def validate_test():
from preprocess import PreProcess
p = PreProcess()
global model, train_indices, val_indices, train_loader, test_loader, optimizer
model = MonocularVelocityNN(initial_depth=config["depth"])
dataset = VideoDataLoader(directory=str(Path.cwd() / "data/testprocdir"),
delta=config["delta"],
Y=p.labels,
depth=config["depth"])
# Creating data indices for training and validation splits:
dataset_size = len(dataset)
val_indices = list(
range(1, dataset_size - config["delta"] - config["depth"]))
# valid_sampler = Sampler(val_indices)
test_loader = DataLoader(dataset,
batch_size=config["test_batch_size"],
sampler=val_indices)
load_weights(Path.cwd() / "data/weights.pt")
if not device:
raise RuntimeError("Only use model with Cuda")
model.to(device)
validate(is_labels=False)
def main(self):
preprocess = PreProcess()
self.wordsList, self.wordVectors = preprocess.load_word2vec()
data, list_id, labels, types, max_length = preprocess.load_mutations()
self.maxSeqLength = max_length + 1
# create dictionary of type and it's respective value in int
count = 0
for i in types:
dic = {i: count}
self.types.update(dic)
count = count + 1
self.numClasses = len(self.types)
train_df, test_df, labels_train, labels_test = self.split_data(
data, labels)
ids_train, ids_test = self.create_matrix(train_df, test_df)
# Spit out details about data
print("\n=================================\nData details:")
print("- Training-set:\t{}".format(len(train_df)))
print("- Test-set:\t\t{}".format(len(test_df)))
print("- Classes:\t\t{}".format(self.types))
print("=================================\n\n")
self.train_model(ids_train, labels_train, ids_test, labels_test)
def submit():
if request.form['text_input'] == "" or len(
request.form['text_input']) < 10:
return "Please provide input large enough, Classifier can understand :)"
# todo: change column name to be dynamically taken from training file
test_data = pd.DataFrame([request.form['text_input']],
columns=['Document'])
session_id = request.cookies['session']
path = os.path.join(app.config['UPLOAD_FOLDER'], session_id)
trained_classifier = [i for i in os.listdir(path) if '.pkl' in i]
vectorizer = os.path.join(path, 'tfidf_vectorizer.pk')
tfidf_transformer = joblib.load(vectorizer)
pre_processor = PreProcess(test_data, column_name='Document')
test_data = pre_processor.clean_html()
test_data = pre_processor.remove_non_ascii()
test_data = pre_processor.remove_spaces()
test_data = pre_processor.remove_punctuation()
test_data = pre_processor.stemming()
test_data = pre_processor.lemmatization()
test_data = pre_processor.stop_words()
test_data1 = tfidf_transformer.transform(test_data.Document)
result = {}
for clf in trained_classifier:
model = joblib.load(os.path.join(path, clf))
print(clf, model.predict(test_data1)[0])
classifier_name = clf.split('/')[-1].split('.')[0]
result[classifier_name] = model.predict(test_data1)[0]
print(result)
return render_template('results.html', result=result)
def interence_main():
CHECKPOINT_PATH = './model/seq2seq_ckpt-7800'
preprocess = PreProcess()
# with tf.variable_scope('nmt_model', reuse=None):
model = NMTModel()
test_sentence = 'this is a test.'
test_sentence = preprocess.english2id(test_sentence)
print(preprocess.id2english(test_sentence))
output_op = model.inference(test_sentence)
sess = tf.Session()
saver = tf.train.Saver()
saver.restore(sess, CHECKPOINT_PATH)
with tf.variable_scope('nmt_model', reuse=None):
model = NMTModel()
test_sentence = 'this is a test.'
test_sentence = preprocess.english2id(test_sentence)
output_op = model.inference(test_sentence)
sess = tf.Session()
saver = tf.train.Saver()
saver.restore(sess, CHECKPOINT_PATH)
output = sess.run(output_op)
print(output)
print(preprocess.id2chinese(output))
sess.close()
def __init__(self, bus_stop_distance, traffic_light_distance, array_stops,
array_trfl):
super().__init__()
self.bst_dist = bus_stop_distance
self.trfl_dist = traffic_light_distance
self.array_stops = array_stops
self.array_trfl = array_trfl
self.prepro = PreProcess()
def execute_ALPR(event):
"""
runs the full license plate recognition process.
