added test for optimizers

tests/test_model_functioning/test_optimizers.py

+import numpy as np
+import string
+import torch
+import pytest
+
+from torch import nn
+from pytorch_widedeep.models.wide import Wide
+from pytorch_widedeep.models.deep_dense import DeepDense
+from pytorch_widedeep.models.deep_text import DeepText
+from pytorch_widedeep.models.deep_image import DeepImage
+
+from pytorch_widedeep.models.wide_deep import WideDeep
+from pytorch_widedeep.optimizers import Adam, RAdam, SGD, RMSprop
+from copy import deepcopy as c
+
+import pdb
+
+# Wide array
+X_wide=np.random.choice(2, (100, 100), p = [0.8, 0.2])
+
+# Deep Array
+colnames    = list(string.ascii_lowercase)[:10]
+embed_cols  = [np.random.choice(np.arange(5), 100) for _ in range(5)]
+embed_input = [(u,i,j) for u,i,j in zip(colnames[:5], [5]*5, [16]*5)]
+cont_cols   = [np.random.rand(100) for _ in range(5)]
+deep_column_idx={k:v for v,k in enumerate(colnames)}
+X_deep = np.vstack(embed_cols+cont_cols).transpose()
+
+# Text Array
+padded_sequences = np.random.choice(np.arange(1,100), (100, 48))
+vocab_size = 1000
+X_text = np.hstack((np.repeat(np.array([[0,0]]), 100, axis=0), padded_sequences))
+
+# Image Array
+X_img = np.random.choice(256, (100, 224, 224, 3))
+
+optimizers_1 = { 'wide': Adam, 'deepdense':RAdam, 'deeptext': SGD, 'deepimage':RMSprop}
+optimizers_2 = { 'wide': RAdam, 'deepdense':SGD, 'deeptext': RMSprop}
+
+###############################################################################
+# Test that the MultipleOptimizer class functions as expected
+###############################################################################
+@pytest.mark.parametrize("optimizers, expected_opt",
+    [
+        (optimizers_1, { 'wide': 'Adam', 'deepdense':'RAdam', 'deeptext': 'SGD', 'deepimage':'RMSprop'}),
+        (optimizers_2, { 'wide': 'RAdam', 'deepdense':'SGD', 'deeptext': 'RMSprop', 'deepimage': 'Adam'}),
+    ],
+)
+def test_optimizers(optimizers, expected_opt):
+	wide = Wide(100, 1)
+	deepdense = DeepDense(hidden_layers=[32,16], dropout=[0.5], deep_column_idx=deep_column_idx,
+	    embed_input=embed_input, continuous_cols=colnames[-5:], output_dim=1)
+	deeptext = DeepText( vocab_size=vocab_size, embed_dim=32, padding_idx=0)
+	deepimage=DeepImage(pretrained=True)
+	model = WideDeep(wide=wide, deepdense=deepdense, deeptext=deeptext, deepimage=deepimage)
+	model.compile(method='logistic', optimizers=optimizers)
+	out = []
+	for name, opt in model.optimizer._optimizers.items():
+		out.append(expected_opt[name] == opt.__class__.__name__	)
+	assert all(out)
+