Restored default types in user-facing functions for convenience.

Users no longer have to fill out the templated types that can't be deduced from the arguments, which makes the library a bit more user-friendly. To keep things a bit more rigorous, the definitions of the default types are stored in typedefs so that we keep them consistent throughout the library.
SingleR-inc · Aug 26, 2024 · fba1770 · fba1770
1 parent 168cba6
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diff --git a/README.md b/README.md
@@ -31,12 +31,12 @@ ref_labels;
 ref_markers;
 
 // Building the classifier.
-singlepp::TrainSingleOptions<int, double> train_opt;
+singlepp::TrainSingleOptions train_opt;
 auto trained = singlepp::train_single(ref_mat, ref_labels.data(), train_opt);
 
 // Running the classification on the test matrix.
-singlepp::ClassifySingleOptions<double> class_opt;
-auto res = singlepp::classify_single<int>(test_mat, trained, class_opt);
+singlepp::ClassifySingleOptions class_opt;
+auto res = singlepp::classify_single(test_mat, trained, class_opt);
 ```
 
 See the [reference documentation](https://singler-inc.github.io/singlepp) for more details.
@@ -48,7 +48,7 @@ This is based on ranking the differences in median log-expression values between
 
 ```cpp
 singlepp::ChooseClassicMarkersOptions mrk;
-auto classic_markers = singlepp::choose_classic_markers<int>(
+auto classic_markers = singlepp::choose_classic_markers(
     ref_mat.get(),
     ref_labels.data(),
     m_opt
@@ -99,7 +99,7 @@ auto trained_intersect = singlepp::train_single_intersect(
 Then, `classify_single_intersect()` will perform classification using only the intersection of genes:
 
 ```cpp
-auto res_intersect = singlepp::classify_single_intersect<int>(
+auto res_intersect = singlepp::classify_single_intersect(
     test_mat,
     trained_intersect,
     class_opt
@@ -116,16 +116,16 @@ Let's say we have two references A and B:
 
 ```cpp
 auto trainA = singlepp::train_single(refA_mat, refA_labels.data(), refA_markers, train_opt);
-auto resA = singlepp::classify_single<int>(test_mat, trainA, class_opt);
+auto resA = singlepp::classify_single(test_mat, trainA, class_opt);
 
 auto trainB = singlepp::train_single(refB_mat, refB_labels.data(), refB_markers, train_opt);
-auto resB = singlepp::classify_single<int>(test_mat, trainB, class_opt);
+auto resB = singlepp::classify_single(test_mat, trainB, class_opt);
 ```
 
 We build the integrated classifier:
 
 ```cpp
-std::vector<singlepp::TrainIntegratedInput<double, int, int> > inputs;
+std::vector<singlepp::TrainIntegratedInput<> > inputs;
 inputs.push_back(singlepp::prepare_integrated_input(refA_mat, refA_labels.data(), preA));
 inputs.push_back(singlepp::prepare_integrated_input(refB_mat, refB_labels.data(), preB));
 
@@ -137,7 +137,7 @@ And then we can finally run the scoring.
 For each cell in the test dataset, `classify_integrated()` picks the best label among the assignments from each individual reference.
 
 ```cpp
-singlepp::ClassifyIntegratedOptions<double> ci_opt;
+singlepp::ClassifyIntegratedOptions ci_opt;
 auto ires = single.run(test_mat, train_integrated, ci_opt);
 ires.best; // index of the best reference.
 ```

diff --git a/docs/Doxyfile b/docs/Doxyfile
@@ -924,6 +924,7 @@ INPUT                  = ../include/singlepp/classify_single.hpp \
                          ../include/singlepp/Markers.hpp \
                          ../include/singlepp/Intersection.hpp \
                          ../include/singlepp/choose_classic_markers.hpp \
+                         ../include/singlepp/defs.hpp \
                          ../include/singlepp/singlepp.hpp \
                          ../README.md
 

diff --git a/include/singlepp/Intersection.hpp b/include/singlepp/Intersection.hpp
@@ -1,7 +1,7 @@
 #ifndef SINGLEPP_INTERSECTION_HPP
 #define SINGLEPP_INTERSECTION_HPP
 
-#include "macros.hpp"
+#include "defs.hpp"
 
