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import scrapetube
import random
import pandas as pd

AllVideoList = []
videos = scrapetube.get_channel("UCJIfeSCssxSC_Dhc5s7woww") # channel ID

titleList = []
urlList = []
while True:

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from bayes_opt import BayesianOptimization
from bayes_opt.logger import JSONLogger
from bayes_opt.event import Events

logger = JSONLogger(path="./tau_logs.json")


pbounds = {'tau1': (0.1, 0.2), 'tau2': (0.3, 0.4),
           'tau3': (0.5, 0.6), 'tau4': (0.7, 0.8),
           }

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# ref: https://blog.csdn.net/MachineLearner/article/details/104587288
def plot_decision_boundary(pred_func, X, y, figure=None):
    """Plot a decision boundary"""

    if figure is None:  # If no figure is given, create a new one
        plt.figure()
    # Set min and max values and give it some padding
    x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
    y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
    h = 0.01

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# 無重複數字
# input: name_list = [0, 1, 2, 3, 8]
for idx, num in enumerate(set(name_list)):
  if idx != num:
    name_list[idx] = idx
# output: name_list = [0, 1, 2, 3, 4]

# 有重複數字
# input: name_list = [0, 2, 2, 1, 6, 6]
for idx, num in enumerate(set(name_list)):

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# evaluate the clustering performance
from sklearn.cluster import KMeans
from sklearn.metrics.cluster import normalized_mutual_info_score
import numpy as np

def evaluation(X, Y):
    classN = np.max(Y)+1
    kmeans = KMeans(n_clusters=classN).fit(X)
    nmi = normalized_mutual_info_score(Y, kmeans.labels_, average_method='arithmetic')
    return nmi

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# https://stackoverflow.com/questions/63761717/load-image-dataset
# https://stackoverflow.com/questions/60655280/how-to-split-an-image-dataset-in-x-train-y-train-x-test-y-test-by-tensorflow

import tensorflow as tf
import pandas as pd

train_df = pd.read_csv('train.csv')
train_df['class'] = train_df['class'].apply(str)

train_datagen = tf.keras.preprocessing.image.ImageDataGenerator(horizontal_flip=True, vertical_flip=True,)

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import numpy as np
from sklearn import metrics
from sklearn.metrics import roc_auc_score
y = np.array([1, 1, 2, 2])
#scores可以是模型預測結果(Label)
#scores也可以是模型預測的confidence(softmax probability)
scores = np.array([1, 1, 2, 2])
scores = np.array([0.1, 0.4, 0.35, 0.8])
area_under_curve = roc_auc_score(y, scores)

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 def eval(loader, gt_labels_t, output_file="output.txt"):
    G.eval()  # 特徵提取器
    F1.eval() # 分類器
    size = 0
    correct = 0
    
    y_pred=[]
    y_true=[]
    pred_prob = None
    pred_result = None

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import torch.nn.functional as F
import torch
import numpy as np
from torch.autograd import Function
import torch.nn as nn
from pdb import set_trace as breakpoint
import sys
import math
from torch.nn.parameter import Parameter
from torch.nn import init

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# LabelSmoothing.py

# https://www.aiuai.cn/aifarm1333.html 示例 3
# From: Github - NVIDIA/DeepLearningExamples/PyTorch/Classification
# smoothing.py
import torch
import torch.nn as nn

# 一般版本LabelSmoothing
class LabelSmoothing(nn.Module):