""" Module of functions defining program parameters """ import sys import argparse def makeArgParserCodeML(description, task = "classification"): """ Make program arguments parser for machine learning for analysis of source code problems Parameters: - description -- string describing the parser - task -- task for which dataset is used: either 'classification' or 'similarity' Returns: constructed argument parser """ parser = argparse.ArgumentParser(description = description) parser.add_argument("dataset", type=str, help="dataset directory") parser.add_argument("--min_solutions", type=int, default=8, help = "min number of problem solutions") parser.add_argument("--problems", default=None, type=int, help = "max number of problems to load") parser.add_argument("--short_code", default = 4, type=int, help = "shortest code to load") parser.add_argument("--long_code", default = None, type=int, help = "longest code to load") parser.add_argument("--testpart", default=0, type=float, help="part of whole dataset for testing") parser.add_argument("--valpart", default=0.2, type=float, help="fraction of training dataset for validation") parser.add_argument("--batch", default=32, type=int, help="batch size") parser.add_argument("--seed_ds", type=int, default=101, help = "seed for making randomized dataset") parser.add_argument("--seed_model", type=int, default=101, help = "seed for making randomized model") parser.add_argument("--dnn", default="basic", type=str, help="dnn model") parser.add_argument('--progress', default=1, type=int, choices=[0, 1, 2], help="mode of keras training progress bar") parser.add_argument("--epochs", default=32, type=int, help="number of train epochs") parser.add_argument("--patience", default=32, type=int, help="number of epoch to train without improvements") parser.add_argument("--dense", type=int, nargs='*', help="sizes of dense layers") parser.add_argument("--regul_dense_only", action="store_true", default=False, help="regulirize only dense layers") parser.add_argument("--history", type=str, default="./train_history.pcl", help="filename to write history of training") parser.add_argument("--optimizer", type=str, default="rmsprop", choices=["sgd", "rmsprop", "adam", "adadelta", "adagrad", "adamax", "nadam", "ftrl"], help="training optimizer") parser.add_argument("--ckpt_dir", default = None, type=str, help="checkpoint directory") if task == "similarity": parser.add_argument("--validation", type=str, default="same", choices=["same", "different"], help="validation with same or different " + "as training problems") parser.add_argument("--valsize", default=5000, type=int, help="size of validation dataset") parser.add_argument("--trainsize", default=20000, type=int, help="size of training dataset") parser.add_argument("--similpart", default=0.5, type=float, help="fraction of all samples, " + "representing pairs of similar source code") elif task == "classification": parser.add_argument("--balanced_split", action="store_true", default=False, help="balanced split of problem solutions " + "for training, validation and testing") else: sys.exit(f"Wrong machine learning task {task}. " + "It should be either 'classification' or 'similarity'") return parser def addSeqTokensArgs(parser): """ Add arguments defining DNN processing sequences of tokens Parameters: - parser -- argument parser as an object of ArgumentParser to add arguments Returns: constructed argument parser """ parser.add_argument('--seq_len', default=None, type=int, help='maximum lengths of token sequence') parser.add_argument("--coding", type=str, default="categorical", choices=["categorical", "trainable", "one_hot"], help="dnn input data coding: " + "either one-hot or categorical or trainable") parser.add_argument("--embed", default=None, type=int, help="size of embedding vectors; " + "number of tokens if it is not defined" ) parser.add_argument("--kernels", required=True, type=int, nargs='+', help="width of convolution kernels") parser.add_argument("--filters", required=True, type=int, nargs='+', help="number of convolution filters") parser.add_argument("--strides", default = None, type=int, nargs='+', help="strides of convolutions") parser.add_argument("--conv_act", type=str, default= None, choices=["relu", "sigmoid", "tanh"], help="activation of convolutional layers") parser.add_argument("--pool", type=str, default="max", choices=["max", "aver"], help="operation of pooling layer") parser.add_argument("--dropout", default=0, type=float, help="rate of dropout") return parser def addRegularizationArgs(parser): """ Add arguments defining regularization Parameters: - parser -- argument parser as an object of ArgumentParser to add arguments Returns: constructed argument parser """ parser.add_argument("--l1", default=0, type=float, help="l1 regularization") parser.add_argument("--l2", default=0, type=float, help="l2 regularization") return parser def parseArguments(parser): """ Parse program arguments Parameters: - parser -- argument parser as an object of ArgumentParser to add arguments Returns: object with program arguments """ args = parser.parse_args() print("Parameter settings used:") for k,v in sorted(vars(args).items()): print("{}: {}".format(k,v)) return args def checkConvolution(args): """ Check definition of convolution by program arguments Parameters: - args object with program arguments """ if len(args.kernels) != len(args.filters): print("Error: Numbers of convolution kernels and filters are different") sys.exit(1) if args.strides and len(args.strides) != len(args.filters): print("Error: Numbers of convolution kernels and strides are different") sys.exit(1)