BatchRegressor

class capymoa.base.BatchRegressor

Bases: Regressor

Base class for batch trained regression algorithms.

>>> class MyBatchRegressor(BatchRegressor):
...
...     def batch_train(self, x, y):
...         with np.printoptions(precision=2):
...             print(x)
...             print(y)
...             print()
...
...     def predict(self, instance):
...         return 0.0
...
>>> from capymoa.datasets import FriedTiny
>>> print("downloading stream"); stream = FriedTiny()
downloading stream...
>>> learner = MyBatchRegressor(stream.schema, batch_size=2)
>>> for _ in range(4):
...     learner.train(stream.next_instance())
[[0.49 0.07 0.   0.83 0.76 0.6  0.13 0.89 0.07 0.34]
 [0.22 0.4  0.66 0.53 0.84 0.71 0.58 0.47 0.57 0.53]]
[17.95 13.81]

[[0.9  0.91 0.94 0.98 0.56 0.74 0.63 0.82 0.31 0.51]
 [0.79 0.86 0.36 0.84 0.16 0.95 0.11 0.29 0.41 0.99]]
[20.77 18.3 ]

__init__(

schema: Schema,

batch_size: int,

random_seed: int = 1,

) → None

Initialize the batch classifier.

Parameters:

• schema – A schema used to allocate memory for the batch.

• batch_size – The size of the batch.

• random_seed – The random seed for reproducibility.

train(instance: RegressionInstance) → None

Collate instances into a batch and call batch_train().

abstract batch_train(

x: ndarray[Any, dtype[number]],

y: ndarray[Any, dtype[integer]],

) → None

Train the classifier with a batch of instances.

Parameters:

• x – A real valued matrix of shape (batch_size, num_attributes) containing a batch of feature vectors.

• y – A real valued vector of shape (batch_size,) containing a batch of target values.

abstract predict(instance: RegressionInstance) → float64