class PerceptronImpl():
def __init__(self, penalty=None, alpha=0.0001, fit_intercept=True, max_iter=None, tol=None, shuffle=True, verbose=0, eta0=1.0, n_jobs=None, random_state=None, early_stopping=False, validation_fraction=0.1, n_iter_no_change=5, class_weight='balanced', warm_start=False, n_iter=None):
self._hyperparams = {
'penalty': penalty,
'alpha': alpha,
'fit_intercept': fit_intercept,
'max_iter': max_iter,
'tol': tol,
'shuffle': shuffle,
'verbose': verbose,
'eta0': eta0,
'n_jobs': n_jobs,
'random_state': random_state,
'early_stopping': early_stopping,
'validation_fraction': validation_fraction,
'n_iter_no_change': n_iter_no_change,
'class_weight': class_weight,
'warm_start': warm_start,
'n_iter': n_iter}
def fit(self, X, y=None):
self._sklearn_model = SKLModel(**self._hyperparams)
if (y is not None):
self._sklearn_model.fit(X, y)
else:
self._sklearn_model.fit(X)
return self
def predict(self, X):
return self._sklearn_model.predict(X)
def fit(self, X, y=None):
self._sklearn_model = SKLModel(**self._hyperparams)
if (y is not None):
self._sklearn_model.fit(X, y)
else:
self._sklearn_model.fit(X)
return self
def simulation(n, runs, margin=0, p_runs=100, d=2):
'''
Run a a given number of simulations to compare svm and perceptron error rates. Generates a set of training and testing points, runs a
single svm and a given number of perceptrons (avg error is taken)
:param n: number of points
:param p_runs: number of perceptrons to average
:param runs: number of times to sun simulation
:param d: dimensionality of points
:return: pandas dataframe, each row
'''
all_data = []
for i in range(runs):
# Get test data and its gamma, split 80-20 test train
train_dat, test_dat, margin = generate_labeled_points(n_train=n,
n_test=ceil(n *
25),
gamma=margin,
dim=d)
# Separate train points from labels
train_points = [x[0] for x in train_dat]
train_labels = [x[1] for x in train_dat]
# Separate test points from their labels
test_points = [x[0] for x in test_dat]
test_labels = [x[1] for x in test_dat]
# Run k = p_runs number of perceptrons on this same training data, take their mean error
p_errors = []
seed = np.random.RandomState()
for k in range(p_runs):
perceptron = Perceptron(random_state=seed)
perceptron.fit(train_points, train_labels)
p_errors.append(perceptron.score(test_points, test_labels))
p_error = np.mean(p_errors)
# Train and test with single SVM
svm = SVC(kernel="linear")
svm.fit(train_points, train_labels)
svm_error = svm.score(test_points, test_labels)
all_data.append([n, margin, p_error, svm_error])
df = pd.DataFrame(
all_data, columns=['n', 'margin', 'avg perceptron_error', 'svm_error'])
return df
def __init__(self,
penalty=None,
alpha=0.0001,
fit_intercept=True,
max_iter=1000,
tol=1e-3,
shuffle=True,
verbose=0,
eta0=1.0,
n_jobs=1,
random_state=0,
class_weight=None,
warm_start=False):
self.penalty = penalty
self.alpha = alpha
self.fit_intercept = fit_intercept
self.max_iter = max_iter
self.tol = tol
self.shuffle = shuffle
self.verbose = verbose
self.eta0 = eta0
self.n_jobs = n_jobs
self.random_state = random_state
self.class_weight = class_weight
self.warm_start = warm_start
super().__init__()
self.classifier = Perceptron(penalty=self.penalty,
alpha=self.alpha,
fit_intercept=self.fit_intercept,
max_iter=self.max_iter,
tol=self.tol,
shuffle=self.shuffle,
verbose=self.verbose,
random_state=self.random_state,
eta0=self.eta0,
warm_start=self.warm_start,
class_weight=self.class_weight,
n_jobs=self.n_jobs)
def demo():
""" _test_mol
This demo tests the MOL learner on a file stream, which reads from
the music.csv file.
The test computes the performance of the MOL learner as well as
the time to create the structure and classify all the samples in
the file.
"""
# Setup logging
logging.basicConfig(format='%(message)s', level=logging.INFO)
# Setup the file stream
stream = FileStream("../data/datasets/music.csv", 0, 6)
stream.prepare_for_use()
# Setup the classifier, by default it uses Logistic Regression
# classifier = MultiOutputLearner()
# classifier = MultiOutputLearner(base_estimator=SGDClassifier(n_iter=100))
classifier = MultiOutputLearner(base_estimator=Perceptron())
# Setup the pipeline
pipe = Pipeline([('classifier', classifier)])
pretrain_size = 150
logging.info('Pre training on %s samples', str(pretrain_size))
logging.info('Total %s samples', str(stream.n_samples))
X, y = stream.next_sample(pretrain_size)
# classifier.fit(X, y)
classes = stream.target_values
classes_flat = list(set([item for sublist in classes for item in sublist]))
pipe.partial_fit(X, y, classes=classes_flat)
count = 0
true_labels = []
predicts = []
init_time = timer()
logging.info('Evaluating...')
