Pure-ldp

Fixed and updated package dependencies on pip

Fixed a pip installation issue

Added new frequency oracles:
* Implemented Google's RAPPOR under pure_ldp.frequency_oracles.rappor
* Hadamard Mechanism (HM) under pure_ldp.frequency_oracles.hadamard_mechanism
* Direct Encoding (DE) under pure_ldp.frequency_oracles.direct_encoding
* A heuristic version of OLH called Fast Local Hashing (FLH) under pure_ldp.frequency_oracles.local_hashing``
* Private Count Sketch (PCS) an older version of private sketching under pure_ldp.frequency_oracles.priv_count_sketch
* SketchRespones (SR) a sketching oracle that uses any generic frequency oracle to perturb sketch rows under pure_ldp.frequency_oracles.sketch_response
* Zero oracle that just predicts 0 no matter what is aggregated (under pure_ldp.frequency_oracles.zero_oracle) - Mainly used for testing/experimenting

Added new heavy hitter implementations:
* Apple's Sequence Fragment Puzzle (SFP) under pure_ldp.heavy_hitters.apple_sfp
* Bassily et als TreeHistogram under pure_ldp.heavy_hitters.treehistogram

Other additions:
* Added pre-processing techniques to estimating over the whole domain for any frequency oracle (see the estimate_all of pure_ldp.core._freq_oracle_server )
* Added helpers for frequency oracles and heavy hitters for simulations (under pure_ldp.simulations.helpers)
* Added a long list of experiments and examples (under pure_ldp.simulations.paper_experiments.py)

Bug Fixes and Performance Improvements:
* Fixed wrong perturbation probability being used in Apple's HCMS
* Fixed a whole range of bugs where various parameters of frequency oracles wouldn't update properly
* Performance improvements for local_hashing and unary_encoding
* Fixed bugs in PEM

Bug fixes:
* Hadamard Response now works in low privacy mode (epsilon < 1)
* Hadamard response should properly work via PyPI

New:
* Hadamard Response server has an additional normalisation parameter -
* If normalization = 0 then no normalisation is applied (default)
* If normalization = 1, the estimates are clipped to 0 and normalised
* if normalization=2 then the estimates are projected onto the probability simplex

Changes:
* Added Apple's CMS algorithm under pure_ldp_frequency_oracles.apple_cms
* Added helper methods to create instances of FOs/HHs in pure_ldp.core
* PEMServer aggregate method now takes a single tuple as an argument (i.e the output of PEMClient.privatise)
* HadamardResponseClient requires hash_funcs generated from HadamardResponseServer

Fixes:
* Updated some docstrings
* Fixed bug with HadamardResponse not permuting data properly
* Fixed typos in the README
* Fixed bugs in examples.py

Breaking changes:
* Package structure has changed, all frequency oracles are now under pure_ldp.frequency_oracles i.e for local hashing it is now from pure_ldp.frequency_oracles.local_hashing import LHClient, LHServer
* Heavy hitters are also in their own package (just PEM at the moment)

Bugfixes:
* Fixed examples in example.py
* Added docstrings to PEM package
* Fixed raising errors in base class methods