From 3f05e193d1a492956486bddca28cf392de3158b4 Mon Sep 17 00:00:00 2001
From: Mathias Leys <mathias.leys@nillion.com>
Date: Thu, 6 Jun 2024 12:46:40 +0200
Subject: [PATCH 01/28] Restructure models & add prophet boilerplate

---
 nada_ai/linear_model/__init__.py                           | 1 +
 .../{nn/models.py => linear_model/linear_regression.py}    | 2 +-
 nada_ai/nn/__init__.py                                     | 1 -
 nada_ai/time_series/__init__.py                            | 1 +
 nada_ai/time_series/prophet.py                             | 7 +++++++
 pyproject.toml                                             | 1 +
 6 files changed, 11 insertions(+), 2 deletions(-)
 create mode 100644 nada_ai/linear_model/__init__.py
 rename nada_ai/{nn/models.py => linear_model/linear_regression.py} (96%)
 create mode 100644 nada_ai/time_series/__init__.py
 create mode 100644 nada_ai/time_series/prophet.py

diff --git a/nada_ai/linear_model/__init__.py b/nada_ai/linear_model/__init__.py
new file mode 100644
index 0000000..da620d7
--- /dev/null
+++ b/nada_ai/linear_model/__init__.py
@@ -0,0 +1 @@
+from .linear_regression import LinearRegression
\ No newline at end of file
diff --git a/nada_ai/nn/models.py b/nada_ai/linear_model/linear_regression.py
similarity index 96%
rename from nada_ai/nn/models.py
rename to nada_ai/linear_model/linear_regression.py
index a74d69f..828e9f7 100644
--- a/nada_ai/nn/models.py
+++ b/nada_ai/linear_model/linear_regression.py
@@ -1,4 +1,4 @@
-"""Model implementations"""
+"""Linear regression implementation"""
 
 import nada_algebra as na
 from nada_ai.nn.module import Module
diff --git a/nada_ai/nn/__init__.py b/nada_ai/nn/__init__.py
index c7176d8..9924b14 100644
--- a/nada_ai/nn/__init__.py
+++ b/nada_ai/nn/__init__.py
@@ -2,4 +2,3 @@
 from .parameter import Parameter
 from .layers import *
 from .activations import *
-from .models import *
diff --git a/nada_ai/time_series/__init__.py b/nada_ai/time_series/__init__.py
new file mode 100644
index 0000000..74a3000
--- /dev/null
+++ b/nada_ai/time_series/__init__.py
@@ -0,0 +1 @@
+from .prophet import Prophet
\ No newline at end of file
diff --git a/nada_ai/time_series/prophet.py b/nada_ai/time_series/prophet.py
new file mode 100644
index 0000000..98e2277
--- /dev/null
+++ b/nada_ai/time_series/prophet.py
@@ -0,0 +1,7 @@
+"""Facebook Prophet implementation"""
+
+from nada_ai.nn.module import Module
+from nada_ai.nn.parameter import Parameter
+
+class Prophet(Module):
+    ...
diff --git a/pyproject.toml b/pyproject.toml
index 048d218..9fb5917 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -8,6 +8,7 @@ readme = "README.md"
 [tool.poetry.dependencies]
 python = "^3.10"
 numpy = "^1.26.4"
+pandas = "^2.2.2"
 nada-dsl = "^0.2.1"
 py-nillion-client = "^0.2.1"
 nada-algebra = "^0.3.2"

From 0217be87e6dd1739d37332a6477b3d4c26739be3 Mon Sep 17 00:00:00 2001
From: Mathias Leys <mathias.leys@nillion.com>
Date: Fri, 7 Jun 2024 12:56:17 +0200
Subject: [PATCH 02/28] Baseline prophet skeleton

---
 nada_ai/linear_model/__init__.py          |   2 +-
 nada_ai/time_series/__init__.py           |   2 +-
 nada_ai/time_series/helpers.py            |  48 +++++
 nada_ai/time_series/prophet.py            | 202 +++++++++++++++++++++-
 tests/nada-tests/nada-project.toml        |   4 +
 tests/nada-tests/src/linear_regression.py |   2 +-
 tests/nada-tests/src/prophet.py           |  26 +++
 7 files changed, 282 insertions(+), 4 deletions(-)
 create mode 100644 nada_ai/time_series/helpers.py
 create mode 100644 tests/nada-tests/src/prophet.py

diff --git a/nada_ai/linear_model/__init__.py b/nada_ai/linear_model/__init__.py
index da620d7..5303cce 100644
--- a/nada_ai/linear_model/__init__.py
+++ b/nada_ai/linear_model/__init__.py
@@ -1 +1 @@
-from .linear_regression import LinearRegression
\ No newline at end of file
+from .linear_regression import LinearRegression
diff --git a/nada_ai/time_series/__init__.py b/nada_ai/time_series/__init__.py
index 74a3000..2f2ffd5 100644
--- a/nada_ai/time_series/__init__.py
+++ b/nada_ai/time_series/__init__.py
@@ -1 +1 @@
-from .prophet import Prophet
\ No newline at end of file
+from .prophet import Prophet
diff --git a/nada_ai/time_series/helpers.py b/nada_ai/time_series/helpers.py
new file mode 100644
index 0000000..0a4f13e
--- /dev/null
+++ b/nada_ai/time_series/helpers.py
@@ -0,0 +1,48 @@
+"""Time series helper functions"""
+
+from collections import OrderedDict
+import pandas as pd
+import numpy as np
+import nada_algebra as na
+
+
+def fourier_series(
+    dates: pd.Series,
+    period: int | float,
+    series_order: int,
+) -> np.ndarray:
+    x_T = dates * np.pi * 2
+    fourier_components = np.empty((dates.shape[0], 2 * series_order))
+    for i in range(series_order):
+        c = x_T * (i + 1) / period
+        fourier_components[:, 2 * i] = np.sin(c)
+        fourier_components[:, (2 * i) + 1] = np.cos(c)
+    return fourier_components
+
+
+def decode_component_matrix(component_matrix: na.NadaArray):
+    additive_terns, multiplicative_terms, yearly, weekly, daily = component_matrix.inner
+    return {
+        "additive_terms": additive_terns,
+        "multiplicative_terms": multiplicative_terms,
+        "yearly": yearly,
+        "weekly": weekly,
+        "daily": daily,
+    }
+
+
+def decode_seasonality_matrix(seasonality_matrix: na.NadaArray):
+    period_names = ["yearly", "yearly", "weekly", "weekly", "daily", "daily"]
+    modes = ["additive", "multiplicative"] * 3
+
+    result = {}
+    for row, period_name, mode in zip(seasonality_matrix.inner, period_names, modes):
+        period, fourier_order, prior_scale = row
+        result[f"{period_name}_{mode}"] = {
+            "period": period,
+            "fourier_order": fourier_order,
+            "prior_scale": prior_scale,
+            "mode": mode,
+        }
+
+    return OrderedDict(result)
diff --git a/nada_ai/time_series/prophet.py b/nada_ai/time_series/prophet.py
index 98e2277..f8f3dc9 100644
--- a/nada_ai/time_series/prophet.py
+++ b/nada_ai/time_series/prophet.py
@@ -1,7 +1,207 @@
 """Facebook Prophet implementation"""
 
+from typing import override
+import pandas as pd
+import nada_algebra as na
 from nada_ai.nn.module import Module
 from nada_ai.nn.parameter import Parameter
+from nada_ai.time_series.helpers import (
+    fourier_series,
+    decode_seasonality_matrix,
+    decode_component_matrix,
+)
+
 
 class Prophet(Module):
-    ...
+    """Prophet forecasting implementation"""
+
+    def __init__(
+        self,
+        growth: str = "linear",
+        n_changepoints: int = 25,  # TODO: somehow enforce a fixed number of changepoints in case n_changepoints < len(sequence)
+        num_seasonality_features: int = 1,  # TODO: somehow enforce a fixed number of seasonalities
+    ) -> None:
+        if growth not in {"linear"}:
+            raise NotImplementedError(f"Growth mode `{growth}` is not supported")
+
+        self.growth = growth
+
+        M = 1  # NOTE: MAP estimation is assumed, so M=1 guaranteed
+
+        self.k = Parameter((M, 1))
+        self.m = Parameter((M, 1))
+        self.delta = Parameter((M, n_changepoints))
+        self.beta = Parameter((M, num_seasonality_features))
+        self.changepoints_t = Parameter(n_changepoints)
+
+        self.y_scale = Parameter(1)
+
+        self.seasonality_matrix = Parameter((6, 3))
+        self.component_matrix = Parameter(5)
+
+        self.seasonalities = decode_seasonality_matrix(self.seasonality_matrix)
+        self.component_modes = decode_component_matrix(self.component_matrix)
+
+    def predict_seasonal_comps(self, df):
+        seasonal_features, _, component_cols, _ = self.make_all_seasonality_features(df)
+
+        X = seasonal_features.values
+        data = {}
+        for component in component_cols.columns:
+            component_values = component_cols[component].values
+            beta_c = self.beta * component_values
+
+            comp = X @ beta_c.T
+
+            comp = (
+                comp
+                * self.component_modes[
+                    component.replace("_additive", "").replace("_multiplicative", "")
+                ]
+            )
+            data[component] = comp.mean(axis=1)
+
+        return pd.DataFrame(data)
+
+    def make_all_seasonality_features(self, df):
+        seasonal_features = []
+        prior_scales = []
+        modes = {"additive": [], "multiplicative": []}
+
+        for name, props in self.seasonalities.items():
+            features = fourier_series(df["ds"], props["period"], props["fourier_order"])
+            columns = [
+                "{}_delim_{}".format(name, i + 1) for i in range(features.shape[1])
+            ]
+            features = pd.DataFrame(features, columns=columns)
+
+            seasonal_features.append(features)
+            prior_scales.extend([props["prior_scale"]] * features.shape[1])
+            modes[props["mode"]].append(name)
+
+        if len(seasonal_features) == 0:
+            seasonal_features.append(pd.DataFrame({"zeros": na.zeros(df.shape[0])}))
+            prior_scales.append(1.0)
+
+        seasonal_features = pd.concat(seasonal_features, axis=1)
+        component_cols, modes = self.regressor_column_matrix(seasonal_features, modes)
+        return seasonal_features, prior_scales, component_cols, modes
+
+    def regressor_column_matrix(self, seasonal_features, modes):
+        components = pd.DataFrame(
+            {
+                "col": na.arange(seasonal_features.shape[1]),
+                "component": [x.split("_delim_")[0] for x in seasonal_features.columns],
+            }
+        )
+
+        for mode in ["additive", "multiplicative"]:
+            components = self.add_group_component(
+                components, mode + "_terms", modes[mode]
+            )
+            modes[mode].append(mode + "_terms")
+
+        component_cols = pd.crosstab(
+            components["col"],
+            components["component"],
+        ).sort_index(level="col")
+
+        for name in ["additive_terms", "multiplicative_terms"]:
+            if name not in component_cols:
+                component_cols[name] = 0
+
+        component_cols.drop("zeros", axis=1, inplace=True, errors="ignore")
+
+        return component_cols, modes
+
+    def add_group_component(self, components, name, group):
+        new_comp = components[components["component"].isin(set(group))].copy()
+        group_cols = new_comp["col"].unique()
+        if len(group_cols) > 0:
+            new_comp = pd.DataFrame({"col": group_cols, "component": name})
+            components = pd.concat([components, new_comp])
+        return components
+
+    def predict_trend(self, df: pd.DataFrame) -> pd.Series:
+        # NOTE: this indexing is possible because M=1 guaranteed
+        # If this were not the case, we should take the arithmetic mean
+        [[k]] = self.k
+        [[m]] = self.m
+        [delta] = self.delta
+
+        t = df["t"].to_numpy()
+        if self.growth == "linear":
+            mask = self.changepoints_t[None, :] <= t[..., None]
+            deltas_t = delta * mask
+            k_t = deltas_t.sum(axis=1) + k
+            m_t = (-self.changepoints_t * deltas_t).sum(axis=1) + m
+            trend = k_t * t + m_t
+        else:
+            raise NotImplementedError(self.growth + " is not supported")
+
+        return trend * self.y_scale + df["floor"]
+
+    def predict(self, df: pd.DataFrame) -> na.NadaArray:
+        df["trend"] = self.predict_trend(df)
+        seasonal_comps = self.predict_seasonal_comps(df)
+
+        result = pd.concat((df[["ds", "trend"]], seasonal_comps), axis=1)
+
+        return (result["trend"] * (result["multiplicative_terms"] + 1)) + result[
+            "additive_terms"
+        ]
+
+    @override
+    def __call__(
+        self,
+        dates: na.NadaArray,
+        floor: na.NadaArray,
+        t: na.NadaArray,
+        trend: na.NadaArray,
+    ) -> na.NadaArray:
+        """
+        Forward pass.
+        Note: requires multiple input arrays due special nature of forecasting.
+
+        Args:
+            dates (na.NadaArray): Array of timestamp values.
+            floor (na.NadaArray): Array of floor values.
+            t (na.NadaArray): Array of t values.
+            trend (na.NadaArray): Array of trend values.
+
+        Returns:
+            na.NadaArray: Forecasted values.
+        """
+        return self.forward(dates=dates, floor=floor, t=t, trend=trend)
+
+    @override
+    def forward(
+        self,
+        dates: na.NadaArray,
+        floor: na.NadaArray,
+        t: na.NadaArray,
+        trend: na.NadaArray,
+    ) -> na.NadaArray:
+        """
+        Forward pass.
+        Note: requires multiple input arrays due special nature of forecasting.
+
+        Args:
+            dates (na.NadaArray): Array of timestamp values.
+            floor (na.NadaArray): Array of floor values.
+            t (na.NadaArray): Array of t values.
+            trend (na.NadaArray): Array of trend values.
+
+        Returns:
+            na.NadaArray: Forecasted values.
+        """
+        return self.predict(
+            pd.DataFrame(
+                {
+                    "ds": dates.inner,
+                    "floor": floor.inner,
+                    "t": t.inner,
+                    "trend": trend.inner,
+                }
+            )
+        )
diff --git a/tests/nada-tests/nada-project.toml b/tests/nada-tests/nada-project.toml
index f79b2da..f8c9e6a 100644
--- a/tests/nada-tests/nada-project.toml
+++ b/tests/nada-tests/nada-project.toml
@@ -41,3 +41,7 @@ prime_size = 128
 [[programs]]
 path = "src/end-to-end.py"
 prime_size = 128
+
+[[programs]]
+path = "src/prophet.py"
+prime_size = 128
diff --git a/tests/nada-tests/src/linear_regression.py b/tests/nada-tests/src/linear_regression.py
index bf3b2cf..b347c7d 100644
--- a/tests/nada-tests/src/linear_regression.py
+++ b/tests/nada-tests/src/linear_regression.py
@@ -1,6 +1,6 @@
 from nada_dsl import *
 import nada_algebra as na
-from nada_ai.nn import LinearRegression
+from nada_ai.linear_model import LinearRegression
 
 
 def nada_main():
diff --git a/tests/nada-tests/src/prophet.py b/tests/nada-tests/src/prophet.py
new file mode 100644
index 0000000..6c4686d
--- /dev/null
+++ b/tests/nada-tests/src/prophet.py
@@ -0,0 +1,26 @@
+import pytest
+from nada_dsl import *
+import nada_algebra as na
+from nada_ai.time_series import Prophet
+
+
+def nada_main():
+    party = Party("party")
+
+    dates = na.array([4], party, "dates", SecretInteger)
+    floor = na.array([4], party, "floor", SecretInteger)
+    t = na.array([4], party, "t", na.SecretRational)
+    trend = na.array([4], party, "floor", na.SecretRational)
+
+    prophet = Prophet(
+        growth="linear",
+        n_changepoints=2,
+        num_seasonality_features=1,
+    )
+
+    with pytest.raises(NotImplementedError):
+        Prophet(growth="to_the_moon")
+
+    result = prophet(dates, floor, t, trend)
+
+    return result.output(party, "forecast")

