Cleaning up some docstring formatting and removing unnecessary property

src/elexsolver/TransitionMatrixSolver.py

@@ -21,11 +21,11 @@ def __init__(self, strict: bool = True, lam: float | None = None):
         """
         Parameters
         ----------
-        `strict` : bool, default True
-            If True, solution will be constrainted so that all coefficients are >= 0,
+        strict : bool, default True
+            If `True`, solution will be constrainted so that all coefficients are >= 0,
             <= 1, and the sum of each row equals 1.
-        `lam` : float, optional
-            `lam` != 0 will enable L2 regularization (Ridge).
+        lam : float, optional
+            `lam != 0` will enable L2 regularization (Ridge).
         """
         super().__init__()
         self._strict = strict

src/elexsolver/TransitionSolver.py

@@ -17,17 +17,23 @@ class TransitionSolver(LinearSolver):
     """
 
     def __init__(self):
+        """
+        After model-fit, `self.coefficients` will contain
+        the solved coefficients, an np.ndarray matrix of float of shape
+        (number of columns in `X`) x (number of columns in `Y`).
+        Each float represents the percent of how much of row x is part of column y.
+        """
         super().__init__()
 
     def fit(self, X: np.ndarray, Y: np.ndarray, sample_weight: np.ndarray | None = None):
         """
         Parameters
         ----------
-        `X` : np.ndarray matrix or pandas.DataFrame of int
+        X : np.ndarray matrix or pandas.DataFrame of int
             Must have the same number of rows as `Y` but can have any number of columns greater than the number of rows.
-        `Y` : np.ndarray matrix or pandas.DataFrame of int
+        Y : np.ndarray matrix or pandas.DataFrame of int
             Must have the same number of rows as `X` but can have any number of columns greater than the number of rows.
-        `sample_weight` : list, np.ndarray, or pandas.Series of int, optional
+        sample_weight : list or np.ndarray or pandas.Series of int, optional
             Must have the same length (number of rows) as both `X` and `Y`.
 
         Returns
@@ -42,7 +48,7 @@ def predict(self, X: np.ndarray) -> np.ndarray:
         """
         Parameters
         ----------
-        `X` : np.ndarray matrix or pandas.DataFrame of int
+        X : np.ndarray matrix or pandas.DataFrame of int
             Must have the same dimensions as the `X` supplied to `fit()`.
 
         Returns
@@ -53,19 +59,11 @@ def predict(self, X: np.ndarray) -> np.ndarray:
             raise RuntimeError("Solver must be fit before prediction can be performed.")
         return X @ self.coefficients
 
-    @property
-    def betas(self) -> np.ndarray:
+    def _check_data_type(self, A: np.ndarray):
         """
-        Returns
-        -------
-        The solved coefficients, an np.ndarray matrix of float of shape
-        (number of columns in `X`) x (number of columns in `Y`).
-        Each float represents the percent of how much of row x is part of column y.
-        Will return `None` if `fit()` hasn't been called yet.
+        Make sure we're starting with count data which we'll standardize to percentages
+        by calling `self._rescale(A)` later.
         """
-        return self.coefficients
-
-    def _check_data_type(self, A: np.ndarray):
         if not np.all(A.astype("int64") == A):
             raise ValueError("Matrix must contain integers.")
 
@@ -97,9 +95,9 @@ def _check_and_prepare_weights(self, X: np.ndarray, Y: np.ndarray, weights: np.n
 
         Parameters
         ----------
-        `X` : np.ndarray matrix of int (same number of rows as `Y`)
-        `Y` : np.ndarray matrix of int (same number of rows as `X`)
-        `weights` : np.ndarray of int of the shape (number of rows in `X` and `Y`, 1), optional
+        X : np.ndarray matrix of int (same number of rows as `Y`)
+        Y : np.ndarray matrix of int (same number of rows as `X`)
+        weights : np.ndarray of int of the shape (number of rows in `X` and `Y`, 1), optional
         """
 
         if weights is not None:

tests/test_transition_matrix_solver.py

@@ -44,7 +44,7 @@ def test_matrix_fit_predict():
 
     tms = TransitionMatrixSolver().fit(X, Y)
     current_yhat = tms.predict(X)
-    np.testing.assert_allclose(expected_betas, tms.betas, rtol=RTOL, atol=ATOL)
+    np.testing.assert_allclose(expected_betas, tms.coefficients, rtol=RTOL, atol=ATOL)
     np.testing.assert_allclose(expected_yhat, current_yhat, rtol=RTOL, atol=ATOL)
 
 
@@ -76,7 +76,7 @@ def test_matrix_fit_predict_with_weights():
     expected_betas = np.array([[0.737329, 0.262671], [0.230589, 0.769411]])
 
     tms = TransitionMatrixSolver().fit(X, Y, sample_weight=weights)
-    np.testing.assert_allclose(expected_betas, tms.betas, rtol=RTOL, atol=ATOL)
+    np.testing.assert_allclose(expected_betas, tms.coefficients, rtol=RTOL, atol=ATOL)
 
 
 def test_matrix_fit_predict_not_strict():
@@ -105,7 +105,7 @@ def test_matrix_fit_predict_not_strict():
     expected_betas = np.array([[0.760451, 0.239558], [0.216624, 0.783369]])
 
     tms = TransitionMatrixSolver(strict=False).fit(X, Y)
-    np.testing.assert_allclose(expected_betas, tms.betas, rtol=RTOL, atol=ATOL)
+    np.testing.assert_allclose(expected_betas, tms.coefficients, rtol=RTOL, atol=ATOL)
 
 
 def test_ridge_matrix_fit_predict():
@@ -134,7 +134,7 @@ def test_ridge_matrix_fit_predict():
     expected_betas = np.array([[0.479416, 0.520584], [0.455918, 0.544082]])
 
     tms = TransitionMatrixSolver(lam=1).fit(X, Y)
-    np.testing.assert_allclose(expected_betas, tms.betas, rtol=RTOL, atol=ATOL)
+    np.testing.assert_allclose(expected_betas, tms.coefficients, rtol=RTOL, atol=ATOL)
 
 
 def test_matrix_fit_predict_pivoted():
@@ -172,7 +172,7 @@ def test_matrix_fit_predict_pivoted():
     )
 
     tms = TransitionMatrixSolver().fit(X, Y)
-    np.testing.assert_allclose(expected_betas, tms.betas, rtol=RTOL, atol=ATOL)
+    np.testing.assert_allclose(expected_betas, tms.coefficients, rtol=RTOL, atol=ATOL)
 
 
 def test_matrix_fit_predict_bad_dimensions():
@@ -232,7 +232,7 @@ def test_matrix_fit_predict_pandas():
         expected_betas = np.array([[0.760428, 0.239572], [0.216642, 0.783358]])
 
         tms = TransitionMatrixSolver().fit(X, Y)
-        np.testing.assert_allclose(expected_betas, tms.betas, rtol=RTOL, atol=ATOL)
+        np.testing.assert_allclose(expected_betas, tms.coefficients, rtol=RTOL, atol=ATOL)
 
     except ImportError:
         # pass this test through since pandas isn't a requirement for elex-solver

tests/test_transition_solver.py

@@ -21,9 +21,9 @@ def test_superclass_predict():
 
 
 @patch.object(TransitionSolver, "__abstractmethods__", set())
-def test_superclass_get_betas():
+def test_superclass_get_coefficients():
     ts = TransitionSolver()
-    assert ts.betas is None
+    assert ts.coefficients is None
 
 
 @patch.object(TransitionSolver, "__abstractmethods__", set())