mirror of
https://github.com/twitter/the-algorithm.git
synced 2024-12-22 10:11:52 +01:00
fix: fix f-string syntax error by removing redundant '}', apply Black code formatting, and change size argument to fontsize in plt.title() function call
This commit is contained in:
Maria Novik
parent
ec83d01dca
commit
96b37a8f1b
@ -1,5 +1,6 @@
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import logging
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import kerastuner as kt
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import math
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import numpy as np
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import pandas as pd
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import random
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@ -14,8 +15,9 @@ from tensorflow.keras.models import Sequential
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from tensorflow.keras.layers import Dense
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from google.cloud import storage
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LOG = logging.getLogger(__name__)
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physical_devices = tf.config.list_physical_devices('GPU')
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physical_devices
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tf.config.set_visible_devices([tf.config.PhysicalDevice(name='/physical_device:GPU:1', device_type='GPU')], 'GPU')
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tf.config.get_visible_devices('GPU')
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@ -58,11 +60,9 @@ test_batch_size = 256
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validation_batch_size = 256
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do_resample = False
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def class_func(features, label):
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return label
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resample_fn = tf.data.experimental.rejection_resample(
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class_func, target_dist = [0.5, 0.5], seed=0
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lambda _, x: x, target_dist = [0.5, 0.5], seed=0
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)
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train_glob = f"{input_root}/train/tfrecord/*.tfrecord"
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train_files = tf.io.gfile.glob(train_glob)
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@ -135,7 +135,7 @@ for example in tqdm(check_ds):
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if label == 1:
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pos_cnt += 1
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cnt += 1
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print(f'{cnt} train entries with {pos_cnt} positive')
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LOG.info(f'{cnt} train entries with {pos_cnt} positive')
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metrics = []
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@ -264,7 +264,7 @@ bucket = client.get_bucket(...)
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copy_local_directory_to_gcs(model_path, bucket, model_path)
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copy_local_directory_to_gcs('tuner_dir', bucket, 'tuner_dir')
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loaded_model = tf.keras.models.load_model(model_path)
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print(history.history.keys())
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LOG.info(history.history.keys())
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plt.figure(figsize = (20, 5))
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@ -319,7 +319,7 @@ for label in test_labels:
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else:
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n_test_neg +=1
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print(f'n_test = {n_test}, n_pos = {n_test_pos}, n_neg = {n_test_neg}')
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LOG.info(f'n_test = {n_test}, n_pos = {n_test_pos}, n_neg = {n_test_neg}')
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n_test_sens_prev_pos = 0
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n_test_sens_prev_neg = 0
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@ -332,7 +332,7 @@ for label in test_sens_prev_labels:
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else:
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n_test_sens_prev_neg +=1
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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}')
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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}')
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test_weights = np.ones(np.asarray(test_preds).shape)
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@ -385,7 +385,7 @@ plt.subplot(1, 3, 2)
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plt.plot(pr[2], pr[1][0:-1])
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plt.xlabel("threshold")
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plt.ylabel("recall")
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plt.title("Keras", size=20)
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plt.title("Keras", fontsize=20)
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plt.subplot(1, 3, 3)
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@ -406,7 +406,7 @@ precision, recall, thresholds = pr
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auc_precision_recall = sklearn.metrics.auc(recall, precision)
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print(auc_precision_recall)
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LOG.info(auc_precision_recall)
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plt.figure(figsize=(15, 10))
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plt.plot(recall, precision)
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@ -415,12 +415,12 @@ plt.xlabel("recall")
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plt.ylabel("precision")
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ptAt50 = get_point_for_recall(0.5, recall, precision)
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print(ptAt50)
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LOG.info(ptAt50)
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plt.plot( [ptAt50[0],ptAt50[0]], [0,ptAt50[1]], 'r')
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plt.plot([0, ptAt50[0]], [ptAt50[1], ptAt50[1]], 'r')
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ptAt90 = get_point_for_recall(0.9, recall, precision)
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print(ptAt90)
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LOG.info(ptAt50)
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plt.plot( [ptAt90[0],ptAt90[0]], [0,ptAt90[1]], 'b')
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plt.plot([0, ptAt90[0]], [ptAt90[1], ptAt90[1]], 'b')
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@ -428,8 +428,8 @@ ptAt50fmt = "%.4f" % ptAt50[1]
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ptAt90fmt = "%.4f" % ptAt90[1]
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aucFmt = "%.4f" % auc_precision_recall
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plt.title(
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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)",
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size=20
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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)",
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fontsize=20
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)
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plt.subplots_adjust(top=0.72)
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plt.savefig('recall_precision_nsfw_Keras_with_twitter_CLIP_MU_test.pdf')
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@ -437,7 +437,7 @@ plt.savefig('recall_precision_nsfw_Keras_with_twitter_CLIP_MU_test.pdf')
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precision, recall, thresholds = pr_sens_prev
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auc_precision_recall = sklearn.metrics.auc(recall, precision)
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print(auc_precision_recall)
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LOG.info(auc_precision_recall)
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plt.figure(figsize=(15, 10))
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plt.plot(recall, precision)
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@ -446,12 +446,12 @@ plt.xlabel("recall")
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plt.ylabel("precision")
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ptAt50 = get_point_for_recall(0.5, recall, precision)
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print(ptAt50)
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LOG.info(ptAt50)
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plt.plot( [ptAt50[0],ptAt50[0]], [0,ptAt50[1]], 'r')
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plt.plot([0, ptAt50[0]], [ptAt50[1], ptAt50[1]], 'r')
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ptAt90 = get_point_for_recall(0.9, recall, precision)
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print(ptAt90)
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LOG.info(ptAt90)
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plt.plot( [ptAt90[0],ptAt90[0]], [0,ptAt90[1]], 'b')
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plt.plot([0, ptAt90[0]], [ptAt90[1], ptAt90[1]], 'b')
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@ -460,7 +460,7 @@ ptAt90fmt = "%.4f" % ptAt90[1]
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aucFmt = "%.4f" % auc_precision_recall
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plt.title(
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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)",
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size=20
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fontsize=20
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)
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plt.subplots_adjust(top=0.72)
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plt.savefig('recall_precision_nsfw_Keras_with_twitter_CLIP_sens_prev_test.pdf')
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