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[FSTORE-1682] Change default behaviour of get_feature_vector to return on-demand features by default (logicalclocks#447)
* reformatting code with black and correcting snippets
* adding documentation for the parameters transform and on-demand features
* adding documentation for the parameters transform and on-demand features
The `get_feature_vector` and `get_feature_vectors` can also return untransformed features by setting the parameter `transform` to `False`.
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#### Retrieving feature vector without on-demand features
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The `get_feature_vector` and `get_feature_vectors` methods can return untransformed feature vectors without on-demand features by disabling model-dependent transformations and excluding on-demand features. To achieve this, set the parameters `transform` and `on_demand_features` to `False`.
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=== "Python"
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!!! example "Returning untransformed feature vectors"
It is important to note that in addition to the filters defined in feature view, [extra filters](./training-data.md#Extra-filters) will be applied if they are defined in the given training dataset version.
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## Retrieving untransformed batch data
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By default, the `get_batch_data` function returns batch data with model-dependent transformations applied. However, you can retrieve untransformed batch data—while still including on-demand features—by setting the `transform` parameter to `False`.
## Passing Context Variables to Transformation Functions
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After [defining a transformation function using a context variable](../transformation_functions.md#passing-context-variables-to-transformation-function), you can pass the necessary context variables through the `transformation_context` parameter when fetching batch data.
Copy file name to clipboardExpand all lines: docs/user_guides/fs/feature_view/feature-vectors.md
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@@ -191,6 +191,41 @@ You can also use the parameter to provide values for all the features which are
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)
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```
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## Retrieving untransformed feature vectors
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By default, the `get_feature_vector` and `get_feature_vectors` functions return transformed feature vectors, which has model-dependent transformations applied and includes on-demand features.
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However, you can retrieve the untransformed feature vectors without applying model-dependent transformations while still including on-demand features by setting the `transform` parameter to False.
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=== "Python"
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!!! example "Returning untransformed feature vectors"
## Retrieving feature vector without on-demand features
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The `get_feature_vector` and `get_feature_vectors` methods can also return untransformed feature vectors without on-demand features by disabling model-dependent transformations and excluding on-demand features. To achieve this, set the parameters `transform` and `on_demand_features` to `False`.
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=== "Python"
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!!! example "Returning untransformed feature vectors"
## Passing Context Variables to Transformation Functions
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After [defining a transformation function using a context variable](../transformation_functions.md#passing-context-variables-to-transformation-function), you can pass the required context variables using the `transformation_context` parameter when fetching the feature vectors.
#### Retrieving untransformed feature vector and batch inference data
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The `get_feature_vector`, `get_feature_vectors`, and `get_batch_data` methods can return untransformed feature vectors and batch data without applying model-dependent transformations while still including on-demand features. To achieve this, set the `transform` parameter to False.
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=== "Python"
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!!! example "Returning untransformed feature vectors and batch data."
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