Hill-Valley 0.5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "from torch.nn import functional as F\n",
    "from torch import optim\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from stochastic_rnn import StochasticLSTM\n",
    "from variational_module import MCSingleMixin\n",
    "from train_utils import train_model, BCLogitMetric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import minmax_scale\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import roc_auc_score, precision_score, accuracy_score, recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from classification_uncertainty_utils import aleatoric_uncertainty, epistemic_uncertainty, uncertainty_avg, score_avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x7f812a146c30>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(1234)\n",
    "torch.manual_seed(1234)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_table = pd.read_table(\"./data/hill-valley/Hill_Valley_with_noise_Training.data\", sep=',', dtype=np.float32)\n",
    "testing_table = pd.read_table(\"./data/hill-valley/Hill_Valley_with_noise_Testing.data\", sep=',', dtype=np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "dim = 1\n",
    "seq_length = 100\n",
    "DROP = 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = minmax_scale(training_table.drop(\"class\", axis=1).values, axis=1)\n",
    "y_train = training_table[\"class\"].values\n",
    "x_test = minmax_scale(testing_table.drop(\"class\", axis=1).values, axis=1)\n",
    "y_test = testing_table[\"class\"].values\n",
    "\n",
    "x_train = np.expand_dims(x_train, axis=-1)\n",
    "x_test = np.expand_dims(x_test, axis=-1)\n",
    "\n",
    "x_train, x_val, y_train, y_val = train_test_split(x_train, y_train, test_size=0.3, random_state=42)\n",
    "\n",
    "N = len(y_train)\n",
    "\n",
    "train_dl = torch.utils.data.DataLoader(\n",
    "        [(x_train[i], y_train[i]) for i in range(len(y_train))],\n",
    "        batch_size=10,\n",
    "        shuffle=True\n",
    ")\n",
    "val_dl = torch.utils.data.DataLoader(\n",
    "        [(x_val[i], y_val[i]) for i in range(len(y_val))],\n",
    "        batch_size=10,\n",
    "        shuffle=False\n",
    ")\n",
    "test_dl = torch.utils.data.DataLoader(\n",
    "        [(x_test[i], y_test[i]) for i in range(len(y_test))],\n",
    "        batch_size=len(y_test),\n",
    "        shuffle=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Net(nn.Module, MCSingleMixin):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        self.rnn = StochasticLSTM(1, 10, DROP)\n",
    "        self.fc = nn.Linear(10, 1)\n",
    "\n",
    "    def regularizer(self):        \n",
    "        # Weight and bias regularizer\n",
    "        weight_sum, bias_sum, dropout_reg = self.rnn.regularizer()\n",
    "        \n",
    "        return weight_sum + bias_sum + dropout_reg\n",
    "    \n",
    "    def get_output_shape(self, x: torch.Tensor):\n",
    "        B = x.shape[1]\n",
    "        return (B,)\n",
    "\n",
    "    def forward(self, x: torch.Tensor):\n",
    "        out, _ = self.rnn(x)\n",
    "        out = out[-1]\n",
    "        out = torch.relu(out)\n",
    "        out = self.fc(out).flatten()\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "net = Net()\n",
    "criterion = nn.BCEWithLogitsLoss()\n",
    "\n",
    "optimizer = optim.Adam(net.parameters(), lr=1e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics = [BCLogitMetric()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 100.00%] train loss: 0.698799\n",
      "[1, 100.00%] validate loss: 0.711887\n",
      "Accuracy = 0.533\n",
      "[2, 100.00%] train loss: 0.682905\n",
      "[2, 100.00%] validate loss: 0.688852\n",
      "Accuracy = 0.712\n",
      "[3, 100.00%] train loss: 0.633740\n",
      "[3, 100.00%] validate loss: 0.619421\n",
      "Accuracy = 0.771\n",
      "[4, 100.00%] train loss: 0.548805\n",
      "[4, 100.00%] validate loss: 0.537875\n",
      "Accuracy = 0.816\n",
