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Commit 8f1c9a3e authored by Baptiste Bauvin's avatar Baptiste Bauvin
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Refactored

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Code/MonoMultiViewClassifiers/Monoview/ExecClassifMonoView.py
+ 175
− 150
View file @ 8f1c9a3e
......@@ -24,29 +24,10 @@ __author__ = "Nikolas Huelsmann, Baptiste BAUVIN"
__status__ = "Prototype" # Production, Development, Prototype
# __date__ = 2016 - 03 - 25
def ExecMonoview_multicore(directory, name, labelsNames, classificationIndices, KFolds, datasetFileIndex, databaseType,
path, randomState, hyperParamSearch="randomizedSearch",
metrics=[["accuracy_score", None]], nIter=30, **args):
DATASET = h5py.File(path + name + str(datasetFileIndex) + ".hdf5", "r")
kwargs = args["args"]
views = [DATASET.get("View" + str(viewIndex)).attrs["name"] for viewIndex in
range(DATASET.get("Metadata").attrs["nbView"])]
neededViewIndex = views.index(kwargs["feat"])
X = DATASET.get("View" + str(neededViewIndex))
Y = DATASET.get("Labels").value
return ExecMonoview(directory, X, Y, name, labelsNames, classificationIndices, KFolds, 1, databaseType, path,
randomState, hyperParamSearch=hyperParamSearch,
metrics=metrics, nIter=nIter, **args)
def ExecMonoview(directory, X, Y, name, labelsNames, classificationIndices, KFolds, nbCores, databaseType, path,
randomState, hyperParamSearch="randomizedSearch",
metrics=[["accuracy_score", None]], nIter=30, **args):
logging.debug("Start:\t Loading data")
def initConstants(args, X, classificationIndices, labelsNames, name, directory):
try:
kwargs = args["args"]
except:
except KeyError:
kwargs = args
t_start = time.time()
feat = X.attrs["name"]
......@@ -56,6 +37,7 @@ def ExecMonoview(directory, X, Y, name, labelsNames, classificationIndices, KFol
labelsString = "-".join(labelsNames)
timestr = time.strftime("%Y%m%d-%H%M%S")
CL_type_string = CL_type
outputFileName = directory + "/" + CL_type_string + "/" + "/" + feat + "/" + timestr + "Results-" + CL_type_string + "-" + labelsString + \
'-learnRate' + str(learningRate) + '-' + name + "-" + feat + "-"
if not os.path.exists(os.path.dirname(outputFileName)):
......@@ -64,26 +46,18 @@ def ExecMonoview(directory, X, Y, name, labelsNames, classificationIndices, KFol
except OSError as exc:
if exc.errno != errno.EEXIST:
raise
logging.debug("Done:\t Loading data")
# Determine the Database to extract features
logging.debug("Info:\t Classification - Database:" + str(name) + " Feature:" + str(feat) + " train ratio:"
+ str(learningRate) + ", CrossValidation k-folds: " + str(KFolds.n_splits) + ", cores:"
+ str(nbCores) + ", algorithm : " + CL_type)
return kwargs, t_start, feat, CL_type, X, learningRate, labelsString, timestr, outputFileName
def initTrainTest(X, Y, classificationIndices):
trainIndices, testIndices = classificationIndices
# Calculate Train/Test data
logging.debug("Start:\t Determine Train/Test split")
X_train = extractSubset(X, trainIndices)
X_test = extractSubset(X, testIndices)
y_train = Y[trainIndices]
y_test = Y[testIndices]
return X_train, y_train, X_test, y_test
logging.debug("Info:\t Shape X_train:" + str(X_train.shape) + ", Length of y_train:" + str(len(y_train)))
logging.debug("Info:\t Shape X_test:" + str(X_test.shape) + ", Length of y_test:" + str(len(y_test)))
logging.debug("Done:\t Determine Train/Test split")
classifierModule = getattr(MonoviewClassifiers, CL_type)
