upgrade version

.github/workflows/documentation.yml

@@ -21,7 +21,7 @@ jobs:
 
       - uses: actions/setup-python@v4
         with:
-          python-version: '3.11'
+          python-version: '3.12'
 
       - uses: tlylt/install-graphviz@v1
 

MANIFEST.in

@@ -1,4 +1,3 @@
-recursive-include onnx_extended *.c *.cpp *.h *.pyx *.pxd *.pxi *.py
 include pyproject.toml
 include MANIFEST.in
 include setup.cfg

_doc/conf.py

@@ -24,7 +24,6 @@
     "sphinx_runpython.epkg",
     "sphinx_runpython.gdot",
     "sphinx_runpython.runpython",
-    "sphinxcontrib.blockdiag",
     "matplotlib.sphinxext.plot_directive",
 ]
 
@@ -73,7 +72,7 @@
 """
 
 # The following is used by sphinx.ext.linkcode to provide links to github
-linkcode_resolve = make_linkcode_resolve(
+_linkcode_resolve = make_linkcode_resolve(
     "mlstatpy",
     (
         "https://github.com/sdpython/mlstatpy/"
@@ -82,6 +81,11 @@
     ),
 )
 
+
+def linkcode_resolve(domain, info):
+    return _linkcode_resolve(domain, info)
+
+
 latex_elements = {
     "papersize": "a4",
     "pointsize": "10pt",

_doc/index.rst

@@ -10,7 +10,7 @@ Les maths d'abord, la programmation ensuite
 Le livre `The Elements of Statistical Learning <http://statweb.stanford.edu/~tibs/ElemStatLearn/>`_
 est considéré comme la bible en matière de machine learning. Ce site aborde des sujets connexes.
 Le site est aussi disponible (format brut de fonderie) sur
-`GitHub/mlstatpy <https://github.com/sdpython/mlstatpy>`_ |gitlogo|.
+`github/mlstatpy <https://github.com/sdpython/mlstatpy>`_ |gitlogo|.
 
 .. toctree::
     :maxdepth: 1
@@ -78,4 +78,5 @@ Xavier Dupré
 Older versions
 ++++++++++++++
 
+* `0.5.0 <../v0.5.0/index.html>`_
 * `0.4.0 <../v0.4.0/index.html>`_

_doc/notebooks/dsgarden/correlation_non_lineaire.ipynb

@@ -940,7 +940,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": null,
    "metadata": {
     "scrolled": false
    },
@@ -962,7 +962,7 @@
     "\n",
     "def pairplot_cross_val(data, model=None, ax=None, **params):\n",
     "    if ax is None:\n",
-    "        fig, ax = plt.subplots(\n",
+    "        _fig, ax = plt.subplots(\n",
     "            data.shape[1], data.shape[1], figsize=params.get(\"figsize\", (10, 10))\n",
     "        )\n",
     "    if \"figsize\" in params:\n",
@@ -3040,4 +3040,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

_doc/notebooks/dsgarden/discret_gradient.ipynb

@@ -3910,4 +3910,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

_doc/notebooks/dsgarden/quantization_f8.ipynb

@@ -833,4 +833,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}

_doc/notebooks/dsgarden/regression_lineaire.ipynb

@@ -2385,4 +2385,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

_doc/notebooks/dsgarden/split_train_test.ipynb

@@ -1348,4 +1348,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

_doc/notebooks/image/segment_detection.ipynb

@@ -470,4 +470,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

_doc/notebooks/metric/pvalues_examples.ipynb

@@ -1045,7 +1045,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1087,7 +1087,7 @@
     "    if ax is None:\n",
     "        import matplotlib.pyplot as plt\n",
     "\n",
-    "        fig, ax = plt.subplots(1, 1, figsize=figsize)\n",
+    "        _fig, ax = plt.subplots(1, 1, figsize=figsize)\n",
     "\n",
     "    smarker = {\n",
     "        (True, True): \"o-\",\n",

_doc/notebooks/ml/logreg_voronoi.ipynb

@@ -167,7 +167,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -233,7 +233,7 @@
     "    cmap = plt.cm.tab20\n",
     "    Z = Z.reshape(xx.shape)\n",
     "    if ax is None:\n",
-    "        fig, ax = plt.subplots(1, 1, figsize=figsize or (4, 3))\n",
+    "        _fig, ax = plt.subplots(1, 1, figsize=figsize or (4, 3))\n",
     "    ax.pcolormesh(xx, yy, Z, cmap=cmap)\n",
     "\n",
     "    # Plot also the training points\n",
@@ -1963,4 +1963,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

_doc/notebooks/ml/neural_tree.ipynb

@@ -1826,4 +1826,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

_doc/notebooks/ml/neural_tree_cost.ipynb

@@ -351,7 +351,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "ecef383a",
    "metadata": {},
    "outputs": [
@@ -773,7 +773,7 @@
    ],
    "source": [
     "from tqdm import tqdm\n",
-    "from onnx_array_api.ext_test_case import measure_time\n",
+    "from mlstatpy.ext_test_case import measure_time\n",
     "\n",
     "data = []\n",
     "for d in tqdm(range(2, 10)):\n",
@@ -870,4 +870,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}