Update version info

Add Discretizer to Datasets
Add excel to report datasets
Add report datasets sheet to benchmark excel

.env.dist

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

.flake8

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

.github/workflows/build.yml

@@ -8,7 +8,7 @@ jobs:
     name: Build
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v3
         with:
           fetch-depth: 0
       - run: echo "project_version=$(git describe --tags --abbrev=0)" >> $GITHUB_ENV
@@ -22,7 +22,8 @@ jobs:
             -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
-        timeout-minutes: 5
-        env:
-          SONAR_TOKEN: ${{ secrets.SONAR_TOKEN }}
+      #- uses: sonarsource/sonarqube-quality-gate-action@master
+      #  timeout-minutes: 5
+      #  env:
+      #    SONAR_TOKEN: ${{ secrets.SONAR_TOKEN }}
+      #    SONAR_HOST_URL: ${{ secrets.SONAR_HOST_URL }}

.github/workflows/main.yml

@@ -13,10 +13,13 @@ jobs:
     strategy:
       matrix:
         os: [macos-latest, ubuntu-latest]
-        python: ["3.10"]
+        python: ["3.10", "3.11"]
+        exclude:
+          - os: macos-latest
+            python: "3.11"
 
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v3
       - name: Set up Python ${{ matrix.python }}
         uses: actions/setup-python@v2
         with:

README.md

@@ -1,7 +1,7 @@
 [![CI](https://github.com/Doctorado-ML/benchmark/actions/workflows/main.yml/badge.svg)](https://github.com/Doctorado-ML/benchmark/actions/workflows/main.yml)
 [![codecov](https://codecov.io/gh/Doctorado-ML/benchmark/branch/main/graph/badge.svg?token=ZRP937NDSG)](https://codecov.io/gh/Doctorado-ML/benchmark)
-[![Quality Gate Status](https://haystack.rmontanana.es:25000/api/project_badges/measure?project=benchmark&metric=alert_status&token=336a6e501988888543c3153baa91bad4b9914dd2)](http://haystack.local:25000/dashboard?id=benchmark)
-[![Technical Debt](https://haystack.rmontanana.es:25000/api/project_badges/measure?project=benchmark&metric=sqale_index&token=336a6e501988888543c3153baa91bad4b9914dd2)](http://haystack.local:25000/dashboard?id=benchmark)
+[![Quality Gate Status](https://haystack.rmontanana.es:25000/api/project_badges/measure?project=benchmark&metric=alert_status&token=336a6e501988888543c3153baa91bad4b9914dd2)](https://haystack.rmontanana.es:25000/dashboard?id=benchmark)
+[![Technical Debt](https://haystack.rmontanana.es:25000/api/project_badges/measure?project=benchmark&metric=sqale_index&token=336a6e501988888543c3153baa91bad4b9914dd2)](https://haystack.rmontanana.es:25000/dashboard?id=benchmark)
 ![https://img.shields.io/badge/python-3.8%2B-blue](https://img.shields.io/badge/python-3.8%2B-brightgreen)
 
 # benchmark

benchmark/Datasets.py

@@ -1,8 +1,10 @@
 import os
 import pandas as pd
+import numpy as np
 from scipy.io import arff
 from .Utils import Files
 from .Arguments import EnvData
+from mdlp.discretization import MDLP
 
 
 class Diterator:
@@ -28,9 +30,12 @@ class DatasetsArff:
         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()
+        df.dropna(axis=0, how="any", inplace=True)
+        X = df.drop(class_name, axis=1)
+        self.features = X.columns
+        self.class_name = class_name
         y, _ = pd.factorize(df[class_name])
+        X = X.to_numpy()
         return X, y
 
 
@@ -43,7 +48,7 @@ class DatasetsTanveer:
     def folder():
         return "data"
 
-    def load(self, name, _):
+    def load(self, name, *args):
         file_name = os.path.join(self.folder(), self.dataset_names(name))
         data = pd.read_csv(
             file_name,
@@ -64,7 +69,7 @@ class DatasetsSurcov:
     def folder():
         return "datasets"
 
-    def load(self, name, _):
+    def load(self, name, *args):
         file_name = os.path.join(self.folder(), self.dataset_names(name))
         data = pd.read_csv(
             file_name,
@@ -80,15 +85,19 @@ class DatasetsSurcov:
 
 class Datasets:
     def __init__(self, dataset_name=None):
-
         envData = EnvData.load()
         class_name = getattr(
             __import__(__name__),
             f"Datasets{envData['source_data']}",
         )
+        self.load = (
+            self.load_discretized
+            if envData["discretize"] == "1"
+            else self.load_continuous
+        )
         self.dataset = class_name()
         self.class_names = []
-        self.load_names()
+        self._load_names()
         if dataset_name is not None:
             try:
                 class_name = self.class_names[
@@ -99,7 +108,7 @@ class Datasets:
                 raise ValueError(f"Unknown dataset: {dataset_name}")
             self.data_sets = [dataset_name]
 
-    def load_names(self):
+    def _load_names(self):
         file_name = os.path.join(self.dataset.folder(), Files.index)
         default_class = "class"
         with open(file_name) as f:
@@ -115,12 +124,63 @@ class Datasets:
             self.data_sets = result
             self.class_names = class_names
 
-    def load(self, name):
+    def get_attributes(self, name):
+        class Attributes:
+            pass
+
+        X, y = self.load_continuous(name)
+        attr = Attributes()
+        values, counts = np.unique(y, return_counts=True)
+        comp = ""
+        sep = ""
+        for count in counts:
+            comp += f"{sep}{count/sum(counts)*100:5.2f}%"
+            sep = "/ "
+        attr.balance = comp
+        attr.classes = len(np.unique(y))
+        attr.samples = X.shape[0]
+        attr.features = X.shape[1]
+        return attr
+
+    def get_features(self):
+        return self.dataset.features
+
+    def get_class_name(self):
+        return self.dataset.class_name
+
+    def load_continuous(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 discretize(self, X, y):
+        """Supervised discretization with Fayyad and Irani's MDLP algorithm.
+
+        Parameters
+        ----------
+        X : np.ndarray
+            array (n_samples, n_features) of features
+        y : np.ndarray
+            array (n_samples,) of labels
+
+        Returns
+        -------
+        tuple (X, y) of numpy.ndarray
+        """
+        discretiz = MDLP(random_state=17, dtype=np.int32)
+        Xdisc = discretiz.fit_transform(X, y)
+        return Xdisc
+
+    def load_discretized(self, name, dataframe=False):
+        X, yd = self.load_continuous(name)
+        Xd = self.discretize(X, yd)
+        dataset = pd.DataFrame(Xd, columns=self.get_features())
+        dataset[self.get_class_name()] = yd
+        if dataframe:
+            return dataset
+        return Xd, yd
+
     def __iter__(self) -> Diterator:
         return Diterator(self.data_sets)

benchmark/Experiments.py

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

benchmark/Models.py

@@ -8,9 +8,12 @@ from sklearn.ensemble import (
 )
 from sklearn.svm import SVC
 from stree import Stree
+from bayesclass import TAN, KDB, AODE
 from wodt import Wodt
 from odte import Odte
 from xgboost import XGBClassifier
+import sklearn
+import xgboost
 
 
 class Models:
@@ -18,6 +21,9 @@ class Models:
     def define_models(random_state):
         return {
             "STree": Stree(random_state=random_state),
+            "TAN": TAN(random_state=random_state),
+            "KDB": KDB(k=3),
+            "AODE": AODE(random_state=random_state),
             "Cart": DecisionTreeClassifier(random_state=random_state),
             "ExtraTree": ExtraTreeClassifier(random_state=random_state),
             "Wodt": Wodt(random_state=random_state),
@@ -89,3 +95,15 @@ 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"

benchmark/Results.py

@@ -1,4 +1,5 @@
 import os
+import sys
 from operator import itemgetter
 import math
 import json
@@ -6,6 +7,7 @@ import abc
 import shutil
 import subprocess
 import xlsxwriter
+from xlsxwriter.exceptions import DuplicateWorksheetName
 import numpy as np
 from .Experiments import BestResults
 from .Datasets import Datasets
@@ -17,6 +19,11 @@ from .Utils import (
     TextColor,
     NO_RESULTS,
 )
+from ._version import __version__
+
+
+def get_input(is_test):
+    return "test" if is_test else input()
 
 
 class BestResultsEver:
@@ -33,7 +40,7 @@ class BestResultsEver:
         ]
         self.data["Arff"]["accuracy"] = [
             "STree_default (linear-ovo)",
-            21.9765,
+            22.109799,
         ]
 
     def get_name_value(self, key, score):
@@ -123,7 +130,7 @@ class BaseReport(abc.ABC):
 
 
 class Report(BaseReport):
-    header_lengths = [30, 6, 5, 3, 7, 7, 7, 15, 16, 15]
+    header_lengths = [30, 6, 5, 3, 7, 7, 7, 15, 17, 15]
     header_cols = [
         "Dataset",
         "Sampl.",
@@ -182,7 +189,7 @@ class Report(BaseReport):
         )
         i += 1
         print(
-            f"{result['time']:9.6f}±{result['time_std']:6.4f} ",
+            f"{result['time']:10.6f}±{result['time_std']:6.4f} ",
             end="",
         )
         i += 1
@@ -196,7 +203,8 @@ 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']}"
@@ -325,7 +333,17 @@ class Excel(BaseReport):
         else:
             self.book = book
             self.close = False
-        self.sheet = self.book.add_worksheet(self.data["model"])
+        suffix = ""
+        num = 1
+        while True:
+            try:
+                self.sheet = self.book.add_worksheet(
+                    self.data["model"] + suffix
+                )
+                break
+            except DuplicateWorksheetName:
+                num += 1
+                suffix = str(num)
         self.max_hyper_width = 0
         self.col_hyperparams = 0
 
@@ -347,7 +365,8 @@ 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']}"
@@ -564,37 +583,251 @@ class Excel(BaseReport):
             self.sheet.set_row(c, 20)
         self.sheet.set_row(0, 25)
         self.sheet.freeze_panes(6, 1)
-        self.sheet.hide_gridlines()
+        self.sheet.hide_gridlines(2)
         if self.close:
             self.book.close()
 
 
 class ReportDatasets:
+    row = 6
+    # alternate lines colors
+    color1 = "#DCE6F1"
+    color2 = "#FDE9D9"
+    color3 = "#B1A0C7"
+
+    def __init__(self, excel=False, book=None):
+        self.excel = excel
+        self.env = EnvData().load()
+        self.close = False
+        self.output = True
+        self.header_text = f"Datasets used in benchmark ver. {__version__}"
+        if excel:
+            self.max_length = 0
+            if book is None:
+                self.excel_file_name = Files.datasets_report_excel
+                self.book = xlsxwriter.Workbook(
+                    self.excel_file_name, {"nan_inf_to_errors": True}
+                )
+                self.set_properties(self.get_title())
+                self.close = True
+            else:
+                self.book = book
+                self.output = False
+            self.sheet = self.book.add_worksheet("Datasets")
+
+    def set_properties(self, title):
+        self.book.set_properties(
+            {
+                "title": title,
+                "subject": "Machine learning results",
+                "author": "Ricardo Montañana Gómez",
+                "manager": "Dr. J. A. Gámez, Dr. J. M. Puerta",
+                "company": "UCLM",
+                "comments": "Created with Python and XlsxWriter",
+            }
+        )
+
     @staticmethod
-    def report():
+    def get_python_version():
+        return "{}.{}".format(sys.version_info.major, sys.version_info.minor)
+
+    def get_title(self):
+        return (
+            f" Benchmark ver. {__version__} - "
+            f" Python ver. {self.get_python_version()}"
+            f" with {self.env['n_folds']} Folds cross validation "
+            f" Discretization: {self.env['discretize']}  "
+            f"Stratification: {self.env['stratified']}"
+        )
+
+    def get_file_name(self):
+        return self.excel_file_name
+
+    def header(self):
+        merge_format = self.book.add_format(
+            {
+                "border": 1,
+                "bold": 1,
+                "align": "center",
+                "valign": "vcenter",
+                "font_size": 18,
+                "bg_color": self.color3,
+            }
+        )
+        merge_format_subheader = self.book.add_format(
+            {
+                "border": 1,
+                "bold": 1,
+                "align": "center",
+                "valign": "vcenter",
+                "font_size": 16,
+                "bg_color": self.color1,
+            }
+        )
+        merge_format_subheader_right = self.book.add_format(
+            {
+                "border": 1,
+                "bold": 1,
+                "align": "right",
+                "valign": "vcenter",
+                "font_size": 16,
+                "bg_color": self.color1,
+            }
+        )
+        merge_format_subheader_left = self.book.add_format(
+            {
+                "border": 1,
+                "bold": 1,
+                "align": "left",
+                "valign": "vcenter",
+                "font_size": 16,
+                "bg_color": self.color1,
+            }
+        )
+        self.sheet.merge_range(0, 0, 0, 4, self.header_text, merge_format)
+        self.sheet.merge_range(
+            1,
+            0,
+            4,
+            0,
+            f" Default score {self.env['score']}",
+            merge_format_subheader,
+        )
+        self.sheet.merge_range(
+            1,
+            1,
+            1,
+            3,
+            "Cross validation",
+            merge_format_subheader_right,
+        )
+        self.sheet.write(
+            1, 4, f"{self.env['n_folds']} Folds", merge_format_subheader_left
+        )
+        self.sheet.merge_range(
+            2,
+            1,
+            2,
+            3,
+            "Stratified",
+            merge_format_subheader_right,
+        )
+        self.sheet.write(
+            2,
+            4,
+            f"{'True' if self.env['stratified']=='1' else 'False'}",
+            merge_format_subheader_left,
+        )
+        self.sheet.merge_range(
+            3,
+            1,
+            3,
+            3,
+            "Discretized",
+            merge_format_subheader_right,
+        )
+        self.sheet.write(
+            3,
+            4,
+            f"{'True' if self.env['discretize']=='1' else 'False'}",
+            merge_format_subheader_left,
+        )
+        self.sheet.merge_range(
+            4,
+            1,
+            4,
+            3,
+            "Seeds",
+            merge_format_subheader_right,
+        )
+        self.sheet.write(
+            4, 4, f"{self.env['seeds']}", merge_format_subheader_left
+        )
+        self.update_max_length(len(self.env["seeds"]) + 1)
+        header_cols = [
+            ("Dataset", 30),
+            ("Samples", 10),
+            ("Features", 10),
+            ("Classes", 10),
+            ("Balance", 50),
+        ]
+        bold = self.book.add_format(
+            {
+                "bold": True,
+                "font_size": 14,
+                "bg_color": self.color3,
+                "border": 1,
+            }
+        )
+        i = 0
+        for item, length in header_cols:
+            self.sheet.write(5, i, item, bold)
+            self.sheet.set_column(i, i, length)
+            i += 1
+
+    def footer(self):
+        # set Balance column width to max length
+        self.sheet.set_column(4, 4, self.max_length)
+        self.sheet.freeze_panes(6, 1)
+        self.sheet.hide_gridlines(2)
+        if self.close:
+            self.book.close()
+
+    def print_line(self, result):
+        size_n = 14
+        integer = self.book.add_format(
+            {"num_format": "#,###", "font_size": size_n, "border": 1}
+        )
+        normal = self.book.add_format({"font_size": size_n, "border": 1})
+        col = 0
+        if self.row % 2 == 0:
+            normal.set_bg_color(self.color1)
+            integer.set_bg_color(self.color1)
+        else:
+            normal.set_bg_color(self.color2)
+            integer.set_bg_color(self.color2)
+        self.sheet.write(self.row, col, result.dataset, normal)
+        self.sheet.write(self.row, col + 1, result.samples, integer)
+        self.sheet.write(self.row, col + 2, result.features, integer)
+        self.sheet.write(self.row, col + 3, result.classes, normal)
+        self.sheet.write(self.row, col + 4, result.balance, normal)
+        self.update_max_length(len(result.balance))
+        self.row += 1
+
+    def update_max_length(self, value):
+        if value > self.max_length:
+            self.max_length = value
+
+    def report(self):
         data_sets = Datasets()
         color_line = TextColor.LINE1
-        print(color_line, end="")
-        print(f"{'Dataset':30s} Sampl. Feat. Cls Balance")
-        print("=" * 30 + " ===== ====== === " + "=" * 40)
+        if self.excel:
+            self.header()
+        if self.output:
+            print(color_line, end="")
+            print(self.header_text)
+            print("")
+            print(f"{'Dataset':30s} Sampl. Feat. Cls Balance")
+            print("=" * 30 + " ====== ===== === " + "=" * 60)
         for dataset in data_sets:
-            X, y = data_sets.load(dataset)
+            attributes = data_sets.get_attributes(dataset)
+            attributes.dataset = dataset
+            if self.excel:
+                self.print_line(attributes)
             color_line = (
                 TextColor.LINE2
                 if color_line == TextColor.LINE1
                 else TextColor.LINE1
             )
-            values, counts = np.unique(y, return_counts=True)
-            comp = ""
-            sep = ""
-            for count in counts:
-                comp += f"{sep}{count/sum(counts)*100:5.2f}%"
-                sep = "/ "
-            print(color_line, end="")
-            print(
-                f"{dataset:30s} {X.shape[0]:6,d} {X.shape[1]:5,d} "
-                f"{len(np.unique(y)):3d} {comp:40s}"
-            )
+            if self.output:
+                print(color_line, end="")
+                print(
+                    f"{dataset:30s} {attributes.samples:6,d} "
+                    f"{attributes.features:5,d} {attributes.classes:3d} "
+                    f"{attributes.balance:40s}"
+                )
+        if self.excel:
+            self.footer()
 
 
 class SQL(BaseReport):
@@ -1066,7 +1299,12 @@ class Benchmark:
                 k = Excel(file_name=file_name, book=book)
                 k.report()
             sheet.freeze_panes(6, 1)
-            sheet.hide_gridlines()
+            sheet.hide_gridlines(2)
+
+        def add_datasets_sheet():
+            # Add datasets sheet
+            re = ReportDatasets(excel=True, book=book)
+            re.report()
 
         def exreport_output():
             file_name = os.path.join(
@@ -1094,6 +1332,7 @@ class Benchmark:
         footer()
         models_files()
         exreport_output()
+        add_datasets_sheet()
         book.close()
 
 
@@ -1117,6 +1356,7 @@ class Summary:
     def __init__(self, hidden=False) -> None:
         self.results = Files().get_all_results(hidden=hidden)
         self.data = []
+        self.data_filtered = []
         self.datasets = {}
         self.models = set()
         self.hidden = hidden
@@ -1193,13 +1433,14 @@ class Summary:
         number=0,
     ) -> None:
         """Print the list of results"""
-        data = self.get_results_criteria(
-            score, model, input_data, sort_key, number
-        )
-        if data == []:
+        if self.data_filtered == []:
+            self.data_filtered = self.get_results_criteria(
+                score, model, input_data, sort_key, number
+            )
+        if self.data_filtered == []:
             raise ValueError(NO_RESULTS)
-        max_file = max(len(x["file"]) for x in data)
-        max_title = max(len(x["title"]) for x in data)
+        max_file = max(len(x["file"]) for x in self.data_filtered)
+        max_title = max(len(x["title"]) for x in self.data_filtered)
         if self.hidden:
             color1 = TextColor.GREEN
             color2 = TextColor.YELLOW
@@ -1208,10 +1449,11 @@ class Summary:
             color2 = TextColor.LINE2
         print(color1, end="")
         print(
-            f"{'Date':10s} {'File':{max_file}s} {'Score':8s} {'Time(h)':7s} "
-            f"{'Title':s}"
+            f" #  {'Date':10s} {'File':{max_file}s} {'Score':8s} "
+            f"{'Time(h)':7s} {'Title':s}"
         )
         print(
+            "===",
             "=" * 10
             + " "
             + "=" * max_file
@@ -1220,21 +1462,60 @@ class Summary:
             + " "
             + "=" * 7
             + " "
-            + "=" * max_title
+            + "=" * max_title,
         )
         print(
             "\n".join(
                 [
-                    (color2 if n % 2 == 0 else color1)
-                    + f"{x['date']} {x['file']:{max_file}s} "
+                    (color2 if n % 2 == 0 else color1) + f"{n:3d} "
+                    f"{x['date']} {x['file']:{max_file}s} "
                     f"{x['metric']:8.5f} "
                     f"{x['duration']/3600:7.3f} "
                     f"{x['title']}"
-                    for n, x in enumerate(data)
+                    for n, x in enumerate(self.data_filtered)
                 ]
             )
         )
 
+    def manage_results(self, excel, is_test):
+        """Manage results showed in the summary
+        return True if excel file is created False otherwise
+        """
+        num = ""
+        book = None
+        while True:
+            print(
+                "Which result do you want to report? (q to quit, r to list "
+                "again, number to report): ",
+                end="",
+            )
+            num = get_input(is_test)
+            if num == "r":
+                self.list_results()
+            if num == "q":
+                if excel:
+                    if book is not None:
+                        book.close()
+                        return True
+                return False
+            if num.isdigit() and int(num) < len(self.data) and int(num) >= 0:
+                rep = Report(self.data_filtered[int(num)]["file"], self.hidden)
+                rep.report()
+                if excel and not self.hidden:
+                    if book is None:
+                        file_name = Files.be_list_excel
+                        book = xlsxwriter.Workbook(
+                            file_name, {"nan_inf_to_errors": True}
+                        )
+                    excel = Excel(
+                        file_name=self.data_filtered[int(num)]["file"],
+                        book=book,
+                    )
+                    excel.report()
+            else:
+                if num not in ("r", "q"):
+                    print(f"Invalid option {num}. Try again!")
+
     def show_result(self, data: dict, title: str = "") -> None:
         def whites(n: int) -> str:
             return " " * n + color1 + "*"

benchmark/Utils.py

@@ -1,6 +1,8 @@
 import os
+import sys
 import subprocess
 
+PYTHON_VERSION = "{}.{}".format(sys.version_info.major, sys.version_info.minor)
 NO_RESULTS = "** No results found **"
 NO_ENV = "File .env not found"
 
@@ -25,6 +27,8 @@ class Files:
     exreport_pdf = "Rplots.pdf"
     benchmark_r = "benchmark.r"
     dot_env = ".env"
+    datasets_report_excel = "ReportDatasets.xlsx"
+    be_list_excel = "some_results.xlsx"
 
     @staticmethod
     def exreport_output(score):

benchmark/__init__.py

@@ -1,10 +1,16 @@
-from .Datasets import Datasets, DatasetsSurcov, DatasetsTanveer, DatasetsArff
+from .Datasets import (
+    Datasets,
+    DatasetsSurcov,
+    DatasetsTanveer,
+    DatasetsArff,
+)
 from .Experiments import Experiment
 from .Results import Report, Summary
+from ._version import __version__
 
 __author__ = "Ricardo Montañana Gómez"
-__copyright__ = "Copyright 2020-2022, Ricardo Montañana Gómez"
+__copyright__ = "Copyright 2020-2023, Ricardo Montañana Gómez"
 __license__ = "MIT License"
 __author_email__ = "ricardo.montanana@alu.uclm.es"
 
-__all__ = ["Experiment", "Datasets", "Report", "Summary"]
+__all__ = ["Experiment", "Datasets", "Report", "Summary", __version__]

benchmark/_version.py

@@ -1 +1 @@
-__version__ = "0.1.1"
+__version__ = "0.4.0"

benchmark/scripts/be_list.py

@@ -1,7 +1,7 @@
 #! /usr/bin/env python
 import os
 from benchmark.Results import Summary
-from benchmark.Utils import Folders
+from benchmark.Utils import Folders, Files
 from benchmark.Arguments import Arguments
 
 """List experiments of a model
@@ -12,6 +12,7 @@ def main(args_test=None):
     arguments = Arguments()
     arguments.xset("number").xset("model", required=False).xset("key")
     arguments.xset("hidden").xset("nan").xset("score", required=False)
+    arguments.xset("excel")
     args = arguments.parse(args_test)
     data = Summary(hidden=args.hidden)
     data.acquire()
@@ -22,32 +23,39 @@ def main(args_test=None):
             sort_key=args.key,
             number=args.number,
         )
+        is_test = args_test is not None
+        if not args.nan:
+            excel_generated = data.manage_results(args.excel, is_test)
+            if args.excel and excel_generated:
+                print(f"Generated file: {Files.be_list_excel}")
+                Files.open(Files.be_list_excel, is_test)
     except ValueError as e:
         print(e)
-    else:
-        if args.nan:
-            results_nan = []
-            results = data.get_results_criteria(
-                score=args.score,
-                model=args.model,
-                input_data=None,
-                sort_key=args.key,
-                number=args.number,
+        return
+    if args.nan:
+        results_nan = []
+        results = data.get_results_criteria(
+            score=args.score,
+            model=args.model,
+            input_data=None,
+            sort_key=args.key,
+            number=args.number,
+        )
+        for result in results:
+            if result["metric"] != result["metric"]:
+                results_nan.append(result)
+        if results_nan != []:
+            print(
+                "\n"
+                + "*" * 30
+                + " Results with nan moved to hidden "
+                + "*" * 30
             )
-            for result in results:
-                if result["metric"] != result["metric"]:
-                    results_nan.append(result)
-            if results_nan != []:
-                print(
-                    "\n"
-                    + "*" * 30
-                    + " Results with nan moved to hidden "
-                    + "*" * 30
+            data.data_filtered = []
+            data.list_results(input_data=results_nan)
+            for result in results_nan:
+                name = result["file"]
+                os.rename(
+                    os.path.join(Folders.results, name),
+                    os.path.join(Folders.hidden_results, name),
                 )
-                data.list_results(input_data=results_nan)
-                for result in results_nan:
-                    name = result["file"]
-                    os.rename(
-                        os.path.join(Folders.results, name),
-                        os.path.join(Folders.hidden_results, name),
-                    )

benchmark/scripts/be_report.py

@@ -21,7 +21,11 @@ def main(args_test=None):
     if args.grid:
         args.best = None
     if args.file is None and args.best is None and args.grid is None:
-        ReportDatasets.report()
+        report = ReportDatasets(args.excel)
+        report.report()
+        if args.excel:
+            is_test = args_test is not None
+            Files.open(report.get_file_name(), is_test)
     else:
         if args.best is not None or args.grid is not None:
             report = ReportBest(args.score, args.model, args.best, args.grid)

benchmark/tests/.env

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

benchmark/tests/.env.arff

@@ -4,3 +4,5 @@ n_folds=5
 model=ODTE
 stratified=0
 source_data=Arff
+seeds=[271, 314, 171]
+discretize=1

benchmark/tests/.env.dist

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

benchmark/tests/.env.surcov

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

benchmark/tests/.gitignore

@@ -0,0 +1,2 @@
+ReportDatasets.xlsx 
+some_results.xlsx

benchmark/tests/Benchmark_test.py

@@ -5,6 +5,7 @@ from openpyxl import load_workbook
 from .TestBase import TestBase
 from ..Utils import Folders, Files, NO_RESULTS
 from ..Results import Benchmark
+from .._version import __version__
 
 
 class BenchmarkTest(TestBase):
@@ -89,6 +90,15 @@ class BenchmarkTest(TestBase):
         self.assertTrue(os.path.exists(benchmark.get_tex_file()))
         self.check_file_file(benchmark.get_tex_file(), "exreport_tex")
 
