Version: Develop 🚧

Python SDK

In this document are exposed all classes implemented inside the Python SDK.

Client

To interact with the Radicalbit AI platform via the SDK, the first thing that must be done is to create the client. The only required parameter is the base_url, which is the URL of the running platform.

from radicalbit_platform_sdk.client import Client

base_url = "http://localhost:9000/"
client = Client(base_url)

Once you have a client instance, you can interact with models inside the platform. ß The available methods of a client instance are:

create_model(model: CreateModel): it is used to create a brand new model inside the platform.

It requires a CreateModel instance and returns the created Model.

from radicalbit_platform_sdk.models import (
	CreateModel,
	DataType,
	FieldType,
	ModelType,
	ColumnDefinition,
	OutputType,
	Granularity,
	SupportedTypes,
)

model_definition = CreateModel(
	name="My model",
	modelType=ModelType.BINARY,
	dataType=DataType.TABULAR,
	granularity=Granularity.HOUR,
	features=[
		ColumnDefinition(
			name="first_name",
			type=SupportedTypes.string,
			field_type=FieldType.categorical
		),
		ColumnDefinition(
			name="last_name",
			type=SupportedTypes.string,
			field_type=FieldType.categorical
		),
		ColumnDefinition(
			name="age",
			type=SupportedTypes.int,
			field_type=FieldType.numerical
		),
	],
	outputs=OutputType(
		prediction=ColumnDefinition(
			name="prediction",
			type=SupportedTypes.float,
			field_type=FieldType.numerical
		),
		output=[
			ColumnDefinition(
				name="adult",
				type=SupportedTypes.string,
				field_type=FieldType.categorical
			)
		],
	),
	target=ColumnDefinition(
		name="prediction",
		type=SupportedTypes.float,
		field_type=FieldType.numerical
	),
	timestamp=ColumnDefinition(
		name="prediction_timestamp",
		type=SupportedTypes.datetime,
		field_type=FieldType.datetime
	),
)

model = client.create_model(model_definition)

get_model(): It gets a specific and existing model by its identifier. It requires the id of an existing model and returns the Model instance.
```
model = client.get_model(model_uuid)
```
search_models(): It gets a list of models. It returns a list of Model.
```
models = client.search_models()
```

Model

It represents an instance of a monitored model.

The available methods of a model instance are:

uuid(): It returns the UUID identifier of the model
name(): It returns the name of the model
description(): It returns the model’s description, if provided
model_type(): It returns the ModelType
data_type(): It returns the DataType
granularity(): It returns the Granularity used by metrics aggregation
features(): It returns a list of ColumnDefinition representing all the feature definitions
target(): It returns a ColumnDefinition representing the ground truth
timestamp(): It returns a ColumnDefinition representing the prediction timestamp. This field is used as reconciliation between reference and current datasets
outputs(): It returns an OutputType representing the model outputs, including prediction and possibly prediction probability fields
frameworks(): It returns the used frameworks, if defined
algorithm(): It returns the used algorithm, if defined
delete(): It deletes the actual model from the platform
update_features(features: List[ColumnDefinition]): Update the model features definition if reference dataset is not provided.
load_reference_dataset(file_name: str, bucket: str, object_name: Optional[str] = None, aws_credentials: Optional[AwsCredentials] = None, separator: str = ‘,’): It uploads a reference dataset file to an S3 bucket and then binds it to the model. It returns a ModelReferenceDataset.

Method properties are:
- file_name: The name of the reference file
- bucket: The name of the S3 bucket.
- object_name: The optional name of the object uploaded to S3. Default value is None.
- aws_credentials: AwsCredentials used to connect to S3 bucket. Default value is None.
- separator: Optional value to define separator used inside CSV file. Default value is ","
```
reference_dataset = model.load_reference_dataset(
	file_name="reference.csv", bucket="my-bucket"
)
```
bind_reference_dataset(dataset_url: str, aws_credentials: Optional[AwsCredentials] = None, separator: str = ‘,’): It binds an existing reference dataset file already uploded to S3 to the model. It returns a ModelReferenceDataset.

Method properties are:
- dataset_url: The url of the file already uploaded inside S3
- aws_credentials: AwsCredentials used to connect to S3 bucket. Default value is None.
- separator: Optional value to define separator used inside CSV file. Default value is ","
```
reference_dataset = model.bind_reference_dataset(
	dataset_url="s3://my-bucket/reference.csv"
)
```
load_current_dataset(file_name: str, bucket: str, correlation_id_column: Optional[str] = None, object_name: Optional[str] = None, aws_credentials: Optional[AwsCredentials] = None, separator: str = ‘,’): It uploads a current dataset file to an S3 bucket and then bind it to the model. It returns a ModelCurrentDataset.

