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import time
import math
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import gradio as gr
from datasets import load_dataset
from sklearn.datasets import fetch_openml
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
LOGS_DATASET_URI = 'pgurazada1/machine-failure-mlops-demo-logs'
def get_data():
"""
Connect to the HuggingFace dataset where the logs are stored.
Pull the data into a dataframe
"""
data = load_dataset(LOGS_DATASET_URI)
sample_df = data['train'].to_pandas().sample(100)
return sample_df
def load_training_data():
dataset = fetch_openml(data_id=42890, as_frame=True, parser="auto")
data_df = dataset.data
target = 'Machine failure'
numeric_features = [
'Air temperature [K]',
'Process temperature [K]',
'Rotational speed [rpm]',
'Torque [Nm]',
'Tool wear [min]'
]
categorical_features = ['Type']
X = data_df[numeric_features + categorical_features]
y = data_df[target]
Xtrain, Xtest, ytrain, ytest = train_test_split(
X, y,
test_size=0.2,
random_state=42
)
return Xtrain, ytrain
def check_model_drift():
sample_df = get_data()
p_pos_label_training_data = 0.03475
training_data_size = 8000
p_0 = sample_df.prediction.value_counts()[0]
p_1 = sample_df.prediction.value_counts()[1]
p_pos_label_sample_logs = p_1/(p_0+p_1)
variance = (p_pos_label_training_data * (1-p_pos_label_training_data))/training_data_size
p_diff = abs(p_pos_label_training_data - p_pos_label_sample_logs)
if p_diff > 2 * math.sqrt(variance):
return "Model Drift Detected! Check logs!"
else:
return "No Model Drift!"
with gr.Blocks() as demo:
gr.Markdown("# Real-time Monitoring Dashboard")
gr.Markdown("Model drift detection (every 5 seconds)")
with gr.Row():
with gr.Column():
gr.Textbox(check_model_drift, every=5)
demo.queue().launch() |