dev.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# enable autoreload\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "from TorchMetricLogger import TorchMetricLogger as TML\n",
    "from TorchMetricLogger import TmlF1, TmlHistogram\n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([12, 27, 22, 16, 24, 15, 14, 28, 18, 24]),\n",
       " array([0.00547985, 0.10447088, 0.2034619 , 0.30245293, 0.40144396,\n",
       "        0.50043499, 0.59942602, 0.69841704, 0.79740807, 0.8963991 ,\n",
       "        0.99539013]))"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "arr = np.random.rand(100, 2)\n",
    "\n",
    "plt.hist(arr)\n",
    "np.histogram(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mp-sodmann\u001b[0m (\u001b[33minexen\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "wandb version 0.15.2 is available!  To upgrade, please run:\n",
       " $ pip install wandb --upgrade"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Tracking run with wandb version 0.13.2"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Run data is saved locally in <code>/media/lux_t1/BigHDD/phil/tml/TorchMetricLogger/wandb/run-20230510_110637-3efyh05e</code>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Syncing run <strong><a href=\"https://wandb.ai/inexen/test_torchmetriclogger/runs/3efyh05e\" target=\"_blank\">crisp-music-8</a></strong> to <a href=\"https://wandb.ai/inexen/test_torchmetriclogger\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://wandb.me/run\" target=\"_blank\">docs</a>)<br/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<button onClick=\"this.nextSibling.style.display='block';this.style.display='none';\">Display W&B run</button><iframe src=\"https://wandb.ai/inexen/test_torchmetriclogger/runs/3efyh05e?jupyter=true\" style=\"border:none;width:100%;height:420px;display:none;\"></iframe>"
      ],
      "text/plain": [
       "<wandb.sdk.wandb_run.Run at 0x7fc5010068b0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import wandb\n",
    "\n",
    "wandb.init(project=\"test_torchmetriclogger\", entity=\"inexen\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reduce histogram\n"
     ]
    }
   ],
   "source": [
    "tml = TML(log_function=wandb.log)\n",
    "\n",
    "p_1d = torch.ones((100, 2))\n",
    "y_1d = torch.ones((100, 2))\n",
    "\n",
    "p_1d[:, 0] = torch.randn(100)\n",
    "y_1d[0:50, 0] = 0\n",
    "y_1d[0, 1] = 0\n",
    "\n",
    "def histogram_log_function(result_dict):\n",
    "    np_hist_scores = {key: np.histogram(result_dict[key], range=(0., 1.), bins=25) for key in result_dict.keys()}\n",
    "\n",
    "    return {\n",
    "        key: wandb.Histogram(np_histogram=np_hist_scores[key]) for key in result_dict.keys()\n",
    "    }\n",
    "\n",
    "tml(\n",
    "    miau=TmlHistogram(p_1d, y_1d, class_names=[\"0\", \"1\"], log_function=histogram_log_function),\n",
    ")\n",
    "\n",
    "result = tml.on_batch_end()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'miau_1_neg'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 0, 1, 0, 1, 0, 0, 0, 0, 0]),\n",
       " array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ],\n",
       "       dtype=float32))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_hist_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TmlHistogram(predictions=array([[0., 1.],\n",
       "       [0., 1.],\n",
       "       [0., 1.],\n",
       "       [0., 1.],\n",
       "       [0., 1.],\n",
       "       [1., 0.],\n",
       "       [1., 0.],\n",
       "       [1., 0.],\n",
       "       [1., 0.],\n",
       "       [1., 0.]], dtype=float32), gold_labels=array([[0., 0.],\n",
       "       [0., 1.],\n",
       "       [0., 1.],\n",
       "       [0., 1.],\n",
       "       [0., 1.],\n",
       "       [1., 1.],\n",
       "       [1., 1.],\n",
       "       [1., 1.],\n",
       "       [1., 1.],\n",
       "       [1., 1.]], dtype=float32), values=None, metric_class=None, class_names=['0', '1'], weights=None, is_metric=False)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tml.metrics[\"miau\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'TMLF1' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m/media/lux_t1/BigHDD/phil/tml/TorchMetricLogger/dev.ipynb Cell 9\u001b[0m in \u001b[0;36m<cell line: 6>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell:/media/lux_t1/BigHDD/phil/tml/TorchMetricLogger/dev.ipynb#W3sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m p_1d \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mones((\u001b[39m10\u001b[39m, \u001b[39m1\u001b[39m))\n\u001b[1;32m      <a href='vscode-notebook-cell:/media/lux_t1/BigHDD/phil/tml/TorchMetricLogger/dev.ipynb#W3sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m y_1d \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mones((\u001b[39m10\u001b[39m, \u001b[39m1\u001b[39m))\n\u001b[1;32m      <a href='vscode-notebook-cell:/media/lux_t1/BigHDD/phil/tml/TorchMetricLogger/dev.ipynb#W3sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m tml(\n\u001b[0;32m----> <a href='vscode-notebook-cell:/media/lux_t1/BigHDD/phil/tml/TorchMetricLogger/dev.ipynb#W3sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m     miau\u001b[39m=\u001b[39mTMLF1(p_1d, y_1d, class_names\u001b[39m=\u001b[39m[\u001b[39m\"\u001b[39m\u001b[39m0\u001b[39m\u001b[39m\"\u001b[39m]),\n\u001b[1;32m      <a href='vscode-notebook-cell:/media/lux_t1/BigHDD/phil/tml/TorchMetricLogger/dev.ipynb#W3sZmlsZQ%3D%3D?line=7'>8</a>\u001b[0m )\n\u001b[1;32m     <a href='vscode-notebook-cell:/media/lux_t1/BigHDD/phil/tml/TorchMetricLogger/dev.ipynb#W3sZmlsZQ%3D%3D?line=9'>10</a>\u001b[0m tml(\n\u001b[1;32m     <a href='vscode-notebook-cell:/media/lux_t1/BigHDD/phil/tml/TorchMetricLogger/dev.ipynb#W3sZmlsZQ%3D%3D?line=10'>11</a>\u001b[0m     miau\u001b[39m=\u001b[39mTMLF1(p_1d, y_1d, class_names\u001b[39m=\u001b[39m[\u001b[39m\"\u001b[39m\u001b[39m0\u001b[39m\u001b[39m\"\u001b[39m]),\n\u001b[1;32m     <a href='vscode-notebook-cell:/media/lux_t1/BigHDD/phil/tml/TorchMetricLogger/dev.ipynb#W3sZmlsZQ%3D%3D?line=11'>12</a>\u001b[0m )\n\u001b[1;32m     <a href='vscode-notebook-cell:/media/lux_t1/BigHDD/phil/tml/TorchMetricLogger/dev.ipynb#W3sZmlsZQ%3D%3D?line=13'>14</a>\u001b[0m tml(\n\u001b[1;32m     <a href='vscode-notebook-cell:/media/lux_t1/BigHDD/phil/tml/TorchMetricLogger/dev.ipynb#W3sZmlsZQ%3D%3D?line=14'>15</a>\u001b[0m     miau\u001b[39m=\u001b[39mTMLF1(p_1d, y_1d, class_names\u001b[39m=\u001b[39m[\u001b[39m\"\u001b[39m\u001b[39m0\u001b[39m\u001b[39m\"\u001b[39m]),\n\u001b[1;32m     <a href='vscode-notebook-cell:/media/lux_t1/BigHDD/phil/tml/TorchMetricLogger/dev.ipynb#W3sZmlsZQ%3D%3D?line=15'>16</a>\u001b[0m )\n",
      "\u001b[0;31mNameError\u001b[0m: name 'TMLF1' is not defined"
     ]
    }
   ],
   "source": [
    "tml = TML()\n",
    "\n",
    "p_1d = torch.ones((10, 1))\n",
    "y_1d = torch.ones((10, 1))\n",
    "\n",
    "tml(\n",
    "    miau=TMLF1(p_1d, y_1d, class_names=[\"0\"]),\n",
    ")\n",
    "\n",
    "tml(\n",
    "    miau=TMLF1(p_1d, y_1d, class_names=[\"0\"]),\n",
    ")\n",
    "\n",
    "tml(\n",
    "    miau=TMLF1(p_1d, y_1d, class_names=[\"0\"]),\n",
    ")\n",
    "\n",
    "result = tml.on_batch_end()\n",
    "\n",
    "assert(result[\"miau\"][\"micro\"] == 1.0)\n",
    "assert (result[\"miau\"][\"precision\"] == 1.0)\n",
    "assert (result[\"miau\"][\"recall\"] == 1.0)\n",
    "assert (result[\"miau\"][\"tp\"] == 30)\n",
    "assert (result[\"miau\"][\"fp\"] == 0)\n",
