IIS_2023_1/volkov_rafael_lab_6/app.py
2023-12-05 12:28:39 +04:00

78 lines
3.0 KiB
Python

from flask import Flask, render_template, request
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neural_network import MLPRegressor
from sklearn.pipeline import Pipeline
import joblib
app = Flask(__name__)
# Загрузка данных
data_bgg = pd.read_csv("bgg_dataset.csv", delimiter=";")
# Выбор нужных столбцов
selected_columns_bgg = ['Year Published', 'Users Rated', 'Rating Average', 'BGG Rank', 'Owned Users', 'Complexity Average']
features = data_bgg[selected_columns_bgg]
# Замена запятых на точки в столбцах 'Rating Average' и 'Complexity Average'
features['Rating Average'] = features['Rating Average'].str.replace(',', '.').astype(float)
features['Complexity Average'] = features['Complexity Average'].str.replace(',', '.').astype(float)
# Замена пропущенных значений средними значениями по столбцам
features = features.fillna(features.mean())
# Разделение данных
X_bgg = features.drop('Rating Average', axis=1)
y_bgg = features['Rating Average']
X_train_bgg, X_test_bgg, y_train_bgg, y_test_bgg = train_test_split(X_bgg, y_bgg, test_size=0.2, random_state=42)
# Масштабирование данных
scaler = StandardScaler()
X_train_bgg_scaled = scaler.fit_transform(X_train_bgg)
X_test_bgg_scaled = scaler.transform(X_test_bgg)
# Обучение модели нейронной сети
mlp_regressor_model = Pipeline([
('scaler', StandardScaler()),
('mlp_regressor', MLPRegressor(hidden_layer_sizes=(100, 50), max_iter=2000, random_state=42))
])
mlp_regressor_model.fit(X_train_bgg_scaled, y_train_bgg)
# Сохранение модели
joblib.dump(mlp_regressor_model, 'mlp_regressor_model.joblib')
# Загрузка модели
mlp_regressor_model = joblib.load('mlp_regressor_model.joblib')
# Обновление маршрута для предсказания
@app.route('/')
def index():
return render_template('index.html')
@app.route('/predict', methods=['POST'])
def predict():
if request.method == 'POST':
# Получение данных из формы
input_data_bgg = {
'Year Published': int(request.form['Year Published']),
'Users Rated': int(request.form['Users Rated']),
'BGG Rank': int(request.form['BGG Rank']),
'Owned Users': int(request.form['Owned Users']),
'Complexity Average': float(request.form['Complexity Average'])
}
# Преобразование данных в DataFrame
input_df_bgg = pd.DataFrame([input_data_bgg])
# Масштабирование входных данных
input_data_scaled = scaler.transform(input_df_bgg)
# Предсказание
prediction_bgg = mlp_regressor_model.predict(input_data_scaled)[0]
return render_template('index.html', prediction_bgg=prediction_bgg)
if __name__ == '__main__':
app.run(debug=True)