This project is a research expriment on metabolites pathway classification. Given a labled data of metabolites pathway and a metabolic profile, we classify postivie and negative pathways by using graph embedding.
The project used the following versions for the packages. other versions needs to be test but should work fine pandas 0.023 numpy 1.15.4 networkx 2.3 scikit-learn 0.21.3 gensim 3.80
Run the run.py file with the config.ini file as an argument to the program. Change the needed parameters in the config.ini file (listed below).
run results will be saved in data/classifier_results
num_of_runs = num of times to run the classifier
input_file_path - metabolic profile as in xlsx format where metabolites are the rows and proteins are the columns.
output_file_path - post process path of metabolic profile
input_file_path - input path of the correlation matrix
output_path_folder - output path of the correlation matrix
matrix_output_path - output path of the matrix to be used
matrix_output_name - name of the matrix to be used.
corr_matrix_path- correlation matrix input path
labels_dir_path- subgraph labels path (to determine if they are positive or negative)
threshold - the treshold from which we add an edge
sub_graphs_output_directory - output directory of subgraph in case you want to save them as .gml files
main_graph_output_directory - output path of main_graph to be saved as .gml
adj_matrix_extensions - dictionary of extensions to be used in the graph. options : power_graph , adj_matrix_power , adj_matrix_and_add. they all take parameter p which is the power of the graph used.
sub2vecMethod - determine the sub2vec mode going to be used. structural or neighberhood
random_walk_length = the number of steps in the random walks
random_walk_number - the number of times we do random walk from each node
random_walk_directory_path_output - random walks output as .gml file
classifier_files_directory - path to save the location of the classifier files
statistic_output - path to save the statistics (if turned on in the code) in order to debug the classifier results
doc2vec_args- pass arguments to doc2vec as a dictionary in python
randomwalk_args - support weighted random walk multiplier if defined
rw_extensions - can define weighted_random_walk or rw_on_main_graph - (see)[https://radimrehurek.com/gensim/models/doc2vec.html]
metrics_list - support metrics of accuracy, precision, recall, f1score, TP, TN , FP, FN - they all will be showed in the xlsx results
validation_list - takes the name of the validation you wish to use. support multiple validations. options : KFold , LeaveOneOut and StratifiedKfold
vaidation_args - define args for the validation wrappers i.e : {"kfold_split": 10, "stratifiedkfold_split": 5}
train_directory_path - embeddings path
train_label_directory_path - embeddings label path
classifiers_list - define which classifier you want to use. can use multiple classifiers at once. options - RandomForest, lightgbm, Svm, CatBoost
classifier_args - pass arguments to the classifier - for example classifiers_args={"random_forest_max_depth": 7,"random_forest_n_estimators": 500}
csv_output_directory - results output path
output_file_name - results file name
output_model_path - saved model in pkl file
to make sure you run the project without any interferance of previous runs you should enable cleaner to delete temporary files.