mirror of
https://github.com/twitter/the-algorithm.git
synced 2025-06-14 16:48:18 -05:00
Twitter Recommendation Algorithm
Please note we have force-pushed a new initial commit in order to remove some publicly-available Twitter user information. Note that this process may be required in the future.
This commit is contained in:
twitter-team
276
trust_and_safety_models/abusive/abusive_model.py
Normal file
276
trust_and_safety_models/abusive/abusive_model.py
Normal file
@ -0,0 +1,276 @@
|
||||
import tensorflow as tf
|
||||
|
||||
physical_devices = tf.config.list_physical_devices('GPU')
|
||||
for device in physical_devices:
|
||||
tf.config.experimental.set_memory_growth(device, True)
|
||||
|
||||
from twitter.hmli.nimbus.modeling.model_config import FeatureType, EncodingType, Feature, Model, LogType
|
||||
from twitter.hmli.nimbus.modeling.feature_loader import BigQueryFeatureLoader
|
||||
from twitter.cuad.representation.models.text_encoder import TextEncoder
|
||||
from twitter.cuad.representation.models.optimization import create_optimizer
|
||||
from twitter.hmli.nimbus.modeling.feature_encoder import FeatureEncoder
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import utils
|
||||
|
||||
cat_names = [
|
||||
...
|
||||
]
|
||||
|
||||
category_features = [Feature(name=cat_name, ftype=FeatureType.CONTINUOUS) for cat_name in cat_names]
|
||||
features = [
|
||||
Feature(name="tweet_text_with_media_annotations", ftype=FeatureType.STRING, encoding=EncodingType.BERT),
|
||||
Feature(name="precision_nsfw", ftype=FeatureType.CONTINUOUS),
|
||||
Feature(name="has_media", ftype=FeatureType.BINARY),
|
||||
Feature(name="num_media", ftype=FeatureType.DISCRETE)
|
||||
] + category_features
|
||||
|
||||
ptos_prototype = Model(
|
||||
name='ptos_prototype',
|
||||
export_path="...",
|
||||
features=features,
|
||||
)
|
||||
print(ptos_prototype)
|
||||
|
||||
cq_loader = BigQueryFeatureLoader(gcp_project=COMPUTE_PROJECT)
|
||||
labels = [
|
||||
"has_non_punitive_action",
|
||||
"has_punitive_action",
|
||||
"has_punitive_action_contains_self_harm",
|
||||
"has_punitive_action_encourage_self_harm",
|
||||
"has_punitive_action_episodic",
|
||||
"has_punitive_action_episodic_hateful_conduct",
|
||||
"has_punitive_action_other_abuse_policy",
|
||||
"has_punitive_action_without_self_harm"
|
||||
]
|
||||
|
||||
train_query = f"""
|
||||
SELECT
|
||||
{{feature_names}},
|
||||
{",".join(labels)},
|
||||
...
|
||||
"""
|
||||
val_query = f"""
|
||||
SELECT
|
||||
{{feature_names}},
|
||||
{",".join(labels)},
|
||||
...
|
||||
"""
|
||||
|
||||
print(train_query)
|
||||
train = cq_loader.load_features(ptos_prototype, "", "", custom_query=train_query)
|
||||
val = cq_loader.load_features(ptos_prototype, "", "", custom_query=val_query)
|
||||
print(train.describe(model=ptos_prototype))
|
||||
|
||||
params = {
|
||||
'max_seq_lengths': 128,
|
||||
'batch_size': 196,
|
||||
'lr': 1e-5,
|
||||
'optimizer_type': 'adamw',
|
||||
'warmup_steps': 0,
|
||||
'cls_dropout_rate': 0.1,
|
||||
'epochs': 30,
|
||||
'steps_per_epoch': 5000,
|
||||
'model_type': 'twitter_multilingual_bert_base_cased_mlm',
|
||||
'mixed_precision': True,
|
||||
}
|
||||
params
|
||||
|
||||
def parse_labeled_data(row_dict):
|
||||
label = [row_dict.pop(l) for l in labels]
|
||||
return row_dict, label
|
||||
|
||||
mirrored_strategy = tf.distribute.MirroredStrategy()
|
||||
BATCH_SIZE = params['batch_size'] * mirrored_strategy.num_replicas_in_sync
|
||||
|
||||
train_ds = train.to_tf_dataset().map(parse_labeled_data).shuffle(BATCH_SIZE*100).batch(BATCH_SIZE).repeat()
|
||||
val_ds = val.to_tf_dataset().map(parse_labeled_data).batch(BATCH_SIZE)
|
||||
|
||||
for record in train_ds:
|
||||
tf.print(record)
|
||||
break
|
||||
|
||||
def get_positive_weights():
|
||||
"""Computes positive weights used for class imbalance from training data."""
