├── assets └── img │ ├── test │ ├── logo.png │ ├── LUCIFER-ML.gif │ └── logo-LuciferML.png ├── luciferml ├── __init__.py ├── supervised │ ├── __init__.py │ ├── utils │ │ ├── __init__.py │ │ ├── tuner │ │ │ ├── optuna │ │ │ │ ├── optuna_base.py │ │ │ │ └── objectives │ │ │ │ │ ├── regression_objectives.py │ │ │ │ │ └── classification_objectives.py │ │ │ └── luciferml_tuner.py │ │ ├── best.py │ │ ├── validator.py │ │ ├── configs.py │ │ ├── preprocesser.py │ │ └── predictors.py │ ├── README │ │ ├── Preprocessing.md │ │ ├── Regression.md │ │ └── Classification.md │ ├── classification.py │ └── regression.py ├── README.md └── preprocessing.py ├── examples ├── Folds5x2_pp.xlsx ├── README.md ├── Salary_Data.csv └── Social_Network_Ads.csv ├── _config.yml ├── requirements.txt ├── .github ├── workflows │ └── publish-package.yml └── FUNDING.yml ├── docs └── index.rst ├── setup.py ├── .gitignore ├── README.md └── LICENSE /assets/img/test: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /assets/img/logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/d4rk-lucif3r/LuciferML/HEAD/assets/img/logo.png -------------------------------------------------------------------------------- /luciferml/__init__.py: -------------------------------------------------------------------------------- 1 | # __version__ = "0.0.11" 2 | 3 | from luciferml import preprocessing 4 | -------------------------------------------------------------------------------- /assets/img/LUCIFER-ML.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/d4rk-lucif3r/LuciferML/HEAD/assets/img/LUCIFER-ML.gif -------------------------------------------------------------------------------- /examples/Folds5x2_pp.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/d4rk-lucif3r/LuciferML/HEAD/examples/Folds5x2_pp.xlsx -------------------------------------------------------------------------------- /luciferml/supervised/__init__.py: -------------------------------------------------------------------------------- 1 | # __version__ = "0.0.11" 2 | 3 | from luciferml.supervised import * 4 | -------------------------------------------------------------------------------- /luciferml/supervised/utils/__init__.py: -------------------------------------------------------------------------------- 1 | # __version__ = "0.0.11" 2 | from luciferml.supervised.utils import * 3 | -------------------------------------------------------------------------------- /assets/img/logo-LuciferML.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/d4rk-lucif3r/LuciferML/HEAD/assets/img/logo-LuciferML.png -------------------------------------------------------------------------------- /_config.yml: -------------------------------------------------------------------------------- 1 | title: LuciferML 2 | theme: jekyll-theme-minimal 3 | logo: /assets/img/LUCIFER-ML.gif 4 | description: Semi-Auto Machine Learning Library by d4rk-lucif3r. 5 | show_downloads: true 6 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | numpy 2 | pandas 3 | scipy 4 | seaborn 5 | matplotlib 6 | scikit-learn 7 | imblearn 8 | xgboost 9 | tensorflow 10 | catboost 11 | lightgbm 12 | optuna 13 | shap 14 | colorama -------------------------------------------------------------------------------- /luciferml/README.md: -------------------------------------------------------------------------------- 1 | # Learn More About 2 | 3 | 1) [Preprocessing](https://github.com/d4rk-lucif3r/LuciferML/blob/master/luciferml/supervised/README/Preprocessing.md) 4 | 5 | 2) [Classification](https://github.com/d4rk-lucif3r/LuciferML/blob/master/luciferml/supervised/README/Classification.md) 6 | 7 | 3) [Regression](https://github.com/d4rk-lucif3r/LuciferML/blob/master/luciferml/supervised/README/Regression.md) 8 | -------------------------------------------------------------------------------- /examples/README.md: -------------------------------------------------------------------------------- 1 | # Examples 2 | 3 | ## Notebooks 4 | 5 | 1. [Heart Attack Analysis](https://www.kaggle.com/d4rklucif3r/heart-attack-analysis-eda-luciferml-88) 6 | 2. [Water Quality Prediction](https://www.kaggle.com/d4rklucif3r/water-quality-eda-luciferml-73-accuracy) 7 | 3. [Salary Prediction](https://www.kaggle.com/d4rklucif3r/salary-eda-luciferml-plotly) 8 | 4. [Stars Prediction](https://www.kaggle.com/d4rklucif3r/spectral-classes-plotly-luciferml-93) 9 | -------------------------------------------------------------------------------- /examples/Salary_Data.csv: -------------------------------------------------------------------------------- 1 | YearsExperience,Salary 2 | 1.1,39343.00 3 | 1.3,46205.00 4 | 1.5,37731.00 5 | 2.0,43525.00 6 | 2.2,39891.00 7 | 2.9,56642.00 8 | 3.0,60150.00 9 | 3.2,54445.00 10 | 3.2,64445.00 11 | 3.7,57189.00 12 | 3.9,63218.00 13 | 4.0,55794.00 14 | 4.0,56957.00 15 | 4.1,57081.00 16 | 4.5,61111.00 17 | 4.9,67938.00 18 | 5.1,66029.00 19 | 5.3,83088.00 20 | 5.9,81363.00 21 | 6.0,93940.00 22 | 6.8,91738.00 23 | 7.1,98273.00 24 | 7.9,101302.00 25 | 8.2,113812.00 26 | 8.7,109431.00 27 | 9.0,105582.00 28 | 9.5,116969.00 29 | 9.6,112635.00 30 | 10.3,122391.00 31 | 10.5,121872.00 32 | -------------------------------------------------------------------------------- /luciferml/supervised/utils/tuner/optuna/optuna_base.py: -------------------------------------------------------------------------------- 1 | import optuna 2 | 3 | 4 | class Tuner: 5 | def __init__(self, sampler, n_trials=100, direction="maximize"): 6 | self.n_trials = n_trials 7 | self.sampler = sampler 8 | self.direction = direction 9 | 10 | def tune(self, objective): 11 | study = optuna.create_study(direction=self.direction, sampler=self.sampler) 12 | study.optimize(objective, n_trials=self.n_trials, n_jobs=-1, gc_after_trial=True) 13 | params = study.best_params 14 | best_score = study.best_value 15 | return params, best_score 16 | -------------------------------------------------------------------------------- /.github/workflows/publish-package.yml: -------------------------------------------------------------------------------- 1 | name: Publish LuciferML to PyPI 2 | 3 | on: 4 | push: 5 | tags: 6 | - '*' 7 | 8 | jobs: 9 | deploy: 10 | runs-on: ubuntu-20.04 11 | 12 | steps: 13 | - uses: actions/checkout@v2 14 | - uses: actions/setup-python@v2 15 | - name: Install Dependencies 16 | run: | 17 | python -m pip install --upgrade pip 18 | pip install setuptools wheel twine 19 | - name: Build Package and Publish it. 20 | env: 21 | TWINE_USERNAME: __token__ 22 | TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }} 23 | run: | 24 | python setup.py sdist bdist_wheel 25 | twine upload dist/* 26 | -------------------------------------------------------------------------------- /.github/FUNDING.yml: -------------------------------------------------------------------------------- 1 | # These are supported funding model platforms 2 | 3 | github: [d4rk-lucif3r] # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2] 4 | patreon: # Replace with a single Patreon username 5 | open_collective: # Replace with a single Open Collective username 6 | ko_fi: # Replace with a single Ko-fi username 7 | tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel 8 | community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry 9 | liberapay: # Replace with a single Liberapay username 10 | issuehunt: # Replace with a single IssueHunt username 11 | otechie: # Replace with a single Otechie username 12 | custom: #['lucifer78908@okaxis', 'https://paypal.me/d4rklucif3r '] # Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2'] 13 | -------------------------------------------------------------------------------- /docs/index.rst: -------------------------------------------------------------------------------- 1 | The LuciferML is a Semi-Automated Machine Learning Python Library that works with tabular data. It is designed to save time while doing data analysis. It will help you right from data preprocessing to Data Prediction. 2 | 3 | The LuciferML will help you with 4 | 5 | 1) Preprocessing Data: 6 | 7 | - Encoding 8 | - Splitting 9 | - Scaling 10 | - Dimensionality Reduction 11 | - Resampling 12 | 13 | 2) Trying many different machine learning models with hyperparameter tuning, 14 | 15 | ## Learn About 16 | 1) [Preprocessing](https://github.com/d4rk-lucif3r/LuciferML/blob/master/luciferml/supervised/README/Preprocessing.md) 17 | 2) [Classification](https://github.com/d4rk-lucif3r/LuciferML/blob/master/luciferml/supervised/README/Classification.md) 18 | 3) [Regression](https://github.com/d4rk-lucif3r/LuciferML/blob/master/luciferml/supervised/README/Regression.md) 19 | -------------------------------------------------------------------------------- /luciferml/supervised/README/Preprocessing.md: -------------------------------------------------------------------------------- 1 | # Preprocessing 2 | 3 | ## Available Methods 4 | 5 | 1) skewcorrect 6 | 7 | Plots distplot and probability plot for non-normalized data and after normalizing the provided data. 8 | Normalizes data using boxcox normalization 9 | 10 | Parameters: 11 | 12 | dataset : pd.DataFrame 13 | 14 | Dataset on which skewness correction has to be done. 15 | 16 | except_columns : list 17 | 18 | Columns for which skewness correction need not to be done.Default = [] 19 | 20 | :returns: Scaled Dataset 21 | :rtype: pd.DataFrame 22 | 23 | Example: 24 | 25 | 1) All Columns 26 | 27 | from luciferml.preprocessing import Preprocess as pp 28 | 29 | import pandas as pd 30 | 31 | dataset = pd.read_csv('/examples/Social_Network_Ads.csv') 32 | prep = pp(dataset, dataset.columns) 33 | dataset = prep.skewcorrect(dataset) 34 | 35 | 2) Except column/columns 36 | 37 | from luciferml.preprocessing import Preprocess as pp 38 | 39 | import pandas as pd 40 | 41 | dataset = pd.read_csv('/examples/Social_Network_Ads.csv') 42 | prep = pp(dataset, dataset.columns, except_columns=['Purchased']) 43 | dataset = prep.skewcorrect() 44 | -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | from setuptools import setup, find_packages 2 | from codecs import open 3 | from os import path 4 | 5 | here = path.abspath(path.dirname(__file__)) 6 | 7 | # Get the long description from the README file 8 | with open(path.join(here, "README.md"), encoding="utf-8") as f: 9 | long_description = f.read() 10 | 11 | setup( 12 | name="lucifer-ml", 13 | packages=[ 14 | "luciferml", 15 | "luciferml.supervised", 16 | "luciferml.supervised.utils", 17 | "luciferml.supervised.utils.tuner", 18 | "luciferml.supervised.utils.tuner.optuna", 19 | "luciferml.supervised.utils.tuner.optuna.objectives", 20 | ], 21 | version="0.0.81a", 22 | license="MIT", 23 | description="Automated ML by d4rk-lucif3r", 24 | long_description=long_description, 25 | long_description_content_type="text/markdown", 26 | author="Arsh Anwar", 27 | author_email="lucifer78908@gmail.com", 28 | url="https://github.com/d4rk-lucif3r/LuciferML", 29 | keywords=["luciferML", "AutoML", "Python"], 30 | install_requires=open("requirements.txt").readlines(), 31 | classifiers=[ 32 | "Development Status :: 3 - Alpha", 33 | "Intended Audience :: Developers", 34 | "Topic :: Software Development :: Build Tools", 35 | "License :: OSI Approved :: MIT License", 36 | "Programming Language :: Python :: 3", 37 | "Programming Language :: Python :: 3.4", 38 | "Programming Language :: Python :: 3.5", 39 | "Programming Language :: Python :: 3.6", 40 | ], 41 | ) 42 | -------------------------------------------------------------------------------- /luciferml/supervised/utils/best.py: -------------------------------------------------------------------------------- 1 | from colorama import Fore 2 | 3 | 4 | class Best: 5 | """ 6 | Best is used to utilise the best model when predictor = 'all' is used. 7 | 8 | """ 9 | 10 | def __init__(self, best_model, tune, isReg=False): 11 | self.__best_model = best_model 12 | self.model = self.__best_model["Model"] 13 | self.name = self.__best_model["Name"] 14 | self.tune = tune 15 | if isReg: 16 | self.r2_score = self.__best_model["R2 Score"] 17 | self.mae = self.__best_model["Mean Absolute Error"] 18 | self.rmse = self.__best_model["Root Mean Squared Error"] 19 | if not isReg: 20 | self.accuracy = self.__best_model["Accuracy"] 21 | self.kfold_acc = self.__best_model["KFold Accuracy"] 22 | if tune == True: 23 | self.best_params = self.__best_model["Best Parameters"] 24 | self.best_accuracy = self.__best_model["Best Accuracy"] 25 | else: 26 | self.best_params = "Run with tune = True to get best parameters" 27 | self.isReg = isReg 28 | 29 | def summary(self): 30 | """Returns a summary of the best model""" 31 | print("\nBest Model Summary:") 32 | print(Fore.CYAN + "Name: ", self.name) 33 | if self.isReg: 34 | print(Fore.CYAN + "R2 Score: ", self.r2_score) 35 | print(Fore.CYAN + "Mean Absolute Error: ", self.mae) 36 | print(Fore.CYAN + "Root Mean Squared Error: ", self.rmse) 37 | else: 38 | print(Fore.CYAN + "Accuracy: ", self.accuracy) 39 | print(Fore.CYAN + "KFold Accuracy: ", self.kfold_acc) 40 | if self.tune: 41 | print(Fore.CYAN + "Best Parameters: ", self.best_params) 42 | print(Fore.CYAN + "Best Accuracy: ", self.best_accuracy) 43 | print("\n") 44 | 45 | def predict(self, pred): 46 | """Predicts the output of the best model""" 47 | prediction = self.__best_model["Model"].predict(pred) 48 | return prediction 49 | -------------------------------------------------------------------------------- /luciferml/supervised/utils/tuner/luciferml_tuner.py: -------------------------------------------------------------------------------- 1 | import traceback 2 | 3 | from luciferml.supervised.utils.predictors import classification_predictor 4 | from luciferml.supervised.utils.predictors import regression_predictor 5 | from luciferml.supervised.utils.tuner.optuna.optuna_base import Tuner 6 | from colorama import Fore 7 | 8 | 9 | def luciferml_tuner( 10 | predictor, 11 | objective, 12 | n_trials, 13 | sampler, 14 | direction, 15 | X_train, 16 | y_train, 17 | cv_folds, 18 | random_state, 19 | metric, 20 | all_mode=False, 21 | verbose=False, 22 | isReg=False, 23 | ): 24 | """ 25 | Takes classifier, tune-parameters, Training Data and no. of folds as input and Performs GridSearch Crossvalidation. 26 | """ 27 | tuner = Tuner(n_trials=n_trials, sampler=sampler, direction=direction) 28 | try: 29 | best_params, best_score = tuner.tune(objective) 30 | if isReg: 31 | model, _ = regression_predictor( 32 | predictor, 33 | best_params, 34 | X_train, 35 | y_train, 36 | cv_folds, 37 | random_state, 38 | metric, 39 | mode="tune", 40 | verbose=verbose, 41 | ) 42 | if not isReg: 43 | model, _ = classification_predictor( 44 | predictor, 45 | best_params, 46 | X_train, 47 | y_train, 48 | cv_folds, 49 | random_state, 50 | metric, 51 | mode="tune", 52 | verbose=verbose, 53 | ) 54 | if not all_mode: 55 | print(Fore.CYAN + " Best Params: ", best_params) 56 | print(Fore.CYAN + " Best Score: ", best_score * 100, "\n") 57 | return best_params, best_score, model 58 | except Exception as error: 59 | print(Fore.RED + "HyperParam Tuning Failed with Error: ", error, "\n") 60 | return None, 0, None 61 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | 131 | # models 132 | */catboost_info/* 133 | */lucifer_ml_info/* 134 | #venv 135 | *venv/* 136 | #misc 137 | *test* -------------------------------------------------------------------------------- /luciferml/supervised/utils/validator.py: -------------------------------------------------------------------------------- 1 | from sklearn.model_selection import cross_val_score 2 | import scipy 3 | from luciferml.supervised.utils.configs import * 4 | from colorama import Fore 5 | 6 | 7 | def pred_check(predictor, pred_type): 8 | if pred_type == "regression": 9 | avlbl_predictors = list(regressors_ver.keys()) 10 | elif pred_type == "classification": 11 | avlbl_predictors = list(classifiers_ver.keys()) 12 | if type(predictor) == str: 13 | if predictor in avlbl_predictors: 14 | return True, predictor 15 | else: 16 | return False, predictor 17 | elif type(predictor) == list: 18 | for i in predictor: 19 | if i not in avlbl_predictors: 20 | return False, i 21 | return True, None 22 | 23 | 24 | def sparse_check(features, labels): 25 | features = features 26 | labels = labels 27 | """ 28 | Takes features and labels as input and checks if any of those is sparse csr_matrix. 29 | """ 30 | try: 31 | if scipy.sparse.issparse(features[()]): 32 | features = features[()].toarray() 33 | elif scipy.sparse.issparse(labels[()]): 34 | labels = labels[()].toarray() 35 | except Exception as error: 36 | pass 37 | return (features, labels) 38 | 39 | 40 | def kfold(model, predictor, X_train, y_train, cv_folds, isReg=False, all_mode=False): 41 | """ 42 | Takes predictor, input_units, epochs, batch_size, X_train, y_train, cv_folds, and accuracy_scores dictionary. 43 | Performs K-Fold Cross validation and stores result in accuracy_scores dictionary and returns it. 44 | """ 45 | if not isReg: 46 | name = classifiers 47 | scoring = "accuracy" 48 | if isReg: 49 | name = regressors 50 | scoring = "r2" 51 | try: 52 | accuracies = cross_val_score( 53 | estimator=model, X=X_train, y=y_train, cv=cv_folds, scoring=scoring 54 | ) 55 | if not all_mode: 56 | if not isReg: 57 | print(" KFold Accuracy: {:.2f} %".format(accuracies.mean() * 100)) 58 | if isReg: 59 | print(" R2 Score: {:.2f} %".format(accuracies.mean() * 100)) 60 | model_name = name[predictor] 61 | accuracy = accuracies.mean() * 100 62 | if not all_mode: 63 | print( 64 | " Standard Deviation: {:.2f} %".format(accuracies.std() * 100), 65 | "\n", 66 | ) 67 | return (model_name, accuracy) 68 | 69 | except Exception as error: 70 | print(Fore.RED + "K-Fold Cross Validation failed with error: ", error, "\n") 71 | -------------------------------------------------------------------------------- /luciferml/supervised/utils/configs.py: -------------------------------------------------------------------------------- 1 | intro = """ 2 | 3 | ██╗░░░░░██╗░░░██╗░█████╗░██╗███████╗███████╗██████╗░░░░░░░███╗░░░███╗██╗░░░░░ 4 | ██║░░░░░██║░░░██║██╔══██╗██║██╔════╝██╔════╝██╔══██╗░░░░░░████╗░████║██║░░░░░ 5 | ██║░░░░░██║░░░██║██║░░╚═╝██║█████╗░░█████╗░░██████╔╝█████╗██╔████╔██║██║░░░░░ 6 | ██║░░░░░██║░░░██║██║░░██╗██║██╔══╝░░██╔══╝░░██╔══██╗╚════╝██║╚██╔╝██║██║░░░░░ 7 | ███████╗╚██████╔╝╚█████╔╝██║██║░░░░░███████╗██║░░██║░░░░░░██║░╚═╝░██║███████╗ 8 | ╚══════╝░╚═════╝░░╚════╝░╚═╝╚═╝░░░░░╚══════╝╚═╝░░╚═╝░░░░░░╚═╝░░░░░╚═╝╚══════╝ 9 | """ 10 | 11 | classifiers = { 12 | "lr": "Logistic Regression", 13 | "sgd": "Stochastic Gradient Descent", 14 | "perc": "Perceptron", 15 | "pass": "Passive Aggressive Classifier", 16 | "ridg": "Ridge Classifier", 17 | "svm": "Support Vector Machine", 18 | "knn": "K-Nearest Neighbours", 19 | "dt": "Decision Trees", 20 | "nb": "Naive Bayes", 21 | "rfc": "Random Forest Classifier", 22 | "gbc": "Gradient Boosting Classifier", 23 | "ada": "AdaBoost Classifier", 24 | "bag": "Bagging Classifier", 25 | "extc": "Extra Trees Classifier", 26 | "lgbm": "LightGBM Classifier", 27 | "cat": "CatBoost Classifier", 28 | "xgb": "XGBoost Classifier", 29 | "ann": "Multi Layer Perceptron Classifier", 30 | } 31 | 32 | regressors = { 33 | "lin": "Linear Regression", 34 | "sgd": "Stochastic Gradient Descent Regressor", 35 | "krr": "Kernel Ridge Regressor", 36 | "elas": "Elastic Net Regressor", 37 | "br": "Bayesian Ridge Regressor", 38 | "svr": "Support Vector Regressor", 39 | "knr": "K-Neighbors Regressor", 40 | "dt": "Decision Trees Regressor", 41 | "rfr": "Random Forest Regressor", 42 | "gbr": "Gradient Boost Regressor", 43 | "ada": "AdaBoost Regressor", 44 | "bag": "Bagging Regressor", 45 | "extr": "Extra Trees Regressor", 46 | "lgbm": "LightGBM Regressor", 47 | "xgb": "XGBoost Regressor", 48 | "cat": "Catboost Regressor", 49 | "ann": "Multi-Layer Perceptron Regressor", 50 | } 51 | 52 | classifiers_ver = { 53 | "lr": "Logistic Regression", 54 | "sgd": "Stochastic Gradient Descent", 55 | "perc": "Perceptron", 56 | "pass": "Passive Aggressive Classifier", 57 | "ridg": "Ridge Classifier", 58 | "svm": "Support Vector Machine", 59 | "knn": "K-Nearest Neighbours", 60 | "dt": "Decision Trees", 61 | "nb": "Naive Bayes", 62 | "rfc": "Random Forest Classifier", 63 | "gbc": "Gradient Boosting Classifier", 64 | "ada": "AdaBoost Classifier", 65 | "bag": "Bagging Classifier", 66 | "extc": "Extra Trees Classifier", 67 | "lgbm": "LightGBM Classifier", 68 | "cat": "CatBoost Classifier", 69 | "xgb": "XGBoost Classifier", 70 | "ann": "Multi Layer Perceptron Classifier", 71 | "all": "All Classifiers", 72 | } 73 | 74 | regressors_ver = { 75 | "lin": "Linear Regression", 76 | "sgd": "Stochastic Gradient Descent Regressor", 77 | "krr": "Kernel Ridge Regressor", 78 | "elas": "Elastic Net Regressor", 79 | "br": "Bayesian Ridge Regressor", 80 | "svr": "Support Vector Regressor", 81 | "knr": "K-Neighbors Regressor", 82 | "dt": "Decision Trees Regressor", 83 | "rfr": "Random Forest Regressor", 84 | "gbr": "Gradient Boost Regressor", 85 | "ada": "AdaBoost Regressor", 86 | "bag": "Bagging Regressor", 87 | "extr": "Extra Trees Regressor", 88 | "lgbm": "LightGBM Regressor", 89 | "xgb": "XGBoost Regressor", 90 | "cat": "Catboost Regressor", 91 | "ann": "Multi Layer Perceptron Regressor", 92 | "all": "All Regressors", 93 | } 94 | params_use_warning = ( 95 | "Params will not work with predictor = 'all'. Settings params = {} " 96 | ) 97 | 98 | unsupported_pred_warning = """Predictor not available. Please use the predictor which is supported by LuciferML. 99 | Check the documentation for more details.\nConflicting Predictor is : {}""" 100 | -------------------------------------------------------------------------------- /luciferml/supervised/README/Regression.md: -------------------------------------------------------------------------------- 1 | # Regression 2 | 3 | Encodes Categorical Data then Applies SMOTE , Splits the features and labels in training and validation sets with test_size = .2 4 | scales X_train, X_val using StandardScaler. 5 | Fits every model on training set and predicts results,Finds R2 Score and mean square error 6 | finds accuracy of model applies K-Fold Cross Validation 7 | and stores its accuracies in a dictionary containing Model name as Key and accuracies as values and returns it 8 | Applies HyperParam Tuning and gives best params and accuracy. 9 | 10 | Parameters: 11 | 12 | features : array 13 | features array 14 | lables : array 15 | labels array 16 | predictor : str 17 | Predicting model to be used 18 | Default 'lin' 19 | Available Predictors: 20 | lin - Linear Regression 21 | sgd - Stochastic Gradient Descent Regressor 22 | elas - Elastic Net Regressor 23 | krr - Kernel Ridge Regressor 24 | br - Bayesian Ridge Regressor 25 | svr - Support Vector Regressor 26 | knr - K-Nearest Regressor 27 | dt - Decision Trees 28 | rfr - Random Forest Regressor 29 | gbr - Gradient Boost Regressor 30 | ada - AdaBoost Regressor, 31 | bag - Bagging Regressor, 32 | extr - Extra Trees Regressor, 33 | lgbm - LightGB Regressor 34 | xgb - XGBoost Regressor 35 | cat - Catboost Regressor 36 | ann - Multi Layer Perceptron Regressor 37 | all - Applies all above regressors 38 | params : dict 39 | contains parameters for model 40 | tune : boolean 41 | when True Applies GridSearch CrossValidation 42 | Default is False 43 | 44 | test_size: float or int, default=.2 45 | If float, should be between 0.0 and 1.0 and represent 46 | the proportion of the dataset to include in 47 | the test split. 48 | If int, represents the absolute number of test samples. 49 | 50 | cv_folds : int 51 | No. of cross validation folds. Default = 10 52 | pca : str 53 | if 'y' will apply PCA on Train and Validation set. Default = 'n' 54 | lda : str 55 | if 'y' will apply LDA on Train and Validation set. Default = 'n' 56 | pca_kernel : str 57 | Kernel to be use in PCA. Default = 'linear' 58 | n_components_lda : int 59 | No. of components for LDA. Default = 1 60 | n_components_pca : int 61 | No. of components for PCA. Default = 2 62 | loss : str 63 | loss method for ann. Default = 'mean_squared_error' 64 | smote : str, 65 | Whether to apply SMOTE. Default = 'y' 66 | k_neighbors : int 67 | No. of neighbours for SMOTE. Default = 1 68 | verbose : boolean 69 | Verbosity of models. Default = False 70 | exclude_models : list 71 | List of models to be excluded when using predictor = 'all' . Default = [] 72 | path : list 73 | List containing path to saved model and scaler. Default = None 74 | Example: [model.pkl, scaler.pkl] 75 | random_state : int 76 | Random random_state for reproducibility. Default = 42 77 | optuna_sampler : Function 78 | Sampler to be used in optuna. Default = TPESampler() 79 | optuna_direction : str 80 | Direction of optimization. Default = 'maximize' 81 | Available Directions: 82 | maximize : Maximize 83 | minimize : Minimize 84 | optuna_n_trials : int 85 | No. of trials for optuna. Default = 100 86 | optuna_metric: str 87 | Metric to be used in optuna. Default = 'r2' 88 | 89 | Returns: 90 | 91 | Dict Containing Name of Regressor, Its K-Fold Cross Validated Accuracy, RMSE, Prediction set 92 | Dataframe containing all the models and their accuracies when predictor is 'all' 93 | 94 | Example: 95 | 96 | from luciferml.supervised.regression import Regression 97 | dataset = pd.read_excel('examples\Folds5x2_pp.xlsx') 98 | X = dataset.iloc[:, :-1] 99 | y = dataset.iloc[:, -1] 100 | regressor = Regression(predictor = 'lin') 101 | regressor.fit(X, y) 102 | result = regressor.result() 103 | -------------------------------------------------------------------------------- /luciferml/supervised/README/Classification.md: -------------------------------------------------------------------------------- 1 | # Classification 2 | 3 | Encode Categorical Data then Applies SMOTE , Splits the features and labels in training and validation sets with test_size = .2 , scales X_train, X_val using StandardScaler. 4 | Fits every model on training set and predicts results find and plots Confusion Matrix, 5 | finds accuracy of model applies K-Fold Cross Validation 6 | and stores accuracy in variable name accuracy and model name in self.classifier name and returns both as a tuple. 7 | Applies HyperParam Tuning and gives best params and accuracy. 8 | 9 | Parameters: 10 | 11 | features : array 12 | features array 13 | lables : array 14 | labels array 15 | predictor : list 16 | Predicting model to be used 17 | Default ['lr'] 18 | Available Predictors: 19 | lr - Logisitic Regression 20 | sgd - Stochastic Gradient Descent Classifier 21 | perc - Perceptron 22 | pass - Passive Aggressive Classifier 23 | ridg - Ridge Classifier 24 | svm -SupportVector Machine 25 | knn - K-Nearest Neighbours 26 | dt - Decision Trees 27 | nb - GaussianNaive bayes 28 | rfc- Random Forest self.Classifier 29 | gbc - Gradient Boosting Classifier 30 | ada - AdaBoost Classifier 31 | bag - Bagging Classifier 32 | extc - Extra Trees Classifier 33 | lgbm - LightGBM Classifier 34 | cat - CatBoost Classifier 35 | xgb- XGBoost self.Classifier 36 | ann - Multi Layer Perceptron Classifier 37 | all - Applies all above classifiers 38 | 39 | params : dict 40 | contains parameters for model 41 | tune : boolean 42 | when True Applies GridSearch CrossValidation 43 | Default is False 44 | 45 | test_size: float or int, default=.2 46 | If float, should be between 0.0 and 1.0 and represent 47 | the proportion of the dataset to include in 48 | the test split. 49 | If int, represents the absolute number of test samples. 50 | 51 | cv_folds : int 52 | No. of cross validation folds. Default = 10 53 | pca : str 54 | if 'y' will apply PCA on Train and Validation set. Default = 'n' 55 | lda : str 56 | if 'y' will apply LDA on Train and Validation set. Default = 'n' 57 | pca_kernel : str 58 | Kernel to be use in PCA. Default = 'linear' 59 | n_components_lda : int 60 | No. of components for LDA. Default = 1 61 | n_components_pca : int 62 | No. of components for PCA. Default = 2 63 | loss : str 64 | loss method for ann. Default = 'binary_crossentropy' 65 | rate for dropout layer. Default = 0 66 | tune_mode : int 67 | HyperParam tune modes. Default = 1 68 | Available Modes: 69 | 1 : Basic Tune 70 | 2 : Intermediate Tune 71 | 3 : Extreme Tune (Can Take Much Time) 72 | smote : str, 73 | Whether to apply SMOTE. Default = 'y' 74 | k_neighbors : int 75 | No. of neighbours for SMOTE. Default = 1 76 | verbose : boolean 77 | Verbosity of models. Default = False 78 | exclude_models : list 79 | List of models to be excluded when using predictor = 'all' . Default = [] 80 | path : list 81 | List containing path to saved model and scaler. Default = None 82 | Example: [model.pkl, scaler.pkl] 83 | random_state : int 84 | Random random_state for reproducibility. Default = 42 85 | optuna_sampler : Function 86 | Sampler to be used in optuna. Default = TPESampler() 87 | optuna_direction : str 88 | Direction of optimization. Default = 'maximize' 89 | Available Directions: 90 | maximize : Maximize 91 | minimize : Minimize 92 | optuna_n_trials : int 93 | No. of trials for optuna. Default = 100 94 | optuna_metric: str 95 | Metric to be used in optuna. Default = 'r2' 96 | lgbm_objective : str 97 | Objective for lgbm classifier. Default = 'binary' 98 | Returns: 99 | 100 | Dict Containing Name of Classifiers, Its K-Fold Cross Validated Accuracy and Prediction set 101 | 102 | Dataframe containing all the models and their accuracies when predictor is 'all' 103 | 104 | Example : 105 | 106 | from luciferml.supervised.classification import Classification 107 | dataset = pd.read_csv('Social_Network_Ads.csv') 108 | X = dataset.iloc[:, :-1] 109 | y = dataset.iloc[:, -1] 110 | classifier = Classification(predictor = 'lr') 111 | classifier.fit(X, y) 112 | result = classifier.result() 113 | -------------------------------------------------------------------------------- /examples/Social_Network_Ads.csv: -------------------------------------------------------------------------------- 1 | Age,EstimatedSalary,Purchased 2 | 19,19000,0 3 | 35,20000,0 4 | 26,43000,0 5 | 27,57000,0 6 | 19,76000,0 7 | 27,58000,0 8 | 27,84000,0 9 | 32,150000,1 10 | 25,33000,0 11 | 35,65000,0 12 | 26,80000,0 13 | 26,52000,0 14 | 20,86000,0 15 | 32,18000,0 16 | 18,82000,0 17 | 29,80000,0 18 | 47,25000,1 19 | 45,26000,1 20 | 46,28000,1 21 | 48,29000,1 22 | 45,22000,1 23 | 47,49000,1 24 | 48,41000,1 25 | 45,22000,1 26 | 46,23000,1 27 | 47,20000,1 28 | 49,28000,1 29 | 47,30000,1 30 | 29,43000,0 31 | 31,18000,0 32 | 31,74000,0 33 | 27,137000,1 34 | 21,16000,0 35 | 28,44000,0 36 | 27,90000,0 37 | 35,27000,0 38 | 33,28000,0 39 | 30,49000,0 40 | 26,72000,0 41 | 27,31000,0 42 | 27,17000,0 43 | 33,51000,0 44 | 35,108000,0 45 | 30,15000,0 46 | 28,84000,0 47 | 23,20000,0 48 | 25,79000,0 49 | 27,54000,0 50 | 30,135000,1 51 | 31,89000,0 52 | 24,32000,0 53 | 18,44000,0 54 | 29,83000,0 55 | 35,23000,0 56 | 27,58000,0 57 | 24,55000,0 58 | 23,48000,0 59 | 28,79000,0 60 | 22,18000,0 61 | 32,117000,0 62 | 27,20000,0 63 | 25,87000,0 64 | 23,66000,0 65 | 32,120000,1 66 | 59,83000,0 67 | 24,58000,0 68 | 24,19000,0 69 | 23,82000,0 70 | 22,63000,0 71 | 31,68000,0 72 | 25,80000,0 73 | 24,27000,0 74 | 20,23000,0 75 | 33,113000,0 76 | 32,18000,0 77 | 34,112000,1 78 | 18,52000,0 79 | 22,27000,0 80 | 28,87000,0 81 | 26,17000,0 82 | 30,80000,0 83 | 39,42000,0 84 | 20,49000,0 85 | 35,88000,0 86 | 30,62000,0 87 | 31,118000,1 88 | 24,55000,0 89 | 28,85000,0 90 | 26,81000,0 91 | 35,50000,0 92 | 22,81000,0 93 | 30,116000,0 94 | 26,15000,0 95 | 29,28000,0 96 | 29,83000,0 97 | 35,44000,0 98 | 35,25000,0 99 | 28,123000,1 100 | 35,73000,0 101 | 28,37000,0 102 | 27,88000,0 103 | 28,59000,0 104 | 32,86000,0 105 | 33,149000,1 106 | 19,21000,0 107 | 21,72000,0 108 | 26,35000,0 109 | 27,89000,0 110 | 26,86000,0 111 | 38,80000,0 112 | 39,71000,0 113 | 37,71000,0 114 | 38,61000,0 115 | 37,55000,0 116 | 42,80000,0 117 | 40,57000,0 118 | 35,75000,0 119 | 36,52000,0 120 | 40,59000,0 121 | 41,59000,0 122 | 36,75000,0 123 | 37,72000,0 124 | 40,75000,0 125 | 35,53000,0 126 | 41,51000,0 127 | 39,61000,0 128 | 42,65000,0 129 | 26,32000,0 130 | 30,17000,0 131 | 26,84000,0 132 | 31,58000,0 133 | 33,31000,0 134 | 30,87000,0 135 | 21,68000,0 136 | 28,55000,0 137 | 23,63000,0 138 | 20,82000,0 139 | 30,107000,1 140 | 28,59000,0 141 | 19,25000,0 142 | 19,85000,0 143 | 18,68000,0 144 | 35,59000,0 145 | 30,89000,0 146 | 34,25000,0 147 | 24,89000,0 148 | 27,96000,1 149 | 41,30000,0 150 | 29,61000,0 151 | 20,74000,0 152 | 26,15000,0 153 | 41,45000,0 154 | 31,76000,0 155 | 36,50000,0 156 | 40,47000,0 157 | 31,15000,0 158 | 46,59000,0 159 | 29,75000,0 160 | 26,30000,0 161 | 32,135000,1 162 | 32,100000,1 163 | 25,90000,0 164 | 37,33000,0 165 | 35,38000,0 166 | 33,69000,0 167 | 18,86000,0 168 | 22,55000,0 169 | 35,71000,0 170 | 29,148000,1 171 | 29,47000,0 172 | 21,88000,0 173 | 34,115000,0 174 | 26,118000,0 175 | 34,43000,0 176 | 34,72000,0 177 | 23,28000,0 178 | 35,47000,0 179 | 25,22000,0 180 | 24,23000,0 181 | 31,34000,0 182 | 26,16000,0 183 | 31,71000,0 184 | 32,117000,1 185 | 33,43000,0 186 | 33,60000,0 187 | 31,66000,0 188 | 20,82000,0 189 | 33,41000,0 190 | 35,72000,0 191 | 28,32000,0 192 | 24,84000,0 193 | 19,26000,0 194 | 29,43000,0 195 | 19,70000,0 196 | 28,89000,0 197 | 34,43000,0 198 | 30,79000,0 199 | 20,36000,0 200 | 26,80000,0 201 | 35,22000,0 202 | 35,39000,0 203 | 49,74000,0 204 | 39,134000,1 205 | 41,71000,0 206 | 58,101000,1 207 | 47,47000,0 208 | 55,130000,1 209 | 52,114000,0 210 | 40,142000,1 211 | 46,22000,0 212 | 48,96000,1 213 | 52,150000,1 214 | 59,42000,0 215 | 35,58000,0 216 | 47,43000,0 217 | 60,108000,1 218 | 49,65000,0 219 | 40,78000,0 220 | 46,96000,0 221 | 59,143000,1 222 | 41,80000,0 223 | 35,91000,1 224 | 37,144000,1 225 | 60,102000,1 226 | 35,60000,0 227 | 37,53000,0 228 | 36,126000,1 229 | 56,133000,1 230 | 40,72000,0 231 | 42,80000,1 232 | 35,147000,1 233 | 39,42000,0 234 | 40,107000,1 235 | 49,86000,1 236 | 38,112000,0 237 | 46,79000,1 238 | 40,57000,0 239 | 37,80000,0 240 | 46,82000,0 241 | 53,143000,1 242 | 42,149000,1 243 | 38,59000,0 244 | 50,88000,1 245 | 56,104000,1 246 | 41,72000,0 247 | 51,146000,1 248 | 35,50000,0 249 | 57,122000,1 250 | 41,52000,0 251 | 35,97000,1 252 | 44,39000,0 253 | 37,52000,0 254 | 48,134000,1 255 | 37,146000,1 256 | 50,44000,0 257 | 52,90000,1 258 | 41,72000,0 259 | 40,57000,0 260 | 58,95000,1 261 | 45,131000,1 262 | 35,77000,0 263 | 36,144000,1 264 | 55,125000,1 265 | 35,72000,0 266 | 48,90000,1 267 | 42,108000,1 268 | 40,75000,0 269 | 37,74000,0 270 | 47,144000,1 271 | 40,61000,0 272 | 43,133000,0 273 | 59,76000,1 274 | 60,42000,1 275 | 39,106000,1 276 | 57,26000,1 277 | 57,74000,1 278 | 38,71000,0 279 | 49,88000,1 280 | 52,38000,1 281 | 50,36000,1 282 | 59,88000,1 283 | 35,61000,0 284 | 37,70000,1 285 | 52,21000,1 286 | 48,141000,0 287 | 37,93000,1 288 | 37,62000,0 289 | 48,138000,1 290 | 41,79000,0 291 | 37,78000,1 292 | 39,134000,1 293 | 49,89000,1 294 | 55,39000,1 295 | 37,77000,0 296 | 35,57000,0 297 | 36,63000,0 298 | 42,73000,1 299 | 43,112000,1 300 | 45,79000,0 301 | 46,117000,1 302 | 58,38000,1 303 | 48,74000,1 304 | 37,137000,1 305 | 37,79000,1 306 | 40,60000,0 307 | 42,54000,0 308 | 51,134000,0 309 | 47,113000,1 310 | 36,125000,1 311 | 38,50000,0 312 | 42,70000,0 313 | 39,96000,1 314 | 38,50000,0 315 | 49,141000,1 316 | 39,79000,0 317 | 39,75000,1 318 | 54,104000,1 319 | 35,55000,0 320 | 45,32000,1 321 | 36,60000,0 322 | 52,138000,1 323 | 53,82000,1 324 | 41,52000,0 325 | 48,30000,1 326 | 48,131000,1 327 | 41,60000,0 328 | 41,72000,0 329 | 42,75000,0 330 | 36,118000,1 331 | 47,107000,1 332 | 38,51000,0 333 | 48,119000,1 334 | 42,65000,0 335 | 40,65000,0 336 | 57,60000,1 337 | 36,54000,0 338 | 58,144000,1 339 | 35,79000,0 340 | 38,55000,0 341 | 39,122000,1 342 | 53,104000,1 343 | 35,75000,0 344 | 38,65000,0 345 | 47,51000,1 346 | 47,105000,1 347 | 41,63000,0 348 | 53,72000,1 349 | 54,108000,1 350 | 39,77000,0 351 | 38,61000,0 352 | 38,113000,1 353 | 37,75000,0 354 | 42,90000,1 355 | 37,57000,0 356 | 36,99000,1 357 | 60,34000,1 358 | 54,70000,1 359 | 41,72000,0 360 | 40,71000,1 361 | 42,54000,0 362 | 43,129000,1 363 | 53,34000,1 364 | 47,50000,1 365 | 42,79000,0 366 | 42,104000,1 367 | 59,29000,1 368 | 58,47000,1 369 | 46,88000,1 370 | 38,71000,0 371 | 54,26000,1 372 | 60,46000,1 373 | 60,83000,1 374 | 39,73000,0 375 | 59,130000,1 376 | 37,80000,0 377 | 46,32000,1 378 | 46,74000,0 379 | 42,53000,0 380 | 41,87000,1 381 | 58,23000,1 382 | 42,64000,0 383 | 48,33000,1 384 | 44,139000,1 385 | 49,28000,1 386 | 57,33000,1 387 | 56,60000,1 388 | 49,39000,1 389 | 39,71000,0 390 | 47,34000,1 391 | 48,35000,1 392 | 48,33000,1 393 | 47,23000,1 394 | 45,45000,1 395 | 60,42000,1 396 | 39,59000,0 397 | 46,41000,1 398 | 51,23000,1 399 | 50,20000,1 400 | 36,33000,0 401 | 49,36000,1 -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Note: LuciferML is now deprecated and is being extended as [ANAI](https://github.com/Revca-ANAI/ANAI). 2 | 3 |

