Merge pull request #18 from hachmannlab/wrapper_aatish

Updated documentation, tutorials, regression metrics

README.md

@@ -22,10 +22,7 @@ Please check the [ChemML website](https://hachmannlab.github.io/chemml) for more
 ChemML is developed in the Python 3 programming language and makes use of a host of data analysis and ML libraries(accessible through the Anaconda distribution), as well as domain-specific libraries. 
 The development follows a strictly modular and object-oriented design to make the overall code as flexible and versatile as possible.
 
-The format of library is similar to the well known libraries like Scikit-learn. ChemML will be soon available 
-via graphical user interface provided by [ChemEco](https://github.com/hachmannlab/chemeco).
-ChemEco is a general-purpose framework for data mining without coding. It also interfaces with many of the libraries that supply methods for the 
-representation, preprocessing, analysis, mining, and modeling of large-scale chemical data sets.
+The format of library is similar to the well known libraries like Scikit-learn. 
 
 
 ## Latest Version:
@@ -44,12 +41,14 @@ Here is a list of external libraries that will be installed with chemml:
  - matplotlib
  - seaborn
  - lxml
+ - openpyxl
+ - ipywidgets
 
-Since conda installation is not available for ChemML yet, we recommend installing rdkit and openbabel (please install openbabel 2.x not openbabel 3.x) in a conda virtual environment prior to installing ChemML. For doing so, you need to follow the conda installer:
+We strongly recommend you to install ChemML in an Anaconda environment. The instructions to create the environment, install ChemML’s dependencies, and subsequently install Chemml using the Python Package Index (PyPI) via pip are as follows:
 
- conda create --name my_chemml_env python=3.6
- source activate my_chemml_env
- conda install -c conda-forge rdkit openbabel
+ conda create --name chemml_env python=3.8
+ source activate chemml_env
+ conda install -c conda-forge openbabel rdkit nb_conda_kernels python-graphviz
  pip install chemml
 
 ## Citation:
@@ -93,6 +92,13 @@ Please cite the use of ChemML as:
  year = {2018}
  }
 
+ @article{vishwakarma2019towards,
+ title={Towards autonomous machine learning in chemistry via evolutionary algorithms},
+ author={Vishwakarma, Gaurav and Haghighatlari, Mojtaba and Hachmann, Johannes},
+ journal={ChemRxiv preprint},
+ year={2019}
+ }
+
 ## License:
 ChemML is copyright (C) 2014-2018 Johannes Hachmann and Mojtaba Haghighatlari, all rights reserved.
 ChemML is distributed under 3-Clause BSD License (https://opensource.org/licenses/BSD-3-Clause).
@@ -102,17 +108,20 @@ ChemML is distributed under 3-Clause BSD License (https://opensource.org/license
 ### Maintainers:
  - Johannes Hachmann, hachmann@buffalo.edu
  - Mojtaba Haghighatlari
- - Aditya Sonpal
+ - Aditya Sonpal, adityaso@buffalo.edu
+ - Aatish Pradhan, aatishpr@buffalo.edu
  University at Buffalo - The State University of New York (UB)
 
 ### Contributors:
  - Doaa Altarawy (MolSSI): scientific advice and software mentor 
  - Gaurav Vishwakarma (UB): automated model optimization
  - Ramachandran Subramanian (UB): Magpie descriptor library port
  - Bhargava Urala Kota (UB): library database
+ - Aditya Sonpal (UB): graph convolution NNs
  - Srirangaraj Setlur (UB): scientific advice
  - Venugopal Govindaraju (UB): scientific advice
  - Krishna Rajan (UB): scientific advice
+ - Aatish Pradhan (UB): Jupyter GUI developer
 
  - We encourage any contributions and feedback. Feel free to fork and make pull-request to the "development" branch.
 

