def fit_predict(self, X_train, y_train, X_test, n_more_iter=0):
"""Return average of posterior estimates of the test samples.
Parameters
----------
X_train : scipy.sparse.csc_matrix, (n_samples, n_features)
y_train : array, shape (n_samples)
X_test : scipy.sparse.csc_matrix, (n_test_samples, n_features)
n_more_iter : int
Number of iterations to continue from the current Coefficients.
Returns
-------
T : array, shape (n_test_samples)
"""
self.task = "regression"
X_train, y_train, X_test = _validate_mcmc_fit_input(X_train, y_train,
X_test)
self.n_iter = self.n_iter + n_more_iter
if n_more_iter > 0:
_check_warm_start(self, X_train)
assert self.prediction_.shape[0] == X_test.shape[0]
assert self.hyper_param_.shape
self.warm_start = True
else:
self.iter_count = 0
coef, y_pred = ffm.ffm_mcmc_fit_predict(self, X_train,
X_test, y_train)
self.w0_, self.w_, self.V_ = coef
self.prediction_ = y_pred
self.warm_start = False
if self.iter_count != 0:
self.iter_count = self.iter_count + n_more_iter
else:
self.iter_count = self.n_iter
return y_pred
def fit_predict(self, X_train, y_train, X_test, n_more_iter=0):
"""Return average of posterior estimates of the test samples.
Parameters
----------
X_train : scipy.sparse.csc_matrix, (n_samples, n_features)
y_train : array, shape (n_samples)
X_test : scipy.sparse.csc_matrix, (n_test_samples, n_features)
n_more_iter : int
Number of iterations to continue from the current Coefficients.
Returns
------
T : array, shape (n_test_samples)
"""
self.task = "regression"
X_train, y_train, X_test = _validate_mcmc_fit_input(
X_train, y_train, X_test)
self.n_iter = self.n_iter + n_more_iter
if n_more_iter > 0:
_check_warm_start(self, X_train)
assert self.prediction_.shape[0] == X_test.shape[0]
assert self.hyper_param_.shape
self.warm_start = True
else:
self.iter_count = 0
coef, y_pred = ffm.ffm_mcmc_fit_predict(self, X_train, X_test, y_train)
self.w0_, self.w_, self.V_ = coef
self.prediction_ = y_pred
self.warm_start = False
if self.iter_count != 0:
self.iter_count = self.iter_count + n_more_iter
else:
self.iter_count = self.n_iter
return y_pred
def fit_predict_proba(self, X_train, y_train, X_test):
"""Return average class probabilities of posterior estimates of the
test samples.
Use only with MCMC!
Parameters
----------
X_train : scipy.sparse.csc_matrix, (n_samples, n_features)
y_train : array, shape (n_samples)
the targets have to be encodes as {-1, 1}.
X_test : scipy.sparse.csc_matrix, (n_test_samples, n_features)
Returns
-------
y_pred : array, shape (n_test_samples)
Returns probability estimates for the class with lowest
classification label.
"""
self.task = "classification"
self.classes_ = np.unique(y_train)
if len(self.classes_) != 2:
raise ValueError("This solver only supports binary classification"
" but the data contains"
" class: %r" % self.classes_)
# fastFM-core expects labels to be in {-1,1}
y_train = y_train.copy()
i_class1 = (y_train == self.classes_[0])
y_train[i_class1] = -1
y_train[~i_class1] = 1
X_train, y_train, X_test = _validate_mcmc_fit_input(X_train, y_train,
X_test)
y_train = _validate_class_labels(y_train)
coef, y_pred = ffm.ffm_mcmc_fit_predict(self, X_train,
X_test, y_train)
self.w0_, self.w_, self.V_ = coef
return y_pred
def fit_predict_proba(self, X_train, y_train, X_test):
"""Return average class probabilities of posterior estimates of the
test samples.
Use only with MCMC!
Parameters
----------
X_train : scipy.sparse.csc_matrix, (n_samples, n_features)
y_train : array, shape (n_samples)
the targets have to be encodes as {-1, 1}.
X_test : scipy.sparse.csc_matrix, (n_test_samples, n_features)
Returns
------
y_pred : array, shape (n_test_samples)
Returns probability estimates for the class with lowest
classification label.
"""
self.task = "classification"
self.classes_ = np.unique(y_train)
if len(self.classes_) != 2:
raise ValueError("This solver only supports binary classification"
" but the data contains"
" class: %r" % self.classes_)
# fastFM-core expects labels to be in {-1,1}
y_train = y_train.copy()
i_class1 = (y_train == self.classes_[0])
y_train[i_class1] = -1
y_train[-i_class1] = 1
X_train, y_train, X_test = _validate_mcmc_fit_input(
X_train, y_train, X_test)
y_train = _validate_class_labels(y_train)
coef, y_pred = ffm.ffm_mcmc_fit_predict(self, X_train, X_test, y_train)
self.w0_, self.w_, self.V_ = coef
return y_pred
