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eval_BiLSTM-DOC.py
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eval_BiLSTM-DOC.py
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# Preprocessing
import numpy as np
import pandas as pd
from nltk.tokenize import word_tokenize
from sklearn.preprocessing import LabelEncoder
from keras.utils import to_categorical
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
import pickle
import sys
# Modeling
from keras.models import Model
from keras import backend as K
import os
# Evaluation
from keras.models import load_model
from sklearn.metrics import confusion_matrix
from utils import *
from sklearn.svm import OneClassSVM
from tqdm import tqdm
from sklearn.neighbors import LocalOutlierFactor
import pymysql.cursors
# GPU setting
dataset = sys.argv[1]
proportion = int(sys.argv[2])
logger = create_logger('BiLSTM_' + dataset)
os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3"
if proportion==25:
gpu_id = "0"
elif proportion==50:
gpu_id = "2"
elif proportion==75:
gpu_id = "3"
set_allow_growth(gpu_id)
df, partition_to_n_row = load_single(dataset)
df['content_words'] = df['text'].apply(lambda s: word_tokenize(s))
df['words_len'] = df['content_words'].apply(lambda s: len(s))
texts = df['content_words'].tolist()
MAX_SEQ_LEN = None
MAX_NUM_WORDS = 10000
# filters without "," and "."
tokenizer = Tokenizer(num_words=MAX_NUM_WORDS, oov_token="<UNK>", filters='!"#$%&()*+-/:;<=>@[\]^_`{|}~')
tokenizer.fit_on_texts(texts)
word_index = tokenizer.word_index
sequences = tokenizer.texts_to_sequences(texts)
sequences_pad = pad_sequences(sequences, maxlen=MAX_SEQ_LEN, padding='post', truncating='post')
idx_train = (None, partition_to_n_row['train'])
idx_valid = (partition_to_n_row['train'], partition_to_n_row['train'] + partition_to_n_row['valid'])
idx_test = (partition_to_n_row['train'] + partition_to_n_row['valid'], None)
X_train = sequences_pad[idx_train[0]:idx_train[1]]
X_valid = sequences_pad[idx_valid[0]:idx_valid[1]]
X_test = sequences_pad[idx_test[0]:idx_test[1]]
df_train = df[idx_train[0]:idx_train[1]]
df_valid = df[idx_valid[0]:idx_valid[1]]
df_test = df[idx_test[0]:idx_test[1]]
y_train = df_train.label.reset_index(drop=True)
y_valid = df_valid.label.reset_index(drop=True)
y_test = df_test.label.reset_index(drop=True)
n_class = y_train.unique().shape[0]
n_class_seen = round(n_class * proportion/100)
for number in range(10):
print("start:", dataset, proportion, number)
with open('data/y_cols_' + dataset + "_" + str(proportion) + '_' + str(number) + '.pickle', 'rb') as handle:
d = pickle.load(handle)
y_cols_seen = d['y_cols_seen']
y_cols_unseen = d['y_cols_unseen']
print(y_cols_seen)
train_seen_idx = y_train[y_train.isin(y_cols_seen)].index
valid_seen_idx = y_valid[y_valid.isin(y_cols_seen)].index
X_train_seen = X_train[train_seen_idx]
y_train_seen = y_train[train_seen_idx]
X_valid_seen = X_valid[valid_seen_idx]
y_valid_seen = y_valid[valid_seen_idx]
le = LabelEncoder()
le.fit(y_train_seen)
y_train_idx = le.transform(y_train_seen)
y_valid_idx = le.transform(y_valid_seen)
y_train_onehot = to_categorical(y_train_idx)
y_valid_onehot = to_categorical(y_valid_idx)
y_test_mask = y_test.copy()
y_test_mask[y_test_mask.isin(y_cols_unseen)] = 'unseen'
train_data = (X_train_seen, y_train_onehot)
valid_data = (X_valid_seen, y_valid_onehot)
test_data = (X_test, y_test_mask)
# Load model
model = load_model('data/BiLSTM-DOC_' + dataset + "_" + str(proportion) + '_' + str(number) + '.h5')
y_pred_proba = model.predict(test_data[0])
y_pred_proba_train = model.predict(train_data[0])
classes = list(le.classes_) + ['unseen']
d_result = {
'all': defaultdict(dict),
'seen': defaultdict(dict),
'unseen': defaultdict(dict),
}
method = "2DOC"
df_seen = pd.DataFrame(y_pred_proba, columns=le.classes_)
df_seen_train = pd.DataFrame(y_pred_proba_train, columns=le.classes_)
df_seen_train['y_true'] = y_train_seen.values
col_to_threshold = {}
alpha = 2
for col in y_cols_seen:
tmp = df_seen_train[df_seen_train['y_true']==col][[col, 'y_true']]
tmp = np.hstack([tmp[col], 2-tmp[col]])
threshold = 1 - alpha*tmp.std()
col_to_threshold[col] = threshold
col_to_threshold = {k: max([0.5, v])for k, v in col_to_threshold.items()}
masks = [df_seen[col]<threshold for col, threshold in col_to_threshold.items()]
is_reject = masks[0]
for mask in masks:
is_reject &= mask
df_seen['unseen'] = is_reject.astype(int)
y_pred = df_seen.idxmax(axis=1)
cm = confusion_matrix(test_data[1], y_pred, classes)
f, d_result = get_score(cm, d_result, method)
# Save the result
results = []
for part, d in d_result.items():
for method, score in d.items():
results.append([dataset, proportion, number, part, method, float(score)])
connection = pymysql.connect(host='localhost', user='root', password='', db='KBS',
charset='utf8mb4', cursorclass=pymysql.cursors.DictCursor)
with connection.cursor() as cursor:
# Create a new record
sql = "INSERT INTO `result` (`dataset`, `proportion`, `number`, `part`, `method`, `score`) VALUES (%s, %s, %s, %s, %s, %s)"
for result in results:
cursor.execute(sql, result)
connection.commit()
connection.close()