Serving
Features
Returns the feature values for the specified entities.
Examples:
client = ff.Client()
fpf = client.features([("avg_transactions", "quickstart")], {"user": "C1410926"})
# Run features through model
Parameters:
Name | Type | Description | Default |
---|---|---|---|
features |
(list[str, str], list[str])
|
List of Name Variant Tuples |
required |
entities |
dict
|
Dictionary of entity name/value pairs |
required |
Returns:
Name | Type | Description |
---|---|---|
features |
Array
|
An Numpy array of feature values in the order given by the inputs |
Training Sets
Return an iterator that iterates through the specified training set.
Examples:
client = ff.Client()
dataset = client.training_set("fraud_training", "quickstart")
training_dataset = dataset.repeat(10).shuffle(1000).batch(8)
for feature_batch in training_dataset:
# Train model
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name |
str
|
Name of training set to be retrieved |
required |
variant |
str
|
Variant of training set to be retrieved |
''
|
Returns:
Name | Type | Description |
---|---|---|
training_set |
Dataset
|
A training set iterator |
Sources
Return a dataframe from a registered source or transformation
Example:
transactions_df = client.dataframe("transactions", "quickstart")
avg_user_transaction_df = transactions_df.groupby("CustomerID")["TransactionAmount"].mean()
Parameters:
Name | Type | Description | Default |
---|---|---|---|
source |
Union[SourceRegistrar, LocalSource, SubscriptableTransformation, str]
|
The source or transformation to compute the dataframe from |
required |
variant |
str
|
The source variant; can't be None if source is a string |
None
|
limit |
int
|
The maximum number of records to return; defaults to NO_RECORD_LIMIT |
NO_RECORD_LIMIT
|
asynchronous |
bool
|
Flag to determine whether the client should wait for resources to be in either a READY or FAILED state before returning. Defaults to False to ensure that newly registered resources are in a READY state prior to serving them as dataframes. |
False
|
Returns:
Name | Type | Description |
---|---|---|
df |
DataFrame
|
The dataframe computed from the source or transformation |
Nearest Neighbors
Query the K nearest neighbors of a provider vector in the index of a registered feature variant
Example:
# Get the 5 nearest neighbors of the vector [0.1, 0.2, 0.3] in the index of the feature "my_feature" with variant "my_variant"
nearest_neighbors = client.nearest("my_feature", "my_variant", [0.1, 0.2, 0.3], 5)
print(nearest_neighbors) # prints a list of entities (e.g. ["entity1", "entity2", "entity3", "entity4", "entity5"])
Parameters:
Name | Type | Description | Default |
---|---|---|---|
feature |
Union[FeatureColumnResource, tuple(str, str)]
|
Feature object or tuple of Feature name and variant |
required |
vector |
List[float]
|
Query vector |
required |
k |
int
|
Number of nearest neighbors to return |
required |