Matching Nets.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"IterResLSTM.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true,"machine_shape":"hm","mount_file_id":"18ufznr1D1G7j_wYQ_eBkt0Qg5cOn9TNs","authorship_tag":"ABX9TyOV8S4jt3nsf/lS1Hrnk4VI"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SqOnVRENlALC","executionInfo":{"status":"ok","timestamp":1637701971455,"user_tz":-60,"elapsed":905,"user":{"displayName":"Daniel Vella","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GitatTDk380OCMvv2bDBT7Lsunn79WNpphzKI_dytU=s64","userId":"08737126295476082217"}},"outputId":"0cc1f300-1305-4e28-c56c-2ccfe4da9ba9"},"source":["!pip install -q condacolab\n","import condacolab\n","condacolab.install()"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["✨🍰✨ Everything looks OK!\n"]}]},{"cell_type":"code","metadata":{"id":"CrNXDls2ms3f"},"source":["!conda install -c chembl chembl_structure_pipeline\n","import chembl_structure_pipeline\n","from chembl_structure_pipeline import standardizer"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qlknBMk_u_fB","executionInfo":{"status":"ok","timestamp":1638273671335,"user_tz":-60,"elapsed":48739,"user":{"displayName":"Daniel Vella","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GitatTDk380OCMvv2bDBT7Lsunn79WNpphzKI_dytU=s64","userId":"08737126295476082217"}},"outputId":"725795d2-71b1-4d76-91b0-dfd6d7cd7065"},"source":["from IPython.display import clear_output\n","\n","# https://www.dgl.ai/pages/start.html\n","\n","# !pip install dgl\n","!pip install dgl-cu111 -f https://data.dgl.ai/wheels/repo.html # FOR CUDA VERSION\n","!pip install dgllife\n","!pip install rdkit-pypi\n","!pip install --pre deepchem\n","!pip install ipython-autotime\n","!pip install gputil\n","!pip install psutil\n","!pip install humanize\n","\n","%load_ext autotime\n","\n","clear = clear_output()"],"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["time: 4.98 ms (started: 2021-11-30 12:01:10 +00:00)\n"]}]},{"cell_type":"code","metadata":{"id":"KKaliKKjuaEB","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1638273681544,"user_tz":-60,"elapsed":10211,"user":{"displayName":"Daniel Vella","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GitatTDk380OCMvv2bDBT7Lsunn79WNpphzKI_dytU=s64","userId":"08737126295476082217"}},"outputId":"7763e671-931a-45f9-ef55-f29190bd6706"},"source":["import os\n","from os import path\n","import statistics\n","import warnings\n","import random\n","import time \n","import itertools\n","import psutil\n","import humanize\n","import GPUtil as GPU\n","import subprocess\n","from datetime import datetime, timedelta\n","\n","import matplotlib.pyplot as plt\n","\n","import pandas as pd\n","import numpy as np\n","\n","import tqdm\n","from tqdm import trange, tqdm_notebook, tnrange\n","\n","import deepchem as dc\n","import rdkit\n","from rdkit import Chem\n","from rdkit.Chem.MolStandardize import rdMolStandardize\n","\n","import dgl\n","from dgl.dataloading import GraphDataLoader\n","from dgl.nn import GraphConv, SumPooling, MaxPooling\n","import dgl.function as fn\n","import dgllife\n","from dgllife import utils\n","\n","# embedding\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","from torch.autograd import Variable\n","from torch.optim.lr_scheduler import ReduceLROnPlateau\n","from torch.profiler import profile, record_function, ProfilerActivity\n","from torch.utils.tensorboard import SummaryWriter\n","\n","\n","import sklearn\n","from sklearn.metrics import (auc, roc_curve, roc_auc_score, average_precision_score, \n","                             accuracy_score, ConfusionMatrixDisplay, confusion_matrix, precision_recall_curve, \n","                             f1_score, PrecisionRecallDisplay)\n","from sklearn.ensemble import RandomForestClassifier\n","\n","warnings.filterwarnings(\"ignore\", message=\"DGLGraph.__len__\")\n","\n","DGLBACKEND = 'pytorch'\n","\n","clear\n","\n","def get_cmd_output(command):\n","  return subprocess.check_output(command,\n","                        stderr=subprocess.STDOUT,\n","                        shell=True).decode('UTF-8')"],"execution_count":2,"outputs":[{"output_type":"stream","name":"stderr","text":["DGL backend not selected or invalid.  Assuming PyTorch for now.\n"]},{"output_type":"stream","name":"stdout","text":["Setting the default backend to \"pytorch\". You can change it in the ~/.dgl/config.json file or export the DGLBACKEND environment variable.  Valid options are: pytorch, mxnet, tensorflow (all lowercase)\n"]},{"output_type":"stream","name":"stderr","text":["Using backend: pytorch\n"]},{"output_type":"stream","name":"stdout","text":["time: 10.2 s (started: 2021-11-30 12:01:10 +00:00)\n"]}]},{"cell_type":"markdown","metadata":{"id":"QjBkn6NV6fZ2"},"source":["## Create Dataset"]},{"cell_type":"code","metadata":{"id":"Wo5FGOnIaipm"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jbuXwHAZfO4m","executionInfo":{"status":"ok","timestamp":1638273895767,"user_tz":-60,"elapsed":310,"user":{"displayName":"Daniel Vella","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GitatTDk380OCMvv2bDBT7Lsunn79WNpphzKI_dytU=s64","userId":"08737126295476082217"}},"outputId":"95becab2-9a77-4d32-f7d7-d8ab11af79ef"},"source":["def create_dataset(df, name, bonds):\n","    print(f\"Creating Dataset and Saving to {drive_path}/data/{name}.pkl\")\n","    data = df.sample(frac=1)\n","    data = data.reset_index(drop=True)\n","    data['mol'] = data['smiles'].apply(lambda x: create_dgl_features(x, bonds))\n","    data.to_pickle(f\"{drive_path}/data/{name}.pkl\")\n","    return data\n","\n","def featurize_atoms(mol):\n","  feats = []\n","  \n","  atom_features = utils.ConcatFeaturizer([\n","    utils.atom_type_one_hot,\n","    