distill_decision_tree.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "import functools\n",
    "\n",
    "import torch\n",
    "\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "import torch.optim as optim\n",
    "\n",
    "from torchvision import datasets, transforms\n",
    "import torchvision.transforms as transforms\n",
    "\n",
    "import os\n",
    "import torch\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import random\n",
    "\n",
    "import copy\n",
    "import math\n",
    "import pickle\n",
    "import argparse\n",
    "\n",
    "from torch.optim.lr_scheduler import StepLR\n",
    "import torchvision\n",
    "import imageio.v2 as imageio\n",
    "\n",
    "from mup_nets import ReLUResNetMUP_manual\n",
    "from train_utils import get_random_data_unif_binary, train_mse, test_mse, ERMDatasetFromFuncBinary\n",
    "from decision_tree_utils import DecisionTree, AndOfLiterals, comp_and_list, \\\n",
    "                                tup_to_literals, literals_to_tup\n",
    "from probing_utils import batch_linear_probe_adam\n",
    "from tqdm import tqdm\n",
    "\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def load_net(save_model_name):\n",
    "    net = torch.load(save_model_name + '.pt')\n",
    "    return net\n",
    "\n",
    "\n",
    "def load_tree_and_net(dtreefile,netfile=None,loss_type='mse'):\n",
    "    # Load network and decision tree\n",
    "    dtree = pickle.load(open(f'dectrees/{dtreefile}.pkl', 'rb'))\n",
    "    if netfile is None:\n",
    "        save_model_name = 'saved_models/' + dtreefile + f'_{loss_type}'\n",
    "        net = load_net(save_model_name)\n",
    "    else:\n",
    "        net = load_net(netfile)\n",
    "    return dtree, net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_activs(model,x):\n",
    "    memorized_activs = {}\n",
    "    with torch.no_grad():\n",
    "        y = model.forward(x, return_activations=memorized_activs)\n",
    "        memorized_activs[('output')] = torch.sign(y)\n",
    "    return memorized_activs\n",
    "\n",
    "def get_norm_concat_activs(model,x):\n",
    "    activ = get_activs(model,x)\n",
    "    # activ_list = [activ[k] / torch.norm(activ[k]) for k in activ.keys()]\n",
    "    activ_list = [activ[k] for k in activ.keys()]\n",
    "    newactiv = torch.cat(activ_list,dim=1)\n",
    "    newactiv = newactiv / torch.norm(newactiv)\n",
    "    newactiv = newactiv * math.sqrt(newactiv.shape[0])\n",
    "    return newactiv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision tree distillation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device(\"cuda\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def flatten(xss):\n",
    "    return [x for xs in xss for x in xs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "def get_best_tree_dyn_prog(x, outfn, tup_list, s_list=None,verbose=False):\n",
    "\n",
    "    if s_list is None:\n",
    "        s = len(tup_list)\n",
    "        s_list = [s]\n",
    "    else:\n",
    "        s = max(s_list)\n",
    "\n",
    "    # Get loss value from having an AND as a leaf\n",
    "    if verbose:\n",
    "        print('Starting AND computation')\n",
    "        starttime = time.time()\n",
    "\n",
    "    # Compute and for list of tuples\n",
    "    tupmat = torch.tensor(tup_list, dtype=torch.float).to(device)\n",
    "    nnz = torch.count_nonzero(tupmat,dim=1).view(1,-1)\n",
    "    muls = x @ tupmat.T\n",
    "    tupcomp = (muls > nnz - 0.5) * 1\n",
    "\n",
    "    # tupcomp = (comp_and_list(x, [tup_to_literals(tup) for tup in tup_list])+1)/2\n",
    "    if verbose:\n",
    "        endtime = time.time()\n",
    "        print('Done with AND computation', endtime - starttime)\n",
    "        print('Assigning values to tuples')\n",
    "        starttime = time.time()\n",
    "\n",
    "    tup_coverage = torch.sum(tupcomp,dim=0)\n",
    "    tup_vals = tup_coverage / (2 * outfn.shape[0]) + (1/2) * torch.abs(torch.sum(outfn * tupcomp,dim=0)) / outfn.shape[0]\n",
    "    tup_sgn = torch.sign(torch.sum(outfn * tupcomp,dim=0)) # Best sign if it is a leaf\n",
    "    if verbose:\n",
    "        endtime = time.time()\n",
    "        print('Done assigning values to tuples', endtime - starttime)\n",
    "        print('Constructing tree of ANDs')\n",
    "        starttime = time.time()\n",
    "\n",
    "    # Construct tree \n",
    "    tup_to_idx = {}\n",
    "    for idx, tup in enumerate(tup_list):\n",
    "        tup_to_idx[tup] = idx\n",
    "    d = len(tup_list[0])\n",
    "\n",
    "    # Slow -- currently main bottleneck\n",
    "    tup_to_children_pairs = {}\n",
    "    for tup in tqdm(tup_list):\n",
    "        tup_to_children_pairs[tup] = []\n",
    "        for i in range(d):\n",
    "            if tup[i] == 0:\n",
    "                # Are successors in the list?\n",
    "                l = list(tup)\n",
    "                l[i] = 1\n",
    "                tup1 = tuple(l)\n",
    "                l[i] = -1\n",
    "                tupm1 = tuple(l)\n",
    "                if tup1 in tup_to_idx.keys() and tupm1 in tup_to_idx.keys():\n",
    "                    tup_to_children_pairs[tup].append((tup1,tupm1))\n",
    "\n",
    "    # Relatively fast conversion to indices\n",
    "    idx_to_children_pairs = {}\n",
    "    for tup in tqdm(tup_to_children_pairs):\n",
    "        ci = tup_to_idx[tup]\n",
    "        idx_to_children_pairs[ci] = [(tup_to_idx[t1], tup_to_idx[t2]) for (t1,t2) in tup_to_children_pairs[tup]]\n",
    "\n",
    "\n",
    "    if verbose:\n",
    "        endtime = time.time()\n",
    "        print('Tree of ANDs constructed', endtime - starttime)\n",
    "        print('Setting up dynamic program')\n",
    "        starttime = time.time()\n",
    "\n",
    "    idx_depth_r = {}\n",
    "    rs = set()\n",
    "    for tup in tqdm(tup_list):\n",
    "        tupr = sum([tup[i] != 0 for i in range(d)])\n",
    "        if tupr not in idx_depth_r:\n",
    "            idx_depth_r[tupr] = []\n",
    "        idx_depth_r[tupr].append(tup_to_idx[tup])\n",
    "        rs.add(tupr)\n",
    "\n",
    "    ## Construct list of edges -- relatively efficient\n",
    "    rs = list(rs)\n",
    "    rs.sort()\n",
    "    edge_r_par = {}\n",
    "    edge_r_c1 = {}\n",
    "    edge_r_c2 = {}\n",
    "\n",
    "    for r in tqdm(rs):\n",
    "        edge_r_par[r] = flatten([[pidx]*len(idx_to_children_pairs[pidx]) for pidx in idx_depth_r[r]])\n",
    "        edge_r_c1[r] = flatten([[a[0] for a in idx_to_children_pairs[pidx]] for pidx in idx_depth_r[r]])\n",
    "        edge_r_c2[r] = flatten([[a[1] for a in idx_to_children_pairs[pidx]] for pidx in idx_depth_r[r]])\n",
    "        # print(edge_r_par[r])\n",
    "\n",
    "    rs.sort()\n",
    "    rs.reverse()\n",
    "    mem_vals = tup_vals.repeat(s,1) # size x num_tuples\n",
    "    mem_inds = torch.zeros(mem_vals.shape,dtype=torch.long).to(device)-1\n",
    "    for r in tqdm(rs):\n",
    "        if len(edge_r_c1[r]) > 0: # If there are edges to earlier layers,\n",
    "            num_edges = len(edge_r_c1[r])\n",
    "            e1 = mem_vals[:,edge_r_c1[r]]\n",
    "            e2 = mem_vals[:,edge_r_c2[r]]\n",
    "            # Compute the min-plus\n",
    "            e1 = e1.view(e1.shape[0],1,-1)\n",
    "            e2 = e2.view(1,e2.shape[0],-1)\n",
    "            edge_sum = e1 + e2\n",
    "            edge_sum = edge_sum.view(-1,num_edges)\n",
    "\n",
    "            idxsquare = torch.arange(0,s).view(-1,1) + torch.arange(0,s).view(1,-1)\n",
    "            idxsquare = idxsquare.view(-1)\n",
    "\n",
    "            etot = torch.zeros((s,num_edges)).to(device)\n",
    "            einds = torch.zeros((s,num_edges),dtype=torch.long).to(device)\n",
    "            for cs in range(0,s-2):\n",
    "                idxsub = idxsquare == cs\n",
    "                edgemax = torch.max(edge_sum[idxsub,:],dim=0)\n",
    "                # esumcurrind = edgemax.indices\n",
    "                # in general, can only start at cs+2 = 2 (which is size 3)\n",
    "                # if cs+2 = 10, this is size 11, so want sums of 0,8 and 1,7 (which is 1,9), etc...\n",
    "                # einds[cs+2,:] = edgemax.indices\n",
    "                etot[cs+2,:] = edgemax.values\n",
    "\n",
    "            # This is the main bottleneck, and not worth it to optimize right now,\n",
    "            # since overall program is at least 20x faster than previously\n",
    "            for idx, p in enumerate(edge_r_par[r]):\n",
    "                upd_edge = etot[:,idx] > mem_vals[:,p]\n",
    "                mem_vals[:,p] = torch.maximum(mem_vals[:,p], etot[:,idx])\n",
