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parameters.yml
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parameters.yml
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trainer:
DATA_PATH: "data/" #data location
SAVE_BEST_MODEL: True
MODEL_SAVE_FOLDER: "models"
HIDDEN_CHANNELS: 256
BATCH_SIZE: 32
EPOCHS: 100
SEED: 42
CLEAN_DATA: True
MIN_AFFINITY: 5
MAX_AFFINITY: 11
MEAN_LOWER_BOUND: 6.5
MEAN_UPPER_BOUND: 7.5
LOW_BOUND: 6
HIGH_BOUND: 8
GNN_MODEL: "GCN" # "GC_GNN" "GCN" "GAT" "GIN" "GINE" "GraphSAGE"
EDGE_WEIGHT: True
SCALING: True
SEED: 42
HIDDEN_CHANNELS: 256
EPOCHS: 100
LEARNING_RATE: 1e-3
WEIGHT_DECAY: 5e-4
NODE_FEATURES: True
explainer:
DATA_PATH: "data/" #data location
SAVE_FOLDER: 'results/explanations/' #folder where explanations will be saved
MODEL_PATH: 'models/pretrained_models/model_GCN_best_92.ckpt' #model to use for the explanation phase
GNN_MODEL: "GCN" # "GC_GNN" "GCN" "GAT" "GIN" "GINE" "GraphSAGE"
SAMPLES_TO_EXPLAIN: 20 #number of test samples to explain
AFFINITY_SET: "low" #affinity set for which to perform the explanations (low, medium, high)
HIDDEN_CHANNELS: 256
BATCH_SIZE: 32
EPOCHS: 100
SEED: 42
CLEAN_DATA: True
MIN_AFFINITY: 5
MAX_AFFINITY: 11
MEAN_LOWER_BOUND: 6.5
MEAN_UPPER_BOUND: 7.5
LOW_BOUND: 6
HIGH_BOUND: 8
EDGE_WEIGHT: True #NOTE: If set to True, this has no effect with GraphSAGE and GIN since they do not support edge weights
SCALING: True
SEED: 42
HIDDEN_CHANNELS: 256
EPOCHS: 100
LEARNING_RATE: 1e-3
WEIGHT_DECAY: 5e-4
NODE_FEATURES: True
top_k_computation:
DATA_PATH: "data/" #data location
PLOT: True #if to save top_k graph images
EXPLANATIONS_FOLDER: "results/explanations/GraphSAGE/" #folder where the explanations computed with explainer_script.py are
TOP_K_VALUES: [5,10,15,20,25] #values for the top-k edges computation
NODE_LABELS: False #if to show node labels in the top-k graph images
IMAGE_FORMAT: "svg" #image format for the top-k graph images
#parameters for additional scripts
statistics:
DATA_PATH: "data/" #data location
EXPLANATIONS_FOLDER: "results/explanations/GC_GNN/"
TOP_K_VALUES: [25] #values for the top-k edges computation
MIN_AFFINITY: 5
MAX_AFFINITY: 11
MEAN_LOWER_BOUND: 6.5
MEAN_UPPER_BOUND: 7.5
LOW_BOUND: 6
HIGH_BOUND: 8
CLEAN_DATA: True
affinity_shifting:
DATA_PATH: "data/" #data location
MODEL_PATH: 'models/pretrained_models/model_GAT_best_98.ckpt' #model to use
GNN_MODEL: "GAT" # "GC_GNN" "GCN" "GAT" "GIN" "GINE" "GraphSAGE"
HIDDEN_CHANNELS: 256
BATCH_SIZE: 32
EPOCHS: 100
SEED: 42
CLEAN_DATA: True
MIN_AFFINITY: 5
MAX_AFFINITY: 11
MEAN_LOWER_BOUND: 6.5
MEAN_UPPER_BOUND: 7.5
LOW_BOUND: 6
HIGH_BOUND: 8
EDGE_WEIGHT: True
SCALING: True
SEED: 42
HIDDEN_CHANNELS: 256
EPOCHS: 100
LEARNING_RATE: 1e-3
WEIGHT_DECAY: 5e-4
NODE_FEATURES: True
AFFINITY_SHIFT: 0.5
gnn_explainer:
DATA_PATH: "data/" #data location
SAVE_FOLDER: 'results/additional_experiments/gnn_explainer_explanations/' #folder where explanations will be saved
MODEL_PATH: 'models/pretrained_models/model_GraphSAGE_best_71.ckpt' #model to use for the explanation phase
GNN_MODEL: "GraphSAGE" # "GC_GNN" "GCN" "GAT" "GIN" "GINE" "GraphSAGE"
SAMPLES_TO_EXPLAIN: 20 #number of test samples to explain
AFFINITY_SET: "low" #affinity set for which to perform the explanations (low, medium, high)
HIDDEN_CHANNELS: 256
BATCH_SIZE: 32
EPOCHS: 100
SEED: 42
CLEAN_DATA: True
MIN_AFFINITY: 5
MAX_AFFINITY: 11
MEAN_LOWER_BOUND: 6.5
MEAN_UPPER_BOUND: 7.5
LOW_BOUND: 6
HIGH_BOUND: 8
EDGE_WEIGHT: True #NOTE: If set to True, this has no effect with GraphSAGE and GIN since they do not support edge weights
SCALING: True
SEED: 42
HIDDEN_CHANNELS: 256
EPOCHS: 100
LEARNING_RATE: 1e-3
WEIGHT_DECAY: 5e-4
NODE_FEATURES: True