BNN_new.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.insert(0, './new_src')\n",
    "\n",
    "from varfamily import IAF, MeanField\n",
    "from data import Dataset\n",
    "from layers import BLinear\n",
    "from trainer import train_vi_regression_kl_explicit\n",
    "from priors import StandardNormal\n",
    "\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib widget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = 'cuda:1'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nkotelevskii/anaconda3/envs/condatorch/lib/python3.7/site-packages/alpaca/dataloader/boston_housing.py:37: FutureWarning: read_table is deprecated, use read_csv instead, passing sep='\\t'.\n",
      "  self.df = pd.read_table(data_path, names=self.column_names, header=None, delim_whitespace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape 404\n"
     ]
    }
   ],
   "source": [
    "dataset = Dataset('boston_housing', device, train_batch_size=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class BNN(nn.Module):\n",
    "    def __init__(self, in_features, last_features=10, bias=True, vf_class=MeanField, **kwargs):\n",
    "        super().__init__()\n",
    "\n",
    "        self.layer1 = BLinear(in_features, 4*in_features, bias=True, vf_class=vf_class, **kwargs)\n",
    "        self.layer2 = BLinear(4*in_features, last_features, bias=True, vf_class=vf_class, **kwargs)\n",
    "        self.layer3 = BLinear(last_features, 1, bias=True, vf_class=vf_class, **kwargs)\n",
    "\n",
    "    def log_prob(self):\n",
    "        return self.layer1.log_prob() + self.layer2.log_prob() + self.layer3.log_prob()\n",
    "\n",
    "    def forward(self, input, sample=True):\n",
    "        h = F.leaky_relu(self.layer1(input, sample=sample))\n",
    "        h = F.leaky_relu(self.layer2(h, sample=sample))\n",
    "        h = self.layer3(h, sample=sample)\n",
    "        return h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nkotelevskii/anaconda3/envs/condatorch/lib/python3.7/site-packages/pyro/nn/auto_reg_nn.py:163: UserWarning: ConditionalAutoRegressiveNN input_dim = 1. Consider using an affine transformation instead.\n",
      "  warnings.warn('ConditionalAutoRegressiveNN input_dim = 1. Consider using an affine transformation instead.')\n"
     ]
    }
   ],
   "source": [
    "model = BNN(in_features=dataset.in_features[0], vf_class=IAF, t=1, train_initial=False).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "layer1.vf.weight_mu\n",
      "layer1.vf.weight_rho\n",
      "layer1.vf.bias_mu\n",
      "layer1.vf.bias_rho\n",
      "layer1.vf.transform_w.0.arn.layers.0.weight\n",
      "layer1.vf.transform_w.0.arn.layers.0.bias\n",
      "layer1.vf.transform_w.0.arn.layers.1.weight\n",
      "layer1.vf.transform_w.0.arn.layers.1.bias\n",
      "layer1.vf.transform_b.0.arn.layers.0.weight\n",
      "layer1.vf.transform_b.0.arn.layers.0.bias\n",
      "layer1.vf.transform_b.0.arn.layers.1.weight\n",
      "layer1.vf.transform_b.0.arn.layers.1.bias\n",
      "layer2.vf.weight_mu\n",
      "layer2.vf.weight_rho\n",
      "layer2.vf.bias_mu\n",
      "layer2.vf.bias_rho\n",
      "layer2.vf.transform_w.0.arn.layers.0.weight\n",
      "layer2.vf.transform_w.0.arn.layers.0.bias\n",
      "layer2.vf.transform_w.0.arn.layers.1.weight\n",
      "layer2.vf.transform_w.0.arn.layers.1.bias\n",
      "layer2.vf.transform_b.0.arn.layers.0.weight\n",
      "layer2.vf.transform_b.0.arn.layers.0.bias\n",
      "layer2.vf.transform_b.0.arn.layers.1.weight\n",
      "layer2.vf.transform_b.0.arn.layers.1.bias\n",
      "layer3.vf.weight_mu\n",
      "layer3.vf.weight_rho\n",
      "layer3.vf.bias_mu\n",
      "layer3.vf.bias_rho\n",
      "layer3.vf.transform_w.0.arn.layers.0.weight\n",
      "layer3.vf.transform_w.0.arn.layers.0.bias\n",
      "layer3.vf.transform_w.0.arn.layers.1.weight\n",
      "layer3.vf.transform_w.0.arn.layers.1.bias\n",
      "layer3.vf.transform_b.0.arn.layers.0.weight\n",
      "layer3.vf.transform_b.0.arn.layers.0.bias\n",
      "layer3.vf.transform_b.0.arn.layers.1.weight\n",
      "layer3.vf.transform_b.0.arn.layers.1.bias\n"
     ]
    }
   ],
   "source": [
    "for p in model.named_parameters():\n",
