support for new version of discomll

requirements.txt

@@ -17,4 +17,3 @@ liac-arff==2.0.1
 networkx==1.9.1
 djangorestframework==3.0.3
 django-filter==0.9.1
-discomll

workflows/big_data/library.py

-
 def file_url(input_dict):
     from discomll import dataset
-    
+
     if input_dict["range"] == "true":
         urls = [url.strip() for url in input_dict["url"].split("\n") if url != ""]
     else:
-        urls = [[url.strip()] for url in input_dict["url"].split("\n") if url != ""] 
+        urls = [[url.strip()] for url in input_dict["url"].split("\n") if url != ""]
         for url in urls:
             if url[0].split("://")[0] == "https":
                 raise Exception("Dataset should be accessible over HTTP.")
-    del(input_dict["url"])
-
+    del (input_dict["url"])
 
-    X_indices_splited = input_dict["X_indices"].replace(" ","").split("-")
+    X_indices_splited = input_dict["X_indices"].replace(" ", "").split("-")
     if len(X_indices_splited) == 2:
         a, b = X_indices_splited
         if not a.isdigit() or not b.isdigit():
             raise Exception("Feature indices should be integers. Example: 1-10")
         X_indices = range(int(a), int(b))
     else:
-        X_indices = [int(v) for v in input_dict["X_indices"].replace(" ","").split(",") if v != ""]
-    del(input_dict["X_indices"])
+        X_indices = [int(v) for v in input_dict["X_indices"].replace(" ", "").split(",") if v != ""]
+    del (input_dict["X_indices"])
 
     input_dict["data_type"] = "gzip" if input_dict["data_type"] == "true" else ""
 
@@ -29,20 +27,20 @@ def file_url(input_dict):
     elif input_dict["atr_meta"] == "discrete":
         X_meta = ["d" for i in range(len(X_indices))]
     else:
-        X_meta = input_dict["custom"]        
+        X_meta = input_dict["custom"]
 
-    data = dataset.Data(data_tag = urls,
-                            X_indices = X_indices,
-                            X_meta = X_meta,
-                            generate_urls = True if input_dict["range"] == "true" else False,
-                            **input_dict)
+    data = dataset.Data(data_tag=urls,
+                        X_indices=X_indices,
+                        X_meta=X_meta,
+                        generate_urls=True if input_dict["range"] == "true" else False,
+                        **input_dict)
 
     print data.params
-    
-    return {"dataset" : data}
+
+    return {"dataset": data}
+
 
 def big_data_apply_classifier(input_dict):
-    
     if "naivebayes_fitmodel" in input_dict["fitmodel_url"]:
         return naivebayes_predict(input_dict)
     elif "logreg_fitmodel" in input_dict["fitmodel_url"]:
@@ -60,206 +58,224 @@ def big_data_apply_classifier(input_dict):
     elif "linreg_fitmodel" in input_dict["fitmodel_url"]:
         return linreg_predict(input_dict)
 
+
 def lwlr_fit_predict(input_dict):
     from discomll.regression import locally_weighted_linear_regression
 
     predictions_url = locally_weighted_linear_regression.fit_predict(
-                                                    fitting_data = input_dict["fitting_dataset"],
-                                                    training_data = input_dict["training_dataset"],
-                                                    tau = input_dict["tau"],
-                                                    save_results = True)
+        fitting_data=input_dict["fitting_dataset"],
+        training_data=input_dict["training_dataset"],
+        tau=input_dict["tau"],
+        save_results=True)
     return {"string": predictions_url}
 
