To hypertune the given XGBoost model and display a progress bar using tqdm

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Nov 24, 2024

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To hypertune the given XGBoost model and display a progress bar using tqdm, you can follow these steps: First, make sure you have the necessary libraries installed. You can install tqdm using pip: ```shell pip install tqdm ``` Then, import the required libraries and initialize tqdm: ```python import xgboost as xgb from tqdm import tqdm # Initialize tqdm tqdm.pandas() ``` Next, define the parameters for hypertuning. You can use a grid search or random search to explore different parameter combinations. Here, we'll use a random search as an example: ```python from sklearn.model_selection import RandomizedSearchCV # Define the parameter grid for random search param_grid = { 'subsample': [0.8, 0.9, 1], 'colsample_bytree': [0.6, 0.7, 0.8], 'max_depth': [2, 3, 4], 'min_child_weight': [10, 15, 20], 'learning_rate': [0.01, 0.05, 0.1], 'n_estimators': [500, 1000, 2000] } ``` Now, create an instance of the XGBoost classifier: ```python xgb_model = xgb.XGBClassifier(objective='binary:logistic', n_jobs=-1) ``` Perform the hypertuning using `RandomizedSearchCV` and tqdm to display the progress bar: ```python # Create a RandomizedSearchCV object random_search = RandomizedSearchCV(

estimator=xgb_model, param_distributions=param_grid, n_iter=10, # Number of parameter combinations to try scoring='accuracy', # Use an appropriate scoring metric cv=5, # Number of cross-validation folds n_jobs=-1, # Use all available CPU cores verbose=2 ) # Fit the model with progress bar random_search.fit(train_x, train_y, eval_metric=['error', 'logloss'], early_stopping_rounds=25, eval_set=[(train_x, train_y), (val_x, val_y)], verbose=False, callbacks=[tqdm_callback]) ``` In this example, `RandomizedSearchCV` performs a randomized search over the parameter grid, using cross-validation to evaluate different combinations of parameters. The `verbose=2` argument enables detailed logging, and the `callbacks=[tqdm_callback]` argument ensures that the progress bar is displayed during the search. Note: You need to define a custom callback, `tqdm_callback`, to update the progress bar during the hyperparameter search. Here's an example of how to define it: ```python from sklearn.model_selection import ParameterGrid class TqdmCallback: def __init__(self, total): self.total = total def __call__(self, search_cv): n_iter = search_cv.n_iter_ if search_cv.random_state is not None: rng = np.random.RandomState(search_cv.random_state) random_state = rng.randint(0, self.total) n_iter += random_state tqdm.write(f'Iteration {n_iter}/{self.total}') # Create a TqdmCallback instance tqdm_callback = TqdmCallback(n_iter) ``` Remember to replace `train_x`, `train_y`, `val_x`, and `val_y` with your actual training and validation data.

By following these steps, you can hypertune the XGBoost model while displaying a progress bar using tqdm.

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