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test plot examples
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‎coverage.txt‎

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platform darwin -- Python 3.9.7, pytest-6.2.4, py-1.10.0, pluggy-0.13.1
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rootdir: /Users/johnmount/Documents/work/wvpy/pkg
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plugins: cov-2.12.1, anyio-2.2.0, typeguard-2.13.0
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collected 13 items
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collected 14 items
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tests/test_cross_plan1.py . [ 7%]
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tests/test_cross_predict.py .. [ 23%]
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tests/test_deviance_calc.py . [ 30%]
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tests/test_eval_fn_pre_row.py . [ 38%]
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tests/test_match_auc.py . [ 46%]
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tests/test_onehot.py .. [ 61%]
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tests/test_perm_score_vars.py . [ 69%]
14-
tests/test_search_grid.py .. [ 84%]
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tests/test_cross_predict.py .. [ 21%]
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tests/test_deviance_calc.py . [ 28%]
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tests/test_eval_fn_pre_row.py . [ 35%]
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tests/test_match_auc.py . [ 42%]
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tests/test_onehot.py .. [ 57%]
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tests/test_perm_score_vars.py . [ 64%]
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tests/test_plots.py . [ 71%]
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tests/test_search_grid.py .. [ 85%]
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tests/test_stats1.py . [ 92%]
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tests/test_threshold_stats.py . [100%]
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---------- coverage: platform darwin, python 3.9.7-final-0 -----------
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Name Stmts Miss Cover
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--------------------------------------
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wvpy/__init__.py 3 0 100%
22-
wvpy/util.py 307 137 55%
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wvpy/util.py 307 7 98%
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--------------------------------------
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TOTAL 310 137 56%
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TOTAL 310 7 98%
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============================== 13 passed in 5.59s ==============================
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============================== 14 passed in 7.09s ==============================

