def __CVD( data, exp ):
kn = Evaluation.Evaluator()
X,Y,y_raw = Features.getSamples( kn, data )
data.maxWords = 10000
kf = StratifiedKFold( n_splits=10, shuffle=True )
k = 0
for train, test in kf.split( X, y_raw ):
print( "K-Fold: " + str( k + 1 ) );
x_train_raw, x_test_raw = X[train], X[test]
y_train, y_test = Y[train], Y[test]
x_train, x_test = Features.getCVvectors( x_train_raw, x_test_raw, data )
denseSizes = [512,1024]
batches = [64,128]
dropouts = [2,3]
param_grid = dict( batch_size=batches, denseSize=denseSizes,
dropout=dropouts, input_length=[len(x_train[0])],
output_length=[len(y_train[0])] )
model = KerasClassifier(build_fn=Models.create_ann_model, epochs=30, verbose=2)
y_ints = [y.argmax() for y in y_train]
cweights = class_weight.compute_class_weight( 'balanced', np.unique( y_ints ), y_ints )
grid = GridSearchCV(estimator=model, param_grid=param_grid)
grid_result = grid.fit(x_train, y_train, class_weight=cweights)
print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))
model, scores = kn.evaluateModel( x_test, y_test, grid.best_estimator_.model, data, k )
k = k + 1
kn.saveResults( exp )
def __EmbeddedRNN( data, exp, filepath, network ):
kn = Evaluation.Evaluator()
X,Y,y_raw = Features.getSamples( kn, data )
data.maxWords = 10000
kf = StratifiedKFold( n_splits=10, shuffle=True )
k = 0
for train, test in kf.split( X, y_raw ):
print( "K-Fold: " + str( k + 1 ) );
x_train_raw, x_test_raw = X[train], X[test]
y_train, y_test = Y[train], Y[test]
Models.embedding, x_train, x_test = Features.getEmbedded( x_train_raw, x_test_raw,
y_train, y_test, y_raw, filepath, kn )
batches = [64,218]
neurons = [100,200]
dropouts = [2,3]
param_grid = dict( batch_size=batches, neuron=neurons, dropout=dropouts, output_size=[len(y_train[0])] )
model = None
if network == 'lstm':
model = KerasClassifier(build_fn=Models.create_lstm_model, epochs=30, verbose=2)
else:
model = KerasClassifier(build_fn=Models.create_gru_model, epochs=30, verbose=2)
y_ints = [y.argmax() for y in y_train]
cweights = class_weight.compute_class_weight( 'balanced', np.unique( y_ints ), y_ints )
grid = GridSearchCV(estimator=model, param_grid=param_grid)
grid_result = grid.fit(x_train, y_train, class_weight=cweights)
print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))
model, scores = kn.evaluateModel( x_test, y_test, grid.best_estimator_.model, data, k )
k = k + 1
kn.saveResults( exp )
def WStack():
import Wesleyan
data = Wesleyan.Wesleyan()
kn = Evaluation.Evaluator()
exp = "WStack"
filepath = 'enwiki_20180420_300d.txt'
X,Y,y_raw = Features.getSamples( kn, data )
data.maxWords = 10000
kf = StratifiedKFold( n_splits=10, shuffle=True )
k = 0
for train, test in kf.split( X, y_raw ):
print( "K-Fold: " + str( k + 1 ) );
x_train_raw, x_test_raw = X[train], X[test]
y_train, y_test = Y[train], Y[test]
y_ints = [y.argmax() for y in y_train]
cweights = class_weight.compute_class_weight( 'balanced', np.unique( y_ints ), y_ints )
Models.embedding, x_train, x_test = Features.getEmbedded( x_train_raw, x_test_raw,
y_train, y_test, y_raw, filepath, kn )
y_pred_train = None
y_pred_test = None
def do_cnn():
model1 = Models.create_cnn_model( pool_size=3, layer_size=128, output_size=len(y_train[0]) )
model1.fit(x_train, y_train, epochs=15, verbose=2, batch_size=32, class_weight=cweights)
y_pred_train = model1.predict( x_train, verbose=0 )
y_pred_test = model1.predict( x_test, verbose=0 )
model1 = None
do_cnn()
model2 = Models.create_ann_model( dropout=3, denseSize=512, output_length=len(y_train[0]) )
model2.fit(x_train, y_train, batch_size=64, verbose=2, epochs=30, class_weight=cweights)
y_pred_train2 = model2.predict( x_train, verbose=0 )
y_pred_test2 = model2.predict( x_test, verbose=0 )
model2 = None
x_train, x_test = Features.getCVvectors( x_train_raw, x_test_raw, data )
model3 = Models.create_ann_model( batch_size=64, denseSize=1024,
dropout=3, input_length=[len(x_train[0])],
output_length=[len(y_train[0])] )
model3.fit(x_train, y_train, epochs=100, class_weight=cweights)
y_pred_train3 = model3.predict( x_train, verbose=0 )
y_pred_test3 = model3.predict( x_test, verbose=0 )
new_x_train = np.stack( (y_pred_train, y_pred_train2, y_train_3 ), axis=-1)
new_x_test = np.stack( (y_pred_test, y_pred_test2, y_test_3 ), axis=-1)
model = Models.create_stack_model( input_size=len(new_x_train[0]), output_size=len(y_train[0]) )
history = model.fit(new_x_train, y_train, epochs=100, verbose=2, batch_size=128, class_weight=cweights )
model, scores = kn.evaluateModel( new_x_test, y_test, model, data, k )
k = k + 1
kn.saveResults( exp )
