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add multiprocessing support #13

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add multiprocessing support #13

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78 changes: 57 additions & 21 deletions combo/models/classifier_stacking.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,6 +20,8 @@
from ..utils.utility import list_diff
from .base import BaseAggregator

from concurrent.futures import ProcessPoolExecutor, Future, wait


def split_datasets(X, y, n_folds=3, shuffle_data=False, random_state=None):
"""Utility function to split the data for stacking. The data is split
Expand Down Expand Up @@ -124,7 +126,7 @@ class Stacking(BaseAggregator):
def __init__(self, base_estimators, meta_clf=None, n_folds=2,
keep_original=True,
use_proba=False, shuffle_data=False, random_state=None,
threshold=None, pre_fitted=None):
threshold=None, pre_fitted=None, n_jobs=None):

super(Stacking, self).__init__(
base_estimators=base_estimators, pre_fitted=pre_fitted)
Expand All @@ -147,6 +149,11 @@ def __init__(self, base_estimators, meta_clf=None, n_folds=2,
self.shuffle_data = shuffle_data

self.random_state = random_state
self.n_jobs = n_jobs
# if n_jobs is not None:
# self._pool = ProcessPoolExecutor(max_workers=n_jobs)
# else:
# self._pool = None

if threshold is not None:
warnings.warn(
Expand Down Expand Up @@ -183,26 +190,55 @@ def fit(self, X, y):
X, y, n_folds=self.n_folds, shuffle_data=self.shuffle_data,
random_state=self.random_state)

# iterate over all base classifiers
for i, clf in enumerate(self.base_estimators):
# iterate over all folds
for j in range(self.n_folds):
# build train and test index
full_idx = list(range(n_samples))
test_idx = index_lists[j]
train_idx = list_diff(full_idx, test_idx)
X_train, y_train = X_new[train_idx, :], y_new[train_idx]
X_test, y_test = X_new[test_idx, :], y_new[test_idx]

# train the classifier
clf.fit(X_train, y_train)

# generate the new features on the pseudo test set
if self.use_proba:
new_features[test_idx, i] = clf.predict_proba(
X_test)[:, 1]
else:
new_features[test_idx, i] = clf.predict(X_test)
if self.n_jobs is not None:
futures_map = dict()
pool = ProcessPoolExecutor(self.n_jobs)
for i, clf in enumerate(self.base_estimators):
# iterate over all folds
for j in range(self.n_folds):
# build train and test index
full_idx = list(range(n_samples))
test_idx = index_lists[j]
train_idx = list_diff(full_idx, test_idx)
X_train, y_train = X_new[train_idx, :], y_new[train_idx]
X_test, y_test = X_new[test_idx, :], y_new[test_idx]

fut: Future = pool.submit(clf.fit, X_train, y_train)
futures_map[(i, j)] = fut

def callback(fut):
if self.use_proba:
new_features[test_idx, i] = \
fut.result().predict_proba(X_test)[:, 1]
else:
new_features[test_idx, i] = \
fut.result().predict(X_test)
print(fut.result())

fut.add_done_callback(callback)

wait(futures_map.values())
else:
# iterate over all base classifiers
for i, clf in enumerate(self.base_estimators):
# iterate over all folds
for j in range(self.n_folds):
# build train and test index
full_idx = list(range(n_samples))
test_idx = index_lists[j]
train_idx = list_diff(full_idx, test_idx)
X_train, y_train = X_new[train_idx, :], y_new[train_idx]
X_test, y_test = X_new[test_idx, :], y_new[test_idx]

# train the classifier
clf.fit(X_train, y_train)

# generate the new features on the pseudo test set
if self.use_proba:
new_features[test_idx, i] = clf.predict_proba(
X_test)[:, 1]
else:
new_features[test_idx, i] = clf.predict(X_test)

# build the new dataset for training
if self.keep_original:
Expand Down