---
title: "Machine Learning Workflow: Boosting for Classification"
subtitle: "Econ 425T"
author: "Dr. Hua Zhou @ UCLA"
date: "`r format(Sys.time(), '%d %B, %Y')`"
format:
  html:
    theme: cosmo
    number-sections: true
    toc: true
    toc-depth: 4
    toc-location: left
    code-fold: false
engine: knitr
knitr:
  opts_chunk: 
    fig.align: 'center'
    # fig.width: 6
    # fig.height: 4
    message: FALSE
    cache: false
---

Display system information for reproducibility.

::: {.panel-tabset}

#### Python

```{python}
import IPython
print(IPython.sys_info())
```

:::

## Overview

![](https://www.tidymodels.org/start/resampling/img/resampling.svg)


We illustrate the typical machine learning workflow for regression trees using the `Heart` data set from R `ISLR2` package. 

1. Initial splitting to test and non-test sets.

2. Pre-processing of data: not much is needed for regression trees.

3. Tune the cost complexity pruning hyper-parameter(s) using 10-fold cross-validation (CV) on the non-test data.

4. Choose the best model by CV and refit it on the whole non-test data.

5. Final prediction on the test data.

## Heart data

The goal is to predict the binary outcome `AHD` (`Yes` or `No`) of patients.

::: {.panel-tabset}

#### Python

```{python}
# Load the pandas library
import pandas as pd
# Load numpy for array manipulation
import numpy as np
# Load seaborn plotting library
import seaborn as sns
import matplotlib.pyplot as plt

# Set font sizes in plots
sns.set(font_scale = 1.2)
# Display all columns
pd.set_option('display.max_columns', None)

Heart = pd.read_csv("../data/Heart.csv")
Heart
```

```{python}
# Numerical summaries
Heart.describe(include = 'all')
```

Graphical summary:
```{python}
#| eval: false
# Graphical summaries
plt.figure()
sns.pairplot(data = Heart);
plt.show()
```

:::


## Initial split into test and non-test sets

We randomly split the data in half of test data and another half of non-test data. Stratify on `AHD`.

::: {.panel-tabset}

#### Python

```{python}
from sklearn.model_selection import train_test_split

Heart_other, Heart_test = train_test_split(
  Heart, 
  train_size = 0.75,
  random_state = 425, # seed
  stratify = Heart.AHD
  )
Heart_test.shape
Heart_other.shape
```

Separate $X$ and $y$. We will use 13 features.
```{python}
num_features = ['Age', 'Sex', 'RestBP', 'Chol', 'Fbs', 'RestECG', 'MaxHR', 'ExAng', 'Oldpeak', 'Slope', 'Ca']
cat_features = ['ChestPain', 'Thal']
features = np.concatenate([num_features, cat_features])
# Non-test X and y
X_other = Heart_other[features]
y_other = Heart_other.AHD
# Test X and y
X_test = Heart_test[features]
y_test = Heart_test.AHD
```

:::

## Preprocessing (Python) or recipe (R)

::: {.panel-tabset}

#### Python

There are missing values in `Ca` (quantitative) and `Thal` (qualitative) variables. We are going to use simple `mean` imputation for `Ca` and `most_frequent` imputation for `Thal`. This is suboptimal. Better strategy is to use multiple imputation.
```{python}
# How many NaNs
Heart.isna().sum()
```

In principle, decision trees should be able to handle categorical predictors. However scikit-learn and xgboost implementations don't allow categorical predictors and require one-hot encoding.

```{python}
from sklearn.preprocessing import OneHotEncoder
from sklearn.impute import SimpleImputer
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline

# Transformer for categorical variables
categorical_tf = Pipeline(steps = [
  ("cat_impute", SimpleImputer(strategy = 'most_frequent')),
  ("encoder", OneHotEncoder())
])

# Transformer for continuous variables
numeric_tf = Pipeline(steps = [
  ("num_impute", SimpleImputer(strategy = 'mean')),
])

# Column transformer
col_tf = ColumnTransformer(transformers = [
  ('num', numeric_tf, num_features),
  ('cat', categorical_tf, cat_features)
])
```

