def get_size(data_dir):
if not exists(os.path.join(data_dir, "train_parquet")) or \
not exists(os.path.join(data_dir, "test_parquet")):
raise Exception("Not train and test data parquet specified")
else:
train_tbl = FeatureTable.read_parquet(
os.path.join(data_dir, "train_parquet"))
test_tbl = FeatureTable.read_parquet(
os.path.join(data_dir, "test_parquet"))
# get cat sizes
with tempfile.TemporaryDirectory() as local_path:
get_remote_file_to_local(
os.path.join(data_dir, "meta/categorical_sizes.pkl"),
os.path.join(local_path, "categorical_sizes.pkl"))
with open(os.path.join(local_path, "categorical_sizes.pkl"),
'rb') as f:
cat_sizes_dic = pickle.load(f)
indicator_sizes = [cat_sizes_dic[c] for c in indicator_cols]
print("indicator sizes: ", indicator_sizes)
embedding_sizes = [cat_sizes_dic[c] for c in embedding_cols]
print("embedding sizes: ", embedding_sizes)
cross_sizes = [cat_sizes_dic[c] for c in cross_cols]
return train_tbl, test_tbl, indicator_sizes, embedding_sizes, cross_sizes
def test_exists_s3(self):
access_key_id = os.getenv("AWS_ACCESS_KEY_ID")
secret_access_key = os.getenv("AWS_SECRET_ACCESS_KEY")
if access_key_id and secret_access_key:
file_path = "s3://analytics-zoo-data/nyc_taxi.csv"
assert exists(file_path)
file_path = "s3://analytics-zoo-data/abc.csv"
assert not exists(file_path)
def test_mkdirs_local_2(self):
temp = tempfile.mkdtemp()
path = os.path.join(temp, "dir1")
makedirs("file://" + path)
assert exists("file://" + path)
path = os.path.join(temp, "dir2/dir3")
makedirs("file://" + path)
assert exists("file://" + path)
shutil.rmtree(temp)
def test_mkdirs_s3(self):
access_key_id = os.getenv("AWS_ACCESS_KEY_ID")
secret_access_key = os.getenv("AWS_SECRET_ACCESS_KEY")
if access_key_id and secret_access_key:
file_path = "s3://analytics-zoo-data/temp/abc/"
makedirs(file_path)
assert exists(file_path)
import boto3
s3_client = boto3.Session(
aws_access_key_id=access_key_id,
aws_secret_access_key=secret_access_key).client('s3',
verify=False)
s3_client.delete_object(Bucket='analytics-zoo-data',
Key='temp/abc/')
for i in range(len(list_cols)):
cat_sizes_dict[list_cols[i]] = indexes[i].size()
cat_sizes_dict['engaged_with_user_id'] = user_index.size()
cat_sizes_dict['enaging_user_id'] = user_index.size()
cross_sizes_dict = dict(
zip(["_".join(cross_names) for cross_names in cross_cols],
args.cross_sizes))
cat_sizes_dict.update(cross_sizes_dict)
count_sizes_dict = dict(zip(count_cols, [len(bins)] * len(count_cols)))
cat_sizes_dict.update(count_sizes_dict)
print("cat size dict: ", cat_sizes_dict)
if not exists(os.path.join(args.output_folder, "meta")):
makedirs(os.path.join(args.output_folder, "meta"))
with tempfile.TemporaryDirectory() as local_path:
with open(os.path.join(local_path, "categorical_sizes.pkl"),
'wb') as f:
pickle.dump(cat_sizes_dict, f)
put_local_file_to_remote(os.path.join(local_path,
"categorical_sizes.pkl"),
os.path.join(args.output_folder,
"meta/categorical_sizes.pkl"),
over_write=True)
end = time()
print("Preprocessing and save time: ", end - start)
"intra_op_parallelism": 40
}
est = Estimator.from_keras(model_creator=model_creator,
verbose=True,
config=config,
backend="tf2")
train_count = train_tbl.size()
print("train size: ", train_count)
steps = math.ceil(train_count / options.batch_size)
test_count = test_tbl.size()
print("test size: ", test_count)
val_steps = math.ceil(test_count / options.batch_size)
if not exists(options.model_dir):
makedirs(options.model_dir)
callbacks = []
# early stopping
earlystopping = options.early_stopping
if earlystopping:
from tensorflow.keras.callbacks import EarlyStopping
callbacks.append(
EarlyStopping(monitor='val_auc',
mode='max',
verbose=1,
patience=earlystopping))
def test_exists_local(self):
file_path = os.path.join(self.resource_path, "orca/data/random.npy")
assert exists("file://" + file_path)
file_path = os.path.join(self.resource_path, "orca/data/abc.npy")
