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v2.1 notebooks excluding 10,13

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Jonathan Taylor
2023-08-20 19:31:42 -07:00
parent 5c29f1c9e4
commit fc0c9152cb
20 changed files with 3663 additions and 3808 deletions
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21
Ch02-statlearn-lab.Rmd
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@@ -1,16 +1,3 @@
---
jupyter:
jupytext:
cell_metadata_filter: -all
formats: ipynb,Rmd
main_language: python
text_representation:
extension: .Rmd
format_name: rmarkdown
format_version: '1.2'
jupytext_version: 1.14.7
---
# Chapter 2
@@ -78,7 +65,7 @@ print('fit a model with', 11, 'variables')
The following command will provide information about the `print()` function.
```{python}
# print?
print?
```
@@ -228,7 +215,7 @@ documentation associated with the function `fun`, if it exists.
We can try this for `np.array()`.
```{python}
# np.array?
np.array?
```
This documentation indicates that we could create a floating point array by passing a `dtype` argument into `np.array()`.
@@ -1303,8 +1290,8 @@ The plot methods of a data frame return a familiar object:
an axes. We can use it to update the plot as we did previously:
```{python}
ax = Auto.plot.scatter('horsepower', 'mpg');
ax.set_title('Horsepower vs. MPG')
ax = Auto.plot.scatter('horsepower', 'mpg')
ax.set_title('Horsepower vs. MPG');
```
If we want to save
the figure that contains a given axes, we can find the relevant figure

1490
Ch02-statlearn-lab.ipynb
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19
Ch03-linreg-lab.Rmd
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@@ -1,22 +1,7 @@
---
jupyter:
jupytext:
cell_metadata_filter: -all
formats: ipynb,Rmd
main_language: python
text_representation:
extension: .Rmd
format_name: rmarkdown
format_version: '1.2'
jupytext_version: 1.14.7
---
# Chapter 3
# Lab: Linear Regression
## Importing packages
@@ -277,7 +262,7 @@ value of 25.05 for `medv` when `lstat` equals
10), but the latter are substantially wider.
Next we will plot `medv` and `lstat`
using `DataFrame.plot.scatter()`,
using `DataFrame.plot.scatter()`, \definelongblankMR{plot.scatter()}{plot.slashslashscatter()}
and wish to
add the regression line to the resulting plot.
@@ -399,7 +384,7 @@ Notice how we have compacted the first line into a succinct expression describin
The `Boston` data set contains 12 variables, and so it would be cumbersome
to have to type all of these in order to perform a regression using all of the predictors.
Instead, we can use the following short-hand:
Instead, we can use the following short-hand:\definelongblankMR{columns.drop()}{columns.slashslashdrop()}
```{python}
terms = Boston.columns.drop('medv')

473
Ch03-linreg-lab.ipynb
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15
Ch04-classification-lab.Rmd
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---
jupyter:
jupytext:
cell_metadata_filter: -all
formats: ipynb,Rmd
main_language: python
text_representation:
extension: .Rmd
format_name: rmarkdown
format_version: '1.2'
jupytext_version: 1.14.7
---
# Chapter 4
@@ -807,7 +794,7 @@ feature_std.std()
```
Notice that the standard deviations are not quite $1$ here; this is again due to some procedures using the $1/n$ convention for variances (in this case `scaler()`), while others use $1/(n-1)$ (the `std()` method). See the footnote on page 103.
Notice that the standard deviations are not quite $1$ here; this is again due to some procedures using the $1/n$ convention for variances (in this case `scaler()`), while others use $1/(n-1)$ (the `std()` method). See the footnote on page 200.
In this case it does not matter, as long as the variables are all on the same scale.
