add ch 17 pdp profiles (#22)

* add ch 17 pdp

* Rename duplicate chunks.

---------

Co-authored-by: Jon Harmon <[email protected]>

Author: spitakiss

09_local-interpretable-model-agnostic-explanations-lime.Rmd

@@ -168,7 +168,7 @@ Solution:
 * Examples below use K-LASSO for glass-box model  
 
 
-```{r load-data, warning=FALSE,message=FALSE}
+```{r 09-load-data, warning=FALSE,message=FALSE}
 # core libraries
 library(randomForest)
 library(DALEX)

17_partial-dependence-profiles.Rmd

@@ -1,24 +1,230 @@
 # Partial-dependence Profiles
 
-**Learning objectives:**
+**Sections:**
 
-- THESE ARE NICE TO HAVE BUT NOT ABSOLUTELY NECESSARY
+-   Overview\
+-   Intuition\
+-   Method\ 
+-   Feature Importance\
+-   Example Apartment Prices\
+-   Pros and Cons\
+-   R Examples
 
-## SLIDE 1 {-}
+## Overview {.unnumbered}
 
-- ADD SLIDES AS SECTIONS (`##`).
-- TRY TO KEEP THEM RELATIVELY SLIDE-LIKE; THESE ARE NOTES, NOT THE BOOK ITSELF.
+-   This chapter focuses on partial dependence plots (PDP), also referred to as partial- dependence (PD) profiles.\
+-   Extremely popular technique as global model explainer\
+-   Available in multiple packages such as `DALEX`, `iml`, and `pdp`, and `PDPbox`\
+-   Core idea: PDP plots show how model predictions change as a function of an explanatory variable\
+-   Can be produced used on all observations or focusing on key subsets\
+-   Useful for comparing multiple models:
+    -   Agreement between profiles establishes confidence
+    -   Disagreement may suggest a way to improve a model\
+    -   Evaluation of model performance at boundaries
 
