classifierpromax is a scikit-learn wrapper library that helps to train and optimize multiple classifier models in parallel.
classifier_trainer():
This function trains three classifier models using default hyperparameter values. These are used as a baseline for model performance.
feature_selector():
This function will perform feature selection on all input models.
classifier_optimizer():
This function will perform hyperparameter optimization on all input models.
results_handler():
This function will return the score and hyperparameters for all the input models.
In a machine learning pipeline, code can often be repeated when working with multiple models, violating the DRY (Don’t-Repeat-Yourself) principle. This Python library is to promote DRY principles in machine learning code and create cleaner code.
$ pip install classifierpromax- Training baseline models
import pandas as pd
import numpy as np
from classifierpromax.Classifier_Trainer import Classifier_Trainer
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
# Dummy data
X = pd.DataFrame(np.random.rand(100, 5), columns=[f"feature_{i}" for i in range(5)])
y = pd.Series(np.random.randint(0, 2, size=100))
# Define preprocessor
preprocessor = StandardScaler()
# Function will return a dictionary of models
baseline_models, baseline_score = Classifier_Trainer(preprocessor, X, y, pos_label=1, seed=123)- Feature selection
from classifierpromax.Feature_Selector import Feature_Selector
# Function will return a dictionary of models
fs_models = Feature_Selector(preprocessor, models, X, y, n_features_to_select=3)- Hyperparameter optimization
from classifierpromax.Classifier_Optimizer import Classifier_Optimizer
# Function will return a dictionary of optimized models and another dictionary with the scores
opt_models, opt_score = Classifier_Optimizer(fs_models, X, y)- Results summary
from classifierpromax.Results_Handler import Results_Handler
# Function will score the models and return a summary table
summary = Result_Handler(score, opt_models)
print(summary)Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.
Long Nguyen, Jenson Chang, Gunisha Kaur, Han Wang
classifierpromax was created by Long Nguyen, Jenson Chang, Gunisha Kaur, Han Wang. It is licensed under the terms of the MIT license.
classifierpromax was created with cookiecutter and the py-pkgs-cookiecutter template.