tech.libs.tablesaw.tablesaw-column
Bindings so that you can do math on tablesaw columns.
Bindings so that you can do math on tablesaw columns.
- available-stats
- boolean-col-cast
- boolean-col-data
- boolean-data-field
- datatype->column-cast-fn
- datatype->column-data-cast-fn
- double-col-cast
- double-col-data
- double-data-field
- extend-tablesaw-type
- float-col-cast
- float-col-data
- float-data-field
- int-col-cast
- int-col-data
- int-data-field
- long-col-cast
- long-col-data
- long-data-field
- make-column
- make-empty-column
- short-col-cast
- short-col-data
- short-data-field
- string-col-cast
- string-col-data
- string-data-field
tech.ml.dataset
Column major dataset abstraction for efficiently manipulating in memory datasets.
Column major dataset abstraction for efficiently manipulating in memory datasets.
- ->dataset
- ->flyweight
- ->k-fold-datasets
- ->row-major
- ->train-test-split
- add-column
- add-or-update-column
- column
- column-label-map
- column-map
- column-names
- column-values->categorical
- columns
- columns-with-missing-seq
- compute-centroid-and-global-means
- correlation-table
- dataset->string
- dataset-label-map
- dataset-name
- ds-column-map
- ds-concat
- ds-filter
- ds-group-by
- ds-map-values
- ds-sort-by
- ds-take-nth
- feature-ecount
- from-prototype
- g-means
- has-column-label-map?
- impute-missing-by-centroid-averages
- inference-target-label-inverse-map
- inference-target-label-map
- k-means
- labels
- maybe-column
- metadata
- model-type
- new-column
- num-inference-classes
- order-column-names
- reduce-column-names
- remove-column
- remove-columns
- select
- select-columns
- set-inference-target
- set-metadata
- update-column
- update-columns
- x-means
tech.ml.dataset.base
- ->dataset
- add-column
- add-or-update-column
- column
- column-map
- column-names
- columns
- columns-with-missing-seq
- dataset-name
- ds-column-map
- ds-concat
- ds-filter
- ds-group-by
- ds-map-values
- ds-sort-by
- ds-take-nth
- from-prototype
- index-value-seq
- map-seq->dataset
- maybe-column
- metadata
- new-column
- order-column-names
- remove-column
- remove-columns
- select
- select-columns
- set-metadata
- supported-column-stats
- unordered-select
- update-column
- update-columns
tech.ml.dataset.categorical
Dealing with categorical dataset data involves having two mapping systems. The first is a map of category to integer within the same column. The second is a 'one-hot' encoding where you generate more columns but those have a reduced number of possible categories, usually one categorical value per column.
Dealing with categorical dataset data involves having two mapping systems. The first is a map of category to integer within the same column. The second is a 'one-hot' encoding where you generate more columns but those have a reduced number of possible categories, usually one categorical value per column.
tech.ml.dataset.options
The etl pipeline and dataset operators are built to produce a metadata options map. Their API access to the options is centralized in this file.
The etl pipeline and dataset operators are built to produce a metadata options map. Their API access to the options is centralized in this file.
- column-label-map
- dataset-label-map
- expand-column-names
- feature-column-names
- feature-ecount
- has-column-label-map?
- inference-target-label-inverse-map
- inference-target-label-map
- label-column-names
- model-type-for-column
- model-type-map
- num-classes
- reduce-column-names
- set-dataset-label-map
- set-feature-column-names
- set-label-column-names
tech.ml.dataset.pca
PCA and K-PCA using smile implementations.
PCA and K-PCA using smile implementations.
tech.ml.dataset.pipeline
A set of common 'pipeline' operations you probably will want to run on a dataset.
A set of common 'pipeline' operations you probably will want to run on a dataset.
tech.ml.dataset.pipeline.pipeline-operators
- *pipeline-context*
- *pipeline-env*
- *pipeline-training?*
- ->datatype
- assoc-metadata
- def-multiple-column-etl-operator
- def-single-column-etl-operator
- default-etl-context-columns
- default-perform-etl-columns
- filter
- impute-missing
- inline-perform-operator
- m=
- one-hot
- pca
- pif
- pipeline-inference-context
- pipeline-train-context
- pwhen
- range-scaler
- read-var
- remove-columns
- replace
- replace-missing
- std-scaler
- store-variables
- string->number
- training?
- update-column
- without-recording
tech.ml.dataset.seq-of-maps
Helper functions for dealing with a sequence of maps.
Helper functions for dealing with a sequence of maps.
tech.ml.dataset.tensor
Conversion mechanisms from dataset to tensor and back
Conversion mechanisms from dataset to tensor and back
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