def create_and_save(self):
"""
Create and save the dataset using the arguments passed in from the console. This will shuffle the validation
data, create a classifier, and select the correct classifications that are seen. The resulting list
of images that are correctly classified is then saved to an NPY file.
:return:
"""
parser = argparse.ArgumentParser()
parser.add_argument('-rs',
'--random-state',
default=1024,
help='Random state for the shuffling.')
parser.add_argument('-n',
'--number',
default=100,
help='Number of correct classifications to store.')
arguments = vars(parser.parse_args())
random_state = int(arguments['random_state'])
number = int(arguments['number'])
print('')
print('Starting %s Image and Label Extraction...' % self.name)
print('')
print('Parameters:')
print(' Random State: %s' % random_state)
print(' Number: %s' % number)
print('')
use_project_path()
# Load and shuffle the validation dataset
image_df = pd.read_csv('data/full_image_dataset.csv')
image_df = image_df.sample(
frac=1.0, random_state=random_state).reset_index(drop=True)
# Create the CNN to be tested
model = self.network(weights='imagenet')
accumulator = list()
# Step through the dataframe and keep all images correctly classified.
for index, row in image_df.iterrows():
scaled_image = image.img_to_array(
image.load_img(row['image'], target_size=(224, 224)))
input = self.preprocessor(
np.expand_dims(scaled_image.copy(), axis=0))
raw_predictions = model.predict(input)
if int(self.class_index[row['label']]) == int(
np.argmax(raw_predictions)):
accumulator.append(
[scaled_image,
int(self.class_index[row['label']])])
if len(accumulator) >= number:
break
np.save('data/%s_%s_correct.npy' % (self.name, number),
np.array(accumulator))
from keras.applications.vgg19 import VGG19
from keras.applications.vgg19 import preprocess_input as vgg19_preprocess_input
from keras.applications.densenet import DenseNet201
from keras.applications.densenet import preprocess_input as densenet_preprocess_input
from keras.applications.resnet_v2 import ResNet152V2
from keras.applications.resnet_v2 import preprocess_input as resnet_preprocess_input
import numpy as np
from filter import FourierUniformFilter
from utility import OptimizationSearch
from utility import use_project_path
from utility import save_filter_search_scores
if __name__ == '__main__':
use_project_path()
parser = argparse.ArgumentParser()
parser.add_argument('-n', '--network', default='vgg16',
help='The network architecture to test: vgg16, vgg19, densenet, or resnet')
parser.add_argument('-s', '--sample', default=100,
help='The sample file to use for testing.')
parser.add_argument('-i', '--iterations', default=25,
help='The number of iterations to use for testing.')
arguments = vars(parser.parse_args())
NETWORK = arguments['network']
SAMPLE = arguments['sample']
ITERATIONS = int(arguments['iterations'])
print('')
def load_data_frame():
use_project_path()
data_frame = pd.read_csv(data_filename, dtype='float')
return data_frame
def run_classification_search_experiment(
self,
scoring,
sample=None,
random_state=None,
test_size=0.25,
n_jobs=-1,
n_iter=2,
cv=5,
verbose=3,
multiclass=False,
record_predict_proba=False):
"""
The classification search makes use of a bayesian search to find the best hyper-parameters.
"""
use_project_path()
logger = Logger('%s.txt' % self.name)
search = BayesSearchCV(
self.estimator,
self.hyper_parameters.search_space,
n_jobs=n_jobs,
n_iter=n_iter,
cv=cv,
verbose=verbose,
scoring=scoring,
return_train_score=True
)
data_frame = self.df
if sample is not None:
data_frame = data_frame.sample(n=sample, random_state=random_state)
x_train, x_test, y_train, y_test = train_test_split(data_frame, data_frame[self.target], test_size=test_size)
logger.time_log('Starting HyperParameter Search...')
results = search.fit(x_train, y_train)
logger.time_log('Search Complete.\n')
logger.time_log('Testing Training Partition...')
y_train_predict = batch_predict(results.best_estimator_, x_train)
logger.time_log('Testing Complete.\n')
train_evaluation_frame = EvaluationFrame(y_train, y_train_predict)
logger.time_log('Testing Holdout Partition...')
y_test_predict = batch_predict(results.best_estimator_, x_test)
logger.time_log('Testing Complete.\n')
test_evaluation_frame = EvaluationFrame(y_test, y_test_predict)
test_evaluation_frame.save('%s_predict.p' % self.name)
test_proba_evaluation_frame = None
if record_predict_proba:
logger.time_log('Testing Holdout Partition (probability)...')
y_test_predict_proba = batch_predict_proba(results.best_estimator_, x_test)
test_proba_evaluation_frame = EvaluationFrame(y_test, y_test_predict_proba)
test_proba_evaluation_frame.save('%s_predict_proba.p' % self.name)
logger.time_log('Testing Complete.\n')
evaluator = Evaluator(logger)
evaluator.evaluate_classifier_result(
results,
test_evaluation_frame,
train=train_evaluation_frame,
test_proba=test_proba_evaluation_frame,
multiclass=multiclass
)
logger.close()
self.hyper_parameters.params = results.best_params_
self.hyper_parameters.save('%s_params.p' % self.name)
self.trained_estimator = results.best_estimator_
def run_classification_experiment(
self,
sample=None,
random_state=None,
test_size=0.25,
multiclass=False,
record_predict_proba=False):
"""
Running a classification experiment is used when only a single model run and fit is necessary.
