绘制Logistic回归非比例值

Question

我是新来的Python和一般编程。我正在上一堂关于Logistic回归的课。下面的代码是正确的，情节比较不错的（不是很漂亮，但是OK）：

# ------ LOGISTIC REGRESSION ------ # 

# --- Importing the Libraries --- # 

import numpy as np 
import matplotlib.pyplot as plt 
import pandas as pd 

from sklearn.model_selection import train_test_split 
from sklearn.preprocessing import StandardScaler 
from sklearn.linear_model import LogisticRegression 
from sklearn.metrics import confusion_matrix 
from matplotlib.colors import ListedColormap 

# --- Importing the Dataset --- # 

path = '/home/bohrz/Desktop/Programação/Machine Learning/Part 3 - ' \ 
     'Classification/Section 14 - Logistic Regression/Social_Network_Ads.csv' 
dataset = pd.read_csv(path) 
X = dataset.iloc[:, 2:4].values 
y = dataset.iloc[:, -1].values 

# --- Splitting the Dataset into Training and Test set --- # 

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, 
                random_state=0) 

# --- Feature Scaling --- # 

sc_X = StandardScaler() 
X_train = sc_X.fit_transform(X_train) 
X_test = sc_X.transform(X_test) 

# --- Fitting the Logistic Regression Model to the Dataset --- # 

classifier = LogisticRegression(random_state=0) 
classifier.fit(X_train, y_train) 

# --- Predicting the Test set results --- # 

y_pred = classifier.predict(X_test) 

# --- Making the Confusion Matrix --- # 

cm = confusion_matrix(y_test, y_pred) 

# --- Visualizing Logistic Regression results --- # 

# --- Visualizing the Training set results --- # 

X_set_train, y_set_train = X_train, y_train 
X1, X2 = np.meshgrid(np.arange(start=X_set_train[:, 0].min(), 
           stop=X_set_train[:, 0].max(), step=0.01), 
        np.arange(start=X_set_train[:, 1].min(), 
           stop=X_set_train[:, 1].max(), step=0.01)) 

# Building the graph contour based on classification method 
Z_train = np.array([X1.ravel(), X2.ravel()]).T 
plt.contourf(X1, X2, classifier.predict(Z_train).reshape(X1.shape), alpha=0.75, 
                 cmap=ListedColormap(
                  ('red', 'green'))) 

# Apply limits when outliers are present 
plt.xlim(X1.min(), X1.max()) 
plt.ylim(X2.min(), X2.max()) 

# Creating the scatter plot of the Training set results 
for i, j in enumerate(np.unique(y_set_train)): 
    plt.scatter(X_set_train[y_set_train == j, 0], X_set_train[y_set_train == j, 
                   1], 
       c=ListedColormap(('red', 'green'))(i), label=j) 

plt.title('Logistic Regression (Trainning set results)') 
plt.xlabel('Age') 
plt.ylabel('Estimated Salary') 
plt.legend() 
plt.show() 

我的问题是：我怎么绘制，没有规模的结果？我在代码的几个地方尝试使用invert_transform（）方法，但它没有帮助。

预先感谢您。

Answer 1

你的任务只是跟踪缩放数据和非缩放数据。

虽然没有详细分析您的代码，但基本思路是：查看使用了缩放/非缩放值以及是否需要调整！

• 答：训练结束后，我们不需要缩放X了，所以让我们改变一切恢复
• B：但是那种情节是使用一些np.mesh分类，这本身就是创建通过非标定数据，所以我们需要再次使用变压器
• C：小心：基于网格的方法创建密集网格，如果边界在保持步长的情况下发生变化，则会导致计算机崩溃到内存消耗
  • 这一个实际上可以调整（不知道原始值来自哪里）作为剧情会得到微妙的变化

所以需要的变化是：

答：

y_pred = classifier.predict(X_test) # YOUR CODE 
X_train = sc_X.inverse_transform(X_train) # transform back 
X_test = sc_X.inverse_transform(X_test) # """ 

C：

X1, X2 = np.meshgrid(np.arange(start=X_set_train[:, 0].min(), 
           stop=X_set_train[:, 0].max(), step=10.), #!!! 0.01), 
        np.arange(start=X_set_train[:, 1].min(), 
           stop=X_set_train[:, 1].max(), step=0.1)) #!!! 0.01)) 

B：

Z_train = np.array([X1.ravel(), X2.ravel()]).T 
plt.contourf(X1, X2, classifier.predict(sc_X.transform(Z_train)).reshape(X1.shape), # TRANFORM Z 
            alpha=0.75, 
            cmap=ListedColormap(
            ('red', 'green'))) 

而原来的情节显示走样直线（精细模式楼梯），现在我们看到不同的东西。我将把这个开放给感兴趣的读者（它与缩放相关联！）。