作者 |THILAKADIBOINA
编译 |Flin
起源 |analyticsvidhya
介绍
本文介绍了生成性反抗网络（Generative attersarial Networks，GAN）的应用，这是一种对实在的 Covid-19 数据进行过采样的技术，用于预测死亡率。这个故事让咱们更好地了解数据筹备步骤（如解决不均衡的数据）如何进步模型性能。
本文的数据和外围模型来自 Celestine Iwendi、Ali Kashif Bashir、Atharva Peshkar 最近的一项钻研（2020 年 7 月）“应用加强随机森林算法预测 COVID-19 患者衰弱”。本钻研应用 ADABOST 模型加强的随机森林算法预测个体患者的死亡率，准确率为 94%。本文思考雷同的模型和模型参数，明确剖析了采纳基于 GAN 的过采样技术对现有模型的改良。
对于有抱负的数据科学家来说，学习良好实际的最好办法之一就是加入不同论坛上的黑客比赛，比方 Vidhya、Kaggle 或其余论坛。
此外，从这些论坛或出版的钻研出版物中获取已解决的案例和数据；理解他们的办法，并尝试通过额定的步骤来进步准确性或缩小误差。这将造成一个松软的根底，使咱们可能深刻思考咱们在数据迷信价值链中所学的其余技术的利用。
钻研中应用的数据是用 222 个病人的 13 个特色来训练的。数据有偏差，159 例（72%）属于“0”类或“已复原”类。因为其偏差性质，各种欠采样 / 过采样技术可利用于数据。偏态数据的问题会导致预测模型的适度拟合。
为了克服这一局限性，许多钻研采纳过采样办法来均衡数据集，从而失去更准确的模型训练。过采样是一种通过减少多数数据中的样本数量来弥补数据集不均衡的技术。
惯例办法包含随机过采样（ROS）、合成多数过采样技术（SMOTE）等。无关应用惯例办法解决不均衡类的更多信息，请参阅：
- https://www.analyticsvidhya.c…
最近，一种基于对抗性学习概念的生成性网络的机器学习模型被提出，即生成性对抗性网络。生成性反抗网络（Generative atterial Networks，GAN）的特点使其较易利用于过采样钻研，因为基于反抗训练的神经网络的性质容许生成与原始数据类似的人工数据。基于生成性反抗网络的过采样克服了传统办法（如过拟合）的局限性，容许建设一个高精度的不均衡数据预测模型。
如何生成合成数据？
两个神经网络相互竞争，学习指标散布并生成人工数据
发生器网络 G：模仿训练样本坑骗鉴别器
判断网络 D：判断训练样本和生成样本
生成性对抗性网络是基于博弈论的场景，其中生成网络必须与对手竞争。随着 GAN 学会模仿数据的散布，它被利用于各个领域，如音乐、视频和自然语言，最近还用于解决不均衡的数据问题。
钻研中应用的数据和根本模型能够在这里找到
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import tensorflow as tf
from keras.layers import Input, Dense, Reshape, Flatten, Dropout, BatchNormalization, Embedding
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.merge import concatenate
from keras.models import Sequential, Model
from keras.optimizers import Adam
from keras.utils import to_categorical
from keras.layers.advanced_activations import LeakyReLU
from keras.utils.vis_utils import plot_model
from sklearn.preprocessing import MinMaxScaler, OneHotEncoder, LabelEncoder
import scipy.stats
import datetime as dt
import pydot
import warnings
warnings.filterwarnings("ignore")
%matplotlib inline
df = pd.read_csv('Covid_Train_Oct32020.csv')
df = df.drop('id',axis=1)
df = df.fillna(np.nan,axis=0)
df['age'] = df['age'].fillna(value=df['age'].mean())
df['sym_on'] = pd.to_datetime(df['sym_on'])
df['hosp_vis'] = pd.to_datetime(df['hosp_vis'])
df['sym_on']= df['sym_on'].map(dt.datetime.toordinal)
df['hosp_vis']= df['hosp_vis'].map(dt.datetime.toordinal)
df['diff_sym_hos']= df['hosp_vis'] - df['sym_on']
df=df.drop(['sym_on', 'hosp_vis'], axis=1)
df['location'] = df['location'].astype(str)
df['country'] = df['country'].astype(str)
df['gender'] = df['gender'].astype(str)
df['vis_wuhan'] = df['vis_wuhan'].astype(str)
df['from_wuhan'] = df['from_wuhan'].astype(str)
df['symptom1'] = df['symptom1'].astype(str)
df['symptom2'] = df['symptom2'].astype(str)
df['symptom3'] = df['symptom3'].astype(str)
df['symptom4'] = df['symptom4'].astype(str)
df['symptom5'] = df['symptom5'].astype(str)
df['symptom6'] = df['symptom6'].astype(str)
df.dtypes数据阐明
| 列 | 形容 | 值（用于分类变量） | 类型 |
| id | 患者编号 | 不实用 | 数字 |
| location | 患者所属的地位 | 遍布寰球的多个城市 | 字符串，分类 |
| country | 患者的国家 | 多个国家 | 字符串，分类 |
| gender | 患者性别 | 男，女 | 字符串，分类 |
| age | 患者年龄 | 不实用 | 数字 |
| sym_on | 患者开始留神到症状的日期 | 不实用 | 日期 |
| hosp_vis | 病人去医院的日期 | 不实用 | 日期 |
| vis_wuhan | 患者是否去过中国武汉 | 是（1），否（0） | 数值，分类 |
| from_wuhan | 患者是否属于中国武汉 | 是（1），否（0） | 数值，分类 |
| death | 患者是否因 COVID-19 而逝世 | 是（1），否（0） | 数值，分类 |
