style: update black rules to allow lines up to 100 characters

cmnemoi_learn/_abstract_model.py

@@ -61,8 +61,4 @@ def _reshape_ndarray(self, ndarray: np.ndarray) -> np.ndarray:
         Returns:
             np.ndarray: Reshaped ndarray
         """
-        return (
-            ndarray.reshape((ndarray.shape[0], 1))
-            if len(ndarray.shape) == 1
-            else ndarray
-        )
+        return ndarray.reshape((ndarray.shape[0], 1)) if len(ndarray.shape) == 1 else ndarray

cmnemoi_learn/classification/_abstract_classifier.py

@@ -31,10 +31,5 @@ def score(self, X: np.ndarray, y: np.ndarray) -> float:
         """
         y_pred = self.predict(X)
         return float(
-            np.mean(
-                [
-                    1 if yi_pred == yi_true else 0
-                    for (yi_pred, yi_true) in zip(y_pred, y)
-                ]
-            )
+            np.mean([1 if yi_pred == yi_true else 0 for (yi_pred, yi_true) in zip(y_pred, y)])
         )

cmnemoi_learn/classification/_logistic_regression.py

@@ -56,9 +56,7 @@ def predict(self, X: np.ndarray) -> np.ndarray:
         """
         X = self._get_inputs_with_bias_column(X)
         probabilities = self._logistic_function(X @ self.weights)
-        return np.array(
-            [1 if probability > self.threshold else 0 for probability in probabilities]
-        )
+        return np.array([1 if probability > self.threshold else 0 for probability in probabilities])
 
     def _gradient_descent(self, n_iter: int, learning_rate: float = 0.5) -> np.ndarray:
         """Gradient descent algorithm.
@@ -76,9 +74,7 @@ def _gradient_descent(self, n_iter: int, learning_rate: float = 0.5) -> np.ndarr
         weights_gradient = np.array([])
         for _ in range(n_iter):
             weights_gradient = (
-                (1 / n)
-                * self.X.T
-                @ (self._logistic_function(self.X @ weights) - self.y)
+                (1 / n) * self.X.T @ (self._logistic_function(self.X @ weights) - self.y)
             )
             weights = weights - learning_rate * weights_gradient
 