    "    print(p[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "prior = StandardNormal()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
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      "  1%|          | 101/10000 [00:07<12:36, 13.08it/s]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 100 | ELBO -403997.125 | LL -637.0092163085938 | KL 2016800.625\n",
      "MSE 15322.578125\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 200 | ELBO -570389.0 | LL -166880.28125 | KL 2017543.75\n",
      "MSE 8352.818359375\n"
     ]
    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 300 | ELBO -414698.375 | LL -11290.7392578125 | KL 2017038.125\n",
      "MSE 4937.85498046875\n"
     ]
    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 400 | ELBO -447860.5 | LL -44525.078125 | KL 2016677.125\n",
      "MSE 40116.42578125\n"
     ]
    },
    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 500 | ELBO -599479.5625 | LL -196032.875 | KL 2017233.375\n",
      "MSE 5197.05224609375\n"
     ]
    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 600 | ELBO -499444.65625 | LL -95879.5625 | KL 2017825.5\n",
      "MSE 32557.81640625\n"
     ]
    },
    {
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     "output_type": "stream",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 700 | ELBO -415087.3125 | LL -11762.5244140625 | KL 2016623.875\n",
      "MSE 28765.673828125\n"
     ]
    },
    {
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     "output_type": "stream",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 800 | ELBO -409888.46875 | LL -6552.1455078125 | KL 2016681.5\n",
      "MSE 10828.3154296875\n"
     ]
    },
    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 900 | ELBO -406896.59375 | LL -3599.541015625 | KL 2016485.25\n",
      "MSE 946.1932373046875\n"
     ]
    },
    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 1000 | ELBO -406471.46875 | LL -3579.86865234375 | KL 2014457.875\n",
      "MSE 3212.802490234375\n"
     ]
    },
    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 1100 | ELBO -403128.75 | LL -164.39654541015625 | KL 2014821.625\n",
      "MSE 605.4736328125\n"
     ]
    },
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    },
    {
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     "text": [
      "Epoch 1200 | ELBO -404012.0625 | LL -1270.51611328125 | KL 2013707.625\n",
      "MSE 1399.44189453125\n"
     ]
    },
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 1300 | ELBO -411294.75 | LL -9283.52734375 | KL 2010056.125\n",
      "MSE 2573.38720703125\n"
     ]
    },
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    {
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     "text": [
      "Epoch 1400 | ELBO -398177.6875 | LL -319.2717590332031 | KL 1989292.0\n",
      "MSE 1119.8958740234375\n"
     ]
    },
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 1500 | ELBO -374898.75 | LL -434.8957824707031 | KL 1872319.25\n",
      "MSE 2086.26806640625\n"
     ]
    },
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 1600 | ELBO -319912.90625 | LL -29032.3515625 | KL 1454402.75\n",
      "MSE 4989.212890625\n"
     ]
    },
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 1700 | ELBO 4778116.5 | LL -314.92767333984375 | KL -23892158.0\n",
      "MSE 272.9710998535156\n"
     ]
    },
    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 1800 | ELBO 182209392.0 | LL -1603684.5 | KL -919065344.0\n",
      "MSE 88694.2421875\n"
     ]
    },
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    {
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     "text": [
      "Epoch 1900 | ELBO 281650016.0 | LL -23777.3125 | KL -1408368896.0\n",
      "MSE 47542.65625\n"
     ]
    },
    {
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      " 20%|██        | 2001/10000 [02:28<10:15, 12.99it/s]"
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 2000 | ELBO 4128868864.0 | LL -87390.875 | KL -20644780032.0\n",
      "MSE 145496.15625\n"
     ]
    },
    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 2100 | ELBO 23390654464.0 | LL -770724.5 | KL -116957118464.0\n",
      "MSE 197943.9375\n"
     ]
    },
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 2200 | ELBO 86576463872.0 | LL -569037.375 | KL -432885137408.0\n",
