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import json
import re
import os
import pandas as pd
import numpy as np
import pickle
from urllib.parse import quote
from pathlib import Path
import re
import html
from typing import Dict, Any
from scipy.stats import sem
from utils.constants import (NORM_BASE_SUBMISSION, DATASETS, DIGITS_FOR_VALUES, DIGITS_FOR_ERRORS,
DIMENSIONS, COLUMN_ORDER, MODEL_INFO_FILE, RESULTS_DIR)
from utils import compute_tools
def load_results(folder: str = RESULTS_DIR,
items_to_ignore: list = ["__pycache__", "compiled.pkl", ".DS_Store"]
):
"""
loads results from results folder.
Args:
folder: folder containing results
items_to_ignore: list of items in results folder to ignore
"""
#read model info
with open(MODEL_INFO_FILE) as f:
model_info = json.load(f)
model_size = model_info["MODEL_SIZE"]
backbone_names = model_info["BACKBONE_NAMES"]
#read submission info
all_submissions = os.listdir(folder)
for item in items_to_ignore:
if item in all_submissions: all_submissions.remove(item)
all_submission_results = {}
#TODO: add some info to json files and read here also
all_full_ft_model_names = []
all_frozen_model_names = []
all_submission_results["frozen"] = {}
all_submission_results["full_ft"] = {}
for submission in all_submissions:
combined_results = pd.read_csv(f"{folder}/{submission}/results_and_parameters.csv")
combined_results = combined_results.drop(["index"], errors='ignore')
try:
frozen_or_full_ft = combined_results["frozen_or_full_ft"][0]
except KeyError as e:
KeyError(f"{combined_results=}")
all_submission_results[frozen_or_full_ft][submission] = {}
combined_results["# params"] = combined_results.apply(lambda row: model_size[row.backbone], axis=1)
combined_results["Model"] = combined_results.apply(lambda row: backbone_names[row.backbone], axis=1)
combined_results["Config Settings"] = combined_results.apply(lambda row: get_config_setting_string(row), axis=1)
#TODO: read json info
all_backbones = list(set(combined_results["backbone"].tolist()))
all_submission_results[frozen_or_full_ft][submission]["results"] = combined_results
all_submission_results[frozen_or_full_ft][submission]["all_backbones"] = all_backbones
config_settings = combined_results[["early_stop_patience", "decoder", "n_trials", "data_percentages", "batch_size_selection"]].iloc[0]
config_settings = config_settings.replace("early_stopping_50", "50").replace("n_trials_16", "16").replace("data_100_perc", "100")
all_submission_results[frozen_or_full_ft][submission]["config_info"] = config_settings
#all_submission_results[submission]["json_info"] = json_info
if frozen_or_full_ft =="frozen":
all_frozen_model_names.extend(all_backbones)
else:
all_full_ft_model_names.extend(all_backbones)
all_frozen_model_names = list(set(all_frozen_model_names))
all_full_ft_model_names = list(set(all_full_ft_model_names))
all_model_names = {"full_ft": all_full_ft_model_names, "frozen": all_frozen_model_names}
return all_submission_results, all_model_names, all_submissions
def compute_all_iqms(
all_submission_results: dict,
benchmark_name: str,
dataset_group_keys:list =["backbone", "dataset"],
overall_group_keys:list = ["backbone"],
metric:str ="test metric",
) -> Dict:
"""
- reads combined results from repeated seeds for multiple models
- computes the raw and normalized IQM by dataset for each model by task type
- computes the raw and normalized overall IQM across multiple datasets in each each task type
Args:
all_submission_results: dict containing all results
benchmark_name: name of normalizer file to be used
dataset_group_keys: grouping for computing dataset IQM
overall_group_keys: grouping for computing overall IQM
metric: the column containing scores/values in the combined results tables
"""
output = {}
for submission in all_submission_results:
output[submission] = {}
print(f'\n\n\n{submission=}')
submission_backbones = all_submission_results[submission]["all_backbones"]
#TODO: remove
partition_name = "0.10x train" if "data_10_perc" in submission else "1.00x train"
submission_results = all_submission_results[submission]["results"]
