def fit(self, X, y=None):
X = infer_feature_types(X)
if not is_all_numeric(X):
raise ValueError("PCA input must be all numeric")
X = _convert_woodwork_types_wrapper(X.to_dataframe())
self._component_obj.fit(X)
return self
def transform(self, X, y=None):
X_ww = infer_feature_types(X)
if not is_all_numeric(X_ww):
raise ValueError("LDA input must be all numeric")
X = _convert_woodwork_types_wrapper(X_ww.to_dataframe())
X_t = self._component_obj.transform(X)
X_t = pd.DataFrame(X_t, index=X.index, columns=[f"component_{i}" for i in range(X_t.shape[1])])
return _retain_custom_types_and_initalize_woodwork(X_ww, X_t)
def fit(self, X, y):
X = infer_feature_types(X)
if not is_all_numeric(X):
raise ValueError("LDA input must be all numeric")
y = infer_feature_types(y)
X = _convert_woodwork_types_wrapper(X.to_dataframe())
y = _convert_woodwork_types_wrapper(y.to_series())
n_features = X.shape[1]
n_classes = y.nunique()
n_components = self.parameters['n_components']
if n_components is not None and n_components > min(n_classes, n_features):
raise ValueError(f"n_components value {n_components} is too large")
self._component_obj.fit(X, y)
return self