ML-Ensemble

author:Sebastian Flennerhag
copyright:2017
licence:MIT

Auxiliary transformers to support computational graphs.

mlens.preprocessing

Subset

class mlens.preprocessing.Subset(subset=None)[source]

Bases: mlens.externals.sklearn.base.BaseEstimator, mlens.externals.sklearn.base.TransformerMixin

Select a subset of features.

The Subset class acts as a transformer that reduces the feature set to a subset specified by the user.

Parameters:subset (list) – list of columns indexes to select subset with. Indexes can either be of type str if data accepts slicing on a list of strings, otherwise the list should be of type int.
fit(X, y=None)[source]

Learn what format the data is stored in.

Parameters:
  • X (array-like of shape = [n_samples, n_features]) – The whose type will be inferred.
  • y (array-like of shape = [n_samples, n_features]) – pass-through for Scikit-learn pipeline compatibility.
transform(X, y=None, copy=False)[source]

Return specified subset of X.

Parameters:
  • X (array-like of shape = [n_samples, n_features]) – The whose type will be inferred.
  • y (array-like of shape = [n_samples, n_features]) – pass-through for Scikit-learn pipeline compatibility.
  • copy (bool (default = None)) – whether to copy X before transforming.

Shift

class mlens.preprocessing.Shift(s)[source]

Bases: mlens.externals.sklearn.base.BaseEstimator, mlens.externals.sklearn.base.TransformerMixin

Lag operator.

Shift an input array \(X\) with \(s\) steps, i.e. for some time series \(\mathbf{X} = (X_t, X_{t-1}, ..., X_{0})\),

\[L^{s} \mathbf{X} = (X_{t-s}, X_{t-1-s}, ..., X_{s - s})\]
Parameters:s (int) – number of lags to generate

Examples

>>> import numpy as np
>>> from mlens.preprocessing import Shift
>>> X = np.arange(10)
>>> L = Shift(2)
>>> Z = L.fit_transform(X)
>>> print("X : {}".format(X[2:]))
>>> print("Z : {}".format(Z))
X : [2 3 4 5 6 7 8 9]
Z : [0 1 2 3 4 5 6 7]
fit(X, y=None)[source]

Pass through for compatability.

transform(X)[source]

Return lagged dataset.