function is called when user clicks on the execut button on the gui
"""
#time the function execution
start_time = time.time()
root_folder = os.path.dirname(os.path.realpath(__file__))
models_folder = os.path.join(root_folder, 'ml_models')
pre_process = PreProcess(imagepath)
plate_like_objects = pre_process.get_plate_like_objects()
# plotting.plot_cca(pre_process.full_car_image,
# pre_process.plate_objects_cordinates)
license_plate = license_plate_extract(plate_like_objects, pre_process)
if len(license_plate) == 0:
return False
ocr_instance = OCROnObjects(license_plate)
if ocr_instance.candidates == {}:
wx.MessageBox("No character was segmented",
"Character Segmentation" ,wx.OK|wx.ICON_ERROR)
return False
# plotting.plot_cca(license_plate, ocr_instance.candidates['coordinates'])
deep_learn = DeepMachineLearning()
text_result = deep_learn.learn(ocr_instance.candidates['fullscale'],
os.path.join(models_folder, 'SVC_model', 'SVC_model.pkl'),
(20, 20))
text_phase = TextClassification()
scattered_plate_text = text_phase.get_text(text_result)
plate_text = text_phase.text_reconstruction(scattered_plate_text,
ocr_instance.candidates['columnsVal'])
print 'ALPR process took '+ str(time.time() - start_time) + ' seconds'
listResult.InsertStringItem(listRow, plate_text)
plate_num = Mvrd(plate_text)
details = plate_num.get_data()
if details == False or details == {}:
wx.MessageBox("Vehicle Information could not be retrieved",
"Information Retrieval", wx.OK|wx.ICON_ERROR)
return False;
listResult.SetStringItem(listRow, 1, details['Owner Name'])
listResult.SetStringItem(listRow, 2, details['Isssue Date'])
listResult.SetStringItem(listRow, 3, details['Expiry Date'])
listResult.SetStringItem(listRow, 4, details['Chasis Number'])
listResult.SetStringItem(listRow, 5, details['Model'])
#db_aspect.save_alpr(plate_text, str(datetime.today()))
def main():
data = get_data(
'/Users/aditya1/Documents/Document_Classification/bbc-dataset')
###############################################################################
# Data Pre-processing steps
###############################################################################
column_name = data.columns[0]
# print(column_name)
pre_processor = PreProcess(data, column_name)
# todo: change code to provide all functions in class definition.
pre_processor_operations = ['clean_html']
data = pre_processor.clean_html()
data = pre_processor.remove_non_ascii()
data = pre_processor.remove_spaces()
data = pre_processor.remove_punctuation()
data = pre_processor.stemming()
data = pre_processor.lemmatization()
data = pre_processor.stop_words()
###############################################################################
# Feature extraction
###############################################################################
train_x, test_x, train_y, test_y = train_test_split(data.Document,
data.Category,
test_size=0.20)
# print(train_x.shape, train_y.shape)
# print(test_x.shape, test_y.shape)
tfidf_transformer = TfidfVectorizer(min_df=1)
train_vectors = tfidf_transformer.fit_transform(train_x)
joblib.dump(tfidf_transformer, 'vectorizer.pkl')
test_vectors = tfidf_transformer.transform(test_x)
print(data.head())
###############################################################################
# Perform classification with SVM, kernel=linear
model1 = svm.SVC(kernel='linear')
model1.fit(train_vectors, train_y)
joblib.dump(model1, 'SVM.pkl')
y_pred_class = model1.predict(test_vectors)
print(metrics.accuracy_score(test_y, y_pred_class))
print("Prediction score for classifier %s:\n%s\n" %
(model1, metrics.accuracy_score(test_y, y_pred_class)))
print("Classification report for classifier %s:\n%s\n" %
(model1, metrics.classification_report(test_y, y_pred_class)))
model2 = MultinomialNB()
model2.fit(train_vectors, train_y)
joblib.dump(model2, 'MultinomialNB.pkl')
y_pred_class = model2.predict(test_vectors)
print("Accuracy score:", metrics.accuracy_score(test_y, y_pred_class))
print("Confusion Matrix for classifier %s:\n%s\n" %
(model2, metrics.confusion_matrix(test_y, y_pred_class)))
print("Classification report for classifier %s:\n%s\n" %
(model2, metrics.classification_report(test_y, y_pred_class)))
def train():
from preprocess import PreProcess
p = PreProcess()
global model, train_indices, val_indices, train_loader, test_loader, optimizer
model = MonocularVelocityNN(initial_depth=config["depth"])
dataset = VideoDataLoader(directory=str(Path.cwd() / "data/processed"),
delta=config["delta"],
Y=p.labels,
depth=config["depth"])
# Creating data indices for training and validation splits:
dataset_size = len(dataset)
indices = list(range(1, dataset_size - config["delta"] - config["depth"]))
split = int(np.floor(config["split"] * dataset_size))
if config["randomize"]:
np.random.seed(0)
np.random.shuffle(indices)
train_indices, val_indices = indices[split:], indices[:split]
train_sampler = SubsetRandomSampler(train_indices)
valid_sampler = SubsetRandomSampler(val_indices)
train_loader = DataLoader(dataset,
batch_size=config["batch_size"],
sampler=train_sampler)
test_loader = DataLoader(dataset,
batch_size=config["test_batch_size"],
sampler=valid_sampler)
if config["TRAIN"]:
if not device:
raise RuntimeError("Only use model with Cuda")
model.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=config["lr"])
for epoch in range(1, config["epochs"] + 1):
try:
train(epoch)
validate()
except:
raise
else:
load_weights(Path.cwd() / "data/weights.pt")
if not device:
raise RuntimeError("Only use model with Cuda")
model.to(device)
validate()
def train(config, device, RS='Supervised'):
# Init tokenizer.
tokenizer = Tokenizer(config.temp_dir, config.jieba_dict_file,
config.remove_stopwords, config.stopwords_file,
config.ivr)
# Init feature index.
feature_index = FeatureIndex(config, tokenizer=tokenizer)
file_list = [config.labeled_file]
if config.extra_train_file is not None:
file_list.append(config.extra_train_file)
if config.valid_file is not None:
file_list.append(config.valid_file)
feature_index.build_index(file_list)
# Preprocess data.
pre_process = PreProcess(config)
train_data_dir, valid_data_dir, final_train_file, final_valid_file = pre_process.train_preprocess(
)
# Get PyTorch dataset.
train_dataset = MixnetDataset(config, train_data_dir, feature_index,
tokenizer)
valid_dataset = MixnetDataset(config, valid_data_dir, feature_index,
tokenizer, True)
# Get NER model if necessary and compatible.
need_ner = False
for (feature, feature_config) in config.feature_config_dict.items():
need_ner = need_ner or ("text" in feature_config.get(
"type", "") and feature_config.get("seg_type", "word") == "char"
and feature_config.get("ner", False))
if need_ner:
logger.info("Enable NER, loading NER model...")
# Use predict mode since we cannot train it without tag information.
ner_model = NERModel(device, "predict")
else:
logger.info("Disable NER.")
ner_model = None
# Get PyTorch data loader.
train_data_loader = DataLoader(train_dataset,
batch_size=1,
shuffle=False,
num_workers=config.read_workers)
valid_data_loader = DataLoader(valid_dataset,
batch_size=1,
shuffle=False,
num_workers=config.read_workers)
# Init model.
model = MixNet(config.model_config_dict,
config.output_config_dict,
feature_index.feature_info_dict,
feature_index.label_info_dict,
ner_model=ner_model)
# Train model.
solver = Solver(config, train_data_loader, valid_data_loader,
feature_index, model, device, RS)
solver.build()
solver.train()
def train(start=1, end=31):
# preprocess training data
preprocess = PreProcess()
preprocess.set_date_range(start, end)
preprocess.process()
la = LineAnalysis()
la.set_date_range(start, end)
la.process()
# train model
train = Train()
train.fit()
return
def execute_ALPR():
"""
runs the full license plate recognition process.
function is called when user clicks on the execute button on the gui
"""
# time the function execution
start_time = time.time()
root_folder = os.path.dirname(os.path.realpath(__file__))
models_folder = os.path.join(root_folder, 'ml_models')
# import requests
# r = requests.get(url, auth=('admin', 'admin'))
# file = open(imagepath, "w")
# file.write(r.content)
# file.close()
imagepath=url
pre_process = PreProcess(imagepath)
plate_like_objects = pre_process.get_plate_like_objects()
# plotting.plot_cca(pre_process.full_car_image,
# pre_process.plate_objects_cordinates)
license_plate = license_plate_extract(plate_like_objects, pre_process)
if len(license_plate) == 0:
return False
ocr_instance = OCROnObjects(license_plate)
if ocr_instance.candidates == {}:
# print("No Characters Was Segmented")
# wx.MessageBox("No character was segmented",
# "Character Segmentation" ,wx.OK|wx.ICON_ERROR)
return False
# plotting.plot_cca(license_plate, ocr_instance.candidates['coordinates'])
deep_learn = DeepMachineLearning()
text_result = deep_learn \
.learn(ocr_instance.candidates['fullscale'], os.path.join(models_folder, 'SVC_model', 'SVC_model.pkl'),
(20, 20))
text_phase = TextClassification()
scattered_plate_text = text_phase.get_text(text_result)
plate_text = text_phase.text_reconstruction(scattered_plate_text, ocr_instance.candidates['columnsVal'])
# print('ALPR process took ' + str(time.time() - start_time) + ' seconds')
print(plate_text)
def __init__(self):
self.preprocess = PreProcess()
self.CDBG_map = None
self.precincts_wards_map = None
self.directory_path = 'result'
if not os.path.exists(self.directory_path):
os.mkdir(self.directory_path)
self.refuse_routes_directory_path = os.path.join(self.directory_path, 'refuse_routes')
if not os.path.exists(self.refuse_routes_directory_path):
os.mkdir(self.refuse_routes_directory_path)
def run(self, hand, rgb=False, box=None, hand_crop=None, depth=None):
if rgb and box and hand_crop.any() and depth.any():
phand = self._rgb_hand_seg(hand, box, hand_crop, depth)
else:
phand = PreProcess().median_smooth(hand, self.MEDIAN_DIM)
tmp = phand.copy()
cont = self._get_largest_contour(phand)
box = cv2.boundingRect(cont)
crop = self._crop_box(tmp, box)
self._contour = cont
self._box = box
return (cont, box, crop)
def main(self):
preprocess = PreProcess()
self.wordsList, self.wordVectors = preprocess.load_word2vec()
data, list_id, labels, types, max_length = preprocess.load_mutations()
self.maxSeqLength = max_length + 1
# create dictionary of type and it's respective value in int
count = 0
for i in types:
dic = {i: count}
self.types.update(dic)
count = count + 1
self.numClasses = len(self.types)
train_df, test_df, labels_train, labels_test = self.split_data(
data, labels)
# remove last element to make it even number
train_df = train_df[:-1]
labels_train = labels_train[:-1]
div = int(len(train_df) / self.k)
# get K = 5 batches
train_1, train_2, train_3, train_4, train_5 = [
train_df[i:i + div] for i in range(0, len(train_df), div)
]
labels_1, labels_2, labels_3, labels_4, labels_5 = [
labels_train[i:i + div] for i in range(0, len(labels_train), div)
]
ids_test = self.create_matrix_teste(test_df)
ids_train1 = self.create_matrix_train(train_1)
ids_train2 = self.create_matrix_train(train_2)
ids_train3 = self.create_matrix_train(train_3)
ids_train4 = self.create_matrix_train(train_4)
ids_train5 = self.create_matrix_train(train_5)
train_bins = [train_1, train_2, train_3, train_4, train_5]
label_bins = [labels_1, labels_2, labels_3, labels_4, labels_5]
ids_train = [
ids_train1, ids_train2, ids_train3, ids_train4, ids_train5
]
self.train_model(train_bins, label_bins, ids_train)
def execute_ALPR(event):
"""
runs the full license plate recognition process.
function is called when user clicks on the execut button on the gui
"""
#time the function execution
start_time = time.time()
root_folder = os.path.dirname(os.path.realpath(__file__))
models_folder = os.path.join(root_folder, 'ml_models')
pre_process = PreProcess(imagepath)
plate_like_objects = pre_process.get_plate_like_objects()
# plotting.plot_cca(pre_process.full_car_image,
# pre_process.plate_objects_cordinates)
license_plate = license_plate_extract(plate_like_objects, pre_process)
if len(license_plate) == 0:
return False
ocr_instance = OCROnObjects(license_plate)
#plotting.plot_cca(license_plate, ocr_instance.candidates['coordinates'])
deep_learn = DeepMachineLearning()
text_result = deep_learn.learn(ocr_instance.candidates['fullscale'],
os.path.join(models_folder, 'SVC_model', 'SVC_model.pkl'),
(20, 20))
text_phase = TextClassification()
scattered_plate_text = text_phase.get_text(text_result)
plate_text = text_phase.text_reconstruction(scattered_plate_text,
ocr_instance.candidates['columnsVal'])
print 'ALPR process took '+ str(time.time() - start_time) + ' seconds'
listResult.InsertStringItem(listRow, plate_text)
listResult.SetStringItem(listRow, 1, str(datetime.today()))
db_aspect.save_alpr(plate_text, str(datetime.today()))
def read_process_data(path, files_path):
data = pd.read_csv(path)
column_name = data.columns[0]
# print(column_name)
pre_processor = PreProcess(data, column_name)
# todo: change code to provide all functions in class definition.
data = pre_processor.clean_html()
data = pre_processor.remove_non_ascii()
data = pre_processor.remove_spaces()
data = pre_processor.remove_punctuation()
data = pre_processor.stemming()
data = pre_processor.lemmatization()
data = pre_processor.stop_words()
train_x, test_x, train_y, test_y = train_test_split(data.Document,
data.Category,
test_size=0.20)
tfidf_transformer = TfidfVectorizer(min_df=1)
train_vectors = tfidf_transformer.fit_transform(train_x)
vectorizer_path = os.path.join(files_path, 'tfidf_vectorizer.pk')
joblib.dump(tfidf_transformer, vectorizer_path)
return train_vectors, train_y
def main():
dowload_dataset()
preprocess = PreProcess()
df = preprocess.load_tsv()
features = preprocess.clean_data(df)
features = preprocess.balance_data(features, 15000)
x_train, y_train, x_test, y_test = preprocess.split_data(features)
y_train_round, y_test_round = preprocess.round_labels(y_train, y_test)
y_train_one, y_test_one = preprocess.labels_to_one_hot(
y_train_round, y_test_round)
lr = Logistic_Regression()
lr.fit_and_evaluate(x_train, y_train_round, x_test, y_test_round)
nn1 = NeuralNetwork1()
nn1.fit_and_evaluate(x_train, y_train_one, x_test, y_test_one)
linr = Linear_Regression()
linr.fit_and_evaluate(x_train, y_train, x_test, y_test)
nn2 = NeuralNetwork2()
nn2.fit_and_evaluate(x_train, y_train, x_test, y_test)
print("\n___________________End of the output___________________")
def __init__(self):
super().__init__()
self.folderPath = "textData"
# without transforming string
# List structure: [[data1][data1_label][data2][data2_label][data3][data3_label]]
#self.lemmatizedList = self.preProcess.load_data1(self.folderPath)
#Extract data
#self.dataList = self.getDataList()
#print(self.dataList.__len__())
#self.allWords = self.extractSentence()
#print(self.allWords.__len__())
#Extract label
#self.Labels = self.extractLabels()
#print(self.Labels.__len__())
# transform lemmatized_list(3 generators) into a long string separate by space
#self.sparseWords = self.preProcess.cleanByFrequency(self.allWords)
# filter by sequence
#self.cleanedSentences = self.preProcess.getCleanedSent(self.sparseWords,self.dataList)
'''Pre Process'''
self.preProcess = PreProcess(self.folderPath)
'''i = 0
for x in self.preProcess.cleanedSentences:
i+=1
print(str(i) + ": ")
print(x)'''
# vectorise remnant of sentences
#self.X_train = self.preProcess.vector_Data(self.cleanedSentences,self.Labels)
'''Process Sentence Level'''
self.sentenceLevel = SentenceLevel(self.preProcess.cleanedSentences)
'''Process Document Level'''
# Start TensorFlow Session
self.documentLevel = DocumentLevel(self.sentenceLevel.docInput,self.preProcess.Labels)
# display results
#self.displayData()
'''Summary All Process'''
def post(self):
popu.drop
abc = PreProcess()
return {'inserted':'everything'},301
from preprocess import PreProcess
import time
start_time = time.time()
thisTime = start_time
files = []
dataFolder = os.path.dirname(os.path.abspath(__file__)) + "/data"
resultFolder = os.path.dirname(os.path.abspath(__file__)) + "/result"
count = 0
commonWordList = {}
for i in os.listdir(dataFolder):
if i.endswith('.txt'):
thisFile = os.path.join(dataFolder, i)
reflection = open(thisFile, "r", encoding="utf8")
processData = PreProcess(reflection.read())
wordList = processData.getWordList(reflection.read(), True)
wordFrequency = processData.wordFrequency(wordList)
for wordTuple in wordFrequency:
commonWordList[wordTuple[0]] = commonWordList[
wordTuple[0]] + wordTuple[1] if wordTuple[
0] in commonWordList else wordTuple[1]
print("--- %s seconds ---" % (time.time() - thisTime))
thisTime = time.time()
reflection.close()
result = open(resultFolder + "/wordfrequency.csv", "a+")
result.write("Word,WordCount\n")
def setup_class(self):
image_path = path.join(path.dirname(path.realpath(__file__)))
image_path = path.join(
path.split(image_path)[0], 'test_images', 'car6.jpg')
self.image_array = imread(image_path, as_grey=True)
self.pre_process = PreProcess(image_path)
# -*- coding: utf-8 -*-
"""
@author: Samip
"""
from preprocess import PreProcess
from classifier import Classifier
from modelEvaluation import ModelEvaluation
#Create the objects
pp = PreProcess()
classifier = Classifier()
me = ModelEvaluation()
#Preprocess the data
x_train, x_test, y_train, y_test = pp.scale_data()
choice = int(
input(
"Enter 1 for Logistic Regression, 2 for Decision Tree Classifier, 3 for KNN, 4 for Naive Bayes, 5 for Random Forest, 6 for SVM, 7 for XG Boost, 8 for Adaptive Boosting, 9 for LDA: "
))
clf = {
1: classifier.logistic_regression,
2: classifier.decision_tree_classifer,
3: classifier.knn,
4: classifier.naive_bayes,
5: classifier.random_forest,
6: classifier.svm,
7: classifier.xg_boost,
8: classifier.ada_boost,
pca = RandomizedPCA(n_components=2)
X = pca.fit_transform(X)
fig = pyplot.figure()
pyplot.plot(X[y==False,0],X[y==False,1],'ro')
pyplot.plot(X[y==True,0],X[y==True,1],'bo')
pyplot.title('2D Visualization, Crossmatch LivDet 2013 Testing, ConvNet 5 Layers+PCA')
pyplot.show()
"""
testing = Testing()
testing.divide_by = 5
testing.n_processes_pproc = 3
lstFilesX, y = testing.load_dataset('Training', 'LivDet2011', 'digital')
#PCA only
pproc = PreProcess('',1,False,False,False,False,1.0,None,\
None,None,None,None,None,None,None,None,None)
X = pproc.transform(lstFilesX)
pca = RandomizedPCA(n_components=2)
X = pca.fit_transform(X)
fig = pyplot.figure()
pyplot.plot(X[y == False, 0], X[y == False, 1], 'ro')
pyplot.plot(X[y == True, 0], X[y == True, 1], 'bo')
pyplot.title('2D Visualization, Digital LivDet 2011 Training, PCA only')
pyplot.show()
#LBP+PCA
pproc = PreProcess('LBP',1,False,False,False,False,1.0,None,\
None,None,None,None,None,None,'uniform',[7,7],False)
X = pproc.transform(lstFilesX)
pca = RandomizedPCA(n_components=2)
X = pca.fit_transform(X)
class NB():
classification = {}
number_of_docs = None
vocab_unique = None
classes = None
number_of_docs = None
inputData = None
vocab_unique = None
classes = None
words = {}
process = PreProcess()
testing = {}
train_file = None
test_file = None
def __init__(self, train=None, test=None, train_dir=None, test_dir=None):
"""
:aim: Classify for either a file or given directory
:param train: Train file if a file is given
:param test: Test file is a file is given
