def test_flow_scaler_transform_uniform():
x = numpy.random.uniform(50, 100, size=100).astype(numpy.float32)
scaler = FlowScaler(5)
scaler.fit(x)
x_scaled = scaler.transform(x)
assert scipy.stats.kstest(x_scaled, 'norm').pvalue > 0.05
def test_flow_scaler_transform_uniform():
x = numpy.random.uniform(50, 100, size=100).astype(numpy.float32)
scaler = FlowScaler(5)
scaler.fit(x) # fit takes time
x_scaled = scaler.transform(x)
assert scipy.stats.kstest(x_scaled, 'norm').pvalue > 0.05
def test_flow_scaler_pipeline():
# Only to test each method without fail, for fast testing.
x = numpy.random.uniform(50, 100, size=100).astype(numpy.float32)
scaler = FlowScaler(5)
scaler.fit(x, iteration=1)
x_scaled = scaler.transform(x)
assert x_scaled.shape == x.shape
def test_flow_scaler_transform_variable():
x = numpy.random.uniform(50, 100, size=100).astype(numpy.float32)
xvar = Variable(x)
scaler = FlowScaler(5)
scaler.fit(xvar) # fit takes time
x_scaled = scaler.transform(xvar)
assert isinstance(x_scaled, Variable)
assert scipy.stats.kstest(x_scaled.array, 'norm').pvalue > 0.05
def test_flow_scaler_transform_variable():
x = numpy.random.uniform(50, 100, size=100).astype(numpy.float32)
xvar = Variable(x)
scaler = FlowScaler(5)
scaler.fit(xvar)
x_scaled = scaler.transform(xvar)
assert isinstance(x_scaled, Variable)
assert scipy.stats.kstest(x_scaled.array, 'norm').pvalue > 0.05
def test_flow_scaler_transform_mix_gaussian():
plus = numpy.random.binomial(n=1, p=0.6, size=100).astype(numpy.float32)
x = plus * numpy.random.normal(10, 5, size=100).astype(numpy.float32)
x += (1 - plus) * numpy.random.normal(-10, 5, size=100).astype(
numpy.float32)
scaler = FlowScaler(5)
scaler.fit(x) # fit takes time
x_scaled = scaler.transform(x)
assert scipy.stats.kstest(x_scaled, 'norm').pvalue > 0.05
def test_flow_scaler_transform_mix_gaussian():
plus = numpy.random.binomial(n=1, p=0.6, size=100).astype(numpy.float32)
x = plus * numpy.random.normal(10, 5, size=100).astype(numpy.float32)
x += (1 - plus) * numpy.random.normal(
-10, 5, size=100).astype(numpy.float32)
scaler = FlowScaler(5)
scaler.fit(x)
x_scaled = scaler.transform(x)
assert scipy.stats.kstest(x_scaled, 'norm').pvalue > 0.05
def test_flow_scaler_transform_gpu():
x = numpy.random.uniform(50, 100, size=100).astype(numpy.float32)
scaler = FlowScaler(5)
scaler.to_gpu()
x = cuda.to_gpu(x)
scaler.fit(x) # fit takes time
x_scaled = scaler.transform(x)
assert isinstance(x_scaled, cuda.cupy.ndarray)
assert scipy.stats.kstest(cuda.to_cpu(x_scaled), 'norm').pvalue > 0.05
def test_flow_scaler_pipeline_gpu():
# Only to test each method without fail, for fast testing.
x = numpy.random.uniform(50, 100, size=100).astype(numpy.float32)
x = cuda.to_gpu(x)
scaler = FlowScaler(5)
scaler.to_gpu()
scaler.fit(x, iteration=1)
x_scaled = scaler.transform(x)
assert isinstance(x_scaled, cuda.cupy.ndarray)
assert x_scaled.shape == x.shape
def test_flow_scaler_serialize(tmpdir):
x = numpy.random.uniform(50, 100, size=100).astype(numpy.float32)
scaler = FlowScaler(5)
scaler.fit(x) # fit takes time
x_scaled = scaler.transform(x)
scaler_filepath = os.path.join(str(tmpdir), 'scaler.npz')
serializers.save_npz(scaler_filepath, scaler)
scaler2 = FlowScaler(5)
serializers.load_npz(scaler_filepath, scaler2)
x_scaled2 = scaler2.transform(x)
assert numpy.allclose(scaler.W1.array, scaler2.W1.array)
assert numpy.allclose(scaler.b1.array, scaler2.b1.array)
assert numpy.allclose(scaler.W2.array, scaler2.W2.array)
assert numpy.allclose(scaler.b2.array, scaler2.b2.array)
assert numpy.allclose(x_scaled, x_scaled2)
def test_flow_scaler_transform_gpu():
x = numpy.random.uniform(50, 100, size=100).astype(numpy.float32)
scaler = FlowScaler(5)
scaler.to_gpu()
x = cuda.to_gpu(x)
scaler.fit(x)
x_scaled = scaler.transform(x)
assert isinstance(x_scaled, cuda.cupy.ndarray)
assert scipy.stats.kstest(cuda.to_cpu(x_scaled), 'norm').pvalue > 0.05
def test_flow_scaler_serialize(tmpdir):
x = numpy.random.uniform(50, 100, size=100).astype(numpy.float32)
scaler = FlowScaler(5)
scaler.fit(x)
x_scaled = scaler.transform(x)
scaler_filepath = os.path.join(str(tmpdir), 'scaler.npz')
serializers.save_npz(scaler_filepath, scaler)
scaler2 = FlowScaler(5)
serializers.load_npz(scaler_filepath, scaler2)
x_scaled2 = scaler2.transform(x)
assert numpy.allclose(scaler.W1.array, scaler2.W1.array)
assert numpy.allclose(scaler.b1.array, scaler2.b1.array)
assert numpy.allclose(scaler.W2.array, scaler2.W2.array)
assert numpy.allclose(scaler.b2.array, scaler2.b2.array)
assert numpy.allclose(x_scaled, x_scaled2)