+
 def dt_fit(input_dict):
     from discomll.ensemble import forest_distributed_decision_trees
-    
+
     random_state = None if input_dict["seed"] == "None" else int(input_dict["seed"])
 
     fitmodel_url = forest_distributed_decision_trees.fit(input_dict["dataset"],
-                                       trees_per_chunk = input_dict["trees_per_subset"],
-                                       max_tree_nodes = input_dict["tree_nodes"],
-                                       min_samples_leaf = input_dict["min_samples_leaf"],
-                                       min_samples_split = input_dict["min_samples_split"],
-                                       class_majority = input_dict["majority"],
-                                       bootstrap = input_dict["bootstrap"] == "true",
-                                       measure = input_dict["measure"],
-                                       accuracy = input_dict["accuracy"],
-                                       separate_max = input_dict["separate_max"],
-                                       random_state = random_state,
-                                        save_results = True)
-    return {"fitmodel_url" : fitmodel_url}
+                                                         trees_per_chunk=input_dict["trees_per_subset"],
+                                                         max_tree_nodes=input_dict["tree_nodes"],
+                                                         min_samples_leaf=input_dict["min_samples_leaf"],
+                                                         min_samples_split=input_dict["min_samples_split"],
+                                                         class_majority=input_dict["majority"],
+                                                         bootstrap=input_dict["bootstrap"] == "true",
+                                                         measure=input_dict["measure"],
+                                                         accuracy=input_dict["accuracy"],
+                                                         separate_max=input_dict["separate_max"] == "true",
+                                                         random_state=random_state,
+                                                         save_results=True)
+    return {"fitmodel_url": fitmodel_url}
+
 
 def dt_predict(input_dict):
     from discomll.ensemble import forest_distributed_decision_trees
 
     predictions_url = forest_distributed_decision_trees.predict(input_dict["dataset"],
-                                            fitmodel_url = input_dict["fitmodel_url"],
-                                            save_results = True)
+                                                                fitmodel_url=input_dict["fitmodel_url"],
+                                                                save_results=True)
     return {"string": predictions_url}
 
+
 def rf_fit(input_dict):
     from discomll.ensemble import distributed_random_forest
 
     random_state = None if input_dict["seed"] == "None" else int(input_dict["seed"])
 
     fitmodel_url = distributed_random_forest.fit(input_dict["dataset"],
-                                        trees_per_chunk = input_dict["trees_per_subset"],
-                                        max_tree_nodes = input_dict["tree_nodes"],
-                                        min_samples_leaf = input_dict["min_samples_leaf"],
-                                        min_samples_split = input_dict["min_samples_split"],
-                                        class_majority = input_dict["majority"],
-                                        measure = input_dict["measure"],
-                                        accuracy = input_dict["accuracy"],
-                                        separate_max = input_dict["separate_max"],
-                                        random_state = random_state,
-                                        save_results = True)
-
-    return {"fitmodel_url" : fitmodel_url}
+                                                 trees_per_chunk=input_dict["trees_per_subset"],
+                                                 max_tree_nodes=input_dict["tree_nodes"],
+                                                 min_samples_leaf=input_dict["min_samples_leaf"],
+                                                 min_samples_split=input_dict["min_samples_split"],
+                                                 class_majority=input_dict["majority"],
+                                                 measure=input_dict["measure"],
+                                                 accuracy=input_dict["accuracy"],
+                                                 separate_max=input_dict["separate_max"] == "true",
+                                                 random_state=random_state,
+                                                 save_results=True)
+
+    return {"fitmodel_url": fitmodel_url}
+
 
 def rf_predict(input_dict):
     from discomll.ensemble import distributed_random_forest
-    
-    random_state = None if input_dict["seed"] == "None" else int(input_dict["seed"])
 
     predictions_url = distributed_random_forest.predict(input_dict["dataset"],
-                                            fitmodel_url = input_dict["fitmodel_url"],
-                                            random_state = random_state,
-                                            save_results = True)
+                                                        fitmodel_url=input_dict["fitmodel_url"],
+                                                        save_results=True)
     return {"string": predictions_url}
 
