diff --git a/CHANGELOG.md b/CHANGELOG.md index e46e0bac..28f09793 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,8 @@ # Changelog - +# 7.009 + * Small optimizations to bring back some performance for group-by-column-agg that was lost + between 6 and 7. + # 7.007 * row-at defaults to a copying operation so you can safely use this with datasets that may be zero-copied from other sources. diff --git a/deps.edn b/deps.edn index 0f95038e..03ddf651 100644 --- a/deps.edn +++ b/deps.edn @@ -1,8 +1,7 @@ {:paths ["src" "resources" "target/classes"] - :deps {cnuernber/dtype-next {:mvn/version "10.012"} + :deps {cnuernber/dtype-next {:mvn/version "10.013"} techascent/tech.io {:mvn/version "4.31" :exclusions [org.apache.commons/commons-compress]} - com.cnuernber/ham-fisted {:mvn/version "1.006-SNAPSHOT"} org.apache.datasketches/datasketches-java {:mvn/version "3.3.0"}} :aliases {:codox @@ -13,7 +12,7 @@ :exec-fn codox.main/-main :exec-args {:group-id "techascent" :artifact-id "tech.ml.dataset" - :version "7.008" + :version "7.009" :name "TMD" :description "A Clojure high performance data processing system" :metadata {:doc/format :markdown} diff --git a/docs/000-getting-started.html b/docs/000-getting-started.html index 71ba6f2a..21af13bf 100644 --- a/docs/000-getting-started.html +++ b/docs/000-getting-started.html @@ -4,7 +4,7 @@ function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); - gtag('config', 'G-95TVFC1FEB');

Project

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Namespaces

tech.ml.dataset Getting Started

+ gtag('config', 'G-95TVFC1FEB');

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tech.ml.dataset Getting Started

What kind of data?

TMD processes tabular data, that is, data logically arranged in rows and columns. Similar to a spreadsheet (but handling much larger datasets) or a database (but much more convenient), TMD accelerates exploring, cleaning, and processing data tables. TMD inherits Clojure's data-orientation and flexible dynamic typing, without compromising on being functional; thereby extending the language's reach to new problems and domains.

> (ds/->dataset "lucy.csv")
diff --git a/docs/100-walkthrough.html b/docs/100-walkthrough.html
index d58125eb..27ea2df5 100644
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tech.ml.dataset Walkthrough

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tech.ml.dataset Walkthrough

 
tech.ml.dataset (TMD) is a Clojure library designed to ease working with tabular data, similar to data.table in R or Python's Pandas. TMD takes inspiration from the design of those tools, but does not aim to copy their functionality. Instead, TMD is a building block that increases Clojure's already considerable data processing power.

 
High Level Design

 
In TMD, a dataset is logically a map of column name to column data. Column data is typed (e.g., a column of 16 bit integers, or a column of 64 bit floating point numbers), similar to a database. Column names may be any Java object - keywords and strings are typical - and column values may be any Java primitive type, or type supported by tech.datatype, datetimes, or arbitrary objects. Column data is stored contiguously in JVM arrays, and missing values are indicated with bitsets.

diff --git a/docs/200-quick-reference.html b/docs/200-quick-reference.html
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tech.ml.dataset Quick Reference

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tech.ml.dataset Quick Reference

 
This topic summarizes many of the most frequently used TMD functions, together with some quick notes about their use. Functions here are linked to further documentation, or their source. Note, unless a namespace is specified, each function is accessible via the tech.ml.dataset namespace.

 
For a more thorough treatment, the API docs list every available function.

 
Table of Contents

diff --git a/docs/columns-readers-and-datatypes.html b/docs/columns-readers-and-datatypes.html
index 09af0fd9..47f1f552 100644
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tech.ml.dataset Columns, Readers, and Datatypes

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tech.ml.dataset Columns, Readers, and Datatypes

 
In tech.ml.dataset, columns are composed of three things:
 data, metadata, and the missing set.
 The column's datatype is the datatype of the data member.  The data member can
diff --git a/docs/index.html b/docs/index.html
index cdf9e835..c9b72fd3 100644
--- a/docs/index.html
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@@ -1,10 +1,10 @@
 
-TMD 7.008
Project
Topics
Namespaces
TMD 7.008
A Clojure high performance data processing system.
Topics
Namespaces
tech.v3.dataset
Column major dataset abstraction for efficiently manipulating
+  gtag('config', 'G-95TVFC1FEB');
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Topics
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TMD 7.009
A Clojure high performance data processing system.
Topics
Namespaces
tech.v3.dataset
Column major dataset abstraction for efficiently manipulating
 in memory datasets.

