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eval_HCNN-DOC.py
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eval_HCNN-DOC.py
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# Preprocessing
import os
import pickle
import pandas as pd
import numpy as np
from sklearn.preprocessing import LabelEncoder
from keras.utils import to_categorical
from utils import *
from models import HCNN, Metrics_HCNN
import sys
# Evaluation
from keras import backend as K
from keras.models import load_model, Model
from sklearn.metrics import f1_score, precision_score, recall_score, accuracy_score
from sklearn.metrics import confusion_matrix
from sklearn.neighbors import LocalOutlierFactor
from sklearn.svm import OneClassSVM
import pymysql.cursors
from tqdm import tqdm
dataset = 'SwDA'
proportion = int(sys.argv[1])
logger = create_logger('HCNN_w3')
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)
# Un-serialize
with open('data/df.pickle', 'rb') as handle:
df = pickle.load(handle)
with open('data/word_index.pickle', 'rb') as handle:
word_index = pickle.load(handle)
with open('data/X_train_0.pickle', 'rb') as handle:
X_train = pickle.load(handle)
with open('data/X_valid_0.pickle', 'rb') as handle:
X_valid = pickle.load(handle)
with open('data/X_test_0.pickle', 'rb') as handle:
X_test = pickle.load(handle)
with open('data/X_train_-1.pickle', 'rb') as handle:
X_train_n1 = pickle.load(handle)
with open('data/X_valid_-1.pickle', 'rb') as handle:
X_valid_n1 = pickle.load(handle)
with open('data/X_test_-1.pickle', 'rb') as handle:
X_test_n1 = pickle.load(handle)
with open('data/X_train_1.pickle', 'rb') as handle:
X_train_p1 = pickle.load(handle)
with open('data/X_valid_1.pickle', 'rb') as handle:
X_valid_p1 = pickle.load(handle)
with open('data/X_test_1.pickle', 'rb') as handle:
X_test_p1 = pickle.load(handle)
with open('data/X_train_2.pickle', 'rb') as handle:
X_train_p2 = pickle.load(handle)
with open('data/X_valid_2.pickle', 'rb') as handle:
X_valid_p2 = pickle.load(handle)
with open('data/X_test_2.pickle', 'rb') as handle:
X_test_p2 = pickle.load(handle)
with open('data/X_train_-2.pickle', 'rb') as handle:
X_train_n2 = pickle.load(handle)
with open('data/X_valid_-2.pickle', 'rb') as handle:
X_valid_n2 = pickle.load(handle)
with open('data/X_test_-2.pickle', 'rb') as handle:
X_test_n2 = pickle.load(handle)
with open('data/y_train.pickle', 'rb') as handle:
y_train = pickle.load(handle)
with open('data/y_valid.pickle', 'rb') as handle:
y_valid = pickle.load(handle)
with open('data/y_test.pickle', 'rb') as handle:
y_test = pickle.load(handle)
speaker_change_train = np.load('data/speaker_change_train_3.npy')
speaker_change_valid = np.load('data/speaker_change_valid_3.npy')
speaker_change_test = np.load('data/speaker_change_test_3.npy')
n_class = y_train.unique().shape[0]
n_class_seen = int(n_class * proportion/100)
for number in range(10):
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]
X_train_n1_seen = X_train_n1[train_seen_idx]
X_train_p1_seen = X_train_p1[train_seen_idx]
X_train_n2_seen = X_train_n2[train_seen_idx]
X_train_p2_seen = X_train_p2[train_seen_idx]
y_train_seen = y_train[train_seen_idx]
X_valid_seen = X_valid[valid_seen_idx]
X_valid_n1_seen = X_valid_n1[valid_seen_idx]
X_valid_p1_seen = X_valid_p1[valid_seen_idx]
X_valid_n2_seen = X_valid_n2[valid_seen_idx]
X_valid_p2_seen = X_valid_p2[valid_seen_idx]
y_valid_seen = y_valid[valid_seen_idx]
speaker_change_train_seen = speaker_change_train[train_seen_idx]
speaker_change_valid_seen = speaker_change_valid[valid_seen_idx]
le = LabelEncoder()
le.fit(y_train_seen)
y_train_idx = le.transform(y_train_seen)
y_train_onehot = to_categorical(y_train_idx)
y_valid_idx = le.transform(y_valid_seen)
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'
metrics_earlystop = Metrics_HCNN(logger)
targets_train = np.expand_dims(np.tile([0,0,1,0,0], (X_train_seen.shape[0],1)), axis=2)
targets_valid = np.expand_dims(np.tile([0,0,1,0,0], (X_valid_seen.shape[0],1)), axis=2)
targets_test = np.expand_dims(np.tile([0,0,1,0,0], (X_test.shape[0],1)), axis=2)
train_data = ([X_train_seen, X_train_n1_seen, X_train_p1_seen, X_train_n2_seen, X_train_p2_seen, speaker_change_train_seen, targets_train], y_train_onehot)
valid_data = ([X_valid_seen, X_valid_n1_seen, X_valid_p1_seen, X_valid_n2_seen, X_valid_p2_seen, speaker_change_valid_seen, targets_valid], y_valid_onehot)
test_data = ([X_test, X_test_n1, X_test_p1, X_test_n2, X_test_p2, speaker_change_test, targets_test], y_test_mask)
# Load model
model = load_model('data/HCNN-DOC_w3_' + 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()