+
 def wrf_fit(input_dict):
     from discomll.ensemble import distributed_weighted_forest_rand
-    
+
     random_state = None if input_dict["seed"] == "None" else int(input_dict["seed"])
-    
+
     fitmodel_url = distributed_weighted_forest_rand.fit(input_dict["dataset"],
-                                        trees_per_chunk = input_dict["trees_per_subset"],
-                                        max_tree_nodes = input_dict["tree_nodes"],
-                                        num_medoids = input_dict["num_medoids"],
-                                        min_samples_leaf = input_dict["min_samples_leaf"],
-                                        min_samples_split = input_dict["min_samples_split"],
-                                        class_majority = input_dict["majority"],
-                                        measure = input_dict["measure"],
-                                        accuracy = input_dict["accuracy"],
-                                        separate_max = input_dict["separate_max"],
-                                        random_state = random_state,
-                                        save_results = True)
-    return {"fitmodel_url" : fitmodel_url}
+                                                        trees_per_chunk=input_dict["trees_per_subset"],
+                                                        max_tree_nodes=input_dict["tree_nodes"],
+                                                        num_medoids=input_dict["num_medoids"],
+                                                        min_samples_leaf=input_dict["min_samples_leaf"],
+                                                        min_samples_split=input_dict["min_samples_split"],
+                                                        class_majority=input_dict["majority"],
+                                                        measure=input_dict["measure"],
+                                                        accuracy=input_dict["accuracy"],
+                                                        separate_max=input_dict["separate_max"] == "true",
+                                                        random_state=random_state,
+                                                        save_results=True)
+    return {"fitmodel_url": fitmodel_url}
+
 
 def wrf_predict(input_dict):
     from discomll.ensemble import distributed_weighted_forest_rand
 
     predictions_url = distributed_weighted_forest_rand.predict(input_dict["dataset"],
-                                            fitmodel_url = input_dict["fitmodel_url"],
-                                            coeff = input_dict["coeff"],
-                                            save_results = True)
+                                                               fitmodel_url=input_dict["fitmodel_url"],
+                                                               coeff=input_dict["coeff"],
+                                                               save_results=True)
     return {"string": predictions_url}
 
+
 def linsvm_fit(input_dict):
     from discomll.classification import linear_svm
     fitmodel_url = linear_svm.fit(input_dict["dataset"],
-                                            nu = input_dict["nu"],
-                                            save_results = True)
-    return {"fitmodel_url" : fitmodel_url}    
+                                  nu=input_dict["nu"],
+                                  save_results=True)
+    return {"fitmodel_url": fitmodel_url}
+
 
 def linsvm_predict(input_dict):
     from discomll.classification import linear_svm
 
     predictions_url = linear_svm.predict(input_dict["dataset"],
-                                  fitmodel_url = input_dict["fitmodel_url"],
-                                  save_results = True)
+                                         fitmodel_url=input_dict["fitmodel_url"],
+                                         save_results=True)
     return {"string": predictions_url}
 
+
 def linreg_fit(input_dict):
     from discomll.regression import linear_regression
 
     fitmodel_url = linear_regression.fit(input_dict["dataset"],
-                    save_results = True)
-    
-    return {"fitmodel_url" : fitmodel_url}
+                                         save_results=True)
+
+    return {"fitmodel_url": fitmodel_url}
+
 
 def linreg_predict(input_dict):
     from discomll.regression import linear_regression
-    
+
     predictions_url = linear_regression.predict(input_dict["dataset"],
-                                    fitmodel_url = input_dict["fitmodel_url"],
-                                    save_results = True)
+                                                fitmodel_url=input_dict["fitmodel_url"],
+                                                save_results=True)
 
     return {"string": predictions_url}
 
+
 def kmeans_fit(input_dict):
     from discomll.clustering import kmeans
 
     random_state = None if input_dict["seed"] == "None" else int(input_dict["seed"])
-    
+
     fitmodel_url = kmeans.fit(input_dict["dataset"],
-                                n_clusters = input_dict["clusters"],
-                                 max_iterations = input_dict["itr"],
-                                 random_state = random_state,
-                                 save_results = True)
+                              n_clusters=input_dict["clusters"],
+                              max_iterations=input_dict["itr"],
+                              random_state=random_state,
+                              save_results=True)
+
+    return {"fitmodel_url": fitmodel_url}
 
