/
overfitting.py
517 lines (438 loc) · 28.5 KB
/
overfitting.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Jun 17 21:30:36 2018
@author: vedanshu
"""
"""
overfitting
~~~~~~~~~~~
Plot graphs to illustrate the problem of overfitting.
"""
# Standard library
import json
# My library
import network as nt
# Third-party libraries
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn.model_selection import TimeSeriesSplit
from os import path
def make_plots(filename, num_epochs,
training_cost_xmin=200,
test_accuracy_xmin=200,
test_cost_xmin=0,
training_accuracy_xmin=0,
plt_training_cost = False,
plt_training_accuracy = False,
plt_test_cost = False,
plt_test_accuracy = True,
plt_overlay = False):
"""Load the results from ``filename``, and generate the corresponding
plots. """
f = open(filename, "r")
test_cost, test_mape, test_rmse, test_mae, training_cost, training_mape, \
training_rmse, training_mae = json.load(f)
f.close()
if plt_training_cost:
plot_training_cost(training_cost, num_epochs, training_cost_xmin)
if plt_test_accuracy:
plot_test_accuracy(test_mape, num_epochs, test_accuracy_xmin)
if plt_test_cost:
plot_test_cost(test_cost, num_epochs, test_cost_xmin)
if plt_training_accuracy:
plot_training_accuracy(training_mape, num_epochs,
training_accuracy_xmin)
if plt_overlay:
plot_overlay(test_mape, training_mape, num_epochs,
min(test_accuracy_xmin, training_accuracy_xmin))
def plot_training_cost(training_cost, num_epochs, training_cost_xmin):
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(np.arange(training_cost_xmin, num_epochs),
training_cost[training_cost_xmin:num_epochs],
color='#2A6EA6')
ax.set_xlim([training_cost_xmin, num_epochs])
ax.grid(True)
ax.set_xlabel('Epoch')
ax.set_title('Cost on the training data')
plt.show()
def plot_test_accuracy(test_accuracy, num_epochs, test_accuracy_xmin):
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(np.arange(test_accuracy_xmin, num_epochs),
[accuracy
for accuracy in test_accuracy[test_accuracy_xmin:num_epochs]],
color='#2A6EA6')
ax.set_xlim([test_accuracy_xmin, num_epochs])
ax.grid(True)
ax.set_xlabel('Epoch')
ax.set_title('Error on the test data')
plt.show()
def plot_test_cost(test_cost, num_epochs, test_cost_xmin):
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(np.arange(test_cost_xmin, num_epochs),
test_cost[test_cost_xmin:num_epochs],
color='#2A6EA6')
ax.set_xlim([test_cost_xmin, num_epochs])
ax.grid(True)
ax.set_xlabel('Epoch')
ax.set_title('Cost on the test data')
plt.show()
def plot_training_accuracy(training_accuracy, num_epochs,
training_accuracy_xmin):
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(np.arange(training_accuracy_xmin, num_epochs),
[accuracy
for accuracy in training_accuracy[training_accuracy_xmin:num_epochs]],
color='#2A6EA6')
ax.set_xlim([training_accuracy_xmin, num_epochs])
ax.grid(True)
ax.set_xlabel('Epoch')
ax.set_title('Error on the training data')
plt.show()