Using the function `train_test_split()` we now split the observations into a test set,

1114
Ch04-classification-lab.ipynb
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17
Ch05-resample-lab.Rmd
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---
jupyter:
jupytext:
cell_metadata_filter: -all
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---
# Chapter 5
@@ -518,13 +505,13 @@ slope. Interestingly, these are somewhat different from the estimates
obtained using the bootstrap. Does this indicate a problem with the
bootstrap? In fact, it suggests the opposite. Recall that the
standard formulas given in
{Equation 3.8 on page 80}
{Equation 3.8 on page 82}
rely on certain assumptions. For example,
they depend on the unknown parameter $\sigma^2$, the noise
variance. We then estimate $\sigma^2$ using the RSS. Now although the
formula for the standard errors do not rely on the linear model being
correct, the estimate for $\sigma^2$ does. We see
{in Figure 3.8 on page 106} that there is
{in Figure 3.8 on page 108} that there is
a non-linear relationship in the data, and so the residuals from a
linear fit will be inflated, and so will $\hat{\sigma}^2$. Secondly,
the standard formulas assume (somewhat unrealistically) that the $x_i$

330
Ch05-resample-lab.ipynb
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@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "markdown",
"id": "3a3f2f85",
"id": "85ad9863",
"metadata": {},
"source": [
"\n",
@@ -12,7 +12,7 @@
},
{
"cell_type": "markdown",
"id": "bb22af17",
"id": "ac8b08af",
"metadata": {},
"source": [
"# Lab: Cross-Validation and the Bootstrap\n",
@@ -26,13 +26,13 @@
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@@ -49,7 +49,7 @@
},
{
"cell_type": "markdown",
"id": "78fcfe7a",
"id": "784a2ba3",
"metadata": {},
"source": [
"There are several new imports needed for this lab."
@@ -58,13 +58,13 @@
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"id": "21c2ed4f",
"metadata": {
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@@ -81,7 +81,7 @@
},
{
"cell_type": "markdown",
"id": "713d30db",
"id": "9ac3acd5",
"metadata": {},
"source": [
"## The Validation Set Approach\n",
@@ -102,13 +102,13 @@
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"id": "99c95faf",
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@@ -121,7 +121,7 @@
},
{
"cell_type": "markdown",
"id": "57be35df",
"id": "e76383f0",
"metadata": {},
"source": [
"Now we can fit a linear regression using only the observations corresponding to the training set `Auto_train`."
@@ -130,13 +130,13 @@
{
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"id": "41b0717d",
"id": "d9b0b7c8",
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@@ -150,7 +150,7 @@
},
{
"cell_type": "markdown",
"id": "7f1bef95",
"id": "d196dd08",
"metadata": {},
"source": [
"We now use the `predict()` method of `results` evaluated on the model matrix for this model\n",
@@ -160,13 +160,13 @@
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"id": "d7ea3c0d",
"id": "3e77d831",
"metadata": {
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"outputs": [
@@ -190,7 +190,7 @@
},
{
"cell_type": "markdown",
"id": "6dba5d55",
"id": "f4369ee6",
"metadata": {},
"source": [
"Hence our estimate for the validation MSE of the linear regression\n",
@@ -204,13 +204,13 @@
{
"cell_type": "code",
"execution_count": 6,
"id": "a02a2d05",
"id": "0aa4bfcc",
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@@ -235,7 +235,7 @@
},
{
"cell_type": "markdown",
"id": "39ab59b1",
"id": "0271dc50",
"metadata": {},
"source": [
"Let’s use this function to estimate the validation MSE\n",
@@ -247,13 +247,13 @@
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"id": "a0dbd55f",
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@@ -280,7 +280,7 @@
},
{
"cell_type": "markdown",
"id": "936e168a",
"id": "a7401536",
"metadata": {},
"source": [
"These error rates are $23.62, 18.76$, and $18.80$, respectively. If we\n",
@@ -291,13 +291,13 @@
{
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"id": "83432f06",
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@@ -327,7 +327,7 @@
},
{
"cell_type": "markdown",
"id": "f5ceb357",
"id": "00785402",
"metadata": {},
"source": [
"Using this split of the observations into a training set and a validation set,\n",
@@ -341,7 +341,7 @@
},
{
"cell_type": "markdown",
"id": "6d624a5c",
"id": "21c071b8",
"metadata": {},
"source": [
"## Cross-Validation\n",
@@ -374,13 +374,13 @@
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@@ -410,7 +410,7 @@
},
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"cell_type": "markdown",