-    return {"fitmodel_url" : fitmodel_url}
 
 def kmeans_predict(input_dict):
     from discomll.clustering import kmeans
 
     predictions_url = kmeans.predict(input_dict["dataset"],
-                                        fitmodel_url = input_dict["fitmodel_url"],
-                                        save_results = True)
+                                     fitmodel_url=input_dict["fitmodel_url"],
+                                     save_results=True)
     return {"string": predictions_url}
 
+
 def logreg_fit(input_dict):
     from discomll.classification import logistic_regression
 
     fitmodel_url = logistic_regression.fit(input_dict["dataset"],
-                                            alpha = input_dict["alpha"],
-                                            max_iterations = input_dict["itr"],
-                                            save_results = True)
-    return {"fitmodel_url" : fitmodel_url}
+                                           alpha=input_dict["alpha"],
+                                           max_iterations=input_dict["itr"],
+                                           save_results=True)
+    return {"fitmodel_url": fitmodel_url}
+
 
 def logreg_predict(input_dict):
     from discomll.classification import logistic_regression
-    
+
     predictions_url = logistic_regression.predict(input_dict["dataset"],
-                                                fitmodel_url = input_dict["fitmodel_url"],
-                                                save_results = True)
+                                                  fitmodel_url=input_dict["fitmodel_url"],
+                                                  save_results=True)
     return {"string": predictions_url}
 
+
 def naivebayes_fit(input_dict):
     from discomll.classification import naivebayes
 
-    fitmodel_url = naivebayes.fit(input_dict["dataset"], save_results = True)
-    
-    return {"fitmodel_url" : fitmodel_url}
+    fitmodel_url = naivebayes.fit(input_dict["dataset"], save_results=True)
+
+    return {"fitmodel_url": fitmodel_url}
+
 
 def naivebayes_predict(input_dict):
     from discomll.classification import naivebayes
     m = 1 if input_dict["m"] == "" else input_dict["m"]
-    
+
     predictions_url = naivebayes.predict(input_dict["dataset"],
-                                fitmodel_url = input_dict["fitmodel_url"],
-                                 m = input_dict["m"],
-                                save_results = True )
-    
+                                         fitmodel_url=input_dict["fitmodel_url"],
+                                         m=input_dict["m"],
+                                         save_results=True)
+
     return {"string": predictions_url}
 
+
 def results_to_file(input_dict):
-    #implementation is in visualization_views.py
-    return {} 
+    # implementation is in visualization_views.py
+    return {}
+
 
 def measure_distribution(input_dict):
-    #implementation is in visualization_views.py
+    # implementation is in visualization_views.py
     return {}
 
+
 def model_view(input_dict):
-    #implementation is in visualization_views.py
+    # implementation is in visualization_views.py
     return {}
 
+
 def bigdata_ca(input_dict):
-    #implementation is in visualization_views.py
+    # implementation is in visualization_views.py
     return {}
 
+
 def bigdata_mse(input_dict):
-    #implementation is in visualization_views.py
+    # implementation is in visualization_views.py
     return {}
-

workflows/big_data/package_data/widgets/4f2ce923-62e6-4be1-a394-72ac52988386.json

@@ -5,7 +5,7 @@
       "category": "1be7b5eb-c1b2-485a-8dbe-56abce63fc73", 
       "treeview_image": "", 
       "uid": "4f2ce923-62e6-4be1-a394-72ac52988386", 
-      "is_streaming": false, 
+      "windows_queue": false, 
       "package": "big_data", 
       "interaction_view": "", 
       "has_progress_bar": false, 
@@ -19,7 +19,7 @@
       "wsdl_method": "", 
       "wsdl": "", 
       "interactive": false, 
-      "windows_queue": false, 
+      "is_streaming": false, 
       "order": 1, 
       "name": "Distributed Random Forest"
     }
@@ -171,7 +171,7 @@
       "required": true, 
       "multi": false, 
       "parameter_type": "text", 
-      "variable": "leaf_min_inst", 
+      "variable": "min_samples_split", 
       "parameter": true, 
       "order": 4, 
       "uid": "a2f366a9-af74-4b3f-90ed-33c1fcad4c3a"

workflows/big_data/package_data/widgets/72a39fab-5433-493f-ae22-12a264075356.json

