openml
diff --git a/‎doc/progress.rst‎
Lines changed: 1 addition & 0 deletions b/‎doc/progress.rst‎
Lines changed: 1 addition & 0 deletions
diff --git a/‎openml/datasets/data_feature.py‎
Lines changed: 6 additions & 0 deletions b/‎openml/datasets/data_feature.py‎
Lines changed: 6 additions & 0 deletions
diff --git a/‎openml/datasets/dataset.py‎
Lines changed: 112 additions & 42 deletions b/‎openml/datasets/dataset.py‎
Lines changed: 112 additions & 42 deletions
@@ -9,6 +9,7 @@ Changelog
 0.13.1
 ~~~~~~
 
+ * ADD #1081 #1132: Add additional options for (not) downloading datasets ``openml.datasets.get_dataset`` and cache management.
  * ADD #1028: Add functions to delete runs, flows, datasets, and tasks (e.g., ``openml.datasets.delete_dataset``).
  * ADD #1144: Add locally computed results to the ``OpenMLRun`` object's representation if the run was created locally and not downloaded from the server.
  * ADD #1180: Improve the error message when the checksum of a downloaded dataset does not match the checksum provided by the API.
 
@@ -62,5 +62,11 @@ def __init__(
     def __repr__(self):
         return "[%d - %s (%s)]" % (self.index, self.name, self.data_type)
 
+    def __eq__(self, other):
+        if not isinstance(other, OpenMLDataFeature):
+            return False
+
+        return self.__dict__ == other.__dict__
+
     def _repr_pretty_(self, pp, cycle):
         pp.text(str(self))
@@ -7,6 +7,7 @@
 import os
 import pickle
 from typing import List, Optional, Union, Tuple, Iterable, Dict
+import warnings
 
 import arff
 import numpy as np
@@ -18,7 +19,6 @@
 from .data_feature import OpenMLDataFeature
 from ..exceptions import PyOpenMLError
 
-
 logger = logging.getLogger(__name__)
 
 
@@ -212,17 +212,22 @@ def find_invalid_characters(string, pattern):
         self._dataset = dataset
         self._minio_url = minio_url
 
+        self._features = None  # type: Optional[Dict[int, OpenMLDataFeature]]
+        self._qualities = None  # type: Optional[Dict[str, float]]
+        self._no_qualities_found = False
+
         if features_file is not None:
-            self.features = _read_features(
-                features_file
-            )  # type: Optional[Dict[int, OpenMLDataFeature]]
-        else:
-            self.features = None
+            self._features = _read_features(features_file)
+
+        if qualities_file == "":
+            # TODO(0.15): to switch to "qualities_file is not None" below and remove warning
+            warnings.warn(
+                "Starting from Version 0.15 `qualities_file` must be None and not an empty string.",
+                FutureWarning,
+            )
 
         if qualities_file:
-            self.qualities = _read_qualities(qualities_file)  # type: Optional[Dict[str, float]]
-        else:
-            self.qualities = None
+            self._qualities = _read_qualities(qualities_file)
 
         if data_file is not None:
             rval = self._compressed_cache_file_paths(data_file)
@@ -234,12 +239,36 @@ def find_invalid_characters(string, pattern):
             self.data_feather_file = None
             self.feather_attribute_file = None
 
+    @property
+    def features(self):
+        # Lazy loading of features
+        if self._features is None:
+            self._load_metadata(features=True)
+
+        return self._features
+
+    @property
+    def qualities(self):
+        # Lazy loading of qualities
+        # We have to check `_no_qualities_found` as there might not be qualities for a dataset
+        if self._qualities is None and (not self._no_qualities_found):
+            self._load_metadata(qualities=True)
+
+        return self._qualities
+
     @property
     def id(self) -> Optional[int]:
         return self.dataset_id
 
     def _get_repr_body_fields(self) -> List[Tuple[str, Union[str, int, List[str]]]]:
         """Collect all information to display in the __repr__ body."""
+
+        # Obtain number of features in accordance with lazy loading.
+        if self._qualities is not None and self._qualities["NumberOfFeatures"] is not None:
+            n_features = int(self._qualities["NumberOfFeatures"])  # type: Optional[int]
+        else:
+            n_features = len(self._features) if self._features is not None else None
+
         fields = {
             "Name": self.name,
             "Version": self.version,
@@ -248,14 +277,14 @@ def _get_repr_body_fields(self) -> List[Tuple[str, Union[str, int, List[str]]]]:
             "Download URL": self.url,
             "Data file": self.data_file,
             "Pickle file": self.data_pickle_file,
-            "# of features": len(self.features) if self.features is not None else None,
+            "# of features": n_features,
         }
         if self.upload_date is not None:
             fields["Upload Date"] = self.upload_date.replace("T", " ")
         if self.dataset_id is not None:
             fields["OpenML URL"] = self.openml_url
-        if self.qualities is not None and self.qualities["NumberOfInstances"] is not None:
-            fields["# of instances"] = int(self.qualities["NumberOfInstances"])
+        if self._qualities is not None and self._qualities["NumberOfInstances"] is not None:
+            fields["# of instances"] = int(self._qualities["NumberOfInstances"])
 
         # determines the order in which the information will be printed
         order = [
@@ -773,6 +802,40 @@ def get_data(
 
         return data, targets, categorical, attribute_names
 
+    def _load_metadata(self, features: bool = False, qualities: bool = False):
+        """Load the missing metadata information from the server and store it in the
+        dataset object.
+
+        The purpose of the function is to support lazy loading.
+
+        Parameters
+        ----------
+        features : bool (default=False)
+            If True, load the `self.features` data if not already loaded.