def plot_overlay(test_accuracy, training_accuracy, num_epochs, xmin):
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(np.arange(xmin, num_epochs),
[accuracy for accuracy in test_accuracy],
color='#2A6EA6',
label="Error on the test data")
ax.plot(np.arange(xmin, num_epochs),
[accuracy
for accuracy in training_accuracy],
color='#FFA933',
label="Error on the training data")
ax.grid(True)
ax.set_xlim([xmin, num_epochs])
ax.set_xlabel('Epoch')
ax.set_ylim([0, 20])
plt.legend(loc="lower right")
plt.show()
def timeSeriesSplit(cso = False):
state = {0: 'NSW', 1: 'QLD', 2: 'SA', 3: 'TAS', 4: 'VIC'}
year = {0: '2015', 1: '2016', 2: '2017'}
df_nsw = pd.DataFrame()
df_qld = pd.DataFrame()
df_sa = pd.DataFrame()
df_tas = pd.DataFrame()
df_vic = pd.DataFrame()
df = {'NSW': df_nsw, 'QLD': df_qld, 'SA': df_sa, 'TAS': df_tas, 'VIC': df_vic}
df_nsw_test = pd.DataFrame()
df_qld_test = pd.DataFrame()
df_sa_test = pd.DataFrame()
df_tas_test = pd.DataFrame()
df_vic_test = pd.DataFrame()
df_test = {'NSW': df_nsw_test, 'QLD': df_qld_test, 'SA': df_sa_test, 'TAS': df_tas_test, 'VIC': df_vic_test}
for st in state.values():
for ye in year.values():
for mn in range(1,13):
if mn < 10:
dataset = pd.read_csv('./datasets/train/' + st + '/PRICE_AND_DEMAND_' + ye + '0' + str(mn) +'_' + st + '1.csv')
else:
dataset = pd.read_csv('./datasets/train/' + st + '/PRICE_AND_DEMAND_' + ye + str(mn) +'_' + st + '1.csv')
df[st] = df[st].append(dataset.iloc[:,1:3])
df[st] = df[st].set_index('SETTLEMENTDATE')
for st in state.values():
dataset = pd.read_csv('./datasets/test/' + st + '/PRICE_AND_DEMAND_201801_' + st + '1.csv')
df_test[st] = df_test[st].append(dataset.iloc[:,1:3])
df_test[st] = df_test[st].set_index('SETTLEMENTDATE')
# numpy array
list_hourly_load_NSW = np.array(df['NSW'])
list_hourly_load_QLD = np.array(df['QLD'])
list_hourly_load_SA = np.array(df['SA'])
list_hourly_load_TAS = np.array(df['TAS'])
list_hourly_load_VIC = np.array(df['VIC'])
# the length of the sequnce for predicting the future value
sequence_length = 84
x_size = 36
hidden = 10
y_size = 48
# normalizing
matrix_load_NSW = list_hourly_load_NSW / np.linalg.norm(list_hourly_load_NSW)
matrix_load_QLD = list_hourly_load_QLD / np.linalg.norm(list_hourly_load_QLD)
matrix_load_SA = list_hourly_load_SA / np.linalg.norm(list_hourly_load_SA)
matrix_load_TAS = list_hourly_load_TAS / np.linalg.norm(list_hourly_load_TAS)
matrix_load_VIC = list_hourly_load_VIC / np.linalg.norm(list_hourly_load_VIC)
matrix_load_NSW = matrix_load_NSW[:-(len(matrix_load_NSW) % sequence_length)]
matrix_load_QLD = matrix_load_QLD[:-(len(matrix_load_QLD) % sequence_length)]
matrix_load_SA = matrix_load_SA[:-(len(matrix_load_SA) % sequence_length)]
matrix_load_TAS = matrix_load_TAS[:-(len(matrix_load_TAS) % sequence_length)]
matrix_load_VIC = matrix_load_VIC[:-(len(matrix_load_VIC) % sequence_length)]
matrix_load_NSW = matrix_load_NSW.reshape(-1, sequence_length)
matrix_load_QLD = matrix_load_QLD.reshape(-1, sequence_length)
