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Merge pull request #4 from Doctorado-ML/language_version

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Add Language and language version to reports
Add custom seeds to .env
This commit is contained in:
Ricardo Montañana Gómez
2022-11-01 14:07:59 +01:00
committed by GitHub
parent 5532beb88a 1e83db7956
commit c77feff54b
33 changed files with 631 additions and 21 deletions
Download Patch File Download Diff File Expand all files Collapse all files

1
.env.dist
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@@ -4,3 +4,4 @@ n_folds=5
model=ODTE
stratified=0
source_data=Tanveer
seeds=[57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]

1
.flake8
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@@ -1,2 +1,3 @@
[flake8]
exclude = .git,__init__.py
ignore = E203, W503

526
Untitled.ipynb Normal file
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@@ -0,0 +1,526 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "0e48f7d2-7481-4eca-9c38-56d21c203093",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"DEBUG:weka.core.jvm:Adding bundled jars\n",
"DEBUG:weka.core.jvm:Classpath=['/Users/rmontanana/miniconda3/envs/pyweka/lib/python3.10/site-packages/javabridge/jars/rhino-1.7R4.jar', '/Users/rmontanana/miniconda3/envs/pyweka/lib/python3.10/site-packages/javabridge/jars/runnablequeue.jar', '/Users/rmontanana/miniconda3/envs/pyweka/lib/python3.10/site-packages/javabridge/jars/cpython.jar', '/Users/rmontanana/miniconda3/envs/pyweka/lib/python3.10/site-packages/weka/lib/python-weka-wrapper.jar', '/Users/rmontanana/miniconda3/envs/pyweka/lib/python3.10/site-packages/weka/lib/weka.jar']\n",
"DEBUG:weka.core.jvm:MaxHeapSize=default\n",
"DEBUG:weka.core.jvm:Package support disabled\n",
"WARNING: An illegal reflective access operation has occurred\n",
"WARNING: Illegal reflective access by weka.core.WekaPackageClassLoaderManager (file:/Users/rmontanana/miniconda3/envs/pyweka/lib/python3.10/site-packages/weka/lib/weka.jar) to method java.lang.ClassLoader.defineClass(java.lang.String,byte[],int,int,java.security.ProtectionDomain)\n",
"WARNING: Please consider reporting this to the maintainers of weka.core.WekaPackageClassLoaderManager\n",
"WARNING: Use --illegal-access=warn to enable warnings of further illegal reflective access operations\n",
"WARNING: All illegal access operations will be denied in a future release\n"
]
}
],
"source": [
"import weka.core.jvm as jvm\n",
"jvm.start()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "2ac4e479-3818-4562-a967-bb303d8dd573",
"metadata": {},
"outputs": [],
"source": [
"from weka.core.converters import Loader\n",
"data_dir = \"/Users/rmontanana/Code/discretizbench/datasets/\"\n",
"loader = Loader(classname=\"weka.core.converters.ArffLoader\")\n",
"data = loader.load_file(data_dir + \"iris.arff\")\n",
"data.class_is_last()\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "ceb9f912-db42-4cbc-808f-48b5a9d89d44",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"@relation iris\n",
"\n",
"@attribute sepallength numeric\n",
"@attribute sepalwidth numeric\n",
"@attribute petallength numeric\n",
"@attribute petalwidth numeric\n",
"@attribute class {Iris-setosa,Iris-versicolor,Iris-virginica}\n",
"\n",
"@data\n",
"5.1,3.5,1.4,0.2,Iris-setosa\n",
"4.9,3,1.4,0.2,Iris-setosa\n",
"4.7,3.2,1.3,0.2,Iris-setosa\n",
"4.6,3.1,1.5,0.2,Iris-setosa\n",
"5,3.6,1.4,0.2,Iris-setosa\n",
"5.4,3.9,1.7,0.4,Iris-setosa\n",
"4.6,3.4,1.4,0.3,Iris-setosa\n",
"5,3.4,1.5,0.2,Iris-setosa\n",
"4.4,2.9,1.4,0.2,Iris-setosa\n",
"4.9,3.1,1.5,0.1,Iris-setosa\n",
"5.4,3.7,1.5,0.2,Iris-setosa\n",
"4.8,3.4,1.6,0.2,Iris-setosa\n",
"4.8,3,1.4,0.1,Iris-setosa\n",
"4.3,3,1.1,0.1,Iris-setosa\n",
"5.8,4,1.2,0.2,Iris-setosa\n",
"5.7,4.4,1.5,0.4,Iris-setosa\n",
"5.4,3.9,1.3,0.4,Iris-setosa\n",
"5.1,3.5,1.4,0.3,Iris-setosa\n",
"5.7,3.8,1.7,0.3,Iris-setosa\n",
"5.1,3.8,1.5,0.3,Iris-setosa\n",
"5.4,3.4,1.7,0.2,Iris-setosa\n",
"5.1,3.7,1.5,0.4,Iris-setosa\n",
"4.6,3.6,1,0.2,Iris-setosa\n",
"5.1,3.3,1.7,0.5,Iris-setosa\n",
"4.8,3.4,1.9,0.2,Iris-setosa\n",
"5,3,1.6,0.2,Iris-setosa\n",
"5,3.4,1.6,0.4,Iris-setosa\n",
"5.2,3.5,1.5,0.2,Iris-setosa\n",
"5.2,3.4,1.4,0.2,Iris-setosa\n",
"4.7,3.2,1.6,0.2,Iris-setosa\n",
"4.8,3.1,1.6,0.2,Iris-setosa\n",
"5.4,3.4,1.5,0.4,Iris-setosa\n",
"5.2,4.1,1.5,0.1,Iris-setosa\n",
"5.5,4.2,1.4,0.2,Iris-setosa\n",
"4.9,3.1,1.5,0.1,Iris-setosa\n",
"5,3.2,1.2,0.2,Iris-setosa\n",
"5.5,3.5,1.3,0.2,Iris-setosa\n",
"4.9,3.1,1.5,0.1,Iris-setosa\n",
"4.4,3,1.3,0.2,Iris-setosa\n",
"5.1,3.4,1.5,0.2,Iris-setosa\n",
"5,3.5,1.3,0.3,Iris-setosa\n",
"4.5,2.3,1.3,0.3,Iris-setosa\n",
"4.4,3.2,1.3,0.2,Iris-setosa\n",
"5,3.5,1.6,0.6,Iris-setosa\n",
"5.1,3.8,1.9,0.4,Iris-setosa\n",
"4.8,3,1.4,0.3,Iris-setosa\n",
"5.1,3.8,1.6,0.2,Iris-setosa\n",
"4.6,3.2,1.4,0.2,Iris-setosa\n",
"5.3,3.7,1.5,0.2,Iris-setosa\n",
"5,3.3,1.4,0.2,Iris-setosa\n",
"7,3.2,4.7,1.4,Iris-versicolor\n",
"6.4,3.2,4.5,1.5,Iris-versicolor\n",
"6.9,3.1,4.9,1.5,Iris-versicolor\n",
"5.5,2.3,4,1.3,Iris-versicolor\n",
"6.5,2.8,4.6,1.5,Iris-versicolor\n",
"5.7,2.8,4.5,1.3,Iris-versicolor\n",
"6.3,3.3,4.7,1.6,Iris-versicolor\n",
"4.9,2.4,3.3,1,Iris-versicolor\n",
"6.6,2.9,4.6,1.3,Iris-versicolor\n",
