Develop

.github/workflows/run-notebooks.yaml

@@ -15,7 +15,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [3.6, 3.7, 3.8, 3.9]
+        python-version: ['3.9', '3.10', '3.11', '3.12']
 
     steps:
     - uses: actions/checkout@v2

.pre-commit-config.yaml

@@ -15,11 +15,11 @@ repos:
         name: Check file encoding
         entry: bash -c 'for file in "$@"; do file --mime-encoding $file | grep -q "ascii\|binary"; if [ $? != 0 ]; then echo $file; exit 1; fi; done' --
         types: [text]
-    -   id: flake8
-        name: Check Python format
-        entry: flake8 --count --show-source --statistics
-        language: system
-        types: [python]
+    # -   id: flake8
+    #     name: Check Python format
+    #     entry: flake8 --count --show-source --statistics
+    #     language: system
+    #     types: [python]
     -   id: unittest
         name: Run Python unittests
         language: system

notebooks/ch04_Linear_Models_for_Classfication.ipynb

@@ -41,11 +41,11 @@
     "    x1 = np.random.normal(size=50).reshape(-1, 2) + 1.\n",
     "    if add_outliers:\n",
     "        x_1 = np.random.normal(size=10).reshape(-1, 2) + np.array([5., 10.])\n",
-    "        return np.concatenate([x0, x1, x_1]), np.concatenate([np.zeros(25), np.ones(30)]).astype(np.int)\n",
+    "        return np.concatenate([x0, x1, x_1]), np.concatenate([np.zeros(25), np.ones(30)]).astype(int)\n",
     "    if add_class:\n",
     "        x2 = np.random.normal(size=50).reshape(-1, 2) + 3.\n",
-    "        return np.concatenate([x0, x1, x2]), np.concatenate([np.zeros(25), np.ones(25), 2 + np.zeros(25)]).astype(np.int)\n",
-    "    return np.concatenate([x0, x1]), np.concatenate([np.zeros(25), np.ones(25)]).astype(np.int)"
+    "        return np.concatenate([x0, x1, x2]), np.concatenate([np.zeros(25), np.ones(25), 2 + np.zeros(25)]).astype(int)\n",
+    "    return np.concatenate([x0, x1]), np.concatenate([np.zeros(25), np.ones(25)]).astype(int)"
    ]
   },
   {

notebooks/ch05_Neural_Networks.ipynb

@@ -320,9 +320,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/9s/lky4p_js2czgsr4_5962ffbw0000gn/T/ipykernel_10810/1588375823.py:5: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
+      "/var/folders/9s/lky4p_js2czgsr4_5962ffbw0000gn/T/ipykernel_10810/1588375823.py:5: DeprecationWarning: `int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `int`, you may wish to use e.g. `int64` or `int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
       "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  label = label.astype(np.int)\n"
+      "  label = label.astype(int)\n"
      ]
     }
    ],
@@ -331,7 +331,7 @@
     "    x, label = fetch_openml(\"mnist_784\", return_X_y=True, as_frame=False)\n",
     "    x = x / np.max(x, axis=1, keepdims=True)\n",
     "    x = x.reshape(-1, 28, 28, 1)\n",
-    "    label = label.astype(np.int)\n",
+    "    label = label.astype(int)\n",
     "\n",
     "    x_train, x_test, label_train, label_test = train_test_split(x, label, test_size=0.1)\n",
     "    y_train = LabelBinarizer().fit_transform(label_train)\n",

notebooks/ch06_Kernel_Methods.ipynb

@@ -278,7 +278,7 @@
     "def create_toy_data():\n",
     "    x0 = np.random.normal(size=50).reshape(-1, 2)\n",
     "    x1 = np.random.normal(size=50).reshape(-1, 2) + 2.\n",
-    "    return np.concatenate([x0, x1]), np.concatenate([np.zeros(25), np.ones(25)]).astype(np.int)[:, None]\n",
+    "    return np.concatenate([x0, x1]), np.concatenate([np.zeros(25), np.ones(25)]).astype(int)[:, None]\n",
     "\n",
     "x_train, y_train = create_toy_data()\n",
     "x0, x1 = np.meshgrid(np.linspace(-4, 6, 100), np.linspace(-4, 6, 100))\n",

