def _compute_targets_distribution(self, conditions):
"""Given conditions dict {feature_col:val}, returns the
distribution and (class mapping for lookup) of the random label
self.targets|conditions.
"""
if not set(self.conditions).issubset(set(conditions.keys())):
raise BLE(ValueError(
'Must specify values for all the conditionals.\n'
'Received: {}\n'
'Expected: {}'.format(conditions, self.conditions_numerical +
self.conditions_categorical)))
# Are there any category values in conditions which never appeared during
# training? If yes, we need to run the partial RF.
unseen = any([conditions[cat] not in self.categories_to_val_map[cat]
for cat in self.conditions_categorical])
X_numerical = [conditions[col] for col in self.conditions_numerical]
if unseen:
distribution = self.rf_partial.predict_proba(X_numerical)
classes = self.rf_partial.classes_
else:
X_categorical = [conditions[col] for col in
self.conditions_categorical]
X_categorical = utils.binarize_categorical_row(
self.conditions_categorical, self.categories_to_val_map,
X_categorical)
distribution = self.rf_full.predict_proba(
np.hstack((X_numerical, X_categorical)))
classes = self.rf_partial.classes_
return distribution[0], classes
def _compute_targets_distribution(self, conditions):
"""Given conditions dict {feature_col:val}, returns the
distribution and (class mapping for lookup) of the random label
self.targets|conditions.
"""
if not set(self.conditions).issubset(set(conditions.keys())):
raise BLE(
ValueError('Must specify values for all the conditionals.\n'
'Received: {}\n'
'Expected: {}'.format(
conditions, self.conditions_numerical +
self.conditions_categorical)))
# Are there any category values in conditions which never appeared during
# training? If yes, we need to run the partial RF.
unseen = any([
conditions[cat] not in self.categories_to_val_map[cat]
for cat in self.conditions_categorical
])
X_numerical = [conditions[col] for col in self.conditions_numerical]
if unseen:
distribution = self.rf_partial.predict_proba(X_numerical)
classes = self.rf_partial.classes_
else:
X_categorical = [
conditions[col] for col in self.conditions_categorical
]
X_categorical = utils.binarize_categorical_row(
self.conditions_categorical, self.categories_to_val_map,
X_categorical)
distribution = self.rf_full.predict_proba(
np.hstack((X_numerical, X_categorical)))
classes = self.rf_partial.classes_
return distribution[0], classes
def _compute_targets_distribution(self, conditions):
"""Given conditions dict {feature_col:val}, returns the conditional
mean of the `targets`, and the scale of the Gaussian noise.
"""
if not set(self.conditions).issubset(set(conditions.keys())):
raise ValueError('Must specify values for all the conditionals.\n'
'Received: {}\n'
'Expected: {}'.format(conditions, self.conditions_numerical +
self.conditions_categorical))
# Are there any category values in conditions which never appeared during
# training? If yes, we need to run the partial RF.
unseen = any([conditions[cat] not in self.categories_to_val_map[cat]
for cat in self.conditions_categorical])
X_numerical = [conditions[col] for col in self.conditions_numerical]
if unseen:
inputs = np.array([X_numerical])
assert inputs.shape == (1, len(self.conditions_numerical))
predictions = self.mr_partial.predict(inputs)
noise = self.mr_partial_noise
else:
X_categorical = [conditions[col] for col in
self.conditions_categorical]
X_categorical = utils.binarize_categorical_row(
self.conditions_categorical, self.categories_to_val_map,
X_categorical)
inputs = np.concatenate(([X_numerical], [X_categorical]), axis=1)
assert inputs.shape == \
(1, len(self.conditions_numerical) + len(X_categorical))
predictions = self.mr_full.predict(inputs)
noise = self.mr_full_noise
return predictions[0], noise
def _compute_targets_distribution(self, conditions):
"""Given conditions dict {feature_col:val}, returns the conditional
mean of the `targets`, and the scale of the Gaussian noise.
"""
if not set(self.conditions).issubset(set(conditions.keys())):
raise BLE(
ValueError('Must specify values for all the conditionals.\n'
'Received: {}\n'
'Expected: {}'.format(
conditions, self.conditions_numerical +
self.conditions_categorical)))
# Are there any category values in conditions which never appeared during
# training? If yes, we need to run the partial RF.
unseen = any([
conditions[cat] not in self.categories_to_val_map[cat]
for cat in self.conditions_categorical
])
X_numerical = [conditions[col] for col in self.conditions_numerical]
if unseen:
inputs = np.array([X_numerical])
assert inputs.shape == (1, len(self.conditions_numerical))
predictions = self.mr_partial.predict(inputs)
noise = self.mr_partial_noise
else:
X_categorical = [
conditions[col] for col in self.conditions_categorical
]
X_categorical = utils.binarize_categorical_row(
self.conditions_categorical, self.categories_to_val_map,
X_categorical)
inputs = np.concatenate(([X_numerical], [X_categorical]), axis=1)
assert inputs.shape == \
(1, len(self.conditions_numerical) + len(X_categorical))
predictions = self.mr_full.predict(inputs)
noise = self.mr_full_noise
return predictions[0], noise
