From da11222bcce0fa94d373749f6b493cfb52a40323 Mon Sep 17 00:00:00 2001 From: Michael Fu Date: Wed, 13 Apr 2022 18:15:38 +1000 Subject: [PATCH] pyexplainer version 1.1.7 --- .ipynb_checkpoints/TUTORIAL-checkpoint.ipynb | 2589 ----------------- dist/pyexplainer-1.1.4-py3-none-any.whl | Bin 2409912 -> 0 bytes dist/pyexplainer-1.1.4.tar.gz | Bin 2357230 -> 0 bytes dist/pyexplainer-1.1.5.tar.gz | Bin 2357280 -> 0 bytes dist/pyexplainer-1.1.6-py3-none-any.whl | Bin 2409979 -> 0 bytes dist/pyexplainer-1.1.6.tar.gz | Bin 2357320 -> 0 bytes ...whl => pyexplainer-1.1.7-py3-none-any.whl} | Bin 2409937 -> 2410089 bytes dist/pyexplainer-1.1.7.tar.gz | Bin 0 -> 2357367 bytes pyexplainer/__init__.py | 2 +- .../__pycache__/__init__.cpython-39.pyc | Bin 187 -> 187 bytes pyproject.toml | 2 +- tests/__init__.py | 2 +- tests/__pycache__/__init__.cpython-39.pyc | Bin 171 -> 181 bytes .../test_predict.cpython-39-pytest-6.2.5.pyc | Bin 3065 -> 3075 bytes ...st_pyexplainer.cpython-39-pytest-6.2.5.pyc | Bin 40441 -> 40621 bytes .../test_rulefit.cpython-39-pytest-6.2.5.pyc | Bin 8139 -> 8149 bytes tests/test_pyexplainer.py | 2 +- 17 files changed, 4 insertions(+), 2593 deletions(-) delete mode 100644 .ipynb_checkpoints/TUTORIAL-checkpoint.ipynb delete mode 100644 dist/pyexplainer-1.1.4-py3-none-any.whl delete mode 100644 dist/pyexplainer-1.1.4.tar.gz delete mode 100644 dist/pyexplainer-1.1.5.tar.gz delete mode 100644 dist/pyexplainer-1.1.6-py3-none-any.whl delete mode 100644 dist/pyexplainer-1.1.6.tar.gz rename dist/{pyexplainer-1.1.5-py3-none-any.whl => pyexplainer-1.1.7-py3-none-any.whl} (99%) create mode 100644 dist/pyexplainer-1.1.7.tar.gz diff --git a/.ipynb_checkpoints/TUTORIAL-checkpoint.ipynb b/.ipynb_checkpoints/TUTORIAL-checkpoint.ipynb deleted file mode 100644 index b8e6320..0000000 --- a/.ipynb_checkpoints/TUTORIAL-checkpoint.ipynb +++ /dev/null @@ -1,2589 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "italic-seafood", - "metadata": {}, - "source": [ - "#
Welcome to PyExplainer Quickstart Guide
" - ] - }, - { - "cell_type": "markdown", - "id": "northern-birmingham", - "metadata": {}, - "source": [ - "# Top Note - MUST READ !!\n", - "#### 1. When initialising the PyExplainer object, you should prepare 5 necessary parameters and follow the data type \n", - "(1) X_train (pd.core.frame.DataFrame) - feature columns from training data

\n", - "(2) y_train (pd.core.series.Series) - label column from training data

\n", - "(3) indep (pd.core.indexes.base.Index) - names of feature columns > most of the time, you can get it by 'X_explain.columns'

\n", - "(4) dep (str) - name of label column

\n", - "(5) blackbox_model (any supervised classification model trained from sklearn lib) - model trained from sklearn lib

\n", - "\n", - "#### 2. When using the explain() function under PyExplainer object, you should prepare 2 parameters and follow the data type\n", - "(1) X_explain (pd.core.frame.DataFrame) - one row of feature data

\n", - "(2) y_explain (pd.core.series.Series) - one row of predicted data \n", - "\n", - "#### 3. Be careful when using the custom pandas index for Series and DataFrame \n", - "In our Full Tutorial (PART B) example, the FileName column was used as the custom index.
\n", - "However, it is fine if you don't have custom index, pandas will generate default row index starting from 0.

\n", - "If you do want to make use of custom index, make sure to use it consistently, whenever you do the data processing.

