Version bump: 3.2.3 -> 3.3.0

ecnet/models/mlp.py

@@ -2,25 +2,27 @@
 # -*- coding: utf-8 -*-
 #
 # ecnet/models/mlp.py
-# v.3.2.3
-# Developed in 2019 by Travis Kessler <[email protected]>
+# v.3.3.0
+# Developed in 2020 by Travis Kessler <[email protected]>
 #
 # Contains the "MultilayerPerceptron" (feed-forward neural network) class
 #
 
+# Stdlib imports
 from os import environ
 from re import compile, IGNORECASE
 
+# 3rd party imports
 from h5py import File
 from numpy import array, string_, zeros
-
 from tensorflow import config, Tensor
 from tensorflow.keras.callbacks import EarlyStopping
 from tensorflow.keras.layers import Dense
 from tensorflow.keras.losses import MeanSquaredError
 from tensorflow.keras.models import Model
 from tensorflow.keras.optimizers import Adam
 
+# ECNet imports
 from ecnet.utils.logging import logger
 
 environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

ecnet/server.py

@@ -2,8 +2,8 @@
 # -*- coding: utf-8 -*-
 #
 # ecnet/server.py
-# v.3.2.3
-# Developed in 2019 by Travis Kessler <[email protected]>
+# v.3.3.0
+# Developed in 2020 by Travis Kessler <[email protected]>
 #
 # Contains the "Server" class, which handles ECNet project creation, neural
 # network model creation, data hand-off to models, prediction error
@@ -26,8 +26,8 @@
 
 class Server:
 
-    def __init__(self, model_config: str='config.yml', prj_file: str=None,
-                 num_processes: int=1):
+    def __init__(self, model_config: str = 'config.yml', prj_file: str = None,
+                 num_processes: int = 1):
         '''Server object: handles data loading, model creation, data-to-model
         hand-off, data input parameter selection, hyperparameter tuning
 
@@ -69,8 +69,8 @@ def __init__(self, model_config: str='config.yml', prj_file: str=None,
             self._vars = default_config()
             save_config(self._vars, self._cf_file)
 
-    def load_data(self, filename: str, random: bool=False, split: list=None,
-                  normalize: bool=False):
+    def load_data(self, filename: str, random: bool = False,
+                  split: list = None, normalize: bool = False):
         '''Loads data from an ECNet-formatted CSV database
 
         Args:
@@ -90,8 +90,8 @@ def load_data(self, filename: str, random: bool=False, split: list=None,
         self._df.create_sets(random, split)
         self._sets = self._df.package_sets()
 
-    def create_project(self, project_name: str, num_pools: int=1,
-                       num_candidates: int=1):
+    def create_project(self, project_name: str, num_pools: int = 1,
+                       num_candidates: int = 1):
         '''Creates folder hierarchy for a new project
 
         Args:
@@ -111,8 +111,8 @@ def create_project(self, project_name: str, num_pools: int=1,
         logger.log('debug', 'Number of candidates/pool: {}'.format(
                    num_candidates), call_loc='PROJECT')
 
-    def limit_inputs(self, limit_num: int, num_estimators: int=None,
-                     eval_set: str='learn', output_filename: str=None,
+    def limit_inputs(self, limit_num: int, num_estimators: int = None,
+                     eval_set: str = 'learn', output_filename: str = None,
                      **kwargs) -> list:
         '''Selects `limit_num` influential input parameters using random
         forest regression
@@ -149,9 +149,9 @@ def limit_inputs(self, limit_num: int, num_estimators: int=None,
         return result
 
     def tune_hyperparameters(self, num_employers: int, num_iterations: int,
-                             shuffle: bool=None, split: list=None,
-                             validate: bool=True, eval_set: str=None,
-                             eval_fn: str='rmse', epochs: int=300):
+                             shuffle: bool = None, split: list = None,
+                             validate: bool = True, eval_set: str = None,
+                             eval_fn: str = 'rmse', epochs: int = 300):
         '''Tunes neural network learning hyperparameters using an artificial
         bee colony algorithm; tuned hyperparameters are saved to Server's
         model configuration file
@@ -185,10 +185,10 @@ def tune_hyperparameters(self, num_employers: int, num_iterations: int,
         )
         save_config(self._vars, self._cf_file)
 
