add ClassificationReport docs

CHANGELOG.md

@@ -3,7 +3,7 @@ CHANGELOG
 
 This changelog references the relevant changes done in PHP-ML library.
 
-* 0.2.0 (in plan/progress)
+* 0.1.2 (in plan/progress)
     * feature [Dataset] - FilesDataset - load dataset from files (folder names as targets)
     * feature [Metric] - ClassificationReport - report about trained classifier
     * bug [Feature Extraction] - fix problem with token count vectorizer array order

README.md

@@ -51,6 +51,7 @@ composer require php-ai/php-ml
 * Metric
     * [Accuracy](http://php-ml.readthedocs.io/en/latest/machine-learning/metric/accuracy/)
     * [Confusion Matrix](http://php-ml.readthedocs.io/en/latest/machine-learning/metric/confusion-matrix/)
+    * [Classification Report](http://php-ml.readthedocs.io/en/latest/machine-learning/metric/classification-report/)
 * Workflow
     * [Pipeline](http://php-ml.readthedocs.io/en/latest/machine-learning/workflow/pipeline)
 * Cross Validation

docs/index.md

@@ -51,6 +51,7 @@ composer require php-ai/php-ml
 * Metric
     * [Accuracy](machine-learning/metric/accuracy/)
     * [Confusion Matrix](machine-learning/metric/confusion-matrix/)
+    * [Classification Report](machine-learning/metric/classification-report/)
 * Workflow
     * [Pipeline](machine-learning/workflow/pipeline)
 * Cross Validation

docs/machine-learning/metric/classification-report.md

@@ -0,0 +1,61 @@
+# Classification Report
+
+Class for calculate main classifier metrics: precision, recall, F1 score and support.
+
+### Report
+
+To generate report you must provide the following parameters:
+
+* $actualLabels - (array) true sample labels
+* $predictedLabels - (array) predicted labels (e.x. from test group)
+
+```
+use Phpml\Metric\ClassificationReport;
+
+$actualLabels = ['cat', 'ant', 'bird', 'bird', 'bird'];
+$predictedLabels = ['cat', 'cat', 'bird', 'bird', 'ant'];
+
+$report = new ClassificationReport($actualLabels, $predictedLabels);
+```
+
+### Metrics
+
+After creating the report you can draw its individual metrics:
+
+* precision (`getPrecision()`) - fraction of retrieved instances that are relevant
+* recall (`getRecall()`) - fraction of relevant instances that are retrieved
+* F1 score (`getF1score()`) - measure of a test's accuracy
+* support (`getSupport()`) - count of testes samples
+
+```
+$precision = $report->getPrecision();
+
+// $precision = ['cat' => 0.5, 'ant' => 0.0, 'bird' => 1.0];
+```
+
+### Example
+
+```
+use Phpml\Metric\ClassificationReport;
+
+$actualLabels = ['cat', 'ant', 'bird', 'bird', 'bird'];
+$predictedLabels = ['cat', 'cat', 'bird', 'bird', 'ant'];
+
+$report = new ClassificationReport($actualLabels, $predictedLabels);
+
+$report->getPrecision();
+// ['cat' => 0.5, 'ant' => 0.0, 'bird' => 1.0]
+
+$report->getRecall();
+// ['cat' => 1.0, 'ant' => 0.0, 'bird' => 0.67]
+
+$report->getF1score();
+// ['cat' => 0.67, 'ant' => 0.0, 'bird' => 0.80]
+
+$report->getSupport();
+// ['cat' => 1, 'ant' => 1, 'bird' => 3]
+
+$report->getAverage();
+// ['precision' => 0.75, 'recall' => 0.83, 'f1score' => 0.73]
+
+```

mkdocs.yml

@@ -15,6 +15,7 @@ pages:
     - Metric:
       - Accuracy: machine-learning/metric/accuracy.md
       - Confusion Matrix: machine-learning/metric/confusion-matrix.md
+      - Classification Report: machine-learning/metric/classification-report.md
     - Workflow:
       - Pipeline: machine-learning/workflow/pipeline.md
     - Cross Validation: