Integrate with LightGBM¶

Comet can automatically log your model graph, logging metrics, and parameters from your LightGBM code. You need only add the following lines of code to your LightGBM script:

from comet_ml import Experiment
import lightgbm as lgbm

experiment = Experiment()

# Your code here...

gbm = lgbm.train()

Log automatically¶

The Comet LightGBM auto-logger can automatically log:

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

Configure Comet for LightGBM¶

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¶

Here is a simple end-to-end LightGBM 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 lightgbm as lgb
import os
import pandas as pd
from sklearn.metrics import mean_squared_error

experiment = comet_ml.Experiment(
    api_key="<Your API Key>",
    project_name="<Your Project Name>"
)

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)

lgb_train = lgb.Dataset(X_train, y_train)
lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train)

params = {
    "boosting_type": "gbdt",
    "objective": "regression",
    "metric": {"rmse", "l2", "l1", "huber"},
    "num_leaves": 31,
    "learning_rate": 0.05,
    "feature_fraction": 0.9,
    "bagging_fraction": 0.8,
    "bagging_freq": 5,
    "verbosity": -1,
}

gbm = lgb.train(
    params,
    lgb_train,
    num_boost_round=20,
    valid_sets=lgb_eval,
    valid_names=("validation"),
    early_stopping_rounds=5,
)

y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration)
print("The rmse of prediction is:", mean_squared_error(y_test, y_pred) ** 0.5)

Note

There are alternatives to setting the API key programatically. See more here.

Try it out!¶

Here's an example for using Comet with LightGBM.

Apr. 24, 2024