def main(date, modelType):
"""
Runs the training script. Trains the specified model type, saves the
model to a prefined location (specified in the Constants file), and
runs basic accuracy tests on the trained model.
:param date: Date the training and testing data was collected (YYYY_MMDD)
:param modelType: (string) type of machine learning model to train
:return: (None)
"""
# Make sure that the model is a valid choice
if (not (modelType in MODELS.keys())) and (modelType != ALL):
print "Invalid model type:", modelType
return
# Allow for training more than one model at a time
if modelType == ALL:
modelsToTrain = MODELS.keys()
else:
modelsToTrain = [modelType]
# Load the training and testing data into memory
trainX, trainY = FileIO.loadTrainingData(date)
testX, testY = FileIO.loadTestingData(date)
trainX = np.nan_to_num(trainX)
testX = np.nan_to_num(testX)
for modelType in modelsToTrain:
# Train the desired ML model
name, clfType = MODELS[modelType]
hyperparameters = HYPERPARAMETERS[modelType]
print "Training the", name
clf = clfType(**hyperparameters)
clf.fit(trainX, trainY)
# Perform some very basic accuracy testing
trainResult = clf.predict(trainX)
testResult = clf.predict(testX)
trainingAccuracy = accuracy_score(trainY, trainResult)
testingAccuracy = accuracy_score(testY, testResult)
confusionMatrix = confusion_matrix(testY, testResult)
print "Training Accuracy:", trainingAccuracy
print "Testing Accuracy:", testingAccuracy
print "Confusion Matrix:"
print confusionMatrix
print " "
# Save the model to disk
FileIO.saveModel(clf, modelType, date)
def main(date, modelType):
"""
Runs the training script. Trains the specified model type, saves the
model to a prefined location (specified in the Constants file), and
runs basic accuracy tests on the trained model.
:param date: Date the training and testing data was collected (YYYY_MMDD)
:param modelType: (string) type of machine learning model to train
:return: (None)
"""
# Make sure that the model is a valid choice
if (not (modelType in MODELS.keys())) and (modelType != ALL):
print "Invalid model type:", modelType
return
# Allow for training more than one model at a time
if modelType == ALL:
modelsToTrain = MODELS.keys()
else:
modelsToTrain = [modelType]
# Load the training and testing data into memory
trainX, trainY = FileIO.loadTrainingData(date)
testX, testY = FileIO.loadTestingData(date)
trainX = np.nan_to_num(trainX)
testX = np.nan_to_num(testX)
for modelType in modelsToTrain:
# Train the desired ML model
name, clfType = MODELS[modelType]
hyperparameters = HYPERPARAMETERS[modelType]
print "Training the", name
clf = clfType(**hyperparameters)
clf.fit(trainX, trainY)
# Perform some very basic accuracy testing
trainResult = clf.predict(trainX)
testResult = clf.predict(testX)
trainingAccuracy = accuracy_score(trainY, trainResult)
testingAccuracy = accuracy_score(testY, testResult)
confusionMatrix = confusion_matrix(testY, testResult)
print "Training Accuracy:", trainingAccuracy
print "Testing Accuracy:", testingAccuracy
print "Confusion Matrix:"
print confusionMatrix
print " "
# Save the model to disk
FileIO.saveModel(clf, modelType, date)
def main(date, modelType, iterations):
"""
Determines the optimal hyperparameters for a given machine learning
model for a set of training data.
:param date: Date the training and testing data was collected (YYYY_MMDD)
:param modelType: (string) type of machine learning model to train
:param iterations: (int) number of iterations for hyperparameter searching
:return: (None)
"""
# Make sure that the model is a valid choice
if (not (modelType in MODELS.keys())) and (modelType != ALL):
print "Invalid model type:", modelType
return
# Allow for training more than one model at a time
if modelType == ALL:
modelsToTrain = MODELS.keys()
else:
modelsToTrain = [modelType]
# Load the training and testing data into memory
trainX, trainY = FileIO.loadTrainingData(date)
testX, testY = FileIO.loadTestingData(date)
trainX = np.nan_to_num(trainX)
testX = np.nan_to_num(testX)
for modelType in modelsToTrain:
# Train the desired ML model
name, clfType = MODELS[modelType]
print "Training the", name
baseClassifier = clfType()
clf = RandomizedSearchCV(baseClassifier, param_distributions=PARAMETERS[modelType],
n_iter=iterations,
n_jobs=4)
clf.fit(trainX, trainY)
# Perform some very basic accuracy testing
trainResult = clf.predict(trainX)
testResult = clf.predict(testX)
trainingAccuracy = accuracy_score(trainY, trainResult)
testingAccuracy = accuracy_score(testY, testResult)
confusionMatrix = confusion_matrix(testY, testResult)
print "Training Accuracy:", trainingAccuracy
print "Testing Accuracy:", testingAccuracy
print "Confusion Matrix:"
print confusionMatrix
print " "
print "Hyperparameters:"
for param in PARAMETERS[modelType].keys():
print param + ':', clf.best_estimator_.get_params()[param]
print " "
# Save the model to disk
FileIO.saveModel(clf.best_estimator_, modelType, date)
def main(date, modelType, iterations):
"""
Determines the optimal hyperparameters for a given machine learning
model for a set of training data.
:param date: Date the training and testing data was collected (YYYY_MMDD)
:param modelType: (string) type of machine learning model to train
:param iterations: (int) number of iterations for hyperparameter searching
:return: (None)
"""
# Make sure that the model is a valid choice
if (not (modelType in MODELS.keys())) and (modelType != ALL):
print "Invalid model type:", modelType
return
# Allow for training more than one model at a time
if modelType == ALL:
modelsToTrain = MODELS.keys()
else:
modelsToTrain = [modelType]
# Load the training and testing data into memory
trainX, trainY = FileIO.loadTrainingData(date)
testX, testY = FileIO.loadTestingData(date)
trainX = np.nan_to_num(trainX)
testX = np.nan_to_num(testX)
for modelType in modelsToTrain:
# Train the desired ML model
name, clfType = MODELS[modelType]
print "Training the", name
baseClassifier = clfType()
clf = RandomizedSearchCV(baseClassifier,
param_distributions=PARAMETERS[modelType],
n_iter=iterations,
n_jobs=4)
clf.fit(trainX, trainY)
# Perform some very basic accuracy testing
trainResult = clf.predict(trainX)
testResult = clf.predict(testX)
trainingAccuracy = accuracy_score(trainY, trainResult)
testingAccuracy = accuracy_score(testY, testResult)
confusionMatrix = confusion_matrix(testY, testResult)
print "Training Accuracy:", trainingAccuracy
print "Testing Accuracy:", testingAccuracy
print "Confusion Matrix:"
print confusionMatrix
print " "
print "Hyperparameters:"
for param in PARAMETERS[modelType].keys():
print param + ':', clf.best_estimator_.get_params()[param]
print " "
# Save the model to disk
FileIO.saveModel(clf.best_estimator_, modelType, date)
