def __init__(self):
'''
Constructor
'''
self.dsr = DatasetReader()
self.fenc = FreemanEncoder()
self.training_data = []
class KNN_strings(object):
'''
classdocs
'''
def __init__(self, n_neighbors=1):
'''
Constructor
'''
self.dsr = DatasetReader()
self.fenc = FreemanEncoder()
self.data = []
self.knn = KNeighborsClassifier(n_neighbors=n_neighbors, algorithm='auto', metric=self.lev_metric)
def lev_metric(self, x, y):
i, j = int(x[0]), int(y[0]) # extract indices
# if self.data[i] == self.data[j]:
# print self.data[i], self.data[j], edit_dist(self.data[i], self.data[j])
return edit_dist(self.data[i], self.data[j])
def knn_train(self, dataset, cv=1, datasplit=0.7):
images_dataset= self.dsr.read_dataset_images(dataset)
freeman_code_dict = self.fenc.encode_freeman_dataset(images_dataset)
_, codes, labels = self.dsr.gen_labelled_arrays(freeman_code_dict)
self.data = codes
X = np.arange(len(self.data)).reshape(-1, 1)
if cv <= 1:
self.knn.fit(X, labels)
elif cv > 1:
cv_result = cross_validation.cross_val_score(self.knn, X, labels, cv=cv)
print cv_result
print 'Training Done!'
def knn_predict(self, test_data, score=False):
images_dataset= self.dsr.read_dataset_images(test_data)
freeman_code_dict = self.fenc.encode_freeman_dataset(images_dataset)
_, codes, labels = self.dsr.gen_labelled_arrays(freeman_code_dict)
X_pred = np.arange(len(codes)).reshape(-1, 1)
predictions = self.knn.predict(X_pred)
if score == True:
accuracy = self.knn.score(X_pred, labels)
print "Test Accuracy: ", accuracy
return predictions
def knn_predict_one(self, test_image):
image_code = self.fenc.encode_freeman(test_image)
print image_code
data = [image_code]
X_pred = np.arange(len(data)).reshape(-1, 1)
prediction = self.knn.predict(X_pred)
return prediction
def __init__(self):
'''
Constructor
'''
ml_alg_base.__init__(self)
self.dsr = DatasetReader()
self.learning_model = naive_bayes.GaussianNB()
def __init__(self, n_neighbors=1):
'''
Constructor
'''
self.dsr = DatasetReader()
self.fenc = FreemanEncoder()
self.data = []
self.knn = KNeighborsClassifier(n_neighbors=n_neighbors,
algorithm='auto',
metric=self.lev_metric)
def __init__(self):
'''
Constructor
'''
self.dsr = DatasetReader()
self.fenc = FreemanEncoder()
states = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
symbols = ['0', '1', '2', '3', '4', '5', '6', '7']
self.learning_model = HiddenMarkovModelTrainer(states=states,
symbols=symbols)
self.model = None
def build_stacked_ae(path):
"""
Build the stacked auto-encoder neural network, and evaluate its performance
:param path: Path to the genetic dataset
:return: Accuracy of classification of cell cycle phase.
"""
############### Stacked Auto-Encoders ##############
dr = DatasetReader(path)
train = dr.load_data()
ae = StackedAutoencoder(train[0], train[1], train[2], 3)
ae.create_autoencoder()
result = ae.evaluate_autoencoder()
return result[1] * 100
print("Accuracy: %.2f%%" % (result[1] * 100))
def __init__(self):
'''
Constructor
'''
self.dsr = DatasetReader()
self.fenc = FreemanEncoder()
self.training_data = []
def __init__(self, n_neighbors=1):
'''
Constructor
'''
self.dsr = DatasetReader()
self.fenc = FreemanEncoder()
self.data = []
self.knn = KNeighborsClassifier(n_neighbors=n_neighbors, algorithm='auto', metric=self.lev_metric)
def __init__(self):
'''
Constructor
'''
self.dsr = DatasetReader()
self.fenc = FreemanEncoder()
states = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
symbols = ['0', '1', '2', '3', '4', '5', '6', '7']
self.learning_model = HiddenMarkovModelTrainer(states=states, symbols=symbols)
self.model = None
def evaluationMode(settings, read, detailEva):
print("Evaluation mode!")
clinicalNotes = DatasetReader.readClinicalNotes(settings["dataset"]["directory"], settings["dataset"]["name"])
if read:
nejiAnnotations = Annotator.readNejiAnnotations(settings["dataset"]["neji_annotations"])
else:
nejiAnnotations = Annotator.annotate(clinicalNotes)
Evaluator.evaluateNeji(clinicalNotes, nejiAnnotations, detailEva)
annotations = Annotator.postProcessing(clinicalNotes, nejiAnnotations, settings["post_vocabularies"])
Evaluator.evaluateAnnotations(clinicalNotes, annotations, detailEva)
print("Done!")
