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clj-boost.core

The main clj-boost namespace. By requiring this namespace you get all functions for serializing, training and predicting XGBoost models.

The main clj-boost namespace.
By requiring this namespace you get all functions for serializing,
training and predicting XGBoost models.

cross-validation

(cross-validation dmatrix
                  {:keys [params rounds nfold metrics], :as config})

Perform cross-validation on the training set.

The training set must be a DMatrix instance (see dmatrix) while config is a regular map.

It returns a sequence of maps containing train and test error for every round as defined in the config map.

config fields:

Example:

(cross-validation (dmatrix x y)
                  {:params {:eta 1.0}
                   :rounds 5
                   :nfold 3})
Perform cross-validation on the training set.

The training set must be a **DMatrix** instance (see
[[dmatrix]]) while `config` is a regular map.

It returns a sequence of maps containing train and test
error for every round as defined in the `config` map.

`config` fields:

- `:params`  -> training parameters (see https://xgboost.readthedocs.io/en/latest/parameter.html)
- `:rounds`  -> number of boosting iterations
- `:nfold`   -> number of folds for cross-validation
- `:metrics` -> metrics to evaluate goodness of fit, must be a vector

Example:

```
(cross-validation (dmatrix x y)
                  {:params {:eta 1.0}
                   :rounds 5
                   :nfold 3})
```
sourceraw docstring

dmatrixmultimethod

Serializes given data to DMatrix.

It is required by the XGBoost API to serialize data structures to use the library (https://xgboost.readthedocs.io/en/latest/jvm/java_intro.html).

dmatrix tries to make the process as painless as possible internally dealing with types and variable arguments.

It is possible to pass a map with :x and optionally :y keys, their values must be either a sequence of sequences or a vector of vectors for :x and a flat vector or sequence for :y.

The input can also be a vector of vectors/sequence of sequences for x and optionally a flat vector/sequence for y.

If given a string as input dmatrix loads a DMatrix from the given path in string form.

y is required only for training, not for prediction.

Examples:

(def map-with-y {:x [[1 0] [0 1]] :y [1 0]})
(dmatrix m)

(def map-without-y {:x [[1 0] [0 1]]})
(dmatrix m)

(def vec-x [[1 0] [0 1]])
(def vec-y [1 0])
(dmatrix vec-x vec-y)
(dmatrix vec-x)

(def seq-x '((1 0) (0 1)))
(def seq-y '(1 0))
(dmatrix seq-x seq-y)
(dmatrix seq-x)

(dmatrix "path/to/stored/dmatrix")
Serializes given data to **DMatrix**.

It is required by the XGBoost API to serialize data structures to use
the library (https://xgboost.readthedocs.io/en/latest/jvm/java_intro.html).

`dmatrix` tries to make the process as painless as possible internally
dealing with types and variable arguments.

It is possible to pass a map with `:x` and optionally `:y` keys,
their values must be either a sequence of sequences or a vector of vectors
for `:x` and a flat vector or sequence for `:y`.

The input can also be a vector of vectors/sequence of sequences for *x*
and optionally a flat vector/sequence for *y*.

If given a string as input `dmatrix` loads a **DMatrix** from the
given path in string form.

*y* is required only for training, not for prediction.

Examples:

```
(def map-with-y {:x [[1 0] [0 1]] :y [1 0]})
(dmatrix m)

(def map-without-y {:x [[1 0] [0 1]]})
(dmatrix m)

(def vec-x [[1 0] [0 1]])
(def vec-y [1 0])
(dmatrix vec-x vec-y)
(dmatrix vec-x)

(def seq-x '((1 0) (0 1)))
(def seq-y '(1 0))
(dmatrix seq-x seq-y)
(dmatrix seq-x)

(dmatrix "path/to/stored/dmatrix")
```
sourceraw docstring

fit

(fit dmatrix
     {:keys [params rounds watches early-stopping booster],
      :as config})

Train an XGBoost model on the given data.