    "assert (result[\"miau\"][\"fn\"] == 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m/media/inexen/Extreme SSD/TML/TorchMetricLogger/dev.ipynb Cell 3\u001b[0m in \u001b[0;36m<cell line: 24>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     <a href='vscode-notebook-cell:/media/inexen/Extreme%20SSD/TML/TorchMetricLogger/dev.ipynb#W2sZmlsZQ%3D%3D?line=21'>22</a>\u001b[0m \u001b[39massert\u001b[39;00m (result[\u001b[39m\"\u001b[39m\u001b[39mmiau\u001b[39m\u001b[39m\"\u001b[39m][\u001b[39m\"\u001b[39m\u001b[39mprecision\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m==\u001b[39m \u001b[39m0.5\u001b[39m)\n\u001b[1;32m     <a href='vscode-notebook-cell:/media/inexen/Extreme%20SSD/TML/TorchMetricLogger/dev.ipynb#W2sZmlsZQ%3D%3D?line=22'>23</a>\u001b[0m \u001b[39massert\u001b[39;00m (result[\u001b[39m\"\u001b[39m\u001b[39mmiau\u001b[39m\u001b[39m\"\u001b[39m][\u001b[39m\"\u001b[39m\u001b[39mrecall\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m==\u001b[39m \u001b[39m1.0\u001b[39m)\n\u001b[0;32m---> <a href='vscode-notebook-cell:/media/inexen/Extreme%20SSD/TML/TorchMetricLogger/dev.ipynb#W2sZmlsZQ%3D%3D?line=23'>24</a>\u001b[0m \u001b[39massert\u001b[39;00m (result[\u001b[39m\"\u001b[39m\u001b[39mmiau\u001b[39m\u001b[39m\"\u001b[39m][\u001b[39m\"\u001b[39m\u001b[39mtp\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m==\u001b[39m \u001b[39m30\u001b[39m)\n\u001b[1;32m     <a href='vscode-notebook-cell:/media/inexen/Extreme%20SSD/TML/TorchMetricLogger/dev.ipynb#W2sZmlsZQ%3D%3D?line=24'>25</a>\u001b[0m \u001b[39massert\u001b[39;00m (result[\u001b[39m\"\u001b[39m\u001b[39mmiau\u001b[39m\u001b[39m\"\u001b[39m][\u001b[39m\"\u001b[39m\u001b[39mfp\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m)\n\u001b[1;32m     <a href='vscode-notebook-cell:/media/inexen/Extreme%20SSD/TML/TorchMetricLogger/dev.ipynb#W2sZmlsZQ%3D%3D?line=25'>26</a>\u001b[0m \u001b[39massert\u001b[39;00m (result[\u001b[39m\"\u001b[39m\u001b[39mmiau\u001b[39m\u001b[39m\"\u001b[39m][\u001b[39m\"\u001b[39m\u001b[39mfn\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m)\n",
      "\u001b[0;31mAssertionError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "tml = TML()\n",
    "\n",
    "p_1d = torch.ones((10, 1))\n",
    "y_1d = torch.ones((10, 1))\n",
    "y_1d[5:] = 0\n",
    "\n",
    "tml(\n",
    "    miau=TMLF1(p_1d, y_1d, class_names=[\"0\"]),\n",
    ")\n",
    "\n",
    "tml(\n",
    "    miau=TMLF1(p_1d, y_1d, class_names=[\"0\"]),\n",
    ")\n",
    "\n",
    "tml(\n",
    "    miau=TMLF1(p_1d, y_1d, class_names=[\"0\"]),\n",
    ")\n",
    "\n",
    "result = tml.on_batch_end()\n",
    "\n",
    "# assert (result[\"miau\"][\"micro\"] == 0.5)\n",
    "assert (result[\"miau\"][\"precision\"] == 0.5)\n",
    "assert (result[\"miau\"][\"recall\"] == 1.0)\n",
    "assert (result[\"miau\"][\"tp\"] == 15)\n",
    "assert (result[\"miau\"][\"fp\"] == 15)\n",
    "assert (result[\"miau\"][\"fn\"] == 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'miau': {'macro': 0.6666666666666666,\n",
       "  'precision': 0.5,\n",
       "  'recall': 1.0,\n",
       "  'micro': 0.6666666666666666,\n",
       "  'soft_micro': 0.6666666666666666,\n",
       "  'tp': 15,\n",
       "  'fp': 15,\n",
       "  'fn': 0},\n",
       " 'miau_0': {'macro': 0.6666666666666666,\n",
       "  'precision': 0.5,\n",
       "  'recall': 1.0,\n",
       "  'micro': 0.6666666666666666,\n",
       "  'soft_micro': 0.6666666666666666,\n",
       "  'tp': 15,\n",
       "  'fp': 15,\n",
       "  'fn': 0}}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(list, {'mean': [0.9462365591397646]})"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tml.metrics[\"miau\"].history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tml = TML()\n",
    "p_2d = torch.ones((10, 5))\n",
