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docs: align SDK indentations (#153)
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@maocorte
maocorte authored Jul 31, 2024
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Expand Up @@ -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
Expand All @@ -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
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