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---
jupyter:
jupytext:
cell_metadata_filter: -all
formats: notebooks///ipynb,Rmarkdown///Rmd
main_language: python
text_representation:
extension: .Rmd
format_name: rmarkdown
format_version: '1.2'
jupytext_version: 1.14.7
---
# Chapter 8
# Lab: Tree-Based Methods
We import some of our usual libraries at this top
level.
```{python}
import numpy as np
import pandas as pd
from matplotlib.pyplot import subplots
import sklearn.model_selection as skm
from ISLP import load_data, confusion_table
from ISLP.models import ModelSpec as MS
```
We also collect the new imports
needed for this lab.
```{python}
from sklearn.tree import (DecisionTreeClassifier as DTC,
DecisionTreeRegressor as DTR,
plot_tree,
export_text)
from sklearn.metrics import (accuracy_score,
log_loss)
from sklearn.ensemble import \
(RandomForestRegressor as RF,
GradientBoostingRegressor as GBR)
from ISLP.bart import BART
```
## Fitting Classification Trees
We first use classification trees to analyze the `Carseats` data set.
In these data, `Sales` is a continuous variable, and so we begin
by recoding it as a binary variable. We use the `where()`
function to create a variable, called `High`, which takes on a
value of `Yes` if the `Sales` variable exceeds 8, and takes
on a value of `No` otherwise.
```{python}
Carseats = load_data('Carseats')
High = np.where(Carseats.Sales > 8,
"Yes",
"No")
```
We now use `DecisionTreeClassifier()` to fit a classification tree in
order to predict `High` using all variables but `Sales`.
To do so, we must form a model matrix as we did when fitting regression
models.
```{python}
model = MS(Carseats.columns.drop('Sales'), intercept=False)
D = model.fit_transform(Carseats)
feature_names = list(D.columns)
X = np.asarray(D)
```
We have converted `D` from a data frame to an array `X`, which is needed in some of the analysis below. We also need the `feature_names` for annotating our plots later.
There are several options needed to specify the classifier,
such as `max_depth` (how deep to grow the tree), `min_samples_split`
(minimum number of observations in a node to be eligible for splitting)
and `criterion` (whether to use Gini or cross-entropy as the split criterion).
We also set `random_state` for reproducibility; ties in the split criterion are broken at random.
```{python}
clf = DTC(criterion='entropy',
max_depth=3,
random_state=0)
clf.fit(X, High)
```
In our discussion of qualitative features in Section 3.3,
we noted that for a linear regression model such a feature could be
represented by including a matrix of dummy variables (one-hot-encoding) in the model
matrix, using the formula notation of `statsmodels`.
As mentioned in Section 8.1, there is a more
natural way to handle qualitative features when building a decision
tree, that does not require such dummy variables; each split amounts to partitioning the levels into two groups.
However,
the `sklearn` implementation of decision trees does not take
advantage of this approach; instead it simply treats the one-hot-encoded levels as separate variables.
```{python}
accuracy_score(High, clf.predict(X))
```
With only the default arguments, the training error rate is
21%.
For classification trees, we can
access the value of the deviance using `log_loss()`,
\begin{equation*}
\begin{split}
-2 \sum_m \sum_k n_{mk} \log \hat{p}_{mk},
\end{split}
\end{equation*}
where $n_{mk}$ is the number of observations in the $m$th terminal
node that belong to the $k$th class.
```{python}
resid_dev = np.sum(log_loss(High, clf.predict_proba(X)))
resid_dev
```
This is closely related to the *entropy*, defined in (8.7).
A small deviance indicates a
tree that provides a good fit to the (training) data.
One of the most attractive properties of trees is that they can
be graphically displayed. Here we use the `plot()` function
to display the tree structure.
```{python}
ax = subplots(figsize=(12,12))[1]
plot_tree(clf,
feature_names=feature_names,
ax=ax);
```
The most important indicator of `Sales` appears to be `ShelveLoc`.
We can see a text representation of the tree using
`export_text()`, which displays the split
criterion (e.g. `Price <= 92.5`) for each branch.
For leaf nodes it shows the overall prediction
(`Yes` or `No`).
We can also see the number of observations in that
leaf that take on values of `Yes` and `No` by specifying `show_weights=True`.
```{python}
print(export_text(clf,
feature_names=feature_names,
show_weights=True))
```
In order to properly evaluate the performance of a classification tree
on these data, we must estimate the test error rather than simply
computing the training error. We split the observations into a
training set and a test set, build the tree using the training set,
and evaluate its performance on the test data. This pattern is
similar to that in Chapter 6, with the linear models
replaced here by decision trees --- the code for validation
is almost identical. This approach leads to correct predictions
for 68.5% of the locations in the test data set.
```{python}
validation = skm.ShuffleSplit(n_splits=1,
test_size=200,
random_state=0)
results = skm.cross_validate(clf,
D,
High,
cv=validation)
results['test_score']
```
Next, we consider whether pruning the tree might lead to improved
classification performance. We first split the data into a training and
test set. We will use cross-validation to prune the tree on the training
set, and then evaluate the performance of the pruned tree on the test
set.
