def run_example(stats=False):
encoder = OneHotEncoder({'animal': 2, 'color': 1}, ['weight', 'height'])
data = [{'animal': 'cat', 'color': 'blue', 'weight': 6.0, 'height': 88.9, 'extra_junk': 'blah'},
{'animal': 'cat', 'color': 'red', 'weight': 3.0, 'height': 44.9},
{'animal': 'dog', 'color': 'yellow', 'weight': 5.5, 'height': 2.5},
{'animal': 'fish', 'color': 'blue', 'weight': 7.0, 'height': 3233.2},
{'animal': 'cat', 'color': 'magenta', 'weight': 2.0, 'height': 666.6},
{'animal': 'mouse', 'color': 'red', 'weight': 0.0, 'height': 55.5},
{'animal': 'mouse', 'color': 'blah', 'weight': 99.9, 'height': 33}]
encoder.load_from_data_stream(data)
encoded_data = encoder.encode_data(data)
data_decoded = encoder.decode_data(encoded_data)
expected = [{'height': 88.9, 'weight': 6.0, 'animal': 'cat', 'color': 'blue'},
{'height': 44.9, 'weight': 3.0, 'animal': 'cat', 'color': 'UNKNOWN_CATEGORICAL_LEVEL'},
{'height': 2.5, 'weight': 5.5, 'color': 'UNKNOWN_CATEGORICAL_LEVEL', 'animal': 'UNKNOWN_CATEGORICAL_LEVEL'},
{'height': 3233.2, 'weight': 7.0, 'color': 'blue', 'animal': 'UNKNOWN_CATEGORICAL_LEVEL'},
{'height': 666.6, 'weight': 2.0, 'animal': 'cat', 'color': 'UNKNOWN_CATEGORICAL_LEVEL'},
{'height': 55.5, 'weight': 0.0, 'animal': 'mouse', 'color': 'UNKNOWN_CATEGORICAL_LEVEL'},
{'height': 33, 'weight': 99.9, 'animal': 'mouse', 'color': 'UNKNOWN_CATEGORICAL_LEVEL'}]
assert data_decoded == expected
# add number stats?
if stats:
encoder.add_numeric_stats(data)
# check the package
packaged = encoder.package_data()
return packaged
def test_load_from_data_encodes_data():
encoder = OneHotEncoder(['animal', 'color'], ['weight'], max_levels_default=100)
data = [{'animal': 'cat', 'color': 'blue', 'weight': 1.0},
{'animal': 'cat', 'color': 'red', 'weight': 3.0},
{'animal': 'dog', 'color': 'yellow', 'weight': 5.5},
{'animal': 'fish', 'color': 'blue', 'weight': 7.0},
{'animal': 'cat', 'color': 'blue', 'weight': 2.0},
{'animal': 'cat', 'color': 'blue', 'weight': 0.0},
{'animal': 'cat', 'color': 'blue', 'weight': 99.9}]
encoder.load_from_data_stream(data)
encoded_data = [encoder.encode_row(row) for row in data]
assert len(encoded_data) == len(data)
assert len(encoded_data[0]) != len(data[0])
def test_load_from_data():
encoder = OneHotEncoder(['animal', 'color'], ['weight'], max_levels_default=100)
data = [{'animal': 'cat', 'color': 'blue', 'weight': 1.0},
{'animal': 'cat', 'color': 'red', 'weight': 3.0},
{'animal': 'dog', 'color': 'yellow', 'weight': 5.5},
{'animal': 'fish', 'color': 'blue', 'weight': 7.0},
{'animal': 'cat', 'color': 'blue', 'weight': 2.0},
{'animal': 'cat', 'color': 'blue', 'weight': 0.0},
{'animal': 'cat', 'color': 'blue', 'weight': 99.9}]
assert encoder.encoder is None
assert encoder.decoder is None
assert encoder.one_hot_encoder_dicts is None
encoder.load_from_data_stream(data)
assert encoder.encoder is not None
assert encoder.decoder is not None
assert encoder.one_hot_encoder_dicts is not None
def test_inversion_more_complicated_with_max_levels():
encoder = OneHotEncoder({'animal': 2, 'color': 2}, ['weight', 'height'])
data = [{'animal': 'cat', 'color': 'blue', 'weight': 6.0, 'height': 88.9},
{'animal': 'cat', 'color': 'red', 'weight': 3.0, 'height': 44.9},
{'animal': 'dog', 'color': 'yellow', 'weight': 5.5, 'height': 2.5},
{'animal': 'fish', 'color': 'blue', 'weight': 7.0, 'height': 3233.2},
{'animal': 'cat', 'color': 'magenta', 'weight': 2.0, 'height': 666.6},
{'animal': 'mouse', 'color': 'red', 'weight': 0.0, 'height': 55.5},
{'animal': 'mouse', 'color': 'blah', 'weight': 99.9, 'height': 33}]
encoder.load_from_data_stream(data)
encoded_data = encoder.encode_data(data)
data_decoded = encoder.decode_data(encoded_data)
expected = [{'height': 88.9, 'weight': 6.0, 'animal': 'cat', 'color': 'blue'},
{'height': 44.9, 'weight': 3.0, 'animal': 'cat', 'color': 'red'},
{'height': 2.5, 'weight': 5.5, 'color': 'UNKNOWN_CATEGORICAL_LEVEL', 'animal': 'UNKNOWN_CATEGORICAL_LEVEL'},
{'height': 3233.2, 'weight': 7.0, 'color': 'blue', 'animal': 'UNKNOWN_CATEGORICAL_LEVEL'},
