Merge pull request #17 from hachmannlab/published_architectures

Save and load chemml models, get keras model from chemml model, trans…

chemml/__init__.py

@@ -1,5 +1,5 @@
 # __name__ = "chemml"
-__version__ = "0.7.0"
+__version__ = "0.8"
 __author__ = ["Mojtaba Haghighatlari (mojtabah@buffalo.edu)", "Johannes Hachmann (hachmann@buffalo.edu)"]
 
 

chemml/initialization/initialization.py

@@ -521,4 +521,4 @@ def fit(self, X, main_directory='.'):
  else:
  self.file_path = '%s/%s.%s' %(main_directory,self.filename,self.format)
  with open(self.file_path, 'a') as file:
- file.write('%s\n' % str(X))
+ file.write('%s\n' % str(X))

chemml/models/__init__.py

@@ -5,13 +5,15 @@
 """
 
 from chemml.models.keras.mlp import MLP
-from chemml.models.keras.trained import OrganicLorentzLorenz
+
 from chemml.models.keras.graphconvlayers import NeuralGraphHidden, NeuralGraphOutput
 
+from chemml.models.keras.transfer import TransferLearning
+
 
 __all__ = [
- 'OrganicLorentzLorenz',
  'MLP',
  'NeuralGraphHidden',
- 'NeuralGraphOutput'
+ 'NeuralGraphOutput',
+ 'TransferLearning'
 ]

chemml/models/keras/__init__.py

@@ -5,9 +5,9 @@
 
 from .mlp import MLP
 from .graphconvlayers import NeuralGraphHidden, NeuralGraphOutput
-
+from .transfer import TransferLearning
 
 
 __all__ = [
- 'MLP', 'NeuralGraphHidden','NeuralGraphOutput'
+ 'MLP', 'NeuralGraphHidden','NeuralGraphOutput','TransferLearning'
  ]

chemml/models/keras/mlp.py

@@ -1,7 +1,7 @@
 import numpy as np
 import pandas as pd # required to load tensorflow properly
 
-from tensorflow.keras.models import Sequential
+from tensorflow.keras.models import Sequential, Model, load_model
 from tensorflow.keras.optimizers import SGD
 
 from json import load
@@ -44,8 +44,9 @@ class MLP(object):
  Path to the file that specifies layer configuration
  Refer MLP test to see a sample file
 
- opt_config_file: str, optional, default: None
- Path to the file that specifies optimizer configuration
+ opt_config: list, optional, default: None
+ optimizer configuration (for e.g., ["Adam",{"learning_rate":0.01}] or 
+ ["SGD",{"lr":0.01, "momentum":0.9, "lr_decay":0.0, nesterov=False)
  Refer MLP test to see a sample file
 
