Refactoring

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`.

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

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

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

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

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

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

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

examples/time_series/tests/time_series.yaml

@@ -1,5 +1,5 @@
 ---
-program: main
+program: time_series
 inputs:
   secrets:
     my_prophet_changepoints_t_8:

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.
 

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)

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):