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linear
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test_linear.py
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# coding: utf8 from __future__ import unicode_literals, division import numpy import pytest from ...linear.linear import LinearModel from ...neural.optimizers import SGD from ...neural.ops import NumpyOps from ...neural.util import to_categorical @pytest.fixture def instances(): lengths = numpy.asarray([5, 4], dtype="int32") keys = numpy.arange(9, dtype="uint64") values = numpy.ones(9, dtype="float") X = (keys, values, lengths) y = numpy.asarray([0, 2], dtype="int32") return X, to_categorical(y, nb_classes=3) @pytest.fixture def sgd(): return SGD(NumpyOps(), 0.001) @pytest.mark.xfail def test_basic(instances, sgd): X, y = instances nr_class = 3 model = LinearModel(nr_class) yh, backprop = model.begin_update(X) loss1 = ((yh - y) ** 2).sum() backprop(yh - y, sgd) yh, backprop = model.begin_update(X) loss2 = ((yh - y) ** 2).sum() assert loss2 < loss1 print(loss2, loss1) # @pytest.fixture # def model(instances): # templates = [] # for batch in instances: # for _, feats in batch: # for key in feats: # templates.append((key,)) # templates = tuple(set(templates)) # model = AveragedPerceptron(templates) # for batch in instances: # model.time += 1 # for clas, feats in batch: # for key, value in feats.items(): # model.update_weight(key, clas, value) # return model # # def get_score(nr_class, model, feats, clas): # eg = Example(nr_class) # eg.features = feats # eg.costs = [i != clas for i in range(nr_class)] # model(eg) # return eg.scores[clas] # # # def get_scores(nr_class, model, feats): # eg = Example(nr_class) # eg.features = feats # model(eg) # return list(eg.scores) # # # def test_averaging(model): # model.end_training() # nr_class = 4 # # Feature 1 # assert_near_eq(get_score(nr_class, model, {1: 1}, 1), sum([-1, -2, -3]) / 3.0) # assert_near_eq(get_score(nr_class, model, {1: 1}, 2), sum([5, 4, 9]) / 3.0) # assert_near_eq(get_score(nr_class, model, {1: 1}, 3), sum([3, 6, 6]) / 3.0) # # Feature 2 # assert_near_eq(get_score(nr_class, model, {2: 1}, 1), sum([1, 2, 4]) / 3.0) # assert_near_eq(get_score(nr_class, model, {2: 1}, 2), sum([-5, -3, -8]) / 3.0) # assert_near_eq(get_score(nr_class, model, {2: 1}, 3), sum([-3, -6, -5]) / 3.0) # # Feature 3 (absent) # assert_near_eq(get_score(nr_class, model, {3: 1}, 1), 0) # assert_near_eq(get_score(nr_class, model, {3: 1}, 2), 0) # assert_near_eq(get_score(nr_class, model, {3: 1}, 3), 0) # # Feature 4 # assert_near_eq(get_score(nr_class, model, {4: 1}, 1), sum([0, 0, 0]) / 3.0) # assert_near_eq(get_score(nr_class, model, {4: 1}, 2), sum([0, 0, 0]) / 3.0) # assert_near_eq(get_score(nr_class, model, {4: 1}, 3), sum([0, 0, 1]) / 3.0) # # Feature 5 # assert_near_eq(get_score(nr_class, model, {5: 1}, 1), sum([0, 0, 0]) / 3.0) # assert_near_eq(get_score(nr_class, model, {5: 1}, 2), sum([0, 0, 0]) / 3.0) # assert_near_eq(get_score(nr_class, model, {5: 1}, 3), sum([0, 0, -7]) / 3.0) # # # def test_dump_load(model): # loc = tempfile.mkstemp()[1] # model.end_training() # model.dump(loc) # string = open(loc, 'rb').read() # assert string # new_model = AveragedPerceptron([(1,), (2,), (3,), (4,)]) # nr_class = 5 # assert get_scores(nr_class, model, {1: 1, 3: 1, 4: 1}) != \ # get_scores(nr_class, new_model, {1:1, 3:1, 4:1}) # assert get_scores(nr_class, model, {2:1, 5:1}) != \ # get_scores(nr_class, new_model, {2:1, 5:1}) # assert get_scores(nr_class, model, {2:1, 3:1, 4:1}) != \ # get_scores(nr_class, new_model, {2:1, 3:1, 4:1}) # new_model.load(loc) # assert get_scores(nr_class, model, {1:1, 3:1, 4:1}) == \ # get_scores(nr_class, new_model, {1:1, 3:1, 4:1}) # assert get_scores(nr_class, model, {2:1, 5:1}) == \ # get_scores(nr_class, new_model, {2:1, 5:1}) # assert get_scores(nr_class, model, {2:1, 3:1, 4:1}) == \ # get_scores(nr_class, new_model, {2:1, 3:1, 4:1}) # # ### TODO: Need a test that exercises multiple lines. Example bug: ### in gather_weights, don't increment f_i per row, only per feature ### (so overwrite some lines we're gathering)