MSMBuilder 3.8.0

We're pleased to annoounce the release of MSMBuilder 3.8. This release features updates and improvements to contact featurizers, kernel tICA, HMMs, and preprocessing. There are also some bugfixes and API hygiene improvements. We recommend all users upgrade to MSMBuilder 3.8.

New Features

• ContactFeaturizer now lets you use a soft_min option for closest contact distances.

Improvements

• The stride parameter in KernelTICA now works as intended to automatically generate a set of landmark points (gh-972).
• The contacts parameter in CommonContactFeaturizer now performs as the contacts method in regular ContactFeaturizer albeit after validating all the contacts.
• GaussianHMM and VonMisesHMM are now compatible with sklearn.pipeline.Pipeline workflows (gh-980).
• msmbuilder.preprocessing is now compatible with sklearn.pipeline.Pipeline workflows (gh-987).
• Fixed error in pickling HMMs (gh-996).

MSMBuilder 3.7.0

We're pleased to announce the release of MSMBuilder 3.7. This release introduces several new featurizers that can handle multiple sequences or multiple chains within a topology file. There are also some bugfixes and API hygiene improvements. We recommend all users upgrade to MSMBuilder 3.7.

API Changes

• TrajFeatureUnion and SubsetFeatureUnion have been removed due to incompatibilities with the scikit-learn API.

New Features

• KSparseTICA lets you specify the number of non-zero entries, k rather than a regularization strength (gh-916).
• BootStrapMarkovStateModel optionally saves all the models that it generates (gh-919).
• tICA supports commute mapping (see 10.1021/acs.jctc.6b00762) (gh-925).
• CommonContactFeaturizer featurizes different trajectories with different topologies using a common set of inter-residue contacts (gh-876).
• msmbuilder.tpt.mfpt.mfpts can now compute distributions of MFPTs, accounting for the model error due to finite sampling.
• Three new featurization schemes for protein-ligand trajectories are now available: LigandContactFeaturizer, BinaryLigandContactFeaturizer``, andLigandRMSDFeaturizer` (gh-883).

Improvements

• Compatibility with scikit-learn 0.18 (gh-915).
• FeatureSelector feature order is deterministic (gh-920).
• SASAFeaturizer supports the describe_features method (gh-913).
• All LandmarkAgglomerative clusterers now have cluster_centers_ except when metric = rmsd (gh-958)

MSMBuilder 3.6.1

This is a bug-fix release.

• Compatibility with sklearn 0.18 (gh-915)
• Deterministic ordering in feature union (gh-920)

MSMBuilder 3.6

We're pleased to announce the release of MSMBuilder 3.6. This release
introduces project templating and a whole host of new sklearn estimators.
There are also some bugfixes and API hygiene improvements. We recommend all
users upgrade to MSMBuilder 3.6.

API Changes

• version.short_version is now 3.y instead of 3.y.z (gh-829).
• weighted_transform is no longer supported in tICA methods (gh-807). Please
  used kinetic_mapping.
• The cached filenames and formats for DoubleWell, QuadWell,
  and MullerPotential example datasets have changed. The API through
  msmbuilder.example_datasets is still the same, but the data may
  be re-generated instead of using a cached version from a previous installation
  of MSMBuilder (gh-854).
• The alias for Ward clustering has been removed. Modelers should now use
  LandmarkAgglomerative(linkage='ward') (gh-874). Ward clustering is also
  available in AgglomerativeClustering, but without a prediction algorithm.

