126 lines
8.1 KiB
Python
126 lines
8.1 KiB
Python
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import os.path
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import pandas as pd
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from matplotlib import pyplot as plt
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from sklearn.feature_selection import RFE
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from sklearn.linear_model import LinearRegression
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from sklearn.model_selection import train_test_split
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#Задачи анализа, решаемые регрессией: предсказать диапазон окладов или компенсаций
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# на основе других признаков: опыт, квалификация, тип работы и т.д.
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picfld = os.path.join('static', 'charts')
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data = pd.read_csv('D:/Интеллектуальные информационные системы/Dataset/updated_job_descriptions.csv')
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def linear_regression_min_salary():
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y = data['Min Salary']
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df = data.copy()
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# удаляем целевое значение и наименее важные параметры
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df.drop(['Min Salary', 'Preference', 'day', 'month', 'Job Portal', 'State', 'location', 'Country', 'Industry', 'skills',
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'Role', 'Job Title', 'City', 'Sector', 'Ticker', 'Company Size', 'Company', 'Max Salary'],
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axis=1, inplace=True)
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X_train, X_test, y_train, y_test = train_test_split(df.values, y.values, test_size=0.0002, train_size=0.2)
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model = LinearRegression()
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model.fit(X_train, y_train)
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y_pred = model.predict(X_test)
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r_sq = model.score(X_test, y_test)
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plt.plot(y_test, c="#bd0000", label="\"y\" исходная")
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plt.plot(y_pred, c="#00BFFF",
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label="\"y\" предсказанная \n" "Кд = " + str(r_sq))
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plt.legend(loc='lower left')
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plt.title("Линейная регрессия")
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plt.savefig('static/charts/MinSalaryChart.png')
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plt.close()
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def linear_regression_max_salary():
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y = data['Max Salary']
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df = data.copy()
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# удаляем целевое значение и наименее важные параметры
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df.drop(['Max Salary', 'Max Experience', 'Job Portal', 'day', 'Sector', 'Industry', 'Country', 'location', 'Job Title',
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'Ticker', 'Company', 'City', 'State', 'Role', 'skills', 'Qualifications', 'Company Size', 'Min Salary'], axis=1, inplace=True)
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X_train, X_test, y_train, y_test = train_test_split(df.values, y.values, test_size=0.0002, train_size=0.2)
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model = LinearRegression()
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model.fit(X_train, y_train)
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y_pred = model.predict(X_test)
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r_sq = model.score(X_test, y_test)
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plt.plot(y_test, c="#bd0000", label="\"y\" исходная")
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plt.plot(y_pred, c="#00BFFF",
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label="\"y\" предсказанная \n" "Кд = " + str(r_sq))
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plt.legend(loc='lower left')
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plt.title("Линейная регрессия")
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plt.savefig('static/charts/MaxSalaryChart.png')
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plt.close()
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# оценка важности параметров
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def RFE_max_salary():
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df = data.copy()
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y = data['Max Salary']
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df.drop(["Max Salary"], axis=1, inplace=True)
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X_train, X_test, y_train, y_test = train_test_split(df.values, y.values, test_size=0.2)
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column_names = ['Qualifications', 'Country', 'location', 'Work Type', 'Company Size', 'Preference', 'Job Title', 'Role', 'Job Portal',
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'skills', 'Company', 'Min Experience', 'Max Experience', 'Min Salary',
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'Sector', 'Industry', 'City', 'State', 'Ticker', 'year', 'month', 'day',
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"'Casual Dress Code, Social and Recreational Activities, Employee Referral Programs, Health and Wellness Facilities, Life and Disability Insurance'",
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"'Childcare Assistance, Paid Time Off (PTO), Relocation Assistance, Flexible Work Arrangements, Professional Development'",
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"'Employee Assistance Programs (EAP), Tuition Reimbursement, Profit-Sharing, Transportation Benefits, Parental Leave'",
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"'Employee Referral Programs, Financial Counseling, Health and Wellness Facilities, Casual Dress Code, Flexible Spending Accounts (FSAs)'",
