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QuaPy
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missing param in documentation of some protocols

This commit is contained in:
Alejandro Moreo Fernandez 2022-12-13 16:57:11 +01:00
parent c20d9d5ea4
commit bb7a77c7c0
1 changed files with 6 additions and 0 deletions
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6
quapy/protocol.py
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@ -134,6 +134,8 @@ class APP(AbstractStochasticSeededProtocol, OnLabelledCollectionProtocol):
:param repeats: number of copies for each valid prevalence vector (default is 10)
:param smooth_limits_epsilon: the quantity to add and subtract to the limits 0 and 1
:param random_state: allows replicating samples across runs (default None)
:param return_type: set to "sample_prev" (default) to get the pairs of (sample, prevalence) at each iteration, or
to "labelled_collection" to get instead instances of LabelledCollection
"""
def __init__(self, data:LabelledCollection, sample_size, n_prevalences=21, repeats=10, smooth_limits_epsilon=0, random_state=None, return_type='sample_prev'):
@ -192,6 +194,8 @@ class NPP(AbstractStochasticSeededProtocol, OnLabelledCollectionProtocol):
:param sample_size: integer, the number of instances in each sample
:param repeats: the number of samples to generate. Default is 100.
:param random_state: allows replicating samples across runs (default None)
:param return_type: set to "sample_prev" (default) to get the pairs of (sample, prevalence) at each iteration, or
to "labelled_collection" to get instead instances of LabelledCollection
"""
def __init__(self, data:LabelledCollection, sample_size, repeats=100, random_state=None, return_type='sample_prev'):
@ -229,6 +233,8 @@ class USimplexPP(AbstractStochasticSeededProtocol, OnLabelledCollectionProtocol)
:param sample_size: integer, the number of instances in each sample
:param repeats: the number of samples to generate. Default is 100.
:param random_state: allows replicating samples across runs (default None)
:param return_type: set to "sample_prev" (default) to get the pairs of (sample, prevalence) at each iteration, or
to "labelled_collection" to get instead instances of LabelledCollection
"""
def __init__(self, data: LabelledCollection, sample_size, repeats=100, random_state=None, return_type='sample_prev'):