Merge pull request #56 from NiklasMelton/add-parameter-type-checks

assert parameter types

biclustering/BARTMAP.py

@@ -123,6 +123,7 @@ def validate_params(params):
 
         """
         assert "eta" in params
+        assert isinstance(params["eta"], float)
 
     @property
     def column_labels_(self):
@@ -140,9 +141,20 @@ def n_row_clusters(self):
     def n_column_clusters(self):
         return self.module_b.n_clusters
 
-    def _get_x_cb(self, X: np.ndarray, c_b: int):
+    def _get_x_cb(self, x: np.ndarray, c_b: int):
+        """
+        get the components of a vector belonging to a b-side cluster
+
+        Parameters:
+        - x: a sample vector
+        - c_b: b-side cluster label
+
+        Returns:
+            x filtered to features belonging to the b-side cluster c_b
+
+        """
         b_components = self.module_b.labels_ == c_b
-        return X[b_components]
+        return x[b_components]
 
     @staticmethod
     def _pearsonr(a: np.ndarray, b: np.ndarray):

elementary/ART1.py

@@ -46,6 +46,9 @@ def validate_params(params: dict):
         assert 1. >= params["rho"] >= 0.
         assert 1. >= params["beta"] >= 0.
         assert params["L"] >= 1.
+        assert isinstance(params["rho"], float)
+        assert isinstance(params["beta"], float)
+        assert isinstance(params["L"], float)
 
     def validate_data(self, X: np.ndarray):
         """

elementary/ART2.py

@@ -57,6 +57,9 @@ def validate_params(params: dict):
         assert 1. >= params["rho"] >= 0.
         assert 1. >= params["alpha"] >= 0.
         assert 1. >= params["beta"] >= 0.
+        assert isinstance(params["rho"], float)
+        assert isinstance(params["alpha"], float)
+        assert isinstance(params["beta"], float)
 
     def check_dimensions(self, X: np.ndarray):
         """

elementary/BayesianART.py

@@ -43,6 +43,8 @@ def validate_params(params: dict):
         assert "rho" in params
         assert "cov_init" in params
         assert params["rho"] > 0
+        assert isinstance(params["rho"], float)
+        assert isinstance(params["cov_init"], np.ndarray)
 
     def check_dimensions(self, X: np.ndarray):
         """

elementary/EllipsoidART.py

@@ -54,6 +54,11 @@ def validate_params(params: dict):
         assert 1.0 >= params["alpha"] >= 0.
         assert 1.0 >= params["beta"] >= 0.
         assert 1.0 >= params["mu"] > 0.
+        assert isinstance(params["rho"], float)
+        assert isinstance(params["alpha"], float)
+        assert isinstance(params["beta"], float)
+        assert isinstance(params["mu"], float)
+        assert isinstance(params["r_hat"], float)
 
     @staticmethod
     def category_distance(i: np.ndarray, centroid: np.ndarray, major_axis: np.ndarray, params):

elementary/FuzzyART.py

@@ -98,6 +98,9 @@ def validate_params(params: dict):
         assert 1.0 >= params["rho"] >= 0.
         assert params["alpha"] >= 0.
         assert 1.0 >= params["beta"] > 0.
+        assert isinstance(params["rho"], float)
+        assert isinstance(params["alpha"], float)
+        assert isinstance(params["beta"], float)
 
     def check_dimensions(self, X: np.ndarray):
         """

elementary/GaussianART.py

@@ -39,6 +39,8 @@ def validate_params(params: dict):
         assert "rho" in params
         assert "sigma_init" in params
         assert 1.0 >= params["rho"] > 0.
+        assert isinstance(params["rho"], float)
+        assert isinstance(params["sigma_init"], np.ndarray)
 
 
     def category_choice(self, i: np.ndarray, w: np.ndarray, params: dict) -> tuple[float, Optional[dict]]:

elementary/HypersphereART.py

@@ -45,6 +45,10 @@ def validate_params(params: dict):
         assert 1.0 >= params["rho"] >= 0.
         assert params["alpha"] >= 0.
         assert 1.0 >= params["beta"] >= 0.
+        assert isinstance(params["rho"], float)
+        assert isinstance(params["alpha"], float)
+        assert isinstance(params["beta"], float)
+        assert isinstance(params["r_hat"], float)
 
     @staticmethod
     def category_distance(i: np.ndarray, centroid: np.ndarray, radius: float, params) -> float:

elementary/QuadraticNeuronART.py

@@ -55,6 +55,14 @@ def validate_params(params: dict):
         assert "lr_w" in params
         assert "lr_s" in params
         assert 1.0 >= params["rho"] >= 0.
+        assert 1.0 >= params["lr_b"] > 0.
+        assert 1.0 >= params["lr_w"] >= 0.
+        assert 1.0 >= params["lr_s"] >= 0.
+        assert isinstance(params["rho"], float)
+        assert isinstance(params["s_init"], float)
+        assert isinstance(params["lr_b"], float)
+        assert isinstance(params["lr_w"], float)
+        assert isinstance(params["lr_s"], float)
 
     def category_choice(self, i: np.ndarray, w: np.ndarray, params: dict) -> tuple[float, Optional[dict]]:
         """

fusion/FusionART.py

@@ -89,6 +89,7 @@ def validate_params(params: dict):
         assert "gamma_values" in params
         assert all([1.0 >= g >= 0.0 for g in params["gamma_values"]])
         assert sum(params["gamma_values"]) == 1.0
+        assert isinstance(params["gamma_values"], np.ndarray)
 
 
     def validate_data(self, X: np.ndarray):