def __init__(self, fname, *args, **kargs):
Classifier.__init__(self, fname, *args, **kargs)
# sometimes a threshold value is trained during Bayesian
# classification to avoid classifying too many 'documents' as
# one kind or the other
self.thresholds = [1.0, 1.0]
def main():
parser = argparse.ArgumentParser(description='Clasificador de musica.\nToma los datos de entrenamiento de un archivo y utiliza algoritmos evolutivos para crear y mejorar las reglas de clasificación.')
parser.add_argument('-d', '--data', help='Archivo donde se encuentra la información fuente para el clasificador.')
args = vars(parser.parse_args())
"""
Los valores default son:
tamaño discretizacion - 100
poblacion de generacion - 10
min fitness para terminar - 0.9
numero a seleccionar - 4
porcentaje de mutacion - 0.05
maximo de generaciones - 10000
tipo de seleccion - ROULETTE_WHEEL_SELECTION
"""
defaults = [100, 10, 0.9, 4, 0.05, 10000, selection.ROULETTE_WHEEL_SELECTION]
classifier = Classifier(args['data'], discrete_intervals=defaults[0], size_rule_generation=defaults[1], filter_list=["skewness", "spectral_rolloff", "energy", "sv", "spread", "centroid", "obsi", "kurtosis"], log_results=True)
start = time.clock()
best_results = classifier.train(req_min_fitness=defaults[2], gen_select=defaults[3], mutation_prob=defaults[4], limit_generations=defaults[5])
duration = (time.clock() - start)*1000
print "Duration\t", duration, "ms"
print "Training endend."
print "Best results:", ', '.join([str(key) + " fitness: " + str(value['fitness']) for key, value in best_results.items()])
print "Testing:"
classifier.test()
print "Testing ended."
def main():
me=Classifier()
feature_counter=Counter()
feature_set=pickle.load(open('validation_set.pkl', 'rb'))
feature_set_labels=[]
for tweet, rating in feature_set:
print rating
try:
float(rating)
except:
continue
if float(rating)>0:
label='positive'
elif float(rating)<0:
label='negative'
else:
label='neutral'
feature_set_labels.append((tweet, label))
feature_list=chain.from_iterable([word_tokenize(process_tweet(tweet)) for tweet, sentiment in feature_set_labels])
for feat in feature_list:
feature_counter[feat]+=1
me.feature_list=[feat for feat, count in feature_counter.most_common(1000)]
ts=[(me.extract_features(tweet), label) for tweet, label in feature_set]
print 'training Maxent'
me.classifier=MaxentClassifier.train(ts)
return me
def main():
dbinfo = recover()
conn = MySQLdb.connect(**dbinfo)
cur = conn.cursor()
#Learn
sql = "SELECT id,article_text,trainpos,trainneg,trainneutral FROM articles WHERE trainset=1 AND (trainpos>0 OR trainneg>0 OR trainneutral>0)"
cur.execute(sql)
a = Learner()
for aid,article_text,trainpos,trainneg,trainneutral in cur.fetchall():
aid = int(aid)
items = [ (1, int(trainpos)),(0, int(trainneutral)),(-1, int(trainneg)) ]
classification = max(items, key=lambda x : x[1])[0]
a.add_string(article_text, classification)
a.train()
#Predict
sql = "SELECT id,article_text FROM articles"
cur.execute(sql)
b = Classifier(a)
for aid,article_text in cur.fetchall():
aid = int(aid)
classification = b.classify(article_text)
sql = "UPDATE articles SET score=%s WHERE id=%s"
args = [classification,aid]
cur.execute(sql,args)
print aid,classification
conn.commit()
def eval_classifier(classifierToUse, featuresToUse, testOrTrain="train"):
print("Chosen feature: {0}".format(featuresToUse) )
print("Chosen classifier: {0}".format(classifierToUse))
fe = FeatureExtractor(featuresToUse)
dataset = DataSet(fe)
classifier = Classifier()
evaluate = Evaluation()
print "test or Train %s" % testOrTrain
for feature_class, files in getTestData(testOrTrain).items():
print "%s" % testOrTrain
for f in files:
dataset.addFile(feature_class, f)
print "Dataset initialized"
print_class_stats(dataset.classes)
print "Test set created."
a_train, a_test, c_train, c_test = train_test_split(dataset.featureVector, dataset.classes, test_size=0.9)
c_pred = classifier.classification(a_train,a_test,c_train,c_test,classifierToUse)
evaluate.evaluate(c_pred,c_test,featuresToUse,classifierToUse)
def create_predict(HudongItem_csv):
# 读取neo4j内容
db = Neo4j()
db.connectDB()
data_set = db.getLabeledHudongItem('labels.txt')
classifier = Classifier('wiki.zh.bin')
classifier.load_trainSet(data_set)
classifier.set_parameter(weight=[1.0, 3.0, 0.2, 4.0, 0],k=10)
predict_List = readCSVbyColumn(HudongItem_csv, 'title')
file_object = open('predict_labels2.txt','a')
count = 0
vis = set()
for p in predict_List:
cur = HudongItem(db.matchHudongItembyTitle(p))
count += 1
title = cur.title
if title in vis:
continue
vis.add(title)
label = classifier.KNN_predict(cur)
print(str(title)+" "+str(label)+": "+str(count)+"/"+str(len(predict_List)))
file_object.write(str(title)+" "+str(label)+"\n")
file_object.close()
#create_predict('hudong_pedia2.csv')
def __init__(self, D, H, W, K, iternum):
Classifier.__init__(self, D, H, W, K, iternum)
self.L = 100 # size of hidden layer
""" Layer 1 Parameters """
# weight matrix: [M * L]
self.A1 = 0.01 * np.random.randn(self.M, self.L)
# bias: [1 * L]
self.b1 = np.zeros((1,self.L))
""" Layer 3 Parameters """
# weight matrix: [L * K]
self.A3 = 0.01 * np.random.randn(self.L, K)
# bias: [1 * K]
self.b3 = np.zeros((1,K))
""" Hyperparams """
# learning rate
self.rho = 1e-2
# momentum
self.mu = 0.9
# reg strencth
self.lam = 0.1
# velocity for A1: [M * L]
self.v1 = np.zeros((self.M, self.L))
# velocity for A3: [L * K]
self.v3 = np.zeros((self.L, K))
return
def runNeuralNetwork(train, test, batchSize, classNum, hLayer=None, mode=None, momentumFactor=0.0):
"""
A function that call the the classifier to train a learning model.
Args:
train: training examples (numpy)
test: testing examples (numpy)
batchSize: the number of training example for each iteration
classNum: the number of classes
hLayer: number of the hidden layer nodes (list)
mode: weight initializing mode
momentumFactor: momentum factor
"""
print ""
print "Neural Network =============================="
print " - number of hidden layer nodes:",
if hLayer is not None:
print hLayer
else:
print " default (one hidden layer with node number = 2 * feature number)"
print " - weight initialization mode:",
if mode is not None:
print mode
else:
print "default"
print " - momentum factor", momentumFactor
nn = Classifier("neural_network", hidden_layer=hLayer, weightInitMode=mode, momentumFactor=momentumFactor)
nn.train(train, test, classNum, batchSize)
nn.test(test, "test")
def build_model_mnist():
# CNN
filter_size = (5, 5)
activation = Rectifier().apply
pooling_size = (2, 2)
num_filters = 50
layer0 = ConvolutionalLayer(activation=activation, filter_size=filter_size, num_filters=num_filters,
pooling_size=pooling_size,
weights_init=Uniform(width=0.1),
biases_init=Uniform(width=0.01), name="layer_0")
filter_size = (3, 3)
activation = Rectifier().apply
num_filters = 20
layer1 = ConvolutionalLayer(activation=activation, filter_size=filter_size, num_filters=num_filters,
pooling_size=pooling_size,
weights_init=Uniform(width=0.1),
biases_init=Uniform(width=0.01), name="layer_1")
conv_layers = [layer0, layer1]
convnet = ConvolutionalSequence(conv_layers, num_channels= 1,
image_size=(28, 28))
convnet.initialize()
output_dim = np.prod(convnet.get_dim('output'))
mlp = MLP(activations=[Identity()], dims=[output_dim, 10],
weights_init=Uniform(width=0.1),
biases_init=Uniform(width=0.01), name="layer_2")
mlp.initialize()
classifier = Classifier(convnet, mlp)
classifier.initialize()
return classifier
def average_multiple_runs(num_runs, options, args):
for num, option in enumerate(options):
print "Running", num_runs, "iterations with options:", option
list_best_results = []
list_test_results = []
list_correct_results = []
for i in range(num_runs):
print "Running #" + str(i + 1)
classifier = Classifier(args['data'], discrete_intervals=option[0], size_rule_generation=option[1], filter_list=["skewness", "spectral_rolloff", "energy", "sv", "spread", "centroid", "obsi", "kurtosis"], log_results=False)
best_results = classifier.train(req_min_fitness=option[2], gen_select=option[3], mutation_prob=option[4], limit_generations=option[5], selection_type=option[6])
test_results, correct_results = classifier.test()
list_best_results.append(best_results)
list_test_results.append(test_results)
list_correct_results.append(correct_results)
print "Results for option: ", option
print "run\ttype\tgen\tfitness"
for i, results in enumerate(list_best_results):
for rule, result in results.items():
print str(i + 1) + "\t" + rule[:7] + "\t" + str(result["generation"]) + "\t" + str(result["fitness"])
print "run\ttype\tavg correct rules"
for i, results in enumerate(list_test_results):
for avg_map in results:
print str(i + 1) + "\t" + avg_map.keys()[0][:7] + "\t" + str(avg_map[avg_map.keys()[0]])
print "run\ttype\tavg correct results"
for i, results in enumerate(list_correct_results):
for avg_map in results:
print str(i + 1) + "\t" + avg_map.keys()[0][:7] + "\t" + str(avg_map[avg_map.keys()[0]])
def run(self):
"""
Function: Run
-------------
This function will evaluate your solution! You do not need to
write any code in this file, however you SHOULD understand this
function!
"""
print "Running the full pipeline!"
K=25
trainImages = util.loadTrainImages()[:1000]
testImages = util.loadTestImages()
classifier = Classifier()
print 'Training..........'
classifier.train(trainImages, K)
trainPredictions = classifier.test(trainImages)
trainAccuracy = self.evaluate(trainPredictions, trainImages)
print 'Testing...........'
