ppforest2 v0.1.0
Projection Pursuit Decision Trees and Random Forests
Loading...
Searching...
No Matches
Presentation.hpp
Go to the documentation of this file.
1
6#pragma once
7
9#include "io/Output.hpp"
10#include "models/Evaluation.hpp"
12#include "stats/Metrics.hpp"
13
14#include <nlohmann/json.hpp>
15
16namespace ppforest2::io {
17
23
36 Output& out, VariableImportance const& vi, types::Names const& feature_names = {}, int max_rows = 20
37 );
38
49 Output& out,
50 stats::ConfusionMatrix const& cm,
51 std::string const& title = "Confusion Matrix",
52 types::Names const& group_names = {}
53 );
54
65 Output& out, stats::RegressionMetrics const& rm, std::string const& title = "Regression Metrics"
66 );
67
83 Output& out,
85 std::string const& label,
86 types::Names const& group_names = {}
87 );
89 Output& out, stats::RegressionMetrics const& rm, std::string const& label, types::Names const& group_names = {}
90 );
92 Output& out, stats::Metrics const& metrics, std::string const& label, types::Names const& group_names = {}
93 );
94
96 void print_metrics_block(Output& out, stats::ClassificationMetrics const& m, types::Names const& group_names = {});
97 void print_metrics_block(Output& out, stats::RegressionMetrics const& m, types::Names const& group_names = {});
98 void print_metrics_block(Output& out, stats::Metrics const& m, types::Names const& group_names = {});
99
108 float vars_percent = -1;
109 bool default_vars = false;
110 bool default_threads = false;
111 bool default_seed = false;
112 std::string training_samples;
113 std::string test_samples;
114 };
115
123 void print_configuration(Output& out, nlohmann::json const& config, ConfigDisplayHints const& hints = {});
124
133 void print_data_summary(Output& out, nlohmann::json const& meta);
134
142 void print_summary(Output& out, nlohmann::json const& model_data, ConfigDisplayHints const& hints = {});
143}
Confusion matrix for classification model evaluation.
Evaluation data types: per-iteration stats and run summaries.
Free-function API for evaluating trained models — variable importance, out-of-bag diagnostics,...
Mode-specific evaluation metric blocks.
Quiet-aware output context for CLI subcommands.
Definition Color.hpp:31
void print_results(Output &out, ModelStats const &stats)
Print evaluation results (timing, errors, memory) to stdout.
void print_variable_importance(Output &out, VariableImportance const &vi, types::Names const &feature_names={}, int max_rows=20)
Print a ranked variable importance table to stdout.
void print_regression_metrics(Output &out, stats::RegressionMetrics const &rm, std::string const &title="Regression Metrics")
Print regression metrics to stdout.
void print_data_summary(Output &out, nlohmann::json const &meta)
Print a data summary table from a JSON meta object.
void print_summary(Output &out, nlohmann::json const &model_data, ConfigDisplayHints const &hints={})
Display a full model summary from its JSON representation.
void print_metrics_block(Output &out, stats::ClassificationMetrics const &cm, std::string const &label, types::Names const &group_names={})
Print a labeled metrics block — error rate + confusion matrix for classification, MSE + regression me...
void print_configuration(Output &out, nlohmann::json const &config, ConfigDisplayHints const &hints={})
Print model configuration table from a JSON config object.
void print_confusion_matrix(Output &out, stats::ConfusionMatrix const &cm, std::string const &title="Confusion Matrix", types::Names const &group_names={})
Print a formatted confusion matrix to stdout.
Statistical infrastructure for training and evaluation.
Definition ConfusionMatrix.hpp:11
std::variant< ClassificationMetrics, RegressionMetrics > Metrics
Mode-polymorphic metrics block.
Definition Metrics.hpp:116
std::vector< std::string > Names
Vector of name strings — used uniformly for class labels (group_names[i] is the label for GroupId == ...
Definition Types.hpp:49
Grouped result of the variable importance measures.
Definition Evaluation.hpp:39
Optional display hints for print_configuration.
Definition Presentation.hpp:107
std::string test_samples
Definition Presentation.hpp:113
bool default_threads
Definition Presentation.hpp:110
float vars_percent
Definition Presentation.hpp:108
bool default_seed
Definition Presentation.hpp:111
bool default_vars
Definition Presentation.hpp:109
std::string training_samples
Definition Presentation.hpp:112
Per-iteration training statistics.
Definition EvaluateResult.hpp:56
Quiet-aware, indentation-aware output context.
Definition Output.hpp:25
Classification evaluation metrics.
Definition Metrics.hpp:25
A confusion matrix comparing predicted vs actual group labels.
Definition ConfusionMatrix.hpp:38
Regression evaluation metrics.
Definition Metrics.hpp:52