def splitDataset(data, name, fileCategory):
# uses slicing, one of the most useful and least-well-known features of scipy sparse matrices
# you pass in a list of row indices you want to keep, and it will create a sliced copy that includes only those rows
# slicing also works on column indices
# callout to the person who first opened my eyes to them:
# http://stackoverflow.com/questions/13352280/slicing-sparse-matrices-in-scipy-which-types-work-best
# if this "sparse" matrix only has a single value for each row, we have to treat it as a column matrix, and slice it accordingly
# this is the case for our idColumn, and frequently our y values as well.
if data.shape[0] == 1:
validation = data[:,validationIndices]
trainingData = data[:,trainingIndices]
else:
validation = data[validationIndices,:]
trainingData = data[trainingIndices,:]
# ntpath theoretically works really well across systems
name = ntpath.basename(name)
# remove the file extension
name = name[0:-4]
validationFile = path.join(outputDirectory, name + 'validationData.npz')
trainingDataFile = path.join(outputDirectory, name + 'trainingData.npz')
save_sparse_csr(trainingDataFile, trainingData)
save_sparse_csr(validationFile, validation)
# send the file names back to the parent process, where we aggregate and save them
fileNameDict = {
fileCategory + 'trainingData': trainingDataFile,
fileCategory + 'validationData': validationFile
}
messageParent(fileNameDict, 'splitFileNames')
def writeDataDense(X, args, headerRow, nn):
# grab the name of the training and testing files from the full path to those datasets
trainingFileName = args['trainingPrettyName'] + '.csv'
testingFileName = args['testingPrettyName'] + args[
'trainingPrettyName'] + '.csv'
if (nn):
trainingFileName = 'nn_' + trainingFileName
testingFileName = 'nn_' + testingFileName
# save the file names into variables- we will use them to create the file and in the fileNames hash messaged out to the parent.
X_train = path.join(args['outputFolder'], 'X_train_' + trainingFileName)
X_test = path.join(args['outputFolder'], 'X_test_' + testingFileName)
with open(X_train, 'w+') as outputFile:
csvOutputFile = csv.writer(outputFile)
csvOutputFile.writerow(headerRow)
# grab only the rows that were part of our training file from the combined X dataset
csvOutputFile.writerows(X[0:args['trainingLength']])
with open(X_test, 'w+') as outputFile:
csvOutputFile = csv.writer(outputFile)
csvOutputFile.writerow(headerRow)
# grab the rest of the rows from our X dataset, which comprise the testing dataset
csvOutputFile.writerows(X[args['trainingLength']:])
if (nn):
fileNames = {'X_train_nn': X_train, 'X_test_nn': X_test}
else:
fileNames = {'X_train': X_train, 'X_test': X_test}
messageParent(fileNames, 'fileNames')
def splitDataset(data, name, fileCategory):
# uses slicing, one of the most useful and least-well-known features of scipy sparse matrices
# you pass in a list of row indices you want to keep, and it will create a sliced copy that includes only those rows
# slicing also works on column indices
# callout to the person who first opened my eyes to them:
# http://stackoverflow.com/questions/13352280/slicing-sparse-matrices-in-scipy-which-types-work-best
# if this "sparse" matrix only has a single value for each row, we have to treat it as a column matrix, and slice it accordingly
