Open simpsus opened 3 years ago
training is a pd.DataFrame features is a list of column names
model = AdaGradRegressor() model.fit(training[features], training['target']) throws ~\Anaconda3\envs\numerai\lib\site-packages\lightning\impl\adagrad.py in fit(self, X, y) 130 def fit(self, X, y): 131 self.outputs2d = len(y.shape) > 1 --> 132 Y = y.reshape(-1, 1) if not self.outputs2d else y 133 Y = Y.astype(np.float64) 134 return self._fit(X, Y)
~\Anaconda3\envs\numerai\lib\site-packages\pandas\core\generic.py in getattr(self, name) 5137 if self._info_axis._can_hold_identifiers_and_holds_name(name): 5138 return self[name] -> 5139 return object.getattribute(self, name) 5140 5141 def setattr(self, name: str, value) -> None:
AttributeError: 'Series' object has no attribute 'reshape'
when I pass training['target'].values it works. My normal scikit-learn workflow is to pass the pandas objects and that never is an issue.
model.predict(training[features]) has the same issue
training is a pd.DataFrame features is a list of column names
model = AdaGradRegressor() model.fit(training[features], training['target']) throws ~\Anaconda3\envs\numerai\lib\site-packages\lightning\impl\adagrad.py in fit(self, X, y) 130 def fit(self, X, y): 131 self.outputs2d = len(y.shape) > 1 --> 132 Y = y.reshape(-1, 1) if not self.outputs2d else y 133 Y = Y.astype(np.float64) 134 return self._fit(X, Y)
~\Anaconda3\envs\numerai\lib\site-packages\pandas\core\generic.py in getattr(self, name) 5137 if self._info_axis._can_hold_identifiers_and_holds_name(name): 5138 return self[name] -> 5139 return object.getattribute(self, name) 5140 5141 def setattr(self, name: str, value) -> None:
AttributeError: 'Series' object has no attribute 'reshape'
when I pass training['target'].values it works. My normal scikit-learn workflow is to pass the pandas objects and that never is an issue.
model.predict(training[features]) has the same issue