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tp.py
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# -*- coding: utf-8 -*-
"""
______________________________
Author: iMyFish
Email : [email protected]
Time : 2023/11/12 3:20
File: tp.py
Software: PyCharm
______________________________
"""
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
import p_data
# 预处理
p_data.data_for_zz()
p_data.data_for_rl('data/人力资源专员.csv', 'data/人力.csv')
p_data.data_for_rl('data/销售总监.csv', 'data/销售.csv')
# 载入数据
# def tp(data):
# file_zai_zhi = 'data/在职.csv'
# file_ren_li = data
# df_zai_zhi = pd.read_csv(file_zai_zhi, encoding='GBK')
# df_ren_li = pd.read_csv(file_ren_li, encoding='GBK')
# #
# # # 选择相关特征
# # features = ['学历', '经验_encoding', '平均月薪_encoding']
# #
# # # 对 '学历' 进行数值编码
# # label_encoder = LabelEncoder()
# # combined_education = pd.concat([df_zai_zhi['学历'], df_ren_li['学历']], ignore_index=True)
# # label_encoder.fit(combined_education)
# # df_zai_zhi['学历_encoded'] = label_encoder.transform(df_zai_zhi['学历'])
# # df_ren_li['学历_encoded'] = label_encoder.transform(df_ren_li['学历'])
# #
# # # 选择特征和目标变量
# # df_zai_zhi_encoded = df_zai_zhi[features + ['学历_encoded']]
# # df_ren_li_encoded = df_ren_li[features + ['学历_encoded']]
# # if data == 'data/人力.csv':
# # df_zai_zhi['target'] = (df_zai_zhi['职级'] == '2A').astype(int)
# # if data == 'data/销售.csv':
# # df_zai_zhi['target'] = (df_zai_zhi['职级'] == '4B').astype(int)
# #
# # # 分割数据集
# # X = df_zai_zhi_encoded.drop('学历', axis=1)
# # y = df_zai_zhi['target']
# # X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# #
# # # 训练 kNN 模型
# # knn = KNeighborsClassifier(n_neighbors=3)
# # knn.fit(X_train, y_train)
# #
# # # 模型在测试集上的准确率
# # accuracy = knn.score(X_test, y_test)
# #
# # # 对候选人进行预测
# # df_ren_li_encoded = df_ren_li_encoded.drop('学历', axis=1)
# # predictions = knn.predict(df_ren_li_encoded)
# # df_ren_li['Prediction'] = predictions
# #
# # # 筛选出与 "2A" 职级相似的候选人
# # similar_candidates = df_ren_li[df_ren_li['Prediction'] == 1]
# #
# # # 输出结果
# # similar_candidates.head()
#
# # 处理逗号分隔的数字字符串
# df_zai_zhi['平均月薪_encoding'] = df_zai_zhi['平均月薪_encoding'].apply(clean_numeric)
# df_ren_li['平均月薪_encoding'] = df_ren_li['平均月薪_encoding'].apply(clean_numeric)
#
# # 选择相关特征
# features = ['学历', '经验_encoding', '平均月薪_encoding']
#
# # 对 '学历' 进行数值编码
# label_encoder = LabelEncoder()
# combined_education = pd.concat([df_zai_zhi['学历'], df_ren_li['学历']], ignore_index=True)
# label_encoder.fit(combined_education)
# df_zai_zhi['学历_encoded'] = label_encoder.transform(df_zai_zhi['学历'])
# df_ren_li['学历_encoded'] = label_encoder.transform(df_ren_li['学历'])
#
# # 选择特征和目标变量
# df_zai_zhi_encoded = df_zai_zhi[features + ['学历_encoded']]
# df_ren_li_encoded = df_ren_li[features + ['学历_encoded']]
# # df_zai_zhi['target'] = (df_zai_zhi['职级'] == '2A').astype(int)
# if data == 'data/人力.csv':
# df_zai_zhi['target'] = (df_zai_zhi['职级'] == '2A').astype(int)
# output_file_path = 'data/人力推荐.csv'
# if data == 'data/销售.csv':
# df_zai_zhi['target'] = (df_zai_zhi['职级'] == '4B').astype(int)
# output_file_path = 'data/销售推荐.csv'
#
# # 分割数据集
# X = df_zai_zhi_encoded.drop('学历', axis=1)
# y = df_zai_zhi['target']
# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
