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JunoBench

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xet

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Yiran Wang commited on Jun 24

Commit

ba37a6c

1 Parent(s): f5ec8a8

polish numpy buggy and fixed versions

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Files changed (42) hide show

.gitattributes +1 -0
.gitignore +2 -0
benchmark/numpy_1/numpy_1_fixed.ipynb +0 -0
benchmark/numpy_1/numpy_1_reproduced.ipynb +0 -0
benchmark/numpy_10/checkpoint.pth +1 -1
benchmark/numpy_10/numpy_10_fixed.ipynb +0 -0
benchmark/numpy_10/numpy_10_reproduced.ipynb +18 -16
benchmark/numpy_11/data/training_data_64_64.npy +0 -3
benchmark/numpy_11/numpy_11_fixed.ipynb +12 -5
benchmark/numpy_11/numpy_11_reproduced.ipynb +4 -4
benchmark/numpy_12/numpy_12_fixed.ipynb +14 -14
benchmark/numpy_12/numpy_12_reproduced.ipynb +14 -14
benchmark/numpy_13/numpy_13_fixed.ipynb +4 -354
benchmark/numpy_14/numpy_14_fixed.ipynb +27 -48
benchmark/numpy_14/numpy_14_reproduced.ipynb +28 -28
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benchmark/numpy_6/numpy_6_reproduced.ipynb +10 -96
benchmark/numpy_7/learning_curve.png +2 -2
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benchmark/numpy_7/numpy_7_fixed.ipynb +0 -0
benchmark/numpy_7/numpy_7_reproduced.ipynb +0 -0
benchmark/numpy_7/training_accuracy.png +2 -2
benchmark/numpy_8/numpy_8_fixed.ipynb +87 -113
benchmark/numpy_8/numpy_8_reproduced.ipynb +98 -112
benchmark/numpy_9/numpy_9_fixed.ipynb +2 -2
benchmark/numpy_9/numpy_9_reproduced.ipynb +3 -3

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@@ -221,7 +221,7 @@
   },
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@@ -246,7 +246,7 @@
   },
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@@ -308,7 +308,7 @@
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@@ -360,7 +360,7 @@
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@@ -404,7 +404,7 @@
     "optimizer = optim.Adam(model.classifier.parameters(), lr=0.001)\n",
     "\n",
     "# Train the model\n",
-    "epochs = 2 #10\n",
     "print_every = 100\n",
     "steps = 0\n",
     "\n",
@@ -466,7 +466,7 @@
   },
   {
    "cell_type": "code",
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@@ -486,8 +486,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Test Loss: 5.703.. \n",
-      "Test Accuracy: 0.016\n"
      ]
     }
    ],
@@ -541,7 +541,7 @@
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     "_uuid": "0fa599e9-adda-4420-a447-2193f78d8813",
@@ -989,13 +989,15 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-18-c10d9ac8d8ed>\u001b[0m in \u001b[0;36m<cell line: 9>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;31m# Testing with a random image from the test set\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mtest_image_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_ds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimgs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0mdisplay_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_image_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32mnumpy/random/mtrand.pyx\u001b[0m in \u001b[0;36mnumpy.random.mtrand.RandomState.choice\u001b[0;34m()\u001b[0m\n",
       "\u001b[0;31mValueError\u001b[0m: a must be 1-dimensional"
      ]
     }
    ],
    "source": [
     "# Display an image along with the top 5 classes\n",
     "def display_image(image_path):\n",
     "    img = process_image(image_path)\n",

   },
   {
    "cell_type": "code",
+   "execution_count": 2,
    "metadata": {
     "_cell_guid": "f4013f5e-5c3f-4a75-bd6a-6211e7a0cb61",
     "_uuid": "0a583a63-ddc6-4355-bb5a-a8de36e88d13",
   },
   {
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+   "execution_count": 3,
    "metadata": {
     "_cell_guid": "e4186edb-09f5-4886-861b-720d933bca67",
     "_uuid": "5356a5e6-a838-42b9-8eda-4f03f0b7b12c",
   },
   {
    "cell_type": "code",
+   "execution_count": 4,
    "metadata": {
     "_cell_guid": "8a70867d-ab72-4f06-ae03-d21cb3179072",
     "_uuid": "3a7f792a-4ace-41ce-99b0-ce047357a9dd",
   },
   {
    "cell_type": "code",
+   "execution_count": 5,
    "metadata": {
     "_cell_guid": "38580579-2091-4577-8deb-677c6622c46b",
     "_uuid": "f3986cbc-151e-44fd-9bab-27a1c5e95e25",
     "optimizer = optim.Adam(model.classifier.parameters(), lr=0.001)\n",
     "\n",
     "# Train the model\n",
+    "epochs = 1 #10\n",
     "print_every = 100\n",
     "steps = 0\n",
     "\n",
   },
   {
    "cell_type": "code",
+   "execution_count": 6,
    "metadata": {
     "_cell_guid": "8fc1e356-fa0e-43f1-923d-c4df3fb4d156",
     "_uuid": "eb317077-974c-4f54-90b3-2d2135ca6f3c",
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Test Loss: 4.927.. \n",
+      "Test Accuracy: 0.000\n"
      ]
     }
    ],
   },
   {
    "cell_type": "code",
+   "execution_count": 7,
    "metadata": {
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      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-12-c10d9ac8d8ed>\u001b[0m in \u001b[0;36m<cell line: 9>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;31m# Testing with a random image from the test set\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mtest_image_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_ds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimgs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0mdisplay_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_image_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32mnumpy/random/mtrand.pyx\u001b[0m in \u001b[0;36mnumpy.random.mtrand.RandomState.choice\u001b[0;34m()\u001b[0m\n",
       "\u001b[0;31mValueError\u001b[0m: a must be 1-dimensional"
      ]
     }
    ],
    "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
     "# Display an image along with the top 5 classes\n",
     "def display_image(image_path):\n",
     "    img = process_image(image_path)\n",

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@@ -262,7 +262,7 @@
   },
   {
    "cell_type": "code",
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    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-03-06T08:36:33.489978Z",
@@ -285,7 +285,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      " 24%|██▍       | 28/116 [00:00<00:03, 27.07it/s]"
      ]
     },
     {
@@ -299,7 +299,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      " 64%|██████▍   | 74/116 [00:03<00:01, 25.12it/s]"
      ]
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     {
@@ -313,7 +313,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 116/116 [00:04<00:00, 26.47it/s]\n"
      ]
     },
     {
@@ -322,6 +322,13 @@
      "text": [
       "Saving training image binary...\n"
      ]
     }
    ],
    "source": [
@@ -354,7 +361,7 @@
     "\n",
     "\n",
     "  print(\"Saving training image binary...\")\n",
-    "  np.save(training_binary_path,training_data)\n",
     "  elapsed = time.time()-start\n",
     "  \n",
     "else:\n",

   },
   {
    "cell_type": "code",
+   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-03-06T08:36:33.489978Z",
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      " 23%|██▎       | 27/116 [00:00<00:01, 65.54it/s]"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      " 69%|██████▉   | 80/116 [00:01<00:00, 51.75it/s]"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "100%|██████████| 116/116 [00:02<00:00, 50.18it/s]"
      ]
     },
     {
      "text": [
       "Saving training image binary...\n"
      ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
     }
    ],
    "source": [
     "\n",
     "\n",
     "  print(\"Saving training image binary...\")\n",
+    "#   np.save(training_binary_path,training_data)\n",
     "  elapsed = time.time()-start\n",
     "  \n",
     "else:\n",

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-      "100%|██████████| 116/116 [00:05<00:00, 19.67it/s]\n"
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       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-4-bb50660df13a>\u001b[0m in \u001b[0;36m<cell line: 6>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     15\u001b[0m             GENERATE_SQUARE),Image.LANCZOS)\n\u001b[1;32m     16\u001b[0m       \u001b[0mtraining_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m   training_data = np.reshape(training_data,(-1,GENERATE_SQUARE,\n\u001b[0m\u001b[1;32m     18\u001b[0m             GENERATE_SQUARE,3))\n\u001b[1;32m     19\u001b[0m   \u001b[0mtraining_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtraining_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36mreshape\u001b[0;34m(a, newshape, order)\u001b[0m\n\u001b[1;32m    283\u001b[0m            [5, 6]])\n\u001b[1;32m    284\u001b[0m     \"\"\"\n\u001b[0;32m--> 285\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_wrapfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'reshape'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnewshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    286\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    287\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36m_wrapfunc\u001b[0;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[1;32m     54\u001b[0m     \u001b[0mbound\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     55\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mbound\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 56\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_wrapit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     57\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36m_wrapit\u001b[0;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[1;32m     43\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m         \u001b[0mwrap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 45\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     46\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mwrap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
@@ -327,7 +327,7 @@
     "\n",
     "\n",
     "  print(\"Saving training image binary...\")\n",
-    "  np.save(training_binary_path,training_data)\n",
     "  elapsed = time.time()-start\n",
     "  \n",
     "else:\n",

   },
   {
    "cell_type": "code",
+   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-03-06T08:36:33.489978Z",
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "100%|██████████| 116/116 [00:02<00:00, 53.55it/s]\n"
      ]
     },
     {
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-3-3b94fba6246d>\u001b[0m in \u001b[0;36m<cell line: 6>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     15\u001b[0m             GENERATE_SQUARE),Image.LANCZOS)\n\u001b[1;32m     16\u001b[0m       \u001b[0mtraining_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m   training_data = np.reshape(training_data,(-1,GENERATE_SQUARE,\n\u001b[0m\u001b[1;32m     18\u001b[0m             GENERATE_SQUARE,3))\n\u001b[1;32m     19\u001b[0m   \u001b[0mtraining_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtraining_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36mreshape\u001b[0;34m(a, newshape, order)\u001b[0m\n\u001b[1;32m    283\u001b[0m            [5, 6]])\n\u001b[1;32m    284\u001b[0m     \"\"\"\n\u001b[0;32m--> 285\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_wrapfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'reshape'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnewshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    286\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    287\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36m_wrapfunc\u001b[0;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[1;32m     54\u001b[0m     \u001b[0mbound\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     55\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mbound\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 56\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_wrapit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     57\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36m_wrapit\u001b[0;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[1;32m     43\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m         \u001b[0mwrap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 45\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     46\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mwrap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
     "\n",
     "\n",
     "  print(\"Saving training image binary...\")\n",
+    "#   np.save(training_binary_path,training_data)\n",
     "  elapsed = time.time()-start\n",
     "  \n",
     "else:\n",

