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clf_nolearn_2_levels.py
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clf_nolearn_2_levels.py
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"""
"""
import numpy as np
from nolearn.lasagne import BatchIterator
from lasagne.layers import InputLayer, DenseLayer, DropoutLayer
from lasagne.nonlinearities import LeakyRectify, softmax
from lasagne.init import HeNormal
from lasagne.updates import adagrad
from theano import shared
from sklearn.preprocessing import LabelEncoder
from clf_nolearn import Clf_nolearn, My_NeuralNet, EarlyStopping, AdjustVariable, float32
class ansi:
BLUE = '\033[94m'
GREEN = '\033[32m'
ENDC = '\033[0m'
class Clf_nolearn_2_levels(Clf_nolearn):
"""
Simple nolearn based classifier...
"""
def __init__(self, num_features=93, num_classes=9):
"""
"""
self.prefix = 'nl_2lv'
self.num_features = num_features
self.num_classes = num_classes
self.layers0 = [('input', InputLayer),
('dropoutn', DropoutLayer),
('dense0', DenseLayer),
('dropout0', DropoutLayer),
('dense1', DenseLayer),
('dropout1', DropoutLayer),
('dense2', DenseLayer),
('dropout2', DropoutLayer),
('dense3', DenseLayer),
('dropout3', DropoutLayer),
('output', DenseLayer)]
self.early_stopping0 = EarlyStopping(patience=20)
self.nn0 = My_NeuralNet(layers=self.layers0,
input_shape=(None, self.num_features),
dropoutn_p=0.12,
dense0_num_units=900,
dense0_W=HeNormal(),
dense0_nonlinearity = LeakyRectify(leakiness=0.002),
dropout0_p=0.35,
dense1_num_units=600,
dense1_W=HeNormal(),
dense1_nonlinearity = LeakyRectify(leakiness=0.002),
dropout1_p=0.2,
dense2_num_units=400,
dense2_W=HeNormal(),
dense2_nonlinearity = LeakyRectify(leakiness=0.002),
dropout2_p=0.1,
dense3_num_units=300,
dense3_W=HeNormal(),
dense3_nonlinearity = LeakyRectify(leakiness=0.002),
dropout3_p=0.1,
output_num_units=2,
output_nonlinearity=softmax,
update=adagrad,
update_learning_rate=shared(float32(0.01)),
batch_iterator_train=BatchIterator(batch_size=64),
on_epoch_finished=[
AdjustVariable('update_learning_rate', start=0.01, stop=0.001),
# AdjustVariable('update_momentum', start=0.1, stop=0.0),
self.early_stopping0,
],
eval_size=0,
verbose=1,
max_epochs=36
)
self.layers1 = [('input', InputLayer),
('dropoutn', DropoutLayer),
('dense0', DenseLayer),
# ('gaussian0', GaussianNoiseLayer),
('dropout0', DropoutLayer),
('dense1', DenseLayer),
# ('gaussian1', GaussianNoiseLayer),
('dropout1', DropoutLayer),
('dense2', DenseLayer),
('dropout2', DropoutLayer),
# ('dense3', DenseLayer),
# ('dropout3', DropoutLayer),
('output', DenseLayer)]
self.early_stopping1 = EarlyStopping(patience=20)
self.nn1 = My_NeuralNet(layers=self.layers1,
input_shape=(None, self.num_features),
dropoutn_p=0.18,
dense0_num_units=900,
dense0_W=HeNormal(),
dense0_nonlinearity = LeakyRectify(leakiness=0.002),
dropout0_p=0.35,
dense1_num_units=600,
dense1_W=HeNormal(),
dense1_nonlinearity = LeakyRectify(leakiness=0.002),
dropout1_p=0.15,
dense2_num_units=400,
dense2_W=HeNormal(),
dense2_nonlinearity = LeakyRectify(leakiness=0.002),
dropout2_p=0.05,
output_num_units=3,
output_nonlinearity=softmax,
update=adagrad,
update_learning_rate=shared(float32(0.01)),
batch_iterator_train=BatchIterator(batch_size=64),
on_epoch_finished=[
AdjustVariable('update_learning_rate', start=0.01, stop=0.001),
# AdjustVariable('update_momentum', start=0.1, stop=0.0),
self.early_stopping1,
],
eval_size=0,
verbose=1,
max_epochs=60
)
self.layers2 = [('input', InputLayer),
('dropoutn', DropoutLayer),
('dense0', DenseLayer),
# ('gaussian0', GaussianNoiseLayer),
('dropout0', DropoutLayer),
('dense1', DenseLayer),
# ('gaussian1', GaussianNoiseLayer),
('dropout1', DropoutLayer),
('dense2', DenseLayer),
('dropout2', DropoutLayer),
# ('dense3', DenseLayer),
# ('dropout3', DropoutLayer),
('output', DenseLayer)]
self.early_stopping2 = EarlyStopping(patience=20)
self.nn2 = My_NeuralNet(layers=self.layers2,
