first pass in specifying API and ADAM parametes

python/smooth/adam_general/adam.py

@@ -0,0 +1,156 @@
+"""Python wrapper for the ADAM forecasting model.
+
+ * Equ
+valent code in R is 
+"""
+
+from typing import Union, List, Literal, Optional
+
+import numpy as np
+
+from numpy.typing import NDArray
+
+DISTRIBUTON_OPTIONS = Literal[
+    "default", "dnorm", "dlaplace", "ds", "dgnorm", "dlnorm", "dinvgauss", "dgamma"
+]
+
+LOSS_OPTIONS = Literal[
+    "likelihood",
+    "MSE",
+    "MAE",
+    "HAM",
+    "LASSO",
+    "RIDGE",
+    "MSEh",
+    "TMSE" "GTMSE",
+    "MSCE",
+]
+
+
+class ADAM:
+    def __init__(
+        self,
+        model: str = "ZXZ",
+        lags: NDArray = None,
+        ar_order: Union[int, List[int]] = 0,
+        i_order: Union[int, List[int]] = 0,
+        ma_order: Union[int, List[int]] = 0,
+        # SELECT: skipping this for now (auto.arima thingy)
+        constant: bool = False,
+        regressors: Literal["use", "select", "adapt"] = "use",
+        distribution: DISTRIBUTON_OPTIONS = None,
+        loss: LOSS_OPTIONS = "likelihood",
+        # outliers: we're skipping this for now
+        loss_horizon: Optional[int] = None,
+        ic: Literal["AIC", "AICc", "BIC", "BICc"] = "AICc",
+        bounds: Literal["usual", "admissible", "none"] = "usual",
+        # occurrence: skipping this for now,
+        # ---- These are the estimated parameters that we can choose to fix ----
+        # Dictionary of terms e.g. {"alpha": 0.5, "beta": 0.5}
+        persistence: Optional[dict] = None,
+        phi: Optional[float] = None,
+        initial: Optional[dict] = None,
+        # TODO: enforce the structure of this
+        arma: Optional[dict] = None,
+        # ----- End of parameters----
+        verbose: int = 0,
+        # Fisher information matrix: We're skipping for now and we'll use composition
+        # for it like Grid Search in scikit-learn.
+        # initial values for optimization parameters:
+        nlopt_initial: Optional[dict] = None,
+        nlopt_upper: Optional[dict] = None,
+        nlopt_lower: Optional[dict] = None,
+        nlopt_kargs: Optional[dict] = None,
+        # specific to losses or distributions
+        reg_lambda: Optional[float] = None,
+        gnorm_shape: Optional[float] = None,
+    ) -> None:
+        """_summary_
+
+        Parameters
+        ----------
+        model : str, optional
+            _description_, by default "ZXZ"
+        lags : NDArray, optional
+            _description_, by default None
+        ar_order : Union[int, List[int]], optional
+            _description_, by default 0
+        i_order : Union[int, List[int]], optional
+            _description_, by default 0
+        ma_order : Union[int, List[int]], optional
+            _description_, by default 0
+        constant : bool, optional
+            _description_, by default False
+        regressors : Literal["use", "select", "adapt"], optional
+            _description_, by default "use"
+        distribution : DISTRIBUTON_OPTIONS, optional
+            _description_, by default None
+        loss : LOSS_OPTIONS, optional
+            _description_, by default "likelihood"
+        loss_horizon : Optional[int], optional
+            _description_, by default None
+        ic : Literal["AIC", "AICc", "BIC", "BICc"], optional
+            _description_, by default "AICc"
+        bounds : Literal["usual", "admissible", "none"], optional
+            _description_, by default "usual"
+        persistence : Optional[dict], optional
+            _description_, by default None
+        phi : Optional[float], optional
+            _description_, by default None
+        initial : Optional[dict], optional
+            _description_, by default None
+        arma : Optional[dict], optional
+            _description_, by default None
+        verbose : int, optional
+            _description_, by default 0
+        nlopt_initial : Optional[dict], optional
+            _description_, by default None
+        nlopt_upper : Optional[dict], optional
+            _description_, by default None
+        nlopt_lower : Optional[dict], optional
+            _description_, by default None
+        nlopt_kargs : Optional[dict], optional
+            _description_, by default None
+        reg_lambda : Optional[float], optional
+            _description_, by default None
+        gnorm_shape : Optional[float], optional
+            _description_, by default None
+        """
+        self.model = model
+        self.lags = lags
+        self.ar_order = ar_order
+        self.i_order = i_order
+        self.ma_order = ma_order
+        self.constant = constant
+        self.regressors = regressors
+        self.distribution = distribution
+        self.loss = loss
+        self.loss_horizon = loss_horizon
+        self.ic = ic
+        self.bounds = bounds
+        self.persistence = persistence
+        self.phi = phi
+        self.initial = initial
+        self.arma = arma
+        self.verbose = verbose
+        self.nlopt_initial = nlopt_initial
+        self.nlopt_upper = nlopt_upper
+        self.nlopt_lower = nlopt_lower
+        self.nlopt_kargs = nlopt_kargs
+        self.reg_lambda = reg_lambda
+        self.gnorm_shape = gnorm_shape
+
+    def fit(self, y: NDArray, X: Optional[NDArray] = None):
+        """Fit"""
+        return self
+
+    def predict(
+        self,
+        h: int,
+        X: Optional[NDArray] = None,
+    ) -> NDArray:
+        """Point forecasts only."""
+        pass
+
+
+# TODO: Add methods for intervals and simulated future paths