Merge pull request #4 from Doctorado-ML/language_version

Add Language and language version to reports Add custom seeds to .env
Fix lint errors and update version info
2025-08-17 16:35:54 +00:00 · 2022-11-01 14:07:59 +01:00 · 2022-11-01 13:22:53 +01:00 · 2022-11-01 12:24:50 +01:00 · 2022-11-01 02:07:24 +01:00 · 2022-10-25 16:55:04 +02:00
58 changed files with 1822 additions and 241 deletions
--- a/.env.dist
+++ b/.env.dist
@@ -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]
--- a/.flake8
+++ b/.flake8
@@ -1,2 +1,3 @@
 [flake8]
 exclude = .git,__init__.py
+ignore = E203, W503
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -11,19 +11,15 @@ jobs:
      - uses: actions/checkout@v2
        with:
          fetch-depth: 0
-      - name: Get project version
-        id: step_one
-        run: |
-          version=$(git describe --tags --abbrev=0)
-          echo "project_version=$version" >> $GITHUB_ENV
+      - run: echo "project_version=$(git describe --tags --abbrev=0)" >> $GITHUB_ENV
      - uses: sonarsource/sonarqube-scan-action@master
        env:
          SONAR_TOKEN: ${{ secrets.SONAR_TOKEN }}
          SONAR_HOST_URL: ${{ secrets.SONAR_HOST_URL }}
-          with:
-            args: >
-              -Dsonar.projectVersion=${{ env.project_version }}
-              -Dsonar.python.version=3.10
+        with:
+          args: >
+            -Dsonar.projectVersion=${{ env.project_version }}
+            -Dsonar.python.version=3.10
        # If you wish to fail your job when the Quality Gate is red, uncomment the
        # following lines. This would typically be used to fail a deployment.
      - uses: sonarsource/sonarqube-quality-gate-action@master
--- a/.github/workflows/main.yml
+++ b/.github/workflows/main.yml
@@ -46,7 +46,7 @@ jobs:
      - name: Lint
        run: |
          black --check --diff benchmark
-          flake8 --count benchmark
+          flake8 --count benchmark --ignore=E203,W503
      - name: Tests
        run: |
          coverage run -m unittest -v benchmark.tests
--- a/Untitled.ipynb
+++ b/Untitled.ipynb
@@ -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",
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+      "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
+}
--- a/benchmark/Arguments.py
+++ b/benchmark/Arguments.py
@@ -1,6 +1,6 @@
 import sys
 import argparse
-from .Experiments import Models
+from .Models import Models
 from .Utils import Files, NO_ENV

 ALL_METRICS = (
--- a/benchmark/Datasets.py
+++ b/benchmark/Datasets.py
@@ -0,0 +1,126 @@
+import os
+import pandas as pd
+from scipy.io import arff
+from .Utils import Files
+from .Arguments import EnvData
+
+
+class Diterator:
+    def __init__(self, data):
+        self._stack = data.copy()
+
+    def __next__(self):
+        if len(self._stack) == 0:
+            raise StopIteration()
+        return self._stack.pop(0)
+
+
+class DatasetsArff:
+    @staticmethod
+    def dataset_names(name):
+        return f"{name}.arff"
+
+    @staticmethod
+    def folder():
+        return "datasets"
+
+    def load(self, name, class_name):
+        file_name = os.path.join(self.folder(), self.dataset_names(name))
+        data = arff.loadarff(file_name)
+        df = pd.DataFrame(data[0])
+        df = df.dropna()
+        X = df.drop(class_name, axis=1).to_numpy()
+        y, _ = pd.factorize(df[class_name])
+        return X, y
+
+
+class DatasetsTanveer:
+    @staticmethod
+    def dataset_names(name):
+        return f"{name}_R.dat"
+
+    @staticmethod
+    def folder():
+        return "data"
+
+    def load(self, name, _):
+        file_name = os.path.join(self.folder(), self.dataset_names(name))
+        data = pd.read_csv(
+            file_name,
+            sep="\t",
+            index_col=0,
+        )
+        X = data.drop("clase", axis=1).to_numpy()
+        y = data["clase"].to_numpy()
+        return X, y
+
+
+class DatasetsSurcov:
+    @staticmethod
+    def dataset_names(name):
+        return f"{name}.csv"
+
+    @staticmethod
+    def folder():
+        return "datasets"
+
+    def load(self, name, _):
+        file_name = os.path.join(self.folder(), self.dataset_names(name))
+        data = pd.read_csv(
+            file_name,
+            index_col=0,
+        )
+        data.dropna(axis=0, how="any", inplace=True)
+        self.columns = data.columns
+        col_list = ["class"]
+        X = data.drop(col_list, axis=1).to_numpy()
+        y = data["class"].to_numpy()
+        return X, y
+
+
+class Datasets:
+    def __init__(self, dataset_name=None):
+
+        envData = EnvData.load()
+        class_name = getattr(
+            __import__(__name__),
+            f"Datasets{envData['source_data']}",
+        )
+        self.dataset = class_name()
+        self.class_names = []
+        self.load_names()
+        if dataset_name is not None:
+            try:
+                class_name = self.class_names[
+                    self.data_sets.index(dataset_name)
+                ]
+                self.class_names = [class_name]
+            except ValueError:
+                raise ValueError(f"Unknown dataset: {dataset_name}")
+            self.data_sets = [dataset_name]
+
+    def load_names(self):
+        file_name = os.path.join(self.dataset.folder(), Files.index)
+        default_class = "class"
+        with open(file_name) as f:
+            self.data_sets = f.read().splitlines()
+            self.class_names = [default_class] * len(self.data_sets)
+        if "," in self.data_sets[0]:
+            result = []
+            class_names = []
+            for data in self.data_sets:
+                name, class_name = data.split(",")
+                result.append(name)
+                class_names.append(class_name)
+            self.data_sets = result
+            self.class_names = class_names
+
+    def load(self, name):
+        try:
+            class_name = self.class_names[self.data_sets.index(name)]
+            return self.dataset.load(name, class_name)
+        except (ValueError, FileNotFoundError):
+            raise ValueError(f"Unknown dataset: {name}")
+
+    def __iter__(self) -> Diterator:
+        return Diterator(self.data_sets)
--- a/benchmark/Experiments.py
+++ b/benchmark/Experiments.py
@@ -1,4 +1,5 @@
 import os
+import sys
 import json
 import random
 import warnings
@@ -6,7 +7,6 @@ import time
 from datetime import datetime
 from tqdm import tqdm
 import numpy as np
-import pandas as pd
 from sklearn.model_selection import (
    StratifiedKFold,
    KFold,
@@ -14,91 +14,15 @@ from sklearn.model_selection import (
    cross_validate,
 )
 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]
-
-
-class Diterator:
-    def __init__(self, data):
-        self._stack = data.copy()
-
-    def __next__(self):
-        if len(self._stack) == 0:
-            raise StopIteration()
-        return self._stack.pop(0)
-
-
-class DatasetsTanveer:
    @staticmethod
-    def dataset_names(name):
-        return f"{name}_R.dat"
-
-    @staticmethod
-    def folder():
-        return "data"
-
-    def load(self, name):
-        file_name = os.path.join(self.folder(), self.dataset_names(name))
-        data = pd.read_csv(
-            file_name,
-            sep="\t",
-            index_col=0,
-        )
-        X = data.drop("clase", axis=1).to_numpy()
-        y = data["clase"].to_numpy()
-        return X, y
-
-
-class DatasetsSurcov:
-    @staticmethod
-    def dataset_names(name):
-        return f"{name}.csv"
-
-    @staticmethod
-    def folder():
-        return "datasets"
-
-    def load(self, name):
-        file_name = os.path.join(self.folder(), self.dataset_names(name))
-        data = pd.read_csv(
-            file_name,
-            index_col=0,
-        )
-        data.dropna(axis=0, how="any", inplace=True)
-        self.columns = data.columns
-        col_list = ["class"]
-        X = data.drop(col_list, axis=1).to_numpy()
-        y = data["class"].to_numpy()
-        return X, y
-
-
-class Datasets:
-    def __init__(self, dataset_name=None):
-        envData = EnvData.load()
-        class_name = getattr(
-            __import__(__name__),
-            f"Datasets{envData['source_data']}",
-        )
-        self.dataset = class_name()
-        if dataset_name is None:
-            file_name = os.path.join(self.dataset.folder(), Files.index)
-            with open(file_name) as f:
-                self.data_sets = f.read().splitlines()
-        else:
-            self.data_sets = [dataset_name]
-
-    def load(self, name):
-        try:
-            return self.dataset.load(name)
-        except FileNotFoundError:
-            raise ValueError(f"Unknown dataset: {name}")
-
-    def __iter__(self) -> Diterator:
-        return Diterator(self.data_sets)
+    def seeds():
+        return json.loads(EnvData.load()["seeds"])


 class BestResults:
@@ -234,7 +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()
@@ -242,6 +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
@@ -273,7 +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(
@@ -323,6 +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)
@@ -381,7 +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)
        )
--- a/benchmark/Models.py
+++ b/benchmark/Models.py
@@ -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"
--- a/benchmark/Results.py
+++ b/benchmark/Results.py
@@ -7,17 +7,39 @@ import shutil
 import subprocess
 import xlsxwriter
 import numpy as np
-from .Experiments import Datasets, BestResults
+from .Experiments import BestResults
+from .Datasets import Datasets
+from .Arguments import EnvData, ALL_METRICS
 from .Utils import (
    Folders,
    Files,
    Symbols,
-    BEST_ACCURACY_STREE,
    TextColor,
    NO_RESULTS,
 )


+class BestResultsEver:
+    def __init__(self):
+        self.data = {}
+        for i in ["Tanveer", "Surcov", "Arff"]:
+            self.data[i] = {}
+            for metric in ALL_METRICS:
+                self.data[i][metric.replace("-", "_")] = ["self", 1.0]
+                self.data[i][metric] = ["self", 1.0]
+        self.data["Tanveer"]["accuracy"] = [
+            "STree_default (liblinear-ovr)",
+            40.282203,
+        ]
+        self.data["Arff"]["accuracy"] = [
+            "STree_default (linear-ovo)",
+            21.9765,
+        ]
+
+    def get_name_value(self, key, score):
+        return self.data[key][score]
+
+
 class BaseReport(abc.ABC):
    def __init__(self, file_name, best_file=False):
        self.file_name = file_name
@@ -29,7 +51,20 @@ class BaseReport(abc.ABC):
        with open(self.file_name) as f:
            self.data = json.load(f)
        self.best_acc_file = best_file
-        self.lines = self.data if best_file else self.data["results"]
+        if best_file:
+            self.lines = self.data
+        else:
+            self.lines = self.data["results"]
+            self.score_name = self.data["score_name"]
+        self.__compute_best_results_ever()
+
+    def __compute_best_results_ever(self):
+        args = EnvData.load()
+        key = args["source_data"]
+        best = BestResultsEver()
+        self.best_score_name, self.best_score_value = best.get_name_value(
+            key, self.score_name
+        )

    def _get_accuracy(self, item):
        return self.data[item][0] if self.best_acc_file else item["score"]
@@ -68,6 +103,12 @@ class BaseReport(abc.ABC):
        }
        return meaning[status]

+    def _get_best_accuracy(self):
+        return self.best_score_value
+
+    def _get_message_best_accuracy(self):
+        return f"{self.score_name} compared to {self.best_score_name} .:"
+
    @abc.abstractmethod
    def header(self) -> None:
        pass
@@ -82,10 +123,10 @@ class BaseReport(abc.ABC):


 class Report(BaseReport):
-    header_lengths = [30, 5, 5, 3, 7, 7, 7, 15, 16, 15]
+    header_lengths = [30, 6, 5, 3, 7, 7, 7, 15, 16, 15]
    header_cols = [
        "Dataset",
-        "Samp",
+        "Sampl.",
        "Feat.",
        "Cls",
        "Nodes",
@@ -155,7 +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']}"
@@ -187,8 +229,8 @@ class Report(BaseReport):
                    f" {key} {self._status_meaning(key)} .....: {value:2d}"
                )
        self.header_line(
-            f" Accuracy compared to stree_default (liblinear-ovr) .: "
-            f"{accuracy/BEST_ACCURACY_STREE:7.4f}"
+            f" {self._get_message_best_accuracy()} "
+            f"{accuracy/self._get_best_accuracy():7.4f}"
        )
        self.header_line("*")

@@ -208,12 +250,12 @@ class ReportBest(BaseReport):
            if best
            else Files.grid_output(score, model)
        )
+        file_name = os.path.join(Folders.results, name)
        self.best = best
        self.grid = grid
-        file_name = os.path.join(Folders.results, name)
-        super().__init__(file_name, best_file=True)
        self.score_name = score
        self.model = model
+        super().__init__(file_name, best_file=True)

    def header_line(self, text: str) -> None:
        length = sum(self.header_lengths) + len(self.header_lengths) - 3
@@ -253,8 +295,8 @@ class ReportBest(BaseReport):
    def footer(self, accuracy):
        self.header_line("*")
        self.header_line(
-            f" Scores compared to stree_default accuracy (liblinear-ovr) .: "
-            f"{accuracy/BEST_ACCURACY_STREE:7.4f}"
+            f" {self._get_message_best_accuracy()} "
+            f"{accuracy/self._get_best_accuracy():7.4f}"
        )
        self.header_line("*")

@@ -306,7 +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']}"
@@ -508,8 +551,8 @@ class Excel(BaseReport):
                self.sheet.write(self.row, 3, self._status_meaning(key), bold)
                self.row += 1
        message = (
-            f"** Accuracy compared to stree_default (liblinear-ovr) .: "
-            f"{accuracy/BEST_ACCURACY_STREE:7.4f}"
+            f"** {self._get_message_best_accuracy()} "
+            f"{accuracy/self._get_best_accuracy():7.4f}"
        )
        bold = self.book.add_format({"bold": True, "font_size": 14})
        # set width of the hyperparams column with the maximum width
@@ -534,8 +577,8 @@ class ReportDatasets:
        data_sets = Datasets()
        color_line = TextColor.LINE1
        print(color_line, end="")
-        print(f"{'Dataset':30s} Samp. Feat. Cls Balance")
-        print("=" * 30 + " ===== ===== === " + "=" * 40)
+        print(f"{'Dataset':30s} Sampl. Feat. Cls Balance")
+        print("=" * 30 + " ===== ====== === " + "=" * 40)
        for dataset in data_sets:
            X, y = data_sets.load(dataset)
            color_line = (
@@ -551,7 +594,7 @@ class ReportDatasets:
                sep = "/ "
            print(color_line, end="")
            print(
-                f"{dataset:30s} {X.shape[0]:5,d} {X.shape[1]:5,d} "
+                f"{dataset:30s} {X.shape[0]:6,d} {X.shape[1]:5,d} "
                f"{len(np.unique(y)):3d} {comp:40s}"
            )

@@ -633,6 +676,13 @@ class Benchmark:
        self._report = {}
        self._datasets = set()
        self.visualize = visualize
+        self.__compute_best_results_ever()
+
+    def __compute_best_results_ever(self):
+        args = EnvData.load()
+        key = args["source_data"]
+        best = BestResultsEver()
+        _, self.best_score_value = best.get_name_value(key, self._score)

    def get_result_file_name(self):
        return os.path.join(Folders.exreport, Files.exreport(self._score))
@@ -970,7 +1020,7 @@ class Benchmark:
                sheet.write_formula(
                    row,
                    col + 1,
-                    f"=sum({range_metric})/{BEST_ACCURACY_STREE}",
+                    f"=sum({range_metric})/{self.best_score_value}",
                    decimal_total,
                )
                range_rank = (
@@ -1062,7 +1112,7 @@ class StubReport(BaseReport):

    def footer(self, accuracy: float) -> None:
        self.accuracy = accuracy
-        self.score = accuracy / BEST_ACCURACY_STREE
+        self.score = accuracy / self._get_best_accuracy()


 class Summary:
--- a/benchmark/Utils.py
+++ b/benchmark/Utils.py
@@ -1,7 +1,8 @@
 import os
+import sys
 import subprocess

-BEST_ACCURACY_STREE = 40.282203
+PYTHON_VERSION = "{}.{}".format(sys.version_info.major, sys.version_info.minor)
 NO_RESULTS = "** No results found **"
 NO_ENV = "File .env not found"

--- a/benchmark/init.py
+++ b/benchmark/init.py
@@ -1,4 +1,5 @@
-from .Experiments import Experiment, Datasets, DatasetsSurcov, DatasetsTanveer
+from .Datasets import Datasets, DatasetsSurcov, DatasetsTanveer, DatasetsArff
+from .Experiments import Experiment
 from .Results import Report, Summary

 __author__ = "Ricardo Montañana Gómez"
--- a/benchmark/_version.py
+++ b/benchmark/_version.py
@@ -1 +1 @@
-__version__ = "0.1.1"
+__version__ = "0.2.0"
--- a/benchmark/scripts/be_build_best.py
+++ b/benchmark/scripts/be_build_best.py
@@ -1,6 +1,7 @@
 #!/usr/bin/env python
 from benchmark.Results import ReportBest
-from benchmark.Experiments import Datasets, BestResults
+from benchmark.Experiments import BestResults
+from benchmark.Datasets import Datasets
 from benchmark.Arguments import Arguments

 """Build a json file with the best results of a model and its hyperparameters
--- a/benchmark/scripts/be_grid.py
+++ b/benchmark/scripts/be_grid.py
@@ -1,5 +1,6 @@
 #!/usr/bin/env python
-from benchmark.Experiments import GridSearch, Datasets
+from benchmark.Experiments import GridSearch
+from benchmark.Datasets import Datasets
 from benchmark.Arguments import Arguments

 """Do experiment and build result file, optionally print report with results
--- a/benchmark/scripts/be_main.py
+++ b/benchmark/scripts/be_main.py
@@ -1,6 +1,7 @@
 #!/usr/bin/env python
 import os
-from benchmark.Experiments import Experiment, Datasets
+from benchmark.Experiments import Experiment
+from benchmark.Datasets import Datasets
 from benchmark.Results import Report
 from benchmark.Arguments import Arguments

--- a/benchmark/scripts/be_print_strees.py
+++ b/benchmark/scripts/be_print_strees.py
@@ -3,7 +3,7 @@ import os
 import json
 from stree import Stree
 from graphviz import Source
-from benchmark.Experiments import Datasets
+from benchmark.Datasets import Datasets
 from benchmark.Utils import Files, Folders
 from benchmark.Arguments import Arguments

--- a/benchmark/tests/.env
+++ b/benchmark/tests/.env
@@ -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]
--- a/benchmark/tests/.env.arff
+++ b/benchmark/tests/.env.arff
@@ -0,0 +1,7 @@
+score=accuracy
+platform=MacBookpro16
+n_folds=5
+model=ODTE
+stratified=0
+source_data=Arff
+seeds=[271, 314, 171]
--- a/benchmark/tests/.env.dist
+++ b/benchmark/tests/.env.dist
@@ -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]
--- a/benchmark/tests/.env.surcov
+++ b/benchmark/tests/.env.surcov
@@ -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]
--- a/benchmark/tests/BestResults_test.py
+++ b/benchmark/tests/BestResults_test.py
@@ -1,6 +1,7 @@
 import os
 from .TestBase import TestBase
-from ..Experiments import BestResults, Datasets
+from ..Experiments import BestResults
+from ..Datasets import Datasets


 class BestResultTest(TestBase):
--- a/benchmark/tests/Dataset_test.py
+++ b/benchmark/tests/Dataset_test.py
@@ -1,6 +1,7 @@
 import shutil
 from .TestBase import TestBase
-from ..Experiments import Randomized, Datasets
+from ..Experiments import Randomized
+from ..Datasets import Datasets


 class DatasetTest(TestBase):
@@ -22,12 +23,18 @@ 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 = {
            ".env.dist": ["balance-scale", "balloons"],
            ".env.surcov": ["iris", "wine"],
+            ".env.arff": ["iris", "wine"],
        }
        for key, value in test.items():
            self.set_env(key)
@@ -51,6 +58,11 @@ class DatasetTest(TestBase):
        self.assertSequenceEqual(X.shape, (625, 4))
        self.assertSequenceEqual(y.shape, (625,))

+    def test_create_with_unknown_dataset(self):
+        with self.assertRaises(ValueError) as msg:
+            Datasets("unknown")
+        self.assertEqual(str(msg.exception), "Unknown dataset: unknown")
+
    def test_load_unknown_dataset(self):
        dt = Datasets()
        with self.assertRaises(ValueError) as msg:
@@ -61,6 +73,7 @@ class DatasetTest(TestBase):
        test = {
            ".env.dist": "balloons",
            ".env.surcov": "wine",
+            ".env.arff": "iris",
        }
        for key, value in test.items():
            self.set_env(key)
--- a/benchmark/tests/Experiment_test.py
+++ b/benchmark/tests/Experiment_test.py
@@ -1,6 +1,7 @@
 import json
 from .TestBase import TestBase
-from ..Experiments import Experiment, Datasets
+from ..Experiments import Experiment
+from ..Datasets import Datasets


 class ExperimentTest(TestBase):
--- a/benchmark/tests/GridSearch_test.py
+++ b/benchmark/tests/GridSearch_test.py
@@ -1,6 +1,7 @@
 import json
 from .TestBase import TestBase
-from ..Experiments import GridSearch, Datasets
+from ..Experiments import GridSearch
+from ..Datasets import Datasets


 class GridSearchTest(TestBase):
@@ -77,7 +78,9 @@ class GridSearchTest(TestBase):
                "v. 1.2.4, Computed on Test on 2022-02-22 at 12:00:00 took 1s",
            ],
        }
-        self.assertSequenceEqual(data, expected)
+        for key, value in expected.items():
+            self.assertEqual(data[key][0], value[0])
+            self.assertDictEqual(data[key][1], value[1])

    def test_duration_message(self):
        expected = ["47.234s", "5.421m", "1.177h"]
--- a/benchmark/tests/Models_test.py
+++ b/benchmark/tests/Models_test.py
@@ -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)
--- a/benchmark/tests/Report_test.py
+++ b/benchmark/tests/Report_test.py
@@ -75,7 +75,17 @@ class ReportTest(TestBase):
        report = ReportBest("accuracy", "STree", best=False, grid=True)
        with patch(self.output, new=StringIO()) as stdout:
            report.report()
-        self.check_output_file(stdout, "report_grid")
+        file_name = "report_grid.test"
+        with open(os.path.join(self.test_files, file_name)) as f:
+            expected = f.read().splitlines()
+        output_text = stdout.getvalue().splitlines()
+        # Compare replacing STree version
+        for line, index in zip(expected, range(len(expected))):
+            if "1.2.4" in line:
+                # replace STree version
+                line = self.replace_STree_version(line, output_text, index)
+
+            self.assertEqual(line, output_text[index])

    def test_report_best_both(self):
        report = ReportBest("accuracy", "STree", best=True, grid=True)
--- a/benchmark/tests/TestBase.py
+++ b/benchmark/tests/TestBase.py
@@ -14,6 +14,7 @@ class TestBase(unittest.TestCase):
        os.chdir(os.path.dirname(os.path.abspath(__file__)))
        self.test_files = "test_files"
        self.output = "sys.stdout"
+        self.ext = ".test"
        super().__init__(*args, **kwargs)

    def remove_files(self, files, folder):
@@ -31,7 +32,7 @@ class TestBase(unittest.TestCase):
                        print(f'{row};{col};"{value}"', file=f)

    def check_excel_sheet(self, sheet, file_name):
-        file_name += ".test"
+        file_name += self.ext
        with open(os.path.join(self.test_files, file_name), "r") as f:
            expected = csv.reader(f, delimiter=";")
            for row, col, value in expected:
@@ -45,15 +46,20 @@ class TestBase(unittest.TestCase):
                self.assertEqual(sheet.cell(int(row), int(col)).value, value)

    def check_output_file(self, output, file_name):
-        file_name += ".test"
+        file_name += self.ext
        with open(os.path.join(self.test_files, file_name)) as f:
            expected = f.read()
        self.assertEqual(output.getvalue(), expected)

+    @staticmethod
+    def replace_STree_version(line, output, index):
+        idx = line.find("1.2.4")
+        return line.replace("1.2.4", output[index][idx : idx + 5])
+
    def check_file_file(self, computed_file, expected_file):
        with open(computed_file) as f:
            computed = f.read()
-        expected_file += ".test"
+        expected_file += self.ext
        with open(os.path.join(self.test_files, expected_file)) as f:
            expected = f.read()
        self.assertEqual(computed, expected)
--- a/benchmark/tests/Util_test.py
+++ b/benchmark/tests/Util_test.py
@@ -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)
--- a/benchmark/tests/datasets/all.txt
+++ b/benchmark/tests/datasets/all.txt
@@ -1,2 +1,2 @@
-iris
-wine
+iris,class
+wine,class
--- a/benchmark/tests/datasets/hayes-roth.arff
+++ b/benchmark/tests/datasets/hayes-roth.arff
@@ -0,0 +1,305 @@
+% 1. Title: Hayes-Roth & Hayes-Roth (1977) Database
+% 
+% 2. Source Information:
+%    (a) Creators: Barbara and Frederick Hayes-Roth
+%    (b) Donor: David W. Aha (aha@ics.uci.edu) (714) 856-8779   
+%    (c) Date: March, 1989
+% 
+% 3. Past Usage:
+%     1. Hayes-Roth, B., & Hayes-Roth, F. (1977).  Concept learning and the
+%        recognition and classification of exemplars.  Journal of Verbal Learning
+%        and Verbal Behavior, 16, 321-338.
+%        -- Results: 
+%           -- Human subjects classification and recognition performance:
+% 	       1. decreases with distance from the prototype,
+% 	       2. is better on unseen prototypes than old instances, and
+% 	       3. improves with presentation frequency during learning.
+%     2. Anderson, J.R., & Kline, P.J. (1979).  A learning system and its 
+%        psychological implications.  In Proceedings of the Sixth International
+%        Joint Conference on Artificial Intelligence (pp. 16-21).  Tokyo, Japan:
+%        Morgan Kaufmann.
+%        -- Partitioned the results into 4 classes:
+% 	    1. prototypes
+% 	    2. near-prototypes with high presentation frequency during learning
+% 	    3. near-prototypes with low presentation frequency during learning
+% 	    4. instances that are far from protoypes
+%        -- Described evidence that ACT's classification confidence and
+%           recognition behaviors closely simulated human subjects' behaviors.
+%     3. Aha, D.W. (1989).  Incremental learning of independent, overlapping, and
+%        graded concept descriptions with an instance-based process framework.
+%        Manuscript submitted for publication.
+%        -- Used same partition as Anderson & Kline
+%        -- Described evidence that Bloom's classification confidence behavior
+% 	  is similar to the human subjects' behavior.  Bloom fitted the data
+% 	  more closely than did ACT. 
+% 
+% 4. Relevant Information:
+%      This database contains 5 numeric-valued attributes.  Only a subset of
+%      3 are used during testing (the latter 3).  Furthermore, only 2 of the
+%      3 concepts are "used" during testing (i.e., those with the prototypes
+%      000 and 111).  I've mapped all values to their zero-indexing equivalents.
+% 
+%      Some instances could be placed in either category 0 or 1.  I've followed
+%      the authors' suggestion, placing them in each category with equal
+%      probability.
+% 
+%      I've replaced the actual values of the attributes (i.e., hobby has values
+%      chess, sports and stamps) with numeric values.  I think this is how 
+%      the authors' did this when testing the categorization models described
+%      in the paper.  I find this unfair.  While the subjects were able to bring
+%      background knowledge to bear on the attribute values and their
+%      relationships, the algorithms were provided with no such knowledge.  I'm
+%      uncertain whether the 2 distractor attributes (name and hobby) are
+%      presented to the authors' algorithms during testing.  However, it is clear
+%      that only the age, educational status, and marital status attributes are
+%      given during the human subjects' transfer tests.  
+%     
+% 5. Number of Instances: 132 training instances, 28 test instances
+% 
+% 6. Number of Attributes: 5 plus the class membership attribute.  3 concepts.
+% 
+% 7. Attribute Information:
+%       -- 1. name: distinct for each instance and represented numerically
+%       -- 2. hobby: nominal values ranging between 1 and 3
+%       -- 3. age: nominal values ranging between 1 and 4
+%       -- 4. educational level: nominal values ranging between 1 and 4
+%       -- 5. marital status: nominal values ranging between 1 and 4
+%       -- 6. class: nominal value between 1 and 3
+% 
+% 9. Missing Attribute Values: none
+% 
+% 10. Class Distribution: see below
+% 
+% 11. Detailed description of the experiment:
+%   1. 3 categories (1, 2, and neither -- which I call 3)
+%      -- some of the instances could be classified in either class 1 or 2, and
+%         they have been evenly distributed between the two classes
+%   2. 5 Attributes
+%      -- A. name (a randomly-generated number between 1 and 132)
+%      -- B. hobby (a randomly-generated number between 1 and 3)
+%      -- C. age (a number between 1 and 4)
+%      -- D. education level (a number between 1 and 4)
+%      -- E. marital status (a number between 1 and 4)
+%   3. Classification: 
+%      -- only attributes C-E are diagnostic; values for A and B are ignored
+%      -- Class Neither: if a 4 occurs for any attribute C-E
+%      -- Class 1: Otherwise, if (# of 1's)>(# of 2's) for attributes C-E
+%      -- Class 2: Otherwise, if (# of 2's)>(# of 1's) for attributes C-E
+%      -- Either 1 or 2: Otherwise, if (# of 2's)=(# of 1's) for attributes C-E
+%   4. Prototypes:
+%      -- Class 1: 111
+%      -- Class 2: 222
+%      -- Class Either: 333
+%      -- Class Neither: 444  
+%   5. Number of training instances: 132
+%      -- Each instance presented 0, 1, or 10 times
+%      -- None of the prototypes seen during training
+%      -- 3 instances from each of categories 1, 2, and either are repeated 
+%         10 times each
+%      -- 3 additional instances from the Either category are shown during
+%         learning
+%   5. Number of test instances: 28
+%      -- All 9 class 1
+%      -- All 9 class 2
+%      -- All 6 class Either
+%      -- All 4 prototypes
+%      --------------------
+%      --    28 total
+% 
+% Observations of interest:
+%   1. Relative classification confidence of 
+%      -- prototypes for classes 1 and 2 (2 instances)
+%         (Anderson calls these Class 1 instances)
+%      -- instances of class 1 with frequency 10 during training and
+%         instances of class 2 with frequency 10 during training that
+%         are 1 value away from their respective prototypes (6 instances)
+%         (Anderson calls these Class 2 instances)
+%      -- instances of class 1 with frequency 1 during training and 
+%         instances of class 2 with frequency 1 during training that
+%         are 1 value away from their respective prototypes (6 instances)
+%         (Anderson calls these Class 3 instances)
+%      -- instances of class 1 with frequency 1 during training and 
+%         instances of class 2 with frequency 1 during training that
+%         are 2 values away from their respective prototypes (6 instances)
+%         (Anderson calls these Class 4 instances)
+%  2. Relative classification recognition of them also
+% 
+% Some Expected results:
+%    Both frequency and distance from prototype will effect the classification
+%    accuracy of instances.  Greater the frequency, higher the classification
+%    confidence.  Closer to prototype, higher the classification confidence.
+%
+% Information about the dataset
+% CLASSTYPE: nominal
+% CLASSINDEX: last
+%
+
+@relation hayes-roth
+
+@attribute hobby INTEGER
+@attribute age INTEGER
+@attribute educational_level INTEGER
+@attribute marital_status INTEGER
+@attribute class {1,2,3,4}
+
+@data
+2,1,1,2,1
+2,1,3,2,2
+3,1,4,1,3
+2,4,2,2,3
+1,1,3,4,3
+1,1,3,2,2
+3,1,3,2,2
+3,4,2,4,3
+2,2,1,1,1
+3,2,1,1,1
+1,2,1,1,1
+2,2,3,4,3
+1,1,2,1,1
+2,1,2,2,2
+2,4,1,4,3
+1,1,3,3,1
+3,2,1,2,2
+1,2,1,1,1
+3,3,2,1,1
+3,1,3,2,1
+1,2,2,1,2
+3,2,1,3,1
+2,1,2,1,1
+3,2,1,3,1
+2,3,2,1,1
+3,2,2,1,2
+3,2,1,3,2
+2,1,2,2,2
+1,1,3,2,1
+3,2,1,1,1
+1,4,1,1,3
+2,2,1,3,1
+1,2,1,3,2
+1,1,1,2,1
+2,4,3,1,3
+3,1,2,2,2
+1,1,2,2,2
+3,2,2,1,2
+1,2,1,2,2
+3,4,3,2,3
+2,2,2,1,2
+2,2,1,2,2
+3,2,1,3,2
+3,2,1,1,1
+3,1,2,1,1
+1,2,1,3,2
+2,1,1,2,1
+1,1,1,2,1
+1,2,2,3,2
+3,3,1,1,1
+3,3,3,1,1
+3,2,1,2,2
+3,2,1,2,2
+3,1,2,1,1
+1,1,1,2,1
+2,1,3,2,1
+2,2,2,1,2
+2,1,2,1,1
+2,2,1,3,1
+2,1,2,2,2
+1,2,4,2,3
+2,2,1,2,2
+1,1,2,4,3
+1,3,2,1,1
+2,4,4,2,3
+2,3,2,1,1
+3,1,2,2,2
+1,1,2,2,2
+1,3,2,4,3
+1,1,2,2,2
+3,1,4,2,3
+2,1,3,2,2
+1,1,3,2,2
+3,1,3,2,1
+1,2,4,4,3
+1,4,2,1,3
+2,1,2,1,1
+3,4,1,2,3
+2,2,1,1,1
+1,1,2,1,1
+2,2,4,3,3
+3,1,2,2,2
+1,1,3,2,1
+1,2,1,3,1
+1,4,4,1,3
+3,3,3,2,2
+2,2,1,3,2
+3,3,2,1,2
+1,1,1,3,1
+2,2,1,2,2
+2,2,2,1,2
+2,3,2,3,2
+1,3,2,1,2
+2,2,1,2,2
+1,1,1,2,1
+3,2,2,1,2
+3,2,1,1,1
+1,1,2,1,1
+3,1,4,4,3
+3,3,2,1,2
+2,3,2,1,2
+2,1,3,1,1
+1,2,1,2,2
+3,1,1,2,1
+2,2,4,1,3
+1,2,2,1,2
+2,3,2,1,2
+2,2,1,4,3
+1,4,2,3,3
+2,2,1,1,1
+1,2,1,1,1
+2,2,3,2,2
+1,3,2,1,1
+3,1,2,1,1
+3,1,1,2,1
+3,3,1,4,3
+2,3,4,1,3
+1,2,3,3,2
+3,3,2,2,2
+3,3,4,2,3
+1,2,2,1,2
+2,1,1,4,3
+3,1,2,2,2
+3,2,2,4,3
+2,3,1,3,1
+2,1,1,2,1
+3,4,1,3,3
+1,1,4,3,3
+2,1,2,1,1
+1,2,1,2,2
+1,2,2,1,2
+3,1,1,2,1
+1,1,1,2,1
+1,1,2,1,1
+1,2,1,1,1
+1,1,1,3,1
+1,1,3,1,1
+1,3,1,1,1
+1,1,3,3,1
+1,3,1,3,1
+1,3,3,1,1
+1,2,2,1,2
+1,2,1,2,2
+1,1,2,2,2
+1,2,2,3,2
+1,2,3,2,2
+1,3,2,2,2
+1,2,3,3,2
+1,3,2,3,2
+1,3,3,2,2
+1,1,3,2,1
+1,3,2,1,2
+1,2,1,3,1
+1,2,3,1,2
+1,1,2,3,1
+1,3,1,2,2
+1,1,1,1,1
+1,2,2,2,2
+1,3,3,3,1
+1,4,4,4,3
--- a/benchmark/tests/datasets/iris.arff
+++ b/benchmark/tests/datasets/iris.arff
@@ -0,0 +1,225 @@
+% 1. Title: Iris Plants Database
+% 
+% 2. Sources:
+%      (a) Creator: R.A. Fisher
+%      (b) Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
+%      (c) Date: July, 1988
+% 
+% 3. Past Usage:
+%    - Publications: too many to mention!!!  Here are a few.
+%    1. Fisher,R.A. "The use of multiple measurements in taxonomic problems"
+%       Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions
+%       to Mathematical Statistics" (John Wiley, NY, 1950).
+%    2. Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.
+%       (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.
+%    3. Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System
+%       Structure and Classification Rule for Recognition in Partially Exposed
+%       Environments".  IEEE Transactions on Pattern Analysis and Machine
+%       Intelligence, Vol. PAMI-2, No. 1, 67-71.
+%       -- Results:
+%          -- very low misclassification rates (0% for the setosa class)
+%    4. Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule".  IEEE 
+%       Transactions on Information Theory, May 1972, 431-433.
+%       -- Results:
+%          -- very low misclassification rates again
+%    5. See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al's AUTOCLASS II
+%       conceptual clustering system finds 3 classes in the data.
+% 
+% 4. Relevant Information:
+%    --- This is perhaps the best known database to be found in the pattern
+%        recognition literature.  Fisher's paper is a classic in the field
+%        and is referenced frequently to this day.  (See Duda & Hart, for
+%        example.)  The data set contains 3 classes of 50 instances each,
+%        where each class refers to a type of iris plant.  One class is
+%        linearly separable from the other 2; the latter are NOT linearly
+%        separable from each other.
+%    --- Predicted attribute: class of iris plant.
+%    --- This is an exceedingly simple domain.
+% 
+% 5. Number of Instances: 150 (50 in each of three classes)
+% 
+% 6. Number of Attributes: 4 numeric, predictive attributes and the class
+% 
+% 7. Attribute Information:
+%    1. sepal length in cm
+%    2. sepal width in cm
+%    3. petal length in cm
+%    4. petal width in cm
+%    5. class: 
+%       -- Iris Setosa
+%       -- Iris Versicolour
+%       -- Iris Virginica
+% 
+% 8. Missing Attribute Values: None
+% 
+% Summary Statistics:
+%  	           Min  Max   Mean    SD   Class Correlation
+%    sepal length: 4.3  7.9   5.84  0.83    0.7826   
+%     sepal width: 2.0  4.4   3.05  0.43   -0.4194
+%    petal length: 1.0  6.9   3.76  1.76    0.9490  (high!)
+%     petal width: 0.1  2.5   1.20  0.76    0.9565  (high!)
+% 
+% 9. Class Distribution: 33.3% for each of 3 classes.
+
+@RELATION iris
+
+@ATTRIBUTE sepallength	REAL
+@ATTRIBUTE sepalwidth 	REAL
+@ATTRIBUTE petallength 	REAL
+@ATTRIBUTE petalwidth	REAL
+@ATTRIBUTE class 	{Iris-setosa,Iris-versicolor,Iris-virginica}
+
+@DATA
+5.1,3.5,1.4,0.2,Iris-setosa
+4.9,3.0,1.4,0.2,Iris-setosa
+4.7,3.2,1.3,0.2,Iris-setosa
+4.6,3.1,1.5,0.2,Iris-setosa
+5.0,3.6,1.4,0.2,Iris-setosa
+5.4,3.9,1.7,0.4,Iris-setosa
+4.6,3.4,1.4,0.3,Iris-setosa
+5.0,3.4,1.5,0.2,Iris-setosa
+4.4,2.9,1.4,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.4,3.7,1.5,0.2,Iris-setosa
+4.8,3.4,1.6,0.2,Iris-setosa
+4.8,3.0,1.4,0.1,Iris-setosa
+4.3,3.0,1.1,0.1,Iris-setosa
+5.8,4.0,1.2,0.2,Iris-setosa
+5.7,4.4,1.5,0.4,Iris-setosa
+5.4,3.9,1.3,0.4,Iris-setosa
+5.1,3.5,1.4,0.3,Iris-setosa
+5.7,3.8,1.7,0.3,Iris-setosa
+5.1,3.8,1.5,0.3,Iris-setosa
+5.4,3.4,1.7,0.2,Iris-setosa
+5.1,3.7,1.5,0.4,Iris-setosa
+4.6,3.6,1.0,0.2,Iris-setosa
+5.1,3.3,1.7,0.5,Iris-setosa
+4.8,3.4,1.9,0.2,Iris-setosa
+5.0,3.0,1.6,0.2,Iris-setosa
+5.0,3.4,1.6,0.4,Iris-setosa
+5.2,3.5,1.5,0.2,Iris-setosa
+5.2,3.4,1.4,0.2,Iris-setosa
+4.7,3.2,1.6,0.2,Iris-setosa
+4.8,3.1,1.6,0.2,Iris-setosa
+5.4,3.4,1.5,0.4,Iris-setosa
+5.2,4.1,1.5,0.1,Iris-setosa
+5.5,4.2,1.4,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.0,3.2,1.2,0.2,Iris-setosa
+5.5,3.5,1.3,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.4,3.0,1.3,0.2,Iris-setosa
+5.1,3.4,1.5,0.2,Iris-setosa
+5.0,3.5,1.3,0.3,Iris-setosa
+4.5,2.3,1.3,0.3,Iris-setosa
+4.4,3.2,1.3,0.2,Iris-setosa
+5.0,3.5,1.6,0.6,Iris-setosa
+5.1,3.8,1.9,0.4,Iris-setosa
+4.8,3.0,1.4,0.3,Iris-setosa
+5.1,3.8,1.6,0.2,Iris-setosa
+4.6,3.2,1.4,0.2,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+5.0,3.3,1.4,0.2,Iris-setosa
+7.0,3.2,4.7,1.4,Iris-versicolor
+6.4,3.2,4.5,1.5,Iris-versicolor
+6.9,3.1,4.9,1.5,Iris-versicolor
+5.5,2.3,4.0,1.3,Iris-versicolor
+6.5,2.8,4.6,1.5,Iris-versicolor
+5.7,2.8,4.5,1.3,Iris-versicolor
+6.3,3.3,4.7,1.6,Iris-versicolor
+4.9,2.4,3.3,1.0,Iris-versicolor
+6.6,2.9,4.6,1.3,Iris-versicolor
+5.2,2.7,3.9,1.4,Iris-versicolor
+5.0,2.0,3.5,1.0,Iris-versicolor
+5.9,3.0,4.2,1.5,Iris-versicolor
+6.0,2.2,4.0,1.0,Iris-versicolor
+6.1,2.9,4.7,1.4,Iris-versicolor
+5.6,2.9,3.6,1.3,Iris-versicolor
+6.7,3.1,4.4,1.4,Iris-versicolor
+5.6,3.0,4.5,1.5,Iris-versicolor
+5.8,2.7,4.1,1.0,Iris-versicolor
+6.2,2.2,4.5,1.5,Iris-versicolor
+5.6,2.5,3.9,1.1,Iris-versicolor
+5.9,3.2,4.8,1.8,Iris-versicolor
+6.1,2.8,4.0,1.3,Iris-versicolor
+6.3,2.5,4.9,1.5,Iris-versicolor
+6.1,2.8,4.7,1.2,Iris-versicolor
+6.4,2.9,4.3,1.3,Iris-versicolor
+6.6,3.0,4.4,1.4,Iris-versicolor
+6.8,2.8,4.8,1.4,Iris-versicolor
+6.7,3.0,5.0,1.7,Iris-versicolor
+6.0,2.9,4.5,1.5,Iris-versicolor
+5.7,2.6,3.5,1.0,Iris-versicolor
+5.5,2.4,3.8,1.1,Iris-versicolor
+5.5,2.4,3.7,1.0,Iris-versicolor
+5.8,2.7,3.9,1.2,Iris-versicolor
+6.0,2.7,5.1,1.6,Iris-versicolor
+5.4,3.0,4.5,1.5,Iris-versicolor
+6.0,3.4,4.5,1.6,Iris-versicolor
+6.7,3.1,4.7,1.5,Iris-versicolor
+6.3,2.3,4.4,1.3,Iris-versicolor
+5.6,3.0,4.1,1.3,Iris-versicolor
+5.5,2.5,4.0,1.3,Iris-versicolor
+5.5,2.6,4.4,1.2,Iris-versicolor
+6.1,3.0,4.6,1.4,Iris-versicolor
+5.8,2.6,4.0,1.2,Iris-versicolor
+5.0,2.3,3.3,1.0,Iris-versicolor
+5.6,2.7,4.2,1.3,Iris-versicolor
+5.7,3.0,4.2,1.2,Iris-versicolor
+5.7,2.9,4.2,1.3,Iris-versicolor
+6.2,2.9,4.3,1.3,Iris-versicolor
+5.1,2.5,3.0,1.1,Iris-versicolor
+5.7,2.8,4.1,1.3,Iris-versicolor
+6.3,3.3,6.0,2.5,Iris-virginica
+5.8,2.7,5.1,1.9,Iris-virginica
+7.1,3.0,5.9,2.1,Iris-virginica
+6.3,2.9,5.6,1.8,Iris-virginica
+6.5,3.0,5.8,2.2,Iris-virginica
+7.6,3.0,6.6,2.1,Iris-virginica
+4.9,2.5,4.5,1.7,Iris-virginica
+7.3,2.9,6.3,1.8,Iris-virginica
+6.7,2.5,5.8,1.8,Iris-virginica
+7.2,3.6,6.1,2.5,Iris-virginica
+6.5,3.2,5.1,2.0,Iris-virginica
+6.4,2.7,5.3,1.9,Iris-virginica
+6.8,3.0,5.5,2.1,Iris-virginica
+5.7,2.5,5.0,2.0,Iris-virginica
+5.8,2.8,5.1,2.4,Iris-virginica
+6.4,3.2,5.3,2.3,Iris-virginica
+6.5,3.0,5.5,1.8,Iris-virginica
+7.7,3.8,6.7,2.2,Iris-virginica
+7.7,2.6,6.9,2.3,Iris-virginica
+6.0,2.2,5.0,1.5,Iris-virginica
+6.9,3.2,5.7,2.3,Iris-virginica
+5.6,2.8,4.9,2.0,Iris-virginica
+7.7,2.8,6.7,2.0,Iris-virginica
+6.3,2.7,4.9,1.8,Iris-virginica
+6.7,3.3,5.7,2.1,Iris-virginica
+7.2,3.2,6.0,1.8,Iris-virginica
+6.2,2.8,4.8,1.8,Iris-virginica
+6.1,3.0,4.9,1.8,Iris-virginica
+6.4,2.8,5.6,2.1,Iris-virginica
+7.2,3.0,5.8,1.6,Iris-virginica
+7.4,2.8,6.1,1.9,Iris-virginica
+7.9,3.8,6.4,2.0,Iris-virginica
+6.4,2.8,5.6,2.2,Iris-virginica
+6.3,2.8,5.1,1.5,Iris-virginica
+6.1,2.6,5.6,1.4,Iris-virginica
+7.7,3.0,6.1,2.3,Iris-virginica
+6.3,3.4,5.6,2.4,Iris-virginica
+6.4,3.1,5.5,1.8,Iris-virginica
+6.0,3.0,4.8,1.8,Iris-virginica
+6.9,3.1,5.4,2.1,Iris-virginica
+6.7,3.1,5.6,2.4,Iris-virginica
+6.9,3.1,5.1,2.3,Iris-virginica
+5.8,2.7,5.1,1.9,Iris-virginica
+6.8,3.2,5.9,2.3,Iris-virginica
+6.7,3.3,5.7,2.5,Iris-virginica
+6.7,3.0,5.2,2.3,Iris-virginica
+6.3,2.5,5.0,1.9,Iris-virginica
+6.5,3.0,5.2,2.0,Iris-virginica
+6.2,3.4,5.4,2.3,Iris-virginica
+5.9,3.0,5.1,1.8,Iris-virginica
+%
+%
+%
--- a/benchmark/tests/datasets/wine.arff
+++ b/benchmark/tests/datasets/wine.arff
@@ -0,0 +1,302 @@
+% 1. Title of Database: Wine recognition data
+% 	Updated Sept 21, 1998 by C.Blake : Added attribute information
+% 
+% 2. Sources:
+%    (a) Forina, M. et al, PARVUS - An Extendible Package for Data
+%        Exploration, Classification and Correlation. Institute of Pharmaceutical
+%        and Food Analysis and Technologies, Via Brigata Salerno, 
+%        16147 Genoa, Italy.
+% 
+%    (b) Stefan Aeberhard, email: stefan@coral.cs.jcu.edu.au
+%    (c) July 1991
+% 3. Past Usage:
+% 
+%    (1)
+%    S. Aeberhard, D. Coomans and O. de Vel,
+%    Comparison of Classifiers in High Dimensional Settings,
+%    Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of
+%    Mathematics and Statistics, James Cook University of North Queensland.
+%    (Also submitted to Technometrics).
+% 
+%    The data was used with many others for comparing various 
+%    classifiers. The classes are separable, though only RDA 
+%    has achieved 100% correct classification.
+%    (RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data))
+%    (All results using the leave-one-out technique)
+% 
+%    In a classification context, this is a well posed problem 
+%    with "well behaved" class structures. A good data set 
+%    for first testing of a new classifier, but not very 
+%    challenging.
+% 
+%    (2) 
+%    S. Aeberhard, D. Coomans and O. de Vel,
+%    "THE CLASSIFICATION PERFORMANCE OF RDA"
+%    Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of
+%    Mathematics and Statistics, James Cook University of North Queensland.
+%    (Also submitted to Journal of Chemometrics).
+% 
+%    Here, the data was used to illustrate the superior performance of
+%    the use of a new appreciation function with RDA. 
+% 
+% 4. Relevant Information:
+% 
+%    -- These data are the results of a chemical analysis of
+%       wines grown in the same region in Italy but derived from three
+%       different cultivars.
+%       The analysis determined the quantities of 13 constituents
+%       found in each of the three types of wines. 
+% 
+%    -- I think that the initial data set had around 30 variables, but 
+%       for some reason I only have the 13 dimensional version. 
+%       I had a list of what the 30 or so variables were, but a.) 
+%       I lost it, and b.), I would not know which 13 variables
+%       are included in the set.
+% 
+%    -- The attributes are (dontated by Riccardo Leardi, 
+% 	riclea@anchem.unige.it )
+%  	1) Alcohol
+%  	2) Malic acid
+%  	3) Ash
+% 	4) Alcalinity of ash  
+%  	5) Magnesium
+% 	6) Total phenols
+%  	7) Flavanoids
+%  	8) Nonflavanoid phenols
+%  	9) Proanthocyanins
+% 	10)Color intensity
+%  	11)Hue
+%  	12)OD280/OD315 of diluted wines
+%  	13)Proline            
+% 
+% 5. Number of Instances
+% 
+%       	class 1 59
+% 	class 2 71
+% 	class 3 48
+% 
+% 6. Number of Attributes 
+% 	
+% 	13
+% 
+% 7. For Each Attribute:
+% 
+% 	All attributes are continuous
+% 	
+% 	No statistics available, but suggest to standardise
+% 	variables for certain uses (e.g. for us with classifiers
+% 	which are NOT scale invariant)
+% 
+% 	NOTE: 1st attribute is class identifier (1-3)
+% 
+% 8. Missing Attribute Values:
+% 
+% 	None
+% 
+% 9. Class Distribution: number of instances per class
+% 
+%       	class 1 59
+% 	class 2 71
+% 	class 3 48
+%
+% Information about the dataset
+% CLASSTYPE: nominal
+% CLASSINDEX: first
+%
+
+@relation wine
+
+@attribute class {1,2,3}
+@attribute Alcohol REAL
+@attribute Malic_acid REAL
+@attribute Ash REAL
+@attribute Alcalinity_of_ash REAL
+@attribute Magnesium INTEGER
+@attribute Total_phenols REAL
+@attribute Flavanoids REAL
+@attribute Nonflavanoid_phenols REAL
+@attribute Proanthocyanins REAL
+@attribute Color_intensity REAL
+@attribute Hue REAL
+@attribute OD280/OD315_of_diluted_wines REAL
+@attribute Proline INTEGER
+
+@data
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+2,12.07,2.16,2.17,21,85,2.6,2.65,.37,1.35,2.76,.86,3.28,378
+2,12.43,1.53,2.29,21.5,86,2.74,3.15,.39,1.77,3.94,.69,2.84,352
+2,11.79,2.13,2.78,28.5,92,2.13,2.24,.58,1.76,3,.97,2.44,466
+2,12.37,1.63,2.3,24.5,88,2.22,2.45,.4,1.9,2.12,.89,2.78,342
+2,12.04,4.3,2.38,22,80,2.1,1.75,.42,1.35,2.6,.79,2.57,580
+3,12.86,1.35,2.32,18,122,1.51,1.25,.21,.94,4.1,.76,1.29,630
+3,12.88,2.99,2.4,20,104,1.3,1.22,.24,.83,5.4,.74,1.42,530
+3,12.81,2.31,2.4,24,98,1.15,1.09,.27,.83,5.7,.66,1.36,560
+3,12.7,3.55,2.36,21.5,106,1.7,1.2,.17,.84,5,.78,1.29,600
+3,12.51,1.24,2.25,17.5,85,2,.58,.6,1.25,5.45,.75,1.51,650
+3,12.6,2.46,2.2,18.5,94,1.62,.66,.63,.94,7.1,.73,1.58,695
+3,12.25,4.72,2.54,21,89,1.38,.47,.53,.8,3.85,.75,1.27,720
+3,12.53,5.51,2.64,25,96,1.79,.6,.63,1.1,5,.82,1.69,515
+3,13.49,3.59,2.19,19.5,88,1.62,.48,.58,.88,5.7,.81,1.82,580
+3,12.84,2.96,2.61,24,101,2.32,.6,.53,.81,4.92,.89,2.15,590
+3,12.93,2.81,2.7,21,96,1.54,.5,.53,.75,4.6,.77,2.31,600
+3,13.36,2.56,2.35,20,89,1.4,.5,.37,.64,5.6,.7,2.47,780
+3,13.52,3.17,2.72,23.5,97,1.55,.52,.5,.55,4.35,.89,2.06,520
+3,13.62,4.95,2.35,20,92,2,.8,.47,1.02,4.4,.91,2.05,550
+3,12.25,3.88,2.2,18.5,112,1.38,.78,.29,1.14,8.21,.65,2,855
+3,13.16,3.57,2.15,21,102,1.5,.55,.43,1.3,4,.6,1.68,830
+3,13.88,5.04,2.23,20,80,.98,.34,.4,.68,4.9,.58,1.33,415
+3,12.87,4.61,2.48,21.5,86,1.7,.65,.47,.86,7.65,.54,1.86,625
+3,13.32,3.24,2.38,21.5,92,1.93,.76,.45,1.25,8.42,.55,1.62,650
+3,13.08,3.9,2.36,21.5,113,1.41,1.39,.34,1.14,9.40,.57,1.33,550
+3,13.5,3.12,2.62,24,123,1.4,1.57,.22,1.25,8.60,.59,1.3,500
+3,12.79,2.67,2.48,22,112,1.48,1.36,.24,1.26,10.8,.48,1.47,480
+3,13.11,1.9,2.75,25.5,116,2.2,1.28,.26,1.56,7.1,.61,1.33,425
+3,13.23,3.3,2.28,18.5,98,1.8,.83,.61,1.87,10.52,.56,1.51,675
+3,12.58,1.29,2.1,20,103,1.48,.58,.53,1.4,7.6,.58,1.55,640
+3,13.17,5.19,2.32,22,93,1.74,.63,.61,1.55,7.9,.6,1.48,725
+3,13.84,4.12,2.38,19.5,89,1.8,.83,.48,1.56,9.01,.57,1.64,480
+3,12.45,3.03,2.64,27,97,1.9,.58,.63,1.14,7.5,.67,1.73,880
+3,14.34,1.68,2.7,25,98,2.8,1.31,.53,2.7,13,.57,1.96,660
+3,13.48,1.67,2.64,22.5,89,2.6,1.1,.52,2.29,11.75,.57,1.78,620
+3,12.36,3.83,2.38,21,88,2.3,.92,.5,1.04,7.65,.56,1.58,520
+3,13.69,3.26,2.54,20,107,1.83,.56,.5,.8,5.88,.96,1.82,680
+3,12.85,3.27,2.58,22,106,1.65,.6,.6,.96,5.58,.87,2.11,570
+3,12.96,3.45,2.35,18.5,106,1.39,.7,.4,.94,5.28,.68,1.75,675
+3,13.78,2.76,2.3,22,90,1.35,.68,.41,1.03,9.58,.7,1.68,615
+3,13.73,4.36,2.26,22.5,88,1.28,.47,.52,1.15,6.62,.78,1.75,520
+3,13.45,3.7,2.6,23,111,1.7,.92,.43,1.46,10.68,.85,1.56,695
+3,12.82,3.37,2.3,19.5,88,1.48,.66,.4,.97,10.26,.72,1.75,685
+3,13.58,2.58,2.69,24.5,105,1.55,.84,.39,1.54,8.66,.74,1.8,750
+3,13.4,4.6,2.86,25,112,1.98,.96,.27,1.11,8.5,.67,1.92,630
+3,12.2,3.03,2.32,19,96,1.25,.49,.4,.73,5.5,.66,1.83,510
+3,12.77,2.39,2.28,19.5,86,1.39,.51,.48,.64,9.899999,.57,1.63,470
+3,14.16,2.51,2.48,20,91,1.68,.7,.44,1.24,9.7,.62,1.71,660
+3,13.71,5.65,2.45,20.5,95,1.68,.61,.52,1.06,7.7,.64,1.74,740
+3,13.4,3.91,2.48,23,102,1.8,.75,.43,1.41,7.3,.7,1.56,750
+3,13.27,4.28,2.26,20,120,1.59,.69,.43,1.35,10.2,.59,1.56,835
+3,13.17,2.59,2.37,20,120,1.65,.68,.53,1.46,9.3,.6,1.62,840
+3,14.13,4.1,2.74,24.5,96,2.05,.76,.56,1.35,9.2,.61,1.6,560
--- a/benchmark/tests/results/grid_output_accuracy_STree.json
+++ b/benchmark/tests/results/grid_output_accuracy_STree.json
@@ -6,7 +6,7 @@
            "kernel": "liblinear",
            "multiclass_strategy": "ovr"
        },
-        "v. 1.2.4, Computed on Test on 2022-02-22 at 12:00:00 took 1s"
+        "v. 1.3.0, Computed on Test on 2022-02-22 at 12:00:00 took 1s"
    ],
    "balloons": [
        0.625,
@@ -15,6 +15,6 @@
            "kernel": "linear",
            "multiclass_strategy": "ovr"
        },
-        "v. 1.2.4, Computed on Test on 2022-02-22 at 12:00:00 took 1s"
+        "v. 1.3.0, Computed on Test on 2022-02-22 at 12:00:00 took 1s"
    ]
 }
--- a/benchmark/tests/results/results_accuracy_ODTE_Galgo_2022-04-20_10:52:20_0.json
+++ b/benchmark/tests/results/results_accuracy_ODTE_Galgo_2022-04-20_10:52:20_0.json
@@ -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",
--- a/benchmark/tests/results/results_accuracy_RandomForest_iMac27_2022-01-14_12:39:30_0.json
+++ b/benchmark/tests/results/results_accuracy_RandomForest_iMac27_2022-01-14_12:39:30_0.json
@@ -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",
--- a/benchmark/tests/results/results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json
+++ b/benchmark/tests/results/results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json
@@ -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,
--- a/benchmark/tests/results/results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json
+++ b/benchmark/tests/results/results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json
@@ -1,6 +1,8 @@
 {
  "score_name": "accuracy",
  "model": "STree",
+  "language": "Python",
+  "language_version": "3.11x",
  "stratified": false,
  "folds": 5,
  "date": "2021-10-27",
--- a/benchmark/tests/results/results_accuracy_STree_macbook-pro_2021-11-01_19:17:07_0.json
+++ b/benchmark/tests/results/results_accuracy_STree_macbook-pro_2021-11-01_19:17:07_0.json
@@ -1,6 +1,8 @@
 {
  "score_name": "accuracy",
  "model": "STree",
+  "language_version": "3.11x",
+  "language": "Python",
  "stratified": false,
  "folds": 5,
  "date": "2021-11-01",
--- a/benchmark/tests/scripts/Be_Report_test.py
+++ b/benchmark/tests/scripts/Be_Report_test.py
@@ -55,7 +55,16 @@ class BeReportTest(TestBase):
            "be_report", ["-s", "accuracy", "-m", "STree", "-g", "1"]
        )
        self.assertEqual(stderr.getvalue(), "")
-        self.check_output_file(stdout, "report_grid")
+        file_name = "report_grid.test"
+        with open(os.path.join(self.test_files, file_name)) as f:
+            expected = f.read().splitlines()
+        output_text = stdout.getvalue().splitlines()
+        # Compare replacing STree version
+        for line, index in zip(expected, range(len(expected))):
+            if "1.2.4" in line:
+                # replace STree version
+                line = self.replace_STree_version(line, output_text, index)
+            self.assertEqual(line, output_text[index])