From 65b433ed1f9efbd2c5d68bc72876e7e1d26383d9 Mon Sep 17 00:00:00 2001
From: Mathias Leys <mathias.leys@nillion.com>
Date: Tue, 11 Jun 2024 12:41:26 +0100
Subject: [PATCH 03/28] Implement prophet model

---
 nada_ai/__init__.py            |   2 +-
 nada_ai/client.py              |  27 +++-
 nada_ai/time_series/helpers.py |  46 ++----
 nada_ai/time_series/prophet.py | 273 +++++++++++++++++++--------------
 4 files changed, 192 insertions(+), 156 deletions(-)

diff --git a/nada_ai/__init__.py b/nada_ai/__init__.py
index 0cbc867..de41c0d 100644
--- a/nada_ai/__init__.py
+++ b/nada_ai/__init__.py
@@ -1 +1 @@
-from nada_ai.client import StateClient, TorchClient, SklearnClient
+from nada_ai.client import StateClient, TorchClient, SklearnClient, ProphetClient
diff --git a/nada_ai/client.py b/nada_ai/client.py
index 35147c6..50eff14 100644
--- a/nada_ai/client.py
+++ b/nada_ai/client.py
@@ -12,6 +12,7 @@
     LogisticRegression,
     LogisticRegressionCV,
 )
+import prophet
 import torch
 from torch import nn
 import sklearn
@@ -101,9 +102,9 @@ def __ensure_numpy(self, array_like: Any) -> np.ndarray:
             return np.array(array_like)
         if isinstance(array_like, (float, int, np.floating)):
             return np.array([array_like])
-        raise TypeError(
-            "Could not convert type `%s` to NumPy array" % type(array_like).__name__
-        )
+
+        error_msg = f"Could not convert `{type(array_like).__name__}` to NumPy array"
+        raise TypeError(error_msg)
 
 
 class StateClient(ModelClient):
@@ -153,3 +154,23 @@ def __init__(self, model: sklearn.base.BaseEstimator) -> None:
             )
 
         self.state_dict = state_dict
+
+
+class ProphetClient(ModelClient):
+    """ModelClient for Prophet models"""
+
+    def __init__(self, model) -> None:
+        """
+        Client initialization.
+
+        Args:
+            model (prophet.forecaster.Prophet): Prophet model.
+        """
+        self.state_dict = {
+            "k": model.params["k"],
+            "m": model.params["m"],
+            "delta": model.params["delta"],
+            "beta": model.params["beta"],
+            "changepoints_t": model.changepoints_t,
+            "y_scale": model.y_scale,
+        }
diff --git a/nada_ai/time_series/helpers.py b/nada_ai/time_series/helpers.py
index 0a4f13e..9a753b3 100644
--- a/nada_ai/time_series/helpers.py
+++ b/nada_ai/time_series/helpers.py
@@ -1,48 +1,28 @@
 """Time series helper functions"""
 
-from collections import OrderedDict
-import pandas as pd
 import numpy as np
-import nada_algebra as na
 
 
 def fourier_series(
-    dates: pd.Series,
+    dates: np.ndarray,
     period: int | float,
     series_order: int,
 ) -> np.ndarray:
-    x_T = dates * np.pi * 2
+    """
+    Generates (plain-text) Fourier series.
+
+    Args:
+        dates (np.ndarray): Array of timestamp values.
+        period (int | float): Fourier period.
+        series_order (int): Order of Fourier series.
+
+    Returns:
+        np.ndarray: Generates Fourier series.
+    """
+    x_T = dates * 2 * np.pi
     fourier_components = np.empty((dates.shape[0], 2 * series_order))
     for i in range(series_order):
         c = x_T * (i + 1) / period
         fourier_components[:, 2 * i] = np.sin(c)
         fourier_components[:, (2 * i) + 1] = np.cos(c)
     return fourier_components
-
-
-def decode_component_matrix(component_matrix: na.NadaArray):
-    additive_terns, multiplicative_terms, yearly, weekly, daily = component_matrix.inner
-    return {
-        "additive_terms": additive_terns,
-        "multiplicative_terms": multiplicative_terms,
-        "yearly": yearly,
-        "weekly": weekly,
-        "daily": daily,
-    }
-
-
-def decode_seasonality_matrix(seasonality_matrix: na.NadaArray):
-    period_names = ["yearly", "yearly", "weekly", "weekly", "daily", "daily"]
-    modes = ["additive", "multiplicative"] * 3
-
-    result = {}
-    for row, period_name, mode in zip(seasonality_matrix.inner, period_names, modes):
-        period, fourier_order, prior_scale = row
-        result[f"{period_name}_{mode}"] = {
-            "period": period,
-            "fourier_order": fourier_order,
-            "prior_scale": prior_scale,
-            "mode": mode,
-        }
-
-    return OrderedDict(result)
diff --git a/nada_ai/time_series/prophet.py b/nada_ai/time_series/prophet.py
index f8f3dc9..1f2ed71 100644
--- a/nada_ai/time_series/prophet.py
+++ b/nada_ai/time_series/prophet.py
@@ -1,15 +1,12 @@
 """Facebook Prophet implementation"""
 
-from typing import override
-import pandas as pd
+import numpy as np
+from typing import Dict, Tuple, override
+
 import nada_algebra as na
 from nada_ai.nn.module import Module
 from nada_ai.nn.parameter import Parameter
-from nada_ai.time_series.helpers import (
-    fourier_series,
-    decode_seasonality_matrix,
-    decode_component_matrix,
-)
+from nada_ai.time_series.helpers import fourier_series
 
 
 class Prophet(Module):
@@ -17,191 +14,229 @@ class Prophet(Module):
 
     def __init__(
         self,
+        n_changepoints: int,
         growth: str = "linear",
-        n_changepoints: int = 25,  # TODO: somehow enforce a fixed number of changepoints in case n_changepoints < len(sequence)
-        num_seasonality_features: int = 1,  # TODO: somehow enforce a fixed number of seasonalities
+        yearly_seasonality: bool = True,
+        weekly_seasonality: bool = True,
+        daily_seasonality: bool = False,
+        seasonality_mode: str = "additive",
     ) -> None:
-        if growth not in {"linear"}:
-            raise NotImplementedError(f"Growth mode `{growth}` is not supported")
+        """
+        Prophet model initialization.
 
+        Args:
+            n_changepoints (int): Number of changepoints.
+            growth (str, optional): Forecasting growth mode. Defaults to "linear".
+            yearly_seasonality (bool, optional): Whether or not to include a yearly
+                seasonality term. Defaults to True.
+            weekly_seasonality (bool, optional): Whether or not to include a weekly
+                seasonality term. Defaults to True.
+            daily_seasonality (bool, optional): Whether or not to include a daily
+                seasonality term. Defaults to False.
+            seasonality_mode (str, optional): Seasonality mode. Defaults to 'additive'.
+        """
         self.growth = growth
 
+        self.seasonalities = {
+            "additive": {},
+            "multiplicative": {},
+        }
+        if yearly_seasonality:
+            self.seasonalities[seasonality_mode]["yearly"] = {
+                "period": 365.25,
+                "fourier_order": 10,
+            }
+        if weekly_seasonality:
+            self.seasonalities[seasonality_mode]["weekly"] = {
+                "period": 7,
+                "fourier_order": 3,
+            }
+        if daily_seasonality:
+            self.seasonalities[seasonality_mode]["daily"] = {
+                "period": 1,
+                "fourier_order": 4,
+            }
+
+        num_fourier = 0
+        for _, mode_seasonality in self.seasonalities.items():
+            for _, period_seasonality in mode_seasonality.items():
+                # NOTE: times two because there is always a term for both sin and cos
+                num_fourier += period_seasonality["fourier_order"] * 2
+
         M = 1  # NOTE: MAP estimation is assumed, so M=1 guaranteed
 
         self.k = Parameter((M, 1))
         self.m = Parameter((M, 1))
+        self.beta = (
+            Parameter((M, num_fourier))
+            if num_fourier != 0
+            else na.NadaArray(np.array([None]))
+        )
         self.delta = Parameter((M, n_changepoints))
-        self.beta = Parameter((M, num_seasonality_features))
         self.changepoints_t = Parameter(n_changepoints)
-
         self.y_scale = Parameter(1)
 
-        self.seasonality_matrix = Parameter((6, 3))
-        self.component_matrix = Parameter(5)
+    def predict_seasonal_comps(
+        self, dates: np.ndarray
+    ) -> Tuple[na.NadaArray, na.NadaArray]:
+        """
+        Predicts seasonal components.
 
-        self.seasonalities = decode_seasonality_matrix(self.seasonality_matrix)
-        self.component_modes = decode_component_matrix(self.component_matrix)
+        Args:
+            dates (np.ndarray): Array of timestamp values.
 
-    def predict_seasonal_comps(self, df):
-        seasonal_features, _, component_cols, _ = self.make_all_seasonality_features(df)
+        Returns:
+            Tuple[na.NadaArray, na.NadaArray]: Additive and multiplicative
+                seasonal components.
+        """
+        [beta] = self.beta
 
-        X = seasonal_features.values
-        data = {}
-        for component in component_cols.columns:
-            component_values = component_cols[component].values
-            beta_c = self.beta * component_values
+        seasonal_components = {}
+        for mode in ["additive", "multiplicative"]:
+            seasonal_features = self.make_seasonality_features(
+                dates, self.seasonalities[mode]
+            )
 
-            comp = X @ beta_c.T
+            components = []
+            for _, features in seasonal_features.items():
+                if features is None:
+                    continue
 
-            comp = (
-                comp
-                * self.component_modes[
-                    component.replace("_additive", "").replace("_multiplicative", "")
-                ]
-            )
-            data[component] = comp.mean(axis=1)
-
-        return pd.DataFrame(data)
-
-    def make_all_seasonality_features(self, df):
-        seasonal_features = []
-        prior_scales = []
-        modes = {"additive": [], "multiplicative": []}
-
-        for name, props in self.seasonalities.items():
-            features = fourier_series(df["ds"], props["period"], props["fourier_order"])
-            columns = [
-                "{}_delim_{}".format(name, i + 1) for i in range(features.shape[1])
-            ]
-            features = pd.DataFrame(features, columns=columns)
-
-            seasonal_features.append(features)
-            prior_scales.extend([props["prior_scale"]] * features.shape[1])
-            modes[props["mode"]].append(name)
-
-        if len(seasonal_features) == 0:
-            seasonal_features.append(pd.DataFrame({"zeros": na.zeros(df.shape[0])}))
-            prior_scales.append(1.0)
-
-        seasonal_features = pd.concat(seasonal_features, axis=1)
-        component_cols, modes = self.regressor_column_matrix(seasonal_features, modes)
-        return seasonal_features, prior_scales, component_cols, modes
-
-    def regressor_column_matrix(self, seasonal_features, modes):
-        components = pd.DataFrame(
-            {
-                "col": na.arange(seasonal_features.shape[1]),
-                "component": [x.split("_delim_")[0] for x in seasonal_features.columns],
-            }
+                comp = (features @ beta.T) * self.y_scale
+                components.append(comp)
+
+            if len(components) == 0:
+                seasonal_components[mode] = na.zeros(dates.shape, na.Rational)
+            else:
+                seasonal_components[mode] = na.NadaArray(np.array(components))
+
+        additive_component = (seasonal_components["additive"] * self.y_scale).sum(
+            axis=0
         )
+        multiplicative_component = (
+            -seasonal_components["multiplicative"] + na.rational(1)
+        ).prod(axis=0)
 
-        for mode in ["additive", "multiplicative"]:
-            components = self.add_group_component(
-                components, mode + "_terms", modes[mode]
-            )
-            modes[mode].append(mode + "_terms")
+        return additive_component, multiplicative_component
 
-        component_cols = pd.crosstab(
-            components["col"],
-            components["component"],
-        ).sort_index(level="col")
+    def make_seasonality_features(
+        self, dates: np.ndarray, seasonalities: Dict[str, Dict[str, int | float]]
+    ) -> Dict[str, na.NadaArray]:
+        """
+        Generates seasonality features per seasonal component.
 
-        for name in ["additive_terms", "multiplicative_terms"]:
-            if name not in component_cols:
-                component_cols[name] = 0
+        Args:
+            dates (np.ndarray): Array of timestamp values.
+            seasonalities (Dict[str, Dict[str, int  |  float]]): Seasonality config.
 
-        component_cols.drop("zeros", axis=1, inplace=True, errors="ignore")
+        Returns:
+            Dict[str, na.NadaArray]: Generated seasonality features.
+        """
+        features = {}
+        for name, props in seasonalities.items():
+            period, fourier_order = props["period"], props["fourier_order"]
+            if fourier_order == 0:
+                feats = None
+            else:
+                feats = fourier_series(dates, period, fourier_order)
+            features[name] = na.frompyfunc(na.rational, 1, 1)(feats)
+        return features
+
+    def predict_trend(self, floor: na.NadaArray, t: na.NadaArray) -> na.NadaArray:
+        """
+        Predicts trend values.
 
-        return component_cols, modes
+        Args:
+            floor (na.NadaArray): Array of floor values.
+            t (na.NadaArray): Array of t values.
 
