decagon-pytorch/experiments/triacontagon_run/triacontagon_run.py

#!/usr/bin/env python3

from triacontagon.data import Data
from triacontagon.split import split_data
from triacontagon.model import Model
from triacontagon.loop import TrainLoop
from triacontagon.decode import dedicom_decoder
from triacontagon.util import common_one_hot_encoding

import os
import pandas as pd
from bisect import bisect_left
import torch
import sys


def index(a, x):
    i = bisect_left(a, x)
    if i != len(a) and a[i] == x:
        return i
    raise ValueError


def load_data(dev):
    path = '/pstore/data/data_science/ref/decagon'

    df_combo = pd.read_csv(os.path.join(path, 'bio-decagon-combo.csv'))
    df_effcat = pd.read_csv(os.path.join(path, 'bio-decagon-effectcategories.csv'))
    df_mono = pd.read_csv(os.path.join(path, 'bio-decagon-mono.csv'))
    df_ppi = pd.read_csv(os.path.join(path, 'bio-decagon-ppi.csv'))
    df_tgtall = pd.read_csv(os.path.join(path, 'bio-decagon-targets-all.csv'))
    df_tgt = pd.read_csv(os.path.join(path, 'bio-decagon-targets.csv'))

    lst = [ 'df_combo', 'df_effcat', 'df_mono', 'df_ppi', 'df_tgtall', 'df_tgt' ]

    for nam in lst:
        print(f'len({nam}): {len(locals()[nam])}')
        print(f'{nam}.columns: {locals()[nam].columns}')

    genes = set()
    genes = genes.union(df_ppi['Gene 1']).union(df_ppi['Gene 2']) \
        .union(df_tgtall['Gene']).union(df_tgt['Gene'])
    genes = sorted(genes)
    print('len(genes):', len(genes))

    drugs = set()
    drugs = drugs.union(df_combo['STITCH 1']).union(df_combo['STITCH 2']) \
        .union(df_mono['STITCH']).union(df_tgtall['STITCH']).union(df_tgt['STITCH'])
    drugs = sorted(drugs)
    print('len(drugs):', len(drugs))

    data = Data()
    data.add_vertex_type('Gene', len(genes))
    data.add_vertex_type('Drug', len(drugs))

    print('Preparing PPI...')
    print('Indexing rows...')
    rows = [index(genes, g) for g in df_ppi['Gene 1']]
    print('Indexing cols...')
    cols = [index(genes, g) for g in df_ppi['Gene 2']]
    indices = list(zip(rows, cols))
    indices = torch.tensor(indices).transpose(0, 1)
    values = torch.ones(len(rows))
    print('indices.shape:', indices.shape, 'values.shape:', values.shape)
    adj_mat = torch.sparse_coo_tensor(indices, values, size=(len(genes),) * 2,
        device=dev)
    adj_mat = (adj_mat + adj_mat.transpose(0, 1)) / 2
    print('adj_mat created')
    data.add_edge_type('PPI', 0, 0, [ adj_mat ], dedicom_decoder)
    print('OK')

    print('Preparing Drug-Gene (Target) edges...')
    rows = [index(drugs, d) for d in df_tgtall['STITCH']]
    cols = [index(genes, g) for g in df_tgtall['Gene']]
    indices = list(zip(rows, cols))
    indices = torch.tensor(indices).transpose(0, 1)
    values = torch.ones(len(rows))
    adj_mat = torch.sparse_coo_tensor(indices, values, size=(len(drugs), len(genes)),
        device=dev)
    data.add_edge_type('Drug-Gene', 1, 0, [ adj_mat ], dedicom_decoder)
    data.add_edge_type('Gene-Drug', 0, 1, [ adj_mat.transpose(0, 1) ], dedicom_decoder)
    print('OK')

    print('Preparing Drug-Drug (Side Effect) edges...')
    fam = data.add_relation_family('Drug-Drug (Side Effect)', 1, 1, True)
    print('# of side effects:', len(df_combo), 'unique:', len(df_combo['Polypharmacy Side Effect'].unique()))
    adjacency_matrices = []
    side_effect_names = []
    for eff, df in df_combo.groupby('Polypharmacy Side Effect'):
        sys.stdout.write('.') # print(eff, '...')
        sys.stdout.flush()
        rows = [index(drugs, d) for d in df['STITCH 1']]
        cols = [index(drugs, d) for d in df['STITCH 2']]
        indices = list(zip(rows, cols))
        indices = torch.tensor(indices).transpose(0, 1)
        values = torch.ones(len(rows))
        adj_mat = torch.sparse_coo_tensor(indices, values, size=(len(drugs), len(drugs)),
            device=dev)
        adj_mat = (adj_mat + adj_mat.transpose(0, 1)) / 2
        adjacency_matrices.append(adj_mat)
        side_effect_names.append(df['Polypharmacy Side Effect'])
    fam.add_edge_type('Drug-Drug', 1, 1, adjacency_matrices, dedicom_decoder)
    print()
    print('OK')

    return data


def _wrap(obj, method_name):
    orig_fn = getattr(obj, method_name)
    def fn(*args, **kwargs):
        print(f'{method_name}() :: ENTER')
        res = orig_fn(*args, **kwargs)
        print(f'{method_name}() :: EXIT')
        return res
    setattr(obj, method_name, fn)


def main():
    dev = torch.device('cuda:0')
    data = load_data(dev)

    train_data, val_data, test_data = split_data(data, (.8, .1, .1))

    n = sum(vt.count for vt in data.vertex_types)

    model = Model(data, [n, 32, 64], keep_prob=.9,
        conv_activation=torch.sigmoid,
        dec_activation=torch.sigmoid).to(dev)

    initial_repr = common_one_hot_encoding([ vt.count \
        for vt in data.vertex_types ], device=dev)

    loop = TrainLoop(model, val_data, test_data,
        initial_repr, max_epochs=50, batch_size=512,
        loss=torch.nn.functional.binary_cross_entropy_with_logits,
        lr=0.001)

    loop.run()

    with open('/pstore/data/data_science/year/2020/adaszews/models/triacontagon/basic_run.pth', 'wb') as f:
        torch.save(model.state_dict(), f)


if __name__ == '__main__':
    main()