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Fix nan in sorted

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Fix liblinear experiments
This commit is contained in:
Ricardo Montañana Gómez
2022-02-09 12:21:43 +01:00
parent 778007e1a6
commit 60389c6212
2 changed files with 29 additions and 23 deletions
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43
experiments/experiments.txt
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@@ -12,28 +12,29 @@
#ODTE_accuracy_slurm_0202_5.sh, ODTE&ODTE Random Forest rbf-trandom&{"n_jobs": 10, "n_estimators": 100, "max_features": "sqrt", "max_samples": 0.75, "be_hyperparams": "{\"kernel\": \"rbf\", \"splitter\": \"trandom\", \"max_features\": \"sqrt\"}"} #ODTE_accuracy_slurm_0202_5.sh, ODTE&ODTE Random Forest rbf-trandom&{"n_jobs": 10, "n_estimators": 100, "max_features": "sqrt", "max_samples": 0.75, "be_hyperparams": "{\"kernel\": \"rbf\", \"splitter\": \"trandom\", \"max_features\": \"sqrt\"}"}
#ODTE_accuracy_slurm_0202_6.sh, ODTE&ODTE Random Forest linear-trandom&{"n_jobs": 10, "n_estimators": 100, "max_features": "sqrt", "max_samples": 0.75, "be_hyperparams": "{\"kernel\": \"linear\", \"splitter\": \"trandom\", \"max_features\": \"sqrt\"}"} #ODTE_accuracy_slurm_0202_6.sh, ODTE&ODTE Random Forest linear-trandom&{"n_jobs": 10, "n_estimators": 100, "max_features": "sqrt", "max_samples": 0.75, "be_hyperparams": "{\"kernel\": \"linear\", \"splitter\": \"trandom\", \"max_features\": \"sqrt\"}"}
# Pseudo Random Forest # Pseudo Random Forest
ODTE&ODTE pseudo Random Forest rbf-best max_features-sqrt&{"n_jobs": 10, "n_estimators": 100, "max_features": "sqrt" "be_hyperparams": "{\"kernel\": \"rbf\", \"splitter\": \"best\", \"max_features\": \"sqrt\"}"} #ODTE&ODTE pseudo Random Forest rbf-best max_features-sqrt&{"n_jobs": 10, "n_estimators": 100, "max_features": "sqrt" "be_hyperparams": "{\"kernel\": \"rbf\", \"splitter\": \"best\", \"max_features\": \"sqrt\"}"}
ODTE&ODTE pseudo Random Forest linear-best max_features-sqrt&{"n_jobs": 10, "n_estimators": 100, "max_features": "sqrt", "be_hyperparams": "{\"kernel\": \"linear\", \"splitter\": \"best\", \"max_features\": \"sqrt\"}"} #ODTE&ODTE pseudo Random Forest linear-best max_features-sqrt&{"n_jobs": 10, "n_estimators": 100, "max_features": "sqrt", "be_hyperparams": "{\"kernel\": \"linear\", \"splitter\": \"best\", \"max_features\": \"sqrt\"}"}
ODTE&ODTE pseudo Random Forest rbf-random max_features-sqrt&{"n_jobs": 10, "n_estimators": 100, "max_features": "sqrt", "be_hyperparams": "{\"kernel\": \"rbf\", \"splitter\": \"random\", \"max_features\": \"sqrt\"}"} #ODTE&ODTE pseudo Random Forest rbf-random max_features-sqrt&{"n_jobs": 10, "n_estimators": 100, "max_features": "sqrt", "be_hyperparams": "{\"kernel\": \"rbf\", \"splitter\": \"random\", \"max_features\": \"sqrt\"}"}