if not "partition name" in list(submission_results.columns):
submission_results["partition name"] = partition_name
submission_results["partition name"] = partition_name
#get raw values per dataset
series = submission_results.groupby(dataset_group_keys)[metric].apply(np.mean)
raw_per_dataset = series.to_frame().reset_index()
raw_per_dataset = raw_per_dataset.drop(columns=["partition name"], errors='ignore')
included_datsets = [d for d in DATASETS if d in set(raw_per_dataset["dataset"])]
raw_per_dataset_final = pd.DataFrame(columns=["backbone"] + included_datsets)
#get raw errors per dataset
series = submission_results.groupby(dataset_group_keys)[metric].apply(sem)
raw_per_dataset_err = series.to_frame().reset_index()
raw_per_dataset_err = raw_per_dataset_err.drop(columns=["partition name"], errors='ignore')
raw_per_dataset_final_err = pd.DataFrame(columns=["backbone"] + included_datsets)
#rearrange
for backbone in submission_backbones:
#get values
data = raw_per_dataset.loc[raw_per_dataset["backbone"] == backbone]
data = data.drop(columns=["backbone"]).rename(columns={metric: backbone, "dataset": "backbone"})
data = data.set_index(['backbone']).T.reset_index()
data = data.rename(columns={"index": "backbone"})
try:
data = data.loc[:, ["backbone"] + included_datsets]
except KeyError as e:
print(f'{backbone} {e=}')
continue
raw_per_dataset_final = data.copy() if len(raw_per_dataset_final.index)==0 else pd.concat([raw_per_dataset_final, data], ignore_index=True)
#get errors
data_err = raw_per_dataset_err.loc[raw_per_dataset_err["backbone"] == backbone]
data_err = data_err.drop(columns=["backbone"]).rename(columns={metric: backbone, "dataset": "backbone"})
data_err = data_err.set_index(['backbone']).T.reset_index()
data_err = data_err.rename(columns={"index": "backbone"})
data_err = data_err.loc[:, ["backbone"] + included_datsets]
raw_per_dataset_final_err = data_err.copy() if len(raw_per_dataset_final_err.index)==0 else pd.concat([raw_per_dataset_final_err, data_err], ignore_index=True)
raw_per_dataset_final = raw_per_dataset_final.reset_index(drop=True).rename_axis(mapper=None, axis='columns')
raw_per_dataset_final_err = raw_per_dataset_final_err.reset_index(drop=True).rename_axis(mapper=None, axis='columns')
raw_per_dataset_final = raw_per_dataset_final.reindex(columns=["backbone"]+DATASETS, fill_value=np.nan)
raw_per_dataset_final_err = raw_per_dataset_final_err.reindex(columns=["backbone"]+DATASETS, fill_value=np.nan)
#normalize results
normalizer = compute_tools.load_normalizer(benchmark_name=benchmark_name)
new_metric = normalizer.normalize_data_frame(df=submission_results, metric=metric)
#get normalized values per dataset
series = submission_results.groupby(dataset_group_keys)[new_metric].apply(compute_tools.iqm)
normalized_per_dataset = series.to_frame().reset_index()
normalized_per_dataset = normalized_per_dataset.drop(columns=["partition name"], errors='ignore')
included_datsets = [d for d in DATASETS if d in set(normalized_per_dataset["dataset"])]
normalized_per_dataset_final = pd.DataFrame(columns=["backbone"] + included_datsets)
#get normalized errors per dataset
series = submission_results.groupby(dataset_group_keys)[new_metric].apply(compute_tools.trimmed_sem)
normalized_per_dataset_err = series.to_frame().reset_index()
normalized_per_dataset_err = normalized_per_dataset_err.drop(columns=["partition name"], errors='ignore')
normalized_per_dataset_final_err = pd.DataFrame(columns=["backbone"] + included_datsets)
#rearrange
for backbone in submission_backbones:
#get values
data = normalized_per_dataset.loc[normalized_per_dataset["backbone"] == backbone]
data = data.drop(columns=["backbone"]).rename(columns={new_metric: backbone, "dataset": "backbone"})
data = data.set_index(['backbone']).T.reset_index()
data = data.rename(columns={"index": "backbone"})
try:
data = data.loc[:, ["backbone"] + included_datsets]
except KeyError as e:
print(f'{backbone} {e=}')
continue
normalized_per_dataset_final = data.copy() if len(normalized_per_dataset_final.index)==0 else pd.concat([normalized_per_dataset_final, data], ignore_index=True)
#get errors
