文件列表:

experiment-1.x (0, 2022-02-12)
experiment-1.x\fcc_experiment_1.x_both_fixed_and_variable_sized_graphs.ipynb (38327, 2022-02-12)
experiment-1.x\fcc_experiment_1.x_fixed_size_graphs.ipynb (39681, 2022-02-12)
experiment-1.x\fcc_experiment_1.x_variable_size_graphs.ipynb (26940, 2022-02-12)
experiment-1.x\fcc_experiment_1_x_graph_metrics_for_100_events_and_node_metrics_for_event_id_9_visualisation.ipynb (1309606, 2022-02-12)
experiment-1.x\fcc_experiment_1_x_save_variable_graphs_as_torch_data_objects.ipynb (33900, 2022-02-12)
experiment-1.x\info.md (1, 2022-02-12)
experiment-10.x (0, 2022-02-12)
experiment-10.x\fcc_experiment_10_x_model_MLP_2_4_8.ipynb (99509, 2022-02-12)
experiment-10.x\fcc_experiment_10_x_model_performance_MLP_2_4_8.ipynb (29852, 2022-02-12)
experiment-10.x\info.md (1, 2022-02-12)
experiment-2.x (0, 2022-02-12)
experiment-2.x\fcc_experiment_2_x_model_GCNX_2.ipynb (295836, 2022-02-12)
experiment-2.x\fcc_experiment_2_x_model_GCNX_2_calculate_invariant_mass_of_H_Z_O.ipynb (187742, 2022-02-12)
experiment-2.x\fcc_experiment_2_x_model_GCNX_2_calculate_invariant_mass_of_H_Z_O_un_normalised_values.ipynb (187775, 2022-02-12)
experiment-2.x\fcc_experiment_2_x_model_GCNX_2_calculate_sum_of_mom_x_on_colab_save_physics_performance_metrics.ipynb (177319, 2022-02-12)
experiment-2.x\fcc_experiment_2_x_model_GCNX_2_hyperparameters_dropout_0_1_0_2_0_3_0_4_0_5.ipynb (59950, 2022-02-12)
experiment-2.x\fcc_experiment_2_x_model_GCNX_2_hyperparameters_hidden_channels_10_16_32_64_128.ipynb (60798, 2022-02-12)
experiment-2.x\fcc_experiment_2_x_model_GCNX_2_hyperparameters_lr_0_1_to_0_00001.ipynb (59939, 2022-02-12)
experiment-2.x\fcc_experiment_2_x_model_GCNX_2_hyperparameters_non_linearity_elu_selu_gelu_leaky_relu_tanh.ipynb (62472, 2022-02-12)
experiment-2.x\fcc_experiment_2_x_model_GCNX_2_hyperparameters_optimizer_adam_sgd_rmsprop.ipynb (56445, 2022-02-12)
experiment-2.x\fcc_experiment_2_x_model_GCNX_2_hyperparameters_wd_0_5_to_0_00005.ipynb (60022, 2022-02-12)
experiment-2.x\fcc_experiment_2_x_model_GCNX_2_on_20_40_60_80_percentage_of_dataset.ipynb (113812, 2022-02-12)
experiment-2.x\fcc_experiment_2_x_model_final_GCNX_16_32_on_variable_sized.ipynb (811669, 2022-02-12)
experiment-2.x\fcc_experiment_2_x_model_final_GCNX_4_8_on_variable_sized.ipynb (816432, 2022-02-12)
experiment-2.x\fcc_experiment_2_x_model_performance_GCNX_16.ipynb (30231, 2022-02-12)
experiment-2.x\fcc_experiment_2_x_model_performance_GCNX_2.ipynb (29850, 2022-02-12)
experiment-2.x\fcc_experiment_2_x_model_performance_GCNX_2_calculate_invariant_mass_on_colab.ipynb (15570, 2022-02-12)
experiment-2.x\fcc_experiment_2_x_model_performance_GCNX_2_calculate_invariant_mass_un_normalised.ipynb (15141, 2022-02-12)
experiment-2.x\fcc_experiment_2_x_model_performance_GCNX_2_hyperparameters_dropout_0_1_0_2_0_3_0_4_0_5.ipynb (34681, 2022-02-12)
experiment-2.x\fcc_experiment_2_x_model_performance_GCNX_2_hyperparameters_hidden_channels_10_16_32_64_128.ipynb (35086, 2022-02-12)
experiment-2.x\fcc_experiment_2_x_model_performance_GCNX_2_hyperparameters_lr_0_1_to_0_00001.ipynb (34998, 2022-02-12)
