IUM_05
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parent
ffdbe0a365
commit
a6be9a7295
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@ -1,4 +1,5 @@
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creditcardfraud.zip
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creditcard.csv
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data
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model/model.keras
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model/model.keras
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stats_data
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@ -1,9 +1,6 @@
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import os
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import pandas as pd
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import numpy as np
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from sklearn.preprocessing import StandardScaler
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from sklearn.model_selection import train_test_split
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@ -18,72 +15,27 @@ def normalize_data(df):
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return df
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def create_undersample_data(df):
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# Determine the number of instances in the minority class
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fraud_count = len(df[df.Class == 1])
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fraud_indices = np.array(df[df.Class == 1].index)
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# Select indices corresponding to majority class instances
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normal_indices = df[df.Class == 0].index
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# Randomly sample the same number of instances from the majority class
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random_normal_indices = np.random.choice(normal_indices, fraud_count, replace=False)
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random_normal_indices = np.array(random_normal_indices)
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# Combine indices of both classes
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undersample_indice = np.concatenate([fraud_indices, random_normal_indices])
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# Undersample dataset
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undersample_data = df.iloc[undersample_indice, :]
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X_undersample = undersample_data.iloc[:, undersample_data.columns != "Class"]
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y_undersample = undersample_data.iloc[:, undersample_data.columns == "Class"]
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return undersample_data, X_undersample, y_undersample
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def split_undersample_data(X_undersample, y_undersample):
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X_train_undersample, X_test_undersample, y_train_undersample, y_test_undersample = (
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train_test_split(X_undersample, y_undersample, test_size=0.3, random_state=0)
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)
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return (
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X_train_undersample,
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X_test_undersample,
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y_train_undersample,
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y_test_undersample,
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)
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def save_undersample_data(
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undersample_data,
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X_train_undersample,
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X_test_undersample,
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y_train_undersample,
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y_test_undersample,
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):
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undersample_data.to_csv("data/undersample_data.csv", index=False)
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X_train_undersample.to_csv("data/X_train_undersample.csv", index=False)
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X_test_undersample.to_csv("data/X_test_undersample.csv", index=False)
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y_train_undersample.to_csv("data/y_train_undersample.csv", index=False)
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y_test_undersample.to_csv("data/y_test_undersample.csv", index=False)
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def split_whole_data(df):
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def split_data(df):
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X = df.iloc[:, df.columns != "Class"]
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y = df.iloc[:, df.columns == "Class"]
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X_train, X_test, y_train, y_test = train_test_split(
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X, y, test_size=0.3, random_state=0
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X, y, test_size=0.2, random_state=0
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)
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return X_train, X_test, y_train, y_test
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X_train, X_val, y_train, y_val = train_test_split(
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X_train, y_train, test_size=0.25, random_state=0
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)
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return X_train, X_val, X_test, y_train, y_val, y_test
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def save_whole_data(df, X_train, X_test, y_train, y_test):
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def save_data(df, X_train, X_val, X_test, y_train, y_val, y_test):
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df.to_csv("data/creditcard.csv", index=False)
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X_train.to_csv("data/X_train.csv", index=False)
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X_val.to_csv("data/X_val.csv", index=False)
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X_test.to_csv("data/X_test.csv", index=False)
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y_train.to_csv("data/y_train.csv", index=False)
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y_val.to_csv("data/y_val.csv", index=False)
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y_test.to_csv("data/y_test.csv", index=False)
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@ -94,20 +46,8 @@ def main():
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df = load_data("creditcard.csv")
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df = normalize_data(df)
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undersample_data, X_undersample, y_undersample = create_undersample_data(df)
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X_train_undersample, X_test_undersample, y_train_undersample, y_test_undersample = (
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split_undersample_data(X_undersample, y_undersample)
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)
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save_undersample_data(
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undersample_data,
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X_train_undersample,
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X_test_undersample,
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y_train_undersample,
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y_test_undersample,
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)
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X_train, X_test, y_train, y_test = split_whole_data(df)
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save_whole_data(df, X_train, X_test, y_train, y_test)
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X_train, X_val, X_test, y_train, y_val, y_test = split_data(df)
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save_data(df, X_train, X_val, X_test, y_train, y_val, y_test)
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if __name__ == "__main__":
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@ -7,16 +7,12 @@ def write_to_file(file_name):
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df = pd.read_csv("data/creditcard.csv")
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pd.set_option("display.max_columns", None)