```{python}
(X_train,
X_test,
High_train,
High_test) = skm.train_test_split(X,
High,
test_size=0.5,
random_state=0)
```
We first refit the full tree on the training set; here we do not set a `max_depth` parameter, since we will learn that through cross-validation.
```{python}
clf = DTC(criterion='entropy', random_state=0)
clf.fit(X_train, High_train)
accuracy_score(High_test, clf.predict(X_test))
```
Next we use the `cost_complexity_pruning_path()` method of
`clf` to extract cost-complexity values.
```{python}
ccp_path = clf.cost_complexity_pruning_path(X_train, High_train)
kfold = skm.KFold(10,
random_state=1,
shuffle=True)
```
This yields a set of impurities and $\alpha$ values
from which we can extract an optimal one by cross-validation.
```{python}
grid = skm.GridSearchCV(clf,
{'ccp_alpha': ccp_path.ccp_alphas},
refit=True,
cv=kfold,
scoring='accuracy')
grid.fit(X_train, High_train)
grid.best_score_
```
Let’s take a look at the pruned true.
```{python}
ax = subplots(figsize=(12, 12))[1]
best_ = grid.best_estimator_
plot_tree(best_,
feature_names=feature_names,
ax=ax);
```
This is quite a bushy tree. We could count the leaves, or query
`best_` instead.
```{python}
best_.tree_.n_leaves
```
The tree with 30 terminal
nodes results in the lowest cross-validation error rate, with an accuracy of
68.5%. How well does this pruned tree perform on the test data set? Once
again, we apply the `predict()` function.
```{python}
print(accuracy_score(High_test,
best_.predict(X_test)))
confusion = confusion_table(best_.predict(X_test),
High_test)
confusion
```
Now 72.0% of the test observations are correctly classified, which is slightly worse than the error for the full tree (with 35 leaves). So cross-validation has not helped us much here; it only pruned off 5 leaves, at a cost of a slightly worse error. These results would change if we were to change the random number seeds above; even though cross-validation gives an unbiased approach to model selection, it does have variance.
## Fitting Regression Trees
Here we fit a regression tree to the `Boston` data set. The
steps are similar to those for classification trees.
```{python}
Boston = load_data("Boston")
model = MS(Boston.columns.drop('medv'), intercept=False)
D = model.fit_transform(Boston)
feature_names = list(D.columns)
X = np.asarray(D)
```
First, we split the data into training and test sets, and fit the tree
to the training data. Here we use 30% of the data for the test set.
```{python}
(X_train,
X_test,
y_train,
y_test) = skm.train_test_split(X,
Boston['medv'],
test_size=0.3,
random_state=0)
```
Having formed our training and test data sets, we fit the regression tree.
```{python}
reg = DTR(max_depth=3)
reg.fit(X_train, y_train)
ax = subplots(figsize=(12,12))[1]
plot_tree(reg,
feature_names=feature_names,
ax=ax);
```
The variable `lstat` measures the percentage of individuals with
lower socioeconomic status. The tree indicates that lower
values of `lstat` correspond to more expensive houses.
The tree predicts a median house price of $12,042 for small-sized homes (`rm < 6.8`), in
suburbs in which residents have low socioeconomic status (`lstat > 14.4`) and the crime-rate is moderate (`crim > 5.8`).
Now we use the cross-validation function to see whether pruning
the tree will improve performance.
```{python}
ccp_path = reg.cost_complexity_pruning_path(X_train, y_train)
kfold = skm.KFold(5,
shuffle=True,
random_state=10)
grid = skm.GridSearchCV(reg,
{'ccp_alpha': ccp_path.ccp_alphas},
refit=True,
cv=kfold,
scoring='neg_mean_squared_error')
G = grid.fit(X_train, y_train)
```
In keeping with the cross-validation results, we use the pruned tree
to make predictions on the test set.
```{python}
best_ = grid.best_estimator_
np.mean((y_test - best_.predict(X_test))**2)
```
In other words, the test set MSE associated with the regression tree
is 28.07. The square root of
the MSE is therefore around
5.30,
indicating that this model leads to test predictions that are within around
$5300
of the true median home value for the suburb.
Let’s plot the best tree to see how interpretable it is.
```{python}
ax = subplots(figsize=(12,12))[1]
plot_tree(G.best_estimator_,
feature_names=feature_names,
ax=ax);
```
## Bagging and Random Forests
Here we apply bagging and random forests to the `Boston` data, using
the `RandomForestRegressor()` from the `sklearn.ensemble` package. Recall
that bagging is simply a special case of a random forest with
$m=p$. Therefore, the `RandomForestRegressor()` function can be used to
perform both bagging and random forests. We start with bagging.
```{python}
bag_boston = RF(max_features=X_train.shape[1], random_state=0)
bag_boston.fit(X_train, y_train)
```
The argument `max_features` indicates that all 12 predictors should
be considered for each split of the tree --- in other words, that
bagging should be done. How well does this bagged model perform on
the test set?