+    @staticmethod
+    def generate_excel_sheet(test, sheet, file_name):
+        with open(os.path.join("test_files", file_name), "w") as f:
+            for row in range(1, sheet.max_row + 1):
+                for col in range(1, sheet.max_column + 1):
+                    value = sheet.cell(row=row, column=col).value
+                    if value is not None:
+                        print(f'{row};{col};"{value}"', file=f)
+
     def test_excel_output(self):
         benchmark = Benchmark("accuracy", visualize=False)
         benchmark.compile_results()
@@ -98,9 +108,16 @@ class BenchmarkTest(TestBase):
         benchmark.excel()
         file_name = benchmark.get_excel_file_name()
         book = load_workbook(file_name)
+        replace = None
+        with_this = None
         for sheet_name in book.sheetnames:
             sheet = book[sheet_name]
-            self.check_excel_sheet(sheet, f"exreport_excel_{sheet_name}")
-            # ExcelTest.generate_excel_sheet(
-            #     self, sheet, f"exreport_excel_{sheet_name}"
-            # )
+            if sheet_name == "Datasets":
+                replace = self.benchmark_version
+                with_this = __version__
+            self.check_excel_sheet(
+                sheet,
+                f"exreport_excel_{sheet_name}",
+                replace=replace,
+                with_this=with_this,
+            )

benchmark/tests/Dataset_test.py

@@ -23,7 +23,25 @@ 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_load_dataframe(self):
+        self.set_env(".env.arff")
+        dt = Datasets()
+        X, y = dt.load_discretized("iris", dataframe=False)
+        dataset = dt.load_discretized("iris", dataframe=True)
+        class_name = dt.get_class_name()
+        features = dt.get_features()
+        self.assertListEqual(y.tolist(), dataset[class_name].tolist())
+        for i in range(len(features)):
+            self.assertListEqual(
+                X[:, i].tolist(), dataset[features[i]].tolist()
+            )
 
     def test_Datasets_iterator(self):
         test = {

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)

benchmark/tests/Report_test.py

@@ -2,11 +2,14 @@ import os
 from io import StringIO
 from unittest.mock import patch
 from .TestBase import TestBase
-from ..Results import Report, BaseReport, ReportBest, ReportDatasets
+from ..Results import Report, BaseReport, ReportBest, ReportDatasets, get_input
 from ..Utils import Symbols
 
 
 class ReportTest(TestBase):
+    def test_get_input(self):
+        self.assertEqual(get_input(is_test=True), "test")
+
     def test_BaseReport(self):
         with patch.multiple(BaseReport, __abstractmethods__=set()):
             file_name = os.path.join(
@@ -81,7 +84,7 @@ class ReportTest(TestBase):
         output_text = stdout.getvalue().splitlines()
         # Compare replacing STree version
         for line, index in zip(expected, range(len(expected))):
-            if "1.2.4" in line:
+            if self.stree_version in line:
                 # replace STree version
                 line = self.replace_STree_version(line, output_text, index)
 
@@ -97,4 +100,12 @@ class ReportTest(TestBase):
     def test_report_datasets(self, mock_output):
         report = ReportDatasets()
         report.report()
-        self.check_output_file(mock_output, "report_datasets")
+        file_name = f"report_datasets{self.ext}"
+        with open(os.path.join(self.test_files, file_name)) as f:
+            expected = f.read()
+        output_text = mock_output.getvalue().splitlines()
+        for line, index in zip(expected.splitlines(), range(len(expected))):
+            if self.benchmark_version in line:
+                # replace benchmark version
+                line = self.replace_benchmark_version(line, output_text, index)
+            self.assertEqual(line, output_text[index])

benchmark/tests/TestBase.py

@@ -15,6 +15,8 @@ class TestBase(unittest.TestCase):
         self.test_files = "test_files"
         self.output = "sys.stdout"
         self.ext = ".test"
+        self.benchmark_version = "0.2.0"
+        self.stree_version = "1.2.4"
         super().__init__(*args, **kwargs)
 
     def remove_files(self, files, folder):
@@ -31,7 +33,9 @@ class TestBase(unittest.TestCase):
                     if value is not None:
                         print(f'{row};{col};"{value}"', file=f)
 
-    def check_excel_sheet(self, sheet, file_name):
+    def check_excel_sheet(
+        self, sheet, file_name, replace=None, with_this=None
+    ):
         file_name += self.ext
         with open(os.path.join(self.test_files, file_name), "r") as f:
             expected = csv.reader(f, delimiter=";")
@@ -43,6 +47,9 @@ class TestBase(unittest.TestCase):
                         value = float(value)
                     except ValueError:
                         pass
+                if replace is not None and isinstance(value, str):
+                    if replace in value:
+                        value = value.replace(replace, with_this)
                 self.assertEqual(sheet.cell(int(row), int(col)).value, value)
 
     def check_output_file(self, output, file_name):
@@ -51,10 +58,15 @@ class TestBase(unittest.TestCase):
             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 replace_STree_version(self, line, output, index):
+        idx = line.find(self.stree_version)
+        return line.replace(self.stree_version, output[index][idx : idx + 5])
+
+    def replace_benchmark_version(self, line, output, index):
+        idx = line.find(self.benchmark_version)
+        return line.replace(
+            self.benchmark_version, output[index][idx : idx + 5]
+        )
 
     def check_file_file(self, computed_file, expected_file):
         with open(computed_file) as f:

benchmark/tests/Util_test.py

@@ -178,6 +178,8 @@ class UtilTest(TestBase):
             "model": "ODTE",
             "stratified": "0",
             "source_data": "Tanveer",
+            "seeds": "[57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]",
+            "discretize": "0",
         }
         computed = EnvData().load()
         self.assertDictEqual(computed, expected)

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",

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",

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,

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",

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",

benchmark/tests/scripts/Be_Benchmark_test.py

@@ -2,6 +2,7 @@ import os
 from openpyxl import load_workbook
 from ...Utils import NO_RESULTS, Folders, Files
 from ..TestBase import TestBase
+from ..._version import __version__
 
 
 class BeBenchmarkTest(TestBase):
@@ -43,9 +44,19 @@ class BeBenchmarkTest(TestBase):
             Folders.exreport, Files.exreport_excel(self.score)
         )
         book = load_workbook(file_name)
+        replace = None
+        with_this = None
         for sheet_name in book.sheetnames:
             sheet = book[sheet_name]
-            self.check_excel_sheet(sheet, f"exreport_excel_{sheet_name}")
+            if sheet_name == "Datasets":
+                replace = self.benchmark_version
+                with_this = __version__
+            self.check_excel_sheet(
+                sheet,
+                f"exreport_excel_{sheet_name}",
+                replace=replace,
+                with_this=with_this,
+            )
 
     def test_be_benchmark_single(self):
         stdout, stderr = self.execute_script(

benchmark/tests/scripts/Be_Grid_test.py

@@ -4,6 +4,10 @@ from ...Utils import Folders, Files
 from ..TestBase import TestBase
 
 
+def get_test():
+    return "hola"
+
+
 class BeGridTest(TestBase):
     def setUp(self):
         self.prepare_scripts_env()

benchmark/tests/scripts/Be_List_test.py

@@ -1,5 +1,7 @@
 import os
-from ...Utils import Folders, NO_RESULTS
+from unittest.mock import patch
+from openpyxl import load_workbook
+from ...Utils import Folders, Files, NO_RESULTS
 from ..TestBase import TestBase
 
 
@@ -7,12 +9,64 @@ class BeListTest(TestBase):
     def setUp(self):
         self.prepare_scripts_env()
 
-    def test_be_list(self):
+    @patch("benchmark.Results.get_input", return_value="q")
+    def test_be_list(self, input_data):
         stdout, stderr = self.execute_script("be_list", ["-m", "STree"])
         self.assertEqual(stderr.getvalue(), "")
-        self.check_output_file(stdout, "summary_list_model")
+        self.check_output_file(stdout, "be_list_model")
 
-    def test_be_list_no_data(self):
+    @patch("benchmark.Results.get_input", side_effect=iter(["x", "q"]))
+    def test_be_list_invalid_option(self, input_data):
+        stdout, stderr = self.execute_script("be_list", ["-m", "STree"])
+        self.assertEqual(stderr.getvalue(), "")
+        self.check_output_file(stdout, "be_list_model_invalid")
+
+    @patch("benchmark.Results.get_input", side_effect=iter(["0", "q"]))
+    def test_be_list_report(self, input_data):
+        stdout, stderr = self.execute_script("be_list", ["-m", "STree"])
+        self.assertEqual(stderr.getvalue(), "")
+        self.check_output_file(stdout, "be_list_report")
+
+    @patch("benchmark.Results.get_input", side_effect=iter(["q"]))
+    def test_be_list_report_excel_none(self, input_data):
+        stdout, stderr = self.execute_script(
+            "be_list", ["-m", "STree", "-x", "1"]
+        )
+        self.assertEqual(stderr.getvalue(), "")
+        self.check_output_file(stdout, "be_list_model")
+
+    @patch("benchmark.Results.get_input", side_effect=iter(["r", "q"]))
+    def test_be_list_twice(self, input_data):
+        stdout, stderr = self.execute_script("be_list", ["-m", "STree"])
+        self.assertEqual(stderr.getvalue(), "")
+        self.check_output_file(stdout, "be_list_model_2")
+
+    @patch("benchmark.Results.get_input", side_effect=iter(["2", "q"]))
+    def test_be_list_report_excel(self, input_data):
+        stdout, stderr = self.execute_script(
+            "be_list", ["-m", "STree", "-x", "1"]
+        )
+        self.assertEqual(stderr.getvalue(), "")
+        self.check_output_file(stdout, "be_list_report_excel")
+        book = load_workbook(Files.be_list_excel)
+        sheet = book["STree"]
+        self.check_excel_sheet(sheet, "excel")
+
+    @patch("benchmark.Results.get_input", side_effect=iter(["2", "1", "q"]))
+    def test_be_list_report_excel_twice(self, input_data):
+        stdout, stderr = self.execute_script(
+            "be_list", ["-m", "STree", "-x", "1"]
+        )
+        self.assertEqual(stderr.getvalue(), "")
+        self.check_output_file(stdout, "be_list_report_excel_2")
+        book = load_workbook(Files.be_list_excel)
+        sheet = book["STree"]
+        self.check_excel_sheet(sheet, "excel")
+        sheet = book["STree2"]
+        self.check_excel_sheet(sheet, "excel2")
+
+    @patch("benchmark.Results.get_input", return_value="q")
+    def test_be_list_no_data(self, input_data):
         stdout, stderr = self.execute_script(
             "be_list", ["-m", "Wodt", "-s", "f1-macro"]
         )
@@ -41,7 +95,8 @@ class BeListTest(TestBase):
             swap_files(Folders.results, Folders.hidden_results, file_name)
             self.fail("test_be_list_nan() should not raise exception")
 
-    def test_be_list_nan_no_nan(self):
+    @patch("benchmark.Results.get_input", return_value="q")
+    def test_be_list_nan_no_nan(self, input_data):
         stdout, stderr = self.execute_script("be_list", ["--nan", "1"])
         self.assertEqual(stderr.getvalue(), "")
         self.check_output_file(stdout, "be_list_no_nan")

benchmark/tests/scripts/Be_Report_test.py

@@ -1,7 +1,8 @@
 import os
 from openpyxl import load_workbook
-from ...Utils import Folders
+from ...Utils import Folders, Files
 from ..TestBase import TestBase
+from ..._version import __version__
 
 
 class BeReportTest(TestBase):
@@ -14,6 +15,7 @@ class BeReportTest(TestBase):
             "results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.xlsx",
         ]
         self.remove_files(files, Folders.results)
+        self.remove_files([Files.datasets_report_excel], os.getcwd())
         return super().tearDown()
 
     def test_be_report(self):
@@ -41,7 +43,37 @@ class BeReportTest(TestBase):
     def test_be_report_datatsets(self):
         stdout, stderr = self.execute_script("be_report", [])
         self.assertEqual(stderr.getvalue(), "")
-        self.check_output_file(stdout, "report_datasets")
+        file_name = f"report_datasets{self.ext}"
+        with open(os.path.join(self.test_files, file_name)) as f:
+            expected = f.read()
+        output_text = stdout.getvalue().splitlines()
+        for line, index in zip(expected.splitlines(), range(len(expected))):
+            if self.benchmark_version in line:
+                # replace benchmark version
+                line = self.replace_benchmark_version(line, output_text, index)
+            self.assertEqual(line, output_text[index])
+
+    def test_be_report_datasets_excel(self):
+        stdout, stderr = self.execute_script("be_report", ["-x", "1"])
+        self.assertEqual(stderr.getvalue(), "")
+        file_name = f"report_datasets{self.ext}"
+        with open(os.path.join(self.test_files, file_name)) as f:
+            expected = f.read()
+        output_text = stdout.getvalue().splitlines()
+        for line, index in zip(expected.splitlines(), range(len(expected))):
+            if self.benchmark_version in line:
+                # replace benchmark version
+                line = self.replace_benchmark_version(line, output_text, index)
+            self.assertEqual(line, output_text[index])
+        file_name = os.path.join(os.getcwd(), Files.datasets_report_excel)
+        book = load_workbook(file_name)
+        sheet = book["Datasets"]
+        self.check_excel_sheet(
+            sheet,
+            "exreport_excel_Datasets",
+            replace=self.benchmark_version,
+            with_this=__version__,
+        )
 
     def test_be_report_best(self):
         stdout, stderr = self.execute_script(

benchmark/tests/test_files/be_list_model.test

@@ -0,0 +1,6 @@
+ #  Date       File                                                          Score    Time(h) Title
+=== ========== ============================================================= ======== ======= =================================
+  0 2021-11-01 results_accuracy_STree_macbook-pro_2021-11-01_19:17:07_0.json  0.03790   1.143 default B
+  1 2021-10-27 results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json       0.04158   0.943 default A
+  2 2021-09-30 results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json       0.04544   0.173 With gridsearched hyperparameters
+Which result do you want to report? (q to quit, r to list again, number to report): 