notebooks/notebook.ipynb

@@ -0,0 +1,339 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'matplotlib'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[1], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mnumpy\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mnp\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mmatplotlib\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mpyplot\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mplt\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39msklearn\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mdatasets\u001b[39;00m \u001b[39mimport\u001b[39;00m make_blobs, make_circles\n\u001b[1;32m      4\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39msklearn\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mmetrics\u001b[39;00m \u001b[39mimport\u001b[39;00m accuracy_score, log_loss\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.datasets import make_blobs, make_circles\n",
+    "from sklearn.metrics import accuracy_score, log_loss\n",
+    "from tqdm import tqdm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fonctions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mRunning cells with 'cmnemoi-learn-sm69zs_S-py3.11' requires the ipykernel package.\n",
+      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+      "\u001b[1;31mCommand: '/Users/charles/Library/Caches/pypoetry/virtualenvs/cmnemoi-learn-sm69zs_S-py3.11/bin/python -m pip install ipykernel -U --force-reinstall'"
+     ]
+    }
+   ],
+   "source": [
+    "def initialisation(dimensions):\n",
+    "    \n",
+    "    parametres = {}\n",
+    "    C = len(dimensions)\n",
+    "\n",
+    "    np.random.seed(1)\n",
+    "\n",
+    "    for c in range(1, C):\n",
+    "        parametres['W' + str(c)] = np.random.randn(dimensions[c], dimensions[c - 1])\n",
+    "        parametres['b' + str(c)] = np.random.randn(dimensions[c], 1)\n",
+    "\n",
+    "    return parametres"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mRunning cells with 'cmnemoi-learn-sm69zs_S-py3.11' requires the ipykernel package.\n",
+      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+      "\u001b[1;31mCommand: '/Users/charles/Library/Caches/pypoetry/virtualenvs/cmnemoi-learn-sm69zs_S-py3.11/bin/python -m pip install ipykernel -U --force-reinstall'"
+     ]
+    }
+   ],
+   "source": [
+    "def forward_propagation(X, parametres):\n",
+    "  \n",
+    "  activations = {'A0': X}\n",
+    "\n",
+    "  C = len(parametres) // 2\n",
+    "\n",
+    "  for c in range(1, C + 1):\n",
+    "\n",
+    "    Z = parametres['W' + str(c)].dot(activations['A' + str(c - 1)]) + parametres['b' + str(c)]\n",
+    "    activations['A' + str(c)] = 1 / (1 + np.exp(-Z))\n",
+    "\n",
+    "  return activations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mRunning cells with 'cmnemoi-learn-sm69zs_S-py3.11' requires the ipykernel package.\n",
+      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+      "\u001b[1;31mCommand: '/Users/charles/Library/Caches/pypoetry/virtualenvs/cmnemoi-learn-sm69zs_S-py3.11/bin/python -m pip install ipykernel -U --force-reinstall'"
+     ]
+    }
+   ],
+   "source": [
+    "def back_propagation(y, parametres, activations):\n",
+    "\n",
+    "  m = y.shape[1]\n",
+    "  C = len(parametres) // 2\n",
+    "\n",
+    "  dZ = activations['A' + str(C)] - y\n",
+    "  gradients = {}\n",
+    "\n",
+    "  for c in reversed(range(1, C + 1)):\n",
+    "    gradients['dW' + str(c)] = 1/m * np.dot(dZ, activations['A' + str(c - 1)].T)\n",
+    "    gradients['db' + str(c)] = 1/m * np.sum(dZ, axis=1, keepdims=True)\n",
+    "    if c > 1:\n",
+    "      dZ = np.dot(parametres['W' + str(c)].T, dZ) * activations['A' + str(c - 1)] * (1 - activations['A' + str(c - 1)])\n",
+    "\n",
+    "  return gradients"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mRunning cells with 'cmnemoi-learn-sm69zs_S-py3.11' requires the ipykernel package.\n",
+      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+      "\u001b[1;31mCommand: '/Users/charles/Library/Caches/pypoetry/virtualenvs/cmnemoi-learn-sm69zs_S-py3.11/bin/python -m pip install ipykernel -U --force-reinstall'"
+     ]
+    }
+   ],
+   "source": [
+    "def update(gradients, parametres, learning_rate):\n",
+    "\n",
+    "    C = len(parametres) // 2\n",
+    "\n",
+    "    for c in range(1, C + 1):\n",
+    "        parametres['W' + str(c)] = parametres['W' + str(c)] - learning_rate * gradients['dW' + str(c)]\n",
+    "        parametres['b' + str(c)] = parametres['b' + str(c)] - learning_rate * gradients['db' + str(c)]\n",
+    "\n",
