Lecture 12 Supplemental Notebook

Data 100, Spring 2023

Acknowledgments Page

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
import plotly.express as px
import plotly.graph_objs as go

Multiple Linear Regression

Let's load in a new dataset. This is aggregate per-player data from the 2018-19 NBA season, downloaded from Kaggle.

nba = pd.read_csv('nba18-19.csv', index_col=0)
nba.index.name = None # Drops name of index (players are ordered by rank)

nba.head(5)

	Player	Pos	Age	Tm	G	GS	MP	FG	FGA	FG%	...	FT%	ORB	DRB	TRB	AST	STL	BLK	TOV	PF	PTS
1	Álex Abrines\abrinal01	SG	25	OKC	31	2	19.0	1.8	5.1	0.357	...	0.923	0.2	1.4	1.5	0.6	0.5	0.2	0.5	1.7	5.3
2	Quincy Acy\acyqu01	PF	28	PHO	10	0	12.3	0.4	1.8	0.222	...	0.700	0.3	2.2	2.5	0.8	0.1	0.4	0.4	2.4	1.7
3	Jaylen Adams\adamsja01	PG	22	ATL	34	1	12.6	1.1	3.2	0.345	...	0.778	0.3	1.4	1.8	1.9	0.4	0.1	0.8	1.3	3.2
4	Steven Adams\adamsst01	C	25	OKC	80	80	33.4	6.0	10.1	0.595	...	0.500	4.9	4.6	9.5	1.6	1.5	1.0	1.7	2.6	13.9
5	Bam Adebayo\adebaba01	C	21	MIA	82	28	23.3	3.4	5.9	0.576	...	0.735	2.0	5.3	7.3	2.2	0.9	0.8	1.5	2.5	8.9

5 rows × 29 columns

We are interested in predicting the number of points (PTS) an athlete will score in a basketball game from this season.

Suppose we want to fit a linear model by using some characteristics, or features of a player. Specifically, we'll focus on field goals, assists, and 3 point attempts.

• FG, the number of (2 point) field goals per game
• AST, the average number of assists per game, and
• 3PA, the number of 3 point field goals attempted per game

This is how we express that model:

$$ \hat{y} = \theta_0 + \theta_1 \cdot \textbf{FG} + \theta_2 \cdot \textbf{AST} + \theta_3 \cdot \textbf{3PA} $$

nba[['FG', 'AST', '3PA', 'PTS']]

	FG	AST	3PA	PTS
1	1.8	0.6	4.1	5.3
2	0.4	0.8	1.5	1.7
3	1.1	1.9	2.2	3.2
4	6.0	1.6	0.0	13.9
5	3.4	2.2	0.2	8.9
...	...	...	...	...
528	4.0	0.8	0.0	11.5
529	3.1	0.9	0.0	7.8
530	3.6	1.1	0.0	8.9
530	3.4	0.8	0.0	8.5
530	3.8	1.5	0.0	9.4

708 rows × 4 columns

In lecture, we saw that the predictions for the entire data set $\hat{\mathbb{Y}}$ can be computed as:

$$ \Large \hat{\mathbb{Y}} = \mathbb{X} \theta $$

The design matrix $\mathbb{X} \in \mathbb{R}^{n,d+1}$ has $n$ rows corresponding to each record in the dataset and $d+1$ columns corresponding to the original $d$ columns in the data plus an additional 1s column vector (bias/intercept feature).

Let's build this design matrix using Pandas:

X = nba[['FG', 'AST', '3PA']]
X.insert(0, 'Bias', 1)
X

	Bias	FG	AST	3PA
1	1	1.8	0.6	4.1
2	1	0.4	0.8	1.5
3	1	1.1	1.9	2.2
4	1	6.0	1.6	0.0
5	1	3.4	2.2	0.2
...	...	...	...	...
528	1	4.0	0.8	0.0
529	1	3.1	0.9	0.0
530	1	3.6	1.1	0.0
530	1	3.4	0.8	0.0
530	1	3.8	1.5	0.0

708 rows × 4 columns

X = X.to_numpy()
X.shape

(708, 4)

While we're at it, let's build the $\mathbb{Y}$ vector of our true PTS values.

# For nba data
Y = nba[["PTS"]].to_numpy()
n = len(Y)
print("number datapoints", n)
Y[:5]

number datapoints 708

array([[ 5.3],
       [ 1.7],
       [ 3.2],
       [13.9],
       [ 8.9]])

---

Example prediction

Suppose we decide to pick an arbitrary parameter $\theta$:

$$\theta = \begin{bmatrix}0.50 \\ -0.14 \\ 0.65 \\ 1.52 \end{bmatrix}$$

(For those interested: I drew these from random values simulated from a standard normal distribution using np.random.randn.)

theta_arbitrary = np.array([[0.5], [-1.14], [0.65], [1.52]])
theta_arbitrary

array([[ 0.5 ],
       [-1.14],
       [ 0.65],
       [ 1.52]])

For this value of $\theta$ we can make a prediction using our model with matrix multiplication.

