Tests and documents for new JSON routines.

doc/tutorials/index.rst

@@ -10,6 +10,7 @@ See `Awesome XGBoost <https://github.com/dmlc/xgboost/tree/master/demo>`_ for mo
   :caption: Contents:
 
   model
+  saving_model
   Distributed XGBoost with AWS YARN <aws_yarn>
   kubernetes
   Distributed XGBoost with XGBoost4J-Spark <https://xgboost.readthedocs.io/en/latest/jvm/xgboost4j_spark_tutorial.html>

doc/tutorials/saving_model.rst

@@ -0,0 +1,195 @@
+########################
+Introduction to Model IO
+########################
+
+In XGBoost 1.0.0, we introduced experimental support of using `JSON
+<https://www.json.org/json-en.html>`_ for saving/loading XGBoost models and related
+hyper-parameters for training, aiming to replace the old binary internal format with an
+open format that can be easily reused.  The support for binary format will be continued in
+the future until JSON format is no-longer experimental and has satisfying performance.
+This tutorial aims to share some basic insights into the JSON serialisation method used in
+XGBoost.  Without explicitly mentioned, the following sections assume you are using the
+experimental JSON format, which can be enabled by passing
+``enable_experimental_json_serialization=True`` as training parameter, or provide the file
+name with ``.json`` as file extension when saving/loading model:
+``booster.save_model('model.json')``.  More details below.
+
+Before we get started, XGBoost is a gradient boosting library with focus on tree model,
+which means inside XGBoost, there are 2 distinct parts: the model consisted of trees and
+algorithms used to build it.  If you come from Deep Learning community, then it should be
+clear to you that there are differences between the neural network structures composed of
+weights with fixed tensor operations, and the optimizers (like RMSprop) used to train
+them.
+
+So when one calls ``booster.save_model``, XGBoost saves the trees, some model parameters
+like number of input columns in trained trees, and the objective function, which combined
+to represent the concept of "model" in XGBoost.  As for why are we saving the objective as
+part of model, that's because objective controls transformation of global bias (called
+``base_score`` in XGBoost).  Users can share this model with others for prediction,
+evaluation or continue the training with a different set of hyper-parameters etc.
+However, this is not the end of story.  There are cases where we need to save something
+more than just the model itself.  For example, in distrbuted training, XGBoost performs
+checkpointing operation.  Or for some reasons, your favorite distributed computing
+framework decide to copy the model from one worker to another and continue the training in
+there.  In such cases, the serialisation output is required to contain enougth information
+to continue previous training without user providing any parameters again.  We consider
+such scenario as memory snapshot (or memory based serialisation method) and distinguish it
+with normal model IO operation.  In Python, this can be invoked by pickling the
+``Booster`` object.  Other language bindings are still working in progress.
+
+.. note::
+
+  The old binary format doesn't distinguish difference between model and raw memory
+  serialisation format, it's a mix of everything, which is part of the reason why we want
+  to replace it with a more robust serialisation method.  JVM Package has its own memory
+  based serialisation methods.
+
+To enable JSON format support for model IO (saving only the trees and objective), provide
+a filename with ``.json`` as file extension:
+
+.. code-block:: python
+
+  bst.save_model('model_file_name.json')
+
+While for enabling JSON as memory based serialisation format, pass
+``enable_experimental_json_serialization`` as a training parameter.  In Python this can be
+done by:
+
+.. code-block:: python
+
+  bst = xgboost.train({'enable_experimental_json_serialization': True}, dtrain)
+  with open('filename', 'wb') as fd:
+      pickle.dump(bst, fd)
+
+Notice the ``filename`` is for Python intrinsic function ``open``, not for XGBoost.  Hence
+parameter ``enable_experimental_json_serialization`` is required to enable JSON format.
+As the name suggested, memory based serialisation captures many stuffs internal to
+XGBoost, so it's only suitable to be used for checkpoints, which doesn't require stable
+output format.  That being said, loading pickled booster (memory snapshot) in a different
+XGBoost version may lead to errors or undefined behaviors.  But we promise the stable
+output format of binary model and JSON model (once it's no-longer experimental) as they
+are designed to be reusable.  This scheme fits as Python itself doesn't guarantee pickled
+bytecode can be used in different Python version.
+
+***************************
+Custom objective and metric
+***************************
+
+XGBoost accepts user provided objective and metric functions as an extension.  These
+functions are not saved in model file as they are language dependent feature.  With
+Python, user can pickle the model to include these functions in saved binary.  One
+drawback is, the output from pickle is not a stable serialization format and doesn't work
+on different Python version or XGBoost version, not to mention different language
+environment.  Another way to workaround this limitation is to provide these functions
+again after the model is loaded. If the customized function is useful, please consider
+making a PR for implementing it inside XGBoost, this way we can have your functions
+working with different language bindings.
+
+********************************************************
+Saving and Loading the internal parameters configuration
+********************************************************
+
+XGBoost's ``C API`` and ``Python API`` supports saving and loading the internal
+configuration directly as a JSON string.  In Python package:
+
+.. code-block:: python
+
+  bst = xgboost.train(...)
+  config = bst.save_config()
+  print(config)
+
+Will print out something similiar to (not actual output as it's too long for demonstration):
+
+.. code-block:: json
+
+    {
+      "Learner": {
+        "generic_parameter": {
+          "enable_experimental_json_serialization": "0",
+          "gpu_id": "0",
+          "gpu_page_size": "0",
+          "n_jobs": "0",
+          "random_state": "0",
+          "seed": "0",
+          "seed_per_iteration": "0"
+        },
+        "gradient_booster": {
+          "gbtree_train_param": {
+            "num_parallel_tree": "1",
+            "predictor": "gpu_predictor",
+            "process_type": "default",
+            "tree_method": "gpu_hist",
+            "updater": "grow_gpu_hist",
+            "updater_seq": "grow_gpu_hist"
+          },
+          "name": "gbtree",
+          "updater": {
+            "grow_gpu_hist": {
+              "gpu_hist_train_param": {
+                "debug_synchronize": "0",
+                "gpu_batch_nrows": "0",
+                "single_precision_histogram": "0"
+              },
+              "train_param": {
+                "alpha": "0",
+                "cache_opt": "1",
+                "colsample_bylevel": "1",
+                "colsample_bynode": "1",
+                "colsample_bytree": "1",
+                "default_direction": "learn",
+                "enable_feature_grouping": "0",
+                "eta": "0.300000012",
+                "gamma": "0",
+                "grow_policy": "depthwise",
+                "interaction_constraints": "",
+                "lambda": "1",
+                "learning_rate": "0.300000012",
+                "max_bin": "256",
+                "max_conflict_rate": "0",
+                "max_delta_step": "0",
+                "max_depth": "6",
+                "max_leaves": "0",
+                "max_search_group": "100",
+                "refresh_leaf": "1",
+                "sketch_eps": "0.0299999993",
+                "sketch_ratio": "2",
+                "subsample": "1"
+              }
+            }
+          }
+        },
+        "learner_train_param": {
+          "booster": "gbtree",
+          "disable_default_eval_metric": "0",
+          "dsplit": "auto",
+          "objective": "reg:squarederror"
+        },
+        "metrics": [],
+        "objective": {
+          "name": "reg:squarederror",
+          "reg_loss_param": {
+            "scale_pos_weight": "1"
+          }
+        }
+      },
+      "version": [1, 0, 0]
+    }
+
+
+You can load it back to the model generated by same version of XGBoost by:
+
+.. code-block:: python
+
+  bst.load_config(config)
+
+This way users can study the internal representation more closely.
+
+************
+Future Plans
+************
+
+Right now using the JSON format incurs longer serialisation time, we have been working on
+optimizing the JSON implementation to close the gap between binary format and JSON format.
+You can track the progress in `#5046 <https://github.com/dmlc/xgboost/pull/5046>`_.
+Another important item for JSON format support is a stable and documented `schema
+<https://json-schema.org/>`_, based on which one can easily reuse the saved model.