chemml/__init__.py

@@ -1,6 +1,6 @@
 # __name__ = "chemml"
-__version__ = "0.8"
-__author__ = ["Mojtaba Haghighatlari (mojtabah@buffalo.edu)", "Johannes Hachmann (hachmann@buffalo.edu)"]
+__version__ = "1.0"
+__author__ = ["Aditya Sonpal (adityaso@buffalo.edu)", "Garuav Vishwakarma (gvishwak@buffalo.edu) ", "Aatish Pradhan (aatishpr@buffalo.edu)","Mojtaba Haghighatlari (mojtabah@buffalo.edu)", "Johannes Hachmann (hachmann@buffalo.edu)"]
 
 
 # import sys

chemml/datasets/GA_files/error_metric.txt

@@ -0,0 +1,11 @@
+def error_metric(y_true,y_pred):
+ y_true = np.asarray(y_true)
+ y_pred = np.asarray(y_pred)
+ ndata = len(y_true)
+ y_mean = np.mean(y_true)
+ e = y_true - y_pred
+ ae = np.absolute(e)
+ se = np.square(e)
+ var = np.mean(np.square(y_true - y_mean))
+ MAE = np.mean(ae)
+ return MAE

chemml/datasets/GA_files/ga_eval.txt

@@ -0,0 +1,27 @@
+def ga_eval(indi):
+
+ layers = [indi[i] for i in range(2,5) if indi[i] != 0]
+ #print(np.exp(indi[0]))
+
+ #count iterations of GA
+ count=open("tmp.txt", "a")
+ count.write("GA search iteration in process... \n")
+ count.close()
+ file = open("tmp.txt","r")
+ Counter = 0
+ # Reading number of lines from file
+ Content = file.read()
+ CoList = Content.split("\n")
+ for i in CoList:
+ if i:
+ Counter += 1
+ print("GA search iteration in process... ",Counter)
+ mlp = MLPRegressor(alpha=np.exp(indi[0]), activation=indi[1], hidden_layer_sizes=tuple(layers),learning_rate='invscaling', max_iter=10,early_stopping=True)
+ ga_search = single_obj(mlp=mlp, x=X.values, y=Y.values,n_splits=n_splits)
+ #print("GA search iteration in process...")
+ f=open("GA.txt", "a")
+ f.write("%f %s %d %d %d %f \n" %(float(np.exp(indi[0])), str(indi[1]), int(indi[2]), int(indi[3]), int(indi[4]),float(ga_search)))
+ f.close()
+ #gui_return ={"ga_search": ga_search}
+ #print(gui_return)
+ return ga_search

chemml/datasets/GA_files/single_obj.txt

@@ -0,0 +1,12 @@
+def single_obj(mlp, x, y, n_splits=n_splits):
+ n_splits=n_splits
+ kf = KFold(n_splits) # cross validation based on Kfold (creates 5 validation train-test sets)
+ accuracy_kfold = []
+ for training, testing in kf.split(x):
+ mlp.fit(x[training], y[training])
+ y_pred = mlp.predict(x[testing])
+ y_pred, y_act =y_pred.reshape(-1,1), y[testing].reshape(-1,1)
+ model_accuracy=mae(y_act,y_pred) # evaluation metric: mae
+ accuracy_kfold.append(model_accuracy) # creates list of accuracies for each fold
+ #print("def single_obj - completed")
+ return np.mean(accuracy_kfold)

chemml/datasets/GA_files/space.txt

@@ -0,0 +1 @@
+space = ({'alpha': {'uniform': [np.log(0.0001), np.log(0.1)], 'mutation': [0, 1]}},{'activation': {'choice': ['identity', 'logistic', 'tanh', 'relu']}},{'neurons1': {'choice': range(0,220,20)}},{'neurons2': {'choice': range(0,220,20)}},{'neurons3': {'choice': range(0,220,20)}})

chemml/datasets/GA_files/test_hyperparameters.txt

@@ -0,0 +1,6 @@
+def test_hyp(mlp, x, y, xtest, ytest): 
+ mlp.fit(x, y)
+ ypred = mlp.predict(xtest)
+ acc=mae(ytest,ypred)
+ # print(" test_hyp completed ")
+ return np.mean(acc)