 #include <vector>
 #include <algorithm>
@@ -27,7 +27,7 @@ namespace singlepp {
  *
  * @tparam Index_ Integer type for the gene (row) indices.
  */
-template<typename Index_>
+template<typename Index_ = DefaultIndex>
 using Intersection = std::vector<std::pair<Index_, Index_> >;
 
 /**

diff --git a/include/singlepp/Markers.hpp b/include/singlepp/Markers.hpp
@@ -1,7 +1,7 @@
 #ifndef SINGLEPP_MARKERS_HPP
 #define SINGLEPP_MARKERS_HPP
 
-#include "macros.hpp"
+#include "defs.hpp"
 
 #include <vector>
 
@@ -36,7 +36,7 @@ namespace singlepp {
  *
  * @tparam Index_ Integer type for the gene (row) indices.
  */
-template<typename Index_>
+template<typename Index_ = DefaultIndex>
 using Markers = std::vector<std::vector<std::vector<Index_> > >;
 
 }

diff --git a/include/singlepp/annotate_cells_integrated.hpp b/include/singlepp/annotate_cells_integrated.hpp
@@ -1,7 +1,7 @@
 #ifndef SINGLEPP_ANNOTATE_CELLS_INTEGRATED_HPP
 #define SINGLEPP_ANNOTATE_CELLS_INTEGRATED_HPP
 
-#include "macros.hpp"
+#include "defs.hpp"
 
 #include "tatami/tatami.hpp"
 

diff --git a/include/singlepp/annotate_cells_single.hpp b/include/singlepp/annotate_cells_single.hpp
@@ -1,7 +1,7 @@
 #ifndef SINGLEPP_ANNOTATE_CELLS_SINGLE_HPP
 #define SINGLEPP_ANNOTATE_CELLS_SINGLE_HPP
 
-#include "macros.hpp"
+#include "defs.hpp"
 
 #include "tatami/tatami.hpp"
 

diff --git a/include/singlepp/build_indices.hpp b/include/singlepp/build_indices.hpp
@@ -1,7 +1,7 @@
 #ifndef SINGLEPP_BUILD_INDICES_HPP
 #define SINGLEPP_BUILD_INDICES_HPP
 
-#include "macros.hpp"
+#include "defs.hpp"
 
 #include "knncolle/knncolle.hpp"
 #include "tatami/tatami.hpp"

diff --git a/include/singlepp/choose_classic_markers.hpp b/include/singlepp/choose_classic_markers.hpp
@@ -1,7 +1,7 @@
 #ifndef SINGLEPP_CHOOSE_CLASSIC_MARKERS_HPP
 #define SINGLEPP_CHOOSE_CLASSIC_MARKERS_HPP
 
-#include "macros.hpp"
+#include "defs.hpp"
 
 #include "tatami/tatami.hpp"
 

diff --git a/include/singlepp/classify_integrated.hpp b/include/singlepp/classify_integrated.hpp
@@ -1,7 +1,7 @@
 #ifndef SINGLEPP_CLASSIFY_INTEGRATED_HPP
 #define SINGLEPP_CLASSIFY_INTEGRATED_HPP
 
-#include "macros.hpp"
+#include "defs.hpp"
 
 #include "tatami/tatami.hpp"
 
@@ -23,7 +23,7 @@ namespace singlepp {
  * @brief Options for `classify_integrated()`.
  * @tparam Float_ Floating-point type for the correlations and scores.
  */
-template<typename Float_>
+template<typename Float_ = DefaultFloat>
 struct ClassifyIntegratedOptions {
     /**
      * Quantile to use to compute a per-reference score from the correlations.
@@ -55,7 +55,7 @@ struct ClassifyIntegratedOptions {
  * @tparam RefLabel_ Integer type for the label to represent each reference.
  * @tparam Float_ Floating-point type for the correlations and scores.
  */
-template<typename RefLabel_, typename Float_>
+template<typename RefLabel_ = DefaultRefLabel, typename Float_ = DefaultFloat>
 struct ClassifyIntegratedBuffers {
     /** 
      * Pointer to an array of length equal to the number of test cells.
@@ -149,7 +149,7 @@ void classify_integrated(
  * @tparam RefLabel_ Integer type for the label to represent each reference.
  * @tparam Float_ Floating-point type for the correlations and scores.
  */
-template<typename RefLabel_, typename Float_>
+template<typename RefLabel_ = DefaultRefLabel, typename Float_ = DefaultFloat>
 struct ClassifyIntegratedResults {
     /**
      * @cond
@@ -198,7 +198,7 @@ struct ClassifyIntegratedResults {
  *
  * @return Object containing the best reference and associated scores for each cell in `test`.
  */
-template<typename RefLabel_, typename Value_, typename Index_, typename Label_, typename Float_>
+template<typename RefLabel_ = DefaultRefLabel, typename Value_, typename Index_, typename Label_, typename Float_>
 ClassifyIntegratedResults<RefLabel_, Float_> classify_integrated(
     const tatami::Matrix<Value_, Index_>& test,
     const std::vector<const Label_*>& assigned,

