Feat sklearn pca50

conf/real_data.config

@@ -25,7 +25,7 @@ params {
   skip_mogonet                = false
   skip_mofa                   = false
   // Run feature selection
-  select_feature              = true
+  selectFeature               = true
   k_fold_number               = 5
   filter_low_var	      = "1" // This get casted as bool in both Python and R
   // number of component

modules/calculate_metrics/main.nf

@@ -1,11 +1,7 @@
 process CALCULATE_METRICS {
-  def onSockeye = workflow.projectDir.toString().contains('/scratch')
 	debug true
 	label 'process_single'
-	container "${ onSockeye  ?
-		'mogonet.sif' :
-		'tonyliang19/mogonet:latest' }"
-
+	label 'mogonet' // Should be a generic python container?
 	publishDir (
 		path: "${params.outdir}/${task.process.tokenize(':').join('/').toLowerCase()}",
 		mode: 'copy',
@@ -28,4 +24,4 @@ process CALCULATE_METRICS {
       --threshold=${threshold} > \
       ${task.process.tokenize(':')[-1].toLowerCase()}.log
     """
-}
+}

modules/calculate_metrics/resources/usr/bin/calculate_metrics.py

@@ -42,29 +42,35 @@ def calculate_metrics(group, threshold, average='binary', round_digit=3):
     y_pred = np.where(phat >= threshold, 1, 0)
     # Initialize dict for metrics
     metrics = {
-        'auc': 0.0,
+        'roc_auc': 0.0,
+        'pr_auc': 0.0,
+        'f1_score': 0.0,
+        'log_loss': 0.0,
+        'matthews_corrcoef': 0.0,
         'accuracy': 0.0,
         'balanced_accuracy': 0.0,
         'precision': 0.0,
-        'average_precision_score': 0.0,
         'recall': 0.0,
-        'f1_score': 0.0,
-	'log_loss': 0.0,
+
     }
 
     try:
         # Check if both classes are present in the current group
         if len(set(y_true)) > 1:  # More than one unique value in y_true
             # AUC requires predicted probability (phat) not y_pred
-            metrics['auc'] = roc_auc_score(y_true, phat)
-            metrics['accuracy'] = accuracy_score(y_true, y_pred)
-            metrics['balanced_accuracy'] = balanced_accuracy_score(y_true, y_pred)
-            metrics['precision'] = precision_score(y_true, y_pred, average=average)
-            metrics['average_precision_score'] = average_precision_score(y_true, y_pred)
-            metrics['recall'] = recall_score(y_true, y_pred, average=average)
-            metrics['f1_score'] = f1_score(y_true, y_pred, average=average)
-            # For the losses as well
-            metrics['log_loss'] = log_loss(y_true, y_pred)
+            metrics['roc_auc']                = roc_auc_score(y_true, phat)
+            # Same thing with precision-recall auc
+            metrics['pr_auc']                 = average_precision_score(y_true, phat)
+            # Rest could use the predicted class (y_pred) to calculate metrics
+            metrics['f1_score']               = f1_score(y_true, y_pred, average=average)
+            metrics['log_loss']               = log_loss(y_true, phat)
+            metrics['matthews_corrcoef']      = matthews_corrcoef(y_true, y_pred)
+            metrics['accuracy']               = accuracy_score(y_true, y_pred)
+            metrics['balanced_accuracy']      = balanced_accuracy_score(y_true, y_pred)
+            metrics['precision']              = precision_score(y_true, y_pred, average=average)
+            metrics['recall']                 = recall_score(y_true, y_pred, average=average)
+
+
         else:
             raise ValueError("Only one class present")
 

modules/caret_multimodal/predict/main.nf

@@ -3,12 +3,9 @@ include { getPublishPath } from "${modulesDir}/functions"
 
 process CARET_MULTIMODAL_PREDICT {
 	// Vars stuff
-	def onSockeye = workflow.projectDir.toString().contains('/scratch')
 	tag "${dataset_name}-${fold_name}"
 	debug "${params.debug}"
-	container "${ onSockeye  ?
-		'caret_multimodal.sif' :
-		'tonyliang19/caret_multimodal:latest' }"
+  label 'caret_multimodal'
 
 	publishDir (
 		path: "${params.outdir}/${getPublishPath(task.process)}/${dataset_name}/${fold_name}",

modules/caret_multimodal/preprocess/main.nf

@@ -21,17 +21,12 @@ include { getPublishPath } from "${modulesDir}/functions"
 