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undersample_data = pd.read_csv("data/undersample_data.csv")
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X_test_undersample = pd.read_csv("data/X_test_undersample.csv")
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y_test_undersample = pd.read_csv("data/y_test_undersample.csv")
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X_train_undersample = pd.read_csv("data/X_train_undersample.csv")
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y_train_undersample = pd.read_csv("data/y_train_undersample.csv")
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X_test = pd.read_csv("data/X_test.csv")
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y_test = pd.read_csv("data/y_test.csv")
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X_train = pd.read_csv("data/X_train.csv")
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X_val = pd.read_csv("data/X_val.csv")
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X_test = pd.read_csv("data/X_test.csv")
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y_train = pd.read_csv("data/y_train.csv")
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y_val = pd.read_csv("data/y_val.csv")
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y_test = pd.read_csv("data/y_test.csv")
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with open("stats_data/" + file_name, "w") as f:
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sys.stdout = f
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@ -41,24 +37,7 @@ def write_to_file(file_name):
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f.write("\n\n")
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f.write("Size of undersampled dataset\n")
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undersample_data.info()
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f.write("\n\n")
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f.write("Summary statistics of the undersampled dataset\n")
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f.write(str(undersample_data.describe()))
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f.write("\n\n")
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f.write(
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"Distribution of legitimate and fraudulent transactions in an undersampled dataset\n"
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)
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f.write(str(undersample_data["Class"].value_counts()))
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f.write("\n\n")
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f.write("Statistical measures of the training dataset of whole data\n")
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f.write("Statistical measures of the training dataset\n")
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pd.concat([X_train, y_train], axis=1).info()
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f.write("\n")
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f.write(str(pd.concat([X_train, y_train], axis=1).describe()))
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@ -67,49 +46,22 @@ def write_to_file(file_name):
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f.write("\n\n")
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f.write("Statistical measures of the test dataset of whole data\n")
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f.write("Statistical measures of the validation dataset\n")
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pd.concat([X_val, y_val], axis=1).info()
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f.write("\n")
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f.write(str(pd.concat([X_val, y_val], axis=1).describe()))
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f.write("\n")
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f.write(str(pd.concat([X_val, y_val], axis=1)["Class"].value_counts()))
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f.write("\n\n")
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f.write("Statistical measures of the test dataset\n")
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pd.concat([X_test, y_test], axis=1).info()
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f.write("\n")
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f.write(str(pd.concat([X_test, y_test], axis=1).describe()))
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f.write("\n")
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f.write(str(pd.concat([X_test, y_test], axis=1)["Class"].value_counts()))
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f.write("\n\n")
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f.write("Statistical measures of the training dataset of undersampled data\n")
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pd.concat([X_train_undersample, y_train_undersample], axis=1).info()
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f.write("\n")
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f.write(
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str(
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pd.concat([X_train_undersample, y_train_undersample], axis=1).describe()
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)
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)
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f.write("\n")
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f.write(
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str(
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pd.concat([X_train_undersample, y_train_undersample], axis=1)[
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"Class"
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].value_counts()
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)
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)
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f.write("\n\n")
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f.write("Statistical measures of the test dataset of undersampled data\n")
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pd.concat([X_test_undersample, y_test_undersample], axis=1).info()
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f.write("\n")
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f.write(
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str(pd.concat([X_test_undersample, y_test_undersample], axis=1).describe())
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)
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f.write("\n")
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f.write(
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str(
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pd.concat([X_test_undersample, y_test_undersample], axis=1)[
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"Class"
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].value_counts()
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)
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)
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sys.stdout = sys.__stdout__
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@ -10,12 +10,17 @@ import pandas as pd
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def main():
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X_train = pd.read_csv("data/X_train.csv")
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X_val = pd.read_csv("data/X_val.csv")
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y_train = pd.read_csv("data/y_train.csv")
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y_val = pd.read_csv("data/y_val.csv")
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X_train = X_train.to_numpy()
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X_val = X_val.to_numpy()
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y_train = y_train.to_numpy()
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y_val = y_val.to_numpy()
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X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)
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X_val = X_val.reshape(X_val.shape[0], X_val.shape[1], 1)
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model = Sequential(
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[
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@ -41,6 +46,7 @@ def main():
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model.fit(
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X_train,
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y_train,
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validation_data=(X_val, y_val),
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epochs=5,
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verbose=1,
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)
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