🌿 Fern Regeneration -- April 6, 2024 (#287)

Co-authored-by: fern-api <115122769+fern-api[bot]@users.noreply.github.com>

nixtlats/client.py

@@ -61,6 +61,24 @@ def validate_token_front(self) -> typing.Any:
             raise ApiError(status_code=_response.status_code, body=_response.text)
         raise ApiError(status_code=_response.status_code, body=_response_json)
 
+    def health_health_get(self) -> typing.Any:
+        """
+        Check if server is healthy. Used by the readiness probe to check server is healthy.
+        """
+        _response = self._client_wrapper.httpx_client.request(
+            "GET",
+            urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "health"),
+            headers=self._client_wrapper.get_headers(),
+            timeout=60,
+        )
+        if 200 <= _response.status_code < 300:
+            return pydantic.parse_obj_as(typing.Any, _response.json())  # type: ignore
+        try:
+            _response_json = _response.json()
+        except JSONDecodeError:
+            raise ApiError(status_code=_response.status_code, body=_response.text)
+        raise ApiError(status_code=_response.status_code, body=_response_json)
+
     def forecast(self, *, request: SingleSeriesForecast) -> typing.Any:
         """
         This endpoint predicts the future values of a single time series based on the provided data. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains the predicted values based on the input arguments. Get your token for private beta at https://dashboard.nixtla.io
@@ -476,6 +494,24 @@ async def validate_token_front(self) -> typing.Any:
             raise ApiError(status_code=_response.status_code, body=_response.text)
         raise ApiError(status_code=_response.status_code, body=_response_json)
 
+    async def health_health_get(self) -> typing.Any:
+        """
+        Check if server is healthy. Used by the readiness probe to check server is healthy.
+        """
+        _response = await self._client_wrapper.httpx_client.request(
+            "GET",
+            urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "health"),
+            headers=self._client_wrapper.get_headers(),
+            timeout=60,
+        )
+        if 200 <= _response.status_code < 300:
+            return pydantic.parse_obj_as(typing.Any, _response.json())  # type: ignore
+        try:
+            _response_json = _response.json()
+        except JSONDecodeError:
+            raise ApiError(status_code=_response.status_code, body=_response.text)
+        raise ApiError(status_code=_response.status_code, body=_response_json)
+
     async def forecast(self, *, request: SingleSeriesForecast) -> typing.Any:
         """
         This endpoint predicts the future values of a single time series based on the provided data. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains the predicted values based on the input arguments. Get your token for private beta at https://dashboard.nixtla.io

nixtlats/types/multi_series_anomaly.py

@@ -12,7 +12,7 @@
 
 class MultiSeriesAnomaly(pydantic.BaseModel):
     model: typing.Optional[MultiSeriesAnomalyModel] = pydantic.Field(
-        description="Model to use as a string. Options are: `short-horizon`, and `long-horizon.` We recommend using `long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data."
+        description="Model to use as a string. Options are: `timegpt-1`, and `timegpt-1-long-horizon.` We recommend using `timegpt-1-long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data."
     )
     freq: typing.Optional[str] = pydantic.Field(
         description="The frequency of the data represented as a string. 'D' for daily, 'M' for monthly, 'H' for hourly, and 'W' for weekly frequencies are available."

nixtlats/types/multi_series_anomaly_model.py

@@ -8,26 +8,30 @@
 
 class MultiSeriesAnomalyModel(str, enum.Enum):
     """
-    Model to use as a string. Options are: `short-horizon`, and `long-horizon.` We recommend using `long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data.
+    Model to use as a string. Options are: `timegpt-1`, and `timegpt-1-long-horizon.` We recommend using `timegpt-1-long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data.
     """
 
