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add graphviz to predict client

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Kosterix08 2024-06-18 23:24:03 +02:00
parent a2b1a1e4c0
commit cf186c2ef1
1 changed files with 11 additions and 9 deletions
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20
decisiontree.py
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@ -34,7 +34,7 @@ X_test = encoder.transform(X_test)
#print(X_train.head()) #print(X_train.head())
#print(X_test.head()) #print(X_test.head())
clf_en = DecisionTreeClassifier(criterion='entropy', max_depth=3, random_state=0) clf_en = DecisionTreeClassifier(criterion='entropy', max_depth=200, random_state=0)
clf_en.fit(X_train, y_train) clf_en.fit(X_train, y_train)
y_pred_en = clf_en.predict(X_test) y_pred_en = clf_en.predict(X_test)
@ -46,12 +46,6 @@ y_pred_train_en = clf_en.predict(X_train)
#print('Training set score: {:.4f}'.format(clf_en.score(X_train, y_train))) #print('Training set score: {:.4f}'.format(clf_en.score(X_train, y_train)))
#print('Test set score: {:.4f}'.format(clf_en.score(X_test, y_test))) #print('Test set score: {:.4f}'.format(clf_en.score(X_test, y_test)))
dot_data = tree.export_graphviz(clf_en, out_file=None,
feature_names=X_train.columns,
class_names=y_train.unique(),
filled=True, rounded=True,
special_characters=True)
#nowy klient testowo #nowy klient testowo
"""new_client = { """new_client = {
"Wrinkles": random.choice(['Yes', 'No']), "Wrinkles": random.choice(['Yes', 'No']),
@ -84,11 +78,19 @@ def predict_client(client_data,glasses):
print('Training set score: {:.4f}'.format(clf_en.score(X_train, y_train))) print('Training set score: {:.4f}'.format(clf_en.score(X_train, y_train)))
print('Test set score: {:.4f}'.format(clf_en.score(X_test, y_test))) print('Test set score: {:.4f}'.format(clf_en.score(X_test, y_test)))
dot_data = tree.export_graphviz(clf_en, out_file=None,
feature_names=X_train.columns,
class_names=y_train.unique(),
filled=True, rounded=True,
special_characters=True)
graph = graphviz.Source(dot_data)
graph.render("decision_tree", format='png')
return prediction[0] return prediction[0]
#print("\nNew client:") #print("\nNew client:")
#print(new_client_df) #print(new_client_df)
#print("Prediction:", prediction[0]) #print("Prediction:", prediction[0])
#graph = graphviz.Source(dot_data)
#graph.render("decision_tree", format='png')