@inproceedings{
    poklukar2022delaunay,
    title={Delaunay Component Analysis for Evaluation of Data Representations},
    author={Petra Poklukar and Vladislav Polianskii and Anastasiia Varava and Florian T. Pokorny and Danica Kragic Jensfelt},
    booktitle={International Conference on Learning Representations},
    year={2022},
    url={https://openreview.net/forum?id=HTVch9AMPa}
}

poetry install
chmod +x dca/approximate_Delaunay_graph

poetry run python examples/first_example.py 

experiments/first_example/
  /precomputed
    - clusterer.pkl               # HDBSCAN clusterer object
    - input_array.npy             # array of R and E points
    - input_array_comp_labels.npy # array of component labels corresponding to R and E points
    - unfiltered_edges.npy        # array of unfiltered approximated Delaunay edges
    - unfiltered_edges_len.npy    # array of unfiltered approximated Delaunay edge lengths
  /template_id1
    - output.json                 # dca scores 
    /DCA
        - components_stats.pkl    # Local evaluation scores
        - network_stats.pkl       # Global evaluation scores
    /visualization
        - graph visualizations
    /logs
        - version0_elapsed_time.log      # empirical runtime 
        - version0_input.json            # specific input parameters
        - version0_output_formatted.log  # all evaluation scores in a pretty format
        - version0_experiment_info.log   # console logs
        - # output files from qDCA
        - # any additional logs that should not be shared across experiment_ids in precomputed folder

[mm/dd/yyyy hh:mm:ss] :: num_R: 20                            # total number of R points
[mm/dd/yyyy hh:mm:ss] :: num_E: 20                            # total number of E points
[mm/dd/yyyy hh:mm:ss] :: precision: 0.95                      
[mm/dd/yyyy hh:mm:ss] :: recall: 0.4
[mm/dd/yyyy hh:mm:ss] :: network_consistency: 1.0
[mm/dd/yyyy hh:mm:ss] :: network_quality: 0.2
[mm/dd/yyyy hh:mm:ss] :: first_trivial_component_idx: 2       # idx of the first outlier
[mm/dd/yyyy hh:mm:ss] :: num_R_points_in_fundcomp: 8          # number of vertices in F^R
[mm/dd/yyyy hh:mm:ss] :: num_E_points_in_fundcomp: 19         # number of vertices in F^E
[mm/dd/yyyy hh:mm:ss] :: num_RE_edges: 19                     # number of heterogeneous edges in G_DD
[mm/dd/yyyy hh:mm:ss] :: num_total_edges: 95                  # number of all edges in G_DD
[mm/dd/yyyy hh:mm:ss] :: num_R_outliers: 0                    
[mm/dd/yyyy hh:mm:ss] :: num_E_outliers: 1
[mm/dd/yyyy hh:mm:ss] :: num_fundcomp: 1                      # number of fundamental components |F|
[mm/dd/yyyy hh:mm:ss] :: num_comp: 3                          # number of all connected components
[mm/dd/yyyy hh:mm:ss] :: num_outliercomp: 1                   # number of trivial components
# Local scores for each component G_i: consistency and quality (Def 3.2) as well as number of R and E points contained in it
[mm/dd/yyyy hh:mm:ss] :: c(G0): 0.59, q(G0): 0.27, |G0^R|_v: 8   , |G0^E|_v: 19  , |G0|_v: 27  
[mm/dd/yyyy hh:mm:ss] :: c(G1): 0.00, q(G1): 0.00, |G1^R|_v: 12  , |G1^E|_v: 0   , |G1|_v: 12  
[mm/dd/yyyy hh:mm:ss] :: c(G2): 0.00, q(G2): 0.00, |G2^R|_v: 0   , |G2^E|_v: 1   , |G2|_v: 1   

poetry run python examples/first_example.py --cleanup 1

# Generate input parameters
data_config = REData(R=R, E=E)
experiment_config = ExperimentDirs(
    experiment_dir=experiment_path,
    experiment_id=experiment_id,
)
graph_config = DelaunayGraphParams()
hdbscan_config = HDBSCANParams()
geomCA_config = GeomCAParams()

# Initialize loggers
exp_loggers = DCALoggers(experiment_config.logs_dir)

# Run DCA
dca = DCA(
    experiment_config,
    graph_config,
    hdbscan_config,
    geomCA_config,
    loggers=exp_loggers,
)
dca_scores = dca.fit(data_config)
dca.cleanup()  # Optional cleanup

poetry run python experiments/contrastive_learning/CL_varying_component_density.py --n-iterations 10 --perc-to-discard 0.5 --cleanup 1

poetry run python experiments/contrastive_learning/CL_qDCA.py Df query_Df_holdout_c7_to_c11 --run-dca 1 --run-qdca 1 --several-assignments 1 --cleanup 1

poetry run python experiments/contrastive_learning/CL_mode_truncation.py --cleanup 1

poetry run python experiments/contrastive_learning/CL_ablation_study.py cl-ablation-delaunay-edge-approximation --cleanup 1
poetry run python experiments/contrastive_learning/CL_ablation_study.py cl-ablation-delaunay-edge-filtering --cleanup 1
poetry run python experiments/contrastive_learning/CL_ablation_study.py cl-ablation-hdbscan --cleanup 1

poetry run python experiments/stylegan/StyleGAN_mode_truncation.py 0.5 --num-samples "5000" --cleanup 1

poetry run python experiments/stylegan/StyleGAN_qDCA.py --run-dca 1 --run-qdca 1 --cleanup 1

poetry run python experiments/vgg16/VGG16_class_separability.py --version-id 1 --cleanup 1 

poetry run python experiments/vgg16/VGG16_qDCA.py --version-id 1 --run-dca 1 --run-qdca 1 --cleanup 1