Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

change meta estimation #409

Merged
merged 2 commits into from
Sep 13, 2023
Merged

change meta estimation #409

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
33f1372
change meta estimation
PhilippeMoussalli Sep 5, 2023
2f914d5
remove print statements
PhilippeMoussalli Sep 5, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
38 changes: 28 additions & 10 deletions components/load_from_hf_hub/src/main.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2,24 +2,31 @@
import logging
import typing as t

import dask
import dask.dataframe as dd
import pandas as pd
from fondant.component import DaskLoadComponent
from fondant.component_spec import ComponentSpec

logger = logging.getLogger(__name__)

dask.config.set({"dataframe.convert-string": False})


class LoadFromHubComponent(DaskLoadComponent):

def __init__(self, *_,
dataset_name: str,
column_name_mapping: dict,
image_column_names: t.Optional[list],
n_rows_to_load: t.Optional[int],
index_column:t.Optional[str],
def __init__(self,
spec: ComponentSpec,
*_,
dataset_name: str,
column_name_mapping: dict,
image_column_names: t.Optional[list],
n_rows_to_load: t.Optional[int],
index_column: t.Optional[str],
) -> None:
"""
Args:
spec: the component spec
dataset_name: name of the dataset to load.
column_name_mapping: Mapping of the consumed hub dataset to fondant column names
image_column_names: A list containing the original hub image column names. Used to
Expand All @@ -34,6 +41,7 @@ def __init__(self, *_,
self.image_column_names = image_column_names
self.n_rows_to_load = n_rows_to_load
self.index_column = index_column
self.spec = spec

def load(self) -> dd.DataFrame:
# 1) Load data, read as Dask dataframe
Expand Down Expand Up @@ -74,14 +82,24 @@ def _set_unique_index(dataframe: pd.DataFrame, partition_info=None):
"""Function that sets a unique index based on the partition and row number."""
dataframe["id"] = 1
dataframe["id"] = (
str(partition_info["number"])
+ "_"
+ (dataframe.id.cumsum()).astype(str)
str(partition_info["number"])
+ "_"
+ (dataframe.id.cumsum()).astype(str)
Copy link
Collaborator

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

we set the id as str here. Was there no problem with inferring divisions with this ?

Copy link
Contributor Author

@PhilippeMoussalli PhilippeMoussalli Sep 13, 2023 •
edited
Loading

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

I think it's mainly an issue if you cast an index that is already an integer to a string (or vice-versa) because you then change the order/sorting from numerical to lexicographical.

image

This shouldn't be the case here since we're creating a string index from the start. Dask will infer the divisions based on the lexicographical order of the index from the start (and then it should be preserved).

#391 (comment)

Copy link
Collaborator

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

makes sense, thx!

)
dataframe.index = dataframe.pop("id")
return dataframe

dask_df = dask_df.map_partitions(_set_unique_index, meta=dask_df.head())
def _get_meta_df() -> pd.DataFrame:
meta_dict = {"id": pd.Series(dtype="object")}
for subset_name, subset in self.spec.produces.items():
for field_name, field in subset.fields.items():
meta_dict[f"{subset_name}_{field_name}"] = pd.Series(
dtype=pd.ArrowDtype(field.type.value),
)
return pd.DataFrame(meta_dict).set_index("id")

meta = _get_meta_df()
dask_df = dask_df.map_partitions(_set_unique_index, meta=meta)
else:
logger.info(f"Setting `{self.index_column}` as index")
dask_df = dask_df.set_index(self.index_column, drop=True)
Expand Down