      "MSE 647043.3125\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 2300 | ELBO 180369784832.0 | LL -9302873.0 | KL -901895421952.0\n",
      "MSE 2129300.25\n"
     ]
    },
    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 2400 | ELBO 543320604672.0 | LL -9603784.0 | KL -2716650962944.0\n",
      "MSE 9542110.0\n"
     ]
    },
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 2500 | ELBO 590882537472.0 | LL -24160204.0 | KL -2954533535744.0\n",
      "MSE 5307645.0\n"
     ]
    },
    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 2600 | ELBO 909593739264.0 | LL -1725090.0 | KL -4547977150464.0\n",
      "MSE 5924958.0\n"
     ]
    },
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 2700 | ELBO 1714371821568.0 | LL -9319020.0 | KL -8571905376256.0\n",
      "MSE 35281540.0\n"
     ]
    },
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 2800 | ELBO 3099517255680.0 | LL -86496496.0 | KL -15498018291712.0\n",
      "MSE 26336736.0\n"
     ]
    },
    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 2900 | ELBO 5013609381888.0 | LL -516318560.0 | KL -25070627979264.0\n",
      "MSE 25097342.0\n"
     ]
    },
    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 3000 | ELBO 5709722812416.0 | LL -30702012.0 | KL -28548769251328.0\n",
      "MSE 34652336.0\n"
     ]
    },
    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 3100 | ELBO 7219982106624.0 | LL -30783198.0 | KL -36100064149504.0\n",
      "MSE 150422368.0\n"
     ]
    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3200 | ELBO 10954634952704.0 | LL -18846336.0 | KL -54773269135360.0\n",
      "MSE 192315920.0\n"
     ]
    },
    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 3300 | ELBO 10483741491200.0 | LL -1695614848.0 | KL -52427185192960.0\n",
      "MSE 409956832.0\n"
     ]
    },
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 3400 | ELBO 11197142269952.0 | LL -76146688.0 | KL -55986094080000.0\n",
      "MSE 251240000.0\n"
     ]
    },
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 3500 | ELBO 28510687068160.0 | LL -373200352.0 | KL -142555299708928.0\n",
      "MSE 75473032.0\n"
     ]
    },
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      "Epoch 3600 | ELBO 18414187839488.0 | LL -800186.375 | KL -92070937100288.0\n",
      "MSE 237702928.0\n"
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      "Epoch 3700 | ELBO 14294970269696.0 | LL -541733696.0 | KL -71477558771712.0\n",
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      "Epoch 3800 | ELBO 37175081041920.0 | LL -22397144.0 | KL -185875514261504.0\n",
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      "Epoch 3900 | ELBO 20433967513600.0 | LL -173891120.0 | KL -102170703691776.0\n",
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      "Epoch 4000 | ELBO 56769946583040.0 | LL -493483040.0 | KL -283852207554560.0\n",
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      "Epoch 4100 | ELBO 73839908225024.0 | LL -205962656.0 | KL -369200589701120.0\n",
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      "Epoch 4200 | ELBO 29276105605120.0 | LL -1175839232.0 | KL -146386410536960.0\n",
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      "Epoch 4300 | ELBO 69031256129536.0 | LL -477612640.0 | KL -345158671400960.0\n",
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      "Epoch 4400 | ELBO 137403486437376.0 | LL -1497466880.0 | KL -687024914825216.0\n",
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      "Epoch 4600 | ELBO 133586585911296.0 | LL -26114736.0 | KL -667933046996992.0\n",
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      "Epoch 4700 | ELBO 86512494444544.0 | LL -557548288.0 | KL -432565215297536.0\n",
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      "Epoch 4800 | ELBO 134210346024960.0 | LL -617881280.0 | KL -671054817132544.0\n",
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     "output_type": "stream",
     "text": [
      "Epoch 8400 | ELBO 2239832289968128.0 | LL -12774678528.0 | KL -1.1199224934825984e+16\n",