| Recov | 患者是否痊愈 | 是（1），否（0） | 数值，分类 |
| symptom1. symptom2, symptom3, symptom4, symptom5, symptom6 | 患者留神到的症状 | 患者留神到多种症状 | 字符串，分类 |
该钻研思考了 11 个分类输出特色和 2 个数字输出特色。指标变量是死亡 / 复原。已填充新列“diff_sym_hos”，以提供当天在医院发现和承受的症状之间的差别。
钻研的重点是改善多数类别数据，即死亡 == 1，从训练数据中提取了一个子集。子集按类别和数字离开，并传递给 GAN 模型。
df_minority_data=df.loc[df['death'] == 1]
#Subsetting input features without target variable
df_minority_data_withouttv=df_minority_data.loc[:, df_minority_data.columns != 'death']
numerical_df = df_minority_data_withouttv.select_dtypes("number")
categorical_df = df_minority_data_withouttv.select_dtypes("object")
scaling = MinMaxScaler()
numerical_df_rescaled = scaling.fit_transform(numerical_df)
get_dummy_df = pd.get_dummies(categorical_df)
#Seperating Each Category
location_dummy_col = [col for col in get_dummy_df.columns if 'location' in col]
location_dummy = get_dummy_df[location_dummy_col]
country_dummy_col = [col for col in get_dummy_df.columns if 'country' in col]
country_dummy = get_dummy_df[country_dummy_col]
gender_dummy_col = [col for col in get_dummy_df.columns if 'gender' in col]
gender_dummy = get_dummy_df[gender_dummy_col]
vis_wuhan_dummy_col = [col for col in get_dummy_df.columns if 'vis_wuhan' in col]
vis_wuhan_dummy = get_dummy_df[vis_wuhan_dummy_col]
from_wuhan_dummy_col = [col for col in get_dummy_df.columns if 'from_wuhan' in col]
from_wuhan_dummy = get_dummy_df[from_wuhan_dummy_col]
symptom1_dummy_col = [col for col in get_dummy_df.columns if 'symptom1' in col]
symptom1_dummy = get_dummy_df[symptom1_dummy_col]
symptom2_dummy_col = [col for col in get_dummy_df.columns if 'symptom2' in col]
symptom2_dummy = get_dummy_df[symptom2_dummy_col]
symptom3_dummy_col = [col for col in get_dummy_df.columns if 'symptom3' in col]
symptom3_dummy = get_dummy_df[symptom3_dummy_col]
symptom4_dummy_col = [col for col in get_dummy_df.columns if 'symptom4' in col]
symptom4_dummy = get_dummy_df[symptom4_dummy_col]
symptom5_dummy_col = [col for col in get_dummy_df.columns if 'symptom5' in col]
symptom5_dummy = get_dummy_df[symptom5_dummy_col]
symptom6_dummy_col = [col for col in get_dummy_df.columns if 'symptom6' in col]
symptom6_dummy = get_dummy_df[symptom6_dummy_col]
定义生成器
生成器从潜在空间获取输出并生成新的合成样本。泄露修改线性单元（LeakyReLU）是在发生器和鉴别器模型中用于解决某些负值的函数。
它应用默认倡议值 0.2 和适当的权重初始化程序“he_uniform”应用。此外，在不同的层之间应用批处理归一化来标准化来自先前层的激活（零均值和单位方差）并稳固训练过程。
在输入层中，softmax 激活函数用于分类变量，而 sigmoid 函数用于连续变量。
def define_generator (catsh1,catsh2,catsh3,catsh4,catsh5,catsh6,catsh7,catsh8,catsh9,catsh10,catsh11,numerical):
#Inputting noise from latent space
noise = Input(shape = (70,))
hidden_1 = Dense(8, kernel_initializer = "he_uniform")(noise)
hidden_1 = LeakyReLU(0.2)(hidden_1)
hidden_1 = BatchNormalization(momentum = 0.8)(hidden_1)
hidden_2 = Dense(16, kernel_initializer = "he_uniform")(hidden_1)
hidden_2 = LeakyReLU(0.2)(hidden_2)
hidden_2 = BatchNormalization(momentum = 0.8)(hidden_2)
#Branch 1 for generating location data
branch_1 = Dense(32, kernel_initializer = "he_uniform")(hidden_2)
branch_1 = LeakyReLU(0.2)(branch_1)
branch_1 = BatchNormalization(momentum = 0.8)(branch_1)
branch_1 = Dense(64, kernel_initializer = "he_uniform")(branch_1)
branch_1 = LeakyReLU(0.2)(branch_1)
branch_1 = BatchNormalization(momentum=0.8)(branch_1)