    def test_be_report_best_both(self):
        stdout, stderr = self.execute_script(
--- a/benchmark/tests/test_files/be_build_best_report.test
+++ b/benchmark/tests/test_files/be_build_best_report.test
@@ -7,5 +7,5 @@ Dataset                        Score    File/Message
 balance-scale                  0.963520 results_accuracy_ODTE_Galgo_2022-04-20_10:52:20_0.json                       {'base_estimator__C': 57, 'base_estimator__gamma': 0.1, 'base_estimator__kernel': 'rbf', 'base_estimator__multiclass_strategy': 'ovr', 'n_estimators': 100, 'n_jobs': -1} 
 balloons                       0.785000 results_accuracy_ODTE_Galgo_2022-04-20_10:52:20_0.json                       {'base_estimator__C': 5, 'base_estimator__gamma': 0.14, 'base_estimator__kernel': 'rbf', 'base_estimator__multiclass_strategy': 'ovr', 'n_estimators': 100, 'n_jobs': -1} 
 ******************************************************************************************************************************************************************
-* Scores compared to stree_default accuracy (liblinear-ovr) .:  0.0434                                                                                           *
+* accuracy compared to STree_default (liblinear-ovr) .:  0.0434                                                                                                  *
 ******************************************************************************************************************************************************************
--- a/benchmark/tests/test_files/be_main_best.test
+++ b/benchmark/tests/test_files/be_main_best.test
@@ -1,16 +1,16 @@
-[94m***********************************************************************************************************************
-[94m* Report STree ver. 1.2.4 with 5 Folds cross validation and 10 random seeds. 2022-05-09 00:15:25                      *
-[94m* test                                                                                                                *
-[94m* Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False                                            *
-[94m* Execution took    0.80 seconds,   0.00 hours, on iMac27                                                             *
-[94m* Score is accuracy                                                                                                   *
-[94m***********************************************************************************************************************
+[94m************************************************************************************************************************
+[94m* STree ver. 1.2.4 Python ver. 3.11x with 5 Folds cross validation and 10 random seeds. 2022-05-09 00:15:25            *
+[94m* test                                                                                                                 *
+[94m* Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False                                             *
+[94m* Execution took    0.80 seconds,   0.00 hours, on iMac27                                                              *
+[94m* Score is accuracy                                                                                                    *
+[94m************************************************************************************************************************