    "                mem_inds[upd_edge,p] = idx\n",
    "\n",
    "    if verbose:\n",
    "        endtime = time.time()\n",
    "        print('Time for dynamic program', endtime - starttime)\n",
    "        print('Converting to DecisionTree format')\n",
    "        starttime = time.time()\n",
    "\n",
    "    out_trees = {}\n",
    "    for true_size in s_list:\n",
    "        s = true_size - 1\n",
    "\n",
    "        # Convert to DecisionTree format\n",
    "        root_idx = idx_depth_r[0][0]\n",
    "        and_list = []\n",
    "        child_dict = {}\n",
    "        idx_to_size = {}\n",
    "        and_len = []\n",
    "\n",
    "        and_list.append(root_idx)\n",
    "        and_len.append(0)\n",
    "        idx_to_size[root_idx] = s\n",
    "        sgn_dict = {}\n",
    "        i = 0\n",
    "        while i < len(and_list):\n",
    "            ct = and_list[i]\n",
    "            r = and_len[i]\n",
    "            cs = idx_to_size[ct]\n",
    "            if mem_inds[cs,ct] >= 0: # i.e., not -1, which indicates no child\n",
    "                curredge = mem_inds[cs,ct]\n",
    "                l_idx = edge_r_c1[r][curredge]\n",
    "                r_idx = edge_r_c2[r][curredge]\n",
    "\n",
    "                # Compute sl, sr\n",
    "                l_vals = mem_vals[:,l_idx]\n",
    "                r_vals = mem_vals[:,r_idx]\n",
    "                mval = 0\n",
    "                mind = -1\n",
    "                for sl in range(0,cs-1):\n",
    "                    sr = cs - sl - 2\n",
    "                    cval = l_vals[sl] + r_vals[sr]\n",
    "                    if cval > mval:\n",
    "                        mval = cval\n",
    "                        mind = sl\n",
    "                sl = mind\n",
    "                sr = cs - sl - 2\n",
    "                assert(mval == mem_vals[cs,ct])\n",
    "                idx_to_size[l_idx] = sl\n",
    "                idx_to_size[r_idx] = sr\n",
    "                and_list.append(l_idx)\n",
    "                and_len.append(r+1)\n",
    "                and_list.append(r_idx)\n",
    "                and_len.append(r+1)\n",
    "                child_dict[tup_list[ct]] = (tup_list[l_idx],tup_list[r_idx])\n",
    "            else:\n",
    "                sgn_dict[tup_list[ct]] = round(tup_sgn[ct].item())\n",
    "                child_dict[tup_list[ct]] = None\n",
    "            i += 1\n",
    "\n",
    "\n",
    "        newtree = DecisionTree(root_tup=tup_list[root_idx], child_dict = child_dict, sgn_dict = sgn_dict)\n",
    "        out_trees[true_size] = (newtree, mem_vals[s,root_idx])\n",
    "\n",
    "    if verbose:\n",
    "        endtime = time.time()\n",
    "        print('Converted to DecisionTree format', endtime-starttime)\n",
    "\n",
    "    return out_trees"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distillation procedure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_successor_tups(tup):\n",
    "    new_tups = []\n",
    "    for i in range(len(tup)):\n",
    "        if tup[i] != 0:\n",
    "            continue\n",
    "        for j in [1,-1]:\n",
    "            tup1 = copy.deepcopy(list(tup))\n",
    "            tup1[i] = j\n",
    "            tup1 = tuple(tup1)\n",
    "            new_tups.append(tup1)\n",
    "    return new_tups\n",
    "\n",
    "def search_net_for_tree_ands(dtree, net, top_k=500,max_depth=5, num_concurrent_probes=20000,\n",
    "                      num_samples=1000,val_num_samples=10000,\n",
    "                            probe_iters=1001,probe_iter_log=-1,probe_eta=0.01):\n",
    "    \n",
    "    x = get_random_data_unif_binary(d=d,num_samples=num_samples).to(device)\n",
    "    valx = get_random_data_unif_binary(d=d,num_samples=val_num_samples).to(device)\n",
    "    activs = get_norm_concat_activs(net,x)\n",
    "    valactivs = get_norm_concat_activs(net,valx)\n",
    "\n",
    "    ## Only branch out on top-k tuples at each depth, so only at most 2*k*d tuples ever probed per layer\n",
    "\n",
    "    tups_to_probe = {}\n",
    "    probe_vals = {}\n",
    "    start_tup = tuple([0]*d)\n",
    "    tups_to_probe[0] = [start_tup]\n",
    "\n",
    "    for r in range(max_depth+1):\n",
    "        print('Current depth ',r)\n",
    "\n",
    "        currlits = [tup_to_literals(qa) for qa in tups_to_probe[r]]\n",
    "        probe_vals[r] = []\n",
    "\n",
    "        i = 0\n",
    "        while i < len(currlits):\n",
    "            num_to_test = min(num_concurrent_probes,len(currlits)-i)\n",
    "            litsub = currlits[i:i+num_to_test]\n",
    "            fns = comp_and_list(x, litsub)\n",
    "            valfns = comp_and_list(valx, litsub)\n",
    "            print('Testing # of tups:', fns.shape[1])\n",
    "            train_errs, errs = batch_linear_probe_adam(activs, fns, valrep=valactivs,valfns=valfns,\n",
    "                                                  eta=probe_eta,num_iters=probe_iters, norm_bound=math.inf, iter_log = probe_iter_log)\n",
    "            probe_vals[r].extend(errs.tolist())\n",
    "            i += num_to_test\n",
    "        if r == max_depth:\n",
    "            break\n",
    "\n",
    "        errs = torch.tensor(probe_vals[r])\n",
    "        top_idx = torch.topk(errs,min(top_k,len(errs)),largest=False).indices\n",
    "        top_tups = [tups_to_probe[r][idx] for idx in top_idx]\n",
    "        # print(top_tups)\n",
    "        newtups = set()\n",
    "        for qa in tqdm(top_tups):\n",
    "            new_tups = get_successor_tups(qa)\n",
    "            newtups.update(new_tups)\n",
    "\n",
    "        tups_to_probe[r+1] = list(newtups)\n",
    "        print(len(tups_to_probe[r+1]))\n",
    "        \n",
    "        \n",
    "    return tups_to_probe, probe_vals\n",
    "\n",
    "def extract_tup_list_from_probe_output(tups_to_probe, probe_vals,verbose=True):\n",
    "    tup_list = []\n",
    "    ks = list(tups_to_probe.keys())\n",
    "    ks.sort()\n",
    "    for r in ks:\n",
    "        if verbose:\n",
    "            print('-'*50)\n",
    "            print(len(tups_to_probe[r]))\n",
    "        curr_l = [(probe_vals[r][idx], tups_to_probe[r][idx]) for idx in range(len(tups_to_probe[r]))]\n",
    "        curr_l.sort()\n",
    "        for i in range(min(1000,len(curr_l))):\n",
    "            p = curr_l[i]\n",
    "            if verbose:\n",
    "                print(p[0],i, tup_to_literals(p[1]))\n",
    "        tup_list.extend(curr_l)\n",
    "    return tup_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100.0\n",
      "100.0\n",
      "100.0\n",
      "100.0\n",
      "100.0\n"
     ]
    }
   ],
   "source": [
    "models_to_distill = []\n",
    "\n",
    "with open('testaccs.csv', 'r') as f:\n",
    "    trained_trees_and_models = f.readlines()\n",
    "    header = trained_trees_and_models[0]\n",
    "    trained_trees_and_models = trained_trees_and_models[1:]\n",
    "\n",
    "for l in trained_trees_and_models:\n",
    "    # print(l)\n",
    "    a = l.replace('\\n','').split(',')\n",
    "    save_model_name = a[0]\n",
    "    dtreename = a[1]\n",
    "    modelloss = float(a[2])\n",
    "    modelacc = a[3]\n",
    "    # print(trained_trees_and_models)\n",
    "    # print(save_model_name,dtreename,modelloss,modelacc)\n",
    "    \n",
    "    if modelloss < 1e-3 and 'class' in save_model_name and 'dectrees_100_' in save_model_name:\n",
    "        print(modelacc)\n",
    "        # dtree, net = load_tree_and_net(dtreename,save_model_name)\n",
    "        # dot = dtree.visualize(zero_ones=True)\n",
    "        models_to_distill.append((dtreename,save_model_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dectrees_100_2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7/7 [00:00<00:00, 5714.31it/s]\n",
      "100%|██████████| 7/7 [00:00<00:00, 167772.16it/s]\n",
      "100%|██████████| 7/7 [00:00<00:00, 84368.18it/s]\n",
      "100%|██████████| 3/3 [00:00<00:00, 67288.30it/s]\n",
      "100%|██████████| 3/3 [00:00<00:00, 2277.86it/s]\n"
     ]
    },
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dectrees_100_2 10\n",
      "saved_reconstructions/dectrees_100_2_class_erm100000_sgd0.05_epochs10_reconstructed_tree_top10_0.pkl\n",
      "num probed ands 2141\n",
      "dectrees_100_2 50\n",
      "saved_reconstructions/dectrees_100_2_class_erm100000_sgd0.05_epochs10_reconstructed_tree_top50_0.pkl\n",
      "num probed ands 8901\n",
      "dectrees_100_2 100\n",
      "saved_reconstructions/dectrees_100_2_class_erm100000_sgd0.05_epochs10_reconstructed_tree_top100_0.pkl\n",
      "num probed ands 15101\n",
      "dectrees_100_2 200\n",