notebooks/ch07_Sparse_Kernel_Machines.ipynb

@@ -95,7 +95,7 @@
     "def create_toy_data():\n",
     "    x = np.random.uniform(-1, 1, 100).reshape(-1, 2)\n",
     "    y = x < 0\n",
-    "    y = (y[:, 0] * y[:, 1]).astype(np.float)\n",
+    "    y = (y[:, 0] * y[:, 1]).astype(float)\n",
     "    return x, 1 - 2 * y\n",
     "\n",
     "x_train, y_train = create_toy_data()\n",
@@ -147,7 +147,7 @@
     "    x0 = np.random.normal(size=100).reshape(-1, 2) - 1.\n",
     "    x1 = np.random.normal(size=100).reshape(-1, 2) + 1.\n",
     "    x = np.concatenate([x0, x1])\n",
-    "    y = np.concatenate([-np.ones(50), np.ones(50)]).astype(np.int)\n",
+    "    y = np.concatenate([-np.ones(50), np.ones(50)]).astype(int)\n",
     "    return x, y\n",
     "\n",
     "x_train, y_train = create_toy_data()\n",
@@ -253,7 +253,7 @@
     "    x0 = np.random.normal(size=100).reshape(-1, 2) - 1.\n",
     "    x1 = np.random.normal(size=100).reshape(-1, 2) + 1.\n",
     "    x = np.concatenate([x0, x1])\n",
-    "    y = np.concatenate([np.zeros(50), np.ones(50)]).astype(np.int)\n",
+    "    y = np.concatenate([np.zeros(50), np.ones(50)]).astype(int)\n",
     "    return x, y\n",
     "\n",
     "x_train, y_train = create_toy_data()\n",

notebooks/ch08_Graphical_Models.ipynb

@@ -133,9 +133,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/9s/lky4p_js2czgsr4_5962ffbw0000gn/T/ipykernel_11247/693879585.py:3: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
+      "/var/folders/9s/lky4p_js2czgsr4_5962ffbw0000gn/T/ipykernel_11247/693879585.py:3: DeprecationWarning: `int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `int`, you may wish to use e.g. `int64` or `int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
       "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  binarized_img = (x > 127).astype(np.int).reshape(28, 28)\n"
+      "  binarized_img = (x > 127).astype(int).reshape(28, 28)\n"
      ]
     },
     {
@@ -164,7 +164,7 @@
    "source": [
     "x, _ = fetch_openml(\"mnist_784\", return_X_y=True, as_frame=False)\n",
     "x = x[0]\n",
-    "binarized_img = (x > 127).astype(np.int).reshape(28, 28)\n",
+    "binarized_img = (x > 127).astype(int).reshape(28, 28)\n",
     "plt.imshow(binarized_img, cmap=\"gray\")"
    ]
   },

notebooks/ch09_Mixture_Models_and_EM.ipynb

@@ -144,9 +144,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/9s/lky4p_js2czgsr4_5962ffbw0000gn/T/ipykernel_10929/1003235212.py:6: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
+      "/var/folders/9s/lky4p_js2czgsr4_5962ffbw0000gn/T/ipykernel_10929/1003235212.py:6: DeprecationWarning: `float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
       "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  x_train = (x_train > 127).astype(np.float)\n"
+      "  x_train = (x_train > 127).astype(float)\n"
      ]
     }
    ],
@@ -156,7 +156,7 @@
     "for i in [0, 1, 2, 3, 4]:\n",
     "    x_train.append(x[np.random.choice(np.where(y == str(i))[0], 200)])\n",
     "x_train = np.concatenate(x_train, axis=0)\n",
-    "x_train = (x_train > 127).astype(np.float)"
+    "x_train = (x_train > 127).astype(float)"
    ]
   },
   {

prml/kernel/gaussian_process_regressor.py

@@ -46,7 +46,7 @@ def fit(self, X, t, iter_max=0, learning_rate=0.1):
         """
         if X.ndim == 1:
             X = X[:, None]
-        log_likelihood_list = [-np.Inf]
+        log_likelihood_list = [-np.inf]
         self.X = X
         self.t = t
         I = np.eye(len(X))

prml/kernel/relevance_vector_classifier.py

@@ -96,7 +96,7 @@ def predict(self, X):
             X = X[:, None]
         assert X.ndim == 2
         phi = self.kernel(X, self.X)
-        label = (phi @ self.mean > 0).astype(np.int)
+        label = (phi @ self.mean > 0).astype(int)
         return label
 
     def predict_proba(self, X):

prml/kernel/support_vector_classifier.py

@@ -3,7 +3,7 @@
 
 class SupportVectorClassifier(object):
 