"""
use_project_path()
logger = Logger('%s.txt' % self.name)
data_frame = self.df
if sample is not None:
data_frame = data_frame.sample(n=sample, random_state=random_state)
x_train, x_test, y_train, y_test = train_test_split(data_frame, data_frame[self.target], test_size=test_size)
if self.hyper_parameters is not None:
self.estimator.set_params(**self.hyper_parameters.params)
logger.time_log('Training Model...')
self.estimator.fit(x_train, y_train)
logger.time_log('Training Complete.\n')
logger.time_log('Testing Training Partition...')
y_train_predict = batch_predict(self.estimator, x_train)
logger.time_log('Testing Complete.\n')
train_evaluation_frame = EvaluationFrame(y_train, y_train_predict)
logger.time_log('Testing Holdout Partition...')
y_test_predict = batch_predict(self.estimator, x_test)
logger.time_log('Testing Complete.\n')
test_evaluation_frame = EvaluationFrame(y_test, y_test_predict)
test_evaluation_frame.save('%s_predict.p' % self.name)
test_proba_evaluation_frame = None
if record_predict_proba:
logger.time_log('Testing Holdout Partition (probability)...')
y_test_predict_proba = batch_predict_proba(self.estimator, x_test)
test_proba_evaluation_frame = EvaluationFrame(y_test, y_test_predict_proba)
test_proba_evaluation_frame.save('%s_predict_proba.p' % self.name)
logger.time_log('Testing Complete.\n')
evaluator = Evaluator(logger)
evaluator.evaluate_classifier_result(
self.estimator,
test_evaluation_frame,
train=train_evaluation_frame,
test_proba=test_proba_evaluation_frame,
multiclass=multiclass
)
logger.close()
if self.hyper_parameters is not None:
self.hyper_parameters.save('%s_params.p' % self.name)
def run_classification_experiment(self,
sample=None,
random_state=None,
test_size=0.20,
multiclass=False,
record_predict_proba=False,
sampling=None,
cv=5,
verbose=True,
transformer=None,
fit_increment=None,
warm_start=False,
max_iters=None,
n_jobs=-1):
use_project_path()
logger = Logger('%s.txt' % self.name)
evaluator = Evaluator(logger)
data_frame = self.df
if sample is not None:
data_frame = data_frame.sample(n=sample, random_state=random_state)
x_train, x_test, y_train, y_test = train_test_split(
data_frame, data_frame[self.target], test_size=test_size)
if transformer is not None:
logger.time_log('Fitting Transformer...')
transformer.fit(x_train)
logger.time_log('Transformer Fit Complete.\n')
if sampling is not None:
logger.time_log('Starting Data Re-Sampling...')
logger.log('Original Training Shape is %s' % Counter(y_train))
x_new, y_new = sampling.fit_resample(x_train, y_train)
logger.log('Balanced Training Shape is %s' % Counter(y_new))
if hasattr(x_train, 'columns'):
x_new = pd.DataFrame(x_new, columns=x_train.columns)
x_train, y_train = x_new, y_new
logger.time_log('Re-Sampling Complete.\n')
logger.time_log('Shuffling Re-Sampled Data.\n')
x_train, y_train = shuffle(x_train,
y_train,
random_state=random_state)
logger.time_log('Shuffling Complete.\n')
if self.hyper_parameters is not None:
self.estimator.set_params(**self.hyper_parameters.params)
if cv is not None:
kfold = StratifiedKFold(n_splits=cv, random_state=random_state)
logger.time_log('Cross Validating Model...')
fold_scores = Parallel(n_jobs=n_jobs, verbose=3)(
delayed(crossfold_classifier)
(clone(self.estimator), transformer, x_train, y_train,
train_index, test_index, record_predict_proba, verbose,
fit_increment, warm_start, max_iters, random_state)
for train_index, test_index in kfold.split(x_train, y_train))
logger.time_log('Cross Validation Complete.\n')
logger.time_log('Training Model...')