      "saved_reconstructions/dectrees_100_2_class_erm100000_sgd0.05_epochs10_reconstructed_tree_top200_0.pkl\n",
      "num probed ands 20001\n"
     ]
    },
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
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      "saved_reconstructions/dectrees_100_4_class_erm100000_sgd0.05_epochs10_reconstructed_tree_top10_0.pkl\n",
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      "text/plain": [
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     "metadata": {},
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     "name": "stdout",
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      "dectrees_100_5\n"
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    },
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     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "100%|██████████| 63/63 [00:00<00:00, 421437.24it/s]\n",
      "100%|██████████| 63/63 [00:00<00:00, 100053.45it/s]\n",
      "100%|██████████| 6/6 [00:00<00:00, 68200.07it/s]\n",
      "100%|██████████| 6/6 [00:00<00:00, 267.45it/s]\n"
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      "dectrees_100_5 10\n",
      "saved_reconstructions/dectrees_100_5_class_erm100000_sgd0.05_epochs10_reconstructed_tree_top10_0.pkl\n",
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      "dectrees_100_5 10\n",
      "saved_reconstructions/dectrees_100_5_class_erm1000000_sgd0.05_epochs10_reconstructed_tree_top10_0.pkl\n",
      "num probed ands 7936\n",
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      "dectrees_100_5 100\n",
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      "dectrees_100_5 200\n",
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      "dectrees_100_5 500\n",
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "    \n",
    "for dtreename, save_model_name in models_to_distill:\n",
    "    \n",
    "    top_ks = [10,50,100,200]\n",
    "    if 'dectrees_100_5' in dtreename:\n",
    "        top_ks = [10,50,100,200,500,1000]\n",
    "        # continue\n",
    "    \n",
    "    print(dtreename)\n",
    "    dtree, net = load_tree_and_net(dtreename,save_model_name)\n",
    "    \n",
    "    d = len(dtree.ands[0].get_tup())\n",
    "    x = get_random_data_unif_binary(d=d,num_samples=1000).to(device)\n",
    "    tup_list = [a.get_tup() for a in dtree.ands]\n",
    "    outfn = dtree.compute(x).view(-1,1)\n",
    "    num_tups = len(tup_list)\n",
    "    size_range =range(1,num_tups+1)\n",
    "    # size_range = [1]\n",
    "    out_trees = get_best_tree_dyn_prog(x, outfn, tup_list,s_list=size_range)\n",
    "    # out_trees[1][0].visualize()\n",
    "    plt.plot(size_range, [out_trees[k][1].item() for k in size_range])\n",
    "    plt.xlabel('Pruned tree size')\n",
    "    plt.ylabel('Accuracy of approximation')\n",
    "    plt.title('Accuracy of best subtree vs. size')\n",
    "    min_s = min([k for k in out_trees.keys() if out_trees[k][1] > 0.999])\n",
    "    prunedtree = out_trees[min_s][0]\n",
    "    pruneddot = prunedtree.visualize()\n",
    "    \n",
    "    r = max([len(x.get_literals()[0]) for x in dtree.ands]) # depth of tree\n",
    "    \n",
    "    for top_k in top_ks: \n",
    "        print(dtreename,top_k)\n",
    "        d = net.linears[0].in_features\n",
    "        \n",
    "        short_model_name = save_model_name.replace('saved_models/', '')\n",
    "\n",
    "        probed_and_file = f'saved_reconstructions/{short_model_name}_probed_ands_top{top_k}_0.pkl'\n",
    "        rec_file = f'saved_reconstructions/{short_model_name}_reconstructed_tree_top{top_k}_0.pkl'\n",
    "\n",
    "        print(rec_file)\n",
    "\n",
    "        if os.path.exists(probed_and_file):\n",
    "            probed_ands = pickle.load(open(probed_and_file, 'rb'))\n",
    "        else:\n",
    "            tups_to_probe, probe_vals = search_net_for_tree_ands(dtree, net, top_k=top_k,max_depth=r,\n",
    "                                                             num_concurrent_probes=20000,\n",
    "                                                             num_samples=1000,val_num_samples=10000,\n",
    "                                                                probe_iters=101, probe_eta=0.05)\n",
    "            probed_ands = extract_tup_list_from_probe_output(tups_to_probe, probe_vals, verbose=False)\n",