"5.2,2.7,3.9,1.4,Iris-versicolor\n",
"5,2,3.5,1,Iris-versicolor\n",
"5.9,3,4.2,1.5,Iris-versicolor\n",
"6,2.2,4,1,Iris-versicolor\n",
"6.1,2.9,4.7,1.4,Iris-versicolor\n",
"5.6,2.9,3.6,1.3,Iris-versicolor\n",
"6.7,3.1,4.4,1.4,Iris-versicolor\n",
"5.6,3,4.5,1.5,Iris-versicolor\n",
"5.8,2.7,4.1,1,Iris-versicolor\n",
"6.2,2.2,4.5,1.5,Iris-versicolor\n",
"5.6,2.5,3.9,1.1,Iris-versicolor\n",
"5.9,3.2,4.8,1.8,Iris-versicolor\n",
"6.1,2.8,4,1.3,Iris-versicolor\n",
"6.3,2.5,4.9,1.5,Iris-versicolor\n",
"6.1,2.8,4.7,1.2,Iris-versicolor\n",
"6.4,2.9,4.3,1.3,Iris-versicolor\n",
"6.6,3,4.4,1.4,Iris-versicolor\n",
"6.8,2.8,4.8,1.4,Iris-versicolor\n",
"6.7,3,5,1.7,Iris-versicolor\n",
"6,2.9,4.5,1.5,Iris-versicolor\n",
"5.7,2.6,3.5,1,Iris-versicolor\n",
"5.5,2.4,3.8,1.1,Iris-versicolor\n",
"5.5,2.4,3.7,1,Iris-versicolor\n",
"5.8,2.7,3.9,1.2,Iris-versicolor\n",
"6,2.7,5.1,1.6,Iris-versicolor\n",
"5.4,3,4.5,1.5,Iris-versicolor\n",
"6,3.4,4.5,1.6,Iris-versicolor\n",
"6.7,3.1,4.7,1.5,Iris-versicolor\n",
"6.3,2.3,4.4,1.3,Iris-versicolor\n",
"5.6,3,4.1,1.3,Iris-versicolor\n",
"5.5,2.5,4,1.3,Iris-versicolor\n",
"5.5,2.6,4.4,1.2,Iris-versicolor\n",
"6.1,3,4.6,1.4,Iris-versicolor\n",
"5.8,2.6,4,1.2,Iris-versicolor\n",
"5,2.3,3.3,1,Iris-versicolor\n",
"5.6,2.7,4.2,1.3,Iris-versicolor\n",
"5.7,3,4.2,1.2,Iris-versicolor\n",
"5.7,2.9,4.2,1.3,Iris-versicolor\n",
"6.2,2.9,4.3,1.3,Iris-versicolor\n",
"5.1,2.5,3,1.1,Iris-versicolor\n",
"5.7,2.8,4.1,1.3,Iris-versicolor\n",
"6.3,3.3,6,2.5,Iris-virginica\n",
"5.8,2.7,5.1,1.9,Iris-virginica\n",
"7.1,3,5.9,2.1,Iris-virginica\n",
"6.3,2.9,5.6,1.8,Iris-virginica\n",
"6.5,3,5.8,2.2,Iris-virginica\n",
"7.6,3,6.6,2.1,Iris-virginica\n",
"4.9,2.5,4.5,1.7,Iris-virginica\n",
"7.3,2.9,6.3,1.8,Iris-virginica\n",
"6.7,2.5,5.8,1.8,Iris-virginica\n",
"7.2,3.6,6.1,2.5,Iris-virginica\n",
"6.5,3.2,5.1,2,Iris-virginica\n",
"6.4,2.7,5.3,1.9,Iris-virginica\n",
"6.8,3,5.5,2.1,Iris-virginica\n",
"5.7,2.5,5,2,Iris-virginica\n",
"5.8,2.8,5.1,2.4,Iris-virginica\n",
"6.4,3.2,5.3,2.3,Iris-virginica\n",
"6.5,3,5.5,1.8,Iris-virginica\n",
"7.7,3.8,6.7,2.2,Iris-virginica\n",
"7.7,2.6,6.9,2.3,Iris-virginica\n",
"6,2.2,5,1.5,Iris-virginica\n",
"6.9,3.2,5.7,2.3,Iris-virginica\n",
"5.6,2.8,4.9,2,Iris-virginica\n",
"7.7,2.8,6.7,2,Iris-virginica\n",
"6.3,2.7,4.9,1.8,Iris-virginica\n",
"6.7,3.3,5.7,2.1,Iris-virginica\n",
"7.2,3.2,6,1.8,Iris-virginica\n",
"6.2,2.8,4.8,1.8,Iris-virginica\n",
"6.1,3,4.9,1.8,Iris-virginica\n",
"6.4,2.8,5.6,2.1,Iris-virginica\n",
"7.2,3,5.8,1.6,Iris-virginica\n",
"7.4,2.8,6.1,1.9,Iris-virginica\n",
"7.9,3.8,6.4,2,Iris-virginica\n",
"6.4,2.8,5.6,2.2,Iris-virginica\n",
"6.3,2.8,5.1,1.5,Iris-virginica\n",
"6.1,2.6,5.6,1.4,Iris-virginica\n",
"7.7,3,6.1,2.3,Iris-virginica\n",
"6.3,3.4,5.6,2.4,Iris-virginica\n",
"6.4,3.1,5.5,1.8,Iris-virginica\n",
"6,3,4.8,1.8,Iris-virginica\n",
"6.9,3.1,5.4,2.1,Iris-virginica\n",
"6.7,3.1,5.6,2.4,Iris-virginica\n",
"6.9,3.1,5.1,2.3,Iris-virginica\n",
"5.8,2.7,5.1,1.9,Iris-virginica\n",
"6.8,3.2,5.9,2.3,Iris-virginica\n",
"6.7,3.3,5.7,2.5,Iris-virginica\n",
"6.7,3,5.2,2.3,Iris-virginica\n",
"6.3,2.5,5,1.9,Iris-virginica\n",
"6.5,3,5.2,2,Iris-virginica\n",
"6.2,3.4,5.4,2.3,Iris-virginica\n",
"5.9,3,5.1,1.8,Iris-virginica\n"
]
}
],
"source": [
"print(data)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "ded59d25-c34c-4fb8-a35f-1162f1218414",
"metadata": {},
"outputs": [],
"source": [
"from weka.classifiers import Classifier\n",
"cls = Classifier(classname=\"weka.classifiers.trees.J48\", options=[\"-C\", \"0.3\"])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "4c82f2ae-4071-4571-9a19-433b98463143",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['-C', '0.3', '-M', '2']\n"
]
}
],
"source": [
"print(cls.options)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "4c5c7893-ebbe-407d-872c-fd0bf41f8dc8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"weka.classifiers.trees.J48 -C 0.3 -M 2\n"
]
}
],
"source": [
"print(cls.to_commandline())"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "7b73c18d-e0b0-469d-8a60-03bae8e01128",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"70: label index=1.0, class distribution=[0.00308382 0.98338244 0.01353374]\n",
"71: label index=2.0, class distribution=[0.00920087 0.06127297 0.92952615]\n",
"72: label index=1.0, class distribution=[0.00308382 0.98338244 0.01353374]\n",
"73: label index=2.0, class distribution=[0.00409632 0.47019227 0.5257114 ]\n",
"74: label index=1.0, class distribution=[0.00308382 0.98338244 0.01353374]\n",
"75: label index=1.0, class distribution=[0.00228744 0.97269152 0.02502104]\n",
"76: label index=1.0, class distribution=[0.00545355 0.97466198 0.01988447]\n",
"77: label index=2.0, class distribution=[0.00409632 0.47019227 0.5257114 ]\n",
"78: label index=1.0, class distribution=[0.010867 0.52425197 0.46488102]\n",
"79: label index=1.0, class distribution=[0.00308382 0.98338244 0.01353374]\n",
"80: label index=1.0, class distribution=[0.00308382 0.98338244 0.01353374]\n",
"81: label index=1.0, class distribution=[0.00725727 0.94287877 0.04986396]\n",
"82: label index=1.0, class distribution=[0.00725727 0.94287877 0.04986396]\n",
"83: label index=1.0, class distribution=[0.00308382 0.98338244 0.01353374]\n",
