From d29fbc8f3405fe37f500eaee90473063307f075c Mon Sep 17 00:00:00 2001
From: manu-sj <manu.joseph@logicalclocks.com>
Date: Wed, 12 Feb 2025 21:55:59 +0100
Subject: [PATCH 1/3] reformatting code with black and correcting snippets
---
.../on_demand_transformations.md | 99 ++++++++++++++-----
.../model-dependent-transformations.md | 4 +-
2 files changed, 78 insertions(+), 25 deletions(-)
diff --git a/docs/user_guides/fs/feature_group/on_demand_transformations.md b/docs/user_guides/fs/feature_group/on_demand_transformations.md
index 81efb413..1bf61db6 100644
--- a/docs/user_guides/fs/feature_group/on_demand_transformations.md
+++ b/docs/user_guides/fs/feature_group/on_demand_transformations.md
@@ -103,8 +103,13 @@ The `get_feature_vector` function retrieves a single feature vector based on the
=== "Python"
!!! example "Computing on-demand features while retrieving a feature vector"
```python
-
- 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()})
+ 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(),
+ },
+ )
```
#### Retrieving feature vectors
@@ -114,24 +119,44 @@ The `get_feature_vectors` function retrieves multiple feature vectors using a li
=== "Python"
!!! example "Computing on-demand features while retrieving a feature vectors"
```python
-
# Specify unique request parameters for each serving key.
- 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()},
- {"transaction_time":datetime(2022, 11, 20, 12, 50, 00), "current_time":datetime.now()}])
+ 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(),
+ },
+ {
+ "transaction_time": datetime(2022, 11, 20, 12, 50, 00),
+ "current_time": datetime.now(),
+ },
+ ],
+ )
# Specify common request parameters for all serving key.
- 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()})
+ 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(),
+ },
+ )
```
-The `get_feature_vector` and `get_feature_vectors` can also return untransformed features by setting the parameter `transform` to `False`.
+#### Retrieving untransformed feature vector
+
+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`.
=== "Python"
!!! example "Returning untransformed feature vectors"
```python
-
- untransformed_feature_vector = feature_view.get_feature_vector(entry={"id":1}, transform=False)
-
- untransformed_feature_vectors = feature_view.get_feature_vectors(entry=[{"id":1}, {"id":2}], transform=False)
+ untransformed_feature_vector = feature_view.get_feature_vector(
+ entry={"id": 1}, transform=False
+ )
+ untransformed_feature_vectors = feature_view.get_feature_vectors(
+ entry=[{"id": 1}, {"id": 2}], transform=False
+ )
```
#### Compute all on-demand features
@@ -143,27 +168,51 @@ The `request_parameter` in this case, can be a list of dictionaries that specifi
=== "Python"
!!! example "Computing all on-demand features and manually applying model dependent transformations."
```python
-
# Specify request parameters for each serving key.
- untransformed_feature_vector = feature_view.get_feature_vector(entry={"id":1}, transform=False)
+ untransformed_feature_vector = feature_view.get_feature_vector(
+ entry={"id": 1}, transform=False
+ )
# re-compute and add on-demand features to the feature vector
- feature_vector_with_on_demand_features = fv.compute_on_demand_features(untransformed_feature_vector,
- request_parameter={"transaction_time":datetime(2022, 12, 28, 23, 55, 59), "current_time":datetime.now()})
+ feature_vector_with_on_demand_features = fv.compute_on_demand_features(
+ untransformed_feature_vector,
+ request_parameter={
+ "transaction_time": datetime(2022, 12, 28, 23, 55, 59),
+ "current_time": datetime.now(),
+ },
+ )
# Applying model dependent transformations
encoded_feature_vector = fv.transform(feature_vector_with_on_demand_features)
# Specify request parameters for each serving key.
- untransformed_feature_vectors = feature_view.get_feature_vectors(entry=[{"id":1}, {"id":2}], transform=False)
+ untransformed_feature_vectors = feature_view.get_feature_vectors(
+ entry=[{"id": 1}, {"id": 2}], transform=False
+ )
# re-compute and add on-demand features to the feature vectors - Specify unique request parameter for each feature vector
- 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()},
- {"transaction_time":datetime(2022, 11, 20, 12, 50, 00), "current_time":datetime.now()}])
+ 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(),
+ },
+ {
+ "transaction_time": datetime(2022, 11, 20, 12, 50, 00),
+ "current_time": datetime.now(),
+ },
+ ],
+ )
# re-compute and add on-demand feature to the feature vectors - Specify common request parameter for all feature vectors
- 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()})
+ 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(),
+ },
+ )
# Applying model dependent transformations
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
=== "Python"
!!! example "Executing each on-demand transformation function"
