def main():
baseDir = '/Users/nkridler/Desktop/whale/' # Base directory
############################## PARAMETERS ###############################
dataDir = baseDir + 'data2/' # Data directory
oldDir = baseDir + 'data/' # Data directory
params = {
'NFFT': 256,
'Fs': 2000,
'noverlap': 192
} # Spectogram parameters
######################## BUILD A TrainData OBJECT #######################
train = fileio.TrainData(dataDir + 'train2.csv', dataDir + 'train2/')
old = fileio.TrainData(oldDir + 'train.csv', oldDir + 'train/')
###################### SET OUTPUT FILE NAME HERE ########################
trainOutFile = baseDir + 'workspace/trainMetricsBase.csv'
tmplFile = baseDir + 'moby2/templateBase.csv'
makeMetrics(tmplFile,
train=train,
old=old,
params=params,
trainOutFile=trainOutFile)
###################### SET OUTPUT FILE NAME HERE ########################
tmplFile = baseDir + 'moby2/manyMoreTemplates.csv'
trainOutFile = baseDir + 'moby2/trainMetricsMoreTemplates.csv'
makeMetrics(tmplFile,
train=train,
old=old,
params=params,
trainOutFile=trainOutFile)
def main():
# baseDir = '/home/nick/whale/' # Base directory
baseDir = globalConst.BASE_DIR
###################### SET OUTPUT FILE NAME HERE ########################
trainOutFile = baseDir+'workspace/trainMetrics.csv'
############################## PARAMETERS ###############################
dataDir = baseDir+'data/' # Data directory
templateDataDir = baseDir+'template_data/' # Data directory
params = {'NFFT':256, 'Fs':2000, 'noverlap':192} # Spectogram parameters
maxTime = 60 # Number of time slice metrics
######################## BUILD A TrainData OBJECT #######################
train = fileio.TrainData( dataDir+'train.csv',dataDir+'train/')
templateTrain = fileio.TrainData(templateDataDir+'train.csv',templateDataDir+'train/')
##################### BUILD A TemplateManager OBJECT ####################
tmplFile = baseDir+'moby/templateReduced.csv'
tmpl = templateManager.TemplateManager(fileName=tmplFile,
trainObj=templateTrain, params=params)
##################### PLOT the templates ####################
for ix in range(tmpl.size):
print "Plotting template:",ix
tmpl.PlotTemplates(index=ix)
def main():
###################### WORKING DIRECTORY ########################
baseDir = globalConst.BASE_DIR
###################### SET OUTPUT FILE NAME HERE ########################
testOutFile = baseDir + 'workspace/testMetrics.csv'
############################## PARAMETERS ###############################
dataDir = baseDir + 'data/' # Data directory
templateDataDir = baseDir + 'template_data/' # Data directory
params = {
'NFFT': 256,
'Fs': 2000,
'noverlap': 192
} # Spectogram parameters
maxTime = 60 # Number of time slice metrics
######################## BUILD A TestData OBJECT #######################
train = fileio.TrainData(dataDir + 'train.csv', dataDir + 'train/')
templateTrain = fileio.TrainData(templateDataDir + 'train.csv',
templateDataDir + 'train/')
test = fileio.TestData(dataDir + 'test/')
##################### BUILD A TemplateManager OBJECT ####################
tmplFile = baseDir + 'moby/templateReduced.csv'
tmpl = templateManager.TemplateManager(fileName=tmplFile,
trainObj=templateTrain,
params=params)
################## VERTICAL BARS FOR HIFREQ METRICS #####################
bar_ = np.zeros((12, 9), dtype='Float32')
bar1_ = np.zeros((12, 12), dtype='Float32')
bar2_ = np.zeros((12, 6), dtype='Float32')
bar_[:, 3:6] = 1.
bar1_[:, 4:8] = 1.
bar2_[:, 2:4] = 1.
########################### CREATE THE HEADER ###########################
outHdr = metrics.buildHeader(tmpl)
hL = []
####################### LOOP THROUGH THE FILE ###########################
for i in range(test.nTest):
printStatus(i)
P, freqs, bins = test.TestSample(i + 1, params=params)
out = metrics.computeMetrics(P, tmpl, bins, maxTime)
out += metrics.highFreqTemplate(P, bar_)
out += metrics.highFreqTemplate(P, bar1_)
out += metrics.highFreqTemplate(P, bar2_)
hL.append(out)
hL = np.array(hL)
########################## WRITE TO FILE ################################
file = open(testOutFile, 'w')
file.write(outHdr + "\n")
np.savetxt(file, hL, delimiter=',')
file.close()
def main():
#baseDir = '/home/nick/whale/'
baseDir = globalConst.BASE_DIR
dataDir = baseDir + 'data/'
corDir = baseDir + 'moby/'
# Open up the train file
train = fileio.TrainData(dataDir + 'train.csv', dataDir + 'train/')
t_ = pd.read_csv(corDir + 'trainPredictions.csv')
order32_ = orderMetric(t_.label, 32)
order64_ = orderMetric(t_.label, 64)
# Reorder the data
reorder32 = order32_.copy()
reorder64 = order64_.copy()
# NOTE disabled reordering below, because modified genTrainMetrics
# now writes in chronological order, not 'reordered' as below.
"""
k = 0
for i in xrange(train.numH1):
j = int(train.h1[i].split('.')[0][5:]) - 1
reorder32[k] = order32_[j]
reorder64[k] = order64_[j]
k += 1
for i in xrange(train.numH0):
j = int(train.h0[i].split('.')[0][5:]) - 1
reorder32[k] = order32_[j]
reorder64[k] = order64_[j]
k += 1
"""
writeToFile(reorder32, 'corr32.csv', outdir=corDir)
writeToFile(reorder64, 'corr64.csv', outdir=corDir)
"""
# There are 84503 samples
trainSize = globalConst.N_TRAIN
testSize = globalConst.N_TEST
size_ = trainSize + testSize
tt_ = size_*2. # since these are 2 second clips
xs_ = np.linspace(0,tt_,testSize)
xt_ = np.linspace(0,tt_,trainSize)
test32_ = np.interp(xs_,xt_,order32_)
test64_ = np.interp(xs_,xt_,order64_)
"""
t_ = pd.read_csv(corDir + 'testPredictions.csv')
if 'label' in t_.columns: # similar to train.csv
test32_ = orderMetric(t_.label, 32)
test64_ = orderMetric(t_.label, 64)
elif 'probability' in t_.columns: # similar to submissions file
test32_ = orderMetric(t_.probability, 32)
test64_ = orderMetric(t_.probability, 64)
else:
raise RuntimeError, "label or probability not found in testPredictions.csv"
writeToFile(test32_, 'testCorr32.csv', outdir=corDir)
writeToFile(test64_, 'testCorr64.csv', outdir=corDir)
def main():
baseDir = '/home/nick/whale/' # Base directory
###################### SET OUTPUT FILE NAME HERE ########################
trainOutFile = baseDir + 'workspace/trainMetrics.csv'
############################## PARAMETERS ###############################
dataDir = baseDir + 'data/' # Data directory
params = {
'NFFT': 256,
'Fs': 2000,
'noverlap': 192
} # Spectogram parameters
maxTime = 60 # Number of time slice metrics
######################## BUILD A TrainData OBJECT #######################
train = fileio.TrainData(dataDir + 'train.csv', dataDir + 'train/')
##################### BUILD A TemplateManager OBJECT ####################
tmplFile = baseDir + 'moby/templateReduced.csv'
tmpl = templateManager.TemplateManager(fileName=tmplFile,
trainObj=train,
params=params)
################## VERTICAL BARS FOR HIFREQ METRICS #####################
bar_ = np.zeros((12, 9), dtype='Float32')
bar1_ = np.zeros((12, 12), dtype='Float32')
bar2_ = np.zeros((12, 6), dtype='Float32')
bar_[:, 3:6] = 1.
bar1_[:, 4:8] = 1.
bar2_[:, 2:4] = 1.