benchmark/tests/test_files/be_list_model_2.test

@@ -0,0 +1,11 @@
+ #  Date       File                                                          Score    Time(h) Title
+=== ========== ============================================================= ======== ======= =================================
+  0 2021-11-01 results_accuracy_STree_macbook-pro_2021-11-01_19:17:07_0.json  0.03790   1.143 default B
+  1 2021-10-27 results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json       0.04158   0.943 default A
+  2 2021-09-30 results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json       0.04544   0.173 With gridsearched hyperparameters
+Which result do you want to report? (q to quit, r to list again, number to report):  #  Date       File                                                          Score    Time(h) Title
+=== ========== ============================================================= ======== ======= =================================
+  0 2021-11-01 results_accuracy_STree_macbook-pro_2021-11-01_19:17:07_0.json  0.03790   1.143 default B
+  1 2021-10-27 results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json       0.04158   0.943 default A
+  2 2021-09-30 results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json       0.04544   0.173 With gridsearched hyperparameters
+Which result do you want to report? (q to quit, r to list again, number to report): 

benchmark/tests/test_files/be_list_model_invalid.test

@@ -0,0 +1,7 @@
+ #  Date       File                                                          Score    Time(h) Title
+=== ========== ============================================================= ======== ======= =================================
+  0 2021-11-01 results_accuracy_STree_macbook-pro_2021-11-01_19:17:07_0.json  0.03790   1.143 default B
+  1 2021-10-27 results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json       0.04158   0.943 default A
+  2 2021-09-30 results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json       0.04544   0.173 With gridsearched hyperparameters
+Which result do you want to report? (q to quit, r to list again, number to report): Invalid option x. Try again!
+Which result do you want to report? (q to quit, r to list again, number to report): 

benchmark/tests/test_files/be_list_nan.test

@@ -1,13 +1,13 @@
-Date       File                                                             Score    Time(h) Title
-========== ================================================================ ======== ======= ============================================
-2022-05-04 results_accuracy_XGBoost_MacBookpro16_2022-05-04_11:00:35_0.json      nan   3.091 Default hyperparameters
-2022-04-20 results_accuracy_ODTE_Galgo_2022-04-20_10:52:20_0.json            0.04341   6.275 Gridsearched hyperparams v022.1b random_init
-2022-01-14 results_accuracy_RandomForest_iMac27_2022-01-14_12:39:30_0.json   0.03627   0.076 Test default paramters with RandomForest
-2021-11-01 results_accuracy_STree_macbook-pro_2021-11-01_19:17:07_0.json     0.03790   1.143 default B
-2021-10-27 results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json          0.04158   0.943 default A
-2021-09-30 results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json          0.04544   0.173 With gridsearched hyperparameters
+ #  Date       File                                                             Score    Time(h) Title
+=== ========== ================================================================ ======== ======= ============================================
+  0 2022-05-04 results_accuracy_XGBoost_MacBookpro16_2022-05-04_11:00:35_0.json      nan   3.091 Default hyperparameters
+  1 2022-04-20 results_accuracy_ODTE_Galgo_2022-04-20_10:52:20_0.json            0.04341   6.275 Gridsearched hyperparams v022.1b random_init
+  2 2022-01-14 results_accuracy_RandomForest_iMac27_2022-01-14_12:39:30_0.json   0.03627   0.076 Test default paramters with RandomForest
+  3 2021-11-01 results_accuracy_STree_macbook-pro_2021-11-01_19:17:07_0.json     0.03790   1.143 default B
+  4 2021-10-27 results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json          0.04158   0.943 default A
+  5 2021-09-30 results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json          0.04544   0.173 With gridsearched hyperparameters
 
 ****************************** Results with nan moved to hidden ******************************
-Date       File                                                             Score    Time(h) Title
-========== ================================================================ ======== ======= =======================
-2022-05-04 results_accuracy_XGBoost_MacBookpro16_2022-05-04_11:00:35_0.json      nan   3.091 Default hyperparameters
+ #  Date       File                                                             Score    Time(h) Title
+=== ========== ================================================================ ======== ======= =======================
+  0 2022-05-04 results_accuracy_XGBoost_MacBookpro16_2022-05-04_11:00:35_0.json      nan   3.091 Default hyperparameters

benchmark/tests/test_files/be_list_no_nan.test

@@ -1,7 +1,7 @@
-Date       File                                                            Score    Time(h) Title
-========== =============================================================== ======== ======= ============================================
-2022-04-20 results_accuracy_ODTE_Galgo_2022-04-20_10:52:20_0.json           0.04341   6.275 Gridsearched hyperparams v022.1b random_init
-2022-01-14 results_accuracy_RandomForest_iMac27_2022-01-14_12:39:30_0.json  0.03627   0.076 Test default paramters with RandomForest
-2021-11-01 results_accuracy_STree_macbook-pro_2021-11-01_19:17:07_0.json    0.03790   1.143 default B
-2021-10-27 results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json         0.04158   0.943 default A
-2021-09-30 results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json         0.04544   0.173 With gridsearched hyperparameters
+ #  Date       File                                                            Score    Time(h) Title
+=== ========== =============================================================== ======== ======= ============================================
+  0 2022-04-20 results_accuracy_ODTE_Galgo_2022-04-20_10:52:20_0.json           0.04341   6.275 Gridsearched hyperparams v022.1b random_init
+  1 2022-01-14 results_accuracy_RandomForest_iMac27_2022-01-14_12:39:30_0.json  0.03627   0.076 Test default paramters with RandomForest
+  2 2021-11-01 results_accuracy_STree_macbook-pro_2021-11-01_19:17:07_0.json    0.03790   1.143 default B
+  3 2021-10-27 results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json         0.04158   0.943 default A
+  4 2021-09-30 results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json         0.04544   0.173 With gridsearched hyperparameters

benchmark/tests/test_files/be_list_report.test

@@ -0,0 +1,21 @@
+ #  Date       File                                                          Score    Time(h) Title
+=== ========== ============================================================= ======== ======= =================================
+  0 2021-11-01 results_accuracy_STree_macbook-pro_2021-11-01_19:17:07_0.json  0.03790   1.143 default B
+  1 2021-10-27 results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json       0.04158   0.943 default A
+  2 2021-09-30 results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json       0.04544   0.173 With gridsearched hyperparameters
+Which result do you want to report? (q to quit, r to list again, number to report): *************************************************************************************************************************
+* STree ver. 1.2.3 Python ver. 3.11x with 5 Folds cross validation and 10 random seeds. 2021-11-01 19:17:07             *
+* default B                                                                                                             *
+* Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False                                              *
+* Execution took 4115.04 seconds,   1.14 hours, on macbook-pro                                                          *
+* Score is accuracy                                                                                                     *
+*************************************************************************************************************************
+
+Dataset                        Sampl. Feat. Cls Nodes   Leaves  Depth   Score           Time              Hyperparameters 
+============================== ====== ===== === ======= ======= ======= =============== ================= =============== 
+balance-scale                     625     4   3   18.78    9.88    5.90 0.970000±0.0020   0.233304±0.0481 {'max_features': 'auto', 'splitter': 'mutual'} 
+balloons                           16     4   2    4.72    2.86    2.78 0.556667±0.2941   0.021352±0.0058 {'max_features': 'auto', 'splitter': 'mutual'} 
+*************************************************************************************************************************
+* accuracy compared to STree_default (liblinear-ovr) .:  0.0379                                                         *
+*************************************************************************************************************************
+Which result do you want to report? (q to quit, r to list again, number to report): 

benchmark/tests/test_files/be_list_report_excel.test

@@ -0,0 +1,21 @@
+ #  Date       File                                                          Score    Time(h) Title
+=== ========== ============================================================= ======== ======= =================================
+  0 2021-11-01 results_accuracy_STree_macbook-pro_2021-11-01_19:17:07_0.json  0.03790   1.143 default B
+  1 2021-10-27 results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json       0.04158   0.943 default A
+  2 2021-09-30 results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json       0.04544   0.173 With gridsearched hyperparameters
+Which result do you want to report? (q to quit, r to list again, number to report): *************************************************************************************************************************
+* STree ver. 1.2.3 Python ver. 3.11x with 5 Folds cross validation and 10 random seeds. 2021-09-30 11:42:07             *
+* With gridsearched hyperparameters                                                                                     *
+* Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False                                              *
+* Execution took  624.25 seconds,   0.17 hours, on iMac27                                                               *
+* Score is accuracy                                                                                                     *
+*************************************************************************************************************************
+
+Dataset                        Sampl. Feat. Cls Nodes   Leaves  Depth   Score           Time              Hyperparameters 
+============================== ====== ===== === ======= ======= ======= =============== ================= =============== 
+balance-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'} 
+balloons                           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'} 
+*************************************************************************************************************************
+* accuracy compared to STree_default (liblinear-ovr) .:  0.0454                                                         *
+*************************************************************************************************************************
+Which result do you want to report? (q to quit, r to list again, number to report): Generated file: some_results.xlsx

benchmark/tests/test_files/be_list_report_excel_2.test

@@ -0,0 +1,36 @@
+ #  Date       File                                                          Score    Time(h) Title
+=== ========== ============================================================= ======== ======= =================================
+  0 2021-11-01 results_accuracy_STree_macbook-pro_2021-11-01_19:17:07_0.json  0.03790   1.143 default B
+  1 2021-10-27 results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json       0.04158   0.943 default A
+  2 2021-09-30 results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json       0.04544   0.173 With gridsearched hyperparameters
+Which result do you want to report? (q to quit, r to list again, number to report): *************************************************************************************************************************
+* STree ver. 1.2.3 Python ver. 3.11x with 5 Folds cross validation and 10 random seeds. 2021-09-30 11:42:07             *
+* With gridsearched hyperparameters                                                                                     *
+* Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False                                              *
+* Execution took  624.25 seconds,   0.17 hours, on iMac27                                                               *
+* Score is accuracy                                                                                                     *
+*************************************************************************************************************************
+
+Dataset                        Sampl. Feat. Cls Nodes   Leaves  Depth   Score           Time              Hyperparameters 
+============================== ====== ===== === ======= ======= ======= =============== ================= =============== 
+balance-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'} 
+balloons                           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'} 
+*************************************************************************************************************************
+* accuracy compared to STree_default (liblinear-ovr) .:  0.0454                                                         *
+*************************************************************************************************************************
+Which result do you want to report? (q to quit, r to list again, number to report): *************************************************************************************************************************
+* STree ver. 1.2.3 Python ver. 3.11x with 5 Folds cross validation and 10 random seeds. 2021-10-27 09:40:40             *
+* default A                                                                                                             *
+* Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False                                              *
+* Execution took 3395.01 seconds,   0.94 hours, on iMac27                                                               *
+* Score is accuracy                                                                                                     *
+*************************************************************************************************************************
+
+Dataset                        Sampl. Feat. Cls Nodes   Leaves  Depth   Score           Time              Hyperparameters 
+============================== ====== ===== === ======= ======= ======= =============== ================= =============== 
+balance-scale                     625     4   3   11.08    5.90    5.90 0.980000±0.0010   0.285207±0.0603 {'splitter': 'best', 'max_features': 'auto'} 
+balloons                           16     4   2    4.12    2.56    2.56 0.695000±0.2757   0.021201±0.0035 {'splitter': 'best', 'max_features': 'auto'} 
+*************************************************************************************************************************
+* accuracy compared to STree_default (liblinear-ovr) .:  0.0416                                                         *
+*************************************************************************************************************************
+Which result do you want to report? (q to quit, r to list again, number to report): Generated file: some_results.xlsx

benchmark/tests/test_files/be_main_best.test

@@ -1,16 +1,16 @@
-************************************************************************************************************************
-* Report STree ver. 1.2.4 with 5 Folds cross validation and 10 random seeds. 2022-05-09 00:15:25                       *
-* test                                                                                                                 *
-* Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False                                             *
-* Execution took    0.80 seconds,   0.00 hours, on iMac27                                                              *
-* Score is accuracy                                                                                                    *
-************************************************************************************************************************
+*************************************************************************************************************************
+* STree ver. 1.2.4 Python ver. 3.11x with 5 Folds cross validation and 10 random seeds. 2022-05-09 00:15:25             *
+* test                                                                                                                  *
+* Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False                                              *
+* Execution took    0.80 seconds,   0.00 hours, on iMac27                                                               *
+* Score is accuracy                                                                                                     *
+*************************************************************************************************************************
 
-Dataset                        Sampl. Feat. Cls Nodes   Leaves  Depth   Score           Time             Hyperparameters 
-============================== ====== ===== === ======= ======= ======= =============== ================ =============== 
-balance-scale                      625     4   3   23.32   12.16    6.44 0.840160±0.0304  0.013745±0.0019 {'splitter': 'best', 'max_features': 'auto'} 
-balloons                            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'} 
-************************************************************************************************************************
-* accuracy compared to STree_default (liblinear-ovr) .:  0.0422                                                        *
-************************************************************************************************************************
+Dataset                        Sampl. Feat. Cls Nodes   Leaves  Depth   Score           Time              Hyperparameters 
+============================== ====== ===== === ======= ======= ======= =============== ================= =============== 
+balance-scale                      625     4   3   23.32   12.16    6.44 0.840160±0.0304   0.013745±0.0019 {'splitter': 'best', 'max_features': 'auto'} 
+balloons                            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'} 
+*************************************************************************************************************************
+* accuracy compared to STree_default (liblinear-ovr) .:  0.0422                                                         *
+*************************************************************************************************************************
 Results in results/results_accuracy_STree_iMac27_2022-05-09_00:15:25_0.json

benchmark/tests/test_files/be_main_complete.test

@@ -1,16 +1,16 @@
-************************************************************************************************************************
-* Report STree ver. 1.2.4 with 5 Folds cross validation and 10 random seeds. 2022-05-08 20:14:43                       *
-* test                                                                                                                 *
-* Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False                                             *
-* Execution took    0.48 seconds,   0.00 hours, on iMac27                                                              *
-* Score is accuracy                                                                                                    *
-************************************************************************************************************************
+*************************************************************************************************************************
+* STree ver. 1.2.4 Python ver. 3.11x with 5 Folds cross validation and 10 random seeds. 2022-05-08 20:14:43             *
+* test                                                                                                                  *
+* Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False                                              *
+* Execution took    0.48 seconds,   0.00 hours, on iMac27                                                               *
+* Score is accuracy                                                                                                     *
+*************************************************************************************************************************
 
