#importing required libraries
import pandas as pd
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from keras.layers import Input, Dense
from keras.models import Model
from sklearn.metrics import precision_recall_fscore_support
#Load data and perform required operations to clean data ready for further processing
data= pd.read_csv('ambient_temperature_system_failure.csv')
#Exclude datatime column
data_values= data.drop('timestamp' , axis=1).values
#convert data to float type
data_values= data_values.astype('float32')
#create new dataframe with converted values
data_converted= pd.DataFrame(data_values, columns=data.columns[1:])
#Add back datetime column
data_converted.insert(0, 'timestamp', data['timestamp'])
#Remove NAN values from dataset
data_converted= data_converted.dropna()
# Exclude datetime column again
data_tensor = tf.convert_to_tensor(data_converted.drop('timestamp', axis=1).values, dtype=tf.float32)
# Define the autoencoder model
input_dim = data_converted.shape[1] - 1
encoding_dim = 10
input_layer = Input(shape=(input_dim,))
encoder = Dense(encoding_dim, activation='relu')(input_layer)
decoder = Dense(input_dim, activation='relu')(encoder)
autoencoder = Model(inputs=input_layer, outputs=decoder)
# Compile and fit the model
autoencoder.compile(optimizer='adam', loss='mse')
autoencoder.fit(data_tensor, data_tensor, epochs=100, batch_size=32, shuffle=True)
# Calculate the reconstruction error for each data point
reconstructions = autoencoder.predict(data_tensor)
mse = tf.reduce_mean(tf.square(data_tensor - reconstructions), axis=1)
anomaly_scores = pd.Series(mse.numpy(), name='anomaly_scores')
anomaly_scores.index = data_converted.index
#define anomaly detection threshold & assess the model’s effectiveness using precision
threshold = anomaly_scores.quantile(0.99)
anomalous = anomaly_scores > threshold
binary_labels = anomalous.astype(int)
precision, recall,f1_score, _ = precision_recall_fscore_support(binary_labels, anomalous, average='binary')
test= data_converted['value'].values
predictions = anomaly_scores.values
print("Precision: " , precision)
print("Recall: " , recall)
print("F1 Score: " , f1_score)
#Visualizing Anomaly results
# Plot the data with anomalies marked in red
plt.figure(figsize=(8, 8))
plt.plot(data_converted['timestamp'], data_converted['value'])
plt.plot(data_converted['timestamp'][anomalous], data_converted['value'][anomalous], 'ro')
plt.title('Anomaly Detection')
plt.xlabel('Time')
plt.ylabel('Value')
plt.show()
知乎学术咨询：https://www.zhihu.com/consult/people/792359672131756032?isMe=1担任《Mechanical System and Signal Processing》等审稿专家，擅长领域：现代信号处理，机器学习，深度学习，数字孪生，时间序列分析，设备缺陷检测、设备异常检测、设备智能故障诊断与健康管理PHM等。