Python for Finance Cookbook

How to do it...

Execute the following steps to run Bayesian hyperparameter optimization of a LightGBM model.

1. Import the libraries:

import pandas as pdfrom sklearn.model_selection import train_test_splitfrom hyperopt import hp, fmin, tpe, STATUS_OK, Trialsfrom sklearn.model_selection import (cross_val_score,                                     StratifiedKFold)from lightgbm import LGBMClassifierfrom chapter_9_utils import performance_evaluation_report

2. Define parameters for later use:

N_FOLDS = 5MAX_EVALS = 200

3. Load and prepare the data:

df = pd.read_csv('credit_card_fraud.csv')X = df.copy()y = X.pop('Class')X_train, X_test, y_train, y_test = train_test_split(X, y,                                                     test_size=0.2,                                                     stratify=y)

4. Define the objective function:

def objective(params, n_folds = N_FOLDS, random_state=42): ...