pip install git+https://github.com/unina-sfere/NN4OCMSP#egg=NN4OCMSP

pip install NN4OCMSP

# Import libraries

import math 
import pandas as pd
import numpy as np
import sklearn
import keras
from keras import Sequential
from keras.layers import Dense

from sklearn import preprocessing
from sklearn.model_selection import train_test_split

import matplotlib as mpl
import matplotlib.pyplot as plt
%matplotlib inline
from matplotlib.ticker import ScalarFormatter, AutoMinorLocator

from itertools import combinations
import random as python_random
import tensorflow as tf

from NN4OCMSP.functions import *
import NN4OCMSP.dataset

# Import HVAC data 

HVAC_data = NN4OCMSP.dataset.load_HVAC_data()

# Filter train A for Phase I estimatin

train_1_data = HVAC_data[HVAC_data["Vehicle"] == "Train_A"]

# Select the DeltaT variables 
train_1_data = train_1_data.iloc[:,-6:]
# Convert pandas dataframe to NumPy array
train_1_data = train_1_data.to_numpy()

# MSP

s = 6 # number of streams corresponding to the number of train coaches
n = 5 # sample size

# Estimate the mean, the process variability and the variability between streams from the Phase I
# data using standard one-way ANOVA techniques 

mu, sigmaA, sigmae = phaseI_estimation(train_1_data)
print('mean', mu)
print('standard deviation of the common stream component', sigmaA)
print('standard deviation of the individual stream component', sigmae)

# Plot the ΔT signals from the six train coaches

train_1_data = train_1_data.transpose().reshape(-1,n).mean(1).reshape(s,-1).transpose() # Average every 5 rows 

# Plot the ΔT signals from the six train coaches

fig = plt.figure(figsize=(12, 8))

x = np.arange(1, 26 ,1)

a = 0
b = 25

plt.plot(x,train_1_data[a:b,0], label = 'Coach 1', color='black', ls='-', marker='*')
plt.plot(x,train_1_data[a:b,1], label = 'Coach 2', color='blue', ls='-', marker='.')
plt.plot(x,train_1_data[a:b,2], label = 'Coach 3', color='red', ls='-.', marker= 's')
plt.plot(x,train_1_data[a:b,3], label = 'Coach 4', color='green', ls='-', marker='D')
plt.plot(x,train_1_data[a:b,4], label = 'Coach 5', color='orange', ls='-', marker='+')
plt.plot(x,train_1_data[a:b,5], label = 'Coach 6', color='violet', ls='-', marker='P')
# plt.axhline(0, color="red")
plt.xlabel('Sample', fontsize=12)
plt.ylabel('$ \Delta$T', fontsize=12)
plt.legend(fontsize=10)

plt.tick_params(axis='both', which='major', size = 7, width = 1 , direction = 'out', labelsize = 10)
plt.xticks(np.arange(1, 26, 1))

plt.show()

# Set the simulation parameters to properly generate the data set to train the Neural Network (NN)

num_samples = 10000 # number of samples for each out-of-control (oc) scenario
alpha_sim = 0.05 # Type-I error

# Generate data
X, y = dataset_generator(s,n,num_samples, mu, sigmaA, sigmae,alpha_sim)

# Train/validation split

X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.3, stratify = y ,random_state=27)

# Data are normalized to have unit variance and zero mean

scaler = preprocessing.StandardScaler().fit(X_train)
X_train = scaler.transform(X_train)
X_val = scaler.transform(X_val)

# NN definition 

num_hidden_layer = 2 # Number of hidden layers
hidden_activation_function = ['relu', 'relu'] # activation function in the hidden layers
number_hidden_neuron = [20,10] # number of neurons in the hidden layers
num_output_neuron = s # The output layer consists of 𝑠 neurons, corresponding to the 𝑠 possible streams 
                      # responsible for the OC signal
    
epochs = 10 # Number of epochs to train the model. An epoch is an iteration over the entire training data
batch_size = 516 # Number of samples per gradient update

# NN Training 

classifier = NN_model(hidden_activation_function, num_hidden_layer,number_hidden_neuron, num_output_neuron) 

# Compiling the neural network

classifier.compile(optimizer ='adam', loss='binary_crossentropy', metrics = ['accuracy']) # Configures the model for training

# Fitting 

history = classifier.fit(X_train, y_train, batch_size = batch_size, epochs = epochs, validation_data=(X_val, y_val)) # Trains the model

train_2_data = HVAC_data[HVAC_data["Vehicle"] == "Train_B"] # Filter Vehicle by Train B 
train_2_data = train_2_data.iloc[0:-2,-6:] # Select the DeltaTemp variables 
train_2_data = train_2_data.to_numpy() # Convert pandas dataframe to NumPy array
train_2_data = train_2_data.transpose().reshape(-1,n).mean(1).reshape(s,-1).transpose() # Average every 5 rows   

# Plot the ΔT signals from the six train coaches 

fig = plt.figure(figsize=(12, 6))

x = np.arange(1,16,1)

a = 8
b = 23

plt.plot(x,train_2_data[a:b,0], label = 'Coach 1', color='black', ls='-', marker='*')
plt.plot(x,train_2_data[a:b,1], label = 'Coach 2', color='blue', ls='-', marker='.')
plt.plot(x,train_2_data[a:b,2], label = 'Coach 3', color='red', ls='-.', marker= 's')
plt.plot(x,train_2_data[a:b,3], label = 'Coach 4', color='green', ls='-', marker='D')
plt.plot(x,train_2_data[a:b,4], label = 'Coach 5', color='orange', ls='-', marker='+')
plt.plot(x,train_2_data[a:b,5], label = 'Coach 6', color='violet', ls='-', marker='P')
plt.xlabel('Sample', fontsize=12)
plt.ylabel('$ \Delta$T', fontsize=12)
plt.legend(fontsize=10)

plt.xticks(np.arange(1, 16, 1))
plt.tick_params(axis='both', which='major', size = 7, width = 1 , direction = 'out', labelsize = 10)

plt.show()

# Compute the overall mean and the range for each sample

overall_mean, sample_range  = range_overall_mean(train_2_data[a:b,])

# Design and plot of the overall mean and range control charts

fig_size = (12, 6)
fig_control_chart = plt.figure(figsize=fig_size)
fig_control_chart= control_charts(fig_control_chart, s, n, mu, sigmaA, sigmae, alpha_sim, overall_mean, sample_range)

#  Predict the OC stream(s) at the time of the first signal

pred = prediction(train_2_data[a:b,], classifier, scaler, overall_mean, sample_range)

# The range control chart starts signaling an OC state from sample 9. 
pred[8]