Compose

The gtime.compose module contains meta-estimators for building composite models with transformers.

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

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.

fit_transform(X: DataFrame, y: Optional[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.

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

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.

transform(X: 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.