-    def add_group_component(self, components, name, group):
-        new_comp = components[components["component"].isin(set(group))].copy()
-        group_cols = new_comp["col"].unique()
-        if len(group_cols) > 0:
-            new_comp = pd.DataFrame({"col": group_cols, "component": name})
-            components = pd.concat([components, new_comp])
-        return components
+        Raises:
+            NotImplementedError: Raised when unsupported growth mode is provided.
 
-    def predict_trend(self, df: pd.DataFrame) -> pd.Series:
-        # NOTE: this indexing is possible because M=1 guaranteed
+        Returns:
+            na.NadaArray: Predicted trend.
+        """
+        # NOTE: this indexing is possible because M=1 is guaranteed
         # If this were not the case, we should take the arithmetic mean
         [[k]] = self.k
         [[m]] = self.m
         [delta] = self.delta
 
-        t = df["t"].to_numpy()
         if self.growth == "linear":
-            mask = self.changepoints_t[None, :] <= t[..., None]
+            mask = na.frompyfunc(
+                lambda a, b: (a <= b).if_else(na.rational(1), na.rational(0)), 2, 1
+            )(self.changepoints_t[None, :], t[..., None])
             deltas_t = delta * mask
             k_t = deltas_t.sum(axis=1) + k
             m_t = (-self.changepoints_t * deltas_t).sum(axis=1) + m
             trend = k_t * t + m_t
+        elif self.growth == "flat":
+            trend = na.ones_like(t, na.rational) * m
         else:
             raise NotImplementedError(self.growth + " is not supported")
 
-        return trend * self.y_scale + df["floor"]
+        return trend * self.y_scale + floor
 
-    def predict(self, df: pd.DataFrame) -> na.NadaArray:
-        df["trend"] = self.predict_trend(df)
-        seasonal_comps = self.predict_seasonal_comps(df)
+    def predict(
+        self,
+        dates: np.ndarray,
+        floor: na.NadaArray,
+        t: na.NadaArray,
+    ) -> na.NadaArray:
+        """
+        Generates time series forecasts.
 
-        result = pd.concat((df[["ds", "trend"]], seasonal_comps), axis=1)
+        Args:
+            dates (np.ndarray): Array of timestamp values.
+            floor (na.NadaArray): Array of floor values.
+            t (na.NadaArray): Array of t values.
 
-        return (result["trend"] * (result["multiplicative_terms"] + 1)) + result[
-            "additive_terms"
-        ]
+        Returns:
+            na.NadaArray: Forecasted values.
+        """
+        trend = self.predict_trend(floor, t)
+        additive_comps, multiplicative_comps = self.predict_seasonal_comps(dates)
+        yhat = trend * (multiplicative_comps + na.rational(1)) + additive_comps
+        return yhat
 
     @override
     def __call__(
         self,
-        dates: na.NadaArray,
+        dates: np.ndarray,
         floor: na.NadaArray,
         t: na.NadaArray,
-        trend: na.NadaArray,
     ) -> na.NadaArray:
         """
         Forward pass.
-        Note: requires multiple input arrays due special nature of forecasting.
+        Note: requires multiple input arrays due to special nature of forecasting.
 
         Args:
-            dates (na.NadaArray): Array of timestamp values.
+            dates (np.ndarray): Array of timestamp values.
             floor (na.NadaArray): Array of floor values.
             t (na.NadaArray): Array of t values.
-            trend (na.NadaArray): Array of trend values.
 
         Returns:
             na.NadaArray: Forecasted values.
         """
-        return self.forward(dates=dates, floor=floor, t=t, trend=trend)
+        return self.predict(dates=dates, floor=floor, t=t)
 
     @override
     def forward(
         self,
-        dates: na.NadaArray,
+        dates: np.ndarray,
         floor: na.NadaArray,
         t: na.NadaArray,
-        trend: na.NadaArray,
     ) -> na.NadaArray:
         """
         Forward pass.
-        Note: requires multiple input arrays due special nature of forecasting.
+        Note: requires multiple input arrays due to special nature of forecasting.
 
         Args:
-            dates (na.NadaArray): Array of timestamp values.
+            dates (np.ndarray): Array of timestamp values.
             floor (na.NadaArray): Array of floor values.
             t (na.NadaArray): Array of t values.
-            trend (na.NadaArray): Array of trend values.
 
         Returns:
             na.NadaArray: Forecasted values.
         """
-        return self.predict(
-            pd.DataFrame(
-                {
-                    "ds": dates.inner,
-                    "floor": floor.inner,
-                    "t": t.inner,
-                    "trend": trend.inner,
-                }
-            )
-        )
+        return self.predict(dates=dates, floor=floor, t=t)

From e76f593005d75361ae9c88281bbfe5cd4aa165e2 Mon Sep 17 00:00:00 2001
From: Mathias Leys <mathias.leys@nillion.com>
Date: Tue, 11 Jun 2024 12:41:46 +0100
Subject: [PATCH 04/28] Update module & parameter logic

---
 nada_ai/nn/layers.py            | 12 ++++--------
 nada_ai/nn/module.py            | 10 +++++-----
 nada_ai/nn/parameter.py         |  3 +--
 pyproject.toml                  |  6 +++---
 tests/nada-tests/src/prophet.py | 19 ++++++++++---------
 5 files changed, 23 insertions(+), 27 deletions(-)

diff --git a/nada_ai/nn/layers.py b/nada_ai/nn/layers.py
index 5d87fdd..28f32e1 100644
--- a/nada_ai/nn/layers.py
+++ b/nada_ai/nn/layers.py
@@ -1,13 +1,13 @@
 """NN layers logic"""
 
-from typing import Iterable, Union
+from typing import Sequence, Union
 import numpy as np
 import nada_algebra as na
 from nada_ai.nn.module import Module
 from nada_ai.nn.parameter import Parameter
 from nada_dsl import Integer
 
-_ShapeLike = Union[int, Iterable[int]]
+_ShapeLike = Union[int, Sequence[int]]
 
 
 class Linear(Module):
@@ -193,7 +193,7 @@ def forward(self, x: na.NadaArray) -> na.NadaArray:
             unbatched = True
 
         batch_size, channels, input_height, input_width = x.shape
-        is_rational = x.is_rational
+        pool_type = na.rational if x.is_rational else Integer
 
         if any(pad > 0 for pad in self.padding):
             x = na.pad(
@@ -225,11 +225,7 @@ def forward(self, x: na.NadaArray) -> na.NadaArray:
                         end_w = start_w + self.kernel_size[1]
 
                         pool_region = x[b, c, start_h:end_h, start_w:end_w]
-
-                        if is_rational:
-                            pool_size = na.rational(pool_region.size)
-                        else:
-                            pool_size = Integer(pool_region.size)
+                        pool_size = pool_type(pool_region.size)
 
                         output_tensor[b, c, i, j] = na.sum(pool_region) / pool_size
 
diff --git a/nada_ai/nn/module.py b/nada_ai/nn/module.py
index e248dbb..0a7465e 100644
--- a/nada_ai/nn/module.py
+++ b/nada_ai/nn/module.py
@@ -27,7 +27,7 @@ class Module(ABC):
     """Generic neural network module"""
 
     @abstractmethod
-    def forward(self, x: na.NadaArray, *args, **kwargs) -> na.NadaArray:
+    def forward(self, x: na.NadaArray) -> na.NadaArray:
         """
         Forward pass.
 
@@ -39,7 +39,7 @@ def forward(self, x: na.NadaArray, *args, **kwargs) -> na.NadaArray:
         """
         ...
 
-    def __call__(self, x: na.NadaArray, *args, **kwargs) -> na.NadaArray:
+    def __call__(self, x: na.NadaArray) -> na.NadaArray:
         """
         Proxy for forward pass.
 
@@ -49,7 +49,7 @@ def __call__(self, x: na.NadaArray, *args, **kwargs) -> na.NadaArray:
         Returns:
             na.NadaArray: Output array.
         """
-        return self.forward(x, *args, **kwargs)
+        return self.forward(x)
 
     def __named_parameters(self, prefix: str) -> Iterator[Tuple[str, Parameter]]:
         """
@@ -122,6 +122,6 @@ def load_state_from_network(
             raise NotImplementedError("Loading non-rational state is not supported")
 
         for param_name, param in self.named_parameters():
-            state_name = f"{name}_{param_name}"
-            param_state = na.array(param.shape, party, state_name, nada_type)
+            param_state_name = f"{name}_{param_name}"
+            param_state = na.array(param.shape, party, param_state_name, nada_type)
             param.load_state(param_state)
diff --git a/nada_ai/nn/parameter.py b/nada_ai/nn/parameter.py
index b0c2df8..1d3a7ff 100644
--- a/nada_ai/nn/parameter.py
+++ b/nada_ai/nn/parameter.py
@@ -19,8 +19,7 @@ def __init__(self, shape: _ShapeLike) -> None:
         Args:
             shape (_ShapeLike, optional): Parameter array shape.
         """
-        zeros = na.zeros(shape)
-        super().__init__(inner=zeros.inner)
+        super().__init__(inner=np.empty(shape))
 
     def numel(self) -> int:
         """
diff --git a/pyproject.toml b/pyproject.toml
index 9fb5917..5589266 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -8,7 +8,9 @@ readme = "README.md"
 [tool.poetry.dependencies]
 python = "^3.10"
 numpy = "^1.26.4"
-pandas = "^2.2.2"
+torch = "^2.3.0"
+scikit-learn = "^1.4.2"
+prophet = "^1.1.5"
 nada-dsl = "^0.2.1"
 py-nillion-client = "^0.2.1"
 nada-algebra = "^0.3.2"
@@ -17,8 +19,6 @@ nada-algebra = "^0.3.2"
 [tool.poetry.group.dev.dependencies]
 pytest = "^8.2.0"
 black = "^24.4.2"
-torch = "^2.3.0"
-scikit-learn = "^1.4.2"
 
 [build-system]
 requires = ["poetry-core"]
diff --git a/tests/nada-tests/src/prophet.py b/tests/nada-tests/src/prophet.py
index 6c4686d..b39a54c 100644
--- a/tests/nada-tests/src/prophet.py
+++ b/tests/nada-tests/src/prophet.py
@@ -1,5 +1,5 @@
-import pytest
 from nada_dsl import *
+import numpy as np
 import nada_algebra as na
 from nada_ai.time_series import Prophet
 
@@ -7,20 +7,21 @@
 def nada_main():
     party = Party("party")
 
-    dates = na.array([4], party, "dates", SecretInteger)
-    floor = na.array([4], party, "floor", SecretInteger)
+    dates = np.linspace(1, 10, 4)
+    floor = na.array([4], party, "floor", na.SecretRational)
     t = na.array([4], party, "t", na.SecretRational)
-    trend = na.array([4], party, "floor", na.SecretRational)
 
     prophet = Prophet(
-        growth="linear",
         n_changepoints=2,
-        num_seasonality_features=1,
+        growth="linear",
+        yearly_seasonality=False,
+        weekly_seasonality=True,
+        daily_seasonality=False,
+        seasonality_mode="additive",
     )
 
-    with pytest.raises(NotImplementedError):
-        Prophet(growth="to_the_moon")
+    prophet.load_state_from_network("my_prophet", party, na.SecretRational)
 
-    result = prophet(dates, floor, t, trend)
+    result = prophet(dates, floor, t)
 
     return result.output(party, "forecast")

From bc5eee6ed633cc164f3592affcc027de1a919aea Mon Sep 17 00:00:00 2001
From: Mathias Leys <mathias.leys@nillion.com>
Date: Tue, 11 Jun 2024 12:42:18 +0100
Subject: [PATCH 05/28] Update tests

---
 tests/nada-tests/tests/prophet.yaml | 56 +++++++++++++++++++++++++++++
 1 file changed, 56 insertions(+)
 create mode 100644 tests/nada-tests/tests/prophet.yaml

diff --git a/tests/nada-tests/tests/prophet.yaml b/tests/nada-tests/tests/prophet.yaml
new file mode 100644
index 0000000..b5d6047
--- /dev/null
+++ b/tests/nada-tests/tests/prophet.yaml
@@ -0,0 +1,56 @@
+---
+program: prophet
+inputs:
+  secrets:
+    my_prophet_m_0_0:
+      SecretInteger: "3"
+    my_prophet_beta_0_4:
+      SecretInteger: "3"
+    my_prophet_beta_0_5:
+      SecretInteger: "3"
+    my_prophet_changepoints_t_1:
+      SecretInteger: "3"
+    my_prophet_delta_0_1:
+      SecretInteger: "3"
+    my_prophet_y_scale_0:
+      SecretInteger: "3"
+    floor_2:
+      SecretInteger: "3"
+    my_prophet_delta_0_0:
+      SecretInteger: "3"
+    floor_3:
+      SecretInteger: "3"
+    t_2:
+      SecretInteger: "3"
+    floor_0:
+      SecretInteger: "3"
+    my_prophet_changepoints_t_0:
+      SecretInteger: "3"
+    my_prophet_beta_0_1:
+      SecretInteger: "3"
+    t_1:
+      SecretInteger: "3"
+    my_prophet_beta_0_3:
+      SecretInteger: "3"
+    my_prophet_k_0_0:
+      SecretInteger: "3"
+    my_prophet_beta_0_2:
+      SecretInteger: "3"
+    floor_1:
+      SecretInteger: "3"
+    t_0:
+      SecretInteger: "3"
+    t_3:
+      SecretInteger: "3"
+    my_prophet_beta_0_0:
+      SecretInteger: "3"
+  public_variables: {}
+expected_outputs:
+  forecast_2:
+    SecretInteger: "6"
+  forecast_0:
+    SecretInteger: "6"
+  forecast_3:
+    SecretInteger: "6"
+  forecast_1:
+    SecretInteger: "5"

From 688e0b05b379194a72995ce8bb1d58812f5e6468 Mon Sep 17 00:00:00 2001
From: Mathias Leys <mathias.leys@nillion.com>
Date: Tue, 11 Jun 2024 18:11:20 +0100
Subject: [PATCH 06/28] Update poetry

