"""
This module encapsulates the execution logic for the Detectron method, managing the orchestration of the entire pipeline. It includes the ``DetectronExperiment`` abstract class,
which outlines the protocol for setting up and running experiments.
Additionally, the ``DetectronResult`` class is responsible for storing and managing the outcomes of these experiments,
providing methods to access and analyze the trajectories and outcomes of this method's evaluation.
"""
from __future__ import annotations
from typing import Union, List, Type
import json
import os
from warnings import warn
from .ensemble import BaseModelManager, DatasetsManager, DetectronEnsemble
from .record import DetectronRecordsManager
from .strategies import *
[docs]
class DetectronResult:
"""
A class to store the results of a Detectron test.
"""
strategy_mapping = {
'original_disagreement_strategy': OriginalDisagreementStrategy,
'mannwhitney_strategy': MannWhitneyStrategy,
'enhanced_disagreement_strategy': EnhancedDisagreementStrategy,
}
def __init__(self, cal_record: DetectronRecordsManager, test_record: DetectronRecordsManager):
"""
Initializes the DetectronResult with calibration and test records.
Args:
cal_record (DetectronRecordsManager): Manager storing the results of running the Detectron on the 'reference' set.
test_record (DetectronRecordsManager): Manager storing the results of running the Detectron on the 'testing' set.
"""
self.cal_record = cal_record
self.test_record = test_record
self.test_results = []
self.experiment_config = {}
[docs]
def calibration_trajectories(self):
"""
Retrieves the results for each run and each model in the ensemble from the reference set.
Returns:
DataFrame: A DataFrame containing seed, model_id, and rejection_rate from the calibration records.
"""
rec = self.cal_record.get_record()
return rec[['seed', 'model_id', 'rejection_rate']]
[docs]
def test_trajectories(self):
"""
Retrieves the results for each run and each model in the ensemble from the testing set.
Returns:
DataFrame: A DataFrame containing seed, model_id, and rejection_rate from the test records.
"""
rec = self.test_record.get_record()
return rec[['seed', 'model_id', 'rejection_rate']]
[docs]
def get_experiments_results(self):
"""
Executes the Detectron tests using the specified strategy and records.
Returns:
dict: Results from executing the Detectron test.
"""
return self.test_results
[docs]
def analyze_results(self, strategies: Union[str, List[str]]= ["enhanced_disagreement_strategy", "mannwhitney_strategy", "original_disagreement_strategy"]) -> list:
"""
Appends the results of the Detectron tests for each strategy to self.test_results.
Args:
strategies (Union[str, List[str]]): Strategy name or list of strategy names.
Returns:
list: Updated list containing results for each strategy.
"""
# Ensure strategies is a list of strategy names
if isinstance(strategies, str):
strategies = [strategies] # Convert single strategy name to list
self.experiment_config['detectron_params']['test_strategies'] = strategies
for strategy_name in strategies:
if strategy_name not in self.strategy_mapping:
raise ValueError(f"Unrecognized strategy name: {strategy_name}. Available strategies: {list(self.strategy_mapping.keys())}")
strategy_class = self.strategy_mapping[strategy_name]
strategy_results = strategy_class.execute(self.cal_record, self.test_record)
strategy_results['Strategy'] = strategy_name
self.test_results.append(strategy_results)
return self.test_results
[docs]
def set_experiment_config(self, config: dict):
"""
Sets or updates the configuration for the Detectron experiment.
Args:
config (dict): A dictionary of hyperparameters used in the experiment.
"""
self.experiment_config.update(config)
[docs]
def save(self, file_path: str, file_name: str = 'detectron_results', save_config=True):
"""
Saves the Detectron results to JSON format.
Args:
file_path (str): The file path where the results should be saved.
file_name (str): The file name.
"""
# Ensure the main directory exists
os.makedirs(file_path, exist_ok=True)
file_name_path = os.path.join(file_path, f'{file_name}.json')
with open(file_name_path, 'w') as file:
json.dump(self.test_results, file, indent=4)
counts_dict = {}
counts_dict['reference'] = np.sort(self.cal_record.rejected_counts()).tolist()
counts_dict['test'] = np.sort(self.test_record.rejected_counts()).tolist()
file_name_path_counts = os.path.join(file_path, 'rejection_counts.json')
with open(file_name_path_counts, 'w') as file:
json.dump(counts_dict, file, indent=4)
eval_dict = {}
eval_dict['reference'] = self.cal_record.get_evaluation()
eval_dict['test'] = self.test_record.get_evaluation()
file_name_path_evaluation = os.path.join(file_path, 'model_evaluation.json')
with open(file_name_path_evaluation, 'w') as file:
json.dump(eval_dict, file, indent=4)
if save_config:
config_file_path = os.path.join(file_path, 'experiment_config.json')
with open(config_file_path, 'w') as file:
json.dump(self.experiment_config, file, indent=4)
[docs]
@classmethod
def get_supported_strategies(cls) -> list:
"""
Returns a list of supported strategy names.
