28 lines
842 B
Python
28 lines
842 B
Python
import os
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import joblib
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import pandas as pd
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from sklearn.impute import SimpleImputer
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TEST_DATA_DIR = "datasets_test"
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test_df_list = []
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for file in os.listdir(TEST_DATA_DIR):
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file_path = os.path.join(TEST_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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test_df_list.append(df)
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data_test = pd.concat(test_df_list, ignore_index=True).sample(frac=1, random_state=42)
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X_test = data_test.iloc[:, 1:-1].values
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imputer = SimpleImputer(strategy='mean')
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X_imputed = imputer.fit_transform(X_test)
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model_filename = 'trained_model.pkl'
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model = joblib.load(model_filename)
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predictions = model.predict(X_test)
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print(predictions)
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