Merge pull request #38 from ECRL/dev

Better MLP validation, moved multiprocessing checks

docs/conf.py

@@ -32,7 +32,7 @@
 # The short X.Y version
 version = ''
 # The full version, including alpha/beta/rc tags
-release = '3.2.0'
+release = '3.2.1'
 
 
 # -- General configuration ---------------------------------------------------

ecnet/__init__.py

@@ -1,2 +1,2 @@
 from ecnet.server import Server
-__version__ = '3.2.0'
+__version__ = '3.2.1'

ecnet/models/mlp.py

@@ -2,7 +2,7 @@
 # -*- coding: utf-8 -*-
 #
 # ecnet/models/mlp.py
-# v.3.2.0
+# v.3.2.1
 # Developed in 2019 by Travis Kessler <[email protected]>
 #
 # Contains the "MultilayerPerceptron" (feed-forward neural network) class
@@ -19,6 +19,7 @@
 stderr = sys.stderr
 sys.stderr = open(devnull, 'w')
 from keras.backend import clear_session, reset_uids
+from keras.callbacks import EarlyStopping
 from keras.layers import Dense
 from keras.losses import mean_squared_error
 from keras.metrics import mae
@@ -109,31 +110,28 @@ def fit(self, l_x: array, l_y: array, v_x: array=None, v_y: array=None,
         )
 
         if v_x is not None and v_y is not None:
-            valid_mae_lowest = self._model.evaluate(v_x, v_y, verbose=v)[1]
-            steps = int(epochs / 250)
-            for e in range(steps):
-                h = self._model.fit(
-                    l_x,
-                    l_y,
-                    validation_data=(v_x, v_y),
-                    epochs=250,
-                    verbose=v
-                )
-                valid_mae = h.history['val_mean_absolute_error'][-1]
-                if valid_mae < valid_mae_lowest:
-                    valid_mae_lowest = valid_mae
-                elif valid_mae > (valid_mae_lowest + 0.05 * valid_mae_lowest):
-                    logger.log('debug', 'Validation cutoff after {} epochs'
-                               .format(e * 250), call_loc='MLP')
-                    return
-
+            self._model.fit(
+                l_x,
+                l_y,
+                validation_data=(v_x, v_y),
+                callbacks=[EarlyStopping(
+                    monitor='val_loss',
+                    patience=250,
+                    verbose=v,
+                    mode='min',
+                    restore_best_weights=True
+                )],
+                epochs=epochs,
+                verbose=v
+            )
         else:
             self._model.fit(
                 l_x,
                 l_y,
                 epochs=epochs,
                 verbose=v
             )
+
         logger.log('debug', 'Training complete after {} epochs'.format(epochs),
                    call_loc='MLP')
 

ecnet/server.py

@@ -2,7 +2,7 @@
 # -*- coding: utf-8 -*-
 #
 # ecnet/server.py
-# v.3.2.0
+# v.3.2.1
 # Developed in 2019 by Travis Kessler <[email protected]>
 #
 # Contains the "Server" class, which handles ECNet project creation, neural
@@ -24,10 +24,6 @@
     resave_df, resave_model, save_config, save_df, save_project, train_model,\
     use_model, use_project
 
-# Stdlib imports
-from multiprocessing import set_start_method
-from os import name
-
 
 class Server:
 
@@ -52,9 +48,6 @@ def __init__(self, model_config: str='config.yml', prj_file: str=None,
 
         self._num_processes = num_processes
 
-        if name != 'nt':
-            set_start_method('spawn', force=True)
-
         if prj_file is not None:
             self._prj_name, self._num_pools, self._num_candidates, self._df,\
                 self._cf_file, self._vars = open_project(prj_file)

ecnet/tasks/limit_inputs.py

@@ -2,7 +2,7 @@
 # -*- coding: utf-8 -*-
 #
 # ecnet/tasks/limit_inputs.py
-# v.3.2.0
+# v.3.2.1
 # Developed in 2019 by Travis Kessler <[email protected]>
 #
 # Contains functions for selecting influential input parameters

ecnet/tasks/remove_outliers.py

@@ -2,7 +2,7 @@
 # -*- coding: utf-8 -*-
 #
 # ecnet/tasks/remove_outliers.py
-# v.3.2.0
+# v.3.2.1
 # Developed in 2019 by Travis Kessler <[email protected]>
 #
 # Contains function for removing outliers from ECNet DataFrame

ecnet/tasks/training.py

@@ -1,20 +1,17 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 #
-# ecnet/server.py
-# v.3.2.0
+# ecnet/tasks/training.py
+# v.3.2.1
 # Developed in 2019 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
-# calculation, input parameter selection, hyperparameter tuning.
-#
-# For example scripts, refer to https://ecnet.readthedocs.io/en/latest/
+# Contains function for project training (multiprocessed training)
 #
 