:::

## Model

::: {.panel-tabset}

#### Python

```{python}
from sklearn.ensemble import AdaBoostClassifier
from sklearn.tree import DecisionTreeClassifier

bst_mod = AdaBoostClassifier(
  estimator = DecisionTreeClassifier(max_depth = 3),
  # Number of trees (to be tuned)
  n_estimators = 50, 
  # Learning rate (to be tuned)
  learning_rate = 1.0,
  random_state = 425
  )
```

:::

## Pipeline (Python) or workflow (R)

Here we bundle the preprocessing step (Python) or recipe (R) and model.

::: {.panel-tabset}

#### Python

```{python}
from sklearn.pipeline import Pipeline

pipe = Pipeline(steps = [
  ("col_tf", col_tf),
  ("model", bst_mod)
  ])
pipe
```


:::

## Tuning grid

Here we tune the number of trees `n_estimators` and the learning rate `learning_rate`.

::: {.panel-tabset}

#### Python

```{python}
# Tune hyper-parameter(s)
d_grid = [
  DecisionTreeClassifier(max_depth = 1),
  DecisionTreeClassifier(max_depth = 2),
  DecisionTreeClassifier(max_depth = 3),
  DecisionTreeClassifier(max_depth = 4)
  ]
B_grid = np.linspace(10, 100, 10).astype(int)
lambda_grid = [0.2, 0.4, 0.6, 0.8, 1.0]
tuned_parameters = {
  "model__estimator": d_grid,
  "model__n_estimators": B_grid,
  "model__learning_rate": lambda_grid
  }
tuned_parameters 
```

:::

## Cross-validation (CV)

::: {.panel-tabset}

#### Python

Set up CV partitions and CV criterion.

```{python}
from sklearn.model_selection import GridSearchCV

# Set up CV
n_folds = 5
search = GridSearchCV(
  pipe,
  tuned_parameters,
  cv = n_folds, 
  scoring = "roc_auc",
  # Refit the best model on the whole data set
  refit = True
  )
```

Fit CV. This is typically the most time-consuming step.
```{python}
# Fit CV
search.fit(X_other, y_other)
```

Visualize CV results.
```{python}
#| eval: true
#| code-fold: true
cv_res = pd.DataFrame({
  "B": np.array(search.cv_results_["param_model__n_estimators"]),
  "auc": search.cv_results_["mean_test_score"],
  "lambda": search.cv_results_["param_model__learning_rate"],
  "depth": search.cv_results_["param_model__estimator"],
  })

plt.figure()
sns.relplot(
  # kind = "line",
  data = cv_res,
  x = "B",
  y = "auc",
  hue = "lambda",
  style = "depth"
  ).set(
    xlabel = "B",
    ylabel = "CV AUC"
);
plt.show()
```

Best CV AUC:
```{python}
search.best_score_
```

The training accuracy is
```{python}
from sklearn.metrics import accuracy_score, roc_auc_score

accuracy_score(
  y_other,
  search.best_estimator_.predict(X_other)
  )
```

:::

## Finalize our model

Now we are done tuning. Finally, let’s fit this final model to the whole training data and use our test data to estimate the model performance we expect to see with new data.

::: {.panel-tabset}

#### Python

Since we called `GridSearchCV` with `refit = True`, the best model fit on the whole non-test data is readily available.
```{python}
search.best_estimator_
```
The final AUC on the test set is
```{python}
roc_auc_score(
  y_test,
  search.best_estimator_.predict_proba(X_test)[:, 1]
  )
```

The final classification accuracy on the test set is
```{python}
accuracy_score(
  y_test,
  search.best_estimator_.predict(X_test)
  )
```

:::