ODTE&ODTE pseudo Random Forest linear-random max_features-sqrt&{"n_jobs": 10, "n_estimators": 100, "max_features": "sqrt", "be_hyperparams": "{\"kernel\": \"linear\", \"splitter\": \"random\", \"max_features\": \"sqrt\"}"} #ODTE&ODTE pseudo Random Forest linear-random max_features-sqrt&{"n_jobs": 10, "n_estimators": 100, "max_features": "sqrt", "be_hyperparams": "{\"kernel\": \"linear\", \"splitter\": \"random\", \"max_features\": \"sqrt\"}"}
ODTE&ODTE pseudo Random Forest rbf-trandom max_features-sqrt&{"n_jobs": 10, "n_estimators": 100, "max_features": "sqrt", "be_hyperparams": "{\"kernel\": \"rbf\", \"splitter\": \"trandom\", \"max_features\": \"sqrt\"}"} #ODTE&ODTE pseudo Random Forest rbf-trandom max_features-sqrt&{"n_jobs": 10, "n_estimators": 100, "max_features": "sqrt", "be_hyperparams": "{\"kernel\": \"rbf\", \"splitter\": \"trandom\", \"max_features\": \"sqrt\"}"}
ODTE&ODTE pseudo Random Forest linear-trandom max_features-sqrt&{"n_jobs": 10, "n_estimators": 100, "max_features": "sqrt", "be_hyperparams": "{\"kernel\": \"linear\", \"splitter\": \"trandom\", \"max_features\": \"sqrt\"}"} #ODTE&ODTE pseudo Random Forest linear-trandom max_features-sqrt&{"n_jobs": 10, "n_estimators": 100, "max_features": "sqrt", "be_hyperparams": "{\"kernel\": \"linear\", \"splitter\": \"trandom\", \"max_features\": \"sqrt\"}"}
ODTE&ODTE pseudo Random Forest rbf-best max_samples-0.75&{"n_jobs": 10, "n_estimators": 100, "max_samples": 0.75, "be_hyperparams": "{\"kernel\": \"rbf\", \"splitter\": \"best\", \"max_features\": \"sqrt\"}"} #ODTE&ODTE pseudo Random Forest rbf-best max_samples-0.75&{"n_jobs": 10, "n_estimators": 100, "max_samples": 0.75, "be_hyperparams": "{\"kernel\": \"rbf\", \"splitter\": \"best\", \"max_features\": \"sqrt\"}"}
ODTE&ODTE pseudo Random Forest linear-best max_samples-0.75&{"n_jobs": 10, "n_estimators": 100, "max_samples": 0.75, "be_hyperparams": "{\"kernel\": \"linear\", \"splitter\": \"best\", \"max_features\": \"sqrt\"}"} #ODTE&ODTE pseudo Random Forest linear-best max_samples-0.75&{"n_jobs": 10, "n_estimators": 100, "max_samples": 0.75, "be_hyperparams": "{\"kernel\": \"linear\", \"splitter\": \"best\", \"max_features\": \"sqrt\"}"}
ODTE&ODTE pseudo Random Forest rbf-random max_samples-0.75&{"n_jobs": 10, "n_estimators": 100, "max_samples": 0.75, "be_hyperparams": "{\"kernel\": \"rbf\", \"splitter\": \"random\", \"max_features\": \"sqrt\"}"} #ODTE&ODTE pseudo Random Forest rbf-random max_samples-0.75&{"n_jobs": 10, "n_estimators": 100, "max_samples": 0.75, "be_hyperparams": "{\"kernel\": \"rbf\", \"splitter\": \"random\", \"max_features\": \"sqrt\"}"}
ODTE&ODTE pseudo Random Forest linear-random max_samples-0.75&{"n_jobs": 10, "n_estimators": 100, "max_samples": 0.75, "be_hyperparams": "{\"kernel\": \"linear\", \"splitter\": \"random\", \"max_features\": \"sqrt\"}"} #ODTE&ODTE pseudo Random Forest linear-random max_samples-0.75&{"n_jobs": 10, "n_estimators": 100, "max_samples": 0.75, "be_hyperparams": "{\"kernel\": \"linear\", \"splitter\": \"random\", \"max_features\": \"sqrt\"}"}
