Merge pull request #9 from Doctorado-ML/continuous_features

Continuous features

.github/workflows/main.yml

@@ -18,7 +18,7 @@ jobs:
     steps:
       - uses: actions/checkout@v3
       - name: Set up Python ${{ matrix.python }}
-        uses: actions/setup-python@v2
+        uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python }}
       # Make dot command available in the environment
@@ -53,7 +53,7 @@ jobs:
           coverage run -m unittest -v benchmark.tests
           coverage xml
       - name: Upload coverage to Codecov
-        uses: codecov/codecov-action@v1
+        uses: codecov/codecov-action@v3
         with:
           token: ${{ secrets.CODECOV_TOKEN }}
           files: ./coverage.xml

benchmark/Arguments.py

@@ -123,6 +123,15 @@ class Arguments(argparse.ArgumentParser):
                 ("-p", "--hyperparameters"),
                 {"type": str, "required": False, "default": "{}"},
             ],
+            "ignore_nan": [
+                ("--ignore-nan",),
+                {
+                    "default": False,
+                    "action": "store_true",
+                    "required": False,
+                    "help": "Ignore nan results",
+                },
+            ],
             "key": [
                 ("-k", "--key"),
                 {

benchmark/Datasets.py

@@ -2,10 +2,11 @@ import os
 from types import SimpleNamespace
 import pandas as pd
 import numpy as np
+import json
 from scipy.io import arff
 from .Utils import Files
 from .Arguments import EnvData
-from mdlp.discretization import MDLP
+from fimdlp.mdlp import FImdlp
 
 
 class Diterator:
@@ -27,6 +28,12 @@ class DatasetsArff:
     def folder():
         return "datasets"
 
+    @staticmethod
+    def get_range_features(X, c_features):
+        if c_features.strip() == "all":
+            return list(range(X.shape[1]))
+        return json.loads(c_features)
+
     def load(self, name, class_name):
         file_name = os.path.join(self.folder(), self.dataset_names(name))
         data = arff.loadarff(file_name)
@@ -34,7 +41,7 @@ class DatasetsArff:
         df.dropna(axis=0, how="any", inplace=True)
         self.dataset = df
         X = df.drop(class_name, axis=1)
-        self.features = X.columns
+        self.features = X.columns.to_list()
         self.class_name = class_name
         y, _ = pd.factorize(df[class_name])
         X = X.to_numpy()
@@ -50,6 +57,10 @@ class DatasetsTanveer:
     def folder():
         return "data"
 
+    @staticmethod
+    def get_range_features(X, name):
+        return []
+
     def load(self, name, *args):
         file_name = os.path.join(self.folder(), self.dataset_names(name))
         data = pd.read_csv(
@@ -75,6 +86,10 @@ class DatasetsSurcov:
     def folder():
         return "datasets"
 
+    @staticmethod
+    def get_range_features(X, name):
+        return []
+
     def load(self, name, *args):
         file_name = os.path.join(self.folder(), self.dataset_names(name))
         data = pd.read_csv(
@@ -102,16 +117,16 @@ class Datasets:
         )
         self.discretize = envData["discretize"] == "1"
         self.dataset = source_name()
-        self.class_names = []
-        self.data_sets = []
         # initialize self.class_names & self.data_sets
         class_names, sets = self._init_names(dataset_name)
         self.class_names = class_names
         self.data_sets = sets
+        self.states = {}  # states of discretized variables
 
     def _init_names(self, dataset_name):
         file_name = os.path.join(self.dataset.folder(), Files.index)
         default_class = "class"
+        self.continuous_features = {}
         with open(file_name) as f:
             sets = f.read().splitlines()
             class_names = [default_class] * len(sets)
@@ -119,10 +134,14 @@ class Datasets:
             result = []
             class_names = []
             for data in sets:
-                name, class_name = data.split(",")
+                name, class_name, features = data.split(",", 2)
                 result.append(name)
                 class_names.append(class_name)
+                self.continuous_features[name] = features
             sets = result
+        else:
+            for name in sets:
+                self.continuous_features[name] = None
         # Set as dataset list the dataset passed as argument
         if dataset_name is None:
             return class_names, sets
@@ -137,6 +156,7 @@ class Datasets:
         self.discretize = False
         X, y = self.load(name)
         attr = SimpleNamespace()
+        attr.dataset = name
         values, counts = np.unique(y, return_counts=True)
         comp = ""
         sep = ""
@@ -147,24 +167,41 @@ class Datasets:
         attr.classes = len(np.unique(y))
         attr.samples = X.shape[0]
         attr.features = X.shape[1]
+        attr.cont_features = len(self.get_continuous_features())
         self.discretize = tmp
         return attr
 
     def get_features(self):
         return self.dataset.features
 
+    def get_states(self, name):
+        return self.states[name] if name in self.states else None
+
+    def get_continuous_features(self):
+        return self.continuous_features_dataset
+
     def get_class_name(self):
         return self.dataset.class_name
 
     def get_dataset(self):
         return self.dataset.dataset
 
+    def build_states(self, name, X):
+        features = self.get_features()
+        self.states[name] = {
+            features[i]: np.unique(X[:, i]).tolist() for i in range(X.shape[1])
+        }
+
     def load(self, name, dataframe=False):
         try:
             class_name = self.class_names[self.data_sets.index(name)]
             X, y = self.dataset.load(name, class_name)
+            self.continuous_features_dataset = self.dataset.get_range_features(
+                X, self.continuous_features[name]
+            )
             if self.discretize:
                 X = self.discretize_dataset(X, y)
+                self.build_states(name, X)
                 dataset = pd.DataFrame(X, columns=self.get_features())
                 dataset[self.get_class_name()] = y
                 self.dataset.dataset = dataset
@@ -188,9 +225,8 @@ class Datasets:
         -------
         tuple (X, y) of numpy.ndarray
         """
-        discretiz = MDLP(random_state=17, dtype=np.int32)
+        discretiz = FImdlp(algorithm=0)
-        Xdisc = discretiz.fit_transform(X, y)
+        return discretiz.fit_transform(X, y)
-        return Xdisc
 
     def __iter__(self) -> Diterator:
         return Diterator(self.data_sets)

benchmark/Experiments.py

@@ -112,6 +112,7 @@ class Experiment:
         platform,
         title,
         progress_bar=True,
+        ignore_nan=True,
         folds=5,
     ):
         today = datetime.now()
@@ -131,6 +132,7 @@ class Experiment:
         self.score_name = score_name
         self.model_name = model_name
         self.title = title
+        self.ignore_nan = ignore_nan
         self.stratified = stratified == "1"
         self.stratified_class = StratifiedKFold if self.stratified else KFold
         self.datasets = datasets
@@ -184,7 +186,14 @@ class Experiment:
         self.leaves = []
         self.depths = []
 
-    def _n_fold_crossval(self, X, y, hyperparameters):
+    def _build_fit_params(self, name):
+        states = self.datasets.get_states(name)
+        if states is None:
+            return None
+        features = self.datasets.get_features()
+        return {"state_names": states, "features": features}
+
+    def _n_fold_crossval(self, name, X, y, hyperparameters):
         if self.scores != []:
             raise ValueError("Must init experiment before!")
         loop = tqdm(
@@ -201,6 +210,7 @@ class Experiment:
                 shuffle=True, random_state=random_state, n_splits=self.folds
             )
             clf = self._build_classifier(random_state, hyperparameters)
+            fit_params = self._build_fit_params(name)
             self.version = Models.get_version(self.model_name, clf)
             with warnings.catch_warnings():
                 warnings.filterwarnings("ignore")
@@ -209,11 +219,19 @@ class Experiment:
                     X,
                     y,
                     cv=kfold,
+                    fit_params=fit_params,
                     return_estimator=True,
                     scoring=self.score_name,
                 )
-            self.scores.append(res["test_score"])
+            if np.isnan(res["test_score"]).any():
-            self.times.append(res["fit_time"])
+                if not self.ignore_nan:
+                    print(res["test_score"])
+                    raise ValueError("NaN in results")
+                results = res["test_score"][~np.isnan(res["test_score"])]
+            else:
+                results = res["test_score"]
+            self.scores.extend(results)
+            self.times.extend(res["fit_time"])
             for result_item in res["estimator"]:
                 nodes_item, leaves_item, depth_item = Models.get_complexity(
                     self.model_name, result_item
@@ -273,7 +291,7 @@ class Experiment:
             n_classes = len(np.unique(y))
             hyperparameters = self.hyperparameters_dict[name][1]
             self._init_experiment()
-            self._n_fold_crossval(X, y, hyperparameters)
+            self._n_fold_crossval(name, X, y, hyperparameters)
             self._add_results(name, hyperparameters, samp, feat, n_classes)
             self._output_results()
         self.duration = time.time() - now

benchmark/Models.py

@@ -15,6 +15,24 @@ from xgboost import XGBClassifier
 import sklearn
 import xgboost
 
