doc(demo): fix docstrings

SDeMo/docs/src/demo.jl

@@ -26,7 +26,7 @@ size(X)
 
 # ## Setting up the model
 
-## We will start with an initial model that uses a PCA to transform the data, and
+# We will start with an initial model that uses a PCA to transform the data, and
 # then a Naive Bayes Classifier for the classification. Note that this is the
 # partial syntax where we use the default threshold, and all the variables:
 
@@ -53,11 +53,11 @@ cv = crossvalidate(sdm, folds);
 measures = [mcc, balancedaccuracy, ppv, npv, trueskill, markedness]
 cvresult = [mean(measure.(set)) for measure in measures, set in cv]
 pretty_table(
-    hcat(string.(measures), cvresult);
-    alignment=[:l, :c, :c],
-    backend=Val(:markdown),
-    header=["Measure", "Validation", "Training"],
-    formatters=ft_printf("%5.3f", [2, 3])
+        hcat(string.(measures), cvresult);
+        alignment=[:l, :c, :c],
+        backend=Val(:markdown),
+        header=["Measure", "Validation", "Training"],
+        formatters=ft_printf("%5.3f", [2, 3])
 )
 
 # Assuming we want to get a simple idea of what the MCC is for the validation
@@ -69,6 +69,14 @@ mcc.(cv.validation)
 
 ci(cv.validation, mcc)
 
+# We can also get the same output by calling a function on a vector of `ConfusionMatrix`, *e.g.*
+
+mcc(cv.validation)
+
+# Adding the `true` argument returns a tuple with the 95% CI:
+
+mcc(cv.validation, true)
+
 # ## Variable selection
 
 # We will now select variables using forward selection, but with the added
@@ -87,24 +95,16 @@ cv2 = crossvalidate(sdm, folds)
 measures = [mcc, balancedaccuracy, ppv, npv, trueskill, markedness]
 cvresult = [mean(measure.(set)) for measure in measures, set in cv2]
 pretty_table(
-    hcat(string.(measures), cvresult);
-    alignment=[:l, :c, :c],
-    backend=Val(:markdown),
-    header=["Measure", "Validation", "Training"],
-    formatters=ft_printf("%5.3f", [2, 3])
+        hcat(string.(measures), cvresult);
+        alignment=[:l, :c, :c],
+        backend=Val(:markdown),
+        header=["Measure", "Validation", "Training"],
+        formatters=ft_printf("%5.3f", [2, 3])
 )
 
 # Quite clearly! Before thinking about the relative importance of variables, we
 # will take a look at the thresold.
 
-# As a side-note, we can get the average of many performance measures using *e.g.*
-
-f1(cv2.validation)
-
-# And the mean and 95% confidence interval with
-
-balancedaccuracy(cv2.validation, true)
-
 # ## Moving threshold classification
 
 # The `crossvalidate` function comes with an optional argument to specify the
@@ -144,11 +144,11 @@ varimp = variableimportance(sdm, folds)
 # In relative terms, this is:
 
 pretty_table(
-    hcat(variables(sdm), varimp ./ sum(varimp));
-    alignment=[:l, :c],
-    backend=Val(:markdown),
-    header=["Variable", "Importance"],
-    formatters=(ft_printf("%5.3f", 2), ft_printf("%d", 1))
+        hcat(variables(sdm), varimp ./ sum(varimp));
+        alignment=[:l, :c],
+        backend=Val(:markdown),
+        header=["Variable", "Importance"],
+        formatters=(ft_printf("%5.3f", 2), ft_printf("%d", 1))
 )
 
 # ## Partial response curve
@@ -192,8 +192,8 @@ f = Figure()
 ax = Axis(f[1, 1])
 prx, pry = partialresponse(sdm, 1; inflated=false, threshold=false)
 for i in 1:200
-    ix, iy = partialresponse(sdm, 1; inflated=true, threshold=false)
-    lines!(ax, ix, iy, color=(:grey, 0.5))
+        ix, iy = partialresponse(sdm, 1; inflated=true, threshold=false)
+        lines!(ax, ix, iy, color=(:grey, 0.5))
 end
 lines!(ax, prx, pry, color=:black, linewidth=4)
 current_figure() #hide
@@ -287,11 +287,11 @@ cf = counterfactual(sdm, instance(sdm, inst; strict=false), target, 200.0; thres
 # is:
 
 pretty_table(
-    hcat(variables(sdm), instance(sdm, inst), cf[variables(sdm)]);
-    alignment=[:l, :c, :c],
-    backend=Val(:markdown),
-    header=["Variable", "Obs.", "Counterf."],
-    formatters=(ft_printf("%4.1f", [2, 3]), ft_printf("%d", 1))
+        hcat(variables(sdm), instance(sdm, inst), cf[variables(sdm)]);
+        alignment=[:l, :c, :c],
+        backend=Val(:markdown),
+        header=["Variable", "Obs.", "Counterf."],
+        formatters=(ft_printf("%4.1f", [2, 3]), ft_printf("%d", 1))
 )
 
 # We can check the prediction that would be made on the counterfactual:

SDeMo/src/crossvalidation/crossvalidation.jl

@@ -131,9 +131,9 @@ for op in (:leaveoneout, :holdout, :montecarlo, :kfold)
             $op(sdm::SDM, args...; kwargs...) =
                 $op(labels(sdm), features(sdm), args...; kwargs...)
             """
-            $($op)(sdm::Bagging)
+                $($op)(sdm::Bagging)
 
-            Version of `$($op)` using the instances and labels of a bagged SDM.
+            Version of `$($op)` using the instances and labels of a bagged SDM. In this case, the instances of the model used as a reference to build the bagged model are used.
             """
             $op(sdm::Bagging, args...; kwargs...) = $op(sdm.model, args...; kwargs...)
         end,