48 lines
1.6 KiB
Python
48 lines
1.6 KiB
Python
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import pandas as pd
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import MinMaxScaler
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# Read the dataset from the CSV file
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dataset_path = '25k_IMDb_movie_Dataset.csv'
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df = pd.read_csv(dataset_path)
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# Drop the 'path' column
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df.drop('path', axis=1, inplace=True)
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# Drop rows that contain 'Top Gun: Maverick' in any column
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df = df[~df.apply(lambda row: row.astype(str).str.contains('not-released').any(), axis=1)]
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df = df[~df.apply(lambda row: row.astype(str).str.contains('no-rating').any(), axis=1)]
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# Split the dataset into train, dev, and test subsets
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train_df, test_df = train_test_split(df, test_size=0.2, random_state=42)
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dev_df, test_df = train_test_split(test_df, test_size=0.5, random_state=42)
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# Print the sizes of the dataset and subsets
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print(f"Dataset size: {len(df)} examples")
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print(f"Train set size: {len(train_df)} examples")
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print(f"Dev set size: {len(dev_df)} examples")
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print(f"Test set size: {len(test_df)} examples")
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# Compute statistics for the dataset
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statistics = df.describe()
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# Print mean, minimum, maximum, standard deviations, and median values of individual parameters
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print("Statistics:")
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print(statistics)
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# Normalize the data in the dataset
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scaler = MinMaxScaler()
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#df_normalized = pd.DataFrame(scaler.fit_transform(df), columns=df.columns)
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# Compute frequency distribution of examples for each class
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#class_frequency = df_normalized['class_column'].value_counts()
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# Print frequency distribution of examples for each class
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#print("Frequency distribution of examples for each class:")
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#print(class_frequency)
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