From f998f5e51c75100b95a4c74a69bf50deea27e5e2 Mon Sep 17 00:00:00 2001 From: Antoine Barbez Date: Sat, 20 Apr 2019 17:02:55 -0400 Subject: [PATCH] new tuning script for smad --- .DS_Store | Bin 6148 -> 6148 bytes data/.DS_Store | Bin 14340 -> 14340 bytes data/antipatterns/.DS_Store | Bin 6148 -> 6148 bytes experiments/training/__init__.py | 0 experiments/training/train_smad.py | 20 ++-- experiments/tuning/context.py | 9 ++ .../tuning/results/smad_god_class_jedit.csv | 101 ++++++++++++++++ experiments/tuning/tune_smad.py | 113 ++++++++++++++++++ neural_networks/smad/.DS_Store | Bin 6148 -> 6148 bytes neural_networks/smad/model.py | 1 - neural_networks/smad/trained_models/.DS_Store | Bin 6148 -> 6148 bytes .../trained_models/feature_envy/checkpoint | 11 -- .../feature_envy/network0.data-00000-of-00001 | Bin 884 -> 0 bytes .../feature_envy/network0.index | Bin 280 -> 0 bytes .../trained_models/feature_envy/network0.meta | Bin 67295 -> 0 bytes .../feature_envy/network1.data-00000-of-00001 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.../trained_models/god_class/network6.meta | Bin 68280 -> 0 bytes .../god_class/network7.data-00000-of-00001 | Bin 844 -> 0 bytes .../trained_models/god_class/network7.index | Bin 280 -> 0 bytes .../trained_models/god_class/network7.meta | Bin 68431 -> 0 bytes .../god_class/network8.data-00000-of-00001 | Bin 844 -> 0 bytes .../trained_models/god_class/network8.index | Bin 280 -> 0 bytes .../trained_models/god_class/network8.meta | Bin 68582 -> 0 bytes .../god_class/network9.data-00000-of-00001 | Bin 844 -> 0 bytes .../trained_models/god_class/network9.index | Bin 280 -> 0 bytes .../trained_models/god_class/network9.meta | Bin 68733 -> 0 bytes utils/dataUtils.py | 5 +- utils/nnUtils.py | 10 +- 90 files changed, 244 insertions(+), 39 deletions(-) create mode 100644 experiments/training/__init__.py create mode 100644 experiments/tuning/context.py create mode 100644 experiments/tuning/results/smad_god_class_jedit.csv create mode 100644 experiments/tuning/tune_smad.py delete mode 100644 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diff --git a/data/.DS_Store b/data/.DS_Store index 9378e31c917a8147a30b30ada5d6a11cb3c8f965..c667e56ae9ad3ba3f3b67d938600a7b152201abd 100644 GIT binary patch delta 14 VcmZoEXerpBpvcIuSyAz^7yu_n1jzsZ delta 14 VcmZoEXerpBpvcI$SyAz^7yu_t1j+ya diff --git a/data/antipatterns/.DS_Store b/data/antipatterns/.DS_Store index 8f9e0acbe074149463c3a3e5c28c311b5d55397e..34cda19def25b571b852b49f725dfc7685cc034a 100644 GIT binary patch delta 14 VcmZoMXffDez{1F|*^uS3FaRL61RMYW delta 14 VcmZoMXffDez{1G5*^uS3FaRLC1RVeX diff --git a/experiments/training/__init__.py b/experiments/training/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/experiments/training/train_smad.py b/experiments/training/train_smad.py index 8aab55b..3f2d8f5 100644 --- a/experiments/training/train_smad.py +++ b/experiments/training/train_smad.py @@ -29,13 +29,16 @@ def parse_args(): parser.add_argument('-dense_sizes', nargs='+', type=int, help="The sizes of each (dense) hidden layer in the network.") parser.add_argument("-n_net", type=int, default=10, help="The number of distinct networks to be trained and saved.") parser.add_argument("-n_step", type=int, default=300, help="The number of training steps.") - parser.add_argument("-decay_step", type=int, default=300, help="The number of training steps after which the learning rate is decayed") + parser.add_argument("-decay_step", type=int, default=100, help="The number of training steps after which the learning rate is decayed") parser.add_argument("-lr_decay", type=float, default=0.5, help="The factor by which the learning rate is multiplied every 'decay_step' steps") return parser.parse_args() # Get the path of a trained model -def get_save_path(antipattern, net_number): - return os.path.join(ROOT_DIR, 'neural_networks', 'smad', 'trained_models', antipattern, 'network' + str(net_number)) +def get_save_path(antipattern, test_system, net_number): + directory = os.path.join(ROOT_DIR, 