‎pkg/Examples/example_graphs.ipynb‎ renamed to ‎examples/example_graphs.ipynb‎

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‎examples/graph.ipynb‎

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‎examples/stats.ipynb‎

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"cells": [
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{
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"cell_type": "code",
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"execution_count": 0,
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
@@ -15,7 +15,7 @@
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"outputs": [
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{
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"data": {
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"text/plain": "'0.2.2'"
18+
"text/plain": "'0.2.8'"
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},
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"execution_count": 1,
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"metadata": {},
@@ -70,8 +70,8 @@
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"outputs": [
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{
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"data": {
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"text/plain": " threshold count fraction precision true_positive_rate \\\n0 0.999999 5 1.0 0.400000 1.0 \n1 1.000000 5 1.0 0.400000 1.0 \n2 2.000000 4 0.8 0.500000 1.0 \n3 3.000000 3 0.6 0.666667 1.0 \n4 4.000000 2 0.4 0.500000 0.5 \n5 5.000000 1 0.2 0.000000 0.0 \n6 5.000001 0 0.0 NaN 0.0 \n\n false_positive_rate true_negative_rate false_negative_rate enrichment \\\n0 1.000000 0.000000 0.0 1.400000 \n1 1.000000 0.000000 0.0 1.400000 \n2 0.666667 0.333333 0.0 1.750000 \n3 0.333333 0.666667 0.0 2.333333 \n4 0.333333 0.666667 0.5 1.750000 \n5 0.333333 0.666667 1.0 0.000000 \n6 0.000000 1.000000 1.0 NaN \n\n gain lift recall sensitivity specificity \n0 1.400000 1.400000 1.0 1.0 0.000000 \n1 1.400000 1.400000 1.0 1.0 0.000000 \n2 1.750000 2.187500 1.0 1.0 0.333333 \n3 2.333333 3.888889 1.0 1.0 0.666667 \n4 1.750000 4.375000 0.5 0.5 0.666667 \n5 0.000000 0.000000 0.0 0.0 0.666667 \n6 NaN NaN 0.0 0.0 1.000000 ",
74-
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>threshold</th>\n <th>count</th>\n <th>fraction</th>\n <th>precision</th>\n <th>true_positive_rate</th>\n <th>false_positive_rate</th>\n <th>true_negative_rate</th>\n <th>false_negative_rate</th>\n <th>enrichment</th>\n <th>gain</th>\n <th>lift</th>\n <th>recall</th>\n <th>sensitivity</th>\n <th>specificity</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>0.999999</td>\n <td>5</td>\n <td>1.0</td>\n <td>0.400000</td>\n <td>1.0</td>\n <td>1.000000</td>\n <td>0.000000</td>\n <td>0.0</td>\n <td>1.400000</td>\n <td>1.400000</td>\n <td>1.400000</td>\n <td>1.0</td>\n <td>1.0</td>\n <td>0.000000</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1.000000</td>\n <td>5</td>\n <td>1.0</td>\n <td>0.400000</td>\n <td>1.0</td>\n <td>1.000000</td>\n <td>0.000000</td>\n <td>0.0</td>\n <td>1.400000</td>\n <td>1.400000</td>\n <td>1.400000</td>\n <td>1.0</td>\n <td>1.0</td>\n <td>0.000000</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2.000000</td>\n <td>4</td>\n <td>0.8</td>\n <td>0.500000</td>\n <td>1.0</td>\n <td>0.666667</td>\n <td>0.333333</td>\n <td>0.0</td>\n <td>1.750000</td>\n <td>1.750000</td>\n <td>2.187500</td>\n <td>1.0</td>\n <td>1.0</td>\n <td>0.333333</td>\n </tr>\n <tr>\n <th>3</th>\n <td>3.000000</td>\n <td>3</td>\n <td>0.6</td>\n <td>0.666667</td>\n <td>1.0</td>\n <td>0.333333</td>\n <td>0.666667</td>\n <td>0.0</td>\n <td>2.333333</td>\n <td>2.333333</td>\n <td>3.888889</td>\n <td>1.0</td>\n <td>1.0</td>\n <td>0.666667</td>\n </tr>\n <tr>\n <th>4</th>\n <td>4.000000</td>\n <td>2</td>\n <td>0.4</td>\n <td>0.500000</td>\n <td>0.5</td>\n <td>0.333333</td>\n <td>0.666667</td>\n <td>0.5</td>\n <td>1.750000</td>\n <td>1.750000</td>\n <td>4.375000</td>\n <td>0.5</td>\n <td>0.5</td>\n <td>0.666667</td>\n </tr>\n <tr>\n <th>5</th>\n <td>5.000000</td>\n <td>1</td>\n <td>0.2</td>\n <td>0.000000</td>\n <td>0.0</td>\n <td>0.333333</td>\n <td>0.666667</td>\n <td>1.0</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.666667</td>\n </tr>\n <tr>\n <th>6</th>\n <td>5.000001</td>\n <td>0</td>\n <td>0.0</td>\n <td>NaN</td>\n <td>0.0</td>\n <td>0.000000</td>\n <td>1.000000</td>\n <td>1.0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1.000000</td>\n </tr>\n </tbody>\n</table>\n</div>"
73+
"text/plain": " threshold count fraction precision true_positive_rate \\\n0 0.999999 5 1.0 0.400000 1.0 \n1 1.000000 5 1.0 0.400000 1.0 \n2 2.000000 4 0.8 0.500000 1.0 \n3 3.000000 3 0.6 0.666667 1.0 \n4 4.000000 2 0.4 0.500000 0.5 \n5 5.000000 1 0.2 0.000000 0.0 \n6 5.000001 0 0.0 0.000000 0.0 \n\n false_positive_rate true_negative_rate false_negative_rate accuracy \\\n0 1.000000 0.000000 0.0 0.4 \n1 1.000000 0.000000 0.0 0.4 \n2 0.666667 0.333333 0.0 0.6 \n3 0.333333 0.666667 0.0 0.8 \n4 0.333333 0.666667 0.5 0.6 \n5 0.333333 0.666667 1.0 0.4 \n6 0.000000 1.000000 1.0 0.6 \n\n cdf recall sensitivity specificity \n0 0.0 1.0 1.0 0.000000 \n1 0.0 1.0 1.0 0.000000 \n2 0.2 1.0 1.0 0.333333 \n3 0.4 1.0 1.0 0.666667 \n4 0.6 0.5 0.5 0.666667 \n5 0.8 0.0 0.0 0.666667 \n6 1.0 0.0 0.0 1.000000 ",
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"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>threshold</th>\n <th>count</th>\n <th>fraction</th>\n <th>precision</th>\n <th>true_positive_rate</th>\n <th>false_positive_rate</th>\n <th>true_negative_rate</th>\n <th>false_negative_rate</th>\n <th>accuracy</th>\n <th>cdf</th>\n <th>recall</th>\n <th>sensitivity</th>\n <th>specificity</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>0.999999</td>\n <td>5</td>\n <td>1.0</td>\n <td>0.400000</td>\n <td>1.0</td>\n <td>1.000000</td>\n <td>0.000000</td>\n <td>0.0</td>\n <td>0.4</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>1.0</td>\n <td>0.000000</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1.000000</td>\n <td>5</td>\n <td>1.0</td>\n <td>0.400000</td>\n <td>1.0</td>\n <td>1.000000</td>\n <td>0.000000</td>\n <td>0.0</td>\n <td>0.4</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>1.0</td>\n <td>0.000000</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2.000000</td>\n <td>4</td>\n <td>0.8</td>\n <td>0.500000</td>\n <td>1.0</td>\n <td>0.666667</td>\n <td>0.333333</td>\n <td>0.0</td>\n <td>0.6</td>\n <td>0.2</td>\n <td>1.0</td>\n <td>1.0</td>\n <td>0.333333</td>\n </tr>\n <tr>\n <th>3</th>\n <td>3.000000</td>\n <td>3</td>\n <td>0.6</td>\n <td>0.666667</td>\n <td>1.0</td>\n <td>0.333333</td>\n <td>0.666667</td>\n <td>0.0</td>\n <td>0.8</td>\n <td>0.4</td>\n <td>1.0</td>\n <td>1.0</td>\n <td>0.666667</td>\n </tr>\n <tr>\n <th>4</th>\n <td>4.000000</td>\n <td>2</td>\n <td>0.4</td>\n <td>0.500000</td>\n <td>0.5</td>\n <td>0.333333</td>\n <td>0.666667</td>\n <td>0.5</td>\n <td>0.6</td>\n <td>0.6</td>\n <td>0.5</td>\n <td>0.5</td>\n <td>0.666667</td>\n </tr>\n <tr>\n <th>5</th>\n <td>5.000000</td>\n <td>1</td>\n <td>0.2</td>\n <td>0.000000</td>\n <td>0.0</td>\n <td>0.333333</td>\n <td>0.666667</td>\n <td>1.0</td>\n <td>0.4</td>\n <td>0.8</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.666667</td>\n </tr>\n <tr>\n <th>6</th>\n <td>5.000001</td>\n <td>0</td>\n <td>0.0</td>\n <td>0.000000</td>\n <td>0.0</td>\n <td>0.000000</td>\n <td>1.000000</td>\n <td>1.0</td>\n <td>0.6</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1.000000</td>\n </tr>\n </tbody>\n</table>\n</div>"
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},
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"execution_count": 3,
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"metadata": {},
@@ -96,21 +96,34 @@
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 4,
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"outputs": [
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{
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"data": {
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"text/plain": "\"pandas.DataFrame({\\n 'threshold': [0.999999, 1.0, 2.0, 3.0, 4.0, 5.0, 5.000001],\\n 'count': [5, 5, 4, 3, 2, 1, 0],\\n 'fraction': [1.0, 1.0, 0.8, 0.6, 0.4, 0.2, 0.0],\\n 'precision': [0.4, 0.4, 0.5, 0.6666666666666666, 0.5, 0.0, None],\\n 'true_positive_rate': [1.0, 1.0, 1.0, 1.0, 0.5, 0.0, 0.0],\\n 'false_positive_rate': [1.0, 1.0, 0.6666666666666666, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.0],\\n 'true_negative_rate': [0.0, 0.0, 0.3333333333333333, 0.6666666666666666, 0.6666666666666666, 0.6666666666666666, 1.0],\\n 'false_negative_rate': [0.0, 0.0, 0.0, 0.0, 0.5, 1.0, 1.0],\\n 'enrichment': [1.4000000000000001, 1.4000000000000001, 1.75, 2.3333333333333335, 1.75, 0.0, None],\\n 'gain': [1.4000000000000001, 1.4000000000000001, 1.75, 2.3333333333333335, 1.75, 0.0, None],\\n 'lift': [1.4000000000000001, 1.4000000000000001, 2.1875, 3.8888888888888893, 4.375, 0.0, None],\\n 'recall': [1.0, 1.0, 1.0, 1.0, 0.5, 0.0, 0.0],\\n 'sensitivity': [1.0, 1.0, 1.0, 1.0, 0.5, 0.0, 0.0],\\n 'specificity': [0.0, 0.0, 0.33333333333333337, 0.6666666666666667, 0.6666666666666667, 0.6666666666666667, 1.0],\\n })\""
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"pd.DataFrame({\n",
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" 'threshold': [0.999999, 1.0, 2.0, 3.0, 4.0, 5.0, 5.000001],\n",
107+
" 'count': [5, 5, 4, 3, 2, 1, 0],\n",
108+
" 'fraction': [1.0, 1.0, 0.8, 0.6, 0.4, 0.2, 0.0],\n",
109+
" 'precision': [0.4, 0.4, 0.5, 0.6666666666666666, 0.5, 0.0, 0.0],\n",
110+
" 'true_positive_rate': [1.0, 1.0, 1.0, 1.0, 0.5, 0.0, 0.0],\n",
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" 'false_positive_rate': [1.0, 1.0, 0.6666666666666666, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.0],\n",
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" 'true_negative_rate': [0.0, 0.0, 0.3333333333333333, 0.6666666666666666, 0.6666666666666666, 0.6666666666666666, 1.0],\n",
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" 'false_negative_rate': [0.0, 0.0, 0.0, 0.0, 0.5, 1.0, 1.0],\n",
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" 'accuracy': [0.4, 0.4, 0.6, 0.8, 0.6, 0.4, 0.6],\n",
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" 'cdf': [0.0, 0.0, 0.19999999999999996, 0.4, 0.6, 0.8, 1.0],\n",
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" 'recall': [1.0, 1.0, 1.0, 1.0, 0.5, 0.0, 0.0],\n",
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" 'sensitivity': [1.0, 1.0, 1.0, 1.0, 0.5, 0.0, 0.0],\n",
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" 'specificity': [0.0, 0.0, 0.33333333333333337, 0.6666666666666667, 0.6666666666666667, 0.6666666666666667, 1.0],\n",
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" })\n"
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]
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}
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],
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"source": [
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"import data_algebra.util\n",
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"\n",
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"data_algebra.util.pandas_to_example_str(stats)"
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"print(data_algebra.util.pandas_to_example_str(stats))"
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],
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"metadata": {
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"collapsed": false,

‎pkg/build/lib/wvpy/util.py‎

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Original file line numberDiff line numberDiff line change
@@ -206,6 +206,7 @@ def plot_roc(
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wvpy.util.plot_roc(
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prediction=d['x'],
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istrue=d['y'],
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ideal_line_color='lightgrey',
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extra_points=pandas.DataFrame({
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'tpr': [0, 1],
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'fpr': [0, 1],
@@ -235,7 +236,7 @@ def plot_roc(
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matplotlib.pyplot.fill_between(fpr, tpr, color="orange", alpha=0.3)
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matplotlib.pyplot.plot([0, 1], [0, 1], color="navy", lw=lw, linestyle="--")
237238
if extra_points is not None:
238-
matplotlib.pyplot.plot(extra_points.fpr, extra_points.tpr, "bo", color="red")
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matplotlib.pyplot.scatter(extra_points.fpr, extra_points.tpr, color="red")
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if "label" in extra_points.columns:
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tpr = extra_points.tpr.to_list()
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fpr = extra_points.fpr.to_list()

‎pkg/dist/wvpy-0.2.8-py3-none-any.whl‎

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‎pkg/dist/wvpy-0.2.8.tar.gz‎

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‎pkg/docs/search.js‎

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