Bug fixes, enhancements

• ecnet.Server.remove_outliers and ecnet.tasks.remove_outliers have been removed
  • while detecting outliers may be beneficial in determining abnormalities in data, removing them entirely is likely not the right approach (in terms of fuel property prediction). Once a viable usage has been determined, outlier detection will be included.
• Added the batch_size hyper-parameter, included in the default model configuration and hyper-parameter tuning process
  • Relevant unit tests updated
• Any missing model configuration variables from config files generated with previous versions of ECNet will now be set to their default values
  • Additional unit tests added
• Added option to convert SMILES to MDL during PaDEL-based database creation
  • Additional unit test added
• Added PaDEL-generated databases for all properties
• ecnet.tasks.limit_inputs.limit_rforest now relies on sklearn.ensemble.RandomForestRegressor as its only dependency
  • limit_rforest now returns list of parameter names/importances instead of a modified DataFrame
  • Server.limit_inputs also returns a list of parameter names/importances
  • Removed the ditto-lib dependency
• Bug fixes:
  • Server._sets now loads when a PRJ file is opened via ecnet.Server
  • ecnet.utils.data_utils.DataFrame.set_inputs now immediately applies selected inputs to L/V/T sets
  • ParityPlot parity lines now scale to reflect data minimum/maximum
• More robust unit tests for MultilayerPerceptron, database creation, input parameter limiting
• All unit tests may now be run individually

Better MLP validation, moved multiprocessing checks

• Training an MLP using a validation set now uses Keras' early stopping callback to determine learning cutoff, preserves weights at best validation loss
• Moved multiprocessing.set_start_method to multiprocessed tasks

Removal of conversion functions, slight Server rework

1.) The following conversions have been removed from ECNet:

• get_smiles
• smiles_to_descriptors
• smiles_to_mdl
• mdl_to_descriptors

*Note: these were adding clutter, and were not within the main scope of ECNet.

2.) PaDEL-Descriptor is no longer bundled into ECNet

*Note: with the removal of conversion functions, this is no longer needed.

3.) Database creation functions now rely on two separate packages:

• PaDELPy (https://github.com/ECRL/PaDELPy) - QSPR descriptor generation using PaDEL-Descriptor
• alvaDescPy (https://github.com/ECRL/alvaDescPy) - QSPR descriptor generation using alvaDesc

*Note: it made sense to create separate packages for interfacing with these software, a Python interface for generating QSPR descriptors is generally quite handy.

4.) ecnet.tools.database.create_db's arguments have been changed:

>>> ecnet.tools.database.create_db(['CC', 'CCC'], 'my_database.csv', targets=[13, 47])

Construct using alvaDesc:

>>> ecnet.tools.database.create_db(['CC', 'CCC'], 'my_database.csv', targets=[13, 47], backend='alvadesc')

*Note: supplying SMILES strings and targets using lists makes more sense than requiring the user to create a separate file - this change allows the user to choose where the data comes from.

5.) ecnet.tools.project.predict's arguments have been changed:

>>> results = ecnet.tools.project.predict(['CC', 'CCC'], 'my_project.prj')
>>> print(results)
[[13], [47]]

*Note: similar to why we switched to lists as inputs in database creation, makes more sense

6.) ecnet.Server has been rearranged a bit:

• project training has been moved to a separate function at ecnet.tasks.training.train_project
• various functions have been moved to ecnet.utils.server_utils:
  • creating a project folder structure
  • saving a project as a .prj file
  • opening a .prj file to use
• task-specific logging messages have been moved to their respective functions in ecnet.tasks

*Note: ecnet.Server needed to be shrunk down, and functions that were obviously utilities were moved into utility files. This should also provide more direct access to the "back-end" of ECNet (subverting Server usage), allowing greater variation in experimental procedure.

7.) Added a suite of unit tests implemented with the unittest library:

• in addition to Server unit tests, individual utilities of ECNet are tested
• added a Python script, /tests/test_all.py, to automatically run all unit tests and report a summary of successes/failures

*Note: it's time for "proper" unit testing, and that means implementing a unit testing package. I'm looking forward to expanding ECNet's tests and introduce more automation into the testing process.

8.) Installation now forces TensorFlow 1.13.1 to be installed

*Note: I've encountered pip install tensorflow installing the 2.0.0 beta, which ECNet does not currently support - we'll make the change when we're ready (and so is Keras)

9.) Changed/added a variety of databases to the /databases/ directory

• All databases constructed using alvaDesc
• All SMILES strings have been validated with respect to compound name
  • PubChemPy (https://github.com/mcs07/PubChemPy) is a lifesaver
  • Compounds not found on PubChem were validated in-house by an ECRL research assistant

*Note: in order to ensure accurate QSPR-descriptor to experimental value correlation, accurate SMILES strings are necessary (assuming descriptors are being generated using them).

Type checking, improved unit testing

• All methods/functions now enforce specific types for arguments, return values
• calc_r2 function now uses scikit-learn's r2_score function
• Changed unit testing scheme, now uses unittest library
  • added a suite of unit tests

Addition to conversion tools, update to database creation function

• Addition of the "smiles_to_descriptors" function
• Database creation functions now use the "smiles_to_descriptors" function, bypassing the use of OpenBabel (used for SMILES -> MDL -> descriptors)
• Updated relevant documentation

Updates to DataFrame, DataPoint classes and their functionality

• STRING and GROUP attributes for DataPoints (rows in an ECNet-formatted database) can now be accessed as object attributes. For example:

>>> from ecnet.utils.data_utils import DataFrame
>>> df = DataFrame('my_database.csv')
>>> first_entry = df.data_points[0]
>>> print(first_entry.SMILES)  # SMILES is a STRING column in the supplied database
C
>>> print(getattr(first_entry, 'Compound Name')  # STRINGs with spaces are obtained like this
Methane

• Additional STRING columns can be supplied when creating an ECNet-formatted database
• Fixed issue where YAML package was throwing a loader warning
• Suppressed TensorFlow warnings about deprecation
• Updates to documentation
• Other minor changes

Bug fixes, database creation improvements

• Added "set_spawn_method", fixes multiprocessing on Unix systems
• Databases can now be constructed with fingerprints instead of descriptors
• "get_smiles" function now returns an empty string if the molecule is not found on PubChem
• Slight updates to logging
• Hyperparameter tuning bug fix

3.0.0 Release

• Server object refactor
  • Includes API changes
• Update to ML back end (raw TensorFlow -> Keras)
• Logging moved to separate module
• Input descriptor limiting now uses random forest regression, via ditto-lib 1.0.0
• Implemented ReadTheDocs page
• Added classes for parity plot generation
• Updated hyperparameter tuning for ECabc 2.2.2 release
• Implemented methods for removing outliers, via ditto-lib 1.0.0

Bug fixes, GA improvements, data sorting options, optimizations

• Updated parameter limiting with GA, per 0.6.0 PyGenetics update
• Fixed bug with MultilayerPerceptron returning "NaN" values
• Changed default parameter bounds for ABC tuning
• Added "sort_string" argument for Server.import_data
• Added "Getting Started" tutorial for new users

Tool integrations, database additions

• Integrated various tools:
  • Database creation tool (wrappers for Open Babel, PaDEL-Descriptor)
  • Using project tool (supply text w/ molecules, ECNet .prj file)
  • Get SMILES from molecule name (PubChemPy)
  • Convert SMILES to MDL/SDF
  • Convert MDL/SDF to QSPR descriptors
• Added unit tests for database creation tool, using project tool
• Removed command line tools (integrated, above)
• Added various databases:
  • Cloud point
  • Pour point
  • Yield sooting index