-    SHORT_HORIZON = "short-horizon"
-    LONG_HORIZON = "long-horizon"
     TIMEGPT_1 = "timegpt-1"
     TIMEGPT_1_LONG_HORIZON = "timegpt-1-long-horizon"
+    SHORT_HORIZON = "short-horizon"
+    LONG_HORIZON = "long-horizon"
+    AZUREAI = "azureai"
 
     def visit(
         self,
-        short_horizon: typing.Callable[[], T_Result],
-        long_horizon: typing.Callable[[], T_Result],
         timegpt_1: typing.Callable[[], T_Result],
         timegpt_1_long_horizon: typing.Callable[[], T_Result],
+        short_horizon: typing.Callable[[], T_Result],
+        long_horizon: typing.Callable[[], T_Result],
+        azureai: typing.Callable[[], T_Result],
     ) -> T_Result:
-        if self is MultiSeriesAnomalyModel.SHORT_HORIZON:
-            return short_horizon()
-        if self is MultiSeriesAnomalyModel.LONG_HORIZON:
-            return long_horizon()
         if self is MultiSeriesAnomalyModel.TIMEGPT_1:
             return timegpt_1()
         if self is MultiSeriesAnomalyModel.TIMEGPT_1_LONG_HORIZON:
             return timegpt_1_long_horizon()
+        if self is MultiSeriesAnomalyModel.SHORT_HORIZON:
+            return short_horizon()
+        if self is MultiSeriesAnomalyModel.LONG_HORIZON:
+            return long_horizon()
+        if self is MultiSeriesAnomalyModel.AZUREAI:
+            return azureai()

nixtlats/types/multi_series_cross_validation.py

@@ -13,8 +13,12 @@
 
 
 class MultiSeriesCrossValidation(pydantic.BaseModel):
+    fewshot_steps: typing.Optional[int] = pydantic.Field(description="Deprecated. Please use finetune_steps instead.")
+    fewshot_loss: typing.Optional[MultiSeriesCrossValidationFewshotLoss] = pydantic.Field(
+        description="Deprecated. Please use finetune_loss instead."
+    )
     model: typing.Optional[MultiSeriesCrossValidationModel] = pydantic.Field(
-        description="Model to use as a string. Options are: `short-horizon`, and `long-horizon.` We recommend using `long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data."
+        description="Model to use as a string. Options are: `timegpt-1`, and `timegpt-1-long-horizon.` We recommend using `timegpt-1-long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data."
     )
     freq: typing.Optional[str] = pydantic.Field(
         description="The frequency of the data represented as a string. 'D' for daily, 'M' for monthly, 'H' for hourly, and 'W' for weekly frequencies are available."
@@ -36,15 +40,11 @@ class MultiSeriesCrossValidation(pydantic.BaseModel):
     clean_ex_first: typing.Optional[bool] = pydantic.Field(
         description="A boolean flag that indicates whether the API should preprocess (clean) the exogenous signal before applying the large time model. If True, the exogenous signal is cleaned; if False, the exogenous variables are applied after the large time model."
     )
-    fewshot_steps: typing.Optional[int] = pydantic.Field(
+    finetune_steps: typing.Optional[int] = pydantic.Field(
         description="The number of tuning steps used to train the large time model on the data. Set this value to 0 for zero-shot inference, i.e., to make predictions without any further model tuning."
     )
-    fewshot_loss: typing.Optional[MultiSeriesCrossValidationFewshotLoss] = pydantic.Field(
-        description="The loss used to train the large time model on the data. Select from ['default', 'mae', 'mse', 'rmse', 'mape', 'smape']. It will only be used if finetune_steps larger than 0. Default is a robust loss function that is less sensitive to outliers."
-    )
-    finetune_steps: typing.Optional[int] = pydantic.Field(description="Deprecated. Please use fewshot_steps instead.")
     finetune_loss: typing.Optional[MultiSeriesCrossValidationFinetuneLoss] = pydantic.Field(
-        description="Deprecated. Please use fewshot_loss instead."
+        description="The loss used to train the large time model on the data. Select from ['default', 'mae', 'mse', 'rmse', 'mape', 'smape']. It will only be used if finetune_steps larger than 0. Default is a robust loss function that is less sensitive to outliers."
     )
 
     def json(self, **kwargs: typing.Any) -> str:

nixtlats/types/multi_series_cross_validation_fewshot_loss.py

@@ -8,7 +8,7 @@
 
 class MultiSeriesCrossValidationFewshotLoss(str, enum.Enum):
     """
-    The loss used to train the large time model on the data. Select from ['default', 'mae', 'mse', 'rmse', 'mape', 'smape']. It will only be used if finetune_steps larger than 0. Default is a robust loss function that is less sensitive to outliers.
+    Deprecated. Please use finetune_loss instead.
     """
 
     DEFAULT = "default"

nixtlats/types/multi_series_cross_validation_finetune_loss.py

@@ -8,7 +8,7 @@
 
 class MultiSeriesCrossValidationFinetuneLoss(str, enum.Enum):
     """
-    Deprecated. Please use fewshot_loss instead.
+    The loss used to train the large time model on the data. Select from ['default', 'mae', 'mse', 'rmse', 'mape', 'smape']. It will only be used if finetune_steps larger than 0. Default is a robust loss function that is less sensitive to outliers.
     """
 
     DEFAULT = "default"

nixtlats/types/multi_series_cross_validation_model.py

@@ -8,26 +8,30 @@
 
 class MultiSeriesCrossValidationModel(str, enum.Enum):
     """
-    Model to use as a string. Options are: `short-horizon`, and `long-horizon.` We recommend using `long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data.
+    Model to use as a string. Options are: `timegpt-1`, and `timegpt-1-long-horizon.` We recommend using `timegpt-1-long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data.
     """
 
-    SHORT_HORIZON = "short-horizon"
-    LONG_HORIZON = "long-horizon"
     TIMEGPT_1 = "timegpt-1"
     TIMEGPT_1_LONG_HORIZON = "timegpt-1-long-horizon"
+    SHORT_HORIZON = "short-horizon"
+    LONG_HORIZON = "long-horizon"
+    AZUREAI = "azureai"
 
     def visit(
         self,
-        short_horizon: typing.Callable[[], T_Result],
-        long_horizon: typing.Callable[[], T_Result],
         timegpt_1: typing.Callable[[], T_Result],
         timegpt_1_long_horizon: typing.Callable[[], T_Result],
+        short_horizon: typing.Callable[[], T_Result],
+        long_horizon: typing.Callable[[], T_Result],
+        azureai: typing.Callable[[], T_Result],
     ) -> T_Result:
-        if self is MultiSeriesCrossValidationModel.SHORT_HORIZON:
-            return short_horizon()
-        if self is MultiSeriesCrossValidationModel.LONG_HORIZON:
-            return long_horizon()
         if self is MultiSeriesCrossValidationModel.TIMEGPT_1:
             return timegpt_1()
         if self is MultiSeriesCrossValidationModel.TIMEGPT_1_LONG_HORIZON:
             return timegpt_1_long_horizon()
+        if self is MultiSeriesCrossValidationModel.SHORT_HORIZON:
+            return short_horizon()
+        if self is MultiSeriesCrossValidationModel.LONG_HORIZON:
+            return long_horizon()
+        if self is MultiSeriesCrossValidationModel.AZUREAI:
+            return azureai()

