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VectorTCM.py
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# %%
from collections import Counter
import copy as cp
import tkinter as tk
from tkinter import filedialog
from tkinter import ttk
from tkinter import font as tkFont
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from matplotlib import font_manager
import matplotlib as mpl
from regex import R
from sklearn import decomposition as dcp
mpl.use("TkAgg")
from matplotlib import backends as mpb
btk = mpb.backend_tkagg
import seaborn as sns
import altair as alt
import os
import pandas as pd
import numpy as np
import gensim
import sys
sys.setrecursionlimit(1000000)
font = font_manager.FontProperties(fname='simhei.ttf',size=10)
parameters = {'xtick.labelsize': 17,
'ytick.labelsize': 17,
'font.family':'SimHei',
'axes.unicode_minus':False}
plt.rcParams.update(parameters)
plt.style.use('ggplot')
#--------------------------------------------------------------
# %%
# UI界面
root = tk.Tk()
root.title("Vector-TCM")
root.geometry("1200x800")
root.resizable(True, True)
root.configure(bg='#aed0ee')
root.iconbitmap("i.ico")
# 缺省值
fontsty = tkFont.Font(family='Arial', size=14, weight=tkFont.BOLD,slant='roman')
fonttip = tkFont.Font(family='Arial', size=12, weight=tkFont.NORMAL,slant='roman')
fontsmall = tkFont.Font(family='Arial', size=9, weight=tkFont.NORMAL,slant='roman')
fontresult = tkFont.Font(family='Arial', size=12, weight=tkFont.BOLD,slant='roman')
fontplot = font_manager.FontProperties(fname='simhei.ttf')
fontplotsize = 14
button_width = 12
long_button_width = 50
entry_width = 70
button_height = 1
framecolor='#f6f9e4'
# 数据与字符串容器
df = pd.DataFrame()
data = pd.DataFrame()
filename = tk.StringVar()
filepath = tk.StringVar()
class openfile:
def __init__(self):
self.path = filedialog.askopenfilename(title='选择Excel文件', filetypes=[('Excel', '*.xlsx')])
filename.set(self.path)
filepath.set('File path: ' + self.path)
if filename !='':
global df
df = pd.read_excel(filename.get())
global data
data = pd.DataFrame(df)
label_load.configure(text='Please load the selected file')
else:
pass
def clear_data():
global data
global filename
global filepath
global opted_list
global list_box1
global list_box2
global listToBeSelected
global tab4_button3
global tab4_button4
global tab4_savepath1
global tab4_savepath2
global pres_df
global herb_df
global tab5_button3
global tab5_button4
global tab5_savepath1
global tab5_savepath2
global ldia_pres_df
global ldia_herb_df
global toolbar1
global toolbar2
global toolbar3
global toolbar4
global toolbar5
filename = tk.StringVar()
filepath = tk.StringVar()
data = pd.DataFrame()
list_box1.delete(0, 'end')
list_box2.delete(0, 'end')
opted_list=[]
listToBeSelected=[]
label_load.configure(text='Please select a file to open')
Label1.configure(textvariable=filepath)
tab3_table1.delete(*tab3_table1.get_children())
try:
canvas1.get_tk_widget().place_forget()
canvas1.get_tk_widget().destroy()
except:
pass
try:
canvas2.get_tk_widget().place_forget()
canvas2.get_tk_widget().destroy()
except:
pass
try:
canvas3.get_tk_widget().place_forget()
canvas3.get_tk_widget().destroy()
except:
pass
try:
canvas4.get_tk_widget().place_forget()
canvas4.get_tk_widget().destroy()
except:
pass
try:
canvas5.get_tk_widget().place_forget()
canvas5.get_tk_widget().destroy()
except:
pass
try:
tab4_button3.destroy()
except:
pass
try:
tab4_button4.destroy()
except:
pass
tab4_savepath1.set('')
tab4_savepath2.set('')
pres_df = pd.DataFrame()
herb_df = pd.DataFrame()
try:
tab5_button3.destroy()
except:
pass
try:
tab5_button4.destroy()
except:
pass
tab5_savepath1.set('')
tab5_savepath2.set('')
ldia_pres_df = pd.DataFrame()
ldia_herb_df = pd.DataFrame()
try:
toolbar1.destroy()
except:
pass
try:
toolbar2.destroy()
except:
pass
try:
toolbar3.destroy()
except:
pass
try:
toolbar4.destroy()
except:
pass
try:
toolbar5.destroy()
except:
pass
button1=tk.Button(root,text="Open file",command=lambda:openfile(),width=button_width, )
button1.place(x=17,y=39)
Label1 = tk.Label(root, textvariable=filepath, font=fontsty,bg='#aed0ee')
Label1.place(x=300,y=10)
button3 = tk.Button(root, text='All Clear', command=lambda:clear_data(), width=button_width)
button3.place(x=1060,y=7)
button2=tk.Button(root,text="Start load",command=lambda:StartExecution.excute(),width=button_width, )
button2.place(x=17,y=79)
class download_exmplr:
def exmp():
Exmp = pd.read_excel('English example.xlsx')
path = tk.StringVar()
savepath = filedialog.asksaveasfilename(title='Save as', initialfile='English example.xlsx', filetypes=[('Excel', '*.xlsx')])
path.set(savepath)
#Exmp.to_excel(path.get()+'.xlsx', index=False)
Exmp.to_excel(path.get(), index=False)
def cxmp():
Cxmp = pd.read_excel('Chinese example.xlsx')