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"source": [
"The arguments to `cross_validate()` are as follows: an\n",
@@ -426,7 +426,7 @@
},
{
"cell_type": "markdown",
"id": "b527f67f",
"id": "5c7901f2",
"metadata": {},
"source": [
"We can repeat this procedure for increasingly complex polynomial fits.\n",
@@ -442,13 +442,13 @@
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@@ -480,7 +480,7 @@
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"id": "792f1304",
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"As in Figure 5.4, we see a sharp drop in the estimated test MSE between the linear and\n",
@@ -499,13 +499,13 @@
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@@ -530,7 +530,7 @@
},
{
"cell_type": "markdown",
"id": "983625b2",
"id": "f5092f1b",
"metadata": {},
"source": [
"In the CV example above, we used $K=n$, but of course we can also use $K<n$. The code is very similar\n",
@@ -541,13 +541,13 @@
{
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@@ -580,7 +580,7 @@
},
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"id": "32bf6662",
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"metadata": {},
"source": [
"Notice that the computation time is much shorter than that of LOOCV.\n",
@@ -595,7 +595,7 @@
},
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"id": "1e89127b",
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"metadata": {},
"source": [
"The `cross_validate()` function is flexible and can take\n",
@@ -606,13 +606,13 @@
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@@ -641,7 +641,7 @@
},
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"source": [
"One can estimate the variability in the test error by running the following:"
@@ -650,13 +650,13 @@
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@@ -684,7 +684,7 @@
},
{
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"metadata": {},
"source": [
"Note that this standard deviation is not a valid estimate of the\n",
@@ -724,13 +724,13 @@
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@@ -745,7 +745,7 @@
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"This function returns an estimate for $\\alpha$\n",
@@ -758,13 +758,13 @@
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@@ -785,7 +785,7 @@
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@@ -797,13 +797,13 @@
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@@ -840,13 +840,13 @@
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@@ -872,7 +872,7 @@
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"Notice the use of `_` as a loop variable in `for _ in range(B)`. This is often used if the value of the counter is\n",
@@ -885,13 +885,13 @@
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@@ -916,7 +916,7 @@
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"The final output shows that the bootstrap estimate for ${\\rm SE}(\\hat{\\alpha})$ is $0.0912$.\n",
@@ -951,13 +951,13 @@
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@@ -986,13 +986,13 @@
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"Typing `hp_func?` will show that it has two arguments `D`\n",
@@ -1019,13 +1019,13 @@
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@@ -1070,13 +1070,13 @@
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"This indicates that the bootstrap estimate for ${\\rm SE}(\\hat{\\beta}_0)$ is\n",
@@ -1120,13 +1120,13 @@
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"The standard error estimates for $\\hat{\\beta}_0$ and $\\hat{\\beta}_1$\n",
@@ -1164,13 +1164,13 @@
"obtained using the bootstrap. Does this indicate a problem with the\n",
"bootstrap? In fact, it suggests the opposite. Recall that the\n",
"standard formulas given in\n",
" {Equation 3.8 on page 80}\n",
" {Equation 3.8 on page 82}\n",
"rely on certain assumptions. For example,\n",
"they depend on the unknown parameter $\\sigma^2$, the noise\n",
"variance. We then estimate $\\sigma^2$ using the RSS. Now although the\n",
"formula for the standard errors do not rely on the linear model being\n",
"correct, the estimate for $\\sigma^2$ does. We see\n",
" {in Figure 3.8 on page 106} that there is\n",
" {in Figure 3.8 on page 108} that there is\n",
"a non-linear relationship in the data, and so the residuals from a\n",
"linear fit will be inflated, and so will $\\hat{\\sigma}^2$. Secondly,\n",
"the standard formulas assume (somewhat unrealistically) that the $x_i$\n",
@@ -1192,13 +1192,13 @@
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@@ -1226,7 +1226,7 @@
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"We compare the results to the standard errors computed using `sm.OLS()`."
@@ -1235,13 +1235,13 @@
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---
jupyter:
jupytext:
cell_metadata_filter: -all
formats: ipynb,Rmd
main_language: python
text_representation:
extension: .Rmd
format_name: rmarkdown
format_version: '1.2'
jupytext_version: 1.14.7
---
# Chapter 6
@@ -45,7 +32,7 @@ from ISLP.models import \
(Stepwise,
sklearn_selected,
sklearn_selection_path)
# !pip install l0bnb
!pip install l0bnb
from l0bnb import fit_path
```
@@ -540,7 +527,7 @@ grid.best_params_['ridge__alpha']
grid.best_estimator_
```
Recall we set up the `kfold` object for 5-fold cross-validation on page 296. We now plot the cross-validated MSE as a function of $-\log(\lambda)$, which has shrinkage decreasing from left
Recall we set up the `kfold` object for 5-fold cross-validation on page 298. We now plot the cross-validated MSE as a function of $-\log(\lambda)$, which has shrinkage decreasing from left
to right.
```{python}
@@ -728,7 +715,7 @@ ax.set_ylabel('Standardized coefficiients', fontsize=20);
```
The smallest cross-validated error is lower than the test set MSE of the null model
and of least squares, and very similar to the test MSE of 115526.71 of ridge
regression (page 303) with $\lambda$ chosen by cross-validation.
regression (page 305) with $\lambda$ chosen by cross-validation.
```{python}
np.min(tuned_lasso.mse_path_.mean(1))

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---
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# Chapter 7
@@ -83,7 +70,7 @@ for feature transformations such as `PCA()` seen in Section 6.5.3) which
allows for easy evaluation of the polynomial at new data points. Here `poly()` is referred to as a *helper* function, and sets up the transformation; `Poly()` is the actual workhorse that computes the transformation. See also
the
discussion of transformations on
page 127.
page 129.
In the code above, the first line executes the `fit()` method
using the dataframe
@@ -748,7 +735,6 @@ ANOVA, $\mathcal{M}_2$ is preferred.
We can repeat the same process for `age` as well. We see there is very clear evidence that
a non-linear term is required for `age`.
\newpage
```{python}
gam_0 = LinearGAM(year_term +

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# Chapter 8

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# Chapter 9

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@@ -2,7 +2,7 @@
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},
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"# Lab: Support Vector Machines\n",
@@ -25,13 +25,13 @@
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"We also collect the new imports\n",
@@ -55,13 +55,13 @@
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@@ -73,7 +73,7 @@
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"We will use the function `RocCurveDisplay.from_estimator()` to\n",
@@ -83,13 +83,13 @@
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@@ -99,7 +99,7 @@
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"## Support Vector Classifier\n",
@@ -123,13 +123,13 @@
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"They are not. We now fit the classifier."
