Add test_model_01().

src/icosagon/model.py

@@ -1,5 +1,6 @@
 from .data import Data
-from typing import List
+from typing import List, \
+    Callable
 from .trainprep import prepare_training, \
     TrainValTest
 import torch
@@ -19,13 +20,13 @@ class Model(object):
         layer_activation: Callable[[torch.Tensor], torch.Tensor] = torch.nn.functional.relu,
         dec_activation: Callable[[torch.Tensor], torch.Tensor] = lambda x: x,
         lr: float = 0.001,
-        loss = Callable[[torch.Tensor, torch.Tensor], torch.Tensor] = torch.nn.functional.binary_cross_entropy_with_logits,
+        loss: Callable[[torch.Tensor, torch.Tensor], torch.Tensor] = torch.nn.functional.binary_cross_entropy_with_logits,
         batch_size: int = 100) -> None:
 
         if not isinstance(data, Data):
             raise TypeError('data must be an instance of Data')
 
-        if not isinstance(layer_sizes, list):
+        if not isinstance(layer_dimensions, list):
             raise TypeError('layer_dimensions must be a list')
 
         if not isinstance(ratios, TrainValTest):
@@ -60,6 +61,10 @@ class Model(object):
         self.loss = loss
         self.batch_size = batch_size
 
+        self.prep_d = None
+        self.seq = None
+        self.opt = None
+
         self.build()
 
     def build(self):

tests/icosagon/test_model.py

@@ -0,0 +1,35 @@
+from icosagon.data import Data
+from icosagon.model import Model
+from icosagon.trainprep import PreparedData
+import torch
+import ast
+
+
+def _is_identity_function(f):
+    for x in range(-100, 101):
+        if f(x) != x:
+            return False
+    return True
+
+
+def test_model_01():
+    d = Data()
+    d.add_node_type('Dummy', 10)
+    fam = d.add_relation_family('Dummy-Dummy', 0, 0, False)
+    fam.add_relation_type('Dummy Rel', torch.rand(10, 10).round())
+
+    m = Model(d)
+
+    assert m.data == d
+    assert m.layer_dimensions == [32, 64]
+    assert (m.ratios.train, m.ratios.val, m.ratios.test) == (.8, .1, .1)
+    assert m.keep_prob == 1.
+    assert _is_identity_function(m.rel_activation)
+    assert m.layer_activation == torch.nn.functional.relu
+    assert _is_identity_function(m.dec_activation)
+    assert m.lr == 0.001
+    assert m.loss == torch.nn.functional.binary_cross_entropy_with_logits
+    assert m.batch_size == 100
+    assert isinstance(m.prep_d, PreparedData)
+    assert isinstance(m.seq, torch.nn.Sequential)
+    assert isinstance(m.opt, torch.optim.Optimizer)