Update openapi json file (#445)

nixtla/_modidx.py

@@ -106,10 +106,17 @@
                                                                                'nixtla/nixtla_client.py'),
                                       'nixtla.nixtla_client._validate_input_size': ( 'nixtla_client.html#_validate_input_size',
                                                                                      'nixtla/nixtla_client.py')},
+            'nixtla.types.anomaly_detection_output': {},
+            'nixtla.types.cross_validation_input_finetune_loss': {},
+            'nixtla.types.cross_validation_input_level_item': {},
+            'nixtla.types.cross_validation_output': {},
             'nixtla.types.forecast_input_finetune_loss': {},
             'nixtla.types.forecast_input_level_item': {},
             'nixtla.types.forecast_output': {},
             'nixtla.types.http_validation_error': {},
+            'nixtla.types.in_sample_input_level_item': {},
+            'nixtla.types.in_sample_output': {},
+            'nixtla.types.level': {},
             'nixtla.types.model': {},
             'nixtla.types.multi_series_anomaly': {},
             'nixtla.types.multi_series_cross_validation': {},
@@ -123,6 +130,7 @@
             'nixtla.types.multi_series_input': {},
             'nixtla.types.multi_series_insample_forecast': {},
             'nixtla.types.multi_series_insample_forecast_level_item': {},
+            'nixtla.types.series': {},
             'nixtla.types.series_with_future_exogenous': {},
             'nixtla.types.single_series_forecast': {},
             'nixtla.types.single_series_forecast_fewshot_loss': {},

nixtla/types/__init__.py

@@ -1,9 +1,16 @@
 # This file was auto-generated by Fern from our API Definition.
 
+from .anomaly_detection_output import AnomalyDetectionOutput
+from .cross_validation_input_finetune_loss import CrossValidationInputFinetuneLoss
+from .cross_validation_input_level_item import CrossValidationInputLevelItem
+from .cross_validation_output import CrossValidationOutput
 from .forecast_input_finetune_loss import ForecastInputFinetuneLoss
 from .forecast_input_level_item import ForecastInputLevelItem
 from .forecast_output import ForecastOutput
 from .http_validation_error import HttpValidationError
+from .in_sample_input_level_item import InSampleInputLevelItem
+from .in_sample_output import InSampleOutput
+from .level import Level
 from .model import Model
 from .multi_series_anomaly import MultiSeriesAnomaly
 from .multi_series_cross_validation import MultiSeriesCrossValidation
@@ -17,6 +24,7 @@
 from .multi_series_input import MultiSeriesInput
 from .multi_series_insample_forecast import MultiSeriesInsampleForecast
 from .multi_series_insample_forecast_level_item import MultiSeriesInsampleForecastLevelItem
+from .series import Series
 from .series_with_future_exogenous import SeriesWithFutureExogenous
 from .single_series_forecast import SingleSeriesForecast
 from .single_series_forecast_fewshot_loss import SingleSeriesForecastFewshotLoss
@@ -28,10 +36,17 @@
 from .validation_error_loc_item import ValidationErrorLocItem
 
 __all__ = [
+    "AnomalyDetectionOutput",
+    "CrossValidationInputFinetuneLoss",
+    "CrossValidationInputLevelItem",
+    "CrossValidationOutput",
     "ForecastInputFinetuneLoss",
     "ForecastInputLevelItem",
     "ForecastOutput",
     "HttpValidationError",
+    "InSampleInputLevelItem",
+    "InSampleOutput",
+    "Level",
     "Model",
     "MultiSeriesAnomaly",
     "MultiSeriesCrossValidation",
@@ -45,6 +60,7 @@
     "MultiSeriesInput",
     "MultiSeriesInsampleForecast",
     "MultiSeriesInsampleForecastLevelItem",
+    "Series",
     "SeriesWithFutureExogenous",
     "SingleSeriesForecast",
     "SingleSeriesForecastFewshotLoss",