---
 poetry.lock    | 650 ++++++++++++++++++++++++++++++++++++++++++++++++-
 pyproject.toml |   1 +
 2 files changed, 643 insertions(+), 8 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index e09e6aa..701b24c 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -78,6 +78,28 @@ files = [
 [package.dependencies]
 colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
+[[package]]
+name = "cmdstanpy"
+version = "1.2.3"
+description = "Python interface to CmdStan"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "cmdstanpy-1.2.3-py3-none-any.whl", hash = "sha256:9cd927496462dff5f9f8075fac13f22b370834de93d18387f204bd93418fc08a"},
+    {file = "cmdstanpy-1.2.3.tar.gz", hash = "sha256:4491265f11d2bc8efa3ba0628e36d673ab995f30b168c06ee8e9c297a7ce3569"},
+]
+
+[package.dependencies]
+numpy = ">=1.21"
+pandas = "*"
+stanio = ">=0.4.0,<2.0.0"
+tqdm = "*"
+
+[package.extras]
+all = ["xarray"]
+docs = ["ipykernel", "ipython", "ipywidgets", "matplotlib", "nbsphinx", "pydata-sphinx-theme (<0.9)", "sphinx (>5,<6)", "sphinx-copybutton", "xarray"]
+test = ["flake8", "mypy", "pylint", "pytest", "pytest-cov", "pytest-order", "xarray"]
+
 [[package]]
 name = "colorama"
 version = "0.4.6"
@@ -89,6 +111,84 @@ files = [
     {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
 ]
 
+[[package]]
+name = "contourpy"
+version = "1.2.1"
+description = "Python library for calculating contours of 2D quadrilateral grids"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "contourpy-1.2.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:bd7c23df857d488f418439686d3b10ae2fbf9bc256cd045b37a8c16575ea1040"},
+    {file = "contourpy-1.2.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:5b9eb0ca724a241683c9685a484da9d35c872fd42756574a7cfbf58af26677fd"},
+    {file = "contourpy-1.2.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4c75507d0a55378240f781599c30e7776674dbaf883a46d1c90f37e563453480"},
+    {file = "contourpy-1.2.1-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:11959f0ce4a6f7b76ec578576a0b61a28bdc0696194b6347ba3f1c53827178b9"},
+    {file = "contourpy-1.2.1-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:eb3315a8a236ee19b6df481fc5f997436e8ade24a9f03dfdc6bd490fea20c6da"},
+    {file = "contourpy-1.2.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:39f3ecaf76cd98e802f094e0d4fbc6dc9c45a8d0c4d185f0f6c2234e14e5f75b"},
+    {file = "contourpy-1.2.1-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:94b34f32646ca0414237168d68a9157cb3889f06b096612afdd296003fdd32fd"},
+    {file = "contourpy-1.2.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:457499c79fa84593f22454bbd27670227874cd2ff5d6c84e60575c8b50a69619"},
+    {file = "contourpy-1.2.1-cp310-cp310-win32.whl", hash = "sha256:ac58bdee53cbeba2ecad824fa8159493f0bf3b8ea4e93feb06c9a465d6c87da8"},
+    {file = "contourpy-1.2.1-cp310-cp310-win_amd64.whl", hash = "sha256:9cffe0f850e89d7c0012a1fb8730f75edd4320a0a731ed0c183904fe6ecfc3a9"},
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 version = "1.2.1"
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 name = "fsspec"
 version = "2024.6.0"
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 tqdm = ["tqdm"]
 
+[[package]]
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+version = "0.50"
+description = "Generate and work with holidays in Python"
+optional = false
+python-versions = ">=3.8"
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 version = "2.0.0"
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 testing = ["pytest", "pytest-benchmark"]
 
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+parallel = ["dask[dataframe]", "distributed"]
+
 [[package]]
 name = "py-nillion-client"
 version = "0.2.1"
@@ -654,6 +1200,20 @@ files = [
 [package.extras]
 dev = ["maturin (==1.4.0)", "maturin[zig] (==1.4.0)", "pytest (==8.0.0)", "pyyaml (==6.0.1)"]
 
+[[package]]
+name = "pyparsing"
+version = "3.1.2"
+description = "pyparsing module - Classes and methods to define and execute parsing grammars"
+optional = false
+python-versions = ">=3.6.8"
+files = [
+    {file = "pyparsing-3.1.2-py3-none-any.whl", hash = "sha256:f9db75911801ed778fe61bb643079ff86601aca99fcae6345aa67292038fb742"},
+    {file = "pyparsing-3.1.2.tar.gz", hash = "sha256:a1bac0ce561155ecc3ed78ca94d3c9378656ad4c94c1270de543f621420f94ad"},
+]
+
+[package.extras]
+diagrams = ["jinja2", "railroad-diagrams"]
+
 [[package]]
 name = "pytest"
 version = "8.2.2"
@@ -676,6 +1236,31 @@ tomli = {version = ">=1", markers = "python_version < \"3.11\""}
 [package.extras]
 dev = ["argcomplete", "attrs (>=19.2)", "hypothesis (>=3.56)", "mock", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
 
+[[package]]
+name = "python-dateutil"
+version = "2.9.0.post0"
+description = "Extensions to the standard Python datetime module"
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
+files = [
+    {file = "python-dateutil-2.9.0.post0.tar.gz", hash = "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3"},
+    {file = "python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427"},
+]
+
+[package.dependencies]
+six = ">=1.5"
+
+[[package]]
+name = "pytz"
+version = "2024.1"
+description = "World timezone definitions, modern and historical"
+optional = false
+python-versions = "*"
+files = [
+    {file = "pytz-2024.1-py2.py3-none-any.whl", hash = "sha256:328171f4e3623139da4983451950b28e95ac706e13f3f2630a879749e7a8b319"},
+    {file = "pytz-2024.1.tar.gz", hash = "sha256:2a29735ea9c18baf14b448846bde5a48030ed267578472d8955cd0e7443a9812"},
+]
+
 [[package]]
 name = "richreports"
 version = "0.2.0"
@@ -792,6 +1377,24 @@ files = [
     {file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
 ]
 
+[[package]]
+name = "stanio"
+version = "0.5.0"
+description = "Utilities for preparing Stan inputs and processing Stan outputs"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "stanio-0.5.0-py3-none-any.whl", hash = "sha256:412f8ede127968be51e074be6a6ca1e66d5a8ed50805e2fed89b76868e7332ae"},
+    {file = "stanio-0.5.0.tar.gz", hash = "sha256:dee429314d965f390181afa8ff7ff8144446eeb58cc2547ccc108ba73e6744e2"},
+]
+
+[package.dependencies]
+numpy = "*"
+
+[package.extras]
+test = ["pandas", "pytest", "pytest-cov"]
+ujson = ["ujson (>=5.5.0)"]
+
 [[package]]
 name = "sympy"
 version = "1.12.1"
@@ -895,6 +1498,26 @@ typing-extensions = ">=4.8.0"
 opt-einsum = ["opt-einsum (>=3.3)"]
 optree = ["optree (>=0.9.1)"]
 
+[[package]]
+name = "tqdm"
+version = "4.66.4"
+description = "Fast, Extensible Progress Meter"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "tqdm-4.66.4-py3-none-any.whl", hash = "sha256:b75ca56b413b030bc3f00af51fd2c1a1a5eac6a0c1cca83cbb37a5c52abce644"},
+    {file = "tqdm-4.66.4.tar.gz", hash = "sha256:e4d936c9de8727928f3be6079590e97d9abfe8d39a590be678eb5919ffc186bb"},
+]
+
+[package.dependencies]
+colorama = {version = "*", markers = "platform_system == \"Windows\""}
+
+[package.extras]
+dev = ["pytest (>=6)", "pytest-cov", "pytest-timeout", "pytest-xdist"]
+notebook = ["ipywidgets (>=6)"]
+slack = ["slack-sdk"]
+telegram = ["requests"]
+
 [[package]]
 name = "triton"
 version = "2.3.1"
@@ -920,16 +1543,27 @@ tutorials = ["matplotlib", "pandas", "tabulate", "torch"]
 
 [[package]]
 name = "typing-extensions"
-version = "4.12.1"
+version = "4.12.2"
 description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "typing_extensions-4.12.1-py3-none-any.whl", hash = "sha256:6024b58b69089e5a89c347397254e35f1bf02a907728ec7fee9bf0fe837d203a"},
-    {file = "typing_extensions-4.12.1.tar.gz", hash = "sha256:915f5e35ff76f56588223f15fdd5938f9a1cf9195c0de25130c627e4d597f6d1"},
+    {file = "typing_extensions-4.12.2-py3-none-any.whl", hash = "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d"},
+    {file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
+]
+
+[[package]]
+name = "tzdata"
+version = "2024.1"
+description = "Provider of IANA time zone data"
+optional = false
+python-versions = ">=2"
+files = [
+    {file = "tzdata-2024.1-py2.py3-none-any.whl", hash = "sha256:9068bc196136463f5245e51efda838afa15aaeca9903f49050dfa2679db4d252"},
+    {file = "tzdata-2024.1.tar.gz", hash = "sha256:2674120f8d891909751c38abcdfd386ac0a5a1127954fbc332af6b5ceae07efd"},
 ]
 
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "6e39c59ae056502f6a0512ca45e3f7502694d1c91518b37d223f390baf51e712"
+content-hash = "5fbd199d20ff748b0f4b8b98c3b1b6967824e9bcf2b87300b112f37572412d87"
diff --git a/pyproject.toml b/pyproject.toml
index 5589266..47efe38 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -19,6 +19,7 @@ nada-algebra = "^0.3.2"
 [tool.poetry.group.dev.dependencies]
 pytest = "^8.2.0"
 black = "^24.4.2"
+pandas = "^2.2.2"
 
 [build-system]
 requires = ["poetry-core"]

From a771eed0cbcb6702bca398a7f29b8aadbb3de4a7 Mon Sep 17 00:00:00 2001
From: Mathias Leys <mathias.leys@nillion.com>
Date: Tue, 11 Jun 2024 18:11:49 +0100
Subject: [PATCH 07/28] Update examples

---
 examples/README.md                            |   1 +
 examples/complex_model/network/compute.py     |   8 +-
 examples/neural_net/network/compute.py        |   8 +-
 examples/time_series/nada-project.toml        |   7 +
 examples/time_series/network/compute.py       | 189 +++++++++++++++++
 .../network/helpers/nillion_client_helper.py  |  12 ++
 .../network/helpers/nillion_keypath_helper.py |  10 +
 .../helpers/nillion_payments_helper.py        |  12 ++
 examples/time_series/src/main.py              |  35 ++++
 examples/time_series/tests/time_series.yaml   | 192 ++++++++++++++++++
 10 files changed, 472 insertions(+), 2 deletions(-)
 create mode 100644 examples/time_series/nada-project.toml
 create mode 100644 examples/time_series/network/compute.py
 create mode 100644 examples/time_series/network/helpers/nillion_client_helper.py
 create mode 100644 examples/time_series/network/helpers/nillion_keypath_helper.py
 create mode 100644 examples/time_series/network/helpers/nillion_payments_helper.py
 create mode 100644 examples/time_series/src/main.py
 create mode 100644 examples/time_series/tests/time_series.yaml

diff --git a/examples/README.md b/examples/README.md
index 1ace6fc..74161c1 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -5,6 +5,7 @@ The following are the currently available examples:
 - [Linear Regression](./linear_regression): shows how to run a linear regression using Nada AI
 - [Neural Net](./neural_net): shows how to build & run a simple Feed-Forward Neural Net (both linear layers & activations) using Nada AI
 - [Complex Model](./complex_model): shows how to build more intricate model architectures using Nada AI. Contains convolutions, pooling operations, linear layers and activations
+- [Time Series](./time_series): shows how to run a Facebook Prophet time series forecasting model using Nada AI
 
 The Nada program source code is stored in `src/main.py`.
 
diff --git a/examples/complex_model/network/compute.py b/examples/complex_model/network/compute.py
index 8e53180..61f126d 100644
--- a/examples/complex_model/network/compute.py
+++ b/examples/complex_model/network/compute.py
@@ -189,7 +189,13 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
     )
 
     # Sort & rescale the obtained results by the quantization scale (here: 16)
-    outputs = [result[1] / 2**16 for result in sorted(result.items())]
+    outputs = outputs = [
+        result[1] / 2**16
+        for result in sorted(
+            result.items(),
+            key=lambda x: int(x[0].replace("my_output", "").replace("_", "")),
+        )
+    ]
 
     print(f"🖥️  The result is {outputs}")
 
diff --git a/examples/neural_net/network/compute.py b/examples/neural_net/network/compute.py
index 677682a..0d5c771 100644
--- a/examples/neural_net/network/compute.py
+++ b/examples/neural_net/network/compute.py
@@ -167,7 +167,13 @@ def forward(self, x: na.NadaArray) -> na.NadaArray:
     )
 
     # Sort & rescale the obtained results by the quantization scale (here: 16)
-    outputs = [result[1] / 2**16 for result in sorted(result.items())]
+    outputs = [
+        result[1] / 2**16
+        for result in sorted(
+            result.items(),
+            key=lambda x: int(x[0].replace("my_output", "").replace("_", "")),
+        )
+    ]
 
     print(f"🖥️  The result is {outputs}")
 
