def __init__(self, verbosity=1, numLabels=3):
"""
Initialize the encoder as CioEncoder; requires a valid API key.
"""
super(ClassificationModelEndpoint, self).__init__(verbosity, numLabels)
self.encoder = CioEncoder(cacheDir="./experiments/cache")
self.compareEncoder = LanguageEncoder()
self.n = self.encoder.n
self.w = int((self.encoder.targetSparsity / 100) * self.n)
self.categoryBitmaps = {}
self.negatives = defaultdict(list)
self.positives = defaultdict(list)
def __init__(self,
verbosity=1,
numLabels=3,
modelDir="ClassificationModelEndpoint"):
"""
Initializes the encoder as CioEncoder; requires a valid API key.
"""
super(ClassificationModelEndpoint, self).__init__(
verbosity=verbosity, numLabels=numLabels, modelDir=modelDir)
self.encoder = CioEncoder(cacheDir="./experiments/cache")
self.compareEncoder = LanguageEncoder()
self.n = self.encoder.n
self.w = int((self.encoder.targetSparsity/100) * self.n)
self.categoryBitmaps = {}
self.negatives = defaultdict(list)
self.positives = defaultdict(list)
class ClassificationModelEndpoint(ClassificationModel):
"""
Class to run the survey response classification task with Cortical.io
text endpoint encodings and classification system.
From the experiment runner, the methods expect to be fed one sample at a time.
"""
def __init__(self,
verbosity=1,
numLabels=3,
modelDir="ClassificationModelEndpoint",
unionSparsity=20.0):
"""
Initializes the encoder as CioEncoder; requires a valid API key.
"""
super(ClassificationModelEndpoint, self).__init__(
verbosity=verbosity, numLabels=numLabels, modelDir=modelDir)
self.encoder = CioEncoder(cacheDir="./experiments/cache",
unionSparsity=unionSparsity)
self.compareEncoder = LanguageEncoder()
self.n = self.encoder.n
self.w = int((self.encoder.targetSparsity/100) * self.n)
self.categoryBitmaps = {}
self.negatives = defaultdict(list)
self.positives = defaultdict(list)
def encodeSample(self, sample):
"""
Encode an SDR of the input string by querying the Cortical.io API.
@param sample (list) Tokenized sample, where each item is
a string
@return fp (dict) The sample text, sparsity, and bitmap.
Example return dict:
{
"text": "Example text",
"sparsity": 0.03,
"bitmap": numpy.array([])
}
"""
sample = " ".join(sample)
fpInfo = self.encoder.encode(sample)
if fpInfo:
fp = {"text":fpInfo["text"] if "text" in fpInfo else fpInfo["term"],
"sparsity":fpInfo["sparsity"],
"bitmap":numpy.array(fpInfo["fingerprint"]["positions"])}
else:
fp = {"text":sample,
"sparsity":float(self.w)/self.n,
"bitmap":self.encodeRandomly(sample)}
return fp
def resetModel(self):
"""Reset the model"""
self.positives.clear()
self.negatives.clear()
self.categoryBitmaps.clear()
def trainModel(self, i, negatives=None):
# TODO: add batch training, where i is a list; note we should only add
# negatives when training on one sample so we know which labels to use.
"""
Train the classifier on the sample and labels for record i. Use
Cortical.io's createClassification() to make a bitmap that represents the
class. The list sampleReference is populated to correlate classifier
prototypes to sample IDs.
@param negative (list) Each item is the dictionary containing
text, sparsity and bitmap for the
negative samples.
"""
record = self.patterns[i]
labelsToUpdateBitmaps = set()
for label in record["labels"]:
if record["pattern"]["text"] and record["pattern"]["bitmap"].any():
self.positives[label].append(record["pattern"]["text"])
if negatives:
for neg in negatives:
if neg["text"]:
self.negatives[label].append(neg["text"])
labelsToUpdateBitmaps.add(label)
for label in labelsToUpdateBitmaps:
self.categoryBitmaps[label] = self.encoder.createCategory(
str(label), self.positives[label], self.negatives[label])["positions"]
self.sampleReference.append(i)
def testModel(self, i, numLabels=3, metric="overlappingAll"):
"""
Test on record i. The Cortical.io classifier returns a dictionary
containing various distance metrics between the sample and the classes.
@param numLabels (int) Number of classification predictions.
@param metric (str) Distance metric use by classifier.
@return (numpy array) numLabels most-frequent classifications
for the data samples; int or empty.