    "\n",
    "            pickle.dump(probed_ands, open(probed_and_file, 'wb'))\n",
    "\n",
    "        print('num probed ands', len(probed_ands))\n",
    "\n",
    "        check_cover=False\n",
    "        if check_cover:\n",
    "            print('Do these probed ands cover the tree?')\n",
    "            true_tups = [a.get_tup() for a in dtree.ands]\n",
    "            for tup in true_tups:\n",
    "                found = False\n",
    "                for v in probed_ands:\n",
    "                    if v[1] == tup:\n",
    "                        print(v[0],tup_to_literals(tup))\n",
    "                        found = True\n",
    "                        break\n",
    "                if not found:\n",
    "                    print('NOT FOUND', tup_to_literals(tup))\n",
    "\n",
    "\n",
    "                valx = get_random_data_unif_binary(d=d,num_samples=5000)\n",
    "        if os.path.exists(rec_file):\n",
    "            rectrees = pickle.load(open(rec_file, 'rb'))\n",
    "        else:\n",
    "            tup_list = [qa[1] for qa in probed_ands]\n",
    "            x = get_random_data_unif_binary(d=d,num_samples=1000).to(device)\n",
    "            outfn = dtree.compute(x).view(-1,1)\n",
    "            rectrees = get_best_tree_dyn_prog(x, outfn, tup_list,s_list=range(1,64),verbose=True)\n",
    "            # reconstructedtree.visualize()\n",
    "            pickle.dump(rectrees, open(rec_file, 'wb'))\n",
    "\n",
    "        show_out = False\n",
    "        # if show_out:\n",
    "        valx = get_random_data_unif_binary(d=d,num_samples=5000).to(device)\n",
    "        outfn = dtree.compute(valx).view(-1,1)\n",
    "        sizes = rectrees.keys()\n",
    "        train_accs = []\n",
    "        test_accs = []\n",
    "        for s in rectrees.keys():\n",
    "            reconstructedtree = rectrees[s][0]\n",
    "            reconstructed = reconstructedtree.compute(valx).view(-1,1)\n",
    "            test_acc = torch.sum((torch.sign(outfn * reconstructed)+1)/2) / outfn.shape[0]\n",
    "            train_acc = rectrees[s][1]\n",
    "            train_accs.append(train_acc.item())\n",
    "            test_accs.append(test_acc.item())\n",
    "            if show_out:\n",
    "                print(f'size {s}\\ttrain {train_acc:.3f}\\ttest {test_acc:.3f}')\n",
    "        ax = plt.gca()\n",
    "        color=next(ax._get_lines.prop_cycler)['color']\n",
    "        plt.plot(sizes, test_accs, label=f'test, k {top_k}', color=color)\n",
    "        plt.plot(sizes, train_accs, '--', label=f'train, k {top_k}', color=color)\n",
    "    plt.title(dtreename)\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "        # Is there a way to batch these computations?\n",
    "        # It seems that we can construct a list of possible edges, create a matrix that is of dimensions EDGE x TREE SIZE, and \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dectrees_100_2\n",
      "saved_models/dectrees_100_2_class_erm100000_sgd0.05_epochs10\n",
      "num probed ands 2141\n",
      "size 63\ttrain 1.000\ttest 1.000\n",
      "num probed ands 8901\n",
      "size 63\ttrain 1.000\ttest 1.000\n",
      "num probed ands 15101\n",
      "size 63\ttrain 1.000\ttest 1.000\n",
      "num probed ands 20001\n",
      "size 63\ttrain 1.000\ttest 1.000\n",
      "dectrees_100_3\n",
      "saved_models/dectrees_100_3_class_erm100000_sgd0.05_epochs10\n",
      "num probed ands 4091\n",
      "size 63\ttrain 0.886\ttest 0.873\n",
      "num probed ands 18459\n",
      "size 63\ttrain 1.000\ttest 1.000\n",
      "num probed ands 33122\n",
      "size 63\ttrain 1.000\ttest 1.000\n",
      "num probed ands 48213\n",
      "size 63\ttrain 1.000\ttest 1.000\n",
      "dectrees_100_4\n",
      "saved_models/dectrees_100_4_class_erm100000_sgd0.05_epochs10\n",
      "num probed ands 6022\n",
      "size 63\ttrain 0.831\ttest 0.814\n",
      "num probed ands 28042\n",
      "size 63\ttrain 0.888\ttest 0.874\n",
      "num probed ands 52856\n",
      "size 63\ttrain 1.000\ttest 1.000\n",
      "num probed ands 94227\n",
      "size 63\ttrain 1.000\ttest 1.000\n",
      "dectrees_100_5\n",
      "saved_models/dectrees_100_5_class_erm100000_sgd0.05_epochs10\n",
      "num probed ands 7925\n",
      "size 63\ttrain 0.729\ttest 0.728\n",
