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Non stratified experiments

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Remove reference column in analysis
This commit is contained in:
Ricardo Montañana Gómez
2021-03-22 11:02:53 +01:00
parent 6d68c81920
commit 08fb237001
6 changed files with 318 additions and 281 deletions
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44
analysis_mysql.py
View File

@@ -15,7 +15,7 @@ models_tree = [
] ]
models_ensemble = ["odte", "adaBoost", "bagging", "TBRaF", "TBRoF", "TBRRoF"] models_ensemble = ["odte", "adaBoost", "bagging", "TBRaF", "TBRoF", "TBRRoF"]
title = "Best model results" title = "Best model results"
lengths = (30, 9, 11, 11, 11, 11, 11, 11) lengths = (30, 12, 12, 12, 12, 12, 12)
def parse_arguments() -> Tuple[str, str, str, bool, bool]: def parse_arguments() -> Tuple[str, str, str, bool, bool]:
@@ -63,7 +63,7 @@ def report_header_content(title, experiment, model_type):
output += "*" * length + "\n\n" output += "*" * length + "\n\n"
lines = "" lines = ""
for item, data in enumerate(fields): for item, data in enumerate(fields):
output += f"{fields[item]:{lengths[item]}} " output += f"{fields[item]:^{lengths[item]}} "
lines += "=" * lengths[item] + " " lines += "=" * lengths[item] + " "
output += f"\n{lines}" output += f"\n{lines}"
return output return output
@@ -80,31 +80,17 @@ def report_header(title, experiment, model_type):
def report_line(line): def report_line(line):
output = f"{line['dataset']:{lengths[0] + 5}s} " output = f"{line['dataset']:{lengths[0] + 5}s} "
data = models.copy() data = models.copy()
data.insert(0, "reference")
for key, model in enumerate(data): for key, model in enumerate(data):
output += f"{line[model]:{lengths[key + 1]}s} " output += f"{line[model]:{lengths[key + 1]}s} "
return output return output
def report_footer(agg): def report_footer(agg):
print( length = sum(lengths) + len(lengths) - 1
TextColor.GREEN print("-" * length)
+ f"we have better results {agg['better']['items']:2d} times"
)
print(
TextColor.RED
+ f"we have worse results {agg['worse']['items']:2d} times"
)
color = TextColor.LINE1 color = TextColor.LINE1
for item in models: for item in models:
print( print(color + f"{item:10s} ", end="")
color + f"{item:10s} used {agg[item]['items']:2d} times ", end=""
)
print(
color + f"better than reference {agg[item]['better']:2d} times ",
end="",
)
print(color + f"worse {agg[item]['worse']:2d} times ", end="")
print(color + f"best of models {agg[item]['best']:2d} times") print(color + f"best of models {agg[item]['best']:2d} times")
color = ( color = (
TextColor.LINE2 if color == TextColor.LINE1 else TextColor.LINE1 TextColor.LINE2 if color == TextColor.LINE1 else TextColor.LINE1
@@ -115,7 +101,7 @@ def report_footer(agg):
dbh = MySQL() dbh = MySQL()
database = dbh.get_connection() database = dbh.get_connection()
dt = Datasets(False, False, "tanveer") dt = Datasets(False, False, "tanveer")
fields = ("Dataset", "Reference") fields = ("Dataset",)
models = models_tree if model_type == "tree" else models_ensemble models = models_tree if model_type == "tree" else models_ensemble
for item in models: for item in models:
fields += (f"{item}",) fields += (f"{item}",)
@@ -127,9 +113,6 @@ for item in [
"worse", "worse",
] + models: ] + models:
agg[item] = {} agg[item] = {}
agg[item]["items"] = 0
agg[item]["better"] = 0
agg[item]["worse"] = 0
agg[item]["best"] = 0 agg[item]["best"] = 0
if csv_output: if csv_output:
f = open(report_csv, "w") f = open(report_csv, "w")
@@ -143,22 +126,13 @@ for dataset in dt:
for model in models: for model in models:
record = dbh.find_best(dataset[0], model, experiment) record = dbh.find_best(dataset[0], model, experiment)
if record is None: if record is None:
line[model] = color + "-" * 9 + " " line[model] = color + "-" * 12
else: else:
reference = record[13] reference = record[13]
accuracy = record[5] accuracy = record[5]
acc_std = record[11]
find_one = True find_one = True
agg[model]["items"] += 1 item = f"{accuracy:.4f}±{acc_std:.3f}"
if accuracy > reference:
sign = "+"
agg["better"]["items"] += 1
agg[model]["better"] += 1
else:
sign = "-"
agg["worse"]["items"] += 1
agg[model]["worse"] += 1
item = f"{accuracy:9.7} {sign}"
line["reference"] = f"{reference:9.7}"
if accuracy == max_accuracy: if accuracy == max_accuracy:
line[model] = ( line[model] = (
TextColor.GREEN + TextColor.BOLD + item + TextColor.ENDC TextColor.GREEN + TextColor.BOLD + item + TextColor.ENDC

15
comparewodt/compare.sh Executable file
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@@ -0,0 +1,15 @@
#!/bin/bash
function busca_resultado() {
res=`grep -w $2 $1|cut -d";" -f2`
}
estratificado="estratificado.txt"
no_estratificado="no_estratificado.txt"
busca_resultado $estratificado "wine"
echo $res
busca_resultado $no_estratificado "zoo"
echo $res
cat $estratificado|while read dataset accuracy
do
busca_resultado $no_estratificado $dataset
echo "$dataset E[$accuracy] NE[$res]"
done

11
experimentation/Experiments.py
View File

@@ -6,8 +6,9 @@ import warnings
from sklearn.model_selection import GridSearchCV, cross_validate from sklearn.model_selection import GridSearchCV, cross_validate
from . import Models from . import Models
from .Database import Hyperparameters, Outcomes, MySQL from .Database import Hyperparameters, MySQL, Outcomes
from .Sets import Datasets from .Sets import Datasets
from sklearn.model_selection._split import KFold
class Experiment: class Experiment:
@@ -81,6 +82,7 @@ class Experiment:
for item in outcomes: for item in outcomes:
total[item] = [] total[item] = []
for random_state in [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]: for random_state in [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]:
kfold = KFold(shuffle=True, random_state=random_state, n_splits=5)
model.set_params(**{"random_state": random_state}) model.set_params(**{"random_state": random_state})
print(f"{random_state}, ", end="", flush=True) print(f"{random_state}, ", end="", flush=True)
with warnings.catch_warnings(): with warnings.catch_warnings():
@@ -88,7 +90,12 @@ class Experiment:
# Also affect subprocesses # Also affect subprocesses
os.environ["PYTHONWARNINGS"] = "ignore" os.environ["PYTHONWARNINGS"] = "ignore"
results = cross_validate( results = cross_validate(
model, X, y, return_train_score=True, n_jobs=self._threads model,
X,
y,
return_train_score=True,
n_jobs=self._threads,
cv=kfold,
) )
for item in outcomes: for item in outcomes:
total[item].append(results[item]) total[item].append(results[item])

484
report.csv
View File

@@ -1,295 +1,295 @@
dataset, classifier, accuracy dataset, classifier, accuracy
balance-scale, stree, 0.9488 balance-scale, stree, 0.97056
balance-scale, wodt, 0.86016 balance-scale, wodt, 0.912
balance-scale, j48svm, 0.94128 balance-scale, j48svm, 0.94
balance-scale, oc1, 0.9192 balance-scale, oc1, 0.9192
balance-scale, cart, 0.57312 balance-scale, cart, 0.78816
balance-scale, baseRaF, 0.790543 balance-scale, baseRaF, 0.706738
balloons, stree, 0.866667 balloons, stree, 0.86
balloons, wodt, 0.626667 balloons, wodt, 0.688333
balloons, j48svm, 0.511667 balloons, j48svm, 0.595
balloons, oc1, 0.62 balloons, oc1, 0.62
balloons, cart, 0.683333 balloons, cart, 0.671667
balloons, baseRaF, 0.5375 balloons, baseRaF, 0.605
breast-cancer-wisc-diag, stree, 0.978932 breast-cancer-wisc-diag, stree, 0.972764
breast-cancer-wisc-diag, wodt, 0.962546 breast-cancer-wisc-diag, wodt, 0.967317
breast-cancer-wisc-diag, j48svm, 0.956397 breast-cancer-wisc-diag, j48svm, 0.952878
breast-cancer-wisc-diag, oc1, 0.933477 breast-cancer-wisc-diag, oc1, 0.933477
breast-cancer-wisc-diag, cart, 0.933925 breast-cancer-wisc-diag, cart, 0.93953
breast-cancer-wisc-diag, baseRaF, 0.94808 breast-cancer-wisc-diag, baseRaF, 0.965694
breast-cancer-wisc-prog, stree, 0.828462 breast-cancer-wisc-prog, stree, 0.811128
breast-cancer-wisc-prog, wodt, 0.689654 breast-cancer-wisc-prog, wodt, 0.710141
breast-cancer-wisc-prog, j48svm, 0.697013 breast-cancer-wisc-prog, j48svm, 0.724038
breast-cancer-wisc-prog, oc1, 0.71 breast-cancer-wisc-prog, oc1, 0.71
breast-cancer-wisc-prog, cart, 0.749462 breast-cancer-wisc-prog, cart, 0.699833
breast-cancer-wisc-prog, baseRaF, 0.70087 breast-cancer-wisc-prog, baseRaF, 0.74485
breast-cancer-wisc, stree, 0.965694 breast-cancer-wisc, stree, 0.965802
breast-cancer-wisc, wodt, 0.936199 breast-cancer-wisc, wodt, 0.946208
breast-cancer-wisc, j48svm, 0.967529 breast-cancer-wisc, j48svm, 0.967674
breast-cancer-wisc, oc1, 0.940194 breast-cancer-wisc, oc1, 0.940194
breast-cancer-wisc, cart, 0.937074 breast-cancer-wisc, cart, 0.940629
breast-cancer-wisc, baseRaF, 0.946961 breast-cancer-wisc, baseRaF, 0.942857
breast-cancer, stree, 0.730853 breast-cancer, stree, 0.733158
breast-cancer, wodt, 0.619673 breast-cancer, wodt, 0.650236
breast-cancer, j48svm, 0.712976 breast-cancer, j48svm, 0.707719
breast-cancer, oc1, 0.649728 breast-cancer, oc1, 0.649728
breast-cancer, cart, 0.637205 breast-cancer, cart, 0.65444
breast-cancer, baseRaF, 0.685839 breast-cancer, baseRaF, 0.656438
cardiotocography-10clases, stree, 0.666522 cardiotocography-10clases, stree, 0.712009
cardiotocography-10clases, wodt, 0.627577 cardiotocography-10clases, wodt, 0.773706
cardiotocography-10clases, j48svm, 0.832552 cardiotocography-10clases, j48svm, 0.830812
cardiotocography-10clases, oc1, 0.795528 cardiotocography-10clases, oc1, 0.795528
cardiotocography-10clases, cart, 0.716373 cardiotocography-10clases, cart, 0.818864
cardiotocography-10clases, baseRaF, 0.679912 cardiotocography-10clases, baseRaF, 0.774788
cardiotocography-3clases, stree, 0.848074 cardiotocography-3clases, stree, 0.891956
cardiotocography-3clases, wodt, 0.803063 cardiotocography-3clases, wodt, 0.897509
cardiotocography-3clases, j48svm, 0.9278 cardiotocography-3clases, j48svm, 0.927327
cardiotocography-3clases, oc1, 0.899811 cardiotocography-3clases, oc1, 0.899811
cardiotocography-3clases, cart, 0.844726 cardiotocography-3clases, cart, 0.929258
cardiotocography-3clases, baseRaF, 0.880937 cardiotocography-3clases, baseRaF, 0.896715
