MLWatcher is a python agent that records a large variety of time-serie metrics of your running ML classification algorithm.
It enables you to monitor in real time :
- the predictions : monitor the repartition of classes, the distribution of the
predict_proba_matrixvalues, anomalies in your predictions
- the features : monitor concept drift, anomalies in your data
- the labels : monitor accuracy, precision, recall, f1 of your predictions vs labels if applicable
The statistics derived from the data are :
- range, mean, std, median, q25, q50, q75, iqr for any continuous values (probabilities, features)
- count, frequency for any discrete values (labels, classes)
Some additional data are derived from the
predict_proba_matrix and monitored as continuous values :
- pred1 : the maximum prediction for each line in the
- spread12 : the spread between the maximum prediction(pred1) and the second maximum prediction for each line in the
predict_proba_matrix. Drops in the pred1 timeserie and jumps in the spread12 can indicate a decrease of the algorithm average degree of certainty of its predictions.
MLWatcher minimal input is the
predict_proba_matrix of your algorithm (for each line in the batch of data, the probabilities of each class).
input_matrix are optional to monitor.
In case of binary classification with only 2 classes, a threshold value can be fed to monitor the labels-related and prediction-related metrics.
MLWatcher use cases
Monitoring your Machine Learning metrics can be used to achieve multiple goals:
- alert on concept drift : the production data can be significatively different from the training data as time passes by. Analyze the distribution of the production features through time (mean, std, median, iqr etc).
Example of concept drift for MNIST dataset where the input pixels values get suddenly inverted. An anomaly in the distribution of the features is raised:
- analyze the performance of the model : the model may no longer be accurate with respect to the production data. Some unlabeled metrics can display this staleness : pred1, spread12, class_frequency. Drops in the pred1 and spread12 timeseries can indicate a decrease of the algorithm average degree of certainty of its predictions.
Example of how the model predictions metrics change when a new set of input data comes into production:
- check that your model is numerically stable : analyze the pred1 and spread12 stability, but also the different classes frequency stability.
Example of putting into production a weakly trained model (trained with a highly unbalanced training set) and how this affects the stability of the predictions distribution for production:
- canary process new models : monitor multiple ML models with the production inputs and compare metrics of the production algorithm vs the tested ones, analyze the stability of each model through time, etc.
Example of monitoring the accuracy metric for multiple concurrent algorithms:
- if labels are available, analyze the evolution of the classic ML metrics and correlate with other time series (features, predictions).
The size of each buffer of data is also monitored, so it is important to also correlate the computed metrics with the sample size. (ie : the sample size is not always statiscally significant).
0- Install the libs in requirements.txt.
python -m pip install -r /path/to/requirements.txt
1- Add the MLWatcher folder in the same folder of your algorithm script.
2- Personalize some technical parameters in file conf.py (rotating logs specs, filenames, token if applicable, etc).
3- Load the MLWatcher libs in your import lines :
from MLWatcher.agent import MonitoringAgent
4- Instanciate a MonitoringAgent object, and run the agent-server side:
agent = MonitoringAgent(frequency=5, max_buffer_size=500, n_classes=10, agent_id='1', server_IP='127.0.0.1', server_port=8000)
frequency : (int) Time in minutes to collect data. Frequency of monitoring
max_buffer_size : (int) Upper limit of number of inputs in buffer. Sampling of incoming data is done if limit is reached
n_classes : (int) Number of classes for classification. Must be equal to the number of columns of your predict_proba matrix
agent_id : (string) ID. Used in case of multiple agent monitors (default '1')
server_IP : (string) IP of the server ('127.0.0.1' if local server)
server_port : (int) Port of the server (default 8000)
For LOCAL Server. Local server would be listening on previously defined port, on localhost interface (127.0.0.1).
For DISTANT Server : Hosted server would be listening on a defined port, on localhost interface (--listen localhost) or all interfaces (--listen all). Recommended :
python /path/to/server.py --listen all --port 8000 --n_sockets 5
See --help for server.py options.
5- Monitor the running ML process for each batch of data
agent.collect_data( predict_proba_matrix = <your pred_proba matrix>, ##mandatory input_matrix = <your feature matrix>, ##optional label_matrix = <your label matrix> ##optional )
TOKEN=None is provided in conf.py, you can analyze your data stored locally in the PROD folder with the given jupyter notebook (ANALYTICS folder)
7- For advanced analytics of the metrics and detect anomalies in your data, the agent output is compatible with Anodot Rest API by using a valid
You can use the Anodot API script as follows :
- Asynchronously from json files written on disk:
python anodot_api.py --input <path/to/PROD/XXX_MLmetrics.json> --token <TOKEN>
- Synchronously without storing any production file :
TOKEN='123ad..dfg' instead of None in conf.py In case the data is not correctly sent to Anodot(connection problems), the agent will start writing the metrics directly on disk in PROD folder. Please contact Anodot to get a TOKEN through a trial session.
The agent is fully writen in Python 3.X. It was tested with Python >= 3.5
The input format for the agent collector are :
predict_proba_matrix size (batch_size x n_classes)
label_matrix binary matrix of shape (batch_size x n_classes) or (int matrix of shape (batch_size x 1)
features (optional) :
input_matrix size (batch_size x n_features)
n_classes must be >= 2
See Getting Started section. You can also have a look and run the example given with the MNIST dataset in the EXAMPLE folder (requirement:tensorflow).
Deployment and technical features.
The agent structure is as follows:
- a light agent-client side that collects the data of the algorithm and sends it the agent-server running in background (don't forget to launch agent.run_local_server() or use server.py)
- a agent-server side that asynchronously handles the data received to compute a wide variety of time series metrics.
It skips the data if a problem is met and records logs accordingly.
The agent is a light weight collector that stores up to
max_buffer_size datapoints every period.
Above this limit, sampling is done using a 'Reservoir sampling' algorithm so the sampled data remains statistically significant.
To tackle bottleneck issues, you can adjust the number of threads that the server can run in parallel with the volume of batches you want to monitor synchronously.
You can also adjust
frequency parameters accordingly to your volumetry.
For Anodot usage, a limit from Anodot API is defined as 2000 metric-datapoints per second. Please make sure that the volumetry is below this limit, else some monitored data would be lost (no storage case).
Before going to production, a phase of tests for implementing the agent and server to your production running algorithm is highly recommended.
This agent was developped by Anodot to help the data science community to monitor in real time the performance, the anomalies and the lifecycle of running ML algorithms.
Please also refer to the paper of Google 'The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction' to have a global view of the good practices in production ML algorithm design and monitoring.
- Garry B - ITC Project for Anodot
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