mojolearn.HostGBDT#

class mojolearn.HostGBDT(*, loss, text, n_features_in, approx_dim, n_classes=None, numeric_mode=None, bias=None, estimator='GradientBoosting')#

CPU inference for a saved gradient-boosting model.

Loads a file written by GradientBoosting.save and predicts on the CPU, with no GPU needed. predict returns what GradientBoosting.predict returns for the same file. Models with categorical (CTR) columns are supported; a model that uses CTR table types with no prediction-time formula (Buckets, BinarizedTargetMeanValue, FloatTargetMeanValue) is refused at load.

Build one with from_file(). The constructor takes the archive’s fields directly and refuses a file whose fields disagree with its model text as corrupt.

Parameters:

  • loss (str) – The loss the model was fitted with.

  • text (str) – The model text (GradientBoosting.model_).

  • n_features_in (int)

  • approx_dim (int) – Number of raw outputs per row.

  • n_classes (int or None, default None)

  • numeric_mode (str or None, default None) – The numeric mode recorded with the model.

  • bias (float or None, default None)

  • estimator (str, default ‘GradientBoosting’) – The class that saved the model.

Variables:

  • n_features_in_ (int)

  • approx_dim_ (int)

  • bias_ (float)

  • n_classes_ (int or None)

  • model_ (str)

Examples

>>> import numpy as np, mojolearn
>>> X = np.random.default_rng(0).random((1000, 8), dtype=np.float32)
>>> y = (X[:, 0] > 0.5).astype(np.float32)
>>> mojolearn.GradientBoosting(loss="Logloss", n_estimators=50).fit(X, y).save("gb.npz")
>>> host = mojolearn.HostGBDT.from_file("gb.npz")
>>> proba = host.predict_proba(X)

See also

User guide: Trees and gradient boosting

Choosing an estimator

Methods

from_file(path)

Load a model from a file written by GradientBoosting.save.

model_sha256()

Return the SHA-256 hex digest of the model text (UTF-8).

predict(X)

Return raw float32 scores, as GradientBoosting.predict does.

predict_proba(X)

Return float64 class probabilities [1 - p, p] for Logloss and CrossEntropy models.

Attributes

estimator

inference_engine

Name of the prediction engine, 'sequential'.

n_trees

Number of trees in the model.

n_model_columns_

the input columns, or more for a CTR model
n_model_columns_#

the input columns, or more for a CTR model

Type:

model columns

classmethod from_file(path)#

Load a model from a file written by GradientBoosting.save.

Parameters:

path (str or path-like)

Returns:

HostGBDT

Raises:

ValueError – If the file was saved by another class or is corrupt.

property n_trees#

Number of trees in the model.

property inference_engine#

Name of the prediction engine, 'sequential'.

model_sha256()#

Return the SHA-256 hex digest of the model text (UTF-8).

It equals the digest of the archive’s model member.

predict(X)#

Return raw float32 scores, as GradientBoosting.predict does.

Parameters:

X (array-like of shape (n_samples, n_features))

Returns:

mojolearn.Array – Shape (n_samples,) for a single-output loss, otherwise (n_samples, approx_dim).

predict_proba(X)#

Return float64 class probabilities [1 - p, p] for Logloss and CrossEntropy models.

Matches GradientBoosting.predict_proba for these losses. MultiClass and MultiClassOneVsAll models raise NotImplementedError; any other loss raises ValueError.

Parameters:

X (array-like of shape (n_samples, n_features))

Returns:

mojolearn.Array – Shape (n_samples, 2).