Add nodes, leaves, depth to mysql

Add nodes, leaves, depth, samples, features and classes to analysis

analysis_mysql.py

@@ -1,5 +1,6 @@
 import argparse
 from typing import Tuple
+import numpy as np
 from experimentation.Sets import Datasets
 from experimentation.Utils import TextColor
 from experimentation.Database import MySQL
@@ -14,8 +15,10 @@ models_tree = [
     "baseRaF",
 ]
 models_ensemble = ["odte", "adaBoost", "bagging", "TBRaF", "TBRoF", "TBRRoF"]
+description = ["samp", "var", "cls"]
+complexity = ["nodes", "leaves", "depth"]
 title = "Best model results"
-lengths = (30, 12, 12, 12, 12, 12, 12)
+lengths = (30, 4, 3, 3, 3, 3, 3, 12, 12, 12, 12, 12, 12)
 
 
 def parse_arguments() -> Tuple[str, str, str, bool, bool]:
@@ -79,9 +82,11 @@ def report_header(title, experiment, model_type):
 
 def report_line(line):
     output = f"{line['dataset']:{lengths[0] + 5}s} "
+    for key, item in enumerate(description + complexity):
+        output += f"{line[item]:{lengths[key + 1]}d} "
     data = models.copy()
     for key, model in enumerate(data):
-        output += f"{line[model]:{lengths[key + 1]}s} "
+        output += f"{line[model]:{lengths[key + 7]}s} "
     return output
 
 
@@ -101,7 +106,15 @@ def report_footer(agg):
 dbh = MySQL()
 database = dbh.get_connection()
 dt = Datasets(False, False, "tanveer")
-fields = ("Dataset",)
+fields = (
+    "Dataset",
+    "Samp",
+    "Var",
+    "Cls",
+    "Nod",
+    "Lea",
+    "Dep",
+)
 models = models_tree if model_type == "tree" else models_ensemble
 for item in models:
     fields += (f"{item}",)
@@ -121,13 +134,23 @@ for dataset in dt:
     find_one = False
     # Look for max accuracy for any given dataset
     line = {"dataset": color + dataset[0]}
+    X, y = dt.load(dataset[0])  # type: ignore
+    line["samp"], line["var"] = X.shape
+    line["cls"] = len(np.unique(y))
     record = dbh.find_best(dataset[0], models, experiment)
     max_accuracy = 0.0 if record is None else record[5]
+    line["nodes"] = 0
+    line["leaves"] = 0
+    line["depth"] = 0
     for model in models:
         record = dbh.find_best(dataset[0], model, experiment)
         if record is None:
             line[model] = color + "-" * 12
         else:
+            if model == "stree":
+                line["nodes"] = record[12]
+                line["leaves"] = record[13]
+                line["depth"] = record[14]
             reference = record[13]
             accuracy = record[5]
             acc_std = record[11]

experimentation/Database.py

@@ -176,6 +176,7 @@ class BD(ABC):
         accuracy,
         time_spent,
         parameters,
+        complexity,
     ) -> None:
         """Create a record in MySQL database
 
@@ -187,8 +188,8 @@ class BD(ABC):
         command_insert = (
             "replace into results (date, time, type, accuracy, "
             "dataset, classifier, norm, stand, parameters, accuracy_std, "
-            "time_spent, time_spent_std) values (%s, %s, "
-            "%s, %s, %s, %s, %s, %s, %s, %s, %s, %s)"
+            "time_spent, time_spent_std, nodes, leaves, depth) values (%s, %s,"
+            " %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)"
         )
         now = datetime.now()
         date = now.strftime("%Y-%m-%d")
@@ -206,6 +207,9 @@ class BD(ABC):
             accuracy[1],
             time_spent[0],
             time_spent[1],
+            complexity["nodes"],
+            complexity["leaves"],
+            complexity["depth"],
         )
         cursor = database.cursor()
         cursor.execute(command_insert, values)
@@ -319,7 +323,9 @@ class Outcomes(BD):
         self._table = "outcomes"
         super().__init__(host=host, model=model)
 
-    def store(self, dataset, normalize, standardize, parameters, results):
+    def store(
+        self, dataset, normalize, standardize, parameters, results, complexity
+    ):
         outcomes = ["fit_time", "score_time", "train_score", "test_score"]
         data = ""
         for index in outcomes:
@@ -350,6 +356,7 @@ class Outcomes(BD):
                 float(np.std(results["fit_time"])),
             ],
             parameters,
+            complexity,
         )
 
     def report(self, dataset, exclude_params):

experimentation/Experiments.py

@@ -2,9 +2,8 @@ import json
 import os
 import time
 import warnings
-
+import numpy as np
 from sklearn.model_selection import GridSearchCV, cross_validate
-
 from . import Models
 from .Database import Hyperparameters, MySQL, Outcomes
 from .Sets import Datasets
@@ -94,15 +93,25 @@ class Experiment:
                     X,
                     y,
                     return_train_score=True,
+                    return_estimator=True,
                     n_jobs=self._threads,
                     cv=kfold,
                 )
                 for item in outcomes:
                     total[item].append(results[item])
         print("end")
+        if type(model).__name__ == "Stree":
+            best_model = results["estimator"][np.argmax(results["test_score"])]
+            nodes, leaves = best_model.nodes_leaves()
+            depth = best_model.depth_
+        else:
+            nodes = leaves = depth = 0
+        complexity = dict(nodes=nodes, leaves=leaves, depth=depth)
         outcomes = Outcomes(host=self._host, model=self._model_name)
         parameters = json.dumps(parameters, sort_keys=True)
-        outcomes.store(dataset, normalize, standardize, parameters, total)
+        outcomes.store(
+            dataset, normalize, standardize, parameters, total, complexity
+        )
         if self._num_warnings > 0:
             print(f"{self._num_warnings} warnings have happend")
 