 
Public variables and functions:
tech.v3.dataset.categorical
Conversions of categorical values into numbers and back.  Two forms of conversions
 are supported, a straight value->integer map and one-hot encoding.

diff --git a/docs/nippy-serialization-rocks.html b/docs/nippy-serialization-rocks.html
index f33f3821..419d3ea1 100644
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tech.ml.dataset And nippy

+  gtag('config', 'G-95TVFC1FEB');
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tech.ml.dataset And nippy

 
We are big fans of the nippy system for
 freezing/thawing data.  So we were pleasantly surprized with how well it performs
 with dataset and how easy it was to extend the dataset object to support nippy
diff --git a/docs/supported-datatypes.html b/docs/supported-datatypes.html
index 4fb69ed3..58c7085f 100644
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tech.ml.dataset Supported Datatypes

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tech.ml.dataset Supported Datatypes

 
tech.ml.dataset supports a wide range of datatypes and has a system for expanding
 the supported datatype set, aliasing new names to existing datatypes, and packing
 object datatypes into primitive containers.  Let's walk through each of these topics
diff --git a/docs/tech.v3.dataset.categorical.html b/docs/tech.v3.dataset.categorical.html
index 4053aa6f..6aa655d5 100644
--- a/docs/tech.v3.dataset.categorical.html
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tech.v3.dataset.categorical
Conversions of categorical values into numbers and back.  Two forms of conversions
+  gtag('config', 'G-95TVFC1FEB');
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Topics
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Public Vars
tech.v3.dataset.categorical
Conversions of categorical values into numbers and back.  Two forms of conversions
 are supported, a straight value->integer map and one-hot encoding.

 
The functions in this namespace manipulate the metadata on the columns of the dataset, wich can be inspected  via clojure.core/meta

 
fit-categorical-map
(fit-categorical-map dataset colname & [table-args res-dtype])
Given a column, map it into an numeric space via a discrete map of values
diff --git a/docs/tech.v3.dataset.clipboard.html b/docs/tech.v3.dataset.clipboard.html
index c0d4fece..00fbe662 100644
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tech.v3.dataset.clipboard
Optional namespace that copies a dataset to the clipboard for pasting into
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tech.v3.dataset.clipboard
Optional namespace that copies a dataset to the clipboard for pasting into
 applications such as excel or google sheets.

 
Reading defaults to 'csv' format while writing defaults to 'tsv' format.

 
clipboard
(clipboard)
Get the system clipboard.

diff --git a/docs/tech.v3.dataset.column-filters.html b/docs/tech.v3.dataset.column-filters.html
index 06b6f2fc..76861e67 100644
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   function gtag(){dataLayer.push(arguments);}
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tech.v3.dataset.column-filters
Queries to select column subsets that have various properites such as all numeric
+  gtag('config', 'G-95TVFC1FEB');
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tech.v3.dataset.column-filters
Queries to select column subsets that have various properites such as all numeric
 columns, all feature columns, or columns that have a specific datatype.

 
Further a few set operations (union, intersection, difference) are provided
 to further manipulate subsets of columns.

diff --git a/docs/tech.v3.dataset.column.html b/docs/tech.v3.dataset.column.html
index aa7fa91d..505e0386 100644
--- a/docs/tech.v3.dataset.column.html
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-  gtag('config', 'G-95TVFC1FEB');
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tech.v3.dataset.column
clone
(clone col)
Clone this column not changing anything.