The training set must be a DMatrix instance (see dmatrix) while config is a regular map.

It returns a trained model (a Booster instance) that can be used for prediction or as a base margin for further training.

config fields:

  • :params -> training parameters (see https://xgboost.readthedocs.io/en/latest/parameter.html)
  • :rounds -> number of boosting iterations
  • :watches -> a map of data to be evaluated during training. Usually either {:train train-set} to evaluate only on the training set or {:train train-set :valid validation-set}
  • :early-stopping -> training stops after this number of rounds of consecutive increases in any evaluation metric
  • :booster -> Optionally set an existing model to use as base margin

Example:

(fit (dmatrix x y)
  {:params {:eta 1.0}
   :rounds 2
   :watches {:train (dmatrix x y)}
   :early-stopping 10})
Train an **XGBoost** model on the given data.

The training set must be a **DMatrix** instance (see
[[dmatrix]]) while `config` is a regular map.

It returns a trained model (a **Booster** instance) that can
be used for prediction or as a base margin for further training.

`config` fields:

- `:params` -> training parameters (see https://xgboost.readthedocs.io/en/latest/parameter.html)
- `:rounds` -> number of boosting iterations
- `:watches` -> a map of data to be evaluated during training. Usually either
  `{:train train-set}` to evaluate only on the training set or
  `{:train train-set :valid validation-set}`
- `:early-stopping` -> training stops after this number of rounds of consecutive
  increases in any evaluation metric
- `:booster` -> Optionally set an existing model to use as base margin

Example:

```
(fit (dmatrix x y)
  {:params {:eta 1.0}
   :rounds 2
   :watches {:train (dmatrix x y)}
   :early-stopping 10})
```
sourceraw docstring

load-model

(load-model path)

Loads a saved XGBoost model.

Given a path to a saved XGBoost model loads it and makes it usable. Returns a Booster instance.

Example:

(load-model "path/to/model")
Loads a saved **XGBoost** model.

Given a path to a saved **XGBoost** model loads it and makes it usable.
Returns a **Booster** instance.

Example:

```
(load-model "path/to/model")
```
sourceraw docstring

nrow

(nrow dmatrix)

Returns the number of rows in a DMatrix.

Example:

(nrow (dmatrix train))
;; 50
Returns the number of rows in a **DMatrix**.

Example:

```
(nrow (dmatrix train))
;; 50
```
sourceraw docstring

persistmultimethod

Save datasets and models in a format suited for XGBoost.

Save either a DMatrix or a Booster instance to retrieve it for later use.

Example:

(persist (dmatrix dataset) "path/to/dataset")

(persist (fit (dmatrix dataset) config) "path/to/model")
Save datasets and models in a format suited for **XGBoost**.

Save either a **DMatrix** or a **Booster** instance to retrieve it for later use.

Example:

```
(persist (dmatrix dataset) "path/to/dataset")

(persist (fit (dmatrix dataset) config) "path/to/model")
```
sourceraw docstring

pipe

(pipe train-dmatrix test-dmatrix config & [path])

Train-test-persist pipeline.

This tries to reproduce a typical workflow: train a model on training data, test the model on the test dataset and optionally save the trained model.

Example:

(pipe (dmatrix train) (dmatrix test) config "path/to/save/model")
Train-test-persist pipeline.

This tries to reproduce a typical workflow: train a model on training data,
test the model on the test dataset and optionally save the trained model.

Example:

```
(pipe (dmatrix train) (dmatrix test) config "path/to/save/model")
```
sourceraw docstring

predict

(predict model dmatrix)

Use a trained model to make predictions.

Given a Booster model and a DMatrix dataset uses the former to make predictions on the latter.

Example:

(predict (fit (dmatrix train) config)
         test-dmatrix)
Use a trained model to make predictions.

Given a **Booster** model and a **DMatrix** dataset uses
the former to make predictions on the latter.

Example:

```
(predict (fit (dmatrix train) config)
         test-dmatrix)
```
sourceraw docstring

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