{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-22T05:32:54.107372Z",
     "start_time": "2021-03-22T05:32:53.400242Z"
    },
    "tags": [
     "remove_output"
    ]
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from tqdm.autonotebook import tqdm\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-22T05:32:54.114216Z",
     "start_time": "2021-03-22T05:32:54.109474Z"
    },
    "tags": [
     "remove_cell"
    ]
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib_inline\n",
    "matplotlib_inline.backend_inline.set_matplotlib_formats('svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-22T05:32:54.669963Z",
     "start_time": "2021-03-22T05:32:54.116147Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-22T05:32:54.676062Z",
     "start_time": "2021-03-22T05:32:54.671481Z"
    }
   },
   "outputs": [],
   "source": [
    "torch_scalar = torch.tensor(3.14)\n",
    "torch_vector = torch.tensor([1, 2, 3, 4])\n",
    "torch_matrix = torch.tensor([[1, 2,],\n",
    "                             [3, 4,],\n",
    "                             [5, 6,], \n",
    "                             [7, 8,]])\n",
    "#You don't have to format it like I did, thats just for clarity\n",
    "torch_tensor3d = torch.tensor([\n",
    "                            [\n",
    "                            [ 1,  2,  3], \n",
    "                            [ 4,  5,  6],\n",
    "                            ],\n",
    "                            [\n",
    "                            [ 7,  8,  9], \n",
    "                            [10, 11, 12],\n",
    "                            ],\n",
    "                            [\n",
    "                            [13, 14, 15], \n",
    "                            [16, 17, 18],\n",
    "                            ],\n",
    "                            [\n",
    "                            [19, 20, 21], \n",
    "                            [22, 23, 24],\n",
    "                            ]\n",
    "                              ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-22T05:32:54.688664Z",
     "start_time": "2021-03-22T05:32:54.677220Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([])\n",
      "torch.Size([4])\n",
      "torch.Size([4, 2])\n",
      "torch.Size([4, 2, 3])\n"
     ]
    }
   ],
   "source": [
    "print(torch_scalar.shape)\n",
    "print(torch_vector.shape)\n",
    "print(torch_matrix.shape)\n",
    "print(torch_tensor3d.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-22T05:32:54.694456Z",
     "start_time": "2021-03-22T05:32:54.690164Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.13200003 0.0054197  0.24716025 0.08458665]\n",
      " [0.51738806 0.517676   0.33316974 0.07034239]\n",
      " [0.53272871 0.51833686 0.73074206 0.82302625]\n",
      " [0.37334327 0.59914251 0.82853404 0.51186258]]\n"
     ]
    }
   ],
   "source": [
    "x_np = np.random.random((4,4))\n",
    "print(x_np)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-22T05:32:54.701736Z",
     "start_time": "2021-03-22T05:32:54.697048Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.1320, 0.0054, 0.2472, 0.0846],\n",
      "        [0.5174, 0.5177, 0.3332, 0.0703],\n",
      "        [0.5327, 0.5183, 0.7307, 0.8230],\n",
      "        [0.3733, 0.5991, 0.8285, 0.5119]], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "x_pt = torch.tensor(x_np)\n",
    "print(x_pt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-22T05:32:54.708524Z",
     "start_time": "2021-03-22T05:32:54.703648Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "float64 torch.float64\n"
     ]
    }
   ],
   "source": [
    "print(x_np.dtype, x_pt.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-22T05:32:54.715713Z",
     "start_time": "2021-03-22T05:32:54.710165Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "float32 torch.float32\n"
     ]
    }
   ],
   "source": [
    "#Lets force them to be 32-bit floats\n",
    "x_np = np.asarray(x_np, dtype=np.float32)\n",
    "x_pt = torch.tensor(x_np, dtype=torch.float32)\n",
    "print(x_np.dtype, x_pt.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-22T05:32:54.722368Z",
     "start_time": "2021-03-22T05:32:54.717373Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[False False False False]\n",
      " [ True  True False False]\n",
      " [ True  True  True  True]\n",
      " [False  True  True  True]]\n",
      "bool\n"
     ]
    }
   ],
   "source": [
    "b_np = (x_np > 0.5)\n",
    "print(b_np)\n",
    "print(b_np.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-22T05:32:54.729593Z",
     "start_time": "2021-03-22T05:32:54.724132Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[False, False, False, False],\n",
      "        [ True,  True, False, False],\n",
      "        [ True,  True,  True,  True],\n",
      "        [False,  True,  True,  True]])\n",
      "torch.bool\n"
     ]
    }
   ],
   "source": [
    "b_pt = (x_pt > 0.5)\n",
    "print(b_pt)\n",
    "print(b_pt.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-22T05:32:54.737835Z",
     "start_time": "2021-03-22T05:32:54.730952Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float32(6.8254595)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(x_np)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-22T05:32:54.743037Z",
     "start_time": "2021-03-22T05:32:54.739198Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(6.8255)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.sum(x_pt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-22T05:32:54.747879Z",
     "start_time": "2021-03-22T05:32:54.744383Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.13200003, 0.51738805, 0.53272873, 0.37334326],\n",
       "       [0.0054197 , 0.517676  , 0.5183369 , 0.5991425 ],\n",
       "       [0.24716026, 0.33316973, 0.73074204, 0.82853407],\n",
       "       [0.08458665, 0.07034239, 0.82302624, 0.5118626 ]], dtype=float32)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.transpose(x_np)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-22T05:32:54.755058Z",
     "start_time": "2021-03-22T05:32:54.749723Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.1320, 0.5174, 0.5327, 0.3733],\n",
       "        [0.0054, 0.5177, 0.5183, 0.5991],\n",
       "        [0.2472, 0.3332, 0.7307, 0.8285],\n",
       "        [0.0846, 0.0703, 0.8230, 0.5119]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.transpose(x_pt, 0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-22T05:32:54.763796Z",
     "start_time": "2021-03-22T05:32:54.756945Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([3, 2, 4])\n"
     ]
    }
   ],
   "source": [
    "print(torch.transpose(torch_tensor3d, 0, 2).shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-22T05:32:59.532902Z",
     "start_time": "2021-03-22T05:32:54.765163Z"
    }
   },
   "outputs": [],
   "source": [
    "import timeit\n",
    "x = torch.rand(2**11, 2**11)\n",
    "time_cpu = timeit.timeit(\"x@x\", globals=globals(), number=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-22T05:32:59.578188Z",
     "start_time": "2021-03-22T05:32:59.539627Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Is CUDA available? : True\n"
     ]
    }
   ],
   "source": [
    "print(\"Is CUDA available? :\", torch.cuda.is_available())\n",
    "device = torch.device(\"cuda\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-22T05:33:01.740576Z",
     "start_time": "2021-03-22T05:32:59.580308Z"
    }
   },
   "outputs": [],
   "source": [
    "x = x.to(device)\n",
    "time_gpu = timeit.timeit(\"x@x\", globals=globals(), number=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-22T05:33:01.957208Z",
     "start_time": "2021-03-22T05:33:01.750393Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[tensor(1), tensor(2)]\n",
      "[tensor(1, device='cuda:0'), tensor(2, device='cuda:0')]\n"
     ]
    }
   ],
   "source": [
    "def moveTo(obj, device):\n",
    "    \"\"\"\n",
    "    obj: the python object to move to a device, or to move its contents to a device\n",
    "    device: the compute device to move objects to\n",
    "    \"\"\"\n",
    "    if isinstance(obj, list):\n",
    "        return [moveTo(x, device) for x in obj]\n",
    "    elif isinstance(obj, tuple):\n",
    "        return tuple(moveTo(list(obj), device))\n",
    "    elif isinstance(obj, set):\n",
    "        return set(moveTo(list(obj), device))\n",
    "    elif isinstance(obj, dict):\n",
    "        to_ret = dict()\n",
    "        for key, value in obj.items():\n",
    "            to_ret[moveTo(key, device)] = moveTo(value, device)\n",
    "        return to_ret\n",
    "    elif hasattr(obj, \"to\"):\n",
    "        return obj.to(device)\n",
    "    else:\n",
    "        return obj\n",
    "    \n",
    "some_tensors = [torch.tensor(1), torch.tensor(2)]\n",
    "print(some_tensors)\n",
    "print(moveTo(some_tensors, device))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-22T05:33:02.498610Z",
     "start_time": "2021-03-22T05:33:01.960934Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
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    "def f(x):\n",
    "    return torch.pow((x-2.0), 2)\n",
    "\n",
    "x_axis_vals = np.linspace(-7,9,100) \n",
    "y_axis_vals = f(torch.tensor(x_axis_vals)).numpy()\n",
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def fP(x): #Defining the derivative of f(x) manually\n",
    "    return 2*x-4\n",
    "\n",
    "y_axis_vals_p = fP(torch.tensor(x_axis_vals)).numpy()\n",
    "\n",
    "#First, lets draw a black line at 0, so that we can easily tell if something is positive or negative\n",
