Metadata-Version: 2.1
Name: kowalsky
Version: 0.0.35
Summary: A small package for all useful ML things
Home-page: https://github.com/NikitaGordia/Kowalsky
Author: Nikita Gordia
Author-email: nikita.gordia@gmail.com
License: UNKNOWN
Description: # Kowalsky, analysis!
        
        A simple package for handful ML things and more.
        
        ## What's new? [v0.0.35]
        * add ```feature``` package with two types of analysis + support для остальных функций
           * Recursive Feature Elimination
           * Sequential Feature Selection
        * improve optimize:
           * ```EarlyStopping``` mechanism
           * optimization graph
           * multitasks with ```n_jobs=-1```
        * add ```logs``` package    
        
        ## What's inside?
        
        1. ```analysis``` - method for evaluation of specified model with
           given dataframe. With ```export_test_set=True``` it exports
           ready for submission predictions.
           
        2. df - module for working with dataframe:
            * ```corr``` - sort all correlated features.
            * ```handle_outliers``` - fill or drop columns with outliers.
            * ```log_transform``` - transform columns with log function.
            * ```group_by_mean``` - make additional columns with aggregated mean
            * ```group_by_max``` - make additional columns with aggregated max
            * ```group_by_min``` - make additional columns with aggregated min
            * ```apply_with_progress``` - apply heavy function for each row of dataset.
            * ```scale``` - scale columns with Standard of MinMax scalers
            
        3. kaggle:
            * ```submit``` - make submit-file for kaggle based on sample
            
        4. metrics:
            *  ```rmse``` - RMSE scorer
            *  ```rmsle``` - RMSLE scorer
            
        5. optuna - handful methods for working with optuna:
            * ```optimize``` - optimize model with given dataframe
            * ```optimize_super_learner``` - optimize super learner configuration
           with given set of models and set of heads (meta_model)
              
        6. colab:
            *  ```csv``` - read csv file located at Google Drive with
               specified id
            *  ```path``` - get path to Google Drive file
        
        7. feature:
            *  ```rfe_analysis``` - Recursive Feature Elimination analysis
            *  ```sfs_analysis``` - Sequential Feature Selection analysis
            
        8. logs:
            *  ```profile_memory``` - logs all heavy variables
            *  ```make_pretty_pyplot``` - makes pyplot look better :)
           
        ## Example:
        ```
        !pip install kowalsky --upgrade
        from kowalsky.optuna import optimize
        optimize('RFR',
                 path='../input/project/feed.csv',
                 scorer='acc',
                 y_label='y_label',
                 trials=3000)
        ```
        
        ## Avaliable models:
        #### Gradient Boosts
        ```
            'XGBR': XGBRegressor
            'XGBC': XGBClassifier
            'LGBR': LGBMRegressor
            'LGBC': LGBMClassifier
        ```
        
        #### Trees
        ```
            'RFR': RandomForestRegressor
            'RFC': RandomForestClassifier
            'DTR': DecisionTreeRegressor
            'DTC': DecisionTreeClassifier
            'ETR': ExtraTreeRegressor
            'ETC': ExtraTreeClassifier
        ```
        
        #### Ensemble
        ```
            'BC': BaggingClassifier
            'BR': BaggingRegressor
            'ADAR': AdaBoostRegressor
            'ADAC': AdaBoostClassifier
            'CBR': CatBoostRegressor
            'CBC': CatBoostClassifier
        ```
        
        #### KNeighbors
        ```
            'KNC': KNeighborsClassifier
            'KNR': KNeighborsRegressor
        ```
        
        #### SVM
        ```
            'SVR': SVR
            'SVC': SVC
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
