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cfg.yaml
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dataset_path: '../../FiFAR/alert_data/processed_data/alerts.parquet'
data_cfg_path: '../alert_data/dataset_cfg.yaml' # Dataset config file
expert_folder_path: '../../FiFAR/synthetic_experts' # Path containing the outputs from expert_gen.py
destination_path_train: '../../FiFAR/testbed/train_alert' # Output directory of the generated training scenarios
destination_path_test: '../../FiFAR/testbed/test' # Output directory of the generated test scenarios
random_seed: 42
#Define which partitions of the dataset should be used to generate the training and test scenarios
#If the dataset has a timestamp column, training_set should be defined as the dates that delimit the partition
#If the dataset does not have a timestamp column, training_set should be defined as the indexes that delimit the partition
#Note - The intervals are defined as [start,end) - the last value is not included
training_set: [3,7]
test_set: [7,8]
# Set this value to true if you wish for batches to contain only instances with the same value for the TIMESTAMP column
# i.e. in our experiments, a batch can only contain instances belonging to the same month
timestamp_constraint: True
#The following dictionaries define the capacity constraints for training and testing.
#Each batch configuration is combined with each capacity configuration.
environments_train:
batch:
###################################################
#-----------Defining the batch vector-------------#
#To define the batch vector, the user must set:
# - 'size'
# - 'seed'
#The user may generate several batch configurations
###################################################
shuffle_1:
size: 5000
seed: 42
shuffle_2:
size: 5000
seed: 43
shuffle_3:
size: 5000
seed: 44
shuffle_4:
size: 5000
seed: 45
shuffle_5:
size: 5000
seed: 46
capacity:
###################################################
#-----------Defining the capacity matrix-------------#
#To define the capacity matrix, the user must set:
# - 'deferral_rate' - [0,1]
# - 'distribution' - {'variable','homogeneous'}
#If the distribution is variable, the user must set:
# - 'distribution_stdev' - [0,1]
# - 'distribution_seed' - [0,1]
# - 'variable_capacity_per_batch' - {True,False}
#The user may also set the value of 'n_experts',
#limiting the number of experts available in each batch.
#If 'n_experts' is set, the user must set
# - 'n_experts_seed'
# - 'variable experts_per_batch' - {True, False}
#The user may generate several capacity configurations
###################################################
team_1:
deferral_rate: 1
n_experts: 10
n_experts_seed: 42
variable_experts_per_batch: False
distribution: 'homogeneous'
team_2:
deferral_rate: 1
n_experts: 10
n_experts_seed: 43
variable_experts_per_batch: False
distribution: 'homogeneous'
team_3:
deferral_rate: 1
n_experts: 10
n_experts_seed: 44
variable_experts_per_batch: False
distribution: 'homogeneous'
team_4:
deferral_rate: 1
n_experts: 10
n_experts_seed: 45
variable_experts_per_batch: False
distribution: 'homogeneous'
team_5:
deferral_rate: 1
n_experts: 10
n_experts_seed: 46
variable_experts_per_batch: False
distribution: 'homogeneous'
environments_test:
batch:
testsize:
size: 4457
seed: 42
capacity:
team_1-hom:
deferral_rate: 0.9090909090
n_experts: 10
n_experts_seed: 42
variable_experts_per_batch: False
distribution: 'homogeneous'
team_2-hom:
deferral_rate: 0.9090909090
n_experts: 10
n_experts_seed: 43
variable_experts_per_batch: False
distribution: 'homogeneous'
team_3-hom:
deferral_rate: 0.9090909090
n_experts: 10
n_experts_seed: 44
variable_experts_per_batch: False
distribution: 'homogeneous'
team_4-hom:
deferral_rate: 0.9090909090
n_experts: 10
n_experts_seed: 45
variable_experts_per_batch: False
distribution: 'homogeneous'
team_5-hom:
deferral_rate: 0.9090909090
n_experts: 10
n_experts_seed: 46
variable_experts_per_batch: False
distribution: 'homogeneous'
#-----
team_1-var_1:
deferral_rate: 0.9090909090
n_experts: 10
n_experts_seed: 42
variable_experts_per_batch: False
distribution: 'variable'
distribution_stdev: 0.2
distribution_seed: 42
