Dual graph #76
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| { | ||
| "benchmark_setup": { | ||
| "data": [ | ||
| { | ||
| "data_id": "example1", | ||
| "graph_id": "random-1-ultra-sparse", | ||
| "parameters_id": "gwi", | ||
| "seed_range": [ | ||
| 1, | ||
| 5 | ||
| ] | ||
| }, | ||
| { | ||
| "data_id": "example1", | ||
| "graph_id": "random-1-sparse", | ||
| "parameters_id": "gwi", | ||
| "seed_range": [ | ||
| 1, | ||
| 5 | ||
| ] | ||
| }, | ||
| { | ||
| "data_id": "example1", | ||
| "graph_id": "lattice", | ||
| "parameters_id": "gwi", | ||
| "seed_range": [ | ||
| 1, | ||
| 5 | ||
| ] | ||
| }, | ||
| { | ||
| "data_id": "example1", | ||
| "graph_id": "ar5", | ||
| "parameters_id": "gwi", | ||
| "seed_range": [ | ||
| 1, | ||
| 5 | ||
| ] | ||
| }, | ||
| { | ||
| "data_id": "example1", | ||
| "graph_id": "circle", | ||
| "parameters_id": "gwi", | ||
| "seed_range": [ | ||
| 1, | ||
| 5 | ||
| ] | ||
| } | ||
| ], | ||
| "evaluation": { | ||
| "benchmarks": { | ||
| "filename_prefix": "dualGraph_p200_sparse/", | ||
| "show_seed": false, | ||
| "errorbar": true, | ||
| "errorbarh": false, | ||
| "scatter": true, | ||
| "path": true, | ||
| "text": false, | ||
| "ids": [ | ||
| "dualGL-gt13", | ||
| "jtsampler_gts", | ||
| "psi-learn", | ||
| "glasso", | ||
| "mb" | ||
| ] | ||
| }, | ||
| "graph_true_plots": true, | ||
| "graph_true_stats": false, | ||
| "ggally_ggpairs": false, | ||
| "graph_plots": [], | ||
| "mcmc_traj_plots": [ | ||
| { | ||
| "id": "jtsampler_gts", | ||
| "burn_in": 0.0, | ||
| "thinning": 100, | ||
| "functional": [ | ||
| "score" | ||
| ], | ||
| "active": true | ||
| } | ||
| ], | ||
| "mcmc_heatmaps": [], | ||
| "mcmc_autocorr_plots": [] | ||
| } | ||
| }, | ||
| "resources": { | ||
| "data": { | ||
| "iid": [ | ||
| { | ||
| "id": "example1", | ||
| "standardized": false, | ||
| "n": [ | ||
| 20, 50, 100 | ||
| ] | ||
| } | ||
| ] | ||
| }, | ||
| "graph": { | ||
| "trilearn_rand_bandmat": [ | ||
| { | ||
| "id": "ar5", | ||
| "max_bandwidth": 5, | ||
| "dim": 200 | ||
| } | ||
| ], | ||
| "bdgraph_graphsim": [ | ||
| { | ||
| "id": "random-1-sparse", | ||
| "p": 200, | ||
| "graph": "random", | ||
| "class": null, | ||
| "size": null, | ||
| "prob": 0.1 | ||
| }, | ||
| { | ||
| "id": "random-1-ultra-sparse", | ||
| "p": 200, | ||
| "graph": "random", | ||
| "class": null, | ||
| "size": null, | ||
| "prob": 0.01 | ||
| }, | ||
| { | ||
| "id": "lattice", | ||
| "p": 200, | ||
| "graph": "lattice", | ||
| "class": null, | ||
| "size": null, | ||
| "prob": 0.1 | ||
| }, | ||
| { | ||
| "id": "circle", | ||
| "p": 200, | ||
| "graph": "circle", | ||
| "class": null, | ||
| "size": null, | ||
| "prob": 0.2 | ||
| } | ||
| ] | ||
| }, | ||
| "parameters": { | ||
| "bdgraph_rgwish": [ | ||
| { | ||
| "id": "gwi", | ||
| "b": 3, | ||
| "threshold_conv": 0.000001 | ||
| } | ||
| ] | ||
| }, | ||
| "structure_learning_algorithms": { | ||
| "equsa_psilearner": [ | ||
| { | ||
| "id": "psi-learn", | ||
| "timeout": null, | ||
| "alpha1": 0.3, | ||
| "alpha2": [0.01, 0.025, 0.05, 0.1, 0.15, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.9, 0.98] | ||
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| }], | ||
| "dualgl": [ | ||
| { | ||
| "id": "dualGL-gt13", | ||
| "timeout": null, | ||
| "startalg": "jtsampler_gts", | ||
| "alpha": [0.01, 0.025, 0.05, 0.1, 0.15, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.9, 0.99] | ||
| } | ||
| ], | ||
| "athomas_jtsampler": [ | ||
| { | ||
| "id": "jtsampler_gts", | ||
| "burnin_frac": 0.5, | ||
| "mcmc_estimator": ["threshold"], | ||
| "timeout": null, | ||
| "mcmc_seed": 1, | ||