while stream.has_more_samples():
X, y = stream.next_sample()
# p = classifier.predict(X)
p = pipe.predict(X)
predicts.extend(p)
true_labels.extend(y)
count += 1
perf = hamming_score(true_labels, predicts)
logging.info('Evaluation time: %s s', str(timer() - init_time))
logging.info('Total samples analyzed: %s', str(count))
logging.info('The classifier\'s static Hamming score : %0.3f' % perf)
def preceptron(self):
# Perceptron
perceptron = Perceptron(penalty='l2', max_iter=1000, shuffle=True)
perceptron.fit(self.X_train, self.y_train)
acc = round(perceptron.score(self.X_train, self.y_train) * 100, 2)
print("acc with Perceptron:", acc)
self.y_pred = perceptron.predict(self.X_test)
def fit(self, x, y):
'''
metodo para treinar a arquitetura de dois niveis
:x: dados para treinamento
:y: rotulo dos dados
:dsel_x: padroes da janela de validacao
:dsel_y: rotulos da janela de validacao
'''
# salvando os dados de trainamento
self.x_train = x
self.y_train = y
# salvando as dificuldades das instancias
self.H = self.kDN(x, y)
# treinando o nivel 1 #########################################
self.levelone = KNeighborsClassifier(self.n_vizinhos)
self.levelone.fit(x, y)
# realizando a previsao para o conjunto de treinamento
y_pred = self.levelone.predict(x)
# salvando os indices das instancias que foram classificadas erradas
indices = [i for i in range(len(y)) if y_pred[i] != y[i]]
# obtendo o limiar de dificuldade do problema
self.limiar = self.defineThreshold(indices)
###############################################################
# treinando o nivel 2 #########################################
# obtendo as instancias dificeis
x_dificeis, y_dificeis = self.hardInstances(x, y, self.limiar)
# criando o ensemble
self.ensemble = BaggingClassifier(base_estimator=Perceptron(),
max_samples=0.9,
max_features=1.0,
n_estimators=100)
self.ensemble.fit(x_dificeis, y_dificeis)
# treinando o modelo 2
self.leveltwo = KNORAU(self.ensemble.estimators_, self.n_vizinhos)
self.leveltwo.fit(x_dificeis, y_dificeis)
def main():
#create the training & test sets, skipping the header row with [1:]
dataset_T = genfromtxt(open('Data/demoTrain.csv', 'r'),
delimiter=',',
dtype='f8')[:]
dataset_R = genfromtxt(open('Data/demoTarget.csv', 'r'),
delimiter=',',
dtype='f8')[:]
dataset_v = genfromtxt(open('Data/demoTest.csv', 'r'),
delimiter=',',
dtype='f8')[:]
trueData = genfromtxt(open('Data/validate.csv', 'r'),
delimiter=',',
dtype='f8')[:]
target = [x for x in dataset_R]
train = [x[:] for x in dataset_T]
validate = [x[:] for x in dataset_v]
y = [x for x in trueData]
test = genfromtxt(open('Data/demoTest.csv', 'r'),
delimiter=',',
dtype='f8')[:]
per = Perceptron(n_iter=2, shuffle=True)
per.fit(train, target)
#val = per.decision_function(validate)
val = per.predict(validate)
score = per.score(validate, y)
print str(score) + "\n"
for v in val:
print v
a = per.fit_transform(train, target)
print a
def __init__(self,
penalty=None,
alpha=0.0001,
fit_intercept=True,
max_iter=None,
tol=None,
shuffle=True,
verbose=0,
eta0=1.0,
n_jobs=None,
random_state=None,
early_stopping=False,
validation_fraction=0.1,
n_iter_no_change=5,
class_weight='balanced',
warm_start=False,
n_iter=None):
self._hyperparams = {
'penalty': penalty,
'alpha': alpha,
'fit_intercept': fit_intercept,
'max_iter': max_iter,
'tol': tol,
'shuffle': shuffle,
'verbose': verbose,
'eta0': eta0,
'n_jobs': n_jobs,
'random_state': random_state,
'early_stopping': early_stopping,
'validation_fraction': validation_fraction,
'n_iter_no_change': n_iter_no_change,
'class_weight': class_weight,
'warm_start': warm_start,
'n_iter': n_iter
}
self._wrapped_model = Op(**self._hyperparams)
def main():
#create the training & test sets, skipping the header row with [1:]
dataset_T = genfromtxt(open('Data/demoTrain.csv','r'), delimiter=',', dtype='f8')[:]
dataset_R = genfromtxt(open('Data/demoTarget.csv','r'), delimiter=',', dtype='f8')[:]
dataset_v = genfromtxt(open('Data/demoTest.csv','r'), delimiter=',', dtype='f8')[:]
trueData = genfromtxt(open('Data/validate.csv','r'), delimiter=',', dtype='f8')[:]
target = [x for x in dataset_R]
train = [x[:] for x in dataset_T]
validate = [x[:] for x in dataset_v]
y = [x for x in trueData]
test = genfromtxt(open('Data/demoTest.csv','r'), delimiter=',', dtype='f8')[:]
per = Perceptron(n_iter=2, shuffle=True)
per.fit(train, target)
#val = per.decision_function(validate)
val = per.predict(validate)
score = per.score(validate, y)
print str(score) +"\n"
for v in val:
print v
a= per.fit_transform(train,target)
print a
def test_perceptron(x: list, y: list, learning_rate: float, max_iter: int) -> None:
perceptron = Perceptron(max_iter=max_iter, alpha=learning_rate)
perceptron.fit(x, y)
plot_model(np.array(x), perceptron)
x_val, y_val = validacaoCompleta(x_train, y_train)
elif(validacao[k] == validacao[1]):
x_val, y_val = validacaoInstanciasFaceis(x_train, y_train, n_vizinhos)
elif(validacao[k] == validacao[2]):
x_val, y_val = validacaoInstanciasDificeis(x_train, y_train, n_vizinhos)
# 3.3. End ################################################################################################
# 3.4. Instanciando os classificadores ##########################################################
########## instanciando o modelo Bagging+REP ###########################################
# definindo o numero do modelo na tabela
num_model = 0
# intanciando o classificador
ensemble = BaggingClassifier(base_estimator=Perceptron(),
max_samples=qtd_amostras,
max_features=1.0,
n_estimators = qtd_modelos)
# treinando o modelo
ensemble.fit(x_train, y_train)
# realizando a poda
ensemble = REP(x_val, y_val, ensemble)
# computando a previsao
pred = ensemble.predict(x_test)
# computando a diversidade do ensemble
q_statistic = MedidasDiversidade('q', x_val, y_val, ensemble)
y_test, pred,
nome_datasets[h] + '-pct-' + str(pct_trainamento[i]) +
'- Bagging com DecisionTree [' + str(j) + ']')
# escrevendo os resultados obtidos
tabela.Adicionar_Sheet_Linha(num_model, j,
[acuracia, auc, f1measure, gmean])
# 3.2.1. End ###################################################################################
# 3.2.2. Bagging com Main ################################################################
# numero do modelo na tabela
num_model = 1
# modelo
bg = BaggingClassifier(base_estimator=Perceptron(),
max_samples=pct_trainamento[i],
max_features=1.0,
n_estimators=qtd_modelos)
# treinando o modelo
bg.fit(x_train, y_train)
# computando a previsao
pred = bg.predict(x_test)
# printando os resultados
acuracia, auc, f1measure, gmean = printar_resultados(
y_test, pred,
nome_datasets[h] + '-pct-' + str(pct_trainamento[i]) +
'- Bagging com Main [' + str(j) + ']')
from sklearn.linear_model.perceptron import Perceptron
from numbers_mass import one, two
import itertools
from generate_picture import read_image
x = [
list(itertools.chain.from_iterable(one)),
list(itertools.chain.from_iterable(two))
]
print(x)
y = [1, 2]
clf = Perceptron(random_state=241)
clf.fit(x, y)
if __name__ == "__main__":
print(clf.predict([
list(itertools.chain.from_iterable(one)),
]))
print(
clf.predict([
list(itertools.chain.from_iterable(read_image('1.png'))),
]))
def main():
while True:
intro = Text(
Point(250, 300),
"2048 TRAINER\n\nTrain model...R\nFull game train...G\n\n>>>Delays between moves<<<\nTest KNN model...E\nTest Perceptron model...P\nRandom model...N\n\n>>>No delays<<<\nTest KNN model...F\nTest Perceptron model...S\nRandom model...M\n\nSimple learning model...L\n\nPRESS Q TO QUIT"
)
intro.setSize(20)
intro.setTextColor(color_rgb(255, 255, 255))
if os.path.isfile("2048_train.csv"):
data = pd.read_csv("2048_train.csv",
header=None,
usecols=[
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
14, 15, 16
])
direction = pd.read_csv("2048_train.csv", header=None, usecols=[0])
splitRatio = 0.7
datatrainingSet, datatestSet = splitData(data, splitRatio)
directiontrainingSet, directiontestSet = splitData(
direction, splitRatio)
splitRatio = 0.5
datatestSet, datadevelopementSet = splitData(
datatestSet, splitRatio)
directiontestSet, directiondevelopementSet = splitData(
directiontestSet, splitRatio)
direction = np.transpose(direction)
isPrevData = True
else:
isPrevData = False
if os.path.isfile("best_weights.txt"):
fileContent = open("best_weights.txt", 'r')
weightString = fileContent.readlines()
bestWeightsX = np.array(weightString[0:17])
bestWeightsX = bestWeightsX.astype(np.float)
bestWeightsY = np.array(weightString[17:34])
bestWeightsY = bestWeightsY.astype(np.float)
randomRadius = float(weightString[34])
bestLearnScore = float(weightString[35])
fileContent.close()
else:
bestWeightsX = np.empty(0)
bestWeightsY = np.empty(0)
for i in range(0, 17):
bestWeightsX = np.append(bestWeightsX, 0)
bestWeightsY = np.append(bestWeightsY, 0)
randomRadius = 100
bestLearnScore = 0
win = GraphWin("2048", 500, 600)
win.setBackground(color_rgb(0, 103, 105))
intro.draw(win)
if isPrevData:
options = ['r', 'g', 'e', 'n', 'f', 'm', 'l', 'q', 's', 'p']
else:
options = ['r', 'g', 'n', 'm', 'l', 'q']
mode = '-'
while mode not in options:
mode = win.getKey()
if mode == 'q':
win.close()
return 0
if mode == 'e' or mode == 'f':
knn = KNeighborsClassifier(n_neighbors=10)
knn.fit(datatrainingSet,
np.ravel(np.transpose(directiontrainingSet)))
if mode == 'p' or mode == 's':
ppn = Perceptron(eta0=0.01, n_iter=10000)
ppn.fit(datatrainingSet,
np.ravel(np.transpose(directiontrainingSet)))
intro.undraw()
win.setBackground(color_rgb(100, 100, 100))
score = 0
scoreText = Text(Point(250, 550), str(score))
scoreText.setTextColor(color_rgb(255, 255, 255))
scoreText.setSize(30)
board = np.array([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0],
[0, 0, 0, 0]])
isChangeBoard = np.array([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0],
[0, 0, 0, 0]])
#DO THE SAME THING FOR THE TILE NUMBERS
tileList = []
numberList = []
board = spawn(board)
board = spawn(board)
for i in range(0, 4):
for j in range(0, 4):
tileList.append(
Rectangle(Point(i * 125 + 5, j * 125 + 5),
Point(i * 125 + 120, j * 125 + 120)))
numberList.append(
Text(Point(i * 125 + 60, j * 125 + 60), str(board[j][i])))
numberList[i * 4 + j].setSize(20)
tileList[i * 4 + j].setWidth(4)
tileList[i * 4 + j].setOutline(color_rgb(255, 255, 255))
numberList[i * 4 + j].setTextColor(color_rgb(0, 0, 0))
tileList[i * 4 + j].draw(win)
numberList[i * 4 + j].draw(win)
#THE TRAINING DATA
if not isPrevData:
data = np.empty((0, 16), float)
direction = np.empty(0)
drawBoard(board, win, tileList, numberList)
move = '-'
classes = ['w', 's', 'd', 'a']
moveIter = 0
nMoves = 0
totalIterations = 1
iteration = 1
currentWeightsX = generateRandomWeights(bestWeightsX,
math.floor(randomRadius))
currentWeightsY = generateRandomWeights(bestWeightsY,
math.floor(randomRadius))
currentBestWeightsX = currentWeightsX
currentBestWeightsY = currentWeightsY
if mode == 'l':
maxIterations = int(
input("Enter number of generations to simulate:"))
while True:
score = calcScore(board)
if mode != 'l':
drawBoard(board, win, tileList, numberList)
scoreText.undraw()
if mode != 'l':
scoreText.setSize(30)
scoreText.setText(str(score))
scoreText.draw(win)
for i in range(0, 4):
for j in range(0, 4):
isChangeBoard[i][j] = board[i][j]
if mode == 'r' or mode == 'g':
move = win.getKey()
if nMoves % 30 == 0 and mode == 'r':
board = generateRandomGrid(board)
nMoves += 1
else:
if mode == 'e' or mode == 'n' or mode == 'p':
time.sleep(0.5)
if mode == 'e' or mode == 'f':
probs = np.ravel(
knn.predict_proba(gridToData(board).reshape(1, -1)))
ranks = [0] * len(probs)
for i, x in enumerate(
sorted(range(len(probs)), key=lambda y: probs[y])):
ranks[x] = i
move = classes[ranks[moveIter]]
moveIter += 1
elif mode == 'm':
move = classes[random.randint(0, 3)]
elif mode == 'l':
move = classes[calculateDirection(currentWeightsX, board) *
2 +
calculateDirection(currentWeightsY, board)]
if moveIter > 0:
move = classes[random.randint(0, 3)]
moveIter += 1
elif mode == 'p' or mode == 's':
move = ppn.predict(gridToData(board).reshape(1, -1))
if moveIter > 0:
move = classes[random.randint(0, 3)]
moveIter += 1
if move == 'w':
board = shift(board, 0)
elif move == 'd':
board = shift(board, 1)
elif move == 's':
board = shift(board, 2)
elif move == 'a':
board = shift(board, 3)
elif move == 'q':
