def make_neural_network():
dataset = np.loadtxt("/Users/BARNES_3/Documents/niki/courses/Decision making/riot_predictor/data_for_neural.csv", delimiter=",")
score_total = 0
for i in xrange(0, 5):
msk = np.random.rand(len(dataset)) < 0.8
train = dataset[msk]
test = dataset[~msk]
x_train = train[:,0:6]
y_train = train[:,6]
x_test = test[:,0:6]
y_test = test[:,6]
# print type(x_test)
# score = 0.797035347777
# 0.801596351197
nn = Classifier(
layers=[
# Layer("Tanh", units = 1000),
# Layer("Sigmoid", units = 1000),
# Layer("Linear")],
Layer("ExpLin", units = 800),
Layer("Softmax"),
],
learning_rate=0.0002,
n_iter=20)
nn.fit(x_train, y_train)
score = nn.score(x_test, y_test)
score_total += score
print score_total/5
# print score
return nn
def autoEncoderOptimization(data):
rbm = ae.AutoEncoder(layers=[
ae.Layer("Tanh", units=300),
ae.Layer("Sigmoid", units=200),
ae.Layer("Tanh", units=100)
],
learning_rate=0.002,
n_iter=10)
rbm.fit(data["train"])
model = Classifier(layers=[
Layer("Tanh", units=300),
Layer("Sigmoid", units=200),
Layer("Tanh", units=100),
Layer("Rectifier", units=100),
Layer("Rectifier", units=50),
Layer("Softmax")
], )
rbm.transfer(model)
model.fit(data["train"], data["label"])
prediction = model.predict(data["train"])
print accuracy_score(data["label"], prediction)
def mlp(number_layers, number_neurons_1, number_neurons_2, number_neurons_3,
number_neurons_4, dropout_rate):
layers = []
number_neurons = []
number_neurons.append(number_neurons_1)
number_neurons.append(number_neurons_2)
number_neurons.append(number_neurons_3)
number_neurons.append(number_neurons_4)
for i in np.arange(number_layers):
layers.append(
Layer("Sigmoid", units=number_neurons[i], dropout=dropout_rate))
layers.append(Layer("Softmax", units=2))
scores = []
for i in np.arange(n_validations):
X_train, X_test, Y_train, Y_test = sklearn.cross_validation.train_test_split(
X, Y, test_size=0.3, random_state=1)
predictor = Classifier(layers=layers, learning_rate=0.001, n_iter=25)
predictor.fit(X_train, Y_train)
scores.append(metrics.accuracy_score(Y_test,
predictor.predict(X_test)))
return -median(scores)
def batch_train(train, val, model_path):
trainX, trainY = train
valX, valY = val
nn = Classifier(
layers=[
Convolution('Rectifier',
channels=100,
kernel_shape=(5, WORD_DIM),
border_mode='valid'
#pool_shape=(3,1),
#pool_type='max'
),
Layer('Rectifier', units=900, dropout=0.5),
Layer('Softmax')
],
batch_size=50,
learning_rate=0.02,
normalize='dropout',
verbose=True)
nn.n_iter = 100
print 'Net created...'
try:
nn.fit(trainX, trainY)
except KeyboardInterrupt:
pickle.dump(nn, open(model_path, 'wb'))
pickle.dump(nn, open(model_path, 'wb'))
print 'Done, final model saved'
print 'Testing'
#Accuracy on the validation set
print 'Validation accuracy:', batch_test(model_path, val)
def train_neural_network(samples, nn=None, learning_rate=0.001, n_iter=25): #pylint:disable=invalid-name
"""Trains a neural network using the given sample data.
Args:
samples: Tuple containing (sample inputs, sample outputs).
nn: Neural network that should be trained. If this is none, a new NN
will be created.
learning_rate: Neural network learning rate.
n_iter: Number of training iterations to use.
Returns:
The trained neural network.
"""
sample_inputs, sample_outputs = check_samples(samples)
# Create a new classifier if necessary.
if nn is None:
n_features = len(sample_inputs[0])
nn = Classifier(
layers=[
Layer("Maxout", units=n_features, pieces=2),
Layer("Softmax")],
learning_rate=learning_rate,
n_iter=n_iter)