"84: label index=1.0, class distribution=[0.02353491 0.65433551 0.32212958]\n",
"85: label index=1.0, class distribution=[0.01727259 0.943168 0.03955941]\n",
"86: label index=1.0, class distribution=[0.06513736 0.90310001 0.03176263]\n",
"87: label index=1.0, class distribution=[0.00545355 0.97466198 0.01988447]\n",
"88: label index=1.0, class distribution=[0.00228744 0.97269152 0.02502104]\n",
"89: label index=1.0, class distribution=[0.00732671 0.98195521 0.01071808]\n",
"90: label index=1.0, class distribution=[0.00725727 0.94287877 0.04986396]\n",
"91: label index=1.0, class distribution=[0.00725727 0.94287877 0.04986396]\n",
"92: label index=1.0, class distribution=[0.00732671 0.98195521 0.01071808]\n",
"93: label index=1.0, class distribution=[0.00308382 0.98338244 0.01353374]\n",
"94: label index=1.0, class distribution=[0.00725727 0.94287877 0.04986396]\n",
"95: label index=1.0, class distribution=[0.00308382 0.98338244 0.01353374]\n",
"96: label index=1.0, class distribution=[0.00732671 0.98195521 0.01071808]\n",
"97: label index=1.0, class distribution=[0.00308382 0.98338244 0.01353374]\n",
"98: label index=1.0, class distribution=[0.00228744 0.97269152 0.02502104]\n",
"99: label index=1.0, class distribution=[0.00725727 0.94287877 0.04986396]\n",
"100: label index=1.0, class distribution=[0.00308382 0.98338244 0.01353374]\n",
"101: label index=2.0, class distribution=[0.00102485 0.02817698 0.97079816]\n",
"102: label index=2.0, class distribution=[0.01274667 0.02829538 0.95895795]\n",
"103: label index=2.0, class distribution=[0.00102485 0.02817698 0.97079816]\n",
"104: label index=2.0, class distribution=[0.00139749 0.01280739 0.98579512]\n",
"105: label index=2.0, class distribution=[0.00102485 0.02817698 0.97079816]\n",
"106: label index=2.0, class distribution=[0.00102485 0.02817698 0.97079816]\n",
"107: label index=1.0, class distribution=[0.00725727 0.94287877 0.04986396]\n",
"108: label index=2.0, class distribution=[0.00139749 0.01280739 0.98579512]\n",
"109: label index=2.0, class distribution=[0.00139749 0.01280739 0.98579512]\n",
"110: label index=2.0, class distribution=[0.00431289 0.0395258 0.95616131]\n",
"111: label index=2.0, class distribution=[0.00102485 0.02817698 0.97079816]\n",
"112: label index=2.0, class distribution=[0.00139749 0.01280739 0.98579512]\n",
"113: label index=2.0, class distribution=[0.00102485 0.02817698 0.97079816]\n",
"114: label index=2.0, class distribution=[0.01274667 0.02829538 0.95895795]\n",
"115: label index=2.0, class distribution=[0.01274667 0.02829538 0.95895795]\n",
"116: label index=2.0, class distribution=[0.00102485 0.02817698 0.97079816]\n",
"117: label index=2.0, class distribution=[0.00102485 0.02817698 0.97079816]\n",
"118: label index=2.0, class distribution=[0.00431289 0.0395258 0.95616131]\n",
"119: label index=2.0, class distribution=[0.00139749 0.01280739 0.98579512]\n",
"120: label index=1.0, class distribution=[0.02353491 0.65433551 0.32212958]\n",
"121: label index=2.0, class distribution=[0.00102485 0.02817698 0.97079816]\n",
"122: label index=2.0, class distribution=[0.01274667 0.02829538 0.95895795]\n",
"123: label index=2.0, class distribution=[0.00139749 0.01280739 0.98579512]\n",
"124: label index=2.0, class distribution=[0.00139749 0.01280739 0.98579512]\n",
"125: label index=2.0, class distribution=[0.00102485 0.02817698 0.97079816]\n",
"126: label index=2.0, class distribution=[0.00102485 0.02817698 0.97079816]\n",
"127: label index=2.0, class distribution=[0.00139749 0.01280739 0.98579512]\n",
"128: label index=2.0, class distribution=[0.00920087 0.06127297 0.92952615]\n",
"129: label index=2.0, class distribution=[0.00139749 0.01280739 0.98579512]\n",
"130: label index=1.0, class distribution=[0.010867 0.52425197 0.46488102]\n",
"131: label index=2.0, class distribution=[0.00139749 0.01280739 0.98579512]\n",
"132: label index=2.0, class distribution=[0.00431289 0.0395258 0.95616131]\n",
"133: label index=2.0, class distribution=[0.00139749 0.01280739 0.98579512]\n",
"134: label index=2.0, class distribution=[0.00409632 0.47019227 0.5257114 ]\n",
"135: label index=1.0, class distribution=[0.02353491 0.65433551 0.32212958]\n",
"136: label index=2.0, class distribution=[0.00102485 0.02817698 0.97079816]\n",
"137: label index=2.0, class distribution=[0.00431289 0.0395258 0.95616131]\n",
"138: label index=2.0, class distribution=[0.00102485 0.02817698 0.97079816]\n",
"139: label index=2.0, class distribution=[0.00920087 0.06127297 0.92952615]\n",
"140: label index=2.0, class distribution=[0.00102485 0.02817698 0.97079816]\n",
"141: label index=2.0, class distribution=[0.00102485 0.02817698 0.97079816]\n",
"142: label index=2.0, class distribution=[0.00102485 0.02817698 0.97079816]\n",
"143: label index=2.0, class distribution=[0.01274667 0.02829538 0.95895795]\n",
"144: label index=2.0, class distribution=[0.00102485 0.02817698 0.97079816]\n",
"145: label index=2.0, class distribution=[0.00102485 0.02817698 0.97079816]\n",
"146: label index=2.0, class distribution=[0.00102485 0.02817698 0.97079816]\n",
"147: label index=2.0, class distribution=[0.00139749 0.01280739 0.98579512]\n",
"148: label index=2.0, class distribution=[0.00102485 0.02817698 0.97079816]\n",
