Decision Trees

Decision Tree Classifier

class snapml.DecisionTreeClassifier(criterion='gini', splitter='best', max_depth=None, min_samples_leaf=1, max_features=None, random_state=None, n_jobs=1, use_histograms=True, hist_nbins=256, use_gpu=False, gpu_id=0, verbose=False)

Decision Tree Classifier

This class implements a decision tree classifier using the IBM Snap ML library. It can be used for binary and multi-class classification problems.

Parameters:

criterionstring, default=”gini”

This function measures the quality of a split. Possible values: “gini” and “entropy” for information gain. “entropy” is currently not supported.

splitterstring, default=”best”

This parameter defines the strategy used to choose the split at each node. Possible values: “best” and “random”. “random” is currently not supported.

max_depthint or None, default=None

The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_leaf samples.

min_samples_leafint or float, default=1

The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it generates at least min_samples_leaf training samples in each of the left and right branches. - If int, then consider min_samples_leaf as the minimum number. - If float, then consider ceil(min_samples_leaf * n_samples) as the minimum number.

max_featuresint, float, string or None, default=None

The number of features to consider when looking for the best split:

If int, then consider max_features features at each split.
If float, then consider int(max_features * n_features) features at each split.
If “auto”, then max_features=sqrt(n_features).
If “sqrt”, then max_features=sqrt(n_features).
If “log2”, then max_features=log2(n_features).
If None, then max_features=n_features.

random_stateint, or None, default=None

If int, random_state is the seed used by the random number generator; If None, the random number generator is the RandomState instance used by np.random.

n_jobsinteger, default=1

The number of CPU threads to use.

use_histogramsboolean, default=True

Use histogram-based splits rather than exact splits.

hist_nbinsint, default=256

Number of histogram bins.

use_gpuboolean, default=False

Use GPU acceleration (only supported for histogram-based splits).

gpu_idint, default=0

Device ID of the GPU which will be used when GPU acceleration is enabled.

verbosebool, default=False

If True, it prints debugging information while training. Warning: this will increase the training time. For performance evaluation, use verbose=False.

Attributes:

classes_array of shape = [n_classes]: The classes labels (single output problem)
n_classes_int: The number of classes (for single output problems)
feature_importances_array-like, shape=(n_features,): Feature importances.

fit(X_train, y_train, sample_weight=None)

Fit the model according to the given train data.

Parameters:

X_traindense matrix (ndarray): Train dataset
y_trainarray-like, shape = (n_samples,): The target vector corresponding to X_train.
sample_weightarray-like, shape = [n_samples] or None: Sample weights. If None, then samples are equally weighted. TODO: Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node.

Returns:

selfobject

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict: Parameter names mapped to their values.

predict(X, n_jobs=None)

Class/Regression predictions

The returned class/regression estimates.

Parameters:

Xdense matrix (ndarray) or memmap (np.memmap): Dataset used for predicting class/regression estimates.
n_jobsint, default=None: Number of threads used to run inference. By default the value of the class attribute is used..

Returns:

pred/proba: array-like, shape = (n_samples,): Returns the predicted class/values of the sample.

predict_proba(X, n_jobs=None)

Predict class probabilities.

Parameters:

Xdense matrix (ndarray): Dataset used for predicting probabilities.
n_jobsint, default=None: Number of threads used to run inference. By default the value of the class attribute is used..
Returns
——-
proba: array-like, shape = (n_samples, n_classes): Returns the predicted probabilities the sample.

score(X, y, sample_weight=None)

Return the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters:

Xarray-like of shape (n_samples, n_features): Test samples.
yarray-like of shape (n_samples,) or (n_samples, n_outputs): True labels for X.
sample_weightarray-like of shape (n_samples,), default=None: Sample weights.

Returns:

scorefloat: Mean accuracy of self.predict(X) w.r.t. y.

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to fit.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

X_trainstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for X_train parameter in fit.
sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in fit.
y_trainstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for y_train parameter in fit.

Returns:

selfobject: The updated object.

set_params(**params)

Set the parameters of this model.

Valid parameter keys can be listed with get_params().

Returns:

self

set_predict_proba_request(*, n_jobs: bool | None | str = '$UNCHANGED$') → DecisionTreeClassifier

Request metadata passed to the predict_proba method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to predict_proba if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to predict_proba.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

n_jobsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for n_jobs parameter in predict_proba.