data_err = normalized_per_dataset_err.loc[normalized_per_dataset["backbone"] == backbone]
data_err = data_err.drop(columns=["backbone"]).rename(columns={new_metric: backbone, "dataset": "backbone"})
data_err = data_err.set_index(['backbone']).T.reset_index()
data_err = data_err.rename(columns={"index": "backbone"})
data_err = data_err.loc[:, ["backbone"] + included_datsets]
normalized_per_dataset_final_err = data_err.copy() if len(normalized_per_dataset_final_err.index)==0 else pd.concat([normalized_per_dataset_final_err, data_err], ignore_index=True)
normalized_per_dataset_final = normalized_per_dataset_final.reset_index(drop=True).rename_axis(mapper=None, axis='columns')
normalized_per_dataset_final_err = normalized_per_dataset_final_err.reset_index(drop=True).rename_axis(mapper=None, axis='columns')
normalized_per_dataset_final =normalized_per_dataset_final.reindex(columns=["backbone"]+DATASETS, fill_value=np.nan)
normalized_per_dataset_final_err =normalized_per_dataset_final_err.reindex(columns=["backbone"]+DATASETS, fill_value=np.nan)
#get normalized values by dimension
normalized_overall = pd.DataFrame(columns=["backbone"])
normalized_overall_std_err = pd.DataFrame(columns=["backbone"])
submission_dimensions = []
for dimension in DIMENSIONS:
dimension_data = submission_results.loc[submission_results["dataset"].isin(DIMENSIONS[dimension])].copy()
dimension_datasets = sorted(set(dimension_data["dataset"]))
dimension_backbones = sorted(set(dimension_data["backbone"]))
exclude_backbone = []
for backbone in dimension_backbones:
backbone_datasets = dimension_data.loc[dimension_data["backbone"] == backbone]["dataset"].tolist()
if set(backbone_datasets) != set(dimension_datasets):
#if backbone is missing datasets, drop from table
exclude_backbone.append(backbone)
dimension_datasets = [True if d in dimension_datasets else False for d in DIMENSIONS[dimension]]
# dimension_data = dimension_data[~dimension_data["backbone"].isin(exclude_backbone)]
if all(dimension_datasets):
submission_dimensions.append(dimension)
#get values
normalized_iqms_dimension = compute_tools.bootstrap_iqm_aggregate(dimension_data, metric= new_metric)
series = normalized_iqms_dimension.groupby(overall_group_keys)[new_metric].apply(np.mean)
normalized_iqms_dimension = series.to_frame().reset_index()
normalized_iqms_dimension = normalized_iqms_dimension.rename(columns={new_metric: dimension})
normalized_iqms_dimension.loc[normalized_iqms_dimension["backbone"].isin(exclude_backbone), dimension, ] = np.nan
#get errors
normalized_dimension_std_err = compute_tools.bootstrap_iqm_aggregate(dimension_data, metric=new_metric)
series = normalized_dimension_std_err.groupby(["backbone"])[new_metric].apply(sem)
# series = submission_results.loc[submission_results["dataset"].isin(DIMENSIONS[dimension])].copy()
# series = series[~series["dataset"].isin(exclude_backbone)]
# series = series.groupby(overall_group_keys)[new_metric].apply(sem)
normalized_dimension_std_err = series.to_frame().reset_index()
normalized_dimension_std_err = normalized_dimension_std_err.drop(columns=["partition name"], errors='ignore')
normalized_dimension_std_err = normalized_dimension_std_err.rename(columns={new_metric: dimension})
normalized_dimension_std_err.loc[normalized_dimension_std_err["backbone"].isin(exclude_backbone), dimension] = np.nan
# series = dimension_data.groupby(overall_group_keys)[new_metric].apply(sem)
# normalized_dimension_std_err = series.to_frame().reset_index()
# normalized_dimension_std_err = normalized_dimension_std_err.rename(columns={new_metric: dimension})
else:
normalized_iqms_dimension = pd.DataFrame({
"backbone": submission_backbones,
dimension: [np.nan] * len(submission_backbones),
})
normalized_dimension_std_err = pd.DataFrame({
"backbone": submission_backbones,
dimension: [np.nan] * len(submission_backbones),
})
normalized_iqms_dimension.sort_values(by=['backbone'], inplace=True)
normalized_dimension_std_err.sort_values(by=['backbone'], inplace=True)
normalized_overall = normalized_iqms_dimension.copy() if len(normalized_overall.index)==0 else normalized_overall.merge(normalized_iqms_dimension, how="left", on="backbone")
normalized_overall_std_err = normalized_dimension_std_err.copy() if len(normalized_overall_std_err.index)==0 else normalized_overall_std_err.merge(normalized_dimension_std_err, how="left", on="backbone")
output[submission]["raw_per_dataset"] = raw_per_dataset_final
output[submission]["normalized_per_dataset"] = normalized_per_dataset_final
output[submission]["normalized_overall"] = normalized_overall
output[submission]["raw_per_dataset_err"] = raw_per_dataset_final_err
output[submission]["normalized_per_dataset_err"] = normalized_per_dataset_final_err
output[submission]["normalized_overall_err"] = normalized_overall_std_err
output[submission]["submission_dimensions"] = submission_dimensions
return output
def format_values(x):
x = x*100
x = round(x,1)
return x
def format_errors(x):
x = x*100
x = round(x,1)
return x
def get_config_setting_string(row) -> str:
config_settings = f"""
Early Stop Patience: {row.early_stop_patience} /
Decoder: {row.decoder} /
# trials: {row.n_trials} /
Data : {row.data_percentages}% /
Batch Size Selection: {row.batch_size_selection}
"""
config_settings = config_settings.replace("early_stopping_50", "50").replace("n_trials_16", "16").replace("data_100_perc", "100")
return config_settings
def get_overall_performance_table(all_submission_results: dict,
all_iqms: dict
) -> Dict:
"""
create tables for 'Aggregated Performance' page.
Args:
all_submission_results: dict containing all results
all_iqms: dict containing all computed results
"""
output = {}
result_type = ["normalized"]
for value in result_type:
all_tables = []
all_tables_err = []
for submission in all_submission_results:
#get results
submission_data = all_iqms[submission][f"{value}_overall"].copy()
submission_data["Model"] = "-"
submission_data["# params"] = "-"
submission_data["submission"] = submission
submission_data_err = all_iqms[submission][f"{value}_overall_err"].copy()
submission_data_err["Config Settings"] = "-"
submission_data_err["Model"] = "-"
submission_data_err["# params"] = "-"
submission_data_err["submission"] = submission
#get parameters
parameters = all_submission_results[submission]["results"]
for backbone in all_submission_results[submission]["all_backbones"]:
submission_data.loc[submission_data["backbone"] == backbone, "Model"] = parameters.loc[parameters["backbone"] == backbone]["Model"].tolist()[0]
submission_data.loc[submission_data["backbone"] == backbone, "# params"] = parameters.loc[parameters["backbone"] == backbone]["# params"].tolist()[0]
submission_data_err.loc[submission_data_err["backbone"] == backbone, "Model"] = parameters.loc[parameters["backbone"] == backbone]["Model"].tolist()[0]
submission_data_err.loc[submission_data_err["backbone"] == backbone, "# params"] = parameters.loc[parameters["backbone"] == backbone]["# params"].tolist()[0]
all_tables.append(submission_data)
all_tables_err.append(submission_data_err)
print(f'\n\n\n {submission} {value} {submission_data[["Core", "Detection (Object/Instance)", "Model", "submission"]].head(50)=}')
all_tables = pd.concat(all_tables)
all_tables_err = pd.concat(all_tables_err)
all_tables.loc[:, COLUMN_ORDER[value]["overall_table"]] = all_tables[COLUMN_ORDER[value]["overall_table"]].round(DIGITS_FOR_VALUES).apply(lambda series: series.apply(format_values))
all_tables_err.loc[:, COLUMN_ORDER[value]["overall_table"]]= all_tables_err[COLUMN_ORDER[value]["overall_table"]].round(DIGITS_FOR_ERRORS).apply(lambda series: series.apply(format_errors))
all_tables = all_tables[COLUMN_ORDER["all_tables"] + COLUMN_ORDER[value]["overall_table"]]
all_tables_err = all_tables_err[COLUMN_ORDER["all_tables"] + COLUMN_ORDER[value]["overall_table"]]
for col in COLUMN_ORDER[value]["overall_table"]:
new_column = f"{col}"
all_tables = all_tables.rename(columns={col: new_column})
all_tables_err = all_tables_err.rename(columns={col: new_column})
output[value] = all_tables
output[f"{value}_err"] = all_tables_err
return output
def get_performance_by_dimension_table(all_submission_results: dict,
all_iqms: dict
) -> Dict:
"""
create tables for 'Capabilities' page.