conn-bench-sonar-mines-rocks, stree, 0.597433 conn-bench-sonar-mines-rocks, stree, 0.71439
conn-bench-sonar-mines-rocks, wodt, 0.649501 conn-bench-sonar-mines-rocks, wodt, 0.824959
conn-bench-sonar-mines-rocks, j48svm, 0.728897 conn-bench-sonar-mines-rocks, j48svm, 0.73892
conn-bench-sonar-mines-rocks, oc1, 0.710798 conn-bench-sonar-mines-rocks, oc1, 0.710798
conn-bench-sonar-mines-rocks, cart, 0.630511 conn-bench-sonar-mines-rocks, cart, 0.728711
conn-bench-sonar-mines-rocks, baseRaF, 0.727885 conn-bench-sonar-mines-rocks, baseRaF, 0.772981
cylinder-bands, stree, 0.628081 cylinder-bands, stree, 0.687101
cylinder-bands, wodt, 0.570849 cylinder-bands, wodt, 0.704074
cylinder-bands, j48svm, 0.736126 cylinder-bands, j48svm, 0.726351
cylinder-bands, oc1, 0.67106 cylinder-bands, oc1, 0.67106
cylinder-bands, cart, 0.583602 cylinder-bands, cart, 0.712703
cylinder-bands, baseRaF, 0.647188 cylinder-bands, baseRaF, 0.675117
dermatology, stree, 0.975454 dermatology, stree, 0.971833
dermatology, wodt, 0.951925 dermatology, wodt, 0.965557
dermatology, j48svm, 0.955472 dermatology, j48svm, 0.955735
dermatology, oc1, 0.916087 dermatology, oc1, 0.916087
dermatology, cart, 0.918064 dermatology, cart, 0.932766
dermatology, baseRaF, 0.886384 dermatology, baseRaF, 0.970723
echocardiogram, stree, 0.82094 echocardiogram, stree, 0.814758
echocardiogram, wodt, 0.723077 echocardiogram, wodt, 0.733875
echocardiogram, j48svm, 0.835726 echocardiogram, j48svm, 0.805527
echocardiogram, oc1, 0.748291 echocardiogram, oc1, 0.748291
echocardiogram, cart, 0.757493 echocardiogram, cart, 0.745043
echocardiogram, baseRaF, 0.775732 echocardiogram, baseRaF, 0.753522
fertility, stree, 0.88 fertility, stree, 0.88
fertility, wodt, 0.763 fertility, wodt, 0.785
fertility, j48svm, 0.864 fertility, j48svm, 0.857
fertility, oc1, 0.793 fertility, oc1, 0.793
fertility, cart, 0.752 fertility, cart, 0.8
fertility, baseRaF, 0.837 fertility, baseRaF, 0.798
haberman-survival, stree, 0.764675 haberman-survival, stree, 0.727795
haberman-survival, wodt, 0.647827 haberman-survival, wodt, 0.664707
haberman-survival, j48svm, 0.708847 haberman-survival, j48svm, 0.714056
haberman-survival, oc1, 0.651634 haberman-survival, oc1, 0.651634
haberman-survival, cart, 0.640899 haberman-survival, cart, 0.65
haberman-survival, baseRaF, 0.733443 haberman-survival, baseRaF, 0.720133
heart-hungarian, stree, 0.829924 heart-hungarian, stree, 0.827522
heart-hungarian, wodt, 0.758328 heart-hungarian, wodt, 0.764909
heart-hungarian, j48svm, 0.785061 heart-hungarian, j48svm, 0.785026
heart-hungarian, oc1, 0.758298 heart-hungarian, oc1, 0.758298
heart-hungarian, cart, 0.731297 heart-hungarian, cart, 0.760508
heart-hungarian, baseRaF, 0.778289 heart-hungarian, baseRaF, 0.779804
hepatitis, stree, 0.83871 hepatitis, stree, 0.824516
hepatitis, wodt, 0.774839 hepatitis, wodt, 0.785806
hepatitis, j48svm, 0.761935 hepatitis, j48svm, 0.761935
hepatitis, oc1, 0.756774 hepatitis, oc1, 0.756774
hepatitis, cart, 0.766452 hepatitis, cart, 0.765161
hepatitis, baseRaF, 0.764477 hepatitis, baseRaF, 0.773671
ilpd-indian-liver, stree, 0.742691 ilpd-indian-liver, stree, 0.719207
ilpd-indian-liver, wodt, 0.650159 ilpd-indian-liver, wodt, 0.676176
ilpd-indian-liver, j48svm, 0.692116 ilpd-indian-liver, j48svm, 0.690339
ilpd-indian-liver, oc1, 0.660139 ilpd-indian-liver, oc1, 0.660139
ilpd-indian-liver, cart, 0.653587 ilpd-indian-liver, cart, 0.663423
ilpd-indian-liver, baseRaF, 0.698181 ilpd-indian-liver, baseRaF, 0.696685
ionosphere, stree, 0.948732 ionosphere, stree, 0.953276
ionosphere, wodt, 0.857328 ionosphere, wodt, 0.88008
ionosphere, j48svm, 0.891445 ionosphere, j48svm, 0.891984
ionosphere, oc1, 0.879742 ionosphere, oc1, 0.879742
ionosphere, cart, 0.876125 ionosphere, cart, 0.895771
ionosphere, baseRaF, 0.87236 ionosphere, baseRaF, 0.875389
iris, stree, 0.98 iris, stree, 0.965333
iris, wodt, 0.96 iris, wodt, 0.946
iris, j48svm, 0.941333 iris, j48svm, 0.947333
iris, oc1, 0.948 iris, oc1, 0.948
iris, cart, 0.956667 iris, cart, 0.938667
iris, baseRaF, 0.944726 iris, baseRaF, 0.953413
led-display, stree, 0.7071 led-display, stree, 0.703
led-display, wodt, 0.7053 led-display, wodt, 0.7049
led-display, j48svm, 0.7177 led-display, j48svm, 0.7204
led-display, oc1, 0.6993 led-display, oc1, 0.6993
led-display, cart, 0.7073 led-display, cart, 0.7037
led-display, baseRaF, 0.56058 led-display, baseRaF, 0.70178
libras, stree, 0.761111 libras, stree, 0.788333
libras, wodt, 0.671111 libras, wodt, 0.764167
libras, j48svm, 0.664167 libras, j48svm, 0.66
libras, oc1, 0.645 libras, oc1, 0.645
libras, cart, 0.555556 libras, cart, 0.655
libras, baseRaF, 0.657278 libras, baseRaF, 0.726722
low-res-spect, stree, 0.879492 low-res-spect, stree, 0.865713
low-res-spect, wodt, 0.845585 low-res-spect, wodt, 0.856459
low-res-spect, j48svm, 0.831852 low-res-spect, j48svm, 0.83358
low-res-spect, oc1, 0.824671 low-res-spect, oc1, 0.824671
low-res-spect, cart, 0.826327 low-res-spect, cart, 0.829206
low-res-spect, baseRaF, 0.765601 low-res-spect, baseRaF, 0.790875