def annotationMode(settings, read):
print("Annotation mode!")
clinicalNotes = DatasetReader.readClinicalNotes(settings["dataset"]["directory"], settings["dataset"]["name"])
if read:
nejiAnnotations = Annotator.readNejiAnnotations(settings["dataset"]["neji_annotations"])
else:
nejiAnnotations = Annotator.annotate(clinicalNotes)
Writer.writeAnnotations(nejiAnnotations, settings["dataset"]["neji_annotations"])
annotations = Annotator.postProcessing(clinicalNotes, nejiAnnotations, settings["post_vocabularies"])
matrix = Writer.writeMatrix(annotations, settings["dataset"]["matrix_location"])
print("Done!")
return matrix, clinicalNotes
def build_binary_classifiers(path_g1_sg2m, path_g1s_g2m):
"""
Build the stacked neural network with single output neuron for binary classification to G1 vs. S+G2M and
G1+S vs. G2M phases, and evaluate its performance
:param path_g1_sg2m: Path to the labeled dataset in two labels : G1 and SG2M
:param path_g1s_g2m: Path to the labeled dataset in two labels : G1S and G2M
:return: Accuracy of classification of each model.
"""
############### Ordinal Classifier #################
dr1 = DatasetReader(path_g1_sg2m)
dr2 = DatasetReader(path_g1s_g2m)
binary_train1 = dr1.load_data()
binary_train2 = dr2.load_data()
oc1 = OrdinalClassifier(binary_train1[0], binary_train1[1])
oc2 = OrdinalClassifier(binary_train2[0], binary_train2[1])
r1 = oc1.classify()
r2 = oc2.classify()
return r1, r2
class HMM(object):
'''
classdocs
'''
def __init__(self):
'''
Constructor
'''
self.dsr = DatasetReader()
self.fenc = FreemanEncoder()
states = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
symbols = ['0', '1', '2', '3', '4', '5', '6', '7']
self.learning_model = HiddenMarkovModelTrainer(states=states,
symbols=symbols)
self.model = None
def generate_labelled_sequences(self, freeman_codes_dict):
labeled_sequences = []
labeled_symbols = []
codes_list = freeman_codes_dict.items()
for tup in codes_list:
for code in tup[1]:
temp = []
for symbol in code:
temp.append((symbol, tup[0]))
labeled_symbols.append(temp)
for tup in codes_list:
for code in tup[1]:
labeled_sequences.append((code, tup[0]))
codes = numpy.array([x[0] for x in labeled_sequences])
labels = numpy.array([y[1] for y in labeled_sequences])
return labeled_symbols, labeled_sequences, codes, labels
def learning_curve(self,
dataset,
n_iter,
train_sizes=numpy.linspace(0.1, 1.0, 5)):
cv_scores = []
train_scores = []
for i in train_sizes:
data = dataset[:int(len(dataset) * i)]
cv_score = []
t_score = []
for j in range(n_iter):
cv_score.extend(self.training(dataset, cv=10, n_iter=1))
train_score, test_score = self.training(dataset, n_iter=1)
t_score.extend(train_score)
cv_scores.append(cv_score)
train_scores.append(t_score)
cv_scores = numpy.array(cv_scores)
train_scores = numpy.array(train_scores)
print cv_scores.shape
print train_scores.shape
return train_scores, cv_scores
def get_data(self, dataset_path):
dataset = self.dsr.read_dataset_images(dataset_path)
freeman_codes_dict = self.fenc.encode_freeman_dataset(dataset)
labeled_symbols, labeled_sequence, codes, labels = self.generate_labelled_sequences(
freeman_codes_dict)
return labeled_symbols, labeled_sequence, codes, labels
def training(self, dataset, cv=1, n_iter=1):
if isinstance(dataset, basestring):
labeled_symbols, labeled_sequence, codes, labels = self.get_data(
dataset)
else:
labeled_symbols, labeled_sequence, codes, labels = dataset
self.model = self.learning_model.train(labeled_symbols)
if cv > 1:
cv_scores = []
for i in range(n_iter):
skf = cross_validation.KFold(len(labels),
n_folds=10,
shuffle=True)
iter_score = []
for train_index, test_index in skf:
train_data = list(
numpy.array(labeled_symbols)[train_index])
test_data = list(numpy.array(labeled_symbols)[test_index])
self.model = self.learning_model.train(train_data)
fold_score = self.model.evaluate(test_data)
iter_score.append(fold_score)