 # stdlib. imports
-from multiprocessing import Pool
+from multiprocessing import Pool, set_start_method
 from operator import itemgetter
+from os import name
 
 # ECNet imports
 from ecnet.utils.logging import logger
@@ -48,6 +45,9 @@ def train_project(prj_name: str, num_pools: int, num_candidates: int,
         num_processes (int): number of concurrent processes used to train
     '''
 
+    if name != 'nt':
+        set_start_method('spawn', force=True)
+
     logger.log('info', 'Training {}x{} models'.format(
         num_pools, num_candidates
     ), call_loc='TRAIN')

ecnet/tasks/tuning.py

@@ -2,12 +2,16 @@
 # -*- coding: utf-8 -*-
 #
 # ecnet/tasks/tuning.py
-# v.3.2.0
+# v.3.2.1
 # Developed in 2019 by Travis Kessler <[email protected]>
 #
 # Contains functions/fitness functions for tuning hyperparameters
 #
 
+# stdlib. imports
+from multiprocessing import set_start_method
+from os import name
+
 # 3rd party imports
 from ecabc.abc import ABC
 
@@ -42,6 +46,9 @@ def tune_hyperparameters(df: DataFrame, vars: dict, num_employers: int,
         dict: tuned hyperparameters
     '''
 
+    if name != 'nt':
+        set_start_method('spawn', force=True)
+
     logger.log('info', 'Tuning architecture/learning hyperparameters',
                call_loc='TUNE')
     logger.log('debug', 'Arguments:\n\t| num_employers:\t{}\n\t| '

ecnet/tools/database.py

@@ -2,7 +2,7 @@
 # -*- coding: utf-8 -*-
 #
 # ecnet/tools/database.py
-# v.3.2.0
+# v.3.2.1
 # Developed in 2019 by Travis Kessler <[email protected]>
 #
 # Contains functions for creating ECNet-formatted databases

ecnet/tools/plotting.py

@@ -2,7 +2,7 @@
 # -*- coding: utf-8 -*-
 #
 # ecnet/tools/plotting.py
-# v.3.2.0
+# v.3.2.1
 # Developed in 2019 by Travis Kessler <[email protected]>
 #
 # Contains functions/classes for creating various plots

ecnet/tools/project.py

@@ -2,7 +2,7 @@
 # -*- coding: utf-8 -*-
 #
 # ecnet/tools/project.py
-# v.3.2.0
+# v.3.2.1
 # Developed in 2019 by Travis Kessler <[email protected]>
 #
 # Contains functions for predicting data using pre-existing .prj files

ecnet/utils/data_utils.py

@@ -2,7 +2,7 @@
 # -*- coding: utf-8 -*-
 #
 # ecnet/utils/data_utils.py
-# v.3.2.0
+# v.3.2.1
 # Developed in 2019 by Travis Kessler <[email protected]>
 #
 # Contains functions/classes for loading data, saving data, saving results

ecnet/utils/error_utils.py

@@ -2,7 +2,7 @@
 # -*- coding: utf-8 -*-
 #
 # ecnet/utils/error_utils.py
-# v.3.2.0
+# v.3.2.1
 # Developed in 2019 by Travis Kessler <[email protected]>
 #
 # Contains functions for error calculations

ecnet/utils/logging.py

@@ -2,7 +2,7 @@
 # -*- coding: utf-8 -*-
 #
 # ecnet/utils/logging.py
-# v.3.2.0
+# v.3.2.1
 # Developed in 2019 by Travis Kessler <[email protected]>
 #
 # Contains logger used by ECNet

ecnet/utils/server_utils.py

@@ -2,7 +2,7 @@
 # -*- coding: utf-8 -*-
 #
 # ecnet/utils/server_utils.py
-# v.3.2.0
+# v.3.2.1
 # Developed in 2019 by Travis Kessler <[email protected]>
 #
 # Contains functions used by ecnet.Server

setup.py

@@ -2,7 +2,7 @@
 
 setup(
     name='ecnet',
-    version='3.2.0',
+    version='3.2.1',
     description='UMass Lowell Energy and Combustion Research Laboratory Neural'
                 ' Network Software',
     url='http://github.com/tjkessler/ecnet',