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[FIX] Preprocess: pickle when saving #2289

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[FIX] Preprocess: pickle when saving #2289

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40 changes: 30 additions & 10 deletions Orange/preprocess/preprocess.py
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Original file line number Diff line number Diff line change
Expand Up @@ -34,12 +34,20 @@ def __call__(self, data):


class Continuize(Preprocess):
(Indicators, FirstAsBase, FrequentAsBase, Remove, RemoveMultinomial,
ReportError, AsOrdinal, AsNormalizedOrdinal, Leave) = Enum(
"Continuize",
"Indicators, FirstAsBase, FrequentAsBase,"
"Remove, RemoveMultinomial, ReportError, AsOrdinal,"
"AsNormalizedOrdinal, Leave")
class ContinuizeDV(Enum):
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@kernc kernc May 12, 2017 •
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Before, you'd get:

>>> from Orange.preprocess import Continuize
>>> preproc = Continuize(multinomial_treatment=Continuize.FrequentAsBase)
>>> preproc
Continuize(multinomial_treatment=Continuize.FrequentAsBase)

With this change, it's:

>>> preproc = Continuize(multinomial_treatment=Continuize.FrequentAsBase)
>>> preproc
Continuize(multinomial_treatment=2)

Is there some way to keep the customized Enum and still pickle numbers?

Or, as an alternative, sklearn uses expressive strings. I wouldn't mind that also.

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@kernc: I did some changes.

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Can name this MultinomialTreatment is it's an enumeration of such methods?

Indicators = "Indicators"
FirstAsBase = "FirstAsBase"
FrequentAsBase = "FrequentAsBase"
Remove = "Remove"
RemoveMultinomial = "RemoveMultinomial"
ReportError = "ReportError"
AsOrdinal = "AsOrdinal"
AsNormalizedOrdinal = "AsNormalizedOrdinal"
Leave = "Leave"
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@kernc kernc Jun 12, 2017 •
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I don't oppose strings at all. They are simple and robust. Sklearn is all strings in params, and nobody is complaining. But sklearn checks their user input for invalid values. Could we check the passed argument is valid? E.g.:

assert multinomial_treatment in self._MultinomialTreatment


Indicators, FirstAsBase, FrequentAsBase, Remove, RemoveMultinomial, ReportError,\
AsOrdinal, AsNormalizedOrdinal, Leave\
= ContinuizeDV

def __init__(self, zero_based=True,
multinomial_treatment=Indicators):
Expand Down Expand Up @@ -327,8 +335,12 @@ class Randomize(Preprocess):
>>> randomizer = Randomize(Randomize.RandomizeClasses)
>>> randomized_data = randomizer(data)
"""
Type = Enum("Randomize", dict(RandomizeClasses=1, RandomizeAttributes=2,
RandomizeMetas=4), type=int)
from enum import IntEnum
class Type(IntEnum):
RandomizeClasses = 1
RandomizeAttributes = 2
RandomizeMetas = 4

RandomizeClasses, RandomizeAttributes, RandomizeMetas = Type

def __init__(self, rand_type=RandomizeClasses, rand_seed=None):
Expand Down Expand Up @@ -399,8 +411,16 @@ class _MethodEnum(Enum):
def __call__(self, *args, **kwargs):
return getattr(Scale, '_' + self.name)(*args, **kwargs)

CenteringType = _MethodEnum('Scale', 'NoCentering, Mean, Median')
ScalingType = _MethodEnum('Scale', 'NoScaling, Std, Span')
class CenteringType(_MethodEnum):
NoCentering = "NoCentering"
Mean = "Mean"
Median = "Median"

class ScalingType(_MethodEnum):
NoScaling = "NoScaling"
Std = "Std"
Span = "Span"

NoCentering, Mean, Median = CenteringType
NoScaling, Std, Span = ScalingType

Expand Down