matrix_load_SA = matrix_load_SA.reshape(-1, sequence_length)
matrix_load_TAS = matrix_load_TAS.reshape(-1, sequence_length)
matrix_load_VIC = matrix_load_VIC.reshape(-1, sequence_length)
# shuffle the training set (but do not shuffle the test set)
np.random.shuffle(matrix_load_NSW)
np.random.shuffle(matrix_load_QLD)
np.random.shuffle(matrix_load_SA)
np.random.shuffle(matrix_load_TAS)
np.random.shuffle(matrix_load_VIC)
# the training set
X_NSW = matrix_load_NSW[:, :x_size]
X_QLD = matrix_load_QLD[:, :x_size]
X_SA = matrix_load_SA[:, :x_size]
X_TAS = matrix_load_TAS[:, :x_size]
X_VIC = matrix_load_VIC[:, :x_size]
# the last column is the true value to compute the mean-squared-error loss
y_NSW = matrix_load_NSW[:, x_size:]
y_QLD = matrix_load_QLD[:, x_size:]
y_SA = matrix_load_SA[:, x_size:]
y_TAS = matrix_load_TAS[:, x_size:]
y_VIC = matrix_load_VIC[:, x_size:]
tscv = TimeSeriesSplit(n_splits=5)
X = {'NSW': X_NSW, 'QLD': X_QLD, 'SA': X_SA, 'TAS': X_TAS, 'VIC': X_VIC}
y = {'NSW': y_NSW, 'QLD': y_QLD, 'SA': y_SA, 'TAS': y_TAS, 'VIC': y_VIC}
for st in state.values():
print("State: ", st)
i = 1
for train_index, test_index in tscv.split(X[st]):
X_train, X_test = X[st][train_index], X[st][test_index]
y_train, y_test = y[st][train_index], y[st][test_index]
print("Train and validation from state ", st, " split ", i)
net = nt.Network([x_size, hidden, y_size], nt.Activation.tanh, nt.QuadraticCost)
if cso:
fname = "kernelBiasTimeSeries" + st + ".npy"
if not path.exists(fname):
print("Weights and biases initialization for state ",st, " in progress...")
randInt = np.random.randint(X_train.shape[0])
net.cso(100,X_train[randInt].reshape(x_size,1),y_train[randInt].reshape(y_size,1),
net.multiObjectiveFunction,-0.6,0.6,net.dim ,100)
net.set_weight_bias(np.array(net.get_Gbest()))
np.save(fname, np.array(net.get_Gbest()))
net.set_weight_bias(np.load(fname))
if cso:
fname = "results_" + st + "_TS_" + str(i) + "CSO"
else:
fname = "results_" + st + "_TS_" + str(i) + "GD"
num_epochs = 1500
lmbda = 2
evaluation_cost, eval_mape, eval_rmse, eval_mae, training_cost, training_mape, training_rmse, training_mae = net.SGD(
X_train.transpose(),y_train.transpose(), num_epochs,
10, 0.01,
X_test.transpose(), y_test.transpose(),
lmbda, monitor_evaluation_cost = True,
monitor_evaluation_accuracy = True,
monitor_training_cost = True,
monitor_training_accuracy = True,
output2D = True)
f = open(fname, "w")
json.dump([evaluation_cost, eval_mape, eval_rmse, eval_mae, training_cost, training_mape, training_rmse, training_mae], f)
f.close()
# make_plots(fname, num_epochs,
# training_cost_xmin = 0,
# test_accuracy_xmin = 0,
# test_cost_xmin = 0,
# training_accuracy_xmin = 0)
i = i+1
def fiveFoldCrossValidation(cso = False):
#State and year to use for training and testing
state = {0: 'NSW', 1: 'QLD', 2: 'SA', 3: 'TAS', 4: 'VIC'}
# state = {0: 'NSW'}
year = {0: '2015', 1: '2016', 2: '2017'}
# year = {0: '2015'}
hidden = 10