+    "    return parametres"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mRunning cells with 'cmnemoi-learn-sm69zs_S-py3.11' requires the ipykernel package.\n",
+      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+      "\u001b[1;31mCommand: '/Users/charles/Library/Caches/pypoetry/virtualenvs/cmnemoi-learn-sm69zs_S-py3.11/bin/python -m pip install ipykernel -U --force-reinstall'"
+     ]
+    }
+   ],
+   "source": [
+    "def predict(X, parametres):\n",
+    "  activations = forward_propagation(X, parametres)\n",
+    "  C = len(parametres) // 2\n",
+    "  Af = activations['A' + str(C)]\n",
+    "  return Af >= 0.5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mRunning cells with 'cmnemoi-learn-sm69zs_S-py3.11' requires the ipykernel package.\n",
+      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+      "\u001b[1;31mCommand: '/Users/charles/Library/Caches/pypoetry/virtualenvs/cmnemoi-learn-sm69zs_S-py3.11/bin/python -m pip install ipykernel -U --force-reinstall'"
+     ]
+    }
+   ],
+   "source": [
+    "def deep_neural_network(X, y, hidden_layers = (16, 16, 16), learning_rate = 0.001, n_iter = 3000):\n",
+    "    \n",
+    "    # initialisation parametres\n",
+    "    dimensions = list(hidden_layers)\n",
+    "    dimensions.insert(0, X.shape[0])\n",
+    "    dimensions.append(y.shape[0])\n",
+    "    np.random.seed(1)\n",
+    "    parametres = initialisation(dimensions)\n",
+    "\n",
+    "    # tableau numpy contenant les futures accuracy et log_loss\n",
+    "    training_history = np.zeros((int(n_iter), 2))\n",
+    "\n",
+    "    C = len(parametres) // 2\n",
+    "\n",
+    "    # gradient descent\n",
+    "    for i in tqdm(range(n_iter)):\n",
+    "\n",
+    "        activations = forward_propagation(X, parametres)\n",
+    "        gradients = back_propagation(y, parametres, activations)\n",
+    "        parametres = update(gradients, parametres, learning_rate)\n",
+    "        Af = activations['A' + str(C)]\n",
+    "\n",
+    "        # calcul du log_loss et de l'accuracy\n",
+    "        training_history[i, 0] = (log_loss(y.flatten(), Af.flatten()))\n",
+    "        y_pred = predict(X, parametres)\n",
+    "        training_history[i, 1] = (accuracy_score(y.flatten(), y_pred.flatten()))\n",
+    "\n",
+    "    # Plot courbe d'apprentissage\n",
+    "    plt.figure(figsize=(12, 4))\n",
+    "    plt.subplot(1, 2, 1)\n",
+    "    plt.plot(training_history[:, 0], label='train loss')\n",
+    "    plt.legend()\n",
+    "    plt.subplot(1, 2, 2)\n",
+    "    plt.plot(training_history[:, 1], label='train acc')\n",
+    "    plt.legend()\n",
+    "    plt.show()\n",
+    "\n",
+    "    return training_history"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mRunning cells with 'cmnemoi-learn-sm69zs_S-py3.11' requires the ipykernel package.\n",
+      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+      "\u001b[1;31mCommand: '/Users/charles/Library/Caches/pypoetry/virtualenvs/cmnemoi-learn-sm69zs_S-py3.11/bin/python -m pip install ipykernel -U --force-reinstall'"
+     ]
+    }
+   ],
+   "source": [
+    "X, y = make_circles(n_samples=100, noise=0.1, factor=0.3, random_state=0)\n",
+    "X = X.T\n",
+    "y = y.reshape((1, y.shape[0]))\n",
+    "\n",
+    "print('dimensions de X:', X.shape)\n",
+    "print('dimensions de y:', y.shape)\n",
+    "\n",
+    "plt.scatter(X[0, :], X[1, :], c=y, cmap='summer')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mRunning cells with 'cmnemoi-learn-sm69zs_S-py3.11' requires the ipykernel package.\n",
+      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+      "\u001b[1;31mCommand: '/Users/charles/Library/Caches/pypoetry/virtualenvs/cmnemoi-learn-sm69zs_S-py3.11/bin/python -m pip install ipykernel -U --force-reinstall'"
+     ]
+    }
+   ],
+   "source": [
+    "deep_neural_network(X, y, hidden_layers = (16, 16, 16), learning_rate = 0.1, n_iter = 3000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mRunning cells with 'cmnemoi-learn-sm69zs_S-py3.11' requires the ipykernel package.\n",
+      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+      "\u001b[1;31mCommand: '/Users/charles/Library/Caches/pypoetry/virtualenvs/cmnemoi-learn-sm69zs_S-py3.11/bin/python -m pip install ipykernel -U --force-reinstall'"
+     ]
+    }
+   ],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "038c04557dfd72b4d6039cb7951b93ffe7520921b6515cb88d8784deedfaf89f"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.7.9 ('base')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

pyproject.toml

@@ -29,6 +29,9 @@ pytest = "^7.4.0"
 pytest-cov = "^4.1.0"
 scikit-learn = "^1.3.0"
 
+[tool.black]
+line-length = 100
+
 [build-system]
 requires = ["poetry-core"]
 build-backend = "poetry.core.masonry.api"