lymphography, stree, 0.864828 lymphography, stree, 0.823425
lymphography, wodt, 0.784598 lymphography, wodt, 0.808782
lymphography, j48svm, 0.772552 lymphography, j48svm, 0.778552
lymphography, oc1, 0.734634 lymphography, oc1, 0.734634
lymphography, cart, 0.79331 lymphography, cart, 0.766276
lymphography, baseRaF, 0.718919 lymphography, baseRaF, 0.761622
mammographic, stree, 0.819062 mammographic, stree, 0.817068
mammographic, wodt, 0.76379 mammographic, wodt, 0.759839
mammographic, j48svm, 0.816863 mammographic, j48svm, 0.821435
mammographic, oc1, 0.768805 mammographic, oc1, 0.768805
mammographic, cart, 0.766706 mammographic, cart, 0.757131
mammographic, baseRaF, 0.802937 mammographic, baseRaF, 0.780206
molec-biol-promoter, stree, 0.810822 molec-biol-promoter, stree, 0.767056
molec-biol-promoter, wodt, 0.741905 molec-biol-promoter, wodt, 0.798528
molec-biol-promoter, j48svm, 0.785455 molec-biol-promoter, j48svm, 0.744935
molec-biol-promoter, oc1, 0.734805 molec-biol-promoter, oc1, 0.734805
molec-biol-promoter, cart, 0.739437 molec-biol-promoter, cart, 0.748701
molec-biol-promoter, baseRaF, 0.644409 molec-biol-promoter, baseRaF, 0.667239
musk-1, stree, 0.75432 musk-1, stree, 0.916388
musk-1, wodt, 0.734763 musk-1, wodt, 0.838914
musk-1, j48svm, 0.806143 musk-1, j48svm, 0.82693
musk-1, oc1, 0.776401 musk-1, oc1, 0.776401
musk-1, cart, 0.683419 musk-1, cart, 0.780215
musk-1, baseRaF, 0.764916 musk-1, baseRaF, 0.834034
oocytes_merluccius_nucleus_4d, stree, 0.812142 oocytes_merluccius_nucleus_4d, stree, 0.835125
oocytes_merluccius_nucleus_4d, wodt, 0.723538 oocytes_merluccius_nucleus_4d, wodt, 0.737673
oocytes_merluccius_nucleus_4d, j48svm, 0.740807 oocytes_merluccius_nucleus_4d, j48svm, 0.741766
oocytes_merluccius_nucleus_4d, oc1, 0.743199 oocytes_merluccius_nucleus_4d, oc1, 0.743199
oocytes_merluccius_nucleus_4d, cart, 0.706999 oocytes_merluccius_nucleus_4d, cart, 0.728265
oocytes_merluccius_nucleus_4d, baseRaF, 0.743156 oocytes_merluccius_nucleus_4d, baseRaF, 0.792313
oocytes_merluccius_states_2f, stree, 0.921688 oocytes_merluccius_states_2f, stree, 0.87359
oocytes_merluccius_states_2f, wodt, 0.884993 oocytes_merluccius_states_2f, wodt, 0.895115
oocytes_merluccius_states_2f, j48svm, 0.900002 oocytes_merluccius_states_2f, j48svm, 0.901374
oocytes_merluccius_states_2f, oc1, 0.889223 oocytes_merluccius_states_2f, oc1, 0.889223
oocytes_merluccius_states_2f, cart, 0.877563 oocytes_merluccius_states_2f, cart, 0.891193
oocytes_merluccius_states_2f, baseRaF, 0.87948 oocytes_merluccius_states_2f, baseRaF, 0.910551
oocytes_trisopterus_nucleus_2f, stree, 0.747691 oocytes_trisopterus_nucleus_2f, stree, 0.799995
oocytes_trisopterus_nucleus_2f, wodt, 0.654345 oocytes_trisopterus_nucleus_2f, wodt, 0.751431
oocytes_trisopterus_nucleus_2f, j48svm, 0.755697 oocytes_trisopterus_nucleus_2f, j48svm, 0.756587
oocytes_trisopterus_nucleus_2f, oc1, 0.747697 oocytes_trisopterus_nucleus_2f, oc1, 0.747697
oocytes_trisopterus_nucleus_2f, cart, 0.704823 oocytes_trisopterus_nucleus_2f, cart, 0.734313
oocytes_trisopterus_nucleus_2f, baseRaF, 0.721601 oocytes_trisopterus_nucleus_2f, baseRaF, 0.76193
oocytes_trisopterus_states_5b, stree, 0.845361 oocytes_trisopterus_states_5b, stree, 0.924441
oocytes_trisopterus_states_5b, wodt, 0.769139 oocytes_trisopterus_states_5b, wodt, 0.89165
oocytes_trisopterus_states_5b, j48svm, 0.885075 oocytes_trisopterus_states_5b, j48svm, 0.887943
oocytes_trisopterus_states_5b, oc1, 0.86393 oocytes_trisopterus_states_5b, oc1, 0.86393
oocytes_trisopterus_states_5b, cart, 0.757974 oocytes_trisopterus_states_5b, cart, 0.870263
oocytes_trisopterus_states_5b, baseRaF, 0.862434 oocytes_trisopterus_states_5b, baseRaF, 0.922149
parkinsons, stree, 0.835897 parkinsons, stree, 0.865641
parkinsons, wodt, 0.811795 parkinsons, wodt, 0.901538
parkinsons, j48svm, 0.859487 parkinsons, j48svm, 0.844615
parkinsons, oc1, 0.865641 parkinsons, oc1, 0.865641
parkinsons, cart, 0.725128 parkinsons, cart, 0.855897
parkinsons, baseRaF, 0.847298 parkinsons, baseRaF, 0.87924
pima, stree, 0.780002 pima, stree, 0.764053
pima, wodt, 0.697832 pima, wodt, 0.681591
pima, j48svm, 0.748314 pima, j48svm, 0.749876
pima, oc1, 0.693027 pima, oc1, 0.693027
pima, cart, 0.712883 pima, cart, 0.701172
pima, baseRaF, 0.70849 pima, baseRaF, 0.697005
pittsburg-bridges-MATERIAL, stree, 0.886147 pittsburg-bridges-MATERIAL, stree, 0.867749
pittsburg-bridges-MATERIAL, wodt, 0.762208 pittsburg-bridges-MATERIAL, wodt, 0.79961
pittsburg-bridges-MATERIAL, j48svm, 0.84645 pittsburg-bridges-MATERIAL, j48svm, 0.855844
pittsburg-bridges-MATERIAL, oc1, 0.81026 pittsburg-bridges-MATERIAL, oc1, 0.81026
pittsburg-bridges-MATERIAL, cart, 0.730087 pittsburg-bridges-MATERIAL, cart, 0.783593
pittsburg-bridges-MATERIAL, baseRaF, 0.800316 pittsburg-bridges-MATERIAL, baseRaF, 0.81136
pittsburg-bridges-REL-L, stree, 0.578143 pittsburg-bridges-REL-L, stree, 0.564048
pittsburg-bridges-REL-L, wodt, 0.574429 pittsburg-bridges-REL-L, wodt, 0.617143
pittsburg-bridges-REL-L, j48svm, 0.653571 pittsburg-bridges-REL-L, j48svm, 0.645048
