The code for running the model accuracy for kmeans clustering takes a long time to execute - k-means

Question

I've used kmeans clustering algorithm for training the data and then try to get accuracy with some Classification algorithms such as decision tree, random forest, KNN algorithm etc,. After training of data while running model accuracy it takes long time for running. I've attached the code below.
# lets import the warnings library so that we can avoid warnings
import warnings
warnings.filterwarnings('ignore')
# Lets select the Spending score, and Annual Income Columns from the Data
x = data.loc[:, ['Time', 'V1','V2','V3','V4','V5','V6','V7','V8','V9','V10','V11','V12','V13','V14','V15','V16','V17','V18','V19','V20']].values
# let's check the shape of x
print(x.shape)
# lets convert this data into a dataframe
x_data = pd.DataFrame(x)
x_data.head()
km = KMeans(n_clusters = 2, init = 'k-means++', max_iter = 300, n_init = 10, random_state = 0)
y_means = km.fit_predict(x)
# lets find out the Results
a = data['Class']
y_means = pd.DataFrame(y_means)
z = pd.concat([y_means, a], axis = 1)
z = z.rename(columns = {0: 'cluster'})
# lets check the Clusters of each Crops
print("Lets check the Results After Applying the K Means Clustering Analysis \n")
print("First Cluster:", z[z['cluster'] == 0]['Class'].unique())
print("---------------------------------------------------------------")
print("Second Cluster:", z[z['cluster'] == 1]['Class'].unique())
print("---------------------------------------------------------------")
from sklearn.cluster import KMeans
hc=KMeans
hc= KMeans(n_clusters=2)
y_her= hc.fit_predict(x)
# lets find out the Results
b = data['Class']
y_herr = pd.DataFrame(y_her)
w = pd.concat([y_herr, b], axis = 1)
w= w.rename(columns = {0: 'cluster'})
# lets check the Clusters of each Crops
print("K-Means Clustering Analysis \n")
print("Zero Cluster:", w[w['cluster'] == 0]['Class'].unique())
print("---------------------------------------------------------------")
print("First Cluster:", w[w['cluster'] == 1]['Class'].unique())
print("---------------------------------------------------------------")
y = data['Class']
x = data.drop(['Class'], axis = 1)
print("Shape of x:", x.shape)
print("Shape of y:", y.shape)
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, random_state = 0)
print("The Shape of x train:", x_train.shape)
print("The Shape of x test:", x_test.shape)
print("The Shape of y train:", y_train.shape)
print("The Shape of y test:", y_test.shape)
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import AdaBoostClassifier
from xgboost import XGBClassifier
import xgboost as xgb
from sklearn.metrics import classification_report
from sklearn.metrics import accuracy_score,confusion_matrix,roc_auc_score
from mlxtend.plotting import plot_confusion_matrix
def evaluator(y_test, y_pred):
# Accuracy:
print('Accuracy is: ', accuracy_score(y_test,y_pred))
print('')
# Classification Report:
print('Classification Report: \n',classification_report(y_test,y_pred))
print('Confusion Matrix: \n\n')
plt.style.use("ggplot")
cm = confusion_matrix(y_test,y_pred)
plot_confusion_matrix(conf_mat = cm,figsize=(10,10),show_normed=True)
plt.title('Confusion Matrix for Logistic Regression', fontsize = 15)
plt.show()
#In this below part where the code is running for a long time.
model_accuracy = pd.DataFrame(columns=['Model','Accuracy'])
models = {
"KNN" : KNeighborsClassifier(),
"DT" : DecisionTreeClassifier(),
'RFC' : RandomForestClassifier(),
'GBC' : GradientBoostingClassifier(),
'XGB' : XGBClassifier()
}
for test, clf in models.items():
clf.fit(x_train, y_train)
y_pred = clf.predict(x_test)
acc = accuracy_score(y_test,y_pred)
train_pred = clf.predict(x_train)
train_acc = accuracy_score(y_train, train_pred)
print("\n", test + ' scores')
print(acc)
print(classification_report(y_test,y_pred))
print(confusion_matrix(y_test,y_pred))
print('*' * 100,"\n")
model_accuracy = model_accuracy.append({'Model': test, 'Accuracy': acc, 'Train_acc': train_acc}, ignore_index=True)
I want a detailed output as it mentioned in the code.
For KNN algorithm:
enter image description here
enter image description here
same it goes for other algorithms..