diff --git a/deps.edn b/deps.edn index e1e59214..fc12d4aa 100644 --- a/deps.edn +++ b/deps.edn @@ -1,6 +1,6 @@ {:paths ["src" "resources" "target/classes"] :deps {;;org.clojure/clojure {:mvn/version "1.11.1"} - cnuernber/dtype-next {:mvn/version "10.111"} + cnuernber/dtype-next {:mvn/version "10.113"} techascent/tech.io {:mvn/version "4.31" :exclusions [org.apache.commons/commons-compress]} org.apache.datasketches/datasketches-java {:mvn/version "4.2.0"} @@ -14,7 +14,7 @@ :exec-fn codox.main/-main :exec-args {:group-id "techascent" :artifact-id "tech.ml.dataset" - :version "7.029" + :version "7.030" :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 a9f79fa5..0c0a46c7 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

Topics

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
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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.

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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
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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
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-TMD 7.029
Project
Topics
Namespaces
TMD 7.029
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 7.030
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
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tech.ml.dataset And nippy

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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
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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 3e0edca0..b105f280 100644
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tech.v3.dataset.categorical
Conversions of categorical values into numbers and back.  Two forms of conversions
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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

 
dataset->categorical-maps
(dataset->categorical-maps dataset)
Given a dataset, return a sequence of categorical map entries.

diff --git a/docs/tech.v3.dataset.clipboard.html b/docs/tech.v3.dataset.clipboard.html
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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 22e34cf2..b12d634a 100644
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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 0427928a..69a0ad97 100644
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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 7360c2f3..86c1e361 100644
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tech.v3.dataset
Column major dataset abstraction for efficiently manipulating
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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 467ad54f..0fe3a0a4 100644
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tech.v3.dataset.io.csv
CSV parsing based on charred.api/read-csv.

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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 9b260cb4..15312b00 100644
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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 4c63d6d6..8742ba10 100644
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   function gtag(){dataLayer.push(arguments);}
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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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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
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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
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tech.v3.dataset.join
implementation of join algorithms, both exact (hash-join) and near.

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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
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tech.v3.dataset.math
Various mathematic transformations of datasets such as (inefficiently)
+  gtag('config', 'G-95TVFC1FEB');
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Public Vars
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
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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
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tech.v3.dataset.modelling
Methods related specifically to machine learning such as setting the inference
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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 71614d26..26ad749b 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
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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
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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
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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
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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
diff --git a/docs/tech.v3.dataset.rolling.html b/docs/tech.v3.dataset.rolling.html
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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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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.

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tech.v3.dataset.set
Extensions to datasets to do per-row bag-semantics set/union and intersection.

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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
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tech.v3.dataset.tensor
Conversion mechanisms from dataset to tensor and back.

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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
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tech.v3.dataset.zip
Load zip data.  Zip files with a single file entry can be loaded with ->dataset.  When
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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

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tech.v3.libs.arrow
Support for reading/writing apache arrow datasets.  Datasets may be memory mapped
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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: