class TestCData(unittest.TestCase):
"""
Dear Categorical Dataframe,
I would like you to:
+ hold categorical data for me.
+ partition the data to learning and testing cases
+ be able to generate weights based on the representation ratio of different classes
+ transform (whiten, autoencode, standardize) the independent variables
and adjust the <inputs_required> accordingly.
These transformations should fitted only on the learning data!
+ dummycode/embed the categorical variable:
create the one-hot vector representations of categories OR
embed the categorical variable into N-space,
adjust <outputs_required> accordingly,
and be able to translate the network output back to human readable class names
+ be able to reset transformations and embeddings if this is desirable
without the loss of information.
+ create a learning table from the data
+ generate random batches from the data
- Handle multiple labels and be able to average similarily labelled samples
"""
def setUp(self):
self.X_, self.y_, headers = parse_csv(etalonroot + "/input.csv")
self.data = CData((self.X_, self.y_), cross_val=0)
def test_initialization_on_etalon_with_given_parameters(self):
new_data = CData((self.X_, self.y_), cross_val=0.5)
self.assertEqual(
new_data.embedding, "onehot",
"<embedding> property is faulty after initialization!")
self.assertEqual(
len(new_data.categories), 3,
"Invalid determination of categories! (got {})".format(
new_data.categories))
self.assertEqual(new_data.crossval, 0.5,
"Wrong <crossval> value in data!")
self.assertEqual(new_data.N, 5)
self.assertEqual(new_data.N, new_data.learning.shape[0],
"Validation data splitting went wrong @ learning!")
self.assertEqual(new_data.n_testing, 5)
self.assertEqual(new_data.n_testing, new_data.testing.shape[0],
"Validation data splitting went wrong @ testing!")
def test_reset(self):
data2 = CData((self.X_, self.y_), cross_val=0)
er = "Difference detected in data shapes"
self.data.crossval = 3
self.data.embedding = 10
self.data.transformation = ("pca", 1)
self.data.reset_data(shuff=False)
sm1, sm2 = np.sum(self.data.data), np.sum(data2.data)
self.assertEqual(self.data.learning.shape, (7, 3), msg=er)
self.assertEqual(
sm1,
sm2,
msg="The sums of learning data differ by {}!\n{}\n{}".format(
abs(sm1 - sm2), sm1, sm2))
def test_core_data_is_readonly(self):
with self.assertRaises(ValueError):
self.data.data[0][0] = 2.0
def test_setter_sets_crossval_getter_right(self):
self.data.crossval = 5
self.assertEqual(self.data.crossval, 0.5,
"Wrong <crossval> value in data!")
self.assertEqual(self.data.N, 5)
self.assertEqual(self.data.N, self.data.learning.shape[0],
"Validation data splitting went wrong @ learning!")
self.assertEqual(self.data.n_testing, 5)
self.assertEqual(self.data.n_testing, self.data.testing.shape[0],
"Validation data splitting went wrong @ testing!")
def test_weights_sum_to_N(self):
w = self.data.sample_weights
self.assertEqual(round(w.sum()), self.data.N)
def test_concatenate_produces_right_shape(self):
newdata = CData((self.X_, self.y_), cross_val=0)
newdata.concatenate(self.data)
self.assertEqual(newdata.N, 20,
"Split after concatenation went wrong!")
self.assertEqual(newdata.data.shape, (20, 3),
"Shapes went haywire after concatenation!")
def test_batches_from_generator_are_shaped_and_distributed_right(self):
i = 0
self.data.crossval = 2
self.data.transformation = ("ae", 15)
self.data.embedding = 3
for X, y, w in self.data.batchgen(2, weigh=True):
self.assertEqual(X.shape, (2, 15))
self.assertEqual(y.shape, (2, 3))
self.assertEqual(w.shape, (2, ))
i += 1
self.assertEqual(
i,
4,
msg="Number of batches differ. Got {} expected {}".format(i, 4))
def test_neurons_required_property_on_untransformed_data(self):
inshape, outputs = self.data.neurons_required
self.assertIsInstance(inshape, tuple, "Input shape is not in a tuple!")
self.assertIsInstance(inshape, tuple,
"Output shape is not in a tuple!")
self.assertEqual(inshape, (3, ))
self.assertEqual(outputs, (3, ))
def test_neurons_required_proprety_after_heavy_transformation_then_resetting(
self):
self.data.embedding = 10
self.data.transformation = ("pca", 2)
inshape, outputs = self.data.neurons_required
self.assertIsInstance(inshape, tuple, "Input shape is not in a tuple!")
self.assertIsInstance(outputs, tuple,
"Output shape is not in a tuple!")
self.assertEqual(inshape, (2, ),
"Wrong input shape after transformation/embedding!")
self.assertEqual(outputs, (10, ),
"Wrong output shape after transformation/embedding!")
self.data.reset_data(shuff=False)
inshape, outputs = self.data.neurons_required
self.assertIsInstance(inshape, tuple, "Input shape is not in a tuple!")
self.assertIsInstance(outputs, tuple,
"Output shape is not in a tuple!")
self.assertEqual(inshape, (3, ), "Wrong input shape after resetting!")
self.assertEqual(outputs, (3, ), "Wrong output shape after resetting!")
from csxdata import CData, roots
from brainforge import BackpropNetwork
from brainforge.layers import ConvLayer, PoolLayer, Flatten, DenseLayer, Activation
from brainforge.optimization import RMSprop
data = CData(roots["misc"] + "mnist.pkl.gz", cross_val=10000, fold=True)
ins, ous = data.neurons_required
net = BackpropNetwork(input_shape=ins, layerstack=[
ConvLayer(3, 8, 8, compiled=False),
PoolLayer(3, compiled=False), Activation("tanh"),
Flatten(), DenseLayer(60, activation="tanh"),
DenseLayer(ous, activation="softmax")
], cost="xent", optimizer=RMSprop(eta=0.01))
net.fit_generator(data.batchgen(bsize=20, infinite=True), lessons_per_epoch=60000, epochs=30,
validation=data.table("testing"))
