8000 [MRG] MNT remove coupling between pipeline methods by NicolasHug · Pull Request #16777 · scikit-learn/scikit-learn · GitHub
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[MRG] MNT remove coupling between pipeline methods #16777

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Merged
merged 1 commit into from
Mar 27, 2020
Merged

[MRG] MNT remove coupling between pipeline methods #16777

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54 changes: 33 additions & 21 deletions sklearn/pipeline.py
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8000 86D1
Original file line number Diff line number Diff line change
Expand Up @@ -258,17 +258,7 @@ def _log_message(self, step_idx):
len(self.steps),
8000 name)

# Estimator interface

def _fit(self, X, y=None, **fit_params):
# shallow copy of steps - this should really be steps_
self.steps = list(self.steps)
self._validate_steps()
# Setup the memory
memory = check_memory(self.memory)

fit_transform_one_cached = memory.cache(_fit_transform_one)

def _check_fit_params(self, **fit_params):
fit_params_steps = {name: {} for name, step in self.steps
if step is not None}
for pname, pval in fit_params.items():
Expand All @@ -281,6 +271,19 @@ def _fit(self, X, y=None, **fit_params):
"=sample_weight)`.".format(pname))
step, param = pname.split('__', 1)
fit_params_steps[step][param] = pval
return fit_params_steps

# Estimator interface

def _fit(self, X, y=None, **fit_params_steps):
# shallow copy of steps - this should really be steps_
self.steps = list(self.steps)
self._validate_steps()
# Setup the memory
memory = check_memory(self.memory)

fit_transform_one_cached = memory.cache(_fit_transform_one)

for (step_idx,
name,
transformer) in self._iter(with_final=False,
Expand Down Expand Up @@ -318,9 +321,7 @@ def _fit(self, X, y=None, **fit_params):
# transformer. This is necessary when loading the transformer
# from the cache.
self.steps[step_idx] = (name, fitted_transformer)
if self._final_estimator == 'passthrough':
return X, {}
return X, fit_params_steps[self.steps[-1][0]]
return X

def fit(self, X, y=None, **fit_params):
"""Fit the model
Expand Down Expand Up @@ -348,11 +349,14 @@ def fit(self, X, y=None, **fit_params):
self : Pipeline
This estimator
"""
Xt, fit_params = self._fit(X, y, **fit_params)
fit_params_steps = self._check_fit_params(**fit_params)
Xt = self._fit(X, y, **fit_params_steps)
with _print_elapsed_time('Pipeline',
self._log_message(len(self.steps) - 1)):
if self._final_estimator != 'passthrough':
self._final_estimator.fit(Xt, y, **fit_params)
fit_params_last_step = fit_params_steps[self.steps[-1][0]]
self._final_estimator.fit(Xt, y, **fit_params_last_step)

return self

def fit_transform(self, X, y=None, **fit_params):
Expand Down Expand Up @@ -382,16 +386,20 @@ def fit_transform(self, X, y=None, **fit_params):
Xt : array-like of shape (n_samples, n_transformed_features)
Transformed samples
"""
fit_params_steps = self._check_fit_params(**fit_params)
Xt = self._fit(X, y, **fit_params_steps)

last_step = self._final_estimator
Xt, fit_params = self._fit(X, y, **fit_params)
with _print_elapsed_time('Pipeline',
self._log_message(len(self.steps) - 1)):
if last_step == 'passthrough':
return Xt
fit_params_last_step = fit_params_steps[self.steps[-1][0]]
if hasattr(last_step, 'fit_transform'):
return last_step.fit_transform(Xt, y, **fit_params)
return last_step.fit_transform(Xt, y, **fit_params_last_step)
else:
return last_step.fit(Xt, y, **fit_params).transform(Xt)
return last_step.fit(Xt, y,
**fit_params_last_step).transform(Xt)

@if_delegate_has_method(delegate='_final_estimator')
def predict(self, X, **predict_params):
Expand Down Expand Up @@ -447,10 +455,14 @@ def fit_predict(self, X, y=None, **fit_params):
-------
y_pred : array-like
"""
Xt, fit_params = self._fit(X, y, **fit_params)
fit_params_steps = self._check_fit_params(**fit_params)
Xt = self._fit(X, y, **fit_params_steps)

fit_params_last_step = fit_params_steps[self.steps[-1][0]]
with _print_elapsed_time('Pipeline',
self._log_message(len(self.steps) - 1)):
y_pred = self.steps[-1][-1].fit_predict(Xt, y, **fit_params)
y_pred = self.steps[-1][-1].fit_predict(Xt, y,
**fit_params_last_step)
return y_pred

@if_delegate_has_method(delegate='_final_estimator')
Expand Down
0