def _loadData(self):
""" Load data, returning a dict of text data objects.
Keys are line numbers at which the text appears in the CSV file.
"""
return readCSV(
self.dataPath,
numLabels=0) # 0 to train models in unsupervised fashion
def generateDataFile(inputData, outputDataDir, type):
"""
Generates a samples data file with all of the words in the sample
reversed.
@param (str) Path to input original samples data file
@param (TextPreprocess) Processor to perform some text cleanup.
"""
if not os.path.exists(outputDataDir):
os.makedirs(outputDataDir)
fileName = string.join(inputData.split(".")[:-1], ".") + "_" + type + ".csv"
dataDict = readCSV(inputData, numLabels=3)
headers = ["QID", "QuestionText", "Response", "Classification1",
"Classification2", "Classification3"]
data = []
for sample in dataDict.items():
response = sample[1][0]
tokens = response.split(" ")
tokens = cleanTokens(tokens)
response = None
if type == "scrambled":
random.shuffle(tokens)
response = " ".join(tokens)
elif type == "reversed":
response = " ".join(tokens[::-1])
dataToWrite = [sample[0], "", response]
dataToWrite.extend(sample[1][1])
data.append(dataToWrite)
writeCSV(data, headers, os.path.join(outputDataDir, fileName))
def split(self,
filePath=None,
numLabels=3,
textPreprocess=False,
dataDict=None,
abbrCSV="",
contrCSV="",
ignoreCommon=100,
removeStrings="[identifier deleted]",
correctSpell=True):
"""
Split all the comments in a file into tokens, w/ or w/o preprocessing.
Specifying both filePath and dataDict will prefer filePath.
@param filePath (str) Path to csv file
@param dataDict (dict) Data as returned by readCSV()
@param numLabels (int) Number of columns of category labels.
@param textPreprocess (bool) True will preprocess text while tokenizing.
@return dataDict (dict) Data as read in from filePath.
Please see TextPreprocess tokenize() for the other parameters; they're only
used when textPreprocess is True.
"""
if filePath:
dataDict = readCSV(filePath, numLabels=numLabels)
if dataDict is None:
raise Exception("No data given, or could not read CSV.")
preprocessor = TextPreprocess(abbrCSV=abbrCSV, contrCSV=contrCSV)
expandAbbr = (abbrCSV != "")
expandContr = (contrCSV != "")
for recordNum, record in dataDict.iteritems():
comment, categories, uniqueID = record
# Convert the categories to a string of their IDs
categories = string.join(
[str(self.categoryToId[c]) for c in categories])
if textPreprocess:
tokens, _ = preprocessor.tokenizeAndFilter(
comment, ignoreCommon, removeStrings, correctSpell,
expandAbbr, expandContr)
else:
tokens = preprocessor.tokenize(comment)
data = self._formatSequence(tokens, categories, recordNum,
uniqueID)
self.records.append(data)
self.sequenceCount += 1
return dataDict
def split(
self,
filePath=None,
numLabels=3,
textPreprocess=False,
dataDict=None,
abbrCSV="",
contrCSV="",
ignoreCommon=100,
removeStrings="[identifier deleted]",
correctSpell=True,
):
"""
Split all the comments in a file into tokens, w/ or w/o preprocessing.
Specifying both filePath and dataDict will prefer filePath.
@param filePath (str) Path to csv file
@param dataDict (dict) Data as returned by readCSV()
@param numLabels (int) Number of columns of category labels.
@param textPreprocess (bool) True will preprocess text while tokenizing.
@return dataDict (dict) Data as read in from filePath.
Please see TextPreprocess tokenize() for the other parameters; they're only
used when textPreprocess is True.
"""
if filePath:
dataDict = readCSV(filePath, numLabels=numLabels)
if dataDict is None:
raise Exception("No data given, or could not read CSV.")
preprocessor = TextPreprocess(abbrCSV=abbrCSV, contrCSV=contrCSV)
expandAbbr = abbrCSV != ""
expandContr = contrCSV != ""
for recordNum, record in dataDict.iteritems():
comment, categories, uniqueID = record
# Convert the categories to a string of their IDs
categories = string.join([str(self.categoryToId[c]) for c in categories])
if textPreprocess:
tokens, _ = preprocessor.tokenizeAndFilter(
comment, ignoreCommon, removeStrings, correctSpell, expandAbbr, expandContr
)
else:
tokens = preprocessor.tokenize(comment)
data = self._formatSequence(tokens, categories, recordNum, uniqueID)
self.records.append(data)
self.sequenceCount += 1
return dataDict
def run(args):
if args.loadPath:
model = loadModel(args.loadPath)
elif args.modelName == "HTMNetwork":
networkConfig = loadJSON(_NETWORK_JSON)
print "Creating the network model..."
model = _createModel(modelName=args.modelName,
savePath=args.savePath,
networkConfig=networkConfig,
inputFilePath=args.dataPath,
prepData=True,
numLabels=0,
stripCats=True,
retinaScaling=1.0)
numRecords = sum(
model.networkDataGen.getNumberOfTokens(model.networkDataPath))
print "Training the model..."
model.trainModel(
iterations=numRecords) # TODO: switch to using trainNetwork
else:
model = _createModel(modelName=args.modelName, savePath=args.savePath)
dataDict = readCSV(args.dataPath, numLabels=0)
print "Preparing and encoding the data..."
samples = model.prepData(dataDict, args.preprocess)
patterns = model.encodeSamples(samples)
print "Training the model..."
for i in xrange(len(samples)):
model.trainModel(i)
if args.savePath:
model.saveModel()
# Query the model.
printTemplate = "{0:<10}|{1:<30}"
while 1 < 2:
print "Now we query the model for samples (quit with 'q')..."
input = raw_input("Enter a query: ")
if input == "q": break
sortedDistances = model.queryModel(input, args.preprocess)
print printTemplate.format("Sample ID", "Distance from query")
for sID, dist in sortedDistances:
print printTemplate.format(sID, dist)
return
def readData(args):
"""
Read data file and print out some statistics
Return a training set, test set, labelId to text map, and docId to categories
map.
Return format:
trainingData = [
["fox eats carrots", [0], docId],
["fox eats peppers", [0], docId],
["carrots are healthy", [1], docId],
["peppers is healthy", [1], docId],
]
"""
# Read data
dataDict = readCSV(args.dataPath, 1)
labelRefs, dataDict = mapLabelRefs(dataDict)
categoriesInOrderOfInterest = [8, 9, 10, 5, 6, 11, 13][0 : args.numLabels]
# Select data based on categories of interest. Shift category indices down
# so we go from 0 to numLabels-1
trainingData = []
counts = numpy.zeros(len(labelRefs))
for document in dataDict.itervalues():
docId = document[2]
oldCategoryIndex = document[1][0]
if oldCategoryIndex in categoriesInOrderOfInterest:
newIndex = categoriesInOrderOfInterest.index(oldCategoryIndex)
trainingData.append([document[0], [newIndex], docId])
counts[newIndex] += 1
# For each document, figure out which categories it belongs to
# Include the shifted category index
documentCategoryMap = {}
for doc in dataDict.iteritems():
docId = doc[1][2]
oldCategoryIndex = doc[1][1][0]
if oldCategoryIndex in categoriesInOrderOfInterest:
newIndex = categoriesInOrderOfInterest.index(oldCategoryIndex)
v = documentCategoryMap.get(docId, [])
v.append(newIndex)
documentCategoryMap[docId] = v
labelRefs = [labelRefs[i] for i in categoriesInOrderOfInterest]
print "Total number of unique documents", len(documentCategoryMap)
print "Category counts: ", counts
print "Categories in training/test data:", labelRefs
return trainingData, trainingData, labelRefs, documentCategoryMap
def __init__(self, dataPath="data.csv"):
"""
initializes imbu model with given sample data
:param str dataPath: Path to sample data file.
Must be a CSV file having 'ID and 'Sample' columns
"""
g_log.info("Initialize imbu model")
csvdata = readCSV(dataPath, numLabels=0)
self.samples = OrderedDict()
for dataID, text in csvdata.iteritems():
self.samples[dataID] = text
self.models = {modelName: createModel(modelName, dataPath, csvdata)
for modelName, modelFactory in _MODEL_MAPPING.iteritems()}
def run(args):
if args.loadPath:
model = loadModel(args.loadPath)
elif args.modelName == "HTMNetwork":
networkConfig = loadJSON(_NETWORK_JSON)
print "Creating the network model..."
model = _createModel(modelName=args.modelName, savePath=args.savePath,
networkConfig=networkConfig, inputFilePath=args.dataPath, prepData=True,
numLabels=0, stripCats=True, retinaScaling=1.0)
numRecords = sum(
model.networkDataGen.getNumberOfTokens(model.networkDataPath))
print "Training the model..."
model.trainModel(iterations=numRecords) # TODO: switch to using trainNetwork
else:
model = _createModel(modelName=args.modelName, savePath=args.savePath)
dataDict = readCSV(args.dataPath, numLabels=0)
print "Preparing and encoding the data..."
samples = model.prepData(dataDict, args.preprocess)
patterns = model.encodeSamples(samples)
print "Training the model..."
for i in xrange(len(samples)):
model.trainModel(i)
if args.savePath:
model.saveModel()
# Query the model.
printTemplate = "{0:<10}|{1:<30}"
while 1<2:
print "Now we query the model for samples (quit with 'q')..."
input = raw_input("Enter a query: ")
if input == "q": break
sortedDistances = model.queryModel(input, args.preprocess)
print printTemplate.format("Sample ID", "Distance from query")
for sID, dist in sortedDistances:
print printTemplate.format(sID, dist)
return
def __init__(self, dataPath="data.csv"):
"""
initializes imbu model with given sample data
:param str dataPath: Path to sample data file.