-Dataset                        Sampl. Feat. Cls Nodes   Leaves  Depth   Score           Time             Hyperparameters 
-============================== ====== ===== === ======= ======= ======= =============== ================ =============== 
-balance-scale                     625     4   3   17.36    9.18    6.18 0.908480±0.0247  0.007388±0.0013 {}              
-balloons                           16     4   2    4.64    2.82    2.66 0.663333±0.3009  0.000664±0.0002 {}              
-************************************************************************************************************************
-* accuracy compared to STree_default (liblinear-ovr) .:  0.0390                                                        *
-************************************************************************************************************************
+Dataset                        Sampl. Feat. Cls Nodes   Leaves  Depth   Score           Time              Hyperparameters 
+============================== ====== ===== === ======= ======= ======= =============== ================= =============== 
+balance-scale                     625     4   3   17.36    9.18    6.18 0.908480±0.0247   0.007388±0.0013 {}              
+balloons                           16     4   2    4.64    2.82    2.66 0.663333±0.3009   0.000664±0.0002 {}              
+*************************************************************************************************************************
+* accuracy compared to STree_default (liblinear-ovr) .:  0.0390                                                         *
+*************************************************************************************************************************
 Results in results/results_accuracy_STree_iMac27_2022-05-08_20:14:43_0.json

benchmark/tests/test_files/be_main_dataset.test

@@ -1,15 +1,15 @@
-************************************************************************************************************************
-* Report STree ver. 1.2.4 with 5 Folds cross validation and 10 random seeds. 2022-05-08 19:38:28                       *
-* test                                                                                                                 *
-* Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False                                             *
-* Execution took    0.06 seconds,   0.00 hours, on iMac27                                                              *
-* Score is accuracy                                                                                                    *
-************************************************************************************************************************
+*************************************************************************************************************************
+* STree ver. 1.2.4 Python ver. 3.11x with 5 Folds cross validation and 10 random seeds. 2022-05-08 19:38:28             *
+* test                                                                                                                  *
+* Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False                                              *
+* Execution took    0.06 seconds,   0.00 hours, on iMac27                                                               *
+* Score is accuracy                                                                                                     *
+*************************************************************************************************************************
 
-Dataset                        Sampl. Feat. Cls Nodes   Leaves  Depth   Score           Time             Hyperparameters 
-============================== ====== ===== === ======= ======= ======= =============== ================ =============== 
-balloons                           16     4   2    4.64    2.82    2.66 0.663333±0.3009  0.000671±0.0001 {}              
-************************************************************************************************************************
-* accuracy compared to STree_default (liblinear-ovr) .:  0.0165                                                        *
-************************************************************************************************************************
+Dataset                        Sampl. Feat. Cls Nodes   Leaves  Depth   Score           Time              Hyperparameters 
+============================== ====== ===== === ======= ======= ======= =============== ================= =============== 
+balloons                           16     4   2    4.64    2.82    2.66 0.663333±0.3009   0.000671±0.0001 {}              
+*************************************************************************************************************************
+* accuracy compared to STree_default (liblinear-ovr) .:  0.0165                                                         *
+*************************************************************************************************************************
 Partial result file removed: results/results_accuracy_STree_iMac27_2022-05-08_19:38:28_0.json

benchmark/tests/test_files/be_main_grid.test

@@ -1,16 +1,16 @@
-************************************************************************************************************************
-* Report STree ver. 1.2.4 with 5 Folds cross validation and 10 random seeds. 2022-05-09 00:21:06                       *
-* test                                                                                                                 *
-* Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False                                             *
-* Execution took    0.89 seconds,   0.00 hours, on iMac27                                                              *
-* Score is accuracy                                                                                                    *
-************************************************************************************************************************
+*************************************************************************************************************************
+* STree ver. 1.2.4 Python ver. 3.11x with 5 Folds cross validation and 10 random seeds. 2022-05-09 00:21:06             *
+* test                                                                                                                  *
+* Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False                                              *
+* Execution took    0.89 seconds,   0.00 hours, on iMac27                                                               *
+* Score is accuracy                                                                                                     *
+*************************************************************************************************************************
 
-Dataset                        Sampl. Feat. Cls Nodes   Leaves  Depth   Score           Time             Hyperparameters 
-============================== ====== ===== === ======= ======= ======= =============== ================ =============== 
-balance-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'} 
-balloons                           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'} 
-************************************************************************************************************************
-* accuracy compared to STree_default (liblinear-ovr) .:  0.0391                                                        *
-************************************************************************************************************************
+Dataset                        Sampl. Feat. Cls Nodes   Leaves  Depth   Score           Time              Hyperparameters 
+============================== ====== ===== === ======= ======= ======= =============== ================= =============== 
+balance-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'} 
+balloons                           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'} 
+*************************************************************************************************************************
+* accuracy compared to STree_default (liblinear-ovr) .:  0.0391                                                         *
+*************************************************************************************************************************
 Results in results/results_accuracy_STree_iMac27_2022-05-09_00:21:06_0.json

benchmark/tests/test_files/be_summary_list_model.test

@@ -26,10 +26,10 @@
 * results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json                      *
 *                                                                               *
 *********************************************************************************
-Date       File                                                            Score    Time(h) Title
-========== =============================================================== ======== ======= ============================================
-2021-09-30 results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json         0.04544   0.173 With gridsearched hyperparameters
-2022-04-20 results_accuracy_ODTE_Galgo_2022-04-20_10:52:20_0.json           0.04341   6.275 Gridsearched hyperparams v022.1b random_init
-2021-10-27 results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json         0.04158   0.943 default A
-2021-11-01 results_accuracy_STree_macbook-pro_2021-11-01_19:17:07_0.json    0.03790   1.143 default B
-2022-01-14 results_accuracy_RandomForest_iMac27_2022-01-14_12:39:30_0.json  0.03627   0.076 Test default paramters with RandomForest
+ #  Date       File                                                            Score    Time(h) Title
+=== ========== =============================================================== ======== ======= ============================================
+  0 2021-09-30 results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json         0.04544   0.173 With gridsearched hyperparameters
+  1 2022-04-20 results_accuracy_ODTE_Galgo_2022-04-20_10:52:20_0.json           0.04341   6.275 Gridsearched hyperparams v022.1b random_init
+  2 2021-10-27 results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json         0.04158   0.943 default A
+  3 2021-11-01 results_accuracy_STree_macbook-pro_2021-11-01_19:17:07_0.json    0.03790   1.143 default B
+  4 2022-01-14 results_accuracy_RandomForest_iMac27_2022-01-14_12:39:30_0.json  0.03627   0.076 Test default paramters with RandomForest

benchmark/tests/test_files/be_summary_list_score.test

@@ -26,10 +26,10 @@
 * results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json                      *
 *                                                                               *
 *********************************************************************************
-Date       File                                                            Score    Time(h) Title
-========== =============================================================== ======== ======= ============================================
-2021-09-30 results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json         0.04544   0.173 With gridsearched hyperparameters
-2022-04-20 results_accuracy_ODTE_Galgo_2022-04-20_10:52:20_0.json           0.04341   6.275 Gridsearched hyperparams v022.1b random_init
-2021-10-27 results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json         0.04158   0.943 default A
-2021-11-01 results_accuracy_STree_macbook-pro_2021-11-01_19:17:07_0.json    0.03790   1.143 default B
-2022-01-14 results_accuracy_RandomForest_iMac27_2022-01-14_12:39:30_0.json  0.03627   0.076 Test default paramters with RandomForest
+ #  Date       File                                                            Score    Time(h) Title
+=== ========== =============================================================== ======== ======= ============================================
+  0 2021-09-30 results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json         0.04544   0.173 With gridsearched hyperparameters
+  1 2022-04-20 results_accuracy_ODTE_Galgo_2022-04-20_10:52:20_0.json           0.04341   6.275 Gridsearched hyperparams v022.1b random_init
+  2 2021-10-27 results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json         0.04158   0.943 default A
+  3 2021-11-01 results_accuracy_STree_macbook-pro_2021-11-01_19:17:07_0.json    0.03790   1.143 default B
+  4 2022-01-14 results_accuracy_RandomForest_iMac27_2022-01-14_12:39:30_0.json  0.03627   0.076 Test default paramters with RandomForest

benchmark/tests/test_files/be_summary_list_score_all.test

@@ -26,13 +26,13 @@
 * results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json                      *
 *                                                                               *
 *********************************************************************************
-Date       File                                                            Score    Time(h) Title
-========== =============================================================== ======== ======= ============================================
-2021-09-30 results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json         0.04544   0.173 With gridsearched hyperparameters
-2022-04-20 results_accuracy_ODTE_Galgo_2022-04-20_10:52:20_0.json           0.04341   6.275 Gridsearched hyperparams v022.1b random_init
-2021-10-27 results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json         0.04158   0.943 default A
-2021-11-01 results_accuracy_STree_macbook-pro_2021-11-01_19:17:07_0.json    0.03790   1.143 default B
-2022-01-14 results_accuracy_RandomForest_iMac27_2022-01-14_12:39:30_0.json  0.03627   0.076 Test default paramters with RandomForest
+ #  Date       File                                                            Score    Time(h) Title
+=== ========== =============================================================== ======== ======= ============================================
+  0 2021-09-30 results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json         0.04544   0.173 With gridsearched hyperparameters
+  1 2022-04-20 results_accuracy_ODTE_Galgo_2022-04-20_10:52:20_0.json           0.04341   6.275 Gridsearched hyperparams v022.1b random_init
+  2 2021-10-27 results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json         0.04158   0.943 default A
+  3 2021-11-01 results_accuracy_STree_macbook-pro_2021-11-01_19:17:07_0.json    0.03790   1.143 default B
+  4 2022-01-14 results_accuracy_RandomForest_iMac27_2022-01-14_12:39:30_0.json  0.03627   0.076 Test default paramters with RandomForest
 ** No results found **
 ** No results found **
 ** No results found **

benchmark/tests/test_files/be_summary_list_score_model.test

@@ -26,10 +26,10 @@
 * results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json                      *
 *                                                                               *
 *********************************************************************************
-Date       File                                                            Score    Time(h) Title
-========== =============================================================== ======== ======= ============================================
-2021-09-30 results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json         0.04544   0.173 With gridsearched hyperparameters
-2022-04-20 results_accuracy_ODTE_Galgo_2022-04-20_10:52:20_0.json           0.04341   6.275 Gridsearched hyperparams v022.1b random_init
-2021-10-27 results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json         0.04158   0.943 default A
-2021-11-01 results_accuracy_STree_macbook-pro_2021-11-01_19:17:07_0.json    0.03790   1.143 default B
-2022-01-14 results_accuracy_RandomForest_iMac27_2022-01-14_12:39:30_0.json  0.03627   0.076 Test default paramters with RandomForest
+ #  Date       File                                                            Score    Time(h) Title
+=== ========== =============================================================== ======== ======= ============================================
+  0 2021-09-30 results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json         0.04544   0.173 With gridsearched hyperparameters
+  1 2022-04-20 results_accuracy_ODTE_Galgo_2022-04-20_10:52:20_0.json           0.04341   6.275 Gridsearched hyperparams v022.1b random_init
+  2 2021-10-27 results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json         0.04158   0.943 default A
+  3 2021-11-01 results_accuracy_STree_macbook-pro_2021-11-01_19:17:07_0.json    0.03790   1.143 default B
+  4 2022-01-14 results_accuracy_RandomForest_iMac27_2022-01-14_12:39:30_0.json  0.03627   0.076 Test default paramters with RandomForest

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"

benchmark/tests/test_files/excel2.test

@@ -0,0 +1,48 @@
+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"
+3;5;"3,395.01 s"
+3;7;" "
+3;8;"Platform"
+3;9;"iMac27"
+3;10;"Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]"
+4;5;"  0.94 h"
+4;10;"Stratified: False"
+6;1;"Dataset"
+6;2;"Samples"
+6;3;"Features"
+6;4;"Classes"
+6;5;"Nodes"
+6;6;"Leaves"
+6;7;"Depth"
+6;8;"Score"
+6;9;"Score Std."
+6;10;"Time"
+6;11;"Time Std."
+6;12;"Hyperparameters"
+7;1;"balance-scale"
+7;2;"625"
+7;3;"4"
+7;4;"3"
+7;5;"11.08"
+7;6;"5.9"
+7;7;"5.9"
+7;8;"0.98"
+7;9;"0.001"
+7;10;"0.2852065515518188"
+7;11;"0.06031593282605064"
+7;12;"{'splitter': 'best', 'max_features': 'auto'}"
+8;1;"balloons"
+8;2;"16"
+8;3;"4"
+8;4;"2"
+8;5;"4.12"
+8;6;"2.56"
+8;7;"2.56"
+8;8;"0.695"
+8;9;"0.2756860130252853"
+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"

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"

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"

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"

benchmark/tests/test_files/exreport_excel_Datasets.test

@@ -0,0 +1,25 @@
+1;1;"Datasets used in benchmark ver. 0.2.0"
+2;1;" Default score accuracy"
+2;2;"Cross validation"
+2;5;"5 Folds"
+3;2;"Stratified"
+3;5;"False"
+4;2;"Discretized"
+4;5;"False"
+5;2;"Seeds"
+5;5;"[57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]"
+6;1;"Dataset"
+6;2;"Samples"
+6;3;"Features"
+6;4;"Classes"
+6;5;"Balance"
+7;1;"balance-scale"
+7;2;"625"
+7;3;"4"
+7;4;"3"
+7;5;" 7.84%/ 46.08%/ 46.08%"
+8;1;"balloons"
+8;2;"16"
+8;3;"4"
+8;4;"2"
+8;5;"56.25%/ 43.75%"

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"

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"

