8000 Elapsed step times for inference stage in Pipeline and FeatureUnion by MezentsevIlya · Pull Request #19293 · scikit-learn/scikit-learn · GitHub
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Elapsed step times for inference stage in Pipeline and FeatureUnion #19293

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Elapsed step times for inference stage in Pipeline and FeatureUnion #19293

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d8b3c97
add possibility for log elapsed time in inference stage for Pipeline …
Jan 28, 2021
3f6d454
add default value
Jan 28, 2021
37729f3
add default value for second case
Jan 28, 2021
a782b3c
fix linting
Jan 28, 2021
798644d
fix linting
Jan 28, 2021
d2d5871
another linting fix
Jan 28, 2021
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142 changes: 97 additions & 45 deletions sklearn/pipeline.py
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8000
Original file line number Diff line number Diff line change
Expand Up @@ -73,9 +73,12 @@ class Pipeline(_BaseComposition):
inspect estimators within the pipeline. Caching the
transformers is advantageous when fitting is time consuming.

verbose : bool, default=False
If True, the time elapsed while fitting each step will be printed as it
is completed.
verbose : bool or int, default=False
If True or less than 10, the time elapsed while fitting
each step will be printed as it is completed.
If greater than or equal to 10, the time elapsed
while performing each step (fitting or inference)
will be printed as it is completed.

Attributes
----------
Expand Down Expand Up @@ -237,8 +240,8 @@ def _final_estimator(self):
estimator = self.steps[-1][1]
return 'passthrough' if estimator is None else estimator

def _log_message(self, step_idx):
if not self.verbose:
def _log_message(self, step_idx, is_fitting=True):
if not self.verbose or (self.verbose < 10 and not is_fitting):
return None
name, _ = self.steps[step_idx]

Expand Down Expand Up @@ -278,7 +281,7 @@ def _fit(self, X, y=None, **fit_params_steps):
filter_passthrough=False):
if (transformer is None or transformer == 'passthrough'):
with _print_elapsed_time('Pipeline',
self._log_message(step_idx)):
self._log_message(step_idx, True)):
continue

if hasattr(memory, 'location'):
Expand All @@ -303,7 +306,7 @@ def _fit(self, X, y=None, **fit_params_steps):
X, fitted_transformer = fit_transform_one_cached(
cloned_transformer, X, y, None,
message_clsname='Pipeline',
message=self._log_message(step_idx),
message=self._log_message(step_idx, True),
**fit_params_steps[name])
# Replace the transformer of the step with the fitted
# transformer. This is necessary when loading the transformer
Expand Down Expand Up @@ -340,7 +343,7 @@ def fit(self, X, y=None, **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)):
self._log_message(len(self.steps) - 1, True)):
if self._final_estimator != 'passthrough':
fit_params_last_step = fit_params_steps[self.steps[-1][0]]
self._final_estimator.fit(Xt, y, **fit_params_last_step)
Expand Down Expand Up @@ -379,7 +382,7 @@ def fit_transform(self, X, y=None, **fit_params):

last_step = self._final_estimator
with _print_elapsed_time('Pipeline',
self._log_message(len(self.steps) - 1)):
self._log_message(len(self.steps) - 1, True)):
if last_step == 'passthrough':
return Xt
fit_params_last_step = fit_params_steps[self.steps[-1][0]]
Expand Down Expand Up @@ -414,9 +417,17 @@ def predict(self, X, **predict_params):
y_pred : array-like
"""
Xt = X
for _, name, transform in self._iter(with_final=False):
Xt = transform.transform(Xt)
return self.steps[-1][-1].predict(Xt, **predict_params)
for idx, name, transform in self._iter(with_final=False,
filter_passthrough=False):
with _print_elapsed_time('Pipeline',
self._log_message(idx, False)):
if (transform is None or transform == 'passthrough'):
continue
Xt = transform.transform(Xt)
with _print_elapsed_time(
'Pipeline', self._log_message(len(self.steps) - 1, False)):
y_pred = self.steps[-1][-1].predict(Xt, **predict_params)
return y_pred

