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@martin-sicho martin-sicho released this 21 Sep 07:23
· 1215 commits to main since this release
v2.1.0
124effb

Change Log

From v2.0.1 to v2.1.0.a2

Fixes

  • fixed error with serialization of the DataFrameDescriptorSet (#63)
  • Papyrus descriptors are not fetched by default anymore from the Papyrus adapter, which caused fetching of unnecessary data.
  • A potential bug in new version of pandas broke scaffold generation so a workaround was implemented.

Changes

  • QSPRModel.evaluate moved to a separate class EvaluationMethod in qsprpred.models.interfaces, with subclasses for cross-validation and making predictions on a test set in qsprpred.models.evaluation_methods (CrossValidation and EvaluateTestSetPerformance respectively).
  • QSPRModel attribute scoreFunc is removed.
  • 'qspr/models' is no longer added to the output path of QSPRModel.save, allowing for complete control over the output path.
  • SKlearnMetrics.supportsTask now uses a dictionary like dict[ModelTasks, list[str]] to map tasks to supported metric names. (#53)
  • GBMTRandomSplit and ScaffoldSplit now use the GBMTDataSplit to create balanced splits. RandomSplit still functions the same way as a completely random test split.
  • PCMSplit replaces StratifiedPerTarget and is compatible with RandomSplit, ScaffoldSplit and ClusterSplit.
  • DuplicatesFilter refactored toRepeatsFilter, as it also captures scenarios where triplicates/quadruplicates are found in the dataset. These scenarios are now also covered by the respective UnitTest.
  • The versioning scheme of development snapshots has changed from devX to alphaX/betaX, where X is an integer that increments with each release.
  • The following model class have been renamed and moved:
    • models.models.QSPRsklearn > models.sklearn.SklearnModel
    • deep.models.QSPRDNN > extra.gpu.models.dnn.DNNModel
    • extra.models.pcm.ModelPCM > extra.models.pcm.PCMModel
    • extra.models.pcm.QSPRsklearnPCM > extra.models.pcm.SklearnPCMModel
  • The command line interface modules now use input and output file paths instead
    of automatically placing all files in a subfolder qspr, allowing for more
    control over the output and input paths.

New Features

  • GBMTDataSplit - parent class to create globally balanced splits with the gbmt-split package.
  • ClusterSplit - splits data based clustering of molecular fingerprints (uses GBMTDataSplit).
  • Raise error if search space for optuna optimization is missing search space type annotation or if type not in list.
  • When installing package with pip, the commit hash and date of the installation is saved into qsprpred._version
  • HyperParameterOptimization classes now accept a evaluation_method argument, which is an instance of EvaluationMethod (see above). This allows for hyperparameter optimization to be performed on a test set, or on a cross-validation set. (#11)
  • HyperParameterOptimization now accepts score_aggregation argument, which is a function that takes a list of scores and returns a single score. This allows for the use of different aggregation functions, such as np.mean or np.median to combine scores from different folds. (#45)
  • A new tutorial adding_new_components.ipynb has been added to the tutorials folder, which demonstrates how to add new model to QSPRpred.
  • A new function Metrics.checkMetricCompatibility has been added, which checks if a metric is compatible with a given task and a given prediction methods (i.e. predict or predictProba)
  • In EvaluationMethod (see above), an attribute use_proba has been added, which determines whether the predict or predictProba method is used to make predictions (#56).
  • Add new descriptorset SmilesDesc to use the smiles strings as a descriptor.
  • New module early_stopping with classes EarlyStopping and EarlyStoppingMode has been added. This module allows for more control over early stopping in models that support it.
  • Add new descriptorset SmilesDesc to use the smiles strings as a descriptor.
  • Refactoring of the test suite under qsprpred.data and improvement of temporary file handling (!114).
  • PyBoostModel - QSPRpred wrapper for py-boost models. Requires optional pyboost dependencies.
  • ChempropModel - QSPRpred wrapper for Chemprop models. Requires optional deep dependencies.
  • The data_CLI argument --log_transform (-lt) has been changed to --transform_data (-t), which now accepts a number of transformations to apply to the target data. Available transformations are log, log10, log2, sqrt, cbrt, exp, exp2, exp10, square, cube, reciprocal.
  • New data_CLI, model_CLI and predict_CLI argument --skip_backup (-sb) to skip the backup of the output files. WARNING: This will overwrite existing files.

Removed Features

  • StratifiedPerTarget is replaced by PCMSplit.
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