cv_scores.append(numpy.mean(iter_score))
return cv_scores
else:
skf = cross_validation.ShuffleSplit(len(labels),
n_iter=n_iter,
test_size=0.2,
random_state=0)
training_score = []
test_score = []
for train_index, test_index in skf:
train_data = list(numpy.array(labeled_symbols)[train_index])
test_data = list(numpy.array(labeled_symbols)[test_index])
self.model = self.learning_model.train(train_data)
training_score.append(self.model.evaluate(train_data))
test_score.append(self.model.evaluate(test_data))
if n_iter == 1:
predict_labels = []
for i in range(len(list(codes[test_index]))):
predicted_states = self.model.tag(
list(codes[test_index])[i])
predict_labels.append(predicted_states[0][1])
self.ConfusionMatrix = ConfusionMatrix(
list(labels[test_index]), predict_labels)
return training_score, test_score
def predict(self, image_path):
if os.path.isfile(image_path):
image_array = self.dsr.read_img_bw(image_path)
freeman_code = self.fenc.encode_freeman(image_array)
else:
freeman_code = image_path
predicted_states = self.model.tag(freeman_code)
predicted_states = [x[1] for x in predicted_states]
if len(set(predicted_states)) == 1:
predicted_class = list(set(predicted_states))[0]
return predicted_class
## TESTING CODE (WILL BE REMOVED) ##
# from HMM import HMM
# hmm = HMM()
# cv_scores = hmm.training('I:\\eclipse_workspace\\CharacterRecognition\\teams_dataset', cv=10, n_iter=50)
# train_score, test_score = hmm.training('I:\\eclipse_workspace\\CharacterRecognition\\teams_dataset', n_iter=1)
# with open('hmm_confusion_matrix.txt', 'w') as fp:
# fp.write(hmm.ConfusionMatrix.__str__())
#
# with open("./Results/hmm.txt", 'w') as fp:
# for i in range(len(cv_scores)):
# text = str(cv_scores[i]) + ',' + str(train_score[i]) + ',' + str(test_score[i]) + '\n'
# print text
# print '--------------------------------'
# fp.write(text)
if (dropAll):
m20Connector.dropAll()
m20Connector.close()
sys.exit(0)
if (initDB):
"""
Create tables
insert first level tables = movies, genome_tags
"""
m20Connector.initDB()
# Init to read
moviesDS = DatasetReader.initWithFraction(dataset_path + '/movies.csv',
1.0,
',',
init=True)
gtagsDS = DatasetReader.initWithFraction(dataset_path +
'/genome-tags.csv',
1.0,
',',
init=True)
linksDS = DatasetReader.initWithFraction(dataset_path + '/links.csv',
1.0,
',',
init=True)
#Just init
ratingsDS = DatasetReader(dataset_path + "/ratings.csv", init=True)
tagsDS = DatasetReader(dataset_path + "/tags.csv", init=True)
gscoresDS = DatasetReader(dataset_path + "/genome-scores.csv",
class HMM(object):
'''
classdocs
'''
def __init__(self):
'''
Constructor
'''
self.dsr = DatasetReader()
self.fenc = FreemanEncoder()
states = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
symbols = ['0', '1', '2', '3', '4', '5', '6', '7']
self.learning_model = HiddenMarkovModelTrainer(states=states, symbols=symbols)
self.model = None
def generate_labelled_sequences(self, freeman_codes_dict):
labeled_sequences = []
labeled_symbols = []
codes_list = freeman_codes_dict.items()
for tup in codes_list:
for code in tup[1]:
temp = []
for symbol in code:
temp.append((symbol, tup[0]))
labeled_symbols.append(temp)
for tup in codes_list:
for code in tup[1]:
labeled_sequences.append((code,tup[0]))
codes = numpy.array([x[0] for x in labeled_sequences])
labels = numpy.array([y[1] for y in labeled_sequences])
return labeled_symbols, labeled_sequences, codes, labels
def learning_curve(self, dataset, n_iter, train_sizes=numpy.linspace(0.1, 1.0, 5)):
cv_scores = []
train_scores = []
for i in train_sizes:
data = dataset[:int(len(dataset)*i)]
cv_score = []
t_score = []
for j in range(n_iter):
cv_score.extend(self.training(dataset, cv=10, n_iter=1))
train_score, test_score = self.training(dataset, n_iter=1)
t_score.extend(train_score)
cv_scores.append(cv_score)
train_scores.append(t_score)
cv_scores = numpy.array(cv_scores)
train_scores = numpy.array(train_scores)
print cv_scores.shape
print train_scores.shape
return train_scores, cv_scores
def get_data(self, dataset_path):
dataset = self.dsr.read_dataset_images(dataset_path)
freeman_codes_dict = self.fenc.encode_freeman_dataset(dataset)
labeled_symbols, labeled_sequence, codes, labels = self.generate_labelled_sequences(freeman_codes_dict)
return labeled_symbols, labeled_sequence, codes, labels
def training(self, dataset, cv=1, n_iter=1):
if isinstance(dataset, basestring):
labeled_symbols, labeled_sequence, codes, labels = self.get_data(dataset)
else:
labeled_symbols, labeled_sequence, codes, labels = dataset
self.model = self.learning_model.train(labeled_symbols)
if cv > 1:
cv_scores = []
for i in range(n_iter):
skf = cross_validation.KFold(len(labels), n_folds=10, shuffle=True)
iter_score = []
for train_index, test_index in skf:
train_data = list(numpy.array(labeled_symbols)[train_index])
test_data = list(numpy.array(labeled_symbols)[test_index])
self.model = self.learning_model.train(train_data)
fold_score = self.model.evaluate(test_data)
iter_score.append(fold_score)
cv_scores.append(numpy.mean(iter_score))
return cv_scores
else:
skf = cross_validation.ShuffleSplit(len(labels), n_iter=n_iter, test_size=0.2, random_state=0)
training_score = []
test_score = []
for train_index, test_index in skf:
train_data = list(numpy.array(labeled_symbols)[train_index])
test_data = list(numpy.array(labeled_symbols)[test_index])
self.model = self.learning_model.train(train_data)
training_score.append(self.model.evaluate(train_data))
test_score.append(self.model.evaluate(test_data))
if n_iter==1:
predict_labels = []
for i in range(len(list(codes[test_index]))):
predicted_states = self.model.tag(list(codes[test_index])[i])
predict_labels.append(predicted_states[0][1])
self.ConfusionMatrix = ConfusionMatrix(list(labels[test_index]), predict_labels)
return training_score, test_score
def predict(self, image_path):
if os.path.isfile(image_path):
image_array = self.dsr.read_img_bw(image_path)
freeman_code = self.fenc.encode_freeman(image_array)
else:
freeman_code = image_path
predicted_states = self.model.tag(freeman_code)
predicted_states = [x[1] for x in predicted_states]
if len(set(predicted_states)) == 1:
predicted_class = list(set(predicted_states))[0]
return predicted_class
## TESTING CODE (WILL BE REMOVED) ##
# from HMM import HMM
# hmm = HMM()
# cv_scores = hmm.training('I:\\eclipse_workspace\\CharacterRecognition\\teams_dataset', cv=10, n_iter=50)
# train_score, test_score = hmm.training('I:\\eclipse_workspace\\CharacterRecognition\\teams_dataset', n_iter=1)
# with open('hmm_confusion_matrix.txt', 'w') as fp:
# fp.write(hmm.ConfusionMatrix.__str__())
#
# with open("./Results/hmm.txt", 'w') as fp:
# for i in range(len(cv_scores)):
# text = str(cv_scores[i]) + ',' + str(train_score[i]) + ',' + str(test_score[i]) + '\n'
# print text
# print '--------------------------------'
# fp.write(text)
class KNN_strings(object):
'''
classdocs
'''
def __init__(self, n_neighbors=1):
'''
Constructor
'''
self.dsr = DatasetReader()
self.fenc = FreemanEncoder()
self.data = []
self.knn = KNeighborsClassifier(n_neighbors=n_neighbors,
algorithm='auto',
metric=self.lev_metric)
def lev_metric(self, x, y):
i, j = int(x[0]), int(y[0]) # extract indices
# if self.data[i] == self.data[j]:
# print self.data[i], self.data[j], edit_dist(self.data[i], self.data[j])
return edit_dist(self.data[i], self.data[j])
def knn_train(self, dataset, cv=1, datasplit=0.7):
images_dataset = self.dsr.read_dataset_images(dataset)
freeman_code_dict = self.fenc.encode_freeman_dataset(images_dataset)
_, codes, labels = self.dsr.gen_labelled_arrays(freeman_code_dict)
self.data = codes
X = np.arange(len(self.data)).reshape(-1, 1)
if cv <= 1:
self.knn.fit(X, labels)
elif cv > 1:
cv_result = cross_validation.cross_val_score(self.knn,
X,
labels,
cv=cv)
print cv_result
print 'Training Done!'
