def fit(self, X_train, y_train):
""" Fit model with specified loss.
Parameters
----------
X : scipy.sparse.csc_matrix, (n_samples, n_features)
y : float | ndarray, shape = (n_samples, )
the targets have to be encodes as {-1, 1}.
"""
check_consistent_length(X_train, y_train)
X_train = check_array(X_train,
accept_sparse="csc",
dtype=np.float64,
order="F")
y_train = _validate_class_labels(y_train)
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
self.w0_, self.w_, self.V_ = ffm.ffm_als_fit(self, X_train, y_train)
return self
def fit(self, X_train, y_train, n_more_iter=0):
""" Fit model with specified loss.
Parameters
----------
X : scipy.sparse.csc_matrix, (n_samples, n_features)
y : float | ndarray, shape = (n_samples, )
n_more_iter : int
Number of iterations to continue from the current Coefficients.
"""
check_consistent_length(X_train, y_train)
y_train = check_array(y_train, ensure_2d=False, dtype=np.float64)
X_train = check_array(X_train, accept_sparse="csc", dtype=np.float64,
order="F")
self.n_iter = self.n_iter + n_more_iter
if n_more_iter > 0:
_check_warm_start(self, X_train)
self.warm_start = True
self.w0_, self.w_, self.V_ = ffm.ffm_als_fit(self, X_train, y_train)
if self.iter_count != 0:
self.iter_count = self.iter_count + n_more_iter
else:
self.iter_count = self.n_iter
# reset to default setting
self.warm_start = False
return self
def fit(self, X_train, y_train):
""" Fit model with specified loss.
Parameters
----------
X : scipy.sparse.csc_matrix, (n_samples, n_features)
y : float | ndarray, shape = (n_samples, )
the targets have to be encodes as {-1, 1}.
"""
check_consistent_length(X_train, y_train)
X_train = check_array(X_train, accept_sparse="csc", dtype=np.float64,
order="F")
y_train = _validate_class_labels(y_train)
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
self.w0_, self.w_, self.V_ = ffm.ffm_als_fit(self, X_train, y_train)
return self
def fit(self, X_train, y_train, n_more_iter=0):
""" Fit model with specified loss.
Parameters
----------
X : scipy.sparse.csc_matrix, (n_samples, n_features)
y : float | ndarray, shape = (n_samples, )
the targets have to be encodes as {-1, 1}.
n_more_iter : int
Number of iterations to continue from the current Coefficients.
"""
check_consistent_length(X_train, y_train)
X_train = check_array(X_train,
accept_sparse="csc",
dtype=np.float64,
order="F")
y_train = _validate_class_labels(y_train)
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
self.n_iter = self.n_iter + n_more_iter
if n_more_iter > 0:
_check_warm_start(self, X_train)
self.warm_start = True
self.w0_, self.w_, self.V_ = ffm.ffm_als_fit(self, X_train, y_train)
if self.iter_count != 0:
self.iter_count = self.iter_count + n_more_iter
else:
self.iter_count = self.n_iter
# reset to default setting
self.warm_start = False
return self
def fit(self, X_train, y_train, n_more_iter=0):
""" Fit model with specified loss.
Parameters
----------
X : scipy.sparse.csc_matrix, (n_samples, n_features)
y : float | ndarray, shape = (n_samples, )
the targets have to be encodes as {-1, 1}.
n_more_iter : int
Number of iterations to continue from the current Coefficients.
"""
check_consistent_length(X_train, y_train)
X_train = check_array(X_train, accept_sparse="csc", dtype=np.float64,
order="F")
y_train = _validate_class_labels(y_train)
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
self.n_iter = self.n_iter + n_more_iter
if n_more_iter > 0:
_check_warm_start(self, X_train)
self.warm_start = True
self.w0_, self.w_, self.V_ = ffm.ffm_als_fit(self, X_train, y_train)
if self.iter_count != 0:
self.iter_count = self.iter_count + n_more_iter
else:
self.iter_count = self.n_iter
# reset to default setting
self.warm_start = False
return self