```python
-
# Specify request parameters for each serving key.
- feature_vector = feature_view.get_feature_vector(entry={"id":1}, transform=False, return_type="pandas")
+ feature_vector = feature_view.get_feature_vector(
+ entry={"id": 1}, transform=False, return_type="pandas"
+ )
# Applying model dependent transformations
- feature_vector["on_demand_feature1"] = fv.on_demand_transformations["on_demand_feature1"](feature_vector["transaction_time"], datetime.now())
+ feature_vector["on_demand_feature1"] = fv.on_demand_transformations[
+ "on_demand_feature1"
+ ](feature_vector["transaction_time"], datetime.now())
+
```
\ No newline at end of file
diff --git a/docs/user_guides/fs/feature_view/model-dependent-transformations.md b/docs/user_guides/fs/feature_view/model-dependent-transformations.md
index 314bd14e..4072b88c 100644
--- a/docs/user_guides/fs/feature_view/model-dependent-transformations.md
+++ b/docs/user_guides/fs/feature_view/model-dependent-transformations.md
@@ -123,9 +123,9 @@ Model-dependent transformation functions can also be manually applied to a featu
fv.init_serving(training_dataset_version)
# Get untransformed feature Vector
- feature_vector = fv.get_feature_vector(entry={"index":10}, transformed=False, return_type="pandas")
+ feature_vector = fv.get_feature_vector(entry={"index":10}, transform=False, return_type="pandas")
# Apply Model Dependent transformations
- encode_feature_vector = fv.transform(feature_vector)
+ encoded_feature_vector = fv.transform(feature_vector)
```
From ae47f6302f2119dd3628892cc71e84a862e892d2 Mon Sep 17 00:00:00 2001
From: manu-sj <manu.joseph@logicalclocks.com>
Date: Thu, 13 Feb 2025 10:32:17 +0100
Subject: [PATCH 2/3] adding documentation for the parameters transform and
on-demand features
---
.../on_demand_transformations.md | 14 ++++----
.../user_guides/fs/feature_view/batch-data.md | 11 ++++++
.../fs/feature_view/feature-vectors.md | 35 +++++++++++++++++++
.../model-dependent-transformations.md | 23 ++++++++++++
4 files changed, 76 insertions(+), 7 deletions(-)
diff --git a/docs/user_guides/fs/feature_group/on_demand_transformations.md b/docs/user_guides/fs/feature_group/on_demand_transformations.md
index 1bf61db6..d961e77a 100644
--- a/docs/user_guides/fs/feature_group/on_demand_transformations.md
+++ b/docs/user_guides/fs/feature_group/on_demand_transformations.md
@@ -144,18 +144,18 @@ The `get_feature_vectors` function retrieves multiple feature vectors using a li
)
```
-#### Retrieving untransformed feature vector
+#### Retrieving feature vector without on-demand features
-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`.
+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`.
=== "Python"
!!! example "Returning untransformed feature vectors"
```python
untransformed_feature_vector = feature_view.get_feature_vector(
- entry={"id": 1}, transform=False
+ entry={"id": 1}, transform=False, on_demand_features=False
)
untransformed_feature_vectors = feature_view.get_feature_vectors(
- entry=[{"id": 1}, {"id": 2}], transform=False
+ entry=[{"id": 1}, {"id": 2}], transform=False, on_demand_features=False
)
```
@@ -170,7 +170,7 @@ The `request_parameter` in this case, can be a list of dictionaries that specifi
```python
# Specify request parameters for each serving key.
untransformed_feature_vector = feature_view.get_feature_vector(
- entry={"id": 1}, transform=False
+ entry={"id": 1}, transform=False, on_demand_features=False
)
# re-compute and add on-demand features to the feature vector
@@ -187,7 +187,7 @@ The `request_parameter` in this case, can be a list of dictionaries that specifi
# Specify request parameters for each serving key.
untransformed_feature_vectors = feature_view.get_feature_vectors(
- entry=[{"id": 1}, {"id": 2}], transform=False
+ entry=[{"id": 1}, {"id": 2}], transform=False, on_demand_features=False
)
# re-compute and add on-demand features to the feature vectors - Specify unique request parameter for each feature vector
@@ -228,7 +228,7 @@ On-demand transformation functions can also be accessed and executed as normal f
```python
# Specify request parameters for each serving key.
feature_vector = feature_view.get_feature_vector(
- entry={"id": 1}, transform=False, return_type="pandas"
+ entry={"id": 1}, transform=False, on_demand_features=False, return_type="pandas"
)
# Applying model dependent transformations
diff --git a/docs/user_guides/fs/feature_view/batch-data.md b/docs/user_guides/fs/feature_view/batch-data.md
index 5f1fd9a7..7b80ae95 100644
--- a/docs/user_guides/fs/feature_view/batch-data.md
+++ b/docs/user_guides/fs/feature_view/batch-data.md
@@ -55,6 +55,17 @@ feature_view.init_batch_scoring(training_dataset_version=1)
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.
