Integrate with XGBoost¶

Comet integrates with XGBoost.

XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. The same code runs on major distributed environment (Kubernetes, Hadoop, SGE, MPI, Dask) and can solve problems beyond billions of examples.

from comet_ml import Experiment
import xgboost as xgb

experiment = Experiment()

# Your code here...
model = xgb.fit()

Log automatically¶

The Comet XGBoost auto-logger can automatically log:

Model/graph description
Metrics (such as loss and accuracy)
Hyperparameters
Command-line arguments

Configure Comet for XGBoost¶

Item	Experiment Parameter	Environment Setting	Configuration File Setting
Model/graph description	log_graph	COMET_AUTO_LOG_GRAPH	comet.auto_log.graph
Metrics	auto_metric_logging	COMET_AUTO_LOG_METRICS	comet.auto_log.metrics
Hyperparameters	auto_param_logging	COMET_AUTO_LOG_PARAMETERS	comet.auto_log.parameters
Command-line arguments	parse_args	COMET_AUTO_LOG_CLI_ARGUMENTS	comet.auto_log.cli_arguments

End-to-end Example¶

# Get the data for this script:
# wget https://raw.githubusercontent.com/microsoft/LightGBM/master/examples/regression/regression.train -qq
# wget https://raw.githubusercontent.com/microsoft/LightGBM/master/examples/regression/regression.test -qq
import comet_ml

import xgboost as xgb
import os
import pandas as pd
from sklearn.metrics import mean_squared_error

experiment = comet_ml.Experiment()

dirname = os.path.dirname(__file__)
df_train = pd.read_csv(os.path.join(dirname, "regression.train"), header=None, sep="\t")
df_test = pd.read_csv(os.path.join(dirname, "regression.test"), header=None, sep="\t")

y_train = df_train[0]
y_test = df_test[0]

X_train = df_train.drop(0, axis=1)
X_test = df_test.drop(0, axis=1)

param = {
    "objective": "reg:squarederror",
    "colsample_bytree": 0.3,
    "learning_rate": 0.1,
    "max_depth": 5,
    "alpha": 10,
    "n_estimators": 50,
}

model = xgb.XGBRegressor(**param)
model.fit(
    X_train,
    y_train,
    eval_set=[(X_train, y_train), (X_test, y_test)],
    eval_metric="rmse"
)

Try it out!¶

Here's an example for using Comet with XGBoost.

Apr. 25, 2024