Exemplos de plot_lines_graph em Python

Exemplo n.º 1

Arquivo: main_window_elasticnet_standa.py Projeto: davis-9fv/Project

    # ElasticNet
    y_predicted_en = algorithm.elastic_net2(x_train, y_train, x_train)
    rmse = compare(y_train, y_predicted_en)
    print('RMSE Elastic %.3f' % (rmse))

    print('------- Test --------')
    # No Prediction
    y_hat_predicted = y_test
    rmse = compare(y_test, y_hat_predicted)
    print('RMSE NoPredic  %.3f' % (rmse))

    # Dummy
    y_predicted_dummy = x_test[:, 0]
    rmse = compare(y_test, y_predicted_dummy)
    print('RMSE Dummy   %.3f' % (rmse))

    # ElasticNet
    y_predicted_en, y_future_en = algorithm.elastic_net(
        x_train, y_train, x_test, y_test)
    rmse = compare(y_test, y_predicted_en)
    print('RMSE Elastic %.3f' % (rmse))
    rmse = compare(y_test, y_future_en)
    print('RMSE Elastic Future %.3f' % (rmse))
    print('  ')

    titles = ['Y test', 'ElasticNet', 'ElasticNet Future']
    data = [y_test, y_predicted_en, y_future_en]
    date_test = date[split:]
    misc.plot_lines_graph('Raw Data, Test Data, Window size ' + str(x),
                          date_test, titles, data)

Exemplo n.º 2

# https://machinelearningmastery.com/time-series-data-stationary-python/
from matplotlib import pyplot
from pandas import read_csv
from Util import data_misc
from Util import misc
from Util import data_misc
import pandas as pd

series = read_csv('../Thesis/Bitcoin_historical_data_processed_1f.csv',
                  header=0,
                  sep='\t')
# transform data to be stationary
raw_values = series['Avg'].values
date = series['Date'].values
diff = data_misc.difference(raw_values, 1)

rolmean = pd.rolling_mean(raw_values, window=15)
rolstd = pd.rolling_std(raw_values, window=15)

titles = ['Raw Data', 'Rolling Mean', 'Standard Mean']
data = [raw_values, rolmean, rolstd]
misc.plot_lines_graph('Data ', date, titles, data)
misc.plot_data_graph2('Stationary ', date, diff)

Exemplo n.º 3

Arquivo: main_window_passangers_stat.py Projeto: davis-9fv/Project

y_predicted_la_es = algorithm.lasso(x_train, y_train, x_test, normalize=False)
rmse, y_predicted_la = compare_test(y_test, y_predicted_la_es)
print('RMSE Lasso    %.3f' % (rmse))

# titles = ['Y', 'ElasticNet', 'ElasticNet Future', 'KNN5', 'KNN10']
# y_future_en = y_future_en[1]
# data = [y_hat_predicted, y_predicted_en, y_future_en, y_predicted_knn5, y_predicted_knn10]

titles = ['Y', 'ElasticNet', 'KNN5', 'KNN10', 'SGD', 'Lasso']
data = [y_hat_predicted, y_predicted_en, y_predicted_knn5, y_predicted_knn10, [], y_predicted_la]

date_test = date[split + 1:]
print('Length date test:' + str(len(date_test)))
print('Length data test:' + str(len(y_test)))

misc.plot_lines_graph('Stationary, Test Data ', date_test, titles, data)

data = [y_test, y_predicted_en_es, y_future_en_es, y_predicted_knn5_es, y_predicted_knn10_es]
misc.plot_lines_graph('Stationary, Test Data ', date_test, titles, data)

"""
y = list()
y_1 = list()
y_2 = list()
y_3 = list()
y_4 = list()

for i in range(len(y_train)):
    y.append(float(y_train[i]))
    y_1.append(float(y_train[i]))
    y_2.append(float(y_train[i]))

Exemplo n.º 4

Arquivo: main_window_passangers.py Projeto: davis-9fv/Project

print('RMSE SGD     %.3f' % (rmse))

# print('Y_test')
# print(y_test)

titles = [' ', 'Y', 'ElasticNet', 'KNN5', 'KNN10']
data = [[], y_test, y_predicted_en, y_predicted_knn5, y_predicted_knn10]

#titles = [' ', 'Y', 'ElasticNet', 'ElasticNet Future', 'KNN5', 'KNN10']
#data = [[], y_test, y_predicted_en, y_future_en, y_predicted_knn5, y_predicted_knn10]

date_test = date[split:]
print('Length date test:' + str(len(date_test)))
print('Length data test:' + str(len(y_test)))

misc.plot_lines_graph('Raw Data, Test Data ', date_test, titles, data)

y = list()
y_1 = list()
y_2 = list()
for i in range(len(y_train)):
    y.append(float(y_train[i]))
    y_1.append(float(y_train[i]))
    y_2.append(float(y_train[i]))

for i in range(len(y_test)):
    y.append(float(y_test[i]))
    y_1.append(float(y_predicted_en[i]))
    y_2.append(float(y_future_en[i]))

titles = ['Y', 'ElasticNet', 'ElasticNet Future']

Exemplo n.º 5

Arquivo: main_passangers_stat_norm.py Projeto: davis-9fv/Project

# KNN5
y_hat_predicted = algorithm.knn_regressor(x_train, y_train, x_test, 5)
rmse, y_predicted_knn5 = compare_test(test_scaled, y_hat_predicted)
print('RMSE KNN(5)  %.3f' % (rmse))

# KNN10
y_hat_predicted = algorithm.knn_regressor(x_train, y_train, x_test, 10)
rmse, y_predicted_knn10 = compare_test(test_scaled, y_hat_predicted)
print('RMSE KNN(10) %.3f' % (rmse))

# SGD
y_hat_predicted = algorithm.sgd_regressor(x_train, y_train, x_test)
rmse, y_predicted_sgd = compare_test(test_scaled, y_hat_predicted)
print('RMSE SGD     %.3f' % (rmse))

# Lasso
y_hat_predicted = algorithm.lasso(x_train, y_train, x_test, normalize=False)
rmse, y_predicted_la = compare_test(test_scaled, y_hat_predicted)
print('RMSE Lasso   %.3f' % (rmse))

# LSTM
y_hat_predicted = algorithm.lstm(x_train, y_train, x_test, batch_size=1, nb_epoch=200, neurons=3)
rmse, y_predicted_lstm = compare_test(test_scaled, y_hat_predicted)
print('RMSE LSTM   %.3f' % (rmse))

titles = ['X', 'Y', 'ElasticNet', 'KNN5', 'KNN10', 'SGD', 'Lasso','LSTM']
data = [raw_values[split:-1], y_predicted_real, y_predicted_en, y_predicted_knn5, y_predicted_knn10, y_predicted_sgd,
        y_predicted_la,y_predicted_lstm]
misc.plot_lines_graph('Stationary - Normalization, Test Data ', date[split:], titles, data)

Exemplo n.º 6

Arquivo: main_passangers_norm.py Projeto: davis-9fv/Project

rmse, y_predicted_knn5 = compare_test(test_scaled, y_hat_predicted)
print('RMSE KNN(5)  %.3f' % (rmse))

# KNN10
y_hat_predicted = algorithm.knn_regressor(x_train, y_train, x_test, 10)
rmse, y_predicted_knn10 = compare_test(test_scaled, y_hat_predicted)
print('RMSE KNN(10) %.3f' % (rmse))

# SGD
y_hat_predicted = algorithm.sgd_regressor(x_train, y_train, x_test)
rmse, y_predicted_sgd = compare_test(test_scaled, y_hat_predicted)
print('RMSE SGD     %.3f' % (rmse))

# LSTM
y_hat_predicted = algorithm.lstm(x_train,
                                 y_train,
                                 x_test,
                                 batch_size=1,
                                 nb_epoch=3,
                                 neurons=1)
rmse, y_predicted_lstm = compare_test(test_scaled, y_hat_predicted)
print('RMSE LSTM   %.3f' % (rmse))

titles = ['X', 'Y', 'ElasticNet', 'KNN5', 'KNN10', 'SGD', 'LSTM']
data = [
    test[:, 0], test[:, 1], y_predicted_en, y_predicted_knn5,
    y_predicted_knn10, y_predicted_sgd, y_predicted_lstm
]
misc.plot_lines_graph('Normalization, Test Data ', date[split + 1:], titles,
                      data)

Exemplo n.º 7

Arquivo: Main.py Projeto: davis-9fv/Project

rmse, y_predicted_knn5 = compare_test(test_scaled, y_hat_predicted)
print('RMSE KNN(5)  %.3f' % (rmse))