@@ -168,13 +168,13 @@
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"The support vector classifier with two features can\n",
@@ -212,13 +212,13 @@
{
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@@ -243,7 +243,7 @@
},
{
"cell_type": "markdown",
"id": "f6ce1246",
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"metadata": {},
"source": [
"The decision\n",
@@ -257,13 +257,13 @@
{
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@@ -291,7 +291,7 @@
},
{
"cell_type": "markdown",
"id": "906f4bb8",
"id": "301d764d",
"metadata": {},
"source": [
"With a smaller value of the cost parameter, we\n",
@@ -303,13 +303,13 @@
{
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@@ -331,7 +331,7 @@
},
{
"cell_type": "markdown",
"id": "90a0ee53",
"id": "0693b3eb",
"metadata": {},
"source": [
"Since the support vector machine is an estimator in `sklearn`, we\n",
@@ -341,13 +341,13 @@
{
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"id": "b65e80d6",
"id": "9adb3793",
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@@ -378,7 +378,7 @@
},
{
"cell_type": "markdown",
"id": "d390528c",
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"metadata": {},
"source": [
"We can easily access the cross-validation errors for each of these models\n",
@@ -389,13 +389,13 @@
{
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"id": "bba8fad7",
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@@ -417,7 +417,7 @@
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{
"cell_type": "markdown",
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"metadata": {},
"source": [
"We see that `C=1` results in the highest cross-validation\n",
@@ -430,13 +430,13 @@
{
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@@ -448,7 +448,7 @@
},
{
"cell_type": "markdown",
"id": "db41f5e2",
"id": "ddbda9de",
"metadata": {},
"source": [
"Now we predict the class labels of these test observations. Here we\n",
@@ -459,13 +459,13 @@
{
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@@ -534,7 +534,7 @@
},
{
"cell_type": "markdown",
"id": "bbfc8005",
"id": "7f002ea6",
"metadata": {},
"source": [
"Thus, with this value of `C`,\n",
@@ -546,13 +546,13 @@
{
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"id": "0320d9e0",
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@@ -622,7 +622,7 @@
},
{
"cell_type": "markdown",
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"source": [
"In this case 60% of test observations are correctly classified.\n",
@@ -637,13 +637,13 @@
{
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@@ -666,7 +666,7 @@
},
{
"cell_type": "markdown",
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"source": [
"Now the observations are just barely linearly separable."
@@ -675,13 +675,13 @@
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@@ -750,7 +750,7 @@
},
{
"cell_type": "markdown",
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"We fit the\n",
@@ -762,13 +762,13 @@
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@@ -794,7 +794,7 @@
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"source": [
"Indeed no training errors were made and only three support vectors were used.\n",
@@ -807,13 +807,13 @@
{
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@@ -882,7 +882,7 @@
},
{
"cell_type": "markdown",
"id": "39a432d1",
"id": "d0684844",
"metadata": {},
"source": [
"Using `C=0.1`, we again do not misclassify any training observations, but we\n",
@@ -894,13 +894,13 @@
{
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"id": "c67591a1",
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@@ -926,7 +926,7 @@
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{
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"metadata": {},
"source": [
"## Support Vector Machine\n",
@@ -947,13 +947,13 @@
{
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"id": "322be574",
"id": "2fee8df5",
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@@ -966,7 +966,7 @@
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{
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"source": [
"Plotting the data makes it clear that the class boundary is indeed non-linear."
@@ -975,13 +975,13 @@
{
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@@ -989,7 +989,7 @@
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@@ -1017,7 +1017,7 @@
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{
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"id": "64913fe3",
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"metadata": {},
"source": [
"The data is randomly split into training and testing groups. We then\n",
@@ -1028,13 +1028,13 @@
{
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"id": "0c2690d1",
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@@ -1066,7 +1066,7 @@
},
{
"cell_type": "markdown",
"id": "5da9efdb",
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"metadata": {},
"source": [
"The plot shows that the resulting SVM has a decidedly non-linear\n",
@@ -1076,13 +1076,13 @@
{
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"id": "3eb171e8",
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@@ -1107,7 +1107,7 @@
},
{
"cell_type": "markdown",
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"source": [
"We can see from the figure that there are a fair number of training\n",
@@ -1120,13 +1120,13 @@
{
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"id": "9a6b905b",
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@@ -1153,7 +1153,7 @@
},
{
"cell_type": "markdown",
"id": "300c1b8b",
"id": "5bc77e3f",
"metadata": {},
"source": [
"We can perform cross-validation using `skm.GridSearchCV()` to select the\n",
@@ -1164,13 +1164,13 @@
{
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"id": "5ab01d6c",
"id": "bcbd15a4",
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@@ -1201,7 +1201,7 @@
},
{
"cell_type": "markdown",
"id": "1bb987ae",
"id": "997bbfbd",
"metadata": {},
"source": [
"The best choice of parameters under five-fold CV is achieved at `C=1`\n",
@@ -1212,13 +1212,13 @@
{
"cell_type": "code",
"execution_count": 25,
"id": "166a6acb",
"id": "28ca551e",
"metadata": {
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@@ -1303,7 +1303,7 @@
},
{
"cell_type": "markdown",
"id": "39ee6f32",
"id": "48e671f4",
"metadata": {},
"source": [
"With these parameters, 12% of test\n",
@@ -1312,7 +1312,7 @@
},
{
"cell_type": "markdown",
"id": "f0ea699d",
"id": "eaed0a87",
"metadata": {},
"source": [
"## ROC Curves\n",
@@ -1346,13 +1346,13 @@
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" In this example, the SVM appears to provide accurate predictions. By increasing\n",
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@@ -1428,7 +1428,7 @@
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"cell_type": "markdown",
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"However, these ROC curves are all on the training data. We are really\n",
@@ -1440,13 +1440,13 @@
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@@ -1462,7 +1462,7 @@
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"Let’s look at our tuned SVM."
@@ -1471,13 +1471,13 @@
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"## SVM with Multiple Classes\n",
@@ -1526,13 +1526,13 @@
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@@ -1558,7 +1558,7 @@
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"We now fit an SVM to the data:"
@@ -1567,13 +1567,13 @@
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"The `sklearn.svm` library can also be used to perform support vector\n",
@@ -1614,7 +1614,7 @@
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"## Application to Gene Expression Data\n",
@@ -1631,13 +1631,13 @@
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"This data set consists of expression measurements for 2,308\n",
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"We see that there are *no* training\n",
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"We see that using `C=10` yields two test set errors on these data.\n",
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13
Ch11-surv-lab.Rmd
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# Chapter 11

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Ch11-surv-lab.ipynb
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"\n",
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"# Lab: Survival Analysis\n",
@@ -31,13 +31,13 @@
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@@ -51,7 +51,7 @@
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"We also collect the new imports\n",
@@ -61,13 +61,13 @@
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"## Brain Cancer Data\n",
@@ -94,13 +94,13 @@
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"The rows index the 88 patients, while the 8 columns contain the predictors and outcome variables.\n",
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"Before beginning an analysis, it is important to know how the\n",
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@@ -292,7 +292,7 @@
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"Next we create Kaplan-Meier survival curves that are stratified by\n",
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@@ -350,7 +350,7 @@
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"As discussed in Section 11.4, we can perform a\n",
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"The resulting $p$-value is $0.23$, indicating no evidence of a\n",
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@@ -553,7 +553,7 @@
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"outputs": [
@@ -659,7 +659,7 @@
},
{
"cell_type": "markdown",
"id": "2820f486",
"id": "2e6163ca",
"metadata": {},
"source": [
"Regardless of which test we use, we see that there is no clear\n",
@@ -675,13 +675,13 @@
{
"cell_type": "code",
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"id": "c2767d88",
"id": "c26a3499",
"metadata": {
"execution": {
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}
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"outputs": [
@@ -800,7 +800,7 @@
},
{
"cell_type": "markdown",
"id": "eee4ab1f",
"id": "a96e311c",
"metadata": {},
"source": [
" The `diagnosis` variable has been coded so that the baseline\n",
@@ -823,13 +823,13 @@
{
"cell_type": "code",
"execution_count": 13,
"id": "ede1d219",
"id": "8d999f26",
"metadata": {
"execution": {
"iopub.execute_input": "2023-08-07T00:19:35.030313Z",
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}
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"outputs": [],
@@ -845,7 +845,7 @@
},
{
"cell_type": "markdown",
"id": "e1c307ae",
"id": "bf628fd8",
"metadata": {},
"source": [
"We make four\n",
@@ -856,13 +856,13 @@
{
"cell_type": "code",
"execution_count": 14,
"id": "dc032a71",
"id": "a1f6b355",
"metadata": {
"execution": {
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}
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@@ -974,7 +974,7 @@
},
{
"cell_type": "markdown",
"id": "84da2586",
"id": "3f6334e4",
"metadata": {},
"source": [
"We then construct the model matrix based on the model specification `all_MS` used to fit\n",
@@ -984,13 +984,13 @@
{
"cell_type": "code",
"execution_count": 15,
"id": "e7c1fe43",
"id": "a1a9d5b3",
"metadata": {
"execution": {
"iopub.execute_input": "2023-08-07T00:19:35.043454Z",
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}
},
"outputs": [
@@ -1117,7 +1117,7 @@
},
{
"cell_type": "markdown",
"id": "3cfe1ec4",
"id": "3eaec7e6",
"metadata": {},
"source": [
"We can use the `predict_survival_function()` method to obtain the estimated survival function."
@@ -1126,13 +1126,13 @@
{
"cell_type": "code",
"execution_count": 16,
"id": "f89fbed7",
"id": "1a18b56a",
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"execution": {
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@@ -1276,7 +1276,7 @@
},
{
"cell_type": "markdown",
"id": "29afd641",
"id": "7d533f90",
"metadata": {},
"source": [
"This returns a data frame,\n",
@@ -1287,13 +1287,13 @@
{
"cell_type": "code",
"execution_count": 17,
"id": "8f0329b4",
"id": "ff3de29c",
"metadata": {
"execution": {
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@@ -1316,7 +1316,7 @@
},
{
"cell_type": "markdown",
"id": "12723ce5",
"id": "e660793e",
"metadata": {},
"source": [
"## Publication Data\n",
@@ -1330,13 +1330,13 @@
{
"cell_type": "code",
"execution_count": 18,
"id": "3045bfc0",
"id": "cd9060c1",
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"execution": {
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@@ -1363,7 +1363,7 @@
},
{
"cell_type": "markdown",
"id": "6fcb22f7",
"id": "d8f0f687",
"metadata": {},
"source": [
"As discussed previously, the $p$-values from fitting Cox’s\n",
@@ -1375,13 +1375,13 @@
{
"cell_type": "code",
"execution_count": 19,
"id": "d070f716",
"id": "6af7106e",
"metadata": {
"execution": {
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@@ -1453,7 +1453,7 @@
},
{
"cell_type": "markdown",
"id": "513a55b1",
"id": "8ef28b8b",
"metadata": {},
"source": [
"However, the results change dramatically when we include other\n",
@@ -1464,13 +1464,13 @@
{
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"execution_count": 20,
"id": "2bbcdd0c",
"id": "b6ebefa7",
"metadata": {
"execution": {
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}
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"outputs": [
@@ -1573,7 +1573,7 @@
},
{
"cell_type": "markdown",
"id": "75bb8aa6",
"id": "d463e623",
"metadata": {},
"source": [
"We see that there are a number of statistically significant variables,\n",
@@ -1583,7 +1583,7 @@
},
{
"cell_type": "markdown",
"id": "bfe236e5",
"id": "a23c38e0",
"metadata": {},
"source": [
"## Call Center Data\n",
@@ -1608,13 +1608,13 @@
{
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"execution_count": 21,
"id": "b8ece43a",
"id": "098f42ea",
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@@ -1637,7 +1637,7 @@
},
{
"cell_type": "markdown",
"id": "c93e44f3",
"id": "2f54ed03",
"metadata": {},
"source": [
"We then build a model matrix (omitting the intercept)"
@@ -1646,13 +1646,13 @@
{
"cell_type": "code",
"execution_count": 22,
"id": "3e4f766f",
"id": "26d5d0d0",
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@@ -1666,7 +1666,7 @@
},
{
"cell_type": "markdown",
"id": "cad1ed19",
"id": "a1a8f65d",
"metadata": {},
"source": [
"It is worthwhile to take a peek at the model matrix `X`, so\n",
@@ -1678,13 +1678,13 @@
{
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"execution_count": 23,
"id": "72f42d14",
"id": "77500663",
"metadata": {
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@@ -1781,7 +1781,7 @@
},
{
"cell_type": "markdown",
"id": "38c40ae1",
"id": "fd7bd61c",
"metadata": {},
"source": [
"Next, we specify the coefficients and the hazard function."
@@ -1790,13 +1790,13 @@
{
"cell_type": "code",
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"id": "8b921536",
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@@ -1808,7 +1808,7 @@
},
{
"cell_type": "markdown",
"id": "a0698ffd",
"id": "cfe879e6",
"metadata": {},
"source": [
"Here, we have set the coefficient associated with `Operators` to\n",
@@ -1837,13 +1837,13 @@
{
"cell_type": "code",
"execution_count": 25,
"id": "96ce0f99",
"id": "d4be10c2",
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@@ -1854,7 +1854,7 @@
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{
"cell_type": "markdown",
"id": "1956e4c2",
"id": "6095cfc1",
"metadata": {},
"source": [
"We are now ready to generate data under the Cox proportional hazards\n",
@@ -1868,13 +1868,13 @@
{
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"id": "63d78ff9",
"id": "c98d396f",
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@@ -1886,7 +1886,7 @@
},
{
"cell_type": "markdown",
"id": "035e4ecf",
"id": "ed2e23ea",
"metadata": {},
"source": [
"We now simulate our censoring variable, for which we assume\n",
@@ -1897,13 +1897,13 @@
{
"cell_type": "code",
"execution_count": 27,
"id": "fe008dbf",
"id": "caf627bc",
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@@ -2005,13 +2005,13 @@
{
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"id": "c3a2bec7",
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}
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@@ -2032,7 +2032,7 @@
},
{
"cell_type": "markdown",
"id": "207937e5",
"id": "5f345011",
"metadata": {},
"source": [
"We now plot Kaplan-Meier survival curves. First, we stratify by `Center`."
@@ -2041,13 +2041,13 @@
{
"cell_type": "code",
"execution_count": 29,
"id": "2b27af56",
"id": "338db71d",
"metadata": {
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@@ -2084,7 +2084,7 @@
},
{
"cell_type": "markdown",
"id": "be6d37f7",
"id": "1b5a1230",
"metadata": {},
"source": [
"Next, we stratify by `Time`."
@@ -2093,13 +2093,13 @@
{
"cell_type": "code",
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"id": "9625598d",
"id": "c1db6e15",
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@@ -2136,7 +2136,7 @@
},
{
"cell_type": "markdown",
"id": "1408ebc0",
"id": "deb73d38",
"metadata": {},
"source": [
"It seems that calls at Call Center B take longer to be answered than\n",
@@ -2149,13 +2149,13 @@
{
"cell_type": "code",
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"id": "75a744ef",
"id": "02ea4212",
"metadata": {
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@@ -2247,7 +2247,7 @@
},
{
"cell_type": "markdown",
"id": "be5055e4",
"id": "db9cc6ee",
"metadata": {},
"source": [
"Next, we consider the effect of `Time`."
@@ -2256,13 +2256,13 @@
{
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"id": "9badb3e3",
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"The $p$-values for Center B and evening time\n",
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# Chapter 12
# Lab: Unsupervised Learning
# Lab: Unsupervised Learning
In this lab we demonstrate PCA and clustering on several datasets.
As in other labs, we import some of our libraries at this top
level. This makes the code more readable, as scanning the first few
@@ -220,7 +207,7 @@ We can plot the PVE explained by each component, as well as the cumulative PVE.
plot the proportion of variance explained.
```{python}
# %%capture
%%capture
fig, axes = plt.subplots(1, 2, figsize=(15, 6))
ticks = np.arange(pcaUS.n_components_)+1
ax = axes[0]

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