42 lines
1.2 KiB
Python
42 lines
1.2 KiB
Python
import pandas as pd
|
|
import numpy as np
|
|
from sklearn.model_selection import train_test_split
|
|
from sklearn import metrics
|
|
import matplotlib.pyplot as plt
|
|
import tensorflow as tf
|
|
import math
|
|
|
|
from tensorflow import keras
|
|
from process_dataset import process_data_and_get_x_y
|
|
|
|
|
|
def show_result(x, y):
|
|
plt.title('Usage kWh Model', fontsize=15, color='g', pad=12)
|
|
plt.plot(x, y, 'o', color='r')
|
|
|
|
m, b = np.polyfit(x, y, 1)
|
|
plt.plot(x, m * x + b, color='darkblue')
|
|
plt.xlabel('Actual')
|
|
plt.ylabel('Predicted')
|
|
plt.show()
|
|
|
|
|
|
model = keras.models.load_model('steel_industry_model')
|
|
|
|
energy_data_test = pd.read_csv('Steel_industry_data_test.csv')
|
|
energy_data_test, x_test, y_test = process_data_and_get_x_y(energy_data_test)
|
|
|
|
y_predicted = model.predict(x_test)
|
|
test_results = {}
|
|
test_results['usage_model'] = model.evaluate(
|
|
x_test,
|
|
y_test, verbose=0)
|
|
|
|
print('Mean Absolute Error : ', metrics.mean_absolute_error(y_test, y_predicted))
|
|
print('Mean Squared Error : ', metrics.mean_squared_error(y_test, y_predicted))
|
|
print('Root Mean Squared Error : ', math.sqrt(metrics.mean_squared_error(y_test, y_predicted)))
|
|
|
|
print(test_results['usage_model'])
|
|
|
|
show_result(y_test, y_predicted)
|