# KNN10
y_hat_predicted = algorithm.knn_regressor(x_train, y_train, x_test, 10)
rmse, y_predicted_knn10 = compare_test(test_scaled, y_hat_predicted)
print('RMSE KNN(10) %.3f' % (rmse))

# SGD
y_hat_predicted = algorithm.sgd_regressor(x_train, y_train, x_test)
rmse, y_predicted_sgd = compare_test(test_scaled, y_hat_predicted)
print('RMSE SGD     %.3f' % (rmse))

# LSTM
y_hat_predicted = algorithm.lstm(x_train,
                                 y_train,
                                 x_test,
                                 batch_size=1,
                                 nb_epoch=3,
                                 neurons=1)
rmse, y_predicted_lstm = compare_test(test_scaled, y_hat_predicted)
print('RMSE LSTM   %.3f' % (rmse))

titles = ['X', 'Y', 'y_predicted_Real', 'KNN5', 'KNN10', 'SGD', 'LSTM']

#data = [x_test, y_test,y_predicted_real, y_predicted_knn5, y_predicted_knn10, y_predicted_sgd, y_predicted_lstm]
#data = [y_predicted_real, y_predicted_en, y_predicted_knn5, y_predicted_knn10, y_predicted_sgd, y_predicted_lstm]
#data = [raw_values[1+split:], y_predicted_en, y_predicted_knn5, y_predicted_knn10, y_predicted_sgd,y_predicted_lstm]

misc.plot_lines_graph('Models, Test Data ', date[1 + split:], titles, data)

Exemplo n.º 8

Arquivo: main_passangers.py Projeto: davis-9fv/Project

print('RMSE Elastic %.3f' % (rmse))

# KNN5
y_hat_predicted = algorithm.knn_regressor(x_train, y_train, x_test, 5)
rmse, y_predicted_knn5 = compare_test(test, y_hat_predicted)
print('RMSE KNN(5)  %.3f' % (rmse))

# KNN10
y_hat_predicted = algorithm.knn_regressor(x_train, y_train, x_test, 10)
rmse, y_predicted_knn10 = compare_test(test, y_hat_predicted)
print('RMSE KNN(10) %.3f' % (rmse))

# SGD
y_hat_predicted = algorithm.sgd_regressor(x_train, y_train, x_test)
rmse, y_predicted_sgd = compare_test(test, y_hat_predicted)
print('RMSE SGD     %.3f' % (rmse))

# LSTM
y_hat_predicted = algorithm.lstm(x_train,
                                 y_train,
                                 x_test,
                                 batch_size=1,
                                 nb_epoch=3,
                                 neurons=1)
rmse, y_predicted_lstm = compare_test(test, y_hat_predicted)
print('RMSE LSTM   %.3f' % (rmse))

titles = ['X', 'Y', 'ElasticNet', 'KNN5', 'KNN10']
data = [x_test, y_test, y_predicted_en, y_predicted_knn5, y_predicted_knn10]
misc.plot_lines_graph('Raw Data, Test Data ', date[split:], titles, data)
misc.plot_data_graph2('Realvalue', date, raw_values)

Exemplo n.º 9

Arquivo: main_window_passangers_norm_shuffle.py Projeto: davis-9fv/Project

#rmse, y_predicted_lstm = compare_test(y_test, y_predicted_lstm)
#print('RMSE LSTM    %.3f' % (rmse))

# print('Y_test')
# print(y_test)

for i in range(0,len(y_hat_predicted)):
    titles = ['Y', 'ElasticNet', 'Lasso']
    data = [y_hat_predicted[i], y_predicted_en[i], y_predicted_la[i]]
    # titles = ['', 'Y', 'ElasticNet', 'KNN5', 'KNN10', 'SGD']
    # data = [[], y_test, y_predicted_en, y_predicted_knn5, y_predicted_knn10, y_predicted_sgd]
    date_test = date[split:]
    print('Length date test:' + str(len(date_test)))
    print('Length data test:' + str(len(y_test)))

    misc.plot_lines_graph('Normalization, Test Data ', date_test, titles, data)

"""
y = list()
y_1 = list()
y_2 = list()
for i in range(len(y_train)):
    y.append(float(y_train[i]))
    y_1.append(float(y_train[i]))
#    y_2.append(float(y_train[i]))

for i in range(len(y_test)):
    y.append(float(y_test[i]))
    y_1.append(float(y_predicted_en[i]))
#    y_2.append(float(y_future_en[i]))