def test_character_pos_tracker():
x = StringTracker()
data = ["abc abc", "93341-1", "912254", None]
no_nulls = [a for a in data if a is not None]
count = len(no_nulls)
n_unique = len(set(no_nulls))
for record in data:
x.update(record)
assert x.items.get_num_active_items() == n_unique
assert x.items.get_total_weight() == count
assert pytest.approx(x.length.ints.mean(), 0.001) == 6.666
assert pytest.approx(x.token_length.ints.mean(), 0.001) == 1.333
assert x.theta_sketch.get_result().get_estimate() == float(n_unique)
assert x.count == count
assert x.char_pos_tracker is not None
assert x.length.count == 3
assert x.char_pos_tracker.char_pos_map["a"].histogram.get_min_value() == 0
assert x.char_pos_tracker.char_pos_map["a"].count == 2
assert x.char_pos_tracker.char_pos_map["-"].histogram.get_min_value() == 5
def test_merge_mod_character_lists():
x = StringTracker()
y = StringTracker()
data = ["abc abc", "93341-1", "912254", "bac tralalala"]
data_2 = [
"geometric inference ", "93341-1", "912254", "bac tralalala",
"😀 this is a sale! a ❄️ sale!",
"this is a long sentence that ends in an A", None
]
for record in data:
x.update(record, character_list="ab")
for record in data_2:
y.update(record, character_list="a")
assert x.char_pos_tracker.char_pos_map["NITL"].count == 23
assert x.char_pos_tracker.char_pos_map["NITL"].histogram.get_max_value(
) == 11
assert x.char_pos_tracker.char_pos_map["a"].histogram.get_max_value() == 12
assert y.char_pos_tracker.char_pos_map["NITL"].count == 102
assert y.char_pos_tracker.char_pos_map["a"].histogram.get_max_value() == 40
assert y.char_pos_tracker.char_pos_map["NITL"].histogram.get_max_value(
) == 39
x = x.merge(y)
assert x.char_pos_tracker.char_pos_map["a"].histogram.get_max_value() == 40
assert x.char_pos_tracker.char_pos_map["NITL"].histogram.get_max_value(
) == 39
assert x.char_pos_tracker.char_pos_map["NITL"].count == 125
assert x.token_length.histogram.get_max_value() == 10
def test_merge_character_pos():
x = StringTracker()
y = StringTracker()
data = ["abc abc", "93341-1", "912254", "bac tralalala"]
data_2 = ["geometric inference ", "93341-1", "912254", "bac tralalala", "😀 this is a sale! a ❄️ sale!", "this is a long sentence that ends in an A", None]
for record in data:
x.update(record)
for record in data_2:
y.update(record)
assert x.char_pos_tracker.char_pos_map["NITL"].count == 2
assert x.char_pos_tracker.char_pos_map["NITL"].histogram.get_max_value() == 3
assert x.char_pos_tracker.char_pos_map["NITL"].histogram.get_min_value() == 3
assert x.char_pos_tracker.char_pos_map["a"].histogram.get_max_value() == 12
assert x.char_pos_tracker.char_pos_map["a"].histogram.get_min_value() == 0
assert y.char_pos_tracker.char_pos_map["NITL"].count == 22
assert y.char_pos_tracker.char_pos_map["NITL"].histogram.get_max_value() == 39
new_tracker = x.merge(y)
assert new_tracker.char_pos_tracker.char_pos_map["a"].histogram.get_max_value() == 40
assert new_tracker.char_pos_tracker.char_pos_map["NITL"].histogram.get_max_value() == 39
assert new_tracker.char_pos_tracker.char_pos_map["NITL"].histogram.get_min_value() == 0
assert new_tracker.char_pos_tracker.char_pos_map["NITL"].count == 24
def test_unicode_character_pos():
x = StringTracker()
data = ["👷♀️ 🤺"]
for record in data:
x.update(record, character_list="🤺")
assert x.char_pos_tracker.char_pos_map["NITL"].count == 5
assert x.char_pos_tracker.char_pos_map["🤺"].count == 1
assert x.char_pos_tracker.char_pos_map["🤺"].histogram.get_min_value() == 5
def test_protobuf():
x = StringTracker()
data = ["one", "two", "three", "one", "one", "One", "six", None, None]
for record in data:
x.update(record)
x2 = StringTracker.from_protobuf(x.to_protobuf())
assert x.count == x2.count
assert x.items.get_total_weight() == x2.items.get_total_weight()
assert x.theta_sketch.get_result().get_estimate() == x2.theta_sketch.get_result().get_estimate()
def test_string_tracker_merge():
x = StringTracker()
data = ["one", "two", "three", "one", "one", "One test", "six", None, None]
for record in data:
x.update(record)
assert x.token_length.histogram.get_max_value() == 2
assert x.token_length.histogram.get_min_value() == 1
x2 = StringTracker()
data = ["this is a long sentence that ends in an A"]
for record in data:
x2.update(record)
assert x2.token_length.histogram.get_max_value() == 10
assert x2.token_length.histogram.get_min_value() == 10
new_string_track = x2.merge(x)
assert new_string_track.token_length.histogram.get_max_value() == 10
assert new_string_track.token_length.histogram.get_min_value() == 1
def test_tracking():
x = StringTracker()
data = ["one", "two", "three", "one", "one", "One", "six", None, None]
no_nulls = [a for a in data if a is not None]
count = len(no_nulls)
n_unique = len(set(no_nulls))
for record in data:
x.update(record)
assert x.items.get_num_active_items() == n_unique
assert x.items.get_total_weight() == count
assert [("one", 3, 3, 3)] == x.items.get_frequent_items(datasketches.frequent_items_error_type.NO_FALSE_NEGATIVES, 2)
assert x.theta_sketch.get_result().get_estimate() == float(n_unique)
assert x.count == count
# check case insensitive tracking
assert x.char_pos_tracker.char_pos_map["o"].count == 5
def test_summary():
import pandas as pd
x = StringTracker()
data = ["one", "two", "three", "one", "one", "One", "six", None, None]
for record in data:
x.update(record)
# Check the full output. NOTE: the order of the "items" below should
# really be arbitrary
expected = {
"uniqueCount": {"estimate": 5.0, "upper": 5.0, "lower": 5.0},
"frequent": {
"items": [
{"value": "one", "estimate": 3.0},
{"value": "three", "estimate": 1.0},
{"value": "six", "estimate": 1.0},
{"value": "One", "estimate": 1.0},
{"value": "two", "estimate": 1.0},
]
},
"length": {
"count": "7",
"min": 3.0,
"max": 5.0,
"mean": 3.2857142857142856,
"stddev": 0.7559289460184544,
"histogram": {
"start": 3.0,
"end": 5.0000005,
"counts": ["6", "1"],
"max": 5.0,
"min": 3.0,
"bins": [3.0, 4.000000249999999, 5.0000005],
"n": "7",
"width": 0.0,
},
"uniqueCount": {"estimate": 2.0, "upper": 2.0, "lower": 2.0},
"quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 5.0, 5.0, 5.0]},
"frequentNumbers": {"longs": [{"estimate": "6", "value": "3", "rank": 0}, {"estimate": "1", "value": "5", "rank": 1}], "doubles": []},
"isDiscrete": False,
},
"tokenLength": {
"count": "7",
"min": 1.0,
"max": 1.0,
"mean": 1.0,
"histogram": {"start": 1.0, "end": 1.0000001, "counts": ["7"], "max": 1.0, "min": 1.0, "bins": [1.0, 1.0000001], "n": "7", "width": 0.0},
"uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0},
"quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]},
"frequentNumbers": {"longs": [{"estimate": "7", "value": "1", "rank": 0}], "doubles": []},
"stddev": 0.0,
"isDiscrete": False,
},
"charPosTracker": {
"characterList": "!#$%&()*+,-./0123456789?@[]^_abcdefghijklmnopqrstuvwyz{}",
"charPosMap": {
"i": {
"count": "1",
"min": 1.0,
"max": 1.0,
"mean": 1.0,
"histogram": {"start": 1.0, "end": 1.0000001, "counts": ["1"], "max": 1.0, "min": 1.0, "bins": [1.0, 1.0000001], "n": "1", "width": 0.0},
"uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0},
"quantiles": {
"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0],
"quantileValues": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
},
"frequentNumbers": {"longs": [{"estimate": "1", "value": "1", "rank": 0}], "doubles": []},
"stddev": 0.0,
"isDiscrete": False,
},
"t": {
"count": "2",
"histogram": {"counts": ["2"], "bins": [0.0, 0.0], "n": "2", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0},
"uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0},
"quantiles": {
"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0],
"quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
},
"frequentNumbers": {"longs": [{"estimate": "2", "value": "0", "rank": 0}], "doubles": []},
"min": 0.0,
"max": 0.0,
"mean": 0.0,
"stddev": 0.0,
"isDiscrete": False,
},
"s": {
"count": "1",
"histogram": {"counts": ["1"], "bins": [0.0, 0.0], "n": "1", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0},
"uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0},
"quantiles": {
"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0],
"quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
},
"frequentNumbers": {"longs": [{"estimate": "1", "value": "0", "rank": 0}], "doubles": []},
"min": 0.0,
"max": 0.0,
"mean": 0.0,
"stddev": 0.0,
"isDiscrete": False,
},
"n": {
"count": "4",
"min": 1.0,
"max": 1.0,
"mean": 1.0,
"histogram": {"start": 1.0, "end": 1.0000001, "counts": ["4"], "max": 1.0, "min": 1.0, "bins": [1.0, 1.0000001], "n": "4", "width": 0.0},
"uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0},
"quantiles": {
"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0],
"quantileValues": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
},
"frequentNumbers": {"longs": [{"estimate": "4", "value": "1", "rank": 0}], "doubles": []},
"stddev": 0.0,
"isDiscrete": False,
},
"h": {