 // TODO: Replace name here
 process CARET_MULTIMODAL_PREPROCESS {
-  // temp variables to use
-  def onSockeye = workflow.projectDir.toString().contains('/scratch')
   // process level configuration
   debug "${params.debug}" // debugs true or false by param in MESSI.config
   tag "${dataset_name}" // identifier of process when ran in parallel
   // By var before to determine what container to use
   // Uses apptainer if true otherwise docker
-	container "${ onSockeye  ?
-		'caret_multimodal.sif' :
-		'tonyliang19/caret_multimodal:latest' }"
-
+  label "caret_multimodal"
   label "process_low"
 
   /* outputs store to outdir for saving and inspecting purpose */

modules/caret_multimodal/select_feature/main.nf

@@ -4,13 +4,10 @@ include { getPublishPath } from "${modulesDir}/functions"
 
 process CARET_MULTIMODAL_SELECT_FEATURE {
   // Vars stuff
-	def onSockeye = workflow.projectDir.toString().contains('/scratch')
 	tag "${dataset_name}"
 	debug true
   label 'process_high'
-	container "${ onSockeye  ?
-		'caret_multimodal.sif' :
-		'tonyliang19/caret_multimodal:latest' }"
+	label 'caret_multimodal'
 
 	publishDir (
 		path: "${params.outdir}/${task.process.tokenize(':').join('/').toLowerCase()}/${dataset_name}",

modules/caret_multimodal/train/main.nf

@@ -17,14 +17,9 @@
 include { getPublishPath } from "${modulesDir}/functions"
 
 process CARET_MULTIMODAL_TRAIN {
-	// Vars stuff
-	def onSockeye = workflow.projectDir.toString().contains('/scratch')
 	// Identifier for each dataset and fold combination
 	tag "${dataset_name}-${fold_path.name}"
-	// TODO: rename to the actual image name used
-	container "${ onSockeye  ?
-		'caret_multimodal.sif' :
-		'tonyliang19/caret_multimodal:latest' }"
+
 	// Parse the output directory to migrate results to
 	publishDir (
 		path: "${params.outdir}/${getPublishPath(task.process)}/${dataset_name}/${fold_path.name}",
@@ -34,6 +29,7 @@ process CARET_MULTIMODAL_TRAIN {
 	// TODO: add your custom labels to tell if process
 	// consumes large RAM/ROM and gpu access or not
 	label 'process_medium'
+	label 'caret_multimodal'
 
 
 	// TODO: Change arg name to mae_path or mu_path

modules/cooperative_learning/predict/main.nf

@@ -2,13 +2,10 @@
 include { getPublishPath } from "${modulesDir}/functions"
 process COOPERATIVE_LEARNING_PREDICT {
 	// Vars stuff
-	def onSockeye = workflow.projectDir.toString().contains('/scratch')
 	tag "${dataset_name}-${fold_name}"
 	debug true
 	label 'process_single'
-	container "${ onSockeye  ?
-		'codia.sif' :
-		'tonyliang19/codia:latest' }"
+	label 'codia'
 
 	publishDir (
 		path: "${params.outdir}/${getPublishPath(task.process)}/${dataset_name}/${fold_name}",

modules/cooperative_learning/preprocess/main.nf

@@ -5,14 +5,11 @@ include { getPublishPath } from "${modulesDir}/functions"
 
 process COOPERATIVE_LEARNING_PREPROCESS {
   // Temp variables
-  def onSockeye = workflow.projectDir.toString().contains('/scratch')
   /* Directives for process */
   debug "${params.debug}" // default is true
   tag "${dataset_name}"
   label 'process_single'
-  container "${ onSockeye ?
-    'codia.sif' :
-    'tonyliang19/codia:latest'}"
+  label 'codia'
 
