dodal orange widgete za statistiko in testiranje

workflows/cforange/library.py

@@ -52,12 +52,149 @@ def cforange_filter_relieff(input_dict):
     output_dict['new_dataset'] = new_dataset
     return output_dict   
 
-def cforange_cross_validation(input_dict):
+def cforange_multiple_cross_validation(input_dict):
+    import orange, orngTest, orngStat
+    learners = input_dict['learners']
+    data = input_dict['dataset']
+    folds = int(input_dict['folds'])
+    results = orngTest.crossValidation(learners, data, folds=folds)
+    output_dict = {}
+    output_dict['results']=results
+    return output_dict
+
+def cforange_proportion_test(input_dict):
+    import orange, orngTest, orngStat
+    learners = input_dict['learners']
+    data = input_dict['dataset']
+    learnProp = float(input_dict['learnProp'])
+    times = int(input_dict['times'])
+    results = orngTest.proportionTest(learners, data, learnProp, times=times)
+    output_dict = {}
+    output_dict['results']=results
+    return output_dict  
+
+def cforange_leave_one_out(input_dict):
     import orange, orngTest, orngStat
     learners = input_dict['learners']
     data = input_dict['dataset']
+    results = orngTest.leaveOneOut(learners, data)
+    output_dict = {}
+    output_dict['results']=results
+    return output_dict       
+
+def cforange_cross_validation(input_dict):
+    import orange, orngTest, orngStat
+    learners = [input_dict['learner']]
+    data = input_dict['dataset']
     folds = int(input_dict['folds'])
     results = orngTest.crossValidation(learners, data, folds=folds)
     output_dict = {}
     output_dict['results']=results
-    return output_dict
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+    return output_dict
+
+def cforange_classification_accuracy(input_dict):
+    import orngStat
+    results = input_dict['results']
+    CAs = orngStat.CA(results)
+    if len(CAs)==1:
+        CAs = CAs[0]
+    output_dict = {}
+    output_dict['ca']=CAs
+    return output_dict
+
+def cforange_classification_accuracy(input_dict):
+    import orngStat
+    results = input_dict['results']
+    if input_dict['reportSE']=='true':
+        reportSE = True
+    else:
+        reportSE = False
+    CAs = orngStat.CA(results,reportSE=reportSE)
+    if len(CAs)==1:
+        CAs = CAs[0]
+    output_dict = {}
+    output_dict['ca']=CAs
+    return output_dict 
+
+def cforange_average_probability(input_dict):
+    import orngStat
+    results = input_dict['results']
+    if input_dict['reportSE']=='true':
+        reportSE = True
+    else:
+        reportSE = False
+    APs = orngStat.AP(results,reportSE=reportSE)
+    if len(APs)==1:
+        APs = APs[0]
+    output_dict = {}
+    output_dict['ap']=APs
+    return output_dict
+
+def cforange_brier_score(input_dict):
+    import orngStat
+    results = input_dict['results']
+    if input_dict['reportSE']=='true':
+        reportSE = True
+    else:
+        reportSE = False
+    BSs = orngStat.BrierScore(results,reportSE=reportSE)
+    if len(BSs)==1:
+        BSs = BSs[0]
+    output_dict = {}
+    output_dict['bs']=BSs
+    return output_dict 
+
+def cforange_information_score(input_dict):
+    import orngStat
+    results = input_dict['results']
+    if input_dict['reportSE']=='true':
+        reportSE = True
+    else:
+        reportSE = False
+    ISs = orngStat.IS(results,apriori=None,reportSE=reportSE)
+    if len(ISs)==1:
+        ISs = ISs[0]
+    output_dict = {}
+    output_dict['is']=ISs
+    return output_dict
+
+def cforange_confusion_matrix(input_dict):
+    import orngStat
+    results = input_dict['results']
+    classIndex = int(input_dict['classIndex'])
+    if input_dict['cutoff']!='':
+        cutoff = float(input_dict['cutoff'])
+        cm = orngStat.confusionMatrices(results,classIndex=classIndex,cutoff=cutoff)
+    else:
+        cm = orngStat.confusionMatrices(results,classIndex=classIndex)
+    if len(cm)==1:
+        cm = cm[0]
+    print cm
+    output_dict = {}
+    output_dict['cm']=cm
+    return output_dict
+
+def cforange_confusion_matrix_computations(input_dict):
+    import orngStat
+    cm = input_dict['cm']
+    alpha = float(input_dict['alpha'])
+    output_dict = {}
+    output_dict['sens']=orngStat.sens(cm)
+    output_dict['spec']=orngStat.spec(cm)
+    output_dict['PPV']=orngStat.PPV(cm)
+    output_dict['NPV']=orngStat.NPV(cm)
+    output_dict['precision']=orngStat.precision(cm)
+    output_dict['recall']=orngStat.recall(cm)
+    output_dict['F1']=orngStat.F1(cm)
+    output_dict['Falpha']=orngStat.Falpha(cm,alpha=alpha)
+    output_dict['MCC']=orngStat.MCC(cm)
+    return output_dict  
+
+def cforange_auc(input_dict):
+    import orngStat
+    results = input_dict['results']
+    method = int(input_dict['method'])
+    auc = orngStat.AUC(results,method)
+    output_dict = {}
+    output_dict['AUC']=auc
+    return output_dict  
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