break
if lose(board):
print(score)
if mode == 'l':
board = np.array([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0],
[0, 0, 0, 0]])
isChangeBoard = np.array([[0, 0, 0, 0], [0, 0, 0, 0],
[0, 0, 0, 0], [0, 0, 0, 0]])
board = spawn(board)
board = spawn(board)
scoreText.setSize(16)
scoreText.setText("Score: " + str(score) +
" -- iteration: " + str(iteration) +
" -- r: " + str(randomRadius))
if score > bestLearnScore:
bestLearnScore = score
currentBestWeightsX = currentWeightsX
currentBestWeightsY = currentWeightsY
print("current best" + str(currentBestWeightsX) +
str(currentBestWeightsY))
if iteration == 1000:
drawBoard(board, win, tileList, numberList)
print("best score: " + str(bestLearnScore))
iteration = 1
randomRadius *= 0.9
bestWeightsX = currentBestWeightsX
bestWeightsY = currentBestWeightsY
print("best weights:" + str(bestWeightsX) +
str(bestWeightsY))
if totalIterations / 1000 >= maxIterations:
toWrite = np.empty(0)
toWrite = np.append(toWrite, bestWeightsX)
toWrite = np.append(toWrite, bestWeightsY)
toWrite = np.append(toWrite, randomRadius)
toWrite = np.append(toWrite, bestLearnScore)
np.savetxt("best_weights.txt", toWrite)
#WRITE WEIGHTS N STUFF
break
totalIterations += 1
iteration += 1
currentWeightsX = generateRandomWeights(
bestWeightsX, math.floor(randomRadius))
currentWeightsY = generateRandomWeights(
bestWeightsY, math.floor(randomRadius))
else:
break
isChanged = False
for i in range(0, 4):
for j in range(0, 4):
if isChangeBoard[i][j] != board[i][j]:
isChanged = True
if isChanged:
moveIter = 0
if mode == 'r' or mode == 'g':
direction = np.append(direction, move)
data = np.vstack([data, gridToData2(board)])
board = spawn(board)
score = calcScore(board)
scoreText.setSize(16)
scoreText.setText(str(score) + " -- YOU LOSE (Press Q)")
while move != 'q':
move = win.getKey()
win.close()
if mode == 'r' or mode == 'g':
pd.DataFrame.to_csv(pd.DataFrame(
np.hstack([
np.reshape(directiontrainingSet,
[np.shape(directiontrainingSet)[0], 1]),
datatrainingSet
])),
"2048_train.csv",
index=False,
header=False)
data = [
array([ # Vector de [forma;textura;peso]
[1, 1, -1], # Manzana
[1, -1, -1], # Naranja
[1, 1, -1], # Manzana
[1, -1, -1], # Naranja
]), array([
1,
-1,
1,
1
])
]
# Crear una instancia de Perceptron con un máximo de 100 épocas
perceptron = Perceptron(max_iter=1000)
# Entrenar la red neuronal
perceptron.fit(data[0], data[1])
def test_net_accuracy():
# Probar la certeza de la red neuronal con la data previamente alimentada
# para filtrar una manzana y una naranja.
data = [
array([
[1, 1, -1], # Manzana
[1, -1, -1], # Naranja
[-1, -1, -1] # Una naranja que es elíptica
]), array([
1,
import numpy as np
from matplotlib import pyplot
from sklearn.linear_model.perceptron import Perceptron
from sklearn.metrics import accuracy_score
import usps
import perceptron # Pour la fonction two_classes
data, labels = usps.load_train()
data_test, labels_test = usps.load_test()
for k in range(10):
labels_k = perceptron.two_classes(labels, k)
net = Perceptron()
net.fit(data, labels_k)
output_train = net.predict(data)
output_test = net.predict(data_test)
print(k)
print(" Score (train)", accuracy_score(labels_k, output_train))
labels_k_test = perceptron.two_classes(labels_test, k)
print(" Score (test)", accuracy_score(labels_k_test, output_test))
def main():
# 1. Definindo variaveis para o experimento #########################################################################
qtd_modelos = 100
qtd_execucoes = 30
qtd_amostras = 0.9
qtd_folds = 10
n_vizinhos = 7
nome_datasets = ['kc1', 'kc2']
# 1. End ############################################################################################################
# for para variar entre os datasets
for h in range(len(nome_datasets)):
# 2. Lendo os datasets ############################################################################################
# lendo o dataset
data = pd.read_csv('dataset/'+nome_datasets[h]+'.csv')
# obtendo os padroes e seus respectivos rotulos
df_x = np.asarray(data.iloc[:,0:-1])
df_y = np.asarray(data.iloc[:,-1])
# 2.1. Criando a tabela para salvar os dados #################################################
# criando a tabela que vai acomodar o modelo
tabela = Tabela_excel()
tabela.Criar_tabela(nome_tabela='arquivos_lista03/'+nome_datasets[h],
folhas=['OLA', 'LCA', 'KNORA-E', 'KNORA-U', 'Arquitetura'],
cabecalho=['acuracy', 'auc', 'fmeasure', 'gmean'],
largura_col=5000)
# 2.1. End #####################################################################################
# 2. End ############################################################################################################
# executando os algoritmos x vezes
for j in range(qtd_execucoes):
# 3. Dividindo os dados para treinamento e teste ################################################################
# quebrando o dataset sem sobreposicao em 90% para treinamento e 10% para teste
skf = StratifiedKFold(df_y, n_folds=qtd_folds)
# tomando os indices para treinamento e teste
train_index, test_index = next(iter(skf))
# obtendo os conjuntos de dados para treinamento e teste
x_train = df_x[train_index]
y_train = df_y[train_index]
x_test = df_x[test_index]
y_test = df_y[test_index]
# 3. End #########################################################################################################
# 4. Gerando o pool de classificadores ##########################################################################
# intanciando o classificador
ensemble = BaggingClassifier(base_estimator=Perceptron(),
max_samples=qtd_amostras,
max_features=1.0,
n_estimators = qtd_modelos)
# treinando o modelo
ensemble.fit(x_train, y_train)
# 4. End ########################################################################################################
# 5. Instanciando os classificadores ##########################################################
################################### OLA ########################################################
executar_modelo('OLA', x_train, y_train, x_test, y_test, ensemble.estimators_, n_vizinhos, nome_datasets, h, j, tabela)
################################################################################################
