diff --git a/Code/MonoMultiViewClassifiers/ExecClassif.py b/Code/MonoMultiViewClassifiers/ExecClassif.py
index c0c311984fd2b2cadf22b6b926fdde829558810a..e7e6ca9d4d40272a181d1cfcf6d9a0aa3a5ae48d 100644
--- a/Code/MonoMultiViewClassifiers/ExecClassif.py
+++ b/Code/MonoMultiViewClassifiers/ExecClassif.py
@@ -167,7 +167,7 @@ def execOneBenchmark(coreIndex=-1, LABELS_DICTIONARY=None, directory=None, class
resultsMonoview += [ExecMonoview_multicore(directory, args.name, labelsNames, classificationIndices, kFolds,
coreIndex, args.type, args.pathF, randomState, labels,
hyperParamSearch=hyperParamSearch, metrics=metrics,
- nIter=args.CL_GS_iter, **argument)
+ nIter=args.CL_HPS_iter, **argument)
for argument in argumentDictionaries["Monoview"]]
logging.debug("Done:\t Monoview benchmark")
@@ -181,7 +181,7 @@ def execOneBenchmark(coreIndex=-1, LABELS_DICTIONARY=None, directory=None, class
resultsMultiview += [
ExecMultiview_multicore(directory, coreIndex, args.name, classificationIndices, kFolds, args.type,
args.pathF, LABELS_DICTIONARY, randomState, labels, hyperParamSearch=hyperParamSearch,
- metrics=metrics, nIter=args.CL_GS_iter, **arguments)
+ metrics=metrics, nIter=args.CL_HPS_iter, **arguments)
for arguments in argumentDictionaries["Multiview"]]
logging.debug("Done:\t Multiview benchmark")
@@ -220,7 +220,7 @@ def execOneBenchmark_multicore(nbCores=-1, LABELS_DICTIONARY=None, directory=Non
delayed(ExecMonoview_multicore)(directory, args.name, labelsNames, classificationIndices, kFolds,
coreIndex, args.type, args.pathF, randomState, labels,
hyperParamSearch=hyperParamSearch,
- metrics=metrics, nIter=args.CL_GS_iter,
+ metrics=metrics, nIter=args.CL_HPS_iter,
**argumentDictionaries["Monoview"][coreIndex + stepIndex * nbCores])
for coreIndex in range(min(nbCores, nbExperiments - stepIndex * nbCores))))
logging.debug("Done:\t Monoview benchmark")
@@ -238,7 +238,7 @@ def execOneBenchmark_multicore(nbCores=-1, LABELS_DICTIONARY=None, directory=Non
resultsMultiview += Parallel(n_jobs=nbCores)(
delayed(ExecMultiview_multicore)(directory, coreIndex, args.name, classificationIndices, kFolds,
args.type, args.pathF, LABELS_DICTIONARY, randomState, labels,
- hyperParamSearch=hyperParamSearch, metrics=metrics, nIter=args.CL_GS_iter,
+ hyperParamSearch=hyperParamSearch, metrics=metrics, nIter=args.CL_HPS_iter,
**argumentDictionaries["Multiview"][stepIndex * nbCores + coreIndex])
for coreIndex in range(min(nbCores, nbExperiments - stepIndex * nbCores)))
logging.debug("Done:\t Multiview benchmark")
@@ -280,7 +280,7 @@ def execOneBenchmarkMonoCore(DATASET=None, LABELS_DICTIONARY=None, directory=Non
resultsMonoview += [ExecMonoview(directory, X, Y, args.name, labelsNames, classificationIndices, kFolds,
1, args.type, args.pathF, randomState,
hyperParamSearch=hyperParamSearch, metrics=metrics,
- nIter=args.CL_GS_iter, **arguments)]
+ nIter=args.CL_HPS_iter, **arguments)]
logging.debug("Done:\t Monoview benchmark")
logging.debug("Start:\t Multiview arguments initialization")
@@ -294,7 +294,7 @@ def execOneBenchmarkMonoCore(DATASET=None, LABELS_DICTIONARY=None, directory=Non
resultsMultiview += [
ExecMultiview(directory, DATASET, args.name, classificationIndices, kFolds, 1, args.type,
args.pathF, LABELS_DICTIONARY, randomState, labels, hyperParamSearch=hyperParamSearch,
- metrics=metrics, nIter=args.CL_GS_iter, **arguments)]
+ metrics=metrics, nIter=args.CL_HPS_iter, **arguments)]
logging.debug("Done:\t Multiview benchmark")
return [flag, resultsMonoview, resultsMultiview]
@@ -444,7 +444,7 @@ def execClassif(arguments):
# resultsMonoview += [ExecMonoview_multicore(directory, args.name, labelsNames, classificationIndices, kFolds,
# coreIndex, args.type, args.pathF, randomState,
# hyperParamSearch=hyperParamSearch,
-# metrics=metrics, nIter=args.CL_GS_iter,
+# metrics=metrics, nIter=args.CL_HPS_iter,
# **arguments)
# for arguments in argumentDictionaries["Monoview"]]
# monoviewTime = time.time() - dataBaseTime - start
@@ -456,7 +456,7 @@ def execClassif(arguments):
# resultsMultiview += [
# ExecMultiview_multicore(directory, coreIndex, args.name, classificationIndices, kFolds, args.type,
# args.pathF, LABELS_DICTIONARY, randomState, hyperParamSearch=hyperParamSearch,
-# metrics=metrics, nIter=args.CL_GS_iter, **arguments)
+# metrics=metrics, nIter=args.CL_HPS_iter, **arguments)
# for arguments in argumentDictionaries["Multiview"]]
# multiviewTime = time.time() - monoviewTime - dataBaseTime - start
#
@@ -501,14 +501,14 @@ def execClassif(arguments):
# args.type,
# args.pathF,
# LABELS_DICTIONARY, randomState, hyperParamSearch=hyperParamSearch,
-# metrics=metrics, nIter=args.CL_GS_iter,
+# metrics=metrics, nIter=args.CL_HPS_iter,
# **argumentDictionaries["Multiview"][stepIndex * nbCores + coreIndex])
# for coreIndex in range(min(nbCores, nbExperiments - stepIndex * nbCores)))
# else:
# resultsMultiview = [
# ExecMultiview(directory, DATASET, args.name, classificationIndices, kFolds, 1, args.type, args.pathF,
# LABELS_DICTIONARY, randomState, hyperParamSearch=hyperParamSearch,
-# metrics=metrics, nIter=args.CL_GS_iter, **arguments) for arguments in
+# metrics=metrics, nIter=args.CL_HPS_iter, **arguments) for arguments in
# argumentDictionaries["Multiview"]]
# multiviewTime = time.time() - monoviewTime - dataBaseTime - start
# if nbCores > 1:
diff --git a/Code/MonoMultiViewClassifiers/Monoview/ExecClassifMonoView.py b/Code/MonoMultiViewClassifiers/Monoview/ExecClassifMonoView.py
index fc0102a0aa970d2398ac9281a6a6fc0ffc5a7c05..4f6d844a5d69d32d53b81d6fc4576df49fec9809 100644
--- a/Code/MonoMultiViewClassifiers/Monoview/ExecClassifMonoView.py
+++ b/Code/MonoMultiViewClassifiers/Monoview/ExecClassifMonoView.py
@@ -196,85 +196,106 @@ if __name__ == '__main__':
import argparse
import pickle
+ from ..utils import Dataset
+
parser = argparse.ArgumentParser(
description='This methods is used to execute a multiclass classification with one single view. ',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
groupStandard = parser.add_argument_group('Standard arguments')
groupStandard.add_argument('-log', action='store_true', help='Use option to activate Logging to Console')
- groupStandard.add_argument('--type', metavar='STRING', action='store', help='Type of Dataset', default=".hdf5")
groupStandard.add_argument('--name', metavar='STRING', action='store',
- help='Name of Database (default: %(default)s)', default='DB')
+ help='Name of Database', default='Plausible')
+ groupStandard.add_argument('--cl_name', metavar='STRING', action='store',
+ help='THe name of the monoview classifier to use', default='DecisionTree')
groupStandard.add_argument('--view', metavar='STRING', action='store',
- help='Name of Feature for Classification (default: %(default)s)', default='View0')
+ help='Name of the view used', default='View0')
groupStandard.add_argument('--pathF', metavar='STRING', action='store',
- help='Path to the views (default: %(default)s)', default='Results-FeatExtr/')
+ help='Path to the database hdf5 file', default='../../../Data/Plausible')
groupStandard.add_argument('--directory', metavar='STRING', action='store',
- help='Path to the views (default: %(default)s)', default='Results-FeatExtr/')
+ help='Path of the output directory', default='')
groupStandard.add_argument('--labelsNames', metavar='STRING', action='store', nargs='+',
- help='Name of classLabels CSV-file (default: %(default)s)', default='classLabels.csv')
+ help='Name of the labels used for classification', default=['Yes', 'No'])
groupStandard.add_argument('--classificationIndices', metavar='STRING', action='store',
- help='Name of classLabels-Description CSV-file (default: %(default)s)',
- default='classLabels-Description.csv')
- groupStandard.add_argument('--nbCores', metavar='INT', action='store', help='Number of cores, -1 for all', type=int,
- default=1)
+ help='Path to the classificationIndices pickle file',
+ default='')
+ groupStandard.add_argument('--KFolds', metavar='STRING', action='store',
+ help='Path to the kFolds pickle file',
+ default='')
+ groupStandard.add_argument('--nbCores', metavar='INT', action='store', help='Number of cores, -1 for all',
+ type=int, default=1)
groupStandard.add_argument('--randomState', metavar='INT', action='store',
help='Seed for the random state or pickable randomstate file', default=42)
groupStandard.add_argument('--hyperParamSearch', metavar='STRING', action='store',
- help='The type of method used tosearch the best set of hyper parameters', default='randomizedSearch')
- groupStandard.add_argument('--metrics', metavar='STRING', action='store', nargs="+",
- help='Metrics used in the experimentation, the first will be the one used in CV',
- default=[''])
- groupStandard.add_argument('--nIter', metavar='INT', action='store', help='Number of itetarion in hyper parameter search', type=int,
+ help='The type of method used to search the best set of hyper parameters',
+ default='randomizedSearch')
+ groupStandard.add_argument('--metrics', metavar='STRING', action='store',
+ help='Path to the pickle file describing the metricsused to analyze the performance',
+ default='')
+ groupStandard.add_argument('--kwargs', metavar='STRING', action='store',
+ help='Path to the pickle file containing the key-words arguments used for classification',
+ default='')
+ groupStandard.add_argument('--nIter', metavar='INT', action='store',
+ help='Number of itetarion in hyper parameter search', type=int,
default=10)
args = parser.parse_args()
directory = args.directory
name = args.name
+ classifierName = args.cl_name
labelsNames = args.labelsNames
- classificationIndices = args.classificationIndices
- KFolds = args.KFolds
+ viewName = args.view
+ with open(args.classificationIndices, 'rb') as handle:
+ classificationIndices = pickle.load(handle)
+ with open(args.KFolds, 'rb') as handle:
+ KFolds = pickle.load(handle)
nbCores = args.nbCores
- databaseType = None
path = args.pathF
- randomState = args.randomState
+ with open(args.randomState, 'rb') as handle:
+ randomState = pickle.load(handle)
hyperParamSearch = args.hyperParamSearch
- metrics = args.metrics
+ with open(args.metrics, 'rb') as handle:
+ metrics = pickle.load(handle)
nIter = args.nIter
- kwargs = args.kwargs
-
- # Extract the data using MPI
- X = None
- Y = None
+ with open(args.kwargs, 'rb') as handle:
+ kwargs = pickle.load(handle)
- logfilename = "gen a goodlogfilename"
+ databaseType = None
+ # Extract the data using MPI
+ X, Y = Dataset.getMonoviewShared(path, name, viewName)
- logfile = directory + logfilename
- if os.path.isfile(logfile + ".log"):
+ # Init log
+ logFileName = time.strftime("%Y_%m_%d-%H:%M:%S") + "-" + name + "-"+ viewName +"-" + classifierName +'-LOG'
+ if not os.path.exists(os.path.dirname(directory + logFileName)):
+ try:
+ os.makedirs(os.path.dirname(directory + logFileName))
+ except OSError as exc:
+ if exc.errno != errno.EEXIST:
+ raise
+ logFile = directory + logFileName
+ if os.path.isfile(logFile + ".log"):
for i in range(1, 20):
- testFileName = logfilename + "-" + str(i) + ".log"
- if not os.path.isfile(directory + testFileName):
- logfile = directory + testFileName
+ testFileName = logFileName + "-" + str(i) + ".log"
+ if not (os.path.isfile(directory + testFileName)):
+ logFile = directory + testFileName
break
else:
- logfile += ".log"
-
- logging.basicConfig(format='%(asctime)s %(levelname)s: %(message)s', filename=logfile, level=logging.DEBUG,
+ logFile += ".log"
+ logging.basicConfig(format='%(asctime)s %(levelname)s: %(message)s', filename=logFile, level=logging.DEBUG,
filemode='w')
-
if args.log:
logging.getLogger().addHandler(logging.StreamHandler())
-
+ # Computing on multiple cores
res = ExecMonoview(directory, X, Y, name, labelsNames, classificationIndices, KFolds, nbCores, databaseType, path,
randomState, hyperParamSearch=hyperParamSearch,
metrics=metrics, nIter=nIter, **kwargs)
with open(directory + "res.pickle", "wb") as handle:
- pickle.dump(randomState, handle)
+ pickle.dump(res, handle)
# Pickle the res in a file to be reused.
diff --git a/Code/MonoMultiViewClassifiers/utils/Dataset.py b/Code/MonoMultiViewClassifiers/utils/Dataset.py
index 738136b6453a0395535745936c6d97b6a8709181..c2ab4805eed7d0187625ae58d8d782bc1d5c0592 100644
--- a/Code/MonoMultiViewClassifiers/utils/Dataset.py
+++ b/Code/MonoMultiViewClassifiers/utils/Dataset.py
@@ -2,7 +2,7 @@ import logging
import os
import select
import sys
-
+import h5py
import numpy as np
from scipy import sparse
@@ -114,3 +114,10 @@ def input_(timeout=15):
else:
return "y"
+
+def getMonoviewShared(path, name, viewName, labelsNames, classificationIndices):
+ """ATM is not used with shared memory, but soon :)"""
+ HDF5_dataset_file = h5py.File(path + name + ".hdf5", "w")
+ X = HDF5_dataset_file.get(viewName).value
+ Y = HDF5_dataset_file.get("Labels").value
+ return X, Y
\ No newline at end of file
diff --git a/Code/MonoMultiViewClassifiers/utils/execution.py b/Code/MonoMultiViewClassifiers/utils/execution.py
index e6cf110737b9ca98b8710ae34d8e1b6a8ee5b0b4..25ae201ffe33260a3d7ad1160a489c6afc68b790 100644
--- a/Code/MonoMultiViewClassifiers/utils/execution.py
+++ b/Code/MonoMultiViewClassifiers/utils/execution.py
@@ -76,7 +76,7 @@ def parseTheArgs(arguments):
, default=[''])
groupClass.add_argument('--CL_metric_princ', metavar='STRING', action='store',
help='Determine which metric to use for randomSearch and optimization', default="f1_score")
- groupClass.add_argument('--CL_GS_iter', metavar='INT', action='store',
+ groupClass.add_argument('--CL_HPS_iter', metavar='INT', action='store',
help='Determine how many hyper parameters optimization tests to do', type=int, default=2)
groupClass.add_argument('--CL_HPS_type', metavar='STRING', action='store',
help='Determine which hyperparamter search function use', default="randomizedSearch")
@@ -239,7 +239,7 @@ def initRandomState(randomStateArg, directory):
def initLogFile(args):
"""Used to init the directory where the preds will be stored and the log file"""
resultDirectory = "../Results/" + args.name + "/started_" + time.strftime("%Y_%m_%d-%H_%M") + "/"
- logFileName = time.strftime("%Y%m%d-%H%M%S") + "-" + ''.join(args.CL_type) + "-" + "_".join(
+ logFileName = time.strftime("%Y_%m_%d-%H:%M:%S") + "-" + ''.join(args.CL_type) + "-" + "_".join(
args.views) + "-" + args.name + "-LOG"
if not os.path.exists(os.path.dirname(resultDirectory + logFileName)):
try: