糖尿病数据分析风险预测

# import pandas as pd # import requests # import math # import re # import jieba # # pip install wordcloud -i https://pypi.tuna.tsinghua.edu.cn/simple --trusted-host pypi.tuna.tsinghua.edu.cn # from collections import Counter # from wordcloud import WordCloud # import matplotlib.pyplot as plt # import random # import matplotlib # # matplotlib.rcParams['font.sans-serif'] = ['SimHei'] # matplotlib.rcParams['axes.unicode_minus'] = False # import sklearn # from sklearn.feature_extraction.text import CountVectorizer # from sklearn.feature_extraction.text import TfidfTransformer # import matplotlib.pyplot as plt # import matplotlib # from sklearn.feature_extraction.text import TfidfVectorizer # # matplotlib.rcParams['font.sans-serif'] = ['SimHei'] # matplotlib.rcParams['axes.unicode_minus'] = False # import random # import numpy as np # import matplotlib.mlab as mlab # import matplotlib.pyplot as plt # from collections import Counter # import bertvec # from bertvec import bert_vec # def plt_picture_ciyun(n): # """ # 词云 # 传入的n是:我 老婆 走 了 四年 总会 不经意 想起 那 感觉 没 经历 的 很 难 体会 # """ # wc = WordCloud( # # 设置字体，不指定就会出现乱码 # font_path='simhei.ttf', # # 设置背景色 # background_color='white', # # 设置背景宽 # width=500, # # 设置背景高 # height=350, # # 最大字体 # max_font_size=50, # # 最小字体 # min_font_size=10, # mode='RGBA' # # colormap='pink' # ) # # 产生词云 # wc.generate(n) # # 显示图片 # # 指定所绘图名称 # plt.figure("jay") # # # 以图片的形式显示词云 # plt.imshow(wc) # # 关闭图像坐标系 # plt.axis("off") # # 保存词云图片 # # plt.savefig("2209070221.png") # plt.show() # def tfidf_word(corpus): # """ # tfodf 提取关键词： # import sklearn # from sklearn.feature_extraction.text import CountVectorizer # from sklearn.feature_extraction.text import TfidfTransformer # from sklearn.feature_extraction.text import TfidfVectorizer # corpus = ['TF-IDF 主要 思想 是', '算法 一个 重要 特点 可以 脱离 语料库 背景', # '如果 一个 网页 被 很多 其他 网页 链接 说明 网页 重要', # '原始 文本 进行 标记', # '主要 思想'] # """ # vectorizer = TfidfVectorizer() # X = vectorizer.fit_transform(corpus) # data = {'word': vectorizer.get_feature_names_out(), # 'tfidf': X.toarray().sum(axis=0).tolist()} # df = pd.DataFrame(data) # df = df.sort_values(by='tfidf', ascending=True) # True是从小达到 False是从大到小 # res = {} # for i in df.values: # res[i[0]] = i[1] # # print(res){'链接': 0.2556396904397093, '说明': 0.2556396904397093...} # return res # def dict_sort(dic): # """ # 字典排序 # """ # count = sorted(dic.items(), key=lambda x: x[1], reverse=True) # True 是降序 False是升序 # return count # date=pd.read_excel('待处理数据 0812.xlsx') # # date=pd.read_excel('已评分数据.xlsx') # train_data=date['Comment'].values # # datas=[] # for line in train_data: # try: # line =line.split(' ') # temp='' # for i in line: # temp=temp+i+" " # # print(temp) # datas.append(temp) # except: # pass # # # print(len(datas)) # res=tfidf_word(datas) # # print(res) # res=dict_sort(res) # word_list=[] # n='' # for idx,(k ,v) in enumerate(res): # # print(k,v) # word_list.append(k) # n=n+k+" " # if idx==200:break # # # 词云 # plt_picture_ciyun(n) # word_list_vec=[] # for word in word_list: # vec = bert_vec(word) # # print(vec) # word_list_vec.append([word,vec]) import matplotlib.pyplot as plt import matplotlib matplotlib.rcParams['font.sans-serif'] = ['SimHei'] matplotlib.rcParams['axes.unicode_minus'] = False import random import numpy as np import matplotlib.mlab as mlab import matplotlib.pyplot as plt from collections import Counter from sklearn.manifold import TSNE from pylab import mpl mpl.rcParams['font.sans-serif'] = ['SimHei'] #中文字体 mpl.rcParams['axes.unicode_minus'] = False #防止负号出现异常显示 def tsne_em(train_x,train_y): plt.figure(figsize=(15,15)) #定义画布大小 color=['b',"r","g","k"] # 定义颜色 参数c 可以等于：['c', 'b', 'g', 'r', 'm', 'y', 'k', 'w'] color_label=['b',"r","g","k"] marker=[" "," "," "," "] tokens = [] labels = [] for idx,line in enumerate(train_x): # print(str(train_y[idx])) labels.append(train_y[idx]) tokens.append(train_x[idx]) # 存储的是向量 tsne_model = TSNE(perplexity=10, n_components=2, init='pca', n_iter=2500, random_state=23) # perplexity: 默认为30，数据集越大，需要参数值越大，建议值位5-50 , n_components=2 默认为2，嵌入空间的维度（嵌入空间的意思就是结果空间）,别的参数估计不重要 print(len(tokens)) print(tokens) new_values = tsne_model.fit_transform(np.array(tokens)) # 将X投影到一个嵌入空间并返回转换结果 # 降维处理 # print(new_values) x = [] y = [] for value in new_values: x.append(value[0]) y.append(value[1]) for i in range(len(x)): plt.scatter(x[i],y[i],c=color[1],marker=marker[1]) plt.text(x[i],y[i], labels[i], fontsize=10,color=color_label[1]) plt.xticks(fontsize=20) plt.yticks(fontsize=20) plt.show()