      "MSE 6899598848.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 85%|████████▌ | 8501/10000 [10:33<01:55, 12.95it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8500 | ELBO 1908598103867392.0 | LL -1587509248.0 | KL -9542998572400640.0\n",
      "MSE 1502988928.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 86%|████████▌ | 8601/10000 [10:41<01:48, 12.89it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8600 | ELBO 2267599119319040.0 | LL -9150151680.0 | KL -1.1338041499058176e+16\n",
      "MSE 4111524096.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 87%|████████▋ | 8701/10000 [10:48<01:40, 12.90it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8700 | ELBO 2851762184650752.0 | LL -4967967744.0 | KL -1.4258836156186624e+16\n",
      "MSE 10977541120.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 88%|████████▊ | 8801/10000 [10:56<01:33, 12.88it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8800 | ELBO 2484927685722112.0 | LL -28754464768.0 | KL -1.242478204157952e+16\n",
      "MSE 4884555776.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 89%|████████▉ | 8901/10000 [11:03<01:23, 13.09it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8900 | ELBO 2327517134323712.0 | LL -5578785280.0 | KL -1.1637613320470528e+16\n",
      "MSE 4145645568.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 90%|█████████ | 9001/10000 [11:11<01:16, 13.10it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9000 | ELBO 2823997100130304.0 | LL -3115934208.0 | KL -1.4120001338343424e+16\n",
      "MSE 115597803520.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 91%|█████████ | 9101/10000 [11:18<01:09, 12.95it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9100 | ELBO 4170731877302272.0 | LL -11356545024.0 | KL -2.0853715489521664e+16\n",
      "MSE 2604446777344.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 92%|█████████▏| 9201/10000 [11:26<01:01, 12.95it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9200 | ELBO 2574620024635392.0 | LL -54597816320.0 | KL -1.2873372048293888e+16\n",
      "MSE 3465740544.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 93%|█████████▎| 9301/10000 [11:33<00:54, 12.90it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9300 | ELBO 2796351200952320.0 | LL -16359976960.0 | KL -1.3981837609140224e+16\n",
      "MSE 4320216064.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 94%|█████████▍| 9401/10000 [11:41<00:46, 12.91it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9400 | ELBO 2169634002305024.0 | LL -2000642816.0 | KL -1.084818007785472e+16\n",
      "MSE 3087990016.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 95%|█████████▌| 9501/10000 [11:48<00:38, 12.90it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9500 | ELBO 1987634494701568.0 | LL -24016693248.0 | KL -9938292329938944.0\n",
      "MSE 6936428544.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 96%|█████████▌| 9601/10000 [11:56<00:30, 12.93it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9600 | ELBO 2152224620806144.0 | LL -22166224896.0 | KL -1.076123383365632e+16\n",
      "MSE 40243511296.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 97%|█████████▋| 9701/10000 [12:04<00:23, 12.98it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9700 | ELBO 3319039594070016.0 | LL -3689137152.0 | KL -1.6595216760832e+16\n",
      "MSE 122773037056.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 98%|█████████▊| 9801/10000 [12:11<00:15, 12.93it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9800 | ELBO 2955783339769856.0 | LL -79580184576.0 | KL -1.4779313178017792e+16\n",
      "MSE 5755292352512.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 99%|█████████▉| 9901/10000 [12:19<00:07, 13.15it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9900 | ELBO 2023208500854784.0 | LL -7692854784.0 | KL -1.0116080353673216e+16\n",
      "MSE 927534284800.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [12:26<00:00, 13.40it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10000 | ELBO 4719391132352512.0 | LL -10335716352.0 | KL -2.3597005590757376e+16\n",
      "MSE 4221171924992.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "train_vi_regression_kl_explicit(model, prior, dataset, device, num_epochs=10000, report_freq=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_pred_to_true(model, dataset, name=''):\n",