#Output Layer1
branch_1_output = Dense(catsh1, activation = "softmax")(branch_1)
#Likewise, for all remaining 10 categories branches will be defined
#Branch 12 for generating numerical data
branch_12 = Dense(64, kernel_initializer = "he_uniform")(hidden_2)
branch_12 = LeakyReLU(0.2)(branch_3)
branch_12 = BatchNormalization(momentum=0.8)(branch_12)
branch_12 = Dense(128, kernel_initializer = "he_uniform")(branch_12)
branch_12 = LeakyReLU(0.2)(branch_12)
branch_12 = BatchNormalization(momentum=0.8)(branch_12)
#Output Layer12
branch_12_output = Dense(numerical, activation = "sigmoid")(branch_12)
#Combined output
combined_output = concatenate([branch_1_output, branch_2_output, branch_3_output,branch_4_output,branch_5_output,branch_6_output,branch_7_output,branch_8_output,branch_9_output,branch_10_output,branch_11_output,branch_12_output])
#Return model
return Model(inputs = noise, outputs = combined_output)
generator = define_generator(location_dummy.shape[1],country_dummy.shape[1],gender_dummy.shape[1],vis_wuhan_dummy.shape[1],from_wuhan_dummy.shape[1],symptom1_dummy.shape[1],symptom2_dummy.shape[1],symptom3_dummy.shape[1],symptom4_dummy.shape[1],symptom5_dummy.shape[1],symptom6_dummy.shape[1],numerical_df_rescaled.shape[1])
generator.summary()定义鉴别器
鉴别器模型将从咱们的数据（例如矢量）中获取样本，并输入对于样本是实在还是假的分类预测。这是一个二进制分类问题，因而在输入层中应用 sigmoid 激活函数，在模型编译中应用二进制穿插熵损失函数。应用学习率 LR 为 0.0002 且倡议的 beta1 动量值为 0.5 的 Adam 优化算法。
def define_discriminator(inputs_n):
#Input from generator
d_input = Input(shape = (inputs_n,))
d = Dense(128, kernel_initializer="he_uniform")(d_input)
d = LeakyReLU(0.2)(d)
d = Dense(64, kernel_initializer="he_uniform")(d)
d = LeakyReLU(0.2)(d)
d = Dense(32, kernel_initializer="he_uniform")(d)
d = LeakyReLU(0.2)(d)
d = Dense(16, kernel_initializer="he_uniform")(d)
d = LeakyReLU(0.2)(d)
d = Dense(8, kernel_initializer="he_uniform")(d)
d = LeakyReLU(0.2)(d)
#Output Layer
d_output = Dense(1, activation = "sigmoid")(d)
#compile and return model
model = Model(inputs = d_input, outputs = d_output)
model.compile(loss = "binary_crossentropy", optimizer = Adam(lr=0.0002, beta_1=0.5), metrics = ["accuracy"])
return model
inputs_n = location_dummy.shape[1]+country_dummy.shape[1]+gender_dummy.shape[1]+vis_wuhan_dummy.shape[1]+from_wuhan_dummy.shape[1]+symptom1_dummy.shape[1]+symptom2_dummy.shape[1]+symptom3_dummy.shape[1]+symptom4_dummy.shape[1]+symptom5_dummy.shape[1]+symptom6_dummy.shape[1]+numerical_df_rescaled.shape[1]
discriminator = define_discriminator(inputs_n)
discriminator.summary()将生成器和鉴别器组合为 GAN 模型并实现训练。思考了 7,000 个期间，并思考了残缺的少数派训练数据。
Def define_complete_gan(generator, discriminator):
discriminator.trainable = False
gan_output = discriminator(generator.output)
#Initialize gan
model = Model(inputs = generator.input, outputs = gan_output)
#Model Compilation
model.compile(loss = "binary_crossentropy", optimizer = Adam(lr=0.0002, beta_1=0.5))
return model
completegan = define_complete_gan(generator, discriminator)
def gan_train(gan, generator, discriminator, catsh1,catsh2,catsh3,catsh4,catsh5,catsh6,catsh7,catsh8,catsh9,catsh10,catsh11,numerical, latent_dim, n_epochs, n_batch, n_eval):
#Upddte Discriminator with half batch size
half_batch = int(n_batch / 2)
discriminator_loss = []
generator_loss = []
#generate class labels for fake and real
valid = np.ones((half_batch, 1))
y_gan = np.ones((n_batch, 1))
fake = np.zeros((half_batch, 1))
#training
for i in range(n_epochs):
#select random batch from real categorical and numerical data
idx = np.random.randint(0, catsh1.shape[0], half_batch)
location_real = catsh1[idx]
country_real = catsh2[idx]
gender_real = catsh3[idx]
vis_wuhan_real = catsh4[idx]
from_wuhan_real = catsh5[idx]
symptom1_real = catsh6[idx]
symptom2_real = catsh7[idx]
symptom3_real = catsh8[idx]
symptom4_real = catsh9[idx]
symptom5_real = catsh10[idx]
symptom6_real = catsh11[idx]
numerical_real = numerical_df_rescaled[idx]
#concatenate categorical and numerical data for the discriminator
real_data = np.concatenate([location_real, country_real, gender_real,vis_wuhan_real,from_wuhan_real,symptom1_real,symptom2_real,symptom3_real,symptom4_real,symptom5_real,symptom6_real,numerical_real], axis = 1)
#generate fake samples from the noise
noise = np.random.normal(0, 1, (half_batch, latent_dim))
fake_data = generator.predict(noise)
#train the discriminator and return losses and acc
d_loss_real, da_real = discriminator.train_on_batch(real_data, valid)
d_loss_fake, da_fake = discriminator.train_on_batch(fake_data, fake)
d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
discriminator_loss.append(d_loss)
#generate noise for generator input and train the generator (to have the discriminator label samples as valid)
noise = np.random.normal(0, 1, (n_batch, latent_dim))
g_loss = gan.train_on_batch(noise, y_gan)
generator_loss.append(g_loss)
#evaluate progress
if (i+1) % n_eval == 0:
print ("Epoch: %d [Discriminator loss: %f] [Generator loss: %f]" % (i + 1, d_loss, g_loss))
plt.figure(figsize = (20, 10))
plt.plot(generator_loss, label = "Generator loss")
plt.plot(discriminator_loss, label = "Discriminator loss")
plt.title("Stats from training GAN")
plt.grid()
plt.legend()
latent_dim = 100
gan_train(completegan, generator, discriminator, location_dummy.values,country_dummy.values,gender_dummy.values,vis_wuhan_dummy.values,from_wuhan_dummy.values,symptom1_dummy.values,symptom2_dummy.values,symptom3_dummy.values,symptom4_dummy.values,symptom5_dummy.values,symptom6_dummy.values,numerical_df_rescaled, latent_dim, n_epochs = 7000, n_batch = 63, n_eval = 200)
训练后的模型用于生成少数类的其余 96 条记录，以对每个类进行均等宰割（159）。当初将生成的数值数据与原始数据的均值，标准差和方差进行比拟；并依据每个类别的计数比拟类别数据。
noise = np.random.normal(0, 1, (96, 100))
generated_mixed_data = generator.predict(noise)
columns=list(location_dummy.columns)+list(country_dummy.columns)+list(gender_dummy.columns)+list(vis_wuhan_dummy.columns)+list(from_wuhan_dummy.columns)+list(symptom1_dummy.columns)+list(symptom2_dummy.columns)+list(symptom3_dummy.columns)+list(symptom4_dummy.columns)+list(symptom5_dummy.columns)+list(symptom6_dummy.columns)+list(numerical_df.columns)
mixed_gen_df = pd.DataFrame(data = generated_mixed_data, columns = columns)
mixed_gen_df.iloc[:,:-3] = np.round(mixed_gen_df.iloc[:,:-3])
mixed_gen_df.iloc[:,-2:] = scaling.inverse_transform(mixed_gen_df.iloc[:,-2:])
#Original Data
original_df = pd.concat([location_dummy,country_dummy,gender_dummy,vis_wuhan_dummy,from_wuhan_dummy,symptom1_dummy,symptom2_dummy,symptom3_dummy,symptom4_dummy,symptom5_dummy,symptom6_dummy,numerical_df], axis = 1)
