Neptune_LightGBM_train_summary.py

import lightgbm as lgb
import neptune
import numpy as np
from neptune.integrations.lightgbm import NeptuneCallback, create_booster_summary
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split

# Create run
run = neptune.init_run(
    project="common/lightgbm-integration",
    api_token=neptune.ANONYMOUS_API_TOKEN,
    name="train-cls",
    tags=["lgbm-integration", "train", "cls"],
)

# Create neptune callback
neptune_callback = NeptuneCallback(run=run)

# Prepare data
X, y = load_digits(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)
lgb_train = lgb.Dataset(X_train, y_train)
lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train)

# Define parameters
params = {
    "boosting_type": "gbdt",
    "objective": "multiclass",
    "num_class": 10,
    "metric": ["multi_logloss", "multi_error"],
    "num_leaves": 21,
    "learning_rate": 0.05,
    "feature_fraction": 0.9,
    "bagging_fraction": 0.8,
    "bagging_freq": 5,
    "max_depth": 12,
}

# Train the model
gbm = lgb.train(
    params,
    lgb_train,
    num_boost_round=200,
    valid_sets=[lgb_train, lgb_eval],
    valid_names=["training", "validation"],
    callbacks=[neptune_callback],
)

y_pred = np.argmax(gbm.predict(X_test), axis=1)

# Log summary metadata to the same run under the "lgbm_summary" namespace
run["lgbm_summary"] = create_booster_summary(
    booster=gbm,
    log_trees=True,
    list_trees=[0, 1, 2, 3, 4],
    log_confusion_matrix=True,
    y_pred=y_pred,
    y_true=y_test,
)