#Training and testing batches
x_batches = {}
y_batches = {}
#parameters for 5 fold validation
set_size = 84
x_size = 36
y_size = 48
x_batches_validation_fold ={}
y_batches_validation_fold ={}
x_batches_train_fold = {}
y_batches_train_fold = {}
df_nsw = pd.DataFrame()
df_qld = pd.DataFrame()
df_sa = pd.DataFrame()
df_tas = pd.DataFrame()
df_vic = pd.DataFrame()
df = {'NSW': df_nsw, 'QLD': df_qld, 'SA': df_sa, 'TAS': df_tas, 'VIC': df_vic}
for st in state.values():
for ye in year.values():
for mn in range(1,13):
if mn < 10:
dataset = pd.read_csv('./datasets/train/' + st + '/PRICE_AND_DEMAND_' + ye + '0' + str(mn) +'_' + st + '1.csv')
else:
dataset = pd.read_csv('./datasets/train/' + st + '/PRICE_AND_DEMAND_' + ye + str(mn) +'_' + st + '1.csv')
df[st] = df[st].append(dataset.iloc[:,1:3])
df[st] = df[st].set_index('SETTLEMENTDATE')
TS_NSW = np.array(df['NSW'])
TS_QLD = np.array(df['QLD'])
TS_SA = np.array(df['SA'])
TS_TAS = np.array(df['TAS'])
TS_VIC = np.array(df['VIC'])
#Normalizing the dataset
TS_NSW = TS_NSW / np.linalg.norm(TS_NSW)
TS_QLD = TS_QLD / np.linalg.norm(TS_QLD)
TS_SA = TS_SA / np.linalg.norm(TS_SA)
TS_TAS = TS_TAS / np.linalg.norm(TS_TAS)
TS_VIC = TS_VIC / np.linalg.norm(TS_VIC)
""" Making the dataset size divisible by num_period """
TS_NSW = TS_NSW[:(len(TS_NSW) -(len(TS_NSW) % set_size))]
TS_QLD = TS_QLD[:(len(TS_QLD)- (len(TS_QLD) % set_size))]
TS_SA = TS_SA[:(len(TS_SA) -(len(TS_SA) % set_size))]
TS_TAS = TS_TAS[:(len(TS_TAS) -(len(TS_TAS) % set_size))]
TS_VIC = TS_VIC[:(len(TS_VIC) - (len(TS_VIC) % set_size))]
""" Making our training dataset with batch size of num_period """
TS_batches = {'NSW': TS_NSW.reshape(-1, set_size).transpose(),
'QLD': TS_QLD.reshape(-1, set_size).transpose(),
'SA': TS_SA.reshape(-1, set_size).transpose(),
'TAS': TS_TAS.reshape(-1, set_size).transpose(),
'VIC': TS_VIC.reshape(-1, set_size).transpose()}
x_batches = {'NSW': TS_batches['NSW'][:x_size,:],
'QLD': TS_batches['QLD'][:x_size,:],
'SA': TS_batches['SA'][:x_size,:],
'TAS': TS_batches['TAS'][:x_size,:],
'VIC': TS_batches['VIC'][:x_size,:]}
y_batches = {'NSW': TS_batches['NSW'][x_size:,:],
'QLD': TS_batches['QLD'][x_size:,:],
'SA': TS_batches['SA'][x_size:,:],
'TAS': TS_batches['TAS'][x_size:,:],
'VIC': TS_batches['VIC'][x_size:,:]}
#Making validation set
x_batches_validation_fold[1] = {'NSW': x_batches['NSW'][:, np.arange(0,x_batches['NSW'].shape[1],5)],
'QLD': x_batches['QLD'][:, np.arange(0,x_batches['QLD'].shape[1],5)],
'SA': x_batches['SA'][:, np.arange(0,x_batches['SA'].shape[1],5)],
'TAS': x_batches['TAS'][:, np.arange(0,x_batches['TAS'].shape[1],5)],
'VIC': x_batches['VIC'][:, np.arange(0,x_batches['VIC'].shape[1],5)]}
x_batches_validation_fold[2] = {'NSW': x_batches['NSW'][:, np.arange(1,x_batches['NSW'].shape[1],5)],
'QLD': x_batches['QLD'][:, np.arange(1,x_batches['QLD'].shape[1],5)],
'SA': x_batches['SA'][:, np.arange(1,x_batches['SA'].shape[1],5)],