| "num_samples": 10000000, | ||
| "sampler": 1, | ||
| "edge_penalty": 0.0, | ||
| "size_maxclique": 100, | ||
| "full_output": true, | ||
| "threshold": [ | ||
| 0.0, | ||
| 0.1, | ||
| 0.2, | ||
| 0.3, | ||
| 0.4, | ||
| 0.5, | ||
| 0.6, | ||
| 0.7, | ||
| 0.8, | ||
| 0.9, | ||
| 1.0 | ||
| ] | ||
| } | ||
| ], | ||
| "huge_glasso": [ | ||
| { | ||
| "id": "glasso", | ||
| "lambda": [2, 1, 0.8, 0.6, 0.4, 0.2, 0.1, 0.05, 0.01], | ||
| "timeout": null | ||
| } | ||
| ], | ||
| "huge_mb": [ | ||
| { | ||
| "id": "mb", | ||
| "lambda": [2, 1, 0.8, 0.6, 0.4, 0.2, 0.1, 0.05, 0.01], | ||
| "timeout": null | ||
| } | ||
| ] | ||
| } | ||
| } | ||
| } | ||
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| Provides a way to create a precision matrix with entries assigned randomly from a given list. | ||
| The genrated precision is then converted to a correlation matrix. | ||
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| { | ||
| "title": "Random-precision", | ||
| "version": "", | ||
| "package": { | ||
| "title": "", | ||
| "url": "" | ||
| }, | ||
| "docs_url": "", | ||
| "papers": [ | ||
| ], | ||
| "graph_types": [ | ||
| "UG" | ||
| ], | ||
| "language": "R" | ||
| } |
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| # Samples and inverts a precision matrics from the G-Wishart distribution. | ||
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| seed <- as.integer(snakemake@wildcards[["seed"]]) | ||
| set.seed(seed) | ||
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| df <- read.csv(snakemake@input[["adjmat"]], header = TRUE, check.names = FALSE) | ||
| adjmat <- as.matrix(df) | ||
| p <- nrow(adjmat) | ||
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| K_values <-as.numeric(snakemake@input[['precision_values']]) | ||
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| print("Simulating randam-precision matrix") | ||
| if(length(K_values) == 1) | ||
| { | ||
| precmat <- 1*(adjmat !=0) * K_values | ||
| } | ||
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| if(length(K_values) > 1) | ||
| { | ||
| M = sum(adjmat !=0) | ||
| v = sample.int(length(K_values), M, replace=TRUE) | ||
| precmat<- 1*(adjmat !=0) | ||
| precmat[which(adjmat!=0)] <- K_values[v] | ||
| } | ||
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| print("Inverting the precision matrix") | ||
| covmat <- cov2cor(solve(precmat)) | ||
| colnames(covmat) <- colnames(df) | ||
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| filename <- snakemake@output[["params"]] | ||
| write.table(covmat, | ||
| file = filename, row.names = FALSE, | ||
| quote = FALSE, col.names = TRUE, sep = "," | ||
| ) | ||
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| rule bdgraph_rgwish: | ||
|
There was a problem hiding this comment. This name should probably be changed |
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| input: | ||
| adjmat = "{output_dir}/adjmat/{adjmat}.csv" | ||
| output: | ||
| params = "{output_dir}/parameters/" + \ | ||
| pattern_strings["random_precmat"] + "/" \ | ||
| "seed={seed}/"+\ | ||
| "adjmat=/{adjmat}.csv" | ||
| container: | ||
| None | ||
| script: | ||
| "random_precmat.R" | ||
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| { | ||
| "title": "random_precision", | ||
| "description": "Generates a random precision matrix", | ||
| "type": "array", | ||
| "items": { | ||
| "title": "random_precision", | ||
| "description": "Generates a precision matrix from the given list of values, assigned randomly, then coverts it to a correlation matrix", | ||
| "type": "object", | ||
| "properties": { | ||
| "id": { | ||
| "type": "string" | ||
| }, | ||
| "precision_values": { | ||
| "type": "array", | ||
| "items": { | ||
| "type": "string" | ||
| } | ||
| } | ||
| }, | ||
| "required": [ | ||
| "id", | ||
| "precision_values" | ||
| ], | ||
| "additionalProperties": false, | ||
| "examples": [ | ||
| { | ||
| "id": "rand_K", | ||
| "precision_values": [0.5, 0.25] | ||
| } | ||
| ] | ||
| }, | ||
| "uniqueItems": true | ||
| } |
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| import os | ||
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| def change_filename(original_string): | ||
| return original_string.replace("adjvecs_tobecompressed.csv", "adjvecs_fulloutput.tar.gz") | ||
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| rule athomas_jtsampler: | ||
| input: | ||
| data=alg_input_data(), | ||
| output: | ||
| seqgraph=touch(alg_output_seqgraph_path(module_name)), | ||
| seqgraph_full=touch(change_filename(alg_output_seqgraph_path(module_name))), | ||
| time=touch(alg_output_time_path(module_name)), | ||
| ntests=touch(alg_output_ntests_path(module_name)) | ||
| container: | ||
| "docker://hallawalla/athomas_jtsampler:1.5" | ||
| script: | ||
| "script.sh" | ||
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| #!/bin/bash | ||
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| #CP=$(pwd)/workflow/rules/structure_learning_algorithms/athomas_jtsampler/jtsampler | ||
| CP=/jtsampler | ||
| TEMP_FILENAME=${snakemake_output[seqgraph_full]/fulloutput.tar.gz/fulloutput_tobecompressed.csv} | ||
| if [ ${snakemake_wildcards[timeout]} = "None" ]; then | ||
| if [ ${snakemake_wildcards[full_output]} = "True" ]; then | ||
| /usr/bin/time -f "%e" -o ${snakemake_output[time]} java -classpath $CP EstimateGM \ | ||
| -r ${snakemake_wildcards[mcmc_seed]} \ | ||
| -n ${snakemake_wildcards[num_samples]} \ | ||
| -s ${snakemake_wildcards[sampler]} \ | ||
| -a ${snakemake_wildcards[edge_penalty]} \ | ||
| -c ${snakemake_wildcards[size_maxclique]} \ | ||
| -pd 5 \ | ||
| -F < ${snakemake_input[data]} > $TEMP_FILENAME | ||
| ## convet to benchpress file | ||
| ## copying the first 4 lines and every line afterwards that has a successfull move | ||
| awk -F, -v OFS=',' 'NR <= 4 || $5 == 0 {print $1, $2, $3, $4}' $TEMP_FILENAME > ${snakemake_output[seqgraph]} | ||
| ## compressing the files | ||
| tar -czf ${snakemake_output[seqgraph_full]} $TEMP_FILENAME | ||
| rm -f $TEMP_FILENAME | ||
| else | ||
| /usr/bin/time -f "%e" -o ${snakemake_output[time]} java -classpath $CP EstimateGM \ | ||
| -r ${snakemake_wildcards[mcmc_seed]} \ | ||
| -n ${snakemake_wildcards[num_samples]} \ | ||
| -s ${snakemake_wildcards[sampler]} \ | ||
| -a ${snakemake_wildcards[edge_penalty]} \ | ||
| -pd 5 \ | ||
| -c ${snakemake_wildcards[size_maxclique]} < ${snakemake_input[data]} > ${snakemake_output[seqgraph]} | ||
| fi | ||
| else | ||
| /usr/bin/time -f "%e" -o ${snakemake_output[time]} timeout -s SIGINT ${snakemake_wildcards[timeout]} bash -c 'java -classpath $CP EstimateGM -r ${snakemake_wildcards[mcmc_seed]} < ${snakemake_input[data]} > ${snakemake_output[seqgraph]}' | ||
| fi | ||
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| #java -classpath $CP EstimateGM -r 1 -n 1000 -s 2 -a 10000 < results/data/adjmat\=/bdgraph_graphsim/p\=25/graph\=random/class\=None/size\=None/prob\=0.5/seed\=1/parameters\=/bdgraph_rgwish/b\=3/threshold_conv\=1e-07/seed\=1/data\=/iid/n\=100/seed\=1.csv |
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There should also be a rule in the iiid data module, telling how to sample from this. It will probably be in the sam way as for the intra class parameters module.