LSTM_Dropout_LSTM.py

import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM
from tensorflow.keras.layers import Dense, Dropout
import pandas as pd
import tensorflow as tf
from matplotlib import pyplot as plt
from sklearn.preprocessing import MinMaxScaler
import seaborn as sns
from keras.wrappers.scikit_learn import KerasRegressor
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error
import matplotlib.pyplot as plt
from method import createXY, rmse


def build_model(optimizer, neurons, dropout_rate, learn_rate):
    """
    Build and configure a LSTM_Dropout_LSTM neural network model for time series prediction.
    Parameters:
    - optimizer (tf.keras.optimizers.Optimizer): The optimizer used for model training.
    - neurons (int): The number of LSTM units in each layer, which affects model complexity.
    - dropout_rate (float): The dropout rate applied to the model to reduce overfitting.
    Returns:
    - grid_model (tf.keras.models.Sequential): The configured LSTM model.
    """
    grid_model = Sequential()
    grid_model.add(LSTM(neurons, return_sequences=True, input_shape=(3, 1)))
    grid_model.add(Dropout(dropout_rate))
    grid_model.add(LSTM(neurons))
    grid_model.add(Dense(1))
    grid_model.compile(loss='mse', optimizer=optimizer)
    return grid_model


if __name__ == '__main__':
    outIndex = r'.\result\LSTM_Dropout_LSTM_Index.csv'
    outPrediction = r'.\result\LSTM_Dropout_LSTM_Prediction.csv'
    outTotalIndex = r'.\result\LSTM_Dropout_LSTM_Total_Index.csv'
    outDataArr = []
    totalPrediction = []
    totalOriginal = []
    outPredictionArr = []
    outSingleOriginalArr = []
    outSinglePredictionArr = []
    outTotalIndexArr = []
    for i in range(0, 16):
        data = pd.read_excel(r'./data/well-all-Original.xls', sheet_name='Sheet1', index_col=[0])
        df = data.iloc[:, [i]]
        # df = data.iloc[:,0:1]
        print(df)
        # Split the data into training and testing sets
        df_for_training = df[:-216]
        df_for_testing = df[-216:]
        print(df_for_training.shape)
        print(df_for_testing.shape)
        # Data normalization
        scaler = MinMaxScaler(feature_range=(0, 1))
        df_for_training_scaled = scaler.fit_transform(df_for_training)
        df_for_testing_scaled = scaler.transform(df_for_testing)
        # Create input sequences (X) and output labels (Y) for the model
        trainX, trainY = createXY(df_for_training_scaled, 3)
        testX, testY = createXY(df_for_testing_scaled, 3)
        # Create and train the model
        grid_model = KerasRegressor(build_fn=build_model, verbose=1)
        parameters = {'batch_size': [64],
                      'epochs': [150],
                      'optimizer': ['adam'],
                      'neurons': [64],
                      'dropout_rate': [0.1],
                      'learn_rate': [0.001]
                      }

        grid_search = GridSearchCV(estimator=grid_model,
                                   param_grid=parameters,
                                   cv=2)
        grid_search = grid_search.fit(trainX, trainY, validation_data=(testX, testY))
        grid_search.best_params_
        my_model = grid_search.best_estimator_.model
        # # Save the model
        my_model.save(r"./model/LSTM_Dropout_LSTM.h5")
        prediction = my_model.predict(testX)
        print("prediction\n", prediction)
        # Repeat the predictions to match the original data format
        prediction_copies_array = np.repeat(prediction, 2, axis=-1)
        # Inverse transform the predictions and original data to their original scale
        pred = scaler.inverse_transform(np.reshape(prediction_copies_array, (len(prediction), 2)))[:, 0]
        original_copies_array = np.repeat(testY, 2, axis=-1)
        original = scaler.inverse_transform(np.reshape(original_copies_array, (len(testY), 2)))[:, 0]

        # total Prediction
        pred_list = pred.tolist()
        original_list = original.tolist()
        j = 0
        for j in range(len(pred_list)):
            outPredictionData = pred_list[j]
            outOriginalData = original_list[j]
            totalPrediction.append(outPredictionData)
            totalOriginal.append(outOriginalData)
            Abs = abs(pred_list[j] - original_list[j])
            outSinglePredictionData = [pred_list[j], original_list[j], Abs]
            outPredictionArr.append(outSinglePredictionData)

        print("pred_list--", pred_list)
        print("original_list--", original_list)
        outSinglePredictionArr.append(pred_list)
        outSingleOriginalArr.append(original_list)

        r2 = r2_score(pred, original)
        mse = mean_squared_error(pred, original)
        Rmse = rmse(pred, original)
        mae = mean_absolute_error(pred, original)
        print("r2", r2_score(pred, original))
        print("mse", mean_squared_error(pred, original))
        print("rmse", rmse(pred, original))
        print("mae", mean_absolute_error(pred, original))

        # Output evaluation metrics
        outdata = ['Well-{}'.format(i + 1), r2, mse, Rmse, mae]
        outDataArr.append(outdata)
    total_r2 = r2_score(totalPrediction, totalOriginal)
    total_mse = mean_squared_error(totalPrediction, totalOriginal)
    total_Rmse = rmse(totalPrediction, totalOriginal)
    total_mae = mean_absolute_error(totalPrediction, totalOriginal)
    # total_rRMSE = rRMSE(Original, Prediction)
    print("Total Index--")
    print("r2", total_r2)
    print("mse", total_mse)
    print("rmse", total_Rmse)
    print("mae", total_mae)
    # print("rRMSE", total_rRMSE)

    # Output evaluation metrics
    outIndexdata = [total_r2, total_mse, total_Rmse, total_mae]
    outTotalIndexArr.append(outIndexdata)

    outDataArr = pd.DataFrame(outDataArr)
    outDataArr.rename(columns={0: 'well', 1: 'R2', 2: 'mse', 3: 'rmse', 4: 'mae'}, inplace=True)
    print(outDataArr)
    outDataArr.to_csv(outIndex)

    # Save prediction to a file
    # Total well
    outPredictionArr = pd.DataFrame(outPredictionArr)
    outPredictionArr.rename(columns={0: 'WellPrediction', 1: 'WellOriginal', 2: 'WellAbs',
                                     }, inplace=True)
    print(outPredictionArr)
    outPredictionArr.to_csv(outPrediction)

    # Save evaluation metrics to a file
    outTotalIndexArr = pd.DataFrame(outTotalIndexArr)
    outTotalIndexArr.rename(columns={0: 'R2', 1: 'mse', 2: 'rmse', 3: 'mae', 4: 'rRMSE'}, inplace=True)
    print(outTotalIndexArr)
    outTotalIndexArr.to_csv(outTotalIndex)