def tpo_cv_cnn_other():
maxlen = 200
nb_words = 6500
filter_size = 20
k = 4
folds = range(1, 11)
trains = ['data/tpov4/train_'+str(fold)+'.csv' for fold in folds]
tests = ['data/tpov4/test_'+str(fold)+'.csv' for fold in folds]
tas_other = ['data/tpov4/train_'+str(fold)+'_other.csv' for fold in folds]
tss_other = ['data/tpov4/test_'+str(fold)+'_other.csv' for fold in folds]
pairs = zip(trains, tests, tas_other, tss_other)
accs = []
for (train, test, ta_other, ts_other) in pairs:
print(train + '=>' + test)
X_train, Y_train, X_test, Y_test, nb_classes = load_csvs(train, test,
nb_words, maxlen, embd_type='self', w2v=None)
Other_train = load_other(ta_other, maxlen, k)
Other_test = load_other(ts_other, maxlen, k)
acc = cnn_other(Y_train, Y_test, nb_classes,
Other_train, Other_test, k,
maxlen,
50, filter_size, 32, 25, 'rmsprop')
accs.append(acc)
acc_cv = np.mean(accs)
print('after 10-fold cv:' + str(acc_cv))
def keras_model():
import pandas as pd
import numpy as np
from keras.preprocessing import sequence
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Convolution1D, MaxPooling1D
from keras.callbacks import EarlyStopping
from keras.utils import np_utils
from data_util import load_csvs, load_other
import ml_metrics as metrics
nb_words = 6500
maxlen = 175
filter_length = 10
other_col_dim = 4
X_train, Y_train, X_test, Y_test, nb_classes = load_csvs('data/tpov4/train_1.csv',
'data/tpov4/test_1.csv',
nb_words, maxlen, 'self', w2v=None)
# read _other.csv
other_train = load_other('data/tpov4/train_1_other.csv', maxlen, other_col_dim)
other_test = load_other('data/tpov4/test_1_other.csv', maxlen, other_col_dim)
print('other tensor:', other_train.shape)
pool_length = maxlen - filter_length + 1
model = Sequential()
model.add(Convolution1D(nb_filter=50,
filter_length=filter_length,
border_mode="valid", activation="relu",
input_shape=(maxlen, other_col_dim)))
model.add(MaxPooling1D(pool_length=pool_length))
model.add(Flatten())
model.add(Dropout(0.05))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy',
optimizer={{choice(['rmsprop', 'adam', 'adadelta', 'adagrad'])}})
earlystop = EarlyStopping(monitor='val_loss', patience=1, verbose=1)
model.fit(other_train, Y_train, batch_size=32, nb_epoch=25,
validation_split=0.1, show_accuracy=True, callbacks=[earlystop])
classes = earlystop.model.predict_classes(other_test, batch_size=32)
org_classes = np_utils.categorical_probas_to_classes(Y_test)
acc = np_utils.accuracy(classes, org_classes) # accuracy only supports classes
print('Test accuracy:', acc)
kappa = metrics.quadratic_weighted_kappa(classes, org_classes)
print('Test Kappa:', kappa)
return {'loss': -acc, 'status': STATUS_OK}
"loading data..."
train_df = pd.read_csv('data/tpov4/train_1.csv')
test_df = pd.read_csv('data/tpov4/test_1.csv')
nb_words = 6500
maxlen = 175
embd_dim = 100
other_col_dim = 4
X_train, Y_train, X_test, Y_test, nb_classes = load_csvs('data/tpov4/train_1.csv',
'data/tpov4/test_1.csv',
nb_words, maxlen, 'self', w2v=None)
# read _other.csv
pos_train = load_other('data/tpov4/train_1_other.csv', maxlen, other_col_dim)
pos_test = load_other('data/tpov4/test_1_other.csv', maxlen, other_col_dim)
print('other tensor:', pos_train.shape)
# get #char to be a feature.
len_char_train = np.array([len(x.split()) for x in train_df.text.values.tolist()], dtype='float32')
len_char_test = np.array([len(x.split()) for x in test_df.text.values.tolist()], dtype='float32')
# normalize
len_max = np.max(len_char_train)
len_char_train /= len_max
len_char_test /= len_max
# IMPORTANT! reshape to make sure dim=2 (#sample, 1)
len_char_train = len_char_train.reshape(len_char_train.shape[0], 1)