\n", - "Otherwise, some of your data may have pandas default index while the others have your custom index,

\n", - "which will trigger errors whenever you try to combine your DataFrame and Series. " - ] - }, - { - "cell_type": "markdown", - "id": "conscious-roommate", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "id": "employed-intensity", - "metadata": {}, - "source": [ - "# PART A - Quick Start" - ] - }, - { - "cell_type": "markdown", - "id": "deadly-immigration", - "metadata": {}, - "source": [ - "## 1. Prepare data and model\n", - "\n", - "Note. We use the default data and model here for an example" - ] - }, - { - "cell_type": "markdown", - "id": "higher-cooling", - "metadata": {}, - "source": [ - "### 1.1 Import required library" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "harmful-morocco", - "metadata": {}, - "outputs": [], - "source": [ - "from pyexplainer import pyexplainer_pyexplainer\n", - "from sklearn.ensemble import RandomForestClassifier" - ] - }, - { - "cell_type": "markdown", - "id": "practical-jenny", - "metadata": {}, - "source": [ - "### 1.2 Obtain default dataset and train a global model (Random Forest)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "employed-adobe", - "metadata": {}, - "outputs": [], - "source": [ - "default_data_and_model = pyexplainer_pyexplainer.get_dflt()\n", - "rf_model = RandomForestClassifier(random_state=0)\n", - "rf_model.fit(default_data_and_model['X_train'],\n", - " default_data_and_model['y_train'])\n", - "py_explainer = pyexplainer_pyexplainer.PyExplainer(X_train = default_data_and_model['X_train'],\n", - " y_train = default_data_and_model['y_train'],\n", - " indep = default_data_and_model['indep'],\n", - " dep = default_data_and_model['dep'],\n", - " blackbox_model = rf_model)" - ] - }, - { - "cell_type": "markdown", - "id": "informational-disclosure", - "metadata": {}, - "source": [ - "## 🔧2. Create PyExplainer object " - ] - }, - { - "cell_type": "markdown", - "id": "anticipated-tourism", - "metadata": {}, - "source": [ - "### 2.1 Prepare data for creating PyExplainer" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "abstract-disposal", - "metadata": {}, - "outputs": [], - "source": [ - "X_explain = default_data_and_model['X_explain']\n", - "y_explain = default_data_and_model['y_explain']" - ] - }, - { - "cell_type": "markdown", - "id": "inside-republic", - "metadata": {}, - "source": [ - "### 2.2 Create rules" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "spatial-wrong", - "metadata": {}, - "outputs": [], - "source": [ - "created_rules = py_explainer.explain(X_explain=X_explain,\n", - " y_explain=y_explain,\n", - " search_function='crossoverinterpolation',\n", - " random_state=0,\n", - " reuse_local_model=True)" - ] - }, - { - "cell_type": "markdown", - "id": "engaging-ridge", - "metadata": {}, - "source": [ - "## 3. Create interactive visualization\n", - "\n", - "You can change feature values at the slider bar to observe change of risk score." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "second-trout", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "46699c0d6c604c6b8c8f74b9e113a8ad", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(Label(value='Risk Score: '), FloatProgress(value=0.0, bar_style='info', layout=Layout(width='40…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "c4db65e8679d454ba41fe13d04188d45", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Output(layout=Layout(border='3px solid black'), outputs=({'output_type': 'display_data', 'data': {'text/plain'…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "8bf04d43b65c4bb1bf30db0deb621d63", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatSlider(value=246.0, continuous_update=False, description='#1 The value of AddedLOC is more than 246.0', l…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "4b3db091da534290bfb9e027f836b8d9", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatSlider(value=11.0, continuous_update=False, description='#2 The value of LOC is more than 11.0', layout=L…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "py_explainer.visualise(created_rules)" - ] - }, - { - "cell_type": "markdown", - "id": "894f3a9a", - "metadata": {}, - "source": [ - "##### *** If the widget is not displayed, please run the code cell below, restart the notebook, and rerun from the top" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9db21a4d", - "metadata": {}, - "outputs": [], - "source": [ - "import os \n", - "os.system(\"jupyter nbextension enable --py widgetsnbextension\")" - ] - }, - { - "cell_type": "markdown", - "id": "a15de81b", - "metadata": {}, - "source": [ - "# PART B - Full Tutorial" - ] - }, - { - "cell_type": "markdown", - "id": "italic-services", - "metadata": {}, - "source": [ - "## 1. Prepare sample data and model" - ] - }, - { - "cell_type": "markdown", - "id": "front-programmer", - "metadata": {}, - "source": [ - "### 1.1 For the simplicity, we load the sample DataFrame that is included in the package already" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "removable-problem", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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DDEV Added_lines \\\n", - "File ... \n", - "activemq-console/src/main/java/org/apache/activ... ... 1 32 \n", - "activemq-console/src/main/java/org/apache/activ... ... 2 30 \n", - "activemq-console/src/main/java/org/apache/activ... ... 1 8 \n", - "\n", - " Del_lines OWN_LINE \\\n", - "File \n", - "activemq-console/src/main/java/org/apache/activ... 18 1.00000 \n", - "activemq-console/src/main/java/org/apache/activ... 28 0.98374 \n", - "activemq-console/src/main/java/org/apache/activ... 8 1.00000 \n", - "\n", - " OWN_COMMIT MINOR_COMMIT \\\n", - "File \n", - "activemq-console/src/main/java/org/apache/activ... 1.0 0 \n", - "activemq-console/src/main/java/org/apache/activ... 0.5 0 \n", - "activemq-console/src/main/java/org/apache/activ... 1.0 0 \n", - "\n", - " MINOR_LINE MAJOR_COMMIT \\\n", - "File \n", - "activemq-console/src/main/java/org/apache/activ... 1 1 \n", - "activemq-console/src/main/java/org/apache/activ... 1 2 \n", - "activemq-console/src/main/java/org/apache/activ... 1 1 \n", - "\n", - " MAJOR_LINE RealBug \n", - "File \n", - "activemq-console/src/main/java/org/apache/activ... 0 False \n", - "activemq-console/src/main/java/org/apache/activ... 1 False \n", - "activemq-console/src/main/java/org/apache/activ... 0 False \n", - "\n", - "[3 rows x 66 columns]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = df.set_index(df['File'])\n", - "df = df.drop(['File', 'HeuBug', 'HeuBugCount', 'RealBugCount'], axis=1)\n", - "df.head(3)" - ] - }, - { - "cell_type": "markdown", - "id": "advance-wilderness", - "metadata": {}, - "source": [ - "### 1.3 Define feature cols (X), and label col (y)\n", - "##### the function AutoSpearman is used as a feature selection method to reduce number of features\n", - "##### for more information about the algorithm please refer to [this paper](https://ieeexplore.ieee.org/document/8530020)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "growing-scale", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(Part 1) Automatically select non-correlated metrics based on a Spearman rank correlation test\n", - "> Step 1 comparing between CountDeclMethod and CountDeclFunction\n", - ">> CountDeclMethod has the average correlation of 0.433 with other metrics\n", - ">> CountDeclFunction has the average correlation of 0.433 with other metrics\n", - ">> Exclude CountDeclMethod\n", - "> Step 2 comparing between MAJOR_COMMIT and DDEV\n", - ">> MAJOR_COMMIT has the average correlation of 0.274 with other metrics\n", - ">> DDEV has the average correlation of 0.274 with other metrics\n", - ">> Exclude DDEV\n", - "> Step 3 comparing between SumCyclomatic and SumCyclomaticModified\n", - ">> SumCyclomatic has the average correlation of 0.501 with other metrics\n", - ">> SumCyclomaticModified has the average correlation of 0.501 with other metrics\n", - ">> Exclude SumCyclomatic\n", - "> Step 4 comparing between AvgCyclomatic and AvgCyclomaticModified\n", - ">> AvgCyclomatic has the average correlation of 0.387 with other metrics\n", - ">> AvgCyclomaticModified has the average correlation of 0.387 with other metrics\n", - ">> Exclude AvgCyclomatic\n", - "> Step 5 comparing between MaxCyclomatic and MaxCyclomaticModified\n", - ">> MaxCyclomatic has the average correlation of 0.476 with other metrics\n", - ">> MaxCyclomaticModified has the average correlation of 0.476 with other metrics\n", - ">> Exclude MaxCyclomatic\n", - "> Step 6 comparing between SumCyclomaticModified and SumCyclomaticStrict\n", - ">> SumCyclomaticModified has the average correlation of 0.488 with other metrics\n", - ">> SumCyclomaticStrict has the average correlation of 0.489 with other metrics\n", - ">> Exclude SumCyclomaticStrict\n", - "> Step 7 comparing between CountStmtDecl and CountLineCodeDecl\n", - ">> CountStmtDecl has the average correlation of 0.49 with other metrics\n", - ">> CountLineCodeDecl has the average correlation of 0.487 with other metrics\n", - ">> Exclude CountStmtDecl\n", - "> Step 8 comparing between CountLineCode and CountStmt\n", - ">> CountLineCode has the average correlation of 0.504 with other metrics\n", - ">> CountStmt has the average correlation of 0.501 with other metrics\n", - ">> Exclude CountLineCode\n", - "> Step 9 comparing between CountSemicolon and CountStmt\n", - ">> CountSemicolon has the average correlation of 0.484 with other metrics\n", - ">> CountStmt has the average correlation of 0.492 with other metrics\n", - ">> Exclude CountStmt\n", - "> Step 10 comparing between OWN_COMMIT and MAJOR_COMMIT\n", - ">> OWN_COMMIT has the average correlation of 0.238 with other metrics\n", - ">> MAJOR_COMMIT has the average correlation of 0.249 with other metrics\n", - ">> Exclude MAJOR_COMMIT\n", - "> Step 11 comparing between CountPath_Max and MaxCyclomaticModified\n", - ">> CountPath_Max has the average correlation of 0.447 with other metrics\n", - ">> MaxCyclomaticModified has the average correlation of 0.448 with other metrics\n", - ">> Exclude MaxCyclomaticModified\n", - "> Step 12 comparing between CountStmtExe and CountLineCodeExe\n", - ">> CountStmtExe has the average correlation of 0.473 with other metrics\n", - ">> CountLineCodeExe has the average correlation of 0.475 with other metrics\n", - ">> Exclude CountLineCodeExe\n", - "> Step 13 comparing between SumEssential and CountDeclFunction\n", - ">> SumEssential has the average correlation of 0.397 with other metrics\n", - ">> CountDeclFunction has the average correlation of 0.379 with other metrics\n", - ">> Exclude SumEssential\n", - "> Step 14 comparing between CountPath_Max and MaxCyclomaticStrict\n", - ">> CountPath_Max has the average correlation of 0.427 with other metrics\n", - ">> MaxCyclomaticStrict has the average correlation of 0.428 with other metrics\n", - ">> Exclude MaxCyclomaticStrict\n", - "> Step 15 comparing between CountPath_Max and CountPath_Mean\n", - ">> CountPath_Max has the average correlation of 0.416 with other metrics\n", - ">> CountPath_Mean has the average correlation of 0.399 with other metrics\n", - ">> Exclude CountPath_Max\n", - "> Step 16 comparing between AvgCyclomaticStrict and AvgCyclomaticModified\n", - ">> AvgCyclomaticStrict has the average correlation of 0.337 with other metrics\n", - ">> AvgCyclomaticModified has the average correlation of 0.33 with other metrics\n", - ">> Exclude AvgCyclomaticStrict\n", - "> Step 17 comparing between CountDeclFunction and CountDeclInstanceMethod\n", - ">> CountDeclFunction has the average correlation of 0.364 with other metrics\n", - ">> CountDeclInstanceMethod has the average correlation of 0.342 with other metrics\n", - ">> Exclude CountDeclFunction\n", - "> Step 18 comparing between CountSemicolon and CountLineCodeDecl\n", - ">> CountSemicolon has the average correlation of 0.436 with other metrics\n", - ">> CountLineCodeDecl has the average correlation of 0.421 with other metrics\n", - ">> Exclude CountSemicolon\n", - "> Step 19 comparing between CountLine and CountLineBlank\n", - ">> CountLine has the average correlation of 0.413 with other metrics\n", - ">> CountLineBlank has the average correlation of 0.372 with other metrics\n", - ">> Exclude CountLine\n", - "> Step 20 comparing between MaxNesting_Mean and CountPath_Mean\n", - ">> MaxNesting_Mean has the average correlation of 0.33 with other metrics\n", - ">> CountPath_Mean has the average correlation of 0.365 with other metrics\n", - ">> Exclude CountPath_Mean\n", - "> Step 21 comparing between MaxNesting_Max and MaxNesting_Mean\n", - ">> MaxNesting_Max has the average correlation of 0.337 with other metrics\n", - ">> MaxNesting_Mean has the average correlation of 0.316 with other metrics\n", - ">> Exclude MaxNesting_Max\n", - "> Step 22 comparing between CountOutput_Mean and AvgLineCode\n", - ">> CountOutput_Mean has the average correlation of 0.284 with other metrics\n", - ">> AvgLineCode has the average correlation of 0.317 with other metrics\n", - ">> Exclude AvgLineCode\n", - "> Step 23 comparing between CountLineCodeDecl and SumCyclomaticModified\n", - ">> CountLineCodeDecl has the average correlation of 0.385 with other metrics\n", - ">> SumCyclomaticModified has the average correlation of 0.375 with other metrics\n", - ">> Exclude CountLineCodeDecl\n", - "> Step 24 comparing between CountPath_Min and MaxNesting_Min\n", - ">> CountPath_Min has the average correlation of 0.083 with other metrics\n", - ">> MaxNesting_Min has the average correlation of 0.077 with other metrics\n", - ">> Exclude CountPath_Min\n", - "> Step 25 comparing between CountDeclInstanceMethod and SumCyclomaticModified\n", - ">> CountDeclInstanceMethod has the average correlation of 0.304 with other metrics\n", - ">> SumCyclomaticModified has the average correlation of 0.371 with other metrics\n", - ">> Exclude SumCyclomaticModified\n", - "> Step 26 comparing between RatioCommentToCode and CountStmtExe\n", - ">> RatioCommentToCode has the average correlation of 0.341 with other metrics\n", - ">> CountStmtExe has the average correlation of 0.379 with other metrics\n", - ">> Exclude CountStmtExe\n", - "> Step 27 comparing between CountInput_Max and CountInput_Mean\n", - ">> CountInput_Max has the average correlation of 0.293 with other metrics\n", - ">> CountInput_Mean has the average correlation of 0.232 with other metrics\n", - ">> Exclude CountInput_Max\n", - "> Step 28 comparing between CountOutput_Max and CountOutput_Mean\n", - ">> CountOutput_Max has the average correlation of 0.329 with other metrics\n", - ">> CountOutput_Mean has the average correlation of 0.259 with other metrics\n", - ">> Exclude CountOutput_Max\n", - "> Step 29 comparing between MaxNesting_Mean and AvgCyclomaticModified\n", - ">> MaxNesting_Mean has the average correlation of 0.257 with other metrics\n", - ">> AvgCyclomaticModified has the average correlation of 0.247 with other metrics\n", - ">> Exclude MaxNesting_Mean\n", - "> Step 30 comparing between Added_lines and Del_lines\n", - ">> Added_lines has the average correlation of 0.294 with other metrics\n", - ">> Del_lines has the average correlation of 0.291 with other metrics\n", - ">> Exclude Added_lines\n", - "> Step 31 comparing between CountLineBlank and CountDeclInstanceMethod\n", - ">> CountLineBlank has the average correlation of 0.299 with other metrics\n", - ">> CountDeclInstanceMethod has the average correlation of 0.258 with other metrics\n", - ">> Exclude CountLineBlank\n", - "> Step 32 comparing between MINOR_LINE and OWN_LINE\n", - ">> MINOR_LINE has the average correlation of 0.08 with other metrics\n", - ">> OWN_LINE has the average correlation of 0.078 with other metrics\n", - ">> Exclude MINOR_LINE\n", - "> Step 33 comparing between CountDeclInstanceMethod and CountDeclMethodPublic\n", - ">> CountDeclInstanceMethod has the average correlation of 0.246 with other metrics\n", - ">> CountDeclMethodPublic has the average correlation of 0.232 with other metrics\n", - ">> Exclude CountDeclInstanceMethod\n", - "> Step 34 comparing between AvgLine and CountOutput_Mean\n", - ">> AvgLine has the average correlation of 0.234 with other metrics\n", - ">> CountOutput_Mean has the average correlation of 0.239 with other metrics\n", - ">> Exclude CountOutput_Mean\n", - "> Step 35 comparing between CountLineComment and AvgLineComment\n", - ">> CountLineComment has the average correlation of 0.149 with other metrics\n", - ">> AvgLineComment has the average correlation of 0.112 with other metrics\n", - ">> Exclude CountLineComment\n", - "> Step 36 comparing between Del_lines and ADEV\n", - ">> Del_lines has the average correlation of 0.265 with other metrics\n", - ">> ADEV has the average correlation of 0.233 with other metrics\n", - ">> Exclude Del_lines\n", - "According to Part 1 of AutoSpearman, ['ADEV', 'CountClassCoupled', 'AvgLine', 'OWN_LINE', 'CountDeclMethodProtected', 'CountDeclInstanceVariable', 'PercentLackOfCohesion', 'CountDeclClass', 'MAJOR_LINE', 'AvgLineBlank', 'CountDeclMethodPublic', 'CountInput_Mean', 'MaxNesting_Min', 'CountOutput_Min', 'CountDeclMethodDefault', 'AvgCyclomaticModified', 'CountInput_Min', 'CountDeclClassMethod', 'CountClassDerived', 'AvgLineComment', 'CountDeclClassVariable', 'CountClassBase', 'OWN_COMMIT', 'MaxInheritanceTree', 'CountDeclMethodPrivate', 'MINOR_COMMIT', 'AvgEssential', 'COMM', 'RatioCommentToCode'] are selected.\n", - "(Part 2) Automatically select non-correlated metrics based on a Variance Inflation Factor analysis\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\micha\\miniconda3\\lib\\site-packages\\statsmodels\\tsa\\tsatools.py:142: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only\n", - " x = pd.concat(x[::order], 1)\n", - "C:\\Users\\micha\\miniconda3\\lib\\site-packages\\statsmodels\\stats\\outliers_influence.py:193: RuntimeWarning: divide by zero encountered in double_scalars\n", - " vif = 1. / (1. - r_squared_i)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> Step 1 - exclude ADEV\n", - "> Step 2 - exclude AvgLine\n", - "Finally, according to Part 2 of AutoSpearman, Index(['CountClassCoupled', 'OWN_LINE', 'CountDeclMethodProtected',\n", - " 'CountDeclInstanceVariable', 'PercentLackOfCohesion', 'CountDeclClass',\n", - " 'MAJOR_LINE', 'AvgLineBlank', 'CountDeclMethodPublic',\n", - " 'CountInput_Mean', 'MaxNesting_Min', 'CountOutput_Min',\n", - " 'CountDeclMethodDefault', 'AvgCyclomaticModified', 'CountInput_Min',\n", - " 'const', 'CountDeclClassMethod', 'CountClassDerived', 'AvgLineComment',\n", - " 'CountDeclClassVariable', 'CountClassBase', 'OWN_COMMIT',\n", - " 'MaxInheritanceTree', 'CountDeclMethodPrivate', 'MINOR_COMMIT',\n", - " 'AvgEssential', 'COMM', 'RatioCommentToCode'],\n", - " dtype='object') are selected.\n", - "27 out of 65 were selected via AutoSpearman feature selection process\n", - "feature cols: \n", - "\n", - " CountClassCoupled \\\n", - "File \n", - "activemq-console/src/main/java/org/apache/activ... 2 \n", - "\n", - " OWN_LINE \\\n", - "File \n", - "activemq-console/src/main/java/org/apache/activ... 1.0 \n", - "\n", - " CountDeclMethodProtected \\\n", - "File \n", - "activemq-console/src/main/java/org/apache/activ... 7 \n", - "\n", - " CountDeclInstanceVariable \\\n", - "File \n", - "activemq-console/src/main/java/org/apache/activ... 3 \n", - "\n", - " PercentLackOfCohesion \\\n", - "File \n", - "activemq-console/src/main/java/org/apache/activ... 61 \n", - "\n", - " CountDeclClass \\\n", - "File \n", - "activemq-console/src/main/java/org/apache/activ... 1 \n", - "\n", - " MAJOR_LINE AvgLineBlank \\\n", - "File \n", - "activemq-console/src/main/java/org/apache/activ... 0 1 \n", - "\n", - " CountDeclMethodPublic \\\n", - "File \n", - "activemq-console/src/main/java/org/apache/activ... 0 \n", - "\n", - " CountInput_Mean ... \\\n", - "File ... \n", - "activemq-console/src/main/java/org/apache/activ... 2.714286 ... \n", - "\n", - " AvgLineComment \\\n", - "File \n", - "activemq-console/src/main/java/org/apache/activ... 6 \n", - "\n", - " CountDeclClassVariable \\\n", - "File \n", - "activemq-console/src/main/java/org/apache/activ... 0 \n", - "\n", - " CountClassBase \\\n", - "File \n", - "activemq-console/src/main/java/org/apache/activ... 1 \n", - "\n", - " OWN_COMMIT \\\n", - "File \n", - "activemq-console/src/main/java/org/apache/activ... 1.0 \n", - "\n", - " MaxInheritanceTree \\\n", - "File \n", - "activemq-console/src/main/java/org/apache/activ... 2 \n", - "\n", - " CountDeclMethodPrivate \\\n", - "File \n", - "activemq-console/src/main/java/org/apache/activ... 0 \n", - "\n", - " MINOR_COMMIT \\\n", - "File \n", - "activemq-console/src/main/java/org/apache/activ... 0 \n", - "\n", - " AvgEssential COMM \\\n", - "File \n", - "activemq-console/src/main/java/org/apache/activ... 2 1 \n", - "\n", - " RatioCommentToCode \n", - "File \n", - "activemq-console/src/main/java/org/apache/activ... 0.7 \n", - "\n", - "[1 rows x 27 columns] \n", - "\n", - "\n", - "label col: \n", - "\n", - " File\n", - "activemq-console/src/main/java/org/apache/activemq/console/command/AbstractAmqCommand.java False\n", - "Name: RealBug, dtype: bool\n" - ] - } - ], - "source": [ - "from pyexplainer.pyexplainer_pyexplainer import AutoSpearman\n", - "# select all rows, and all feature cols\n", - "# the last col, which is label col, is not selected\n", - "X = df.iloc[:, :-1]\n", - "total_features = len(X.columns)\n", - "\n", - "# apply feature selection function to our feature DataFrame\n", - "X = AutoSpearman(X)\n", - "selected = len(X.columns)\n", - "\n", - "# select all rows, and the last label col\n", - "y = df.iloc[:, -1]\n", - "\n", - "print(selected, \" out of \", total_features, \" were selected via AutoSpearman feature selection process\")\n", - "print('feature cols:', '\\n\\n', X.head(1), '\\n\\n')\n", - "print('label col:', '\\n\\n', y.head(1))" - ] - }, - { - "cell_type": "markdown", - "id": "offshore-cattle", - "metadata": {}, - "source": [ - "### 1.4 Split data into training and testing set" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "absolute-diversity", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.model_selection import train_test_split\n", - "# 70% training and 30% test\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)" - ] - }, - { - "cell_type": "markdown", - "id": "fewer-webmaster", - "metadata": {}, - "source": [ - "## 2. Training and Predicting" - ] - }, - { - "cell_type": "markdown", - "id": "increased-queensland", - "metadata": {}, - "source": [ - "### 2.1 Train a RandomForest model using sklearn" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "lesser-coupon", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "RandomForestClassifier(random_state=0)" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.ensemble import RandomForestClassifier\n", - "\n", - "rf_model = RandomForestClassifier(n_estimators=100, max_depth=None, min_samples_split=2, random_state=0)\n", - "rf_model.fit(X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "id": "early-admission", - "metadata": {}, - "source": [ - "### 2.2 Generate predictions" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "detailed-isaac", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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activemq-core/src/main/java/org/apache/activemq/kaha/MapContainer.javaFalse