Returns:
list: A list of strings representing the names of the supported strategies.
"""
return list(cls.strategy_mapping.keys())
[docs]
class DetectronExperiment:
"""
Abstract base class that defines the protocol for running Detectron experiments.
Methods:
run: Orchestrates the entire process of a Detectron experiment using specified parameters and strategies.
"""
[docs]
@staticmethod
def run(datasets: DatasetsManager,
base_model_manager: BaseModelManager,
training_params: dict=None,
samples_size : int = 20,
calib_result:DetectronRecordsManager=None,
ensemble_size=10,
num_calibration_runs=100,
patience=3,
sampling: str = "uniform",
allow_margin : bool = False,
margin = 0.05):
"""
Orchestrates the process of a Detectron experiment, including ensemble training and testing, and strategy evaluation.
Args:
datasets (DatasetsManager): Manages the datasets used in the experiment.
training_params (dict): Parameters for training the cdcs within the ensembles.
base_model_manager (BaseModelManager): Manager for the base model operations.
samples_size (int): Number of samples to use in each Detectron run. Defaults to 20.
calib_result (Optional[DetectronRecordsManager]): Calibration results, if provided. Defaults to None.
ensemble_size (int): Number of models in each ensemble. Defaults to 10.
num_calibration_runs (int): Number of calibration runs. Defaults to 100.
patience (int): Number of iterations with no improvement before stopping. Defaults to 3.
allow_margin (bool): Allow a margin of error when comparing model outputs. Defaults to False.
margin (float): Threshold for considering differences significant when margin is allowed. Defaults to 0.05.
sampling (str, optional): Specifies the method for sampling the data, by default set to 'uniform'.
Returns:
tuple: A tuple containing the Detectron results, experimental strategy results, and Detectron evaluation results, if conducted.
"""
# create a calibration ensemble
calibration_ensemble = DetectronEnsemble(base_model_manager, ensemble_size)
# create a testing ensemble
testing_ensemble = DetectronEnsemble(base_model_manager, ensemble_size)
# ensure the reference set is larger compared to testing set
_, y_reference = datasets.get_dataset_by_type(dataset_type="reference")
reference_size = len(y_reference)
if reference_size < samples_size:
print("The reference set must be larger than the sample size, ", reference_size,", ", samples_size)
raise ValueError("Not enough samples in the reference set!")
if reference_size < 2 * samples_size:
warn("The reference set is smaller than twice the testing set, this may lead to poor calibration")
if calib_result is not None:
print("Calibration record on reference set provided, skipping Detectron execution on reference set.")
cal_record = calib_result
else:
# evaluate the calibration ensemble
cal_record = calibration_ensemble.evaluate_ensemble(datasets=datasets,
n_runs=num_calibration_runs,
samples_size=samples_size,
training_params=training_params,
set='reference',
patience=patience,
allow_margin=allow_margin,
margin=margin,
sampling=sampling)
print("Detectron execution on reference set completed.")
test_record = testing_ensemble.evaluate_ensemble(datasets=datasets,
n_runs=num_calibration_runs,
samples_size=samples_size,
training_params=training_params,
set='testing',
patience=patience,
allow_margin=allow_margin,
margin=margin,
sampling=sampling)
print("Detectron execution on testing set completed.")
assert cal_record.sample_size == test_record.sample_size, \
"The calibration record must have been generated with the same sample size as the observation set"
# save the detectron runs results
detectron_results = DetectronResult(cal_record, test_record)
detectron_params = {
'additional_training_params': training_params,
'samples_size': samples_size,
'cdcs_ensemble_size': ensemble_size,
'num_runs': num_calibration_runs,
'patience': patience,
'allow_margin': allow_margin,
'margin': margin,
'sampling-method':sampling,
}
experiment_config = {
'experiment_name': "DetectronExperiment",
'datasets':datasets.get_info(),
'base_model': base_model_manager.get_instance().get_info(),
'detectron_params': detectron_params
}
detectron_results.set_experiment_config(experiment_config)
# return the Detectron results
return detectron_results