Add first triacontagon experiment.

experiments/triacontagon_run/triacontagon_run.py

@@ -0,0 +1,146 @@
+#!/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()

src/triacontagon/util.py

@@ -225,7 +225,7 @@ def _select_rows(a: torch.Tensor, rows: torch.Tensor):
     return res
 
 
-def common_one_hot_encoding(vertex_type_counts: List[int]) -> \
+def common_one_hot_encoding(vertex_type_counts: List[int], device=None) -> \
     List[torch.Tensor]:
 
     tot = sum(vertex_type_counts)
@@ -241,6 +241,7 @@ def common_one_hot_encoding(vertex_type_counts: List[int]) -> \
         ])
         val = torch.ones(cnt)
         x = _sparse_coo_tensor(ind, val, size=(cnt, tot))
+        x = x.to(device)
         res.append(x)
         ofs += cnt