xgbsearch 0.1.6

An implementation of grid search, random search for XGBoost with eval_set and early stopping support.

xgbsearch

A pacakge implementing grid search and random search for XGBoost.

Description

An implementation of grid search and random search for hyper parameter tunning for xgboost that allows for specification of eval_sets and usage of early stopping.

Installation

Install it via PyPI using pip command.

# Install or Upgrade to newest available version
$ pip install -U xgbsearch

Motivation

Current implementation of grid search and random search in scikit-learn (using GridSearchCV/RandomSearchCV) does not allow for specifying evaluation sets that can be easily passed to XGboost.fit() and used in early stopping.

This package significantly simplifies implementation of grid search and random search.

Additionally, it makes implementation of your own, new search algorithms very simple and provides a consistent interface for running various hyper parameter tunning algorightms while retaining early stopping and eval sets functionality provided by xgboost.

Example usage

The package provides 3 classes:

• XgbSearch - the base super class that contains most of the implementation details.
• XgbGridSearch - implementation of grid search (exchaustive search) algorithm for hyper parameter tunning.
• XgbRandomSearch - implementation of random search algorithm for hyper parameter tunning.

from xgbsearch import XgbGridSearch, XgbRandomSearch
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
import pandas as pd
from sklearn.metrics import roc_auc_score

X, y = make_classification(random_state=42)
X = pd.DataFrame(X)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

# These parameters will be passed to xgb.fit as is.
fit_params = {
    "device": "cuda",
    "objective": "binary:logistic",
    "eval_metric": ["auc"],
}

# The parameters here will be tuned. If the parameter is a single value, it will be passed as is.
# If the parameter is a list, all possible combinations will be searched using grid search.
tune_params_grid = {
    "eta": [0.01, 0.001],
    "max_depth": [5, 11],
    "min_child_weight": 3,
}

grid_search = XgbGridSearch(tune_params_grid, fit_params)
eval_set = [(X_train, y_train, "train"), (X_test, y_test, "test")]
grid_search.fit(X_train, y_train, eval_set, 10000, 100, verbose_eval=25)

# The parameters here will be tuned. If the parameter is a single value, it will be passed as is.
# If the parameter is a list, during each iteration a single value will be picked from that list.
# If the parameter is a tuple of two floats, a random value between the two ends will be picked.
# If the parameter is a tuple of two ints, a random int value between the two ends will be picked.
tune_params_random = {
    "eta": (0.1, 0.005),
    "max_depth": (5, 11),
    "min_child_weight": [1, 2, 3],
}

random_search = XgbRandomSearch(tune_params_random, fit_params, max_iter_count=3)
eval_set = [(X_train, y_train, "train"), (X_test, y_test, "test")]
random_search.fit(X_train, y_train, eval_set, 10000, 100, verbose_eval=25)

# You can access the results like this.
print(random_search.get_best_model())  # returns the best model object
print(
    random_search.get_best_model_results()
)  # returns best model results dict with complete results
print(random_search.predict(X_test))  # generates predictions for the BEST model
print(
    random_search.score(X_test, y_test, roc_auc_score)
)  # calculates the score using given function; note the function needs to accept X, y as input

Output

After fitting, XgbGridSearch and XgbRandomSearch will populate a result list. This will contain one dict per iteration with complete result of the model run.

{'model': <xgboost.core.Booster at 0x7f3b00bcd070>, # actual model object, if early stopping is enabled this will be the LAST model fitted, not the best one
 'parameters': {'eta': 0.05,'colsample_bytree': 0.8,'max_depth': 11,'min_child_weight': 3,'device': 'cuda','objective': 'binary:logistic','eval_metric': ['auc']}, # Parameters passed to xgb.fit
 'num_boost_round': 10000,'verbose_eval': 10,'early_stopping_rounds': 100},
 # Results of the model fitting process by boosting round
 'model_training_results': {'TRAIN': OrderedDict([('auc',
                [0.8943848819579814,
                 ...
                 0.9805122373835824,
                 0.9805555555555555,
                 0.9806530214424951])]),
  'train': OrderedDict([('auc',
                [0.8943848819579814,
                 ...
                 0.9805122373835824,
                 0.9805555555555555,
                 0.9806530214424951])]),
  'test': OrderedDict([('auc',
                [0.8711075249536788,
                 ...
                 0.8837786145478453,
                 0.8839579224194609,
                 0.8836590759667683])])},
 'best_iteration': 204, # index of the best iteration, this is significant when using early_stopping
 'best_score': 0.8865877712031558, # value of last eval_metric on last eval_set for the best model
 'best_model': <xgboost.core.Booster at 0x7f3b01a75a30>} # actual best model object

Implementing your own search

It is very easy to implement your own search using XgbSearch class.

from xgbsearch import XgbSearch

class MyOwnSearch(XgbSearch):

    def __init__(self, tune_params, fit_params, add_value, maximise_score=True):
        super().__init__(tune_params, fit_params, maximise_score)
        self.add_value = add_value

    def _generate_params(self):
        # Toy example. Will just take the first parameter and add to it a value specified in the constructor.
        # this method needs to return a list dicts of parameters that will be passed into xgb.fit()
        result = []
        for i in range(3):
            first_key = list(self.tune_params.keys())[0]
            loop_result = self.tune_params | self.fit_params
            loop_result[first_key] = loop_result[first_key] + i * self.add_value
            result.append(loop_result)

        return result


# Run it!
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
import pandas as pd
from sklearn.metrics import roc_auc_score

X, y = make_classification(random_state=42)
X = pd.DataFrame(X)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

# These parameters will be passed to xgb.fit as is.
fit_params = {
    "device": "cuda",
    "objective": "binary:logistic",
    "eval_metric": ["auc"],
}

# The parameters here will be tuned. If the parameter is a single value, it will be passed as is.
# If the parameter is a list, all possible combinations will be searched using grid search.
tune_params_grid = {
    "eta": 0.01,
    "max_depth": 5,
    "min_child_weight": 3,
}

my_search = MyOwnSearch(tune_params_grid, fit_params, 0.01)
eval_set = [(X_train, y_train, "train"), (X_test, y_test, "test")]
my_search.fit(X_train, y_train, eval_set, 10000, 100, verbose_eval=25)

Additional classes

This package also contains a supplement class XgbResultDisplay which makes accessing results of grid search/random search easier. This class uses static methods.

See display-demo.ipynb for examples.