diff --git a/Code/MonoMutliViewClassifiers/ExecClassif.py b/Code/MonoMutliViewClassifiers/ExecClassif.py index 649a55486e52674ced145dc925798562361fe7d1..fc98998c5e9aaa039a92f939e3b4a73fa0805deb 100644 --- a/Code/MonoMutliViewClassifiers/ExecClassif.py +++ b/Code/MonoMutliViewClassifiers/ExecClassif.py @@ -14,7 +14,7 @@ import matplotlib # Import own modules import Multiview -import Metrics +# import Metrics import MonoviewClassifiers from Multiview.ExecMultiview import ExecMultiview, ExecMultiview_multicore from Monoview.ExecClassifMonoView import ExecMonoview, ExecMonoview_multicore @@ -27,7 +27,7 @@ from utils import execution, Dataset __author__ = "Baptiste Bauvin" __status__ = "Prototype" # Production, Development, Prototype -matplotlib.use('Agg') # Anti-Grain Geometry C++ library to make a raster (pixel) image of the figure +# matplotlib.use('Agg') # Anti-Grain Geometry C++ library to make a raster (pixel) image of the figure def initBenchmark(args): @@ -154,7 +154,7 @@ def classifyOneIter_multicore(LABELS_DICTIONARY, argumentDictionaries, nbCores, benchmark, views): resultsMonoview = [] - np.savetxt(directories + "train_indices.csv", classificationIndices[0], delimiter=",") + np.savetxt(directory + "train_indices.csv", classificationIndices[0], delimiter=",") labelsNames = LABELS_DICTIONARY.values() resultsMonoview += [ExecMonoview_multicore(directory, args.name, labelsNames, classificationIndices, kFolds, coreIndex, args.type, args.pathF, randomState, @@ -199,8 +199,6 @@ def classifyOneIter(LABELS_DICTIONARY, argumentDictionaries, nbCores, directory, randomState, hyperParamSearch, metrics, DATASET, viewsIndices, dataBaseTime, start, benchmark, views): print classificationIndices[0] - import pdb; - pdb.set_trace() np.savetxt(directory + "train_indices.csv", classificationIndices[0], delimiter=",") resultsMonoview = []