testPredictions = classifier.test(testImages)
testAccuracy = self.evaluate(testPredictions, testImages)
print 'All done. Here is your summary:'
self.reportAccuracy(trainAccuracy, 'Train Accuracy')
self.reportAccuracy(testAccuracy, 'Test Accuracy')
def GetNewArticles(request):
# Get the articles from RSS
# aggregator = NewsAggregator()
# list_of_articles = aggregator.feedreader()
classifier = Classifier("filename.pkl")
# Predict
list_of_classes = []
# with open("articles_dump", "wb") as dump:
# pickle.dump(list_of_articles, dump, pickle.HIGHEST_PROTOCOL)
with open("articles_dump") as dump:
list_of_articles = pickle.load(dump)
for article in list_of_articles:
list_of_classes.append(article["content"])
# print list_of_classes
res = classifier.predict(np.asarray(list_of_classes))
for i in range(0, len(list_of_articles)):
if res[i] == 1:
cat = "Sports"
elif res[i] == 2:
cat = "Economy_business_finance"
elif res[i] == 3:
cat = "Science_technology"
else:
cat = "Lifestyle_leisure"
element = list_of_articles[i]
list_of_articles[i]["category"] = cat
article = Article(article_title=element["title"], article_content=element["content"], article_category=cat)
article.save()
json_object = json.dumps(list_of_articles)
return HttpResponse(json_object)
def test_classify_by_randomforest():
stock_d = testdata()
ti = TechnicalIndicators(stock_d)
filename = 'test_N225_randomforest.pickle'
clffile = os.path.join(os.path.dirname(
os.path.abspath(__file__)),
'..', 'clf',
filename)
if os.path.exists(clffile):
os.remove(clffile)
clf = Classifier(filename)
ti.calc_ret_index()
ret = ti.stock['ret_index']
train_X, train_y = clf.train(ret, classifier="Random Forest")
eq_(filename, os.path.basename(clf.filename))
r = round(train_X[-1][-1], 5)
expected = 1.35486
eq_(r, expected)
r = round(train_X[0][0], 5)
expected = 1.08871
eq_(r, expected)
expected = 14
r = len(train_X[0])
eq_(r, expected)
expected = 120
r = len(train_X)
eq_(r, expected)
expected = [1, 0, 0, 0, 1, 1, 0, 0, 0, 0,
0, 0, 1, 0, 0, 1, 0, 1, 0, 1,
1, 0, 1, 1, 1, 1, 1, 0, 1, 0,
1, 1, 1, 1, 0, 1, 0, 1, 1, 0,
1, 0, 0, 1, 1, 1, 1, 1, 1, 1,
0, 0, 0, 1, 0, 0, 1, 1, 1, 1,
1, 0, 1, 0, 0, 0, 0, 0, 0, 1,
1, 1, 0, 0, 1, 0, 1, 1, 0, 1,
1, 0, 1, 1, 0, 1, 0, 0, 1, 0,
1, 1, 0, 0, 1, 0, 1, 0, 1, 1,
1, 1, 1, 0, 1, 1, 1, 0, 0, 1,
1, 0, 0, 1, 1, 1, 0, 1, 1, 0]
for r, e in zip(train_y, expected):
eq_(r, e)
expected = 1
test_y = clf.classify(ret)
assert(test_y[0] == 0 or test_y[0] == 1)
if os.path.exists(clffile):
os.remove(clffile)
def main(mode='test'):
cl = Classifier()
cl.create_db('bunyk.db')
if mode == 'test':
test(cl)
else:
train(cl, 'http://bunyk.wordpress.com')
def setUp(self):
text = u"Comment Google classe les pages Internet"
c = Classifier(CleanTextUtil("french"))
c.add_text(text)
self.dictionary_db = c.dictionary_db
self.vi = VectorItem("googl", "1")
def askfunc():
options=\
{
"login": False,
"username": "",
"status": 0
}
error = None
if session.has_key('username'):
options["login"]=True
options["username"]=session["username"]
else:
return render_template('errorpage.html', error = error)
if request.method=='POST':
op=request.form['op']
print ("enter the post")
if op == "submit":
newpic = request.files['photo']
picAdded = True;
cur=mysql.connection.cursor()
cur.execute("select userid from user where username = "******"'" + session["username"] + "'")
useridresult = [];
useridresult=cur.fetchall()
userid = useridresult[0][0]
userid = str(userid)
currentuserid = userid;
currentuserid = str(currentuserid)
pic_location = "static/pictures/" + currentuserid + "/" +newpic.filename
newpic.save(pic_location)
title = request.form['title']
ori = request.form['ori']
ori = str(ori)
print ori
if ori == "I dont know": #TODO: update with proper default value
print ("classifier entered")
cc = Classifier(pic_location)
ori = cc.classify_text()
ori = ori.title()
tar = request.form['tar']
#if tar == 'NA'
des = request.form['des']
cur=mysql.connection.cursor()
cur.execute("insert into post (title, description, origin, target, pathtophoto, userid) values ('" + title + "', '" + des + "', '" + ori + "', '" + tar + "', '" + newpic.filename +"', "+ currentuserid +");")
print ("insert into post (title, description, origin, target, pathtophoto, userid) values ('" + title + "', '" + des + "', '" + ori + "', '" + tar + "', '" + newpic.filename +"', "+ currentuserid +");")
cur.execute("commit")
return render_template('redirect.html', error=error)
return render_template('ask.html', error=error, **options)
def test_combinations(args, graph=False):
py = plotly.plotly(username='******', key='uzkqabvlzm', verbose=False)
options = [100, 10, 0.9, 4, 0.05, 10000]
features = ["skewness", "spectral_rolloff", "energy", "sv", "spread", "centroid", "zcr", "obsi", "kurtosis"]
electronic_y = []
classical_y = []
categories = []
print '\t'.join([feature[:2] for feature in features] + ["meta", "acou", "regg", "elec", "class"])
for i in range(1, len(features) + 1):
combinations = [list(comb) for comb in itertools.combinations(features, i)]
for comb in combinations:
comb_name = ', '.join(comb)
classifier = Classifier(args['data'], discrete_intervals=options[0], size_rule_generation=options[1], filter_list=comb)
top_fitness = classifier.train(req_min_fitness=options[2], gen_select=options[3], mutation_prob=options[4], limit_generations=options[5])
for feature in features:
if feature in comb:
sys.stdout.write("X\t")
else:
sys.stdout.write("\t")
sys.stdout.write(str(top_fitness['metal']["fitness"])[:4] + "\t")
sys.stdout.write(str(top_fitness['acoustic']["fitness"])[:4] + "\t")
sys.stdout.write(str(top_fitness['reggae']["fitness"])[:4] + "\t")
sys.stdout.write(str(top_fitness['electronic']["fitness"])[:4] + "\t")
sys.stdout.write(str(top_fitness['classical']["fitness"])[:4] + "\n")
if graph:
print "Training ended\nFinal fitness:", top_fitness
electronic_y.append(top_fitness['metal'])
classical_y.append(top_fitness['classical'])
categories.append(comb_name)
if len(categories) > 20:
electronic = {
"name": "Metal",
"x": categories,
"y": electronic_y,
"type": "bar"
}
classical = {
"name": "Classical",
"x": categories,
"y": classical_y,
"type": "bar"
}
layout = {
"barmode": "group",
'xaxis': {'type': 'combination'},
'catagories': categories
}
response = py.plot([electronic, classical], layout=layout)
print response['url']
electronic_y = []
classical_y = []
categories = []
def cl_button_clicked_cb(self, button):
"""Classify button callback
:param button: signal came from this button
"""
if not len(self.sel_files):
return
self.counter = -1
for row in self.sel_files:
Classifier.classify(self.all_files[row], MainWindow.SR, row, self.update_classify_progress_cb)
def test_classify():
proxy = ReviewsMongoProxy("tripadvisor_train")
review = proxy.find_review_by_id(proxy.next_random_review_id())
classifier = Classifier("../tripadvisor/aspect_nltk_nb.pkl")
classifier.classify(review)
print_review(review)
def classifier(search_query) :
cls = Classifier(' '.join(search_query.split('_')))
classified_output = cls.classify()
if classified_output != None and len(classified_output) > 0 :
with open("output/" + search_query+".json","w") as out :
out.write(json.dumps(classified_output))
return json.dumps({"query" : search_query, "status": "Success"})
else :
return json.dumps({"query" : search_query, "status": "Failed"})
def main(c = "decision_tree", option = "IG", dataset = "iris", ratio = 0.8):
classifier_types = {0: "decision_tree", 1: "naive_bayes", 2: "neural_net"}
options = {0:["IG", "IGR"], 1:["normal"], 2:["shallow", "medium"]}
ratio = float(ratio)
if dataset == "monks":
(training, test) = load_data.load_monks(ratio)
elif dataset == "congress":
(training, test) = load_data.load_congress_data(ratio)
elif dataset == "iris":
(training, test) = load_data.load_iris(ratio)
else:
print "Error: Cannot find dataset name."
return
print "Training... Please hold."
# classifier_types = {0: "decision_tree", 2: "neural_net"}
# options = {0:["IG", "IGR"], 2:["shallow", "medium"]}
# (training, test) = load_data.load_iris(0.8)
# nn_classifier = Classifier(classifier_type="neural_net", option = "medium")
# nn_classifier.train(training)
# nn_classifier.test(test)
# print test
# (training, test) = load_data.load_congress_data(0.8)
# print test
# (training, test) = load_data.load_monks(1)
# print test
# (training, test) = load_data.load_iris(0.8)
# print training
# "option = IG/IGR"
# dt_classifier = Classifier(classifier_type="decision_tree", weights=[], option="IG")
# dt_classifier.train(training)
# dt_classifier.test(test)
# for i, c in classifier_types.iteritems():
# for option in options[i]:
print " "
print "================================================================="
print "Dataset = ", dataset
print "Classifier = ", c
print "Option = ", option
classifier = Classifier(classifier_type=c, weights = [], option = option)
classifier.train(training)
classifier.test(test)
print "================================================================="
print " "
# option value could be either shallow(3 layers) or medium(5)
# nn_classifier = Classifier(classifier_type="neural_net", option = "medium")
# nn_classifier.train(training)
# nn_classifier.test(test)
return
def test_performance(args, num_runs):
#Features:
features = ["skewness", "spectral_rolloff", "energy", "sv", "spread", "centroid", "zcr", "obsi", "kurtosis"]
option = [100, 10, 0.9, 2, 0.05, 1000, selection.ROULETTE_WHEEL_SELECTION]
for i in range(1, len(features) + 1):
print "Num of features:", i
for num_run in range(num_runs):
classifier = Classifier(args['data'], discrete_intervals=option[0], size_rule_generation=option[1], filter_list=features[:i], log_results=False)
start = time.clock()
classifier.train(req_min_fitness=option[2], gen_select=option[3], mutation_prob=option[4], limit_generations=option[5], selection_type=option[6])
duration = (time.clock() - start)*1000
print num_run, "\t", duration
def main():
me=Classifier()
feature_counter=Counter()
feature_set=pickle.load(open('undersampled_emoticon.pkl', 'rb'))
feature_list=chain.from_iterable([word_tokenize(process_tweet(tweet)) for tweet, sentiment in feature_set])
for feat in feature_list:
feature_counter[feat]+=1
me.feature_list=[feat for feat, count in feature_counter.most_common(1000)]
ts=[(me.extract_features(tweet), label) for tweet, label in feature_set]
print 'training Maxent, algorithm CG'
me.classifier=MaxentClassifier.train(ts)
return me
def makeClassifier():
jiraGitMapper = Mapper()
# Create a mapping of jira commits to git tickets
ticketsToCommits = jiraGitMapper.mapCommitsToTickets(gitData, jiraData, "SONAR-")
# Take the git commits and associate them with java class names
ticketsAndCommitsToClasses = jiraGitMapper.mapCommitsToClasses(ticketsToCommits)
ticketsToClasses = ticketsAndCommitsToClasses[0]
commitsToClasses = ticketsAndCommitsToClasses[1]
classifier = Classifier()
results = classifier.classifyClasses(ticketsToClasses)
# results = classifier.randomClassifyClasses(ticketsToClasses)
print("Precision: %.3f, Recall: %.3f, Accuracy: %.3f, f1 score: %.3f, hamming loss: %.3f" % (results[0], results[1], results[2], results[3], results[4]))
def get_predictions(sample_x):
c=Classifier()
predictions=[]
try:
for x in sample_x:
row=x
labels=get_labels(x)
p=c.get_prediction(row,labels)
p=1 if p else 0
predictions.append(p)
except Exception as e:
show_exception(e)
return []
return predictions
def run(self):
global worksqueue, spectImg
classifier = Classifier()
while True:
sample = worksqueue.get()
worksqueue.task_done()
result = classifier.classify(sample)
print "\nPreciction: %s\n" % result
spectImg = writeMFCC(sample, RATE)
def main():
args = parser.parse_args()
data_json = read_dataset(args.data)
processor = TextProcessor()
classifier = Classifier(processor)
classifier.train(data_json)
serialized_classifier = classifier.dump()
ensure_directory(args.output)
with open(args.output, 'w') as f:
f.write(serialized_classifier)
f.write(os.linesep)
def test(args):
test_performance(args, 5)
return
#return
options = [
[100, 10, 0.9, 4, 0.05, 10000, selection.ROULETTE_WHEEL_SELECTION], # discrete_intervals, size_rule_generation, req_min_fitness, gen_select, limit_generations
[1000, 10, 0.9, 4, 0.05, 10000, selection.ROULETTE_WHEEL_SELECTION],
[100, 20, 0.9, 4, 0.05, 10000, selection.ROULETTE_WHEEL_SELECTION],
[100, 5, 0.9, 2, 0.05, 10000, selection.ROULETTE_WHEEL_SELECTION],
[100, 10, 0.9, 2, 0.05, 10000, selection.ROULETTE_WHEEL_SELECTION],
[100, 10, 0.9, 6, 0.05, 10000, selection.ROULETTE_WHEEL_SELECTION],
[200, 50, 0.9, 10, 0.05, 10000, selection.ROULETTE_WHEEL_SELECTION],
[300, 10, 0.9, 4, 0.1, 10000, selection.ROULETTE_WHEEL_SELECTION],
[500, 15, 0.9, 2, 0.005, 10000, selection.ROULETTE_WHEEL_SELECTION],
[50, 20, 0.9, 4, 0.1, 10000, selection.ROULETTE_WHEEL_SELECTION]
]
#prueba Tamaño de población
options = [
[100, 5, 0.9, 2, 0.05, 10000, selection.ROULETTE_WHEEL_SELECTION],
[100, 10, 0.9, 2, 0.05, 10000, selection.ROULETTE_WHEEL_SELECTION],
[100, 15, 0.9, 2, 0.05, 10000, selection.ROULETTE_WHEEL_SELECTION],
[100, 20, 0.9, 2, 0.05, 10000, selection.ROULETTE_WHEEL_SELECTION],
[100, 30, 0.9, 2, 0.05, 10000, selection.ROULETTE_WHEEL_SELECTION],
[100, 50, 0.9, 2, 0.05, 10000, selection.ROULETTE_WHEEL_SELECTION]
]
#prueba Proceso de seleccion
options = [
[100, 10, 0.9, 2, 0.05, 10000, selection.ROULETTE_WHEEL_SELECTION],
[100, 10, 0.9, 2, 0.05, 10000, selection.RANK_SELECTION],
[100, 10, 0.9, 2, 0.05, 10000, selection.TOURNAMENT_SELECTION]
]
options = [
[100, 10, 0.9, 2, 0.05, 10000, selection.ROULETTE_WHEEL_SELECTION],
]
average_multiple_runs(30, options, args)
test_combinations(args)
for num, option in enumerate(options):
print "Option num:", num, ", val:", option
classifier = Classifier(args['data'], discrete_intervals=option[0], size_rule_generation=option[1], filter_list=["skewness", "spectral_rolloff", "energy", "sv", "spread", "centroid", "obsi", "kurtosis"], log_results=True)
best_results = classifier.train(req_min_fitness=option[2], gen_select=option[3], mutation_prob=option[4], limit_generations=option[5])
print "Testing"
classifier.test()
# classifier.guess_genre([7.53659769442,1389.49121537,0.0166588959174,0.355062895642,1480.75635175,769.172547276,3.47303203307,69.8220939453])
print "Training ended\nFinal fitness:", best_results
class EmojiRecommender():
def __init__(self, fname_model, fname_embed, fname_dataset):
print >> sys.stderr, 'EmojiRecommender: [info] loading word index...'