########################### CREATE THE HEADER ###########################
outHdr = metrics.buildHeader(tmpl)
###################### LOOP THROUGH THE FILES ###########################
hL = []
for i in range(train.numH1):
P, freqs, bins = train.H1Sample(i, params=params)
out = metrics.computeMetrics(P, tmpl, bins, maxTime)
out += metrics.highFreqTemplate(P, bar_)
out += metrics.highFreqTemplate(P, bar1_)
out += metrics.highFreqTemplate(P, bar2_)
hL.append([1, i] + out)
for i in range(train.numH0):
P, freqs, bins = train.H0Sample(i, params=params)
out = metrics.computeMetrics(P, tmpl, bins, maxTime)
out += metrics.highFreqTemplate(P, bar_)
out += metrics.highFreqTemplate(P, bar1_)
out += metrics.highFreqTemplate(P, bar2_)
hL.append([0, i] + out)
hL = np.array(hL)
file = open(trainOutFile, 'w')
file.write("Truth,Index," + outHdr + "\n")
np.savetxt(file, hL, delimiter=',')
file.close()
def main():
baseDir = '/home/nick/whale/'
dataDir = baseDir + 'data/'
# Open up the train file
train = fileio.TrainData(dataDir + 'train.csv', dataDir + 'train/')
t_ = pd.read_csv(dataDir + 'train.csv')
order32_ = orderMetric(t_.label, 32)
order64_ = orderMetric(t_.label, 64)
# Reorder the data
reorder32 = order32_.copy()
reorder64 = order64_.copy()
k = 0
for i in xrange(train.numH1):
j = int(train.h1[i].split('.')[0][5:]) - 1
reorder32[k] = order32_[j]
reorder64[k] = order64_[j]
k += 1
for i in xrange(train.numH0):
j = int(train.h0[i].split('.')[0][5:]) - 1
reorder32[k] = order32_[j]
reorder64[k] = order64_[j]
k += 1
writeToFile(reorder32, 'corr32.csv')
writeToFile(reorder64, 'corr64.csv')
# There are 84503 samples
trainSize = 30000
testSize = 54503
size_ = trainSize + testSize
tt_ = size_ * 2. # since these are 2 second clips
xs_ = np.linspace(0, tt_, testSize)
xt_ = np.linspace(0, tt_, trainSize)
test32_ = np.interp(xs_, xt_, order32_)
test64_ = np.interp(xs_, xt_, order64_)
writeToFile(test32_, 'testCorr32.csv')
writeToFile(test64_, 'testCorr64.csv')
def create_dirs(Ntrain, Nval, Ntest, original_dataset_dir, base_dir):
#original_dataset_dir = 'C:\\svn\\dwatts\\dev\\datasets\\whale_data\\data'
#original_test_dataset_dir = 'C:\\svn\\dwatts\\dev\\datasets\\whale_data\\data\\test'
# smaller dataset
#base_dir = 'C:\\svn\\dwatts\\dev\\dl_with_python\\whale_small\\'
if os.path.exists(base_dir):
shutil.rmtree(base_dir)
os.mkdir(base_dir)
# define train, validationa and test splits
train_dir = os.path.join(base_dir, 'train')
os.mkdir(train_dir)
validation_dir = os.path.join(base_dir, 'validation')
os.mkdir(validation_dir)
test_dir = os.path.join(base_dir, 'test')
os.mkdir(test_dir)
# Right and non-right whale training recording
train_right_dir = os.path.join(train_dir, 'right')
os.mkdir(train_right_dir)
train_nonright_dir = os.path.join(train_dir, 'nonright')
os.mkdir(train_nonright_dir)
# Right and non-right whale validation recordings
validation_right_dir = os.path.join(validation_dir, 'right')
os.mkdir(validation_right_dir)
validation_nonright_dir = os.path.join(validation_dir, 'nonright')
os.mkdir(validation_nonright_dir)
# Right and non-right whale training recordings
test_right_dir = os.path.join(test_dir, 'right')
os.mkdir(test_right_dir)
test_nonright_dir = os.path.join(test_dir, 'nonright')
os.mkdir(test_nonright_dir)
# first work out which files are right/non-right and
# then copy smaller set to the whale_small dir
# build a train object
original_train_dir = os.path.join(original_dataset_dir, 'train')
train = fileio.TrainData(original_dataset_dir+'/train.csv',base_dir+'train')
# Copy Ntrain right whale recordings to train_whale_dir
train_right_fnames = [train.h1[i] for i in range(Ntrain) ]
for fname in train_right_fnames:
src = os.path.join(original_train_dir, fname)
dst = os.path.join(train_right_dir, fname)
shutil.copyfile(src, dst)
# Copy Ntrain non-right whale recordings to train_whale_dir
train_nonright_fnames = [train.h0[i] for i in range(Ntrain) ]
for fname in train_nonright_fnames:
src = os.path.join(original_train_dir, fname)
dst = os.path.join(train_nonright_dir, fname)
shutil.copyfile(src, dst)
# Copy next Nval right whale recordings to validation_whale_dir
val_right_fnames = [train.h1[i] for i in range(Ntrain, Ntrain+Nval) ]
for fname in val_right_fnames:
src = os.path.join(original_train_dir, fname)
dst = os.path.join(validation_right_dir, fname)
