interactive_losses.ipynb

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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b5dcc28",
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.express as px\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import torch\n",
    "import seaborn as sns\n",
    "from rindti.losses import GeneralisedLiftedStructureLoss, SoftNearestNeighborLoss\n",
    "from sklearn.manifold import TSNE\n",
    "\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "import numpy as np\n",
    "from ipywidgets import interact\n",
    "from sklearn.decomposition import PCA\n",
    "from umap import UMAP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5492b47",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compress(dist, scale):\n",
    "    centre = dist.mean(dim=0)\n",
    "    displacement = (centre - dist).norm(dim=1, keepdim=True)\n",
    "    return dist / (1 + displacement * scale)\n",
    "\n",
    "class Plotter:\n",
    "    def __init__(self, num=100, dims=2, loss=GeneralisedLiftedStructureLoss):\n",
    "        self.num = num\n",
    "        self.dims = dims\n",
    "        self.loss = loss\n",
    "        self.dist = torch.normal(0,1,size=(num, dims))\n",
    "\n",
    "    def get_data(self, distance, scale, *args, **kwargs):\n",
    "        a = compress(self.dist, scale) + torch.ones(self.dims) * distance\n",
    "        b = compress(self.dist, scale) - torch.ones(self.dims) * distance\n",
    "        data = torch.cat((a, b))\n",
    "        fam_idx = torch.tensor([0] * self.num + [3] * self.num).view(-1, 1)\n",
    "        loss = self.loss(*args, **kwargs)\n",
    "        losses = loss.forward(data, fam_idx).tolist()\n",
    "        if data.shape[1] > 2:\n",
    "            data = PCA(n_components=2).fit_transform(data)\n",
    "        else:\n",
    "            data = data.numpy()\n",
    "        data = pd.DataFrame(data, columns=[\"x\", \"y\"])\n",
    "        data[\"distance\"] = distance\n",
    "        data[\"scale\"] = scale\n",
    "        data['loss'] = losses\n",
    "        data['symbol'] = fam_idx.view(-1).tolist()\n",
    "        return data\n",
    "    \n",
    "    def get_loss(self, *args, **kwargs):\n",
    "        data = self.get_data(*args, **kwargs)\n",
    "        return data['loss'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c5d773f",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = go.FigureWidget()\n",
    "scatt = fig.add_trace(go.Scatter())\n",
    "fig.update_layout(\n",
    "                  width=1000,\n",
    "                  height=600,\n",
    "                 )\n",
    "fig.update_traces(marker=dict(size=12), mode=\"markers\")\n",
    "p = Plotter(num=100, dims=32, loss=SoftNearestNeighborLoss)\n",
    "@interact(distance=(0, 1, 0.1), scale=(0, 1.0, 0.1), temperature=(-3, 3, 1))\n",
    "def update(distance=0, scale=0, temperature=0, optim_temperature=False):\n",
    "    with fig.batch_update():\n",
    "        data = p.get_data(distance, scale, temperature=10**temperature, optim_temperature=optim_temperature)\n",
    "        scatt[\"data\"][0].x= data['x']\n",
    "        scatt[\"data\"][0].y= data['y']\n",
    "        scatt[\"data\"][0].marker.color = data['loss'] + 1\n",
    "        scatt[\"data\"][0].marker.symbol = data['symbol']\n",
    "        scatt[\"data\"][0].hovertext = data['loss']\n",
    "        fig['layout'].title = \"Loss = {}\".format(data['loss'].mean())\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76ea9901",
   "metadata": {},
   "outputs": [
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    "p = Plotter(dims=2, num=25, loss=SoftNearestNeighborLoss)\n",
    "t = []\n",
    "for distance in np.linspace(0,1,25):\n",
    "    for scale in np.linspace(0,1,25):\n",
    "        t.append((distance, scale, p.get_loss(distance, scale)))\n",
    "t = pd.DataFrame(t, columns=['distance', \"scale\", \"loss\"])\n",
    "\n",
    "fig = px.scatter_3d(data_frame=t, x=\"distance\", y=\"scale\", z=\"loss\", color=\"loss\", width=1000, height=1000)\n",
    "fig.update_coloraxes(showscale=False)\n",
    "fig.update_layout(title=\"How distance between 2 clusters and their std affect SNN Loss\")\n",
    "# fig.write_html(\"scale_dist_loss_2d.html\")\n",
    "fig.show()"
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   "execution_count": null,
   "id": "63e1ae77",
   "metadata": {},
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   "execution_count": null,
   "id": "514bb886",
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   "source": [
    "n = 256\n",
    "t = torch.normal(0,1, size=(n, 32))\n",
    "fam_idx = torch.tensor([0] * (n//2) + [1] * (n//2))\n",
    "\n",
    "loss = SoftNearestNeighborLoss(optim_temperature=False)\n",
    "\n",
    "%%profile\n",
    "loss(t, fam_idx)"
   ]
  },
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   "execution_count": null,
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        "encoding": "base64",
        "path": [
         "_data",
         0,
         "marker",
         "color",
         "value"
        ]
       },
       {
        "data": 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        "encoding": "base64",
        "path": [
         "_data",
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         "value"
        ]
       },
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          "\u001b[0;32m<ipython-input-3-b219d5380f8d>\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(distance, scale, temperature)\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0minteract\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdistance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemperature\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdistance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemperature\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0;32mwith\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch_update\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdistance\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mtemperature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m         \u001b[0mscatt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"data\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'x'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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