|
||||
label_weights_df = utils.get_label_weights(
|
||||
"tos-data-media-full",
|
||||
project_id="twttr-abusive-interact-prod",
|
||||
dataset_id="tos_policy"
|
||||
)
|
||||
pos_weight_tensor = tf.cast(
|
||||
label_weights_df.sort_values(by='label').positive_class_weight,
|
||||
dtype=tf.float32
|
||||
)
|
||||
return pos_weight_tensor
|
||||
|
||||
pos_weight_tensor = get_positive_weights()
|
||||
print(pos_weight_tensor)
|
||||
|
||||
class TextEncoderPooledOutput(TextEncoder):
|
||||
def call(self, x):
|
||||
return super().call([x])["pooled_output"]
|
||||
|
||||
def get_config(self):
|
||||
return super().get_config()
|
||||
|
||||
with mirrored_strategy.scope():
|
||||
text_encoder_pooled_output = TextEncoderPooledOutput(
|
||||
params['max_seq_lengths'],
|
||||
model_type=params['model_type'],
|
||||
trainable=True
|
||||
)
|
||||
|
||||
fe = FeatureEncoder(train)
|
||||
inputs, preprocessing_head = fe.build_model_head(model=ptos_prototype, text_encoder=text_encoder_pooled_output)
|
||||
|
||||
cls_dropout = tf.keras.layers.Dropout(params['cls_dropout_rate'], name="cls_dropout")
|
||||
outputs = cls_dropout(preprocessing_head)
|
||||
outputs = tf.keras.layers.Dense(8, name="output", dtype="float32")(outputs)
|
||||
|
||||
model = tf.keras.Model(
|
||||
inputs=inputs,
|
||||
outputs=outputs
|
||||
)
|
||||
pr_auc = tf.keras.metrics.AUC(curve="PR", num_thresholds=1000, multi_label=True, from_logits=True)
|
||||
|
||||
custom_loss = lambda y_true, y_pred: utils.multilabel_weighted_loss(y_true, y_pred, weights=pos_weight_tensor)
|
||||
optimizer = create_optimizer(
|
||||
init_lr=params["lr"],
|
||||
num_train_steps=(params["epochs"] * params["steps_per_epoch"]),
|
||||
num_warmup_steps=params["warmup_steps"],
|
||||
optimizer_type=params["optimizer_type"],
|
||||
)
|
||||
if params.get("mixed_precision"):
|
||||
optimizer = tf.train.experimental.enable_mixed_precision_graph_rewrite(optimizer)
|
||||
|
||||
model.compile(
|
||||
optimizer=optimizer,
|
||||
loss=custom_loss,
|
||||
metrics=[pr_auc]
|
||||
)
|
||||
|
||||
model.weights
|
||||
model.summary()
|
||||
pr_auc.name
|
||||
|
||||
import getpass
|
||||
import wandb
|
||||
from wandb.keras import WandbCallback
|
||||
try:
|
||||
wandb_key = ...
|
||||
wandb.login(...)
|
||||
run = wandb.init(project='ptos_with_media',
|
||||
group='new-split-trains',
|
||||
notes='tweet text with only (num_media, precision_nsfw). on full train set, new split.',
|
||||
entity='absv',
|
||||
config=params,
|
||||
name='tweet-text-w-nsfw-1.1',
|
||||
sync_tensorboard=True)
|
||||
except FileNotFoundError:
|
||||
print('Wandb key not found')
|
||||
run = wandb.init(mode='disabled')
|
||||
import datetime
|
||||
import os
|
||||
|
||||
start_train_time = datetime.datetime.now()
|
||||
print(start_train_time.strftime("%m-%d-%Y (%H:%M:%S)"))
|
||||
checkpoint_path = os.path.join("...")
|
||||
print("Saving model checkpoints here: ", checkpoint_path)
|
||||
|
||||
cp_callback = tf.keras.callbacks.ModelCheckpoint(
|
||||
filepath=os.path.join(checkpoint_path, "model.{epoch:04d}.tf"),
|
||||
verbose=1,
|
||||
monitor=f'val_{pr_auc.name}',
|
||||
mode='max',
|
||||
save_freq='epoch',
|
||||
save_best_only=True
|
||||
)
|
||||
|
||||
early_stopping_callback = tf.keras.callbacks.EarlyStopping(patience=7,
|
||||
monitor=f"val_{pr_auc.name}",
|
||||
mode="max")
|
||||
|
||||
model.fit(train_ds, epochs=params["epochs"], validation_data=val_ds, callbacks=[cp_callback, early_stopping_callback],
|
||||
steps_per_epoch=params["steps_per_epoch"],
|
||||
verbose=2)
|
||||
|
||||
import tensorflow_hub as hub
|
||||
|
||||
gs_model_path = ...
