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casper-hansen / Nested-Cross-Validation

Nested cross-validation for unbiased predictions. Can be used with Scikit-Learn, XGBoost, Keras and LightGBM, or any other estimator that implements the scikit-learn interface.
MIT License
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'list' object has no attribute 'set_params' #15

Open bharath8847 opened 3 years ago

bharath8847 commented 3 years ago

from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.cluster import KMeans,MiniBatchKMeans from sklearn.metrics import adjusted_rand_score import matplotlib.pyplot as plt import matplotlib.cm as cm from sklearn.decomposition import TruncatedSVD

vectorizer = TfidfVectorizer(

min_df = 0.3,

#max_df = 0.95,
max_features = 8000,
stop_words = 'english'

) X = vectorizer.fit_transform(data.keywords) X=X.toarray() from sklearn.preprocessing import LabelEncoder Encoder = LabelEncoder() Train_Y = Encoder.fit_transform(data['keys']) models_to_run = [MultinomialNB(), LinearSVC(),LogisticRegression(),RandomForestClassifier(),MLPClassifier()]

param_grid = [{'alpha': (1e-2, 1e-3) }, {'C': (np.logspace(-5, 1, 5))},

          {'C': (np.logspace(-5, 1, 5))},

          {'max_depth': (3, 6, 10, 15, 20),
           'n_estimators':(10, 50, 100, 200)},

          {'alpha': (1e-2, 1e-3)}]

nested_CV_search = NestedCV(model=models_to_run, params_grid=param_grid[i] , outer_kfolds=4, inner_kfolds=4, cv_options={'sqrt_of_score':True, 'randomized_search_iter':30}) nested_CV_search.fit(X=X,y=Train_Y) grid_nested_cv.score_vs_variance_plot() print('\nCumulated best parameter grid was:\n{0}'.format(nested_CV_search.best_params))

AttributeError Traceback (most recent call last)

in () 39 nested_CV_search = NestedCV(model=models_to_run, params_grid=param_grid[i] , outer_kfolds=4, inner_kfolds=4, 40 cv_options={'sqrt_of_score':True, 'randomized_search_iter':30}) ---> 41 nested_CV_search.fit(X=X,y=Train_Y) 42 grid_nested_cv.score_vs_variance_plot() 43 print('\nCumulated best parameter grid was:\n{0}'.format(nested_CV_search.best_params)) 8 frames /usr/local/lib/python3.7/dist-packages/nested_cv/nested_cv.py in _parallel_fitting(X_train_inner, X_test_inner, y_train_inner, y_test_inner, param_dict) 233 '\n\tFitting these parameters:\n\t{0}'.format(param_dict)) 234 # Set hyperparameters, train model on inner split, predict results. --> 235 self.model.set_params(**param_dict) 236 237 # Fit model with current hyperparameters and score it AttributeError: 'list' object has no attribute 'set_params'
bharath8847 commented 3 years ago

Doesnt work for multiple models. Please add it .