benchmark/numpy_12/numpy_12_fixed.ipynb CHANGED Viewed

@@ -561,34 +561,34 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "84/84 - 2s - 22ms/step - loss: 0.0521\n",
       "Epoch 2/10\n",
-      "84/84 - 0s - 2ms/step - loss: 0.0203\n",
       "Epoch 3/10\n",
-      "84/84 - 0s - 1ms/step - loss: 0.0136\n",
       "Epoch 4/10\n",
-      "84/84 - 0s - 1ms/step - loss: 0.0116\n",
       "Epoch 5/10\n",
-      "84/84 - 0s - 1ms/step - loss: 0.0099\n",
       "Epoch 6/10\n",
-      "84/84 - 0s - 1ms/step - loss: 0.0080\n",
       "Epoch 7/10\n",
-      "84/84 - 0s - 1ms/step - loss: 0.0061\n",
       "Epoch 8/10\n",
-      "84/84 - 0s - 1ms/step - loss: 0.0046\n",
       "Epoch 9/10\n",
-      "84/84 - 0s - 1ms/step - loss: 0.0035\n",
       "Epoch 10/10\n",
-      "84/84 - 0s - 1ms/step - loss: 0.0027\n",
-      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step\n",
       "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
-      "Train Score: 25.55 RMSE\n",
-      "Test Score: 64.38 RMSE\n"
      ]
     },
     {
      "data": {
-      "image/png": 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T0OLCZunflQG9gyemi40CdSnRWgZlyNWgXHDBBcb306ZNY+7cuYwcOZLXXnuN8PDwPr843cMPP8z999/fb+8vhBBi4PvyUIXRsQKh25VX7+CJtlswm5RWz1nMJuwWEy0uD/Utri5nk/RWlXeZqzv1J+Bb4hkMGZRetRnHxcVx0kkncfDgQVJTU3E4HFRXV7c6pqSkxKhZSU1NPa6rR/+5vboW3T333ENNTY3xlZeX15vLFkIIMQi9u60QAMUbE4Qqg1LTwUaBOv+Jrf1ND9i6U38CkOJd4hmSNSj+6uvrOXToEGlpacyaNQur1cqqVauM5/ft20dubi7Z2dkAZGdns3PnTkpLS41jVq5cSUxMDFlZWR2ex263ExMT0+pLCCHEiaPZ6ebj3dryzmnjkgCoD1UGxTtFtqMAJZidPFUN3e/gAUiJ8S3xDPRx9wEFKD/5yU9Ys2YNR48eZd26dVx++eWYzWauueYaYmNjufnmm7nrrrtYvXo1OTk53HjjjWRnZzNv3jwAzjvvPLKysrjuuuvYvn07H330Effeey/Lli3Dbrf3yw0KIYQY/D7bV0pdi4u02DDOPGkYEMIaFKPFuP0qCd+Ggf0fQOktxt2ZgQKQ7K1Bcbg9xvLQQBVQDUp+fj7XXHMNFRUVDBs2jNNOO40NGzYwbJj2H8tjjz2GyWRiyZIltLS0sGjRIv72t78ZrzebzaxYsYJbb72V7OxsIiMjueGGG3jggQf69q6EEEIMKe9u15Z3Lp6e7rchX2gyKF0v8QRv1+Bqowal6w4eAJvFRGKkjYoGB8U1zd3OvIRCQAHKK6+80unzYWFhLF++nOXLl3d4zMiRI3n//fcDOa0QQogT3OcHygG4cGoauZWNQHCHofnTu3jazkDR+TIoA68GBSA5JoyKBgcldc1kMXBLJmQvHiGEEANas9NNnbfuY3RSJJE2LUMRuiUebw1K2OCrQQFI1QtlawZ2oawEKEIIIQa0Cu+HsNWsEBNm8QUAQdyQz19NBxsF6vRhbcEIUAKtQQFfoWzJAJ+FIgGKEEKIAa2iXvsgTYy0oygKkUEMANrj2ygw9EWy1QHOQQFfgDLQZ6FIgCKEEGJAK/cGKEnR2odwRBCLUNtT28FGgbpgFsnqNSjx3Rhzr/O1GkuAIoQQQvRYeb32IZwYqdVO6F08jQ53SGZ51HaxxBOsGhT/jQIDmVibGjs49uORAEUIIcSAVqEHKFHeDIq3SNblUWkJwoZ8bXV3UFt/d/FUN/nmmMR1cC3t0WehSA2KEEII0Qt6DUpSlPY3/wibr/ajMQSFsl0WyQZp1H21t0A2NtyKpRsbBer0JZ6Khhac7uAHeN0lAYoQQogBTe/iSfJmUMwmhTCr9vEV7DoUVVV9NSghLpKtbPAu7wRQfwKQGGnDYlJQVSirG7hZFAlQhBBCDGjlfl08umBuyOevyenG5dHqXjqegxKcIlmjQDbAabAmk0Jy9MDfNFACFCGEEANaeZsaFPAt8wR73L2+vGMxKUYtTFtRQSqSrfIu8SQEMANFlxKr16FIgCKEEEL0SNsaFPAVygZ7mqwxRTbciqIo7R4TrCLZnmZQAFIGQaGsBChCCCEGLI9HNT6I/QOUYGUp2qrpYidjaH1t/dkGXVjdBARegwKQGjvwh7VJgCKEEGLAqm12GjUf/tNSI0K0o7Ex1dYvWGpLz6B4VGh29k+XTEOLy9jhed6YxIBfnxwjNShCCCFEj+kFsjFhFmwW30dWqDYMLPNez7BOApQIq682pb+WeV7fnEdds4sxSZGcPSE54NenxkgNihBCiEHG5fbw4a4irv3nRqb86iM2HK4I2bXoBbJJbQKCYO53409vyx0W3XGAYjIpRgDVH0tQbo/Ks+uOAnDj/FGYTO3XwnRmMGwY2PEimhBCiBPOh7uK+dW7u1p9cK07VNGjZYS+UNFRgBKqDEo3AhTQAqgGh7tf2qBX7S3hWEUjseFWlswa0aP3MAKUGsmgCCGEGASWrz5ISW0LSVE2stJiAN/E0lCoaNBrPlp3qoSqBqW7AUpUP17fM18cAeCaUzJbTdUNRIq3BqWuxRWyTRe7IgGKEEIIQ4G3M+TfN53ClbO1v53rXTSh0N4MFAhdF095Oy3P7YnydvnUNTs7PS5Quwpq2HikEotJ4YZTR/b4faLDrEYWaqDWoUiAIoQQAoBmp9sIRobHhRPvHQBWFcIMSntTZME3ByXYk2S7m0HRf3d9Hdy9sOEYABdOTSMtNrxX76Uv8wzUVmMJUIQQQgBQ5K1HCLeaiQ23GgPA9D1fQsEY0hbdtgZFy1AEc7NAVVV9XTxdBCh6xqeijwOUPUW1ACyeltbr99KvsSqE/347I0WyQgghACiq0ZZ30uLCUBTFGKEe0hoUvUi2zbTUYE1r9VfT5MTp1mayJEV1Pr1VXwLSA6y+UlitBZHD47qXPXF73Dy66VHcqpvvTP0OqZGpxnOx4do96MPnBhoJUIQQQgBQ5P3wS/cuHcRHahNKKxscqKra4Wj3/qRnINoORouwB7+LR1/eiQ23Yre0vw+PLtEbUOk1NH2h2ek2lry6G6D899B/eenrlwB45+A7XJ91PTdNuYkoWxSx4dq/3+qm0AWgnZElHiGEEIBfBsU7Bl2vo2hxeWhyBrdbRlde134Xj7HEE8Qunu7Wn4AvoCrvwwxKsd8SXFw3xts3Ohv569a/ApAemU6Lu4V/7PwH33jvG9Q56oz3GKgZFAlQhBBCAFDo/QBM8/7tPMJmNqa3hqKTp9npps67hJMU2XZQm5bBCOYST3emyOr0JaCKPsygFLZZguvK83uep6ypjOFRw3nv8vf489l/Jjk8mYL6At468JaRQalplABFCCHEAFbkbTFO92ZQWtehBP9DTA+KrGaFmPDWFQmhKJINJINi1KA09F0GJZD6k/Kmcp7d9SwAt598OzazjXMyz+HWGbcC8PLXLxMTroUAkkERQggxoOldPPpOt4BfJ0/wMyh69iEx0n5cxkCvQWlw9O+Owf4CW+LxZVA8nr65vkIjgOw6QHlq+1M0uhqZkjiF80edbzy+eMxiYu2xFNQXUOTcAoQm+OwOCVCEEEIAfh+Afn9Dj/fWKYRiFkp5B1NkwZdBUVWCVh8TSICi77zs8qjU9tGwNv8uq87k1eXxxv43ALhr9l2tgrtwSzjfGP8NAL6q+C8gGRQhhBADWEOLi9pmrZ4jbYBkUHwFsscHBOFWM/rnbrDG3QdSg2K3mInxTpPtq06eAr3Lqoslno+OfoRbdTM3dS5zUucc9/zVE6/GrJg5WLcdk71QAhQhhBADl/6382i7hegwX4dIgjFNNvgfYnqLcXszR0wmhQhrcFuN9QxK26FxHenrWSjdXeL55NgnAJw/+vx2n0+NTOXckecCYEv4UgIUIYQQA5degNl2+UDPoFSFpAal831vIoI8rK08gAwK9O00WVVVfUXMnSzxFNUXsbtiNwoKZ2ec3eFxSyctBcASs50GVzVOt6fX19jXJEARQgjhNwOl9d/O9RqUyhDUoPiKZNuf2qpvGBiMTh6X22MEGt2pQQHf/kF9MQultslFg/c+O1viWZW7CoCTU04mMTyxw+OmD5vOpIQsFJMLS8xOagdgFkUCFCGEEEYGpe3fzhNCmEEp72CKrM7YMDAIGRRtmi6YFN/vpCt6BqUvalD0XaYTIm2EWTueYvtJrra8szBzYafvpygKi0adB4Alai/VEqAIIYQYiDrOoISuBkUvku1o35tgzkIp9SvYNZu6N/K/L2tQ9H8/nS3vlDeVs6VEax1ekLmgy/c8O1NbAjJHHqK4rrrX19jXJEARQghhzEDx7+CB0GZQ9CFnHdWgBHOabCAdPLq+nCbbnQLZz/I+Q0UlKzGLtKiudzseEzsGizsZRXGzoWhdr6+xr0mAIoQQot0ZKICxX0tloyNoA9FAKwo1alA6yKDoRbKNwQhQApiBouvL/Xi602Ks1590tbzjL44ZAGwp/6LnF9dPJEARQogTnKqqXWZQHC5PUMfK1za5cHknsHZU8xGp16AE4bp6FKBE9l0XT1dLPHWOOjYUbQBgwciul3d0w23anJR9tV/h9AysOhQJUIQQ4gRX2+Qygo+2NSjhVjN274aBwZwmq58r0mbGbmm/KDTSm0EJRpFsTwIUfV5KX2RQOspw6V7f/zouj4sxsWMYEzum2+87InwSHlckLZ4Go37lQEkdK3YUsr+krtfX3RsSoAghxAlO3yU3LsJKuK11MKAoil8dSvD+hq0PD4uL6LhjJphFsj2qQfG2Gdc1u2hx9e4ajTk17dSgbCjawBNbngB88026Kz7Sjrt+IgCr81YD8PGeEm57aStPrzncm0vuNQlQhBDiBNdRB49ODxKCOQtFD1Biwq0dHmNsGDhAMygx4RYs3o6f3hTKuj0qxbXt72ScV5fHT9b8BLfq5pKxl3DlSVcG9N6x4VZcdVkArM5djaqqRrZmeBd7/vQ3CVCEEOIEZ8xAiW3/Aykh0rthYBA7efS5HLHhlg6P0Qe1NQRh1H15NwKUtw+8zZ82/4laRy2gZZ8S+6CTp7SuGbdHxWJSWp2/0dnIjz79ETUtNUxJnMJ92fcdt+tzV+LCrbgaxqOoVgobCtlftd8IUNK62POnv0mAIoQQJ7jimvbH3Ot8s1CCn0GJ7SyDYtNrUIJXJNtRy3NxQzG/Xv9rnt39LFe9dxV7KvYAftNkG3peh6IHkCkxYa1msPxz5z85WH2QpPAkHj/7cezm7md3dLHhVlBtRLq1LMr96++noKYa6HpTwv4mAYoQQpzgCrtY4gnFLBR99HpceGc1KMHZLLDZ6abOu4zUUQblrQNv4VG1/Wzy6/O57v3reH3/60ahbG8yKL4ll9b/fvSpsXfPvpuUyJQevXest43cXncxcfY4dpbvpCjsb6A4O8yoBYsEKEIIcYIr6mDMvS6UNSj6B2h7Io3NAvs3g6JnT2wWEzFhxy85uTwu3jrwFgD/N/f/OCvjLBweBw+sfwAlbD/Qu06ewnY2CcytzeVIzREsioXTR5ze4/fWM1QNDYk8tfApIiyREH6I8OEvMSym4+W1YJAARQghTnBdFckmROg1KMHr4qn2BkOdLfHok2T7O4Pi38HTXo3HFwVfUNJYQpw9jivGX8ETZz/BJWMv0V6rfAb0bty9MaPGL4OyJn8NALNSZhFti+7xe+vBZ02jk6zELH5+8qOoHguW6L08svn+oA7na0sCFCGEOMHp+8ykxHRQgxJ5YtegdNXB88b+NwC4dOyl2Mw2FEXh+qzrAShw5KCY63u1xFPQzgwUPUA5Y8QZPX5f8P1+HW4PzU4PcaaJNOVfC6qFOSlzAi667UsSoAghxAmsxeU25ojEd7CcotegVAaxBqU7AUqkLTiD2vQlluR2ApTihmI+L/gcgCUnLTEen5AwgazELDy4sMRsM3Zm7s359bbfekc9OcU5AJyVcVaP3xe0Oh69Fbq6yUFRdTPuhonM4Het7icUJEARQogTWI13l2KTAjFh7QcDoejiqW7sRoDiXeJpcrpxe/pvKWJXgdY2PCkt5rjn9OLYOalzGB07utVzV4y7AgBr3CbK65s7PYeqqrz8VS7rDpYf9/ixikYARsRHAPBl4Ze4VBejYkaRGZPZs5vyUhTF+B3XNDmNYGhUXHqv3rcvSIAihBAnsCq/QMBkaj+dH+83STZYNQm13cmg2H1FnE3O/lvm2VlQDcC0EbGtHnd5XLx54E0AvjH+G8e97oIxF2A12TCHlVDacrDTc6w9UM49b+3ktpe3tvodF1Q3Ud/iwmpWGJ0UqR2bvxaAM0ec2eN78qcXIlc3Ov1moIS2gwckQBFCiBOaXoza2Uj5BO9zDnfwNgz0jbrvOECxW0zoMVV/LfM0tLg4WFoPwNThrQOUz/M/p7SxlHh7PAtHHr+DcIwthtPTz9Hex7au0+DuxQ3HAG0ZTa85AdhXrO2HM3ZYFFazCbfHzef52pLSmRl9FKD4Z1Bq2m9pDgUJUIQQIgRW7Chk3kOrWLu/LKTXoWdQOgsEwm1mwqzax0Uw6lCcbo+xQ3FnGRRFUfp9w8A9RbV4VEiNCSO5TRHxGwe8xbHjtOLY9lw5QVvmMUdvo7Su/c33imqaWPV1qe+chbXG9/u8G/adlKJ16uws30lVSxXRtmhmJM/o2U21EacHKI3OTvf8CTYJUIQQIgRWbC+iuLaZn7+5o9/bZDtT0+TNoHQSCEBw61D07AlAdAd1Mbr+3jBwR34NAFPbLO8U1RfxRcEXACwZ33Ex6anD54IzAcXcwv8Or2z3mFc35bWqodlT5BegeDMoE1K1AEXv3jkt/TSsps5/N92lB4FVjQ6j5byjmTjBJAGKEEKEQH61VvhYWNPME6s6r0/oT3oGJb6TJR7/54ORQTE2CgyztBrt3h59w8D6fsqg7CrQApRpbZZ33jqoFceeknoKo2JHdfh6k2Ii3DEbgHWFXxz3vMvt4ZWv8gCYkREHwN72ApSUaJxuJ+8eehfou+Ud8C3vHSqrx+lWMSkdt5wHkwQoQggRAvlVvjqDf35+mAMl7af/+5ueEelsYiv4Wo317pr+1J0psjp9w8D+ykLtyK8GYIpfBsXlcfHWfm1y7DdOOr44tq0k8xQAdlflHFeHsnpfGcW1zSRE2rjz3JMAXwbF6fZwuKwB0DIo7x95n9LGUoaFD+Pckef27sb86BmUvUXaf4PJ0WFYzaEPD0J/BUIIcYKpa3YaH/TzxyXi8qjc+86ukEztrOluBiWIs1BqutFiDFDnqKMxfBVh6S9TVF/a6bHdUVrbzAc7i/B4l1vqmp0cLtcCBP8C2c/zP6e0SSuOXZC5oMv3HR4+EdVjpd5VxYHqA62ee3GjVhx75ewRzBgRB0BeZRM1TU6OljfgcHuItJlJi7Xz3O7nALg269oOa156Qv896/UuA2F5B3oZoDzyyCMoisIdd9xhPNbc3MyyZctITEwkKiqKJUuWUFJS0up1ubm5LF68mIiICJKTk7n77rtxuUK3BiuEEMGkd2nER1h55IpphFlNbDxSyXs7ioJ+LXoGpaMhbTr9+WDWoHQUoFQ2V/L7Tb/n3DfOpcTyBtbY7XxW9Eavz/vb9/dy64tb+NeXRwDYXViLqmodLf67GL++/3Wg8+JYf6kx0bgbtRkpGwo3GI/nVTayxlskfc2cTGIjrEb3zNdFtUbAMD4lmnVFX3Kw+iCR1kiuPOnKXt+rP71A2uHSNjsM9S7Guh4HKJs2beLpp59m2rRprR6/8847ee+993j99ddZs2YNhYWFXHHFFcbzbrebxYsX43A4WLduHf/+97957rnnuO+++3p+F0IIMYjkV2oByoj4CDISIrhpvvbh9fHu4qBfizEQbQDWoLS3k7Hb4+b7K7/P83uep8HZgJUoAI427Or1efWBaE+vPUyz081OvUDWL3vS3eJYf6mxYbgaxgGwvmi98fhXRypRVZg9Mp5R3hkn+jC4PUW1Rv3JxNRo/rXrXwB886Rv9mrvnfa0DQQHdYBSX1/P0qVL+cc//kF8fLzxeE1NDc888wx/+tOfOOecc5g1axbPPvss69atY8MGLWr8+OOP2bNnDy+88AIzZszgggsu4MEHH2T58uU4HMGbUiiEEKGSX6V9EOp/W57o/VDS98QJpmpjiafzDEpiVAiKZNvJoLx18C32Vu4l2hbN3xb8jQUxDwFQ0nKIRmdjr86rZ4fK6lp4dVMeOwqO7+B588CbqKhdFsf6S40Jw90wHoCckhycbu3+9I4ZfQAbQFa6N0Ap9AUo0bEF5JTkYDFZWDppaS/usH1tW8zTYwfxEs+yZctYvHgxCxe2HkyTk5OD0+ls9fjEiRPJzMxk/Xotaly/fj1Tp04lJSXFOGbRokXU1taye/fuds/X0tJCbW1tqy8hhBis9ALZEfFagKLv8VIWigDFaDPuPIOSGKldY282vev2NXVQg1LnqOOvW/8KwA+m/4DTR5zOmPgMPM5YVNzsKN/Rq/NW+QVfT605xNbcKsA3QdblcfH2gbcBAlpmSYsNw9OSguKJpsnVxLaybYDWwQW+XYr3VOwhLVF7bG+xb4nn66b3ALhozEWkRKbQ14ZMBuWVV15hy5YtPPzww8c9V1xcjM1mIy4urtXjKSkpFBcXG8f4Byf68/pz7Xn44YeJjY01vjIyMgK9bCGEGDD0GhQ9QNFbOktqO9+vpa+pqtqtQW3gy6CUN/R/ENXRFNm/7/g7lc2VjIoZxVUTrwK0gWJ6fceWki09PqfL7aG2WauFjAmzUFTTbASSU9K1AGVt/lqjOPaczHO6/d6psWGACY93mWdDkbaiUKTvUhwbxifHPuGqFVfx9IE7QHGwr7iO3MpGTLYStlVqk2O/PfnbPb6/zsS2CU4HZYCSl5fH7bffzosvvkhYWPBSQPfccw81NTXGV15eXtDOLYQQfc2XQdE2f9MzKI0Od7/N82hPk9NtFEbqXTodSQxmF087RbK5tbm8sPcFAO6ec7cxpCw1Ngx34yhAWz7pqWrvORUFfnjOeOPxjIRw43fzxn6tEPeycZcF1EWT6l0yaa4bC/gFKN4MCrZifvHFLwCoaC4jOnk9TreKqkJ0qjaYbWHmQsbGje3p7XVqSGRQcnJyKC0t5eSTT8ZisWCxWFizZg1PPPEEFouFlJQUHA4H1dXVrV5XUlJCamoqAKmpqcd19eg/68e0ZbfbiYmJafUlhBCDlV6DMiJB+yCItFuItGkDx0qDmEXRl1IsJsU4f0cSvV0s1Y1OnG5Pv16XPt3W/4PzTzl/wuVxMT99PqcPP914PC02zMig7CjbYdR3BErfkygmzMq180YaAdm04XEAFNYX+opjT+pecawuwmYhJsyC25tB2VW+i1pHrbYxn6mJfx34NU2uJpIjkgEwxa8BUyOKtRxPxFYAvjvtuz26r+6wWUxEeP/92y2mLuuRgiWgAGXBggXs3LmTbdu2GV+zZ89m6dKlxvdWq5VVq1YZr9m3bx+5ublkZ2cDkJ2dzc6dOykt9fWsr1y5kpiYGLKysvrotoQQYmCqb3EZyyr+G7Lp+7wEs1C2ym+jQEXpfGJrXLjV2Jivqp+zKG0zKEdrjrIqdxUKCnfPubvVtabEhOFxJONxRdLsbmZ3Rfu1jF2pbNDOmRBpI9xm5u5FE1AUuHBqGuArjp2bOpeRMSMDfv+02HBUVxwp4Rl4VA9Pb/snjfYNhA9/iaLGPNIi03j1olcZFzcOj9KILXEttqTPQFE5ffjpZCX27+ej/rseHhfe5X8LwWLp+hCf6OhopkyZ0uqxyMhIEhMTjcdvvvlm7rrrLhISEoiJieGHP/wh2dnZzJs3D4DzzjuPrKwsrrvuOh599FGKi4u59957WbZsGXa7/bhzCiHEUFLgXd6Ji7C22mdmWLSdI+UNQQ1QarpZfwJgMikkRNopr2+hvN5x3MZ5fXpdbQKU1/a/BsDpI04/bpkjzGomPsJGU9NITNF7yCnJ6dEmer5gTTvn1adkcsXJI7BZTK2KY7szObY9KbFh7CupIyN8OiVNeTy/91nC07Xn7GY7j5/9OEnhSfxo5o/40eofYUv4EhQtU3XLtFt6dM5AxIZbKappHjDLO9APk2Qfe+wxLrroIpYsWcIZZ5xBamoqb731lvG82WxmxYoVmM1msrOzufbaa7n++ut54IEH+vpShBBiwGnbYqzT61CCucRT1c0WY12St1C2op8LZf0DlCZXE+8cfAeAqydc3e7xqf6FsqU9K5TVs0L+E3VtFu0j8qOjH1HWVEZCWEK3Jse2J80b0I2xXcCZI85kYuwsXPUTCHPMYPmC5UaG5KyMsxgfOwXF5ERR3GTFndxnuxZ3Rg8G0wZIizEEmEFpz2effdbq57CwMJYvX87y5cs7fM3IkSN5//33e3tqIYQYdNq2GOuSo7UPhmC2GlcbtR7dK/jUO3n6s9W42emm2allDmIjrHxw5D3qHHUMjxrO/OHz231Naoydfce0AGVryVbcHjdmU+c1NW11tGlidXM1j256FNACJKu5Z/UZKd4P/qbGeP56+V95dVMumzbsZO6EYcxNO8U4TlEUfjzndr7/iVZzsmzm93t0vkDpmaOBlEHpdYAihBCi+4wCWW8Hjy4lRsugBLPVuLtD2nQJ+iyUfqxBqfVmT8wmhSibmVe+fgWAqyZchUlpP+mfGhuOpzkNixJGnbOOg9UHmZAwIaDzdjTy/3ebfkdlcyVjY8dy89SbA70dg56ZKPZ27hRWe2egxB4fEMwfPo+bs36E0+3i9Ix5PT5nIBZOSiHnWDVnT0wOyvm6QwIUIYQIog4zKN4AJZg1KHrnSlctxjq9s6Wivv+u0ZgiG2ZhZ8VO9lbuxWaycfm4yzt8jfbhbybONJ5y9042l2wOPEBpOP53sSZvDSsOr8CkmHhg/gO92qAvtU2Aok+R7Whq6x1z+q9rpz1Xzs7gG7NGDJgCWZDdjIUQIqjazkDR6Us8we3i6d6uwbqkICzx+NefvPr1qwCcP/p84sLiOnxNqre+w+bU5pfoc0YC4cugaPdY66jlgfVabeT1Wdczbdi0Dl/bHfo1FtfqAUrrKbIDwUAKTkACFCGECCrfEk/oi2SrG48vDO2MPgulP4tk9WWnqIhmPjz6IQDXTLym09fo2QlnnZY12VC4gSZXU0Dn1YO1hEgtWHtu13OUNpUyMmYky2YsC+i92qMv8VQ2OGh2urUZKAysotSBRgIUIYQIkgb/GSgdFMnWNrtodrqDcj3VAbQZg2+JpzwIGRRHxDqcHidTEqcwJWlKp6/RP+TLKhNIj0yn2d3MusJ1AZ1XX+KJ8wZrq3K1eV63Tr+VMEvvg4jYcCthVu0jt6S22ZdBkQClQxKgCCFEkOh78MSGW4kJax0UxIRbjLbWYHXytJ390ZXEILQZawGKiwrTZwAszep69169Q6au2c3pw88CYHXu6oDOq/8uEiJt5NXmcbjmMBbFwukjTu/ild2jKIqxzLO/pJ5GhxaEtlckKzQSoAghRJB0NAMFtA8wY5mnLjjLPMamfN1tM/Z28VT2YwalusmJJWYXDqpJCk9i0chFXb4m2m+rgCnxpwLaxn5uT/cyUW6P2mqDwjX52v43J6ecTIyt77ZW0Zeitnh3SY6PsBLexRYDJzIJUIQQIkg66uDR+XY17v8MiqqqvjbjyMAyKA0ON02O/lmGqm1yalNUgW9O+Ga35o4oimJ8+CeaJxJji6GqpYptZdu6fU6Pqn0fH2Hjs/zPADhjxBkBX39n9GzJlmNVrX4W7ZMARQghgqSjDh5dMAtl61tcuLyfyt0tko2y+5ah+muZJ7dhL+bwPExYuPKkK7v9Oj1AKatzcuaIMwH4NPfTbr1WX96JtltodjeQU6ztinxWxlkBXHnX9AB0R34NAOlxUn/SGQlQhBAiSDrq4NH5lnj6P4OiZ0/sFhNh1u4tMyiKQlJk/7YaH2r5CIApsWeSFJ7U7delxmi/06KaZs7OPBuA1XmrUVW1y9catTiRVtYVrsOluhgVM6pHmwJ2Ri+IbXJK/Ul3SIAihBBBUuCdHtrROPFg7mjcdu5HdyX0U6GsR/Xw7qF3qeQrAM5OuyKg1/tPap2fPh+byUZeXR6Hqg91+doqfSfjCBtr8rT6k77OnoAvg6JLkwxKpyRAEUKIINEnsA6Lbn/n9mEhyKB0t4NHpxfK9mWr8abiTVy94mr+74v/A8WDq34CU4ZNDug99E6e4tpmIqwRzE2bC2hZlK5UeoO12AgLnxd8DvR9/Qkc31KcLhmUTkmAIoQQQaIvi+gTWdsKZg1KoC3GOr1QtrKP9uNZk7eGmz66ib2Ve4myRmGuvoim/Gu7Pd1Wp+8WrI+SPyfzHACe3f0sO8t2dvpafWCdOTyX6pZqYmwxzEyeGeitdH2NbQIUmYHSOQlQhBAiCBodLqP2QJ/I2lYwdzTW22oDXeJJ0qfJ9sF+PKqq8rftfwPg3JHnsuLyFTSUng6qNeAARS+S1QegXTTmImYmz6TOUcd3V36XbaXbOnxtpXeJp860HYDThp+GxdT3W9UlRtkxm3zj5AfSzsEDkQQoQggRBHr2xG4xGTM72tJ3NK5ocOBwefr1evS6i8CXePquSHZT8Sb2VOzBbrZz77x7CTfH4nRrRa09DVAqGlpwuDyEWcJ4auFTzEmdQ4OzgVtW3sJ/D/6XozVHj5uPomVQPBQ6tT18zs44u9f31h6zSSHFmyVTlONrUkRrEqAIIYa80tpmPJ6uuzn6U5k345AUZe9wU7b4CBsW79+wy/txx2CA6qbWo927S8/+lPfBEs+zu58F4LJxl5EQlmBkdaxmhYgAB5glRNiwmU2oqm/QXYQ1guULlpOdlk2Tq4l7v7yXi9+5mLkvzeWO1XcYgUplgwNzxBHq3KVEWaP6pUBWpwdSSVF2o2VbtE9+O0KIIW31vlJOeWgVf119MKTX0VX9CYDJpAStUNYokg0wU+HLoPTu+vZV7uOLgi8wKSZuyLoBaL2TcaA765pMCimx2u9Or0MBCLeE85cFf+Hbk79NVmIWYeYwWtwtrMpdZWxGWN3oxBq7GdB2Tu6LvXc6ogco6VJ/0iUJUIQQQ9qmI5UAvJ6T162ZGP1F/0DvqP5EF6xC2Z62GRv78fRiiSfnWCXXv/kooNWeZMRkAHCsQpsTkxAZ2DXp9L1uitv87uxmOz+e/WNevehVNnxrA9+b9j0A/r7j73hUD+VNNVhidgFaNqc/6fNaZAZK1yRAEUIMafrY+LzKJg6XN4TsOiq8SyKJXXz4DosOziwUPYMSG3AXj3c/ngZHjwO+/2zeToNVy1jcOPlG4/GPdhcDcOrY7g9o85fq/dD3z6C0ZTaZSVPOJ8wUyeGaw6w8tpIqNqGYnAyPHMm0pGk9Ond3zRkVD8Bs7z9Fx/q+TFkIIQYQ/433PttXxthhUSG5jvLuZlBigrXE08MMijfAcrg91LW4jtuVuTt2132AonhwN4wl2T5Oez+Xh0/2lABwwZTUgN8TfG27RZ0EKDVNTn762n7MCfOwD1vF09ufpiXcgRk4f+TFAS8tBeqCqWls/eW5xPcwS3QikQyKEGJIK/XbeO+zfaUhu47ybtSggG+Jp6yPdjTuKMtRbbQZBxZghFnNRNm1v9v2ZJnHo3ooU7VuGUfVPN7fWQTA+sMV1Da7SIqyMXtUQsDvC77fXUkny2M782twe1QclfOxmcI5UH0Ac/gxVFVhyUmX9Oi8gZLgpHskQBFCDGn+GZSNhytpdLhCch0Vfl08nenLHY0PltYx+zefsLxNgbDbo/oKUgMMUMBXI9KTQtltpdtwm6pQ3XZc9RN5d3shAB/u0gKV8yantpoVEgj9d1faye9uW562kzCeCFLUBb4nGieQEZvWo/OK/iEBihBiyGpxualq9GUKHG4P6w9VhORa9GxDYjczKKV9kEFZu7+cigYHf/x4H3sKa43Ht+VVo6raLI648MD/Nq/fQ0/G3b936H8AuOomg2ol51gVuRWNfLy7d8s70L3f3ba8GuP7/GNzsJu0upUIx7wen1f0DwlQhBBDlj6R1WY2ceFU7W/Hn+0rC8m16Jvr6XvZdESfJttZFiDQc3pU+OV/d+HxqDQ6XPzkdW1i6iXT03s0i0O/h0A3DHR6nHx87GMAzI0nM2+MtpRz37u7qGhwEBtuZd6YxICvR+effWpvaUtVVbblVRs/1zbYOTP+TlpKz2OYeXaPzyv6hwQoQoghSy80HRZt5+wJyQB8tr806O3Gbo9q7F2TFN1FBiVG34yvBXcvh8uV1/kyHDnHqngjJ5+H3/+aI+UNpMaEcf8lgW3Ip0vqYavxxqKN1Dqq8bgiSbVP5ZLpwwFf0HhuVgpWc88/lvTfXZPTTX3L8Ut5RTXNlNe3YDYpnHHSMAC27kvDUXEOCREyl2SgkQBFCDFk6bNEkmPsnDouEZvZFJJ246pGB3qskdBF10xipA1F0bIegWYo2tI7hyamRgNw/3u7+c+GYwD8/sppAU+RNa6xhxsGfnDkAwBctVNJj43kgimpxuRc6N3yDkCEzWIU8LbXBbXdmz2ZkBLNuZO0gPVwmfbfQqDdTKL/SYAihBiy9A+p5Gg7ETYLc71LCqu/Dm43j55piI+wYukiQ2D5+r98bP85t5jf6/Uyjz6O/o6F45mQEk2DQxvt/u1TR3H6+GE9fl99iSeQcfzNrmY+OfYJAK7aGaTGhBEfaTMyGVF2C6eN79n8E396FqW9Tp5t+dUATM+I47Q29x9oN5PofxKgCCGGLP0DXq9NONP7Ybj2QHlQr6NbU2QbK+GNm+D1bzOeXO6yvEFVeUmvzlte57v/31w+BYtJ4aSUKH52/sReva+vSLb7Acra/LU0uhoJV5JwN2WS5t3J91unZAJw2cx07JbA9t9pj69Nu+MMysyMOEYlRjAi3jfNVVp/Bx4Z1CaEGLL0v0XrH1qzRmrTOw+U1AX1OvRMRoczUPI3wyvfgvoSUMzUKVFEe2qI/Pp1mP5/PTqnqqpGAJEUZScjIYLVPzmLuAgr4QFuxNeWXsgbSCu0vrwT55lDKSZjqNrCrBTW3H1Wn41+76jI2O1R2ZmvdfBMz4hDURROH5/Ey1/lAbLEMxBJBkUIMWT5lni0D62RiZGAVizZ7HQH7Tr0TEa7GZT8HPjP5VpwknQSfGclK1O/A8Coo69CDwt6GxxuWlwe73m1D9+MhAiiezD5ta3h3uxHYXVTtwuOM2MySQxLxF0/A/Btmgfav5e+2tk3pYMlnkNl9TQ43ETYzIxL1qYJnzbOt8wjGZSBRwIUIcSQVeJXJAtanUF0mJY4zqtsDNp16MWuSW0/BAu2aMFJSy2MnA/fXQ3DZ5GfcRH1ahgJTcfgyNoenVMPiiJsZiJsfZssT4m1oyjQ4vJ0u1D2zll3surKVVRUam3Eaf20m29yB3sZ6e3FU4fHGoPgTh2biD7ZXmpQBh4JUIQQQ1ZZmwyKoiiMTIwA4GhFEAMUY0ibXwalZDf85zJoqYHMbPjWa2DX/mYfH5fA2+7TtOM2/6tH5yzv5uTanrBbzMb7FlZ3PBRtxY5C/vTxPqNd2umG6kat/be/dvPtqEhWrz+ZkRFnPBYfaeOCKalEh1nISovpl+sRPSc1KEKIIcnp9hg7COtpf9CWE3YV1HKsInitxuXtTZFd/RA010DGXFj6uhGcgLaj8ePuhVxn+QS+XgF1xRAdWAtud/f+6an0uHDK6loorGli6ojYVs+53B5+87+9PLfuKACzRyVwxknDjF2GI2xmYsL65+NHD0bbFsnqGZTpfgEKwBNXzwTosrtKBJ/8GxFCDEn6B5TFpLQqgByZoGVQjgUzg9LQJpvhcsDhz7Tvz38E7NGtjk+OsfO1mskOZSJ4XLDlPwGfs7u7J/fU8DgtECisbmr1eE2jkxuf22QEJ6ANiQMorNGOTY0N67ddg9vbDbrZ6ebrYq0wum2AYjGbJDgZoOTfihBiSPKfImvyGwY2ylsoeyyINSi+5RZvoJS3ARz1EDkM0mYcd7zedfRv5znaA1ueD7hYtsLIoPRPgKIv0fgHKKqqcv2/NvL5gXLCrWbOn6xlfbZ6sxd6BqW/6k/A11Je3+KiwTtNdm9RLW6PSlKUjfR+PLfoWxKgCCGGJF+BbOsPpMxEPYMSvCUeowZF34fngLYfDeMWgun4P4aHeQOUFa5TUC3hUJMLpXsCOudxQVEfS9c7eWp8tR6ldS1sz6/BpMCbt57KbeeMA2BrbhUej0qREaD0T/0JaAPfIrxt1HqQqmdPJqXF9FvmRvQ9CVCEEEOS/xRZf3oGpaCqCafb0+/X0ehw0eid4GrUoBxYqf1z/LntvsZuMRMfYaUFGw1p3l12D34S0Hn7s0gWMDIR/hmUQ6X1gFbnk5Uew8TUaMKsJuqaXRwqq6fIu8TTnxkU8GVR9K0Ovi7SdnKeJIWwg4oEKEKIIamszZA2XXK0HbvFhMujHlc/0R/07IndYtL2ianOhbKvQTHB2HM6fJ1e7FmcfLr2gB7UBHjefgtQ4o5f4jlUpgUoY4dpQaDFbGLaiDgAtuZWG0s8qf0coOgZqBJvkLrXm0HR9yQSg4MEKEKIIamkzZh7nckU3FbjigZfoKAoii/QyJgL4fEdvk4v9jwQ682g5G6Alu5PwPUVyfbvEk9pXYuRiTrk3Xhv7DBfR9LJmdo9bsmt8lvi6d8ARQ9KS2ubUVXVyKBMTJUMymAiAYoQYkgqrWs/gwKQmaD9DT83CHUovimybZZ3xi3s9HV6FuCIJwUSxoDHCYfXdPu8Zf28xJMYacNmNqGqvuJXXwbFF6DMzIwDWmdQ+rMGBfyWeOpaKKppprbZhcWkMDY5sl/PK/qWBChCiCGpowwKwKigZlC8AUqkDZzNcMQbZIw/r9PXtdpTZpy3VuVg95Z5Wlxu6pq1Dpb+KpI1mRTS2rQa6zUoY5OPD1D2l9YZ2aRgZlD2erMnY4dF9clmhCJ4JEARQgxJ/m3GbY1MDN4slHL/WpDcdeBshKhUSJ3a6eta7cqrZ1sOfNKtdmO9/sRqVogN778R7uneTEhRTTMNLS6jo0evQQEt0BoRH25cdpjV1K/XBK0zKHoHz8Q0qT8ZbCRAEUIMOS63x8hcJMe0F6B4Z6EEYYmn1Zh7o3tnIXTR7qp/yJbUNsOo08Bsh9p8rcAWwOOBou3tvtaoP4m092tbrZ5BKahu4rC3/iQpykZcm52B9ToU0JZ3+rvVVw/uSvwyKFJ/MvhIgCKEGHLK6x2oKphNim/2iB89g5Jb2YjH07PdgrtLD5QyLVW+ibAnnd/l61pNRLVFaEEKaO3GBTnwzLnwz4VQefj4c7Y3Wr8f+O9qrNefjPGrP9Gd7F3mgf5f3oHWvzvfDBTJoAw2EqAIIYYcvUA2Kcpm7Fzrb3hcOBaTQovLQ0ldx5vd9QUtm6Fy9qFHwFEHI+bAhAu7fJ1RR1GndaIYM1M+/yP84xwo2AxmG5QcP8CtvwtkdXonT1FNc7sFsrqZfhmU/m4xBt9wvrpmF4e91yUzUAYfCVCEEEOOXiCrF5q2ZTGbGBGvfbgeLe/fOpSKegeLTRtJK/4MTFa45C9g6rpYU7/2ZqeHuhaXr1C2SdvXhmlXww9zYNJF7Z4T+j+DkuY3rK3tDBR/k9JisFtMrV7Tn6LtFsKs2vk8KsRHWNvt5hIDmwQoQoghR8+gpLRTf6LL9Nah5Fb2vg7F5fZw7zs7+fe6o1q2wyvnWBUV5SX82vqc9sDpP4bkSd16z3CbmWi7tuNvaW0LJI6FWTfCmLPgpo/hiqc73OFYr0EZ1s8ZlFZLPKXa73Fc8vEZFJvFxDTvjsd61qU/KYrSqntrYqqMuB+M+me/ayGECKHSWr2Dp+O/rY9KjGAtfdNqvOloFS9syAW0oOTRb0xjf3Etj/3rBf6ovMowpRY1aQLK6XcF9L7DYuzUlbkorWvWPvgvfrzd46oaHKw/XMH5k1MxmZR+H3OvS/MGG7XNLhodHS/xANy9aCJvbcnnwilp/XpNuuRou9GlJR08g5MEKEKIIaezIW26zARvoWwfBCh5Vb73eHd7Ien573Nlw4u8oBSAGVTFjHLJX8ASWMCQHG3ncFmD1mrcAVVV+c7zm8k5VsVDl0/lW3Mzg7bEE2W3EBNmobbZhcujYreYjKxKW6eMTuCU0Qn9ej3+/Jf3JkkHz6AkSzxCiCGnuBsj1fVNA4/2QatxQZU2qGxGRhxZ4dXcXf97xlJAC3acU65CuXklZM4N+H1btRp34KPdJeQc0+pS3t6aD/T/RoH+/JdsxgyLwtROUXIo+LeXSwZlcJIMihBiyCnqxqZ0/sPaVFXtVY1CgXeS6sJJyXxL2Y55jcph2wSSln1ITGzPswa+iajtZ1Bcbg+PfvS18fOmo1UUVDf1+z48/tLjwo1W3vYKZENFz6CYFDgpRQKUwUgyKEKIIae4tusAZUS8FqDUt7iobnT26nx6BmV4fDgJBZ8CMOaMa3oVnIDfuPsOlnhez8nncFkD8RFWowj13W2FVHpHyvd3kSxAepzvd9xR/Uko6AXSo5MiCbPKiPvBSDIoQoghpdnpNgKOtJiOO0bCbWaGRdspq2shr6qR+MieZxv0DEpGlAJHP9ce7MYwtq74Bo4dv8TT5HDz2Mr9ANx2znjsFhM78mt4YcMx9NlzCb24p+7yX+IZ204HT6icNj6J6RlxXDlrRKgvRfSQZFCEEEOKXn8SZjURE97538EyvLNQ8iqbenw+j0elqEZ7/ei6zeBqhthMGDaxx++pGxbtN022jX99eYTSuhZGxIdz7bxMLpyahsWkGMFSfIQVi7n//4hP99uZeNwAyqAkR4fx32XzuXbeyFBfiuihgP7rffLJJ5k2bRoxMTHExMSQnZ3NBx98YDzf3NzMsmXLSExMJCoqiiVLllBSUtLqPXJzc1m8eDEREREkJydz991343K5+uZuhBAnPH15pzt7vmR4O3n8u3ACVVrXgtOtYjYpxBes1h486bwu99rpDr1ItrimudV8FYDXN+cBcOfCk7BbzCRE2jhtfJLxfDAKZMGXQVEUbTlFiL4SUIAyYsQIHnnkEXJycti8eTPnnHMOl156Kbt37wbgzjvv5L333uP1119nzZo1FBYWcsUVVxivd7vdLF68GIfDwbp16/j3v//Nc889x3333de3dyWEOGHpGZTUmK4nlmZ461DyKnseoOR7g5vUaDumAx9rD45f1OP38zciPhxFgUaHmwpvXQmAw+Uh13vN/kHJJdPTje+DFaBMSI0mLsLKKaMSCLdJrYfoOwEFKBdffDEXXngh48eP56STTuK3v/0tUVFRbNiwgZqaGp555hn+9Kc/cc455zBr1iyeffZZ1q1bx4YNGwD4+OOP2bNnDy+88AIzZszgggsu4MEHH2T58uU4HI4uzi6EGMjK6lo48/ereeSDr7s+uB91p4NHZ8xC6UWAoi+pZEcXQ20BWMJh9Ok9fj9/dovZWELx33m5oLoJj6otY/nPejlvcqoxUj4YHTwAseFW1v38HF78TuBt1EJ0pscLlG63m1deeYWGhgays7PJycnB6XSycOFC45iJEyeSmZnJ+vXrAVi/fj1Tp04lJSXFOGbRokXU1tYaWZj2tLS0UFtb2+pLCDGwfHmwnGMVjTy15hC7CmpCdh0lnXXweDyw+VnY8RrUlzIiQfvwz6/qeQ2K/tqzla3aA2POBGvfjXPXg6hjfgPl9GBlZEJkq2WsKLuFBZOSgY73IeoPETZLUOpdxIkl4C6enTt3kp2dTXNzM1FRUbz99ttkZWWxbds2bDYbcXFxrY5PSUmhuLgYgOLi4lbBif68/lxHHn74Ye6///5AL1UIEUT+yyS/+/Br/nNzaP5GrRestjukbftLsOIO48fZSVO42jyPt6oW4vGoPRoypmdQZjR/pT0w/ryA36Mzo5IiWH+4otVIfj1YyfTOcvH38/MnEW61cMOpUhwqBreAQ94JEyawbds2Nm7cyK233soNN9zAnj3Hb/fdl+655x5qamqMr7y8vH49nxAicP6Fpp8fKOeLA+UhuQ69BiWlbQ2KqsKGJ7Xvo7VaDVv5Lh6x/pNvsJKSdlp5u6Ogqok46kir26k90McBSmaCVnjqv8SjByij2glQMhMj+OM3pzMyUQpWxeAWcIBis9kYN24cs2bN4uGHH2b69On8+c9/JjU1FYfDQXV1davjS0pKSE3VdtxMTU09rqtH/1k/pj12u93oHNK/hBADi77Uoe/F8siHe/F41M5e0i98XTxtApSjX0DJLrBGwA/WwU8Owqk/BOBBy7+o2/5ej85XUN3EOaatmPBAyhSIy+jV9bc1KrHjJZ5MCULEENbrRUOPx0NLSwuzZs3CarWyatUq47l9+/aRm5tLdnY2ANnZ2ezcuZPS0lLjmJUrVxITE0NWVlZvL0UIEUJ6BuWXF2URZbewq6CWFTuLgnoNTrfHmBlyXA3Kxqe0f06/GsLjIWoYnPsgqyPOx6yojPnsh5C/OaDzqapKQVUT55s3aQ9MvKi3t3AcPRPSKoNS2XEGRYihIqAA5Z577mHt2rUcPXqUnTt3cs899/DZZ5+xdOlSYmNjufnmm7nrrrtYvXo1OTk53HjjjWRnZzNv3jwAzjvvPLKysrjuuuvYvn07H330Effeey/Lli3Dbg9OS5wQou+53B4Kq7XMxYyMOG45YwwAf/5kf1Cvo6yuBVUFi0khKdLvz5SqY7Dvfe37U77ne1xRWDnmZ6x2T8fiaYaXroKWum6fr6rRCc4GzjDt0B6Y1PcBil5nUtXopKbJicejGl1HoySDIoawgAKU0tJSrr/+eiZMmMCCBQvYtGkTH330Eeeeey4Ajz32GBdddBFLlizhjDPOIDU1lbfeest4vdlsZsWKFZjNZrKzs7n22mu5/vrreeCBB/r2roQQQVVU04zbo2KzaG2v12drBZqHyhqobwneIEZ9eSclJqx1weumf4DqgTFnQ3LrCa/DE2NY5rydcms6NJbD7ne6fb78qkbONO0gTHFC/ChtiaePRdktxkyT3IpGimubcbg8WExKp7s1CzHYBdTF88wzz3T6fFhYGMuXL2f58uUdHjNy5Ejef//9QE4rhBjg9PqTEXHhmEwKcRE2EiJtVDY4OFbRwOT02KBcR3F7M1AcDbDlee37ud8/7jUj4sNpJIyP7Oez1Pkv2PoCnHxdt85XUNXEIv/lnT6YHtuekYkRlNe3cKyygboWbZ+hjIQIae0VQ5r81y2E6DW9/mR4vG/+h14fcbS850PQAlXU3hTZna9Dcw0kjGm3w0Yfd/+y41RQzJC3AcoPtH8CRyPsehOatVlMRZW1LDB5559MuqTvbqSNkX6FskaLcYLUn4ihTQIUIUSv5XtrIjL8PjT1+oijfsWd/a3dIW0739D+efL1YDr+jzx93P3uugjc47yDJre+cPybqyq8+R144yZ4/lJoqcea9wUxSiP11kQYMadP78XfSL9W485ajIUYSiRAEUL0Wp53iUf/sAcY5d047mh58AKU4zIodSVw7Evt+8lXtPuapCgb4VYzqgplY7+hPbj9FXC3qZ3Z+Trs+5/2feEWeP3bjC7R9t4pSD2n3eCnr4xK8majKhqlxVicMCRAEUL0mj5FdoT/Ek9S8DMoxd4pskYGZc9/teLY4bMhvv3JqoqikOEdeb8/dj5EJEJ9MRzyjUygrhj1/bsBeNs9H4dih4MrOa3uQwAax17YT3ekMfYM8lvikQyKGOokQBFC9JpeJNt6icf3t/5gOW5I2+63tX9OvrzT1+mZn9waF0y7Wntw63+0f6oqvHcHSnM1Ozyjudv5PW5tuQ3V+8dnjRpBxPiz+vQ+2tKXy4prmzlcXg/46lKEGKokQBFC9EqLy22Mic9oJ4NSVtcSlFZjVVUpqdGGtKXEhEFtIeRqG5Uy+bJOX6sHVnlVjTDzWu3Br9+HJ0+Dp8+A/R/Qolr4ifP7TM1MYpVnFvdzCy7VxJvuMxie1L/TreMirMSEaU2XzU4PigIj4iVAEUObBChCiF4pqGpCVSHCZiYh0mY8HhNmJdH7czDqUCobHDjcHsAboOx+B1AhYx7Ejuj0tfrSVH5lE6Rkwcj5oLqhZCcUa0PY/uxawqw5p/LqLdlMTo/hueYzmNXyFE9YbiTKHvC+qwFRFKXV3jppMWGEWc39ek4hQq1//68SQgx5eoHsiPhwlDZzQEYmRlDR4OBYRSNThvfvLBS9QDYpyo7NYoLd3iGRU9ovjvXXKoMCsPR1KNoOzib+sXoPnx+po2HEmbx8yRRsFhOPXTWDi/7yBTWuKLLig1OsOjIxgp0FNd7vpUBWDH2SQRFC9Eq+90M9o50lh2AWyvqGtNmhOhfyNwFKt+aTGEWo3mJfbJEw8lQYt4B3m2ey1jOd7581Tgt8gJNSornnAm0i7fSM4Ayh8685kfoTcSKQDIoQolfyKo8vkNUZs1CCsMSjF8iOjXTAJ/drD46cDzFpXb5Wv/bqRid1zU6iw6zGcwXVrXdp1t04fzRzRycaLcD9zT9rIhkUcSKQAEWIQe7Lg+XUNbs4f0pqSM6vL4v4txjrgplBKauq4Rbze9yZ/x54tE4X5tzUrddG2S3ER1ipanSSV9lEVroWoDQ53FQ2OIDjAxSArPT+LY71NzJBMijixCIBihCD2L7iOm7411d4VJX19yzQikODLN+YgXL8h+Zo79/0j/T3uPuCLXxr6w2kWHPBg7Zp37n3gz4ZthsyEiKoaqwhr6rRCDwKvXNVIm1mYsJD+8elHuyBBCjixCABihCDlKqq/PK/u3B5VAAOldaHJkAxZqAcn2EY6V3+KK/XWo37stulrtnJf3OOEbXpL1xU/R9ScFOqxnFs5t3MueRWMAXW5ZIRH8GO/Bpj6BxoHUqg7THUtgA42JKj7YxKjKC+xcXYYVEhvRYhgkECFCEGqbe3FvDVkUrj56MVjZw6LrjX0NDiosK7BNJeBkVvNa5ocHC0vKFPOnkOltbxry+P8s7WAv6o/oELvLsJ/889l1dT7uSPC84JODgBXx2KHnABFHrrT9LbWd4JNkVReP/203F5VGkxFicECVCEGIRqmpw89P5eQBviVd3o5Fhl8EbK6/QP85gwC7Hh1naP6ctW42anm0v/+iUNDjcZSgkX2DfhwcTR0//A2addz2J7+9fQHXoGKNcvg1LYQYFsqETY5I9sceKQNmMhBqE/fryP8noHY4dF8oOzxgJwrL/rPNqR184uxm31ZaHs4bIGGhxuouwWXphbAIBpzBmMWXAzEb0ITsDXJu2/xJM/gDIoQpxoJBwXYpApqW3mhQ3HAHjw0im0uLTpqccqQxCgdDIDRecrlO19gKLv5DsuOYqRRdpGfUxZ0uv3hdZLPKqqoijKgMugCHEikQyKEIPMV0cq8agwOT2GU8clkent6DhW0YCqqkG9lkNlWjvv6GFt5nI4GuDvZ8N/rmBctMO4vt464n2PedFlULILTFaYeFGv3xcgPS4MRYEmp5vyeu2aC6u12SrD22mhFkL0LwlQhBhkNh/VCmPnjEoAtPkjJgUaHb4P1mA5UKIFKOOT23SV7H4HCrfAoVWc/eV1jFBK+6TVWB/4dpbzc+2BcQsgIqHX7wtgt5hJ9XZB5VU14vGoFNXIEo8QoSIBihCDzOZjVQDMHhUPaB+sabHaB2hfZCkCoWdQxrUNULb+R/unYias5hBv235FasNe6pqdvTrf0YpGQGVy1SrtgT5a3tEZe/JUNlJW34LTrWI2KaRE2/v0PEKIrkmAIsQgUt/iYm9RLQCzR/oyB/q49WMVwatDqWpwGBmbVnM5yg9C7npQTPCdlZAylWFKDS/bfkv+oT29OufR8gaylGNE1x8BSxhMuKBX79eWf6GsPuI+NSYMi1n+qBQi2OT/OiEGka25VXhUrWgzNdY3lE3fmyWYGZSD3uzJ8LhwIv0HsOnZk3HnwvBZcOP77LdOIlppYtiqO8Dj7tH5Gh0uSutauNi8Xntg/Hlgj+7FHRxPbzXOq2wyhrSlxwV/+J0QQgIUIQaVzUe15Z053uUdnb5Py9EgZlD0+pNWyztuF2x/Wft+5rXaP8NieG/cA9SrYSRVboH1y3t0vqPl2vLOpZYN2gN9vLwDfhmUqsYBNaRNiBORBChCDCI53vqTWaNaF4YaGZQgthofLG0nQDm4EupLICIJTjrfeHhY5kk86LpO++HTB6Ek8KWeoxUNTFWOkE4ZWCO1DEofM2pQ/AIUaTEWIjQkQBFikHC5PWzN9RbIjmyTQfFrNQ6WA6V1QJsOnq0vaP+cfjVYbMbD44ZF8ar7LNabZ4PbAW9/D9yBFcwerWjgfPNX2g/jzwVb32+Yl+kNUAqrm41gTzIoQoSGBChCDBJfF9fR4HATHWbhpJTWtRd6gFLd6KSmsXedMt11qG0GpbYQ9nuHp828rtWx2jEKdzTeiBoeD8U7tFbkABwtb2CRabP2w6SLe3HlHUuOtmOzmHB7VLZ4s1WSQREiNCRAEWKQ0OefnJwZj9nUemfdCJuFYd5W2GDsyVPf4qKwRhtiNi45Clwt8NoN4HFBxjxIntjq+GHRdmLCLJSo8ZRPukF7UC+m7SZn8deMMxXiNln7ZXkHwGRSGOENSGqbXYAMaRMiVCRAEWKQMOaftFne0Y1KDF6rsZ49SYqyExduhRV3Qv5XEBYLlx5fBKsoipFp2Z50EaDAkTVQdbTb5xxf+RkADenzISymt7fQoRFt9hVKi5UuHiFCQQIUIQYBVVWNDp7Zo9qfnJqZ0I+txlVHYdMzUJADHjcHSv0myK7/K2x7ERQzXPkcJI1r9y3GJ2vLUjsaYmHMmdqDW1/s1ukbWlyc5tLaiy2TL+3VrXQlwy9jEhNmITqsd5sQCiF6RjYLFKKb9H1uFEXp4si+V1DdRHFtMxaTwoyMuOMP8Hj6N4Py1i2Qt1H7PiyWk+0TecNWxZjyevi4SHt80UMw9pwO30LPoBwqrddqVA5/pgU2Z/0cTObWB1fn4jn0GcqYM1DiR1F4dD/TTEdwoxAxtX/qT3T+OzMP72QTRCFE/5IARYhu+sXbu/hwVxEf3H5GqyFpwfDZvjIApgyPJdzm92HudsG/L4KCHK6PHM0IaxL5+dmgToO+CqRqCnzBiT0WmmsY07yRMSZA3/rnlO/B3O91+jbjUrQA5UBpnbbBX1gc1BbAodUwfiE0VUPOs7Dnv1C4FRPQZI4m/OrncO7OAeBraxaTo4b1zX11INM/QJEhbUKEjAQoQnRDXmUjr2zKRVVh3aFyrjh5RFDP/+72QgAunJra+okdr2hj5YHY2n1cbt4HNV/C1lFw8nX0ia//p/0zYy58+30o2s4fnn+dQ/U2fnDRqUzNmgxxGV2+zTjvOPwj5Q24TDYs066Cr56Grc9DSy188DNoKAXAg4kSNY40dyXqi99glFWru9mfcDaT++auOpThlzWRFmMhQkdqUITohlc35eFd4TF21A2WopomNnk7eC6alu57wtkMqx/Wvj/9JzRc/h/ecJ8BgGf1b8HRR0s9X7+n/XPiRWC20Jwyg7/Vnc4HnrmkTD2rW8EJaO264VYzTreqzRjRA6g9/4U3btSCk8TxNJ//R05zP8mZLY/xkuscFFQinNr9V2Wc2zf31Al93L1+zUKI0JAARYguON0eXtmUZ/x8uI8DFI9HpbC6CbdHbff5FduLUFU4ZVRC67/Rb/4X1OZDdDqc8RMip1/C78zfI19NwlRXBBv+1vuLa6yEo19q30+6CNAyIB5VKyAdFtX9XX5NJoWxyVoh78HSekidCmnTtSfNNjjz53Drl/zXfB6FzmgSYqK51/0d7nHejAMLX7onkzhifO/vqQux4VaivXsLSQZFiNCRJR4hurByTwnl9S3Gz0f6OED59Xu7eX79MSJsZqakx3LyyHi+d8YY4iO1Saz68s7FM/yyJ8218PkftO/P+jlYtQ/S4cPi+X3BN/mz7W/wxeMw69sQmdSj69qSWwVbX+Jk1Q0pUyBhDICvgyclOuCC4fHJ0ewqqOVgaT2LJgOXPaUVyp58PQybAMAbOfkA3HDqKHIrG3j5qwW8755LA2G86R3p358URWFGZhxfHixnyvDYfj+fEKJ9kkERogsvbjwGwAVTtPqPo+UNRkdPX9hwuAKARoebr45W8tSaQ3zn+c20uNwcKW9gZ0ENZpPChVP86k/WL4fGCkgcBzOWGg9PSovhXc+plERMAEcdrHm0x9f1o5e3UrH5Le8b+zpn9hXXAr6akkDonTz6Pj6kZMGi3xrBydHyBjYdrcKkwOUzh3P7gpMIs5qoIQoXFkYFIUAB+Pt1s1lz99mMTgrO+YQQx5MARYhOHClv4MuDFSgK/PT8iZgUaHC4Katr6frF3aCqKgVV2qZ0/7x+No9+YxoxYRZyjlXx63d38+42LXsyf1wSiVF2UFXY/iqse0J7g3PuBbMvEZqVHoOKiX9H3aQ9sPkZKD8Q8HU53R6qqqs4w7QDgPox2sZ/eZWN/HudFrDN6mBgXGf0AEXfx6ett7Zo2ZPTxw8jNTaM1Ngwbpo/GoD4CCuxEcGZSRJuM7dqNxZCBJ8EKEJ04uWvcgE466RhjE6KZIS3w6Ov6lBqmpw0ONwAnDY+iW/OzuCJa2ZiUuDlr/J4eu0hAC6Zng51JfDKUnj7FnA2wpizYFLroWVZadqE1Terx8G4hdro+ddugJb6gK6rrK6FM5Tt2BUnRz0p/GGrBbdH5cevb6e+xcWskfEsmRV4J5NvFkoDnjY1Nx6PyptbCgD4ht9733rWWC6cmsrtC/q//kQIMXBIgCJEB5qdbl7frBXHLp07EsBI+fdVHUq+N3uSFGUnzKrNNzlrQjI/O1/by6bR4SbW4uDiuldh+Smw739gssLZ98LSN8DU+n/hianRKAqU1LZQueAPEJkMpbvhvz+AtstSjkbY+DT8LRtW3NXq+eLaZhaZNwHwoWcOz284xj1v7eCrI5VE2Mz86ZvTj9sPqDtGJkRgNSs0Od0UVDe1em7D4QoKqpuIDrNwblaK8Xh0mJW/LZ3Ft72ZFCHEiUECFCE68OGuYqoanaTHhnH2xGTAF6D0Vaux/iHddkO6W84Yw+XThnGz+X98Yb8D+2cPQHO11vlyy2dw5t1gPn65I9JuYbS3TmN3fRRc9YIW0Oz5L3z+R2ip07pyPnsEHp8KH/wUSvdoS0F7/mu8T2Pedi4wfQVAw5gL8Kjw2mZt+eW+i7IY2cNaEIvZxJgkLYuyv6T1Ms8ne7UZKIunphnBmhDixCUBihAdeGmjtrxz1ZxMI1ugByh9tcSj15+MaNPOqtQW8MeGX/BL64tEu6shfjRc9iR89zNIndLpe07yLvPsKayFzLmw2Nvt8+mD8HAGPHchfPYwNJZD3EiYsFh7/v27oakKXC1krb8bu+JiR8Q8rl2yhChv2+3CSclcNad7c086vj5tT569RbWtHt9TVAN0vNeQEOLEIm3GQrRjf0kdXx2txGxSWn0g99cST6sMyqFP4c3vYGqs0HYHPu83MP2adjMm7clKj+F/O4vYowcAs74NxTth0z8BVZubkj4DJl8Ok68A1Q1PnQbl+2HlfRCRSEL9firUaD4aey93x4bz2FUz+HBXMb+4cGKv9yLKSo/hnW2FvutDKxbeU6j9rNfRCCFObBKgCNEOPXuycFJyq3139AAlt6IRt0ftUR2Gv4JqbdqrMbF011vwxk2ACqnT4JvPQ0JgtRdZ/hkU3QW/hxnfgtgMiEpu8woLXPwEPHs+bHke0O7pF87vMCNRm71yblZKq7qQ3pjUzvUVVDdR2+zCalaMQlohxIlNlniEaKPJ4eZNb7urXhyrS48Lx2Y24XB7KGxT5NkTRg1KXLhWpPrZI4AK066Gm1cGHJyAlqEAOFRWT7NT6xDCZILhs9oJTrxGZsOsG70/qKyNOJePPHNIje3+pNju0gOUY5WN1Le4AF+wMi45GptF/lgSQkiAIsRx3ttRSF2zi8yECE4b13oKq9mkMDKx71qNC/yXeI6tg/J9YI2EC38P1p7tpJscbScx0oZHPb4QtVPn3q8Nfhs2kT+YtGAlJbrvd/NNirKTEmNHVX1D3/TlHlneEULoJEARoo0Xvcs715ySiamdJRyjDqUssNkibTU6XFQ1OgFvgLL5X9oTU78BYT3/oFYUpd1llC6FxcKyTXDrOo7Uaau/KbF9H6DA8ctQRv1JugQoQgiNBChC+CmobmJ7XjUWk8KVs9sfRGa0Glf0brdgPXsSHWYhxlXta/OdfVOv3hd8H/R7igIIUABMJhqcKnXepZeUmH4KUNpcn2RQhBBtSYAihJ8jZdqyzcjECJI62Km3r1qN8/3rT7a9AB6nVieSPqNX7wsdFMp2U0ltMwBRdovRXtzXstK0Tfj2FNZS0+Q0upkkQBFC6CRAEcLP0Qo9QOl4EJmv1bh3Szx6BiUjLgw2P6s92AfZE/BlKPYW1R43Ur4rxd4AJTmm7wtkdfr1fV1cx+4Cbf7J8LjwoO21I4QY+CRAEcJPbqW2bKMXwrZn9DAtQCmoaqLF5e7xufSswRmWnVB9DOyx2lySPjAmKRKbxUSDw23cU3eV1mobIab20/IOaCPvI2xmWlwe/rezCJD6EyFEaxKgCOFHH2E/qpMMyrAoO5E2EylqBXkBfvj701uMT6/21p7MuAZsfbODrsVsYmKqNrE10DoUPYPSX/UnACaTYlzfu9u1HZtleUcI4U8CFCH86NmGzE4yKIqq8k/746wP+yHuL//a43MVVDUyRilkZPla7YHZN/f4vdozIUULAAJqNcZXg9KfAQr4MiZ1zVpB7iQJUIQQfiRAEcJLVVWjBqWzDAprHyXbuQGA8dt/D8fW9+h8BdVN3Gz+AAUVJlwIw07q0ft0RJ/IeqgssGJefYknpR9rUMBXKKubLEs8Qgg/EqAI4VVa10Kz04NJ8Rs939a+D7WN9oC9nkxMuOGNG6GhPKBzOVwe3HWlLDF7syen/rA3l96uscO8AUppYMW8+hJPf9agQOuak2i7hRHxHfzOhRAnpIAClIcffpg5c+YQHR1NcnIyl112Gfv27Wt1THNzM8uWLSMxMZGoqCiWLFlCSUlJq2Nyc3NZvHgxERERJCcnc/fdd+NyuXp/N0L0wjHvXJPh8eHtj1svPwhv3QJA/bRvs8Txaw6q6VBXBG99FzzdL5gtqmniWvNKwhQn6vBZkJndJ/fgb6w3g3K4vL7DTh6PR2Xt/jKOVfiyLCVGF0//BigTUqLR5+BNSo/p9SaEQoihJaAAZc2aNSxbtowNGzawcuVKnE4n5513Hg0Nvj/c7rzzTt577z1ef/111qxZQ2FhIVdc4etMcLvdLF68GIfDwbp16/j3v//Nc889x3333dd3dyVEDxgtxgl+yzvOJtj1Jrx0NfxtLrTUwIhTiLrk96QkJXKr4w7c5jBtB+LHpsDr34YNT0JNfus3V1XI36yNs/e4KSqr5DrzSgCUU38I/fDhnBEfjtWs0Oz0GAW5/nYX1nDFk+u4/l9fcf2/vkJVVVRV9XXx9NMUWV24zcwYb5ZHCmSFEG0FNIXpww8/bPXzc889R3JyMjk5OZxxxhnU1NTwzDPP8NJLL3HOOecA8OyzzzJp0iQ2bNjAvHnz+Pjjj9mzZw+ffPIJKSkpzJgxgwcffJCf/exn/PrXv8Zms/Xd3QkRgNyKNi3Gzmb457lQstN30Ig52g7DFhunjUviP+UNvJHxC67KfwjqCmH329rXx/dqG/7Nvx0qD8HaP0DBZu09otMYGTaWRKWOUnMqyRMv7pf7sZhNjEqM5EBpPYfK6slI0O7L41F5+IO9PPPFEfTEyrGKRnYX1pIeF47D7QG0bqX+dtq4JA6W1nP6+KSuDxZCnFB6VYNSU6MNWEpISAAgJycHp9PJwoULjWMmTpxIZmYm69drhYTr169n6tSppKT4tm5ftGgRtbW17N69u93ztLS0UFtb2+pLDC1Ot4evi2vZU6h99aZ9t6d8Q9q8AcqXf9aCk7A4OP3H8ION8J1PICYNgNO8H6pPl0+Hnx6GG1bAgvsg81TwuLTpsMvnwMtXa8GJJUx7r7oi0sq+AGBDylVg7p9preBXh+JXKPvejkL+8bkWnCyelkb2mEQAPtlbQnGNtryTFGULyq7CP79gIh/cfjoLJqV0fbAQ4oTS4z8ZPR4Pd9xxB/Pnz2fKlCkAFBcXY7PZiIuLa3VsSkoKxcXFxjH+wYn+vP5cex5++GHuv//+nl6qGAR+9PJWPtjV+t//U9fO4vwpqUG7Bt+QtkioPAJf/El74qI/wZQlxx2fPTYRs0nhcHkDBY0mho8+HUafrgUzeZu01+97H2xRMOc7kL1MC1AOfMz29/9BfnUjhaOv7Nd7GpccBbvhkN/GhusOVgDw7VNH8etLJvPa5jzWH67gk70lTM+IAyC5H3Yxbk+Y1SztxUKIdvU4QFm2bBm7du3iiy++6Mvradc999zDXXfdZfxcW1tLRkZGv59XBIfD5eHTr0sBSIqy0+JyU9fs4o2cvKAGKPqQtpGJEfDhHeBqhtFndDjdNSbMyoyMOHKOVfHFgTKumpPpezJjDlzzMtSXgTUc7FEUVjeRn1cPYafyoC2Onc4aHk9K6Nd7Gpus1dP4d/JsPlYJaMsrAOdMTEZRYFdBLdtyq4H+bzEWQoiu9ChAue2221ixYgVr165lxAjfjq+pqak4HA6qq6tbZVFKSkpITU01jvnqq69avZ/e5aMf05bdbsdulz8wh6rdhTW0uDzER1jZ9H8L2F9Sz6LH17L2QDn1La5+27AOALcTDn1Ky673+IGrmvWmLEYXN8L+D8BkgQv/0GkB62njksg5VsXnB8pbByj620cksfrrUl7YuIc1+8tQ2zTTDO/n1tq2SzxVDQ7j+1kj4wEtKJyZEceW3Gpe25wH9H+BrBBCdCWgRWZVVbntttt4++23+fTTTxk9enSr52fNmoXVamXVqlXGY/v27SM3N5fsbK2NMjs7m507d1JaWmocs3LlSmJiYsjKyurNvYhBKudYFaB9YCqKwkkpUYxJimyVWekLLreHHfnVqKoKzTXw4T3wx4nw0jex7/gP37e8x79tv8P+zne0F2Qvg2ETOn1Pvbhz3aGK41p565qdnPvYGr7z/GY+26cFJ6MSIxgzLJIxwyI5NyuF6SPi+uz+2qN3yZTXt1DT6DR+1+OSo4iP9BWkL8zSllmLvDUowVriEUKIjgT0V9Nly5bx0ksv8d///pfo6GijZiQ2Npbw8HBiY2O5+eabueuuu0hISCAmJoYf/vCHZGdnM2/ePADOO+88srKyuO6663j00UcpLi7m3nvvZdmyZZIlOUFtOqotOcwaqS13KIrC+VNS+dtnh/hwVxGXTE/vk/M8/skB/rr6IL86L4MbD98F+Zu0JyKHcTj5XDYfKOAs216SPaUQNxLO+GmX7zk9I44ou4XKBgd7imqZMtw3HXXdoQoOlzUQaTOzdN5IvnVKJqOSOplQ2w+i7BZSY8Iorm3mYFk9m7zLO7O92RPduZNSePRD30wjyaAIIUItoAzKk08+SU1NDWeddRZpaWnG16uvvmoc89hjj3HRRRexZMkSzjjjDFJTU3nrrbeM581mMytWrMBsNpOdnc21117L9ddfzwMPPNB3dyUGDVVVjb/Vzx7l+9C8YIrWKbP66zKaHD3fMVjndHt4+atcwmhh+uff14KTsDi4+mW4ay8rRtzFT13f49FJb8Cdu+H7X4A9qsv3tZpNzPN2waw9UNbquS252n1dMmM4v7hwUtCDE51Rh1JWT85RX7bK37jkqFY7OEsNihAi1ALKoKhtF9DbERYWxvLly1m+fHmHx4wcOZL3338/kFOLIepYRSPl9Q5sZhNT/bIPU4bHMDwunILqJtbsL+t1sexn+8qoa2jgn9Y/cbK6G5c1Cst1b8PwkwH89uCJgNgRnb3VcU4fn8Qne0tYf6iCH5w1znh8q7fgdGZmXK+uvbfGDYviy4MV7C2qZUeBNhpg9qjWxbmKorBwUgrPfHEE6P+NAoUQoiuyF48Iqc3e7MnUEbGEWc3G4/oyD8CHu4p6fZ43cvL4seU1zjDvpFG182jib4zgBHxD2jI72ySwA9ljtQzKpqOVOFzakDOnt94F4OTM+I5eGhT6yPsVO4pwuDwkRdm0QKyNhX6zSCRAEUKEmgQoIqQ2H22/JgLgAm+AsmpvKS2uni/zVNS3sG3vfq73jpa/w/kD/nEsmfwq3zC4o94Apb0P7q6MT44iKcpGs9PDtrxqAPYV19Hs9BATZmFMiJZ2dHonT1mdNsJeL0Zua86oeE4ZncAZJw0jMVImOgshQksCFBFSm4+1XxMBWuYhOdpOXYuLdYcqenyOd7cXcqPpf4QrDhg+m4bRi1BVeHWT1lLb0OKivF778G61D083KYpi1KGsO6TtaqzXn8zMjMdkCu0meHqAops9sv3ZKxazide+l83zN50iG/cJIUJOAhQRMtWNDg56B4i1F6CYTAqLJmtZlE/2lBz3fHet3Lzb2JiPM3/Kt+aOAuCVTXnUt7h4xRuoxEVYiY2w9ugc+jLPem8gteWYHqDE9fi6+0pKjJ1Im2/5bNao0C45CSFEd/TjBCwhOqd374wZFkliexvTtdRzQXwBZaavOHzUDUwN+Bx7i2o5tew1Ii0tuFKmYRl/Hue6VZKi7JTVtTD7Nytpdmp1I/5FuoE6daw2D2VrbjXNTjdbvUs9oa4/AS3DMzY5ih35NdgtJqak9/w+hRAiWCRAESGzydvyelz9yc434NPfQNURTgVOtYGj2ozztcuxzvsuZMztdLorNflQXwpxmazYeIDvmz8GwHL2z0FRsFkUrpozguWrD9Hs9DA8Lpxvzc3k2rkje3wvoxIjjHkjH+0u5lhFI4oCMwZABgW0Tp4d+TVMz4gLyiaAQgjRWxKgiD7X4nLz/LpjXDgtjeFxHY9yzzGGhvnVROx6E976LqhaVoPIZI402hhNPux5Q/uasgQu/3vrXYD3fwS734ZjX0J1rvHw7aoZm+KmLm4i0RMuNB6/7ezxRNotTEiJ5qwJyZh7WSeiKAqnjk3kra0FPPnZIUALCmLCerZk1NdOPymJt7YWsHhqWqgvRQghukUCFNHnXtuUx2/f38vaA2X85+a57R6z+utSo0B2zmhvgPL1+/DWLVpwcvINsOBXEJnIH17awrGdX/LoyE1klb6vBTFmG1z6Ny2T8ulv4PM/+N5cMUNkEp76UmyK1v0Tdf59rbIu4TZzq5klfWGeN0D5urgOGBjLO7rLZgxn7uhE0mRCrBBikJAARfS5XQW1gFYwWt3oIC6idcvq4bJ6fvTKFm4wfcg3Eo8x+vP3wGKHbS+BxwXTroKLHgeTthRxcmY8/9sxhj+Fz+OfV34TXrsetr8MljBoqYNdb2hvPOtGmHQxZJzC4VqFxY+tIlUt4/ErpzJ9YvuBUl861VsoqxsIBbI6RVFI7ySbJYQQA40EKKLPfV2iZRBcHpWVe0q4cnaG8Vxds5Pv/3sDv3b/lSXWz6EW2O734kmXaJkRk69OQv+g35JbjXr9YpTLn9aWgXKe1Q4wWeDiP8PMa43X/P6jHJo8FsZMnM70k+f01622MiI+goyEcPIqmwA4uZ3OJCGEEN0jAYroUx6PygFvgAKQs3ULV362AMJiUadfwyP7RvOLmsc4y7wdVTGjnH4X2KLAUQ9RKdrSjrn1f5aT02OwmU1UNjg4VtHIqGlXgrMR3vsR2GPhqudhzFnG8Vtyq/hgVzGKAj89f2Kwbh2A7DGJ5FXmE223MG5Y13v5CCGEaJ8EKCeYumYnn35daoxkt1lMLJyUQqS9b/5TyK9qotHhRlFAVWF23r/AVAL1JSir7ue3AGbwWMIxffN5OOm8Lt/TbjEzZXgMW3Kr2ZpXpW26N+sGGDEbIpIg2jeivdHh4v53dwOw5OQRTEiN7pP76q5zJqbw2uZ8sscmhnxAmxBCDGYSoJxgHv7ga17amNvqsW/NzeShywOfMdKefd7sycTUGGIdxVxa/zkA7jN+xq4vVjDds5smSyzh335LCzC6aWZmPFtyq9lyrJrLZ3o380uZ3OqYZqeb7/x7M9vza4i2W7jr3JP65J4CsWhyCs/fdAqT02OCfm4hhBhKZCDCCeaLA9oo9tkj45k/TivqfHtLAbXNzj55/33FWoHsxNRo7o7+GKvi5uvwmbwcsZRLG/+PC0xP41q2OaDgBHwdMVvzqtp9vsXl5nv/yWHdoQoibWaeu+mUkBSFKoqi7WXT3uA5IYQQ3SYBygmktLaZ3EptgNi/bpzDCzfP5aSUKJqcbt7ZWtAn59hXoo2unx7vYGbZfwF4pP5CHv/kAABXLZhLdHxywO+rF8ruLaqj0eE67vm7Xt3Omv1lhFvN/Ovbc9odnS+EEGLwkADlBKLPHZmQEk1MmBVFUfjWKZkAvLghF1VVe30OPYNydtUbmNwt7FZO4jNnFuX1LWQmRPCtHk5rTY8LJzUmDLdHZWd+Tavn8iob+d/OIswmhX/eMJu5YxI7eBchhBCDhQQoJ5DN3tHyc0b5JrdefvIIwqwm9pXUGXvj9JTD5eFwWQMxNJBx6EUAdo/9DqAVi/5k0YRejVn3bzf2t/GINpF2+ohY5o9L6vH7CyGEGDgkQDmBGKPl/XazjQ23cvG0dEDl1Q2HevX+h8vrcXlU7rS/i8lRD8lZTDn7KiwmhVkj47mol2PW9ToU/T50Xx3RdhA+ZbRkToQQYqiQLp4TRKPDxe5Cbfll1sh4qCmAjU/Brjd5pLGS39mbMX2t4nr6ZCzzb4NJlx43j6Qr+4rrmKQc43rlf9oDC35F1vA4Vv/kLBKjbL1uu832Tmpdd6iCZqebMKsZ8GVQ5o5O6PC1QgghBhcJUE4Q2/KqcXlUZsbUMnz1Hdp+Nh6t2NQM+ioMlqIt8MZNEJsJp96mDU6zdm//lv1F1TxkfQYzHsi6FCacD0BGQkSf3MPk9Bhjx+D1hys4e0IyxTXNHKtoxKTArFFSGCuEEEOFLPEMJY5GqDqq7U/TpuA152gVM5UDPO/+OcqOV7XgZNTpcPXLcPt23jh7NdnNf+GViG9BRCLU5MIHP4UnZsDGv4OzufW5VBXqS6HiEHi0oW9pB19mpukgDnMknP+7Pr89RVFYMEnrAFq1twSAr45q2ZOs9JgBs3OwEEKI3pMMylBx9Et45Rpo9na4mG2QMBamLoFpV6HsfYeXbb8jzO2EtOnaZnzDTzZePjMriaIPivh13SV84//+gGXnK/D5n6A2Hz64WwtWIhIhKhlQtEDI2aC9OCIRRs7nsopPAMifdTdjYnpXb9KRhVkpvLgxl0/2lPLgpaqv/mSU1J8IIcRQIgHKUHBgJbx6LbiaQTGD6ga3A8r2wqe/gU9/w20ACtRmLiTm2ufBFtnqLUYnRhJpM9PgcHO4xsNJc27WNt/b+h/4/DEtUGks174MihYINVbA3neJArZ5xpB5+vf77VazxyQSYTNTXNvM7sJaNh7WMiinSP2JEEIMKRKgDHa734Y3vwseJ4xfBN/8t7b80lgOR9bC9lfgqDZu/j/qBVxz3X/AevxSiMmkMCkths3HqthdWMNJKdFgscOc78Csm6ChzPtVqi3pxI+CuAxAgcItFG77mPWbNvKC/Wreju6/Ca5hVjNnjB/Gh7uLeW1zHgdKtcFwEqAIIcTQIgHKYHZwlVbQqnpg8hVwxd/B7A0+bJlaBmTmtby5egPPf7yR6LHzuK6d4EQ3ZXgsm49Vsauglstn+j1hMmkb8vltytdK5jzWFqfzc+csTh/V/3NIFkxK5sPdxcaeQielRJEQaev38wohhAgeKZLtRzWNToprmimuaaa0rrnnk1o9Hqg61vqx6lx482YtOJl2FSz5py84aePzEjvb1XFdjn/XN7jbVVDT6XFt1TY7eWGjdn0TUvp/9+BzJiajKODyaL9PyZ4IIcTQIxmUfvLhriJufXFLq2aaHu8avO1F+N9dkL0MTv+xVvfx2vXQVAVpM+DiJ8Bk7vDlW/OqAboMUKYMjwVgT2EtHo/arbkl9S0uvv2vr9hVUEt8hJVr5/VslH0gEqPszMqMN0b3y4A2IYQYeiSD0k+e+eIIqgomBSzeD/rXN+dRVtcS+Jsd/Vwrev3iMfjLLHj5GijcCuHxcNV/Op1TUtPo5FhFIwDTR8R1eppxyVHYLCbqWlzkVTV2eVmNDhc3PbeJLbnVxIZbeeE7cxmVFNnl6/rCwizfcpMMaBNCiKFHApR+cLS8gU1HqzApsO7nCzj40IXMyIjD6VZ5PScv8De8/Gm45hVIGAP1JXBoFaBoyzpxmZ2+dKd3uWZkYgSxEZ3PCbGaTUxM1ZZodhXUdnpss9PNd5/fzFdHKom2W/jPzacwOT22+/fUSxdMSSXMamJ6RhwpMd0bJCeEEGLwkAClH7y5JR+A08cPIzVW+/BcOlcLJF7amIvHE2AtiqLAhAvgBxvg3AcgbiSc/wiMW9jlS3cUVAMwdXj3ggc9yNhV2HEdSovLzff+k8OXByuIsJl57qZTmNZFdqavjUyM5JO7zuT5G08J6nmFEEIEhwQofczjUXlrSwEAS2aNMB6/aFo6MWEW8quaWHugrGdvbrHD/Nvhjh0w7/t4PCrPfnnkuM3z/O3M1wKNaSO6F6BMGa4Vyur79rTlcHlY9uJW1uwvI8xq4tlvz+mytqW/jIjvOiskhBBicJIApY9tOFxBQXUT0WEWzvOrkwi3mY2ARW+P7a2Ve0u4/709LP3nxg47b/QlnindzKBM8WZQdhfUtNt19Kt3d/PJ3hJsFhP/vH4Oc8dIgaoQQoi+JwFKH3sjR1veuXh6urHbrk5f5ln1dSlFNU29PtcHO4sAaHZ6+N5/cqiob12AW9ngIL9KO093A5QJqdGYTQoVDQ6Ka1vvv+P2qLyzVcsO/eWamZw2vv9nngghhDgxSYDSh+pbXHywqxiAb/gt7+jGJUdzyugE3B6VVzf1oFjWT4vLzaq9pQDER1gpqG7iBy9uwen2GMfo2ZMxSZHd3kgvzGpmfHIUcHyh7NGKBpqcbsKsJhZO6mBomxBCCNEHJEDpQ+/vLKLJ6WZMUiQzM+LaPUbPory+Ob/ng9uAdQcrqGtxkRJj59XvZRNpM7PxSCW//d9e45id+dUATO1m/YlOL5Td3aZQdm+RFrBMTI3B3I0ZKUIIIURPSYDSh972K45VlPY/wBdN1tpjC6qb2FdS1+NzfbCryHi/k1KiefxqbTb9c+uOst/7vju8BbLd7eDR6YWybTMoe7yFs1neibNCCCFEf5EApRtUVWX56oP86eN91DY72z2mpLaZDUcqALhkenqH7xVmNTPPW1j62b6edfO43B5W7ikB4PwpqQCcm5XCBd7v//rpQcC3xBNoC7Ber7Ijv7pVlmePN4OSlSYBihBCiP4lAUo3bMmt5vcf7eOJTw+y8I9rWLGj8LjlmRU7ilBVODkzjoyEiE7f76yThgHw2b7SHl3PxiOVVDU6SYi0ccoo3xTVZWeP815LIV8dqaSophlF8e2x011Th8diM5sorWvhaIVvoqyeQZkkAYoQQoh+JgFKN7y3vRDQxtaX1rVw20tb+e7zm3G4fAWp73qP6Sx7ojtrQjIAm49WUddBRqYz+vLOeVkpWMy+f4VThseyYGIyHhV+8vp2AMYNiyLSHtiWS2FWMzMz4wBYd6gcgLK6FkrrWlAUjGmzQgghRH+RAKULLreHFTu0gOBvS0/mjoXjsZlNfLK3lGe+OALAsYoGtudVY1Jg8bSuA5RRSZGMTorE5VH58mB5QNfj8ah8tLv18o6/287Rsii5lVrmI9D6E92pY7UW4vWHtGUrvUB2dGJkwAGPEEIIESgJULqw8Ugl5fUtxEVYOWdiCncsPIlHlmg7Ej+x6gAF1U1GhuXUsUkMi7Z3633PNJZ5AqtDycmtoqyuhegwixFE+JuZGc/pfvNJAu3g0WWP1epk1h+qQFVVo/5kkhTICiGECAIJULrw7jYt+LhgSho2i/brunzmcE4ZnUCT080D7+0OaHlHd9YEX4ASSLuxPoX2vKxU43raus1biwLdH3Hf1oyMOMKsJioaHOwvqfd18Ej9iRBCiCCQAKUTLS63Ue9x8fQ043FFUXjw0ilYTAof7S5hf0k9NrOJRe0suXRk3phE7BYTxbXN3W43LqltNrI1N5w6ssPj5o5J5NunjmLR5JQeb+Jns5iY4y3AXX+o3NfBIxkUIYQQQSABSifW7i+nttlFcrSduaNb7zkzITWam04bbfx85oRhxIZ3f+O6MKvZWEZZ/XX3lnmeX38Ul0fllFEJXQYev75kMk9fNxuruef/ivXr+3RfGYfL6gGYLBkUIYQQQSABSif0pZuLpqW3Ozn19gXjSYsNA7Rln0Cd7e3m6U67cZPDzYve5R3/wKg/ZXvntazdX4ZHhcRIW7drbIQQQojekHaMDjS0uPjEOwztkhnt15ZE2i28+J25bMurNoakBUKvQ8k5VkVNk7NVBmZPYS3v7SjkqtkZjEqK5M0t+VQ3OslMiODcrODsgzN1eCxRdgv1LS5AW97paEKuEEII0Zckg9IOVVX55Tu7aHK6GZUYwfROCk3HDIviipM7Hm3fmZGJkYxLjsLlUfn065JWz/349e08+dkhznt8LU+sOsC/vtRamr996qig7YNjMZs4ZbRvEJwUyAohhAgWCVDa8cwXR3hrawFmk8JDl0/t16yBnnn5YGex8diBkjpj7ojD5eFPK/dzuKyBaLuFb87J6Ldrac+pY321N1IgK4QQIlgkQGnj8wNlPPS+tiPwvYsnceq442eN9CV92Nqa/WU0eJdS9NqXcyYm88Q1M0mK0uo+ls4bSVSQh6Tp+waBZFCEEEIEj9Sg+DlW0cBtL23Fo8I3Zo3g26eO6vdzZqXFkJkQQW5lI5/tK+PCqalGK/El09O5ZHo6Z540jK25VZzWz8FSR9c3f1wibo/KmGFRQT+/EEKIE5MEKH6eX3+MmiYn0zPi+M1lU4JSEKooChdMSeXptYf5YFcRGQnhHK1oJMxqMophY8Otxv49wWYyKbz4nXkhObcQQogTlwQofv7vwkkkRNr4xqwRhFnNQTvv+d4AZfXXpcRH2ABYMClF9rwRQghxwpJPQD8mk8IyvzHxwTJ9RBxpsWEU1TTz4sZjQGBj84UQQoihRopkBwCTSWHRZK1Y1qNCdJjFmJEihBBCnIgCDlDWrl3LxRdfTHp6Ooqi8M4777R6XlVV7rvvPtLS0ggPD2fhwoUcOHCg1TGVlZUsXbqUmJgY4uLiuPnmm6mvr+/VjQx2/oPezp+cit0SvCUmIYQQYqAJOEBpaGhg+vTpLF++vN3nH330UZ544gmeeuopNm7cSGRkJIsWLaK5udk4ZunSpezevZuVK1eyYsUK1q5dyy233NLzuxgCZo9KICVGaye+rAdj84UQQoihRFFVVe3xixWFt99+m8suuwzQsifp6en8+Mc/5ic/+QkANTU1pKSk8Nxzz3H11Vezd+9esrKy2LRpE7Nnzwbgww8/5MILLyQ/P5/09K5rL2pra4mNjaWmpoaYmKEzm2NXQQ1Hyhu4WOpPhBBCDEGBfH73aQ3KkSNHKC4uZuHChcZjsbGxzJ07l/Xr1wOwfv164uLijOAEYOHChZhMJjZu3Nju+7a0tFBbW9vqayiaMjxWghMhhBCCPg5Qiou1ce0pKa03s0tJSTGeKy4uJjm59UwPi8VCQkKCcUxbDz/8MLGxscZXRkZwx70LIYQQIrgGRRfPPffcQ01NjfGVl5cX6ksSQgghRD/q0wAlNVXrRCkpab0zb0lJifFcamoqpaWlrZ53uVxUVlYax7Rlt9uJiYlp9SWEEEKIoatPA5TRo0eTmprKqlWrjMdqa2vZuHEj2dnZAGRnZ1NdXU1OTo5xzKefforH42Hu3Ll9eTlCCCGEGKQCniRbX1/PwYMHjZ+PHDnCtm3bSEhIIDMzkzvuuIPf/OY3jB8/ntGjR/PLX/6S9PR0o9Nn0qRJnH/++Xz3u9/lqaeewul0ctttt3H11Vd3q4NHCCGEEENfwAHK5s2bOfvss42f77rrLgBuuOEGnnvuOX7605/S0NDALbfcQnV1NaeddhoffvghYWFhxmtefPFFbrvtNhYsWIDJZGLJkiU88cQTfXA7QgghhBgKejUHJVSG6hwUIYQQYigL2RwUIYQQQoi+IAGKEEIIIQYcCVCEEEIIMeBIgCKEEEKIAUcCFCGEEEIMOBKgCCGEEGLACXgOykCgd0YP1V2NhRBCiKFI/9zuzoSTQRmg1NXVAciuxkIIIcQgVFdXR2xsbKfHDMpBbR6Ph8LCQqKjo1EUpU/fu7a2loyMDPLy8k64IXBy73Lvcu8nDrl3ufdQ3LuqqtTV1ZGeno7J1HmVyaDMoJhMJkaMGNGv5ziRd02We5d7P9HIvcu9n2hCee9dZU50UiQrhBBCiAFHAhQhhBBCDDgSoLRht9v51a9+hd1uD/WlBJ3cu9z7iUbuXe79RDOY7n1QFskKIYQQYmiTDIoQQgghBhwJUIQQQggx4EiAIoQQQogBRwIUIYQQQgw4EqD4Wb58OaNGjSIsLIy5c+fy1VdfhfqS+tzDDz/MnDlziI6OJjk5mcsuu4x9+/a1Oqa5uZlly5aRmJhIVFQUS5YsoaSkJERX3H8eeeQRFEXhjjvuMB4byvdeUFDAtddeS2JiIuHh4UydOpXNmzcbz6uqyn333UdaWhrh4eEsXLiQAwcOhPCK+4bb7eaXv/wlo0ePJjw8nLFjx/Lggw+22gtkqNz72rVrufjii0lPT0dRFN55551Wz3fnPisrK1m6dCkxMTHExcVx8803U19fH8S76JnO7t3pdPKzn/2MqVOnEhkZSXp6Otdffz2FhYWt3mMo3ntb3//+91EUhccff7zV4wPx3iVA8Xr11Ve56667+NWvfsWWLVuYPn06ixYtorS0NNSX1qfWrFnDsmXL2LBhAytXrsTpdHLeeefR0NBgHHPnnXfy3nvv8frrr7NmzRoKCwu54oorQnjVfW/Tpk08/fTTTJs2rdXjQ/Xeq6qqmD9/PlarlQ8++IA9e/bwxz/+kfj4eOOYRx99lCeeeIKnnnqKjRs3EhkZyaJFi2hubg7hlffe7373O5588kn++te/snfvXn73u9/x6KOP8pe//MU4Zqjce0NDA9OnT2f58uXtPt+d+1y6dCm7d+9m5cqVrFixgrVr13LLLbcE6xZ6rLN7b2xsZMuWLfzyl79ky5YtvPXWW+zbt49LLrmk1XFD8d79vf3222zYsIH09PTjnhuQ964KVVVV9ZRTTlGXLVtm/Ox2u9X09HT14YcfDuFV9b/S0lIVUNesWaOqqqpWV1erVqtVff31141j9u7dqwLq+vXrQ3WZfaqurk4dP368unLlSvXMM89Ub7/9dlVVh/a9/+xnP1NPO+20Dp/3eDxqamqq+vvf/954rLq6WrXb7erLL78cjEvsN4sXL1ZvuummVo9dccUV6tKlS1VVHbr3Dqhvv/228XN37nPPnj0qoG7atMk45oMPPlAVRVELCgqCdu291fbe2/PVV1+pgHrs2DFVVYf+vefn56vDhw9Xd+3apY4cOVJ97LHHjOcG6r1LBgVwOBzk5OSwcOFC4zGTycTChQtZv359CK+s/9XU1ACQkJAAQE5ODk6ns9XvYuLEiWRmZg6Z38WyZctYvHhxq3uEoX3v7777LrNnz+bKK68kOTmZmTNn8o9//MN4/siRIxQXF7e699jYWObOnTvo7/3UU09l1apV7N+/H4Dt27fzxRdfcMEFFwBD+979dec+169fT1xcHLNnzzaOWbhwISaTiY0bNwb9mvtTTU0NiqIQFxcHDO1793g8XHfdddx9991Mnjz5uOcH6r0Pys0C+1p5eTlut5uUlJRWj6ekpPD111+H6Kr6n8fj4Y477mD+/PlMmTIFgOLiYmw2m/E/rS4lJYXi4uIQXGXfeuWVV9iyZQubNm067rmhfO+HDx/mySef5K677uIXv/gFmzZt4kc/+hE2m40bbrjBuL/2/h8Y7Pf+85//nNraWiZOnIjZbMbtdvPb3/6WpUuXAgzpe/fXnfssLi4mOTm51fMWi4WEhIQh9btobm7mZz/7Gddcc42xYd5Qvvff/e53WCwWfvSjH7X7/EC9dwlQTmDLli1j165dfPHFF6G+lKDIy8vj9ttvZ+XKlYSFhYX6coLK4/Ewe/ZsHnroIQBmzpzJrl27eOqpp7jhhhtCfHX967XXXuPFF1/kpZdeYvLkyWzbto077riD9PT0IX/v4nhOp5NvfvObqKrKk08+GerL6Xc5OTn8+c9/ZsuWLSiKEurLCYgs8QBJSUmYzebjujVKSkpITU0N0VX1r9tuu40VK1awevVqRowYYTyempqKw+Ggurq61fFD4XeRk5NDaWkpJ598MhaLBYvFwpo1a3jiiSewWCykpKQM2XtPS0sjKyur1WOTJk0iNzcXwLi/ofj/wN13383Pf/5zrr76aqZOncp1113HnXfeycMPPwwM7Xv31537TE1NPa4xwOVyUVlZOSR+F3pwcuzYMVauXGlkT2Do3vvnn39OaWkpmZmZxp97x44d48c//jGjRo0CBu69S4AC2Gw2Zs2axapVq4zHPB4Pq1atIjs7O4RX1vdUVeW2227j7bff5tNPP2X06NGtnp81axZWq7XV72Lfvn3k5uYO+t/FggUL2LlzJ9u2bTO+Zs+ezdKlS43vh+q9z58//7h28v379zNy5EgARo8eTWpqaqt7r62tZePGjYP+3hsbGzGZWv9RZzab8Xg8wNC+d3/duc/s7Gyqq6vJyckxjvn000/xeDzMnTs36Nfcl/Tg5MCBA3zyySckJia2en6o3vt1113Hjh07Wv25l56ezt13381HH30EDOB7D1l57gDzyiuvqHa7XX3uuefUPXv2qLfccosaFxenFhcXh/rS+tStt96qxsbGqp999plaVFRkfDU2NhrHfP/731czMzPVTz/9VN28ebOanZ2tZmdnh/Cq+49/F4+qDt17/+qrr1SLxaL+9re/VQ8cOKC++OKLakREhPrCCy8YxzzyyCNqXFyc+t///lfdsWOHeumll6qjR49Wm5qaQnjlvXfDDTeow4cPV1esWKEeOXJEfeutt9SkpCT1pz/9qXHMULn3uro6devWrerWrVtVQP3Tn/6kbt261ehU6c59nn/++erMmTPVjRs3ql988YU6fvx49ZprrgnVLXVbZ/fucDjUSy65RB0xYoS6bdu2Vn/2tbS0GO8xFO+9PW27eFR1YN67BCh+/vKXv6iZmZmqzWZTTznlFHXDhg2hvqQ+B7T79eyzzxrHNDU1qT/4wQ/U+Ph4NSIiQr388svVoqKi0F10P2oboAzle3/vvffUKVOmqHa7XZ04caL697//vdXzHo9H/eUvf6mmpKSodrtdXbBggbpv374QXW3fqa2tVW+//XY1MzNTDQsLU8eMGaP+3//9X6sPpqFy76tXr273/+8bbrhBVdXu3WdFRYV6zTXXqFFRUWpMTIx64403qnV1dSG4m8B0du9Hjhzp8M++1atXG+8xFO+9Pe0FKAPx3hVV9RunKIQQQggxAEgNihBCCCEGHAlQhBBCCDHgSIAihBBCiAFHAhQhhBBCDDgSoAghhBBiwJEARQghhBADjgQoQgghhBhwJEARQgghxIAjAYoQQgghBhwJUIQQQggx4EiAIoQQQogBRwIUIYQQQgw4/w+jEUXL1/ZDFQAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]

      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "84/84 - 2s - 22ms/step - loss: 0.0425\n",
       "Epoch 2/10\n",
+      "84/84 - 0s - 2ms/step - loss: 0.0124\n",
       "Epoch 3/10\n",
+      "84/84 - 0s - 1ms/step - loss: 0.0088\n",
       "Epoch 4/10\n",
+      "84/84 - 0s - 1ms/step - loss: 0.0074\n",
       "Epoch 5/10\n",
+      "84/84 - 0s - 1ms/step - loss: 0.0065\n",
       "Epoch 6/10\n",
+      "84/84 - 0s - 1ms/step - loss: 0.0053\n",
       "Epoch 7/10\n",
+      "84/84 - 0s - 1ms/step - loss: 0.0045\n",
       "Epoch 8/10\n",
+      "84/84 - 0s - 1ms/step - loss: 0.0039\n",
       "Epoch 9/10\n",
+      "84/84 - 0s - 1ms/step - loss: 0.0036\n",
       "Epoch 10/10\n",
+      "84/84 - 0s - 1ms/step - loss: 0.0034\n",
+      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step\n",
       "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
+      "Train Score: 28.98 RMSE\n",
+      "Test Score: 82.74 RMSE\n"
      ]
     },
     {
      "data": {
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]

benchmark/numpy_12/numpy_12_reproduced.ipynb CHANGED Viewed

@@ -561,29 +561,29 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "84/84 - 2s - 24ms/step - loss: 0.0662\n",
       "Epoch 2/10\n",
-      "84/84 - 0s - 2ms/step - loss: 0.0493\n",
       "Epoch 3/10\n",
-      "84/84 - 0s - 1ms/step - loss: 0.0271\n",
       "Epoch 4/10\n",
-      "84/84 - 0s - 1ms/step - loss: 0.0130\n",
       "Epoch 5/10\n",
-      "84/84 - 0s - 1ms/step - loss: 0.0090\n",
       "Epoch 6/10\n",
-      "84/84 - 0s - 1ms/step - loss: 0.0077\n",
       "Epoch 7/10\n",
-      "84/84 - 0s - 1ms/step - loss: 0.0069\n",
       "Epoch 8/10\n",
-      "84/84 - 0s - 1ms/step - loss: 0.0061\n",
       "Epoch 9/10\n",
-      "84/84 - 0s - 1ms/step - loss: 0.0055\n",
       "Epoch 10/10\n",
-      "84/84 - 0s - 1ms/step - loss: 0.0050\n",
-      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step\n",
-      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
-      "Train Score: 39.87 RMSE\n",
-      "Test Score: 166.56 RMSE\n"
      ]
     },
     {

      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "84/84 - 2s - 25ms/step - loss: 0.0641\n",
       "Epoch 2/10\n",
+      "84/84 - 0s - 2ms/step - loss: 0.0434\n",
       "Epoch 3/10\n",
+      "84/84 - 0s - 2ms/step - loss: 0.0277\n",
       "Epoch 4/10\n",
+      "84/84 - 0s - 2ms/step - loss: 0.0210\n",
       "Epoch 5/10\n",
+      "84/84 - 0s - 2ms/step - loss: 0.0153\n",
       "Epoch 6/10\n",
+      "84/84 - 0s - 2ms/step - loss: 0.0109\n",
       "Epoch 7/10\n",
+      "84/84 - 0s - 2ms/step - loss: 0.0082\n",
       "Epoch 8/10\n",
+      "84/84 - 0s - 2ms/step - loss: 0.0068\n",
       "Epoch 9/10\n",
+      "84/84 - 0s - 2ms/step - loss: 0.0061\n",
       "Epoch 10/10\n",
+      "84/84 - 0s - 2ms/step - loss: 0.0056\n",
+      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step \n",
+      "Train Score: 32.59 RMSE\n",
+      "Test Score: 119.71 RMSE\n"
      ]
     },
     {

benchmark/numpy_13/numpy_13_fixed.ipynb CHANGED Viewed

@@ -1138,7 +1138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.status.busy": "2023-04-24T20:38:49.427002Z",
@@ -1154,7 +1154,7 @@
        "21"
       ]
      },
-     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1165,7 +1165,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.status.busy": "2023-04-24T20:38:49.435465Z",
@@ -1174,357 +1174,7 @@
      "shell.execute_reply.started": "2023-04-24T20:38:49.436224Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 1.01888657e+01,  1.40744915e+01,  1.53015804e+01,\n",
-       "         1.69883270e+01,  1.79913826e+01,  1.94246330e+01,\n",
-       "         1.98932781e+01,  2.04561520e+01,  2.02658005e+01,\n",
-       "         2.03706818e+01,  2.03248425e+01,  2.00651283e+01,\n",
-       "         1.97179489e+01,  1.94197636e+01,  1.90902290e+01,\n",
-       "         1.87754097e+01,  1.84560223e+01,  1.81027870e+01,\n",
-       "         1.77419052e+01,  1.74265709e+01,  1.70849838e+01,\n",
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-       "        -5.95438461e+01, -5.95438461e+01, -5.95438461e+01,\n",
-       "        -5.95438461e+01, -5.95438461e+01, -5.95438461e+01,\n",
-       "        -5.95438461e+01, -5.95438461e+01, -5.95438461e+01,\n",
-       "        -5.95438461e+01, -5.95438461e+01, -5.95438461e+01,\n",
-       "        -5.95438461e+01, -5.95438461e+01, -5.95438461e+01,\n",
-       "        -5.95438461e+01, -5.95438461e+01, -5.95438461e+01,\n",
-       "        -5.95438461e+01,  0.00000000e+00,  0.00000000e+00,\n",
-       "         0.00000000e+00]], dtype=float32)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "NX[0]"
    ]

   },
   {
    "cell_type": "code",
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.status.busy": "2023-04-24T20:38:49.427002Z",
        "21"
       ]
      },
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.status.busy": "2023-04-24T20:38:49.435465Z",
      "shell.execute_reply.started": "2023-04-24T20:38:49.436224Z"
     }
    },
+   "outputs": [],
    "source": [
     "NX[0]"
    ]

benchmark/numpy_14/numpy_14_fixed.ipynb CHANGED Viewed

@@ -250,13 +250,6 @@
     }
    },
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/2\n"
-     ]
-    },
     {
      "name": "stderr",
      "output_type": "stream",
@@ -269,20 +262,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4s/step - accuracy: 0.5000 - loss: 0.9583\n",
       "Epoch 1: val_accuracy improved from -inf to 0.50000, saving model to data_small/best_weights_V19-01-0.5000.keras\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m31s\u001b[0m 31s/step - accuracy: 0.5000 - loss: 0.9583 - val_accuracy: 0.5000 - val_loss: 0.7054\n",
-      "Epoch 2/2\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 1.0000 - loss: 0.6814\n",
-      "Epoch 2: val_accuracy improved from 0.50000 to 0.83333, saving model to data_small/best_weights_V19-02-0.8333.keras\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m27s\u001b[0m 27s/step - accuracy: 1.0000 - loss: 0.6814 - val_accuracy: 0.8333 - val_loss: 0.6884\n"
      ]
     }
    ],
    "source": [
     "# Dont run again model is saved @ /kaggle/working/save_weights/best_weights_V19-47-0.9566.hdf5\n",
     "\n",
-    "history_V19 = modelV19.fit(train_generator, epochs=2, validation_data=validation_generator, callbacks=[early_stop, checkpoint_V19])"
    ]
   },
   {
@@ -348,20 +337,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/2\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8s/step - accuracy: 0.6667 - loss: 1.1260\n",
-      "Epoch 1: val_accuracy improved from -inf to 0.50000, saving model to data_small/best_weights_M2-01-0.5000.keras\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m52s\u001b[0m 52s/step - accuracy: 0.6667 - loss: 1.1260 - val_accuracy: 0.5000 - val_loss: 0.6371\n",
-      "Epoch 2/2\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 672ms/step - accuracy: 0.8333 - loss: 0.8860\n",
-      "Epoch 2: val_accuracy improved from 0.50000 to 0.83333, saving model to data_small/best_weights_M2-02-0.8333.keras\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 44s/step - accuracy: 0.8333 - loss: 0.8860 - val_accuracy: 0.8333 - val_loss: 0.4877\n"
      ]
     }
    ],
    "source": [
     "# Dont run again model is saved @ /kaggle/working/save_weights/best_weights_M2-49-0.9681.hdf5\n",
-    "history_M2 = modelM2.fit(train_generator, epochs=2, validation_data=validation_generator, callbacks=[early_stop, checkpoint_M2])"
    ]
   },
   {
@@ -472,7 +456,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-17T12:07:00.234155Z",
@@ -497,27 +481,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/2\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━���━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10s/step - accuracy: 1.0000 - loss: 0.0000e+00\n",
-      "Epoch 1: val_accuracy improved from -inf to 1.00000, saving model to data_small/best_weights_ensemble-01-1.0000.keras\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m112s\u001b[0m 112s/step - accuracy: 1.0000 - loss: 0.0000e+00 - val_accuracy: 1.0000 - val_loss: 0.0000e+00\n",
-      "Epoch 2/2\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 1.0000 - loss: 0.0000e+00\n",
-      "Epoch 2: val_accuracy did not improve from 1.00000\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step - accuracy: 1.0000 - loss: 0.0000e+00 - val_accuracy: 0.8333 - val_loss: 0.0000e+00\n"
      ]
     }
    ],
    "source": [
     "# only fit when there are trainable parameters\n",
-    "history_ensemble = ensemble_model.fit(train_generator, epochs=2, validation_data=validation_generator, callbacks=[early_stop, checkpoint_ensemble])"
    ]
   },
   {
@@ -560,7 +539,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-17T12:14:59.727959Z",
@@ -575,7 +554,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1s/step - accuracy: 1.0000 - loss: 0.0000e+00\n"
      ]
     },
     {
@@ -584,7 +563,7 @@
        "[0.0, 0.0, 1.0, 0.0, 1.0, 1.0]"
       ]
      },
-     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -598,7 +577,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-17T12:20:24.753793Z",
@@ -615,13 +594,13 @@
      "text": [
       "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step\n",
       "One-hot encoded predicted labels:\n",
-      "[[0.6874518  0.3125482 ]\n",
-      " [0.3015358  0.6984642 ]\n",
-      " [0.28066972 0.71933025]\n",
-      " [0.8681891  0.13181089]\n",
-      " [0.15508811 0.84491193]\n",
-      " [0.8854995  0.11450046]]\n",
-      "[0 1 1 0 1 0]\n"
      ]
     }
    ],
@@ -649,7 +628,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-17T12:16:41.235043Z",
@@ -664,7 +643,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1s/step\n"
      ]
     }
    ],
@@ -689,7 +668,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-16T20:46:57.948345Z",

     }
    },
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3s/step - accuracy: 0.6667 - loss: 0.5391\n",
       "Epoch 1: val_accuracy improved from -inf to 0.50000, saving model to data_small/best_weights_V19-01-0.5000.keras\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 17s/step - accuracy: 0.6667 - loss: 0.5391 - val_accuracy: 0.5000 - val_loss: 0.6312\n"
      ]
     }
    ],
    "source": [
     "# Dont run again model is saved @ /kaggle/working/save_weights/best_weights_V19-47-0.9566.hdf5\n",
     "\n",
+    "history_V19 = modelV19.fit(train_generator, epochs=1, validation_data=validation_generator, callbacks=[early_stop, checkpoint_V19])"
    ]
   },
   {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7s/step - accuracy: 0.6667 - loss: 0.9756\n",
+      "Epoch 1: val_accuracy improved from -inf to 0.83333, saving model to data_small/best_weights_M2-01-0.8333.keras\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 30s/step - accuracy: 0.6667 - loss: 0.9756 - val_accuracy: 0.8333 - val_loss: 0.5959\n"
      ]
     }
    ],
    "source": [
     "# Dont run again model is saved @ /kaggle/working/save_weights/best_weights_M2-49-0.9681.hdf5\n",
+    "history_M2 = modelM2.fit(train_generator, epochs=1, validation_data=validation_generator, callbacks=[early_stop, checkpoint_M2])"
    ]
   },
   {
   },
   {
    "cell_type": "code",
+   "execution_count": 12,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-17T12:07:00.234155Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7s/step - accuracy: 0.6667 - loss: 0.0000e+00\n",
+      "Epoch 1: val_accuracy improved from -inf to 0.83333, saving model to data_small/best_weights_ensemble-01-0.8333.keras\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m64s\u001b[0m 64s/step - accuracy: 0.6667 - loss: 0.0000e+00 - val_accuracy: 0.8333 - val_loss: 0.0000e+00\n"
      ]
     }
    ],
    "source": [
     "# only fit when there are trainable parameters\n",
+    "history_ensemble = ensemble_model.fit(train_generator, epochs=1, validation_data=validation_generator, callbacks=[early_stop, checkpoint_ensemble])"
    ]
   },
   {
   },
   {
    "cell_type": "code",
+   "execution_count": 14,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-17T12:14:59.727959Z",
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 912ms/step - accuracy: 1.0000 - loss: 0.0000e+00\n"
      ]
     },
     {
        "[0.0, 0.0, 1.0, 0.0, 1.0, 1.0]"
       ]
      },
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
   },
   {
    "cell_type": "code",
+   "execution_count": 15,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-17T12:20:24.753793Z",
      "text": [
       "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step\n",
       "One-hot encoded predicted labels:\n",
+      "[[0.6766326  0.32336748]\n",
+      " [0.13196638 0.8680336 ]\n",
+      " [0.61891085 0.38108918]\n",
+      " [0.07593525 0.92406476]\n",
+      " [0.11446559 0.88553447]\n",
+      " [0.7296946  0.27030542]]\n",
+      "[0 1 0 1 1 0]\n"
      ]
     }
    ],
   },
   {
    "cell_type": "code",
+   "execution_count": 16,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-17T12:16:41.235043Z",
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 850ms/step\n"
      ]
     }
    ],
   },
   {
    "cell_type": "code",
+   "execution_count": 17,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-16T20:46:57.948345Z",

benchmark/numpy_14/numpy_14_reproduced.ipynb CHANGED Viewed

@@ -269,13 +269,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4s/step - accuracy: 0.5000 - loss: 0.9583\n",
       "Epoch 1: val_accuracy improved from -inf to 0.50000, saving model to data_small/best_weights_V19-01-0.5000.keras\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m31s\u001b[0m 31s/step - accuracy: 0.5000 - loss: 0.9583 - val_accuracy: 0.5000 - val_loss: 0.7054\n",
       "Epoch 2/2\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 1.0000 - loss: 0.6814\n",
-      "Epoch 2: val_accuracy improved from 0.50000 to 0.83333, saving model to data_small/best_weights_V19-02-0.8333.keras\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m27s\u001b[0m 27s/step - accuracy: 1.0000 - loss: 0.6814 - val_accuracy: 0.8333 - val_loss: 0.6884\n"
      ]
     }
    ],
@@ -349,13 +349,13 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/2\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8s/step - accuracy: 0.6667 - loss: 1.1260\n",
       "Epoch 1: val_accuracy improved from -inf to 0.50000, saving model to data_small/best_weights_M2-01-0.5000.keras\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m52s\u001b[0m 52s/step - accuracy: 0.6667 - loss: 1.1260 - val_accuracy: 0.5000 - val_loss: 0.6371\n",
       "Epoch 2/2\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 672ms/step - accuracy: 0.8333 - loss: 0.8860\n",
       "Epoch 2: val_accuracy improved from 0.50000 to 0.83333, saving model to data_small/best_weights_M2-02-0.8333.keras\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 44s/step - accuracy: 0.8333 - loss: 0.8860 - val_accuracy: 0.8333 - val_loss: 0.4877\n"
      ]
     }
    ],
@@ -472,7 +472,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-17T12:07:00.234155Z",
@@ -497,7 +497,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -505,13 +505,13 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/2\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10s/step - accuracy: 1.0000 - loss: 0.0000e+00\n",
       "Epoch 1: val_accuracy improved from -inf to 1.00000, saving model to data_small/best_weights_ensemble-01-1.0000.keras\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m112s\u001b[0m 112s/step - accuracy: 1.0000 - loss: 0.0000e+00 - val_accuracy: 1.0000 - val_loss: 0.0000e+00\n",
       "Epoch 2/2\n",
       "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 1.0000 - loss: 0.0000e+00\n",
       "Epoch 2: val_accuracy did not improve from 1.00000\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step - accuracy: 1.0000 - loss: 0.0000e+00 - val_accuracy: 0.8333 - val_loss: 0.0000e+00\n"
      ]
     }
    ],
@@ -560,7 +560,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-17T12:14:59.727959Z",
@@ -575,7 +575,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1s/step - accuracy: 1.0000 - loss: 0.0000e+00\n"
      ]
     },
     {
@@ -584,7 +584,7 @@
        "[0.0, 0.0, 1.0, 0.0, 1.0, 1.0]"
       ]
      },
-     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -598,7 +598,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-17T12:20:24.753793Z",
@@ -615,13 +615,13 @@
      "text": [
       "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step\n",
       "One-hot encoded predicted labels:\n",
-      "[[0.6874518  0.3125482 ]\n",
-      " [0.3015358  0.6984642 ]\n",
-      " [0.28066972 0.71933025]\n",
-      " [0.8681891  0.13181089]\n",
-      " [0.15508811 0.84491193]\n",
-      " [0.8854995  0.11450046]]\n",
-      "[0 1 1 0 1 0]\n"
      ]
     }
    ],
@@ -649,7 +649,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-17T12:16:41.235043Z",
@@ -664,7 +664,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 976ms/step\n"
      ]
     },
     {
@@ -674,7 +674,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mAxisError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-20-e36d626cb2d3>\u001b[0m in \u001b[0;36m<cell line: 13>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;31m# Convert one-hot encoded labels to integer labels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0my_true_onehot\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest_generator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0my_true_classes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true_onehot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36margmax\u001b[0;34m(a, axis, out, keepdims)\u001b[0m\n\u001b[1;32m   1227\u001b[0m     \"\"\"\n\u001b[1;32m   1228\u001b[0m     \u001b[0mkwds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'keepdims'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m}\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mkeepdims\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NoValue\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1229\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_wrapfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'argmax'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1230\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1231\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36m_wrapfunc\u001b[0;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[1;32m     57\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 59\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mbound\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     60\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     61\u001b[0m         \u001b[0;31m# A TypeError occurs if the object does have such a method in its\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mAxisError\u001b[0m: axis 1 is out of bounds for array of dimension 1"

      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3s/step - accuracy: 0.3333 - loss: 0.7429\n",
       "Epoch 1: val_accuracy improved from -inf to 0.50000, saving model to data_small/best_weights_V19-01-0.5000.keras\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 16s/step - accuracy: 0.3333 - loss: 0.7429 - val_accuracy: 0.5000 - val_loss: 0.7080\n",
       "Epoch 2/2\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 927ms/step - accuracy: 0.5000 - loss: 0.7603\n",
+      "Epoch 2: val_accuracy improved from 0.50000 to 1.00000, saving model to data_small/best_weights_V19-02-1.0000.keras\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 13s/step - accuracy: 0.5000 - loss: 0.7603 - val_accuracy: 1.0000 - val_loss: 0.6773\n"
      ]
     }
    ],
      "output_type": "stream",
      "text": [
       "Epoch 1/2\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7s/step - accuracy: 0.3333 - loss: 1.3967\n",
       "Epoch 1: val_accuracy improved from -inf to 0.50000, saving model to data_small/best_weights_M2-01-0.5000.keras\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 28s/step - accuracy: 0.3333 - loss: 1.3967 - val_accuracy: 0.5000 - val_loss: 0.8811\n",
       "Epoch 2/2\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 631ms/step - accuracy: 0.5000 - loss: 1.0401\n",
       "Epoch 2: val_accuracy improved from 0.50000 to 0.83333, saving model to data_small/best_weights_M2-02-0.8333.keras\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 7s/step - accuracy: 0.5000 - loss: 1.0401 - val_accuracy: 0.8333 - val_loss: 0.3798\n"
      ]
     }
    ],
   },
   {
    "cell_type": "code",
+   "execution_count": 12,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-17T12:07:00.234155Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "output_type": "stream",
      "text": [
       "Epoch 1/2\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━���━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7s/step - accuracy: 0.8333 - loss: 0.0000e+00\n",
       "Epoch 1: val_accuracy improved from -inf to 1.00000, saving model to data_small/best_weights_ensemble-01-1.0000.keras\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m59s\u001b[0m 59s/step - accuracy: 0.8333 - loss: 0.0000e+00 - val_accuracy: 1.0000 - val_loss: 0.0000e+00\n",
       "Epoch 2/2\n",
       "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 1.0000 - loss: 0.0000e+00\n",
       "Epoch 2: val_accuracy did not improve from 1.00000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step - accuracy: 1.0000 - loss: 0.0000e+00 - val_accuracy: 1.0000 - val_loss: 0.0000e+00\n"
      ]
     }
    ],
   },
   {
    "cell_type": "code",
+   "execution_count": 14,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-17T12:14:59.727959Z",
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 814ms/step - accuracy: 1.0000 - loss: 0.0000e+00\n"
      ]
     },
     {
        "[0.0, 0.0, 1.0, 0.0, 1.0, 1.0]"
       ]
      },
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
   },
   {
    "cell_type": "code",
+   "execution_count": 15,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-17T12:20:24.753793Z",
      "text": [
       "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step\n",
       "One-hot encoded predicted labels:\n",
+      "[[0.31029052 0.6897094 ]\n",
+      " [0.835801   0.16419895]\n",
+      " [0.8169173  0.1830827 ]\n",
+      " [0.23521721 0.76478285]\n",
+      " [0.26234603 0.737654  ]\n",
+      " [0.6919892  0.30801076]]\n",
+      "[1 0 0 1 1 0]\n"
      ]
     }
    ],
   },
   {
    "cell_type": "code",
+   "execution_count": 16,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-17T12:16:41.235043Z",
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 889ms/step\n"
      ]
     },
     {
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mAxisError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-16-e36d626cb2d3>\u001b[0m in \u001b[0;36m<cell line: 13>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;31m# Convert one-hot encoded labels to integer labels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0my_true_onehot\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest_generator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0my_true_classes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true_onehot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36margmax\u001b[0;34m(a, axis, out, keepdims)\u001b[0m\n\u001b[1;32m   1227\u001b[0m     \"\"\"\n\u001b[1;32m   1228\u001b[0m     \u001b[0mkwds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'keepdims'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m}\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mkeepdims\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NoValue\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1229\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_wrapfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'argmax'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1230\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1231\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36m_wrapfunc\u001b[0;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[1;32m     57\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 59\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mbound\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     60\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     61\u001b[0m         \u001b[0;31m# A TypeError occurs if the object does have such a method in its\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mAxisError\u001b[0m: axis 1 is out of bounds for array of dimension 1"

benchmark/numpy_15/numpy_15_fixed.ipynb CHANGED Viewed

@@ -531,7 +531,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:07:23.138154Z",
@@ -541,16 +541,7 @@
      "shell.execute_reply.started": "2023-04-30T09:07:23.138117Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Input Shape (75, 4)\n",
-      "Output Shape (19, 4)\n"
-     ]
-    }
-   ],
    "source": [
     "print('Input Shape', (X_tr_arr.shape))\n",
     "print('Output Shape', X_test.shape)"
@@ -558,7 +549,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:34:17.065851Z",
@@ -578,7 +569,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:34:20.799015Z",
@@ -597,7 +588,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:34:23.422913Z",
@@ -630,7 +621,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:37:06.380805Z",
@@ -700,7 +691,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:33:59.300321Z",
@@ -738,7 +729,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:37:08.958874Z",
@@ -760,7 +751,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:37:11.076162Z",
@@ -811,7 +802,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:35:38.572927Z",

   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:07:23.138154Z",
      "shell.execute_reply.started": "2023-04-30T09:07:23.138117Z"
     }
    },
+   "outputs": [],
    "source": [
     "print('Input Shape', (X_tr_arr.shape))\n",
     "print('Output Shape', X_test.shape)"
   },
   {
    "cell_type": "code",
+   "execution_count": 11,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:34:17.065851Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 12,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:34:20.799015Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 13,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:34:23.422913Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 14,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:37:06.380805Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 15,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:33:59.300321Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 16,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:37:08.958874Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 17,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:37:11.076162Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 18,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:35:38.572927Z",

benchmark/numpy_15/numpy_15_reproduced.ipynb CHANGED Viewed

@@ -36,7 +36,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T15:07:54.776437Z",
@@ -104,7 +104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:07:06.608808Z",
@@ -209,7 +209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:07:13.136214Z",
@@ -230,7 +230,7 @@
        "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -256,7 +256,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:07:13.153535Z",
@@ -353,7 +353,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:07:17.991761Z",
@@ -370,7 +370,7 @@
        "array([[<Axes: title={'center': 'sepal_length_cm'}>]], dtype=object)"
       ]
      },
-     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -399,7 +399,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:07:18.456682Z",
@@ -447,7 +447,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:07:23.074983Z",
@@ -488,7 +488,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:07:23.108304Z",
@@ -511,7 +511,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:07:23.128879Z",
@@ -531,7 +531,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:07:23.138154Z",
@@ -541,16 +541,7 @@
      "shell.execute_reply.started": "2023-04-30T09:07:23.138117Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Input Shape (75, 4)\n",
-      "Output Shape (19, 4)\n"
-     ]
-    }
-   ],
    "source": [
     "print('Input Shape', (X_tr_arr.shape))\n",
     "print('Output Shape', X_test.shape)"
@@ -558,7 +549,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:34:17.065851Z",
@@ -578,7 +569,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:34:20.799015Z",
@@ -597,7 +588,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:34:23.422913Z",
@@ -630,7 +621,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:37:06.380805Z",
@@ -690,7 +681,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:33:59.300321Z",
@@ -728,7 +719,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:37:08.958874Z",
@@ -750,7 +741,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:37:11.076162Z",
@@ -775,9 +766,9 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-21-2e390b463717>\u001b[0m in \u001b[0;36m<cell line: 6>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mweightInitialization\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_features\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m#Gradient Descent\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mcoeff\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcosts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_tr_arr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_tr_arr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlearning_rate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.0001\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mno_iterations\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4500\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;31m#Final prediction\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcoeff\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"w\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-19-f28af5fdf927>\u001b[0m in \u001b[0;36mmodel_predict\u001b[0;34m(w, b, X, Y, learning_rate, no_iterations)\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mno_iterations\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m         \u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m         \u001b[0mgrads\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcost\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_optimize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m         \u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m         \u001b[0mdw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgrads\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"dw\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-16-654cc97431c9>\u001b[0m in \u001b[0;36mmodel_optimize\u001b[0;34m(w, b, X, Y)\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0;31m# Prediction\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m     \u001b[0mfinal_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msigmoid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m     \u001b[0mcost\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mY\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfinal_result\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mfinal_result\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mValueError\u001b[0m: shapes (75,4) and (1,4) not aligned: 4 (dim 1) != 1 (dim 0)"
      ]
     }

   },
   {
    "cell_type": "code",
+   "execution_count": 2,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T15:07:54.776437Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:07:06.608808Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:07:13.136214Z",
        "Name: count, dtype: int64"
       ]
      },
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
   },
   {
    "cell_type": "code",
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:07:13.153535Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:07:17.991761Z",
        "array([[<Axes: title={'center': 'sepal_length_cm'}>]], dtype=object)"
       ]
      },
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     },
   },
   {
    "cell_type": "code",
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:07:18.456682Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:07:23.074983Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 9,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:07:23.108304Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 10,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:07:23.128879Z",
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:07:23.138154Z",
      "shell.execute_reply.started": "2023-04-30T09:07:23.138117Z"
     }
    },
+   "outputs": [],
    "source": [
     "print('Input Shape', (X_tr_arr.shape))\n",
     "print('Output Shape', X_test.shape)"
   },
   {
    "cell_type": "code",
+   "execution_count": 11,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:34:17.065851Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 12,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:34:20.799015Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 13,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:34:23.422913Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 14,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:37:06.380805Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 15,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:33:59.300321Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 16,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:37:08.958874Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 17,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-30T09:37:11.076162Z",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-17-2e390b463717>\u001b[0m in \u001b[0;36m<cell line: 6>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mweightInitialization\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_features\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m#Gradient Descent\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mcoeff\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcosts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_tr_arr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_tr_arr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlearning_rate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.0001\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mno_iterations\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4500\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;31m#Final prediction\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcoeff\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"w\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-15-f28af5fdf927>\u001b[0m in \u001b[0;36mmodel_predict\u001b[0;34m(w, b, X, Y, learning_rate, no_iterations)\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mno_iterations\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m         \u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m         \u001b[0mgrads\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcost\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_optimize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m         \u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m         \u001b[0mdw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgrads\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"dw\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-14-654cc97431c9>\u001b[0m in \u001b[0;36mmodel_optimize\u001b[0;34m(w, b, X, Y)\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0;31m# Prediction\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m     \u001b[0mfinal_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msigmoid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m     \u001b[0mcost\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mY\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfinal_result\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mfinal_result\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mValueError\u001b[0m: shapes (75,4) and (1,4) not aligned: 4 (dim 1) != 1 (dim 0)"
      ]
     }

benchmark/numpy_2/numpy_2_fixed.ipynb CHANGED Viewed

@@ -91,7 +91,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-23T19:58:29.534349Z",
@@ -177,7 +177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-23T19:58:29.718801Z",
@@ -253,7 +253,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-23T19:58:29.809363Z",
@@ -339,7 +339,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-23T19:58:29.913294Z",
@@ -349,22 +349,14 @@
      "shell.execute_reply.started": "2023-05-23T19:58:29.913261Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'numpy.ndarray'> <class 'numpy.ndarray'> <class 'numpy.ndarray'> <class 'numpy.ndarray'>\n"
-     ]
-    }
-   ],
    "source": [
     "print(type(X_train),type(Y_train),type(X_test),type(Y_test))\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-23T19:58:29.921946Z",
@@ -383,7 +375,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
    "metadata": {
     "execution": {
      "iopub.status.busy": "2023-05-23T19:58:29.966294Z",
@@ -392,260 +384,7 @@
      "shell.execute_reply.started": "2023-05-23T19:58:29.966531Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>bedrooms</th>\n",
-       "      <th>bathrooms</th>\n",
-       "      <th>sqft_living</th>\n",
-       "      <th>sqft_lot</th>\n",
-       "      <th>floors</th>\n",
-       "      <th>waterfront</th>\n",
-       "      <th>view</th>\n",
-       "      <th>sqft_above</th>\n",
-       "      <th>sqft_basement</th>\n",
-       "      <th>yr_built</th>\n",
-       "      <th>yr_renovated</th>\n",
-       "      <th>country</th>\n",
-       "      <th>price</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>4.000</td>\n",
-       "      <td>2.500</td>\n",
-       "      <td>2510</td>\n",
-       "      <td>7992</td>\n",
-       "      <td>1.000</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1610</td>\n",
-       "      <td>900</td>\n",
-       "      <td>1978</td>\n",
-       "      <td>0</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>319000.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2.000</td>\n",
-       "      <td>1.000</td>\n",
-       "      <td>700</td>\n",
-       "      <td>4800</td>\n",
-       "      <td>1.000</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>700</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1922</td>\n",
-       "      <td>2008</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>260000.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>4.000</td>\n",
-       "      <td>2.250</td>\n",
-       "      <td>2750</td>\n",
-       "      <td>6180</td>\n",
-       "      <td>1.000</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1500</td>\n",
-       "      <td>1250</td>\n",
-       "      <td>1948</td>\n",
-       "      <td>0</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>635000.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3.000</td>\n",
-       "      <td>2.750</td>\n",
-       "      <td>2820</td>\n",
-       "      <td>5348</td>\n",
-       "      <td>2.000</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2820</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2008</td>\n",
-       "      <td>0</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>749000.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>3.000</td>\n",
-       "      <td>1.000</td>\n",
-       "      <td>910</td>\n",
-       "      <td>4500</td>\n",
-       "      <td>1.000</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>910</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1948</td>\n",
-       "      <td>1994</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>465000.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3675</th>\n",
-       "      <td>3.000</td>\n",
-       "      <td>2.500</td>\n",
-       "      <td>1840</td>\n",
-       "      <td>3035</td>\n",
-       "      <td>1.000</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>920</td>\n",
-       "      <td>920</td>\n",
-       "      <td>1926</td>\n",
-       "      <td>2003</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>678333.333</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3676</th>\n",
-       "      <td>3.000</td>\n",
-       "      <td>2.500</td>\n",
-       "      <td>1880</td>\n",
-       "      <td>7000</td>\n",
-       "      <td>2.000</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1880</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1993</td>\n",
-       "      <td>0</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>315000.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3677</th>\n",
-       "      <td>3.000</td>\n",
-       "      <td>2.000</td>\n",
-       "      <td>1170</td>\n",
-       "      <td>5360</td>\n",
-       "      <td>1.000</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1170</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1919</td>\n",
-       "      <td>2001</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>335000.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3678</th>\n",
-       "      <td>3.000</td>\n",
-       "      <td>2.500</td>\n",
-       "      <td>1620</td>\n",
-       "      <td>7686</td>\n",
-       "      <td>2.000</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1620</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1989</td>\n",
-       "      <td>0</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>246500.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3679</th>\n",
-       "      <td>7.000</td>\n",
-       "      <td>4.000</td>\n",
-       "      <td>3150</td>\n",
-       "      <td>34830</td>\n",
-       "      <td>1.000</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3150</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1957</td>\n",
-       "      <td>2005</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>999000.000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>3680 rows × 13 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     bedrooms bathrooms sqft_living sqft_lot floors waterfront view  \\\n",
-       "0       4.000     2.500        2510     7992  1.000          0    0   \n",
-       "1       2.000     1.000         700     4800  1.000          0    0   \n",
-       "2       4.000     2.250        2750     6180  1.000          0    0   \n",
-       "3       3.000     2.750        2820     5348  2.000          0    0   \n",
-       "4       3.000     1.000         910     4500  1.000          0    0   \n",
-       "...       ...       ...         ...      ...    ...        ...  ...   \n",
-       "3675    3.000     2.500        1840     3035  1.000          0    0   \n",
-       "3676    3.000     2.500        1880     7000  2.000          0    0   \n",
-       "3677    3.000     2.000        1170     5360  1.000          0    0   \n",
-       "3678    3.000     2.500        1620     7686  2.000          0    0   \n",
-       "3679    7.000     4.000        3150    34830  1.000          0    0   \n",
-       "\n",
-       "     sqft_above sqft_basement yr_built yr_renovated country      price  \n",
-       "0          1610           900     1978            0     USA 319000.000  \n",
-       "1           700             0     1922         2008     USA 260000.000  \n",
-       "2          1500          1250     1948            0     USA 635000.000  \n",
-       "3          2820             0     2008            0     USA 749000.000  \n",
-       "4           910             0     1948         1994     USA 465000.000  \n",
-       "...         ...           ...      ...          ...     ...        ...  \n",
-       "3675        920           920     1926         2003     USA 678333.333  \n",
-       "3676       1880             0     1993            0     USA 315000.000  \n",
-       "3677       1170             0     1919         2001     USA 335000.000  \n",
-       "3678       1620             0     1989            0     USA 246500.000  \n",
-       "3679       3150             0     1957         2005     USA 999000.000  \n",
-       "\n",
-       "[3680 rows x 13 columns]"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "train_houseprice"
    ]

   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-23T19:58:29.534349Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-23T19:58:29.718801Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-23T19:58:29.809363Z",
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-23T19:58:29.913294Z",
      "shell.execute_reply.started": "2023-05-23T19:58:29.913261Z"
     }
    },
+   "outputs": [],
    "source": [
     "print(type(X_train),type(Y_train),type(X_test),type(Y_test))\n"
    ]
   },
   {
    "cell_type": "code",
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-23T19:58:29.921946Z",
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.status.busy": "2023-05-23T19:58:29.966294Z",
      "shell.execute_reply.started": "2023-05-23T19:58:29.966531Z"
     }
    },
+   "outputs": [],
    "source": [
     "train_houseprice"
    ]

benchmark/numpy_2/numpy_2_reproduced.ipynb CHANGED Viewed

@@ -91,7 +91,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-23T19:58:29.534349Z",
@@ -177,7 +177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-23T19:58:29.718801Z",
@@ -253,7 +253,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-23T19:58:29.809363Z",
@@ -339,7 +339,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-23T19:58:29.913294Z",
@@ -349,22 +349,14 @@
      "shell.execute_reply.started": "2023-05-23T19:58:29.913261Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'numpy.ndarray'> <class 'numpy.ndarray'> <class 'numpy.ndarray'> <class 'numpy.ndarray'>\n"
-     ]
-    }
-   ],
    "source": [
     "print(type(X_train),type(Y_train),type(X_test),type(Y_test))\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-23T19:58:29.921946Z",
@@ -382,7 +374,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-8-54bcb5c62015>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain_houseprice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mY_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'join'"
      ]
     }

   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-23T19:58:29.534349Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-23T19:58:29.718801Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-23T19:58:29.809363Z",
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-23T19:58:29.913294Z",
      "shell.execute_reply.started": "2023-05-23T19:58:29.913261Z"
     }
    },
+   "outputs": [],
    "source": [
     "print(type(X_train),type(Y_train),type(X_test),type(Y_test))\n"
    ]
   },
   {
    "cell_type": "code",
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-23T19:58:29.921946Z",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-5-54bcb5c62015>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain_houseprice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mY_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'join'"
      ]
     }

benchmark/numpy_3/numpy_3_fixed.ipynb CHANGED Viewed

The diff for this file is too large to render. See raw diff

benchmark/numpy_3/numpy_3_reproduced.ipynb CHANGED Viewed

@@ -534,11 +534,11 @@
       "application/vnd.holoviews_exec.v0+json": "",
       "text/html": [
        "<div id='p1002'>\n",
-       "  <div id=\"d66d3c77-2212-4283-b2f5-3c43cd966475\" data-root-id=\"p1002\" style=\"display: contents;\"></div>\n",
        "</div>\n",
        "<script type=\"application/javascript\">(function(root) {\n",
-       "  var docs_json = {\"ccd1c5a2-4f4e-4bbc-b40d-4c85d4698144\":{\"version\":\"3.4.3\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.browser.BrowserInfo\",\"id\":\"p1002\"},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"p1003\",\"attributes\":{\"plot_id\":\"p1002\",\"comm_id\":\"a8232a867dcd460c88af691fac204dc1\",\"client_comm_id\":\"06d257117477455a824a7c2271f1092b\"}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"gap\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"TemplateEditor1\",\"properties\":[{\"name\":\"layout\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"copy_to_clipboard1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":null}]}]}};\n",
-       "  var render_items = [{\"docid\":\"ccd1c5a2-4f4e-4bbc-b40d-4c85d4698144\",\"roots\":{\"p1002\":\"d66d3c77-2212-4283-b2f5-3c43cd966475\"},\"root_ids\":[\"p1002\"]}];\n",
        "  var docs = Object.values(docs_json)\n",
        "  if (!docs) {\n",
        "    return\n",
@@ -1070,7 +1070,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-4-5d99f9ebae80>\u001b[0m in \u001b[0;36m<cell line: 15>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcols\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m     \u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0medges\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistogram\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m     \u001b[0mdd\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mHistogram\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0medges\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ALL Loans'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mredim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m' '\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/numpy/lib/histograms.py\u001b[0m in \u001b[0;36mhistogram\u001b[0;34m(a, bins, range, density, weights)\u001b[0m\n\u001b[1;32m    778\u001b[0m     \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweights\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_ravel_and_check_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    779\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 780\u001b[0;31m     \u001b[0mbin_edges\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muniform_bins\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_bin_edges\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    781\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    782\u001b[0m     \u001b[0;31m# Histogram is an integer or a float array depending on the weights.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/numpy/lib/histograms.py\u001b[0m in \u001b[0;36m_get_bin_edges\u001b[0;34m(a, bins, range, weights)\u001b[0m\n\u001b[1;32m    424\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'`bins` must be positive, when an integer'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    425\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 426\u001b[0;31m         \u001b[0mfirst_edge\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlast_edge\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_outer_edges\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    427\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    428\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbins\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/numpy/lib/histograms.py\u001b[0m in \u001b[0;36m_get_outer_edges\u001b[0;34m(a, range)\u001b[0m\n\u001b[1;32m    321\u001b[0m         \u001b[0mfirst_edge\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlast_edge\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    322\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfirst_edge\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlast_edge\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 323\u001b[0;31m             raise ValueError(\n\u001b[0m\u001b[1;32m    324\u001b[0m                 \"autodetected range of [{}, {}] is not finite\".format(first_edge, last_edge))\n\u001b[1;32m    325\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
@@ -1079,7 +1079,7 @@
     }
    ],
    "source": [
-    "%%opts Histogram[width=700 height=400 tools=['hover'] xrotation=0]{+axiswise +framewise}\n",
     "\n",
     "g = df.groupby('STATUS')\n",
     "\n",

       "application/vnd.holoviews_exec.v0+json": "",
       "text/html": [
        "<div id='p1002'>\n",
+       "  <div id=\"e3338bff-f56c-496b-bcfd-1b9add1b31a8\" data-root-id=\"p1002\" style=\"display: contents;\"></div>\n",
        "</div>\n",
        "<script type=\"application/javascript\">(function(root) {\n",
+       "  var docs_json = {\"3ac6855c-36e5-4df8-b07b-6956602cec8e\":{\"version\":\"3.4.3\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.browser.BrowserInfo\",\"id\":\"p1002\"},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"p1003\",\"attributes\":{\"plot_id\":\"p1002\",\"comm_id\":\"39fbdea63f5f4ae98de8533cd32f4dcf\",\"client_comm_id\":\"cf5ee6fce66a4a6795359cc0cbf4157f\"}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"gap\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"TemplateEditor1\",\"properties\":[{\"name\":\"layout\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"copy_to_clipboard1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":null}]}]}};\n",
+       "  var render_items = [{\"docid\":\"3ac6855c-36e5-4df8-b07b-6956602cec8e\",\"roots\":{\"p1002\":\"e3338bff-f56c-496b-bcfd-1b9add1b31a8\"},\"root_ids\":[\"p1002\"]}];\n",
        "  var docs = Object.values(docs_json)\n",
        "  if (!docs) {\n",
        "    return\n",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-4-f80eddf9cd65>\u001b[0m in \u001b[0;36m<cell line: 17>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcols\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m     \u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0medges\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistogram\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     20\u001b[0m     \u001b[0mdd\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mHistogram\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0medges\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ALL Loans'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mredim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m' '\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/numpy/lib/histograms.py\u001b[0m in \u001b[0;36mhistogram\u001b[0;34m(a, bins, range, density, weights)\u001b[0m\n\u001b[1;32m    778\u001b[0m     \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweights\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_ravel_and_check_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    779\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 780\u001b[0;31m     \u001b[0mbin_edges\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muniform_bins\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_bin_edges\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    781\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    782\u001b[0m     \u001b[0;31m# Histogram is an integer or a float array depending on the weights.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/numpy/lib/histograms.py\u001b[0m in \u001b[0;36m_get_bin_edges\u001b[0;34m(a, bins, range, weights)\u001b[0m\n\u001b[1;32m    424\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'`bins` must be positive, when an integer'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    425\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 426\u001b[0;31m         \u001b[0mfirst_edge\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlast_edge\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_outer_edges\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    427\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    428\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbins\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/numpy/lib/histograms.py\u001b[0m in \u001b[0;36m_get_outer_edges\u001b[0;34m(a, range)\u001b[0m\n\u001b[1;32m    321\u001b[0m         \u001b[0mfirst_edge\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlast_edge\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    322\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfirst_edge\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlast_edge\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 323\u001b[0;31m             raise ValueError(\n\u001b[0m\u001b[1;32m    324\u001b[0m                 \"autodetected range of [{}, {}] is not finite\".format(first_edge, last_edge))\n\u001b[1;32m    325\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
     }
    ],
    "source": [
+    "# %%opts Histogram[width=700 height=400 tools=['hover'] xrotation=0]{+axiswise +framewise}\n",
     "\n",
     "g = df.groupby('STATUS')\n",
     "\n",

benchmark/numpy_4/data_small/best_weights.keras CHANGED Viewed

@@ -1,3 +1,3 @@
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benchmark/numpy_4/numpy_4_fixed.ipynb CHANGED Viewed

@@ -43,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:17.445317Z",
@@ -53,18 +53,7 @@
      "shell.execute_reply.started": "2023-04-05T10:18:17.445259Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "125"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "train_normal = glob.glob('data_small/chest-xray-pneumonia/chest_xray/train/NORMAL/*.jpeg')\n",
     "a = len(train_normal)\n",
@@ -80,7 +69,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:17.820239Z",
@@ -98,7 +87,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:18.108254Z",
@@ -108,22 +97,14 @@
      "shell.execute_reply.started": "2023-04-05T10:18:18.108213Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total nos. of training images are: 527\n"
-     ]
-    }
-   ],
    "source": [
     "print(\"Total nos. of training images are: {}\".format(a + b))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:18.120784Z",
@@ -147,7 +128,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:18.129958Z",
@@ -176,7 +157,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:22.576329Z",
@@ -205,7 +186,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:23.629025Z",
@@ -215,25 +196,14 @@
      "shell.execute_reply.started": "2023-04-05T10:18:23.628982Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'NORMAL': 0, 'PNEUMONIA': 1}"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "train_dataset.class_indices"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:23.639332Z",
@@ -250,7 +220,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:23.648339Z",
@@ -268,7 +238,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:23.659988Z",
@@ -304,7 +274,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:23.671809Z",
@@ -325,7 +295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:32.061048Z",
@@ -360,7 +330,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:32.087621Z",
@@ -410,7 +380,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:32.239629Z",
@@ -433,7 +403,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:32.262881Z",
@@ -480,7 +450,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:32.271157Z",
@@ -503,9 +473,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6s/step - auc: 0.7909 - loss: 0.7056\n",
-      "Epoch 1: val_auc improved from -inf to 0.97100, saving model to data_small/best_weights.keras\n",
-      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 10s/step - auc: 0.8016 - loss: 0.6840 - val_auc: 0.9710 - val_loss: 0.2057\n"
      ]
     }
    ],
@@ -520,7 +490,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:21:23.875033Z",
@@ -537,7 +507,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:32:34.305363Z",
@@ -552,15 +522,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 709ms/step\n",
       "              precision    recall  f1-score   support\n",
       "\n",
-      "   PNEUMONIA     0.2195    1.0000    0.3600         9\n",
-      "      NORMAL     0.0000    0.0000    0.0000        32\n",
       "\n",
-      "    accuracy                         0.2195        41\n",
-      "   macro avg     0.1098    0.5000    0.1800        41\n",
-      "weighted avg     0.0482    0.2195    0.0790        41\n",
       "\n"
      ]
     },

   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:17.445317Z",
      "shell.execute_reply.started": "2023-04-05T10:18:17.445259Z"
     }
    },
+   "outputs": [],
    "source": [
     "train_normal = glob.glob('data_small/chest-xray-pneumonia/chest_xray/train/NORMAL/*.jpeg')\n",
     "a = len(train_normal)\n",
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:17.820239Z",
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:18.108254Z",
      "shell.execute_reply.started": "2023-04-05T10:18:18.108213Z"
     }
    },
+   "outputs": [],
    "source": [
     "print(\"Total nos. of training images are: {}\".format(a + b))"
    ]
   },
   {
    "cell_type": "code",
+   "execution_count": 2,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:18.120784Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:18.129958Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:22.576329Z",
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:23.629025Z",
      "shell.execute_reply.started": "2023-04-05T10:18:23.628982Z"
     }
    },
+   "outputs": [],
    "source": [
     "train_dataset.class_indices"
    ]
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:23.639332Z",
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:23.648339Z",
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:23.659988Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:23.671809Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:32.061048Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:32.087621Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:32.239629Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 9,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:32.262881Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 10,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:32.271157Z",
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6s/step - auc: 0.8211 - loss: 0.5703\n",
+      "Epoch 1: val_auc improved from -inf to 0.97200, saving model to data_small/best_weights.keras\n",
+      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m92s\u001b[0m 12s/step - auc: 0.8283 - loss: 0.5611 - val_auc: 0.9720 - val_loss: 0.5673\n"
      ]
     }
    ],
   },
   {
    "cell_type": "code",
+   "execution_count": 11,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:21:23.875033Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 12,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:32:34.305363Z",
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 3s/step\n",
       "              precision    recall  f1-score   support\n",
       "\n",
+      "   PNEUMONIA     0.2656    1.0000    0.4198        17\n",
+      "      NORMAL     0.0000    0.0000    0.0000        47\n",
       "\n",
+      "    accuracy                         0.2656        64\n",
+      "   macro avg     0.1328    0.5000    0.2099        64\n",
+      "weighted avg     0.0706    0.2656    0.1115        64\n",
       "\n"
      ]
     },

benchmark/numpy_4/numpy_4_reproduced.ipynb CHANGED Viewed

@@ -43,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:17.445317Z",
@@ -53,18 +53,7 @@
      "shell.execute_reply.started": "2023-04-05T10:18:17.445259Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "125"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "train_normal = glob.glob('data_small/chest-xray-pneumonia/chest_xray/train/NORMAL/*.jpeg')\n",
     "a = len(train_normal)\n",
@@ -80,7 +69,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:17.820239Z",
@@ -98,7 +87,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:18.108254Z",
@@ -108,22 +97,14 @@
      "shell.execute_reply.started": "2023-04-05T10:18:18.108213Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total nos. of training images are: 527\n"
-     ]
-    }
-   ],
    "source": [
     "print(\"Total nos. of training images are: {}\".format(a + b))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:18.120784Z",
@@ -147,7 +128,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:18.129958Z",
@@ -176,7 +157,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:22.576329Z",
@@ -205,7 +186,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:23.629025Z",
@@ -215,25 +196,14 @@
      "shell.execute_reply.started": "2023-04-05T10:18:23.628982Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'NORMAL': 0, 'PNEUMONIA': 1}"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "train_dataset.class_indices"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:23.639332Z",
@@ -250,7 +220,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:23.648339Z",
@@ -268,7 +238,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:23.659988Z",
@@ -304,7 +274,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:23.671809Z",
@@ -325,7 +295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:32.061048Z",
@@ -360,7 +330,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:32.087621Z",
@@ -410,7 +380,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:32.239629Z",
@@ -433,7 +403,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:32.262881Z",
@@ -480,7 +450,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:32.271157Z",
@@ -503,9 +473,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6s/step - auc: 0.7909 - loss: 0.7056\n",
-      "Epoch 1: val_auc improved from -inf to 0.97100, saving model to data_small/best_weights.keras\n",
-      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 10s/step - auc: 0.8016 - loss: 0.6840 - val_auc: 0.9710 - val_loss: 0.2057\n"
      ]
     }
    ],
@@ -520,7 +490,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:21:23.875033Z",
@@ -537,7 +507,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:32:34.305363Z",
@@ -552,7 +522,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3s/step\n"
      ]
     },
     {
@@ -562,7 +532,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-21-a341cad50e80>\u001b[0m in \u001b[0;36m<cell line: 7>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m   prediction_classes = np.concatenate([prediction_classes,\n\u001b[1;32m      9\u001b[0m                        np.argmax(model.predict(x), axis = -1)])\n\u001b[0;32m---> 10\u001b[0;31m   \u001b[0mtrue_classes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtrue_classes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'numpy'"
      ]
     }

   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:17.445317Z",
      "shell.execute_reply.started": "2023-04-05T10:18:17.445259Z"
     }
    },
+   "outputs": [],
    "source": [
     "train_normal = glob.glob('data_small/chest-xray-pneumonia/chest_xray/train/NORMAL/*.jpeg')\n",
     "a = len(train_normal)\n",
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:17.820239Z",
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:18.108254Z",
      "shell.execute_reply.started": "2023-04-05T10:18:18.108213Z"
     }
    },
+   "outputs": [],
    "source": [
     "print(\"Total nos. of training images are: {}\".format(a + b))"
    ]
   },
   {
    "cell_type": "code",
+   "execution_count": 2,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:18.120784Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:18.129958Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:22.576329Z",
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:23.629025Z",
      "shell.execute_reply.started": "2023-04-05T10:18:23.628982Z"
     }
    },
+   "outputs": [],
    "source": [
     "train_dataset.class_indices"
    ]
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:23.639332Z",
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:23.648339Z",
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:23.659988Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:23.671809Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:32.061048Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:32.087621Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:32.239629Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 9,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:32.262881Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 10,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:18:32.271157Z",
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6s/step - auc: 0.8234 - loss: 0.5202\n",
+      "Epoch 1: val_auc improved from -inf to 0.95125, saving model to data_small/best_weights.keras\n",
+      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m96s\u001b[0m 12s/step - auc: 0.8311 - loss: 0.5095 - val_auc: 0.9512 - val_loss: 0.2403\n"
      ]
     }
    ],
   },
   {
    "cell_type": "code",
+   "execution_count": 11,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:21:23.875033Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 12,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-05T10:32:34.305363Z",
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 6s/step\n"
      ]
     },
     {
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
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-       "breed\n",
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-       "maltese_dog             117\n",
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     "breed_all = labels_all['breed']\n",
     "breed_count = breed_all.value_counts()\n",
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     "execution": {
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@@ -476,7 +390,7 @@
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      "text": [
-      "100%|██████████| 588/588 [00:00<00:00, 982.23it/s] \n"
      ]
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     {
@@ -487,6 +401,13 @@
       "Train Images shape:  (3, 1, 224, 224, 3)  size: 451,584\n",
       "One-hot encoded output shape:  (3, 5)  size: 15\n"
      ]
     }
    ],
    "source": [

   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-31T11:55:45.863383Z",
      "shell.execute_reply.started": "2023-05-31T11:55:45.863351Z"
     }
    },
+   "outputs": [],
    "source": [
     "breed_all = labels_all['breed']\n",
     "breed_count = breed_all.value_counts()\n",
   },
   {
    "cell_type": "code",
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-31T11:55:45.890591Z",
        "4     88  021b5a49189665c0442c19b5b33e8cf1         entlebucher"
       ]
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   },
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      "shell.execute_reply.started": "2023-05-31T11:55:46.081439Z"
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    ]
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     "execution": {
      "iopub.execute_input": "2023-05-31T11:51:03.032746Z",
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "100%|██████████| 588/588 [00:00<00:00, 630.73it/s]"
      ]
     },
     {
       "Train Images shape:  (3, 1, 224, 224, 3)  size: 451,584\n",
       "One-hot encoded output shape:  (3, 5)  size: 15\n"
      ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
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+      "\n"
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    ],
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-    {
-     "data": {
-      "text/plain": [
-       "breed\n",
-       "scottish_deerhound      126\n",
-       "maltese_dog             117\n",
-       "afghan_hound            116\n",
-       "entlebucher             115\n",
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     "breed_count = breed_all.value_counts()\n",
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-       "                                 id             breed\n",
-       "0  000bec180eb18c7604dcecc8fe0dba07       boston_bull\n",
-       "1  001513dfcb2ffafc82cccf4d8bbaba97             dingo\n",
-       "2  001cdf01b096e06d78e9e5112d419397          pekinese\n",
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-       "4  0021f9ceb3235effd7fcde7f7538ed62  golden_retriever"
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-     "metadata": {},
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    "source": [
     "train_labels.head()"
    ]
@@ -461,7 +375,7 @@
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-   "execution_count": 10,
    "metadata": {
     "execution": {
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@@ -476,7 +390,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 588/588 [00:00<00:00, 768.71it/s]\n"
      ]
     },
     {
@@ -486,7 +400,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-10-86af726456f6>\u001b[0m in \u001b[0;36m<cell line: 16>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     14\u001b[0m     \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpand_dims\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0mX_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m255.0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0mX_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'id'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_labels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'id'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;31m# Printing train image and one hot encode shape & size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mIndexError\u001b[0m: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices"
      ]
     }

   },
   {
    "cell_type": "code",
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-31T11:55:45.831997Z",
        "4  0021f9ceb3235effd7fcde7f7538ed62  golden_retriever"
       ]
      },
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-31T11:55:45.863383Z",
      "shell.execute_reply.started": "2023-05-31T11:55:45.863351Z"
     }
    },
+   "outputs": [],
    "source": [
     "breed_all = labels_all['breed']\n",
     "breed_count = breed_all.value_counts()\n",
   },
   {
    "cell_type": "code",
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-31T11:55:45.934744Z",
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-31T11:55:46.081478Z",
      "shell.execute_reply.started": "2023-05-31T11:55:46.081439Z"
     }
    },
+   "outputs": [],
    "source": [
     "train_labels.head()"
    ]
   },
   {
    "cell_type": "code",
+   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-31T11:51:03.032746Z",
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "100%|██████████| 588/588 [00:00<00:00, 657.49it/s]\n"
      ]
     },
     {
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-8-86af726456f6>\u001b[0m in \u001b[0;36m<cell line: 16>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     14\u001b[0m     \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpand_dims\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0mX_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m255.0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0mX_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'id'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_labels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'id'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;31m# Printing train image and one hot encode shape & size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mIndexError\u001b[0m: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices"
      ]
     }

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@@ -167,7 +167,7 @@
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    "cell_type": "code",
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    "metadata": {
     "execution": {
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@@ -177,25 +177,14 @@
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-     "execution_count": 6,
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-    }
-   ],
    "source": [
     "app_train.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:42.482990Z",
@@ -205,18 +194,7 @@
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-       "(48744, 72)"
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-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "app_test.shape"
    ]
@@ -259,7 +237,7 @@
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   {
    "cell_type": "code",
-   "execution_count": 8,
    "metadata": {
     "execution": {
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@@ -284,7 +262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:48.487653Z",
@@ -303,7 +281,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:48.901595Z",
@@ -321,7 +299,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:48.911188Z",
@@ -343,7 +321,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:49.050761Z",
@@ -360,7 +338,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:49.070174Z",
@@ -396,7 +374,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:49.172092Z",
@@ -414,7 +392,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:49.181041Z",
@@ -432,7 +410,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:49.239903Z",
@@ -492,7 +470,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:49.399844Z",
@@ -536,7 +514,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:49.615247Z",
@@ -673,7 +651,7 @@
        "freq                        53900                   67991  "
       ]
      },
-     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -720,7 +698,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:50.800859Z",
@@ -736,52 +714,52 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "<ipython-input-19-3e482c76d290>:1: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_train['OCCUPATION_TYPE'][app_train['NAME_EDUCATION_TYPE']=='Secondary / secondary special'] = app_train['OCCUPATION_TYPE'][app_train['NAME_EDUCATION_TYPE']=='Secondary / secondary special'].fillna('Laborers')\n",
-      "<ipython-input-19-3e482c76d290>:2: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_train['OCCUPATION_TYPE'][app_train['NAME_EDUCATION_TYPE']=='Higher education'] =  app_train['OCCUPATION_TYPE'][app_train['NAME_EDUCATION_TYPE']=='Higher education'].fillna('Core staff')\n",
-      "<ipython-input-19-3e482c76d290>:3: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_train['OCCUPATION_TYPE'][app_train['NAME_EDUCATION_TYPE']=='Incomplete higher'] = app_train['OCCUPATION_TYPE'][app_train['NAME_EDUCATION_TYPE']=='Incomplete higher'].fillna('Laborers')\n",
-      "<ipython-input-19-3e482c76d290>:4: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_train['OCCUPATION_TYPE'][app_train['NAME_EDUCATION_TYPE']=='Lower secondary'] = app_train['OCCUPATION_TYPE'][app_train['NAME_EDUCATION_TYPE']=='Lower secondary'].fillna('Laborers')\n",
-      "<ipython-input-19-3e482c76d290>:5: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_train['OCCUPATION_TYPE'][app_train['NAME_EDUCATION_TYPE']=='Academic degree'] = app_train['OCCUPATION_TYPE'][app_train['NAME_EDUCATION_TYPE']=='Academic degree'].fillna('Managers')\n",
-      "<ipython-input-19-3e482c76d290>:7: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_test['OCCUPATION_TYPE'][app_test['NAME_EDUCATION_TYPE']=='Secondary / secondary special'] = app_test['OCCUPATION_TYPE'][app_test['NAME_EDUCATION_TYPE']=='Secondary / secondary special'].fillna('Laborers')\n",
-      "<ipython-input-19-3e482c76d290>:8: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_test['OCCUPATION_TYPE'][app_test['NAME_EDUCATION_TYPE']=='Higher education'] =  app_test['OCCUPATION_TYPE'][app_test['NAME_EDUCATION_TYPE']=='Higher education'].fillna('Core staff')\n",
-      "<ipython-input-19-3e482c76d290>:9: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_test['OCCUPATION_TYPE'][app_test['NAME_EDUCATION_TYPE']=='Incomplete higher'] = app_test['OCCUPATION_TYPE'][app_test['NAME_EDUCATION_TYPE']=='Incomplete higher'].fillna('Laborers')\n",
-      "<ipython-input-19-3e482c76d290>:10: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_test['OCCUPATION_TYPE'][app_test['NAME_EDUCATION_TYPE']=='Lower secondary'] = app_test['OCCUPATION_TYPE'][app_test['NAME_EDUCATION_TYPE']=='Lower secondary'].fillna('Laborers')\n",
-      "<ipython-input-19-3e482c76d290>:11: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
@@ -822,7 +800,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:51.675082Z",
@@ -838,42 +816,42 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "<ipython-input-20-eee1247958f3>:1: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_train['ORGANIZATION_TYPE'][(app_train['OCCUPATION_TYPE'] == 'Accountants') |\n",
-      "<ipython-input-20-eee1247958f3>:23: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_test['ORGANIZATION_TYPE'][(app_test['OCCUPATION_TYPE'] == 'Accountants') |\n",
-      "<ipython-input-20-eee1247958f3>:46: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_train['ORGANIZATION_TYPE'][(app_train['OCCUPATION_TYPE'] == 'Medicine staff')|\n",
-      "<ipython-input-20-eee1247958f3>:49: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_test['ORGANIZATION_TYPE'][(app_test['OCCUPATION_TYPE'] == 'Medicine staff')|\n",
-      "<ipython-input-20-eee1247958f3>:52: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_train['ORGANIZATION_TYPE'][(app_train['OCCUPATION_TYPE'] == 'Private service staff')|\n",
-      "<ipython-input-20-eee1247958f3>:57: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_test['ORGANIZATION_TYPE'][(app_test['OCCUPATION_TYPE'] == 'Private service staff')|\n",
-      "<ipython-input-20-eee1247958f3>:63: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_train['ORGANIZATION_TYPE'][(app_train['OCCUPATION_TYPE'] == 'Security staff')] = app_train['ORGANIZATION_TYPE'][(app_train['OCCUPATION_TYPE'] == 'Security staff')].fillna('Security')\n",
-      "<ipython-input-20-eee1247958f3>:64: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
@@ -950,7 +928,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:54.947839Z",
@@ -967,7 +945,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:54.976259Z",
@@ -982,7 +960,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "<ipython-input-22-0a2c7bd4f24d>:2: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame.\n",
       "Try using .loc[row_indexer,col_indexer] = value instead\n",
       "\n",
@@ -998,7 +976,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:55.085687Z",
@@ -1013,7 +991,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "<ipython-input-23-012c43242dab>:2: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame.\n",
       "Try using .loc[row_indexer,col_indexer] = value instead\n",
       "\n",
@@ -1029,7 +1007,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:55.192859Z",
@@ -1047,7 +1025,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:55.235212Z",
@@ -1085,7 +1063,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:55.502923Z",
@@ -1158,7 +1136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:57.927525Z",
@@ -1192,7 +1170,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:58.048903Z",
@@ -1210,7 +1188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:58.153738Z",
@@ -1226,35 +1204,35 @@
       "text/plain": [
        "['DAYS_EMPLOYED',\n",
        " 'AMT_ANNUITY',\n",
        " 'AMT_GOODS_PRICE',\n",
-       " 'REG_REGION_NOT_LIVE_REGION',\n",
        " 'CNT_CHILDREN',\n",
-       " 'AMT_REQ_CREDIT_BUREAU_HOUR',\n",
-       " 'DAYS_ID_PUBLISH',\n",
-       " 'DAYS_BIRTH',\n",
        " 'REGION_POPULATION_RELATIVE',\n",
-       " 'REGION_RATING_CLIENT_W_CITY',\n",
-       " 'EXT_SOURCE_2',\n",
-       " 'AMT_INCOME_TOTAL',\n",
        " 'DAYS_REGISTRATION',\n",
        " 'DEF_60_CNT_SOCIAL_CIRCLE',\n",
        " 'AMT_REQ_CREDIT_BUREAU_YEAR',\n",
-       " 'REG_CITY_NOT_LIVE_CITY',\n",
-       " 'AMT_REQ_CREDIT_BUREAU_QRT',\n",
-       " 'AMT_REQ_CREDIT_BUREAU_WEEK',\n",
-       " 'HOUR_APPR_PROCESS_START',\n",
-       " 'REG_CITY_NOT_WORK_CITY',\n",
-       " 'DEF_30_CNT_SOCIAL_CIRCLE',\n",
-       " 'AMT_REQ_CREDIT_BUREAU_DAY',\n",
        " 'DAYS_LAST_PHONE_CHANGE',\n",
-       " 'EXT_SOURCE_3',\n",
        " 'AMT_REQ_CREDIT_BUREAU_MON',\n",
        " 'LIVE_CITY_NOT_WORK_CITY',\n",
-       " 'OBS_30_CNT_SOCIAL_CIRCLE',\n",
-       " 'REGION_RATING_CLIENT']"
       ]
      },
-     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1267,7 +1245,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:58.164735Z",
@@ -1299,7 +1277,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:58.183609Z",
@@ -1318,7 +1296,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:59.697622Z",
@@ -1345,7 +1323,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:32:00.313206Z",
@@ -1387,7 +1365,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:32:01.830751Z",
@@ -1405,7 +1383,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:32:09.294612Z",
@@ -1422,7 +1400,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:32:09.651347Z",
@@ -1440,7 +1418,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:32:09.718228Z",
@@ -1782,7 +1760,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T14:18:30.867184Z",
@@ -1991,7 +1969,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T14:49:11.499710Z",
@@ -2024,7 +2002,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T14:47:45.251432Z",
@@ -2061,7 +2039,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T14:49:14.993876Z",
@@ -2071,18 +2049,7 @@
      "shell.execute_reply.started": "2023-05-20T14:49:14.993845Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.10/dist-packages/deap/creator.py:185: RuntimeWarning: A class named 'FitnessMax' has already been created and it will be overwritten. Consider deleting previous creation of that class or rename it.\n",
-      "  warnings.warn(\"A class named '{0}' has already been created and it \"\n",
-      "/usr/local/lib/python3.10/dist-packages/deap/creator.py:185: RuntimeWarning: A class named 'Individual' has already been created and it will be overwritten. Consider deleting previous creation of that class or rename it.\n",
-      "  warnings.warn(\"A class named '{0}' has already been created and it \"\n"
-     ]
-    }
-   ],
    "source": [
     "# Создание класса для управления эволюцией\n",
     "creator.create(\"FitnessMax\", base.Fitness, weights=(1.0,))\n",
@@ -2105,7 +2072,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T14:58:23.426108Z",
@@ -2142,7 +2109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T14:58:26.121638Z",
@@ -2193,6 +2160,13 @@
     "    # Замена старого поколения потомками\n",
     "    population[:] = offspring"
    ]
   }
  ],
  "metadata": {

   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:42.474671Z",
      "shell.execute_reply.started": "2023-05-20T13:31:42.474643Z"
     }
    },
+   "outputs": [],
    "source": [
     "app_train.shape"
    ]
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:42.482990Z",
      "shell.execute_reply.started": "2023-05-20T13:31:42.482965Z"
     }
    },
+   "outputs": [],
    "source": [
     "app_test.shape"
    ]
   },
   {
    "cell_type": "code",
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:43.880911Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:48.487653Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:48.901595Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 9,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:48.911188Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 10,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:49.050761Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 11,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:49.070174Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 12,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:49.172092Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 13,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:49.181041Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 14,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:49.239903Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 15,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:49.399844Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 16,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:49.615247Z",
        "freq                        53900                   67991  "
       ]
      },
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
   },
   {
    "cell_type": "code",
+   "execution_count": 17,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:50.800859Z",
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "<ipython-input-17-3e482c76d290>:1: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_train['OCCUPATION_TYPE'][app_train['NAME_EDUCATION_TYPE']=='Secondary / secondary special'] = app_train['OCCUPATION_TYPE'][app_train['NAME_EDUCATION_TYPE']=='Secondary / secondary special'].fillna('Laborers')\n",
+      "<ipython-input-17-3e482c76d290>:2: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_train['OCCUPATION_TYPE'][app_train['NAME_EDUCATION_TYPE']=='Higher education'] =  app_train['OCCUPATION_TYPE'][app_train['NAME_EDUCATION_TYPE']=='Higher education'].fillna('Core staff')\n",
+      "<ipython-input-17-3e482c76d290>:3: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_train['OCCUPATION_TYPE'][app_train['NAME_EDUCATION_TYPE']=='Incomplete higher'] = app_train['OCCUPATION_TYPE'][app_train['NAME_EDUCATION_TYPE']=='Incomplete higher'].fillna('Laborers')\n",
+      "<ipython-input-17-3e482c76d290>:4: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_train['OCCUPATION_TYPE'][app_train['NAME_EDUCATION_TYPE']=='Lower secondary'] = app_train['OCCUPATION_TYPE'][app_train['NAME_EDUCATION_TYPE']=='Lower secondary'].fillna('Laborers')\n",
+      "<ipython-input-17-3e482c76d290>:5: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_train['OCCUPATION_TYPE'][app_train['NAME_EDUCATION_TYPE']=='Academic degree'] = app_train['OCCUPATION_TYPE'][app_train['NAME_EDUCATION_TYPE']=='Academic degree'].fillna('Managers')\n",
+      "<ipython-input-17-3e482c76d290>:7: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_test['OCCUPATION_TYPE'][app_test['NAME_EDUCATION_TYPE']=='Secondary / secondary special'] = app_test['OCCUPATION_TYPE'][app_test['NAME_EDUCATION_TYPE']=='Secondary / secondary special'].fillna('Laborers')\n",
+      "<ipython-input-17-3e482c76d290>:8: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_test['OCCUPATION_TYPE'][app_test['NAME_EDUCATION_TYPE']=='Higher education'] =  app_test['OCCUPATION_TYPE'][app_test['NAME_EDUCATION_TYPE']=='Higher education'].fillna('Core staff')\n",
+      "<ipython-input-17-3e482c76d290>:9: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_test['OCCUPATION_TYPE'][app_test['NAME_EDUCATION_TYPE']=='Incomplete higher'] = app_test['OCCUPATION_TYPE'][app_test['NAME_EDUCATION_TYPE']=='Incomplete higher'].fillna('Laborers')\n",
+      "<ipython-input-17-3e482c76d290>:10: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_test['OCCUPATION_TYPE'][app_test['NAME_EDUCATION_TYPE']=='Lower secondary'] = app_test['OCCUPATION_TYPE'][app_test['NAME_EDUCATION_TYPE']=='Lower secondary'].fillna('Laborers')\n",
+      "<ipython-input-17-3e482c76d290>:11: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
   },
   {
    "cell_type": "code",
+   "execution_count": 18,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:51.675082Z",
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "<ipython-input-18-eee1247958f3>:1: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_train['ORGANIZATION_TYPE'][(app_train['OCCUPATION_TYPE'] == 'Accountants') |\n",
+      "<ipython-input-18-eee1247958f3>:23: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_test['ORGANIZATION_TYPE'][(app_test['OCCUPATION_TYPE'] == 'Accountants') |\n",
+      "<ipython-input-18-eee1247958f3>:46: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_train['ORGANIZATION_TYPE'][(app_train['OCCUPATION_TYPE'] == 'Medicine staff')|\n",
+      "<ipython-input-18-eee1247958f3>:49: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_test['ORGANIZATION_TYPE'][(app_test['OCCUPATION_TYPE'] == 'Medicine staff')|\n",
+      "<ipython-input-18-eee1247958f3>:52: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_train['ORGANIZATION_TYPE'][(app_train['OCCUPATION_TYPE'] == 'Private service staff')|\n",
+      "<ipython-input-18-eee1247958f3>:57: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_test['ORGANIZATION_TYPE'][(app_test['OCCUPATION_TYPE'] == 'Private service staff')|\n",
+      "<ipython-input-18-eee1247958f3>:63: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_train['ORGANIZATION_TYPE'][(app_train['OCCUPATION_TYPE'] == 'Security staff')] = app_train['ORGANIZATION_TYPE'][(app_train['OCCUPATION_TYPE'] == 'Security staff')].fillna('Security')\n",
+      "<ipython-input-18-eee1247958f3>:64: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
   },
   {
    "cell_type": "code",
+   "execution_count": 19,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:54.947839Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 20,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:54.976259Z",
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "<ipython-input-20-0a2c7bd4f24d>:2: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame.\n",
       "Try using .loc[row_indexer,col_indexer] = value instead\n",
       "\n",
   },
   {
    "cell_type": "code",
+   "execution_count": 21,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:55.085687Z",
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "<ipython-input-21-012c43242dab>:2: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame.\n",
       "Try using .loc[row_indexer,col_indexer] = value instead\n",
       "\n",
   },
   {
    "cell_type": "code",
+   "execution_count": 22,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:55.192859Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 23,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:55.235212Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 24,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:55.502923Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 25,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:57.927525Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 26,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:58.048903Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 27,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:58.153738Z",
       "text/plain": [
        "['DAYS_EMPLOYED',\n",
        " 'AMT_ANNUITY',\n",
+       " 'REGION_RATING_CLIENT',\n",
+       " 'REG_CITY_NOT_WORK_CITY',\n",
+       " 'DAYS_ID_PUBLISH',\n",
+       " 'EXT_SOURCE_3',\n",
+       " 'REG_CITY_NOT_LIVE_CITY',\n",
        " 'AMT_GOODS_PRICE',\n",
+       " 'REGION_RATING_CLIENT_W_CITY',\n",
+       " 'AMT_REQ_CREDIT_BUREAU_WEEK',\n",
+       " 'AMT_REQ_CREDIT_BUREAU_DAY',\n",
        " 'CNT_CHILDREN',\n",
        " 'REGION_POPULATION_RELATIVE',\n",
+       " 'AMT_REQ_CREDIT_BUREAU_QRT',\n",
+       " 'AMT_REQ_CREDIT_BUREAU_HOUR',\n",
        " 'DAYS_REGISTRATION',\n",
+       " 'AMT_INCOME_TOTAL',\n",
+       " 'OBS_30_CNT_SOCIAL_CIRCLE',\n",
+       " 'DAYS_BIRTH',\n",
        " 'DEF_60_CNT_SOCIAL_CIRCLE',\n",
        " 'AMT_REQ_CREDIT_BUREAU_YEAR',\n",
        " 'DAYS_LAST_PHONE_CHANGE',\n",
+       " 'DEF_30_CNT_SOCIAL_CIRCLE',\n",
+       " 'REG_REGION_NOT_LIVE_REGION',\n",
        " 'AMT_REQ_CREDIT_BUREAU_MON',\n",
        " 'LIVE_CITY_NOT_WORK_CITY',\n",
+       " 'EXT_SOURCE_2',\n",
+       " 'HOUR_APPR_PROCESS_START']"
       ]
      },
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
   },
   {
    "cell_type": "code",
+   "execution_count": 28,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:58.164735Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 29,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:58.183609Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 30,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:59.697622Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 31,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:32:00.313206Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 32,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:32:01.830751Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 33,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:32:09.294612Z",
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:32:09.651347Z",
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:32:09.718228Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 34,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T14:18:30.867184Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 35,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T14:49:11.499710Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 36,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T14:47:45.251432Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 38,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T14:49:14.993876Z",
      "shell.execute_reply.started": "2023-05-20T14:49:14.993845Z"
     }
    },
+   "outputs": [],
    "source": [
     "# Создание класса для управления эволюцией\n",
     "creator.create(\"FitnessMax\", base.Fitness, weights=(1.0,))\n",
   },
   {
    "cell_type": "code",
+   "execution_count": 37,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T14:58:23.426108Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 39,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T14:58:26.121638Z",
     "    # Замена старого поколения потомками\n",
     "    population[:] = offspring"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

