mirror of
https://github.com/Doctorado-ML/benchmark.git
synced 2025-08-16 07:55:54 +00:00
Begin refactor arguments
This commit is contained in:
147
benchmark/Arguments.py
Normal file
147
benchmark/Arguments.py
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@@ -0,0 +1,147 @@
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import argparse
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from .Experiments import Models
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from .Utils import Files
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ALL_METRICS = (
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"accuracy",
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"f1-macro",
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"f1-micro",
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"f1-weighted",
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"roc-auc-ovr",
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)
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class EnvData:
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@staticmethod
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def load():
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args = {}
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with open(Files.dot_env) as f:
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for line in f.read().splitlines():
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if line == "" or line.startswith("#"):
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continue
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key, value = line.split("=")
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args[key] = value
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return args
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class EnvDefault(argparse.Action):
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# Thanks to https://stackoverflow.com/users/445507/russell-heilling
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def __init__(self, envvar, required=True, default=None, **kwargs):
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self._args = EnvData.load()
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default = self._args[envvar]
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required = False
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super(EnvDefault, self).__init__(
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default=default, required=required, **kwargs
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)
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def __call__(self, parser, namespace, values, option_string=None):
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setattr(namespace, self.dest, values)
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class Arguments:
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def __init__(self):
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self.ap = argparse.ArgumentParser()
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models_data = Models.define_models(random_state=0)
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models = "{" + ", ".join(models_data) + "}"
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self.parameters = {
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"best": [
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("-b", "--best"),
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{
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"type": str,
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"required": False,
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"help": "best results of models",
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},
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],
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"color": [],
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"compare": [
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("-c", "--compare"),
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{
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"type": bool,
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"required": False,
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"help": "Compare accuracy with best results",
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},
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],
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"dataset": [],
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"excel": [
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("-x", "--excel"),
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{
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"type": bool,
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"required": False,
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"default": False,
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"help": "Generate Excel File",
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},
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],
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"file": [
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("-f", "--file"),
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{"type": str, "required": False, "help": "Result file"},
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],
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"grid": [
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("-g", "--grid"),
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{
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"type": str,
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"required": False,
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"help": "grid results of model",
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},
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],
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"grid_paramfile": [],
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"hidden": [],
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"hyperparameters": [],
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"key": [],
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"lose": [],
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"model": [
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("-m", "--model"),
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{
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"type": str,
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"required": True,
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"choices": list(models_data),
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"help": f"model name: {models}",
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},
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],
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"model1": [],
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"model2": [],
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"nan": [],
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"number": [],
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"n_folds": [],
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"paramfile": [],
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"platform": [],
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"quiet": [],
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"report": [],
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"score": [
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("-s", "--score"),
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{
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"action": EnvDefault,
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"envvar": "score",
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"type": str,
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"required": True,
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"choices": ALL_METRICS,
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},
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],
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"sql": [
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("-q", "--sql"),
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{"type": bool, "required": False, "help": "Generate SQL File"},
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],
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"stratified": [],
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"tex_output": [
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("-t", "--tex-output"),
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{
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"type": bool,
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"required": False,
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"default": False,
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"help": "Generate Tex file with the table",
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},
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],
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"title": [],
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"win": [],
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}
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def xset(self, *arg_name, **kwargs):
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print("parameters", arg_name[0])
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names, default = self.parameters[arg_name[0]]
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self.ap.add_argument(
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*names,
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**{**default, **kwargs},
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)
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return self
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def parse(self):
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return self.ap.parse_args()
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@@ -13,7 +13,8 @@ from sklearn.model_selection import (
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GridSearchCV,
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cross_validate,
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)
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from .Utils import Folders, Files, EnvData
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from .Utils import Folders, Files
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from .Arguments import EnvData
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from .Models import Models
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@@ -1,6 +1,5 @@
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import os
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import subprocess
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import argparse
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BEST_ACCURACY_STREE = 40.282203
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ALL_METRICS = (
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@@ -132,33 +131,6 @@ class Symbols:
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better_best = black_star
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class EnvData:
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@staticmethod
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def load():
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args = {}
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with open(Files.dot_env) as f:
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for line in f.read().splitlines():
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if line == "" or line.startswith("#"):
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continue
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key, value = line.split("=")
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args[key] = value
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return args
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class EnvDefault(argparse.Action):
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# Thanks to https://stackoverflow.com/users/445507/russell-heilling
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def __init__(self, envvar, required=True, default=None, **kwargs):
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self._args = EnvData.load()
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default = self._args[envvar]
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required = False
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super(EnvDefault, self).__init__(
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default=default, required=required, **kwargs
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)
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def __call__(self, parser, namespace, values, option_string=None):
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setattr(namespace, self.dest, values)
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class TextColor:
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BLUE = "\033[94m"
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CYAN = "\033[96m"
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@@ -1,6 +1,6 @@
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from .Experiments import Experiment, Datasets, DatasetsSurcov, DatasetsTanveer
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from .Results import Report, Summary
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from .Utils import EnvDefault
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from .Arguments import EnvDefault
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__author__ = "Ricardo Montañana Gómez"
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__copyright__ = "Copyright 2020-2022, Ricardo Montañana Gómez"
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@@ -1,47 +1,19 @@
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#!/usr/bin/env python
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from benchmark.Results import Benchmark
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from benchmark.Utils import ALL_METRICS, Files, EnvDefault
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import argparse
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from benchmark.Utils import Files
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from benchmark.Arguments import Arguments
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def parse_arguments():
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ap = argparse.ArgumentParser()
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ap.add_argument(
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"-s",
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"--score",
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action=EnvDefault,
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envvar="score",
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type=str,
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required=True,
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choices=ALL_METRICS,
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help="score name {accuracy, f1_macro, ...}",
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)
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ap.add_argument(
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"-x",
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"--excel",
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type=bool,
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required=False,
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help="Generate Excel File",
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)
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ap.add_argument(
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"-t",
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"--tex-output",
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type=bool,
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required=False,
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default=False,
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)
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args = ap.parse_args()
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return (args.score, args.excel, args.tex_output)
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(score, excel, tex_output) = parse_arguments()
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benchmark = Benchmark(score=score, visualize=True)
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arguments = Arguments()
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arguments.xset("score").xset("excel").xset("tex_output")
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ar = arguments.parse()
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benchmark = Benchmark(score=ar.score, visualize=True)
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benchmark.compile_results()
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benchmark.save_results()
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benchmark.report(tex_output)
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benchmark.report(ar.tex_output)
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benchmark.exreport()
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if excel:
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if ar.excel:
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benchmark.excel()
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Files.open(benchmark.get_excel_file_name())
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if tex_output:
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if ar.tex_output:
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print(f"File {benchmark.get_tex_file()} generated")
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@@ -1,29 +1,17 @@
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#!/usr/bin/env python
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import argparse
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import json
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from benchmark.Results import Summary
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from benchmark.Utils import EnvDefault, ALL_METRICS
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from benchmark.Utils import ALL_METRICS, Arguments
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def parse_arguments():
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ap = argparse.ArgumentParser()
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ap.add_argument(
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"-s",
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"--score",
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type=str,
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action=EnvDefault,
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envvar="score",
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required=True,
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choices=ALL_METRICS,
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help="score name {accuracy, f1-macro, f1-weighted, roc-auc-ovr}",
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)
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args = ap.parse_args()
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return (args.score,)
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arguments = Arguments()
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metrics = list(ALL_METRICS)
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metrics.append("all")
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arguments.xset("score", choices=metrics)
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args = arguments.parse()
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(score,) = parse_arguments()
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metrics = ALL_METRICS if score == "all" else [score]
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metrics = ALL_METRICS if args.score == "all" else [args.score]
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summary = Summary()
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summary.acquire()
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@@ -1,9 +1,12 @@
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#!/usr/bin/env python
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import argparse
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import numpy as np
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from benchmark.Experiments import Datasets
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from benchmark.Results import Report, Excel, SQL, ReportBest
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from benchmark.Utils import ALL_METRICS, Files, TextColor, EnvDefault
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from benchmark.Utils import (
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Files,
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TextColor,
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)
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from benchmark.Arguments import Arguments
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"""Build report on screen of a result file, optionally generate excel and sql
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@@ -12,83 +15,6 @@ If no argument is set, displays the datasets and its characteristics
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"""
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def parse_arguments():
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ap = argparse.ArgumentParser()
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ap.add_argument(
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"-f",
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"--file",
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type=str,
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required=False,
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help="Result file",
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)
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ap.add_argument(
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"-x",
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"--excel",
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type=bool,
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required=False,
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help="Generate Excel file",
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)
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ap.add_argument(
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"-q",
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"--sql",
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type=bool,
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required=False,
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help="Generate sql file",
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)
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ap.add_argument(
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"-c",
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"--compare",
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type=bool,
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required=False,
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help="Compare accuracy with best results",
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)
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ap.add_argument(
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"-b",
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"--best",
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type=str,
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required=False,
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help="best results of models",
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)
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ap.add_argument(
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"-g",
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"--grid",
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type=str,
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required=False,
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help="grid results of model",
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)
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ap.add_argument(
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"-m",
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"--model",
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action=EnvDefault,
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envvar="model",
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type=str,
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required=True,
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help="model name",
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)
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ap.add_argument(
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"-s",
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"--score",
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action=EnvDefault,
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envvar="score",
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type=str,
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required=True,
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choices=ALL_METRICS,
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help="score name {accuracy, f1_macro, ...}",
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)
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args = ap.parse_args()
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return (
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args.file,
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args.excel,
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args.sql,
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args.compare,
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args.best,
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args.grid,
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args.score,
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args.model,
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)
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def default_report():
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sets = Datasets()
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color_line = TextColor.LINE1
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@@ -116,22 +42,26 @@ def default_report():
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if __name__ == "__main__":
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(file, excel, sql, compare, best, grid, score, model) = parse_arguments()
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if grid:
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best = False
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if file is None and best is None:
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arguments = Arguments()
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arguments.xset("file").xset("excel").xset("sql").xset("compare")
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arguments.xset("best").xset("grid").xset("model").xset("score")
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args = arguments.parse()
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if args.grid:
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args.best = False
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if args.file is None and args.best is None:
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default_report()
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else:
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if best is not None or grid is not None:
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report = ReportBest(score, model, best, grid)
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if args.best is not None or args.grid is not None:
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report = ReportBest(args.score, args.model, args.best, args.grid)
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report.report()
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else:
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report = Report(file, compare)
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report = Report(args.file, args.compare)
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report.report()
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if excel:
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excel = Excel(file, compare)
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if args.excel:
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excel = Excel(args.file, args.compare)
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excel.report()
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Files.open(excel.get_file_name())
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if sql:
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sql = SQL(file)
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if args.sql:
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sql = SQL(args.file)
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sql.report()
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@@ -266,3 +266,8 @@ class UtilTest(TestBase):
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self.assertEqual(TextColor.ENDC, "\033[0m")
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self.assertEqual(TextColor.BOLD, "\033[1m")
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self.assertEqual(TextColor.UNDERLINE, "\033[4m")
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def test_Arguments(self):
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arguments = Arguments()
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arg_list = ["score", "excel", "tex_output"]
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arguments.set_arguments(arg_list)
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