https://github.com/d4rk-lucif3r/LuciferML/blob/master/assets/img/LUCIFER-ML.gif

4 | 5 | # LuciferML a Semi-Automated Machine Learning Library by d4rk-lucif3r 6 | 7 | [![Downloads](https://static.pepy.tech/personalized-badge/lucifer-ml?period=total&units=international_system&left_color=black&right_color=green&left_text=Total%20Downloads)](https://pepy.tech/project/lucifer-ml) 8 | [![Downloads](https://static.pepy.tech/personalized-badge/lucifer-ml?period=month&units=international_system&left_color=black&right_color=green&left_text=Downloads%20per%20Month)](https://pepy.tech/project/lucifer-ml) 9 | ![ReadTheDocs](https://img.shields.io/readthedocs/luciferml?style=plastic) 10 | 11 | ## About 12 | 13 | The LuciferML is a Semi-Automated Machine Learning Python Library that works with tabular data. It is designed to save time while doing data analysis. It will help you right from data preprocessing to Data Prediction. 14 | 15 | ### The LuciferML will help you with 16 | 17 | 1. Preprocessing Data: 18 | - Encoding 19 | - Splitting 20 | - Scaling 21 | - Dimensionality Reduction 22 | - Resampling 23 | 2. Trying many different machine learning models with hyperparameter tuning, 24 | 25 | ## Installation 26 | 27 | pip install lucifer-ml 28 | 29 | ## Available Preprocessing Techniques 30 | 31 | 1) Skewness Correction 32 | 33 | Takes Pandas Dataframe as input. Transforms each column in dataset except the columns given as an optional parameter. 34 | Returns Transformed Data. 35 | 36 | Example: 37 | 38 | 1) All Columns 39 | 40 | from luciferml.preprocessing import Preprocess as pp 41 | import pandas as pd 42 | dataset = pd.read_csv('/examples/Social_Network_Ads.csv') 43 | prep = pp(dataset, dataset.columns) 44 | dataset = prep.skewcorrect(dataset) 45 | 46 | 2) Except column/columns 47 | 48 | from luciferml.preprocessing import Preprocess as pp 49 | import pandas as pd 50 | dataset = pd.read_csv('/examples/Social_Network_Ads.csv') 51 | prep = pp(dataset, dataset.columns, except_columns=['Purchased']) 52 | dataset = prep.skewcorrect() 53 | 54 | More about Preprocessing [here](https://github.com/d4rk-lucif3r/LuciferML/blob/master/luciferml/supervised/README/Preprocessing.md) 55 | 56 | ## Available Modelling Techniques 57 | 58 | 1) Classification 59 | 60 | Available Models for Classification 61 | 62 | - 'lr' : 'Logistic Regression', 63 | - 'sgd' : 'Stochastic Gradient Descent', 64 | - 'perc': 'Perceptron', 65 | - 'pass': 'Passive Aggressive Classifier', 66 | - 'ridg': 'Ridge Classifier', 67 | - 'svm' : 'Support Vector Machine', 68 | - 'knn' : 'K-Nearest Neighbours', 69 | - 'dt' : 'Decision Trees', 70 | - 'nb' : 'Naive Bayes', 71 | - 'rfc' : 'Random Forest Classifier', 72 | - 'gbc' : 'Gradient Boosting Classifier', 73 | - 'ada' : 'AdaBoost Classifier', 74 | - 'bag' : 'Bagging Classifier', 75 | - 'extc': 'Extra Trees Classifier', 76 | - 'lgbm': 'LightGBM Classifier', 77 | - 'cat' : 'CatBoost Classifier', 78 | - 'xgb' : 'XGBoost Classifier', 79 | - 'ann' : 'Multilayer Perceptron Classifier', 80 | - 'all' : 'Applies all above classifiers' 81 | 82 | Example: 83 | 84 | from luciferml.supervised.classification import Classification 85 | dataset = pd.read_csv('Social_Network_Ads.csv') 86 | X = dataset.iloc[:, :-1] 87 | y = dataset.iloc[:, -1] 88 | classifier = Classification(predictor = ['lr']) 89 | classifier.fit(X, y) 90 | result = classifier.result() 91 | 92 | More About [Classification](https://github.com/d4rk-lucif3r/LuciferML/blob/master/luciferml/supervised/README/Classification.md) 93 | 94 | 2) Regression 95 | 96 | Available Models for Regression 97 | 98 | - 'lin' : 'Linear Regression', 99 | - 'sgd' : 'Stochastic Gradient Descent Regressor', 100 | - 'elas': 'Elastic Net Regressot', 101 | - 'krr' : 'Kernel Ridge Regressor', 102 | - 'br' : 'Bayesian Ridge Regressor', 103 | - 'svr' : 'Support Vector Regressor', 104 | - 'knr' : 'K-Nearest Regressor', 105 | - 'dt' : 'Decision Trees', 106 | - 'rfr' : 'Random Forest Regressor', 107 | - 'gbr' : 'Gradient Boost Regressor', 108 | - 'ada' : 'AdaBoost Regressor', 109 | - 'bag' : 'Bagging Regressor', 110 | - 'extr': 'Extra Trees Regressor', 111 | - 'lgbm': 'LightGBM Regressor', 112 | - 'xgb' : 'XGBoost Regressor', 113 | - 'cat' : 'Catboost Regressor', 114 | - 'ann' : 'Multilayer Perceptron Regressor', 115 | - 'all' : 'Applies all above regressors' 116 | 117 | Example: 118 | 119 | from luciferml.supervised.regression import Regression 120 | dataset = pd.read_excel('examples\Folds5x2_pp.xlsx') 121 | X = dataset.iloc[:, :-1] 122 | y = dataset.iloc[:, -1] 123 | regressor = Regression(predictor = ['lin']) 124 | regressor.fit(X, y) 125 | result = regressor.result() 126 | 127 | More about Regression [here](https://github.com/d4rk-lucif3r/LuciferML/blob/master/luciferml/supervised/README/Regression.md) 128 | 129 | ## Hyperparameter Tuning 130 | 131 | LuciferML is powered by [Optuna](https://github.com/optuna/optuna) for Hyperparam tuning. Just add "tune = True" in either Regressor or Classifier it will start tuning the model/s with Optuna. 132 | 133 | ## Persistence 134 | 135 | LuciferML's model can be saved as a pickle file. It will save both the model and the scaler to the pickle file. 136 | 137 | 138 | - Saving 139 | 140 | Ex: 141 | regressor.save([, ]) 142 | 143 | A new LuciferML Object can be loaded as well by specifying path of model and scaler 144 | 145 | - Loading 146 | 147 | Ex: 148 | regressor = Regression(path = [, ]) 149 | 150 | These are applicable for both Classification and Regression. 151 | 152 | ## Examples 153 | 154 | Please refer to more examples [here](https://github.com/d4rk-lucif3r/LuciferML/blob/master/examples/example.ipynb) 155 | 156 | --- 157 | 158 | ## [To-Do's](https://github.com/d4rk-lucif3r/LuciferML/issues/10) 159 | -------------------------------------------------------------------------------- /luciferml/supervised/utils/preprocesser.py: -------------------------------------------------------------------------------- 1 | import time 2 | 3 | import matplotlib.pyplot as plt 4 | import numpy as np 5 | import seaborn as sns 6 | import shap 7 | from imblearn.over_sampling import SMOTE 8 | from luciferml.supervised.utils.configs import * 9 | from sklearn.compose import ColumnTransformer 10 | from sklearn.decomposition import PCA, KernelPCA 11 | from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA 12 | from sklearn.inspection import permutation_importance 13 | from sklearn.metrics import confusion_matrix 14 | from sklearn.model_selection import train_test_split 15 | from sklearn.preprocessing import LabelEncoder, OneHotEncoder, StandardScaler 16 | from colorama import Fore 17 | 18 | 19 | class PreProcesser: 20 | def data_preprocess( 21 | self, features, labels, test_size, random_state, smote, k_neighbors 22 | ): 23 | try: 24 | if smote == "y": 25 | sm = SMOTE(k_neighbors=k_neighbors, random_state=random_state) 26 | features, labels = sm.fit_resample(features, labels) 27 | # Splitting --------------------------------------------------------------------- 28 | X_train, X_val, y_train, y_val = train_test_split( 29 | features, labels, test_size=test_size, random_state=random_state 30 | ) 31 | # Scaling --------------------------------------------------------------------- 32 | sc = StandardScaler() 33 | X_train = sc.fit_transform(X_train) 34 | X_val = sc.transform(X_val) 35 | return (X_train, X_val, y_train, y_val, sc) 36 | except Exception as error: 37 | print(Fore.RED + "Preprocessing Failed with error: ", error, "\n") 38 | 39 | def confusion_matrix(self, y_pred, y_val): 40 | """ 41 | Takes Predicted data and Validation data as input and prepares and plots Confusion Matrix. 42 | """ 43 | try: 44 | cm = confusion_matrix(y_val, y_pred) 45 | ax = plt.subplot() 46 | sns.heatmap(cm, annot=True, fmt="g", ax=ax) 47 | ax.set_xlabel("Predicted labels") 48 | ax.set_ylabel("True labels") 49 | ax.set_title("Confusion Matrix") 50 | ax.xaxis.set_ticklabels(np.unique(y_val)) 51 | ax.yaxis.set_ticklabels(np.unique(y_val)) 52 | plt.show() 53 | except Exception as error: 54 | print( 55 | Fore.RED + "Building Confusion Matrix Failed with error :", error, "\n" 56 | ) 57 | 58 | def dimensionality_reduction( 59 | self, 60 | lda, 61 | pca, 62 | X_train, 63 | X_val, 64 | y_train, 65 | n_components_lda, 66 | n_components_pca, 67 | pca_kernel, 68 | start, 69 | ): 70 | """ 71 | Performs Dimensionality Reduction on Training and Validation independent variables. 72 | """ 73 | try: 74 | if lda == "y": 75 | lda = LDA(n_components=n_components_lda) 76 | X_train = lda.fit_transform(X_train, y_train) 77 | X_val = lda.transform(X_val) 78 | if pca == "y" and not lda == "y": 79 | if not pca_kernel == "linear": 80 | try: 81 | 82 | kpca = KernelPCA( 83 | n_components=n_components_pca, kernel=pca_kernel 84 | ) 85 | X_train = kpca.fit_transform(X_train) 86 | X_val = kpca.transform(X_val) 87 | except MemoryError as error: 88 | print(error) 89 | end = time.time() 90 | print("Time Elapsed :", end - start) 91 | return 92 | 93 | elif pca_kernel == "linear": 94 | pca = PCA(n_components=n_components_pca) 95 | X_train = pca.fit_transform(X_train) 96 | X_val = pca.transform(X_val) 97 | else: 98 | print("Un-identified PCA Kernel\n") 99 | return 100 | return (X_train, X_val) 101 | except Exception as error: 102 | print( 103 | Fore.RED + "Dimensionality Reduction Failed with error :", error, "\n" 104 | ) 105 | return (X_train, X_val) 106 | 107 | def encoder(self, features, labels): 108 | """ 109 | Takes features and labels as arguments and encodes features using onehot encoding and labels with label encoding. 110 | Returns Encoded Features and Labels. 111 | """ 112 | try: 113 | cat_features = [ 114 | i for i in features.columns if features.dtypes[i] == "object" 115 | ] 116 | if len(cat_features) >= 1: 117 | index = [] 118 | for i in range(0, len(cat_features)): 119 | index.append(features.columns.get_loc(cat_features[i])) 120 | ct = ColumnTransformer( 121 | transformers=[("encoder", OneHotEncoder(), index)], 122 | remainder="passthrough", 123 | ) 124 | print("Encoding Features [*]\n") 125 | features = np.array(ct.fit_transform(features)) 126 | if labels.dtype == "O": 127 | le = LabelEncoder() 128 | labels = le.fit_transform(labels) 129 | return (features, labels) 130 | except Exception as error: 131 | print(Fore.RED + "Encoding Failed with error :", error) 132 | 133 | def permutational_feature_imp(self, features, X_test, y_test, model): 134 | perm_importance = permutation_importance(model, X_test, y_test) 135 | sorted_idx = perm_importance.importances_mean.argsort() 136 | plt.barh( 137 | features.columns[sorted_idx], perm_importance.importances_mean[sorted_idx] 138 | ) 139 | plt.xlabel("Feature Importance") 140 | 141 | def shap_feature_imp(self, features, X_train, model, *args, **kwargs): 142 | explainer = shap.TreeExplainer(model) 143 | shap_values = explainer.shap_values(X_train) 144 | shap.summary_plot( 145 | shap_values, 146 | X_train, 147 | feature_names=features.columns, 148 | plot_type="bar", 149 | *args, 150 | **kwargs 151 | ) 152 | shap.summary_plot( 153 | shap_values, X_train, feature_names=features.columns, *args, **kwargs 154 | ) 155 | for i in range(len(features.columns)): 156 | shap.dependence_plot( 157 | i, shap_values, X_train, feature_names=features.columns, *args, **kwargs 158 | ) 159 | -------------------------------------------------------------------------------- /luciferml/preprocessing.py: -------------------------------------------------------------------------------- 1 | import time 2 | from collections import Counter 3 | 4 | import matplotlib.pyplot as plt 5 | import numpy as np 6 | import pandas as pd 7 | import seaborn as sns 8 | from colorama import Fore 9 | from IPython.display import display 10 | from scipy.special import boxcox1p 11 | from scipy.stats import norm, probplot, skew 12 | 13 | from luciferml.supervised.utils.configs import intro 14 | 15 | 16 | class Preprocess: 17 | 18 | def __init__(self, dataset, columns, except_columns=[]): 19 | self.__dataset = dataset 20 | self.__columns = columns 21 | self.__except_columns = except_columns 22 | 23 | def __plotter(self, name, text, color): 24 | plt.figure(figsize=(20, 10)) 25 | plt.subplot(1, 2, 1) 26 | sns.distplot( 27 | self.__dataset[name], 28 | fit=norm, 29 | color=color, 30 | label="Skewness: %.2f" % (self.__dataset[name].skew()), 31 | ) 32 | plt.title( 33 | name.capitalize() 34 | + " Distplot for {} {} Skewness Transformation".format(name, text), 35 | color="black", 36 | ) 37 | plt.legend() 38 | plt.subplot(1, 2, 2) 39 | 40 | probplot(self.__dataset[name], plot=plt) 41 | plt.show() 42 | 43 | def __skewcheck(self): 44 | numeric_feats = self.__dataset.dtypes[self.__dataset.dtypes != 45 | "object"].index 46 | if not len(self.__except_columns) == 0: 47 | if len(self.__except_columns) > len(numeric_feats): 48 | numeric_feats = set(self.__except_columns) - set(numeric_feats) 49 | else: 50 | numeric_feats = set(numeric_feats) - set(self.__except_columns) 51 | skewed_feats = ( 52 | self.__dataset[numeric_feats] 53 | .apply(lambda x: skew(x.dropna())) 54 | .sort_values(ascending=False) 55 | ) 56 | print(Fore.GREEN + "\nSkewness in numerical features: \n") 57 | skewness = pd.DataFrame(skewed_feats, columns=["Skewness"]) 58 | display(skewness) 59 | skew_dict = dict(skewness["Skewness"]) 60 | skewed_features = skewness.index 61 | return (skewed_features, skew_dict) 62 | 63 | def skewcorrect(self) -> pd.DataFrame: 64 | """ 65 | Plots distplot and probability plot for non-normalized data and after normalizing the provided data. 66 | Normalizes data using boxcox normalization 67 | 68 | :returns: Scaled Dataset 69 | :rtype: pd.DataFrame 70 | 71 | Example: 72 | 73 | 1) All Columns 74 | 75 | from luciferml.preprocessing import Preprocess as pp 76 | 77 | import pandas as pd 78 | 79 | dataset = pd.read_csv('/examples/Social_Network_Ads.csv') 80 | prep = pp(dataset, dataset.columns) 81 | dataset = prep.skewcorrect(dataset) 82 | 83 | 2) Except column/columns 84 | 85 | from luciferml.preprocessing import Preprocess as pp 86 | 87 | import pandas as pd 88 | 89 | dataset = pd.read_csv('/examples/Social_Network_Ads.csv') 90 | prep = pp(dataset, dataset.columns, except_columns=['Purchased']) 91 | dataset = prep.skewcorrect() 92 | 93 | 94 | """ 95 | try: 96 | start = time.time() 97 | print(Fore.MAGENTA + intro, "\n") 98 | print(Fore.GREEN + "Started LuciferML [", "\u2713", "]\n") 99 | if not isinstance(self.__dataset, pd.DataFrame): 100 | print( 101 | Fore.RED + "TypeError: This Function expects Pandas Dataframe but {}".format( 102 | type(self.__dataset) 103 | ), 104 | " is given \n", 105 | ) 106 | end = time.time() 107 | print(Fore.GREEN + "Elapsed Time: ", end - start, "seconds\n") 108 | return 109 | 110 | (skewed_features, skew_dict) = self.__skewcheck() 111 | for column_name in skewed_features: 112 | lam = 0 113 | (mu, sigma) = norm.fit(self.__dataset[column_name]) 114 | print( 115 | Fore.CYAN + 116 | "Skewness Before Transformation for {}: ".format( 117 | column_name), 118 | self.__dataset[column_name].skew(), 119 | "\n", 120 | ) 121 | print( 122 | Fore.CYAN + "Mean before Transformation for {} : {}, Standard Deviation before Transformation for {} : {}".format( 123 | column_name.capitalize(), mu, column_name.capitalize(), sigma 124 | ), 125 | "\n", 126 | ) 127 | self.__plotter( 128 | column_name, "Before", "lightcoral") 129 | try: 130 | if skew_dict[column_name] > 0.75: 131 | lam = 0.15 132 | self.__dataset[column_name] = boxcox1p( 133 | self.__dataset[column_name], lam) 134 | print( 135 | Fore.GREEN + 136 | "Skewness After Transformation for {}: ".format( 137 | column_name), 138 | self.__dataset[column_name].skew(), 139 | "\n", 140 | ) 141 | (mu, sigma) = norm.fit(self.__dataset[column_name]) 142 | print( 143 | Fore.GREEN + "Mean before Transformation for {} : {}, Standard Deviation before Transformation for {} : {}".format( 144 | column_name.capitalize(), 145 | mu, 146 | column_name.capitalize(), 147 | sigma, 148 | ), 149 | "\n", 150 | ) 151 | self.__plotter( 152 | column_name, "After", "orange") 153 | except Exception as error: 154 | print( 155 | Fore.RED + "\nPlease check your dataset's column :", 156 | column_name, 157 | "Raised Error: ", 158 | error, 159 | "\n", 160 | ) 161 | pass 162 | end = time.time() 163 | print(Fore.GREEN + "Elapsed Time: ", end - start, "seconds\n") 164 | return self.__dataset 165 | 166 | except Exception as error: 167 | print(Fore.RED + "Skewness Correction Failed with error : ", error, "\n") 168 | 169 | def detect_outliers(self): 170 | """ 171 | This function takes dataset and columns as input and finds Q1, Q3 and IQR for that list of column 172 | Detects the outlier and it index and stores them in a list. 173 | Then it creates as counter object with that list and stores it 174 | in Multiple Outliers list if the value of outlier is greater than 1.5 175 | 176 | Ex: 177 | 1) For printing no. of outliers. 178 | print("number of outliers detected --> ", 179 | len(dataset.loc[detect_outliers(dataset, dataset.columns[:-1])])) 180 | 2) Printing rows and columns collecting the outliers 181 | dataset.loc[detect_outliers(dataset.columns[:-1])] 182 | 3) Dropping those detected outliers 183 | dataset = dataset.drop(detect_outliers(dataset.columns[:-1]),axis = 0).reset_index(drop = True) 184 | """ 185 | outlier_indices = [] 186 | for column in self.__columns: 187 | Q1 = np.percentile(self.