scicloj.ml.metamorph — scicloj/scicloj.ml 0.2.1

This namespace contains functions, which operate on a metamorph context. They all return the context as well.

So all functions in this namespace are metamorph compliant and can be placed in a metamorph pipeline.

Most functions here are only manipulating the dataset, which is in the ctx map under the key :metamorph/data. And they behave the same in pipeline mode :fit and :transform.

A few functions manipulate other keys inside the ctx map, and/or behave different in :fit and :transform.

This is documented per function in this form:

metamorph	.
Behaviour in mode :fit	.
Behaviour in mode :transform	.
Reads keys from ctx	.
Writes keys to ctx	.

The namespaces scicloj.ml.metamorph and scicloj.ml.dataset contain functions with the same name. But they operate on either a context map (ns metamorph) or on a dataset (ns dataset)

The functions in this namesspaces are re-exported from :

• tablecloth.pipeline
• tech.v3.libs.smile.metamorph
• scicloj.metamorph.ml
• tech.v3.dataset.metamorph

->array clj

(->array colname)

(->array colname datatype)

Convert numerical column(s) to java array

add-column clj

(add-column column-name column)

(add-column column-name column size-strategy)

Add or update (modify) column under column-name.

column can be sequence of values or generator function (which gets ds as input).

• ds - a dataset
• column-name - if it's existing column name, column will be replaced
• column - can be column (from other dataset), sequence, single value or function. Too big columns are always trimmed. Too small are cycled or extended with missing values (according to size-strategy argument)
• size-strategy (optional) - when new column is shorter than dataset row count, following strategies are applied:
  • :cycle - repeat data
  • :na - append missing values
  • :strict - (default) throws an exception when sizes mismatch

add-columns clj

(add-columns columns-map)

(add-columns columns-map size-strategy)

Add or updade (modify) columns defined in columns-map (mapping: name -> column)

add-or-replace-column clj

(add-or-replace-column column-name column)

(add-or-replace-column column-name column size-strategy)

add-or-replace-columns clj

(add-or-replace-columns columns-map)

(add-or-replace-columns columns-map size-strategy)

add-or-update-column clj

(add-or-update-column column)

(add-or-update-column colname column)

If column exists, replace. Else append new column.

aggregate clj

(aggregate aggregator)

(aggregate aggregator options)

Aggregate dataset by providing:

• aggregation function
• map with column names and functions
• sequence of aggregation functions

Aggregation functions can return:

• single value
• seq of values
• map of values with column names

aggregate-columns clj

(aggregate-columns columns-selector column-aggregators)

(aggregate-columns columns-selector column-aggregators options)

Aggregates each column separately

anti-join clj

(anti-join ds-right columns-selector)

(anti-join ds-right columns-selector options)

append clj

(append & args)

append-columns clj

(append-columns column-seq)

as-regular-dataset clj

(as-regular-dataset)

Remove grouping tag

asof-join clj

(asof-join ds-right colname)

(asof-join ds-right colname options)

assoc-ds clj

(assoc-ds cname cdata & args)

If dataset is not nil, calls clojure.core/assoc. Else creates a new empty dataset and then calls clojure.core/assoc. Guaranteed to return a dataset (unlike assoc).

assoc-metadata clj

(assoc-metadata filter-fn-or-ds k v & args)

Set metadata across a set of columns.

bind clj

(bind & args)

bow->something-sparse clj

(bow->something-sparse bow-col indices-col bow->sparse-fn options)

Converts a bag-of-word column bow-col to a sparse data column indices-col. The exact transformation to the sparse representtaion is given by bow->sparse-fn

metamorph	.
Behaviour in mode :fit	normal
Behaviour in mode :transform	normal
Reads keys from ctx	none
Writes keys to ctx	:scicloj.ml.smile.metamorph/bow->sparse-vocabulary

bow->sparse-array clj

(bow->sparse-array bow-col indices-col)

(bow->sparse-array bow-col indices-col options)

Converts a bag-of-word column bow-col to sparse indices column indices-col, as needed by the Maxent model. Options can be of:

create-vocab-fn A function which converts the bow map to a list of tokens. Defaults to scicloj.ml.smile.nlp/create-vocab-all

The sparse data is represented as primitive int arrays, of which entries are the indices against the vocabulary of the present tokens.

metamorph	.
Behaviour in mode :fit	normal
Behaviour in mode :transform	normal
Reads keys from ctx	none
Writes keys to ctx	:scicloj.ml.smile.metamorph/bow->sparse-vocabulary

bow->SparseArray clj

(bow->SparseArray bow-col indices-col)

(bow->SparseArray bow-col indices-col options)

Converts a bag-of-word column bow-col to sparse indices column indices-col, as needed by the discrete naive bayes model.

Options can be of:

create-vocab-fn A function which converts the bow map to a list of tokens. Defaults to scicloj.ml.smile.nlp/create-vocab-all

The sparse data is represented as smile.util.SparseArray.

metamorph	.
Behaviour in mode :fit	normal
Behaviour in mode :transform	normal
Reads keys from ctx	none
Writes keys to ctx	:scicloj.ml.smile.metamorph/bow->sparse-vocabulary

bow->tfidf clj

(bow->tfidf bow-column tfidf-column)

Calculates the tfidf score from bag-of-words (as token frequency maps) in column bow-column and stores them in a new column tfid-column as maps of token->tfidf-score.

metamorph	.
Behaviour in mode :fit	normal
Behaviour in mode :transform	normal
Reads keys from ctx	none
Writes keys to ctx	none

brief clj

(brief)

(brief options)

Get a brief description, in mapseq form of a dataset. A brief description is the mapseq form of descriptive stats.

by-rank clj

(by-rank columns-selector rank-predicate)

(by-rank columns-selector rank-predicate options)

Select rows using rank on a column, ties are resolved using :dense method.

See R docs. Rank uses 0 based indexing.