Must be a CSV file having 'ID and 'Sample' columns
"""
g_log.info("Initialize imbu model")
csvdata = readCSV(dataPath, numLabels=0)
self.samples = OrderedDict()
for dataID, text in csvdata.iteritems():
self.samples[dataID] = text
self.models = {
modelName: createModel(modelName, dataPath, csvdata)
for modelName, modelFactory in _MODEL_MAPPING.iteritems()
}
def validateExperiment(self, expectationFilePath):
"""Returns accuracy of predicted labels against expected labels."""
dataDict = readCSV(expectationFilePath, numLabels=self.numClasses)
accuracies = numpy.zeros((len(self.results)))
for i, trial in enumerate(self.results):
for j, predictionList in enumerate(trial[0]):
predictions = [self.labelRefs[p] for p in predictionList]
if predictions == []:
predictions = ["(none)"]
expected = dataDict.items()[j+self.trainSizes[i]][1]
accuracies[i] += (float(len(set(predictions) & set(expected[1])))
/ len(expected[1]))
accuracies[i] = accuracies[i] / len(trial[0])
return accuracies
def getExpectedClassifications(runner, expectationFilePath):
"""
Return a list of the labels predicted by runner and a list of expected
labels from the expected classifications file path.
"""
dataDict = readCSV(expectationFilePath, numLabels=3)
expectedClasses = []
resultClasses = []
for trial, trialResults in enumerate(runner.results):
for i, predictionList in enumerate(trialResults[0]):
predictions = [runner.labelRefs[p] for p in predictionList]
if predictions == []:
predictions = ["(none)"]
resultClasses.append(predictions)
expectedClasses.append(dataDict.items()[i+runner.trainSizes[trial]][1][1])
return expectedClasses, resultClasses
def validateExperiment(self, expectationFilePath):
"""Returns accuracy of predicted labels against expected labels."""
dataDict = readCSV(expectationFilePath, numLabels=self.numClasses)
accuracies = numpy.zeros((len(self.results)))
for i, trial in enumerate(self.results):
for j, predictionList in enumerate(trial[0]):
predictions = [self.labelRefs[p] for p in predictionList]
if predictions == []:
predictions = ["(none)"]
expected = dataDict.items()[j + self.trainSizes[i]][1]
accuracies[i] += (
float(len(set(predictions) & set(expected[1]))) /
len(expected[1]))
accuracies[i] = accuracies[i] / len(trial[0])
return accuracies
def getExpectedClassifications(runner, expectationFilePath):
"""
Return a list of the labels predicted by runner and a list of expected
labels from the expected classifications file path.
"""
dataDict = readCSV(expectationFilePath, numLabels=3)
expectedClasses = []
resultClasses = []
for trial, trialResults in enumerate(runner.results):
for i, predictionList in enumerate(trialResults[0]):
predictions = [runner.labelRefs[p] for p in predictionList]
if predictions == []:
predictions = ["(none)"]
resultClasses.append(predictions)
expectedClasses.append(
dataDict.items()[i + runner.trainSizes[trial]][1][1])
return expectedClasses, resultClasses
def setupData(self, preprocess=False):
"""
Get the data from CSV and preprocess if specified. The call to readCSV()
assumes a specific CSV format, detailed in its docstring.
@param preprocess (bool) Whether or not to preprocess the data when
reading in samples.
"""
self.dataDict = readCSV(self.dataPath, numLabels=self.numClasses)
if self.experimentType == "incremental":
# stop now if the data won't work for the specified experiment
if (not isinstance(self.trainSizes, list) or not
all([0 <= size <= len(self.dataDict) for size in self.trainSizes])):
raise ValueError("Invalid size(s) for training set(s).")
self.labelRefs, self.dataDict = mapLabelRefs(self.dataDict)
self.samples = self.model.prepData(self.dataDict, preprocess)
if self.verbosity > 1:
for i, s in self.samples.iteritems():
print i, s
def generateDataFile(inputData, outputDataDir, type):
"""
Generates a samples data file with all of the words in the sample
reversed.
@param (str) Path to input original samples data file
@param (TextPreprocess) Processor to perform some text cleanup.
