def fit(self, X, y):
assert self.__check_valid(X, y), 'input is invalid.'
if self._is_trained is False:
self._nFeat = X.shape[1]
if self._max_depth is None:
self._max_depth = self._nFeat
if self._is_prune is True:
spliter = ShuffleSpliter(X.shape[0], ratio=0.1)
train_ix, val_ix = spliter.split()
Xtrain, ytrain = X[train_ix], y[train_ix]
Xval, yval = X[val_ix], y[val_ix]
self._parameter['tree'] = self._build_tree(Xtrain, ytrain, depth=0)
self._tree_prune(self._parameter['tree'], Xval, yval)
else:
self._parameter['tree'] = self._build_tree(X, y, depth=0)
self._is_trained = True
def fit(self, X, y):
assert self.__check_valid(X, y), 'input is invalid.'
if self._is_trained is False:
self._nFeat = X.shape[1]
self._class_label = list(np.unique(y))
self._class_label.sort()
self._nClass = len(self._class_label)
if self._is_prune is True:
spliter = ShuffleSpliter(X.shape[0], ratio=0.1)
train_ix, val_ix = spliter.split()
Xtrain, ytrain = X[train_ix], y[train_ix]
Xval, yval = X[val_ix], y[val_ix]
used_feat = set()
self._parameter['tree'] = self._build_tree(Xtrain, ytrain, used_feat)
self._tree_prune(self._parameter['tree'], Xval, yval)
else:
used_feat = set()
self._parameter['tree'] = self._build_tree(X, y, used_feat)
self._is_trained = True
def fit(self, X, y):
assert self.__check_valid(X, y), 'input is invalid.'
if self._is_trained is False:
self._nFeat = X.shape[1]
self._class_label = list(np.unique(y))
self._class_label.sort()
self._nClass = len(self._class_label)
if self._is_prune is True:
spliter = ShuffleSpliter(X.shape[0], ratio=0.1)
train_ix, val_ix = spliter.split()
Xtrain, ytrain = X[train_ix], y[train_ix]
Xval, yval = X[val_ix], y[val_ix]
used_feat = set()
self._parameter['tree'] = self._build_tree(Xtrain, ytrain,
used_feat)
self._tree_prune(self._parameter['tree'], Xval, yval)
else:
used_feat = set()
self._parameter['tree'] = self._build_tree(X, y, used_feat)
self._is_trained = True
