def conduct_auto_sklearn(experimentdatafile,resamplingstrategy,resamplingstrategyarguments,timefortask):
# Load data files
lunchbox = pickle.load(open(experimentdatafile,"rb"))
# Set up autosklearn and run against Pythia feature sets
clf = autosklearn.classification.AutoSklearnClassifier(time_left_for_this_task=timefortask, per_run_time_limit=360, \
initial_configurations_via_metalearning=25, ensemble_size=50, ensemble_nbest=50, seed=1, \
ml_memory_limit=3000, include_estimators=None, include_preprocessors=None, \
resampling_strategy=resamplingstrategy, resampling_strategy_arguments=resamplingstrategyarguments, \
tmp_folder=None, output_folder=None, delete_tmp_folder_after_terminate=True, \
delete_output_folder_after_terminate=True, shared_mode=False)
# TODO Find way to suppress voluminous INFO messages from autosklearn
clf.fit(numpy.asarray(lunchbox['train_data']), numpy.asarray(lunchbox['train_target']))
# Print autoasklearn results
print("Models",clf.show_models(), file=sys.stderr)
# Get performance metrics of autolearn models against testing data
predictions = clf.predict(numpy.asarray(lunchbox['test_data']))
performresults = performance_metrics.get_perform_metrics(numpy.asarray(lunchbox['test_target']), predictions)
# Fill results dictionary to return to Sacred for logging
results = dict()
results['autosklearn_models'] = clf.show_models()
results['autosklearn_perform_results'] = performresults
results['directory'] = lunchbox['directory']
results['features'] = lunchbox['features']
results['algorithms'] = lunchbox['algorithms']
results['parameters'] = lunchbox['parameters']
return results
def run_model(train_data, train_labels, test_data, test_labels):
'''
Algorithm which will take in a set of training text and labels to train a bag of words model
This model is then used with a logistic regression algorithm to predict the labels for a second set of text
Method modified from code available at:
https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-1-for-beginners-bag-of-words
Args:
train_data_text: Text training set. Needs to be iterable
train_labels: Training set labels
test_data_text: The text to
Returns:
pred_labels: The predicted labels as determined by logistic regression
'''
#use Logistic Regression to train a model
logreg = linear_model.LogisticRegression(C=1e5)
# we create an instance of Neighbours Classifier and fit the data.
logreg.fit(train_data, train_labels)
#Now that we have something trained we can check if it is accurate with the test set
pred_labels = logreg.predict(test_data)
perform_results = performance_metrics.get_perform_metrics(test_labels, pred_labels)
#Perform_results is a dictionary, so we should add other pertinent information to the run
perform_results['vector'] = 'Bag_of_Words'
perform_results['alg'] = 'Logistic_Regression'
return pred_labels, perform_results
def run_model(train_data, train_labels, test_data, test_labels):
'''
Algorithm which will take in a set of training text and labels to train a bag of words model
This model is then used with a logistic regression algorithm to predict the labels for a second set of text
Method modified from code available at:
https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-1-for-beginners-bag-of-words
Args:
train_data_text: Text training set. Needs to be iterable
train_labels: Training set labels
test_data_text: The text to
Returns:
pred_labels: The predicted labels as determined by logistic regression
'''
#use Logistic Regression to train a model
logreg = linear_model.LogisticRegression(C=1e5)