   // More special publish dir by getting method/method_abc to method/abc
   publishDir (

modules/cooperative_learning/select_feature/main.nf

@@ -4,13 +4,10 @@ include { getPublishPath } from "${modulesDir}/functions"
 
 process COOPERATIVE_LEARNING_SELECT_FEATURE {
   // Vars stuff
-	def onSockeye = workflow.projectDir.toString().contains('/scratch')
 	tag "${dataset_name}"
 	debug true
   label 'process_high'
-	container "${ onSockeye  ?
-		'codia.sif' :
-		'tonyliang19/codia:latest' }"
+  label 'codia'
 
 	publishDir (
 		path: "${params.outdir}/${task.process.tokenize(':').join('/').toLowerCase()}/${dataset_name}",
@@ -21,7 +18,7 @@ process COOPERATIVE_LEARNING_SELECT_FEATURE {
 	// Labels
 	label 'low_mem'
 	label 'cpu'
-	label 'codia'
+  label 'codia'
 
 
   /* Input and output blocks*/

modules/cooperative_learning/train/main.nf

@@ -7,13 +7,10 @@
 include { getPublishPath } from "${modulesDir}/functions"
 process COOPERATIVE_LEARNING_TRAIN {
   // Vars stuff
-	def onSockeye = workflow.projectDir.toString().contains('/scratch')
 	tag "${dataset_name}-${fold_path.name}"
 	debug	"${params.debug}"
 	label 'process_high'
-	container "${ onSockeye  ? 
-		'codia.sif' : 
-		'tonyliang19/codia:latest' }"
+	label 'codia'
 	// More special publish dir by getting method/method_abc to method/abc
   publishDir (
     path: "${params.outdir}/${getPublishPath(task.process)}/${dataset_name}/${fold_path.name}",

modules/diablo/downstream/main.nf

@@ -22,16 +22,13 @@ include { getPublishPath } from "${modulesDir}/functions"
 // TODO: Replace name here
 process DIABLO_DOWNSTREAM {
   // temp variables to use
-  def onSockeye = workflow.projectDir.toString().contains('/scratch')
   // process level configuration
   debug "${params.debug}" // debugs true or false by param in MESSI.config
   label 'process_single'
   tag "${dataset_name}" // identifier of process when ran in parallel
   // By var before to determine what container to use
   // Uses apptainer if true otherwise docker
-  container "${ onSockeye ?
-    'codia.sif' :
-    'tonyliang19/codia:latest'}"
+  label 'codia'
 
   /* outputs store to outdir for saving and inspecting purpose */
   publishDir (

modules/diablo/predict/main.nf

@@ -3,13 +3,10 @@ include { getPublishPath } from "${modulesDir}/functions"
 
 process DIABLO_PREDICT {
 	// Vars stuff
-	def onSockeye = workflow.projectDir.toString().contains('/scratch')
 	tag "${dataset_name}-${fold_name}-${method}"
 	debug true
 	label 'process_single'
-	container "${ onSockeye  ?
-		'codia.sif' :
-		'tonyliang19/codia:latest' }"
+	label 'codia'
 
 	publishDir (
 		//path: "${params.outdir}/${task.process.tokenize(':')[-1].toLowerCase()}/${dataset_name}/${fold_name}",

modules/diablo/preprocess/main.nf

@@ -27,16 +27,13 @@ include { getPublishPath } from "${modulesDir}/functions"
 
 process DIABLO_PREPROCESS {
   // temp variables to use
-  def onSockeye = workflow.projectDir.toString().contains('/scratch')
   // process level configuration
   debug "${params.debug}" // debugs true or false by param in MESSI.config
   label 'process_single'
   tag "${dataset_name}" // identifier of process when ran in parallel
   // By var before to determine what container to use
   // Uses apptainer if true otherwise docker
-  container "${ onSockeye ?
-    'codia.sif' :
-    'tonyliang19/codia:latest'}"
+  label 'codia'
 
   /* outputs store to outdir for saving and inspecting purpose */
   publishDir (

modules/diablo/select_feature/main.nf

@@ -4,13 +4,10 @@ include { getPublishPath } from "${modulesDir}/functions"
 
 process DIABLO_SELECT_FEATURE {
   // Vars stuff
-	def onSockeye = workflow.projectDir.toString().contains('/scratch')
 	tag "${dataset_name}-design_${design}-ncomp_${ncomp}"
 	debug true
   label 'process_high'
-	container "${ onSockeye  ?
-		'codia.sif' :
-		'tonyliang19/codia:latest' }"
+  label 'codia'
 
 	publishDir (
 		path: "${params.outdir}/${task.process.tokenize(':').join('/').toLowerCase()}/${dataset_name}",

modules/diablo/train/main.nf

@@ -6,15 +6,12 @@
 include { getPublishPath } from "${modulesDir}/functions"
 process DIABLO_TRAIN {
 	// Vars stuff
-	def onSockeye = workflow.projectDir.toString().contains('/scratch')
 	def method_name = "diablo"
 	tag "${dataset_name}-${fold_path.name}-design_${design}-ncomp_${ncomp}"
 	//debug	"${params.debug}"
 	debug true
 	label 'process_medium'
-	container "${ onSockeye  ?
-		'codia.sif' :
-		'tonyliang19/codia:latest' }"
+	label 'codia'
 	// Parse this path
 	publishDir (
 		//path: "${params.outdir}/${task.process.tokenize(':')[-1].toLowerCase()}/${dataset_name}/${fold_path.name}",

modules/integrao/predict/main.nf

@@ -3,12 +3,9 @@ include { getPublishPath } from "${modulesDir}/functions"
 
 process INTEGRAO_PREDICT {
 	// Vars stuff
-	def onSockeye = workflow.projectDir.toString().contains('/scratch')
 	tag "${dataset_name}-${fold_name}"
 	debug "${params.debug}"
-	container "${ onSockeye  ?
-		'integrao.sif' :
-		'tonyliang19/integrao:latest' }"
+	label 'integrao'
 
 	publishDir (
 		path: "${params.outdir}/${getPublishPath(task.process)}/${dataset_name}/${fold_name}",

modules/integrao/preprocess/main.nf

@@ -22,15 +22,12 @@ include { getPublishPath } from "${modulesDir}/functions"
 // TODO: Replace name here
 process INTEGRAO_PREPROCESS {
   // temp variables to use
-  def onSockeye = workflow.projectDir.toString().contains('/scratch')
   // process level configuration
   debug "${params.debug}" // debugs true or false by param in MESSI.config
   tag "${dataset_name}" // identifier of process when ran in parallel
   // By var before to determine what container to use
   // Uses apptainer if true otherwise docker
-	container "${ onSockeye  ?
-		'integrao.sif' :
-		'tonyliang19/integrao:latest' }"
+  label 'integrao'
 
   label "process_low"
 

modules/integrao/select_feature/main.nf

@@ -4,14 +4,11 @@ include { getPublishPath } from "${modulesDir}/functions"
 
 process INTEGRAO_SELECT_FEATURE {
   // Vars stuff
-	def onSockeye = workflow.projectDir.toString().contains('/scratch')
 	tag "${dataset_name}"
 	debug true
         label 'process_high'
         label 'gpu'
-	container "${ onSockeye  ?
-		'integrao.sif' :
-		'tonyliang19/integrao:latest' }"
+        label 'integrao'
 
 	publishDir (
 		path: "${params.outdir}/${task.process.tokenize(':').join('/').toLowerCase()}/${dataset_name}",