/
base.py
72 lines (60 loc) · 2.4 KB
/
base.py
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"""Implements a TransformerResamplerMixin for transformers that have a resample method."""
# License: Apache 2.0
class TransformerResamplerMixin:
"""Mixin class for all transformers resamplers in giotto."""
_estimator_type = 'transformer_resampler'
def fit_transform(self, X, y=None, **fit_params):
"""Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params
and returns a transformed version of X.
Parameters
----------
X : numpy array of shape [n_samples, n_features]
Training set.
y : numpy array of shape [n_samples]
Target values.
Returns
-------
X_new : numpy array of shape [n_samples, n_features_new]
Transformed array.
"""
# non-optimized default implementation; override when a better
# method is possible for a given clustering algorithm
if y is None:
# fit method of arity 1 (unsupervised transformation)
return self.fit(X, **fit_params).transform(X)
else:
# fit method of arity 2 (supervised transformation)
return self.fit(X, y, **fit_params).transform(X, y)
def transform_resample(self, X, y):
"""Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params
and returns a transformed version of X.
Parameters
----------
X : numpy array of shape [n_samples, n_features]
Training set.
y : numpy array of shape [n_samples]
Target values.
Returns
-------
X_new : numpy array of shape [n_samples, n_features_new]
Transformed array.
"""
return self.transform(X), self.resample(y, X)
def fit_transform_resample(self, X, y, **fit_params):
"""Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params
and returns a transformed version of X.
Parameters
----------
X : numpy array of shape [n_samples, n_features]
Training set.
y : numpy array of shape [n_samples]
Target values.
Returns
-------
X_new : numpy array of shape [n_samples, n_features_new]
Transformed array.
"""
return self.fit(X, y, **fit_params).transform_resample(X, y)