"149: label index=2.0, class distribution=[0.00431289 0.0395258 0.95616131]\n",
"150: label index=2.0, class distribution=[0.00920087 0.06127297 0.92952615]\n"
]
}
],
"source": [
"from weka.classifiers import Classifier\n",
"cls = Classifier(classname=\"weka.classifiers.bayes.BayesNet\", options=[\"-Q\", \"weka.classifiers.bayes.net.search.local.TAN\"])\n",
"cls.build_classifier(data)\n",
"\n",
"for index, inst in enumerate(data):\n",
" pred = cls.classify_instance(inst)\n",
" dist = cls.distribution_for_instance(inst)\n",
" print(str(index+1) + \": label index=\" + str(pred) + \", class distribution=\" + str(dist))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "0b74f00a-15b3-4177-bb8c-e02ed1a3fd38",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Bayes Network Classifier\n",
"Using ADTree\n",
"#attributes=5 #classindex=4\n",
"Network structure (nodes followed by parents)\n",
"sepallength(3): class \n",
"sepalwidth(3): class petalwidth \n",
"petallength(3): class sepallength \n",
"petalwidth(3): class petallength \n",
"class(3): \n",
"LogScore Bayes: -484.0749140715054\n",
"LogScore BDeu: -653.8524681760015\n",
"LogScore MDL: -654.6252712234647\n",
"LogScore ENTROPY: -499.2955771064808\n",
"LogScore AIC: -561.2955771064808\n",
"\n"
]
},
{
"ename": "OSError",
"evalue": "[Errno 63] File name too long: '<?xml version=\"1.0\"?>\\n<!-- DTD for the XMLBIF 0.3 format -->\\n<!DOCTYPE BIF [\\n\\t<!ELEMENT BIF ( NETWORK )*>\\n\\t <!ATTLIST BIF VERSION CDATA #REQUIRED>\\n\\t<!ELEMENT NETWORK ( NAME, ( PROPERTY | VARIABLE | DEFINITION )* )>\\n\\t<!ELEMENT NAME (#PCDATA)>\\n\\t<!ELEMENT VARIABLE ( NAME, ( OUTCOME | PROPERTY )* ) >\\n\\t <!ATTLIST VARIABLE TYPE (nature|decision|utility) \"nature\">\\n\\t<!ELEMENT OUTCOME (#PCDATA)>\\n\\t<!ELEMENT DEFINITION ( FOR | GIVEN | TABLE | PROPERTY )* >\\n\\t<!ELEMENT FOR (#PCDATA)>\\n\\t<!ELEMENT GIVEN (#PCDATA)>\\n\\t<!ELEMENT TABLE (#PCDATA)>\\n\\t<!ELEMENT PROPERTY (#PCDATA)>\\n]>\\n\\n\\n<BIF VERSION=\"0.3\">\\n<NETWORK>\\n<NAME>iris-weka.filters.supervised.attribute.Discretize-Rfirst-last-precision6-weka.filters.unsupervised.attribute.ReplaceMissingValues</NAME>\\n<VARIABLE TYPE=\"nature\">\\n<NAME>sepallength</NAME>\\n<OUTCOME>&apos;\\\\&apos;(-inf-5.55]\\\\&apos;&apos;</OUTCOME>\\n<OUTCOME>&apos;\\\\&apos;(5.55-6.15]\\\\&apos;&apos;</OUTCOME>\\n<OUTCOME>&apos;\\\\&apos;(6.15-inf)\\\\&apos;&apos;</OUTCOME>\\n</VARIABLE>\\n<VARIABLE TYPE=\"nature\">\\n<NAME>sepalwidth</NAME>\\n<OUTCOME>&apos;\\\\&apos;(-inf-2.95]\\\\&apos;&apos;</OUTCOME>\\n<OUTCOME>&apos;\\\\&apos;(2.95-3.35]\\\\&apos;&apos;</OUTCOME>\\n<OUTCOME>&apos;\\\\&apos;(3.35-inf)\\\\&apos;&apos;</OUTCOME>\\n</VARIABLE>\\n<VARIABLE TYPE=\"nature\">\\n<NAME>petallength</NAME>\\n<OUTCOME>&apos;\\\\&apos;(-inf-2.45]\\\\&apos;&apos;</OUTCOME>\\n<OUTCOME>&apos;\\\\&apos;(2.45-4.75]\\\\&apos;&apos;</OUTCOME>\\n<OUTCOME>&apos;\\\\&apos;(4.75-inf)\\\\&apos;&apos;</OUTCOME>\\n</VARIABLE>\\n<VARIABLE TYPE=\"nature\">\\n<NAME>petalwidth</NAME>\\n<OUTCOME>&apos;\\\\&apos;(-inf-0.8]\\\\&apos;&apos;</OUTCOME>\\n<OUTCOME>&apos;\\\\&apos;(0.8-1.75]\\\\&apos;&apos;</OUTCOME>\\n<OUTCOME>&apos;\\\\&apos;(1.75-inf)\\\\&apos;&apos;</OUTCOME>\\n</VARIABLE>\\n<VARIABLE TYPE=\"nature\">\\n<NAME>class</NAME>\\n<OUTCOME>Iris-setosa</OUTCOME>\\n<OUTCOME>Iris-versicolor</OUTCOME>\\n<OUTCOME>Iris-virginica</OUTCOME>\\n</VARIABLE>\\n<DEFINITION>\\n<FOR>sepallength</FOR>\\n<GIVEN>class</GIVEN>\\n<TABLE>\\n0.9223300970873787 0.06796116504854369 0.009708737864077669 \\n0.22330097087378642 0.4563106796116505 0.32038834951456313 \\n0.02912621359223301 0.20388349514563106 0.7669902912621359 \\n</TABLE>\\n</DEFINITION>\\n<DEFINITION>\\n<FOR>sepalwidth</FOR>\\n<GIVEN>class</GIVEN>\\n<GIVEN>petalwidth</GIVEN>\\n<TABLE>\\n0.04854368932038835 0.3592233009708738 0.5922330097087378 \\n0.3333333333333333 0.3333333333333333 0.3333333333333333 \\n0.3333333333333333 0.3333333333333333 0.3333333333333333 \\n0.3333333333333333 0.3333333333333333 0.3333333333333333 \\n0.6831683168316832 0.2871287128712871 0.0297029702970297 \\n0.2 0.6 0.2 \\n0.3333333333333333 0.3333333333333333 0.3333333333333333 \\n0.6923076923076923 0.23076923076923078 0.07692307692307693 \\n0.3763440860215054 0.5053763440860215 0.11827956989247312 \\n</TABLE>\\n</DEFINITION>\\n<DEFINITION>\\n<FOR>petallength</FOR>\\n<GIVEN>class</GIVEN>\\n<GIVEN>sepallength</GIVEN>\\n<TABLE>\\n0.979381443298969 0.010309278350515464 0.010309278350515464 \\n0.7777777777777778 0.1111111111111111 0.1111111111111111 \\n0.3333333333333333 0.3333333333333333 0.3333333333333333 \\n0.04 0.92 0.04 \\n0.02040816326530612 0.8775510204081632 0.10204081632653061 \\n0.02857142857142857 0.7142857142857143 0.2571428571428571 \\n0.2 0.6 0.2 \\n0.043478260869565216 0.043478260869565216 0.9130434782608695 \\n0.012345679012345678 0.012345679012345678 0.9753086419753086 \\n</TABLE>\\n</DEFINITION>\\n<DEFINITION>\\n<FOR>petalwidth</FOR>\\n<GIVEN>class</GIVEN>\\n<GIVEN>petallength</GIVEN>\\n<TABLE>\\n0.9805825242718447 0.009708737864077669 0.009708737864077669 \\n0.3333333333333333 0.3333333333333333 0.3333333333333333 \\n0.3333333333333333 0.3333333333333333 0.3333333333333333 \\n0.3333333333333333 0.3333333333333333 0.3333333333333333 \\n0.01098901098901099 0.978021978021978 0.01098901098901099 \\n0.06666666666666667 0.7333333333333333 0.2 \\n0.3333333333333333 0.3333333333333333 0.3333333333333333 \\n0.2 0.6 0.2 \\n0.009900990099009901 0.0891089108910891 0.900990099009901 \\n</TABLE>\\n</DEFINITION>\\n<DEFINITION>\\n<FOR>class</FOR>\\n<TABLE>\\n0.3333333333333333 0.3333333333333333 0.3333333333333333 \\n</TABLE>\\n</DEFINITION>\\n</NETWORK>\\n</BIF>\\n'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mOSError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn [13], line 9\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;28mcls\u001b[39m)\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mweka\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mplot\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgraph\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mgraph\u001b[39;00m \u001b[38;5;66;03m# NB: pygraphviz and PIL are required\u001b[39;00m\n\u001b[0;32m----> 9\u001b[0m \u001b[43mgraph\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot_dot_graph\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgraph\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/miniconda3/envs/pyweka/lib/python3.10/site-packages/weka/plot/graph.py:49\u001b[0m, in \u001b[0;36mplot_dot_graph\u001b[0;34m(graph, filename)\u001b[0m\n\u001b[1;32m 46\u001b[0m logger\u001b[38;5;241m.\u001b[39merror(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPIL is not installed, cannot display graph plot!\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 47\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[0;32m---> 49\u001b[0m agraph \u001b[38;5;241m=\u001b[39m \u001b[43mAGraph\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgraph\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 50\u001b[0m agraph\u001b[38;5;241m.\u001b[39mlayout(prog\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdot\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 51\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m filename \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
"File \u001b[0;32m~/miniconda3/envs/pyweka/lib/python3.10/site-packages/pygraphviz/agraph.py:157\u001b[0m, in \u001b[0;36mAGraph.__init__\u001b[0;34m(self, thing, filename, data, string, handle, name, strict, directed, **attr)\u001b[0m\n\u001b[1;32m 154\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_owns_handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m 155\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m filename \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 156\u001b[0m \u001b[38;5;66;03m# load new graph from file (creates self.handle)\u001b[39;00m\n\u001b[0;32m--> 157\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 158\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m string \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 159\u001b[0m \u001b[38;5;66;03m# load new graph from string (creates self.handle)\u001b[39;00m\n\u001b[1;32m 160\u001b[0m \u001b[38;5;66;03m# get the charset from the string to properly encode it for\u001b[39;00m\n\u001b[1;32m 161\u001b[0m \u001b[38;5;66;03m# writing to the temporary file in from_string()\u001b[39;00m\n\u001b[1;32m 162\u001b[0m match \u001b[38;5;241m=\u001b[39m re\u001b[38;5;241m.\u001b[39msearch(\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcharset\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124ms*=\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124ms*\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m([^\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m]+)\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m, string)\n",
"File \u001b[0;32m~/miniconda3/envs/pyweka/lib/python3.10/site-packages/pygraphviz/agraph.py:1243\u001b[0m, in \u001b[0;36mAGraph.read\u001b[0;34m(self, path)\u001b[0m\n\u001b[1;32m 1233\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mread\u001b[39m(\u001b[38;5;28mself\u001b[39m, path):\n\u001b[1;32m 1234\u001b[0m \u001b[38;5;124;03m\"\"\"Read graph from dot format file on path.\u001b[39;00m\n\u001b[1;32m 1235\u001b[0m \n\u001b[1;32m 1236\u001b[0m \u001b[38;5;124;03m path can be a file name or file handle\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1241\u001b[0m \n\u001b[1;32m 1242\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1243\u001b[0m fh \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_fh\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1244\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1245\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_close_handle()\n",
"File \u001b[0;32m~/miniconda3/envs/pyweka/lib/python3.10/site-packages/pygraphviz/agraph.py:1791\u001b[0m, in \u001b[0;36mAGraph._get_fh\u001b[0;34m(self, path, mode)\u001b[0m\n\u001b[1;32m 1789\u001b[0m fh \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpopen(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbzcat \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m path) \u001b[38;5;66;03m# probably not portable\u001b[39;00m\n\u001b[1;32m 1790\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1791\u001b[0m fh \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1792\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(path, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwrite\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 1793\u001b[0m \u001b[38;5;66;03m# Note, mode of file handle is unchanged.\u001b[39;00m\n\u001b[1;32m 1794\u001b[0m fh \u001b[38;5;241m=\u001b[39m path\n",