utils.atomic_number_one_hot,\n","    utils.atom_degree_one_hot,\n","    utils.atom_explicit_valence_one_hot,\n","    utils.atom_formal_charge_one_hot,\n","    utils.atom_num_radical_electrons_one_hot,\n","    utils.atom_hybridization_one_hot,\n","    utils.atom_is_aromatic_one_hot\n","  ])\n","\n","  for atom in mol.GetAtoms():\n","    feats.append(atom_features(atom))\n","  return {'feats': torch.tensor(feats).float()}\n","\n","def featurize_bonds(mol):\n","  feats = []\n","  \n","  bond_features = utils.ConcatFeaturizer([\n","    utils.bond_type_one_hot,\n","    utils.bond_is_conjugated_one_hot,\n","    utils.bond_is_in_ring_one_hot,\n","    utils.bond_stereo_one_hot,\n","    utils.bond_direction_one_hot,\n","  ])\n","\n","  for bond in mol.GetBonds():\n","    feats.append(bond_features(bond))\n","    feats.append(bond_features(bond))\n","  return {'edge_feats': torch.tensor(feats).float()}\n","\n","def create_dgl_features(smiles, bonds):\n","    mol = Chem.MolFromSmiles(smiles)\n","    mol = standardizer.standardize_mol(mol)\n","    \n","    if bonds:\n","        dgl_graph = utils.mol_to_bigraph(mol=mol,\n","                                    node_featurizer=featurize_atoms,\n","                                    edge_featurizer=featurize_bonds,\n","                                    canonical_atom_order=True)\n","    else:\n","      dgl_graph = utils.mol_to_bigraph(mol=mol,\n","                                    node_featurizer=featurize_atoms,\n","                                    canonical_atom_order=True)\n","    \n","    dgl_graph = dgl.add_self_loop(dgl_graph)\n","    \n","    return dgl_graph\n","\n","def load_dataset(dataset, bonds=False, feat='graph', create_new=False):\n","  \"\"\"\n","    dataset values: muv, tox21, dude-gpcr\n","    feat values: graph, ecfp\n","  \"\"\"\n","  dataset_test_tasks = {\n","    'tox21': ['SR-HSE', 'SR-MMP', 'SR-p53'],\n","    'muv': ['MUV-832', 'MUV-846', 'MUV-852', 'MUV-858', 'MUV-859'],\n","    'dude-gpcr': ['adrb2', 'cxcr4']\n","  }\n","\n","  dataset_original = dataset\n","\n","  if bonds:\n","    dataset = dataset + \"_with_bonds\"\n","\n","  if path.exists(f\"{drive_path}/data/{dataset}_dgl.pkl\") and not create_new:\n","    # Load Dataset\n","    print(\"Reading Pickle\")\n","    if feat == 'graph':\n","      data = pd.read_pickle(f\"{drive_path}/data/{dataset}_dgl.pkl\")\n","    else:\n","      data = pd.read_pickle(f\"{drive_path}/data/{dataset}_ecfp.pkl\")\n","  else:\n","    # Create Dataset\n","    df = pd.read_csv(f\"{drive_path}/data/raw/{dataset_original}.csv\")\n","    if feat == 'graph':\n","      data = create_dataset(df, f\"{dataset}_dgl\", bonds)\n","    else:\n","      data = create_ecfp_dataset(df, f\"{dataset}_ecfp\")\n","\n","  test_tasks = dataset_test_tasks.get(dataset_original)\n","  drop_cols = test_tasks.copy()\n","  drop_cols.extend(['mol_id', 'smiles', 'mol'])\n","  train_tasks = [x for x in list(data.columns) if x not in drop_cols]\n","\n","  train_dfs = dict.fromkeys(train_tasks)\n","  for task in train_tasks:\n","      df = data[[task, 'mol']].dropna()\n","      df.columns = ['y', 'mol']\n","      # FOR BOND INFORMATION\n","      if with_bonds:\n","        for index, r in df.iterrows():\n","          if r.mol.edata['edge_feats'].shape[-1] < 17:\n","            df.drop(index, inplace=True)\n","      train_dfs[task] = df\n","  for key in train_dfs:\n","    print(key, len(train_dfs[key]))\n","\n","  if feat == 'graph':\n","    feat_length = data.iloc[0].mol.ndata['feats'].shape[-1]\n","    print(\"Feature Length\", feat_length)\n","\n","    if with_bonds:\n","      feat_length = data.iloc[0].mol.edata['edge_feats'].shape[-1]\n","      print(\"Feature Length\", feat_length)\n","    else:\n","      print(\"Edge Features: \", with_bonds)\n","\n","  test_dfs = dict.fromkeys(test_tasks)\n","\n","  for task in test_tasks:\n","      df = data[[task, 'mol']].dropna()\n","      df.columns = ['y', 'mol']\n","\n","      # FOR BOND INFORMATION\n","      if with_bonds:\n","        for index, r in df.iterrows():\n","          if r.mol.edata['edge_feats'].shape[-1] < 17:\n","            df.drop(index, inplace=True)\n","\n","      test_dfs[task] = df\n","\n","  for key in test_dfs:\n","    print(key, len(test_dfs[key]))\n","\n","  # return data, train_tasks, test_tasks\n","  return train_tasks, train_dfs, test_tasks, test_dfs"],"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["time: 166 ms (started: 2021-11-30 12:04:55 +00:00)\n"]}]},{"cell_type":"code","metadata":{"id":"7t3rDXr3L5Ps"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Kk2W2nRnBghe"},"source":["## Create Episode"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dHdJ65wMBh_u","executionInfo":{"status":"ok","timestamp":1638273898075,"user_tz":-60,"elapsed":403,"user":{"displayName":"Daniel Vella","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GitatTDk380OCMvv2bDBT7Lsunn79WNpphzKI_dytU=s64","userId":"08737126295476082217"}},"outputId":"e1c3577f-c177-4af9-a412-f6657b95ee10"},"source":["def create_episode(n_support_pos, n_support_neg, n_query, data, test=False, train_balanced=True):\n","  \"\"\"\n","    n_query = per class data points\n","    Xy = dataframe dataset in format [['y', 'mol']]\n","  \"\"\"\n","  support = []\n","  query = []\n","\n","  n_query_pos = n_query\n","  n_query_neg = n_query\n","\n","  support_neg = data[data['y'] == 0].sample(n_support_neg)\n","  support_pos = data[data['y'] == 1].sample(n_support_pos)\n","\n","  # organise support by class in array dimensions\n","  support.append(support_neg.to_numpy())\n","  support.append(support_pos.to_numpy())\n","  support = np.array(support, dtype=object)\n","  support_X = [rec[1] for sup_class in support for rec in sup_class]\n","  