    "y_2d = torch.ones((10, 5))\n",
    "\n",
    "p_2d[0] = 0\n",
    "\n",
    "p_2d[3, 3] = 0\n",
    "\n",
    "w = torch.ones(10, 5)\n",
    "w[3,3] = 0\n",
    "\n",
    "tml(\n",
    "    miau=TmlMetric(p_2d, y_2d, class_names=[\"0\", \"1\", \"2\", \"3\", \"4\"], metric_class=TMLDiceCoefficient),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tml(\n",
    "    miau=TmlMetric(p_2d, y_2d, class_names=[\"0\", \"1\", \"2\", \"3\", \"4\"], metric_class=TMLDiceCoefficient),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[7]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.array([7]).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['miau', 'miau_0', 'miau_1', 'miau_2', 'miau_3', 'miau_4'])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tml.metrics.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(list,\n",
       "            {'metric': [0.5,\n",
       "              1.0,\n",
       "              1.0,\n",
       "              0.5,\n",
       "              1.0,\n",
       "              1.0,\n",
       "              1.0,\n",
       "              1.0,\n",
       "              1.0,\n",
       "              1.0,\n",
       "              0.5,\n",
       "              1.0,\n",
       "              1.0,\n",
       "              0.5,\n",
       "              1.0,\n",
       "              1.0,\n",
       "              1.0,\n",
       "              1.0,\n",
       "              1.0,\n",
       "              1.0],\n",
       "             'weights': [1.0,\n",
       "              1.0,\n",
       "              1.0,\n",
       "              1.0,\n",
       "              1.0,\n",
       "              1.0,\n",
       "              1.0,\n",
       "              1.0,\n",
       "              1.0,\n",
       "              1.0,\n",
       "              1.0,\n",
       "              1.0,\n",
       "              1.0,\n",
       "              1.0,\n",
       "              1.0,\n",
       "              1.0,\n",
       "              1.0,\n",
       "              1.0,\n",
       "              1.0,\n",
       "              1.0]})"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tml.metrics[\"miau_3\"].partial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean': 0.9066666647791862, 'median': 1.0, 'min': 0.16666667, 'max': 1.0}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tml.metrics[\"miau\"].reduce()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(list,\n",
       "            {'mean': [0.9066666647791862],\n",
       "             'median': [1.0],\n",
       "             'min': [0.16666667],\n",
       "             'max': [1.0]})"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tml.metrics[\"miau\"].history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'loss': {'mean': 1.0000000000000002, 'median': 1.0, 'min': 0.0, 'max': 2.0}}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from TorchMetricLogger import TorchMetricLogger as TML\n",
    "from TorchMetricLogger import TmlMetric, TmlMetricFunction, TMLBinaryAccuracy, TMLDiceCoefficient, TMLF1, TMLMean\n",
    "import torch\n",
    "import numpy as np\n",
    "\n",
    "# create a list of fake losses between 2 and 1\n",
    "loss = np.linspace(2.0, 0.0, num=50)\n",
    "\n",
    "tml = TML()\n",
    "\n",
    "# add them together as one batch\n",
    "for l in loss:\n",
    "    tml(loss=TmlMetric(None, l, TMLMean))\n",
    "\n",
    "tml.on_batch_end()\n",
    "\n",
    "# assert the average loss is 1\n",
    "#assert np.allclose(tml.metrics[\"loss\"].history, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(list, {})"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tml.metrics[\"loss\"].partial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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