def knn_predict(self, test_data, score=False):
images_dataset = self.dsr.read_dataset_images(test_data)
freeman_code_dict = self.fenc.encode_freeman_dataset(images_dataset)
_, codes, labels = self.dsr.gen_labelled_arrays(freeman_code_dict)
X_pred = np.arange(len(codes)).reshape(-1, 1)
predictions = self.knn.predict(X_pred)
if score == True:
accuracy = self.knn.score(X_pred, labels)
print "Test Accuracy: ", accuracy
return predictions
def knn_predict_one(self, test_image):
image_code = self.fenc.encode_freeman(test_image)
print image_code
data = [image_code]
X_pred = np.arange(len(data)).reshape(-1, 1)
prediction = self.knn.predict(X_pred)
return prediction
from datasets.caltechpedestrian import CaltechPedestrian
from datasets.bdd100k import BDD100K
from datasets.citypersons import CityPersons
from DatasetReader import DatasetReader
import logging
import os
logging.basicConfig(filename='example.log',level=logging.DEBUG)
base_dir_bdd100k = '/data/stars/share/STARSDATASETS/bdd100k'
for subset in ['train', 'val']:
db = BDD100K(name='bdd100k-{}'.format(subset), base_dir=base_dir_bdd100k, save_dir='./bdd100k-{}'.format(subset), subset=subset)
db.writedataframe()
reader = DatasetReader('./bdd100k-{}'.format(subset))
df = reader.get_annotations(query='category == "person"')
reader.plot_annotations(df=df, plot_cols=['xmin', 'ymin', 'xmax', 'ymax', 'category'])
base_dir_caltech = '/data/stars/user/uujjwal/datasets/pedestrian/caltech/caltechall-train'
db = CaltechPedestrian(name='caltechall-train', base_dir=base_dir_caltech, save_dir='./caltechall-train')
db.writedataframe()
reader = DatasetReader('./caltechall-train')
df = reader.get_annotations(query='object == "person"')
reader.plot_annotations(df=df, plot_cols=['xmin_full', 'ymin_full', 'xmax_full', 'ymax_full', 'object'])
base_dir_caltech = '/data/stars/user/uujjwal/datasets/pedestrian/caltech/caltechall-test'
db = CaltechPedestrian(name='caltechall-test', base_dir=base_dir_caltech, save_dir='./caltechall-test')
db.writedataframe()
reader = DatasetReader('./caltechall-test')
if (dropAll):
m20Connector.dropAll()
m20Connector.close()
sys.exit(0)
if (initDB):
"""
Create tables
insert first level tables = movies, genome_tags
"""
m20Connector.initDB()
# Init to read
moviesDS = DatasetReader.initWithFraction('datasets/data/movies.csv',
1.0,
',',
init=True)
gtagsDS = DatasetReader.initWithFraction(
'datasets/data/genome-tags.csv', 1.0, ',', init=True)
linksDS = DatasetReader.initWithFraction('datasets/data/links.csv',
1.0,
',',
init=True)
#Just init
ratingsDS = DatasetReader("datasets/data/ratings.csv", init=True)
tagsDS = DatasetReader("datasets/data/tags.csv", init=True)
gscoresDS = DatasetReader("datasets/data/genome-scores.csv", init=True)
if (init_clear == False):
for movie in moviesDS.readPercentage():
from DatasetReader import DatasetReader
if __name__ == '__main__':
moviesDS = DatasetReader.initWithFraction('datasets/movies.csv', 1.0, ',')
for m in moviesDS.readPercentage()[:10]:
print m
def __init__(self):
self.reader = DatasetReader()
class KNN(object):
'''
classdocs
'''
def __init__(self):
'''
Constructor
'''
self.dsr = DatasetReader()
self.fenc = FreemanEncoder()
self.training_data = []
def generate_labelled_sequences(self, freeman_codes_dict):
labelled_sequences = []
codes_list = freeman_codes_dict.items()
for tup in codes_list:
for code in tup[1]:
labelled_sequences.append((tup[0], code))
return labelled_sequences
def prepare_data(self, datas, training=[], test=[], split=0.80):
# Separate data into 2 sets, 1 is training and 1 is test,split is the ratio (the default is 0.70)
for data in range(len(datas) - 1):
if random.random() < split:
training.append(datas[data])
else:
test.append(datas[data])
def get_neighbors(self, training, test_instance, k):
# Get the list of nearest neighbors to a test instance
distances = []
for i in range(len(training) - 1):
dist = edit_dist(test_instance, training[i][1])
distances.append((training[i], dist))
distances.sort(key=operator.itemgetter(1))
neighbors = []
for x in range(0, k):
neighbors.append(distances[x][0])
return neighbors
def get_label(self, neighbors):
# Determine the label of a test instance base on its nearest neighbors
max = 0
labels = {}
for neighbor in neighbors:
if neighbor[0] not in labels:
labels[neighbor[0]] = 1
else:
labels[neighbor[0]] += 1
sorted_labels = sorted(labels.items(),
key=operator.itemgetter(1),
reverse=True)
return sorted_labels[0][0]
def evaluation(self, training, test):