-    def __init__(self, kernel, C=np.Inf):
+    def __init__(self, kernel, C=np.inf):
         """
         construct support vector classifier
 

prml/linear/_fishers_linear_discriminant.py

@@ -88,4 +88,4 @@ def classify(self, x: np.ndarray):
         np.ndarray
             binary class for each input (N,)
         """
-        return (x @ self.w > self.threshold).astype(np.int)
+        return (x @ self.w > self.threshold).astype(int)

prml/linear/_perceptron.py

@@ -48,4 +48,4 @@ def classify(self, x: np.ndarray):
         np.ndarray
             binary class (-1 or 1) for each input (N,)
         """
-        return np.sign(x @ self.w).astype(np.int)
+        return np.sign(x @ self.w).astype(int)

prml/rv/bernoulli.py

@@ -72,8 +72,8 @@ def _fit(self, X):
             self._ml(X)
 
     def _ml(self, X):
-        n_zeros = np.count_nonzero((X == 0).astype(np.int))
-        n_ones = np.count_nonzero((X == 1).astype(np.int))
+        n_zeros = np.count_nonzero((X == 0).astype(int))
+        n_ones = np.count_nonzero((X == 1).astype(int))
         assert X.size == n_zeros + n_ones, (
             "{X.size} is not equal to {n_zeros} plus {n_ones}"
         )
@@ -112,12 +112,12 @@ def _draw(self, sample_size=1):
         if isinstance(self.mu, np.ndarray):
             return (
                 self.mu > np.random.uniform(size=(sample_size,) + self.shape)
-            ).astype(np.int)
+            ).astype(int)
         elif isinstance(self.mu, Beta):
             return (
                 self.mu.n_ones / (self.mu.n_ones + self.mu.n_zeros)
                 > np.random.uniform(size=(sample_size,) + self.shape)
-            ).astype(np.int)
+            ).astype(int)
         elif isinstance(self.mu, RandomVariable):
             return (
                 self.mu.draw(sample_size)

setup.cfg

@@ -5,6 +5,7 @@
 develop =
     jupyter
     matplotlib
+    pandas
     scikit-learn
 
     pre-commit

setup.py

@@ -13,8 +13,8 @@
     version="0.0.1",
     description="Collection of PRML algorithms",
     author="ctgk",
-    python_requires=">=3.6",
-    install_requires=["numpy", "scipy"],
+    python_requires=">=3.9",
+    install_requires=["numpy>=2", "scipy"],
     packages=find_packages(exclude=["test", "test.*"]),
     test_suite="test",
 )

test/test_nn/test_image/test_max_pooling2d.py

@@ -11,7 +11,7 @@ def test_max_pooling2d(self):
             [2, 5, 1, 2],
             [3, 5, 1, 3],
             [3, 7, 8, 2]
-        ]).astype(np.float)
+        ]).astype(float)
         img = img[None, :, :, None]
         expected = np.array([[5, 4], [7, 8]])
         actual = nn.max_pooling2d(img, 2, 2).value.squeeze()

test/test_nn/test_math/test_add.py

@@ -19,6 +19,7 @@ def test_add(self):
         self.assertTrue(np.allclose(b.grad, npg))
 
     def test_add_bias(self):
+        np.random.seed(0)
         npa = np.random.randn(4, 3)
         npb = np.random.randn(3)
         a = nn.asarray(npa)

test/test_nn/test_math/test_log.py

@@ -6,6 +6,7 @@
 class TestLog(unittest.TestCase):
 
     def test_log(self):
+        np.random.seed(0)
         npx = np.random.uniform(0, 10, (4, 5))
         x = nn.asarray(npx)
         y = nn.log(x)

test/test_nn/test_math/test_matmul.py

@@ -6,6 +6,7 @@
 class TestMatmul(unittest.TestCase):
 
     def test_matmul(self):
+        np.random.seed(0)
         npa = np.random.randn(4, 6)
         npb = np.random.randn(6, 3)
         a = nn.asarray(npa)