self.windexer = WordIndexer.load(fname_embed)
print >> sys.stderr, 'EmojiRecommender: [info] loading model...'
self.clf = Classifier()
self.clf.load_model(fname_model)
print >> sys.stderr, 'EmojiRecommender: [info] loading emojis...'
ecode_split = cPickle.load(open(fname_dataset, 'r'))
self.emojis = [emo for emo, split in ecode_split]
self.ydim = len(self.emojis)
print >> sys.stderr, 'EmojiRecommender: [info] initialization done'
def preprocess(self, text):
text = text.decode('utf8')
seq = zhtokenizer.tokenize(text)
idxs = self.windexer.seq2idx(seq)
return idxs
def predict_proba(self, text):
idxs = self.preprocess(text)
if len(idxs) == 0:
return None
else:
return self.clf.predict_proba(idxs)
def recommend(self, text, n = 5):
proba = self.predict_proba(text)
if proba is None:
eids = [i for i in range(n)]
scores = [0. for i in range(n)]
else:
ranks = [(i, proba[i]) for i in range(self.ydim)]
ranks = sorted(ranks, key = lambda k:-k[1])
eids = [ranks[i][0] for i in range(n)]
scores = [ranks[i][1] for i in range(n)]
res = [{'emoji':self.emojis[eid], 'score':'%.2f'%(score)} for eid, score in zip(eids, scores)]
return res
def runDev(self):
print "Running in development mode"
K=5
trainImages = util.loadTrainImages()[:100]
testImages = util.loadTestImages()[:100]
classifier = Classifier()
print 'Training..........'
classifier.train(trainImages, K)
trainPredictions = classifier.test(trainImages)
trainAccuracy = self.evaluate(trainPredictions, trainImages)
print 'All done. Here is your summary:'
self.reportAccuracy(trainAccuracy, 'Train Accuracy')
fscore_top100 = Queue.Queue()
fscore_feat = Queue.Queue()
fscore_nofeat = Queue.Queue()
else:
fscore_top100 = np.zeros(num_folds)
fscore_feat = np.zeros(num_folds)
fscore_nofeat = np.zeros(num_folds)
for fold in range(num_folds):
print "Training and testing fold " + str(fold + 1) + "..."
# Split dataset into train and set based on current fold
train_set, train_labels, test_set, test_labels = utils.split_set(
full_set, labels, thresholds[fold], thresholds[fold + 1])
if args.t:
t_feat = Thread(target=Classifier(clf(), True, False,
args.t).learn_classifier,
args=(train_set, train_labels, test_set, test_labels,
fscore_feat))
t_nofeat = Thread(target=Classifier(clf(), False, False,
args.t).learn_classifier,
args=(train_set, train_labels, test_set, test_labels,
fscore_nofeat))
t_100 = Thread(target=Classifier(clf(), True, True,
args.t).learn_classifier,
args=(train_set, train_labels, test_set, test_labels,
fscore_top100))
t_feat.start()
t_nofeat.start()
t_100.start()
t_feat.join()
t_nofeat.join()
def test_with_image(img_path):
VGG_Face = Vgg_face_dag()
VGG_Face = vgg_face_dag(VGG_Face, "src/models/vgg_face_dag.pth")
thicc = 2
score = 0 # To evaluate the state of the driver(drowsy or not)
frame_count = 0
frames = []
path = os.getcwd()
font = cv2.FONT_HERSHEY_COMPLEX_SMALL
img = cv2.imread(img_path)
height, width = img.shape[:2]
classifier = Classifier(img)
left_eye_pred = classifier.left_eye()
right_eye_pred = classifier.right_eye()
frames.append(img)
drunk_pred = classifier.drunk_pred(frames, VGG_Face)
if drunk_pred == 1:
cv2.putText(img, "Drunk", (10, 20), font, 1, (255, 255, 255), 1,
cv2.LINE_AA)
else:
cv2.putText(img, "Sober", (10, 20), font, 1, (255, 255, 255), 1,
cv2.LINE_AA)
if left_eye_pred == 0 and right_eye_pred == 0:
score += 1
cv2.putText(img, "Asleep", (10, height - 20), font, 1, (255, 255, 255),
1, cv2.LINE_AA)
else:
score = -1
cv2.putText(img, "Awake", (10, height - 20), font, 1, (255, 255, 255),
1, cv2.LINE_AA)
if score < 0:
score = 0
cv2.putText(img, "Score: " + str(score), (100, height - 20), font, 1,
(255, 255, 255), 1, cv2.LINE_AA)
if score > 8: # Using 15 as threshold to say the driver has had his/her eyes closed for too long
# Driver is feeling sleepy so we play the alarm
cv2.imwrite(os.path.join(path, str(datetime.now) + '.jpg'), img)
#playsound() # Play sound
if thicc < 16:
thicc += 2
else:
thicc -= 2
if thicc < 2:
thicc = 2
cv2.readOpticalFlow(img, (0, 0), (width, height), (0, 0, 255), thicc)
cv2.imshow('frame', img)
k = cv2.waitKey(0)
if k == 27:
cv2.destroyAllWindows()
import os
import time
from flask import Flask, request, redirect, url_for
from classifier import Classifier
UPLOAD_FOLDER = 'uploads/'
app = Flask(__name__)
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
cf = Classifier()
@app.route('/', methods=['GET', 'POST'])
def upload_file():
if request.method == 'POST':
file = request.files['file']
filepath = os.path.join(app.config['UPLOAD_FOLDER'], file.filename)
file.save(filepath)
print(filepath)
res = cf.classify(filepath)
print(res)
if res is not None:
return res
else:
return 'nope'
return '''
<!doctype html>
<title>Upload</title>
<h1>Upload image</h1>
<form method=post enctype=multipart/form-data>
from classifier import Classifier
import numpy
import pandas as pd
#prepare data
data = numpy.load("data.npz")
train = data['train']
test = data['test']
labels = data['labels']
#create classifier
clf = Classifier()
clf.TreeClassifier()
clf.load_data(training=train, labels=labels, test=test)
results = clf.predict()
df = pd.read_csv("pair&average.csv", sep='\t')
def TF(x):
if x == 0:
return False
else:
return True
results = [TF(x) for x in results]
TF = pd.Series(results)
df['Need_normalize'] = TF
newdf = df[df['Need_normalize'] == True]
def main(displayHistory=True):
#Window for past frames
framesDiffHistory = [(getBlankFrameDiff(), getBlankFrameDiff())
for i in range(framesInHistory)]
lastEyes = None
#Load model classifier
classifier = Classifier()
#Start thread to make predictions
classifier.startPredictions()
#Initialize webcam
vs = WebcamVideoStream(src=0).start()
#For FPS computation
t0 = -1
#Face/eyes detector
detector = Detector()
print "Starting eye recognition..."
while True:
#Compute FPS
dt = time.time() - t0
fps = 1 / dt
t0 = time.time()
#Limit FPS with wait
waitMs = 5
key = cv2.waitKey(waitMs) & 0xFF
#Get image from webcam, convert to grayscale and resize
fullFrame = vs.read()
fullFrame = cv2.cvtColor(fullFrame, cv2.COLOR_BGR2GRAY)
frame = imutils.resize(fullFrame, width=300)
#Find face
faceBB = detector.getFace(frame)
if faceBB is None:
#Invalidate eyes bounding box as all will change
lastEyes = None
detector.resetEyesBB()
continue
#Get low resolution face coordinates
x, y, w, h = faceBB
face = frame[y:y + h, x:x + w]
#Apply to high resolution frame
xScale = fullFrame.shape[1] / frame.shape[1]
yScale = fullFrame.shape[0] / frame.shape[0]
x, y, w, h = x * xScale, y * yScale, w * xScale, h * yScale
fullFace = fullFrame[y:y + h, x:x + w]
#Find eyes on high resolution face
eyes = detector.getEyes(fullFace)
if eyes is None:
#Reset last eyes
lastEyes = None
continue
eye0, eye1 = eyes
#Process (normalize, resize)
eye0 = process(eye0)
eye1 = process(eye1)
#Reshape for dataset
eye0 = np.reshape(eye0, [datasetImageSize, datasetImageSize, 1])
eye1 = np.reshape(eye1, [datasetImageSize, datasetImageSize, 1])
#We have a recent picture of the eyes
if lastEyes is not None:
#Load previous eyes
eye0previous, eye1previous = lastEyes
#Compute diffs
diff0 = getDifferenceFrame(eye0, eye0previous)
diff1 = getDifferenceFrame(eye1, eye1previous)
#Display/debug
displayDiff = False
if displayDiff:
displayCurrentDiff(eye0,
eye1,
eye0previous,
eye1previous,
stopFrame=False)
#Crop beginning then add new to end
framesDiffHistory = framesDiffHistory[1:]
framesDiffHistory.append([diff0, diff1])
#Keep current as last frame
lastEyes = [eye0, eye1]
#Note: this is not time consuming
if displayHistory:
displayHistoryDiffs(framesDiffHistory, fps)
#Extract each eyes
X0, X1 = zip(*framesDiffHistory)
#Reshape as a tensor (NbExamples,SerieLength,Width,Height,Channels)
X0 = np.reshape(X0, [
-1,
len(framesDiffHistory), datasetImageSize, datasetImageSize, 1
])
X1 = np.reshape(X1, [
-1,
len(framesDiffHistory), datasetImageSize, datasetImageSize, 1
])
#Save history to Classifier
classifier.X0 = X0
classifier.X1 = X1
class Spider(object):
def __init__(self):
self.verifyCodeUrl = "http://jwgl.buct.edu.cn/CheckCode.aspx" #验证码获取地址
self.jwglLoginUrl = "http://jwgl.buct.edu.cn/default2.aspx" #教务网登录地址
self.getGradeUrl = "http://jwgl.buct.edu.cn/xscjcx.aspx" #成绩获取地址
self.getScheduleUrl = "http://jwgl.buct.edu.cn/xskbcx.aspx" #课程表获取地址
self.postClassUrl = "http://jwgl.buct.edu.cn/xsxk.aspx" #选课地址
self.studentID = #学号
self.username = #姓名
self.jwglPassword = #教务网密码
self.major = '0202高分子材料与工程'
self.session = requests.Session() #实例化 session 对象
self.response = self.session.send(self.prepareJwglFirst(), timeout=5) # GET 方法获取登录网站的 '__VIEWSTATE'
# 实例化验证码识别器对象
from classifier import Classifier
self.classifier = Classifier()
self.classifier.loadTrainingMat()
self.remainList = [0, 1, 3, 4, 6, 7, 8, 10, 11, 12, 14]
def formatHeaders(self, referer=None):
"""
生成请求的 headers，referer 参数的默认值为 None
若 referer 为 None，则 headers 不包括 referer 参数
"""
headers = {
'Host': 'jwgl.buct.edu.cn',
'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64; rv:52.0) Gecko/20100101 Firefox/52.0',
'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
'Accept-Language': 'en-US,en;q=0.5',
'Accept-Encoding': 'gzip, deflate, br',
'Connection': 'keep-alive',
'Upgrade-Insecure-Request': '1',
}
if referer:
headers['Referer'] = referer
return headers
def getVIEWSTATE(self):
"""
正则获取登录页面的 "__VIEWSTATE"
"""
import re
return re.findall('<.*name="__VIEWSTATE".*value="(.*)?".*/>', self.response.text)[0]
def prepareJwglFirst(self):
headers = self.formatHeaders()
req = Request('GET', self.jwglLoginUrl, headers=headers)
return self.session.prepare_request(req)
def prepareJwglLogin(self):
"""
实例化登录 jwgl 需要的 request
"""
postdata = {
'__VIEWSTATE': self.getVIEWSTATE(), #此参数非常重要，通过函数从当前网页源代码获取
'txtUserName': self.studentID,
'TextBox2': self.jwglPassword,
'txtSecretCode': self.verCode,
'RadioButtonList1': '学生',
'Button1': '',
'lbLanguage': '',
'hidPdrs': '',
'hidsc': '',
}
headers = self.formatHeaders(self.jwglLoginUrl)
req = Request('POST', self.jwglLoginUrl, headers=headers, data=postdata)
return self.session.prepare_request(req)
def prepareGetGrade(self):
headers = self.formatHeaders(self.response.url)
params = {
'xh': self.studentID,
'xm': self.username,
'gnmkdm': 'N121605',
}
req = Request('GET', self.getGradeUrl, headers=headers, params=params)
return self.session.prepare_request(req)
def preparePastGrade(self):
headers = self.formatHeaders(self.response.url)
params = {
'xh': self.studentID,
'xm': self.username,
'gnmkdm': 'N121605',
}
postdata = {
'__EVENTTARGET': '',
'__EVENTARGUMENT': '',
'__VIEWSTATE': self.getVIEWSTATE(), #此参数非常重要，通过函数从当前网页源代码获取
'hidLanguage': '',
'ddlXN': '',
'ddlXQ': '',
'ddl_kcxz': '',
'btn_zcj': '历年成绩',
}
req = Request('POST', self.getGradeUrl, headers=headers, params=params, data=postdata)
return self.session.prepare_request(req)
def prepareSchedule(self):
headers = self.formatHeaders(self.response.url)
params = {
'xh': self.studentID,
'xm': self.username,
'gnmkdm': 'N121603',
}
req = Request('GET', self.getScheduleUrl, headers=headers, params=params)
return self.session.prepare_request(req)
def preparePastSchedule(self, xn_, xq_):
headers = self.formatHeaders(self.response.url)
params = {
'xh': self.studentID,
'xm': self.username,
'gnmkdm': 'N121603',
}
postdata = {
'__EVENTTARGET': 'xnd',
'__EVENTARGUMENT': '',
'__VIEWSTATE': self.getVIEWSTATE(), #此参数非常重要，通过函数从当前网页源代码获取
'xnd': xn_,
'xqd': xq_,
}
req = Request('POST', self.getScheduleUrl, headers=headers, params=params, data=postdata)
return self.session.prepare_request(req)
def prepareClass(self):
headers = self.formatHeaders(self.response.url)
params = {
'xh': self.studentID,
'xm': self.username,
'gnmkdm': 'N121101',
}
req = Request('GET', self.postClassUrl, headers=headers, params=params)
return self.session.prepare_request(req)
def prepareGetClass(self):
headers = self.formatHeaders(self.response.url)
params = {
'xh': self.studentID,
'xm': self.username,
'gnmkdm': 'N121101',
}
postdata = {
'__EVENTTARGET': '',
'__EVENTARGUMENT': '',
'__VIEWSTATE': self.getVIEWSTATE(), #此参数非常重要，通过函数从当前网页源代码获取
'DrDl_Nj': self.studentID[:4],
'zymc': self.major + '主修专业||' + self.studentID[:4],
'xx': '',
'Button5': '本专业选课'
}
req = Request('POST', self.postClassUrl, headers=headers, params=params, data=postdata)
return self.session.prepare_request(req)
def jwglLogin(self, tryNum=10):
"""
教务网登录函数
tryNum --> 尝试登录的最大次数，防止因递归深度过大导致溢出
"""
import re
tryNum -= 1
if tryNum < 0:
print('\n*** stack overflow! exiting...')
