Commit d8c2cd53 authored by Amir MOHAMMADI's avatar Amir MOHAMMADI
Browse files

Fix docs

parent 639cb0c6
Pipeline #61026 passed with stage
in 12 minutes and 12 seconds
......@@ -60,13 +60,11 @@ def get_bob_tags(estimator=None, force_tags=None):
Relies on the tags API of sklearn to set and retrieve the tags.
Specify an estimator tag values with ``estimator._more_tags``:
Specify an estimator tag values with ``estimator._more_tags``::
```
class My_annotator_transformer(sklearn.base.BaseEstimator):
def _more_tags(self):
return {"bob_output": "annotations"}
```
class My_annotator_transformer(sklearn.base.BaseEstimator):
def _more_tags(self):
return {"bob_output": "annotations"}
The returned tags will take their value with the following priority:
......@@ -74,69 +72,75 @@ def get_bob_tags(estimator=None, force_tags=None):
2. key:value in `estimator` tags (set with `estimator._more_tags()`) if it exists;
3. the default value for that tag if none of the previous exist.
Tags format
-----------
Examples
--------
bob_input: str
The Sample attribute passed to the first argument of the fit or transform method.
Example:
`{"bob_input": ("annotations")}`
will result in:
`estimator.transform(sample.annotations)`
Default:
`{"bob_input": "data"}`
Default value is ``data``.
Example::
{"bob_input": ("annotations")}
will result in::
estimator.transform(sample.annotations)
bob_transform_extra_input: tuple of str
Each element of the tuple is a str representing an attribute of a Sample
object. Each attribute of the sample will be passed as argument to the transform
method in that order.
Example:
`{"bob_transform_extra_input": (("arg_0","data"), ("arg_1","annotations"))}`
will result in:
`estimator.transform(sample.data, arg_0=sample.data, arg_1=sample.annotations)`
Default:
`{"bob_transform_extra_input": tuple()}`
method in that order. Default value is an empty tuple ``(,)``.
Example::
{"bob_transform_extra_input": (("arg_0","data"), ("arg_1","annotations"))}
will result in::
estimator.transform(sample.data, arg_0=sample.data, arg_1=sample.annotations)
bob_fit_extra_input: tuple of str
Each element of the tuple is a str representing an attribute of a Sample
object. Each attribute of the sample will be passed as argument to the fit
method in that order.
Example:
`{"bob_fit_extra_input": (("y", "annotations"), ("extra "metadata"))}`
will result in:
`estimator.fit(sample.data, y=sample.annotations, extra=sample.metadata)`
Default:
`{"bob_fit_extra_input": tuple()}`
method in that order. Default value is an empty tuple ``(,)``.
Example::
{"bob_fit_extra_input": (("y", "annotations"), ("extra "metadata"))}
will result in::
estimator.fit(sample.data, y=sample.annotations, extra=sample.metadata)
bob_output: str
The Sample attribute in which the output of the transform is stored.
Default:
`{"bob_output": "data"}`
Default value is ``data``.
bob_checkpoint_extension: str
The extension of each checkpoint file.
Default:
`{"bob_checkpoint_extension": ".h5"}`
Default value is ``.h5``.
bob_features_save_fn: func
The function used to save each checkpoint file.
Default:
`{"bob_features_save_fn": bob.io.base.save}`
Default value is :any:`bob.io.base.save`.
bob_features_load_fn: func
The function used to load each checkpoint file.
Default:
`{"bob_features_load_fn": bob.io.base.load}`
Default value is :any:`bob.io.base.load`.
bob_fit_supports_dask_array: bool
Indicates that the fit method of that estimator accepts dask arrays as
input. You may only use this tag if you accept X (N, M) and optionally y
(N) as input. The fit function may not accept any other input.
Default:
`{"bob_fit_supports_dask_array": False}`
Default value is ``False``.
bob_fit_supports_dask_bag: bool
Indicates that the fit method of that estimator accepts dask bags as
input. If true, each input parameter of the fit will be a dask bag. You
still can (and normally you should) wrap your estimator with the
SampleWrapper so the same code runs with and without dask.
Default:
`{"bob_fit_supports_dask_bag": False}`
Default value is ``False``.
bob_checkpoint_features: bool
If False, the features of the estimator will never be saved.
Default:
`{"bob_checkpoint_features": True}`
Default value is ``True``.
Parameters
----------
......
......@@ -11,3 +11,4 @@ py:class PeriodicCallback
py:class defaultdict
py:class WorkerState
py:class deque
py:class sklearn.BaseEstimator
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