{'height': 666.6, 'weight': 2.0, 'animal': 'cat', 'color': 'UNKNOWN_CATEGORICAL_LEVEL'},
{'height': 55.5, 'weight': 0.0, 'animal': 'mouse', 'color': 'red'},
{'height': 33, 'weight': 99.9, 'animal': 'mouse', 'color': 'UNKNOWN_CATEGORICAL_LEVEL'}]
assert data_decoded == expected
def get_round_trip_decoded(stats=False, omit_cols=None):
encoder = OneHotEncoder({'animal': 2, 'color': 1}, ['weight', 'height'], omit_cols=omit_cols)
data = [{'animal': 'cat', 'color': 'blue', 'weight': 6.0, 'height': 88.9, 'extra_junk': 'blah'},
{'animal': 'cat', 'color': 'red', 'weight': 3.0, 'height': 44.9},
{'animal': 'dog', 'color': 'yellow', 'weight': 5.5, 'height': 2.5},
{'animal': 'fish', 'color': 'blue', 'weight': 7.0, 'height': 3233.2},
{'animal': 'cat', 'color': 'magenta', 'weight': 2.0, 'height': 666.6},
{'animal': 'mouse', 'color': 'red', 'weight': 0.0, 'height': 55.5},
{'animal': 'mouse', 'color': 'blah', 'weight': 99.9, 'height': 33}]
encoder.load_from_data_stream(data)
encoded_data = encoder.encode_data(data)
data_decoded = encoder.decode_data(encoded_data)
# add number stats?
if stats:
encoder.add_numeric_stats(data)
# check the package
packaged = encoder.package_data()
return data_decoded, packaged
def test_inversion_more_complicated():
encoder = OneHotEncoder(['animal', 'color'], ['weight', 'height'], max_levels_default=100)
data = [{'animal': 'cat', 'color': 'blue', 'weight': 6.0, 'height': 88.9},
{'animal': 'cat', 'color': 'red', 'weight': 3.0, 'height': 44.9},
{'animal': 'dog', 'color': 'yellow', 'weight': 5.5, 'height': 2.5},
{'animal': 'fish', 'color': 'blue', 'weight': 7.0, 'height': 3233.2},
{'animal': 'cat', 'color': 'magenta', 'weight': 2.0, 'height': 666.6},
{'animal': 'mouse', 'color': 'red', 'weight': 0.0, 'height': 55.5},
{'animal': 'mouse', 'color': 'blah', 'weight': 99.9, 'height': 33}]
encoder.load_from_data_stream(data)
encoded_data = encoder.encode_data(data)
data_decoded = encoder.decode_data(encoded_data)
assert data_decoded == data
data_recoded = encoder.encode_data(data_decoded)
assert data_recoded == encoded_data
def test_inversion():
encoder = OneHotEncoder(['animal', 'color'], ['weight'], max_levels_default=100)
data = [{'animal': 'cat', 'color': 'blue', 'weight': 6.0},
{'animal': 'cat', 'color': 'red', 'weight': 3.0},
{'animal': 'dog', 'color': 'yellow', 'weight': 5.5},
{'animal': 'fish', 'color': 'blue', 'weight': 7.0},
{'animal': 'cat', 'color': 'magenta', 'weight': 2.0},
{'animal': 'mouse', 'color': 'purple', 'weight': 0.0},
{'animal': 'mouse', 'color': 'black', 'weight': 99.9}]
encoder.load_from_data_stream(data)
encoded_data = encoder.encode_data(data)
data_decoded = encoder.decode_data(encoded_data)
assert data_decoded == data
data_recoded = encoder.encode_data(data_decoded)
assert data_recoded == encoded_data
def test_html_form():
encoder = OneHotEncoder({'animal': 2, 'color': 1}, ['weight', 'height'])
data = [{'animal': 'cat', 'color': 'blue', 'weight': 6.0, 'height': 88.9, 'extra_junk': 'blah'},
{'animal': 'cat', 'color': 'red', 'weight': 3.0, 'height': 44.9},
{'animal': 'dog', 'color': 'yellow', 'weight': 5.5, 'height': 2.5},
{'animal': 'fish', 'color': 'blue', 'weight': 7.0, 'height': 3233.2},
{'animal': 'cat', 'color': 'magenta', 'weight': 2.0, 'height': 666.6},
{'animal': 'mouse', 'color': 'red', 'weight': 0.0, 'height': 55.5},
{'animal': 'mouse', 'color': 'blah', 'weight': 99.9, 'height': 33}]
encoder.load_from_data_stream(data)
package = encoder.package_data()
expected = {'max_levels_default': 10000,
'numeric_cols': ['weight', 'height'],
'categorical_n_levels_dict': {'animal': 2, 'color': 1},
'one_hot_encoder_dicts': {'animal': {'cat': 0, 'mouse': 1}, 'color': {'blue': 0}},
'numeric_stats': None,
'omit_cols': None}
assert package == expected
html_header, form_div = encoder.get_form_html_elements()
form_tags = ['id="form"', 'schema', 'alpaca', 'script']
for tag in form_tags:
assert tag in form_div
header_tags = ['jquery', 'bootstrap', 'alpaca', 'script']
for tag in header_tags:
assert tag in html_header
html_page = encoder.get_form_html_page()
assert "<html" in html_page
assert html_header in html_page