# Evaluate the accuracy of knn
correct_count = 0
# k = int(math.ceil(len(training)/10))
k = 1
for test_data in test:
neighbors = self.get_neighbors(training, test_data[1], k)
label = self.get_label(neighbors)
if int(label) == int(test_data[0]):
correct_count += 1
print(float(correct_count) / len(test)) * 100
def knn_train(self, dataset_path, train_test_split=0.8):
dataset = self.dsr.read_dataset_images(dataset_path)
freeman_codes_dict = self.fenc.encode_freeman_dataset(dataset)
labelled_sequences = self.generate_labelled_sequences(
freeman_codes_dict)
training = []
test = []
# print labelled_sequences
self.prepare_data(labelled_sequences,
training,
test,
split=train_test_split)
self.training_data = training
if train_test_split != 1.0:
print "Training:" + len(training).__str__()
print "Test:" + len(test).__str__()
self.evaluation(training, test)
def knn_predict_one(self, image, k=1):
if os.path.isfile(image):
image_array = self.dsr.read_img_bw(image)
test = self.fenc.encode_freeman(image_array)
else:
test = image
# Try to find the nearest neighbors of the first sequences in training
neighbors = self.get_neighbors(self.training_data, test, k)
label = self.get_label(neighbors)
return label
class NaiveBayes(ml_alg_base):
'''
classdocs
'''
def __init__(self):
'''
Constructor
'''
ml_alg_base.__init__(self)
self.dsr = DatasetReader()
self.learning_model = naive_bayes.GaussianNB()
def get_data(self, dataset_path="./teams_dataset"):
data_dict = self.dsr.read_dataset_images(dataset_path)
_, data_set_x, data_set_y = self.dsr.gen_labelled_arrays(data_dict)
data_set_x = data_set_x.reshape(len(data_set_x), -1)
return data_set_x, data_set_y
def training(self, dataset_path, cv=1):
dataset = self.dsr.read_dataset_images(dataset_path)
_, images, labels = self.dsr.gen_labelled_arrays(dataset)
images = numpy.array(images)
#reshape images for input
data = images.reshape(len(images), -1)
if cv <= 1:
self.learning_model.fit(data, labels)
elif cv > 1:
cv_result = cross_validation.cross_val_score(self.learning_model,
data,
labels,
cv=cv)
return cv_result
pickle.dump(self.learning_model,
open("./Models/naivebayes_model.p", "wb"))
def predict(self, image_path):
try:
self.learning_model = pickle.load(
open("./Models/naivebayes_model.p", "rb"))
except:
print "Please train the Naive Bayes model first"
if isbasestring(image_path):
image = self.dsr.read_img_bw(image_path)
else:
image = image_path
image = image.reshape(-1, image.shape[0] * image.shape[1])
result = self.learning_model.predict(image)
return result
# from NaiveBayes import NaiveBayes
# NB = NaiveBayes()
# # NB.training('I:\\eclipse_workspace\\CharacterRecognition\\digits_dataset_clean', cv=5)
# # print NB.predict('I:\\eclipse_workspace\\CharacterRecognition\\test1.jpg')
# data_x, data_y = NB.get_data()
# print data_x.shape, data_y.shape
# NB.first_exp(data_x, data_y, NB.learning_model, algorithm_name='NaiveBayes' ,num_iter=50)
class KNN_statistic(object):
'''
classdocs
'''
def __init__(self):
'''
Constructor
'''
self.dsr = DatasetReader()
self.fenc = FreemanEncoder()
self.training_data = []
def generate_labelled_sequences(self, freeman_codes_dict):
labelled_sequences = []
codes_list = freeman_codes_dict.items()
for tup in codes_list:
for code in tup[1]:
labelled_sequences.append((tup[0], code))
return labelled_sequences
def prepare_data(self, arrays_data=[], arrays_labels=[], split=0.2):
# Separate data into 2 sets, 1 is training and 1 is test,split is the ratio (the default is 0.20)
ad_train, ad_test, al_train, al_test = train_test_split(
arrays_data, arrays_labels, test_size=split, random_state=42)
return ad_train, ad_test, al_train, al_test
def get_neighbors(self, data, data_label, test_instance, k):
# Get the list of nearest neighbors to a test instance
distances = []
for i in range(len(data)):
dist = edit_dist(test_instance, data[i])
distances.append((data[i], data_label[i], dist))
distances.sort(key=operator.itemgetter(2))
neighbors = []
for x in range(0, k):
neighbors.append([distances[x][0], distances[x][1]])
return neighbors
def get_label(self, neighbors):
# Determine the label of a test instance base on its nearest neighbors
labels = {}
for neighbor in neighbors:
if neighbor[1] not in labels:
labels[neighbor[1]] = 1
else:
labels[neighbor[1]] += 1
sorted_labels = sorted(labels.items(),
key=operator.itemgetter(1),
reverse=True)
return sorted_labels[0][0]
def evaluation(self,
data,
data_for_distance_caculation,
