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import numpy as np |
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import pandas as pd |
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from sklearn.model_selection import train_test_split |
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df_positive = pd.read_csv("./intermediate/cleaned_positive.csv") |
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df_negative = pd.read_csv("./intermediate/cleaned_negative.csv") |
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pos_train, pos_test = train_test_split(df_positive, test_size=0.2, random_state=42) |
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neg_train_full, neg_test = train_test_split(df_negative, test_size=0.2, random_state=42) |
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neg_train = neg_train_full.sample(n=len(pos_train), random_state=42) |
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neg_remaining = neg_train_full.drop(neg_train.index) |
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neg_test = pd.concat([neg_test, neg_remaining]).reset_index(drop=True) |
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train_df = pd.concat([pos_train, neg_train]).sample(frac=1, random_state=42).reset_index(drop=True) |
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test_df = pd.concat([pos_test, neg_test]).sample(frac=1, random_state=42).reset_index(drop=True) |
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train_df.to_csv("./intermediate/train.csv", index=False) |
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test_df.to_csv("./intermediate/test.csv", index=False) |