@@ -5,7 +5,7 @@
       "category": "1be7b5eb-c1b2-485a-8dbe-56abce63fc73", 
       "treeview_image": "", 
       "uid": "72a39fab-5433-493f-ae22-12a264075356", 
-      "is_streaming": false, 
+      "windows_queue": false, 
       "package": "big_data", 
       "interaction_view": "", 
       "has_progress_bar": false, 
@@ -19,7 +19,7 @@
       "wsdl_method": "", 
       "wsdl": "", 
       "interactive": false, 
-      "windows_queue": false, 
+      "is_streaming": false, 
       "order": 1, 
       "name": "Distributed Weighted Forest"
     }
@@ -205,7 +205,7 @@
       "required": true, 
       "multi": false, 
       "parameter_type": "text", 
-      "variable": "leaf_min_inst", 
+      "variable": "min_samples_split", 
       "parameter": true, 
       "order": 5, 
       "uid": "ac032f38-f4a4-44ea-8c02-96506d4f8e86"

workflows/big_data/package_data/widgets/d9de579e-fdd9-47da-948e-69183f24340f.json

@@ -5,7 +5,7 @@
       "category": "08c90036-fd13-4ff3-ac20-01982262a782", 
       "treeview_image": "", 
       "uid": "d9de579e-fdd9-47da-948e-69183f24340f", 
-      "is_streaming": false, 
+      "windows_queue": false, 
       "package": "big_data", 
       "interaction_view": "", 
       "has_progress_bar": false, 
@@ -19,7 +19,7 @@
       "wsdl_method": "", 
       "wsdl": "", 
       "interactive": false, 
-      "windows_queue": false, 
+      "is_streaming": false, 
       "order": 1, 
       "name": "Apply Classifier"
     }
@@ -37,7 +37,7 @@
       "parameter_type": null, 
       "variable": "dataset", 
       "parameter": false, 
-      "order": 5, 
+      "order": 4, 
       "uid": "0c174cfd-0d61-44ff-8ce2-9c32f8791293"
     }
   }, 
@@ -58,23 +58,6 @@
       "uid": "46e9a02d-a613-4a01-a8ee-51d578a32fea"
     }
   }, 
-  {
-    "model": "workflows.abstractinput", 
-    "fields": {
-      "widget": "d9de579e-fdd9-47da-948e-69183f24340f", 
-      "name": "Random forest  - random state", 
-      "short_name": "rds", 
-      "default": "None", 
-      "description": "Define a random state for predict phase.", 
-      "required": true, 
-      "multi": false, 
-      "parameter_type": "text", 
-      "variable": "seed", 
-      "parameter": true, 
-      "order": 3, 
-      "uid": "8ec2b906-2b9e-4cda-9455-09ccd7d134fb"
-    }
-  }, 
   {
     "model": "workflows.abstractinput", 
     "fields": {
@@ -96,7 +79,7 @@
     "model": "workflows.abstractinput", 
     "fields": {
       "widget": "d9de579e-fdd9-47da-948e-69183f24340f", 
-      "name": "Weighted forest - similarity coeff", 
+      "name": "Distributed Weighted forest - similarity coeff", 
       "short_name": "coe", 
       "default": "0.5", 
       "description": "Percentage of most similar treees to include in prediction (0 - 1)", 
@@ -105,7 +88,7 @@
       "parameter_type": "text", 
       "variable": "coeff", 
       "parameter": true, 
-      "order": 4, 
+      "order": 3, 
       "uid": "d979ff23-eb11-40cf-9d81-2a71ddf5d790"
     }
   }, 

workflows/big_data/visualization_views.py

 from django.shortcuts import render
 
-def measure_distribution(request,input_dict,output_dict,widget):
+
+def measure_distribution(request, input_dict, output_dict, widget):
     from disco.core import result_iterator
     from discomll.utils import distribution
 
