bump xarray

MaxHalford · Jan 4, 2025 · f913425 · f913425
1 parent 1ac324b
commit f913425
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Showing 5 changed files with 21 additions and 19 deletions.
diff --git a/poetry.lock b/poetry.lock
diff --git a/prince/famd.py b/prince/famd.py
@@ -35,7 +35,7 @@ def __init__(
 
     def _check_input(self, X):
         if self.check_input:
-            sklearn.utils.check_array(X, dtype=[str, np.number])
+            sklearn.utils.check_array(X, dtype=[str, "numeric"])
 
     @utils.check_is_dataframe_input
     def fit(self, X, y=None):

diff --git a/prince/mca.py b/prince/mca.py
@@ -55,7 +55,7 @@ def fit(self, X, y=None):
         """
 
         if self.check_input:
-            sklearn.utils.check_array(X, dtype=[str, np.number])
+            sklearn.utils.check_array(X, dtype=[str, "numeric"])
 
         # K is the number of actual variables, to apply the Benzécri correction
         self.K_ = X.shape[1]
@@ -99,5 +99,5 @@ def column_cosine_similarities(self, X):
     def transform(self, X):
         """Computes the row principal coordinates of a dataset."""
         if self.check_input:
-            sklearn.utils.check_array(X, dtype=[str, np.number])
+            sklearn.utils.check_array(X, dtype=[str, "numeric"])
         return self.row_coordinates(X)
diff --git a/pyproject.toml b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "prince"
-version = "0.14.0"
+version = "0.15.0"
 description = "Factor analysis in Python: PCA, CA, MCA, MFA, FAMD, GPA"
 authors = ["Max Halford <[email protected]>"]
 license = "MIT"
@@ -19,7 +19,7 @@ ipykernel = "^6.13.0"
 rpy2 = "^3.5.2"
 ruff = "^0.8.5"
 pre-commit = "^4.0.1"
-xarray = "^2023.11.0"
+xarray = "^2025.1.0"
 
 [tool.ruff]
 lint.select = ["E", "F", "I", "UP"]  # https://beta.ruff.rs/docs/rules/

diff --git a/tests/test_famd.py b/tests/test_famd.py
@@ -120,8 +120,8 @@ def test_issue_169():
     0         -1.000920 -0.669274
     1         -0.092001  0.669274
 
-    >>> famd.transform(df[3:])
-    component         0             1
-    3         -0.869173 -8.050546e-17
+    >>> famd.transform(df[3:]).round(6)
+    component         0    1
+    3         -0.869173 -0.0
 
     """