@if_delegate_has_method(delegate='_final_estimator')
def fit_predict(self, X, y=None, **fit_params):
Expand Down Expand Up @@ -449,8 +460,8 @@ def fit_predict(self, X, y=None, **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)):
with _print_elapsed_time(
'Pipeline', self._log_m 10000 essage(len(self.steps) - 1, True)):
y_pred = self.steps[-1][-1].fit_predict(Xt, y,
**fit_params_last_step)
return y_pred
Expand All @@ -470,9 +481,14 @@ def predict_proba(self, X):
y_proba : array-like of shape (n_samples, n_classes)
"""
Xt = X
for _, name, transform in self._iter(with_final=False):
Xt = transform.transform(Xt)
return self.steps[-1][-1].predict_proba(Xt)
for idx, name, transform in self._iter(with_final=False):
with _print_elapsed_time(
'Pipeline', self._log_message(idx, False)):
Xt = transform.transform(Xt)
with _print_elapsed_time(
'Pipeline', self._log_message(len(self.steps) - 1, False)):
y_proba = self.steps[-1][-1].predict_proba(Xt)
return y_proba

@if_delegate_has_method(delegate='_final_estimator')
def decision_function(self, X):
Expand All @@ -489,9 +505,14 @@ def decision_function(self, X):
y_score : array-like of shape (n_samples, n_classes)
"""
Xt = X
for _, name, transform in self._iter(with_final=False):
Xt = transform.transform(Xt)
return self.steps[-1][-1].decision_function(Xt)
for idx, name, transform in self._iter(with_final=False):
with _print_elapsed_time('Pipeline',
self._log_message(idx, False)):
Xt = transform.transform(Xt)
with _print_elapsed_time(
'Pipeline', self._log_message(len(self.steps) - 1, False)):
y_score = self.steps[-1][-1].decision_function(Xt)
return y_score

@if_delegate_has_method(delegate='_final_estimator')
def score_samples(self, X):
Expand All @@ -508,9 +529,14 @@ def score_samples(self, X):
y_score : ndarray of shape (n_samples,)
"""
Xt = X
for _, _, transformer in self._iter(with_final=False):
Xt = transformer.transform(Xt)
return self.steps[-1][-1].score_samples(Xt)
for idx, _, transformer in self._iter(with_final=False):
with _print_elapsed_time('Pipeline',
self._log_message(idx, False)):
Xt = transformer.transform(Xt)
with _print_elapsed_time(
'Pipeline', self._log_message(len(self.steps) - 1, False)):
y_score = self.steps[-1][-1].score_samples(Xt)
return y_score

@if_delegate_has_method(delegate='_final_estimator')
def predict_log_proba(self, X):
Expand All @@ -527,9 +553,14 @@ def predict_log_proba(self, X):
y_score : array-like of shape (n_samples, n_classes)
"""
Xt = X
for _, name, transform in self._iter(with_final=False):
Xt = transform.transform(Xt)
return self.steps[-1][-1].predict_log_proba(Xt)
for idx, name, transform in self._iter(with_final=False):
with _print_elapsed_time('Pipeline',
self._log_message(idx, False)):
Xt = transform.transform(Xt)
with _print_elapsed_time(
'Pipeline', self._log_message(len(self.steps) - 1, False)):
y_score = self.steps[-1][-1].predict_log_proba(Xt)
return y_score

@property
def transform(self):
Expand All @@ -556,8 +587,12 @@ def transform(self):

def _transform(self, X):
Xt = X
for _, _, transform in self._iter():
Xt = transform.transform(Xt)
for idx, _, transform in self._iter(filter_passthrough=False):
with _print_elapsed_time('Pipeline',
self._log_message(idx, False)):
if (transform is None or transform == 'passthrough'):
continue
Xt = transform.transform(Xt)
return Xt

@property
Expand Down ED4F Expand Up @@ -614,12 +649,18 @@ def score(self, X, y=None, sample_weight=None):
score : float
"""
Xt = X
for _, name, transform in self._iter(with_final=False):
Xt = transform.transform(Xt)
for idx, name, transform in self._iter(with_final=False):
with _print_elapsed_time('Pipeline',
self._log_message(idx, False)):
Xt = transform.transform(Xt)
score_params = {}
if sample_weight is not None:
score_params['sample_weight'] = sample_weight
return self.steps[-1][-1].score(Xt, y, **score_params)

with _print_elapsed_time(
'Pipeline', self._log_message(len(self.steps) - 1, False)):
score = self.steps[-1][-1].score(Xt, y, **score_params)
return score

@property
def classes_(self):
Expand Down Expand Up @@ -705,9 +746,12 @@ def make_pipeline(*steps, memory=None, verbose=False):
inspect estimators within the pipeline. Caching the
transformers is advantageous when fitting is time consuming.

verbose : bool, default=False
If True, the time elapsed while fitting each step will be printed as it
is completed.
verbose : bool or int, default=False
If True or less than 10, the time elapsed while fitting
each step will be printed as it is completed.
If greater than or equal to 10, the time elapsed
while performing each step (fitting or inference)
will be printed as it is completed.