support_y = np.asarray([rec[0] for sup_class in support for rec in sup_class], dtype=np.float16).flatten()\n","\n","  data = data.drop(support_neg.index)\n","  data = data.drop(support_pos.index)\n","\n","  if len(data[data['y'] == 1]) < n_query:\n","    n_query_pos = len(data[data['y'] == 1])\n","\n","  if test:\n","    # test uses all data remaining\n","    query_neg = data[data['y'] == 0].to_numpy()\n","    query_pos = data[data['y'] == 1].to_numpy()\n","  elif (not test) and train_balanced:\n","    # for balanced queries, same size as support\n","    query_neg = data[data['y'] == 0].sample(n_query_neg).to_numpy()\n","    query_pos = data[data['y'] == 1].sample(n_query_pos).to_numpy()\n","  elif (not test) and (not train_balanced):\n","    # print('test')\n","    query_neg = data[data['y'] == 0].sample(1).to_numpy()\n","    query_pos = data[data['y'] == 1].sample(1).to_numpy()\n","\n","    query_rem = data.sample(n_query*2 - 2)\n","    query_neg_rem = query_rem[query_rem['y'] == 0].to_numpy()\n","    query_pos_rem = query_rem[query_rem['y'] == 1].to_numpy()\n","\n","    query_neg = np.concatenate((query_neg, query_neg_rem))\n","    query_pos = np.concatenate((query_pos, query_pos_rem), axis=0)\n","\n","  query_X = np.concatenate([query_neg[:, 1], query_pos[:, 1]])\n","  query_y = np.concatenate([query_neg[:, 0], query_pos[:, 0]])\n","  \n","  return support_X, support_y, query_X, query_y"],"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["time: 40.9 ms (started: 2021-11-30 12:04:57 +00:00)\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3vMHH8BIBsaJ","executionInfo":{"status":"ok","timestamp":1638054595153,"user_tz":-60,"elapsed":25,"user":{"displayName":"Daniel Vella","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GitatTDk380OCMvv2bDBT7Lsunn79WNpphzKI_dytU=s64","userId":"08737126295476082217"}},"outputId":"cd602161-0071-47e2-e368-508a3d4bcacc"},"source":["# task = 'NR-AR'\n","# df = data[[task, 'mol']]\n","# df = df.dropna()\n","# df.columns = ['y', 'mol']\n","\n","# support_X, support_y, query_X, query_y = create_episode(1, 1, 64, df)\n","\n","# support_y\n","\n","\n","# testing\n","# support = []\n","# query = []\n","\n","# support_neg = df[df['y'] == 0].sample(2)\n","# support_pos = df[df['y'] == 1].sample(2)\n","\n","# # organise support by class in array dimensions\n","# support.append(support_neg.to_numpy())\n","# support.append(support_pos.to_numpy())\n","# support = np.array(support)\n","# support.shape\n","\n","# support[:, :, 1]"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["time: 2.4 ms (started: 2021-11-27 23:09:54 +00:00)\n"]}]},{"cell_type":"markdown","metadata":{"id":"xJK4aI5N60N4"},"source":["## Graph Embedding"]},{"cell_type":"code","metadata":{"id":"YjSMfBvkeYmk","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1638273901071,"user_tz":-60,"elapsed":337,"user":{"displayName":"Daniel Vella","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GitatTDk380OCMvv2bDBT7Lsunn79WNpphzKI_dytU=s64","userId":"08737126295476082217"}},"outputId":"33329ce1-9cbe-4007-e67b-615185b7876d"},"source":["class GCN(nn.Module):\n","    def __init__(self, in_channels, out_channels=128):\n","      super(GCN, self).__init__()\n","      self.conv1 = GraphConv(in_channels, 64)\n","      self.conv2 = GraphConv(64, 128)\n","      self.conv3 = GraphConv(128, 64)\n","      self.sum_pool = SumPooling()\n","      self.dense = nn.Linear(64, out_channels)\n","\n","    def forward(self, graph, in_feat):\n","        h = self.conv1(graph, in_feat)\n","        h = F.relu(h)\n","        graph.ndata['h'] = h       \n","        graph.update_all(fn.copy_u('h', 'm'), fn.max('m', 'h'))\n","      \n","        h = self.conv2(graph, graph.ndata['h'])\n","        h = F.relu(h)\n","        graph.ndata['h'] = h\n","        graph.update_all(fn.copy_u('h', 'm'), fn.max('m', 'h'))\n","\n","        h = self.conv3(graph, graph.ndata['h'])\n","        h = F.relu(h)\n","        graph.ndata['h'] = h\n","        graph.update_all(fn.copy_u('h', 'm'), fn.max('m', 'h'))\n","\n","        output = self.sum_pool(graph, graph.ndata['h'])\n","        output = torch.tanh(output)\n","        output = self.dense(output)\n","        output = torch.tanh(output)\n","        return output"],"execution_count":7,"outputs":[{"output_type":"stream","name":"stdout","text":["time: 19 ms (started: 2021-11-30 12:05:00 +00:00)\n"]}]},{"cell_type":"markdown","metadata":{"id":"ytH7laPC68s1"},"source":["## Matching Networks\n","\n","[GitABCWorld - Matching Networks](https://github.com/gitabcworld/MatchingNetworks)\n","\n"]},{"cell_type":"markdown","metadata":{"id":"JVwXEmOl6zXV"},"source":["### Distance Fn"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YamfmesX7BFj","executionInfo":{"status":"ok","timestamp":1638273903963,"user_tz":-60,"elapsed":731,"user":{"displayName":"Daniel Vella","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GitatTDk380OCMvv2bDBT7Lsunn79WNpphzKI_dytU=s64","userId":"08737126295476082217"}},"outputId":"0809292b-0fec-4738-b4c5-b389703ce9ed"},"source":["class DistanceNetwork(nn.Module):\n","  \"\"\"\n","  Produces pdfs over the support set classes for the target set image.\n","  :param support_set: The embeddings of the support set images, tensor of shape [sequence_length, batch_size, 64]\n","  :param input_image: The embedding of the target image, tensor of shape [batch_size, 64]\n","  :return: Softmax pdf. Tensor with cosine similarities of shape [batch_size, sequence_length]\n","  \"\"\"\n","\n","  def __init__(self):\n","    super(DistanceNetwork, self).