Hello everyone
I am running XGBoost in Google colab Pro environment
I use the following parameter settings
BorutaPy(alpha=0.05,
estimator=XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1, colsample_bynode=1,
colsample_bytree=1, gamma=0, learning_rate=0.1,
max_delta_step=0, max_depth=3,
min_child_weight=1, missing=None,
n_estimators=100, n_jobs=-1, nthread=None,
objective='binary:logistic', random_state=0,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
seed=None, silent=None, subsample=1,
verbosity=1),
max_iter=100, n_estimators='auto', perc=100, random_state=1,
two_step=True, verbose=2)
When I call the fit method XGBoost throwing value exception
find all relevant features
feat_selector.fit(X_train, y_train)
XGBoostError Traceback (most recent call last)
/usr/local/lib/python3.7/dist-packages/boruta/boruta_py.py in _get_imp(self, X, y)
383 try:
–> 384 self.estimator.fit(X, y)
385 except Exception as e:
9 frames
/usr/local/lib/python3.7/dist-packages/xgboost/sklearn.py in fit(self, X, y, sample_weight, eval_set, eval_metric, early_stopping_rounds, verbose, xgb_model, sample_weight_eval_set, callbacks)
731 verbose_eval=verbose, xgb_model=xgb_model,
–> 732 callbacks=callbacks)
733
/usr/local/lib/python3.7/dist-packages/xgboost/training.py in train(params, dtrain, num_boost_round, evals, obj, feval, maximize, early_stopping_rounds, evals_result, verbose_eval, xgb_model, callbacks, learning_rates)
215 obj=obj, feval=feval,
–> 216 xgb_model=xgb_model, callbacks=callbacks)
217
/usr/local/lib/python3.7/dist-packages/xgboost/training.py in _train_internal(params, dtrain, num_boost_round, evals, obj, feval, xgb_model, callbacks)
73 if version % 2 == 0:
—> 74 bst.update(dtrain, i, obj)
75 bst.save_rabit_checkpoint()
/usr/local/lib/python3.7/dist-packages/xgboost/core.py in update(self, dtrain, iteration, fobj)
1108 _check_call(_LIB.XGBoosterUpdateOneIter(self.handle, ctypes.c_int(iteration),
-> 1109 dtrain.handle))
1110 else:
/usr/local/lib/python3.7/dist-packages/xgboost/core.py in _check_call(ret)
175 if ret != 0:
–> 176 raise XGBoostError(py_str(_LIB.XGBGetLastError()))
177
XGBoostError: Invalid Parameter format for seed expect int but value=‘RandomState(MT19937)’
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
in ()
1 # find all relevant features
----> 2 feat_selector.fit(X_train, y_train)
/usr/local/lib/python3.7/dist-packages/boruta/boruta_py.py in fit(self, X, y)
199 “”"
200
–> 201 return self._fit(X, y)
202
203 def transform(self, X, weak=False):
/usr/local/lib/python3.7/dist-packages/boruta/boruta_py.py in _fit(self, X, y)
283
284 # add shadow attributes, shuffle them and train estimator, get imps
–> 285 cur_imp = self._add_shadows_get_imps(X, y, dec_reg)
286
287 # get the threshold of shadow importances we will use for rejection
/usr/local/lib/python3.7/dist-packages/boruta/boruta_py.py in _add_shadows_get_imps(self, X, y, dec_reg)
410 x_sha = np.apply_along_axis(self._get_shuffle, 0, x_sha)
411 # get importance of the merged matrix
–> 412 imp = self._get_imp(np.hstack((x_cur, x_sha)), y)
413 # separate importances of real and shadow features
414 imp_sha = imp[x_cur_w:]
/usr/local/lib/python3.7/dist-packages/boruta/boruta_py.py in get_imp(self, X, y)
385 except Exception as e:
386 raise ValueError(‘Please check your X and y variable. The provided’
–> 387 ‘estimator cannot be fitted to your data.\n’ + str(e))
388 try:
389 imp = self.estimator.feature_importances
ValueError: Please check your X and y variable. The providedestimator cannot be fitted to your data.
Invalid Parameter format for seed expect int but value=‘RandomState(MT19937)’
I appreciate your help