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LSTM_1_binary_Y_Y.py
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# -*- coding: utf-8 -*-
"""
Created on Sat Oct 29 21:06:30 2022
@author: ssb60
"""
import numpy as np
import matplotlib.pyplot as plt
import keras
from tensorflow import keras
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers import SimpleRNN
from keras.layers import Dropout
from keras.layers import BatchNormalization
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OrdinalEncoder
import pandas as pd
import tensorflow as tf
print(tf.__version__)
print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))
#Loading data
Time_series_X_train = np.load(r'T_s_X_train.npy')
Time_series_Y_train = np.load(r'T_s_Y_train_flattened.npy')
Time_series_X_test = np.load(r'T_s_X_test.npy')
Time_series_Y_test = np.load(r'T_s_Y_test_flattened.npy')
def my_metric_fn(y_true, y_pred):
y_pred = tf.convert_to_tensor(y_pred)
y_true = tf.cast(y_true, y_pred.dtype)
#print(y_pred.dtype)
y_true = tf.cast(y_true > 0.33, y_true.dtype) * 1
y_pred_r = tf.cast(y_pred > 0.33, y_pred.dtype) * 1
#return (y_pred.get_shape().as_list()[1]*y_pred.get_shape().as_list()[0] - tf.math.count_nonzero((y_true - y_pred_r)*100))/(y_pred.get_shape().as_list()[1]*y_pred.get_shape().as_list()[0])
return 1-(tf.math.count_nonzero( y_true- y_pred_r)/(y_pred.get_shape().as_list()[1]*y_pred.get_shape().as_list()[0]))
'''Time_series_X_train =np.sin(Time_series_X_train )
Time_series_X_test=np.sin(Time_series_X_test)'''
#Creating folder for o/p
# importing os module
import os
# # Leaf directory
directory = "X_to_Y_8_days__reg_test_adam" # change
#
# # Parent Directories
parent_dir = r".\Outputs_from_models"
# # "D:\TL_11_18\Transmission_Line_Screen\Outputs_from_models"
# path = os.path.join(parent_dir, directory)
# os.makedirs(path)
# print("Directory '% s' created" % directory)
#
location = parent_dir + "\\" + directory
# path=location+"\Testing_data"
# os.makedirs(path)
# path=location+"\Model"
# os.makedirs(path)
# path=location+"\Plots"
# os.makedirs(path)
#
LSTM_model = Sequential()
LSTM_model.add(LSTM(1000, activation='sigmoid',return_sequences=True, input_shape=(192, 120)))
LSTM_model.add(LSTM(500, activation='tanh',return_sequences=False, input_shape=(192, 120)))
LSTM_model.add(Dense(3000, activation='relu'))
#LSTM_model.add(BatchNormalization())
#LSTM_model.add(Dropout(0.1))
LSTM_model.add(Dense(1000, activation='relu'))
#LSTM_model.add(BatchNormalization())
#LSTM_model.add(Dropout(0.1))
LSTM_model.add(Dense(3000, activation='relu'))
#LSTM_model.add(BatchNormalization())
#LSTM_model.add(Dropout(0.1))
LSTM_model.add(Dense(2880, activation='sigmoid'))
#opt = keras.optimizers.Adam(learning_rate=0.1e-8,decay=0.8)
opt = keras.optimizers.Adam(learning_rate=0.001,
beta_1=0.9,
beta_2=0.99,
epsilon=1e-07,
amsgrad=False) #1e-6 0.0001 0.9
#opt=keras.optimizers.SGD(learning_rate=0.00001,decay=0.8)
LSTM_model.compile(loss='mse', optimizer=opt, metrics=['accuracy', 'mae'],run_eagerly=True)
LSTM_model.summary()
#metrics=[tf.keras.metrics.BinaryAccuracy()]
callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=10,restore_best_weights=True)
history=LSTM_model.fit(Time_series_X_train, Time_series_Y_train, epochs=0, batch_size=200,validation_data=(Time_series_X_test,Time_series_Y_test),callbacks=[callback])#
'''plt.plot(history.history['accuracy'])
plt.plot(history.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'val'], loc='upper left')
plt.savefig(location+r'Plots'+r'\acc.png')
plt.show()'''
# plt.plot(history.history['loss'])
# plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'val'], loc='upper left')
plt.savefig(location+r'\Plots'+r'\loss.png')
plt.show()
predictions = LSTM_model.predict(Time_series_X_test)
#y_pred_r=tf.round(predictions )
#y_pred_r=tf.math.minimum(y_pred_r,1)
predictions[predictions<=0.3333]=0
predictions[predictions > 0.3333]=1
pred=predictions
#pred=y_pred_r.numpy()
idx_y=np.load('y_test_idx.npy')
df=pd.DataFrame(pred)
df.insert(loc=0,column='day',value=idx_y[:,0])
#df.insert(loc=1,column='hour',value=idx_y[:,1])
#df['hour']=idx_y[:,1]
mask1 = df.duplicated(subset=['day'],keep = "first") # this line is to get the first occ.