# Train the classifier.
nn.fit(sample_inputs, sample_outputs)
return nn
def MLP_Evaluation(sample,
lable,
n_hidden=10,
activation_func='Tanh',
n_updates=20,
k_fold=5):
X = sample
y = lable
kf = KFold(n_splits=k_fold, shuffle=True)
split_num = kf.get_n_splits(X)
k = 1
G1, G2, S, Total = 0, 0, 0, 0
(AUC, p, r, f1) = (0, 0, 0, 0)
for train_index, test_index in kf.split(X):
# print("TRAIN:", train_index, "TEST:", test_index)
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
nn = Classifier(
layers=[
Layer(activation_func, units=n_hidden),
Layer(activation_func, units=n_hidden),
####################Consider mutilpe layer condition#############################
# Layer(activation_func, units=n_hidden),
# Layer(activation_func, units=n_hidden),
# Layer(activation_func, units=n_hidden),
# Layer(activation_func, units=n_hidden),
# Layer(activation_func, units=n_hidden),
# Layer(activation_func, units=n_hidden),
Layer("Softmax")
],
learning_rate=0.001,
n_iter=n_updates)
nn.fit(X_train, y_train)
y_test_vector = np.zeros((X_test.shape[0], 3), dtype='int64')
for count in range(0, X_test.shape[0]):
if (y_test[count][0] == 0):
y_test_vector[count][0] = 1
elif (y_test[count][0] == 1):
y_test_vector[count][1] = 1
else:
y_test_vector[count][2] = 1
(AUC_k, p_k, r_k, f1_k) = evaluation.evaluate(nn, X_test,
y_test_vector, 0.8)
print("%s / %s Iteration:AUC: %s, Prec: %s, Rec: %s, F1: %s" %
(k, k_fold, AUC_k, p_k, r_k, f1_k))
AUC = AUC + AUC_k
p = p + p_k
r = r + r_k
f1 = f1 + f1_k
print("Average: AUC: %s, Prec: %s, Rec: %s, F1: %s" %
(AUC / k, p / k, r / k, f1 / k))
k = k + 1
AUC = AUC / k_fold
p = p / k_fold
r = r / k_fold
f1 = f1 / k_fold
return AUC, p, r, f1
def train_sknn(X, y):
'''
NeuralNet with sknn
'''
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.25,
random_state=5)
X_train, X_test = impute_nan(X_train, X_test)
X_train, X_test = normalize_features(X_train, X_test)
nn = Classifier(layers=[Layer("Tanh", units=12),
Layer("Softmax")],
learning_rate=0.005,
n_iter=25)
# gs = GridSearchCV(nn, param_grid={
# 'learning_rate': [0.05, 0.01, 0.005, 0.001],
# 'hidden0__units': [4, 8, 12,100],
# 'hidden0__type': ["Rectifier", "Sigmoid", "Tanh"]})
# gs.fit(X_train, y_train)
# print(gs.best_estimator_)
nn.fit(X_train, y_train)
predicted = nn.predict(X_test).flatten()
labels = y_test
return predicted, labels
def learn_data(self):
first_half = []
fh_wins = []
second_half = []
sh_wins = []
key_t = []
for key, stats in self.results.items():
if key[0] < 2006:
first_half += [stats.stat_arr()]
fh_wins += [stats.wins]
else:
second_half += [stats.stat_arr()]
sh_wins += [stats.wins]
key_t += [key]
x_ = np.array([second_half])
x = np.array([first_half])
y_learn = np.array([fh_wins])
nn = Classifier(layers=[Layer("Sigmoid", units=100),
Layer("Softmax")],
learning_rate=0.01,
n_iter=50)
nn.fit(x, y_learn)
prdt = nn.predict(x_)
for i in range(len(second_half)):
if prdt[0][i] >= 10 or sh_wins[i] >= 11:
print((str(key_t[i]) + " actually won " + str(sh_wins[i]) +
" and " + "was predicted with " + str(prdt[0][i])))
def main():
vals, actions = matrixFromCSV("C:\\Users\\Chrisd\\Documents\\College\\Spring 2016\\379K\\Kaggle\\Kaggle\\train.csv")
X_train, X_test, y_train, y_test = train_test_split(vals, actions, test_size=0.33, random_state=22)
totalTest, totalAns = matrixFromCSV("C:\\Users\\Chrisd\\Documents\\College\\Spring 2016\\379K\\Kaggle\\Kaggle\\test.csv")
nn = Classifier(
layers=[
Layer("Softmax", units=10),
Layer("Linear", units=10),
Layer("Sigmoid")],
learning_rate=0.001,
n_iter=20)
nn.fit(X_train,y_train)
pickle.dump(nn, open('nn.pkl', 'wb'))
'''rs = RandomizedSearchCV(nn, param_distributions={
'learning_rate': stats.uniform(0.001, 0.05),
'hidden0__units': stats.randint(4, 100),
'hidden1__units': stats.randint(4, 100),
'hidden1__type': ["Linear","Rectifier", "Sigmoid", "Tanh"]})
rs.fit(X_train, y_train)
pickle.dump(rs, open('rs.pkl', 'wb'))
rs = pickle.load(open('rs.pkl', 'rb'))'''
#print(X_test.shape)
#X_test.reshape(9,1)'''
nn = pickle.load(open('nn.pkl', 'rb'))
answer = nn.predict(X_test)
writeToCSV(answer)
print(getPercent(answer,y_test))
def autoEncoderOptimization(data):
rbm = ae.AutoEncoder(
layers=[
ae.Layer("Tanh", units=300),
ae.Layer("Sigmoid", units=200),
ae.Layer("Tanh", units=100)
],
learning_rate=0.002,
n_iter=10
)
rbm.fit(data["train"])
model = Classifier(
layers=[
Layer("Tanh", units=300),
Layer("Sigmoid", units=200),
Layer("Tanh", units=100),
Layer("Rectifier", units=100),
Layer("Rectifier", units=50),
Layer("Softmax")
],
)
rbm.transfer(model)
model.fit(data["train"], data["label"])
prediction = model.predict(data["train"])
print accuracy_score(data["label"], prediction)
def trainMLP(trainX, trainY, validationX, validationY, activation='Tanh', algorithm='adam',
hidden_layer_size=2048, alpha=0.001):
print('Learning...')