Possible :ties strategies: :average, :first, :last, :random, :min, :max, :dense. :dense is the same as in data.table::frank from R

:desc? set to true (default) order descending before calculating rank

categorical->number clj

(categorical->number filter-fn-or-ds)

(categorical->number filter-fn-or-ds table-args)

(categorical->number filter-fn-or-ds table-args result-datatype)

Convert columns into a discrete , numeric representation See tech.v3.dataset.categorical/fit-categorical-map.

categorical->one-hot clj

(categorical->one-hot filter-fn-or-ds)

(categorical->one-hot filter-fn-or-ds table-args)

(categorical->one-hot filter-fn-or-ds table-args result-datatype)

Convert string columns to numeric columns. See tech.v3.dataset.categorical/fit-one-hot

clone clj

(clone)

Clone an object. Can clone anything convertible to a reader.

cluster clj

(cluster clustering-method clustering-method-args target-column)

Metamorph transformer, which clusters the data and creates a new column with the cluster id.

clustering-method can be any of:

• :spectral
• :dbscan
• :k-means
• :mec
• :clarans
• :g-means
• :lloyd
• :x-means
• :deterministic-annealing
• :denclue

The clustering-args is a vector with the positional arguments for each cluster function, as documented here: https://cljdoc.org/d/generateme/fastmath/2.1.5/api/fastmath.clustering (but minus the data argument, which will be passed in automatically)

The cluster id of each row gets written to the column in target-column

metamorph	.
Behaviour in mode :fit	Calculates cluster centers of the rows dataset at key :metamorph/data and stores them in ctx under key at :metamorph/id. Adds as wll column in target-column with cluster centers into the dataset.
Behaviour in mode :transform	Reads cluster centers from ctx and applies it to data in :metamorph/data
Reads keys from ctx	In mode :transform : Reads cluster centers to use from ctx at key in :metamorph/id.
Writes keys to ctx	In mode :fit : Stores cluster centers in ctx under key in :metamorph/id.

column clj

(column colname)

column->dataset clj

(column->dataset colname transform-fn)

(column->dataset colname transform-fn options)

Transform a column into a sequence of maps using transform-fn. Return dataset created out of the sequence of maps.

column-cast clj

(column-cast colname datatype)

Cast a column to a new datatype. This is never a lazy operation. If the old and new datatypes match and no cast-fn is provided then dtype/clone is called on the column.

colname may be a scalar or a tuple of [src-col dst-col].

datatype may be a datatype enumeration or a tuple of [datatype cast-fn] where cast-fn may return either a new value, :tech.v3.dataset/missing, or :tech.v3.dataset/parse-failure. Exceptions are propagated to the caller. The new column has at least the existing missing set (if no attempt returns :missing or :cast-failure). :cast-failure means the value gets added to metadata key :unparsed-data and the index gets added to :unparsed-indexes.

If the existing datatype is string, then tech.v3.datatype.column/parse-column is called.

Casts between numeric datatypes need no cast-fn but one may be provided. Casts to string need no cast-fn but one may be provided. Casts from string to anything will call tech.v3.dataset.column/parse-column.

column-count clj

(column-count)

column-labeled-mapseq clj

(column-labeled-mapseq value-colname-seq)

Given a dataset, return a sequence of maps where several columns are all stored in a :value key and a :label key contains a column name. Used for quickly creating timeseries or scatterplot labeled graphs. Returns a lazy sequence, not a reader!

See also columnwise-concat

Return a sequence of maps with

  {... - columns not in colname-seq
   :value - value from one of the value columns
   :label - name of the column the value came from
  }

column-map clj

(column-map result-colname map-fn)

(column-map result-colname map-fn filter-fn-or-ds)

(column-map result-colname map-fn res-dtype-or-opts filter-fn-or-ds)

Produce a new (or updated) column as the result of mapping a fn over columns. This function is never lazy - all results are immediately calculated.

• dataset - dataset.
• result-colname - Name of new (or existing) column.
• map-fn - function to map over columns. Same rules as tech.v3.datatype/emap.
• res-dtype-or-opts - If not given result is scanned to infer missing and datatype. If using an option map, options are described below.
• filter-fn-or-ds - A dataset, a sequence of columns, or a tech.v3.datasets/column-filters column filter function. Defaults to all the columns of the existing dataset.

Returns a new dataset with a new or updated column.

Options:

• :datatype - Set the dataype of the result column. If not given result is scanned to infer result datatype and missing set.
• :missing-fn - if given, columns are first passed to missing-fn as a sequence and this dictates the missing set. Else the missing set is by scanning the results during the inference process. See tech.v3.dataset.column/union-missing-sets and tech.v3.dataset.column/intersect-missing-sets for example functions to pass in here.

Examples:

  ;;From the tests --

  (let [testds (ds/->dataset [{:a 1.0 :b 2.0} {:a 3.0 :b 5.0} {:a 4.0 :b nil}])]
    ;;result scanned for both datatype and missing set
    (is (= (vec [3.0 6.0 nil])
           (:b2 (ds/column-map testds :b2 #(when % (inc %)) [:b]))))
    ;;result scanned for missing set only.  Result used in-place.
    (is (= (vec [3.0 6.0 nil])
           (:b2 (ds/column-map testds :b2 #(when % (inc %))
                               {:datatype :float64} [:b]))))
    ;;Nothing scanned at all.
    (is (= (vec [3.0 6.0 nil])
           (:b2 (ds/column-map testds :b2 #(inc %)
                               {:datatype :float64
                                :missing-fn ds-col/union-missing-sets} [:b]))))
    ;;Missing set scanning causes NPE at inc.
    (is (thrown? Throwable
                 (ds/column-map testds :b2 #(inc %)
                                {:datatype :float64}
                                [:b]))))