path = tk.StringVar()
savepath = filedialog.asksaveasfilename(title='Save as', initialfile='Chinese example.xlsx', filetypes=[('Excel', '*.xlsx')])
path.set(savepath)
#Cxmp.to_excel(path.get()+'.xlsx', index=False)
Cxmp.to_excel(path.get(), index=False)
button4 = tk.Button(root, text='Example data', command=lambda:download_exmplr.exmp(), width=button_width)
button4.place(x=17,y=179)
button5 = tk.Button(root, text='示例数据', command=lambda:download_exmplr.cxmp(), width=button_width)
button5.place(x=17,y=219)
# 读取数据与预处理
herb_list = []
file_dict = dict()
list_vect = []
class PreProcessing:
def data_pre():
global data
col = data.columns
data = data.set_index(col[0])
global herb_list
global file_dict
global list_vect
# herb_list
sentence = ""
for index, row in data.iterrows():
for sen in row:
sentence = sentence + sen + ','
herb_list = sentence.split(sep=',')
# feil_dict
file_dict = dict()
for index, row in data.iterrows():
for sen in row:
per_vect = []
ws = sen.split(sep=',')
for herb in ws:
per_vect.append(herb)
file_dict[index] = per_vect
# list_vect
list_vect = []
for index, row in data.iterrows():
for sen in row:
sen_row = []
sent = sen.split(sep=',')
','.join(sent)
for herb in sent:
sen_row.append(herb)
list_vect.append(sen_row)
process1 = ttk.Progressbar(root, length=100, mode='indeterminate',value=0, max=100)
process1.place(x=13,y=119)
label_load = tk.Label(root, text='Please select a file to open', font=fontsty,bg='#aed0ee')
label_load.place(x=17,y=10)
class StartExecution(PreProcessing):
global data
def excute():
if filename.get() !='':
process1.step(50)
process1.update()
process1.start('idle')
PreProcessing.data_pre()
displayIndex()
label_load.configure(text='File loaded successfully')
process1.stop()
else:
tk.Messagebox.showinfo(title='Warning', message='Please select a file to open')
# tab1------------------------------------------------------------------------
MultiGroupTab=ttk.Notebook(root)
tab1 = tk.Frame(bg=framecolor,relief='ridge',borderwidth=2)
ta1_button4 = tk.Button(tab1,text="Descriptive statistical results",command=lambda:DescriptiveStatistics.herb_count(),width=long_button_width, )
ta1_button4.pack(side='top', anchor='nw',padx=5, pady=5)
Illustrate1 = tk.Label(tab1, text='1.The total number of different herbs:____', font=fontsty,bg=framecolor)
Illustrate1.pack(side='top', anchor='nw',padx=5, pady=5)
Illustrate2 = tk.Label(tab1, text='2.The total number of herbs:____', font=fontsty,bg=framecolor)
Illustrate2.pack(side='top', anchor='nw',padx=5, pady=5)
Illustrate3 = tk.Label(tab1, text='3.The average number of herbs in each prescription:____', font=fontsty,bg=framecolor)
Illustrate3.pack(side='top', anchor='nw',padx=5, pady=5)
Illustrate4 = tk.Label(tab1, text='4.The most common herb', font=fontsty,bg=framecolor)
Illustrate4.pack(side='top', anchor='nw',padx=5, pady=5)
Illustrate5 = tk.Label(tab1, text='How many herbs do you need to display by frequency?', font=fonttip,bg=framecolor)
Illustrate5.pack(side='top', anchor='nw',padx=5, pady=5)
# 位置需要调整
SpinBar = tk.Spinbox(tab1, from_=0, to=20, width=button_width,increment=5,bg=framecolor)
SpinBar.pack(side='top', anchor='nw',padx=5, pady=5,after=Illustrate5)
# 描述性统计类
class DescriptiveStatistics:
def herb_count():
global herb_list
total_herb_list = len(Counter(herb_list))
Illustrate1.config(text='1.The total number of different herbs: {}'.format(total_herb_list))
total_herb_word_list = len(herb_list)
Illustrate2.config(text='2.The total number of herbs: {}'.format(total_herb_word_list))
global file_dict
len_herb_list = 0
for index in file_dict.keys():
local_herb_list = file_dict.get(index)
local_herb_list = list(set(local_herb_list))
len_list = len(local_herb_list)
len_herb_list = len_herb_list + len_list
avg_len = len_herb_list / (len(file_dict.keys()))
Illustrate3.config(text='3.The average number of herbs in each prescription: {}'.format(round(avg_len, 0)))
def most_common_herb():
global herb_list
global most_common_herb1
Counter_every_herb = Counter(herb_list)
most_common_herb2 = Counter_every_herb.most_common(int(SpinBar.get()))
most_common_herb1 = pd.DataFrame(most_common_herb2, columns=['herb', 'count'])
fig1, ax1 = plt.subplots()
x = most_common_herb1['herb']
y = most_common_herb1['count']
y = list(y)
y.reverse() # 倒序
ax1.barh(x, y, align='center', color='dodgerblue', tick_label=list(x))
ax1.margins(y=.01, x=.01)
ax1.ignore_existing_data_limits = True
ax1.autoscale_view(tight=False, scalex=False, scaley=True)
for a,b in zip(x,y):
plt.text(b+0.1,a,b,ha = 'center',va = 'center',fontsize=14)
plt.ylabel('herbs', fontsize=fontplotsize, fontproperties=fontplot)
plt.yticks(x,fontsize=fontplotsize,fontproperties=fontplot)
global canvas1
canvas1 = btk.FigureCanvasTkAgg(fig1, master=tab1) # A tk.DrawingArea.
canvas1.draw()
global toolbar1
toolbar1 = btk.NavigationToolbar2Tk(canvas1, root)
toolbar1.update()
canvas1.get_tk_widget().pack(side='bottom', anchor='s',padx=0, pady=30,fill='both',expand=True)
def forget_plot():
global canvas1
global toolbar1
global most_common_herb1
if most_common_herb1.empty==True and canvas1.figure==None:
tk.messagebox.showinfo('Tips', 'Please choose how many herbs you need to display first.')
else:
most_common_herb1=pd.DataFrame()
try:
canvas1.get_tk_widget().place_forget()
canvas1.get_tk_widget().destroy()
except:
pass
try:
toolbar1.destroy()
except:
pass
def Descript_save():
global herb_list
Counter_every_herb = Counter(herb_list)
full_common_data = Counter_every_herb.most_common()
full_common_data = pd.DataFrame(full_common_data,columns=['herb', 'count'])
save_path=tk.StringVar()
path = filedialog.asksaveasfilename(title=u'保存文件', filetypes=[('Excel', '*.xlsx')], initialdir=(os.path.expanduser('~/Desktop')))
save_path.set(path)
full_common_data.to_excel(save_path.get()+'.xlsx',index=True)
tab1_button1=tk.Button(tab1,text="Display",command=lambda:DescriptiveStatistics.most_common_herb(),width=button_width, )
tab1_button1.place(x=115,y=225)
tab1_button2=tk.Button(tab1,text="Clear",command=lambda:DescriptiveStatistics.forget_plot(),width=button_width, )
tab1_button2.place(x=225,y=225)
tips = tk.Label(tab1, text='Tips: Please clear the canvas before each redraw', font=fonttip,bg=framecolor)
tips.place(x=325,y=225)
tab1_button3=tk.Button(tab1,text="Download herbal frequency data",command=lambda:DescriptiveStatistics.Descript_save(),width=long_button_width,)
tab1_button3.place(x=400,y=5)
tab1_label1_tip=tk.Label(tab1,text='A toolbar will appear at the bottom when drawing, which can be used to adjust or save the image. Before redrawing, please click "Clear" to clear the old image and toolbar',font=fonttip,bg=framecolor,wraplength=1050,justify='left')
tab1_label1_tip.place(relx=0.0, rely=0.94)
# tab2 ---------------------------------------------------------------------
tab2 = tk.Frame(bg=framecolor,relief='ridge', borderwidth=2)
# 已选定的列表
opted_list = []
# 展示函数（该函数调用在button-"Start analysis"）
def displayIndex():
global data
global listToBeSelected
listToBeSelected=data.index
for pres in listToBeSelected:
list_box1.insert('end', pres)
def PlusAndDecrease():
global list_box1
if list_box1.curselection()==():
pass
else:
global opted_list
global listToBeSelected
for herb in list_box1.curselection():
list_box2.insert('end', list_box1.get(herb))
opted_list.append(list_box1.get(herb))
list_box1.delete(herb)
# 相似度计算
class similarity:
global file_dict
global opted_list
global herb_list
def __init__(self) :
herb_dense_dataframe = pd.DataFrame(columns=['pres_name', 'herb_name'])
for pres_name in file_dict.keys():
temp_simi_herb_list = file_dict.get(pres_name)
pres_name = [pres_name]
pres_name = pd.DataFrame(pres_name, columns=['pres_name'])
herb_dense_dataframe = pd.concat([herb_dense_dataframe, pres_name], axis=0, join='outer')
for herb in temp_simi_herb_list:
herb_df = pd.DataFrame(columns=['herb_name'])
herb = [herb]
herb = pd.DataFrame(herb, columns=['herb_name'])
herb_df = pd.concat([herb_df, herb], axis=0, join='outer')
herb_dense_dataframe = pd.concat([herb_dense_dataframe, herb_df], axis=0, join='outer')
herb_dense_dataframe['count'] = 1
herb_dense_dataframe['pres_name'] = herb_dense_dataframe['pres_name'].fillna(method='ffill')
herb_dense_dataframe.dropna(subset=['herb_name'], axis=0, inplace=True, how="any")
herb_dense_dataframe = herb_dense_dataframe.pivot_table(
'count', index=herb_dense_dataframe['pres_name'], columns=['herb_name']).fillna(0)
herb_dense_dataframe = herb_dense_dataframe.astype('int')
self.df = herb_dense_dataframe
def dot_cos(self):
dot_df = pd.DataFrame()
cos_df = pd.DataFrame()
for res1 in opted_list:
dot_matrix = pd.DataFrame()
cos_matrix = pd.DataFrame()
for res2 in opted_list:
vec1 = self.df.loc[res1]
vec2 = self.df.loc[res2]
dot = np.dot(vec1, vec2)
cos = dot / (np.linalg.norm(vec1) * np.linalg.norm(vec2))
dot_matrix = dot_matrix.join(pd.DataFrame(dot,columns=[res2],index=[res1]), how='right')
cos_matrix = cos_matrix.join(pd.DataFrame(cos,columns=[res2],index=[res1]), how='right')
dot_df = pd.concat([dot_df, dot_matrix], axis=0, join="outer")
cos_df = pd.concat([cos_df, cos_matrix], axis=0, join="outer")
return dot_df, cos_df
def dot(self):
dot_df = pd.DataFrame()
for index1, row1 in self.df.iterrows():
dot_matrix = pd.DataFrame()
series1 = np.array(self.df.loc[index1])
for index2, row2 in self.df.iterrows():
series2 = np.array(self.df.loc[index2])
series1_2_dot = np.dot(series1, series2)
dot_matrix = dot_matrix.join(pd.DataFrame([series1_2_dot], columns=[index2], index=[index1]), how='right')
dot_df = pd.concat([dot_df, dot_matrix], axis=0, join="outer")
return dot_df
def cos(self):
cos_df = pd.DataFrame()
for index1, row1 in self.df.iterrows():
cos_matrix = pd.DataFrame()
series1 = np.array(self.df.loc[index1])
for index2, row2 in self.df.iterrows():
series2 = np.array(self.df.loc[index2])
series1_2_cos = np.dot(series1, series2) / \
(np.linalg.norm(series1) * np.linalg.norm(series2))
cos_matrix = cos_matrix.join(pd.DataFrame([series1_2_cos], columns=[index2], index=[index1]), how='right')
cos_df = pd.concat([cos_df, cos_matrix], axis=0, join="outer")
return cos_df
canvas2 = btk.FigureCanvasTkAgg(master=tab2)
canvas3 = btk.FigureCanvasTkAgg(master=tab2)
def cal_dot():
global font
cal=similarity()
dot=(cal.dot_cos())[0]
fig2, ax2 = plt.subplots(1,1,figsize=(5,4))
sns.heatmap(dot, annot=True, fmt=".2g", linewidths=.5, cmap='YlOrRd')
ax2.set_title('Dot product')
plt.xticks(font=font, rotation=45)
plt.yticks(font=font, rotation=45)
global canvas2
canvas2 = btk.FigureCanvasTkAgg(fig2, master=tab2) # A tk.DrawingArea.
canvas2.draw()
global toolbar2
toolbar2 = btk.NavigationToolbar2Tk(canvas2, tab2)
toolbar2.update()
canvas2.get_tk_widget().place(x=20,y=250)
def cal_cos():
global font
cal=similarity()
cos=(cal.dot_cos())[1]
fig3, ax3 = plt.subplots(1,1,figsize=(5,4))
sns.heatmap(cos, annot=True, fmt=".2g", linewidths=.5, cmap='YlGnBu')
ax3.set_title('Cosine similarity')
plt.xticks(font=font, rotation=45)
plt.yticks(font=font, rotation=45)
global canvas3
canvas3 = btk.FigureCanvasTkAgg(fig3, master=tab2) # A tk.DrawingArea.
canvas3.draw()
global toolbar3
toolbar3 = btk.NavigationToolbar2Tk(canvas3, tab2)
toolbar3.update()
canvas3.get_tk_widget().place(x=550,y=250)
tab2_box1 = tk.LabelFrame(tab2,relief='groove',bg=framecolor)
tab2_box1.place(x=460,y=77,width=600,height=165)
tab2_label1 = tk.Label(tab2_box1, text="The button on the below is used to calculate the dot value and cos value between the prescriptions of the entire dataset, it will take a long time, please perform this task when you are idle.", font=fonttip,wraplength=600,bg=framecolor,justify='left')
tab2_label1.place(x=0,y=0)
tab2_button5 = tk.Button(tab2_box1, text="Calculate dot matrix for all prescriptions", command=lambda:excute_dot(),width=35)
tab2_button6 = tk.Button(tab2_box1, text="Calculate cos matrix for all prescriptions", command=lambda:excute_cos(),width=35)
tab2_button5.place(x=0,y=70)
tab2_button6.place(x=270,y=70)
tab2_button7 = tk.Button(tab2_box1, text="Download dot", command=lambda:save_dot())
tab2_button8 = tk.Button(tab2_box1, text="Download cos", command=lambda:save_cos())
def cal_all_dot():
all_cal = similarity()
dot = all_cal.dot()
return dot
def cal_all_cos():
all_cal = similarity()
cos = all_cal.cos()
return cos
dot_matrix = pd.DataFrame()
def excute_dot():
process1.step(50)
process1.update()
process1.start('idle')
dot_m = cal_all_dot()
global dot_matrix
dot_matrix = dot_m
process1.stop()
tab2_button7.place(x=0,y=120)
cos_matrix = pd.DataFrame()
def excute_cos():
process1.step(50)
process1.update()
process1.start('idle')
cos_m = cal_all_cos()
global cos_matrix
cos_matrix = cos_m
process1.stop()
tab2_button8.place(x=270,y=120)
def save_dot():
global dot_matrix
path = tk.StringVar()
savep = filedialog.asksaveasfilename(title=u'保存文件', filetypes=[('CSV', '*.csv')], initialdir=(os.path.expanduser('~/Desktop')))
path.set(savep)
dot_matrix.to_csv(path.get()+'.csv',index=True)
def save_cos():
global cos_matrix
path = tk.StringVar()
savep = filedialog.asksaveasfilename(title=u'保存文件', filetypes=[('CSV', '*.csv')], initialdir=(os.path.expanduser('~/Desktop')))
path.set(savep)
cos_matrix.to_csv(path.get()+'.csv',index=True)
def tab2_clear():
global opted_list
global canvas2
global canvas3
global dot_matrix
global cos_matrix
dot_matrix = pd.DataFrame()
cos_matrix = pd.DataFrame()
if opted_list==[] and canvas2.figure == None and canvas3.figure == None:
tk.messagebox.showinfo('Tips', 'Please select a prescription first')
else:
opted_list = []
list_box2.delete(0, tk.END)
for herb in opted_list:
list_box1.insert(tk.END, herb)
try:
canvas2.get_tk_widget().place_forget()
canvas2.get_tk_widget().destroy()
canvas2.figure = None
except:
pass
try:
canvas3.get_tk_widget().place_forget()
canvas3.get_tk_widget().destroy()
canvas3.figure = None
except:
pass
try:
toolbar2.destroy()
except:
pass
try:
toolbar3.destroy()
except:
pass
try:
tab2_button7.destroy()
except:
pass
try:
tab2_button8.destroy()
except:
pass
#tab2的选择栏和说明栏
tab2_illustrate1 = tk.Label(tab2, text='Focus on dot product and cosine similarity for a specific prescription', font=fontsty,bg=framecolor)
tab2_illustrate1.pack(side='top', anchor='nw',padx=5, pady=5)
tab2_illustrate2 = tk.Label(tab2, text='Please select the prescription you want to analyze,choose at least 2 or more.', font=fonttip,bg=framecolor)
tab2_illustrate2.pack(side='top', anchor='nw',padx=5, pady=5)
list_box1=tk.Listbox(tab2,selectmode='multiple',width=15,height=9,)
list_box1.pack(side='left', anchor='nw',padx=5, pady=5)
tab2_button1 = tk.Button(tab2, text='Add to', command=lambda:PlusAndDecrease(), width=button_width)
tab2_button1.pack(side='left', anchor='nw',padx=5, pady=5)
list_box2=tk.Listbox(tab2,selectmode='multiple',width=15,height=9,)
list_box2.pack(side='left', anchor='nw',padx=5, pady=5)
tab2_button2 = tk.Button(tab2, text="Calculate cos", command=cal_cos,width=button_width)
tab2_button2.pack(side='top', anchor='nw',padx=5, pady=5,after=list_box2)
tab2_button3 = tk.Button(tab2, text="Calculate dot", command=cal_dot,width=button_width)
tab2_button3.pack(side='top', anchor='nw',padx=5, pady=5,after=list_box2)
# tab2的清屏按钮
tab2_button4 = tk.Button(tab2, text="Clear", command=tab2_clear,width=button_width)
tab2_button4.pack(side='top', anchor='nw',padx=5, pady=5,after=tab2_button2)
tab2_label_tip=tk.Label(tab2,text='The program will pop up a window when drawing, you can adjust or save the image through this window, when you want to redraw, you must first close the pop-up window',font=fonttip,bg=framecolor,wraplength=1050,justify='left')
tab2_label_tip.place(relx=0.0, rely=0.94)
# --------------------------------------------------------------------------------------
# tab3开始
tab3=tk.Frame(bg=framecolor,relief='ridge', borderwidth=2)
def tf_idf():
global herb_list
global file_dict
global list_vect
tf_idf_dict = dict()
lexicon=sorted(set(herb_list))
for tf_pres_name in file_dict.keys():
ini_tf_vect = dict()
herbs = file_dict.get(tf_pres_name)
herbs_counts = Counter(herbs)
for index, value in herbs_counts.items():
docs_contain_key = 0
for herb_row in list_vect:
if (index in herb_row) == True:
docs_contain_key = docs_contain_key + 1
tf = value / len(lexicon)
if docs_contain_key != 0:
idf = len(file_dict.keys()) / docs_contain_key
else:
idf = 0
ini_tf_vect[index] = tf * idf
tf_idf_dict[tf_pres_name] = ini_tf_vect
tf_idf_dataframe = pd.DataFrame(columns=['pres_name', 'herb_name'])
for pres_name in tf_idf_dict.keys():
herb_tf_idf_dict = tf_idf_dict.get(pres_name)
pres_name = [pres_name]
pres_name = pd.DataFrame(pres_name, columns=['pres_name'])
tf_idf_dataframe = pd.concat([tf_idf_dataframe, pres_name], axis=0, join='outer')
for herb_name in herb_tf_idf_dict:
herb_df = pd.DataFrame(columns=['herb_name', 'herb_tf_idf_value'])
herb_tf_value = herb_tf_idf_dict.get(herb_name)
herb_name = [herb_name]
herb_name = pd.DataFrame(herb_name, columns=['herb_name'])
herb_df = pd.concat([herb_df, herb_name], axis=0, join='outer')
herb_tf_value = round(herb_tf_value, 3)
herb_tf_value = [herb_tf_value]
herb_tf_value = pd.DataFrame(herb_tf_value, columns=['herb_tf_idf_value'])
herb_df = pd.concat([herb_df, herb_tf_value], axis=0, join='outer')
tf_idf_dataframe = pd.concat([tf_idf_dataframe, herb_df], axis=0, join='outer')
idf_df = cp.copy(tf_idf_dataframe)
idf_df['pres_name'] = idf_df['pres_name'].fillna(method='ffill')
idf_df['herb_name'] = idf_df['herb_name'].fillna(method='ffill')
idf_df.dropna(subset=['herb_tf_idf_value'], axis=0, inplace=True, how="any")
idf_df = idf_df.pivot_table('herb_tf_idf_value', index=['pres_name'], columns=['herb_name']).fillna(round(0, 3))
idf_df['tf_idf_sum'] = idf_df.apply(lambda x: x.sum(),axis=1)
sum_table=pd.DataFrame(idf_df['tf_idf_sum'])
tf_idf_sort_dict=dict()
for index, row in sum_table.iterrows():
for i in row:
temp_tfidf_herb_list = file_dict.get(index)
len_pres = len(temp_tfidf_herb_list)
mean_tf_idf = i / len_pres
tf_idf_sort_dict[index] = mean_tf_idf
tf_idf_mean_value=pd.DataFrame.from_dict(tf_idf_sort_dict, orient='index')
tf_idf_mean_value.columns=['tf_idf_mean']
global data
tf_idf_herb_list=data
tf_idf_mean_value_herb_list=pd.concat([tf_idf_mean_value, tf_idf_herb_list], axis=1)
tf_idf_sort = tf_idf_mean_value_herb_list.sort_values(by=['tf_idf_mean'], ascending=False)
return tf_idf_sort, idf_df,tf_idf_dict
tab3_radio=tk.IntVar()
def tf_idf_diplay(tab3_num):
if filename.get()!="":
if tab3_table1.get_children() != (): # 如果表格存在数据，清空
for item in tab3_table1.get_children():
tab3_table1.delete(item)
sort_table = tf_idf()[0]
sort_table = sort_table.round({'tf_idf_mean':3})
if tab3_radio.get()==1:
sort_head = sort_table.head(tab3_num)
sort_head = sort_head.astype(str)
for pres,row in sort_head.iterrows():
tf=sort_head.loc[pres]['tf_idf_mean']
col_0=sort_head.columns[1]
herb = sort_head.loc[pres][col_0]
value = (pres,tf,herb)
tab3_table1.insert('', 'end', values=value)
elif tab3_radio.get()==2:
sort_tail = sort_table.tail(tab3_num)
sort_tail = sort_tail.astype(str)
for pres,row in sort_tail.iterrows():
tf=sort_tail.loc[pres]['tf_idf_mean']
col_0=sort_tail.columns[1]
herb = sort_tail.loc[pres][col_0]
value = (pres,tf,herb)
tab3_table1.insert('', 'end', values=value)
else:
tk.messagebox.showinfo("Tips","Please select the sorting method")
else:
tk.messagebox.showinfo("Tips","Please select a file to open")
def download_tf_idf():
if filename.get()!="":
sort_table = tf_idf()[0]
sort_table = sort_table.round({'tf_idf_mean':3})
save_path=tk.StringVar()