pittsburg-bridges-REL-L, oc1, 0.604957 pittsburg-bridges-REL-L, oc1, 0.604957
pittsburg-bridges-REL-L, cart, 0.581762 pittsburg-bridges-REL-L, cart, 0.625333
pittsburg-bridges-REL-L, baseRaF, 0.623964 pittsburg-bridges-REL-L, baseRaF, 0.622107
pittsburg-bridges-SPAN, stree, 0.677193 pittsburg-bridges-SPAN, stree, 0.658713
pittsburg-bridges-SPAN, wodt, 0.529357 pittsburg-bridges-SPAN, wodt, 0.606959
pittsburg-bridges-SPAN, j48svm, 0.626784 pittsburg-bridges-SPAN, j48svm, 0.621579
pittsburg-bridges-SPAN, oc1, 0.579333 pittsburg-bridges-SPAN, oc1, 0.579333
pittsburg-bridges-SPAN, cart, 0.536023 pittsburg-bridges-SPAN, cart, 0.557544
pittsburg-bridges-SPAN, baseRaF, 0.593913 pittsburg-bridges-SPAN, baseRaF, 0.630217
pittsburg-bridges-T-OR-D, stree, 0.902381 pittsburg-bridges-T-OR-D, stree, 0.849952
pittsburg-bridges-T-OR-D, wodt, 0.79 pittsburg-bridges-T-OR-D, wodt, 0.818429
pittsburg-bridges-T-OR-D, j48svm, 0.835619 pittsburg-bridges-T-OR-D, j48svm, 0.838333
pittsburg-bridges-T-OR-D, oc1, 0.831545 pittsburg-bridges-T-OR-D, oc1, 0.831545
pittsburg-bridges-T-OR-D, cart, 0.721667 pittsburg-bridges-T-OR-D, cart, 0.821619
pittsburg-bridges-T-OR-D, baseRaF, 0.841081 pittsburg-bridges-T-OR-D, baseRaF, 0.821007
planning, stree, 0.725525 planning, stree, 0.73527
planning, wodt, 0.552192 planning, wodt, 0.576847
planning, j48svm, 0.711246 planning, j48svm, 0.711381
planning, oc1, 0.566988 planning, oc1, 0.566988
planning, cart, 0.574384 planning, cart, 0.586712
planning, baseRaF, 0.626404 planning, baseRaF, 0.590586
post-operative, stree, 0.722222 post-operative, stree, 0.703333
post-operative, wodt, 0.56 post-operative, wodt, 0.535556
post-operative, j48svm, 0.692222 post-operative, j48svm, 0.701111
post-operative, oc1, 0.542222 post-operative, oc1, 0.542222
post-operative, cart, 0.586667 post-operative, cart, 0.567778
post-operative, baseRaF, 0.669413 post-operative, baseRaF, 0.539375
seeds, stree, 0.949048 seeds, stree, 0.952857
seeds, wodt, 0.925238 seeds, wodt, 0.940476
seeds, j48svm, 0.912381 seeds, j48svm, 0.909524
seeds, oc1, 0.932381 seeds, oc1, 0.932381
seeds, cart, 0.879524 seeds, cart, 0.900476
seeds, baseRaF, 0.904209 seeds, baseRaF, 0.942518
statlog-australian-credit, stree, 0.678116 statlog-australian-credit, stree, 0.678261
statlog-australian-credit, wodt, 0.571739 statlog-australian-credit, wodt, 0.561594
statlog-australian-credit, j48svm, 0.655652 statlog-australian-credit, j48svm, 0.66029
statlog-australian-credit, oc1, 0.573913 statlog-australian-credit, oc1, 0.573913
statlog-australian-credit, cart, 0.606377 statlog-australian-credit, cart, 0.595507
statlog-australian-credit, baseRaF, 0.678261 statlog-australian-credit, baseRaF, 0.678261
statlog-german-credit, stree, 0.7472 statlog-german-credit, stree, 0.7569
statlog-german-credit, wodt, 0.6878 statlog-german-credit, wodt, 0.6929
statlog-german-credit, j48svm, 0.7261 statlog-german-credit, j48svm, 0.7244
statlog-german-credit, oc1, 0.6874 statlog-german-credit, oc1, 0.6874
statlog-german-credit, cart, 0.6834 statlog-german-credit, cart, 0.6738
statlog-german-credit, baseRaF, 0.69528 statlog-german-credit, baseRaF, 0.68762
statlog-heart, stree, 0.848148 statlog-heart, stree, 0.822222
statlog-heart, wodt, 0.773333 statlog-heart, wodt, 0.777778
statlog-heart, j48svm, 0.815556 statlog-heart, j48svm, 0.795926
statlog-heart, oc1, 0.749259 statlog-heart, oc1, 0.749259
statlog-heart, cart, 0.758519 statlog-heart, cart, 0.762222
statlog-heart, baseRaF, 0.767883 statlog-heart, baseRaF, 0.747605
statlog-image, stree, 0.959307 statlog-image, stree, 0.956623
statlog-image, wodt, 0.955671 statlog-image, wodt, 0.954632
statlog-image, j48svm, 0.966797 statlog-image, j48svm, 0.967403
statlog-image, oc1, 0.95013 statlog-image, oc1, 0.95013
statlog-image, cart, 0.963377 statlog-image, cart, 0.964892
statlog-image, baseRaF, 0.825938 statlog-image, baseRaF, 0.953604
statlog-vehicle, stree, 0.801413 statlog-vehicle, stree, 0.788537
statlog-vehicle, wodt, 0.731811 statlog-vehicle, wodt, 0.726492
statlog-vehicle, j48svm, 0.730389 statlog-vehicle, j48svm, 0.729651
statlog-vehicle, oc1, 0.708496 statlog-vehicle, oc1, 0.708496
statlog-vehicle, cart, 0.728592 statlog-vehicle, cart, 0.728367
statlog-vehicle, baseRaF, 0.683698 statlog-vehicle, baseRaF, 0.789572
synthetic-control, stree, 0.971667 synthetic-control, stree, 0.95
synthetic-control, wodt, 0.979 synthetic-control, wodt, 0.973167
synthetic-control, j48svm, 0.921667 synthetic-control, j48svm, 0.922333
synthetic-control, oc1, 0.863167 synthetic-control, oc1, 0.863167
synthetic-control, cart, 0.906333 synthetic-control, cart, 0.908333
synthetic-control, baseRaF, 0.8999 synthetic-control, baseRaF, 0.971567
tic-tac-toe, stree, 0.987435 tic-tac-toe, stree, 0.984444
tic-tac-toe, wodt, 0.849967 tic-tac-toe, wodt, 0.93905
tic-tac-toe, j48svm, 0.983301 tic-tac-toe, j48svm, 0.983295
tic-tac-toe, oc1, 0.91849 tic-tac-toe, oc1, 0.91849
tic-tac-toe, cart, 0.836177 tic-tac-toe, cart, 0.951558
