def create_Scenario(request):
dataset = request.param[0]()
samples_split_option = request.param[1]
corruption = request.param[2]
params = {"dataset": dataset}
params.update({
"partners_count": 3,
"amounts_per_partner": [0.3, 0.5, 0.2],
"samples_split_option": samples_split_option,
"corruption_parameters": corruption,
})
params.update({
"contributivity_methods": ["Shapley values", "Independent scores"],
"multi_partner_learning_approach":
"fedavg",
"aggregation":
"uniform",
})
params.update({
"gradient_updates_per_pass_count": 5,
"epoch_count": 2,
"minibatch_count": 2,
"is_early_stopping": True,
})
params.update({"init_model_from": "random_initialization"})
params.update({"is_quick_demo": False})
# scenario_.dataset object is created inside the Scenario constructor
scenario_ = Scenario(**params, scenario_id=0)
scenario_.mpl = scenario_._multi_partner_learning_approach(
scenario_, is_save_data=True)
return scenario_
def create_Scenario(request):
dataset = request.param[0]()
samples_split_option = request.param[1]
params = {"dataset": dataset}
params.update({
"partners_count": 3,
"amounts_per_partner": [0.2, 0.5, 0.3],
"samples_split_option": samples_split_option,
"corrupted_datasets": ["not_corrupted"] * 3,
})
params.update({
"methods": ["Shapley values", "Independent scores"],
"multi_partner_learning_approach": "fedavg",
"aggregation": "uniform",
})
params.update({
"gradient_updates_per_pass_count": 5,
"epoch_count": 2,
"minibatch_count": 2,
"is_early_stopping": True,
})
params.update({"init_model_from": "random_initialization"})
params.update({"is_quick_demo": False})
full_experiment_name = "unit-test-pytest"
experiment_path = (Path.cwd() / constants.EXPERIMENTS_FOLDER_NAME /
full_experiment_name)
# scenario_.dataset object is created inside the Scenario constructor
scenario_ = Scenario(**params,
experiment_path=experiment_path,
scenario_id=0,
repeats_count=1)
scenario_.mpl = scenario_.multi_partner_learning_approach(
scenario_, is_save_data=True)
scenario_.instantiate_scenario_partners()
# Split data according to scenario and then pre-process successively...
# ... train data, early stopping validation data, test data
if scenario_.samples_split_type == "basic":
scenario_.split_data()
elif scenario_.samples_split_type == "advanced":
scenario_.split_data_advanced()
scenario_.compute_batch_sizes()
scenario_.data_corruption()
return scenario_