  ;;Ad-hoc repl --

user> (require '[tech.v3.dataset :as ds]))
nil
user> (def ds (ds/->dataset "test/data/stocks.csv"))
#'user/ds
user> (ds/head ds)
test/data/stocks.csv [5 3]:

| symbol |       date | price |
|--------|------------|-------|
|   MSFT | 2000-01-01 | 39.81 |
|   MSFT | 2000-02-01 | 36.35 |
|   MSFT | 2000-03-01 | 43.22 |
|   MSFT | 2000-04-01 | 28.37 |
|   MSFT | 2000-05-01 | 25.45 |
user> (-> (ds/column-map ds "price^2" #(* % %) ["price"])
          (ds/head))
test/data/stocks.csv [5 4]:

| symbol |       date | price |   price^2 |
|--------|------------|-------|-----------|
|   MSFT | 2000-01-01 | 39.81 | 1584.8361 |
|   MSFT | 2000-02-01 | 36.35 | 1321.3225 |
|   MSFT | 2000-03-01 | 43.22 | 1867.9684 |
|   MSFT | 2000-04-01 | 28.37 |  804.8569 |
|   MSFT | 2000-05-01 | 25.45 |  647.7025 |



user> (def ds1 (ds/->dataset [{:a 1} {:b 2.0} {:a 2 :b 3.0}]))
#'user/ds1
user> ds1
_unnamed [3 2]:

|  :b | :a |
|----:|---:|
|     |  1 |
| 2.0 |    |
| 3.0 |  2 |
user> (ds/column-map ds1 :c (fn [a b]
                              (when (and a b)
                                (+ (double a) (double b))))
                     [:a :b])
_unnamed [3 3]:

|  :b | :a |  :c |
|----:|---:|----:|
|     |  1 |     |
| 2.0 |    |     |
| 3.0 |  2 | 5.0 |
user> (ds/missing (*1 :c))
{0,1}

column-names clj

(column-names)

(column-names columns-selector)

(column-names columns-selector meta-field)

column-values->categorical clj

(column-values->categorical src-column)

Given a column encoded via either string->number or one-hot, reverse map to the a sequence of the original string column values. In the case of one-hot mappings, src-column must be the original column name before the one-hot map

columns clj

(columns)

(columns result-type)

Returns columns of dataset. Result type can be any of:

• :as-map
• :as-double-arrays
• :as-seqs

columns-with-missing-seq clj

(columns-with-missing-seq)

Return a sequence of:

  {:column-name column-name
   :missing-count missing-count
  }

or nil of no columns are missing data.

columnwise-concat clj

(columnwise-concat colnames)

(columnwise-concat colnames options)

Given a dataset and a list of columns, produce a new dataset with the columns concatenated to a new column with a :column column indicating which column the original value came from. Any columns not mentioned in the list of columns are duplicated.

Example:

user> (-> [{:a 1 :b 2 :c 3 :d 1} {:a 4 :b 5 :c 6 :d 2}]
          (ds/->dataset)
          (ds/columnwise-concat [:c :a :b]))
null [6 3]:

| :column | :value | :d |
|---------+--------+----|
|      :c |      3 |  1 |
|      :c |      6 |  2 |
|      :a |      1 |  1 |
|      :a |      4 |  2 |
|      :b |      2 |  1 |
|      :b |      5 |  2 |

Options:

value-column-name - defaults to :value colname-column-name - defaults to :column

concat clj

(concat & args)

concat-copying clj

(concat-copying & args)

concat-inplace clj

(concat-inplace)

(concat-inplace & args)

Concatenate datasets in place. Respects missing values. Datasets must all have the same columns. Result column datatypes will be a widening cast of the datatypes.

convert-types clj

(convert-types coltype-map-or-columns-selector)

(convert-types columns-selector new-types)

Convert type of the column to the other type.

count-vectorize clj

(count-vectorize text-col bow-col)

(count-vectorize text-col bow-col options)

Transforms the text column text-col into a map of token frequencies in column bow-col

options can be any of

text->bow-fn A functions which takes as input a

metamorph	.
Behaviour in mode :fit	normal
Behaviour in mode :transform	normal
Reads keys from ctx	none
Writes keys to ctx	none

data->dataset clj

(data->dataset)

Convert a data-ized dataset created via dataset->data back into a full dataset

dataset->categorical-xforms clj

(dataset->categorical-xforms)

Given a dataset, return a map of column-name->xform information.

dataset->data clj

(dataset->data)

Convert a dataset to a pure clojure datastructure. Returns a map with two keys: {:metadata :columns}. :columns is a vector of column definitions appropriate for passing directly back into new-dataset. A column definition in this case is a map of {:name :missing :data :metadata}.

dataset->str clj

(dataset->str)

(dataset->str options)

Convert a dataset to a string. Prints a single line header and then calls dataset-data->str.

For options documentation see dataset-data->str.

dataset-name clj

(dataset-name)

dataset? clj

(dataset?)

Is ds a dataset type?

descriptive-stats clj

(descriptive-stats)

(descriptive-stats options)

Get descriptive statistics across the columns of the dataset. In addition to the standard stats. Options: :stat-names - defaults to (remove #{:values :num-distinct-values} (all-descriptive-stats-names)) :n-categorical-values - Number of categorical values to report in the 'values' field. Defaults to 21.

difference clj

(difference ds-right)

(difference ds-right options)

drop clj

(drop columns-selector rows-selector)

Drop columns and rows.

drop-columns clj

(drop-columns)

(drop-columns columns-selector)

(drop-columns columns-selector meta-field)

Drop columns by (returns dataset):

• name
• sequence of names
• map of names with new names (rename)
• function which filter names (via column metadata)

drop-missing clj

(drop-missing)

(drop-missing columns-selector)

Drop rows with missing values

columns-selector selects columns to look at missing values

drop-rows clj

(drop-rows)

(drop-rows rows-selector)

(drop-rows rows-selector options)

Drop rows using:

• row id
• seq of row ids
• seq of true/false
• fn with predicate

empty-dataset clj

(empty-dataset)

empty-ds? clj

(empty-ds?)

ensure-array-backed clj

(ensure-array-backed)

(ensure-array-backed options)

Ensure the column data in the dataset is stored in pure java arrays. This is sometimes necessary for interop with other libraries and this operation will force any lazy computations to complete. This also clears the missing set for each column and writes the missing values to the new arrays.

Columns that are already array backed and that have no missing values are not changed and retuned.

The postcondition is that dtype/->array will return a java array in the appropriate datatype for each column.

Options:

• :unpack? - unpack packed datetime types. Defaults to true

feature-ecount clj

(feature-ecount)

Number of feature columns. Feature columns are columns that are not inference targets.

fill-range-replace clj

(fill-range-replace colname max-span)

(fill-range-replace colname max-span missing-strategy)

(fill-range-replace colname max-span missing-strategy missing-value)

filter clj

(filter predicate)

dataset->dataset transformation. Predicate is passed a map of colname->column-value.

filter-column clj

(filter-column colname)

(filter-column colname predicate)

Filter a given column by a predicate. Predicate is passed column values. If predicate is not an instance of Ifn it is treated as a value and will be used as if the predicate is #(= value %).

The 2-arity form of this function reads the column as a boolean reader so for instance numeric 0 values are false in that case as are Double/NaN, Float/NaN. Objects are only false if nil?.

Returns a dataset.

filter-dataset clj

(filter-dataset filter-fn-or-ds)

Filter the columns of the dataset returning a new dataset. This pathway is designed to work with the tech.v3.dataset.column-filters namespace.

• If filter-fn-or-ds is a dataset, it is returned.
• If filter-fn-or-ds is sequential, then select-columns is called.
• If filter-fn-or-ds is :all, all columns are returned
• If filter-fn-or-ds is an instance of IFn, the dataset is passed into it.

first clj

(first)

fold-by clj

(fold-by columns-selector)

(fold-by columns-selector folding-function)

Group-by and pack columns into vector - the output data set has a row for each unique combination of the provided columns while each remaining column has its valu(es) collected into a vector, similar to how clojure.core/group-by works. See https://scicloj.github.io/tablecloth/index.html#Fold-by

full-join clj

(full-join ds-right columns-selector)

(full-join ds-right columns-selector options)

group-by clj

(group-by grouping-selector)

(group-by grouping-selector options)

Group dataset by:

• column name
• list of columns
• map of keys and row indexes
• function getting map of values

Options are:

• select-keys - when grouping is done by function, you can limit fields to a select-keys seq.
• result-type - return results as dataset (:as-dataset, default) or as map of datasets (:as-map) or as map of row indexes (:as-indexes) or as sequence of (sub)datasets
• other parameters which are passed to dataset fn

When dataset is returned, meta contains :grouped? set to true. Columns in dataset:

• name - group name
• group-id - id of the group (int)
• data - group as dataset

group-by->indexes clj

(group-by->indexes key-fn)

(Non-lazy) - Group a dataset and return a map of key-fn-value->indexes where indexes is an in-order contiguous group of indexes.

group-by-column clj

(group-by-column colname)

Return a map of column-value->dataset.

group-by-column->indexes clj

(group-by-column->indexes colname)

(Non-lazy) - Group a dataset by a column return a map of column-val->indexes where indexes is an in-order contiguous group of indexes.

grouped? clj

(grouped?)

Is dataset represents grouped dataset (result of group-by)?

groups->map clj

(groups->map)

Convert grouped dataset to the map of groups

groups->seq clj

(groups->seq)

has-column? clj

(has-column? column-name)

head clj

(head)

(head n)

induction clj

(induction induct-fn & args)

Given a dataset and a function from dataset->row produce a new dataset. The produced row will be merged with the current row and then added to the dataset.

Options are same as the options used for [[->dataset]] in order for the user to control the parsing of the return values of induct-fn. A new dataset is returned.

Example:

user> (def ds (ds/->dataset {:a [0 1 2 3] :b [1 2 3 4]}))
#'user/ds
user> ds
_unnamed [4 2]:

| :a | :b |
|---:|---:|
|  0 |  1 |
|  1 |  2 |
|  2 |  3 |
|  3 |  4 |
user> (ds/induction ds (fn [ds]
                         {:sum-of-previous-row (dfn/sum (ds/rowvec-at ds -1))
                          :sum-a (dfn/sum (ds :a))
                          :sum-b (dfn/sum (ds :b))}))
_unnamed [4 5]:

| :a | :b | :sum-b | :sum-a | :sum-of-previous-row |
|---:|---:|-------:|-------:|---------------------:|
|  0 |  1 |    0.0 |    0.0 |                  0.0 |
|  1 |  2 |    1.0 |    0.0 |                  1.0 |
|  2 |  3 |    3.0 |    1.0 |                  5.0 |
|  3 |  4 |    6.0 |    3.0 |                 14.0 |

inference-column? clj

(inference-column?)

inference-target-column-names clj

(inference-target-column-names)

Return the names of the columns that are inference targets.

inference-target-ds clj

(inference-target-ds)

Given a dataset return reverse-mapped inference target columns or nil in the case where there are no inference targets.

inference-target-label-inverse-map clj

(inference-target-label-inverse-map & args)

Given options generated during ETL operations and annotated with :label-columns sequence container 1 label column, generate a reverse map that maps from a dataset value back to the label that generated that value.

inference-target-label-map clj

(inference-target-label-map & args)

info clj

(info)

(info result-type)

inner-join clj

(inner-join ds-right columns-selector)

(inner-join ds-right columns-selector options)

intersect clj

(intersect ds-right)

(intersect ds-right options)

join-columns clj

(join-columns target-column columns-selector)

(join-columns target-column columns-selector conf)

labels clj

(labels)

Return the labels. The labels sequence is the reverse mapped inference column. This returns a single column of data or errors out.

last clj

(last)

left-join clj

(left-join ds-right columns-selector)

(left-join ds-right columns-selector options)

map-columns clj

(map-columns column-name map-fn)

(map-columns column-name columns-selector map-fn)

(map-columns column-name new-type columns-selector map-fn)

mapseq-reader clj

(mapseq-reader)

(mapseq-reader options)

Return a reader that produces a map of column-name->column-value upon read.

mark-as-group clj

(mark-as-group)

Add grouping tag

min-max-scale clj

(min-max-scale columns-selector
               {:keys [min max] :or {min -0.5 max 0.5} :as options})

Metamorph transfomer, which scales the column data into a given range.

columns-selector tablecloth columns-selector to choose columns to work on meta-field tablecloth meta-field working with columns-selector

options Options for scaler, can take:

min Minimal value to scale to (default -0.5)

max Maximum value to scale to (default 0.5)

metamorph	.
Behaviour in mode :fit	Scales the dataset at key :metamorph/data and stores the trained model in ctx under key at :metamorph/id
Behaviour in mode :transform	Reads trained min-max-scale model from ctx and applies it to data in :metamorph/data
Reads keys from ctx	In mode :transform : Reads trained model to use for from key in :metamorph/id.
Writes keys to ctx	In mode :fit : Stores trained model in key $id

min-n-by-column clj

(min-n-by-column cname N)

(min-n-by-column cname N comparator)

(min-n-by-column cname N comparator options)

Find the minimum N entries (unsorted) by column. Resulting data will be indexed in original order. If you want a sorted order then sort the result.

See options to sort-by-column.

Example:

user> (ds/min-n-by-column ds "price" 10 nil nil)
test/data/stocks.csv [10 3]:

| symbol |       date | price |
|--------|------------|------:|
|   AMZN | 2001-09-01 |  5.97 |
|   AMZN | 2001-10-01 |  6.98 |
|   AAPL | 2000-12-01 |  7.44 |
|   AAPL | 2002-08-01 |  7.38 |
|   AAPL | 2002-09-01 |  7.25 |
|   AAPL | 2002-12-01 |  7.16 |
|   AAPL | 2003-01-01 |  7.18 |
|   AAPL | 2003-02-01 |  7.51 |
|   AAPL | 2003-03-01 |  7.07 |
|   AAPL | 2003-04-01 |  7.11 |
user> (ds/min-n-by-column ds "price" 10 > nil)
test/data/stocks.csv [10 3]:

| symbol |       date |  price |
|--------|------------|-------:|
|   GOOG | 2007-09-01 | 567.27 |
|   GOOG | 2007-10-01 | 707.00 |
|   GOOG | 2007-11-01 | 693.00 |
|   GOOG | 2007-12-01 | 691.48 |
|   GOOG | 2008-01-01 | 564.30 |
|   GOOG | 2008-04-01 | 574.29 |
|   GOOG | 2008-05-01 | 585.80 |
|   GOOG | 2009-11-01 | 583.00 |
|   GOOG | 2009-12-01 | 619.98 |
|   GOOG | 2010-03-01 | 560.19 |

missing clj

(missing)

Given a dataset or a column, return the missing set as a roaring bitmap

model clj

(model options)

Executes a machine learning model in train/predict (depending on :mode) from the metamorph.ml model registry.

The model is passed between both invocation via the shared context ctx in a key (a step indentifier) which is passed in key :metamorph/id and guarantied to be unique for each pipeline step.

The function writes and reads into this common context key.

Options:

• :model-type - Keyword for the model to use

Further options get passed to train functions and are model specific.

See here for an overview for the models build into scicloj.ml:

https://scicloj.github.io/scicloj.ml-tutorials/userguide-models.html

Other libraries might contribute other models, which are documented as part of the library.

metamorph	.
Behaviour in mode :fit	Calls scicloj.metamorph.ml/train using data in :metamorph/data and optionsand stores trained model in ctx under key in :metamorph/id
Behaviour in mode :transform	Reads trained model from ctx and calls scicloj.metamorph.ml/predict with the model in $id and data in :metamorph/data
Reads keys from ctx	In mode :transform : Reads trained model to use for prediction from key in :metamorph/id.
Writes keys to ctx	In mode :fit : Stores trained model in key $id and writes feature-ds and target-ds before prediction into ctx at :scicloj.metamorph.ml/feature-ds /:scicloj.metamorph.ml/target-ds

See as well:

• scicloj.metamorph.ml/train
• scicloj.metamorph.ml/predict

model-type clj

(model-type & args)

Check the label column after dataset processing. Return either :regression :classification

new-column clj

(new-column)

(new-column data)

(new-column data metadata)

(new-column data metadata missing)

Create a new column. Data will scanned for missing values unless the full 4-argument pathway is used.

new-dataset clj

(new-dataset)

(new-dataset column-seq)

(new-dataset ds-metadata column-seq)

Create a new dataset from a sequence of columns. Data will be converted into columns using ds-col-proto/ensure-column-seq. If the column seq is simply a collection of vectors, for instance, columns will be named ordinally. options map - :dataset-name - Name of the dataset. Defaults to "_unnamed". :key-fn - Key function used on all column names before insertion into dataset.

The return value fulfills the dataset protocols.

num-inference-classes clj

(num-inference-classes)

Given a dataset and correctly built options from pipeline operations, return the number of classes used for the label. Error if not classification dataset.

order-by clj

(order-by columns-or-fn)

(order-by columns-or-fn comparators)

(order-by columns-or-fn comparators options)

Order dataset by:

• column name
• columns (as sequence of names)
• key-fn
• sequence of columns / key-fn Additionally you can ask the order by:
• :asc
• :desc
• custom comparator function

order-column-names clj

(order-column-names colname-seq)

Order a sequence of columns names so they match the order in the original dataset. Missing columns are placed last.

pivot->longer clj

(pivot->longer)

(pivot->longer columns-selector)

(pivot->longer columns-selector options)

tidyr pivot_longer api

pivot->wider clj

(pivot->wider columns-selector value-columns)

(pivot->wider columns-selector value-columns options)

pmap-ds clj

(pmap-ds ds-map-fn)

(pmap-ds ds-map-fn options)

Parallelize mapping a function from dataset->dataset across a single dataset. Results are coalesced back into a single dataset. The original dataset is simple sliced into n-core results and map-fn is called n-core times. ds-map-fn must be a function from dataset->dataset although it may return nil.

Options:

• :max-batch-size - this is a default for tech.v3.parallel.for/indexed-map-reduce. You can control how many rows are processed in a given batch - the default is 64000. If your mapping pathway produces a large expansion in the size of the dataset then it may be good to reduce the max batch size and use :as-seq to produce a sequence of datasets.
• :result-type
  • :as-seq - Return a sequence of datasets, one for each batch.
  • :as-ds - Return a single datasets with all results in memory (default option).

print-dataset clj

(print-dataset)

(print-dataset options)

probability-distributions->label-column clj

(probability-distributions->label-column dst-colname)

Given a dataset that has columns in which the column names describe labels and the rows describe a probability distribution, create a label column by taking the max value in each row and assign column that row value.

process-group-data clj

(process-group-data f)

(process-group-data f parallel?)

rand-nth clj

(rand-nth)

(rand-nth options)

random clj

(random)

(random n)

(random n options)

read-nippy clj

(read-nippy)

reduce-dimensions clj

(reduce-dimensions algorithm target-dims cnames opts)

Metamorph transformer, which reduces the dimensions of a given dataset.

algorithm can be any of:

• :pca-cov
• :pca-cor
• :pca-prob
• :kpca
• :gha
• :random

target-dims is number of dimensions to reduce to.

cnames is a sequence of column names on which the reduction get performed

opts are the options of the algorithm

metamorph	.
Behaviour in mode :fit	Reduces dimensions of the dataset at key :metamorph/data and stores the trained model in ctx under key at :metamorph/id
Behaviour in mode :transform	Reads trained reduction model from ctx and applies it to data in :metamorph/data
Reads keys from ctx	In mode :transform : Reads trained model to use from ctx at key in :metamorph/id.
Writes keys to ctx	In mode :fit : Stores trained model in ctx under key in :metamorph/id.

remove-column clj

(remove-column col-name)

Same as:

(dissoc dataset col-name)

remove-columns clj

(remove-columns colname-seq-or-fn)

Remove columns indexed by column name seq or column filter function. For example:

  (remove-columns DS [:A :B])
  (remove-columns DS cf/categorical)

remove-rows clj

(remove-rows row-indexes)

Same as drop-rows.

rename-columns clj

(rename-columns columns-mapping)

(rename-columns columns-selector columns-map-fn)

Rename columns with provided old -> new name map

reorder-columns clj

(reorder-columns columns-selector & args)

Reorder columns using column selector(s). When column names are incomplete, the missing will be attached at the end.

replace-missing clj

(replace-missing)

(replace-missing strategy)

(replace-missing columns-selector strategy)

(replace-missing columns-selector strategy value)

replace-missing-value clj

(replace-missing-value scalar-value)

(replace-missing-value filter-fn-or-ds scalar-value)

reverse-rows clj

(reverse-rows)

Reverse the rows in the dataset or column.

right-join clj

(right-join ds-right columns-selector)

(right-join ds-right columns-selector options)

row-at clj

(row-at idx)

Get the row at an individual index. If indexes are negative then the dataset is indexed from the end.

user> (ds/row-at stocks 1)
{"date" #object[java.time.LocalDate 0x534cb03b "2000-02-01"],
 "symbol" "MSFT",
 "price" 36.35}
user> (ds/row-at stocks -1)
{"date" #object[java.time.LocalDate 0x6bf60ed5 "2010-03-01"],
 "symbol" "AAPL",
 "price" 223.02}

row-count clj

(row-count)

row-map clj

(row-map map-fn)

(row-map map-fn options)

Map a function across the rows of the dataset producing a new dataset that is merged back into the original potentially replacing existing columns. Options are passed into the [[->dataset]] function so you can control the resulting column types by the usual dataset parsing options described there.

Options:

See options for pmap-ds. In particular, note that you can produce a sequence of datasets as opposed to a single large dataset.

Examples:

user> (def stocks (ds/->dataset "test/data/stocks.csv"))
#'user/stocks
user> (ds/head stocks)
test/data/stocks.csv [5 3]:

| symbol |       date | price |
|--------|------------|------:|
|   MSFT | 2000-01-01 | 39.81 |
|   MSFT | 2000-02-01 | 36.35 |
|   MSFT | 2000-03-01 | 43.22 |
|   MSFT | 2000-04-01 | 28.37 |
|   MSFT | 2000-05-01 | 25.45 |
user> (ds/head (ds/row-map stocks (fn [row]
                                    {"symbol" (keyword (row "symbol"))
                                     :price2 (* (row "price")(row "price"))})))
test/data/stocks.csv [5 4]:

| symbol |       date | price |   :price2 |
|--------|------------|------:|----------:|
|  :MSFT | 2000-01-01 | 39.81 | 1584.8361 |
|  :MSFT | 2000-02-01 | 36.35 | 1321.3225 |
|  :MSFT | 2000-03-01 | 43.22 | 1867.9684 |
|  :MSFT | 2000-04-01 | 28.37 |  804.8569 |
|  :MSFT | 2000-05-01 | 25.45 |  647.7025 |

row-mapcat clj

(row-mapcat mapcat-fn)

(row-mapcat mapcat-fn options)

Map a function across the rows of the dataset. The function must produce a sequence of maps and the original dataset rows will be duplicated and then merged into the result of calling (->> (apply concat) (->>dataset options) on the result of mapcat-fn. Options are the same as [[->dataset]].

The smaller the maps returned from mapcat-fn the better, perhaps consider using records. In the case that a mapcat-fn result map has a key that overlaps a column name the column will be replaced with the output of mapcat-fn. The returned map will have the key :_row-id assoc'd onto it so for absolutely minimal gc usage include this as a member variable in your map.

Options:

• See options for pmap-ds. Especially note :max-batch-size and :result-type. In order to conserve memory it may be much more efficient to return a sequence of datasets rather than one large dataset. If returning sequences of datasets perhaps consider a transducing pathway across them or the [[tech.v3.dataset.reductions]] namespace.

Example:

user> (def ds (ds/->dataset {:rid (range 10)
                             :data (repeatedly 10 #(rand-int 3))}))
#'user/ds
user> (ds/head ds)
_unnamed [5 2]:

| :rid | :data |
|-----:|------:|
|    0 |     0 |
|    1 |     2 |
|    2 |     0 |
|    3 |     1 |
|    4 |     2 |
user> (def mapcat-fn (fn [row]
                       (for [idx (range (row :data))]
                         {:idx idx})))
#'user/mapcat-fn
user> (mapcat mapcat-fn (ds/rows ds))
({:idx 0} {:idx 1} {:idx 0} {:idx 0} {:idx 1} {:idx 0} {:idx 1} {:idx 0} {:idx 1})
user> (ds/row-mapcat ds mapcat-fn)
_unnamed [9 3]:

| :rid | :data | :idx |
|-----:|------:|-----:|
|    1 |     2 |    0 |
|    1 |     2 |    1 |
|    3 |     1 |    0 |
|    4 |     2 |    0 |
|    4 |     2 |    1 |
|    6 |     2 |    0 |
|    6 |     2 |    1 |
|    8 |     2 |    0 |
|    8 |     2 |    1 |
user>

rows clj

(rows)

(rows result-type)

Returns rows of dataset. Result type can be any of:

• :as-maps
• :as-double-arrays
• :as-seqs

rowvec-at clj

(rowvec-at idx)

Return a persisent-vector-like row at a given index. Negative indexes index from the end.

user> (ds/rowvec-at stocks 1)
["MSFT" #object[java.time.LocalDate 0x5848b8b3 "2000-02-01"] 36.35]
user> (ds/rowvec-at stocks -1)
["AAPL" #object[java.time.LocalDate 0x4b70b0d5 "2010-03-01"] 223.02]

rowvecs clj

(rowvecs)

(rowvecs options)

Return a randomly addressable list of rows in persistent vector-like form.

Options:

• copying? - When true the data is copied out of the dataset row by row upon read of that row. When false the data is only referenced upon each read of a particular key. Copying is appropriate if you want to use the row values as keys a map and it is inappropriate if you are only going to read a given key for a given row once.

user> (take 5 (ds/rowvecs stocks))
(["MSFT" #object[java.time.LocalDate 0x5be9e4c8 "2000-01-01"] 39.81]
 ["MSFT" #object[java.time.LocalDate 0xf758e5 "2000-02-01"] 36.35]
 ["MSFT" #object[java.time.LocalDate 0x752cc84d "2000-03-01"] 43.22]
 ["MSFT" #object[java.time.LocalDate 0x7bad4827 "2000-04-01"] 28.37]
 ["MSFT" #object[java.time.LocalDate 0x3a62c34a "2000-05-01"] 25.45])

sample clj

(sample)

(sample n)

(sample n options)

Sample n-rows from a dataset. Defaults to sampling without replacement.

For the definition of seed, see the argshuffle documentation](https://cnuernber.github.io/dtype-next/tech.v3.datatype.argops.html#var-argshuffle)

The returned dataset's metadata is altered merging {:print-index-range (range n)} in so you will always see the entire returned dataset. If this isn't desired, vary-meta a good pathway.

Options:

• :replacement? - Do sampling with replacement. Defaults to false.
• :seed - Provide a seed as a number or provide a Random implementation.

select clj

(select columns-selector rows-selector)

Select columns and rows.

select-by-index clj

(select-by-index col-index row-index)

Trim dataset according to this sequence of indexes. Returns a new dataset.

col-index and row-index - one of:

• :all - all the columns
• list of indexes. May contain duplicates. Negative values will be counted from the end of the sequence.

select-columns clj

(select-columns)

(select-columns columns-selector)

(select-columns columns-selector meta-field)

Select columns by (returns dataset):

• name
• sequence of names
• map of names with new names (rename)
• function which filter names (via column metadata)

select-columns-by-index clj

(select-columns-by-index col-index)

Select columns from the dataset by seq of index(includes negative) or :all.

See documentation for select-by-index.

select-missing clj

(select-missing)

(select-missing columns-selector)

Select rows with missing values

columns-selector selects columns to look at missing values

select-rows clj

(select-rows)

(select-rows rows-selector)

(select-rows rows-selector options)

Select rows using:

• row id
• seq of row ids
• seq of true/false
• fn with predicate

select-rows-by-index clj

(select-rows-by-index row-index)

Select rows from the dataset or column by seq of index(includes negative) or :all.

See documentation for select-by-index.

semi-join clj

(semi-join ds-right columns-selector)

(semi-join ds-right columns-selector options)

separate-column clj

(separate-column column separator)

(separate-column column target-columns separator)

(separate-column column target-columns separator conf)

set-dataset-name clj

(set-dataset-name ds-name)

set-inference-target clj

(set-inference-target target-name-or-target-name-seq)

Set the inference target on the column. This sets the :column-type member of the column metadata to :inference-target?.

shape clj

(shape)

Returns shape of the dataset [rows, cols]

shuffle clj

(shuffle)

(shuffle options)

sort-by clj

(sort-by key-fn)

(sort-by key-fn compare-fn & args)

Sort a dataset by a key-fn and compare-fn.

• key-fn - function from map to sort value.
• compare-fn may be one of:
  • a clojure operator like clojure.core/<
  • :tech.numerics/<, :tech.numerics/> for unboxing comparisons of primitive values.
  • clojure.core/compare
  • A custom java.util.Comparator instantiation.

Options:

• :nan-strategy - General missing strategy. Options are :first, :last, and :exception.
• :parallel? - Uses parallel quicksort when true and regular quicksort when false.

sort-by-column clj

(sort-by-column colname)

(sort-by-column colname compare-fn & args)

Sort a dataset by a given column using the given compare fn.

• compare-fn may be one of:
  • a clojure operator like clojure.core/<
  • :tech.numerics/<, :tech.numerics/> for unboxing comparisons of primitive values.
  • clojure.core/compare
  • A custom java.util.Comparator instantiation.

Options:

• :nan-strategy - General missing strategy. Options are :first, :last, and :exception.
• :parallel? - Uses parallel quicksort when true and regular quicksort when false.

std-scale clj

(std-scale columns-selector options)

(std-scale columns-selector
           meta-field
           {:keys [mean? stddev?] :or {mean? true stddev? true} :as options})

Metamorph transfomer, which centers and scales the dataset per column.

columns-selector tablecloth columns-selector to choose columns to work on meta-field tablecloth meta-field working with columns-selector

options are the options for the scaler and can take:

mean? If true (default), the data gets shifted by the column means, so 0 centered

stddev? If true (default), the data gets scaled by the standard deviation of the column

metamorph	.
Behaviour in mode :fit	Centers and scales the dataset at key :metamorph/data and stores the trained model in ctx under key at :metamorph/id
Behaviour in mode :transform	Reads trained std-scale model from ctx and applies it to data in :metamorph/data
Reads keys from ctx	In mode :transform : Reads trained model to use for from key in :metamorph/id.
Writes keys to ctx	In mode :fit : Stores trained model in key $id

tail clj

(tail)

(tail n)

take-nth clj

(take-nth n-val)

transform-one-hot clj

(transform-one-hot column-selector strategy)

(transform-one-hot column-selector strategy options)

Transformer which mapps categorical variables to numbers. Each value of the column gets its won column in one-hot-encoding.

To handle different levls of a variable between train an dtets data, three strategies are available:

• :full The levels are retrieved from a dataset at key :metamorph.ml/full-ds in the context
• :independent One-hot columns are fitted and transformed independently for train and test data
• :fit The mapping fitted in mode :fit is used in :transform, and it is assumed that all levels are present in the data during :fit

ungroup clj

(ungroup)

(ungroup options)

Concat groups into dataset.

When add-group-as-column or add-group-id-as-column is set to true or name(s), columns with group name(s) or group id is added to the result.

Before joining the groups groups can be sorted by group name.

union clj

(union & args)

unique-by clj

(unique-by)

(unique-by columns-selector)

(unique-by columns-selector options)

unique-by-column clj

(unique-by-column colname)

(unique-by-column options colname)

Map-fn function gets passed map for each row, rows are grouped by the return value. Keep-fn is used to decide the index to keep.

:keep-fn - Function from key, idx-seq->idx. Defaults to #(first %2).

unmark-group clj

(unmark-group)

Remove grouping tag

unordered-select clj

(unordered-select colname-seq index-seq)

Perform a selection but use the order of the columns in the existing table; do not reorder the columns based on colname-seq. Useful when doing selection based on sets or persistent hash maps.

unroll clj

(unroll columns-selector)

(unroll columns-selector options)

Unfolds sequences stored inside a column(s), turning it into multiple columns. Opposite of fold-by. Add each of the provided columns to the set that defines the "uniqe key" of each row. Thus there will be a new row for each value inside the target column(s)' value sequence. If you want instead to split the content of the columns into a set of new columns, look at separate-column. See https://scicloj.github.io/tablecloth/index.html#Unroll

unroll-column clj

(unroll-column column-name)

(unroll-column column-name options)

Unroll a column that has some (or all) sequential data as entries. Returns a new dataset with same columns but with other columns duplicated where the unroll happened. Column now contains only scalar data.

Any missing indexes are dropped.

user> (-> (ds/->dataset [{:a 1 :b [2 3]}
                              {:a 2 :b [4 5]}
                              {:a 3 :b :a}])
               (ds/unroll-column :b {:indexes? true}))
  _unnamed [5 3]:

| :a | :b | :indexes |
|----+----+----------|
|  1 |  2 |        0 |
|  1 |  3 |        1 |
|  2 |  4 |        0 |
|  2 |  5 |        1 |
|  3 | :a |        0 |

Options - :datatype - datatype of the resulting column if one aside from :object is desired. :indexes? - If true, create a new column that records the indexes of the values from the original column. Can also be a truthy value (like a keyword) and the column will be named this.

update clj

(update filter-fn-or-ds update-fn & args)

Update this dataset. Filters this dataset into a new dataset, applies update-fn, then merges the result into original dataset.

This pathways is designed to work with the tech.v3.dataset.column-filters namespace.

• filter-fn-or-ds is a generalized parameter. May be a function, a dataset or a sequence of column names.
• update-fn must take the dataset as the first argument and must return a dataset.

(ds/bind-> (ds/->dataset dataset) ds
           (ds/remove-column "Id")
           (ds/update cf/string ds/replace-missing-value "NA")
           (ds/update-elemwise cf/string #(get {"" "NA"} % %))
           (ds/update cf/numeric ds/replace-missing-value 0)
           (ds/update cf/boolean ds/replace-missing-value false)
           (ds/update-columnwise (cf/union (cf/numeric ds) (cf/boolean ds))
                                 #(dtype/elemwise-cast % :float64)))

update-column clj

(update-column col-name update-fn)

Update a column returning a new dataset. update-fn is a column->column transformation. Error if column does not exist.

update-columns clj

(update-columns columns-map)

(update-columns columns-selector update-functions)

update-columnwise clj

(update-columnwise filter-fn-or-ds cwise-update-fn & args)

Call update-fn on each column of the dataset. Returns the dataset. See arguments to update

update-elemwise clj

(update-elemwise map-fn)

(update-elemwise filter-fn-or-ds map-fn)

Replace all elements in selected columns by calling selected function on each element. column-name-seq must be a sequence of column names if provided. filter-fn-or-ds has same rules as update. Implicitly clears the missing set so function must deal with type-specific missing values correctly. Returns new dataset

value-reader clj

(value-reader)

(value-reader options)

Return a reader that produces a reader of column values per index. Options: :copying? - Default to false - When true row values are copied on read.

write! clj

(write! output-path)

(write! output-path options)

Write a dataset out to a file. Supported forms are:

(ds/write! test-ds "test.csv")
(ds/write! test-ds "test.tsv")
(ds/write! test-ds "test.tsv.gz")
(ds/write! test-ds "test.nippy")
(ds/write! test-ds out-stream)

Options:

• :max-chars-per-column - csv,tsv specific, defaults to 65536 - values longer than this will cause an exception during serialization.
• :max-num-columns - csv,tsv specific, defaults to 8192 - If the dataset has more than this number of columns an exception will be thrown during serialization.
• :quoted-columns - csv specific - sequence of columns names that you would like to always have quoted.
• :file-type - Manually specify the file type. This is usually inferred from the filename but if you pass in an output stream then you will need to specify the file type.
• :headers? - if csv headers are written, defaults to true.

write-nippy! clj

(write-nippy! filename)