'TAS': x_batches['TAS'][:, np.arange(1,x_batches['TAS'].shape[1],5)],
'VIC': x_batches['VIC'][:, np.arange(1,x_batches['VIC'].shape[1],5)]}
x_batches_validation_fold[3] = {'NSW': x_batches['NSW'][:, np.arange(2,x_batches['NSW'].shape[1],5)],
'QLD': x_batches['QLD'][:, np.arange(2,x_batches['QLD'].shape[1],5)],
'SA': x_batches['SA'][:, np.arange(2,x_batches['SA'].shape[1],5)],
'TAS': x_batches['TAS'][:, np.arange(2,x_batches['TAS'].shape[1],5)],
'VIC': x_batches['VIC'][:, np.arange(2,x_batches['VIC'].shape[1],5)]}
x_batches_validation_fold[4] = {'NSW': x_batches['NSW'][:,np.arange(3,x_batches['NSW'].shape[1],5)],
'QLD': x_batches['QLD'][:, np.arange(3,x_batches['QLD'].shape[1],5)],
'SA': x_batches['SA'][:, np.arange(3,x_batches['SA'].shape[1],5)],
'TAS': x_batches['TAS'][:, np.arange(3,x_batches['TAS'].shape[1],5)],
'VIC': x_batches['VIC'][:, np.arange(3,x_batches['VIC'].shape[1],5)]}
x_batches_validation_fold[5] = {'NSW': x_batches['NSW'][:, np.arange(4,x_batches['NSW'].shape[1],5)],
'QLD': x_batches['QLD'][:, np.arange(4,x_batches['QLD'].shape[1],5)],
'SA': x_batches['SA'][:, np.arange(4,x_batches['SA'].shape[1],5)],
'TAS': x_batches['TAS'][:, np.arange(4,x_batches['TAS'].shape[1],5)],
'VIC': x_batches['VIC'][:, np.arange(4,x_batches['VIC'].shape[1],5)]}
y_batches_validation_fold[1] = {'NSW': y_batches['NSW'][:, np.arange(0,y_batches['NSW'].shape[1],5)],
'QLD': y_batches['QLD'][:, np.arange(0,y_batches['QLD'].shape[1],5)],
'SA': y_batches['SA'][:, np.arange(0,y_batches['SA'].shape[1],5)],
'TAS': y_batches['TAS'][:, np.arange(0,y_batches['TAS'].shape[1],5)],
'VIC': y_batches['VIC'][:, np.arange(0,y_batches['VIC'].shape[1],5)]}
y_batches_validation_fold[2] = {'NSW': y_batches['NSW'][:, np.arange(1,y_batches['NSW'].shape[1],5)],
'QLD': y_batches['QLD'][:, np.arange(1,y_batches['QLD'].shape[1],5)],
'SA': y_batches['SA'][:, np.arange(1,y_batches['SA'].shape[1],5)],
'TAS': y_batches['TAS'][:, np.arange(1,y_batches['TAS'].shape[1],5)],
'VIC': y_batches['VIC'][:, np.arange(1,y_batches['VIC'].shape[1],5)]}
y_batches_validation_fold[3] = {'NSW': y_batches['NSW'][:, np.arange(2,y_batches['NSW'].shape[1],5)],
'QLD': y_batches['QLD'][:, np.arange(2,y_batches['QLD'].shape[1],5)],
'SA': y_batches['SA'][:, np.arange(2,y_batches['SA'].shape[1],5)],
'TAS': y_batches['TAS'][:, np.arange(2,y_batches['TAS'].shape[1],5)],
'VIC': y_batches['VIC'][:, np.arange(2,y_batches['VIC'].shape[1],5)]}
y_batches_validation_fold[4] = {'NSW': y_batches['NSW'][:, np.arange(3,y_batches['NSW'].shape[1],5)],
'QLD': y_batches['QLD'][:, np.arange(3,y_batches['QLD'].shape[1],5)],
'SA': y_batches['SA'][:, np.arange(3,y_batches['SA'].shape[1],5)],
'TAS': y_batches['TAS'][:, np.arange(3,y_batches['TAS'].shape[1],5)],
'VIC': y_batches['VIC'][:, np.arange(3,y_batches['VIC'].shape[1],5)]}
y_batches_validation_fold[5] = {'NSW': y_batches['NSW'][:, np.arange(4,y_batches['NSW'].shape[1],5)],
'QLD': y_batches['QLD'][:, np.arange(4,y_batches['QLD'].shape[1],5)],
'SA': y_batches['SA'][:, np.arange(4,y_batches['SA'].shape[1],5)],
'TAS': y_batches['TAS'][:, np.arange(4,y_batches['TAS'].shape[1],5)],
'VIC': y_batches['VIC'][:, np.arange(4,y_batches['VIC'].shape[1],5)]}
#Making training sets
x_batches_train_fold[1] = {'NSW': x_batches['NSW'][:, [x for x in np.arange(0,x_batches['NSW'].shape[1]) if x not in np.arange(0,x_batches['NSW'].shape[1],5)] ],
'QLD': x_batches['QLD'][:, [x for x in np.arange(0,x_batches['QLD'].shape[1]) if x not in np.arange(0,x_batches['QLD'].shape[1],5)] ],
'SA': x_batches['SA'][:, [x for x in np.arange(0,x_batches['SA'].shape[1]) if x not in np.arange(0,x_batches['SA'].shape[1],5)] ],
'TAS': x_batches['TAS'][:, [x for x in np.arange(0,x_batches['TAS'].shape[1]) if x not in np.arange(0,x_batches['TAS'].shape[1],5)] ],
'VIC': x_batches['VIC'][:, [x for x in np.arange(0,x_batches['VIC'].shape[1]) if x not in np.arange(0,x_batches['VIC'].shape[1],5)] ]}
x_batches_train_fold[2] = {'NSW': x_batches['NSW'][:, [x for x in np.arange(1,x_batches['NSW'].shape[1]) if x not in np.arange(1,x_batches['NSW'].shape[1],5)] ],
'QLD': x_batches['QLD'][:, [x for x in np.arange(1,x_batches['QLD'].shape[1]) if x not in np.arange(1,x_batches['QLD'].shape[1],5)] ],
'SA': x_batches['SA'][:, [x for x in np.arange(1,x_batches['SA'].shape[1]) if x not in np.arange(1,x_batches['SA'].shape[1],5)] ],
'TAS': x_batches['TAS'][:, [x for x in np.arange(1,x_batches['TAS'].shape[1]) if x not in np.arange(1,x_batches['TAS'].shape[1],5)] ],
'VIC': x_batches['VIC'][:, [x for x in np.arange(1,x_batches['VIC'].shape[1]) if x not in np.arange(1,x_batches['VIC'].shape[1],5)] ]}
x_batches_train_fold[3] = {'NSW': x_batches['NSW'][:, [x for x in np.arange(2,x_batches['NSW'].shape[1]) if x not in np.arange(2,x_batches['NSW'].shape[1],5)] ],
'QLD': x_batches['QLD'][:, [x for x in np.arange(2,x_batches['QLD'].shape[1]) if x not in np.arange(2,x_batches['QLD'].shape[1],5)] ],
'SA': x_batches['SA'][:, [x for x in np.arange(2,x_batches['SA'].shape[1]) if x not in np.arange(2,x_batches['SA'].shape[1],5)] ],
'TAS': x_batches['TAS'][:, [x for x in np.arange(2,x_batches['TAS'].shape[1]) if x not in np.arange(2,x_batches['TAS'].shape[1],5)] ],
'VIC': x_batches['VIC'][:, [x for x in np.arange(2,x_batches['VIC'].shape[1]) if x not in np.arange(2,x_batches['VIC'].shape[1],5)] ]}
x_batches_train_fold[4] = {'NSW': x_batches['NSW'][:, [x for x in np.arange(3,x_batches['NSW'].shape[1]) if x not in np.arange(3,x_batches['NSW'].shape[1],5)] ],
'QLD': x_batches['QLD'][:, [x for x in np.arange(3,x_batches['QLD'].shape[1]) if x not in np.arange(3,x_batches['QLD'].shape[1],5)] ],
'SA': x_batches['SA'][:, [x for x in np.arange(3,x_batches['SA'].shape[1]) if x not in np.arange(3,x_batches['SA'].shape[1],5)] ],
'TAS': x_batches['TAS'][:, [x for x in np.arange(3,x_batches['TAS'].shape[1]) if x not in np.arange(3,x_batches['TAS'].shape[1],5)]],
'VIC': x_batches['VIC'][:, [x for x in np.arange(3,x_batches['VIC'].shape[1]) if x not in np.arange(3,x_batches['VIC'].shape[1],5)]]}
x_batches_train_fold[5] = {'NSW': x_batches['NSW'][:, [x for x in np.arange(4,x_batches['NSW'].shape[1]) if x not in np.arange(4,x_batches['NSW'].shape[1],5)] ],
'QLD': x_batches['QLD'][:, [x for x in np.arange(4,x_batches['QLD'].shape[1]) if x not in np.arange(4,x_batches['QLD'].shape[1],5)] ],
'SA': x_batches['SA'][:, [x for x in np.arange(4,x_batches['SA'].shape[1]) if x not in np.arange(4,x_batches['SA'].shape[1],5)] ],
'TAS': x_batches['TAS'][:, [x for x in np.arange(4,x_batches['TAS'].shape[1]) if x not in np.arange(4,x_batches['TAS'].shape[1],5)] ],
'VIC': x_batches['VIC'][:, [x for x in np.arange(4,x_batches['VIC'].shape[1]) if x not in np.arange(4,x_batches['VIC'].shape[1],5)] ]}
y_batches_train_fold[1] = {'NSW': y_batches['NSW'][:, [x for x in np.arange(0,y_batches['NSW'].shape[1]) if x not in np.arange(0,y_batches['NSW'].shape[1],5)] ],
'QLD': y_batches['QLD'][:, [x for x in np.arange(0,y_batches['QLD'].shape[1]) if x not in np.arange(0,y_batches['QLD'].shape[1],5)] ],
'SA': y_batches['SA'][:, [x for x in np.arange(0,y_batches['SA'].shape[1]) if x not in np.arange(0,y_batches['SA'].shape[1],5)] ],
'TAS': y_batches['TAS'][:, [x for x in np.arange(0,y_batches['TAS'].shape[1]) if x not in np.arange(0,y_batches['TAS'].shape[1],5)] ],
'VIC': y_batches['VIC'][:, [x for x in np.arange(0,y_batches['VIC'].shape[1]) if x not in np.arange(0,y_batches['VIC'].shape[1],5)] ]}
y_batches_train_fold[2] = {'NSW': y_batches['NSW'][:, [x for x in np.arange(1,y_batches['NSW'].shape[1]) if x not in np.arange(1,y_batches['NSW'].shape[1],5)] ],
'QLD': y_batches['QLD'][:, [x for x in np.arange(1,y_batches['QLD'].shape[1]) if x not in np.arange(1,y_batches['QLD'].shape[1],5)] ],
'SA': y_batches['SA'][:, [x for x in np.arange(1,y_batches['SA'].shape[1]) if x not in np.arange(1,y_batches['SA'].shape[1],5)] ],
'TAS': y_batches['TAS'][:, [x for x in np.arange(1,y_batches['TAS'].shape[1]) if x not in np.arange(1,y_batches['TAS'].shape[1],5)] ],
'VIC': y_batches['VIC'][:, [x for x in np.arange(1,y_batches['VIC'].shape[1]) if x not in np.arange(1,y_batches['VIC'].shape[1],5)] ]}
y_batches_train_fold[3] = {'NSW': y_batches['NSW'][:, [x for x in np.arange(2,y_batches['NSW'].shape[1]) if x not in np.arange(2,y_batches['NSW'].shape[1],5)] ],
'QLD': y_batches['QLD'][:, [x for x in np.arange(2,y_batches['QLD'].shape[1]) if x not in np.arange(2,y_batches['QLD'].shape[1],5)] ],
'SA': y_batches['SA'][:, [x for x in np.arange(2,y_batches['SA'].shape[1]) if x not in np.arange(2,y_batches['SA'].shape[1],5)] ],
'TAS': y_batches['TAS'][:, [x for x in np.arange(2,y_batches['TAS'].shape[1]) if x not in np.arange(2,y_batches['TAS'].shape[1],5)] ],
'VIC': y_batches['VIC'][:, [x for x in np.arange(2,y_batches['VIC'].shape[1]) if x not in np.arange(2,y_batches['VIC'].shape[1],5)] ]}
