Release 0.11.0

[yannikschaelte]

Diverse:

• Shorten date-time log (Shorten datetime log #456)
• Add look-ahead example notebook (Add look-ahead example notebook #461)
• Fix decoration of plot_acceptance_rates_trajectory (fix decoration of the 'plot_acceptance_rates_trajectory' funcction #465)
• Hot-fix redis clean-up (Hotfix redis clean-up #475)

Semi-automatic summary statistics and robust sample weighting (#429)

Breaking changes:

• API of the (Adaptive)PNormDistance was altered substantially to allow
  cutom definition of update indices.
• Internal weighting of samples (should not affect users).

Semi-automatic summary statistics:

• Implement (Adaptive)PNormDistance with the ability to learn summary
  statistics from simulations.
• Add sumstat submodule for generic mappings (id, trafos), and especially a
  PredictorSumstat summary statistic that can make use of Predictor objects.
• Add subsetting routines that allow restricting predictor model training
  samples.
• Add predictor submodule with generic Predictor class and concrete
  implementations including linear regression, Lasso, Gaussian Process,
  Neural Network.
• Add InfoWeightedPNormDistance that allows using predictor models to weight
  data not only by scale, but also by information content.

Outlier-robust adaptive distances:

• Update documentation towards robust distances.
• Add section in the corresponding notebook.
• Implement PCMAD outlier correction scheme.

Changes to internal sample weighting:

• Do not normalize weights of in-memory particles by model; this allows to
  more easily use the sampling weights and the list of particles for
  adaptive components (e.g. distance functions)
• Normalization of population to 1 is applied on sample level in the
  sampler wrapper function
• In the database, normalization is still by sample to not break old db
  support; would be nicer to also there only normalize by total sum
  -- requires a db update though.

Changes to internal object instruction from samples:

• Pass sample instead of weighted_sum_stats to distance function.
  This is because thus the distance can choose on its own what it wants
  -- all or only accepted particles; distances; weights; parameters;
  summary statistics.

Visualization:

• Function to plot adaptive distance weights from log file.

[codecov-commenter]

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