exit(0)
codeImg = self.session.get(self.verifyCodeUrl, timeout=5) #获取验证码图片
with open('check.gif', 'wb') as fr: #保存验证码图片
for chunk in codeImg:
fr.write(chunk)
self.verCode = self.classifier.recognizer("check.gif") #识别验证码
try:
self.response = self.session.send(self.prepareJwglLogin(), timeout=5)
if re.search(self.studentID, self.response.url): #若 response.url 中匹配到学号，则认为登录成功
print("login successfully!")
print(self.response.url)
else:
raise VerifyError("Wrong Verification code!")
except VerifyError as e:
print(e)
print("retry...")
self.jwglLogin(tryNum) #若登录不成功则递归调用自身
def getPastGrade(self):
"""
获取历年成绩
"""
self.response = self.session.send(self.prepareGetGrade(), timeout=5)
self.response = self.session.send(self.preparePastGrade(), timeout=5)
gradeMat = self.formatTable(self.response.text)
gradeMat = [[row[i] for i in range(len(row)) if i in self.remainList] for row in gradeMat]
self.outputTable(gradeMat, outputPath='grade.md')
def getPastSchedule(self, xn_ ,xq_):
self.response = self.session.send(self.prepareSchedule(), timeout=5)
self.response = self.session.send(self.preparePastSchedule(xn_, xq_), timeout=5)
scheduleMat = self.formatTable(self.response.text)
with open('schedule.md', 'w') as fr:
fr.write(str(scheduleMat))
#self.outputTable(scheduleMat, outputPath='schedule.md')
def getClassList(self):
self.response = self.session.send(self.prepareClass(), timeout=5)
self.response = self.session.send(self.prepareGetClass(), timeout=5)
def outputTable(self, tableMat, outputPath):
"""
将成绩输出成 md 格式
"""
tableMat.insert(1, [':------' for i in range(len(tableMat[0]))])
with open(outputPath, 'w') as fr:
for row in tableMat:
fr.write('|')
for each in row:
fr.write(each)
fr.write('|')
fr.write('\n')
def formatTable(self, tableBody):
"""
将抓取到的成绩解析成列表
"""
from bs4 import BeautifulSoup
import re
soup = BeautifulSoup(tableBody, 'html.parser')
return soup.br.table
tableRow = soup.br.table.find_all('tr')
tableMat = [i.find_all('td') for i in tableRow]
return [[each.get_text().strip() for each in row] for row in tableMat]
def clean(self):
"""
爬取结束关闭会话
"""
self.session.close()
from flask_socketio import SocketIO
import socket
from PIL import Image
import numpy as np
import struct
import sys
import time
import log
import os
from classifier import Classifier
from datastore import DataStore
classifier = Classifier(16, 16, 3)
app = Flask(__name__, static_url_path='')
socketio = SocketIO(app)
ds = DataStore('/var/pood/ds')
last_frame_time = 0
frames_received = 100
positives_last_frame = 0
force_training = False
collecting_negs = False
def has_cli_arg(arg_str):
return arg_str in sys.argv
def classify_req(sock):
global last_frame_time, frames_received, positives_last_frame, collecting_negs
from itertools import count
import torch
from torchvision import utils
import random
import glob
from shutil import copyfile
from mask_loader import load_image
from classifier import Classifier
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
CLASSIFIER_FILENAME = 'trained_models/classifier.to'
classifier = Classifier()
classifier.cuda()
classifier.load_state_dict(torch.load(CLASSIFIER_FILENAME))
classifier.eval()
file_names = glob.glob('data/raw/**.jpg', recursive=True)
while True:
file_name = random.choice(file_names)
hash = file_name.split('/')[-1][:-4]
image = load_image(file_name).to(device)
image = classifier.apply(image)
if image is None:
continue
# game stuff
from game_logic import GameLogic
from levels import LevelMic
from path_collector import PathCollector
# yaml config file
cfg = yaml.safe_load(open("../config.yaml"))
# init path collector
path_coll = PathCollector(cfg, root_path='.')
# --
# mic
# create classifier
classifier = Classifier(path_coll=path_coll, verbose=True)
# create mic instance
mic = Mic(classifier=classifier,
feature_params=cfg['feature_params'],
mic_params=cfg['mic_params'],
is_audio_record=True)
# --
# game setup
# init pygame
pygame.init()
# init display
screen = pygame.display.set_mode(cfg['game']['screen_size'])
def train():
if args.dataset == 'baidu_VH':
dataset = baidu_VH(PROJECT_METAROOT)
elif args.dataset == 'summe':
pass
#dataset=
else:
raise ValueError('No such dataset')
log.l.info(dataset.print_info())
train_data = AsyncReader(dataset,
root_path=BAIDU_VH_ROOT,
mode='train',
modality=args.modality)
train_data.set_params({
'limitedfiles':
None,
'sample_rate':
100,
'save_path':
'tmp_results/train_{}_sampled.pkl'.format(args.modality)
})
X_train, Y_train = train_data.read_data(k=args.thread)
val_data = AsyncReader(dataset,
root_path=BAIDU_VH_ROOT,
mode='val',
modality=args.modality)
val_data.set_params({
'limitedfiles':
None,
'sample_rate':
1,
'save_path':
'tmp_results/val_{}_sampled.pkl'.format(args.modality)
})
X_val, Y_val = val_data.read_data(k=args.thread)
model = Classifier(model_name=args.model_name,
if_grid_search=args.if_grid_search,
model_kernel=args.model_kernel)
if args.if_grid_search:
model.set_grid_search_params(grid_search_params[args.model_name])
X_train_grid_search, Y_train_grid_search = Sample_data(
X_train, Y_train, args.grid_search_sample_rate)
model.grid_search(X_train_grid_search, Y_train_grid_search)
model.fit(X_train, Y_train)
X_val_metric, Y_val_metric = Sample_data(X_val, Y_val, 0.1)
predict_val = model.predict(X_val_metric)
metrics = get_metrics(predict_val, Y_val_metric, metrics=METRICS)
# print metrics
log.l.info('the metrics of {} is :{}'.format(METRICS, metrics))
del X_train, Y_train #,X_train_grid_search,Y_train_grid_search,X_val_metric,Y_val_metric
if args.create_curves:
# for test set:
val_curves_dic = dict()
for k, v in val_data.data_dic.items():
val_curves_dic[k] = model.predict(v)
test_data = AsyncReader(dataset,
root_path=BAIDU_VH_ROOT,
mode='test',
modality=args.modality)
test_data.set_params({
'limitedfiles':
None,
'sample_rate':
1,
'save_path':
'tmp_results/test_{}_sampled.pkl'.format(args.modality)
})
_, _ = test_data.read_data(k=args.thread)
test_curves_dic = dict()
for k, v in test_data.data_dic.items():
test_curves_dic[k] = model.predict(v)
return_info = {'val': val_curves_dic, 'test': test_curves_dic}
if args.save_curves:
joblib.dump(
return_info,
'tmp_results/val_test_{}_curves.pkl'.format(args.modality))
return return_info
return None
# Library import
import uvicorn
from fastapi import FastAPI, HTTPException, Request
from fastapi.templating import Jinja2Templates
from fastapi.responses import HTMLResponse
from classifier import Classifier
from helper import SentimentRequest, SentimentResponse
# Create APP intance of FastAPI
app = FastAPI()
model = Classifier()
templates = Jinja2Templates(directory="templates")
# Index route. Default: http://127.0.0.1:8000
@app.get("/", response_class=HTMLResponse)
async def read_item(request: Request):
context = {
"request" : request,
'title' : "Form Input for News Classifier"
}
return templates.TemplateResponse("index.html", context=context)
@app.post('/predict/', response_model=SentimentResponse, status_code=200)
async def predict_text(request: SentimentRequest):
if not model:
raise HTTPException(status_code=404, detail="Model not found.")
pred = model.process(request.text)
return SentimentResponse(text=request.text, prediction=pred)
from classifier import Classifier
import time
from flask import Flask, render_template, request
app = Flask(__name__)
print("Load classifier")
start_time = time.time()
classifier = Classifier()
print("Classifier is successfully loaded")
print(time.time() - start_time, "seconds")
@app.route("/", methods=["POST", "GET"])
def index_page(text="", prediction_message=""):
if request.method == "POST":
text = request.form["text"]
prediction_message = classifier.get_result_message(text)
return render_template('simple_page.html',
text=text,
prediction_message=prediction_message)
if __name__ == "__main__":
app.run(host='127.0.0.1', port=8080, debug=True)
# Chunks should never include caption data from multiple videos, cut off before 500 if at the end of the caption, then start again for the next caption.
# Idea: after question answering, compare the comment's video-ID to the video-ID of the caption chunk which answered the question.
# this will give us an idea of where the answers are coming from (i.e. is it always from the associated video or not)
# Change these to get best results with a static response to positive and negative comments.
positive_threshold = 0.75
negative_threshold = -0.75
####################################
# #
# Object Calls #
# #
####################################
classifier = Classifier() # Question classifier class.
YTObj = CommentCollection(API_SERVICE_NAME, API_VERSION,
DEVELOPER_KEY) # youtube acess object
####################################
# #
# Main Code Body #
# #
####################################
# dataframe for comments and captions with format
"""
---------------
| | 'commentList' | 'captionList' |
| VIDEO_IDS[0] | list(...) | list(...) |
...