activemq-core/src/main/java/org/apache/activemq/openwire/v3/MessageAckMarshaller.javaFalse
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" - ], - "text/plain": [ - " PredictedBug\n", - "File \n", - "activemq-core/src/main/java/org/apache/activemq... False\n", - "activemq-core/src/main/java/org/apache/activemq... False\n", - "activemq-core/src/main/java/org/apache/activemq... False" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# generate prediction from the model, which will return a list of predicted labels\n", - "y_preds = rf_model.predict(X_test) \n", - "# create a DataFrame which only has predicted label column\n", - "y_preds = pd.DataFrame(data={'PredictedBug': y_preds}, index=y_test.index) \n", - "y_preds.head(3)" - ] - }, - { - "cell_type": "markdown", - "id": "inappropriate-decline", - "metadata": {}, - "source": [ - "## 3. Prediction post processing" - ] - }, - { - "cell_type": "markdown", - "id": "weekly-homeless", - "metadata": {}, - "source": [ - "### 3.1 Combine feature cols, label col, and the predicted col in testing set" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "steady-comparison", - "metadata": {}, - "outputs": [], - "source": [ - "combined_testing_data = X_test.join(y_test.to_frame())\n", - "combined_testing_data = combined_testing_data.join(y_preds)\n", - "combined_testing_data.head(3)\n", - "# total num of rows\n", - "total_rows = len(combined_testing_data)" - ] - }, - { - "cell_type": "markdown", - "id": "viral-limit", - "metadata": {}, - "source": [ - "### 3.2 Filter out wronly predicted rows " - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "median-element", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The model correctly predicted 90.60000000000001 % of testing data\n" - ] - } - ], - "source": [ - "correctly_predicted_data = combined_testing_data[combined_testing_data['RealBug']==combined_testing_data['PredictedBug']]\n", - "correctly_predicted_rows = len(correctly_predicted_data)\n", - "print('The model correctly predicted ', round((correctly_predicted_rows / total_rows), 3) * 100, '% of testing data')" - ] - }, - { - "cell_type": "markdown", - "id": "single-compression", - "metadata": {}, - "source": [ - "### 3.3 We focus on the bug file, therefore, filter out the non-buggy file" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "welsh-aspect", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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True True \n", - "activemq-core/src/main/java/org/apache/activemq... True True \n", - "activemq-openwire-generator/src/main/java/org/a... True True \n", - "\n", - "[3 rows x 29 columns]" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "correctly_predicted_bug = correctly_predicted_data[correctly_predicted_data['RealBug']==True]\n", - "correctly_predicted_bug.head(3)" - ] - }, - { - "cell_type": "markdown", - "id": "systematic-equipment", - "metadata": {}, - "source": [ - "### 3.4 Define feature cols and label col using correctly predicted testing data" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "shaped-confirmation", - "metadata": {}, - "outputs": [], - "source": [ - "# select all rows and feature cols\n", - "feature_cols = correctly_predicted_bug.iloc[:, :-2]\n", - "# selected all rows and one label col (either RealBug or PredictedBug is fine since they are the same)\n", - "label_col = correctly_predicted_bug.iloc[:, -2]" - ] - }, - { - "cell_type": "markdown", - "id": "vocational-spending", - "metadata": {}, - "source": [ - "### 3.5 Select one row of correctly predicted bug to be explained" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "cross-rhythm", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "one row of feature: \n", - "\n", - " CountClassCoupled \\\n", - "File \n", - "activemq-core/src/test/java/org/apache/activemq... 12 \n", - "\n", - " OWN_LINE \\\n", - "File \n", - "activemq-core/src/test/java/org/apache/activemq... 0.738916 \n", - "\n", - " CountDeclMethodProtected \\\n", - "File \n", - "activemq-core/src/test/java/org/apache/activemq... 3 \n", - "\n", - " CountDeclInstanceVariable \\\n", - "File \n", - "activemq-core/src/test/java/org/apache/activemq... 2 \n", - "\n", - " PercentLackOfCohesion \\\n", - "File \n", - "activemq-core/src/test/java/org/apache/activemq... 77 \n", - "\n", - " CountDeclClass \\\n", - "File \n", - "activemq-core/src/test/java/org/apache/activemq... 3 \n", - "\n", - " MAJOR_LINE AvgLineBlank \\\n", - "File \n", - "activemq-core/src/test/java/org/apache/activemq... 1 1 \n", - "\n", - " CountDeclMethodPublic \\\n", - "File \n", - "activemq-core/src/test/java/org/apache/activemq... 8 \n", - "\n", - " CountInput_Mean ... \\\n", - "File ... \n", - "activemq-core/src/test/java/org/apache/activemq... 2.181818 ... \n", - "\n", - " AvgLineComment \\\n", - "File \n", - "activemq-core/src/test/java/org/apache/activemq... 2 \n", - "\n", - " CountDeclClassVariable \\\n", - "File \n", - "activemq-core/src/test/java/org/apache/activemq... 1 \n", - "\n", - " CountClassBase \\\n", - "File \n", - "activemq-core/src/test/java/org/apache/activemq... 2 \n", - "\n", - " OWN_COMMIT \\\n", - "File \n", - "activemq-core/src/test/java/org/apache/activemq... 0.8 \n", - "\n", - " MaxInheritanceTree \\\n", - "File \n", - "activemq-core/src/test/java/org/apache/activemq... 6 \n", - "\n", - " CountDeclMethodPrivate \\\n", - "File \n", - "activemq-core/src/test/java/org/apache/activemq... 0 \n", - "\n", - " MINOR_COMMIT \\\n", - "File \n", - "activemq-core/src/test/java/org/apache/activemq... 