29 lines
956 B
Python
29 lines
956 B
Python
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import os
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import pandas as pd
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from sklearn.naive_bayes import GaussianNB
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from sklearn import preprocessing
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import joblib
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TRAIN_DATA_DIR = "datasets_train_raw"
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train_df_list = []
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for file in os.listdir(TRAIN_DATA_DIR):
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file_path = os.path.join(TRAIN_DATA_DIR, file)
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df = pd.read_csv(file_path, delim_whitespace=True, skiprows=1,
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names=["tbid", "tphys", "r", "vr", "vt", "ik1", "ik2", "sm1", "sm2", "a", "e",
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"collapsed"])
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train_df_list.append(df)
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data_train = pd.concat(train_df_list, ignore_index=True).sample(frac=1, random_state=42)
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X_train = data_train.iloc[:, 1:-1].values
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y_train = data_train.iloc[:, -1].values
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lab = preprocessing.LabelEncoder()
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y_train_transformed = lab.fit_transform(y_train)
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clf = GaussianNB(var_smoothing=1e-07)
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clf.fit(X_train, y_train_transformed)
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joblib.dump(clf, 'trained_model.pkl')
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joblib.dump(lab, 'label_encoder.pkl')
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