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Clustering_oras.yml
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id: clustering_example_oras
description: "Clustering benchmark on Gagolewski's, true number of clusters plus minus 2."
version: "1.5.0"
benchmarker: "Izaskun Mallona, Daniel Incicau, Ben Carrillo"
storage:
api: S3
endpoint: http://omnibenchmark.mls.uzh.ch:9000
bucket_name: clusteringexampleoras
software_backend: apptainer
software_environments:
clustbench:
description: "clustbench on py3.12.6"
conda: envs/clustbench.yml
envmodule: clustbench
apptainer: oras://quay.io/imallona/clustering_example/clustbench:latest
fcps:
description: "CRAN's FCPS"
conda: envs/fcps.yml
apptainer: oras://quay.io/imallona/clustering_example/fcps:latest
envmodule: fcps
metric_collectors:
- id: plotting
name: "Single-backend metric collector."
software_environment: fcps
repository:
url: https://github.com/imallona/clustering_report
commit: "040"
inputs:
- metrics.scores
outputs:
- id: plotting.html
path: "{name}/plotting_report.html"
stages:
## clustbench data ##########################################################
- id: data
modules:
- id: clustbench
name: "clustbench datasets, from https://www.sciencedirect.com/science/article/pii/S0020025521010082#t0005 Table1"
software_environment: "clustbench"
repository:
url: https://github.com/imallona/clustbench_data
commit: fc67ebd
parameters: # comments depict the possible cardinalities and the number of curated labelsets
- dataset_generator: "fcps"
dataset_name: ["atom", "chainlink"] # 2 1
# - dataset_generator: "fcps"
# dataset_name: "engytime" # 2 2
# - dataset_generator: "fcps"
# dataset_name: "hepta" # 7 1
# - dataset_generator: "fcps"
# dataset_name: "lsun" # 3 1
# - dataset_generator: "fcps"
# dataset_name: "target" # 2, 6 2
# - dataset_generator: "fcps"
# dataset_name: "tetra" # 4 1
# - dataset_generator: "fcps"
# dataset_name: "twodiamonds" # 2 1
# - dataset_generator: "fcps"
# dataset_name: "wingnut" # 2 1
# - dataset_generator: "graves"
# dataset_name: "dense" # 2 1
# - dataset_generator: "graves"
# dataset_name: "fuzzyx" # 2, 4, 5 6
# - dataset_generator: "graves"
# dataset_name: "line" # 2 1
# - dataset_generator: "graves"
# dataset_name: "parabolic" # 2, 42
# - dataset_generator: "graves"
# dataset_name: "ring" # 2 1
# - dataset_generator: "graves"
# dataset_name: "ring_noisy" # 2 1
# - dataset_generator: "graves"
# dataset_name: "ring_outliers" # 2, 52
# - dataset_generator: "graves"
# dataset_name: "zigzag" # 3, 5 2
# - dataset_generator: "graves"
# dataset_name: "zigzag_noisy" # 3, 52
# - dataset_generator: "graves"
# dataset_name: "zigzag_outliers" # 3, 52
# - dataset_generator: "other"
# dataset_name: "chameleon_t4_8k" # 6 1
# - dataset_generator: "other"
# dataset_name: "chameleon_t5_8k" # 6 1
# - dataset_generator: "other"
# dataset_name: "hdbscan" # 6 1
# - dataset_generator: "other"
# dataset_name: "iris" # 3 1
# - dataset_generator: "other"
# dataset_name: "iris5" # 3 1
# - dataset_generator: "other"
# dataset_name: "square" # 2 1
# - dataset_generator: "sipu"
# dataset_name: "aggregation" # 7 1
# - dataset_generator: "sipu"
# dataset_name: "compound" # 4, 5, 6 5
# - dataset_generator: "sipu"
# dataset_name: "flame" # 2 2
# - dataset_generator: "sipu"
# dataset_name: "jain" # 2 1
# - dataset_generator: "sipu"
# dataset_name: "pathbased" # 3, 4 2
# - dataset_generator: "sipu"
# dataset_name: "r15" # 8, 9, 15 3
# - dataset_generator: "sipu"
# dataset_name: "spiral" # 3 1
# - dataset_generator: "sipu"
# dataset_name: "unbalance" # 8 1
# - dataset_generator: "uci"
# dataset_name: "ecoli" # 8 1
# - dataset_generator: "uci"
# dataset_name: "ionosphere" # 2 1
# - dataset_generator: "uci"
# dataset_name: "sonar" # 2 1
# - dataset_generator: "uci"
# dataset_name: "statlog" # 7 1
# - dataset_generator: "uci"
# dataset_name: "wdbc" # 2 1
# - dataset_generator: "uci"
# dataset_name: "wine" # 3 1
# - dataset_generator: "uci"
# dataset_name: "yeast" # 10 1
# - dataset_generator: "wut"
# dataset_name: "circles" # 4 1
# - dataset_generator: "wut"
# dataset_name: "cross" # 4 1
# - dataset_generator: "wut"
# dataset_name: "graph" # 10 1
# - dataset_generator: "wut"
# dataset_name: "isolation" # 3 1
# - dataset_generator: "wut"
# dataset_name: "labirynth" # 6 1
# - dataset_generator: "wut"
# dataset_name: "mk1" # 3 1
# - dataset_generator: "wut"
# dataset_name: "mk2" # 2 1
# - dataset_generator: "wut"
# dataset_name: "mk3" # 3 1
# - dataset_generator: "wut"
# dataset_name: "mk4" # 3 1
# - dataset_generator: "wut"
# dataset_name: "olympic" # 5 1
# - dataset_generator: "wut"
# dataset_name: "smile" # 4, 6 2
# - dataset_generator: "wut"
# dataset_name: "stripes" # 2 1
# - dataset_generator: "wut"
# dataset_name: "trajectories" # 4 1
# - dataset_generator: "wut"
# dataset_name: "trapped_lovers" # 3 1
# - dataset_generator: "wut"
# dataset_name: "twosplashes" # 2 1
# - dataset_generator: "wut"
# dataset_name: "windows" # 5 1
# - dataset_generator: "wut"
# dataset_name: "x1" # 3 1
# - dataset_generator: "wut"
# dataset_name: "x2" # 3 1
# - dataset_generator: "wut"
# dataset_name: "x3" # 4 1
# - dataset_generator: "wut"
# dataset_name: "z1" # 3 1
# - dataset_generator: "wut"
# dataset_name: "z2" # 5 1
# - dataset_generator: "wut"
# dataset_name: "z3" # 4 1
outputs:
- id: data.matrix
path: "{dataset}.data.gz"
- id: data.true_labels
path: "{dataset}.labels0.gz"
## clustbench methods (fastcluster) ###################################################################
- id: clustering
modules:
- id: fastcluster
name: "fastcluster algorithm"
software_environment: "clustbench"
repository:
url: https://github.com/imallona/clustbench_fastcluster
commit: e644ce5
parameters:
- linkage: "complete"
#- linkage: ["ward", "average", "weighted", "median", "centroid"]
- id: sklearn
name: "sklearn"
software_environment: "clustbench"
repository:
url: https://github.com/imallona/clustbench_sklearn
commit: dcf35e1
parameters:
- method: "birch"
# ["kmeans, "gm"]
# ["spectral"] ## too slow
- id: agglomerative
name: "agglomerative"
software_environment: "clustbench"
repository:
url: https://github.com/imallona/clustbench_agglomerative
commit: 9d086a9
parameters:
- linkage: "average"
# ["complete", "ward"]
- id: genieclust
name: "genieclust"
software_environment: "clustbench"
repository:
url: https://github.com/imallona/clustbench_genieclust
commit: 7d9e799
parameters:
- method: "genie"
# method: ["gic", "ica"]
gini_threshold: 0.5
- id: fcps
name: "fcps"
software_environment: "fcps"
repository:
url: https://github.com/imallona/clustbench_fcps
commit: e780fed
parameters:
- method: "FCPS_Minimax"
seed: 2
# - "FCPS_AdaptiveDensityPeak" # not in Conda
# - "FCPS_MinEnergy",
# - "FCPS_HDBSCAN_2",
# - "FCPS_HDBSCAN_4",
# - "FCPS_HDBSCAN_8",
# - "FCPS_Diana",
# - "FCPS_Fanny",
# - "FCPS_Hardcl",
# - "FCPS_Softcl",
# - "FCPS_Clara",
# - "FCPS_PAM"
inputs:
- data.matrix
- data.true_labels
outputs:
- id: clustering.predicted_ks_range
path: "{dataset}_ks_range.labels.gz"
- id: metrics
modules:
- id: partition_metrics
name: "clustbench partition metrics"
software_environment: "clustbench"
repository:
url: https://github.com/imallona/clustbench_metrics
commit: c4eda85
parameters:
- metric: ["normalized_clustering_accuracy", "adjusted_fm_score"]
# - "adjusted_mi_score"
# - "adjusted_rand_score"
# - "fm_score"
# - "mi_score"
# - "normalized_clustering_accuracy"
# - "normalized_mi_score"
# - "normalized_pivoted_accuracy"
# - "pair_sets_index"
# - "rand_score"
inputs:
- clustering.predicted_ks_range
- data.true_labels
outputs:
- id: metrics.scores
path: "{dataset}.scores.gz"