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import datetime
import json
import numpy as np
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import shutil
import sklearn.metrics
import sys
import tensorflow as tf
from collections import OrderedDict
from csv import DictWriter
from keras.callbacks import CSVLogger
from scipy.io import arff
from sklearn import model_selection
from sklearn.model_selection import StratifiedKFold, KFold
from tensorflow.keras import Sequential
from tensorflow.keras import layers
from tensorflow.keras.layers import Dense, LSTM, LeakyReLU, Dropout, Conv1D, MaxPooling1D, ReLU, Bidirectional
import matplotlib.pyplot as plt
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
def convertArffToDataFrame(fileName):
dataset = arff.loadarff(open(fileName))
data_list = [list(item) for item in dataset[0]]
return np.array(data_list, dtype=np.float32)
def get_weight_bias(y_data):
neg = len([i for i in y_data if i == 0])
# print(neg)
pos = len([i for i in y_data if i == 1])
# print(pos)
total = len(y_data)
if (neg != 0):
weight_for_0 = (1 / neg) * (total)
else:
weight_for_0 = 0
if (pos != 0):
weight_for_1 = (1 / pos) * (total) # TODO, pay more attention to this weight distrib.
else:
weight_for_1 = 0
return {0: weight_for_0, 1: weight_for_1}
def convertDataToLTSMFormat(data, timeSequences, numMetaAttrs):
x = []
y = []
ts_index = 0
window = []
for i in data:
correct = i[-1]
reshaped = np.array(i[:-1], dtype=np.float32)
window.append(reshaped.flatten())
ts_index += 1
if (ts_index == timeSequences):
x.append(np.array(window))
y.append(correct)
ts_index = 0
window = []
# split_data = np.array(np.array_split(np.array(reshaped).flatten(), windowSize, axis=0))
# for j in range(windowSize):
x = np.array(x)
y = np.array(y)
return [x, y]
def calc_conf_matrix_rates(conf_matrix):
true_positive = conf_matrix[0][0] / (sum(conf_matrix[0]))
true_negative = conf_matrix[1][1] / (sum(conf_matrix[1]))
return {
'true_positive': true_positive,
'true_negative': true_negative
}
def transformer_encoder(inputs, head_size, num_heads, ff_dim, dropout=0):
# Norm and Attention
x = layers.LayerNormalization(epsilon=1e-6)(inputs) # What does passing inputs do to x?
x = layers.MultiHeadAttention(key_dim=head_size, num_heads=num_heads, dropout=dropout)(x,
x) # What does passing x,x do?
x = layers.Dropout(dropout)(x)
res = x + inputs # res?
# feed foward
x = layers.LayerNormalization(epsilon=1e-6)(res)
x = layers.Conv1D(filters=ff_dim, kernel_size=1, activation='relu')(
x) # Might need to put the activation layer as separate var for d4j
x = layers.Dropout(dropout)(x)
x = layers.Conv1D(filters=inputs.shape[-1], kernel_size=1)(x)
return x + res
def build_transformer_model(input_shape, head_size, num_heads, ff_dim, num_transformer_blocks, mlp_units, dropout=0,
mlp_dropout=0):
inputs = tf.keras.Input(shape=input_shape)
x = inputs
for _ in range(num_transformer_blocks):
x = transformer_encoder(x, head_size, num_heads, ff_dim, dropout)
x = layers.GlobalAveragePooling1D(data_format="channels_first")(x) # What does channels_first do?
for dim in mlp_units:
x = layers.Dense(dim, activation='relu')(x) # What does passing x do here?
x = layers.Dropout(mlp_dropout)(x)
outputs = layers.Dense(1, activation='sigmoid')(x) # 2 here is because we have a binary class.
return tf.keras.Model(inputs, outputs)
if __name__ == '__main__':
timeSequences = 5
numAttributes = 150
windowSize = int(numAttributes / 2);
numMetaAttrs = 0
testPID = 11
trainDir = "/home/notroot/Desktop/d2lab/gazepoint/train_test_data_output/bpog only/" + "/trainData_500.0mssec_P"+str(testPID)+" p"+str(testPID)+".conf.list.csv.arff"
testDataDir = "/home/notroot/Desktop/d2lab/gazepoint/train_test_data_output/bpog only anat/"
testData = convertArffToDataFrame(testDataDir + "/trainData_500.0mssec_P"+str(testPID)+" p"+str(testPID)+".anatomy.list.csv.arff")
trainData = convertArffToDataFrame(trainDir)
models = []
input_shape = (timeSequences, numAttributes)
# models.append(tf.keras.models.load_model("/home/notroot/Desktop/d2lab/gazepoint/python/2023-12-01 09_16_12,973606/stacked_lstm_v2-Adagrad0,008 wdecay: None ema:False.h5"));
transformer = build_transformer_model(input_shape, head_size=numAttributes, num_heads=14, ff_dim=numAttributes,
num_transformer_blocks=2, mlp_units=[numAttributes * 2], mlp_dropout=0.15,
dropout=0.1)
kernelSize = 2 # filters is the num windows, and 2 b/c (x,y)
filterSize = int(windowSize/2)
conv_stacked_lstm = Sequential()
conv_stacked_lstm.add(Conv1D(2, kernelSize, input_shape=input_shape)) # filter size of 25 to split the window into three frames.
conv_stacked_lstm.add(MaxPooling1D(pool_size=1))
conv_stacked_lstm.add(Dropout(0.10))
conv_stacked_lstm.add(Bidirectional(LSTM(int(numAttributes), dropout=0.2, return_sequences=True, activation='relu')))
conv_stacked_lstm.add(Bidirectional(LSTM(int(numAttributes), dropout=0.2, return_sequences=True, activation='relu')))
conv_stacked_lstm.add(Bidirectional(LSTM(int(numAttributes), dropout=0.2, return_sequences=True, activation='relu')))
conv_stacked_lstm.add(Bidirectional(LSTM(int(numAttributes), dropout=0.2, activation='relu')))
conv_stacked_lstm.add(Dropout(0.20))
conv_stacked_lstm.add(Dense(int(numAttributes)))
conv_stacked_lstm.add(LeakyReLU())
conv_stacked_lstm.add(Dense(1, activation='sigmoid'))
conv_stacked_lstm.compile( optimizer=tf.keras.optimizers.Adam(learning_rate=1.4e-3),
loss="binary_crossentropy",
metrics=[ tf.keras.metrics.BinaryAccuracy(name='accuracy'),
tf.keras.metrics.TruePositives(name='tp'),
tf.keras.metrics.TrueNegatives(name='tn'),
])
models.append(conv_stacked_lstm);
xTest, yTest = convertDataToLTSMFormat(testData, timeSequences, numAttributes)
# xTest = normalizeData(xTest, windowSize, numAttributes, numMetaAttrs, attr_min_max)
xTrain, yTrain = convertDataToLTSMFormat(trainData, timeSequences, numAttributes)
# inputs = np.concatenate((xTrain, xTest), axis=0)
# targets = np.concatenate((yTest, yTrain), axis=0)
'''
Done fitting on multiple participants, time for real world data testing
'''
y_hats = []
acc_rates = []
for model in models:
for i in range(0,30):
kfold = StratifiedKFold(n_splits=14, shuffle=False)
fold_no = 0
for train, test in kfold.split(xTrain, yTrain):
weights = get_weight_bias(yTrain[train])
model.fit(xTrain[train], yTrain[train],
# class_weight=weights
epochs=20
)
print(xTest.shape)
y_hat = model.predict(xTest)
# results = model.evlauate(xVal, yTest)
y_hat = [(1.0 if y_pred >= 0.5 else 0.0) for y_pred in y_hat]
conf_matrix = sklearn.metrics.confusion_matrix(yTest, y_hat, labels=[1.0, 0.0])
conf_matrix_rates = calc_conf_matrix_rates(conf_matrix)
print(conf_matrix)
tp_rate = conf_matrix_rates['true_positive']
tn_rate = conf_matrix_rates['true_negative']
acc_rate = (tp_rate + tn_rate) / 2
acc_rates.append(acc_rate)
print("acc rate: " + str(acc_rate))
print("tp: " + str(tp_rate) + " tn: " + str(tn_rate))
scores = model.evaluate(xTrain[test], yTrain[test], verbose=0)
print(
f'Score for fold {fold_no}: {model.metrics_names[0]} of {scores[0]}; {model.metrics_names[1]} of {scores[1] * 100}%')
fold_no += 1
plt.plot(np.array(range(0,len(acc_rates))), np.array(acc_rates))
plt.show()
conf_matrix = sklearn.metrics.confusion_matrix(yTest, outcomes, labels=[1.0, 0.0])