"""
os.environ['PYTHONHASHSEED'] = '0'
np.random.seed(17)
rn.seed(12345)
if __name__ == "__main__":
set_reproductible()
datadir = "../data/"
trainfile = datadir + "traindata.csv"
devfile = datadir + "devdata.csv"
testfile = None
# Basic checking
start_time = time.perf_counter()
classifier = Classifier()
print("\n")
# Training
print("1. Training the classifier...\n")
classifier.train(trainfile)
# Evaluation on the dev dataset
print("\n2. Evaluation on the dev dataset...\n")
slabels = classifier.predict(devfile)
glabels = load_label_output(devfile)
eval_list(glabels, slabels)
if testfile is not None:
# Evaluation on the test data
print("\n3. Evaluation on the test dataset...\n")
slabels = classifier.predict(testfile)
glabels = load_label_output(testfile)
eval_list(glabels, slabels)
from flask import Flask, request
import sys
sys.path.append('./scripts/classifier')
sys.path.append('./scripts/server/sql')
from classifier import Classifier
import utils, instructor, course, program_outcomes, learning_objectives, lab_schedule, \
assignment_schedule, project_schedule, mid_term_schedule, final_exam_schedule, \
course_grading
txt_clf = Classifier()
app = Flask(__name__)
# http://localhost:5000/classify?text=who is doing it
@app.route("/classify")
def classify():
text = request.args.get('text')
text = text.lower()
text = utils.map_words_to_digits_in_text(text)
question_type = txt_clf.classify(text)
return _return_response_for_question(text, question_type)
def _return_response_for_question(text, label):
if label == 'instructor':
return instructor.get_instructor_details(text)
elif label == 'course_name':
return course.get_course_details(text)
elif label == 'course_learning_objectives':
return learning_objectives.get_learning_objectives(text)
elif label == 'program_outcome':
def main(is_interactive=True,
k=64,
des_option=constants.ORB_FEAT_OPTION,
svm_kernel=cv2.SVM_LINEAR):
if not is_interactive:
experiment_start = time.time()
# Check for the dataset of images
if not os.path.exists(constants.DATASET_PATH):
print("Dataset not found, please copy one.")
return
dataset = Dataset(constants.DATASET_PATH)
dataset.generate_sets()
# Check for the directory where stores generated files
if not os.path.exists(constants.FILES_DIR_NAME):
os.makedirs(constants.FILES_DIR_NAME)
if is_interactive:
des_option = input(
"Enter [1] for using ORB features or [2] to use SIFT features.\n")
k = input(
"Enter the number of cluster centers you want for the codebook.\n")
svm_option = input(
"Enter [1] for using SVM kernel Linear or [2] to use RBF.\n")
svm_kernel = cv2.SVM_LINEAR if svm_option == 1 else cv2.SVM_RBF
des_name = constants.ORB_FEAT_NAME if des_option == constants.ORB_FEAT_OPTION else constants.SIFT_FEAT_NAME
log = Log(k, des_name, svm_kernel)
codebook_filename = filenames.codebook(k, des_name)
if is_interactive:
codebook_option = input(
"Enter [1] for generating a new codebook or [2] to load one.\n")
else:
codebook_option = constants.GENERATE_OPTION
if codebook_option == constants.GENERATE_OPTION:
# Calculate all the training descriptors to generate the codebook
start = time.time()
des = descriptors.all_descriptors(dataset, dataset.get_train_set(),
des_option)
end = time.time()
log.train_des_time(end - start)
# Generates the codebook using K Means
print("Generating a codebook using K-Means with k={0}".format(k))
start = time.time()
codebook = descriptors.gen_codebook(dataset, des, k)
end = time.time()
log.codebook_time(end - start)
# Stores the codebook in a file
utils.save(codebook_filename, codebook)
print("Codebook saved in {0}".format(codebook_filename))
else:
# Load a codebook from a file
print("Loading codebook ...")
codebook = utils.load(codebook_filename)
print("Codebook with shape = {0} loaded.".format(codebook.shape))
# Train and test the dataset
classifier = Classifier(dataset, log)
svm = classifier.train(svm_kernel,
codebook,
des_option=des_option,
is_interactive=is_interactive)
print("Training ready. Now beginning with testing")
result, labels = classifier.test(codebook,
svm,
des_option=des_option,
is_interactive=is_interactive)
# Store the results from the test
classes = dataset.get_classes()
log.classes(classes)
log.classes_counts(dataset.get_classes_counts())
result_filename = filenames.result(k, des_name, svm_kernel)
test_count = len(dataset.get_test_set()[0])
result_matrix = np.reshape(result, (len(classes), test_count))
utils.save_csv(result_filename, result_matrix)
# Create a confusion matrix
confusion_matrix = np.zeros((len(classes), len(classes)), dtype=np.uint32)
for i in range(len(result)):
predicted_id = int(result[i])
real_id = int(labels[i])
confusion_matrix[real_id][predicted_id] += 1
print("Confusion Matrix =\n{0}".format(confusion_matrix))
log.confusion_matrix(confusion_matrix)
log.save()
print("Log saved on {0}.".format(filenames.log(k, des_name, svm_kernel)))
if not is_interactive:
experiment_end = time.time()
elapsed_time = utils.humanize_time(experiment_end - experiment_start)
print("Total time during the experiment was {0}".format(elapsed_time))
else:
# Show a plot of the confusion matrix on interactive mode
utils.show_conf_mat(confusion_matrix)
raw_input("Press [Enter] to exit ...")
wordCounter[word] += 1
else:
wordCounter[word] = 1
popularWords = sorted(wordCounter, key = wordCounter.get, reverse = True)
lexicon = popularWords[:4000]
# After learning lexicon here OOV words are replaced by UNK sign
trainingSet0 = unkWord(trainingSet0, lexicon)
trainingSet1 = unkWord(trainingSet1, lexicon)
# positive and negative tweets are passed to training onject
for word in trainingSet0:
tweetTrainer.train(word, '0')
for word in trainingSet1:
tweetTrainer.train(word, '1')
# a classifier instance
sentimentClassifier = Classifier(tweetTrainer.data, tokenizer.Tokenizer(stop_words = [], signs_to_remove = []))
# storage of lexicon and sentiment classifier on disk
c = open('sentimentClassifier.pickle', 'wb')
l = open('lexicon.pickle', 'wb')
pickle.dump(sentimentClassifier, c)
pickle.dump(lexicon, l)
c.close()
l.close()
file.close()
# test section
# loading lexicon and sentimentClassifier for evaluation over random samples
# samples are chosen randomly and not within training samples
c = open('sentimentClassifier.pickle', 'rb')
l = open('lexicon.pickle', 'rb')
fileEval=open('test2.csv', 'r', encoding="Latin-1")#.csv
"Test10(3Good3Bad).mp4"
# "Detector/DLTest.mp4"
# "Train1(1Good5Bad).mp4",
# "Train2(1Good6Bad).mp4"
# "Train3(1Good5Bad).mp4",
# "Train4(1Good5Bad).mp4",
]
for video in videoList:
videoPath = "/home/eamonn/FYP/Videos/" + video
gymObjects = {
'Gym_Plate': {
'Location': '',
'Frame': 0
},
'FootWear': {
'Location': [],
'Frame': 0
}
# 'Person': {'Location': [],
# 'Frame': 0}
}
classifier = Classifier()
classifier.createDecisionTreeClassifier()
god = GymObjectDetector(gymObjects, videoPath)
trackedObjects = god.getNormalisedObjectLocations()
CSRTTracker = MultiTracker(gymObjects, videoPath, classifier)
barbellPosition, footwearPosition = CSRTTracker.displayAndTrack()
class FitAndPredict:
'''
Class contains function for the training and classification pipeline
'''
def __init__(self):
self.train_file = config.TRAIN_FILE
self.test_file = config.TEST_FILE
self.predicted_test_file = config.PREDICTED_TEST_FILE
self.model_folder = config.MODELS_FOLDER
self.target_map = config.TARGET_MAP
self.map_sensors = config.MAP_SENSORS
self.load_cell_theshold = config.LOAD_CELL_THRESHOLD
self.weight_threshold = config.WEIGHT_THRESHOLD
self.outliers_threshold = config.OUTLIERS_THRESHOLD
self.feature_names = config.FEATURE_NAMES
self.dev_map = config.DEV_MAP
self.plank_dict = config.PLANK_DICT
self.position_to_remove = config.POSITION_TO_REMOVE
self.sensor_details_file = sensor_details_file
self.norm_sensor_details_file = norm_sensor_details_file
self.random_state = config.RANDOM_STATE
self.min_samples_split = config.MIN_SAMPLES_SPLIT
self.min_samples_leaf = config.MIN_SAMPLES_LEAF
self.n_estimators = config.N_ESTIMATORS
self.model_name = model_name
self.target_column = config.TARGET_COLUMN
self.preprocess = PreProcess(self.load_cell_theshold, self.weight_threshold, self.outliers_threshold, \
self.map_sensors, self.target_map, self.position_to_remove, \
self.sensor_details_file, self.norm_sensor_details_file, self.model_folder)
self.fe = FeatureExtractor(self.plank_dict)
def read_train_data(self):
'''
Function to read the train data in the training pipeline
'''
logging.debug(__name__ + ' : ' + ' Start read_train_data()')
try:
self.input_data = read_csv(self.train_file)
logging.debug(__name__ + ' shape : ' + str(self.input_data.shape))
logging.debug(__name__ + ' : ' + ' End read_train_data()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Input file not found ')
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
def check_train_data(self):
'''
Function to check if all load cell columns and target column are present in the train data
'''
logging.debug(__name__ + ' : ' + ' Start check_train_data()')
try:
train_columns = self.input_data.columns.values
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
# check if all load cell columns are present in the train data
try:
if (set(self.map_sensors.keys()).issubset(set(train_columns))) or \
(set(self.map_sensors.values()).issubset(set(train_columns))):
pass
else:
print ("LOAD CELL COLUMNS NOT PRESENT IN TRAIN DATA")
logging.debug(__name__ + ' : ' + ' LOAD CELL COLUMNS NOT PRESENT IN TRAIN DATA')
logging.debug(__name__ + ' : ' + ' End read_train_data()')
sys.exit()
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
# check if target column is present in the data
try:
if (set([self.target_column]).issubset(set(train_columns))):
pass
else:
print ("TARGET COLUMN NOT PRESENT IN TRAIN DATA")
logging.debug(__name__ + ' : ' + ' TARGET COLUMN NOT PRESENT IN TRAIN DATA')
logging.debug(__name__ + ' : ' + ' End read_train_data()')
sys.exit()
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
# check if all 5 positions are present in target column of train data
try:
if (set(self.input_data[self.target_column].unique()).issubset(set([1, 2, 3, 4, 5]))):
pass
else:
print ("VALUES OTHER THAN PRESPECIFIED POSITION VALUES PRESENT IN TRAIN DATA")
logging.debug(__name__ + ' : ' + ' VALUES OTHER THAN PRESPECIFIED POSITION VALUES PRESENT IN TRAIN DATA')
logging.debug(__name__ + ' : ' + ' End read_train_data()')
sys.exit()
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
logging.debug(__name__ + ' : ' + ' End check_train_data()')
return
def preprocess_train_data(self):
'''
Function to preprocess the train data in the training pipeline
'''
logging.debug(__name__ + ' : ' + ' Start preprocess_train_data()')
try:
logging.debug(__name__ + ' : ' + ' Start rename_columns_if_needed()')
self.preprocessed_input_data = self.preprocess.rename_columns_if_needed(self.input_data)
logging.debug(__name__ + ' : ' + ' End rename_columns_if_needed()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
try:
logging.debug(__name__ + ' : ' + ' Start rem_missing_train()')
self.preprocessed_input_data = self.preprocess.rem_missing_train(self.preprocessed_input_data)
logging.debug(__name__ + ' : ' + ' End rem_missing_train()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
try:
logging.debug(__name__ + ' : ' + ' Start rem_load_cell_threshold()')
self.preprocessed_input_data = self.preprocess.rem_load_cell_threshold(self.preprocessed_input_data)
logging.debug(__name__ + ' : ' + ' End rem_load_cell_threshold()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
try:
logging.debug(__name__ + ' : ' + ' Start rem_less_weights()')
self.preprocessed_input_data = self.preprocess.rem_less_weights(self.preprocessed_input_data)
logging.debug(__name__ + ' : ' + ' End rem_less_weights()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
try:
logging.debug(__name__ + ' : ' + ' Start rem_sitting()')
self.preprocessed_input_data = self.preprocess.rem_sitting(self.preprocessed_input_data)
logging.debug(__name__ + ' : ' + ' End rem_sitting()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
try:
logging.debug(__name__ + ' : ' + ' Start normalize()')
self.preprocessed_input_data = self.preprocess.normalize(self.preprocessed_input_data)
logging.debug(__name__ + ' : ' + ' End normalize()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
try:
logging.debug(__name__ + ' : ' + ' Start treat_outliers_train()')
self.preprocessed_input_data = self.preprocess.treat_outliers_train(self.preprocessed_input_data)
logging.debug(__name__ + ' : ' + ' End treat_outliers_train()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
logging.debug(__name__ + ' shape : ' + str(self.preprocessed_input_data.shape))
logging.debug(__name__ + ' : ' + ' End preprocess_train_data()')
return
def read_test_data(self):
'''
Function to read the test data in the classification pipeline
'''
logging.debug(__name__ + ' : ' + ' Start read_test_data()')
try:
self.test_data = read_csv(self.test_file, header = None)
logging.debug(__name__ + ' shape : ' + str(self.test_data.shape))
logging.debug(__name__ + ' : ' + ' End read_test_data()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Test file not found ')
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