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"

benchmark/tests/test_files/report.test

@@ -1,15 +1,15 @@
-************************************************************************************************************************
-* Report STree ver. 1.2.3 with 5 Folds cross validation and 10 random seeds. 2021-09-30 11:42:07                       *
-* With gridsearched hyperparameters                                                                                    *
-* Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False                                             *
-* Execution took  624.25 seconds,   0.17 hours, on iMac27                                                              *
-* Score is accuracy                                                                                                    *
-************************************************************************************************************************
+*************************************************************************************************************************
+* STree ver. 1.2.3 Python ver. 3.11x with 5 Folds cross validation and 10 random seeds. 2021-09-30 11:42:07             *
+* With gridsearched hyperparameters                                                                                     *
+* Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False                                              *
+* Execution took  624.25 seconds,   0.17 hours, on iMac27                                                               *
+* Score is accuracy                                                                                                     *
+*************************************************************************************************************************
 
-Dataset                        Sampl. Feat. Cls Nodes   Leaves  Depth   Score           Time             Hyperparameters 
-============================== ====== ===== === ======= ======= ======= =============== ================ =============== 
-balance-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'} 
-balloons                           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'} 
-************************************************************************************************************************
-* accuracy compared to STree_default (liblinear-ovr) .:  0.0454                                                        *
-************************************************************************************************************************
+Dataset                        Sampl. Feat. Cls Nodes   Leaves  Depth   Score           Time              Hyperparameters 
+============================== ====== ===== === ======= ======= ======= =============== ================= =============== 
+balance-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'} 
+balloons                           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'} 
+*************************************************************************************************************************
+* accuracy compared to STree_default (liblinear-ovr) .:  0.0454                                                         *
+*************************************************************************************************************************

benchmark/tests/test_files/report_compared.test

@@ -1,16 +1,16 @@
-************************************************************************************************************************
-* Report STree ver. 1.2.3 with 5 Folds cross validation and 10 random seeds. 2021-09-30 11:42:07                       *
-* With gridsearched hyperparameters                                                                                    *
-* Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False                                             *
-* Execution took  624.25 seconds,   0.17 hours, on iMac27                                                              *
-* Score is accuracy                                                                                                    *
-************************************************************************************************************************
+*************************************************************************************************************************
+* STree ver. 1.2.3 Python ver. 3.11x with 5 Folds cross validation and 10 random seeds. 2021-09-30 11:42:07             *
+* With gridsearched hyperparameters                                                                                     *
+* Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False                                              *
+* Execution took  624.25 seconds,   0.17 hours, on iMac27                                                               *
+* Score is accuracy                                                                                                     *
+*************************************************************************************************************************
 
-Dataset                        Sampl. Feat. Cls Nodes   Leaves  Depth   Score           Time             Hyperparameters 
-============================== ====== ===== === ======= ======= ======= =============== ================ =============== 
-balance-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'} 
-balloons                           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'} 
-************************************************************************************************************************
-* ✔ Equal to best .....:  1                                                                                            *
-* accuracy compared to STree_default (liblinear-ovr) .:  0.0454                                                        *
-************************************************************************************************************************
+Dataset                        Sampl. Feat. Cls Nodes   Leaves  Depth   Score           Time              Hyperparameters 
+============================== ====== ===== === ======= ======= ======= =============== ================= =============== 
+balance-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'} 
+balloons                           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'} 
+*************************************************************************************************************************
+* ✔ Equal to best .....:  1                                                                                             *
+* accuracy compared to STree_default (liblinear-ovr) .:  0.0454                                                         *
+*************************************************************************************************************************

benchmark/tests/test_files/report_datasets.test

@@ -1,4 +1,6 @@
-Dataset                        Sampl. Feat. Cls Balance
-============================== ===== ====== === ========================================
+Datasets used in benchmark ver. 0.2.0
+
+Dataset                        Sampl. Feat. Cls Balance
+============================== ====== ===== === ============================================================
 balance-scale                     625     4   3  7.84%/ 46.08%/ 46.08%                  
 balloons                           16     4   2 56.25%/ 43.75%                          

benchmark/tests/test_files/summary_list_hidden.test

@@ -1,4 +1,4 @@
-Date       File                                                             Score    Time(h) Title
-========== ================================================================ ======== ======= =======================
-2022-05-04 results_accuracy_XGBoost_MacBookpro16_2022-05-04_11:00:35_0.json      nan   3.091 Default hyperparameters
-2021-11-01 results_accuracy_STree_iMac27_2021-11-01_23:55:16_0.json          0.97446   0.098 default
+ #  Date       File                                                             Score    Time(h) Title
+=== ========== ================================================================ ======== ======= =======================
+  0 2022-05-04 results_accuracy_XGBoost_MacBookpro16_2022-05-04_11:00:35_0.json      nan   3.091 Default hyperparameters
+  1 2021-11-01 results_accuracy_STree_iMac27_2021-11-01_23:55:16_0.json          0.97446   0.098 default

benchmark/tests/test_files/summary_list_model.test

@@ -1,5 +1,5 @@
-Date       File                                                          Score    Time(h) Title
-========== ============================================================= ======== ======= =================================
-2021-11-01 results_accuracy_STree_macbook-pro_2021-11-01_19:17:07_0.json  0.03790   1.143 default B
-2021-10-27 results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json       0.04158   0.943 default A
-2021-09-30 results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json       0.04544   0.173 With gridsearched hyperparameters
+ #  Date       File                                                          Score    Time(h) Title
+=== ========== ============================================================= ======== ======= =================================
+  0 2021-11-01 results_accuracy_STree_macbook-pro_2021-11-01_19:17:07_0.json  0.03790   1.143 default B
+  1 2021-10-27 results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json       0.04158   0.943 default A
+  2 2021-09-30 results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json       0.04544   0.173 With gridsearched hyperparameters

benchmark/tests/test_files/summary_list_n.test

@@ -1,5 +1,5 @@
-Date       File                                                            Score    Time(h) Title
-========== =============================================================== ======== ======= ============================================
-2022-04-20 results_accuracy_ODTE_Galgo_2022-04-20_10:52:20_0.json           0.04341   6.275 Gridsearched hyperparams v022.1b random_init
-2022-01-14 results_accuracy_RandomForest_iMac27_2022-01-14_12:39:30_0.json  0.03627   0.076 Test default paramters with RandomForest
-2021-11-01 results_accuracy_STree_macbook-pro_2021-11-01_19:17:07_0.json    0.03790   1.143 default B
+ #  Date       File                                                            Score    Time(h) Title
+=== ========== =============================================================== ======== ======= ============================================
+  0 2022-04-20 results_accuracy_ODTE_Galgo_2022-04-20_10:52:20_0.json           0.04341   6.275 Gridsearched hyperparams v022.1b random_init
+  1 2022-01-14 results_accuracy_RandomForest_iMac27_2022-01-14_12:39:30_0.json  0.03627   0.076 Test default paramters with RandomForest
+  2 2021-11-01 results_accuracy_STree_macbook-pro_2021-11-01_19:17:07_0.json    0.03790   1.143 default B

benchmark/tests/test_files/summary_list_score.test

@@ -1,7 +1,7 @@
-Date       File                                                            Score    Time(h) Title
-========== =============================================================== ======== ======= ============================================
-2022-04-20 results_accuracy_ODTE_Galgo_2022-04-20_10:52:20_0.json           0.04341   6.275 Gridsearched hyperparams v022.1b random_init
-2022-01-14 results_accuracy_RandomForest_iMac27_2022-01-14_12:39:30_0.json  0.03627   0.076 Test default paramters with RandomForest
-2021-11-01 results_accuracy_STree_macbook-pro_2021-11-01_19:17:07_0.json    0.03790   1.143 default B
-2021-10-27 results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json         0.04158   0.943 default A
-2021-09-30 results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json         0.04544   0.173 With gridsearched hyperparameters
+ #  Date       File                                                            Score    Time(h) Title
+=== ========== =============================================================== ======== ======= ============================================
+  0 2022-04-20 results_accuracy_ODTE_Galgo_2022-04-20_10:52:20_0.json           0.04341   6.275 Gridsearched hyperparams v022.1b random_init
+  1 2022-01-14 results_accuracy_RandomForest_iMac27_2022-01-14_12:39:30_0.json  0.03627   0.076 Test default paramters with RandomForest
+  2 2021-11-01 results_accuracy_STree_macbook-pro_2021-11-01_19:17:07_0.json    0.03790   1.143 default B
+  3 2021-10-27 results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json         0.04158   0.943 default A
+  4 2021-09-30 results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json         0.04544   0.173 With gridsearched hyperparameters

benchmark/tests/test_files/summary_show_top.test

@@ -1,7 +1,7 @@
-Date       File                                                            Score    Time(h) Title
-========== =============================================================== ======== ======= ============================================
-2021-09-30 results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json         0.04544   0.173 With gridsearched hyperparameters
-2022-04-20 results_accuracy_ODTE_Galgo_2022-04-20_10:52:20_0.json           0.04341   6.275 Gridsearched hyperparams v022.1b random_init
-2021-10-27 results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json         0.04158   0.943 default A
-2021-11-01 results_accuracy_STree_macbook-pro_2021-11-01_19:17:07_0.json    0.03790   1.143 default B
-2022-01-14 results_accuracy_RandomForest_iMac27_2022-01-14_12:39:30_0.json  0.03627   0.076 Test default paramters with RandomForest
+ #  Date       File                                                            Score    Time(h) Title
+=== ========== =============================================================== ======== ======= ============================================
+  0 2021-09-30 results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json         0.04544   0.173 With gridsearched hyperparameters
+  1 2022-04-20 results_accuracy_ODTE_Galgo_2022-04-20_10:52:20_0.json           0.04341   6.275 Gridsearched hyperparams v022.1b random_init
+  2 2021-10-27 results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json         0.04158   0.943 default A
+  3 2021-11-01 results_accuracy_STree_macbook-pro_2021-11-01_19:17:07_0.json    0.03790   1.143 default B
+  4 2022-01-14 results_accuracy_RandomForest_iMac27_2022-01-14_12:39:30_0.json  0.03627   0.076 Test default paramters with RandomForest

requirements.txt

@@ -2,7 +2,10 @@ pandas
 scikit-learn
 scipy
 odte
+cython
+mdlp-discretization
 mufs
+bayesclass @ git+ssh://git@github.com/doctorado-ml/bayesclass.git
 xlsxwriter
 openpyxl
 tqdm

setup.py

@@ -49,15 +49,14 @@ setuptools.setup(
     name="benchmark",
     version=get_data("version", "_version.py"),
     license=get_data("license"),
-    description="Oblique decision tree with svm nodes",
+    description="Benchmark of models with different datasets",
     long_description=readme(),
     long_description_content_type="text/markdown",
     packages=setuptools.find_packages(),
     url="https://github.com/Doctorado-ML/benchmark",
     author=get_data("author"),
     author_email=get_data("author_email"),
-    keywords="scikit-learn oblique-classifier oblique-decision-tree decision-\
-    tree svm svc",
+    keywords="scikit-learn benchmark",
     classifiers=[
         "Development Status :: 4 - Beta",
         "License :: OSI Approved :: " + get_data("license"),