-    def train(self, shuffle: str=None, split: list=None, retrain: bool=False,
-              validate: bool=False, selection_set: str=None,
-              selection_fn: str='rmse', model_filename: str='model.h5',
-              verbose=0) -> list:
+    def train(self, shuffle: str = None, split: list = None,
+              retrain: bool = False, validate: bool = False,
+              selection_set: str = None, selection_fn: str = 'rmse',
+              model_filename: str = 'model.h5', verbose: int = 0) -> tuple:
         '''Trains neural network(s) using currently-loaded data; single NN if
         no project is created, all candidates if created
 
@@ -210,8 +210,8 @@ def train(self, shuffle: str=None, split: list=None, retrain: bool=False,
                 model only)
 
         Returns:
-            list: if training single model, returns list of learn/valid losses,
-                else None
+            tuple: if training single model, returns tuple of learn/valid
+                losses, else None
         '''
 
         if self._prj_name is None:
@@ -246,8 +246,8 @@ def train(self, shuffle: str=None, split: list=None, retrain: bool=False,
             )
             return None
 
-    def use(self, dset: str=None, output_filename: str=None,
-            model_filename: str='model.h5') -> list:
+    def use(self, dset: str = None, output_filename: str = None,
+            model_filename: str = 'model.h5') -> list:
         '''Uses trained neural network(s) to predict for specified set; single
         NN if no project created, best pool candidates if created
 
@@ -277,8 +277,8 @@ def use(self, dset: str=None, output_filename: str=None,
                        call_loc='USE')
         return results
 
-    def errors(self, *args, dset: str=None,
-               model_filename: str='model.h5') -> dict:
+    def errors(self, *args, dset: str = None,
+               model_filename: str = 'model.h5') -> dict:
         '''Obtains various errors for specified set
 
         Args:
@@ -304,8 +304,8 @@ def errors(self, *args, dset: str=None,
         logger.log('debug', 'Errors: {}'.format(errors), call_loc='ERRORS')
         return errors
 
-    def save_project(self, filename: str=None, clean_up: bool=True,
-                     del_candidates: bool=False):
+    def save_project(self, filename: str = None, clean_up: bool = True,
+                     del_candidates: bool = False):
         '''Saves current state of project to a .prj file
 
         Args:

ecnet/tasks/limit_inputs.py

@@ -2,8 +2,8 @@
 # -*- coding: utf-8 -*-
 #
 # ecnet/tasks/limit_inputs.py
-# v.3.2.3
-# Developed in 2019 by Travis Kessler <[email protected]>
+# v.3.3.0
+# Developed in 2020 by Travis Kessler <[email protected]>
 #
 # Contains functions for selecting influential input parameters
 #
@@ -21,8 +21,8 @@
 from ecnet.utils.server_utils import get_x, get_y
 
 
-def limit_rforest(df: DataFrame, limit_num: int, num_estimators: int=None,
-                  num_processes: int=1, eval_set: str='learn',
+def limit_rforest(df: DataFrame, limit_num: int, num_estimators: int = None,
+                  num_processes: int = 1, eval_set: str = 'learn',
                   **kwargs) -> list:
     '''Uses random forest regression to select input parameters
 

ecnet/tasks/training.py

@@ -2,8 +2,8 @@
 # -*- coding: utf-8 -*-
 #
 # ecnet/tasks/training.py
-# v.3.2.3
-# Developed in 2019 by Travis Kessler <[email protected]>
+# v.3.3.0
+# Developed in 2020 by Travis Kessler <[email protected]>
 #
 # Contains function for project training (multiprocessed training)
 #

