diff --git a/Pilot1/Attn/attn.py b/Pilot1/Attn/attn.py index cc4b4dd6..e2ffb7a6 100644 --- a/Pilot1/Attn/attn.py +++ b/Pilot1/Attn/attn.py @@ -8,8 +8,6 @@ import numpy as np file_path = os.path.dirname(os.path.realpath(__file__)) -lib_path2 = os.path.abspath(os.path.join(file_path, '..', '..', 'common')) -sys.path.append(lib_path2) import candle diff --git a/Pilot1/Attn/attn_abstention_keras2.py b/Pilot1/Attn/attn_abstention_keras2.py index 564019ea..9783acd1 100644 --- a/Pilot1/Attn/attn_abstention_keras2.py +++ b/Pilot1/Attn/attn_abstention_keras2.py @@ -8,7 +8,7 @@ import tensorflow as tf from tensorflow.keras import backend as K -from tensorflow.keras.models import model_from_json, model_from_yaml +from tensorflow.keras.models import model_from_json from tensorflow.keras.callbacks import ModelCheckpoint, CSVLogger, ReduceLROnPlateau, EarlyStopping, TensorBoard from sklearn.utils.class_weight import compute_class_weight @@ -214,7 +214,7 @@ def run(params): # Try class weight and abstention classifier y_integers = np.argmax(Y_train, axis=1) - class_weights = compute_class_weight('balanced', np.unique(y_integers), y_integers) + class_weights = compute_class_weight(class_weight='balanced', classes=np.unique(y_integers), y=y_integers) d_class_weights = dict(enumerate(class_weights)) print('X_train shape:', X_train.shape) @@ -444,12 +444,6 @@ def save_and_test_saved_model(params, model, root_fname, nb_classes, alpha, mask with open(params['save_path'] + root_fname + '.model.json', "w") as json_file: json_file.write(model_json) - # serialize model to YAML - model_yaml = model.to_yaml() - with open(params['save_path'] + root_fname + '.model.yaml', "w") as yaml_file: - - yaml_file.write(model_yaml) - # serialize weights to HDF5 model.save_weights(params['save_path'] + root_fname + '.model.h5') print("Saved model to disk") @@ -460,18 +454,8 @@ def save_and_test_saved_model(params, model, root_fname, nb_classes, alpha, mask json_file.close() loaded_model_json = model_from_json(loaded_model_json) - # load yaml and create model - yaml_file = open(params['save_path'] + root_fname + '.model.yaml', 'r') - loaded_model_yaml = yaml_file.read() - yaml_file.close() - loaded_model_yaml = model_from_yaml(loaded_model_yaml) - # yaml.load(input, Loader=yaml.FullLoader) - # load weights into new model loaded_model_json.load_weights(params['save_path'] + root_fname + '.model.h5') - # input = params['save_path'] + root_fname + '.model.h5' - # loaded_model_json.load(input, Loader=yaml.FullLoader) - # print("Loaded json model from disk") # evaluate json loaded model on test data loaded_model_json.compile(loss=candle.abstention_loss(alpha, mask), optimizer='SGD', metrics=[candle.abstention_acc_metric(nb_classes)]) @@ -480,27 +464,17 @@ def save_and_test_saved_model(params, model, root_fname, nb_classes, alpha, mask print('json Validation abstention accuracy:', score_json[1]) print("json %s: %.2f%%" % (loaded_model_json.metrics_names[1], score_json[1] * 100)) - # load weights into new model - loaded_model_yaml.load_weights(params['save_path'] + root_fname + '.model.h5') - print("Loaded yaml model from disk") - # evaluate yaml loaded model on test data - loaded_model_yaml.compile(loss=candle.abstention_loss(alpha, mask), optimizer='SGD', metrics=[candle.abstention_acc_metric(nb_classes)]) - score_yaml = loaded_model_yaml.evaluate(X_test, Y_test, verbose=0) - print('yaml Validation abstention loss:', score_yaml[0]) - print('yaml Validation abstention accuracy:', score_yaml[1]) - print("yaml %s: %.2f%%" % (loaded_model_yaml.metrics_names[1], score_yaml[1] * 100)) - # predict using loaded yaml model on test and training data - predict_yaml_train = loaded_model_yaml.predict(X_train) - predict_yaml_test = loaded_model_yaml.predict(X_test) - print('Yaml_train_shape:', predict_yaml_train.shape) - print('Yaml_test_shape:', predict_yaml_test.shape) - predict_yaml_train_classes = np.argmax(predict_yaml_train, axis=1) - predict_yaml_test_classes = np.argmax(predict_yaml_test, axis=1) - np.savetxt(params['save_path'] + root_fname + '_predict_yaml_train.csv', predict_yaml_train, delimiter=",", fmt="%.3f") - np.savetxt(params['save_path'] + root_fname + '_predict_yaml_test.csv', predict_yaml_test, delimiter=",", fmt="%.3f") - np.savetxt(params['save_path'] + root_fname + '_predict_yaml_train_classes.csv', predict_yaml_train_classes, delimiter=",", fmt="%d") - np.savetxt(params['save_path'] + root_fname + '_predict_yaml_test_classes.csv', predict_yaml_test_classes, delimiter=",", fmt="%d") + predict_train = loaded_model_json.predict(X_train) + predict_test = loaded_model_json.predict(X_test) + print('train_shape:', predict_train.shape) + print('test_shape:', predict_test.shape) + predict_train_classes = np.argmax(predict_train, axis=1) + predict_test_classes = np.argmax(predict_test, axis=1) + np.savetxt(params['save_path'] + root_fname + '_predict_train.csv', predict_train, delimiter=",", fmt="%.3f") + np.savetxt(params['save_path'] + root_fname + '_predict_test.csv', predict_test, delimiter=",", fmt="%.3f") + np.savetxt(params['save_path'] + root_fname + '_predict_train_classes.csv', predict_train_classes, delimiter=",", fmt="%d") + np.savetxt(params['save_path'] + root_fname + '_predict_test_classes.csv', predict_test_classes, delimiter=",", fmt="%d") def main(): diff --git a/Pilot1/Attn/attn_baseline_keras2.py b/Pilot1/Attn/attn_baseline_keras2.py index 1059537c..9bed2c39 100644 --- a/Pilot1/Attn/attn_baseline_keras2.py +++ b/Pilot1/Attn/attn_baseline_keras2.py @@ -10,7 +10,7 @@ from tensorflow.keras import backend as K from tensorflow.keras.layers import Input, Dense, Dropout, BatchNormalization -from tensorflow.keras.models import Model, model_from_json, model_from_yaml +from tensorflow.keras.models import Model, model_from_json from tensorflow.keras.utils import to_categorical from tensorflow.keras.callbacks import Callback, ModelCheckpoint, CSVLogger, ReduceLROnPlateau, EarlyStopping, TensorBoard @@ -197,7 +197,7 @@ def run(params): Y_val = to_categorical(Y_val, nb_classes) y_integers = np.argmax(Y_train, axis=1) - class_weights = compute_class_weight('balanced', np.unique(y_integers), y_integers) + class_weights = compute_class_weight(class_weight='balanced', classes=np.unique(y_integers), y=y_integers) d_class_weights = dict(enumerate(class_weights)) print('X_train shape:', X_train.shape) @@ -363,11 +363,6 @@ def save_and_test_saved_model(params, model, root_fname, X_train, X_test, Y_test with open(params['save_path'] + root_fname + ".model.json", "w") as json_file: json_file.write(model_json) - # serialize model to YAML - model_yaml = model.to_yaml() - with open(params['save_path'] + root_fname + ".model.yaml", "w") as yaml_file: - yaml_file.write(model_yaml) - # serialize weights to HDF5 model.save_weights(params['save_path'] + root_fname + ".model.h5") print("Saved model to disk") @@ -378,12 +373,6 @@ def save_and_test_saved_model(params, model, root_fname, X_train, X_test, Y_test json_file.close() loaded_model_json = model_from_json(loaded_model_json) - # load yaml and create model - yaml_file = open(params['save_path'] + root_fname + '.model.yaml', 'r') - loaded_model_yaml = yaml_file.read() - yaml_file.close() - loaded_model_yaml = model_from_yaml(loaded_model_yaml) - # load weights into new model loaded_model_json.load_weights(params['save_path'] + root_fname + ".model.h5") print("Loaded json model from disk") @@ -397,30 +386,19 @@ def save_and_test_saved_model(params, model, root_fname, X_train, X_test, Y_test print("json %s: %.2f%%" % (loaded_model_json.metrics_names[1], score_json[1] * 100)) - # load weights into new model - loaded_model_yaml.load_weights(params['save_path'] + root_fname + ".model.h5") - print("Loaded yaml model from disk") - - # evaluate loaded model on test data - loaded_model_yaml.compile(loss='binary_crossentropy', optimizer=params['optimizer'], metrics=['accuracy']) - score_yaml = loaded_model_yaml.evaluate(X_test, Y_test, verbose=0) - print('yaml Validation loss:', score_yaml[0]) - print('yaml Validation accuracy:', score_yaml[1]) - print("yaml %s: %.2f%%" % (loaded_model_yaml.metrics_names[1], score_yaml[1] * 100)) - - # predict using loaded yaml model on test and training data - predict_yaml_train = loaded_model_yaml.predict(X_train) - predict_yaml_test = loaded_model_yaml.predict(X_test) - print('Yaml_train_shape:', predict_yaml_train.shape) - print('Yaml_test_shape:', predict_yaml_test.shape) - - predict_yaml_train_classes = np.argmax(predict_yaml_train, axis=1) - predict_yaml_test_classes = np.argmax(predict_yaml_test, axis=1) - np.savetxt(params['save_path'] + root_fname + "_predict_yaml_train.csv", predict_yaml_train, delimiter=",", fmt="%.3f") - np.savetxt(params['save_path'] + root_fname + "_predict_yaml_test.csv", predict_yaml_test, delimiter=",", fmt="%.3f") - - np.savetxt(params['save_path'] + root_fname + "_predict_yaml_train_classes.csv", predict_yaml_train_classes, delimiter=",", fmt="%d") - np.savetxt(params['save_path'] + root_fname + "_predict_yaml_test_classes.csv", predict_yaml_test_classes, delimiter=",", fmt="%d") + # predict using loaded model on test and training data + predict_train = loaded_model_json.predict(X_train) + predict_test = loaded_model_json.predict(X_test) + print('train_shape:', predict_train.shape) + print('test_shape:', predict_test.shape) + + predict_train_classes = np.argmax(predict_train, axis=1) + predict_test_classes = np.argmax(predict_test, axis=1) + np.savetxt(params['save_path'] + root_fname + "_predict_train.csv", predict_train, delimiter=",", fmt="%.3f") + np.savetxt(params['save_path'] + root_fname + "_predict_test.csv", predict_test, delimiter=",", fmt="%.3f") + + np.savetxt(params['save_path'] + root_fname + "_predict_train_classes.csv", predict_train_classes, delimiter=",", fmt="%d") + np.savetxt(params['save_path'] + root_fname + "_predict_test_classes.csv", predict_test_classes, delimiter=",", fmt="%d") def main(): diff --git a/Pilot1/Attn/test.sh b/Pilot1/Attn/test.sh index 57dac5bd..f94ea140 100755 --- a/Pilot1/Attn/test.sh +++ b/Pilot1/Attn/test.sh @@ -1,3 +1,4 @@ +set -x python attn_baseline_keras2.py -# python attn_abstention_keras2.py --epochs 1 +python attn_abstention_keras2.py --epochs 1 python attn_bin_working_jan7_h5.py --in ../../Data/Pilot1/top_21_1fold_001.h5 --ep 1 --save_dir "./save" diff --git a/Pilot1/Combo/NCI60.py b/Pilot1/Combo/NCI60.py index 601a7c12..7baed44c 100644 --- a/Pilot1/Combo/NCI60.py +++ b/Pilot1/Combo/NCI60.py @@ -2,7 +2,6 @@ import collections import os -import sys import numpy as np import pandas as pd @@ -14,8 +13,6 @@ from sklearn.preprocessing import StandardScaler, MinMaxScaler, MaxAbsScaler file_path = os.path.dirname(os.path.realpath(__file__)) -lib_path = os.path.abspath(os.path.join(file_path, '..', '..', 'common')) -sys.path.append(lib_path) import candle @@ -286,7 +283,7 @@ def load_drug_set_descriptors(drug_set='ALMANAC', ncols=None, scaling='std', add # df1['NAME'] = df1['NAME'].map(lambda x: x[4:]) df1.rename(columns={'NAME': 'Drug'}, inplace=True) - df2 = df.drop('NAME', 1) + df2 = df.drop('NAME', axis=1) if add_prefix: df2 = df2.add_prefix('dragon7.') @@ -336,7 +333,7 @@ def load_drug_descriptors_new(ncols=None, scaling='std', add_prefix=True): # df1['NAME'] = df1['NAME'].map(lambda x: x[4:]) df1.rename(columns={'NAME': 'Drug'}, inplace=True) - df2 = df.drop('NAME', 1) + df2 = df.drop('NAME', axis=1) if add_prefix: df2 = df2.add_prefix('dragon7.') @@ -383,7 +380,7 @@ def load_drug_descriptors(ncols=None, scaling='std', add_prefix=True): df1['NAME'] = df1['NAME'].map(lambda x: x[4:]) df1.rename(columns={'NAME': 'NSC'}, inplace=True) - df2 = df.drop('NAME', 1) + df2 = df.drop('NAME', axis=1) if add_prefix: df2 = df2.add_prefix('dragon7.') @@ -427,7 +424,7 @@ def load_drug_descriptors_old(ncols=None, scaling='std', add_prefix=True): df1 = pd.DataFrame(df.loc[:, 'NAME'].astype(int).astype(str)) df1.rename(columns={'NAME': 'NSC'}, inplace=True) - df2 = df.drop('NAME', 1) + df2 = df.drop('NAME', axis=1) if add_prefix: df2 = df2.add_prefix('dragon7.') @@ -489,7 +486,7 @@ def load_sample_rnaseq(ncols=None, scaling='std', add_prefix=True, use_landmark_ df1 = df['Sample'] - df2 = df.drop('Sample', 1) + df2 = df.drop('Sample', axis=1) if add_prefix: df2 = df2.add_prefix('rnaseq.') @@ -541,7 +538,7 @@ def load_cell_expression_rnaseq(ncols=None, scaling='std', add_prefix=True, use_ df1 = df['CELLNAME'] df1 = df1.map(lambda x: x.replace(':', '.')) - df2 = df.drop('CELLNAME', 1) + df2 = df.drop('CELLNAME', axis=1) if add_prefix: df2 = df2.add_prefix('rnaseq.') @@ -589,7 +586,7 @@ def load_cell_expression_u133p2(ncols=None, scaling='std', add_prefix=True, use_ df1 = df['CELLNAME'] df1 = df1.map(lambda x: x.replace(':', '.')) - df2 = df.drop('CELLNAME', 1) + df2 = df.drop('CELLNAME', axis=1) if add_prefix: df2 = df2.add_prefix('expr.') @@ -639,7 +636,7 @@ def load_cell_expression_5platform(ncols=None, scaling='std', add_prefix=True, u df1 = df['CellLine'] df1.name = 'CELLNAME' - df2 = df.drop('CellLine', 1) + df2 = df.drop('CellLine', axis=1) if add_prefix: df2 = df2.add_prefix('expr_5p.') @@ -680,7 +677,7 @@ def load_cell_mirna(ncols=None, scaling='std', add_prefix=True): df1 = df['CellLine'] df1.name = 'CELLNAME' - df2 = df.drop('CellLine', 1) + df2 = df.drop('CellLine', axis=1) if add_prefix: df2 = df2.add_prefix('mRNA.') @@ -775,7 +772,7 @@ def load_drug_autoencoded_AG(ncols=None, scaling='std', add_prefix=True): global_cache[path] = df df1 = pd.DataFrame(df.loc[:, 'NSC'].astype(int).astype(str)) - df2 = df.drop('NSC', 1) + df2 = df.drop('NSC', axis=1) if add_prefix: df2 = df2.add_prefix('smiles_latent_AG.') diff --git a/Pilot1/Combo/combo.py b/Pilot1/Combo/combo.py index 14b8944d..b5d5a97f 100644 --- a/Pilot1/Combo/combo.py +++ b/Pilot1/Combo/combo.py @@ -1,12 +1,9 @@ from __future__ import print_function import os -import sys import logging file_path = os.path.dirname(os.path.realpath(__file__)) -lib_path2 = os.path.abspath(os.path.join(file_path, '..', '..', 'common')) -sys.path.append(lib_path2) import candle diff --git a/Pilot1/Combo/combo_baseline_keras2.py b/Pilot1/Combo/combo_baseline_keras2.py index e1c8381c..5791adb4 100644 --- a/Pilot1/Combo/combo_baseline_keras2.py +++ b/Pilot1/Combo/combo_baseline_keras2.py @@ -4,6 +4,7 @@ import collections import logging +import sys import os import threading @@ -46,7 +47,7 @@ def set_up_logger(logfile, verbose): fh.setFormatter(logging.Formatter("[%(asctime)s %(process)d] %(message)s", datefmt="%Y-%m-%d %H:%M:%S")) fh.setLevel(logging.DEBUG) - sh = logging.StreamHandler() + sh = logging.StreamHandler(sys.stdout) sh.setFormatter(logging.Formatter('')) sh.setLevel(logging.DEBUG if verbose else logging.INFO) diff --git a/Pilot1/Combo/combo_dose.py b/Pilot1/Combo/combo_dose.py index 75159d01..87d62992 100644 --- a/Pilot1/Combo/combo_dose.py +++ b/Pilot1/Combo/combo_dose.py @@ -6,6 +6,7 @@ import collections import logging import os +import sys import threading import numpy as np @@ -49,7 +50,7 @@ def set_up_logger(logfile, verbose): fh.setFormatter(logging.Formatter("[%(asctime)s %(process)d] %(message)s", datefmt="%Y-%m-%d %H:%M:%S")) fh.setLevel(logging.DEBUG) - sh = logging.StreamHandler() + sh = logging.StreamHandler(sys.stdout) sh.setFormatter(logging.Formatter('')) sh.setLevel(logging.DEBUG if verbose else logging.INFO) diff --git a/Pilot1/Combo/test.sh b/Pilot1/Combo/test.sh index b66b678c..6a1c12b6 100755 --- a/Pilot1/Combo/test.sh +++ b/Pilot1/Combo/test.sh @@ -1,7 +1,9 @@ #!/bin/bash +set -x + python combo_baseline_keras2.py --use_landmark_genes True --warmup_lr True --reduce_lr True -z 256 --epochs 4 -python infer.py --sample_set NCIPDM --drug_set ALMANAC --use_landmark_genes -m ./save/combo.A=relu.B=256.E=10.O=adam.LR=None.CF=e.DF=d.wu_lr.re_lr.L1000.D1=1000.D2=1000.D3=1000.model.h5 -w ./save/combo.A=relu.B=256.E=10.O=adam.LR=None.CF=e.DF=d.wu_lr.re_lr.L1000.D1=1000.D2=1000.D3=1000.weights.h5 --epochs 4 +python infer.py --sample_set NCIPDM --drug_set ALMANAC --use_landmark_genes -m ./save/combo.A=relu.B=256.E=4.O=adam.LR=None.CF=e.DF=d.wu_lr.re_lr.L1000.D1=1000.D2=1000.D3=1000.model.h5 -w ./save/combo.A=relu.B=256.E=4.O=adam.LR=None.CF=e.DF=d.wu_lr.re_lr.L1000.D1=1000.D2=1000.D3=1000.weights.h5 # Need to revisit combo_dose.py and infer_dose.py # python combo_dose.py --use_landmark_genes True -z 256 diff --git a/Pilot1/NT3/abstain_functions.py b/Pilot1/NT3/abstain_functions.py index 01f86b70..339962ea 100644 --- a/Pilot1/NT3/abstain_functions.py +++ b/Pilot1/NT3/abstain_functions.py @@ -45,6 +45,9 @@ 'nargs': '+', 'type': int, 'help': 'list of names corresponding to each task to use'}, + {'name': 'cf_noise', + 'type': str, + 'help': 'input file with cf noise'} ] diff --git a/Pilot1/NT3/nt3.py b/Pilot1/NT3/nt3.py index 9c73ab52..e85f8bf0 100644 --- a/Pilot1/NT3/nt3.py +++ b/Pilot1/NT3/nt3.py @@ -1,9 +1,6 @@ import os -import sys file_path = os.path.dirname(os.path.realpath(__file__)) -lib_path2 = os.path.abspath(os.path.join(file_path, '..', '..', 'common')) -sys.path.append(lib_path2) import candle @@ -17,14 +14,22 @@ 'type': float}, {'name': 'feature_col', 'type': int}, + {'name': 'sample_ids', + 'type': int}, {'name': 'feature_threshold', 'type': float}, {'name': 'add_noise', 'type': candle.str2bool}, {'name': 'noise_correlated', 'type': candle.str2bool}, + {'name': 'noise_column', + 'type': candle.str2bool}, + {'name': 'noise_cluster', + 'type': candle.str2bool}, {'name': 'noise_gaussian', - 'type': candle.str2bool} + 'type': candle.str2bool}, + {'name': 'noise_type', + 'type': str} ] required = [ @@ -63,4 +68,4 @@ def set_locals(self): self.required = set(required) if additional_definitions is not None: self.additional_definitions = self.additional_definitions + additional_definitions - print(self.additional_definitions) + # print(self.additional_definitions) diff --git a/Pilot1/NT3/nt3_abstention_keras2.py b/Pilot1/NT3/nt3_abstention_keras2.py index e9cafc37..7563d258 100644 --- a/Pilot1/NT3/nt3_abstention_keras2.py +++ b/Pilot1/NT3/nt3_abstention_keras2.py @@ -7,7 +7,7 @@ from tensorflow.keras import backend as K from tensorflow.keras.layers import Dense, Dropout, Activation, Conv1D, MaxPooling1D, Flatten, LocallyConnected1D -from tensorflow.keras.models import Sequential, model_from_json, model_from_yaml +from tensorflow.keras.models import Sequential, model_from_json from tensorflow.keras.utils import to_categorical from tensorflow.keras.callbacks import CSVLogger, ReduceLROnPlateau @@ -34,6 +34,7 @@ def set_locals(self): self.required = set(bmk.required) if additional_definitions is not None: self.additional_definitions = abs_definitions + bmk.additional_definitions + # print("Additional definitions:", self.additional_definitions) def initialize_parameters(default_model='nt3_noise_model.txt'): @@ -67,11 +68,8 @@ def load_data(train_path, test_path, gParameters): df_y_train = df_train[:, 0].astype('int') df_y_test = df_test[:, 0].astype('int') - # only training set has noise Y_train = to_categorical(df_y_train, gParameters['classes']) Y_test = to_categorical(df_y_test, gParameters['classes']) -# Y_train, y_train_noise_gen = candle.label_flip(df_y_train, gParameters['label_noise']) -# Y_test, y_test_noise_gen = candle.label_flip(df_y_test, gParameters['label_noise']) df_x_train = df_train[:, 1:seqlen].astype(np.float32) df_x_test = df_test[:, 1:seqlen].astype(np.float32) @@ -86,29 +84,11 @@ def load_data(train_path, test_path, gParameters): X_train = mat[:X_train.shape[0], :] X_test = mat[X_train.shape[0]:, :] - # check if noise is on - if gParameters['add_noise']: - # check if we want noise correlated with a feature - if gParameters['noise_correlated']: - Y_train, y_train_noise_gen = candle.label_flip_correlated(Y_train, - gParameters['label_noise'], X_train, - gParameters['feature_col'], - gParameters['feature_threshold']) - # else add uncorrelated noise - else: - Y_train, y_train_noise_gen = candle.label_flip(Y_train, gParameters['label_noise']) - # check if noise is on for RNA-seq data - elif gParameters['noise_gaussian']: - print("adding gnoise", gParameters['std_dev']) - X_train = candle.add_gaussian_noise(X_train, 0, gParameters['std_dev']) - return X_train, Y_train, X_test, Y_test def run(gParameters): - print('Params:', gParameters) - file_train = gParameters['train_data'] file_test = gParameters['test_data'] url = gParameters['data_url'] @@ -118,6 +98,9 @@ def run(gParameters): X_train, Y_train, X_test, Y_test = load_data(train_file, test_file, gParameters) + # only training set has noise + X_train, Y_train = candle.add_noise(X_train, Y_train, gParameters) + # add extra class for abstention # first reverse the to_categorical Y_train = np.argmax(Y_train, axis=1) @@ -265,9 +248,9 @@ def run(gParameters): # path = '{}/{}.autosave.model.h5'.format(output_dir, model_name) # checkpointer = ModelCheckpoint(filepath=path, verbose=1, save_weights_only=False, save_best_only=True) csv_logger = CSVLogger('{}/training.log'.format(output_dir)) - reduce_lr = ReduceLROnPlateau(monitor='abs_crossentropy', + reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=10, verbose=1, mode='auto', - epsilon=0.0001, cooldown=0, min_lr=0) + min_delta=0.0001, cooldown=0, min_lr=0) candleRemoteMonitor = candle.CandleRemoteMonitor(params=gParameters) timeoutMonitor = candle.TerminateOnTimeOut(gParameters['timeout']) @@ -304,11 +287,6 @@ def run(gParameters): with open("{}/{}.model.json".format(output_dir, model_name), "w") as json_file: json_file.write(model_json) - # serialize model to YAML - model_yaml = model.to_yaml() - with open("{}/{}.model.yaml".format(output_dir, model_name), "w") as yaml_file: - yaml_file.write(model_yaml) - # serialize weights to HDF5 model.save_weights("{}/{}.weights.h5".format(output_dir, model_name)) print("Saved model to disk") @@ -319,12 +297,6 @@ def run(gParameters): json_file.close() loaded_model_json = model_from_json(loaded_model_json) - # load yaml and create model - yaml_file = open('{}/{}.model.yaml'.format(output_dir, model_name), 'r') - loaded_model_yaml = yaml_file.read() - yaml_file.close() - loaded_model_yaml = model_from_yaml(loaded_model_yaml) - # load weights into new model loaded_model_json.load_weights('{}/{}.weights.h5'.format(output_dir, model_name)) print("Loaded json model from disk") @@ -340,21 +312,6 @@ def run(gParameters): print("json %s: %.2f%%" % (loaded_model_json.metrics_names[1], score_json[1] * 100)) - # load weights into new model - loaded_model_yaml.load_weights('{}/{}.weights.h5'.format(output_dir, model_name)) - print("Loaded yaml model from disk") - - # evaluate loaded model on test data - loaded_model_yaml.compile(loss=gParameters['loss'], - optimizer=gParameters['optimizer'], - metrics=[gParameters['metrics']]) - score_yaml = loaded_model_yaml.evaluate(X_test, Y_test, verbose=0) - - print('yaml Test score:', score_yaml[0]) - print('yaml Test accuracy:', score_yaml[1]) - - print("yaml %s: %.2f%%" % (loaded_model_yaml.metrics_names[1], score_yaml[1] * 100)) - return history diff --git a/Pilot1/NT3/nt3_baseline_keras2.py b/Pilot1/NT3/nt3_baseline_keras2.py index 4ab49ce7..3eceba0a 100644 --- a/Pilot1/NT3/nt3_baseline_keras2.py +++ b/Pilot1/NT3/nt3_baseline_keras2.py @@ -7,7 +7,7 @@ from tensorflow.keras import backend as K from tensorflow.keras.layers import Dense, Dropout, Activation, Conv1D, MaxPooling1D, Flatten, LocallyConnected1D -from tensorflow.keras.models import Sequential, model_from_json, model_from_yaml +from tensorflow.keras.models import Sequential, model_from_json from tensorflow.keras.utils import to_categorical from tensorflow.keras.callbacks import CSVLogger, ReduceLROnPlateau @@ -48,7 +48,6 @@ def load_data(train_path, test_path, gParameters): df_y_train = df_train[:, 0].astype('int') df_y_test = df_test[:, 0].astype('int') - # only training set has noise Y_train = to_categorical(df_y_train, gParameters['classes']) Y_test = to_categorical(df_y_test, gParameters['classes']) @@ -65,22 +64,6 @@ def load_data(train_path, test_path, gParameters): X_train = mat[:X_train.shape[0], :] X_test = mat[X_train.shape[0]:, :] - # TODO: Add better names for noise boolean, make a featue for both RNA seq and label noise together - # check if noise is on (this is for label) - if gParameters['add_noise']: - # check if we want noise correlated with a feature - if gParameters['noise_correlated']: - Y_train, y_train_noise_gen = candle.label_flip_correlated(Y_train, - gParameters['label_noise'], X_train, - gParameters['feature_col'], - gParameters['feature_threshold']) - # else add uncorrelated noise - else: - Y_train, y_train_noise_gen = candle.label_flip(Y_train, gParameters['label_noise']) - # check if noise is on for RNA-seq data - elif gParameters['noise_gaussian']: - X_train = candle.add_gaussian_noise(X_train, 0, gParameters['std_dev']) - return X_train, Y_train, X_test, Y_test @@ -100,6 +83,9 @@ def run(gParameters): X_train, Y_train, X_test, Y_test = load_data(train_file, test_file, gParameters) + # only training set has noise + X_train, Y_train = candle.add_noise(X_train, Y_train, gParameters) + print('X_train shape:', X_train.shape) print('X_test shape:', X_test.shape) @@ -204,7 +190,7 @@ def run(gParameters): # path = '{}/{}.autosave.model.h5'.format(output_dir, model_name) # checkpointer = ModelCheckpoint(filepath=path, verbose=1, save_weights_only=False, save_best_only=True) csv_logger = CSVLogger('{}/training.log'.format(output_dir)) - reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=10, verbose=1, mode='auto', epsilon=0.0001, cooldown=0, min_lr=0) + reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=10, verbose=1, mode='auto', min_delta=0.0001, cooldown=0, min_lr=0) candleRemoteMonitor = candle.CandleRemoteMonitor(params=gParameters) timeoutMonitor = candle.TerminateOnTimeOut(gParameters['timeout']) @@ -227,11 +213,6 @@ def run(gParameters): with open("{}/{}.model.json".format(output_dir, model_name), "w") as json_file: json_file.write(model_json) - # serialize model to YAML - model_yaml = model.to_yaml() - with open("{}/{}.model.yaml".format(output_dir, model_name), "w") as yaml_file: - yaml_file.write(model_yaml) - # serialize weights to HDF5 model.save_weights("{}/{}.weights.h5".format(output_dir, model_name)) print("Saved model to disk") @@ -242,12 +223,6 @@ def run(gParameters): json_file.close() loaded_model_json = model_from_json(loaded_model_json) - # load yaml and create model - yaml_file = open('{}/{}.model.yaml'.format(output_dir, model_name), 'r') - loaded_model_yaml = yaml_file.read() - yaml_file.close() - loaded_model_yaml = model_from_yaml(loaded_model_yaml) - # load weights into new model loaded_model_json.load_weights('{}/{}.weights.h5'.format(output_dir, model_name)) print("Loaded json model from disk") @@ -263,21 +238,6 @@ def run(gParameters): print("json %s: %.2f%%" % (loaded_model_json.metrics_names[1], score_json[1] * 100)) - # load weights into new model - loaded_model_yaml.load_weights('{}/{}.weights.h5'.format(output_dir, model_name)) - print("Loaded yaml model from disk") - - # evaluate loaded model on test data - loaded_model_yaml.compile(loss=gParameters['loss'], - optimizer=gParameters['optimizer'], - metrics=[gParameters['metrics']]) - score_yaml = loaded_model_yaml.evaluate(X_test, Y_test, verbose=0) - - print('yaml Test score:', score_yaml[0]) - print('yaml Test accuracy:', score_yaml[1]) - - print("yaml %s: %.2f%%" % (loaded_model_yaml.metrics_names[1], score_yaml[1] * 100)) - return history diff --git a/Pilot1/NT3/nt3_candle_wrappers_baseline_keras2.py b/Pilot1/NT3/nt3_candle_wrappers_baseline_keras2.py index 6ca195a5..036913f4 100644 --- a/Pilot1/NT3/nt3_candle_wrappers_baseline_keras2.py +++ b/Pilot1/NT3/nt3_candle_wrappers_baseline_keras2.py @@ -7,7 +7,7 @@ from tensorflow.keras import backend as K from tensorflow.keras.layers import Dense, Dropout, Activation, Conv1D, MaxPooling1D, Flatten, LocallyConnected1D -from tensorflow.keras.models import Sequential, model_from_json, model_from_yaml +from tensorflow.keras.models import Sequential, model_from_json from tensorflow.keras.utils import to_categorical from tensorflow.keras.callbacks import CSVLogger, ReduceLROnPlateau @@ -51,7 +51,6 @@ def load_data(train_path, test_path, gParameters): df_y_train = df_train[:, 0].astype('int') df_y_test = df_test[:, 0].astype('int') - # only training set has noise Y_train = to_categorical(df_y_train, gParameters['classes']) Y_test = to_categorical(df_y_test, gParameters['classes']) @@ -68,29 +67,11 @@ def load_data(train_path, test_path, gParameters): X_train = mat[:X_train.shape[0], :] X_test = mat[X_train.shape[0]:, :] - # TODO: Add better names for noise boolean, make a featue for both RNA seq and label noise together - # check if noise is on (this is for label) - if gParameters['add_noise']: - # check if we want noise correlated with a feature - if gParameters['noise_correlated']: - Y_train, y_train_noise_gen = candle.label_flip_correlated(Y_train, - gParameters['label_noise'], X_train, - gParameters['feature_col'], - gParameters['feature_threshold']) - # else add uncorrelated noise - else: - Y_train, y_train_noise_gen = candle.label_flip(Y_train, gParameters['label_noise']) - # check if noise is on for RNA-seq data - elif gParameters['noise_gaussian']: - X_train = candle.add_gaussian_noise(X_train, 0, gParameters['std_dev']) - return X_train, Y_train, X_test, Y_test def run(gParameters): - print('Params:', gParameters) - file_train = gParameters['train_data'] file_test = gParameters['test_data'] url = gParameters['data_url'] @@ -100,6 +81,9 @@ def run(gParameters): X_train, Y_train, X_test, Y_test = load_data(train_file, test_file, gParameters) + # only training set has noise + X_train, Y_train = candle.add_noise(X_train, Y_train, gParameters) + print('X_train shape:', X_train.shape) print('X_test shape:', X_test.shape) @@ -222,7 +206,7 @@ def run(gParameters): # path = '{}/{}.autosave.model.h5'.format(output_dir, model_name) # checkpointer = ModelCheckpoint(filepath=path, verbose=1, save_weights_only=False, save_best_only=True) csv_logger = CSVLogger('{}/training.log'.format(output_dir)) - reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=10, verbose=1, mode='auto', epsilon=0.0001, cooldown=0, min_lr=0) + reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=10, verbose=1, mode='auto', min_delta=0.0001, cooldown=0, min_lr=0) candleRemoteMonitor = candle.CandleRemoteMonitor(params=gParameters) timeoutMonitor = candle.TerminateOnTimeOut(gParameters['timeout']) history = model.fit(X_train, Y_train, @@ -242,11 +226,6 @@ def run(gParameters): with open("{}/{}.model.json".format(output_dir, model_name), "w") as json_file: json_file.write(model_json) - # serialize model to YAML - model_yaml = model.to_yaml() - with open("{}/{}.model.yaml".format(output_dir, model_name), "w") as yaml_file: - yaml_file.write(model_yaml) - # serialize weights to HDF5 model.save_weights("{}/{}.weights.h5".format(output_dir, model_name)) print("Saved model to disk") @@ -257,12 +236,6 @@ def run(gParameters): json_file.close() loaded_model_json = model_from_json(loaded_model_json) - # load yaml and create model - yaml_file = open('{}/{}.model.yaml'.format(output_dir, model_name), 'r') - loaded_model_yaml = yaml_file.read() - yaml_file.close() - loaded_model_yaml = model_from_yaml(loaded_model_yaml) - # load weights into new model loaded_model_json.load_weights('{}/{}.weights.h5'.format(output_dir, model_name)) print("Loaded json model from disk") @@ -278,21 +251,6 @@ def run(gParameters): print("json %s: %.2f%%" % (loaded_model_json.metrics_names[1], score_json[1] * 100)) - # load weights into new model - loaded_model_yaml.load_weights('{}/{}.weights.h5'.format(output_dir, model_name)) - print("Loaded yaml model from disk") - - # evaluate loaded model on test data - loaded_model_yaml.compile(loss=gParameters['loss'], - optimizer=gParameters['optimizer'], - metrics=[gParameters['metrics']]) - score_yaml = loaded_model_yaml.evaluate(X_test, Y_test, verbose=0) - - print('yaml Test score:', score_yaml[0]) - print('yaml Test accuracy:', score_yaml[1]) - - print("yaml %s: %.2f%%" % (loaded_model_yaml.metrics_names[1], score_yaml[1] * 100)) - return history diff --git a/Pilot1/NT3/nt3_default_model.txt b/Pilot1/NT3/nt3_default_model.txt index 61f00b4d..708b7051 100644 --- a/Pilot1/NT3/nt3_default_model.txt +++ b/Pilot1/NT3/nt3_default_model.txt @@ -20,10 +20,14 @@ output_dir = '.' add_noise = False label_noise = 0.3 noise_correlated = False +noise_cluster = False +noise_column = False +noise_type = 'gaussian' feature_col = 50 feature_threshold = 0.01 noise_gaussian = False std_dev = 0.5 timeout = 3600 ckpt_restart_mode = 'off' +ckpt_save_interval = 0 ckpt_checksum = True diff --git a/Pilot1/NT3/nt3_noise_model.txt b/Pilot1/NT3/nt3_noise_model.txt index 88c5f19d..6c8f1d73 100644 --- a/Pilot1/NT3/nt3_noise_model.txt +++ b/Pilot1/NT3/nt3_noise_model.txt @@ -3,14 +3,14 @@ data_url = 'http://ftp.mcs.anl.gov/pub/candle/public/benchmarks/Pilot1/normal-tu train_data = 'nt_train2.csv' test_data = 'nt_test2.csv' model_name = 'nt3' -conv = [64, 20, 1, 64, 10, 1] +conv = [128, 20, 1, 128, 10, 1] dense = [200,20] activation = 'elu' out_activation = 'softmax' loss = 'categorical_crossentropy' optimizer = 'sgd' metrics = 'accuracy' -epochs = 100 +epochs = 200 batch_size = 16 learning_rate = 0.002 dropout = 0.0 @@ -18,18 +18,24 @@ classes = 2 pool = [1, 10] output_dir = '.' timeout = 3600 -add_noise = True -noise_correlated = True +add_noise = False +noise_correlated = False +noise_gaussian = True +noise_cluster = False +noise_column = False +noise_type = 'gaussian' +std_dev = 0.5 label_noise = 0.3 -feature_col = 1000 +feature_col = [ 8570, 9054, 11206, 11493, 12122, 12660, 14837, 16467, 18122, 24384, 30810, 34180, 36520, 38698, 42393, 45322, 48671, 52177, 53259, 55111, 58261, 58361, 59900] +sample_ids = [ 1, 2, 3, 4, 7, 10, 15, 33, 38, 39, 40, 41, 45, 53, 55, 56, 58, 62, 63, 64, 65, 70, 75, 77, 80, 89, 90, 93, 95, 99, 102, 103, 104, 113, 116, 117, 126, 133, 143, 145, 149, 152, 154, 157, 161, 164, 168, 169, 170, 175, 181, 183, 184, 187, 189, 191, 200, 202, 203, 206, 213, 217, 218, 220, 223, 228, 233, 235, 237, 244, 249, 255, 257, 259, 261, 262, 264, 265, 267, 268, 273, 280, 282, 283, 289, 290, 292, 294, 295, 297, 303, 304, 305, 309, 310, 311, 317, 321, 322, 323, 324, 327, 328, 330, 331, 332, 334, 335, 336, 337, 339, 340, 342, 351, 353, 354, 360, 363, 364, 365, 368, 378, 379, 380, 381, 383, 384, 387, 390, 395, 398, 403, 405, 407, 414, 415, 432, 434, 435, 438, 441, 451, 453, 455, 456, 457, 459, 462, 464, 465, 467, 468, 469, 470, 471, 472, 473, 476, 479, 482, 484, 486, 488, 489, 493, 497, 499, 503, 506, 511, 514, 517, 518, 521, 524, 525, 529, 530, 531, 532, 534, 539, 540, 543, 544, 547, 549, 550, 551, 559, 560, 563, 572, 574, 575, 577, 580, 582, 585, 589, 591, 595, 597, 600, 601, 606, 607, 609, 615, 623, 628, 629, 631, 637, 643, 645, 646, 647, 649, 651, 653, 667, 674, 677, 679, 685, 687, 689, 692, 693, 696, 698, 702, 703, 706, 707, 711, 712, 714, 721, 722, 724, 728, 729, 731, 733, 741, 743, 745, 747, 751, 754, 755, 759, 760, 761, 765, 768, 770, 771, 780, 781, 792, 793, 802, 806, 810, 812, 817, 818, 820, 822, 824, 827, 828, 832, 835, 842, 848, 851, 852, 854, 855, 861, 863, 864, 871, 875, 877, 881, 883, 885, 888, 892, 893, 900, 902, 905, 908, 910, 911, 916, 922, 923, 924, 932, 940, 941, 942, 950, 953, 957, 958, 960, 962, 968, 971, 976, 978, 979, 993, 994, 995, 1006, 1008, 1009, 1013, 1015, 1018, 1025, 1033, 1034, 1036, 1039, 1041, 1048, 1052, 1053, 1058, 1062, 1068, 1075, 1076, 1078, 1079, 1082, 1083, 1089, 1091, 1093, 1103, 1105, 1106, 1115, 1118, 1119] feature_threshold = 0.01 shuffle = False -acc_gain = 0.5 +acc_gain = 1.5 abs_gain = 0.5 min_acc = 0.99 max_abs = 0.30 alpha = 0.3 -alpha_scale_factor = 0.95 -init_abs_epoch = 2 +alpha_scale_factor = 0.8 +init_abs_epoch = 5 task_list = 0 -task_names = ['activation_5'] +task_names = ['activation_4'] diff --git a/Pilot1/NT3/nt3_perf_bench_model.txt b/Pilot1/NT3/nt3_perf_bench_model.txt index 41af2c32..f7df59a6 100644 --- a/Pilot1/NT3/nt3_perf_bench_model.txt +++ b/Pilot1/NT3/nt3_perf_bench_model.txt @@ -5,6 +5,7 @@ test_data = 'nt_test2.csv' model_name = 'nt3' conv = [128, 20, 1, 128, 10, 1] dense = [200,20] +pool = [1, 10] activation = 'relu' out_activation = 'softmax' loss = 'categorical_crossentropy' @@ -15,6 +16,6 @@ batch_size = 5 learning_rate = 0.001 dropout = 0.1 classes = 2 -pool = [1, 10] output_dir = '.' +add_noise = False timeout = 7200 diff --git a/Pilot1/NT3/test.sh b/Pilot1/NT3/test.sh index 11db9499..28ac1144 100755 --- a/Pilot1/NT3/test.sh +++ b/Pilot1/NT3/test.sh @@ -1,5 +1,7 @@ #!/bin/bash +set -x + python nt3_baseline_keras2.py -e 2 # python nt3_baseline_keras2_tensorrt.py -e 2 python nt3_abstention_keras2.py -e 2 diff --git a/Pilot1/P1B1/p1b1.py b/Pilot1/P1B1/p1b1.py index eb35c2dc..3bf84a31 100644 --- a/Pilot1/P1B1/p1b1.py +++ b/Pilot1/P1B1/p1b1.py @@ -1,7 +1,6 @@ from __future__ import print_function import os -import sys import logging import pandas as pd @@ -11,8 +10,6 @@ from scipy.stats.stats import pearsonr file_path = os.path.dirname(os.path.realpath(__file__)) -lib_path2 = os.path.abspath(os.path.join(file_path, '..', '..', 'common')) -sys.path.append(lib_path2) import candle diff --git a/Pilot1/P1B1/test.sh b/Pilot1/P1B1/test.sh index 8af81f13..7f591aa7 100755 --- a/Pilot1/P1B1/test.sh +++ b/Pilot1/P1B1/test.sh @@ -1 +1,2 @@ +set -x python p1b1_baseline_keras2.py --use_landmark_genes True --train_samples 100 --epochs 5 diff --git a/Pilot1/P1B2/p1b2.py b/Pilot1/P1B2/p1b2.py index 4f3036c1..f9b0d1a3 100644 --- a/Pilot1/P1B2/p1b2.py +++ b/Pilot1/P1B2/p1b2.py @@ -5,14 +5,9 @@ from sklearn.metrics import accuracy_score import os -import sys import logging file_path = os.path.dirname(os.path.realpath(__file__)) -lib_path = os.path.abspath(os.path.join(file_path, '..', 'common')) -sys.path.append(lib_path) -lib_path2 = os.path.abspath(os.path.join(file_path, '..', '..', 'common')) -sys.path.append(lib_path2) import candle diff --git a/Pilot1/P1B2/test.sh b/Pilot1/P1B2/test.sh index fbd43711..a2506ee0 100755 --- a/Pilot1/P1B2/test.sh +++ b/Pilot1/P1B2/test.sh @@ -1 +1,2 @@ +set -x python p1b2_baseline_keras2.py --epochs 3 diff --git a/Pilot1/P1B3/p1b3.py b/Pilot1/P1B3/p1b3.py index 21d2e6bd..c42cfeab 100644 --- a/Pilot1/P1B3/p1b3.py +++ b/Pilot1/P1B3/p1b3.py @@ -18,14 +18,12 @@ from sklearn.preprocessing import StandardScaler, MinMaxScaler, MaxAbsScaler file_path = os.path.dirname(os.path.realpath(__file__)) -lib_path = os.path.abspath(os.path.join(file_path, '..')) -sys.path.append(lib_path) -lib_path2 = os.path.abspath(os.path.join(file_path, '..', '..', 'common')) -sys.path.append(lib_path2) import candle logger = logging.getLogger(__name__) +logger.setLevel(logging.INFO) +logger.addHandler(logging.StreamHandler(sys.stdout)) # Number of data generator workers WORKERS = 1 @@ -258,7 +256,7 @@ def load_cellline_expressions(path, dtype, ncols=None, scaling='std'): df1 = df1.map(lambda x: x.replace('.', ':')) df1.name = 'CELLNAME' - df2 = df.drop('CellLine', 1) + df2 = df.drop('CellLine', axis=1) total = df2.shape[1] if ncols and ncols < total: @@ -296,7 +294,7 @@ def load_cellline_mirna(path, dtype, ncols=None, scaling='std'): df1 = df1.map(lambda x: x.replace('.', ':')) df1.name = 'CELLNAME' - df2 = df.drop('CellLine', 1) + df2 = df.drop('CellLine', axis=1) total = df2.shape[1] if ncols and ncols < total: @@ -381,7 +379,7 @@ def load_drug_descriptors(path, dtype, ncols=None, scaling='std'): df1 = pd.DataFrame(df.loc[:, 'NAME']) df1.rename(columns={'NAME': 'NSC'}, inplace=True) - df2 = df.drop('NAME', 1) + df2 = df.drop('NAME', axis=1) # # Filter columns if requested @@ -419,7 +417,7 @@ def load_drug_autoencoded(path, dtype, ncols=None, scaling='std'): df = pd.read_csv(path, engine='c', converters={'NSC': str}, dtype=dtype) df1 = pd.DataFrame(df.loc[:, 'NSC']) - df2 = df.drop('NSC', 1) + df2 = df.drop('NSC', axis=1) total = df2.shape[1] if ncols and ncols < total: @@ -735,7 +733,7 @@ def flow(self): elif fea == 'noise': df = df.merge(self.data.df_drug_rand, on='NSC') - df = df.drop(['CELLNAME', 'NSC'], 1) + df = df.drop(['CELLNAME', 'NSC'], axis=1) x = np.array(df.iloc[:, 1:]) if self.cell_noise_sigma: diff --git a/Pilot1/P1B3/p1b3_baseline_keras2.py b/Pilot1/P1B3/p1b3_baseline_keras2.py index e1765053..e4aa6b46 100644 --- a/Pilot1/P1B3/p1b3_baseline_keras2.py +++ b/Pilot1/P1B3/p1b3_baseline_keras2.py @@ -137,7 +137,7 @@ def on_epoch_end(self, batch, logs={}): class MyProgbarLogger(ProgbarLogger): def __init__(self, samples): - super(MyProgbarLogger, self).__init__(count_mode='samples') + super(MyProgbarLogger, self).