def getKWARGS(classifierModule, hyperParamSearch, nIter, CL_type, X_train, y_train, randomState,
outputFileName, KFolds, nbCores, metrics, kwargs):
if hyperParamSearch != "None":
classifierHPSearch = getattr(classifierModule, hyperParamSearch)
logging.debug("Start:\t RandomSearch best settings with " + str(nIter) + " iterations for " + CL_type)
......@@ -92,42 +66,15 @@ def ExecMonoview(directory, X, Y, name, labelsNames, classificationIndices, KFol
clKWARGS = dict((str(index), desc) for index, desc in enumerate(cl_desc))
logging.debug("Done:\t RandomSearch best settings")
else:
clKWARGS = kwargs[kwargs["CL_type"] + "KWARGS"]
logging.debug("Start:\t Training")
cl_res = classifierModule.fit(X_train, y_train, randomState, NB_CORES=nbCores, **clKWARGS)
logging.debug("Done:\t Training")
logging.debug("Start:\t Predicting")
# Stats Result
y_train_pred = cl_res.predict(X_train)
y_test_pred = cl_res.predict(X_test)
full_labels = cl_res.predict(X)
logging.debug("Done:\t Predicting")
t_end = time.time() - t_start
logging.debug("Info:\t Time for training and predicting: " + str(t_end) + "[s]")
clKWARGS = kwargs[CL_type + "KWARGS"]
return clKWARGS
logging.debug("Start:\t Getting Results")
stringAnalysis, imagesAnalysis, metricsScores = execute(name, classificationIndices, KFolds, nbCores,
hyperParamSearch, metrics, nIter, feat, CL_type,
clKWARGS, labelsNames, X.shape,
y_train, y_train_pred, y_test, y_test_pred, t_end,
randomState, cl_res, outputFileName)
cl_desc = [value for key, value in sorted(clKWARGS.items())]
logging.debug("Done:\t Getting Results")
def saveResults(stringAnalysis, outputFileName, full_labels_pred, y_train_pred, y_train, imagesAnalysis):
logging.info(stringAnalysis)
# labelsString = "-".join(labelsNames)
# timestr = time.strftime("%Y%m%d-%H%M%S")
# CL_type_string = CL_type
# outputFileName = directory + "/"+CL_type_string+"/"+"/"+feat+"/"+timestr +"Results-" + CL_type_string + "-" + labelsString + \
# '-learnRate' + str(learningRate) + '-' + name + "-" + feat + "-"
outputTextFile = open(outputFileName + '.txt', 'w')
outputTextFile.write(stringAnalysis)
outputTextFile.close()
np.savetxt(outputFileName + "full_pred.csv", full_labels.astype(np.int16), delimiter=",")
np.savetxt(outputFileName + "full_pred.csv", full_labels_pred.astype(np.int16), delimiter=",")
np.savetxt(outputFileName + "train_pred.csv", y_train_pred.astype(np.int16), delimiter=",")
np.savetxt(outputFileName + "train_labels.csv", y_train.astype(np.int16), delimiter=",")
......@@ -142,92 +89,170 @@ def ExecMonoview(directory, X, Y, name, labelsNames, classificationIndices, KFol
imagesAnalysis[imageName].savefig(outputFileName + imageName + '.png')
logging.info("Done:\t Result Analysis")
def ExecMonoview_multicore(directory, name, labelsNames, classificationIndices, KFolds, datasetFileIndex, databaseType,
path, randomState, hyperParamSearch="randomizedSearch",
metrics=[["accuracy_score", None]], nIter=30, **args):
DATASET = h5py.File(path + name + str(datasetFileIndex) + ".hdf5", "r")
kwargs = args["args"]
views = [DATASET.get("View" + str(viewIndex)).attrs["name"] for viewIndex in
range(DATASET.get("Metadata").attrs["nbView"])]
neededViewIndex = views.index(kwargs["feat"])
X = DATASET.get("View" + str(neededViewIndex))
Y = DATASET.get("Labels").value
return ExecMonoview(directory, X, Y, name, labelsNames, classificationIndices, KFolds, 1, databaseType, path,
randomState, hyperParamSearch=hyperParamSearch,
metrics=metrics, nIter=nIter, **args)
def ExecMonoview(directory, X, Y, name, labelsNames, classificationIndices, KFolds, nbCores, databaseType, path,
randomState, hyperParamSearch="randomizedSearch",
metrics=[["accuracy_score", None]], nIter=30, **args):
logging.debug("Start:\t Loading data")
kwargs, \
t_start, \
feat, \
CL_type, \
X, \
learningRate, \
labelsString, \
timestr, \
outputFileName = initConstants(args, X, classificationIndices, labelsNames, name, directory)
logging.debug("Done:\t Loading data")
logging.debug("Info:\t Classification - Database:" + str(name) + " Feature:" + str(feat) + " train ratio:"
+ str(learningRate) + ", CrossValidation k-folds: " + str(KFolds.n_splits) + ", cores:"
+ str(nbCores) + ", algorithm : " + CL_type)
logging.debug("Start:\t Determine Train/Test split")
X_train, y_train, X_test, y_test = initTrainTest(X, Y, classificationIndices)
logging.debug("Info:\t Shape X_train:" + str(X_train.shape) + ", Length of y_train:" + str(len(y_train)))
logging.debug("Info:\t Shape X_test:" + str(X_test.shape) + ", Length of y_test:" + str(len(y_test)))
logging.debug("Done:\t Determine Train/Test split")
logging.debug("Start:\t Generate classifier args")
classifierModule = getattr(MonoviewClassifiers, CL_type)
clKWARGS = getKWARGS(classifierModule, hyperParamSearch,
nIter, CL_type, X_train, y_train,
randomState, outputFileName,
KFolds, nbCores, metrics, kwargs)
logging.debug("Done:\t Generate classifier args")
logging.debug("Start:\t Training")
cl_res = classifierModule.fit(X_train, y_train, randomState, NB_CORES=nbCores, **clKWARGS)
logging.debug("Done:\t Training")
logging.debug("Start:\t Predicting")
full_labels_pred = cl_res.predict(X)
y_train_pred = full_labels_pred[classificationIndices[0]]
y_test_pred = full_labels_pred[classificationIndices[1]]
logging.debug("Done:\t Predicting")
t_end = time.time() - t_start
logging.debug("Info:\t Time for training and predicting: " + str(t_end) + "[s]")
logging.debug("Start:\t Getting Results")
stringAnalysis, \
imagesAnalysis, \
metricsScores = execute(name, classificationIndices, KFolds, nbCores,
hyperParamSearch, metrics, nIter, feat, CL_type,
clKWARGS, labelsNames, X.shape,
y_train, y_train_pred, y_test, y_test_pred, t_end,
randomState, cl_res, outputFileName)
cl_desc = [value for key, value in sorted(clKWARGS.items())]
logging.debug("Done:\t Getting Results")
logging.debug("Start:\t Saving results")
saveResults(stringAnalysis, outputFileName, full_labels_pred, y_train_pred, y_train, imagesAnalysis)
logging.info("Done:\t Saving Results")
viewIndex = args["viewIndex"]
return viewIndex, [CL_type, cl_desc + [feat], metricsScores, full_labels, clKWARGS]
return viewIndex, [CL_type, cl_desc + [feat], metricsScores, full_labels_pred, clKWARGS]
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description='This methods permits to execute a multiclass classification with one single view. At this point the used classifier is a RandomForest. The GridSearch permits to vary the number of trees and CrossValidation with k-folds. The result will be a plot of the score per class and a CSV with the best classifier found by the GridSearch.',
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')
groupStandard.add_argument('--feat', metavar='STRING', action='store',
help='Name of Feature for Classification (default: %(default)s)', default='RGB')
groupStandard.add_argument('--pathF', metavar='STRING', action='store',
