
training_data_
: arraylike, shape (n_samples, n_features) Stores the training data.
nbrs_
: sklearn.neighbors.NearestNeighbors instance Stores nearest neighbors instance, including BallTree or KDtree if applicable.
dist_matrix_
: arraylike, shape (n_samples, n_samples) Stores the geodesic distance matrix of training data.
References
[1] Tenenbaum, J.B.; De Silva, V.; & Langford, J.C. A global geometric framework for nonlinear dimensionality reduction. Science 290 (5500) Full API documentation: IsomapScikitsLearnNode

class
mdp.nodes.
BinarizerScikitsLearnNode
¶ Binarize data (set feature values to 0 or 1) according to a threshold
This node has been automatically generated by wrapping the
sklearn.preprocessing.data.Binarizer
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Values greater than the threshold map to 1, while values less than or equal to the threshold map to 0. With the default threshold of 0, only positive values map to 1.
Binarization is a common operation on text count data where the analyst can decide to only consider the presence or absence of a feature rather than a quantified number of occurrences for instance.
It can also be used as a preprocessing step for estimators that consider boolean random variables (e.g. modelled using the Bernoulli distribution in a Bayesian setting).
Read more in the User Guide.
Parameters
 threshold : float, optional (0.0 by default)
 Feature values below or equal to this are replaced by 0, above it by 1. Threshold may not be less than 0 for operations on sparse matrices.
 copy : boolean, optional, default True
 set to False to perform inplace binarization and avoid a copy (if the input is already a numpy array or a scipy.sparse CSR matrix).
Notes
If the input is a sparse matrix, only the nonzero values are subject to update by the Binarizer class.
This estimator is stateless (besides constructor parameters), the fit method does nothing but is useful when used in a pipeline.
Full API documentation: BinarizerScikitsLearnNode

class
mdp.nodes.
MiniBatchDictionaryLearningScikitsLearnNode
¶ Minibatch dictionary learning
This node has been automatically generated by wrapping the
sklearn.decomposition.dict_learning.MiniBatchDictionaryLearning
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Finds a dictionary (a set of atoms) that can best be used to represent data using a sparse code.
Solves the optimization problem:
(U^*,V^*) = argmin 0.5  Y  U V _2^2 + alpha *  U _1 (U,V) with  V_k _2 = 1 for all 0 <= k < n_components
Read more in the User Guide.
Parameters
 n_components : int,
 number of dictionary elements to extract
 alpha : float,
 sparsity controlling parameter
 n_iter : int,
 total number of iterations to perform
 fit_algorithm : {‘lars’, ‘cd’}
 lars: uses the least angle regression method to solve the lasso problem (linear_model.lars_path) cd: uses the coordinate descent method to compute the Lasso solution (linear_model.Lasso). Lars will be faster if the estimated components are sparse.
 transform_algorithm : {‘lasso_lars’, ‘lasso_cd’, ‘lars’, ‘omp’, ‘threshold’}
 Algorithm used to transform the data. lars: uses the least angle regression method (linear_model.lars_path) lasso_lars: uses Lars to compute the Lasso solution lasso_cd: uses the coordinate descent method to compute the Lasso solution (linear_model.Lasso). lasso_lars will be faster if the estimated components are sparse. omp: uses orthogonal matching pursuit to estimate the sparse solution threshold: squashes to zero all coefficients less than alpha from the projection dictionary * X’
 transform_n_nonzero_coefs : int,
0.1 * n_features
by default  Number of nonzero coefficients to target in each column of the solution. This is only used by algorithm=’lars’ and algorithm=’omp’ and is overridden by alpha in the omp case.
 transform_alpha : float, 1. by default
 If algorithm=’lasso_lars’ or algorithm=’lasso_cd’, alpha is the penalty applied to the L1 norm. If algorithm=’threshold’, alpha is the absolute value of the threshold below which coefficients will be squashed to zero. If algorithm=’omp’, alpha is the tolerance parameter: the value of the reconstruction error targeted. In this case, it overrides n_nonzero_coefs.
 split_sign : bool, False by default
 Whether to split the sparse feature vector into the concatenation of its negative part and its positive part. This can improve the performance of downstream classifiers.
 n_jobs : int,
 number of parallel jobs to run
 dict_init : array of shape (n_components, n_features),
 initial value of the dictionary for warm restart scenarios
verbose :
 degree of verbosity of the printed output
 batch_size : int,
 number of samples in each minibatch
 shuffle : bool,
 whether to shuffle the samples before forming batches
 random_state : int or RandomState
 Pseudo number generator state used for random sampling.
Attributes
components_
: array, [n_components, n_features] components extracted from the data
inner_stats_
: tuple of (A, B) ndarrays Internal sufficient statistics that are kept by the algorithm. Keeping them is useful in online settings, to avoid loosing the history of the evolution, but they shouldn’t have any use for the end user. A (n_components, n_components) is the dictionary covariance matrix. B (n_features, n_components) is the data approximation matrix
n_iter_
: int Number of iterations run.
Notes
References:
J. Mairal, F. Bach, J. Ponce, G. Sapiro, 2009: Online dictionary learning for sparse coding (http://www.di.ens.fr/sierra/pdfs/icml09.pdf)
See also
SparseCoder DictionaryLearning SparsePCA MiniBatchSparsePCA
Full API documentation: MiniBatchDictionaryLearningScikitsLearnNode

class
mdp.nodes.
TfidfVectorizerScikitsLearnNode
¶ Convert a collection of raw documents to a matrix of TFIDF features.
This node has been automatically generated by wrapping the
sklearn.feature_extraction.text.TfidfVectorizer
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Equivalent to CountVectorizer followed by TfidfTransformer.
Read more in the User Guide.
Parameters
 input : string {‘filename’, ‘file’, ‘content’}
If ‘filename’, the sequence passed as an argument to fit is expected to be a list of filenames that need reading to fetch the raw content to analyze.
If ‘file’, the sequence items must have a ‘read’ method (filelike object) that is called to fetch the bytes in memory.
Otherwise the input is expected to be the sequence strings or bytes items are expected to be analyzed directly.
 encoding : string, ‘utf8’ by default.
 If bytes or files are given to analyze, this encoding is used to decode.
 decode_error : {‘strict’, ‘ignore’, ‘replace’}
 Instruction on what to do if a byte sequence is given to analyze that contains characters not of the given encoding. By default, it is ‘strict’, meaning that a UnicodeDecodeError will be raised. Other values are ‘ignore’ and ‘replace’.
 strip_accents : {‘ascii’, ‘unicode’, None}
 Remove accents during the preprocessing step. ‘ascii’ is a fast method that only works on characters that have an direct ASCII mapping. ‘unicode’ is a slightly slower method that works on any characters. None (default) does nothing.
 analyzer : string, {‘word’, ‘char’} or callable
Whether the feature should be made of word or character ngrams.
If a callable is passed it is used to extract the sequence of features out of the raw, unprocessed input.
 preprocessor : callable or None (default)
 Override the preprocessing (string transformation) stage while preserving the tokenizing and ngrams generation steps.
 tokenizer : callable or None (default)
 Override the string tokenization step while preserving the
preprocessing and ngrams generation steps.
Only applies if
analyzer == 'word'
.  ngram_range : tuple (min_n, max_n)
 The lower and upper boundary of the range of nvalues for different ngrams to be extracted. All values of n such that min_n <= n <= max_n will be used.
 stop_words : string {‘english’}, list, or None (default)
If a string, it is passed to _check_stop_list and the appropriate stop list is returned. ‘english’ is currently the only supported string value.
If a list, that list is assumed to contain stop words, all of which will be removed from the resulting tokens. Only applies if
analyzer == 'word'
.If None, no stop words will be used. max_df can be set to a value in the range [0.7, 1.0) to automatically detect and filter stop words based on intra corpus document frequency of terms.
 lowercase : boolean, default True
 Convert all characters to lowercase before tokenizing.
 token_pattern : string
 Regular expression denoting what constitutes a “token”, only used
if
analyzer == 'word'
. The default regexp selects tokens of 2 or more alphanumeric characters (punctuation is completely ignored and always treated as a token separator).  max_df : float in range [0.0, 1.0] or int, default=1.0
 When building the vocabulary ignore terms that have a document frequency strictly higher than the given threshold (corpusspecific stop words). If float, the parameter represents a proportion of documents, integer absolute counts. This parameter is ignored if vocabulary is not None.
 min_df : float in range [0.0, 1.0] or int, default=1
 When building the vocabulary ignore terms that have a document frequency strictly lower than the given threshold. This value is also called cutoff in the literature. If float, the parameter represents a proportion of documents, integer absolute counts. This parameter is ignored if vocabulary is not None.
 max_features : int or None, default=None
If not None, build a vocabulary that only consider the top max_features ordered by term frequency across the corpus.
This parameter is ignored if vocabulary is not None.
 vocabulary : Mapping or iterable, optional
 Either a Mapping (e.g., a dict) where keys are terms and values are indices in the feature matrix, or an iterable over terms. If not given, a vocabulary is determined from the input documents.
 binary : boolean, default=False
 If True, all nonzero term counts are set to 1. This does not mean outputs will have only 0/1 values, only that the tf term in tfidf is binary. (Set idf and normalization to False to get 0/1 outputs.)
 dtype : type, optional
 Type of the matrix returned by fit_transform() or transform().
 norm : ‘l1’, ‘l2’ or None, optional
 Norm used to normalize term vectors. None for no normalization.
 use_idf : boolean, default=True
 Enable inversedocumentfrequency reweighting.
 smooth_idf : boolean, default=True
 Smooth idf weights by adding one to document frequencies, as if an extra document was seen containing every term in the collection exactly once. Prevents zero divisions.
 sublinear_tf : boolean, default=False
 Apply sublinear tf scaling, i.e. replace tf with 1 + log(tf).
Attributes
idf_
: array, shape = [n_features], or None The learned idf vector (global term weights)
when
use_idf
is set to True, None otherwise. stop_words_
: setTerms that were ignored because they either:
 occurred in too many documents (max_df)
 occurred in too few documents (min_df)
 were cut off by feature selection (max_features).
This is only available if no vocabulary was given.
See also
 CountVectorizer
 Tokenize the documents and count the occurrences of token and return them as a sparse matrix
 TfidfTransformer
 Apply Term Frequency Inverse Document Frequency normalization to a sparse matrix of occurrence counts.
Notes
The
stop_words_
attribute can get large and increase the model size when pickling. This attribute is provided only for introspection and can be safely removed using delattr or set to None before pickling.Full API documentation: TfidfVectorizerScikitsLearnNode

class
mdp.nodes.
RandomizedPCAScikitsLearnNode
¶ Principal component analysis (PCA) using randomized SVD
This node has been automatically generated by wrapping the
sklearn.decomposition.pca.RandomizedPCA
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Linear dimensionality reduction using approximated Singular Value Decomposition of the data and keeping only the most significant singular vectors to project the data to a lower dimensional space.
Read more in the User Guide.
Parameters
 n_components : int, optional
 Maximum number of components to keep. When not given or None, this is set to n_features (the second dimension of the training data).
 copy : bool
 If False, data passed to fit are overwritten and running fit(X).transform(X) will not yield the expected results, use fit_transform(X) instead.
 iterated_power : int, optional
 Number of iterations for the power method. 3 by default.
 whiten : bool, optional
When True (False by default) the components_ vectors are divided by the singular values to ensure uncorrelated outputs with unit componentwise variances.
Whitening will remove some information from the transformed signal (the relative variance scales of the components) but can sometime improve the predictive accuracy of the downstream estimators by making their data respect some hardwired assumptions.
 random_state : int or RandomState instance or None (default)
 Pseudo Random Number generator seed control. If None, use the numpy.random singleton.
Attributes
components_
: array, [n_components, n_features] Components with maximum variance.
explained_variance_ratio_
: array, [n_components] Percentage of variance explained by each of the selected components. k is not set then all components are stored and the sum of explained variances is equal to 1.0
mean_
: array, [n_features] Perfeature empirical mean, estimated from the training set.
Examples
>>> import numpy as np >>> from sklearn.decomposition import RandomizedPCA >>> X = np.array([[1, 1], [2, 1], [3, 2], [1, 1], [2, 1], [3, 2]]) >>> pca = RandomizedPCA(n_components=2) >>> pca.fit(X) RandomizedPCA(copy=True, iterated_power=3, n_components=2, random_state=None, whiten=False) >>> print(pca.explained_variance_ratio_) [ 0.99244... 0.00755...]
See also
PCA TruncatedSVD
References
[Halko2009] Finding structure with randomness: Stochastic algorithms for constructing approximate matrix decompositions Halko, et al., 2009 (arXiv:909) [MRT] A randomized algorithm for the decomposition of matrices PerGunnar Martinsson, Vladimir Rokhlin and Mark Tygert Full API documentation: RandomizedPCAScikitsLearnNode

class
mdp.nodes.
IncrementalPCAScikitsLearnNode
¶ Incremental principal components analysis (IPCA).
This node has been automatically generated by wrapping the
sklearn.decomposition.incremental_pca.IncrementalPCA
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Linear dimensionality reduction using Singular Value Decomposition of centered data, keeping only the most significant singular vectors to project the data to a lower dimensional space.
Depending on the size of the input data, this algorithm can be much more memory efficient than a PCA.
This algorithm has constant memory complexity, on the order of
batch_size
, enabling use of np.memmap files without loading the entire file into memory.The computational overhead of each SVD is
O(batch_size * n_features ** 2)
, but only 2 * batch_size samples remain in memory at a time. There will ben_samples / batch_size
SVD computations to get the principal components, versus 1 large SVD of complexityO(n_samples * n_features ** 2)
for PCA.Read more in the User Guide.
Parameters
 n_components : int or None, (default=None)
 Number of components to keep. If
n_components `` is ``None
, thenn_components
is set tomin(n_samples, n_features)
.  batch_size : int or None, (default=None)
 The number of samples to use for each batch. Only used when calling
fit
. Ifbatch_size
isNone
, thenbatch_size
is inferred from the data and set to5 * n_features
, to provide a balance between approximation accuracy and memory consumption.  copy : bool, (default=True)
 If False, X will be overwritten.
copy=False
can be used to save memory but is unsafe for general use.  whiten : bool, optional
When True (False by default) the
components_
vectors are divided byn_samples
timescomponents_
to ensure uncorrelated outputs with unit componentwise variances.Whitening will remove some information from the transformed signal (the relative variance scales of the components) but can sometimes improve the predictive accuracy of the downstream estimators by making data respect some hardwired assumptions.
Attributes
components_
: array, shape (n_components, n_features) Components with maximum variance.
explained_variance_
: array, shape (n_components,) Variance explained by each of the selected components.
explained_variance_ratio_
: array, shape (n_components,) Percentage of variance explained by each of the selected components. If all components are stored, the sum of explained variances is equal to 1.0
mean_
: array, shape (n_features,) Perfeature empirical mean, aggregate over calls to
partial_fit
. var_
: array, shape (n_features,) Perfeature empirical variance, aggregate over calls to
partial_fit
. noise_variance_
: float The estimated noise covariance following the Probabilistic PCA model from Tipping and Bishop 1999. See “Pattern Recognition and Machine Learning” by C. Bishop, 12.2.1 p. 574 or http://www.miketipping.com/papers/metmppca.pdf.
n_components_
: int The estimated number of components. Relevant when
n_components=None
. n_samples_seen_
: int The number of samples processed by the estimator. Will be reset on
new calls to fit, but increments across
partial_fit
calls.
Notes
Implements the incremental PCA model from:
D. Ross, J. Lim, R. Lin, M. Yang, Incremental Learning for Robust Visual Tracking, International Journal of Computer Vision, Volume 77, Issue 13, pp. 125141, May 2008. See http://www.cs.toronto.edu/~dross/ivt/RossLimLinYang_ijcv.pdf
This model is an extension of the Sequential KarhunenLoeve Transform from:
A. Levy and M. Lindenbaum, Sequential KarhunenLoeve Basis Extraction and its Application to Images, IEEE Transactions on Image Processing, Volume 9, Number 8, pp. 13711374, August 2000. See http://www.cs.technion.ac.il/~mic/doc/sklip.pdf
We have specifically abstained from an optimization used by authors of both papers, a QR decomposition used in specific situations to reduce the algorithmic complexity of the SVD. The source for this technique is Matrix Computations, Third Edition, G. Holub and C. Van Loan, Chapter 5, section 5.4.4, pp 252253.. This technique has been omitted because it is advantageous only when decomposing a matrix with
n_samples
(rows) >= 5/3 *n_features
(columns), and hurts the readability of the implemented algorithm. This would be a good opportunity for future optimization, if it is deemed necessary.References
 Ross, J. Lim, R. Lin, M. Yang. Incremental Learning for Robust Visual
Tracking, International Journal of Computer Vision, Volume 77, Issue 13, pp. 125141, May 2008.
 Golub and C. Van Loan. Matrix Computations, Third Edition, Chapter 5,
Section 5.4.4, pp. 252253.
See also
PCA RandomizedPCA KernelPCA SparsePCA TruncatedSVD
Full API documentation: IncrementalPCAScikitsLearnNode

class
mdp.nodes.
MiniBatchSparsePCAScikitsLearnNode
¶ Minibatch Sparse Principal Components Analysis
This node has been automatically generated by wrapping the
sklearn.decomposition.sparse_pca.MiniBatchSparsePCA
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Finds the set of sparse components that can optimally reconstruct the data. The amount of sparseness is controllable by the coefficient of the L1 penalty, given by the parameter alpha.
Read more in the User Guide.
Parameters
 n_components : int,
 number of sparse atoms to extract
 alpha : int,
 Sparsity controlling parameter. Higher values lead to sparser components.
 ridge_alpha : float,
 Amount of ridge shrinkage to apply in order to improve conditioning when calling the transform method.
 n_iter : int,
 number of iterations to perform for each mini batch
 callback : callable,
 callable that gets invoked every five iterations
 batch_size : int,
 the number of features to take in each mini batch
verbose :
 degree of output the procedure will print
 shuffle : boolean,
 whether to shuffle the data before splitting it in batches
 n_jobs : int,
 number of parallel jobs to run, or 1 to autodetect.
 method : {‘lars’, ‘cd’}
 lars: uses the least angle regression method to solve the lasso problem (linear_model.lars_path) cd: uses the coordinate descent method to compute the Lasso solution (linear_model.Lasso). Lars will be faster if the estimated components are sparse.
 random_state : int or RandomState
 Pseudo number generator state used for random sampling.
Attributes
components_
: array, [n_components, n_features] Sparse components extracted from the data.
error_
: array Vector of errors at each iteration.
n_iter_
: int Number of iterations run.
See also
PCA SparsePCA DictionaryLearning
Full API documentation: MiniBatchSparsePCAScikitsLearnNode

class
mdp.nodes.
FactorAnalysisScikitsLearnNode
¶ Factor Analysis (FA)
This node has been automatically generated by wrapping the
sklearn.decomposition.factor_analysis.FactorAnalysis
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.A simple linear generative model with Gaussian latent variables.
The observations are assumed to be caused by a linear transformation of lower dimensional latent factors and added Gaussian noise. Without loss of generality the factors are distributed according to a Gaussian with zero mean and unit covariance. The noise is also zero mean and has an arbitrary diagonal covariance matrix.
If we would restrict the model further, by assuming that the Gaussian noise is even isotropic (all diagonal entries are the same) we would obtain
PPCA
.FactorAnalysis performs a maximum likelihood estimate of the socalled loading matrix, the transformation of the latent variables to the observed ones, using expectationmaximization (EM).
Read more in the User Guide.
Parameters
 n_components : int  None
 Dimensionality of latent space, the number of components
of
X
that are obtained aftertransform
. If None, n_components is set to the number of features.  tol : float
 Stopping tolerance for EM algorithm.
 copy : bool
 Whether to make a copy of X. If
False
, the input X gets overwritten during fitting.  max_iter : int
 Maximum number of iterations.
 noise_variance_init : None  array, shape=(n_features,)
 The initial guess of the noise variance for each feature. If None, it defaults to np.ones(n_features)
 svd_method : {‘lapack’, ‘randomized’}
 Which SVD method to use. If ‘lapack’ use standard SVD from
scipy.linalg, if ‘randomized’ use fast
randomized_svd
function. Defaults to ‘randomized’. For most applications ‘randomized’ will be sufficiently precise while providing significant speed gains. Accuracy can also be improved by setting higher values for iterated_power. If this is not sufficient, for maximum precision you should choose ‘lapack’.  iterated_power : int, optional
 Number of iterations for the power method. 3 by default. Only used
if
svd_method
equals ‘randomized’  random_state : int or RandomState
 Pseudo number generator state used for random sampling. Only used
if
svd_method
equals ‘randomized’
Attributes
components_
: array, [n_components, n_features] Components with maximum variance.
loglike_
: list, [n_iterations] The log likelihood at each iteration.
noise_variance_
: array, shape=(n_features,) The estimated noise variance for each feature.
n_iter_
: int Number of iterations run.
References
See also
 PCA: Principal component analysis is also a latent linear variable model
 which however assumes equal noise variance for each feature. This extra assumption makes probabilistic PCA faster as it can be computed in closed form.
 FastICA: Independent component analysis, a latent variable model with
 nonGaussian latent variables.
Full API documentation: FactorAnalysisScikitsLearnNode

class
mdp.nodes.
FunctionTransformerScikitsLearnNode
¶ Constructs a transformer from an arbitrary callable.
This node has been automatically generated by wrapping the
sklearn.preprocessing._function_transformer.FunctionTransformer
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.A FunctionTransformer forwards its X (and optionally y) arguments to a userdefined function or function object and returns the result of this function. This is useful for stateless transformations such as taking the log of frequencies, doing custom scaling, etc.
A FunctionTransformer will not do any checks on its function’s output.
Note: If a lambda is used as the function, then the resulting transformer will not be pickleable.
New in version 0.17.
Parameters
 func : callable, optional default=None
 The callable to use for the transformation. This will be passed the same arguments as transform, with args and kwargs forwarded. If func is None, then func will be the identity function.
 validate : bool, optional default=True
 Indicate that the input X array should be checked before calling func. If validate is false, there will be no input validation. If it is true, then X will be converted to a 2dimensional NumPy array or sparse matrix. If this conversion is not possible or X contains NaN or infinity, an exception is raised.
 accept_sparse : boolean, optional
 Indicate that func accepts a sparse matrix as input. If validate is False, this has no effect. Otherwise, if accept_sparse is false, sparse matrix inputs will cause an exception to be raised.
 pass_y: bool, optional default=False
 Indicate that transform should forward the y argument to the inner callable.
Full API documentation: FunctionTransformerScikitsLearnNode

