def make_user_item_regression(random_state=123, n_user=20, n_item=20,
label_stdev=0.4, rank=2, bias=True,
first_order=True, stdev_w0=.2, stdev_w=0.3,
stdev_V=0.4, mean_w0=2, mean_w=5, mean_V=10):
n_features = n_user + n_item
n_samples = n_user * n_item
# create design matrix
user_cols = np.repeat(range(n_user), n_item)
item_cols = np.array(list(range(n_item)) * n_user) + n_user
cols = np.hstack((user_cols, item_cols))
rows = np.hstack((np.arange(n_item*n_user), np.arange(n_item*n_user)))
X = sp.coo_matrix((np.ones_like(cols, dtype=np.float64), (rows, cols)))
X = sp.csc_matrix(X)
assert X.shape[0] == n_samples
assert X.shape[1] == n_features
# sample the model parameter
random_state = check_random_state(random_state)
w0 = random_state.normal(mean_w0, stdev_w0)
w = random_state.normal(mean_w, stdev_w, n_features)
V = random_state.normal(mean_V, stdev_V, (rank, n_features))
y = ffm_predict(w0, w, V, X)
if label_stdev > 0:
y = random_state.normal(y, label_stdev)
return X, y, (w0, w, V)
def make_user_item_regression(random_state=123, n_user=20, n_item=20,
label_stdev=0.4, rank=2, bias=True,
first_order=True, stdev_w0=.2, stdev_w=0.3,
stdev_V=0.4, mean_w0=2, mean_w=5, mean_V=10):
n_features = n_user + n_item
n_samples = n_user * n_item
# create design matrix
user_cols = np.repeat(range(n_user), n_item)
item_cols = np.array(range(n_item) * n_user) + n_user
cols = np.hstack((user_cols, item_cols))
rows = np.hstack((np.arange(n_item*n_user), np.arange(n_item*n_user)))
X = sp.coo_matrix((np.ones_like(cols, dtype=np.float64), (rows, cols)))
X = sp.csc_matrix(X)
assert X.shape[0] == n_samples
assert X.shape[1] == n_features
# sample the model parameter
random_state = check_random_state(random_state)
w0 = random_state.normal(mean_w0, stdev_w0)
w = random_state.normal(mean_w, stdev_w, n_features)
V = random_state.normal(mean_V, stdev_V, (rank, n_features))
y = ffm_predict(w0, w, V, X)
if label_stdev > 0:
y = random_state.normal(y, label_stdev)
return X, y, (w0, w, V)
def predict(self, X_test):
""" Return predictions
Parameters
----------
X : scipy.sparse.csc_matrix, (n_samples, n_features)
Returns
------
T : array, shape (n_samples)
The labels are returned for classification.
"""
X_test = check_array(X_test, accept_sparse="csc", dtype=np.float64, order="F")
assert sp.isspmatrix_csc(X_test)
assert X_test.shape[1] == len(self.w_)
return ffm.ffm_predict(self.w0_, self.w_, self.V_, X_test)
def predict(self, X_test):
""" Return predictions
Parameters
----------
X : scipy.sparse.csc_matrix, (n_samples, n_features)
Returns
------
T : array, shape (n_samples)
The labels are returned for classification.
"""
X_test = check_array(X_test, accept_sparse="csc", dtype=np.float64,
order="F")
assert sp.isspmatrix_csc(X_test)
assert X_test.shape[1] == len(self.w_)
return ffm.ffm_predict(self.w0_, self.w_, self.V_, X_test)
def test_ffm_predict():
w0, w, V, y, X = get_test_problem()
y_pred = ffm.ffm_predict(w0, w, V, X)
assert_equal(y_pred, y)
def test_ffm_predict():
w0, w, V, y, X = get_test_problem()
y_pred = ffm.ffm_predict(w0, w, V, X)
assert_equal(y_pred, y)