def __call__(self, X, y, *args, **kwargs):
if None in (self.function, self.gradient):
raise NotBuilt("you must build the optimizer before calling it")
if not check_dataset_consistency(X, y):
raise InvalidInput(
"the features set and target set must have as many rows")
X, y = shuffle_dataset(X, y)
m = y.shape[0]
self.parameters = np.zeros((X.shape[1], 1))
for _ in range(self.iterations):
for i in range(0, m, self.batch_size):
self.parameters = self.parameters - (
self.learning_rate / m) * self.gradient(
X[i:i + self.batch_size],
y[i:i + self.batch_size],
self.parameters,
*args,
**kwargs,
)
if self.is_history_enabled:
self.history.append((1 / self.batch_size) *
self.function(X, y, self.parameters))
return self.parameters
def fit(self, X, y):
"""Fit the model."""
X = features_reshape(X)
if not check_dataset_consistency(X, y):
raise InvalidInput("the features set and target set must have as many rows")
if self.standardize is not None:
X = self.standardize(X)
X = np.concatenate((np.ones((X.shape[0], 1)), X), axis=1)
self.optimizer(X, y)
def fit(self, X, y):
"""Fit the model."""
X = features_reshape(X)
if not check_dataset_consistency(X, y):
raise InvalidInput(
"the features set and target set must have as many rows")
if self.standardize is not None:
X = self.standardize(X)
X = np.concatenate((np.ones((X.shape[0], 1)), X), axis=1)
self.labels = np.unique(y)
n_labels = np.size(self.labels)
if n_labels < 2:
raise InvalidInput(
"target must have at least two different classes")
elif n_labels == 2:
self.optimizer(X, y)
else:
self.optimizer(X, (y == self.labels).astype(int))
def test_check_dataset_consistency_invalid():
"""Test of `check_dataset_consistency` with invalid input ."""
X = np.arange(9).reshape(3, 3)
y = np.arange(4).reshape(2, 2)
assert not check_dataset_consistency(X, y)
def test_check_dataset_consistency_valid():
"""Test of `check_dataset_consistency` with valid input."""
X = np.arange(9).reshape(3, 3)
y = np.arange(3).reshape(3, 1)
assert check_dataset_consistency(X, y)