weka_test.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",
+      "37: label index=0.0, class distribution=[0.99688403 0.00188598 0.00122999]\n",
+      "38: label index=0.0, class distribution=[0.99487322 0.00310305 0.00202373]\n",
+      "39: label index=0.0, class distribution=[0.99487322 0.00310305 0.00202373]\n",
+      "40: label index=0.0, class distribution=[0.99688403 0.00188598 0.00122999]\n",
+      "41: label index=0.0, class distribution=[0.99688403 0.00188598 0.00122999]\n",
+      "42: label index=0.0, class distribution=[0.96326708 0.02223308 0.01449983]\n",
+      "43: label index=0.0, class distribution=[0.99487322 0.00310305 0.00202373]\n",
+      "44: label index=0.0, class distribution=[0.99688403 0.00188598 0.00122999]\n",
+      "45: label index=0.0, class distribution=[0.99688403 0.00188598 0.00122999]\n",
+      "46: label index=0.0, class distribution=[0.99487322 0.00310305 0.00202373]\n",
+      "47: label index=0.0, class distribution=[0.99688403 0.00188598 0.00122999]\n",
+      "48: label index=0.0, class distribution=[0.99487322 0.00310305 0.00202373]\n",
+      "49: label index=0.0, class distribution=[0.99688403 0.00188598 0.00122999]\n",
+      "50: label index=0.0, class distribution=[0.99487322 0.00310305 0.00202373]\n",
+      "51: label index=1.0, class distribution=[0.00545355 0.97466198 0.01988447]\n",
+      "52: label index=1.0, class distribution=[0.00545355 0.97466198 0.01988447]\n",
+      "53: label index=1.0, class distribution=[0.010867   0.52425197 0.46488102]\n",
+      "54: label index=1.0, class distribution=[0.00725727 0.94287877 0.04986396]\n",
+      "55: label index=1.0, class distribution=[0.00228744 0.97269152 0.02502104]\n",
+      "56: label index=1.0, class distribution=[0.00308382 0.98338244 0.01353374]\n",
+      "57: label index=1.0, class distribution=[0.00545355 0.97466198 0.01988447]\n",
+      "58: label index=1.0, class distribution=[0.00725727 0.94287877 0.04986396]\n",
+      "59: label index=1.0, class distribution=[0.00228744 0.97269152 0.02502104]\n",
+      "60: label index=1.0, class distribution=[0.00725727 0.94287877 0.04986396]\n",
+      "61: label index=1.0, class distribution=[0.00725727 0.94287877 0.04986396]\n",
+      "62: label index=1.0, class distribution=[0.00732671 0.98195521 0.01071808]\n",
+      "63: label index=1.0, class distribution=[0.00308382 0.98338244 0.01353374]\n",
+      "64: label index=1.0, class distribution=[0.00308382 0.98338244 0.01353374]\n",
+      "65: label index=1.0, class distribution=[0.00308382 0.98338244 0.01353374]\n",
+      "66: label index=1.0, class distribution=[0.00545355 0.97466198 0.01988447]\n",
+      "67: label index=1.0, class distribution=[0.00732671 0.98195521 0.01071808]\n",
+      "68: label index=1.0, class distribution=[0.00308382 0.98338244 0.01353374]\n",
+      "69: label index=1.0, class distribution=[0.00228744 0.97269152 0.02502104]\n",
+      "70: label index=1.0, class distribution=[0.00308382 0.98338244 0.01353374]\n",
+      "71: label index=2.0, class distribution=[0.00920087 0.06127297 0.92952615]\n",
+      "72: label index=1.0, class distribution=[0.00308382 0.98338244 0.01353374]\n",
+      "73: label index=2.0, class distribution=[0.00409632 0.47019227 0.5257114 ]\n",
+      "74: label index=1.0, class distribution=[0.00308382 0.98338244 0.01353374]\n",
+      "75: label index=1.0, class distribution=[0.00228744 0.97269152 0.02502104]\n",
+      "76: label index=1.0, class distribution=[0.00545355 0.97466198 0.01988447]\n",
+      "77: label index=2.0, class distribution=[0.00409632 0.47019227 0.5257114 ]\n",
+      "78: label index=1.0, class distribution=[0.010867   0.52425197 0.46488102]\n",
+      "79: label index=1.0, class distribution=[0.00308382 0.98338244 0.01353374]\n",
+      "80: label index=1.0, class distribution=[0.00308382 0.98338244 0.01353374]\n",
+      "81: label index=1.0, class distribution=[0.00725727 0.94287877 0.04986396]\n",
+      "82: label index=1.0, class distribution=[0.00725727 0.94287877 0.04986396]\n",
+      "83: label index=1.0, class distribution=[0.00308382 0.98338244 0.01353374]\n",
+      "84: label index=1.0, class distribution=[0.02353491 0.65433551 0.32212958]\n",
+      "85: label index=1.0, class distribution=[0.01727259 0.943168   0.03955941]\n",
+      "86: label index=1.0, class distribution=[0.06513736 0.90310001 0.03176263]\n",
+      "87: label index=1.0, class distribution=[0.00545355 0.97466198 0.01988447]\n",
+      "88: label index=1.0, class distribution=[0.00228744 0.97269152 0.02502104]\n",
+      "89: label index=1.0, class distribution=[0.00732671 0.98195521 0.01071808]\n",
+      "90: label index=1.0, class distribution=[0.00725727 0.94287877 0.04986396]\n",
+      "91: label index=1.0, class distribution=[0.00725727 0.94287877 0.04986396]\n",
+      "92: label index=1.0, class distribution=[0.00732671 0.98195521 0.01071808]\n",
+      "93: label index=1.0, class distribution=[0.00308382 0.98338244 0.01353374]\n",
+      "94: label index=1.0, class distribution=[0.00725727 0.94287877 0.04986396]\n",
+      "95: label index=1.0, class distribution=[0.00308382 0.98338244 0.01353374]\n",
+      "96: label index=1.0, class distribution=[0.00732671 0.98195521 0.01071808]\n",
+      "97: label index=1.0, class distribution=[0.00308382 0.98338244 0.01353374]\n",
+      "98: label index=1.0, class distribution=[0.00228744 0.97269152 0.02502104]\n",
+      "99: label index=1.0, class distribution=[0.00725727 0.94287877 0.04986396]\n",
+      "100: label index=1.0, class distribution=[0.00308382 0.98338244 0.01353374]\n",
+      "101: label index=2.0, class distribution=[0.00102485 0.02817698 0.97079816]\n",
+      "102: label index=2.0, class distribution=[0.01274667 0.02829538 0.95895795]\n",
+      "103: label index=2.0, class distribution=[0.00102485 0.02817698 0.97079816]\n",
+      "104: label index=2.0, class distribution=[0.00139749 0.01280739 0.98579512]\n",
+      "105: label index=2.0, class distribution=[0.00102485 0.02817698 0.97079816]\n",
+      "106: label index=2.0, class distribution=[0.00102485 0.02817698 0.97079816]\n",
+      "107: label index=1.0, class distribution=[0.00725727 0.94287877 0.04986396]\n",
+      "108: label index=2.0, class distribution=[0.00139749 0.01280739 0.98579512]\n",
+      "109: label index=2.0, class distribution=[0.00139749 0.01280739 0.98579512]\n",
+      "110: label index=2.0, class distribution=[0.00431289 0.0395258  0.95616131]\n",
+      "111: label index=2.0, class distribution=[0.00102485 0.02817698 0.97079816]\n",
+      "112: label index=2.0, class distribution=[0.00139749 0.01280739 0.98579512]\n",
+      "113: label index=2.0, class distribution=[0.00102485 0.02817698 0.97079816]\n",
+      "114: label index=2.0, class distribution=[0.01274667 0.02829538 0.95895795]\n",
+      "115: label index=2.0, class distribution=[0.01274667 0.02829538 0.95895795]\n",
+      "116: label index=2.0, class distribution=[0.00102485 0.02817698 0.97079816]\n",
+      "117: label index=2.0, class distribution=[0.00102485 0.02817698 0.97079816]\n",
+      "118: label index=2.0, class distribution=[0.00431289 0.0395258  0.95616131]\n",
+      "119: label index=2.0, class distribution=[0.00139749 0.01280739 0.98579512]\n",
+      "120: label index=1.0, class distribution=[0.02353491 0.65433551 0.32212958]\n",
+      "121: label index=2.0, class distribution=[0.00102485 0.02817698 0.97079816]\n",
+      "122: label index=2.0, class distribution=[0.01274667 0.02829538 0.95895795]\n",
+      "123: label index=2.0, class distribution=[0.00139749 0.01280739 0.98579512]\n",
+      "124: label index=2.0, class distribution=[0.00139749 0.01280739 0.98579512]\n",
+      "125: label index=2.0, class distribution=[0.00102485 0.02817698 0.97079816]\n",
+      "126: label index=2.0, class distribution=[0.00102485 0.02817698 0.97079816]\n",
+      "127: label index=2.0, class distribution=[0.00139749 0.01280739 0.98579512]\n",
+      "128: label index=2.0, class distribution=[0.00920087 0.06127297 0.92952615]\n",
+      "129: label index=2.0, class distribution=[0.00139749 0.01280739 0.98579512]\n",
+      "130: label index=1.0, class distribution=[0.010867   0.52425197 0.46488102]\n",
+      "131: label index=2.0, class distribution=[0.00139749 0.01280739 0.98579512]\n",
+      "132: label index=2.0, class distribution=[0.00431289 0.0395258  0.95616131]\n",
+      "133: label index=2.0, class distribution=[0.00139749 0.01280739 0.98579512]\n",
+      "134: label index=2.0, class distribution=[0.00409632 0.47019227 0.5257114 ]\n",
+      "135: label index=1.0, class distribution=[0.02353491 0.65433551 0.32212958]\n",
+      "136: label index=2.0, class distribution=[0.00102485 0.02817698 0.97079816]\n",
+      "137: label index=2.0, class distribution=[0.00431289 0.0395258  0.95616131]\n",
+      "138: label index=2.0, class distribution=[0.00102485 0.02817698 0.97079816]\n",
+      "139: label index=2.0, class distribution=[0.00920087 0.06127297 0.92952615]\n",
+      "140: label index=2.0, class distribution=[0.00102485 0.02817698 0.97079816]\n",
+      "141: label index=2.0, class distribution=[0.00102485 0.02817698 0.97079816]\n",
+      "142: label index=2.0, class distribution=[0.00102485 0.02817698 0.97079816]\n",
+      "143: label index=2.0, class distribution=[0.01274667 0.02829538 0.95895795]\n",
+      "144: label index=2.0, class distribution=[0.00102485 0.02817698 0.97079816]\n",
+      "145: label index=2.0, class distribution=[0.00102485 0.02817698 0.97079816]\n",
+      "146: label index=2.0, class distribution=[0.00102485 0.02817698 0.97079816]\n",
+      "147: label index=2.0, class distribution=[0.00139749 0.01280739 0.98579512]\n",
+      "148: label index=2.0, class distribution=[0.00102485 0.02817698 0.97079816]\n",
+      "149: label index=2.0, class distribution=[0.00431289 0.0395258  0.95616131]\n",
+      "150: label index=2.0, class distribution=[0.00920087 0.06127297 0.92952615]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from weka.classifiers import Classifier\n",
+    "cls = Classifier(classname=\"weka.classifiers.bayes.BayesNet\", options=[\"-Q\", \"weka.classifiers.bayes.net.search.local.TAN\"])\n",
+    "cls.build_classifier(data)\n",
+    "\n",
+    "for index, inst in enumerate(data):\n",
+    "    pred = cls.classify_instance(inst)\n",
+    "    dist = cls.distribution_for_instance(inst)\n",
+    "    print(str(index+1) + \": label index=\" + str(pred) + \", class distribution=\" + str(dist))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "0b74f00a-15b3-4177-bb8c-e02ed1a3fd38",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Bayes Network Classifier\n",
+      "Using ADTree\n",
+      "#attributes=5 #classindex=4\n",
+      "Network structure (nodes followed by parents)\n",
+      "sepallength(3): class \n",
+      "sepalwidth(3): class petalwidth \n",
+      "petallength(3): class sepallength \n",
+      "petalwidth(3): class petallength \n",
+      "class(3): \n",
+      "LogScore Bayes: -484.0749140715054\n",
+      "LogScore BDeu: -653.8524681760015\n",
+      "LogScore MDL: -654.6252712234647\n",
+      "LogScore ENTROPY: -499.2955771064808\n",
+      "LogScore AIC: -561.2955771064808\n",
+      "\n"
+     ]
+    },
+    {
+     "ename": "OSError",