def normal_distribution(org, noise):
org_x = np.linspace(org.min(), org.max(), len(org))
noise_x = np.linspace(noise.min(), noise.max(), len(noise))
org_y = scipy.stats.norm.pdf(org_x, org.mean(), org.std())
noise_y = scipy.stats.norm.pdf(noise_x, noise.mean(), noise.std())
n, bins, patches = plt.hist([org, noise], density = True, alpha = 0.5, color = ["green", "red"])
xmin, xmax = plt.xlim()
plt.plot(org_x, org_y, color = "green", label = "Original data", alpha = 0.5)
plt.plot(noise_x, noise_y, color = "red", label = "Generated data", alpha = 0.5)
title = f"Original data mean {np.round(org.mean(), 4)}, Original data std {np.round(org.std(), 4)}, Original data var {np.round(org.var(), 4)}\nGenerated data mean {np.round(noise.mean(), 4)}, Generated data {np.round(noise.std(), 4)}, Generated data var {np.round(noise.var(), 2)}"
plt.title(title)
plt.legend()
plt.grid()
plt.show()
Numeric_columns=numerical_df.columns
for column in numerical_df.columns:
print(column, "Comparison between Original Data and Generated Data")
normal_distribution(original_df
, mixed_gen_df
)原始数据和生成数据之间的年龄比拟
原始数据与生成的数据之间的比拟
原始数据和生成的数据之间的类别比拟
特色 |
原始数据 | 产生的数据 | ||
| 0 | 1 | 0 | 1 | |
| location_Hokkaido | 61 | 2 | 95 | 1 |
| gender_female | 49 | 14 | 60 | 36 |
| symptom2_ cough | 62 | 1 | 96 | 0 |
GAN 过采样办法生成的数据简直相似于原始数据，原始数据的误差约为 1%。对于一些罕见类别，不会在所有类别值上生成数据。
遵循与原始钻研中提到的雷同的数据筹备步骤，以查看通过应用 GAN 超采样与原始办法相比模型性能如何进步。所生成样本的独热编码数据被转换为原始数据帧格局。
# Getting Back Categorical Data in Original_Format from Dummies
location_filter_col = [col for col in mixed_gen_df if col.startswith('location')]
location=mixed_gen_df[location_filter_col]
location= pd.get_dummies(location).idxmax(1)
location= location.replace('location_', '', regex=True)
df_generated_data = pd.DataFrame()
df_generated_data['location']=location
country_filter_col = [col for col in mixed_gen_df if col.startswith('country')]
country=mixed_gen_df[country_filter_col]
country= pd.get_dummies(country).idxmax(1)
country= country.replace('country_', '', regex=True)
df_generated_data['country']=country
gender_filter_col = [col for col in mixed_gen_df if col.startswith('gender')]
gender=mixed_gen_df[gender_filter_col]
gender= pd.get_dummies(gender).idxmax(1)
gender= gender.replace('gender_', '', regex=True)
df_generated_data['gender']=gender
vis_wuhan_filter_col = [col for col in mixed_gen_df if col.startswith('vis_wuhan')]
vis_wuhan=mixed_gen_df[vis_wuhan_filter_col]
vis_wuhan= pd.get_dummies(vis_wuhan).idxmax(1)
vis_wuhan= vis_wuhan.replace('vis_wuhan_', '', regex=True)
df_generated_data['vis_wuhan']=vis_wuhan
from_wuhan_filter_col = [col for col in mixed_gen_df if col.startswith('from_wuhan')]
from_wuhan=mixed_gen_df[from_wuhan_filter_col]
from_wuhan= pd.get_dummies(from_wuhan).idxmax(1)
from_wuhan= from_wuhan.replace('from_wuhan_', '', regex=True)
df_generated_data['from_wuhan']=from_wuhan
symptom1_filter_col = [col for col in mixed_gen_df if col.startswith('symptom1')]
symptom1=mixed_gen_df[symptom1_filter_col]
symptom1= pd.get_dummies(symptom1).idxmax(1)
symptom1= symptom1.replace('symptom1_', '', regex=True)
df_generated_data['symptom1']=symptom1