diff --git a/include/singlepp/classify_single.hpp b/include/singlepp/classify_single.hpp
@@ -1,7 +1,7 @@
 #ifndef SINGLEPP_CLASSIFY_SINGLE_HPP
 #define SINGLEPP_CLASSIFY_SINGLE_HPP
 
-#include "macros.hpp"
+#include "defs.hpp"
 
 #include "tatami/tatami.hpp"
 
@@ -22,7 +22,7 @@ namespace singlepp {
  * @brief Options for `classify_single()` and friends.
  * @tparam Float_ Floating-point type for the correlations and scores.
  */
-template<typename Float_>
+template<typename Float_ = DefaultFloat>
 struct ClassifySingleOptions {
     /**
      * Quantile to use to define a per-label score for each test cell,
@@ -60,7 +60,7 @@ struct ClassifySingleOptions {
  * @tparam Label_ Integer type for the reference labels.
  * @tparam Float_ Floating-point type for the correlations and scores.
  */
-template<typename Label_, typename Float_>
+template<typename Label_ = DefaultLabel, typename Float_ = DefaultFloat>
 struct ClassifySingleBuffers {
     /** 
      * Pointer to an array of length equal to the number of test cells.
@@ -194,7 +194,7 @@ void classify_single_intersect(
  * @tparam Label_ Integer type for the reference labels.
  * @tparam Float_ Floating-point type for the correlations and scores.
  */
-template<typename Label_, typename Float_>
+template<typename Label_ = DefaultLabel, typename Float_ = DefaultFloat>
 struct ClassifySingleResults {
     /**
      * @cond
@@ -266,7 +266,7 @@ ClassifySingleBuffers<Label_, Float_> results_to_buffers(ClassifySingleResults<L
  *
  * @return Results of the classification for each cell in the test dataset.
  */
-template<typename Label_, typename Value_, typename Index_, typename Float_>
+template<typename Label_ = DefaultLabel, typename Value_, typename Index_, typename Float_>
 ClassifySingleResults<Label_, Float_> classify_single(
     const tatami::Matrix<Value_, Index_>& test,
     const TrainedSingle<Index_, Float_>& trained,
@@ -293,7 +293,7 @@ ClassifySingleResults<Label_, Float_> classify_single(
  *
  * @return Results of the classification for each cell in the test dataset.
  */ 
-template<typename Label_, typename Value_, typename Index_, typename Float_>
+template<typename Label_ = DefaultLabel, typename Value_, typename Index_, typename Float_>
 ClassifySingleResults<Label_, Float_> classify_single_intersect(
     const tatami::Matrix<Value_, Index_>& test,
     const TrainedSingleIntersect<Index_, Float_>& trained,

diff --git a/include/singlepp/correlations_to_score.hpp b/include/singlepp/correlations_to_score.hpp
@@ -1,8 +1,6 @@
 #ifndef SINGLEPP_CORRELATIONS_TO_SCORE_HPP
 #define SINGLEPP_CORRELATIONS_TO_SCORE_HPP
 
