def decision_function(self, X):
"""Decision function of the linear model
Parameters
----------
X : numpy array of shape [n_samples, n_features]
Returns
-------
C : array, shape = [n_samples]
Returns predicted values.
"""
X = safe_asarray(X)
return safe_sparse_dot(X, self.coef_.T) + self.intercept_
def fit(self, X, y_clf, y_regression):
"""
Fit the multiclass model.
Parameters
----------
X : numpy array of shape [n_samples,n_features]
Training data
y_clf : numpy array of shape [n_samples]
Target classes for classification model
y_regression: numpy array of shape [n_samples]
Target values for regression model
Returns
-------
self : returns an instance of self.
"""
X = safe_asarray(X)
y_clf = np.asarray(y_clf)
y_regression = np.asarray(y_regression)
self.clf_model = self.clf.fit(X, y_clf)
classes = set(y_clf)
regr = self.regr
def _generator():
for class_ in classes:
examples = y_clf == class_
yield class_, X[examples], y_regression[examples], regr
out = Parallel(self.n_jobs, self.verbose, self.pre_dispatch)(\
delayed(_fit_helper)(*params) for params in _generator())
self.regression_models = {}
for class_, regr_model in out:
self.regression_models[class_] = regr_model
return self
def fit(self, X, y_clf, y_regression):
"""
Fit the multiclass model.
Parameters
----------
X : numpy array of shape [n_samples,n_features]
Training data
y_clf : numpy array of shape [n_samples]
Target classes for classification model
y_regression: numpy array of shape [n_samples]
Target values for regression model
Returns
-------
self : returns an instance of self.
"""
X = safe_asarray(X)
y_clf = np.asarray(y_clf)
y_regression = np.asarray(y_regression)
self.clf_model = self.clf.fit(X, y_clf)
classes = set(y_clf)
regr = self.regr
def _generator():
for class_ in classes:
examples = y_clf == class_
yield class_, X[examples], y_regression[examples], regr
out = Parallel(self.n_jobs, self.verbose, self.pre_dispatch)(\
delayed(_fit_helper)(*params) for params in _generator())
self.regression_models = {}
for class_, regr_model in out:
self.regression_models[class_] = regr_model
return self
def predict(self, X, return_class_prediction=False):
"""
Predict using the muticlass regression model
Parameters
----------
X : numpy array of shape [n_samples, n_features]
Returns
-------
C : array, shape = [n_samples]
Returns predicted values.
"""
X = safe_asarray(X)
y_clf_predicted = np.asarray(self.clf_model.predict(X))
classes = set(y_clf_predicted)
def _generator():
for class_ in classes:
examples = y_clf_predicted == class_
yield examples, X[examples], self.regression_models[class_]
out = Parallel(self.n_jobs, self.verbose, self.pre_dispatch)(\
delayed(_predict_helper)(*params) for params in _generator())
y_regr_predicted = None
for examples, predicted in out:
if y_regr_predicted is None:
y_regr_predicted = np.zeros(X.shape[0], predicted.dtype)
y_regr_predicted[examples] = predicted
if return_class_prediction:
return y_clf_predicted, y_regr_predicted
else:
return y_regr_predicted
def predict(self, X, return_class_prediction=False):
"""
Predict using the muticlass regression model
Parameters
----------
X : numpy array of shape [n_samples, n_features]
Returns
-------
C : array, shape = [n_samples]
Returns predicted values.
"""
X = safe_asarray(X)
y_clf_predicted = np.asarray(self.clf_model.predict(X))
classes = set(y_clf_predicted)
def _generator():
for class_ in classes:
examples = y_clf_predicted == class_
yield examples, X[examples], self.regression_models[class_]
out = Parallel(self.n_jobs, self.verbose, self.pre_dispatch)(\
delayed(_predict_helper)(*params) for params in _generator())
y_regr_predicted = None
for examples, predicted in out:
if y_regr_predicted is None:
y_regr_predicted = np.zeros(X.shape[0], predicted.dtype)
y_regr_predicted[examples] = predicted
if return_class_prediction:
return y_clf_predicted, y_regr_predicted
else:
return y_regr_predicted
def fit(self, X, y):
X = safe_asarray(X)
y = np.asarray(y)
X = (X.T / y).T
return super(RSELinearRegression, self).fit(X, y / y)
def fit(self, X, y):
X = safe_asarray(X)
y = np.asarray(y)
X = (X.T / y).T
return super(RSELinearRegression, self).fit(X, y / y)