Returns:

selfobject: The updated object.

set_predict_request(*, n_jobs: bool | None | str = '$UNCHANGED$') → DecisionTreeClassifier

Request metadata passed to the predict method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to predict if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to predict.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

n_jobsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for n_jobs parameter in predict.

Returns:

selfobject: The updated object.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → DecisionTreeClassifier

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to score.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in score.

Returns:

selfobject: The updated object.

Decision Tree Regressor

class snapml.DecisionTreeRegressor(criterion='mse', splitter='best', max_depth=None, min_samples_leaf=1, max_features=None, random_state=None, n_jobs=1, use_histograms=True, hist_nbins=256, use_gpu=False, gpu_id=0, verbose=False)

Decision Tree Regressor

This class implements a decision tree regressor using the IBM Snap ML library. It can be used for regression tasks.

Parameters:

criterion{‘mse’}, default=”mse”

This function measures the quality of a split.

splitterstring, default=”best”

This parameter defines the strategy used to choose the split at each node. Possible values: “best” and “random”. “random” is currently not supported.

max_depthint or None, default=None

The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_leaf samples.

min_samples_leafint or float, default=1

The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it generates at least min_samples_leaf training samples in each of the left and right branches. - If int, then consider min_samples_leaf as the minimum number. - If float, then consider ceil(min_samples_leaf * n_samples) as the minimum number.

max_featuresint, float, string or None, default=None

The number of features to consider when looking for the best split:

If int, then consider max_features features at each split.
If float, then consider int(max_features * n_features) features at each split.
If “auto”, then max_features=n_features.
If “sqrt”, then max_features=sqrt(n_features).
If “log2”, then max_features=log2(n_features).
If None, then max_features=n_features.

random_stateint, or None, default=None

If int, random_state is the seed used by the random number generator; If None, the random number generator is the RandomState instance used by np.random.

n_jobsinteger, default=1

The number of CPU threads to use.

use_histogramsboolean, default=True

Use histogram-based splits rather than exact splits.

hist_nbinsint, default=256

Number of histogram bins.

use_gpuboolean, default=False

Use GPU acceleration (only supported for histogram-based splits).

gpu_idint, default=0

Device ID of the GPU which will be used when GPU acceleration is enabled.

verbosebool, default=False

If True, it prints debugging information while training. Warning: this will increase the training time. For performance evaluation, use verbose=False.

Attributes:

feature_importances_array-like, shape=(n_features,): Feature importances.

fit(X_train, y_train, sample_weight=None)

Fit the model according to the given train data.

Parameters:

X_traindense matrix (ndarray): Train dataset
y_trainarray-like, shape = (n_samples,): The target vector corresponding to X_train.
sample_weightarray-like, shape = [n_samples] or None: Sample weights. If None, then samples are equally weighted. TODO: Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node.

Returns:

selfobject

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict: Parameter names mapped to their values.

predict(X, n_jobs=None)

Class/Regression predictions

The returned class/regression estimates.

Parameters:

Xdense matrix (ndarray) or memmap (np.memmap): Dataset used for predicting class/regression estimates.
n_jobsint, default=None: Number of threads used to run inference. By default the value of the class attribute is used..

Returns:

pred/proba: array-like, shape = (n_samples,): Returns the predicted class/values of the sample.

score(X, y, sample_weight=None)

Return the coefficient of determination of the prediction.

The coefficient of determination $R^2$ is defined as $(1 - \frac{u}{v})$, where $u$ is the residual sum of squares ((y_true - y_pred)** 2).sum() and $v$ is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a $R^2$ score of 0.0.

Parameters:

Xarray-like of shape (n_samples, n_features): Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
yarray-like of shape (n_samples,) or (n_samples, n_outputs): True values for X.
sample_weightarray-like of shape (n_samples,), default=None: Sample weights.

Returns:

scorefloat: $R^2$ of self.predict(X) w.r.t. y.

Notes

The $R^2$ score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score(). This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to fit.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

X_trainstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for X_train parameter in fit.
sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in fit.
y_trainstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for y_train parameter in fit.

Returns:

selfobject: The updated object.

set_params(**params)

Set the parameters of this model.

Valid parameter keys can be listed with get_params().

Returns:

self

set_predict_request(*, n_jobs: bool | None | str = '$UNCHANGED$') → DecisionTreeRegressor

Request metadata passed to the predict method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to predict if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to predict.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

n_jobsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for n_jobs parameter in predict.

Returns:

selfobject: The updated object.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → DecisionTreeRegressor

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to score.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in score.

Returns:

selfobject: The updated object.