__dataset[column], 25) 188 | Q3 = np.percentile(self.__dataset[column], 75) 189 | IQR = Q3 - Q1 190 | outlier_step = IQR * 1.5 191 | outlier_list_col = self.__dataset[(self.__dataset[column] < Q1 - outlier_step) 192 | | (self.__dataset[column] > Q3 + outlier_step)].index 193 | outlier_indices.extend(outlier_list_col) 194 | outlier_indices = Counter(outlier_indices) 195 | multiple_outliers = list( 196 | i for i, v in outlier_indices.items() if v > 1.5) 197 | return multiple_outliers 198 | 199 | def preprocess(self): 200 | 201 | display(self.__datasetdescribe().T.style.bar( 202 | subset=['mean'], 203 | color='#606ff2').background_gradient( 204 | subset=['std'], cmap='PuBu').background_gradient(subset=['50%'], cmap='PuBu')) 205 | -------------------------------------------------------------------------------- /luciferml/supervised/utils/predictors.py: -------------------------------------------------------------------------------- 1 | from tkinter import N 2 | from catboost import CatBoostClassifier, CatBoostRegressor 3 | from colorama import Fore 4 | from lightgbm import LGBMClassifier, LGBMRegressor 5 | from luciferml.supervised.utils.tuner.optuna.objectives.classification_objectives import ( 6 | ClassificationObjectives, 7 | ) 8 | from luciferml.supervised.utils.tuner.optuna.objectives.regression_objectives import ( 9 | RegressionObjectives, 10 | ) 11 | from sklearn.ensemble import ( 12 | AdaBoostClassifier, 13 | AdaBoostRegressor, 14 | BaggingClassifier, 15 | BaggingRegressor, 16 | ExtraTreesClassifier, 17 | ExtraTreesRegressor, 18 | GradientBoostingClassifier, 19 | GradientBoostingRegressor, 20 | RandomForestClassifier, 21 | RandomForestRegressor, 22 | ) 23 | from sklearn.kernel_ridge import KernelRidge 24 | from sklearn.linear_model import ( 25 | BayesianRidge, 26 | ElasticNet, 27 | LinearRegression, 28 | LogisticRegression, 29 | PassiveAggressiveClassifier, 30 | Perceptron, 31 | RidgeClassifier, 32 | SGDClassifier, 33 | SGDRegressor, 34 | ) 35 | from sklearn.naive_bayes import GaussianNB 36 | from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor 37 | from sklearn.neural_network import MLPClassifier, MLPRegressor 38 | from sklearn.svm import SVC, SVR 39 | from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor 40 | from xgboost import XGBClassifier, XGBRegressor 41 | 42 | 43 | def classification_predictor( 44 | predictor, 45 | params, 46 | X_train, 47 | y_train, 48 | cv_folds, 49 | random_state, 50 | metric, 51 | mode="single", 52 | verbose=False, 53 | lgbm_objective="binary", 54 | ): 55 | """ 56 | Takes Predictor string , parameters , Training and Validation set and Returns a classifier for the Choosen Predictor. 57 | """ 58 | try: 59 | objective = ClassificationObjectives( 60 | X_train, 61 | y_train, 62 | cv=cv_folds, 63 | random_state=random_state, 64 | metric=metric, 65 | lgbm_objective=lgbm_objective, 66 | ) 67 | if predictor == "lr": 68 | if mode == "single": 69 | print( 70 | Fore.YELLOW + "Training Logistic Regression on Training Set [*]\n" 71 | ) 72 | classifier = LogisticRegression(**params) 73 | objective_to_be_tuned = objective.lr_classifier_objective 74 | 75 | elif predictor == "sgd": 76 | if mode == "single": 77 | print( 78 | Fore.YELLOW 79 | + "Training Stochastic Gradient Descent on Training Set [*]\n" 80 | ) 81 | classifier = SGDClassifier(**params) 82 | objective_to_be_tuned = objective.sgd_classifier_objective 83 | 84 | elif predictor == "perc": 85 | if mode == "single": 86 | print(Fore.YELLOW + "Training Perceptron on Training Set [*]\n") 87 | classifier = Perceptron(**params) 88 | objective_to_be_tuned = objective.perc_classifier_objective 89 | 90 | elif predictor == "pass": 91 | if mode == "single": 92 | print(Fore.YELLOW + "Training Passive Aggressive on Training Set [*]\n") 93 | classifier = PassiveAggressiveClassifier(**params) 94 | objective_to_be_tuned = objective.pass_classifier_objective 95 | 96 | elif predictor == "ridg": 97 | if mode == "single": 98 | print(Fore.YELLOW + "Training Ridge Classifier on Training Set [*]\n") 99 | classifier = RidgeClassifier(**params) 100 | objective_to_be_tuned = objective.ridg_classifier_objective 101 | 102 | elif predictor == "svm": 103 | if mode == "single": 104 | print( 105 | Fore.YELLOW 106 | + "Training Support Vector Machine on Training Set [*]\n" 107 | ) 108 | classifier = SVC(**params) 109 | objective_to_be_tuned = objective.svm_classifier_objective 110 | 111 | elif predictor == "knn": 112 | if mode == "single": 113 | print( 114 | Fore.YELLOW + "Training K-Nearest Neighbours on Training Set [*]\n" 115 | ) 116 | classifier = KNeighborsClassifier(**params) 117 | objective_to_be_tuned = objective.knn_classifier_objective 118 | 119 | elif predictor == "dt": 120 | if mode == "single": 121 | print( 122 | Fore.YELLOW 123 | + "Training Decision Tree Classifier on Training Set [*]\n" 124 | ) 125 | classifier = DecisionTreeClassifier(**params) 126 | objective_to_be_tuned = objective.dt_classifier_objective 127 | 128 | elif predictor == "nb": 129 | if mode == "single": 130 | print( 131 | Fore.YELLOW 132 | + "Training Naive Bayes Classifier on Training Set [*]\n" 133 | ) 134 | classifier = GaussianNB(**params) 135 | objective_to_be_tuned = None 136 | 137 | elif predictor == "rfc": 138 | if mode == "single": 139 | print( 140 | Fore.YELLOW 141 | + "Training Random Forest Classifier on Training Set [*]\n" 142 | ) 143 | classifier = RandomForestClassifier(**params) 144 | objective_to_be_tuned = objective.rfc_classifier_objective 145 | 146 | elif predictor == "gbc": 147 | if mode == "single": 148 | print( 149 | Fore.YELLOW 150 | + "Training Gradient Boosting Classifier on Training Set [*]\n" 151 | ) 152 | classifier = GradientBoostingClassifier(**params) 153 | objective_to_be_tuned = objective.gbc_classifier_objective 154 | 155 | elif predictor == "ada": 156 | if mode == "single": 157 | print( 158 | Fore.YELLOW + "Training AdaBoost Classifier on Training Set [*]\n" 159 | ) 160 | classifier = AdaBoostClassifier(**params) 161 | objective_to_be_tuned = objective.ada_classifier_objective 162 | 163 | elif predictor == "bag": 164 | if mode == "single": 165 | print(Fore.YELLOW + "Training Bagging Classifier on Training Set [*]\n") 166 | classifier = BaggingClassifier(**params) 167 | objective_to_be_tuned = objective.bag_classifier_objective 168 | 169 | elif predictor == "extc": 170 | if mode == "single": 171 | print( 172 | Fore.YELLOW 173 | + "Training Extra Trees Classifier on Training Set [*]\n" 174 | ) 175 | classifier = ExtraTreesClassifier(**params) 176 | objective_to_be_tuned = objective.extc_classifier_objective 177 | 178 | elif predictor == "lgbm": 179 | if mode == "single": 180 | print(Fore.YELLOW + "Training LightGBM on Training Set [*]\n") 181 | classifier = LGBMClassifier(**params) 182 | objective_to_be_tuned = objective.lgbm_classifier_objective 183 | 184 | elif predictor == "cat": 185 | if mode == "single": 186 | print(Fore.YELLOW + "Training CatBoostClassifier on Training Set [*]\n") 187 | params["verbose"] = verbose 188 | classifier = CatBoostClassifier(**params) 189 | params.pop("verbose") 190 | objective_to_be_tuned = objective.cat_classifier_objective 191 | 192 | elif predictor == "xgb": 193 | if mode == "single": 194 | print(Fore.YELLOW + "Training XGBClassifier on Training Set [*]\n") 195 | if verbose: 196 | params["verbosity"] = 2 197 | if not verbose: 198 | params["verbosity"] = 0 199 | 200 | classifier = XGBClassifier(**params) 201 | params.pop("verbosity") 202 | objective_to_be_tuned = objective.xgb_classifier_objective 203 | 204 | elif predictor == "ann": 205 | classifier = MLPClassifier(**params) 206 | objective_to_be_tuned = objective.mlp_classifier_objective 207 | return (classifier, objective_to_be_tuned) 208 | except Exception as error: 209 | print(Fore.RED + "Model Build Failed with error :", error, "\n") 210 | 211 | 212 | def regression_predictor( 213 | predictor, 214 | params, 215 | X_train, 216 | y_train, 217 | cv_folds, 218 | random_state, 219 | metric, 220 | mode="single", 221 | verbose=False, 222 | ): 223 | """ 224 | Takes Predictor string , parameters , Training and Validation set and Returns a regressor for the Choosen Predictor. 225 | """ 226 | try: 227 | objective = RegressionObjectives( 228 | X_train, y_train, cv=cv_folds, random_state=random_state, metric=metric 229 | ) 230 | if predictor == "lin": 231 | if mode == "single": 232 | print(Fore.YELLOW + "Training Linear Regression on Training Set [*]\n") 233 | regressor = LinearRegression(**params) 234 | objective_to_be_tuned = objective.lin_regressor_objective 235 | elif predictor == "sgd": 236 | if mode == "single": 237 | print( 238 | "Training Stochastic Gradient Descent Regressor on Training Set [*]\n" 239 | ) 240 | regressor = SGDRegressor(**params) 241 | objective_to_be_tuned = objective.sgd_regressor_objective 242 | elif predictor == "krr": 243 | if mode == "single": 244 | print( 245 | Fore.YELLOW 246 | + "Training Kernel Ridge Regressor on Training Set [*]\n" 247 | ) 248 | regressor = KernelRidge(**params) 249 | objective_to_be_tuned = objective.krr_regressor_objective 250 | elif predictor == "elas": 251 | if mode == "single": 252 | print( 253 | Fore.YELLOW + "Training ElasticNet Regressor on Training Set [*]\n" 254 | ) 255 | regressor = ElasticNet(**params) 256 | objective_to_be_tuned = objective.elas_regressor_objective 257 | elif predictor == "br": 258 | if mode == "single": 259 | print( 260 | Fore.YELLOW 261 | + "Training BayesianRidge Regressor on Training Set [*]\n" 262 | ) 263 | regressor = BayesianRidge(**params) 264 | objective_to_be_tuned = objective.br_regressor_objective 265 | elif predictor == "svr": 266 | if mode == "single": 267 | print( 268 | Fore.YELLOW 269 | + "Training Support Vector Machine on Training Set [*]\n" 270 | ) 271 | regressor = SVR(**params) 272 | objective_to_be_tuned = objective.svr_regressor_objective 273 | elif predictor == "knr": 274 | if mode == "single": 275 | print( 276 | Fore.YELLOW + "Training KNeighbors Regressor on Training Set [*]\n" 277 | ) 278 | regressor = KNeighborsRegressor(**params) 279 | objective_to_be_tuned = objective.knr_regressor_objective 280 | elif predictor == "dt": 281 | if mode == "single": 282 | print( 283 | Fore.YELLOW 284 | + "Training Decision Tree regressor on Training Set [*]\n" 285 | ) 286 | regressor = DecisionTreeRegressor(**params) 287 | objective_to_be_tuned = objective.dt_regressor_objective 288 | elif predictor == "rfr": 289 | if mode == "single": 290 | print( 291 | Fore.YELLOW 292 | + "Training Random Forest regressor on Training Set [*]\n" 293 | ) 294 | regressor = RandomForestRegressor(**params) 295 | objective_to_be_tuned = objective.rfr_regressor_objective 296 | elif predictor == "gbr": 297 | if mode == "single": 298 | print( 299 | Fore.YELLOW 300 | + "Training Gradient Boosting Regressor on Training Set [*]\n" 301 | ) 302 | regressor = GradientBoostingRegressor(**params) 303 | objective_to_be_tuned = objective.gbr_regressor_objective 304 | 305 | elif predictor == "ada": 306 | if mode == "single": 307 | print(Fore.YELLOW + "Training AdaBoost Regressor on Training Set [*]\n") 308 | regressor = AdaBoostRegressor(**params) 309 | objective_to_be_tuned = objective.ada_regressor_objective 310 | elif predictor == "bag": 311 | if mode == "single": 312 | print(Fore.YELLOW + "Training Bagging Regressor on Training Set [*]\n") 313 | regressor = BaggingRegressor(**params) 314 | objective_to_be_tuned = objective.bag_regressor_objective 315 | elif predictor == "extr": 316 | if mode == "single": 317 | print( 318 | Fore.YELLOW + "Training Extra Trees Regressor on Training Set [*]\n" 319 | ) 320 | regressor = ExtraTreesRegressor(**params) 321 | objective_to_be_tuned = objective.extr_regressor_objective 322 | elif predictor == "xgb": 323 | if mode == "single": 324 | print(Fore.YELLOW + "Training XGBregressor on Training Set [*]\n") 325 | regressor = XGBRegressor(**params) 326 | objective_to_be_tuned = objective.xgb_regressor_objective 327 | elif predictor == "lgbm": 328 | if mode == "single": 329 | print(Fore.YELLOW + "Training LGBMRegressor on Training Set [*]\n") 330 | regressor = LGBMRegressor(**params) 331 | objective_to_be_tuned = objective.lgbm_regressor_objective 332 | elif predictor == "cat": 333 | if mode == "single": 334 | print(Fore.YELLOW + "Training CatBoost Regressor on Training Set [*]\n") 335 | params["verbose"] = verbose 336 | regressor = CatBoostRegressor(**params) 337 | params.pop("verbose") 338 | objective_to_be_tuned = objective.cat_regressor_objective 339 | elif predictor == "ann": 340 | if mode == "single": 341 | print( 342 | Fore.YELLOW 343 | + "Training Multi Layered Perceptron on Training Set [*]\n" 344 | ) 345 | regressor = MLPRegressor(**params) 346 | objective_to_be_tuned = objective.mlp_regressor_objective 347 | return (regressor, objective_to_be_tuned) 348 | except Exception as error: 349 | print(Fore.RED + "Model Build Failed with error :", error, "\n") 350 | -------------------------------------------------------------------------------- /luciferml/supervised/utils/tuner/optuna/objectives/regression_objectives.py: -------------------------------------------------------------------------------- 1 | from catboost import CatBoostRegressor 2 | from lightgbm import LGBMRegressor 3 | from sklearn.ensemble import ( 4 | AdaBoostRegressor, 5 | BaggingRegressor, 6 | ExtraTreesRegressor, 7 | GradientBoostingRegressor, 8 | RandomForestRegressor, 9 | ) 10 | from sklearn.kernel_ridge import KernelRidge 11 | from sklearn.linear_model import ( 12 | BayesianRidge, 13 | ElasticNet, 14 | LinearRegression, 15 | SGDRegressor, 16 | ) 17 | from sklearn.model_selection import cross_val_score 18 | from sklearn.neighbors import KNeighborsRegressor 19 | from sklearn.neural_network import MLPRegressor 20 | from sklearn.svm import SVR 21 | from sklearn.tree import DecisionTreeRegressor 22 | from xgboost import XGBRegressor 23 | 24 | 25 | class RegressionObjectives: 26 | def __init__(self, X, y, cv=5, random_state=42, metric="r2"): 27 | self.metric = metric 28 | self.cv = cv 29 | self.X = X 30 | self.y = y 31 | self.random_state = random_state 32 | 33 | def lin_regressor_objective(self, trial): 34 | param = { 35 | "fit_intercept": trial.suggest_categorical("fit_intercept", [True, False]), 36 | "copy_X": trial.suggest_categorical("copy_X", [True, False]), 37 | } 38 | regressor = LinearRegression(**param, n_jobs=-1) 39 | scores = cross_val_score( 40 | regressor, self.X, self.y, cv=self.cv, scoring=self.metric 41 | ) 42 | return scores.mean() 43 | 44 | def sgd_regressor_objective(self, trial): 45 | param = { 46 | "loss": trial.suggest_categorical( 47 | "loss", ["squared_loss", "huber", "epsilon_insensitive"] 48 | ), 49 | "penalty": trial.suggest_categorical( 50 | "penalty", ["none", "l2", "l1", "elasticnet"] 51 | ), 52 | "alpha": trial.suggest_float("alpha", 1e-10, 1e-3), 53 | "l1_ratio": trial.suggest_float("l1_ratio", 0.0, 1.0), 54 | "learning_rate": trial.suggest_categorical( 55 | "learning_rate", ["constant", "optimal", "invscaling", "adaptive"] 56 | ), 57 | "eta0": trial.suggest_float("eta0", 0.0, 1.0), 58 | "power_t": trial.suggest_float("power_t", 0.0, 1.0), 59 | "warm_start": trial.suggest_categorical("warm_start", [True, False]), 60 | "average": trial.suggest_categorical("average", [True, False]), 61 | "random_state": self.random_state, 62 | } 63 | regressor = SGDRegressor(**param) 64 | scores = cross_val_score( 65 | regressor, self.X, self.y, cv=self.cv, n_jobs=-1, scoring=self.metric 66 | ) 67 | return scores.mean() 68 | 69 | def krr_regressor_objective(self, trial): 70 | param = { 71 | "alpha": trial.suggest_loguniform("alpha", 1e-10, 1e-3), 72 | "kernel": trial.suggest_categorical("kernel", ["linear", "rbf"]), 73 | "degree": trial.suggest_int("degree", 1, 3), 74 | "gamma": trial.suggest_loguniform("gamma", 1e-10, 1e-3), 75 | "coef0": trial.suggest_loguniform("coef0", 1e-10, 1e-3), 76 | } 77 | regressor = KernelRidge(**param) 78 | scores = cross_val_score( 79 | regressor, self.X, self.y, cv=self.cv, n_jobs=-1, scoring=self.metric 80 | ) 81 | return scores.mean() 82 | 83 | def elas_regressor_objective(self, trial): 84 | param = { 85 | "alpha": trial.suggest_loguniform("alpha", 1e-10, 1e-3), 86 | "l1_ratio": trial.suggest_float("l1_ratio", 0.0, 1.0), 87 | "max_iter": trial.suggest_int("max_iter", 100, 1000), 88 | "selection": trial.suggest_categorical("selection", ["cyclic", "random"]), 89 | "tol": trial.suggest_loguniform("tol", 1e-10, 1e-3), 90 | "random_state": self.random_state, 91 | } 92 | regressor = ElasticNet(**param) 93 | scores = cross_val_score( 94 | regressor, self.X, self.y, cv=self.cv, n_jobs=-1, scoring=self.metric 95 | ) 96 | return scores.mean() 97 | 98 | def br_regressor_objective(self, trial): 99 | param = { 100 | "alpha_1": trial.suggest_loguniform("alpha_1", 1e-10, 1e-3), 101 | "alpha_2": trial.suggest_loguniform("alpha_2", 1e-10, 1e-3), 102 | "lambda_1": trial.suggest_loguniform("lambda_1", 1e-10, 1e-3), 103 | "lambda_2": trial.suggest_loguniform("lambda_2", 1e-10, 1e-3), 104 | "fit_intercept": trial.suggest_categorical("fit_intercept", [True, False]), 105 | "normalize": trial.suggest_categorical("normalize", [True, False]), 106 | "copy_X": trial.suggest_categorical("copy_X", [True, False]), 107 | } 108 | regressor = BayesianRidge(**param) 109 | scores = cross_val_score( 110 | regressor, self.X, self.y, cv=self.cv, n_jobs=-1, scoring=self.metric 111 | ) 112 | return scores.mean() 113 | 114 | def svr_regressor_objective(self, trial): 115 | param = { 116 | "C": trial.suggest_loguniform("C", 1e-10, 1e-3), 117 | "kernel": trial.suggest_categorical("kernel", ["linear", "rbf"]), 118 | "degree": trial.suggest_int("degree", 1, 3), 119 | "gamma": trial.suggest_loguniform("gamma", 1e-10, 1e-3), 120 | "coef0": trial.suggest_loguniform("coef0", 1e-10, 1e-3), 121 | "shrinking": trial.suggest_categorical("shrinking", [True, False]), 122 | "tol": trial.suggest_loguniform("tol", 1e-10, 1e-3), 123 | "cache_size": trial.suggest_loguniform("cache_size", 1e-10, 1e-3), 124 | "verbose": trial.suggest_categorical("verbose", [True, False]), 