benchmark/numpy_8/numpy_8_reproduced.ipynb CHANGED Viewed

@@ -167,7 +167,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:42.474671Z",
@@ -177,25 +177,14 @@
      "shell.execute_reply.started": "2023-05-20T13:31:42.474643Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(307511, 73)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "app_train.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:42.482990Z",
@@ -205,18 +194,7 @@
      "shell.execute_reply.started": "2023-05-20T13:31:42.482965Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(48744, 72)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "app_test.shape"
    ]
@@ -259,7 +237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:43.880911Z",
@@ -284,7 +262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:48.487653Z",
@@ -303,7 +281,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:48.901595Z",
@@ -321,7 +299,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:48.911188Z",
@@ -343,7 +321,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:49.050761Z",
@@ -360,7 +338,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:49.070174Z",
@@ -396,7 +374,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:49.172092Z",
@@ -414,7 +392,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:49.181041Z",
@@ -432,7 +410,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:49.239903Z",
@@ -492,7 +470,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:49.399844Z",
@@ -536,7 +514,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:49.615247Z",
@@ -673,7 +651,7 @@
        "freq                        53900                   67991  "
       ]
      },
-     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -720,7 +698,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:50.800859Z",
@@ -736,52 +714,52 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "<ipython-input-19-3e482c76d290>:1: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_train['OCCUPATION_TYPE'][app_train['NAME_EDUCATION_TYPE']=='Secondary / secondary special'] = app_train['OCCUPATION_TYPE'][app_train['NAME_EDUCATION_TYPE']=='Secondary / secondary special'].fillna('Laborers')\n",
-      "<ipython-input-19-3e482c76d290>:2: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_train['OCCUPATION_TYPE'][app_train['NAME_EDUCATION_TYPE']=='Higher education'] =  app_train['OCCUPATION_TYPE'][app_train['NAME_EDUCATION_TYPE']=='Higher education'].fillna('Core staff')\n",
-      "<ipython-input-19-3e482c76d290>:3: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_train['OCCUPATION_TYPE'][app_train['NAME_EDUCATION_TYPE']=='Incomplete higher'] = app_train['OCCUPATION_TYPE'][app_train['NAME_EDUCATION_TYPE']=='Incomplete higher'].fillna('Laborers')\n",
-      "<ipython-input-19-3e482c76d290>:4: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_train['OCCUPATION_TYPE'][app_train['NAME_EDUCATION_TYPE']=='Lower secondary'] = app_train['OCCUPATION_TYPE'][app_train['NAME_EDUCATION_TYPE']=='Lower secondary'].fillna('Laborers')\n",
-      "<ipython-input-19-3e482c76d290>:5: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_train['OCCUPATION_TYPE'][app_train['NAME_EDUCATION_TYPE']=='Academic degree'] = app_train['OCCUPATION_TYPE'][app_train['NAME_EDUCATION_TYPE']=='Academic degree'].fillna('Managers')\n",
-      "<ipython-input-19-3e482c76d290>:7: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_test['OCCUPATION_TYPE'][app_test['NAME_EDUCATION_TYPE']=='Secondary / secondary special'] = app_test['OCCUPATION_TYPE'][app_test['NAME_EDUCATION_TYPE']=='Secondary / secondary special'].fillna('Laborers')\n",
-      "<ipython-input-19-3e482c76d290>:8: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_test['OCCUPATION_TYPE'][app_test['NAME_EDUCATION_TYPE']=='Higher education'] =  app_test['OCCUPATION_TYPE'][app_test['NAME_EDUCATION_TYPE']=='Higher education'].fillna('Core staff')\n",
-      "<ipython-input-19-3e482c76d290>:9: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_test['OCCUPATION_TYPE'][app_test['NAME_EDUCATION_TYPE']=='Incomplete higher'] = app_test['OCCUPATION_TYPE'][app_test['NAME_EDUCATION_TYPE']=='Incomplete higher'].fillna('Laborers')\n",
-      "<ipython-input-19-3e482c76d290>:10: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_test['OCCUPATION_TYPE'][app_test['NAME_EDUCATION_TYPE']=='Lower secondary'] = app_test['OCCUPATION_TYPE'][app_test['NAME_EDUCATION_TYPE']=='Lower secondary'].fillna('Laborers')\n",
-      "<ipython-input-19-3e482c76d290>:11: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
@@ -822,7 +800,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:51.675082Z",
@@ -838,42 +816,42 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "<ipython-input-20-eee1247958f3>:1: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_train['ORGANIZATION_TYPE'][(app_train['OCCUPATION_TYPE'] == 'Accountants') |\n",
-      "<ipython-input-20-eee1247958f3>:23: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_test['ORGANIZATION_TYPE'][(app_test['OCCUPATION_TYPE'] == 'Accountants') |\n",
-      "<ipython-input-20-eee1247958f3>:46: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_train['ORGANIZATION_TYPE'][(app_train['OCCUPATION_TYPE'] == 'Medicine staff')|\n",
-      "<ipython-input-20-eee1247958f3>:49: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_test['ORGANIZATION_TYPE'][(app_test['OCCUPATION_TYPE'] == 'Medicine staff')|\n",
-      "<ipython-input-20-eee1247958f3>:52: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_train['ORGANIZATION_TYPE'][(app_train['OCCUPATION_TYPE'] == 'Private service staff')|\n",
-      "<ipython-input-20-eee1247958f3>:57: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_test['ORGANIZATION_TYPE'][(app_test['OCCUPATION_TYPE'] == 'Private service staff')|\n",
-      "<ipython-input-20-eee1247958f3>:63: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_train['ORGANIZATION_TYPE'][(app_train['OCCUPATION_TYPE'] == 'Security staff')] = app_train['ORGANIZATION_TYPE'][(app_train['OCCUPATION_TYPE'] == 'Security staff')].fillna('Security')\n",
-      "<ipython-input-20-eee1247958f3>:64: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
@@ -950,7 +928,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:54.947839Z",
@@ -967,7 +945,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:54.976259Z",
@@ -982,7 +960,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "<ipython-input-22-0a2c7bd4f24d>:2: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame.\n",
       "Try using .loc[row_indexer,col_indexer] = value instead\n",
       "\n",
@@ -998,7 +976,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:55.085687Z",
@@ -1013,7 +991,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "<ipython-input-23-012c43242dab>:2: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame.\n",
       "Try using .loc[row_indexer,col_indexer] = value instead\n",
       "\n",
@@ -1029,7 +1007,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:55.192859Z",
@@ -1047,7 +1025,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:55.235212Z",
@@ -1085,7 +1063,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:55.502923Z",
@@ -1158,7 +1136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:57.927525Z",
@@ -1192,7 +1170,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:58.048903Z",
@@ -1210,7 +1188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:58.153738Z",
@@ -1224,37 +1202,37 @@
     {
      "data": {
       "text/plain": [
-       "['DAYS_EMPLOYED',\n",
-       " 'AMT_ANNUITY',\n",
-       " 'AMT_GOODS_PRICE',\n",
-       " 'REG_REGION_NOT_LIVE_REGION',\n",
-       " 'CNT_CHILDREN',\n",
-       " 'AMT_REQ_CREDIT_BUREAU_HOUR',\n",
-       " 'DAYS_ID_PUBLISH',\n",
        " 'DAYS_BIRTH',\n",
-       " 'REGION_POPULATION_RELATIVE',\n",
-       " 'REGION_RATING_CLIENT_W_CITY',\n",
-       " 'EXT_SOURCE_2',\n",
        " 'AMT_INCOME_TOTAL',\n",
-       " 'DAYS_REGISTRATION',\n",
-       " 'DEF_60_CNT_SOCIAL_CIRCLE',\n",
-       " 'AMT_REQ_CREDIT_BUREAU_YEAR',\n",
-       " 'REG_CITY_NOT_LIVE_CITY',\n",
-       " 'AMT_REQ_CREDIT_BUREAU_QRT',\n",
-       " 'AMT_REQ_CREDIT_BUREAU_WEEK',\n",
        " 'HOUR_APPR_PROCESS_START',\n",
-       " 'REG_CITY_NOT_WORK_CITY',\n",
        " 'DEF_30_CNT_SOCIAL_CIRCLE',\n",
        " 'AMT_REQ_CREDIT_BUREAU_DAY',\n",
        " 'DAYS_LAST_PHONE_CHANGE',\n",
-       " 'EXT_SOURCE_3',\n",
-       " 'AMT_REQ_CREDIT_BUREAU_MON',\n",
        " 'LIVE_CITY_NOT_WORK_CITY',\n",
-       " 'OBS_30_CNT_SOCIAL_CIRCLE',\n",
-       " 'REGION_RATING_CLIENT']"
       ]
      },
-     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1267,7 +1245,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:58.164735Z",
@@ -1299,7 +1277,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:58.183609Z",
@@ -1318,7 +1296,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:59.697622Z",
@@ -1345,7 +1323,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:32:00.313206Z",
@@ -1387,7 +1365,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:32:01.830751Z",
@@ -1405,7 +1383,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:32:09.294612Z",
@@ -1422,7 +1400,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:32:09.651347Z",
@@ -1440,7 +1418,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:32:09.718228Z",
@@ -1782,7 +1760,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T14:18:30.867184Z",
@@ -1792,7 +1770,15 @@
      "shell.execute_reply.started": "2023-05-20T14:18:30.867141Z"
     }
    },
-   "outputs": [],
    "source": [
     "import pandas as pd\n",
     "from sklearn.model_selection import train_test_split\n",
@@ -1983,7 +1969,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T14:49:11.499710Z",
@@ -2016,7 +2002,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T14:47:45.251432Z",
@@ -2049,7 +2035,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T14:49:14.993876Z",
@@ -2082,7 +2068,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T14:58:23.426108Z",
@@ -2112,7 +2098,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T14:58:26.121638Z",
@@ -2125,16 +2111,16 @@
    "outputs": [
     {
      "ename": "TypeError",
-     "evalue": "<lambda>() takes 2 positional arguments but 18 were given",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-42-c2fff693920c>\u001b[0m in \u001b[0;36m<cell line: 8>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;31m# Оценка фитнеса\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0mfitnesses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevaluate_fitness\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpopulation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mindividual\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfitness\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpopulation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfitnesses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m         \u001b[0mindividual\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfitness\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfitness\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-40-c74419f37cb5>\u001b[0m in \u001b[0;36mevaluate_fitness\u001b[0;34m(individual)\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mevaluate_fitness\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindividual\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0;31m# Вычислите значения признаков на основе individual\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m     \u001b[0mX_train_gp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransform_gp_structure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindividual\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# Вычисление новых признаков на обучающей выборке\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m     \u001b[0mrf_model_gp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train_gp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# Обучение модели случайного леса с новыми признаками\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0mX_test_gp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransform_gp_structure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindividual\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# Вычисление новых признаков на тестовой выборке\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-40-c74419f37cb5>\u001b[0m in \u001b[0;36mtransform_gp_structure\u001b[0;34m(individual, X)\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtransform_gp_structure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindividual\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mexpr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindividual\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mexpr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m# Определение функции оценки фитнеса (ваша собственная функция)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-40-c74419f37cb5>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtransform_gp_structure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindividual\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mexpr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindividual\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mexpr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m# Определение функции оценки фитнеса (ваша собственная функция)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mTypeError\u001b[0m: <lambda>() takes 2 positional arguments but 18 were given"
      ]
     }
    ],

   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:42.474671Z",
      "shell.execute_reply.started": "2023-05-20T13:31:42.474643Z"
     }
    },
+   "outputs": [],
    "source": [
     "app_train.shape"
    ]
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:42.482990Z",
      "shell.execute_reply.started": "2023-05-20T13:31:42.482965Z"
     }
    },
+   "outputs": [],
    "source": [
     "app_test.shape"
    ]
   },
   {
    "cell_type": "code",
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:43.880911Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:48.487653Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:48.901595Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 9,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:48.911188Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 10,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:49.050761Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 11,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:49.070174Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 12,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:49.172092Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 13,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:49.181041Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 14,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:49.239903Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 15,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:49.399844Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 16,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:49.615247Z",
        "freq                        53900                   67991  "
       ]
      },
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
   },
   {
    "cell_type": "code",
+   "execution_count": 17,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:50.800859Z",
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "<ipython-input-17-3e482c76d290>:1: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_train['OCCUPATION_TYPE'][app_train['NAME_EDUCATION_TYPE']=='Secondary / secondary special'] = app_train['OCCUPATION_TYPE'][app_train['NAME_EDUCATION_TYPE']=='Secondary / secondary special'].fillna('Laborers')\n",
+      "<ipython-input-17-3e482c76d290>:2: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_train['OCCUPATION_TYPE'][app_train['NAME_EDUCATION_TYPE']=='Higher education'] =  app_train['OCCUPATION_TYPE'][app_train['NAME_EDUCATION_TYPE']=='Higher education'].fillna('Core staff')\n",
+      "<ipython-input-17-3e482c76d290>:3: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_train['OCCUPATION_TYPE'][app_train['NAME_EDUCATION_TYPE']=='Incomplete higher'] = app_train['OCCUPATION_TYPE'][app_train['NAME_EDUCATION_TYPE']=='Incomplete higher'].fillna('Laborers')\n",
+      "<ipython-input-17-3e482c76d290>:4: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_train['OCCUPATION_TYPE'][app_train['NAME_EDUCATION_TYPE']=='Lower secondary'] = app_train['OCCUPATION_TYPE'][app_train['NAME_EDUCATION_TYPE']=='Lower secondary'].fillna('Laborers')\n",
+      "<ipython-input-17-3e482c76d290>:5: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_train['OCCUPATION_TYPE'][app_train['NAME_EDUCATION_TYPE']=='Academic degree'] = app_train['OCCUPATION_TYPE'][app_train['NAME_EDUCATION_TYPE']=='Academic degree'].fillna('Managers')\n",
+      "<ipython-input-17-3e482c76d290>:7: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_test['OCCUPATION_TYPE'][app_test['NAME_EDUCATION_TYPE']=='Secondary / secondary special'] = app_test['OCCUPATION_TYPE'][app_test['NAME_EDUCATION_TYPE']=='Secondary / secondary special'].fillna('Laborers')\n",
+      "<ipython-input-17-3e482c76d290>:8: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_test['OCCUPATION_TYPE'][app_test['NAME_EDUCATION_TYPE']=='Higher education'] =  app_test['OCCUPATION_TYPE'][app_test['NAME_EDUCATION_TYPE']=='Higher education'].fillna('Core staff')\n",
+      "<ipython-input-17-3e482c76d290>:9: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_test['OCCUPATION_TYPE'][app_test['NAME_EDUCATION_TYPE']=='Incomplete higher'] = app_test['OCCUPATION_TYPE'][app_test['NAME_EDUCATION_TYPE']=='Incomplete higher'].fillna('Laborers')\n",
+      "<ipython-input-17-3e482c76d290>:10: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_test['OCCUPATION_TYPE'][app_test['NAME_EDUCATION_TYPE']=='Lower secondary'] = app_test['OCCUPATION_TYPE'][app_test['NAME_EDUCATION_TYPE']=='Lower secondary'].fillna('Laborers')\n",
+      "<ipython-input-17-3e482c76d290>:11: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
   },
   {
    "cell_type": "code",
+   "execution_count": 18,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:51.675082Z",
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "<ipython-input-18-eee1247958f3>:1: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_train['ORGANIZATION_TYPE'][(app_train['OCCUPATION_TYPE'] == 'Accountants') |\n",
+      "<ipython-input-18-eee1247958f3>:23: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_test['ORGANIZATION_TYPE'][(app_test['OCCUPATION_TYPE'] == 'Accountants') |\n",
+      "<ipython-input-18-eee1247958f3>:46: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_train['ORGANIZATION_TYPE'][(app_train['OCCUPATION_TYPE'] == 'Medicine staff')|\n",
+      "<ipython-input-18-eee1247958f3>:49: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_test['ORGANIZATION_TYPE'][(app_test['OCCUPATION_TYPE'] == 'Medicine staff')|\n",
+      "<ipython-input-18-eee1247958f3>:52: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_train['ORGANIZATION_TYPE'][(app_train['OCCUPATION_TYPE'] == 'Private service staff')|\n",
+      "<ipython-input-18-eee1247958f3>:57: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_test['ORGANIZATION_TYPE'][(app_test['OCCUPATION_TYPE'] == 'Private service staff')|\n",
+      "<ipython-input-18-eee1247958f3>:63: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  app_train['ORGANIZATION_TYPE'][(app_train['OCCUPATION_TYPE'] == 'Security staff')] = app_train['ORGANIZATION_TYPE'][(app_train['OCCUPATION_TYPE'] == 'Security staff')].fillna('Security')\n",
+      "<ipython-input-18-eee1247958f3>:64: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
   },
   {
    "cell_type": "code",
+   "execution_count": 19,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:54.947839Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 20,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:54.976259Z",
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "<ipython-input-20-0a2c7bd4f24d>:2: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame.\n",
       "Try using .loc[row_indexer,col_indexer] = value instead\n",
       "\n",
   },
   {
    "cell_type": "code",
+   "execution_count": 21,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:55.085687Z",
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "<ipython-input-21-012c43242dab>:2: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame.\n",
       "Try using .loc[row_indexer,col_indexer] = value instead\n",
       "\n",
   },
   {
    "cell_type": "code",
+   "execution_count": 22,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:55.192859Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 23,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:55.235212Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 24,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:55.502923Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 25,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:57.927525Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 26,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:58.048903Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 27,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:58.153738Z",
     {
      "data": {
       "text/plain": [
+       "['CNT_CHILDREN',\n",
        " 'DAYS_BIRTH',\n",
        " 'AMT_INCOME_TOTAL',\n",
        " 'HOUR_APPR_PROCESS_START',\n",
+       " 'AMT_GOODS_PRICE',\n",
        " 'DEF_30_CNT_SOCIAL_CIRCLE',\n",
+       " 'AMT_REQ_CREDIT_BUREAU_QRT',\n",
+       " 'AMT_REQ_CREDIT_BUREAU_YEAR',\n",
+       " 'DEF_60_CNT_SOCIAL_CIRCLE',\n",
+       " 'AMT_REQ_CREDIT_BUREAU_HOUR',\n",
+       " 'AMT_REQ_CREDIT_BUREAU_MON',\n",
+       " 'REGION_RATING_CLIENT_W_CITY',\n",
+       " 'EXT_SOURCE_2',\n",
        " 'AMT_REQ_CREDIT_BUREAU_DAY',\n",
+       " 'OBS_30_CNT_SOCIAL_CIRCLE',\n",
+       " 'DAYS_ID_PUBLISH',\n",
        " 'DAYS_LAST_PHONE_CHANGE',\n",
+       " 'REG_CITY_NOT_WORK_CITY',\n",
+       " 'DAYS_EMPLOYED',\n",
+       " 'REGION_RATING_CLIENT',\n",
        " 'LIVE_CITY_NOT_WORK_CITY',\n",
+       " 'REG_REGION_NOT_LIVE_REGION',\n",
+       " 'REGION_POPULATION_RELATIVE',\n",
+       " 'DAYS_REGISTRATION',\n",
+       " 'REG_CITY_NOT_LIVE_CITY',\n",
+       " 'EXT_SOURCE_3',\n",
+       " 'AMT_ANNUITY',\n",
+       " 'AMT_REQ_CREDIT_BUREAU_WEEK']"
       ]
      },
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
   },
   {
    "cell_type": "code",
+   "execution_count": 28,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:58.164735Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 29,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:58.183609Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 30,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:31:59.697622Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 31,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:32:00.313206Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 32,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:32:01.830751Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 33,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:32:09.294612Z",
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:32:09.651347Z",
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T13:32:09.718228Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 34,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T14:18:30.867184Z",
      "shell.execute_reply.started": "2023-05-20T14:18:30.867141Z"
     }
    },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.84\n"
+     ]
+    }
+   ],
    "source": [
     "import pandas as pd\n",
     "from sklearn.model_selection import train_test_split\n",
   },
   {
    "cell_type": "code",
+   "execution_count": 35,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T14:49:11.499710Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 36,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T14:47:45.251432Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 38,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T14:49:14.993876Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 37,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T14:58:23.426108Z",
   },
   {
    "cell_type": "code",
+   "execution_count": 39,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-20T14:58:26.121638Z",
    "outputs": [
     {
      "ename": "TypeError",
+     "evalue": "<lambda>() takes 2 positional arguments but 72 were given",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-39-c2fff693920c>\u001b[0m in \u001b[0;36m<cell line: 8>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;31m# Оценка фитнеса\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0mfitnesses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevaluate_fitness\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpopulation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mindividual\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfitness\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpopulation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfitnesses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m         \u001b[0mindividual\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfitness\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfitness\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-37-c74419f37cb5>\u001b[0m in \u001b[0;36mevaluate_fitness\u001b[0;34m(individual)\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mevaluate_fitness\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindividual\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0;31m# Вычислите значения признаков на основе individual\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m     \u001b[0mX_train_gp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransform_gp_structure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindividual\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# Вычисление новых признаков на обучающей выборке\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m     \u001b[0mrf_model_gp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train_gp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# Обучение модели случайного леса с новыми признаками\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0mX_test_gp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransform_gp_structure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindividual\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# Вычисление новых признаков на тестовой выборке\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-37-c74419f37cb5>\u001b[0m in \u001b[0;36mtransform_gp_structure\u001b[0;34m(individual, X)\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtransform_gp_structure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindividual\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mexpr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindividual\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mexpr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m# Определение функции оценки фитнеса (ваша собственная функция)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-37-c74419f37cb5>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtransform_gp_structure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindividual\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mexpr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindividual\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mexpr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m# Определение функции оценки фитнеса (ваша собственная функция)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: <lambda>() takes 2 positional arguments but 72 were given"
      ]
     }
    ],

benchmark/numpy_9/numpy_9_fixed.ipynb CHANGED Viewed

@@ -734,7 +734,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-07-29T17:57:32.925654Z",
@@ -784,7 +784,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "<ipython-input-4-b586dafd4291>:49: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
       "  regression_weight = torch.tensor(regression_weight, dtype=torch.float32, device=device)\n"
      ]
     }

   },
   {
    "cell_type": "code",
+   "execution_count": 1,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-07-29T17:57:32.925654Z",
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "<ipython-input-1-b586dafd4291>:49: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
       "  regression_weight = torch.tensor(regression_weight, dtype=torch.float32, device=device)\n"
      ]
     }

benchmark/numpy_9/numpy_9_reproduced.ipynb CHANGED Viewed

@@ -734,7 +734,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-07-29T17:57:32.925654Z",
@@ -752,8 +752,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-2-cb771ae86354>\u001b[0m in \u001b[0;36m<cell line: 20>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;31m# Load initial regression loss weights from the directory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m \u001b[0mregression_weight\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_loss_weights_from_directory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mregression_weights_directory\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;31m# Training loop for regression task\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-2-cb771ae86354>\u001b[0m in \u001b[0;36mload_loss_weights_from_directory\u001b[0;34m(directory_path)\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mweight_files\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfilename\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfilename\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdirectory_path\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\".npy\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0mweights\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdirectory_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfilename\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mweight_files\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweights\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;31m# Function to save the updated weights to a directory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mValueError\u001b[0m: zero-dimensional arrays cannot be concatenated"
      ]
     }

   },
   {
    "cell_type": "code",
+   "execution_count": 1,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-07-29T17:57:32.925654Z",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-1-cb771ae86354>\u001b[0m in \u001b[0;36m<cell line: 20>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;31m# Load initial regression loss weights from the directory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m \u001b[0mregression_weight\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_loss_weights_from_directory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mregression_weights_directory\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;31m# Training loop for regression task\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-1-cb771ae86354>\u001b[0m in \u001b[0;36mload_loss_weights_from_directory\u001b[0;34m(directory_path)\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mweight_files\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfilename\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfilename\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdirectory_path\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\".npy\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0mweights\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdirectory_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfilename\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mweight_files\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweights\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;31m# Function to save the updated weights to a directory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mValueError\u001b[0m: zero-dimensional arrays cannot be concatenated"
      ]
     }