|
||||
reloaded_keras_layer = hub.KerasLayer(gs_model_path)
|
||||
inputs = tf.keras.layers.Input(name="tweet__core__tweet__text", shape=(1,), dtype=tf.string)
|
||||
output = reloaded_keras_layer(inputs)
|
||||
v7_model = tf.keras.models.Model(inputs=inputs, outputs=output)
|
||||
pr_auc = tf.keras.metrics.AUC(curve="PR", name="pr_auc")
|
||||
roc_auc = tf.keras.metrics.AUC(curve="ROC", name="roc_auc")
|
||||
v7_model.compile(metrics=[pr_auc, roc_auc])
|
||||
|
||||
model.load_weights("...")
|
||||
candidate_model = model
|
||||
|
||||
with mirrored_strategy.scope():
|
||||
candidate_eval = candidate_model.evaluate(val_ds)
|
||||
|
||||
test_query = f"""
|
||||
SELECT
|
||||
{",".join(ptos_prototype.feature_names())},
|
||||
has_media,
|
||||
precision_nsfw,
|
||||
{",".join(labels)},
|
||||
...
|
||||
"""
|
||||
|
||||
test = cq_loader.load_features(ptos_prototype, "", "", custom_query=test_query)
|
||||
test = test.to_tf_dataset().map(parse_labeled_data)
|
||||
|
||||
print(test)
|
||||
|
||||
test_only_media = test.filter(lambda x, y: tf.equal(x["has_media"], True))
|
||||
test_only_nsfw = test.filter(lambda x, y: tf.greater_equal(x["precision_nsfw"], 0.95))
|
||||
test_no_media = test.filter(lambda x, y: tf.equal(x["has_media"], False))
|
||||
test_media_not_nsfw = test.filter(lambda x, y: tf.logical_and(tf.equal(x["has_media"], True), tf.less(x["precision_nsfw"], 0.95)))
|
||||
for d in [test, test_only_media, test_only_nsfw, test_no_media, test_media_not_nsfw]:
|
||||
print(d.reduce(0, lambda x, _: x + 1).numpy())
|
||||
|
||||
from notebook_eval_utils import SparseMultilabelEvaluator, EvalConfig
|
||||
from dataclasses import asdict
|
||||
|
||||
def display_metrics(probs, targets, labels=labels):
|
||||
eval_config = EvalConfig(prediction_threshold=0.5, precision_k=0.9)
|
||||
for eval_mode, y_mask in [("implicit", np.ones(targets.shape))]:
|
||||
print("Evaluation mode", eval_mode)
|
||||
metrics = SparseMultilabelEvaluator.evaluate(
|
||||
targets, np.array(probs), y_mask, classes=labels, eval_config=eval_config
|
||||
)
|
||||
metrics_df = pd.DataFrame.from_dict(asdict(metrics)["per_topic_metrics"]).transpose()
|
||||
metrics_df["pos_to_neg"] = metrics_df["num_pos_samples"] / (metrics_df["num_neg_samples"] + 1)
|
||||
display(metrics_df.median())
|
||||
display(metrics_df)
|
||||
return metrics_df
|
||||
|
||||
|
||||
def eval_model(model, df):
|
||||
with mirrored_strategy.scope():
|
||||
targets = np.stack(list(df.map(lambda x, y: y).as_numpy_iterator()), axis=0)
|
||||
df = df.padded_batch(BATCH_SIZE)
|
||||
preds = model.predict(df)
|
||||
return display_metrics(preds, targets)
|
||||
|
||||
subsets = {"test": test,
|
||||
"test_only_media": test_only_media,
|
||||
"test_only_nsfw": test_only_nsfw,
|
||||
"test_no_media": test_no_media,
|
||||
"test_media_not_nsfw": test_media_not_nsfw}
|
||||
|
||||
metrics = {}
|
||||
for name, df in subsets.items():
|
||||
metrics[name] = eval_model(candidate_model, df)
|
||||
[(name, m.pr_auc) for name, m in metrics.items()]
|
||||
for name, x in [(name, m.pr_auc.to_string(index=False).strip().split("\n")) for name, m in metrics.items()]:
|
||||
print(name)
|
||||
for y in x:
|
||||
print(y.strip(), end="\t")
|
||||
print(".")
|
||||
for d in [test, test_only_media, test_only_nsfw, test_no_media, test_media_not_nsfw]:
|
||||
print(d.reduce(0, lambda x, _: x + 1).numpy())
|
||||
Reference in New Issue
Block a user