      "[5, 100.00%] train loss: 0.481695\n",
      "[5, 100.00%] validate loss: 0.460680\n",
      "Accuracy = 0.845\n",
      "[6, 100.00%] train loss: 0.415644\n",
      "[6, 100.00%] validate loss: 0.404528\n",
      "Accuracy = 0.864\n",
      "[7, 100.00%] train loss: 0.380502\n",
      "[7, 100.00%] validate loss: 0.366852\n",
      "Accuracy = 0.877\n",
      "[8, 100.00%] train loss: 0.341207\n",
      "[8, 100.00%] validate loss: 0.315712\n",
      "Accuracy = 0.891\n",
      "[9, 100.00%] train loss: 0.272960\n",
      "[9, 100.00%] validate loss: 0.289936\n",
      "Accuracy = 0.900\n",
      "[10, 100.00%] train loss: 0.252959\n",
      "[10, 100.00%] validate loss: 0.258560\n",
      "Accuracy = 0.908\n",
      "[11, 100.00%] train loss: 0.233153\n",
      "[11, 100.00%] validate loss: 0.239731\n",
      "Accuracy = 0.915\n",
      "[12, 100.00%] train loss: 0.234611\n",
      "[12, 100.00%] validate loss: 0.231398\n",
      "Accuracy = 0.919\n",
      "[13, 100.00%] train loss: 0.207900\n",
      "[13, 100.00%] validate loss: 0.183715\n",
      "Accuracy = 0.925\n",
      "[14, 100.00%] train loss: 0.188711\n",
      "[14, 100.00%] validate loss: 0.182985\n",
      "Accuracy = 0.930\n",
      "[15, 100.00%] train loss: 0.174805\n",
      "[15, 100.00%] validate loss: 0.168486\n",
      "Accuracy = 0.933\n",
      "[16, 100.00%] train loss: 0.150754\n",
      "[16, 100.00%] validate loss: 0.168721\n",
      "Accuracy = 0.936\n",
      "Retry 1/5\n",
      "[17, 100.00%] train loss: 0.136307\n",
      "[17, 100.00%] validate loss: 0.162329\n",
      "Accuracy = 0.939\n",
      "[18, 100.00%] train loss: 0.124022\n",
      "[18, 100.00%] validate loss: 0.159111\n",
      "Accuracy = 0.941\n",
      "[19, 100.00%] train loss: 0.131994\n",
      "[19, 100.00%] validate loss: 0.120479\n",
      "Accuracy = 0.944\n",
      "[20, 100.00%] train loss: 0.122889\n",
      "[20, 100.00%] validate loss: 0.108349\n",
      "Accuracy = 0.946\n",
      "[21, 100.00%] train loss: 0.129059\n",
      "[21, 100.00%] validate loss: 0.106996\n",
      "Accuracy = 0.948\n",
      "[22, 100.00%] train loss: 0.115657\n",
      "[22, 100.00%] validate loss: 0.114976\n",
      "Accuracy = 0.950\n",
      "Retry 1/5\n",
      "[23, 100.00%] train loss: 0.094206\n",
      "[23, 100.00%] validate loss: 0.093340\n",
      "Accuracy = 0.952\n",
      "[24, 100.00%] train loss: 0.116503\n",
      "[24, 100.00%] validate loss: 0.092324\n",
      "Accuracy = 0.954\n",
      "[25, 100.00%] train loss: 0.094530\n",
      "[25, 100.00%] validate loss: 0.089465\n",
      "Accuracy = 0.956\n",
      "[26, 100.00%] train loss: 0.108885\n",
      "[26, 100.00%] validate loss: 0.081190\n",
      "Accuracy = 0.957\n",
      "[27, 100.00%] train loss: 0.081926\n",
      "[27, 100.00%] validate loss: 0.080634\n",
      "Accuracy = 0.958\n",
      "[28, 100.00%] train loss: 0.085784\n",
      "[28, 100.00%] validate loss: 0.066940\n",
      "Accuracy = 0.960\n",
      "[29, 100.00%] train loss: 0.071026\n",
      "[29, 100.00%] validate loss: 0.072586\n",
      "Accuracy = 0.961\n",
      "Retry 1/5\n",
      "[30, 100.00%] train loss: 0.065672\n",
      "[30, 100.00%] validate loss: 0.085894\n",
      "Accuracy = 0.962\n",
      "Retry 2/5\n",
      "[31, 100.00%] train loss: 0.074114\n",
      "[31, 100.00%] validate loss: 0.096009\n",
      "Accuracy = 0.963\n",
      "Retry 3/5\n",
      "[32, 100.00%] train loss: 0.052317\n",
      "[32, 100.00%] validate loss: 0.067808\n",
      "Accuracy = 0.964\n",
      "Retry 4/5\n",
      "[33, 100.00%] train loss: 0.089013\n",
      "[33, 100.00%] validate loss: 0.078600\n",
      "Accuracy = 0.964\n",
      "Retry 5/5\n",
      "CPU times: user 3min 27s, sys: 2.1 s, total: 3min 29s\n",
      "Wall time: 3min 55s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "net = train_model(net, train_dl, val_dl, criterion, optimizer, \"hill-valley-0.5.pt\", 100, metrics=metrics, patience=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 1.00\n",
