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