diff --git a/examples/time_series/nada-project.toml b/examples/time_series/nada-project.toml
new file mode 100644
index 0000000..15f098a
--- /dev/null
+++ b/examples/time_series/nada-project.toml
@@ -0,0 +1,7 @@
+name = "time_series"
+version = "0.1.0"
+authors = [""]
+
+[[programs]]
+path = "src/main.py"
+prime_size = 128
diff --git a/examples/time_series/network/compute.py b/examples/time_series/network/compute.py
new file mode 100644
index 0000000..ef1c293
--- /dev/null
+++ b/examples/time_series/network/compute.py
@@ -0,0 +1,189 @@
+import asyncio
+import py_nillion_client as nillion
+import os
+import sys
+import time
+import numpy as np
+import nada_algebra as na
+import pandas as pd
+from nada_ai import ProphetClient
+from prophet import Prophet
+from dotenv import load_dotenv
+
+# Add the parent directory to the system path to import modules from it
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "../..")))
+
+# Import helper functions for creating nillion client and getting keys
+from neural_net.network.helpers.nillion_client_helper import create_nillion_client
+from neural_net.network.helpers.nillion_keypath_helper import (
+    getUserKeyFromFile,
+    getNodeKeyFromFile,
+)
+import nada_algebra.client as na_client
+
+# Load environment variables from a .env file
+load_dotenv()
+
+
+# Decorator function to measure and log the execution time of asynchronous functions
+def async_timer(file_path):
+    def decorator(func):
+        async def wrapper(*args, **kwargs):
+            start_time = time.time()
+            result = await func(*args, **kwargs)
+            end_time = time.time()
+            elapsed_time = end_time - start_time
+
+            # Log the execution time to a file
+            with open(file_path, "a") as file:
+                file.write(f"{elapsed_time:.6f},\n")
+            return result
+
+        return wrapper
+
+    return decorator
+
+
+# Asynchronous function to store a program on the nillion client
+@async_timer("bench/store_program.txt")
+async def store_program(client, user_id, cluster_id, program_name, program_mir_path):
+    action_id = await client.store_program(cluster_id, program_name, program_mir_path)
+    program_id = f"{user_id}/{program_name}"
+    print("Stored program. action_id:", action_id)
+    print("Stored program_id:", program_id)
+    return program_id
+
+
+# Asynchronous function to store secrets on the nillion client
+@async_timer("bench/store_secrets.txt")
+async def store_secrets(client, cluster_id, program_id, party_id, party_name, secrets):
+    secret_bindings = nillion.ProgramBindings(program_id)
+    secret_bindings.add_input_party(party_name, party_id)
+
+    # Store the secret for the specified party
+    store_id = await client.store_secrets(cluster_id, secret_bindings, secrets, None)
+    return store_id
+
+
+# Asynchronous function to perform computation on the nillion client
+@async_timer("bench/compute.txt")
+async def compute(
+    client, cluster_id, compute_bindings, store_ids, computation_time_secrets
+):
+    compute_id = await client.compute(
+        cluster_id,
+        compute_bindings,
+        store_ids,
+        computation_time_secrets,
+        nillion.PublicVariables({}),
+    )
+
+    # Monitor and print the computation result
+    print(f"The computation was sent to the network. compute_id: {compute_id}")
+    while True:
+        compute_event = await client.next_compute_event()
+        if isinstance(compute_event, nillion.ComputeFinishedEvent):
+            print(f"✅  Compute complete for compute_id {compute_event.uuid}")
+            return compute_event.result.value
+
+
+# Main asynchronous function to coordinate the process
+async def main():
+    cluster_id = os.getenv("NILLION_CLUSTER_ID")
+    userkey = getUserKeyFromFile(os.getenv("NILLION_USERKEY_PATH_PARTY_1"))
+    nodekey = getNodeKeyFromFile(os.getenv("NILLION_NODEKEY_PATH_PARTY_1"))
+    client = create_nillion_client(userkey, nodekey)
+    party_id = client.party_id
+    user_id = client.user_id
+    party_names = na_client.parties(2)
+    program_name = "main"
+    program_mir_path = f"./target/{program_name}.nada.bin"
+
+    if not os.path.exists("bench"):
+        os.mkdir("bench")
+
+    na.set_log_scale(50)
+
+    # Store the program
+    program_id = await store_program(
+        client, user_id, cluster_id, program_name, program_mir_path
+    )
+
+    # Train prophet model
+    model = Prophet()
+
+    ds = pd.date_range("2024-05-01", "2024-05-17").tolist()
+    y = np.arange(1, 18).tolist()
+
+    fit_model = model.fit(df=pd.DataFrame({"ds": ds, "y": y}))
+
+    print("Model params are:", fit_model.params)
+    print("Number of detected changepoints:", fit_model.n_changepoints)
+
+    # Create and store model secrets via ModelClient
+    model_client = ProphetClient(fit_model)
+    model_secrets = nillion.Secrets(
+        model_client.export_state_as_secrets("my_prophet", na.SecretRational)
+    )
+
+    model_store_id = await store_secrets(
+        client, cluster_id, program_id, party_id, party_names[0], model_secrets
+    )
+
+    # Store inputs to perform inference for
+    future_df = fit_model.make_future_dataframe(periods=3)
+    inference_ds = fit_model.setup_dataframe(future_df.copy())
+
+    my_input = {}
+    my_input.update(
+        na_client.array(inference_ds["floor"].to_numpy(), "floor", na.SecretRational)
+    )
+    my_input.update(
+        na_client.array(inference_ds["t"].to_numpy(), "t", na.SecretRational)
+    )
+
+    input_secrets = nillion.Secrets(my_input)
+
+    data_store_id = await store_secrets(
+        client, cluster_id, program_id, party_id, party_names[1], input_secrets
+    )
+
+    # Set up the compute bindings for the parties
+    compute_bindings = nillion.ProgramBindings(program_id)
+    [
+        compute_bindings.add_input_party(party_name, party_id)
+        for party_name in party_names
+    ]
+    compute_bindings.add_output_party(party_names[1], party_id)
+
+    print(f"Computing using program {program_id}")
+    print(f"Use secret store_id: {model_store_id} {data_store_id}")
+
+    # Perform the computation and return the result
+    result = await compute(
+        client,
+        cluster_id,
+        compute_bindings,
+        [model_store_id, data_store_id],
+        nillion.Secrets({}),
+    )
+
+    # Sort & rescale the obtained results by the quantization scale
+    outputs = [
+        result[1] / 2**50
+        for result in sorted(
+            result.items(),
+            key=lambda x: int(x[0].replace("my_output", "").replace("_", "")),
+        )
+    ]
+
+    print(f"🖥️  The result is {outputs}")
+
+    expected = fit_model.predict(inference_ds)["yhat"].to_numpy()
+    print(f"🖥️  VS expected plain-text result {expected}")
+    return result
+
+
+# Run the main function if the script is executed directly
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/examples/time_series/network/helpers/nillion_client_helper.py b/examples/time_series/network/helpers/nillion_client_helper.py
new file mode 100644
index 0000000..5247aee
--- /dev/null
+++ b/examples/time_series/network/helpers/nillion_client_helper.py
@@ -0,0 +1,12 @@
+import os
+import py_nillion_client as nillion
+from helpers.nillion_payments_helper import create_payments_config
+
+
+def create_nillion_client(userkey, nodekey):
+    bootnodes = [os.getenv("NILLION_BOOTNODE_MULTIADDRESS")]
+    payments_config = create_payments_config()
+
+    return nillion.NillionClient(
+        nodekey, bootnodes, nillion.ConnectionMode.relay(), userkey, payments_config
+    )
diff --git a/examples/time_series/network/helpers/nillion_keypath_helper.py b/examples/time_series/network/helpers/nillion_keypath_helper.py
new file mode 100644
index 0000000..4a45413
--- /dev/null
+++ b/examples/time_series/network/helpers/nillion_keypath_helper.py
@@ -0,0 +1,10 @@
+import os
+import py_nillion_client as nillion
+
+
+def getUserKeyFromFile(userkey_filepath):
+    return nillion.UserKey.from_file(userkey_filepath)
+
+
+def getNodeKeyFromFile(nodekey_filepath):
+    return nillion.NodeKey.from_file(nodekey_filepath)
diff --git a/examples/time_series/network/helpers/nillion_payments_helper.py b/examples/time_series/network/helpers/nillion_payments_helper.py
new file mode 100644
index 0000000..f68b33c
--- /dev/null
+++ b/examples/time_series/network/helpers/nillion_payments_helper.py
@@ -0,0 +1,12 @@
+import os
+import py_nillion_client as nillion
+
+
+def create_payments_config():
+    return nillion.PaymentsConfig(
+        os.getenv("NILLION_BLOCKCHAIN_RPC_ENDPOINT"),
+        os.getenv("NILLION_WALLET_PRIVATE_KEY"),
+        int(os.getenv("NILLION_CHAIN_ID")),
+        os.getenv("NILLION_PAYMENTS_SC_ADDRESS"),
+        os.getenv("NILLION_BLINDING_FACTORS_MANAGER_SC_ADDRESS"),
+    )
diff --git a/examples/time_series/src/main.py b/examples/time_series/src/main.py
new file mode 100644
index 0000000..b0347c4
--- /dev/null
+++ b/examples/time_series/src/main.py
@@ -0,0 +1,35 @@
+import nada_algebra as na
+import numpy as np
+from nada_ai.time_series import Prophet
+
+
+def nada_main():
+    na.set_log_scale(50)
+
+    # Step 1: We use Nada Algebra wrapper to create "Party0" and "Party1"
+    parties = na.parties(2)
+
+    # Step 2: Instantiate model object
+    my_prophet = Prophet(
+        n_changepoints=12,  # NOTE: this is a learned hyperparameter
+        yearly_seasonality=False,
+        weekly_seasonality=True,
+        daily_seasonality=False,
+    )
+
+    # Step 3: Load model weights from Nillion network by passing model name (acts as ID)
+    # In this examples Party0 provides the model and Party1 runs inference
+    my_prophet.load_state_from_network("my_prophet", parties[0], na.SecretRational)
+
+    # Step 4: Load input data to be used for inference (provided by Party1)
+    dates = np.arange(np.datetime64("2024-05-01"), np.datetime64("2024-05-21"))
+
+    floor = na.array((20,), parties[1], "floor", na.SecretRational)
+    t = na.array((20,), parties[1], "t", na.SecretRational)
+
+    # Step 5: Compute inference
+    # Note: completely equivalent to `my_model.forward(...)` or `model.predict(...)`
+    result = my_prophet(dates, floor, t)
+
+    # Step 6: We can use result.output() to produce the output for Party1 and variable name "my_output"
+    return result.output(parties[1], "my_output")
diff --git a/examples/time_series/tests/time_series.yaml b/examples/time_series/tests/time_series.yaml
new file mode 100644
index 0000000..7497d2b
--- /dev/null
+++ b/examples/time_series/tests/time_series.yaml
@@ -0,0 +1,192 @@
+---
+program: main
+inputs:
+  secrets:
+    my_prophet_changepoints_t_8:
+      SecretInteger: "3"
+    my_prophet_changepoints_t_11:
+      SecretInteger: "3"
+    floor_17:
+      SecretInteger: "3"
+    floor_4:
+      SecretInteger: "3"
+    my_prophet_beta_0_3:
+      SecretInteger: "3"
+    t_7:
+      SecretInteger: "3"
+    t_3:
+      SecretInteger: "3"
+    my_prophet_beta_0_0:
+      SecretInteger: "3"
+    t_1:
+      SecretInteger: "3"
+    floor_5:
+      SecretInteger: "3"
+    my_prophet_changepoints_t_6:
+      SecretInteger: "3"
+    floor_16:
+      SecretInteger: "3"
+    my_prophet_y_scale_0:
+      SecretInteger: "3"
+    floor_2:
+      SecretInteger: "3"
+    floor_19:
+      SecretInteger: "3"
+    my_prophet_beta_0_5:
+      SecretInteger: "3"
+    my_prophet_changepoints_t_7:
+      SecretInteger: "3"
+    my_prophet_delta_0_5:
+      SecretInteger: "3"
+    t_8:
+      SecretInteger: "3"
+    floor_18:
+      SecretInteger: "3"
+    my_prophet_beta_0_2:
+      SecretInteger: "3"
+    floor_10:
+      SecretInteger: "3"
+    t_12:
+      SecretInteger: "3"
+    my_prophet_delta_0_4:
+      SecretInteger: "3"
+    t_14:
+      SecretInteger: "3"
+    t_16:
+      SecretInteger: "3"
+    t_2:
+      SecretInteger: "3"
+    t_5:
+      SecretInteger: "3"
+    floor_11:
+      SecretInteger: "3"
+    my_prophet_delta_0_2:
+      SecretInteger: "3"
+    my_prophet_delta_0_7:
+      SecretInteger: "3"
+    my_prophet_k_0_0:
+      SecretInteger: "3"
+    my_prophet_changepoints_t_1:
+      SecretInteger: "3"
+    t_13:
+      SecretInteger: "3"
+    t_18:
+      SecretInteger: "3"
+    my_prophet_delta_0_0:
+      SecretInteger: "3"
+    my_prophet_changepoints_t_9:
+      SecretInteger: "3"
+    floor_13:
+      SecretInteger: "3"
+    t_15:
+      SecretInteger: "3"
+    t_17:
+      SecretInteger: "3"
+    my_prophet_delta_0_6:
+      SecretInteger: "3"
+    t_10:
+      SecretInteger: "3"
+    my_prophet_changepoints_t_10:
+      SecretInteger: "3"
+    my_prophet_beta_0_4:
+      SecretInteger: "3"
+    floor_3:
+      SecretInteger: "3"
+    my_prophet_changepoints_t_2:
+      SecretInteger: "3"
+    t_9:
+      SecretInteger: "3"
+    floor_9:
+      SecretInteger: "3"
+    t_11:
+      SecretInteger: "3"
+    my_prophet_delta_0_3:
+      SecretInteger: "3"
+    my_prophet_delta_0_10:
+      SecretInteger: "3"
+    my_prophet_changepoints_t_3:
+      SecretInteger: "3"
+    floor_1:
+      SecretInteger: "3"
+    my_prophet_delta_0_8:
+      SecretInteger: "3"
+    t_19:
+      SecretInteger: "3"
+    floor_7:
+      SecretInteger: "3"
+    floor_0:
+      SecretInteger: "3"
+    floor_14:
+      SecretInteger: "3"
+    floor_8:
+      SecretInteger: "3"
+    my_prophet_changepoints_t_4:
+      SecretInteger: "3"
+    floor_12:
+      SecretInteger: "3"
+    my_prophet_changepoints_t_0:
+      SecretInteger: "3"
+    my_prophet_delta_0_9:
+      SecretInteger: "3"
+    my_prophet_m_0_0:
+      SecretInteger: "3"
+    my_prophet_changepoints_t_5:
+      SecretInteger: "3"
+    t_6:
+      SecretInteger: "3"
+    my_prophet_beta_0_1:
+      SecretInteger: "3"
+    t_0:
+      SecretInteger: "3"
+    floor_15:
+      SecretInteger: "3"
+    t_4:
+      SecretInteger: "3"
+    my_prophet_delta_0_11:
+      SecretInteger: "3"
+    my_prophet_delta_0_1:
+      SecretInteger: "3"
+    floor_6:
+      SecretInteger: "3"
+  public_variables: {}
+expected_outputs:
+  my_output_1:
+    SecretInteger: "3"
+  my_output_16:
+    SecretInteger: "3"
+  my_output_17:
+    SecretInteger: "3"
+  my_output_14:
+    SecretInteger: "3"
+  my_output_19:
+    SecretInteger: "3"
+  my_output_3:
+    SecretInteger: "3"
+  my_output_2:
+    SecretInteger: "3"
+  my_output_0:
+    SecretInteger: "3"
+  my_output_11:
+    SecretInteger: "3"
+  my_output_10:
+    SecretInteger: "3"
+  my_output_5:
+    SecretInteger: "3"
+  my_output_6:
+    SecretInteger: "3"
+  my_output_7:
+    SecretInteger: "3"
+  my_output_12:
+    SecretInteger: "3"
+  my_output_9:
+    SecretInteger: "3"
+  my_output_4:
+    SecretInteger: "3"
+  my_output_8:
+    SecretInteger: "3"
+  my_output_15:
+    SecretInteger: "3"
+  my_output_18:
+    SecretInteger: "3"
+  my_output_13:
+    SecretInteger: "3"

From eee449a5a513bca541c2ac521d28b54d448ecb5e Mon Sep 17 00:00:00 2001
From: Mathias Leys <mathias.leys@nillion.com>
Date: Tue, 11 Jun 2024 18:12:06 +0100
Subject: [PATCH 08/28] Update tests