trainX, trainY = shuffle(trainX, trainY)
validationX, validationY = shuffle(validationX, validationY)
mlp = Classifier(
layers=[
Layer(activation, units=hidden_layer_size, dropout=0.1),
Layer("Softmax", units=len(np.unique(trainY)), dropout=0.2)
], learning_rule=algorithm,
learning_rate=0.0005,
learning_momentum=0.9,
batch_size=256,
n_stable=10,
n_iter=200,
regularize="L2",
weight_decay=alpha,
loss_type="mcc", #?
valid_set=(validationX, validationY),
verbose=True)
print(mlp)
mlp.fit(trainX, trainY)
return mlp
def batch_train(train,val,model_path):
trainX,trainY = train
valX,valY = val
nn = Classifier(layers = [
Convolution('Rectifier',
channels=100,
kernel_shape=(5,WORD_DIM),
border_mode='valid'
#pool_shape=(3,1),
#pool_type='max'
),
Layer('Rectifier',units=900,dropout=0.5),
Layer('Softmax')],
batch_size = 50,
learning_rate = 0.02,
normalize='dropout',
verbose = True)
nn.n_iter = 100
print 'Net created...'
try:
nn.fit(trainX,trainY)
except KeyboardInterrupt:
pickle.dump(nn,open(model_path,'wb'))
pickle.dump(nn,open(model_path,'wb'))
print 'Done, final model saved'
print 'Testing'
#Accuracy on the validation set
print 'Validation accuracy:',batch_test(model_path,val)
def train(X, ty):
nn = Classifier(
layers=[Layer("Sigmoid", units=5000), Layer("Sigmoid", units=5)], learning_rate=0.001, n_iter=100, verbose=1
)
nn.fit(X, ty)
print "Train Done!"
return nn
def test_VerboseClassifier(self):
nn = MLPC(layers=[L("Softmax")], verbose=1, n_iter=1)
a_in, a_out = numpy.zeros((8,16)), numpy.zeros((8,1), dtype=numpy.int32)
nn.fit(a_in, a_out)
assert_in("Epoch Training Error Validation Error Time", self.buf.getvalue())
assert_in(" 1 ", self.buf.getvalue())
assert_in(" N/A ", self.buf.getvalue())
def mlp(number_layers, number_neurons_1, number_neurons_2, number_neurons_3, number_neurons_4, dropout_rate):
layers = []
number_neurons = []
number_neurons.append(number_neurons_1)
number_neurons.append(number_neurons_2)
number_neurons.append(number_neurons_3)
number_neurons.append(number_neurons_4)
for i in np.arange(number_layers):
layers.append(Layer("Sigmoid", units=number_neurons[i], dropout = dropout_rate))
layers.append(Layer("Softmax", units=2))
scores = []
for i in np.arange(n_validations):
X_train, X_test, Y_train, Y_test = sklearn.cross_validation.train_test_split(X,Y, test_size=0.3, random_state=1)
predictor = Classifier(
layers=layers,
learning_rate=0.001,
n_iter=25)
predictor.fit(X_train, Y_train)
scores.append(metrics.accuracy_score(Y_test, predictor.predict(X_test)))
return -median(scores)
def check(self, a_in, a_out, a_mask, act='Softmax'):
nn = MLPC(layers=[L(act)],
learning_rule='adam',
learning_rate=0.05,
n_iter=250,
n_stable=25)
nn.fit(a_in, a_out, a_mask)
return nn.predict_proba(a_in)
def train_sknn(data, labels):
# layer one:
hidden_layer = Layer(type="Sigmoid", name="hidden", units=10)
output_layer = Layer(type="Softmax", name="output")
layers = [hidden_layer, output_layer]
mlp = Classifier(layers=layers, random_state=1)
mlp.fit(data, labels)
return mlp
def MLP_leave_one_cross_validation(sample, lable):
length = len(sample)
right, first, second, third = 0, 0, 0, 0
false_list = []
for k in range(0, length):
nn = Classifier(layers=[Layer("ExpLin", units=1000),
Layer("Softmax")],
learning_rate=0.001,
n_iter=27)
train_sample = copy.deepcopy(sample)
lable_sample = copy.deepcopy(lable)
test_sample = np.array([sample[k]])
test_lable = lable[k]
train_sample = np.delete(train_sample, k, 0)
lable_sample = np.delete(lable_sample, k, 0)
nn.fit(train_sample, lable_sample)
test_result = nn.predict(test_sample)
print "predict_label: ", test_result[0][0]
print "true_label: ", test_lable[0]
if (test_lable[0] == 0):
if (test_result[0][0] == test_lable[0]):
print True
first += 1
right += 1
else:
print False
false_list.append(k)
elif (test_lable[0] == 1):
if (test_result[0][0] == test_lable[0]):
print True
second += 1
right += 1
else:
print False
false_list.append(k)
else:
if (test_result[0][0] == test_lable[0]):
print True
third += 1
right += 1
else:
print False
false_list.append(k)
print "...................................................................................................."