'neural_networks', 'smad', 'trained_models', antipattern, test_system) + if not os.path.exists(directory): + os.makedirs(directory) + return os.path.join(directory, 'network' + str(net_number)) def build_dataset(antipattern, systems): input_size = {'god_class':8, 'feature_envy':9} @@ -60,14 +63,13 @@ def train(session, model, x_train, y_train, x_test, y_test, num_step, start_lr, feed_dict_train = { model.input_x: x_train, model.input_y: y_train, - model.training: True, model.learning_rate:learning_rate, model.beta:beta} session.run(model.learning_step, feed_dict=feed_dict_train) - loss_train = session.run(model.loss, feed_dict={model.input_x:x_train, model.input_y:y_train, model.training: False}) - loss_test = session.run(model.loss, feed_dict={model.input_x:x_test, model.input_y:y_test, model.training: False}) + loss_train = session.run(model.loss, feed_dict={model.input_x:x_train, model.input_y:y_train}) + loss_test = session.run(model.loss, feed_dict={model.input_x:x_test, model.input_y:y_test}) losses_train.append(loss_train) losses_test.append(loss_test) return losses_train, losses_test @@ -83,7 +85,7 @@ def train(session, model, x_train, y_train, x_test, y_test, num_step, start_lr, # Create model model = md.SMAD( shape=args.dense_sizes, - input_size=x_train[0].shape[-1]) + input_size=x_train.shape[-1]) # To save and restore a trained model saver = tf.train.Saver(max_to_keep=args.n_net) @@ -116,13 +118,13 @@ def train(session, model, x_train, y_train, x_test, y_test, num_step, start_lr, all_losses_test.append(losses_test) # Save the model - saver.save(sess=session, save_path=get_save_path(args.antipattern, i)) + saver.save(sess=session, save_path=get_save_path(args.antipattern, args.test_system, i)) # Compute the ensemble prediction on the test system ensemble_prediction = nnUtils.ensemble_prediction( model=model, - save_paths=[get_save_path(args.antipattern, i) for i in range(args.n_net)], + save_paths=[get_save_path(args.antipattern, args.test_system, i) for i in range(args.n_net)], input_x=x_test) # Print Ensemble performances diff --git a/experiments/tuning/context.py b/experiments/tuning/context.py new file mode 100644 index 0000000..e47e0de --- /dev/null +++ b/experiments/tuning/context.py @@ -0,0 +1,9 @@ +import os +import sys + +ROOT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), '../..')) +sys.path.insert(0, ROOT_DIR) + +import utils.nnUtils as nnUtils +import experiments.training.train_smad as train_smad +import neural_networks.smad.model as md \ No newline at end of file diff --git a/experiments/tuning/results/smad_god_class_jedit.csv b/experiments/tuning/results/smad_god_class_jedit.csv new file mode 100644 index 0000000..22f9461 --- /dev/null +++ b/experiments/tuning/results/smad_god_class_jedit.csv @@ -0,0 +1,101 @@ +Learning rate;Beta;Dense sizes;F-measure +0.187625311004;0.14675747497;[37];0.5205479452054794 +0.242540118664;0.111539495108;[92, 30, 7];0.5165562913907285 +0.0576728013128;0.0279349021;[73, 22, 5];0.5151515151515151 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a/experiments/tuning/tune_smad.py b/experiments/tuning/tune_smad.py new file mode 100644 index 0000000..beb3161 --- /dev/null +++ b/experiments/tuning/tune_smad.py @@ -0,0 +1,113 @@ +from context import ROOT_DIR, nnUtils, train_smad, md + +import tensorflow as tf +import numpy as np + +import argparse +import os +import progressbar +import random +os.environ['TF_CPP_MIN_LOG_LEVEL']='2' + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument("antipattern", help="Either 'god_class' or 'feature_envy'") + parser.add_argument("test_system", help="The name of the system to be used for testing.