+import random
+
+
+class MockModel(SVC):
+    # Only used for testing
+    def predict(self, X):
+        if random.random() < 0.1:
+            return [float("NaN")] * len(X)
+        return super().predict(X)
+
+    def nodes_leaves(self):
+        return 0, 0
+
+    def fit(self, X, y, **kwargs):
+        kwargs.pop("state_names", None)
+        kwargs.pop("features", None)
+        return super().fit(X, y, **kwargs)
+
 
 class Models:
     @staticmethod
@@ -22,27 +40,27 @@ class Models:
         return {
             "STree": Stree(random_state=random_state),
             "TAN": TAN(random_state=random_state),
-            "KDB": KDB(k=3),
+            "KDB": KDB(k=2),
             "AODE": AODE(random_state=random_state),
             "Cart": DecisionTreeClassifier(random_state=random_state),
             "ExtraTree": ExtraTreeClassifier(random_state=random_state),
             "Wodt": Wodt(random_state=random_state),
             "SVC": SVC(random_state=random_state),
             "ODTE": Odte(
-                base_estimator=Stree(random_state=random_state),
+                estimator=Stree(random_state=random_state),
                 random_state=random_state,
             ),
             "BaggingStree": BaggingClassifier(
-                base_estimator=Stree(random_state=random_state),
+                estimator=Stree(random_state=random_state),
                 random_state=random_state,
             ),
             "BaggingWodt": BaggingClassifier(
-                base_estimator=Wodt(random_state=random_state),
+                estimator=Wodt(random_state=random_state),
                 random_state=random_state,
             ),
             "XGBoost": XGBClassifier(random_state=random_state),
             "AdaBoostStree": AdaBoostClassifier(
-                base_estimator=Stree(
+                estimator=Stree(
                     random_state=random_state,
                 ),
                 algorithm="SAMME",
@@ -50,6 +68,7 @@ class Models:
             ),
             "GBC": GradientBoostingClassifier(random_state=random_state),
             "RandomForest": RandomForestClassifier(random_state=random_state),
+            "Mock": MockModel(random_state=random_state),
         }
 
     @staticmethod

benchmark/Results.py

@@ -684,7 +684,7 @@ class ReportDatasets:
                 "bg_color": self.color1,
             }
         )
-        self.sheet.merge_range(0, 0, 0, 4, self.header_text, merge_format)
+        self.sheet.merge_range(0, 0, 0, 5, self.header_text, merge_format)
         self.sheet.merge_range(
             1,
             0,
@@ -697,24 +697,24 @@ class ReportDatasets:
             1,
             1,
             1,
-            3,
+            4,
             "Cross validation",
             merge_format_subheader_right,
         )
         self.sheet.write(
-            1, 4, f"{self.env['n_folds']} Folds", merge_format_subheader_left
+            1, 5, f"{self.env['n_folds']} Folds", merge_format_subheader_left
         )
         self.sheet.merge_range(
             2,
             1,
             2,
-            3,
+            4,
             "Stratified",
             merge_format_subheader_right,
         )
         self.sheet.write(
             2,
-            4,
+            5,
             f"{'True' if self.env['stratified']=='1' else 'False'}",
             merge_format_subheader_left,
         )
@@ -722,13 +722,13 @@ class ReportDatasets:
             3,
             1,
             3,
-            3,
+            4,
             "Discretized",
             merge_format_subheader_right,
         )
         self.sheet.write(
             3,
-            4,
+            5,
             f"{'True' if self.env['discretize']=='1' else 'False'}",
             merge_format_subheader_left,
         )
@@ -736,18 +736,19 @@ class ReportDatasets:
             4,
             1,
             4,
-            3,
+            4,
             "Seeds",
             merge_format_subheader_right,
         )
         self.sheet.write(
-            4, 4, f"{self.env['seeds']}", merge_format_subheader_left
+            4, 5, 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),
+            ("Continuous", 10),
             ("Classes", 10),
             ("Balance", 50),
         ]
@@ -767,7 +768,7 @@ class ReportDatasets:
 