:param train_dir: Train directory if directory is given
:param test_dir: Test directory if directory is given
"""
if train is not None or test is not None:
self.train_file = train
self.test_file = test
self.number_of_docs = self.process.getNumberOfDocs(train)
self.inputData = self.process.processFile(train)
self.vocab_unique = self.process.createVocab(self.inputData)
self.classes = self.process.getClasses(self.inputData)
else:
for i in os.listdir(train_dir + "neg"):
self.number_of_docs += self.process.getNumberOfDocs(train)
self.inputData += self.process.processFile(train)
self.vocab_unique += self.process.createVocab(self.inputData)
self.classes += self.process.getClasses(self.inputData)
for i in os.listdir(train_dir + "pos"):
self.number_of_docs += self.process.getNumberOfDocs(train)
self.inputData += self.process.processFile(train)
self.vocab_unique += self.process.createVocab(self.inputData)
self.classes += self.process.getClasses(self.inputData)
"""
for i in os.listdir(test_dir + "neg"):
for i in os.listdir(test_dir + "pos"):
"""
def classify(self):
"""
:aim: Classify training
:return: None
"""
for c in self.classes:
temp_dict = {}
for word in self.vocab_unique:
temp_dict[word] = 0
self.classification[c] = temp_dict
# keys = classes, values = count of words in document given class
for key in self.classification:
for line in self.inputData:
if line[len(line) - 1] != key:
continue
else:
for word in line:
if word not in self.classification[key]:
continue
(self.classification[key])[word] += 1
def count_words(self):
"""
:aim: Count words per class
:return: None
"""
for cl in self.classes:
count = 0
for k in self.classification[cl]:
count += self.classification[cl][k]
self.words[cl] = count
def classify_smoothed(self):
"""
:aim: Add one smoothing to probability
:return: None
"""
self.dict_likelihood = self.classification.copy()
for cl in self.classes:
for k in self.dict_likelihood[cl]:
self.dict_likelihood[cl][k] = (self.dict_likelihood[cl][k] + 1) / float(self.words[cl] +
len(self.vocab_unique))
def test_probability(self):
"""
:aim: Test data on training
:return: None
"""
# Number of documents, given a class
for cl in self.classes:
d = self.process.numberOfDocsGivenClass(cl, self.inputData)
self.process.docs[cl] = d
# prior probabilities
self.priors = {}
for cl in self.classes:
if cl not in self.priors:
self.priors[cl] = self.process.docs[cl] / float(self.number_of_docs)
with open(self.test_file) as f:
content = f.readlines()
content = [x.strip() for x in content]
test_data = content
# add classes as keys to testing dict, values = prior probabilities
for cl in self.classes:
if cl not in self.testing:
self.testing[cl] = self.priors[cl]
for cl in self.classes:
for w in test_data:
for key in self.dict_likelihood[cl]:
if key != w:
continue
elif key == w:
self.testing[cl] *= self.dict_likelihood[cl][w]
# Compute the most likely class
self.result = max(self.testing, key=self.testing.get)
def output(self):
"""
:aim: Write final classification output to file
:return: None
"""
string = \
"Total number of documents/lines in input" + str(self.number_of_docs) + "\n" + \
"Vocabulary of unique words: " + str(self.vocab_unique) + "\n"+ \
"Classes:" + str(self.classes) + "\n"+ \
"Count of words, given class: " + str(self.words) + "\n"+ \
"Word likelihoods with add - 1 smoothing with respect to class: " + str(self.dict_likelihood) + "\n"+ \
"Prior probabilities: ", str(self.priors) + "\n" + \
"Probabilities of test data: " + str(self.testing) + "\n" + \
"The most likely class for the test document: " + self.result
if self.train_file is not None:
f = open("movies-small.txt", "w")
f.write("\n".join(string))
f.close()
else:
#for multiple directory implementation
pass
def run_pipeline(self,
feat_extract_name,
X_train,
y_train,
X_test=None,
y_test=None):
if self.DEBUG >= 1:
print 'training...'
