New features (#136)

Three major features:

The ability to incorporate background knowledge in terms of edge Constraints.
The ability to specify multiple benchmark setups in the same config file.
A new module gcastle_dag, for generating DAGs using the gCastle package

Author: felixleopoldo

.gitignore

@@ -11,4 +11,6 @@ causal-cmd.log
 .java/
 .cache/
 __pycache__
-workflow/schemas/tmp.config.schema.json
+workflow/schemas/tmp.config.schema.json
+.RData
+.Rhistory

VERSION

@@ -1,2 +1 @@
-2.8.0
-
+2.9.0

config/chordal_algos.json

@@ -1,75 +1,97 @@
 {
-  "benchmark_setup": {
-    "data": [
-      {
-        "data_id": "example1",
-        "graph_id": "AR1-5",
-        "parameters_id": "intra-class",
-        "seed_range": [1, 1]
-      }
-    ],
-    "evaluation": {
-      "benchmarks": {
-        "filename_prefix": "mcmc_chordal/",
-        "show_seed": false,
-        "errorbar": true,
-        "errorbarh": false,
-        "scatter": true,
-        "path": true,
-        "text": false,
-        "ids": ["gg99", "trilearn"]
-      },
-      "graph_true_plots": true,
-      "graph_true_stats": false,
-      "ggally_ggpairs": false,
-      "graph_plots": ["gg99"],
-      "mcmc_traj_plots": [
-        {
-          "id": "gg99",
-          "burn_in": 0.5,
-          "thinning": 1000,
-          "functional": ["score", "size"],
-          "active": true
-        },
+  "benchmark_setup": [
+    {
+      "data": [
         {
-          "id": "trilearn",
-          "burn_in": 0.5,
-          "thinning": 1,
-          "functional": ["score", "size"],
-          "active": true
+          "data_id": "example1",
+          "graph_id": "AR1-5",
+          "parameters_id": "intra-class",
+          "seed_range": [
+            1,
+            1
+          ]
         }
       ],
-      "mcmc_heatmaps": [
-        {
-          "id": "gg99",
-          "burn_in": 0.5,
-          "active": true
+      "evaluation": {
+        "benchmarks": {
+          "filename_prefix": "mcmc_chordal/",
+          "show_seed": false,
+          "errorbar": true,
+          "errorbarh": false,
+          "scatter": true,
+          "path": true,
+          "text": false,
+          "ids": [
+            "gg99",
+            "trilearn"
+          ]
         },
-        {
-          "id": "trilearn",
-          "burn_in": 0.5,
-          "active": true
-        }
-      ],
-      "mcmc_autocorr_plots": [
-        {
-          "id": "gg99",
-          "burn_in": 0.5,
-          "thinning": 1000,
-          "lags": 50,
-          "functional": ["score", "size"],
-          "active": true
-        }
-      ]
+        "graph_true_plots": true,
+        "graph_true_stats": false,
+        "ggally_ggpairs": false,
+        "graph_plots": [
+          "gg99"
+        ],
+        "mcmc_traj_plots": [
+          {
+            "id": "gg99",
+            "burn_in": 0.5,
+            "thinning": 1000,
+            "functional": [
+              "score",
+              "size"
+            ],
+            "active": true
+          },
+          {
+            "id": "trilearn",
+            "burn_in": 0.5,
+            "thinning": 1,
+            "functional": [
+              "score",
+              "size"
+            ],
+            "active": true
+          }
+        ],
+        "mcmc_heatmaps": [
+          {
+            "id": "gg99",
+            "burn_in": 0.5,
+            "active": true
+          },
+          {
+            "id": "trilearn",
+            "burn_in": 0.5,
+            "active": true
+          }
+        ],
+        "mcmc_autocorr_plots": [
+          {
+            "id": "gg99",
+            "burn_in": 0.5,
+            "thinning": 1000,
+            "lags": 50,
+            "functional": [
+              "score",
+              "size"
+            ],
+            "active": true
+          }
+        ]
+      },
+      "title": "chordal_algos"
     }
-  },
+  ],
   "resources": {
     "data": {
       "iid": [
         {
           "id": "example1",
           "standardized": false,
-          "n": [200]
+          "n": [
+            200
+          ]
         }
       ]
     },
@@ -102,7 +124,7 @@
         {
           "id": "gwi",
           "b": 3,
-          "threshold_conv": 1e-8
+          "threshold_conv": 1e-08
         }
       ]
     },
@@ -113,10 +135,20 @@
           "burnin_frac": 0.5,
           "mcmc_estimator": "threshold",
           "timeout": null,
-          "threshold": [0.2, 0.5, 0.9],
-          "mcmc_seed": [1, 2, 3],
+          "threshold": [
+            0.2,
+            0.5,
+            0.9
+          ],
+          "mcmc_seed": [
+            1,
+            2,
+            3
+          ],
           "num_samples": 1000000,
-          "sampler": [1],
+          "sampler": [
+            1
+          ],
           "edge_penalty": 1.0,
           "size_maxclique": 10000
         }
@@ -125,9 +157,15 @@
         {
           "id": "parallelDG_test",
           "M": 10000,
-          "R": [100, 200],
+          "R": [
+            100,
+            200
+          ],
           "datatype": "continuous",
-          "mcmc_seed": [1, 2],
+          "mcmc_seed": [
+            1,
+            2
+          ],
           "graph_prior": "uniform",
           "graph_prior_param": 1.0,
           "graph_prior_param1": 3.0,
@@ -149,13 +187,21 @@
           "n_particles": 20,
           "M": 2000,
           "pseudo_obs": 1,
-          "mcmc_seed": [1, 2, 3],
+          "mcmc_seed": [
+            1,
+            2,
+            3
+          ],
           "mcmc_estimator": "threshold",
-          "threshold": [0.2, 0.5, 0.9],
+          "threshold": [
+            0.2,
+            0.5,
+            0.9
+          ],
           "burnin_frac": 0.5,
           "timeout": null
         }
       ]
     }
   }
-}
+}