tic-tac-toe, baseRaF, 0.836562 tic-tac-toe, baseRaF, 0.974906
vertebral-column-2clases, stree, 0.829032 vertebral-column-2clases, stree, 0.851936
vertebral-column-2clases, wodt, 0.793548 vertebral-column-2clases, wodt, 0.801935
vertebral-column-2clases, j48svm, 0.850645 vertebral-column-2clases, j48svm, 0.84871
vertebral-column-2clases, oc1, 0.815161 vertebral-column-2clases, oc1, 0.815161
vertebral-column-2clases, cart, 0.775161 vertebral-column-2clases, cart, 0.784839
vertebral-column-2clases, baseRaF, 0.794591 vertebral-column-2clases, baseRaF, 0.822601
wine, stree, 0.977778 wine, stree, 0.949333
wine, wodt, 0.968079 wine, wodt, 0.973048
wine, j48svm, 0.983778 wine, j48svm, 0.979143
wine, oc1, 0.916165 wine, oc1, 0.916165
wine, cart, 0.897524 wine, cart, 0.921937
wine, baseRaF, 0.923513 wine, baseRaF, 0.97748
zoo, stree, 0.96 zoo, stree, 0.955524
zoo, wodt, 0.945 zoo, wodt, 0.954429
zoo, j48svm, 0.920857 zoo, j48svm, 0.92381
zoo, oc1, 0.890952 zoo, oc1, 0.890952
zoo, cart, 0.958 zoo, cart, 0.957476
zoo, baseRaF, 0.8861 zoo, baseRaF, 0.936262
1 dataset classifier accuracy
2 balance-scale stree 0.9488 0.97056
3 balance-scale wodt 0.86016 0.912
4 balance-scale j48svm 0.94128 0.94
5 balance-scale oc1 0.9192
6 balance-scale cart 0.57312 0.78816
7 balance-scale baseRaF 0.790543 0.706738
8 balloons stree 0.866667 0.86
9 balloons wodt 0.626667 0.688333
10 balloons j48svm 0.511667 0.595
11 balloons oc1 0.62
12 balloons cart 0.683333 0.671667
13 balloons baseRaF 0.5375 0.605
14 breast-cancer-wisc-diag stree 0.978932 0.972764
15 breast-cancer-wisc-diag wodt 0.962546 0.967317
16 breast-cancer-wisc-diag j48svm 0.956397 0.952878
17 breast-cancer-wisc-diag oc1 0.933477
18 breast-cancer-wisc-diag cart 0.933925 0.93953
19 breast-cancer-wisc-diag baseRaF 0.94808 0.965694
20 breast-cancer-wisc-prog stree 0.828462 0.811128
21 breast-cancer-wisc-prog wodt 0.689654 0.710141
22 breast-cancer-wisc-prog j48svm 0.697013 0.724038
23 breast-cancer-wisc-prog oc1 0.71
24 breast-cancer-wisc-prog cart 0.749462 0.699833
25 breast-cancer-wisc-prog baseRaF 0.70087 0.74485
26 breast-cancer-wisc stree 0.965694 0.965802
27 breast-cancer-wisc wodt 0.936199 0.946208
28 breast-cancer-wisc j48svm 0.967529 0.967674
29 breast-cancer-wisc oc1 0.940194
30 breast-cancer-wisc cart 0.937074 0.940629
31 breast-cancer-wisc baseRaF 0.946961 0.942857
32 breast-cancer stree 0.730853 0.733158
33 breast-cancer wodt 0.619673 0.650236
34 breast-cancer j48svm 0.712976 0.707719
35 breast-cancer oc1 0.649728
36 breast-cancer cart 0.637205 0.65444
37 breast-cancer baseRaF 0.685839 0.656438
38 cardiotocography-10clases stree 0.666522 0.712009
39 cardiotocography-10clases wodt 0.627577 0.773706
40 cardiotocography-10clases j48svm 0.832552 0.830812
41 cardiotocography-10clases oc1 0.795528
42 cardiotocography-10clases cart 0.716373 0.818864
43 cardiotocography-10clases baseRaF 0.679912 0.774788
44 cardiotocography-3clases stree 0.848074 0.891956
45 cardiotocography-3clases wodt 0.803063 0.897509
46 cardiotocography-3clases j48svm 0.9278 0.927327
47 cardiotocography-3clases oc1 0.899811
48 cardiotocography-3clases cart 0.844726 0.929258
49 cardiotocography-3clases baseRaF 0.880937 0.896715
50 conn-bench-sonar-mines-rocks stree 0.597433 0.71439
51 conn-bench-sonar-mines-rocks wodt 0.649501 0.824959
52 conn-bench-sonar-mines-rocks j48svm 0.728897 0.73892
53 conn-bench-sonar-mines-rocks oc1 0.710798
54 conn-bench-sonar-mines-rocks cart 0.630511 0.728711
55 conn-bench-sonar-mines-rocks baseRaF 0.727885 0.772981
56 cylinder-bands stree 0.628081 0.687101
57 cylinder-bands wodt 0.570849 0.704074
58 cylinder-bands j48svm 0.736126 0.726351
59 cylinder-bands oc1 0.67106
60 cylinder-bands cart 0.583602 0.712703
61 cylinder-bands baseRaF 0.647188 0.675117
62 dermatology stree 0.975454 0.971833
63 dermatology wodt 0.951925 0.965557
64 dermatology j48svm 0.955472 0.955735
65 dermatology oc1 0.916087
66 dermatology cart 0.918064 0.932766
67 dermatology baseRaF 0.886384 0.970723
68 echocardiogram stree 0.82094 0.814758
69 echocardiogram wodt 0.723077 0.733875
70 echocardiogram j48svm 0.835726 0.805527
71 echocardiogram oc1 0.748291
72 echocardiogram cart 0.757493 0.745043
73 echocardiogram baseRaF 0.775732 0.753522
74 fertility stree 0.88
75 fertility wodt 0.763 0.785
76 fertility j48svm 0.864 0.857
77 fertility oc1 0.793
78 fertility cart 0.752 0.8
79 fertility baseRaF 0.837 0.798
80 haberman-survival stree 0.764675 0.727795
81 haberman-survival wodt 0.647827 0.664707
82 haberman-survival j48svm 0.708847 0.714056
83 haberman-survival oc1 0.651634
84 haberman-survival cart 0.640899 0.65
85 haberman-survival baseRaF 0.733443 0.720133
86 heart-hungarian stree 0.829924 0.827522
87 heart-hungarian wodt 0.758328 0.764909
88 heart-hungarian j48svm 0.785061 0.785026
89 heart-hungarian oc1 0.758298
90 heart-hungarian cart 0.731297 0.760508
91 heart-hungarian baseRaF 0.778289 0.779804
92 hepatitis stree 0.83871 0.824516
93 hepatitis wodt 0.774839 0.785806
94 hepatitis j48svm 0.761935
95 hepatitis oc1 0.756774
96 hepatitis cart 0.766452 0.765161