      "num probed ands 37569\n",
      "size 63\ttrain 0.903\ttest 0.879\n",
      "num probed ands 71850\n",
      "size 63\ttrain 1.000\ttest 1.000\n",
      "num probed ands 131976\n",
      "size 63\ttrain 1.000\ttest 1.000\n",
      "num probed ands 291339\n",
      "size 63\ttrain 1.000\ttest 1.000\n",
      "num probed ands 540090\n",
      "size 63\ttrain 1.000\ttest 1.000\n",
      "dectrees_100_5\n",
      "saved_models/dectrees_100_5_class_erm1000000_sgd0.05_epochs10\n",
      "num probed ands 7936\n",
      "size 63\ttrain 0.783\ttest 0.787\n",
      "num probed ands 37563\n",
      "size 63\ttrain 0.895\ttest 0.880\n",
      "num probed ands 71717\n",
      "size 63\ttrain 0.975\ttest 0.974\n",
      "num probed ands 131455\n",
      "size 63\ttrain 1.000\ttest 1.000\n",
      "num probed ands 283887\n",
      "size 63\ttrain 1.000\ttest 1.000\n",
      "num probed ands 527532\n",
      "size 63\ttrain 1.000\ttest 1.000\n"
     ]
    }
   ],
   "source": [
    "results = []\n",
    "    \n",
    "for dtreename, save_model_name in models_to_distill:\n",
    "    \n",
    "    print(dtreename)\n",
    "    print(save_model_name)\n",
    "    dtree, net = load_tree_and_net(dtreename,save_model_name)\n",
    "    \n",
    "    d = len(dtree.ands[0].get_tup())\n",
    "    r = max([len(x.get_literals()[0]) for x in dtree.ands]) # depth of tree\n",
    "\n",
    "    top_ks = [10,50,100,200]\n",
    "    if 'dectrees_100_5' in dtreename:\n",
    "        top_ks = [10,50,100,200,500,1000]\n",
    "        \n",
    "        \n",
    "    for top_k in top_ks:   \n",
    "        short_model_name = save_model_name.replace('saved_models/', '')\n",
    "        probed_and_file = f'saved_reconstructions/{short_model_name}_probed_ands_top{top_k}_0.pkl'\n",
    "        rec_file = f'saved_reconstructions/{short_model_name}_reconstructed_tree_top{top_k}_0.pkl'\n",
    "\n",
    "        probed_ands = pickle.load(open(probed_and_file, 'rb'))\n",
    "        num_probed_ands = len(probed_ands)\n",
    "        print('num probed ands', num_probed_ands)\n",
    "        rectrees = pickle.load(open(rec_file, 'rb'))\n",
    "\n",
    "        valx = get_random_data_unif_binary(d=d,num_samples=5000).to(device)\n",
    "        outfn = dtree.compute(valx).view(-1,1)\n",
    "        sizes = rectrees.keys()\n",
    "        train_accs = []\n",
    "        test_accs = []\n",
    "        s = max(rectrees.keys())\n",
    "        # for s in rectrees.keys():\n",
    "        reconstructedtree = rectrees[s][0]\n",
    "        reconstructed = reconstructedtree.compute(valx).view(-1,1)\n",
    "        test_acc = torch.sum((torch.sign(outfn * reconstructed)+1)/2) / outfn.shape[0]\n",
    "        train_acc = rectrees[s][1]\n",
    "        train_accs.append(train_acc.item())\n",
    "        test_accs.append(test_acc.item())\n",
    "        \n",
    "        results.append((dtreename, save_model_name, top_k, train_acc, test_acc))\n",
    "        \n",
    "        print(f'size {s}\\ttrain {train_acc:.3f}\\ttest {test_acc:.3f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(results, open('results.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = pickle.load(open('results.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "treenames = list(set([x[0] for x in models_to_distill]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_dict = {}\n",
    "for dtreename in treenames:\n",
    "    results_dict[dtreename] = [x for x in results if x[0] == dtreename]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/state/partition1/llgrid/pkg/anaconda/anaconda3-2022b/lib/python3.8/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.4\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    }
   ],
   "source": [
    "import scipy.stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "3\n",
      "4\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "res_by_r_k = {}\n",
    "\n",
    "for r in [2,3,4,5]:\n",
    "    print(r)\n",
    "    for suffix in ['']: #,'_a','_b','_c','_d'\n",
    "        dtn = f'dectrees_100_{r}{suffix}'\n",
    "        a = results_dict[dtn]\n",
    "        \n",
    "        num_samples = 100000\n",
    "        if r == 5:\n",
    "            num_samples = 1000000\n",
    "        a = [x for x in a if f'erm{num_samples}_' in x[1]]\n",
    "        topk = [x[2] for x in a]\n",