#
# # 训练 kNN 模型
# knn = KNeighborsClassifier(n_neighbors=6)
# knn.fit(X_train, y_train)
#
# # 模型在测试集上的准确率
# accuracy = knn.score(X_test, y_test)
# print(accuracy)
#
# # 对候选人进行预测
# df_ren_li_encoded = df_ren_li_encoded.drop('学历', axis=1)
# predictions = knn.predict(df_ren_li_encoded)
# df_ren_li['Prediction'] = predictions
#
# # 筛选出与 "2A" 职级相似的候选人
# similar_candidates = df_ren_li[df_ren_li['Prediction'] == 1]
#
# # 输出结果
#
# similar_candidates.to_csv(output_file_path, index=False, encoding='GBK')
# # 数据清洗函数：移除数字中的逗号，并转换为浮点数
# def clean_numeric(x):
# if isinstance(x, str):
# return float(x.replace(',', ''))
# return x
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
import p_data
# 数据清洗函数：移除数字中的逗号，并转换为浮点数
def clean_numeric(x):
if isinstance(x, str):
return float(x.replace(',', ''))
return x
# 修改后的tp函数
def tp(data):
file_zai_zhi = 'data/在职.csv'
file_ren_li = data
df_zai_zhi = pd.read_csv(file_zai_zhi, encoding='GBK')
df_ren_li = pd.read_csv(file_ren_li, encoding='GBK')
df_zai_zhi['平均月薪_encoding'] = df_zai_zhi['平均月薪_encoding'].apply(clean_numeric)
df_ren_li['平均月薪_encoding'] = df_ren_li['平均月薪_encoding'].apply(clean_numeric)
features = ['学历', '经验_encoding', '平均月薪_encoding']
label_encoder = LabelEncoder()
combined_education = pd.concat([df_zai_zhi['学历'], df_ren_li['学历']], ignore_index=True)
label_encoder.fit(combined_education)
df_zai_zhi['学历_encoded'] = label_encoder.transform(df_zai_zhi['学历'])
df_ren_li['学历_encoded'] = label_encoder.transform(df_ren_li['学历'])
df_zai_zhi_encoded = df_zai_zhi[features + ['学历_encoded']]
df_ren_li_encoded = df_ren_li[features + ['学历_encoded']]
if data == 'data/人力.csv':
df_zai_zhi['target'] = (df_zai_zhi['职级'] == '2A').astype(int)
output_file_path = 'data/人力推荐.csv'
if data == 'data/销售.csv':
df_zai_zhi['target'] = (df_zai_zhi['职级'] == '4B').astype(int)
output_file_path = 'data/销售推荐.csv'
X = df_zai_zhi_encoded.drop('学历', axis=1)
y = df_zai_zhi['target']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# 使用随机森林
rf = RandomForestClassifier(n_estimators=100, random_state=42)
rf.fit(X_train, y_train)
rf_accuracy = rf.score(X_test, y_test)
print("随机森林准确率:", rf_accuracy)
# # 使用梯度提升树
# gb = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1, random_state=42)
# gb.fit(X_train, y_train)
# gb_accuracy = gb.score(X_test, y_test)
# print("梯度提升树准确率:", gb_accuracy)
df_ren_li_encoded = df_ren_li_encoded.drop('学历', axis=1)
df_ren_li['RF_Prediction'] = rf.predict(df_ren_li_encoded)
# df_ren_li['GB_Prediction'] = gb.predict(df_ren_li_encoded)
similar_candidates_rf = df_ren_li[df_ren_li['RF_Prediction'] == 1]
# similar_candidates_gb = df_ren_li[df_ren_li['GB_Prediction'] == 1]
# 输出随机森林和梯度提升树的预测结果
similar_candidates_rf.to_csv(output_file_path.replace('.csv', '_rf.csv'), index=False, encoding='GBK')
# similar_candidates_gb.to_csv(output_file_path.replace('.csv', '_gb.csv'), index=False, encoding='GBK')
def print_out_rl():
# duqu renli tuijian rf csv
file_ren_li = 'data/人力推荐_rf.csv'
df_ren_li = pd.read_csv(file_ren_li, encoding='GBK')
# shuchu RF_Prediction = 1 de renli
print(df_ren_li[df_ren_li['RF_Prediction'] == 1])
def print_out_xs():
# duqu renli tuijian rf csv
file_ren_li = 'data/销售推荐_rf.csv'
df_ren_li = pd.read_csv(file_ren_li, encoding='GBK')
# shuchu RF_Prediction = 1 de renli
print(df_ren_li[df_ren_li['RF_Prediction'] == 1])
if __name__ == '__main__':
tp('data/人力.csv')
print('------------------')
tp('data/销售.csv')
print_out_rl()
print_out_xs()