\n Hence, the cross-validation will be performed using all the systems except this one.") + parser.add_argument("-n_fold", type=int, default=5, help="Number of folds (k) for a k-fold-cross-validation") + parser.add_argument("-n_step", type=int, default=100, help="Number of training steps (i.e., epochs) to be performed for each fold") + parser.add_argument("-n_test", type=int, default=100, help="Number of random hyper-parameters sets to be tested") + return parser.parse_args() + +def generateRandomHyperParameters(): + learning_rate = 10**-random.uniform(0.0, 2.5) + beta = 10**-random.uniform(0.0, 2.5) + + minBound = 4 + maxBound = 100 + dense_sizes = [] + nb_dense_layer = random.randint(1, 3) + for _ in range(nb_dense_layer): + dense_size = random.randint(minBound, maxBound) + dense_sizes.append(dense_size) + maxBound = dense_size + + return learning_rate, beta, dense_sizes + +def get_cross_validation_dataset(X, Y, fold_index, n_fold): + folds_x, folds_y = nnUtils.split(X, Y, n_fold) + x_train = np.empty(shape=[0, X.shape[-1]]) + y_train = np.empty(shape=[0, 1]) + for i in range(n_fold): + if i != fold_index: + x_train = np.concatenate((x_train, folds_x[i]), axis=0) + y_train = np.concatenate((y_train, folds_y[i]), axis=0) + + return x_train, y_train, folds_x[fold_index], folds_y[fold_index] + +def train(session, model, x_train, y_train, num_step, lr, beta): + for step in range(num_step): + feed_dict_train = { + model.input_x: x_train, + model.input_y: y_train, + model.learning_rate:lr, + model.beta:beta} + + session.run(model.learning_step, feed_dict=feed_dict_train) + +if __name__ == "__main__": + args = parse_args() + + # Remove the test system from the training set and build dataset + train_smad.training_systems.remove(args.test_system) + data_x, data_y = train_smad.build_dataset(args.antipattern, train_smad.training_systems) + data_x, data_y = nnUtils.shuffle(data_x, data_y) + + bar = progressbar.ProgressBar(maxval=args.n_test, \ + widgets=['Performing cross validation for ' + args.test_system + ': ' ,progressbar.Percentage()]) + bar.start() + + output_file_path = os.path.join(ROOT_DIR, 'experiments', 'tuning', 'results', 'smad_' + args.antipattern + '_' + args.test_system + '.csv') + + params = [] + perfs = [] + for i in range(args.n_test): + learning_rate, beta, dense_sizes = generateRandomHyperParameters() + params.append([learning_rate, beta, dense_sizes]) + + predictions = np.empty(shape=[0, 1]) + for j in range(args.n_fold): + x_train, y_train, x_test, y_test = get_cross_validation_dataset(data_x, data_y, j, args.n_fold) + + # New graph + tf.reset_default_graph() + + # Create model + model = md.SMAD( + shape=dense_sizes, + input_size=x_train.shape[-1]) + + with tf.Session() as session: + # Initialize the variables of the TensorFlow graph. + session.run(tf.global_variables_initializer()) + + train( + session=session, + model=model, + x_train=x_train, + y_train=y_train, + num_step=args.n_step, + lr=learning_rate, + beta=beta) + + predictions = np.concatenate((predictions, session.run(model.inference, feed_dict={model.input_x: x_test})), axis=0) + + perfs.append(nnUtils.f_measure(predictions, data_y)) + indexes = np.argsort(np.array(perfs)) + with open(output_file_path, 'w') as file: + file.write("Learning rate;Beta;Dense sizes;F-measure\n") + for j in reversed(indexes): + for k in range(len(params[j])): + file.write(str(params[j][k]) + ';') + file.write(str(perfs[j]) + '\n') + bar.update(i+1) + bar.finish() \ No newline at end of file diff --git a/neural_networks/smad/.DS_Store b/neural_networks/smad/.DS_Store index c36193f625d080acbd4ceec5c516cb12662d7d59..24d0c2b3d71d59eae7e05a1784d63468531b8aeb 100644 GIT binary patch delta 37 tcmZoMXfc@J&&WD4U^gS{WFAHZ&XnThoTU8x9EQz2jF;Fpvvd6A2LRQz3vmDd delta 30 mcmZoMXfc@J&&V<{U^gSnWFAI^&7q8&*d{h;Zf58B%MSpMq6v2Z diff --git a/neural_networks/smad/model.py b/neural_networks/smad/model.py index d4ac83b..8a5bf48 100644 --- a/neural_networks/smad/model.py +++ b/neural_networks/smad/model.py @@ -11,7 +11,6 @@ def __init__(self, shape, input_size): self.input_y = tf.placeholder(tf.float32,[None, 1], name="input_y") # Placeholders for training parameters - self.training = tf.placeholder(tf.bool, name="training") self.learning_rate = tf.placeholder(tf.float32, name="learning_rate") self.beta = tf.placeholder(tf.float32, name="beta") 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