def test_isotonic_dtype():
y = [2, 1, 4, 3, 5]
weights = np.array([.9, .9, .9, .9, .9], dtype=np.float64)
reg = IsotonicRegression()
for dtype in (np.int32, np.int64, np.float32, np.float64):
for sample_weight in (None, weights.astype(np.float32), weights):
y_np = np.array(y, dtype=dtype)
expected_dtype = \
check_array(y_np, dtype=[np.float64, np.float32],
ensure_2d=False).dtype
res = isotonic_regression(y_np, sample_weight=sample_weight)
assert res.dtype == expected_dtype
X = np.arange(len(y)).astype(dtype)
reg.fit(X, y_np, sample_weight=sample_weight)
res = reg.predict(X)
assert res.dtype == expected_dtype
def predict(self, X):
X = check_array(X)
self.key = 1000
return np.ones(X.shape[0])
# Data
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('-e',
'--estimators',
nargs="+",
required=True,
choices=ESTIMATORS)
args = vars(parser.parse_args())
data_train = fetch_20newsgroups_vectorized(subset="train")
data_test = fetch_20newsgroups_vectorized(subset="test")
X_train = check_array(data_train.data,
dtype=np.float32,
accept_sparse="csc")
X_test = check_array(data_test.data, dtype=np.float32, accept_sparse="csr")
y_train = data_train.target
y_test = data_test.target
print("20 newsgroups")
print("=============")
print("X_train.shape = {0}".format(X_train.shape))
print("X_train.format = {0}".format(X_train.format))
print("X_train.dtype = {0}".format(X_train.dtype))
print("X_train density = {0}"
"".format(X_train.nnz / np.product(X_train.shape)))
print("y_train {0}".format(y_train.shape))
print("X_test {0}".format(X_test.shape))
print("X_test.format = {0}".format(X_test.format))
def fit(self, X, y=None):
self.X_shape_ = check_array(X).shape
return self
def transform(self, X):
X = check_array(X)
if X.shape[1] != self.X_shape_[1]:
raise ValueError('Bad number of features')
return sp.csr_matrix(X)
def predict(self, X):
# return 1 if X has more than one element else return 0
X = check_array(X)
if X.shape[0] > 1:
return np.ones(X.shape[0])
return np.zeros(X.shape[0])
def transform(self, X):
X = check_array(X)
return X
def fit(self, X, y=None):
X = check_array(X)
return self
def predict(self, X):
X = check_array(X)
return np.ones(X.shape[0])
def predict(self, X):
check_is_fitted(self)
X = check_array(X)
return np.ones(X.shape[0])