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September 8, 2024 14:14
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| import os | |
| import time | |
| import warnings | |
| import neptune | |
| from dotenv import load_dotenv | |
| from lightgbm import LGBMClassifier | |
| from xgboost import XGBClassifier | |
| from sklearn.datasets import make_classification | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.metrics import accuracy_score | |
| from tqdm.notebook import tqdm | |
| load_dotenv() | |
| warnings.filterwarnings('ignore') | |
| api_token = os.getenv("NEPTUNE_API_TOKEN") | |
| project = os.getenv("NEPTUNE_PROJECT_NAME") | |
| def create_run(name): | |
| run = neptune.init_run( | |
| project=os.getenv("NEPTUNE_PROJECT_NAME"), | |
| api_token=os.getenv("NEPTUNE_API_TOKEN"), | |
| custom_run_id=name | |
| ) | |
| return run | |
| # Creating two separate experiments | |
| lgbm_run = create_run('LightGBM') | |
| xgb_run = create_run('XGBoost') | |
| # Configuration for our custom dataset | |
| min_samples = 1000 | |
| max_samples = 20000 | |
| step = 1000 | |
| for sample_size in tqdm(range(min_samples, max_samples + step, step)): | |
| # Generating the dataset of custom sample size | |
| X, y = make_classification(n_samples=sample_size) | |
| X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y) | |
| # XGBoost training | |
| xgb_model = XGBClassifier(random_state=42, verbosity=0) | |
| start = time.time() | |
| xgb_model.fit(X_train, y_train) | |
| end = time.time() | |
| xgb_runtime = end - start | |
| xgb_accuracy = accuracy_score(y_test, xgb_model.predict(X_test)) | |
| # LightGBM training | |
| lgbm_model = LGBMClassifier(random_state=42, verbosity=-1) | |
| start = time.time() | |
| lgbm_model.fit(X_train, y_train) | |
| end = time.time() | |
| lgbm_runtime = end - start | |
| lgbm_accuracy = accuracy_score(y_test, lgbm_model.predict(X_test)) | |
| # Logging | |
| lgbm_run["metrics/comparison/runtime"].append(lgbm_runtime, step=sample_size) | |
| lgbm_run["metrics/comparison/accuracy"].append(lgbm_accuracy, step=sample_size) | |
| xgb_run["metrics/comparison/accuracy"].append(xgb_accuracy, step=sample_size) | |
| xgb_run["metrics/comparison/runtime"].append(xgb_runtime, step=sample_size) | |
| xgb_run.stop() | |
| lgbm_run.stop() |
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