[scikit-learn] ANN Scikit-learn 0.18 released

[Piotr Bialecki]

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<mailto: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 <<mailto:t3kcit at gmail.com>t3kcit at gmail.com<mailto: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(OriginBasePareto())
 59
 60         >>> from metrics.TwoClassMetrics import Accuracy, Sensitivity
 61         >>> from metrics.metricSet import EvaluatedMetricSet
 62         >>> met1 = EvaluatedMetricSet.BuildByExplicitValue(
 63         ...           [(Accuracy, 0.7), (Sensitivity, 0.9)])
 64         >>> met1.SetTitle("Example1")
 65         >>> met1.associatedData = range(5)  # property set/get
 66         >>> met2 = EvaluatedMetricSet.BuildByExplicitValue(
 67         ...           [(Accuracy, 0.8), (Sensitivity, 0.6)])
 68         >>> met2.SetTitle("Example2")
 69         >>> met2.SetAssociatedData("abcdef")  # explicit method call
 70         >>> met3 = EvaluatedMetricSet.BuildByExplicitValue(
 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,met3),
 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,met3),
 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.BuildByExplicitValue(
100         ...         [(Accuracy, 0.7), (PositivePredictiveValue, 0.5)])
101         >>> best = ranker.SelectBest((met1,met2,met3,met4),
102         ...                          pAlg, OptDistConverter())
103         Traceback (most recent call last):
104         ...
105         ValueError: Metric sets contain differing Metrics.




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