@@ -11,9 +12,11 @@ def measure_distribution(request,input_dict,output_dict,widget):
         dist += str(k.split("/")[-1][:-2]) + " " + str(v) + "\n"
 
     input_dict["string"] = dist
-    return render(request, 'visualizations/display_string.html',{'widget':widget,'input_dict':input_dict,'output_dict':output_dict})
+    return render(request, 'visualizations/display_string.html',
+                  {'widget': widget, 'input_dict': input_dict, 'output_dict': output_dict})
+
 
-def bigdata_ca(request,input_dict,output_dict,widget):
+def bigdata_ca(request, input_dict, output_dict, widget):
     from discomll.utils import accuracy
     import os.path
     from mothra.settings import MEDIA_ROOT
@@ -21,70 +24,72 @@ def bigdata_ca(request,input_dict,output_dict,widget):
 
     folder = 'discomll_measures'
     tag = input_dict["predictions"]
-    destination = MEDIA_ROOT+'/'+folder+"/"+tag[0][6:]+'.txt'
+    destination = MEDIA_ROOT + '/' + folder + "/" + tag[0][6:] + '.txt'
     ensure_dir(destination)
 
     if input_dict["dataset"].params["id_index"] == -1:
         input_dict["string"] = "ID index should be defined."
-    elif not os.path.isfile(destination): #file doesnt exists
-        results = accuracy.measure(test_data = input_dict["dataset"],
-                                predictions = input_dict["predictions"],
-                                measure = "ca")
-        string = "Classification Accuracy \n" 
-        for k, v in results:
-            string += str(v) + "\n"
+    elif not os.path.isfile(destination):  # file doesnt exists
+        measure, acc = accuracy.measure(test_data=input_dict["dataset"],
+                                   predictions=input_dict["predictions"],
+                                   measure="ca")
+        string = "Classification Accuracy \n"
+        score = str(measure) + " " + str(acc) + "\n"
+        string += score
         input_dict["string"] = string
 
-        f = open(destination,'w')
-        f.write(str(v))
+        f = open(destination, 'w')
+        f.write(score)
         f.close()
-        
+
     else:
-        string = "Classification Accuracy \n" 
-        f = open(destination,'r')
+        #ca results are cached
+        string = "Classification Accuracy \n"
+        f = open(destination, 'r')
         input_dict["string"] = string + str(f.readlines()[0])
         f.close()
-    
-    return render(request, 'visualizations/display_string.html',{'widget':widget,'input_dict':input_dict,'output_dict':output_dict})
 
-def bigdata_mse(request,input_dict,output_dict,widget):
+    return render(request, 'visualizations/display_string.html',
+                  {'widget': widget, 'input_dict': input_dict, 'output_dict': output_dict})
+
+
+def bigdata_mse(request, input_dict, output_dict, widget):
     from discomll.utils import accuracy
-    from disco.core import result_iterator
     import os.path
     from mothra.settings import MEDIA_ROOT
     from workflows.helpers import ensure_dir
 
     folder = 'discomll_measures'
     tag = input_dict["predictions"]
-    destination = MEDIA_ROOT+'/'+folder+"/"+tag[0][6:]+'.txt'
+    destination = MEDIA_ROOT + '/' + folder + "/" + tag[0][6:] + '.txt'
     ensure_dir(destination)
 
     if input_dict["dataset"].params["id_index"] == -1:
         input_dict["string"] = "ID index should be defined."
-    elif not os.path.isfile(destination): #file doesnt exists