analysis/README.md

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+# Price_Pulse
+
+python -m venv venv
+
+.\venv\Scripts\activate
+
+pip install -r requirements.txt
+
+python app.py
+
+/predict_price
+/plot

analysis/app.py

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

analysis/docker-compose.yml

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+version: '3'
+services:
+  clickhouse:
+    image: yandex/clickhouse-server:latest
+    ports:
+      - "8123:8123"
+      - "9000:9000"
+    volumes:
+      - ./clickhouse-data:/var/lib/clickhouse

analysis/platforms_train_v2.py

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+# -*- coding: utf-8 -*-
+"""Platforms_train_v2.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1yD7QxO8rUrHXvYLn_z5eofUKenJqXZoU
+"""
+
+import os
+import numpy as np
+import pandas as pd
+from datetime import datetime
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_squared_error
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
+import joblib
+from sklearn.preprocessing import MinMaxScaler
+from keras.models import Sequential
+from keras.layers import Dense, Dropout, LSTM
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+column_names = ['product_url', 'price', 'datetime']
+
+df = pd.read_csv('parsed_data_public_price_history_all.csv')
+
+# Преобразуем колонку 'datetime' в тип данных datetime
+df['datetime'] = pd.to_datetime(df['datetime'], format='mixed', utc=True)
+df['price'] = df['price'].astype(float)
+
+
+q_low = df['price'].quantile(0.55)
+q_hi = df['price'].quantile(0.75)
+q_range = q_hi - q_low
+df = df[(df['price'] < q_hi + 1.5 * q_range) & (df['price'] > q_low - 1.5 * q_range)]
+df.describe()
+
+# Оставляем только колонки 'price' и 'datetime'
+df_hourly_avg = df[['price', 'datetime']]
+
+# Округляем время до дня
+df_hourly_avg['datetime'] = df_hourly_avg['datetime'].dt.floor('1H')
+
+
+# Группируем по каждому часу и вычисляем среднее значение цены
+df_hourly_avg = df_hourly_avg.groupby('datetime').agg({'price': 'mean'}).reset_index()
+
+df_hourly_avg.set_index('datetime', inplace=True)
+
+#only values
+df_hourly_avg_arr = df_hourly_avg.values
+
+#Split
+split = int(0.8*len(df_hourly_avg_arr))
+
+train, test = df_hourly_avg_arr[:split], df_hourly_avg_arr[split:]
+
+
+#Normalise data by scaling to a range of 0 to 1 to improve learning and convergence of model.
+# Feature scaling and fitting scaled data
+scaler = MinMaxScaler(feature_range=(0, 1))
+scaled_data = scaler.fit_transform(df_hourly_avg_arr)
+
+# Creating a data structure with n time-steps and 1 output
+n = 3
+X_train, y_train = [], []
+for i in range(n,len(train)):
+    X_train.append(scaled_data[i-n:i,0])
+    y_train.append(scaled_data[i,0])
+
+# Convert X_train and y_train to numpy arrays for training LSTM model
+
+X_train, y_train = np.array(X_train), np.array(y_train)
+
+# Reshape the data as LSTM expects 3-D data (samples, time steps, features)
+
+X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
+
+# create and fit the LSTM network
+model = Sequential()
+model.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1],1)))
+model.add(LSTM(units=50))
+model.add(Dense(1))
+
+model.compile(loss='mean_squared_error', optimizer='adam')
+model.fit(X_train, y_train, epochs=1000, batch_size=1, verbose=2)
+
+inputs = df_hourly_avg_arr [len(df_hourly_avg_arr) - len(test) - n:]
+inputs = inputs.reshape(-1,1)
+inputs = scaler.transform(inputs)
+
+# Create test data set
+X_test = []
+for i in range(n, inputs.shape[0]):
+    X_test.append(inputs[i-n:i, 0])
+
+# Convert data to numpy array
+X_test = np.array(X_test)
+
+# Reshape data to be 3-D
+X_test = np.reshape(X_test, (X_test.shape[0],X_test.shape[1],1))
+predict_price = model.predict(X_test)
+predict_price = scaler.inverse_transform(predict_price)
+
+print(X_test.shape)
+
+rmse = np.sqrt(np.mean(np.power((test - predict_price),2)))
+
+# Plot predicted vs actual values
+train = df_hourly_avg[:split]
+test = df_hourly_avg[split:]
+test['Predictions'] = predict_price
+
+plt.figure(figsize=(20,10))
+sns.set_style("whitegrid")
+plt.plot(train['price'], label='Training')
+plt.plot(test['price'], label='Actual')
+plt.plot(test['Predictions'], label='Predicted')
+plt.title("AZN Close Price - LSTM", color = 'black', fontsize = 20)
+plt.xlabel('Date', color = 'black', fontsize = 15)
+plt.ylabel('Price', color = 'black', fontsize = 15)
+plt.legend()
+
+model.save("/content/drive/MyDrive/Colab Notebooks/Platforms/my_model_.keras")