assert not exists("file://" + file_path)
train_data, test_data, n_uid, n_mid, n_cat = load_dien_data(args.data_dir)
model = build_model(args.model_type, n_uid, n_mid, n_cat, args.lr, args.data_type)
[inputs, feature_cols] = align_input_features(model)
estimator = Estimator.from_graph(inputs=inputs, outputs=[model.y_hat],
labels=[model.target_ph], loss=model.loss,
optimizer=model.optim, model_dir=args.model_dir,
metrics={'loss': model.loss, 'accuracy': model.accuracy})
estimator.fit(train_data.df, epochs=args.epochs, batch_size=args.batch_size,
feature_cols=feature_cols, label_cols=['label'], validation_data=test_data.df)
ckpts_dir = os.path.join(args.model_dir, 'ckpts/')
if not exists(ckpts_dir):
makedirs(ckpts_dir)
snapshot_path = ckpts_dir + "ckpt_" + args.model_type
estimator.save_tf_checkpoint(snapshot_path)
time_train = time.time()
print(f"perf training time: {(time_train - time_start):.2f}")
result = estimator.evaluate(test_data.df, args.batch_size, feature_cols=feature_cols,
label_cols=['label'])
evaluator = BinaryClassificationEvaluator(rawPredictionCol="prediction",
labelCol="label_t",
metricName="areaUnderROC")
prediction_df = estimator.predict(test_data.df, feature_cols=feature_cols)
prediction_df.cache()
transform_label = udf(lambda x: int(x[1]), "int")
def prepare_features(train_tbl, test_tbl, reindex_tbls):
def add_ratio_features(tbl):
cal_ratio = (lambda x: x[1] / x[0] if x[0] > 0 else 0.0)
tbl = tbl.apply(["engaged_with_user_follower_count", "engaged_with_user_following_count"],
"engaged_with_user_follower_following_ratio", cal_ratio, "float")\
.apply(["enaging_user_follower_count", "enaging_user_following_count"],
"enaging_user_follower_following_ratio", cal_ratio, "float")
return tbl
def organize_cols(tbl):
tbl = tbl.select(
array("enaging_user_follower_count",
"enaging_user_following_count",
"enaging_user_follower_following_ratio").alias("user_num"),
array("len_hashtags", "len_domains", "len_links",
"engaged_with_user_follower_count",
"engaged_with_user_following_count",
"engaged_with_user_follower_following_ratio").alias(
"item_num"), *cat_cols, *embed_cols, "label")
return tbl
print("reindexing embedding cols")
train_tbl = train_tbl.reindex(embed_cols, reindex_tbls)
test_tbl = test_tbl.reindex(embed_cols, reindex_tbls)
embed_in_dims = {}
for i, c, in enumerate(embed_cols):
embed_in_dims[c] = max(reindex_tbls[i].df.agg({
c + "_new": "max"
}).collect()[0])
print("add ratio features")
train_tbl = add_ratio_features(train_tbl)
test_tbl = add_ratio_features(test_tbl)
print("scale numerical features")
train_tbl, min_max_dic = train_tbl.min_max_scale(num_cols + ratio_cols)
test_tbl = test_tbl.transform_min_max_scale(num_cols + ratio_cols,
min_max_dic)
stats_dir = os.path.join(args.model_dir, 'stats')
if not exists(stats_dir):
makedirs(stats_dir)
with open(os.path.join(stats_dir, "min_max.pkl"), 'wb') as f:
pickle.dump(min_max_dic, f)
user_col_info = ColumnInfoTower(
indicator_cols=["enaging_user_is_verified"],
indicator_dims=[2],
embed_cols=["enaging_user_id"],
embed_in_dims=[embed_in_dims["enaging_user_id"]],
embed_out_dims=[16],
numerical_cols=["user_num"],
numerical_dims=[3],
name="user")
item_col_info = ColumnInfoTower(
indicator_cols=[
"engaged_with_user_is_verified", "present_media", "tweet_type",
"language"
],
indicator_dims=[2, 13, 3, 67], # max + 1
embed_cols=[
"engaged_with_user_id", "hashtags", "present_links",
"present_domains"
],
embed_in_dims=[
embed_in_dims["engaged_with_user_id"], embed_in_dims["hashtags"],
embed_in_dims["present_links"], embed_in_dims["present_domains"]
],
embed_out_dims=[16, 16, 16, 16],
numerical_cols=["item_num"],
numerical_dims=[6],
name="item")
print("organize columns and specify user_col_info and item_col_info")
train_tbl = organize_cols(train_tbl)
test_tbl = organize_cols(test_tbl)
return train_tbl, test_tbl, user_col_info, item_col_info