      "Precision = 1.00\n",
      "AUCROC = 1.00\n"
     ]
    }
   ],
   "source": [
    "net.eval()\n",
    "with torch.no_grad():\n",
    "    for i, (x, y) in enumerate(test_dl):\n",
    "        pre_sigmoids = net.sample(10, x.transpose(-2,-3))\n",
    "        scores = torch.sigmoid(pre_sigmoids)\n",
    "        score_average = score_avg(scores)\n",
    "        predictions = torch.round(score_average)\n",
    "        uncertainties = uncertainty_avg(scores)\n",
    "        print(\"Accuracy = %.2f\" % accuracy_score(y, predictions))\n",
    "        print(\"Precision = %.2f\" % precision_score(y, predictions))\n",
    "        print(\"AUCROC = %.2f\" % roc_auc_score(y, score_average))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = aleatoric_uncertainty(scores)\n",
    "E = epistemic_uncertainty(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f812e29d7b8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dist = torch.abs(y - score_average)\n",
    "sns.scatterplot(x=dist, y=uncertainties)\n",
    "sns.scatterplot(x=dist, y=A)\n",
    "sns.scatterplot(x=dist, y=E)\n",
    "plt.xlabel(\"diff\")\n",
    "plt.ylabel(\"uncertainty\")\n",
    "plt.legend(labels=['uncertainty', 'aleatoric', 'epistemic'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Spread pre and post sigmoid analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "T, B = pre_sigmoids.shape\n",
    "pre_sigmoid_df = pd.DataFrame(columns=[\"entry\",\"value\"])\n",
    "post_sigmoid_df = pd.DataFrame(columns=[\"entry\",\"value\"])\n",
    "for i in range(T):\n",
    "    for j in range(20):\n",
    "        pre_sigmoid_df.loc[i*B + j] = (j+1, pre_sigmoids[i,j])\n",
    "        post_sigmoid_df.loc[i*B + j] = (j+1, scores[i,j])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = sns.catplot(x=\"entry\", y=\"value\", data=pre_sigmoid_df)\n",
    "plot.fig.set_figwidth(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = sns.catplot(x=\"entry\", y=\"value\", data=post_sigmoid_df)\n",
    "plot.fig.set_figwidth(12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Flat line test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predictions tensor([0.9308, 0.9284])\n",
      "uncertainties tensor([0.0644, 0.0665])\n"
     ]
    }
   ],
   "source": [
    "new_x_test = minmax_scale(np.full((2, 100), 0., dtype=np.float32), axis=1)\n",
    "new_x_test = np.expand_dims(new_x_test, axis=-1)\n",
    "new_x_test = new_x_test.swapaxes(-2,-3)\n",
    "new_x_test = torch.tensor(new_x_test)\n",
    "\n",
    "net.eval()\n",
    "with torch.no_grad():\n",
    "    outputs = net.sample(10, new_x_test)\n",
    "    outputs = torch.sigmoid(outputs)\n",
    "    uncertainties = uncertainty_avg(outputs)\n",
    "    scores = score_avg(outputs)\n",
    "\n",
    "plt.plot(new_x_test[:,0])\n",
    "plt.show()\n",
    "print(\"predictions\", scores)\n",
    "print(\"uncertainties\", uncertainties)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Noise test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predictions tensor([0.5574, 0.6510])\n",
      "uncertainties tensor([0.2467, 0.2272])\n"
     ]
    }
   ],
   "source": [
    "new_x_test = minmax_scale(np.random.randn(2, 100).astype(np.float32), axis=1)\n",
    "new_x_test = np.expand_dims(new_x_test, axis=-1)\n",
    "new_x_test = new_x_test.swapaxes(-2,-3)\n",
    "new_x_test = torch.tensor(new_x_test)\n",
    "\n",
    "net.eval()\n",
    "with torch.no_grad():\n",
    "    outputs = net.sample(10, new_x_test)\n",
    "    outputs = torch.sigmoid(outputs)\n",
    "    uncertainties = uncertainty_avg(outputs)\n",
    "    scores = score_avg(outputs)\n",
    "\n",
    "plt.plot(new_x_test[:,0])\n",
    "plt.show()\n",
    "print(\"predictions\", scores)\n",
    "print(\"uncertainties\", uncertainties)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Note\n",
    "In binary classification, the maximum uncertainty is 0.25 (need to verify this), and the minimum is 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}