return
def check_test_data(self):
'''
Function to check if all load cell columns are present in the test data
'''
logging.debug(__name__ + ' : ' + ' Start check_test_data()')
# check if all load cell columns are present in the test data
#try:
#test_columns = self.test_data.columns
#except Exception as e:
#logging.error(__name__ + ' : ' + ' Error: ' + str(e))
#finally:
#pass
try:
#if (set(self.map_sensors.keys()).issubset(set(test_columns))) or (set(self.map_sensors.values()).issubset(set(test_columns))):
test_columns = ['LC' + str(x) for x in range(1, 17)]
if self.test_data.shape[1] == len(test_columns):
self.test_data.columns = test_columns
pass
else:
print ("TEST DATA DO NOT HAVE ALL LOAD CELLS DATA")
logging.debug(__name__ + ' : ' + ' TEST DATA DO NOT HAVE ALL LOAD CELLS DATA')
logging.debug(__name__ + ' : ' + ' End check_test_data()')
sys.exit()
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
logging.debug(__name__ + ' : ' + ' End check_test_data()')
return
def preprocess_test_data(self):
'''
Function to preprocess the test data in the classification pipeline
'''
logging.debug(__name__ + ' : ' + ' Start preprocess_test_data()')
try:
self.preprocessed_test_data = self.test_data
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
#try:
# logging.debug(__name__ + ' : ' + ' Start rename_columns_if_needed()')
# self.preprocessed_test_data = self.preprocess.rename_columns_if_needed(self.test_data)
# logging.debug(__name__ + ' : ' + ' End rename_columns_if_needed()')
#except Exception as e:
# logging.error(__name__ + ' : ' + ' Error: ' + str(e))
#finally:
# pass
try:
logging.debug(__name__ + ' : ' + ' Start treat_missing_test()')
self.preprocessed_test_data = self.preprocess.treat_missing_test(self.preprocessed_test_data)
logging.debug(__name__ + ' : ' + ' End treat_missing_test()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
try:
logging.debug(__name__ + ' : ' + ' Start normalize()')
self.preprocessed_test_data = self.preprocess.normalize(self.preprocessed_test_data)
logging.debug(__name__ + ' : ' + ' End normalize()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
try:
logging.debug(__name__ + ' : ' + ' Start treat_outliers_test()')
self.preprocessed_test_data = self.preprocess.treat_outliers_test(self.preprocessed_test_data)
logging.debug(__name__ + ' : ' + ' End treat_outliers_test()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
logging.debug(__name__ + ' shape : ' + str(self.preprocessed_test_data.shape))
logging.debug(__name__ + ' : ' + ' End preprocess_test_data()')
return
def transform_train_data_into_features(self):
'''
Function to create features from train data in the training pipeline
'''
logging.debug(__name__ + ' : ' + ' Start transform_train_data_into_features()')
try:
# left_sensors_pct
logging.debug(__name__ + ' : ' + ' Start left_percent()')
self.preprocessed_input_data['left_sensors_pct'] = self.preprocessed_input_data.apply(lambda x : self.fe.left_percent(x), axis = 1)
logging.debug(__name__ + ' : ' + ' End left_percent()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
try:
# plank_1_std
logging.debug(__name__ + ' : ' + ' Start plank_1_std_cal()')
self.preprocessed_input_data['plank_1_std'] = self.preprocessed_input_data.apply(lambda x : self.fe.plank_1_std_cal(x), axis = 1)
logging.debug(__name__ + ' : ' + ' End plank_1_std_cal()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
try:
# plank_2_std
logging.debug(__name__ + ' : ' + ' Start plank_2_std_cal()')
self.preprocessed_input_data['plank_2_std'] = self.preprocessed_input_data.apply(lambda x : self.fe.plank_2_std_cal(x), axis = 1)
logging.debug(__name__ + ' : ' + ' End plank_2_std_cal()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
try:
# plank_3_std
logging.debug(__name__ + ' : ' + ' Start plank_3_std_cal()')
self.preprocessed_input_data['plank_3_std'] = self.preprocessed_input_data.apply(lambda x : self.fe.plank_3_std_cal(x), axis = 1)
logging.debug(__name__ + ' : ' + ' End plank_3_std_cal()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
try:
# plank_4_std
logging.debug(__name__ + ' : ' + ' Start plank_4_std_cal()')
self.preprocessed_input_data['plank_4_std'] = self.preprocessed_input_data.apply(lambda x : self.fe.plank_4_std_cal(x), axis = 1)
logging.debug(__name__ + ' : ' + ' End plank_4_std_cal()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
try:
# com_1_x
logging.debug(__name__ + ' : ' + ' Start get_com_1_x()')
self.preprocessed_input_data['plank_1_com_x'] = self.preprocessed_input_data.apply(lambda x: self.fe.get_com_1_x(x), axis = 1)
logging.debug(__name__ + ' : ' + ' End get_com_1_x()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
try:
# com_2_x
logging.debug(__name__ + ' : ' + ' Start get_com_2_x()')
self.preprocessed_input_data['plank_2_com_x'] = self.preprocessed_input_data.apply(lambda x: self.fe.get_com_2_x(x), axis = 1)
logging.debug(__name__ + ' : ' + ' End get_com_2_x()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
try:
# com_3_x
logging.debug(__name__ + ' : ' + ' Start get_com_3_x()')
self.preprocessed_input_data['plank_3_com_x'] = self.preprocessed_input_data.apply(lambda x: self.fe.get_com_3_x(x), axis = 1)
logging.debug(__name__ + ' : ' + ' End get_com_3_x()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
try:
# com_4_x
logging.debug(__name__ + ' : ' + ' Start get_com_4_x()')
self.preprocessed_input_data['plank_4_com_x'] = self.preprocessed_input_data.apply(lambda x: self.fe.get_com_4_x(x), axis = 1)
logging.debug(__name__ + ' : ' + ' End get_com_4_x()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
try:
# com_1_y
logging.debug(__name__ + ' : ' + ' Start get_com_1_y()')
self.preprocessed_input_data['plank_1_com_y'] = self.preprocessed_input_data.apply(lambda x: self.fe.get_com_1_y(x), axis = 1)
logging.debug(__name__ + ' : ' + ' End get_com_1_y()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
try:
# com_2_y
logging.debug(__name__ + ' : ' + ' Start get_com_2_y()')
self.preprocessed_input_data['plank_2_com_y'] = self.preprocessed_input_data.apply(lambda x: self.fe.get_com_2_y(x), axis = 1)
logging.debug(__name__ + ' : ' + ' End get_com_2_y()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
try:
# com_3_y
logging.debug(__name__ + ' : ' + ' Start get_com_3_y()')
self.preprocessed_input_data['plank_3_com_y'] = self.preprocessed_input_data.apply(lambda x: self.fe.get_com_3_y(x), axis = 1)
logging.debug(__name__ + ' : ' + ' End get_com_3_y()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
try:
# com_4_y
logging.debug(__name__ + ' : ' + ' Start get_com_4_y()')
self.preprocessed_input_data['plank_4_com_y'] = self.preprocessed_input_data.apply(lambda x: self.fe.get_com_4_y(x), axis = 1)
logging.debug(__name__ + ' : ' + ' End get_com_4_y()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
try:
# y_errors
logging.debug(__name__ + ' : ' + ' Start get_errors_from_fitted_line()')
self.preprocessed_input_data['y_errors'] = self.preprocessed_input_data.apply(lambda x: self.fe.get_errors_from_fitted_line(x), axis = 1)
logging.debug(__name__ + ' : ' + ' End get_errors_from_fitted_line()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
try:
# plank_3_deviation from fitted line through COMs of first two planks
logging.debug(__name__ + ' : ' + ' Start get_deviation_plank_3()')
self.preprocessed_input_data['plank_3_dev'] = self.preprocessed_input_data.apply(lambda x: self.fe.get_deviation_plank_3(x), axis = 1)
logging.debug(__name__ + ' : ' + ' End get_deviation_plank_3()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
try:
# plank_4_deviation from fitted line through COMs of first two planks
logging.debug(__name__ + ' : ' + ' Start get_deviation_plank_4()')
self.preprocessed_input_data['plank_4_dev'] = self.preprocessed_input_data.apply(lambda x: self.fe.get_deviation_plank_4(x), axis = 1)
logging.debug(__name__ + ' : ' + ' End get_deviation_plank_4()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
try:
# plank_3_dev_bucket
logging.debug(__name__ + ' : ' + ' Start bucketize_plank_3_dev()')
self.preprocessed_input_data['plank_3_dev_bucket'] = self.preprocessed_input_data['plank_3_dev'].apply(lambda x: self.fe.bucketize_plank_dev(x))
self.preprocessed_input_data['plank_3_dev_bucket'] = self.preprocessed_input_data['plank_3_dev_bucket'].apply(lambda x: self.dev_map[x])
logging.debug(__name__ + ' : ' + ' End bucketize_plank_3_dev()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
try:
# plank_4_dev_bucket
logging.debug(__name__ + ' : ' + ' Start bucketize_plank_4_dev()')
self.preprocessed_input_data['plank_4_dev_bucket'] = self.preprocessed_input_data['plank_4_dev'].apply(lambda x: self.fe.bucketize_plank_dev(x))
self.preprocessed_input_data['plank_4_dev_bucket'] = self.preprocessed_input_data['plank_4_dev_bucket'].apply(lambda x: self.dev_map[x])
logging.debug(__name__ + ' : ' + ' End bucketize_plank_4_dev()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
try:
# plank_4_deviation from fitted line through COMs of 2nd and 3rd planks
logging.debug(__name__ + ' : ' + ' Start plank_4_wrt_3_2()')
self.preprocessed_input_data['plank_4_wrt_3_2'] = self.preprocessed_input_data.apply(lambda x: self.fe.plank_4_wrt_3_2(x), axis = 1)
logging.debug(__name__ + ' : ' + ' End plank_4_wrt_3_2()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
logging.debug(__name__ + ' shape : ' + str(self.preprocessed_input_data.shape))
logging.debug(__name__ + ' : ' + ' End transform_train_data_into_features()')
return
def transform_test_data_into_features(self):
'''
Function to create features from test data in the classification pipeline
'''
logging.debug(__name__ + ' : ' + ' Start transform_test_data_into_features()')
try:
# left_sensors_pct
logging.debug(__name__ + ' : ' + ' Start left_percent()')
self.preprocessed_test_data['left_sensors_pct'] = self.preprocessed_test_data.apply(lambda x : self.fe.left_percent(x), axis = 1)
logging.debug(__name__ + ' : ' + ' End left_percent()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
try:
# plank_1_std
logging.debug(__name__ + ' : ' + ' Start plank_1_std_cal()')
self.preprocessed_test_data['plank_1_std'] = self.preprocessed_test_data.apply(lambda x : self.fe.plank_1_std_cal(x), axis = 1)
logging.debug(__name__ + ' : ' + ' End plank_1_std_cal()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
try:
# plank_2_std
logging.debug(__name__ + ' : ' + ' Start plank_2_std_cal()')
self.preprocessed_test_data['plank_2_std'] = self.preprocessed_test_data.apply(lambda x : self.fe.plank_2_std_cal(x), axis = 1)
logging.debug(__name__ + ' : ' + ' End plank_2_std_cal()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
try:
# plank_3_std
logging.debug(__name__ + ' : ' + ' Start plank_3_std_cal()')
self.preprocessed_test_data['plank_3_std'] = self.preprocessed_test_data.apply(lambda x : self.fe.plank_3_std_cal(x), axis = 1)
logging.debug(__name__ + ' : ' + ' End plank_3_std_cal()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
try:
# plank_4_std
logging.debug(__name__ + ' : ' + ' Start plank_4_std_cal()')
self.preprocessed_test_data['plank_4_std'] = self.preprocessed_test_data.apply(lambda x : self.fe.plank_4_std_cal(x), axis = 1)
logging.debug(__name__ + ' : ' + ' End plank_4_std_cal()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
try:
# com_1_x
logging.debug(__name__ + ' : ' + ' Start get_com_1_x()')
self.preprocessed_test_data['plank_1_com_x'] = self.preprocessed_test_data.apply(lambda x: self.fe.get_com_1_x(x), axis = 1)
logging.debug(__name__ + ' : ' + ' End get_com_1_x()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
try:
# com_2_x
logging.debug(__name__ + ' : ' + ' Start get_com_2_x()')
self.preprocessed_test_data['plank_2_com_x'] = self.preprocessed_test_data.apply(lambda x: self.fe.get_com_2_x(x), axis = 1)
logging.debug(__name__ + ' : ' + ' End get_com_2_x()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
try:
# com_3_x
logging.debug(__name__ + ' : ' + ' Start get_com_3_x()')
self.preprocessed_test_data['plank_3_com_x'] = self.preprocessed_test_data.apply(lambda x: self.fe.get_com_3_x(x), axis = 1)
logging.debug(__name__ + ' : ' + ' End get_com_3_x()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
try:
# com_4_x
logging.debug(__name__ + ' : ' + ' Start get_com_4_x()')
self.preprocessed_test_data['plank_4_com_x'] = self.preprocessed_test_data.apply(lambda x: self.fe.get_com_4_x(x), axis = 1)
logging.debug(__name__ + ' : ' + ' End get_com_4_x()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
try:
# com_1_y
logging.debug(__name__ + ' : ' + ' Start get_com_1_y()')
self.preprocessed_test_data['plank_1_com_y'] = self.preprocessed_test_data.apply(lambda x: self.fe.get_com_1_y(x), axis = 1)
logging.debug(__name__ + ' : ' + ' End get_com_1_y()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
try:
# com_2_y
logging.debug(__name__ + ' : ' + ' Start get_com_2_y()')
self.preprocessed_test_data['plank_2_com_y'] = self.preprocessed_test_data.apply(lambda x: self.fe.get_com_2_y(x), axis = 1)
logging.debug(__name__ + ' : ' + ' End get_com_2_y()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
try:
# com_3_y
logging.debug(__name__ + ' : ' + ' Start get_com_3_y()')
