Add xgboost documentation

docs/api.md

@@ -101,18 +101,18 @@ will automatically place weights into the `smd.CollectionKeys.WEIGHTS` collectio
 | `GRADIENTS` | TensorFlow, PyTorch, MXNet | Matches all gradients tensors. In TensorFlow non-DLC, must use `hook.wrap_optimizer()`.  |
 | `LOSSES` | TensorFlow, PyTorch, MXNet | Matches all loss tensors. |
 | `SCALARS` | TensorFlow, PyTorch, MXNet | Matches all scalar tensors, such as loss or accuracy. |
-| `METRICS` | TensorFlow, XGBoost | ??? |
+| `METRICS` | TensorFlow, XGBoost | Evaluation metrics computed by the algorithm. |
 | `INPUTS` | TensorFlow | Matches all inputs to a layer (outputs of the previous layer). |
 | `OUTPUTS` | TensorFlow | Matches all outputs of a layer (inputs of the following layer). |
 | `SEARCHABLE_SCALARS` | TensorFlow | Scalars that will go to SageMaker Metrics. |
 | `OPTIMIZER_VARIABLES` | TensorFlow | Matches all optimizer variables. |
-| `HYPERPARAMETERS` | XGBoost | ... |
-| `PREDICTIONS` | XGBoost | ... |
-| `LABELS` | XGBoost | ... |
-| `FEATURE_IMPORTANCE` | XGBoost | ... |
-| `AVERAGE_SHAP` | XGBoost | ... |
-| `FULL_SHAP` | XGBoost | ... |
-| `TREES` | XGBoost | ... |
+| `HYPERPARAMETERS` | XGBoost | [Booster paramameters](https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost_hyperparameters.html) |
+| `PREDICTIONS` | XGBoost | Predictions on validation set (if provided) |
+| `LABELS` | XGBoost | Labels on validation set (if provided) |
+| `FEATURE_IMPORTANCE` | XGBoost | Feature importance given by [get_score()](https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.Booster.get_score) |
+| `FULL_SHAP` | XGBoost | A matrix of (nsmaple, nfeatures + 1) with each record indicating the feature contributions ([SHAP values](https://github.com/slundberg/shap)) for that prediction. Computed on training data with [predict()](https://github.com/slundberg/shap) |
+| `AVERAGE_SHAP` | XGBoost | The sum of SHAP value magnitudes over all samples. Represents the impact each feature has on the model output. |
+| `TREES` | XGBoost | Boosted tree model given by [trees_to_dataframe()](https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.Booster.trees_to_dataframe) |
 
 
 

docs/xgboost.md

@@ -1,3 +1,99 @@
 # XGBoost
 
-TODO: Fill this out (ask Edward for examples).
+## Contents
+
+- [SageMaker Example](#sagemaker-example)
+- [Full API](#full-api)
+
+## SageMaker Example
+
+### Use XGBoost as a built-in algorithm
+
+The XGBoost algorithm can be used 1) as a built-in algorithm, or 2) as a framework such as MXNet, PyTorch, or Tensorflow.
+If SageMaker XGBoost is used as a built-in algorithm in container verision `0.90-2` or later, Amazon SageMaker Debugger will be available by default (i.e., zero code change experience).
+See [XGBoost Algorithm AWS docmentation](https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.html) for more information on how to use XGBoost as a built-in algorithm.
+See [Amazon SageMaker Debugger examples](https://github.com/awslabs/amazon-sagemaker-examples/tree/master/sagemaker-debugger) for sample notebooks that demonstrate debugging and monitoring capabilities of Aamazon SageMaker Debugger.
+See [SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable/) for more information on how to configure the Amazon SageMaker Debugger from the Python SDK.
+
+### Use XGBoost as a framework
+
+When SageMaker XGBoost is used as a framework, it is recommended that the hook is configured from the [SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable/).
+By using SageMaker Python SDK, you can run different jobs (e.g., Processing jobs) on the SageMaker platform.
+You can retrieve the hook as follows.
+```python
+import xgboost as xgb
+from smdebug.xgboost import Hook
+
+dtrain = xgb.DMatrix("train.libsvm")
+dtest = xgb.DMatrix("test.libsmv")
+
+hook = Hook.create_from_json_file()
+hook.train_data = dtrain  # required
+hook.validation_data = dtest  # optional
+hook.hyperparameters = params  # optional
+
+bst = xgb.train(
+    params,
+    dtrain,
+    callbacks=[hook],
+    evals_result=[(dtrain, "train"), (dvalid, "validation")]
+)
+```
+
+Alternatively, you can also create the hook from `smdebug`'s Python API as shown in the next section.
+
+### Use the Debugger hook
+
+If you are in a non-SageMaker environment, or even in SageMaker, if you want to configure the hook in a certain way in script mode, you can use the full Debugger hook API as follows.
+```python
+import xgboost as xgb
+from smdebug.xgboost import Hook
+
+dtrain = xgb.DMatrix("train.libsvm")
+dvalid = xgb.DMatrix("validation.libsmv")
+
+hook = Hook(
+    out_dir=out_dir,  # required
+    train_data=dtrain,  # required
+    validation_data=dvalid,  # optional
+    hyperparameters=hyperparameters,  # optional
+)
+```
+
+## Full API
+
+```python
+def __init__(
+    self,
+    out_dir,
+    export_tensorboard = False,
+    tensorboard_dir = None,
+    dry_run = False,
+    reduction_config = None,
+    save_config = None,
+    include_regex = None,
+    include_collections = None,
+    save_all = False,
+    include_workers = "one",
+    hyperparameters = None,
+    train_data = None,
+    validation_data = None,
+)
+```
+Initializes the hook. Pass this object as a callback to `xgboost.train()`.
+* `out_dir` (str): A path into which tensors and metadata will be written.
+* `export_tensorboard` (bool): Whether to use TensorBoard logs.
+* `tensorboard_dir` (str): Where to save TensorBoard logs.
+* `dry_run` (bool): If true, evaluations are not actually saved to disk.
+* `reduction_config` (ReductionConfig object): Not supported in XGBoost and will be ignored.
+* `save_config` (SaveConfig object): See the [Common API](https://github.com/awslabs/sagemaker-debugger/blob/master/docs/api.md).
+* `include_regex` (list[str]): List of additional regexes to save.
+* `include_collections` (list[str]): List of collections to save.
+* `save_all` (bool): Saves all tensors and collections. **WARNING: May be memory-intensive and slow.**
+* `include_workers` (str): Used for distributed training, can also be "all".
+* `hyperparameters` (dict): Booster params.
+* `train_data` (DMatrix object): Data to be trained.
+* `validation_data` (DMatrix object): Validation set for which metrics will evaluated during training.
+
+See the [Common API](https://github.com/awslabs/sagemaker-debugger/blob/master/docs/api.md) page for details about Collection, SaveConfig, and ReductionConfig.\
+See the [Analysis](https://github.com/awslabs/sagemaker-debugger/blob/master/docs/analysis.md) page for details about analyzing a training job.