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base: Doctorado-ML:discretiz
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Doctorado-ML:main Doctorado-ML:covbench Doctorado-ML:rmontanana-patch-1 Doctorado-ML:flask Doctorado-ML:flask-new Doctorado-ML:refactor_args Doctorado-ML:add_excel_belist Doctorado-ML:language_version Doctorado-ML:discretiz Doctorado-ML:refactor_arguments
Doctorado-ML:v0.2.0 Doctorado-ML:v0.1.1
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compare: Doctorado-ML:v0.2.0
Branches Tags
Doctorado-ML:covbench Doctorado-ML:main Doctorado-ML:rmontanana-patch-1 Doctorado-ML:flask Doctorado-ML:flask-new Doctorado-ML:refactor_args Doctorado-ML:add_excel_belist Doctorado-ML:language_version Doctorado-ML:discretiz Doctorado-ML:refactor_arguments
Doctorado-ML:v0.2.0 Doctorado-ML:v0.1.1

5 Commits
discretiz ... v0.2.0

Author SHA1 Message Date
Ricardo Montañana Gómez
c77feff54b Merge pull request #4 from Doctorado-ML/language_version 
Add Language and language version to reports
Add custom seeds to .env
 2022-11-01 14:07:59 +01:00
Ricardo Montañana
1e83db7956 Fix lint errors and update version info 2022-11-01 13:22:53 +01:00
Ricardo Montañana
8cf823e843 Add custom seeds to .env 2022-11-01 12:24:50 +01:00
Ricardo Montañana
97718e6e82 Add Language and language version to reports 2022-11-01 02:07:24 +01:00
Ricardo Montañana Gómez
5532beb88a Merge pull request #3 from Doctorado-ML/discretiz 
Add Arff data source for experiments
Add consistent comparative results to reports
 2022-10-25 16:55:04 +02:00
33 changed files with 631 additions and 21 deletions
Expand all files Collapse all files

1
.env.dist
View File

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

1
.flake8
View File

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

526
Untitled.ipynb Normal file
View File

@@ -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": [
"1: label index=0.0, class distribution=[0.99688403 0.00188598 0.00122999]\n",
"2: label index=0.0, class distribution=[0.99487322 0.00310305 0.00202373]\n",
"3: label index=0.0, class distribution=[0.99487322 0.00310305 0.00202373]\n",
"4: label index=0.0, class distribution=[0.99487322 0.00310305 0.00202373]\n",
"5: label index=0.0, class distribution=[0.99688403 0.00188598 0.00122999]\n",
"6: label index=0.0, class distribution=[0.99688403 0.00188598 0.00122999]\n",
"7: label index=0.0, class distribution=[0.99688403 0.00188598 0.00122999]\n",
"8: label index=0.0, class distribution=[0.99688403 0.00188598 0.00122999]\n",
"9: label index=0.0, class distribution=[0.96326708 0.02223308 0.01449983]\n",
"10: label index=0.0, class distribution=[0.99487322 0.00310305 0.00202373]\n",
"11: label index=0.0, class distribution=[0.99688403 0.00188598 0.00122999]\n",
"12: label index=0.0, class distribution=[0.99688403 0.00188598 0.00122999]\n",
"13: label index=0.0, class distribution=[0.99487322 0.00310305 0.00202373]\n",
"14: label index=0.0, class distribution=[0.99487322 0.00310305 0.00202373]\n",
"15: label index=0.0, class distribution=[0.9382677 0.03162683 0.03010547]\n",
"16: label index=0.0, class distribution=[0.9382677 0.03162683 0.03010547]\n",
"17: label index=0.0, class distribution=[0.99688403 0.00188598 0.00122999]\n",
"18: label index=0.0, class distribution=[0.99688403 0.00188598 0.00122999]\n",
"19: label index=0.0, class distribution=[0.9382677 0.03162683 0.03010547]\n",
"20: label index=0.0, class distribution=[0.99688403 0.00188598 0.00122999]\n",
"21: label index=0.0, class distribution=[0.99688403 0.00188598 0.00122999]\n",
"22: label index=0.0, class distribution=[0.99688403 0.00188598 0.00122999]\n",
"23: label index=0.0, class distribution=[0.99688403 0.00188598 0.00122999]\n",
"24: label index=0.0, class distribution=[0.99487322 0.00310305 0.00202373]\n",
"25: label index=0.0, class distribution=[0.99688403 0.00188598 0.00122999]\n",
"26: label index=0.0, class distribution=[0.99487322 0.00310305 0.00202373]\n",
"27: label index=0.0, class distribution=[0.99688403 0.00188598 0.00122999]\n",
"28: label index=0.0, class distribution=[0.99688403 0.00188598 0.00122999]\n",
"29: label index=0.0, class distribution=[0.99688403 0.00188598 0.00122999]\n",
"30: label index=0.0, class distribution=[0.99487322 0.00310305 0.00202373]\n",
"31: label index=0.0, class distribution=[0.99487322 0.00310305 0.00202373]\n",
"32: label index=0.0, class distribution=[0.99688403 0.00188598 0.00122999]\n",
"33: label index=0.0, class distribution=[0.99688403 0.00188598 0.00122999]\n",
"34: label index=0.0, class distribution=[0.99688403 0.00188598 0.00122999]\n",
"35: label index=0.0, class distribution=[0.99487322 0.00310305 0.00202373]\n",
"36: label index=0.0, class distribution=[0.99487322 0.00310305 0.00202373]\n",
"37: label index=0.0, class distribution=[0.99688403 0.00188598 0.00122999]\n",
"38: label index=0.0, class distribution=[0.99487322 0.00310305 0.00202373]\n",
"39: label index=0.0, class distribution=[0.99487322 0.00310305 0.00202373]\n",
"40: label index=0.0, class distribution=[0.99688403 0.00188598 0.00122999]\n",
"41: label index=0.0, class distribution=[0.99688403 0.00188598 0.00122999]\n",
"42: label index=0.0, class distribution=[0.96326708 0.02223308 0.01449983]\n",
"43: label index=0.0, class distribution=[0.99487322 0.00310305 0.00202373]\n",
"44: label index=0.0, class distribution=[0.99688403 0.00188598 0.00122999]\n",
"45: label index=0.0, class distribution=[0.99688403 0.00188598 0.00122999]\n",
"46: label index=0.0, class distribution=[0.99487322 0.00310305 0.00202373]\n",
"47: label index=0.0, class distribution=[0.99688403 0.00188598 0.00122999]\n",
"48: label index=0.0, class distribution=[0.99487322 0.00310305 0.00202373]\n",
"49: label index=0.0, class distribution=[0.99688403 0.00188598 0.00122999]\n",
"50: label index=0.0, class distribution=[0.99487322 0.00310305 0.00202373]\n",
"51: label index=1.0, class distribution=[0.00545355 0.97466198 0.01988447]\n",
"52: label index=1.0, class distribution=[0.00545355 0.97466198 0.01988447]\n",
"53: label index=1.0, class distribution=[0.010867 0.52425197 0.46488102]\n",
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"82: label index=1.0, class distribution=[0.00725727 0.94287877 0.04986396]\n",
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"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",
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"98: label index=1.0, class distribution=[0.00228744 0.97269152 0.02502104]\n",
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"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",
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"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",
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"126: label index=2.0, class distribution=[0.00102485 0.02817698 0.97079816]\n",
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"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",
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"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 os
import sys
import json import json
import random import random
import warnings import warnings
@@ -15,10 +16,13 @@ from sklearn.model_selection import (
from .Utils import Folders, Files, NO_RESULTS from .Utils import Folders, Files, NO_RESULTS
from .Datasets import Datasets from .Datasets import Datasets
from .Models import Models from .Models import Models
from .Arguments import EnvData
class Randomized: class Randomized:
seeds = [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] @staticmethod
def seeds():
return json.loads(EnvData.load()["seeds"])
class BestResults: class BestResults:
@@ -154,7 +158,7 @@ class Experiment:
self.platform = platform self.platform = platform
self.progress_bar = progress_bar self.progress_bar = progress_bar
self.folds = folds self.folds = folds
self.random_seeds = Randomized.seeds self.random_seeds = Randomized.seeds()
self.results = [] self.results = []
self.duration = 0 self.duration = 0
self._init_experiment() self._init_experiment()
@@ -162,6 +166,10 @@ class Experiment:
def get_output_file(self): def get_output_file(self):
return self.output_file 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): def _build_classifier(self, random_state, hyperparameters):
self.model = Models.get_model(self.model_name, random_state) self.model = Models.get_model(self.model_name, random_state)
clf = self.model clf = self.model
@@ -193,7 +201,7 @@ class Experiment:
shuffle=True, random_state=random_state, n_splits=self.folds shuffle=True, random_state=random_state, n_splits=self.folds
) )
clf = self._build_classifier(random_state, hyperparameters) 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(): with warnings.catch_warnings():
warnings.filterwarnings("ignore") warnings.filterwarnings("ignore")
res = cross_validate( res = cross_validate(
@@ -243,6 +251,8 @@ class Experiment:
output["duration"] = self.duration output["duration"] = self.duration
output["seeds"] = self.random_seeds output["seeds"] = self.random_seeds
output["platform"] = self.platform output["platform"] = self.platform
output["language_version"] = self.get_python_version()
output["language"] = "Python"
output["results"] = self.results output["results"] = self.results
with open(self.output_file, "w") as f: with open(self.output_file, "w") as f:
json.dump(output, f) json.dump(output, f)
@@ -301,7 +311,7 @@ class GridSearch:
self.progress_bar = progress_bar self.progress_bar = progress_bar
self.folds = folds self.folds = folds
self.platform = platform self.platform = platform
self.random_seeds = Randomized.seeds self.random_seeds = Randomized.seeds()
self.grid_file = os.path.join( self.grid_file = os.path.join(
Folders.results, Files.grid_input(score_name, model_name) 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 wodt import Wodt
from odte import Odte from odte import Odte
from xgboost import XGBClassifier from xgboost import XGBClassifier
import sklearn
import xgboost
class Models: class Models:
@@ -89,3 +91,15 @@ class Models:
nodes, leaves = result.nodes_leaves() nodes, leaves = result.nodes_leaves()
depth = result.depth_ if hasattr(result, "depth_") else 0 depth = result.depth_ if hasattr(result, "depth_") else 0
return nodes, leaves, depth 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._compare_totals = {}
self.header_line("*") self.header_line("*")
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" with {self.data['folds']} Folds "
f"cross validation and {len(self.data['seeds'])} random seeds. " f"cross validation and {len(self.data['seeds'])} random seeds. "
f"{self.data['date']} {self.data['time']}" f"{self.data['date']} {self.data['time']}"
@@ -347,7 +348,8 @@ class Excel(BaseReport):
def get_title(self): def get_title(self):
return ( 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" with {self.data['folds']} Folds "
f"cross validation and {len(self.data['seeds'])} random seeds. " f"cross validation and {len(self.data['seeds'])} random seeds. "
f"{self.data['date']} {self.data['time']}" f"{self.data['date']} {self.data['time']}"