"\u001b[0;31mOSError\u001b[0m: [Errno 63] File name too long: '<?xml version=\"1.0\"?>\\n<!-- DTD for the XMLBIF 0.3 format -->\\n<!DOCTYPE BIF [\\n\\t<!ELEMENT BIF ( NETWORK )*>\\n\\t <!ATTLIST BIF VERSION CDATA #REQUIRED>\\n\\t<!ELEMENT NETWORK ( NAME, ( PROPERTY | VARIABLE | DEFINITION )* )>\\n\\t<!ELEMENT NAME (#PCDATA)>\\n\\t<!ELEMENT VARIABLE ( NAME, ( OUTCOME | PROPERTY )* ) >\\n\\t <!ATTLIST VARIABLE TYPE (nature|decision|utility) \"nature\">\\n\\t<!ELEMENT OUTCOME (#PCDATA)>\\n\\t<!ELEMENT DEFINITION ( FOR | GIVEN | TABLE | PROPERTY )* >\\n\\t<!ELEMENT FOR (#PCDATA)>\\n\\t<!ELEMENT GIVEN (#PCDATA)>\\n\\t<!ELEMENT TABLE (#PCDATA)>\\n\\t<!ELEMENT PROPERTY (#PCDATA)>\\n]>\\n\\n\\n<BIF VERSION=\"0.3\">\\n<NETWORK>\\n<NAME>iris-weka.filters.supervised.attribute.Discretize-Rfirst-last-precision6-weka.filters.unsupervised.attribute.ReplaceMissingValues</NAME>\\n<VARIABLE TYPE=\"nature\">\\n<NAME>sepallength</NAME>\\n<OUTCOME>&apos;\\\\&apos;(-inf-5.55]\\\\&apos;&apos;</OUTCOME>\\n<OUTCOME>&apos;\\\\&apos;(5.55-6.15]\\\\&apos;&apos;</OUTCOME>\\n<OUTCOME>&apos;\\\\&apos;(6.15-inf)\\\\&apos;&apos;</OUTCOME>\\n</VARIABLE>\\n<VARIABLE TYPE=\"nature\">\\n<NAME>sepalwidth</NAME>\\n<OUTCOME>&apos;\\\\&apos;(-inf-2.95]\\\\&apos;&apos;</OUTCOME>\\n<OUTCOME>&apos;\\\\&apos;(2.95-3.35]\\\\&apos;&apos;</OUTCOME>\\n<OUTCOME>&apos;\\\\&apos;(3.35-inf)\\\\&apos;&apos;</OUTCOME>\\n</VARIABLE>\\n<VARIABLE TYPE=\"nature\">\\n<NAME>petallength</NAME>\\n<OUTCOME>&apos;\\\\&apos;(-inf-2.45]\\\\&apos;&apos;</OUTCOME>\\n<OUTCOME>&apos;\\\\&apos;(2.45-4.75]\\\\&apos;&apos;</OUTCOME>\\n<OUTCOME>&apos;\\\\&apos;(4.75-inf)\\\\&apos;&apos;</OUTCOME>\\n</VARIABLE>\\n<VARIABLE TYPE=\"nature\">\\n<NAME>petalwidth</NAME>\\n<OUTCOME>&apos;\\\\&apos;(-inf-0.8]\\\\&apos;&apos;</OUTCOME>\\n<OUTCOME>&apos;\\\\&apos;(0.8-1.75]\\\\&apos;&apos;</OUTCOME>\\n<OUTCOME>&apos;\\\\&apos;(1.75-inf)\\\\&apos;&apos;</OUTCOME>\\n</VARIABLE>\\n<VARIABLE TYPE=\"nature\">\\n<NAME>class</NAME>\\n<OUTCOME>Iris-setosa</OUTCOME>\\n<OUTCOME>Iris-versicolor</OUTCOME>\\n<OUTCOME>Iris-virginica</OUTCOME>\\n</VARIABLE>\\n<DEFINITION>\\n<FOR>sepallength</FOR>\\n<GIVEN>class</GIVEN>\\n<TABLE>\\n0.9223300970873787 0.06796116504854369 0.009708737864077669 \\n0.22330097087378642 0.4563106796116505 0.32038834951456313 \\n0.02912621359223301 0.20388349514563106 0.7669902912621359 \\n</TABLE>\\n</DEFINITION>\\n<DEFINITION>\\n<FOR>sepalwidth</FOR>\\n<GIVEN>class</GIVEN>\\n<GIVEN>petalwidth</GIVEN>\\n<TABLE>\\n0.04854368932038835 0.3592233009708738 0.5922330097087378 \\n0.3333333333333333 0.3333333333333333 0.3333333333333333 \\n0.3333333333333333 0.3333333333333333 0.3333333333333333 \\n0.3333333333333333 0.3333333333333333 0.3333333333333333 \\n0.6831683168316832 0.2871287128712871 0.0297029702970297 \\n0.2 0.6 0.2 \\n0.3333333333333333 0.3333333333333333 0.3333333333333333 \\n0.6923076923076923 0.23076923076923078 0.07692307692307693 \\n0.3763440860215054 0.5053763440860215 0.11827956989247312 \\n</TABLE>\\n</DEFINITION>\\n<DEFINITION>\\n<FOR>petallength</FOR>\\n<GIVEN>class</GIVEN>\\n<GIVEN>sepallength</GIVEN>\\n<TABLE>\\n0.979381443298969 0.010309278350515464 0.010309278350515464 \\n0.7777777777777778 0.1111111111111111 0.1111111111111111 \\n0.3333333333333333 0.3333333333333333 0.3333333333333333 \\n0.04 0.92 0.04 \\n0.02040816326530612 0.8775510204081632 0.10204081632653061 \\n0.02857142857142857 0.7142857142857143 0.2571428571428571 \\n0.2 0.6 0.2 \\n0.043478260869565216 0.043478260869565216 0.9130434782608695 \\n0.012345679012345678 0.012345679012345678 0.9753086419753086 \\n</TABLE>\\n</DEFINITION>\\n<DEFINITION>\\n<FOR>petalwidth</FOR>\\n<GIVEN>class</GIVEN>\\n<GIVEN>petallength</GIVEN>\\n<TABLE>\\n0.9805825242718447 0.009708737864077669 0.009708737864077669 \\n0.3333333333333333 0.3333333333333333 0.3333333333333333 \\n0.3333333333333333 0.3333333333333333 0.3333333333333333 \\n0.3333333333333333 0.3333333333333333 0.3333333333333333 \\n0.01098901098901099 0.978021978021978 0.01098901098901099 \\n0.06666666666666667 0.7333333333333333 0.2 \\n0.3333333333333333 0.3333333333333333 0.3333333333333333 \\n0.2 0.6 0.2 \\n0.009900990099009901 0.0891089108910891 0.900990099009901 \\n</TABLE>\\n</DEFINITION>\\n<DEFINITION>\\n<FOR>class</FOR>\\n<TABLE>\\n0.3333333333333333 0.3333333333333333 0.3333333333333333 \\n</TABLE>\\n</DEFINITION>\\n</NETWORK>\\n</BIF>\\n'"
]
}
],
"source": [
"from weka.classifiers import Classifier\n",
"\n",
"cls = Classifier(classname=\"weka.classifiers.bayes.BayesNet\", options=[\"-Q\", \"weka.classifiers.bayes.net.search.local.TAN\"])\n",
"cls.build_classifier(data)\n",
"\n",
"print(cls)\n",
"\n",
"import weka.plot.graph as graph # NB: pygraphviz and PIL are required\n",
"graph.plot_dot_graph(cls.graph)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3f59f200-4f23-4add-86ae-6df1494ede82",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.10.6"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