experiment-2.x\fcc_experiment_2_x_model_performance_GCNX_2_hyperparameters_non_linearity_elu_selu_gelu_leaky_relu_tanh.ipynb (37864, 2022-02-12)
experiment-2.x\fcc_experiment_2_x_model_performance_GCNX_2_hyperparameters_optimizer_adam_sgd_rmsprop.ipynb (29153, 2022-02-12)
experiment-2.x\fcc_experiment_2_x_model_performance_GCNX_2_hyperparameters_wd_0_5_to_0_00005.ipynb (35118, 2022-02-12)
experiment-2.x\fcc_experiment_2_x_model_performance_GCNX_4.ipynb (29996, 2022-02-12)
experiment-2.x\fcc_experiment_2_x_model_performance_GCNX_8.ipynb (30140, 2022-02-12)
experiment-2.x\fcc_experiment_2_x_model_performance_GCN_20_percent_dataset.ipynb (30207, 2022-02-12)
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# fccml-thesis Graph Neural Networks for Particle Physics # Acknowledgements: - Prof Daniela Bortoletto, University of Oxford - Prof Phil Blunsom, University of Oxford - Dr Michele Selvaggi, CERN - Dr Loukas Gouskos, CERN # Experiment 1.x: Data processing, analysis, visualisation - Builds the 3 graph datasets for two processing schemes: fixed size graph data sets and variable size datasets: ```fcc_experiment_1.x_both_fixed_and_variable_sized_graphs.ipynb``` - Builds the 3 graph datasets and saves graphs as torch objects ```fcc_experiment_1_x_save_variable_graphs_as_torch_data_objects.ipynb``` - Visualisation of Node Metrics for Event ID:9 and Global Metrics of 100 Graphs ```fccml_experiment_1_x_graph_metrics_for_100_events_and_node_metrics_for_event_id_9_visualisation.ipynb``` # Experiment 2.x GCN ## Network Depth - Model Training and Testing on GCN-2 on Fixed and Variable Sized Graphs and explanations of node predictions using GNNExplainer ```fcc_experiment_2_x_model_GCNX_2.ipynb``` - Model Training and Testing of GCN-16 on Variable Sized Graphs across 3 datasets ```fcc_experiment_2_x_model_final_GCNX_16_32_on_variable_sized.ipynb``` - Model Training and Testing of On GCN-4 and GCN-8 on Variable Sized Graphs across 3 datasets ```fcc_experiment_2_x_model_final_GCNX_4_8_on_variable_sized.ipynb``` - Model Performance of GCN-16 across 3 datasets ```fcc_experiment_2_x_model_performance_GCNX_16.ipynb``` - Model Performance of GCN-4 across 3 datasets ```fcc_experiment_2_x_model_performance_GCNX_4.ipynb``` - Model Performance of GCN-8 across 3 datasets ```fcc_experiment_2_x_model_performance_GCNX_8.ipynb``` - Model Performance of GCN-2 across 3 datasets ```fcc_experiment_2_x_model_performance_GCNX_2.ipynb``` - Model Performance by calculating the sum of 4 predicted properties and sum of 4 true properties of each of the clustered particles: H, Z and others separately in the test dataset and returning their MSE and MAE scores across each class for the 3 graph datasets created. ```fcc_experiment_2_x_model_GCNX_2_calculate_sum_of_mom_x_on_colab_save_physics_performance_metrics.ipynb``` ## Hyperparameters - Model Training and Testing on GCN-2 on the KNN dataset: Hyperparameter Dropout ```fcc_experiment_2_x_model_GCNX_2_hyperparameters_dropout_0_1_0_2_0_3_0_4_0_5.ipynb``` - Model Training and Testing on GCN-2 on the KNN dataset: Hidden Channels ```fcc_experiment_2_x_model_GCNX_2_hyperparameters_hidden_channels_10_16_32_***_128.ipynb``` - Model Training and Testing on GCN-2 on the KNN dataset: Learning Rate ```fcc_experiment_2_x_model_GCNX_2_hyperparameters_lr_0_1_to_0_00001.ipynb``` - Model Training and Testing on GCN-2 on the KNN dataset: Non-linearity ```fcc_experiment_2_x_model_GCNX_2_hyperparameters_non_linearity_elu_selu_gelu_leaky_relu_tanh.ipynb``` - Model Training and Testing on GCN-2 on the KNN dataset: Optimizers ```fcc_experiment_2_x_model_GCNX_2_hyperparameters_optimizer_adam_sgd_rmsprop.ipynb``` - Model Training and Testing on GCN-2 on the KNN dataset: Weight Decay ```fcc_experiment_2_x_model_GCNX_2_hyperparameters_wd_0_5_to_0_00005.ipynb``` - Model Performance of GCN-2 dropout ```fcc_experiment_2_x_model_performance_GCNX_2_hyperparameters_dropout_0_1_0_2_0_3_0_4_0_5.ipynb``` - Model Performance of GCN-2 hidden channels ```fcc_experiment_2_x_model_performance_GCNX_2_hyperparameters_hidden_channels_10_16_32_***_128.ipynb``` - Model Performance of GCN-2 Learning Rate ```fcc_experiment_2_x_model_performance_GCNX_2_hyperparameters_lr_0_1_to_0_00001.ipynb``` - Model Performance of GCN-2 Non Linearity ```fcc_experiment_2_x_model_performance_GCNX_2_hyperparameters_non_linearity_elu_selu_gelu_leaky_relu_tanh.ipynb``` - Model Performance of GCN-2 optimizers ```fcc_experiment_2_x_model_performance_GCNX_2_hyperparameters_optimizer_adam_sgd_rmsprop.ipynb``` - Model Performance of GCN-2 Weight decay ```fcc_experiment_2_x_model_performance_GCNX_2_hyperparameters_wd_0_5_to_0_00005.ipynb``` ## Dataset Size - Model Training and Testing on GCN-2 on various dataset sizes: ```fcc_experiment_2_x_model_GCNX_2_on_20_40_60_80_percentage_of_dataset.ipynb``` - Model Performance of 20% of dataset ```fcc_experiment_2_x_model_performance_GCN_20_percent_dataset.ipynb``` - Model Performance of 40% of dataset ```fcc_experiment_2_x_model_performance_GCN_40_percent_dataset.ipynb``` - Model Performance of 60% of dataset ```fcc_experiment_2_x_model_performance_GCN_60_percent_dataset.ipynb``` - Model Performance of 80% of dataset ```fcc_experiment_2_x_model_performance_GCN_80_percent_dataset.ipynb``` # Experiment 3.x ChebNet - Model Training and Testing of ChebNet-2 and ChebNet-4 on Variable Sized Graphs across 3 datasets ```fcc_experiment_3_x_model_ChebNet_2_4.ipynb``` - ChebNet-2 with k=3, 4 on all 3 datasets ```fcc_experiment_3_x_model_ChebNet_2_hyperparameters_k_3_4.ipynb``` - Model performance of ChebNet-2 on all 3 datasets ```fcc_experiment_3_x_model_performance_ChebX_2.ipynb``` - Model performance ChebNet-2 with k=3, 4 on all 3 datasets ```fcc_experiment_3_x_model_performance_ChebX_2_hyperparameters_k_3_4.ipynb``` - Model performance of ChebNet-4 on all 3 datasets ```fcc_experiment_3_x_model_performance_ChebX_4.ipynb``` # Experiment 4.x SAGE - Model Training and Testing of SAGE-2 and SAGE-4 on Variable Sized Graphs across 3 datasets ```fcc_experiment_4_x_model_SAGE_2_4.ipynb``` - Model