data_label,
data_for_distance_calculation_label,
k=3):
# Evaluate the accuracy of knn
correct_count = 0
for instance in range(0, len(data) - 1):
neighbors = self.get_neighbors(
data_for_distance_caculation,
data_for_distance_calculation_label, data[instance], k)
label = self.get_label(neighbors)
if int(label) == int(data_label[instance]):
correct_count += 1
return (float(correct_count) / len(data))
def knn_train(self, dataset_path, train_test_split=0.2):
dataset = self.dsr.read_dataset_images(dataset_path)
freeman_codes_dict = self.fenc.encode_freeman_dataset(dataset)
_, arrays_data, arrays_label = self.dsr.gen_labelled_arrays(
freeman_codes_dict)
arrays_data, arrays_label = shuffle(arrays_data, arrays_label)
ad_train, ad_test, al_train, al_test = self.prepare_data(
arrays_data, arrays_label, split=train_test_split)
# Cross validation with 5 folds
kf = KFold(len(ad_train), 5)
result = 0
for train_index, test_index in kf:
ad_train_kfold, ad_test_kfold = ad_train[train_index], ad_train[
test_index]
al_train_kfold, al_test_kfold = al_train[train_index], al_train[
test_index]
result += self.evaluation(ad_test_kfold,
ad_train_kfold,
al_test_kfold,
al_train_kfold,
k=2)
result_average = result / 5
# Result with the training
result_training = self.evaluation(ad_train,
ad_train,
al_train,
al_train,
k=2)
# Result with the test
result_test = self.evaluation(ad_test,
ad_train,
al_test,
al_train,
k=2)
return result_average, result_training, result_test
# knn = KNN_strings(n_neighbors=1)
# knn = KNN_statistic()
# results = []
# for x in range(50):
# result_average, result_training, result_test = knn.knn_train("/home/thovo/PycharmProjects/CharacterRecognition/digits_dataset", 0.2)
# text = result_average.__str__() + " , " + result_training.__str__() + " , " + result_test.__str__() + "\n"
# results.append(text)
#
#
# f = open("Results/knn.txt", "w")
# for item in results:
# f.write(item)
#
# f.close()
class KNN(object):
'''
classdocs
'''
def __init__(self):
'''
Constructor
'''
self.dsr = DatasetReader()
self.fenc = FreemanEncoder()
self.training_data = []
def generate_labelled_sequences(self, freeman_codes_dict):
labelled_sequences = []
codes_list = freeman_codes_dict.items()
for tup in codes_list:
for code in tup[1]:
labelled_sequences.append((tup[0],code))
return labelled_sequences
def prepare_data(self, datas, training=[], test=[], split=0.80):
# Separate data into 2 sets, 1 is training and 1 is test,split is the ratio (the default is 0.70)
for data in range(len(datas)-1):
if random.random() < split:
training.append(datas[data])
else:
test.append(datas[data])
def get_neighbors(self, training, test_instance, k):
# Get the list of nearest neighbors to a test instance
distances =[]
for i in range(len(training)-1):
dist = edit_dist(test_instance, training[i][1])
distances.append((training[i], dist))
distances.sort(key=operator.itemgetter(1))
neighbors = []
for x in range(0, k):
neighbors.append(distances[x][0])
return neighbors
def get_label(self, neighbors):
# Determine the label of a test instance base on its nearest neighbors
max = 0
labels = {}
for neighbor in neighbors:
if neighbor[0] not in labels:
labels[neighbor[0]] = 1
else:
labels[neighbor[0]] += 1
sorted_labels = sorted(labels.items(), key=operator.itemgetter(1), reverse=True)
return sorted_labels[0][0]
def evaluation(self, training, test):
# Evaluate the accuracy of knn
correct_count = 0
# k = int(math.ceil(len(training)/10))
k = 1
for test_data in test:
neighbors = self.get_neighbors(training, test_data[1], k)
label = self.get_label(neighbors)
if int(label) == int(test_data[0]):
correct_count += 1
print (float(correct_count)/len(test))*100
def knn_train(self, dataset_path, train_test_split=0.8):
dataset = self.dsr.read_dataset_images(dataset_path)
freeman_codes_dict = self.fenc.encode_freeman_dataset(dataset)
labelled_sequences = self.generate_labelled_sequences(freeman_codes_dict)
training = []
test = []
# print labelled_sequences
self.prepare_data(labelled_sequences,training,test, split=train_test_split)
self.training_data = training
if train_test_split != 1.0:
print "Training:" + len(training).__str__()
print "Test:" + len(test).__str__()
self.evaluation(training,test)
def knn_predict_one(self, image, k=1):
if os.path.isfile(image):
image_array = self.dsr.read_img_bw(image)
test = self.fenc.encode_freeman(image_array)
else:
test = image
# Try to find the nearest neighbors of the first sequences in training
neighbors = self.get_neighbors(self.training_data, test, k)
