Latino package improved and debugged (now it works from start to show vocabulary table widget)

workflows/latino/import_dotnet.py

 import logging
 import os
 import sys
-from settings import package_root
+from settings import PACKAGE_ROOT
 
 #------------------------------------------------------------------------------
 # prepare environment for loading latino (Python.net interpreter should be used)
 # see: http://pythonnet.sourceforge.net/
 #------------------------------------------------------------------------------
 
-sys.path.append(os.path.join(package_root, 'bin'))
+sys.path.append(os.path.join(PACKAGE_ROOT, 'bin'))
 
 try:
     import System
     import Latino
-    from LatinoCloudFlows import *
+    from LatinoClowdFlows import *
 except Exception:
     logging.warning("DotNet assemblies could not be loaded! Probable reasons: missing dlls or wrong interpreter (see http://pythonnet.sourceforge.net). "
                     "Other functionality of ClowdFlows (besides .Net assemblies) should be OK!")

workflows/latino/library_gen.py

@@ -5,6 +5,26 @@
 from serialization_utils import *
 from import_dotnet import *
 
+def sentiment_analysis_con_cat(inputDict):
+    _str1 = ToString(inputDict['str1'])
+    _str2 = ToString(inputDict['str2'])
+    execResult = SentimentAnalysisCF.ConCat(_str1, _str2)
+    execResultPy = ToPyObj(execResult)
+    outputDict = {}
+    outputDict['out'] = execResultPy
+    return outputDict
+
+def sentiment_analysis_ena_cudna_funk(inputDict):
+    _str1 = ToString(inputDict['str1'])
+    _str2 = ToString(inputDict['str2'])
+    _str3 = ToString(inputDict['str3'])
+    _str4 = ToString(inputDict['str4'])
+    execResult = SentimentAnalysisCF.EnaCudnaFunk(_str1, _str2, _str3, _str4)
+    execResultPy = ToPyObj(execResult)
+    outputDict = {}
+    outputDict['out'] = execResultPy
+    return outputDict
+
 def latino_flatten_object_to_string_array(inputDict):
     _data = ToNetObj(inputDict['data'])
     execResult = LatinoCF.FlattenObjectToStringArray(_data)
@@ -270,7 +290,7 @@ def latino_construct_snowball_stemmer(inputDict):
     return outputDict
 
 def latino_construct_stop_words_tagger(inputDict):
-    _stopWords = ToString(inputDict['stopWords'])
+    _stopWords = ToNetObj(inputDict['stopWords'])
     _ignoreCase = ToBool(inputDict['ignoreCase'])
     execResult = LatinoCF.ConstructStopWordsTagger(_stopWords, _ignoreCase)
     execResultPy = ToPyObj(execResult)
@@ -341,7 +361,7 @@ def latino_tag_string_stopwords(inputDict):
     outputDict['string'] = execResultPy
     return outputDict
 
-def latino_construct_bow_space(inputDict):
+def latino_construct_bow_space_1(inputDict):
     _documents = ToNetObj(inputDict['documents'])
     _tokenizer = ToNetObj(inputDict['tokenizer'])
     _stemmer = ToNetObj(inputDict['stemmer'])
@@ -358,7 +378,7 @@ def latino_construct_bow_space(inputDict):
     outputDict['ds'] = execResultPy['ds']
     return outputDict
 
-def latino_construct_bow_space(inputDict):
+def latino_construct_bow_space_2(inputDict):
     _adc = ToNetObj(inputDict['adc'])
     _tokenId = ToString(inputDict['tokenId'])
     _stemId = ToString(inputDict['stemId'])
@@ -473,7 +493,7 @@ def latino_sparse_matrix_to_table(inputDict):
 def latino_construct_kmeans_clusterer(inputDict):
     _k = ToInt(inputDict['k'])
     _centroidType = ToEnum(Latino.Model.CentroidType, inputDict['centroidType'], Latino.Model.CentroidType.NrmL2)
-    _similarityModel = ToEnum(LatinoCloudFlows.SimilarityModel, inputDict['similarityModel'], LatinoCloudFlows.SimilarityModel.Cosine)
+    _similarityModel = ToEnum(LatinoClowdFlows.SimilarityModel, inputDict['similarityModel'], LatinoClowdFlows.SimilarityModel.Cosine)
     _randomSeed = ToInt(inputDict['randomSeed'])
     _eps = ToFloat(inputDict['eps'])
     _trials = ToInt(inputDict['trials'])
@@ -521,7 +541,7 @@ def latino_clustering_results_info(inputDict):
     return outputDict
 
 def latino_construct_centroid_classifier(inputDict):
-    _similarityModel = ToEnum(LatinoCloudFlows.SimilarityModel, inputDict['similarityModel'], LatinoCloudFlows.SimilarityModel.Cosine)
+    _similarityModel = ToEnum(LatinoClowdFlows.SimilarityModel, inputDict['similarityModel'], LatinoClowdFlows.SimilarityModel.Cosine)
     _normalizeCentorids = ToBool(inputDict['normalizeCentorids'])
     execResult = LatinoCF.ConstructCentroidClassifier(_similarityModel, _normalizeCentorids)
     execResultPy = ToPyObj(execResult)
@@ -596,7 +616,7 @@ def latino_construct_maximum_entropy_classifier_fast(inputDict):
     return outputDict
 
 def latino_construct_knn_classifier(inputDict):
-    _similarityModel = ToEnum(LatinoCloudFlows.SimilarityModel, inputDict['similarityModel'], LatinoCloudFlows.SimilarityModel.Cosine)
+    _similarityModel = ToEnum(LatinoClowdFlows.SimilarityModel, inputDict['similarityModel'], LatinoClowdFlows.SimilarityModel.Cosine)
     _k = ToInt(inputDict['k'])
     _softVoting = ToBool(inputDict['softVoting'])
     execResult = LatinoCF.ConstructKnnClassifier(_similarityModel, _k, _softVoting)

workflows/latino/serialization_utils.py

@@ -276,7 +276,10 @@ def ToPyObj(data):
                 l.append(ToPyObj(val))
             return tuple(l)
     if hasattr(data, "GetType"):
-        for interface in data.GetType().GetInterfaces():
+        type = data.GetType()
+        if type.IsArray:
+            return [ToPyObj(x) for x in data]
+        for interface in type.GetInterfaces():
             if interface.Name == u'ISerializable':
                 return LSO(data)
     return data

workflows/latino/visualization_views.py

@@ -34,5 +34,5 @@ def advanced_object_viewer(request,input_dict,output_dict,widget):
 
 def show_table(request,input_dict,output_dict,widget):
     tbl= input_dict['tbl']
-    output_dict = {'attrs':list(tbl.items()[0][0]), 'metas':[], 'data':tbl.values()[0], 'title':'Vocabulary Table'}
+    output_dict = {'attrs':list(tbl.items()[0][0]), 'metas':[], 'data':(((None, v) for v in inst) for inst in tbl.values()[0]), 'title':'Vocabulary Table'}
     return render(request, 'visualizations/table_viewer.html',{'widget':widget,'input_dict':input_dict,'output_dict':output_dict})
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