97 hepatitis baseRaF 0.764477 0.773671
98 ilpd-indian-liver stree 0.742691 0.719207
99 ilpd-indian-liver wodt 0.650159 0.676176
100 ilpd-indian-liver j48svm 0.692116 0.690339
101 ilpd-indian-liver oc1 0.660139
102 ilpd-indian-liver cart 0.653587 0.663423
103 ilpd-indian-liver baseRaF 0.698181 0.696685
104 ionosphere stree 0.948732 0.953276
105 ionosphere wodt 0.857328 0.88008
106 ionosphere j48svm 0.891445 0.891984
107 ionosphere oc1 0.879742
108 ionosphere cart 0.876125 0.895771
109 ionosphere baseRaF 0.87236 0.875389
110 iris stree 0.98 0.965333
111 iris wodt 0.96 0.946
112 iris j48svm 0.941333 0.947333
113 iris oc1 0.948
114 iris cart 0.956667 0.938667
115 iris baseRaF 0.944726 0.953413
116 led-display stree 0.7071 0.703
117 led-display wodt 0.7053 0.7049
118 led-display j48svm 0.7177 0.7204
119 led-display oc1 0.6993
120 led-display cart 0.7073 0.7037
121 led-display baseRaF 0.56058 0.70178
122 libras stree 0.761111 0.788333
123 libras wodt 0.671111 0.764167
124 libras j48svm 0.664167 0.66
125 libras oc1 0.645
126 libras cart 0.555556 0.655
127 libras baseRaF 0.657278 0.726722
128 low-res-spect stree 0.879492 0.865713
129 low-res-spect wodt 0.845585 0.856459
130 low-res-spect j48svm 0.831852 0.83358
131 low-res-spect oc1 0.824671
132 low-res-spect cart 0.826327 0.829206
133 low-res-spect baseRaF 0.765601 0.790875
134 lymphography stree 0.864828 0.823425
135 lymphography wodt 0.784598 0.808782
136 lymphography j48svm 0.772552 0.778552
137 lymphography oc1 0.734634
138 lymphography cart 0.79331 0.766276
139 lymphography baseRaF 0.718919 0.761622
140 mammographic stree 0.819062 0.817068
141 mammographic wodt 0.76379 0.759839
142 mammographic j48svm 0.816863 0.821435
143 mammographic oc1 0.768805
144 mammographic cart 0.766706 0.757131
145 mammographic baseRaF 0.802937 0.780206
146 molec-biol-promoter stree 0.810822 0.767056
147 molec-biol-promoter wodt 0.741905 0.798528
148 molec-biol-promoter j48svm 0.785455 0.744935
149 molec-biol-promoter oc1 0.734805
150 molec-biol-promoter cart 0.739437 0.748701
151 molec-biol-promoter baseRaF 0.644409 0.667239
152 musk-1 stree 0.75432 0.916388
153 musk-1 wodt 0.734763 0.838914
154 musk-1 j48svm 0.806143 0.82693
155 musk-1 oc1 0.776401
156 musk-1 cart 0.683419 0.780215
157 musk-1 baseRaF 0.764916 0.834034
158 oocytes_merluccius_nucleus_4d stree 0.812142 0.835125
159 oocytes_merluccius_nucleus_4d wodt 0.723538 0.737673
160 oocytes_merluccius_nucleus_4d j48svm 0.740807 0.741766
161 oocytes_merluccius_nucleus_4d oc1 0.743199
162 oocytes_merluccius_nucleus_4d cart 0.706999 0.728265
163 oocytes_merluccius_nucleus_4d baseRaF 0.743156 0.792313
164 oocytes_merluccius_states_2f stree 0.921688 0.87359
165 oocytes_merluccius_states_2f wodt 0.884993 0.895115
166 oocytes_merluccius_states_2f j48svm 0.900002 0.901374
167 oocytes_merluccius_states_2f oc1 0.889223
168 oocytes_merluccius_states_2f cart 0.877563 0.891193
169 oocytes_merluccius_states_2f baseRaF 0.87948 0.910551
170 oocytes_trisopterus_nucleus_2f stree 0.747691 0.799995
171 oocytes_trisopterus_nucleus_2f wodt 0.654345 0.751431
172 oocytes_trisopterus_nucleus_2f j48svm 0.755697 0.756587
173 oocytes_trisopterus_nucleus_2f oc1 0.747697
174 oocytes_trisopterus_nucleus_2f cart 0.704823 0.734313
175 oocytes_trisopterus_nucleus_2f baseRaF 0.721601 0.76193
176 oocytes_trisopterus_states_5b stree 0.845361 0.924441
177 oocytes_trisopterus_states_5b wodt 0.769139 0.89165
178 oocytes_trisopterus_states_5b j48svm 0.885075 0.887943
179 oocytes_trisopterus_states_5b oc1 0.86393
180 oocytes_trisopterus_states_5b cart 0.757974 0.870263
181 oocytes_trisopterus_states_5b baseRaF 0.862434 0.922149
182 parkinsons stree 0.835897 0.865641
183 parkinsons wodt 0.811795 0.901538
184 parkinsons j48svm 0.859487 0.844615
185 parkinsons oc1 0.865641
186 parkinsons cart 0.725128 0.855897
187 parkinsons baseRaF 0.847298 0.87924
188 pima stree 0.780002 0.764053
189 pima wodt 0.697832 0.681591
190 pima j48svm 0.748314 0.749876
191 pima oc1 0.693027
192 pima cart 0.712883 0.701172
193 pima baseRaF 0.70849 0.697005
194 pittsburg-bridges-MATERIAL stree 0.886147 0.867749
195 pittsburg-bridges-MATERIAL wodt 0.762208 0.79961
196 pittsburg-bridges-MATERIAL j48svm 0.84645 0.855844
197 pittsburg-bridges-MATERIAL oc1 0.81026
198 pittsburg-bridges-MATERIAL cart 0.730087 0.783593
199 pittsburg-bridges-MATERIAL baseRaF 0.800316 0.81136
200 pittsburg-bridges-REL-L stree 0.578143 0.564048
201 pittsburg-bridges-REL-L wodt 0.574429 0.617143
202 pittsburg-bridges-REL-L j48svm 0.653571 0.645048
203 pittsburg-bridges-REL-L oc1 0.604957
204 pittsburg-bridges-REL-L cart 0.581762 0.625333
205 pittsburg-bridges-REL-L baseRaF 0.623964 0.622107
206 pittsburg-bridges-SPAN stree 0.677193 0.658713
207 pittsburg-bridges-SPAN wodt 0.529357 0.606959
208 pittsburg-bridges-SPAN j48svm 0.626784 0.621579
209 pittsburg-bridges-SPAN oc1 0.579333
210 pittsburg-bridges-SPAN cart 0.536023 0.557544
211 pittsburg-bridges-SPAN baseRaF 0.593913 0.630217
212 pittsburg-bridges-T-OR-D stree 0.902381 0.849952
213 pittsburg-bridges-T-OR-D wodt 0.79 0.818429
214 pittsburg-bridges-T-OR-D j48svm 0.835619 0.838333
215 pittsburg-bridges-T-OR-D oc1 0.831545
216 pittsburg-bridges-T-OR-D cart 0.721667 0.821619
217 pittsburg-bridges-T-OR-D baseRaF 0.841081 0.821007
218 planning stree 0.725525 0.73527
219 planning wodt 0.552192 0.576847
220 planning j48svm 0.711246 0.711381
221 planning oc1 0.566988
222 planning cart 0.574384 0.586712
223 planning baseRaF 0.626404 0.590586
224 post-operative stree 0.722222 0.703333
225 post-operative wodt 0.56 0.535556
226 post-operative j48svm 0.692222 0.701111
227 post-operative oc1 0.542222
228 post-operative cart 0.586667 0.567778
229 post-operative baseRaF 0.669413 0.539375
230 seeds stree 0.949048 0.952857
231 seeds wodt 0.925238 0.940476
232 seeds j48svm 0.912381 0.909524
233 seeds oc1 0.932381
234 seeds cart 0.879524 0.900476
235 seeds baseRaF 0.904209 0.942518
236 statlog-australian-credit stree 0.678116 0.678261
237 statlog-australian-credit wodt 0.571739 0.561594
238 statlog-australian-credit j48svm 0.655652 0.66029
239 statlog-australian-credit oc1 0.573913
240 statlog-australian-credit cart 0.606377 0.595507
241 statlog-australian-credit baseRaF 0.678261
242 statlog-german-credit stree 0.7472 0.7569
243 statlog-german-credit wodt 0.6878 0.6929
244 statlog-german-credit j48svm 0.7261 0.7244
245 statlog-german-credit oc1 0.6874
246 statlog-german-credit cart 0.6834 0.6738
247 statlog-german-credit baseRaF 0.69528 0.68762
248 statlog-heart stree 0.848148 0.822222
249 statlog-heart wodt 0.773333 0.777778
250 statlog-heart j48svm 0.815556 0.795926
251 statlog-heart oc1 0.749259
252 statlog-heart cart 0.758519 0.762222
253 statlog-heart baseRaF 0.767883 0.747605
254 statlog-image stree 0.959307 0.956623
255 statlog-image wodt 0.955671 0.954632
256 statlog-image j48svm 0.966797 0.967403
257 statlog-image oc1 0.95013
258 statlog-image cart 0.963377 0.964892
259 statlog-image baseRaF 0.825938 0.953604
260 statlog-vehicle stree 0.801413 0.788537
261 statlog-vehicle wodt 0.731811 0.726492
262 statlog-vehicle j48svm 0.730389 0.729651
263 statlog-vehicle oc1 0.708496
264 statlog-vehicle cart 0.728592 0.728367
265 statlog-vehicle baseRaF 0.683698 0.789572
266 synthetic-control stree 0.971667 0.95
267 synthetic-control wodt 0.979 0.973167
268 synthetic-control j48svm 0.921667 0.922333
269 synthetic-control oc1 0.863167
270 synthetic-control cart 0.906333 0.908333
271 synthetic-control baseRaF 0.8999 0.971567
272 tic-tac-toe stree 0.987435 0.984444
273 tic-tac-toe wodt 0.849967 0.93905
274 tic-tac-toe j48svm 0.983301 0.983295
275 tic-tac-toe oc1 0.91849
276 tic-tac-toe cart 0.836177 0.951558
277 tic-tac-toe baseRaF 0.836562 0.974906
278 vertebral-column-2clases stree 0.829032 0.851936
279 vertebral-column-2clases wodt 0.793548 0.801935
280 vertebral-column-2clases j48svm 0.850645 0.84871
281 vertebral-column-2clases oc1 0.815161
282 vertebral-column-2clases cart 0.775161 0.784839
283 vertebral-column-2clases baseRaF 0.794591 0.822601
284 wine stree 0.977778 0.949333
285 wine wodt 0.968079 0.973048
286 wine j48svm 0.983778 0.979143
287 wine oc1 0.916165
288 wine cart 0.897524 0.921937
289 wine baseRaF 0.923513 0.97748
290 zoo stree 0.96 0.955524
291 zoo wodt 0.945 0.954429
292 zoo j48svm 0.920857 0.92381
293 zoo oc1 0.890952
294 zoo cart 0.958 0.957476
295 zoo baseRaF 0.8861 0.936262

40
stats_stree.py Normal file
View File

@@ -0,0 +1,40 @@
from stree import Stree
from experimentation.Sets import Datasets
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_depth(node, depth):
if node is None:
return depth
if node.is_leaf():
return depth + 1
return max(
compute_depth(node.get_up(), depth + 1),
compute_depth(node.get_down(), depth + 1),
)
dt = Datasets(True, False, "tanveer")
for dataset in dt:
dataset_name = dataset[0]
X, y = dt.load(dataset_name)
clf = Stree(random_state=1)
clf.fit(X, y)
accuracy = clf.score(X, y)
nodes, leaves = nodes_leaves(clf)
depth = compute_depth(clf.tree_, 0)
print(
f"{dataset_name:30s} {nodes:5d} {leaves:5d} {clf.depth_:5d} "
f"{depth:5d} {accuracy:7.5f}"
)

5
testwodt.py
View File

@@ -1,6 +1,6 @@
import argparse import argparse
from wodt import TreeClassifier from wodt import TreeClassifier
from sklearn.model_selection import cross_val_score from sklearn.model_selection import KFold, cross_val_score
import numpy as np import numpy as np
import random import random
from experimentation.Sets import Datasets from experimentation.Sets import Datasets
@@ -85,8 +85,9 @@ def process_dataset(dataset, verbose):
for random_state in random_seeds: for random_state in random_seeds:
random.seed(random_state) random.seed(random_state)
np.random.seed(random_state) np.random.seed(random_state)
kfold = KFold(shuffle=True, random_state=random_state, n_splits=5)
clf = TreeClassifier(random_state=random_state) clf = TreeClassifier(random_state=random_state)
res = cross_val_score(clf, X, y, cv=5) res = cross_val_score(clf, X, y, cv=kfold)
scores.append(res) scores.append(res)
if verbose: if verbose:
print( print(