    "    '''\n",
    "    The function plots prediction values (with predicted variance) versus true ones\n",
    "    '''\n",
    "    torchType = torch.float32\n",
    "    n_IS = 1000\n",
    "    \n",
    "    examples = torch.tensor([], device=device, dtype=torchType)\n",
    "    true_ans = torch.tensor([], device=device, dtype=torchType)\n",
    "    pred_ans = torch.tensor([], device=device, dtype=torchType)\n",
    "    pred_std = torch.tensor([], device=device, dtype=torchType)\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        for val_batch, val_label in tqdm(dataset.val_dataloader):\n",
    "            pred_matrix = torch.empty((n_IS, val_batch.shape[0]), device=device, dtype=torchType)\n",
    "            for i in range(n_IS):\n",
    "                preds = model(val_batch)\n",
    "                pred_matrix[i, :] = preds.view(-1)\n",
    "            means = pred_matrix.mean(0)\n",
    "            stds = pred_matrix.std(0)\n",
    "            examples = torch.cat([examples, val_batch], dim=0)\n",
    "            true_ans = torch.cat([true_ans, val_label.squeeze()], dim=0)\n",
    "            pred_ans = torch.cat([pred_ans, means], dim=0)\n",
    "            pred_std = torch.cat([pred_std, stds], dim=0)\n",
    "        idx = torch.argsort(true_ans)\n",
    "        examples = examples[idx].cpu().numpy()\n",
    "        true_ans = true_ans[idx].cpu().numpy()\n",
    "        pred_ans = pred_ans[idx].cpu().numpy()\n",
    "        pred_std = pred_std[idx].cpu().numpy()\n",
    "        \n",
    "        plt.figure(figsize=(15, 8), dpi=300)\n",
    "        plt.title(name)\n",
    "        plt.fill_between(x=true_ans, y1=pred_ans+2*pred_std, y2=pred_ans-2*pred_std, alpha=0.5, label='std', )\n",
    "        plt.plot(true_ans, pred_ans, '-..')\n",
    "        plt.xlabel('y_true')\n",
    "        plt.ylabel('y_pred')\n",
    "        plt.axis('equal')\n",
    "        plt.xlim(true_ans.min(), true_ans.max())\n",
    "        plt.ylim(true_ans.min(), true_ans.max())\n",
    "        plt.xticks(ticks=np.linspace(true_ans.min(), true_ans.max(), 10))\n",
    "        plt.yticks(ticks=np.linspace(true_ans.min(), true_ans.max(), 10))\n",
    "        plt.plot([true_ans.min(), true_ans.max()], [true_ans.min(), true_ans.max()], linewidth=6, c='r', label='perfect fit')\n",
    "        plt.legend()\n",
    "        plt.tight_layout()\n",
    "#         plt.savefig(f'./figs/{name}.png', format='png')\n",
    "        plt.show();\n",
    "        return examples, true_ans, pred_ans, pred_std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 4/4 [00:08<00:00,  2.22s/it]\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 4500x2400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(array([[6.79208e+01, 0.00000e+00, 1.81000e+01, ..., 2.02000e+01,\n",
       "         3.84970e+02, 2.29800e+01],\n",
       "        [2.50461e+01, 0.00000e+00, 1.81000e+01, ..., 2.02000e+01,\n",
       "         3.96900e+02, 2.67700e+01],\n",
       "        [4.57461e+01, 0.00000e+00, 1.81000e+01, ..., 2.02000e+01,\n",
       "         8.82700e+01, 3.69800e+01],\n",
       "        ...,\n",
       "        [1.83377e+00, 0.00000e+00, 1.95800e+01, ..., 1.47000e+01,\n",
       "         3.89610e+02, 1.92000e+00],\n",
       "        [1.38100e-02, 8.00000e+01, 4.60000e-01, ..., 1.44000e+01,\n",
       "         3.94230e+02, 2.97000e+00],\n",
       "        [5.78340e-01, 2.00000e+01, 3.97000e+00, ..., 1.30000e+01,\n",
       "         3.84540e+02, 7.44000e+00]], dtype=float32),\n",
       " array([ 5. ,  5.6,  7. ,  7.2,  7.2,  8.5,  8.8, 10.2, 10.5, 11.3, 11.7,\n",
       "        11.7, 11.9, 12.6, 12.7, 13.1, 13.2, 13.3, 13.5, 13.8, 13.8, 14.6,\n",
       "        15. , 15.2, 15.2, 15.4, 15.6, 16.2, 17.2, 17.3, 17.5, 17.8, 17.8,\n",
       "        18.5, 18.7, 18.8, 18.9, 18.9, 19.1, 19.4, 19.4, 19.6, 19.6, 20.1,\n",
       "        20.2, 20.4, 20.6, 20.6, 20.6, 20.8, 20.8, 21.1, 21.2, 21.4, 21.8,\n",
       "        21.9, 22. , 22.2, 22.3, 22.6, 23. , 23. , 23.1, 23.1, 23.2, 23.2,\n",
       "        23.3, 23.3, 23.4, 23.7, 23.8, 23.9, 23.9, 24. , 24.4, 24.6, 24.8,\n",
       "        25. , 25. , 26.4, 26.6, 27.9, 28.4, 28.7, 28.7, 32. , 32.2, 33.1,\n",