```{python}
ax = subplots(figsize=(8,8))[1]
y_hat_bag = bag_boston.predict(X_test)
ax.scatter(y_hat_bag, y_test)
np.mean((y_test - y_hat_bag)**2)
```
The test set MSE associated with the bagged regression tree is
14.63, about half that obtained using an optimally-pruned single
tree. We could change the number of trees grown from the default of
100 by
using the `n_estimators` argument:
```{python}
bag_boston = RF(max_features=X_train.shape[1],
n_estimators=500,
random_state=0).fit(X_train, y_train)
y_hat_bag = bag_boston.predict(X_test)
np.mean((y_test - y_hat_bag)**2)
```
There is not much change. Bagging and random forests cannot overfit by
increasing the number of trees, but can underfit if the number is too small.
Growing a random forest proceeds in exactly the same way, except that
we use a smaller value of the `max_features` argument. By default,
`RandomForestRegressor()` uses $p$ variables when building a random
forest of regression trees (i.e. it defaults to bagging), and `RandomForestClassifier()` uses
$\sqrt{p}$ variables when building a
random forest of classification trees. Here we use `max_features=6`.
```{python}
RF_boston = RF(max_features=6,
random_state=0).fit(X_train, y_train)
y_hat_RF = RF_boston.predict(X_test)
np.mean((y_test - y_hat_RF)**2)
```
The test set MSE is 20.04;
this indicates that random forests did somewhat worse than bagging
in this case. Extracting the `feature_importances_` values from the fitted model, we can view the
importance of each variable.
```{python}
feature_imp = pd.DataFrame(
{'importance':RF_boston.feature_importances_},
index=feature_names)
feature_imp.sort_values(by='importance', ascending=False)
```
This
is a relative measure of the total decrease in node impurity that results from
splits over that variable, averaged over all trees (this was plotted in Figure 8.9 for a model fit to the `Heart` data).
The results indicate that across all of the trees considered in the
random forest, the wealth level of the community (`lstat`) and the
house size (`rm`) are by far the two most important variables.
## Boosting
Here we use `GradientBoostingRegressor()` from `sklearn.ensemble`
to fit boosted regression trees to the `Boston` data
set. For classification we would use `GradientBoostingClassifier()`.
The argument `n_estimators=5000`
indicates that we want 5000 trees, and the option
`max_depth=3` limits the depth of each tree. The
argument `learning_rate` is the $\lambda$
mentioned earlier in the description of boosting.
```{python}
boost_boston = GBR(n_estimators=5000,
learning_rate=0.001,
max_depth=3,
random_state=0)
boost_boston.fit(X_train, y_train)
```
We can see how the training error decreases with the `train_score_` attribute.
To get an idea of how the test error decreases we can use the
`staged_predict()` method to get the predicted values along the path.
```{python}
test_error = np.zeros_like(boost_boston.train_score_)
for idx, y_ in enumerate(boost_boston.staged_predict(X_test)):
test_error[idx] = np.mean((y_test - y_)**2)
plot_idx = np.arange(boost_boston.train_score_.shape[0])
ax = subplots(figsize=(8,8))[1]
ax.plot(plot_idx,
boost_boston.train_score_,
'b',
label='Training')
ax.plot(plot_idx,
test_error,
'r',
label='Test')
ax.legend();
```
We now use the boosted model to predict `medv` on the test set:
```{python}
y_hat_boost = boost_boston.predict(X_test);
np.mean((y_test - y_hat_boost)**2)
```
The test MSE obtained is 14.48,
similar to the test MSE for bagging. If we want to, we can
perform boosting with a different value of the shrinkage parameter
$\lambda$ in (8.10). The default value is 0.001, but
this is easily modified. Here we take $\lambda=0.2$.
```{python}
boost_boston = GBR(n_estimators=5000,
learning_rate=0.2,
max_depth=3,
random_state=0)
boost_boston.fit(X_train,
y_train)
y_hat_boost = boost_boston.predict(X_test);
np.mean((y_test - y_hat_boost)**2)
```
In this case, using $\lambda=0.2$ leads to a almost the same test MSE
as when using $\lambda=0.001$.
## Bayesian Additive Regression Trees
In this section we demonstrate a `Python` implementation of BART found in the
`ISLP.bart` package. We fit a model
to the `Boston` housing data set. This `BART()` estimator is
designed for quantitative outcome variables, though other implementations are available for
fitting logistic and probit models to categorical outcomes.
```{python}
bart_boston = BART(random_state=0, burnin=5, ndraw=15)
bart_boston.fit(X_train, y_train)
```
On this data set, with this split into test and training, we see that the test error of BART is similar to that of random forest.
```{python}
yhat_test = bart_boston.predict(X_test.astype(np.float32))
np.mean((y_test - yhat_test)**2)
```
We can check how many times each variable appeared in the collection of trees.
This gives a summary similar to the variable importance plot for boosting and random forests.
```{python}
var_inclusion = pd.Series(bart_boston.variable_inclusion_.mean(0),
index=D.columns)
var_inclusion
```