dislib.trees

class dislib.trees.DecisionTreeClassifier(try_features, max_depth, distr_depth, sklearn_max, bootstrap, random_state, n_classes=None, range_max=None, range_min=None, n_split_points='auto', split_computation='raw', sync_after_fit=True, mmap=True, nested=False)

Bases: BaseDecisionTree

A distributed decision tree classifier.

Parameters

• try_features (int) – The number of features to consider when looking for the best split.

  Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than try_features features.

• max_depth (int) – The maximum depth of the tree. If np.inf, then nodes are expanded until all leaves are pure.

• distr_depth (int) – Number of levels of the tree in which the nodes are split in a distributed way.

• bootstrap (bool) – Randomly select n_instances samples with repetition (used in random forests).

• random_state (RandomState instance) – The random number generator.

• n_classes (int) – Number of classes that appear on the dataset. Only needed on distributed random forest.

• range_min (ds-array or np.array) – Contains the minimum values of the different attributes of the dataset Only used on distributed random forest (it is an optional parameter)

• range_max (ds-array or np.array) – Contains the maximum values of the different attributes of the dataset Only used on distributed random forest (it is an optional parameter)

• n_split_points (String or int) – Number of split points to evaluate. “auto”, “sqrt” or integer value. Used on distributed random forest (non memory map version)

• split_computation (String) – “raw”, “gaussian_approximation” or “uniform_approximation” distribution of the values followed by the split points selected. Used on distributed random forest (non memory map version)

• sync_after_fit (bool) – Synchronize or not after the training. Used on distributed random forest (non memory map version)

• mmap (bool) – Use the memory map version or not

• nested (bool) – Use the nested version or not

Variables

• n_features (int) – The number of features of the dataset. It can be a pycompss.runtime.Future object.

• n_classes (int) – The number of classes of this RfDataset. It can be a pycompss.runtime.Future object.

• tree (None or _Node) – The root node of the tree after the tree is fitted.

• nodes_info (None or list of _InnerNodeInfo and _LeafInfo) – List of the node information for the nodes of the tree in the same order as obtained in the fit() method, up to distr_depth depth. After fit(), it is a pycompss.runtime.Future object.

• subtrees (None or list of _Node) – List of subtrees of the tree at distr_depth depth obtained in the fit() method. After fit(), it is a list of pycompss.runtime.Future objects.

fit(dataset)

Fits the DecisionTreeClassifier.

predict(x_row)

Predicts classes for the given samples using a fitted tree.

predict_proba(x_row)

Predicts class probabilities for the given samples using a fitted tree.

predict_proba(x_row, collect=False)

Predicts class probabilities for a row block using a fitted tree.

Parameters

• x_row (ds-array) – A row block of samples.

• collect (boolean) – Only affects nested and distributed versions of the algorithm. When True, the results are synchronized before the returning, when False, no synchronization is done, but the user should do it manually when he/she wants the results.

Returns

predicted_proba – An array with the predicted probabilities for the given samples. The shape is (len(subset.samples), self.n_classes), with the index of the column being codes of the fitted dislib.classification.rf.data.RfDataset. The returned object can be a pycompss.runtime.Future object.

Return type

ndarray

class dislib.trees.DecisionTreeRegressor(try_features, max_depth, distr_depth, sklearn_max, bootstrap, random_state, range_max=None, range_min=None, n_split_points='auto', split_computation='raw', sync_after_fit=True, mmap=True, nested=False)

Bases: BaseDecisionTree

A distributed decision tree regressor.

Parameters

• try_features (int) – The number of features to consider when looking for the best split.

  Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than try_features features.

• max_depth (int) – The maximum depth of the tree. If np.inf, then nodes are expanded until all leaves are pure.

• distr_depth (int) – Number of levels of the tree in which the nodes are split in a distributed way.

• bootstrap (bool) – Randomly select n_instances samples with repetition (used in random forests).

• random_state (RandomState instance) – The random number generator.

• range_min (ds-array or np.array) – Contains the minimum values of the different attributes of the dataset Only used on distributed random forest (it is an optional parameter)

• range_max (ds-array or np.array) – Contains the maximum values of the different attributes of the dataset Only used on distributed random forest (it is an optional parameter)

• n_split_points (String or int) – Number of split points to evaluate. “auto”, “sqrt” or integer value. Used on distributed random forest (non memory map version)

• split_computation (String) – “raw”, “gaussian_approximation” or “uniform_approximation” distribution of the values followed by the split points selected. Used on distributed random forest (non memory map version)

• sync_after_fit (bool) – Synchronize or not after the training. Used on distributed random forest (non memory map version)

• mmap (bool) – Use the memory map version or not

• nested (bool) – Use the nested version or not

Variables

• n_features (int) – The number of features of the dataset. It can be a pycompss.runtime.Future object.