125 | "max_iter": trial.suggest_int("max_iter", 100, 1000), 126 | } 127 | regressor = SVR(**param) 128 | scores = cross_val_score( 129 | regressor, self.X, self.y, cv=self.cv, n_jobs=-1, scoring=self.metric 130 | ) 131 | return scores.mean() 132 | 133 | def knr_regressor_objective(self, trial): 134 | param = { 135 | "n_neighbors": trial.suggest_int("n_neighbors", 1, 10), 136 | "weights": trial.suggest_categorical("weights", ["uniform", "distance"]), 137 | "algorithm": trial.suggest_categorical( 138 | "algorithm", ["auto", "ball_tree", "kd_tree", "brute"] 139 | ), 140 | "leaf_size": trial.suggest_int("leaf_size", 1, 100), 141 | "p": trial.suggest_int("p", 1, 3), 142 | "n_jobs": -1, 143 | } 144 | regressor = KNeighborsRegressor(**param) 145 | scores = cross_val_score( 146 | regressor, self.X, self.y, cv=self.cv, n_jobs=-1, scoring=self.metric 147 | ) 148 | return scores.mean() 149 | 150 | def dt_regressor_objective(self, trial): 151 | param = { 152 | "criterion": trial.suggest_categorical( 153 | "criterion", ["mse", "friedman_mse", "mae"] 154 | ), 155 | "splitter": trial.suggest_categorical("splitter", ["best", "random"]), 156 | "max_depth": trial.suggest_int("max_depth", 1, 10), 157 | "min_samples_split": trial.suggest_int("min_samples_split", 2, 10), 158 | "min_samples_leaf": trial.suggest_int("min_samples_leaf", 1, 10), 159 | "min_weight_fraction_leaf": trial.suggest_loguniform( 160 | "min_weight_fraction_leaf", 1e-10, 1e-3 161 | ), 162 | "max_features": trial.suggest_categorical( 163 | "max_features", ["auto", "sqrt", "log2", None] 164 | ), 165 | "max_leaf_nodes": trial.suggest_int("max_leaf_nodes", 2, 10), 166 | "min_impurity_decrease": trial.suggest_loguniform( 167 | "min_impurity_decrease", 1e-10, 1e-3 168 | ), 169 | "random_state": self.random_state, 170 | } 171 | regressor = DecisionTreeRegressor(**param) 172 | scores = cross_val_score( 173 | regressor, self.X, self.y, cv=self.cv, n_jobs=-1, scoring=self.metric 174 | ) 175 | return scores.mean() 176 | 177 | def gbr_regressor_objective(self, trial): 178 | param = { 179 | "loss": trial.suggest_categorical( 180 | "loss", ["ls", "lad", "huber", "quantile"] 181 | ), 182 | "learning_rate": trial.suggest_loguniform("learning_rate", 1e-10, 1e-3), 183 | "n_estimators": trial.suggest_int("n_estimators", 10, 1000), 184 | "criterion": trial.suggest_categorical( 185 | "criterion", ["friedman_mse", "mae"] 186 | ), 187 | "max_depth": trial.suggest_int("max_depth", 1, 10), 188 | "min_samples_split": trial.suggest_int("min_samples_split", 2, 10), 189 | "min_samples_leaf": trial.suggest_int("min_samples_leaf", 1, 10), 190 | "min_weight_fraction_leaf": trial.suggest_loguniform( 191 | "min_weight_fraction_leaf", 1e-10, 1e-3 192 | ), 193 | "max_features": trial.suggest_categorical( 194 | "max_features", ["auto", "sqrt", "log2", None] 195 | ), 196 | "max_leaf_nodes": trial.suggest_int("max_leaf_nodes", 2, 10), 197 | "min_impurity_decrease": trial.suggest_loguniform( 198 | "min_impurity_decrease", 1e-10, 1e-3 199 | ), 200 | "random_state": self.random_state, 201 | } 202 | regressor = GradientBoostingRegressor(**param) 203 | scores = cross_val_score( 204 | regressor, self.X, self.y, cv=self.cv, n_jobs=-1, scoring=self.metric 205 | ) 206 | return scores.mean() 207 | 208 | def ada_regressor_objective(self, trial): 209 | param = { 210 | "learning_rate": trial.suggest_loguniform("learning_rate", 1e-10, 1e-3), 211 | "n_estimators": trial.suggest_int("n_estimators", 10, 1000), 212 | "loss": trial.suggest_categorical( 213 | "loss", ["linear", "square", "exponential"] 214 | ), 215 | "random_state": self.random_state, 216 | } 217 | regressor = AdaBoostRegressor(**param) 218 | scores = cross_val_score( 219 | regressor, self.X, self.y, cv=self.cv, n_jobs=-1, scoring=self.metric 220 | ) 221 | return scores.mean() 222 | 223 | def bag_regressor_objective(self, trial): 224 | param = { 225 | "n_estimators": trial.suggest_int("n_estimators", 10, 1000), 226 | "bootstrap_features": trial.suggest_categorical( 227 | "bootstrap_features", [True, False] 228 | ), 229 | "oob_score": trial.suggest_categorical("oob_score", [True, False]), 230 | "max_samples": trial.suggest_uniform("max_samples", 0.0, 1.0), 231 | "max_features": trial.suggest_uniform("max_features", 0.0, 1.0), 232 | "random_state": self.random_state, 233 | "n_jobs": -1, 234 | } 235 | regressor = BaggingRegressor(**param) 236 | scores = cross_val_score( 237 | regressor, self.X, self.y, cv=self.cv, n_jobs=-1, scoring=self.metric 238 | ) 239 | return scores.mean() 240 | 241 | def extr_regressor_objective(self, trial): 242 | param = { 243 | "n_estimators": trial.suggest_int("n_estimators", 10, 1000), 244 | "criterion": trial.suggest_categorical( 245 | "criterion", ["mse", "friedman_mse", "mae"] 246 | ), 247 | "max_depth": trial.suggest_int("max_depth", 1, 10), 248 | "min_samples_split": trial.suggest_int("min_samples_split", 2, 10), 249 | "min_samples_leaf": trial.suggest_int("min_samples_leaf", 1, 10), 250 | "min_weight_fraction_leaf": trial.suggest_loguniform( 251 | "min_weight_fraction_leaf", 1e-10, 1e-3 252 | ), 253 | "max_features": trial.suggest_categorical( 254 | "max_features", ["auto", "sqrt", "log2", None] 255 | ), 256 | "max_leaf_nodes": trial.suggest_int("max_leaf_nodes", 2, 10), 257 | "min_impurity_decrease": trial.suggest_loguniform( 258 | "min_impurity_decrease", 1e-10, 1e-3 259 | ), 260 | "bootstrap": True, 261 | "oob_score": trial.suggest_categorical("oob_score", [True, False]), 262 | "random_state": self.random_state, 263 | "n_jobs": -1, 264 | } 265 | regressor = ExtraTreesRegressor(**param) 266 | scores = cross_val_score( 267 | regressor, self.X, self.y, cv=self.cv, n_jobs=-1, scoring=self.metric 268 | ) 269 | return scores.mean() 270 | 271 | def rfr_regressor_objective(self, trial): 272 | param = { 273 | "n_estimators": trial.suggest_int("n_estimators", 200, 1500), 274 | "max_features": trial.suggest_categorical("max_features", ["auto", "sqrt"]), 275 | "max_depth": trial.suggest_int("max_depth", 10, 80, log=True), 276 | "min_samples_split": trial.suggest_int("min_samples_split", 2, 15), 277 | "min_samples_leaf": trial.suggest_int("min_samples_leaf", 1, 9), 278 | "bootstrap": trial.suggest_categorical("bootstrap", [True, False]), 279 | } 280 | regressor = RandomForestRegressor(**param, n_jobs=-1, verbose=0) 281 | scores = cross_val_score( 282 | regressor, self.X, self.y, cv=self.cv, n_jobs=-1, scoring=self.metric 283 | ) 284 | return scores.mean() 285 | 286 | def xgb_regressor_objective(self, trial): 287 | param = { 288 | "n_estimators": trial.suggest_int("n_estimators", 500, 4000), 289 | "max_depth": trial.suggest_int("max_depth", 8, 16), 290 | "min_child_weight": trial.suggest_int("min_child_weight", 1, 300), 291 | "gamma": trial.suggest_int("gamma", 1, 3), 292 | "learning_rate": 0.01, 293 | "colsample_bytree": trial.suggest_discrete_uniform( 294 | "colsample_bytree", 0.5, 1, 0.1 295 | ), 296 | "lambda": trial.suggest_loguniform("lambda", 1e-3, 10.0), 297 | "alpha": trial.suggest_loguniform("alpha", 1e-3, 10.0), 298 | "subsample": trial.suggest_categorical("subsample", [0.6, 0.7, 0.8, 1.0]), 299 | "random_state": 42, 300 | } 301 | regressor = XGBRegressor(**param) 302 | scores = cross_val_score( 303 | regressor, self.X, self.y, cv=self.cv, n_jobs=-1, scoring=self.metric 304 | ) 305 | return scores.mean() 306 | 307 | def cat_regressor_objective(self, trial): 308 | params = { 309 | "iterations": trial.suggest_int("iterations", 50, 300), 310 | "depth": trial.suggest_int("depth", 4, 10), 311 | "random_strength": trial.suggest_int("random_strength", 0, 100), 312 | "bagging_temperature": trial.suggest_loguniform( 313 | "bagging_temperature", 0.01, 100.00 314 | ), 315 | "learning_rate": trial.suggest_loguniform("learning_rate", 1e-3, 1e-1), 316 | "od_type": trial.suggest_categorical("od_type", ["IncToDec", "Iter"]), 317 | } 318 | regressor = CatBoostRegressor(**params) 319 | scores = cross_val_score( 320 | regressor, self.X, self.y, cv=self.cv, n_jobs=-1, scoring=self.metric 321 | ) 322 | return scores.mean() 323 | 324 | def lgbm_regressor_objective(self, trial): 325 | 326 | param = { 327 | "boosting_type": "gbdt", 328 | "objective": "regression", 329 | "metric": "rmse", 330 | "learning_rate": trial.suggest_categorical( 331 | "learning_rate", [0.0125, 0.025, 0.05, 0.1] 332 | ), 333 | "num_leaves": trial.suggest_int("num_leaves", 2, 2048), 334 | "lambda_l1": trial.suggest_float("lambda_l1", 1e-8, 10.0, log=True), 335 | "lambda_l2": trial.suggest_float("lambda_l2", 1e-8, 10.0, log=True), 336 | "colsample_bytree": min( 337 | trial.suggest_float("colsample_bytree", 0.3, 1.0 + 1e-8), 1.0 338 | ), 339 | "bagging_fraction": min( 340 | trial.suggest_float("bagging_fraction", 0.3, 1.0 + 1e-8), 1.0 341 | ), 342 | "bagging_freq": trial.suggest_int("bagging_freq", 1, 7), 343 | "min_data_in_leaf": trial.suggest_int("min_data_in_leaf", 1, 100), 344 | "feature_pre_filter": False, 345 | "random_state": self.random_state, 346 | "num_threads": -1, 347 | "extra_trees": trial.suggest_categorical("extra_trees", [True, False]), 348 | } 349 | regressor = LGBMRegressor(**param) 350 | scores = cross_val_score( 351 | regressor, self.X, self.y, cv=self.cv, n_jobs=-1, scoring=self.metric 352 | ) 353 | return scores.mean() 354 | 355 | def mlp_regressor_objective(self, trial): 356 | param = { 357 | "hidden_layer_sizes": trial.suggest_int("hidden_layer_sizes", 2, 10), 358 | "activation": trial.suggest_categorical("activation", ["logistic", "tanh"]), 359 | "solver": trial.suggest_categorical("solver", ["lbfgs", "adam"]), 360 | "alpha": trial.suggest_loguniform("alpha", 1e-8, 1e-1), 361 | "learning_rate": trial.suggest_categorical( 362 | "learning_rate", ["constant", "adaptive"] 363 | ), 364 | "max_iter": trial.suggest_int("max_iter", 1, 2000), 365 | "random_state": self.random_state, 366 | "verbose": 0, 367 | "early_stopping": True, 368 | "validation_fraction": 0.2, 369 | "n_iter_no_change": 10, 370 | } 371 | regressor = MLPRegressor(**param) 372 | scores = cross_val_score( 373 | regressor, self.X, self.y, cv=self.cv, n_jobs=-1, scoring=self.metric 374 | ) 375 | return scores.mean() 376 | -------------------------------------------------------------------------------- /luciferml/supervised/utils/tuner/optuna/objectives/classification_objectives.py: -------------------------------------------------------------------------------- 1 | from catboost import CatBoostClassifier 2 | from lightgbm import LGBMClassifier 3 | from sklearn.ensemble import ( 4 | AdaBoostClassifier, 5 | BaggingClassifier, 6 | ExtraTreesClassifier, 7 | GradientBoostingClassifier, 8 | RandomForestClassifier, 9 | ) 10 | from sklearn.linear_model import ( 11 | LogisticRegression, 12 | PassiveAggressiveClassifier, 13 | Perceptron, 14 | RidgeClassifier, 15 | SGDClassifier, 16 | ) 17 | from sklearn.model_selection import cross_val_score 18 | from sklearn.neighbors import KNeighborsClassifier 19 | from sklearn.neural_network import MLPClassifier 20 | from sklearn.svm import SVC 21 | from sklearn.tree import DecisionTreeClassifier 22 | from xgboost import XGBClassifier 23 | 24 | 25 | class ClassificationObjectives: 26 | def __init__( 27 | self, X, y, cv=5, random_state=42, metric="accuracy", lgbm_objective="binary" 28 | ): 29 | self.metric = metric 30 | self.cv = cv 31 | self.X = X 32 | self.y = y 33 | self.random_state = random_state 34 | self.lgbm_objective = lgbm_objective 35 | 36 | def lr_classifier_objective(self, trial): 37 | param = { 38 | "C": trial.suggest_loguniform("C", 1e-5, 1e5), 39 | "solver": trial.suggest_categorical( 40 | "solver", ["newton-cg", "lbfgs", "liblinear", "sag", "saga"] 41 | ), 42 | "max_iter": trial.suggest_int("max_iter", 1, 1000), 43 | "tol": trial.suggest_loguniform("tol", 1e-5, 1e-2), 44 | "random_state": self.random_state, 45 | } 46 | clf = LogisticRegression(**param) 47 | scores = cross_val_score( 48 | clf, 49 | self.X, 50 | self.y, 51 | cv=self.cv, 52 | scoring=self.metric, 53 | n_jobs=-1, 54 | ) 55 | return scores.mean() 56 | 57 | def sgd_classifier_objective(self, trial): 58 | param = { 59 | "loss": trial.suggest_categorical( 60 | "loss", 61 | ["hinge", "log", "modified_huber", "squared_hinge", "perceptron"], 62 | ), 63 | "penalty": trial.suggest_categorical("penalty", ["l2", "l1", "elasticnet"]), 64 | "alpha": trial.suggest_loguniform("alpha", 1e-5, 1e5), 65 | "l1_ratio": trial.suggest_uniform("l1_ratio", 0, 1), 66 | "fit_intercept": trial.suggest_categorical("fit_intercept", [True, False]), 67 | "max_iter": trial.suggest_int("max_iter", 1, 1000), 68 | "tol": trial.suggest_loguniform("tol", 1e-5, 1e-2), 69 | "random_state": self.random_state, 70 | } 71 | clf = SGDClassifier(**param) 72 | scores = cross_val_score( 73 | clf, self.X, self.y, cv=self.cv, scoring=self.metric, n_jobs=-1 74 | ) 75 | return scores.mean() 76 | 77 | def ridg_classifier_objective(self, trial): 78 | param = { 79 | "alpha": trial.suggest_loguniform("alpha", 1e-5, 1e5), 80 | "fit_intercept": trial.suggest_categorical("fit_intercept", [True, False]), 81 | "max_iter": trial.suggest_int("max_iter", 1, 1000), 82 | "tol": trial.suggest_loguniform("tol", 1e-5, 1e-2), 83 | "random_state": self.random_state, 84 | } 85 | clf = RidgeClassifier(**param) 86 | scores = cross_val_score( 87 | clf, 88 | self.X, 89 | self.y, 90 | cv=self.cv, 91 | scoring=self.metric, 92 | n_jobs=-1, 93 | ) 94 | return scores.mean() 95 | 96 | def perc_classifier_objective(self, trial): 97 | param = { 98 | "penalty": trial.suggest_categorical("penalty", ["l2", "l1", "elasticnet"]), 99 | "alpha": trial.suggest_loguniform("alpha", 1e-5, 1e5), 100 | "fit_intercept": trial.suggest_categorical("fit_intercept", [True, False]), 101 | "max_iter": trial.suggest_int("max_iter", 1, 1000), 102 | "tol": trial.suggest_loguniform("tol", 1e-5, 1e-2), 103 | "random_state": self.random_state, 104 | } 105 | clf = Perceptron(**param) 106 | scores = cross_val_score( 107 | clf, 108 | self.X, 109 | self.y, 110 | cv=self.cv, 111 | scoring=self.metric, 112 | n_jobs=-1, 113 | ) 114 | return scores.mean() 115 | 116 | def pass_classifier_objective(self, trial): 117 | param = { 118 | "C": trial.suggest_loguniform("C", 1e-5, 1e5), 119 | "fit_intercept": trial.suggest_categorical("fit_intercept", [True, False]), 120 | "max_iter": trial.suggest_int("max_iter", 1, 1000), 121 | "tol": trial.suggest_loguniform("tol", 1e-5, 1e-2), 122 | "random_state": self.random_state, 123 | } 124 | clf = PassiveAggressiveClassifier(**param) 125 | scores = cross_val_score( 126 | clf, 127 | self.X, 128 | self.y, 129 | cv=self.cv, 130 | scoring=self.metric, 131 | n_jobs=-1, 132 | ) 133 | return scores.mean() 134 | 135 | def svm_classifier_objective(self, trial): 136 | param = { 137 | "C": trial.suggest_loguniform("C", 1e-5, 1e5), 138 | "kernel": trial.suggest_categorical( 139 | "kernel", ["rbf", "linear", "poly", "sigmoid"] 140 | ), 141 | "gamma": trial.suggest_loguniform("gamma", 1e-5, 1e5), 142 | "degree": trial.suggest_int("degree", 1, 10), 143 | "coef0": trial.suggest_loguniform("coef0", 1e-5, 1e5), 144 | "shrinking": trial.suggest_categorical("shrinking", [True, False]), 145 | "tol": trial.suggest_loguniform("tol", 1e-5, 1e-2), 146 | "random_state": self.random_state, 147 | } 148 | clf = SVC(**param) 149 | scores = cross_val_score( 150 | clf, 151 | self.X, 152 | self.y, 153 | cv=self.cv, 154 | scoring=self.metric, 155 | n_jobs=-1, 156 | ) 157 | return scores.mean() 158 | 159 | def knn_classifier_objective(self, trial): 160 | param = { 161 | "n_neighbors": trial.suggest_int("n_neighbors", 1, 256), 162 | "weights": trial.suggest_categorical("weights", ["uniform", "distance"]), 163 | "p": trial.suggest_int("p", 1, 10), 164 | "n_jobs": -1, 165 | "rows_limit": 100000, 166 | } 167 | clf = KNeighborsClassifier(**param) 168 | scores = cross_val_score( 169 | clf, 170 | self.X, 171 | self.y, 172 | cv=self.cv, 173 | scoring=self.metric, 174 | n_jobs=-1, 175 | ) 176 | return scores.mean() 177 | 178 | def dt_classifier_objective(self, trial): 179 | param = { 180 | "criterion": trial.suggest_categorical("criterion", ["gini", "entropy"]), 181 | "splitter": trial.suggest_categorical("splitter", ["best", "random"]), 182 | "max_depth": trial.suggest_int("max_depth", 1, 10), 183 | "min_samples_split": trial.suggest_int("min_samples_split", 1, 10), 184 | "min_samples_leaf": trial.suggest_int("min_samples_leaf", 1, 10), 185 | "min_weight_fraction_leaf": trial.suggest_float( 186 | "min_weight_fraction_leaf", 0, 0.5 187 | ), 188 | "max_features": trial.suggest_categorical( 189 | "max_features", ["auto", "sqrt", "log2"] 190 | ), 191 | "random_state": self.random_state, 192 | } 193 | clf = DecisionTreeClassifier(**param) 194 | scores = cross_val_score( 195 | clf, 196 | self.X, 197 | self.y, 198 | cv=self.cv, 199 | scoring=self.metric, 200 | n_jobs=-1, 201 | ) 202 | return scores.mean() 203 | 204 | def rfc_classifier_objective(self, trial): 205 | param = { 206 | "n_estimators": trial.suggest_int("n_estimators", 1, 100), 207 | "criterion": trial.suggest_categorical("criterion", ["gini", "entropy"]), 208 | "min_weight_fraction_leaf": trial.suggest_float( 209 | "min_weight_fraction_leaf", 0, 0.5 210 | ), 211 | "max_depth": trial.suggest_int("max_depth", 2, 32), 212 | "min_samples_split": trial.suggest_int("min_samples_split", 2, 100), 213 | "min_samples_leaf": trial.suggest_int("min_samples_leaf", 1, 100), 214 | "max_features": trial.suggest_float("max_features", 0.01, 1), 215 | "seed": self.random_state, 216 | "n_jobs": -1, 217 | "max_steps": 10, 218 | } 219 | clf = RandomForestClassifier(**param) 220 | scores = cross_val_score( 221 | clf, 222 | self.X, 223 | self.y, 224 | cv=self.cv, 225 | scoring=self.metric, 226 | n_jobs=-1, 227 | ) 228 | return scores.mean() 229 | 230 | def gbc_classifier_objective(self, trial): 231 | param = { 232 | "n_estimators": trial.suggest_int("n_estimators", 1, 100), 233 | "learning_rate": trial.suggest_loguniform("learning_rate", 1e-5, 1e5), 234 | "max_depth": trial.suggest_int("max_depth", 1, 10), 235 | "min_samples_split": trial.suggest_int("min_samples_split", 1, 10), 236 | "min_samples_leaf": trial.suggest_int("min_samples_leaf", 