print k
print "...................................................................................................."
# G1_rate = 1.0 * first / 59
# S_rate = 1.0 * second / 58
# G2_rate = 1.0 * third / 65
print "class G1:", 1.0 * first / 59
print "class S:", 1.0 * second / 58
print "class G2:", 1.0 * third / 65
print "class total:", 1.0 * right / 182
print false_list
def fit_network():
x,y = datasplit.data()
x_normalized = normalize(x,norm='l2')
nn = Classifier(layers=[Layer("Softmax" , units=1000),Layer("Softmax" , units=62)],learning_rate=0.02,n_iter=1)
le= LabelEncoder()
le.fit(y)
y = le.transform(y)
nn.fit(x_normalized , y)
return nn
def wrapper_for_backprop_neural_network_code(train_x, train_y, test_x, test_y):
score = None
nn = Classifier(
layers=[Layer('Sigmoid', units=5),
Layer('Softmax')], learning_rate=.001, n_iter=25)
nn.fit(train_x, train_y)
predicted = nn.predict(test_x)
score = accuracy_score(predicted, test_y)
return score
def train_model(values, labels):
model = Classifier(layers=[
Convolution("Rectifier", channels=8, kernel_shape=(3, 3)),
Layer("Softmax")
],
learning_rate=0.02,
n_iter=5)
model.fit(values, labels)
return model
def _ann_n_iter(data, data_test, target, target_test, n_units):
nn = Classifier(
layers=[
Layer("Sigmoid", units=n_units),
Layer("Softmax")],
n_iter=4000)
nn.fit(data, target)
test_score = nn.score(data_test, target_test)
print n_units, test_score
def test_GetParamValues(self):
nn = MLPC(layers=[L("Linear")],
learning_rate=0.05,
n_iter=456,
n_stable=123,
valid_size=0.2,
dropout_rate=0.25)
params = nn.get_params()
self.check_values(params)
def _ann_n_iter(data, data_test, target, target_test, n_iter):
nn = Classifier(
layers=[
Layer("Sigmoid", units=100),
Layer("Softmax")],
n_iter=n_iter)
train_score = np.mean(cross_validation.cross_val_score(nn, data, target, cv=10))
nn.fit(data, target)
test_score = nn.score(data_test, target_test)
print n_iter, train_score, test_score
def CNN(X_train, y_train, X_test):
nn = Classifier(
layers=[
Convolution("Rectifier", channels=20, kernel_shape=(5,5), dropout=0.25),
Layer("Tanh", units=300),
Layer("Tanh", units=100),
Layer("Softmax")], learning_rate=0.02, n_iter=10)
nn.fit(X_train, y_train)
print('\nTRAIN SCORE', nn.score(X_train, y_train))
return list(nn.predict(X_test))
def wrapper_for_backprop_neural_network_code(train_x, train_y, test_x, test_y):
score = None
nn = Classifier(layers=[Layer('Sigmoid', units=5),
Layer('Softmax')],
learning_rate=.001,
n_iter=25)
nn.fit(train_x, train_y)
predicted = nn.predict(test_x)
score = accuracy_score(predicted, test_y)
return score
def train_model(values,labels):
model = Classifier(
layers=[
Convolution("Rectifier", channels=8, kernel_shape=(3,3)),
Layer("Softmax")
],
learning_rate=0.02,
n_iter=5)
model.fit(values, labels)
return model
def train(X, ty):
nn = Classifier(
layers=[Layer("Sigmoid", units=15),
Layer("Softmax", units=2)],
learning_rate=0.001,
n_iter=10,
verbose=1)
nn.fit(X, ty)
print "Train Done!"
return nn
class SoftmaxNeuralNetwork:
def __init__(self):
# learning rate
self.nn = Classifier(layers=[Layer("Softmax", units=100), Layer("Softmax")], learning_rate=0.001, n_iter=25)
def train(self, training_input, correct_output):
self.nn.fit(training_input, correct_output)
def predict(self, training_example):
return self.nn.predict(training_example)
def train():
res = createArrays()
X_train = res[0]
Y_train = res[1]
samples = res[2]
nn = Classifier(layers=[Layer("Sigmoid", units=150),
Layer("Softmax")],
learning_rate=0.001,
n_iter=samples)
nn.fit(X_train, Y_train)
pickle.dump(nn, open('nn.pkl', 'wb'))
def covnetTrain(train_bmi , train_labels , ite =10 , kernel =3 ,learn_rate =0.02, channel = 8):
nn = Classifier(
layers = [
Convolution("Rectifier", channels=channel, kernel_shape=(kernel,kernel)),
Layer("Softmax")],
learning_rate=learn_rate,
n_iter=ite
)
neuralnet = nn.fit(train_bmi , train_labels)
return neuralnet
def neural_classifier(X, y, classes=2):
from sknn.mlp import Classifier, Layer
nn = Classifier(layers=[Layer("Rectifier", units=100),
Layer("Softmax")],
learning_rate=0.02,
n_iter=10)
if classes == 2:
model = nn.fit(X, y)
else:
model = OneVsRestClassifier(nn).fit(X, y)
return model
def _nn(tx, ty, rx, ry, n_iter):
print "_nn"
nn = Classifier(
layers=[
Layer("Tanh", units=100),
Layer("Softmax")],
n_iter=n_iter)
nn.fit(tx, ty)
resultst = nn.score(tx, ty)
resultsr = nn.score(rx, ry)
print "_nn done"
return n_iter, resultst, resultsr
def train(X, y, num_classes, model=None):
if model is None:
model = Classifier(
layers=[
Layer("Sigmoid", units=args.num_hidden),
Layer("Softmax", units=num_classes)],
learning_rule='sgd',
learning_rate=args.lr,
n_iter=args.n_iter,
verbose=1)
model.fit(X, y)
pickle.dump(model, open(args.outfile, "w"))
def nn_model(x, y):
nn = Classifier(
layers=[
Layer("Sigmoid", units=500),
Layer("Sigmoid", units=500),
Layer("Softmax")],
learning_rate=0.008,
weight_decay = 0.0001,
dropout_rate=0.1,
n_iter=400)
nn.fit(x.as_matrix(), y)
return nn
def fit_network():
x, y = datasplit.data()
x_normalized = normalize(x, norm='l2')
nn = Classifier(
layers=[Layer("Softmax", units=1000),
Layer("Softmax", units=62)],
learning_rate=0.02,
n_iter=1)
le = LabelEncoder()
le.fit(y)
y = le.transform(y)
nn.fit(x_normalized, y)
return nn
def get_X_Y(filetrain,filetest):
y_train,x_train=readCSV(filetrain)
y_test,x_test=readCSV(filetest)
# print f_score.f_score(X,Y)
#print t_score.t_score(X,Y)
nn=Classifier(layers=[Layer("Rectifier",units=100),Layer("Softmax")],learning_rate=0.02,n_iter=10)
#pdb.set_trace()
nn.fit(x_train,y_train)
score=nn.score(x_test,y_test)
def auto(X_, act, units_):
## Neural Network Classifier -- 3 Hidden Layer
myae = Classifier(layers=[Layer(act, units=units_[0]),
Layer("Softmax")],
n_iter=100,
verbose=True,
regularize="L2",
batch_size=32,
learning_rule="adagrad")
## Fit the Classifier
np.random.seed(1)
myae.fit(np.asarray(X_), np.asarray(X_))
return myae
def auto(X_, act, units_):
## Neural Network Classifier -- 3 Hidden Layer
myae = Classifier(layers = [Layer(act, units=units_[0]),
Layer("Softmax")],
n_iter = 100,
verbose=True,
regularize="L2",
batch_size=32,
learning_rule="adagrad")
## Fit the Classifier
np.random.seed(1)
myae.fit(np.asarray(X_), np.asarray(X_))
return myae
def _ann_train_size(data, data_test, target, target_test, train_size):
nn = Classifier(
layers=[
Layer("Sigmoid", units=100),
Layer("Softmax")])
if train_size < 1:
X_train, _, y_train, _ = cross_validation.train_test_split(
data, target, train_size=train_size, stratify=target)
else:
X_train, y_train = data, target
nn.fit(X_train, y_train)
train_score = nn.score(X_train, y_train)
test_score = nn.score(data_test, target_test)
print train_size, train_score, test_score
def find_meta_parameters(X_, y_, classifier_type, **kwargs):
if kwargs.get('act'):
act = kwargs['act']
print("\n Finding meta parameters for classifier: {0}".format(classifier_type))
if classifier_type == "NN":
## Neural Network Classifier -- 2 Hidden Layer
NN = Classifier(layers = [Layer(act, units=20),