18
benchmark/Experiments.py
View File

@@ -1,4 +1,5 @@
import os
import sys
import json
import random
import warnings
@@ -15,10 +16,13 @@ from sklearn.model_selection import (
from .Utils import Folders, Files, NO_RESULTS
from .Datasets import Datasets
from .Models import Models
from .Arguments import EnvData
class Randomized:
seeds = [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]
@staticmethod
def seeds():
return json.loads(EnvData.load()["seeds"])
class BestResults:
@@ -154,7 +158,7 @@ class Experiment:
self.platform = platform
self.progress_bar = progress_bar
self.folds = folds
self.random_seeds = Randomized.seeds
self.random_seeds = Randomized.seeds()
self.results = []
self.duration = 0
self._init_experiment()
@@ -162,6 +166,10 @@ class Experiment:
def get_output_file(self):
return self.output_file
@staticmethod
def get_python_version():
return "{}.{}".format(sys.version_info.major, sys.version_info.minor)
def _build_classifier(self, random_state, hyperparameters):
self.model = Models.get_model(self.model_name, random_state)
clf = self.model
@@ -193,7 +201,7 @@ class Experiment:
shuffle=True, random_state=random_state, n_splits=self.folds
)
clf = self._build_classifier(random_state, hyperparameters)
self.version = clf.version() if hasattr(clf, "version") else "-"
self.version = Models.get_version(self.model_name, clf)
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
res = cross_validate(
@@ -243,6 +251,8 @@ class Experiment:
output["duration"] = self.duration
output["seeds"] = self.random_seeds
output["platform"] = self.platform
output["language_version"] = self.get_python_version()
output["language"] = "Python"
output["results"] = self.results
with open(self.output_file, "w") as f:
json.dump(output, f)
@@ -301,7 +311,7 @@ class GridSearch:
self.progress_bar = progress_bar
self.folds = folds
self.platform = platform
self.random_seeds = Randomized.seeds
self.random_seeds = Randomized.seeds()
self.grid_file = os.path.join(
Folders.results, Files.grid_input(score_name, model_name)
)

14
benchmark/Models.py
View File

@@ -11,6 +11,8 @@ from stree import Stree
from wodt import Wodt
from odte import Odte
from xgboost import XGBClassifier
import sklearn
import xgboost
class Models:
@@ -89,3 +91,15 @@ class Models:
nodes, leaves = result.nodes_leaves()
depth = result.depth_ if hasattr(result, "depth_") else 0
return nodes, leaves, depth
@staticmethod
def get_version(name, clf):
if hasattr(clf, "version"):
return clf.version()
if name in ["Cart", "ExtraTree", "RandomForest", "GBC", "SVC"]:
return sklearn.__version__
elif name.startswith("Bagging") or name.startswith("AdaBoost"):
return sklearn.__version__
elif name == "XGBoost":
return xgboost.__version__
return "Error"

6
benchmark/Results.py
View File

@@ -196,7 +196,8 @@ class Report(BaseReport):
self._compare_totals = {}
self.header_line("*")
self.header_line(
f" Report {self.data['model']} ver. {self.data['version']}"
f" {self.data['model']} ver. {self.data['version']}"
f" {self.data['language']} ver. {self.data['language_version']}"
f" with {self.data['folds']} Folds "
f"cross validation and {len(self.data['seeds'])} random seeds. "
f"{self.data['date']} {self.data['time']}"
@@ -347,7 +348,8 @@ class Excel(BaseReport):
def get_title(self):
return (
f" Report {self.data['model']} ver. {self.data['version']}"
f" {self.data['model']} ver. {self.data['version']}"
f" {self.data['language']} ver. {self.data['language_version']}"
f" with {self.data['folds']} Folds "
f"cross validation and {len(self.data['seeds'])} random seeds. "
f"{self.data['date']} {self.data['time']}"

2
benchmark/Utils.py
View File

@@ -1,6 +1,8 @@
import os
import sys
import subprocess
PYTHON_VERSION = "{}.{}".format(sys.version_info.major, sys.version_info.minor)
NO_RESULTS = "** No results found **"
NO_ENV = "File .env not found"

2
benchmark/_version.py
View File

@@ -1 +1 @@
__version__ = "0.1.1"
__version__ = "0.2.0"

1
benchmark/tests/.env
View File

@@ -5,3 +5,4 @@ model=ODTE
stratified=0
# Source of data Tanveer/Surcov
source_data=Tanveer
seeds=[57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]

1
benchmark/tests/.env.arff
View File

@@ -4,3 +4,4 @@ n_folds=5
model=ODTE
stratified=0
source_data=Arff
seeds=[271, 314, 171]

1
benchmark/tests/.env.dist
View File

@@ -5,3 +5,4 @@ model=ODTE
stratified=0
# Source of data Tanveer/Surcov
source_data=Tanveer
seeds=[57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]

1
benchmark/tests/.env.surcov
View File

@@ -5,3 +5,4 @@ model=ODTE
stratified=0
# Source of data Tanveer/Surcov
source_data=Surcov
seeds=[57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]

7
benchmark/tests/Dataset_test.py
View File

@@ -23,7 +23,12 @@ class DatasetTest(TestBase):
def test_Randomized(self):
expected = [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]
self.assertSequenceEqual(Randomized.seeds, expected)
self.assertSequenceEqual(Randomized.seeds(), expected)
def test_Randomized_3_seeds(self):
self.set_env(".env.arff")
expected = [271, 314, 171]
self.assertSequenceEqual(Randomized.seeds(), expected)
def test_Datasets_iterator(self):
test = {

34
benchmark/tests/Models_test.py
View File

@@ -15,6 +15,8 @@ from odte import Odte
from xgboost import XGBClassifier
from .TestBase import TestBase
from ..Models import Models
import xgboost
import sklearn
class ModelTest(TestBase):
@@ -33,6 +35,38 @@ class ModelTest(TestBase):
for key, value in test.items():
self.assertIsInstance(Models.get_model(key), value)
def test_Models_version(self):
def ver_stree():
return "1.2.3"
def ver_wodt():
return "h.j.k"
def ver_odte():
return "4.5.6"
test = {
"STree": [ver_stree, "1.2.3"],
"Wodt": [ver_wodt, "h.j.k"],
"ODTE": [ver_odte, "4.5.6"],
"RandomForest": [None, "7.8.9"],
"BaggingStree": [None, "x.y.z"],
"AdaBoostStree": [None, "w.x.z"],
"XGBoost": [None, "10.11.12"],
}
for key, value in test.items():
clf = Models.get_model(key)
if key in ["STree", "Wodt", "ODTE"]:
clf.version = value[0]
elif key == "XGBoost":
xgboost.__version__ = value[1]
else:
sklearn.__version__ = value[1]
self.assertEqual(Models.get_version(key, clf), value[1])
def test_bogus_Model_Version(self):
self.assertEqual(Models.get_version("unknown", None), "Error")
def test_BaggingStree(self):
clf = Models.get_model("BaggingStree")
self.assertIsInstance(clf, BaggingClassifier)

1
benchmark/tests/Util_test.py
View File

@@ -178,6 +178,7 @@ class UtilTest(TestBase):
"model": "ODTE",
"stratified": "0",
"source_data": "Tanveer",
"seeds": "[57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]",
}
computed = EnvData().load()
self.assertDictEqual(computed, expected)

2
benchmark/tests/results/results_accuracy_ODTE_Galgo_2022-04-20_10:52:20_0.json
View File

@@ -3,6 +3,8 @@
"title": "Gridsearched hyperparams v022.1b random_init",
"model": "ODTE",
"version": "0.3.2",
"language_version": "3.11x",
"language": "Python",
"stratified": false,
"folds": 5,
"date": "2022-04-20",

2
benchmark/tests/results/results_accuracy_RandomForest_iMac27_2022-01-14_12:39:30_0.json
View File