input_shape=(None, self.num_features),
dropoutn_p=0.13,
dense0_num_units=900,
dense0_W=HeNormal(),
dense0_nonlinearity = LeakyRectify(leakiness=0.002),
dropout0_p=0.35,
dense1_num_units=600,
dense1_W=HeNormal(),
dense1_nonlinearity = LeakyRectify(leakiness=0.002),
dropout1_p=0.15,
dense2_num_units=400,
dense2_W=HeNormal(),
dense2_nonlinearity = LeakyRectify(leakiness=0.002),
dropout2_p=0.05,
output_num_units=6,
output_nonlinearity=softmax,
update=adagrad,
update_learning_rate=shared(float32(0.01)),
batch_iterator_train=BatchIterator(batch_size=64),
on_epoch_finished=[
AdjustVariable('update_learning_rate', start=0.01, stop=0.001),
# AdjustVariable('update_momentum', start=0.1, stop=0.0),
self.early_stopping2,
],
# eval_size=0.2,
eval_size=0,
verbose=1,
max_epochs=31
)
def train_validate(self, X_train, y_train, X_valid, y_valid):
"""
"""
le = LabelEncoder()
id_123 = np.logical_or(np.logical_or(y_train==1, y_train==2),
y_train==3)
y0 = np.zeros(len(y_train), dtype=np.int32)
y0[id_123] = 1
X0 = np.copy(X_train)
y0 = le.fit_transform(y0).astype(np.int32)
X1 = X_train[id_123]
y1 = y_train[id_123]
y1 = le.fit_transform(y1).astype(np.int32)
X2 = X_train[np.logical_not(id_123)]
y2 = y_train[np.logical_not(id_123)]
y2 = le.fit_transform(y2).astype(np.int32)
#Validation
id_123_valid = np.logical_or(np.logical_or(y_valid==1, y_valid==2),
y_valid==3)
y0_valid = np.zeros(len(y_valid), dtype=np.int32)
y0_valid[id_123_valid] = 1
X0_valid = np.copy(X_valid)
y0_valid = le.fit_transform(y0_valid).astype(np.int32)
X1_valid = X_valid[id_123_valid]
y1_valid = y_valid[id_123_valid]
y1_valid = le.fit_transform(y1_valid).astype(np.int32)
X2_valid = X_valid[np.logical_not(id_123_valid)]
y2_valid = y_valid[np.logical_not(id_123_valid)]
y2_valid = le.fit_transform(y2_valid).astype(np.int32)
self.nn0.max_epochs = 300
self.nn0.verbose=1
self.nn0.fit(X0, y0, X0_valid, y0_valid)
y0_pred = self.nn0.predict_proba(X_valid)
self.nn1.max_epochs = 300
self.nn1.verbose=1
self.nn1.fit(X1, y1, X1_valid, y1_valid)
y1_pred = self.nn1.predict_proba(X_valid)
self.nn2.max_epochs = 300
self.nn2.verbose=1
self.nn2.fit(X2, y2, X2_valid, y2_valid)
y2_pred = self.nn2.predict_proba(X_valid)
y_pred = np.zeros((y0_pred.shape[0], 9))
y_pred[:,0] = y0_pred[:,0]*y2_pred[:,0]
y_pred[:,1] = y0_pred[:,1]*y1_pred[:,0]
y_pred[:,2] = y0_pred[:,1]*y1_pred[:,1]
y_pred[:,3] = y0_pred[:,1]*y1_pred[:,2]
y_pred[:,4] = y0_pred[:,0]*y2_pred[:,1]
y_pred[:,5] = y0_pred[:,0]*y2_pred[:,2]
y_pred[:,6] = y0_pred[:,0]*y2_pred[:,3]
y_pred[:,7] = y0_pred[:,0]*y2_pred[:,4]
y_pred[:,8] = y0_pred[:,0]*y2_pred[:,5]
return y_pred
def train_test(self, X, y, X_test):
"""
"""
le = LabelEncoder()
id_123 = np.logical_or(np.logical_or(y==1, y==2), y==3)
y0 = np.zeros(len(y), dtype=np.int32)
y0[id_123] = 1
X0 = np.copy(X)
y0 = le.fit_transform(y0).astype(np.int32)
X1 = X[id_123]
y1 = y[id_123]
y1 = le.fit_transform(y1).astype(np.int32)
X2 = X[np.logical_not(id_123)]
y2 = y[np.logical_not(id_123)]
y2 = le.fit_transform(y2).astype(np.int32)
print 'working on nn0...'
self.nn0.max_epochs = self.early_stopping0.best_valid_epoch
self.nn0.verbose=0
self.nn0.fit(X0, y0)
y0_pred = self.nn0.predict_proba(X_test)
print 'working on nn1...'
self.nn1.max_epochs = self.early_stopping1.best_valid_epoch
self.nn1.verbose=0
self.nn1.fit(X1, y1)
y1_pred = self.nn1.predict_proba(X_test)
print 'working on nn2...'
self.nn2.max_epochs = self.early_stopping2.best_valid_epoch
self.nn2.verbose=0
self.nn2.fit(X2, y2)
y2_pred = self.nn2.predict_proba(X_test)
y_pred = np.zeros((y0_pred.shape[0], 9))
y_pred[:,0] = y0_pred[:,0]*y2_pred[:,0]
y_pred[:,1] = y0_pred[:,1]*y1_pred[:,0]
y_pred[:,2] = y0_pred[:,1]*y1_pred[:,1]
y_pred[:,3] = y0_pred[:,1]*y1_pred[:,2]
y_pred[:,4] = y0_pred[:,0]*y2_pred[:,1]
y_pred[:,5] = y0_pred[:,0]*y2_pred[:,2]
y_pred[:,6] = y0_pred[:,0]*y2_pred[:,3]
y_pred[:,7] = y0_pred[:,0]*y2_pred[:,4]
y_pred[:,8] = y0_pred[:,0]*y2_pred[:,5]
return y_pred