See Also
--------
Expand All @@ -729,8 +773,15 @@ def make_pipeline(*steps, memory=None, verbose=False):
return Pipeline(_name_estimators(steps), memory=memory, verbose=verbose)


def _transform_one(transformer, X, y, weight, **fit_params):
res = transformer.transform(X)
def _transform_one(transformer,
X,
y,
weight,
message_clsname='',
message=None,
**fit_params):
with _print_elapsed_time(message_clsname, message):
res = transformer.transform(X)
# if we have a weight for this transformer, multiply output
if weight is None:
return res
Expand Down Expand Up @@ -951,7 +1002,7 @@ def fit(self, X, y=None, **fit_params):
self : FeatureUnion
This estimator
"""
transformers = self._parallel_func(X, y, fit_params, _fit_one)
transformers = self._parallel_func(X, y, fit_params, _fit_one, True)
if not transformers:
# All transformers are None
return self
Expand All @@ -977,7 +1028,8 @@ def fit_transform(self, X, y=None, **fit_params):
hstack of results of transformers. sum_n_components is the
sum of n_components (output dimension) over transformers.
"""
results = self._parallel_func(X, y, fit_params, _fit_transform_one)
results = self._parallel_func(X, y, fit_params,
_fit_transform_one, True)
if not results:
# All transformers are None
return np.zeros((X.shape[0], 0))
Expand All @@ -987,12 +1039,13 @@ def fit_transform(self, X, y=None, **fit_params):

return self._hstack(Xs)

def _log_message(self, name, idx, total):
if not self.verbose:
def _log_message(self, name, idx, total, is_fitting=True):
if not self.verbose or (self.verbose < 10 and not is_fitting):
return None

return '(step %d of %d) Processing %s' % (idx, total, name)

def _parallel_func(self, X, y, fit_params, func):
def _parallel_func(self, X, y, fit_params, func, is_fitting):
"""Runs func in parallel on X and y"""
self.transformer_list = list(self.transformer_list)
self._validate_transformers()
Expand All @@ -1002,7 +1055,8 @@ def _parallel_func(self, X, y, fit_params, func):
return Parallel(n_jobs=self.n_jobs)(delayed(func)(
transformer, X, y, weight,
message_clsname='FeatureUnion',
message=self._log_message(name, idx, len(transformers)),
message=self._log_message(name, idx,
len(transformers), is_fitting),
**fit_params) for idx, (name, transformer,
weight) in enumerate(transformers, 1))

Expand All @@ -1021,9 +1075,7 @@ def transform(self, X):
hstack of results of transformers. sum_n_components is the
sum of n_components (output dimension) over transformers.
"""
Xs = Parallel(n_jobs=self.n_jobs)(
delayed(_transform_one)(trans, X, None, weight)
for name, trans, weight in self._iter())
Xs = self._parallel_func(X, None, {}, _transform_one, False)
if not Xs:
# All transformers are None
return np.zeros((X.shape[0], 0))
Expand Down
21 changes: 18 additions & 3 deletions sklearn/tests/test_pipeline.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1157,7 +1157,7 @@ def test_pipeline_param_error():
r'\[FeatureUnion\].*\(step 2 of 2\) Processing mult2.* total=.*\n$'),
(FeatureUnion([('mult1', 'drop'), ('mult2', Mult()), ('mult3', 'drop')]),
r'\[FeatureUnion\].*\(step 1 of 1\) Processing mult2.* total=.*\n$')
], ['fit', 'fit_transform', 'fit_predict'])
], ['fit', 'fit_transform', 'fit_predict', 'transform', 'predict'])
if hasattr(est, method) and not (
method == 'fit_transform' and hasattr(est, 'steps') and
isinstance(est.steps[-1][1], FitParamT))
Expand All @@ -1172,11 +1172,26 @@ def test_verbose(est, method, pattern, capsys):
y = [[7], [8]]

est.set_params(verbose=False)
func(X, y)
if method in ['transform', 'predict']:
est.fit(X, y)
func(X)
else:
func(X, y)
assert not capsys.readouterr().out, 'Got output for verbose=False'

est.set_params(verbose=True)
func(X, y)
if method in ['transform', 'predict']:
func(X)
assert not capsys.readouterr().out, 'Got output for verbose=False'
else:
func(X, y)
assert re.match(pattern, capsys.readouterr().out)

est.set_params(verbose=10)
if method in ['transform', 'predict']:
func(X)
else:
func(X, y)
assert re.match(pattern, capsys.readouterr().out)


Expand Down
0