chemml/models/keras/mlp.py

@@ -130,6 +130,7 @@ def save(self, path, filename):
  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')
+ print("File saved as "+path+"/"+filename+"_chemml_model.csv")
 
  def load(self, path_to_model):
  """

chemml/utils/utilities.py

@@ -384,7 +384,7 @@ def regression_metrics(y_true, y_predicted, nfeatures = None):
  metrics_dict['AE'] = [list(ae)]
  metrics_dict['SE'] = [list(se)]
 
- var = np.mean(np.square(y_predicted - y_mean))
+ var = np.mean(np.square(y_true - y_mean))
 
  metrics_dict['ME'] = np.mean(e)
  # mean absolute error

chemml/wrapper/base.py

@@ -339,7 +339,7 @@ def references(self,host,function):
  # ref_p = "@misc{chollet2015keras,title={Keras},author={Chollet, Fran\c{c}ois and others},year={2015},publisher={GitHub},howpublished={\url{https://github.com/keras-team/keras}},}"
  ref_p = "ABCD"
  self.refs['keras'] = {'url': ref_g, 'paper': ref_p}
- elif function in ['GA_DEAP']:
+ elif function in ['GA']:
  ref_g = "https://github.com/deap/deap"
  ref_p = """@article{DEAP_JMLR2012,
  author = " F\'elix-Antoine Fortin and Fran\c{c}ois-Michel {De Rainville} and Marc-Andr\'e Gardner and Marc Parizeau and Christian Gagn\'e ",

chemml/wrapper/chemml_cml/chemml_wrapper.py

@@ -1868,3 +1868,229 @@ def fit(self):
  # step7: delete all inputs from memory
  del self.inputs
 
