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timeseries-anomaly-detection-autoencoders / app.py

remeajayi

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	import gradio as gr
	from huggingface_hub import from_pretrained_keras
	import pandas as pd
	import numpy as np
	import json
	from matplotlib import pyplot as plt

	f = open('scaler.json')
	scaler = json.load(f)

	TIME_STEPS = 288

	# Generated training sequences for use in the model.
	def create_sequences(values, time_steps=TIME_STEPS):
	output = []
	for i in range(len(values) - time_steps + 1):
	output.append(values[i : (i + time_steps)])
	return np.stack(output)


	def normalize_data(data):
	df_test_value = (data - scaler["mean"]) / scaler["std"]
	return df_test_value

	def plot_test_data(df_test_value):
	fig, ax = plt.subplots()
	df_test_value.plot(legend=False, ax=ax)
	return fig

	def get_anomalies(df_test_value):
	# Create sequences from test values.
	x_test = create_sequences(df_test_value.values)
	model = from_pretrained_keras("keras-io/timeseries-anomaly-detection")

	# Get test MAE loss.
	x_test_pred = model.predict(x_test)
	test_mae_loss = np.mean(np.abs(x_test_pred - x_test), axis=1)
	test_mae_loss = test_mae_loss.reshape((-1))

	# Detect all the samples which are anomalies.
	anomalies = test_mae_loss > scaler["threshold"]
	return anomalies

	def plot_anomalies(df_test_value, data, anomalies):
	# data i is an anomaly if samples [(i - timesteps + 1) to (i)] are anomalies
	anomalous_data_indices = []
	for data_idx in range(TIME_STEPS - 1, len(df_test_value) - TIME_STEPS + 1):
	if np.all(anomalies[data_idx - TIME_STEPS + 1 : data_idx]):
	anomalous_data_indices.append(data_idx)
	df_subset = data.iloc[anomalous_data_indices]
	fig, ax = plt.subplots()
	data.plot(legend=False, ax=ax)
	df_subset.plot(legend=False, ax=ax, color="r")
	return fig

	def master(file):
	# read file
	data = pd.read_csv(file, parse_dates=True, index_col="timestamp")
	df_test_value = normalize_data(data)
	# plot input test data
	plot1 = plot_test_data(df_test_value)
	# predict
	anomalies = get_anomalies(df_test_value)
	#plot anomalous data points
	plot2 = plot_anomalies(df_test_value, data, anomalies)
	return plot2

	outputs = gr.Plot()


	iface = gr.Interface(master,
	gr.inputs.File(label="csv file"),
	outputs=outputs,
	examples=["art_daily_jumpsup.csv"], title="Timeseries Anomaly Detection Using an Autoencoder",
	description = "Anomaly detection of timeseries data.",
	article = "Space by: <a href=\"https://www.linkedin.com/in/olohireme-ajayi/\">Reme Ajayi</a> <br> Keras Example by <a href=\"https://github.com/pavithrasv/\"> Pavithra Vijay</a>")



	iface.launch()