Changelog¶

API Changes¶

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).

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, and LigandRMSDFeaturizer (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)

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 (#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 I/O 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.

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).

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).

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).