Xgboost Logging

Import necessary packages

from katonic.ml import MyClient
import os
import numpy as np
from xgboost import XGBClassifier
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
from sklearn.metrics import accuracy_score, roc_auc_score, log_loss, recall_score, f1_score, precision_score
from sklearn.model_selection import train_test_split
import pandas as pd

Create client object using MyClient

myclient = MyClient()
mlflow = myclient.mlflow
client = myclient.client
mlflow.create_experiment("test_exp")

Define metric function

def metric(actual, pred):
    acc_score = accuracy_score(actual, pred)
    recall = recall_score(actual, pred)
    precision_scr = precision_score(actual, pred)
    f1_scr = f1_score(actual, pred)
    auc_roc = roc_auc_score(actual, pred)
    log_los = log_loss(actual, pred)

    return (
        acc_score,
        auc_roc,
        log_los,
        recall,
        f1_scr,
        precision_scr
    )

Implement model training and logging steps

exp_name = "mlflow-test-XGB2"
mlflow.set_experiment(exp_name)
exp_details = mlflow.get_experiment_by_name(exp_name)
with mlflow.start_run(run_name=exp_name):
    df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/diabetes.csv')
    x = df.drop(columns=['Outcome'], axis=1)
    y = df['Outcome']
    X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=.20, random_state=98)
    # The predicted column is "quality" which is a scalar from [3, 9]
    model_clf = XGBClassifier(random_state=0)
    #mlflow.log_param("")
    model_clf.fit(X_train, y_train)
    
    y_pred = model_clf.predict(X_test)
    (acc_score, auc_roc, log_los, recall, f1_scr, precision_scr) = metric(y_test, y_pred)

    model_metrics = {
        "accuracy_score": acc_score,
        "roc_auc_score": auc_roc,
        "log_loss": log_los,
        "recall": recall,
        "f1_score": f1_scr,
        "precision_score": precision_scr
    }

    for metric_name, score in model_metrics.items():
        mlflow.log_metric(metric_name, score)

    mlflow.xgboost.log_model(xgb_model=model_clf, artifact_path="model")