[英]error ::sklearn.exceptions.NotFittedError: CountVectorizer - Vocabulary wasn't fitted
[英]sklearn.exceptions.NotFittedError how to get rid of this
基本上,我正在尝试为多元线性回归模型创建一个 GUI,用户可以在其中添加他的一些值并从中获得电影分数。 这是代码:
import tkinter as tk
import sklearn as sk
root= tk.Tk()
canvas1 = tk.Canvas(root, width = 1200, height = 450)
canvas1.pack()
print_model = model.summary()
label_model = tk.Label(root, text=print_model, justify = 'center', relief = 'solid', bg='LightSkyBlue1')
canvas1.create_window(800, 220, window=label_model)
label1 = tk.Label(root, text=' Taper le nombre de vote: ')
label1.pack(side="left")
canvas1.create_window(80, 100, window=label1)
entry1 = tk.Entry (root)
canvas1.create_window(300, 100, window=entry1)
label2 = tk.Label(root, text=' Taper la durée du film (en minutes): ')
label2.pack(side="left")
canvas1.create_window(80, 120, window=label2)
entry2 = tk.Entry (root)
canvas1.create_window(300, 120, window=entry2)
label3 = tk.Label(root, text=' Est-ce que le fim est en anglais? (1=oui, 0=non): ')
label3.pack(side="left")
canvas1.create_window(80, 140, window=label3)
entry3 = tk.Entry (root)
canvas1.create_window(300, 140, window=entry3)
label4 = tk.Label(root, text=' Est-ce que le film est PG-13? (1=oui,0=non): ')
label4.pack(side="left")
canvas1.create_window(80, 160, window=label4)
entry4 = tk.Entry (root)
canvas1.create_window(300, 160, window=entry4)
label5 = tk.Label(root, text=' Est-ce que le film est PG? (1=oui,0=non): ')
label5.pack(side="left")
canvas1.create_window(80, 180, window=label5)
entry5 = tk.Entry (root)
canvas1.create_window(300, 180, window=entry5)
label6 = tk.Label(root, text=' Est-ce que le film est G? (1=oui,0=non): ')
label6.pack(side="left")
canvas1.create_window(80, 200, window=label6)
entry6 = tk.Entry (root)
canvas1.create_window(300, 200, window=entry6)
label7 = tk.Label(root, text=' Est-ce que le film est R? (1=oui,0=non): ')
label7.pack(side="left")
canvas1.create_window(80, 220, window=label7)
entry7 = tk.Entry (root)
canvas1.create_window(300, 220, window=entry7)
regr = linear_model.LinearRegression()
def values():
global New_Nb_Vote #our 1st input variable
New_Nb_Vote = float(entry1.get())
global New_Durée
New_Durée = float(entry2.get())
global New_Anglais
New_Anglais = float(entry3.get())
global New_PG_13
New_PG_13 = float(entry4.get())
global New_PG
New_PG = float(entry5.get())
global New_G
New_G = float(entry6.get())
global New_R
New_R = float(entry7.get())
Prediction_result = ('Score IMDb prédit: ', regr.predict([[New_Nb_Vote,New_Durée,New_Anglais,New_PG_13,New_PG,New_G,New_R]]))
label_Prediction = tk.Label(root, text= Prediction_result, bg='orange')
canvas1.create_window(80, 250, window=label_Prediction)
button1 = tk.Button (root, text='Prédire score IMDb',command=values, bg='orange')
canvas1.create_window(270, 250, window=button1)
root.mainloop()
基本上,我使用的是我事先导入的 CSV,并且已经找到了上述代码的必要值:
regr = linear_model.LinearRegression()
regr.fit(first, second)
print('Intercept: \n', regr.intercept_)
print('Coefficients: \n', regr.coef_)
**Gives me this**
Intercept:
37.26301371428632
Coefficients:
[ 0.00000000e+00 3.18885131e-06 8.98583585e-03 -1.55836415e-02
-7.78758275e-01 -3.63227977e-01 -2.33707300e-01 -7.10371314e-02
-5.81148889e-02]
GUI 看起来不错,但是每当我向其中输入值时,我都会收到如下所示的错误:
Exception in Tkinter callback
Traceback (most recent call last):
File "C:\Users\owner\Anaconda3\lib\tkinter\__init__.py", line 1705, in __call__
return self.func(*args)
File "<ipython-input-199-b62d64111f11>", line 25, in values
Prediction_result = ('Score IMDb prédit: ', regr.predict([[New_Nb_Vote,New_Durée,New_Anglais,New_PG_13,New_PG,New_G,New_R]]))
File "C:\Users\owner\Anaconda3\lib\site-packages\sklearn\linear_model\base.py", line 221, in predict
return self._decision_function(X)
File "C:\Users\owner\Anaconda3\lib\site-packages\sklearn\linear_model\base.py", line 202, in _decision_function
check_is_fitted(self, "coef_")
File "C:\Users\owner\Anaconda3\lib\site-packages\sklearn\utils\validation.py", line 914, in check_is_fitted
raise NotFittedError(msg % {'name': type(estimator).__name__})
sklearn.exceptions.NotFittedError: This LinearRegression instance is not fitted yet. Call 'fit' with appropriate arguments before using this method.