nixtlats/types/multi_series_forecast.py

@@ -13,8 +13,12 @@
 
 
 class MultiSeriesForecast(pydantic.BaseModel):
+    fewshot_steps: typing.Optional[int] = pydantic.Field(description="Deprecated. Please use finetune_steps instead.")
+    fewshot_loss: typing.Optional[MultiSeriesForecastFewshotLoss] = pydantic.Field(
+        description="Deprecated. Please use finetune_loss instead."
+    )
     model: typing.Optional[MultiSeriesForecastModel] = pydantic.Field(
-        description="Model to use as a string. Options are: `short-horizon`, and `long-horizon.` We recommend using `long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data."
+        description="Model to use as a string. Options are: `timegpt-1`, and `timegpt-1-long-horizon.` We recommend using `timegpt-1-long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data."
     )
     freq: typing.Optional[str] = pydantic.Field(
         description="The frequency of the data represented as a string. 'D' for daily, 'M' for monthly, 'H' for hourly, and 'W' for weekly frequencies are available."
@@ -32,15 +36,11 @@ class MultiSeriesForecast(pydantic.BaseModel):
     clean_ex_first: typing.Optional[bool] = pydantic.Field(
         description="A boolean flag that indicates whether the API should preprocess (clean) the exogenous signal before applying the large time model. If True, the exogenous signal is cleaned; if False, the exogenous variables are applied after the large time model."
     )
-    fewshot_steps: typing.Optional[int] = pydantic.Field(
+    finetune_steps: typing.Optional[int] = pydantic.Field(
         description="The number of tuning steps used to train the large time model on the data. Set this value to 0 for zero-shot inference, i.e., to make predictions without any further model tuning."
     )
-    fewshot_loss: typing.Optional[MultiSeriesForecastFewshotLoss] = pydantic.Field(
-        description="The loss used to train the large time model on the data. Select from ['default', 'mae', 'mse', 'rmse', 'mape', 'smape']. It will only be used if finetune_steps larger than 0. Default is a robust loss function that is less sensitive to outliers."
-    )
-    finetune_steps: typing.Optional[int] = pydantic.Field(description="Deprecated. Please use fewshot_steps instead.")
     finetune_loss: typing.Optional[MultiSeriesForecastFinetuneLoss] = pydantic.Field(
-        description="Deprecated. Please use fewshot_loss instead."
+        description="The loss used to train the large time model on the data. Select from ['default', 'mae', 'mse', 'rmse', 'mape', 'smape']. It will only be used if finetune_steps larger than 0. Default is a robust loss function that is less sensitive to outliers."
     )
 
     def json(self, **kwargs: typing.Any) -> str:

nixtlats/types/multi_series_forecast_fewshot_loss.py

@@ -8,7 +8,7 @@
 
 class MultiSeriesForecastFewshotLoss(str, enum.Enum):
     """
-    The loss used to train the large time model on the data. Select from ['default', 'mae', 'mse', 'rmse', 'mape', 'smape']. It will only be used if finetune_steps larger than 0. Default is a robust loss function that is less sensitive to outliers.
+    Deprecated. Please use finetune_loss instead.
     """
 
     DEFAULT = "default"

nixtlats/types/multi_series_forecast_finetune_loss.py

@@ -8,7 +8,7 @@
 
 class MultiSeriesForecastFinetuneLoss(str, enum.Enum):
     """
-    Deprecated. Please use fewshot_loss instead.
+    The loss used to train the large time model on the data. Select from ['default', 'mae', 'mse', 'rmse', 'mape', 'smape']. It will only be used if finetune_steps larger than 0. Default is a robust loss function that is less sensitive to outliers.
     """
 
     DEFAULT = "default"

nixtlats/types/multi_series_forecast_model.py

@@ -8,26 +8,30 @@
 
 class MultiSeriesForecastModel(str, enum.Enum):
     """
-    Model to use as a string. Options are: `short-horizon`, and `long-horizon.` We recommend using `long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data.
+    Model to use as a string. Options are: `timegpt-1`, and `timegpt-1-long-horizon.` We recommend using `timegpt-1-long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data.
     """
 