Layer(act, units=20),
Layer("Softmax")],
regularize="L2",
n_iter = 1000,
verbose=True,
batch_size=25,
learning_rule="adagrad",
random_state=0)
## Meta Parameters Grid Search with Cross Validation
param_grid = {"learning_rate": [0.001, 0.01, 0.05, 0.075],
"weight_decay": [0.0001, 0.001, 0.005, 0.01],
"hidden0__units": [75, 100],
"hidden1__units": [75, 100]}
NN = GridSearchCV(NN, param_grid, refit=True, verbose=True, scoring='roc_auc', n_jobs=1, cv=5)
## Fit the Classifier
np.random.seed(1)
NN.fit(np.asarray(X_), np.asarray(y_, dtype=np.int8))
## Best Fit Estimator
Best_Model = NN.best_estimator_
elif classifier_type == "RF":
## Random Forest
rf = RandomForestClassifier(random_state=0, verbose=1, n_estimators=1000)
## Meta Parameters Grid Search with Cross Validation
param_grid = {'max_features': ["auto", "log2", np.int(np.shape(X_)[1]/2)],
'n_estimators': [100,500,1000]}
rf = GridSearchCV(rf, param_grid, refit=True, verbose=True, scoring='roc_auc', n_jobs=1, cv=5)
## Fit the Classifier
np.random.seed(1)
rf.fit(np.asarray(X_), np.asarray(y_, dtype=np.int8))
## Best Fit Estimator
Best_Model = rf.best_estimator_
#Best_NN,
else:
raise ValueError("classifier_type undefined in find_meta_parameter")
return Best_Model
class ClassifierNeuralNet():
def __init__(self):
self.nn = Classifier(
layers=[
Layer("Sigmoid", units =100),
Layer("Softmax")],
learning_rate = 0.001,
n_iter = 200)
def train(self):
data = parser.load_echo_data('data/training_data.csv')
self.nn.fit(data.data, data.target)
def predictData(self, data):
return self.nn.predict(data)
def train(X, y, w, num_classes, model=None, lr=0.01):
if model is None:
model = Classifier(
layers=[
Layer("Sigmoid", units=args.num_hidden),
Layer("Softmax", units=num_classes)],
learning_rule='sgd',
learning_rate=lr,
n_iter=1,
verbose=1)
model.fit(X, y)#, w=w)
pickle.dump(model, open(args.outfile, "w"))
labels = model.predict(X).flatten()
print "Split accuracy", float(np.sum(labels == y))/X.shape[0]
return model
def train():
res = createArrays()
X_train = res[0]
Y_train = res[1]
samples = res[2]
nn = Classifier(
layers=[
Layer("Sigmoid", units=150),
Layer("Softmax")
],
learning_rate=0.001,
n_iter=samples
)
nn.fit(X_train, Y_train)
pickle.dump(nn, open('nn.pkl', 'wb'))
def mlp(
number_layers,
number_neurons_1,
number_neurons_2,
number_neurons_3,
number_neurons_4,
dropout_rate_1,
dropout_rate_2,
dropout_rate_3,
dropout_rate_4,
weight_decay,
activation_1,
activation_2,
activation_3,
activation_4,
learning_rate,
):
layers = []
number_neurons = []
activation = []
dropout = []
number_neurons.append(number_neurons_1)
number_neurons.append(number_neurons_2)
number_neurons.append(number_neurons_3)
number_neurons.append(number_neurons_4)
activation.append(activation_1)
activation.append(activation_2)
activation.append(activation_3)
activation.append(activation_4)
dropout.append(dropout_rate_1)
dropout.append(dropout_rate_2)
dropout.append(dropout_rate_3)
dropout.append(dropout_rate_4)
for i in np.arange(number_layers):
layers.append(Layer(activation[i], units=number_neurons[i], dropout=dropout[i], weight_decay=weight_decay))
layers.append(Layer("Softmax", units=2))
predictor = Classifier(layers=layers, learning_rate=learning_rate, n_iter=25)
predictor.fit(X_train, Y_train)
return -metrics.accuracy_score(Y_test, predictor.predict(X_test))
def buildClassifier(clf='random_forest'):
if (clf == 'neuron_network'):
return Classifier(
layers=[Layer("Sigmoid", units=100),
Layer("Softmax", units=2)],
learning_rule='sgd',
learning_rate=0.01,
n_iter=10)
elif (clf == 'SVC'):
return svm.SVC()
elif (clf == 'Linear Discriminant Analysis'):
return LinearDiscriminantAnalysis()
elif (clf == 'Quadratic Discriminant Analysis'):
return QuadraticDiscriminantAnalysis()
elif (clf == 'AdaBoost'):
return AdaBoostClassifier(n_estimators=100)
elif (clf == 'extra_trees_classifier'):
return ExtraTreesClassifier(n_estimators=10)
elif (clf == 'gradient_boosting_classifier'):
return GradientBoostingClassifier(n_estimators=10)
else:
return RandomForestClassifier(n_estimators=10)
