Metadata-Version: 2.1
Name: automl-tools
Version: 0.1.6
Summary: automl_tools
Home-page: https://github.com/jonaqp/automl_tools/
Author: Jonathan Quiza
Author-email: jony327@gmail.com
License: UNKNOWN
Download-URL: https://github.com/jonaqp/automl_tools/archive/main.zip
Description: # Automl_tools: automl binary classification
        
        
        [![Github License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
        [![Updates](https://pyup.io/repos/github/woctezuma/google-colab-transfer/shield.svg)](pyup)
        [![Python 3](https://pyup.io/repos/github/woctezuma/google-colab-transfer/python-3-shield.svg)](pyup)
        [![Code coverage](https://codecov.io/gh/woctezuma/google-colab-transfer/branch/master/graph/badge.svg)](codecov)
        
        
        
        
        Automl_tools is a Python library that implements Gradient Boosting
        ## Installation
        
        The code is packaged for PyPI, so that the installation consists in running:
        ```sh
        pip install automl-tools
        ```
        
        ## Colab
        
        [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/10DFkSmzMO1GqLX-mgBWfDjS9OIVmEy6O?usp=sharing)
        
        
        ## Usage
        
        Probabilistic binary example on the Boston housing dataset:
        
        ```python
        import pandas as pd
        from automl_tools import automl_run
        
        train = pd.read_csv("https://raw.githubusercontent.com/jonaqp/automl_tools/main/automl_tools/examples/train.csv?token=AAN2ZBDWF77QITK4ARSFIFDABUGAU")
        test = pd.read_csv("https://raw.githubusercontent.com/jonaqp/automl_tools/main/automl_tools/examples/test.csv?token=AAN2ZBD6TMUC5XSGRTJNVPDABUGCO")
        
        automl_run(train=train,
                   test=test,
                   id_col=None, 
                   target_col="Survived",
                   imp_num="knn",
                   imp_cat="knn",
                   processing="binding",
                   mutual_information=False,
                   correlation_drop=False,
                   model_feature_selection=None,
                   model_run="LR",
                   augmentation=True,
                   Stratified=True,
                   cv=5)
        
        
        
        
        
        
        
        ```
        
        ## Parameter
        ```sh
        imp_num : "gaussian", "arbitrary", "median", "mean", "random", "knn"
        imp_cat : "frequent", "constant", "rare", "knn"
        processing:  "woe", "binding" 
        ```
        
        ## Support Binary
        ```sh
        model_feature_selection: 
            default: ["LR", "RF", "LGB"]
                LR  : LogisticRegression
                RF  : RandomForestClassifier
                SVM : SVC
                LS  : LASSO
                RD  : RIDGE
                NET : Elasticnet
                DT  : DecisionTreeClassifier
                ET  : ExtraTreesClassifier
                GB  : GradientBoostingClassifier
                AB  : AdaBoostClassifier
                XGB  : XGBClassifier
                LGB  : LGBMClassifier
                CTB  : CatBoostClassifier
                NGB  : NGBClassifier
        
        model_run:
            default: "LR"
                LR  : LogisticRegression
                RF  : RandomForestClassifier
                SVM : SVC
                LS  : LASSO
                RD  : RIDGE
                NET : Elasticnet
                DT  : DecisionTreeClassifier
                ET  : ExtraTreesClassifier
                GB  : GradientBoostingClassifier
                AB  : AdaBoostClassifier
                XGB  : XGBClassifier
                LGB  : LGBMClassifier
                CTB  : CatBoostClassifier
                NGB  : NGBClassifier
        ```
        
        ## License
        
        [Apache License 2.0](https://www.dropbox.com/s/8t6xtgk06o3ij61/LICENSE?dl=0).
        
        
        ## New features v1.0
         * multi_class
         * regression
         * integrations GCP deploy model CI/CD
         * integrations AWS deploy model CI/CD
         
        ## BugFix
         - 0.1.5
           - fix imputer
           - fix space hyperparameter
           - update catboost test
           
         - 0.1.4
           - add parameter cv
           - add confusion Matrix
           - add comments readme.txt
           
         - 0.1.3
           - add parameter id_col
           - add comments readme.txt
        
        
        
        ## Reference
        
         - Jonathan Quiza [github](https://github.com/jonaqp).
         - Jonathan Quiza [RumiMLSpark](http://rumi-ml.herokuapp.com/).
         - Jonathan Quiza [linkedin](https://www.linkedin.com/in/jonaqp/).
        
        
Keywords: automl,binary
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
