Stacking refers to a model that repeats the process of "creating multiple predictive models and inputting their outputs into yet another predictive model. On this page, we will run stacking and see which model was valid for the first stage of stacking.
import matplotlib.pyplot as plt
from sklearn.datasets import make_classification
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import StackingClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score
from sklearn.tree import export_graphviz
from subprocess import call
Create data for experiment #
# Create data with 20 features
n_features = 20
X, y = make_classification(
n_samples=2500,
n_features=n_features,
n_informative=10,
n_classes=2,
n_redundant=0,
n_clusters_per_class=4,
random_state=777,
)
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.33, random_state=777
)
Plot the data with respect to multiple features #
Confirm that it does not appear to be classifiable by simple rules.
plt.figure(figsize=(10, 10))
plt.subplot(2, 2, 1)
plt.scatter(X[:, 2], X[:, 7], c=y)
plt.xlabel("x2")
plt.ylabel("x7")
plt.subplot(2, 2, 2)
plt.scatter(X[:, 4], X[:, 9], c=y)
plt.xlabel("x4")
plt.ylabel("x9")
plt.subplot(2, 2, 3)
plt.scatter(X[:, 5], X[:, 1], c=y)
plt.xlabel("x5")
plt.ylabel("x1")
plt.subplot(2, 2, 4)
plt.scatter(X[:, 1], X[:, 3], c=y)
plt.xlabel("x1")
plt.ylabel("x3")
plt.show()
Stacking vs. Random Forest #
Classification with Random Forest #
model = RandomForestClassifier(n_estimators=50, max_depth=5, random_state=777)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
rf_score = roc_auc_score(y_test, y_pred)
print(f"ROC-AUC = {rf_score}")
ROC-AUC = 0.855797033310609
When stacking with multiple trees #
We can confirm that stacking with only DecisionTreeClassifier does not improve accuracy much.
# Models used in base learner
estimators = [
("dt1", DecisionTreeClassifier(max_depth=3, random_state=777)),
("dt2", DecisionTreeClassifier(max_depth=4, random_state=777)),
("dt3", DecisionTreeClassifier(max_depth=5, random_state=777)),
("dt4", DecisionTreeClassifier(max_depth=6, random_state=777)),
]
# Number of models included in base learner
n_estimators = len(estimators)
# aggregation model
final_estimator = DecisionTreeClassifier(max_depth=3, random_state=777)
# train base-learner and aggregation model
clf = StackingClassifier(estimators=estimators, final_estimator=final_estimator)
clf.fit(X_train, y_train)
# evaluate
y_pred = clf.predict(X_test)
clf_score = roc_auc_score(y_test, y_pred)
print("ROC-AUC")
print(f"Decision Tree Stacking＝{clf_score}, Random Forest＝{rf_score}")
ROC-AUC
Decision Tree Stacking＝0.7359716965608031, Random Forest＝0.855797033310609
Visualize trees used for stacking #
export_graphviz(
clf.final_estimator_,
out_file="tree_final_estimator.dot",
class_names=["A", "B"],
feature_names=[e[0] for e in estimators],
proportion=True,
filled=True,
)
call(
[
"dot",
"-Tpng",
"tree_final_estimator.dot",
"-o",
f"tree_final_estimator.png",
"-Gdpi=200",
]
)
display(Image(filename="tree_final_estimator.png"))
Look at the feature importance of the trees used in the stacking. #
We see that although stacked, in the end only the fourth tree is used almost exclusively in the forecast.
plt.figure(figsize=(6, 3))
plot_index = [i for i in range(n_estimators)]
plt.bar(plot_index, clf.final_estimator_.feature_importances_)
plt.xticks(plot_index, [e[0] for e in estimators])
plt.xlabel("model name")
plt.ylabel("feature-importance")
plt.show()
Check the performance of each tree in the base learner #
scores = []
for clf_estim in clf.estimators_:
print("====")
y_pred = clf_estim.predict(X_test)
scr = roc_auc_score(y_test, y_pred)
scores.append(scr)
print(clf_estim)
print(scr)
n_estimators = len(estimators)
plot_index = [i for i in range(n_estimators)]
plt.figure(figsize=(8, 4))
plt.bar(plot_index, scores)
plt.xticks(plot_index, [e[0] for e in estimators])
plt.xlabel("model name")
plt.ylabel("roc-auc")
plt.show()
====
DecisionTreeClassifier(max_depth=3, random_state=777)
0.7660117774277722
====
DecisionTreeClassifier(max_depth=4, random_state=777)
0.7744128916993818
====
DecisionTreeClassifier(max_depth=5, random_state=777)
0.8000158677919086
====
DecisionTreeClassifier(max_depth=6, random_state=777)
0.8084639977432473
