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LabMate.AI was designed to help identifying optimized conditions for chemical reactions. The software is written in the Python 2.7 programming language and uses the following dependencies, which should be installed: - NumPy (>= 1.11.3) - Pandas (>= 0.19.2) - Scikit-learn (>= 0.18.1) ======================================================================================== random_reaction_gen.py This script creates two text files that are required to run LabMate.AI: 1) "all_combos.txt" - the file contains all possible combinations of reaction conditions. 2) "train_data.txt" - the file contains 10 random reaction sampled from all possible combinations. The script requires minor editing and instructions are available once the script is opened with a text editor. To run the script, open a terminal in your destiny folder and type: >>> python random_reaction_gen.py After performing the reactions, add a column in the end of the "train_data.txt" file mentioning the reaction yield/conversion or similar (sample file available) ======================================================================================== LabMate.AI.py This script implements a routine to search for the next best experiment to be carried out. To run LabMate.AI, open a terminal and type: >>> python LabMate-AI.py in the folder where the Python script, the "train_data.txt" and the "all_combos.txt" files are located. The columns in the txt files must be tab separated. The grid search routine will run in 6 CPUs (n_jobs=6 as in line 56), so make sure there are enough computational resources available. Train data txt file: - Fist column is the reaction identifier - Last column is the reaction yield/conversion - Columns in the middle correspond to the descriptor set - File must be named "train_data.txt", otherwise it will not be recognised by the script. Search space txt file: - Fist column is the reaction identifier - Following columns correspond to the descriptor set - File must be named "all_combos.txt", otherwise it will not be recognised by the script. Output files: - "best_score.txt" - saves the negative mean absolute error value (lower absolute value is better) - "feature_importances.txt" - importance (given in the range of 0-1) for each descriptor, according to the random forest algorithm - "selected_reaction.txt" - this is the next best experiment, as suggested by LabMate.AI - "predictions.txt" - predictions for all possible reactions - "random_forest_model_grid.sav" - saves the model