move data science tools wvu https://github.com/WinVector/wvu

Author: JohnMount

README.ipynb

@@ -1,258 +1,9 @@
-[wvpy](https://github.com/WinVector/wvpy) is a simple 
-set of utilities for teaching data science and machine learning methods.
-They are not replacements for the obvious methods in sklearn.
 
-Some notes on the Jupyter sheet runner can be found [here](https://win-vector.com/2022/08/20/an-effective-personal-jupyter-data-science-workflow/)
+wvpy tools for converting Jupyter notebooks to and from Python files.
 
+Text and video tutotials here: [https://win-vector.com/2022/08/20/an-effective-personal-jupyter-data-science-workflow/](https://win-vector.com/2022/08/20/an-effective-personal-jupyter-data-science-workflow/).
 
-```python
-import numpy.random
-import pandas
-import wvpy.util
+Many of the data science functions have been moved to wvu [https://github.com/WinVector/wvu](https://win-vector.com/2022/08/20/an-effective-personal-jupyter-data-science-workflow/).
 
-wvpy.__version__
-```
 
 
-
-
-    '0.2.7'
-
-
-
-Illustration of cross-method plan.
-
-
-```python
-wvpy.util.mk_cross_plan(10,2)
-```
-
-
-
-
-    [{'train': [1, 2, 3, 4, 9], 'test': [0, 5, 6, 7, 8]},
-     {'train': [0, 5, 6, 7, 8], 'test': [1, 2, 3, 4, 9]}]
-
-
-
-Plotting example
-
-
-```python
-help(wvpy.util.plot_roc)
-```
-
-    Help on function plot_roc in module wvpy.util:
-    
-    plot_roc(prediction, istrue, title='Receiver operating characteristic plot', *, truth_target=True, ideal_line_color=None, extra_points=None, show=True)
-        Plot a ROC curve of numeric prediction against boolean istrue.
-        
-        :param prediction: column of numeric predictions
-        :param istrue: column of items to predict
-        :param title: plot title
-        :param truth_target: value to consider target or true.
-        :param ideal_line_color: if not None, color of ideal line
-        :param extra_points: data frame of additional point to annotate graph, columns fpr, tpr, label
-        :param show: logical, if True call matplotlib.pyplot.show()
-        :return: calculated area under the curve, plot produced by call.
-        
-        Example:
-        
-        import pandas
-        import wvpy.util
-        
-        d = pandas.DataFrame({
-            'x': [1, 2, 3, 4, 5],
-            'y': [False, False, True, True, False]
-        })
-        
-        wvpy.util.plot_roc(
-            prediction=d['x'],
-            istrue=d['y'],
-            ideal_line_color='lightgrey'
-        )
-        
-        wvpy.util.plot_roc(
-            prediction=d['x'],
-            istrue=d['y'],
-            extra_points=pandas.DataFrame({
-                'tpr': [0, 1],
-                'fpr': [0, 1],
-                'label': ['AAA', 'BBB']
-            })
-        )
-    
-
-
-
-```python
-d = pandas.concat([
-    pandas.DataFrame({
-        'x': numpy.random.normal(size=1000),
-        'y': numpy.random.choice([True, False], 
-                                 p=(0.02, 0.98), 
-                                 size=1000, 
-                                 replace=True)}),
-    pandas.DataFrame({
-        'x': numpy.random.normal(size=200) + 5,
-        'y': numpy.random.choice([True, False], 
-                                 size=200, 
-                                 replace=True)}),
-])
-```
-
-
-```python
-wvpy.util.plot_roc(
-    prediction=d.x,
-    istrue=d.y,
-    ideal_line_color="DarkGrey",
-    title='Example ROC plot')
-```
-
-
-    <Figure size 432x288 with 0 Axes>
-
-
-
-    
-![png](output_7_1.png)
-    
-
-
-
-
-
-    0.903298366883511
-
-
-
-
-```python
-help(wvpy.util.threshold_plot)
-```
-
-    Help on function threshold_plot in module wvpy.util:
-    
-    threshold_plot(d: pandas.core.frame.DataFrame, pred_var, truth_var, truth_target=True, threshold_range=(-inf, inf), plotvars=('precision', 'recall'), title='Measures as a function of threshold', *, show=True)
-        Produce multiple facet plot relating the performance of using a threshold greater than or equal to
-        different values at predicting a truth target.
-        
-        :param d: pandas.DataFrame to plot
-        :param pred_var: name of column of numeric predictions
-        :param truth_var: name of column with reference truth
-        :param truth_target: value considered true
-        :param threshold_range: x-axis range to plot
-        :param plotvars: list of metrics to plot, must come from ['threshold', 'count', 'fraction', 'precision',
-            'true_positive_rate', 'false_positive_rate', 'true_negative_rate', 'false_negative_rate',
-            'recall', 'sensitivity', 'specificity']
-        :param title: title for plot
-        :param show: logical, if True call matplotlib.pyplot.show()
-        :return: None, plot produced as a side effect
-        
-        Example:
-        
-        import pandas
-        import wvpy.util
-        
-        d = pandas.DataFrame({
-            'x': [1, 2, 3, 4, 5],
-            'y': [False, False, True, True, False]
-        })
-        
-        wvpy.util.threshold_plot(
-            d,
-            pred_var='x',
-            truth_var='y',
-            plotvars=("sensitivity", "specificity"),
-        )
-    
-
-
-
-```python
-wvpy.util.threshold_plot(
-        d,
-        pred_var='x',
-        truth_var='y',
-        plotvars=("sensitivity", "specificity"),
-        title = "example plot"
-    )
-```
-
-
-    
-![png](output_9_0.png)
-    
-
-
-
-```python
-
-wvpy.util.threshold_plot(
-        d,
-        pred_var='x',
-        truth_var='y',
-        plotvars=("precision", "recall"),
-        title = "example plot"
-    )
-```
-
-
-    
-![png](output_10_0.png)
-    
-
-
-
-```python
-help(wvpy.util.gain_curve_plot)
-```
-
-    Help on function gain_curve_plot in module wvpy.util:
-    
-    gain_curve_plot(prediction, outcome, title='Gain curve plot', *, show=True)
-        plot cumulative outcome as a function of prediction order (descending)
-        
-        :param prediction: vector of numeric predictions
-        :param outcome: vector of actual values
-        :param title: plot title
-        :param show: logical, if True call matplotlib.pyplot.show()
-        :return: None
-    
-
-
-
-```python
-wvpy.util.gain_curve_plot(
-        prediction=d['x'],
-        outcome=d['y'],
-        title = "gain curve plot"
-)
-```
-
-
-    
-![png](output_12_0.png)
-    
-
-
-
-```python
-wvpy.util.lift_curve_plot(
-        prediction=d['x'],
-        outcome=d['y'],
-        title = "lift curve plot"
-)
-```
-
-
-    
-![png](output_13_0.png)
-    
-
-
-
-```python
-
-```