self.preprocessed_test_data['plank_3_com_y'] = self.preprocessed_test_data.apply(lambda x: self.fe.get_com_3_y(x), axis = 1)
logging.debug(__name__ + ' : ' + ' End get_com_3_y()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
try:
# com_4_y
logging.debug(__name__ + ' : ' + ' Start get_com_4_y()')
self.preprocessed_test_data['plank_4_com_y'] = self.preprocessed_test_data.apply(lambda x: self.fe.get_com_4_y(x), axis = 1)
logging.debug(__name__ + ' : ' + ' End get_com_4_y()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
try:
# y_errors
logging.debug(__name__ + ' : ' + ' Start get_errors_from_fitted_line()')
self.preprocessed_test_data['y_errors'] = self.preprocessed_test_data.apply(lambda x: self.fe.get_errors_from_fitted_line(x), axis = 1)
logging.debug(__name__ + ' : ' + ' End get_errors_from_fitted_line()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
try:
# plank_3_deviation from fitted line through COMs of first two planks
logging.debug(__name__ + ' : ' + ' Start get_deviation_plank_3()')
self.preprocessed_test_data['plank_3_dev'] = self.preprocessed_test_data.apply(lambda x: self.fe.get_deviation_plank_3(x), axis = 1)
logging.debug(__name__ + ' : ' + ' End get_deviation_plank_3()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
try:
# plank_4_deviation from fitted line through COMs of first two planks
logging.debug(__name__ + ' : ' + ' Start get_deviation_plank_4()')
self.preprocessed_test_data['plank_4_dev'] = self.preprocessed_test_data.apply(lambda x: self.fe.get_deviation_plank_4(x), axis = 1)
logging.debug(__name__ + ' : ' + ' End get_deviation_plank_4()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
try:
# plank_3_dev_bucket
logging.debug(__name__ + ' : ' + ' Start bucketize_plank_3_dev()')
self.preprocessed_test_data['plank_3_dev_bucket'] = self.preprocessed_test_data['plank_3_dev'].apply(lambda x: self.fe.bucketize_plank_dev(x))
self.preprocessed_test_data['plank_3_dev_bucket'] = self.preprocessed_test_data['plank_3_dev_bucket'].apply(lambda x: self.dev_map[x])
logging.debug(__name__ + ' : ' + ' End bucketize_plank_3_dev()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
try:
# plank_4_dev_bucket
logging.debug(__name__ + ' : ' + ' Start bucketize_plank_4_dev()')
self.preprocessed_test_data['plank_4_dev_bucket'] = self.preprocessed_test_data['plank_4_dev'].apply(lambda x: self.fe.bucketize_plank_dev(x))
self.preprocessed_test_data['plank_4_dev_bucket'] = self.preprocessed_test_data['plank_4_dev_bucket'].apply(lambda x: self.dev_map[x])
logging.debug(__name__ + ' : ' + ' End bucketize_plank_4_dev()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
try:
# plank_4_deviation from fitted line through COMs of 2nd and 3rd planks
logging.debug(__name__ + ' : ' + ' Start plank_4_wrt_3_2()')
self.preprocessed_test_data['plank_4_wrt_3_2'] = self.preprocessed_test_data.apply(lambda x: self.fe.plank_4_wrt_3_2(x), axis = 1)
logging.debug(__name__ + ' : ' + ' End plank_4_wrt_3_2()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
pass
logging.debug(__name__ + ' shape : ' + str(self.preprocessed_test_data.shape))
logging.debug(__name__ + ' : ' + ' End transform_test_data_into_features()')
return
def train_model(self):
'''
Function to train the model on train data for training pipeline
'''
logging.debug(__name__ + ' : ' + ' Start train_model()')
try:
# train the model
self.learner = Learner(self.n_estimators, self.min_samples_split, \
self.min_samples_leaf, self.random_state, \
self.model_folder, self.model_name)
X_train = self.preprocessed_input_data[self.feature_names]
Y_train = self.preprocessed_input_data[self.target_column]
predictions, pred_prob = self.learner.train_model(X_train, Y_train)
#print (np.round(accuracy_score(Y_train, predictions), 4) * 100)
#print (np.round(log_loss(Y_train, pred_prob), 2))
logging.debug(__name__ + ' : ' + ' End train_model()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
return
def classify(self):
'''
Function to make classifications on test data for the classification pipeline
'''
logging.debug(__name__ + ' : ' + ' Start classify()')
try:
# classify using the model
self.classifier = Classifier(self.model_folder)
X_test = self.preprocessed_test_data[self.feature_names]
#Y_test = self.preprocessed_test_data[self.target_column]
#print (X_test.shape, Y_test.shape)
predictions, pred_prob = self.classifier.classify_model(X_test)
# saving the test dataset with the predicted values
#self.test_data[self.target_column] = predictions
#self.test_data.to_csv(self.predicted_test_file, index = False)
self.predicted_test_data = DataFrame({self.target_column:predictions})
self.predicted_test_data.to_csv(self.predicted_test_file, index = False, header = False)
# printing the accuracy score
#print (np.round(accuracy_score(Y_test, predictions), 4) * 100)
#print (np.round(log_loss(Y_test, pred_prob), 2))
logging.debug(__name__ + ' : ' + ' End classify()')
except Exception as e:
logging.error(__name__ + ' : ' + ' Error: ' + str(e))
finally:
return
class Recognizer:
def __init__(self, agent_id):
self.clf = Classifier(
model_path='./models/model.augmented.pkl',
vectorizer_path='./models/vectorizer.augmented.pkl',
label_dict_path='./models/label_dict.augmented.pkl')
self.q_detector = QuestionDetector(agent_id)
self.tokenizer = MeCab.Tagger()
def recognize(self, sentence):
sentences = self.split_sentence(sentence)
analyzed_sentences = [self.normalize(s) for s in sentences]
sentences = [''.join([w.word for w in s]) for s in analyzed_sentences]
intents = self.clf.predict(sentences)
is_q = any(self.q_detector.detect(s) for s in sentences)
results = [
self.get_detail(s, i) for s, i in zip(analyzed_sentences, intents)
]
return results
def get_detail(self, sentence, intent):
if intent == 'DIVINE':
agent_id = self.get_agent_id(''.join([w.word for w in sentence]))
result = self.get_white_black(sentence)
result = 'HUMAN' if result is None else result
return ('DIVINE', agent_id, result)
elif intent == 'VOTE':
agent_id = self.get_agent_id(''.join([w.word for w in sentence]))
return ('VOTE', agent_id)
elif intent == 'ESTIMATE':
agent_id = self.get_agent_id(''.join([w.word for w in sentence]))
role = self.get_white_black(sentence)
role = self.get_role(sentence) if role is None else role
return ('ESTIMATE', agent_id, role)
elif intent == 'CO':
role = self.get_role(sentence)
return ('CO', role)
elif intent == 'REQUEST':
return ('REQUEST')
else:
return ('CHAT')
def get_agent_id(self, sentence):
m = re.search(r'Agent\[(\d+)\]', sentence)
if m:
return m.group(1)
else:
return False
def get_role(self, sentence):
if any(w.word == '人狼' for w in sentence):
return '狼'
elif any(w.word == '狂人' for w in sentence):
return '狂'
elif any(w.word == '占い師' for w in sentence):
return '占'
else:
return '村'
def get_white_black(self, sentence):
if any(w.word == '人狼' for w in sentence):
return 'WEREWOLF'
elif any(w.word == '村人' for w in sentence):
return 'HUMAN'
else:
return None
def normalize(self, sentence):
sentence = self.norm_token(sentence)
words = self.tokenize(sentence)
words = [self.norm_role(w) for w in words]
return words
def tokenize(self, sentence):
result = []
for line in self.tokenizer.parse(sentence).strip().split('\n'):
if line == 'EOS': break
word, feature = line.split('\t')
result.append(Morph(word, feature))
return result
def norm_role(self, s):
if s.pos == '名詞':
if re.match(r'人狼|狼|黒', s.word):
s.word = '人狼'
elif re.match(r'狂人|狂', s.word):
s.word = '狂人'
elif re.match(r'占い師|占い|占', s.word):
s.word = '占い師'
elif re.match(r'村人|人間|白', s.word):
s.word = '村人'
return s
def norm_token(self, sentence):
sentence = re.sub(r'[\.．。]', '。', sentence)
sentence = re.sub(r'[\,，、]', '、', sentence)
sentence = re.sub(r'\?|？', '？', sentence)
sentence = re.sub(r'\!|！', '！', sentence)
return sentence
def split_sentence(self, sentence):
sentence = self.norm_token(sentence)
sentence = re.sub(r'？', '？[SEP]', sentence)
sentence = re.sub(r'！', '！[SEP]', sentence)
sentence = re.sub(r'。', '。[SEP]', sentence)
sentence = re.sub(r'\[SEP\]$', '', sentence)
sentences = sentence.split('[SEP]')
return sentences
def iter_oger_nn_test_data(self):
"""Iter annotations of OGER filtered by the NN on the CRAFT corpus.
yields:
tuple: (pmid, sspan, espan, n_gram, label, entity ID)
"""
# perform concept recognition or not
cr = int(self.config['other']['concept_recognition'])
# load the classifier
c = Classifier(self.config_path)
c.restore_model()
# create a feature extractor
fextr = FeatureExtractor(self.config_path)
# back mapping from integer to ontology
mapping = {int(self.config['classes'][o]):
o for o in self.config['classes']}
# initialize feature names with empty arrays
features = {}
for name in c.column_names[:-1]:
features[name] = []
# lists of term data
tlists = []
# go through all OGER test annotations
for pmid, sspan, espan, n_gram, label, entity_id in \
self.iter_oger_test_data():
# get list of feature values
for i, val in enumerate(fextr.iter_feature_values(n_gram)):
name = c.column_names[i]
features[name].append(val)
tlists.append((pmid, sspan, espan, n_gram, label, entity_id))
predictions = c.classifier.predict(
input_fn=lambda: c.eval_input_fn(features))
# get predictions and zip them with other annotation data
for i, pred_dict in enumerate(predictions):
# list of (probability, entity type label)-tuples
probs = []
# go through the probabilities of the entity types
for index, p in enumerate(pred_dict['probabilities']):
# append (probability, entity type label)
probs.append((p, mapping[index]))
# sort tuples by probability in decreasing order
probs = sorted(probs, reverse=True)
# labels to consider, by default only the one with highest prob
labels = [probs[0][1]]
# check if difference between the highest and second-highest
# probability is smaller than 0.3
threshold = float(self.config['parameters']['threshold'])
prob_diff = probs[0][0] - probs[1][0]
if prob_diff < threshold:
labels.append(probs[1][1])
# go through entity type labels
for label in labels:
# ignore entity types classified as normal nouns
if label != 'nn':
# check if concept recognition should be performed
if cr:
# ignore entity types where OGER and NN give different
# labels
if label == tlists[i][4]:
yield tlists[i]
else:
yield tlists[i][:4] + (label,) + (tlists[i][-1],)
class WGAN(tf.keras.Model):
def __init__(self, batch_size=64):
super().__init__(name="WGAN")
self.generator = Generator(100, batch_size)
self.critic = Critic()
self.classifier_m = Classifier()
self.train_dataset = None
self.test_dataset = None
self.train_labels = None
self.test_labels = None
self.batch_size = batch_size
def load_dataset(self, dataset, n_classes):
self.train_dataset, self.train_labels, self.test_dataset, self.test_labels = dataset
self.num_classes = n_classes
@tf.function
def predict_batch(self, images, type_class):
images_predictions = tf.TensorArray(tf.float32,
size=10,
dynamic_size=True)
ys = tf.TensorArray(tf.float32, size=10, dynamic_size=True)
matched_images = tf.TensorArray(tf.float32, size=0, dynamic_size=True)
index = 0
for i in tf.range(len(images)):
gen_image = data_access.normalize(
data_access.de_standardize(images[i]))
img = tf.expand_dims(gen_image, axis=0)
c_type = self.classifier_m.predict_image(img)
w_list = tf.one_hot(c_type, self.num_classes)
w_list = tf.reshape(w_list, (w_list.shape[1], ))
images_predictions = images_predictions.write(i, w_list)
y_list = tf.one_hot(type_class, self.num_classes)
ys = ys.write(i, y_list)
if (tf.reduce_all(tf.equal(w_list, y_list))):
matched_images = matched_images.write(index, images[i])
index += 1
return images_predictions.stack(), ys.stack(), matched_images.stack()
@tf.function
def gradient_penalty(self, generated_samples, real_images, half_batch):
alpha = backend.random_uniform(shape=[half_batch, 1, 1, 1],
minval=0.0,
maxval=1.0)
differences = generated_samples - real_images
interpolates = real_images + (alpha * differences)
gradients = tf.gradients(self.critic(interpolates), [interpolates])[0]
slopes = tf.sqrt(tf.reduce_sum(tf.square(gradients), axis=[1, 2, 3]))
gradient_p = tf.reduce_mean((slopes - 1.)**2)
return gradient_p
@tf.function
def training_step_critic(self, real_imgs, gen_imgs, real_labels,
gen_labels, half_batch):
lambda_ = 10.0
with tf.GradientTape() as tape:
d_x_real = self.critic(real_imgs, training=True)
d_x_gen = self.critic(gen_imgs, training=True)
critic_r_loss = self.critic.compute_loss(real_labels, d_x_real)
critic_g_loss = self.critic.compute_loss(gen_labels, d_x_gen)
total_loss = critic_r_loss + critic_g_loss + (
lambda_ *
self.gradient_penalty(gen_imgs, real_imgs, half_batch))
gradients_of_critic = tape.gradient(total_loss,
self.critic.trainable_variables)
self.critic.backPropagate(gradients_of_critic,
self.critic.trainable_variables)
return total_loss
@tf.function
def training_step_generator(self, noise_size, class_type):
# prepare points in latent space as input for the generator
X_g = self.generator.generate_noise(self.batch_size, noise_size)
# create inverted labels for the fake samples
y_g = -np.ones((self.batch_size, 1)).astype(np.float32)
with tf.GradientTape() as tape:
d_x = self.generator(X_g, training=True) # Trainable?