-#include "macros.hpp"
-
 #include <vector>
 #include <algorithm>
 #include <limits>

diff --git a/include/singlepp/defs.hpp b/include/singlepp/defs.hpp
@@ -0,0 +1,48 @@
+#ifndef SINGLEPP_DEFS_HPP
+#define SINGLEPP_DEFS_HPP
+
+/**
+ * @file defs.hpp
+ * @brief Common definitions for **singlepp**.
+ */
+
+#ifndef SINGLEPP_CUSTOM_PARALLEL
+#include "subpar/subpar.hpp"
+#define SINGLEPP_CUSTOM_PARALLEL subpar::parallelize
+#endif
+
+namespace singlepp {
+
+/**
+ * Default type for the `Index_` template argument.
+ * This is the type for the gene (and sample) indices, typically from the row/column indices of a `tatami::Matrix`.
+ */
+typedef int DefaultIndex;
+
+/**
+ * Default type for the `Label_` template argument.
+ * This is the type for the label identifiers within each reference.
+ */
+typedef int DefaultLabel;
+
+/**
+ * Default type for the `RefLabel_` template argument.
+ * This is the type for the reference identifiers during integrated classification.
+ */
+typedef int DefaultRefLabel;
+
+/**
+ * Default type for the `Float_` template argument.
+ * This is the type for the correlations and classification scores.
+ */
+typedef double DefaultFloat;
+
+/**
+ * Default type for the `Value_` template argument.
+ * This is the type for input data in the `tatami::Matrix`.
+ */
+typedef double DefaultValue;
+
+};
+
+#endif
diff --git a/include/singlepp/fill_labels_in_use.hpp b/include/singlepp/fill_labels_in_use.hpp
@@ -1,8 +1,6 @@
 #ifndef SINGLEPP_FILL_LABELS_IN_USE_HPP
 #define SINGLEPP_FILL_LABELS_IN_USE_HPP
 
-#include "macros.hpp"
-
 #include <vector>
 #include <algorithm>
 #include <type_traits>

diff --git a/include/singlepp/macros.hpp b/include/singlepp/macros.hpp
diff --git a/include/singlepp/scaled_ranks.hpp b/include/singlepp/scaled_ranks.hpp
@@ -1,8 +1,6 @@
 #ifndef SINGLEPP_SCALED_RANKS_HPP
 #define SINGLEPP_SCALED_RANKS_HPP
 
-#include "macros.hpp"
-
 #include <algorithm>
 #include <vector>
 #include <cmath>

diff --git a/include/singlepp/subset_to_markers.hpp b/include/singlepp/subset_to_markers.hpp
@@ -1,8 +1,6 @@
 #ifndef SINGLEPP_SUBSET_TO_MARKERS_HPP
 #define SINGLEPP_SUBSET_TO_MARKERS_HPP
 
-#include "macros.hpp"
-
 #include "Markers.hpp"
 #include "Intersection.hpp"
 

diff --git a/include/singlepp/train_integrated.hpp b/include/singlepp/train_integrated.hpp
@@ -1,7 +1,7 @@
 #ifndef SINGLEPP_TRAIN_INTEGRATED_HPP
 #define SINGLEPP_TRAIN_INTEGRATED_HPP
 
-#include "macros.hpp"
+#include "defs.hpp"
 
 #include "scaled_ranks.hpp"
 #include "train_single.hpp"
@@ -30,7 +30,7 @@ namespace singlepp {
  * @tparam Index_ Integer type for the row/column indices of the matrix.
  * @tparam Label_ Integer type for the reference labels.
  */
-template<typename Value_, typename Index_, typename Label_>
+template<typename Value_ = DefaultValue, typename Index_ = DefaultIndex, typename Label_ = DefaultLabel>
 struct TrainIntegratedInput {
     /**
      * @cond

diff --git a/include/singlepp/train_single.hpp b/include/singlepp/train_single.hpp
@@ -1,7 +1,7 @@
 #ifndef SINGLEPP_TRAIN_SINGLE_HPP
 #define SINGLEPP_TRAIN_SINGLE_HPP
 
-#include "macros.hpp"
+#include "defs.hpp"
 
 #include "knncolle/knncolle.hpp"
 #include "tatami/tatami.hpp"
@@ -24,7 +24,7 @@ namespace singlepp {
  * @tparam Index_ Integer type for the row/column indices of the matrix.
  * @tparam Float_ Floating-point type for the correlations and scores.
  */
-template<typename Index_, typename Float_>
+template<typename Index_ = DefaultIndex, typename Float_ = DefaultFloat>
 struct TrainSingleOptions {
     /**
      * Number of top markers to use from each pairwise comparison between labels.