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153 lines
4.1 KiB
Python
153 lines
4.1 KiB
Python
from datetime import datetime
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from functools import reduce
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import os
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import pandas as pd
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import re
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from sklearn.metrics import average_precision_score, classification_report, precision_recall_curve, PrecisionRecallDisplay
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from sklearn.model_selection import train_test_split
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import tensorflow as tf
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import matplotlib.pyplot as plt
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import re
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from twitter.cuad.representation.models.optimization import create_optimizer
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from twitter.cuad.representation.models.text_encoder import TextEncoder
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pd.set_option('display.max_colwidth', None)
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pd.set_option('display.expand_frame_repr', False)
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print(tf.__version__)
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print(tf.config.list_physical_devices())
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log_path = os.path.join('pnsfwtweettext_model_runs', datetime.now().strftime('%Y-%m-%d_%H.%M.%S'))
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tweet_text_feature = 'text'
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params = {
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'batch_size': 32,
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'max_seq_lengths': 256,
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'model_type': 'twitter_bert_base_en_uncased_augmented_mlm',
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'trainable_text_encoder': True,
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'lr': 5e-5,
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'epochs': 10,
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}
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REGEX_PATTERNS = [
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r'^RT @[A-Za-z0-9_]+: ',
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r"@[A-Za-z0-9_]+",
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r'https:\/\/t\.co\/[A-Za-z0-9]{10}',
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r'@\?\?\?\?\?',
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]
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EMOJI_PATTERN = re.compile(
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"(["
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"\U0001F1E0-\U0001F1FF"
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"\U0001F300-\U0001F5FF"
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"\U0001F600-\U0001F64F"
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"\U0001F680-\U0001F6FF"
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"\U0001F700-\U0001F77F"
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"\U0001F780-\U0001F7FF"
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"\U0001F800-\U0001F8FF"
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"\U0001F900-\U0001F9FF"
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"\U0001FA00-\U0001FA6F"
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"\U0001FA70-\U0001FAFF"
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"\U00002702-\U000027B0"
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"])"
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)
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def clean_tweet(text):
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for pattern in REGEX_PATTERNS:
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text = re.sub(pattern, '', text)
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text = re.sub(EMOJI_PATTERN, r' \1 ', text)
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text = re.sub(r'\n', ' ', text)
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return text.strip().lower()
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df['processed_text'] = df['text'].astype(str).map(clean_tweet)
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df.sample(10)
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X_train, X_val, y_train, y_val = train_test_split(df[['processed_text']], df['is_nsfw'], test_size=0.1, random_state=1)
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def df_to_ds(X, y, shuffle=False):
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ds = tf.data.Dataset.from_tensor_slices((
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X.values,
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tf.one_hot(tf.cast(y.values, tf.int32), depth=2, axis=-1)
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))
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if shuffle:
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ds = ds.shuffle(1000, seed=1, reshuffle_each_iteration=True)
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return ds.map(lambda text, label: ({ tweet_text_feature: text }, label)).batch(params['batch_size'])
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ds_train = df_to_ds(X_train, y_train, shuffle=True)
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ds_val = df_to_ds(X_val, y_val)
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X_train.values
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inputs = tf.keras.layers.Input(shape=(), dtype=tf.string, name=tweet_text_feature)
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encoder = TextEncoder(
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max_seq_lengths=params['max_seq_lengths'],
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model_type=params['model_type'],
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trainable=params['trainable_text_encoder'],
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local_preprocessor_path='demo-preprocessor'
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)
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embedding = encoder([inputs])["pooled_output"]
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predictions = tf.keras.layers.Dense(2, activation='softmax')(embedding)
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model = tf.keras.models.Model(inputs=inputs, outputs=predictions)
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model.summary()
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optimizer = create_optimizer(
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params['lr'],
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params['epochs'] * len(ds_train),
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0,
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weight_decay_rate=0.01,
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optimizer_type='adamw'
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)
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bce = tf.keras.losses.BinaryCrossentropy(from_logits=False)
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pr_auc = tf.keras.metrics.AUC(curve='PR', num_thresholds=1000, from_logits=False)
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model.compile(optimizer=optimizer, loss=bce, metrics=[pr_auc])
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callbacks = [
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tf.keras.callbacks.EarlyStopping(
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monitor='val_loss',
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mode='min',
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patience=1,
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restore_best_weights=True
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),
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tf.keras.callbacks.ModelCheckpoint(
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filepath=os.path.join(log_path, 'checkpoints', '{epoch:02d}'),
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save_freq='epoch'
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),
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tf.keras.callbacks.TensorBoard(
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log_dir=os.path.join(log_path, 'scalars'),
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update_freq='batch',
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write_graph=False
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)
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]
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history = model.fit(
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ds_train,
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epochs=params['epochs'],
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callbacks=callbacks,
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validation_data=ds_val,
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steps_per_epoch=len(ds_train)
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)
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model.predict(["xxx 🍑"])
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preds = X_val.processed_text.apply(apply_model)
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print(classification_report(y_val, preds >= 0.90, digits=4))
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precision, recall, thresholds = precision_recall_curve(y_val, preds)
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fig = plt.figure(figsize=(15, 10))
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plt.plot(precision, recall, lw=2)
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plt.grid()
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plt.xlim(0.2, 1)
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plt.ylim(0.3, 1)
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plt.xlabel("Recall", size=20)
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plt.ylabel("Precision", size=20)
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average_precision_score(y_val, preds)
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