the-algorithm/trust_and_safety_models/nsfw/nsfw_text.py

import os
import re
from datetime import datetime
from functools import reduce

import matplotlib.pyplot as plt
import pandas as pd
import tensorflow as tf
from sklearn.metrics import (
    PrecisionRecallDisplay,
    average_precision_score,
    classification_report,
    precision_recall_curve,
)
from sklearn.model_selection import train_test_split
from twitter.cuad.representation.models.optimization import create_optimizer
from twitter.cuad.representation.models.text_encoder import TextEncoder

pd.set_option("display.max_colwidth", None)
pd.set_option("display.expand_frame_repr", False)

print(tf.__version__)
print(tf.config.list_physical_devices())

log_path = os.path.join(
    "pnsfwtweettext_model_runs", datetime.now().strftime("%Y-%m-%d_%H.%M.%S")
)

tweet_text_feature = "text"

params = {
    "batch_size": 32,
    "max_seq_lengths": 256,
    "model_type": "twitter_bert_base_en_uncased_augmented_mlm",
    "trainable_text_encoder": True,
    "lr": 5e-5,
    "epochs": 10,
}

REGEX_PATTERNS = [
    r"^RT @[A-Za-z0-9_]+: ",
    r"@[A-Za-z0-9_]+",
    r"https:\/\/t\.co\/[A-Za-z0-9]{10}",
    r"@\?\?\?\?\?",
]

EMOJI_PATTERN = re.compile(
    "(["
    "\U0001F1E0-\U0001F1FF"
    "\U0001F300-\U0001F5FF"
    "\U0001F600-\U0001F64F"
    "\U0001F680-\U0001F6FF"
    "\U0001F700-\U0001F77F"
    "\U0001F780-\U0001F7FF"
    "\U0001F800-\U0001F8FF"
    "\U0001F900-\U0001F9FF"
    "\U0001FA00-\U0001FA6F"
    "\U0001FA70-\U0001FAFF"
    "\U00002702-\U000027B0"
    "])"
)


def clean_tweet(text):
    for pattern in REGEX_PATTERNS:
        text = re.sub(pattern, "", text)

    text = re.sub(EMOJI_PATTERN, r" \1 ", text)

    text = re.sub(r"\n", " ", text)

    return text.strip().lower()


df["processed_text"] = df["text"].astype(str).map(clean_tweet)
df.sample(10)

X_train, X_val, y_train, y_val = train_test_split(
    df[["processed_text"]], df["is_nsfw"], test_size=0.1, random_state=1
)


def df_to_ds(X, y, shuffle=False):
    ds = tf.data.Dataset.from_tensor_slices(
        (X.values, tf.one_hot(tf.cast(y.values, tf.int32), depth=2, axis=-1))
    )

    if shuffle:
        ds = ds.shuffle(1000, seed=1, reshuffle_each_iteration=True)

    return ds.map(lambda text, label: ({tweet_text_feature: text}, label)).batch(
        params["batch_size"]
    )


ds_train = df_to_ds(X_train, y_train, shuffle=True)
ds_val = df_to_ds(X_val, y_val)
X_train.values

inputs = tf.keras.layers.Input(shape=(), dtype=tf.string, name=tweet_text_feature)
encoder = TextEncoder(
    max_seq_lengths=params["max_seq_lengths"],
    model_type=params["model_type"],
    trainable=params["trainable_text_encoder"],
    local_preprocessor_path="demo-preprocessor",
)
embedding = encoder([inputs])["pooled_output"]
predictions = tf.keras.layers.Dense(2, activation="softmax")(embedding)
model = tf.keras.models.Model(inputs=inputs, outputs=predictions)

model.summary()

optimizer = create_optimizer(
    params["lr"],
    params["epochs"] * len(ds_train),
    0,
    weight_decay_rate=0.01,
    optimizer_type="adamw",
)
bce = tf.keras.losses.BinaryCrossentropy(from_logits=False)
pr_auc = tf.keras.metrics.AUC(curve="PR", num_thresholds=1000, from_logits=False)
model.compile(optimizer=optimizer, loss=bce, metrics=[pr_auc])

callbacks = [
    tf.keras.callbacks.EarlyStopping(
        monitor="val_loss", mode="min", patience=1, restore_best_weights=True
    ),
    tf.keras.callbacks.ModelCheckpoint(
        filepath=os.path.join(log_path, "checkpoints", "{epoch:02d}"), save_freq="epoch"
    ),
    tf.keras.callbacks.TensorBoard(
        log_dir=os.path.join(log_path, "scalars"),
        update_freq="batch",
        write_graph=False,
    ),
]
history = model.fit(
    ds_train,
    epochs=params["epochs"],
    callbacks=callbacks,
    validation_data=ds_val,
    steps_per_epoch=len(ds_train),
)

model.predict(["xxx 🍑"])

preds = X_val.processed_text.apply(apply_model)
print(classification_report(y_val, preds >= 0.90, digits=4))

precision, recall, thresholds = precision_recall_curve(y_val, preds)

fig = plt.figure(figsize=(15, 10))
plt.plot(precision, recall, lw=2)
plt.grid()
plt.xlim(0.2, 1)
plt.ylim(0.3, 1)
plt.xlabel("Recall", size=20)
plt.ylabel("Precision", size=20)

average_precision_score(y_val, preds)