y_batches_train_fold[4] = {'NSW': y_batches['NSW'][:, [x for x in np.arange(3,y_batches['NSW'].shape[1]) if x not in np.arange(3,y_batches['NSW'].shape[1],5)] ],
'QLD': y_batches['QLD'][:, [x for x in np.arange(3,y_batches['QLD'].shape[1]) if x not in np.arange(3,y_batches['QLD'].shape[1],5)] ],
'SA': y_batches['SA'][:, [x for x in np.arange(3,y_batches['SA'].shape[1]) if x not in np.arange(3,y_batches['SA'].shape[1],5)] ],
'TAS': y_batches['TAS'][:, [x for x in np.arange(3,y_batches['TAS'].shape[1]) if x not in np.arange(3,y_batches['TAS'].shape[1],5)] ],
'VIC': y_batches['VIC'][:, [x for x in np.arange(3,y_batches['VIC'].shape[1]) if x not in np.arange(3,y_batches['VIC'].shape[1],5)] ]}
y_batches_train_fold[5] = {'NSW': y_batches['NSW'][:, [x for x in np.arange(4,y_batches['NSW'].shape[1]) if x not in np.arange(4,y_batches['NSW'].shape[1],5)] ],
'QLD': y_batches['QLD'][:, [x for x in np.arange(4,y_batches['QLD'].shape[1]) if x not in np.arange(4,y_batches['QLD'].shape[1],5)] ],
'SA': y_batches['SA'][:, [x for x in np.arange(4,y_batches['SA'].shape[1]) if x not in np.arange(4,y_batches['SA'].shape[1],5)] ],
'TAS': y_batches['TAS'][:, [x for x in np.arange(4,y_batches['TAS'].shape[1]) if x not in np.arange(4,y_batches['TAS'].shape[1],5)] ],
'VIC': y_batches['VIC'][:, [x for x in np.arange(4,y_batches['VIC'].shape[1]) if x not in np.arange(4,y_batches['VIC'].shape[1],5)] ]}
for st in state.values():
for fold in np.arange(1,6):
print("Train and validation from state ", st, " fold ", fold)
net = nt.Network([x_size, hidden, y_size], nt.Activation.tanh, nt.QuadraticCost)
if cso:
fname = "kernelBias5Fold" + st + ".npy"
if not path.exists(fname):
print("Weights and biases initialization for state ",st ," in progress...")
randInt = np.random.randint(x_batches[st].shape[1])
net.cso(100,x_batches[st][:, randInt].reshape(x_size,1),
y_batches[st][:, randInt].reshape(y_size,1),
net.multiObjectiveFunction,-0.6,0.6,net.dim ,100)
net.set_weight_bias(np.array(net.get_Gbest()))
np.save(fname, np.array(net.get_Gbest()))
net.set_weight_bias(np.load(fname))
num_epochs = 1500
lmbda = 2
if cso:
fname = "results_"+ st + "_5Fold_" + str(fold) + "CSO"
else:
fname = "results_"+ st + "_5Fold_" + str(fold) + "GD" #GD: Gaussian Distribution
evaluation_cost, eval_mape, eval_rmse, eval_mae, training_cost, training_mape, training_rmse, training_mae = net.SGD(
x_batches_train_fold[fold][st],y_batches_train_fold[fold][st],
num_epochs, 10, 0.01, x_batches_validation_fold[fold][st],
y_batches_validation_fold[fold][st],
lmbda, monitor_evaluation_cost = True,
monitor_evaluation_accuracy = True,
monitor_training_cost = True,
monitor_training_accuracy = True,
output2D = True)
f = open(fname, "w")
json.dump([evaluation_cost, eval_mape, eval_rmse, eval_mae, training_cost, training_mape, training_rmse, training_mae], f)
f.close()
# make_plots(fname, num_epochs,
# training_cost_xmin = 0,
# test_accuracy_xmin = 0,
# test_cost_xmin = 0,
# training_accuracy_xmin = 0)