"""
sampleBitmap = self.patterns[i]["pattern"]["bitmap"].tolist()
distances = defaultdict(list)
for cat, catBitmap in self.categoryBitmaps.iteritems():
distances[cat] = self.compareEncoder.compare(sampleBitmap, catBitmap)
return self.getWinningLabels(distances, numLabels=numLabels, metric=metric)
@staticmethod
def compareCategories(catDistances, metric="overlappingAll"):
"""
Calculate category distances. Returns a defaultdict of category keys, where
values are OrderedDicts sorted such that the most similar categories
(according to the input metric) are listed first.
"""
descendingOrder = ("overlappingAll", "overlappingLeftRight",
"overlappingRightLeft", "cosineSimilarity",
"weightedScoring")
categoryComparisons = defaultdict(list)
for k, v in catDistances.iteritems():
# Create a dict for this category
metricDict = {compareCat: distances[metric]
for compareCat, distances in v.iteritems()}
# Sort the dict by the metric
reverse = True if metric in descendingOrder else False
categoryComparisons[k] = OrderedDict(
sorted(metricDict.items(), key=lambda k: k[1], reverse=reverse))
return categoryComparisons
def getCategoryDistances(self, sort=True, save=None, labelRefs=None):
"""
Return a dict where keys are categories and values are dicts of distances.
@param sort (bool) Sort the inner dicts with compareCategories()
@param save (str) Dump catDistances to a JSON in this dir.
@return (defaultdict)
E.g. w/ categories 0 and 1:
catDistances = {
0: {
0: {"cosineSimilarity": 1.0, ...},
1: {"cosineSimilarity": 0.33, ...}
},
1: {
0: {"cosineSimilarity": 0.33, ...},
1: {"cosineSimilarity": 1.0, ...}
}
Note the inner-dicts of catDistances are OrderedDict objects.
"""
catDistances = defaultdict(list)
for cat, catBitmap in self.categoryBitmaps.iteritems():
catDistances[cat] = OrderedDict()
for compareCat, compareBitmap in self.categoryBitmaps.iteritems():
# List is in order of self.categoryBitmaps.keys()
catDistances[cat][compareCat] = self.compareEncoder.compare(
catBitmap, compareBitmap)
if sort:
# Order each inner dict of catDistances such that the ranking is most to
# least similar.
catDistances = self.compareCategories(catDistances)
if save is not None:
self.writeOutCategories(
save, comparisons=catDistances, labelRefs=labelRefs)
return catDistances
@staticmethod
def getWinningLabels(distances, numLabels, metric):
"""
Return indices of winning categories, based off of the input metric.
Overrides the base class implementation.
"""
metricValues = numpy.array([v[metric] for v in distances.values()])
sortedIdx = numpy.argsort(metricValues)
# euclideanDistance and jaccardDistance are ascending
descendingOrder = ("overlappingAll", "overlappingLeftRight",
"overlappingRightLeft", "cosineSimilarity",
"weightedScoring")
if metric in descendingOrder:
sortedIdx = sortedIdx[::-1]
return numpy.array(
[distances.keys()[catIdx] for catIdx in sortedIdx[:numLabels]])
@staticmethod
def query():
print "The Classification Endpoint model doesn't support this method."
@staticmethod
def infer():
print "The Classification Endpoint model doesn't support this method."
class ClassificationModelEndpoint(ClassificationModel):
"""
Class to run the survey response classification task with Cortical.io
text endpoint encodings and classification system.
From the experiment runner, the methods expect to be fed one sample at a time.
"""
def __init__(self, verbosity=1, numLabels=3):
"""
Initialize the encoder as CioEncoder; requires a valid API key.
"""
super(ClassificationModelEndpoint, self).__init__(verbosity, numLabels)
self.encoder = CioEncoder(cacheDir="./experiments/cache")
self.compareEncoder = LanguageEncoder()
self.n = self.encoder.n
self.w = int((self.encoder.targetSparsity / 100) * self.n)
self.categoryBitmaps = {}
self.negatives = defaultdict(list)
self.positives = defaultdict(list)
def encodePattern(self, sample):
"""
Encode an SDR of the input string by querying the Cortical.io API.
@param sample (list) Tokenized sample, where each item is
a string
@return fp (dict) The sample text, sparsity, and bitmap.
Example return dict:
{
"text": "Example text",
"sparsity": 0.03,
"bitmap": numpy.array([])
}
"""
sample = " ".join(sample)
fpInfo = self.encoder.encode(sample)
if fpInfo:
fp = {
"text": fpInfo["text"] if "text" in fpInfo else fpInfo["term"],
"sparsity": fpInfo["sparsity"],
"bitmap": numpy.array(fpInfo["fingerprint"]["positions"])
}
else:
fp = {
"text": sample,
"sparsity": float(self.w) / self.n,
"bitmap": self.encodeRandomly(sample)
}
return fp
def resetModel(self):
"""Reset the model"""
self.positives.clear()
self.negatives.clear()
self.categoryBitmaps.clear()
def trainModel(self, samples, labels, negatives=None):
"""
Train the classifier on the input sample and label. Use Cortical.io's
createClassification to make a bitmap that represents the class
@param samples (list) List of dictionaries containing the
sample text, sparsity, and bitmap.
