def fit(self, X, pairs):
""" Fit model with specified loss.
Parameters
----------
X : scipy.sparse.csc_matrix, (n_samples, n_features)
y : float | ndarray, shape = (n_compares, 2)
Each row `i` defines a pair of samples such that
the first returns a high value then the second
FM(X[i,0]) > FM(X[i, 1]).
"""
# 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)
assert_all_finite(pairs)
pairs = pairs.astype(np.float64)
# check that pairs contain no real values
assert_array_equal(pairs, pairs.astype(np.int32))
assert pairs.max() <= X.shape[1]
assert pairs.min() >= 0
self.w0_, self.w_, self.V_ = ffm.ffm_fit_sgd_bpr(self, X, pairs)
return self
def fit(self, X, pairs):
""" Fit model with specified loss.
Parameters
----------
X : scipy.sparse.csc_matrix, (n_samples, n_features)
y : float | ndarray, shape = (n_compares, 2)
Each row `i` defines a pair of samples such that
the first returns a high value then the second
FM(X[i,0]) > FM(X[i, 1]).
"""
# 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)
assert_all_finite(pairs)
pairs = pairs.astype(np.float64)
# check that pairs contain no real values
assert_array_equal(pairs, pairs.astype(np.int32))
assert pairs.max() <= X.shape[1]
assert pairs.min() >= 0
self.w0_, self.w_, self.V_ = ffm.ffm_fit_sgd_bpr(self, X, pairs)
return self