antes1 = datetime.now()
pproc= PreProcess(feat_extract_name = feat_extract_name, n_processes = self.n_processes_pproc, size_percentage = self.size_percentage, \
roi = self.roi, high_pass = self.high_pass,low_pass = self.low_pass, gauss_noise = self.gauss_noise, \
feature_extractor__method = self.lbp__method, feature_extractor__n_tiles = self.lbp__n_tiles, \
feature_extractor__n_filters = self.n_filters, feature_extractor__shape_norm = self.shape_norm, \
feature_extractor__shape_conv = self.shape_conv, feature_extractor__shape_pool = self.shape_pool, \
feature_extractor__stride_pool = self.stride_pool, feature_extractor__stoc_pool = self.stoc_pool, \
feature_extractor__div_norm = self.div_norm, \
feature_extractor__region_shape = self.region_shape, feature_extractor__region_stride = self.region_stride, \
feature_extractor__top_regions = self.top_regions, feature_extractor__stride_pool_recurrent = self.stride_pool_recurrent, \
feature_extractor__analysis_shape = self.analysis_shape, multi_column = self.multi_column, \
augmentation = self.augmentation, aug_rotate = self.aug_rotate
)
norm = preprocessing.StandardScaler(copy=True)
piplist = []
if self.cross_validation:
piplist.append(('pproc', pproc))
piplist.append(('norm', norm))
if self.use_pca:
pca = RandomizedPCA2(
whiten=True,
random_state=0,
n_components=self.pca__n_components,
copy=True
) #Must use fit_transform instead of fit() and then transform() when copy=false
#from sklearn.decomposition import PCA
#pca = PCA(whiten=True, n_components=self.pca__n_components, copy=True)
#pca = FastICA(whiten=True,random_state=0, n_components=self.pca__n_components, max_iter=400)
#pca = SparsePCA(random_state=0, n_components=self.pca__n_components) #Must use fit_transform instead of fit() and then transform() when copy=false
piplist.append(('pca', pca))
if self.use_lda:
lda = LDA(n_components=self.lda__n_components)
piplist.append(('lda', lda))
if self.predict.lower() == 'svm':
if self.svm__kernel.lower() == 'rbf':
pred = SVC2(kernel='rbf',
class_weight='auto',
random_state=0,
C=self.svm__C,
gamma=self.svm__gamma,
multi_column=self.multi_column,
augmentation=self.augmentation,
aug_rotate=self.aug_rotate)
else:
pred = LinearSVC2(random_state=0,
fit_intercept=False,
class_weight='auto',
C=self.svm__C,
augmentation=self.augmentation)
elif self.predict.lower() == 'sgd':
pred = SGD2(loss="hinge",
penalty="l2",
l1_ratio=0.05,
random_state=0,
n_iter=5,
shuffle=True,
augmentation=self.augmentation,
alpha=self.sgd__alpha)
elif self.predict.lower() == 'knn':
pred = KNeighborsClassifier(n_neighbors=self.knn__n_neighbors,
weights=self.knn__weights)
piplist.append(('pred', pred))
pipeline = Pipeline(piplist)
if self.cross_validation:
params_grid = self.params_auto.copy()
params_grid.update(self.dicPredict[self.predict])
params_grid.update(self.params_pproc)
if feat_extract_name.lower() != 'none':
params_grid.update(self.dicfeat_extract[feat_extract_name])
pipelineGridSearch = GridSearchCV2(
pipeline,
params_grid,
cv=self.n_folds,
verbose=0,
n_jobs=self.n_processes_cv,
n_jobs_last_estimator=self.n_processes_cv_last_estimator,
augmentation=self.augmentation,
auto_adjust_params=None,
testing=self)
pipelineGridSearch.fit(X_train, y_train)
#gridsearchRef = GridSearchCV(pipeline, params_grid, cv=self.n_folds, iid=True, scoring = 'roc_auc', verbose=0, n_jobs=1)
#gridsearchRef.fit(X_train,y_train)
#print 'ReF=== score=', gridsearchRef.best_score_,'params=', gridsearchRef.best_params_
return pipelineGridSearch.best_score_, pipelineGridSearch.best_params_
else:
antes = datetime.now()
X_train = pproc.transform(X_train)
antes2 = datetime.now()
X_test = pproc.transform(X_test)
time_pproc = datetime.now() - antes2
if self.multi_column:
y_pred_train = []
y_pred_test = []
if self.aug_rotate:
multiply = 30
else:
multiply = 10
for i in range(multiply):
pipeline = pipeline.fit(X_train[i::multiply, :], y_train)
y_pred_train.append(
pipeline.predict(X_train[i::multiply, :]))
y_pred_test.append(pipeline.predict(
X_test[i::multiply, :]))
y_pred_train = np.mean(np.asarray(y_pred_train), axis=0)
y_pred_train[y_pred_train >= 0] = 1
y_pred_train[y_pred_train < 0] = 0
y_pred_test = np.mean(np.asarray(y_pred_test), axis=0)
y_pred_test[y_pred_test >= 0] = 1
y_pred_test[y_pred_test < 0] = 0
else:
pipeline = pipeline.fit(X_train, y_train)
#save the classifier
with open(
self.temp_dir + 'clf_' + self.datasettrain.lower() +
'_' + self.sensortrain.lower() + '_' +
self.feat_extract_name.lower() + '.pkl',
'wb') as output:
pickle.dump(pipeline, output, pickle.HIGHEST_PROTOCOL)
y_pred_train = pipeline.predict(X_train)
antes2 = datetime.now()
y_pred_test = []
for i in range(0, len(X_test), self.mini_batch_size_test):
y_pred_test.extend(
list(
pipeline.predict(
X_test[i:i + self.mini_batch_size_test])))
test_time = (datetime.now() - antes2) + time_pproc
print 'Tempo Predict= ', test_time # DEBUG
print 'Numero de amostras', str(len(X_test))
score_training = 100. - (100. *
roc_auc_score(y_train, y_pred_train))
print 'score_training=', score_training
score = 100. - (100. *
roc_auc_score(y_test, np.asarray(y_pred_test)))
total_time = datetime.now() - antes # DEBUG
pca = pipeline.steps[-2][1]
pca_total_variance = None
if hasattr(pca, 'explained_variance_ratio_'):
pca_total_variance = np.sum(pca.explained_variance_ratio_)
pred = pipeline.steps[-1][1]
n_support_ = None
if hasattr(pred, 'n_support_'):
n_support_ = pred.n_support_
self.append_results(params=None,
score_mean=score,
score_std=None,
total_time=total_time,
test_time=test_time,
n_test_samples=str(len(X_test)),
score_training=score_training,
n_svm_vectors=n_support_,
pca_total_variance=pca_total_variance)
return score, None
if self.DEBUG >= 1:
print 'Tempo Fit Pipeline= ', (datetime.now() - antes1) # DEBUG
from corpus_reader import CorpusReader
from preprocess import PreProcess
from tf_idf import TfIdf
from knn import KNN
from metrics import MetricsGenerator
from pprint import pprint as pp
if __name__ == '__main__':
print('reading...')