+class GA(BASE):
+ def fit(self):
+ self.paramFROMinput()
+ # txt_files = list(self.parameters.keys())[:4] #keys = evaluate, space, error_metric, objecive
+ # ga_eval = self.parameters[txt_files[0]]
+ # space = self.parameters[txt_files[1]]
+ # error_metric = self.parameters[txt_files[2]]
+ # single_obj = self.parameters[txt_files[3]]
+
+ # for key in self.parameters:
+ # print(key," : ", self.parameters[key])
+
+
+ try:
+ from chemml.optimization import GeneticAlgorithm
+ from sklearn.neural_network import MLPRegressor
+ from sklearn.model_selection import train_test_split
+ from datetime import date, datetime
+
+
+ import numpy as np
+
+ # for files in list(self.parameters.keys())[:4]: #keys = evaluate, space, error_metric, objecive
+ # with open(self.parameters[files],'r') as f:
+ # contents = f.read()
+ # # print("files: ", files, "contents: ", contents)
+ # files = compile(contents, "<string>", "exec")
+
+ for key in list(self.parameters.keys()):
+ if key == 'fitness':
+ final_fit=[]
+ fitness = str(self.parameters[key])[-6:-3]
+ fitness = fitness[0]+fitness[1]+fitness[2]
+ final_fit.append(fitness)
+ final_fit = tuple(final_fit)
+ # print("fitness: ",final_fit)
+ # print("type(fitness): ", type(final_fit))
+ elif key == 'pop_size':
+ pop_size = self.parameters[key]
+ elif key == 'crossover_size':
+ crossover_size = self.parameters[key]
+ elif key == 'mutation_size':
+ mutation_size = self.parameters[key]
+ elif key == 'n_splits':
+ global n_splits
+ n_splits = self.parameters[key]
+ elif key == 'crossover_type':
+ crossover_type = self.parameters[key]
+ elif key == 'mutation_prob':
+ mutation_prob = self.parameters[key]
+ elif key == 'initial_population':
+ initial_population = self.parameters[key]
+ elif key == 'n_generations':
+ n_generations = self.parameters[key]
+ elif key == 'early_stopping':
+ early_stopping = self.parameters[key]
+ elif key == 'init_ratio':
+ init_ratio = self.parameters[key]
+ elif key == 'crossover_ratio':
+ crossover_ratio = self.parameters[key]
+ elif key == 'algorithm':
+ global algorithm
+ algorithm = self.parameters[key]
+
+ #default in chemml.optimizaiton.geneticalgorithm
+ if 'early_stopping' not in list(self.parameters.keys())[4:]:
+ early_stopping = 10
+
+ with open(self.parameters['error_metric'],'r') as f:
+ contents = f.read()
+ # print("files: ", files, "contents: ", contents)
+ code = compile(contents, "<string>", "exec")
+ loc = {}
+ try:
+ exec(code,globals(), loc)
+ global mae
+ mae = loc['error_metric']
+ except:
+ print("Something wrong with the code...")
+ print("error_metric: ", mae)
+ print("type(error_metric): ",type(mae))
+
+ with open(self.parameters['space'],'r') as f:
+ contents = f.read()
+ code = compile(contents, "<string>", "exec")
+ loc = {}
+ try:
+ exec(code,globals(), loc)
+ space = loc['space']
+ except:
+ print("Something wrong with the code...")
+ print("Space: ", space)
+ print("type(space): ",type(space))
+
+
+ with open(self.parameters['single_obj'],'r') as f:
+ contents = f.read()
+ # print("files: ", files, "contents: ", contents)
+ code = compile(contents, "<string>", "exec")
+ loc = {}
+ try:
+ exec(code,globals(), loc)
+ global single_obj
+ single_obj = loc['single_obj']
+ except:
+ print("Something wrong with the code...")
+ print("single_obj: ", single_obj)
+ print("type(single_obj): ",type(single_obj))
+
+ # print("single_obj: ", single_obj)
+ # print("type(single_obj): ",type(single_obj))
+
+ with open(self.parameters['evaluate'],'r') as f:
+ contents = f.read()
+ # print("files: ", files, "contents: ", contents)
+ code = compile(contents, "<string>", "exec")
+ loc = {}
+ try:
+ exec(code,globals(), loc)
+ ga_eval = loc['ga_eval']
+ except:
+ print("Something wrong with the code...")
+ print("ga_eval: ", ga_eval)
+ print("type(ga_eval): ",type(ga_eval))
+ # print("ga_eval: ", ga_eval)
+ # print("type(ga_eval): ",type(ga_eval))
+
+ with open(self.parameters['test_hyperparameters'],'r') as f:
+ contents = f.read()
+ # print("files: ", files, "contents: ", contents)
+ code = compile(contents, "<string>", "exec")
+ loc = {}
+ try:
+ exec(code,globals(), loc)
+ test_hyp = loc['test_hyp']
+ except:
+ print("Something wrong with the code...")
+ print("test_hyperparameters: ", test_hyperparameters)
+ print("type(test_hyperparameters): ",type(test_hyperparameters))
+
+
+ ##### GA happening here#########
+ def ga_mlpregressor(x_train, y_train, x_test, y_test, al=algorithm,n_splits=n_splits,n_generations=n_generations,early_stopping=early_stopping): 
+ global X 
+ global Y
+ X=x_train
+ Y=y_train 
+ print("Hyperparameter optimization is a time consuming process - do not shutdown Kernel....\n")
+ print('Total GA search iterations = ', n_generations*pop_size)
+ gann = GeneticAlgorithm(evaluate=ga_eval, space=space, fitness=final_fit, pop_size = pop_size, crossover_size=crossover_size, mutation_size=mutation_size, algorithm=al)
+ global MLPRegressor
+ from sklearn.neural_network import MLPRegressor
+ global KFold
+ from sklearn.model_selection import KFold
+ import warnings
+ warnings.filterwarnings("ignore") 
+ best_ind_df, best_individual = gann.search(n_generations=n_generations, early_stopping=early_stopping) # set pop_size<30, n_generations*pop_size = no. of times GA runs 
+ print("GeneticAlgorithm - complete!")
+
+ all_items = list(gann.fitness_dict.items())
+ all_items_df = pd.DataFrame(all_items, columns=['hyperparameters', 'Accuracy_score'])
+ print("\n\ngenetic algorithm results for each generation: \n", best_ind_df, "\n\nbest particle: ", best_individual, "\n")
+ print("Calculating accuracy on test data....")
+ l = [best_individual['neurons1'], best_individual['neurons2'], best_individual['neurons3']]
+ layers = [i for i in l if i != 0]
+ ga_mlp = MLPRegressor(alpha=np.exp(best_individual['alpha']), activation=best_individual['activation'], hidden_layer_sizes=tuple(layers), learning_rate='invscaling', max_iter=20, early_stopping=True)
+ ga_accuracy_test = test_hyp(mlp=ga_mlp, x=X, y=Y, xtest=x_test, ytest=y_test)
+ print("\n\nTest set error_metric (default = MAE) for the best GA hyperparameter: ", ga_accuracy_test, "\n")
+ return all_items_df , best_ind_df
+
+
+ #Read data here
+ self.required('dfx_train', req=True)
+ dfx_train= self.inputs['dfx_train'].value
+ self.required('dfy_train', req=True)
+ dfy_train= self.inputs['dfy_train'].value
+ self.required('dfx_test', req=True)
+ dfx_test= self.inputs['dfx_test'].value
+ self.required('dfy_test', req=True)
+ dfy_test= self.inputs['dfy_test'].value
+
+
+ # dfx_train = self.inputs['dfx_train'].value
+ # dfy_train = self.inputs['dfy_train'].value
+ # dfx_test = self.inputs['dfx_test'].value
+ # dfy_test = self.inputs['dfy_test'].value
+
+ # type of ML model defined here
+ for key in list(self.parameters.keys()):
+ if key == 'ml_model':
+ ml_model = self.parameters[key]
+ if ml_model == 'MLPRegressor':
+
+ best_ind_df, best_individual = ga_mlpregressor(x_train=dfx_train, y_train=dfy_train, x_test=dfx_test,y_test=dfy_test, al = algorithm, n_splits=n_splits, n_generations=n_generations, early_stopping=early_stopping)
+ # print(all_items_df)
+
+
+ os.remove("tmp.txt") #remove tmp file to count umber of GA iterations
+ os.remove("GA.txt") #remove file with all GA iterations
+
+ # now = datetime.now() #to save with current date and time
+ # dt_string = now.strftime("%m-%d-%Y %H-%M-%S")
+ # all_items_df.to_csv('best_ind_df' + str(dt_string) + '.csv')
+ # best_ind_df.to_csv('best_individual' + str(dt_string) + '.csv')
+ # print("GA DONEE!!!")
+
+ except Exception as err:
+ msg = '@Task #%i(%s): '%(self.iblock+1, self.Task) + type(err).__name__ + ': '+ str(err)
+ raise TypeError(msg)
+
+ order = [edge[1] for edge in self.Base.graph if edge[0] == self.iblock]
+ for token in set(order):
+ if token not in self.outputs:
+ msg = "@Task #%i(%s): not a valid output token '%s'" % (self.iblock + 1, self.Task, token)
+ raise NameError(msg)
+ elif token == 'best_ind_df':
+ self.set_value(token, best_ind_df)
+ self.outputs[token].count = order.count(token)
+ self.Base.send[(self.iblock, token)] = self.outputs[token]
+ elif token == 'best_individual':
+ self.set_value(token, best_individual)
+ self.outputs[token].count = order.count(token)
+ self.Base.send[(self.iblock, token)] = self.outputs[token]
+
+ # step7: delete all inputs from memory
+ del self.inputs