The @ symbol is the matrix multiply operation and is equivalent to writing xt.dot(theta).

display((X @ theta_arbitrary)[:5])

display(Y[:5])

array([[ 5.07 ],
       [ 2.844],
       [ 3.825],
       [-5.3  ],
       [-1.642]])

array([[ 5.3],
       [ 1.7],
       [ 3.2],
       [13.9],
       [ 8.9]])

Computing MSE

For Ordinary Least Squares, the average loss is MSE:

$$ \Large R(\theta) = \frac{1}{n} || \mathbb{Y} - \mathbb{X}\theta||^2_2 $$

NumPy has a handy function np.linalg.norm that computes the norm of a matrix (default is L2 norm).

theta_arbitrary = np.array([0.5, -1.14, 0.65, 1.52])
def mse_nba(theta):
    """
    Y is PTS
    X is intercept, FG, AST, 3PA
    """
    return (1/n) * (np.linalg.norm(Y - X @ theta) ** 2)

theta_arbitrary
mse_nba(theta_arbitrary)

57637.9018454746

Is this good? Is this bad? Let's compute the optimal theta and compare!

---

Implementing Least Squares

From lecture, the Least Squares Estimate $\hat{\theta}$ is: $$ \Large \hat{\theta} = \left( \mathbb{X}^T \mathbb{X} \right)^{-1} \mathbb{X}^T \mathbb{Y} $$

While it is not as numerically stable or efficient. We can compute $\hat{\theta}$ by direction using matrix inversion. To do this, we import the inv function for the numpy linear algebra library:

from numpy.linalg import inv

def least_squares_estimate(X, Y):
    return inv(X.T @ X) @ X.T @ Y

theta_hat = least_squares_estimate(X, Y)
theta_hat

array([[-0.29253798],
       [ 2.51705703],
       [ 0.05075571],
       [ 0.31307653]])

print("Arbitrary theta MSE: ", mse_nba(theta_arbitrary))
print("Optimal theta MSE:"  , mse_nba(theta_hat))

Arbitrary theta MSE:  57637.9018454746
Optimal theta MSE: 0.3963133329080335

Nice!!!

---

Making Least Squares Predictions

Now that we have estimated the model parameters $\hat{\theta}$ we can now also predict the points scored $\hat{\mathbb{Y}}$ for each of our players.

Y_hat = X @ theta_hat

---

Model Performance/Diagnosing the Model

In previous lectures we have plotted the residual vs. our single input feature $x$.

For higher dimensional regression models, we often graph the residual with respect to the fitted values $\hat{y}$.

fig = px.scatter(x = Y_hat.flatten(), y = (Y - Y_hat).flatten(), opacity=0.8, width=600, height=400)
fig.add_trace(go.Scatter(x=[0, 30], y=[0,0], name="$\hat{Y}=Y$"))
fig.update_xaxes(title="$\hat{Y}$")
fig.update_yaxes(title="$\hat{Y} - Y$")
fig

Overall while the residuals are roughly centered around 0 ($\hat{y} = y$), we see heteroskedasticity: Our regression spread is uneven, particularly as predictions get big.

Multiple $R^2$

Let's compute the coefficient of determination, or multiple $R^2$, for our model.

r2_ast_fg_3pa = np.var(Y_hat) / np.var(Y)
r2_ast_fg_3pa

0.9883162128703274

That's super high!!! Wait, what's up with that?

(Hint: Basketball facts)

Let's try reducing the number of features to see how this Multiple $R^2$ metric changes.

# Use intercept, AST, 3PA
X_3d = nba[['AST', '3PA']]
X_3d.insert(0, 'Bias', 1)
X_3d = X_3d.to_numpy()

theta_ast_3pa = least_squares_estimate(X_3d, Y)
Y_hat_ast_3pa = X_3d @ theta_ast_3pa

r2_ast_3pa = np.var(Y_hat_ast_3pa) / np.var(Y)
r2_ast_3pa

0.608786276366571

# Use intercept, AST only (SLR)
X_slr = nba[['AST']]
X_slr.insert(0, 'Bias', 1)
X_slr = X_slr.to_numpy()

theta_ast_only = least_squares_estimate(X_slr, Y)
Y_hat_ast_only = X_slr @ theta_ast_only

r2_ast_only = np.var(Y_hat_ast_only) / np.var(Y)
r2_ast_only

0.4570055507968593

theta_ast_only

array([[3.98332315],
       [2.39888152]])

theta_hat

array([[-0.29253798],
       [ 2.51705703],
       [ 0.05075571],
       [ 0.31307653]])

Comparing these Multiple $R^2$ together:

print("(SLR) intercept, AST:    ", r2_ast_only)
print("intercept, 3PA, AST:     ", r2_ast_3pa)
print("intercept, FG, 3PA, AST: ", r2_ast_fg_3pa)

(SLR) intercept, AST:     0.4570055507968593
intercept, 3PA, AST:      0.608786276366571
intercept, FG, 3PA, AST:  0.9883162128703274

Because of how basketball is scored, knowing Field Goals FG and 3 point goal attempts 3PA will reliably tell you how many total points a player scored in that game. This is assuming NBA players make a good number of their 3 pointers.

Side note, if you wanted to check that Multiple R^2 for Simple Linear Regression is indeed correlation coefficient $r^2$:

r = np.corrcoef(nba['AST'], nba['PTS'])[0,1]
r ** 2

0.4570055507968595