ClassificationModelsComparison.py

from sklearn.datasets import make_classification ,make_moons,make_circles
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import train_test_split
from matplotlib.colors import ListedColormap
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import make_pipeline
from sklearn.inspection import DecisionBoundaryDisplay
import matplotlib.pyplot as plt
import numpy as np


X,y= make_classification(n_features=2,n_redundant=0,n_informative=2,n_clusters_per_class=1 ,random_state=42)
X +=1.2*np.random.uniform(size=X.shape)
Xy=(X,y)
# plt.scatter(X[:,0],X[:,1], c= y)
# plt.show()

# X,y= make_moons(noise=0.2,random_state=42)
# X,y = make_circles(noise=0.1,factor=0.3,random_state=42)

datasets = [Xy,
            make_moons(noise=0.2,random_state=42),
            make_circles(noise=0.1,factor=0.3,random_state=42)
            ]
fig= plt.figure(figsize=(6,9))
i=1
for ds_cnt , ds in enumerate(datasets):
    X,y = ds 
    if ds_cnt ==0:
        colors="darkred"
    elif ds_cnt==1:
        colors="darkblue"
    else:
        colors="darkgreen"
    ax=plt.subplot(len(datasets),1,i)
    ax.scatter(X[:,0],X[:,1], c= y,cmap=plt.cm.coolwarm,edgecolors="black") 
    i+=1


names =["Nearest Neighbors" , "Linear SVM","Decision Tree", "Random Forest", "Naive Bayes"]
classifiers =[KNeighborsClassifier(),
              SVC(),
              DecisionTreeClassifier(),
              RandomForestClassifier(),
              GaussianNB()]

fig= plt.figure(figsize=(6,9))
i=1
for ds_cnt , ds in enumerate(datasets):
    X,y=ds
    X_train , X_test , y_train , y_test =train_test_split(X,y,test_size=0.2,random_state=42)
    cm_bright= ListedColormap(["darkred","darkblue"])
    ax=plt.subplot(len(datasets),len(classifiers)+1,i)
    if ds_cnt==0:
        ax.set_title("Input Data")
    
    ax.scatter(X_train[:,0],X_train[:,1],c=y_train,cmap= cm_bright,edgecolors="black")

    ax.scatter(X_train[:,0],X_train[:,1],c=y_train,cmap= cm_bright,edgecolors="black",alpha=0.6)

    i+=1

    for name, clf in zip(names,classifiers):
        ax=plt.subplot(len(datasets),len(classifiers)+1,i)

        
        clf=make_pipeline(StandardScaler(),clf)
        clf.fit(X_train,y_train)
        score=clf.score(X_test,y_test)
        DecisionBoundaryDisplay.from_estimator(clf,X,cmap=plt.cm.RdBu,alpha=0.7,ax=ax,eps=0.5)

        ax.scatter(X_train[:,0],X_train[:,1],c=y_train,cmap= cm_bright,edgecolors="black")

        ax.scatter(X_train[:,0],X_train[:,1],c=y_train,cmap= cm_bright,edgecolors="black",alpha=0.6)

        if ds_cnt ==0:
            ax.set_title(name)
        ax.text(X[:,0].max()-0.15,
                X[:,1].min()-0.35,
                str(score))
        i+=1

plt.show()