################################### LCA ########################################################
executar_modelo('LCA', x_train, y_train, x_test, y_test, ensemble.estimators_, n_vizinhos, nome_datasets, h, j, tabela)
################################################################################################
################################### KNORAE #####################################################
executar_modelo('KNORAE', x_train, y_train, x_test, y_test, ensemble.estimators_, n_vizinhos, nome_datasets, h, j, tabela)
################################################################################################
################################### KNORAU #####################################################
executar_modelo('KNORAU', x_train, y_train, x_test, y_test, ensemble.estimators_, n_vizinhos, nome_datasets, h, j, tabela)
################################################################################################
################################### Arquitetura ################################################
# importando o metodo
arq = Arquitetura(n_vizinhos)
# treinando o metodo
arq.fit(x_train, y_train)
# realizando a previsao
pred = arq.predict(x_test)
# printando os resultados
nome = 'Arquitetura'
acuracia, auc, f1measure, gmean = printar_resultados(y_test, pred, nome_datasets[h]+'-'+nome+'-['+str(j)+']')
# escrevendo os resultados obtidos
tabela.Adicionar_Sheet_Linha(4, j, [acuracia, auc, f1measure, gmean])
'NearestNeighbors':NearestNeighbors(), 'Normalizer':Normalizer(), 'NuSVC':NuSVC(), 'NuSVR':NuSVR(), 'Nystroem':Nystroem(), 'OAS':OAS(), 'OneClassSVM':OneClassSVM(), 'OrthogonalMatchingPursuit':OrthogonalMatchingPursuit(), 'OrthogonalMatchingPursuitCV':OrthogonalMatchingPursuitCV(), 'PCA':PCA(), 'PLSCanonical':PLSCanonical(), 'PLSRegression':PLSRegression(), 'PLSSVD':PLSSVD(), 'PassiveAggressiveClassifier':PassiveAggressiveClassifier(), 'PassiveAggressiveRegressor':PassiveAggressiveRegressor(), 'Perceptron':Perceptron(), 'ProjectedGradientNMF':ProjectedGradientNMF(), 'QuadraticDiscriminantAnalysis':QuadraticDiscriminantAnalysis(), 'RANSACRegressor':RANSACRegressor(), 'RBFSampler':RBFSampler(), 'RadiusNeighborsClassifier':RadiusNeighborsClassifier(), 'RadiusNeighborsRegressor':RadiusNeighborsRegressor(), 'RandomForestClassifier':RandomForestClassifier(), 'RandomForestRegressor':RandomForestRegressor(), 'RandomizedLasso':RandomizedLasso(), 'RandomizedLogisticRegression':RandomizedLogisticRegression(), 'RandomizedPCA':RandomizedPCA(), 'Ridge':Ridge(), 'RidgeCV':RidgeCV(), 'RidgeClassifier':RidgeClassifier(), 'RidgeClassifierCV':RidgeClassifierCV(),
def task2(sneakers_data: pd.DataFrame, boots_data: pd.DataFrame,
sneakers_labels: pd.DataFrame, boots_labels: pd.DataFrame):
full_data = sneakers_data.append(boots_data)
full_labels = sneakers_labels.append(boots_labels)
train_times = []
predict_times = []
accuracies = []
print("\tTask 2 output")
num_splits = 4
for train_index, test_index in KFold(n_splits=num_splits,
shuffle=True).split(full_data):
train_data = full_data.iloc[train_index]
test_data = full_data.iloc[test_index]
train_labels = full_labels.iloc[train_index]
test_labels = full_labels.iloc[test_index]
pctrn = Perceptron()
train_start_time = timeit.default_timer()
pctrn.fit(X=train_data, y=train_labels)
train_end_time = timeit.default_timer()
train_time = train_end_time - train_start_time
train_times.append(train_time)
print("\tPerceptron took", train_time, "seconds to train on data")
predict_start_time = timeit.default_timer()
prediction = pctrn.predict(test_data)
predict_end_time = timeit.default_timer()
predict_time = predict_end_time - predict_start_time
predict_times.append(predict_time)
print("\tPerceptron took", predict_time,
"seconds to make a prediction")
accuracy = accuracy_score(test_labels, prediction) * 100
accuracies.append(accuracy)
print("\tAccuracy for perceptron", accuracy, "%")
confusion = confusion_matrix(test_labels, prediction)
percent_true_pos = (confusion[0, 0] / len(test_labels)) * 100
percent_false_pos = (confusion[0, 1] / len(test_labels)) * 100
percent_true_neg = (confusion[1, 1] / len(test_labels)) * 100
percent_false_neg = (confusion[1, 0] / len(test_labels)) * 100
print("\tPerceptron confusion matrix true positive:", percent_true_pos,
"%")
print("\tPerceptron confusion matrix false positive:",
percent_false_pos, "%")
print("\tPerceptron confusion matrix true negative:", percent_true_neg,
"%")
print("\tPerceptron confusion matrix false negative:",
percent_false_neg, "%\n")
print("\tThe minimum train time was", np.min(train_times), "seconds")
print("\tThe maximum train time was", np.max(train_times), "seconds")
print("\tThe average train time was", np.mean(train_times), "seconds\n")
print("\tThe minimum prediction time was", np.min(predict_times),
"seconds")
print("\tThe maximum prediction time was", np.max(predict_times),
"seconds")
print("\tThe average prediction time was", np.mean(predict_times),
"seconds\n")
print("\tMinimum Accuracy was", np.min(accuracies), "%")
print("\tMaximum accuracy was", np.max(accuracies), "%")
print("\tAverage accuracy was", np.mean(accuracies), "%\n")
print(
"\tTotal train time for all k-folds in perceptron with gamma value of =",
np.sum(train_times), "seconds")
print(
"\tTotal prediction time for all k-folds in perceptron with gamma of =",
np.sum(predict_times), "seconds\n")
class PerceptronMask(BaseClassifier):
""" PerceptronMask
A mask for scikit-learn's Perceptron classifier.
Because scikit-multiflow's framework require a few interfaces, not present
int scikit-learn, this mask allows the first to use classifiers native to
the latter.
"""
def __init__(self):
super().__init__()
self.classifier = Perceptron(n_iter=50)
def fit(self, X, y, classes = None, weight=None):
""" fit
Calls the Perceptron fit function from sklearn.
Parameters
----------
X: numpy.ndarray of shape (n_samples, n_features)
The feature's matrix.
y: Array-like
The class labels for all samples in X.
classes: Not used.
weight: Instance weight. If not provided, uniform weights are assumed.
Returns
-------
PerceptronMask
self
"""
self.classifier.fit(X, y, sample_weight=weight)
return self
def partial_fit(self, X, y, classes=None, weight=None):
""" partial_fit
Calls the Perceptron partial_fit from sklearn.
Parameters
----------
X: numpy.ndarray of shape (n_samples, n_features)
The feature's matrix.
y: Array-like
The class labels for all samples in X.
classes: list, optional
A list with all the possible labels of the classification problem.
weight: Instance weight. If not provided, uniform weights are assumed.
Returns
-------
PerceptronMask
self
"""
self.classifier.partial_fit(X, y, classes, weight)
return self
def predict(self, X):
""" predict
Uses the current model to predict samples in X.
Parameters
----------
X: numpy.ndarray of shape (n_samples, n_features)
The feature's matrix.
Returns
-------
list
A list containing the predicted labels for all instances in X.
"""
return self.classifier.predict(X)
def predict_proba(self, X):
""" predict_proba
Predicts the probability of each sample belonging to each one of the
known classes.
Parameters
----------
X: Numpy.ndarray of shape (n_samples, n_features)
A matrix of the samples we want to predict.
Returns
-------
numpy.ndarray
An array of shape (n_samples, n_features), in which each outer entry is
associated with the X entry of the same index. And where the list in
index [i] contains len(self.classes) elements, each of which represents
the probability that the i-th sample of X belongs to a certain label.
"""
return self.classifier.predict_proba(X)
def score(self, X, y):
""" score
Returns the predict performance for the samples in X.
Parameters
----------
X: numpy.ndarray of shape (n_sample, n_features)
The features matrix.
y: Array-like
An array-like containing the class labels for all samples in X.
Returns
-------
float
The classifier's score.
"""
return self.classifier.score(X, y)
def get_info(self):
params = self.classifier.get_params()
penalty = params['penalty']
penalty = 'None' if penalty is None else penalty
fit_int = params['fit_intercept']
fit_int = 'True' if fit_int else 'False'
shuffle = params['shuffle']
shuffle = 'True' if shuffle else 'False'
return 'Perceptron: penalty: ' + penalty + \
' - alpha: ' + str(round(params['alpha'], 3)) + \
' - fit_intercept: ' + fit_int + \
' - n_iter: ' + str(params['n_iter']) + \
' - shuffle: ' + shuffle
def get_paramater_grids(data_path):
"""Returns all the Models / Paramters I'm searching over"""
pca_nfeatures = [0, 1, 2, 5, 10, 15, 20, 24]
# 0 is a pass through value that does not do PCA
# 24 is safer because some features might be found in the training set. This might not be the safest design, but it works for now
# some may have more or few features based on training. 24 seems safe
# Number of training points allowed to be on the wrong side of hyperplane.
# A point is fractionally over the line if it violates the margin
# 0 is a lower bound
# The approximent number of training points (170)/2 seems like a reasonable upper bound (n
# The sklearn formulation is different than what was covered in the book
svc_gamma = np.logspace(-7, 0, 8) #kernal parameter
paramater_grids = {}
# SVC
paramater_grids['SVC'] = {}
paramater_grids['SVC']['pipeline']=\
Pipeline([
('cleaner', hdpp.DataCleaner(data_path + 'meta_data.csv').CleaningPipeline),
('feature', PCA()),
('classifier', SVC())
])
paramater_grids['SVC']['parameters'] = \
{'classifier__kernel':('linear','rbf','sigmoid'), #Linear doesn't gues gamma, but whatevs
'classifier__C': np.logspace(-2,4,7),
'classifier__gamma': svc_gamma,
'feature__n_components': pca_nfeatures}
# SVC with a polynomial kernal
paramater_grids['SVC_poly'] = {}
paramater_grids['SVC_poly']['pipeline']=\
Pipeline([
('cleaner', hdpp.DataCleaner(data_path + 'meta_data.csv').CleaningPipeline),
('feature', PCA()),
('classifier', SVC(kernel='poly'))
])
paramater_grids['SVC_poly']['parameters'] = \
{'classifier__degree': [2, 3, 4, 5],
'classifier__C': np.logspace(-2,3,6),
'classifier__gamma': svc_gamma,
'feature__n_components': pca_nfeatures}
# Gaussian Mixture
paramater_grids['GM'] = {}
paramater_grids['GM']['pipeline']=\
Pipeline([
('cleaner', hdpp.DataCleaner(data_path + 'meta_data.csv').CleaningPipeline),
('feature', PCA()),
('classifier', GM(max_iter=250,n_init=25))
])
paramater_grids['GM']['parameters'] = \
{'classifier__n_components': [1, 2, 4, 8, 16, 32],
'classifier__covariance_type': ['full', 'tied', 'diag', 'spherical'],
'feature__n_components': pca_nfeatures}
# Perceptron
paramater_grids['Perceptron_PCA'] = {}
paramater_grids['Perceptron_PCA']['pipeline'] = \
Pipeline([
('cleaner', hdpp.DataCleaner(data_path + 'meta_data.csv').CleaningPipeline),
('feature', PCA()),
('classifier', Perceptron())
])
paramater_grids['Perceptron_PCA']['parameters'] = \
{'feature__n_components': pca_nfeatures}
# Perceptron
paramater_grids['Perceptron_LDA'] = {}
paramater_grids['Perceptron_LDA']['pipeline'] = \
Pipeline([
('cleaner', hdpp.DataCleaner(data_path + 'meta_data.csv').CleaningPipeline),
('feature', LinearDiscriminantAnalysis()),
('classifier', Perceptron())
])
paramater_grids['Perceptron_LDA']['parameters'] = {}
return paramater_grids
#encoding=utf8
import os
from sklearn.linear_model.perceptron import Perceptron
import pandas as pd
if os.path.exists('./step2/result.csv'):
os.remove('./step2/result.csv')
# 获取训练数据
train_data = pd.read_csv('./step2/train_data.csv')
# 获取训练标签
train_label = pd.read_csv('./step2/train_label.csv')
train_label = train_label['target']
# 获取测试数据
test_data = pd.read_csv('./step2/test_data.csv')
# 训练数据
clf = Perceptron(eta0=0.1, max_iter=500)
clf.fit(train_data, train_label)
res = clf.predict(test_data)
# 保存
res = {"result": res}
res = pd.DataFrame(res)
res.to_csv('./step2/result.csv', index=0)
acc_svc = accuracy_score(y_pred, y_test) print(acc_svc) # 0.81218 # 线性支持向量机SVC from sklearn.svm import LinearSVC linear_svc = LinearSVC() linear_svc.fit(x_train, y_train) y_pred = linear_svc.predict(x_test) acc_linear_svc = accuracy_score(y_pred, y_test) print(acc_linear_svc) # 0.77157 # 感知机 from sklearn.linear_model.perceptron import Perceptron perceptron = Perceptron() perceptron.fit(x_train, y_train) y_pred = perceptron.predict(x_test) acc_perceptron = accuracy_score(y_pred, y_test) print(acc_perceptron) # 0.77665 # 决策树 from sklearn.tree import DecisionTreeClassifier decisiontree = DecisionTreeClassifier() decisiontree.fit(x_train, y_train) y_pred = decisiontree.predict(x_test) acc_decisiontree = accuracy_score(y_pred, y_test) print(acc_decisiontree) # 0.82233 # 随机森林
def __init__(self):
super().__init__()
self.classifier = Perceptron(n_iter=50)
class PerceptronMask(StreamModel):
""" PerceptronMask
A mask for scikit-learn's Perceptron classifier.