label = self.get_label(neighbors)
return label
class KNN_statistic(object):
'''
classdocs
'''
def __init__(self):
'''
Constructor
'''
self.dsr = DatasetReader()
self.fenc = FreemanEncoder()
self.training_data = []
def generate_labelled_sequences(self, freeman_codes_dict):
labelled_sequences = []
codes_list = freeman_codes_dict.items()
for tup in codes_list:
for code in tup[1]:
labelled_sequences.append((tup[0],code))
return labelled_sequences
def prepare_data(self, arrays_data=[], arrays_labels=[], split=0.2):
# Separate data into 2 sets, 1 is training and 1 is test,split is the ratio (the default is 0.20)
ad_train, ad_test, al_train, al_test = train_test_split(arrays_data, arrays_labels, test_size=split, random_state=42)
return ad_train, ad_test, al_train, al_test
def get_neighbors(self, data, data_label, test_instance, k):
# Get the list of nearest neighbors to a test instance
distances = []
for i in range(len(data)):
dist = edit_dist(test_instance, data[i])
distances.append((data[i], data_label[i], dist))
distances.sort(key=operator.itemgetter(2))
neighbors = []
for x in range(0, k):
neighbors.append([distances[x][0], distances[x][1]])
return neighbors
def get_label(self, neighbors):
# Determine the label of a test instance base on its nearest neighbors
labels = {}
for neighbor in neighbors:
if neighbor[1] not in labels:
labels[neighbor[1]] = 1
else:
labels[neighbor[1]] += 1
sorted_labels = sorted(labels.items(), key=operator.itemgetter(1), reverse=True)
return sorted_labels[0][0]
def evaluation(self, data, data_for_distance_caculation, data_label, data_for_distance_calculation_label, k=3):
# Evaluate the accuracy of knn
correct_count = 0
for instance in range(0, len(data)-1):
neighbors = self.get_neighbors(data_for_distance_caculation,data_for_distance_calculation_label, data[instance], k)
label = self.get_label(neighbors)
if int(label) == int(data_label[instance]):
correct_count += 1
return (float(correct_count)/len(data))
def knn_train(self, dataset_path, train_test_split=0.2):
dataset = self.dsr.read_dataset_images(dataset_path)
freeman_codes_dict = self.fenc.encode_freeman_dataset(dataset)
_, arrays_data, arrays_label = self.dsr.gen_labelled_arrays(freeman_codes_dict)
arrays_data, arrays_label = shuffle(arrays_data, arrays_label)
ad_train, ad_test, al_train, al_test = self.prepare_data(arrays_data, arrays_label, split=train_test_split)
# Cross validation with 5 folds
kf = KFold(len(ad_train), 5)
result = 0
for train_index, test_index in kf:
ad_train_kfold, ad_test_kfold = ad_train[train_index], ad_train[test_index]
al_train_kfold, al_test_kfold = al_train[train_index], al_train[test_index]
result += self.evaluation(ad_test_kfold, ad_train_kfold, al_test_kfold, al_train_kfold, k=2)
result_average = result/5
# Result with the training
result_training = self.evaluation(ad_train, ad_train, al_train, al_train, k=2)
# Result with the test
result_test = self.evaluation(ad_test, ad_train, al_test, al_train, k=2)
return result_average, result_training, result_test
# knn = KNN_strings(n_neighbors=1)
# knn = KNN_statistic()
# results = []
# for x in range(50):
# result_average, result_training, result_test = knn.knn_train("/home/thovo/PycharmProjects/CharacterRecognition/digits_dataset", 0.2)
# text = result_average.__str__() + " , " + result_training.__str__() + " , " + result_test.__str__() + "\n"
# results.append(text)
#
#
# f = open("Results/knn.txt", "w")
# for item in results:
# f.write(item)
#
# f.close()
def __init__(self, dataset_path, args):
self._dataset_path = dataset_path
self._documents = DatasetReader(dataset_path, args).read_dataset()
import numpy as np
from matplotlib import pyplot as plt
from Args import DIM, ROOT, EPOCHS, BATCH_SIZE, NUM_WORKERS, LEARNING_RATE
from DatasetReader import DatasetReader
from model import UNet
import torch.optim as optim
from copy import deepcopy
from Evaluation import MeanDiceCoefficient
if (__name__ == "__main__"):
model = UNet().cuda()
loss_fn = nn.BCELoss()
optimiser = optim.Adam(model.parameters(), lr=LEARNING_RATE)
trainset = DatasetReader(ROOT + "train/")
testset = deepcopy(trainset)
testset.setTrainMode(False)
trainloader = torch.utils.data.DataLoader(trainset,
batch_size=BATCH_SIZE,
shuffle=True,
num_workers=NUM_WORKERS)
testloader = torch.utils.data.DataLoader(testset,
batch_size=BATCH_SIZE,
shuffle=True,
num_workers=NUM_WORKERS)
for epoch in range(EPOCHS):
# Training phase