From ea26f7cfccd60f8c2e35a95f00bece4775c40b7a Mon Sep 17 00:00:00 2001 From: Mauro Cortellazzi Date: Wed, 31 Jul 2024 15:42:01 +0200 Subject: [PATCH] docs: align SDK indentations (#153) --- docs/docs/l5_python_sdk.md | 228 ++++++++++++++++++------------------- 1 file changed, 114 insertions(+), 114 deletions(-) diff --git a/docs/docs/l5_python_sdk.md b/docs/docs/l5_python_sdk.md index 80691583..1a041b55 100644 --- a/docs/docs/l5_python_sdk.md +++ b/docs/docs/l5_python_sdk.md @@ -24,82 +24,82 @@ 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](#createmodel) instance and returns the created [Model](#model). - -```python -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 + It requires a [CreateModel](#createmodel) instance and returns the created [Model](#model). + + ```python + 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 + ) + ], ), - ColumnDefinition( - name="age", - type=SupportedTypes.int, + target=ColumnDefinition( + name="prediction", + type=SupportedTypes.float, field_type=FieldType.numerical ), - ], - outputs=OutputType( - prediction=ColumnDefinition( - name="prediction", - type=SupportedTypes.float, - field_type=FieldType.numerical + timestamp=ColumnDefinition( + name="prediction_timestamp", + type=SupportedTypes.datetime, + field_type=FieldType.datetime ), - 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) -``` + ) + + 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](#model) instance. -```python -model = client.get_model(model_uuid) -``` + ```python + model = client.get_model(model_uuid) + ``` * **`search_models()`**: It gets a list of models. It returns a list of [Model](#model). -```python -models = client.search_models() -``` + ```python + models = client.search_models() + ``` ### Model @@ -124,66 +124,66 @@ The available methods of a model instance are: * **`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](#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](#awscredentials) used to connect to S3 bucket. Default value is None. -* **`separator`**: Optional value to define separator used inside CSV file. Default value is "," - -```python -reference_dataset = model.load_reference_dataset( - file_name="reference.csv", bucket="my-bucket" -) -``` + 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](#awscredentials) used to connect to S3 bucket. Default value is None. + * **`separator`**: Optional value to define separator used inside CSV file. Default value is "," + + ```python + 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](#modelreferencedataset). -Method properties are: + Method properties are: -* **`dataset_url`**: The url of the file already uploaded inside S3 -* **`aws_credentials`**: [AwsCredentials](#awscredentials) used to connect to S3 bucket. Default value is None. -* **`separator`**: Optional value to define separator used inside CSV file. Default value is "," + * **`dataset_url`**: The url of the file already uploaded inside S3 + * **`aws_credentials`**: [AwsCredentials](#awscredentials) used to connect to S3 bucket. Default value is None. + * **`separator`**: Optional value to define separator used inside CSV file. Default value is "," -```python -reference_dataset = model.bind_reference_dataset( - dataset_url="s3://my-bucket/reference.csv" -) -``` + ```python + 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](#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](#awscredentials) used to connect to S3 bucket. Default value is None. -* **`separator`**: Optional value to define separator used inside CSV file. Default value is "," - -```python -current_dataset = model.load_current_dataset( -file_name="reference.csv", -bucket="my-bucket", -correlation_id_column="prediction_identifier" -) -``` + 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](#awscredentials) used to connect to S3 bucket. Default value is None. + * **`separator`**: Optional value to define separator used inside CSV file. Default value is "," + + ```python + 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](#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](#awscredentials) used to connect to S3 bucket. Default value is None. -* **`separator`**: Optional value to define separator used inside CSV file. Default value is "," - -```python -current_dataset = model.bind_current_dataset( - dataset_url="s3://my-bucket/reference.csv", - correlation_id_column="prediction_identifier" -) -``` + 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](#awscredentials) used to connect to S3 bucket. Default value is None. + * **`separator`**: Optional value to define separator used inside CSV file. Default value is "," + + ```python + 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](#modelreferencedataset) representing all the current datasets and related metrics * **`get_current_datasets()`**: It returns a list of [ModelCurrentDataset](#modelcurrentdataset) representing all the current datasets and related metrics