質問編集履歴
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import pandas as pd
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sample_submission = pd.read_csv("../input/nlp-getting-started/sample_submission.csv")
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test = pd.read_csv("../input/nlp-getting-started/test.csv")
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train = pd.read_csv("../input/nlp-getting-started/train.csv")
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df=train
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```ここに言語を入力
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---------------------------------------------------------------------------
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ValueError Traceback (most recent call last)
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<ipython-input-10-e418a6eefe52> in <module>
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14 scoring="accuracy",cv=3
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15 )
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---> 16 gscv.fit(X_train,y_train)
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17 gscv.best_params_
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/opt/conda/lib/python3.6/site-packages/sklearn/model_selection/_search.py in fit(self, X, y, groups, **fit_params)
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686 return results
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687
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--> 688 self._run_search(evaluate_candidates)
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689
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690 # For multi-metric evaluation, store the best_index_, best_params_ and
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/opt/conda/lib/python3.6/site-packages/sklearn/model_selection/_search.py in _run_search(self, evaluate_candidates)
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1147 def _run_search(self, evaluate_candidates):
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1148 """Search all candidates in param_grid"""
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-> 1149 evaluate_candidates(ParameterGrid(self.param_grid))
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/opt/conda/lib/python3.6/site-packages/sklearn/model_selection/_search.py in evaluate_candidates(candidate_params)
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665 for parameters, (train, test)
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666 in product(candidate_params,
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--> 667 cv.split(X, y, groups)))
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669 if len(out) < 1:
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/opt/conda/lib/python3.6/site-packages/joblib/parallel.py in __call__(self, iterable)
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1001 # remaining jobs.
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1002 self._iterating = False
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-> 1003 if self.dispatch_one_batch(iterator):
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1004 self._iterating = self._original_iterator is not None
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/opt/conda/lib/python3.6/site-packages/joblib/parallel.py in dispatch_one_batch(self, iterator)
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832 return False
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833 else:
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--> 834 self._dispatch(tasks)
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835 return True
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/opt/conda/lib/python3.6/site-packages/joblib/parallel.py in _dispatch(self, batch)
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751 with self._lock:
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752 job_idx = len(self._jobs)
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--> 753 job = self._backend.apply_async(batch, callback=cb)
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754 # A job can complete so quickly than its callback is
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755 # called before we get here, causing self._jobs to
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/opt/conda/lib/python3.6/site-packages/joblib/_parallel_backends.py in apply_async(self, func, callback)
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199 def apply_async(self, func, callback=None):
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200 """Schedule a func to be run"""
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--> 201 result = ImmediateResult(func)
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202 if callback:
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203 callback(result)
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/opt/conda/lib/python3.6/site-packages/joblib/_parallel_backends.py in __init__(self, batch)
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580 # Don't delay the application, to avoid keeping the input
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581 # arguments in memory
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--> 582 self.results = batch()
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584 def get(self):
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/opt/conda/lib/python3.6/site-packages/joblib/parallel.py in __call__(self)
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254 with parallel_backend(self._backend, n_jobs=self._n_jobs):
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255 return [func(*args, **kwargs)
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--> 256 for func, args, kwargs in self.items]
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258 def __len__(self):
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/opt/conda/lib/python3.6/site-packages/joblib/parallel.py in <listcomp>(.0)
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254 with parallel_backend(self._backend, n_jobs=self._n_jobs):
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255 return [func(*args, **kwargs)
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--> 256 for func, args, kwargs in self.items]
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258 def __len__(self):
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/opt/conda/lib/python3.6/site-packages/sklearn/model_selection/_validation.py in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, error_score)
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514 estimator.fit(X_train, **fit_params)
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515 else:
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--> 516 estimator.fit(X_train, y_train, **fit_params)
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518 except Exception as e:
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/opt/conda/lib/python3.6/site-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)
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724 else:
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725 train_dmatrix = DMatrix(X, label=training_labels,
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--> 726 missing=self.missing, nthread=self.n_jobs)
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728 self._Booster = train(xgb_options, train_dmatrix, self.get_num_boosting_rounds(),
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/opt/conda/lib/python3.6/site-packages/xgboost/core.py in __init__(self, data, label, missing, weight, silent, feature_names, feature_types, nthread)
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402 self._init_from_csc(data)
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403 elif isinstance(data, np.ndarray):
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--> 404 self._init_from_npy2d(data, missing, nthread)
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405 elif isinstance(data, DataTable):
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406 self._init_from_dt(data, nthread)
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/opt/conda/lib/python3.6/site-packages/xgboost/core.py in _init_from_npy2d(self, mat, missing, nthread)
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476 # we try to avoid data copies if possible (reshape returns a view when possible
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477 # and we explicitly tell np.array to try and avoid copying)
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--> 478 data = np.array(mat.reshape(mat.size), copy=False, dtype=np.float32)
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479 handle = ctypes.c_void_p()
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480 missing = missing if missing is not None else np.nan
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自然言語処理に取り組んでおります。
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以下のコードを書いておりますが、
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最後の「# GBDTモデルのパラメータ探索」以降のコード入力追加で、
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