ecnet/tasks/tuning.py

@@ -2,8 +2,8 @@
 # -*- coding: utf-8 -*-
 #
 # ecnet/tasks/tuning.py
-# v.3.2.3
-# Developed in 2019 by Travis Kessler <[email protected]>
+# v.3.3.0
+# Developed in 2020 by Travis Kessler <[email protected]>
 #
 # Contains functions/fitness functions for tuning hyperparameters
 #
@@ -22,10 +22,10 @@
 
 
 def tune_hyperparameters(df: DataFrame, vars: dict, num_employers: int,
-                         num_iterations: int, num_processes: int=1,
-                         shuffle: str=None, split: list=None,
-                         validate: bool=True, eval_set: str=None,
-                         eval_fn: str='rmse', epochs: int=300) -> dict:
+                         num_iterations: int, num_processes: int = 1,
+                         shuffle: str = None, split: list = None,
+                         validate: bool = True, eval_set: str = None,
+                         eval_fn: str = 'rmse', epochs: int = 300) -> dict:
     '''Tunes neural network learning/architecture hyperparameters
 
     Args:

ecnet/tools/database.py

@@ -2,8 +2,8 @@
 # -*- coding: utf-8 -*-
 #
 # ecnet/tools/database.py
-# v.3.2.3
-# Developed in 2019 by Travis Kessler <[email protected]>
+# v.3.3.0
+# Developed in 2020 by Travis Kessler <[email protected]>
 #
 # Contains functions for creating ECNet-formatted databases
 #
@@ -20,7 +20,7 @@
 
 try:
     import pybel
-except:
+except ImportError:
     pybel = None
 
 
@@ -35,9 +35,9 @@ def __init__(self, id):
         self.inputs = None
 
 
-def create_db(smiles: list, db_name: str, targets: list=None,
-              id_prefix: str='', extra_strings: dict={}, backend: str='padel',
-              convert_mdl: bool=False):
+def create_db(smiles: list, db_name: str, targets: list = None,
+              id_prefix: str = '', extra_strings: dict = {},
+              backend: str = 'padel', convert_mdl: bool = False):
     ''' create_db: creates an ECNet-formatted database from SMILES strings
     using either PaDEL-Descriptor or alvaDesc software; using alvaDesc
     requires a valid installation/license of alvaDesc

ecnet/tools/plotting.py

@@ -2,8 +2,8 @@
 # -*- coding: utf-8 -*-
 #
 # ecnet/tools/plotting.py
-# v.3.2.3
-# Developed in 2019 by Travis Kessler <[email protected]>
+# v.3.3.0
+# Developed in 2020 by Travis Kessler <[email protected]>
 #
 # Contains functions/classes for creating various plots
 #
@@ -18,9 +18,10 @@
 
 class ParityPlot:
 
-    def __init__(self, title: str='Parity Plot',
-                 x_label: str='Experimental Value',
-                 y_label: str='Predicted Value', font: str='Times New Roman'):
+    def __init__(self, title: str = 'Parity Plot',
+                 x_label: str = 'Experimental Value',
+                 y_label: str = 'Predicted Value',
+                 font: str = 'Times New Roman'):
         ''' ParityPlot: creates a plot of predicted values vs. experimental
         data relative to a 1:1 parity line
 
@@ -39,7 +40,7 @@ def __init__(self, title: str='Parity Plot',
         self._min_val = 0
         self._labels = None
 
-    def add_series(self, x_vals, y_vals, name: str=None, color: str=None):
+    def add_series(self, x_vals, y_vals, name: str = None, color: str = None):
         ''' Adds data to the plot
 
         Args:
@@ -67,7 +68,7 @@ def add_series(self, x_vals, y_vals, name: str=None, color: str=None):
         if y_min < self._min_val:
             self._min_val = y_min
 
-    def add_error_bars(self, error: float, label: str=None):
+    def add_error_bars(self, error: float, label: str = None):
         ''' Adds error bars, +/- the error relative to the 1:1 parity line
 
         Args:
@@ -96,7 +97,7 @@ def save(self, filename: str):
         self._add_parity_line()
         plt.savefig(filename)
 
