Metadata-Version: 2.1
Name: itaewon
Version: 0.1.4
Summary: A personal python library to speed up my workflow
Home-page: https://github.com/benjcabalona1029/itaewon
Author: Benjamin Cabalona Jr
Author-email: benjcabalonajr@gmail.com
License: MIT License
Description: # Itaewon
        
        [![Downloads](https://pepy.tech/badge/itaewon)](https://pepy.tech/project/itaewon)
        [![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/benjcabalona1029/itaewon/blob/master/LICENSE)
        
        My personal python library to speed-up my workflow.
        
        Also, i found myself switching to R and Python quite frequently. This is my attempt to minimize that.
        
        The implementation is quite simple, since i'm simply running `R` scripts in the background.
        
        Sample usage is shown below.
        
        
        
        # Installation
        
        
        ```python
        # !pip install itaewon==0.1.4
        ```
        
        
        ```python
        import pandas as pd
        import numpy as np
        import warnings
        
        warnings.filterwarnings('ignore')
        ```
        
        
        ```python
        data = pd.DataFrame()
        error = np.random.normal(0,5,300)
        data['X'] = np.random.normal(5,2.6,300)
        data['Z'] = np.random.binomial(1,0.3,300)
        data['Y'] = 10*data['X'] + 2*data['Z'] + error
        ```
        
        
        # Testing RModels
        
        
        ```python
        from itaewon.rmodels import RModels
        ```
        
        
        ```python
        formula = 'Y ~ X + Z'
        rmodel = RModels(data,formula)
        model = rmodel.linear_regression()
        rmodel.infer_linear(model)
        ```
        
               Residuals       
             Min.   :-14.6393  
             1st Qu.: -3.4848  
             Median : -0.1583  
             Mean   :  0.0000  
             3rd Qu.:  3.3146  
             Max.   : 16.2795  
        
            $coefficients
                         Estimate Std. Error   t value      Pr(>|t|)
            (Intercept) -1.109010  0.6718716 -1.650627  9.987172e-02
            X           10.163182  0.1150433 88.342270 2.862585e-215
            Z            1.426163  0.6222261  2.292033  2.260318e-02
        
        
            $sigma
            [1] 4.922223
        
        
            $r.squared
            [1] 0.9634044
        
        
            $adj.r.squared
            [1] 0.963158
        
        
            fstatistic 3909.3683074439355
            p_value [1] 4.668023e-214
        
        
        
        
        ```python
        new_data = pd.DataFrame()
        new_data['X'] = [0,1,2,3]
        new_data['Z'] = [1,0,0,1]
        ```
        
        
        ```python
        rmodel.predict(model,new_data,True,'confidence')
        ```
        
        
        
        
        <div>
        
        <table border="1" class="dataframe">
          <thead>
            <tr style="text-align: right;">
              <th></th>
              <th>Predicted</th>
              <th>Lower</th>
              <th>Upper</th>
            </tr>
          </thead>
          <tbody>
            <tr>
              <th>0</th>
              <td>0.317153</td>
              <td>-1.233912</td>
              <td>1.868218</td>
            </tr>
            <tr>
              <th>1</th>
              <td>9.054173</td>
              <td>7.921668</td>
              <td>10.186677</td>
            </tr>
            <tr>
              <th>2</th>
              <td>19.217355</td>
              <td>18.258526</td>
              <td>20.176184</td>
            </tr>
            <tr>
              <th>3</th>
              <td>30.806700</td>
              <td>29.671831</td>
              <td>31.941568</td>
            </tr>
          </tbody>
        </table>
        </div>
        
        
        
        
        ```python
        rmodel.predict(model,new_data,True,'prediction')
        ```
        
        
        
        
        <div>
        
        <table border="1" class="dataframe">
          <thead>
            <tr style="text-align: right;">
              <th></th>
              <th>Predicted</th>
              <th>Lower</th>
              <th>Upper</th>
            </tr>
          </thead>
          <tbody>
            <tr>
              <th>0</th>
              <td>0.317153</td>
              <td>-9.493093</td>
              <td>10.127399</td>
            </tr>
            <tr>
              <th>1</th>
              <td>9.054173</td>
              <td>-0.698657</td>
              <td>18.807003</td>
            </tr>
            <tr>
              <th>2</th>
              <td>19.217355</td>
              <td>9.483164</td>
              <td>28.951546</td>
            </tr>
            <tr>
              <th>3</th>
              <td>30.806700</td>
              <td>21.053595</td>
              <td>40.559804</td>
            </tr>
          </tbody>
        </table>
        </div>
        
        
        
        
        ```python
        rmodel.predict(model,new_data)
        ```
        
        
        
        
        <div>
        <table border="1" class="dataframe">
          <thead>
            <tr style="text-align: right;">
              <th></th>
              <th>Predicted</th>
            </tr>
          </thead>
          <tbody>
            <tr>
              <th>0</th>
              <td>0.317153</td>
            </tr>
            <tr>
              <th>1</th>
              <td>9.054173</td>
            </tr>
            <tr>
              <th>2</th>
              <td>19.217355</td>
            </tr>
            <tr>
              <th>3</th>
              <td>30.806700</td>
            </tr>
          </tbody>
        </table>
        </div>
        
        # Testing the custom module
        
        
        ```python
        from itaewon.custom import Custom
        ```
        
        
        ```python
        from sklearn.linear_model import LinearRegression, LogisticRegression
        ```
        
        
        ```python
        lr = LinearRegression()
        ```
        
        
        ```python
        custom = Custom(data[['X','Z']],data['Y'],True,lr)
        custom.custom()
        ```
        
            RMSE for LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False) : 4.752017508515921
        
        
        
        ```python
        lr = LogisticRegression()
        ```
        
        
        ```python
        custom = Custom(data[['X','Y']],data['Z'],False,lr)
        print(custom.custom()[0])
        print(custom.custom()[1])
        ```
        
            [[70  0]
             [29  0]]
                          precision    recall  f1-score   support
        
                       0       0.71      1.00      0.83        70
                       1       0.00      0.00      0.00        29
        
                accuracy                           0.71        99
               macro avg       0.35      0.50      0.41        99
            weighted avg       0.50      0.71      0.59        99
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
