add docs for Pipeline

README.md

@@ -48,6 +48,8 @@ composer require php-ai/php-ml
     * [DBSCAN](http://php-ml.readthedocs.io/en/latest/machine-learning/clustering/dbscan/)
 * Metric
     * [Accuracy](http://php-ml.readthedocs.io/en/latest/machine-learning/metric/accuracy/)
+* Workflow
+    * [Pipeline](http://php-ml.readthedocs.io/en/latest/machine-learning/workflow/pipeline)
 * Cross Validation
     * [Random Split](http://php-ml.readthedocs.io/en/latest/machine-learning/cross-validation/random-split/)
     * [Stratified Random Split](http://php-ml.readthedocs.io/en/latest/machine-learning/cross-validation/stratified-random-split/)

docs/index.md

@@ -38,35 +38,36 @@ composer require php-ai/php-ml
 
 * Classification
     * [SVC](machine-learning/classification/svc/)
-    * [k-Nearest Neighbors](http://php-ml.readthedocs.io/en/latest/machine-learning/classification/k-nearest-neighbors/)
-    * [Naive Bayes](http://php-ml.readthedocs.io/en/latest/machine-learning/classification/naive-bayes/)
+    * [k-Nearest Neighbors](machine-learning/classification/k-nearest-neighbors/)
+    * [Naive Bayes](machine-learning/classification/naive-bayes/)
 * Regression
-    * [Least Squares](http://php-ml.readthedocs.io/en/latest/machine-learning/regression/least-squares/)
-    * [SVR](http://php-ml.readthedocs.io/en/latest/machine-learning/regression/svr/)
+    * [Least Squares](machine-learning/regression/least-squares/)
+    * [SVR](machine-learning/regression/svr/)
 * Clustering
-    * [k-Means](http://php-ml.readthedocs.io/en/latest/machine-learning/clustering/k-means/)
-    * [DBSCAN](http://php-ml.readthedocs.io/en/latest/machine-learning/clustering/dbscan/)
-* 
+    * [k-Means](machine-learning/clustering/k-means/)
+    * [DBSCAN](machine-learning/clustering/dbscan/)
 * Metric
-    * [Accuracy](http://php-ml.readthedocs.io/en/latest/machine-learning/metric/accuracy/)
+    * [Accuracy](machine-learning/metric/accuracy/)
+* Workflow
+    * [Pipeline](machine-learning/workflow/pipeline)
 * Cross Validation
-    * [Random Split](http://php-ml.readthedocs.io/en/latest/machine-learning/cross-validation/random-split/)
-    * [Stratified Random Split](http://php-ml.readthedocs.io/en/latest/machine-learning/cross-validation/stratified-random-split/)
+    * [Random Split](machine-learning/cross-validation/random-split/)
+    * [Stratified Random Split](machine-learning/cross-validation/stratified-random-split/)
 * Preprocessing
-    * [Normalization](http://php-ml.readthedocs.io/en/latest/machine-learning/preprocessing/normalization/)
-    * [Imputation missing values](http://php-ml.readthedocs.io/en/latest/machine-learning/preprocessing/imputation-missing-values/)
+    * [Normalization](machine-learning/preprocessing/normalization/)
+    * [Imputation missing values](machine-learning/preprocessing/imputation-missing-values/)
 * Feature Extraction
-    * [Token Count Vectorizer](http://php-ml.readthedocs.io/en/latest/machine-learning/feature-extraction/token-count-vectorizer/)
+    * [Token Count Vectorizer](machine-learning/feature-extraction/token-count-vectorizer/)
 * Datasets
-    * [CSV](http://php-ml.readthedocs.io/en/latest/machine-learning/datasets/csv-dataset/)
+    * [CSV](machine-learning/datasets/csv-dataset/)
     * Ready to use:
-        * [Iris](http://php-ml.readthedocs.io/en/latest/machine-learning/datasets/demo/iris/)
-        * [Wine](http://php-ml.readthedocs.io/en/latest/machine-learning/datasets/demo/wine/)
-        * [Glass](http://php-ml.readthedocs.io/en/latest/machine-learning/datasets/demo/glass/)
+        * [Iris](machine-learning/datasets/demo/iris/)
+        * [Wine](machine-learning/datasets/demo/wine/)
+        * [Glass](machine-learning/datasets/demo/glass/)
 * Math
-    * [Distance](http://php-ml.readthedocs.io/en/latest/math/distance/)
-    * [Matrix](http://php-ml.readthedocs.io/en/latest/math/matrix/)
-    * [Statistic](http://php-ml.readthedocs.io/en/latest/math/statistic/)
+    * [Distance](math/distance/)
+    * [Matrix](math/matrix/)
+    * [Statistic](math/statistic/)
     
 
 ## Contribute

docs/machine-learning/workflow/pipeline.md

@@ -0,0 +1,65 @@
+# Pipeline
+
+In machine learning, it is common to run a sequence of algorithms to process and learn from dataset. For example:
+
+    * Split each document’s text into tokens.
+    * Convert each document’s words into a numerical feature vector ([Token Count Vectorizer](machine-learning/feature-extraction/token-count-vectorizer/)).
+    * Learn a prediction model using the feature vectors and labels.
+    
+PHP-ML represents such a workflow as a Pipeline, which consists sequence of transformers and a estimator.
+
+
+### Constructor Parameters
+
+* $transformers (array|Transformer[]) - sequence of objects that implements Transformer interface
+* $estimator (Estimator) - estimator that can train and predict
+
+```
+use Phpml\Classification\SVC;
+use Phpml\FeatureExtraction\TfIdfTransformer;
+use Phpml\Pipeline;
+
+$transformers = [
+    new TfIdfTransformer(),
+];
+$estimator = new SVC();
+
+$pipeline = new Pipeline($transformers, $estimator);
+```
+
+### Example
+
+First our pipeline replace missing value, then normalize samples and finally train SVC estimator. Thus prepared pipeline repeats each transformation step for predicted sample.
+
+```
+use Phpml\Classification\SVC;
+use Phpml\Pipeline;
+use Phpml\Preprocessing\Imputer;
+use Phpml\Preprocessing\Normalizer;
+use Phpml\Preprocessing\Imputer\Strategy\MostFrequentStrategy;
+
+$transformers = [
+    new Imputer(null, new MostFrequentStrategy()),
+    new Normalizer(),
+];
+$estimator = new SVC();
+
+$samples = [
+    [1, -1, 2],
+    [2, 0, null],
+    [null, 1, -1],
+];
+
+$targets = [
+    4,
+    1,
+    4,
+];
+
+$pipeline = new Pipeline($transformers, $estimator);
+$pipeline->train($samples, $targets);
+
+$predicted = $pipeline->predict([[0, 0, 0]]);
+
+// $predicted == 4
+```

mkdocs.yml

@@ -14,6 +14,8 @@ pages:
       - DBSCAN: machine-learning/clustering/dbscan.md
     - Metric:
       - Accuracy: machine-learning/metric/accuracy.md
+    - Workflow:
+      - Pipeline: machine-learning/workflow/pipeline.md
     - Cross Validation:
       - RandomSplit: machine-learning/cross-validation/random-split.md
       - Stratified Random Split: machine-learning/cross-validation/stratified-random-split.md