__init__()\n","\n","  def forward(self, support_set, query_mol):\n","    similarities = []\n","    \n","    for support_mol in support_set:\n","      similarities.append(nn.CosineSimilarity(dim=0)(support_mol.unsqueeze(1), query_mol))\n","\n","    similarities = torch.stack(similarities)\n","    return similarities"],"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["time: 6.28 ms (started: 2021-11-30 12:05:03 +00:00)\n"]}]},{"cell_type":"markdown","metadata":{"id":"2QqrX6P6iWcN"},"source":["### ResiLSTM Embedding\n","\n","Unlike the AttnLSTM model which only modifies the test vectors additively, this model allows for an additive update to be performed to both test and support using information from each other.\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nxHK4ZTfnd53","executionInfo":{"status":"ok","timestamp":1638273906841,"user_tz":-60,"elapsed":15,"user":{"displayName":"Daniel Vella","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GitatTDk380OCMvv2bDBT7Lsunn79WNpphzKI_dytU=s64","userId":"08737126295476082217"}},"outputId":"aa80e55b-aeac-4081-da49-fe898f81a420"},"source":["def cos(x, y):\n","  transpose_shape = tuple(list(range(len(y.shape)))[::-1])\n","\n","  x = x.float()\n","\n","  denom = (\n","      torch.sqrt(torch.sum(torch.square(x)) *\n","                     torch.sum(torch.square(y))) + torch.finfo(torch.float32).eps)\n","\n","  return torch.matmul(x, torch.permute(y, transpose_shape)) / denom\n"],"execution_count":9,"outputs":[{"output_type":"stream","name":"stdout","text":["time: 4.15 ms (started: 2021-11-30 12:05:05 +00:00)\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3asnWE8Kr363","executionInfo":{"status":"ok","timestamp":1638273907200,"user_tz":-60,"elapsed":15,"user":{"displayName":"Daniel Vella","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GitatTDk380OCMvv2bDBT7Lsunn79WNpphzKI_dytU=s64","userId":"08737126295476082217"}},"outputId":"c9d909a2-fb8c-495e-d7ed-f2545488bab5"},"source":["class ResiLSTMEmbedding(nn.Module):\n","  def __init__(self, n_support, n_feat=128, max_depth=3):\n","    super(ResiLSTMEmbedding, self).__init__()\n","\n","    self.max_depth = max_depth\n","    self.n_support = n_support\n","    self.n_feat = n_feat\n","\n","    self.support_lstm = nn.LSTMCell(input_size=2*self.n_feat, hidden_size=self.n_feat)\n","    self.q_init = torch.nn.Parameter(torch.zeros((self.n_support, self.n_feat), dtype=torch.float, device=\"cuda\"))\n","    self.support_states_init_h = torch.nn.Parameter(torch.zeros(self.n_support, self.n_feat))\n","    self.support_states_init_c = torch.nn.Parameter(torch.zeros(self.n_support, self.n_feat))\n","  \n","    self.query_lstm = nn.LSTMCell(input_size=2*self.n_feat, hidden_size=self.n_feat)\n","\n","    if torch.cuda.is_available():\n","      self.support_lstm = self.support_lstm.cuda()\n","      self.query_lstm = self.query_lstm.cuda()\n","      self.q_init = self.q_init.cuda()\n","  \n","  def forward(self, x_support, x_query):\n","    self.p_init = torch.zeros((len(x_query), self.n_feat)).to(device)\n","    self.query_states_init_h = torch.zeros(len(x_query), self.n_feat).to(device)\n","    self.query_states_init_c = torch.zeros(len(x_query), self.n_feat).to(device)\n","\n","    x_support = x_support\n","    x_query = x_query\n","    z_support = x_support\n","    q = self.q_init\n","    p = self.p_init\n","    support_states_h = self.support_states_init_h\n","    support_states_c = self.support_states_init_c\n","    query_states_h = self.query_states_init_h\n","    query_states_c = self.query_states_init_c\n","\n","    for i in range(self.max_depth):\n","      sup_e = cos(z_support + q, x_support)\n","      sup_a = torch.nn.functional.softmax(sup_e, dim=-1)\n","      sup_r = torch.matmul(sup_a, x_support).float()\n","\n","      query_e = cos(x_query + p, z_support)\n","      query_a = torch.nn.functional.softmax(query_e, dim=-1)\n","      query_r = torch.matmul(query_a, z_support).float()\n","\n","      sup_qr = torch.cat((q, sup_r), 1)\n","      support_hidden, support_out = self.support_lstm(sup_qr, (support_states_h, support_states_c))\n","      q = support_hidden\n","\n","      query_pr = torch.cat((p, query_r), 1)\n","      query_hidden, query_out = self.query_lstm(query_pr, (query_states_h, query_states_c))\n","      p = query_hidden\n","\n","      z_support = sup_r\n","\n","    return x_support + q, x_query + p"],"execution_count":10,"outputs":[{"output_type":"stream","name":"stdout","text":["time: 60.3 ms (started: 2021-11-30 12:05:06 +00:00)\n"]}]},{"cell_type":"markdown","metadata":{"id":"PFxygGPfo6oQ"},"source":["### Matching Net"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_F2MNvI9o9K_","executionInfo":{"status":"ok","timestamp":1638273909919,"user_tz":-60,"elapsed":13,"user":{"displayName":"Daniel Vella","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GitatTDk380OCMvv2bDBT7Lsunn79WNpphzKI_dytU=s64","userId":"08737126295476082217"}},"outputId":"72a5abb2-8611-46c7-c575-7c6ac63f5784"},"source":["class MatchingNet(nn.Module):\n","  def __init__(self, with_bonds=False):\n","    \"\"\"\n","    Matching Network\n","    \"\"\"\n","    super(MatchingNet, self).__init__()\n","\n","    self.dn = DistanceNetwork()\n","\n","    if torch.cuda.is_available():\n","      self.dn = self.dn.cuda()\n","\n","  def forward(self, emb_support, emb_query, one_hot_support_y):\n","\n","    y_hats = torch.tensor([])\n","    pred_logits = torch.tensor([])\n","\n","    if torch.cuda.is_available(): \n","      pred_logits = pred_logits.cuda()\n","      y_hats = y_hats.cuda()\n","    \n","    for i, query in enumerate(emb_query):\n","        similarities = self.dn(emb_support, query.unsqueeze(0).permute(1, 0))\n","\n","        # attention = torch.nn.functional.softmax(similarities, dim=-1) # might delete\n","        attention = similarities\n","\n","        prediction_logits = torch.matmul(attention.squeeze(1).float(), one_hot_support_y.float()).unsqueeze(0)\n","\n","        pred_logits = torch.cat((pred_logits, prediction_logits), 0)\n","\n","    return pred_logits"],"execution_count":11,"outputs":[{"output_type":"stream","name":"stdout","text":["time: 17.6 ms (started: 2021-11-30 12:05:09 +00:00)\n"]}]},{"cell_type":"markdown","metadata":{"id":"ch0Z-cFCBnfm"},"source":["## Training Loop"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pShZJvrVhpKI","executionInfo":{"status":"ok","timestamp":1638274114264,"user_tz":-60,"elapsed":267,"user":{"displayName":"Daniel Vella","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GitatTDk380OCMvv2bDBT7Lsunn79WNpphzKI_dytU=s64","userId":"08737126295476082217"}},"outputId":"854aef0e-2b34-4ac6-e604-575ba51d629c"},"source":["def train(train_tasks, train_dfs, balanced_queries, k_pos, k_neg, n_query, episodes, lr):\n","  \n","  writer = SummaryWriter()\n","  start_time = time.time()\n","\n","  node_feat_size = 177\n","  embedding_size = 128\n","\n","  encoder = GCN(node_feat_size, embedding_size)\n","  resi_lstm = ResiLSTMEmbedding(k_pos+k_neg)\n","  matching_net = MatchingNet()\n","  loss_fn = nn.MSELoss()\n","\n","  if torch.cuda.is_available(): \n","    encoder = encoder.cuda()\n","    resi_lstm = resi_lstm.cuda()\n","    matching_net = matching_net.cuda()\n","    loss_fn = loss_fn.cuda()\n","\n","  encoder_optimizer = torch.optim.Adam(encoder.parameters(), lr = lr)\n","  lstm_optimizer = torch.optim.Adam(resi_lstm.parameters(), lr = lr)\n","  \n","  encoder_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(encoder_optimizer, patience=200, verbose=False)\n","  lstm_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(lstm_optimizer, patience=200, verbose=False)\n","  \n","  episode_num = 1\n","  early_stop = False\n","  losses = []\n","  \n","  running_loss = 0.0\n","  running_acc = 0.0\n","  running_roc = 0.0\n","  running_prc = 0.0\n","\n","  pbar = trange(episodes, desc=f\"Training\")\n","  for episode in pbar:\n","    episode_loss = 0.0\n","\n","    # SET TRAINING MODE\n","    encoder.train()\n","    resi_lstm.train()\n","    matching_net.train()\n","\n","    # RANDOMISE ORDER OF TASKS PER EPISODE\n","    shuffled_train_tasks = random.sample(train_tasks, len(train_tasks))\n","\n","    # LOOP OVER TASKS\n","    for task in shuffled_train_tasks:\n","      X = train_dfs[task]\n","      X_support, y_support, X_query, y_query = create_episode(k_pos, k_neg, n_query, X, False, balanced_queries)\n","\n","      total_query = int((y_query == 0).sum() + (y_query == 1).sum())\n","\n","      target_inds = torch.from_numpy(y_query.astype('float32')).float()\n","      target_inds = target_inds.unsqueeze(1).type(torch.int64)\n","      targets = Variable(target_inds, requires_grad=False).to(device)\n","\n","      # ONE HOT SUPPORT TARGETS\n","      y_support = torch.tensor(y_support, dtype=int)\n","      support_set_one_hot_y = F.one_hot(y_support, num_classes=2)\n","      \n","      # ONE HOT QUERY TARGETS\n","      query_targets = torch.from_numpy(y_query.astype('int'))\n","      one_hot_queries = torch.zeros(total_query, 2) # n-way 2\n","      one_hot_queries[torch.arange(total_query), query_targets] = 1\n","      one_hot = torch.autograd.Variable(one_hot_queries)\n","\n","      if torch.cuda.is_available(): \n","        targets=targets.cuda()\n","        one_hot_queries=one_hot_queries.cuda()\n","        support_set_one_hot_y = support_set_one_hot_y.cuda()\n","\n","      n_support = k_pos + k_neg\n","      X = X_support + list(X_query)\n","\n","      # CREATE EMBEDDINGS\n","      dataloader = GraphDataLoader(X, batch_size=(n_support + total_query), shuffle=False, pin_memory=True)\n","      for graph in dataloader:\n","        graph = graph.to(device)\n","        \n","        if with_bonds:\n","          embeddings = encoder.forward(graph, (graph.ndata['feats'].float(), graph.edata['edge_feats'].float())) # FOR NODES + EDGES ONLY\n","        else:\n","          embeddings = encoder.forward(graph, graph.ndata['feats'].float()) # FOR NODES ONLY\n","\n","      # LSTM EMBEDDINGS\n","      emb_support = embeddings[:n_support]\n","      emb_query = embeddings[n_support:]\n","      lstm_support, lstm_query = resi_lstm(emb_support, emb_query)\n","\n","      # def forward(self, emb_support, emb_query, one_hot_support_y, k_pos, k_neg):\n","\n","      # MATCHING NET\n","      logits = matching_net(lstm_support, lstm_query, support_set_one_hot_y)\n","\n","      # LOSS FUNCTION\n","      loss = loss_fn(logits, one_hot_queries)\n","\n","      # BACK PROP\n","      encoder.zero_grad()\n","      resi_lstm.zero_grad()\n","      matching_net.zero_grad()\n","\n","      loss.backward()\n","          \n","      encoder_optimizer.step()\n","      lstm_optimizer.step()\n","\n","      _, y_hats = logits.max(1)\n","      \n","      preds = y_hats.detach().cpu()\n","\n","      roc = roc_auc_score(query_targets, preds)\n","      prc = average_precision_score(query_targets, preds)\n","      acc = accuracy_score(query_targets, preds)\n","\n","      episode_loss += loss.item()\n","      running_loss += loss.item()\n","      running_acc += acc\n","      running_roc += roc\n","      running_prc += prc\n","\n","    pbar.set_description(f\"Episode {episode_num} - Loss {loss.item():.6f} - Acc {acc:.4f} - LR {encoder_optimizer.param_groups[0]['lr']}\")\n","    pbar.refresh()\n","\n","    losses.append(episode_loss / len(train_tasks))\n","    writer.add_scalar('Loss/train', episode_loss / len(train_tasks), episode_num)\n","\n","    if encoder_optimizer.param_groups[0]['lr'] < 0.000001:\n","      break # EARLY STOP\n","    elif episode_num < episodes:\n","      episode_num += 1\n","\n","    encoder_scheduler.step(loss)\n","    