def fit(self, X, y=None):
"""Fit the CategoricalEncoder to X.
Parameters
----------
X : array-like, shape [n_samples, n_features]
The data to determine the categories of each feature.
Returns
-------
self
"""
X = self._check_X(X)
if self.handle_unknown not in ["error", "ignore"]:
template = "handle_unknown should be either 'error' or " "'ignore', got %s"
raise ValueError(template % self.handle_unknown)
if (self.hashing_dim
is not None) and (not isinstance(self.hashing_dim, int)):
raise ValueError("value '%r' was specified for hashing_dim, "
"which has invalid type, expected None or "
"int." % self.hashing_dim)
if self.categories not in ["auto", "most_frequent", "k-means"]:
for cats in self.categories:
if not np.all(np.sort(cats) == np.array(cats)):
raise ValueError("Unsorted categories are not yet "
"supported")
n_samples, n_features = X.shape
self.categories_ = list()
self.random_state_ = check_random_state(self.random_state)
for i in range(n_features):
Xi = X[:, i]
if self.categories == "auto":
self.categories_.append(np.unique(Xi))
elif self.categories == "most_frequent":
self.categories_.append(self.get_most_frequent(Xi))
elif self.categories == "k-means":
uniques, count = np.unique(Xi, return_counts=True)
self.categories_.append(
get_kmeans_prototypes(
uniques,
self.n_prototypes,
sample_weight=count,
random_state=self.random_state_,
))
else:
if self.handle_unknown == "error":
valid_mask = np.in1d(Xi, self.categories[i])
if not np.all(valid_mask):
diff = np.unique(Xi[~valid_mask])
msg = "Found unknown categories {0} in column {1}" " during fit".format(
diff, i)
raise ValueError(msg)
self.categories_.append(
np.array(self.categories[i], dtype=object))
return self
def test_kmeans_protoypes():
X_test = np.array(['cbbba', 'baaac', 'accc'])
proto = get_kmeans_prototypes(X_test, 3)
assert np.array_equal(np.sort(proto), np.sort(X_test))