df2=df[~mask1]
pred_0_hr =df2.to_numpy()
pred_0_hr_list =[]
temp=[]
counter_op=0
for i in range (1,len(pred_0_hr)):
if ( pred_0_hr[i,0]-pred_0_hr[i-1,0])<2:
temp.append(pred_0_hr[i,:])
if i== len(pred_0_hr)-1:
temp_np=np.array(temp)
index_for_temp=2
new_temp_1=[]
for count_reshape_r in range (0,len(temp_np)):
new_temp=[]
new_temp.append(temp_np[count_reshape_r,0])
for count_reshape_c in range (1,temp_np.shape[1]):
if ((count_reshape_c) % 120)==0 :
new_temp.append(temp_np[count_reshape_r,count_reshape_c])
new_temp_1.append(new_temp)
new_temp=[]
new_temp.append(temp_np[count_reshape_r,0])
else:
new_temp.append(temp_np[count_reshape_r,count_reshape_c])
new_temp_1=np.array(new_temp_1)
df_3=pd.DataFrame(new_temp_1)
file_name=location +r'\Testing_data\excel'+str(counter_op)+'.xlsx'
df_3.to_excel(file_name)
else:
temp_np=np.array(temp)
index_for_temp=2
new_temp_1=[]
for count_reshape_r in range (0,len(temp_np)):
new_temp=[]
new_temp.append(temp_np[count_reshape_r,0])
for count_reshape_c in range (1,temp_np.shape[1]):
if ((count_reshape_c ) % 120)==0 :
new_temp.append(temp_np[count_reshape_r,count_reshape_c])
new_temp_1.append(new_temp)
new_temp=[]
new_temp.append(temp_np[count_reshape_r,0])
else:
new_temp.append(temp_np[count_reshape_r,count_reshape_c])
new_temp_1=np.array(new_temp_1)
df_3=pd.DataFrame(new_temp_1)
file_name=location+r'\Testing_data\excel'+str(counter_op)+'.xlsx'
df_3.to_excel(file_name)
temp=[]
counter_op=counter_op+1
#df.to_excel('out_33_10_xy_y_56.xlsx')
np.save(location +'\output.npy',pred)
pred_t=pred
np.save(location +'\output.npy',pred_t)
Time_series_Y_test[Time_series_Y_test<=0.33]=0
Time_series_Y_test[Time_series_Y_test > 0.33]=1
tp=np.count_nonzero(Time_series_Y_test == 1)
tn =np.sum(Time_series_Y_test == 0)
#pp=np.count_nonzero(pred == 1)
#pn =np.sum(pred == 0)
print('trueval_pos')
print(tp)
print('trueval_neg')
print(tn)
print('positive_pred')
'''print(pp)
print('negative_pred')
print(pn)'''
total_positive=np.count_nonzero(Time_series_Y_test == 1)
total_negative =np.sum(Time_series_Y_test == 0)
pred_pos=np.count_nonzero(pred_t == 1)
pred_neg =np.sum(pred_t == 0)
mat_1 = Time_series_Y_test + pred_t
tp =np.sum(mat_1 == 2)
tp_arr=np.sum(mat_1 == 2,0)
tn=np.sum(mat_1 == 0)
tn_arr=np.sum(mat_1 == 0,0)
mat_2 = Time_series_Y_test - pred_t
fn = np.sum(mat_2 == 1)
fn_arr=np.sum(mat_2 == 1,0)
fp =np.sum(mat_2 ==-1)
fp_arr=np.sum(mat_2 == -1,0)
df_cm=pd.DataFrame(tp_arr.T)
#df. rename(columns = {'old_col1':'new_col1', 'old_col2':'new_col2'}, inplace = True)
df_cm.rename(columns={0:'True_Positive'},inplace='True')
df_cm.insert(loc=1,column='True_Negative',value=tn_arr)
df_cm.insert(loc=2,column='False_Positive',value=fp_arr)
df_cm.insert(loc=3,column='False_Negative',value=fn_arr)
lines =np.linspace(1, 120, num=120)
lines_arr = np.tile(lines,24)
df_cm.insert(loc=0,column='Line_No',value=lines_arr)
hour=np.linspace(1, 24, num=24)
hour_arr = np.repeat(hour,120)
df_cm.insert(loc=1,column='Hour',value=hour_arr)
file_name=location+'\confusion_matrix.xlsx'
df_cm.to_excel(file_name)
df_false_neg = df_cm.groupby(["Line_No"]).False_Negative.sum().reset_index()
print('tp')
print(tp)
print('tn')
print(tn)
print('fp')
print(fp)
print('fn')
print(fn)
f1_score = 2*tp/(2*tp + fp + fn)
print('F1-score = ',f1_score)
print('Most false neg line')
print(df_false_neg.loc[df_false_neg.False_Negative.idxmax(),'False_Negative'])
print("No of times")
print(df_false_neg.loc[df_false_neg.False_Negative.idxmax(),'Line_No'])
LSTM_model.evaluate(Time_series_X_test,Time_series_Y_test)
LSTM_model.evaluate(Time_series_X_train,Time_series_Y_train)
# saving model
LSTM_model.save(location+'\Model'+'\LSTM_model.h5')
np.save(location+'\Model'+'\my_history_LSTM_model.npy',history.history,allow_pickle=True)
Y_percentage_test = np.load('T_s_Y_test_flattened.npy')
indices = np.where(mat_2 == 1)
print(Y_percentage_test[indices])
fn_per=Y_percentage_test[indices]
np.save(location+'\False_Negative.npy',fn_per)
plt.hist(fn_per)
plt.savefig(location+r'\Plots'+r'\Histogram_False_Negative.png')
plt.show()
indices = np.where(mat_2 == -1)
fp_per=Y_percentage_test[indices]
np.save(location+'\False_Positive.npy',fp_per)
plt.hist(fp_per)
plt.savefig(location+r'\Plots'+'\Histogram_False_Positive.png')
plt.show()
Errors_true_false =np.append(fn_per,fp_per)
plt.hist(Errors_true_false)
plt.savefig(location+r'\Plots'+'\Histogram_Errors.png')
plt.show()
print('Everything worked!!!')