1, 10), 237 | "min_weight_fraction_leaf": trial.suggest_float( 238 | "min_weight_fraction_leaf", 0, 0.5 239 | ), 240 | "max_features": trial.suggest_categorical( 241 | "max_features", ["auto", "sqrt", "log2"] 242 | ), 243 | } 244 | clf = GradientBoostingClassifier(**param) 245 | scores = cross_val_score( 246 | clf, 247 | self.X, 248 | self.y, 249 | cv=self.cv, 250 | scoring=self.metric, 251 | n_jobs=-1, 252 | ) 253 | return scores.mean() 254 | 255 | def ada_classifier_objective(self, trial): 256 | param = { 257 | "n_estimators": trial.suggest_int("n_estimators", 1, 100), 258 | "learning_rate": trial.suggest_loguniform("learning_rate", 1e-5, 1e5), 259 | "algorithm": trial.suggest_categorical("algorithm", ["SAMME", "SAMME.R"]), 260 | "random_state": self.random_state, 261 | } 262 | clf = AdaBoostClassifier(**param) 263 | scores = cross_val_score( 264 | clf, 265 | self.X, 266 | self.y, 267 | cv=self.cv, 268 | scoring=self.metric, 269 | n_jobs=-1, 270 | ) 271 | return scores.mean() 272 | 273 | def bag_classifier_objective(self, trial): 274 | param = { 275 | "n_estimators": trial.suggest_int("n_estimators", 1, 100), 276 | "bootstrap": trial.suggest_categorical("bootstrap", [True, False]), 277 | "bootstrap_features": trial.suggest_categorical( 278 | "bootstrap_features", [True, False] 279 | ), 280 | "max_samples": trial.suggest_uniform("max_samples", 0.1, 1), 281 | "max_features": trial.suggest_uniform("max_features", 0.1, 1), 282 | "n_jobs": -1, 283 | "random_state": self.random_state, 284 | } 285 | clf = BaggingClassifier(**param) 286 | scores = cross_val_score( 287 | clf, 288 | self.X, 289 | self.y, 290 | cv=self.cv, 291 | scoring=self.metric, 292 | n_jobs=-1, 293 | ) 294 | return scores.mean() 295 | 296 | def extc_classifier_objective(self, trial): 297 | param = { 298 | "criterion": trial.suggest_categorical("criterion", ["gini", "entropy"]), 299 | "min_weight_fraction_leaf": trial.suggest_float( 300 | "min_weight_fraction_leaf", 0, 0.5 301 | ), 302 | "max_depth": trial.suggest_int("max_depth", 2, 32), 303 | "min_samples_split": trial.suggest_int("min_samples_split", 2, 100), 304 | "min_samples_leaf": trial.suggest_int("min_samples_leaf", 1, 100), 305 | "max_features": trial.suggest_float("max_features", 0.01, 1), 306 | "random_state": self.random_state, 307 | "n_jobs": -1, 308 | "max_steps": 10, 309 | } 310 | clf = ExtraTreesClassifier(**param) 311 | scores = cross_val_score( 312 | clf, 313 | self.X, 314 | self.y, 315 | cv=self.cv, 316 | scoring=self.metric, 317 | n_jobs=-1, 318 | ) 319 | return scores.mean() 320 | 321 | def lgbm_classifier_objective(self, trial): 322 | param = { 323 | "learning_rate": trial.suggest_categorical( 324 | "learning_rate", [0.0125, 0.025, 0.05, 0.1] 325 | ), 326 | "num_leaves": trial.suggest_int("num_leaves", 2, 2048), 327 | "lambda_l1": trial.suggest_float("lambda_l1", 1e-8, 10.0, log=True), 328 | "lambda_l2": trial.suggest_float("lambda_l2", 1e-8, 10.0, log=True), 329 | "feature_fraction": min( 330 | trial.suggest_float("feature_fraction", 0.3, 1.0 + 1e-8), 1.0 331 | ), 332 | "bagging_fraction": min( 333 | trial.suggest_float("bagging_fraction", 0.3, 1.0 + 1e-8), 1.0 334 | ), 335 | "bagging_freq": trial.suggest_int("bagging_freq", 1, 7), 336 | "min_data_in_leaf": trial.suggest_int("min_data_in_leaf", 1, 100), 337 | "extra_trees": trial.suggest_categorical("extra_trees", [True, False]), 338 | "feature_pre_filter": False, 339 | "boosting_type": "gbdt", 340 | "seed": self.random_state, 341 | "num_threads": -1, 342 | "objective": self.lgbm_objective, 343 | } 344 | clf = LGBMClassifier(**param) 345 | scores = cross_val_score( 346 | clf, 347 | self.X, 348 | self.y, 349 | cv=self.cv, 350 | scoring=self.metric, 351 | n_jobs=-1, 352 | ) 353 | return scores.mean() 354 | 355 | def cat_classifier_objective(self, trial): 356 | param = { 357 | "objective": trial.suggest_categorical("objective", ["Logloss", "CrossEntropy"]), 358 | "iterations": 1000, 359 | "colsample_bylevel": trial.suggest_float("colsample_bylevel", 0.01, 0.1), 360 | "depth": trial.suggest_int("depth", 1, 12), 361 | "boosting_type": trial.suggest_categorical("boosting_type", ["Ordered", "Plain"]), 362 | "bootstrap_type": trial.suggest_categorical( 363 | "bootstrap_type", ["Bayesian", "Bernoulli", "MVS"] 364 | ), 365 | "min_data_in_leaf": trial.suggest_int("min_data_in_leaf", 1, 100), 366 | "learning_rate": trial.suggest_categorical( 367 | "learning_rate", [0.05, 0.1, 0.2] 368 | ), 369 | "rsm": trial.suggest_float("rsm", 0.1, 1), 370 | "l2_leaf_reg": trial.suggest_float( 371 | "l2_leaf_reg", 0.0001, 10.0, log=False 372 | ), 373 | "random_state": self.random_state, 374 | "verbose": False, 375 | "allow_writing_files": False, 376 | } 377 | 378 | if param["bootstrap_type"] == "Bayesian": 379 | param["bagging_temperature"] = trial.suggest_float("bagging_temperature", 0, 10) 380 | elif param["bootstrap_type"] == "Bernoulli": 381 | param["subsample"] = trial.suggest_float("subsample", 0.1, 1) 382 | clf = CatBoostClassifier(**param) 383 | scores = cross_val_score( 384 | clf, 385 | self.X, 386 | self.y, 387 | cv=self.cv, 388 | scoring=self.metric, 389 | n_jobs=-1, 390 | ) 391 | return scores.mean() 392 | 393 | def xgb_classifier_objective(self, trial): 394 | param = { 395 | "learning_rate": trial.suggest_categorical( 396 | "learning_rate", [0.05, 0.1, 0.2] 397 | ), 398 | "eta": trial.suggest_categorical("eta", [0.0125, 0.025, 0.05, 0.1]), 399 | "max_depth": trial.suggest_int("max_depth", 2, 12), 400 | "lambda": trial.suggest_float("lambda", 1e-8, 10.0, log=True), 401 | "alpha": trial.suggest_float("alpha", 1e-8, 10.0, log=True), 402 | "colsample_bytree": min( 403 | trial.suggest_float("colsample_bytree", 0.3, 1.0 + 1e-8), 1.0 404 | ), 405 | "subsample": min(trial.suggest_float("subsample", 0.3, 1.0 + 1e-8), 1.0), 406 | "min_child_weight": trial.suggest_int("min_child_weight", 1, 100), 407 | "tree_method": "hist", 408 | "booster": "gbtree", 409 | "n_jobs": -1, 410 | "seed": self.random_state, 411 | "verbosity": 0, 412 | 413 | } 414 | clf = XGBClassifier(**param) 415 | scores = cross_val_score( 416 | clf, 417 | self.X, 418 | self.y, 419 | cv=self.cv, 420 | scoring=self.metric, 421 | n_jobs=-1, 422 | ) 423 | return scores.mean() 424 | 425 | def mlp_classifier_objective(self, trial): 426 | param = { 427 | "hidden_layer_sizes": trial.suggest_int("hidden_layer_sizes", 1, 10), 428 | "max_iter": trial.suggest_int("max_iter", 1, 2000), 429 | "dense_1_size": trial.suggest_int("dense_1_size", 4, 100), 430 | "dense_2_size": trial.suggest_int("dense_2_size", 2, 100), 431 | "learning_rate": trial.suggest_categorical( 432 | "learning_rate", [0.005, 0.01, 0.05, 0.1, 0.2] 433 | ), 434 | "learning_rate_type": trial.suggest_categorical( 435 | "learning_rate_type", ["constant", "adaptive"] 436 | ), 437 | "alpha": trial.suggest_float("alpha", 1e-8, 10.0, log=True), 438 | "seed": self.random_state, 439 | } 440 | clf = MLPClassifier(**param) 441 | scores = cross_val_score( 442 | clf, 443 | self.X, 444 | self.y, 445 | cv=self.cv, 446 | scoring=self.metric, 447 | n_jobs=-1, 448 | ) 449 | return scores.mean() 450 | -------------------------------------------------------------------------------- /luciferml/supervised/classification.py: -------------------------------------------------------------------------------- 1 | import copy 2 | import os 3 | from re import S 4 | import time 5 | import warnings 6 | 7 | import numpy as np 8 | import optuna 9 | import pandas as pd 10 | from IPython.display import display 11 | from joblib import dump, load 12 | from luciferml.supervised.utils.best import Best 13 | from luciferml.supervised.utils.configs import * 14 | from luciferml.supervised.utils.predictors import classification_predictor 15 | from luciferml.supervised.utils.preprocesser import PreProcesser 16 | from luciferml.supervised.utils.tuner.luciferml_tuner import luciferml_tuner 17 | from luciferml.supervised.utils.validator import * 18 | from optuna.samplers._tpe.sampler import TPESampler 19 | from sklearn.metrics import accuracy_score 20 | from colorama import Fore 21 | 22 | 23 | class Classification: 24 | def __init__( 25 | self, 26 | predictor=["lr"], 27 | params={}, 28 | tune=False, 29 | test_size=0.2, 30 | cv_folds=10, 31 | random_state=42, 32 | pca_kernel="linear", 33 | n_components_lda=1, 34 | lda="n", 35 | pca="n", 36 | n_components_pca=2, 37 | metrics=[ 38 | "accuracy", 39 | ], 40 | loss="binary_crossentropy", 41 | validation_split=0.20, 42 | tune_mode=1, 43 | smote="n", 44 | k_neighbors=1, 45 | verbose=False, 46 | exclude_models=[], 47 | path=None, 48 | optuna_sampler=TPESampler(multivariate=True), 49 | optuna_direction="maximize", 50 | optuna_n_trials=100, 51 | optuna_metric="accuracy", 52 | lgbm_objective="binary", 53 | ): 54 | """ 55 | Encode Categorical Data then Applies SMOTE , Splits the features and labels in training and validation sets with test_size = .2 , scales self.X_train, self.X_val using StandardScaler.\n 56 | Fits every model on training set and predicts results find and plots Confusion Matrix,\n 57 | finds accuracy of model applies K-Fold Cross Validation\n 58 | and stores accuracy in variable name accuracy and model name in self.classifier name and returns both as a tuple.\n 59 | Applies HyperParam Tuning and gives best params and accuracy.\n 60 | 61 | Parameters: 62 | 63 | features : array 64 | features array 65 | lables : array 66 | labels array 67 | predictor : list 68 | Predicting model to be used 69 | Default ['lr'] - Logistic Regression\n 70 | Available Predictors: 71 | lr - Logisitic Regression\n 72 | sgd - Stochastic Gradient Descent Classifier\n 73 | perc - Perceptron\n 74 | pass - Passive Aggressive Classifier\n 75 | ridg - Ridge Classifier\n 76 | svm -SupportVector Machine\n 77 | knn - K-Nearest Neighbours\n 78 | nb - GaussianNaive bayes\n 79 | rfc- Random Forest self.Classifier\n 80 | gbc - Gradient Boosting Classifier\n 81 | ada - AdaBoost Classifier\n 82 | bag - Bagging Classifier\n 83 | extc - Extra Trees Classifier\n 84 | lgbm - LightGBM Classifier\n 85 | cat - CatBoost Classifier\n 86 | xgb- XGBoost self.Classifier\n 87 | ann - MultiLayer Perceptron Classifier\n 88 | all - Applies all above classifiers\n 89 | 90 | params : dict 91 | contains parameters for model 92 | tune : boolean 93 | when True Applies GridSearch CrossValidation 94 | Default is False 95 | test_size: float or int, default=.2 96 | If float, should be between 0.0 and 1.0 and represent 97 | the proportion of the dataset to include in 98 | the test split. 99 | If int, represents the absolute number of test samples. 100 | cv_folds : int 101 | No. of cross validation folds. Default = 10 102 | pca : str 103 | if 'y' will apply PCA on Train and Validation set. Default = 'n' 104 | lda : str 105 | if 'y' will apply LDA on Train and Validation set. Default = 'n' 106 | pca_kernel : str 107 | Kernel to be use in PCA. Default = 'linear' 108 | n_components_lda : int 109 | No. of components for LDA. Default = 1 110 | n_components_pca : int 111 | No. of components for PCA. Default = 2 112 | loss : str 113 | loss method for ann. Default = 'binary_crossentropy' 114 | rate for dropout layer. Default = 0 115 | smote : str, 116 | Whether to apply SMOTE. Default = 'y' 117 | k_neighbors : int 118 | No. of neighbours for SMOTE. Default = 1 119 | verbose : boolean 120 | Verbosity of models. Default = False 121 | exclude_models : list 122 | List of models to be excluded when using predictor = 'all' . Default = [] 123 | path : list 124 | List containing path to saved model and scaler. Default = None 125 | Example: [model.pkl, scaler.pkl] 126 | random_state : int 127 | Random random_state for reproducibility. Default = 42 128 | optuna_sampler : Function 129 | Sampler to be used in optuna. Default = TPESampler() 130 | optuna_direction : str 131 | Direction of optimization. Default = 'maximize' 132 | Available Directions: 133 | maximize : Maximize 134 | minimize : Minimize 135 | optuna_n_trials : int 136 | No. of trials for optuna. Default = 100 137 | optuna_metric: str 138 | Metric to be used in optuna. Default = 'r2' 139 | lgbm_objective : str 140 | Objective for lgbm classifier. Default = 'binary' 141 | 142 | Returns: 143 | 144 | Dict Containing Name of Classifiers, Its K-Fold Cross Validated Accuracy and Prediction set 145 | 146 | Dataframe containing all the models and their accuracies when predictor is 'all' 147 | 148 | Example: 149 | 150 | from luciferml.supervised.classification import Classification 151 | 152 | dataset = pd.read_csv('Social_Network_Ads.csv') 153 | 154 | X = dataset.iloc[:, :-1] 155 | 156 | y = dataset.iloc[:, -1] 157 | 158 | classifier = Classification(predictor = 'lr') 159 | 160 | classifier.fit(X, y) 161 | 162 | result = classifier.result() 163 | 164 | """ 165 | self.preprocess = PreProcesser() 166 | if type(predictor) == list: 167 | if not "all" in predictor: 168 | self.predictor = predictor[0] if len( 169 | predictor) == 1 else predictor 170 | else: 171 | self.predictor = predictor 172 | else: 173 | self.predictor = predictor 174 | bool_pred, pred = pred_check(predictor, pred_type="classification") 175 | if not bool_pred: 176 | raise ValueError(unsupported_pred_warning.format(pred)) 177 | self.original_predictor = predictor 178 | self.params = params 179 | self.tune = tune 180 | self.test_size = test_size 181 | self.cv_folds = cv_folds 182 | self.random_state = random_state 183 | self.pca_kernel = pca_kernel 184 | self.n_components_lda = n_components_lda 185 | self.lda = lda 186 | self.pca = pca 187 | self.n_components_pca = n_components_pca 188 | self.metrics = metrics 189 | self.loss = loss 190 | self.validation_split = validation_split 191 | self.tune_mode = tune_mode 192 | self.rerun = False 193 | self.smote = smote 194 | self.k_neighbors = k_neighbors 195 | self.verbose = verbose 196 | self.exclude_models = exclude_models 197 | self.sampler = optuna_sampler 198 | self.direction = optuna_direction 199 | self.n_trials = optuna_n_trials 200 | self.metric = optuna_metric 201 | self.lgbm_objective = lgbm_objective 202 | 203 | self.accuracy_scores = {} 204 | self.reg_result = {} 205 | self.accuracy = 0 206 | self.y_pred = [] 207 | self.kfold_accuracy = 0 208 | self.classifier_name = "" 209 | self.sc = 0 210 | 211 | self.kfoldacc = [] 212 | self.acc = [] 213 | self.bestacc = [] 214 | self.bestparams = [] 215 | self.tuned_trained_model = [] 216 | self.best_classifier_path = "" 217 | self.scaler_path = "" 218 | self.classifier_model = [] 219 | self.result_df = pd.DataFrame(index=None) 220 | self.classifiers = copy.deepcopy(classifiers) 221 | for i in self.exclude_models: 222 | self.classifiers.pop(i) 223 | self.best_classifier = "First Run the Predictor in All mode" 224 | self.objective = None 225 | self.pred_mode = "" 226 | self.model_to_predict = [] 227 | 228 | if path != None: 229 | try: 230 | self.classifier, self.sc = self.__load(path) 231 | except Exception as e: 232 | print(Fore.RED + e) 233 | print(Fore.RED + "Model not found") 234 | if not self.verbose: 235 | optuna.logging.set_verbosity(optuna.logging.WARNING) 236 | 237 | def fit(self, features, labels): 238 | """[Takes Features and Labels and Encodes Categorical Data then Applies SMOTE , Splits the features and labels in training and validation sets with test_size = .2 239 | scales X_train, self.X_val using StandardScaler. 240 | Fits every model on training set and predicts results, 241 | finds accuracy of model applies K-Fold Cross Validation 242 | and stores its accuracies in a dictionary containing Model name as Key and accuracies as values and returns it 243 | Applies GridSearch Cross Validation and gives best params out from param list.] 244 | 245 | Args: 246 | features ([Pandas DataFrame]): [DataFrame containing Features] 247 | labels ([Pandas DataFrame]): [DataFrame containing Labels] 248 | """ 249 | self.features = features 250 | self.labels = labels 251 | # Time Function --------------------------------------------------------------------- 252 | 253 | self.start = time.time() 254 | print(Fore.MAGENTA + intro, "\n") 255 | print(Fore.GREEN + "Started LuciferML [", "\u2713", "]\n") 256 | if not self.rerun: 257 | # CHECKUP --------------------------------------------------------------------- 258 | if not isinstance(self.features, pd.DataFrame) and not isinstance( 259 | self.labels, pd.Series 260 | ): 261 | print( 262 | Fore.RED 263 | + "TypeError: This Function take features as Pandas Dataframe and labels as Pandas Series. Please check your implementation.\n" 264 | ) 265 | self.end = time.time() 266 | print(self.end - self.start) 267 | return 268 | 269 | print(Fore.YELLOW + "Preprocessing Started [*]\n") 270 | self.features, self.labels = self.preprocess.encoder( 271 | self.features, self.labels 272 | ) 273 | 274 | self.features, self.labels = sparse_check(self.features, self.labels) 275 | 276 | ( 277 | self.X_train, 278 | self.X_val, 279 | self.y_train, 280 | self.y_val, 281 | self.sc, 282 | ) = self.preprocess.data_preprocess( 283 | self.features, 284 | self.labels, 285 | self.test_size, 286 | self.random_state, 287 | self.smote, 288 | self.k_neighbors, 289 | ) 290 | 291 | self.X_train, self.X_val = self.preprocess.dimensionality_reduction( 292 | self.lda, 293 | self.pca, 294 | self.X_train, 295 | self.X_val, 296 | self.y_train, 297 | self.n_components_lda, 298 | self.n_components_pca, 299 | self.pca_kernel, 300 | self.start, 301 | ) 302 | 303 | print(Fore.GREEN + "Preprocessing Done [", "\u2713", "]\n") 304 | 305 | if self.original_predictor == "all" or type(self.predictor) == list: 306 | if 'all' in self.predictor and type(self.predictor)==list: 307 | self.predictor.remove('all') 308 | self.model_to_predict = ( 309 | self.predictor if len(self.predictor) > 1 and type(self.predictor) == list else self.classifiers 310 | ) 311 | 312 | self.result_df["Name"] = ( 313 | list(self.classifiers[i] for i in self.predictor) 314 | if type(self.predictor) == list and len(self.predictor) > 1 315 | else list(self.classifiers.values()) 316 | ) 317 | self.pred_mode = "all" if len(self.predictor) > 1 and type( 318 | self.predictor) == list else "single" 319 | self.