+        qualities: bool (default=False)
+            If True, load the `self.qualities` data if not already loaded.
+        """
+        # Delayed Import to avoid circular imports or having to import all of dataset.functions to
+        # import OpenMLDataset
+        from openml.datasets.functions import _get_dataset_metadata
+
+        if self.dataset_id is None:
+            raise ValueError(
+                """No dataset id specified. Please set the dataset id.
+                                Otherwise we cannot load metadata."""
+            )
+
+        features_file, qualities_file = _get_dataset_metadata(
+            self.dataset_id, features=features, qualities=qualities
+        )
+
+        if features_file is not None:
+            self._features = _read_features(features_file)
+
+        if qualities_file is not None:
+            self._qualities = _read_qualities(qualities_file)
+            self._no_qualities_found = self._qualities is None
+
     def retrieve_class_labels(self, target_name: str = "class") -> Union[None, List[str]]:
         """Reads the datasets arff to determine the class-labels.
 
@@ -790,10 +853,6 @@ def retrieve_class_labels(self, target_name: str = "class") -> Union[None, List[
         -------
         list
         """
-        if self.features is None:
-            raise ValueError(
-                "retrieve_class_labels can only be called if feature information is available."
-            )
         for feature in self.features.values():
             if (feature.name == target_name) and (feature.data_type == "nominal"):
                 return feature.nominal_values
@@ -922,6 +981,7 @@ def _to_dict(self) -> "OrderedDict[str, OrderedDict]":
         return data_container
 
 
+# -- Code for Features Property
 def _read_features(features_file: str) -> Dict[int, OpenMLDataFeature]:
     features_pickle_file = _get_features_pickle_file(features_file)
     try:
@@ -930,35 +990,41 @@ def _read_features(features_file: str) -> Dict[int, OpenMLDataFeature]:
     except:  # noqa E722
         with open(features_file, encoding="utf8") as fh:
             features_xml_string = fh.read()
-        xml_dict = xmltodict.parse(
-            features_xml_string, force_list=("oml:feature", "oml:nominal_value")
-        )
-        features_xml = xml_dict["oml:data_features"]
-
-        features = {}
-        for idx, xmlfeature in enumerate(features_xml["oml:feature"]):
-            nr_missing = xmlfeature.get("oml:number_of_missing_values", 0)
-            feature = OpenMLDataFeature(
-                int(xmlfeature["oml:index"]),
-                xmlfeature["oml:name"],
-                xmlfeature["oml:data_type"],
-                xmlfeature.get("oml:nominal_value"),
-                int(nr_missing),
-            )
-            if idx != feature.index:
-                raise ValueError("Data features not provided in right order")
-            features[feature.index] = feature
+
+        features = _parse_features_xml(features_xml_string)
 
         with open(features_pickle_file, "wb") as fh_binary:
             pickle.dump(features, fh_binary)
     return features
 
 
+def _parse_features_xml(features_xml_string):
+    xml_dict = xmltodict.parse(features_xml_string, force_list=("oml:feature", "oml:nominal_value"))
+    features_xml = xml_dict["oml:data_features"]
+
+    features = {}
+    for idx, xmlfeature in enumerate(features_xml["oml:feature"]):
+        nr_missing = xmlfeature.get("oml:number_of_missing_values", 0)
+        feature = OpenMLDataFeature(
+            int(xmlfeature["oml:index"]),
+            xmlfeature["oml:name"],
+            xmlfeature["oml:data_type"],
+            xmlfeature.get("oml:nominal_value"),
+            int(nr_missing),
+        )
+        if idx != feature.index:
+            raise ValueError("Data features not provided in right order")
+        features[feature.index] = feature
+
+    return features
+
+
 def _get_features_pickle_file(features_file: str) -> str:
     """This function only exists so it can be mocked during unit testing"""
     return features_file + ".pkl"
 
 
+# -- Code for Qualities Property
 def _read_qualities(qualities_file: str) -> Dict[str, float]:
     qualities_pickle_file = _get_qualities_pickle_file(qualities_file)
     try:
@@ -967,19 +1033,12 @@ def _read_qualities(qualities_file: str) -> Dict[str, float]:
     except:  # noqa E722
         with open(qualities_file, encoding="utf8") as fh:
             qualities_xml = fh.read()
-        xml_as_dict = xmltodict.parse(qualities_xml, force_list=("oml:quality",))
-        qualities = xml_as_dict["oml:data_qualities"]["oml:quality"]
-        qualities = _check_qualities(qualities)
+        qualities = _parse_qualities_xml(qualities_xml)
         with open(qualities_pickle_file, "wb") as fh_binary:
             pickle.dump(qualities, fh_binary)
     return qualities
 
 
-def _get_qualities_pickle_file(qualities_file: str) -> str:
-    """This function only exists so it can be mocked during unit testing"""
-    return qualities_file + ".pkl"
-
-
 def _check_qualities(qualities: List[Dict[str, str]]) -> Dict[str, float]:
     qualities_ = {}
     for xmlquality in qualities:
@@ -992,3 +1051,14 @@ def _check_qualities(qualities: List[Dict[str, str]]) -> Dict[str, float]:
             value = float(xmlquality["oml:value"])
         qualities_[name] = value
     return qualities_
+
+
+def _parse_qualities_xml(qualities_xml):
+    xml_as_dict = xmltodict.parse(qualities_xml, force_list=("oml:quality",))
+    qualities = xml_as_dict["oml:data_qualities"]["oml:quality"]
+    return _check_qualities(qualities)
+
+
+def _get_qualities_pickle_file(qualities_file: str) -> str:
+    """This function only exists so it can be mocked during unit testing"""
+    return qualities_file + ".pkl"