121 lines
4.2 KiB
Python
121 lines
4.2 KiB
Python
import os
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import urllib.request
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import pandas as pd
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import numpy as np
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import StandardScaler
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def download_file():
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url = "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data"
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filename = "adult.data"
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urllib.request.urlretrieve(url, filename)
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csv_file = convert_data_to_csv()
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return csv_file
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def convert_data_to_csv():
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data_file = "adult.data"
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csv_file = "adult.csv"
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df = pd.read_csv(data_file, header=None)
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df.to_csv(csv_file, index=False)
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# delete_data_file()
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return csv_file
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def delete_data_file():
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filename = "adult.data"
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os.remove(filename)
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def add_subsets_to_csv_file(data):
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data.columns = ["age", "workclass", "fnlwgt", "education", "education-num", "marital-status", "occupation",
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"relationship", "race", "sex", "capital-gain", "capital-loss", "hours-per-week", "native-country",
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"income"]
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train_data, test_data = train_test_split(data, test_size=0.2, random_state=42)
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if len(train_data) > len(test_data):
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train_data, dev_data = train_test_split(train_data, test_size=0.25, random_state=42)
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else:
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dev_data = pd.DataFrame()
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train_data.to_csv("adult_train.csv", index=False)
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dev_data.to_csv("adult_dev.csv", index=False)
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test_data.to_csv("adult_test.csv", index=False)
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print("Data set: ", data.shape)
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print("Train Data set: ", train_data.shape)
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print("Dev Data set: ", dev_data.shape)
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print("Test Data set: ", test_data.shape)
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return data
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def check_if_data_set_has_division_into_subsets(file_name):
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data = pd.read_csv(file_name)
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if "train" not in data.columns or "dev" not in data.columns or "test" not in data.columns:
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data_set = add_subsets_to_csv_file(data)
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data_set.to_csv(file_name, index=False)
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def get_statistics(data):
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train_data = pd.read_csv("adult_train.csv", dtype={"income": "category"})
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dev_data = pd.read_csv("adult_dev.csv", dtype={"income": "category"})
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test_data = pd.read_csv("adult_test.csv", dtype={"income": "category"})
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print("Wielkość zbioru: ", len(data))
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print("Wielkość zbioru treningowego: ", len(train_data))
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print("Wielkość zbioru walidacyjnego: ", len(dev_data))
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print("Wielkość zbioru testowego: ", len(test_data))
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print("Średnia wartość wieku: ", np.mean(data["age"]))
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print("Minimalna wartość wieku: ", np.min(data["age"]))
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print("Maksymalna wartość wieku: ", np.max(data["age"]))
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print("Odchylenie standardowe wartości wieku: ", np.std(data["age"]))
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print("Mediana wartości wieku: ", np.median(data["age"]))
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print("Rozkład częstości klas: ")
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freq_dist_all = data['income'].value_counts()
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print('Rozkład częstości etykiet klas na całym zbiorze danych:')
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print(freq_dist_all)
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freq_dist_train = train_data['income'].value_counts()
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print('Rozkład częstości etykiet klas na zbiorze treningowym:')
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print(freq_dist_train)
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freq_dist_test = test_data['income'].value_counts()
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print('Rozkład częstości etykiet klas na zbiorze testowym:')
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print(freq_dist_test)
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freq_dist_dev = dev_data['income'].value_counts()
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print('Rozkład częstości etykiet klas na zbiorze walidacyjnym:')
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print(freq_dist_dev)
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def normalization(data):
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numeric_features = ['age', 'fnlwgt', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week']
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numeric_data = data[numeric_features]
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scaler = StandardScaler()
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normalized_data = scaler.fit_transform(numeric_data)
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data[numeric_features] = normalized_data
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print(data.head())
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def clean(data):
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data.replace('?', np.nan, inplace=True)
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data.dropna(inplace=True)
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data.drop_duplicates(inplace=True)
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data[['age', 'fnlwgt', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week']] = data[
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['age', 'fnlwgt', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week']].apply(pd.to_numeric)
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if __name__ == '__main__':
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csv_file_name = download_file()
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# check_if_data_set_has_division_into_subsets(csv_file_name)
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# data = pd.read_csv(csv_file_name, dtype={"income": "category"})
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# get_statistics(data)
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# normalization(data)
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# clean(data)
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