754f820035
## What changes were proposed in this pull request? Add AL2 license to metadata of all .md files. This seemed to be the tidiest way as it will get ignored by .md renderers and other tools. Attempts to write them as markdown comments revealed that there is no such standard thing. ## How was this patch tested? Doc build Closes #24243 from srowen/SPARK-26918. Authored-by: Sean Owen <sean.owen@databricks.com> Signed-off-by: Sean Owen <sean.owen@databricks.com>
135 lines
5.2 KiB
Markdown
135 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/index.html#org.apache.spark.ml.stat.Correlation$)
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computes the correlation matrix for the input Dataset of Vectors using the specified method.
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The output will be a DataFrame that contains the correlation matrix of the column of vectors.
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{% include_example scala/org/apache/spark/examples/ml/CorrelationExample.scala %}
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</div>
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<div data-lang="java" markdown="1">
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[`Correlation`](api/java/org/apache/spark/ml/stat/Correlation.html)
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computes the correlation matrix for the input Dataset of Vectors using the specified method.
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The output will be a DataFrame that contains the correlation matrix of the column of vectors.
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{% include_example java/org/apache/spark/examples/ml/JavaCorrelationExample.java %}
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</div>
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<div data-lang="python" markdown="1">
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[`Correlation`](api/python/pyspark.ml.html#pyspark.ml.stat.Correlation$)
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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/index.html#org.apache.spark.ml.stat.ChiSquareTest$) for details on the API.
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{% include_example scala/org/apache/spark/examples/ml/ChiSquareTestExample.scala %}
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</div>
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<div data-lang="java" markdown="1">
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Refer to the [`ChiSquareTest` Java docs](api/java/org/apache/spark/ml/stat/ChiSquareTest.html) for details on the API.
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{% include_example java/org/apache/spark/examples/ml/JavaChiSquareTestExample.java %}
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</div>
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<div data-lang="python" markdown="1">
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Refer to the [`ChiSquareTest` Python docs](api/python/index.html#pyspark.ml.stat.ChiSquareTest$) 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, variance, and number of nonzeros, 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/index.html#org.apache.spark.ml.stat.Summarizer$)
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to compute the mean and variance for a vector column of the input dataframe, with and without a weight column.
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{% include_example scala/org/apache/spark/examples/ml/SummarizerExample.scala %}
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</div>
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<div data-lang="java" markdown="1">
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The following example demonstrates using [`Summarizer`](api/java/org/apache/spark/ml/stat/Summarizer.html)
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to compute the mean and variance for a vector column of the input dataframe, with and without a weight column.
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{% include_example java/org/apache/spark/examples/ml/JavaSummarizerExample.java %}
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</div>
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<div data-lang="python" markdown="1">
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Refer to the [`Summarizer` Python docs](api/python/index.html#pyspark.ml.stat.Summarizer$) 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> |