"count": "1",
"min": 1.0,
"max": 1.0,
"mean": 1.0,
"histogram": {"start": 1.0, "end": 1.0000001, "counts": ["1"], "max": 1.0, "min": 1.0, "bins": [1.0, 1.0000001], "n": "1", "width": 0.0},
"uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0},
"quantiles": {
"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0],
"quantileValues": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
},
"frequentNumbers": {"longs": [{"estimate": "1", "value": "1", "rank": 0}], "doubles": []},
"stddev": 0.0,
"isDiscrete": False,
},
"o": {
"count": "5",
"max": 2.0,
"mean": 0.4,
"stddev": 0.894427190999916,
"histogram": {
"end": 2.0000002,
"counts": ["4", "1"],
"max": 2.0,
"bins": [0.0, 1.0000001, 2.0000002],
"n": "5",
"start": 0.0,
"width": 0.0,
"min": 0.0,
},
"uniqueCount": {"estimate": 2.0, "upper": 2.0, "lower": 2.0},
"quantiles": {
"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0],
"quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.0, 2.0, 2.0],
},
"frequentNumbers": {"longs": [{"estimate": "4", "value": "0", "rank": 0}, {"estimate": "1", "value": "2", "rank": 1}], "doubles": []},
"min": 0.0,
"isDiscrete": False,
},
"NITL": {
"count": "1",
"min": 2.0,
"max": 2.0,
"mean": 2.0,
"histogram": {"start": 2.0, "end": 2.0000002, "counts": ["1"], "max": 2.0, "min": 2.0, "bins": [2.0, 2.0000002], "n": "1", "width": 0.0},
"uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0},
"quantiles": {
"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0],
"quantileValues": [2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0],
},
"frequentNumbers": {"longs": [{"estimate": "1", "value": "2", "rank": 0}], "doubles": []},
"stddev": 0.0,
"isDiscrete": False,
},
"w": {
"count": "1",
"min": 1.0,
"max": 1.0,
"mean": 1.0,
"histogram": {"start": 1.0, "end": 1.0000001, "counts": ["1"], "max": 1.0, "min": 1.0, "bins": [1.0, 1.0000001], "n": "1", "width": 0.0},
"uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0},
"quantiles": {
"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0],
"quantileValues": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
},
"frequentNumbers": {"longs": [{"estimate": "1", "value": "1", "rank": 0}], "doubles": []},
"stddev": 0.0,
"isDiscrete": False,
},
"e": {
"count": "6",
"min": 2.0,
"max": 4.0,
"mean": 2.5,
"stddev": 0.8366600265340756,
"histogram": {
"start": 2.0,
"end": 4.0000004,
"counts": ["5", "1"],
"max": 4.0,
"min": 2.0,
"bins": [2.0, 3.0000002, 4.0000004],
"n": "6",
"width": 0.0,
},
"uniqueCount": {"estimate": 3.0, "upper": 3.0, "lower": 3.0},
"quantiles": {
"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0],
"quantileValues": [2.0, 2.0, 2.0, 2.0, 2.0, 3.0, 4.0, 4.0, 4.0],
},
"frequentNumbers": {
"longs": [
{"estimate": "4", "value": "2", "rank": 0},
{"estimate": "1", "value": "4", "rank": 1},
{"estimate": "1", "value": "3", "rank": 2},
],
"doubles": [],
},
"isDiscrete": False,
},
"r": {
"count": "1",
"min": 2.0,
"max": 2.0,
"mean": 2.0,
"histogram": {"start": 2.0, "end": 2.0000002, "counts": ["1"], "max": 2.0, "min": 2.0, "bins": [2.0, 2.0000002], "n": "1", "width": 0.0},
"uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0},
"quantiles": {
"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0],
"quantileValues": [2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0],
},
"frequentNumbers": {"longs": [{"estimate": "1", "value": "2", "rank": 0}], "doubles": []},
"stddev": 0.0,
"isDiscrete": False,
},
},
},
}
expected_items = pd.DataFrame(expected["frequent"]["items"]).sort_values(["value", "estimate"])
expected["frequent"].pop("items")
# removing items that due to their statisctical nature differ in different systems. Need to dig in to see if there is a way to fix the seeds so values dont change from mac os to ubuntu
for char, value in expected["charPosTracker"]["charPosMap"].items():
value.pop("frequentNumbers")
actual = message_to_dict(x.to_summary())
actual_items = pd.DataFrame(actual["frequent"]["items"]).sort_values(["value", "estimate"])
actual["frequent"].pop("items")
# same as above, removing items that due to their statisctical nature differ in different systems. Need to dig in to see if there is a way to fix the seeds so values dont change from mac os to ubuntu
for char, value in actual["charPosTracker"]["charPosMap"].items():
value.pop("frequentNumbers")
assert expected == actual
pd.testing.assert_frame_equal(actual_items.reset_index(drop=True).sort_index(axis=1), expected_items.reset_index(drop=True).sort_index(axis=1))