+## Retrieving untransformed batch data
+
+The `get_batch_data` function by default returns the batch data with model-dependent transformations applied. However it can also return untransformed batch data without applying model-dependent transformations but contains on-demand features by setting the parameter `transform` to `False`.
+
+=== "Python"
+!!! example "Returning untransformed batch data"
+ ```python
+ # Fetching untransformed batch data.
+ untransformed_batch_data = feature_view.get_batch_data(transform=False)
+ ```
+
## Passing Context Variables to Transformation Functions
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.
diff --git a/docs/user_guides/fs/feature_view/feature-vectors.md b/docs/user_guides/fs/feature_view/feature-vectors.md
index ed7f46b1..6af3675c 100644
--- a/docs/user_guides/fs/feature_view/feature-vectors.md
+++ b/docs/user_guides/fs/feature_view/feature-vectors.md
@@ -191,6 +191,41 @@ You can also use the parameter to provide values for all the features which are
)
```
+## Retrieving untransformed feature vectors
+
+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.
+
+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.
+
+=== "Python"
+!!! example "Returning untransformed feature vectors"
+ ```python
+ # Fetching untransformed feature vector.
+ untransformed_feature_vector = feature_view.get_feature_vector(
+ entry={"id": 1}, transform=False
+ )
+
+ # Fetching untransformed feature vectors.
+ untransformed_feature_vectors = feature_view.get_feature_vectors(
+ entry=[{"id": 1}, {"id": 2}], transform=False
+ )
+ ```
+
+## Retrieving feature vector without on-demand features
+
+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`.
+
+=== "Python"
+!!! example "Returning untransformed feature vectors"
+ ```python
+ untransformed_feature_vector = feature_view.get_feature_vector(
+ entry={"id": 1}, transform=False, on_demand_features=False
+ )
+ untransformed_feature_vectors = feature_view.get_feature_vectors(
+ entry=[{"id": 1}, {"id": 2}], transform=False, on_demand_features=False
+ )
+ ```
+
## Passing Context Variables to Transformation Functions
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.
diff --git a/docs/user_guides/fs/feature_view/model-dependent-transformations.md b/docs/user_guides/fs/feature_view/model-dependent-transformations.md
index 4072b88c..b6657ae5 100644
--- a/docs/user_guides/fs/feature_view/model-dependent-transformations.md
+++ b/docs/user_guides/fs/feature_view/model-dependent-transformations.md
@@ -129,3 +129,26 @@ Model-dependent transformation functions can also be manually applied to a featu
encoded_feature_vector = fv.transform(feature_vector)
```
+#### Retrieving untransformed feature vector and batch inference data
+
+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.
+
+=== "Python"
+!!! example "Returning untransformed feature vectors and batch data."
+ ```python
+ # Fetching untransformed feature vector.
+ untransformed_feature_vector = feature_view.get_feature_vector(
+ entry={"id": 1}, transform=False
+ )
+
+ # Fetching untransformed feature vectors.
+ untransformed_feature_vectors = feature_view.get_feature_vectors(
+ entry=[{"id": 1}, {"id": 2}], transform=False
+ )
+
+ # Fetching untransformed batch data.
+ untransformed_batch_data = feature_view.get_batch_data(
+ transform=False
+ )
+ ```
+
From 0d56327e2bb3a1b8ee9ac9df482fbb89094a5c00 Mon Sep 17 00:00:00 2001
From: manu-sj <manu.joseph@logicalclocks.com>
Date: Thu, 13 Feb 2025 10:35:17 +0100
Subject: [PATCH 3/3] adding documentation for the parameters transform and
on-demand features
---
docs/user_guides/fs/feature_view/batch-data.md | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/docs/user_guides/fs/feature_view/batch-data.md b/docs/user_guides/fs/feature_view/batch-data.md
index 7b80ae95..1663da9b 100644
--- a/docs/user_guides/fs/feature_view/batch-data.md
+++ b/docs/user_guides/fs/feature_view/batch-data.md
@@ -57,7 +57,7 @@ It is important to note that in addition to the filters defined in feature view,
## Retrieving untransformed batch data
-The `get_batch_data` function by default returns the batch data with model-dependent transformations applied. However it can also return untransformed batch data without applying model-dependent transformations but contains on-demand features by setting the parameter `transform` to `False`.
+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`.
=== "Python"
!!! example "Returning untransformed batch data"