---
 tests/test_all_nada.py | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/tests/test_all_nada.py b/tests/test_all_nada.py
index 7db2bbb..a20fcf9 100644
--- a/tests/test_all_nada.py
+++ b/tests/test_all_nada.py
@@ -13,9 +13,15 @@
     "pool",
     "linear_regression",
     "end-to-end",
+    "prophet",
 ]
 
-EXAMPLES = []
+EXAMPLES = [
+    "complex_model",
+    "linear_regression",
+    "neural_net",
+    # "time_series",
+]
 
 TESTS = [("tests/nada-tests/", test) for test in TESTS] + [
     ("examples/" + test, test) for test in EXAMPLES

From 50e08539b2c1c076ef6ed4e3dcba86b38cfacbe5 Mon Sep 17 00:00:00 2001
From: Mathias Leys <mathias.leys@nillion.com>
Date: Tue, 11 Jun 2024 18:12:14 +0100
Subject: [PATCH 09/28] Fix prophet bug

---
 nada_ai/time_series/prophet.py | 44 ++++++++++++++++++++++++++++------
 1 file changed, 37 insertions(+), 7 deletions(-)

diff --git a/nada_ai/time_series/prophet.py b/nada_ai/time_series/prophet.py
index 1f2ed71..8b43169 100644
--- a/nada_ai/time_series/prophet.py
+++ b/nada_ai/time_series/prophet.py
@@ -102,7 +102,7 @@ def predict_seasonal_comps(
                 if features is None:
                     continue
 
-                comp = (features @ beta.T) * self.y_scale
+                comp = features @ beta.T
                 components.append(comp)
 
             if len(components) == 0:
@@ -110,12 +110,13 @@ def predict_seasonal_comps(
             else:
                 seasonal_components[mode] = na.NadaArray(np.array(components))
 
-        additive_component = (seasonal_components["additive"] * self.y_scale).sum(
-            axis=0
+        additive_component = seasonal_components["additive"] * self.y_scale
+        additive_component = additive_component.sum(axis=0)
+
+        multiplicative_component = -seasonal_components["multiplicative"] + na.rational(
+            1
         )
-        multiplicative_component = (
-            -seasonal_components["multiplicative"] + na.rational(1)
-        ).prod(axis=0)
+        multiplicative_component = multiplicative_component.prod(axis=0)
 
         return additive_component, multiplicative_component
 
@@ -180,7 +181,9 @@ def predict_trend(self, floor: na.NadaArray, t: na.NadaArray) -> na.NadaArray:
     def predict(
         self,
         dates: np.ndarray,
+        # TODO: often all zero - opportunity to compress
         floor: na.NadaArray,
+        # TODO: can be deterministically generated from len(horizon)
         t: na.NadaArray,
     ) -> na.NadaArray:
         """
@@ -194,11 +197,38 @@ def predict(
         Returns:
             na.NadaArray: Forecasted values.
         """
+        assert len(dates) == len(
+            floor
+        ), "Provided Prophet inputs must be equally sized."
+        assert len(floor) == len(t), "Provided Prophet inputs must be equally sized."
+
+        dates = self.ensure_numeric_dates(dates)
         trend = self.predict_trend(floor, t)
         additive_comps, multiplicative_comps = self.predict_seasonal_comps(dates)
-        yhat = trend * (multiplicative_comps + na.rational(1)) + additive_comps
+        yhat = trend * multiplicative_comps + additive_comps
         return yhat
 
+    def ensure_numeric_dates(self, dates: np.ndarray) -> np.ndarray:
+        """
+        Ensures an array of dates is of the correct data format.
+
+        Args:
+            dates (np.ndarray): Data array.
+
+        Raises:
+            TypeError: Raised when dates array of incompatible type is passed.
+
+        Returns:
+            np.ndarray: Standardized dates.
+        """
+        if isinstance(dates.dtype, np.floating):
+            return dates
+        if np.issubdtype(dates.dtype, np.datetime64):
+            return dates.astype(np.float64)
+        raise TypeError(
+            f"Could not convert dates array of type `{dates}` to a NumPy array of numerics."
+        )
+
     @override
     def __call__(
         self,

From 8e43b3031c236ca02e24faa2d2bbe9bea3b5d1a7 Mon Sep 17 00:00:00 2001
From: Mathias Leys <mathias.leys@nillion.com>
Date: Tue, 11 Jun 2024 18:22:54 +0100
Subject: [PATCH 10/28] Fix type checking

---
 nada_ai/time_series/prophet.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/nada_ai/time_series/prophet.py b/nada_ai/time_series/prophet.py
index 8b43169..38d4e54 100644
--- a/nada_ai/time_series/prophet.py
+++ b/nada_ai/time_series/prophet.py
@@ -221,7 +221,7 @@ def ensure_numeric_dates(self, dates: np.ndarray) -> np.ndarray:
         Returns:
             np.ndarray: Standardized dates.
         """
-        if isinstance(dates.dtype, np.floating):
+        if np.issubdtype(dates.dtype, (np.integer, np.floating)):
             return dates
         if np.issubdtype(dates.dtype, np.datetime64):
             return dates.astype(np.float64)

From 1ef414595622c8775ad6beb8a1292c292af46dcb Mon Sep 17 00:00:00 2001
From: Mathias Leys <mathias.leys@nillion.com>
Date: Tue, 11 Jun 2024 18:48:55 +0100
Subject: [PATCH 11/28] Refactoring

---
 examples/README.md                                     |  2 +-
 examples/complex_model/nada-project.toml               |  2 +-
 .../complex_model/src/{main.py => complex_model.py}    |  0
 examples/complex_model/tests/complex_model.yaml        |  2 +-
 examples/linear_regression/nada-project.toml           |  2 +-
 .../src/{main.py => linear_regression.py}              |  0
 .../linear_regression/tests/linear_regression.yaml     |  2 +-
 examples/neural_net/nada-project.toml                  |  4 ++--
 examples/neural_net/src/{main.py => neural_net.py}     |  0
 examples/neural_net/tests/neural_net.yaml              |  2 +-
 examples/time_series/nada-project.toml                 |  2 +-
 examples/time_series/src/{main.py => time_series.py}   |  0
 examples/time_series/tests/time_series.yaml            |  2 +-
 nada_ai/client.py                                      |  2 +-
 nada_ai/time_series/prophet.py                         | 10 +++++-----
 tests/python-tests/test_nn.py                          | 10 ----------
 16 files changed, 16 insertions(+), 26 deletions(-)
 rename examples/complex_model/src/{main.py => complex_model.py} (100%)
 rename examples/linear_regression/src/{main.py => linear_regression.py} (100%)
 rename examples/neural_net/src/{main.py => neural_net.py} (100%)
 rename examples/time_series/src/{main.py => time_series.py} (100%)

diff --git a/examples/README.md b/examples/README.md
index 74161c1..39f1b1d 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -7,6 +7,6 @@ The following are the currently available examples:
 - [Complex Model](./complex_model): shows how to build more intricate model architectures using Nada AI. Contains convolutions, pooling operations, linear layers and activations
 - [Time Series](./time_series): shows how to run a Facebook Prophet time series forecasting model using Nada AI
 
-The Nada program source code is stored in `src/main.py`.
+The Nada program source code is stored in `src/<EXAMPLE_NAME>.py`.
 
 In order to follow the end-to-end example, head to `network/compute.py`. You can run it by simply running `nada build` to build the Nada program followed by `python network/compute.py`.
diff --git a/examples/complex_model/nada-project.toml b/examples/complex_model/nada-project.toml
index aaa7360..0c95e19 100644
--- a/examples/complex_model/nada-project.toml
+++ b/examples/complex_model/nada-project.toml
@@ -3,5 +3,5 @@ version = "0.1.0"
 authors = [""]
 
 [[programs]]
-path = "src/main.py"
+path = "src/complex_model.py"
 prime_size = 128
diff --git a/examples/complex_model/src/main.py b/examples/complex_model/src/complex_model.py
similarity index 100%
rename from examples/complex_model/src/main.py
rename to examples/complex_model/src/complex_model.py
diff --git a/examples/complex_model/tests/complex_model.yaml b/examples/complex_model/tests/complex_model.yaml
index 43e1974..9a30a2c 100644
--- a/examples/complex_model/tests/complex_model.yaml
+++ b/examples/complex_model/tests/complex_model.yaml
@@ -1,5 +1,5 @@
 ---
-program: main
+program: complex_model
 inputs:
   secrets:
     # We assume all values were originally floats, scaled & rounded by a factor of 2**16
diff --git a/examples/linear_regression/nada-project.toml b/examples/linear_regression/nada-project.toml
index c64354d..3b69c79 100644
--- a/examples/linear_regression/nada-project.toml
+++ b/examples/linear_regression/nada-project.toml
@@ -3,5 +3,5 @@ version = "0.1.0"
 authors = [""]
 
 [[programs]]
-path = "src/main.py"
+path = "src/linear_regression.py"
 prime_size = 128
diff --git a/examples/linear_regression/src/main.py b/examples/linear_regression/src/linear_regression.py
similarity index 100%
rename from examples/linear_regression/src/main.py
rename to examples/linear_regression/src/linear_regression.py
diff --git a/examples/linear_regression/tests/linear_regression.yaml b/examples/linear_regression/tests/linear_regression.yaml
index 6e20d1b..cf8c05d 100644
--- a/examples/linear_regression/tests/linear_regression.yaml
+++ b/examples/linear_regression/tests/linear_regression.yaml
@@ -1,5 +1,5 @@
 ---
-program: main
+program: linear_regression
 inputs:
   secrets:
     # We assume all values were originally floats, scaled & rounded by a factor of 2**16
diff --git a/examples/neural_net/nada-project.toml b/examples/neural_net/nada-project.toml
index aaa7360..1ac31f6 100644
--- a/examples/neural_net/nada-project.toml
+++ b/examples/neural_net/nada-project.toml
@@ -1,7 +1,7 @@
-name = "complex_model"
+name = "neural_net"
 version = "0.1.0"
 authors = [""]
 
 [[programs]]
-path = "src/main.py"
+path = "src/neural_net.py"
 prime_size = 128
diff --git a/examples/neural_net/src/main.py b/examples/neural_net/src/neural_net.py
similarity index 100%
rename from examples/neural_net/src/main.py
rename to examples/neural_net/src/neural_net.py
diff --git a/examples/neural_net/tests/neural_net.yaml b/examples/neural_net/tests/neural_net.yaml
index 7fec66a..bb15100 100644
--- a/examples/neural_net/tests/neural_net.yaml
+++ b/examples/neural_net/tests/neural_net.yaml
@@ -1,5 +1,5 @@
 ---
-program: main
+program: neural_net
 inputs:
   secrets:
     # We assume all values were originally floats, scaled & rounded by a factor of 2**16
diff --git a/examples/time_series/nada-project.toml b/examples/time_series/nada-project.toml
index 15f098a..b8f6a35 100644
--- a/examples/time_series/nada-project.toml
+++ b/examples/time_series/nada-project.toml
@@ -3,5 +3,5 @@ version = "0.1.0"
 authors = [""]
 
 [[programs]]
-path = "src/main.py"
+path = "src/time_series.py"
 prime_size = 128
diff --git a/examples/time_series/src/main.py b/examples/time_series/src/time_series.py
similarity index 100%
rename from examples/time_series/src/main.py
rename to examples/time_series/src/time_series.py
diff --git a/examples/time_series/tests/time_series.yaml b/examples/time_series/tests/time_series.yaml
index 7497d2b..b0e2b98 100644
--- a/examples/time_series/tests/time_series.yaml
+++ b/examples/time_series/tests/time_series.yaml
@@ -1,5 +1,5 @@
 ---
-program: main
+program: time_series
 inputs:
   secrets:
     my_prophet_changepoints_t_8:
diff --git a/nada_ai/client.py b/nada_ai/client.py
index 50eff14..a7bc824 100644
--- a/nada_ai/client.py
+++ b/nada_ai/client.py
@@ -159,7 +159,7 @@ def __init__(self, model: sklearn.base.BaseEstimator) -> None:
 class ProphetClient(ModelClient):
     """ModelClient for Prophet models"""
 
-    def __init__(self, model) -> None:
+    def __init__(self, model: "prophet.forecaster.Prophet") -> None:
         """
         Client initialization.
 
diff --git a/nada_ai/time_series/prophet.py b/nada_ai/time_series/prophet.py
index 38d4e54..7110a46 100644
--- a/nada_ai/time_series/prophet.py
+++ b/nada_ai/time_series/prophet.py
@@ -197,10 +197,8 @@ def predict(
         Returns:
             na.NadaArray: Forecasted values.
         """
-        assert len(dates) == len(
-            floor
-        ), "Provided Prophet inputs must be equally sized."
-        assert len(floor) == len(t), "Provided Prophet inputs must be equally sized."
+        assert len(dates) == len(floor), "Prophet inputs must be equally sized."
+        assert len(floor) == len(t), "Prophet inputs must be equally sized."
 
         dates = self.ensure_numeric_dates(dates)
         trend = self.predict_trend(floor, t)
@@ -221,7 +219,9 @@ def ensure_numeric_dates(self, dates: np.ndarray) -> np.ndarray:
         Returns:
             np.ndarray: Standardized dates.
         """
-        if np.issubdtype(dates.dtype, (np.integer, np.floating)):
+        if np.issubdtype(dates.dtype, np.integer) or np.issubdtype(
+            dates.dtype, np.floating
+        ):
             return dates
         if np.issubdtype(dates.dtype, np.datetime64):
             return dates.astype(np.float64)
diff --git a/tests/python-tests/test_nn.py b/tests/python-tests/test_nn.py
index 92e056b..af18282 100644
--- a/tests/python-tests/test_nn.py
+++ b/tests/python-tests/test_nn.py
@@ -79,8 +79,6 @@ def forward(x: na.NadaArray) -> na.NadaArray: ...
     def test_parameters_5(self):
         param = Parameter((2, 3))
 
-        assert param[0][0] == Integer(0)
-
         alphas = na.alphas((2, 3), alpha=Integer(42))
         param.load_state(alphas)
 
@@ -96,9 +94,6 @@ def forward(x: na.NadaArray) -> na.NadaArray: ...
 
         mod = TestModule()
 
-        for _, param in mod.named_parameters():
-            assert param[0][0] == Integer(0)
-
         alphas = na.alphas((2, 3), alpha=Integer(42))
 
         for _, param in mod.named_parameters():
@@ -124,9 +119,6 @@ def forward(x: na.NadaArray) -> na.NadaArray: ...
 