path = filedialog.asksaveasfilename(title=u'保存文件', filetypes=[('Excel', '*.xlsx')], initialdir=(os.path.expanduser('~/Desktop')))
save_path.set(path)
sort_table.to_excel(save_path.get()+'.xlsx',index=True)
else:
tk.messagebox.showinfo("Tips","Please select a file to open")
tab3_label1 = tk.Label(tab3,text='TF-IDF value calculation has been completed in the background.', font=fontsty,bg=framecolor)
tab3_label1.pack(side='top', anchor='nw',padx=5, pady=5)
tab3_label2 = tk.Label(tab3,text='please select the number of prescriptions you want to display.', font=fonttip,bg=framecolor)
tab3_label2.pack(side='top', anchor='nw',padx=5, pady=5)
tab3_spinbox = tk.Spinbox(tab3, from_=1, to=50, width=5)
tab3_spinbox.pack(side='left', anchor='nw',padx=5, pady=5)
tab3_ratio1 = tk.Radiobutton(tab3, text='General herbal prescription', variable=tab3_radio, value=1,bg=framecolor)
tab3_ratio2 = tk.Radiobutton(tab3, text='Rare herbal prescription', variable=tab3_radio, value=2,bg=framecolor)
tab3_ratio1.pack(side='left', anchor='nw',padx=5, pady=5)
tab3_ratio2.pack(side='left', anchor='nw',padx=5, pady=5,after=tab3_ratio1)
tab3_button1 = tk.Button(tab3, text='Display', command=lambda:tf_idf_diplay(tab3_num=int(tab3_spinbox.get())), width=button_width)
tab3_button1.pack(side='left', anchor='nw',padx=5, pady=5)
tab3_button2 = tk.Button(tab3, text='Clear', command=lambda:tab3_table1.delete(*tab3_table1.get_children()), width=button_width)
tab3_button2.pack(side='left', anchor='nw',padx=5, pady=5)
tab3_button3 = tk.Button(tab3,text='Download the full form', command=lambda:download_tf_idf(), width=long_button_width)
tab3_button3.pack(side='left', anchor='nw',padx=5, pady=5)
tab3_table1 = ttk.Treeview(tab3, columns=('head Prescription', 'Average TF-IDF', 'herbal composition'), show='headings')
tab3_table1.heading('head Prescription', text='head Prescription')
tab3_table1.heading('Average TF-IDF', text='Average TF-IDF')
tab3_table1.heading('herbal composition', text='herbal composition')
tab3_table1.place(x=0, y=120, relwidth=1, relheight=0.83)
# ---------------------------------------------------------------------------
# tab4开始
tab4=tk.Frame(bg=framecolor,relief='ridge', borderwidth=2)
tab4_label1 = tk.Label(tab4,text='Topic classification based on Latent Semantic Analysis (LSA)', font=fontsty,bg=framecolor)
tab4_label1.pack(side='top', anchor='nw',padx=5, pady=5)
tab4_label2 = tk.Label(tab4,text='Please select the number of topics you want to classify.', font=fonttip,bg=framecolor)
tab4_label2.pack(side='top', anchor='nw',padx=5, pady=5)
tab4_spinbox = tk.Spinbox(tab4, from_=1, to=500, width=long_button_width)
tab4_spinbox.pack(side='left', anchor='nw',padx=5, pady=5)
canvas4 = btk.FigureCanvasTkAgg()
pres_df=pd.DataFrame()
herb_df=pd.DataFrame()
class lsa_display():
def __init__(self):
self.idf = tf_idf()[1]
self.num = int(tab4_spinbox.get())
def svd_plot(self):
self.idf.drop(['tf_idf_sum'], axis=1, inplace=True)
self.idf = self.idf.T
svd = dcp.TruncatedSVD(n_components=self.num, n_iter=10,random_state=123)
svd_model = svd.fit(self.idf)
svd_topic = svd.transform(self.idf)
explvara_list = list(svd.explained_variance_ratio_)
sing = svd_model.singular_values_
expl_cum = np.cumsum(explvara_list)
lsa_topic = pd.DataFrame(
{'topic': range(1, self.num + 1), 'explained_variance': explvara_list, 'cumulative_explained_variance': expl_cum,
'singular_values': sing})
lsa_topic = lsa_topic.set_index('topic')
fig4=plt.figure(figsize=(8, 5))
plt.subplot2grid((6,10),(0,0),colspan=5,rowspan=6)
plt.plot(expl_cum,color='red')
plt.xlabel('Number of components',fontsize=fontplotsize)
plt.xticks(fontsize=fontplotsize)
plt.ylabel('Cumulative explained variance',fontsize=fontplotsize)
plt.yticks(fontsize=fontplotsize)
plt.subplot2grid((6,8),(0,5),colspan=4,rowspan=3)
plt.plot(sing,color='blue')
plt.ylabel('Singular values',fontsize=fontplotsize)
plt.yticks(fontsize=fontplotsize)
plt.xticks([])
plt.subplot2grid((6,8),(3,5),colspan=4,rowspan=3)
plt.plot(explvara_list,color='green')
plt.xlabel('Number of components',fontsize=fontplotsize)
plt.xticks(fontsize=fontplotsize)
plt.ylabel('Explained variance',fontsize=fontplotsize)
plt.yticks(fontsize=fontplotsize)
global canvas4
canvas4 = btk.FigureCanvasTkAgg(fig4, master=tab4) # A tk.DrawingArea.