• tree (None or _Node) – The root node of the tree after the tree is fitted.

• nodes_info (None or list of _InnerNodeInfo and _LeafInfo) – List of the node information for the nodes of the tree in the same order as obtained in the fit() method, up to distr_depth depth. After fit(), it is a pycompss.runtime.Future object.

• subtrees (None or list of _Node) – List of subtrees of the tree at distr_depth depth obtained in the fit() method. After fit(), it is a list of pycompss.runtime.Future objects.

fit(dataset)

Fits the DecisionTreeRegressor.

predict(x_row)

Predicts target values for the given samples using a fitted tree.

class dislib.trees.RandomForestClassifier(n_estimators=10, try_features='sqrt', max_depth=inf, distr_depth='auto', sklearn_max=100000000.0, hard_vote=False, random_state=None, n_classes=None, range_max=None, range_min=None, bootstrap=True, n_split_points='auto', split_computation='raw', sync_after_fit=True, mmap=True, nested=False)

Bases: BaseRandomForest

A distributed random forest classifier.

Parameters

• n_estimators (int, optional (default=10)) – Number of trees to fit.

• try_features (int, str or None, optional (default=’sqrt’)) – The number of features to consider when looking for the best split:

  • If “sqrt”, then try_features=sqrt(n_features).

  • If “third”, then try_features=n_features // 3.

  • If None, then try_features=n_features.

  Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than try_features features.

• max_depth (int or np.inf, optional (default=np.inf)) – The maximum depth of the tree. If np.inf, then nodes are expanded until all leaves are pure.

• distr_depth (int or str, optional (default=’auto’)) – Number of levels of the tree in which the nodes are split in a distributed way.

• sklearn_max (int or float, optional (default=1e8)) – Maximum size (len(subsample)*n_features) of the arrays passed to sklearn’s DecisionTreeClassifier.fit(), which is called to fit subtrees (subsamples) of our DecisionTreeClassifier. sklearn fit() is used because it’s faster, but requires loading the data to memory, which can cause memory problems for large datasets. This parameter can be adjusted to fit the hardware capabilities.

• hard_vote (bool, optional (default=False)) – If True, it uses majority voting over the predict() result of the decision tree predictions. If False, it takes the class with the higher probability given by predict_proba(), which is an average of the probabilities given by the decision trees.

• random_state (int, RandomState instance or None, optional (default=None)) – If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

• n_classes (int) – Number of classes that appear on the dataset. Only needed on distributed random forest.

• range_min (ds-array or np.array) – Contains the minimum values of the different attributes of the dataset Only used on distributed random forest (it is an optional parameter)

• range_max (ds-array or np.array) – Contains the maximum values of the different attributes of the dataset Only used on distributed random forest (it is an optional parameter)

• n_split_points (String or int) – Number of split points to evaluate. “auto”, “sqrt” or integer value. Used on distributed random forest (non memory map version)

• split_computation (String) – “raw”, “gaussian_approximation” or “uniform_approximation” distribution of the values followed by the split points selected. Used on distributed random forest (non memory map version)

• sync_after_fit (bool) – Synchronize or not after the training. Used on distributed random forest (non memory map version)

• mmap (bool) – Use the memory map version or not.

• nested (bool) – Use the nested version or not.

Variables

• classes (None or ndarray) – Array of distinct classes, set at fit().

• rf (RandomForestClassifier selected) – Instance of mmap, distributed or nested RandomForestClassifier selected.

load_model(filepath, load_format='json')

Loads a model from a file. The model is reinstantiated in the exact same state in which it was saved, without any of the code used for model definition or fitting. :Parameters: * filepath (str) – Path of the saved the model

• load_format (str, optional (default=’json’)) – Format used to load the model.

Examples

>>> from dislib.trees import RandomForestClassifier
>>> import numpy as np
>>> import dislib as ds
>>> x = np.array([[1, 2], [1, 4], [1, 0], [4, 2], [4, 4], [4, 0]])
>>> y = np.array([1, 1, 2, 2, 2, 1])
>>> x_train = ds.array(x, (2, 2))
>>> y_train = ds.array(y, (2, 1))
>>> model = RandomForestClassifier(n_estimators=2, random_state=0)
>>> model.fit(x_train, y_train)
>>> model.save_model('/tmp/model')
>>> loaded_model = RandomForestClassifier()
>>> loaded_model.load_model('/tmp/model')
>>> x_test = ds.array(np.array([[0, 0], [4, 4]]), (2, 2))
>>> model_pred = model.predict(x_test)
>>> loaded_model_pred = loaded_model.predict(x_test)
>>> assert np.allclose(model_pred.collect(),

predict(x)

Predicts target classes using a fitted forest.

Parameters

x (ds-array, shape=(n_samples, n_features)) – The input samples.

Returns

y_pred – Predicted class labels for x.

Return type

ds-array, shape=(n_samples, 1)

predict_proba(x)

Predicts class probabilities using a fitted forest.

The probabilities are obtained as an average of the probabilities of each decision tree.

Parameters

x (ds-array, shape=(n_samples, n_features)) – The input samples.

Returns

probabilities – Predicted probabilities for the samples to belong to each class. The columns of the array correspond to the classes given at self.classes.

Return type

ds-array, shape=(n_samples, n_classes)

save_model(filepath, overwrite=True, save_format='json')

Saves a model to a file. The model is synchronized before saving and can be reinstantiated in the exact same state, without any of the code used for model definition or fitting. :Parameters: * filepath (str) – Path where to save the model

• overwrite (bool, optional (default=True)) – Whether any existing model at the target location should be overwritten.

• save_format (str, optional (default=’json)) – Format used to save the models.

Examples

>>> from dislib.trees import RandomForestClassifier
>>> import numpy as np
>>> import dislib as ds
>>> x = np.array([[1, 2], [1, 4], [1, 0], [4, 2], [4, 4], [4, 0]])
>>> y = np.array([1, 1, 2, 2, 2, 1])
>>> x_train = ds.array(x, (2, 2))
>>> y_train = ds.array(y, (2, 1))
>>> model = RandomForestClassifier(n_estimators=2, random_state=0)
>>> model.fit(x_train, y_train)
>>> model.save_model('/tmp/model')
>>> loaded_model = RandomForestClassifier()
>>> loaded_model.load_model('/tmp/model')
>>> x_test = ds.array(np.array([[0, 0], [4, 4]]), (2, 2))
>>> model_pred = model.predict(x_test)
>>> loaded_model_pred = loaded_model.predict(x_test)
>>> assert np.allclose(model_pred.collect(),
>>> loaded_model_pred.collect())

score(x, y, collect=False)

Accuracy classification score.

Returns the mean accuracy of the predictions on the given test data.

Parameters

• x (ds-array, shape=(n_samples, n_features)) – The training input samples.

• y (ds-array, shape (n_samples, 1)) – The true labels.

• collect (bool, optional (default=False)) – When True, a synchronized result is returned.

Returns

score – Fraction of correctly classified samples.

Return type

float (as future object)

class dislib.trees.RandomForestRegressor(n_estimators=10, try_features='sqrt', max_depth=inf, distr_depth='auto', sklearn_max=100000000.0, random_state=None, range_max=None, range_min=None, bootstrap=True, n_split_points='auto', split_computation='raw', sync_after_fit=True, mmap=True, nested=False)

Bases: BaseRandomForest

A distributed random forest regressor.

Parameters

• n_estimators (int, optional (default=10)) – Number of trees to fit.

• try_features (int, str or None, optional (default=’sqrt’)) – The number of features to consider when looking for the best split:

  • If “sqrt”, then try_features=sqrt(n_features).

  • If “third”, then try_features=n_features // 3.

  • If None, then try_features=n_features.

  Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than try_features features.

• max_depth (int or np.inf, optional (default=np.inf)) – The maximum depth of the tree. If np.inf, then nodes are expanded until all leaves are pure.

• distr_depth (int or str, optional (default=’auto’)) – Number of levels of the tree in which the nodes are split in a distributed way.

• sklearn_max (int or float, optional (default=1e8)) – Maximum size (len(subsample)*n_features) of the arrays passed to sklearn’s DecisionTreeRegressor.fit(), which is called to fit subtrees (subsamples) of our DecisionTreeRegressor. sklearn fit() is used because it’s faster, but requires loading the data to memory, which can cause memory problems for large datasets. This parameter can be adjusted to fit the hardware capabilities.

• random_state (int, RandomState instance or None, optional (default=None)) – If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

• n_classes (int) – Number of classes that appear on the dataset. Only needed on distributed random forest.

• range_min (ds-array or np.array) – Contains the minimum values of the different attributes of the dataset Only used on distributed random forest (it is an optional parameter)

• range_max (ds-array or np.array) – Contains the maximum values of the different attributes of the dataset Only used on distributed random forest (it is an optional parameter)

• n_split_points (String or int) – Number of split points to evaluate. “auto”, “sqrt” or integer value. Used on distributed random forest (non memory map version)

• split_computation (String) – “raw”, “gaussian_approximation” or “uniform_approximation” distribution of the values followed by the split points selected. Used on distributed random forest (non memory map version)

• sync_after_fit (bool) – Synchronize or not after the training. Used on distributed random forest (non memory map version)

• mmap (bool) – Use the memory map version of the algorithm or not

• nested (bool) – Use the nested version of the algorithm or not

Variables

rf (RandomForestRegressor selected) – Instance of mmap, distributed or nested RandomForestRegressor selected.

load_model(filepath, load_format='json')

Loads a model from a file. The model is reinstantiated in the exact same state in which it was saved, without any of the code used for model definition or fitting. :Parameters: * filepath (str) – Path of the saved the model

• load_format (str, optional (default=’json’)) – Format used to load the model.

Examples

>>> from dislib.trees import RandomForestRegressor
>>> import numpy as np
>>> import dislib as ds
>>> x = np.array([[1, 2], [1, 4], [1, 0], [4, 2], [4, 4], [4, 0]])
>>> y = np.array([1.5, 1.2, 2.7, 2.1, 0.2, 0.6])
>>> x_train = ds.array(x, (2, 2))
>>> y_train = ds.array(y, (2, 1))
>>> model = RandomForestRegressor(n_estimators=2, random_state=0)
>>> model.fit(x_train, y_train)
>>> model.save_model('/tmp/model')
>>> loaded_model = RandomForestRegressor()
>>> loaded_model.load_model('/tmp/model')
>>> x_test = ds.array(np.array([[0, 0], [4, 4]]), (2, 2))
>>> model_pred = model.predict(x_test)
>>> loaded_model_pred = loaded_model.predict(x_test)
>>> assert np.allclose(model_pred.collect(),

predict(x)

Predicts target values using a fitted forest.

Parameters

x (ds-array, shape=(n_samples, n_features)) – The input samples.

Returns

y_pred – Predicted values for x.

Return type

ds-array, shape=(n_samples, 1)

save_model(filepath, overwrite=True, save_format='json')

Saves a model to a file. The model is synchronized before saving and can be reinstantiated in the exact same state, without any of the code used for model definition or fitting. :Parameters: * filepath (str) – Path where to save the model

• overwrite (bool, optional (default=True)) – Whether any existing model at the target location should be overwritten.

• save_format (str, optional (default=’json)) – Format used to save the models.

Examples

>>> from dislib.trees import RandomForestRegressor
>>> import numpy as np
>>> import dislib as ds
>>> x = np.array([[1, 2], [1, 4], [1, 0], [4, 2], [4, 4], [4, 0]])
>>> y = np.array([1.5, 1.2, 2.7, 2.1, 0.2, 0.6])
>>> x_train = ds.array(x, (2, 2))
>>> y_train = ds.array(y, (2, 1))
>>> model = RandomForestRegressor(n_estimators=2, random_state=0)
>>> model.fit(x_train, y_train)
>>> model.save_model('/tmp/model')
>>> loaded_model = RandomForestRegressor()
>>> loaded_model.load_model('/tmp/model')
>>> x_test = ds.array(np.array([[0, 0], [4, 4]]), (2, 2))
>>> model_pred = model.predict(x_test)
>>> loaded_model_pred = loaded_model.predict(x_test)
>>> assert np.allclose(model_pred.collect(),
>>> loaded_model_pred.collect())

score(x, y, collect=False)

R2 regression score.

Returns the coefficient of determination $R^2$ of the prediction. The coefficient $R^2$ is defined as $(1-u/v)$, 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 if the model is 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

• x (ds-array, shape=(n_samples, n_features)) – The training input samples.

• y (ds-array, shape (n_samples, 1)) – The true values.

• collect (bool, optional (default=False)) – When True, a synchronized result is returned.

Returns

score – Coefficient of determination $R^2$.

Return type

float (as future object)