         mod = TestModule2()
 
-        for _, param in mod.named_parameters():
-            assert param[0][0] == Integer(0)
-
         alphas = na.alphas((2, 3), alpha=Integer(42))
 
         for _, param in mod.named_parameters():
@@ -138,8 +130,6 @@ def forward(x: na.NadaArray) -> na.NadaArray: ...
     def test_parameters_8(self):
         param = Parameter((2, 3))
 
-        assert param[0][0] == Integer(0)
-
         alphas = na.alphas((3, 3), alpha=Integer(42))
 
         with pytest.raises(MismatchedShapesException):

From 5b73d516848b82255b41e277d3915ab2cdf36758 Mon Sep 17 00:00:00 2001
From: Mathias Leys <mathias.leys@nillion.com>
Date: Tue, 11 Jun 2024 22:30:29 +0100
Subject: [PATCH 12/28] Fix multiplicative seasonality

---
 nada_ai/time_series/prophet.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/nada_ai/time_series/prophet.py b/nada_ai/time_series/prophet.py
index 7110a46..0cf7cf6 100644
--- a/nada_ai/time_series/prophet.py
+++ b/nada_ai/time_series/prophet.py
@@ -113,7 +113,7 @@ def predict_seasonal_comps(
         additive_component = seasonal_components["additive"] * self.y_scale
         additive_component = additive_component.sum(axis=0)
 
-        multiplicative_component = -seasonal_components["multiplicative"] + na.rational(
+        multiplicative_component = seasonal_components["multiplicative"] + na.rational(
             1
         )
         multiplicative_component = multiplicative_component.prod(axis=0)

From 56add95ebd505ddb9aba2e02fe8ec459eabf67c9 Mon Sep 17 00:00:00 2001
From: Mathias Leys <mathias.leys@nillion.com>
Date: Tue, 11 Jun 2024 22:32:04 +0100
Subject: [PATCH 13/28] fix flat trend

---
 nada_ai/time_series/prophet.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/nada_ai/time_series/prophet.py b/nada_ai/time_series/prophet.py
index 0cf7cf6..7c5317c 100644
--- a/nada_ai/time_series/prophet.py
+++ b/nada_ai/time_series/prophet.py
@@ -172,7 +172,7 @@ def predict_trend(self, floor: na.NadaArray, t: na.NadaArray) -> na.NadaArray:
             m_t = (-self.changepoints_t * deltas_t).sum(axis=1) + m
             trend = k_t * t + m_t
         elif self.growth == "flat":
-            trend = na.ones_like(t, na.rational) * m
+            trend = na.ones_like(t, na.Rational) * m
         else:
             raise NotImplementedError(self.growth + " is not supported")
 

From 83d72068175066e31288da4366ba2d75da4aa8c0 Mon Sep 17 00:00:00 2001
From: Mathias Leys <mathias.leys@nillion.com>
Date: Tue, 11 Jun 2024 22:35:56 +0100
Subject: [PATCH 14/28] Variable naming

---
 nada_ai/time_series/prophet.py | 12 +++++-------
 1 file changed, 5 insertions(+), 7 deletions(-)

diff --git a/nada_ai/time_series/prophet.py b/nada_ai/time_series/prophet.py
index 7c5317c..58e5389 100644
--- a/nada_ai/time_series/prophet.py
+++ b/nada_ai/time_series/prophet.py
@@ -110,15 +110,13 @@ def predict_seasonal_comps(
             else:
                 seasonal_components[mode] = na.NadaArray(np.array(components))
 
-        additive_component = seasonal_components["additive"] * self.y_scale
-        additive_component = additive_component.sum(axis=0)
+        add_component = seasonal_components["additive"] * self.y_scale
+        add_component = add_component.sum(axis=0)
 
-        multiplicative_component = seasonal_components["multiplicative"] + na.rational(
-            1
-        )
-        multiplicative_component = multiplicative_component.prod(axis=0)
+        mult_component = seasonal_components["multiplicative"] + na.rational(1)
+        mult_component = mult_component.prod(axis=0)
 
-        return additive_component, multiplicative_component
+        return add_component, mult_component
 
     def make_seasonality_features(
         self, dates: np.ndarray, seasonalities: Dict[str, Dict[str, int | float]]

From 6726a7aac937ab419e1bb9a89b8968ec488bcf6a Mon Sep 17 00:00:00 2001
From: Mathias Leys <mathias.leys@nillion.com>
Date: Wed, 12 Jun 2024 09:39:45 +0100
Subject: [PATCH 15/28] Refactor fourier

---
 nada_ai/time_series/prophet.py | 20 +++++++++++++++-----
 1 file changed, 15 insertions(+), 5 deletions(-)

diff --git a/nada_ai/time_series/prophet.py b/nada_ai/time_series/prophet.py
index 58e5389..1c95963 100644
--- a/nada_ai/time_series/prophet.py
+++ b/nada_ai/time_series/prophet.py
@@ -57,11 +57,7 @@ def __init__(
                 "fourier_order": 4,
             }
 
-        num_fourier = 0
-        for _, mode_seasonality in self.seasonalities.items():
-            for _, period_seasonality in mode_seasonality.items():
-                # NOTE: times two because there is always a term for both sin and cos
-                num_fourier += period_seasonality["fourier_order"] * 2
+        num_fourier = self._num_fourier_terms()
 
         M = 1  # NOTE: MAP estimation is assumed, so M=1 guaranteed
 
@@ -76,6 +72,20 @@ def __init__(
         self.changepoints_t = Parameter(n_changepoints)
         self.y_scale = Parameter(1)
 
+    def _num_fourier_terms(self) -> int:
+        """
+        Calculates the number of Fourier terms.
+
+        Returns:
+            int: Number of Fourier terms.
+        """
+        num_fourier = 0
+        for _, mode_seasonality in self.seasonalities.items():
+            for _, period_seasonality in mode_seasonality.items():
+                # NOTE: times two because there is always a term for both sin and cos
+                num_fourier += period_seasonality["fourier_order"] * 2
+        return num_fourier
+
     def predict_seasonal_comps(
         self, dates: np.ndarray
     ) -> Tuple[na.NadaArray, na.NadaArray]:

From efab6815d2704a9d8a0313def071b28a51174c3d Mon Sep 17 00:00:00 2001
From: Mathias Leys <mathias.leys@nillion.com>
Date: Wed, 12 Jun 2024 10:00:43 +0100
Subject: [PATCH 16/28] Fix formatting

---
 nada_ai/time_series/prophet.py | 24 +++++++++++++-----------
 1 file changed, 13 insertions(+), 11 deletions(-)

diff --git a/nada_ai/time_series/prophet.py b/nada_ai/time_series/prophet.py
index 1c95963..b0ad5ec 100644
--- a/nada_ai/time_series/prophet.py
+++ b/nada_ai/time_series/prophet.py
@@ -115,10 +115,11 @@ def predict_seasonal_comps(
                 comp = features @ beta.T
                 components.append(comp)
 
-            if len(components) == 0:
-                seasonal_components[mode] = na.zeros(dates.shape, na.Rational)
-            else:
-                seasonal_components[mode] = na.NadaArray(np.array(components))
+            seasonal_components[mode] = (
+                na.NadaArray(np.array(components))
+                if len(components) != 0
+                else na.zeros(dates.shape, na.Rational)
+            )
 
         add_component = seasonal_components["additive"] * self.y_scale
         add_component = add_component.sum(axis=0)
@@ -144,11 +145,11 @@ def make_seasonality_features(
         features = {}
         for name, props in seasonalities.items():
             period, fourier_order = props["period"], props["fourier_order"]
-            if fourier_order == 0:
-                feats = None
-            else:
+            feats = None
+            if fourier_order != 0:
                 feats = fourier_series(dates, period, fourier_order)
-            features[name] = na.frompyfunc(na.rational, 1, 1)(feats)
+                feats = na.frompyfunc(na.rational, 1, 1)(feats)
+            features[name] = feats
         return features
 
     def predict_trend(self, floor: na.NadaArray, t: na.NadaArray) -> na.NadaArray:
@@ -172,9 +173,10 @@ def predict_trend(self, floor: na.NadaArray, t: na.NadaArray) -> na.NadaArray:
         [delta] = self.delta
 
         if self.growth == "linear":
-            mask = na.frompyfunc(
-                lambda a, b: (a <= b).if_else(na.rational(1), na.rational(0)), 2, 1
-            )(self.changepoints_t[None, :], t[..., None])
+            le_fn = lambda a, b: (a <= b).if_else(na.rational(1), na.rational(0))
+            mask = na.frompyfunc(le_fn, 2, 1)(
+                self.changepoints_t[None, :], t[..., None]
+            )
             deltas_t = delta * mask
             k_t = deltas_t.sum(axis=1) + k
             m_t = (-self.changepoints_t * deltas_t).sum(axis=1) + m

From c60da035f8ac7bd0b23f3fd7d2cc072b487ff77a Mon Sep 17 00:00:00 2001
From: Mathias Leys <mathias.leys@nillion.com>
Date: Wed, 12 Jun 2024 10:03:30 +0100
Subject: [PATCH 17/28] Fix formatting

---
 nada_ai/time_series/prophet.py | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/nada_ai/time_series/prophet.py b/nada_ai/time_series/prophet.py
index b0ad5ec..991a93c 100644
--- a/nada_ai/time_series/prophet.py
+++ b/nada_ai/time_series/prophet.py
@@ -173,10 +173,9 @@ def predict_trend(self, floor: na.NadaArray, t: na.NadaArray) -> na.NadaArray:
         [delta] = self.delta
 
         if self.growth == "linear":
-            le_fn = lambda a, b: (a <= b).if_else(na.rational(1), na.rational(0))
-            mask = na.frompyfunc(le_fn, 2, 1)(
-                self.changepoints_t[None, :], t[..., None]
-            )
+            less_than_fn = lambda a, b: (a <= b).if_else(na.rational(1), na.rational(0))
+            less_than_vectorized = na.frompyfunc(less_than_fn, 2, 1)
+            mask = less_than_vectorized(self.changepoints_t[None, :], t[..., None])
             deltas_t = delta * mask
             k_t = deltas_t.sum(axis=1) + k
             m_t = (-self.changepoints_t * deltas_t).sum(axis=1) + m

From 7610e740adfd0607cfd14010bdd47e7ff8a0816b Mon Sep 17 00:00:00 2001
From: Mathias Leys <mathias.leys@nillion.com>
Date: Wed, 12 Jun 2024 10:04:08 +0100
Subject: [PATCH 18/28] Fix formatting

---
 nada_ai/time_series/prophet.py | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/nada_ai/time_series/prophet.py b/nada_ai/time_series/prophet.py
index 991a93c..e881346 100644
--- a/nada_ai/time_series/prophet.py
+++ b/nada_ai/time_series/prophet.py
@@ -228,11 +228,10 @@ def ensure_numeric_dates(self, dates: np.ndarray) -> np.ndarray:
         Returns:
             np.ndarray: Standardized dates.
         """
-        if np.issubdtype(dates.dtype, np.integer) or np.issubdtype(
-            dates.dtype, np.floating
-        ):
+        dtype = dates.dtype
+        if np.issubdtype(dtype, np.integer) or np.issubdtype(dtype, np.floating):
             return dates
-        if np.issubdtype(dates.dtype, np.datetime64):
+        if np.issubdtype(dtype, np.datetime64):
             return dates.astype(np.float64)
         raise TypeError(
             f"Could not convert dates array of type `{dates}` to a NumPy array of numerics."

From 868c9d4247eeff1ebaf62c75c5569d4de82476aa Mon Sep 17 00:00:00 2001
From: Mathias Leys <mathias.leys@nillion.com>
Date: Wed, 12 Jun 2024 10:06:03 +0100
Subject: [PATCH 19/28] Fix formatting

---
 nada_ai/time_series/prophet.py | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/nada_ai/time_series/prophet.py b/nada_ai/time_series/prophet.py
index e881346..ddd86a8 100644
--- a/nada_ai/time_series/prophet.py
+++ b/nada_ai/time_series/prophet.py
@@ -206,8 +206,8 @@ def predict(
         Returns:
             na.NadaArray: Forecasted values.
         """
-        assert len(dates) == len(floor), "Prophet inputs must be equally sized."
-        assert len(floor) == len(t), "Prophet inputs must be equally sized."
+        assert dates.shape == floor.shape, "Prophet inputs must be equally sized."
+        assert floor.shape == t.shape, "Prophet inputs must be equally sized."
 
         dates = self.ensure_numeric_dates(dates)
         trend = self.predict_trend(floor, t)
@@ -233,9 +233,8 @@ def ensure_numeric_dates(self, dates: np.ndarray) -> np.ndarray:
             return dates
         if np.issubdtype(dtype, np.datetime64):
             return dates.astype(np.float64)
-        raise TypeError(
-            f"Could not convert dates array of type `{dates}` to a NumPy array of numerics."
-        )
+        error_msg = f"Could not convert dates of type `{dates}` to a numeric array."
+        raise TypeError(error_msg)
 
     @override
     def __call__(

From 8cc6489ac8613bc99a219218e5f457572a1f2dfd Mon Sep 17 00:00:00 2001
From: Mathias Leys <mathias.leys@nillion.com>
Date: Wed, 12 Jun 2024 15:11:33 +0100
Subject: [PATCH 20/28] Bump nada-algebra version

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 47efe38..326c0ca 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -13,7 +13,7 @@ scikit-learn = "^1.4.2"
 prophet = "^1.1.5"
 nada-dsl = "^0.2.1"
 py-nillion-client = "^0.2.1"
-nada-algebra = "^0.3.2"
+nada-algebra = "^0.3.3"
 
 
 [tool.poetry.group.dev.dependencies]

From d7c205be5a3acd6d8eb3ac63fc0bfe18876a0dc9 Mon Sep 17 00:00:00 2001
From: Mathias Leys <mathias.leys@nillion.com>
Date: Wed, 12 Jun 2024 15:29:46 +0100
Subject: [PATCH 21/28] Update poetry

---
 poetry.lock | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 701b24c..0833d43 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -687,13 +687,13 @@ files = [
 