-    def _add_parity_line(self, offset: float=0.0):
+    def _add_parity_line(self, offset: float = 0.0):
         ''' Adds a 1:1 parity line
 
         Args:

ecnet/tools/project.py

@@ -2,8 +2,8 @@
 # -*- coding: utf-8 -*-
 #
 # ecnet/tools/project.py
-# v.3.2.3
-# Developed in 2019 by Travis Kessler <[email protected]>
+# v.3.3.0
+# Developed in 2020 by Travis Kessler <[email protected]>
 #
 # Contains functions for predicting data using pre-existing .prj files
 #
@@ -20,8 +20,8 @@
 from ecnet.tools.database import create_db
 
 
-def predict(smiles: list, prj_file: str, results_file: str=None,
-            backend: str='padel') -> list:
+def predict(smiles: list, prj_file: str, results_file: str = None,
+            backend: str = 'padel') -> list:
     ''' predict: predicts values for supplied molecules (SMILES strings) using
     pre-existing ECNet project (.prj) file
 

ecnet/utils/data_utils.py

@@ -2,8 +2,8 @@
 # -*- coding: utf-8 -*-
 #
 # ecnet/utils/data_utils.py
-# v.3.2.3
-# Developed in 2019 by Travis Kessler <[email protected]>
+# v.3.3.0
+# Developed in 2020 by Travis Kessler <[email protected]>
 #
 # Contains functions/classes for loading data, saving data, saving results
 #
@@ -33,8 +33,8 @@ def __init__(self):
 class PackagedData:
 
     def __init__(self):
-        '''PackagedData object: contains lists of input and target data for data
-        set assignments
+        '''PackagedData object: contains lists of input and target data for
+        data set assignments
         '''
 
         self.learn_x = []
@@ -112,7 +112,7 @@ def __len__(self):
 
         return len(self.data_points)
 
-    def create_sets(self, random: bool=False, split: list=[0.7, 0.2, 0.1]):
+    def create_sets(self, random: bool = False, split: list = [0.7, 0.2, 0.1]):
         '''Creates learning, validation and test sets
 
         Args:
@@ -167,9 +167,9 @@ def create_sets(self, random: bool=False, split: list=[0.7, 0.2, 0.1]):
         logger.log('debug', 'Number of entries in test set: {}'.format(
                    len(self.test_set)), call_loc='DF')
 
-    def create_sorted_sets(self, sort_str: str, split: list=[0.7, 0.2, 0.1]):
-        '''Creates random learn, validate and test sets, ensuring data points with
-        the supplied sort string are split proportionally between the sets
+    def create_sorted_sets(self, sort_str: str, split: list = [0.7, 0.2, 0.1]):
+        '''Creates random learn, validate and test sets, ensuring data points
+        with the supplied sort string are split proportionally between the sets
 
         Args:
             sort_str (str): database STRING value used to sort data points
@@ -239,7 +239,7 @@ def normalize(self):
                         (float(getattr(pt, inp)) - v_min) / (v_max - v_min)
                     )
 
-    def shuffle(self, sets: str='all', split: list=[0.7, 0.2, 0.1]):
+    def shuffle(self, sets: str = 'all', split: list = [0.7, 0.2, 0.1]):
         '''Shuffles learning, validation and test sets or learning and
         validation sets
 

ecnet/utils/error_utils.py

@@ -2,8 +2,8 @@
 # -*- coding: utf-8 -*-
 #
 # ecnet/utils/error_utils.py
-# v.3.2.3
-# Developed in 2019 by Travis Kessler <[email protected]>
+# v.3.3.0
+# Developed in 2020 by Travis Kessler <[email protected]>
 #
 # Contains functions for error calculations
 #

ecnet/utils/logging.py

@@ -2,8 +2,8 @@
 # -*- coding: utf-8 -*-
 #
 # ecnet/utils/logging.py
-# v.3.2.3
-# Developed in 2019 by Travis Kessler <[email protected]>
+# v.3.3.0
+# Developed in 2020 by Travis Kessler <[email protected]>
 #
 # Contains logger used by ECNet
 #