def test_save_prediction(self):
model = RandomForestClassifier()
model.id = get_model_id(model)
model.fit(self.iris.data, self.iris.target)
indexes = np.fromfunction(lambda x: x, (self.iris.data.shape[0], ), dtype=np.int32)
saving_predict_proba(model, self.iris.data, indexes)
os.remove('RandomForestClassifier_r0_N__m5_0p0__m4_2__m1_auto__m0_N__m3_1__m2_N__n0_10__b0_1__c1_gini__c0_N_190.csv')
def test_save_prediction(self):
model = RandomForestClassifier()
model.id = get_model_id(model)
model.fit(self.iris.data, self.iris.target)
indexes = np.fromfunction(lambda x: x, (self.iris.data.shape[0], ), dtype=np.int32)
saving_predict_proba(model, self.iris.data, indexes)
os.remove('RandomForestClassifier_r0_N__m5_0p0__m4_2__m1_auto__m0_N__m3_1__m2_N__n0_10__b0_1__c1_gini__c0_N_0_149.csv')
def test_save_prediction(self):
model = RandomForestClassifier()
model.id = get_model_id(model)
model.fit(self.iris.data, self.iris.target)
indexes = np.fromfunction(lambda x: x, (self.iris.data.shape[0], ), dtype=np.int32)
saving_predict_proba(model, self.iris.data, indexes)
any_file_removed = False
for filename in os.listdir('.'):
if filename.startswith('RandomForestClassifier'):
os.remove(filename)
any_file_removed = True
self.assertTrue(any_file_removed)
def _get_child_predict(self, clf, X, index=None):
if self.stack_by_proba and hasattr(clf, 'predict_proba'):
if self.save_stage0 and index is not None:
proba = util.saving_predict_proba(clf, X, index)
else:
proba = clf.predict_proba(X)
return proba[:, 1:]
elif hasattr(clf, 'predict'):
predict_result = clf.predict(X)
if isinstance(clf, ClassifierMixin):
lb = LabelBinarizer()
lb.fit(predict_result)
return lb.fit_transform(predict_result)
else:
return predict_result.reshape((predict_result.size, 1))
else:
return clf.fit_transform(X)
def _get_child_predict(self, clf, X, index=None):
if self.stack_by_proba and hasattr(clf, 'predict_proba'):
if self.save_stage0 and index is not None:
proba = util.saving_predict_proba(clf, X, index)
else:
proba = clf.predict_proba(X)
return proba[:, 1:]
elif hasattr(clf, 'predict'):
predict_result = clf.predict(X)
if isinstance(clf, ClassifierMixin):
lb = LabelBinarizer()
lb.fit(predict_result)
return lb.fit_transform(predict_result)
else:
return predict_result.reshape((predict_result.size, 1))
else:
return clf.fit_transform(X)
def predict_proba(self, xs_test, index=None):
"""Predict class probabilities for X.
The predicted class probabilities of an input sample is computed.
Parameters
----------
X : array-like or sparse matrix of shape = [n_samples, n_features]
The input samples.
Returns
-------
p : array of shape = [n_samples, n_classes].
The class probabilities of the input samples.
"""
return util.saving_predict_proba(self.estimator, xs_test, index,
self.cache_dir)
def predict_proba(self, xs_test, index=None):
"""Predict class probabilities for X.
The predicted class probabilities of an input sample is computed.
Parameters
----------
X : array-like or sparse matrix of shape = [n_samples, n_features]
The input samples.
Returns
-------
p : array of shape = [n_samples, n_classes].
The class probabilities of the input samples.
"""
return util.saving_predict_proba(self.estimator,
xs_test,
index,
self.cache_dir)