"""
if not os.path.exists(outputDataDir):
os.makedirs(outputDataDir)
fileName = string.join(inputData.split(".")[:-1],
".") + "_" + type + ".csv"
dataDict = readCSV(inputData, numLabels=3)
headers = [
"QID", "QuestionText", "Response", "Classification1",
"Classification2", "Classification3"
]
data = []
for sample in dataDict.items():
response = sample[1][0]
tokens = response.split(" ")
tokens = cleanTokens(tokens)
response = None
if type == "scrambled":
random.shuffle(tokens)
response = " ".join(tokens)
elif type == "reversed":
response = " ".join(tokens[::-1])
dataToWrite = [sample[0], "", response]
dataToWrite.extend(sample[1][1])
data.append(dataToWrite)
writeCSV(data, headers, os.path.join(outputDataDir, fileName))
def setupData(self, preprocess=False):
"""
Get the data from CSV and preprocess if specified. The call to readCSV()
assumes a specific CSV format, detailed in its docstring.
@param preprocess (bool) Whether or not to preprocess the data when
reading in samples.
"""
self.dataDict = readCSV(self.dataPath, numLabels=self.numClasses)
if self.experimentType == "incremental":
# stop now if the data won't work for the specified experiment
if (not isinstance(self.trainSizes, list) or not all(
[0 <= size <= len(self.dataDict)
for size in self.trainSizes])):
raise ValueError("Invalid size(s) for training set(s).")
self._mapLabelRefs()
self.samples = self.model.prepData(self.dataDict, preprocess)
if self.verbosity > 1:
for i, s in self.samples.iteritems():
print i, s
try:
with pkg_resources.resource_filename(__name__, jsonPath) as f:
return json.load(f)
except IOError as e:
print "Could not find JSON at '{}'.".format(jsonPath)
raise e
# Indicates global ready status of all models. g_ready will transition to
# True when all models have been created, trained, and are ready to handle
# requests
g_ready = False
g_models = {}
g_csvdata = (
readCSV(
os.getenv("IMBU_DATA",
pkg_resources.resource_filename(__name__, "data.csv")),
numLabels=0)
)
# Get data and order by unique ID
g_samples = OrderedDict(
(sample[2], sample[0]) for sample in g_csvdata.values()
)
def createModel(modelName, modelFactory):
"""Return an instantiated model."""
global g_models
def _loadData(self):
""" Load data, returning a dict of text data objects.
Keys are line numbers at which the text appears in the CSV file.
"""
return readCSV(self.dataPath,
numLabels=0) # 0 to train models in unsupervised fashion
def _loadData(self):
""" Load data.
"""
return readCSV(self.dataPath,
numLabels=0) # 0 to train models in unsupervised fashion
with pkg_resources.resource_filename(__name__, jsonPath) as f:
return json.load(f)
except IOError as e:
print "Could not find JSON at '{}'.".format(jsonPath)
raise e
# Indicates global ready status of all models. g_ready will transition to
# True when all models have been created, trained, and are ready to handle
# requests
g_ready = False
g_models = {}
# Get data and order by unique ID
g_csvdata = (readCSV(_DATA_PATH, numLabels=0))
g_samples = OrderedDict(
(int(sample[2]), sample[0]) for sample in g_csvdata.values())
def createModel(modelName, modelFactory):
"""Return an instantiated model."""
global g_models
modelDir = os.path.join(_MODEL_CACHE_DIR_PREFIX, modelName)
try:
print "Attempting to load from", modelDir
model = ClassificationModel.loadModel(modelDir)
modelProxy = SynchronousBackgroundModelProxy(model)
def _loadData(self):
""" Load data.
"""
return readCSV(
self.dataPath,
numLabels=0) # 0 to train models in unsupervised fashion
return json.load(f)
except IOError as e:
print "Could not find JSON at '{}'.".format(jsonPath)
raise e
# Indicates global ready status of all models. g_ready will transition to
# True when all models have been created, trained, and are ready to handle
# requests
g_ready = False
g_models = {}
# Get data and order by unique ID
g_csvdata = (
readCSV(_DATA_PATH, numLabels=0)
)
g_samples = OrderedDict(
(int(sample[2]), sample[0]) for sample in g_csvdata.values()
)
def createModel(modelName, modelFactory):
"""Return an instantiated model."""
global g_models
modelDir = os.path.join(_MODEL_CACHE_DIR_PREFIX, modelName)
try:
try:
with pkg_resources.resource_filename(__name__, jsonPath) as fin:
return json.load(fin)
except IOError as e:
print "Could not find JSON at '{}'.".format(jsonPath)
raise e
# Indicates global ready status of all models. g_ready will transition to
# True when all models have been created, trained, and are ready to handle
# requests
g_ready = False
g_models = {}
g_csvdata = (
readCSV(
os.getenv("IMBU_DATA",
pkg_resources.resource_filename(__name__, "data.csv")),
numLabels=0)
)
# Get data and order by unique ID
g_samples = OrderedDict(
(sample[2], sample[0]) for sample in g_csvdata.values()
)
def createModel(modelName, modelFactory):
"""Return an instantiated model."""
global g_models
