gtime.compose.FeatureCreation

class gtime.compose.FeatureCreation(transformers, remainder='drop', sparse_threshold=0.3, n_jobs=None, transformer_weights=None, verbose=False)

Applies transformers to columns of a pandas DataFrame.

This estimator is a wrapper of sklearn.compose.ColumnTransformer, the only difference is the output type of fit_transform and transform methods which is a DataFrame instead of an array.

Attributes

named_transformers_

Access the fitted transformer by name.

Methods

fit(self, X[, y])	Fit all transformers using X.
fit_transform(self, X, y)	Fit all transformers, transform the data and concatenate results.
get_feature_names(self)	Get feature names from all transformers.
get_params(self[, deep])	Get parameters for this estimator.
set_params(self, \*\*kwargs)	Set the parameters of this estimator.
transform(self, X)	Transform X separately by each transformer, concatenate results.

__init__(self, transformers, remainder='drop', sparse_threshold=0.3, n_jobs=None, transformer_weights=None, verbose=False)

Initialize self. See help(type(self)) for accurate signature.

fit(self, X, y=None)

Fit all transformers using X.

Parameters

Xarray-like or DataFrame of shape [n_samples, n_features]

Input data, of which specified subsets are used to fit the transformers.

yarray-like, shape (n_samples, …), optional

Targets for supervised learning.

Returns

selfColumnTransformer

This estimator

fit_transform(self, X:pandas.core.frame.DataFrame, y:pandas.core.frame.DataFrame=None)

Fit all transformers, transform the data and concatenate results.

Parameters

Xpd.DataFrame, shape (n_samples, n_features), required

Input data, of which specified subsets are used to fit the transformers.

ypd.DataFrame, shape (n_samples, …), optional, default: None

Targets for supervised learning.

Returns

X_t_dfpd.DataFrame, shape (n_samples, sum_n_components)

hstack of results of transformers. sum_n_components is the sum of n_components (output dimension) over transformers.

Examples

>>> import pandas.util.testing as testing
>>> from gtime.compose import FeatureCreation
>>> from gtime.feature_extraction import Shift, MovingAverage
>>> data = testing.makeTimeDataFrame(freq="s")
>>> fc = FeatureCreation([
...     ('s1', Shift(1), ['A']),
...     ('ma3', MovingAverage(window_size=3), ['B']),
... ])
>>> fc.fit_transform(data).head()
                     s1__A__Shift  ma3__B__MovingAverage
2000-01-01 00:00:00           NaN                    NaN
2000-01-01 00:00:01      0.211403                    NaN
2000-01-01 00:00:02     -0.313854               0.085045
2000-01-01 00:00:03      0.502018              -0.239269
2000-01-01 00:00:04     -0.225324              -0.144625

get_feature_names(self)

Get feature names from all transformers.

Returns

feature_nameslist of strings

Names of the features produced by transform.

get_params(self, deep=True)

Get parameters for this estimator.

Parameters

deepboolean, optional

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

Returns

paramsmapping of string to any

Parameter names mapped to their values.

property named_transformers_

Access the fitted transformer by name.

Read-only attribute to access any transformer by given name. Keys are transformer names and values are the fitted transformer objects.

set_params(self, **kwargs)

Set the parameters of this estimator.

Valid parameter keys can be listed with get_params().

Returns

self

transform(self, X:pandas.core.frame.DataFrame)

Transform X separately by each transformer, concatenate results.

Parameters

Xpd.DataFrame, shape (n_samples, n_features), required

The data to be transformed by subset.

Returns

X_t_dfDataFrame, shape (n_samples, sum_n_components)

hstack of results of transformers. sum_n_components is the sum of n_components (output dimension) over transformers. If any result is a sparse matrix, everything will be converted to sparse matrices.