__init__(count_mode='steps') self.samples = samples self.params = {} diff --git a/Pilot1/P1B3/test.sh b/Pilot1/P1B3/test.sh index 03cf635e..16b2e950 100755 --- a/Pilot1/P1B3/test.sh +++ b/Pilot1/P1B3/test.sh @@ -1,2 +1,3 @@ +set -x python p1b3_baseline_keras2.py --feature_subsample 500 -e 5 --train_steps 100 --val_steps 10 --test_steps 10 python p1b3_baseline_keras2.py --conf p1b3_conv_model.txt -e 5 --train_steps 100 --val_steps 10 --test_steps 10 diff --git a/Pilot1/ST1/README.md b/Pilot1/ST1/README.md new file mode 100644 index 00000000..7c0e668f --- /dev/null +++ b/Pilot1/ST1/README.md @@ -0,0 +1,182 @@ +# Simple transformers for classification and regression using SMILE string input + +## Introduction +The ST1 benchmark represent two versions of a simple transformer, one that can perform regression and the other classification. We chose the transformer architecture to see if we could train directly on SMILE strings. This benchmark brings novel capability to the suite of Pilot1 benchmarks in two ways. First, the featureization of a small molecule is simply its SMILE string. The second novel aspect to the set of Pilot1 benchmarks is that the model is based on the Transformer architecture, albeit this benchmark is a simpler version of the large Transformer models that train on billions and greater parameters. + +Both the original code and the CANDLE versions are available. The original examples are retained and can be run as noted below. The CANDLE versions make use of the common network design in `smiles_transformer.py`, and implement the models in `sct_baseline_keras2.py` and `srt_baseline_keras2.py`, for classification and regression, respectively. + +The example classification problem takes as input SMILE strings and trains a model to predict whether or not a compound is 'drug-like' based on Lipinski criteria. The example regression problem takes as input SMILE strings and trains a model to predict the molecular weight. Data are freely downloadable and automatically downloaded by the CANDLE versions. + +For the CANDLE versions, all the relevant arguments are contained in the respective default model files. All variables can be overwritten from the command line. The datasets will be automatically downloaded and stored in the `../../Data/Pilot1 directory`. The respective default model files and commands to invoke the classifier and regressor are: +``` +class_default_model.txt +python sct_baseline_keras2.py +``` +and + +``` +regress_default_model.txt +python srt_baseline_keras2.py +``` + +## Running the original versions +The original code demonstrating a simple transformer regressor and a simple transformer classifier are available as +``` +smiles_regress_transformer.py +``` +and + +``` +smiles_class_transformer.py +``` + +The example data sets are the same as for the CANDLE versions, and allow one to predict whether a small molecule is "drug-like" based on Lipinski criteria (classification problem), or predict the molecular weight (regression) from a SMILE string as input. +The example data sets are downloadable using the information in the `regress_default_model.txt` or `class_default_model.txt` files. +These data files must be downloaded manually and specified on the command line for execution of the original versions. + +``` +# for regression +train_data = https://ftp.mcs.anl.gov/pub/candle/public/benchmarks/Examples/xform-smiles-data/chm.weight.trn.csv +val_data = https://ftp.mcs.anl.gov/pub/candle/public/benchmarks/Examples/xform-smiles-data/chm.weight.val.csv + + +# for classification +train_data = https://ftp.mcs.anl.gov/pub/candle/public/benchmarks/Examples/xform-smiles-data/chm.lipinski.trn.csv +val_data = https://ftp.mcs.anl.gov/pub/candle/public/benchmarks/Examples/xform-smiles-data/chm.lipinski.val.csv +``` + +To run the models +``` +CUDA_VISIBLE_DEVICES=1 python smiles_class_transformer.py --in_train chm.lipinski.trn.csv --in_vali chm.lipinski.val.csv --ep 25 +``` +or + +``` +CUDA_VISIBLE_DEVICES=0 python smiles_regress_transformer.py --in_train chm.weight.trn.csv --in_vali chm.weight.val.csv --ep 25 +``` +The model with the best validation loss is saved in the .h5 dumps. Log files contain the trace. Regression output should look something like this. +``` +Epoch 1/25 +2022-03-21 12:53:11.402337: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.10 +46875/46875 [==============================] - 4441s 95ms/step - loss: 11922.3690 - mae: 77.4828 - r2: 0.3369 - val_loss: 1460.3314 - val_mae: 21.4797 - val_r2: 0.9164 + +Epoch 00001: val_loss improved from inf to 1460.33142, saving model to smile_regress.autosave.model.h5 +Epoch 2/25 +46875/46875 [==============================] - 4431s 95ms/step - loss: 5460.8232 - mae: 55.1082 - r2: 0.6845 - val_loss: 1451.3109 - val_mae: 27.5647 - val_r2: 0.9163 + +Epoch 00002: val_loss improved from 1460.33142 to 1451.31091, saving model to smile_regress.autosave.model.h5 +Epoch 3/25 +46875/46875 [==============================] - 4428s 94ms/step - loss: 5007.1718 - mae: 52.5552 - r2: 0.7112 - val_loss: 1717.9198 - val_mae: 31.0688 - val_r2: 0.9004 + +Epoch 00003: val_loss did not improve from 1451.31091 +Epoch 4/25 +46875/46875 [==============================] - 4419s 94ms/step - loss: 4844.3624 - mae: 51.5771 - r2: 0.7206 - val_loss: 1854.3645 - val_mae: 35.4912 - val_r2: 0.8908 + +Epoch 00004: val_loss did not improve from 1451.31091 +Epoch 5/25 +46875/46875 [==============================] - 4715s 101ms/step - loss: 4754.6214 - mae: 51.0409 - r2: 0.7277 - val_loss: 1404.8254 - val_mae: 25.8863 - val_r2: 0.9200 + +Epoch 00005: val_loss improved from 1451.31091 to 1404.82544, saving model to smile_regress.autosave.model.h5 +Epoch 6/25 +46875/46875 [==============================] - 4474s 95ms/step - loss: 4679.2172 - mae: 50.7018 - r2: 0.7314 - val_loss: 1353.6165 - val_mae: 23.1900 - val_r2: 0.9252 + +Epoch 00006: val_loss improved from 1404.82544 to 1353.61646, saving model to smile_regress.autosave.model.h5 +Epoch 7/25 +46875/46875 [==============================] - 4421s 94ms/step - loss: 4585.5881 - mae: 50.3534 - r2: 0.7359 - val_loss: 1312.0532 - val_mae: 27.0301 - val_r2: 0.9234 + +Epoch 00007: val_loss improved from 1353.61646 to 1312.05322, saving model to smile_regress.autosave.model.h5 +Epoch 8/25 +46875/46875 [==============================] - 4420s 94ms/step - loss: 4587.0872 - mae: 50.2153 - r2: 0.7348 - val_loss: 1064.2247 - val_mae: 19.8623 - val_r2: 0.9399 + +Epoch 00008: val_loss improved from 1312.05322 to 1064.22473, saving model to smile_regress.autosave.model.h5 +Epoch 9/25 +46875/46875 [==============================] - 4423s 94ms/step - loss: 4587.0479 - mae: 50.0801 - r2: 0.7369 - val_loss: 1592.4563 - val_mae: 29.7814 - val_r2: 0.9085 + +Epoch 00009: val_loss did not improve from 1064.22473 +Epoch 10/25 +46875/46875 [==============================] - 4425s 94ms/step - loss: 4558.5997 - mae: 49.9495 - r2: 0.7383 - val_loss: 1683.6605 - val_mae: 33.6503 - val_r2: 0.9023 + +Epoch 00010: val_loss did not improve from 1064.22473 +Epoch 11/25 +46875/46875 [==============================] - 4422s 94ms/step - loss: 4546.4993 - mae: 49.8544 - r2: 0.7401 - val_loss: 950.7401 - val_mae: 17.9388 - val_r2: 0.9465 + +Epoch 00011: val_loss improved from 1064.22473 to 950.74005, saving model to smile_regress.autosave.model.h5 +Epoch 12/25 +46875/46875 [==============================] - 4425s 94ms/step - loss: 4442.8416 - mae: 49.5132 - r2: 0.7436 - val_loss: 1060.0773 - val_mae: 18.5870 - val_r2: 0.9397 + +Epoch 00012: val_loss did not improve from 950.74005 +Epoch 13/25 +46875/46875 [==============================] - 4429s 94ms/step - loss: 4481.1873 - mae: 49.5325 - r2: 0.7434 - val_loss: 878.7765 - val_mae: 14.4166 - val_r2: 0.9515 + +Epoch 00013: val_loss improved from 950.74005 to 878.77649, saving model to smile_regress.autosave.model.h5 +Epoch 14/25 +46875/46875 [==============================] - 4428s 94ms/step - loss: 4474.0927 - mae: 49.4286 - r2: 0.7445 - val_loss: 1116.5386 - val_mae: 23.1262 - val_r2: 0.9360 + +Epoch 00014: val_loss did not improve from 878.77649 +Epoch 15/25 +46875/46875 [==============================] - 4422s 94ms/step - loss: 4426.6833 - mae: 49.2094 - r2: 0.7446 - val_loss: 883.4428 - val_mae: 13.1046 - val_r2: 0.9507 + +Epoch 00015: val_loss did not improve from 878.77649 +Epoch 16/25 +46875/46875 [==============================] - 4416s 94ms/step - loss: 4386.3840 - mae: 49.0872 - r2: 0.7476 - val_loss: 981.3953 - val_mae: 14.7772 - val_r2: 0.9451 + +Epoch 00016: val_loss did not improve from 878.77649 +Epoch 17/25 +46875/46875 [==============================] - 4423s 94ms/step - loss: 4384.2891 - mae: 49.0281 - r2: 0.7464 - val_loss: 887.1695 - val_mae: 14.6686 - val_r2: 0.9506 + +Epoch 00017: val_loss did not improve from 878.77649 +Epoch 18/25 +46875/46875 [==============================] - 4417s 94ms/step - loss: 4398.2363 - mae: 48.9583 - r2: 0.7487 - val_loss: 849.0564 - val_mae: 12.2192 - val_r2: 0.9528 + +Epoch 00018: val_loss improved from 878.77649 to 849.05640, saving model to smile_regress.autosave.model.h5 +Epoch 19/25 +46875/46875 [==============================] - 4422s 94ms/step - loss: 4350.2122 - mae: 48.8221 - r2: 0.7505 - val_loss: 847.2310 - val_mae: 13.4015 - val_r2: 0.9533 + +Epoch 00019: val_loss improved from 849.05640 to 847.23096, saving model to smile_regress.autosave.model.h5 +Epoch 20/25 +46875/46875 [==============================] - 4428s 94ms/step - loss: 4346.0122 - mae: 48.7704 - r2: 0.7513 - val_loss: 964.1797 - val_mae: 17.7863 - val_r2: 0.9453 + +Epoch 00020: val_loss did not improve from 847.23096 +Epoch 21/25 +46875/46875 [==============================] - 4424s 94ms/step - loss: 4293.9141 - mae: 48.5882 - r2: 0.7521 - val_loss: 800.8525 - val_mae: 12.8857 - val_r2: 0.9556 + +Epoch 00021: val_loss improved from 847.23096 to 800.85254, saving model to smile_regress.autosave.model.h5 +Epoch 22/25 +46875/46875 [==============================] - 4427s 94ms/step - loss: 4323.8214 - mae: 48.5665 - r2: 0.7524 - val_loss: 835.3901 - val_mae: 14.5708 - val_r2: 0.9534 + +Epoch 00022: val_loss did not improve from 800.85254 +Epoch 23/25 +46875/46875 [==============================] - 4429s 94ms/step - loss: 4311.1271 - mae: 48.5622 - r2: 0.7528 - val_loss: 820.6389 - val_mae: 14.3753 - val_r2: 0.9547 + +Epoch 00023: val_loss did not improve from 800.85254 +Epoch 24/25 +46875/46875 [==============================] - 4427s 94ms/step - loss: 4286.2930 - mae: 48.3628 - r2: 0.7548 - val_loss: 815.2863 - val_mae: 12.7142 - val_r2: 0.9549 + +Epoch 00024: val_loss did not improve from 800.85254 +Epoch 25/25 +46875/46875 [==============================] - 4422s 94ms/step - loss: 4259.8291 - mae: 48.3112 - r2: 0.7556 - val_loss: 813.1475 - val_mae: 12.2975 - val_r2: 0.9564 + +Epoch 00025: val_loss did not improve from 800.85254 +``` + + +## Example classification problem metrics +CHEMBL -- 1.5M training examples +Predicting Lipinski criteria for drug likeness (1/0) +Validation 100K samples non-overlapping +Classification validation accuracy is about 91% after 10-20 epochs + +## Example regression problem metrics +CHEMBL -- 1.5M training examples (shuffled and resampled so not same 1.5M as classification) +Predicting molecular Weight validation +Is also 100K samples non-overlapping. +Regression problem achieves R^2 about .95 after ~20 epochs. + + + + + + + + + diff --git a/Pilot1/ST1/class_default_model.txt b/Pilot1/ST1/class_default_model.txt new file mode 100644 index 00000000..63ad4a94 --- /dev/null +++ b/Pilot1/ST1/class_default_model.txt @@ -0,0 +1,20 @@ +[Global_Params] +data_url = "https://ftp.mcs.anl.gov/pub/candle/public/benchmarks/Examples/xform-smiles-data/" +train_data = 'chm.lipinski.trn.csv' +val_data = 'chm.lipinski.val.csv' +dropout = 0.1 +dense = [1024, 256, 64, 16] +activation = 'relu' +out_layer = 2 +out_activation = 'softmax' +transformer_depth = 4 +embed_dim = 128 +ff_dim = 128 +maxlen = 250 +num_heads = 16 +vocab_size = 40000 +epochs = 400 +batch_size = 32 +loss = 'sparse_categorical_crossentropy' +learning_rate = 0.000001 +optimizer = 'adam' diff --git a/Pilot1/ST1/regress_default_model.txt b/Pilot1/ST1/regress_default_model.txt new file mode 100644 index 00000000..56417085 --- /dev/null +++ b/Pilot1/ST1/regress_default_model.txt @@ -0,0 +1,20 @@ +[Global_Params] +data_url = "https://ftp.mcs.anl.gov/pub/candle/public/benchmarks/Examples/xform-smiles-data/" +train_data = 'chm.weight.trn.csv' +val_data = 'chm.weight.val.csv' +dropout = 0.1 +dense = [1024, 256, 64, 16] +activation = 'relu' +out_layer = 1 +out_activation = 'relu' +transformer_depth = 4 +embed_dim = 128 +ff_dim = 128 +maxlen = 250 +num_heads = 16 +vocab_size = 40000 +epochs = 400 +batch_size = 32 +loss = 'mean_squared_error' +optimizer = 'adam' +learning_rate = 0.001 diff --git a/Pilot1/ST1/regress_docking_model.txt b/Pilot1/ST1/regress_docking_model.txt new file mode 100644 index 00000000..9252ebcf --- /dev/null +++ b/Pilot1/ST1/regress_docking_model.txt @@ -0,0 +1,19 @@ +[Global_Params] +data_url = "https://ftp.mcs.anl.gov/pub/candle/public/benchmarks/Examples/xform-smiles-data/" +train_data = 'Nsp13.helicase_m1_pocket2.train' +val_data = 'Nsp13.helicase_m1_pocket2.val' +dropout = 0.1 +dense = [1024, 256, 64, 16] +activation = 'relu' +out_layer = 1 +out_activation = 'relu' +transformer_depth = 4 +embed_dim = 128 +ff_dim = 128 +maxlen = 250 +num_heads = 16 +vocab_size = 40000 +epochs = 400 +batch_size = 32 +loss = 'mean_squared_error' +optimizer='Adam' diff --git a/Pilot1/ST1/sct_baseline_keras2.py b/Pilot1/ST1/sct_baseline_keras2.py new file mode 100644 index 00000000..80b06387 --- /dev/null +++ b/Pilot1/ST1/sct_baseline_keras2.py @@ -0,0 +1,70 @@ +# Setup + +import os + +from tensorflow.keras import backend as K +from tensorflow.keras.callbacks import ModelCheckpoint, CSVLogger, ReduceLROnPlateau, EarlyStopping + +file_path = os.path.dirname(os.path.realpath(__file__)) + +import candle +import smiles_transformer as st + + +def initialize_parameters(default_model='class_default_model.txt'): + + # Build benchmark object + sctBmk = st.BenchmarkST(st.file_path, default_model, 'keras', + prog='sct_baseline', + desc='Transformer model for SMILES classification') + + # Initialize parameters + gParameters = candle.finalize_parameters(sctBmk) + + return gParameters + + +# Train and Evaluate + +def run(params): + + x_train, y_train, x_val, y_val = st.load_data(params) + + model = st.transformer_model(params) + + kerasDefaults = candle.keras_default_config() + + optimizer = candle.build_optimizer(params['optimizer'], params['learning_rate'], kerasDefaults) + + model.compile(loss=params['loss'], + optimizer=optimizer, + metrics=['accuracy']) + + # set up a bunch of callbacks to do work during model training.. + + checkpointer = ModelCheckpoint(filepath='smile_class.autosave.model.h5', verbose=1, save_weights_only=True, save_best_only=True) + csv_logger = CSVLogger('smile_class.training.log') + reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.75, patience=20, verbose=1, mode='auto', epsilon=0.0001, cooldown=3, min_lr=0.000000001) + early_stop = EarlyStopping(monitor='val_loss', patience=100, verbose=1, mode='auto') + + history = model.fit(x_train, y_train, + batch_size=params['batch_size'], + epochs=params['epochs'], + verbose=1, + validation_data=(x_val, y_val), + callbacks=[checkpointer, csv_logger, reduce_lr, early_stop]) + + model.load_weights('smile_class.autosave.model.h5') + + return history + + +def main(): + params = initialize_parameters() + run(params) + + +if __name__ == '__main__': + main() + if K.backend() == 'tensorflow': + K.clear_session() diff --git a/Pilot1/ST1/smiles_class_transformer.py b/Pilot1/ST1/smiles_class_transformer.py new file mode 100644 index 00000000..c90ee465 --- /dev/null +++ b/Pilot1/ST1/smiles_class_transformer.py @@ -0,0 +1,171 @@ +# Setup + +import pandas as pd +import os +import argparse + +import matplotlib +matplotlib.use('Agg') + +import tensorflow as tf + +from tensorflow import keras +from tensorflow.keras import backend as K +from tensorflow.keras.optimizers import Adam +from tensorflow.keras.callbacks import ModelCheckpoint, CSVLogger, ReduceLROnPlateau, EarlyStopping +from tensorflow.keras import layers +from tensorflow.keras.preprocessing import sequence +from tensorflow.keras.preprocessing import text + +file_path = os.path.dirname(os.path.realpath(__file__)) + +psr = argparse.ArgumentParser(description='input csv file') +psr.add_argument('--in_train', default='in_train') +psr.add_argument('--in_vali', default='in_vali') +psr.add_argument('--ep', type=int, default=400) +args = vars(psr.parse_args()) +print(args) + +EPOCH = args['ep'] +BATCH = 32 + +data_path_train = args['in_train'] +data_path_vali = args['in_vali'] + +DR = 0.1 # Dropout rate + +# define r2 for reporting + + +def r2(y_true, y_pred): + SS_res = K.sum(K.square(y_true - y_pred)) + SS_tot = K.sum(K.square(y_true - K.mean(y_true))) + return (1 - SS_res / (SS_tot + K.epsilon())) + +# Implement a Transformer block as a layer + + +class TransformerBlock(layers.Layer): + def __init__(self, embed_dim, num_heads, ff_dim, rate=0.1): + super(TransformerBlock, self).__init__() + self.att = layers.MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim) + self.ffn = keras.Sequential( + [layers.Dense(ff_dim, activation="relu"), layers.Dense(embed_dim), ] + ) + self.layernorm1 = layers.LayerNormalization(epsilon=1e-6) + self.layernorm2 = layers.LayerNormalization(epsilon=1e-6) + self.dropout1 = layers.Dropout(rate) + self.dropout2 = layers.Dropout(rate) + + def call(self, inputs, training): + attn_output = self.att(inputs, inputs) + attn_output = self.dropout1(attn_output, training=training) + out1 = self.layernorm1(inputs + attn_output) + ffn_output = self.ffn(out1) + ffn_output = self.dropout2(ffn_output, training=training) + return self.layernorm2(out1 + ffn_output) + +# Implement embedding layer +# Two seperate embedding layers, one for tokens, one for token index (positions). + + +class TokenAndPositionEmbedding(layers.Layer): + def __init__(self, maxlen, vocab_size, embed_dim): + super(TokenAndPositionEmbedding, self).__init__() + self.token_emb = layers.Embedding(input_dim=vocab_size, output_dim=embed_dim) + self.pos_emb = layers.Embedding(input_dim=maxlen, output_dim=embed_dim) + + def call(self, x): + maxlen = tf.shape(x)[-1] + positions = tf.range(start=0, limit=maxlen, delta=1) + positions = self.pos_emb(positions) + x = self.token_emb(x) + return x + positions + + +# Input and prepare dataset + +vocab_size = 40000 # +maxlen = 250 # + + +data_train = pd.read_csv(data_path_train) +data_vali = pd.read_csv(data_path_vali) + +data_train.head() + +# Dataset has type and smiles as the two fields + +y_train = data_train["type"].values.reshape(-1, 1) * 1.0 +y_val = data_vali["type"].values.reshape(-1, 1) * 1.0 + +tokenizer = text.Tokenizer(num_words=vocab_size) +tokenizer.fit_on_texts(data_train["smiles"]) + + +def prep_text(texts, tokenizer, max_sequence_length): + # Turns text into into padded sequences. + text_sequences = tokenizer.texts_to_sequences(texts) + return sequence.pad_sequences(text_sequences, maxlen=maxlen) + + +x_train = prep_text(data_train["smiles"], tokenizer, maxlen) +x_val = prep_text(data_vali["smiles"], tokenizer, maxlen) + +print(x_train.shape) +print(y_train.shape) + +# Create regression/classifier model using N transformer layers + +embed_dim = 128 # Embedding size for each token +num_heads = 16 # Number of attention heads +ff_dim = 128 # Hidden layer size in feed forward network inside transformer + +inputs = layers.Input(shape=(maxlen,)) +embedding_layer = TokenAndPositionEmbedding(maxlen, vocab_size, embed_dim) +x = embedding_layer(inputs) +transformer_block = TransformerBlock(embed_dim, num_heads, ff_dim) +x = transformer_block(x) +x = transformer_block(x) +x = transformer_block(x) +x = transformer_block(x) + +# x = layers.GlobalAveragePooling1D()(x) --- the original model used this but the accuracy was much lower + +x = layers.Reshape((1, 32000), input_shape=(250, 128,))(x) # reshaping increases parameters but improves accuracy a lot +x = layers.Dropout(0.1)(x) +x = layers.Dense(1024, activation="relu")(x) +x = layers.Dropout(0.1)(x) +x = layers.Dense(256, activation="relu")(x) +x = layers.Dropout(0.1)(x) +x = layers.Dense(64, activation="relu")(x) +x = layers.Dropout(0.1)(x) +x = layers.Dense(16, activation="relu")(x) +x = layers.Dropout(0.1)(x) +outputs = layers.Dense(2, activation="softmax")(x) + +model = keras.Model(inputs=inputs, outputs=outputs) + +model.summary() + +# Train and Evaluate + +model.compile(loss='sparse_categorical_crossentropy', + optimizer=Adam(lr=0.000001), + metrics=['accuracy']) + +# set up a bunch of callbacks to do work during model training.. + +checkpointer = ModelCheckpoint(filepath='smile_class.autosave.model.h5', verbose=1, save_weights_only=True, save_best_only=True) +csv_logger = CSVLogger('smile_class.training.log') +reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.75, patience=20, verbose=1, mode='auto', epsilon=0.0001, cooldown=3, min_lr=0.000000001) +early_stop = EarlyStopping(monitor='val_loss', patience=100, verbose=1, mode='auto') + +history = model.fit(x_train, y_train, + batch_size=BATCH, + epochs=EPOCH, + verbose=1, + validation_data=(x_val, y_val), + callbacks=[checkpointer, csv_logger, reduce_lr, early_stop]) + +model.load_weights('smile_class.autosave.model.h5') diff --git a/Pilot1/ST1/smiles_regress_transformer.py b/Pilot1/ST1/smiles_regress_transformer.py new file mode 100644 index 00000000..3cb05f5f --- /dev/null +++ b/Pilot1/ST1/smiles_regress_transformer.py @@ -0,0 +1,172 @@ +# Setup + +import pandas as pd +import os +import argparse + +import matplotlib +matplotlib.use('Agg') + +import tensorflow as tf + +from tensorflow import keras +from tensorflow.keras import backend as K +from tensorflow.keras.optimizers import Adam +from tensorflow.keras.callbacks import ModelCheckpoint, CSVLogger, ReduceLROnPlateau, EarlyStopping +from tensorflow.keras import layers +from tensorflow.keras.preprocessing import sequence +from tensorflow.keras.preprocessing import text + + +file_path = os.path.dirname(os.path.realpath(__file__)) + +psr = argparse.ArgumentParser(description='input csv file') +psr.add_argument('--in_train', default='in_train') +psr.add_argument('--in_vali', default='in_vali') +psr.add_argument('--ep', type=int, default=400) +args = vars(psr.parse_args()) +print(args) + +EPOCH = args['ep'] +BATCH = 32 + +data_path_train = args['in_train'] +data_path_vali = args['in_vali'] + +DR = 0.1 # Dropout rate + +# define r2 for reporting + + +def r2(y_true, y_pred): + SS_res = K.sum(K.square(y_true - y_pred)) + SS_tot = K.sum(K.square(y_true - K.mean(y_true))) + return (1 - SS_res / (SS_tot + K.epsilon())) + +# Implement a Transformer block as a layer + + +class TransformerBlock(layers.Layer): + def __init__(self, embed_dim, num_heads, ff_dim, rate=0.1): + super(TransformerBlock, self).__init__() + self.att = layers.MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim) + self.ffn = keras.Sequential( + [layers.Dense(ff_dim, activation="relu"), layers.Dense(embed_dim), ] + ) + self.layernorm1 = layers.LayerNormalization(epsilon=1e-6) + self.layernorm2 = layers.LayerNormalization(epsilon=1e-6) + self.dropout1 = layers.Dropout(rate) + self.dropout2 = layers.Dropout(rate) + + def call(self, inputs, training): + attn_output = self.att(inputs, inputs) + attn_output = self.dropout1(attn_output, training=training) + out1 = self.layernorm1(inputs + attn_output) + ffn_output = self.ffn(out1) + ffn_output = self.dropout2(ffn_output, training=training) + return self.layernorm2(out1 + ffn_output) + +# Implement embedding layer +# Two seperate embedding layers, one for tokens, one for token index (positions). + + +class TokenAndPositionEmbedding(layers.Layer): + def __init__(self, maxlen, vocab_size, embed_dim): + super(TokenAndPositionEmbedding, self).__init__() + self.token_emb = layers.Embedding(input_dim=vocab_size, output_dim=embed_dim) + self.pos_emb = layers.Embedding(input_dim=maxlen, output_dim=embed_dim) + + def call(self, x): + maxlen = tf.shape(x)[-1] + positions = tf.range(start=0, limit=maxlen, delta=1) + positions = self.pos_emb(positions) + x = self.token_emb(x) + return x + positions + + +# Input and prepare dataset + +vocab_size = 40000 # +maxlen = 250 # + + +data_train = pd.read_csv(data_path_train) +data_vali = pd.read_csv(data_path_vali) + +data_train.head() + +# Dataset has type and smiles as the two fields + +y_train = data_train["type"].values.reshape(-1, 1) * 1.0 +y_val = data_vali["type"].values.reshape(-1, 1) * 1.0 + +tokenizer = text.Tokenizer(num_words=vocab_size) +tokenizer.fit_on_texts(data_train["smiles"]) + + +def prep_text(texts, tokenizer, max_sequence_length): + # Turns text into into padded sequences. + text_sequences = tokenizer.texts_to_sequences(texts) + return sequence.pad_sequences(text_sequences, maxlen=maxlen) + + +x_train = prep_text(data_train["smiles"], tokenizer, maxlen) +x_val = prep_text(data_vali["smiles"], tokenizer, maxlen) + +print(x_train.shape) +print(y_train.shape) + +# Create regression/classifier model using N transformer layers + +embed_dim = 128 # Embedding size for each token +num_heads = 16 # Number of attention heads +ff_dim = 128 # Hidden layer size in feed forward network inside transformer + +inputs = layers.Input(shape=(maxlen,)) +embedding_layer = TokenAndPositionEmbedding(maxlen, vocab_size, embed_dim) +x = embedding_layer(inputs) +transformer_block = TransformerBlock(embed_dim, num_heads, ff_dim) +x = transformer_block(x) +x = transformer_block(x) +x = transformer_block(x) +x = transformer_block(x) + +# x = layers.GlobalAveragePooling1D()(x) --- the original model used this but the accuracy was much lower + +x = layers.Reshape((1, 32000), input_shape=(250, 128,))(x) # reshaping increases parameters but improves accuracy a lot +x = layers.Dropout(0.1)(x) +x = layers.Dense(1024, activation="relu")(x) +x = layers.Dropout(0.1)(x) +x = layers.Dense(256, activation="relu")(x) +x = layers.Dropout(0.1)(x) +x = layers.Dense(64, activation="relu")(x) +x = layers.Dropout(0.1)(x) +x = layers.Dense(16, activation="relu")(x) +x = layers.Dropout(0.1)(x) +outputs = layers.Dense(1, activation="relu")(x) + +model = keras.Model(inputs=inputs, outputs=outputs) + +model.summary() + +# Train and Evaluate + +model.compile(loss='mean_squared_error', + optimizer=Adam(lr=0.00001), + metrics=['mae', r2]) + +# set up a bunch of callbacks to do work during model training.. + +checkpointer = ModelCheckpoint(filepath='smile_regress.autosave.model.h5', verbose=1, save_weights_only=True, save_best_only=True) +csv_logger = CSVLogger('smile_regress.training.log') +reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.75, patience=20, verbose=1, mode='auto', epsilon=0.0001, cooldown=3, min_lr=0.000000001) +early_stop = EarlyStopping(monitor='val_loss', patience=100, verbose=1, mode='auto') + +history = model.fit(x_train, y_train, + batch_size=BATCH, + epochs=EPOCH, + verbose=1, + validation_data=(x_val, y_val), + callbacks=[checkpointer, csv_logger, reduce_lr, early_stop]) + +model.load_weights('smile_regress.autosave.model.h5') diff --git a/Pilot1/ST1/smiles_transformer.py b/Pilot1/ST1/smiles_transformer.py new file mode 100644 index 00000000..92d645ca --- /dev/null +++ b/Pilot1/ST1/smiles_transformer.py @@ -0,0 +1,181 @@ +from __future__ import print_function + +import os + +import pandas as pd + +import tensorflow as tf +from tensorflow import keras +from tensorflow.keras import layers +from tensorflow.keras.preprocessing import sequence +from tensorflow.keras.preprocessing import text +from tensorflow.keras import backend as K + +file_path = os.path.dirname(os.path.realpath(__file__)) + +import candle + +additional_definitions = [ + {'name': 'embed_dim', + 'type': int, + 'help': 'Embedding dimension for each token'}, + {'name': 'ff_dim', + 'type': int, + 'help': 'Hidden layer size in feed forward network inside transformer'}, + {'name': 'maxlen', + 'type': int, + 'help': 'Maximum sequence length'}, + {'name': 'num_heads', + 'type': int, + 'help': 'Number of attention heads'}, + {'name': 'out_layer', + 'type': int, + 'help': 'Size of output layer'}, + {'name': 'transformer_depth', + 'type': int, + 'help': 'Number of transformer layers'}, + {'name': 'vocab_size', + 'type': int, + 'help': 'Vocabulary size'}, +] + +required = [] + + +class BenchmarkST(candle.Benchmark): + + def set_locals(self): + """Functionality to set variables specific for the benchmark + - required: set of required parameters for the benchmark. + - additional_definitions: list of dictionaries describing the additional parameters for the + benchmark. + """ + + if required is not None: + self.required = set(required) + if additional_definitions is not None: + self.additional_definitions = additional_definitions + + +class TransformerBlock(layers.Layer): + + def __init__(self, embed_dim, num_heads, ff_dim, rate=0.1): + super(TransformerBlock, self).__init__() + self.att = layers.MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim) + self.ffn = keras.Sequential( + [layers.Dense(ff_dim, activation="relu"), layers.Dense(embed_dim), ] + ) + self.layernorm1 = layers.LayerNormalization(epsilon=1e-6) + self.layernorm2 = layers.LayerNormalization(epsilon=1e-6) + self.dropout1 = layers.Dropout(rate) + self.dropout2 = layers.Dropout(rate) + + def call(self, inputs, training): + attn_output = self.att(inputs, inputs) + attn_output = self.dropout1(attn_output, training=training) + out1 = self.layernorm1(inputs + attn_output) + ffn_output = self.ffn(out1) + ffn_output = self.dropout2(ffn_output, training=training) + return self.layernorm2(out1 + ffn_output) + +# Implement embedding layer +# Two seperate embedding layers, one for tokens, one for token index (positions). + + +class TokenAndPositionEmbedding(layers.Layer): + + def __init__(self, maxlen, vocab_size, embed_dim): + super(TokenAndPositionEmbedding, self).__init__() + self.token_emb = layers.Embedding(input_dim=vocab_size, output_dim=embed_dim) + self.pos_emb = layers.Embedding(input_dim=maxlen, output_dim=embed_dim) + + def call(self, x): + maxlen = tf.shape(x)[-1] + positions = tf.range(start=0, limit=maxlen, delta=1) + positions = self.pos_emb(positions) + x = self.token_emb(x) + return x + positions + + +# define r2 for reporting + + +def r2(y_true, y_pred): + SS_res = K.sum(K.square(y_true - y_pred)) + SS_tot = K.sum(K.square(y_true - K.mean(y_true))) + return (1 - SS_res / (SS_tot + K.epsilon())) + + +def prep_text(texts, tokenizer, max_sequence_length): + # Turns text into into padded sequences. + text_sequences = tokenizer.texts_to_sequences(texts) + return sequence.pad_sequences(text_sequences, maxlen=max_sequence_length) + + +def load_data(params): + + data_path_train = candle.fetch_file(params['data_url'] + params['train_data'], 'Pilot1') + data_path_val = candle.fetch_file(params['data_url'] + params['val_data'], 'Pilot1') + + vocab_size = params['vocab_size'] + maxlen = params['maxlen'] + + data_train = pd.read_csv(data_path_train) + data_vali = pd.read_csv(data_path_val) + + data_train.head() + +# Dataset has type and smiles as the two fields + + y_train = data_train["type"].values.reshape(-1, 1) * 1.0 + y_val = data_vali["type"].values.reshape(-1, 1) * 1.0 + + tokenizer = text.Tokenizer(num_words=vocab_size) + tokenizer.fit_on_texts(data_train["smiles"]) + + x_train = prep_text(data_train["smiles"], tokenizer, maxlen) + x_val = prep_text(data_vali["smiles"], tokenizer, maxlen) + + print(x_train.shape) + print(y_train.shape) + + return x_train, y_train, x_val, y_val + + +def transformer_model(params): + + embed_dim = params['embed_dim'] # 128 + ff_dim = params['ff_dim'] # 128 + maxlen = params['maxlen'] # 250 + num_heads = params['num_heads'] # 16 + vocab_size = params['vocab_size'] # 40000 + transformer_depth = params['transformer_depth'] # 4 + + inputs = layers.Input(shape=(maxlen,)) + embedding_layer = TokenAndPositionEmbedding(maxlen, vocab_size, embed_dim) + x = embedding_layer(inputs) + transformer_block = TransformerBlock(embed_dim, num_heads, ff_dim) + for i in range(transformer_depth): + x = transformer_block(x) + + # = layers.GlobalAveragePooling1D()(x) --- the original model used this but the accuracy was much lower + + dropout = params['dropout'] # 0.1 + dense_layers = params['dense'] # [1024, 256, 64, 16] + activation = params['activation'] # 'relu' + out_layer = params['out_layer'] # 2 for class, 1 for regress + out_act = params['out_activation'] # 'softmax' for class, 'relu' for regress + + x = layers.Reshape((1, 32000), input_shape=(250, 128,))(x) # reshaping increases parameters but improves accuracy a lot + x = layers.Dropout(0.1)(x) + for dense in dense_layers: + x = layers.Dense(dense, activation=activation)(x) + x = layers.Dropout(dropout)(x) + + outputs = layers.Dense(out_layer, activation=out_act)(x) + + model = keras.Model(inputs=inputs, outputs=outputs) + + model.summary() + + return model diff --git a/Pilot1/ST1/srt_baseline_keras2.py b/Pilot1/ST1/srt_baseline_keras2.py new file mode 100644 index 00000000..d6ad7c7d --- /dev/null +++ b/Pilot1/ST1/srt_baseline_keras2.py @@ -0,0 +1,92 @@ +# Setup + +import os + +from tensorflow.keras import backend as K +from tensorflow.keras.callbacks import ModelCheckpoint, CSVLogger, ReduceLROnPlateau, EarlyStopping + +file_path = os.path.dirname(os.path.realpath(__file__)) + +import candle +import smiles_transformer as st + +import tensorflow.config.experimental +gpus = tensorflow.config.experimental.list_physical_devices('GPU') +try: + for gpu in gpus: + print("setting memory growth") + tensorflow.config.experimental.set_memory_growth(gpu, True) +except RuntimeError as e: + print(e) + + +def initialize_parameters(default_model='regress_default_model.txt'): + + # Build benchmark object + sctBmk = st.BenchmarkST(st.file_path, default_model, 'keras', + prog='p1b1_baseline', + desc='Multi-task (DNN) for data extraction from clinical reports - Pilot 3 Benchmark 1') + + # Initialize parameters + gParameters = candle.finalize_parameters(sctBmk) + + return gParameters + + +# Train and Evaluate + +def run(params): + + x_train, y_train, x_val, y_val = st.load_data(params) + + model = st.transformer_model(params) + + kerasDefaults = candle.keras_default_config() + + optimizer = candle.build_optimizer(params['optimizer'], params['learning_rate'], kerasDefaults) + + # optimizer = optimizers.deserialize({'class_name': params['optimizer'], 'config': {}}) + + # I don't know why we set base_lr. It doesn't appear to be used. + # if 'base_lr' in params and params['base_lr'] > 0: + # base_lr = params['base_lr'] + # else: + # base_lr = K.get_value(optimizer.lr) + + # if 'learning_rate' in params and params['learning_rate'] > 0: + # K.set_value(optimizer.lr, params['learning_rate']) + # print('Done setting optimizer {} learning rate to {}'.format( + # params['optimizer'], params['learning_rate'])) + + model.compile(loss=params['loss'], + optimizer=optimizer, + metrics=['mae', st.r2]) + + # set up a bunch of callbacks to do work during model training.. + + checkpointer = ModelCheckpoint(filepath='smile_regress.autosave.model.h5', verbose=1, save_weights_only=True, save_best_only=True) + csv_logger = CSVLogger('smile_regress.training.log') + reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.75, patience=20, verbose=1, mode='auto', epsilon=0.0001, cooldown=3, min_lr=0.000000001) + early_stop = EarlyStopping(monitor='val_loss', patience=100, verbose=1, mode='auto') + + history = model.fit(x_train, y_train, + batch_size=params['batch_size'], + epochs=params['epochs'], + verbose=1, + validation_data=(x_val, y_val), + callbacks=[checkpointer, csv_logger, reduce_lr, early_stop]) + + model.load_weights('smile_regress.autosave.model.h5') + + return history + + +def main(): + params = initialize_parameters() + run(params) + + +if __name__ == '__main__': + main() + if K.backend() == 'tensorflow': + K.clear_session() diff --git a/Pilot1/T29/test.sh b/Pilot1/T29/test.sh index cd77cead..8574bbb0 100755 --- a/Pilot1/T29/test.sh +++ b/Pilot1/T29/test.sh @@ -1,3 +1,4 @@ ./prep.sh +set -x python t29res.py -e 5 python infer.py --model t29res.model.json --weights t29res.model.h5 --n_pred 10 diff --git a/Pilot1/TC1/tc1.py b/Pilot1/TC1/tc1.py index 562876fa..f33e7529 100644 --- a/Pilot1/TC1/tc1.py +++ b/Pilot1/TC1/tc1.py @@ -1,12 +1,9 @@ from __future__ import print_function import os -import sys import logging file_path = os.path.dirname(os.path.realpath(__file__)) -lib_path2 = os.path.abspath(os.path.join(file_path, '..', '..', 'common')) -sys.path.append(lib_path2) import candle diff --git a/Pilot1/TC1/tc1_baseline_keras2.py b/Pilot1/TC1/tc1_baseline_keras2.py index 979e0f35..684d9c06 100644 --- a/Pilot1/TC1/tc1_baseline_keras2.py +++ b/Pilot1/TC1/tc1_baseline_keras2.py @@ -2,18 +2,15 @@ import numpy as np import os -import sys from tensorflow.keras import backend as K from tensorflow.keras.layers import Dense, Dropout, Activation, Conv1D, MaxPooling1D, Flatten from tensorflow.keras.layers import LocallyConnected1D -from tensorflow.keras.models import Sequential, model_from_json, model_from_yaml +from tensorflow.keras.models import Sequential, model_from_json from tensorflow.keras.callbacks import ModelCheckpoint, CSVLogger, ReduceLROnPlateau file_path = os.path.dirname(os.path.realpath(__file__)) -lib_path2 = os.path.abspath(os.path.join(file_path, '..', '..', 'common')) -sys.path.append(lib_path2) import tc1 as bmk import candle @@ -120,7 +117,7 @@ def run(gParameters): save_weights_only=False, save_best_only=True) csv_logger = CSVLogger('{}/training.log'.format(output_dir)) reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=10, - verbose=1, mode='auto', epsilon=0.0001, cooldown=0, min_lr=0) + verbose=1, mode='auto', min_delta=0.0001, cooldown=0, min_lr=0) history = model.fit(X_train, Y_train, batch_size=gParameters['batch_size'], @@ -139,11 +136,6 @@ def run(gParameters): with open("{}/{}.model.json".format(output_dir, model_name), "w") as json_file: json_file.write(model_json) - # serialize model to YAML - model_yaml = model.to_yaml() - with open("{}/{}.model.yaml".format(output_dir, model_name), "w") as yaml_file: - yaml_file.write(model_yaml) - # serialize weights to HDF5 model.save_weights("{}/{}.model.h5".format(output_dir, model_name)) print("Saved model to disk") @@ -154,12 +146,6 @@ def run(gParameters): json_file.close() loaded_model_json = model_from_json(loaded_model_json) - # load yaml and create model - yaml_file = open('{}/{}.model.yaml'.format(output_dir, model_name), 'r') - loaded_model_yaml = yaml_file.read() - yaml_file.close() - loaded_model_yaml = model_from_yaml(loaded_model_yaml) - # load weights into new model loaded_model_json.load_weights('{}/{}.model.h5'.format(output_dir, model_name)) print("Loaded json model from disk") @@ -175,21 +161,6 @@ def run(gParameters): print("json %s: %.2f%%" % (loaded_model_json.metrics_names[1], score_json[1] * 100)) - # load weights into new model - loaded_model_yaml.load_weights('{}/{}.model.h5'.format(output_dir, model_name)) - print("Loaded yaml model from disk") - - # evaluate loaded model on test data - loaded_model_yaml.compile(loss=gParameters['loss'], - optimizer=gParameters['optimizer'], - metrics=[gParameters['metrics']]) - score_yaml = loaded_model_yaml.evaluate(X_test, Y_test, verbose=0) - - print('yaml Test score:', score_yaml[0]) - print('yaml Test accuracy:', score_yaml[1]) - - print("yaml %s: %.2f%%" % (loaded_model_yaml.metrics_names[1], score_yaml[1] * 100)) - return history diff --git a/Pilot1/TC1/test.sh b/Pilot1/TC1/test.sh index 2403c05d..24fa476d 100755 --- a/Pilot1/TC1/test.sh +++ b/Pilot1/TC1/test.sh @@ -1,2 +1,3 @@ +set -x python tc1_baseline_keras2.py -e 2 python tc1_baseline_keras2.py --conf tc1_modac_model.txt -e 2 diff --git a/Pilot1/Uno/test.sh b/Pilot1/Uno/test.sh index a99adc19..fe88761b 100755 --- a/Pilot1/Uno/test.sh +++ b/Pilot1/Uno/test.sh @@ -1,12 +1,15 @@ #!/bin/bash +set -x + # AUC prediction model -if [ ! -f "top_21_auc_1fold.uno.h5" ]; then - curl -o top_21_auc_1fold.uno.h5 http://ftp.mcs.anl.gov/pub/candle/public/benchmarks/Pilot1/uno/top_21_auc_1fold.uno.h5 +if [ ! -f "$CANDLE_DATA_DIR/Pilot1/Uno/top_21_auc_1fold.uno.h5" ]; then + # download from http://ftp.mcs.anl.gov/pub/candle/public/benchmarks/Pilot1/uno/top_21_auc_1fold.uno.h5 + python -c 'from candle import get_file;get_file(fname="top_21_auc_1fold.uno.h5", origin="https://ftp.mcs.anl.gov/pub/candle/public/benchmarks/Pilot1/uno/top_21_auc_1fold.uno.h5", cache_subdir="Pilot1/Uno")' fi -python uno_baseline_keras2.py --config_file uno_auc_model.txt --use_exported_data top_21_auc_1fold.uno.h5 -e 3 --save_weights save/saved.model.weights.h5 -python uno_infer.py --data top_21_auc_1fold.uno.h5 \ +python uno_baseline_keras2.py --config_file uno_auc_model.txt --use_exported_data "$CANDLE_DATA_DIR/Pilot1/Uno/top_21_auc_1fold.uno.h5" -e 3 --save_weights save/saved.model.weights.h5 +python uno_infer.py --data "$CANDLE_DATA_DIR/Pilot1/Uno/top_21_auc_1fold.uno.h5" \ --model_file save/'uno.A=relu.B=32.E=3.O=adamax.LR=0.0001.CF=r.DF=d.DR=0.1.wu_lr.re_lr.L1000.D1=1000.D2=1000.D3=1000.D4=1000.D5=1000.FD1=1000.FD2=1000.FD3=1000.FD4=1000.FD5=1000.model.json' \ --weights_file save/saved.model.weights.h5 \ --partition val \ @@ -15,5 +18,5 @@ python uno_infer.py --data top_21_auc_1fold.uno.h5 \ --agg_dose AUC # CLR model -python uno_clr_keras2.py --config_file uno_auc_clr_model.txt --use_exported_data top_21_auc_1fold.uno.h5 -e 3 +python uno_clr_keras2.py --config_file uno_auc_clr_model.txt --use_exported_data ../../Data/Pilot1/Uno/top_21_auc_1fold.uno.h5 -e 3 diff --git a/Pilot1/Uno/uno.py b/Pilot1/Uno/uno.py index 91368b06..5fbee8a3 100644 --- a/Pilot1/Uno/uno.py +++ b/Pilot1/Uno/uno.py @@ -1,15 +1,10 @@ from __future__ import print_function import os -import sys import logging import argparse file_path = os.path.dirname(os.path.realpath(__file__)) -lib_path = os.path.abspath(os.path.join(file_path, '..')) -sys.path.append(lib_path) -lib_path2 = os.path.abspath(os.path.join(file_path, '..', '..', 'common')) -sys.path.append(lib_path2) import candle diff --git a/Pilot1/Uno/uno_baseline_keras2.py b/Pilot1/Uno/uno_baseline_keras2.py index c814c630..9c175b91 100644 --- a/Pilot1/Uno/uno_baseline_keras2.py +++ b/Pilot1/Uno/uno_baseline_keras2.py @@ -289,7 +289,7 @@ def run(params): cell_subset_path=args.cell_subset_path, drug_subset_path=args.drug_subset_path) train_gen = CombinedDataGenerator(loader, batch_size=args.batch_size, shuffle=args.shuffle) val_gen = CombinedDataGenerator(loader, partition='val', batch_size=args.batch_size, shuffle=args.shuffle) - store = pd.HDFStore(fname, complevel=9, complib='blosc:snappy') + store = pd.HDFStore(fname) config_min_itemsize = {'Sample': 30, 'Drug1': 10} if not args.single: @@ -302,8 +302,8 @@ def run(params): for j, input_feature in enumerate(x_list): input_feature.columns = [''] * len(input_feature.columns) - store.append('x_{}_{}'.format(partition, j), input_feature.astype('float32'), format='table', data_column=True) - store.append('y_{}'.format(partition), y.astype({target: 'float32'}), format='table', data_column=True, + store.append('x_{}_{}'.format(partition, j), input_feature.astype('float32'), format='table') + store.append('y_{}'.format(partition), y.astype({target: 'float32'}), format='table', min_itemsize=config_min_itemsize) logger.info('Generating {} dataset. {} / {}'.format(partition, i, gen.steps)) diff --git a/Pilot1/Uno/uno_clr_keras2.py b/Pilot1/Uno/uno_clr_keras2.py index dbe9aa9c..8370146e 100644 --- a/Pilot1/Uno/uno_clr_keras2.py +++ b/Pilot1/Uno/uno_clr_keras2.py @@ -304,8 +304,8 @@ def run(params): for j, input_feature in enumerate(x_list): input_feature.columns = [''] * len(input_feature.columns) - store.append('x_{}_{}'.format(partition, j), input_feature.astype('float32'), format='table', data_column=True) - store.append('y_{}'.format(partition), y.astype({target: 'float32'}), format='table', data_column=True, + store.append('x_{}_{}'.format(partition, j), input_feature.astype('float32'), format='table', data_columns=True) + store.append('y_{}'.format(partition), y.astype({target: 'float32'}), format='table', data_columns=True, min_itemsize=config_min_itemsize) logger.info('Generating {} dataset. {} / {}'.format(partition, i, gen.steps)) diff --git a/Pilot1/Uno/uno_contamination_keras2.py b/Pilot1/Uno/uno_contamination_keras2.py index 964c21b4..dcd1a2df 100644 --- a/Pilot1/Uno/uno_contamination_keras2.py +++ b/Pilot1/Uno/uno_contamination_keras2.py @@ -239,8 +239,8 @@ def run(params): for j, input_feature in enumerate(x_list): input_feature.columns = [''] * len(input_feature.columns) - store.append('x_{}_{}'.format(partition, j), input_feature.astype('float32'), format='table', data_column=True) - store.append('y_{}'.format(partition), y.astype({target: 'float32'}), format='table', data_column=True, + store.append('x_{}_{}'.format(partition, j), input_feature.astype('float32'), format='table', data_columns=True) + store.append('y_{}'.format(partition), y.astype({target: 'float32'}), format='table', data_columns=True, min_itemsize=config_min_itemsize) logger.info('Generating {} dataset. {} / {}'.format(partition, i, gen.steps)) diff --git a/Pilot1/Uno/uno_data.py b/Pilot1/Uno/uno_data.py index a821483f..96aa2279 100644 --- a/Pilot1/Uno/uno_data.py +++ b/Pilot1/Uno/uno_data.py @@ -5,7 +5,6 @@ import logging import os import pickle -import sys import numpy as np import pandas as pd @@ -22,11 +21,8 @@ from sklearn.model_selection import ShuffleSplit, KFold file_path = os.path.dirname(os.path.realpath(__file__)) -lib_path = os.path.abspath(os.path.join(file_path, '..', '..', 'common')) -sys.path.append(lib_path) -# import candle -import file_utils +import candle global_cache = {} @@ -40,7 +36,7 @@ def get_file(url): fname = os.path.basename(url) - return file_utils.get_file(fname, origin=url, cache_subdir='Pilot1') + return candle.get_file(fname, origin=url, cache_subdir='Pilot1') def read_IDs_file(fname): @@ -251,15 +247,15 @@ def load_drug_data(ncols=None, scaling='std', imputing='mean', dropna=None, add_ df_desc = load_drug_set_descriptors(drug_set='Combined_PubChem', ncols=ncols) df_fp = load_drug_set_fingerprints(drug_set='Combined_PubChem', ncols=ncols) - df_desc = pd.merge(df_info[['ID', 'Drug']], df_desc, on='Drug').drop('Drug', 1).rename(columns={'ID': 'Drug'}) - df_fp = pd.merge(df_info[['ID', 'Drug']], df_fp, on='Drug').drop('Drug', 1).rename(columns={'ID': 'Drug'}) + df_desc = pd.merge(df_info[['ID', 'Drug']], df_desc, on='Drug').drop('Drug', axis=1).rename(columns={'ID': 'Drug'}) + df_fp = pd.merge(df_info[['ID', 'Drug']], df_fp, on='Drug').drop('Drug', axis=1).rename(columns={'ID': 'Drug'}) df_desc2 = load_drug_set_descriptors(drug_set='NCI60', usecols=df_desc.columns.tolist() if ncols else None) df_fp2 = load_drug_set_fingerprints(drug_set='NCI60', usecols=df_fp.columns.tolist() if ncols else None) df_desc = pd.concat([df_desc, df_desc2]).reset_index(drop=True) df1 = pd.DataFrame(df_desc.loc[:, 'Drug']) - df2 = df_desc.drop('Drug', 1) + df2 = df_desc.drop('Drug', axis=1) df2 = impute_and_scale(df2, scaling=scaling, imputing=imputing, dropna=dropna) if add_prefix: df2 = df2.add_prefix('dragon7.') @@ -267,7 +263,7 @@ def load_drug_data(ncols=None, scaling='std', imputing='mean', dropna=None, add_ df_fp = pd.concat([df_fp, df_fp2]).reset_index(drop=True) df1 = pd.DataFrame(df_fp.loc[:, 'Drug']) - df2 = df_fp.drop('Drug', 1) + df2 = df_fp.drop('Drug', axis=1) df2 = impute_and_scale(df2, scaling=None, imputing=imputing, dropna=dropna) if add_prefix: df2 = df2.add_prefix('dragon7.') @@ -289,7 +285,7 @@ def load_mordred_descriptors(ncols=None, scaling='std', imputing='mean', dropna= df1 = pd.DataFrame(df.loc[:, 'DRUG']) df1.rename(columns={'DRUG': 'Drug'}, inplace=True) - df2 = df.drop('DRUG', 1) + df2 = df.drop('DRUG', axis=1) if add_prefix: df2 = df2.add_prefix('mordred.') @@ -298,7 +294,7 @@ def load_mordred_descriptors(ncols=None, scaling='std', imputing='mean', dropna= df_desc = pd.concat([df1, df2], axis=1) df1 = pd.DataFrame(df_desc.loc[:, 'Drug']) - df2 = df_desc.drop('Drug', 1) + df2 = df_desc.drop('Drug', axis=1) if add_prefix: df2 = df2.add_prefix('mordred.') if feature_subset: @@ -316,13 +312,13 @@ def load_drug_descriptors(ncols=None, scaling='std', imputing='mean', dropna=Non df_info['Drug'] = df_info['PUBCHEM'] df_desc = load_drug_set_descriptors(drug_set='Combined_PubChem', ncols=ncols) - df_desc = pd.merge(df_info[['ID', 'Drug']], df_desc, on='Drug').drop('Drug', 1).rename(columns={'ID': 'Drug'}) + df_desc = pd.merge(df_info[['ID', 'Drug']], df_desc, on='Drug').drop('Drug', axis=1).rename(columns={'ID': 'Drug'}) df_desc2 = load_drug_set_descriptors(drug_set='NCI60', usecols=df_desc.columns.tolist() if ncols else None) df_desc = pd.concat([df_desc, df_desc2]).reset_index(drop=True) df1 = pd.DataFrame(df_desc.loc[:, 'Drug']) - df2 = df_desc.drop('Drug', 1) + df2 = df_desc.drop('Drug', axis=1) if add_prefix: df2 = df2.add_prefix('dragon7.') if feature_subset: @@ -340,13 +336,13 @@ def load_drug_fingerprints(ncols=None, scaling='std', imputing='mean', dropna=No df_info['Drug'] = df_info['PUBCHEM'] df_fp = load_drug_set_fingerprints(drug_set='Combined_PubChem', ncols=ncols) - df_fp = pd.merge(df_info[['ID', 'Drug']], df_fp, on='Drug').drop('Drug', 1).rename(columns={'ID': 'Drug'}) + df_fp = pd.merge(df_info[['ID', 'Drug']], df_fp, on='Drug').drop('Drug', axis=1).rename(columns={'ID': 'Drug'}) df_fp2 = load_drug_set_fingerprints(drug_set='NCI60', usecols=df_fp.columns.tolist() if ncols else None) df_fp = pd.concat([df_fp, df_fp2]).reset_index(drop=True) df1 = pd.DataFrame(df_fp.loc[:, 'Drug']) - df2 = df_fp.drop('Drug', 1) + df2 = df_fp.drop('Drug', axis=1) if add_prefix: df2 = df2.add_prefix('dragon7.') if feature_subset: @@ -431,7 +427,7 @@ def load_drug_set_descriptors(drug_set='Combined_PubChem', ncols=None, usecols=N df1 = pd.DataFrame(df.loc[:, 'NAME']) df1.rename(columns={'NAME': 'Drug'}, inplace=True) - df2 = df.drop('NAME', 1) + df2 = df.drop('NAME', axis=1) if add_prefix: df2 = df2.add_prefix('dragon7.') @@ -471,7 +467,7 @@ def load_drug_set_fingerprints(drug_set='Combined_PubChem', ncols=None, usecols= df1 = pd.DataFrame(df.loc[:, col1]) df1.rename(columns={col1: 'Drug'}, inplace=True) - df2 = df.drop(col1, 1) + df2 = df.drop(col1, axis=1) if add_prefix: df2 = df2.add_prefix('dragon7.') @@ -554,7 +550,7 @@ def with_prefix(x): df1 = df_sample_source.merge(df_source, on='Source', how='left').drop('Source', axis=1) logger.info('Embedding RNAseq data source into features: %d additional columns', df1.shape[1] - 1) - df2 = df.drop('Sample', 1) + df2 = df.drop('Sample', axis=1) if add_prefix: df2 = df2.add_prefix('rnaseq.') diff --git a/Pilot1/Uno/uno_trainUQ_keras2.py b/Pilot1/Uno/uno_trainUQ_keras2.py index 0c228eec..a505c0db 100644 --- a/Pilot1/Uno/uno_trainUQ_keras2.py +++ b/Pilot1/Uno/uno_trainUQ_keras2.py @@ -233,8 +233,8 @@ def run(params): for j, input_feature in enumerate(x_list): input_feature.columns = [''] * len(input_feature.columns) - store.append('x_{}_{}'.format(partition, j), input_feature.astype('float32'), format='table', data_column=True) - store.append('y_{}'.format(partition), y.astype({target: 'float32'}), format='table', data_column=True, + store.append('x_{}_{}'.format(partition, j), input_feature.astype('float32'), format='table', data_columns=True) + store.append('y_{}'.format(partition), y.astype({target: 'float32'}), format='table', data_columns=True, min_itemsize=config_min_itemsize) logger.info('Generating {} dataset. {} / {}'.format(partition, i, gen.steps)) diff --git a/Pilot1/UnoMT/test.sh b/Pilot1/UnoMT/test.sh index 51d66a17..ac1fd311 100755 --- a/Pilot1/UnoMT/test.sh +++ b/Pilot1/UnoMT/test.sh @@ -1 +1,2 @@ +set -x python unoMT_baseline_pytorch.py --epochs 3 --resp_val_start_epoch 2 diff --git a/Pilot1/UnoMT/unoMT.py b/Pilot1/UnoMT/unoMT.py index b64a3f08..b146830d 100644 --- a/Pilot1/UnoMT/unoMT.py +++ b/Pilot1/UnoMT/unoMT.py @@ -6,14 +6,12 @@ file_path = os.path.dirname(os.path.realpath(__file__)) -lib_path = os.path.abspath(os.path.join(file_path, '..')) -sys.path.append(lib_path) -lib_path2 = os.path.abspath(os.path.join(file_path, '..', '..', 'common')) -sys.path.append(lib_path2) import candle logger = logging.getLogger(__name__) +logger.setLevel(logging.INFO) +logger.addHandler(logging.StreamHandler(sys.stdout)) # candle.set_parallelism_threads() additional_definitions = [ diff --git a/Pilot2/P2B1/p2b1.py b/Pilot2/P2B1/p2b1.py index e3311d19..83c190ee 100644 --- a/Pilot2/P2B1/p2b1.py +++ b/Pilot2/P2B1/p2b1.py @@ -20,8 +20,6 @@ from importlib import reload file_path = os.path.dirname(os.path.realpath(__file__)) -lib_path2 = os.path.abspath(os.path.join(file_path, '..', '..', 'common')) -sys.path.append(lib_path2) import helper import random diff --git a/Pilot2/P2B1/p2b1_AE_models.py b/Pilot2/P2B1/p2b1_AE_models.py index 3ffa9f2f..5274098a 100644 --- a/Pilot2/P2B1/p2b1_AE_models.py +++ b/Pilot2/P2B1/p2b1_AE_models.py @@ -9,13 +9,8 @@ from tensorflow.keras.regularizers import l2 import os -import sys file_path = os.path.dirname(os.path.realpath(__file__)) -lib_path = os.path.abspath(os.path.join(file_path, '..', 'common')) -sys.path.append(lib_path) -lib_path2 = os.path.abspath(os.path.join(file_path, '..', '..', 'common')) -sys.path.append(lib_path2) def full_conv_mol_auto(bead_k_size=20, mol_k_size=12, weights_path=None, input_shape=(1, 784), diff --git a/Pilot2/P2B1/p2b1_baseline_keras2.py b/Pilot2/P2B1/p2b1_baseline_keras2.py index c698fc74..f1fa1733 100644 --- a/Pilot2/P2B1/p2b1_baseline_keras2.py +++ b/Pilot2/P2B1/p2b1_baseline_keras2.py @@ -12,10 +12,6 @@ TIMEOUT = 3600 # in sec; set this to -1 for no timeout file_path = os.path.dirname(os.path.realpath(__file__)) -# lib_path = os.path.abspath(os.path.join(file_path, '..', 'common')) -# sys.path.append(lib_path) -lib_path2 = os.path.abspath(os.path.join(file_path, '..', '..', 'common')) -sys.path.append(lib_path2) from tensorflow.keras import backend as K diff --git a/Pilot2/P2B1/test.sh b/Pilot2/P2B1/test.sh new file mode 100755 index 00000000..6f041a9c --- /dev/null +++ b/Pilot2/P2B1/test.sh @@ -0,0 +1,2 @@ +set -x +python p2b1_baseline_keras2.py -e 1 diff --git a/Pilot3/P3B1/p3b1.py b/Pilot3/P3B1/p3b1.py index a9b3f54e..5feb77dc 100644 --- a/Pilot3/P3B1/p3b1.py +++ b/Pilot3/P3B1/p3b1.py @@ -3,11 +3,8 @@ import numpy as np import os -import sys file_path = os.path.dirname(os.path.realpath(__file__)) -lib_path2 = os.path.abspath(os.path.join(file_path, '..', '..', 'common')) -sys.path.append(lib_path2) import candle diff --git a/Pilot3/P3B1/test.sh b/Pilot3/P3B1/test.sh new file mode 100755 index 00000000..2b033cfb --- /dev/null +++ b/Pilot3/P3B1/test.sh @@ -0,0 +1,2 @@ +set -x +python p3b1_baseline_keras2.py -e 1 diff --git a/Pilot3/P3B2/p3b2.py b/Pilot3/P3B2/p3b2.py index 54fb6af4..bc19198b 100644 --- a/Pilot3/P3B2/p3b2.py +++ b/Pilot3/P3B2/p3b2.py @@ -1,11 +1,8 @@ from __future__ import print_function import os -import sys file_path = os.path.dirname(os.path.realpath(__file__)) -lib_path2 = os.path.abspath(os.path.join(file_path, '..', '..', 'common')) -sys.path.append(lib_path2) import candle diff --git a/Pilot3/P3B2/test.sh b/Pilot3/P3B2/test.sh new file mode 100755 index 00000000..b4b3fe0a --- /dev/null +++ b/Pilot3/P3B2/test.sh @@ -0,0 +1,2 @@ +set -x +python p3b2_baseline_keras2.py -e 1 diff --git a/Pilot3/P3B3/p3b3.py b/Pilot3/P3B3/p3b3.py index bb2536b9..dc11bad8 100644 --- a/Pilot3/P3B3/p3b3.py +++ b/Pilot3/P3B3/p3b3.py @@ -1,11 +1,8 @@ from __future__ import print_function import os -import sys file_path = os.path.dirname(os.path.realpath(__file__)) -lib_path2 = os.path.abspath(os.path.join(file_path, '..', '..', 'common')) -sys.path.append(lib_path2) import candle @@ -68,7 +65,15 @@ 'type': int}, {'name': 'num_filters', 'nargs': '+', - 'type': int} + 'type': int}, + {'name': 'task_list', + 'nargs': '+', + 'type': int, + 'help': 'list of task indices to use'}, + {'name': 'task_names', + 'nargs': '+', + 'type': int, + 'help': 'list of names corresponding to each task to use'} ] diff --git a/Pilot3/P3B3/test.sh b/Pilot3/P3B3/test.sh new file mode 100755 index 00000000..e61e80fe --- /dev/null +++ b/Pilot3/P3B3/test.sh @@ -0,0 +1,2 @@ +set -x +python p3b3_baseline_keras2.py -e 1 diff --git a/Pilot3/P3B4/dense_attention.py b/Pilot3/P3B4/dense_attention.py index e0ae8266..464e0df0 100644 --- a/Pilot3/P3B4/dense_attention.py +++ b/Pilot3/P3B4/dense_attention.py @@ -24,10 +24,10 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops +from tensorflow.python.framework import smart_cond from tensorflow.python.framework import tensor_shape from tensorflow.python.keras import backend as K from tensorflow.python.keras.engine.base_layer import Layer -from tensorflow.python.keras.utils import tf_utils from tensorflow.python.ops import array_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import math_ops @@ -127,7 +127,7 @@ def _apply_scores(self, scores, value, scores_mask=None, training=None): def dropped_weights(): return nn.dropout(weights, rate=self.dropout) - weights = tf_utils.smart_cond( + weights = smart_cond.smart_cond( training, dropped_weights, lambda: array_ops.identity(weights)) diff --git a/Pilot3/P3B4/p3b4.py b/Pilot3/P3B4/p3b4.py index ea96722e..9e228140 100644 --- a/Pilot3/P3B4/p3b4.py +++ b/Pilot3/P3B4/p3b4.py @@ -1,15 +1,15 @@ from __future__ import print_function import os -import sys file_path = os.path.dirname(os.path.realpath(__file__)) -lib_path2 = os.path.abspath(os.path.join(file_path, '..', '..', 'common')) -sys.path.append(lib_path2) import candle additional_definitions = [ + {'name': 'attention_heads', + 'action': 'store', + 'type': int}, {'name': 'attention_size', 'action': 'store', 'type': int}, @@ -43,7 +43,7 @@ ] -class BenchmarkP3B3(candle.Benchmark): +class BenchmarkP3B4(candle.Benchmark): def set_locals(self): """Functionality to set variables specific for the benchmark diff --git a/Pilot3/P3B4/p3b4_baseline_keras2.py b/Pilot3/P3B4/p3b4_baseline_keras2.py index 1f044dfa..6e7ef775 100644 --- a/Pilot3/P3B4/p3b4_baseline_keras2.py +++ b/Pilot3/P3B4/p3b4_baseline_keras2.py @@ -16,13 +16,13 @@ def initialize_parameters(default_model='p3b4_default_model.txt'): # Build benchmark object - p3b3Bmk = bmk.BenchmarkP3B3(bmk.file_path, default_model, 'keras', + p3b4Bmk = bmk.BenchmarkP3B4(bmk.file_path, default_model, 'keras', prog='p3b4_baseline', desc='Hierarchical Convolutional Attention Networks for \ data extraction from clinical reports - Pilot 3 Benchmark 4') # Initialize parameters - gParameters = candle.finalize_parameters(p3b3Bmk) + gParameters = candle.finalize_parameters(p3b4Bmk) # bmk.logger.info('Params: {}'.format(gParameters)) return gParameters diff --git a/Pilot3/P3B4/p3b4_baseline_tf2.py b/Pilot3/P3B4/p3b4_baseline_tf2.py index 1db6176a..c49cf9dc 100644 --- a/Pilot3/P3B4/p3b4_baseline_tf2.py +++ b/Pilot3/P3B4/p3b4_baseline_tf2.py @@ -9,13 +9,13 @@ def initialize_parameters(default_model='p3b4_default_model.txt'): # Build benchmark object - p3b3Bmk = bmk.BenchmarkP3B3(bmk.file_path, default_model, 'keras', + p3b4Bmk = bmk.BenchmarkP3B4(bmk.file_path, default_model, 'keras', prog='p3b4_baseline', desc='Hierarchical Self-Attention Network for \ data extraction - Pilot 3 Benchmark 4') # Initialize parameters - gParameters = candle.finalize_parameters(p3b3Bmk) + gParameters = candle.finalize_parameters(p3b4Bmk) return gParameters diff --git a/Pilot3/P3B4/test.sh b/Pilot3/P3B4/test.sh new file mode 100755 index 00000000..68f597e1 --- /dev/null +++ b/Pilot3/P3B4/test.sh @@ -0,0 +1,2 @@ +set -x +python p3b4_baseline_tf2.py -e 1 diff --git a/Pilot3/P3B5/p3b5.py b/Pilot3/P3B5/p3b5.py index 51c57152..ac230b36 100644 --- a/Pilot3/P3B5/p3b5.py +++ b/Pilot3/P3B5/p3b5.py @@ -1,9 +1,6 @@ import os -import sys file_path = os.path.dirname(os.path.realpath(__file__)) -lib_path2 = os.path.abspath(os.path.join(file_path, '..', '..', 'common')) -sys.path.append(lib_path2) import candle diff --git a/Pilot3/P3B5/p3b5_darts.py b/Pilot3/P3B5/p3b5_darts.py index 54d7caf3..883449de 100644 --- a/Pilot3/P3B5/p3b5_darts.py +++ b/Pilot3/P3B5/p3b5_darts.py @@ -11,10 +11,6 @@ file_path = os.path.dirname(os.path.realpath(__file__)) -lib_path = os.path.abspath(os.path.join(file_path, '..')) -sys.path.append(lib_path) -lib_path2 = os.path.abspath(os.path.join(file_path, '..', '..', 'common')) -sys.path.append(lib_path2) import darts diff --git a/Pilot3/P3B5/test.sh b/Pilot3/P3B5/test.sh new file mode 100755 index 00000000..0a0902d6 --- /dev/null +++ b/Pilot3/P3B5/test.sh @@ -0,0 +1,2 @@ +set -x +python p3b5_baseline_pytorch.py -e 1 diff --git a/Pilot3/P3B6/default_model.txt b/Pilot3/P3B6/default_model.txt index e944e6d9..474edc97 100644 --- a/Pilot3/P3B6/default_model.txt +++ b/Pilot3/P3B6/default_model.txt @@ -4,7 +4,7 @@ learning_rate = 2e-5 eps = 1e-8 weight_decay = 0.0 batch_size = 10 -num_epochs = 10 +epochs = 10 rng_seed = 13 num_train_samples = 10000 num_valid_samples = 10000 diff --git a/Pilot3/P3B6/p3b6.py b/Pilot3/P3B6/p3b6.py index 660bded4..3000aa0b 100644 --- a/Pilot3/P3B6/p3b6.py +++ b/Pilot3/P3B6/p3b6.py @@ -1,9 +1,6 @@ import os -import sys file_path = os.path.dirname(os.path.realpath(__file__)) -lib_path2 = os.path.abspath(os.path.join(file_path, '..', '..', 'common')) -sys.path.append(lib_path2) import candle @@ -30,7 +27,7 @@ 'weight_decay', 'rng_seed', 'batch_size', - 'num_epochs', + 'epochs', ] diff --git a/Pilot3/P3B6/p3b6_baseline.py b/Pilot3/P3B6/p3b6_baseline_pytorch.py similarity index 98% rename from Pilot3/P3B6/p3b6_baseline.py rename to Pilot3/P3B6/p3b6_baseline_pytorch.py index b0d430dd..d951da84 100644 --- a/Pilot3/P3B6/p3b6_baseline.py +++ b/Pilot3/P3B6/p3b6_baseline_pytorch.py @@ -134,7 +134,7 @@ def run(args): criterion = nn.BCEWithLogitsLoss() - for epoch in range(args.num_epochs): + for epoch in range(args.epochs): train(train_loader, model, optimizer, criterion, args, epoch) validate(valid_loader, model, args, epoch) diff --git a/Pilot3/P3B7/data.py b/Pilot3/P3B7/data.py index 3753eef2..965bc073 100644 --- a/Pilot3/P3B7/data.py +++ b/Pilot3/P3B7/data.py @@ -200,7 +200,7 @@ def _download(self): self._preprocess(raw_data) def _preprocess(self, raw_data): - print(f"Preprocessing data...") + print("Preprocessing data...") self._make_processed_dirs() with open(raw_data, 'rb') as f: @@ -215,7 +215,7 @@ def _preprocess(self, raw_data): self._save_split('train', corpus.train, y_train) self._save_split('valid', corpus.valid, y_valid) self._save_vocab(corpus.vocab) - print(f"Done!") + print("Done!") def _save_split(self, split, data, target): target = self._create_target(target) diff --git a/Pilot3/P3B7/default_model.txt b/Pilot3/P3B7/default_model.txt index 20964bcb..1ca521d5 100644 --- a/Pilot3/P3B7/default_model.txt +++ b/Pilot3/P3B7/default_model.txt @@ -10,9 +10,8 @@ kernel3 = 5 embed_dim = 300 n_filters = 100 batch_size = 50 -num_epochs = 10 +epochs = 10 rng_seed = 13 weight_decay = 1e-3 use_synthetic_data = 'True' -epochs = 10 -device = 'cpu' \ No newline at end of file +device = 'cpu' diff --git a/Pilot3/P3B7/metrics.py b/Pilot3/P3B7/metrics.py index 2e3b8e88..44c39417 100644 --- a/Pilot3/P3B7/metrics.py +++ b/Pilot3/P3B7/metrics.py @@ -1,4 +1,4 @@ -from pytorch_lightning.metrics.classification import F1 +from torchmetrics import F1Score as F1 class F1Meter: diff --git a/Pilot3/P3B7/p3b7.py b/Pilot3/P3B7/p3b7.py index efaa377b..2f21a6a7 100644 --- a/Pilot3/P3B7/p3b7.py +++ b/Pilot3/P3B7/p3b7.py @@ -1,9 +1,6 @@ import os -import sys file_path = os.path.dirname(os.path.realpath(__file__)) -lib_path2 = os.path.abspath(os.path.join(file_path, '..', '..', 'common')) -sys.path.append(lib_path2) import candle @@ -33,7 +30,7 @@ 'weight_decay', 'rng_seed', 'batch_size', - 'num_epochs', + 'epochs', ] diff --git a/Pilot3/P3B7/p3b7_baseline.py b/Pilot3/P3B7/p3b7_baseline_pytorch.py similarity index 81% rename from Pilot3/P3B7/p3b7_baseline.py rename to Pilot3/P3B7/p3b7_baseline_pytorch.py index 5e1542e0..85d796ee 100644 --- a/Pilot3/P3B7/p3b7_baseline.py +++ b/Pilot3/P3B7/p3b7_baseline_pytorch.py @@ -2,9 +2,9 @@ import p3b7 as bmk import candle -import numpy as np +import pandas as pd +from pathlib import Path -import torch.nn as nn from torch.utils.data import DataLoader from data import P3B3, Egress @@ -13,10 +13,7 @@ from meters import AccuracyMeter from metrics import F1Meter -from prune import ( - negative_prune, min_max_prune, - create_prune_masks, remove_prune_masks -) +from prune import create_prune_masks, remove_prune_masks TASKS = { @@ -134,7 +131,7 @@ def evaluate(model, loader, device): accmeter.update_accuracy() - print(f'Validation accuracy:') + print("Validation accuracy:") accmeter.print_task_accuracies() loss /= len(loader.dataset) @@ -142,39 +139,27 @@ def evaluate(model, loader, device): return loss -def save_dataframe(metrics, filename): +def save_dataframe(metrics, filename, args): """Save F1 metrics""" df = pd.DataFrame(metrics, index=[0]) - path = Path(ARGS.savepath).joinpath(f'f1/{filename}.csv') + path = Path(args.savepath).joinpath(f'f1/{filename}.csv') df.to_csv(path, index=False) def run(args): args = candle.ArgumentStruct(**args) args.cuda = torch.cuda.is_available() - args.device = torch.device(f"cuda" if args.cuda else "cpu") - - if args.use_synthetic_data: - train_data, valid_data = get_synthetic_data(args) - - hparams = Hparams( - kernel1=args.kernel1, - kernel2=args.kernel2, - kernel3=args.kernel3, - embed_dim=args.embed_dim, - n_filters=args.n_filters, - ) - else: - train_data, valid_data = get_egress_data(tasks) - - hparams = Hparams( - kernel1=args.kernel1, - kernel2=args.kernel2, - kernel3=args.kernel3, - embed_dim=args.embed_dim, - n_filters=args.n_filters, - vocab_size=len(train_data.vocab) - ) + args.device = torch.device("cuda" if args.cuda else "cpu") + + train_data, valid_data = get_synthetic_data(args) + + hparams = Hparams( + kernel1=args.kernel1, + kernel2=args.kernel2, + kernel3=args.kernel3, + embed_dim=args.embed_dim, + n_filters=args.n_filters, + ) train_loader = DataLoader(train_data, batch_size=args.batch_size) valid_loader = DataLoader(valid_data, batch_size=args.batch_size) diff --git a/Pilot3/P3B7/prune.py b/Pilot3/P3B7/prune.py index 537baee6..5c85b832 100644 --- a/Pilot3/P3B7/prune.py +++ b/Pilot3/P3B7/prune.py @@ -128,7 +128,6 @@ def compute_mask(self, t, default_mask): # Check that the amount of units to prune is not > than the number of # parameters in t tensor_size = t.nelement() - tNP = t.detach().cpu().numpy() # Compute number of units to prune: amount if int, # else amount * tensor_size nparams_toprune = _compute_nparams_toprune(self.amount, tensor_size) @@ -234,6 +233,6 @@ def remove_prune_masks(model: nn.Module): prune.remove(module, name='weight') # prune 20% of connections in all linear layers elif isinstance(module, torch.nn.Linear): - prune.remove(module, name='weight', amount=0.2) + prune.remove(module, name='weight') return model diff --git a/Pilot3/P3B8/p3b8.py b/Pilot3/P3B8/p3b8.py index adf32c47..655ac6e0 100644 --- a/Pilot3/P3B8/p3b8.py +++ b/Pilot3/P3B8/p3b8.py @@ -1,9 +1,6 @@ import os -import sys file_path = os.path.dirname(os.path.realpath(__file__)) -lib_path2 = os.path.abspath(os.path.join(file_path, '..', '..', 'common')) -sys.path.append(lib_path2) import candle diff --git a/Pilot3/P3B8/p3b8_baseline.py b/Pilot3/P3B8/p3b8_baseline_pytorch.py similarity index 100% rename from Pilot3/P3B8/p3b8_baseline.py rename to Pilot3/P3B8/p3b8_baseline_pytorch.py diff --git a/Pilot3/P3B9/README.md b/Pilot3/P3B9/README.md new file mode 100644 index 00000000..fe1c771a --- /dev/null +++ b/Pilot3/P3B9/README.md @@ -0,0 +1,12 @@ +## P3B9: Pretraining BERT on a small subset of PubMed abstracts + +**Overview**: This benchmark uses masked language learning to pretraing a BERT model. As it is only being trained on a small dataset, the purpose of this benchmark is to evaluate the pretraining performance on new accelerator hardware. + +**Files**: + +1. part-000000.tar is a tar achieve with a set of 1000 PubMed abstracts and formatted for [WebDataset](https://github.com/webdataset/webdataset). +2. pubmed_bert-vocab.txt is the tokenizer vocabulary. +3. bert_webdataset.py contains the model training code, relying heavily on [Huggingface Transformers](https://github.com/huggingface/transformers) +4. run.sh is a sample runscript for running this benchmark locally. + +Benchmark requires webdataset==0.1.62. diff --git a/Pilot3/P3B9/default_model.txt b/Pilot3/P3B9/default_model.txt new file mode 100644 index 00000000..bcbe628f --- /dev/null +++ b/Pilot3/P3B9/default_model.txt @@ -0,0 +1,21 @@ +[Global_Params] +model_name = 'p3b9' +output_dir = './outputs/' +train_bool = True +per_device_train_batch_size = 12 +gradient_accumulation_steps = 1 +max_len = 512 +learning_rate = 1e-4 +weight_decay = 0.0000 +adam_beta2 = 0.98 +adam_epsilon = 2e-8 +max_steps = 10 +warmup_steps = 1 +token_max_len = 128 +vocab_size = 30_000 +hidden_size = 768 +intermediate_size = 3072 +max_position_embeddings = 512 +num_attention_heads = 12 +num_hidden_layers = 12 +type_vocab_size = 2 diff --git a/Pilot3/P3B9/p3b9.py b/Pilot3/P3B9/p3b9.py new file mode 100644 index 00000000..71394034 --- /dev/null +++ b/Pilot3/P3B9/p3b9.py @@ -0,0 +1,125 @@ +import os + +file_path = os.path.dirname(os.path.realpath(__file__)) + +import candle + +additional_definitions = [ + {'name': 'per_device_train_batch_size', + 'type': int, + 'default': 16, + 'help': 'Batch per device in training'}, + {'name': 'gradient_accumulation_steps', + 'type': int, + 'default': 1, + 'help': 'Number of steps for accumulating gradient'}, + {'name': 'max_len', + 'type': int, + 'default': 512, + 'help': 'Max length for truncation'}, + {'name': 'weight_decay', + 'type': float, + 'default': 0.0000, + 'help': 'ADAM weight decay'}, + {'name': 'adam_beta1', + 'type': float, + 'default': 0.9, + 'help': 'ADAM beta1 parameter'}, + {'name': 'adam_beta2', + 'type': float, + 'default': 0.98, + 'help': 'ADAM beta2 parameter'}, + {'name': 'adam_epsilon', + 'type': float, + 'default': 2e-8, + 'help': 'ADAM epsilon parameter'}, + {'name': 'max_steps', + 'type': int, + 'default': 10, + 'help': 'Max training steps'}, + {'name': 'warmup_steps', + 'type': int, + 'default': 1, + 'help': 'Warmup steps'}, + {'name': 'model_name_or_path', + 'type': str, + 'default': None, + 'help': 'model name or path'}, + {'name': 'savepath', + 'type': str, + 'default': './pretrained', + 'help': 'config path for saving'}, + {'name': 'token_max_len', + 'type': int, + 'default': 128, + 'help': 'Max length for tokenizer'}, + {'name': 'name_pretrained_tokenizer', + 'type': str, + 'default': 'pubmed_bert-vocab.txt', + 'help': 'file name where pretrained tokenizer is stored'}, + {'name': 'dataset', + 'type': str, + 'default': 'part-000000.tar', + 'help': 'file name of dataset'}, + {'name': 'data_len_gpu', + 'type': int, + 'default': 1000, + 'help': 'Total data len per gpu'}, + {'name': 'vocab_size', + 'type': int, + 'default': 30_000, + 'help': 'Vocabulary size of the BERT model. Defines the number of different tokens that can be represented by the inputs_ids passed when calling BertModel or TFBertModel.'}, + {'name': 'hidden_size', + 'type': int, + 'default': 768, + 'help': 'Dimensionality of the encoder layers and the pooler layer.'}, + {'name': 'intermediate_size', + 'type': int, + 'default': 3072, + 'help': 'Dimensionality of the “intermediate” (often named feed-forward) layer in the Transformer encoder.'}, + {'name': 'max_position_embeddings', + 'type': int, + 'default': 512, + 'help': 'The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048).'}, + {'name': 'num_attention_heads', + 'type': int, + 'default': 12, + 'help': 'Number of attention heads for each attention layer in the Transformer encoder.'}, + {'name': 'num_hidden_layers', + 'type': int, + 'default': 12, + 'help': 'Number of hidden layers in the Transformer encoder.'}, + {'name': 'type_vocab_size', + 'type': int, + 'default': 2, + 'help': 'The vocabulary size of the token_type_ids passed when calling BertModel or TFBertModel.'}, +] + +required = [ + 'per_device_train_batch_size', + 'gradient_accumulation_steps', + 'max_len', + 'learning_rate', + 'weight_decay', + 'adam_beta2', + 'adam_epsilon', + 'max_steps', + 'warmup_steps', +] + + +class BenchmarkP3B9(candle.Benchmark): + """ Benchmark for BERT """ + + def set_locals(self): + """ Set parameters for the benchmark. + + Args: + required: set of required parameters for the benchmark. + additional_definitions: list of dictionaries describing the additional parameters for the + benchmark. + """ + if required is not None: + self.required = set(required) + if additional_definitions is not None: + self.additional_definitions = additional_definitions diff --git a/Pilot3/P3B9/p3b9_baseline_pytorch.py b/Pilot3/P3B9/p3b9_baseline_pytorch.py new file mode 100644 index 00000000..0c723a58 --- /dev/null +++ b/Pilot3/P3B9/p3b9_baseline_pytorch.py @@ -0,0 +1,102 @@ +import torch +import p3b9 as bmk +import candle + +from transformers import ( + BertTokenizer, BertConfig, BertForMaskedLM, DataCollatorForLanguageModeling, Trainer, TrainingArguments, HfArgumentParser +) + +import webdataset as wd + + +class truncate(object): + + def __init__(self, max_len): + self.max_len = max_len + + def __call__(self, doc): + return doc[:self.max_len] + + +def initialize_parameters(): + """ Initialize the parameters for the P3B5 benchmark """ + + p3b9_bench = bmk.BenchmarkP3B9( + bmk.file_path, + "default_model.txt", + "pytorch", + prog="p3b9", + desc="BERT", + ) + + gParameters = candle.finalize_parameters(p3b9_bench) + return gParameters + + +def run(args): + + # Repair argument overlap by duplication + # (i.e. arguments that have the same function + # but slightly different names between CANDLE + # and hf parsers are copied from CANDLE keyword + # to expected hf keyword) + args['seed'] = args['rng_seed'] + if 'train_bool' in args.keys(): + args['do_train'] = args['train_bool'] + if 'eval_bool' in args.keys(): + args['do_eval'] = args['eval_bool'] + if 'epochs' in args.keys(): + args['num_train_epochs'] = args['epochs'] + + parser = HfArgumentParser((TrainingArguments)) + training_args = parser.parse_dict(args)[0] + print(training_args) + + # Convert from dictionary to structure + args = candle.ArgumentStruct(**args) + + trunc = truncate(args.max_len) + + print('total data len per gpu:', args.data_len_gpu) + dataset = wd.Dataset(args.dataset, + length=args.data_len_gpu, + shuffle=True).decode('torch').rename(input_ids='pth').map_dict(input_ids=trunc).shuffle(1000) + tokenizer = BertTokenizer.from_pretrained(args.name_pretrained_tokenizer) + + data_collator = DataCollatorForLanguageModeling( + tokenizer=tokenizer, mlm=True, mlm_probability=0.15 + ) + + config = BertConfig( + vocab_size=args.vocab_size, + hidden_size=args.hidden_size, + intermediate_size=args.intermediate_size, + max_position_embeddings=args.max_position_embeddings, + num_attention_heads=args.num_attention_heads, + num_hidden_layers=args.num_hidden_layers, + type_vocab_size=args.type_vocab_size, + ) + + if args.model_name_or_path is not None: + model = BertForMaskedLM.from_pretrained(args.model_name_or_path) + else: + model = BertForMaskedLM(config=config) + + trainer = Trainer( + model=model, + args=training_args, + data_collator=data_collator, + train_dataset=dataset + ) + + trainer.train() + trainer.save_model(args.savepath) + + +def main(): + params = initialize_parameters() + run(params) + + +if __name__ == "__main__": + main() diff --git a/Pilot3/P3B9/part-000000.tar b/Pilot3/P3B9/part-000000.tar new file mode 100755 index 00000000..3225133f Binary files /dev/null and b/Pilot3/P3B9/part-000000.tar differ diff --git a/Pilot3/P3B9/pubmed_bert-vocab.txt b/Pilot3/P3B9/pubmed_bert-vocab.txt new file mode 100755 index 00000000..4d6396c9 --- /dev/null +++ b/Pilot3/P3B9/pubmed_bert-vocab.txt @@ -0,0 +1,30000 @@ +[PAD] +[UNK] +[CLS] +[SEP] +[MASK] +! +" +# +$ +% +& +' +( +) +* ++ +, +- +. +/ +0 +1 +2 +3 +4 +5 +6 +7 +8 +9 +: +; +< += +> +? +@ +[ +\ +] +^ +_ +` +a +b +c +d +e +f +g +h +i +j +k +l +m +n +o +p +q +r +s +t +u +v +w +x +y +z +{ +| +} +~ +¡ +¢ +£ +¤ +¥ +¦ +§ +¨ +© +ª +« +¬ +® +¯ +° +± +² +³ +´ +µ +¶ +· +¸ +¹ +º +» +¼ +½ +¾ +¿ +× +ß +æ +ð +÷ +ø +þ +đ +ħ +ı +ĸ +ł +ŋ +œ +ƅ +ƍ +ƒ +ƙ +ƛ +ƞ +ƭ +ƴ +ƶ +ƹ +ƺ +ƿ +ǀ +ǁ +ǂ +ȣ +ȥ +ȶ +ɂ +ɐ +ɑ +ɒ +ɓ +ɔ +ɕ +ɖ +ə +ɚ +ɛ +ɝ +ɡ +ɣ +ɤ +ɦ +ɨ +ɩ +ɪ +ɫ +ɬ +ɭ +ɮ +ɯ +ɲ +ɳ +ɵ +ɶ +ɷ +ɸ +ɹ +ɻ +ɾ +ɿ +ʀ +ʁ +ʂ +ʃ +ʅ +ʈ +ʉ +ʊ +ʋ +ʌ +ʎ +ʏ 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+undertake +##lec +##ovani +##ofovir +##oglycerate +##gry +coxsack +reluct +##cho +clots +tibetan +electrochemically +coliforms +ethiopia +diaphorase +wis +demented +generators +bgl +##001 +hemif +cauda +duk +holter +carbamate +reporters +ercp +puerto +babesia +subchronic +mall +methyltransferases +ltc +dorsi +multich +reconstructing +supplementing +##osamin +fissure +biobank +1m +##ocentric +predi +cytochemical +malarial +##ertz +cvc +clearances +methanogenic +installation +misinterp +11beta +descend +oceanic +##strom +cosmetics +laterality +rhabdomyosarcoma +##adenine +ginsenoside +beach +##obese +homologies +ergonomic +prene +mesodermal +bisulfite +avm +edited +sero +millil +homogenized +expressive +granulocytic +lifestyles +feedstock +scintillation +zucker +lithotr +lns +chondrogenesis +gustatory +mildew +ribonucleic +reinhardtii +metach +chlorination +deplete +tq +##iced +stan +linezolid +cfd +g6p +##case +preg +federation +##uating +##hf +lz +##xe +isg +teq +tfa +separates +speaker +sirolimus +streptokinase +neurofibromatosis +c3n4 +gz +tertile +##egm +ltc4 +disadvantaged +incurable +spectroscopies +neutropenic +dentist +spiders +##431 +##peak +symposium +acv +hemolysin +ssd +gliosis +##azolamide +pvl +geranyl +myofibrils +metazoan +extracranial +acromegaly +##ainide +diptera +##erries +##ilinear +cataracts +##ocarbon +disent +autocl +hepatopancre +myelopathy +inkt +fluoroquinolone +aha +menth +anatase +thermophilus +vacancy +lycopers +hcb +##ophthora +sampler +cpf +cnc +theil +pulmon +##otrophoblast +aestivum +aiv +##iculi +inertia +riv +monogenic +mrm +picryl +budes +redistrib +##uscin +donovani +neuroplastic +20s +japonicus +parasitemia +salam +programmable diff --git a/README.md b/README.md index 7571ee70..57c1dde3 100644 --- a/README.md +++ b/README.md @@ -17,8 +17,6 @@ At this point, we will refer to a benchmark by it's problem area and benchmark n Over time, we will be adding implementations that make use of different tensor frameworks. The primary (baseline) benchmarks are implemented using keras, and are named with '_baseline' in the name, for example p3b1_baseline_keras2.py. -Implementations that use alternative tensor frameworks, such as mxnet or neon, will have the name of the framework in the name. Examples can be seen in the P1B3 benchmark contribs/ directory, for example: - p1b3_mxnet.py - p1b3_neon.py +Implementations that use alternative tensor frameworks, such as pytorch, will have the name of the framework in the name. Examples can be seen in the Pilot1 benchmark UnoMT directory, for example: `unoMT_baseline_pytorch.py` -Documentation: https://ecp-candle.github.io/Candle/html/readme.html +Documentation: https://ecp-candle.github.io/Candle/ diff --git a/common/P1_utils.py b/common/P1_utils.py index 5cc38543..54319ddc 100644 --- a/common/P1_utils.py +++ b/common/P1_utils.py @@ -402,19 +402,19 @@ def design_mat(mod, numerical_covariates, batch_levels): mod = mod.drop(["batch"], axis=1) numerical_covariates = list(numerical_covariates) - sys.stderr.write("found %i batches\n" % design.shape[1]) + sys.stdout.write("found %i batches\n" % design.shape[1]) other_cols = [c for i, c in enumerate(mod.columns) if i not in numerical_covariates] factor_matrix = mod[other_cols] design = pd.concat((design, factor_matrix), axis=1) if numerical_covariates is not None: - sys.stderr.write("found %i numerical covariates...\n" % len(numerical_covariates)) + sys.stdout.write("found %i numerical covariates...\n" % len(numerical_covariates)) for i, nC in enumerate(numerical_covariates): cname = mod.columns[nC] - sys.stderr.write("\t{0}\n".format(cname)) + sys.stdout.write("\t{0}\n".format(cname)) design[cname] = mod[mod.columns[nC]] - sys.stderr.write("found %i categorical variables:" % len(other_cols)) - sys.stderr.write("\t" + ", ".join(other_cols) + '\n') + sys.stdout.write("found %i categorical variables:" % len(other_cols)) + sys.stdout.write("\t" + ", ".join(other_cols) + '\n') return design @@ -508,7 +508,7 @@ def combat_batch_effect_removal(data, batch_labels, model=None, numerical_covari design = design_mat(model, numerical_covariates, batch_levels) - sys.stderr.write("Standardizing Data across genes.\n") + sys.stdout.write("Standardizing Data across genes.\n") B_hat = np.dot(np.dot(la.inv(np.dot(design.T, design)), design.T), data.T) grand_mean = np.dot((n_batches / n_array).T, B_hat[:n_batch, :]) var_pooled = np.dot(((data - np.dot(design, B_hat).T) ** 2), np.ones((int(n_array), 1)) / int(n_array)) @@ -520,7 +520,7 @@ def combat_batch_effect_removal(data, batch_labels, model=None, numerical_covari s_data = ((data - stand_mean) / np.dot(np.sqrt(var_pooled), np.ones((1, int(n_array))))) - sys.stderr.write("Fitting L/S model and finding priors\n") + sys.stdout.write("Fitting L/S model and finding priors\n") batch_design = design[design.columns[:n_batch]] gamma_hat = np.dot(np.dot(la.inv(np.dot(batch_design.T, batch_design)), batch_design.T), s_data.T) @@ -535,7 +535,7 @@ def combat_batch_effect_removal(data, batch_labels, model=None, numerical_covari a_prior = list(map(aprior, delta_hat)) b_prior = list(map(bprior, delta_hat)) - sys.stderr.write("Finding parametric adjustments\n") + sys.stdout.write("Finding parametric adjustments\n") gamma_star, delta_star = [], [] for i, batch_idxs in enumerate(batch_info): temp = it_sol(s_data[batch_idxs], gamma_hat[i], diff --git a/common/benchmark_def.py b/common/benchmark_def.py index 1a6d6f48..98c021f2 100644 --- a/common/benchmark_def.py +++ b/common/benchmark_def.py @@ -150,7 +150,7 @@ def read_config_file(self, file): fileParams = self.format_benchmark_config_arguments(fileParams) - print(fileParams) + # print(fileParams) return fileParams diff --git a/common/candle/__init__.py b/common/candle/__init__.py index 998cf920..b9ea19c6 100644 --- a/common/candle/__init__.py +++ b/common/candle/__init__.py @@ -71,6 +71,10 @@ from noise_utils import label_flip from noise_utils import label_flip_correlated from noise_utils import add_gaussian_noise +from noise_utils import add_column_noise +from noise_utils import add_cluster_noise +from noise_utils import add_noise + # P1-specific from P1_utils import coxen_single_drug_gene_selection from P1_utils import coxen_multi_drug_gene_selection diff --git a/common/ckpt_keras_utils.py b/common/ckpt_keras_utils.py index fae26b25..cf626f78 100644 --- a/common/ckpt_keras_utils.py +++ b/common/ckpt_keras_utils.py @@ -172,6 +172,10 @@ def __init__(self, gParameters, logger="DEFAULT", verbose=True): def report_initial(self): """ Simply report that we are ready to run """ self.info("Callback initialized.") + if self.save_interval == 0: + self.info("Checkpoint save interval == 0 " + + "-> checkpoints are disabled.") + return # Skip the rest of this output if self.metadata is not None: self.info("metadata='%s'" % self.metadata) if self.save_best: @@ -261,6 +265,8 @@ def save_check(self, logs, epoch): model metrics in given logs Also updates epoch_best if appropriate """ + if self.save_interval == 0: + return False # Checkpoints are disabled. # skip early epochs to improve speed if epoch < self.skip_epochs: self.debug("model saving disabled until epoch %d" % @@ -274,7 +280,7 @@ def save_check(self, logs, epoch): if epoch == self.epoch_max: self.info("writing final epoch %i ..." % epoch) return True # Final epoch - save! - if self.save_interval != 0 and epoch % self.save_interval == 0: + if epoch % self.save_interval == 0: return True # We are on the save_interval: save! # else- not saving: self.debug("not writing this epoch.") @@ -393,31 +399,30 @@ def clean(self, epoch_now): kept = 0 # Consider most recent epochs first: for epoch in reversed(self.epochs): - self.debug('checking %s' % epoch) + self.debug('clean(): checking epoch directory: %i' % epoch) if not self.keep(epoch, epoch_now, kept): deleted += 1 self.delete(epoch) - self.debug('deleted') + self.debug('clean(): deleted epoch: %i' % epoch) else: kept += 1 - self.debug('kept %s' % kept) return (kept, deleted) def keep(self, epoch, epoch_now, kept): """ kept: Number of epochs already kept - return True if we are keeping this epoch + return True if we are keeping this epoch, else False """ if epoch == epoch_now: # We just wrote this! - self.debug('latest') + self.debug('keep(): epoch is latest: %i' % epoch) return True if self.epoch_best == epoch: # This is the best epoch - self.debug('best') + self.debug('keep(): epoch is best: %i' % epoch) return True if kept < self.keep_limit: - self.debug('< limit %s' % self.keep_limit) + self.debug('keep(): epoch count is < limit %i' % self.keep_limit) return True # No reason to keep this: delete it: return False @@ -486,12 +491,12 @@ def restart(gParameters, model, verbose=True): model_file = dir_last + "/model.h5" if not os.path.exists(model_file): if param_ckpt_mode == "required": - raise Exception("ckpt_mode=='required' but no checkpoint" + raise Exception("ckpt_restart_mode=='required' but no checkpoint " + "could be found!") # We must be under AUTO - proceed without restart assert param_ckpt_mode == "auto" return None - logger.info("restarting: %s", model_file) + logger.info("restarting: '%s'" % model_file) result = restart_json(gParameters, logger, dir_last) logger.info("restarting: epoch=%i timestamp=%s", result["epoch"], result["timestamp"]) @@ -502,7 +507,7 @@ def restart(gParameters, model, verbose=True): stop = time.time() duration = stop - start rate = MB / duration - logger.info("model read: %0.3f MB in %0.3f seconds (%0.2f MB/s).", + logger.info("restarting: model read: %0.3f MB in %0.3f seconds (%0.2f MB/s).", MB, duration, rate) return result @@ -520,6 +525,7 @@ def restart_json(gParameters, logger, directory): # print(str(J)) logger.debug("ckpt-info.json contains:") logger.debug(json.dumps(J, indent=2)) + logger.info("restarting from epoch: %i" % J["epoch"]) if param(gParameters, "ckpt_checksum", False, ParamType.BOOLEAN): checksum = checksum_file(logger, directory + "/model.h5") if checksum != J["checksum"]: @@ -589,8 +595,8 @@ def param_type_check(key, value, type_): if type_ is ParamType.FLOAT or \ type_ is ParamType.FLOAT_NN: return param_type_check_float(key, value, type_) - raise ValueError("param_type_check(): unknown type: '%s'" % - str(type_)) + raise TypeError("param_type_check(): unknown type: '%s'" % + str(type_)) def param_type_check_bool(key, value): @@ -707,12 +713,13 @@ def ckpt_parser(parser): help="Toggle saving only weights (not optimizer) (NYI)") parser.add_argument("--ckpt_save_interval", type=int, default=1, - help="Interval to save checkpoints") + help="Epoch interval to save checkpoints. " + + "Set to 0 to disable writing checkpoints") # keeping parser.add_argument("--ckpt_keep_mode", choices=['linear', 'exponential'], help="Checkpoint saving mode. " - + "choices are 'linear' or 'exponential' ") + + "Choices are 'linear' or 'exponential' ") parser.add_argument("--ckpt_keep_limit", type=int, default=1000000, help="Limit checkpoints to keep") diff --git a/common/clr_keras_utils.py b/common/clr_keras_utils.py index 5ed880ff..b6ef113e 100644 --- a/common/clr_keras_utils.py +++ b/common/clr_keras_utils.py @@ -50,7 +50,9 @@ class CyclicLR(Callback): """This callback implements a cyclical learning rate policy (CLR). The method cycles the learning rate between two boundaries with some constant frequency. - # Arguments + + Parameters + ---------- base_lr: initial learning rate which is the lower boundary in the cycle. max_lr: upper boundary in the cycle. Functionally, diff --git a/common/data_utils.py b/common/data_utils.py index b30966a2..7fec06dd 100644 --- a/common/data_utils.py +++ b/common/data_utils.py @@ -21,14 +21,19 @@ # TAKEN from tensorflow def to_categorical(y, num_classes=None): """Converts a class vector (integers) to binary class matrix. - E.g. for use with categorical_crossentropy. - Arguments: - y: class vector to be converted into a matrix + E.g. for use with categorical_crossentropy. + Parameters + ---------- + y: numpy array + class vector to be converted into a matrix (integers from 0 to num_classes). - num_classes: total number of classes. - Returns: - A binary matrix representation of the input. The classes axis is placed - last. + num_classes: int + total number of classes. + Returns + ------- + categorical: numpy array + A binary matrix representation of the input. The classes axis is placed + last. """ y = np.array(y, dtype='int') input_shape = y.shape diff --git a/common/default_utils.py b/common/default_utils.py index 8b183c3c..b0403f3e 100644 --- a/common/default_utils.py +++ b/common/default_utils.py @@ -301,6 +301,7 @@ def get_default_neon_parser(parser): parser.add_argument("--model_name", dest='model_name', type=str, default=argparse.SUPPRESS, help="specify model name to use when building filenames for saving") + parser.add_argument("-d", "--data_type", dest='data_type', default=argparse.SUPPRESS, choices=['f16', 'f32', 'f64'], @@ -321,9 +322,11 @@ def get_default_neon_parser(parser): parser.add_argument("-r", "--rng_seed", dest='rng_seed', type=int, default=argparse.SUPPRESS, help="random number generator seed") + parser.add_argument("-e", "--epochs", type=int, default=argparse.SUPPRESS, help="number of training epochs") + parser.add_argument("-z", "--batch_size", type=int, default=argparse.SUPPRESS, help="batch size") @@ -348,6 +351,7 @@ def get_common_parser(parser): parser.add_argument("--train_bool", dest='train_bool', type=str2bool, default=True, help="train model") + parser.add_argument("--eval_bool", dest='eval_bool', type=str2bool, default=argparse.SUPPRESS, help="evaluate model (use it for inference)") @@ -365,6 +369,10 @@ def get_common_parser(parser): default=argparse.SUPPRESS, help="training data filename") + parser.add_argument("--val_data", action="store", + default=argparse.SUPPRESS, + help="validation data filename") + parser.add_argument("--test_data", action="store", default=argparse.SUPPRESS, help="testing data filename") diff --git a/common/helper_utils.py b/common/helper_utils.py index e3f3534b..bd8f3b9b 100644 --- a/common/helper_utils.py +++ b/common/helper_utils.py @@ -86,7 +86,7 @@ def set_up_logger(logfile, logger, verbose=False, fh.setFormatter(logging.Formatter(fmt_line, datefmt=fmt_date)) fh.setLevel(logging.DEBUG) - sh = logging.StreamHandler() + sh = logging.StreamHandler(sys.stdout) sh.setFormatter(logging.Formatter('')) sh.setLevel(logging.DEBUG if verbose else logging.INFO) diff --git a/common/keras_utils.py b/common/keras_utils.py index 79a91835..fb251882 100644 --- a/common/keras_utils.py +++ b/common/keras_utils.py @@ -64,14 +64,14 @@ def get_function(name): return mapped -def build_optimizer(type, lr, kerasDefaults): +def build_optimizer(optimizer, lr, kerasDefaults): """ Set the optimizer to the appropriate Keras optimizer function based on the input string and learning rate. Other required values are set to the Keras default values Parameters ---------- - type : string + optimizer : string String to choose the optimizer Options recognized: 'sgd', 'rmsprop', 'adagrad', adadelta', 'adam' @@ -88,35 +88,35 @@ def build_optimizer(type, lr, kerasDefaults): The appropriate Keras optimizer function """ - if type == 'sgd': + if optimizer == 'sgd': return optimizers.SGD(lr=lr, decay=kerasDefaults['decay_lr'], momentum=kerasDefaults['momentum_sgd'], nesterov=kerasDefaults['nesterov_sgd']) # , # clipnorm=kerasDefaults['clipnorm'], # clipvalue=kerasDefaults['clipvalue']) - elif type == 'rmsprop': + elif optimizer == 'rmsprop': return optimizers.RMSprop(lr=lr, rho=kerasDefaults['rho'], epsilon=kerasDefaults['epsilon'], decay=kerasDefaults['decay_lr']) # , # clipnorm=kerasDefaults['clipnorm'], # clipvalue=kerasDefaults['clipvalue']) - elif type == 'adagrad': + elif optimizer == 'adagrad': return optimizers.Adagrad(lr=lr, epsilon=kerasDefaults['epsilon'], decay=kerasDefaults['decay_lr']) # , # clipnorm=kerasDefaults['clipnorm'], # clipvalue=kerasDefaults['clipvalue']) - elif type == 'adadelta': + elif optimizer == 'adadelta': return optimizers.Adadelta(lr=lr, rho=kerasDefaults['rho'], epsilon=kerasDefaults['epsilon'], decay=kerasDefaults['decay_lr']) # , # clipnorm=kerasDefaults['clipnorm'], # clipvalue=kerasDefaults['clipvalue']) - elif type == 'adam': + elif optimizer == 'adam': return optimizers.Adam(lr=lr, beta_1=kerasDefaults['beta_1'], beta_2=kerasDefaults['beta_2'], epsilon=kerasDefaults['epsilon'], @@ -125,27 +125,27 @@ def build_optimizer(type, lr, kerasDefaults): # clipvalue=kerasDefaults['clipvalue']) # Not generally available -# elif type == 'adamax': +# elif optimizer == 'adamax': # return optimizers.Adamax(lr=lr, beta_1=kerasDefaults['beta_1'], # beta_2=kerasDefaults['beta_2'], # epsilon=kerasDefaults['epsilon'], # decay=kerasDefaults['decay_lr']) -# elif type == 'nadam': +# elif optimizer == 'nadam': # return optimizers.Nadam(lr=lr, beta_1=kerasDefaults['beta_1'], # beta_2=kerasDefaults['beta_2'], # epsilon=kerasDefaults['epsilon'], # schedule_decay=kerasDefaults['decay_schedule_lr']) -def build_initializer(type, kerasDefaults, seed=None, constant=0.): +def build_initializer(initializer, kerasDefaults, seed=None, constant=0.): """ Set the initializer to the appropriate Keras initializer function based on the input string and learning rate. Other required values are set to the Keras default values Parameters ---------- - type : string + initializer : string String to choose the initializer Options recognized: 'constant', 'uniform', 'normal', @@ -167,30 +167,30 @@ def build_initializer(type, kerasDefaults, seed=None, constant=0.): The appropriate Keras initializer function """ - if type == 'constant': + if initializer == 'constant': return initializers.Constant(value=constant) - elif type == 'uniform': + elif initializer == 'uniform': return initializers.RandomUniform(minval=kerasDefaults['minval_uniform'], maxval=kerasDefaults['maxval_uniform'], seed=seed) - elif type == 'normal': + elif initializer == 'normal': return initializers.RandomNormal(mean=kerasDefaults['mean_normal'], stddev=kerasDefaults['stddev_normal'], seed=seed) - elif type == 'glorot_normal': + elif initializer == 'glorot_normal': # aka Xavier normal initializer. keras default return initializers.glorot_normal(seed=seed) - elif type == 'glorot_uniform': + elif initializer == 'glorot_uniform': return initializers.glorot_uniform(seed=seed) - elif type == 'lecun_uniform': + elif initializer == 'lecun_uniform': return initializers.lecun_uniform(seed=seed) - elif type == 'he_normal': + elif initializer == 'he_normal': return initializers.he_normal(seed=seed) diff --git a/common/noise_utils.py b/common/noise_utils.py index f3689e87..4469a5d4 100644 --- a/common/noise_utils.py +++ b/common/noise_utils.py @@ -2,6 +2,74 @@ import numpy as np +def add_noise_new(data, labels, params): + # new refactoring of the noise injection methods + # noise_mode sets the pattern of the noise injection + # cluster: apply to the samples and features defined by noise_samples and noise_features + # conditional : apply to features conditional on a threshold + # + # noise_type sets the form of the noise + # gaussian: Gaussian feature noise with noise_scale as std_dev + # uniform: Uniformly distributed noise on the interval [-noise_scale, noise_scale] + # label: Flip labels + if params['noise_injection']: + if params['label_noise']: + # check if we want noise correlated with a feature + if params['noise_correlated']: + labels, y_noise_gen = label_flip_correlated(labels, + params['label_noise'], data, + params['feature_col'], + params['feature_threshold']) + # else add uncorrelated noise + else: + labels, y_noise_gen = label_flip(labels, params['label_noise']) + # check if noise is on for RNA-seq data + elif params['noise_gaussian']: + data = add_gaussian_noise(data, 0, params['std_dev']) + elif params['noise_cluster']: + data = add_cluster_noise(data, loc=0., scale=params['std_dev'], + col_ids=params['feature_col'], + noise_type=params['noise_type'], + row_ids=params['sample_ids'], + y_noise_level=params['label_noise']) + elif params['noise_column']: + data = add_column_noise(data, 0, params['std_dev'], + col_ids=params['feature_col'], + noise_type=params['noise_type']) + + return data, labels + + +def add_noise(data, labels, params): + # put all the logic under the add_noise switch + if params['add_noise']: + if params['label_noise']: + # check if we want noise correlated with a feature + if params['noise_correlated']: + labels, y_noise_gen = label_flip_correlated(labels, + params['label_noise'], data, + params['feature_col'], + params['feature_threshold']) + # else add uncorrelated noise + else: + labels, y_noise_gen = label_flip(labels, params['label_noise']) + # check if noise is on for RNA-seq data + elif params['noise_gaussian']: + data = add_gaussian_noise(data, 0, params['std_dev']) + elif params['noise_cluster']: + data = add_cluster_noise(data, loc=0., scale=params['std_dev'], + col_ids=params['feature_col'], + noise_type=params['noise_type'], + row_ids=params['sample_ids'], + y_noise_level=params['label_noise']) + elif params['noise_column']: + data = add_column_noise(data, 0, params['std_dev'], + col_ids=params['feature_col'], + noise_type=params['noise_type']) + + return data, labels + + def label_flip(y_data_categorical, y_noise_level): flip_count = 0 for i in range(0, y_data_categorical.shape[0]): @@ -18,19 +86,22 @@ def label_flip(y_data_categorical, y_noise_level): return y_data_categorical, y_noise_generated -def label_flip_correlated(y_data_categorical, y_noise_level, x_data, feature_column, threshold): - flip_count = 0 - for i in range(0, y_data_categorical.shape[0]): - if x_data[i][feature_column] > threshold: - if random.random() < y_noise_level: - flip_count += 1 - for j in range(y_data_categorical.shape[1]): - y_data_categorical[i][j] = int(not y_data_categorical[i][j]) +def label_flip_correlated(y_data_categorical, y_noise_level, x_data, col_ids, threshold): + for col_id in col_ids: + flip_count = 0 + for i in range(0, y_data_categorical.shape[0]): + if x_data[i][col_id] > threshold: + if random.random() < y_noise_level: + print(i, y_data_categorical[i][:]) + flip_count += 1 + for j in range(y_data_categorical.shape[1]): + y_data_categorical[i][j] = int(not y_data_categorical[i][j]) + print(i, y_data_categorical[i][:]) - y_noise_generated = float(flip_count) / float(y_data_categorical.shape[0]) - print("Correlated label noise generation for feature {:d}:\n".format(feature_column)) - print("Labels flipped on {} samples out of {}: {:06.4f} ({:06.4f} requested)\n".format( - flip_count, y_data_categorical.shape[0], y_noise_generated, y_noise_level)) + y_noise_generated = float(flip_count) / float(y_data_categorical.shape[0]) + print("Correlated label noise generation for feature {:d}:\n".format(col_id)) + print("Labels flipped on {} samples out of {}: {:06.4f} ({:06.4f} requested)\n".format( + flip_count, y_data_categorical.shape[0], y_noise_generated, y_noise_level)) return y_data_categorical, y_noise_generated @@ -41,3 +112,44 @@ def add_gaussian_noise(x_data, loc=0., scale=0.5): train_noise = np.random.normal(loc, scale, size=x_data.shape) x_data = x_data + train_noise return x_data + + +# Add simple Gaussian noise to a list of RNA seq values, assume normalized x data +def add_column_noise(x_data, loc=0., scale=0.5, col_ids=[0], noise_type='gaussian'): + for col_id in col_ids: + print("added", noise_type, "noise to column ", col_id) + print(x_data[:, col_id].T) + if noise_type == 'gaussian': + train_noise = np.random.normal(loc, scale, size=x_data.shape[0]) + elif noise_type == 'uniform': + train_noise = np.random.uniform(-1.0 * scale, scale, size=x_data.shape[0]) + print(train_noise) + x_data[:, col_id] = 1.0 * x_data[:, col_id] + 1.0 * train_noise.T + print(x_data[:, col_id].T) + return x_data + + +# Add noise to a list of RNA seq values, for a fraction of samples assume normalized x data +def add_cluster_noise(x_data, loc=0., scale=0.5, col_ids=[0], noise_type='gaussian', row_ids=[0], y_noise_level=0.0): + # loop over all samples + num_samples = len(row_ids) + flip_count = 0 + for row_id in row_ids: + # only perturb a fraction of the samples + if random.random() < y_noise_level: + flip_count += 1 + for col_id in col_ids: + print("added", noise_type, "noise to row, column ", row_id, col_id) + print(x_data[row_id, col_id]) + if noise_type == 'gaussian': + train_noise = np.random.normal(loc, scale) + elif noise_type == 'uniform': + train_noise = np.random.uniform(-1.0 * scale, scale) + print(train_noise) + x_data[row_id, col_id] = 1.0 * x_data[row_id, col_id] + 1.0 * train_noise + print(x_data[row_id, col_id]) + + y_noise_generated = float(flip_count) / float(num_samples) + print("Noise added to {} samples out of {}: {:06.4f} ({:06.4f} requested)\n".format( + flip_count, num_samples, y_noise_generated, y_noise_level)) + return x_data diff --git a/common/parsing_utils.py b/common/parsing_utils.py index ec69f272..f04b0761 100644 --- a/common/parsing_utils.py +++ b/common/parsing_utils.py @@ -74,6 +74,11 @@ 'type': str, 'default': argparse.SUPPRESS, 'help': 'training data filename.'}, + {'name': 'val_data', + 'action': 'store', + 'type': str, + 'default': argparse.SUPPRESS, + 'help': 'validation data filename.'}, {'name': 'test_data', 'type': str, 'action': 'store', @@ -295,9 +300,9 @@ 'type': hutils.str2bool, 'default': False, 'help': 'Toggle saving only weights (not optimizer) (NYI).'}, - {'name': 'ckpt_save_internal', + {'name': 'ckpt_save_interval', 'type': int, - 'default': 1, + 'default': 0, 'help': 'Interval to save checkpoints.'}, {'name': 'ckpt_keep_mode', 'type': str, diff --git a/common/pytorch_utils.py b/common/pytorch_utils.py index e756ef4b..51d39284 100644 --- a/common/pytorch_utils.py +++ b/common/pytorch_utils.py @@ -42,18 +42,18 @@ def get_function(name): return mapped -def build_activation(type): +def build_activation(activation): # activation - if type == 'relu': + if activation == 'relu': return torch.nn.ReLU() - elif type == 'sigmoid': + elif activation == 'sigmoid': return torch.nn.Sigmoid() - elif type == 'tanh': + elif activation == 'tanh': return torch.nn.Tanh() -def build_optimizer(model, type, lr, kerasDefaults, trainable_only=True): +def build_optimizer(model, optimizer, lr, kerasDefaults, trainable_only=True): if trainable_only: params = filter(lambda p: p.requires_grad, model.parameters()) else: @@ -61,57 +61,57 @@ def build_optimizer(model, type, lr, kerasDefaults, trainable_only=True): # schedule = optimizers.optimizer.Schedule() # constant lr (equivalent to default keras setting) - if type == 'sgd': + if optimizer == 'sgd': return torch.optim.GradientDescentMomentum(params, lr=lr, momentum_coef=kerasDefaults['momentum_sgd'], nesterov=kerasDefaults['nesterov_sgd']) - elif type == 'rmsprop': + elif optimizer == 'rmsprop': return torch.optim.RMSprop(model.parameters(), lr=lr, alpha=kerasDefaults['rho'], eps=kerasDefaults['epsilon']) - elif type == 'adagrad': + elif optimizer == 'adagrad': return torch.optim.Adagrad(model.parameters(), lr=lr, eps=kerasDefaults['epsilon']) - elif type == 'adadelta': + elif optimizer == 'adadelta': return torch.optim.Adadelta(params, eps=kerasDefaults['epsilon'], rho=kerasDefaults['rho']) - elif type == 'adam': + elif optimizer == 'adam': return torch.optim.Adam(params, lr=lr, betas=[kerasDefaults['beta_1'], kerasDefaults['beta_2']], eps=kerasDefaults['epsilon']) -def initialize(weights, type, kerasDefaults, seed=None, constant=0.): +def initialize(weights, initializer, kerasDefaults, seed=None, constant=0.): - if type == 'constant': + if initializer == 'constant': return torch.nn.init.constant_(weights, val=constant) - elif type == 'uniform': + elif initializer == 'uniform': return torch.nn.init.uniform(weights, a=kerasDefaults['minval_uniform'], b=kerasDefaults['maxval_uniform']) - elif type == 'normal': + elif initializer == 'normal': return torch.nn.init.normal(weights, mean=kerasDefaults['mean_normal'], std=kerasDefaults['stddev_normal']) - elif type == 'glorot_normal': # not quite Xavier + elif initializer == 'glorot_normal': # not quite Xavier return torch.nn.init.xavier_normal(weights) - elif type == 'glorot_uniform': + elif initializer == 'glorot_uniform': return torch.nn.init.xavier_uniform_(weights) - elif type == 'he_normal': + elif initializer == 'he_normal': return torch.nn.init.kaiming_uniform(weights) diff --git a/common/uq_keras_utils.py b/common/uq_keras_utils.py index ad36a87b..e2029d8a 100644 --- a/common/uq_keras_utils.py +++ b/common/uq_keras_utils.py @@ -759,15 +759,15 @@ def loss(y_true, y_pred): y_shape = K.shape(y_true) if nout > 1: - y_out0 = K.reshape(y_pred[:, 0::nout], y_shape) - y_out1 = K.reshape(y_pred[:, 1::nout], y_shape) - y_out2 = K.reshape(y_pred[:, 2::nout], y_shape) + y_qtl0 = K.reshape(y_pred[:, 0::3], y_shape) + y_qtl1 = K.reshape(y_pred[:, 1::3], y_shape) + y_qtl2 = K.reshape(y_pred[:, 2::3], y_shape) else: - y_out0 = K.reshape(y_pred[:, 0], y_shape) - y_out1 = K.reshape(y_pred[:, 1], y_shape) - y_out2 = K.reshape(y_pred[:, 2], y_shape) + y_qtl0 = K.reshape(y_pred[:, 0], y_shape) + y_qtl1 = K.reshape(y_pred[:, 1], y_shape) + y_qtl2 = K.reshape(y_pred[:, 2], y_shape) - return quantile_loss(lowquantile, y_true, y_out1) + quantile_loss(highquantile, y_true, y_out2) + 2. * quantile_loss(0.5, y_true, y_out0) + return quantile_loss(lowquantile, y_true, y_qtl1) + quantile_loss(highquantile, y_true, y_qtl2) + 2. * quantile_loss(0.5, y_true, y_qtl0) return loss @@ -795,10 +795,10 @@ def metric(y_true, y_pred): """ y_shape = K.shape(y_true) if nout > 1: - y_out = K.reshape(y_pred[:, index::nout], y_shape) + y_qtl = K.reshape(y_pred[:, index::3], y_shape) else: - y_out = K.reshape(y_pred[:, index], y_shape) - return quantile_loss(quantile, y_true, y_out) + y_qtl = K.reshape(y_pred[:, index], y_shape) + return quantile_loss(quantile, y_true, y_qtl) metric.__name__ = 'quantile_{}'.format(quantile) return metric @@ -819,7 +819,11 @@ def add_index_to_output(y_train): """ # Add indices to y y_train_index = range(y_train.shape[0]) - y_train_augmented = np.vstack([y_train, y_train_index]).T + if y_train.ndim > 1: + shp = (y_train.shape[0], 1) + y_train_augmented = np.hstack([y_train, np.reshape(y_train_index, shp)]) + else: + y_train_augmented = np.vstack([y_train, y_train_index]).T return y_train_augmented @@ -884,6 +888,11 @@ def __init__(self, x, y, a_max=0.99): Maximum value of a variable to allow """ super(Contamination_Callback, self).__init__() + if y.ndim > 1: + if y.shape[1] > 1: + raise Exception( + 'ERROR ! Contamination model can be applied to one-output regression, but provided training data has: ' + + str(y.ndim) + 'outpus... Exiting') self.x = x # Features of training set self.y = y # Output of training set diff --git a/examples/ADRP/adrp.py b/examples/ADRP/adrp.py index 3bda4c92..801ec86f 100644 --- a/examples/ADRP/adrp.py +++ b/examples/ADRP/adrp.py @@ -1,7 +1,6 @@ from __future__ import print_function import os -import sys import logging import pandas as pd @@ -12,10 +11,6 @@ from sklearn.preprocessing import StandardScaler file_path = os.path.dirname(os.path.realpath(__file__)) -# lib_path = os.path.abspath(os.path.join(file_path, '..')) -# sys.path.append(lib_path) -lib_path2 = os.path.abspath(os.path.join(file_path, "..", "..", "common")) -sys.path.append(lib_path2) import candle diff --git a/examples/ADRP/adrp_default_model.txt b/examples/ADRP/adrp_default_model.txt index f7b36780..9eba807a 100644 --- a/examples/ADRP/adrp_default_model.txt +++ b/examples/ADRP/adrp_default_model.txt @@ -40,6 +40,5 @@ use_tb = False tsne = False use_sample_weight = False sample_weight_type = 'linear' -framework = 'keras' [Monitor Params] timeout = 36000 diff --git a/examples/ADRP/reg_go2.py b/examples/ADRP/reg_go2.py index 706869b5..50c69784 100644 --- a/examples/ADRP/reg_go2.py +++ b/examples/ADRP/reg_go2.py @@ -1,7 +1,6 @@ import pandas as pd import numpy as np import os -import sys import argparse import math @@ -19,13 +18,10 @@ from tensorflow.keras.callbacks import ModelCheckpoint, CSVLogger, ReduceLROnPlateau, EarlyStopping - from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler file_path = os.path.dirname(os.path.realpath(__file__)) -lib_path = os.path.abspath(os.path.join(file_path, '..', '..', 'common')) -sys.path.append(lib_path) psr = argparse.ArgumentParser(description='input csv file') psr.add_argument('--in', default='in_file') diff --git a/examples/ADRP/test.sh b/examples/ADRP/test.sh new file mode 100755 index 00000000..dfaf2615 --- /dev/null +++ b/examples/ADRP/test.sh @@ -0,0 +1,2 @@ +set -ex +python adrp_baseline_keras2.py -e 1 diff --git a/examples/M16/M16_test.py b/examples/M16/M16_test.py index 6d913a25..91bdabcc 100644 --- a/examples/M16/M16_test.py +++ b/examples/M16/M16_test.py @@ -1,13 +1,10 @@ import os -import sys import pandas as pd import numpy as np import warnings warnings.filterwarnings("ignore") file_path = os.path.dirname(os.path.realpath(__file__)) -lib_path2 = os.path.abspath(os.path.join(file_path, "..", "..", "common")) -sys.path.append(lib_path2) import candle diff --git a/examples/M16/test.sh b/examples/M16/test.sh new file mode 100755 index 00000000..b2bf7885 --- /dev/null +++ b/examples/M16/test.sh @@ -0,0 +1,2 @@ +set -ex +python m16_baseline_keras2.py diff --git a/examples/darts/advanced/example_setup.py b/examples/darts/advanced/example_setup.py index 42ac97a5..0fb1c41d 100644 --- a/examples/darts/advanced/example_setup.py +++ b/examples/darts/advanced/example_setup.py @@ -1,11 +1,7 @@ import os -import sys file_path = os.path.dirname(os.path.realpath(__file__)) -lib_path2 = os.path.abspath(os.path.join(file_path, '..', '..', '..', 'common')) -sys.path.append(lib_path2) - import candle diff --git a/examples/darts/test.sh b/examples/darts/test.sh new file mode 100755 index 00000000..38f0e212 --- /dev/null +++ b/examples/darts/test.sh @@ -0,0 +1,3 @@ +cd uno +set -x +python uno_example.py -e 1 diff --git a/examples/darts/uno/example_setup.py b/examples/darts/uno/example_setup.py index 573dbc2c..de5ae94d 100644 --- a/examples/darts/uno/example_setup.py +++ b/examples/darts/uno/example_setup.py @@ -1,11 +1,6 @@ import os -import sys - file_path = os.path.dirname(os.path.realpath(__file__)) -lib_path2 = os.path.abspath(os.path.join(file_path, '..', '..', '..', 'common')) -sys.path.append(lib_path2) - import candle diff --git a/examples/darts/uno/uno_example.py b/examples/darts/uno/uno_example.py index cca91b35..db08c354 100644 --- a/examples/darts/uno/uno_example.py +++ b/examples/darts/uno/uno_example.py @@ -1,4 +1,5 @@ import logging +import sys import torch import torch.nn as nn @@ -9,8 +10,11 @@ import darts import candle -logging.basicConfig(level=logging.INFO) +# logging.basicConfig(sys.stdout, level=logging.INFO) +# Set up the logger to go to stdout instead of stderr logger = logging.getLogger("darts_uno") +logger.setLevel(logging.INFO) +logger.addHandler(logging.StreamHandler(sys.stdout)) def initialize_parameters(): diff --git a/examples/histogen/extract_code_baseline_pytorch.py b/examples/histogen/extract_code_baseline_pytorch.py index 238ad2df..4f96700a 100755 --- a/examples/histogen/extract_code_baseline_pytorch.py +++ b/examples/histogen/extract_code_baseline_pytorch.py @@ -13,11 +13,6 @@ from vqvae import VQVAE file_path = os.path.dirname(os.path.realpath(__file__)) -lib_path = os.path.abspath(os.path.join(file_path, '..')) -sys.path.append(lib_path) -lib_path2 = os.path.abspath(os.path.join(file_path, '..', '..', 'common')) -sys.path.append(lib_path2) - import candle @@ -82,7 +77,7 @@ def extract(lmdb_env, loader, model, device): index = 0 with lmdb_env.begin(write=True) as txn: - pbar = tqdm(loader) + pbar = tqdm(loader, file=sys.stdout) for img, _, filename in pbar: img = img.to(device) diff --git a/examples/histogen/extract_code_default_model.txt b/examples/histogen/extract_code_default_model.txt index 98559937..a01a929a 100644 --- a/examples/histogen/extract_code_default_model.txt +++ b/examples/histogen/extract_code_default_model.txt @@ -1,7 +1,6 @@ [Global_Params] size = 256 batch_size = 128 -use_gpus = True ckpt_directory = './' ckpt_restart = 'checkpoint/vqvae_001.pt' lmdb_filename = 'lmdb_001' diff --git a/examples/histogen/sample_baseline_pytorch.py b/examples/histogen/sample_baseline_pytorch.py index 67e3b908..871af4df 100755 --- a/examples/histogen/sample_baseline_pytorch.py +++ b/examples/histogen/sample_baseline_pytorch.py @@ -1,5 +1,4 @@ import os -import sys import torch from torchvision.utils import save_image @@ -9,11 +8,6 @@ from pixelsnail import PixelSNAIL file_path = os.path.dirname(os.path.realpath(__file__)) -lib_path = os.path.abspath(os.path.join(file_path, '..')) -sys.path.append(lib_path) -lib_path2 = os.path.abspath(os.path.join(file_path, '..', '..', 'common')) -sys.path.append(lib_path2) - import candle diff --git a/examples/histogen/test.sh b/examples/histogen/test.sh new file mode 100755 index 00000000..fc516a6e --- /dev/null +++ b/examples/histogen/test.sh @@ -0,0 +1,4 @@ +set -ex +python train_vqvae_baseline_pytorch.py -e 1 +python extract_code_baseline_pytorch.py +python train_pixelsnail_baseline_pytorch.py -e 1 diff --git a/examples/histogen/train_pixelsnail_baseline_pytorch.py b/examples/histogen/train_pixelsnail_baseline_pytorch.py index 47fea59f..a2acc423 100755 --- a/examples/histogen/train_pixelsnail_baseline_pytorch.py +++ b/examples/histogen/train_pixelsnail_baseline_pytorch.py @@ -19,11 +19,6 @@ from scheduler import CycleScheduler file_path = os.path.dirname(os.path.realpath(__file__)) -lib_path = os.path.abspath(os.path.join(file_path, '..')) -sys.path.append(lib_path) -lib_path2 = os.path.abspath(os.path.join(file_path, '..', '..', 'common')) -sys.path.append(lib_path2) - import candle @@ -119,7 +114,7 @@ def initialize_parameters(default_model='train_pixelsnail_default_model.txt'): def train(args, epoch, loader, model, optimizer, scheduler, device): - loader = tqdm(loader) + loader = tqdm(loader, file=sys.stdout) criterion = nn.CrossEntropyLoss() diff --git a/examples/histogen/train_pixelsnail_default_model.txt b/examples/histogen/train_pixelsnail_default_model.txt index 152cfe67..48ab34a6 100644 --- a/examples/histogen/train_pixelsnail_default_model.txt +++ b/examples/histogen/train_pixelsnail_default_model.txt @@ -5,11 +5,10 @@ batch_size = 32 learning_rate = 3e-4 hier = 'top' channel = 256 -n_res_block = 4 -n_res_channel = 256 -n_out_res_block = 0 -n_cond_res_block = 3 -dropout = 0.1 +n_res_block = 4 +n_res_channel = 256 +n_out_res_block = 0 +n_cond_res_block = 3 +dropout = 0.1 amp = 'O0' -use_gpus = True ckpt_directory = './' diff --git a/examples/histogen/train_vqvae_baseline_pytorch.py b/examples/histogen/train_vqvae_baseline_pytorch.py index 5c67c8d4..ad01b2bc 100755 --- a/examples/histogen/train_vqvae_baseline_pytorch.py +++ b/examples/histogen/train_vqvae_baseline_pytorch.py @@ -16,10 +16,6 @@ file_path = os.path.dirname(os.path.realpath(__file__)) -lib_path = os.path.abspath(os.path.join(file_path, '..')) -sys.path.append(lib_path) -lib_path2 = os.path.abspath(os.path.join(file_path, '..', '..', 'common')) -sys.path.append(lib_path2) port = (2 ** 15 + 2 ** 14 + hash(os.getuid() if sys.platform != "win32" else 1) % 2 ** 14) @@ -92,7 +88,7 @@ def initialize_parameters(default_model='train_vqvae_default_model.txt'): def train(epoch, loader, model, optimizer, scheduler, device): if dist.is_primary(): - loader = tqdm(loader) + loader = tqdm(loader, file=sys.stdout) criterion = nn.MSELoss() diff --git a/examples/image-vae/image_vae_baseline_pytorch.py b/examples/image-vae/image_vae_baseline_pytorch.py index 60e80cbb..a40e0595 100644 --- a/examples/image-vae/image_vae_baseline_pytorch.py +++ b/examples/image-vae/image_vae_baseline_pytorch.py @@ -13,11 +13,8 @@ from model import GeneralVae, PictureDecoder, PictureEncoder, customLoss from utils import AverageMeter import os -import sys file_path = os.path.dirname(os.path.realpath(__file__)) -lib_path = os.path.abspath(os.path.join(file_path, '..', '..', 'common')) -sys.path.append(lib_path) import candle @@ -97,7 +94,7 @@ def run(gParams): test_file = candle.fetch_file(data_url + test_data, subdir='Examples/image_vae') starting_epoch = 1 - total_epochs = gParams['epochs'] + total_epochs = args.epochs rng_seed = 42 torch.manual_seed(rng_seed) @@ -260,10 +257,10 @@ def test(epoch, args): val_losses.append(test_loss) - if total_epochs is None: + if total_epochs == 0: trn_rng = itertools.count(start=starting_epoch) else: - trn_rng = range(starting_epoch, total_epochs + 1) + trn_rng = range(starting_epoch, total_epochs) for epoch in trn_rng: for param_group in optimizer.param_groups: diff --git a/examples/image-vae/image_vae_default_model.txt b/examples/image-vae/image_vae_default_model.txt index 87069b7a..05de9124 100644 --- a/examples/image-vae/image_vae_default_model.txt +++ b/examples/image-vae/image_vae_default_model.txt @@ -3,7 +3,7 @@ data_url = 'ftp://ftp.mcs.anl.gov/pub/candle/public/benchmarks/Examples/image_va train_data = 'train.csv' test_data = 'test.csv' workers = 16 -epochs = None +epochs = 0 batch_size = 256 grad_clip = 2.0 model_path = 'models' diff --git a/examples/image-vae/sample_baseline_pytorch.py b/examples/image-vae/sample_baseline_pytorch.py index e0b4d9a8..16f41045 100644 --- a/examples/image-vae/sample_baseline_pytorch.py +++ b/examples/image-vae/sample_baseline_pytorch.py @@ -1,6 +1,5 @@ import logging import os -import sys import numpy as np import torch @@ -13,8 +12,6 @@ logger.setLevel(logging.CRITICAL) file_path = os.path.dirname(os.path.realpath(__file__)) -lib_path = os.path.abspath(os.path.join(file_path, '..', '..', 'common')) -sys.path.append(lib_path) import candle @@ -25,6 +22,8 @@ 'default': 'samples/'}, {'name': 'checkpoint', 'type': str, 'help': 'saved model to sample from'}, + {'name': 'model_path', 'type': str, + 'help': 'path to saved model to sample from'}, {'name': 'num_samples', 'type': int, 'default': 64, 'help': 'number of samples to draw'}, {'name': 'image', 'type': candle.str2bool, 'help': 'save images instead of numpy array'} ] diff --git a/examples/image-vae/sample_default_model.txt b/examples/image-vae/sample_default_model.txt index 2ebd61d3..b2f52edc 100644 --- a/examples/image-vae/sample_default_model.txt +++ b/examples/image-vae/sample_default_model.txt @@ -1,9 +1,8 @@ [Global_Params] data_url = 'http://ftp.mcs.anl.gov/pub/candle/public/benchmarks/Examples/image_vae' -test_model = 'model.pt' num_samples = 64 batch_size = 64 model_path = 'models' -checkpoint = 'epoch_6.pt' +checkpoint = 'epoch_1.pt' output_dir = 'samples' image = True diff --git a/examples/image-vae/test.sh b/examples/image-vae/test.sh new file mode 100755 index 00000000..09a239a9 --- /dev/null +++ b/examples/image-vae/test.sh @@ -0,0 +1,3 @@ +set -ex +python image_vae_baseline_pytorch.py -e 2 +python sample_baseline_pytorch.py --checkpoint epoch_1.pt diff --git a/examples/mnist/mnist.py b/examples/mnist/mnist.py index 8b081cfa..d6c68606 100644 --- a/examples/mnist/mnist.py +++ b/examples/mnist/mnist.py @@ -1,8 +1,5 @@ import os -import sys file_path = os.path.dirname(os.path.realpath(__file__)) -lib_path = os.path.abspath(os.path.join(file_path, '..', '..', 'common')) -sys.path.append(lib_path) from tensorflow.keras import backend as K import candle diff --git a/examples/mnist/test.sh b/examples/mnist/test.sh new file mode 100755 index 00000000..bb7b24c9 --- /dev/null +++ b/examples/mnist/test.sh @@ -0,0 +1,3 @@ +set -ex +python mnist_cnn_candle.py -e 3 +python mnist_mlp_candle.py -e 3 diff --git a/examples/rnagen/rnagen.py b/examples/rnagen/rnagen.py index b76ced7e..e27129a0 100644 --- a/examples/rnagen/rnagen.py +++ b/examples/rnagen/rnagen.py @@ -1,5 +1,4 @@ import os -import sys import time import logging import argparse @@ -16,10 +15,7 @@ from tensorflow.keras.metrics import binary_crossentropy, mean_squared_error file_path = os.path.dirname(os.path.realpath(__file__)) -lib_path = os.path.abspath(os.path.join(file_path, '..', '..', 'common')) -sys.path.append(lib_path) -import file_utils import candle DATA_URL = 'http://ftp.mcs.anl.gov/pub/candle/public/benchmarks/Examples/rnagen/' @@ -51,7 +47,7 @@ def parse_args(): def get_file(url): fname = os.path.basename(url) - return file_utils.get_file(fname, origin=url, cache_subdir='Examples') + return candle.get_file(fname, origin=url, cache_subdir='Examples') def impute_and_scale(df, scaling='std', imputing='mean', dropna='all'): @@ -148,7 +144,7 @@ def with_prefix(x): df1 = df_sample_source.merge(df_source, on='Source', how='left').drop('Source', axis=1) logger.info('Embedding RNAseq data source into features: %d additional columns', df1.shape[1] - 1) - df2 = df.drop('Sample', 1) + df2 = df.drop('Sample', axis=1) if add_prefix: df2 = df2.add_prefix('rnaseq.') diff --git a/examples/rnagen/rnagen_baseline_keras2.py b/examples/rnagen/rnagen_baseline_keras2.py index 13ae6ab5..9d52cb71 100644 --- a/examples/rnagen/rnagen_baseline_keras2.py +++ b/examples/rnagen/rnagen_baseline_keras2.py @@ -1,5 +1,4 @@ import os -import sys import time import logging import numpy as np @@ -15,8 +14,6 @@ from tensorflow.keras.metrics import binary_crossentropy, mean_squared_error file_path = os.path.dirname(os.path.realpath(__file__)) -lib_path = os.path.abspath(os.path.join(file_path, '..', '..', 'common')) -sys.path.append(lib_path) import candle @@ -33,6 +30,20 @@ 'help': 'number of top sample types to use'}, {'name': 'n_samples', 'type': int, 'default': 10000, 'help': 'number of RNAseq samples to generate'}, + {'name': 'encoder_layers', + 'nargs': '+', + 'type': int, + 'help': 'encoder network structure'}, + {'name': 'encoder_activation', + 'type': str, + 'help': 'encoder layer activation'}, + {'name': 'decoder_layers', + 'nargs': '+', + 'type': int, + 'help': 'decoder network structure'}, + {'name': 'decoder_activation', + 'type': str, + 'help': 'decoder layer activation'}, {'name': 'plot', 'type': candle.str2bool, 'help': 'plot test performance comparision with and without synthetic training data'} ] @@ -113,7 +124,7 @@ def impute_and_scale(df, scaling='std', imputing='mean', dropna='all'): def load_cell_type(gParams): - link = gParams['data_url'] + gParams['cell_types'] + link = gParams['data_url'] + gParams['train_data'] path = candle.fetch_file(link, subdir='Examples') df = pd.read_csv(path, engine='c', sep='\t', header=None) df.columns = ['Sample', 'type'] @@ -169,7 +180,7 @@ def with_prefix(x): df1 = df_sample_source.merge(df_source, on='Source', how='left').drop('Source', axis=1) logger.info('Embedding RNAseq data source into features: %d additional columns', df1.shape[1] - 1) - df2 = df.drop('Sample', 1) + df2 = df.drop('Sample', axis=1) if add_prefix: df2 = df2.add_prefix('rnaseq.') diff --git a/examples/rnagen/rnagen_default_model.txt b/examples/rnagen/rnagen_default_model.txt index a11dad3a..573db43d 100644 --- a/examples/rnagen/rnagen_default_model.txt +++ b/examples/rnagen/rnagen_default_model.txt @@ -1,6 +1,6 @@ [Global_Params] data_url = 'ftp://ftp.mcs.anl.gov/pub/candle/public/benchmarks/Examples/rnagen/' -cell_types = 'combined_cancer_types' +train_data = 'combined_cancer_types' model = 'cvae' encoder_layers = [100, 100] decoder_layers = [100, 100] diff --git a/examples/rnagen/test.sh b/examples/rnagen/test.sh new file mode 100755 index 00000000..9c183767 --- /dev/null +++ b/examples/rnagen/test.sh @@ -0,0 +1,3 @@ +set -ex +python rnagen_baseline_keras2.py -e 1 +python rnagen_baseline_keras2.py --plot True diff --git a/examples/rnngen/infer_rnngen_baseline_pytorch.py b/examples/rnngen/infer_rnngen_baseline_pytorch.py index ead14a72..b82348f5 100644 --- a/examples/rnngen/infer_rnngen_baseline_pytorch.py +++ b/examples/rnngen/infer_rnngen_baseline_pytorch.py @@ -1,5 +1,4 @@ import os -import sys import time import pandas as pd @@ -13,11 +12,6 @@ from model.vocab import get_vocab_from_file file_path = os.path.dirname(os.path.realpath(__file__)) -lib_path = os.path.abspath(os.path.join(file_path, '..')) -sys.path.append(lib_path) -lib_path2 = os.path.abspath(os.path.join(file_path, '..', '..', 'common')) -sys.path.append(lib_path2) - import candle @@ -182,7 +176,7 @@ def run(params): file = 'pilot1/ft_goodperforming_model.pt' elif args.model == 'ft_poorperforming_model.pt': file = 'pilot1/ft_poorperforming_model.pt' - else: # Corresponding to args.model == 'autosave.model.pt': + else: # Corresponding to args.model == 'autosave.model.pt': file = 'mosesrun/autosave.model.pt' print('Recovering trained model') diff --git a/examples/rnngen/test.sh b/examples/rnngen/test.sh new file mode 100755 index 00000000..a77dc928 --- /dev/null +++ b/examples/rnngen/test.sh @@ -0,0 +1,2 @@ +set -ex +python infer_rnngen_baseline_pytorch.py diff --git a/examples/unet/test.sh b/examples/unet/test.sh new file mode 100755 index 00000000..e69de29b diff --git a/examples/unet/unet.py b/examples/unet/unet.py index d3a00d9a..62c56bb3 100644 --- a/examples/unet/unet.py +++ b/examples/unet/unet.py @@ -1,8 +1,5 @@ import os -import sys file_path = os.path.dirname(os.path.realpath(__file__)) -lib_path = os.path.abspath(os.path.join(file_path, '..', '..', 'common')) -sys.path.append(lib_path) from tensorflow.keras.models import Model from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, Dropout, Cropping2D, Concatenate, ZeroPadding2D diff --git a/test.sh b/test.sh new file mode 100755 index 00000000..995a4f22 --- /dev/null +++ b/test.sh @@ -0,0 +1,42 @@ +#!/bin/bash + +export TF_CPP_MIN_LOG_LEVEL=3 +set +x +if [ $# == 0 ]; then +for pilot in "Pilot1" "Pilot2" "Pilot3" "examples" +do + for dir in $pilot/*/ + do + echo $dir + cd $dir +# more test.sh + ./test.sh > test.log + rm test.log + cd ../../ + done +done +elif [ $# == 1 ]; then + for dir in $1/*/ + do + echo $dir + cd $dir +# more test.sh + ./test.sh > test.log + rm test.log + cd ../../ + done +elif [ $# == 2 ]; then + for dir in $1/$2/ + do + echo $dir + cd $dir +# more test.sh + ./test.sh > test.log + rm test.log + cd ../../ + done +else + echo "Too many arguments" + echo "With no arguments the script will loop over Pilot1, Pilot2, Pilot3 and examples" + echo "Or you can enter a directory, or a directory and benchmark to test a smaller set" +fi