New Features

• Butterworth, DoubleEWMA, StandardScaler, RobustScaler are
  available via the command line (gh-895).
• BinaryContactFeaturizer featurizes a trajectory into a
  boolean array corresponding to whether each residue-residue
  distance is below a cutoff (gh-798).
• LogisticContactFeaturizer produces a logistic transform
  of residue-residue distances about a center distance (gh-798).
• FactorAnalysis, FastICA, and KernelPCA are available in the
  decomposition module (gh-807).
• Butterworth, EWMA, and DoubleEWMA are available in the
  preprocessing module (gh-818).
• We encourage users to download the msmb_data conda package to easily
  install example data. The data can be loaded through existing methods
  in msmbuilder.example_datasets (gh-854, gh-867).
• An example dataset MinimalFsPeptide is available. This is a strided
  version of the existing FsPeptide dataset. We use it for testing,
  when a fully-converged dataset is not required (gh-867).
• Project templates! Read the new tutorial or the :ref:io page for
  details (gh-768).
• LandmarkAgglomerative clustering now features the ward linkage
  option. An algorithm for predicting cluster assignments with the
  ward objective function has been developed and implemented (gh-874).

Improvements

• Remove a unicode character from ktica.py (gh-833)
• msmbuilder.decomposition.KernelTICA now includes all parameters in its
  __init__, making it compatible with Osprey (gh-823).
• msmbuilder.tpt methods can now handle BayesianMarkovStateModels as
  input. Please note that we still do not recommend using this module with
  BootStrapMarkovStateModel.

MSMBuilder 3.5

We're pleased to announce the release of MSMBuilder 3.5. This release wraps more relevant sklearn estimators and transformers. There are also some bugfixes and API hygiene improvements. We recommend all users upgrade to MSMBuilder 3.5.

API Changes

• msmbuilder.featurizer.FeatureUnion is now deprecated. Please use msmbuilder.feature_selection.FeatureSelector instead (#799).
• msmbuilder.feature_extraction has been added to conform to the scikit-learn API. This is essentially an alias of msmbuilder.featurizer (#799).

New Features

• KernelTICA, Nystroem, and LandmarkNystroem are available in the decomposition module (#807).
• FeatureSelector and VarianceThreshold are available in the feature_selection module (#799)
• SparsePCA and MiniBatchSparsePCA are available in the decomposition module (#791).
• Binarizer, FunctionTransformer, Imputer, KernelCenterer, LabelBinarizer, MultiLabelBinarizer, MinMaxScaler, MaxAbsScaler, Normalizer, RobustScaler, StandardScaler,
  and PolynomialFeatures are available in the preprocessing module (#796).

Improvements

• Fix a compilation error on gcc 5 (#783)
• Fix pickle-ing of ContinuousTimeMSM. The optimizer_state_ parameter is not saved (#822).

MSMBuilder 3.4

We're pleased to announce MSMBuilder 3.4. It contains a plethora of new
features, bug fixes, and improvements.

API Changes

• Range-based slicing on dataset objects is no longer allowed. Keys in the
  dataset object don't have to be continuous. The empty slice, e.g. ds[:]
  loads all trajectories in a list (#610).
• Ward clustering has been renamed AgglomerativeClustering in scikit-learn.
  Please use the new msmbuilder wrapper class AgglomerativeClustering. An
  alias for Ward has been made available (#685).
• PCCA.trimmed_microstates_to_macrostates has been removed. This
  dictionary was actually keyed by untrimmed microstate labels.
  PCCA.transform would throw an exception when operating on a system
  with trimming because it was using this misleading dictionary. Please use
  pcca.microstate_mapping_ for this functionality (#709).
• UnionDataset has been removed after deprecation in 3.3. Please use
  FeatureUnion instead (#671).
• SubsetFeaturizer and ilk have been removed from the
  msmbuilder.featurizer namespace. Please import them from
  msmbuilder.featurizer.subset (#738).
• FirstSlicer has been removed. Use Slicer(first=x) for the same
  functionality (#738).
• msmbuilder.featurizer.load has been removed. Featurizer.save
  has been removed. Please use utils.load, utils.dump (#738).

New Features

• Dataset objects can call, fit_transform_with() to simplify the
  common pattern of applying an estimator to a dataset object to produce a
  new dataset object (#610).
• kinetic_mapping is a new option to tICA. It's similar to
  weighted_transform, but based on a better theoretical framework.
  weighted_transform is deprecated (#766).
• VonMisesFeaturizer uses soft bins around the unit-circle to give an
  alternate representation of dihedral angles (#744).
• MarkovStateModel has a partial_transform() method (#707).
• KappaAngleFeaturizer is available via the command line (#681).
• MarkovStateModel has a new attribute, percent_retained_, for
  ergodic trimming (#689).
• AlphaAngleFeaturizer computes the dihedral angles between alpha
  carbons (#691).
• FunctionFeaturizer computes features based on an arbitrary Python
  function or callable (#717).
• Automatic State Partitioning (APM) uses kinetic information to cluster
  conformations (#748).

Improvements

• Consistent counts setup and ergodic cutoff across various flavors of
  Markov models (#718, #729, #701, #705).
• Tests no longer depend on sklearn.hmm, which has been removed (#690).
• Improvements to RSMDFeaturizer (#695, #764).
• SparseTICA is completely re-written with large performance
  improvements when dealing with large numbers of features (#704).
• Links for downloading example data are un-broken after figshare
  changed URLs (#751).

MSMBuilder v3.3.1

This point release is for compatibility with scikit-learn version 0.17

• Ward clustering has been renamed AgglomerativeClustering in scikit-learn.
  Please use the new msmbuilder wrapper class AgglomerativeClustering. An
  alias for Ward has been made available.

MSMBuilder v3.3.0

We're pleased to announce the release of MSMBuilder v3.3.0. The focus of this
release is a completely re-written module for constructing HMMs as well as bug
fixes and incremental improvements.

API Changes

• FeatureUnion is an estimator that deprecates the functionality of
  UnionDataset. Passing a list of paths to dataset() will no longer
  automatically yield a UnionDataset. This behavior is still available by
  specifying fmt="dir-npy-union", but is deprecated (#611).
• The command line flag for featurizers --out (deprecated in 3.2) now saves
  the featurizer as a pickle file (#546). Please use --transformed for the
  old behavior. This is consistent with other command-line commands.
• The default number of timescales in MarkovStateModel is now one less than
  the number of states (was 10). This addresses some bugs with
  implied_timescales and PCCA(+) (#603).

New Features

• GaussianHMM and VonMisesHMM is rewritten to feature higher code reuse
  and code quality (#583, #582, #584, #572, #570).
• KDTree can find n nearest points to e.g. a cluster center (#599).
• Slicer featurizer can slice feature arrays as part of a pipeline
  (#567).

Improvements

• PCCAPlus is compatible with scipy 0.16 (#620).
• Documentation improvements (#618, #608, #604, #602)
• Test improvements, especially for Windows (#593, #590, #588, #579, #578,
  #577, #576)
• Bug fix: MarkovStateModel.sample() produced trajectories of incorrect
  length. This function is still deprecated (#556).
• Bug fix: The muller example dataset did not respect users' specifications for
  initial coordinates (#631).
• MarkovStateModel.draw_samples failed if discrete trajectories did not
  contain every possible state (#638). Function can now accept a single
  trajectory, as well as a list of them.
• SuperposeFeaturizer now respects the topology argument when loading the
  reference trajectory (#555).

MSMBuilder v3.2.0

We're pleased to announce the release of MSMBuilder v3.2.0. With this release:

• tICA ignores too-short trajectories during fitting instead of raising
  an exception
• New methods for sampling from MSM models have been added
• Datasets can be opened in "append" mode
• Compatibility with sklearn 0.16 was addressed
• utils.dump saves using the pickle protocol. utils.load is backwards
  compatible.
• The command line flag for featurizers --out is deprecated. Use
  --transformed instead. This is consistent with other command-line
  commands.
• Various bug fixes were addressed

MSMBuilder 3.1.0

v3.1 (Feb 27, 2015)

• Numerous improvements to ContinuousTimeMSM optimization
• Switch ContinuousTimeMSM.score to transmat-style GMRQ
• New example dataset with Muller potential
• Assorted bug fixes in the command line layer