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Data sources |
Data sources |
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In this section, we introduce how to use data source in ML to load data. Besides some general data sources such as Parquet, CSV, JSON and JDBC, we also provide some specific data sources for ML.
Table of Contents
- This will become a table of contents (this text will be scraped). {:toc}
This image data source is used to load image files from a directory, it can load compressed image (jpeg, png, etc.) into raw image representation via ImageIO in Java library.
The loaded DataFrame has one StructType column: "image", containing image data stored as image schema.
The schema of the image column is:
- origin:
StringType(represents the file path of the image) - height:
IntegerType(height of the image) - width:
IntegerType(width of the image) - nChannels:
IntegerType(number of image channels) - mode:
IntegerType(OpenCV-compatible type) - data:
BinaryType(Image bytes in OpenCV-compatible order: row-wise BGR in most cases)
{% highlight scala %} scala> val df = spark.read.format("image").option("dropInvalid", true).load("data/mllib/images/origin/kittens") df: org.apache.spark.sql.DataFrame = [image: struct<origin: string, height: int ... 4 more fields>]
scala> df.select("image.origin", "image.width", "image.height").show(truncate=false) +-----------------------------------------------------------------------+-----+------+ |origin |width|height| +-----------------------------------------------------------------------+-----+------+ |file:///spark/data/mllib/images/origin/kittens/54893.jpg |300 |311 | |file:///spark/data/mllib/images/origin/kittens/DP802813.jpg |199 |313 | |file:///spark/data/mllib/images/origin/kittens/29.5.a_b_EGDP022204.jpg |300 |200 | |file:///spark/data/mllib/images/origin/kittens/DP153539.jpg |300 |296 | +-----------------------------------------------------------------------+-----+------+ {% endhighlight %}
{% highlight java %} Dataset imagesDF = spark.read().format("image").option("dropInvalid", true).load("data/mllib/images/origin/kittens"); imageDF.select("image.origin", "image.width", "image.height").show(false); /* Will output: +-----------------------------------------------------------------------+-----+------+ |origin |width|height| +-----------------------------------------------------------------------+-----+------+ |file:///spark/data/mllib/images/origin/kittens/54893.jpg |300 |311 | |file:///spark/data/mllib/images/origin/kittens/DP802813.jpg |199 |313 | |file:///spark/data/mllib/images/origin/kittens/29.5.a_b_EGDP022204.jpg |300 |200 | |file:///spark/data/mllib/images/origin/kittens/DP153539.jpg |300 |296 | +-----------------------------------------------------------------------+-----+------+ */ {% endhighlight %}
{% highlight python %}
df = spark.read.format("image").option("dropInvalid", True).load("data/mllib/images/origin/kittens") df.select("image.origin", "image.width", "image.height").show(truncate=False) +-----------------------------------------------------------------------+-----+------+ |origin |width|height| +-----------------------------------------------------------------------+-----+------+ |file:///spark/data/mllib/images/origin/kittens/54893.jpg |300 |311 | |file:///spark/data/mllib/images/origin/kittens/DP802813.jpg |199 |313 | |file:///spark/data/mllib/images/origin/kittens/29.5.a_b_EGDP022204.jpg |300 |200 | |file:///spark/data/mllib/images/origin/kittens/DP153539.jpg |300 |296 | +-----------------------------------------------------------------------+-----+------+ {% endhighlight %}
{% highlight r %}
df = read.df("data/mllib/images/origin/kittens", "image") head(select(df, df$image.origin, df$image.width, df$image.height))
1 file:///spark/data/mllib/images/origin/kittens/54893.jpg 2 file:///spark/data/mllib/images/origin/kittens/DP802813.jpg 3 file:///spark/data/mllib/images/origin/kittens/29.5.a_b_EGDP022204.jpg 4 file:///spark/data/mllib/images/origin/kittens/DP153539.jpg width height 1 300 311 2 199 313 3 300 200 4 300 296
{% endhighlight %}
This LIBSVM data source is used to load 'libsvm' type files from a directory.
The loaded DataFrame has two columns: label containing labels stored as doubles and features containing feature vectors stored as Vectors.
The schemas of the columns are:
- label:
DoubleType(represents the instance label) - features:
VectorUDT(represents the feature vector)
{% highlight scala %} scala> val df = spark.read.format("libsvm").option("numFeatures", "780").load("data/mllib/sample_libsvm_data.txt") df: org.apache.spark.sql.DataFrame = [label: double, features: vector]
scala> df.show(10) +-----+--------------------+ |label| features| +-----+--------------------+ | 0.0|(780,[127,128,129...| | 1.0|(780,[158,159,160...| | 1.0|(780,[124,125,126...| | 1.0|(780,[152,153,154...| | 1.0|(780,[151,152,153...| | 0.0|(780,[129,130,131...| | 1.0|(780,[158,159,160...| | 1.0|(780,[99,100,101,...| | 0.0|(780,[154,155,156...| | 0.0|(780,[127,128,129...| +-----+--------------------+ only showing top 10 rows {% endhighlight %}
{% highlight java %} Dataset df = spark.read.format("libsvm").option("numFeatures", "780").load("data/mllib/sample_libsvm_data.txt"); df.show(10); /* Will output: +-----+--------------------+ |label| features| +-----+--------------------+ | 0.0|(780,[127,128,129...| | 1.0|(780,[158,159,160...| | 1.0|(780,[124,125,126...| | 1.0|(780,[152,153,154...| | 1.0|(780,[151,152,153...| | 0.0|(780,[129,130,131...| | 1.0|(780,[158,159,160...| | 1.0|(780,[99,100,101,...| | 0.0|(780,[154,155,156...| | 0.0|(780,[127,128,129...| +-----+--------------------+ only showing top 10 rows */ {% endhighlight %}
{% highlight python %}
df = spark.read.format("libsvm").option("numFeatures", "780").load("data/mllib/sample_libsvm_data.txt") df.show(10) +-----+--------------------+ |label| features| +-----+--------------------+ | 0.0|(780,[127,128,129...| | 1.0|(780,[158,159,160...| | 1.0|(780,[124,125,126...| | 1.0|(780,[152,153,154...| | 1.0|(780,[151,152,153...| | 0.0|(780,[129,130,131...| | 1.0|(780,[158,159,160...| | 1.0|(780,[99,100,101,...| | 0.0|(780,[154,155,156...| | 0.0|(780,[127,128,129...| +-----+--------------------+ only showing top 10 rows {% endhighlight %}
{% highlight r %}
df = read.df("data/mllib/sample_libsvm_data.txt", "libsvm") head(select(df, df$label, df$features), 10)
label features 1 0 <environment: 0x7fe6d35366e8> 2 1 <environment: 0x7fe6d353bf78> 3 1 <environment: 0x7fe6d3541840> 4 1 <environment: 0x7fe6d3545108> 5 1 <environment: 0x7fe6d354c8e0> 6 0 <environment: 0x7fe6d35501a8> 7 1 <environment: 0x7fe6d3555a70> 8 1 <environment: 0x7fe6d3559338> 9 0 <environment: 0x7fe6d355cc00> 10 0 <environment: 0x7fe6d35643d8>
{% endhighlight %}