fengsxy
commented
2 years ago About the reason, I find that y will be flattern in this code.But for example ExtraTree are supported for the muti-output regression.
assert (
isinstance(X_train_all, np.ndarray)
or issparse(X_train_all)
or isinstance(X_train_all, pd.DataFrame)
), (
"X_train_all must be a numpy array, a pandas dataframe, "
"or Scipy sparse matrix."
)
assert isinstance(y_train_all, np.ndarray) or isinstance(
y_train_all, pd.Series
), "y_train_all must be a numpy array or a pandas series."
assert (
X_train_all.size != 0 and y_train_all.size != 0
), "Input data must not be empty."
if isinstance(X_train_all, np.ndarray) and len(X_train_all.shape) == 1:
X_train_all = np.reshape(X_train_all, (X_train_all.size, 1))
if isinstance(y_train_all, np.ndarray):
**y_train_all = y_train_all.flatten()**
assert (
X_train_all.shape[0] == y_train_all.shape[0]
), "# rows in X_train must match length of y_train."
self._df = isinstance(X_train_all, pd.DataFrame)
self._nrow, self._ndim = X_train_all.shape
if self._state.task == TS_FORECAST:
X_train_all = pd.DataFrame(X_train_all)
assert (
X_train_all[X_train_all.columns[0]].dtype.name == "datetime64[ns]"
), f"For '{TS_FORECAST}' task, the first column must contain timestamp values."
X, y = X_train_all, y_train_all
elif dataframe is not None and label is not None:
assert isinstance(
dataframe, pd.DataFrame
), "dataframe must be a pandas DataFrame"
assert label in dataframe.columns, "label must a column name in dataframe"
self._df = True
if self._state.task == TS_FORECAST:
assert (
dataframe[dataframe.columns[0]].dtype.name == "datetime64[ns]"
), f"For '{TS_FORECAST}' task, the first column must contain timestamp values."
X = dataframe.drop(columns=label)
self._nrow, self._ndim = X.shape
y = dataframe[label]
else:
raise ValueError("either X_train+y_train or dataframe+label are required")`
sonichi
miquelduranfrigola
@sonichi Hi,sonchi. I have two things to share!
Today i am trying to use warm-start,but when i use muti-Reregssor with two out-put first it has some problems.
And with these time's thinking,i have a new a idea,for example ,like Extra-Tree,it needn't use muti-regressor,so we can check the data ,and choose the model which is naturaly adataped to muti-ouput-regreession task.
when i do a muti-output regression task,it says that
but i think the shape is matching.