Zad 05.Biblioteki DL - prosta sieć neuronowa
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Cezary Gałązkiewicz
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@ -1,25 +1,126 @@
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import pandas as pd
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import numpy as np
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from sklearn.model_selection import train_test_split
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from sklearn import metrics
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import matplotlib.pyplot as plt
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import tensorflow as tf
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import math
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energy_data = pd.read_csv('Steel_industry_data.csv')
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from tensorflow import keras
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#from tensorflow.keras import layers
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train_data, test_data = train_test_split(energy_data, test_size=7008, random_state=1)
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test_data, dev_data = train_test_split(test_data, test_size=3504, random_state=1)
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def process_data_and_get_x_y(data):
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data.columns = ["date", "Usage_kWh", "Lagging_Current_Reactive.Power_kVarh", "Leading_Current_Reactive_Power_kVarh",
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"CO2(tCO2)", "Lagging_Current_Power_Factor", "Leading_Current_Power_Factor", "WeekStatus",
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"Day_of_week", "Load_Type"] #without NSM column
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data = data.set_index('date')
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data = pd.get_dummies(data, drop_first=True)
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x = data.drop('Usage_kWh', axis=1)
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#x = data['Lagging_Current_Reactive.Power_kVarh']
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y = data['Usage_kWh']
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return data, x, y
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def plot_loss(history):
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plt.plot(history.history['loss'], label='loss')
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plt.plot(history.history['val_loss'], label='val_loss')
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plt.ylim([0, 10])
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plt.xlabel('Epoch')
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plt.ylabel('Error')
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plt.legend()
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plt.grid(True)
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plt.show()
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def show_result(x, y):
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plt.title('One variable Model', fontsize=15, color='g', pad=12)
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plt.plot(x, y, 'o', color='r')
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m, b = np.polyfit(x, y, 1)
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plt.plot(x, m * x + b, color='darkblue')
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plt.xlabel('Actual')
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plt.ylabel('Predicted')
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plt.show()
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energy_data_train = pd.read_csv('Steel_industry_data_train.csv')
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energy_data_test = pd.read_csv('Steel_industry_data_test.csv')
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energy_data_test, x_test, y_test = process_data_and_get_x_y(energy_data_test)
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energy_data_train, x_train, y_train = process_data_and_get_x_y(energy_data_train)
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#x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size=0.2, random_state=1)
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# x_test, x_dev, y_test, y_dev = train_test_split(x_test, y_test, test_size=0.5, random_state=1)
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# stats
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print(energy_data.describe(include='all'))
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print(energy_data_test.describe(include='all'))
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print(x_train.describe(include='all'))
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#print(np.array(x_train).reshape(-1, 1))
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print('Training set size:')
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print(train_data.shape)
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print('Testing set size:')
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print(test_data.shape)
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print('Dev set size:')
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print(dev_data.shape)
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normalizer = tf.keras.layers.Normalization(axis=1)
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normalizer.adapt(np.array(x_train))
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print(normalizer.mean.numpy())
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#print(train_data.describe(include='all'))
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#print(test_data.describe(include='all'))
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#print(dev_data.describe(include='all'))
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# powinno być niezmienione
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print(np.array(x_train[:1]))
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test_data.to_csv("steel_industry_data_test.csv", encoding="utf-8", index=False)
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dev_data.to_csv("steel_industry_data_dev.csv", encoding="utf-8", index=False)
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train_data.to_csv("steel_industry_data_train.csv", encoding="utf-8", index=False)
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#usage = np.array(x_train)
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#usage_normalizer = keras.layers.Normalization(input_shape=[14, ], axis=1)
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#usage_normalizer.adapt(usage)
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usage_model = tf.keras.Sequential([
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normalizer,
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keras.layers.Dense(units=10, activation='relu'),
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keras.layers.Dense(units=1)
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])
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print(usage_model.summary())
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usage_model.compile(
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optimizer=tf.optimizers.Adam(learning_rate=0.1),
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loss='mean_absolute_error')
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history = usage_model.fit(
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x_train,
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y_train,
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epochs=100,
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# Suppress logging.
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verbose=0,
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# Calculate validation results on 20% of the training data.
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validation_split=0.2)
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hist = pd.DataFrame(history.history)
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hist['epoch'] = history.epoch
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print(hist.tail())
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plot_loss(history)
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y_predicted = usage_model.predict(x_test)
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test_results = {}
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test_results['usage_model'] = usage_model.evaluate(
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x_test,
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y_test, verbose=0)
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print('Mean Absolute Error : ', metrics.mean_absolute_error(y_test, y_predicted))
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print('Mean Squared Error : ', metrics.mean_squared_error(y_test, y_predicted))
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print('Root Mean Squared Error : ', math.sqrt(metrics.mean_squared_error(y_test, y_predicted)))
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print(test_results['usage_model'])
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show_result(y_test, y_predicted)
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#print('Training set size:')
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#print(x_train.shape)
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#print(y_train.shape)
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#print('Testing set size:')
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#print(x_test.shape)
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#print(y_test.shape)
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# print('Dev set size:')
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# print(x_dev.shape)
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# print(y_dev.shape)
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# print(train_data.describe(include='all'))
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# print(test_data.describe(include='all'))
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# print(dev_data.describe(include='all'))
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