53 lines
2.2 KiB
Python
53 lines
2.2 KiB
Python
from sklearn.metrics import recall_score, precision_score
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from sklearn.model_selection import train_test_split
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from sklearn.neural_network import MLPClassifier
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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.preprocessing import StandardScaler
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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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y = data['Country']
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def MLP_classifier_country():
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df = data.copy()
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df.drop(['Country', 'location', 'Company Size', 'Preference', 'Job Title', 'Role', 'Job Portal',
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'skills', 'Company', 'Min Experience', 'Max Experience', 'Min Salary',
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'Max Salary', 'Sector', 'Industry', 'City', 'State', 'Ticker', 'year', 'month', 'day'],
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axis=1, inplace=True)
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X_train, X_test, y_train, y_test = train_test_split(df, y, test_size=0.2)
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mlp = MLPClassifier(hidden_layer_sizes=(100, 50), max_iter=20)
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scaler = StandardScaler()
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scaler.fit(X_train.values)
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X_train_scaler = scaler.transform(X_train.values)
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X_test_scaler = scaler.transform(X_test.values)
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mlp.fit(X_train_scaler, y_train)
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y_pred = mlp.predict(X_test_scaler)
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precision = precision_score(y_test.values, y_pred, average='weighted')
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recall = recall_score(y_test.values, y_pred, average='weighted')
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print("Precision:", precision)
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print("Recall:", recall)
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# Получаем метки классов
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class_labels = mlp.classes_
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print("Class labels:", class_labels)
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print("Уникальных Country :", data['Country'].nunique())
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# Создаем график
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plt.scatter(X_train['Qualifications'].values, X_train['Work Type'].values, c=y_train.values, cmap='viridis', label='Train Data')
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plt.scatter(X_test['Qualifications'].values, X_test['Work Type'].values, c=y_test.values, cmap='viridis', marker='x', label='Test Data')
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plt.xlabel('Qualifications')
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plt.ylabel('Work Type')
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plt.title('MLPClassifier Visualization')
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plt.savefig('static/charts/MLPClassifier.png')
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plt.close()
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return 0
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if __name__ == '__main__':
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MLP_classifier_country()
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