base
Config
¶
Bases: ABC
Base class for all configurations.
All configurations inherit from this class, be they stored in a file or generated on the fly.
Attributes:
Name | Type | Description |
---|---|---|
desc |
str
|
The description from the docstring. |
_repr_content |
dict
|
The items that are displayed in the repr method. |
Source code in spotpython/data/base.py
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|
desc: str
property
¶
Return the description from the docstring.
Returns:
Name | Type | Description |
---|---|---|
str |
str
|
The description from the docstring. |
Examples:
>>> class MyConfig(Config):
... '''My configuration class.'''
... pass
>>> MyConfig().desc
'My configuration class.'
__init__()
¶
Initialize a Config object.
Source code in spotpython/data/base.py
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Dataset
¶
Bases: ABC
Base class for all datasets.
All datasets inherit from this class, be they stored in a file or generated on the fly.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
task |
str
|
Type of task the dataset is meant for. Should be one of: - “Regression” - “Binary classification” - “Multi-class classification” - “Multi-output binary classification” - “Multi-output regression” |
required |
n_features |
int
|
Number of features in the dataset. |
required |
n_samples |
int
|
Number of samples in the dataset. |
None
|
n_classes |
int
|
Number of classes in the dataset, only applies to classification datasets. |
None
|
n_outputs |
int
|
Number of outputs the target is made of, only applies to multi-output datasets. |
None
|
sparse |
bool
|
Whether the dataset is sparse or not. |
False
|
Attributes:
Name | Type | Description |
---|---|---|
desc |
str
|
The description from the docstring. |
_repr_content |
dict
|
The items that are displayed in the repr method. |
Source code in spotpython/data/base.py
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|
desc: str
property
¶
Return the description from the docstring.
Returns:
Name | Type | Description |
---|---|---|
str |
str
|
The description from the docstring. |
Examples:
>>> class MyDataset(Dataset):
... '''My dataset class.'''
... def __init__(self):
... super().__init__('Regression', 10)
... def __iter__(self):
... yield from range(10)
>>> MyDataset().desc
'My dataset class.'
__init__(task, n_features, n_samples=None, n_classes=None, n_outputs=None, sparse=False)
¶
Initialize a Dataset object.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
task |
str
|
Type of task the dataset is meant for. Should be one of: - “Regression” - “Binary classification” - “Multi-class classification” - “Multi-output binary classification” - “Multi-output regression” |
required |
n_features |
int
|
Number of features in the dataset. |
required |
n_samples |
int
|
Number of samples in the dataset. Defaults to None. |
None
|
n_classes |
int
|
Number of classes in the dataset, only applies to classification datasets. Defaults to None. |
None
|
n_outputs |
int
|
Number of outputs the target is made of, only applies to multi-output datasets. Defaults to None. |
None
|
sparse |
bool
|
Whether the dataset is sparse or not. Defaults to False. |
False
|
Source code in spotpython/data/base.py
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__iter__()
abstractmethod
¶
Abstract method for iterating over samples in the dataset.
Source code in spotpython/data/base.py
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take(k)
¶
Iterate over the k samples.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
k |
int
|
The number of samples to iterate over. |
required |
Returns:
Type | Description |
---|---|
islice
|
itertools.islice: An iterator over the first k samples in the dataset. |
Examples:
>>> class MyDataset(Dataset):
... def __init__(self):
... super().__init__('Regression', 10)
... def __iter__(self):
... yield from range(10)
>>> list(MyDataset().take(5))
[0, 1, 2, 3, 4]
Source code in spotpython/data/base.py
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FileConfig
¶
Bases: Config
Base class for configurations that are stored in a local file.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
filename |
str
|
The file’s name. |
required |
directory |
Optional[str]
|
The directory where the file is contained.
Defaults to the location of the |
None
|
desc |
dict
|
Extra config parameters to pass as keyword arguments. |
{}
|
Returns:
Type | Description |
---|---|
FileConfig
|
A FileConfig object. |
Examples:
>>> config = FileConfig(filename="config.json", directory="/path/to/directory")
Source code in spotpython/data/base.py
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|
path: pathlib.Path
property
¶
The path to the configuration file.
Returns:
Type | Description |
---|---|
Path
|
pathlib.Path: The path to the configuration file. |
Examples:
>>> config = FileConfig(filename="config.json", directory="/path/to/directory")
>>> config.path
PosixPath('/path/to/directory/config.json')
FileDataset
¶
Bases: Dataset
Base class for datasets that are stored in a local file.
Small datasets that are part of the spotriver package inherit from this class.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
filename |
str
|
The file’s name. |
required |
directory |
Optional[str]
|
The directory where the file is contained.
Defaults to the location of the |
None
|
desc |
dict
|
Extra dataset parameters to pass as keyword arguments. |
{}
|
Returns:
Type | Description |
---|---|
FileDataset
|
A FileDataset object. |
Examples:
>>> dataset = FileDataset(filename="dataset.csv", directory="/path/to/directory")
Source code in spotpython/data/base.py
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path: pathlib.Path
property
¶
The path to the dataset file.
