Add test_fast_model_01().

src/icosagon/fastmodel.py

@@ -1,12 +1,16 @@
 from .fastconv import FastConvLayer
 from .bulkdec import BulkDecodeLayer
 from .input import OneHotInputLayer
+from .trainprep import PreparedData
 import torch
 import types
+from typing import List, \
+    Union, \
+    Callable
 
 
 class FastModel(torch.nn.Module):
-    def __init(self, prep_d: PreparedData,
+    def __init__(self, prep_d: PreparedData,
         layer_dimensions: List[int] = [32, 64],
         keep_prob: float = 1.,
         rel_activation: Callable[[torch.Tensor], torch.Tensor] = lambda x: x,
@@ -20,6 +24,7 @@ class FastModel(torch.nn.Module):
             layer_activation, dec_activation)
 
         self.prep_d = prep_d
+        self.layer_dimensions = layer_dimensions
         self.keep_prob = float(keep_prob)
         self.rel_activation = rel_activation
         self.layer_activation = layer_activation
@@ -55,7 +60,7 @@ class FastModel(torch.nn.Module):
     def forward(self, _):
         return self.seq(None)
 
-    def self._check_params(self, prep_d, layer_dimensions, rel_activation,
+    def _check_params(self, prep_d, layer_dimensions, rel_activation,
         layer_activation, dec_activation):
 
         if not isinstance(prep_d, PreparedData):

tests/icosagon/test_fastmodel.py

@@ -0,0 +1,50 @@
+from icosagon.fastmodel import FastModel
+from icosagon.data import Data
+from icosagon.trainprep import prepare_training, \
+    TrainValTest
+import torch
+import time
+
+
+def _make_symmetric(x: torch.Tensor):
+    x = (x + x.transpose(0, 1)) / 2
+    return x
+
+
+def _symmetric_random(n_rows, n_columns):
+    return _make_symmetric(torch.rand((n_rows, n_columns),
+        dtype=torch.float32).round().to_sparse())
+
+
+def _some_data_with_interactions():
+    d = Data()
+    d.add_node_type('Gene', 1000)
+    d.add_node_type('Drug', 100)
+
+    fam = d.add_relation_family('Drug-Gene', 1, 0, True)
+    fam.add_relation_type('Target',
+        torch.rand((100, 1000), dtype=torch.float32).round().to_sparse())
+
+    fam = d.add_relation_family('Gene-Gene', 0, 0, True)
+    fam.add_relation_type('Interaction',
+        _symmetric_random(1000, 1000))
+
+    fam = d.add_relation_family('Drug-Drug', 1, 1, True)
+    for i in range(500):
+        fam.add_relation_type('Side Effect: Nausea %d' % i,
+            _symmetric_random(100, 100))
+        fam.add_relation_type('Side Effect: Infertility %d' % i,
+            _symmetric_random(100, 100))
+        fam.add_relation_type('Side Effect: Death %d' % i,
+            _symmetric_random(100, 100))
+    return d
+
+
+def test_fast_model_01():
+    d = _some_data_with_interactions()
+    prep_d = prepare_training(d, TrainValTest(.8, .1, .1))
+    model = FastModel(prep_d)
+    for i in range(10):
+        t = time.time()
+        _ = model(None)
+        print('Model forward took:', time.time() - t)