variable_capacity_per_batch: False
team_2-var_1:
deferral_rate: 0.9090909090
n_experts: 10
n_experts_seed: 43
variable_experts_per_batch: False
distribution: 'variable'
distribution_stdev: 0.2
distribution_seed: 42
variable_capacity_per_batch: False
team_3-var_1:
deferral_rate: 0.9090909090
n_experts: 10
n_experts_seed: 44
variable_experts_per_batch: False
distribution: 'variable'
distribution_stdev: 0.2
distribution_seed: 42
variable_capacity_per_batch: False
team_4-var_1:
deferral_rate: 0.9090909090
n_experts: 10
n_experts_seed: 45
variable_experts_per_batch: False
distribution: 'variable'
distribution_stdev: 0.2
distribution_seed: 42
variable_capacity_per_batch: False
team_5-var_1:
deferral_rate: 0.9090909090
n_experts: 10
n_experts_seed: 46
variable_experts_per_batch: False
distribution: 'variable'
distribution_stdev: 0.2
distribution_seed: 42
variable_capacity_per_batch: False
#-----
team_1-var_2:
deferral_rate: 0.9090909090
n_experts: 10
n_experts_seed: 42
variable_experts_per_batch: False
distribution: 'variable'
distribution_stdev: 0.2
distribution_seed: 43
variable_capacity_per_batch: False
team_2-var_2:
deferral_rate: 0.9090909090
n_experts: 10
n_experts_seed: 43
variable_experts_per_batch: False
distribution: 'variable'
distribution_stdev: 0.2
distribution_seed: 43
variable_capacity_per_batch: False
team_3-var_2:
deferral_rate: 0.9090909090
n_experts: 10
n_experts_seed: 44
variable_experts_per_batch: False
distribution: 'variable'
distribution_stdev: 0.2
distribution_seed: 43
variable_capacity_per_batch: False
team_4-var_2:
deferral_rate: 0.9090909090
n_experts: 10
n_experts_seed: 45
variable_experts_per_batch: False
distribution: 'variable'
distribution_stdev: 0.2
distribution_seed: 43
variable_capacity_per_batch: False
team_5-var_2:
deferral_rate: 0.9090909090
n_experts: 10
n_experts_seed: 46
variable_experts_per_batch: False
distribution: 'variable'
distribution_stdev: 0.2
distribution_seed: 43
variable_capacity_per_batch: False
#-----
team_1-var_3:
deferral_rate: 0.9090909090
n_experts: 10
n_experts_seed: 42
variable_experts_per_batch: False
distribution: 'variable'
distribution_stdev: 0.2
distribution_seed: 44
variable_capacity_per_batch: False
team_2-var_3:
deferral_rate: 0.9090909090
n_experts: 10
n_experts_seed: 43
variable_experts_per_batch: False
distribution: 'variable'
distribution_stdev: 0.2
distribution_seed: 44
variable_capacity_per_batch: False
team_3-var_3:
deferral_rate: 0.9090909090
n_experts: 10
n_experts_seed: 44
variable_experts_per_batch: False
distribution: 'variable'
distribution_stdev: 0.2
distribution_seed: 44
variable_capacity_per_batch: False
team_4-var_3:
deferral_rate: 0.9090909090
n_experts: 10
n_experts_seed: 45
variable_experts_per_batch: False
distribution: 'variable'
distribution_stdev: 0.2
distribution_seed: 44
variable_capacity_per_batch: False
team_5-var_3:
deferral_rate: 0.9090909090
n_experts: 10
n_experts_seed: 46
variable_experts_per_batch: False
distribution: 'variable'
distribution_stdev: 0.2
distribution_seed: 44
variable_capacity_per_batch: False
#-----
team_1-var_4:
deferral_rate: 0.9090909090
n_experts: 10
n_experts_seed: 42
variable_experts_per_batch: False
distribution: 'variable'
distribution_stdev: 0.2
distribution_seed: 45
variable_capacity_per_batch: False
team_2-var_4:
deferral_rate: 0.9090909090
n_experts: 10
n_experts_seed: 43
variable_experts_per_batch: False
distribution: 'variable'
distribution_stdev: 0.2
distribution_seed: 45
variable_capacity_per_batch: False
team_3-var_4:
deferral_rate: 0.9090909090
n_experts: 10
n_experts_seed: 44
variable_experts_per_batch: False
distribution: 'variable'
distribution_stdev: 0.2
distribution_seed: 45
variable_capacity_per_batch: False
team_4-var_4:
deferral_rate: 0.9090909090
n_experts: 10
n_experts_seed: 45
variable_experts_per_batch: False
distribution: 'variable'
distribution_stdev: 0.2
distribution_seed: 45
variable_capacity_per_batch: False
team_5-var_4:
deferral_rate: 0.9090909090
n_experts: 10
n_experts_seed: 46
variable_experts_per_batch: False
distribution: 'variable'
distribution_stdev: 0.2
distribution_seed: 45
variable_capacity_per_batch: False