[scikit-learn] ANN Scikit-learn 0.18 released
Maciek Wójcikowski
maciek at wojcikowski.pl
Tue Oct 11 08:39:07 EDT 2016
Hi Piotr,
I've been there - most probably some package is blocking you to update via
numpy dependency. Try to update numpy first and the conflicting package
should pop up: "conda update numpy=1.11"
----
Pozdrawiam, | Best regards,
Maciek Wójcikowski
maciek at wojcikowski.pl
2016-10-11 14:32 GMT+02:00 Piotr Bialecki <piotr.bialecki at hotmail.de>:
> Congratulations to all contributors!
>
> I would like to update to the new version using conda, but apparently it
> is not available:
>
> ~$ conda update scikit-learn
> Fetching package metadata .......
> Solving package specifications: ..........
>
> # All requested packages already installed.
> # packages in environment at /home/pbialecki/anaconda2:
> #
> scikit-learn 0.17.1 np110py27_2
>
> Should I reinstall scikit?
>
>
> Best regards,
> Piotr
>
>
>
> On 03.10.2016 18:23, Raghav R V wrote:
>
> Hi Brown,
>
> Thanks for the email. There is a working PR here at
> <https://github.com/scikit-learn/scikit-learn/pull/7388>
> https://github.com/scikit-learn/scikit-learn/pull/7388
>
> Would you be kind to take a look at it and comment how helpful the
> proposed API is for your use case?
>
> Thanks
>
>
> On Mon, Oct 3, 2016 at 6:05 AM, Brown J.B. <jbbrown at kuhp.kyoto-u.ac.jp>
> wrote:
>
>> Hello community,
>>
>> Congratulations on the release of 0.19 !
>> While I'm merely a casual user and wish I could contribute more often, I
>> thank everyone for their time and efforts!
>>
>> 2016-10-01 1:58 GMT+09:00 Andreas Mueller < <t3kcit at gmail.com>
>> t3kcit at gmail.com>:
>>
>> We've got a lot in the works already for 0.19.
>>>>
>>>> * multiple metrics for cross validation (#7388 et al.)
>>>>
>>>
>> I've done something like this in my internal model building and selection
>> libraries.
>> My solution has been to have
>> -each metric object be able to explain a "distance from optimal"
>> -a metric collection object, which can be built by either explicit
>> instantiation or calculation using data
>> -a pareto curve calculation object
>> -a ranker for the points on the pareto curve, with the ability to
>> select the N-best points.
>>
>> While there are certainly smarter interfaces and implementations, here is
>> an example of one of my doctests that may help get this PR started.
>> My apologies that my old docstring argument notation doesn't match the
>> commonly used standards.
>>
>> Hope this helps,
>> J.B. Brown
>> Kyoto University
>>
>> 26 class TrialRanker(object):
>>
>> 27 """An object for handling the generic mechanism of selecting
>> optimal
>> 28 trials from a colletion of trials."""
>>
>> 43 def SelectBest(self, metricSets, paretoAlg,
>>
>> 44 preProcessor=None):
>>
>> 45 """Select the best [metricSets] by using
>> the
>> 46 [paretoAlg] pareto selection object. Note that it is
>> actually
>> 47 the [paretoAlg] that specifies how many optimal [metricSets]
>> to
>> 48 select.
>>
>> 49
>>
>> 50 Data may be pre-processed into a form necessary for the
>> [paretoAlg]
>> 51 by using the [preProcessor] that is a
>> MetricSetConverter.
>> 52
>>
>> 53 Return: an EvaluatedMetricSet if [paretoAlg] selects only
>> one
>> 54 metric set, otherwise a list of EvaluatedMetricSet
>> objects.
>> 55
>>
>> 56 >>> from pareto.paretoDecorators import
>> MinNormSelector
>> 57 >>> from pareto import OriginBasePareto
>>
>> 58 >>> pAlg = MinNormSelector(OriginBasePare
>> to())
>> 59
>>
>> 60 >>> from metrics.TwoClassMetrics import Accuracy,
>> Sensitivity
>> 61 >>> from metrics.metricSet import
>> EvaluatedMetricSet
>> 62 >>> met1 = EvaluatedMetricSet.BuildByExpl
>> icitValue(
>> 63 ... [(Accuracy, 0.7), (Sensitivity,
>> 0.9)])
>> 64 >>> met1.SetTitle("Example1")
>>
>> 65 >>> met1.associatedData = range(5) # property
>> set/get
>> 66 >>> met2 = EvaluatedMetricSet.BuildByExpl
>> icitValue(
>> 67 ... [(Accuracy, 0.8), (Sensitivity,
>> 0.6)])
>> 68 >>> met2.SetTitle("Example2")
>>
>> 69 >>> met2.SetAssociatedData("abcdef") # explicit method
>> call
>> 70 >>> met3 = EvaluatedMetricSet.BuildByExpl
>> icitValue(
>> 71 ... [(Accuracy, 0.5), (Sensitivity,
>> 0.5)])
>> 72 >>> met3.SetTitle("Example3")
>>
>> 73 >>> met3.associatedData = float
>>
>> 74
>>
>> 75 >>> from metrics.metricSet.converters import
>> OptDistConverter
>> 76
>>
>> 77 >>> ranker = TrialRanker() # pAlg selects
>> met1
>> 78 >>> best = ranker.SelectBest((met1,met2,m
>> et3),
>> 79 ... pAlg,
>> OptDistConverter())
>> 80 >>> best.VerboseDescription(True)
>>
>> 81 >>> str(best)
>>
>> 82 'Example1: 2 metrics; Accuracy=0.700;
>> Sensitivity=0.900'
>> 83 >>> best.associatedData
>>
>> 84 [0, 1, 2, 3, 4]
>>
>> 85
>>
>> 86 >>> pAlg = MinNormSelector(OriginBasePareto(),
>> nSelect=2)
>> 87 >>> best = ranker.SelectBest((met1,met2,m
>> et3),
>> 88 ... pAlg,
>> OptDistConverter())
>> 89 >>> for metSet in best:
>>
>> 90 ... metSet.VerboseDescription(True
>> )
>> 91 ... str(metSet)
>>
>> 92 ... str(metSet.associatedData)
>>
>> 93 'Example1: 2 metrics; Accuracy=0.700;
>> Sensitivity=0.900'
>> 94 '[0, 1, 2, 3, 4]'
>>
>> 95 'Example2: 2 metrics; Accuracy=0.800;
>> Sensitivity=0.600'
>> 96 'abcdef'
>>
>> 97
>>
>> 98 >>> from metrics.TwoClassMetrics import
>> PositivePredictiveValue
>> 99 >>> met4 = EvaluatedMetricSet.BuildByExpl
>> icitValue(
>> 100 ... [(Accuracy, 0.7), (PositivePredictiveValue,
>> 0.5)])
>> 101 >>> best = ranker.SelectBest((met1,met2,m
>> et3,met4),
>> 102 ... pAlg,
>> OptDistConverter())
>> 103 Traceback (most recent call last):
>>
>> 104 ...
>>
>> 105 ValueError: Metric sets contain differing
>> Metrics.
>>
>>
>>
>>
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>> scikit-learn at python.org
>> https://mail.python.org/mailman/listinfo/scikit-learn
>>
>>
>
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