Update Devoir2.ipynb

TP5/devoir2/Devoir2.ipynb

@@ -78,7 +78,29 @@
     "    \n",
     "    y = np.mean(X, axis=1)\n",
     "\n",
-    "    return X, y\n"
+    "    return X, y\n",
+    "# /!\\ THIS IS A THIRD TEST VERSION, COULD (AND WILL CERTAINLY) CHANGE\n",
+    "\n",
+    "\n",
+    "import random\n",
+    "def generate_data_2(n_samples, n_features):\n",
+    "    X = []\n",
+    "    y = np.ndarray((n_samples,))\n",
+    "    X.append(np.random.geometric(p = 0.5, size = n_features))\n",
+    "    sum_X = np.ndarray((n_features,))\n",
+    "    for i in range(n_samples):\n",
+    "        p = random.random()\n",
+    "        temp = np.random.geometric(p = p, size = n_features)\n",
+    "        #print(temp)\n",
+    "        sum_X = sum_X + temp\n",
+    "        #print(sum_X)\n",
+    "        X.append(sum_X)\n",
+    "    X = np.array(X)\n",
+    "        \n",
+    "    \n",
+    "    y = np.mean(X, axis=1)\n",
+    "\n",
+    "    return X, y"
    ]
   },
   {
@@ -135,6 +157,30 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Arrays to store results\n",
+    "n_selected = []\n",
+    "\n",
+    "# We test our regression n_test time\n",
+    "for i in range(n_tests):\n",
+    "    # Generate the data\n",
+    "    X, y = generate_data_2(n_samples, n_features,)\n",
+    "    # Fit the model (pipeline with the data)\n",
+    "    model.fit(X, y)\n",
+    "    # We can now retrieve selected features :\n",
+    "    selected_features = lasso.coef_ != 0\n",
+    "    n_selected.append(np.count_nonzero(selected_features))\n",
+    "\n",
+    "uniq, count = np.unique(n_selected, return_counts=True)\n",
+    "plt.bar(uniq, count, label='Number of selected features per training')\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -177,7 +223,41 @@
     "\n",
     "uniq, count = np.unique(n_selected, return_counts=True)\n",
     "print(f'Features selected : {uniq}, count : {count}')\n",
-    "print(f'Number of time fist feature was ignored : {zero_removed}')"
+    "print(f'Number of time fist feature was ignored : {zero_removed}')\n",
+    "plt.bar(uniq, count, label='Number of selected features per training')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Arrays to store results\n",
+    "n_selected = []\n",
+    "zero_removed = 0\n",
+    "\n",
+    "# We test our regression n_test time\n",
+    "for i in range(n_tests):\n",
+    "    # Generate the data\n",
+    "    X, y = generate_data_2(n_samples, n_features)\n",
+    "    # Fit the model (pipeline with the data)\n",
+    "    model.fit(X, y)\n",
+    "    # We can now retrieve selected features :\n",
+    "    selected_features = elastic_net.coef_ != 0\n",
+    "    n_selected.append(np.count_nonzero(selected_features))\n",
+    "\n",
+    "    # Fastly show that we always remove X[0]\n",
+    "    if not selected_features[0]:\n",
+    "        zero_removed += 1\n",
+    "\n",
+    "\n",
+    "uniq, count = np.unique(n_selected, return_counts=True)\n",
+    "print(f'Features selected : {uniq}, count : {count}')\n",
+    "print(f'Number of time fist feature was ignored : {zero_removed}')\n",
+    "plt.bar(uniq, count, label='Number of selected features per training')\n",
+    "plt.show()"
    ]
   },
   {
@@ -194,6 +274,13 @@
     "\n",
     "It is **like** the elastic net « found » that each $X[i], i > 0$ were generated from $X[0]$ but did not « found » a link between the elements.\n"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -201,7 +288,8 @@
    "hash": "3abb0a1ef4892304d86bb3a3dfd052bcca35057beadba016173999c775e8d3ba"
   },
   "kernelspec": {
-   "display_name": "Python 3.9.7 64-bit ('AOS1-QteoCFsS': pipenv)",
+   "display_name": "Python 3",
+   "language": "python",
    "name": "python3"
   },
   "language_info": {
@@ -213,7 +301,8 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
   }
  },
  "nbformat": 4,