merge

mothra/build/pip-delete-this-directory.txt

+This file is placed here by pip to indicate the source was put
+here by pip.
+
+Once this package is successfully installed this source code will be
+deleted (unless you remove this file).

mothra/settings.py

@@ -159,59 +159,51 @@ except NameError:
         from local_settings import *
     except ImportError:
         pass
-        
-if USE_CONCURRENCY:
-    INSTALLED_APPS = (
-        'django.contrib.auth',
-        'django.contrib.contenttypes',
-        'django.contrib.sessions',
-        'django.contrib.sites',
-        'django.contrib.messages',
-        'django.contrib.staticfiles',
-        'django.contrib.admin',
-        'south',
-        'workflows',
-        'workflows.latino',
-        'workflows.decision_support',
-        'workflows.bioinformatics',
-        'workflows.subgroup_discovery',
-        'workflows.nlp',
-        'workflows.ilp',
-        'workflows.mysql',
-        'website',
-        'signuplogin',
-        'django_extensions',
-        'django.contrib.humanize',
-        'orderable_inlines',
+
+INSTALLED_APPS_DEFAULT = (
+    'django.contrib.auth',
+    'django.contrib.contenttypes',
+    'django.contrib.sessions',
+    'django.contrib.sites',
+    'django.contrib.messages',
+    'django.contrib.staticfiles',
+    'django.contrib.admin',
+    'south',
+    'website',
+    'signuplogin',
+    'django_extensions',
+    'django.contrib.humanize',
+    'orderable_inlines',
+    'workflows',
+    )
+
+INSTALLED_APPS_CONCUR = (
         'djcelery',
-    )     
-else:
-    INSTALLED_APPS = (
-        'django.contrib.auth',
-        'django.contrib.contenttypes',
-        'django.contrib.sessions',
-        'django.contrib.sites',
-        'django.contrib.messages',
-        'django.contrib.staticfiles',
-        'django.contrib.admin',
-        'south',
-        'workflows',
-        'workflows.latino',
-        'workflows.decision_support',
-        'workflows.bioinformatics',
-        'workflows.subgroup_discovery',
-        'workflows.nlp',
-        'workflows.ilp',
-        'workflows.mysql',
-        'workflows.base',
-        'website',
-        'signuplogin',
-        'django_extensions',
-        'django.contrib.humanize',
-        'orderable_inlines',
+    ) if USE_CONCURRENCY else (
         'streams',
-    )        
-        
+    )
+
+INSTALLED_APPS_WORKFLOWS_SUB = (
+    'workflows.base',
+    'workflows.latino',
+    'workflows.decision_support',
+    'workflows.bioinformatics',
+    'workflows.subgroup_discovery',
+    'workflows.nlp',
+    'workflows.nl_toolkit',
+    'workflows.ilp',
+    'workflows.weka',
+    'workflows.cforange',
+    'workflows.perfeval',
+    'workflows.mysql'
+    #WORKFLOWS_SUBAPP_PLACEHOLDER
+    )
+
+INSTALLED_APPS = \
+    INSTALLED_APPS_DEFAULT +\
+    INSTALLED_APPS_CONCUR +\
+    INSTALLED_APPS_WORKFLOWS_SUB
+
 TEMPLATE_CONTEXT_PROCESSORS = DEFAULT_SETTINGS.TEMPLATE_CONTEXT_PROCESSORS
 
 TEMPLATES_FOLDER = os.path.join(PROJECT_DIR, 'templates')
@@ -226,3 +218,4 @@ LOGIN_URL = '/login/'
 LOGIN_REDIRECT_URL = '/'
 
 STATIC_DOC_ROOT = os.path.join(os.getcwd(), 'mothra/public/media')
+

requirements.txt

@@ -7,4 +7,5 @@ django-extensions==0.9
 django-orderable-inlines==0.0.6
 django-picklefield==0.2.1
 httplib2==0.7.5
+pydot==1.0.28
 wsgiref==0.1.2

workflows/base/library.py

+def base_concatenate_lists(input_dict):
+    lists = input_dict['lists']
+    new_list = []
+    for every_list in lists:
+        new_list = new_list+every_list
+    output_dict = {}
+    output_dict['list']=new_list
+    return output_dict
\ No newline at end of file

workflows/bioinformatics/db/package_data.json

+[\n]
\ No newline at end of file

workflows/bioinformatics/interaction_views.py

@@ -5,6 +5,7 @@ Bioinformatics interaction viewes.
 '''
 from django.shortcuts import render
 
-def insilico_search(request, input_dict, output_dict, widget):
-    #TODOl
-    return render(request, 'interactions/insilico_search.html', {'widget':widget})
+def feature_selection(request, input_dict, output_dict, widget):
+    #TODO
+    return render(request, 'interactions/feature_selection.html', {'widget':widget})
+

workflows/bioinformatics/library.py

@@ -3,9 +3,25 @@ Bioinformatics library.
 
 @author: Anze Vavpetic <anze.vavpetic@ijs.si>
 '''
-def insilico_search(input_dict):
+#
+# Visualization widgets:
+#
+def rank_plotter(input_dict):
     return input_dict
+def segs_rule_browser(input_dict):
+    return input_dict
+
+#
+# Interactions widgets:
+#
+def feature_selection_finished(postdata, input_dict, output_dict):
+    # TODO
+    return output_dict
 
-def insilico_finished(input_dict):
-    #TODO
-    return input_dict
\ No newline at end of file
+#
+# Regular widgets:
+#
+def segs(input_dict):
+    # TODO
+    output_dict = {}
+    return output_dict
\ No newline at end of file

workflows/bioinformatics/settings.py

@@ -6,6 +6,6 @@ package_root = os.path.dirname(__file__)
 package_statics = os.path.join(os.path.dirname(__file__), 'static', package_name)
 package_bin = os.path.join(package_root, 'bin')
 
-auto_update_db = True
+auto_update_db = False
 create_backups = True
 

workflows/bioinformatics/urls.py

+from django.conf.urls.defaults import patterns, include, url
+
+urlpatterns = patterns('',
+    url(r'^get-new-feature-selection-scores/widget(?P<widget_id>[0-9]+)/(?P<method>\w+)/$', 'workflows.bioinformatics.views.get_new_feature_selection_scores', 
+        name='get new feature selection scores'),
+)
\ No newline at end of file

workflows/bioinformatics/views.py

+# helperji, context stvari
+from django.shortcuts import render, get_object_or_404, redirect
+from django.http import Http404, HttpResponse
+
+# modeli
+from workflows.models import *
+# auth fore
+from django.contrib.auth.decorators import login_required
+
+# Orange bioinformatics differential expression
+# from orngBioinformatics.obiExpression import *
+
+# SCORE_METHODS = [("fold change", ExpressionSignificance_FoldChange, twoTailTest, True),
+#                  ("log2 fold change", ExpressionSignificance_Log2FoldChange, twoTailTest, True),
+#                  ("t-test", ExpressionSignificance_TTest_T, twoTailTest, True),
+#                  ("t-test p-value", ExpressionSignificance_TTest_PValue, oneTailTestLow, True),
+#                  ("anova", ExpressionSignificance_ANOVA_F, oneTailTestHi, False),
+#                  ("anova p-value", ExpressionSignificance_ANOVA_PValue, oneTailTestLow, False),
+#                  ("signal to noise ratio", ExpressionSignificance_SignalToNoise, twoTailTest, True),
+#                  ("info gain", ExpressionSignificance_Info, oneTailTestHi, True),
+#                  ("chi-square", ExpressionSignificance_ChiSquare, oneTailTestHi, True),
+#                  ("mann-whitney", ExpressionSignigicance_MannWhitneyu_U, oneTailTestLow, True),
+#                  ("AREA (timeseries)", ExpressionSignificance_AREA, oneTailTestHi, False),
+#                  ("FC (timeseries)", ExpressionSignificance_FCts, oneTailTestHi, False)]
+
+@login_required
+def get_new_feature_selection_scores(request, widget_id, method = 0):
+    w = get_object_or_404(Widget, pk=widget_id)
+    if w.workflow.user == request.user:
+        data = w.inputs.all()[0]
+        _, score_method, _, twotailtest = SCORE_METHODS[method]
+        return HttpResponse(data, mimetype='text/json')
+    else:
+        return HttpResponse(status=400)

workflows/bioinformatics/visualization_views.py

+'''
+Bioinformatics visualization viewes.
+
+@author: Anze Vavpetic <anze.vavpetic@ijs.si>
+'''
+from django.shortcuts import render
+
+def rank_plotter(request, input_dict, output_dict, widget):
+    #TODO
+    return render(request, 'interactions/rank_plotter.html', {'widget':widget})
+
+def segs_rule_browser(request, input_dict, output_dict, widget):
+    #TODO
+    return render(request, 'interactions/segs_rule_browser.html', {'widget':widget})

workflows/cforange/interaction_views.py

+from django.shortcuts import render
+
+def cforange_filter_integers(request,input_dict,output_dict,widget):
+    return render(request, 'interactions/cforange_filter_integers.html',{'widget':widget,'intList':input_dict['intList']})
\ No newline at end of file

