def score(self, X, y, objectives):
"""Evaluate model performance on current and additional objectives.
Arguments:
X (ww.DataTable, pd.DataFrame or np.ndarray): Data of shape [n_samples, n_features]
y (pd.Series, ww.DataColumn): True labels of length [n_samples]
objectives (list): Non-empty list of objectives to score on
Returns:
dict: Ordered dictionary of objective scores
"""
# Only converting X for the call to _score_all_objectives
if X is None:
X = pd.DataFrame()
X = infer_feature_types(X)
X = _convert_woodwork_types_wrapper(X.to_dataframe())
y = infer_feature_types(y)
y = _convert_woodwork_types_wrapper(y.to_series())
y_predicted = self.predict(X, y)
y_predicted = _convert_woodwork_types_wrapper(y_predicted.to_series())
y_shifted = y.shift(-self.gap)
objectives = self.create_objectives(objectives)
y_shifted, y_predicted = drop_rows_with_nans(y_shifted, y_predicted)
return self._score_all_objectives(X,
y_shifted,
y_predicted,
y_pred_proba=None,
objectives=objectives)
def fit(self, X, y):
"""Fit a time series regression pipeline.
Arguments:
X (ww.DataTable, pd.DataFrame or np.ndarray): The input training data of shape [n_samples, n_features]
y (ww.DataColumn, pd.Series, np.ndarray): The target training targets of length [n_samples]
Returns:
self
"""
if X is None:
X = pd.DataFrame()
X = infer_feature_types(X)
y = infer_feature_types(y)
X = _convert_woodwork_types_wrapper(X.to_dataframe())
y = _convert_woodwork_types_wrapper(y.to_series())
X_t = self._compute_features_during_fit(X, y)
X_t = X_t.to_dataframe()
y_shifted = y.shift(-self.gap)
X_t, y_shifted = drop_rows_with_nans(X_t, y_shifted)
self.estimator.fit(X_t, y_shifted)
self.input_feature_names = self._component_graph.input_feature_names
return self
def predict(self, X, y=None, objective=None):
"""Make predictions using selected features.
Arguments:
X (ww.DataTable, pd.DataFrame, or np.ndarray): Data of shape [n_samples, n_features]
y (ww.DataColumn, pd.Series, np.ndarray, None): The target training targets of length [n_samples]
objective (Object or string): The objective to use to make predictions
Returns:
ww.DataColumn: Predicted values.
"""
if X is None:
X = pd.DataFrame()
X = infer_feature_types(X)
y = infer_feature_types(y)
X = _convert_woodwork_types_wrapper(X.to_dataframe())
y = _convert_woodwork_types_wrapper(y.to_series())
features = self.compute_estimator_features(X, y)
features = _convert_woodwork_types_wrapper(features.to_dataframe())
features_no_nan, y = drop_rows_with_nans(features, y)
y_arg = None
if self.estimator.predict_uses_y:
y_arg = y
predictions = self.estimator.predict(features_no_nan,
y_arg).to_series()
predictions = predictions.rename(self.input_target_name)
padded = pad_with_nans(
predictions, max(0, features.shape[0] - predictions.shape[0]))
return infer_feature_types(padded)
def _predict(self, X, y, objective=None, pad=False):
features = self.compute_estimator_features(X, y)
features = _convert_woodwork_types_wrapper(features.to_dataframe())
features_no_nan, y_no_nan = drop_rows_with_nans(features, y)
if objective is not None:
objective = get_objective(objective, return_instance=True)
if not objective.is_defined_for_problem_type(self.problem_type):
raise ValueError(
f"Objective {objective.name} is not defined for time series binary classification."
)
if self.threshold is None:
predictions = self._estimator_predict(features_no_nan,
y_no_nan).to_series()
else:
proba = self._estimator_predict_proba(features_no_nan,
y_no_nan).to_dataframe()
proba = proba.iloc[:, 1]
if objective is None:
predictions = proba > self.threshold
else:
predictions = objective.decision_function(
proba, threshold=self.threshold, X=features_no_nan)
if pad:
predictions = pad_with_nans(
predictions, max(0, features.shape[0] - predictions.shape[0]))
return infer_feature_types(predictions)
def score(self, X, y, objectives):
"""Evaluate model performance on current and additional objectives.
Arguments:
X (ww.DataTable, pd.DataFrame or np.ndarray): Data of shape [n_samples, n_features]
y (ww.DataColumn, pd.Series): True labels of length [n_samples]
objectives (list): Non-empty list of objectives to score on
Returns:
dict: Ordered dictionary of objective scores
"""
X, y = self._convert_to_woodwork(X, y)
X = _convert_woodwork_types_wrapper(X.to_dataframe())
y = _convert_woodwork_types_wrapper(y.to_series())
objectives = [
get_objective(o, return_instance=True) for o in objectives
]
y_encoded = self._encode_targets(y)
y_shifted = y_encoded.shift(-self.gap)
y_predicted, y_predicted_proba = self._compute_predictions(
X, y, objectives, time_series=True)
if y_predicted is not None:
y_predicted = _convert_woodwork_types_wrapper(
y_predicted.to_series())
if y_predicted_proba is not None:
y_predicted_proba = _convert_woodwork_types_wrapper(
y_predicted_proba.to_dataframe())
y_shifted, y_predicted, y_predicted_proba = drop_rows_with_nans(
y_shifted, y_predicted, y_predicted_proba)
return self._score_all_objectives(X,
y_shifted,
y_predicted,
y_pred_proba=y_predicted_proba,
objectives=objectives)
def _predict(self, X, y, objective=None, pad=False):
features = self.compute_estimator_features(X, y)
features = _convert_woodwork_types_wrapper(features.to_dataframe())
features_no_nan, y_no_nan = drop_rows_with_nans(features, y)
predictions = self._estimator_predict(features_no_nan, y_no_nan)
if pad:
padded = pad_with_nans(predictions.to_series(), max(0, features.shape[0] - predictions.shape[0]))
return infer_feature_types(padded)
return predictions
def predict_proba(self, X, y=None):
"""Make probability estimates for labels.
Arguments:
X (ww.DataTable, pd.DataFrame or np.ndarray): Data of shape [n_samples, n_features]
Returns:
ww.DataTable: Probability estimates
"""
X, y = self._convert_to_woodwork(X, y)
X = _convert_woodwork_types_wrapper(X.to_dataframe())
y = _convert_woodwork_types_wrapper(y.to_series())
y = self._encode_targets(y)
features = self.compute_estimator_features(X, y)
features = _convert_woodwork_types_wrapper(features.to_dataframe())
features_no_nan, y_no_nan = drop_rows_with_nans(features, y)
proba = self._estimator_predict_proba(features_no_nan, y_no_nan).to_dataframe()
proba.columns = self._encoder.classes_
padded = pad_with_nans(proba, max(0, features.shape[0] - proba.shape[0]))
return infer_feature_types(padded)
def fit(self, X, y):
"""Fit a time series classification pipeline.
Arguments:
X (ww.DataTable, pd.DataFrame or np.ndarray): The input training data of shape [n_samples, n_features]
y (ww.DataColumn, pd.Series, np.ndarray): The target training targets of length [n_samples]
Returns:
self
"""
X, y = self._convert_to_woodwork(X, y)
X = _convert_woodwork_types_wrapper(X.to_dataframe())
y = _convert_woodwork_types_wrapper(y.to_series())
self._encoder.fit(y)
y = self._encode_targets(y)
X_t = self._compute_features_during_fit(X, y)
X_t = _convert_woodwork_types_wrapper(X_t.to_dataframe())
y_shifted = y.shift(-self.gap)
X_t, y_shifted = drop_rows_with_nans(X_t, y_shifted)
self.estimator.fit(X_t, y_shifted)
return self
