anntestavcount.py

# ANN for av count of tata mills
import math
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mpl
import seaborn as sns
from keras.models import Sequential
from keras.layers import Dense, LSTM
from keras.utils import plot_model
from sklearn.preprocessing import MinMaxScaler

np.random.seed(7)
dataset = pd.read_csv('tata_av_count.csv', parse_dates = True, index_col = 0)
data = dataset.values
data = dataset.astype('float32')
from sklearn.model_selection import train_test_split
train, test = train_test_split(data.values, test_size = 0.2)
def prepare_data(data, lags = 1):
    ''' Create lagged data from an input time series '''
    X, y = [], []
    for row in range(len(data) - lags - 1):
        a = data[row:(row + lags), 0]
        X.append(a)
        y.append(data[row + lags, 0])
    return np.array(X), np.array(y)
lags = 6
X_train, y_train = prepare_data(train, lags)
X_test, y_test = prepare_data(test, lags)
y_true = y_test

# Create and fit Multilayer Perceptron Model
mdl = Sequential()
mdl.add(Dense(units = lags, activation ='relu'))
mdl.add(Dense(1))
mdl.compile(loss = 'mean_squared_error', optimizer = 'adam')
mdl.fit(X_train, y_train, epochs = 100, batch_size = 2, verbose = 2)
         
# estimate model performance
train_score = mdl.evaluate(X_train, y_train, verbose=0)
print('Train Score: {:.2f} MSE ({:.2f} RMSE)'.format(train_score, math.sqrt(train_score)))
test_score = mdl.evaluate(X_test, y_test, verbose=0)
print('Test Score: {:.2f} MSE ({:.2f} RMSE)'.format(test_score, math.sqrt(test_score)))

# generate predictions for training
train_predict = mdl.predict(X_train)
test_predict = mdl.predict(X_test)
 
# shift train predictions for plotting
train_predict_plot = np.empty_like(data)
train_predict_plot[:, :] = np.nan
train_predict_plot[lags: len(train_predict) + lags, :] = train_predict
 
# shift test predictions for plotting
test_predict_plot = np.empty_like(data)
test_predict_plot[:, :] = np.nan
test_predict_plot[len(train_predict)+(lags*2)+1:len(data)-1, :] = test_predict
 
# plot baseline and predictions
plt.plot(data.values, label='Observed', color='#006699');
plt.plot(train_predict_plot, label='Prediction for train', color='#006699', alpha=0.5);
plt.plot(test_predict_plot, label='Prediction for test', color='#ff0066');
plt.legend(loc='best')
plt.title('Multilayer Perceptron with Window')
plt.show()

# plot prediction with observation
mse = ((y_test.reshape(-1, 1) - test_predict.reshape(-1, 1)) ** 2).mean()
plt.title('Prediction quality: {:.2f} MSE ({:.2f} RMSE)'.format(mse, math.sqrt(mse)))
plt.plot(y_test.reshape(-1, 1), label='Observed', color='#006699')
plt.plot(test_predict.reshape(-1, 1), label='Prediction', color='#ff0066')
plt.legend(loc='best')
plt.show()