def create_network(niter, lr, verboseflag):
nn = Classifier(layers=[Layer("Sigmoid", units=5),
Layer("Softmax")],
learning_rate=lr,
n_iter=niter,
verbose=verboseflag)
return nn
def mlp_test():
# layer one:
hidden_layer = Layer(type="Sigmoid", name="hidden", units=10)
output_layer = Layer(type="Softmax", name="output")
layers = [hidden_layer, output_layer]
mlp = Classifier(layers=layers, random_state=1)
classify(classifier=mlp)
def predict():
pipeline = Pipeline([
('min/max scaler', MinMaxScaler(feature_range=(0.0, 1.0))),
('neural network', Classifier(layers=[Layer("ExpLin", units=5), Layer("Softmax")], n_iter=25))])
X = np.load('All_features.npz')['arr_0']
D = np.load('Akunin_features.npz')['arr_0']
all_samples = [1]*141 + [0]*123
y = np.array(all_samples)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.0, random_state=0)
pipeline.fit(X_train, y_train)
pickle.dump(pipeline, open('NeuralNet_model.pkl', 'wb'))
prediction = pipeline.predict(D)
probs = pipeline.predict_proba(D)
gradation = {1.01: 5, 0.9: 4, 0.8: 3, 0.7: 2, 0.6: 1}
ress1 = []
simple_predicts = []
scale_predicts = []
for i in prediction:
simple_predicts.append(i[0])
for i in probs:
scale_predicts.append(i[1]*10)
compare = []
for u in gradation:
if i[1] < u:
compare.append(gradation[u])
ress1.append(min(compare))
return simple_predicts, scale_predicts
def classifyNeuralNetworkClassifier(XTrain, XTest, YTrain, YTest, params):
activation = params['activation']
actLastLayer = params['actLastLayer']
rule = params['rule']
noOfUnits = params['units']
rate = params['rate']
noOfIter = params['iter']
nn = Classifier(layers=[Layer(activation, units=noOfUnits), Layer(actLastLayer)], learning_rule=rule,
learning_rate=0.02,
n_iter=10)
nn.fit(XTrain, YTrain)
YPred = nn.predict(XTest)
diff = YPred - YTest.reshape(YPred.shape)
score = diff[diff == 0].size
score = (100.0 * score) / (YPred.size)
return score
def CNN(X, y):
#l2 normalize
#preprocessing.normalize(X, 'max')
#scale centre to the mean to unit vector
#preprocessing.scale(X_train)
#preprocessing.scale(X_test)
#X = equalize_hist(X)
X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.2, random_state = 42)
nn = Classifier(
layers=[
Convolution("Rectifier", channels=10, kernel_shape=(5,5), dropout=0.25, normalize="batch", weight_decay=0.0001),
#Layer("Tanh", units=10),
Layer("Softmax")], learning_rate=0.05, n_iter=10)
nn.fit(X_train, y_train)
print('\nTRAIN SCORE', nn.score(X_train, y_train))
print('TEST SCORE', nn.score(X_test, y_test))
def mlpclassifier(input_data, output_labels,filename) :
from sknn.mlp import Classifier, Layer
mlpC = Classifier(
layers=[
#Layer("Maxout", units=100, pieces=2),
Layer("Softmax")],
learning_rate=0.001,
n_iter=25)
X_train, X_test, Y_train, Y_test = train_test_split(input_data, output_labels, test_size=0.25, random_state=42)
mlpC.fit(X_train, Y_train)
predictionsMLP= mlpC.predict(X_test)
calc_conf_matrix(Y_test, predictionsMLP, 'Multi Layer Perceptron confusion matrix', filename+'_cm')
roc_plot(input_data,output_labels, mlpC,filename+'_roc')
coeff_of_deterimination(mlpC, input_data, output_labels, 5)
def train_dropout_nn(X, y, model_type='classifier', cv_fold=5):
"""
Parameters
----------
X
y
model_type
cv_fold
Returns
-------
"""
# a grid of hyperparameters from which to search for an optimal combination
param_grid = {
'weight_decay': [0.05, 0.01, 0.005, 0.001],
'dropout_rate': [0.25, 0.50],
'learning_momentum': np.arange(0.1, 1.0, 0.3),
'learning_rate': [0.05, 0.01, 0.005, 0.001],
'hidden0__units': [8, 16, 32, 64],
'hidden0__dropout': [0.25, 0.50]
}
# create appropriate model type
if model_type == 'classifier':
model = Classifier(
layers=[Layer('Sigmoid'), Layer('Softmax')],
regularize='L2',
verbose=True
)
else:
model = Regressor(
layers=[Layer('Sigmoid'), Layer('Linear')],
regularize='L2',
verbose=True
)
# do a grid search for optimal hyperparameters
grid_search = GridSearchCV(
estimator=model,
param_grid=param_grid,
scoring='neg_mean_squared_error',
cv=cv_fold,
refit=True
)
logging.info('Fitting neural networks regularized with dropout ...')