nixtla/types/anomaly_detection_output.py

@@ -0,0 +1,32 @@
+# This file was auto-generated by Fern from our API Definition.
+
+import datetime as dt
+import typing
+
+from ..core.datetime_utils import serialize_datetime
+from ..core.pydantic_utilities import pydantic_v1
+
+
+class AnomalyDetectionOutput(pydantic_v1.BaseModel):
+    input_tokens: int
+    output_tokens: int
+    finetune_tokens: int
+    mean: typing.List[float]
+    sizes: typing.List[int]
+    intervals: typing.Optional[typing.Dict[str, typing.Optional[typing.List[float]]]] = None
+    weights_x: typing.Optional[typing.List[float]] = None
+    anomaly: typing.List[bool]
+
+    def json(self, **kwargs: typing.Any) -> str:
+        kwargs_with_defaults: typing.Any = {"by_alias": True, "exclude_unset": True, **kwargs}
+        return super().json(**kwargs_with_defaults)
+
+    def dict(self, **kwargs: typing.Any) -> typing.Dict[str, typing.Any]:
+        kwargs_with_defaults: typing.Any = {"by_alias": True, "exclude_unset": True, **kwargs}
+        return super().dict(**kwargs_with_defaults)
+
+    class Config:
+        frozen = True
+        smart_union = True
+        extra = pydantic_v1.Extra.allow
+        json_encoders = {dt.datetime: serialize_datetime}

nixtla/types/cross_validation_input_finetune_loss.py

@@ -0,0 +1,7 @@
+# This file was auto-generated by Fern from our API Definition.
+
+import typing
+
+CrossValidationInputFinetuneLoss = typing.Union[
+    typing.Literal["default", "mae", "mse", "rmse", "mape", "smape"], typing.Any
+]

nixtla/types/cross_validation_input_level_item.py

@@ -0,0 +1,5 @@
+# This file was auto-generated by Fern from our API Definition.
+
+import typing
+
+CrossValidationInputLevelItem = typing.Union[int, float]

nixtla/types/cross_validation_output.py

@@ -0,0 +1,31 @@
+# This file was auto-generated by Fern from our API Definition.
+
+import datetime as dt
+import typing
+
+from ..core.datetime_utils import serialize_datetime
+from ..core.pydantic_utilities import pydantic_v1
+
+
+class CrossValidationOutput(pydantic_v1.BaseModel):
+    input_tokens: int
+    output_tokens: int
+    finetune_tokens: int
+    mean: typing.List[float]
+    sizes: typing.List[int]
+    idxs: typing.List[int]
+    intervals: typing.Optional[typing.Dict[str, typing.Optional[typing.List[float]]]] = None
+
+    def json(self, **kwargs: typing.Any) -> str:
+        kwargs_with_defaults: typing.Any = {"by_alias": True, "exclude_unset": True, **kwargs}
+        return super().json(**kwargs_with_defaults)
+
+    def dict(self, **kwargs: typing.Any) -> typing.Dict[str, typing.Any]:
+        kwargs_with_defaults: typing.Any = {"by_alias": True, "exclude_unset": True, **kwargs}
+        return super().dict(**kwargs_with_defaults)
+
+    class Config:
+        frozen = True
+        smart_union = True
+        extra = pydantic_v1.Extra.allow
+        json_encoders = {dt.datetime: serialize_datetime}

nixtla/types/forecast_output.py

@@ -8,6 +8,9 @@
 
 
 class ForecastOutput(pydantic_v1.BaseModel):
+    input_tokens: int
+    output_tokens: int
+    finetune_tokens: int
     mean: typing.List[float]
     intervals: typing.Optional[typing.Dict[str, typing.Optional[typing.List[float]]]] = None
     weights_x: typing.Optional[typing.List[float]] = None

nixtla/types/in_sample_input_level_item.py

@@ -0,0 +1,5 @@
+# This file was auto-generated by Fern from our API Definition.
+
+import typing
+
+InSampleInputLevelItem = typing.Union[int, float]

