wehub-resource-sync
110 lines
3.3 KiB
Python
110 lines
3.3 KiB
Python
import os
|
|
|
|
import pandas as pd
|
|
import numpy as np
|
|
|
|
import ray
|
|
|
|
#
|
|
# Data generation utilities for the parquet files.
|
|
#
|
|
|
|
|
|
def generate_data(
|
|
num_rows: int,
|
|
num_files: int,
|
|
num_row_groups_per_file: int,
|
|
compression: str,
|
|
data_dir: str,
|
|
):
|
|
results = []
|
|
for file_index, global_row_index in enumerate(
|
|
range(0, num_rows, num_rows // num_files)
|
|
):
|
|
num_rows_in_file = min(num_rows // num_files, num_rows - global_row_index)
|
|
results.append(
|
|
generate_file.remote(
|
|
file_index,
|
|
global_row_index,
|
|
num_rows_in_file,
|
|
num_row_groups_per_file,
|
|
compression,
|
|
data_dir,
|
|
)
|
|
)
|
|
filenames, data_sizes = zip(*ray.get(results))
|
|
return filenames, sum(data_sizes)
|
|
|
|
|
|
@ray.remote
|
|
def generate_file(
|
|
file_index: int,
|
|
global_row_index: int,
|
|
num_rows_in_file: int,
|
|
num_row_groups_per_file: int,
|
|
compression: str,
|
|
data_dir: str,
|
|
):
|
|
buffs = []
|
|
for group_index, group_global_row_index in enumerate(
|
|
range(0, num_rows_in_file, num_rows_in_file // num_row_groups_per_file)
|
|
):
|
|
num_rows_in_group = min(
|
|
num_rows_in_file // num_row_groups_per_file,
|
|
num_rows_in_file - group_global_row_index,
|
|
)
|
|
buffs.append(
|
|
generate_row_group(group_index, group_global_row_index, num_rows_in_group)
|
|
)
|
|
df = pd.concat(buffs)
|
|
data_size = df.memory_usage(deep=True).sum()
|
|
file_ext = "gz" if compression == "gzip" else "snappy"
|
|
filename = os.path.join(data_dir, f"input_data_{file_index}.parquet.{file_ext}")
|
|
df.to_parquet(
|
|
filename,
|
|
compression=compression,
|
|
row_group_size=num_rows_in_file // num_row_groups_per_file,
|
|
)
|
|
return filename, data_size
|
|
|
|
|
|
# TODO(jian): Enhance the parquet content:
|
|
# 1) supports more data types;
|
|
# 2) supports data skews.
|
|
|
|
DATA_SPEC = {
|
|
"embeddings_name0": (0, 2385, np.int64),
|
|
"embeddings_name1": (0, 201, np.int64),
|
|
"embeddings_name2": (0, 201, np.int64),
|
|
"embeddings_name3": (0, 6, np.int64),
|
|
"embeddings_name4": (0, 19, np.int64),
|
|
"embeddings_name5": (0, 1441, np.int64),
|
|
"embeddings_name6": (0, 201, np.int64),
|
|
"embeddings_name7": (0, 22, np.int64),
|
|
"embeddings_name8": (0, 156, np.int64),
|
|
"embeddings_name9": (0, 1216, np.int64),
|
|
"embeddings_name10": (0, 9216, np.int64),
|
|
"embeddings_name11": (0, 88999, np.int64),
|
|
"embeddings_name12": (0, 941792, np.int64),
|
|
"embeddings_name13": (0, 9405, np.int64),
|
|
"embeddings_name14": (0, 83332, np.int64),
|
|
"embeddings_name15": (0, 828767, np.int64),
|
|
"embeddings_name16": (0, 945195, np.int64),
|
|
"one_hot0": (0, 3, np.int64),
|
|
"one_hot1": (0, 50, np.int64),
|
|
"labels": (0, 1, np.float64),
|
|
}
|
|
|
|
|
|
def generate_row_group(group_index: int, global_row_index: int, num_rows_in_group: int):
|
|
buffer = {
|
|
"key": np.array(range(global_row_index, global_row_index + num_rows_in_group)),
|
|
}
|
|
for col, (low, high, dtype) in DATA_SPEC.items():
|
|
if dtype in (np.int16, np.int32, np.int64):
|
|
buffer[col] = np.random.randint(low, high, num_rows_in_group, dtype=dtype)
|
|
elif dtype in (np.float32, np.float64):
|
|
buffer[col] = (high - low) * np.random.rand(num_rows_in_group) + low
|
|
|
|
return pd.DataFrame(buffer)
|