+  gtag('config', 'G-95TVFC1FEB');
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tech.v3.dataset.column
clone
(clone col)
Clone this column not changing anything.

 
view source
column-map
(column-map map-fn res-dtype & args)
Map a scalar function across one or more columns.
 This is the semi-missing-set aware version of tech.v3.datatype/emap.  This function
 is never lazy.

diff --git a/docs/tech.v3.dataset.html b/docs/tech.v3.dataset.html
index d87a6d69..3200f39f 100644
--- a/docs/tech.v3.dataset.html
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   function gtag(){dataLayer.push(arguments);}
   gtag('js', new Date());
 
-  gtag('config', 'G-95TVFC1FEB');
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tech.v3.dataset
Column major dataset abstraction for efficiently manipulating
+  gtag('config', 'G-95TVFC1FEB');
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Public Vars
tech.v3.dataset
Column major dataset abstraction for efficiently manipulating
 in memory datasets.

 
->>dataset
(->>dataset options dataset)(->>dataset dataset)
Please see documentation of ->dataset.  Options are the same.

 
view source
->dataset
(->dataset dataset options)(->dataset dataset)
Create a dataset from either csv/tsv or a sequence of maps.

diff --git a/docs/tech.v3.dataset.io.csv.html b/docs/tech.v3.dataset.io.csv.html
index de567ff7..d80cabc2 100644
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tech.v3.dataset.io.csv
CSV parsing based on charred.api/read-csv.

+  gtag('config', 'G-95TVFC1FEB');
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tech.v3.dataset.io.csv
CSV parsing based on charred.api/read-csv.

 
csv->dataset
(csv->dataset input & [options])
Read a csv into a dataset.  Same options as tech.v3.dataset/->dataset.

 
view source
csv->dataset-seq
(csv->dataset-seq input & [options])
Read a csv into a lazy sequence of datasets.  All options of tech.v3.dataset/->dataset
 are suppored aside from :n-initial-skip-rows with an additional option of
diff --git a/docs/tech.v3.dataset.io.datetime.html b/docs/tech.v3.dataset.io.datetime.html
index 493e16cc..79c802ba 100644
--- a/docs/tech.v3.dataset.io.datetime.html
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   function gtag(){dataLayer.push(arguments);}
   gtag('js', new Date());
 
-  gtag('config', 'G-95TVFC1FEB');
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tech.v3.dataset.io.datetime
Helpful and well tested string->datetime pathways.

+  gtag('config', 'G-95TVFC1FEB');
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Public Vars
tech.v3.dataset.io.datetime
Helpful and well tested string->datetime pathways.

 
datatype->general-parse-fn-map
Map of datetime datatype to generalized parse fn.

 
view source
datetime-formatter-or-str->parser-fn
(datetime-formatter-or-str->parser-fn datatype format-string-or-formatter)
Given a datatype and one of fn? string? DateTimeFormatter,
 return a function that takes strings and returns datetime objects
diff --git a/docs/tech.v3.dataset.io.string-row-parser.html b/docs/tech.v3.dataset.io.string-row-parser.html
index c51ebc86..126cb30e 100644
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   function gtag(){dataLayer.push(arguments);}
   gtag('js', new Date());
 
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tech.v3.dataset.io.string-row-parser
Parsing functions based on raw data that is represented by a sequence
+  gtag('config', 'G-95TVFC1FEB');
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Public Vars
tech.v3.dataset.io.string-row-parser
Parsing functions based on raw data that is represented by a sequence
 of string arrays.

 
partition-all-rows
(partition-all-rows {:keys [header-row?], :or {header-row? true}} n row-seq)
Given a sequence of rows, partition into an undefined number of partitions of at most
 N rows but keep the header row as the first for all sequences.

diff --git a/docs/tech.v3.dataset.io.univocity.html b/docs/tech.v3.dataset.io.univocity.html
index 76bae096..8a681fd4 100644
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   function gtag(){dataLayer.push(arguments);}
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-  gtag('config', 'G-95TVFC1FEB');
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tech.v3.dataset.io.univocity
Bindings to univocity.  Transforms csv's, tsv's into sequences
+  gtag('config', 'G-95TVFC1FEB');
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tech.v3.dataset.io.univocity
Bindings to univocity.  Transforms csv's, tsv's into sequences
 of string arrays that are then passed into tech.v3.dataset.io.string-row-parser
 methods.

 
create-csv-parser
(create-csv-parser {:keys [header-row? num-rows column-whitelist column-blacklist column-allowlist column-blocklist separator n-initial-skip-rows], :or {header-row? true}, :as options})
Create an implementation of univocity csv parser.

diff --git a/docs/tech.v3.dataset.join.html b/docs/tech.v3.dataset.join.html
index 32539b5f..a148bc12 100644
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-  gtag('config', 'G-95TVFC1FEB');
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tech.v3.dataset.join
implementation of join algorithms, both exact (hash-join) and near.

+  gtag('config', 'G-95TVFC1FEB');
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tech.v3.dataset.join
implementation of join algorithms, both exact (hash-join) and near.

 
hash-join
(hash-join colname lhs rhs)(hash-join colname lhs rhs {:keys [operation-space], :or {operation-space :int32}, :as options})
Join by column.  For efficiency, lhs should be smaller than rhs.
 colname - may be a single item or a tuple in which is destructures as:
 (let lhs-colname rhs-colname colname] ...)
diff --git a/docs/tech.v3.dataset.math.html b/docs/tech.v3.dataset.math.html
index ceb546b8..1a790243 100644
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tech.v3.dataset.math
Various mathematic transformations of datasets such as (inefficiently)
+  gtag('config', 'G-95TVFC1FEB');
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tech.v3.dataset.math
Various mathematic transformations of datasets such as (inefficiently)
 building simple tables, pca, and normalizing columns to have mean of 0 and variance of 1.
 More in-depth transformations are found at tech.v3.dataset.neanderthal.

 
correlation-table
(correlation-table dataset & {:keys [correlation-type colname-seq]})
Return a map of colname->list of sorted tuple of colname, coefficient.
diff --git a/docs/tech.v3.dataset.metamorph.html b/docs/tech.v3.dataset.metamorph.html
index 1ebcbef1..8e2f47e8 100644
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tech.v3.dataset.metamorph
This is an auto-generated api system - it scans the namespaces and changes the first
+  gtag('config', 'G-95TVFC1FEB');
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tech.v3.dataset.metamorph
This is an auto-generated api system - it scans the namespaces and changes the first
 to be metamorph-compliant which means transforming an argument that is just a dataset into
 an argument that is a metamorph context - a map of {:metamorph/data ds}.  They also return
 their result as a metamorph context.

diff --git a/docs/tech.v3.dataset.modelling.html b/docs/tech.v3.dataset.modelling.html
index 29ef766b..7c18226d 100644
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tech.v3.dataset.modelling
Methods related specifically to machine learning such as setting the inference
+  gtag('config', 'G-95TVFC1FEB');
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tech.v3.dataset.modelling
Methods related specifically to machine learning such as setting the inference
 target.  This file integrates tightly with tech.v3.dataset.categorical which provides
 categorical -> number and one-hot transformation pathways.

 
The functions in this namespace manipulate the metadata on the columns of the dataset, wich can be inspected  via clojure.core/meta

diff --git a/docs/tech.v3.dataset.neanderthal.html b/docs/tech.v3.dataset.neanderthal.html
index 2c92a7db..dcf310bf 100644
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tech.v3.dataset.neanderthal
Conversion of a dataset to/from a neanderthal dense matrix as well as various
+  gtag('config', 'G-95TVFC1FEB');
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tech.v3.dataset.neanderthal
Conversion of a dataset to/from a neanderthal dense matrix as well as various
 dataset transformations such as pca, covariance and correlation matrixes.

 
Please include these additional dependencies in your project:

 
  [uncomplicate/neanderthal "0.45.0"]
diff --git a/docs/tech.v3.dataset.print.html b/docs/tech.v3.dataset.print.html
index a872d568..89d093d2 100644
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tech.v3.dataset.print
dataset->str
(dataset->str ds options)(dataset->str ds)
Convert a dataset to a string.  Prints a single line header and then calls
+  gtag('config', 'G-95TVFC1FEB');
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tech.v3.dataset.print
dataset->str
(dataset->str ds options)(dataset->str ds)
Convert a dataset to a string.  Prints a single line header and then calls
 dataset-data->str.

