ASSESS (Automated SignalS Evaluation Service System)

ASSESS allows you to assess the signal in your dataset, understand the important variables and deploy your model in seconds.

Command line interface

Training a model

python train.py
    --label "label"# the name of the target variable.
    --path ../data/iris2.csv # path to the dataset csv file.
    --explainmodel # optional argument, to store plots explaining the model' decisions during the predictive process.

example:

python train.py --target "label" --path "../data/iris2.csv" --explainmodel

Monitoring the model via the MLflow UI

Place yourself in the ASSESS/src directory then:

mlflow ui

Generate predictions

python predict.py --path ../data/iris2.csv

Deploy the model as a local REST API

from the ASSESS directory:

mlflow models serve -m "src\mlruns\0\\ :raw-html-m2r:`<model id>`\ \artifacts\sk_model" -p 1234

HTTPs query from a Python script

# ... define X as the pandas dataframe containing the observations
http_data = X.to_json(orient='split')
host = '127.0.0.1'
port = '1234'
url = f'http://{host}:{port}/invocations'
headers = {'Content-Type': 'application/json'}
r = requests.post(url=url, headers=headers, data=http_data)
return r.text