0 \n", - "\n", - " AvgEssential COMM \\\n", - "File \n", - "activemq-core/src/test/java/org/apache/activemq... 1 5 \n", - "\n", - " RatioCommentToCode \n", - "File \n", - "activemq-core/src/test/java/org/apache/activemq... 0.27 \n", - "\n", - "[1 rows x 27 columns] \n", - "\n", - "one row of label: \n", - "\n", - " File\n", - "activemq-core/src/test/java/org/apache/activemq/transport/fanout/FanoutTransportBrokerTest.java True\n", - "Name: RealBug, dtype: bool\n" - ] - } - ], - "source": [ - "# decide which row to be selected\n", - "selected_row = 0\n", - "# select the row in X_test which contains all of the feature values\n", - "X_explain = feature_cols.iloc[[selected_row]]\n", - "# select the corresponding label from the DataFrame that we just created above\n", - "y_explain = label_col.iloc[[selected_row]]\n", - "print('one row of feature:', '\\n\\n', X_explain, '\\n')\n", - "print('one row of label:', '\\n\\n', y_explain)" - ] - }, - { - "cell_type": "markdown", - "id": "second-overhead", - "metadata": {}, - "source": [ - "## 4. Create rules (explanations) and visualise it !" - ] - }, - { - "cell_type": "markdown", - "id": "private-symbol", - "metadata": {}, - "source": [ - "### 4.1 Initialise a PyExplainer object" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "coastal-assist", - "metadata": {}, - "outputs": [], - "source": [ - "from pyexplainer import pyexplainer_pyexplainer\n", - "\n", - "py_explainer = pyexplainer_pyexplainer.PyExplainer(X_train = X_train,\n", - " y_train = y_train,\n", - " indep = X_train.columns,\n", - " dep = 'RealBug',\n", - " blackbox_model = rf_model)" - ] - }, - { - "cell_type": "markdown", - "id": "fatal-leather", - "metadata": {}, - "source": [ - "### 4.2 Create rules by triggering explain function under PyExplainer object\n", - "##### Attention: This step can be time-consuming" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "floating-milwaukee", - "metadata": {}, - "outputs": [], - "source": [ - "rules = py_explainer.explain(X_explain=X_explain,\n", - " y_explain=y_explain,\n", - " search_function='crossoverinterpolation')" - ] - }, - { - "cell_type": "markdown", - "id": "interim-banner", - "metadata": {}, - "source": [ - "##### Those created rules are stored in a dictionary, for more information about what is contained in each key, please refer to 'Appendix' part" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "short-atlanta", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_keys(['synthetic_data', 'synthetic_predictions', 'X_explain', 'y_explain', 'indep', 'dep', 'top_k_positive_rules', 'top_k_negative_rules', 'local_rulefit_model'])" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rules.keys()" - ] - }, - { - "cell_type": "markdown", - "id": "desperate-profit", - "metadata": {}, - "source": [ - "### 4.3 Simply trigger visualise function under PyExplainer object to visualise the created rules " - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "fourth-queensland", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "a2baa847c99441bd982b8792e092f003", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(Label(value='Risk Score: '), FloatProgress(value=0.0, bar_style='info', layout=Layout(width='40…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "07d0fafe28994776bc5666a43c492587", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Output(layout=Layout(border='3px solid black'))" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "f6f9d182fd2949dbbcc534fd2bfd17d9", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatSlider(value=1.0, continuous_update=False, description='#1 The value of CountDeclClassVariable is more th…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "25fbbd2765cc4afba015f97b46dfdfc8", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatSlider(value=0.0, continuous_update=False, description='#2 The value of CountDeclMethodPrivate is more th…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "py_explainer.visualise(rules)" - ] - }, - { - "cell_type": "markdown", - "id": "olive-filename", - "metadata": {}, - "source": [ - "# Appendix" - ] - }, - { - "cell_type": "markdown", - "id": "closed-draft", - "metadata": {}, - "source": [ - "## The detail of variables used to to create PyExplainer" - ] - }, - { - "cell_type": "markdown", - "id": "sufficient-vault", - "metadata": {}, - "source": [ - "### Synthetic_data\n", - "\n", - "Synthetic_data is data that are generated by PyExplainer using one of the following approaches.\n", - "\n", - "1. Crossover and Interpolation\n", - "2. Random Perturbation.\n", - "\n", - "After Synthetic_data is generated, it is stored as a pandas DataFrame object. " - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "vital-dynamics", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Type of pyExp_rule_obj['synthetic_data'] - \n", - "\n", - "Example\n" - ] - }, - { - "data": { - "text/html": [ - "
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"\n", - " AvgLineComment \\\n", - "File \n", - "activemq-core/src/test/java/org/apache/activemq... 2 \n", - "\n", - " CountDeclClassVariable \\\n", - "File \n", - "activemq-core/src/test/java/org/apache/activemq... 1 \n", - "\n", - " CountClassBase \\\n", - "File \n", - "activemq-core/src/test/java/org/apache/activemq... 2 \n", - "\n", - " OWN_COMMIT \\\n", - "File \n", - "activemq-core/src/test/java/org/apache/activemq... 0.8 \n", - "\n", - " MaxInheritanceTree \\\n", - "File \n", - "activemq-core/src/test/java/org/apache/activemq... 6 \n", - "\n", - " CountDeclMethodPrivate \\\n", - "File \n", - "activemq-core/src/test/java/org/apache/activemq... 0 \n", - "\n", - " MINOR_COMMIT \\\n", - "File \n", - "activemq-core/src/test/java/org/apache/activemq... 0 \n", - "\n", - " AvgEssential COMM \\\n", - "File \n", - "activemq-core/src/test/java/org/apache/activemq... 