symptom2_filter_col = [col for col in mixed_gen_df if col.startswith('symptom2')]
symptom2=mixed_gen_df[symptom2_filter_col]
symptom2= pd.get_dummies(symptom2).idxmax(1)
symptom2= symptom2.replace('symptom2_', '', regex=True)
df_generated_data['symptom2']=symptom2
symptom3_filter_col = [col for col in mixed_gen_df if col.startswith('symptom3')]
symptom3=mixed_gen_df[symptom3_filter_col]
symptom3= pd.get_dummies(symptom3).idxmax(1)
symptom3= symptom3.replace('symptom3_', '', regex=True)
df_generated_data['symptom3']=symptom3
symptom4_filter_col = [col for col in mixed_gen_df if col.startswith('symptom4')]
symptom4=mixed_gen_df[symptom4_filter_col]
symptom4= pd.get_dummies(symptom4).idxmax(1)
symptom4= symptom4.replace('symptom4_', '', regex=True)
df_generated_data['symptom4']=symptom4
symptom5_filter_col = [col for col in mixed_gen_df if col.startswith('symptom5')]
symptom5=mixed_gen_df[symptom5_filter_col]
symptom5= pd.get_dummies(symptom5).idxmax(1)
symptom5= symptom5.replace('symptom5_', '', regex=True)
df_generated_data['symptom5']=symptom5
symptom6_filter_col = [col for col in mixed_gen_df if col.startswith('symptom6')]
symptom6=mixed_gen_df[symptom6_filter_col]
symptom6= pd.get_dummies(symptom6).idxmax(1)
symptom6= symptom6.replace('symptom6_', '', regex=True)
df_generated_data['symptom6']=symptom6
df_generated_data['death']=1
df_generated_data['death']=1
df_generated_data[['age','diff_sym_hos']]=mixed_gen_df[['age','diff_sym_hos']]
df_generated_data = df_generated_data.fillna(np.nan,axis=0)
#Encoding Data
encoder_location = preprocessing.LabelEncoder()
encoder_country = preprocessing.LabelEncoder()
encoder_gender = preprocessing.LabelEncoder()
encoder_symptom1 = preprocessing.LabelEncoder()
encoder_symptom2 = preprocessing.LabelEncoder()
encoder_symptom3 = preprocessing.LabelEncoder()
encoder_symptom4 = preprocessing.LabelEncoder()
encoder_symptom5 = preprocessing.LabelEncoder()
encoder_symptom6 = preprocessing.LabelEncoder()
# Loading and Preparing Data
df = pd.read_csv('Covid_Train_Oct32020.csv')
df = df.drop('id',axis=1)
df = df.fillna(np.nan,axis=0)
df['age'] = df['age'].fillna(value=tdata['age'].mean())
df['sym_on'] = pd.to_datetime(df['sym_on'])
df['hosp_vis'] = pd.to_datetime(df['hosp_vis'])
df['sym_on']= df['sym_on'].map(dt.datetime.toordinal)
df['hosp_vis']= df['hosp_vis'].map(dt.datetime.toordinal)
df['diff_sym_hos']= df['hosp_vis'] - df['sym_on']
df = df.drop(['sym_on','hosp_vis'],axis=1)
df['location'] = encoder_location.fit_transform(df['location'].astype(str))
df['country'] = encoder_country.fit_transform(df['country'].astype(str))
df['gender'] = encoder_gender.fit_transform(df['gender'].astype(str))
df[['symptom1']] = encoder_symptom1.fit_transform(df['symptom1'].astype(str))
df[['symptom2']] = encoder_symptom2.fit_transform(df['symptom2'].astype(str))
df[['symptom3']] = encoder_symptom3.fit_transform(df['symptom3'].astype(str))
df[['symptom4']] = encoder_symptom4.fit_transform(df['symptom4'].astype(str))
df[['symptom5']] = encoder_symptom5.fit_transform(df['symptom5'].astype(str))
df[['symptom6']] = encoder_symptom6.fit_transform(df['symptom6'].astype(str))
# Encoding Generated Data
df_generated_data['location'] = encoder_location.transform(df_generated_data['location'].astype(str))
df_generated_data['country'] = encoder_country.transform(df_generated_data['country'].astype(str))
df_generated_data['gender'] = encoder_gender.transform(df_generated_data['gender'].astype(str))
df_generated_data[['symptom1']] = encoder_symptom1.transform(df_generated_data['symptom1'].astype(str))
df_generated_data[['symptom2']] = encoder_symptom2.transform(df_generated_data['symptom2'].astype(str))
df_generated_data[['symptom3']] = encoder_symptom3.transform(df_generated_data['symptom3'].astype(str))
df_generated_data[['symptom4']] = encoder_symptom4.transform(df_generated_data['symptom4'].astype(str))
df_generated_data[['symptom5']] = encoder_symptom5.transform(df_generated_data['symptom5'].astype(str))
df_generated_data[['symptom6']] = encoder_symptom6.transform(df_generated_data['symptom6'].astype(str))
df_generated_data[['diff_sym_hos']] = df_generated_data['diff_sym_hos'].astype(int)模型比拟
将原始数据分为训练和测试后，将 GAN 生成的数据增加到训练数据中，以将性能与根本模型进行比拟。在理论（原始）宰割测试数据上测试模型性能。
from sklearn.metrics import recall_score as rs
from sklearn.metrics import precision_score as ps
from sklearn.metrics import f1_score as fs
from sklearn.metrics import balanced_accuracy_score as bas
from sklearn.metrics import confusion_matrix as cm
import numpy as np
import pandas as pd
import datetime as dt
import sklearn
from scipy import stats
from sklearn import preprocessing
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import recall_score as rs
from sklearn.metrics import precision_score as ps
from sklearn.metrics import f1_score as fs
from sklearn.metrics import log_loss
rf = RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
criterion='gini', max_depth=2, max_features='auto',
max_leaf_nodes=None, max_samples=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=2, min_samples_split=2,
min_weight_fraction_leaf=0.0, n_estimators=100,
n_jobs=None, oob_score=False, random_state=None,
verbose=0, warm_start=False)
classifier = AdaBoostClassifier(rf,50,0.01,'SAMME.R',10)
#Seperate TV in Generated Data
X1 = df_generated_data.loc[:, df_generated_data.columns != 'death']
Y1 = df_generated_data['death']
#Seperate TV in Original Data
X = df.loc[:, df.columns != 'death']
Y = df['death']
#Splitting Original Data
X_train, X_test, Y_train, Y_test = train_test_split(X,Y,test_size=0.2,random_state=0)
#Appending Generated Data to X_train
X_train1=X_train.append(X1, sort=False)
Y_train1=Y_train.append(Y1)
classifier.fit(X_train1,np.array(Y_train1).reshape(Y_train1.shape[0],1))
pred = np.array(classifier.predict(X_test))
recall = rs(Y_test,pred)
precision = ps(Y_test,pred)
r1 = fs(Y_test,pred)
ma = classifier.score(X_test,Y_test)
print('*** Evaluation metrics for test dataset ***\n')
print('Recall Score:',recall)
print('Precision Score:',precision)
print('F1 Score:',f1)
print('Accuracy:',ma)| 公制 | 根本模型得分 * | 用加强的生成数据评分 |
| 召回分数 | 0.75 | 0.83 |
| 精度分数 | 1 | 1 |
| F1 分数 | 0.86 | 0.9 |
| 准确性 | 0.9 | 0.95 |
材料起源：表 3 根本模型指标
- https://www.ncbi.nlm.nih.gov/…
论断
与根本模型相比，所提出的模型提供了更加精确和牢靠的后果，表明基于 GAN 的过采样克服了不均衡数据的局限性，并适当地裁减了少数类。
原文链接：https://www.analyticsvidhya.c…
欢送关注磐创 AI 博客站：
http://panchuang.net/
sklearn 机器学习中文官网文档：
http://sklearn123.com/
欢送关注磐创博客资源汇总站：
http://docs.panchuang.net/