 
For options documentation see dataset-data->str.

 
view source
dataset-data->str
(dataset-data->str dataset)(dataset-data->str dataset options)
Convert the dataset values to a string.

diff --git a/docs/tech.v3.dataset.reductions.apache-data-sketch.html b/docs/tech.v3.dataset.reductions.apache-data-sketch.html
index 8bdedf1b..0d238e72 100644
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tech.v3.dataset.reductions.apache-data-sketch
Reduction reducers based on the apache data sketch family of algorithms.

+  gtag('config', 'G-95TVFC1FEB');
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tech.v3.dataset.reductions.apache-data-sketch
Reduction reducers based on the apache data sketch family of algorithms.

 
diff --git a/docs/tech.v3.dataset.reductions.html b/docs/tech.v3.dataset.reductions.html
index d1eef8ca..c924e4e4 100644
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tech.v3.dataset.reductions
Specific high performance reductions intended to be performend over a sequence
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tech.v3.dataset.reductions
Specific high performance reductions intended to be performend over a sequence
 of datasets.  This allows aggregations to be done in situations where the dataset is
 larger than what will fit in memory on a normal machine.  Due to this fact, summation
 is implemented using Kahan algorithm and various statistical methods are done in using
@@ -57,7 +57,7 @@
     :n-dates (ds-reduce/count-distinct :date :int32)}
    [ds-seq])
 

-
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count-distinct
(count-distinct colname op-space)(count-distinct colname)
view source
distinct
(distinct colname finalizer)(distinct colname)
Create a reducer that will return a set of values.

+
view source
count-distinct
(count-distinct colname op-space)(count-distinct colname)
view source
distinct
(distinct colname finalizer)(distinct colname)
Create a reducer that will return a set of values.

 
view source
distinct-int32
(distinct-int32 colname finalizer)(distinct-int32 colname)
Get the set of distinct items given you know the space is no larger than int32
 space.  The optional finalizer allows you to post-process the data.

 
view source
first-value
(first-value colname)
view source
group-by-column-agg
(group-by-column-agg colname agg-map options ds-seq)(group-by-column-agg colname agg-map ds-seq)
Group a sequence of datasets by a column and aggregate down into a new dataset.

@@ -127,7 +127,7 @@
 |  b | 42 |  1 |
 |  a | 22 |  2 |
-
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group-by-column-agg-rf

(group-by-column-agg-rf colname agg-map)(group-by-column-agg-rf colname agg-map options)

Produce a transduce-compatible rf that will perform the group-by-column-agg pathway. +

view source

group-by-column-agg-rf

(group-by-column-agg-rf colname agg-map)(group-by-column-agg-rf colname agg-map options)

Produce a transduce-compatible rf that will perform the group-by-column-agg pathway. See documentation for group-by-column-agg.

tech.v3.dataset.reductions-test> (def stocks (ds/->dataset "test/data/stocks.csv" {:key-fn keyword}))
 #'tech.v3.dataset.reductions-test/stocks
diff --git a/docs/tech.v3.dataset.rolling.html b/docs/tech.v3.dataset.rolling.html
index 8594b4cb..ae8087a9 100644
--- a/docs/tech.v3.dataset.rolling.html
+++ b/docs/tech.v3.dataset.rolling.html
@@ -4,7 +4,7 @@
   function gtag(){dataLayer.push(arguments);}
   gtag('js', new Date());
 
-  gtag('config', 'G-95TVFC1FEB');
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tech.v3.dataset.rolling
Implement a generalized rolling window including support for time-based variable
+  gtag('config', 'G-95TVFC1FEB');
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Topics
Namespaces
Public Vars
tech.v3.dataset.rolling
Implement a generalized rolling window including support for time-based variable
 width windows.

 
expanding
(expanding ds reducer-map)
Run a set of reducers across a dataset with an expanding set of windows.  These
 will produce a cumsum-type operation.

diff --git a/docs/tech.v3.dataset.set.html b/docs/tech.v3.dataset.set.html
index 6b362e81..cbf2d27a 100644
--- a/docs/tech.v3.dataset.set.html
+++ b/docs/tech.v3.dataset.set.html
@@ -4,7 +4,7 @@
   function gtag(){dataLayer.push(arguments);}
   gtag('js', new Date());
 
-  gtag('config', 'G-95TVFC1FEB');
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tech.v3.dataset.set
Extensions to datasets to do per-row bag-semantics set/union and intersection.

