01cc852982
### What changes were proposed in this pull request? Change the link to the Scala API document. ``` $ git grep "#org.apache.spark.package" docs/_layouts/global.html: <li><a href="api/scala/index.html#org.apache.spark.package">Scala</a></li> docs/index.md:* [Spark Scala API (Scaladoc)](api/scala/index.html#org.apache.spark.package) docs/rdd-programming-guide.md:[Scala](api/scala/#org.apache.spark.package), [Java](api/java/), [Python](api/python/) and [R](api/R/). ``` ### Why are the changes needed? The home page link for Scala API document is incorrect after upgrade to 3.0 ### Does this PR introduce any user-facing change? Document UI change only. ### How was this patch tested? Local test, attach screenshots below: Before: ![image](https://user-images.githubusercontent.com/4833765/74335713-c2385300-4dd7-11ea-95d8-f5a3639d2578.png) After: ![image](https://user-images.githubusercontent.com/4833765/74335727-cbc1bb00-4dd7-11ea-89d9-4dcc1310e679.png) Closes #27549 from xuanyuanking/scala-doc. Authored-by: Yuanjian Li <xyliyuanjian@gmail.com> Signed-off-by: Sean Owen <srowen@gmail.com>
137 lines
5.2 KiB
Markdown
137 lines
5.2 KiB
Markdown
---
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layout: global
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title: Basic Statistics
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displayTitle: Basic Statistics
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license: |
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Licensed to the Apache Software Foundation (ASF) under one or more
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contributor license agreements. See the NOTICE file distributed with
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this work for additional information regarding copyright ownership.
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The ASF licenses this file to You under the Apache License, Version 2.0
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(the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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---
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`\[
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\newcommand{\R}{\mathbb{R}}
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\newcommand{\E}{\mathbb{E}}
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\newcommand{\x}{\mathbf{x}}
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\newcommand{\y}{\mathbf{y}}
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\newcommand{\wv}{\mathbf{w}}
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\newcommand{\av}{\mathbf{\alpha}}
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\newcommand{\bv}{\mathbf{b}}
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\newcommand{\N}{\mathbb{N}}
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\newcommand{\id}{\mathbf{I}}
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\newcommand{\ind}{\mathbf{1}}
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\newcommand{\0}{\mathbf{0}}
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\newcommand{\unit}{\mathbf{e}}
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\newcommand{\one}{\mathbf{1}}
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\newcommand{\zero}{\mathbf{0}}
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\]`
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**Table of Contents**
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* This will become a table of contents (this text will be scraped).
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{:toc}
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## Correlation
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Calculating the correlation between two series of data is a common operation in Statistics. In `spark.ml`
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we provide the flexibility to calculate pairwise correlations among many series. The supported
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correlation methods are currently Pearson's and Spearman's correlation.
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<div class="codetabs">
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<div data-lang="scala" markdown="1">
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[`Correlation`](api/scala/org/apache/spark/ml/stat/Correlation$.html)
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computes the correlation matrix for the input Dataset of Vectors using the specified method.
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The output will be a DataFrame that contains the correlation matrix of the column of vectors.
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{% include_example scala/org/apache/spark/examples/ml/CorrelationExample.scala %}
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</div>
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<div data-lang="java" markdown="1">
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[`Correlation`](api/java/org/apache/spark/ml/stat/Correlation.html)
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computes the correlation matrix for the input Dataset of Vectors using the specified method.
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The output will be a DataFrame that contains the correlation matrix of the column of vectors.
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{% include_example java/org/apache/spark/examples/ml/JavaCorrelationExample.java %}
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</div>
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<div data-lang="python" markdown="1">
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[`Correlation`](api/python/pyspark.ml.html#pyspark.ml.stat.Correlation$)
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computes the correlation matrix for the input Dataset of Vectors using the specified method.
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The output will be a DataFrame that contains the correlation matrix of the column of vectors.
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{% include_example python/ml/correlation_example.py %}
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</div>
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</div>
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## Hypothesis testing
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Hypothesis testing is a powerful tool in statistics to determine whether a result is statistically
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significant, whether this result occurred by chance or not. `spark.ml` currently supports Pearson's
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Chi-squared ( $\chi^2$) tests for independence.
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`ChiSquareTest` conducts Pearson's independence test for every feature against the label.
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For each feature, the (feature, label) pairs are converted into a contingency matrix for which
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the Chi-squared statistic is computed. All label and feature values must be categorical.
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<div class="codetabs">
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<div data-lang="scala" markdown="1">
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Refer to the [`ChiSquareTest` Scala docs](api/scala/org/apache/spark/ml/stat/ChiSquareTest$.html) for details on the API.
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{% include_example scala/org/apache/spark/examples/ml/ChiSquareTestExample.scala %}
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</div>
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<div data-lang="java" markdown="1">
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Refer to the [`ChiSquareTest` Java docs](api/java/org/apache/spark/ml/stat/ChiSquareTest.html) for details on the API.
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{% include_example java/org/apache/spark/examples/ml/JavaChiSquareTestExample.java %}
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</div>
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<div data-lang="python" markdown="1">
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Refer to the [`ChiSquareTest` Python docs](api/python/index.html#pyspark.ml.stat.ChiSquareTest$) for details on the API.
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{% include_example python/ml/chi_square_test_example.py %}
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</div>
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</div>
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## Summarizer
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We provide vector column summary statistics for `Dataframe` through `Summarizer`.
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Available metrics are the column-wise max, min, mean, sum, variance, std, and number of nonzeros,
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as well as the total count.
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<div class="codetabs">
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<div data-lang="scala" markdown="1">
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The following example demonstrates using [`Summarizer`](api/scala/org/apache/spark/ml/stat/Summarizer$.html)
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to compute the mean and variance for a vector column of the input dataframe, with and without a weight column.
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{% include_example scala/org/apache/spark/examples/ml/SummarizerExample.scala %}
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</div>
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<div data-lang="java" markdown="1">
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The following example demonstrates using [`Summarizer`](api/java/org/apache/spark/ml/stat/Summarizer.html)
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to compute the mean and variance for a vector column of the input dataframe, with and without a weight column.
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{% include_example java/org/apache/spark/examples/ml/JavaSummarizerExample.java %}
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</div>
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<div data-lang="python" markdown="1">
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Refer to the [`Summarizer` Python docs](api/python/index.html#pyspark.ml.stat.Summarizer$) for details on the API.
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{% include_example python/ml/summarizer_example.py %}
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</div>
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</div>
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