test: add unit tests

This commit is contained in:
Jensun Ravichandran 2022-03-30 15:12:33 +02:00
parent 9da47b1dba
commit 7d3f59e54b
No known key found for this signature in database
GPG Key ID: 4E9348239810B51F
6 changed files with 218 additions and 26 deletions

View File

@ -26,13 +26,11 @@ from .lvq import (
)
from .probabilistic import (
CELVQ,
PLVQ,
RSLVQ,
SLVQ,
)
from .unsupervised import (
GrowingNeuralGas,
HeskesSOM,
KohonenSOM,
NeuralGas,
)

View File

@ -16,7 +16,7 @@ class KNN(SupervisedPrototypeModel):
"""K-Nearest-Neighbors classification algorithm."""
def __init__(self, hparams, **kwargs):
super().__init__(hparams, **kwargs)
super().__init__(hparams, skip_proto_layer=True, **kwargs)
# Default hparams
self.hparams.setdefault("k", 1)
@ -28,7 +28,7 @@ class KNN(SupervisedPrototypeModel):
# Layers
self.proto_layer = LabeledComponents(
distribution=[],
distribution=len(data) * [1],
components_initializer=LiteralCompInitializer(data),
labels_initializer=LiteralLabelsInitializer(targets))
self.competition_layer = KNNC(k=self.hparams.k)

View File

@ -67,8 +67,13 @@ class SLVQ(ProbabilisticLVQ):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# Default hparams
self.hparams.setdefault("variance", 1.0)
variance = self.hparams.get("variance")
self.conditional_distribution = GaussianPrior(variance)
self.loss = LossLayer(nllr_loss)
self.conditional_distribution = GaussianPrior(self.hparams.variance)
class RSLVQ(ProbabilisticLVQ):
@ -76,8 +81,13 @@ class RSLVQ(ProbabilisticLVQ):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# Default hparams
self.hparams.setdefault("variance", 1.0)
variance = self.hparams.get("variance")
self.conditional_distribution = GaussianPrior(variance)
self.loss = LossLayer(rslvq_loss)
self.conditional_distribution = GaussianPrior(self.hparams.variance)
class PLVQ(ProbabilisticLVQ, SiameseGMLVQ):
@ -88,8 +98,12 @@ class PLVQ(ProbabilisticLVQ, SiameseGMLVQ):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.conditional_distribution = RankScaledGaussianPrior(
self.hparams.lambd)
# Default hparams
self.hparams.setdefault("lambda", 1.0)
lam = self.hparams.get("lambda", 1.0)
self.conditional_distribution = RankScaledGaussianPrior(lam)
self.loss = torch.nn.KLDivLoss()
# FIXME

View File

@ -35,7 +35,7 @@ class KohonenSOM(NonGradientMixin, UnsupervisedPrototypeModel):
# Additional parameters
x, y = torch.arange(h), torch.arange(w)
grid = torch.stack(torch.meshgrid(x, y), dim=-1)
grid = torch.stack(torch.meshgrid(x, y, indexing="ij"), dim=-1)
self.register_buffer("_grid", grid)
self._sigma = self.hparams.sigma
self._lr = self.hparams.lr
@ -88,12 +88,12 @@ class NeuralGas(UnsupervisedPrototypeModel):
self.save_hyperparameters(hparams)
# Default hparams
self.hparams.setdefault("agelimit", 10)
self.hparams.setdefault("age_limit", 10)
self.hparams.setdefault("lm", 1)
self.energy_layer = NeuralGasEnergy(lm=self.hparams.lm)
self.topology_layer = ConnectionTopology(
agelimit=self.hparams.agelimit,
agelimit=self.hparams.age_limit,
num_prototypes=self.hparams.num_prototypes,
)

View File

@ -1,15 +0,0 @@
"""prototorch.models test suite."""
import unittest
class TestDummy(unittest.TestCase):
def setUp(self):
pass
def test_dummy(self):
pass
def tearDown(self):
pass

195
tests/test_models.py Normal file
View File

@ -0,0 +1,195 @@
"""prototorch.models test suite."""
import prototorch as pt
import pytest
import torch
def test_glvq_model_build():
model = pt.models.GLVQ(
{"distribution": (3, 2)},
prototypes_initializer=pt.initializers.RNCI(2),
)
def test_glvq1_model_build():
model = pt.models.GLVQ1(
{"distribution": (3, 2)},
prototypes_initializer=pt.initializers.RNCI(2),
)
def test_glvq21_model_build():
model = pt.models.GLVQ1(
{"distribution": (3, 2)},
prototypes_initializer=pt.initializers.RNCI(2),
)
def test_gmlvq_model_build():
model = pt.models.GMLVQ(
{
"distribution": (3, 2),
"input_dim": 2,
"latent_dim": 2,
},
prototypes_initializer=pt.initializers.RNCI(2),
)
def test_grlvq_model_build():
model = pt.models.GRLVQ(
{
"distribution": (3, 2),
"input_dim": 2,
},
prototypes_initializer=pt.initializers.RNCI(2),
)
def test_gtlvq_model_build():
model = pt.models.GTLVQ(
{
"distribution": (3, 2),
"input_dim": 4,
"latent_dim": 2,
},
prototypes_initializer=pt.initializers.RNCI(2),
)
def test_lgmlvq_model_build():
model = pt.models.LGMLVQ(
{
"distribution": (3, 2),
"input_dim": 4,
"latent_dim": 2,
},
prototypes_initializer=pt.initializers.RNCI(2),
)
def test_image_glvq_model_build():
model = pt.models.ImageGLVQ(
{"distribution": (3, 2)},
prototypes_initializer=pt.initializers.RNCI(16),
)
def test_image_gmlvq_model_build():
model = pt.models.ImageGMLVQ(
{
"distribution": (3, 2),
"input_dim": 16,
"latent_dim": 2,
},
prototypes_initializer=pt.initializers.RNCI(16),
)
def test_image_gtlvq_model_build():
model = pt.models.ImageGMLVQ(
{
"distribution": (3, 2),
"input_dim": 16,
"latent_dim": 2,
},
prototypes_initializer=pt.initializers.RNCI(16),
)
def test_siamese_glvq_model_build():
model = pt.models.SiameseGLVQ(
{"distribution": (3, 2)},
prototypes_initializer=pt.initializers.RNCI(4),
)
def test_siamese_gmlvq_model_build():
model = pt.models.SiameseGMLVQ(
{
"distribution": (3, 2),
"input_dim": 4,
"latent_dim": 2,
},
prototypes_initializer=pt.initializers.RNCI(4),
)
def test_siamese_gtlvq_model_build():
model = pt.models.SiameseGTLVQ(
{
"distribution": (3, 2),
"input_dim": 4,
"latent_dim": 2,
},
prototypes_initializer=pt.initializers.RNCI(4),
)
def test_knn_model_build():
train_ds = pt.datasets.Iris(dims=[0, 2])
model = pt.models.KNN(dict(k=3), data=train_ds)
def test_lvq1_model_build():
model = pt.models.LVQ1(
{"distribution": (3, 2)},
prototypes_initializer=pt.initializers.RNCI(2),
)
def test_lvq21_model_build():
model = pt.models.LVQ21(
{"distribution": (3, 2)},
prototypes_initializer=pt.initializers.RNCI(2),
)
def test_median_lvq_model_build():
model = pt.models.MedianLVQ(
{"distribution": (3, 2)},
prototypes_initializer=pt.initializers.RNCI(2),
)
def test_celvq_model_build():
model = pt.models.CELVQ(
{"distribution": (3, 2)},
prototypes_initializer=pt.initializers.RNCI(2),
)
def test_rslvq_model_build():
model = pt.models.RSLVQ(
{"distribution": (3, 2)},
prototypes_initializer=pt.initializers.RNCI(2),
)
def test_slvq_model_build():
model = pt.models.SLVQ(
{"distribution": (3, 2)},
prototypes_initializer=pt.initializers.RNCI(2),
)
def test_growing_neural_gas_model_build():
model = pt.models.GrowingNeuralGas(
{"num_prototypes": 5},
prototypes_initializer=pt.initializers.RNCI(2),
)
def test_kohonen_som_model_build():
model = pt.models.KohonenSOM(
{"shape": (3, 2)},
prototypes_initializer=pt.initializers.RNCI(2),
)
def test_neural_gas_model_build():
model = pt.models.NeuralGas(
{"num_prototypes": 5},
prototypes_initializer=pt.initializers.RNCI(2),
)