ODTE&ODTE pseudo Random Forest rbf-trandom max_samples-0.75&{"n_jobs": 10, "n_estimators": 100, "max_samples": 0.75, "be_hyperparams": "{\"kernel\": \"rbf\", \"splitter\": \"trandom\", \"max_features\": \"sqrt\"}"} #ODTE&ODTE pseudo Random Forest rbf-trandom max_samples-0.75&{"n_jobs": 10, "n_estimators": 100, "max_samples": 0.75, "be_hyperparams": "{\"kernel\": \"rbf\", \"splitter\": \"trandom\", \"max_features\": \"sqrt\"}"}
ODTE&ODTE pseudo Random Forest linear-trandom max_samples-0.75&{"n_jobs": 10, "n_estimators": 100, "max_samples": 0.75, "be_hyperparams": "{\"kernel\": \"linear\", \"splitter\": \"trandom\", \"max_features\": \"sqrt\"}"} #ODTE&ODTE pseudo Random Forest linear-trandom max_samples-0.75&{"n_jobs": 10, "n_estimators": 100, "max_samples": 0.75, "be_hyperparams": "{\"kernel\": \"linear\", \"splitter\": \"trandom\", \"max_features\": \"sqrt\"}"}
#
# kernel liblinear Random Forest and pseudo Random Forest # kernel liblinear Random Forest and pseudo Random Forest
# RF # RF
ODTE&ODTE Random Forest liblinear-best max_features-sqrt&'{"n_jobs": 10, "n_estimators": 100, "max_features": "sqrt", "max_samples": 0.75, "be_hyperparams": "{\"kernel\": \"liblinear\", \"splitter\": \"best\", \"multiclass_strategy\": \"ovr\"}"}' ODTE&ODTE Random Forest liblinear-best max_features-sqrt&{"n_jobs": 10, "n_estimators": 100, "max_features": "sqrt", "max_samples": 0.75, "be_hyperparams": "{\"kernel\": \"liblinear\", \"splitter\": \"best\", \"multiclass_strategy\": \"ovr\"}"}
ODTE&ODTE Random Forest liblinear-random max_features-sqrt&'{"n_jobs": 10, "n_estimators": 100, "max_features": "sqrt", "max_samples": 0.75, "be_hyperparams": "{\"kernel\": \"liblinear\", \"splitter\": \"random\", \"multiclass_strategy\": \"ovr\"}"}' ODTE&ODTE Random Forest liblinear-random max_features-sqrt&{"n_jobs": 10, "n_estimators": 100, "max_features": "sqrt", "max_samples": 0.75, "be_hyperparams": "{\"kernel\": \"liblinear\", \"splitter\": \"random\", \"multiclass_strategy\": \"ovr\"}"}
ODTE&ODTE Random Forest liblinear-trandom max_features-sqrt&'{"n_jobs": 10, "n_estimators": 100, "max_features": "sqrt", "max_samples": 0.75, "be_hyperparams": "{\"kernel\": \"liblinear\", \"splitter\": \"trandom\", \"multiclass_strategy\": \"ovr\"}"}' ODTE&ODTE Random Forest liblinear-trandom max_features-sqrt&{"n_jobs": 10, "n_estimators": 100, "max_features": "sqrt", "max_samples": 0.75, "be_hyperparams": "{\"kernel\": \"liblinear\", \"splitter\": \"trandom\", \"multiclass_strategy\": \"ovr\"}"}
# pseudo RF # pseudo RF
ODTE&ODTE Random Forest liblinear-best max_samples-sqrt&'{"n_jobs": 10, "n_estimators": 100, "max_samples": 0.75, "be_hyperparams": "{\"kernel\": \"liblinear\", \"splitter\": \"trandom\", \"multiclass_strategy\": \"ovr\"}"}' ODTE&ODTE pseudo Random Forest liblinear-best max_samples-sqrt&{"n_jobs": 10, "n_estimators": 100, "max_samples": 0.75, "be_hyperparams": "{\"kernel\": \"liblinear\", \"splitter\": \"best\", \"multiclass_strategy\": \"ovr\"}"}
ODTE&ODTE Random Forest liblinear-random max_samples-sqrt&'{"n_jobs": 10, "n_estimators": 100, "max_samples": 0.75, "be_hyperparams": "{\"kernel\": \"liblinear\", \"splitter\": \"trandom\", \"multiclass_strategy\": \"ovr\"}"}' ODTE&ODTE pseudo Random Forest liblinear-random max_samples-sqrt&{"n_jobs": 10, "n_estimators": 100, "max_samples": 0.75, "be_hyperparams": "{\"kernel\": \"liblinear\", \"splitter\": \"random\", \"multiclass_strategy\": \"ovr\"}"}
ODTE&ODTE Random Forest liblinear-trandom max_samples-sqrt&'{"n_jobs": 10, "n_estimators": 100, "max_samples": 0.75, "be_hyperparams": "{\"kernel\": \"liblinear\", \"splitter\": \"trandom\", \"multiclass_strategy\": \"ovr\"}"}' ODTE&ODTE pseudo Random Forest liblinear-trandom max_samples-sqrt&{"n_jobs": 10, "n_estimators": 100, "max_samples": 0.75, "be_hyperparams": "{\"kernel\": \"liblinear\", \"splitter\": \"trandom\", \"multiclass_strategy\": \"ovr\"}"}
ODTE&ODTE Random Forest liblinear-best max_features-sqrt&'{"n_jobs": 10, "n_estimators": 100, "max_features": "sqrt", "be_hyperparams": "{\"kernel\": \"liblinear\", \"splitter\": \"trandom\", \"multiclass_strategy\": \"ovr\"}"}' ODTE&ODTE pseudo Random Forest liblinear-best max_features-sqrt&{"n_jobs": 10, "n_estimators": 100, "max_features": "sqrt", "be_hyperparams": "{\"kernel\": \"liblinear\", \"splitter\": \"best\", \"multiclass_strategy\": \"ovr\"}"}
ODTE&ODTE Random Forest liblinear-random max_features-sqrt&'{"n_jobs": 10, "n_estimators": 100, "max_features": "sqrt", "be_hyperparams": "{\"kernel\": \"liblinear\", \"splitter\": \"trandom\", \"multiclass_strategy\": \"ovr\"}"}' ODTE&ODTE pseudo Random Forest liblinear-random max_features-sqrt&{"n_jobs": 10, "n_estimators": 100, "max_features": "sqrt", "be_hyperparams": "{\"kernel\": \"liblinear\", \"splitter\": \"random\", \"multiclass_strategy\": \"ovr\"}"}
ODTE&ODTE Random Forest liblinear-trandom max_features-sqrt&'{"n_jobs": 10, "n_estimators": 100, "max_features": "sqrt", "be_hyperparams": "{\"kernel\": \"liblinear\", \"splitter\": \"trandom\", \"multiclass_strategy\": \"ovr\"}"}' ODTE&ODTE pseudo Random Forest liblinear-trandom max_features-sqrt&{"n_jobs": 10, "n_estimators": 100, "max_features": "sqrt", "be_hyperparams": "{\"kernel\": \"liblinear\", \"splitter\": \"trandom\", \"multiclass_strategy\": \"ovr\"}"}# RF

9
src/Results.py
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@@ -1,4 +1,5 @@
import os import os
import math
import json import json
import abc import abc
import shutil import shutil
@@ -741,7 +742,7 @@ class Summary:
file_name = data["file"] file_name = data["file"]
metric = data["metric"] metric = data["metric"]
result = StubReport(os.path.join(Folders.results, file_name)) result = StubReport(os.path.join(Folders.results, file_name))
length = 80 length = 81
print("*" * length) print("*" * length)
if title != "": if title != "":
print(f"*{title:^{length - 2}s}*") print(f"*{title:^{length - 2}s}*")
@@ -786,7 +787,11 @@ class Summary:
else [x for x in haystack if x[criterion] == value] else [x for x in haystack if x[criterion] == value]
) )
return ( return (
sorted(haystack, key=lambda x: x["metric"], reverse=True)[0] sorted(
haystack,
key=lambda x: -1.0 if math.isnan(x["metric"]) else x["metric"],
reverse=True,
)[0]
if len(haystack) > 0 if len(haystack) > 0
else {} else {}
) )