-    SHORT_HORIZON = "short-horizon"
-    LONG_HORIZON = "long-horizon"
     TIMEGPT_1 = "timegpt-1"
     TIMEGPT_1_LONG_HORIZON = "timegpt-1-long-horizon"
+    SHORT_HORIZON = "short-horizon"
+    LONG_HORIZON = "long-horizon"
+    AZUREAI = "azureai"
 
     def visit(
         self,
-        short_horizon: typing.Callable[[], T_Result],
-        long_horizon: typing.Callable[[], T_Result],
         timegpt_1: typing.Callable[[], T_Result],
         timegpt_1_long_horizon: typing.Callable[[], T_Result],
+        short_horizon: typing.Callable[[], T_Result],
+        long_horizon: typing.Callable[[], T_Result],
+        azureai: typing.Callable[[], T_Result],
     ) -> T_Result:
-        if self is MultiSeriesForecastModel.SHORT_HORIZON:
-            return short_horizon()
-        if self is MultiSeriesForecastModel.LONG_HORIZON:
-            return long_horizon()
         if self is MultiSeriesForecastModel.TIMEGPT_1:
             return timegpt_1()
         if self is MultiSeriesForecastModel.TIMEGPT_1_LONG_HORIZON:
             return timegpt_1_long_horizon()
+        if self is MultiSeriesForecastModel.SHORT_HORIZON:
+            return short_horizon()
+        if self is MultiSeriesForecastModel.LONG_HORIZON:
+            return long_horizon()
+        if self is MultiSeriesForecastModel.AZUREAI:
+            return azureai()

nixtlats/types/multi_series_insample_forecast.py

@@ -12,7 +12,7 @@
 
 class MultiSeriesInsampleForecast(pydantic.BaseModel):
     model: typing.Optional[MultiSeriesInsampleForecastModel] = pydantic.Field(
-        description="Model to use as a string. Options are: `short-horizon`, and `long-horizon.` We recommend using `long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data."
+        description="Model to use as a string. Options are: `timegpt-1`, and `timegpt-1-long-horizon.` We recommend using `timegpt-1-long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data."
     )
     freq: typing.Optional[str] = pydantic.Field(
         description="The frequency of the data represented as a string. 'D' for daily, 'M' for monthly, 'H' for hourly, and 'W' for weekly frequencies are available."

nixtlats/types/multi_series_insample_forecast_model.py

@@ -8,26 +8,30 @@
 
 class MultiSeriesInsampleForecastModel(str, enum.Enum):
     """
-    Model to use as a string. Options are: `short-horizon`, and `long-horizon.` We recommend using `long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data.
+    Model to use as a string. Options are: `timegpt-1`, and `timegpt-1-long-horizon.` We recommend using `timegpt-1-long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data.
     """
 
-    SHORT_HORIZON = "short-horizon"
-    LONG_HORIZON = "long-horizon"
     TIMEGPT_1 = "timegpt-1"
     TIMEGPT_1_LONG_HORIZON = "timegpt-1-long-horizon"
+    SHORT_HORIZON = "short-horizon"
+    LONG_HORIZON = "long-horizon"
+    AZUREAI = "azureai"
 
     def visit(
         self,
-        short_horizon: typing.Callable[[], T_Result],
-        long_horizon: typing.Callable[[], T_Result],
         timegpt_1: typing.Callable[[], T_Result],
         timegpt_1_long_horizon: typing.Callable[[], T_Result],
+        short_horizon: typing.Callable[[], T_Result],
+        long_horizon: typing.Callable[[], T_Result],
+        azureai: typing.Callable[[], T_Result],
     ) -> T_Result:
-        if self is MultiSeriesInsampleForecastModel.SHORT_HORIZON:
-            return short_horizon()
-        if self is MultiSeriesInsampleForecastModel.LONG_HORIZON:
-            return long_horizon()
         if self is MultiSeriesInsampleForecastModel.TIMEGPT_1:
             return timegpt_1()
         if self is MultiSeriesInsampleForecastModel.TIMEGPT_1_LONG_HORIZON:
             return timegpt_1_long_horizon()
+        if self is MultiSeriesInsampleForecastModel.SHORT_HORIZON:
+            return short_horizon()
+        if self is MultiSeriesInsampleForecastModel.LONG_HORIZON:
+            return long_horizon()
+        if self is MultiSeriesInsampleForecastModel.AZUREAI:
+            return azureai()