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fixed some typos (#1814)

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This commit is contained in:
Dr. Kashif Rasul
2016-11-25 22:34:57 +01:00
committed by Yuan (Terry) Tang
parent be2f28ec08
commit da2556f58a
14 changed files with 32 additions and 38 deletions
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2
doc/faq.md
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@@ -41,7 +41,7 @@ Most importantly, it pushes the limit of the computation resources we can use.
How can I port the model to my own system
-----------------------------------------
The model and data format of XGBoost is exchangable,
The model and data format of XGBoost is exchangeable,
which means the model trained by one language can be loaded in another.
This means you can train the model using R, while running prediction using
Java or C++, which are more common in production systems.

1
doc/get_started/index.md
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@@ -36,7 +36,6 @@ bst <- xgboost(data = train$data, label = train$label, max.depth = 2, eta = 1, n
nthread = 2, objective = "binary:logistic")
# predict
pred <- predict(bst, test$data)
```
## Julia

2
doc/how_to/contribute.md
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@@ -138,7 +138,7 @@ make the-markdown-to-make.md
- Add the generated figure to the ```dmlc/web-data``` repo.
- If you already cloned the repo to doc, this means a ```git add```
- Create PR for both the markdown and ```dmlc/web-data```
- You can also build the document locally by typing the followig command at ```doc```
- You can also build the document locally by typing the following command at ```doc```
```bash
make html
```

2
doc/how_to/index.md
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@@ -6,7 +6,7 @@ This page contains guidelines to use and develop mxnets.
- [How to Install XGBoost](../build.md)
## Use XGBoost in Specific Ways
- [Parameter tunning guide](param_tuning.md)
- [Parameter tuning guide](param_tuning.md)
- [Use out of core computation for large dataset](external_memory.md)
## Develop and Hack XGBoost

5
doc/input_format.md
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@@ -12,8 +12,7 @@ train.txt
1 0:0.01 1:0.3
0 0:0.2 1:0.3
```
Each line represent a single instance, and in the first line '1' is the instance label,'101' and '102' are feature indices, '1.2' and '0.03' are feature values. In the binary classification case, '1' is used to indicate positive samples, and '0' is used to indicate negative samples. We also support probability values in [0,1] as label, to indicate the probability of the instanc
e being positive.
Each line represent a single instance, and in the first line '1' is the instance label,'101' and '102' are feature indices, '1.2' and '0.03' are feature values. In the binary classification case, '1' is used to indicate positive samples, and '0' is used to indicate negative samples. We also support probability values in [0,1] as label, to indicate the probability of the instance being positive.
Additional Information
----------------------
@@ -54,4 +53,4 @@ train.txt.base_margin
1.0
3.4
```
XGBoost will take these values as intial margin prediction and boost from that. An important note about base_margin is that it should be margin prediction before transformation, so if you are doing logistic loss, you will need to put in value before logistic transformation. If you are using XGBoost predictor, use pred_margin=1 to output margin values.
XGBoost will take these values as initial margin prediction and boost from that. An important note about base_margin is that it should be margin prediction before transformation, so if you are doing logistic loss, you will need to put in value before logistic transformation. If you are using XGBoost predictor, use pred_margin=1 to output margin values.

4
doc/jvm/index.md
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@@ -17,7 +17,7 @@ To publish the artifacts to your local maven repository, run
mvn install
Or, if you would like to skip tests, run
Or, if you would like to skip tests, run
mvn -DskipTests install
@@ -32,7 +32,7 @@ This command will publish the xgboost binaries, the compiled java classes as wel
After integrating with Dataframe/Dataset APIs of Spark 2.0, XGBoost4J-Spark only supports compile with Spark 2.x. You can build XGBoost4J-Spark as a component of XGBoost4J by running `mvn package`, and you can specify the version of spark with `mvn -Dspark.version=2.0.0 package`. (To continue working with Spark 1.x, the users are supposed to update pom.xml by modifying the properties like `spark.version`, `scala.version`, and `scala.binary.version`. Users also need to change the implemention by replacing SparkSession with SQLContext and the type of API parameters from Dataset[_] to Dataframe)
After integrating with Dataframe/Dataset APIs of Spark 2.0, XGBoost4J-Spark only supports compile with Spark 2.x. You can build XGBoost4J-Spark as a component of XGBoost4J by running `mvn package`, and you can specify the version of spark with `mvn -Dspark.version=2.0.0 package`. (To continue working with Spark 1.x, the users are supposed to update pom.xml by modifying the properties like `spark.version`, `scala.version`, and `scala.binary.version`. Users also need to change the implementation by replacing SparkSession with SQLContext and the type of API parameters from Dataset[_] to Dataframe)
Contents
--------

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doc/jvm/java_intro.md
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@@ -133,7 +133,7 @@ Booster booster = new Booster(param, "model.bin");
```
## Prediction
after training and loading a model, you use it to predict other data, the predict results will be a two-dimension float array (nsample, nclass) ,for predict leaf, it would be (nsample, nclass*ntrees)
after training and loading a model, you use it to predict other data, the predict results will be a two-dimension float array (nsample, nclass), for predict leaf, it would be (nsample, nclass*ntrees)
```java
DMatrix dtest = new DMatrix("test.svm.txt");
//predict

2
doc/jvm/xgboost4j-intro.md
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@@ -26,7 +26,7 @@ They are also often [much more efficient](http://arxiv.org/abs/1603.02754).
