Same confusion matrix when changing DecisionTreeClassifier parameters

I’m trying to build my first Decision Tree Classifier using the Iris dataset in the sklearn library. This is my first sample code:

from sklearn.datasets import load_iris
from sklearn.metrics import accuracy_score
from sklearn.metrics import f1_score
from sklearn.model_selection import cross_val_score
from sklearn.metrics import confusion_matrix
from sklearn import tree 
import numpy as np
import graphviz 

iris = load_iris()

clf_ex1 = tree.DecisionTreeClassifier(criterion="entropy",random_state=300,min_samples_leaf=5,
                                      class_weight={0:1,1:10,2:10})

np.random.seed(0)

indices = np.random.permutation(len(iris.data))
indices_training=indices[:-10]
indices_test=indices[-10:]

iris_X_train = iris.data[indices_training]
iris_y_train = iris.target[indices_training]
iris_X_test  = iris.data[indices_test]
iris_y_test  = iris.target[indices_test]

clf_ex1 = clf_ex1.fit(iris_X_train, iris_y_train)

predicted_y_test = clf_ex1.predict(iris_X_test)

print(confusion_matrix(iris_y_test, predicted_y_test))


print("Predictions:")
print(predicted_y_test)
print("True classes:")
print(iris_y_test) 
print("--------")
print(iris.target_names)

# print some metrics results
acc_score = accuracy_score(iris_y_test, predicted_y_test)
print("--------")
print("Accuracy score: "+ str(acc_score))
print("--------")
f1=f1_score(iris_y_test, predicted_y_test, average='macro')
print("F1 score: "+str(f1))
print("--------")

scores = cross_val_score(clf_ex1, iris.data, iris.target, cv=5) 
print(scores)

dot_data = tree.export_graphviz(clf_ex1, out_file=None, 
                         feature_names=iris.feature_names, 
                         class_names=iris.target_names, 
                         filled=True, rounded=True,  
                         special_characters=True)  
graph = graphviz.Source(dot_data)  
graph

As you can see, in my DecisionTreeClassifier function I set the weight of the classes by giving a major value to the second one and the third one and I’ve given 300 to the random_state parameter. Then, I made another example by changing these parameters on this way:

clf_ex2 = tree.DecisionTreeClassifier(criterion="entropy",random_state=300,min_samples_leaf=5,
                                  class_weight={0:1,1:1,2:10})

and on this way:

clf_ex3 = tree.DecisionTreeClassifier(class_weight=None, criterion='entropy', 
                                  max_depth=2, 
                                  max_leaf_nodes=None, 
                                  min_samples_leaf=15, 
                                  min_samples_split=5, 
                                  random_state=100, 
                                  splitter='best')

The problem is that all the values that I print (the confusion matrix, the accuracy, the predicted_y_test and the f1 score) do not change between the three codes. The only value that gets affected is the Cross Validation Score. Why?