README.md

@@ -1,34 +1,20 @@
 ============================= test session starts ==============================
-platform darwin -- Python 3.9.7, pytest-6.2.5, py-1.11.0, pluggy-1.0.0
+platform darwin -- Python 3.9.12, pytest-7.1.1, pluggy-1.0.0
 rootdir: /Users/johnmount/Documents/work/wvpy/pkg
 plugins: anyio-3.5.0, cov-3.0.0
-collected 20 items
+collected 4 items
 
-tests/test_cross_plan1.py .                                              [  5%]
-tests/test_cross_predict.py ..                                           [ 15%]
-tests/test_deviance_calc.py .                                            [ 20%]
-tests/test_eval_fn_pre_row.py .                                          [ 25%]
-tests/test_match_auc.py .                                                [ 30%]
-tests/test_nb_fns.py ....                                                [ 50%]
-tests/test_onehot.py ..                                                  [ 60%]
-tests/test_perm_score_vars.py .                                          [ 65%]
-tests/test_plots.py .                                                    [ 70%]
-tests/test_se.py .                                                       [ 75%]
-tests/test_search_grid.py ..                                             [ 85%]
-tests/test_stats1.py .                                                   [ 90%]
-tests/test_threshold_stats.py .                                          [ 95%]
-tests/test_typs_in_frame.py .                                            [100%]
+tests/test_nb_fns.py ....                                                [100%]
 
----------- coverage: platform darwin, python 3.9.7-final-0 -----------
+---------- coverage: platform darwin, python 3.9.12-final-0 ----------
 Name                      Stmts   Miss  Cover
 ---------------------------------------------
 wvpy/__init__.py              3      0   100%
 wvpy/jtools.py              206     76    63%
 wvpy/pysheet.py              99     99     0%
 wvpy/render_workbook.py      54     54     0%
-wvpy/util.py                321      7    98%
 ---------------------------------------------
-TOTAL                       683    236    65%
+TOTAL                       362    229    37%
 
 
-============================= 20 passed in 12.71s ==============================
+============================== 4 passed in 8.92s ===============================

coverage.txt

@@ -1,7 +1,9 @@
-Win Vector LLC tools for doing and teaching data science in Python 3
+Win Vector LLC tools for converting Python Jupyter to and from Python source files
 https://github.com/WinVector/wvpy
 
 Some notes can be found here: https://github.com/WinVector/wvpy
 and here: https://win-vector.com/2022/08/20/an-effective-personal-jupyter-data-science-workflow/
 
+Many of the data science functions have been moved to wvu https://github.com/WinVector/wvu
+