TrainModel

class protopipe.mva.TrainModel(case, feature_name_list, target_name=None)

Bases: object

Train classification or regressor model.

Parameters

case: str

Possibilities are regressor or classifier

feature_name_list: list

List of features

target_name: str, optional

Regression target

Methods Summary

get_optimal_model(init_model, …)	Get optimal hyperparameters and returns best model.
split_data(data_sig, train_fraction[, …])	Load and split data to build train/test samples.

Methods Documentation

get_optimal_model(init_model, tuned_parameters, scoring, cv)

Get optimal hyperparameters and returns best model.

Parameters

init_model: `~sklearn.base.BaseEstimator`

Model to optimise

tuned_parameters: dict

Contains parameter names and ranges to optimise on

scoring: str

Estimator

cv: int

number of split for x-validation

Returns

——-

best_estimator: `~sklearn.base.BaseEstimator`

Best model

split_data(data_sig, train_fraction, data_bkg=None, force_same_nsig_nbkg=False)

Load and split data to build train/test samples.

Parameters

data_sig: `~pandas.DataFrame`

Data frame

train_fraction: float

Fraction of events to build the training sample

data_bkg: `~pandas.DataFrame`

Data frame

force_same_nsig_nbkg: bool

If true, the same number of signal and bkg events will be used to build a classifier