report.csv

@@ -1,107 +1,107 @@
 dataset, classifier, accuracy
-balance-scale, stree, 0.91184
+balance-scale, stree, 0.97056
 balance-scale, wodt, 0.912
 balance-scale, j48svm, 0.94
 balance-scale, oc1, 0.9192
 balance-scale, cart, 0.78816
 balance-scale, baseRaF, 0.706738
-balloons, stree, 0.653333
+balloons, stree, 0.86
 balloons, wodt, 0.688333
 balloons, j48svm, 0.595
 balloons, oc1, 0.62
 balloons, cart, 0.671667
 balloons, baseRaF, 0.605
-breast-cancer-wisc-diag, stree, 0.968898
+breast-cancer-wisc-diag, stree, 0.972764
 breast-cancer-wisc-diag, wodt, 0.967317
 breast-cancer-wisc-diag, j48svm, 0.952878
 breast-cancer-wisc-diag, oc1, 0.933477
 breast-cancer-wisc-diag, cart, 0.93953
 breast-cancer-wisc-diag, baseRaF, 0.965694
-breast-cancer-wisc-prog, stree, 0.802051
+breast-cancer-wisc-prog, stree, 0.811128
 breast-cancer-wisc-prog, wodt, 0.710141
 breast-cancer-wisc-prog, j48svm, 0.724038
 breast-cancer-wisc-prog, oc1, 0.71
 breast-cancer-wisc-prog, cart, 0.699833
 breast-cancer-wisc-prog, baseRaF, 0.74485
-breast-cancer-wisc, stree, 0.966661
+breast-cancer-wisc, stree, 0.965802
 breast-cancer-wisc, wodt, 0.946208
 breast-cancer-wisc, j48svm, 0.967674
 breast-cancer-wisc, oc1, 0.940194
 breast-cancer-wisc, cart, 0.940629
 breast-cancer-wisc, baseRaF, 0.942857
-breast-cancer, stree, 0.734211
+breast-cancer, stree, 0.733158
 breast-cancer, wodt, 0.650236
 breast-cancer, j48svm, 0.707719
 breast-cancer, oc1, 0.649728
 breast-cancer, cart, 0.65444
 breast-cancer, baseRaF, 0.656438
-cardiotocography-10clases, stree, 0.552558
+cardiotocography-10clases, stree, 0.712009
 cardiotocography-10clases, wodt, 0.773706
 cardiotocography-10clases, j48svm, 0.830812
 cardiotocography-10clases, oc1, 0.795528
 cardiotocography-10clases, cart, 0.818864
 cardiotocography-10clases, baseRaF, 0.774788
-cardiotocography-3clases, stree, 0.35207
+cardiotocography-3clases, stree, 0.891956
 cardiotocography-3clases, wodt, 0.897509
 cardiotocography-3clases, j48svm, 0.927327
 cardiotocography-3clases, oc1, 0.899811
 cardiotocography-3clases, cart, 0.929258
 cardiotocography-3clases, baseRaF, 0.896715
-conn-bench-sonar-mines-rocks, stree, 0.755528
+conn-bench-sonar-mines-rocks, stree, 0.71439
 conn-bench-sonar-mines-rocks, wodt, 0.824959
 conn-bench-sonar-mines-rocks, j48svm, 0.73892
 conn-bench-sonar-mines-rocks, oc1, 0.710798
 conn-bench-sonar-mines-rocks, cart, 0.728711
 conn-bench-sonar-mines-rocks, baseRaF, 0.772981
-cylinder-bands, stree, 0.715049
+cylinder-bands, stree, 0.687101
 cylinder-bands, wodt, 0.704074
 cylinder-bands, j48svm, 0.726351
 cylinder-bands, oc1, 0.67106
 cylinder-bands, cart, 0.712703
 cylinder-bands, baseRaF, 0.675117
-dermatology, stree, 0.966087
+dermatology, stree, 0.971833
 dermatology, wodt, 0.965557
 dermatology, j48svm, 0.955735
 dermatology, oc1, 0.916087
 dermatology, cart, 0.932766
 dermatology, baseRaF, 0.970723
-echocardiogram, stree, 0.808832
+echocardiogram, stree, 0.814758
 echocardiogram, wodt, 0.733875
 echocardiogram, j48svm, 0.805527
 echocardiogram, oc1, 0.748291
 echocardiogram, cart, 0.745043
 echocardiogram, baseRaF, 0.753522
-fertility, stree, 0.866
+fertility, stree, 0.88
 fertility, wodt, 0.785
 fertility, j48svm, 0.857
 fertility, oc1, 0.793
 fertility, cart, 0.8
 fertility, baseRaF, 0.798
-haberman-survival, stree, 0.735637
+haberman-survival, stree, 0.727795
 haberman-survival, wodt, 0.664707
 haberman-survival, j48svm, 0.714056
 haberman-survival, oc1, 0.651634
 haberman-survival, cart, 0.65
 haberman-survival, baseRaF, 0.720133
-heart-hungarian, stree, 0.817674
+heart-hungarian, stree, 0.827522
 heart-hungarian, wodt, 0.764909
 heart-hungarian, j48svm, 0.785026
 heart-hungarian, oc1, 0.758298
 heart-hungarian, cart, 0.760508
 heart-hungarian, baseRaF, 0.779804
-hepatitis, stree, 0.796129
+hepatitis, stree, 0.824516
 hepatitis, wodt, 0.785806
 hepatitis, j48svm, 0.761935
 hepatitis, oc1, 0.756774
 hepatitis, cart, 0.765161
 hepatitis, baseRaF, 0.773671
-ilpd-indian-liver, stree, 0.723498
+ilpd-indian-liver, stree, 0.719207
 ilpd-indian-liver, wodt, 0.676176
 ilpd-indian-liver, j48svm, 0.690339
 ilpd-indian-liver, oc1, 0.660139
 ilpd-indian-liver, cart, 0.663423
 ilpd-indian-liver, baseRaF, 0.696685
-ionosphere, stree, 0.866056
+ionosphere, stree, 0.953276
 ionosphere, wodt, 0.88008
 ionosphere, j48svm, 0.891984
 ionosphere, oc1, 0.879742
@@ -113,49 +113,49 @@ iris, j48svm, 0.947333
 iris, oc1, 0.948
 iris, cart, 0.938667
 iris, baseRaF, 0.953413
-led-display, stree, 0.7007
+led-display, stree, 0.703
 led-display, wodt, 0.7049
 led-display, j48svm, 0.7204
 led-display, oc1, 0.6993
 led-display, cart, 0.7037
 led-display, baseRaF, 0.70178
-libras, stree, 0.747778
+libras, stree, 0.788333
 libras, wodt, 0.764167
 libras, j48svm, 0.66
 libras, oc1, 0.645
 libras, cart, 0.655
 libras, baseRaF, 0.726722
-low-res-spect, stree, 0.853102
+low-res-spect, stree, 0.865713
 low-res-spect, wodt, 0.856459
 low-res-spect, j48svm, 0.83358
 low-res-spect, oc1, 0.824671
 low-res-spect, cart, 0.829206
 low-res-spect, baseRaF, 0.790875
-lymphography, stree, 0.77046
+lymphography, stree, 0.823425
 lymphography, wodt, 0.808782
 lymphography, j48svm, 0.778552
 lymphography, oc1, 0.734634
 lymphography, cart, 0.766276
 lymphography, baseRaF, 0.761622
-mammographic, stree, 0.81915
+mammographic, stree, 0.817068
 mammographic, wodt, 0.759839
 mammographic, j48svm, 0.821435
 mammographic, oc1, 0.768805
 mammographic, cart, 0.757131
 mammographic, baseRaF, 0.780206
-molec-biol-promoter, stree, 0.764416
+molec-biol-promoter, stree, 0.767056
 molec-biol-promoter, wodt, 0.798528
 molec-biol-promoter, j48svm, 0.744935
 molec-biol-promoter, oc1, 0.734805
 molec-biol-promoter, cart, 0.748701
 molec-biol-promoter, baseRaF, 0.667239
-musk-1, stree, 0.843463
+musk-1, stree, 0.916388
 musk-1, wodt, 0.838914
 musk-1, j48svm, 0.82693
 musk-1, oc1, 0.776401
 musk-1, cart, 0.780215
 musk-1, baseRaF, 0.834034
-oocytes_merluccius_nucleus_4d, stree, 0.810657
+oocytes_merluccius_nucleus_4d, stree, 0.835125
 oocytes_merluccius_nucleus_4d, wodt, 0.737673
 oocytes_merluccius_nucleus_4d, j48svm, 0.741766
 oocytes_merluccius_nucleus_4d, oc1, 0.743199
@@ -167,127 +167,127 @@ oocytes_merluccius_states_2f, j48svm, 0.901374
 oocytes_merluccius_states_2f, oc1, 0.889223
 oocytes_merluccius_states_2f, cart, 0.891193
 oocytes_merluccius_states_2f, baseRaF, 0.910551
-oocytes_trisopterus_nucleus_2f, stree, 0.800986
+oocytes_trisopterus_nucleus_2f, stree, 0.799995
 oocytes_trisopterus_nucleus_2f, wodt, 0.751431
 oocytes_trisopterus_nucleus_2f, j48svm, 0.756587
 oocytes_trisopterus_nucleus_2f, oc1, 0.747697
 oocytes_trisopterus_nucleus_2f, cart, 0.734313
 oocytes_trisopterus_nucleus_2f, baseRaF, 0.76193
-oocytes_trisopterus_states_5b, stree, 0.9023
+oocytes_trisopterus_states_5b, stree, 0.924441
 oocytes_trisopterus_states_5b, wodt, 0.89165
 oocytes_trisopterus_states_5b, j48svm, 0.887943
 oocytes_trisopterus_states_5b, oc1, 0.86393
 oocytes_trisopterus_states_5b, cart, 0.870263
 oocytes_trisopterus_states_5b, baseRaF, 0.922149
-parkinsons, stree, 0.882051
+parkinsons, stree, 0.865641
 parkinsons, wodt, 0.901538
 parkinsons, j48svm, 0.844615
 parkinsons, oc1, 0.865641
 parkinsons, cart, 0.855897
 parkinsons, baseRaF, 0.87924
-pima, stree, 0.766651
+pima, stree, 0.764053
 pima, wodt, 0.681591
 pima, j48svm, 0.749876
 pima, oc1, 0.693027
 pima, cart, 0.701172
 pima, baseRaF, 0.697005
-pittsburg-bridges-MATERIAL, stree, 0.787446
+pittsburg-bridges-MATERIAL, stree, 0.867749
 pittsburg-bridges-MATERIAL, wodt, 0.79961
 pittsburg-bridges-MATERIAL, j48svm, 0.855844
 pittsburg-bridges-MATERIAL, oc1, 0.81026
