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_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