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 [email protected] 2016-10-11 14:32 GMT+02:00 Piotr Bialecki <[email protected]>: > 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. <[email protected]> > 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 < <[email protected]> >> [email protected]>: >> >> 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. >> >> >> >> >> _______________________________________________ >> scikit-learn mailing list >> [email protected] >> https://mail.python.org/mailman/listinfo/scikit-learn >> >> > > > _______________________________________________ > scikit-learn mailing > [email protected]https://mail.python.org/mailman/listinfo/scikit-learn > > > > _______________________________________________ > scikit-learn mailing list > [email protected] > https://mail.python.org/mailman/listinfo/scikit-learn > >
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