Performance of SAGE-2 across 3 datasets ```fcc_experiment_4_x_model_performance_SAGE_2.ipynb``` - Model Performance of SAGE-4 across 3 datasets ```fcc_experiment_4_x_model_performance_SAGE_4.ipynb``` # Experiment 5.x TAGCN - Model Training and Testing of TAGCN-2 and TAGCN-4 on Variable Sized Graphs across 3 datasets ```fcc_experiment_5_x_model_TAGCN_2_4.ipynb``` - Model Performance TAGCN-2 on all datasets ```fcc_experiment_5_x_model_performance_TAGCN_2.ipynb ``` - Model Performance of TAGCN-4 across all datasets ```fcc_experiment_5_x_model_performance_TAGCN_4.ipynb``` # Experiment 6.x GAT - Model Training and Testing of GAT-2 and GAT-4 on Variable Sized Graphs across 3 datasets ```fcc_experiment_6_x_model_GAT_2_4.ipynb``` - Model Training and Testing GAT-2 with different heads on KNN dataset ```fcc_experiment_6_x_model_GAT_2_hyperparameters_heads_2_4_6_8_10.ipynb``` - Model performance of GAT-2 across 3 datasets ```fcc_experiment_6_x_model_performance_GAT_2.ipynb``` - Model performance of GAT-2 with different heads on KNN dataset ```fcc_experiment_6_x_model_performance_GAT_2_hyperparameters_heads_2_4_6_8_10.ipynb``` - Model performance of GAT-4 across 3 datasets ```fcc_experiment_6_x_model_performance_GAT_4.ipynb``` # Experiment 7.x GIN - Model Training and Testing of GIN-2 and GIN-4 on Variable Sized Graphs across 3 datasets ```fcc_experiment_7_x_model_GIN_2_4.ipynb ``` - Model performance of GIN-2 across 3 datasets ```fcc_experiment_7_x_model_performance_GIN_2.ipynb``` - Model performance GIN-4 across 3 datasets ```fcc_experiment_7_x_model_performance_GIN_4.ipynb``` # Experiment 8.x JK - Model Training and Testing of JK-2 and JK-4 on Variable Sized Graphs across 3 datasets ```fcc_experiment_8_x_model_JK_2_4.ipynb``` - Model Performance of JK-2 across 3 datasets ```fcc_experiment_8_x_model_performance_JK_2.ipynb``` - Model Performance ofJK-4 across 3 datasets ```fcc_experiment_8_x_model_performance_JK_4.ipynb``` # Experiment 9.x superGAT - Model training and testing of superGAT-2 and superGAT-4 on all datasets ```fcc_experiment_9_x_model_superGAT_2_4.ipynb``` - Model training and testing of superGAT-2: different attentions and heads on KNN dataset ```fcc_experiment_9_x_model_superGAT_2_hyperparameters_attention_MX_SD_head_2_4_8.ipynb``` - Model Performance of superGAT-2 on all datasets ```fcc_experiment_9_x_model_performance_superGAT_2.ipynb``` - Model Performance of superGAT-2: different attentions and heads on KNN dataset ```fcc_experiment_9_x_model_performance_superGAT_2_hyperparameters_heads_2_4_6_8_10.ipynb``` - Model Performance of superGAT-4 on all datasets ```fcc_experiment_9_x_model_performance_superGAT_4.ipynb``` # Experiment 10.x MLP - Model Training and Testing on MLP-2, MLP-4, MLP-8 on Variable Sized Graphs using only node features ```fcc_experiment_10_x_model_MLP_2_4_8.ipynb``` - Model performance of MLP-2, MLP-4, MLP-8 on Variable Sized Graphs using event accuracy ```fcc_experiment_10_x_model_performance_MLP_2_4_8.ipynb```

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