help='Path to the views (default: %(default)s)', default='Results-FeatExtr/')
groupStandard.add_argument('--fileCL', metavar='STRING', action='store',
help='Name of classLabels CSV-file (default: %(default)s)', default='classLabels.csv')
groupStandard.add_argument('--fileCLD', metavar='STRING', action='store',
help='Name of classLabels-Description CSV-file (default: %(default)s)',
default='classLabels-Description.csv')
groupStandard.add_argument('--fileFeat', metavar='STRING', action='store',
help='Name of feature CSV-file (default: %(default)s)', default='feature.csv')
groupClass = parser.add_argument_group('Classification arguments')
groupClass.add_argument('--CL_type', metavar='STRING', action='store', help='Classifier to use',
default="RandomForest")
groupClass.add_argument('--CL_CV', metavar='INT', action='store', help='Number of k-folds for CV', type=int,
default=10)
groupClass.add_argument('--CL_Cores', metavar='INT', action='store', help='Number of cores, -1 for all', type=int,
default=1)
groupClass.add_argument('--CL_split', metavar='FLOAT', action='store', help='Split ratio for train and test',
type=float, default=0.9)
groupClass.add_argument('--CL_metrics', metavar='STRING', action='store',
help='Determine which metrics to use, separate with ":" if multiple, if empty, considering all',
default='')
groupClassifier = parser.add_argument_group('Classifier Config')
groupClassifier.add_argument('--CL_config', metavar='STRING', nargs="+", action='store',
help='GridSearch: Determine the trees', default=['25:75:125:175'])
args = parser.parse_args()
classifierKWARGS = dict((key, value) for key, value in enumerate([arg.split(":") for arg in args.CL_config]))
### Main Programm
# Configure Logger
directory = os.path.dirname(os.path.abspath(__file__)) + "/Results-ClassMonoView/"
logfilename = datetime.datetime.now().strftime("%Y_%m_%d") + "-CMV-" + args.name + "-" + args.feat + "-LOG"
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
break
else:
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())
# Read the features
logging.debug("Start:\t Read " + args.type + " Files")
if args.type == ".csv":
X = np.genfromtxt(args.pathF + args.fileFeat, delimiter=';')
Y = np.genfromtxt(args.pathF + args.fileCL, delimiter=';')
elif args.type == ".hdf5":
dataset = h5py.File(args.pathF + args.name + ".hdf5", "r")
viewsDict = dict((dataset.get("View" + str(viewIndex)).attrs["name"], viewIndex) for viewIndex in
range(dataset.get("Metadata").attrs["nbView"]))
X = dataset["View" + str(viewsDict[args.feat])][...]
Y = dataset["Labels"][...]
logging.debug("Info:\t Shape of Feature:" + str(X.shape) + ", Length of classLabels vector:" + str(Y.shape))
logging.debug("Done:\t Read CSV Files")
arguments = {args.CL_type + "KWARGS": classifierKWARGS, "feat": args.feat, "fileFeat": args.fileFeat,
"fileCL": args.fileCL, "fileCLD": args.fileCLD, "CL_type": args.CL_type}
ExecMonoview(X, Y, args.name, args.CL_split, args.CL_CV, args.CL_Cores, args.type, args.pathF,
metrics=args.CL_metrics, **arguments)
pass
# parser = argparse.ArgumentParser(
# description='This methods permits to execute a multiclass classification with one single view. At this point the used classifier is a RandomForest. The GridSearch permits to vary the number of trees and CrossValidation with k-folds. The result will be a plot of the score per class and a CSV with the best classifier found by the GridSearch.',