workflows/cforange/library.py

+def cforange_split_dataset(input_dict):
+    import orange
+    output_dict = {}
+    data = input_dict['dataset']
+    selection = orange.MakeRandomIndices2(data,float(input_dict['p']))
+    train_data = data.select(selection,0)
+    test_data = data.select(selection,1)
+    output_dict['train_data']=train_data
+    output_dict['test_data']=test_data
+    return output_dict
+
+def cforange_score_estimation(input_dict):
+    import orange
+    import orngFSS
+    data = input_dict['dataset']
+    ma = orngFSS.attMeasure(data,orange.MeasureAttribute_relief(k=int(input_dict['k']), m=int(input_dict['m'])))
+    output_string = ""
+    output_dict = {}
+    output_dict['results'] = ma
+    return output_dict
+
+def cforange_best_natts(input_dict):
+    import orange
+    import orngFSS
+    data = input_dict['dataset']
+    scores = input_dict['scores']
+    n = int(input_dict['n'])
+    new_dataset = orngFSS.selectBestNAtts(data,scores,n)
+    output_dict={}
+    output_dict['new_dataset'] = new_dataset
+    return output_dict
+
+def cforange_atts_above_thresh(input_dict):
+    import orange
+    import orngFSS
+    data = input_dict['dataset']
+    scores = input_dict['scores']
+    thresh = float(input_dict['thresh'])
+    new_dataset = orngFSS.selectAttsAboveThresh(data,scores,thresh)
+    output_dict={}
+    output_dict['new_dataset'] = new_dataset
+    return output_dict
+
+def cforange_filter_relieff(input_dict):
+    import orange
+    import orngFSS
+    data = input_dict['dataset']
+    measure = orange.MeasureAttribute_relief(k=int(input_dict['k']), m=int(input_dict['m']))
+    margin = float(input_dict['margin'])
+    new_dataset = orngFSS.filterRelieff(data,measure,margin)
+    output_dict = {}
+    output_dict['new_dataset'] = new_dataset
+    return output_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
+
+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  
+
+def cforange_MSE(input_dict):
+    import orngStat
+    results = input_dict['results']
+    errors = orngStat.MSE(results)
+    if len(errors)==1:
+        errors = errors[0]
+    output_dict = {}
+    output_dict['MSE']=errors
+    return output_dict
+
+def cforange_RMSE(input_dict):
+    import orngStat
+    results = input_dict['results']
+    errors = orngStat.RMSE(results)
+    if len(errors)==1:
+        errors = errors[0]
+    output_dict = {}
+    output_dict['RMSE']=errors
+    return output_dict    
+
+def cforange_MAE(input_dict):
+    import orngStat
+    results = input_dict['results']
+    errors = orngStat.MAE(results)
+    if len(errors)==1:
+        errors = errors[0]
+    output_dict = {}
+    output_dict['MAE']=errors
+    return output_dict
+
+def cforange_RSE(input_dict):
+    import orngStat
+    results = input_dict['results']
+    errors = orngStat.RSE(results)
+    if len(errors)==1:
+        errors = errors[0]
+    output_dict = {}
+    output_dict['RSE']=errors
+    return output_dict    
+
+def cforange_RRSE(input_dict):
+    import orngStat
+    results = input_dict['results']
+    errors = orngStat.RRSE(results)
+    if len(errors)==1:
+        errors = errors[0]
+    output_dict = {}
+    output_dict['RRSE']=errors
+    return output_dict    
+
+def cforange_RAE(input_dict):
+    import orngStat
+    results = input_dict['results']
+    errors = orngStat.RAE(results)
+    if len(errors)==1:
+        errors = errors[0]
+    output_dict = {}
+    output_dict['RAE']=errors
+    return output_dict    
+
+def cforange_R2(input_dict):
+    import orngStat
+    results = input_dict['results']
+    errors = orngStat.R2(results)
+    if len(errors)==1:
+        errors = errors[0]
+    output_dict = {}
+    output_dict['R2']=errors
+    return output_dict        
+
+def cforange_prepare_results(input_dict):
+    output_dict = {}
+    learners = input_dict['learners']
+    newlist = []
+    for i in range(0,len(learners)):
+        newdict = {}
+        newdict['name']=str(learners[i])
+        newdict['fbeta']=0.5
+        newdict['precision']=input_dict['precision'][i]
+        newdict['recall']=input_dict['recall'][i]
+        newdict['fscore']=input_dict['f'][i]
+        newlist.append(newdict)
+    output_dict['alp']=newlist
+    return output_dict
\ No newline at end of file