def main(self):
if self.eval_method == "training":
self.train(self.arff.get_features(), self.arff.get_labels())
self._print_confusion_matrix(self.arff.get_features(),
self.arff.get_labels())
elif self.eval_method == "random":
train_features, train_labels, test_features, test_labels = self.training_test_split(
train_percent=self.eval_parameter)
self.train(train_features, train_labels)
self.test(test_features, test_labels)
self._print_confusion_matrix(test_features, test_labels)
elif self.eval_method == "static":
self.train(self.arff.get_features(), self.arff.get_labels())
arff_file = self.eval_parameter
test_data = Arff(arff_file)
if self.normalize:
test_data.normalize()
self.test(features=test_data.get_features(),
labels=test_data.get_labels())
self._print_confusion_matrix(features=test_data.get_features(),
labels=test_data.get_labels())
elif self.eval_method == "cross":
# print('PARAMETER')
self.eval_parameter = int(self.eval_parameter)
self.cross_validate(
self.eval_parameter) # confusion matrix not supported for CV
type(self.eval_parameter)
else:
raise Exception("Unrecognized evaluation method '{}'".format(
self.eval_method))
class ToolkitSession:
""" Toolkit session is given a learner with an associated arff file.
Notes:
* A learner class can be passed without instantiation. It will be created when the session is started.
* Learner keyword arguments can be passed to the session
* A learner class can also already be instantiated when passed
"""
def __init__(self,
arff,
learner,
eval_method=None,
eval_parameter=None,
print_confusion_matrix=False,
normalize=False,
random_seed=None,
label_count=1,
**kwargs):
"""
Args:
arff: Can be arff path, numpy array, or arff object
learner: Learner type or instantiated learner type
eval_method: training, static (separate test set), random (random test split), cross (cross-validate)
eval_parameter: "random" - % used for training; static - test set; cross - # of folds;
print_confusion_matrix (bool): True will print the confuction matrix (only makes sense for classification)
normalize: Normalize training/test data
random_seed: Set random seed for deterministic shuffling
**kwargs:
"""
# parse the command-line arguments
if random_seed:
random.seed(random_seed)
np.random.seed(random_seed)
# update class variables
if inspect.isclass(learner):
# Instantiate learner if needed
self.learner = learner(**kwargs)
self.learner_name = learner.__name__
else:
self.learner = learner
self.learner_name = type(learner).__name__
self.print_confusion_matrix_flag = print_confusion_matrix
self.eval_method = eval_method
self.eval_parameter = eval_parameter
self.normalize = normalize
self.training_accuracy = []
self.test_accuracy = []
# load the ARFF file
self.arff = Arff(arff, label_count=label_count)
if isinstance(arff, Arff):
arff = arff.dataset_name
if self.normalize:
print("Using normalized data")
self.arff.normalize()
# print some stats
print("\nDataset name: {}\n"
"Number of instances: {}\n"
"Number of attributes: {}\n"
"Learning algorithm: {}\n"
"Evaluation method: {}\n".format(arff, self.arff.shape[0],
self.arff.shape[1],
self.learner_name,
self.eval_method))
if not eval_method is None:
self.main()
def main(self):
if self.eval_method == "training":
self.train(self.arff.get_features(), self.arff.get_labels())
self._print_confusion_matrix(self.arff.get_features(),
self.arff.get_labels())
elif self.eval_method == "random":
train_features, train_labels, test_features, test_labels = self.training_test_split(
train_percent=self.eval_parameter)
self.train(train_features, train_labels)
self.test(test_features, test_labels)
self._print_confusion_matrix(test_features, test_labels)
elif self.eval_method == "static":
self.train(self.arff.get_features(), self.arff.get_labels())
arff_file = self.eval_parameter
test_data = Arff(arff_file)
if self.normalize:
test_data.normalize()
self.test(features=test_data.get_features(),
labels=test_data.get_labels())
self._print_confusion_matrix(features=test_data.get_features(),
labels=test_data.get_labels())
elif self.eval_method == "cross":
# print('PARAMETER')
self.eval_parameter = int(self.eval_parameter)
self.cross_validate(
self.eval_parameter) # confusion matrix not supported for CV
type(self.eval_parameter)
else:
raise Exception("Unrecognized evaluation method '{}'".format(
self.eval_method))
def training_test_split(self, train_percent=.9):
""" Arff object with labels included
Args:
train_percent:
Returns:
Tuple: train_features, train_labels, test_features, test_labels
"""
self.arff.shuffle()
print("Calculating accuracy on a random hold-out set...")
train_percent = float(train_percent)
if train_percent < 0 or train_percent > 1:
raise Exception(
"Percentage for random evaluation must be between 0 and 1")
print("Percentage used for training: {}".format(rnd4(train_percent)))
print("Percentage used for testing: {}".format(rnd4(1 -
train_percent)))
train_size = int(train_percent * self.arff.shape[0])
train_features = self.arff.get_features(slice(0, train_size))
train_labels = self.arff.get_labels(slice(0, train_size))
test_features = self.arff.get_features(slice(train_size, None))
test_labels = self.arff.get_labels(slice(train_size, None))
return train_features, train_labels, test_features, test_labels
def train(self, features=None, labels=None):
"""By default, this trains on entire arff file. Features and labels options are given to e.g.
train on only a part of the data
Args:
features (array-like):
labels (array-like):
Returns:
"""
print("Calculating accuracy on training set...")
if features is None:
features = self.arff.get_features()
if labels is None:
labels = self.arff.get_labels()
start_time = time.time()
self.learner.train(features, labels)
elapsed_time = time.time() - start_time
print("Time to train (in seconds): {}".format(rnd4(elapsed_time)))
accuracy = self.learner.measure_accuracy(features, labels)
self.training_accuracy.append(accuracy)
print("Training set accuracy: {}".format(rnd4(accuracy)))
def test(self, features, labels):
""" This eval_method 1) creates a 'random' training/test split according to some user-specified percentage,
2) trains the data
3) reports training AND test accuracy
"""
test_accuracy = self.learner.measure_accuracy(features, labels)
self.test_accuracy.append(test_accuracy)
print("Test set accuracy: {}".format(rnd4(test_accuracy)))
def _print_confusion_matrix(self, features, labels):
if self.print_confusion_matrix_flag:
cm = self.learner.get_confusion_matrix(features, labels)
print(cm)
def generate_fold(self, folds):
for i in range(folds):
start_test = int(i * self.arff.shape[0] / folds)
end_test = int((i + 1) * self.arff.shape[0] / folds)
train_features = self.arff.get_features(
row_idx=np.r_[0:start_test, end_test:self.arff.shape[0]])
train_labels = self.arff.get_labels(
row_idx=np.r_[0:start_test, end_test:self.arff.shape[0]])
test_features = self.arff.get_features(slice(start_test, end_test))
test_labels = self.arff.get_labels(slice(start_test, end_test))
yield train_features, train_labels, test_features, test_labels
def cross_validate(self, folds, reps=1):
print("Calculating accuracy using cross-validation...")
if folds <= 0:
raise Exception("Number of folds must be greater than 0")
print("Number of folds: {}".format(folds))
reps = 1
sum_accuracy = 0.0
elapsed_time = 0.0
for rep_counter in range(reps):
self.arff.shuffle()
for fold_counter, [
train_features, train_labels, test_features, test_labels
] in enumerate(self.generate_fold(folds)):
start_time = time.time()
# Train model
self.learner.train(train_features, train_labels)
elapsed_time += time.time() - start_time
training_accuracy = self.learner.measure_accuracy(
train_features, train_labels)
# Get test accuracy
test_accuracy = self.learner.measure_accuracy(
test_features, test_labels)
sum_accuracy += test_accuracy
print("Rep={}, Fold={}, Accuracy={}".format(
rep_counter, fold_counter, rnd4(test_accuracy)))
self.training_accuracy.append(training_accuracy)
self.test_accuracy.append(test_accuracy)
elapsed_time /= (reps * folds)
print("Average time to train (in seconds): {}".format(
rnd4(elapsed_time)))
print("Mean accuracy={}".format(rnd4(sum_accuracy /
(reps * folds))))