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"'Flexible Spending Accounts (FSAs), Relocation Assistance, Legal Assistance, Employee Recognition Programs, Financial Counseling'",
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"'Health Insurance, Retirement Plans, Flexible Work Arrangements, Employee Assistance Programs (EAP), Bonuses and Incentive Programs'",
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"'Health Insurance, Retirement Plans, Paid Time Off (PTO), Flexible Work Arrangements, Employee Assistance Programs (EAP)'",
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"'Legal Assistance, Bonuses and Incentive Programs, Wellness Programs, Employee Discounts, Retirement Plans'",
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"'Life and Disability Insurance, Stock Options or Equity Grants, Employee Recognition Programs, Health Insurance, Social and Recreational Activities'",
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"'Transportation Benefits, Professional Development, Bonuses and Incentive Programs, Profit-Sharing, Employee Discounts'",
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"'Tuition Reimbursement, Stock Options or Equity Grants, Parental Leave, Wellness Programs, Childcare Assistance'"]
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estimator = LinearRegression()
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rfe_model = RFE(estimator)
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rfe_model.fit(X_train, y_train)
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ranks = rank_to_dict_rfe(rfe_model.ranking_, column_names)
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sorted_dict = dict(sorted(ranks.items(), key=lambda x: x[1], reverse=True))
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print("Оценка важности параметров для нахождения максимальной оплаты труда")
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print(sorted_dict)
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def RFE_min_salary():
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df = data.copy()
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y = data['Min Salary']
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df.drop(["Min Salary"], axis=1, inplace=True)
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X_train, X_test, y_train, y_test = train_test_split(df.values, y.values, test_size=0.2)
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column_names = ['Qualifications', 'Country', 'location', 'Work Type', 'Company Size', 'Preference', 'Job Title', 'Role', 'Job Portal',
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'skills', 'Company', 'Min Experience', 'Max Experience', 'Max Salary',
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'Sector', 'Industry', 'City', 'State', 'Ticker', 'year', 'month', 'day',
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"'Casual Dress Code, Social and Recreational Activities, Employee Referral Programs, Health and Wellness Facilities, Life and Disability Insurance'",
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"'Childcare Assistance, Paid Time Off (PTO), Relocation Assistance, Flexible Work Arrangements, Professional Development'",
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"'Employee Assistance Programs (EAP), Tuition Reimbursement, Profit-Sharing, Transportation Benefits, Parental Leave'",
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"'Employee Referral Programs, Financial Counseling, Health and Wellness Facilities, Casual Dress Code, Flexible Spending Accounts (FSAs)'",
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"'Flexible Spending Accounts (FSAs), Relocation Assistance, Legal Assistance, Employee Recognition Programs, Financial Counseling'",
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"'Health Insurance, Retirement Plans, Flexible Work Arrangements, Employee Assistance Programs (EAP), Bonuses and Incentive Programs'",
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"'Health Insurance, Retirement Plans, Paid Time Off (PTO), Flexible Work Arrangements, Employee Assistance Programs (EAP)'",
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"'Legal Assistance, Bonuses and Incentive Programs, Wellness Programs, Employee Discounts, Retirement Plans'",
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"'Life and Disability Insurance, Stock Options or Equity Grants, Employee Recognition Programs, Health Insurance, Social and Recreational Activities'",
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"'Transportation Benefits, Professional Development, Bonuses and Incentive Programs, Profit-Sharing, Employee Discounts'",
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"'Tuition Reimbursement, Stock Options or Equity Grants, Parental Leave, Wellness Programs, Childcare Assistance'"]
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estimator = LinearRegression()
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rfe_model = RFE(estimator)
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rfe_model.fit(X_train, y_train)
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ranks = rank_to_dict_rfe(rfe_model.ranking_, column_names)
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sorted_dict = dict(sorted(ranks.items(), key=lambda x: x[1], reverse=True))
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print("Оценка важности параметров для нахождения минимальной оплаты труда")
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print(sorted_dict)
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def rank_to_dict_rfe(ranking, names):
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n_ranks = [float(1 / i) for i in ranking]
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n_ranks = map(lambda x: round(x, 2), n_ranks)
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return dict(zip(names, n_ranks))
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if __name__ == '__main__':
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# linear_regression_min_salary()
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# linear_regression_max_salary()
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RFE_min_salary()
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RFE_max_salary()
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