def __init__( self, X, y=None, device=None, length=None, ): # TODO: Remove warning in release 0.4 if device is not None: warnings.warn( "device is no longer needed by Dataset and will be ignored.", DeprecationWarning) self.X = X self.y = y self.X_indexing = check_indexing(X) self.y_indexing = check_indexing(y) self.X_is_ndframe = is_pandas_ndframe(X) if length is not None: self._len = length return # pylint: disable=invalid-name len_X = get_len(X) if y is not None: len_y = get_len(y) if len_y != len_X: raise ValueError("X and y have inconsistent lengths.") self._len = len_X
def on_train_begin(self, net, X, y, **kwargs): self.X_indexing_ = check_indexing(X) self.y_indexing_ = check_indexing(y) # Looks for the right most index where `*_best` is True # That index is used to get the best score in `net.history` with suppress(ValueError, IndexError, KeyError): best_name_history = net.history[:, '{}_best'.format(self.name_)] idx_best_reverse = best_name_history[::-1].index(True) idx_best = len(best_name_history) - idx_best_reverse - 1 self.best_score_ = net.history[idx_best, self.name_]
def __init__( self, X, y=None, length=None, ): self.X = X self.y = y self.X_indexing = check_indexing(X) self.y_indexing = check_indexing(y) self.X_is_ndframe = is_pandas_ndframe(X) if length is not None: self._len = length return # pylint: disable=invalid-name len_X = get_len(X) if y is not None: len_y = get_len(y) if len_y != len_X: raise ValueError("X and y have inconsistent lengths.") self._len = len_X
def on_train_begin(self, net, X, y, **kwargs): self.X_indexing_ = check_indexing(X) self.y_indexing_ = check_indexing(y)