Args:
all_submission_results: dict containing all results
all_iqms: dict containing all computed results
"""
output = {}
result_type = ["normalized"]
for value in result_type:
all_tables = {}
all_tables_err = {}
for dimension in DIMENSIONS:
dimension_tables = []
dimension_tables_err = []
for submission in all_submission_results:
#get results
submission_data = all_iqms[submission][f"{value}_per_dataset"][DIMENSIONS[dimension]+["backbone"]].copy()
dimension_results = all_iqms[submission][f"{value}_overall"][[dimension]+["backbone"]].copy()
submission_data = submission_data.merge(dimension_results, how="left", on="backbone")
submission_data["Model"] = "-"
submission_data["# params"] = "-"
submission_data["submission"] = submission
submission_data_err = all_iqms[submission][f"{value}_per_dataset_err"][DIMENSIONS[dimension]+["backbone"]].copy()
dimension_results_err = all_iqms[submission][f"{value}_overall_err"][[dimension]+["backbone"]].copy()
submission_data_err = submission_data_err.merge(dimension_results_err, how="left", on="backbone")
submission_data_err["Model"] = "-"
submission_data_err["# params"] = "-"
submission_data_err["submission"] = submission
#get parameters
parameters = all_submission_results[submission]["results"]
for backbone in all_submission_results[submission]["all_backbones"]:
submission_data.loc[submission_data["backbone"] == backbone, "Model"] = parameters.loc[parameters["backbone"] == backbone]["Model"].tolist()[0]
submission_data.loc[submission_data["backbone"] == backbone, "# params"] = parameters.loc[parameters["backbone"] == backbone]["# params"].tolist()[0]
submission_data_err.loc[submission_data_err["backbone"] == backbone, "Model"] = parameters.loc[parameters["backbone"] == backbone]["Model"].tolist()[0]
submission_data_err.loc[submission_data_err["backbone"] == backbone, "# params"] = parameters.loc[parameters["backbone"] == backbone]["# params"].tolist()[0]
dimension_tables.append(submission_data)
dimension_tables_err.append(submission_data_err)
# print(f'\n\n\n {submission} {dimension} {submission_data[[dimension, "Model", "submission"]].head(50)=}')
dimension_tables = pd.concat(dimension_tables)
dimension_tables.loc[:, DIMENSIONS[dimension]] = dimension_tables[DIMENSIONS[dimension]].round(DIGITS_FOR_VALUES).apply(lambda series: series.apply(format_values))
dimension_tables.loc[:, dimension] = dimension_tables[dimension].round(DIGITS_FOR_VALUES).apply(format_values)
dimension_tables = dimension_tables[COLUMN_ORDER["all_tables"] + [dimension] + COLUMN_ORDER[value]["dimension_tables"] + DIMENSIONS[dimension]]
new_column = f"{dimension}"
dimension_tables = dimension_tables.rename(columns={dimension: new_column})
all_tables[dimension] = dimension_tables
dimension_tables_err = pd.concat(dimension_tables_err)
dimension_tables_err.loc[:, DIMENSIONS[dimension]] = dimension_tables_err[DIMENSIONS[dimension]].round(DIGITS_FOR_ERRORS).apply(lambda series: series.apply(format_errors))
dimension_tables_err.loc[:, dimension] = dimension_tables_err[dimension].round(DIGITS_FOR_ERRORS).apply(format_errors)
dimension_tables_err = dimension_tables_err[COLUMN_ORDER["all_tables"] + [dimension] + COLUMN_ORDER[value]["dimension_tables"] + DIMENSIONS[dimension]]
dimension_tables_err = dimension_tables_err.rename(columns={dimension: new_column})
all_tables_err[f"{dimension}_err"] = dimension_tables_err
output[value] = all_tables
output[f"{value}_err"] = all_tables_err
return output
def get_datasets_tables(all_submission_results: dict,
all_iqms: dict
) -> Dict:
"""
creates tables for dataset tab.