nixtla/types/in_sample_output.py

@@ -0,0 +1,31 @@
+# This file was auto-generated by Fern from our API Definition.
+
+import datetime as dt
+import typing
+
+from ..core.datetime_utils import serialize_datetime
+from ..core.pydantic_utilities import pydantic_v1
+
+
+class InSampleOutput(pydantic_v1.BaseModel):
+    input_tokens: int
+    output_tokens: int
+    finetune_tokens: int
+    mean: typing.List[float]
+    sizes: typing.List[int]
+    intervals: typing.Optional[typing.Dict[str, typing.Optional[typing.List[float]]]] = None
+    weights_x: typing.Optional[typing.List[float]] = None
+
+    def json(self, **kwargs: typing.Any) -> str:
+        kwargs_with_defaults: typing.Any = {"by_alias": True, "exclude_unset": True, **kwargs}
+        return super().json(**kwargs_with_defaults)
+
+    def dict(self, **kwargs: typing.Any) -> typing.Dict[str, typing.Any]:
+        kwargs_with_defaults: typing.Any = {"by_alias": True, "exclude_unset": True, **kwargs}
+        return super().dict(**kwargs_with_defaults)
+
+    class Config:
+        frozen = True
+        smart_union = True
+        extra = pydantic_v1.Extra.allow
+        json_encoders = {dt.datetime: serialize_datetime}

nixtla/types/level.py

@@ -0,0 +1,5 @@
+# This file was auto-generated by Fern from our API Definition.
+
+import typing
+
+Level = typing.Union[int, float]

nixtla/types/series.py

@@ -0,0 +1,36 @@
+# This file was auto-generated by Fern from our API Definition.
+
+import datetime as dt
+import typing
+
+from ..core.datetime_utils import serialize_datetime
+from ..core.pydantic_utilities import pydantic_v1
+
+
+class Series(pydantic_v1.BaseModel):
+    x: typing.Optional[typing.List[typing.List[float]]] = pydantic_v1.Field(alias="X", default=None)
+    y: typing.List[float] = pydantic_v1.Field()
+    """
+    Historic values of the target.
+    """
+
+    sizes: typing.List[int] = pydantic_v1.Field()
+    """
+    Sizes of the individual series.
+    """
+
+    def json(self, **kwargs: typing.Any) -> str:
+        kwargs_with_defaults: typing.Any = {"by_alias": True, "exclude_unset": True, **kwargs}
+        return super().json(**kwargs_with_defaults)
+
+    def dict(self, **kwargs: typing.Any) -> typing.Dict[str, typing.Any]:
+        kwargs_with_defaults: typing.Any = {"by_alias": True, "exclude_unset": True, **kwargs}
+        return super().dict(**kwargs_with_defaults)
+
+    class Config:
+        frozen = True
+        smart_union = True
+        allow_population_by_field_name = True
+        populate_by_name = True
+        extra = pydantic_v1.Extra.allow
+        json_encoders = {dt.datetime: serialize_datetime}

nixtla/types/series_with_future_exogenous.py

@@ -10,8 +10,15 @@
 class SeriesWithFutureExogenous(pydantic_v1.BaseModel):
     x_future: typing.Optional[typing.List[typing.List[float]]] = pydantic_v1.Field(alias="X_future", default=None)
     x: typing.Optional[typing.List[typing.List[float]]] = pydantic_v1.Field(alias="X", default=None)
-    y: typing.List[float]
-    sizes: typing.List[int]
+    y: typing.List[float] = pydantic_v1.Field()
+    """
+    Historic values of the target.
+    """
+
+    sizes: typing.List[int] = pydantic_v1.Field()
+    """
+    Sizes of the individual series.
+    """
 
     def json(self, **kwargs: typing.Any) -> str:
         kwargs_with_defaults: typing.Any = {"by_alias": True, "exclude_unset": True, **kwargs}

setup.py

@@ -11,6 +11,7 @@
     "jupyterlab",
     "nbdev",
     "neuralforecast",
+    "numpy<2",
     "plotly",
     "polars",
     "pre-commit",