Method properties are:
- file_name: The name of the reference file
- bucket: The name of the S3 bucket.
- correlation_id_column: The name of the column used for correlation id
- object_name: The optional name of the object uploaded to S3. Default value is None.
- aws_credentials: AwsCredentials used to connect to S3 bucket. Default value is None.
- separator: Optional value to define separator used inside CSV file. Default value is ","
```
current_dataset = model.load_current_dataset(
	file_name="reference.csv",
	bucket="my-bucket",
	correlation_id_column="prediction_identifier"
)
```
bind_current_dataset(dataset_url: str, correlation_id_column: str, aws_credentials: Optional[AwsCredentials] = None, separator: str = ‘,’): It binds an existing current dataset file already uploded to S3 to the model. It returns a ModelCurrentDataset.

Method properties are:
- dataset_url: The url of the file already uploaded inside S3
- correlation_id_column: The name of the column used for correlation id
- aws_credentials: AwsCredentials used to connect to S3 bucket. Default value is None.
- separator: Optional value to define separator used inside CSV file. Default value is ","
```
current_dataset = model.bind_current_dataset(
	dataset_url="s3://my-bucket/reference.csv",
	correlation_id_column="prediction_identifier"
)
```
get_reference_datasets(): It returns a list of ModelReferenceDataset representing all the current datasets and related metrics
get_current_datasets(): It returns a list of ModelCurrentDataset representing all the current datasets and related metrics

ModelReferenceDataset

It represent an instance of uploaded reference dataset.The available methods are:
uuid(): the UUID identifier of the uploaded dataset
path(): The URL of the dataset in the object storage
date(): When dataset was uploaded
status(): The status job of the while it is calculating metrics
statistics(): If job status is SUCCEEDED then returns the dataset statistics
data_quality(): If job status is SUCCEEDED then returns the data quality metrics of the current dataset
model_quality(): If job status is SUCCEEDED then returns the model quality metrics of the current dataset

ModelCurrentDataset

It represents an instance of uploaded current dataset.

The available methods are:

uuid(): The UUID identifier of the uploaded dataset
path(): The URL of the dataset in the object storage
date(): When dataset was uploaded
status(): The status job while it is calculating metrics
statistics(): If job status is SUCCEEDED then returns the dataset statistics
data_quality(): If job status is SUCCEEDED then returns the data quality metrics of the current dataset
model_quality(): If job status is SUCCEEDED then returns the model quality metrics of the current dataset
drift(): If job status is SUCCEEDED then returns the drift metrics of the current dataset

CreateModel

It contains the definition of a model to be created.

Its properties are:

name: The name of the model.
description: An optional description to explain something about the model.
model_type: The ModelType of the model
data_type: It explains the DataType used by the model
granularity: The Granularity of window used to calculate aggregated metrics
features: A list of ColumnDefinition representing the features set
outputs: An OutputType definition to explain the output of the model
target: The ColumnDefinition used to represent model’s target
timestamp: The ColumnDefinition used to store when prediction was done
frameworks: An optional field to describe the frameworks used by the model
algorithm: An optional field to explain the algorithm used by the model

ModelType

Enumeration used to define the type of the model and to calculate the right metrics. Available values are: REGRESSION, BINARY and MULTI_CLASS.

DataType

Enumeration used to define the type of data managed by the model. Available values are: TABULAR, TEXT and IMAGE

Granularity

Enumeration used to define the granularity used by aggregations inside metrics calculation. Available values are: HOUR, DAY, WEEK and MONTH.

ColumnDefinition

It contains the definition of a single column inside a dataset.

Its properties are:

name: The name of the column
type: The SupportedTypes of the data represented inside this column
field_type: The FieldType of the field

SupportedTypes

Enumeration used to define the available types that a column definition could have. Available values are: int, float, str, bool and datetime

FieldType

Enumeration used to define the categorical type of the field. Available values are: categorical, numerical and datetime.

OutputType

It defines the output of the model.

Its properties are:

output: A list of ColumnDefinition representing the outputs set
prediction: The ColumnDefinition used to represent the prediction
prediction_proba: An optional ColumnDefinition used to represent the prediction probability

AwsCredentials

It defines credentials needed to authenticate to an S3 compatible API service.

Its properties are:

access_key_id: Access key ID needed to authenticate to APIs
secret_access_key: Secret access key needed to authenticate to APIs
default_region: Region to be used
endpoint_url: Optional value to define an S3 compatible API endpoint, if different than AWS. By default is None

Client​

Model​

ModelReferenceDataset​

ModelCurrentDataset​

CreateModel​

ModelType​

DataType​

Granularity​

ColumnDefinition​

SupportedTypes​

FieldType​

OutputType​

AwsCredentials​

Client

Model

ModelReferenceDataset

ModelCurrentDataset

CreateModel

ModelType

DataType

Granularity

ColumnDefinition

SupportedTypes

FieldType

OutputType

AwsCredentials