include/xgboost/c_api.h

@@ -426,6 +426,24 @@ XGB_DLL int XGBoosterPredict(BoosterHandle handle,
                              unsigned ntree_limit,
                              bst_ulong *out_len,
                              const float **out_result);
+/*
+ * Short note for serialization APIs.  There are 3 different sets of serialization API.
+ *
+ * - Functions with the term "Model" handles saving/loading XGBoost model like trees or
+ *   linear weights.  Striping out parameters configuration like training algorithms or
+ *   CUDA device ID helps user to reuse the trained model for different tasks, examples
+ *   are prediction, training continuation or interpretation.
+ *
+ * - Functions with the term "Config" handles save/loading configuration.  It helps user
+ *   to study the internal of XGBoost.  Also user can use the load method for specifying
+ *   paramters in a structured way.  These functions are introduced in 1.0.0, and are not
+ *   yet stable.
+ *
+ * - Functions with the term "Serialization" are combined of above two.  They are used in
+ *   situations like check-pointing, or continuing training task in distributed
+ *   environment.  In these cases the task must be carried out without any user
+ *   intervention.
+ */
 
 /*!
  * \brief Load model from existing file
@@ -506,7 +524,10 @@ XGB_DLL int XGBoosterSaveRabitCheckpoint(BoosterHandle handle);
 
 
 /*!
- * \brief Save XGBoost's internal configuration into a JSON document.
+ * \brief Save XGBoost's internal configuration into a JSON document.  Currently the
+ *        support is experimental, function signature may change in the future without
+ *        notice.
+ *
  * \param handle handle to Booster object.
  * \param out_str A valid pointer to array of characters.  The characters array is
  *                allocated and managed by XGBoost, while pointer to that array needs to
@@ -516,7 +537,10 @@ XGB_DLL int XGBoosterSaveRabitCheckpoint(BoosterHandle handle);
 XGB_DLL int XGBoosterSaveJsonConfig(BoosterHandle handle, bst_ulong *out_len,
                                     char const **out_str);
 /*!
- * \brief Load XGBoost's internal configuration from a JSON document.
+ * \brief Load XGBoost's internal configuration from a JSON document.  Currently the
+ *        support is experimental, function signature may change in the future without
+ *        notice.
+ *
  * \param handle handle to Booster object.
  * \param json_parameters string representation of a JSON document.
  * \return 0 when success, -1 when failure happens

src/learner.cc

@@ -472,7 +472,10 @@ class LearnerImpl : public Learner {
   }
 
   // Save model into binary format.  The code is about to be deprecated by more robust
-  // JSON serialization format.
+  // JSON serialization format.  This function is uneffected by
+  // `enable_experimental_json_serialization` as user might enable this flag for pickle
+  // while still want a binary output.  As we are progressing at replacing the binary
+  // format, there's no need to put too much effort on it.
   void SaveModel(dmlc::Stream* fo) const override {
     LearnerModelParamLegacy mparam = mparam_;  // make a copy to potentially modify
     std::vector<std::pair<std::string, std::string> > extra_attr;