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reformatting code with black and correcting snippets
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‎docs/user_guides/fs/feature_group/on_demand_transformations.md

+76-23
Original file line numberDiff line numberDiff line change
@@ -103,8 +103,13 @@ The `get_feature_vector` function retrieves a single feature vector based on the
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=== "Python"
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!!! example "Computing on-demand features while retrieving a feature vector"
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```python
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feature_vector = feature_view.get_feature_vector(entry={"id":1}, request_parameter={"transaction_time":datetime(2022, 12, 28, 23, 55, 59), "current_time":datetime.now()})
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feature_vector = feature_view.get_feature_vector(
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entry={"id": 1},
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request_parameter={
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"transaction_time": datetime(2022, 12, 28, 23, 55, 59),
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"current_time": datetime.now(),
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},
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)
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```
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#### Retrieving feature vectors
@@ -114,24 +119,44 @@ The `get_feature_vectors` function retrieves multiple feature vectors using a li
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=== "Python"
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!!! example "Computing on-demand features while retrieving a feature vectors"
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```python
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# Specify unique request parameters for each serving key.
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feature_vector = feature_view.get_feature_vectors(entry=[{"id":1}, {"id":2}], request_parameter=[{"transaction_time":datetime(2022, 12, 28, 23, 55, 59), "current_time":datetime.now()},
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{"transaction_time":datetime(2022, 11, 20, 12, 50, 00), "current_time":datetime.now()}])
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feature_vector = feature_view.get_feature_vectors(
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entry=[{"id": 1}, {"id": 2}],
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request_parameter=[
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{
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"transaction_time": datetime(2022, 12, 28, 23, 55, 59),
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"current_time": datetime.now(),
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},
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{
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"transaction_time": datetime(2022, 11, 20, 12, 50, 00),
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"current_time": datetime.now(),
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},
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],
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)
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# Specify common request parameters for all serving key.
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feature_vector = feature_view.get_feature_vectors(entry=[{"id":1}, {"id":2}], request_parameter={"transaction_time":datetime(2022, 12, 28, 23, 55, 59), "current_time":datetime.now()})
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feature_vector = feature_view.get_feature_vectors(
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entry=[{"id": 1}, {"id": 2}],
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request_parameter={
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"transaction_time": datetime(2022, 12, 28, 23, 55, 59),
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"current_time": datetime.now(),
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},
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)
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```
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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 untransformed feature vector
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The `get_feature_vector` and `get_feature_vectors` can also return untransformed features vector without applying model-dependent transformations that contains on-demand features by setting the parameter `transform` to `False`.
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=== "Python"
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!!! example "Returning untransformed feature vectors"
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```python
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untransformed_feature_vector = feature_view.get_feature_vector(entry={"id":1}, transform=False)
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untransformed_feature_vectors = feature_view.get_feature_vectors(entry=[{"id":1}, {"id":2}], transform=False)
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untransformed_feature_vector = feature_view.get_feature_vector(
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entry={"id": 1}, transform=False
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)
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untransformed_feature_vectors = feature_view.get_feature_vectors(
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entry=[{"id": 1}, {"id": 2}], transform=False
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)
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```
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#### Compute all on-demand features
@@ -143,27 +168,51 @@ The `request_parameter` in this case, can be a list of dictionaries that specifi
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=== "Python"
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!!! example "Computing all on-demand features and manually applying model dependent transformations."
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```python
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# Specify request parameters for each serving key.
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untransformed_feature_vector = feature_view.get_feature_vector(entry={"id":1}, transform=False)
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untransformed_feature_vector = feature_view.get_feature_vector(
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entry={"id": 1}, transform=False
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)
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# re-compute and add on-demand features to the feature vector
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feature_vector_with_on_demand_features = fv.compute_on_demand_features(untransformed_feature_vector,
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request_parameter={"transaction_time":datetime(2022, 12, 28, 23, 55, 59), "current_time":datetime.now()})
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feature_vector_with_on_demand_features = fv.compute_on_demand_features(
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untransformed_feature_vector,
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request_parameter={
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"transaction_time": datetime(2022, 12, 28, 23, 55, 59),
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"current_time": datetime.now(),
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},
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)
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# Applying model dependent transformations
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encoded_feature_vector = fv.transform(feature_vector_with_on_demand_features)
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# Specify request parameters for each serving key.
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untransformed_feature_vectors = feature_view.get_feature_vectors(entry=[{"id":1}, {"id":2}], transform=False)
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untransformed_feature_vectors = feature_view.get_feature_vectors(
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entry=[{"id": 1}, {"id": 2}], transform=False
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)
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# re-compute and add on-demand features to the feature vectors - Specify unique request parameter for each feature vector
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feature_vectors_with_on_demand_features = fv.compute_on_demand_features(untransformed_feature_vectors,
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request_parameter=[{"transaction_time":datetime(2022, 12, 28, 23, 55, 59), "current_time":datetime.now()},
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{"transaction_time":datetime(2022, 11, 20, 12, 50, 00), "current_time":datetime.now()}])
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feature_vectors_with_on_demand_features = fv.compute_on_demand_features(
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untransformed_feature_vectors,
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request_parameter=[
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{
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"transaction_time": datetime(2022, 12, 28, 23, 55, 59),
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"current_time": datetime.now(),
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},
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{
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"transaction_time": datetime(2022, 11, 20, 12, 50, 00),
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"current_time": datetime.now(),
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},
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],
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)
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# re-compute and add on-demand feature to the feature vectors - Specify common request parameter for all feature vectors
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feature_vectors_with_on_demand_features = fv.compute_on_demand_features(untransformed_feature_vectors, request_parameter={"transaction_time":datetime(2022, 12, 28, 23, 55, 59), "current_time":datetime.now()})
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feature_vectors_with_on_demand_features = fv.compute_on_demand_features(
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untransformed_feature_vectors,
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request_parameter={
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"transaction_time": datetime(2022, 12, 28, 23, 55, 59),
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"current_time": datetime.now(),
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},
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)
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# Applying model dependent transformations
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encoded_feature_vector = fv.transform(feature_vectors_with_on_demand_features)
@@ -177,10 +226,14 @@ On-demand transformation functions can also be accessed and executed as normal f
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=== "Python"
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!!! example "Executing each on-demand transformation function"
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```python
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# Specify request parameters for each serving key.
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feature_vector = feature_view.get_feature_vector(entry={"id":1}, transform=False, return_type="pandas")
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feature_vector = feature_view.get_feature_vector(
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entry={"id": 1}, transform=False, return_type="pandas"
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)
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# Applying model dependent transformations
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feature_vector["on_demand_feature1"] = fv.on_demand_transformations["on_demand_feature1"](feature_vector["transaction_time"], datetime.now())
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feature_vector["on_demand_feature1"] = fv.on_demand_transformations[
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"on_demand_feature1"
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](feature_vector["transaction_time"], datetime.now())
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```

‎docs/user_guides/fs/feature_view/model-dependent-transformations.md

+2-2
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@@ -123,9 +123,9 @@ Model-dependent transformation functions can also be manually applied to a featu
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fv.init_serving(training_dataset_version)
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# Get untransformed feature Vector
126-
feature_vector = fv.get_feature_vector(entry={"index":10}, transformed=False, return_type="pandas")
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feature_vector = fv.get_feature_vector(entry={"index":10}, transform=False, return_type="pandas")
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# Apply Model Dependent transformations
129-
encode_feature_vector = fv.transform(feature_vector)
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encoded_feature_vector = fv.transform(feature_vector)
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```
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