 [[package]]
 name = "nada-algebra"
-version = "0.3.2"
+version = "0.3.3"
 description = "Nada-Algebra is a Python library designed for algebraic operations on NumPy-like array objects on top of Nada DSL and Nillion Network."
 optional = false
 python-versions = "<4.0,>=3.10"
 files = [
-    {file = "nada_algebra-0.3.2-py3-none-any.whl", hash = "sha256:d83e7c71854bb6230ad51009a273d2eb9c4f4c4117a5808f8cd485f1f9a83d6f"},
-    {file = "nada_algebra-0.3.2.tar.gz", hash = "sha256:1aed21b53ad7e764dccd27aec2d8fd25faaef8159429f81a7341499f5554d09e"},
+    {file = "nada_algebra-0.3.3-py3-none-any.whl", hash = "sha256:d5590a7e5a535df96bcc4fbe58dc8e5200bfe620368a6248f3bdf473f5f7e0da"},
+    {file = "nada_algebra-0.3.3.tar.gz", hash = "sha256:99314f2d3fd17160436ded762d4ff8b6c4e879fcc60468d497768d6161cf3a98"},
 ]
 
 [package.dependencies]
@@ -1566,4 +1566,4 @@ files = [
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "5fbd199d20ff748b0f4b8b98c3b1b6967824e9bcf2b87300b112f37572412d87"
+content-hash = "5b547da890f17807c8b1eb405e22fde1942bb4498c70aa2b01282e35f7b38d1a"

From 4a3ce9e925648c3f4f32c226a7e222da8466510f Mon Sep 17 00:00:00 2001
From: Mathias Leys <mathias.leys@nillion.com>
Date: Wed, 12 Jun 2024 15:29:55 +0100
Subject: [PATCH 22/28] Update examples

---
 examples/time_series/network/compute.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/time_series/network/compute.py b/examples/time_series/network/compute.py
index ef1c293..177932b 100644
--- a/examples/time_series/network/compute.py
+++ b/examples/time_series/network/compute.py
@@ -6,7 +6,7 @@
 import numpy as np
 import nada_algebra as na
 import pandas as pd
-from nada_ai import ProphetClient
+from nada_ai.client import ProphetClient
 from prophet import Prophet
 from dotenv import load_dotenv
 

From b1b64f078f7798a404e9be5dcdb907750f6fbd67 Mon Sep 17 00:00:00 2001
From: Mathias Leys <mathias.leys@nillion.com>
Date: Wed, 12 Jun 2024 15:30:04 +0100
Subject: [PATCH 23/28] Update tests

---
 tests/python-tests/test_model_client.py | 61 ++++++++++++++++++++++++-
 1 file changed, 60 insertions(+), 1 deletion(-)

diff --git a/tests/python-tests/test_model_client.py b/tests/python-tests/test_model_client.py
index ce9f19b..b65ac82 100644
--- a/tests/python-tests/test_model_client.py
+++ b/tests/python-tests/test_model_client.py
@@ -3,11 +3,13 @@
 import pytest
 import torch
 import numpy as np
+import pandas as pd
 
 import nada_algebra as na
 from sklearn.linear_model import LinearRegression, LogisticRegression
 from torch import nn
-from nada_ai.client import ModelClient, TorchClient, SklearnClient
+from prophet import Prophet
+from nada_ai.client import ModelClient, TorchClient, SklearnClient, ProphetClient
 import py_nillion_client as nillion
 
 
@@ -247,3 +249,60 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 
         for expected_layer in expected_layers:
             assert expected_layer in secrets.keys()
+
+    def test_prophet_1(self):
+        model = Prophet()
+
+        ds = pd.date_range("2024-05-01", "2024-05-17").tolist()
+        y = np.arange(1, 18).tolist()
+
+        fit_model = model.fit(df=pd.DataFrame({"ds": ds, "y": y}))
+
+        # Avoid zero secrets - just for testing purposes
+        for param_name in fit_model.params:
+            fit_model.params[param_name] = fit_model.params[param_name] + 1
+
+        model_client = ProphetClient(fit_model)
+
+        secrets = model_client.export_state_as_secrets("test_model", na.SecretRational)
+
+        expected_layers = [
+            "test_model_k_0_0",
+            "test_model_m_0_0",
+            "test_model_delta_0_0",
+            "test_model_delta_0_1",
+            "test_model_delta_0_2",
+            "test_model_delta_0_3",
+            "test_model_delta_0_4",
+            "test_model_delta_0_5",
+            "test_model_delta_0_6",
+            "test_model_delta_0_7",
+            "test_model_delta_0_8",
+            "test_model_delta_0_9",
+            "test_model_delta_0_10",
+            "test_model_delta_0_11",
+            "test_model_beta_0_0",
+            "test_model_beta_0_1",
+            "test_model_beta_0_2",
+            "test_model_beta_0_3",
+            "test_model_beta_0_4",
+            "test_model_beta_0_5",
+            "test_model_changepoints_t_0",
+            "test_model_changepoints_t_1",
+            "test_model_changepoints_t_2",
+            "test_model_changepoints_t_3",
+            "test_model_changepoints_t_4",
+            "test_model_changepoints_t_5",
+            "test_model_changepoints_t_6",
+            "test_model_changepoints_t_7",
+            "test_model_changepoints_t_8",
+            "test_model_changepoints_t_9",
+            "test_model_changepoints_t_10",
+            "test_model_changepoints_t_11",
+            "test_model_y_scale_0",
+        ]
+
+        assert len(secrets) == len(expected_layers)
+
+        for expected_layer in expected_layers:
+            assert expected_layer in secrets.keys()

From 56ca1b851b9336d196e85d0e81daebe07f88e52e Mon Sep 17 00:00:00 2001
From: Mathias Leys <mathias.leys@nillion.com>
Date: Wed, 12 Jun 2024 15:34:10 +0100
Subject: [PATCH 24/28] Fix compatibility with legacy python versions

---
 nada_ai/time_series/prophet.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/nada_ai/time_series/prophet.py b/nada_ai/time_series/prophet.py
index ddd86a8..367a75b 100644
--- a/nada_ai/time_series/prophet.py
+++ b/nada_ai/time_series/prophet.py
@@ -1,7 +1,7 @@
 """Facebook Prophet implementation"""
 
 import numpy as np
-from typing import Dict, Tuple, override
+from typing import Dict, Tuple
 
 import nada_algebra as na
 from nada_ai.nn.module import Module
@@ -236,7 +236,6 @@ def ensure_numeric_dates(self, dates: np.ndarray) -> np.ndarray:
         error_msg = f"Could not convert dates of type `{dates}` to a numeric array."
         raise TypeError(error_msg)
 
-    @override
     def __call__(
         self,
         dates: np.ndarray,
@@ -257,7 +256,6 @@ def __call__(
         """
         return self.predict(dates=dates, floor=floor, t=t)
 
-    @override
     def forward(
         self,
         dates: np.ndarray,

From 19115084218657dda76592919ff2555dfef3d0ae Mon Sep 17 00:00:00 2001
From: Mathias Leys <mathias.leys@nillion.com>
Date: Wed, 12 Jun 2024 15:36:22 +0100
Subject: [PATCH 25/28] Fix example tests

---
 examples/complex_model/target/.gitignore     | 5 +++++
 examples/linear_regression/target/.gitignore | 5 +++++
 examples/neural_net/target/.gitignore        | 5 +++++
 examples/time_series/target/.gitignore       | 5 +++++
 4 files changed, 20 insertions(+)
 create mode 100644 examples/complex_model/target/.gitignore
 create mode 100644 examples/linear_regression/target/.gitignore
 create mode 100644 examples/neural_net/target/.gitignore
 create mode 100644 examples/time_series/target/.gitignore

diff --git a/examples/complex_model/target/.gitignore b/examples/complex_model/target/.gitignore
new file mode 100644
index 0000000..5291a74
--- /dev/null
+++ b/examples/complex_model/target/.gitignore
@@ -0,0 +1,5 @@
+# This directory is kept purposely, so that no compilation errors arise.
+# Ignore everything in this directory
+*
+# Except this file
+!.gitignore
\ No newline at end of file
diff --git a/examples/linear_regression/target/.gitignore b/examples/linear_regression/target/.gitignore
new file mode 100644
index 0000000..5291a74
--- /dev/null
+++ b/examples/linear_regression/target/.gitignore
@@ -0,0 +1,5 @@
+# This directory is kept purposely, so that no compilation errors arise.
+# Ignore everything in this directory
+*
+# Except this file
+!.gitignore
\ No newline at end of file
diff --git a/examples/neural_net/target/.gitignore b/examples/neural_net/target/.gitignore
new file mode 100644
index 0000000..5291a74
--- /dev/null
+++ b/examples/neural_net/target/.gitignore
@@ -0,0 +1,5 @@
+# This directory is kept purposely, so that no compilation errors arise.
+# Ignore everything in this directory
+*
+# Except this file
+!.gitignore
\ No newline at end of file
diff --git a/examples/time_series/target/.gitignore b/examples/time_series/target/.gitignore
new file mode 100644
index 0000000..5291a74
--- /dev/null
+++ b/examples/time_series/target/.gitignore
@@ -0,0 +1,5 @@
+# This directory is kept purposely, so that no compilation errors arise.
+# Ignore everything in this directory
+*
+# Except this file
+!.gitignore
\ No newline at end of file

From 074cde13d779cffba2f82819d1763e755dad63a6 Mon Sep 17 00:00:00 2001
From: Mathias Leys <mathias.leys@nillion.com>
Date: Wed, 12 Jun 2024 16:24:55 +0100
Subject: [PATCH 26/28] Fix rescaling

---
 examples/complex_model/network/compute.py     | 4 ++--
 examples/linear_regression/network/compute.py | 4 ++--
 examples/neural_net/network/compute.py        | 6 +++---
 examples/time_series/network/compute.py       | 2 +-
 4 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/examples/complex_model/network/compute.py b/examples/complex_model/network/compute.py
index 61f126d..ccc4bdc 100644
--- a/examples/complex_model/network/compute.py
+++ b/examples/complex_model/network/compute.py
@@ -188,9 +188,9 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
         nillion.Secrets({}),
     )
 
-    # Sort & rescale the obtained results by the quantization scale (here: 16)
+    # Sort & rescale the obtained results by the quantization scale
     outputs = outputs = [
-        result[1] / 2**16
+        na_client.float_from_rational(result[1])
         for result in sorted(
             result.items(),
             key=lambda x: int(x[0].replace("my_output", "").replace("_", "")),
diff --git a/examples/linear_regression/network/compute.py b/examples/linear_regression/network/compute.py
index b69889c..1bd5aef 100644
--- a/examples/linear_regression/network/compute.py
+++ b/examples/linear_regression/network/compute.py
@@ -162,8 +162,8 @@ async def main():
         [model_store_id, data_store_id],
         nillion.Secrets({}),
     )
-    # Rescale the obtained result by the quantization scale (here: 16)
-    outputs = [result["my_output_0"] / 2**16]
+    # Rescale the obtained result by the quantization scale
+    outputs = [na_client.float_from_rational(result["my_output_0"])]
     print(f"🖥️  The result is {outputs}")
 
     expected = fit_model.predict(np.ones((NUM_FEATS,)).reshape(1, -1))
diff --git a/examples/neural_net/network/compute.py b/examples/neural_net/network/compute.py
index 0d5c771..9999bf9 100644
--- a/examples/neural_net/network/compute.py
+++ b/examples/neural_net/network/compute.py
@@ -5,7 +5,7 @@
 import numpy as np
 import time
 import nada_algebra as na
-from nada_ai import TorchClient
+from nada_ai.client import TorchClient
 import torch
 from dotenv import load_dotenv
 
@@ -166,9 +166,9 @@ def forward(self, x: na.NadaArray) -> na.NadaArray:
         nillion.Secrets({}),
     )
 
-    # Sort & rescale the obtained results by the quantization scale (here: 16)
+    # Sort & rescale the obtained results by the quantization scale
     outputs = [
-        result[1] / 2**16
+        na_client.float_from_rational(result[1])
         for result in sorted(
             result.items(),
             key=lambda x: int(x[0].replace("my_output", "").replace("_", "")),
diff --git a/examples/time_series/network/compute.py b/examples/time_series/network/compute.py
index 177932b..5f5fafb 100644
--- a/examples/time_series/network/compute.py
+++ b/examples/time_series/network/compute.py
@@ -170,7 +170,7 @@ async def main():
 
     # Sort & rescale the obtained results by the quantization scale
     outputs = [
-        result[1] / 2**50
+        na_client.float_from_rational(result[1])
         for result in sorted(
             result.items(),
             key=lambda x: int(x[0].replace("my_output", "").replace("_", "")),

From 38304207086f1d358d0c91b13e7769b4c4064d52 Mon Sep 17 00:00:00 2001
From: Mathias Leys <mathias.leys@nillion.com>
Date: Wed, 12 Jun 2024 16:25:13 +0100
Subject: [PATCH 27/28] Bump nada-algebra version

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 326c0ca..b762363 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -13,7 +13,7 @@ scikit-learn = "^1.4.2"
 prophet = "^1.1.5"
 nada-dsl = "^0.2.1"
 py-nillion-client = "^0.2.1"
-nada-algebra = "^0.3.3"
+nada-algebra = "^0.3.4"
 
 
 [tool.poetry.group.dev.dependencies]

From 295a3ce1c0896b5848e7de5b1f4e1cbd909e71b8 Mon Sep 17 00:00:00 2001
From: Mathias Leys <mathias.leys@nillion.com>
Date: Wed, 12 Jun 2024 16:38:44 +0100
Subject: [PATCH 28/28] Update poetry

---
 poetry.lock | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 0833d43..5b99ba5 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -687,13 +687,13 @@ files = [
 
 [[package]]
 name = "nada-algebra"
-version = "0.3.3"
+version = "0.3.4"
 description = "Nada-Algebra is a Python library designed for algebraic operations on NumPy-like array objects on top of Nada DSL and Nillion Network."
 optional = false
 python-versions = "<4.0,>=3.10"
 files = [
-    {file = "nada_algebra-0.3.3-py3-none-any.whl", hash = "sha256:d5590a7e5a535df96bcc4fbe58dc8e5200bfe620368a6248f3bdf473f5f7e0da"},
-    {file = "nada_algebra-0.3.3.tar.gz", hash = "sha256:99314f2d3fd17160436ded762d4ff8b6c4e879fcc60468d497768d6161cf3a98"},
+    {file = "nada_algebra-0.3.4-py3-none-any.whl", hash = "sha256:b9ac86f35b5ed29b12439c9aacf929287a6eafcbd5075ff51f05b25354794117"},
+    {file = "nada_algebra-0.3.4.tar.gz", hash = "sha256:8fe57e4b2efd6abbda59660aad053fc1c1d13dedfa0e7109d9ecb11a173ca334"},
 ]
 
 [package.dependencies]
@@ -1566,4 +1566,4 @@ files = [
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "5b547da890f17807c8b1eb405e22fde1942bb4498c70aa2b01282e35f7b38d1a"
+content-hash = "352c0a87c207737336e42778f8d9a574bbbda4f8f94b06883aa2fc20a137ad1a"