def fit(self, X, y): """ 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}. """ y = _validate_class_labels(y) self.classes_ = np.unique(y) 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.copy() i_class1 = (y_train == self.classes_[0]) y_train[i_class1] = -1 y_train[-i_class1] = 1 check_consistent_length(X, y) y = y.astype(np.float64) X = X.T X = check_array(X, accept_sparse="csc", dtype=np.float64) self.w0_, self.w_, self.V_ = ffm.ffm_sgd_fit(self, X, y) return self
def fit(self, X, y, 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, ) """ check_consistent_length(X, y) y = check_array(y, ensure_2d=False, dtype=np.float64) # The sgd solver expects a transposed design matrix in column major # order (csc_matrix). X = X.T # creates a copy X = check_array(X, accept_sparse="csc", dtype=np.float64) self.n_iter = self.n_iter + n_more_iter if n_more_iter > 0: _check_warm_start(self, X.T) self.warm_start = True self.w0_, self.w_, self.V_ = ffm.ffm_sgd_fit(self, X, y) 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, y): """ Fit model with specified loss. Parameters ---------- X : scipy.sparse.csc_matrix, (n_samples, n_features) y : float | ndarray, shape = (n_samples, ) """ check_consistent_length(X, y) y = check_array(y, ensure_2d=False, dtype=np.float64) X = X.T X = check_array(X, accept_sparse="csc", dtype=np.float64) self.w0_, self.w_, self.V_ = ffm.ffm_sgd_fit(self, X, y) return self
def fit(self, X, y): """ Fit model with specified loss. Parameters ---------- X : scipy.sparse.csc_matrix, (n_samples, n_features) y : float | ndarray, shape = (n_samples, ) """ check_consistent_length(X, y) y = check_array(y, ensure_2d=False, dtype=np.float64) # The sgd solver expects a transposed design matrix in column major # order (csc_matrix). X = X.T # creates a copy X = check_array(X, accept_sparse="csc", dtype=np.float64) self.w0_, self.w_, self.V_ = ffm.ffm_sgd_fit(self, X, y) return self