[pull] master from madgik:master

.github/workflows/ebrains.yml

@@ -0,0 +1,26 @@
+name: Mirror to Ebrains
+
+on:
+  push:
+    branches: [ master ]
+    tags:
+    - '*'
+
+jobs:
+  to_ebrains:
+    runs-on: ubuntu-latest
+    steps:
+      - name: syncmaster
+        uses: wei/git-sync@v3
+        with:
+          source_repo: "madgik/exareme"
+          source_branch: "master"
+          destination_repo: "https://ghpusher:${{ secrets.EBRAINS_GITLAB_ACCESS_TOKEN }}@gitlab.ebrains.eu/hbp-mip/exareme.git"
+          destination_branch: "master"
+      - name: synctags
+        uses: wei/git-sync@v3
+        with:
+          source_repo: "madgik/exareme"
+          source_branch: "refs/tags/*"
+          destination_repo: "https://ghpusher:${{ secrets.EBRAINS_GITLAB_ACCESS_TOKEN }}@gitlab.ebrains.eu/hbp-mip/exareme.git"
+          destination_branch: "refs/tags/*"

Exareme-Docker/src/mip-algorithms/CALIBRATION_BELT/properties.json

@@ -21,7 +21,7 @@
         {
             "name": "y",
             "label": "y",
-            "desc": "Observed dichotomous outcomes.",
+            "desc": "Observed dichotomous outcome.",
             "type": "column",
             "columnValuesSQLType": "text, integer",
             "columnValuesIsCategorical": "true",

Exareme-Docker/src/mip-algorithms/CART/properties.json

@@ -1,12 +1,12 @@
 {
 	"name": "CART",
-	"desc": "CART, used to generate a decision tree from a dataset",
-	"label": "CART",
+	"desc": "Decision tree-based algorithm that splits the data into smaller subsets based on the feature that provides the most information gain, and then builds the tree recursively on the smaller subsets.",
+	"label": "Classification and Regression Trees (CART)",
 	"type": "python_iterative",
 	"parameters": [{
 			"name": "x",
-			"label": "x",
-			"desc": "Independent variables: A list of variables from database.",
+			"label": "Covariate (independent)",
+			"desc": "One or more variables",
 			"type": "column",
 			"columnValuesSQLType": "real, integer",
 			"columnValuesIsCategorical": "",
@@ -17,8 +17,8 @@
 			"valueType": "string"
 		}, {
 			"name": "y",
-			"label": "y",
-			"desc": "Dependent variable: A variable from database.",
+			"label": "Variable (dependent)",
+			"desc": "A unique variable",
 			"type": "column",
 			"columnValuesSQLType": "real, integer, text",
 			"columnValuesIsCategorical": "",

Exareme-Docker/src/mip-algorithms/ID3/properties.json

@@ -1,12 +1,12 @@
 {
 	"name": "ID3",
-	"desc": "Iterative Dichotomiser 3, used to generate a decision tree from a dataset",
-	"label": "ID3",
+	"desc": "Decision tree-based algorithm that builds the tree by choosing the feature with the highest information gain at each step.",
+	"label": "Iterative Dichotomiser 3 (ID3)",
 	"type": "iterative",
 	"parameters": [{
 			"name": "x",
-			"label": "x",
-			"desc": "Independent variables: A list of categorical variables from database.",
+			"label": "Covariate (independent)",
+			"desc": "One or more categorical variables",
 			"type": "column",
 			"columnValuesSQLType": "text,integer",
 			"columnValuesIsCategorical": "true",
@@ -17,8 +17,8 @@
 			"valueType": "string"
 		}, {
 			"name": "y",
-			"label": "y",
-			"desc": "Dependent variable: A categorical variable from database.",
+			"label": "Variable (dependent)",
+			"desc": "A unique categorical variable",
 			"type": "column",
 			"columnValuesSQLType": "text,integer",
 			"columnValuesIsCategorical": "true",

Exareme-Docker/src/mip-algorithms/KMEANS/properties.json

@@ -1,12 +1,12 @@
 {
 	"name": "KMEANS",
-	"desc": "KMEANS_accurate",
+	"desc": "Unsupervised learning algorithm that partitions a set of data points into k clusters, where k is a user-defined parameter. The algorithm iteratively updates the centroids of each cluster until convergence.",
 	"label": "k-Means Clustering",
 	"type": "iterative",
 	"parameters": [{
 			"name": "y",
-			"label": "y",
-			"desc": "A list of real/integer variables from database.",
+			"label": "Variables (independent)",
+			"desc": "A list of continuous variables",
 			"type": "column",
 			"columnValuesSQLType": "real,integer",
 			"columnValuesIsCategorical": "false",