chapter 4 Load real data.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "84ad733a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import imageio\n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ac5df9fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\WIN10\\AppData\\Local\\Temp\\ipykernel_19516\\1760084411.py:1: DeprecationWarning: Starting with ImageIO v3 the behavior of this function will switch to that of iio.v3.imread. To keep the current behavior (and make this warning disappear) use `import imageio.v2 as imageio` or call `imageio.v2.imread` directly.\n",
      "  img_arr = imageio.imread('../jupyter home/dataset/chapter 4/R-C.jpg')\n"
     ]
    }
   ],
   "source": [
    "img_arr = imageio.imread('../jupyter home/dataset/chapter 4/R-C.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2a065450",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1280, 720, 3)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_arr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b067d3ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 3\n",
    "batch = torch.zeros(batch_size, 3, 256, 256, dtype=torch.uint8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8e1ec0b1",
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       "          [0, 0, 0,  ..., 0, 0, 0],\n",
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      ]
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     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "batch"
   ]
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  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "45b42603",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\WIN10\\AppData\\Local\\Temp\\ipykernel_19516\\385995829.py:9: DeprecationWarning: Starting with ImageIO v3 the behavior of this function will switch to that of iio.v3.imread. To keep the current behavior (and make this warning disappear) use `import imageio.v2 as imageio` or call `imageio.v2.imread` directly.\n",
      "  img_arr = imageio.imread(os.path.join(data_dir, filename))\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "data_dir = 'F:\\jupyter home\\dataset\\chapter 4\\demo_256'\n",
    "filenames = [name for name in os.listdir(data_dir) if os.path.splitext(name)[-1] == '.jpg']\n",
    "\n",
    "# global img_t\n",
    "\n",
    "for i, filename in enumerate(filenames):\n",
    "    img_arr = imageio.imread(os.path.join(data_dir, filename))\n",
    "    img_t = torch.from_numpy(img_arr)\n",
    "    img_t = img_t.permute(2,0,1)\n",
    "    img_t = img_t[:3]\n",
    "#     batch[i].shape, img_t.shape\n",
    "    batch[i] = img_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "32231651",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch = batch.float() "
   ]
  },
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   "cell_type": "code",
   "execution_count": 67,
   "id": "bbd2e280",
   "metadata": {
    "scrolled": true
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   ],
   "source": [
    "batch"
   ]
  },
  {
   "cell_type": "code",
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "27b90f69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([3, 3, 256, 256])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "1652b6ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch /= 255.0"
   ]
  },
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   "cell_type": "code",
   "execution_count": 62,
   "id": "a10f64b1",
   "metadata": {
    "scrolled": true
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      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "1e75aef5",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_channels = batch.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "44376be0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_channels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "0a76a6cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "global mean, std\n",
    "\n",
    "for c in range(n_channels):\n",
    "    mean = torch.mean(batch[:,c])\n",
    "    std = torch.std(batch[:,c]) # 标准差\n",
    "    batch[:,c] = (batch[:,c]-mean)/std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "39e81cf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(0.4040)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "7dfc9e43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(0.5700)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "c062c9a9",
   "metadata": {
    "scrolled": true
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       "          [1.2328, 1.2328, 1.2328,  ..., 1.2328, 1.2328, 1.2328]],\n",
       "\n",
       "         [[1.0887, 1.0887, 1.0887,  ..., 1.0887, 1.0887, 1.0887],\n",
       "          [1.0887, 1.0887, 1.0887,  ..., 1.0887, 1.0887, 1.0887],\n",
       "          [1.0887, 1.0887, 1.0887,  ..., 1.0887, 1.0887, 1.0887],\n",
       "          ...,\n",
       "          [1.0887, 1.0887, 1.0887,  ..., 1.0887, 1.0887, 1.0887],\n",
       "          [1.0887, 1.0887, 1.0887,  ..., 1.0887, 1.0887, 1.0887],\n",
       "          [1.0887, 1.0887, 1.0887,  ..., 1.0887, 1.0887, 1.0887]],\n",
       "\n",
       "         [[0.9769, 0.9769, 0.9769,  ..., 0.9769, 0.9769, 0.9769],\n",
       "          [0.9769, 0.9769, 0.9769,  ..., 0.9769, 0.9769, 0.9769],\n",
       "          [0.9769, 0.9769, 0.9769,  ..., 0.9769, 0.9769, 0.9769],\n",
       "          ...,\n",
       "          [0.9769, 0.9769, 0.9769,  ..., 0.9769, 0.9769, 0.9769],\n",
       "          [0.9769, 0.9769, 0.9769,  ..., 0.9769, 0.9769, 0.9769],\n",
       "          [0.9769, 0.9769, 0.9769,  ..., 0.9769, 0.9769, 0.9769]]],\n",
       "\n",
       "\n",
       "        [[[1.1804, 1.2747, 1.2328,  ..., 1.2328, 1.2852, 1.2223],\n",
       "          [1.2642, 1.1595, 1.2119,  ..., 1.2433, 1.1385, 1.2328],\n",
       "          [1.2538, 1.2328, 1.2538,  ..., 1.2433, 1.3062, 1.2014],\n",
       "          ...,\n",
       "          [1.2223, 1.2014, 1.2852,  ..., 1.2538, 1.2328, 1.2538],\n",
       "          [1.2852, 1.1804, 1.2538,  ..., 1.2538, 1.2433, 1.2014],\n",
       "          [1.2119, 1.2433, 1.2433,  ..., 1.2328, 1.2014, 1.2642]],\n",
       "\n",
       "         [[1.0353, 1.1314, 1.0887,  ..., 1.0887, 1.1421, 1.0780],\n",
       "          [1.1208, 1.0140, 1.0674,  ..., 1.0994, 0.9926, 1.0887],\n",
       "          [1.1101, 1.0887, 1.1101,  ..., 1.0994, 1.1635, 1.0567],\n",
       "          ...,\n",
       "          [1.0780, 1.0567, 1.1421,  ..., 1.1101, 1.0887, 1.1101],\n",
       "          [1.1421, 1.0353, 1.1101,  ..., 1.1101, 1.0994, 1.0567],\n",
       "          [1.0674, 1.0994, 1.0994,  ..., 1.0887, 1.0567, 1.1208]],\n",
       "\n",
       "         [[0.9284, 1.0157, 0.9769,  ..., 0.9769, 1.0254, 0.9672],\n",
       "          [1.0060, 0.9089, 0.9575,  ..., 0.9866, 0.8895, 0.9769],\n",
       "          [0.9963, 0.9769, 0.9963,  ..., 0.9866, 1.0448, 0.9478],\n",
       "          ...,\n",
       "          [0.9672, 0.9478, 1.0254,  ..., 0.9963, 0.9769, 0.9963],\n",
       "          [1.0254, 0.9284, 0.9963,  ..., 0.9963, 0.9866, 0.9478],\n",
       "          [0.9575, 0.9866, 0.9866,  ..., 0.9769, 0.9478, 1.0060]]],\n",
       "\n",
       "\n",
       "        [[[1.2328, 1.2328, 1.2328,  ..., 1.2328, 1.2328, 1.2328],\n",
       "          [1.2328, 1.2328, 1.2328,  ..., 1.2328, 1.2328, 1.2328],\n",
       "          [1.2328, 1.2328, 1.2328,  ..., 1.2328, 1.2328, 1.2328],\n",
       "          ...,\n",
       "          [1.2328, 1.2328, 1.2328,  ..., 1.2328, 1.2328, 1.2328],\n",
       "          [1.2328, 1.2328, 1.2328,  ..., 1.2328, 1.2328, 1.2328],\n",
       "          [1.2328, 1.2328, 1.2328,  ..., 1.2328, 1.2328, 1.2328]],\n",
       "\n",
       "         [[1.0887, 1.0887, 1.0887,  ..., 1.0887, 1.0887, 1.0887],\n",
       "          [1.0887, 1.0887, 1.0887,  ..., 1.0887, 1.0887, 1.0887],\n",
       "          [1.0887, 1.0887, 1.0887,  ..., 1.0887, 1.0887, 1.0887],\n",
       "          ...,\n",
       "          [1.0887, 1.0887, 1.0887,  ..., 1.0887, 1.0887, 1.0887],\n",
       "          [1.0887, 1.0887, 1.0887,  ..., 1.0887, 1.0887, 1.0887],\n",
       "          [1.0887, 1.0887, 1.0887,  ..., 1.0887, 1.0887, 1.0887]],\n",
       "\n",
       "         [[0.9769, 0.9769, 0.9769,  ..., 0.9769, 0.9769, 0.9769],\n",
       "          [0.9769, 0.9769, 0.9769,  ..., 0.9769, 0.9769, 0.9769],\n",
       "          [0.9769, 0.9769, 0.9769,  ..., 0.9769, 0.9769, 0.9769],\n",
       "          ...,\n",
       "          [0.9769, 0.9769, 0.9769,  ..., 0.9769, 0.9769, 0.9769],\n",
       "          [0.9769, 0.9769, 0.9769,  ..., 0.9769, 0.9769, 0.9769],\n",
       "          [0.9769, 0.9769, 0.9769,  ..., 0.9769, 0.9769, 0.9769]]]])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e09d5aca",
   "metadata": {},
   "source": [
    "### 4.2 表格数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ff6f14c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7f190150",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 7.  ,  0.27,  0.36, ...,  0.45,  8.8 ,  6.  ],\n",
       "       [ 6.3 ,  0.3 ,  0.34, ...,  0.49,  9.5 ,  6.  ],\n",
       "       [ 8.1 ,  0.28,  0.4 , ...,  0.44, 10.1 ,  6.  ],\n",
       "       ...,\n",
       "       [ 6.5 ,  0.24,  0.19, ...,  0.46,  9.4 ,  6.  ],\n",
       "       [ 5.5 ,  0.29,  0.3 , ...,  0.38, 12.8 ,  7.  ],\n",
       "       [ 6.  ,  0.21,  0.38, ...,  0.32, 11.8 ,  6.  ]], dtype=float32)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_path = \"F:\\jupyter home\\dataset\\chapter 4\\winequality-white .csv\"\n",
    "wineq_numpy = np.loadtxt(wine_path, dtype=np.float32, delimiter=';', skiprows=1)\n",
    "\n",
    "wineq_numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "064974bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "col_list = next(csv.reader(open(wine_path), delimiter=';'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "908266bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((4898, 12),\n",
       " ['fixed acidity',\n",
       "  'volatile acidity',\n",
       "  'citric acid',\n",
       "  'residual sugar',\n",
       "  'chlorides',\n",
       "  'free sulfur dioxide',\n",
       "  'total sulfur dioxide',\n",
       "  'density',\n",
       "  'pH',\n",
       "  'sulphates',\n",
       "  'alcohol',\n",
       "  'quality'])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wineq_numpy.shape, col_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "30b57da4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([4898, 12]), torch.float32)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wineq = torch.from_numpy(wineq_numpy)\n",
    "wineq.shape, wineq.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8c71322b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([[ 7.0000,  0.2700,  0.3600,  ...,  3.0000,  0.4500,  8.8000],\n",
       "         [ 6.3000,  0.3000,  0.3400,  ...,  3.3000,  0.4900,  9.5000],\n",
       "         [ 8.1000,  0.2800,  0.4000,  ...,  3.2600,  0.4400, 10.1000],\n",
       "         ...,\n",
       "         [ 6.5000,  0.2400,  0.1900,  ...,  2.9900,  0.4600,  9.4000],\n",
       "         [ 5.5000,  0.2900,  0.3000,  ...,  3.3400,  0.3800, 12.8000],\n",
       "         [ 6.0000,  0.2100,  0.3800,  ...,  3.2600,  0.3200, 11.8000]]),\n",
       " torch.Size([4898, 11]))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = wineq[:, :-1]\n",
    "data, data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b158d45b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([6., 6., 6.,  ..., 6., 7., 6.]), torch.Size([4898]))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target = wineq[:, -1]\n",
    "target, target.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "05433641",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([6, 6, 6,  ..., 6, 7, 6])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target = wineq[:, -1].long()\n",
    "target"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15ea47b0",
   "metadata": {},
   "source": [
    "4.3.4 独热编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b833a01c",
   "metadata": {},
   "outputs": [],
   "source": [
    "target_onehot = torch.zeros(target.shape[0],10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "35edeaf1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([[0., 0., 0.,  ..., 0., 0., 0.],\n",
       "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
       "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
       "         ...,\n",
       "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
       "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
       "         [0., 0., 0.,  ..., 0., 0., 0.]]),\n",
       " 4898)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_onehot,target.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "052021ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0., 0., 0.,  ..., 0., 0., 0.],\n",
       "        [0., 0., 0.,  ..., 0., 0., 0.],\n",
       "        [0., 0., 0.,  ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0.,  ..., 0., 0., 0.],\n",
       "        [0., 0., 0.,  ..., 1., 0., 0.],\n",
       "        [0., 0., 0.,  ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_onehot.scatter_(1, target.unsqueeze(1), 1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bbd9bf69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[6],\n",
       "        [6],\n",
       "        [6],\n",
       "        ...,\n",
       "        [6],\n",
       "        [7],\n",
       "        [6]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target.unsqueeze(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "968456e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "target_unssqueezed = target.unsqueeze(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0d43ab15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([6.8548e+00, 2.7824e-01, 3.3419e-01, 6.3914e+00, 4.5772e-02, 3.5308e+01,\n",
       "         1.3836e+02, 9.9403e-01, 3.1883e+00, 4.8985e-01, 1.0514e+01]),\n",
       " tensor([[ 7.0000,  0.2700,  0.3600,  ...,  3.0000,  0.4500,  8.8000],\n",
       "         [ 6.3000,  0.3000,  0.3400,  ...,  3.3000,  0.4900,  9.5000],\n",
       "         [ 8.1000,  0.2800,  0.4000,  ...,  3.2600,  0.4400, 10.1000],\n",
       "         ...,\n",
       "         [ 6.5000,  0.2400,  0.1900,  ...,  2.9900,  0.4600,  9.4000],\n",
       "         [ 5.5000,  0.2900,  0.3000,  ...,  3.3400,  0.3800, 12.8000],\n",
       "         [ 6.0000,  0.2100,  0.3800,  ...,  3.2600,  0.3200, 11.8000]]))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_mean = torch.mean(data, dim = 0)\n",
    "data_mean,data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6b643489",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([7.1211e-01, 1.0160e-02, 1.4646e-02, 2.5726e+01, 4.7733e-04, 2.8924e+02,\n",
       "        1.8061e+03, 8.9455e-06, 2.2801e-02, 1.3025e-02, 1.5144e+00])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_var = torch.var(data, dim=0)\n",