Returns:
Type | Description |
---|---|
Path
|
pathlib.Path: The path to the dataset file. |
Examples:
>>> dataset = FileDataset(filename="dataset.csv", directory="/path/to/directory")
>>> dataset.path
PosixPath('/path/to/directory/dataset.csv')
GenericFileDataset
¶
Bases: Dataset
Base class for datasets that are stored in a local file.
Small datasets that are part of the spotriver package inherit from this class.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
filename |
str
|
The file’s name. |
required |
target |
str
|
The name of the target variable. |
required |
converters |
dict
|
A dictionary specifying how to convert the columns of the dataset. Defaults to None. |
None
|
parse_dates |
list
|
A list of columns to parse as dates. Defaults to None. |
None
|
directory |
str
|
The directory where the file is contained. Defaults to the location of the |
None
|
desc |
dict
|
Extra dataset parameters to pass as keyword arguments. |
{}
|
Examples:
>>> from river.datasets import Iris
>>> dataset = Iris()
>>> for x, y in dataset:
... print(x, y)
... break
({'sepal_length': 5.1,
'sepal_width': 3.5,
'petal_length': 1.4,
'petal_width': 0.2},
'setosa')
Source code in spotpython/data/base.py
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|
path: pathlib.Path
property
¶
Returns the path where the dataset is stored.
RemoteDataset
¶
Bases: FileDataset
Base class for datasets that are stored in a remote file.
Medium and large datasets that are not part of the river package inherit from this class.
The filename doesn’t have to be provided if unpack is False. Indeed in the latter case the filename will be inferred from the URL.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
url |
str
|
The URL the dataset is located at. |
required |
size |
int
|
The expected download size. |
required |
unpack |
bool
|
Whether to unpack the download or not. Defaults to True. |
True
|
filename |
str
|
An optional name to given to the file if the file is unpacked. Defaults to None. |
None
|
desc |
dict
|
Extra dataset parameters to pass as keyword arguments. |
{}
|
Examples:
>>> from river.datasets import AirlinePassengers
>>> dataset = AirlinePassengers()
>>> for x, y in dataset:
... print(x, y)
... break
({'month': datetime.datetime(1949, 1, 1, 0, 0)}, 112)
Source code in spotpython/data/base.py
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is_downloaded: bool
property
¶
Indicate whether or not the data has been correctly downloaded.
path: pathlib.Path
property
¶
Returns the path where the dataset is stored.
__iter__()
¶
Iterates over the samples of a dataset.
Source code in spotpython/data/base.py
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download(force=False, verbose=True)
¶
Downloads the dataset.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
force |
bool
|
Whether to force the download even if the data is already downloaded. Defaults to False. |
False
|
verbose |
bool
|
Whether to display information about the download. Defaults to True. |
True
|
Source code in spotpython/data/base.py
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SyntheticDataset
¶
Bases: Dataset
A synthetic dataset.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
task |
str
|
Type of task the dataset is meant for. Should be one of: - “Regression” - “Binary classification” - “Multi-class classification” - “Multi-output binary classification” - “Multi-output regression” |
required |
n_features |
int
|
Number of features in the dataset. |
required |
n_samples |
int
|
Number of samples in the dataset. |
required |
n_classes |
int
|
Number of classes in the dataset, only applies to classification datasets. |
None
|
n_outputs |
int
|
Number of outputs the target is made of, only applies to multi-output datasets. |
None
|
sparse |
bool
|
Whether the dataset is sparse or not. |
False
|
Returns:
Type | Description |
---|---|
SyntheticDataset
|
A synthetic dataset object. |
Examples:
>>> from sklearn.datasets import make_classification
>>> X, y = make_classification(n_features=4, random_state=0)
>>> dataset = SyntheticDataset(task="Binary classification",
n_features=4,
n_samples=100,
n_classes=2,
n_outputs=1,
sparse=False)
Source code in spotpython/data/base.py
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__repr__()
¶
String representation of the SyntheticDataset object.
Returns:
Name | Type | Description |
---|---|---|
str |
str
|
A string representation of the SyntheticDataset object. |
Examples:
>>> from sklearn.datasets import make_classification
>>> X, y = make_classification(n_features=4, random_state=0)
>>> dataset = SyntheticDataset(task="Binary classification",
n_features=4,
n_samples=100,
n_classes=2,
n_outputs=1,
sparse=False)
>>> print(dataset)
Synthetic data generator
Configuration¶
task Binary classification
n_features 4 n_samples 100 n_classes 2 n_outputs 1 sparse False
Source code in spotpython/data/base.py
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get_data_home(data_home=None)
¶
Return the location where remote datasets are to be stored.
By default the data directory is set to a folder named ‘spotriver_data’ in the user home folder. Alternatively, it can be set by the ‘SPOTRIVER_DATA’ environment variable or programmatically by giving an explicit folder path. The ‘~’ symbol is expanded to the user home folder. If the folder does not already exist, it is automatically created.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
data_home |
str or Path
|
The path to spotriver data directory. If |
None
|
Returns:
Name | Type | Description |
---|---|---|
data_home |
Path
|
The path to the spotriver data directory. |
Examples:
>>> from pathlib import Path
>>> get_data_home()
PosixPath('/home/user/spotriver_data')
>>> get_data_home(Path('/tmp/spotriver_data'))
PosixPath('/tmp/spotriver_data')
Source code in spotpython/data/base.py
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