d_z = self.critic(d_x, training=True)
images_predictions, ys, matched_images = self.predict_batch(
d_x, class_type)
generator_loss = self.generator.compute_loss(
d_z, y_g, ys, images_predictions)
gradients_of_generator = tape.gradient(
generator_loss, self.generator.trainable_variables)
self.generator.backPropagate(gradients_of_generator,
self.generator.trainable_variables)
return generator_loss, matched_images, self.generator(
self.generator.seed, training=False)
def generate_real_samples(self, n_samples):
# choose random instances
ix = np.random.randint(0, self.train_dataset.shape[0], n_samples)
# select images
X = self.train_dataset[ix]
# associate with class labels of -1 for 'real'
y = -np.ones((n_samples, 1)).astype(np.float32)
return X, y
@tf.function
# use the generator to generate n fake examples, with class labels
def generate_fake_samples(self, noise_size, n_samples):
# generate points in latent space
x_input = self.generator.generate_noise(n_samples, noise_size)
# get images generated
X = self.generator(x_input, training=True)
# associate with class labels of 1.0 for 'fake'
y = np.ones((n_samples, 1)).astype(np.float32)
return X, y
def define_loss_tensorboard(self):
logdir = "logs/train/" + datetime.now().strftime("%Y%m%d-%H%M%S")
return tf.summary.create_file_writer(logdir=logdir)
def define_graph_tensorboard(self):
logdir = "logs/graph/" + datetime.now().strftime("%Y%m%d-%H%M%S")
return tf.summary.create_file_writer(logdir=logdir)
def train_model(self, epoches, n_critic=5, noise_size=100, class_type=5):
batch_per_epoch = int(self.train_dataset.shape[0] / self.batch_size)
# calculate the number of training iterations
n_steps = batch_per_epoch * epoches
# calculate the size of half a batch of samples
half_batch = int(self.batch_size / 2)
sum_writer_loss = self.define_loss_tensorboard()
self.classifier_m.load_local_model()
avg_loss_critic = tf.keras.metrics.Mean()
avg_loss_gen = tf.keras.metrics.Mean()
epoch = 0
n_dif_images = 4
directory = 'imgs'
start_time = time.time()
for i in range(n_steps):
for _ in range(n_critic):
# get randomly selected 'real' samples
X_real, y_real = self.generate_real_samples(half_batch)
# generate 'fake' examples
X_fake, y_fake = self.generate_fake_samples(
noise_size, half_batch)
# update critic model weights
c_loss = self.training_step_critic(X_real, X_fake, y_real,
y_fake, half_batch)
avg_loss_critic(c_loss)
gen_loss, matched_images, gen_images = self.training_step_generator(
noise_size, class_type)
avg_loss_gen(gen_loss)
data_access.print_training_output(i, n_steps,
avg_loss_critic.result(),
avg_loss_gen.result())
if ((i % (n_steps / epoches)) == 0):
data_access.store_images_seed(directory,
gen_images[:n_dif_images], epoch)
with sum_writer_loss.as_default():
tf.summary.scalar('loss_gen',
avg_loss_gen.result(),
step=self.generator.optimizer.iterations)
tf.summary.scalar('avg_loss_critic',
avg_loss_critic.result(),
step=self.critic.optimizer.iterations)
epoch += 1
data_access.create_collection(epoches, n_dif_images, directory)
print('Time elapse {}'.format(time.time() - start_time))
def generate_images(self, number_of_samples, directory):
seed = tf.random.normal([number_of_samples, 100])
images = self.generator(seed)
predictions = self.classifier_m.predict_image_vector(
data_access.normalize(data_access.de_standardize(images)))
data_access.produce_generate_figure('imgs', images, predictions)
def __init__(self,
microDataLoc,
clusterNum=1,
macroDataLoc="data/clusterData.txt"):
Classifier.__init__(self, microDataLoc, clusterNum, macroDataLoc)
def start_rules_training(self):
# Generate the tags for the values range
gen_tags = gt(self.df)
tags_ranges = gen_tags.set_tags()
# Split the dataset in 5 random partitions
parts_gen = Part(self.df)
partition_set = parts_gen.gen_partition_set()
# Initialize the best rulesset using full dataset
best_rulesset = pd.DataFrame()
best_rulesset = self.get_initial_rules(self.df, tags_ranges)
# Train the rules with all posible combinations of training and test partitions
for i in range(0, len(partition_set) - 1):
# Select the partition for the test set, using the index of the loop
test_set = partition_set[i]
# Select the partitions for training set, removing test partition from a copy of the partitions list
training_set = partition_set.copy()
training_set.pop(i) # Remove test partition from training_set
# Fuzzify the data from the test set
fuzzifier = FuzGen(test_set)
test_df = fuzzifier.fuzzify_data(tags_ranges)
'''
Deal each training set with the rules set, to get the best rules set.
In each iteration, accumulate the matched rules to the previous rules set.
This will allows to distinct the best rules, which have been matched more times
'''
for training_df in training_set:
# Fuzzify training partition
fuzzifier = FuzGen(training_df)
fuzzy_df = fuzzifier.fuzzify_data(tags_ranges)
# Deal the new rules set to the training partition
classifier = Classifier(fuzzy_df, best_rulesset)
classifier.classify_dataset()
# Check results of classification: matched rules and positives rate
TP_value, matched_rules = classifier.verify_classification()
# Concatenate the matched rules to the current best rules set
best_rulesset = pd.concat([best_rulesset, matched_rules])
'''
Once get the matched rules over the initial set, test the rules set over the test partition
Before this, apply a filter to select only a rule for each antecesors set, based in the matches
got from the training
'''
# Filter the best rules, removing repeated antecesors
best_rulesset = self.reduce_rules(best_rulesset, tags_ranges)
# Try to classify the test set with the rules set get from training
classifier = Classifier(test_df, best_rulesset)
classifier.classify_dataset()
# Check classification results
TP_value, matched_rules = classifier.verify_classification()
# Calculate accuraccy, as the division between the positives rate (matches) and the length of test set
accuraccy = (TP_value / len(test_df))
print(f"Test {i} accuraccy: {accuraccy}")
print(f"Lenght of minimal rules set: {len(best_rulesset)}")
return best_rulesset
time_budget = max_time
overall_time_budget = overall_time_budget + time_budget
vprint( verbose, "[+] Cumulated time budget (all tasks so far) %5.2f sec" % (overall_time_budget))
# We do not add the time left over form previous dataset: time_budget += time_left_over
vprint( verbose, "[+] Time budget for this task %5.2f sec" % time_budget)
time_spent = time.time() - start
vprint( verbose, "[+] Remaining time after reading data %5.2f sec" % (time_budget-time_spent))
if time_spent >= time_budget:
vprint( verbose, "[-] Sorry, time budget exceeded, skipping this task")
execution_success = False
continue
# ========= Creating a model, knowing its assigned task from D.info['task'].
# The model can also select its hyper-parameters based on other elements of info.
vprint( verbose, "======== Creating model ==========")
M = Classifier()
# ========= Reload trained model if it exists.
vprint( verbose, "**********************************************************************")
vprint( verbose, "****** Attempting to reload model (from res/) to avoid training ******")
vprint( verbose, "**********************************************************************")
you_must_train=1
modelname = os.path.join(res_dir,basename)
if os.path.isfile(modelname + '_model.pickle'):
M = M.load(modelname)
you_must_train=0
vprint( verbose, "[+] Model reloaded, no need to train!")
# ========= Train if needed only.
if you_must_train:
vprint( verbose, "======== Trained model not found, proceeding to train!")
ha='center',
color=color)
plt.xlabel('Predicted Value')
plt.xticks(range(CLASSES))
plt.ylabel('Actual Value')
plt.yticks(range(CLASSES))
plt.colorbar()
plt.tight_layout()
plt.savefig(str(plot_name), bbox_inches='tight', pad_inches=0)
plt.close()
# Seed for consistency
np.random.seed(SEED)
# Load best weights back up and make confusion matrices
classifier = Classifier(input_size=INPUTS)
weight_files = sorted(CLASS_MODEL_DIR.iterdir())
for weight_file in weight_files[:-1]:
classifier.addLayer(file_name=weight_file, output=False)
classifier.addLayer(file_name=weight_files[-1], output=True)
train_conf_title = 'Train Confusion Matrix'
makeConfMat(classifier,
train_data,
train_labels,
CLASS_TRAIN_CONF,
title=train_conf_title)
test_conf_title = 'Test Confusion Matrix'
makeConfMat(classifier,
test_data,
test_labels,
CLASS_TEST_CONF,
def run_iteration(iteration, hash_map):
lbp = LocalBinaryPatterns(24, 8)
data = []
labels = []
#Finding all images
images = [os.path.join(root, name) for root, dirs, files in os.walk("../training_images")
for name in files if name.endswith((".jpeg", ".jpg"))]
#Spliting it into training and testing groups
training, testing = train_test_split(images, test_size = 0.25)
#Training Phase
for imagePath in training:
#Load the image, convert it to grayscale, and compute LBP
image = cv2.imread(imagePath)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
if imagePath in hash_map:
hist = hash_map[imagePath]
else:
hist = lbp.compute(gray)
hash_map[imagePath] = hist
print str(iteration) + " DEBUG(Training): Computed LBP Histogram for " + imagePath
#Plotting histogram if needed
#plt.bar(bin_edges[:-1], hist, width = 1)
#plt.xlim(min(bin_edges), max(bin_edges))
#plt.show()
#Extract the label from the image path, then update the label and data lists
labels.append(imagePath.split("/")[-2])
data.append(hist)
#Train classifier
classifier = Classifier("SVM")
print "\n\n" + str(iteration) + " DEBUG: Training Classifier"
classifier.train(data, labels)
print "\n\n" + str(iteration) + " DEBUG: Trained Classifier\n\n"
#Testing Phase
data = []
labels = []
for imagePath in testing:
#Load the image, convert to grayscale, describe it and classify it
image = cv2.imread(imagePath)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
if imagePath in hash_map:
hist = hash_map[imagePath]
else:
hist = lbp.compute(gray)
hash_map[imagePath] = hist
print str(iteration) + " DEBUG(Testing): Computed LBP Histogram for " + imagePath
data.append(hist)
labels.append(imagePath.split("/")[-2])
print "\n\n" + str(iteration) + " DEBUG: Forming predictions"
predictions = classifier.predict(data)
counter = 0
print "\n\n" + str(iteration) + " DEBUG: Printing predictions\n\n"
for index, prediction in enumerate(predictions):
print "Name -> " + testing[index] + " Actual -> " + labels[index] + " Prediction -> " + prediction
if labels[index] == prediction:
counter = counter + 1
accuracy = (float(counter)/float(len(predictions))) * 100.0
print "\n\n" + str(iteration) + " The Classifier Accuracy was " + str(accuracy) + "%"
return accuracy
#Load the image, convert it to grayscale, and compute LBP
image = cv2.imread(imagePath)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
hist = lbp.compute(gray)
#Plotting histogram if needed
#plt.bar(bin_edges[:-1], hist, width = 1)
#plt.xlim(min(bin_edges), max(bin_edges))
#plt.show()
#Extract the label from the image path, then update the label and data lists
labels.append(imagePath.split("/")[-2])
data.append(hist)
#Train classifier
classifier = Classifier("SVM")
classifier.train(data, labels)
#Testing Phase
data = []
testing = [
os.path.join(root, name)
for root, dirs, files in os.walk("../testing_images") for name in files
if name.endswith((".jpeg", ".jpg"))
]
for imagePath in testing:
#Load the image, convert to grayscale, describe it and classify it
image = cv2.imread(imagePath)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
hist = lbp.compute(gray)
"nubank", "ciw", "cef-savings", "itau-cc",
"itau-savings", "bradesco-savings", "generic"
],
required=True,
help="Set account that will be used.")
parser.add_argument("-af",
"--account_src_file",
required=True,
help="Set account source to integrate")
parser.add_argument("-acf", "--account_from", help="Define from import")
parser.add_argument("-act", "--account_to", help="Define to import")
parser.add_argument(
"-cl",
"--classifier",
default=None,
choices=Classifier.AVAILABLE_STRATEGIES().keys(),
help="Define classifier that must be used to import data")
args = parser.parse_args()
if args.verbose:
loglevel = logging.DEBUG
logformat = Util.LOG_FORMAT_DEBUG
elif args.quiet:
loglevel = logging.WARN
# TODO log to file in this case
logformat = Util.LOG_FORMAT_FULL
else:
loglevel = logging.INFO
logformat = Util.LOG_FORMAT_SIMPLE
# TODO config logger by dictnoray - https://realpython.com/python-logging/
from classifier import Classifier
from detector import Detector
from pipeline import *
from steps import *
with open(common.CONFIG_PATH) as f:
config = yaml.load(f)
main_pipeline = Pipeline([
Input("input"),
DetectingSingleFrameStep(
"detector", Detector(snapshot_path=config['paths']['detector']),
EXTRACTORS[config["extractor"]]()),
ClassifyingBoxesStep(
"classifier",
model=Classifier(snapshot_path=config['paths']['classifier']),
input_width=common.CLASSIFIER_INPUT_WIDTH,
input_height=common.CLASSIFIER_INPUT_HEIGHT),
DecodeClassesStep("decoder",
label_encoder=common.unpickle_data(
config['paths']['label_encoder'])),
])
visualisation_pipeline = main_pipeline + [
VisualiseStep("visualise"),
ShowVisualisation("showtime")
]
without_showing = main_pipeline + [VisualiseStep("visualise")]