 
@@ -64,18 +65,21 @@ def __init__(self,
  regression=True,
  nclasses=None,
  layer_config_file=None,
- opt_config_file=None):
+ opt_config=None):
  self.model = Sequential()
  if layer_config_file:
  self.layers = self.parse_layer_config(layer_config_file)
+ self.nhidden = None
+ self.nneurons = None
+ self.activations = None
  else:
  self.layers = []
  self.nhidden = nhidden
  self.nneurons = nneurons if nneurons else [100] * nhidden
  self.activations = activations if activations else ['sigmoid'
  ] * nhidden
- if opt_config_file:
- self.opt = self.parse_opt_config(opt_config_file)
+ if opt_config:
+ self.opt = self.parse_opt_config(opt_config)
  else:
  self.opt = SGD(
  lr=learning_rate, momentum=0.9, decay=lr_decay, nesterov=False)
@@ -84,6 +88,89 @@ def __init__(self,
  self.loss = loss
  self.is_regression = regression
  self.nclasses = nclasses
+
+
+ def get_keras_model(self, include_output=True):
+ """
+ Returns the entire Keras model or the model without the output layer in 
+ its current state (fitted or compiled)
+ 
+ Parameters
+ __________
+ include_output: bool
+ if True it will return the entire model, if False it will return 
+ the model without the output layer
+ 
+ Returns
+ _______
+ self.model: tensorflow.python.keras type object
+ 
+ """
+ if not include_output:
+ head_model = Model(self.model.input, self.model.layers[-2].output)
+ return head_model
+ else:
+ return self.model
+
+ def save(self, path, filename):
+ """
+ Saves the chemml.models.MLP object along with the underlying 
+ tensorflow.python.keras object
+ 
+ Parameters
+ __________
+ path: str
+ the path to the directory where the models should be saved
+ filename: str
+ the name of the model file without the file type
+ 
+ """
+ obj_dict = vars(self)
+ self.model.save(path+'/'+filename+'.h5')
+ obj_dict['path_to_file'] = path +'/'+ filename+'.h5'
+ obj_df = pd.DataFrame.from_dict(obj_dict,orient='index')
+ obj_df.to_csv(path+'/'+filename+'_chemml_model.csv')
+
+ def load(self, path_to_model):
+ """
+ Loads the chemml.models.MLP object along with the underlying 
+ tensorflow.python.keras object
+ 
+ Parameters
+ __________
+ path_to_model: str
+ path to the chemml.models.MLP object
+ 
+ """
+ chemml_model = pd.read_csv(path_to_model,index_col=0)
+ self.model = load_model(chemml_model.loc['path_to_file'][0])
+ # optimizer config
+ opt = self.model.optimizer.get_config()
+ opt_list = [opt['name']]
+ del opt['name']
+ opt_list.append(opt)
+ self.opt = self.parse_opt_config(opt_list)
+
+ self.nepochs=int(chemml_model.loc['nepochs'][0])
+ self.batch_size=int(chemml_model.loc['batch_size'][0])
+ self.loss=chemml_model.loc['loss'][0]
+ self.is_regression=eval(chemml_model.loc['is_regression'][0])
+ self.nclasses=chemml_model.loc['nclasses'][0]
+
+ if str(self.nclasses).lower() == 'nan':
+ self.nclasses = None
+ else:
+ self.nclasses = int(self.nclasses)
+
+ self.feature_size = int(chemml_model.loc['feature_size'][0])
+
+ # layer config
+ self.layers = [(n['class_name'],n['config']) for n in self.model.get_config()['layers']]
+ self.nhidden = None
+ self.nneurons = None
+ self.activations = None
+
+ return self
 
  def fit(self, X, y):
  """
@@ -114,6 +201,8 @@ def fit(self, X, y):
  'units': self.nclasses,
  'activation': 'softmax'
  }))
+
+ self.feature_size = X.shape[-1]
  layer_name, layer_params = self.layers[0]
  layer_params['input_dim'] = X.shape[-1]
  keras_layer_module = import_module('tensorflow.keras.layers')
@@ -191,25 +280,27 @@ def parse_layer_config(self, layer_config_file):
  layers = load(f)
  return layers
 
- def parse_opt_config(self, opt_config_file):
+ def parse_opt_config(self, opt_config):
  """
  Internal method to parse a optimizer config file
 
  Parameters
  ----------
- opt_config_file: str
- Filepath that contains the optimizer configuration file - Refer MLP test to see a sample file
- Refer MLP test to see a sample file and https://keras.io/optimizers/
- for all possible types of optimizers and corresponding optimizer parameters
-
+ opt_config: list
+ optimizer configuration (for e.g., ["Adam",{"learning_rate":0.01}] 
+ or ["SGD",{"lr":0.01, "momentum":0.9, "lr_decay":0.0, nesterov=False)
+ refer https://keras.io/optimizers/ for all possible types of 
+ optimizers and corresponding optimizer parameters
+ 
  Returns
  -------
  opt: keras.optimizers
  keras optimizer created out of contents of optmizer configuration file
 
  """
- with open(opt_config_file, 'r') as f:
- opt_name, opt_params = load(f)
+ if isinstance(opt_config, list):
+ opt_name, opt_params = opt_config[0], opt_config[1]
+
  keras_opt_module = import_module('tensorflow.keras.optimizers')
  opt = getattr(keras_opt_module, opt_name)(**opt_params)
  return opt