# 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')
# groupStandard.add_argument('--feat', metavar='STRING', action='store',
# help='Name of Feature for Classification (default: %(default)s)', default='RGB')
# groupStandard.add_argument('--pathF', metavar='STRING', action='store',
# help='Path to the views (default: %(default)s)', default='Results-FeatExtr/')
# groupStandard.add_argument('--fileCL', metavar='STRING', action='store',
# help='Name of classLabels CSV-file (default: %(default)s)', default='classLabels.csv')
# groupStandard.add_argument('--fileCLD', metavar='STRING', action='store',
# help='Name of classLabels-Description CSV-file (default: %(default)s)',
# default='classLabels-Description.csv')
# groupStandard.add_argument('--fileFeat', metavar='STRING', action='store',
# help='Name of feature CSV-file (default: %(default)s)', default='feature.csv')
#
# groupClass = parser.add_argument_group('Classification arguments')
# groupClass.add_argument('--CL_type', metavar='STRING', action='store', help='Classifier to use',
# default="RandomForest")
# groupClass.add_argument('--CL_CV', metavar='INT', action='store', help='Number of k-folds for CV', type=int,
# default=10)
# groupClass.add_argument('--CL_Cores', metavar='INT', action='store', help='Number of cores, -1 for all', type=int,
# default=1)
# groupClass.add_argument('--CL_split', metavar='FLOAT', action='store', help='Split ratio for train and test',
# type=float, default=0.9)
# groupClass.add_argument('--CL_metrics', metavar='STRING', action='store',
# help='Determine which metrics to use, separate with ":" if multiple, if empty, considering all',
# default='')
#
# groupClassifier = parser.add_argument_group('Classifier Config')
# groupClassifier.add_argument('--CL_config', metavar='STRING', nargs="+", action='store',
# help='GridSearch: Determine the trees', default=['25:75:125:175'])
#
# args = parser.parse_args()
#
# classifierKWARGS = dict((key, value) for key, value in enumerate([arg.split(":") for arg in args.CL_config]))
# ### Main Programm
#
#
# # Configure Logger
# directory = os.path.dirname(os.path.abspath(__file__)) + "/Results-ClassMonoView/"
# logfilename = datetime.datetime.now().strftime("%Y_%m_%d") + "-CMV-" + args.name + "-" + args.feat + "-LOG"
# 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
# break
# else:
# 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())
#
# # Read the features
# logging.debug("Start:\t Read " + args.type + " Files")
#
# if args.type == ".csv":
# X = np.genfromtxt(args.pathF + args.fileFeat, delimiter=';')
# Y = np.genfromtxt(args.pathF + args.fileCL, delimiter=';')
# elif args.type == ".hdf5":
# dataset = h5py.File(args.pathF + args.name + ".hdf5", "r")
# viewsDict = dict((dataset.get("View" + str(viewIndex)).attrs["name"], viewIndex) for viewIndex in
# range(dataset.get("Metadata").attrs["nbView"]))
# X = dataset["View" + str(viewsDict[args.feat])][...]
# Y = dataset["Labels"][...]
#
# logging.debug("Info:\t Shape of Feature:" + str(X.shape) + ", Length of classLabels vector:" + str(Y.shape))
# logging.debug("Done:\t Read CSV Files")
#
# arguments = {args.CL_type + "KWARGS": classifierKWARGS, "feat": args.feat, "fileFeat": args.fileFeat,
# "fileCL": args.fileCL, "fileCLD": args.fileCLD, "CL_type": args.CL_type}
# ExecMonoview(X, Y, args.name, args.CL_split, args.CL_CV, args.CL_Cores, args.type, args.pathF,
# metrics=args.CL_metrics, **arguments)
Code/Versions.py
+ 14
− 44
View file @ 8f1c9a3e
#!/usr/bin/env python
""" Script to render versions of modules used """
# Import built-in modules
# Import 3rd party modules
# Import own modules
# Author-Info
__author__ = "Baptiste Bauvin"
__status__ = "Prototype" # Production, Development, Prototype
# __date__ = 2016 - 03 - 25
def testVersions():
isUpToDate = True
toInstall = []
try:
import sys
# print("Python-V.: " + sys.version)
except:
# print "Please install Python 2.7"
except ImportError:
raise
try:
import pyscm
except:
# print "Please install pyscm"
except ImportError:
isUpToDate = False
toInstall.append("pyscm")
try:
import numpy
# print("Numpy-V.: " + numpy.version.version)
except:
# print "Please install numpy module"
except ImportError:
isUpToDate = False
toInstall.append("numpy")
try:
import scipy
# print("Scipy-V.: " + scipy.__version__)
except:
# print "Please install scipy module"
except ImportError:
isUpToDate = False
toInstall.append("scipy")
try:
import matplotlib
# print("Matplotlib-V.: " + matplotlib.__version__)
except:
# print "Please install matplotlib module"
except ImportError:
isUpToDate = False
toInstall.append("matplotlib")
try:
import sklearn
# print("Sklearn-V.: " + sklearn.__version__)
except:
# print "Please install sklearn module"
except ImportError:
isUpToDate = False
toInstall.append("sklearn")
try:
import logging # To create Log-Files
# print("Logging: " + logging.__version__)
except:
# print "Please install logging module"
import logging
except ImportError:
isUpToDate = False
toInstall.append("logging")
try:
import joblib
# print("joblib: " + joblib.__version__)
except:
# print "Pease install joblib module"
except ImportError:
isUpToDate = False
toInstall.append("joblib")
try:
import argparse
# print("argparse: " + argparse.__version__)
except:
# print "Pease install argparse module"
except ImportError:
isUpToDate = False
toInstall.append("argparse")
try:
import h5py #
# print("h5py: " + h5py.__version__)
except:
# print "Pease install h5py module"
except ImportError:
isUpToDate = False
toInstall.append("h5py")
try:
import graphviz #
except:
except ImportError:
isUpToDate = False
toInstall.append("graphviz")
try:
import pickle #
except:
except ImportError:
isUpToDate = False
toInstall.append("pickle")
......
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