def get_features_for_prediction(features, i, use_pca=False):
X_train, y_train, X_test, X_val, y_val = [],[],[],[],[]
for item in features:
## distinguish twitter glove and common glove
## distinguish deepmoji sum and avg
feature, ty, mode = featureAnalysis(item)
if feature=="glove" and ty=="twitter":
constant.emb_dim = 200
elif: feature=="emoji":
pass
else:
constant.emb_dim = 300
pass
pass
print(feature)
## prepare data for feature-10 folders
vocab = generate_vocab(include_test=True)
train, val, dev_no_lab = read_data(is_shuffle=True, random_state=i, dev_with_label=False, include_test=True)
## Add labels to dev_no_lab for getting features
ind = dev_no_lab[0]
X_text = dev_no_lab[1]
labels = ["others" for i in range(len(ind))]
dev = (ind, X_text, labels)
## feature_list: glove emoji elmo bert deepmoji emo2vec
## if you want twitter glove or common glove use ty='twitter' and ty='common'
print(ty)
Xi_train, yi_train = get_feature(train, vocab, feature_list=[feature], mode=[mode],split="final_train"+str(i),ty=[ty]) ## [29010,3,emb_size] 3 is number of sentence
# Xi_val, yi_val = get_feature(val, vocab, feature_list=[feature], mode=[mode],split="final_valid"+str(i),ty=[ty]) ## [1150,3,emb_size]
Xi_test, _ = get_feature(dev, vocab, feature_list=[feature], mode=[mode],split="final_test"+str(i),ty=[ty]) ## [2755,3,emb_size]
# Xi_train = np.concatenate((Xi_train, Xi_val), axis = 0)
# yi_train = np.concatenate((yi_train, yi_val), axis = 0)
if use_pca:
Xi_train, Xi_val, Xi_test = pca(Xi_train,Xi_val,Xi_test)
pass
# if feature == "bert":
# print(Xi_train.shape)
# Xi_train = np.squeeze(Xi_train,axis = 2)
# Xi_test = np.squeeze(Xi_test,axis = 2)
# Xi_val = np.squeeze(Xi_val,axis = 2)
# pass
if X_train==[]:
X_train = Xi_train
y_train = yi_train
X_test = Xi_test
# X_val = Xi_val
# y_val = yi_val
else:
X_train = np.concatenate((X_train, Xi_train), axis = 2)
X_test = np.concatenate((X_test, Xi_test), axis = 2)
# X_val = np.concatenate((X_val, Xi_val), axis = 2)
return X_train, y_train, X_val, y_val, X_test, ind, X_text
def get_single_feature_for_svm(feature, ty, i):
## prepare data for feature-10 folders
vocab = generate_vocab()
train, val, dev_no_lab = read_data(is_shuffle=True, random_state=i, dev_with_label=constant.dev_with_label, include_test=constant.include_test)
## feature_list: glove emoji elmo bert deepmoji emo2vec
## if you want twitter glove or common glove use ty='twitter' and ty='common'
X_train, y_train = get_feature(train, vocab, feature_list=[feature], mode=['sum'],split="train",ty=ty) ## [29010,3,emb_size] 3 is number of sentence
X_test, y_test = get_feature(val, vocab, feature_list=[feature], mode=['sum'],split="valid",ty=ty) ## [1150,3,emb_size]
X_train_reduced, X_test_reduced, _ = pca(X_train, X_test)
return X_train_reduced, y_train, X_test_reduced, y_test
def get_multi_features(features, i, emb_dim, use_pca=False):
X_train, y_train, X_test, y_test = [],[],[],[]
for item in features:
## distinguish twitter glove and common glove
## distinguish deepmoji sum and avg
feature, ty, mode = featureAnalysis(item)
if item == features[-2]:
constant.emb_dim = emb_dim[0]
elif item == features[-1]:
constant.emb_dim = emb_dim[1]
else:
constant.emb_dim = 300
print(feature)
## prepare data for feature-10 folders
vocab = generate_vocab()
train, val, dev_no_lab = read_data(is_shuffle=True, random_state=i, dev_with_label=constant.dev_with_label, include_test=constant.include_test)
## feature_list: glove emoji elmo bert deepmoji emo2vec
## if you want twitter glove or common glove use ty='twitter' and ty='common'
split_train = "merged_train"+str(i) if constant.include_test else "train"+str(i)
split_val = "merged_val"+str(i) if constant.include_test else "valid"+str(i)
print("Loading split", split_train)
Xi_train, yi_train = get_feature(train, vocab, feature_list=[feature], mode=[mode],split=split_train,ty=ty) ## [29010,3,emb_size] 3 is number of sentence
Xi_test, yi_test = get_feature(val, vocab, feature_list=[feature], mode=[mode],split=split_val,ty=ty) ## [1150,3,emb_size]
if use_pca:
Xi_train, Xi_test, _ = pca(Xi_train, Xi_test)
pass
if feature == "bert":
Xi_train = np.squeeze(Xi_train,axis = 2)
Xi_test = np.squeeze(Xi_test,axis = 2)
pass
if X_train==[]:
X_train = Xi_train
y_train = yi_train
X_test = Xi_test
y_test = yi_test
else:
X_train = np.concatenate((X_train, Xi_train), axis = 2)
X_test = np.concatenate((X_test, Xi_test), axis = 2)
return X_train, y_train, X_test, y_test
for j in range(1, 10):
c = j / 1000
model = get_classifier(ty='LR', c=c)
microF1s = 0
for i in range(constant.num_split):
## prepare data for feature-10 folders
vocab = generate_vocab()
train, val, dev_no_lab = read_data(is_shuffle=True, random_state=i)
## feature_list: glove emoji elmo bert deepmoji emo2vec
## if you want twitter glove or common glove use ty='twitter' and ty='common'
X_train, y_train = get_feature(
train,
vocab,
feature_list=[feature],
mode=['sum'],
split="train",
ty=ty) ## [29010,3,emb_size] 3 is number of sentence
X_test, y_test = get_feature(val,
vocab,
feature_list=[feature],
mode=['sum'],
split="valid",
ty=ty) ## [1150,3,emb_size]
print("###### EXPERIMENT %d when C equals to %f ######" %
((i + 1), c))
print("(EXPERIMENT %d) Create the model" % (i + 1))
## train aval and predict