    "sns.lineplot(x=x_axis_vals, y=[0.0]*len(x_axis_vals), label=\"0\", color='black')\n",
    "sns.lineplot(x=x_axis_vals, y=y_axis_vals, label='$f(x) = (x-2)^2$')\n",
    "sns.lineplot(x=x_axis_vals, y=y_axis_vals_p, label=\"$f'(x)=2 x - 4$\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-22T05:33:02.872628Z",
     "start_time": "2021-03-22T05:33:02.868281Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "x = torch.tensor([-3.5], requires_grad=True)\n",
    "print(x.grad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-22T05:33:02.879284Z",
     "start_time": "2021-03-22T05:33:02.874597Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([30.2500], grad_fn=<PowBackward0>)\n"
     ]
    }
   ],
   "source": [
    "value = f(x)\n",
    "print(value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-22T05:33:02.887619Z",
     "start_time": "2021-03-22T05:33:02.881506Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-11.])\n"
     ]
    }
   ],
   "source": [
    "value.backward()\n",
    "print(x.grad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-22T05:33:02.906233Z",
     "start_time": "2021-03-22T05:33:02.888975Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([2.0000])\n"
     ]
    }
   ],
   "source": [
    "x = torch.tensor([-3.5], requires_grad=True)\n",
    "\n",
    "x_cur = x.clone()\n",
    "x_prev = x_cur*100 #Make the initial \"previous\" solution larger\n",
    "epsilon = 1e-5\n",
    "eta = 0.1\n",
    "\n",
    "while torch.linalg.norm(x_cur-x_prev) > epsilon:\n",
    "    x_prev = x_cur.clone() #We need to make a clone here so that x_prev and x_cur don't point to the same object\n",
    "    \n",
    "    #Compute our function, gradient, and update\n",
    "    value = f(x)\n",
    "    value.backward()\n",
    "    x.data -= eta * x.grad\n",
    "    x.grad.zero_() #We need to zero out the old gradient, as py-torch will not do that for us\n",
    "    \n",
    "    #What are we currently now?\n",
    "    x_cur = x.data\n",
    "    \n",
    "print(x_cur)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-22T05:33:02.911171Z",
     "start_time": "2021-03-22T05:33:02.908110Z"
    }
   },
   "outputs": [],
   "source": [
    "x_param = torch.nn.Parameter(torch.tensor([-3.5]), requires_grad=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-22T05:33:02.916541Z",
     "start_time": "2021-03-22T05:33:02.913522Z"
    }
   },
   "outputs": [],
   "source": [
    "optimizer = torch.optim.SGD([x_param], lr=eta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-22T05:33:02.933325Z",
     "start_time": "2021-03-22T05:33:02.918744Z"
    },
    "tags": [
     "remove_output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([2.0000])\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(60):\n",
    "    optimizer.zero_grad() #x.grad.zero_()\n",
    "    loss_incurred  = f(x_param)\n",
    "    loss_incurred.backward()\n",
    "    optimizer.step() #x.data -= eta * x.grad\n",
    "print(x_param.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-22T05:33:18.679235Z",
     "start_time": "2021-03-22T05:33:02.939654Z"
    }
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'numpy' has no attribute '_no_nep50_warning'",
     "output_type": "error",
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      "\u001b[36mFile \u001b[39m\u001b[32md:\\app\\miniconda\\envs\\dpl\\Lib\\site-packages\\sklearn\\__init__.py:73\u001b[39m\n\u001b[32m     62\u001b[39m \u001b[38;5;66;03m# `_distributor_init` allows distributors to run custom init code.\u001b[39;00m\n\u001b[32m     63\u001b[39m \u001b[38;5;66;03m# For instance, for the Windows wheel, this is used to pre-load the\u001b[39;00m\n\u001b[32m     64\u001b[39m \u001b[38;5;66;03m# vcomp shared library runtime for OpenMP embedded in the sklearn/.libs\u001b[39;00m\n\u001b[32m   (...)\u001b[39m\u001b[32m     67\u001b[39m \u001b[38;5;66;03m# later is linked to the OpenMP runtime to make it possible to introspect\u001b[39;00m\n\u001b[32m     68\u001b[39m \u001b[38;5;66;03m# it and importing it first would fail if the OpenMP dll cannot be found.\u001b[39;00m\n\u001b[32m     69\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (  \u001b[38;5;66;03m# noqa: F401 E402\u001b[39;00m\n\u001b[32m     70\u001b[39m     __check_build,\n\u001b[32m     71\u001b[39m     _distributor_init,\n\u001b[32m     72\u001b[39m )\n\u001b[32m---> \u001b[39m\u001b[32m73\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbase\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m clone  \u001b[38;5;66;03m# noqa: E402\u001b[39;00m\n\u001b[32m     74\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mutils\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_show_versions\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m show_versions  \u001b[38;5;66;03m# noqa: E402\u001b[39;00m\n\u001b[32m     76\u001b[39m _submodules = [\n\u001b[32m     77\u001b[39m     \u001b[33m\"\u001b[39m\u001b[33mcalibration\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m     78\u001b[39m     \u001b[33m\"\u001b[39m\u001b[33mcluster\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m   (...)\u001b[39m\u001b[32m    114\u001b[39m     \u001b[33m\"\u001b[39m\u001b[33mcompose\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m    115\u001b[39m ]\n",
      "\u001b[36mFile \u001b[39m\u001b[32md:\\app\\miniconda\\envs\\dpl\\Lib\\site-packages\\sklearn\\base.py:19\u001b[39m\n\u001b[32m     17\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_config\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m config_context, get_config\n\u001b[32m     18\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mexceptions\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m InconsistentVersionWarning\n\u001b[32m---> \u001b[39m\u001b[32m19\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mutils\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_metadata_requests\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _MetadataRequester, _routing_enabled\n\u001b[32m     20\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mutils\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_missing\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m is_scalar_nan\n\u001b[32m     21\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mutils\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_param_validation\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m validate_parameter_constraints\n",
      "\u001b[36mFile \u001b[39m\u001b[32md:\\app\\miniconda\\envs\\dpl\\Lib\\site-packages\\sklearn\\utils\\__init__.py:9\u001b[39m\n\u001b[32m      7\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m metadata_routing\n\u001b[32m      8\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_bunch\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Bunch\n\u001b[32m----> \u001b[39m\u001b[32m9\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_chunking\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m gen_batches, gen_even_slices\n\u001b[32m     11\u001b[39m \u001b[38;5;66;03m# Make _safe_indexing importable from here for backward compat as this particular\u001b[39;00m\n\u001b[32m     12\u001b[39m \u001b[38;5;66;03m# helper is considered semi-private and typically very useful for third-party\u001b[39;00m\n\u001b[32m     13\u001b[39m \u001b[38;5;66;03m# libraries that want to comply with scikit-learn's estimator API. In particular,\u001b[39;00m\n\u001b[32m     14\u001b[39m \u001b[38;5;66;03m# _safe_indexing was included in our public API documentation despite the leading\u001b[39;00m\n\u001b[32m     15\u001b[39m \u001b[38;5;66;03m# `_` in its name.\u001b[39;00m\n\u001b[32m     16\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_indexing\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[32m     17\u001b[39m     _safe_indexing,  \u001b[38;5;66;03m# noqa: F401\u001b[39;00m\n\u001b[32m     18\u001b[39m     resample,\n\u001b[32m     19\u001b[39m     shuffle,\n\u001b[32m     20\u001b[39m )\n",
      "\u001b[36mFile \u001b[39m\u001b[32md:\\app\\miniconda\\envs\\dpl\\Lib\\site-packages\\sklearn\\utils\\_chunking.py:11\u001b[39m\n\u001b[32m      8\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnp\u001b[39;00m\n\u001b[32m     10\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_config\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m get_config\n\u001b[32m---> \u001b[39m\u001b[32m11\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_param_validation\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Interval, validate_params\n\u001b[32m     14\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mchunk_generator\u001b[39m(gen, chunksize):\n\u001b[32m     15\u001b[39m \u001b[38;5;250m    \u001b[39m\u001b[33;03m\"\"\"Chunk generator, ``gen`` into lists of length ``chunksize``. The last\u001b[39;00m\n\u001b[32m     16\u001b[39m \u001b[33;03m    chunk may have a length less than ``chunksize``.\"\"\"\u001b[39;00m\n",
      "\u001b[36mFile \u001b[39m\u001b[32md:\\app\\miniconda\\envs\\dpl\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py:14\u001b[39m\n\u001b[32m     11\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumbers\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Integral, Real\n\u001b[32m     13\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnp\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m14\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mscipy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msparse\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m csr_matrix, issparse\n\u001b[32m     16\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_config\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m config_context, get_config\n\u001b[32m     17\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mvalidation\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _is_arraylike_not_scalar\n",
      "\u001b[36mFile \u001b[39m\u001b[32md:\\app\\miniconda\\envs\\dpl\\Lib\\site-packages\\scipy\\sparse\\__init__.py:304\u001b[39m\n\u001b[32m    301\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mwarnings\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m_warnings\u001b[39;00m\n\u001b[32m    302\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mimportlib\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m_importlib\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m304\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_base\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m *\n\u001b[32m    305\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_csr\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m *\n\u001b[32m    306\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_csc\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m *\n",
      "\u001b[36mFile \u001b[39m\u001b[32md:\\app\\miniconda\\envs\\dpl\\Lib\\site-packages\\scipy\\sparse\\_base.py:8\u001b[39m\n\u001b[32m      5\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnp\u001b[39;00m\n\u001b[32m      6\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01moperator\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m8\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_sputils\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (asmatrix, check_reshape_kwargs, check_shape,\n\u001b[32m      9\u001b[39m                        get_sum_dtype, isdense, isscalarlike, _todata,\n\u001b[32m     10\u001b[39m                        matrix, validateaxis, getdtype, is_pydata_spmatrix)\n\u001b[32m     11\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mscipy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_lib\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_sparse\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m SparseABC, issparse\n\u001b[32m     13\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_matrix\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m spmatrix\n",
      "\u001b[36mFile \u001b[39m\u001b[32md:\\app\\miniconda\\envs\\dpl\\Lib\\site-packages\\scipy\\sparse\\_sputils.py:10\u001b[39m\n\u001b[32m      8\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmath\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m prod\n\u001b[32m      9\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mscipy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msparse\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msp\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m10\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mscipy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_lib\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_util\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m np_long, np_ulong\n\u001b[32m     13\u001b[39m __all__ = [\u001b[33m'\u001b[39m\u001b[33mupcast\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mgetdtype\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mgetdata\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33misscalarlike\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33misintlike\u001b[39m\u001b[33m'\u001b[39m,\n\u001b[32m     14\u001b[39m            \u001b[33m'\u001b[39m\u001b[33misshape\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33missequence\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33misdense\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mismatrix\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mget_sum_dtype\u001b[39m\u001b[33m'\u001b[39m,\n\u001b[32m     15\u001b[39m            \u001b[33m'\u001b[39m\u001b[33mbroadcast_shapes\u001b[39m\u001b[33m'\u001b[39m]\n\u001b[32m     17\u001b[39m supported_dtypes = [np.bool_, np.byte, np.ubyte, np.short, np.ushort, np.intc,\n\u001b[32m     18\u001b[39m                     np.uintc, np_long, np_ulong, np.longlong, np.ulonglong,\n\u001b[32m     19\u001b[39m                     np.float32, np.float64, np.longdouble,\n\u001b[32m     20\u001b[39m                     np.complex64, np.complex128, np.clongdouble]\n",
      "\u001b[36mFile \u001b[39m\u001b[32md:\\app\\miniconda\\envs\\dpl\\Lib\\site-packages\\scipy\\_lib\\_util.py:14\u001b[39m\n\u001b[32m     11\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtyping\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Literal, TypeAlias, TypeVar\n\u001b[32m     13\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnp\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m14\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mscipy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_lib\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_array_api\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (Array, array_namespace, is_lazy_array,\n\u001b[32m     15\u001b[39m                                    is_numpy, is_marray, xp_result_device,\n\u001b[32m     16\u001b[39m                                    xp_size, xp_result_type)\n\u001b[32m     17\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mscipy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_lib\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_docscrape\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m FunctionDoc, Parameter\n\u001b[32m     18\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mscipy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_lib\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_sparse\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m issparse\n",
      "\u001b[36mFile \u001b[39m\u001b[32md:\\app\\miniconda\\envs\\dpl\\Lib\\site-packages\\scipy\\_lib\\_array_api.py:25\u001b[39m\n\u001b[32m     22\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mtyping\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnpt\u001b[39;00m\n\u001b[32m     24\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mscipy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_lib\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m array_api_compat\n\u001b[32m---> \u001b[39m\u001b[32m25\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mscipy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_lib\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01marray_api_compat\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[32m     26\u001b[39m     is_array_api_obj,\n\u001b[32m     27\u001b[39m     is_lazy_array,\n\u001b[32m     28\u001b[39m     size \u001b[38;5;28;01mas\u001b[39;00m xp_size,\n\u001b[32m     29\u001b[39m     numpy \u001b[38;5;28;01mas\u001b[39;00m np_compat,\n\u001b[32m     30\u001b[39m     device \u001b[38;5;28;01mas\u001b[39;00m xp_device,\n\u001b[32m     31\u001b[39m     is_numpy_namespace \u001b[38;5;28;01mas\u001b[39;00m is_numpy,\n\u001b[32m     32\u001b[39m     is_cupy_namespace \u001b[38;5;28;01mas\u001b[39;00m is_cupy,\n\u001b[32m     33\u001b[39m     is_torch_namespace \u001b[38;5;28;01mas\u001b[39;00m is_torch,\n\u001b[32m     34\u001b[39m     is_jax_namespace \u001b[38;5;28;01mas\u001b[39;00m is_jax,\n\u001b[32m     35\u001b[39m     is_dask_namespace \u001b[38;5;28;01mas\u001b[39;00m is_dask,\n\u001b[32m     36\u001b[39m     is_array_api_strict_namespace \u001b[38;5;28;01mas\u001b[39;00m is_array_api_strict\n\u001b[32m     37\u001b[39m )\n\u001b[32m     38\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mscipy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_lib\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_sparse\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m issparse\n\u001b[32m     39\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mscipy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_lib\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_docscrape\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m FunctionDoc\n",
      "\u001b[36mFile \u001b[39m\u001b[32md:\\app\\miniconda\\envs\\dpl\\Lib\\site-packages\\scipy\\_lib\\array_api_compat\\numpy\\__init__.py:4\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;66;03m# ruff: noqa: PLC0414\u001b[39;00m\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtyping\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Final\n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m *  \u001b[38;5;66;03m# noqa: F403  # pyright: ignore[reportWildcardImportFromLibrary]\u001b[39;00m\n\u001b[32m      6\u001b[39m \u001b[38;5;66;03m# from numpy import * doesn't overwrite these builtin names\u001b[39;00m\n\u001b[32m      7\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;28mabs\u001b[39m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;28mabs\u001b[39m\n",
      "\u001b[36mFile \u001b[39m\u001b[32md:\\app\\miniconda\\envs\\dpl\\Lib\\site-packages\\numpy\\testing\\__init__.py:11\u001b[39m\n\u001b[32m      8\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01munittest\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m TestCase\n\u001b[32m     10\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _private\n\u001b[32m---> \u001b[39m\u001b[32m11\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_private\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mutils\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m *\n\u001b[32m     12\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_private\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mutils\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (_assert_valid_refcount, _gen_alignment_data)\n\u001b[32m     13\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_private\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m extbuild\n",
      "\u001b[36mFile \u001b[39m\u001b[32md:\\app\\miniconda\\envs\\dpl\\Lib\\site-packages\\numpy\\testing\\_private\\utils.py:465\u001b[39m\n\u001b[32m    461\u001b[39m         pprint.pprint(desired, msg)\n\u001b[32m    462\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAssertionError\u001b[39;00m(msg.getvalue())\n\u001b[32m--> \u001b[39m\u001b[32m465\u001b[39m \u001b[38;5;129m@np\u001b[39m\u001b[43m.\u001b[49m\u001b[43m_no_nep50_warning\u001b[49m()\n\u001b[32m    466\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34massert_almost_equal\u001b[39m(actual, desired, decimal=\u001b[32m7\u001b[39m, err_msg=\u001b[33m'\u001b[39m\u001b[33m'\u001b[39m, verbose=\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[32m    467\u001b[39m \u001b[38;5;250m    \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m    468\u001b[39m \u001b[33;03m    Raises an AssertionError if two items are not equal up to desired\u001b[39;00m\n\u001b[32m    469\u001b[39m \u001b[33;03m    precision.\u001b[39;00m\n\u001b[32m   (...)\u001b[39m\u001b[32m    533\u001b[39m \n\u001b[32m    534\u001b[39m \u001b[33;03m    \"\"\"\u001b[39;00m\n\u001b[32m    535\u001b[39m     __tracebackhide__ = \u001b[38;5;28;01mTrue\u001b[39;00m  \u001b[38;5;66;03m# Hide traceback for py.test\u001b[39;00m\n",
      "\u001b[36mFile \u001b[39m\u001b[32md:\\app\\miniconda\\envs\\dpl\\Lib\\site-packages\\numpy\\__init__.py:795\u001b[39m, in \u001b[36m__getattr__\u001b[39m\u001b[34m(attr)\u001b[39m\n\u001b[32m      0\u001b[39m <Error retrieving source code with stack_data see ipython/ipython#13598>\n",
      "\u001b[31mAttributeError\u001b[39m: module 'numpy' has no attribute '_no_nep50_warning'"
     ]
    }
   ],
   "source": [
    "from torch.utils.data import Dataset\n",
    "from sklearn.datasets import fetch_openml\n",
    "\n",
    "# Load data from https://www.openml.org/d/554\n",
    "X, y = fetch_openml('mnist_784', version=1, return_X_y=True)\n",
    "print(X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-22T05:33:18.684096Z",
     "start_time": "2021-03-22T05:33:18.680539Z"
    }
   },
   "outputs": [],
   "source": [
    "class SimpleDataset(Dataset):\n",
    "        \n",
    "    def __init__(self, X, y):\n",
    "        super(SimpleDataset, self).__init__()\n",
    "        self.X = X\n",
    "        self.y = y\n",
    "    \n",
    "    def __getitem__(self, index):\n",
    "        #This \"work\" could have gone in the constructor, but you should get into \n",
    "        inputs = torch.tensor(self.X[index,:], dtype=torch.float32)\n",
    "        targets = torch.tensor(int(self.y[index]), dtype=torch.int64)\n",
    "        return inputs, targets \n",
    "\n",
    "    def __len__(self):\n",
    "        return self.X.shape[0]\n",
    "#Now we can make a PyTorch dataset \n",
    "dataset = SimpleDataset(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-22T05:33:18.698359Z",
     "start_time": "2021-03-22T05:33:18.685197Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length:  70000\n",
      "Features:  torch.Size([784])\n",
      "Label of index 0:  tensor(5)\n"
     ]
    }
   ],
   "source": [
    "print(\"Length: \", len(dataset))\n",
    "example, label = dataset[0]\n",
    "print(\"Features: \", example.shape) #Will return 784\n",
    "print(\"Label of index 0: \", label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-22T05:33:18.934961Z",
     "start_time": "2021-03-22T05:33:18.700260Z"
    },
    "max_h": 0.3,
    "max_w": 0.9
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f3d87846150>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "image/png": 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gzetQb6dp5KPhk5I21f7O7fR7V+irLe8bp8sCSXAGHZAEYQeSIOxAEoQdSIKwA0kQdiAJwg4k8X+zhHFo7nUhhwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(example.reshape((28,28)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-22T05:33:18.943218Z",
     "start_time": "2021-03-22T05:33:18.936102Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56000 examples for training and 14000 for testing\n"
     ]
    }
   ],
   "source": [
    "train_size = int(len(dataset)*0.8)\n",
    "test_size = len(dataset)-train_size\n",
    "\n",
    "train_dataset, test_dataset = torch.utils.data.random_split(dataset, (train_size, test_size))\n",
    "print(\"{} examples for training and {} for testing\".format(len(train_dataset), len(test_dataset)))"
   ]
  }
 ],
 "metadata": {
  "author": "Why PyTorch?",
  "celltoolbar": "Tags",
  "kernelspec": {
   "display_name": "dpl",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.14"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": false,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "latex_metadata": {
   "title": "The Mechanics of Learning"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}