def clusters(dataSet, features, exemplars):
ftrs = list(features)
wI = wellIndex(dataSet)
objectCount = len(wI)
predicted = np.empty(objectCount, dtype=np.int)
# Default to use all features if none habe been selected.
if not ftrs:
ftrs = data.imageFeatures(dataSet)
if ftrs and exemplars: #(exemplars or wellTypes(dataSet)):
# Training feature data.
valueMatrix = np.matrix([scaledArray(dataSet, ftr) for ftr in ftrs], copy=False).transpose()
# Construct from well type annotation.
trainingLabels = np.copy(wI['type'].values)
# Knock out large part of training values (to speed up training).
trainingSample = np.random.rand(trainingLabels.size) < configuration(dataSet).wellTypeSample
trainingLabels = np.where(trainingSample, trainingLabels, np.nan)
# Override well type annotations where exemplars have been chosen by user.
exemplarDict = dict(exemplars)
for popId, exemplars in exemplarDict.iteritems():
for exemplar in exemplars:
trainingLabels[exemplar] = popId
# Prune training features and labels, based on presence of labels.
trainingValues = valueMatrix[~np.isnan(trainingLabels)]
trainingLabels = trainingLabels[~np.isnan(trainingLabels)]
print "Begin training"
#trainingValues = np.take(valueMatrix, exemplarObjects, axis=0)
forest = RandomForestClassifier(
n_estimators=10,
n_jobs=-1,
class_weight="balanced"#,
#min_samples_split=0.01*trainingValues.size
)
forest = forest.fit(trainingValues, trainingLabels) #forest.fit(trainingValues, exemplarLabels)
print "End training"
print "Begin classification"
#predicted = forest.predict(valueMatrix)
confidenceThreshold = data.config(dataSet).classifierConfidenceThreshold
probabilities = forest.predict_proba(valueMatrix)
maxProb = np.max(probabilities, axis=1)
maxArgProb = np.argmax(probabilities, axis=1)
predicted = np.where(maxProb > confidenceThreshold, np.choose(maxArgProb, forest.classes_), 2).astype(np.int)
print "End classification"
else:
predicted.fill(2) # 2 unsure about all input when no training input is provided
# Partition predicted column to object indices.
return predicted
def objectInfo(dataSet, featureSet, column, row, plate, exemplars, probes):
objects = allObjects(dataSet, column, row, plate, exemplars, probes)
combined = wellIndex(dataSet).loc[objects].copy()
# Feature values.
for ftr in featureSet:
combined[ftr] = np.take(scaledArray(dataSet, ftr), objects)
if data.mdsColumnsPresent(dataSet):
for mdsCol in data.mdsColumns:
combined[mdsCol] = np.take(scaledArray(dataSet, mdsCol), objects)
# Predicted population values.
combined["population"] = np.take(clusters(dataSet, featureSet, exemplars), objects)
# Generate well URLs on the spot, based on config.
wellImages = data.config(dataSet).wellImages
for name, urlFunction in wellImages.iteritems():
combined["img_" + name] = combined.apply(
lambda row: urlFunction(int(row['plate']), int(row['column']), int(row['row'])), axis=1)
return combined
def configuration(dataSet):
return data.config(dataSet)