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Clustering_conda.yml
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id: clustering_example_conda
description: Clustering benchmark on Gagolewski's, true number of clusters plus minus 2.
version: 1.4
benchmarker: "Izaskun Mallona, Daniel Incicau"
storage: http://omnibenchmark.org:9000
benchmark_yaml_spec: 0.04
storage_api: S3
storage_bucket_name: clusteringexampleconda
software_backend: conda
software_environments:
clustbench:
description: "clustbench on py3.12.6"
conda: envs/clustbench.yml
envmodule: clustbench
apptainer: envs/clustbench.sif
sklearn:
description: "Daniel's on py3.12.6"
conda: envs/sklearn.yml
apptainer: envs/sklearn.sif
envmodule: clustbench # not true, but
R:
description: "Daniel's R with readr, dplyr, mclust, caret"
conda: envs/r.yml
apptainer: envs/r.sif
envmodule: fcps # not true, but
rmarkdown:
description: "R with some plotting dependencies"
conda: envs/rmarkdown.yml
apptainer: envs/r.sif # not true, but
envmodule: fcps # not true, but
fcps:
description: "CRAN's FCPS"
conda: envs/fcps.yml
apptainer: envs/fcps.sif
envmodule: fcps
metric_collectors:
- id: plotting
name: "Single-backend metric collector."
software_environment: "rmarkdown"
repository:
url: https://github.com/imallona/clustering_report
commit: f1a5876
inputs:
- metrics.scores
outputs:
- id: plotting.html
path: "{input}/{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: 366c5a2
parameters: # comments depict the possible cardinalities and the number of curated labelsets
- values: ["--dataset_generator", "fcps", "--dataset_name", "atom"] # 2 1
- values: ["--dataset_generator", "fcps", "--dataset_name", "chainlink"] # 2 1
- values: ["--dataset_generator", "fcps", "--dataset_name", "engytime"] # 2 2
- values: ["--dataset_generator", "fcps", "--dataset_name", "hepta"] # 7 1
- values: ["--dataset_generator", "fcps", "--dataset_name", "lsun"] # 3 1
- values: ["--dataset_generator", "fcps", "--dataset_name", "target"] # 2, 6 2
- values: ["--dataset_generator", "fcps", "--dataset_name", "tetra"] # 4 1
- values: ["--dataset_generator", "fcps", "--dataset_name", "twodiamonds"] # 2 1
- values: ["--dataset_generator", "fcps", "--dataset_name", "wingnut"] # 2 1
- values: ["--dataset_generator", "graves", "--dataset_name", "dense"] # 2 1
- values: ["--dataset_generator", "graves", "--dataset_name", "fuzzyx"] # 2, 4, 5 6
- values: ["--dataset_generator", "graves", "--dataset_name", "line"] # 2 1
- values: ["--dataset_generator", "graves", "--dataset_name", "parabolic"] # 2, 4 2
- values: ["--dataset_generator", "graves", "--dataset_name", "ring"] # 2 1
- values: ["--dataset_generator", "graves", "--dataset_name", "ring_noisy"] # 2 1
- values: ["--dataset_generator", "graves", "--dataset_name", "ring_outliers"] # 2, 5 2
- values: ["--dataset_generator", "graves", "--dataset_name", "zigzag"] # 3, 5 2
- values: ["--dataset_generator", "graves", "--dataset_name", "zigzag_noisy"] # 3, 5 2
- values: ["--dataset_generator", "graves", "--dataset_name", "zigzag_outliers"] # 3, 5 2
- values: ["--dataset_generator", "other", "--dataset_name", "chameleon_t4_8k"] # 6 1
- values: ["--dataset_generator", "other", "--dataset_name", "chameleon_t5_8k"] # 6 1
- values: ["--dataset_generator", "other", "--dataset_name", "hdbscan"] # 6 1
- values: ["--dataset_generator", "other", "--dataset_name", "iris"] # 3 1
- values: ["--dataset_generator", "other", "--dataset_name", "iris5"] # 3 1
- values: ["--dataset_generator", "other", "--dataset_name", "square"] # 2 1
- values: ["--dataset_generator", "sipu", "--dataset_name", "aggregation"] # 7 1
- values: ["--dataset_generator", "sipu", "--dataset_name", "compound"] # 4, 5, 6 5
- values: ["--dataset_generator", "sipu", "--dataset_name", "flame"] # 2 2
- values: ["--dataset_generator", "sipu", "--dataset_name", "jain"] # 2 1
- values: ["--dataset_generator", "sipu", "--dataset_name", "pathbased"] # 3, 4 2
- values: ["--dataset_generator", "sipu", "--dataset_name", "r15"] # 8, 9, 15 3
- values: ["--dataset_generator", "sipu", "--dataset_name", "spiral"] # 3 1
- values: ["--dataset_generator", "sipu", "--dataset_name", "unbalance"] # 8 1
- values: ["--dataset_generator", "uci", "--dataset_name", "ecoli"] # 8 1
- values: ["--dataset_generator", "uci", "--dataset_name", "ionosphere"] # 2 1
- values: ["--dataset_generator", "uci", "--dataset_name", "sonar"] # 2 1
- values: ["--dataset_generator", "uci", "--dataset_name", "statlog"] # 7 1
- values: ["--dataset_generator", "uci", "--dataset_name", "wdbc"] # 2 1
- values: ["--dataset_generator", "uci", "--dataset_name", "wine"] # 3 1
- values: ["--dataset_generator", "uci", "--dataset_name", "yeast"] # 10 1
- values: ["--dataset_generator", "wut", "--dataset_name", "circles"] # 4 1
- values: ["--dataset_generator", "wut", "--dataset_name", "cross"] # 4 1
- values: ["--dataset_generator", "wut", "--dataset_name", "graph"] # 10 1
- values: ["--dataset_generator", "wut", "--dataset_name", "isolation"] # 3 1
- values: ["--dataset_generator", "wut", "--dataset_name", "labirynth"] # 6 1
- values: ["--dataset_generator", "wut", "--dataset_name", "mk1"] # 3 1
- values: ["--dataset_generator", "wut", "--dataset_name", "mk2"] # 2 1
- values: ["--dataset_generator", "wut", "--dataset_name", "mk3"] # 3 1
- values: ["--dataset_generator", "wut", "--dataset_name", "mk4"] # 3 1
- values: ["--dataset_generator", "wut", "--dataset_name", "olympic"] # 5 1
- values: ["--dataset_generator", "wut", "--dataset_name", "smile"] # 4, 6 2
- values: ["--dataset_generator", "wut", "--dataset_name", "stripes"] # 2 1
- values: ["--dataset_generator", "wut", "--dataset_name", "trajectories"] # 4 1
- values: ["--dataset_generator", "wut", "--dataset_name", "trapped_lovers"] # 3 1
- values: ["--dataset_generator", "wut", "--dataset_name", "twosplashes"] # 2 1
- values: ["--dataset_generator", "wut", "--dataset_name", "windows"] # 5 1
- values: ["--dataset_generator", "wut", "--dataset_name", "x1"] # 3 1
- values: ["--dataset_generator", "wut", "--dataset_name", "x2"] # 3 1
- values: ["--dataset_generator", "wut", "--dataset_name", "x3"] # 4 1
- values: ["--dataset_generator", "wut", "--dataset_name", "z1"] # 3 1
- values: ["--dataset_generator", "wut", "--dataset_name", "z2"] # 5 1
- values: ["--dataset_generator", "wut", "--dataset_name", "z3"] # 4 1
outputs:
- id: data.matrix
path: "{input}/{stage}/{module}/{params}/{dataset}.data.gz"
- id: data.true_labels
path: "{input}/{stage}/{module}/{params}/{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
# url: /home/imallona/src/clustbench_fastcluster/
commit: "45e43d3"
parameters:
- values: ["--linkage", "complete"]
- values: ["--linkage", "ward"]
- values: ["--linkage", "average"]
- values: ["--linkage", "weighted"]
- values: ["--linkage", "median"]
- values: ["--linkage", "centroid"]
- id: sklearn
name: "sklearn"
software_environment: "clustbench"
repository:
url: https://github.com/imallona/clustbench_sklearn
#url: /home/imallona/src/clustbench_sklearn
commit: 5877378
parameters:
- values: ["--method", "birch"]
- values: ["--method", "kmeans"]
# - values: ["--method", "spectral"] ## too slow
- values: ["--method", "gm"]
- id: agglomerative
name: "agglomerative"
software_environment: "clustbench"
repository:
url: https://github.com/imallona/clustbench_agglomerative
commit: 5454368
parameters:
- values: ["--linkage", "average"]
- values: ["--linkage", "complete"]
- values: ["--linkage", "ward"]
- id: genieclust
name: "genieclust"
software_environment: "clustbench"
repository:
url: https://github.com/imallona/clustbench_genieclust
commit: 6090043
parameters:
- values: ["--method", "genie", "--gini_threshold", 0.5]
- values: ["--method", "gic"]
- values: ["--method", "ica"]
- id: fcps
name: "fcps"
software_environment: "fcps"
repository:
url: https://github.com/imallona/clustbench_fcps
commit: 272fa5f
parameters:
# - values: ["--method", "FCPS_AdaptiveDensityPeak"] # not in conda
- values: ["--method", "FCPS_Minimax"]
- values: ["--method", "FCPS_MinEnergy"]
- values: ["--method", "FCPS_HDBSCAN_2"]
- values: ["--method", "FCPS_HDBSCAN_4"]
- values: ["--method", "FCPS_HDBSCAN_8"]
- values: ["--method", "FCPS_Diana"]
- values: ["--method", "FCPS_Fanny"]
- values: ["--method", "FCPS_Hardcl"]
- values: ["--method", "FCPS_Softcl"]
- values: ["--method", "FCPS_Clara"]
- values: ["--method", "FCPS_PAM"]
inputs:
- entries:
- data.matrix
- data.true_labels
outputs:
- id: clustering.predicted_ks_range
path: "{input}/{stage}/{module}/{params}/{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: 9132d45
parameters:
- values: ["--metric", "normalized_clustering_accuracy"]
- values: ["--metric", "adjusted_fm_score"]
- values: ["--metric", "adjusted_mi_score"]
- values: ["--metric", "adjusted_rand_score"]
- values: ["--metric", "fm_score"]
- values: ["--metric", "mi_score"]
- values: ["--metric", "normalized_clustering_accuracy"]
- values: ["--metric", "normalized_mi_score"]
- values: ["--metric", "normalized_pivoted_accuracy"]
- values: ["--metric", "pair_sets_index"]
- values: ["--metric", "rand_score"]
inputs:
- entries:
- clustering.predicted_ks_range
- data.true_labels
outputs:
- id: metrics.scores
path: "{input}/{stage}/{module}/{params}/{dataset}.scores.gz"
# ## daniel's data ###########################################################################
# - id: danielsdata
# modules:
# - id: iris_manual
# name: "Iris Dataset"
# software_environment: "sklearn"
# repository:
# url: https://github.com/omnibenchmark-example/iris.git
# commit: 47c63f0
# - id: penguins
# name: "Penguins Dataset"
# software_environment: "sklearn"
# repository:
# url: https://github.com/omnibenchmark-example/penguins.git
# commit: 9032478
# outputs:
# - id: data.features
# path: "{input}/{stage}/{module}/{params}/{dataset}.features.csv"
# - id: data.labels
# path: "{input}/{stage}/{module}/{params}/{dataset}.labels.csv"
# ## daniel's distances ########################################################################
# - id: distances
# modules:
# - id: D1
# software_environment: "sklearn"
# parameters:
# - values: ["--measure", "cosine"]
# - values: ["--measure", "euclidean"]
# - values: ["--measure", "manhattan"]
# - values: ["--measure", "chebyshev"]
# repository:
# url: https://github.com/omnibenchmark-example/distance.git
# commit: dd99d4f
# inputs:
# - entries:
# - data.features
# outputs:
# - id: distances
# path: "{input}/{stage}/{module}/{params}/{dataset}.distances.csv"
# ## daniel's methods ###################################################################
# - id: danielmethods
# modules:
# - id: kmeans
# software_environment: "sklearn"
# repository:
# url: https://github.com/omnibenchmark-example/kmeans.git
# commit: 049c8b1
# - id: ward
# software_environment: "R"
# repository:
# url: https://github.com/omnibenchmark-example/ward.git
# commit: 976e3f3
# inputs:
# - entries:
# - distances
# outputs:
# - id: methods.clusters
# path: "{input}/{stage}/{module}/{params}/{dataset}.clusters.csv"
# ## daniel's metrics ###################################################################
# - id: danielsmetrics
# modules:
# - id: ari
# software_environment: "R"
# repository:
# url: https://github.com/omnibenchmark-example/ari.git
# commit: 72708f0
# - id: accuracy
# software_environment: "R"
# repository:
# url: https://github.com/omnibenchmark-example/accuracy.git
# commit: e26b32f
# inputs:
# - entries:
# - methods.clusters
# - data.labels
# outputs:
# - id: metrics.mapping
# path: "{input}/{stage}/{module}/{params}/{dataset}.metrics.txt"