Metadata-Version: 2.1
Name: automlkiller
Version: 0.0.29
Summary: Auto machine learning, deep learning library in Python.
Home-page: https://github.com/toandaominh1997/automlkiller
Author: toandaominh1997
Author-email: toandaominh1997@gmail.com
License: MIT
Description: # automlkiller
        
        Automated Machine Learning
        
        ## Usage
        
        1. Step 1: Load data and Preprocessing
        
        ```python
        model = AUTOML(X, y,
                        cleancolumnname = {},
                        datatype = {"categorical_columns": [], "numeric_columns":[], "time_columns":[]},
                        simpleimputer =  {"numeric_strategy": "mean", "categorical_strategy": "most_frequent"},
                        zeronearzerovariance = {"threshold_first" : 0.1, "threshold_second": 20},
                        categoryencoder = {"cols": [], "method": "targetencoder"},
                        groupsimilarfeature = {"group_name": [], "list_of_group_feature": []},
                        binning = {"features_to_discretize": []},
                        maketimefeature = {"time_columns": [], "list_of_feature": ['month',  'dayofweek', 'weekday', 'is_month_end', 'is_month_start', 'hour']},
                        scaling = {"method": "zscore", "numeric_columns": []},
                        # outlier = {"methods": ["pca", "iforest", "knn"], "contamination": 0.2},
                        removeperfectmulticollinearity = {},
                        makenonlinearfeature = {"polynomial_columns": [], "degree": 2, "interaction_only": False, "include_bias": False, "other_nonlinear_feature": ["sin", "cos", "tan"]},
                        # rfe = {"estimator": None, "step": 1, "min_features_to_select": 3, "cv": 3},
                        # reducedimension = {"method": "pca_linear", "n_components": 0.99}
                        )
        ```
        
        2. Step 2: Training Model
        
        
        ```python
        model.create_model(estimator=['classification-lgbmclassifier',
                                    # 'classification-kneighborsclassifier',
                                    'classification-logisticregression',
                                    # 'classification-xgbclassifier',
                                    # 'classification-catboostclassifier',
                                    # 'classification-randomforestclassifier'
                                    ],
                        verbose = True,
                        n_jobs = 2,
                        cv = 2,
                        estimator_params = {
                                    'classification-lgbmclassifier': {'n_jobs': 8},
                        },
                        scoring = ['accuracy', 'roc_auc', 'recall', 'precision', 'f1']
                    )
        model.ensemble_model(scoring = ['accuracy'])
        model.voting_model(scoring = ['accuracy'])
        model.stacking_model(scoring = ['accuracy'])
        ```
        3. Step 3: Model Performance
        ```python
        model.report_tensorboard()
        ```
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