# this is the case for our idColumn, and frequently our y values as well.
if data.shape[0] == 1:
validation = data[:, validationIndices]
trainingData = data[:, trainingIndices]
else:
validation = data[validationIndices, :]
trainingData = data[trainingIndices, :]
# ntpath theoretically works really well across systems
name = ntpath.basename(name)
# remove the file extension
name = name[0:-4]
validationFile = path.join(outputDirectory, name + "validationData.npz")
trainingDataFile = path.join(outputDirectory, name + "trainingData.npz")
save_sparse_csr(trainingDataFile, trainingData)
save_sparse_csr(validationFile, validation)
# send the file names back to the parent process, where we aggregate and save them
fileNameDict = {fileCategory + "trainingData": trainingDataFile, fileCategory + "validationData": validationFile}
messageParent(fileNameDict, "splitFileNames")
def writeMetadata(y, idColumn, args, headerRow, validationSplitColumn, hasCustomValidationSplit):
# these are the file names (with full file paths) that we will be writing to
y_train = path.join(args["outputFolder"], "y_train_" + args["trainingPrettyName"] + ".npz")
id_train = path.join(args["outputFolder"], "id_train_" + args["trainingPrettyName"] + ".npz")
id_test = path.join(
args["outputFolder"], "id_test_" + args["testingPrettyName"] + args["trainingPrettyName"] + ".npz"
)
validation_split_column = path.join(
args["outputFolder"], "validation_split_column_" + args["trainingPrettyName"] + ".npz"
)
trainingLength = args["trainingLength"]
# convert all our data to np arrays, and break apart based on whether it's in the training data or not
idTrainData = np.array(idColumn[0:trainingLength])
idTestData = np.array(idColumn[trainingLength:])
y = np.array(y)
validationSplitColumnData = np.array(validationSplitColumn)
# if our values are not already stored as numbers, convert them to numbers
try:
ySparse = csr_matrix(y)
except:
yInt = [float(i) for i in y[0:trainingLength]]
ySparse = csr_matrix(yInt)
try:
idTrainSparse = csr_matrix(idTrainData)
except:
idTrainInt = [float(i) for i in idTrainData]
idTrainSparse = csr_matrix(idTrainInt)
try:
idTestSparse = csr_matrix(idTestData)
except:
idTestInt = [float(i) for i in idTestData]
idTestSparse = csr_matrix(idTestInt)
try:
validationSplitSparse = csr_matrix(validationSplitColumnData)
except:
validationSplitInt = [float(i) for i in validationSplitColumnData]
validationSplitSparse = csr_matrix(validationSplitInt)
save_sparse_csr(y_train, ySparse)
save_sparse_csr(id_train, idTrainSparse)
save_sparse_csr(id_test, idTestSparse)
save_sparse_csr(validation_split_column, validationSplitSparse)
fileNames = {
"y_train": y_train,
"id_train": id_train,
"id_test": id_test,
"idHeader": args["idHeader"],
"outputHeader": args["outputHeader"],
"validation_split_column": validation_split_column,
"hasCustomValidationSplit": hasCustomValidationSplit,
}
messageParent(fileNames, "fileNames")
def writeDataDense(X, args, headerRow, nn):
# grab the name of the training and testing files from the full path to those datasets
trainingFileName = args["trainingPrettyName"] + ".csv"
testingFileName = args["testingPrettyName"] + args["trainingPrettyName"] + ".csv"
if nn:
trainingFileName = "nn_" + trainingFileName
testingFileName = "nn_" + testingFileName
# save the file names into variables- we will use them to create the file and in the fileNames hash messaged out to the parent.
X_train = path.join(args["outputFolder"], "X_train_" + trainingFileName)
X_test = path.join(args["outputFolder"], "X_test_" + testingFileName)
with open(X_train, "w+") as outputFile:
csvOutputFile = csv.writer(outputFile)
csvOutputFile.writerow(headerRow)
# grab only the rows that were part of our training file from the combined X dataset
csvOutputFile.writerows(X[0 : args["trainingLength"]])
with open(X_test, "w+") as outputFile:
csvOutputFile = csv.writer(outputFile)
csvOutputFile.writerow(headerRow)
# grab the rest of the rows from our X dataset, which comprise the testing dataset
csvOutputFile.writerows(X[args["trainingLength"] :])
if nn:
fileNames = {"X_train_nn": X_train, "X_test_nn": X_test}
else:
fileNames = {"X_train": X_train, "X_test": X_test}
messageParent(fileNames, "fileNames")
def writeDataSparse(X, args, headerRow, nn):
# grab the name of the training and testing files from the full path to those datasets
trainingFileName = args['trainingPrettyName'] + '.npz'
testingFileName = args['testingPrettyName'] + args[
'trainingPrettyName'] + '.npz'
if type(nn) is not bool:
trainingFileName = 'nn_' + trainingFileName
testingFileName = 'nn_' + testingFileName
yFileName = 'y_train_' + 'nn_' + args['trainingPrettyName'] + '.npz'
y_train = path.join(args['outputFolder'], yFileName)
y = np.array(nn)
y = [float(i) for i in y]
# if our values are not already stored as numbers, convert them to numbers
try:
ySparse = csr_matrix(y)
printParent('successfully turned y into a sparse matrix!')
except:
yInt = [float(i) for i in y[0:trainingLength]]
ySparse = csr_matrix(yInt)
save_sparse_csr(y_train, ySparse)
# save the file names into variables- we will use them to create the file and in the fileNames hash messaged out to the parent.