shutil.copyfile(src, dst)
# Copy next Nval non-right whale recordings to validation_whale_dir
val_nonright_fnames = [train.h0[i] for i in range(Ntrain, Ntrain+Nval) ]
for fname in val_nonright_fnames:
src = os.path.join(original_train_dir, fname)
dst = os.path.join(validation_nonright_dir, fname)
shutil.copyfile(src, dst)
# Copy next Ntest right whale recordings to test_whale_dir
test_right_fnames = [train.h1[i] for i in range(Ntrain+Nval, Ntrain+Nval+Ntest) ]
for fname in test_right_fnames:
src = os.path.join(original_train_dir, fname)
dst = os.path.join(test_right_dir, fname)
shutil.copyfile(src, dst)
# Copy next Ntest non-right whale recordings to test_whale_dir
test_nonright_fnames = [train.h0[i] for i in range(Ntrain+Nval, Ntrain+Nval+Ntest) ]
for fname in test_nonright_fnames:
src = os.path.join(original_train_dir, fname)
dst = os.path.join(test_nonright_dir, fname)
shutil.copyfile(src, dst)
# Create dictionary of training, validation and test ids/labels
partition = { 'train': train_right_fnames + train_nonright_fnames,
'validation': val_right_fnames + val_nonright_fnames,
'test': test_right_fnames + test_nonright_fnames}
labels_train_right = {key: 1 for key in train_right_fnames}
labels_train_nonright = {key: 0 for key in train_nonright_fnames}
labels_val_right = {key: 1 for key in val_right_fnames}
labels_val_nonright = {key: 0 for key in val_nonright_fnames}
labels_test_right = {key: 1 for key in test_right_fnames}
labels_test_nonright = {key: 0 for key in test_nonright_fnames}
labels = {**labels_train_right,
**labels_train_nonright,
**labels_val_right,
**labels_val_nonright,
**labels_test_right,
**labels_test_nonright}
return partition, labels
def main():
baseDir = globalConst.BASE_DIR
###################### SET OUTPUT FILE NAME HERE ########################
trainOutFile = baseDir+'workspace/trainMetrics.csv'
############################## PARAMETERS ###############################
dataDir = baseDir+'data/' # Data directory
templateDataDir = baseDir+'template_data/' # Data directory
params = {'NFFT':256, 'Fs':2000, 'noverlap':192} # Spectogram parameters
maxTime = 60 # Number of time slice metrics
######################## BUILD A TrainData OBJECT #######################
train = fileio.TrainData( dataDir+'train.csv',dataDir+'train/')
templateTrain = fileio.TrainData(templateDataDir+'train.csv',templateDataDir+'train/')
##################### BUILD A TemplateManager OBJECT ####################
tmplFile = baseDir+'moby/templateReduced.csv'
tmpl = templateManager.TemplateManager(fileName=tmplFile,
trainObj=templateTrain, params=params)
################## VERTICAL BARS FOR HIFREQ METRICS #####################
bar_ = np.zeros((12,9),dtype='Float32')
bar1_ = np.zeros((12,12),dtype='Float32')
bar2_ = np.zeros((12,6),dtype='Float32')
bar_[:,3:6] = 1.
bar1_[:,4:8] = 1.
bar2_[:,2:4] = 1.
########################### CREATE THE HEADER ###########################
outHdr = metrics.buildHeader(tmpl)
###################### LOOP THROUGH THE FILES ###########################
hL = []
print "\nprocessing whale clips"
for i in range(train.numH1):
printStatus(i)
clip_filename = train.h1[i]
fid = fileID(clip_filename)
P, freqs, bins = train.H1Sample(i,params=params)
out = metrics.computeMetrics(P, tmpl, bins, maxTime)
out += metrics.highFreqTemplate(P, bar_)
out += metrics.highFreqTemplate(P, bar1_)
out += metrics.highFreqTemplate(P, bar2_)
#hL.append([1, i] + out)
hL.append( (fid, [1,i]+out) ) # NOTE added fid for sorting below
print "\nprocessing non-whale clips"
for i in range(train.numH0):
printStatus(i)
clip_filename = train.h0[i]
fid = fileID(clip_filename)
P, freqs, bins = train.H0Sample(i,params=params)
out = metrics.computeMetrics(P, tmpl, bins, maxTime)
out += metrics.highFreqTemplate(P, bar_)
out += metrics.highFreqTemplate(P, bar1_)
out += metrics.highFreqTemplate(P, bar2_)
#hL.append([0, i] + out)
hL.append( (fid, [0,i]+out) ) # NOTE added fid for sorting below
# NOTE Sort by fileID so metrics file lines are in
# sequential order (train1.aiff, train2.aiff....)
print "\nsorting metrics by fileID"
hL_sort = np.array( [metric_lst for fid, metric_lst in sorted(hL)] )
print "writing metrics to:", trainOutFile
file = open(trainOutFile,'w')
file.write("Truth,Index,"+outHdr+"\n")
np.savetxt(file,hL_sort,delimiter=',')
file.close()