@param labels (list) List of numpy arrays containing the
reference indices for the
classifications of each sample.
@param negatives (list) Each item is the dictionary containing
text, sparsity and bitmap for the
negative samples.
"""
labelsToUpdateBitmaps = set()
for sample, sampleLabels in zip(samples, labels):
for label in sampleLabels:
fpInfo = self.encoder.encode(sample["text"])
if sample["text"] and fpInfo:
self.positives[label].append(sample["text"])
# Only add negatives when training on one sample so we know which
# labels to use
if negatives and len(samples) == 1:
for neg in negatives:
if neg["text"]:
self.negatives[label].append(neg["text"])
labelsToUpdateBitmaps.add(label)
for label in labelsToUpdateBitmaps:
self.categoryBitmaps[label] = self.encoder.createCategory(
str(label), self.positives[label],
self.negatives[label])["positions"]
def testModel(self, sample, numLabels=3, metric="overlappingAll"):
"""
Test the Cortical.io classifier on the input sample. Returns a dictionary
containing various distance metrics between the sample and the classes.
@param sample (dict) The sample text, sparsity, and bitmap.
@return (list) Winning classifications based on the
specified metric. The number of items
returned will be <= numLabels.
"""
sampleBitmap = sample["bitmap"].tolist()
distances = defaultdict(list)
for cat, catBitmap in self.categoryBitmaps.iteritems():
distances[cat] = self.compareEncoder.compare(
sampleBitmap, catBitmap)
return self.getWinningLabels(distances,
numLabels=numLabels,
metric=metric)
@staticmethod
def compareCategories(catDistances, metric="overlappingAll"):
"""
Calculate category distances. Returns a defaultdict of category keys, where
values are OrderedDicts sorted such that the most similar categories
(according to the input metric) are listed first.
"""
descendingOrder = ("overlappingAll", "overlappingLeftRight",
"overlappingRightLeft", "cosineSimilarity",
"weightedScoring")
categoryComparisons = defaultdict(list)
for k, v in catDistances.iteritems():
# Create a dict for this category
metricDict = {
compareCat: distances[metric]
for compareCat, distances in v.iteritems()
}
# Sort the dict by the metric
reverse = True if metric in descendingOrder else False
categoryComparisons[k] = OrderedDict(
sorted(metricDict.items(), key=lambda k: k[1],
reverse=reverse))
return categoryComparisons
def getCategoryDistances(self, sort=True, save=None, labelRefs=None):
"""
Return a dict where keys are categories and values are dicts of distances.
@param sort (bool) Sort the inner dicts with compareCategories()
@param save (str) Dump catDistances to a JSON in this dir.
@return (defaultdict)
E.g. w/ categories 0 and 1:
catDistances = {
0: {
0: {"cosineSimilarity": 1.0, ...},
1: {"cosineSimilarity": 0.33, ...}
},
1: {
0: {"cosineSimilarity": 0.33, ...},
1: {"cosineSimilarity": 1.0, ...}
}
Note the inner-dicts of catDistances are OrderedDict objects.
"""
catDistances = defaultdict(list)
for cat, catBitmap in self.categoryBitmaps.iteritems():
catDistances[cat] = OrderedDict()
for compareCat, compareBitmap in self.categoryBitmaps.iteritems():
# List is in order of self.categoryBitmaps.keys()
catDistances[cat][compareCat] = self.compareEncoder.compare(
catBitmap, compareBitmap)
if sort:
# Order each inner dict of catDistances such that the ranking is most to
# least similar.
catDistances = self.compareCategories(catDistances)
if save is not None:
self.writeOutCategories(save,
comparisons=catDistances,
labelRefs=labelRefs)
return catDistances
@staticmethod
def getWinningLabels(distances, numLabels, metric):
"""
Return indices of winning categories, based off of the input metric.
Overrides the base class implementation.
"""
metricValues = numpy.array([v[metric] for v in distances.values()])
sortedIdx = numpy.argsort(metricValues)
# euclideanDistance and jaccardDistance are ascending
descendingOrder = ("overlappingAll", "overlappingLeftRight",
"overlappingRightLeft", "cosineSimilarity",
"weightedScoring")
if metric in descendingOrder:
sortedIdx = sortedIdx[::-1]
return numpy.array(
[distances.keys()[catIdx] for catIdx in sortedIdx[:numLabels]])