169 lines
5.7 KiB
Python
169 lines
5.7 KiB
Python
import numpy as np
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import pandas as pd
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from datetime import timedelta
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from tensorflow.keras.models import load_model
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from sklearn.preprocessing import MinMaxScaler
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import matplotlib.pyplot as plt
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import io
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import joblib
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from flask import Flask, request, jsonify, Blueprint, send_file
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from flasgger import Swagger
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from flask_cors import CORS
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app = Flask(__name__)
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api = Blueprint('api', __name__)
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Swagger(app)
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CORS(app)
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# Загружаем модель и scaler
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model = load_model("my_model_1H.keras")
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scaler = MinMaxScaler(feature_range=(0, 1))
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# Загружаем данные
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column_names = ['product_url', 'price', 'datetime']
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df = pd.read_csv('parsed_data_public_price_history_all.csv')
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# Преобразуем колонку 'datetime' в тип данных datetime
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df['datetime'] = pd.to_datetime(df['datetime'], format='mixed', utc=True)
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df['price'] = df['price'].astype(float)
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q_low = df['price'].quantile(0.55)
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q_hi = df['price'].quantile(0.75)
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q_range = q_hi - q_low
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df = df[(df['price'] < q_hi + 1.5 * q_range) & (df['price'] > q_low - 1.5 * q_range)]
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df_hourly_avg = df[['price', 'datetime']]
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df_hourly_avg['datetime'] = df_hourly_avg['datetime'].dt.floor('1H')
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df_hourly_avg = df_hourly_avg.groupby('datetime').agg({'price': 'mean'}).reset_index()
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df_hourly_avg.set_index('datetime', inplace=True)
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# Подготовка данных для прогнозирования
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def prepare_data(df, days_forward=7):
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last_date = df.index[-1]
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scaled_data = scaler.fit_transform(df[['price']].values)
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n = 3 # число временных шагов (можно менять)
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X_test = []
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# Формируем X_test на основе последних n значений
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for i in range(n, len(scaled_data)):
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X_test.append(scaled_data[i - n:i, 0])
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X_test = np.array(X_test)
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X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
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# Предсказание на 7 дней вперед
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predictions = []
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current_input = X_test[-1] # начальное состояние для прогноза
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for _ in range(days_forward):
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pred = model.predict(np.expand_dims(current_input, axis=0))
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predictions.append(pred[0, 0])
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# Обновляем current_input, добавляя новое предсказание и удаляя старое
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current_input = np.append(current_input[1:], pred).reshape(n, 1)
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# Масштабируем предсказания обратно
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predictions = scaler.inverse_transform(np.array(predictions).reshape(-1, 1)).flatten()
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future_dates = [last_date + timedelta(days=i) for i in range(1, days_forward + 1)]
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forecast_df = pd.DataFrame({'date': future_dates, 'predicted_price': predictions})
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return forecast_df
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# Построение графика
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def plot_price(forecast_df):
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plt.figure(figsize=(14, 7))
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plt.plot(df_hourly_avg.index, df_hourly_avg['price'], label='Actual Price', color='blue')
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plt.plot(forecast_df['date'], forecast_df['predicted_price'], label='Predicted Price', color='orange')
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plt.title("Price Prediction")
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plt.xlabel("Date")
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plt.ylabel("Price")
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plt.legend()
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plt.grid(True)
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img = io.BytesIO()
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plt.savefig(img, format='png')
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img.seek(0)
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plt.close()
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return img
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@api.route('/predict_price', methods=['GET'])
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def predict_price():
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"""
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Предсказание цены на 7 дней вперед
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---
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responses:
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200:
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description: JSON с предсказаниями цен и днем минимальной цены
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schema:
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type: object
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properties:
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forecast:
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type: array
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items:
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type: object
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properties:
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date:
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type: string
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format: date
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predicted_price:
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type: number
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min_price_day:
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type: object
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properties:
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date:
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type: string
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format: date
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price:
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type: number
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"""
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forecast_df = prepare_data(df_hourly_avg)
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forecast_list = forecast_df.to_dict(orient='records') # Преобразование в список словарей
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# Преобразуем значения 'predicted_price' в float
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for record in forecast_list:
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record['predicted_price'] = float(record['predicted_price'])
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# Определяем день с минимальной предсказанной ценой
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min_price_day = forecast_df.loc[forecast_df['predicted_price'].idxmin()]
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# Преобразуем минимальную цену в float
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min_price_day_price = float(min_price_day['predicted_price'])
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# Формируем ответ
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return jsonify({
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'forecast': forecast_list,
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'min_price_day': {
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'date': min_price_day['date'].strftime('%Y-%m-%d'),
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'price': min_price_day_price
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}
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})
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# Эндпоинт для получения графика
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@api.route('/plot', methods=['GET'])
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def plot():
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"""
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Получение графика предсказанных и фактических цен
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---
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responses:
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200:
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description: Возвращает график предсказанных и фактических цен в формате PNG
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content:
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image/png:
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schema:
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type: string
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format: binary
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"""
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forecast_df = prepare_data(df_hourly_avg)
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img = plot_price(forecast_df)
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return send_file(img, mimetype='image/png')
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app.register_blueprint(api, url_prefix='/api')
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if __name__ == "__main__":
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app.run(debug=True) |