refactor: Explicitly name parameter to listing functions

Author: eddiebergman

examples/40_paper/2018_ida_strang_example.py

@@ -17,8 +17,8 @@
 # License: BSD 3-Clause
 
 import matplotlib.pyplot as plt
+
 import openml
-import pandas as pd
 
 ##############################################################################
 # A basic step for each data-mining or machine learning task is to determine
@@ -47,13 +47,16 @@
 
 # Downloads all evaluation records related to this study
 evaluations = openml.evaluations.list_evaluations(
-    measure, size=None, flows=flow_ids, study=study_id, output_format="dataframe"
+    measure,
+    size=None,
+    flows=flow_ids,
+    study=study_id,
 )
 # gives us a table with columns data_id, flow1_value, flow2_value
 evaluations = evaluations.pivot(index="data_id", columns="flow_id", values="value").dropna()
 # downloads all data qualities (for scatter plot)
 data_qualities = openml.datasets.list_datasets(
-    data_id=list(evaluations.index.values), output_format="dataframe"
+    data_id=list(evaluations.index.values),
 )
 # removes irrelevant data qualities
 data_qualities = data_qualities[meta_features]
@@ -86,10 +89,9 @@
 def determine_class(val_lin, val_nonlin):
     if val_lin < val_nonlin:
         return class_values[0]
-    elif val_nonlin < val_lin:
+    if val_nonlin < val_lin:
         return class_values[1]
-    else:
-        return class_values[2]
+    return class_values[2]
 
 
 evaluations["class"] = evaluations.apply(

openml/datasets/functions.py

@@ -5,6 +5,7 @@
 import logging
 import warnings
 from collections import OrderedDict
+from functools import partial
 from pathlib import Path
 from pyexpat import ExpatError
 from typing import TYPE_CHECKING, Any
@@ -81,7 +82,12 @@ def list_datasets(
     size: int | None = None,
     status: str | None = None,
     tag: str | None = None,
-    **kwargs: Any,
+    data_name: str | None = None,
+    data_version: int | None = None,
+    number_instances: int | None = None,
+    number_features: int | None = None,
+    number_classes: int | None = None,
+    number_missing_values: int | None = None,
 ) -> pd.DataFrame:
     """Return a dataframe of all dataset which are on OpenML.
 
@@ -101,10 +107,12 @@ def list_datasets(
         default active datasets are returned, but also datasets
         from another status can be requested.
     tag : str, optional
-    kwargs : dict, optional
-        Legal filter operators (keys in the dict):
-        data_name, data_version, number_instances,
-        number_features, number_classes, number_missing_values.
+    data_name : str, optional
+    data_version : int, optional
+    number_instances : int, optional
+    number_features : int, optional
+    number_classes : int, optional
+    number_missing_values : int, optional
 
     Returns
     -------
@@ -118,19 +126,29 @@ def list_datasets(
         If qualities are calculated for the dataset, some of
         these are also included as columns.
     """
-    batches = openml.utils._list_all(
-        listing_call=_list_datasets,
+    listing_call = partial(
+        _list_datasets,
         data_id=data_id,
-        offset=offset,
-        size=size,
         status=status,
         tag=tag,
-        **kwargs,
+        data_name=data_name,
+        data_version=data_version,
+        number_instances=number_instances,
+        number_features=number_features,
+        number_classes=number_classes,
+        number_missing_values=number_missing_values,
     )
-    return pd.concat(batches, ignore_index=True)
+    batches = openml.utils._list_all(listing_call, offset=offset, limit=size)
+    return pd.concat(batches)
 
 
-def _list_datasets(data_id: list[int] | None = None, **kwargs: Any) -> pd.DataFrame:
+def _list_datasets(
+    limit: int,
+    offset: int,
+    *,
+    data_id: list[int] | None = None,
+    **kwargs: Any,
+) -> pd.DataFrame:
     """
     Perform api call to return a list of all datasets.
 
@@ -141,6 +159,10 @@ def _list_datasets(data_id: list[int] | None = None, **kwargs: Any) -> pd.DataFr
     display_errors is also separated from the kwargs since it has a
     default value.
 
+    limit : int
+        The maximum number of datasets to show.
+    offset : int
+        The number of datasets to skip, starting from the first.
     data_id : list, optional
 
     kwargs : dict, optional
@@ -152,7 +174,7 @@ def _list_datasets(data_id: list[int] | None = None, **kwargs: Any) -> pd.DataFr
     -------
     datasets : dataframe
     """
-    api_call = "data/list"
+    api_call = f"data/list/list/{limit}/offset/{offset}"
 
     if kwargs is not None:
         for operator, value in kwargs.items():
@@ -242,12 +264,13 @@ def check_datasets_active(
     dict
         A dictionary with items {did: bool}
     """
-    datasets = list_datasets(status="all", data_id=dataset_ids, output_format="dataframe")
-    missing = set(dataset_ids) - set(datasets.get("did", []))
+    datasets = list_datasets(status="all", data_id=dataset_ids)
+    missing = set(dataset_ids) - set(datasets.index)
     if raise_error_if_not_exist and missing:
         missing_str = ", ".join(str(did) for did in missing)
         raise ValueError(f"Could not find dataset(s) {missing_str} in OpenML dataset list.")
-    return dict(datasets["status"] == "active")
+    mask = datasets["status"] == "active"
+    return dict(mask)
 
 
 def _name_to_id(
@@ -285,7 +308,6 @@ def _name_to_id(
         data_name=dataset_name,
         status=status,
         data_version=version,
-        output_format="dataframe",
     )
     if error_if_multiple and len(candidates) > 1:
         msg = f"Multiple active datasets exist with name '{dataset_name}'."