2
benchmark/Utils.py
View File

@@ -1,6 +1,8 @@
import os import os
import sys
import subprocess import subprocess
PYTHON_VERSION = "{}.{}".format(sys.version_info.major, sys.version_info.minor)
NO_RESULTS = "** No results found **" NO_RESULTS = "** No results found **"
NO_ENV = "File .env not 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 stratified=0
# Source of data Tanveer/Surcov # Source of data Tanveer/Surcov
source_data=Tanveer 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 model=ODTE
stratified=0 stratified=0
source_data=Arff source_data=Arff
seeds=[271, 314, 171]

1
benchmark/tests/.env.dist
View File

@@ -5,3 +5,4 @@ model=ODTE
stratified=0 stratified=0
# Source of data Tanveer/Surcov # Source of data Tanveer/Surcov
source_data=Tanveer 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 stratified=0
# Source of data Tanveer/Surcov # Source of data Tanveer/Surcov
source_data=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): def test_Randomized(self):
expected = [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] 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): def test_Datasets_iterator(self):
test = { test = {

34
benchmark/tests/Models_test.py
View File

@@ -15,6 +15,8 @@ from odte import Odte
from xgboost import XGBClassifier from xgboost import XGBClassifier
from .TestBase import TestBase from .TestBase import TestBase
from ..Models import Models from ..Models import Models
import xgboost
import sklearn
class ModelTest(TestBase): class ModelTest(TestBase):
@@ -33,6 +35,38 @@ class ModelTest(TestBase):
for key, value in test.items(): for key, value in test.items():
self.assertIsInstance(Models.get_model(key), value) 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): def test_BaggingStree(self):
clf = Models.get_model("BaggingStree") clf = Models.get_model("BaggingStree")
self.assertIsInstance(clf, BaggingClassifier) self.assertIsInstance(clf, BaggingClassifier)

1
benchmark/tests/Util_test.py
View File

@@ -178,6 +178,7 @@ class UtilTest(TestBase):
"model": "ODTE", "model": "ODTE",
"stratified": "0", "stratified": "0",
"source_data": "Tanveer", "source_data": "Tanveer",
"seeds": "[57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]",
} }
computed = EnvData().load() computed = EnvData().load()
self.assertDictEqual(computed, expected) 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", "title": "Gridsearched hyperparams v022.1b random_init",
"model": "ODTE", "model": "ODTE",
"version": "0.3.2", "version": "0.3.2",
"language_version": "3.11x",
"language": "Python",
"stratified": false, "stratified": false,
"folds": 5, "folds": 5,
"date": "2022-04-20", "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", "title": "Test default paramters with RandomForest",
"model": "RandomForest", "model": "RandomForest",
"version": "-", "version": "-",
"language_version": "3.11x",
"language": "Python",
"stratified": false, "stratified": false,
"folds": 5, "folds": 5,
"date": "2022-01-14", "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", "model": "STree",
"stratified": false, "stratified": false,
"folds": 5, "folds": 5,
"language_version": "3.11x",
"language": "Python",
"date": "2021-09-30", "date": "2021-09-30",
"time": "11:42:07", "time": "11:42:07",
"duration": 624.2505249977112, "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", "score_name": "accuracy",
"model": "STree", "model": "STree",
"language": "Python",
"language_version": "3.11x",
"stratified": false, "stratified": false,
"folds": 5, "folds": 5,
"date": "2021-10-27", "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", "score_name": "accuracy",
"model": "STree", "model": "STree",
"language_version": "3.11x",
"language": "Python",
"stratified": false, "stratified": false,
"folds": 5, "folds": 5,
"date": "2021-11-01", "date": "2021-11-01",

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

@@ -1,5 +1,5 @@
************************************************************************************************************************ ************************************************************************************************************************
* 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 * * test *
* Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False * * Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False *
* Execution took 0.80 seconds, 0.00 hours, on iMac27 * * Execution took 0.80 seconds, 0.00 hours, on iMac27 *

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

@@ -1,5 +1,5 @@
************************************************************************************************************************ ************************************************************************************************************************
* 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 * * test *
* Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False * * Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False *
* Execution took 0.48 seconds, 0.00 hours, on iMac27 * * Execution took 0.48 seconds, 0.00 hours, on iMac27 *

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

@@ -1,5 +1,5 @@
************************************************************************************************************************ ************************************************************************************************************************
* 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 * * test *
* Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False * * Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False *
* Execution took 0.06 seconds, 0.00 hours, on iMac27 * * Execution took 0.06 seconds, 0.00 hours, on iMac27 *

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

@@ -1,5 +1,5 @@
************************************************************************************************************************ ************************************************************************************************************************
* 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 * * test *
* Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False * * Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False *
* Execution took 0.89 seconds, 0.00 hours, on iMac27 * * Execution took 0.89 seconds, 0.00 hours, on iMac27 *

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

@@ -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" 2;1;" With gridsearched hyperparameters"
3;1;" Score is accuracy" 3;1;" Score is accuracy"
3;2;" Execution time" 3;2;" Execution time"

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

@@ -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" 2;1;" Gridsearched hyperparams v022.1b random_init"
3;1;" Score is accuracy" 3;1;" Score is accuracy"
3;2;" Execution time" 3;2;" Execution time"

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

@@ -1,4 +1,4 @@
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" 2;1;" default A"
3;1;" Score is accuracy" 3;1;" Score is accuracy"
3;2;" Execution time" 3;2;" Execution time"

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

@@ -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" 2;1;" With gridsearched hyperparameters"
3;1;" Score is accuracy" 3;1;" Score is accuracy"
3;2;" Execution time" 3;2;" Execution time"

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

@@ -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" 2;1;" Gridsearched hyperparams v022.1b random_init"
3;1;" Score is accuracy" 3;1;" Score is accuracy"
3;2;" Execution time" 3;2;" Execution time"

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

@@ -1,4 +1,4 @@
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" 2;1;" Test default paramters with RandomForest"
3;1;" Score is accuracy" 3;1;" Score is accuracy"
3;2;" Execution time" 3;2;" Execution time"

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

@@ -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" 2;1;" With gridsearched hyperparameters"
3;1;" Score is accuracy" 3;1;" Score is accuracy"
3;2;" Execution time" 3;2;" Execution time"

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

@@ -1,5 +1,5 @@
************************************************************************************************************************ ************************************************************************************************************************
* 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 * * With gridsearched hyperparameters *
* Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False * * Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False *
* Execution took 624.25 seconds, 0.17 hours, on iMac27 * * Execution took 624.25 seconds, 0.17 hours, on iMac27 *

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

@@ -1,5 +1,5 @@
************************************************************************************************************************ ************************************************************************************************************************
* 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 * * With gridsearched hyperparameters *
* Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False * * Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False *
* Execution took 624.25 seconds, 0.17 hours, on iMac27 * * Execution took 624.25 seconds, 0.17 hours, on iMac27 *