     def footer(self):
         # set Balance column width to max length
-        self.sheet.set_column(4, 4, self.max_length)
+        self.sheet.set_column(5, 5, self.max_length)
         self.sheet.freeze_panes(6, 1)
         self.sheet.hide_gridlines(2)
         if self.close:
@@ -789,8 +790,9 @@ class ReportDatasets:
         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 + 3, result.cont_features, integer)
-        self.sheet.write(self.row, col + 4, result.balance, normal)
+        self.sheet.write(self.row, col + 4, result.classes, normal)
+        self.sheet.write(self.row, col + 5, result.balance, normal)
         self.update_max_length(len(result.balance))
         self.row += 1
 
@@ -807,11 +809,11 @@ class ReportDatasets:
             print(color_line, end="")
             print(self.header_text)
             print("")
-            print(f"{'Dataset':30s} Sampl. Feat. Cls Balance")
+            print(f"{'Dataset':30s} Sampl. Feat. Cont Cls Balance")
-            print("=" * 30 + " ====== ===== === " + "=" * 60)
+            print("=" * 30 + " ====== ===== ==== === " + "=" * 60)
         for dataset in data_sets:
             attributes = data_sets.get_attributes(dataset)
-            attributes.dataset = dataset
+
             if self.excel:
                 self.print_line(attributes)
             color_line = (
@@ -823,8 +825,8 @@ class ReportDatasets:
                 print(color_line, end="")
                 print(
                     f"{dataset:30s} {attributes.samples:6,d} "
-                    f"{attributes.features:5,d} {attributes.classes:3d} "
+                    f"{attributes.features:5,d} {attributes.cont_features:4,d}"
-                    f"{attributes.balance:40s}"
+                    f" {attributes.classes:3d} {attributes.balance:40s}"
                 )
         if self.excel:
             self.footer()

benchmark/scripts/be_build_grid.py

@@ -46,7 +46,7 @@ def main(args_test=None):
         '{"C": 7, "gamma": 0.1, "kernel": "rbf", "multiclass_strategy": '
         '"ovr"}',
         '{"C": 5, "kernel": "rbf", "gamma": "auto"}',
-        '{"C": 0.05, "max_iter": 10000.0, "kernel": "liblinear", '
+        '{"C": 0.05, "max_iter": 10000, "kernel": "liblinear", '
         '"multiclass_strategy": "ovr"}',
         '{"C":0.0275, "kernel": "liblinear", "multiclass_strategy": "ovr"}',
         '{"C": 7, "gamma": 10.0, "kernel": "rbf", "multiclass_strategy": '
@@ -97,7 +97,7 @@ def main(args_test=None):
     for item in results:
         results_tmp = {"n_jobs": [-1], "n_estimators": [100]}
         for key, value in results[item].items():
-            new_key = f"base_estimator__{key}"
+            new_key = f"estimator__{key}"
             try:
                 results_tmp[new_key] = sorted(value)
             except TypeError:
@@ -111,6 +111,7 @@ def main(args_test=None):
                 t2 = sorted([x for x in value if isinstance(x, str)])
                 results_tmp[new_key] = t1 + t2
         output.append(results_tmp)
+
     # save results
     file_name = Files.grid_input(args.score, args.model)
     file_output = os.path.join(Folders.results, file_name)

benchmark/scripts/be_main.py

@@ -13,7 +13,7 @@ def main(args_test=None):
     arguments = Arguments(prog="be_main")
     arguments.xset("stratified").xset("score").xset("model", mandatory=True)
     arguments.xset("n_folds").xset("platform").xset("quiet").xset("title")
-    arguments.xset("report")
+    arguments.xset("report").xset("ignore_nan")
     arguments.add_exclusive(
         ["grid_paramfile", "best_paramfile", "hyperparameters"]
     )
@@ -35,6 +35,7 @@ def main(args_test=None):
             grid_paramfile=args.grid_paramfile,
             progress_bar=not args.quiet,
             platform=args.platform,
+            ignore_nan=args.ignore_nan,
             title=args.title,
             folds=args.n_folds,
         )

