monitaur 0.1.21

Monitaur Client Library

Monitaur Client Library

Tested with the following versions of Python:

1. 3.8.1
2. 3.7.6
3. 3.6.10

Install

$ pip install monitaur

Client Library Examples

from monitaur import Monitaur
from monitaur.utils import hash_file


# create monitaur instance
monitaur = Monitaur(
    auth_key="changme",
    base_url="http://localhost:8008",
)

# train model
dataset = loadtxt("./_example/data.csv", delimiter=",")
seed = 7
test_size = 0.1
model_data = train_model(dataset, seed, test_size)
trained_model = model_data["trained_model"]
training_data = model_data["training_data"]
dump(trained_model, open(f"./_example/data.joblib", "wb"))


# add model to api
model_data = {
    "name": "Diabetes Classifier",
    "model_type": "xgboost",
    "model_class": "tabular",
    "library": "xg_boost",
    "trained_model_hash": hash_file("./_example/data.joblib"),  # trained model (None is allowed)
    "production_file_hash": hash_file("./_example/prediction.py"),  # production file used for running inputs through the trained model (None is allowed)
    "feature_number": 8,
    "owner": "Anthony Habayeb",
    "developer": "Andrew Clark",
    "influences": True,
}
model_set_id = monitaur.add_model(**model_data)

# get aws credentials
credentials = monitaur.get_credentials(model_set_id)

# record training
record_training_data = {
    "credentials": credentials,
    "model_set_id": model_set_id,
    "trained_model": trained_model,
    "training_data": training_data,
    "feature_names": [
        "Pregnancies",
        "Glucose",
        "BloodPressure",
        "SkinThickness",
        "Insulin",
        "BMI",
        "DiabetesPedigreeF",
        "Age",
    ],
    # "re_train": True
}
monitaur.record_training(**record_training_data)

# record transaction
prediction = get_prediction([2, 84, 68, 27, 0, 26.7, 0.341, 32])
transaction_data = {
    "credentials": credentials,
    "model_set_id": model_set_id,
    "trained_model_hash": hash_file("./_example/data.joblib"),
    "production_file_hash": hash_file("./_example/prediction.py"),
    "prediction": prediction,
    "features": {
        "Pregnancies": 2,
        "Glucose": 84,
        "BloodPressure": 68,
        "SkinThickness": 27,
        "Insulin": 0,
        "BMI": 26.7,
        "DiabetesPedigreeF": 0.341,
        "Age": 32,
    },
}
response = monitaur.record_transaction(**transaction_data)
print(response)

# read transactions by passing model_id and/or model_set_id
# both are optional arguments
transactions = monitaur.read_transactions(model_set_id=model_set_id)
print(transactions)

API Examples

requests:

import requests

API_ENDPOINT = "http://localhost:8000"
TOKEN = "422f7515da18ca21cb16b651098ffbc326ea842d"
HEADERS = {"Authorization": f"Token {TOKEN}"}
MODEL_SET_ID = "8fb12cdb-d8b3-4959-8d43-ec6c1516b28b"

# get model metadata
model = requests.get(f"{API_ENDPOINT}/api/models/set/{MODEL_SET_ID}", headers=HEADERS)
print(model.json())
model_id = model.json()["id"]

# get transactions
transactions = requests.get(f"{API_ENDPOINT}/api/transactions/?model={model_id}", headers=HEADERS)
for transaction in transactions.json():
    print(f"\n{transaction}")

cURL:

$ curl -X GET "http://localhost:8000/api/models/set/8fb12cdb-d8b3-4959-8d43-ec6c1516b28b/" \
    -H "Authorization: Token 54321"

httpie:

$ http GET http://localhost:8000/api/models/set/8fb12cdb-d8b3-4959-8d43-ec6c1516b28b/ Authorization:"Token 54321"

History

TBD