if fit_increment is not None:
if max_iters is not None:
for iter in range(max_iters):
x_iter_train, y_iter_train = shuffle(
x_train, y_train, random_state=random_state)
batch_fit_classifier(self.estimator,
x_iter_train,
y_iter_train,
transformer=transformer,
increment=fit_increment,
verbose=verbose)
else:
batch_fit_classifier(self.estimator,
x_train,
y_train,
transformer=transformer,
increment=fit_increment,
verbose=verbose)
else:
if transformer is not None:
x_train_transformed = transformer.transform(x_train)
self.estimator.fit(x_train_transformed, y_train)
else:
self.estimator.fit(x_train, y_train)
logger.time_log('Training Complete.\n')
logger.time_log('Testing Training Partition...')
y_train_predict = batch_predict(self.estimator,
x_train,
transformer=transformer,
verbose=verbose)
logger.time_log('Testing Complete.\n')
train_evaluation_frame = EvaluationFrame(y_train, y_train_predict)
logger.time_log('Testing Holdout Partition...')
y_test_predict = batch_predict(self.estimator,
x_test,
transformer=transformer,
verbose=verbose)
logger.time_log('Testing Complete.\n')
test_evaluation_frame = EvaluationFrame(y_test, y_test_predict)
test_evaluation_frame.save('%s_predict.p' % self.name)
test_proba_evaluation_frame = None
if record_predict_proba:
logger.time_log('Testing Holdout Partition (probability)...')
y_test_predict_proba = batch_predict_proba(self.estimator,
x_test,
transformer=transformer,
verbose=verbose)
test_proba_evaluation_frame = EvaluationFrame(
y_test, y_test_predict_proba)
test_proba_evaluation_frame.save('%s_predict_proba.p' % self.name)
logger.time_log('Testing Complete.\n')
if cv is not None:
evaluator.evaluate_fold_scores(fold_scores)
evaluator.evaluate_classifier_result(
self.estimator,
test_evaluation_frame,
train=train_evaluation_frame,
test_proba=test_proba_evaluation_frame,
multiclass=multiclass)
logger.close()
if self.hyper_parameters is not None:
self.hyper_parameters.save('%s_params.p' % self.name)
self.trained_estimator = self.estimator
def run_classification_search_experiment(self,
scoring,
sample=None,
random_state=None,
test_size=0.20,
n_jobs=-1,
n_iter=2,
cv=5,
verbose=3,
multiclass=False,
record_predict_proba=False,
sampling=None):
use_project_path()
logger = Logger('%s.txt' % self.name)
search = BayesSearchCV(self.estimator,
self.hyper_parameters.search_space,
n_jobs=n_jobs,
n_iter=n_iter,
cv=cv,
verbose=verbose,
scoring=scoring,
return_train_score=True)
data_frame = self.df
if sample is not None:
data_frame = data_frame.sample(n=sample, random_state=random_state)
x_train, x_test, y_train, y_test = train_test_split(
data_frame, data_frame[self.target], test_size=test_size)
if sampling is not None:
logger.time_log('Starting Data Re-Sampling...')
logger.log('Original Training Shape is %s' % Counter(y_train))
x_new, y_new = sampling.fit_resample(x_train, y_train)
logger.log('Balanced Training Shape is %s' % Counter(y_new))
if hasattr(x_train, 'columns'):
x_new = pd.DataFrame(x_new, columns=x_train.columns)
x_train, y_train = x_new, y_new
logger.time_log('Re-Sampling Complete.\n')
logger.time_log('Shuffling Re-Sampled Data.\n')
x_train, y_train = shuffle(x_train,
y_train,
random_state=random_state)
logger.time_log('Shuffling Complete.\n')
logger.time_log('Starting HyperParameter Search...')
results = search.fit(x_train, y_train)
logger.time_log('Search Complete.\n')
logger.time_log('Testing Training Partition...')
y_train_predict = batch_predict(results.best_estimator_, x_train)
logger.time_log('Testing Complete.\n')
train_evaluation_frame = EvaluationFrame(y_train, y_train_predict)
logger.time_log('Testing Holdout Partition...')
y_test_predict = batch_predict(results.best_estimator_, x_test)
logger.time_log('Testing Complete.\n')
test_evaluation_frame = EvaluationFrame(y_test, y_test_predict)
test_evaluation_frame.save('%s_predict.p' % self.name)
test_proba_evaluation_frame = None
if record_predict_proba:
logger.time_log('Testing Holdout Partition (probability)...')
y_test_predict_proba = batch_predict_proba(results.best_estimator_,
x_test)
test_proba_evaluation_frame = EvaluationFrame(
y_test, y_test_predict_proba)
test_proba_evaluation_frame.save('%s_predict_proba.p' % self.name)
logger.time_log('Testing Complete.\n')
evaluator = Evaluator(logger)
evaluator.evaluate_classifier_result(
results,
test_evaluation_frame,
train=train_evaluation_frame,
test_proba=test_proba_evaluation_frame,
multiclass=multiclass)
logger.close()
self.hyper_parameters.params = results.best_params_
self.hyper_parameters.save('%s_params.p' % self.name)
self.trained_estimator = results.best_estimator_