1 5 \n", - "\n", - " RatioCommentToCode \n", - "File \n", - "activemq-core/src/test/java/org/apache/activemq... 0.27 \n", - "\n", - "[1 rows x 27 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "print(\"Type of pyExp_rule_obj['X_explain'] - \", type(rules['X_explain']), \"\\n\")\n", - "\n", - "print('Example')\n", - "display(rules['X_explain'])" - ] - }, - { - "cell_type": "markdown", - "id": "ongoing-drilling", - "metadata": {}, - "source": [ - "### y_explain\n", - "\n", - "y_explain is a label of X_explain " - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "antique-straight", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Type of pyExp_rule_obj['y_explain'] - \n", - "\n", - "Example \n", - "\n", - " File\n", - "activemq-core/src/test/java/org/apache/activemq/transport/fanout/FanoutTransportBrokerTest.java True\n", - "Name: RealBug, dtype: bool\n" - ] - } - ], - "source": [ - "print(\"Type of pyExp_rule_obj['y_explain'] - \", type(rules['y_explain']), \"\\n\")\n", - "print(\"Example\", \"\\n\\n\", rules['y_explain'])" - ] - }, - { - "cell_type": "markdown", - "id": "narrative-senator", - "metadata": {}, - "source": [ - "### indep\n", - "#### indep is feature names of X_explain" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "sized-valuable", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Type of pyExp_rule_obj['indep'] - \n", - "\n", - "Example \n", - "\n", - " Index(['CountClassCoupled', 'OWN_LINE', 'CountDeclMethodProtected',\n", - " 'CountDeclInstanceVariable', 'PercentLackOfCohesion', 'CountDeclClass',\n", - " 'MAJOR_LINE', 'AvgLineBlank', 'CountDeclMethodPublic',\n", - " 'CountInput_Mean', 'MaxNesting_Min', 'CountOutput_Min',\n", - " 'CountDeclMethodDefault', 'AvgCyclomaticModified', 'CountInput_Min',\n", - " 'CountDeclClassMethod', 'CountClassDerived', 'AvgLineComment',\n", - " 'CountDeclClassVariable', 'CountClassBase', 'OWN_COMMIT',\n", - " 'MaxInheritanceTree', 'CountDeclMethodPrivate', 'MINOR_COMMIT',\n", - " 'AvgEssential', 'COMM', 'RatioCommentToCode'],\n", - " dtype='object')\n" - ] - } - ], - "source": [ - "print(\"Type of pyExp_rule_obj['indep'] - \", type(rules['indep']), \"\\n\")\n", - "print(\"Example\", \"\\n\\n\", rules['indep'])" - ] - }, - { - "cell_type": "markdown", - "id": "atlantic-charles", - "metadata": {}, - "source": [ - "### dep\n", - "#### dep is a label name" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "moral-collectible", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Type of pyExp_rule_obj['dep'] - \n", - "\n", - "Example \n", - "\n", - " RealBug\n" - ] - } - ], - "source": [ - "print(\"Type of pyExp_rule_obj['dep'] - \", type(rules['dep']), \"\\n\")\n", - "print(\"Example\", \"\\n\\n\", rules['dep'])" - ] - }, - { - "cell_type": "markdown", - "id": "compact-willow", - "metadata": {}, - "source": [ - "### top_k_positive_rules\n", - "\n", - "top_k_positive_rules is top-k rules that are genereated by PyExplainer to explain why a commit is predicted as defective.\n", - "\n", - "Here we show top-3 rules that lead to defective commits=" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "measured-miller", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Type of pyExp_rule_obj['top_k_positive_rules'] - \n", - "\n", - "Example\n" - ] - }, - { - "data": { - "text/html": [ - "
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indexruletypecoefsupportimportanceis_satisfy_instance
0161AvgLineComment > -3.6149998903274536 & CountCl...rule3.490109e-230.2715401.552240e-23True
1562OWN_COMMIT <= 0.8349999785423279 & COMM > 2.98...rule3.639777e-230.2088771.479593e-23True
2820CountClassBase <= 2.9850000143051147 & OWN_COM...rule3.484802e-230.2323761.471797e-23True
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ruletypecoefsupportimportanceClass
918OWN_COMMIT > 0.8550000190734863 & CountDeclCla...rule-4.819474e-230.6788512.250298e-23Clean
1652OWN_COMMIT > 0.8650000095367432rule-4.748976e-230.6892952.197742e-23Clean
1107CountDeclMethodPrivate <= 1.8399999737739563 &...rule-4.863102e-230.7258492.169360e-23Clean
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" - ], - "text/plain": [ - " rule type coef \\\n", - "918 OWN_COMMIT > 0.8550000190734863 & CountDeclCla... rule -4.819474e-23 \n", - "1652 OWN_COMMIT > 0.8650000095367432 rule -4.748976e-23 \n", - "1107 CountDeclMethodPrivate <= 1.8399999737739563 &... rule -4.863102e-23 \n", - "\n", - " support importance Class \n", - "918 0.678851 2.250298e-23 Clean \n", - "1652 0.689295 2.197742e-23 Clean \n", - "1107 0.725849 2.169360e-23 Clean " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "print(\"Type of pyExp_rule_obj['top_k_negative_rules'] - \", type(rules['top_k_negative_rules']), \"\\n\")\n", - "print('Example')\n", - "display(rules['top_k_negative_rules'])" - ] - }, - { - "cell_type": "markdown", - "id": "written-insertion", - "metadata": {}, - "source": [ - "# Bug Report Channel\n", - "#### Please report here\n", - "#### 📧 or email your report to michaelfu1998@gmail.com" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - }, - "varInspector": { - "cols": { - "lenName": 16, - "lenType": 16, - "lenVar": 40 - }, - "kernels_config": { - "python": { - "delete_cmd_postfix": "", - "delete_cmd_prefix": "del ", - "library": "var_list.py", - "varRefreshCmd": "print(var_dic_list())" - }, - "r": { - "delete_cmd_postfix": ") ", - "delete_cmd_prefix": "rm(", - "library": "var_list.r", - "varRefreshCmd": "cat(var_dic_list()) " - } - }, - "types_to_exclude": [ - "module", - "function", - "builtin_function_or_method", - "instance", - "_Feature" - ], - "window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/dist/pyexplainer-1.1.4-py3-none-any.whl b/dist/pyexplainer-1.1.4-py3-none-any.whl deleted 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