msmbuilder.preprocessing.PolynomialFeatures¶

class msmbuilder.preprocessing.PolynomialFeatures(degree=2, interaction_only=False, include_bias=True)¶

Generate polynomial and interaction features.

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 degree-2 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 not x[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).

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

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_output_features, n_input_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.

Methods

`fit`(X[, y])	Fit Preprocessing to X.
`fit_transform`(sequences[, y])	Fit the model and apply preprocessing
`get_feature_names`([input_features])	Return feature names for output features
`get_params`([deep])	Get parameters for this estimator.
`partial_fit`(sequence[, y])	Fit Preprocessing to X.
`partial_transform`(sequence)	Apply preprocessing to single sequence
`set_params`(**params)	Set the parameters of this estimator.
`summarize`()	Return some diagnostic summary statistics about this Markov model
`transform`(sequences)	Apply preprocessing to sequences

__init__(degree=2, interaction_only=False, include_bias=True)¶

Methods

`__init__`([degree, interaction_only, ...])
`fit`(X[, y])	Fit Preprocessing to X.
`fit_transform`(sequences[, y])	Fit the model and apply preprocessing
`get_feature_names`([input_features])	Return feature names for output features
`get_params`([deep])	Get parameters for this estimator.
`partial_fit`(sequence[, y])	Fit Preprocessing to X.
`partial_transform`(sequence)	Apply preprocessing to single sequence
`set_params`(**params)	Set the parameters of this estimator.
`summarize`()	Return some diagnostic summary statistics about this Markov model
`transform`(sequences)	Apply preprocessing to sequences

Attributes

powers_

fit(X, y=None)¶

Fit Preprocessing to X.

Parameters:

sequence : array-like, [sequence_length, n_features]

A multivariate timeseries.

y : None

Ignored

Returns:

self

fit_transform(sequences, y=None)¶

Fit the model and apply preprocessing

Parameters:

sequences: list of array-like, each of shape (n_samples_i, n_features)

Training data, where n_samples_i in the number of samples in sequence i and n_features is the number of features.

y : None

Ignored

Returns:

sequence_new : list of array-like, each of shape (n_samples_i, n_components)

get_feature_names(input_features=None)¶

Return feature names for output features

Parameters:

input_features : list of string, length n_features, optional

String names for input features if available. By default, “x0”, “x1”, ... “xn_features” is used.

Returns:

output_feature_names : list of string, length n_output_features

get_params(deep=True)¶

Get parameters for this estimator.

Parameters:

deep : boolean, optional

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params : mapping of string to any

Parameter names mapped to their values.

partial_fit(sequence, y=None)¶

Fit Preprocessing to X. Parameters ———- sequence : array-like, [sequence_length, n_features]

A multivariate timeseries.

y : None: Ignored

self

partial_transform(sequence)¶

Apply preprocessing to single sequence

Parameters:

sequence: array like, shape (n_samples, n_features)

A single sequence to transform

Returns:

out : array like, shape (n_samples, n_features)

set_params(**params)¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:	self

summarize()¶: Return some diagnostic summary statistics about this Markov model

transform(sequences)¶

Apply preprocessing to sequences

Parameters:

sequences: list of array-like, each of shape (n_samples_i, n_features)

Sequence data to transform, where n_samples_i in the number of samples in sequence i and n_features is the number of features.

Returns:

sequence_new : list of array-like, each of shape (n_samples_i, n_components)