-Dataset                        Samp  Feat. Cls Nodes   Leaves  Depth   Score           Time             Hyperparameters 
-============================== ===== ===== === ======= ======= ======= =============== ================ =============== 
-[96mbalance-scale                    625     4   3   23.32   12.16    6.44 0.840160±0.0304  0.013745±0.0019 {'splitter': 'best', 'max_features': 'auto'} 
-[94mballoons                          16     4   2    3.00    2.00    2.00 0.860000±0.2850  0.000388±0.0000 {'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'} 
-[94m***********************************************************************************************************************
-[94m* Accuracy compared to stree_default (liblinear-ovr) .:  0.0422                                                       *
-[94m***********************************************************************************************************************
+Dataset                        Sampl. Feat. Cls Nodes   Leaves  Depth   Score           Time             Hyperparameters 
+============================== ====== ===== === ======= ======= ======= =============== ================ =============== 
+[96mbalance-scale                      625     4   3   23.32   12.16    6.44 0.840160±0.0304  0.013745±0.0019 {'splitter': 'best', 'max_features': 'auto'} 
+[94mballoons                            16     4   2    3.00    2.00    2.00 0.860000±0.2850  0.000388±0.0000 {'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'} 
+[94m************************************************************************************************************************
+[94m* accuracy compared to STree_default (liblinear-ovr) .:  0.0422                                                        *
+[94m************************************************************************************************************************
 Results in results/results_accuracy_STree_iMac27_2022-05-09_00:15:25_0.json
--- a/benchmark/tests/test_files/be_main_complete.test
+++ b/benchmark/tests/test_files/be_main_complete.test
@@ -1,16 +1,16 @@
-[94m***********************************************************************************************************************
-[94m* Report STree ver. 1.2.4 with 5 Folds cross validation and 10 random seeds. 2022-05-08 20:14:43                      *
-[94m* test                                                                                                                *
-[94m* Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False                                            *
-[94m* Execution took    0.48 seconds,   0.00 hours, on iMac27                                                             *
-[94m* Score is accuracy                                                                                                   *
-[94m***********************************************************************************************************************
+[94m************************************************************************************************************************
+[94m* STree ver. 1.2.4 Python ver. 3.11x with 5 Folds cross validation and 10 random seeds. 2022-05-08 20:14:43            *
+[94m* test                                                                                                                 *
+[94m* Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False                                             *
+[94m* Execution took    0.48 seconds,   0.00 hours, on iMac27                                                              *
+[94m* Score is accuracy                                                                                                    *
+[94m************************************************************************************************************************

-Dataset                        Samp  Feat. Cls Nodes   Leaves  Depth   Score           Time             Hyperparameters 
-============================== ===== ===== === ======= ======= ======= =============== ================ =============== 
-[96mbalance-scale                    625     4   3   17.36    9.18    6.18 0.908480±0.0247  0.007388±0.0013 {}              
-[94mballoons                          16     4   2    4.64    2.82    2.66 0.663333±0.3009  0.000664±0.0002 {}              
-[94m***********************************************************************************************************************
-[94m* Accuracy compared to stree_default (liblinear-ovr) .:  0.0390                                                       *
-[94m***********************************************************************************************************************
+Dataset                        Sampl. Feat. Cls Nodes   Leaves  Depth   Score           Time             Hyperparameters 
+============================== ====== ===== === ======= ======= ======= =============== ================ =============== 
+[96mbalance-scale                     625     4   3   17.36    9.18    6.18 0.908480±0.0247  0.007388±0.0013 {}              
+[94mballoons                           16     4   2    4.64    2.82    2.66 0.663333±0.3009  0.000664±0.0002 {}              
+[94m************************************************************************************************************************
+[94m* accuracy compared to STree_default (liblinear-ovr) .:  0.0390                                                        *
+[94m************************************************************************************************************************
 Results in results/results_accuracy_STree_iMac27_2022-05-08_20:14:43_0.json
--- a/benchmark/tests/test_files/be_main_dataset.test
+++ b/benchmark/tests/test_files/be_main_dataset.test
@@ -1,15 +1,15 @@
-[94m***********************************************************************************************************************
-[94m* Report STree ver. 1.2.4 with 5 Folds cross validation and 10 random seeds. 2022-05-08 19:38:28                      *
-[94m* test                                                                                                                *
-[94m* Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False                                            *
-[94m* Execution took    0.06 seconds,   0.00 hours, on iMac27                                                             *
-[94m* Score is accuracy                                                                                                   *
-[94m***********************************************************************************************************************
+[94m************************************************************************************************************************
+[94m* STree ver. 1.2.4 Python ver. 3.11x with 5 Folds cross validation and 10 random seeds. 2022-05-08 19:38:28            *
+[94m* test                                                                                                                 *
+[94m* Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False                                             *
+[94m* Execution took    0.06 seconds,   0.00 hours, on iMac27                                                              *
+[94m* Score is accuracy                                                                                                    *
+[94m************************************************************************************************************************

-Dataset                        Samp  Feat. Cls Nodes   Leaves  Depth   Score           Time             Hyperparameters 
-============================== ===== ===== === ======= ======= ======= =============== ================ =============== 
-[96mballoons                          16     4   2    4.64    2.82    2.66 0.663333±0.3009  0.000671±0.0001 {}              
-[94m***********************************************************************************************************************
-[94m* Accuracy compared to stree_default (liblinear-ovr) .:  0.0165                                                       *
-[94m***********************************************************************************************************************
+Dataset                        Sampl. Feat. Cls Nodes   Leaves  Depth   Score           Time             Hyperparameters 
+============================== ====== ===== === ======= ======= ======= =============== ================ =============== 
+[96mballoons                           16     4   2    4.64    2.82    2.66 0.663333±0.3009  0.000671±0.0001 {}              
+[94m************************************************************************************************************************
+[94m* accuracy compared to STree_default (liblinear-ovr) .:  0.0165                                                        *
+[94m************************************************************************************************************************
 Partial result file removed: results/results_accuracy_STree_iMac27_2022-05-08_19:38:28_0.json
--- a/benchmark/tests/test_files/be_main_grid.test
+++ b/benchmark/tests/test_files/be_main_grid.test
@@ -1,16 +1,16 @@
-[94m***********************************************************************************************************************
-[94m* Report STree ver. 1.2.4 with 5 Folds cross validation and 10 random seeds. 2022-05-09 00:21:06                      *
-[94m* test                                                                                                                *
-[94m* Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False                                            *
-[94m* Execution took    0.89 seconds,   0.00 hours, on iMac27                                                             *
-[94m* Score is accuracy                                                                                                   *
-[94m***********************************************************************************************************************
+[94m************************************************************************************************************************
+[94m* STree ver. 1.2.4 Python ver. 3.11x with 5 Folds cross validation and 10 random seeds. 2022-05-09 00:21:06            *
+[94m* test                                                                                                                 *
+[94m* Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False                                             *
+[94m* Execution took    0.89 seconds,   0.00 hours, on iMac27                                                              *
+[94m* Score is accuracy                                                                                                    *
+[94m************************************************************************************************************************

-Dataset                        Samp  Feat. Cls Nodes   Leaves  Depth   Score           Time             Hyperparameters 
-============================== ===== ===== === ======= ======= ======= =============== ================ =============== 
-[96mbalance-scale                    625     4   3   26.12   13.56    7.94 0.910720±0.0249  0.015852±0.0027 {'C': 1.0, 'kernel': 'liblinear', 'multiclass_strategy': 'ovr'} 
-[94mballoons                          16     4   2    4.64    2.82    2.66 0.663333±0.3009  0.000640±0.0001 {'C': 1.0, 'kernel': 'linear', 'multiclass_strategy': 'ovr'} 
-[94m***********************************************************************************************************************
-[94m* Accuracy compared to stree_default (liblinear-ovr) .:  0.0391                                                       *
-[94m***********************************************************************************************************************
+Dataset                        Sampl. Feat. Cls Nodes   Leaves  Depth   Score           Time             Hyperparameters 
+============================== ====== ===== === ======= ======= ======= =============== ================ =============== 
+[96mbalance-scale                     625     4   3   26.12   13.56    7.94 0.910720±0.0249  0.015852±0.0027 {'C': 1.0, 'kernel': 'liblinear', 'multiclass_strategy': 'ovr'} 
+[94mballoons                           16     4   2    4.64    2.82    2.66 0.663333±0.3009  0.000640±0.0001 {'C': 1.0, 'kernel': 'linear', 'multiclass_strategy': 'ovr'} 
+[94m************************************************************************************************************************
+[94m* accuracy compared to STree_default (liblinear-ovr) .:  0.0391                                                        *
+[94m************************************************************************************************************************
 Results in results/results_accuracy_STree_iMac27_2022-05-09_00:21:06_0.json
--- a/benchmark/tests/test_files/excel.test
+++ b/benchmark/tests/test_files/excel.test
@@ -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"
@@ -45,4 +45,4 @@
 8;10;"0.0008541679382324218"
 8;11;"3.629469326417878e-05"
 8;12;"{'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'}"
-10;1;"** Accuracy compared to stree_default (liblinear-ovr) .:  0.0454"
+10;1;"** accuracy compared to STree_default (liblinear-ovr) .:  0.0454"
--- a/benchmark/tests/test_files/excel_add_ODTE.test
+++ b/benchmark/tests/test_files/excel_add_ODTE.test
@@ -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"
@@ -45,4 +45,4 @@
 8;10;"0.1156062078475952"
 8;11;"0.0127842418285999"
 8;12;"{'base_estimator__C': 5, 'base_estimator__gamma': 0.14, 'base_estimator__kernel': 'rbf', 'base_estimator__multiclass_strategy': 'ovr', 'n_estimators': 100, 'n_jobs': -1}"
-10;1;"** Accuracy compared to stree_default (liblinear-ovr) .:  0.0434"
+10;1;"** accuracy compared to STree_default (liblinear-ovr) .:  0.0434"
--- a/benchmark/tests/test_files/excel_add_STree.test
+++ b/benchmark/tests/test_files/excel_add_STree.test
@@ -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"
 3;1;" Score is accuracy"
 3;2;" Execution time"
@@ -43,4 +43,4 @@
 8;10;"0.02120100021362305"
 8;11;"0.003526023309468471"
 8;12;"{'splitter': 'best', 'max_features': 'auto'}"
-10;1;"** Accuracy compared to stree_default (liblinear-ovr) .:  0.0416"
+10;1;"** accuracy compared to STree_default (liblinear-ovr) .:  0.0416"
--- a/benchmark/tests/test_files/excel_compared.test
+++ b/benchmark/tests/test_files/excel_compared.test
@@ -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"
@@ -49,4 +49,4 @@
 11;2;"✔"
 11;3;1
 11;4;"Equal to best"
-13;1;"** Accuracy compared to stree_default (liblinear-ovr) .:  0.0454"
+13;1;"** accuracy compared to STree_default (liblinear-ovr) .:  0.0454"
--- a/benchmark/tests/test_files/exreport_excel_ODTE.test
+++ b/benchmark/tests/test_files/exreport_excel_ODTE.test
@@ -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"
@@ -45,4 +45,4 @@
 8;10;"0.1156062078475952"
 8;11;"0.0127842418285999"
 8;12;"{'base_estimator__C': 5, 'base_estimator__gamma': 0.14, 'base_estimator__kernel': 'rbf', 'base_estimator__multiclass_strategy': 'ovr', 'n_estimators': 100, 'n_jobs': -1}"
-10;1;"** Accuracy compared to stree_default (liblinear-ovr) .:  0.0434"
+10;1;"** accuracy compared to STree_default (liblinear-ovr) .:  0.0434"
--- a/benchmark/tests/test_files/exreport_excel_RandomForest.test
+++ b/benchmark/tests/test_files/exreport_excel_RandomForest.test
@@ -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"
 3;1;" Score is accuracy"
 3;2;" Execution time"
@@ -45,4 +45,4 @@
 8;10;"0.07016648769378662"
 8;11;"0.002460508923990468"
 8;12;"{}"
-10;1;"** Accuracy compared to stree_default (liblinear-ovr) .:  0.0363"
+10;1;"** accuracy compared to STree_default (liblinear-ovr) .:  0.0363"
--- a/benchmark/tests/test_files/exreport_excel_STree.test
+++ b/benchmark/tests/test_files/exreport_excel_STree.test
@@ -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"
@@ -45,4 +45,4 @@
 8;10;"0.0008541679382324218"
 8;11;"3.629469326417878e-05"
 8;12;"{'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'}"
-10;1;"** Accuracy compared to stree_default (liblinear-ovr) .:  0.0454"
+10;1;"** accuracy compared to STree_default (liblinear-ovr) .:  0.0454"
--- a/benchmark/tests/test_files/report.test
+++ b/benchmark/tests/test_files/report.test
@@ -1,15 +1,15 @@
-[94m***********************************************************************************************************************
-[94m* Report STree ver. 1.2.3 with 5 Folds cross validation and 10 random seeds. 2021-09-30 11:42:07                      *
-[94m* With gridsearched hyperparameters                                                                                   *
-[94m* Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False                                            *
-[94m* Execution took  624.25 seconds,   0.17 hours, on iMac27                                                             *
-[94m* Score is accuracy                                                                                                   *
-[94m***********************************************************************************************************************
+[94m************************************************************************************************************************
+[94m* STree ver. 1.2.3 Python ver. 3.11x with 5 Folds cross validation and 10 random seeds. 2021-09-30 11:42:07            *
+[94m* With gridsearched hyperparameters                                                                                    *
+[94m* Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False                                             *
+[94m* Execution took  624.25 seconds,   0.17 hours, on iMac27                                                              *
+[94m* Score is accuracy                                                                                                    *
+[94m************************************************************************************************************************

-Dataset                        Samp  Feat. Cls Nodes   Leaves  Depth   Score           Time             Hyperparameters 
-============================== ===== ===== === ======= ======= ======= =============== ================ =============== 
-[96mbalance-scale                    625     4   3    7.00    4.00    3.00 0.970560±0.0150  0.014049±0.0020 {'C': 10000.0, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'} 
-[94mballoons                          16     4   2    3.00    2.00    2.00 0.860000±0.2850  0.000854±0.0000 {'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'} 
-[94m***********************************************************************************************************************
-[94m* Accuracy compared to stree_default (liblinear-ovr) .:  0.0454                                                       *
-[94m***********************************************************************************************************************
+Dataset                        Sampl. Feat. Cls Nodes   Leaves  Depth   Score           Time             Hyperparameters 
+============================== ====== ===== === ======= ======= ======= =============== ================ =============== 
+[96mbalance-scale                     625     4   3    7.00    4.00    3.00 0.970560±0.0150  0.014049±0.0020 {'C': 10000.0, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'} 
+[94mballoons                           16     4   2    3.00    2.00    2.00 0.860000±0.2850  0.000854±0.0000 {'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'} 
+[94m************************************************************************************************************************
+[94m* accuracy compared to STree_default (liblinear-ovr) .:  0.0454                                                        *
+[94m************************************************************************************************************************
--- a/benchmark/tests/test_files/report_best.test
+++ b/benchmark/tests/test_files/report_best.test
@@ -7,5 +7,5 @@ Dataset                        Score    File/Message
 balance-scale                  0.980000 results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json                     {'splitter': 'best', 'max_features': 'auto'} 
 balloons                       0.860000 results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json                     {'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'} 
 ******************************************************************************************************************************************************************
-* Scores compared to stree_default accuracy (liblinear-ovr) .:  0.0457                                                                                           *
+* accuracy compared to STree_default (liblinear-ovr) .:  0.0457                                                                                                  *
 ******************************************************************************************************************************************************************
--- a/benchmark/tests/test_files/report_compared.test
+++ b/benchmark/tests/test_files/report_compared.test
@@ -1,16 +1,16 @@
-[94m***********************************************************************************************************************
-[94m* Report STree ver. 1.2.3 with 5 Folds cross validation and 10 random seeds. 2021-09-30 11:42:07                      *
-[94m* With gridsearched hyperparameters                                                                                   *
-[94m* Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False                                            *
-[94m* Execution took  624.25 seconds,   0.17 hours, on iMac27                                                             *
-[94m* Score is accuracy                                                                                                   *
-[94m***********************************************************************************************************************
+[94m************************************************************************************************************************
+[94m* STree ver. 1.2.3 Python ver. 3.11x with 5 Folds cross validation and 10 random seeds. 2021-09-30 11:42:07            *
+[94m* With gridsearched hyperparameters                                                                                    *
+[94m* Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False                                             *
+[94m* Execution took  624.25 seconds,   0.17 hours, on iMac27                                                              *
+[94m* Score is accuracy                                                                                                    *
+[94m************************************************************************************************************************

-Dataset                        Samp  Feat. Cls Nodes   Leaves  Depth   Score           Time             Hyperparameters 
-============================== ===== ===== === ======= ======= ======= =============== ================ =============== 
-[96mbalance-scale                    625     4   3    7.00    4.00    3.00 0.970560±0.0150  0.014049±0.0020 {'C': 10000.0, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'} 
-[94mballoons                          16     4   2    3.00    2.00    2.00 0.860000±0.2850✔ 0.000854±0.0000 {'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'} 
-[94m***********************************************************************************************************************
-[94m* ✔ Equal to best .....:  1                                                                                           *
-[94m* Accuracy compared to stree_default (liblinear-ovr) .:  0.0454                                                       *
-[94m***********************************************************************************************************************
+Dataset                        Sampl. Feat. Cls Nodes   Leaves  Depth   Score           Time             Hyperparameters 
+============================== ====== ===== === ======= ======= ======= =============== ================ =============== 
+[96mbalance-scale                     625     4   3    7.00    4.00    3.00 0.970560±0.0150  0.014049±0.0020 {'C': 10000.0, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'} 
+[94mballoons                           16     4   2    3.00    2.00    2.00 0.860000±0.2850✔ 0.000854±0.0000 {'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'} 
+[94m************************************************************************************************************************
+[94m* ✔ Equal to best .....:  1                                                                                            *
+[94m* accuracy compared to STree_default (liblinear-ovr) .:  0.0454                                                        *
+[94m************************************************************************************************************************
--- a/benchmark/tests/test_files/report_datasets.test
+++ b/benchmark/tests/test_files/report_datasets.test
@@ -1,4 +1,4 @@
-[94mDataset                        Samp. Feat. Cls Balance
-============================== ===== ===== === ========================================
-[96mbalance-scale                    625     4   3  7.84%/ 46.08%/ 46.08%                  
-[94mballoons                          16     4   2 56.25%/ 43.75%                          
+[94mDataset                        Sampl. Feat. Cls Balance
+============================== ===== ====== === ========================================
+[96mbalance-scale                     625     4   3  7.84%/ 46.08%/ 46.08%                  
+[94mballoons                           16     4   2 56.25%/ 43.75%                          
--- a/benchmark/tests/test_files/report_grid.test
+++ b/benchmark/tests/test_files/report_grid.test
@@ -7,5 +7,5 @@ Dataset                        Score    File/Message
 balance-scale                  0.919995 v. 1.2.4, Computed on Test on 2022-02-22 at 12:00:00 took 1s                 {'C': 1.0, 'kernel': 'liblinear', 'multiclass_strategy': 'ovr'} 
 balloons                       0.625000 v. 1.2.4, Computed on Test on 2022-02-22 at 12:00:00 took 1s                 {'C': 1.0, 'kernel': 'linear', 'multiclass_strategy': 'ovr'} 
 ******************************************************************************************************************************************************************
-* Scores compared to stree_default accuracy (liblinear-ovr) .:  0.0384                                                                                           *
+* accuracy compared to STree_default (liblinear-ovr) .:  0.0384                                                                                                  *
 ******************************************************************************************************************************************************************
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,6 @@
 pandas
 scikit-learn
+scipy
 odte
 mufs
 xlsxwriter
@@ -8,3 +9,4 @@ tqdm
 xgboost
 graphviz
 Wodt @ git+ssh://git@github.com/doctorado-ml/Wodt.git#egg=Wodt
+unittest-xml-reporting
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
Ricardo Montañana	db61911ca6	Fix CI error	2022-10-25 15:20:12 +02:00
Ricardo Montañana	b24a508d1c	Add consistent comparative results to reports	2022-10-25 15:01:18 +02:00
Ricardo Montañana	29c4b4ceef	Update E203 in main.yml Create tests	2022-10-25 11:36:04 +02:00
Ricardo Montañana	2362f66c7a	Add nan manage to arff datasets	2022-10-25 00:56:37 +02:00
Ricardo Montañana	8001c7f2eb	Add a space to #Samples in every report	2022-10-24 22:43:46 +02:00
Ricardo Montañana	47bf6eeda6	Add a space to #Samples in dataset report	2022-10-24 21:30:56 +02:00
Ricardo Montañana	34b3bd94de	Add Arff as source_data for datasets	2022-10-24 21:04:07 +02:00
Ricardo Montañana	7875e2e6ac	Merge branch 'main' into discretiz	2022-10-24 19:06:52 +02:00
Ricardo Montañana	34b25756ea	Fix error in tests with STree fixed version	2022-10-24 19:05:13 +02:00
Ricardo Montañana	e15ab3dcab	Split Datasets class from Experiments	2022-10-24 18:21:08 +02:00
Ricardo Montañana	12024df4d8	syntax Issue in gh actions build	2022-05-18 22:10:32 +02:00