编辑:有效的部分
bd_f = bd_f.dropna()
first = bd_f[['Nombre_de_vote','Durée','Année','Langue_English','Type_de_contenu_PG-13','Type_de_contenu_PG','Type_de_contenu_G','Type_de_contenu_R']]
second = bd_f['IMDb']
regr = linear_model.LinearRegression()
regr.fit(first, second)
print('Intercept: \n', regr.intercept_)
print('Coefficients: \n', regr.coef_)
这是一个学校项目,我现在卡住了,任何帮助表示赞赏!
在尝试对回归模型使用predict
之前,您需要使用训练数据拟合/训练模型。
你确定你在打电话吗
regr.fit(first, second)
在调用 predict 函数之前
Prediction_result = ('Score IMDb prédit: ', regr.predict([[New_Nb_Vote,New_Durée,New_Anglais,New_PG_13,New_PG,New_G,New_R]]))
编辑:最终组合代码(因为我没有数据也没有目标,我只能通过做一些假设来提供帮助)
这是组合代码。
import tkinter as tk
import sklearn as sk
import pandas as pd
root= tk.Tk()
canvas1 = tk.Canvas(root, width = 1200, height = 450)
canvas1.pack()
bd_f = pd.read_csv('data.csv')
bd_f = bd_f.dropna()
first = bd_f[['Nombre_de_vote','Durée','Année','Langue_English','Type_de_contenu_PG-13','Type_de_contenu_PG','Type_de_contenu_G','Type_de_contenu_R']]
second = bd_f['IMDb']
regr = linear_model.LinearRegression()
regr.fit(first, second)
print('Intercept: \n', regr.intercept_)
print('Coefficients: \n', regr.coef_)
# Not sure what model is here, did you mean regr here
print_model = model.summary()
label_model = tk.Label(root, text=print_model, justify = 'center', relief = 'solid', bg='LightSkyBlue1')
canvas1.create_window(800, 220, window=label_model)
label1 = tk.Label(root, text=' Taper le nombre de vote: ')
label1.pack(side="left")
canvas1.create_window(80, 100, window=label1)
entry1 = tk.Entry (root)
canvas1.create_window(300, 100, window=entry1)
label2 = tk.Label(root, text=' Taper la durée du film (en minutes): ')
label2.pack(side="left")
canvas1.create_window(80, 120, window=label2)
entry2 = tk.Entry (root)
canvas1.create_window(300, 120, window=entry2)
label3 = tk.Label(root, text=' Est-ce que le fim est en anglais? (1=oui, 0=non): ')
label3.pack(side="left")
canvas1.create_window(80, 140, window=label3)
entry3 = tk.Entry (root)
canvas1.create_window(300, 140, window=entry3)
label4 = tk.Label(root, text=' Est-ce que le film est PG-13? (1=oui,0=non): ')
label4.pack(side="left")
canvas1.create_window(80, 160, window=label4)
entry4 = tk.Entry (root)
canvas1.create_window(300, 160, window=entry4)
label5 = tk.Label(root, text=' Est-ce que le film est PG? (1=oui,0=non): ')
label5.pack(side="left")
canvas1.create_window(80, 180, window=label5)
entry5 = tk.Entry (root)
canvas1.create_window(300, 180, window=entry5)
label6 = tk.Label(root, text=' Est-ce que le film est G? (1=oui,0=non): ')
label6.pack(side="left")
canvas1.create_window(80, 200, window=label6)
entry6 = tk.Entry (root)
canvas1.create_window(300, 200, window=entry6)
label7 = tk.Label(root, text=' Est-ce que le film est R? (1=oui,0=non): ')
label7.pack(side="left")
canvas1.create_window(80, 220, window=label7)
entry7 = tk.Entry (root)
canvas1.create_window(300, 220, window=entry7)
def values():
global New_Nb_Vote #our 1st input variable
New_Nb_Vote = float(entry1.get())
global New_Durée
New_Durée = float(entry2.get())
global New_Anglais
New_Anglais = float(entry3.get())
global New_PG_13
New_PG_13 = float(entry4.get())
global New_PG
New_PG = float(entry5.get())
global New_G
New_G = float(entry6.get())
global New_R
New_R = float(entry7.get())
Prediction_result = ('Score IMDb prédit: ', regr.predict([[New_Nb_Vote,New_Durée,New_Anglais,New_PG_13,New_PG,New_G,New_R]]))
label_Prediction = tk.Label(root, text= Prediction_result, bg='orange')
canvas1.create_window(80, 250, window=label_Prediction)
button1 = tk.Button (root, text='Prédire score IMDb',command=values, bg='orange')
canvas1.create_window(270, 250, window=button1)
root.mainloop()
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