assert form_div in html_page
def test_init():
encoder = OneHotEncoder({'foo': 3, 'bar': 7}, ['buzz_numeric'])
assert encoder.numeric_cols == ['buzz_numeric']
assert encoder.categorical_n_levels_dict == {'foo': 3, 'bar': 7}
def test_load_from_data_encodes_data_correctly():
encoder = OneHotEncoder(['animal', 'color'], ['weight'], max_levels_default=100)
data = [{'animal': 'cat', 'color': 'blue', 'weight': 6.0},
{'animal': 'cat', 'color': 'red', 'weight': 3.0},
{'animal': 'dog', 'color': 'yellow', 'weight': 5.5},
{'animal': 'fish', 'color': 'blue', 'weight': 7.0},
{'animal': 'cat', 'color': 'magenta', 'weight': 2.0},
{'animal': 'mouse', 'color': 'purple', 'weight': 0.0},
{'animal': 'mouse', 'color': 'black', 'weight': 99.9}]
encoder.load_from_data_stream(data)
encoded_data = [encoder.encode_row(row) for row in data]
assert len(encoded_data) == len(data)
assert len(encoded_data[0]) != len(data[0])
first_row = encoded_data[0]
expected = [6.0, # weight is numeric and comes first
1.0, # animal is first categorical and cat is the most common, first row is cat
0.0, # animal, mouse is next most common, not a mouse
0.0, # animal, dog and fish tied for frequency but dog first alphabetically
0.0, # animal, fish, cat is not a fish
1.0, # color is next categorical alphabetically and blue is most common, first row blue
0.0, # black
0.0, # magenta
0.0, # purple
0.0, # red
0.0] # yellow
assert first_row == expected
second_row = encoded_data[1]
expected = [3.0, # weight is numeric and comes first
1.0, # animal is first categorical and cat is the most common, first row is cat
0.0, # animal, mouse is next most common, not a mouse
0.0, # animal, dog and fish tied for frequency but dog first alphabetically
0.0, # animal, fish, cat is not a fish
0.0, # color is next categorical alphabetically and blue is most common, first row blue
0.0, # black next alphabetically for ones with frequency 1
0.0, # magenta next
0.0, # purple
1.0, # red, this is red
0.0] # yellow
assert second_row == expected
last_row = encoded_data[-1]
expected = [99.9, # weight is numeric and comes first
0.0, # animal is first categorical and cat is the most common, first row is cat
1.0, # animal, mouse is next most common, not a mouse
0.0, # animal, dog and fish tied for frequency but dog first alphabetically
0.0, # animal, fish, cat is not a fish
0.0, # color is next categorical alphabetically and blue is most common, first row blue
1.0, # black next alphabetically for ones with frequency 1, this one black
0.0, # magenta next
0.0, # purple
0.0, # red
0.0] # yellow
expected_total = [[6.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[3.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0],
[5.5, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0],
[7.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[2.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
[99.9, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0]]
assert encoded_data == expected_total
def test_init_empty():
OneHotEncoder([], [])
def test_init_do_not_ignore_default():
encoder = OneHotEncoder(['foo', 'bar'], ['buzz_numeric'], max_levels_default=999)
assert encoder.numeric_cols == ['buzz_numeric']
assert encoder.categorical_n_levels_dict == {'foo': 999, 'bar': 999}
def test_save_load():
filename = NamedTemporaryFile().name
encoder = OneHotEncoder(['animal', 'color'], ['weight'], max_levels_default=100)
data = [{'animal': 'cat', 'color': 'blue', 'weight': 6.0},
{'animal': 'cat', 'color': 'red', 'weight': 3.0},
{'animal': 'dog', 'color': 'yellow', 'weight': 5.5},
{'animal': 'fish', 'color': 'blue', 'weight': 7.0},
{'animal': 'cat', 'color': 'magenta', 'weight': 2.0},
{'animal': 'mouse', 'color': 'purple', 'weight': 0.0},
{'animal': 'mouse', 'color': 'black', 'weight': 99.9}]
encoder.load_from_data_stream(data)
encoded_data = encoder.encode_data(data)
encoder.save(filename)
encoder_from_file = OneHotEncoder([], [])
encoder_from_file.load_from_file(filename)
encoded_data_from_file = encoder_from_file.encode_data(data)
assert encoded_data == encoded_data_from_file