The gap between the implementation fundamentals of the general data processing frameworks and the more specific machine learning libraries/systems prohibits the smooth connection between these two types of systems, thus brings unnecessary inconvenience to the end user. The common workflow to the user is to utilize the systems like Spark/Flink to preprocess/clean data, pass the results to machine learning systems like [XGBoost](https://github.com/dmlc/xgboost)/[MxNet](https://github.com/dmlc/mxnet)) via the file systems and then conduct the following machine learning phase. This process jumping across two types of systems creates certain inconvenience for the users and brings additional overhead to the operators of the infrastructure.
We want best of both worlds, so we can use the data processing frameworks like Spark and Flink toghether with
We want best of both worlds, so we can use the data processing frameworks like Spark and Flink together with
the best distributed machine learning solutions.
To resolve the situation, we introduce the new-brewed [XGBoost4J](https://github.com/dmlc/xgboost/tree/master/jvm-packages),
<b>XGBoost</b> for <b>J</b>VM Platform. We aim to provide the clean Java/Scala APIs and the integration with the most popular data processing systems developed in JVM-based languages.

18
doc/jvm/xgboost4j_full_integration.md
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@@ -1,6 +1,6 @@
## Introduction
## Introduction
On March 2016, we released the first version of [XGBoost4J](http://dmlc.ml/2016/03/14/xgboost4j-portable-distributed-xgboost-in-spark-flink-and-dataflow.html), which is a set of packages providing Java/Scala interfaces of XGBoost and the integration with prevalent JVM-based distributed data processing platforms, like Spark/Flink.
On March 2016, we released the first version of [XGBoost4J](http://dmlc.ml/2016/03/14/xgboost4j-portable-distributed-xgboost-in-spark-flink-and-dataflow.html), which is a set of packages providing Java/Scala interfaces of XGBoost and the integration with prevalent JVM-based distributed data processing platforms, like Spark/Flink.
The integrations with Spark/Flink, a.k.a. <b>XGBoost4J-Spark</b> and <b>XGBoost-Flink</b>, receive the tremendous positive feedbacks from the community. It enables users to build a unified pipeline, embedding XGBoost into the data processing system based on the widely-deployed frameworks like Spark. The following figure shows the general architecture of such a pipeline with the first version of <b>XGBoost4J-Spark</b>, where the data processing is based on the low-level [Resilient Distributed Dataset (RDD)](http://spark.apache.org/docs/latest/programming-guide.html#resilient-distributed-datasets-rdds) abstraction.
@@ -12,14 +12,14 @@ In the last months, we have a lot of communication with the users and gain the d
* While Spark is still the mainstream data processing tool in most of scenarios, more and more users are porting their RDD-based Spark programs to [DataFrame/Dataset APIs](http://spark.apache.org/docs/latest/sql-programming-guide.html) for the well-designed interfaces to manipulate structured data and the [significant performance improvement](https://databricks.com/blog/2016/07/26/introducing-apache-spark-2-0.html).
* Spark itself has presented a clear roadmap that DataFrame/Dataset would be the base of the latest and future features, e.g. latest version of [ML pipeline](http://spark.apache.org/docs/latest/ml-guide.html) and [Structured Streaming](http://spark.apache.org/docs/latest/structured-streaming-programming-guide.html).
* Spark itself has presented a clear roadmap that DataFrame/Dataset would be the base of the latest and future features, e.g. latest version of [ML pipeline](http://spark.apache.org/docs/latest/ml-guide.html) and [Structured Streaming](http://spark.apache.org/docs/latest/structured-streaming-programming-guide.html).