-## Meeting Videos {-}
+## Intuition {.unnumbered}
 
-### Cohort 1 {-}
+-   PD profile is constructed by taking arithmetic average of individual ceteris-paribus (CP) profiles.
+-   From previous chapter, recall that CP profiles show instance level model prediction behavior based on varying values of an explanatory variable
+-   When model is additive, CP profiles for all instances are parallel with the same shape.
+-   If model includes interactions, CP profiles may not be parallel
+
+*Example plots using rf model on titanic dataset* ![Source: Figure 17.1](img/17-partial-dependence/overview-plots.png)
+
+Note: A chart with all CP profiles plotted together is referred to as an individual conditional explanation (ICE) plot.
+
+## Method {.unnumbered}
+
+### Basic Equations {.unnumbered}
+
+Mathematical representation of PD profile value for model *f()*, variable *j* at value *z*: $$g_{PD}^{j}(z) = E_{\underline{X}^{-j}}\{f(X^{j|=z})\}$$
+
+where $\underline{X}^{-j}$ refers to joint distribution of all explanatory variables other than $X^J$
+
+We rarely know true distribution of $\underline{X}^{-j}$, so we typically estimate using the empirical distribution in our training data:
+
+$$\hat g_{PD}^{j}(z) =  \frac{1}{n} \sum_{i=1}^{n} f(\underline{x}_i^{j|=z}).$$
+
+The above equation refers to the mean of CP profiles for $X^J$
+
+### Clustered partial-dependence profiles {.unnumbered}
+
+-   Mean of CP profiles might not be a good representation if profiles are not parallel.\
+-   Alternative approach would be to create multiple clusters of CP profiles:
+    -   Use K-means or hierarchical clustering to identify clusters\
+    -   Can use Euclidean distance between CP profiles for identifying similar instances
+
+*Example clustered PDP using rf model on titanic dataset* ![Source: Figure 17.2](img/17-partial-dependence/clustered-pdp.png)
+
+### Grouped partial-dependence profiles {.unnumbered}
+
+-   We can use grouped PDPs if we can explicitly identify features that influence the shape of the CP profile for the explanatory variable of interest
+-   Obvious use case is when model includes interaction between variable of interest and another one.
+
+*Example grouped PDP using rf model on titanic dataset* ![Source: Figure 17.3](img/17-partial-dependence/grouped-pdp.png)   
+  
+### Contrastive partial-dependence profiles {-}  
+  
+We can plot PD profiles for multiple models together on same chart.
+
+*Example grouped PDP using rf model on titanic dataset* ![Source: Figure 17.4](img/17-partial-dependence/contrastive-pdp.png)
+
+## Feature Importance {-}  
+  
+This section references Section 8.1.1 of Christopher Molnar's [Interpretable Machine Learning](https://christophm.github.io/interpretable-ml-book/pdp.html) book 
+
+Can measure partial dependence-based feature importance as follows:
+
+$$I(x_S) =  \sqrt{\frac{1}{K-1}\sum_{k=1}^K(\hat{f}_S(x^{(k)}_S) - \frac{1}{K}\sum_{k=1}^K \hat{f}_S({x^{(k)}_S))^2}}$$
+where $x^{(k)}_S$ are K unique values of feature $X_S$
+
+Formula calculates variation of PD profile values around average PD value.  
+  
+Main idea: A flat PD profile indicates a feature that is not important.  
+
+Limitations:  
+
+- Only captures main effects, ignores feature interactions  
+- Defined over unique values over the explanatory variable. A unique feature with just one instance is given equal weight to a value with many instance.  
+  
+
+  
+
+
+
+## Example: apartment-prices data {.unnumbered}
+
+In this section, we use a random forest model to predict price per square meter for an apartment. Focus on two variables, *surface* and *construction year*
+
+### Partial-dependence-profiles {-}  
+![Source: Figure 17.5](img/17-partial-dependence/apt-pdp.png)
+
+### Clustered partial-dependence profiles {-}  
+![Source: Figure 17.6](img/17-partial-dependence/apt-clustered-pdp.png)
+
+### Grouped partial-dependence profiles {-}  
+  
+In this example, we use *district* as grouping variable to see if relationship between model's prediction with construction year and surface is similar in different geographic areas.
+
+![Source: Figure 17.7](img/17-partial-dependence/apt-grouped-pdp.png)\  
+  
+### Constrastive partial-dependence profiles {-}  
+  
+
+Below we compare the PDP output for from two predictive models: a basic linear regression model and the random forest model.
+
+![Source: Figure 17.8](img/17-partial-dependence/apt-contrastive-pdp.png)
+
+## Pros and Cons {.unnumbered}
+
+| Pros                                                                            | Cons                                                                                                             |
+|------------------------------|------------------------------------------|
+| Popular, well-understood in DS community                                        | Maximum number of features in plot is two                                                                        |
+| Simple, intuitive way to summarize effect of feature on target variable         | Assumption of independence; problematic with correlated explanatory variables                                    |
+| Multiple software packages in R and Python; also easy to implement from scratch | Heterogeneous effects may be hidden in basic PDP plot; may need grouped or clustered profiles for better insight |
+| Can be used to assess variable importance                                       | Can be time-consuming to run for medium and large datasets; likely need to use samplse                           |
+| Calculation has a casual interpretation for the model of interest.              | Can be misleading in areas where data are sparse in the training sample                                          |
+
+  
+**Note:**  
+  
+- Pros/Cons above reference both the EMA text as well as Christopher Molnar's Interpretable ML book: <https://christophm.github.io/interpretable-ml-book/pdp.html>
+
+## Code Snippets in R {.unnumbered}
+
+We use the titanic dataset and a random forest model.
+
+```{r 17-load-data, warning=FALSE, message=FALSE, results='hide'}
+library("DALEX")
+library("randomForest")
+titanic_imputed <- archivist::aread("pbiecek/models/27e5c")
+titanic_rf <- archivist::aread("pbiecek/models/4e0fc")
+explainer_rf <- DALEX::explain(model = titanic_rf,  
+                               data = titanic_imputed[, -9],
+                               y = titanic_imputed$survived, 
+                               label = "Random Forest")
+```
+
+### Partial-dependence profiles {-}   
+
+```{r pdp, warning=FALSE, message=FALSE}
+library("ggplot2")
+pdp_rf <- model_profile(explainer = explainer_rf, variables = "age")
+plot(pdp_rf) +  ggtitle("Partial-dependence profile for age") 
+
+```
+
+-   Only need to supplier explainer and variable arguments
+-   Optional argument `N` allows you to vary the sample size used for calculation, default is 100
+-   We can specify specific grouping variables if creating grouped PDP
+-   We can also create clustered PDP by specifying the `k` argument for number of clusters. Uses hierarchical clustering under the hood.
+
+We can include CP profiles (i.e. an ICE plot) with an additional argument to `plot()`:
+
+```{r pdp-ice}
+plot(pdp_rf, geom = "profiles") + 
+    ggtitle("Ceteris-paribus and partial-dependence profiles for age") 
+
+```
+
+### Clustered partial-dependence profiles {.unnumbered}
+
+This uses `hclust()` function:
+
+```{r clustered-pdp, warning=FALSE,message=FALSE}
+pdp_rf_clust <- model_profile(explainer = explainer_rf, 
+                              variables = "age", k = 3)
+
+plot(pdp_rf_clust, geom = "profiles") + 
+    ggtitle("Clustered partial-dependence profiles for age")
+
+```
+
+Grouped partial dependence profiles {-}
+
+Below we group by `gender`:
+
+```{r grouped-pdp, message=FALSE, warning=FALSE}
+pdp_rf_gender <- model_profile(explainer = explainer_rf, 
+                               variables = "age", groups = "gender")
+
+plot(pdp_rf_gender, geom = "profiles") + 
+    ggtitle("Partial-dependence profiles for age, grouped by gender") 
+
+```
+
+### Contrastive partial-dependence profiles {.unnumbered}
+
+```{r contrastive-pdp, warning=FALSE,message=FALSE,results='hide'}
+library("rms")
+titanic_lmr <- archivist::aread("pbiecek/models/58b24")
+explainer_lmr <- DALEX::explain(model = titanic_lmr, 
+                                data = titanic_imputed[, -9],
+                                y = titanic_imputed$survived, 
+                                label = "Logistic Regression")
+
+pdp_lmr <- model_profile(explainer = explainer_lmr, variables = "age")
+pdp_rf <- model_profile(explainer = explainer_rf, variables = "age")
+
+plot(pdp_rf, pdp_lmr) +
+    ggtitle("Partial-dependence profiles for age for two models") 
+
+
+```
+
+
+## Meeting Videos {.unnumbered}
+
+### Cohort 1 {.unnumbered}
 
 `r knitr::include_url("https://www.youtube.com/embed/URL")`
 
 <details>
-<summary> Meeting chat log </summary>
 
-```
+<summary>Meeting chat log</summary>
+
+```         
 LOG
 ```
+
 </details>