Args:
all_submission_results: dict containing all results
all_iqms: dict containing all computed results
"""
output = {}
result_type = ["normalized","raw"]
for value in result_type:
all_tables = {}
all_tables_err = {}
for dataset in DATASETS:
dataset_tables = []
dataset_tables_err = []
for submission in all_submission_results:
#get results
submission_data = all_iqms[submission][f"{value}_per_dataset"][["backbone", dataset]].copy()
submission_data["Model"] = "-"
submission_data["# params"] = "-"
submission_data["submission"] = submission
submission_data_err = all_iqms[submission][f"{value}_per_dataset_err"][["backbone", dataset]].copy()
submission_data_err["Model"] = "-"
submission_data_err["# params"] = "-"
submission_data_err["submission"] = submission
#get parameters
parameters = all_submission_results[submission]["results"]
new_column = "IQM" if value == "normalized" else "Mean"
for backbone in all_submission_results[submission]["all_backbones"]:
submission_data.loc[submission_data["backbone"] == backbone, "Model"] = parameters.loc[parameters["backbone"] == backbone]["Model"].tolist()[0]
submission_data.loc[submission_data["backbone"] == backbone, "# params"] = parameters.loc[parameters["backbone"] == backbone]["# params"].tolist()[0]
submission_data = submission_data.rename(columns={dataset: new_column})
submission_data_err.loc[submission_data_err["backbone"] == backbone, "Model"] = parameters.loc[parameters["backbone"] == backbone]["Model"].tolist()[0]
submission_data_err.loc[submission_data_err["backbone"] == backbone, "# params"] = parameters.loc[parameters["backbone"] == backbone]["# params"].tolist()[0]
submission_data_err = submission_data_err.rename(columns={dataset: new_column})
#TODO: add columns
dataset_tables.append(submission_data)
dataset_tables_err.append(submission_data_err)
column = "IQM" if value == "normalized" else "Mean"
dataset_tables = pd.concat(dataset_tables)
dataset_tables.loc[:, column] = dataset_tables[column].round(DIGITS_FOR_VALUES).apply(format_values)
all_tables[dataset] = dataset_tables[COLUMN_ORDER["all_tables"] + COLUMN_ORDER[value]["dataset_tables"]]
dataset_tables_err = pd.concat(dataset_tables_err)
dataset_tables_err.loc[:, column] = dataset_tables_err[column].round(DIGITS_FOR_ERRORS).apply(format_errors)
all_tables_err[dataset] = dataset_tables_err[COLUMN_ORDER["all_tables"] + COLUMN_ORDER[value]["dataset_tables"]]
output[value] = all_tables
output[f"{value}_err"] = all_tables_err
return output
def get_submission_tables(all_submission_results: dict):
output = {}
frozen_or_full_ft = ["frozen" ,"full_ft"]
config_info = []
for method in frozen_or_full_ft:
for sub in all_submission_results[method]:
config = all_submission_results[method][sub]["config_info"]
config = config.to_frame().T
config["submission"] = sub
config["backbone method"] = method
config_info.append(config)
output = pd.concat(config_info)
output = output[COLUMN_ORDER["submission_info"]]
return output
if __name__ == "__main__":
#load results
all_submission_results, all_model_names, all_submissions = load_results(folder=RESULTS_DIR)
#COMBINED NORM
norm_base_results= []
for method in NORM_BASE_SUBMISSION:
for sub in NORM_BASE_SUBMISSION[method]:
norm_base_results.append(all_submission_results[method][sub]["results"].copy())
norm_base_results = pd.concat(norm_base_results)
benchmark_name = "leaderboard_combined"
compute_tools.make_normalizer(norm_base_results.reset_index(),
metrics=("test metric",),
benchmark_name=benchmark_name)
overall_performance_tables = {}
performance_by_dimension_tables = {}
datasets_tables = {}
for method in ["full_ft","frozen"]:
method_iqms = compute_all_iqms(
all_submission_results = all_submission_results[method],
benchmark_name = benchmark_name,
)
#create tables to be rendered
overall_performance_tables[method] = get_overall_performance_table(all_submission_results=all_submission_results[method],
all_iqms=method_iqms)
performance_by_dimension_tables[method] = get_performance_by_dimension_table(all_submission_results=all_submission_results[method],
all_iqms=method_iqms)
datasets_tables[method] = get_datasets_tables(all_submission_results=all_submission_results[method],
all_iqms=method_iqms)
submission_info_table = get_submission_tables(all_submission_results=all_submission_results)
compiled_results = {
"overall_performance_tables": overall_performance_tables,
"performance_by_dimension_tables": performance_by_dimension_tables,
"datasets_tables": datasets_tables,
"submission_info_table": submission_info_table
}
with open(f'{RESULTS_DIR}/compiled.pkl', 'wb') as handle:
pickle.dump(compiled_results, handle, protocol=pickle.HIGHEST_PROTOCOL)
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