openml/evaluations/functions.py

@@ -3,6 +3,7 @@
 from __future__ import annotations
 
 import json
+from functools import partial
 from itertools import chain
 from typing import Any
 from typing_extensions import Literal, overload
@@ -56,7 +57,7 @@ def list_evaluations(
 def list_evaluations(
     function: str,
     offset: int | None = None,
-    size: int | None = 10000,
+    size: int | None = None,
     tasks: list[str | int] | None = None,
     setups: list[str | int] | None = None,
     flows: list[str | int] | None = None,
@@ -118,11 +119,9 @@ def list_evaluations(
     if per_fold is not None:
         per_fold_str = str(per_fold).lower()
 
-    eval_collection: list[list[OpenMLEvaluation]] = openml.utils._list_all(
-        listing_call=_list_evaluations,
+    listing_call = partial(
+        _list_evaluations,
         function=function,
-        offset=offset,
-        size=size,
         tasks=tasks,
         setups=setups,
         flows=flows,
@@ -133,8 +132,9 @@ def list_evaluations(
         sort_order=sort_order,
         per_fold=per_fold_str,
     )
-    flattened = list(chain.from_iterable(eval_collection))
+    eval_collection = openml.utils._list_all(listing_call, offset=offset, limit=size)
 
+    flattened = list(chain.from_iterable(eval_collection))
     if output_format == "dataframe":
         records = [item._to_dict() for item in flattened]
         return pd.DataFrame.from_records(records, index="run_id")
@@ -143,6 +143,9 @@ def list_evaluations(
 
 
 def _list_evaluations(
+    limit: int,
+    offset: int,
+    *,
     function: str,
     tasks: list | None = None,
     setups: list | None = None,
@@ -161,6 +164,10 @@ def _list_evaluations(
     The arguments that are lists are separated from the single value
     ones which are put into the kwargs.
 
+    limit : int
+        the number of evaluations to return
+    offset : int
+        the number of evaluations to skip, starting from the first
     function : str
         the evaluation function. e.g., predictive_accuracy
 
@@ -178,7 +185,7 @@ def _list_evaluations(
     study : int, optional
 
     kwargs: dict, optional
-        Legal filter operators: tag, limit, offset.
+        Legal filter operators: tag, per_fold
 
     sort_order : str, optional
         order of sorting evaluations, ascending ("asc") or descending ("desc")
@@ -187,7 +194,7 @@ def _list_evaluations(
     -------
     list of OpenMLEvaluation objects
     """
-    api_call = f"evaluation/list/function/{function}"
+    api_call = f"evaluation/list/function/{function}/limit/{limit}/offset/{offset}"
     if kwargs is not None:
         for operator, value in kwargs.items():
             api_call += f"/{operator}/{value}"
@@ -202,7 +209,7 @@ def _list_evaluations(
     if uploaders is not None:
         api_call += "/uploader/{}".format(",".join([str(int(i)) for i in uploaders]))
     if study is not None:
-        api_call += "/study/%d" % study
+        api_call += f"/study/{study}"
     if sort_order is not None:
         api_call += f"/sort_order/{sort_order}"
 

openml/flows/functions.py

@@ -4,6 +4,7 @@
 import os
 import re
 from collections import OrderedDict
+from functools import partial
 from typing import Any, Dict
 
 import dateutil.parser
@@ -135,7 +136,7 @@ def list_flows(
     offset: int | None = None,
     size: int | None = None,
     tag: str | None = None,
-    **kwargs: Any,
+    uploader: str | None = None,
 ) -> pd.DataFrame:
     """
     Return a list of all flows which are on OpenML.
@@ -164,30 +165,29 @@ def list_flows(
             - external version
             - uploader
     """
-    batches = openml.utils._list_all(
-        listing_call=_list_flows,
-        offset=offset,
-        size=size,
-        tag=tag,
-        **kwargs,
-    )
+    listing_call = partial(_list_flows, tag=tag, uploader=uploader)
+    batches = openml.utils._list_all(listing_call, offset=offset, limit=size)
     return pd.concat(batches, ignore_index=True)
 
 
-def _list_flows(**kwargs: Any) -> pd.DataFrame:
+def _list_flows(limit: int, offset: int, **kwargs: Any) -> pd.DataFrame:
     """
     Perform the api call that return a list of all flows.
 
     Parameters
     ----------
+    limit : int
+        the maximum number of flows to return
+    offset : int
+        the number of flows to skip, starting from the first
     kwargs: dict, optional
-        Legal filter operators: uploader, tag, limit, offset.
+        Legal filter operators: uploader, tag
 
     Returns
     -------
     flows : dataframe
     """
-    api_call = "flow/list"
+    api_call = f"flow/list/limit/{limit}/offset/{offset}"
 
     if kwargs is not None:
         for operator, value in kwargs.items():