HyperGBM is a specific implementation of HyperModel (for HyperModel, please refer to the Hypernets project). It is the core interface of the HyperGBM project. By calling the search method to explore and return the best model in the specified Search Space with the specified Searcher.

Required Parameters

  • searcher: hypernets.searcher.Searcher, A Searcher instance. hypernets.searchers.RandomSearcher hypernets.searcher.MCTSSearcher hypernets.searchers.EvolutionSearcher

Optinal Parameters

  • dispatcher: hypernets.core.Dispatcher, Dispatcher is used to provide different execution modes for search trials, such as in-process mode (InProcessDispatcher), distributed parallel mode (DaskDispatcher), etc. InProcessDispatcher is used by default.

  • callbacks: list of callback functions or None, optional (default=None), List of callback functions that are applied at each trial. See hypernets.callbacks for more information.

  • reward_metric: str or None, optinal(default=accuracy), Set corresponding metric according to task type to guide search direction of searcher.

  • task: str or None, optinal(default=None), Task type(binary,multiclass or regression). If None, inference the type of task automatically

  • param data_cleaner_params: dict, (default=None), Dictionary of parameters to initialize the DataCleaner instance. If None, DataCleaner will initialized with default values.

  • param cache_dir: str or None, (default=None), Path of data cache. If None, uses ‘working directory/tmp/cache’ as cache dir

  • param clear_cache: bool, (default=True), Whether clear the cache dir before searching

Use case

# import HyperGBM, Search Space and Searcher
from hypergbm import HyperGBM
from hypergbm.search_space import search_space_general
from hypernets.searchers.random_searcher import RandomSearcher
import pandas as pd
from sklearn.model_selection import train_test_split

# instantiate related objects
searcher = RandomSearcher(search_space_general, optimize_direction='max')
hypergbm = HyperGBM(searcher, task='binary', reward_metric='accuracy')

# load data into Pandas DataFrame
df = pd.read_csv('[train_data_file]')
y = df.pop('target')

# split data into train set and eval set
# The evaluation set is used to evaluate the reward of the model fitted with the training set
X_train, X_eval, y_train, y_eval = train_test_split(df, y, test_size=0.3)

# search
hypergbm.search(X_train, y_train, X_eval, y_eval, max_trials=30)

# load best model
best_trial = hypergbm.get_best_trial()
estimator = hypergbm.load_estimator(best_trial.model_file)

# predict on real data
pred = estimator.predict(X_real)