# we create an instance of Neighbours Classifier and fit the data.
logreg.fit(train_data, train_labels)
#Now that we have something trained we can check if it is accurate with the test set
pred_labels = logreg.predict(test_data)
perform_results = performance_metrics.get_perform_metrics(
test_labels, pred_labels)
#Perform_results is a dictionary, so we should add other pertinent information to the run
perform_results['vector'] = 'Bag_of_Words'
perform_results['alg'] = 'Logistic_Regression'
return pred_labels, perform_results
def predicter(classifier, test_data, test_labels):
# Handle HDF5 case
if type(test_data) is str:
assert test_data==test_labels
with h5py.File(test_data) as f:
test_data = f['data'][()]
test_labels = f['labels'][()]
pred_labels = classifier.predict(test_data)
perform_results = performance_metrics.get_perform_metrics(test_labels, pred_labels)
return pred_labels, perform_results
def predicter(classifier, test_data, test_labels):
# Handle HDF5 case
if type(test_data) is str:
assert test_data == test_labels
with h5py.File(test_data) as f:
test_data = f['data'][()]
test_labels = f['labels'][()]
pred_labels = classifier.predict(test_data)
perform_results = performance_metrics.get_perform_metrics(
test_labels, pred_labels)
return pred_labels, perform_results
def conduct_auto_sklearn(experimentdatafile, resamplingstrategy,
resamplingstrategyarguments, timefortask):
# Load data files
lunchbox = pickle.load(open(experimentdatafile, "rb"))
# Set up autosklearn and run against Pythia feature sets
clf = autosklearn.classification.AutoSklearnClassifier(time_left_for_this_task=timefortask, per_run_time_limit=360, \
initial_configurations_via_metalearning=25, ensemble_size=50, ensemble_nbest=50, seed=1, \
ml_memory_limit=3000, include_estimators=None, include_preprocessors=None, \
resampling_strategy=resamplingstrategy, resampling_strategy_arguments=resamplingstrategyarguments, \
tmp_folder=None, output_folder=None, delete_tmp_folder_after_terminate=True, \
delete_output_folder_after_terminate=True, shared_mode=False)
# TODO Find way to suppress voluminous INFO messages from autosklearn
clf.fit(numpy.asarray(lunchbox['train_data']),
numpy.asarray(lunchbox['train_target']))
# Print autoasklearn results
print("Models", clf.show_models(), file=sys.stderr)
# Get performance metrics of autolearn models against testing data
predictions = clf.predict(numpy.asarray(lunchbox['test_data']))
performresults = performance_metrics.get_perform_metrics(
numpy.asarray(lunchbox['test_target']), predictions)
# Fill results dictionary to return to Sacred for logging
results = dict()
results['autosklearn_models'] = clf.show_models()
results['autosklearn_perform_results'] = performresults
results['directory'] = lunchbox['directory']
results['features'] = lunchbox['features']
results['algorithms'] = lunchbox['algorithms']
results['parameters'] = lunchbox['parameters']
return results
def do_epoch(dmn, mode, epoch, batch_size, log_every, skipped=0):
'''
This function runs the epochs for the training of the neural network. It calls the steps in the epoch
and allows for the tweaking of the parameter values.
Args:
mode (str): 'train' or 'test' for whether this is the training or testing step
epoch (int): the epoch number currently on
skipped (int): how many steps have been skipped because of no change in gradient
Returns:
avg_loss (double): the new calculated average loss
skipped (int): how many steps skipped since the beginning of running the epoch added to the previous skip value
'''
# mode is 'train' or 'test'
y_true = []
y_pred = []
avg_loss = 0.0
prev_time = time.time()
batches_per_epoch = dmn.get_batches_per_epoch(mode)
#if batches_per_epoch==0: batches_per_epoch=10
print(batches_per_epoch, dmn)
for i in range(0, batches_per_epoch):
step_data = dmn.step(i, mode) # Run step using the dynamic memory network object
prediction = step_data["prediction"]
answers = step_data["answers"]
current_loss = step_data["current_loss"]
current_skip = (step_data["skipped"] if "skipped" in step_data else 0)
log = step_data["log"]
skipped += current_skip
if current_skip == 0:
avg_loss += current_loss
for x in answers:
y_true.append(x)
for x in prediction.argmax(axis=1):
#some predictions are not 0,1 for the first couple of guesses
#TODO figure out why...but until then this catches the issue
if x not in [0,1]:
x = np.random.randint(0,2)
y_pred.append(x)
# TODO: save the state sometimes
if (i % log_every == 0):
cur_time = time.time()
print (" %sing: %d.%d / %d \t loss: %.3f \t avg_loss: %.3f \t skipped: %d \t %s \t time: %.2fs" %
(mode, epoch, i * batch_size, batches_per_epoch * batch_size,
current_loss, avg_loss / (i + 1), skipped, log, cur_time - prev_time))
prev_time = cur_time
if np.isnan(current_loss):
print("==> current loss IS NaN. This should never happen :) " )
exit()
avg_loss /= batches_per_epoch
print("\n %s loss = %.5f" % (mode, avg_loss))
print("confusion matrix:")
print(metrics.confusion_matrix(y_true, y_pred))
perform_results = performance_metrics.get_perform_metrics(y_true, y_pred)
print(perform_results)
accuracy = sum([1 if t == p else 0 for t, p in zip(y_true, y_pred)])
print("accuracy: %.2f percent" % (accuracy * 100.0 / batches_per_epoch / batch_size))
return avg_loss, skipped, perform_results, y_pred
def predicter(classifier, test_data, test_labels):
pred_labels = classifier.predict(test_data)
perform_results = performance_metrics.get_perform_metrics(
test_labels, pred_labels)
return pred_labels, perform_results
def predicter(classifier, test_data, test_labels):
pred_labels = classifier.predict(test_data)
perform_results = performance_metrics.get_perform_metrics(test_labels, pred_labels)
return pred_labels, perform_results