Merge branch 'master' of https://github.com/CIFASIS/VDiscover

tcreator

@@ -30,12 +30,12 @@ concatenate = lambda *lists: reduce((lambda a,b: a.extend(b) or a),lists,[])
 if __name__ == "__main__":
 
     # Arguments
-    parser = argparse.ArgumentParser(description='A script to create new test cases using a name and a command line')
-    parser.add_argument("--name", help="A csv with the features to train or predict", type=str, default=None)
+    parser = argparse.ArgumentParser(description='A small utility to create new test cases using a name and a command line')
+    parser.add_argument("--name", help="The name of the ", type=str, default=None)
     parser.add_argument("--cmd", help="Command-line to execute", type=str, default=None)
     parser.add_argument("--batch", help="A csv with the command lines", type=str, default=None)
 
-    parser.add_argument("--copy", help="A csv with the features to train or predict", action='store_true', default=False)
+    parser.add_argument("--copy", help="Force the copy of the files in command lines instead of symbolic linking", action='store_true', default=False)
 
     parser.add_argument("outdir", help="Output directory to write testcases", type=str, default=None)
 
@@ -79,7 +79,7 @@ if __name__ == "__main__":
         if arg <> '':
           pargs = pargs + arg
       #args = concatenate(args)
-      print pargs 
+      print "Procesing '" + " ".join(pargs) + "'"
       #args = filter(lambda x: x is not '', cmd.split(" "))
       WriteTestcase(name,pargs[0],pargs[1:], copy)
 

vdiscover/Pipeline.py

@@ -47,6 +47,9 @@ def fit_transform(self, X, y=None, **fit_params):
     def fit(self, X, y=None, **fit_params):
         return self
 
+    def get_params(self, deep=True):
+        return []
+
 
 class ItemSelector(BaseEstimator, TransformerMixin):
 
@@ -59,6 +62,9 @@ def fit(self, x, y=None):
     def transform(self, data_dict):
         return data_dict[self.key]
 
+    def get_params(self, deep=True):
+        return []
+
 class CutoffMax(BaseEstimator, TransformerMixin):
 
     def __init__(self, maxv):
@@ -72,6 +78,9 @@ def transform(self, X, y=None, **fit_params):
         X[self.pos] = self.maxv
         return X
 
+    def get_params(self, deep=True):
+        return []
+
 
 
 def make_train_pipeline(ftype):

vdiscover/Recall.py

@@ -11,10 +11,7 @@
 def Recall(model_file, in_file, in_type, out_file, test_mode, probability=False):
 
   model = load_model(model_file)
-  #csvreader = open_csv(in_file)
-
-  outfile = open_csv(out_file)
-  csvwriter = csv.writer(outfile, delimiter='\t')
+  csvwriter = write_csv(out_file)
 
   x = dict()
 
@@ -39,8 +36,9 @@ def Recall(model_file, in_file, in_type, out_file, test_mode, probability=False)
     else:
       err = recall_score(test_classes, predicted_classes, average=None)
 
-    print err[0], err[1], sum(err)/2.0
     print classification_report(test_classes, predicted_classes)
+    print "Accuracy per class:", round(err[0],2), round(err[1],2)
+    print "Average accuracy:", round(sum(err)/2.0,2)
 
   elif test_mode == "aggregated":
 

vdiscover/Train.py

@@ -37,9 +37,9 @@ def TrainScikitLearn(model_file, train_file, valid_file, ftype, nsamples):
   model = make_train_pipeline(ftype)
   model.fit(train_dict,train_classes)
 
-  print "Resulting model:"
-  print model
-  print confusion_matrix(train_classes, model.predict(train_dict))
+  print "Done!"
+  #print model
+  #print confusion_matrix(train_classes, model.predict(train_dict))
 
   print "Saving model to",model_file
   modelfile.write(pickle.dumps(model))

vdiscover/Utils.py

@@ -67,6 +67,14 @@ def load_csv(in_file):
 
   return csv.reader(infile, delimiter='\t')
 
+def write_csv(in_file):
+
+  if ".gz" in in_file:
+    infile = gzip.open(in_file, "w")
+  else:
+    infile = open(in_file, "w")
+
+  return csv.writer(infile, delimiter='\t')
 
 def open_csv(in_file):