Repository: aeon-toolkit/aeon (python)

Issue:
[BUG] 
### Describe the bug

An error with Numba(ConstantInferenceError) is preventing me to use clustering.

### Steps/Code to reproduce the bug

from aeon.clustering.k_means import TimeSeriesKMeans

# This is a just a list of numpy arrays
with open('dataCluster100.pkl', 'rb') as f:
        dataCluster99 = pickle.load(f)

X_train = np.array([arr[np.newaxis, :] for arr in dataCluster99])

k_means = TimeSeriesKMeans(
    n_clusters=13,  # Number of desired centers
    init_algorithm="forgy",  # Center initialisation technique
    max_iter=10,  # Maximum number of iterations for refinement on training set
    metric="dtw",  # Distance metric to use
    averaging_method="dba",  # Averaging technique to use
    random_state=1,
)

k_means.fit(X_train)

### Expected results

N/A

### Actual results

ConstantInferenceError                    Traceback (most recent call last)
[<ipython-input-5-2c4a67d93a28>](https://localhost:8080/#) in <cell line: 15>()
     13 )
     14 
---> 15 k_means.fit(X_train)

6 frames
[/usr/local/lib/python3.10/dist-packages/numba/core/dispatcher.py](https://localhost:8080/#) in error_rewrite(e, issue_type)
    407                 raise e
    408             else:
--> 409                 raise e.with_traceback(None)
    410 
    411         argtypes = []

ConstantInferenceError: Failed in nopython mode pipeline (step: nopython rewrites)
Constant inference not possible for: $const288.2 + $292format_value.5

File "../usr/local/lib/python3.10/dist-packages/aeon/clustering/metrics/averaging/_barycenter_averaging.py", line 137:
def _ba_update(
    <source elided>
        else:
            raise ValueError(f"Metric must be a known string, got {metric}")
            ^

### Versions

<details>
System:
    python: 3.10.12 (main, Jun  7 2023, 12:45:35) [GCC 9.4.0]
executable: /usr/bin/python3
   machine: Linux-5.15.107+-x86_64-with-glibc2.31

Python dependencies:
          pip: 23.1.2
   setuptools: 67.7.2
      sklearn: None
         aeon: 0.3.0
  statsmodels: 0.13.5
        numpy: 1.22.4
        scipy: 1.10.1
       pandas: 1.5.3
   matplotlib: 3.7.1
       joblib: 1.2.0
        numba: 0.56.4
     pmdarima: None
      tsfresh: None
</details>

Pull request:
[BUG] DBA numba error
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#### Reference Issues/PRs
Issue Fixes #530 


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#### What does this implement/fix? Explain your changes.
This PR fixes a bug that was occurring for DBA for anyone using a version <0.57. The reason for the bug was numba
doesnt like f strings (apparently) below 0.57. This was the offending line:

`raise ValueError(f"Metric must be a known string, got {metric}") `

This has now been removed. In addition we suspected at first it was an isinstance call as this is an experimental feature in numba so I also removed the occurence of it (as I thought this was the bug) which also removes the warning.

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#### Does your contribution introduce a new dependency? If yes, which one?
No

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#### Did you add any tests for the change?

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#### Any other comments?

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### PR checklist

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##### For all contributions
- [x] I've added myself to the [list of contributors](https://github.com/aeon-toolkit/aeon/blob/main/.all-contributorsrc).
- [x] Optionally, I've updated aeon's [CODEOWNERS](https://github.com/aeon-toolkit/aeon/blob/main/CODEOWNERS) to receive notifications about future changes to these files.
- [x] The PR title starts with either [ENH], [MNT], [DOC], or [BUG] indicating whether the PR topic is related to enhancement, maintenance, documentation, or bug.
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Patch:
diff --git a/aeon/clustering/metrics/averaging/_barycenter_averaging.py b/aeon/clustering/metrics/averaging/_barycenter_averaging.py
index f788db62..15cca346 100644
--- a/aeon/clustering/metrics/averaging/_barycenter_averaging.py
+++ b/aeon/clustering/metrics/averaging/_barycenter_averaging.py
@@ -110,7 +110,6 @@ def _ba_update(
 ) -> Tuple[np.ndarray, float]:
     X_size, X_dims, X_timepoints = X.shape
     sum = np.zeros(X_timepoints)
-
     alignment = np.zeros((X_dims, X_timepoints))
     cost = 0.0
     for i in range(X_size):
@@ -134,7 +133,9 @@ def _ba_update(
                 curr_ts, center, window, independent, c
             )
         else:
-            raise ValueError(f"Metric must be a known string, got {metric}")
+            # When numba version > 0.57 add more informative error with what metric
+            # was passed.
+            raise ValueError("Metric parameter invalid")
         for j, k in curr_alignment:
             alignment[:, k] += curr_ts[:, j]
             sum[k] += 1
diff --git a/aeon/distances/_distance.py b/aeon/distances/_distance.py
index c2da8f19..6674a685 100644
--- a/aeon/distances/_distance.py
+++ b/aeon/distances/_distance.py
@@ -132,7 +132,9 @@ def distance(
     elif metric == "lcss":
         return lcss_distance(x, y, kwargs.get("window"), kwargs.get("epsilon", 1.0))
     elif metric == "erp":
-        return erp_distance(x, y, kwargs.get("window"), kwargs.get("g", 0.0))
+        return erp_distance(
+            x, y, kwargs.get("window"), kwargs.get("g", 0.0), kwargs.get("g_arr", None)
+        )
     elif metric == "edr":
         return edr_distance(x, y, kwargs.get("window"), kwargs.get("epsilon"))
     elif metric == "twe":
@@ -243,7 +245,9 @@ def pairwise_distance(
             x, y, kwargs.get("window"), kwargs.get("epsilon", 1.0)
         )
     elif metric == "erp":
-        return erp_pairwise_distance(x, y, kwargs.get("window"), kwargs.get("g", 0.0))
+        return erp_pairwise_distance(
+            x, y, kwargs.get("window"), kwargs.get("g", 0.0), kwargs.get("g_arr", None)
+        )
     elif metric == "edr":
         return edr_pairwise_distance(x, y, kwargs.get("window"), kwargs.get("epsilon"))
     elif metric == "twe":
@@ -374,7 +378,9 @@ def alignment_path(
             x, y, kwargs.get("window"), kwargs.get("epsilon", 1.0)
         )
     elif metric == "erp":
-        return erp_alignment_path(x, y, kwargs.get("window"), kwargs.get("g", 0.0))
+        return erp_alignment_path(
+            x, y, kwargs.get("window"), kwargs.get("g", 0.0), kwargs.get("g_arr", None)
+        )
     elif metric == "edr":
         return edr_alignment_path(x, y, kwargs.get("window"), kwargs.get("epsilon"))
     elif metric == "twe":
@@ -460,7 +466,9 @@ def cost_matrix(
     elif metric == "lcss":
         return lcss_cost_matrix(x, y, kwargs.get("window"), kwargs.get("epsilon", 1.0))
     elif metric == "erp":
-        return erp_cost_matrix(x, y, kwargs.get("window"), kwargs.get("g", 0.0))
+        return erp_cost_matrix(
+            x, y, kwargs.get("window"), kwargs.get("g", 0.0), kwargs.get("g_arr", None)
+        )
     elif metric == "edr":
         return edr_cost_matrix(x, y, kwargs.get("window"), kwargs.get("epsilon"))
     elif metric == "twe":
diff --git a/aeon/distances/_erp.py b/aeon/distances/_erp.py
index 56e966ee..fdf426b6 100644
--- a/aeon/distances/_erp.py
+++ b/aeon/distances/_erp.py
@@ -34,7 +34,8 @@ def erp_distance(
     x: np.ndarray,
     y: np.ndarray,
     window: float = None,
-    g: Union[float, np.ndarray] = 0.0,
+    g: float = 0.0,
+    g_arr: np.ndarray = None,
 ) -> float:
     """Compute the ERP distance between two time series.
 
@@ -58,10 +59,10 @@ def erp_distance(
     window: float, defaults=None
         The window to use for the bounding matrix. If None, no bounding matrix
         is used.
-    g: float or np.ndarray of shape (n_channels), defaults=0.
-        The reference value to penalise gaps. The default is 0. If it is an array
-        then it must be the length of the number of channels in x and y. If a single
-        value is provided then that value is used across each channel
+    g: float.
+        The reference value to penalise gaps. The default is 0.
+    g_arr: np.ndarray of shape (n_channels), defaults=None
+        Numpy array that must be the length of the number of channels in x and y.
 
     Returns
     -------
@@ -91,10 +92,10 @@ def erp_distance(
         _x = x.reshape((1, x.shape[0]))
         _y = y.reshape((1, y.shape[0]))
         bounding_matrix = create_bounding_matrix(_x.shape[1], _y.shape[1], window)
-        return _erp_distance(_x, _y, bounding_matrix, g)
+        return _erp_distance(_x, _y, bounding_matrix, g, g_arr)
     if x.ndim == 2 and y.ndim == 2:
         bounding_matrix = create_bounding_matrix(x.shape[1], y.shape[1], window)
-        return _erp_distance(x, y, bounding_matrix, g)
+        return _erp_distance(x, y, bounding_matrix, g, g_arr)
     raise ValueError("x and y must be 1D or 2D")
 
 
@@ -104,6 +105,7 @@ def erp_cost_matrix(
     y: np.ndarray,
     window: float = None,
     g: Union[float, np.ndarray] = 0.0,
+    g_arr: np.ndarray = None,
 ) -> np.ndarray:
     """Compute the ERP cost matrix between two time series.
 
@@ -121,10 +123,10 @@ def erp_cost_matrix(
     window: float, defaults=None
         The window to use for the bounding matrix. If None, no bounding matrix
         is used.
-    g: float or np.ndarray of shape (n_channels), defaults=0.
-        The reference value to penalise gaps. The default is 0. If it is an array
-        then it must be the length of the number of channels in x and y. If a single
-        value is provided then that value is used across each channel.
+    g: float.
+        The reference value to penalise gaps. The default is 0.
+    g_arr: np.ndarray of shape (n_channels), defaults=None
+        Numpy array that must be the length of the number of channels in x and y.
 
     Returns
     -------
@@ -158,10 +160,10 @@ def erp_cost_matrix(
         _x = x.reshape((1, x.shape[0]))
         _y = y.reshape((1, y.shape[0]))
         bounding_matrix = create_bounding_matrix(_x.shape[1], _y.shape[1], window)
-        return _erp_cost_matrix(_x, _y, bounding_matrix, g)
+        return _erp_cost_matrix(_x, _y, bounding_matrix, g, g_arr)
     if x.ndim == 2 and y.ndim == 2:
         bounding_matrix = create_bounding_matrix(x.shape[1], y.shape[1], window)
-        return _erp_cost_matrix(x, y, bounding_matrix, g)
+        return _erp_cost_matrix(x, y, bounding_matrix, g, g_arr)
     raise ValueError("x and y must be 1D or 2D")
 
 
@@ -170,9 +172,12 @@ def _erp_distance(
     x: np.ndarray,
     y: np.ndarray,
     bounding_matrix: np.ndarray,
-    g: Union[float, np.ndarray],
+    g: float,
+    g_arr: np.ndarray,
 ) -> float:
-    return _erp_cost_matrix(x, y, bounding_matrix, g)[x.shape[1] - 1, y.shape[1] - 1]
+    return _erp_cost_matrix(x, y, bounding_matrix, g, g_arr)[
+        x.shape[1] - 1, y.shape[1] - 1
+    ]
 
 
 @njit(cache=True, fastmath=True)
@@ -180,15 +185,16 @@ def _erp_cost_matrix(
     x: np.ndarray,
     y: np.ndarray,
     bounding_matrix: np.ndarray,
-    g: Union[float, np.ndarray],
+    g: float,
+    g_arr: np.ndarray,
 ) -> np.ndarray:
     x_size = x.shape[1]
     y_size = y.shape[1]
 
     cost_matrix = np.zeros((x_size + 1, y_size + 1))
 
-    gx_distance, x_sum = _precompute_g(x, g)
-    gy_distance, y_sum = _precompute_g(y, g)
+    gx_distance, x_sum = _precompute_g(x, g, g_arr)
+    gy_distance, y_sum = _precompute_g(y, g, g_arr)
 
     cost_matrix[1:, 0] = x_sum
     cost_matrix[0, 1:] = y_sum
@@ -208,15 +214,15 @@ def _erp_cost_matrix(
 
 @njit(cache=True, fastmath=True)
 def _precompute_g(
-    x: np.ndarray, g: Union[float, np.ndarray]
+    x: np.ndarray, g: float, g_array: np.ndarray
 ) -> Tuple[np.ndarray, float]:
     gx_distance = np.zeros(x.shape[1])
-    if isinstance(g, float):
+    if g_array is None:
         g_arr = np.full(x.shape[0], g)
     else:
-        if g.shape[0] != x.shape[0]:
+        if g_array.shape[0] != x.shape[0]:
             raise ValueError("g must be a float or an array with shape (x.shape[0],)")
-        g_arr = g
+        g_arr = g_array
     x_sum = 0
 
     for i in range(x.shape[1]):
@@ -231,7 +237,8 @@ def erp_pairwise_distance(
     X: np.ndarray,
     y: np.ndarray = None,
     window: float = None,
-    g: Union[float, np.ndarray] = 0.0,
+    g: float = 0.0,
+    g_arr: np.ndarray = None,
 ) -> np.ndarray:
     """Compute the erp pairwise distance between a set of time series.
 
@@ -251,10 +258,10 @@ def erp_pairwise_distance(
     window: float, default=None
         The window to use for the bounding matrix. If None, no bounding matrix
         is used.
-    g: float or np.ndarray of shape (n_channels), defaults=0
-        The reference value to penalise gaps. The default is 0. If it is an array
-        then it must be the length of the number of channels in x and y. If a single
-        value is provided then that value is used across each channel.
+    g: float.
+        The reference value to penalise gaps. The default is 0.
+    g_arr: np.ndarray of shape (n_channels), defaults=None
+        Numpy array that must be the length of the number of channels in x and y.
 
     Returns
     -------
@@ -297,18 +304,21 @@ def erp_pairwise_distance(
     if y is None:
         # To self
         if X.ndim == 3:
-            return _erp_pairwise_distance(X, window, g)
+            return _erp_pairwise_distance(X, window, g, g_arr)
         if X.ndim == 2:
             _X = X.reshape((X.shape[0], 1, X.shape[1]))
-            return _erp_pairwise_distance(_X, window, g)
+            return _erp_pairwise_distance(_X, window, g, g_arr)
         raise ValueError("x and y must be 2D or 3D arrays")
     _x, _y = reshape_pairwise_to_multiple(X, y)
-    return _erp_from_multiple_to_multiple_distance(_x, _y, window, g)
+    return _erp_from_multiple_to_multiple_distance(_x, _y, window, g, g_arr)
 
 
 @njit(cache=True, fastmath=True)
 def _erp_pairwise_distance(
-    X: np.ndarray, window: float, g: Union[float, np.ndarray]
+    X: np.ndarray,
+    window: float,
+    g: float,
+    g_arr: np.ndarray,
 ) -> np.ndarray:
     n_instances = X.shape[0]
     distances = np.zeros((n_instances, n_instances))
@@ -316,7 +326,7 @@ def _erp_pairwise_distance(
 
     for i in range(n_instances):
         for j in range(i + 1, n_instances):
-            distances[i, j] = _erp_distance(X[i], X[j], bounding_matrix, g)
+            distances[i, j] = _erp_distance(X[i], X[j], bounding_matrix, g, g_arr)
             distances[j, i] = distances[i, j]
 
     return distances
@@ -324,7 +334,11 @@ def _erp_pairwise_distance(
 
 @njit(cache=True, fastmath=True)
 def _erp_from_multiple_to_multiple_distance(
-    x: np.ndarray, y: np.ndarray, window: float, g: Union[float, np.ndarray]
+    x: np.ndarray,
+    y: np.ndarray,
+    window: float,
+    g: float,
+    g_arr: np.ndarray,
 ) -> np.ndarray:
     n_instances = x.shape[0]
     m_instances = y.shape[0]
@@ -333,7 +347,7 @@ def _erp_from_multiple_to_multiple_distance(
 
     for i in range(n_instances):
         for j in range(m_instances):
-            distances[i, j] = _erp_distance(x[i], y[j], bounding_matrix, g)
+            distances[i, j] = _erp_distance(x[i], y[j], bounding_matrix, g, g_arr)
     return distances
 
 
@@ -342,7 +356,8 @@ def erp_alignment_path(
     x: np.ndarray,
     y: np.ndarray,
     window: float = None,
-    g: Union[float, np.ndarray] = 0.0,
+    g: float = 0.0,
+    g_arr: np.ndarray = None,
 ) -> Tuple[List[Tuple[int, int]], float]:
     """Compute the erp alignment path between two time series.
 
@@ -360,10 +375,10 @@ def erp_alignment_path(
     window: float, default=None
         The window to use for the bounding matrix. If None, no bounding matrix
         is used.
-    g: float or np.ndarray of shape (n_channels), defaults=0.
-        The reference value to penalise gaps. The default is 0. If it is an array
-        then it must be the length of the number of channels in x and y. If a single
-        value is provided then that value is used across each channel.
+    g: float.
+        The reference value to penalise gaps. The default is 0.
+    g_arr: np.ndarray of shape (n_channels), defaults=None
+        Numpy array that must be the length of the number of channels in x and y.
 
     Returns
     -------
@@ -390,7 +405,7 @@ def erp_alignment_path(
     """
     bounding_matrix = create_bounding_matrix(x.shape[-1], y.shape[-1], window)
     cost_matrix = _add_inf_to_out_of_bounds_cost_matrix(
-        erp_cost_matrix(x, y, window, g), bounding_matrix
+        erp_cost_matrix(x, y, window, g, g_arr), bounding_matrix
     )
     return (
         compute_min_return_path(cost_matrix),


Test patch:
diff --git a/aeon/distances/tests/test_numba_distance_parameters.py b/aeon/distances/tests/test_numba_distance_parameters.py
index cded18a7..808f5014 100644
--- a/aeon/distances/tests/test_numba_distance_parameters.py
+++ b/aeon/distances/tests/test_numba_distance_parameters.py
@@ -33,7 +33,9 @@ def _test_distance_params(
         curr_results = []
         for x, y in test_ts:
             if g_none:
-                param_dict["g"] = np.std([x, y], axis=0).sum(axis=1)
+                param_dict["g_arr"] = np.std([x, y], axis=0).sum(axis=1)
+                if "g" in param_dict:
+                    del param_dict["g"]
             results = []
             results.append(distance_func(x, y, **param_dict))
             results.append(distance(x, y, metric=distance_str, **param_dict))