Spaces:
Runtime error
Runtime error
File size: 2,671 Bytes
246c955 754b6c6 246c955 f8c0662 246c955 f8c0662 246c955 754b6c6 246c955 754b6c6 246c955 754b6c6 246c955 754b6c6 246c955 f8c0662 246c955 f8c0662 246c955 754b6c6 246c955 754b6c6 246c955 754b6c6 246c955 754b6c6 246c955 754b6c6 246c955 754b6c6 246c955 715d266 246c955 f8c0662 641c113 01b125e c00334e 2d1141c 246c955 641c113 754b6c6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 |
import gradio as gr
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(figsize=(12, 6))
df_test_value.plot(legend=False, ax=ax)
ax.set_xlabel("Time")
ax.set_ylabel("Value")
ax.set_title("Input Test Data")
return fig
def get_anomalies(df_test_value):
# Create sequences from test values.
x_test = create_sequences(df_test_value.values)
# Load the pre-trained model.
model = load_pretrained_model()
# 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(figsize=(12, 6))
data.plot(legend=False, ax=ax)
df_subset.plot(legend=False, ax=ax, color="r")
ax.set_xlabel("Time")
ax.set_ylabel("Value")
ax.set_title("Anomalous Data Points")
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
anomalies = get_anomalies(df_test_value)
# Plot anomalous data points
plot2 = plot_anomalies(df_test_value, data, anomalies)
return plot1, plot2
outputs = gr.outputs.Image()
iface = gr.Interface(
fn=master,
inputs=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."
)
iface.launch()
|