Class Grid\_Search - Documentation

The Grid_Search class provides a simple way to execute a hyperparameter tuning for the convolutional neural network model. Have a look at the Model documentation for an overview of all available hyperparameters. The tuning returns the best model (highest ROC-AUC or PR-AUC on the validation data) and an overview of all trained models.

Methods - Overview

name	description
__init__	Initialize the object with a collection of parameter values.
train	Train all models and return the best one.

__init__

def __init__(self, params)

Initialize the object with a collection of parameter values.

For example: providing {'conv_num': [1,2,3], 'kernel_num': [20,50]} will result in training 6 different models (all possible combinations of the provided values) when the train() method is called later on. Parameters that are not provided here will hold their default values in all 6 models.

parameter	type	description
params	dict	A dict containing parameter names as keys and corresponding values as lists.

train

def train(self, data, pr_auc = False, verbose = True)

Train all models and return the best one.

Models are evaluated and ranked according to their ROC-AUC or PR-AUC (precision-recall) on a validation data set.

parameter	type	description
data	pysster.Data	A Data object providing training and validation data sets.
pr_auc	bool	If True, the area under the precision-recall curve will be maximized instead of the area under the ROC curve
verbose	bool	If True, progress information (train/val loss) will be printed throughout the training.

returns	type	description
results	tuple(pysster.Model, str)	The best performing model and an overview table of all models are returned.