 pittsburg-bridges-MATERIAL, cart, 0.783593
 pittsburg-bridges-MATERIAL, baseRaF, 0.81136
-pittsburg-bridges-REL-L, stree, 0.62519
+pittsburg-bridges-REL-L, stree, 0.564048
 pittsburg-bridges-REL-L, wodt, 0.617143
 pittsburg-bridges-REL-L, j48svm, 0.645048
 pittsburg-bridges-REL-L, oc1, 0.604957
 pittsburg-bridges-REL-L, cart, 0.625333
 pittsburg-bridges-REL-L, baseRaF, 0.622107
-pittsburg-bridges-SPAN, stree, 0.630234
+pittsburg-bridges-SPAN, stree, 0.658713
 pittsburg-bridges-SPAN, wodt, 0.606959
 pittsburg-bridges-SPAN, j48svm, 0.621579
 pittsburg-bridges-SPAN, oc1, 0.579333
 pittsburg-bridges-SPAN, cart, 0.557544
 pittsburg-bridges-SPAN, baseRaF, 0.630217
-pittsburg-bridges-T-OR-D, stree, 0.861619
+pittsburg-bridges-T-OR-D, stree, 0.849952
 pittsburg-bridges-T-OR-D, wodt, 0.818429
 pittsburg-bridges-T-OR-D, j48svm, 0.838333
 pittsburg-bridges-T-OR-D, oc1, 0.831545
 pittsburg-bridges-T-OR-D, cart, 0.821619
 pittsburg-bridges-T-OR-D, baseRaF, 0.821007
-planning, stree, 0.70455
+planning, stree, 0.73527
 planning, wodt, 0.576847
 planning, j48svm, 0.711381
 planning, oc1, 0.566988
 planning, cart, 0.586712
 planning, baseRaF, 0.590586
-post-operative, stree, 0.573333
+post-operative, stree, 0.703333
 post-operative, wodt, 0.535556
 post-operative, j48svm, 0.701111
 post-operative, oc1, 0.542222
 post-operative, cart, 0.567778
 post-operative, baseRaF, 0.539375
-seeds, stree, 0.949048
+seeds, stree, 0.952857
 seeds, wodt, 0.940476
 seeds, j48svm, 0.909524
 seeds, oc1, 0.932381
 seeds, cart, 0.900476
 seeds, baseRaF, 0.942518
-statlog-australian-credit, stree, 0.667246
+statlog-australian-credit, stree, 0.678261
 statlog-australian-credit, wodt, 0.561594
 statlog-australian-credit, j48svm, 0.66029
 statlog-australian-credit, oc1, 0.573913
 statlog-australian-credit, cart, 0.595507
 statlog-australian-credit, baseRaF, 0.678261
-statlog-german-credit, stree, 0.7625
+statlog-german-credit, stree, 0.7569
 statlog-german-credit, wodt, 0.6929
 statlog-german-credit, j48svm, 0.7244
 statlog-german-credit, oc1, 0.6874
 statlog-german-credit, cart, 0.6738
 statlog-german-credit, baseRaF, 0.68762
-statlog-heart, stree, 0.822963
+statlog-heart, stree, 0.822222
 statlog-heart, wodt, 0.777778
 statlog-heart, j48svm, 0.795926
 statlog-heart, oc1, 0.749259
 statlog-heart, cart, 0.762222
 statlog-heart, baseRaF, 0.747605
-statlog-image, stree, 0.850649
+statlog-image, stree, 0.956623
 statlog-image, wodt, 0.954632
 statlog-image, j48svm, 0.967403
 statlog-image, oc1, 0.95013
 statlog-image, cart, 0.964892
 statlog-image, baseRaF, 0.953604
-statlog-vehicle, stree, 0.695151
+statlog-vehicle, stree, 0.788537
 statlog-vehicle, wodt, 0.726492
 statlog-vehicle, j48svm, 0.729651
 statlog-vehicle, oc1, 0.708496
 statlog-vehicle, cart, 0.728367
 statlog-vehicle, baseRaF, 0.789572
-synthetic-control, stree, 0.938833
+synthetic-control, stree, 0.95
 synthetic-control, wodt, 0.973167
 synthetic-control, j48svm, 0.922333
 synthetic-control, oc1, 0.863167
 synthetic-control, cart, 0.908333
 synthetic-control, baseRaF, 0.971567
-tic-tac-toe, stree, 0.983296
+tic-tac-toe, stree, 0.984444
 tic-tac-toe, wodt, 0.93905
 tic-tac-toe, j48svm, 0.983295
 tic-tac-toe, oc1, 0.91849
 tic-tac-toe, cart, 0.951558
 tic-tac-toe, baseRaF, 0.974906
-vertebral-column-2clases, stree, 0.852903
+vertebral-column-2clases, stree, 0.851936
 vertebral-column-2clases, wodt, 0.801935
 vertebral-column-2clases, j48svm, 0.84871
 vertebral-column-2clases, oc1, 0.815161
 vertebral-column-2clases, cart, 0.784839
 vertebral-column-2clases, baseRaF, 0.822601
-wine, stree, 0.97581
+wine, stree, 0.949333
 wine, wodt, 0.973048
 wine, j48svm, 0.979143
 wine, oc1, 0.916165
 wine, cart, 0.921937
 wine, baseRaF, 0.97748
-zoo, stree, 0.947619
+zoo, stree, 0.955524
 zoo, wodt, 0.954429
 zoo, j48svm, 0.92381
 zoo, oc1, 0.890952

report_score.py

@@ -55,22 +55,30 @@ def parse_arguments():
     return (args.set_of_files, args.model, args.dataset, args.sql, args.param)
 
 
-def nodes_leaves(clf):
-    nodes = 0
-    leaves = 0
-    for node in clf:
-        if node.is_leaf():
-            leaves += 1
-        else:
-            nodes += 1
-    return nodes, leaves
-
-
 def compute_auto_hyperparams(X, y):
-    params = {"max_iter": 1e4, "C": 0.1}
-    classes = len(np.unique(y))
-    if classes > 2:
-        params["split_criteria"] = "max_samples"
+    """Propuesta de auto configuración de hiperparámetros
+    max_it = 10e4
+    (1 valor)
+    split = impurity si clases==2 y split=max_samples si clases > 2
+    (1 valor)
+    kernel=linear o polinómico
+    (2 valores)
+    C = 0.1, 0.5 y 1.0
+    (3 valores)
+    Caso 1: C=1, max_iter=1e4 + condicional split_max kernel lineal
+    Caso 2: C=0.5, max_iter=1e4 + condicional split_max kernel lineal
+    Caso 3: C=0.1, max_iter=1e4 + condicional split_max kernel lineal
+    Caso 4: C=1, max_iter=1e4 + condicional split_max kernel poly
+    Caso 5: C=0.5, max_iter=1e4 + condicional split_max kernel poly
+    Caso 6: C=0.1, max_iter=1e4 + condicional split_max kernel poly
+    Caso 7: C=1, max_iter=1e4 + condicional + kernel rbf
+    Caso 8: kernel rbf
+    """
+    # params = {"max_iter": 1e4, "kernel": "rbf"}
+    # classes = len(np.unique(y))
+    # if classes > 2:
+    #     params["split_criteria"] = "max_samples"
+    params = {"kernel": "rbf"}
     return params
 
 
@@ -97,7 +105,7 @@ def process_dataset(dataset, verbose, model, auto_params):
         clf = Stree(random_state=random_state)
         clf.set_params(**hyperparameters)
         res = cross_validate(clf, X, y, cv=kfold, return_estimator=True)
-        nodes, leaves = nodes_leaves(res["estimator"][0])
+        nodes, leaves = res["estimator"][0].nodes_leaves()
         depth = res["estimator"][0].depth_
         scores.append(res["test_score"])
         times.append(res["fit_time"])
@@ -222,6 +230,9 @@ if dataset == "all":
             parameters = json.loads("{}")
             accuracy_best = 0.0
             acc_best_std = 0.0
+        if auto_params:
+            # show parameters computed
+            parameters = json.loads(hyperparameters)
         accuracy_computed = np.mean(scores)
         diff = accuracy_best - accuracy_computed
         print(
@@ -243,12 +254,12 @@ else:
     accuracy_best = record[5] if record is not None else 0.0
     acc_best_std = record[11] if record is not None else 0.0
     print(
-        f"* Accuracy Computed : {accuracy:6.4f}±{np.std(scores):6.4f} "
+        f"* Accuracy Computed .: {accuracy:6.4f}±{np.std(scores):6.4f} "
         f"{np.mean(times):5.3f}s"
     )
     print(f"* Accuracy Best .....: {accuracy_best:6.4f}±{acc_best_std:6.4f}")
     print(f"* Difference ........: {accuracy_best - accuracy:6.4f}")
-    print(f"* Nodes/Leaves/Depth :{nodes:2d} {leaves:2d} " f"{depth:2d} ")
+    print(f"* Nodes/Leaves/Depth : {nodes:2d} {leaves:2d} " f"{depth:2d} ")
 stop = time.time()
 print(f"- Auto Hyperparams ..: {hyperparameters}")
 hours, rem = divmod(stop - start, 3600)