Because scikit-multiflow's framework require a few interfaces, not present
int scikit-learn, this mask allows the first to use classifiers native to
the latter.
"""
def __init__(self,
penalty=None,
alpha=0.0001,
fit_intercept=True,
max_iter=1000,
tol=1e-3,
shuffle=True,
verbose=0,
eta0=1.0,
n_jobs=1,
random_state=0,
class_weight=None,
warm_start=False):
self.penalty = penalty
self.alpha = alpha
self.fit_intercept = fit_intercept
self.max_iter = max_iter
self.tol = tol
self.shuffle = shuffle
self.verbose = verbose
self.eta0 = eta0
self.n_jobs = n_jobs
self.random_state = random_state
self.class_weight = class_weight
self.warm_start = warm_start
super().__init__()
self.classifier = Perceptron(penalty=self.penalty,
alpha=self.alpha,
fit_intercept=self.fit_intercept,
max_iter=self.max_iter,
tol=self.tol,
shuffle=self.shuffle,
verbose=self.verbose,
random_state=self.random_state,
eta0=self.eta0,
warm_start=self.warm_start,
class_weight=self.class_weight,
n_jobs=self.n_jobs)
def fit(self, X, y, classes=None, weight=None):
""" fit
Calls the Perceptron fit function from sklearn.
Parameters
----------
X: numpy.ndarray of shape (n_samples, n_features)
The feature's matrix.
y: Array-like
The class labels for all samples in X.
classes: Not used.
weight: Instance weight. If not provided, uniform weights are assumed.
Returns
-------
PerceptronMask
self
"""
self.classifier.fit(X, y, sample_weight=weight)
return self
def partial_fit(self, X, y, classes=None, weight=None):
""" partial_fit
Calls the Perceptron partial_fit from sklearn.
Parameters
----------
X: numpy.ndarray of shape (n_samples, n_features)
The feature's matrix.
y: Array-like
The class labels for all samples in X.
classes: list, optional
A list with all the possible labels of the classification problem.
weight: Instance weight. If not provided, uniform weights are assumed.
Returns
-------
PerceptronMask
self
"""
self.classifier.partial_fit(X, y, classes, weight)
return self
def predict(self, X):
""" predict
Uses the current model to predict samples in X.
Parameters
----------
X: numpy.ndarray of shape (n_samples, n_features)
The feature's matrix.
Returns
-------
numpy.ndarray
A numpy.ndarray containing the predicted labels for all instances in X.
"""
return np.asarray(self.classifier.predict(X))
def predict_proba(self, X):
""" predict_proba
Predicts the probability of each sample belonging to each one of the
known classes.
Parameters
----------
X: Numpy.ndarray of shape (n_samples, n_features)
A matrix of the samples we want to predict.
Returns
-------
numpy.ndarray
An array of shape (n_samples, n_features), in which each outer entry is
associated with the X entry of the same index. And where the list in
index [i] contains len(self.target_values) elements, each of which represents
the probability that the i-th sample of X belongs to a certain label.
"""
return self.classifier._predict_proba_lr(X)
def score(self, X, y):
""" score
Returns the predict performance for the samples in X.
Parameters
----------
X: numpy.ndarray of shape (n_sample, n_features)
The features matrix.
y: Array-like
An array-like containing the class labels for all samples in X.
Returns
-------
float
The classifier's score.
"""
return self.classifier.score(X, y)
def get_info(self):
params = self.classifier.get_params()
info = type(self).__name__ + ':'
info += ' - penalty: {}'.format(params['penalty'])
info += ' - alpha: {}'.format(params['alpha'])
info += ' - fit_intercept: {}'.format(params['fit_intercept'])
info += ' - max_iter: {}'.format(params['max_iter'])
info += ' - tol: {}'.format(params['tol'])
info += ' - shuffle: {}'.format(params['shuffle'])
info += ' - eta0: {}'.format(params['eta0'])
info += ' - warm_start: {}'.format(params['warm_start'])
info += ' - class_weight: {}'.format(params['class_weight'])
info += ' - n_jobs: {}'.format(params['n_jobs'])
return info
def reset(self):
self.__init__(penalty=self.penalty,
alpha=self.alpha,
fit_intercept=self.fit_intercept,
max_iter=self.max_iter,
tol=self.tol,
shuffle=self.shuffle,
verbose=self.verbose,
random_state=self.random_state,
eta0=self.eta0,
warm_start=self.warm_start,
class_weight=self.class_weight,
n_jobs=self.n_jobs)
def result():
if request.method == 'POST':
path = request.files.get('myFile')
df = pd.read_csv(path, encoding="ISO-8859-1")
filename = request.form['filename']
str1 = request.form['feature']
str2 = request.form['label']
if str1 in list(df) and str2 in list(df):
y = df[str2]
X = df[str1]
else:
return render_template('nameError.html')
x = []
for subject in X:
result = re.sub(r"http\S+", "", subject)
replaced = re.sub(r'[^a-zA-Z0-9 ]+', '', result)
x.append(replaced)
X = pd.Series(x)
X = X.str.lower()
"""
texts = []
for doc in X:
doc = nlp(doc, disable=['parser', 'ner'])
tokens = [tok.lemma_.lower().strip() for tok in doc if tok.lemma_ != '-PRON-']
tokens = [tok for tok in tokens if tok not in stopwords]
tokens = ' '.join(tokens)
texts.append(tokens)
X = pd.Series(texts)
"""
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.33)
tfidfvect = TfidfVectorizer(ngram_range=(1, 1))