canvas4.draw()
global toolbar4
toolbar4 = btk.NavigationToolbar2Tk(canvas4, root)
toolbar4.update()
canvas4.get_tk_widget().place(x=0,y=120)
def lsa_confirm(self):
self.idf.drop(['tf_idf_sum'], axis=1, inplace=True)
self.idf = self.idf.T
svd = dcp.TruncatedSVD(n_components=self.num, n_iter=10, random_state=123)
svd = svd.fit(self.idf)
svd_topic = svd.transform(self.idf)
columns = ['topic{}'.format(i) for i in range(svd.n_components)]
pres_svd_topic = pd.DataFrame(svd_topic, columns=columns, index=self.idf.index)
herb_svd_weight = pd.DataFrame(svd.components_, columns=self.idf.columns,
index=columns)
herb_svd_weight = herb_svd_weight.T
global tab4_button3
global tab4_button4
tab4_button3 = tk.Button(tab4, text='Download prescription classification data', command=lambda:lsa_pres_download(), width=35)
tab4_button3.place(x=800, y=120)
tab4_button4 = tk.Button(tab4, text='Download herb classification data', command=lambda:lsa_herb_download(), width=35)
tab4_button4.place(x=800, y=160)
global pres_df
global herb_df
pres_df = pres_svd_topic
herb_df = herb_svd_weight
def excute_lsa():
global canvas4
try:
canvas4.get_tk_widget().place_forget()
canvas4.get_tk_widget().destroy()
except:
pass
if filename == '':
tk.messagebox.showinfo('Warning', 'Please upload the data first!')
else:
if int(tab4_spinbox.get())<len(data.index):
lsa = lsa_display()
lsa.svd_plot()
else:
tk.messagebox.showinfo('Warning', 'The number of topics is too large!')
def confirm_lsa():
if filename == '':
tk.messagebox.showinfo('Warning', 'Please upload the data first!')
else:
lsa = lsa_display()
lsa.lsa_confirm()
tab4_savepath1 = tk.StringVar()
def lsa_pres_download():
global pres_df
global tab4_savepath1
path = filedialog.asksaveasfilename(title='Save file', filetypes=[('Excel', '*.xlsx')])
tab4_savepath1.set(path)
pres_df.to_excel(tab4_savepath1.get()+'.xlsx',index=True)
tab4_savepath2 = tk.StringVar()
def lsa_herb_download():
global herb_df
global tab4_savepath2
path = filedialog.asksaveasfilename(title='Save file', filetypes=[('Excel', '*.xlsx')])
tab4_savepath2.set(path)
herb_df.to_excel(tab4_savepath2.get()+'.xlsx',index=True)
def lsa_clear():
global canvas4
global tab4_button3
global tab4_button4
global tab4_savepath1
global tab4_savepath2
global pres_df
global herb_df
pres_df=pd.DataFrame()
herb_df=pd.DataFrame()
tab4_savepath1.set('')
tab4_savepath2.set('')
try:
tab4_button3.destroy()
tab4_button4.destroy()
except:
pass
try:
canvas4.get_tk_widget().place_forget()
canvas4.get_tk_widget().destroy()
canvas4.figure = None
except:
pass
try:
toolbar4.destroy()
except:
pass
tab4_button1 = tk.Button(tab4, text='Classification', command=lambda:excute_lsa(), width=button_width)
tab4_button1.pack(side='left', anchor='nw',padx=5, pady=5)
tab4_button2 = tk.Button(tab4, text='Confirm the classification results', command=lambda:confirm_lsa(), width=long_button_width)
tab4_button2.pack(side='left', anchor='nw',padx=5, pady=5)
tab4_button5 = tk.Button(tab4, text='Clear', command=lambda:lsa_clear(), width=button_width)
tab4_button5.pack(side='left', anchor='nw',padx=5, pady=5)
tab4_label_tip=tk.Label(tab4,text='The program will pop up a window when drawing, you can adjust or save the image through this window, when you want to redraw, you must first close the pop-up window',font=fonttip,bg=framecolor,wraplength=1050,justify='left')
tab4_label_tip.place(relx=0.0, rely=0.94)
# -------------------------------------------------------------------------------------------------
# tab5
tab5=tk.Frame(bg=framecolor,relief='ridge', borderwidth=2)
tab5_label1 = tk.Label(tab5,text='Topic classification based on Latent Dirichlet Distribution (LDiA)', font=fontsty,bg=framecolor)
tab5_label1.pack(side='top', anchor='nw',padx=5, pady=5)
tab5_label2 = tk.Label(tab5,text='Please select the number of topics you want to classify.', font=fonttip,bg=framecolor)
tab5_label2.pack(side='top', anchor='nw',padx=5, pady=5)
tab5_spinbox = tk.Spinbox(tab5, from_=1, to=500, width=long_button_width)
tab5_spinbox.pack(side='left', anchor='nw',padx=5, pady=5)
ldia_pres_df=pd.DataFrame()
ldia_herb_df=pd.DataFrame()
class ldia_display(similarity):
def __init__(self):
self.num = int(tab5_spinbox.get())
similarity.__init__(self)
def ldia_plot(self):