reader = CorpusReader()
reader.run()
parser = PreProcess()
parsed_trainning_documents = {}
print('processing...')
for k, v in reader.train.items():
parsed_trainning_documents[k] = parser.process(v)
# Entrada para o tf-idf, devemos anotar os documentos com suas classes.
# Receberá como entrada um array de tuplas: ([tokens], classe)
parsed_trainning_documents_with_classes = []
for k in parsed_trainning_documents.keys():
parsed_trainning_documents_with_classes += [(v, k) for v in parsed_trainning_documents[k]]
# Execução tf-idf
print('generating tf.idf...')
tf_idf_calculator = TfIdf(parsed_trainning_documents_with_classes)
tf_idf_calculator.run()
# testa os parâmetros do knn: métrica de distância e valor de K
for metric in ['cosine', 'euclid']:
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.utils import np_utils
# spark, elephas
from keras.optimizers import SGD, Adam
# classes
from preprocess import PreProcess
from file_io import FileIO
import datetime
## MODEL ##
p = PreProcess('./datasets/ag_dataset.txt')
x_train, x_test, y_train, y_test, num_classes = p.run()
# Convert class vectors to binary class matrices
y_train = np_utils.to_categorical(y_train, num_classes)
y_test = np_utils.to_categorical(y_test, num_classes)
# Reshape
dimension = x_train.shape[1]
x_train = x_train.reshape(x_train.shape[0], 1, dimension, 1)
x_test = x_test.reshape(x_test.shape[0], 1, dimension, 1)
print('# Training Data', x_train.shape, y_train.shape)
print('# Testing Data', x_test.shape, y_test.shape)
# model config
epoch_step = 10
pool_size = (1, 2)
def __init__(self, list_of_features):
super().__init__()
self.features = list_of_features
self.prepro = PreProcess()
"""
Created on Wed May 27 07:11:18 2020
@author: rusha
"""
import models
import shap
import pathlib
from preprocess import PreProcess
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.metrics import f1_score, precision_score, recall_score, accuracy_score
preproc = PreProcess()
preproc.splitStandardize()
X_train, _, y_train, y_test = models.Model('tree').getData()
estimator = models.Model('tree').getXGBModel()
estimator.fit(X_train, y_train)
importances = estimator.feature_importances_
cwd = pathlib.Path().absolute()
indices = np.argsort(importances)[::-1]
cols = X_train.columns.tolist()
std = np.std([tree.feature_importances_ for tree in estimator.estimators_],
axis=0)
tempcols = []
for f in range(X_train.shape[1]):
print("%d. feature %s (%f)" %
(f + 1, cols[indices[f]], importances[indices[f]]))
return cv2.undistort(img, self.mtx, self.dist, None, self.mtx)
def warpperspectiv_img(self, img):
# Perspective Transform
undistort = self.undistort_img(img)
return cv2.warpPerspective(undistort, self.M, undistort.shape[1::-1])
def unwarpperspectiv_img(self, warped_img):
return cv2.warpPerspective(warped_img, self.Minv, warped_img.shape[1::-1])
pipline = PipleLine(camera_wide_dist_file)
img_thresh = ImageThresh(r_thresh=(170, 255), s_thresh=(200, 255))
fun_names = ['s_r_threshhold','r_threshshold', 's_threshhold', 'l_b_threshhold', 'x_b_threshhold']
# fun_names = ['l_b_threshhold']
pre = PreProcess(img_thresh, pipline.Minv, fun_names)
def process_image(image):
undis_image = pipline.undistort_img(image)
warped_image = pipline.warpperspectiv_img(image)
try:
result = pre.draw_data(undis_image, warped_image)
return result
except Exception as e:
cv2.imwrite('not_found_line.jpg', image)
cv2.imwrite('not_found_warp.jpg', warped_image)
print(traceback.format_exc())
project_output = 'harder_challenge_video_output.mp4'
clip1 = VideoFileClip("harder_challenge_video.mp4")