lstm_scheduler.step(loss)\n","\n","  epoch_loss = running_loss / (episode_num*len(train_tasks))\n","  epoch_acc = running_acc / (episode_num*len(train_tasks))\n","  epoch_roc = running_roc / (episode_num*len(train_tasks))\n","  epoch_prc = running_prc / (episode_num*len(train_tasks))\n","\n","  print(f'Loss: {epoch_loss:.5f} Acc: {epoch_acc:.4f} ROC: {epoch_roc:.4f} PRC: {epoch_prc:.4f}')\n","\n","  end_time = time.time()\n","\n","  train_info = {\n","      \"losses\": losses,\n","      \"duration\": str(timedelta(seconds=(end_time - start_time))),\n","      \"episodes\": episode_num,\n","      \"train_roc\": epoch_roc,\n","      \"train_prc\": epoch_prc\n","  }\n","\n","  return encoder, resi_lstm, matching_net, train_info"],"execution_count":18,"outputs":[{"output_type":"stream","name":"stdout","text":["time: 235 ms (started: 2021-11-30 12:08:33 +00:00)\n"]}]},{"cell_type":"markdown","metadata":{"id":"xA5c4_1KtMtW"},"source":["## Testing Loop"]},{"cell_type":"code","metadata":{"id":"KkdoG63lBmyz","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1638273914113,"user_tz":-60,"elapsed":341,"user":{"displayName":"Daniel Vella","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GitatTDk380OCMvv2bDBT7Lsunn79WNpphzKI_dytU=s64","userId":"08737126295476082217"}},"outputId":"1655051d-a73c-41e9-e36c-3276dd75eacc"},"source":["def test(encoder, lstm, matching_net, test_tasks, test_dfs, k_pos, k_neg, rounds):\n","  encoder.eval()\n","  lstm.eval()\n","  matching_net.eval()\n","\n","  test_info = {}\n","\n","  with torch.no_grad():\n","    for task in test_tasks:\n","\n","      Xy = test_dfs[task]\n","\n","      running_loss = []\n","      running_acc = []\n","      running_roc = [0]\n","      running_prc = [0]\n","\n","      running_preds = []\n","      running_targets = []\n","      running_actuals = []\n","\n","      for round in trange(rounds):\n","        X_support, y_support, X_query, y_query = create_episode(k_pos, k_neg, n_query=0, data=Xy, test=True, train_balanced=False)\n","        \n","        total_query = int((y_query == 0).sum() + (y_query == 1).sum())\n","        n_support = k_pos + k_neg\n","        X = X_support + list(X_query)\n","\n","        # ONE HOT SUPPORT TARGETS\n","        y_support = torch.tensor(y_support, dtype=int)\n","        support_set_one_hot_y = F.one_hot(y_support, num_classes=2)\n","\n","        if torch.cuda.is_available(): \n","          support_set_one_hot_y = support_set_one_hot_y.cuda()\n","\n","        dataloader = GraphDataLoader(X, batch_size=(n_support + total_query), shuffle=False, pin_memory=True)\n","        for graph in dataloader:\n","          graph = graph.to(device)\n","          if with_bonds:\n","            embeddings = encoder.forward(graph, (graph.ndata['feats'].float(), graph.edata['edge_feats'].float())) # FOR NODES + EDGES ONLY\n","          else:\n","            embeddings = encoder.forward(graph, graph.ndata['feats'].float()) # FOR NODES ONLY\n","\n","        emb_support = embeddings[:n_support]\n","        emb_query = embeddings[n_support:]\n","        emb_support, emb_query = lstm(emb_support, emb_query)\n","\n","        logits = matching_net(emb_support, emb_query, support_set_one_hot_y)\n","\n","        # _, y_hat = logits.max(1)\n","        _, y_hat_actual = logits.max(1)\n","        y_hat = logits[:, 1]\n","\n","        target_inds = torch.from_numpy(y_query.astype('float32')).float()\n","        target_inds = target_inds.unsqueeze(1).type(torch.int64)\n","        targets = Variable(target_inds, requires_grad=False)\n","        y_hat = y_hat.squeeze().detach().cpu()\n","\n","        roc = roc_auc_score(targets, y_hat)\n","        prc = average_precision_score(targets, y_hat)\n","        # acc = accuracy_score(targets, y_hat)\n","\n","        running_preds.append(y_hat)\n","        running_actuals.append(y_hat_actual)\n","        running_targets.append(targets)\n","\n","        # running_acc.append(acc)\n","        running_roc.append(roc)\n","        running_prc.append(prc)\n","\n","      median_index = running_roc.index(statistics.median(running_roc))\n","      if median_index == rounds:\n","        median_index = median_index - 1\n","      chart_preds = running_preds[median_index]\n","      chart_actuals = running_actuals[median_index].detach().cpu()\n","      chart_targets = running_targets[median_index]\n","\n","      c_auc = roc_auc_score(chart_targets, chart_preds)\n","      c_fpr, c_tpr, _ = roc_curve(chart_targets, chart_preds)\n","      plt.plot(c_fpr, c_tpr, marker='.', label = 'AUC = %0.2f' % c_auc)\n","      plt.plot([0, 1], [0, 1],'r--', label='No Skill')\n","      # plt.plot([0, 0, 1], [0, 1, 1], 'g--', label='Perfect Classifier')\n","      plt.title('Receiver Operating Characteristic')\n","      plt.xlabel('False Positive Rate')\n","      plt.ylabel('True Positive Rate')\n","      plt.legend(loc = 'best')\n","      plt.savefig(f\"{drive_path}/{method_dir}/graphs/roc_{dataset}_{task}_pos{n_pos}_neg{n_neg}.png\")\n","      plt.figure().clear()\n","\n","      # prc_graph = PrecisionRecallDisplay.from_predictions(chart_targets, chart_preds)\n","      c_precision, c_recall, _ = precision_recall_curve(chart_targets, chart_preds)\n","      plt.title('Precision Recall Curve')\n","      # plt.plot([0, 1], [0, 0], 'r--', label='No Skill')\n","      no_skill = len(chart_targets[chart_targets==1]) / len(chart_targets)\n","      plt.plot([0, 1], [no_skill, no_skill], linestyle='--', label='No Skill')\n","      # plt.plot([0, 1, 1], [1, 1, 0], 'g--', label='Perfect Classifier')\n","      plt.plot(c_recall, c_precision, marker='.', label = 'AUC = %0.2f' % auc(c_recall, c_precision))\n","      plt.xlabel('Recall')\n","      plt.ylabel('Precision')\n","      plt.legend(loc = 'best')\n","      plt.savefig(f\"{drive_path}/{method_dir}/graphs/prc_{dataset}_{task}_pos{n_pos}_neg{n_neg}.png\")\n","      plt.figure().clear()\n","\n","      cm = ConfusionMatrixDisplay.from_predictions(chart_targets, chart_actuals)\n","      plt.title('Confusion Matrix')\n","      plt.savefig(f\"{drive_path}/{method_dir}/graphs/cm_{dataset}_{task}_pos{n_pos}_neg{n_neg}.png\")\n","      plt.figure().clear()\n","\n","      running_roc.pop(0) # remove the added 0\n","      running_prc.pop(0) # remove the added 0 \n","      # round_acc = f\"{statistics.mean(running_acc):.3f} \\u00B1 {statistics.stdev(running_acc):.3f}\"\n","      round_roc = f\"{statistics.mean(running_roc):.3f} \\u00B1 {statistics.stdev(running_roc):.3f}\"\n","      round_prc = f\"{statistics.mean(running_prc):.3f} \\u00B1 {statistics.stdev(running_prc):.3f}\"\n","\n","      test_info[task] = {\n","          # \"acc\": round_acc,\n","          \"roc\": round_roc,\n","          \"prc\": round_prc,\n","          \"roc_values\": running_roc,\n","          \"prc_values\": running_prc\n","      }\n","\n","      print(f'Test {task}')\n","      # print(f\"Acc: {round_acc}\")\n","      print(f\"ROC: {round_roc}\")\n","      print(f\"PRC: {round_prc}\")\n","\n","    return targets, y_hat, test_info"],"execution_count":12,"outputs":[{"output_type":"stream","name":"stdout","text":["time: 174 ms (started: 2021-11-30 12:05:13 +00:00)\n"]}]},{"cell_type":"markdown","metadata":{"id":"rH3vv36nFk4F"},"source":["## Initiate Training and Testing"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZWxqavsaRZg7","executionInfo":{"status":"ok","timestamp":1638273917413,"user_tz":-60,"elapsed":313,"user":{"displayName":"Daniel Vella","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GitatTDk380OCMvv2bDBT7Lsunn79WNpphzKI_dytU=s64","userId":"08737126295476082217"}},"outputId":"d242c7ee-f6e1-4b41-e412-d827b10bbff3"},"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":13,"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n","time: 2.49 ms (started: 2021-11-30 12:05:17 +00:00)\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"tBy8ipGqFm5O","executionInfo":{"status":"ok","timestamp":1638273927956,"user_tz":-60,"elapsed":246,"user":{"displayName":"Daniel Vella","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GitatTDk380OCMvv2bDBT7Lsunn79WNpphzKI_dytU=s64","userId":"08737126295476082217"}},"outputId":"49721ff5-8c53-4a68-ef71-05706053bd96"},"source":["# PATHS\n","\n","drive_path = \"/content/drive/MyDrive/Colab Notebooks/MSC_21\"\n","method_dir = \"MatchingNets\"\n","log_path = f\"{drive_path}/{method_dir}/logs/\"\n","\n","# PARAMETERS\n","\n","dataset = 'dude-gpcr'\n","with_bonds = False \n","test_rounds = 20\n","n_query = 64 # per class\n","episodes = 5000\n","lr = 0.001\n","balanced_queries = True"],"execution_count":15,"outputs":[{"output_type":"stream","name":"stdout","text":["time: 5.49 ms (started: 2021-11-30 12:05:27 +00:00)\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dXumlZCPFt-U","executionInfo":{"status":"ok","timestamp":1638274042463,"user_tz":-60,"elapsed":57773,"user":{"displayName":"Daniel Vella","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GitatTDk380OCMvv2bDBT7Lsunn79WNpphzKI_dytU=s64","userId":"08737126295476082217"}},"outputId":"a3d44c76-9e18-401a-df29-c9e57402d139"},"source":["#FOR DETERMINISTIC REPRODUCABILITY\n","randomseed = 12\n","torch.manual_seed(randomseed) \n","np.random.seed(randomseed)\n","random.seed(randomseed)\n","torch.cuda.manual_seed(randomseed)\n","device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n","torch.backends.cudnn.is_available()\n","torch.backends.cudnn.benchmark = False # selects fastest conv algo\n","torch.backends.cudnn.deterministic = True\n","\n","# LOAD DATASET\n","# data, train_tasks, test_tasks = load_dataset(dataset, bonds=with_bonds, create_new=False)\n","\n","train_tasks, train_dfs, test_tasks, test_dfs = load_dataset(dataset, bonds=with_bonds, feat='graph', create_new=False)\n","\n","# print(dataset)\n","\n","# train_dfs = dict.fromkeys(train_tasks)\n","# for task in train_tasks:\n","#     df = data[[task, 'mol']].dropna()\n","#     df.columns = ['y', 'mol']\n","#     # FOR BOND INFORMATION\n","#     if with_bonds:\n","#       for index, r in df.iterrows():\n","#         if r.mol.edata['edge_feats'].shape[-1] < 17:\n","#           df.drop(index, inplace=True)\n","#     train_dfs[task] = df\n","# for key in train_dfs:\n","#   print(key, len(train_dfs[key]))\n","\n","# feat_length = data.iloc[0].mol.ndata['feats'].shape[-1]\n","# print(\"Feature Length\", feat_length)\n","\n","# if with_bonds:\n","#   feat_length = data.iloc[0].mol.edata['edge_feats'].shape[-1]\n","#   print(\"Feature Length\", feat_length)\n","# else:\n","#   print(\"Edge Features: \", with_bonds)\n","\n","# test_dfs = dict.fromkeys(test_tasks)\n","\n","# for task in test_tasks:\n","#     df = data[[task, 'mol']].dropna()\n","#     df.columns = ['y', 'mol']\n","\n","#     # FOR BOND INFORMATION\n","#     if with_bonds:\n","#       for index, r in df.iterrows():\n","#         if r.mol.edata['edge_feats'].shape[-1] < 17:\n","#           df.drop(index, inplace=True)\n","\n","#     test_dfs[task] = df\n","\n","# for key in test_dfs:\n","#   print(key, len(test_dfs[key]))"],"execution_count":16,"outputs":[{"output_type":"stream","name":"stdout","text":["Reading Pickle\n","aa2ar 32032\n","drd3 34530\n","adrb1 16097\n","Feature Length 177\n","Edge Features:  False\n","adrb2 15231\n","cxcr4 3446\n","time: 57.2 s (started: 2021-11-30 12:06:24 +00:00)\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"1SzJ7YiJF_Yn","executionInfo":{"status":"ok","timestamp":1638295154812,"user_tz":-60,"elapsed":8485982,"user":{"displayName":"Daniel