X_train = path.join(args['outputFolder'], 'X_train_' + trainingFileName)
X_test = path.join(args['outputFolder'], 'X_test_' + testingFileName)
# scipy sparse matrices need a list of indices to slice
# http://stackoverflow.com/questions/13352280/slicing-sparse-matrices-in-scipy-which-types-work-best
trainRange = range(args['trainingLength'])
testRange = range(args['trainingLength'],
args['trainingLength'] + args['testingLength'])
save_sparse_csr(X_train, X[trainRange, :])
save_sparse_csr(X_test, X[testRange])
if type(nn) is not bool:
fileNames = {
'X_train_nn': X_train,
'X_test_nn': X_test,
'y_train_nn': y_train
}
else:
fileNames = {'X_train': X_train, 'X_test': X_test}
messageParent(fileNames, 'fileNames')
def writeMetadataDense(y, idColumn, args, headerRow):
# grab the name of the training and testing files from the full path to those datasets
# save the file names into variables- we will use them to create the file and in the fileNames hash messaged out to the parent.
y_train = path.join(args['outputFolder'],
'y_train_' + args['trainingPrettyName'] + '.csv')
id_train = path.join(args['outputFolder'],
'id_train_' + args['trainingPrettyName'] + '.csv')
id_test = path.join(
args['outputFolder'], 'id_test_' + args['testingPrettyName'] +
args['trainingPrettyName'] + '.csv')
with open(y_train, 'w+') as outputFile:
csvOutputFile = csv.writer(outputFile)
# write the pretty name for the header row to the output file
csvOutputFile.writerow([args['outputHeader']])
# grab only the rows that were part of our training file from the combined dataset
for rowIdx, row in enumerate(y):
if (rowIdx < args['trainingLength']):
csvOutputFile.writerow([row])
with open(id_train, 'w+') as outputFile:
csvOutputFile = csv.writer(outputFile)
# write the pretty name for the header row to the output file
csvOutputFile.writerow([args['idHeader']])
# grab only the rows that were part of our training file from the combined dataset
for rowIdx, row in enumerate(idColumn):
if (rowIdx < args['trainingLength']):
csvOutputFile.writerow([row])
with open(id_test, 'w+') as outputFile:
csvOutputFile = csv.writer(outputFile)
# write the pretty name for the header row to the output file
csvOutputFile.writerow([args['idHeader']])
# grab only the rows that were part of our testing file from the combined dataset
for rowIdx, row in enumerate(idColumn):
if (rowIdx >= args['trainingLength']):
csvOutputFile.writerow([row])
fileNames = {'y_train': y_train, 'id_train': id_train, 'id_test': id_test}
messageParent(fileNames, 'fileNames')
def writeMetadataDense(y, idColumn, args, headerRow):
# grab the name of the training and testing files from the full path to those datasets
# save the file names into variables- we will use them to create the file and in the fileNames hash messaged out to the parent.
y_train = path.join(args["outputFolder"], "y_train_" + args["trainingPrettyName"] + ".csv")
id_train = path.join(args["outputFolder"], "id_train_" + args["trainingPrettyName"] + ".csv")
id_test = path.join(
args["outputFolder"], "id_test_" + args["testingPrettyName"] + args["trainingPrettyName"] + ".csv"
)
with open(y_train, "w+") as outputFile:
csvOutputFile = csv.writer(outputFile)
# write the pretty name for the header row to the output file
csvOutputFile.writerow([args["outputHeader"]])
# grab only the rows that were part of our training file from the combined dataset
for rowIdx, row in enumerate(y):
if rowIdx < args["trainingLength"]:
csvOutputFile.writerow([row])
with open(id_train, "w+") as outputFile:
csvOutputFile = csv.writer(outputFile)
# write the pretty name for the header row to the output file
csvOutputFile.writerow([args["idHeader"]])
# grab only the rows that were part of our training file from the combined dataset
for rowIdx, row in enumerate(idColumn):
if rowIdx < args["trainingLength"]:
csvOutputFile.writerow([row])
with open(id_test, "w+") as outputFile:
csvOutputFile = csv.writer(outputFile)
# write the pretty name for the header row to the output file
csvOutputFile.writerow([args["idHeader"]])
# grab only the rows that were part of our testing file from the combined dataset
for rowIdx, row in enumerate(idColumn):
if rowIdx >= args["trainingLength"]:
csvOutputFile.writerow([row])
fileNames = {"y_train": y_train, "id_train": id_train, "id_test": id_test}
messageParent(fileNames, "fileNames")
def writeDataSparse(X, args, headerRow, nn):
# grab the name of the training and testing files from the full path to those datasets
trainingFileName = args["trainingPrettyName"] + ".npz"
testingFileName = args["testingPrettyName"] + args["trainingPrettyName"] + ".npz"
if type(nn) is not bool:
trainingFileName = "nn_" + trainingFileName
testingFileName = "nn_" + testingFileName
yFileName = "y_train_" + "nn_" + args["trainingPrettyName"] + ".npz"
y_train = path.join(args["outputFolder"], yFileName)
y = np.array(nn)
y = [float(i) for i in y]
# if our values are not already stored as numbers, convert them to numbers
try:
ySparse = csr_matrix(y)
printParent("successfully turned y into a sparse matrix!")
except:
yInt = [float(i) for i in y[0:trainingLength]]
ySparse = csr_matrix(yInt)
save_sparse_csr(y_train, ySparse)
# save the file names into variables- we will use them to create the file and in the fileNames hash messaged out to the parent.
X_train = path.join(args["outputFolder"], "X_train_" + trainingFileName)
X_test = path.join(args["outputFolder"], "X_test_" + testingFileName)
# scipy sparse matrices need a list of indices to slice
# http://stackoverflow.com/questions/13352280/slicing-sparse-matrices-in-scipy-which-types-work-best
trainRange = range(args["trainingLength"])
testRange = range(args["trainingLength"], args["trainingLength"] + args["testingLength"])
save_sparse_csr(X_train, X[trainRange, :])
save_sparse_csr(X_test, X[testRange])
if type(nn) is not bool:
fileNames = {"X_train_nn": X_train, "X_test_nn": X_test, "y_train_nn": y_train}
else:
fileNames = {"X_train": X_train, "X_test": X_test}
messageParent(fileNames, "fileNames")
def writeMetadata(y, idColumn, args, headerRow, validationSplitColumn,
hasCustomValidationSplit):
# these are the file names (with full file paths) that we will be writing to
y_train = path.join(args['outputFolder'],
'y_train_' + args['trainingPrettyName'] + '.npz')
id_train = path.join(args['outputFolder'],
'id_train_' + args['trainingPrettyName'] + '.npz')
id_test = path.join(
args['outputFolder'], 'id_test_' + args['testingPrettyName'] +
args['trainingPrettyName'] + '.npz')
validation_split_column = path.join(
args['outputFolder'],
'validation_split_column_' + args['trainingPrettyName'] + '.npz')
trainingLength = args['trainingLength']
# convert all our data to np arrays, and break apart based on whether it's in the training data or not
idTrainData = np.array(idColumn[0:trainingLength])
idTestData = np.array(idColumn[trainingLength:])
y = np.array(y)
validationSplitColumnData = np.array(validationSplitColumn)
# if our values are not already stored as numbers, convert them to numbers
try:
ySparse = csr_matrix(y)
except:
yInt = [float(i) for i in y[0:trainingLength]]
ySparse = csr_matrix(yInt)
try:
idTrainSparse = csr_matrix(idTrainData)
except:
idTrainInt = [float(i) for i in idTrainData]
idTrainSparse = csr_matrix(idTrainInt)
try:
idTestSparse = csr_matrix(idTestData)
except:
idTestInt = [float(i) for i in idTestData]
idTestSparse = csr_matrix(idTestInt)
try:
validationSplitSparse = csr_matrix(validationSplitColumnData)
except:
validationSplitInt = [float(i) for i in validationSplitColumnData]
validationSplitSparse = csr_matrix(validationSplitInt)
save_sparse_csr(y_train, ySparse)
save_sparse_csr(id_train, idTrainSparse)
save_sparse_csr(id_test, idTestSparse)
save_sparse_csr(validation_split_column, validationSplitSparse)
fileNames = {
'y_train': y_train,
'id_train': id_train,
'id_test': id_test,
'idHeader': args['idHeader'],
'outputHeader': args['outputHeader'],
'validation_split_column': validation_split_column,
'hasCustomValidationSplit': hasCustomValidationSplit
}
messageParent(fileNames, 'fileNames')
# when doing the cross-validated search, we potentially been holding out a significant portion of the dataset
# once we have found the best hyperparameters, train on the entire dataset
# we have already verified that this is the best set of hyperparameters using cross-validation
if X.shape[0] != X_train.shape[0] or extendedTraining:
longTrainClassifier.fit(X, y)
finishLongTrainTime = time.time()
printParent(classifierName + "'s training on the longer data set took:")
printParent( round((finishLongTrainTime - startLongTrainTime)/60, 1) )
longTrainClassifierScore = longTrainClassifier.score(X, y)
printParent(classifierName + "'s score against the larger training data set is:")
printParent(longTrainClassifierScore)
messageObj['longTrainScore'] = longTrainClassifierScore
# save our classifiers from the validationRound to a separate folder
if globalArgs['validationRound']:
classifierFolder = path.join(globalArgs['bestClassifiersFolder'], 'ensemblingAlgos', 'best' + classifierName)
else:
classifierFolder = path.join(globalArgs['bestClassifiersFolder'], 'best' + classifierName)
if not os.path.exists(classifierFolder):
os.makedirs(classifierFolder)
joblib.dump(longTrainClassifier, path.join(classifierFolder, 'best' + classifierName + '.pkl') )
messageParent(messageObj, 'trainingResults')
def format( X, y, idColumn, args ):
X = min_max_scaler.fit_transform( X ).tolist()
brainArr = []
for rowIndex, row in enumerate(X):
# brainJS expects a very particular format for each row:
# an object with input and output properties, each of which is an array
rowObj = {}
# we might need to wrap output in a list if the output is a single number, like we have.
rowObj['output'] = []
# if a y value exists for this rowIndex, grab it!
# otherwise, it's likely because the user did not pass in a column for y values for the output dataset, so we'll set it equal to the empty string instead
try:
# grab the output value from the y dataset saved earlier
yRow = y[ rowIndex ]
except:
yRow = ""
# designed to handle the possibility of multiple output columns
if( isinstance( yRow, list )):
rowObj['output'].extend( yRow )
else:
# the output value is expected to be a list, so if y values are not lists, then we need to wrap the y value in a list
rowObj['output'].append( yRow )
rowObj[ 'id' ] = idColumn[ rowIndex ]
for idx, val in enumerate(row):
# python saves floats in their binary representation, which gets written to file in scientific notation
# this scientific notation is not necessarily json compatible
# so we format these values as a string
# unfortunately, these strings will only have 6 decimal places after the 0. this should not be a major limitation, as that still allows 6 orders of magnitude of differentiation. it would be a rather surprising neural network who could draw meaningful distinctions between values that are 7 orders of magnitude different from the max value vs values that are 6 orders of magnitude different from the max value.
# in our case, they'll both just get saved as 0.
row[idx] = '{:f}'.format(val)
rowObj['input'] = row
brainArr.append( rowObj )
trainingFileName = path.split( args['trainingData'] )[ -1 ]
testingFileName = path.split( args['testingData'] )[ -1 ]
brainJS_train = path.join( args['outputFolder'], 'brainJS_train_' + trainingFileName )
brainJS_test = path.join( args['outputFolder'], 'brainJS_test_' + testingFileName )
with open( brainJS_train, 'w+') as outputFile:
csvOutputFile = csv.writer(outputFile)
for rowIndex, row in enumerate(brainArr):
if( rowIndex < args['trainingLength'] ):
# csvWriter.writerow expects each row to be a list
# since our rows are actually just dictionaries, we need to wrap it in a list each time so the writer knows this is a single row
csvOutputFile.writerow( [ json.dumps( row ) ] )
with open( brainJS_test, 'w+') as outputFile:
csvOutputFile = csv.writer(outputFile)
for rowIndex, row in enumerate(brainArr):
if( rowIndex >= args['trainingLength'] ):
csvOutputFile.writerow( [ row ] )
fileNames = {
'brainJS_train': brainJS_train,
'brainJS_test': brainJS_test
}
messageParent( fileNames, 'fileNames' )
return brainArr