       "        35.2, 35.4, 37.9, 38.7, 39.8, 44. , 45.4, 46. , 46.7, 48.5, 50. ,\n",
       "        50. , 50. , 50. ], dtype=float32),\n",
       " array([ -161287.33 ,  -156062.34 ,  -125553.77 ,  -154520.64 ,\n",
       "         -115601.86 ,  -153046.3  , 15254445.   ,  -160861.39 ,\n",
       "         -152743.14 ,  -152820.98 ,  -118535.875,  -126770.77 ,\n",
       "          -95785.81 ,  -147214.38 ,   -90822.12 ,   -50103.773,\n",
       "          -75070.15 ,  -150875.23 ,  -107886.32 ,  -153822.86 ,\n",
       "         -158846.25 ,  -164177.73 , 12759897.   ,  -133102.47 ,\n",
       "         -143027.47 ,   -78264.305,   -75113.5  ,   -94119.766,\n",
       "          -78880.164,  -126080.33 ,  -101045.88 ,  -146432.3  ,\n",
       "          -69255.234,   -95175.69 ,  -101593.05 ,  -137046.45 ,\n",
       "          -90391.86 ,   -95530.19 ,  -163845.58 ,  -104718.41 ,\n",
       "         -100032.195,  -131330.7  ,  -114968.09 ,  -103268.05 ,\n",
       "          -82864.34 ,   -97763.96 ,  -162343.75 ,  -132511.89 ,\n",
       "          -97784.5  ,  -112930.72 ,  -149431.11 ,   -86076.305,\n",
       "          -90510.85 ,   -71098.58 ,   -98532.53 ,  -118262.734,\n",
       "         -110803.67 ,  -100268.484,  -127067.766,   -67579.086,\n",
       "          -91699.555,  -124740.63 ,   -94377.7  ,   -95784.15 ,\n",
       "         -151674.77 ,  -155817.42 ,   -77507.875,   -85435.3  ,\n",
       "          -94661.734,   -88124.38 ,   -98309.91 ,  -109151.695,\n",
       "         -112000.914,   -84261.91 ,   -89892.24 ,   -66873.74 ,\n",
       "         -108476.84 ,   -92790.6  ,  -149739.84 ,  -112423.78 ,\n",
       "          -80249.875,  -151273.1  ,   -96243.664,  -105257.94 ,\n",
       "          -71460.88 ,   -92520.76 ,  -118772.94 ,  -114033.7  ,\n",
       "          -91975.56 ,  -124089.57 ,   -66512.16 ,   -71870.91 ,\n",
       "          -65277.39 ,  -124188.53 ,   -77062.555,   -71871.6  ,\n",
       "          -82727.78 ,   -99287.18 ,   -64354.168,   -95657.86 ,\n",
       "          -95407.76 ,   -67524.35 ], dtype=float32),\n",
       " array([  230060.62 ,   162944.86 ,   262392.9  ,   172796.89 ,\n",
       "          169681.39 ,   140308.61 , 11788519.   ,   142435.23 ,\n",
       "          163369.88 ,   148306.72 ,   139180.75 ,   284515.62 ,\n",
       "          103382.05 ,   134681.23 ,   109115.12 ,   132158.28 ,\n",
       "          119666.19 ,   144728.23 ,   163480.9  ,   184136.44 ,\n",
       "          135173.73 ,   133506.5  ,  9698651.   ,   213314.97 ,\n",
       "          144242.34 ,   142849.77 ,   160859.69 ,    86342.555,\n",
       "           80975.08 ,   221736.14 ,    89069.945,   137973.12 ,\n",
       "          173538.38 ,    84471.95 ,   104763.14 ,   181947.72 ,\n",
       "           91639.85 ,    95536.164,   127667.77 ,   120672.46 ,\n",
       "           95643.16 ,   146973.72 ,    83765.34 ,    98326.84 ,\n",
       "           81183.414,    80356.25 ,   130114.445,   112336.16 ,\n",
       "          102461.33 ,    96075.586,   127254.21 ,    73651.65 ,\n",
       "           73490.18 ,    76562.945,    86949.94 ,   114900.46 ,\n",
       "           87747.27 ,    79002.39 ,    95113.77 ,    83646.695,\n",
       "          101513.03 ,   151324.83 ,    85748.6  ,    81931.05 ,\n",
       "          132173.23 ,   128206.48 ,   131859.   ,   104920.6  ,\n",
       "           82403.914,   174029.62 ,    74646.71 ,    83871.84 ,\n",
       "           99730.73 ,    79168.234,    95036.1  ,    80658.625,\n",
       "           86563.73 ,   122645.4  ,   125609.664,    94645.89 ,\n",
       "           72153.31 ,   130249.31 ,   174176.75 ,    97663.38 ,\n",
       "           70016.15 ,    94288.25 ,   123896.586,    88407.33 ,\n",
       "           80187.49 ,   127427.59 ,   127447.195,    92138.33 ,\n",
       "           78474.586,   428241.56 ,    77234.086,    75907.45 ,\n",
       "           75547.766,   257091.73 ,    66645.375,   130757.85 ,\n",
       "          128633.65 ,    79021.016], dtype=float32))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_pred_to_true(model, dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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