benchmark/tests/.env

@@ -6,4 +6,4 @@ stratified=0
 # Source of data Tanveer/Surcov
 source_data=Tanveer
 seeds=[57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]
-discretize=0
+discretize=0

benchmark/tests/.env.dist

@@ -6,4 +6,4 @@ stratified=0
 # Source of data Tanveer/Surcov
 source_data=Tanveer
 seeds=[57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]
-discretize=0
+discretize=0

benchmark/tests/BestResults_test.py

@@ -18,7 +18,7 @@ class BestResultTest(TestBase):
                     "C": 7,
                     "gamma": 0.1,
                     "kernel": "rbf",
-                    "max_iter": 10000.0,
+                    "max_iter": 10000,
                     "multiclass_strategy": "ovr",
                 },
                 "results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json",

benchmark/tests/Dataset_test.py

@@ -1,4 +1,3 @@
-import shutil
 from .TestBase import TestBase
 from ..Experiments import Randomized
 from ..Datasets import Datasets
@@ -17,10 +16,6 @@ class DatasetTest(TestBase):
         self.set_env(".env.dist")
         return super().tearDown()
 
-    @staticmethod
-    def set_env(env):
-        shutil.copy(env, ".env")
-
     def test_Randomized(self):
         expected = [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]
         self.assertSequenceEqual(Randomized.seeds(), expected)

benchmark/tests/Experiment_test.py

@@ -1,4 +1,6 @@
 import json
+from io import StringIO
+from unittest.mock import patch
 from .TestBase import TestBase
 from ..Experiments import Experiment
 from ..Datasets import Datasets
@@ -8,10 +10,12 @@ class ExperimentTest(TestBase):
     def setUp(self):
         self.exp = self.build_exp()
 
-    def build_exp(self, hyperparams=False, grid=False):
+    def build_exp(
+        self, hyperparams=False, grid=False, model="STree", ignore_nan=False
+    ):
         params = {
             "score_name": "accuracy",
-            "model_name": "STree",
+            "model_name": model,
             "stratified": "0",
             "datasets": Datasets(),
             "hyperparams_dict": "{}",
@@ -21,6 +25,7 @@ class ExperimentTest(TestBase):
             "title": "Test",
             "progress_bar": False,
             "folds": 2,
+            "ignore_nan": ignore_nan,
         }
         return Experiment(**params)
 
@@ -31,6 +36,7 @@ class ExperimentTest(TestBase):
             ],
             ".",
         )
+        self.set_env(".env.dist")
         return super().tearDown()
 
     def test_build_hyperparams_file(self):
@@ -46,7 +52,7 @@ class ExperimentTest(TestBase):
                     "C": 7,
                     "gamma": 0.1,
                     "kernel": "rbf",
-                    "max_iter": 10000.0,
+                    "max_iter": 10000,
                     "multiclass_strategy": "ovr",
                 },
                 "results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json",
@@ -89,7 +95,7 @@ class ExperimentTest(TestBase):
     def test_exception_n_fold_crossval(self):
         self.exp.do_experiment()
         with self.assertRaises(ValueError):
-            self.exp._n_fold_crossval([], [], {})
+            self.exp._n_fold_crossval("", [], [], {})
 
     def test_do_experiment(self):
         self.exp.do_experiment()
@@ -131,3 +137,39 @@ class ExperimentTest(TestBase):
         ):
             for key, value in expected_result.items():
                 self.assertEqual(computed_result[key], value)
+
+    def test_build_fit_parameters(self):
+        self.set_env(".env.arff")
+        expected = {
+            "state_names": {
+                "sepallength": [0, 1, 2],
+                "sepalwidth": [0, 1, 3, 4],
+                "petallength": [0, 1, 2, 3],
+                "petalwidth": [0, 1, 2, 3],
+            },
+            "features": [
+                "sepallength",
+                "sepalwidth",
+                "petallength",
+                "petalwidth",
+            ],
+        }
+        exp = self.build_exp(model="TAN")
+        X, y = exp.datasets.load("iris")
+        computed = exp._build_fit_params("iris")
+        for key, value in expected["state_names"].items():
+            self.assertEqual(computed["state_names"][key], value)
+        for feature in expected["features"]:
+            self.assertIn(feature, computed["features"])
+
+    @patch("sys.stdout", new_callable=StringIO)
+    def test_experiment_with_nan_not_ignored(self, mock_output):
+        exp = self.build_exp(model="Mock")
+        self.assertRaises(ValueError, exp.do_experiment)
+        output_text = mock_output.getvalue().splitlines()
+        expected = "[      nan 0.8974359]"
+        self.assertEqual(expected, output_text[0])
+
+    def test_experiment_with_nan_ignored(self):
+        self.exp = self.build_exp(model="Mock", ignore_nan=True)
+        self.exp.do_experiment()

benchmark/tests/Models_test.py

@@ -70,19 +70,19 @@ class ModelTest(TestBase):
     def test_BaggingStree(self):
         clf = Models.get_model("BaggingStree")
         self.assertIsInstance(clf, BaggingClassifier)
-        clf_base = clf.base_estimator
+        clf_base = clf.estimator
         self.assertIsInstance(clf_base, Stree)
 
     def test_BaggingWodt(self):
         clf = Models.get_model("BaggingWodt")
         self.assertIsInstance(clf, BaggingClassifier)
-        clf_base = clf.base_estimator
+        clf_base = clf.estimator
         self.assertIsInstance(clf_base, Wodt)
 
     def test_AdaBoostStree(self):
         clf = Models.get_model("AdaBoostStree")
         self.assertIsInstance(clf, AdaBoostClassifier)
-        clf_base = clf.base_estimator
+        clf_base = clf.estimator
         self.assertIsInstance(clf_base, Stree)
 
     def test_unknown_classifier(self):

benchmark/tests/TestBase.py

@@ -4,6 +4,7 @@ import pathlib
 import sys
 import csv
 import unittest
+import shutil
 from importlib import import_module
 from io import StringIO
 from unittest.mock import patch
@@ -19,6 +20,10 @@ class TestBase(unittest.TestCase):
         self.stree_version = "1.2.4"
         super().__init__(*args, **kwargs)
 
+    @staticmethod
+    def set_env(env):
+        shutil.copy(env, ".env")
+
     def remove_files(self, files, folder):
         for file_name in files:
             file_name = os.path.join(folder, file_name)

benchmark/tests/datasets/all.txt

@@ -1,2 +1,2 @@
-iris,class
+iris,class,all
-wine,class
+wine,class,[0, 1]

benchmark/tests/results/best_results_accuracy_STree.json

@@ -1 +1 @@
-{"balance-scale": [0.98, {"splitter": "best", "max_features": "auto"}, "results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json"], "balloons": [0.86, {"C": 7, "gamma": 0.1, "kernel": "rbf", "max_iter": 10000.0, "multiclass_strategy": "ovr"}, "results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json"]}
+{"balance-scale": [0.98, {"splitter": "best", "max_features": "auto"}, "results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json"], "balloons": [0.86, {"C": 7, "gamma": 0.1, "kernel": "rbf", "max_iter": 10000, "multiclass_strategy": "ovr"}, "results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json"]}

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

@@ -17,10 +17,10 @@
       "features": 4,
       "classes": 3,
       "hyperparameters": {
-        "C": 10000.0,
+        "C": 10000,
         "gamma": 0.1,
         "kernel": "rbf",
-        "max_iter": 10000.0,
+        "max_iter": 10000,
         "multiclass_strategy": "ovr"
       },
       "nodes": 7.0,
@@ -40,7 +40,7 @@
         "C": 7,
         "gamma": 0.1,
         "kernel": "rbf",
-        "max_iter": 10000.0,
+        "max_iter": 10000,
         "multiclass_strategy": "ovr"
       },
       "nodes": 3.0,

benchmark/tests/scripts/Be_Print_Strees_test.py

@@ -27,7 +27,7 @@ class BePrintStrees(TestBase):
                 stdout.getvalue(), f"File {file_name} generated\n"
             )
             computed_size = os.path.getsize(file_name)
-            self.assertGreater(computed_size, 25000)
+            self.assertGreater(computed_size, 24500)
 
     def test_be_print_strees_dataset_color(self):
         for name in self.datasets:

benchmark/tests/test_files/be_build_grid.test

@@ -6,13 +6,13 @@
         "n_estimators": [
             100
         ],
-        "base_estimator__C": [
+        "estimator__C": [
             1.0
         ],
-        "base_estimator__kernel": [
+        "estimator__kernel": [
             "linear"
         ],
-        "base_estimator__multiclass_strategy": [
+        "estimator__multiclass_strategy": [
             "ovo"
         ]
     },
@@ -23,7 +23,7 @@
         "n_estimators": [
             100
         ],
-        "base_estimator__C": [
+        "estimator__C": [
             0.001,
             0.0275,
             0.05,
@@ -36,10 +36,10 @@
             7,
             10000.0
         ],
-        "base_estimator__kernel": [
+        "estimator__kernel": [
             "liblinear"
         ],
-        "base_estimator__multiclass_strategy": [
+        "estimator__multiclass_strategy": [
             "ovr"
         ]
     },
@@ -50,7 +50,7 @@
         "n_estimators": [
             100
         ],
-        "base_estimator__C": [
+        "estimator__C": [
             0.05,
             1.0,
             1.05,
@@ -62,7 +62,7 @@
             57,
             10000.0
         ],
-        "base_estimator__gamma": [
+        "estimator__gamma": [
             0.001,
             0.1,
             0.14,
@@ -70,10 +70,10 @@
             "auto",
             "scale"
         ],
-        "base_estimator__kernel": [
+        "estimator__kernel": [
             "rbf"
         ],
-        "base_estimator__multiclass_strategy": [
+        "estimator__multiclass_strategy": [
             "ovr"
         ]
     },
@@ -84,20 +84,20 @@
         "n_estimators": [
             100
         ],
-        "base_estimator__C": [
+        "estimator__C": [
             0.05,
             0.2,
             1.0,
             8.25
         ],
-        "base_estimator__gamma": [
+        "estimator__gamma": [
             0.1,
             "scale"
         ],
-        "base_estimator__kernel": [
+        "estimator__kernel": [
             "poly"
         ],
-        "base_estimator__multiclass_strategy": [
+        "estimator__multiclass_strategy": [
             "ovo",
             "ovr"
         ]

benchmark/tests/test_files/be_main_best.test

@@ -9,7 +9,7 @@
 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'} 
+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, 'multiclass_strategy': 'ovr'} 
 *************************************************************************************************************************
 * accuracy compared to STree_default (liblinear-ovr) .:  0.0422                                                         *
 *************************************************************************************************************************

benchmark/tests/test_files/excel.test

@@ -32,7 +32,7 @@
 7;9;"0.0150468069702512"
 7;10;"0.01404867172241211"
 7;11;"0.002026269126958884"
-7;12;"{'C': 10000.0, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'}"
+7;12;"{'C': 10000, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'}"
 8;1;"balloons"
 8;2;"16"
 8;3;"4"
@@ -44,5 +44,5 @@
 8;9;"0.2850146195080759"
 8;10;"0.0008541679382324218"
 8;11;"3.629469326417878e-05"
-8;12;"{'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'}"
+8;12;"{'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'}"
 10;1;"** accuracy compared to STree_default (liblinear-ovr) .:  0.0454"

benchmark/tests/test_files/excel_compared.test

@@ -32,7 +32,7 @@
 7;10;0.0150468069702512
 7;11;0.01404867172241211
 7;12;0.002026269126958884
-7;13;"{'C': 10000.0, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'}"
+7;13;"{'C': 10000, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'}"
 8;1;"balloons"
 8;2;16
 8;3;4
@@ -45,7 +45,7 @@
 8;10;0.2850146195080759
 8;11;0.0008541679382324218
 8;12;3.629469326417878e-05
-8;13;"{'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'}"
+8;13;"{'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'}"
 11;2;"✔"
 11;3;1
 11;4;"Equal to best"