+     "evalue": "[Errno 63] File name too long: '<?xml version=\"1.0\"?>\\n<!-- DTD for the XMLBIF 0.3 format -->\\n<!DOCTYPE BIF [\\n\\t<!ELEMENT BIF ( NETWORK )*>\\n\\t      <!ATTLIST BIF VERSION CDATA #REQUIRED>\\n\\t<!ELEMENT NETWORK ( NAME, ( PROPERTY | VARIABLE | DEFINITION )* )>\\n\\t<!ELEMENT NAME (#PCDATA)>\\n\\t<!ELEMENT VARIABLE ( NAME, ( OUTCOME |  PROPERTY )* ) >\\n\\t      <!ATTLIST VARIABLE TYPE (nature|decision|utility) \"nature\">\\n\\t<!ELEMENT OUTCOME (#PCDATA)>\\n\\t<!ELEMENT DEFINITION ( FOR | GIVEN | TABLE | PROPERTY )* >\\n\\t<!ELEMENT FOR (#PCDATA)>\\n\\t<!ELEMENT GIVEN (#PCDATA)>\\n\\t<!ELEMENT TABLE (#PCDATA)>\\n\\t<!ELEMENT PROPERTY (#PCDATA)>\\n]>\\n\\n\\n<BIF VERSION=\"0.3\">\\n<NETWORK>\\n<NAME>iris-weka.filters.supervised.attribute.Discretize-Rfirst-last-precision6-weka.filters.unsupervised.attribute.ReplaceMissingValues</NAME>\\n<VARIABLE TYPE=\"nature\">\\n<NAME>sepallength</NAME>\\n<OUTCOME>&apos;\\\\&apos;(-inf-5.55]\\\\&apos;&apos;</OUTCOME>\\n<OUTCOME>&apos;\\\\&apos;(5.55-6.15]\\\\&apos;&apos;</OUTCOME>\\n<OUTCOME>&apos;\\\\&apos;(6.15-inf)\\\\&apos;&apos;</OUTCOME>\\n</VARIABLE>\\n<VARIABLE TYPE=\"nature\">\\n<NAME>sepalwidth</NAME>\\n<OUTCOME>&apos;\\\\&apos;(-inf-2.95]\\\\&apos;&apos;</OUTCOME>\\n<OUTCOME>&apos;\\\\&apos;(2.95-3.35]\\\\&apos;&apos;</OUTCOME>\\n<OUTCOME>&apos;\\\\&apos;(3.35-inf)\\\\&apos;&apos;</OUTCOME>\\n</VARIABLE>\\n<VARIABLE TYPE=\"nature\">\\n<NAME>petallength</NAME>\\n<OUTCOME>&apos;\\\\&apos;(-inf-2.45]\\\\&apos;&apos;</OUTCOME>\\n<OUTCOME>&apos;\\\\&apos;(2.45-4.75]\\\\&apos;&apos;</OUTCOME>\\n<OUTCOME>&apos;\\\\&apos;(4.75-inf)\\\\&apos;&apos;</OUTCOME>\\n</VARIABLE>\\n<VARIABLE TYPE=\"nature\">\\n<NAME>petalwidth</NAME>\\n<OUTCOME>&apos;\\\\&apos;(-inf-0.8]\\\\&apos;&apos;</OUTCOME>\\n<OUTCOME>&apos;\\\\&apos;(0.8-1.75]\\\\&apos;&apos;</OUTCOME>\\n<OUTCOME>&apos;\\\\&apos;(1.75-inf)\\\\&apos;&apos;</OUTCOME>\\n</VARIABLE>\\n<VARIABLE TYPE=\"nature\">\\n<NAME>class</NAME>\\n<OUTCOME>Iris-setosa</OUTCOME>\\n<OUTCOME>Iris-versicolor</OUTCOME>\\n<OUTCOME>Iris-virginica</OUTCOME>\\n</VARIABLE>\\n<DEFINITION>\\n<FOR>sepallength</FOR>\\n<GIVEN>class</GIVEN>\\n<TABLE>\\n0.9223300970873787 0.06796116504854369 0.009708737864077669 \\n0.22330097087378642 0.4563106796116505 0.32038834951456313 \\n0.02912621359223301 0.20388349514563106 0.7669902912621359 \\n</TABLE>\\n</DEFINITION>\\n<DEFINITION>\\n<FOR>sepalwidth</FOR>\\n<GIVEN>class</GIVEN>\\n<GIVEN>petalwidth</GIVEN>\\n<TABLE>\\n0.04854368932038835 0.3592233009708738 0.5922330097087378 \\n0.3333333333333333 0.3333333333333333 0.3333333333333333 \\n0.3333333333333333 0.3333333333333333 0.3333333333333333 \\n0.3333333333333333 0.3333333333333333 0.3333333333333333 \\n0.6831683168316832 0.2871287128712871 0.0297029702970297 \\n0.2 0.6 0.2 \\n0.3333333333333333 0.3333333333333333 0.3333333333333333 \\n0.6923076923076923 0.23076923076923078 0.07692307692307693 \\n0.3763440860215054 0.5053763440860215 0.11827956989247312 \\n</TABLE>\\n</DEFINITION>\\n<DEFINITION>\\n<FOR>petallength</FOR>\\n<GIVEN>class</GIVEN>\\n<GIVEN>sepallength</GIVEN>\\n<TABLE>\\n0.979381443298969 0.010309278350515464 0.010309278350515464 \\n0.7777777777777778 0.1111111111111111 0.1111111111111111 \\n0.3333333333333333 0.3333333333333333 0.3333333333333333 \\n0.04 0.92 0.04 \\n0.02040816326530612 0.8775510204081632 0.10204081632653061 \\n0.02857142857142857 0.7142857142857143 0.2571428571428571 \\n0.2 0.6 0.2 \\n0.043478260869565216 0.043478260869565216 0.9130434782608695 \\n0.012345679012345678 0.012345679012345678 0.9753086419753086 \\n</TABLE>\\n</DEFINITION>\\n<DEFINITION>\\n<FOR>petalwidth</FOR>\\n<GIVEN>class</GIVEN>\\n<GIVEN>petallength</GIVEN>\\n<TABLE>\\n0.9805825242718447 0.009708737864077669 0.009708737864077669 \\n0.3333333333333333 0.3333333333333333 0.3333333333333333 \\n0.3333333333333333 0.3333333333333333 0.3333333333333333 \\n0.3333333333333333 0.3333333333333333 0.3333333333333333 \\n0.01098901098901099 0.978021978021978 0.01098901098901099 \\n0.06666666666666667 0.7333333333333333 0.2 \\n0.3333333333333333 0.3333333333333333 0.3333333333333333 \\n0.2 0.6 0.2 \\n0.009900990099009901 0.0891089108910891 0.900990099009901 \\n</TABLE>\\n</DEFINITION>\\n<DEFINITION>\\n<FOR>class</FOR>\\n<TABLE>\\n0.3333333333333333 0.3333333333333333 0.3333333333333333 \\n</TABLE>\\n</DEFINITION>\\n</NETWORK>\\n</BIF>\\n'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn [13], line 9\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;28mcls\u001b[39m)\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mweka\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mplot\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgraph\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mgraph\u001b[39;00m  \u001b[38;5;66;03m# NB: pygraphviz and PIL are required\u001b[39;00m\n\u001b[0;32m----> 9\u001b[0m \u001b[43mgraph\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot_dot_graph\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgraph\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniconda3/envs/pyweka/lib/python3.10/site-packages/weka/plot/graph.py:49\u001b[0m, in \u001b[0;36mplot_dot_graph\u001b[0;34m(graph, filename)\u001b[0m\n\u001b[1;32m     46\u001b[0m     logger\u001b[38;5;241m.\u001b[39merror(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPIL is not installed, cannot display graph plot!\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     47\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[0;32m---> 49\u001b[0m agraph \u001b[38;5;241m=\u001b[39m \u001b[43mAGraph\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgraph\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     50\u001b[0m agraph\u001b[38;5;241m.\u001b[39mlayout(prog\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdot\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     51\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m filename \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+      "File \u001b[0;32m~/miniconda3/envs/pyweka/lib/python3.10/site-packages/pygraphviz/agraph.py:157\u001b[0m, in \u001b[0;36mAGraph.__init__\u001b[0;34m(self, thing, filename, data, string, handle, name, strict, directed, **attr)\u001b[0m\n\u001b[1;32m    154\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_owns_handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m    155\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m filename \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    156\u001b[0m     \u001b[38;5;66;03m# load new graph from file (creates self.handle)\u001b[39;00m\n\u001b[0;32m--> 157\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    158\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m string \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    159\u001b[0m     \u001b[38;5;66;03m# load new graph from string (creates self.handle)\u001b[39;00m\n\u001b[1;32m    160\u001b[0m     \u001b[38;5;66;03m# get the charset from the string to properly encode it for\u001b[39;00m\n\u001b[1;32m    161\u001b[0m     \u001b[38;5;66;03m# writing to the temporary file in from_string()\u001b[39;00m\n\u001b[1;32m    162\u001b[0m     match \u001b[38;5;241m=\u001b[39m re\u001b[38;5;241m.\u001b[39msearch(\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcharset\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124ms*=\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124ms*\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m([^\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m]+)\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m, string)\n",
+      "File \u001b[0;32m~/miniconda3/envs/pyweka/lib/python3.10/site-packages/pygraphviz/agraph.py:1243\u001b[0m, in \u001b[0;36mAGraph.read\u001b[0;34m(self, path)\u001b[0m\n\u001b[1;32m   1233\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mread\u001b[39m(\u001b[38;5;28mself\u001b[39m, path):\n\u001b[1;32m   1234\u001b[0m     \u001b[38;5;124;03m\"\"\"Read graph from dot format file on path.\u001b[39;00m\n\u001b[1;32m   1235\u001b[0m \n\u001b[1;32m   1236\u001b[0m \u001b[38;5;124;03m    path can be a file name or file handle\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1241\u001b[0m \n\u001b[1;32m   1242\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1243\u001b[0m     fh \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_fh\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1244\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1245\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_close_handle()\n",
+      "File \u001b[0;32m~/miniconda3/envs/pyweka/lib/python3.10/site-packages/pygraphviz/agraph.py:1791\u001b[0m, in \u001b[0;36mAGraph._get_fh\u001b[0;34m(self, path, mode)\u001b[0m\n\u001b[1;32m   1789\u001b[0m         fh \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpopen(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbzcat \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m path)  \u001b[38;5;66;03m# probably not portable\u001b[39;00m\n\u001b[1;32m   1790\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1791\u001b[0m         fh \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1792\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(path, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwrite\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m   1793\u001b[0m     \u001b[38;5;66;03m# Note, mode of file handle is unchanged.\u001b[39;00m\n\u001b[1;32m   1794\u001b[0m     fh \u001b[38;5;241m=\u001b[39m path\n",
+      "\u001b[0;31mOSError\u001b[0m: [Errno 63] File name too long: '<?xml version=\"1.0\"?>\\n<!-- DTD for the XMLBIF 0.3 format -->\\n<!DOCTYPE BIF [\\n\\t<!ELEMENT BIF ( NETWORK )*>\\n\\t      <!ATTLIST BIF VERSION CDATA #REQUIRED>\\n\\t<!ELEMENT NETWORK ( NAME, ( PROPERTY | VARIABLE | DEFINITION )* )>\\n\\t<!ELEMENT NAME (#PCDATA)>\\n\\t<!ELEMENT VARIABLE ( NAME, ( OUTCOME |  PROPERTY )* ) >\\n\\t      <!ATTLIST VARIABLE TYPE (nature|decision|utility) \"nature\">\\n\\t<!ELEMENT OUTCOME (#PCDATA)>\\n\\t<!ELEMENT DEFINITION ( FOR | GIVEN | TABLE | PROPERTY )* >\\n\\t<!ELEMENT FOR (#PCDATA)>\\n\\t<!ELEMENT GIVEN (#PCDATA)>\\n\\t<!ELEMENT TABLE (#PCDATA)>\\n\\t<!ELEMENT PROPERTY (#PCDATA)>\\n]>\\n\\n\\n<BIF VERSION=\"0.3\">\\n<NETWORK>\\n<NAME>iris-weka.filters.supervised.attribute.Discretize-Rfirst-last-precision6-weka.filters.unsupervised.attribute.ReplaceMissingValues</NAME>\\n<VARIABLE TYPE=\"nature\">\\n<NAME>sepallength</NAME>\\n<OUTCOME>&apos;\\\\&apos;(-inf-5.55]\\\\&apos;&apos;</OUTCOME>\\n<OUTCOME>&apos;\\\\&apos;(5.55-6.15]\\\\&apos;&apos;</OUTCOME>\\n<OUTCOME>&apos;\\\\&apos;(6.15-inf)\\\\&apos;&apos;</OUTCOME>\\n</VARIABLE>\\n<VARIABLE TYPE=\"nature\">\\n<NAME>sepalwidth</NAME>\\n<OUTCOME>&apos;\\\\&apos;(-inf-2.95]\\\\&apos;&apos;</OUTCOME>\\n<OUTCOME>&apos;\\\\&apos;(2.95-3.35]\\\\&apos;&apos;</OUTCOME>\\n<OUTCOME>&apos;\\\\&apos;(3.35-inf)\\\\&apos;&apos;</OUTCOME>\\n</VARIABLE>\\n<VARIABLE TYPE=\"nature\">\\n<NAME>petallength</NAME>\\n<OUTCOME>&apos;\\\\&apos;(-inf-2.45]\\\\&apos;&apos;</OUTCOME>\\n<OUTCOME>&apos;\\\\&apos;(2.45-4.75]\\\\&apos;&apos;</OUTCOME>\\n<OUTCOME>&apos;\\\\&apos;(4.75-inf)\\\\&apos;&apos;</OUTCOME>\\n</VARIABLE>\\n<VARIABLE TYPE=\"nature\">\\n<NAME>petalwidth</NAME>\\n<OUTCOME>&apos;\\\\&apos;(-inf-0.8]\\\\&apos;&apos;</OUTCOME>\\n<OUTCOME>&apos;\\\\&apos;(0.8-1.75]\\\\&apos;&apos;</OUTCOME>\\n<OUTCOME>&apos;\\\\&apos;(1.75-inf)\\\\&apos;&apos;</OUTCOME>\\n</VARIABLE>\\n<VARIABLE TYPE=\"nature\">\\n<NAME>class</NAME>\\n<OUTCOME>Iris-setosa</OUTCOME>\\n<OUTCOME>Iris-versicolor</OUTCOME>\\n<OUTCOME>Iris-virginica</OUTCOME>\\n</VARIABLE>\\n<DEFINITION>\\n<FOR>sepallength</FOR>\\n<GIVEN>class</GIVEN>\\n<TABLE>\\n0.9223300970873787 0.06796116504854369 0.009708737864077669 \\n0.22330097087378642 0.4563106796116505 0.32038834951456313 \\n0.02912621359223301 0.20388349514563106 0.7669902912621359 \\n</TABLE>\\n</DEFINITION>\\n<DEFINITION>\\n<FOR>sepalwidth</FOR>\\n<GIVEN>class</GIVEN>\\n<GIVEN>petalwidth</GIVEN>\\n<TABLE>\\n0.04854368932038835 0.3592233009708738 0.5922330097087378 \\n0.3333333333333333 0.3333333333333333 0.3333333333333333 \\n0.3333333333333333 0.3333333333333333 0.3333333333333333 \\n0.3333333333333333 0.3333333333333333 0.3333333333333333 \\n0.6831683168316832 0.2871287128712871 0.0297029702970297 \\n0.2 0.6 0.2 \\n0.3333333333333333 0.3333333333333333 0.3333333333333333 \\n0.6923076923076923 0.23076923076923078 0.07692307692307693 \\n0.3763440860215054 0.5053763440860215 0.11827956989247312 \\n</TABLE>\\n</DEFINITION>\\n<DEFINITION>\\n<FOR>petallength</FOR>\\n<GIVEN>class</GIVEN>\\n<GIVEN>sepallength</GIVEN>\\n<TABLE>\\n0.979381443298969 0.010309278350515464 0.010309278350515464 \\n0.7777777777777778 0.1111111111111111 0.1111111111111111 \\n0.3333333333333333 0.3333333333333333 0.3333333333333333 \\n0.04 0.92 0.04 \\n0.02040816326530612 0.8775510204081632 0.10204081632653061 \\n0.02857142857142857 0.7142857142857143 0.2571428571428571 \\n0.2 0.6 0.2 \\n0.043478260869565216 0.043478260869565216 0.9130434782608695 \\n0.012345679012345678 0.012345679012345678 0.9753086419753086 \\n</TABLE>\\n</DEFINITION>\\n<DEFINITION>\\n<FOR>petalwidth</FOR>\\n<GIVEN>class</GIVEN>\\n<GIVEN>petallength</GIVEN>\\n<TABLE>\\n0.9805825242718447 0.009708737864077669 0.009708737864077669 \\n0.3333333333333333 0.3333333333333333 0.3333333333333333 \\n0.3333333333333333 0.3333333333333333 0.3333333333333333 \\n0.3333333333333333 0.3333333333333333 0.3333333333333333 \\n0.01098901098901099 0.978021978021978 0.01098901098901099 \\n0.06666666666666667 0.7333333333333333 0.2 \\n0.3333333333333333 0.3333333333333333 0.3333333333333333 \\n0.2 0.6 0.2 \\n0.009900990099009901 0.0891089108910891 0.900990099009901 \\n</TABLE>\\n</DEFINITION>\\n<DEFINITION>\\n<FOR>class</FOR>\\n<TABLE>\\n0.3333333333333333 0.3333333333333333 0.3333333333333333 \\n</TABLE>\\n</DEFINITION>\\n</NETWORK>\\n</BIF>\\n'"
+     ]
+    }
+   ],
+   "source": [
+    "from weka.classifiers import Classifier\n",
+    "\n",
+    "cls = Classifier(classname=\"weka.classifiers.bayes.BayesNet\", options=[\"-Q\", \"weka.classifiers.bayes.net.search.local.TAN\"])\n",
+    "cls.build_classifier(data)\n",
+    "\n",
+    "print(cls)\n",
+    "\n",
+    "import weka.plot.graph as graph  # NB: pygraphviz and PIL are required\n",
+    "graph.plot_dot_graph(cls.graph)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3f59f200-4f23-4add-86ae-6df1494ede82",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}