Based on these feedbacks from the users, we observe a gap between the original RDD-based XGBoost4J-Spark and the users' latest usage scenario as well as the future direction of Spark ecosystem. To fill this gap, we start working on the <b><i>integration of XGBoost and Spark's DataFrame/Dataset abstraction</i></b> in September. In this blog, we will introduce <b>the latest version of XGBoost4J-Spark</b> which allows the user to work with DataFrame/Dataset directly and embed XGBoost to Spark's ML pipeline seamlessly.
Based on these feedbacks from the users, we observe a gap between the original RDD-based XGBoost4J-Spark and the users' latest usage scenario as well as the future direction of Spark ecosystem. To fill this gap, we start working on the <b><i>integration of XGBoost and Spark's DataFrame/Dataset abstraction</i></b> in September. In this blog, we will introduce <b>the latest version of XGBoost4J-Spark</b> which allows the user to work with DataFrame/Dataset directly and embed XGBoost to Spark's ML pipeline seamlessly.
## A Full Integration of XGBoost and DataFrame/Dataset
The following figure illustrates the new pipeline architecture with the latest XGBoost4J-Spark.
The following figure illustrates the new pipeline architecture with the latest XGBoost4J-Spark.
![XGBoost4J New Architecture](https://raw.githubusercontent.com/dmlc/web-data/master/xgboost/unified_pipeline_new.png)
@@ -49,7 +49,7 @@ import org.apache.spark.ml.feature.StringIndexer
// load sales records saved in json files
val salesDF = spark.read.json("sales.json")
// transfrom the string-represented storeType feature to numeric storeTypeIndex
// transform the string-represented storeType feature to numeric storeTypeIndex
val indexer = new StringIndexer()
.setInputCol("storeType")
.setOutputCol("storeTypeIndex")
@@ -71,7 +71,7 @@ import org.apache.spark.ml.feature.StringIndexer
// load sales records saved in json files
val salesDF = spark.read.json("sales.json")
// transfrom the string-represented storeType feature to numeric storeTypeIndex
// transform the string-represented storeType feature to numeric storeTypeIndex
val indexer = new StringIndexer()
.setInputCol("storeType")
.setOutputCol("storeTypeIndex")
@@ -99,7 +99,7 @@ val salesRecordsWithPred = xgboostModel.transform(salesTestDF)
The most critical operation to maximize the power of XGBoost is to select the optimal parameters for the model. Tuning parameters manually is a tedious and labor-consuming process. With the latest version of XGBoost4J-Spark, we can utilize the Spark model selecting tool to automate this process. The following example shows the code snippet utilizing [TrainValidationSplit](http://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.ml.tuning.TrainValidationSplit) and [RegressionEvaluator](http://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.ml.evaluation.RegressionEvaluator) to search the optimal combination of two XGBoost parameters, [max_depth and eta] (https://github.com/dmlc/xgboost/blob/master/doc/parameter.md). The model producing the minimum cost function value defined by RegressionEvaluator is selected and used to generate the prediction for the test set.
```scala
// create XGBoostEstimator
// create XGBoostEstimator
val xgbEstimator = new XGBoostEstimator(xgboostParam).setFeaturesCol("features").
setLabelCol("sales")
val paramGrid = new ParamGridBuilder()
@@ -137,5 +137,3 @@ If you are interested in knowing more about XGBoost, you can find rich resources
- [Tutorials for the R package](xgboost.readthedocs.org/en/latest/R-package/index.html)
- [Introduction of the Parameters](http://xgboost.readthedocs.org/en/latest/parameter.html)
- [Awesome XGBoost, a curated list of examples, tutorials, blogs about XGBoost usecases](https://github.com/dmlc/xgboost/tree/master/demo)

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doc/tutorials/aws_yarn.md
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@@ -49,7 +49,7 @@ Now we can open the browser, and type(replace the DNS with the master DNS)
```
ec2-xx-xx-xx.us-west-2.compute.amazonaws.com:8088
```
This will show the job tracker of the YARN cluster. Note that we may wait a few minutes before the master finishes bootstraping and starts the
This will show the job tracker of the YARN cluster. Note that we may wait a few minutes before the master finishes bootstrapping and starts the
job tracker.
After master machine gets up, we can freely add more slave machines to the cluster.
@@ -158,7 +158,7 @@ Application application_1456461717456_0015 finished with state FINISHED at 14564
Analyze the Model
-----------------
After the model is trained, we can analyse the learnt model and use it for future prediction task.
XGBoost is a portable framework, the model in all platforms are ***exchangable***.
XGBoost is a portable framework, the model in all platforms are ***exchangeable***.
This means we can load the trained model in python/R/Julia and take benefit of data science pipelines
in these languages to do model analysis and prediction.