Summarizer

class pyspark.ml.stat.Summarizer

Tools for vectorized statistics on MLlib Vectors. The methods in this package provide various statistics for Vectors contained inside DataFrames. This class lets users pick the statistics they would like to extract for a given column.

Examples

>>> from pyspark.ml.stat import Summarizer
>>> from pyspark.sql import Row
>>> from pyspark.ml.linalg import Vectors
>>> summarizer = Summarizer.metrics("mean", "count")
>>> df = sc.parallelize([Row(weight=1.0, features=Vectors.dense(1.0, 1.0, 1.0)),
...                      Row(weight=0.0, features=Vectors.dense(1.0, 2.0, 3.0))]).toDF()
>>> df.select(summarizer.summary(df.features, df.weight)).show(truncate=False)
+-----------------------------------+
|aggregate_metrics(features, weight)|
+-----------------------------------+
|{[1.0,1.0,1.0], 1}                 |
+-----------------------------------+

>>> df.select(summarizer.summary(df.features)).show(truncate=False)
+--------------------------------+
|aggregate_metrics(features, 1.0)|
+--------------------------------+
|{[1.0,1.5,2.0], 2}              |
+--------------------------------+

>>> df.select(Summarizer.mean(df.features, df.weight)).show(truncate=False)
+--------------+
|mean(features)|
+--------------+
|[1.0,1.0,1.0] |
+--------------+

>>> df.select(Summarizer.mean(df.features)).show(truncate=False)
+--------------+
|mean(features)|
+--------------+
|[1.0,1.5,2.0] |
+--------------+

Methods

count(col[, weightCol])	return a column of count summary
max(col[, weightCol])	return a column of max summary
mean(col[, weightCol])	return a column of mean summary
metrics(*metrics)	Given a list of metrics, provides a builder that it turns computes metrics from a column.
min(col[, weightCol])	return a column of min summary
normL1(col[, weightCol])	return a column of normL1 summary
normL2(col[, weightCol])	return a column of normL2 summary
numNonZeros(col[, weightCol])	return a column of numNonZero summary
std(col[, weightCol])	return a column of std summary
sum(col[, weightCol])	return a column of sum summary
variance(col[, weightCol])	return a column of variance summary

Methods Documentation

static count(col: pyspark.sql.column.Column, weightCol: Optional[pyspark.sql.column.Column] = None) → pyspark.sql.column.Column

return a column of count summary

static max(col: pyspark.sql.column.Column, weightCol: Optional[pyspark.sql.column.Column] = None) → pyspark.sql.column.Column

return a column of max summary

static mean(col: pyspark.sql.column.Column, weightCol: Optional[pyspark.sql.column.Column] = None) → pyspark.sql.column.Column

return a column of mean summary

static metrics(*metrics: str) → pyspark.ml.stat.SummaryBuilder

Given a list of metrics, provides a builder that it turns computes metrics from a column.

See the documentation of Summarizer for an example.

The following metrics are accepted (case sensitive):

• mean: a vector that contains the coefficient-wise mean.

• sum: a vector that contains the coefficient-wise sum.

• variance: a vector tha contains the coefficient-wise variance.

• std: a vector tha contains the coefficient-wise standard deviation.

• count: the count of all vectors seen.

• numNonzeros: a vector with the number of non-zeros for each coefficients

• max: the maximum for each coefficient.

• min: the minimum for each coefficient.

• normL2: the Euclidean norm for each coefficient.

• normL1: the L1 norm of each coefficient (sum of the absolute values).

Returns

pyspark.ml.stat.SummaryBuilder

Notes

Currently, the performance of this interface is about 2x~3x slower than using the RDD interface.

Examples

metricsstr

metrics that can be provided.

static min(col: pyspark.sql.column.Column, weightCol: Optional[pyspark.sql.column.Column] = None) → pyspark.sql.column.Column

return a column of min summary

static normL1(col: pyspark.sql.column.Column, weightCol: Optional[pyspark.sql.column.Column] = None) → pyspark.sql.column.Column

return a column of normL1 summary

static normL2(col: pyspark.sql.column.Column, weightCol: Optional[pyspark.sql.column.Column] = None) → pyspark.sql.column.Column

return a column of normL2 summary

static numNonZeros(col: pyspark.sql.column.Column, weightCol: Optional[pyspark.sql.column.Column] = None) → pyspark.sql.column.Column

return a column of numNonZero summary

static std(col: pyspark.sql.column.Column, weightCol: Optional[pyspark.sql.column.Column] = None) → pyspark.sql.column.Column

return a column of std summary

static sum(col: pyspark.sql.column.Column, weightCol: Optional[pyspark.sql.column.Column] = None) → pyspark.sql.column.Column

return a column of sum summary

static variance(col: pyspark.sql.column.Column, weightCol: Optional[pyspark.sql.column.Column] = None) → pyspark.sql.column.Column

return a column of variance summary

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