__fitall() 320 | return 321 | 322 | self.classifier, self.objective = classification_predictor( 323 | self.predictor, 324 | self.params, 325 | self.X_train, 326 | self.y_train, 327 | self.cv_folds, 328 | self.random_state, 329 | self.metric, 330 | verbose=self.verbose, 331 | lgbm_objective=self.lgbm_objective, 332 | ) 333 | try: 334 | self.classifier.fit(self.X_train, self.y_train) 335 | except Exception as error: 336 | print(Fore.RED + "Classifier Build Failed with error: ", error, "\n") 337 | finally: 338 | print(Fore.GREEN + "Model Trained Successfully [", "\u2713", "]\n") 339 | 340 | try: 341 | print(Fore.YELLOW + "Evaluating Model Performance [*]\n") 342 | self.y_pred = self.classifier.predict(self.X_val) 343 | if self.predictor == "ann": 344 | self.y_pred = (self.y_pred > 0.5).astype("int32") 345 | self.accuracy = accuracy_score(self.y_val, self.y_pred) 346 | print(Fore.CYAN + " Validation Accuracy is : {:.2f} %".format(self.accuracy * 100)) 347 | self.classifier_name, self.kfold_accuracy = kfold( 348 | self.classifier, 349 | self.predictor, 350 | self.X_train, 351 | self.y_train, 352 | self.cv_folds, 353 | ) 354 | self.preprocess.confusion_matrix(self.y_pred, self.y_val) 355 | except Exception as error: 356 | print(Fore.RED + "Model Evaluation Failed with error: ", error, "\n") 357 | finally: 358 | print(Fore.GREEN + "Model Evaluation Completed [", "\u2713", "]\n") 359 | 360 | if not self.predictor == "nb" and self.tune: 361 | self.__tuner() 362 | 363 | print(Fore.GREEN + "Completed LuciferML Run [", "\u2713", "]\n") 364 | self.end = time.time() 365 | final_time = self.end - self.start 366 | print(Fore.BLUE + "Time Elapsed : ", f"{final_time:.2f}", "seconds \n") 367 | 368 | def __fitall(self): 369 | print(Fore.YELLOW + "Training LuciferML [*]\n") 370 | if self.params != {}: 371 | warnings.warn(params_use_warning, UserWarning) 372 | self.params = {} 373 | for _, self.predictor in enumerate(self.model_to_predict): 374 | if not self.predictor in self.exclude_models: 375 | try: 376 | self.classifier, self.objective = classification_predictor( 377 | self.predictor, 378 | self.params, 379 | self.X_train, 380 | self.y_train, 381 | self.cv_folds, 382 | self.random_state, 383 | self.metric, 384 | mode="multi", 385 | verbose=self.verbose, 386 | lgbm_objective=self.lgbm_objective, 387 | ) 388 | except Exception as error: 389 | print( 390 | Fore.RED + classifiers[self.predictor], 391 | "Model Train Failed with error: ", 392 | error, 393 | "\n", 394 | ) 395 | try: 396 | self.classifier.fit(self.X_train, self.y_train) 397 | self.y_pred = self.classifier.predict(self.X_val) 398 | if self.predictor == "ann": 399 | self.y_pred = (self.y_pred > 0.5).astype("int32") 400 | self.accuracy = accuracy_score(self.y_val, self.y_pred) 401 | self.acc.append(self.accuracy * 100) 402 | self.classifier_name, self.kfold_accuracy = kfold( 403 | self.classifier, 404 | self.predictor, 405 | self.X_train, 406 | self.y_train, 407 | self.cv_folds, 408 | all_mode=True, 409 | ) 410 | self.kfoldacc.append(self.kfold_accuracy) 411 | self.classifier_model.append(self.classifier) 412 | except Exception as error: 413 | print( 414 | classifiers[self.predictor], 415 | "Evaluation Failed with error: ", 416 | error, 417 | "\n", 418 | ) 419 | if self.tune: 420 | self.__tuner(all_mode=True, single_mode=False) 421 | 422 | self.result_df["Accuracy"] = self.acc 423 | self.result_df["KFold Accuracy"] = self.kfoldacc 424 | self.result_df["Model"] = self.classifier_model 425 | if self.tune: 426 | self.result_df["Best Parameters"] = self.bestparams 427 | self.result_df["Best Accuracy"] = self.bestacc 428 | self.best_classifier = Best( 429 | self.result_df.loc[self.result_df["Best Accuracy"].idxmax()], 430 | self.tune, 431 | ) 432 | else: 433 | self.best_classifier = Best( 434 | self.result_df.loc[self.result_df["KFold Accuracy"].idxmax()], self.tune 435 | ) 436 | print(Fore.GREEN + "Training Done [", "\u2713", "]\n") 437 | print(Fore.CYAN + "Results Below\n") 438 | display(self.result_df) 439 | print(Fore.GREEN + "\nCompleted LuciferML Run [", "\u2713", "]\n") 440 | if len(self.model_to_predict) > 1: 441 | self.best_classifier_path, self.scaler_path = self.save( 442 | best=True, model=self.best_classifier.model, scaler=self.sc 443 | ) 444 | print( 445 | Fore.CYAN 446 | + "Saved Best Model to {} and its scaler to {}".format( 447 | self.best_classifier_path, self.scaler_path 448 | ), 449 | "\n", 450 | ) 451 | self.end = time.time() 452 | final_time = self.end - self.start 453 | print(Fore.BLUE + "Time Elapsed : ", f"{final_time:.2f}", "seconds \n") 454 | return 455 | 456 | def __tuner(self, all_mode=False, single_mode=False): 457 | if not all_mode: 458 | print(Fore.YELLOW + "Tuning Started [*]\n") 459 | if not self.predictor == "nb": 460 | ( 461 | self.best_params, 462 | self.best_accuracy, 463 | self.best_trained_model, 464 | ) = luciferml_tuner( 465 | self.predictor, 466 | self.objective, 467 | self.n_trials, 468 | self.sampler, 469 | self.direction, 470 | self.X_train, 471 | self.y_train, 472 | self.cv_folds, 473 | self.random_state, 474 | self.metric, 475 | all_mode=all_mode, 476 | ) 477 | if self.predictor == "nb": 478 | self.best_params = "Not Applicable" 479 | self.best_accuracy = 0 480 | self.bestparams.append(self.best_params) 481 | self.bestacc.append(self.best_accuracy * 100) 482 | self.tuned_trained_model.append(self.best_trained_model) 483 | if not all_mode or single_mode: 484 | print(Fore.GREEN + "Tuning Done [", "\u2713", "]\n") 485 | 486 | def result(self): 487 | """[Makes a dictionary containing Classifier Name, K-Fold CV Accuracy, RMSE, Prediction set.] 488 | 489 | Returns: 490 | [dict]: [Dictionary containing : 491 | - "Classifier" - Classifier Name 492 | - "Accuracy" - KFold CV Accuracy 493 | - "YPred" - Array for Prediction set 494 | ] 495 | [dataframe] : [Dataset containing accuracy and best_params 496 | for all predictors only when predictor = 'all' is used 497 | ] 498 | """ 499 | if not self.pred_mode == "all": 500 | self.reg_result["Classifier"] = self.classifier_name 501 | self.reg_result["Accuracy"] = self.kfold_accuracy 502 | self.reg_result["YPred"] = self.y_pred 503 | 504 | return self.reg_result 505 | if self.pred_mode == "all": 506 | return self.result_df 507 | 508 | def predict(self, X_test): 509 | """[Takes test set and returns predictions for that test set] 510 | 511 | Args: 512 | X_test ([Array]): [Array Containing Test Set] 513 | 514 | Returns: 515 | [Array]: [Predicted set for given test set] 516 | """ 517 | if not self.pred_mode == "all": 518 | X_test = np.array(X_test) 519 | if X_test.ndim == 1: 520 | X_test = X_test.reshape(1, -1) 521 | 522 | y_test = self.classifier.predict(self.sc.transform(X_test)) 523 | 524 | return y_test 525 | if self.pred_mode == "all": 526 | raise TypeError("Predict is only applicable on single predictor") 527 | 528 | def save(self, path=None, best=False, **kwargs): 529 | """ 530 | Saves the model and its scaler to a file provided with a path. 531 | If no path is provided will create a directory named 532 | lucifer_ml_info/models/ and lucifer_ml_info/scaler/ in current working directory 533 | Args: 534 | path ([list]): [List containing path to save the model and scaler.] 535 | Example: path = ["model.pkl", "scaler.pkl"] 536 | 537 | Returns: 538 | Path to the saved model and its scaler. 539 | """ 540 | if not type(path) == list and path != None: 541 | raise TypeError("Path must be a list") 542 | if self.pred_mode == "all" and best == False: 543 | raise TypeError("Cannot save model for all predictors") 544 | dir_path_model = path[0] if path else "lucifer_ml_info/models/classifier/" 545 | dir_path_scaler = path[1] if path else "lucifer_ml_info/scalers/classifier/" 546 | model_name = classifiers[self.predictor].replace(" ", "_") 547 | if best: 548 | dir_path_model = "lucifer_ml_info/best/classifier/models/" 549 | dir_path_scaler = "lucifer_ml_info/best/classifier/scalers/" 550 | model_name = self.best_classifier.name.replace(" ", "_") 551 | os.makedirs(dir_path_model, exist_ok=True) 552 | os.makedirs(dir_path_scaler, exist_ok=True) 553 | timestamp = str(int(time.time())) 554 | path_model = dir_path_model + model_name + "_" + timestamp + ".pkl" 555 | path_scaler = ( 556 | dir_path_scaler + model_name + "_" + "Scaler" + "_" + timestamp + ".pkl" 557 | ) 558 | if ( 559 | not kwargs.get("model") 560 | and not kwargs.get("best") 561 | and not kwargs.get("scaler") 562 | ): 563 | dump(self.classifier, open(path_model, "wb")) 564 | dump(self.sc, open(path_scaler, "wb")) 565 | else: 566 | dump(kwargs.get("model"), open(path_model, "wb")) 567 | dump(kwargs.get("scaler"), open(path_scaler, "wb")) 568 | if not best: 569 | print("Model Saved at {} and Scaler at {}".format(path_model, path_scaler)) 570 | return path_model, path_scaler 571 | 572 | def __load(self, path=None): 573 | """ 574 | Loads model and scaler from the specified path 575 | Args: 576 | path ([list]): [List containing path to load the model and scaler.] 577 | Example: path = ["model.pkl", "scaler.pkl"] 578 | 579 | Returns: 580 | [Model] : [Loaded model] 581 | [Scaler] : [Loaded scaler] 582 | """ 583 | 584 | model_path = path[0] if path[0] else None 585 | scaler_path = path[1] if path[1] else None 586 | if not ".pkl" in model_path and not model_path == None: 587 | raise TypeError( 588 | "[Error] Model Filetype not supported. Please use .pkl type " 589 | ) 590 | if not ".pkl" in scaler_path and not scaler_path == None: 591 | raise TypeError( 592 | "[Error] Scaler Filetype not supported. Please use .pkl type " 593 | ) 594 | if model_path != None and scaler_path != None: 595 | model = load(open(model_path, "rb")) 596 | scaler = load(open(scaler_path, "rb")) 597 | print( 598 | Fore.GREEN 599 | + "[Info] Model and Scaler Loaded from {} and {}".format( 600 | model_path, scaler_path 601 | ) 602 | ) 603 | return model, scaler 604 | elif model_path != None and scaler_path == None: 605 | model = load(open(model_path, "rb")) 606 | print(Fore.GREEN + "[Info] Model Loaded from {}".format(model_path)) 607 | return model 608 | elif model_path == None and scaler_path != None: 609 | scaler = load(open(scaler_path, "rb")) 610 | print(Fore.GREEN + "[Info] Scaler Loaded from {}".format(scaler_path)) 611 | return scaler 612 | else: 613 | raise ValueError("No path specified.Please provide actual path\n") 614 | 615 | def imp_features(self, extensive=False, *args, **kwargs): 616 | """ 617 | Returns the importance features of the dataset 618 | 619 | Args: 620 | 621 | extensive (bool): [If True shows the importance of all features exitensively and will take more time] [default = False] 622 | **args: [Additional arguments] 623 | **kwargs: [Additional keyword arguments] 624 | """ 625 | if self.original_predictor == "all": 626 | raise TypeError( 627 | "[Error] This method is only applicable on single predictor" 628 | ) 629 | if not extensive: 630 | self.preprocesspermutational_feature_imp( 631 | self.features, self.X_train, self.y_train, model=self.classifier 632 | ) 633 | if extensive: 634 | self.preprocessshap_feature_imp( 635 | self.features, self.X_train, model=self.classifier, *args, **kwargs 636 | ) 637 | -------------------------------------------------------------------------------- /luciferml/supervised/regression.py: -------------------------------------------------------------------------------- 1 | import copy 2 | import os 3 | import time 4 | import warnings 5 | from pickle import dump, load 6 | 7 | import numpy as np 8 | import optuna 9 | import pandas as pd 10 | from colorama import Fore 11 | from IPython.display import display 12 | from luciferml.supervised.utils.best import Best 13 | from luciferml.supervised.utils.configs import * 14 | from luciferml.supervised.utils.predictors import regression_predictor 15 | from luciferml.supervised.utils.preprocesser import PreProcesser 16 | from luciferml.supervised.utils.tuner.luciferml_tuner import luciferml_tuner 17 | from luciferml.supervised.utils.validator import * 18 | from optuna.samplers._tpe.sampler import TPESampler 19 | from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score 20 | 21 | 22 | class Regression: 23 | def __init__( 24 | self, 25 | predictor=["lin"], 26 | params={}, 27 | tune=False, 28 | test_size=0.2, 29 | cv_folds=10, 30 | random_state=42, 31 | pca_kernel="linear", 32 | n_components_lda=1, 33 | lda="n", 34 | pca="n", 35 | n_components_pca=2, 36 | loss="mean_squared_error", 37 | validation_split=0.20, 38 | smote="n", 39 | k_neighbors=1, 40 | verbose=False, 41 | exclude_models=[], 42 | path=None, 43 | optuna_sampler=TPESampler(multivariate=True), 44 | optuna_direction="maximize", 45 | optuna_n_trials=100, 46 | optuna_metric="r2", 47 | ): 48 | """ 49 | Encodes Categorical Data then Applies SMOTE , Splits the features and labels in training and validation sets with test_size = .2\n 50 | scales X_train, X_val using StandardScaler.\n 51 | Fits every model on training set and predicts results,Finds R2 Score and mean square error\n 52 | finds accuracy of model applies K-Fold Cross Validation\n 53 | and stores its accuracies in a dictionary containing Model name as Key and accuracies as values and returns it\n 54 | Applies HyperParam Tuning and gives best params and accuracy.\n 55 | 56 | Parameters: 57 | 58 | features : array 59 | features array 60 | lables : array 61 | labels array 62 | predictor : list 63 | Predicting models to be used 64 | Default ['lin'] - 'Linear Regression'\n 65 | Available Predictors: 66 | lin - Linear Regression\n 67 | sgd - Stochastic Gradient Descent Regressor\n 68 | elas - Elastic Net Regressor\n 69 | krr - Kernel Ridge Regressor\n 70 | br - Bayesian Ridge Regressor\n 71 | svr - Support Vector Regressor\n 72 | knr - K-Nearest Regressor\n 73 | dt - Decision Trees\n 74 | rfr - Random Forest Regressor\n 75 | gbr - Gradient Boost Regressor\n 76 | ada - AdaBoost Regressor,\n 77 | bag - Bagging Regressor,\n 78 | extr - Extra Trees Regressor,\n 79 | lgbm - LightGB Regressor\n 80 | xgb - XGBoost Regressor\n 81 | cat - Catboost Regressor\n 82 | ann - Multi Layer Perceptron Regressor\n 83 | all - Applies all above regressors\n 84 | params : dict 85 | contains parameters for model 86 | tune : boolean 87 | when True Applies GridSearch CrossValidation 88 | Default is False 89 | test_size: float or int, default=.2 90 | If float, should be between 0.0 and 1.0 and represent 91 | the proportion of the dataset to include in 92 | the test split. 93 | If int, represents the absolute number of test samples. 94 | cv_folds : int 95 | No. of cross validation folds. Default = 10 96 | pca : str 97 | if 'y' will apply PCA on Train and Validation set. Default = 'n' 98 | lda : str 99 | if 'y' will apply LDA on Train and Validation set. Default = 'n' 100 | pca_kernel : str 101 | Kernel to be use in PCA. Default = 'linear' 102 | n_components_lda : int 103 | No. of components for LDA. Default = 1 104 | n_components_pca : int 105 | No. of components for PCA. Default = 2 106 | loss : str 107 | loss method for ann. Default = 'mean_squared_error' 108 | smote : str, 109 | Whether to apply SMOTE. Default = 'y' 110 | k_neighbors : int 111 | No. of neighbours for SMOTE. Default = 1 112 | verbose : boolean 113 | Verbosity of models. Default = False 114 | exclude_models : list 115 | List of models to be excluded when using predictor = 'all' . Default = [] 116 | path : list 117 | List containing path to saved model and scaler. Default = None 118 | Example: [model.pkl, scaler.pkl] 119 | random_state : int 120 | Random random_state for reproducibility. Default = 42 121 | optuna_sampler : Function 122 | Sampler to be used in optuna. Default = TPESampler() 123 | optuna_direction : str 124 | Direction of optimization. Default = 'maximize' 125 | Available Directions: 126 | maximize : Maximize 127 | minimize : Minimize 128 | optuna_n_trials : int 129 | No. of trials for optuna. Default = 100 130 | optuna_metric: str 131 | Metric to be used in optuna. Default = 'r2' 132 | Returns: 133 | 134 | Dict Containing Name of Regressor, Its K-Fold Cross Validated Accuracy, RMSE, Prediction set 135 | 136 | Dataframe containing all the models and their accuracies when predictor is 'all' 137 | 138 | Example: 139 | 140 | from luciferml.supervised.regression import Regression 141 | 142 | dataset = pd.read_excel('examples\Folds5x2_pp.xlsx') 143 | 144 | X = dataset.iloc[:, :-1] 145 | 146 | y = dataset.iloc[:, -1] 147 | 148 | regressor = Regression(predictor = 'lin') 149 | 150 | regressor.fit(X, y) 151 | 152 | result = regressor.result() 153 | 154 | """ 155 | self.preprocess = PreProcesser() 156 | if type(predictor) == list: 157 | if not "all" in predictor: 158 | self.predictor = predictor[0] if len(predictor) == 1 else predictor 159 | else: 160 | self.predictor = predictor 161 | else: 162 | self.predictor = predictor 163 | bool_pred, pred = pred_check(predictor, pred_type="regression") 164 | if not bool_pred: 165 | raise ValueError(unsupported_pred_warning.format(pred)) 166 | self.original_predictor = predictor 167 | self.params = params 168 | self.tune = tune 169 | self.test_size = test_size 170 | self.cv_folds = cv_folds 171 | self.random_state = random_state 172 | self.pca_kernel = pca_kernel 173 | self.n_components_lda = n_components_lda 174 | self.lda = lda 175 | self.pca = pca 176 | self.n_components_pca = n_components_pca 177 | self.loss = loss 178 | self.validation_split = validation_split 179 | self.rerun = False 180 | self.smote = smote 181 | self.k_neighbors = k_neighbors 182 | self.verbose = verbose 183 | self.exclude_models = exclude_models 184 | self.sampler = optuna_sampler 185 | self.direction = optuna_direction 186 | self.n_trials = optuna_n_trials 187 | self.metric = optuna_metric 188 | 189 | self.accuracy_scores = {} 190 | self.reg_result = {} 191 | self.rm_squared_error = 0 192 | self.accuracy = 0 193 | self.y_pred = [] 194 | self.kfold_accuracy = 0 195 | self.regressor_name = "" 196 | self.sc = 0 197 | 198 | self.kfoldacc = [] 199 | self.acc = [] 200 | self.mae = [] 201 | self.rmse = [] 202 | self.bestacc = [] 203 | self.bestparams = [] 204 | self.regressor_model = [] 205 | self.tuned_trained_model = [] 206 | self.best_regressor_path = "" 207 | self.scaler_path = "" 208 | self.result_df = pd.DataFrame(index=None) 209 | self.regressors = copy.deepcopy(regressors) 210 | for i in self.exclude_models: 211 | self.regressors.pop(i) 212 | self.best_regressor = "First Run the Predictor in All mode" 213 | self.objective = None 214 | self.pred_mode = "" 215 | self.model_to_predict = None 216 | 217 | if path != None: 218 | try: 219 | self.regressor, self.sc = self.__load(path) 220 | except Exception as e: 221 | print(Fore.RED + e) 222 | print(Fore.RED + "Model not found") 223 | if not self.verbose: 224 | optuna.logging.set_verbosity(optuna.logging.WARNING) 225 | 226 | def fit(self, features, labels): 227 | """[Takes Features and Labels and Encodes Categorical Data then Applies SMOTE , Splits the features and labels in training and validation sets with test_size = .2 228 | scales X_train, X_val using StandardScaler. 229 | Fits model on training set and predicts results, Finds R2 Score and mean square error 230 | finds accuracy of model applies K-Fold Cross Validation 231 | and stores its accuracies in a dictionary containing Model name as Key and accuracies as values and returns it 232 | Applies GridSearch Cross Validation and gives best params out from param list.] 233 | 234 | Args: 235 | 236 | features ([Pandas DataFrame]): [DataFrame containing Features] 237 | labels ([Pandas DataFrame]): [DataFrame containing Labels] 238 | """ 239 | 240 | self.features = features 241 | self.labels = labels 242 | 243 | # Time Function --------------------------------------------------------------------- 244 | 245 | self.start = time.time() 246 | print(Fore.MAGENTA + intro, "\n") 247 | print(Fore.GREEN + "Started LuciferML [", "\u2713", "]\n") 248 | if not self.rerun: 249 | # CHECKUP --------------------------------------------------------------------- 250 | if not isinstance(self.features, pd.DataFrame) and not isinstance( 251 | self.labels, pd.Series 252 | ): 253 | print( 254 | Fore.RED 255 | + "TypeError: This Function take features as Pandas Dataframe and labels as Pandas Series. Please check your implementation.\n" 256 | ) 257 | end = time.time() 258 | print(self.end - self.start) 259 | return 260 | print(Fore.YELLOW + "Preprocessing Started [*]\n") 261 | 262 | self.features, self.labels = self.preprocess.encoder( 263 | self.features, self.labels 264 | ) 265 | self.features, self.labels = sparse_check(self.features, self.labels) 266 | ( 267 | self.X_train, 268 | self.X_val, 269 | self.y_train, 270 | self.y_val, 271 | self.sc, 272 | ) = self.preprocess.data_preprocess( 273 | self.features, 274 | self.labels, 275 | self.test_size, 276 | self.random_state, 277 | self.smote, 278 | self.k_neighbors, 279 | ) 280 | self.X_train, self.X_val = self.preprocess.dimensionality_reduction( 281 | self.lda, 282 | self.pca, 283 | self.X_train, 284 | self.X_val, 285 | self.y_train, 286 | self.n_components_lda, 287 | self.n_components_pca, 288 | self.pca_kernel, 289 | self.start, 290 | ) 291 | 292 | print(Fore.GREEN + "Preprocessing Done [", "\u2713", "]\n") 293 | 294 | if self.original_predictor == "all" or type(self.predictor) == list: 295 | if 'all' in self.predictor and type(self.predictor)==list: 296 | self.predictor.remove('all') 297 | self.model_to_predict = ( 298 | self.predictor if len(self.predictor) > 1 and type(self.predictor) == list else self.regressors 299 | ) 300 | self.result_df["Name"] = ( 301 | list(self.regressors[i] for i in self.predictor) 302 | if type(self.predictor) == list and len(self.predictor) > 1 303 | else list(self.regressors.values()) 304 | ) 305 | self.pred_mode = "all" if type(self.predictor) == list and len( 306 | self.predictor) > 1 else "single" 307 | self.__fitall() 308 | return 309 | self.regressor, self.objective = regression_predictor( 310 | self.predictor, 311 | self.params, 312 | self.X_train, 313 | self.y_train, 314 | self.cv_folds, 315 | self.random_state, 316 | self.metric, 317 | verbose=self.verbose, 318 | ) 319 | try: 320 | self.regressor.fit(self.X_train, self.y_train) 321 | except Exception as error: 322 | print(Fore.RED + "Regressor Build Failed with error: ", error, "\n") 323 | finally: 324 | print(Fore.GREEN + "Model Trained Successfully [", "\u2713", "]\n") 325 | 326 | try: 327 | print(Fore.YELLOW + "Evaluating Model Performance [*]\n") 328 | self.y_pred = self.regressor.predict(self.X_val) 329 | self.accuracy = r2_score(self.y_val, self.y_pred) 330 | self.m_absolute_error = mean_absolute_error(self.y_val, self.y_pred) 331 | self.rm_squared_error = mean_squared_error( 332 | self.y_val, self.y_pred, squared=False 333 | ) 334 | print( 335 | Fore.CYAN 336 | + " Validation R2 Score is {:.2f} %".format(self.accuracy * 100) 337 | ) 338 | print( 339 | Fore.CYAN + " Validation Mean Absolute Error is :", 340 | self.m_absolute_error, 341 | ) 342 | print( 343 | Fore.CYAN + " Validation Root Mean Squared Error is :", 344 | self.rm_squared_error, 345 | ) 346 | self.regressor_name, self.kfold_accuracy = kfold( 347 | self.regressor, 348 | self.predictor, 349 | self.X_train, 350 | self.y_train, 351 | self.cv_folds, 352 | isReg=True, 353 | ) 354 | except Exception as error: 355 | print(Fore.RED + "Model Evaluation Failed with error: ", error, "\n") 356 | finally: 357 | print(Fore.GREEN + "Model Evaluation Completed [", "\u2713", "]\n") 358 | 359 | if not self.predictor == "nb" and self.tune: 360 | self.__tuner() 361 | 362 | print(Fore.GREEN + "Completed LuciferML Run [", "\u2713", "]\n") 363 | self.end = time.time() 364 | final_time = self.end - self.start 365 | print(Fore.BLUE + "Time Elapsed : ", f"{final_time:.2f}", "seconds \n") 366 | 367 | def __fitall(self): 368 | print(Fore.YELLOW + "Training LuciferML [*]\n") 369 | if self.params != {}: 370 | warnings.warn(params_use_warning, UserWarning) 371 | self.params = {} 372 | for _, self.predictor in enumerate(self.model_to_predict): 373 | if not self.predictor in self.exclude_models: 374 | (self.regressor, self.objective,) = regression_predictor( 375 | self.predictor, 376 | self.params, 377 | self.X_train, 378 | self.y_train, 379 | self.cv_folds, 380 | self.random_state, 381 | self.metric, 382 | mode="multi", 383 | verbose=self.verbose, 384 | ) 385 | try: 386 | self.regressor.fit(self.X_train, self.y_train) 387 | except Exception as error: 388 | print( 389 | Fore.RED + regressors[self.predictor], 390 | "Model Train Failed with error: ", 391 | error, 392 | "\n", 393 | ) 394 | try: 395 | self.y_pred = self.regressor.predict(self.X_val) 396 | self.accuracy = r2_score(self.y_val, self.y_pred) 397 | self.m_absolute_error = mean_absolute_error(self.y_val, self.y_pred) 398 | self.rm_squared_error = mean_squared_error( 399 | self.y_val, self.y_pred, squared=False 400 | ) 401 | self.acc.append(self.accuracy * 100) 402 | self.rmse.append(self.rm_squared_error) 403 | self.mae.append(self.m_absolute_error) 404 | self.regressor_name, self.kfold_accuracy = kfold( 405 | self.regressor, 406 | self.predictor, 407 | self.X_train, 408 | self.y_train, 409 | self.cv_folds, 410 | all_mode=True, 411 | isReg=True, 412 | ) 413 | self.kfoldacc.append(self.kfold_accuracy) 414 | self.regressor_model.append(self.regressor) 415 | except Exception as error: 416 | print( 417 | Fore.RED + regressors[self.predictor], 418 | "Evaluation Failed with error: ", 419 | error, 420 | "\n", 421 | ) 422 | 423 | if self.tune: 424 | self.__tuner(all_mode=True, single_mode=False) 425 | if self.predictor == "nb": 426 | self.best_params = "" 427 | self.best_accuracy = self.kfold_accuracy 428 | self.result_df["R2 Score"] = self.acc 429 | self.result_df["Mean Absolute Error"] = self.mae 430 | self.result_df["Root Mean Squared Error"] = self.rmse 431 | self.result_df["KFold Accuracy"] = self.kfoldacc 432 | self.result_df["Model"] = self.regressor_model 433 | 434 | if self.tune: 435 | self.result_df["Best Parameters"] = self.bestparams 436 | self.result_df["Best Accuracy"] = self.bestacc 437 | self.result_df["Trained Model"] = self.tuned_trained_model 438 | self.best_regressor = Best( 439 | self.result_df.loc[self.result_df["Best Accuracy"].idxmax()], 440 | self.tune, 441 | isReg=True, 442 | ) 443 | else: 444 | self.best_regressor = Best( 445 | self.result_df.loc[self.result_df["KFold Accuracy"].idxmax()], 446 | self.tune, 447 | isReg=True, 448 | ) 449 | print(Fore.GREEN + "Training Done [", "\u2713", "]\n") 450 | print(Fore.CYAN + "Results Below\n") 451 | display(self.result_df) 452 | print(Fore.GREEN + "\nCompleted LuciferML Run [", "\u2713", "]\n") 453 | if len(self.model_to_predict) > 1: 454 | self.best_regressor_path, self.scaler_path = self.save( 455 | best=True, model=self.best_regressor.model, scaler=self.sc 456 | ) 457 | print( 458 | Fore.CYAN 459 | + "Saved Best Model to {} and its scaler to {}".format( 460 | self.best_regressor_path, self.scaler_path 461 | ), 462 | "\n", 463 | ) 464 | self.end = time.time() 465 | final_time = self.end - self.start 466 | print(Fore.BLUE + "Time Elapsed : ", f"{final_time:.2f}", "seconds \n") 467 | return 468 | 469 | def __tuner(self, all_mode=False, single_mode=True): 470 | if not all_mode or single_mode: 471 | print(Fore.YELLOW + "Tuning Started [*]\n") 472 | if not self.predictor == "nb": 473 | ( 474 | self.best_params, 475 | self.best_accuracy, 476 | self.best_trained_model, 477 | ) = luciferml_tuner( 478 | self.predictor, 479 | self.objective, 480 | self.n_trials, 481 | self.sampler, 482 | self.direction, 483 | self.X_train, 484 | self.y_train, 485 | self.cv_folds, 486 | self.random_state, 487 | self.metric, 488 | all_mode=all_mode, 489 | isReg=True, 490 | ) 491 | if self.predictor == "nb": 492 | self.best_params = "Not Applicable" 493 | self.best_accuracy = 0 494 | self.bestparams.append(self.best_params) 495 | self.bestacc.append(self.best_accuracy * 100) 496 | self.tuned_trained_model.append(self.best_trained_model) 497 | if not all_mode or single_mode: 498 | print(Fore.GREEN + "Tuning Done [", "\u2713", "]\n") 499 | 500 | def result(self): 501 | """[Makes a dictionary containing Regressor Name, K-Fold CV Accuracy, RMSE, Prediction set.] 502 | 503 | Returns: 504 | 505 | [dict]: [Dictionary containing : 506 | - "Regressor" - Regressor Name 507 | - "Accuracy" - KFold CV Accuracy 508 | - "RMSE" - Root Mean Square 509 | - "YPred" - Array for Prediction set 510 | ] 511 | [dataframe] : [Dataset containing accuracy and best_params 512 | for all predictors only when predictor = 'all' is used 513 | ] 514 | """ 515 | if not self.pred_mode == "all": 516 | self.reg_result["Regressor"] = self.regressor_name 517 | self.reg_result["Accuracy"] = self.kfold_accuracy 518 | self.reg_result["RMSE"] = self.rm_squared_error 519 | self.reg_result["YPred"] = self.y_pred 520 | 521 | return self.reg_result 522 | if self.pred_mode == "all": 523 | return self.result_df 524 | 525 | def predict(self, X_test): 526 | """[Takes test set and returns predictions for that test set] 527 | 528 | Args: 529 | X_test ([Array]): [Array Containing Test Set] 530 | 531 | Returns: 532 | [Array]: [Predicted set for given test set] 533 | """ 534 | if not self.pred_mode == "all": 535 | X_test = np.array(X_test) 536 | if X_test.ndim == 1: 537 | X_test = X_test.reshape(1, -1) 538 | 539 | y_test = self.regressor.predict(self.sc.transform(X_test)) 540 | 541 | return y_test 542 | if self.pred_mode == "all": 543 | raise TypeError("Predict is only applicable on single predictor") 544 | 545 | def save(self, path=None, best=False, **kwargs): 546 | """ 547 | Saves the model and its scaler to a file provided with a path. 548 | If no path is provided will create a directory named 549 | lucifer_ml_info/models/ and lucifer_ml_info/scaler/ in current working directory 550 | 551 | Args: 552 | 553 | path ([list]): [List containing path to save the model and scaler.] 554 | Example: path = ["model.pkl", "scaler.pkl"] 555 | 556 | Returns: 557 | 558 | Path to the saved model and its scaler. 559 | """ 560 | if not type(path) == list and path != None: 561 | raise TypeError("Path must be a list") 562 | if self.pred_mode == "all" and best == False: 563 | raise TypeError("Cannot save model for all predictors") 564 | dir_path_model = path[0] if path else "lucifer_ml_info/models/regression/" 565 | dir_path_scaler = path[1] if path else "lucifer_ml_info/scalers/regression/" 566 | model_name = regressors[self.predictor].replace(" ", "_") 567 | if best: 568 | dir_path_model = "lucifer_ml_info/best/regression/models/" 569 | dir_path_scaler = "lucifer_ml_info/best/regression/scalers/" 570 | model_name = self.best_regressor.name.replace(" ", "_") 571 | os.makedirs(dir_path_model, exist_ok=True) 572 | os.makedirs(dir_path_scaler, exist_ok=True) 573 | timestamp = str(int(time.time())) 574 | path_model = dir_path_model + model_name + "_" + timestamp + ".pkl" 575 | path_scaler = ( 576 | dir_path_scaler + model_name + "_" + "Scaler" + "_" + timestamp + ".pkl" 577 | ) 578 | if ( 579 | not kwargs.get("model") 580 | and not kwargs.get("best") 581 | and not kwargs.get("scaler") 582 | ): 583 | dump(self.regressor, open(path_model, "wb")) 584 | dump(self.sc, open(path_scaler, "wb")) 585 | else: 586 | dump(kwargs.get("model"), open(path_model, "wb")) 587 | dump(kwargs.get("scaler"), open(path_scaler, "wb")) 588 | if not best: 589 | print("Model Saved at {} and Scaler at {}".format(path_model, path_scaler)) 590 | return path_model, path_scaler 591 | 592 | def __load(self, path=None): 593 | """ 594 | Loads model and scaler from the specified path 595 | 596 | Args: 597 | 598 | path ([list]): [List containing path to load the model and scaler.] 599 | Example: path = ["model.pkl", "scaler.pkl"] 600 | 601 | Returns: 602 | [Model] : [Loaded model] 603 | [Scaler] : [Loaded scaler] 604 | """ 605 | model_path = path[0] if path[0] else None 606 | scaler_path = path[1] if path[1] else None 607 | if not ".pkl" in model_path and not model_path == None: 608 | raise TypeError( 609 | "[Error] Model Filetype not supported. Please use .pkl type " 610 | ) 611 | if not ".pkl" in scaler_path and not scaler_path == None: 612 | raise TypeError( 613 | "[Error] Scaler Filetype not supported. Please use .pkl type " 614 | ) 615 | if model_path != None and scaler_path != None: 616 | model = load(open(model_path, "rb")) 617 | scaler = load(open(scaler_path, "rb")) 618 | print( 619 | Fore.GREEN 620 | + "[Info] Model and Scaler Loaded from {} and {}".format( 621 | model_path, scaler_path 622 | ) 623 | ) 624 | return model, scaler 625 | elif model_path != None and scaler_path == None: 626 | model = load(open(model_path, "rb")) 627 | print(Fore.GREEN + "[Info] Model Loaded from {}".format(model_path)) 628 | return model 629 | elif model_path == None and scaler_path != None: 630 | scaler = load(open(scaler_path, "rb")) 631 | print(Fore.GREEN + "[Info] Scaler Loaded from {}".format(scaler_path)) 632 | return scaler 633 | else: 634 | raise ValueError("No path specified.Please provide actual path\n") 635 | 636 | def imp_features(self, extensive=False, *args, **kwargs): 637 | """ 638 | Returns the importance features of the dataset 639 | 640 | Args: 641 | 642 | extensive (bool): [If True shows the importance of all features exitensively and will take more time] [default = False] 643 | **args: [Additional arguments] 644 | **kwargs: [Additional keyword arguments] 645 | """ 646 | if self.original_predictor == "all": 647 | raise TypeError( 648 | "[Error] This method is only applicable on single predictor" 649 | ) 650 | if not extensive: 651 | self.preprocess.permutational_feature_imp( 652 | self.features, self.X_train, self.y_train, model=self.regressor 653 | ) 654 | if extensive: 655 | self.preprocess.shap_feature_imp( 656 | self.features, self.X_train, model=self.regressor, *args, **kwargs 657 | ) 658 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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No Surrender of Others' Freedom. 541 | 542 | If conditions are imposed on you (whether by court order, agreement or 543 | otherwise) that contradict the conditions of this License, they do not 544 | excuse you from the conditions of this License. If you cannot convey a 545 | covered work so as to satisfy simultaneously your obligations under this 546 | License and any other pertinent obligations, then as a consequence you may 547 | not convey it at all. For example, if you agree to terms that obligate you 548 | to collect a royalty for further conveying from those to whom you convey 549 | the Program, the only way you could satisfy both those terms and this 550 | License would be to refrain entirely from conveying the Program. 551 | 552 | 13. Use with the GNU Affero General Public License. 553 | 554 | Notwithstanding any other provision of this License, you have 555 | permission to link or combine any covered work with a work licensed 556 | under version 3 of the GNU Affero General Public License into a single 557 | combined work, and to convey the resulting work. The terms of this 558 | License will continue to apply to the part which is the covered work, 559 | but the special requirements of the GNU Affero General Public License, 560 | section 13, concerning interaction through a network will apply to the 561 | combination as such. 562 | 563 | 14. Revised Versions of this License. 564 | 565 | The Free Software Foundation may publish revised and/or new versions of 566 | the GNU General Public License from time to time. Such new versions will 567 | be similar in spirit to the present version, but may differ in detail to 568 | address new problems or concerns. 569 | 570 | Each version is given a distinguishing version number. If the 571 | Program specifies that a certain numbered version of the GNU General 572 | Public License "or any later version" applies to it, you have the 573 | option of following the terms and conditions either of that numbered 574 | version or of any later version published by the Free Software 575 | Foundation. If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | --------------------------------------------------------------------------------