+  gtag('config', 'G-95TVFC1FEB');
Project
Topics
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tech.v3.dataset.set
Extensions to datasets to do per-row bag-semantics set/union and intersection.

 
difference
(difference a)(difference a b)
Remove tuples from a that also appear in b.

 
view source
intersection
(intersection a)(intersection a b)(intersection a b & args)
Intersect two datasets producing a new dataset with the union of tuples.
 Tuples repeated across all datasets repeated in final dataset at their minimum
diff --git a/docs/tech.v3.dataset.tensor.html b/docs/tech.v3.dataset.tensor.html
index e4c66c17..aaad38c4 100644
--- a/docs/tech.v3.dataset.tensor.html
+++ b/docs/tech.v3.dataset.tensor.html
@@ -4,7 +4,7 @@
   function gtag(){dataLayer.push(arguments);}
   gtag('js', new Date());
 
-  gtag('config', 'G-95TVFC1FEB');
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Public Vars
tech.v3.dataset.tensor
Conversion mechanisms from dataset to tensor and back.

+  gtag('config', 'G-95TVFC1FEB');
Project
Topics
Namespaces
Public Vars
tech.v3.dataset.tensor
Conversion mechanisms from dataset to tensor and back.

 
dataset->tensor
(dataset->tensor dataset datatype)(dataset->tensor dataset)
Convert a dataset to a tensor.  Columns of the dataset will be converted
 to columns of the tensor.  Default datatype is :float64.

 
view source
mean-center-columns!
(mean-center-columns! tens {:keys [nan-strategy means], :or {nan-strategy :remove}})(mean-center-columns! tens)
in-place nan-aware mean-center the rows of the tensor.  If tensor is writeable then this
diff --git a/docs/tech.v3.dataset.zip.html b/docs/tech.v3.dataset.zip.html
index 8e9192d3..de9702f8 100644
--- a/docs/tech.v3.dataset.zip.html
+++ b/docs/tech.v3.dataset.zip.html
@@ -4,7 +4,7 @@
   function gtag(){dataLayer.push(arguments);}
   gtag('js', new Date());
 
-  gtag('config', 'G-95TVFC1FEB');
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Topics
Namespaces
Public Vars
tech.v3.dataset.zip
Load zip data.  Zip files with a single file entry can be loaded with ->dataset.  When
+  gtag('config', 'G-95TVFC1FEB');
Project
Topics
Namespaces
Public Vars
tech.v3.dataset.zip
Load zip data.  Zip files with a single file entry can be loaded with ->dataset.  When
 a zip file has multiple entries you have to call zipfile->dataset-seq.

 
dataset-seq->zipfile!
(dataset-seq->zipfile! output options ds-seq)(dataset-seq->zipfile! output ds-seq)
Write a sequence of datasets to zipfiles.  You can control the inner type with the
 :file-type option which defaults to .tsv

diff --git a/docs/tech.v3.libs.arrow.html b/docs/tech.v3.libs.arrow.html
index 3d96c0fe..5fbd089b 100644
--- a/docs/tech.v3.libs.arrow.html
+++ b/docs/tech.v3.libs.arrow.html
@@ -4,7 +4,7 @@
   function gtag(){dataLayer.push(arguments);}
   gtag('js', new Date());
 
-  gtag('config', 'G-95TVFC1FEB');
Project
Topics
Namespaces
Public Vars
tech.v3.libs.arrow
Support for reading/writing apache arrow datasets.  Datasets may be memory mapped
+  gtag('config', 'G-95TVFC1FEB');
Project
Topics
Namespaces
Public Vars
tech.v3.libs.arrow
Support for reading/writing apache arrow datasets.  Datasets may be memory mapped
 but default to being read via an input stream.

 
Supported datatypes: