tech.ml.dataset.modelling — techascent/tech.ml.dataset 2.13

->k-fold-datasets clj

(->k-fold-datasets dataset k)

(->k-fold-datasets
  dataset
  k
  {:keys [randomize-dataset?] :or {randomize-dataset? true} :as options})

Given 1 dataset, prepary K datasets using the k-fold algorithm. Randomize dataset defaults to true which will realize the entire dataset so use with care if you have large datasets.

->row-major clj

(->row-major dataset)

(->row-major dataset options)

(->row-major dataset
             key-colname-seq-map
             {:keys [datatype] :or {datatype :float64}})

Given a dataset and a map of desired key names to sequences of columns, produce a sequence of maps where each key name points to contiguous vector composed of the column values concatenated. If colname-seq-map is not provided then each row defaults to {:features [feature-columns] :label [label-columns]}

->train-test-split clj

(->train-test-split dataset)

(->train-test-split dataset
                    {:keys [randomize-dataset? train-fraction]
                     :or {randomize-dataset? true train-fraction 0.7}
                     :as options})

column-label-map clj

(column-label-map dataset column-name)

column-values->categorical clj

(column-values->categorical dataset 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.

dataset-label-map clj

(dataset-label-map dataset)

feature-ecount clj

(feature-ecount dataset)

When columns aren't scalars then this will change. For now, just the number of feature columns.

has-column-label-map? clj

(has-column-label-map? dataset column-name)

inference-target-column-names clj

(inference-target-column-names ds)

inference-target-label-inverse-map clj

(inference-target-label-inverse-map dataset & [label-columns])

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 dataset & [label-columns])

model-type clj

(model-type dataset & [column-name-seq])

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

num-inference-classes clj

(num-inference-classes dataset)

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

reduce-column-names clj

(reduce-column-names dataset colname-seq)

Reverse map from the one-hot encoded columns to the original source column.

set-inference-target clj

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