class
mdp.nodes.
DPGMMScikitsLearnNode
¶ Variational Inference for the Infinite Gaussian Mixture Model.
This node has been automatically generated by wrapping the
sklearn.mixture.dpgmm.DPGMM
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.DPGMM stands for Dirichlet Process Gaussian Mixture Model, and it is an infinite mixture model with the Dirichlet Process as a prior distribution on the number of clusters. In practice the approximate inference algorithm uses a truncated distribution with a fixed maximum number of components, but almost always the number of components actually used depends on the data.
Stickbreaking Representation of a Gaussian mixture model probability distribution. This class allows for easy and efficient inference of an approximate posterior distribution over the parameters of a Gaussian mixture model with a variable number of components (smaller than the truncation parameter n_components).
Initialization is with normallydistributed means and identity covariance, for proper convergence.
Read more in the User Guide.
Parameters
 n_components: int, default 1
 Number of mixture components.
 covariance_type: string, default ‘diag’
 String describing the type of covariance parameters to use. Must be one of ‘spherical’, ‘tied’, ‘diag’, ‘full’.
 alpha: float, default 1
 Real number representing the concentration parameter of
the dirichlet process. Intuitively, the Dirichlet Process
is as likely to start a new cluster for a point as it is
to add that point to a cluster with alpha elements. A
higher alpha means more clusters, as the expected number
of clusters is
alpha*log(N)
.  tol : float, default 1e3
 Convergence threshold.
 n_iter : int, default 10
 Maximum number of iterations to perform before convergence.
 params : string, default ‘wmc’
 Controls which parameters are updated in the training process. Can contain any combination of ‘w’ for weights, ‘m’ for means, and ‘c’ for covars.
 init_params : string, default ‘wmc’
 Controls which parameters are updated in the initialization process. Can contain any combination of ‘w’ for weights, ‘m’ for means, and ‘c’ for covars. Defaults to ‘wmc’.
 verbose : int, default 0
 Controls output verbosity.
Attributes
 covariance_type : string
 String describing the type of covariance parameters used by the DPGMM. Must be one of ‘spherical’, ‘tied’, ‘diag’, ‘full’.
 n_components : int
 Number of mixture components.
weights_
: array, shape (n_components,) Mixing weights for each mixture component.
means_
: array, shape (n_components, n_features) Mean parameters for each mixture component.
precs_
: arrayPrecision (inverse covariance) parameters for each mixture component. The shape depends on covariance_type:
(`n_components`, 'n_features') if 'spherical', (`n_features`, `n_features`) if 'tied', (`n_components`, `n_features`) if 'diag', (`n_components`, `n_features`, `n_features`) if 'full'
converged_
: bool True when convergence was reached in fit(), False otherwise.
See Also
GMM : Finite Gaussian mixture model fit with EM
 VBGMM : Finite Gaussian mixture model fit with a variational
 algorithm, better for situations where there might be too little data to get a good estimate of the covariance matrix.
Full API documentation: DPGMMScikitsLearnNode

class
mdp.nodes.
LassoLarsICScikitsLearnNode
¶ Lasso model fit with Lars using BIC or AIC for model selection
This node has been automatically generated by wrapping the
sklearn.linear_model.least_angle.LassoLarsIC
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.The optimization objective for Lasso is:
(1 / (2 * n_samples)) * y  Xw^2_2 + alpha * w_1
AIC is the Akaike information criterion and BIC is the Bayes Information criterion. Such criteria are useful to select the value of the regularization parameter by making a tradeoff between the goodness of fit and the complexity of the model. A good model should explain well the data while being simple.
Read more in the User Guide.
Parameters
 criterion : ‘bic’  ‘aic’
 The type of criterion to use.
 fit_intercept : boolean
 whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered).
 positive : boolean (default=False)
 Restrict coefficients to be >= 0. Be aware that you might want to
remove fit_intercept which is set True by default.
Under the positive restriction the model coefficients do not converge
to the ordinaryleastsquares solution for small values of alpha.
Only coeffiencts up to the smallest alpha value (
alphas_[alphas_ > 0.].min()
when fit_path=True) reached by the stepwise LarsLasso algorithm are typically in congruence with the solution of the coordinate descent Lasso estimator. As a consequence using LassoLarsIC only makes sense for problems where a sparse solution is expected and/or reached.  verbose : boolean or integer, optional
 Sets the verbosity amount
 normalize : boolean, optional, default False
 If True, the regressors X will be normalized before regression.
 copy_X : boolean, optional, default True
 If True, X will be copied; else, it may be overwritten.
 precompute : True  False  ‘auto’  arraylike
 Whether to use a precomputed Gram matrix to speed up
calculations. If set to
'auto'
let us decide. The Gram matrix can also be passed as argument.  max_iter : integer, optional
 Maximum number of iterations to perform. Can be used for early stopping.
 eps : float, optional
 The machineprecision regularization in the computation of the
Cholesky diagonal factors. Increase this for very illconditioned
systems. Unlike the
tol
parameter in some iterative optimizationbased algorithms, this parameter does not control the tolerance of the optimization.
Attributes
coef_
: array, shape (n_features,) parameter vector (w in the formulation formula)
intercept_
: float independent term in decision function.
alpha_
: float the alpha parameter chosen by the information criterion
n_iter_
: int number of iterations run by lars_path to find the grid of alphas.
criterion_
: array, shape (n_alphas,) The value of the information criteria (‘aic’, ‘bic’) across all alphas. The alpha which has the smallest information criteria is chosen.
Examples
>>> from sklearn import linear_model >>> clf = linear_model.LassoLarsIC(criterion='bic') >>> clf.fit([[1, 1], [0, 0], [1, 1]], [1.1111, 0, 1.1111]) ... LassoLarsIC(copy_X=True, criterion='bic', eps=..., fit_intercept=True, max_iter=500, normalize=True, positive=False, precompute='auto', verbose=False) >>> print(clf.coef_) [ 0. 1.11...]
Notes
The estimation of the number of degrees of freedom is given by:
“On the degrees of freedom of the lasso” Hui Zou, Trevor Hastie, and Robert Tibshirani Ann. Statist. Volume 35, Number 5 (2007), 21732192.
http://en.wikipedia.org/wiki/Akaike_information_criterion http://en.wikipedia.org/wiki/Bayesian_information_criterion
See also
lars_path, LassoLars, LassoLarsCV
Full API documentation: LassoLarsICScikitsLearnNode

class
mdp.nodes.
RFEScikitsLearnNode
¶ Feature ranking with recursive feature elimination.
This node has been automatically generated by wrapping the
sklearn.feature_selection.rfe.RFE
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Given an external estimator that assigns weights to features (e.g., the coefficients of a linear model), the goal of recursive feature elimination (RFE) is to select features by recursively considering smaller and smaller sets of features. First, the estimator is trained on the initial set of features and weights are assigned to each one of them. Then, features whose absolute weights are the smallest are pruned from the current set features. That procedure is recursively repeated on the pruned set until the desired number of features to select is eventually reached.
Read more in the User Guide.
Parameters
 estimator : object
A supervised learning estimator with a fit method that updates a coef_ attribute that holds the fitted parameters. Important features must correspond to high absolute values in the coef_ array.
For instance, this is the case for most supervised learning algorithms such as Support Vector Classifiers and Generalized Linear Models from the svm and linear_model modules.
 n_features_to_select : int or None (default=None)
 The number of features to select. If None, half of the features are selected.
 step : int or float, optional (default=1)
 If greater than or equal to 1, then step corresponds to the (integer) number of features to remove at each iteration. If within (0.0, 1.0), then step corresponds to the percentage (rounded down) of features to remove at each iteration.
 estimator_params : dict
 Parameters for the external estimator. This attribute is deprecated as of version 0.16 and will be removed in 0.18. Use estimator initialisation or set_params method instead.
 verbose : int, default=0
 Controls verbosity of output.
Attributes
n_features_
: int The number of selected features.
support_
: array of shape [n_features] The mask of selected features.
ranking_
: array of shape [n_features] The feature ranking, such that
ranking_[i]
corresponds to the ranking position of the ith feature. Selected (i.e., estimated best) features are assigned rank 1. estimator_
: object The external estimator fit on the reduced dataset.
Examples
The following example shows how to retrieve the 5 right informative features in the Friedman #1 dataset.
>>> from sklearn.datasets import make_friedman1 >>> from sklearn.feature_selection import RFE >>> from sklearn.svm import SVR >>> X, y = make_friedman1(n_samples=50, n_features=10, random_state=0) >>> estimator = SVR(kernel="linear") >>> selector = RFE(estimator, 5, step=1) >>> selector = selector.fit(X, y) >>> selector.support_ array([ True, True, True, True, True, False, False, False, False, False], dtype=bool) >>> selector.ranking_ array([1, 1, 1, 1, 1, 6, 4, 3, 2, 5])
References
[1] Guyon, I., Weston, J., Barnhill, S., & Vapnik, V., “Gene selection for cancer classification using support vector machines”, Mach. Learn., 46(13), 389–422, 2002. Full API documentation: RFEScikitsLearnNode

class
mdp.nodes.
PCAScikitsLearnNode
¶ Principal component analysis (PCA)
This node has been automatically generated by wrapping the
sklearn.decomposition.pca.PCA
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Linear dimensionality reduction using Singular Value Decomposition of the data and keeping only the most significant singular vectors to project the data to a lower dimensional space.
This implementation uses the scipy.linalg implementation of the singular value decomposition. It only works for dense arrays and is not scalable to large dimensional data.
The time complexity of this implementation is
O(n ** 3)
assuming n ~ n_samples ~ n_features.Read more in the User Guide.
Parameters
 n_components : int, None or string
Number of components to keep. if n_components is not set all components are kept:
n_components == min(n_samples, n_features)
if n_components == ‘mle’, Minka’s MLE is used to guess the dimension if
0 < n_components < 1
, select the number of components such that the amount of variance that needs to be explained is greater than the percentage specified by n_components copy : bool
 If False, data passed to fit are overwritten and running fit(X).transform(X) will not yield the expected results, use fit_transform(X) instead.
 whiten : bool, optional
When True (False by default) the components_ vectors are divided by n_samples times singular values to ensure uncorrelated outputs with unit componentwise variances.
Whitening will remove some information from the transformed signal (the relative variance scales of the components) but can sometime improve the predictive accuracy of the downstream estimators by making there data respect some hardwired assumptions.
Attributes
components_
: array, [n_components, n_features] Principal axes in feature space, representing the directions of maximum variance in the data.
explained_variance_ratio_
: array, [n_components] Percentage of variance explained by each of the selected components.
If
n_components
is not set then all components are stored and the sum of explained variances is equal to 1.0 mean_
: array, [n_features] Perfeature empirical mean, estimated from the training set.
n_components_
: int The estimated number of components. Relevant when n_components is set to ‘mle’ or a number between 0 and 1 to select using explained variance.
noise_variance_
: float The estimated noise covariance following the Probabilistic PCA model from Tipping and Bishop 1999. See “Pattern Recognition and Machine Learning” by C. Bishop, 12.2.1 p. 574 or http://www.miketipping.com/papers/metmppca.pdf. It is required to computed the estimated data covariance and score samples.
Notes
For n_components=’mle’, this class uses the method of `Thomas P. Minka:
Automatic Choice of Dimensionality for PCA. NIPS 2000: 598604`
Implements the probabilistic PCA model from:
M. Tipping and C. Bishop, Probabilistic Principal Component Analysis, Journal of the Royal Statistical Society, Series B, 61, Part 3, pp. 611622 via the score and score_samples methods. See http://www.miketipping.com/papers/metmppca.pdf
Due to implementation subtleties of the Singular Value Decomposition (SVD), which is used in this implementation, running fit twice on the same matrix can lead to principal components with signs flipped (change in direction). For this reason, it is important to always use the same estimator object to transform data in a consistent fashion.
Examples
>>> import numpy as np >>> from sklearn.decomposition import PCA >>> X = np.array([[1, 1], [2, 1], [3, 2], [1, 1], [2, 1], [3, 2]]) >>> pca = PCA(n_components=2) >>> pca.fit(X) PCA(copy=True, n_components=2, whiten=False) >>> print(pca.explained_variance_ratio_) [ 0.99244... 0.00755...]
See also
RandomizedPCA KernelPCA SparsePCA TruncatedSVD
Full API documentation: PCAScikitsLearnNode

class
mdp.nodes.
MultiTaskLassoScikitsLearnNode
¶ Multitask Lasso model trained with L1/L2 mixednorm as regularizer
This node has been automatically generated by wrapping the
sklearn.linear_model.coordinate_descent.MultiTaskLasso
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.The optimization objective for Lasso is:
(1 / (2 * n_samples)) * Y  XW^2_Fro + alpha * W_21
Where:
W_21 = \sum_i \sqrt{\sum_j w_{ij}^2}
i.e. the sum of norm of earch row.
Read more in the User Guide.
Parameters
 alpha : float, optional
 Constant that multiplies the L1/L2 term. Defaults to 1.0
 fit_intercept : boolean
 whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered).
 normalize : boolean, optional, default False
 If
True
, the regressors X will be normalized before regression.  copy_X : boolean, optional, default True
 If
True
, X will be copied; else, it may be overwritten.  max_iter : int, optional
 The maximum number of iterations
 tol : float, optional
 The tolerance for the optimization: if the updates are
smaller than
tol
, the optimization code checks the dual gap for optimality and continues until it is smaller thantol
.  warm_start : bool, optional
 When set to
True
, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution.  selection : str, default ‘cyclic’
 If set to ‘random’, a random coefficient is updated every iteration rather than looping over features sequentially by default. This (setting to ‘random’) often leads to significantly faster convergence especially when tol is higher than 1e4
 random_state : int, RandomState instance, or None (default)
 The seed of the pseudo random number generator that selects a random feature to update. Useful only when selection is set to ‘random’.
Attributes
coef_
: array, shape (n_tasks, n_features) parameter vector (W in the cost function formula)
intercept_
: array, shape (n_tasks,) independent term in decision function.
n_iter_
: int number of iterations run by the coordinate descent solver to reach the specified tolerance.
Examples
>>> from sklearn import linear_model >>> clf = linear_model.MultiTaskLasso(alpha=0.1) >>> clf.fit([[0,0], [1, 1], [2, 2]], [[0, 0], [1, 1], [2, 2]]) MultiTaskLasso(alpha=0.1, copy_X=True, fit_intercept=True, max_iter=1000, normalize=False, random_state=None, selection='cyclic', tol=0.0001, warm_start=False) >>> print(clf.coef_) [[ 0.89393398 0. ] [ 0.89393398 0. ]] >>> print(clf.intercept_) [ 0.10606602 0.10606602]
See also
Lasso, MultiTaskElasticNet
Notes
The algorithm used to fit the model is coordinate descent.
To avoid unnecessary memory duplication the X argument of the fit method should be directly passed as a Fortrancontiguous numpy array.
Full API documentation: MultiTaskLassoScikitsLearnNode

class
mdp.nodes.
RandomizedLogisticRegressionScikitsLearnNode
¶ Randomized Logistic Regression
This node has been automatically generated by wrapping the
sklearn.linear_model.randomized_l1.RandomizedLogisticRegression
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Randomized Regression works by resampling the train data and computing a LogisticRegression on each resampling. In short, the features selected more often are good features. It is also known as stability selection.
Read more in the User Guide.
Parameters
 C : float, optional, default=1
 The regularization parameter C in the LogisticRegression.
 scaling : float, optional, default=0.5
 The alpha parameter in the stability selection article used to randomly scale the features. Should be between 0 and 1.
 sample_fraction : float, optional, default=0.75
 The fraction of samples to be used in each randomized design. Should be between 0 and 1. If 1, all samples are used.
 n_resampling : int, optional, default=200
 Number of randomized models.
 selection_threshold : float, optional, default=0.25
 The score above which features should be selected.
 fit_intercept : boolean, optional, default=True
 whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered).
 verbose : boolean or integer, optional
 Sets the verbosity amount
 normalize : boolean, optional, default=True
 If True, the regressors X will be normalized before regression.
 tol : float, optional, default=1e3
 tolerance for stopping criteria of LogisticRegression
 n_jobs : integer, optional
 Number of CPUs to use during the resampling. If ‘1’, use all the CPUs
 random_state : int, RandomState instance or None, optional (default=None)
 If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.
 pre_dispatch : int, or string, optional
Controls the number of jobs that get dispatched during parallel execution. Reducing this number can be useful to avoid an explosion of memory consumption when more jobs get dispatched than CPUs can process. This parameter can be:
 None, in which case all the jobs are immediately created and spawned. Use this for lightweight and fastrunning jobs, to avoid delays due to ondemand spawning of the jobs
 An int, giving the exact number of total jobs that are spawned
 A string, giving an expression as a function of n_jobs, as in ‘2*n_jobs’
 memory : Instance of joblib.Memory or string
 Used for internal caching. By default, no caching is done. If a string is given, it is the path to the caching directory.
Attributes
scores_
: array, shape = [n_features] Feature scores between 0 and 1.
all_scores_
: array, shape = [n_features, n_reg_parameter] Feature scores between 0 and 1 for all values of the regularization parameter. The reference article suggests
scores_
is the max ofall_scores_
.
Examples
>>> from sklearn.linear_model import RandomizedLogisticRegression >>> randomized_logistic = RandomizedLogisticRegression()
Notes
See examples/linear_model/plot_sparse_recovery.py for an example.
References
Stability selection Nicolai Meinshausen, Peter Buhlmann Journal of the Royal Statistical Society: Series B Volume 72, Issue 4, pages 417473, September 2010 DOI: 10.1111/j.14679868.2010.00740.x
See also
RandomizedLasso, Lasso, ElasticNet
Full API documentation: RandomizedLogisticRegressionScikitsLearnNode

class
mdp.nodes.
SelectFweScikitsLearnNode
¶ Filter: Select the pvalues corresponding to Familywise error rate
This node has been automatically generated by wrapping the
sklearn.feature_selection.univariate_selection.SelectFwe
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Read more in the User Guide.
Parameters
 score_func : callable
 Function taking two arrays X and y, and returning a pair of arrays (scores, pvalues).
 alpha : float, optional
 The highest uncorrected pvalue for features to keep.
Attributes
scores_
: arraylike, shape=(n_features,) Scores of features.
pvalues_
: arraylike, shape=(n_features,) pvalues of feature scores.
See also
f_classif: ANOVA Fvalue between labe/feature for classification tasks. chi2: Chisquared stats of nonnegative features for classification tasks. f_regression: Fvalue between label/feature for regression tasks. SelectPercentile: Select features based on percentile of the highest scores. SelectKBest: Select features based on the k highest scores. SelectFpr: Select features based on a false positive rate test. SelectFdr: Select features based on an estimated false discovery rate. GenericUnivariateSelect: Univariate feature selector with configurable mode.
Full API documentation: SelectFweScikitsLearnNode

class
mdp.nodes.
MultiTaskElasticNetScikitsLearnNode
¶ Multitask ElasticNet model trained with L1/L2 mixednorm as regularizer
This node has been automatically generated by wrapping the
sklearn.linear_model.coordinate_descent.MultiTaskElasticNet
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.The optimization objective for MultiTaskElasticNet is:
(1 / (2 * n_samples)) * Y  XW^Fro_2 + alpha * l1_ratio * W_21 + 0.5 * alpha * (1  l1_ratio) * W_Fro^2
Where:
W_21 = \sum_i \sqrt{\sum_j w_{ij}^2}
i.e. the sum of norm of each row.
Read more in the User Guide.
Parameters
 alpha : float, optional
 Constant that multiplies the L1/L2 term. Defaults to 1.0
 l1_ratio : float
 The ElasticNet mixing parameter, with 0 < l1_ratio <= 1.
For l1_ratio = 0 the penalty is an L1/L2 penalty. For l1_ratio = 1 it
is an L1 penalty.
For
0 < l1_ratio < 1
, the penalty is a combination of L1/L2 and L2.  fit_intercept : boolean
 whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered).
 normalize : boolean, optional, default False
 If
True
, the regressors X will be normalized before regression.  copy_X : boolean, optional, default True
 If
True
, X will be copied; else, it may be overwritten.  max_iter : int, optional
 The maximum number of iterations
 tol : float, optional
 The tolerance for the optimization: if the updates are
smaller than
tol
, the optimization code checks the dual gap for optimality and continues until it is smaller thantol
.  warm_start : bool, optional
 When set to
True
, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution.  selection : str, default ‘cyclic’
 If set to ‘random’, a random coefficient is updated every iteration rather than looping over features sequentially by default. This (setting to ‘random’) often leads to significantly faster convergence especially when tol is higher than 1e4.
 random_state : int, RandomState instance, or None (default)
 The seed of the pseudo random number generator that selects a random feature to update. Useful only when selection is set to ‘random’.
Attributes
intercept_
: array, shape (n_tasks,) Independent term in decision function.
coef_
: array, shape (n_tasks, n_features) Parameter vector (W in the cost function formula). If a 1D y is passed in at fit (non multitask usage),
coef_
is then a 1D array n_iter_
: int number of iterations run by the coordinate descent solver to reach the specified tolerance.
Examples
>>> from sklearn import linear_model >>> clf = linear_model.MultiTaskElasticNet(alpha=0.1) >>> clf.fit([[0,0], [1, 1], [2, 2]], [[0, 0], [1, 1], [2, 2]]) ... MultiTaskElasticNet(alpha=0.1, copy_X=True, fit_intercept=True, l1_ratio=0.5, max_iter=1000, normalize=False, random_state=None, selection='cyclic', tol=0.0001, warm_start=False) >>> print(clf.coef_) [[ 0.45663524 0.45612256] [ 0.45663524 0.45612256]] >>> print(clf.intercept_) [ 0.0872422 0.0872422]
See also
ElasticNet, MultiTaskLasso
Notes
The algorithm used to fit the model is coordinate descent.
To avoid unnecessary memory duplication the X argument of the fit method should be directly passed as a Fortrancontiguous numpy array.
Full API documentation: MultiTaskElasticNetScikitsLearnNode

class
mdp.nodes.
SparseCoderScikitsLearnNode
¶ Sparse coding
This node has been automatically generated by wrapping the
sklearn.decomposition.dict_learning.SparseCoder
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Finds a sparse representation of data against a fixed, precomputed dictionary.
Each row of the result is the solution to a sparse coding problem. The goal is to find a sparse array code such that:
X ~= code * dictionary
Read more in the User Guide.
Parameters
 dictionary : array, [n_components, n_features]
 The dictionary atoms used for sparse coding. Lines are assumed to be normalized to unit norm.
 transform_algorithm : {‘lasso_lars’, ‘lasso_cd’, ‘lars’, ‘omp’, ‘threshold’}
Algorithm used to transform the data:
 lars: uses the least angle regression method (linear_model.lars_path)
 lasso_lars: uses Lars to compute the Lasso solution
 lasso_cd: uses the coordinate descent method to compute the
 Lasso solution (linear_model.Lasso). lasso_lars will be faster if
 the estimated components are sparse.
 omp: uses orthogonal matching pursuit to estimate the sparse solution
 threshold: squashes to zero all coefficients less than alpha from
 the projection
dictionary * X'
 transform_n_nonzero_coefs : int,
0.1 * n_features
by default  Number of nonzero coefficients to target in each column of the solution. This is only used by algorithm=’lars’ and algorithm=’omp’ and is overridden by alpha in the omp case.
 transform_alpha : float, 1. by default
 If algorithm=’lasso_lars’ or algorithm=’lasso_cd’, alpha is the penalty applied to the L1 norm. If algorithm=’threshold’, alpha is the absolute value of the threshold below which coefficients will be squashed to zero. If algorithm=’omp’, alpha is the tolerance parameter: the value of the reconstruction error targeted. In this case, it overrides n_nonzero_coefs.
 split_sign : bool, False by default
 Whether to split the sparse feature vector into the concatenation of its negative part and its positive part. This can improve the performance of downstream classifiers.
 n_jobs : int,
 number of parallel jobs to run
Attributes
components_
: array, [n_components, n_features] The unchanged dictionary atoms
See also
DictionaryLearning MiniBatchDictionaryLearning SparsePCA MiniBatchSparsePCA sparse_encode
Full API documentation: SparseCoderScikitsLearnNode

class
mdp.nodes.
StandardScalerScikitsLearnNode
¶ Standardize features by removing the mean and scaling to unit variance
This node has been automatically generated by wrapping the
sklearn.preprocessing.data.StandardScaler
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Centering and scaling happen independently on each feature by computing the relevant statistics on the samples in the training set. Mean and standard deviation are then stored to be used on later data using the transform method.
Standardization of a dataset is a common requirement for many machine learning estimators: they might behave badly if the individual feature do not more or less look like standard normally distributed data (e.g. Gaussian with 0 mean and unit variance).
For instance many elements used in the objective function of a learning algorithm (such as the RBF kernel of Support Vector Machines or the L1 and L2 regularizers of linear models) assume that all features are centered around 0 and have variance in the same order. If a feature has a variance that is orders of magnitude larger that others, it might dominate the objective function and make the estimator unable to learn from other features correctly as expected.
This scaler can also be applied to sparse CSR or CSC matrices by passing with_mean=False to avoid breaking the sparsity structure of the data.
Read more in the User Guide.
Parameters
 with_mean : boolean, True by default
 If True, center the data before scaling. This does not work (and will raise an exception) when attempted on sparse matrices, because centering them entails building a dense matrix which in common use cases is likely to be too large to fit in memory.
 with_std : boolean, True by default
 If True, scale the data to unit variance (or equivalently, unit standard deviation).
 copy : boolean, optional, default True
 If False, try to avoid a copy and do inplace scaling instead. This is not guaranteed to always work inplace; e.g. if the data is not a NumPy array or scipy.sparse CSR matrix, a copy may still be returned.
Attributes
scale_
: ndarray, shape (n_features,)Per feature relative scaling of the data.
New in version 0.17: scale_ is recommended instead of deprecated std_.
mean_
: array of floats with shape [n_features] The mean value for each feature in the training set.
var_
: array of floats with shape [n_features] The variance for each feature in the training set. Used to compute scale_
n_samples_seen_
: int The number of samples processed by the estimator. Will be reset on
new calls to fit, but increments across
partial_fit
calls.
See also
sklearn.preprocessing.scale()
to perform centering and scaling without using theTransformer
object oriented APIsklearn.decomposition.RandomizedPCA
with whiten=True to further remove the linear correlation across features.Full API documentation: StandardScalerScikitsLearnNode

class
mdp.nodes.
GMMScikitsLearnNode
¶ Gaussian Mixture Model
This node has been automatically generated by wrapping the
sklearn.mixture.gmm.GMM
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Representation of a Gaussian mixture model probability distribution. This class allows for easy evaluation of, sampling from, and maximumlikelihood estimation of the parameters of a GMM distribution.
Initializes parameters such that every mixture component has zero mean and identity covariance.
Read more in the User Guide.
Parameters
 n_components : int, optional
 Number of mixture components. Defaults to 1.
 covariance_type : string, optional
 String describing the type of covariance parameters to use. Must be one of ‘spherical’, ‘tied’, ‘diag’, ‘full’. Defaults to ‘diag’.
 random_state: RandomState or an int seed (None by default)
 A random number generator instance
 min_covar : float, optional
 Floor on the diagonal of the covariance matrix to prevent overfitting. Defaults to 1e3.
 tol : float, optional
 Convergence threshold. EM iterations will stop when average gain in loglikelihood is below this threshold. Defaults to 1e3.
 n_iter : int, optional
 Number of EM iterations to perform.
 n_init : int, optional
 Number of initializations to perform. the best results is kept
 params : string, optional
 Controls which parameters are updated in the training process. Can contain any combination of ‘w’ for weights, ‘m’ for means, and ‘c’ for covars. Defaults to ‘wmc’.
 init_params : string, optional
 Controls which parameters are updated in the initialization process. Can contain any combination of ‘w’ for weights, ‘m’ for means, and ‘c’ for covars. Defaults to ‘wmc’.
 verbose : int, default: 0
 Enable verbose output. If 1 then it always prints the current initialization and iteration step. If greater than 1 then it prints additionally the change and time needed for each step.
Attributes
weights_
: array, shape (n_components,) This attribute stores the mixing weights for each mixture component.
means_
: array, shape (n_components, n_features) Mean parameters for each mixture component.
covars_
: arrayCovariance parameters for each mixture component. The shape depends on covariance_type:
(n_components, n_features) if 'spherical', (n_features, n_features) if 'tied', (n_components, n_features) if 'diag', (n_components, n_features, n_features) if 'full'
converged_
: bool True when convergence was reached in fit(), False otherwise.
See Also
 DPGMM : Infinite gaussian mixture model, using the dirichlet
 process, fit with a variational algorithm
 VBGMM : Finite gaussian mixture model fit with a variational
 algorithm, better for situations where there might be too little data to get a good estimate of the covariance matrix.
Examples
>>> import numpy as np >>> from sklearn import mixture >>> np.random.seed(1) >>> g = mixture.GMM(n_components=2) >>> # Generate random observations with two modes centered on 0 >>> # and 10 to use for training. >>> obs = np.concatenate((np.random.randn(100, 1), ... 10 + np.random.randn(300, 1))) >>> g.fit(obs) GMM(covariance_type='diag', init_params='wmc', min_covar=0.001, n_components=2, n_init=1, n_iter=100, params='wmc', random_state=None, thresh=None, tol=0.001, verbose=0) >>> np.round(g.weights_, 2) array([ 0.75, 0.25]) >>> np.round(g.means_, 2) array([[ 10.05], [ 0.06]]) >>> np.round(g.covars_, 2) array([[[ 1.02]], [[ 0.96]]]) >>> g.predict([[0], [2], [9], [10]]) array([1, 1, 0, 0]...) >>> np.round(g.score([[0], [2], [9], [10]]), 2) array([2.19, 4.58, 1.75, 1.21]) >>> # Refit the model on new data (initial parameters remain the >>> # same), this time with an even split between the two modes. >>> g.fit(20 * [[0]] + 20 * [[10]]) GMM(covariance_type='diag', init_params='wmc', min_covar=0.001, n_components=2, n_init=1, n_iter=100, params='wmc', random_state=None, thresh=None, tol=0.001, verbose=0) >>> np.round(g.weights_, 2) array([ 0.5, 0.5])
Full API documentation: GMMScikitsLearnNode

class
mdp.nodes.
DecisionTreeClassifierScikitsLearnNode
¶ A decision tree classifier.
This node has been automatically generated by wrapping the
sklearn.tree.tree.DecisionTreeClassifier
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Read more in the User Guide.
Parameters
 criterion : string, optional (default=”gini”)
 The function to measure the quality of a split. Supported criteria are “gini” for the Gini impurity and “entropy” for the information gain.
 splitter : string, optional (default=”best”)
 The strategy used to choose the split at each node. Supported strategies are “best” to choose the best split and “random” to choose the best random split.
 max_features : int, float, string or None, optional (default=None)
The number of features to consider when looking for the best split:
 If int, then consider max_features features at each split.
 If float, then max_features is a percentage and
 int(max_features * n_features) features are considered at each
 split.
 If “auto”, then max_features=sqrt(n_features).
 If “sqrt”, then max_features=sqrt(n_features).
 If “log2”, then max_features=log2(n_features).
 If None, then max_features=n_features.
Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than
max_features
features. max_depth : int or None, optional (default=None)
 The maximum depth of the tree. If None, then nodes are expanded until
all leaves are pure or until all leaves contain less than
min_samples_split samples.
Ignored if
max_leaf_nodes
is not None.  min_samples_split : int, optional (default=2)
 The minimum number of samples required to split an internal node.
 min_samples_leaf : int, optional (default=1)
 The minimum number of samples required to be at a leaf node.
 min_weight_fraction_leaf : float, optional (default=0.)
 The minimum weighted fraction of the input samples required to be at a leaf node.
 max_leaf_nodes : int or None, optional (default=None)
 Grow a tree with
max_leaf_nodes
in bestfirst fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes. If not None thenmax_depth
will be ignored.  class_weight : dict, list of dicts, “balanced” or None, optional (default=None)
Weights associated with classes in the form
{class_label: weight}
. If not given, all classes are supposed to have weight one. For multioutput problems, a list of dicts can be provided in the same order as the columns of y.The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as
n_samples / (n_classes * np.bincount(y))
For multioutput, the weights of each column of y will be multiplied.
Note that these weights will be multiplied with sample_weight (passed through the fit method) if sample_weight is specified.
 random_state : int, RandomState instance or None, optional (default=None)
 If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.
 presort : bool, optional (default=False)
 Whether to presort the data to speed up the finding of best splits in fitting. For the default settings of a decision tree on large datasets, setting this to true may slow down the training process. When using either a smaller dataset or a restricted depth, this may speed up the training.
Attributes
classes_
: array of shape = [n_classes] or a list of such arrays The classes labels (single output problem), or a list of arrays of class labels (multioutput problem).
feature_importances_
: array of shape = [n_features] The feature importances. The higher, the more important the feature. The importance of a feature is computed as the (normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance [4]_.
max_features_
: int, The inferred value of max_features.
n_classes_
: int or list The number of classes (for single output problems), or a list containing the number of classes for each output (for multioutput problems).
n_features_
: int The number of features when
fit
is performed. n_outputs_
: int The number of outputs when
fit
is performed. tree_
: Tree object The underlying Tree object.
See also
DecisionTreeRegressor
References
[1] http://en.wikipedia.org/wiki/Decision_tree_learning [2] L. Breiman, J. Friedman, R. Olshen, and C. Stone, “Classification and Regression Trees”, Wadsworth, Belmont, CA, 1984. [3] T. Hastie, R. Tibshirani and J. Friedman. “Elements of Statistical Learning”, Springer, 2009. [4] L. Breiman, and A. Cutler, “Random Forests”, http://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm Examples
>>> from sklearn.datasets import load_iris >>> from sklearn.cross_validation import cross_val_score >>> from sklearn.tree import DecisionTreeClassifier >>> clf = DecisionTreeClassifier(random_state=0) >>> iris = load_iris() >>> cross_val_score(clf, iris.data, iris.target, cv=10) ... ... array([ 1. , 0.93..., 0.86..., 0.93..., 0.93..., 0.93..., 0.93..., 1. , 0.93..., 1. ])
Full API documentation: DecisionTreeClassifierScikitsLearnNode

class
mdp.nodes.
GenericUnivariateSelectScikitsLearnNode
¶ Univariate feature selector with configurable strategy.
This node has been automatically generated by wrapping the
sklearn.feature_selection.univariate_selection.GenericUnivariateSelect
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Read more in the User Guide.
Parameters
 score_func : callable
 Function taking two arrays X and y, and returning a pair of arrays (scores, pvalues).
 mode : {‘percentile’, ‘k_best’, ‘fpr’, ‘fdr’, ‘fwe’}
 Feature selection mode.
 param : float or int depending on the feature selection mode
 Parameter of the corresponding mode.
Attributes
scores_
: arraylike, shape=(n_features,) Scores of features.
pvalues_
: arraylike, shape=(n_features,) pvalues of feature scores.
See also
f_classif: ANOVA Fvalue between labe/feature for classification tasks. chi2: Chisquared stats of nonnegative features for classification tasks. f_regression: Fvalue between label/feature for regression tasks. SelectPercentile: Select features based on percentile of the highest scores. SelectKBest: Select features based on the k highest scores. SelectFpr: Select features based on a false positive rate test. SelectFdr: Select features based on an estimated false discovery rate. SelectFwe: Select features based on familywise error rate.
Full API documentation: GenericUnivariateSelectScikitsLearnNode

class
mdp.nodes.
BernoulliNBScikitsLearnNode
¶ Naive Bayes classifier for multivariate Bernoulli models.
This node has been automatically generated by wrapping the
sklearn.naive_bayes.BernoulliNB
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Like MultinomialNB, this classifier is suitable for discrete data. The difference is that while MultinomialNB works with occurrence counts, BernoulliNB is designed for binary/boolean features.
Read more in the User Guide.
Parameters
 alpha : float, optional (default=1.0)
 Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing).
 binarize : float or None, optional
 Threshold for binarizing (mapping to booleans) of sample features. If None, input is presumed to already consist of binary vectors.
 fit_prior : boolean
 Whether to learn class prior probabilities or not. If false, a uniform prior will be used.
 class_prior : arraylike, size=[n_classes,]
 Prior probabilities of the classes. If specified the priors are not adjusted according to the data.
Attributes
class_log_prior_
: array, shape = [n_classes] Log probability of each class (smoothed).
feature_log_prob_
: array, shape = [n_classes, n_features] Empirical log probability of features given a class, P(x_iy).
class_count_
: array, shape = [n_classes] Number of samples encountered for each class during fitting. This value is weighted by the sample weight when provided.
feature_count_
: array, shape = [n_classes, n_features] Number of samples encountered for each (class, feature) during fitting. This value is weighted by the sample weight when provided.
Examples
>>> import numpy as np >>> X = np.random.randint(2, size=(6, 100)) >>> Y = np.array([1, 2, 3, 4, 4, 5]) >>> from sklearn.naive_bayes import BernoulliNB >>> clf = BernoulliNB() >>> clf.fit(X, Y) BernoulliNB(alpha=1.0, binarize=0.0, class_prior=None, fit_prior=True) >>> print(clf.predict(X[2:3])) [3]
References
C.D. Manning, P. Raghavan and H. Schuetze (2008). Introduction to Information Retrieval. Cambridge University Press, pp. 234265. http://nlp.stanford.edu/IRbook/html/htmledition/thebernoullimodel1.html
A. McCallum and K. Nigam (1998). A comparison of event models for naive Bayes text classification. Proc. AAAI/ICML98 Workshop on Learning for Text Categorization, pp. 4148.
V. Metsis, I. Androutsopoulos and G. Paliouras (2006). Spam filtering with naive Bayes – Which naive Bayes? 3rd Conf. on Email and AntiSpam (CEAS).
Full API documentation: BernoulliNBScikitsLearnNode

class
mdp.nodes.
LogisticRegressionScikitsLearnNode
¶ Logistic Regression (aka logit, MaxEnt) classifier.
This node has been automatically generated by wrapping the
sklearn.linear_model.logistic.LogisticRegression
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.In the multiclass case, the training algorithm uses the onevsrest (OvR) scheme if the ‘multi_class’ option is set to ‘ovr’ and uses the crossentropy loss, if the ‘multi_class’ option is set to ‘multinomial’. (Currently the ‘multinomial’ option is supported only by the ‘lbfgs’ and ‘newtoncg’ solvers.)
This class implements regularized logistic regression using the liblinear library, newtoncg and lbfgs solvers. It can handle both dense and sparse input. Use Cordered arrays or CSR matrices containing 64bit floats for optimal performance; any other input format will be converted (and copied).
The newtoncg and lbfgs solvers support only L2 regularization with primal formulation. The liblinear solver supports both L1 and L2 regularization, with a dual formulation only for the L2 penalty.
Read more in the User Guide.
Parameters
 penalty : str, ‘l1’ or ‘l2’
 Used to specify the norm used in the penalization. The newtoncg and lbfgs solvers support only l2 penalties.
 dual : bool
 Dual or primal formulation. Dual formulation is only implemented for l2 penalty with liblinear solver. Prefer dual=False when n_samples > n_features.
 C : float, optional (default=1.0)
 Inverse of regularization strength; must be a positive float. Like in support vector machines, smaller values specify stronger regularization.
 fit_intercept : bool, default: True
 Specifies if a constant (a.k.a. bias or intercept) should be added to the decision function.
 intercept_scaling : float, default: 1
 Useful only if solver is liblinear. when self.fit_intercept is True, instance vector x becomes [x, self.intercept_scaling], i.e. a “synthetic” feature with constant value equals to intercept_scaling is appended to the instance vector. The intercept becomes intercept_scaling * synthetic feature weight Note! the synthetic feature weight is subject to l1/l2 regularization as all other features. To lessen the effect of regularization on synthetic feature weight (and therefore on the intercept) intercept_scaling has to be increased.
 class_weight : dict or ‘balanced’, optional
Weights associated with classes in the form
{class_label: weight}
. If not given, all classes are supposed to have weight one.The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as
n_samples / (n_classes * np.bincount(y))
Note that these weights will be multiplied with sample_weight (passed through the fit method) if sample_weight is specified.
New in version 0.17: class_weight=’balanced’ instead of deprecated class_weight=’auto’.
 max_iter : int
 Useful only for the newtoncg, sag and lbfgs solvers. Maximum number of iterations taken for the solvers to converge.
 random_state : int seed, RandomState instance, or None (default)
 The seed of the pseudo random number generator to use when shuffling the data.
 solver : {‘newtoncg’, ‘lbfgs’, ‘liblinear’, ‘sag’}
Algorithm to use in the optimization problem.
 For small datasets, ‘liblinear’ is a good choice, whereas ‘sag’ is
faster for large ones.
 For multiclass problems, only ‘newtoncg’ and ‘lbfgs’ handle
multinomial loss; ‘sag’ and ‘liblinear’ are limited to oneversusrest schemes.
‘newtoncg’, ‘lbfgs’ and ‘sag’ only handle L2 penalty.
Note that ‘sag’ fast convergence is only guaranteed on features with approximately the same scale. You can preprocess the data with a scaler from sklearn.preprocessing.
New in version 0.17: Stochastic Average Gradient descent solver.
 tol : float, optional
 Tolerance for stopping criteria.
 multi_class : str, {‘ovr’, ‘multinomial’}
 Multiclass option can be either ‘ovr’ or ‘multinomial’. If the option chosen is ‘ovr’, then a binary problem is fit for each label. Else the loss minimised is the multinomial loss fit across the entire probability distribution. Works only for the ‘lbfgs’ solver.
 verbose : int
 For the liblinear and lbfgs solvers set verbose to any positive number for verbosity.
 warm_start : bool, optional
When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution. Useless for liblinear solver.
New in version 0.17: warm_start to support lbfgs, newtoncg, sag solvers.
 n_jobs : int, optional
 Number of CPU cores used during the crossvalidation loop. If given a value of 1, all cores are used.
Attributes
coef_
: array, shape (n_classes, n_features) Coefficient of the features in the decision function.
intercept_
: array, shape (n_classes,) Intercept (a.k.a. bias) added to the decision function. If fit_intercept is set to False, the intercept is set to zero.
n_iter_
: array, shape (n_classes,) or (1, ) Actual number of iterations for all classes. If binary or multinomial, it returns only 1 element. For liblinear solver, only the maximum number of iteration across all classes is given.
See also
 SGDClassifier : incrementally trained logistic regression (when given
 the parameter
loss="log"
).
sklearn.svm.LinearSVC : learns SVM models using the same algorithm.
Notes
The underlying C implementation uses a random number generator to select features when fitting the model. It is thus not uncommon, to have slightly different results for the same input data. If that happens, try with a smaller tol parameter.
Predict output may not match that of standalone liblinear in certain cases. See differences from liblinear in the narrative documentation.
References
 LIBLINEAR – A Library for Large Linear Classification
 http://www.csie.ntu.edu.tw/~cjlin/liblinear/
 HsiangFu Yu, FangLan Huang, ChihJen Lin (2011). Dual coordinate descent
 methods for logistic regression and maximum entropy models. Machine Learning 85(12):4175. http://www.csie.ntu.edu.tw/~cjlin/papers/maxent_dual.pdf
Full API documentation: LogisticRegressionScikitsLearnNode

class
mdp.nodes.
MultiLabelBinarizerScikitsLearnNode
¶ Transform between iterable of iterables and a multilabel format
This node has been automatically generated by wrapping the
sklearn.preprocessing.label.MultiLabelBinarizer
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Although a list of sets or tuples is a very intuitive format for multilabel data, it is unwieldy to process. This transformer converts between this intuitive format and the supported multilabel format: a (samples x classes) binary matrix indicating the presence of a class label.
Parameters
 classes : arraylike of shape [n_classes] (optional)
 Indicates an ordering for the class labels
 sparse_output : boolean (default: False),
 Set to true if output binary array is desired in CSR sparse format
Attributes
classes_
: array of labels A copy of the classes parameter where provided, or otherwise, the sorted set of classes found when fitting.
Examples
>>> mlb = MultiLabelBinarizer() >>> mlb.fit_transform([(1, 2), (3,)]) array([[1, 1, 0], [0, 0, 1]]) >>> mlb.classes_ array([1, 2, 3])
>>> mlb.fit_transform([set(['scifi', 'thriller']), set(['comedy'])]) array([[0, 1, 1], [1, 0, 0]]) >>> list(mlb.classes_) ['comedy', 'scifi', 'thriller']
Full API documentation: MultiLabelBinarizerScikitsLearnNode

class
mdp.nodes.
NuSVCScikitsLearnNode
¶ NuSupport Vector Classification.
This node has been automatically generated by wrapping the
sklearn.svm.classes.NuSVC
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Similar to SVC but uses a parameter to control the number of support vectors.
The implementation is based on libsvm.
Read more in the User Guide.
Parameters
 nu : float, optional (default=0.5)
 An upper bound on the fraction of training errors and a lower bound of the fraction of support vectors. Should be in the interval (0, 1].
 kernel : string, optional (default=’rbf’)
 Specifies the kernel type to be used in the algorithm. It must be one of ‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’ or a callable. If none is given, ‘rbf’ will be used. If a callable is given it is used to precompute the kernel matrix.
 degree : int, optional (default=3)
 Degree of the polynomial kernel function (‘poly’). Ignored by all other kernels.
 gamma : float, optional (default=’auto’)
 Kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’. If gamma is ‘auto’ then 1/n_features will be used instead.
 coef0 : float, optional (default=0.0)
 Independent term in kernel function. It is only significant in ‘poly’ and ‘sigmoid’.
 probability : boolean, optional (default=False)
 Whether to enable probability estimates. This must be enabled prior to calling fit, and will slow down that method.
 shrinking : boolean, optional (default=True)
 Whether to use the shrinking heuristic.
 tol : float, optional (default=1e3)
 Tolerance for stopping criterion.
 cache_size : float, optional
 Specify the size of the kernel cache (in MB).
 class_weight : {dict, ‘auto’}, optional
 Set the parameter C of class i to class_weight[i]*C for SVC. If not given, all classes are supposed to have weight one. The ‘auto’ mode uses the values of y to automatically adjust weights inversely proportional to class frequencies.
 verbose : bool, default: False
 Enable verbose output. Note that this setting takes advantage of a perprocess runtime setting in libsvm that, if enabled, may not work properly in a multithreaded context.
 max_iter : int, optional (default=1)
 Hard limit on iterations within solver, or 1 for no limit.
 decision_function_shape : ‘ovo’, ‘ovr’ or None, default=None
Whether to return a onevsrest (‘ovr’) ecision function of shape (n_samples, n_classes) as all other classifiers, or the original onevsone (‘ovo’) decision function of libsvm which has shape (n_samples, n_classes * (n_classes  1) / 2). The default of None will currently behave as ‘ovo’ for backward compatibility and raise a deprecation warning, but will change ‘ovr’ in 0.18.
New in version 0.17: decision_function_shape=’ovr’ is recommended.
Changed in version 0.17: Deprecated decision_function_shape=’ovo’ and None.
 random_state : int seed, RandomState instance, or None (default)
 The seed of the pseudo random number generator to use when shuffling the data for probability estimation.
Attributes
support_
: arraylike, shape = [n_SV] Indices of support vectors.
support_vectors_
: arraylike, shape = [n_SV, n_features] Support vectors.
n_support_
: arraylike, dtype=int32, shape = [n_class] Number of support vectors for each class.
dual_coef_
: array, shape = [n_class1, n_SV] Coefficients of the support vector in the decision function. For multiclass, coefficient for all 1vs1 classifiers. The layout of the coefficients in the multiclass case is somewhat nontrivial. See the section about multiclass classification in the SVM section of the User Guide for details.
coef_
: array, shape = [n_class1, n_features]Weights assigned to the features (coefficients in the primal problem). This is only available in the case of a linear kernel.
coef_ is readonly property derived from dual_coef_ and support_vectors_.
intercept_
: array, shape = [n_class * (n_class1) / 2] Constants in decision function.
Examples
>>> import numpy as np >>> X = np.array([[1, 1], [2, 1], [1, 1], [2, 1]]) >>> y = np.array([1, 1, 2, 2]) >>> from sklearn.svm import NuSVC >>> clf = NuSVC() >>> clf.fit(X, y) NuSVC(cache_size=200, class_weight=None, coef0=0.0, decision_function_shape=None, degree=3, gamma='auto', kernel='rbf', max_iter=1, nu=0.5, probability=False, random_state=None, shrinking=True, tol=0.001, verbose=False) >>> print(clf.predict([[0.8, 1]])) [1]
See also
 SVC
 Support Vector Machine for classification using libsvm.
 LinearSVC
 Scalable linear Support Vector Machine for classification using liblinear.
Full API documentation: NuSVCScikitsLearnNode

class
mdp.nodes.
SparsePCAScikitsLearnNode
¶ Sparse Principal Components Analysis (SparsePCA)
This node has been automatically generated by wrapping the
sklearn.decomposition.sparse_pca.SparsePCA
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Finds the set of sparse components that can optimally reconstruct the data. The amount of sparseness is controllable by the coefficient of the L1 penalty, given by the parameter alpha.
Read more in the User Guide.
Parameters
 n_components : int,
 Number of sparse atoms to extract.
 alpha : float,
 Sparsity controlling parameter. Higher values lead to sparser components.
 ridge_alpha : float,
 Amount of ridge shrinkage to apply in order to improve conditioning when calling the transform method.
 max_iter : int,
 Maximum number of iterations to perform.
 tol : float,
 Tolerance for the stopping condition.
 method : {‘lars’, ‘cd’}
 lars: uses the least angle regression method to solve the lasso problem (linear_model.lars_path) cd: uses the coordinate descent method to compute the Lasso solution (linear_model.Lasso). Lars will be faster if the estimated components are sparse.
 n_jobs : int,
 Number of parallel jobs to run.
 U_init : array of shape (n_samples, n_components),
 Initial values for the loadings for warm restart scenarios.
 V_init : array of shape (n_components, n_features),
 Initial values for the components for warm restart scenarios.
verbose :
 Degree of verbosity of the printed output.
 random_state : int or RandomState
 Pseudo number generator state used for random sampling.
Attributes
components_
: array, [n_components, n_features] Sparse components extracted from the data.
error_
: array Vector of errors at each iteration.
n_iter_
: int Number of iterations run.
See also
PCA MiniBatchSparsePCA DictionaryLearning
Full API documentation: SparsePCAScikitsLearnNode

class
mdp.nodes.
MiniBatchKMeansScikitsLearnNode
¶ MiniBatch KMeans clustering
This node has been automatically generated by wrapping the
sklearn.cluster.k_means_.MiniBatchKMeans
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Parameters
 n_clusters : int, optional, default: 8
 The number of clusters to form as well as the number of centroids to generate.
 max_iter : int, optional
 Maximum number of iterations over the complete dataset before stopping independently of any early stopping criterion heuristics.
 max_no_improvement : int, default: 10
Control early stopping based on the consecutive number of mini batches that does not yield an improvement on the smoothed inertia.
To disable convergence detection based on inertia, set max_no_improvement to None.
 tol : float, default: 0.0
Control early stopping based on the relative center changes as measured by a smoothed, variancenormalized of the mean center squared position changes. This early stopping heuristics is closer to the one used for the batch variant of the algorithms but induces a slight computational and memory overhead over the inertia heuristic.
To disable convergence detection based on normalized center change, set tol to 0.0 (default).
 batch_size : int, optional, default: 100
 Size of the mini batches.
 init_size : int, optional, default: 3 * batch_size
 Number of samples to randomly sample for speeding up the initialization (sometimes at the expense of accuracy): the only algorithm is initialized by running a batch KMeans on a random subset of the data. This needs to be larger than n_clusters.
 init : {‘kmeans++’, ‘random’ or an ndarray}, default: ‘kmeans++’
Method for initialization, defaults to ‘kmeans++’:
‘kmeans++’ : selects initial cluster centers for kmean clustering in a smart way to speed up convergence. See section Notes in k_init for more details.
‘random’: choose k observations (rows) at random from data for the initial centroids.
If an ndarray is passed, it should be of shape (n_clusters, n_features) and gives the initial centers.
 n_init : int, default=3
 Number of random initializations that are tried.
In contrast to KMeans, the algorithm is only run once, using the
best of the
n_init
initializations as measured by inertia.  compute_labels : boolean, default=True
 Compute label assignment and inertia for the complete dataset once the minibatch optimization has converged in fit.
 random_state : integer or numpy.RandomState, optional
 The generator used to initialize the centers. If an integer is given, it fixes the seed. Defaults to the global numpy random number generator.
 reassignment_ratio : float, default: 0.01
 Control the fraction of the maximum number of counts for a center to be reassigned. A higher value means that low count centers are more easily reassigned, which means that the model will take longer to converge, but should converge in a better clustering.
 verbose : boolean, optional
 Verbosity mode.
Attributes
cluster_centers_
: array, [n_clusters, n_features] Coordinates of cluster centers
labels_
: Labels of each point (if compute_labels is set to True).
inertia_
: float The value of the inertia criterion associated with the chosen partition (if compute_labels is set to True). The inertia is defined as the sum of square distances of samples to their nearest neighbor.
Notes
See http://www.eecs.tufts.edu/~dsculley/papers/fastkmeans.pdf
Full API documentation: MiniBatchKMeansScikitsLearnNode

class
mdp.nodes.
LassoLarsScikitsLearnNode
¶ Lasso model fit with Least Angle Regression a.k.a. Lars
This node has been automatically generated by wrapping the
sklearn.linear_model.least_angle.LassoLars
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.It is a Linear Model trained with an L1 prior as regularizer.
The optimization objective for Lasso is:
(1 / (2 * n_samples)) * y  Xw^2_2 + alpha * w_1
Read more in the User Guide.
Parameters
 alpha : float
 Constant that multiplies the penalty term. Defaults to 1.0.
alpha = 0
is equivalent to an ordinary least square, solved byLinearRegression
. For numerical reasons, usingalpha = 0
with the LassoLars object is not advised and you should prefer the LinearRegression object.  fit_intercept : boolean
 whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered).
 positive : boolean (default=False)
 Restrict coefficients to be >= 0. Be aware that you might want to
remove fit_intercept which is set True by default.
Under the positive restriction the model coefficients will not converge
to the ordinaryleastsquares solution for small values of alpha.
Only coeffiencts up to the smallest alpha value (
alphas_[alphas_ > 0.].min()
when fit_path=True) reached by the stepwise LarsLasso algorithm are typically in congruence with the solution of the coordinate descent Lasso estimator.  verbose : boolean or integer, optional
 Sets the verbosity amount
 normalize : boolean, optional, default False
 If True, the regressors X will be normalized before regression.
 copy_X : boolean, optional, default True
 If True, X will be copied; else, it may be overwritten.
 precompute : True  False  ‘auto’  arraylike
 Whether to use a precomputed Gram matrix to speed up
calculations. If set to
'auto'
let us decide. The Gram matrix can also be passed as argument.  max_iter : integer, optional
 Maximum number of iterations to perform.
 eps : float, optional
 The machineprecision regularization in the computation of the
Cholesky diagonal factors. Increase this for very illconditioned
systems. Unlike the
tol
parameter in some iterative optimizationbased algorithms, this parameter does not control the tolerance of the optimization.  fit_path : boolean
 If
True
the full path is stored in thecoef_path_
attribute. If you compute the solution for a large problem or many targets, settingfit_path
toFalse
will lead to a speedup, especially with a small alpha.
Attributes
alphas_
: array, shape (n_alphas + 1,)  list of n_targets such arrays Maximum of covariances (in absolute value) at each iteration.
n_alphas
is eithermax_iter
,n_features
, or the number of nodes in the path with correlation greater thanalpha
, whichever is smaller. active_
: list, length = n_alphas  list of n_targets such lists Indices of active variables at the end of the path.
coef_path_
: array, shape (n_features, n_alphas + 1) or list If a list is passed it’s expected to be one of n_targets such arrays.
The varying values of the coefficients along the path. It is not
present if the
fit_path
parameter isFalse
. coef_
: array, shape (n_features,) or (n_targets, n_features) Parameter vector (w in the formulation formula).
intercept_
: float  array, shape (n_targets,) Independent term in decision function.
n_iter_
: arraylike or int. The number of iterations taken by lars_path to find the grid of alphas for each target.
Examples
>>> from sklearn import linear_model >>> clf = linear_model.LassoLars(alpha=0.01) >>> clf.fit([[1, 1], [0, 0], [1, 1]], [1, 0, 1]) ... LassoLars(alpha=0.01, copy_X=True, eps=..., fit_intercept=True, fit_path=True, max_iter=500, normalize=True, positive=False, precompute='auto', verbose=False) >>> print(clf.coef_) [ 0. 0.963257...]
See also
lars_path lasso_path Lasso LassoCV LassoLarsCV sklearn.decomposition.sparse_encode
Full API documentation: LassoLarsScikitsLearnNode

class
mdp.nodes.
LassoScikitsLearnNode
¶ Linear Model trained with L1 prior as regularizer (aka the Lasso)
This node has been automatically generated by wrapping the
sklearn.linear_model.coordinate_descent.Lasso
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.The optimization objective for Lasso is:
(1 / (2 * n_samples)) * y  Xw^2_2 + alpha * w_1
Technically the Lasso model is optimizing the same objective function as the Elastic Net with
l1_ratio=1.0
(no L2 penalty).Read more in the User Guide.
Parameters
 alpha : float, optional
 Constant that multiplies the L1 term. Defaults to 1.0.
alpha = 0
is equivalent to an ordinary least square, solved by theLinearRegression
object. For numerical reasons, usingalpha = 0
is with the Lasso object is not advised and you should prefer the LinearRegression object.  fit_intercept : boolean
 whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered).
 normalize : boolean, optional, default False
 If
True
, the regressors X will be normalized before regression.  copy_X : boolean, optional, default True
 If
True
, X will be copied; else, it may be overwritten.  precompute : True  False  ‘auto’  arraylike
 Whether to use a precomputed Gram matrix to speed up
calculations. If set to
'auto'
let us decide. The Gram matrix can also be passed as argument. For sparse input this option is alwaysTrue
to preserve sparsity. WARNING : The'auto'
option is deprecated and will be removed in 0.18.  max_iter : int, optional
 The maximum number of iterations
 tol : float, optional
 The tolerance for the optimization: if the updates are
smaller than
tol
, the optimization code checks the dual gap for optimality and continues until it is smaller thantol
.  warm_start : bool, optional
 When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution.
 positive : bool, optional
 When set to
True
, forces the coefficients to be positive.  selection : str, default ‘cyclic’
 If set to ‘random’, a random coefficient is updated every iteration rather than looping over features sequentially by default. This (setting to ‘random’) often leads to significantly faster convergence especially when tol is higher than 1e4.
 random_state : int, RandomState instance, or None (default)
 The seed of the pseudo random number generator that selects a random feature to update. Useful only when selection is set to ‘random’.
Attributes
coef_
: array, shape (n_features,)  (n_targets, n_features) parameter vector (w in the cost function formula)
sparse_coef_
: scipy.sparse matrix, shape (n_features, 1)  (n_targets, n_features)sparse_coef_
is a readonly property derived fromcoef_
intercept_
: float  array, shape (n_targets,) independent term in decision function.
n_iter_
: int  arraylike, shape (n_targets,) number of iterations run by the coordinate descent solver to reach the specified tolerance.
Examples
>>> from sklearn import linear_model >>> clf = linear_model.Lasso(alpha=0.1) >>> clf.fit([[0,0], [1, 1], [2, 2]], [0, 1, 2]) Lasso(alpha=0.1, copy_X=True, fit_intercept=True, max_iter=1000, normalize=False, positive=False, precompute=False, random_state=None, selection='cyclic', tol=0.0001, warm_start=False) >>> print(clf.coef_) [ 0.85 0. ] >>> print(clf.intercept_) 0.15
See also
lars_path lasso_path LassoLars LassoCV LassoLarsCV sklearn.decomposition.sparse_encode
Notes
The algorithm used to fit the model is coordinate descent.
To avoid unnecessary memory duplication the X argument of the fit method should be directly passed as a Fortrancontiguous numpy array.
Full API documentation: LassoScikitsLearnNode

class
mdp.nodes.
RANSACRegressorScikitsLearnNode
¶ RANSAC (RANdom SAmple Consensus) algorithm.
This node has been automatically generated by wrapping the
sklearn.linear_model.ransac.RANSACRegressor
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.RANSAC is an iterative algorithm for the robust estimation of parameters from a subset of inliers from the complete data set. More information can be found in the general documentation of linear models.
A detailed description of the algorithm can be found in the documentation of the
linear_model
subpackage.Read more in the User Guide.
Parameters
 base_estimator : object, optional
Base estimator object which implements the following methods:
 fit(X, y): Fit model to given training data and target values.
 score(X, y): Returns the mean accuracy on the given test data, which is used for the stop criterion defined by stop_score. Additionally, the score is used to decide which of two equally large consensus sets is chosen as the better one.
If base_estimator is None, then
base_estimator=sklearn.linear_model.LinearRegression()
is used for target values of dtype float.Note that the current implementation only supports regression estimators.
 min_samples : int (>= 1) or float ([0, 1]), optional
 Minimum number of samples chosen randomly from original data. Treated
as an absolute number of samples for min_samples >= 1, treated as a
relative number ceil(min_samples * X.shape[0]) for
min_samples < 1. This is typically chosen as the minimal number of
samples necessary to estimate the given base_estimator. By default a
sklearn.linear_model.LinearRegression()
estimator is assumed and min_samples is chosen asX.shape[1] + 1
.  residual_threshold : float, optional
 Maximum residual for a data sample to be classified as an inlier. By default the threshold is chosen as the MAD (median absolute deviation) of the target values y.
 is_data_valid : callable, optional
 This function is called with the randomly selected data before the model is fitted to it: is_data_valid(X, y). If its return value is False the current randomly chosen subsample is skipped.
 is_model_valid : callable, optional
 This function is called with the estimated model and the randomly selected data: is_model_valid(model, X, y). If its return value is False the current randomly chosen subsample is skipped. Rejecting samples with this function is computationally costlier than with is_data_valid. is_model_valid should therefore only be used if the estimated model is needed for making the rejection decision.
 max_trials : int, optional
 Maximum number of iterations for random sample selection.
 stop_n_inliers : int, optional
 Stop iteration if at least this number of inliers are found.
 stop_score : float, optional
 Stop iteration if score is greater equal than this threshold.
 stop_probability : float in range [0, 1], optional
RANSAC iteration stops if at least one outlierfree set of the training data is sampled in RANSAC. This requires to generate at least N samples (iterations):
N >= log(1  probability) / log(1  e**m)
where the probability (confidence) is typically set to high value such as 0.99 (the default) and e is the current fraction of inliers w.r.t. the total number of samples.
 residual_metric : callable, optional
Metric to reduce the dimensionality of the residuals to 1 for multidimensional target values
y.shape[1] > 1
. By default the sum of absolute differences is used:lambda dy: np.sum(np.abs(dy), axis=1)
 random_state : integer or numpy.RandomState, optional
 The generator used to initialize the centers. If an integer is given, it fixes the seed. Defaults to the global numpy random number generator.
Attributes
estimator_
: object Best fitted model (copy of the base_estimator object).
n_trials_
: int Number of random selection trials until one of the stop criteria is
met. It is always
<= max_trials
. inlier_mask_
: bool array of shape [n_samples] Boolean mask of inliers classified as
True
.
References
[1] http://en.wikipedia.org/wiki/RANSAC [2] http://www.cs.columbia.edu/~belhumeur/courses/compPhoto/ransac.pdf [3] http://www.bmva.org/bmvc/2009/Papers/Paper355/Paper355.pdf Full API documentation: RANSACRegressorScikitsLearnNode

class
mdp.nodes.
TruncatedSVDScikitsLearnNode
¶ Dimensionality reduction using truncated SVD (aka LSA).
This node has been automatically generated by wrapping the
sklearn.decomposition.truncated_svd.TruncatedSVD
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.This transformer performs linear dimensionality reduction by means of truncated singular value decomposition (SVD). It is very similar to PCA, but operates on sample vectors directly, instead of on a covariance matrix. This means it can work with scipy.sparse matrices efficiently.
In particular, truncated SVD works on term count/tfidf matrices as returned by the vectorizers in sklearn.feature_extraction.text. In that context, it is known as latent semantic analysis (LSA).
This estimator supports two algorithm: a fast randomized SVD solver, and a “naive” algorithm that uses ARPACK as an eigensolver on (X * X.T) or (X.T * X), whichever is more efficient.
Read more in the User Guide.
Parameters
 n_components : int, default = 2
 Desired dimensionality of output data. Must be strictly less than the number of features. The default value is useful for visualisation. For LSA, a value of 100 is recommended.
 algorithm : string, default = “randomized”
 SVD solver to use. Either “arpack” for the ARPACK wrapper in SciPy (scipy.sparse.linalg.svds), or “randomized” for the randomized algorithm due to Halko (2009).
 n_iter : int, optional
 Number of iterations for randomized SVD solver. Not used by ARPACK.
 random_state : int or RandomState, optional
 (Seed for) pseudorandom number generator. If not given, the numpy.random singleton is used.
 tol : float, optional
 Tolerance for ARPACK. 0 means machine precision. Ignored by randomized SVD solver.
Attributes
components_
: array, shape (n_components, n_features)explained_variance_ratio_
: array, [n_components] Percentage of variance explained by each of the selected components.
explained_variance_
: array, [n_components] The variance of the training samples transformed by a projection to each component.
Examples
>>> from sklearn.decomposition import TruncatedSVD >>> from sklearn.random_projection import sparse_random_matrix >>> X = sparse_random_matrix(100, 100, density=0.01, random_state=42) >>> svd = TruncatedSVD(n_components=5, random_state=42) >>> svd.fit(X) TruncatedSVD(algorithm='randomized', n_components=5, n_iter=5, random_state=42, tol=0.0) >>> print(svd.explained_variance_ratio_) [ 0.0782... 0.0552... 0.0544... 0.0499... 0.0413...] >>> print(svd.explained_variance_ratio_.sum()) 0.279...
See also
PCA RandomizedPCA
References
Finding structure with randomness: Stochastic algorithms for constructing approximate matrix decompositions Halko, et al., 2009 (arXiv:909) http://arxiv.org/pdf/0909.4061
Notes
SVD suffers from a problem called “sign indeterminancy”, which means the sign of the
components_
and the output from transform depend on the algorithm and random state. To work around this, fit instances of this class to data once, then keep the instance around to do transformations.Full API documentation: TruncatedSVDScikitsLearnNode

class
mdp.nodes.
SelectFprScikitsLearnNode
¶ Filter: Select the pvalues below alpha based on a FPR test.
This node has been automatically generated by wrapping the
sklearn.feature_selection.univariate_selection.SelectFpr
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.FPR test stands for False Positive Rate test. It controls the total amount of false detections.
Read more in the User Guide.
Parameters
 score_func : callable
 Function taking two arrays X and y, and returning a pair of arrays (scores, pvalues).
 alpha : float, optional
 The highest pvalue for features to be kept.
Attributes
scores_
: arraylike, shape=(n_features,) Scores of features.
pvalues_
: arraylike, shape=(n_features,) pvalues of feature scores.
See also
f_classif: ANOVA Fvalue between labe/feature for classification tasks. chi2: Chisquared stats of nonnegative features for classification tasks. f_regression: Fvalue between label/feature for regression tasks. SelectPercentile: Select features based on percentile of the highest scores. SelectKBest: Select features based on the k highest scores. SelectFdr: Select features based on an estimated false discovery rate. SelectFwe: Select features based on familywise error rate. GenericUnivariateSelect: Univariate feature selector with configurable mode.
Full API documentation: SelectFprScikitsLearnNode

class
mdp.nodes.
NuSVRScikitsLearnNode
¶ Nu Support Vector Regression.
This node has been automatically generated by wrapping the
sklearn.svm.classes.NuSVR
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Similar to NuSVC, for regression, uses a parameter nu to control the number of support vectors. However, unlike NuSVC, where nu replaces C, here nu replaces the parameter epsilon of epsilonSVR.
The implementation is based on libsvm.
Read more in the User Guide.
Parameters
 C : float, optional (default=1.0)
 Penalty parameter C of the error term.
 nu : float, optional
 An upper bound on the fraction of training errors and a lower bound of the fraction of support vectors. Should be in the interval (0, 1]. By default 0.5 will be taken.
 kernel : string, optional (default=’rbf’)
 Specifies the kernel type to be used in the algorithm. It must be one of ‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’ or a callable. If none is given, ‘rbf’ will be used. If a callable is given it is used to precompute the kernel matrix.
 degree : int, optional (default=3)
 Degree of the polynomial kernel function (‘poly’). Ignored by all other kernels.
 gamma : float, optional (default=’auto’)
 Kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’. If gamma is ‘auto’ then 1/n_features will be used instead.
 coef0 : float, optional (default=0.0)
 Independent term in kernel function. It is only significant in ‘poly’ and ‘sigmoid’.
 shrinking : boolean, optional (default=True)
 Whether to use the shrinking heuristic.
 tol : float, optional (default=1e3)
 Tolerance for stopping criterion.
 cache_size : float, optional
 Specify the size of the kernel cache (in MB).
 verbose : bool, default: False
 Enable verbose output. Note that this setting takes advantage of a perprocess runtime setting in libsvm that, if enabled, may not work properly in a multithreaded context.
 max_iter : int, optional (default=1)
 Hard limit on iterations within solver, or 1 for no limit.
Attributes
support_
: arraylike, shape = [n_SV] Indices of support vectors.
support_vectors_
: arraylike, shape = [nSV, n_features] Support vectors.
dual_coef_
: array, shape = [1, n_SV] Coefficients of the support vector in the decision function.
coef_
: array, shape = [1, n_features]Weights assigned to the features (coefficients in the primal problem). This is only available in the case of a linear kernel.
coef_ is readonly property derived from dual_coef_ and support_vectors_.
intercept_
: array, shape = [1] Constants in decision function.
Examples
>>> from sklearn.svm import NuSVR >>> import numpy as np >>> n_samples, n_features = 10, 5 >>> np.random.seed(0) >>> y = np.random.randn(n_samples) >>> X = np.random.randn(n_samples, n_features) >>> clf = NuSVR(C=1.0, nu=0.1) >>> clf.fit(X, y) NuSVR(C=1.0, cache_size=200, coef0=0.0, degree=3, gamma='auto', kernel='rbf', max_iter=1, nu=0.1, shrinking=True, tol=0.001, verbose=False)
See also
 NuSVC
 Support Vector Machine for classification implemented with libsvm with a parameter to control the number of support vectors.
 SVR
 epsilon Support Vector Machine for regression implemented with libsvm.
Full API documentation: NuSVRScikitsLearnNode

class
mdp.nodes.
LogOddsEstimatorScikitsLearnNode
¶ This node has been automatically generated by wrapping the
sklearn.ensemble.gradient_boosting.LogOddsEstimator
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Full API documentation: LogOddsEstimatorScikitsLearnNode

class
mdp.nodes.
RandomizedSearchCVScikitsLearnNode
¶ Randomized search on hyper parameters.
This node has been automatically generated by wrapping the
sklearn.grid_search.RandomizedSearchCV
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.RandomizedSearchCV implements a “fit” and a “score” method. It also implements “predict”, “predict_proba”, “decision_function”, “transform” and “inverse_transform” if they are implemented in the estimator used.
The parameters of the estimator used to apply these methods are optimized by crossvalidated search over parameter settings.
In contrast to GridSearchCV, not all parameter values are tried out, but rather a fixed number of parameter settings is sampled from the specified distributions. The number of parameter settings that are tried is given by n_iter.
If all parameters are presented as a list, sampling without replacement is performed. If at least one parameter is given as a distribution, sampling with replacement is used. It is highly recommended to use continuous distributions for continuous parameters.
Read more in the User Guide.
Parameters
 estimator : estimator object.
 A object of that type is instantiated for each grid point.
This is assumed to implement the scikitlearn estimator interface.
Either estimator needs to provide a
score
function, orscoring
must be passed.  param_distributions : dict
 Dictionary with parameters names (string) as keys and distributions
or lists of parameters to try. Distributions must provide a
rvs
method for sampling (such as those from scipy.stats.distributions). If a list is given, it is sampled uniformly.  n_iter : int, default=10
 Number of parameter settings that are sampled. n_iter trades off runtime vs quality of the solution.
 scoring : string, callable or None, default=None
 A string (see model evaluation documentation) or
a scorer callable object / function with signature
scorer(estimator, X, y)
. IfNone
, thescore
method of the estimator is used.  fit_params : dict, optional
 Parameters to pass to the fit method.
 n_jobs : int, default=1
 Number of jobs to run in parallel.
 pre_dispatch : int, or string, optional
Controls the number of jobs that get dispatched during parallel execution. Reducing this number can be useful to avoid an explosion of memory consumption when more jobs get dispatched than CPUs can process. This parameter can be:
 None, in which case all the jobs are immediately created and spawned. Use this for lightweight and fastrunning jobs, to avoid delays due to ondemand spawning of the jobs
 An int, giving the exact number of total jobs that are spawned
 A string, giving an expression as a function of n_jobs, as in ‘2*n_jobs’
 iid : boolean, default=True
 If True, the data is assumed to be identically distributed across the folds, and the loss minimized is the total loss per sample, and not the mean loss across the folds.
 cv : int, crossvalidation generator or an iterable, optional
Determines the crossvalidation splitting strategy. Possible inputs for cv are:
 None, to use the default 3fold crossvalidation,
 integer, to specify the number of folds.
 An object to be used as a crossvalidation generator.
 An iterable yielding train/test splits.
For integer/None inputs, if
y
is binary or multiclass,StratifiedKFold
used. If the estimator is a classifier or ify
is neither binary nor multiclass,KFold
is used.Refer User Guide for the various crossvalidation strategies that can be used here.
 refit : boolean, default=True
 Refit the best estimator with the entire dataset. If “False”, it is impossible to make predictions using this RandomizedSearchCV instance after fitting.
 verbose : integer
 Controls the verbosity: the higher, the more messages.
 random_state : int or RandomState
 Pseudo random number generator state used for random uniform sampling from lists of possible values instead of scipy.stats distributions.
 error_score : ‘raise’ (default) or numeric
 Value to assign to the score if an error occurs in estimator fitting. If set to ‘raise’, the error is raised. If a numeric value is given, FitFailedWarning is raised. This parameter does not affect the refit step, which will always raise the error.
Attributes
grid_scores_
: list of named tuplesContains scores for all parameter combinations in param_grid. Each entry corresponds to one parameter setting. Each named tuple has the attributes:
parameters
, a dict of parameter settingsmean_validation_score
, the mean score over the crossvalidation foldscv_validation_scores
, the list of scores for each fold
best_estimator_
: estimator Estimator that was chosen by the search, i.e. estimator which gave highest score (or smallest loss if specified) on the left out data. Not available if refit=False.
best_score_
: float Score of best_estimator on the left out data.
best_params_
: dict Parameter setting that gave the best results on the hold out data.
Notes
The parameters selected are those that maximize the score of the heldout data, according to the scoring parameter.
If n_jobs was set to a value higher than one, the data is copied for each parameter setting(and not n_jobs times). This is done for efficiency reasons if individual jobs take very little time, but may raise errors if the dataset is large and not enough memory is available. A workaround in this case is to set pre_dispatch. Then, the memory is copied only pre_dispatch many times. A reasonable value for pre_dispatch is 2 * n_jobs.
See Also
GridSearchCV
: Does exhaustive search over a grid of parameters.
ParameterSampler
: A generator over parameter settins, constructed from
 param_distributions.
Full API documentation: RandomizedSearchCVScikitsLearnNode

class
mdp.nodes.
ProjectedGradientNMFScikitsLearnNode
¶ NonNegative Matrix Factorization (NMF)
This node has been automatically generated by wrapping the
sklearn.decomposition.nmf.ProjectedGradientNMF
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Find two nonnegative matrices (W, H) whose product approximates the non negative matrix X. This factorization can be used for example for dimensionality reduction, source separation or topic extraction.
The objective function is:
0.5 * X  WH_Fro^2 + alpha * l1_ratio * vec(W)_1 + alpha * l1_ratio * vec(H)_1 + 0.5 * alpha * (1  l1_ratio) * W_Fro^2 + 0.5 * alpha * (1  l1_ratio) * H_Fro^2
Where:
A_Fro^2 = \sum_{i,j} A_{ij}^2 (Frobenius norm) vec(A)_1 = \sum_{i,j} abs(A_{ij}) (Elementwise L1 norm)
The objective function is minimized with an alternating minimization of W and H.
Read more in the User Guide.
Parameters
 n_components : int or None
 Number of components, if n_components is not set all features are kept.
 init : ‘random’  ‘nndsvd’  ‘nndsvda’  ‘nndsvdar’  ‘custom’
Method used to initialize the procedure. Default: ‘nndsvdar’ if n_components < n_features, otherwise random. Valid options:
‘random’: nonnegative random matrices, scaled with:
 sqrt(X.mean() / n_components)
 ‘nndsvd’: Nonnegative Double Singular Value Decomposition (NNDSVD)
initialization (better for sparseness)
 ‘nndsvda’: NNDSVD with zeros filled with the average of X
(better when sparsity is not desired)
 ‘nndsvdar’: NNDSVD with zeros filled with small random values
(generally faster, less accurate alternative to NNDSVDa for when sparsity is not desired)
‘custom’: use custom matrices W and H
 solver : ‘pg’  ‘cd’
Numerical solver to use:
 ‘pg’ is a Projected Gradient solver (deprecated).
 ‘cd’ is a Coordinate Descent solver (recommended).
New in version 0.17: Coordinate Descent solver.
Changed in version 0.17: Deprecated Projected Gradient solver.
 tol : double, default: 1e4
 Tolerance value used in stopping conditions.
 max_iter : integer, default: 200
 Number of iterations to compute.
 random_state : integer seed, RandomState instance, or None (default)
 Random number generator seed control.
 alpha : double, default: 0.
Constant that multiplies the regularization terms. Set it to zero to have no regularization.
New in version 0.17: alpha used in the Coordinate Descent solver.
 l1_ratio : double, default: 0.
The regularization mixing parameter, with 0 <= l1_ratio <= 1. For l1_ratio = 0 the penalty is an elementwise L2 penalty (aka Frobenius Norm). For l1_ratio = 1 it is an elementwise L1 penalty. For 0 < l1_ratio < 1, the penalty is a combination of L1 and L2.
New in version 0.17: Regularization parameter l1_ratio used in the Coordinate Descent solver.
 shuffle : boolean, default: False
If true, randomize the order of coordinates in the CD solver.
New in version 0.17: shuffle parameter used in the Coordinate Descent solver.
 nls_max_iter : integer, default: 2000
Number of iterations in NLS subproblem. Used only in the deprecated ‘pg’ solver.
Changed in version 0.17: Deprecated Projected Gradient solver. Use Coordinate Descent solver instead.
 sparseness : ‘data’  ‘components’  None, default: None
Where to enforce sparsity in the model. Used only in the deprecated ‘pg’ solver.
Changed in version 0.17: Deprecated Projected Gradient solver. Use Coordinate Descent solver instead.
 beta : double, default: 1
Degree of sparseness, if sparseness is not None. Larger values mean more sparseness. Used only in the deprecated ‘pg’ solver.
Changed in version 0.17: Deprecated Projected Gradient solver. Use Coordinate Descent solver instead.
 eta : double, default: 0.1
Degree of correctness to maintain, if sparsity is not None. Smaller values mean larger error. Used only in the deprecated ‘pg’ solver.
Changed in version 0.17: Deprecated Projected Gradient solver. Use Coordinate Descent solver instead.
Attributes
components_
: array, [n_components, n_features] Nonnegative components of the data.
reconstruction_err_
: number Frobenius norm of the matrix difference between
the training data and the reconstructed data from
the fit produced by the model.
 X  WH _2
n_iter_
: int Actual number of iterations.
Examples
>>> import numpy as np >>> X = np.array([[1,1], [2, 1], [3, 1.2], [4, 1], [5, 0.8], [6, 1]]) >>> from sklearn.decomposition import NMF >>> model = NMF(n_components=2, init='random', random_state=0) >>> model.fit(X) NMF(alpha=0.0, beta=1, eta=0.1, init='random', l1_ratio=0.0, max_iter=200, n_components=2, nls_max_iter=2000, random_state=0, shuffle=False, solver='cd', sparseness=None, tol=0.0001, verbose=0)
>>> model.components_ array([[ 2.09783018, 0.30560234], [ 2.13443044, 2.13171694]]) >>> model.reconstruction_err_ 0.00115993...
References
C.J. Lin. Projected gradient methods for nonnegative matrix factorization. Neural Computation, 19(2007), 27562779. http://www.csie.ntu.edu.tw/~cjlin/nmf/
Cichocki, Andrzej, and P. H. A. N. AnhHuy. “Fast local algorithms for large scale nonnegative matrix and tensor factorizations.” IEICE transactions on fundamentals of electronics, communications and computer sciences 92.3: 708721, 2009.
Full API documentation: ProjectedGradientNMFScikitsLearnNode

class
mdp.nodes.
SGDClassifierScikitsLearnNode
¶ Linear classifiers (SVM, logistic regression, a.o.) with SGD training.
This node has been automatically generated by wrapping the
sklearn.linear_model.stochastic_gradient.SGDClassifier
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.This estimator implements regularized linear models with stochastic gradient descent (SGD) learning: the gradient of the loss is estimated each sample at a time and the model is updated along the way with a decreasing strength schedule (aka learning rate). SGD allows minibatch (online/outofcore) learning, see the partial_fit method. For best results using the default learning rate schedule, the data should have zero mean and unit variance.
This implementation works with data represented as dense or sparse arrays of floating point values for the features. The model it fits can be controlled with the loss parameter; by default, it fits a linear support vector machine (SVM).
The regularizer is a penalty added to the loss function that shrinks model parameters towards the zero vector using either the squared euclidean norm L2 or the absolute norm L1 or a combination of both (Elastic Net). If the parameter update crosses the 0.0 value because of the regularizer, the update is truncated to 0.0 to allow for learning sparse models and achieve online feature selection.
Read more in the User Guide.
Parameters
 loss : str, ‘hinge’, ‘log’, ‘modified_huber’, ‘squared_hinge’, ‘perceptron’, or a regression loss: ‘squared_loss’, ‘huber’, ‘epsilon_insensitive’, or ‘squared_epsilon_insensitive’
 The loss function to be used. Defaults to ‘hinge’, which gives a linear SVM. The ‘log’ loss gives logistic regression, a probabilistic classifier. ‘modified_huber’ is another smooth loss that brings tolerance to outliers as well as probability estimates. ‘squared_hinge’ is like hinge but is quadratically penalized. ‘perceptron’ is the linear loss used by the perceptron algorithm. The other losses are designed for regression but can be useful in classification as well; see SGDRegressor for a description.
 penalty : str, ‘none’, ‘l2’, ‘l1’, or ‘elasticnet’
 The penalty (aka regularization term) to be used. Defaults to ‘l2’ which is the standard regularizer for linear SVM models. ‘l1’ and ‘elasticnet’ might bring sparsity to the model (feature selection) not achievable with ‘l2’.
 alpha : float
 Constant that multiplies the regularization term. Defaults to 0.0001 Also used to compute learning_rate when set to ‘optimal’.
 l1_ratio : float
 The Elastic Net mixing parameter, with 0 <= l1_ratio <= 1. l1_ratio=0 corresponds to L2 penalty, l1_ratio=1 to L1. Defaults to 0.15.
 fit_intercept : bool
 Whether the intercept should be estimated or not. If False, the data is assumed to be already centered. Defaults to True.
 n_iter : int, optional
 The number of passes over the training data (aka epochs). The number of iterations is set to 1 if using partial_fit. Defaults to 5.
 shuffle : bool, optional
 Whether or not the training data should be shuffled after each epoch. Defaults to True.
 random_state : int seed, RandomState instance, or None (default)
 The seed of the pseudo random number generator to use when shuffling the data.
 verbose : integer, optional
 The verbosity level
 epsilon : float
 Epsilon in the epsiloninsensitive loss functions; only if loss is ‘huber’, ‘epsilon_insensitive’, or ‘squared_epsilon_insensitive’. For ‘huber’, determines the threshold at which it becomes less important to get the prediction exactly right. For epsiloninsensitive, any differences between the current prediction and the correct label are ignored if they are less than this threshold.
 n_jobs : integer, optional
 The number of CPUs to use to do the OVA (One Versus All, for multiclass problems) computation. 1 means ‘all CPUs’. Defaults to 1.
 learning_rate : string, optional
The learning rate schedule:
 constant: eta = eta0
 optimal: eta = 1.0 / (alpha * (t + t0)) [default]
 invscaling: eta = eta0 / pow(t, power_t)
 where t0 is chosen by a heuristic proposed by Leon Bottou.
 eta0 : double
 The initial learning rate for the ‘constant’ or ‘invscaling’ schedules. The default value is 0.0 as eta0 is not used by the default schedule ‘optimal’.
 power_t : double
 The exponent for inverse scaling learning rate [default 0.5].
 class_weight : dict, {class_label: weight} or “balanced” or None, optional
Preset for the class_weight fit parameter.
Weights associated with classes. If not given, all classes are supposed to have weight one.
The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as
n_samples / (n_classes * np.bincount(y))
 warm_start : bool, optional
 When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution.
 average : bool or int, optional
 When set to True, computes the averaged SGD weights and stores the
result in the
coef_
attribute. If set to an int greater than 1, averaging will begin once the total number of samples seen reaches average. So average=10 will begin averaging after seeing 10 samples.
Attributes
coef_
: array, shape (1, n_features) if n_classes == 2 else (n_classes, n_features) Weights assigned to the features.
intercept_
: array, shape (1,) if n_classes == 2 else (n_classes,) Constants in decision function.
Examples
>>> import numpy as np >>> from sklearn import linear_model >>> X = np.array([[1, 1], [2, 1], [1, 1], [2, 1]]) >>> Y = np.array([1, 1, 2, 2]) >>> clf = linear_model.SGDClassifier() >>> clf.fit(X, Y) ... SGDClassifier(alpha=0.0001, average=False, class_weight=None, epsilon=0.1, eta0=0.0, fit_intercept=True, l1_ratio=0.15, learning_rate='optimal', loss='hinge', n_iter=5, n_jobs=1, penalty='l2', power_t=0.5, random_state=None, shuffle=True, verbose=0, warm_start=False) >>> print(clf.predict([[0.8, 1]])) [1]
See also
LinearSVC, LogisticRegression, Perceptron
Full API documentation: SGDClassifierScikitsLearnNode

class
mdp.nodes.
MeanShiftScikitsLearnNode
¶ Mean shift clustering using a flat kernel.
This node has been automatically generated by wrapping the
sklearn.cluster.mean_shift_.MeanShift
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Mean shift clustering aims to discover “blobs” in a smooth density of samples. It is a centroidbased algorithm, which works by updating candidates for centroids to be the mean of the points within a given region. These candidates are then filtered in a postprocessing stage to eliminate nearduplicates to form the final set of centroids.
Seeding is performed using a binning technique for scalability.
Read more in the User Guide.
Parameters
 bandwidth : float, optional
Bandwidth used in the RBF kernel.
If not given, the bandwidth is estimated using sklearn.cluster.estimate_bandwidth; see the documentation for that function for hints on scalability (see also the Notes, below).
 seeds : array, shape=[n_samples, n_features], optional
 Seeds used to initialize kernels. If not set, the seeds are calculated by clustering.get_bin_seeds with bandwidth as the grid size and default values for other parameters.
 bin_seeding : boolean, optional
 If true, initial kernel locations are not locations of all points, but rather the location of the discretized version of points, where points are binned onto a grid whose coarseness corresponds to the bandwidth. Setting this option to True will speed up the algorithm because fewer seeds will be initialized. default value: False Ignored if seeds argument is not None.
 min_bin_freq : int, optional
 To speed up the algorithm, accept only those bins with at least min_bin_freq points as seeds. If not defined, set to 1.
 cluster_all : boolean, default True
 If true, then all points are clustered, even those orphans that are not within any kernel. Orphans are assigned to the nearest kernel. If false, then orphans are given cluster label 1.
 n_jobs : int
The number of jobs to use for the computation. This works by computing each of the n_init runs in parallel.
If 1 all CPUs are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. For n_jobs below 1, (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = 2, all CPUs but one are used.
Attributes
cluster_centers_
: array, [n_clusters, n_features] Coordinates of cluster centers.
labels_
: Labels of each point.
Notes
Scalability:
Because this implementation uses a flat kernel and a Ball Tree to look up members of each kernel, the complexity will is to O(T*n*log(n)) in lower dimensions, with n the number of samples and T the number of points. In higher dimensions the complexity will tend towards O(T*n^2).
Scalability can be boosted by using fewer seeds, for example by using a higher value of min_bin_freq in the get_bin_seeds function.
Note that the estimate_bandwidth function is much less scalable than the mean shift algorithm and will be the bottleneck if it is used.
References
Dorin Comaniciu and Peter Meer, “Mean Shift: A robust approach toward feature space analysis”. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2002. pp. 603619.
Full API documentation: MeanShiftScikitsLearnNode

class
mdp.nodes.
PassiveAggressiveClassifierScikitsLearnNode
¶ Passive Aggressive Classifier
This node has been automatically generated by wrapping the
sklearn.linear_model.passive_aggressive.PassiveAggressiveClassifier
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Read more in the User Guide.
Parameters
 C : float
 Maximum step size (regularization). Defaults to 1.0.
 fit_intercept : bool, default=False
 Whether the intercept should be estimated or not. If False, the data is assumed to be already centered.
 n_iter : int, optional
 The number of passes over the training data (aka epochs). Defaults to 5.
 shuffle : bool, default=True
 Whether or not the training data should be shuffled after each epoch.
 random_state : int seed, RandomState instance, or None (default)
 The seed of the pseudo random number generator to use when shuffling the data.
 verbose : integer, optional
 The verbosity level
 n_jobs : integer, optional
 The number of CPUs to use to do the OVA (One Versus All, for multiclass problems) computation. 1 means ‘all CPUs’. Defaults to 1.
 loss : string, optional
The loss function to be used:
 hinge: equivalent to PAI in the reference paper.
 squared_hinge: equivalent to PAII in the reference paper.
 warm_start : bool, optional
 When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution.
 class_weight : dict, {class_label: weight} or “balanced” or None, optional
Preset for the class_weight fit parameter.
Weights associated with classes. If not given, all classes are supposed to have weight one.
The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as
n_samples / (n_classes * np.bincount(y))
New in version 0.17: parameter class_weight to automatically weight samples.
Attributes
coef_
: array, shape = [1, n_features] if n_classes == 2 else [n_classes, n_features] Weights assigned to the features.
intercept_
: array, shape = [1] if n_classes == 2 else [n_classes] Constants in decision function.
See also
SGDClassifier Perceptron
References
Online PassiveAggressive Algorithms <http://jmlr.csail.mit.edu/papers/volume7/crammer06a/crammer06a.pdf> K. Crammer, O. Dekel, J. Keshat, S. ShalevShwartz, Y. Singer  JMLR (2006)
Full API documentation: PassiveAggressiveClassifierScikitsLearnNode

class
mdp.nodes.
KernelPCAScikitsLearnNode
¶ Kernel Principal component analysis (KPCA)
This node has been automatically generated by wrapping the
sklearn.decomposition.kernel_pca.KernelPCA
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Nonlinear dimensionality reduction through the use of kernels (see metrics).
Read more in the User Guide.
Parameters
 n_components: int or None
 Number of components. If None, all nonzero components are kept.
 kernel: “linear”  “poly”  “rbf”  “sigmoid”  “cosine”  “precomputed”
 Kernel. Default: “linear”
 degree : int, default=3
 Degree for poly kernels. Ignored by other kernels.
 gamma : float, optional
 Kernel coefficient for rbf and poly kernels. Default: 1/n_features. Ignored by other kernels.
 coef0 : float, optional
 Independent term in poly and sigmoid kernels. Ignored by other kernels.
 kernel_params : mapping of string to any, optional
 Parameters (keyword arguments) and values for kernel passed as callable object. Ignored by other kernels.
 alpha: int
 Hyperparameter of the ridge regression that learns the inverse transform (when fit_inverse_transform=True). Default: 1.0
 fit_inverse_transform: bool
 Learn the inverse transform for nonprecomputed kernels. (i.e. learn to find the preimage of a point) Default: False
 eigen_solver: string [‘auto’’dense’’arpack’]
 Select eigensolver to use. If n_components is much less than the number of training samples, arpack may be more efficient than the dense eigensolver.
 tol: float
 convergence tolerance for arpack. Default: 0 (optimal value will be chosen by arpack)
 max_iter : int
 maximum number of iterations for arpack Default: None (optimal value will be chosen by arpack)
 remove_zero_eig : boolean, default=True
 If True, then all components with zero eigenvalues are removed, so that the number of components in the output may be < n_components (and sometimes even zero due to numerical instability). When n_components is None, this parameter is ignored and components with zero eigenvalues are removed regardless.
Attributes
lambdas_
: Eigenvalues of the centered kernel matrix
alphas_
: Eigenvectors of the centered kernel matrix
dual_coef_
: Inverse transform matrix
X_transformed_fit_
: Projection of the fitted data on the kernel principal components
References
Kernel PCA was introduced in:
 Bernhard Schoelkopf, Alexander J. Smola,
 and KlausRobert Mueller. 1999. Kernel principal
 component analysis. In Advances in kernel methods,
 MIT Press, Cambridge, MA, USA 327352.
Full API documentation: KernelPCAScikitsLearnNode

class
mdp.nodes.
AffinityPropagationScikitsLearnNode
¶ Perform Affinity Propagation Clustering of data.
This node has been automatically generated by wrapping the
sklearn.cluster.affinity_propagation_.AffinityPropagation
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Read more in the User Guide.
Parameters
 damping : float, optional, default: 0.5
 Damping factor between 0.5 and 1.
 convergence_iter : int, optional, default: 15
 Number of iterations with no change in the number of estimated clusters that stops the convergence.
 max_iter : int, optional, default: 200
 Maximum number of iterations.
 copy : boolean, optional, default: True
 Make a copy of input data.
 preference : arraylike, shape (n_samples,) or float, optional
 Preferences for each point  points with larger values of preferences are more likely to be chosen as exemplars. The number of exemplars, ie of clusters, is influenced by the input preferences value. If the preferences are not passed as arguments, they will be set to the median of the input similarities.
 affinity : string, optional, default=``euclidean``
 Which affinity to use. At the moment
precomputed
andeuclidean
are supported.euclidean
uses the negative squared euclidean distance between points.  verbose : boolean, optional, default: False
 Whether to be verbose.
Attributes
cluster_centers_indices_
: array, shape (n_clusters,) Indices of cluster centers
cluster_centers_
: array, shape (n_clusters, n_features) Cluster centers (if affinity !=
precomputed
). labels_
: array, shape (n_samples,) Labels of each point
affinity_matrix_
: array, shape (n_samples, n_samples) Stores the affinity matrix used in
fit
. n_iter_
: int Number of iterations taken to converge.
Notes
See examples/cluster/plot_affinity_propagation.py for an example.
The algorithmic complexity of affinity propagation is quadratic in the number of points.
References
Brendan J. Frey and Delbert Dueck, “Clustering by Passing Messages Between Data Points”, Science Feb. 2007
Full API documentation: AffinityPropagationScikitsLearnNode

class
mdp.nodes.
OneVsOneClassifierScikitsLearnNode
¶ Onevsone multiclass strategy
This node has been automatically generated by wrapping the
sklearn.multiclass.OneVsOneClassifier
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.This strategy consists in fitting one classifier per class pair. At prediction time, the class which received the most votes is selected. Since it requires to fit n_classes * (n_classes  1) / 2 classifiers, this method is usually slower than onevstherest, due to its O(n_classes^2) complexity. However, this method may be advantageous for algorithms such as kernel algorithms which don’t scale well with n_samples. This is because each individual learning problem only involves a small subset of the data whereas, with onevstherest, the complete dataset is used n_classes times.
Read more in the User Guide.
Parameters
 estimator : estimator object
 An estimator object implementing fit and one of decision_function or predict_proba.
 n_jobs : int, optional, default: 1
 The number of jobs to use for the computation. If 1 all CPUs are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. For n_jobs below 1, (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = 2, all CPUs but one are used.
Attributes
estimators_
: list of n_classes * (n_classes  1) / 2 estimators Estimators used for predictions.
classes_
: numpy array of shape [n_classes] Array containing labels.
Full API documentation: OneVsOneClassifierScikitsLearnNode

class
mdp.nodes.
CCAScikitsLearnNode
¶ CCA Canonical Correlation Analysis.
This node has been automatically generated by wrapping the
sklearn.cross_decomposition.cca_.CCA
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.CCA inherits from PLS with mode=”B” and deflation_mode=”canonical”.
Read more in the User Guide.
Parameters
 n_components : int, (default 2).
 number of components to keep.
 scale : boolean, (default True)
 whether to scale the data?
 max_iter : an integer, (default 500)
 the maximum number of iterations of the NIPALS inner loop
 tol : nonnegative real, default 1e06.
 the tolerance used in the iterative algorithm
 copy : boolean
 Whether the deflation be done on a copy. Let the default value to True unless you don’t care about side effects
Attributes
x_weights_
: array, [p, n_components] X block weights vectors.
y_weights_
: array, [q, n_components] Y block weights vectors.
x_loadings_
: array, [p, n_components] X block loadings vectors.
y_loadings_
: array, [q, n_components] Y block loadings vectors.
x_scores_
: array, [n_samples, n_components] X scores.
y_scores_
: array, [n_samples, n_components] Y scores.
x_rotations_
: array, [p, n_components] X block to latents rotations.
y_rotations_
: array, [q, n_components] Y block to latents rotations.
n_iter_
: arraylike Number of iterations of the NIPALS inner loop for each component.
Notes
For each component k, find the weights u, v that maximizes max corr(Xk u, Yk v), such that
u = v = 1
Note that it maximizes only the correlations between the scores.
The residual matrix of X (Xk+1) block is obtained by the deflation on the current X score: x_score.
The residual matrix of Y (Yk+1) block is obtained by deflation on the current Y score.
Examples
>>> from sklearn.cross_decomposition import CCA >>> X = [[0., 0., 1.], [1.,0.,0.], [2.,2.,2.], [3.,5.,4.]] >>> Y = [[0.1, 0.2], [0.9, 1.1], [6.2, 5.9], [11.9, 12.3]] >>> cca = CCA(n_components=1) >>> cca.fit(X, Y) ... CCA(copy=True, max_iter=500, n_components=1, scale=True, tol=1e06) >>> X_c, Y_c = cca.transform(X, Y)
References
Jacob A. Wegelin. A survey of Partial Least Squares (PLS) methods, with emphasis on the twoblock case. Technical Report 371, Department of Statistics, University of Washington, Seattle, 2000.
In french but still a reference:
Tenenhaus, M. (1998). La regression PLS: theorie et pratique. Paris:
Editions Technic.
See also
PLSCanonical PLSSVD
Full API documentation: CCAScikitsLearnNode

class
mdp.nodes.
KernelCentererScikitsLearnNode
¶ Center a kernel matrix
This node has been automatically generated by wrapping the
sklearn.preprocessing.data.KernelCenterer
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Let K(x, z) be a kernel defined by phi(x)^T phi(z), where phi is a function mapping x to a Hilbert space. KernelCenterer centers (i.e., normalize to have zero mean) the data without explicitly computing phi(x). It is equivalent to centering phi(x) with sklearn.preprocessing.StandardScaler(with_std=False).
Read more in the User Guide.
Full API documentation: KernelCentererScikitsLearnNode

class
mdp.nodes.
BernoulliRBMScikitsLearnNode
¶ Bernoulli Restricted Boltzmann Machine (RBM).
This node has been automatically generated by wrapping the
sklearn.neural_network.rbm.BernoulliRBM
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.A Restricted Boltzmann Machine with binary visible units and binary hiddens. Parameters are estimated using Stochastic Maximum Likelihood (SML), also known as Persistent Contrastive Divergence (PCD) [2].
The time complexity of this implementation is
O(d ** 2)
assuming d ~ n_features ~ n_components.Read more in the User Guide.
Parameters
 n_components : int, optional
 Number of binary hidden units.
 learning_rate : float, optional
 The learning rate for weight updates. It is highly recommended to tune this hyperparameter. Reasonable values are in the 10**[0., 3.] range.
 batch_size : int, optional
 Number of examples per minibatch.
 n_iter : int, optional
 Number of iterations/sweeps over the training dataset to perform during training.
 verbose : int, optional
 The verbosity level. The default, zero, means silent mode.
 random_state : integer or numpy.RandomState, optional
 A random number generator instance to define the state of the random permutations generator. If an integer is given, it fixes the seed. Defaults to the global numpy random number generator.
Attributes
intercept_hidden_
: arraylike, shape (n_components,) Biases of the hidden units.
intercept_visible_
: arraylike, shape (n_features,) Biases of the visible units.
components_
: arraylike, shape (n_components, n_features) Weight matrix, where n_features in the number of visible units and n_components is the number of hidden units.
Examples
>>> import numpy as np >>> from sklearn.neural_network import BernoulliRBM >>> X = np.array([[0, 0, 0], [0, 1, 1], [1, 0, 1], [1, 1, 1]]) >>> model = BernoulliRBM(n_components=2) >>> model.fit(X) BernoulliRBM(batch_size=10, learning_rate=0.1, n_components=2, n_iter=10, random_state=None, verbose=0)
References
 [1] Hinton, G. E., Osindero, S. and Teh, Y. A fast learning algorithm for
 deep belief nets. Neural Computation 18, pp 15271554. http://www.cs.toronto.edu/~hinton/absps/fastnc.pdf
 [2] Tieleman, T. Training Restricted Boltzmann Machines using
 Approximations to the Likelihood Gradient. International Conference on Machine Learning (ICML) 2008
Full API documentation: BernoulliRBMScikitsLearnNode

class
mdp.nodes.
AdaBoostRegressorScikitsLearnNode
¶ An AdaBoost regressor.
This node has been automatically generated by wrapping the
sklearn.ensemble.weight_boosting.AdaBoostRegressor
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.An AdaBoost [1] regressor is a metaestimator that begins by fitting a regressor on the original dataset and then fits additional copies of the regressor on the same dataset but where the weights of instances are adjusted according to the error of the current prediction. As such, subsequent regressors focus more on difficult cases.
This class implements the algorithm known as AdaBoost.R2 [2].
Read more in the User Guide.
Parameters
 base_estimator : object, optional (default=DecisionTreeRegressor)
 The base estimator from which the boosted ensemble is built. Support for sample weighting is required.
 n_estimators : integer, optional (default=50)
 The maximum number of estimators at which boosting is terminated. In case of perfect fit, the learning procedure is stopped early.
 learning_rate : float, optional (default=1.)
 Learning rate shrinks the contribution of each regressor by
learning_rate
. There is a tradeoff betweenlearning_rate
andn_estimators
.  loss : {‘linear’, ‘square’, ‘exponential’}, optional (default=’linear’)
 The loss function to use when updating the weights after each boosting iteration.
 random_state : int, RandomState instance or None, optional (default=None)
 If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.
Attributes
estimators_
: list of classifiers The collection of fitted subestimators.
estimator_weights_
: array of floats Weights for each estimator in the boosted ensemble.
estimator_errors_
: array of floats Regression error for each estimator in the boosted ensemble.
feature_importances_
: array of shape = [n_features] The feature importances if supported by the
base_estimator
.
See also
AdaBoostClassifier, GradientBoostingRegressor, DecisionTreeRegressor
References
[1] Y. Freund, R. Schapire, “A DecisionTheoretic Generalization of onLine Learning and an Application to Boosting”, 1995. [2]  Drucker, “Improving Regressors using Boosting Techniques”, 1997.
Full API documentation: AdaBoostRegressorScikitsLearnNode

class
mdp.nodes.
SelectFdrScikitsLearnNode
¶ Filter: Select the pvalues for an estimated false discovery rate
This node has been automatically generated by wrapping the
sklearn.feature_selection.univariate_selection.SelectFdr
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.This uses the BenjaminiHochberg procedure.
alpha
is an upper bound on the expected false discovery rate.Read more in the User Guide.
Parameters
 score_func : callable
 Function taking two arrays X and y, and returning a pair of arrays (scores, pvalues).
 alpha : float, optional
 The highest uncorrected pvalue for features to keep.
Attributes
scores_
: arraylike, shape=(n_features,) Scores of features.
pvalues_
: arraylike, shape=(n_features,) pvalues of feature scores.
References
http://en.wikipedia.org/wiki/False_discovery_rate
See also
f_classif: ANOVA Fvalue between labe/feature for classification tasks. chi2: Chisquared stats of nonnegative features for classification tasks. f_regression: Fvalue between label/feature for regression tasks. SelectPercentile: Select features based on percentile of the highest scores. SelectKBest: Select features based on the k highest scores. SelectFpr: Select features based on a false positive rate test. SelectFwe: Select features based on familywise error rate. GenericUnivariateSelect: Univariate feature selector with configurable mode.
Full API documentation: SelectFdrScikitsLearnNode

class
mdp.nodes.
CalibratedClassifierCVScikitsLearnNode
¶ Probability calibration with isotonic regression or sigmoid.
This node has been automatically generated by wrapping the
sklearn.calibration.CalibratedClassifierCV
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.With this class, the base_estimator is fit on the train set of the crossvalidation generator and the test set is used for calibration. The probabilities for each of the folds are then averaged for prediction. In case that cv=”prefit” is passed to
__init__
, it is it is assumed that base_estimator has been fitted already and all data is used for calibration. Note that data for fitting the classifier and for calibrating it must be disjoint.Read more in the User Guide.
Parameters
 base_estimator : instance BaseEstimator
 The classifier whose output decision function needs to be calibrated to offer more accurate predict_proba outputs. If cv=prefit, the classifier must have been fit already on data.
 method : ‘sigmoid’ or ‘isotonic’
 The method to use for calibration. Can be ‘sigmoid’ which
corresponds to Platt’s method or ‘isotonic’ which is a
nonparameteric approach. It is not advised to use isotonic calibration
with too few calibration samples
(<<1000)
since it tends to overfit. Use sigmoids (Platt’s calibration) in this case.  cv : integer, crossvalidation generator, iterable or “prefit”, optional
Determines the crossvalidation splitting strategy. Possible inputs for cv are:
 None, to use the default 3fold crossvalidation,
 integer, to specify the number of folds.
 An object to be used as a crossvalidation generator.
 An iterable yielding train/test splits.
For integer/None inputs, if
y
is binary or multiclass,StratifiedKFold
used. Ify
is neither binary nor multiclass,KFold
is used.Refer User Guide for the various crossvalidation strategies that can be used here.
If “prefit” is passed, it is assumed that base_estimator has been fitted already and all data is used for calibration.
Attributes
classes_
: array, shape (n_classes) The class labels.
 calibrated_classifiers_: list (len() equal to cv or 1 if cv == “prefit”)
 The list of calibrated classifiers, one for each crossvalidation fold, which has been fitted on all but the validation fold and calibrated on the validation fold.
References
[1] Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers, B. Zadrozny & C. Elkan, ICML 2001 [2] Transforming Classifier Scores into Accurate Multiclass Probability Estimates, B. Zadrozny & C. Elkan, (KDD 2002) [3] Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods, J. Platt, (1999) [4] Predicting Good Probabilities with Supervised Learning, A. NiculescuMizil & R. Caruana, ICML 2005 Full API documentation: CalibratedClassifierCVScikitsLearnNode

class
mdp.nodes.
ExtraTreeClassifierScikitsLearnNode
¶ An extremely randomized tree classifier.
This node has been automatically generated by wrapping the
sklearn.tree.tree.ExtraTreeClassifier
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Extratrees differ from classic decision trees in the way they are built. When looking for the best split to separate the samples of a node into two groups, random splits are drawn for each of the max_features randomly selected features and the best split among those is chosen. When max_features is set 1, this amounts to building a totally random decision tree.
Warning: Extratrees should only be used within ensemble methods.
Read more in the User Guide.
See also
ExtraTreeRegressor, ExtraTreesClassifier, ExtraTreesRegressor
References
[1] P. Geurts, D. Ernst., and L. Wehenkel, “Extremely randomized trees”, Machine Learning, 63(1), 342, 2006. Full API documentation: ExtraTreeClassifierScikitsLearnNode

class
mdp.nodes.
SelectKBestScikitsLearnNode
¶ Select features according to the k highest scores.
This node has been automatically generated by wrapping the
sklearn.feature_selection.univariate_selection.SelectKBest
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Read more in the User Guide.
Parameters
 score_func : callable
 Function taking two arrays X and y, and returning a pair of arrays (scores, pvalues).
 k : int or “all”, optional, default=10
 Number of top features to select. The “all” option bypasses selection, for use in a parameter search.
Attributes
scores_
: arraylike, shape=(n_features,) Scores of features.
pvalues_
: arraylike, shape=(n_features,) pvalues of feature scores.
Notes
Ties between features with equal scores will be broken in an unspecified way.
See also
f_classif: ANOVA Fvalue between labe/feature for classification tasks. chi2: Chisquared stats of nonnegative features for classification tasks. f_regression: Fvalue between label/feature for regression tasks. SelectPercentile: Select features based on percentile of the highest scores. SelectFpr: Select features based on a false positive rate test. SelectFdr: Select features based on an estimated false discovery rate. SelectFwe: Select features based on familywise error rate. GenericUnivariateSelect: Univariate feature selector with configurable mode.
Full API documentation: SelectKBestScikitsLearnNode

class
mdp.nodes.
NormalizerScikitsLearnNode
¶ Normalize samples individually to unit norm.
This node has been automatically generated by wrapping the
sklearn.preprocessing.data.Normalizer
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Each sample (i.e. each row of the data matrix) with at least one non zero component is rescaled independently of other samples so that its norm (l1 or l2) equals one.
This transformer is able to work both with dense numpy arrays and scipy.sparse matrix (use CSR format if you want to avoid the burden of a copy / conversion).
Scaling inputs to unit norms is a common operation for text classification or clustering for instance. For instance the dot product of two l2normalized TFIDF vectors is the cosine similarity of the vectors and is the base similarity metric for the Vector Space Model commonly used by the Information Retrieval community.
Read more in the User Guide.
Parameters
 norm : ‘l1’, ‘l2’, or ‘max’, optional (‘l2’ by default)
 The norm to use to normalize each non zero sample.
 copy : boolean, optional, default True
 set to False to perform inplace row normalization and avoid a copy (if the input is already a numpy array or a scipy.sparse CSR matrix).
Notes
This estimator is stateless (besides constructor parameters), the fit method does nothing but is useful when used in a pipeline.
See also
sklearn.preprocessing.normalize()
equivalent function without the object oriented APIFull API documentation: NormalizerScikitsLearnNode

class
mdp.nodes.
TfidfTransformerScikitsLearnNode
¶ Transform a count matrix to a normalized tf or tfidf representation
This node has been automatically generated by wrapping the
sklearn.feature_extraction.text.TfidfTransformer
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Tf means termfrequency while tfidf means termfrequency times inverse documentfrequency. This is a common term weighting scheme in information retrieval, that has also found good use in document classification.
The goal of using tfidf instead of the raw frequencies of occurrence of a token in a given document is to scale down the impact of tokens that occur very frequently in a given corpus and that are hence empirically less informative than features that occur in a small fraction of the training corpus.
The actual formula used for tfidf is tf * (idf + 1) = tf + tf * idf, instead of tf * idf. The effect of this is that terms with zero idf, i.e. that occur in all documents of a training set, will not be entirely ignored. The formulas used to compute tf and idf depend on parameter settings that correspond to the SMART notation used in IR, as follows:
Tf is “n” (natural) by default, “l” (logarithmic) when sublinear_tf=True. Idf is “t” when use_idf is given, “n” (none) otherwise. Normalization is “c” (cosine) when norm=’l2’, “n” (none) when norm=None.
Read more in the User Guide.
Parameters
 norm : ‘l1’, ‘l2’ or None, optional
 Norm used to normalize term vectors. None for no normalization.
 use_idf : boolean, default=True
 Enable inversedocumentfrequency reweighting.
 smooth_idf : boolean, default=True
 Smooth idf weights by adding one to document frequencies, as if an extra document was seen containing every term in the collection exactly once. Prevents zero divisions.
 sublinear_tf : boolean, default=False
 Apply sublinear tf scaling, i.e. replace tf with 1 + log(tf).
References
[Yates2011] R. BaezaYates and B. RibeiroNeto (2011). Modern Information Retrieval. Addison Wesley, pp. 6874. [MRS2008] C.D. Manning, P. Raghavan and H. Schuetze (2008). Introduction to Information Retrieval. Cambridge University Press, pp. 118120. Full API documentation: TfidfTransformerScikitsLearnNode

class
mdp.nodes.
GradientBoostingClassifierScikitsLearnNode
¶ Gradient Boosting for classification.
This node has been automatically generated by wrapping the
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.GB builds an additive model in a forward stagewise fashion; it allows for the optimization of arbitrary differentiable loss functions. In each stage
n_classes_
regression trees are fit on the negative gradient of the binomial or multinomial deviance loss function. Binary classification is a special case where only a single regression tree is induced.Read more in the User Guide.
Parameters
 loss : {‘deviance’, ‘exponential’}, optional (default=’deviance’)
 loss function to be optimized. ‘deviance’ refers to deviance (= logistic regression) for classification with probabilistic outputs. For loss ‘exponential’ gradient boosting recovers the AdaBoost algorithm.
 learning_rate : float, optional (default=0.1)
 learning rate shrinks the contribution of each tree by learning_rate. There is a tradeoff between learning_rate and n_estimators.
 n_estimators : int (default=100)
 The number of boosting stages to perform. Gradient boosting is fairly robust to overfitting so a large number usually results in better performance.
 max_depth : integer, optional (default=3)
 maximum depth of the individual regression estimators. The maximum
depth limits the number of nodes in the tree. Tune this parameter
for best performance; the best value depends on the interaction
of the input variables.
Ignored if
max_leaf_nodes
is not None.  min_samples_split : integer, optional (default=2)
 The minimum number of samples required to split an internal node.
 min_samples_leaf : integer, optional (default=1)
 The minimum number of samples required to be at a leaf node.
 min_weight_fraction_leaf : float, optional (default=0.)
 The minimum weighted fraction of the input samples required to be at a leaf node.
 subsample : float, optional (default=1.0)
 The fraction of samples to be used for fitting the individual base learners. If smaller than 1.0 this results in Stochastic Gradient Boosting. subsample interacts with the parameter n_estimators. Choosing subsample < 1.0 leads to a reduction of variance and an increase in bias.
 max_features : int, float, string or None, optional (default=None)
The number of features to consider when looking for the best split:
 If int, then consider max_features features at each split.
 If float, then max_features is a percentage and
 int(max_features * n_features) features are considered at each
 split.
 If “auto”, then max_features=sqrt(n_features).
 If “sqrt”, then max_features=sqrt(n_features).
 If “log2”, then max_features=log2(n_features).
 If None, then max_features=n_features.
Choosing max_features < n_features leads to a reduction of variance and an increase in bias.
Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than
max_features
features. max_leaf_nodes : int or None, optional (default=None)
 Grow trees with
max_leaf_nodes
in bestfirst fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes. If not None thenmax_depth
will be ignored.  init : BaseEstimator, None, optional (default=None)
 An estimator object that is used to compute the initial
predictions.
init
has to providefit
andpredict
. If None it usesloss.init_estimator
.  verbose : int, default: 0
 Enable verbose output. If 1 then it prints progress and performance once in a while (the more trees the lower the frequency). If greater than 1 then it prints progress and performance for every tree.
 warm_start : bool, default: False
 When set to
True
, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just erase the previous solution.  random_state : int, RandomState instance or None, optional (default=None)
 If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.
 presort : bool or ‘auto’, optional (default=’auto’)
Whether to presort the data to speed up the finding of best splits in fitting. Auto mode by default will use presorting on dense data and default to normal sorting on sparse data. Setting presort to true on sparse data will raise an error.
New in version 0.17: presort parameter.
Attributes
feature_importances_
: array, shape = [n_features] The feature importances (the higher, the more important the feature).
oob_improvement_
: array, shape = [n_estimators] The improvement in loss (= deviance) on the outofbag samples
relative to the previous iteration.
oob_improvement_[0]
is the improvement in loss of the first stage over theinit
estimator. train_score_
: array, shape = [n_estimators] The ith score
train_score_[i]
is the deviance (= loss) of the model at iterationi
on the inbag sample. Ifsubsample == 1
this is the deviance on the training data. loss_
: LossFunction The concrete
LossFunction
object.  init : BaseEstimator
 The estimator that provides the initial predictions.
Set via the
init
argument orloss.init_estimator
. estimators_
: ndarray of DecisionTreeRegressor, shape = [n_estimators,loss_.K
] The collection of fitted subestimators.
loss_.K
is 1 for binary classification, otherwise n_classes.
See also
sklearn.tree.DecisionTreeClassifier, RandomForestClassifier AdaBoostClassifier
References
J. Friedman, Greedy Function Approximation: A Gradient Boosting Machine, The Annals of Statistics, Vol. 29, No. 5, 2001.
 Friedman, Stochastic Gradient Boosting, 1999
T. Hastie, R. Tibshirani and J. Friedman. Elements of Statistical Learning Ed. 2, Springer, 2009.
Full API documentation: GradientBoostingClassifierScikitsLearnNode

class
mdp.nodes.
RadiusNeighborsClassifierScikitsLearnNode
¶ Classifier implementing a vote among neighbors within a given radius
This node has been automatically generated by wrapping the
sklearn.neighbors.classification.RadiusNeighborsClassifier
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Read more in the User Guide.
Parameters
 radius : float, optional (default = 1.0)
 Range of parameter space to use by default for :meth`radius_neighbors` queries.
 weights : str or callable
weight function used in prediction. Possible values:
 ‘uniform’ : uniform weights. All points in each neighborhood are weighted equally.
 ‘distance’ : weight points by the inverse of their distance. in this case, closer neighbors of a query point will have a greater influence than neighbors which are further away.
 [callable] : a userdefined function which accepts an array of distances, and returns an array of the same shape containing the weights.
Uniform weights are used by default.
 algorithm : {‘auto’, ‘ball_tree’, ‘kd_tree’, ‘brute’}, optional
Algorithm used to compute the nearest neighbors:
 ‘ball_tree’ will use
BallTree
 ‘kd_tree’ will use
KDtree
 ‘brute’ will use a bruteforce search.
 ‘auto’ will attempt to decide the most appropriate algorithm
based on the values passed to
fit()
method.
Note: fitting on sparse input will override the setting of this parameter, using brute force.
 ‘ball_tree’ will use
 leaf_size : int, optional (default = 30)
 Leaf size passed to BallTree or KDTree. This can affect the speed of the construction and query, as well as the memory required to store the tree. The optimal value depends on the nature of the problem.
 metric : string or DistanceMetric object (default=’minkowski’)
 the distance metric to use for the tree. The default metric is minkowski, and with p=2 is equivalent to the standard Euclidean metric. See the documentation of the DistanceMetric class for a list of available metrics.
 p : integer, optional (default = 2)
 Power parameter for the Minkowski metric. When p = 1, this is equivalent to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.
 outlier_label : int, optional (default = None)
 Label, which is given for outlier samples (samples with no neighbors on given radius). If set to None, ValueError is raised, when outlier is detected.
 metric_params : dict, optional (default = None)
 Additional keyword arguments for the metric function.
Examples
>>> X = [[0], [1], [2], [3]] >>> y = [0, 0, 1, 1] >>> from sklearn.neighbors import RadiusNeighborsClassifier >>> neigh = RadiusNeighborsClassifier(radius=1.0) >>> neigh.fit(X, y) RadiusNeighborsClassifier(...) >>> print(neigh.predict([[1.5]])) [0]
See also
KNeighborsClassifier RadiusNeighborsRegressor KNeighborsRegressor NearestNeighbors
Notes
See Nearest Neighbors in the online documentation for a discussion of the choice of
algorithm
andleaf_size
.http://en.wikipedia.org/wiki/Knearest_neighbor_algorithm
Full API documentation: RadiusNeighborsClassifierScikitsLearnNode

class
mdp.nodes.
GaussianRandomProjectionHashScikitsLearnNode
¶ This node has been automatically generated by wrapping the
sklearn.neighbors.approximate.GaussianRandomProjectionHash
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Full API documentation: GaussianRandomProjectionHashScikitsLearnNode

class
mdp.nodes.
VotingClassifierScikitsLearnNode
¶ Soft Voting/Majority Rule classifier for unfitted estimators.
This node has been automatically generated by wrapping the
sklearn.ensemble.voting_classifier.VotingClassifier
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.New in version 0.17.
Read more in the User Guide.
Parameters
 estimators : list of (string, estimator) tuples
 Invoking the
fit
method on theVotingClassifier
will fit clones of those original estimators that will be stored in the class attribute self.estimators_.  voting : str, {‘hard’, ‘soft’} (default=’hard’)
 If ‘hard’, uses predicted class labels for majority rule voting. Else if ‘soft’, predicts the class label based on the argmax of the sums of the predicted probalities, which is recommended for an ensemble of wellcalibrated classifiers.
 weights : arraylike, shape = [n_classifiers], optional (default=`None`)
 Sequence of weights (float or int) to weight the occurances of predicted class labels (hard voting) or class probabilities before averaging (soft voting). Uses uniform weights if None.
Attributes
classes_
: arraylike, shape = [n_predictions]Examples
>>> import numpy as np >>> from sklearn.linear_model import LogisticRegression >>> from sklearn.naive_bayes import GaussianNB >>> from sklearn.ensemble import RandomForestClassifier >>> clf1 = LogisticRegression(random_state=1) >>> clf2 = RandomForestClassifier(random_state=1) >>> clf3 = GaussianNB() >>> X = np.array([[1, 1], [2, 1], [3, 2], [1, 1], [2, 1], [3, 2]]) >>> y = np.array([1, 1, 1, 2, 2, 2]) >>> eclf1 = VotingClassifier(estimators=[ ... ('lr', clf1), ('rf', clf2), ('gnb', clf3)], voting='hard') >>> eclf1 = eclf1.fit(X, y) >>> print(eclf1.predict(X)) [1 1 1 2 2 2] >>> eclf2 = VotingClassifier(estimators=[ ... ('lr', clf1), ('rf', clf2), ('gnb', clf3)], ... voting='soft') >>> eclf2 = eclf2.fit(X, y) >>> print(eclf2.predict(X)) [1 1 1 2 2 2] >>> eclf3 = VotingClassifier(estimators=[ ... ('lr', clf1), ('rf', clf2), ('gnb', clf3)], ... voting='soft', weights=[2,1,1]) >>> eclf3 = eclf3.fit(X, y) >>> print(eclf3.predict(X)) [1 1 1 2 2 2] >>>
Full API documentation: VotingClassifierScikitsLearnNode

class
mdp.nodes.
LassoLarsCVScikitsLearnNode
¶ Crossvalidated Lasso, using the LARS algorithm
This node has been automatically generated by wrapping the
sklearn.linear_model.least_angle.LassoLarsCV
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.The optimization objective for Lasso is:
(1 / (2 * n_samples)) * y  Xw^2_2 + alpha * w_1
Read more in the User Guide.
Parameters
 fit_intercept : boolean
 whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered).
 positive : boolean (default=False)
 Restrict coefficients to be >= 0. Be aware that you might want to
remove fit_intercept which is set True by default.
Under the positive restriction the model coefficients do not converge
to the ordinaryleastsquares solution for small values of alpha.
Only coeffiencts up to the smallest alpha value (
alphas_[alphas_ > 0.].min()
when fit_path=True) reached by the stepwise LarsLasso algorithm are typically in congruence with the solution of the coordinate descent Lasso estimator. As a consequence using LassoLarsCV only makes sense for problems where a sparse solution is expected and/or reached.  verbose : boolean or integer, optional
 Sets the verbosity amount
 normalize : boolean, optional, default False
 If True, the regressors X will be normalized before regression.
 precompute : True  False  ‘auto’  arraylike
 Whether to use a precomputed Gram matrix to speed up
calculations. If set to
'auto'
let us decide. The Gram matrix can also be passed as argument.  max_iter : integer, optional
 Maximum number of iterations to perform.
 cv : int, crossvalidation generator or an iterable, optional
Determines the crossvalidation splitting strategy. Possible inputs for cv are:
 None, to use the default 3fold crossvalidation,
 integer, to specify the number of folds.
 An object to be used as a crossvalidation generator.
 An iterable yielding train/test splits.
For integer/None inputs,
KFold
is used.Refer User Guide for the various crossvalidation strategies that can be used here.
 max_n_alphas : integer, optional
 The maximum number of points on the path used to compute the residuals in the crossvalidation
 n_jobs : integer, optional
 Number of CPUs to use during the cross validation. If
1
, use all the CPUs  eps : float, optional
 The machineprecision regularization in the computation of the Cholesky diagonal factors. Increase this for very illconditioned systems.
 copy_X : boolean, optional, default True
 If True, X will be copied; else, it may be overwritten.
Attributes
coef_
: array, shape (n_features,) parameter vector (w in the formulation formula)
intercept_
: float independent term in decision function.
coef_path_
: array, shape (n_features, n_alphas) the varying values of the coefficients along the path
alpha_
: float the estimated regularization parameter alpha
alphas_
: array, shape (n_alphas,) the different values of alpha along the path
cv_alphas_
: array, shape (n_cv_alphas,) all the values of alpha along the path for the different folds
cv_mse_path_
: array, shape (n_folds, n_cv_alphas) the mean square error on leftout for each fold along the path
(alpha values given by
cv_alphas
) n_iter_
: arraylike or int the number of iterations run by Lars with the optimal alpha.
Notes
The object solves the same problem as the LassoCV object. However, unlike the LassoCV, it find the relevant alphas values by itself. In general, because of this property, it will be more stable. However, it is more fragile to heavily multicollinear datasets.
It is more efficient than the LassoCV if only a small number of features are selected compared to the total number, for instance if there are very few samples compared to the number of features.
See also
lars_path, LassoLars, LarsCV, LassoCV
Full API documentation: LassoLarsCVScikitsLearnNode

class
mdp.nodes.
RobustScalerScikitsLearnNode
¶ Scale features using statistics that are robust to outliers.
This node has been automatically generated by wrapping the
sklearn.preprocessing.data.RobustScaler
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.This Scaler removes the median and scales the data according to the Interquartile Range (IQR). The IQR is the range between the 1st quartile (25th quantile) and the 3rd quartile (75th quantile).
Centering and scaling happen independently on each feature (or each sample, depending on the axis argument) by computing the relevant statistics on the samples in the training set. Median and interquartile range are then stored to be used on later data using the transform method.
Standardization of a dataset is a common requirement for many machine learning estimators. Typically this is done by removing the mean and scaling to unit variance. However, outliers can often influence the sample mean / variance in a negative way. In such cases, the median and the interquartile range often give better results.
New in version 0.17.
Read more in the User Guide.
Parameters
 with_centering : boolean, True by default
 If True, center the data before scaling. This does not work (and will raise an exception) when attempted on sparse matrices, because centering them entails building a dense matrix which in common use cases is likely to be too large to fit in memory.
 with_scaling : boolean, True by default
 If True, scale the data to interquartile range.
 copy : boolean, optional, default is True
 If False, try to avoid a copy and do inplace scaling instead. This is not guaranteed to always work inplace; e.g. if the data is not a NumPy array or scipy.sparse CSR matrix, a copy may still be returned.
Attributes
center_
: array of floats The median value for each feature in the training set.
scale_
: array of floatsThe (scaled) interquartile range for each feature in the training set.
New in version 0.17: scale_ attribute.
See also
sklearn.preprocessing.StandardScaler
to perform centering and scaling using mean and variance.sklearn.decomposition.RandomizedPCA
with whiten=True to further remove the linear correlation across features.Notes
See examples/preprocessing/plot_robust_scaling.py for an example.
http://en.wikipedia.org/wiki/Median_(statistics) http://en.wikipedia.org/wiki/Interquartile_range
Full API documentation: RobustScalerScikitsLearnNode

class
mdp.nodes.
BaggingClassifierScikitsLearnNode
¶ A Bagging classifier.
This node has been automatically generated by wrapping the
sklearn.ensemble.bagging.BaggingClassifier
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.A Bagging classifier is an ensemble metaestimator that fits base classifiers each on random subsets of the original dataset and then aggregate their individual predictions (either by voting or by averaging) to form a final prediction. Such a metaestimator can typically be used as a way to reduce the variance of a blackbox estimator (e.g., a decision tree), by introducing randomization into its construction procedure and then making an ensemble out of it.
This algorithm encompasses several works from the literature. When random subsets of the dataset are drawn as random subsets of the samples, then this algorithm is known as Pasting [1]_. If samples are drawn with replacement, then the method is known as Bagging [2]_. When random subsets of the dataset are drawn as random subsets of the features, then the method is known as Random Subspaces [3]_. Finally, when base estimators are built on subsets of both samples and features, then the method is known as Random Patches [4]_.
Read more in the User Guide.
Parameters
 base_estimator : object or None, optional (default=None)
 The base estimator to fit on random subsets of the dataset. If None, then the base estimator is a decision tree.
 n_estimators : int, optional (default=10)
 The number of base estimators in the ensemble.
 max_samples : int or float, optional (default=1.0)
 The number of samples to draw from X to train each base estimator.
 If int, then draw max_samples samples.
 If float, then draw max_samples * X.shape[0] samples.
 max_features : int or float, optional (default=1.0)
 The number of features to draw from X to train each base estimator.
 If int, then draw max_features features.
 If float, then draw max_features * X.shape[1] features.
 bootstrap : boolean, optional (default=True)
 Whether samples are drawn with replacement.
 bootstrap_features : boolean, optional (default=False)
 Whether features are drawn with replacement.
 oob_score : bool
 Whether to use outofbag samples to estimate the generalization error.
 warm_start : bool, optional (default=False)
When set to True, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new ensemble.
New in version 0.17: warm_start constructor parameter.
 n_jobs : int, optional (default=1)
 The number of jobs to run in parallel for both fit and predict. If 1, then the number of jobs is set to the number of cores.
 random_state : int, RandomState instance or None, optional (default=None)
 If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.
 verbose : int, optional (default=0)
 Controls the verbosity of the building process.
Attributes
base_estimator_
: list of estimators The base estimator from which the ensemble is grown.
estimators_
: list of estimators The collection of fitted base estimators.
estimators_samples_
: list of arrays The subset of drawn samples (i.e., the inbag samples) for each base estimator.
estimators_features_
: list of arrays The subset of drawn features for each base estimator.
classes_
: array of shape = [n_classes] The classes labels.
n_classes_
: int or list The number of classes.
oob_score_
: float Score of the training dataset obtained using an outofbag estimate.
oob_decision_function_
: array of shape = [n_samples, n_classes] Decision function computed with outofbag estimate on the training set. If n_estimators is small it might be possible that a data point was never left out during the bootstrap. In this case, oob_decision_function_ might contain NaN.
References
[1] L. Breiman, “Pasting small votes for classification in large databases and online”, Machine Learning, 36(1), 85103, 1999. [2] L. Breiman, “Bagging predictors”, Machine Learning, 24(2), 123140, 1996. [3] T. Ho, “The random subspace method for constructing decision forests”, Pattern Analysis and Machine Intelligence, 20(8), 832844, 1998. [4] G. Louppe and P. Geurts, “Ensembles on Random Patches”, Machine Learning and Knowledge Discovery in Databases, 346361, 2012. Full API documentation: BaggingClassifierScikitsLearnNode

class
mdp.nodes.
DecisionTreeRegressorScikitsLearnNode
¶ A decision tree regressor.
This node has been automatically generated by wrapping the
sklearn.tree.tree.DecisionTreeRegressor
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Read more in the User Guide.
Parameters
 criterion : string, optional (default=”mse”)
 The function to measure the quality of a split. The only supported criterion is “mse” for the mean squared error, which is equal to variance reduction as feature selection criterion.
 splitter : string, optional (default=”best”)
 The strategy used to choose the split at each node. Supported strategies are “best” to choose the best split and “random” to choose the best random split.
 max_features : int, float, string or None, optional (default=None)
The number of features to consider when looking for the best split:
 If int, then consider max_features features at each split.
 If float, then max_features is a percentage and
 int(max_features * n_features) features are considered at each
 split.
 If “auto”, then max_features=n_features.
 If “sqrt”, then max_features=sqrt(n_features).
 If “log2”, then max_features=log2(n_features).
 If None, then max_features=n_features.
Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than
max_features
features. max_depth : int or None, optional (default=None)
 The maximum depth of the tree. If None, then nodes are expanded until
all leaves are pure or until all leaves contain less than
min_samples_split samples.
Ignored if
max_leaf_nodes
is not None.  min_samples_split : int, optional (default=2)
 The minimum number of samples required to split an internal node.
 min_samples_leaf : int, optional (default=1)
 The minimum number of samples required to be at a leaf node.
 min_weight_fraction_leaf : float, optional (default=0.)
 The minimum weighted fraction of the input samples required to be at a leaf node.
 max_leaf_nodes : int or None, optional (default=None)
 Grow a tree with
max_leaf_nodes
in bestfirst fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes. If not None thenmax_depth
will be ignored.  random_state : int, RandomState instance or None, optional (default=None)
 If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.
 presort : bool, optional (default=False)
 Whether to presort the data to speed up the finding of best splits in fitting. For the default settings of a decision tree on large datasets, setting this to true may slow down the training process. When using either a smaller dataset or a restricted depth, this may speed up the training.
Attributes
feature_importances_
: array of shape = [n_features] The feature importances. The higher, the more important the feature. The importance of a feature is computed as the (normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance [4]_.
max_features_
: int, The inferred value of max_features.
n_features_
: int The number of features when
fit
is performed. n_outputs_
: int The number of outputs when
fit
is performed. tree_
: Tree object The underlying Tree object.
See also
DecisionTreeClassifier
References
[1] http://en.wikipedia.org/wiki/Decision_tree_learning [2] L. Breiman, J. Friedman, R. Olshen, and C. Stone, “Classification and Regression Trees”, Wadsworth, Belmont, CA, 1984. [3] T. Hastie, R. Tibshirani and J. Friedman. “Elements of Statistical Learning”, Springer, 2009. [4] L. Breiman, and A. Cutler, “Random Forests”, http://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm Examples
>>> from sklearn.datasets import load_boston >>> from sklearn.cross_validation import cross_val_score >>> from sklearn.tree import DecisionTreeRegressor >>> boston = load_boston() >>> regressor = DecisionTreeRegressor(random_state=0) >>> cross_val_score(regressor, boston.data, boston.target, cv=10) ... ... array([ 0.61..., 0.57..., 0.34..., 0.41..., 0.75..., 0.07..., 0.29..., 0.33..., 1.42..., 1.77...])
Full API documentation: DecisionTreeRegressorScikitsLearnNode

class
mdp.nodes.
LarsScikitsLearnNode
¶ Least Angle Regression model a.k.a. LAR
This node has been automatically generated by wrapping the
sklearn.linear_model.least_angle.Lars
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Read more in the User Guide.
Parameters
 n_nonzero_coefs : int, optional
 Target number of nonzero coefficients. Use
np.inf
for no limit.  fit_intercept : boolean
 Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered).
 positive : boolean (default=False)
 Restrict coefficients to be >= 0. Be aware that you might want to remove fit_intercept which is set True by default.
 verbose : boolean or integer, optional
 Sets the verbosity amount
 normalize : boolean, optional, default False
 If
True
, the regressors X will be normalized before regression.  precompute : True  False  ‘auto’  arraylike
 Whether to use a precomputed Gram matrix to speed up
calculations. If set to
'auto'
let us decide. The Gram matrix can also be passed as argument.  copy_X : boolean, optional, default True
 If
True
, X will be copied; else, it may be overwritten.  eps : float, optional
 The machineprecision regularization in the computation of the
Cholesky diagonal factors. Increase this for very illconditioned
systems. Unlike the
tol
parameter in some iterative optimizationbased algorithms, this parameter does not control the tolerance of the optimization.  fit_path : boolean
 If True the full path is stored in the
coef_path_
attribute. If you compute the solution for a large problem or many targets, settingfit_path
toFalse
will lead to a speedup, especially with a small alpha.
Attributes
alphas_
: array, shape (n_alphas + 1,)  list of n_targets such arrays Maximum of covariances (in absolute value) at each iteration.
n_alphas
is eithern_nonzero_coefs
orn_features
, whichever is smaller. active_
: list, length = n_alphas  list of n_targets such lists Indices of active variables at the end of the path.
coef_path_
: array, shape (n_features, n_alphas + 1)  list of n_targets such arrays The varying values of the coefficients along the path. It is not
present if the
fit_path
parameter isFalse
. coef_
: array, shape (n_features,) or (n_targets, n_features) Parameter vector (w in the formulation formula).
intercept_
: float  array, shape (n_targets,) Independent term in decision function.
n_iter_
: arraylike or int The number of iterations taken by lars_path to find the grid of alphas for each target.
Examples
>>> from sklearn import linear_model >>> clf = linear_model.Lars(n_nonzero_coefs=1) >>> clf.fit([[1, 1], [0, 0], [1, 1]], [1.1111, 0, 1.1111]) ... Lars(copy_X=True, eps=..., fit_intercept=True, fit_path=True, n_nonzero_coefs=1, normalize=True, positive=False, precompute='auto', verbose=False) >>> print(clf.coef_) [ 0. 1.11...]
See also
lars_path, LarsCV sklearn.decomposition.sparse_encode
Full API documentation: LarsScikitsLearnNode

class
mdp.nodes.
BaggingRegressorScikitsLearnNode
¶ A Bagging regressor.
This node has been automatically generated by wrapping the
sklearn.ensemble.bagging.BaggingRegressor
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.A Bagging regressor is an ensemble metaestimator that fits base regressors each on random subsets of the original dataset and then aggregate their individual predictions (either by voting or by averaging) to form a final prediction. Such a metaestimator can typically be used as a way to reduce the variance of a blackbox estimator (e.g., a decision tree), by introducing randomization into its construction procedure and then making an ensemble out of it.
This algorithm encompasses several works from the literature. When random subsets of the dataset are drawn as random subsets of the samples, then this algorithm is known as Pasting [1]_. If samples are drawn with replacement, then the method is known as Bagging [2]_. When random subsets of the dataset are drawn as random subsets of the features, then the method is known as Random Subspaces [3]_. Finally, when base estimators are built on subsets of both samples and features, then the method is known as Random Patches [4]_.
Read more in the User Guide.
Parameters
 base_estimator : object or None, optional (default=None)
 The base estimator to fit on random subsets of the dataset. If None, then the base estimator is a decision tree.
 n_estimators : int, optional (default=10)
 The number of base estimators in the ensemble.
 max_samples : int or float, optional (default=1.0)
 The number of samples to draw from X to train each base estimator.
 If int, then draw max_samples samples.
 If float, then draw max_samples * X.shape[0] samples.
 max_features : int or float, optional (default=1.0)
 The number of features to draw from X to train each base estimator.
 If int, then draw max_features features.
 If float, then draw max_features * X.shape[1] features.
 bootstrap : boolean, optional (default=True)
 Whether samples are drawn with replacement.
 bootstrap_features : boolean, optional (default=False)
 Whether features are drawn with replacement.
 oob_score : bool
 Whether to use outofbag samples to estimate the generalization error.
 warm_start : bool, optional (default=False)
 When set to True, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new ensemble.
 n_jobs : int, optional (default=1)
 The number of jobs to run in parallel for both fit and predict. If 1, then the number of jobs is set to the number of cores.
 random_state : int, RandomState instance or None, optional (default=None)
 If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.
 verbose : int, optional (default=0)
 Controls the verbosity of the building process.
Attributes
estimators_
: list of estimators The collection of fitted subestimators.
estimators_samples_
: list of arrays The subset of drawn samples (i.e., the inbag samples) for each base estimator.
estimators_features_
: list of arrays The subset of drawn features for each base estimator.
oob_score_
: float Score of the training dataset obtained using an outofbag estimate.
oob_prediction_
: array of shape = [n_samples] Prediction computed with outofbag estimate on the training set. If n_estimators is small it might be possible that a data point was never left out during the bootstrap. In this case, oob_prediction_ might contain NaN.
References
[1] L. Breiman, “Pasting small votes for classification in large databases and online”, Machine Learning, 36(1), 85103, 1999. [2] L. Breiman, “Bagging predictors”, Machine Learning, 24(2), 123140, 1996. [3] T. Ho, “The random subspace method for constructing decision forests”, Pattern Analysis and Machine Intelligence, 20(8), 832844, 1998. [4] G. Louppe and P. Geurts, “Ensembles on Random Patches”, Machine Learning and Knowledge Discovery in Databases, 346361, 2012. Full API documentation: BaggingRegressorScikitsLearnNode

class
mdp.nodes.
SVRScikitsLearnNode
¶ EpsilonSupport Vector Regression.
This node has been automatically generated by wrapping the
sklearn.svm.classes.SVR
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.The free parameters in the model are C and epsilon.
The implementation is based on libsvm.
Read more in the User Guide.
Parameters
 C : float, optional (default=1.0)
 Penalty parameter C of the error term.
 epsilon : float, optional (default=0.1)
 Epsilon in the epsilonSVR model. It specifies the epsilontube within which no penalty is associated in the training loss function with points predicted within a distance epsilon from the actual value.
 kernel : string, optional (default=’rbf’)
 Specifies the kernel type to be used in the algorithm. It must be one of ‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’ or a callable. If none is given, ‘rbf’ will be used. If a callable is given it is used to precompute the kernel matrix.
 degree : int, optional (default=3)
 Degree of the polynomial kernel function (‘poly’). Ignored by all other kernels.
 gamma : float, optional (default=’auto’)
 Kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’. If gamma is ‘auto’ then 1/n_features will be used instead.
 coef0 : float, optional (default=0.0)
 Independent term in kernel function. It is only significant in ‘poly’ and ‘sigmoid’.
 shrinking : boolean, optional (default=True)
 Whether to use the shrinking heuristic.
 tol : float, optional (default=1e3)
 Tolerance for stopping criterion.
 cache_size : float, optional
 Specify the size of the kernel cache (in MB).
 verbose : bool, default: False
 Enable verbose output. Note that this setting takes advantage of a perprocess runtime setting in libsvm that, if enabled, may not work properly in a multithreaded context.
 max_iter : int, optional (default=1)
 Hard limit on iterations within solver, or 1 for no limit.
Attributes
support_
: arraylike, shape = [n_SV] Indices of support vectors.
support_vectors_
: arraylike, shape = [nSV, n_features] Support vectors.
dual_coef_
: array, shape = [1, n_SV] Coefficients of the support vector in the decision function.
coef_
: array, shape = [1, n_features]Weights assigned to the features (coefficients in the primal problem). This is only available in the case of a linear kernel.
coef_ is readonly property derived from dual_coef_ and support_vectors_.
intercept_
: array, shape = [1] Constants in decision function.
Examples
>>> from sklearn.svm import SVR >>> import numpy as np >>> n_samples, n_features = 10, 5 >>> np.random.seed(0) >>> y = np.random.randn(n_samples) >>> X = np.random.randn(n_samples, n_features) >>> clf = SVR(C=1.0, epsilon=0.2) >>> clf.fit(X, y) SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.2, gamma='auto', kernel='rbf', max_iter=1, shrinking=True, tol=0.001, verbose=False)
See also
 NuSVR
 Support Vector Machine for regression implemented using libsvm using a parameter to control the number of support vectors.
 LinearSVR
 Scalable Linear Support Vector Machine for regression implemented using liblinear.
Full API documentation: SVRScikitsLearnNode

class
mdp.nodes.
RFECVScikitsLearnNode
¶ Feature ranking with recursive feature elimination and crossvalidated selection of the best number of features.
This node has been automatically generated by wrapping the
sklearn.feature_selection.rfe.RFECV
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Read more in the User Guide.
Parameters
 estimator : object
A supervised learning estimator with a fit method that updates a coef_ attribute that holds the fitted parameters. Important features must correspond to high absolute values in the coef_ array.
For instance, this is the case for most supervised learning algorithms such as Support Vector Classifiers and Generalized Linear Models from the svm and linear_model modules.
 step : int or float, optional (default=1)
 If greater than or equal to 1, then step corresponds to the (integer) number of features to remove at each iteration. If within (0.0, 1.0), then step corresponds to the percentage (rounded down) of features to remove at each iteration.
 cv : int, crossvalidation generator or an iterable, optional
Determines the crossvalidation splitting strategy. Possible inputs for cv are:
 None, to use the default 3fold crossvalidation,
 integer, to specify the number of folds.
 An object to be used as a crossvalidation generator.
 An iterable yielding train/test splits.
For integer/None inputs, if
y
is binary or multiclass,StratifiedKFold
used. If the estimator is a classifier or ify
is neither binary nor multiclass,KFold
is used.Refer User Guide for the various crossvalidation strategies that can be used here.
 scoring : string, callable or None, optional, default: None
 A string (see model evaluation documentation) or
a scorer callable object / function with signature
scorer(estimator, X, y)
.  estimator_params : dict
 Parameters for the external estimator. This attribute is deprecated as of version 0.16 and will be removed in 0.18. Use estimator initialisation or set_params method instead.
 verbose : int, default=0
 Controls verbosity of output.
Attributes
n_features_
: int The number of selected features with crossvalidation.
support_
: array of shape [n_features] The mask of selected features.
ranking_
: array of shape [n_features] The feature ranking, such that ranking_[i] corresponds to the ranking position of the ith feature. Selected (i.e., estimated best) features are assigned rank 1.
grid_scores_
: array of shape [n_subsets_of_features] The crossvalidation scores such that
grid_scores_[i]
corresponds to the CV score of the ith subset of features. estimator_
: object The external estimator fit on the reduced dataset.
Notes
The size of
grid_scores_
is equal to ceil((n_features  1) / step) + 1, where step is the number of features removed at each iteration.Examples
The following example shows how to retrieve the apriori not known 5 informative features in the Friedman #1 dataset.
>>> from sklearn.datasets import make_friedman1 >>> from sklearn.feature_selection import RFECV >>> from sklearn.svm import SVR >>> X, y = make_friedman1(n_samples=50, n_features=10, random_state=0) >>> estimator = SVR(kernel="linear") >>> selector = RFECV(estimator, step=1, cv=5) >>> selector = selector.fit(X, y) >>> selector.support_ array([ True, True, True, True, True, False, False, False, False, False], dtype=bool) >>> selector.ranking_ array([1, 1, 1, 1, 1, 6, 4, 3, 2, 5])
References
[1] Guyon, I., Weston, J., Barnhill, S., & Vapnik, V., “Gene selection for cancer classification using support vector machines”, Mach. Learn., 46(13), 389–422, 2002. Full API documentation: RFECVScikitsLearnNode

class
mdp.nodes.
BayesianRidgeScikitsLearnNode
¶ Bayesian ridge regression
This node has been automatically generated by wrapping the
sklearn.linear_model.bayes.BayesianRidge
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Fit a Bayesian ridge model and optimize the regularization parameters lambda (precision of the weights) and alpha (precision of the noise).
Read more in the User Guide.
Parameters
 n_iter : int, optional
 Maximum number of iterations. Default is 300.
 tol : float, optional
 Stop the algorithm if w has converged. Default is 1.e3.
 alpha_1 : float, optional
 Hyperparameter : shape parameter for the Gamma distribution prior over the alpha parameter. Default is 1.e6
 alpha_2 : float, optional
 Hyperparameter : inverse scale parameter (rate parameter) for the Gamma distribution prior over the alpha parameter. Default is 1.e6.
 lambda_1 : float, optional
 Hyperparameter : shape parameter for the Gamma distribution prior over the lambda parameter. Default is 1.e6.
 lambda_2 : float, optional
 Hyperparameter : inverse scale parameter (rate parameter) for the Gamma distribution prior over the lambda parameter. Default is 1.e6
 compute_score : boolean, optional
 If True, compute the objective function at each step of the model. Default is False
 fit_intercept : boolean, optional
 whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered). Default is True.
 normalize : boolean, optional, default False
 If True, the regressors X will be normalized before regression.
 copy_X : boolean, optional, default True
 If True, X will be copied; else, it may be overwritten.
 verbose : boolean, optional, default False
 Verbose mode when fitting the model.
Attributes
coef_
: array, shape = (n_features) Coefficients of the regression model (mean of distribution)
alpha_
: float estimated precision of the noise.
lambda_
: array, shape = (n_features) estimated precisions of the weights.
scores_
: float if computed, value of the objective function (to be maximized)
Examples
>>> from sklearn import linear_model >>> clf = linear_model.BayesianRidge() >>> clf.fit([[0,0], [1, 1], [2, 2]], [0, 1, 2]) ... BayesianRidge(alpha_1=1e06, alpha_2=1e06, compute_score=False, copy_X=True, fit_intercept=True, lambda_1=1e06, lambda_2=1e06, n_iter=300, normalize=False, tol=0.001, verbose=False) >>> clf.predict([[1, 1]]) array([ 1.])
Notes
See examples/linear_model/plot_bayesian_ridge.py for an example.
Full API documentation: BayesianRidgeScikitsLearnNode

class
mdp.nodes.
MultiTaskElasticNetCVScikitsLearnNode
¶ Multitask L1/L2 ElasticNet with builtin crossvalidation.
This node has been automatically generated by wrapping the
sklearn.linear_model.coordinate_descent.MultiTaskElasticNetCV
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.The optimization objective for MultiTaskElasticNet is:
(1 / (2 * n_samples)) * Y  XW^Fro_2 + alpha * l1_ratio * W_21 + 0.5 * alpha * (1  l1_ratio) * W_Fro^2
Where:
W_21 = \sum_i \sqrt{\sum_j w_{ij}^2}
i.e. the sum of norm of each row.
Read more in the User Guide.
Parameters
 eps : float, optional
 Length of the path.
eps=1e3
means thatalpha_min / alpha_max = 1e3
.  alphas : arraylike, optional
 List of alphas where to compute the models. If not provided, set automatically.
 n_alphas : int, optional
 Number of alphas along the regularization path
 l1_ratio : float or array of floats
 The ElasticNet mixing parameter, with 0 < l1_ratio <= 1.
For l1_ratio = 0 the penalty is an L1/L2 penalty. For l1_ratio = 1 it
is an L1 penalty.
For
0 < l1_ratio < 1
, the penalty is a combination of L1/L2 and L2. This parameter can be a list, in which case the different values are tested by crossvalidation and the one giving the best prediction score is used. Note that a good choice of list of values for l1_ratio is often to put more values close to 1 (i.e. Lasso) and less close to 0 (i.e. Ridge), as in[.1, .5, .7, .9, .95, .99, 1]
 fit_intercept : boolean
 whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered).
 normalize : boolean, optional, default False
 If
True
, the regressors X will be normalized before regression.  copy_X : boolean, optional, default True
 If
True
, X will be copied; else, it may be overwritten.  max_iter : int, optional
 The maximum number of iterations
 tol : float, optional
 The tolerance for the optimization: if the updates are
smaller than
tol
, the optimization code checks the dual gap for optimality and continues until it is smaller thantol
.  cv : int, crossvalidation generator or an iterable, optional
Determines the crossvalidation splitting strategy. Possible inputs for cv are:
 None, to use the default 3fold crossvalidation,
 integer, to specify the number of folds.
 An object to be used as a crossvalidation generator.
 An iterable yielding train/test splits.
For integer/None inputs,
KFold
is used.Refer User Guide for the various crossvalidation strategies that can be used here.
 verbose : bool or integer
 Amount of verbosity.
 n_jobs : integer, optional
 Number of CPUs to use during the cross validation. If
1
, use all the CPUs. Note that this is used only if multiple values for l1_ratio are given.  selection : str, default ‘cyclic’
 If set to ‘random’, a random coefficient is updated every iteration rather than looping over features sequentially by default. This (setting to ‘random’) often leads to significantly faster convergence especially when tol is higher than 1e4.
 random_state : int, RandomState instance, or None (default)
 The seed of the pseudo random number generator that selects a random feature to update. Useful only when selection is set to ‘random’.
Attributes
intercept_
: array, shape (n_tasks,) Independent term in decision function.
coef_
: array, shape (n_tasks, n_features) Parameter vector (W in the cost function formula).
alpha_
: float The amount of penalization chosen by cross validation
mse_path_
: array, shape (n_alphas, n_folds) or (n_l1_ratio, n_alphas, n_folds) mean square error for the test set on each fold, varying alpha
alphas_
: numpy array, shape (n_alphas,) or (n_l1_ratio, n_alphas) The grid of alphas used for fitting, for each l1_ratio
l1_ratio_
: float best l1_ratio obtained by crossvalidation.
n_iter_
: int number of iterations run by the coordinate descent solver to reach the specified tolerance for the optimal alpha.
Examples
>>> from sklearn import linear_model >>> clf = linear_model.MultiTaskElasticNetCV() >>> clf.fit([[0,0], [1, 1], [2, 2]], ... [[0, 0], [1, 1], [2, 2]]) ... MultiTaskElasticNetCV(alphas=None, copy_X=True, cv=None, eps=0.001, fit_intercept=True, l1_ratio=0.5, max_iter=1000, n_alphas=100, n_jobs=1, normalize=False, random_state=None, selection='cyclic', tol=0.0001, verbose=0) >>> print(clf.coef_) [[ 0.52875032 0.46958558] [ 0.52875032 0.46958558]] >>> print(clf.intercept_) [ 0.00166409 0.00166409]
See also
MultiTaskElasticNet ElasticNetCV MultiTaskLassoCV
Notes
The algorithm used to fit the model is coordinate descent.
To avoid unnecessary memory duplication the X argument of the fit method should be directly passed as a Fortrancontiguous numpy array.
Full API documentation: MultiTaskElasticNetCVScikitsLearnNode

class
mdp.nodes.
PLSRegressionScikitsLearnNode
¶ PLS regression
This node has been automatically generated by wrapping the
sklearn.cross_decomposition.pls_.PLSRegression
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.PLSRegression implements the PLS 2 blocks regression known as PLS2 or PLS1 in case of one dimensional response. This class inherits from _PLS with mode=”A”, deflation_mode=”regression”, norm_y_weights=False and algorithm=”nipals”.
Read more in the User Guide.
Parameters
 n_components : int, (default 2)
 Number of components to keep.
 scale : boolean, (default True)
 whether to scale the data
 max_iter : an integer, (default 500)
 the maximum number of iterations of the NIPALS inner loop (used only if algorithm=”nipals”)
 tol : nonnegative real
 Tolerance used in the iterative algorithm default 1e06.
 copy : boolean, default True
 Whether the deflation should be done on a copy. Let the default value to True unless you don’t care about side effect
Attributes
x_weights_
: array, [p, n_components] X block weights vectors.
y_weights_
: array, [q, n_components] Y block weights vectors.
x_loadings_
: array, [p, n_components] X block loadings vectors.
y_loadings_
: array, [q, n_components] Y block loadings vectors.
x_scores_
: array, [n_samples, n_components] X scores.
y_scores_
: array, [n_samples, n_components] Y scores.
x_rotations_
: array, [p, n_components] X block to latents rotations.
y_rotations_
: array, [q, n_components] Y block to latents rotations.
 coef_: array, [p, q]
 The coefficients of the linear model:
Y = X ``coef_
+ Err`` n_iter_
: arraylike Number of iterations of the NIPALS inner loop for each component.
Notes
Matrices:
T: ``x_scores_`` U: ``y_scores_`` W: ``x_weights_`` C: ``y_weights_`` P: ``x_loadings_`` Q: ``y_loadings__``
Are computed such that:
X = T P.T + Err and Y = U Q.T + Err T[:, k] = Xk W[:, k] for k in range(n_components) U[:, k] = Yk C[:, k] for k in range(n_components) ``x_rotations_`` = W (P.T W)^(1) ``y_rotations_`` = C (Q.T C)^(1)
where Xk and Yk are residual matrices at iteration k.
Slides explaining PLS <http://www.eigenvector.com/Docs/Wise_pls_properties.pdf>
For each component k, find weights u, v that optimizes:
max corr(Xk u, Yk v) * std(Xk u) std(Yk u)
, such thatu = 1
Note that it maximizes both the correlations between the scores and the intrablock variances.
The residual matrix of X (Xk+1) block is obtained by the deflation on the current X score: x_score.
The residual matrix of Y (Yk+1) block is obtained by deflation on the current X score. This performs the PLS regression known as PLS2. This mode is prediction oriented.
This implementation provides the same results that 3 PLS packages provided in the R language (Rproject):
 “mixOmics” with function pls(X, Y, mode = “regression”)
 “plspm ” with function plsreg2(X, Y)
 “pls” with function oscorespls.fit(X, Y)
Examples
>>> from sklearn.cross_decomposition import PLSRegression >>> X = [[0., 0., 1.], [1.,0.,0.], [2.,2.,2.], [2.,5.,4.]] >>> Y = [[0.1, 0.2], [0.9, 1.1], [6.2, 5.9], [11.9, 12.3]] >>> pls2 = PLSRegression(n_components=2) >>> pls2.fit(X, Y) ... PLSRegression(copy=True, max_iter=500, n_components=2, scale=True, tol=1e06) >>> Y_pred = pls2.predict(X)
References
Jacob A. Wegelin. A survey of Partial Least Squares (PLS) methods, with emphasis on the twoblock case. Technical Report 371, Department of Statistics, University of Washington, Seattle, 2000.
In french but still a reference:
Tenenhaus, M. (1998). La regression PLS: theorie et pratique. Paris:
Editions Technic.
Full API documentation: PLSRegressionScikitsLearnNode

class
mdp.nodes.
FeatureHasherScikitsLearnNode
¶ Implements feature hashing, aka the hashing trick.
This node has been automatically generated by wrapping the
sklearn.feature_extraction.hashing.FeatureHasher
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.This class turns sequences of symbolic feature names (strings) into scipy.sparse matrices, using a hash function to compute the matrix column corresponding to a name. The hash function employed is the signed 32bit version of Murmurhash3.
Feature names of type byte string are used asis. Unicode strings are converted to UTF8 first, but no Unicode normalization is done. Feature values must be (finite) numbers.
This class is a lowmemory alternative to DictVectorizer and CountVectorizer, intended for largescale (online) learning and situations where memory is tight, e.g. when running prediction code on embedded devices.
Read more in the User Guide.
Parameters
 n_features : integer, optional
 The number of features (columns) in the output matrices. Small numbers of features are likely to cause hash collisions, but large numbers will cause larger coefficient dimensions in linear learners.
 dtype : numpy type, optional, default np.float64
 The type of feature values. Passed to scipy.sparse matrix constructors as the dtype argument. Do not set this to bool, np.boolean or any unsigned integer type.
 input_type : string, optional, default “dict”
 Either “dict” (the default) to accept dictionaries over (feature_name, value); “pair” to accept pairs of (feature_name, value); or “string” to accept single strings. feature_name should be a string, while value should be a number. In the case of “string”, a value of 1 is implied. The feature_name is hashed to find the appropriate column for the feature. The value’s sign might be flipped in the output (but see non_negative, below).
 non_negative : boolean, optional, default False
 Whether output matrices should contain nonnegative values only; effectively calls abs on the matrix prior to returning it. When True, output values can be interpreted as frequencies. When False, output values will have expected value zero.
Examples
>>> from sklearn.feature_extraction import FeatureHasher >>> h = FeatureHasher(n_features=10) >>> D = [{'dog': 1, 'cat':2, 'elephant':4},{'dog': 2, 'run': 5}] >>> f = h.transform(D) >>> f.toarray() array([[ 0., 0., 4., 1., 0., 0., 0., 0., 0., 2.], [ 0., 0., 0., 2., 5., 0., 0., 0., 0., 0.]])
See also
DictVectorizer : vectorizes stringvalued features using a hash table. sklearn.preprocessing.OneHotEncoder : handles nominal/categorical features
encoded as columns of integers.Full API documentation: FeatureHasherScikitsLearnNode

class
mdp.nodes.
LinearRegressionScikitsLearnNode
¶ Ordinary least squares Linear Regression.
This node has been automatically generated by wrapping the
sklearn.linear_model.base.LinearRegression
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Parameters
 fit_intercept : boolean, optional
 whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered).
 normalize : boolean, optional, default False
 If True, the regressors X will be normalized before regression.
 copy_X : boolean, optional, default True
 If True, X will be copied; else, it may be overwritten.
 n_jobs : int, optional, default 1
 The number of jobs to use for the computation. If 1 all CPUs are used. This will only provide speedup for n_targets > 1 and sufficient large problems.
Attributes
coef_
: array, shape (n_features, ) or (n_targets, n_features) Estimated coefficients for the linear regression problem. If multiple targets are passed during the fit (y 2D), this is a 2D array of shape (n_targets, n_features), while if only one target is passed, this is a 1D array of length n_features.
intercept_
: array Independent term in the linear model.
Notes
From the implementation point of view, this is just plain Ordinary Least Squares (scipy.linalg.lstsq) wrapped as a predictor object.
Full API documentation: LinearRegressionScikitsLearnNode

class
mdp.nodes.
LabelBinarizerScikitsLearnNode
¶ Binarize labels in a onevsall fashion
This node has been automatically generated by wrapping the
sklearn.preprocessing.label.LabelBinarizer
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Several regression and binary classification algorithms are available in the scikit. A simple way to extend these algorithms to the multiclass classification case is to use the socalled onevsall scheme.
At learning time, this simply consists in learning one regressor or binary classifier per class. In doing so, one needs to convert multiclass labels to binary labels (belong or does not belong to the class). LabelBinarizer makes this process easy with the transform method.
At prediction time, one assigns the class for which the corresponding model gave the greatest confidence. LabelBinarizer makes this easy with the inverse_transform method.
Read more in the User Guide.
Parameters
 neg_label : int (default: 0)
 Value with which negative labels must be encoded.
 pos_label : int (default: 1)
 Value with which positive labels must be encoded.
 sparse_output : boolean (default: False)
 True if the returned array from transform is desired to be in sparse CSR format.
Attributes
classes_
: array of shape [n_class] Holds the label for each class.
y_type_
: str, Represents the type of the target data as evaluated by utils.multiclass.type_of_target. Possible type are ‘continuous’, ‘continuousmultioutput’, ‘binary’, ‘multiclass’, ‘mutliclassmultioutput’, ‘multilabelindicator’, and ‘unknown’.
multilabel_
: boolean True if the transformer was fitted on a multilabel rather than a
multiclass set of labels. The
multilabel_
attribute is deprecated and will be removed in 0.18 sparse_input_
: boolean, True if the input data to transform is given as a sparse matrix, False otherwise.
indicator_matrix_
: str ‘sparse’ when the input data to tansform is a multilableindicator and
is sparse, None otherwise. The
indicator_matrix_
attribute is deprecated as of version 0.16 and will be removed in 0.18
Examples
>>> from sklearn import preprocessing >>> lb = preprocessing.LabelBinarizer() >>> lb.fit([1, 2, 6, 4, 2]) LabelBinarizer(neg_label=0, pos_label=1, sparse_output=False) >>> lb.classes_ array([1, 2, 4, 6]) >>> lb.transform([1, 6]) array([[1, 0, 0, 0], [0, 0, 0, 1]])
Binary targets transform to a column vector
>>> lb = preprocessing.LabelBinarizer() >>> lb.fit_transform(['yes', 'no', 'no', 'yes']) array([[1], [0], [0], [1]])
Passing a 2D matrix for multilabel classification
>>> import numpy as np >>> lb.fit(np.array([[0, 1, 1], [1, 0, 0]])) LabelBinarizer(neg_label=0, pos_label=1, sparse_output=False) >>> lb.classes_ array([0, 1, 2]) >>> lb.transform([0, 1, 2, 1]) array([[1, 0, 0], [0, 1, 0], [0, 0, 1], [0, 1, 0]])
See also
 label_binarize : function to perform the transform operation of
 LabelBinarizer with fixed classes.
Full API documentation: LabelBinarizerScikitsLearnNode

class
mdp.nodes.
OneVsRestClassifierScikitsLearnNode
¶ Onevstherest (OvR) multiclass/multilabel strategy
This node has been automatically generated by wrapping the
sklearn.multiclass.OneVsRestClassifier
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Also known as onevsall, this strategy consists in fitting one classifier per class. For each classifier, the class is fitted against all the other classes. In addition to its computational efficiency (only n_classes classifiers are needed), one advantage of this approach is its interpretability. Since each class is represented by one and one classifier only, it is possible to gain knowledge about the class by inspecting its corresponding classifier. This is the most commonly used strategy for multiclass classification and is a fair default choice.
This strategy can also be used for multilabel learning, where a classifier is used to predict multiple labels for instance, by fitting on a 2d matrix in which cell [i, j] is 1 if sample i has label j and 0 otherwise.
In the multilabel learning literature, OvR is also known as the binary relevance method.
Read more in the User Guide.
Parameters
 estimator : estimator object
 An estimator object implementing fit and one of decision_function or predict_proba.
 n_jobs : int, optional, default: 1
 The number of jobs to use for the computation. If 1 all CPUs are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. For n_jobs below 1, (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = 2, all CPUs but one are used.
Attributes
estimators_
: list of n_classes estimators Estimators used for predictions.
classes_
: array, shape = [n_classes] Class labels.
label_binarizer_
: LabelBinarizer object Object used to transform multiclass labels to binary labels and viceversa.
multilabel_
: boolean Whether a OneVsRestClassifier is a multilabel classifier.
Full API documentation: OneVsRestClassifierScikitsLearnNode

class
mdp.nodes.
NystroemScikitsLearnNode
¶ Approximate a kernel map using a subset of the training data.
This node has been automatically generated by wrapping the
sklearn.kernel_approximation.Nystroem
class from thesklearn
library. The wrapped instance can be accessed through thescikits_alg
attribute.Constructs an approximate feature map for an arbitrary kernel using a subset of the data as basis.
Read more in the User Guide.
Parameters
 kernel : string or callable, default=”rbf”
 Kernel map to be approximated. A callable should accept two arguments and the keyword arguments passed to this object as kernel_params, and should return a floating point number.
 n_components : int
 Number of features to construct. How many data points will be used to construct the mapping.
 gamma : float, default=None
 Gamma parameter for the RBF, polynomial, exponential chi2 and sigmoid kernels. Interpretation of the default value is left to the kernel; see the documentation for sklearn.metrics.pairwise. Ignored by other kernels.
 degree : float, default=3
 Deg