    "data_var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9bb84fec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 1.7208e-01, -8.1761e-02,  2.1326e-01,  ..., -1.2468e+00,\n",
       "         -3.4915e-01, -1.3930e+00],\n",
       "        [-6.5743e-01,  2.1587e-01,  4.7996e-02,  ...,  7.3995e-01,\n",
       "          1.3422e-03, -8.2419e-01],\n",
       "        [ 1.4756e+00,  1.7450e-02,  5.4378e-01,  ...,  4.7505e-01,\n",
       "         -4.3677e-01, -3.3663e-01],\n",
       "        ...,\n",
       "        [-4.2043e-01, -3.7940e-01, -1.1915e+00,  ..., -1.3130e+00,\n",
       "         -2.6153e-01, -9.0545e-01],\n",
       "        [-1.6054e+00,  1.1666e-01, -2.8253e-01,  ...,  1.0049e+00,\n",
       "         -9.6251e-01,  1.8574e+00],\n",
       "        [-1.0129e+00, -6.7703e-01,  3.7852e-01,  ...,  4.7505e-01,\n",
       "         -1.4882e+00,  1.0448e+00]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_normalized = (data - data_mean)/torch.sqrt(data_var)\n",
    "data_normalized"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bea968f",
   "metadata": {},
   "source": [
    "4.3.6 寻找阈值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3166d4c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([False, False, False,  ..., False, False, False])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bad_indexes = target <= 3\n",
    "bad_indexes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5416eade",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(20)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bad_index_torch = torch.le(target, 3)\n",
    "bad_index_torch.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "93c4fa3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([4898]), torch.bool, tensor(20))"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bad_indexes.shape, bad_indexes.dtype, bad_indexes.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1b303167",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[8.5000e+00, 2.6000e-01, 2.1000e-01, 1.6200e+01, 7.4000e-02, 4.1000e+01,\n",
       "         1.9700e+02, 9.9800e-01, 3.0200e+00, 5.0000e-01, 9.8000e+00],\n",
       "        [5.8000e+00, 2.4000e-01, 4.4000e-01, 3.5000e+00, 2.9000e-02, 5.0000e+00,\n",
       "         1.0900e+02, 9.9130e-01, 3.5300e+00, 4.3000e-01, 1.1700e+01],\n",
       "        [9.1000e+00, 5.9000e-01, 3.8000e-01, 1.6000e+00, 6.6000e-02, 3.4000e+01,\n",
       "         1.8200e+02, 9.9680e-01, 3.2300e+00, 3.8000e-01, 8.5000e+00],\n",
       "        [7.1000e+00, 3.2000e-01, 3.2000e-01, 1.1000e+01, 3.8000e-02, 1.6000e+01,\n",
       "         6.6000e+01, 9.9370e-01, 3.2400e+00, 4.0000e-01, 1.1500e+01],\n",
       "        [6.9000e+00, 3.9000e-01, 4.0000e-01, 4.6000e+00, 2.2000e-02, 5.0000e+00,\n",
       "         1.9000e+01, 9.9150e-01, 3.3100e+00, 3.7000e-01, 1.2600e+01],\n",
       "        [1.0300e+01, 1.7000e-01, 4.7000e-01, 1.4000e+00, 3.7000e-02, 5.0000e+00,\n",
       "         3.3000e+01, 9.9390e-01, 2.8900e+00, 2.8000e-01, 9.6000e+00],\n",
       "        [7.9000e+00, 6.4000e-01, 4.6000e-01, 1.0600e+01, 2.4400e-01, 3.3000e+01,\n",
       "         2.2700e+02, 9.9830e-01, 2.8700e+00, 7.4000e-01, 9.1000e+00],\n",
       "        [8.3000e+00, 3.3000e-01, 4.2000e-01, 1.1500e+00, 3.3000e-02, 1.8000e+01,\n",
       "         9.6000e+01, 9.9110e-01, 3.2000e+00, 3.2000e-01, 1.2400e+01],\n",
       "        [8.6000e+00, 5.5000e-01, 3.5000e-01, 1.5550e+01, 5.7000e-02, 3.5500e+01,\n",
       "         3.6650e+02, 1.0001e+00, 3.0400e+00, 6.3000e-01, 1.1000e+01],\n",
       "        [7.5000e+00, 3.2000e-01, 2.4000e-01, 4.6000e+00, 5.3000e-02, 8.0000e+00,\n",
       "         1.3400e+02, 9.9580e-01, 3.1400e+00, 5.0000e-01, 9.1000e+00],\n",
       "        [6.7000e+00, 2.5000e-01, 2.6000e-01, 1.5500e+00, 4.1000e-02, 1.1850e+02,\n",
       "         2.1600e+02, 9.9490e-01, 3.5500e+00, 6.3000e-01, 9.4000e+00],\n",
       "        [7.1000e+00, 4.9000e-01, 2.2000e-01, 2.0000e+00, 4.7000e-02, 1.4650e+02,\n",
       "         3.0750e+02, 9.9240e-01, 3.2400e+00, 3.7000e-01, 1.1000e+01],\n",
       "        [1.1800e+01, 2.3000e-01, 3.8000e-01, 1.1100e+01, 3.4000e-02, 1.5000e+01,\n",
       "         1.2300e+02, 9.9970e-01, 2.9300e+00, 5.5000e-01, 9.7000e+00],\n",
       "        [7.6000e+00, 4.8000e-01, 3.7000e-01, 1.2000e+00, 3.4000e-02, 5.0000e+00,\n",
       "         5.7000e+01, 9.9256e-01, 3.0500e+00, 5.4000e-01, 1.0400e+01],\n",
       "        [6.1000e+00, 2.0000e-01, 3.4000e-01, 9.5000e+00, 4.1000e-02, 3.8000e+01,\n",
       "         2.0100e+02, 9.9500e-01, 3.1400e+00, 4.4000e-01, 1.0100e+01],\n",
       "        [4.2000e+00, 2.1500e-01, 2.3000e-01, 5.1000e+00, 4.1000e-02, 6.4000e+01,\n",
       "         1.5700e+02, 9.9688e-01, 3.4200e+00, 4.4000e-01, 8.0000e+00],\n",
       "        [9.4000e+00, 2.4000e-01, 2.9000e-01, 8.5000e+00, 3.7000e-02, 1.2400e+02,\n",
       "         2.0800e+02, 9.9395e-01, 2.9000e+00, 3.8000e-01, 1.1000e+01],\n",
       "        [6.2000e+00, 2.3000e-01, 3.5000e-01, 7.0000e-01, 5.1000e-02, 2.4000e+01,\n",
       "         1.1100e+02, 9.9160e-01, 3.3700e+00, 4.3000e-01, 1.1000e+01],\n",
       "        [6.8000e+00, 2.6000e-01, 3.4000e-01, 1.5100e+01, 6.0000e-02, 4.2000e+01,\n",
       "         1.6200e+02, 9.9705e-01, 3.2400e+00, 5.2000e-01, 1.0500e+01],\n",
       "        [6.1000e+00, 2.6000e-01, 2.5000e-01, 2.9000e+00, 4.7000e-02, 2.8900e+02,\n",
       "         4.4000e+02, 9.9314e-01, 3.4400e+00, 6.4000e-01, 1.0500e+01]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bad_data = data[bad_indexes]\n",
    "bad_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a39f32a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([20, 11])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bad_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "7ba6794d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[8.5000e+00, 2.6000e-01, 2.1000e-01, 1.6200e+01, 7.4000e-02, 4.1000e+01,\n",
       "         1.9700e+02, 9.9800e-01, 3.0200e+00, 5.0000e-01, 9.8000e+00],\n",
       "        [5.8000e+00, 2.4000e-01, 4.4000e-01, 3.5000e+00, 2.9000e-02, 5.0000e+00,\n",
       "         1.0900e+02, 9.9130e-01, 3.5300e+00, 4.3000e-01, 1.1700e+01],\n",
       "        [9.1000e+00, 5.9000e-01, 3.8000e-01, 1.6000e+00, 6.6000e-02, 3.4000e+01,\n",
       "         1.8200e+02, 9.9680e-01, 3.2300e+00, 3.8000e-01, 8.5000e+00],\n",
       "        [7.1000e+00, 3.2000e-01, 3.2000e-01, 1.1000e+01, 3.8000e-02, 1.6000e+01,\n",
       "         6.6000e+01, 9.9370e-01, 3.2400e+00, 4.0000e-01, 1.1500e+01],\n",
       "        [6.9000e+00, 3.9000e-01, 4.0000e-01, 4.6000e+00, 2.2000e-02, 5.0000e+00,\n",
       "         1.9000e+01, 9.9150e-01, 3.3100e+00, 3.7000e-01, 1.2600e+01],\n",
       "        [1.0300e+01, 1.7000e-01, 4.7000e-01, 1.4000e+00, 3.7000e-02, 5.0000e+00,\n",
       "         3.3000e+01, 9.9390e-01, 2.8900e+00, 2.8000e-01, 9.6000e+00],\n",
       "        [7.9000e+00, 6.4000e-01, 4.6000e-01, 1.0600e+01, 2.4400e-01, 3.3000e+01,\n",
       "         2.2700e+02, 9.9830e-01, 2.8700e+00, 7.4000e-01, 9.1000e+00],\n",
       "        [8.3000e+00, 3.3000e-01, 4.2000e-01, 1.1500e+00, 3.3000e-02, 1.8000e+01,\n",
       "         9.6000e+01, 9.9110e-01, 3.2000e+00, 3.2000e-01, 1.2400e+01],\n",
       "        [8.6000e+00, 5.5000e-01, 3.5000e-01, 1.5550e+01, 5.7000e-02, 3.5500e+01,\n",
       "         3.6650e+02, 1.0001e+00, 3.0400e+00, 6.3000e-01, 1.1000e+01],\n",
       "        [7.5000e+00, 3.2000e-01, 2.4000e-01, 4.6000e+00, 5.3000e-02, 8.0000e+00,\n",
       "         1.3400e+02, 9.9580e-01, 3.1400e+00, 5.0000e-01, 9.1000e+00],\n",
       "        [6.7000e+00, 2.5000e-01, 2.6000e-01, 1.5500e+00, 4.1000e-02, 1.1850e+02,\n",
       "         2.1600e+02, 9.9490e-01, 3.5500e+00, 6.3000e-01, 9.4000e+00],\n",
       "        [7.1000e+00, 4.9000e-01, 2.2000e-01, 2.0000e+00, 4.7000e-02, 1.4650e+02,\n",
       "         3.0750e+02, 9.9240e-01, 3.2400e+00, 3.7000e-01, 1.1000e+01],\n",
       "        [1.1800e+01, 2.3000e-01, 3.8000e-01, 1.1100e+01, 3.4000e-02, 1.5000e+01,\n",
       "         1.2300e+02, 9.9970e-01, 2.9300e+00, 5.5000e-01, 9.7000e+00],\n",
       "        [7.6000e+00, 4.8000e-01, 3.7000e-01, 1.2000e+00, 3.4000e-02, 5.0000e+00,\n",
       "         5.7000e+01, 9.9256e-01, 3.0500e+00, 5.4000e-01, 1.0400e+01],\n",
       "        [6.1000e+00, 2.0000e-01, 3.4000e-01, 9.5000e+00, 4.1000e-02, 3.8000e+01,\n",
       "         2.0100e+02, 9.9500e-01, 3.1400e+00, 4.4000e-01, 1.0100e+01],\n",
       "        [4.2000e+00, 2.1500e-01, 2.3000e-01, 5.1000e+00, 4.1000e-02, 6.4000e+01,\n",
       "         1.5700e+02, 9.9688e-01, 3.4200e+00, 4.4000e-01, 8.0000e+00],\n",
       "        [9.4000e+00, 2.4000e-01, 2.9000e-01, 8.5000e+00, 3.7000e-02, 1.2400e+02,\n",
       "         2.0800e+02, 9.9395e-01, 2.9000e+00, 3.8000e-01, 1.1000e+01],\n",
       "        [6.2000e+00, 2.3000e-01, 3.5000e-01, 7.0000e-01, 5.1000e-02, 2.4000e+01,\n",
       "         1.1100e+02, 9.9160e-01, 3.3700e+00, 4.3000e-01, 1.1000e+01],\n",
       "        [6.8000e+00, 2.6000e-01, 3.4000e-01, 1.5100e+01, 6.0000e-02, 4.2000e+01,\n",
       "         1.6200e+02, 9.9705e-01, 3.2400e+00, 5.2000e-01, 1.0500e+01],\n",
       "        [6.1000e+00, 2.6000e-01, 2.5000e-01, 2.9000e+00, 4.7000e-02, 2.8900e+02,\n",
       "         4.4000e+02, 9.9314e-01, 3.4400e+00, 6.4000e-01, 1.0500e+01]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bad_data = data[target <= 3]\n",
    "bad_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d303036e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 7.0000,  0.2700,  0.3600,  ...,  3.0000,  0.4500,  8.8000],\n",
       "        [ 6.3000,  0.3000,  0.3400,  ...,  3.3000,  0.4900,  9.5000],\n",
       "        [ 8.1000,  0.2800,  0.4000,  ...,  3.2600,  0.4400, 10.1000],\n",
       "        ...,\n",
       "        [ 6.6000,  0.3200,  0.3600,  ...,  3.1500,  0.4600,  9.6000],\n",
       "        [ 6.5000,  0.2400,  0.1900,  ...,  2.9900,  0.4600,  9.4000],\n",
       "        [ 6.0000,  0.2100,  0.3800,  ...,  3.2600,  0.3200, 11.8000]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mid_data = data[(target > 3) & (target < 7)]\n",
    "mid_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "79e326b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 6.6000,  0.1600,  0.4000,  ...,  3.5400,  0.5200, 12.4000],\n",
       "        [ 6.6000,  0.1700,  0.3800,  ...,  3.2500,  0.5500, 11.4000],\n",
       "        [ 6.2000,  0.6600,  0.4800,  ...,  3.3300,  0.3900, 12.8000],\n",
       "        ...,\n",
       "        [ 6.2000,  0.2100,  0.2800,  ...,  3.2100,  1.0800, 12.1500],\n",
       "        [ 6.2000,  0.4100,  0.2200,  ...,  3.0400,  0.7900, 13.0000],\n",
       "        [ 5.5000,  0.2900,  0.3000,  ...,  3.3400,  0.3800, 12.8000]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "good_data = data[target >= 7]\n",
    "good_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "def9d544",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([7.6000e+00, 3.3325e-01, 3.3600e-01, 6.3925e+00, 5.4300e-02, 5.3325e+01,\n",
       "        1.7060e+02, 9.9488e-01, 3.1875e+00, 4.7450e-01, 1.0345e+01])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bad_mean = torch.mean(bad_data, dim=0)\n",
    "bad_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "41827be9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([6.8869e+00, 2.8153e-01, 3.3644e-01, 6.7051e+00, 4.7841e-02, 3.5424e+01,\n",
       "        1.4183e+02, 9.9447e-01, 3.1808e+00, 4.8707e-01, 1.0265e+01])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mid_mean = torch.mean(mid_data, dim=0)\n",
    "mid_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "368e73fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([6.7251e+00, 2.6535e-01, 3.2606e-01, 5.2615e+00, 3.8160e-02, 3.4550e+01,\n",
       "        1.2525e+02, 9.9241e-01, 3.2151e+00, 5.0014e-01, 1.1416e+01])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "good_mean = torch.mean(good_data, dim=0)\n",
    "good_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "3c95e939",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([11])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mid_mean = torch.mean(mid_data, dim=0)\n",
    "mid_mean.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "27ac279a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 0 fixed acidity          7.60   6.89   6.73\n",
      " 1 volatile acidity       0.33   0.28   0.27\n",
      " 2 citric acid            0.34   0.34   0.33\n",
      " 3 residual sugar         6.39   6.71   5.26\n",
      " 4 chlorides              0.05   0.05   0.04\n",
      " 5 free sulfur dioxide   53.33  35.42  34.55\n",
      " 6 total sulfur dioxide 170.60 141.83 125.25\n",
      " 7 density                0.99   0.99   0.99\n",
      " 8 pH                     3.19   3.18   3.22\n",
      " 9 sulphates              0.47   0.49   0.50\n",
      "10 alcohol               10.34  10.26  11.42\n"
     ]
    }
   ],
   "source": [
    "for i, args in enumerate(zip(col_list, bad_mean, mid_mean, good_mean)):\n",
    "    print('{:2} {:20} {:6.2f} {:6.2f} {:6.2f}'.format(i, *args))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "0d657e81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([170., 132.,  97.,  ..., 111., 110.,  98.]),\n",
       " tensor([False,  True,  True,  ...,  True,  True,  True]))"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_sulfur_threshold = 141.83\n",
    "total_sulfur_data = data[:,6]\n",
    "predicted_indexes = torch.lt(total_sulfur_data, total_sulfur_threshold)\n",
    "\n",
    "total_sulfur_data, predicted_indexes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "2204c2b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([4898]), torch.bool, tensor(3258))"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actual_indexs = target > 5\n",
    "actual_indexs.shape, actual_indexs.dtype, actual_indexs.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "d8f7b86d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_matches = torch.sum(actual_indexs & predicted_indexes).item()\n",
    "n_matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "a58ec46c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2727"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_predicted = torch.sum(predicted_indexes).item()\n",
    "n_predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "aa63fecd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3258"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_actual = torch.sum(actual_indexs).item()\n",
    "n_actual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "e0990845",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.74000733406674, 0.6193984039287906)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_matches/ n_predicted,  n_matches/ n_actual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e70f10a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "DL_Pytorch",
   "language": "python",
   "name": "dl_pytorch"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.15"
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 },
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