Fix tutorials

docs/src/tutorials/GenerativeDesigns.jl

@@ -48,6 +48,8 @@
 
 # Therefore, given an experimental state with readouts over the feature set $F \subseteq X$, we can calculate the total distance from the entity recorded in the $j$-th row as $d_j = \sum_{x\in F} \rho_x (\hat x, x_j)$, where $\hat x$ and $x_j$ denote the readout for feature $x$ for the entity being tested and the entity recorded in $j$-th column, respectively. 
 
+# Alternatively, we could use the [Mahalanobis distance](https://en.wikipedia.org/wiki/Mahalanobis_distance#Definition).
+
 # Next, we convert distances $d_j$ into probabilistic weights $w_j$. By default, we use a rescaled exponential function, i.e., we put $w_j = \exp(-\lambda d_j)$ for some $\lambda>0$. Notably, $\lambda$'s value determines how belief is distributed across the historical entities. Larger values of $\lambda$ concentrate the belief tightly around the 'closest' historical entities, while smaller values distribute more belief to more distant entities.
 
 # Importantly, the proper choice of the distance functionals and the 'similarity' functional discussed above is a question of hyper-optimization. 
@@ -127,7 +129,13 @@ using CEED, CEED.GenerativeDesigns
 (; sampler, uncertainty, weights) =
     DistanceBased(data, "HeartDisease", Entropy, Exponential(; λ = 5));
 
-# The CEED package offers an additional flexibility by allowing an experiment to yield readouts over multiple features at the same time. In our scenario, we can consider the features `RestingECG`, `Oldpeak`, `ST_Slope`, and `MaxHR` to be obtained from a single experiment `ECG`.
+# You can specify the method for computing the distance using the `distance` keyword. By default, the Kronecker delta and quadratic distance will be utilised for categorical and continuous features, respectively. 
+
+# Alternatively, you can provide a dictionary of `feature => distance` pairs. The implemented distance functionals are `DiscreteMetric(; λ)` and `QuadraticDistance(; λ, standardize=true)`. In that case, the specified distance will be applied to the respective feature, after which the distances will be collated across the range of features.
+
+# You can also use the Mahalanobis distance (`MahalanobisDistance(; diagonal)`).
+
+# The package offers an additional flexibility by allowing an experiment to yield readouts over multiple features at the same time. In our scenario, we can consider the features `RestingECG`, `Oldpeak`, `ST_Slope`, and `MaxHR` to be obtained from a single experiment `ECG`.
 
 # We specify the experiments along with the associated features:
 
@@ -249,7 +257,7 @@ designs = efficient_designs(
     6,
     evidence;
     solver,
-    mdp_options = (; max_parallel = 2, costs_tradeoff = [0, 1.0]),
+    mdp_options = (; max_parallel = 2, costs_tradeoff = (0, 1.0)),
     repetitions = 5,
 );
 

docs/src/tutorials/GenerativeDesigns.md

@@ -52,6 +52,8 @@ For each feature $x\in X$, we consider a function $\rho_x$, which measures the d
 
 Therefore, given an experimental state with readouts over the feature set $F \subseteq X$, we can calculate the total distance from the entity recorded in the $j$-th row as $d_j = \sum_{x\in F} \rho_x (\hat x, x_j)$, where $\hat x$ and $x_j$ denote the readout for feature $x$ for the entity being tested and the entity recorded in $j$-th column, respectively.
 
+Alternatively, we could use the [Mahalanobis distance](https://en.wikipedia.org/wiki/Mahalanobis_distance#Definition).
+
 Next, we convert distances $d_j$ into probabilistic weights $w_j$. By default, we use a rescaled exponential function, i.e., we put $w_j = \exp(-\lambda d_j)$ for some $\lambda>0$. Notably, $\lambda$'s value determines how belief is distributed across the historical entities. Larger values of $\lambda$ concentrate the belief tightly around the 'closest' historical entities, while smaller values distribute more belief to more distant entities.
 
 Importantly, the proper choice of the distance functionals and the 'similarity' functional discussed above is a question of hyper-optimization.
@@ -141,7 +143,13 @@ Note that internally, a state of the decision process is represented as a tuple
 nothing #hide
 ````
 
-The CEED package offers an additional flexibility by allowing an experiment to yield readouts over multiple features at the same time. In our scenario, we can consider the features `RestingECG`, `Oldpeak`, `ST_Slope`, and `MaxHR` to be obtained from a single experiment `ECG`.
+You can specify the method for computing the distance using the `distance` keyword. By default, the Kronecker delta and quadratic distance will be utilised for categorical and continuous features, respectively.
+
+Alternatively, you can provide a dictionary of `feature => distance` pairs. The implemented distance functionals are `DiscreteMetric(; λ)` and `QuadraticDistance(; λ, standardize=true)`. In that case, the specified distance will be applied to the respective feature, after which the distances will be collated across the range of features.
+
+You can also use the Mahalanobis distance (`MahalanobisDistance(; diagonal)`).
+
+The package offers an additional flexibility by allowing an experiment to yield readouts over multiple features at the same time. In our scenario, we can consider the features `RestingECG`, `Oldpeak`, `ST_Slope`, and `MaxHR` to be obtained from a single experiment `ECG`.
 
 We specify the experiments along with the associated features:
 
@@ -287,7 +295,7 @@ designs = efficient_designs(
     6,
     evidence;
     solver,
-    mdp_options = (; max_parallel = 2, costs_tradeoff = [0, 1.0]),
+    mdp_options = (; max_parallel = 2, costs_tradeoff = (0, 1.0)),
     repetitions = 5,
 );
 nothing #hide

tutorials/GenerativeDesigns.jl

@@ -132,7 +132,7 @@ using CEED, CEED.GenerativeDesigns
 # You can specify the method for computing the distance using the `distance` keyword. By default, the Kronecker delta and quadratic distance will be utilised for categorical and continuous features, respectively. 
 
 # Alternatively, you can provide a dictionary of `feature => distance` pairs. The implemented distance functionals are `DiscreteMetric(; λ)` and `QuadraticDistance(; λ, standardize=true)`. In that case, the specified distance will be applied to the respective feature, after which the distances will be collated across the range of features.
-        
+
 # You can also use the Mahalanobis distance (`MahalanobisDistance(; diagonal)`).
 
 # The package offers an additional flexibility by allowing an experiment to yield readouts over multiple features at the same time. In our scenario, we can consider the features `RestingECG`, `Oldpeak`, `ST_Slope`, and `MaxHR` to be obtained from a single experiment `ECG`.
@@ -246,7 +246,7 @@ experiments = Dict(
 seed!(1)
 ## use less number of iterations to speed up build process
 solver = GenerativeDesigns.DPWSolver(;
-    n_iterations = 20_000,
+    n_iterations = 2_000,
     exploration_constant = 5.0,
     tree_in_info = true,
 )
@@ -257,7 +257,7 @@ designs = efficient_designs(
     6,
     evidence;
     solver,
-    mdp_options = (; max_parallel = 2, costs_tradeoff = [0, 1.0]),
+    mdp_options = (; max_parallel = 2, costs_tradeoff = (0, 1.0)),
     repetitions = 5,
 );
 
@@ -281,8 +281,7 @@ end
 #
 seed!(1)
 ## use less number of iterations to speed up build process
-solver =
-    GenerativeDesigns.DPWSolver(; n_iterations = 20_000, depth = 4, tree_in_info = true)
+solver = GenerativeDesigns.DPWSolver(; n_iterations = 2_000, depth = 4, tree_in_info = true)
 design = efficient_value(
     experiments,
     sampler,