    "        \n",
    "        for k in topk:\n",
    "            if (r,k) not in res_by_r_k.keys():\n",
    "                res_by_r_k[(r,k)] = []\n",
    "            aa = [x for x in a if x[2] == k]\n",
    "            assert(len(aa) == 1)\n",
    "            aa = aa[0]\n",
    "            short_model_name = aa[1].replace('saved_models/', '')\n",
    "            probed_and_file = f'saved_reconstructions/{short_model_name}_probed_ands_top{k}_0.pkl'\n",
    "            \n",
    "            probed_ands = pickle.load(open(probed_and_file, 'rb'))\n",
    "            num_probed_ands = len(probed_ands)\n",
    "            res_by_r_k[(r,k)].append((aa, num_probed_ands))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{ccccc}\n",
      "depth & $k$ & \\begin{tabular}{c} fraction of inputs \\\\ distillation is correct \\end{tabular} & \\begin{tabular}{c} average \\\\ number of probes \\end{tabular} & fraction of possible probes\\\\ \n",
      "\\hline\n",
      "2 & 10 & $\\{ 1.00 \\}$ & $2141$ & $0.1070$ \\\\ \n",
      "2 & 50 & $\\{ 1.00 \\}$ & $8901$ & $0.4450$ \\\\ \n",
      "2 & 100 & $\\{ 1.00 \\}$ & $15101$ & $0.7550$ \\\\ \n",
      "\\textbf{2} & \\textbf{  200} & \\textbf{\\{1.00\\} } & \\textbf{20001} & \\textbf{ 1.0000} \\\\ \n",
      "\\hline\n",
      "3 & 10 & $\\{ 0.87 \\}$ & $4091$ & $0.0031$ \\\\ \n",
      "3 & 50 & $\\{ 1.00 \\}$ & $18459$ & $0.0141$ \\\\ \n",
      "3 & 100 & $\\{ 1.00 \\}$ & $33122$ & $0.0252$ \\\\ \n",
      "\\textbf{3} & \\textbf{  200} & \\textbf{\\{1.00\\} } & \\textbf{48213} & \\textbf{ 0.0367} \\\\ \n",
      "\\hline\n",
      "4 & 10 & $\\{ 0.81 \\}$ & $6022$ & $0.0001$ \\\\ \n",
      "4 & 50 & $\\{ 0.87 \\}$ & $28042$ & $0.0004$ \\\\ \n",
      "4 & 100 & $\\{ 1.00 \\}$ & $52856$ & $0.0008$ \\\\ \n",
      "\\textbf{4} & \\textbf{  200} & \\textbf{\\{1.00\\} } & \\textbf{94227} & \\textbf{ 0.0015} \\\\ \n",
      "\\hline\n",
      "5 & 10 & $\\{ 0.79 \\}$ & $7936$ & $0.0000$ \\\\ \n",
      "5 & 50 & $\\{ 0.88 \\}$ & $37563$ & $0.0000$ \\\\ \n",
      "5 & 100 & $\\{ 0.97 \\}$ & $71717$ & $0.0000$ \\\\ \n",
      "5 & 200 & $\\{ 1.00 \\}$ & $131455$ & $0.0001$ \\\\ \n",
      "5 & 500 & $\\{ 1.00 \\}$ & $283887$ & $0.0001$ \\\\ \n",
      "\\textbf{5} & \\textbf{  1000} & \\textbf{\\{1.00\\} } & \\textbf{527532} & \\textbf{ 0.0002} \\\\ \n",
      "\\end{tabular}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/gridsan/eboix/.local/lib/python3.8/site-packages/numpy/core/_methods.py:265: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/gridsan/eboix/.local/lib/python3.8/site-packages/numpy/core/_methods.py:257: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    }
   ],
   "source": [
    "print('\\\\begin{tabular}{ccccc}')\n",
    "print('depth & $k$ & \\\\begin{tabular}{c} fraction of inputs \\\\\\\\ distillation is correct \\\\end{tabular} & \\\\begin{tabular}{c} average \\\\\\\\ number of probes \\\\end{tabular} & fraction of possible probes\\\\\\\\ ')\n",
    "for r in [2,3,4,5]:\n",
    "    print('\\\\hline')\n",
    "    topk = [10, 50, 100, 200]\n",
    "    if r == 5:\n",
    "        topk = [10, 50, 100, 200, 500, 1000]\n",
    "    for k in topk:\n",
    "        accs = [x[0][4].item() for x in res_by_r_k[(r,k)]]\n",
    "        accs = [f'{a:0.2f}' for a in accs]\n",
    "        num_probes = [x[1] for x in res_by_r_k[(r,k)]]\n",
    "        probe_avg = np.mean(num_probes)\n",
    "        probe_std = scipy.stats.sem(num_probes)\n",
    "        possible_probes = 0\n",
    "        for i in range(r+1):\n",
    "            possible_probes += (2**i) * math.comb(100,i)\n",
    "        frac_probes = probe_avg / possible_probes\n",
    "        if k == topk[-1]:\n",
    "            line = '\\\\textbf{' + f'{r}' + '} & \\\\textbf{' + f'  {k}' + '}' + f' & ' + '\\\\textbf{' + '\\\\{' + ', '.join(accs) + '\\\\} '  + '}' + ' & \\\\textbf{' + f'{probe_avg:0.0f}' + '} & \\\\textbf{ ' + f'{frac_probes:0.4f}' + '} \\\\\\\\ '\n",
    "        else:\n",
    "            line = f'{r} & {k} & ' + '$\\\\{ ' + ','.join(accs) + ' \\\\}$ & ' + f'${probe_avg:0.0f}$ & ${frac_probes:0.4f}$' + ' \\\\\\\\ '\n",
    "        print(line)\n",
    "print('\\\\end{tabular}')\n",
    "        # print(res_by_r_k[(r,k)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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