Vella","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GitatTDk380OCMvv2bDBT7Lsunn79WNpphzKI_dytU=s64","userId":"08737126295476082217"}},"outputId":"5c821675-5130-470c-9809-0f8db89cd0de"},"source":["combinations = [\n","    # [10, 10],\n","    # # [5, 10],\n","    [1, 10],\n","    # [1, 5],\n","    # [1, 1]\n","]\n","\n","cols = [\n","  'DATE', 'CPU', 'CPU COUNT',\t'GPU', 'GPU RAM',\t'RAM',\t'CUDA',\t\n","  'REF', 'DATASET', 'ARCHITECTURE',\t\n","  'SPLIT',\t'TARGET',\t'ACCURACY',\t'ROC',\t'PRC',\n","  'ROC_VALUES', 'PRC_VALUES',\n","  'TRAIN ROC',\t'TRAIN PRC',\t'EPISODES',\t'TRAINING TIME'\n","]\n","\n","load_from_saved = False\n","\n","for comb in combinations:\n","  n_pos = comb[0]\n","  n_neg = comb[1]\n","  results = pd.DataFrame(columns=cols)\n","\n","  print(f\"\\nRUNNING {n_pos}+/{n_neg}-\")\n","\n","  if load_from_saved:\n","    encoder = GCN(177, 128)\n","    lstm = ResiLSTMEmbedding(n_pos+n_neg)\n","    matching_net = MatchingNet()\n","    encoder.load_state_dict(torch.load(f\"{drive_path}/{method_dir}/{dataset}_encoder_pos{n_pos}_neg{n_neg}.pt\"))\n","    lstm.load_state_dict(torch.load(f\"{drive_path}/{method_dir}/{dataset}_lstm_pos{n_pos}_neg{n_neg}.pt\"))\n","    matching_net.load_state_dict(torch.load(f\"{drive_path}/{method_dir}/{dataset}_mn_pos{n_pos}_neg{n_neg}.pt\"))\n","    encoder.to(device)\n","    lstm.to(device)\n","    matching_net.to(device)\n","  else:\n","    encoder, lstm, matching_net, train_info = train(train_tasks, train_dfs, balanced_queries, n_pos, n_neg, n_query, episodes, lr)\n","\n","    if with_bonds:\n","      torch.save(encoder.state_dict(), f\"{drive_path}/{method_dir}/{dataset}_encoder_pos{n_pos}_neg{n_neg}_bonds.pt\")\n","      torch.save(lstm.state_dict(), f\"{drive_path}/{method_dir}/{dataset}_lstm_pos{n_pos}_neg{n_neg}_bonds.pt\")\n","      torch.save(matching_net.state_dict(), f\"{drive_path}/{method_dir}/{dataset}_mn_pos{n_pos}_neg{n_neg}_bonds.pt\")\n","    else:\n","      torch.save(encoder.state_dict(), f\"{drive_path}/{method_dir}/{dataset}_encoder_pos{n_pos}_neg{n_neg}.pt\")\n","      torch.save(lstm.state_dict(), f\"{drive_path}/{method_dir}/{dataset}_lstm_pos{n_pos}_neg{n_neg}.pt\")\n","      torch.save(matching_net.state_dict(), f\"{drive_path}/{method_dir}/{dataset}_mn_pos{n_pos}_neg{n_neg}.pt\")\n","    \n","    loss_plot = plt.plot(train_info['losses'])[0]\n","    loss_plot.figure.savefig(f\"{drive_path}/{method_dir}/loss_plots/{dataset}_pos{n_pos}_neg{n_neg}.png\")\n","    plt.figure().clear()\n","\n","  # test_rounds\n","  targets, preds, test_info = test(encoder, lstm, matching_net, test_tasks, test_dfs, n_pos, n_neg, test_rounds)\n","\n","  dt_string = datetime.now().strftime(\"%d/%m/%Y %H:%M:%S\")\n","  cpu = get_cmd_output('cat /proc/cpuinfo | grep -E \"model name\"')\n","  cpu = cpu.split('\\n')[0].split('\\t: ')[-1]\n","  cpu_count = psutil.cpu_count()\n","  cuda_version = get_cmd_output('nvcc --version | grep -E \"Build\"')\n","  gpu = get_cmd_output(\"nvidia-smi -L\")\n","  general_ram_gb = humanize.naturalsize(psutil.virtual_memory().available)\n","  gpu_ram_total_mb = GPU.getGPUs()[0].memoryTotal\n","\n","  for target in test_info:\n","    if load_from_saved:\n","      rec = pd.DataFrame([[dt_string, cpu, cpu_count, gpu, gpu_ram_total_mb, general_ram_gb, cuda_version, \"MSC\", \n","                          dataset, {method_dir}, f\"{n_pos}+/{n_neg}-\", target, 0, test_info[target]['roc'], test_info[target]['prc'], \n","                           test_info[target]['roc_values'], test_info[target]['prc_values'],\n","                          99, 99, 99, 102]], columns=cols)\n","  \n","      results = pd.concat([results, rec])\n","    else:\n","      rec = pd.DataFrame([[dt_string, cpu, cpu_count, gpu, gpu_ram_total_mb, general_ram_gb, cuda_version, \"MSC\", \n","                          dataset, {method_dir}, f\"{n_pos}+/{n_neg}-\", target, 0, test_info[target]['roc'], test_info[target]['prc'],\n","                           test_info[target]['roc_values'], test_info[target]['prc_values'],\n","                          train_info[\"train_roc\"], train_info[\"train_prc\"], train_info[\"episodes\"], train_info[\"duration\"]\n","            ]], columns=cols)\n","  \n","      results = pd.concat([results, rec])\n","\n","  if load_from_saved:\n","    results.to_csv(f\"{drive_path}/results/{dataset}_{method_dir}_pos{n_pos}_neg{n_neg}_from_saved.csv\", index=False)\n","  else:\n","    results.to_csv(f\"{drive_path}/results/{dataset}_{method_dir}_pos{n_pos}_neg{n_neg}.csv\", index=False)"],"execution_count":20,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","RUNNING 1+/10-\n"]},{"output_type":"stream","name":"stderr","text":["Episode 2730 - Loss 0.014905 - Acc 0.9922 - LR 1.0000000000000002e-07:  55%|█████▍    | 2729/5000 [2:09:48<1:48:00,  2.85s/it]\n"]},{"output_type":"stream","name":"stdout","text":["Loss: 0.06800 Acc: 0.9696 ROC: 0.9696 PRC: 0.9571\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 20/20 [09:26<00:00, 28.32s/it]\n"]},{"output_type":"stream","name":"stdout","text":["Test adrb2\n","ROC: 0.999 ± 0.000\n","PRC: 0.975 ± 0.002\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 20/20 [02:08<00:00,  6.41s/it]\n"]},{"output_type":"stream","name":"stdout","text":["Test cxcr4\n","ROC: 0.764 ± 0.021\n","PRC: 0.066 ± 0.027\n"]},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 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\n","text/plain":["<Figure 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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["time: 2h 21min 25s (started: 2021-11-30 15:37:48 +00:00)\n"]}]},{"cell_type":"code","metadata":{"id":"5MYReYaAlVRl"},"source":[""],"execution_count":null,"outputs":[]}]}