Metadata-Version: 2.1
Name: conjurer
Version: 0.0.6
Summary: Python library to help you to perform magic on your data analytics project
Home-page: https://github.com/not-so-fat/conjurer
Author: @not-so-fat
Author-email: conjurer.not.so.fat@gmail.com
License: UNKNOWN
Description: # conjurer
        
        Python library to help you to perform magic on your data analytics project; which helps
        - EDA (load & check data)
        - Automatic machine learning tuning
        
        For detailed background please refer https://github.com/not-so-fat/conjurer/wiki
        
        ## Install
        
        ```
        pip install conjurer
        ```
        
        ## Usage
        
        You can build prediction pipeline from multiple data sources with following simple code. 
        ```
        from conjurer import (
            eda,
            ml
        )
        
        # Load CSVs as pandas.DataFrame
        df_dict = {
            name: eda.read_csv("{}_training.csv".format(name)
            for name in ["target", "demand_history", "product", "customer"]
        }
        
        # Do feature engineering (not implemented)
        feature_training, feature_names = engineer_feature(df_dict)
        
        # Automatic lightgbm tuning 
        model = ml.tune_cv("lightgbm", "rg", feature_training, "sales_amount", feature_names, 5)
        ```
        
        and produce prediction results.
        
        ```
        # Load CSV files for test data set as the same data types as training
        loader = eda.DfDictLoader(df_dict)
        df_dict_test = loader.load({
            name: "{}_test.csv".format(name)
            for name in ["target", "demand_history", "product", "customer"]
        })
        
        # Feature generation for test data set (not implemented)
        feature_test = generate_feature(df_dict)
        
        # Get prediction on test data set
        model.predict(feature_test)
        ```
        
        ## supported ml algorithms
        
        - LightGBM `lightgbm` (`gbm_autosplit.LGBMClassifier` or `gbm_autosplit.LGBMRegressor`)
        - XGBoost `xgboost` (`gbm_autosplit.XGBClassfier` or `gbm_autosplit.XGBRegressor`)
        - Random Forest `random_forest` (`sklearn.ensemble.RandomForestClassifier` or `sklearn.ensemble.RandomForestRegressor`)
        - Lasso / Logistic Regression `linear_model` (`sklearn.linear_model.Lasso` or `sklearn.linear_model.LogisticRegression`)
        
        This module uses CV by `sklearn_cv_pandas.RandomizedSearchCV` or `sklearn_cv_pandas.GridSearchCV` to use 
        pandas.DataFrame for arguments
        
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