X_train_tfidf = tfidfvect.fit_transform(X_train)
start = time()
clf1 = LinearSVC()
clf1.fit(X_train_tfidf, y_train)
pred_SVC = clf1.predict(tfidfvect.transform(X_test))
a1 = accuracy_score(y_test, pred_SVC)
end = time()
print("accuracy SVC: {} and time: {} s".format(a1, (end - start)))
start = time()
clf2 = LogisticRegression(n_jobs=-1,
multi_class='multinomial',
solver='newton-cg')
clf2.fit(X_train_tfidf, y_train)
pred_LR = clf2.predict(tfidfvect.transform(X_test))
a2 = accuracy_score(y_test, pred_LR)
end = time()
print("accuracy LR: {} and time: {}".format(a2, (end - start)))
start = time()
clf3 = RandomForestClassifier(n_jobs=-1)
clf3.fit(X_train_tfidf, y_train)
pred = clf3.predict(tfidfvect.transform(X_test))
a3 = accuracy_score(y_test, pred)
end = time()
print("accuracy RFC: {} and time: {}".format(a3, (end - start)))
start = time()
clf4 = MultinomialNB()
clf4.fit(X_train_tfidf, y_train)
pred = clf4.predict(tfidfvect.transform(X_test))
a4 = accuracy_score(y_test, pred)
end = time()
print("accuracy MNB: {} and time: {}".format(a4, (end - start)))
start = time()
clf5 = GaussianNB()
clf5.fit(X_train_tfidf.toarray(), y_train)
pred = clf5.predict(tfidfvect.transform(X_test).toarray())
a5 = accuracy_score(y_test, pred)
end = time()
print("accuracy GNB: {} and time: {}".format(a5, (end - start)))
start = time()
clf6 = LogisticRegressionCV(n_jobs=-1)
clf6.fit(X_train_tfidf, y_train)
pred_LR = clf6.predict(tfidfvect.transform(X_test))
a6 = accuracy_score(y_test, pred_LR)
end = time()
print("accuracy LRCV: {} and time: {}".format(a6, (end - start)))
start = time()
clf7 = AdaBoostClassifier()
clf7.fit(X_train_tfidf, y_train)
pred_LR = clf7.predict(tfidfvect.transform(X_test))
a7 = accuracy_score(y_test, pred_LR)
end = time()
print("accuracy ABC: {} and time: {}".format(a7, (end - start)))
start = time()
clf8 = BernoulliNB()
clf8.fit(X_train_tfidf.toarray(), y_train)
pred = clf8.predict(tfidfvect.transform(X_test).toarray())
a8 = accuracy_score(y_test, pred)
end = time()
print("accuracy BNB: {} and time: {}".format(a8, (end - start)))
start = time()
clf9 = Perceptron(n_jobs=-1)
clf9.fit(X_train_tfidf.toarray(), y_train)
pred = clf9.predict(tfidfvect.transform(X_test).toarray())
a9 = accuracy_score(y_test, pred)
end = time()
print("accuracy Per: {} and time: {}".format(a9, (end - start)))
start = time()
clf10 = RidgeClassifierCV()
clf10.fit(X_train_tfidf.toarray(), y_train)
pred = clf10.predict(tfidfvect.transform(X_test).toarray())
a10 = accuracy_score(y_test, pred)
end = time()
print("accuracy RidCV: {} and time: {}".format(a10, (end - start)))
start = time()
clf11 = SGDClassifier(n_jobs=-1)
clf11.fit(X_train_tfidf.toarray(), y_train)
pred = clf11.predict(tfidfvect.transform(X_test).toarray())
a11 = accuracy_score(y_test, pred)
end = time()
print("accuracy SGDC: {} and time: {}".format(a11, (end - start)))
start = time()
clf12 = SGDClassifier(n_jobs=-1)
clf12.fit(X_train_tfidf.toarray(), y_train)
pred = clf12.predict(tfidfvect.transform(X_test).toarray())
a12 = accuracy_score(y_test, pred)
end = time()
print("accuracy XGBC: {} and time: {}".format(a12, (end - start)))
acu_list = [a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12]
max_list = max(acu_list)
if max_list == a1:
pickle.dump(clf1, open(filename + '_model', 'wb'))
elif max_list == a2:
pickle.dump(clf2, open(filename + '_model', 'wb'))
elif max_list == a3:
pickle.dump(clf3, open(filename + '_model', 'wb'))
elif max_list == a4:
pickle.dump(clf4, open(filename + '_model', 'wb'))
elif max_list == a5:
pickle.dump(clf5, open(filename + '_model', 'wb'))
elif max_list == a6:
pickle.dump(clf6, open(filename + '_model', 'wb'))
elif max_list == a7:
pickle.dump(clf7, open(filename + '_model', 'wb'))
elif max_list == a8:
pickle.dump(clf8, open(filename + '_model', 'wb'))
elif max_list == a9:
pickle.dump(clf9, open(filename + '_model', 'wb'))
elif max_list == a10:
pickle.dump(clf10, open(filename + '_model', 'wb'))
elif max_list == a11:
pickle.dump(clf11, open(filename + '_model', 'wb'))
elif max_list == a12:
pickle.dump(clf12, open(filename + '_model', 'wb'))
pickle.dump(tfidfvect, open(filename + '_tfidfVect', 'wb'))
return render_template("result.html",
ac1=a1,
ac2=a2,
ac3=a3,
ac4=a4,
ac5=a5,
ac6=a6,
ac7=a7,
ac8=a8,
ac9=a9,
ac10=a10,
ac11=a11,
ac12=a12)
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.20,
random_state=i)
# Normalizando as variaveis de treino e teste
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
# Treinando o Perceptron com o conjunto de treino previamente separado
#Passa-se 100 vezes pelo conjunto de dados e o learning rate = 0.001
from sklearn.linear_model.perceptron import Perceptron
classifier = Perceptron(max_iter=100, eta0=0.1)
classifier.fit(X_train, y_train)
# Apresentando os dados de teste a rede Perceptron e obtendo as predicoes
y_pred = classifier.predict(X_test)
# Fazendo a matriz de confusao
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
'''TP = # True Positives, TN = # True Negatives,
FP = # False Positives, FN = # False Negatives
Acerto = TP / (TP + FP)
'''
#Aplicando o metodo de avaliacao