grid_search.fit(X, y)
# print results from grid search
logging.info('best hyperparameter combination %s' % grid_search.best_params_)
gs_results = grid_search.cv_results_
for params, mean_score in zip(
gs_results['params'], gs_results['mean_test_score']
):
print(params, '%.2f' % np.sqrt(-mean_score))
# return the final model
return grid_search.best_estimator_
def build_mlp(self):
layers = self.__build_layers()
print("Building neural network...")
pipeline = Pipeline([
('min/max scaler', MinMaxScaler(feature_range=(0.0, 1.0))),
('neural network', Classifier(layers=layers, learning_rule='nesterov'))])
print("Network built successfully")
return pipeline
def CNN(X_train, y_train, X_test, X_hidden):
print("CNN")
#l2 normalize preprocessing.normalize(X, 'l2')
preprocessing.normalize(X_train, 'max')
preprocessing.normalize(X_test, 'max')
preprocessing.normalize(X_hidden, 'max')
print("Done normalization")
X_train = equalize_hist(X_train)
X_test = equalize_hist(X_test)
X_hidden = equalize_hist(X_hidden)
nn = Classifier(
layers=[
Convolution("Rectifier", channels=98, kernel_shape=(3, 3)),
#Convolution("Rectifier", channels=100, kernel_shape=(3,3), dropout=0.25,
#weight_decay=0.0001, pool_shape = (2,2), pool_type="max"),
Layer("Softmax")
],
learning_rate=0.01,
n_iter=25,
random_state=42)
nn.fit(X_train, y_train)
print('\nTRAIN SCORE', nn.score(X_train, y_train))
pub_res = list(nn.predict(X_test))
hid_res = list(nn.predict(X_hidden))
return pub_res + hid_res
def train(X, Y):
print X.shape
print Y.shape
trainX = X[:int(X.shape[0] * 0.7), :, :]
trainY = Y[:int(Y.shape[0] * 0.7), :]
valX = X[int(X.shape[0] * 0.7):int(X.shape[0] * 0.8), :, :]
valY = Y[int(Y.shape[0] * 0.7):int(Y.shape[0] * 0.8), :]
testX = X[int(X.shape[0] * 0.8):, :, :]
testY = Y[int(Y.shape[0] * 0.8):, :]
print 'Train, Val, Test'
print trainX.shape, ',', trainY.shape, '--', valX.shape, ',', valY.shape, '--', testX.shape, ',', testY.shape
nn = Classifier(
layers=[
Convolution('Rectifier', channels=1, kernel_shape=(5, WORD_DIM)),
Layer('Rectifier', units=300),
Layer('Rectifier', units=300),
Layer('Softmax')
],
#valid_set = (valX,valY),
learning_rate=0.02, #0.05, #0.001,
#normalize='batch',
verbose=True)
print 'Net created...'
#Load net here --always CHECK HERE before starting -- DO THIS NOW, WE WANT TO CONTINUE FROM HERE ON
nn = pickle.load(open(model_path, 'rb'))
for i in range(100):
try:
nn.n_iter = 5
nn.fit(trainX, trainY)
pickle.dump(nn, open(model_path, 'wb'))
nn = pickle.load(open(model_path, 'rb'))
except KeyboardInterrupt:
pickle.dump(nn, open(model_path, 'wb'))
print 'Saved model after keyboard interrupt'
pickle.dump(nn, open(model_path, 'wb'))
print 'Temp model saved'
#try:
# nn.fit(trainX,trainY)
#except KeyboardInterrupt:
# pickle.dump(nn,open(model_path,'wb'))
print 'Done, final model saved'
def CNN(X_train, y_train, X_test, X_hidden):
print("1 Con, 1 tanh")
#l2 normalize preprocessing.normalize(X, 'l2')
preprocessing.normalize(X_train, 'max')
preprocessing.normalize(X_test, 'max')
preprocessing.normalize(X_hidden, 'max')
print("Done normalization")
X_train = equalize_hist(X_train)
X_test = equalize_hist(X_test)
X_hidden = equalize_hist(X_hidden)
nn = Classifier(layers=[
Layer("Tanh", units=98, weight_decay=0.0001),
Layer("Softmax")
],
learning_rate=0.01,
n_iter=1000,
batch_size=5)
nn.fit(X_train, y_train)
print('\nTRAIN SCORE', nn.score(X_train, y_train))
pub_res = list(nn.predict(X_test))
hid_res = list(nn.predict(X_hidden))
return pub_res + hid_res