benchmark/tests/test_files/exreport_excel_Datasets.test

@@ -1,25 +1,28 @@
-1;1;"Datasets used in benchmark ver. 0.2.0"
+1;1;"Datasets used in benchmark ver. 0.4.0"
 2;1;" Default score accuracy"
 2;2;"Cross validation"
-2;5;"5 Folds"
+2;6;"5 Folds"
 3;2;"Stratified"
-3;5;"False"
+3;6;"False"
 4;2;"Discretized"
-4;5;"False"
+4;6;"False"
 5;2;"Seeds"
-5;5;"[57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]"
+5;6;"[57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]"
 6;1;"Dataset"
 6;2;"Samples"
 6;3;"Features"
-6;4;"Classes"
+6;4;"Continuous"
-6;5;"Balance"
+6;5;"Classes"
+6;6;"Balance"
 7;1;"balance-scale"
 7;2;"625"
 7;3;"4"
-7;4;"3"
+7;4;"0"
-7;5;" 7.84%/ 46.08%/ 46.08%"
+7;5;"3"
+7;6;" 7.84%/ 46.08%/ 46.08%"
 8;1;"balloons"
 8;2;"16"
 8;3;"4"
-8;4;"2"
+8;4;"0"
-8;5;"56.25%/ 43.75%"
+8;5;"2"
+8;6;"56.25%/ 43.75%"

benchmark/tests/test_files/exreport_excel_STree.test

@@ -32,7 +32,7 @@
 7;9;"0.0150468069702512"
 7;10;"0.01404867172241211"
 7;11;"0.002026269126958884"
-7;12;"{'C': 10000.0, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'}"
+7;12;"{'C': 10000, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'}"
 8;1;"balloons"
 8;2;"16"
 8;3;"4"
@@ -44,5 +44,5 @@
 8;9;"0.2850146195080759"
 8;10;"0.0008541679382324218"
 8;11;"3.629469326417878e-05"
-8;12;"{'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'}"
+8;12;"{'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'}"
 10;1;"** accuracy compared to STree_default (liblinear-ovr) .:  0.0454"

benchmark/tests/test_files/report.test

@@ -8,8 +8,8 @@
 
 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'} 
+balance-scale                     625     4   3    7.00    4.00    3.00 0.970560±0.0150   0.014049±0.0020 {'C': 10000, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, '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'} 
+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, 'multiclass_strategy': 'ovr'} 
 *************************************************************************************************************************
 * accuracy compared to STree_default (liblinear-ovr) .:  0.0454                                                         *
 *************************************************************************************************************************

benchmark/tests/test_files/report_best.test

@@ -5,7 +5,7 @@
 Dataset                        Score    File/Message                                                                 Hyperparameters                               
 ============================== ======== ============================================================================ ============================================= 
 balance-scale                  0.980000 results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json                     {'splitter': 'best', 'max_features': 'auto'} 
-balloons                       0.860000 results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json                     {'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'} 
+balloons                       0.860000 results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json                     {'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'} 
 ******************************************************************************************************************************************************************
 * accuracy compared to STree_default (liblinear-ovr) .:  0.0457                                                                                                  *
 ******************************************************************************************************************************************************************

benchmark/tests/test_files/report_compared.test

@@ -8,8 +8,8 @@
 
 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'} 
+balance-scale                     625     4   3    7.00    4.00    3.00 0.970560±0.0150   0.014049±0.0020 {'C': 10000, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, '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'} 
+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, 'multiclass_strategy': 'ovr'} 
 *************************************************************************************************************************
 * ✔ Equal to best .....:  1                                                                                             *
 * accuracy compared to STree_default (liblinear-ovr) .:  0.0454                                                         *

benchmark/tests/test_files/report_datasets.test

@@ -1,6 +1,6 @@
 Datasets used in benchmark ver. 0.2.0
 
-Dataset                        Sampl. Feat. Cls Balance
+Dataset                        Sampl. Feat. Cont Cls Balance
-============================== ====== ===== === ============================================================
+============================== ====== ===== ==== === ============================================================
-balance-scale                     625     4   3  7.84%/ 46.08%/ 46.08%                  
+balance-scale                     625     4    0   3  7.84%/ 46.08%/ 46.08%                  
-balloons                           16     4   2 56.25%/ 43.75%                          
+balloons                           16     4    0   2 56.25%/ 43.75%                          

requirements.txt

@@ -3,7 +3,7 @@ scikit-learn
 scipy
 odte
 cython
-mdlp-discretization
+fimdlp
 mufs
 bayesclass @ git+ssh://git@github.com/doctorado-ml/bayesclass.git
 xlsxwriter