@@ -3,6 +3,8 @@
"title": "Test default paramters with RandomForest",
"model": "RandomForest",
"version": "-",
"language_version": "3.11x",
"language": "Python",
"stratified": false,
"folds": 5,
"date": "2022-01-14",

2
benchmark/tests/results/results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json
View File

@@ -3,6 +3,8 @@
"model": "STree",
"stratified": false,
"folds": 5,
"language_version": "3.11x",
"language": "Python",
"date": "2021-09-30",
"time": "11:42:07",
"duration": 624.2505249977112,

2
benchmark/tests/results/results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json
View File

@@ -1,6 +1,8 @@
{
"score_name": "accuracy",
"model": "STree",
"language": "Python",
"language_version": "3.11x",
"stratified": false,
"folds": 5,
"date": "2021-10-27",

2
benchmark/tests/results/results_accuracy_STree_macbook-pro_2021-11-01_19:17:07_0.json
View File

@@ -1,6 +1,8 @@
{
"score_name": "accuracy",
"model": "STree",
"language_version": "3.11x",
"language": "Python",
"stratified": false,
"folds": 5,
"date": "2021-11-01",

2
benchmark/tests/test_files/be_main_best.test
View File

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************************************************************************************************************************
* Report STree ver. 1.2.4 with 5 Folds cross validation and 10 random seeds. 2022-05-09 00:15:25 *
* STree ver. 1.2.4 Python ver. 3.11x with 5 Folds cross validation and 10 random seeds. 2022-05-09 00:15:25 *
* test *
* Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False *
* Execution took 0.80 seconds, 0.00 hours, on iMac27 *

2
benchmark/tests/test_files/be_main_complete.test
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************************************************************************************************************************
* Report STree ver. 1.2.4 with 5 Folds cross validation and 10 random seeds. 2022-05-08 20:14:43 *
* STree ver. 1.2.4 Python ver. 3.11x with 5 Folds cross validation and 10 random seeds. 2022-05-08 20:14:43 *
* test *
* Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False *
* Execution took 0.48 seconds, 0.00 hours, on iMac27 *

2
benchmark/tests/test_files/be_main_dataset.test
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************************************************************************************************************************
* Report STree ver. 1.2.4 with 5 Folds cross validation and 10 random seeds. 2022-05-08 19:38:28 *
* STree ver. 1.2.4 Python ver. 3.11x with 5 Folds cross validation and 10 random seeds. 2022-05-08 19:38:28 *
* test *
* Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False *
* Execution took 0.06 seconds, 0.00 hours, on iMac27 *

2
benchmark/tests/test_files/be_main_grid.test
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************************************************************************************************************************
* Report STree ver. 1.2.4 with 5 Folds cross validation and 10 random seeds. 2022-05-09 00:21:06 *
* STree ver. 1.2.4 Python ver. 3.11x with 5 Folds cross validation and 10 random seeds. 2022-05-09 00:21:06 *
* test *
* Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False *
* Execution took 0.89 seconds, 0.00 hours, on iMac27 *

2
benchmark/tests/test_files/excel.test
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1;1;" Report STree ver. 1.2.3 with 5 Folds cross validation and 10 random seeds. 2021-09-30 11:42:07"
1;1;" STree ver. 1.2.3 Python ver. 3.11x with 5 Folds cross validation and 10 random seeds. 2021-09-30 11:42:07"
2;1;" With gridsearched hyperparameters"
3;1;" Score is accuracy"
3;2;" Execution time"

2
benchmark/tests/test_files/excel_add_ODTE.test
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1;1;" Report ODTE ver. 0.3.2 with 5 Folds cross validation and 10 random seeds. 2022-04-20 10:52:20"
1;1;" ODTE ver. 0.3.2 Python ver. 3.11x with 5 Folds cross validation and 10 random seeds. 2022-04-20 10:52:20"
2;1;" Gridsearched hyperparams v022.1b random_init"
3;1;" Score is accuracy"
3;2;" Execution time"

2
benchmark/tests/test_files/excel_add_STree.test
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1;1;" Report STree ver. 1.2.3 with 5 Folds cross validation and 10 random seeds. 2021-10-27 09:40:40"
1;1;" STree ver. 1.2.3 Python ver. 3.11x with 5 Folds cross validation and 10 random seeds. 2021-10-27 09:40:40"
2;1;" default A"
3;1;" Score is accuracy"
3;2;" Execution time"

2
benchmark/tests/test_files/excel_compared.test
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1;1;" Report STree ver. 1.2.3 with 5 Folds cross validation and 10 random seeds. 2021-09-30 11:42:07"
1;1;" STree ver. 1.2.3 Python ver. 3.11x with 5 Folds cross validation and 10 random seeds. 2021-09-30 11:42:07"
2;1;" With gridsearched hyperparameters"
3;1;" Score is accuracy"
3;2;" Execution time"

2
benchmark/tests/test_files/exreport_excel_ODTE.test
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@@ -1,4 +1,4 @@
1;1;" Report ODTE ver. 0.3.2 with 5 Folds cross validation and 10 random seeds. 2022-04-20 10:52:20"
1;1;" ODTE ver. 0.3.2 Python ver. 3.11x with 5 Folds cross validation and 10 random seeds. 2022-04-20 10:52:20"
2;1;" Gridsearched hyperparams v022.1b random_init"
3;1;" Score is accuracy"
3;2;" Execution time"

2
benchmark/tests/test_files/exreport_excel_RandomForest.test
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1;1;" Report RandomForest ver. - with 5 Folds cross validation and 10 random seeds. 2022-01-14 12:39:30"
1;1;" RandomForest ver. - Python ver. 3.11x with 5 Folds cross validation and 10 random seeds. 2022-01-14 12:39:30"
2;1;" Test default paramters with RandomForest"
3;1;" Score is accuracy"
3;2;" Execution time"

2
benchmark/tests/test_files/exreport_excel_STree.test
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@@ -1,4 +1,4 @@
1;1;" Report STree ver. 1.2.3 with 5 Folds cross validation and 10 random seeds. 2021-09-30 11:42:07"
1;1;" STree ver. 1.2.3 Python ver. 3.11x with 5 Folds cross validation and 10 random seeds. 2021-09-30 11:42:07"
2;1;" With gridsearched hyperparameters"
3;1;" Score is accuracy"
3;2;" Execution time"

2
benchmark/tests/test_files/report.test
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************************************************************************************************************************
* Report STree ver. 1.2.3 with 5 Folds cross validation and 10 random seeds. 2021-09-30 11:42:07 *
* STree ver. 1.2.3 Python ver. 3.11x with 5 Folds cross validation and 10 random seeds. 2021-09-30 11:42:07 *
* With gridsearched hyperparameters *
* Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False *
* Execution took 624.25 seconds, 0.17 hours, on iMac27 *

2
benchmark/tests/test_files/report_compared.test
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************************************************************************************************************************
* Report STree ver. 1.2.3 with 5 Folds cross validation and 10 random seeds. 2021-09-30 11:42:07 *
* STree ver. 1.2.3 Python ver. 3.11x with 5 Folds cross validation and 10 random seeds. 2021-09-30 11:42:07 *
* With gridsearched hyperparameters *
* Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False *
* Execution took 624.25 seconds, 0.17 hours, on iMac27 *