There are still many challenges in the machine learning modeling process for tabular data, such as imbalanced data, data drift, poor generalization ability, etc. This challenges cannot be completely solved by pipeline search, so we introduced in HyperGBM a more powerful tool is CompeteExperiment.

CompteExperiment is composed of a series of steps and Pipeline Search is just one step. It also includes advanced steps such as data cleaning, data drift handling, two-stage search, ensemble etc., as shown in the figure below: _images/hypergbm-competeexperiment.png

Code example

from hypergbm import make_experiment
from hypergbm.search_space import search_space_general
import pandas as pd
import logging

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

#create an experiment
experiment = make_experiment(df, target=target, 
                 search_space=lambda: search_space_general(class_balancing='SMOTE',n_estimators=300, early_stopping_rounds=10, verbose=0),

#run experiment
estimator =

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

Required Parameters

  • hyper_model: hypergbm.HyperGBM, A HyperGBM instance

  • X_train: Pandas or Dask DataFrame, Feature data for training

  • y_train: Pandas or Dask Series, Target values for training

Optinal Parameters

  • X_eval: (Pandas or Dask DataFrame) or None, (default=None), Feature data for evaluation

  • y_eval: (Pandas or Dask Series) or None, (default=None), Target values for evaluation

  • X_test: (Pandas or Dask Series) or None, (default=None), Unseen data without target values for semi-supervised learning

  • eval_size: float or int, (default=None), Only valid when X_eval or y_eval is None. If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the eval split. If int, represents the absolute number of test samples. If None, the value is set to the complement of the train size.

  • train_test_split_strategy: ‘adversarial_validation’ or None, (default=None), Only valid when X_eval or y_eval is None. If None, use eval_size to split the dataset, otherwise use adversarial validation approach.

  • cv: bool, (default=False), If True, use cross-validation instead of evaluation set reward to guide the search process

  • num_folds: int, (default=3), Number of cross-validated folds, only valid when cv is true

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

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

  • random_state: int or RandomState instance, (default=9527), Controls the shuffling applied to the data before applying the split.

  • scorer: str, callable or None, (default=None), Scorer to used for feature importance evaluation and ensemble. It can be a single string (see get_scorer) or a callable (see make_scorer). If None, exception will occur.

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

  • collinearity_detection: bool, (default=False), Whether to clear multicollinearity features

  • drift_detection: bool,(default=True), Whether to enable data drift detection and processing. Only valid when X_test is provided. Concept drift in the input data is one of the main challenges. Over time, it will worsen the performance of model on new data. We introduce an adversarial validation approach to concept drift problems in HyperGBM. This approach will detect concept drift and identify the drifted features and process them automatically.

  • feature_reselection: bool, (default=True), Whether to enable two stage feature selection and searching

  • feature_reselection_estimator_size: int, (default=10), The number of estimator to evaluate feature importance. Only valid when feature_reselection is True.

  • feature_reselection_threshold: float, (default=1e-5), The threshold for feature selection. Features with importance below the threshold will be dropped. Only valid when feature_reselection is True.

  • ensemble_size: int, (default=20), The number of estimator to ensemble. During the AutoML process, a lot of models will be generated with different preprocessing pipelines, different models, and different hyperparameters. Usually selecting some of the models that perform well to ensemble can obtain better generalization ability than just selecting the single best model.

  • pseudo_labeling: bool, (default=False), Whether to enable pseudo labeling. Pseudo labeling is a semi-supervised learning technique, instead of manually labeling the unlabelled data, we give approximate labels on the basis of the labelled data. Pseudo-labeling can sometimes improve the generalization capabilities of the model.

  • pseudo_labeling_proba_threshold: float, (default=0.8), Confidence threshold of pseudo-label samples. Only valid when feature_reselection is True.

  • pseudo_labeling_resplit: bool, (default=False), Whether to re-split the training set and evaluation set after adding pseudo-labeled data. If False, the pseudo-labeled data is only appended to the training set. Only valid when feature_reselection is True.

  • retrain_on_wholedata: bool, (default=False), Whether to retrain the model with whole data after the search is completed.

  • log_level: int or None, (default=None), Level of logging, possible values:[logging.CRITICAL, logging.FATAL, logging.ERROR, logging.WARNING, logging.WARN, logging.INFO, logging.DEBUG, logging.NOTSET]