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from typing import List
import hypothesis.strategies as st
from caffe2.python import core, workspace
from hypothesis import given
import caffe2.python.hypothesis_test_util as hu
import bisect
import numpy as np
class TestBisectPercentileOp(hu.HypothesisTestCase):
def compare_reference(
self,
raw_data,
pct_raw_data,
pct_mapping,
pct_upper,
pct_lower,
lengths,
):
def bisect_percentile_op_ref(
raw_data,
pct_raw_data,
pct_mapping,
pct_lower,
pct_upper,
lengths
):
results = np.zeros_like(raw_data)
indices = [0]
for j in range(len(lengths)):
indices.append(indices[j] + lengths[j])
for i in range(len(raw_data)):
for j in range(len(raw_data[0])):
start = indices[j]
end = indices[j + 1]
val = raw_data[i][j]
pct_raw_data_i = pct_raw_data[start:end]
pct_lower_i = pct_lower[start:end]
pct_upper_i = pct_upper[start:end]
pct_mapping_i = pct_mapping[start:end]
# Corner cases
if val < pct_raw_data_i[0]:
results[i][j] = 0
continue
if val > pct_raw_data_i[-1]:
results[i][j] = 1.
continue
# interpolation
k = bisect.bisect_left(pct_raw_data_i, val)
if pct_raw_data_i[k] == val:
results[i][j] = pct_mapping_i[k]
else:
k = k - 1
slope = ((pct_lower_i[k + 1] - pct_upper_i[k])
/ (pct_raw_data_i[k + 1] - pct_raw_data_i[k]))
results[i][j] = pct_upper_i[k] + \
slope * (val - pct_raw_data_i[k])
return results
workspace.ResetWorkspace()
workspace.FeedBlob("raw_data", raw_data)
op = core.CreateOperator(
"BisectPercentile",
["raw_data"],
["pct_output"],
percentile_raw=pct_raw_data,
percentile_mapping=pct_mapping,
percentile_lower=pct_lower,
percentile_upper=pct_upper,
lengths=lengths
)
workspace.RunOperatorOnce(op)
expected_output = bisect_percentile_op_ref(
raw_data,
pct_raw_data,
pct_mapping,
pct_lower,
pct_upper,
lengths
)
output = workspace.blobs['pct_output']
np.testing.assert_array_almost_equal(output, expected_output)
def test_bisect_percentil_op_simple(self):
raw_data = np.array([
[1, 1],
[2, 2],
[3, 3],
[3, 1],
[9, 10],
[1.5, 5],
[1.32, 2.4],
[2.9, 5.7],
[-1, -1],
[3, 7]
], dtype=np.float32)
pct_raw_data = np.array([1, 2, 3, 2, 7], dtype=np.float32)
pct_lower = np.array([0.1, 0.2, 0.9, 0.1, 0.5], dtype=np.float32)
pct_upper = np.array([0.1, 0.8, 1.0, 0.4, 1.0], dtype=np.float32)
pct_mapping = np.array([0.1, 0.5, 0.95, 0.25, 0.75], dtype=np.float32)
lengths = np.array([3, 2], dtype=np.int32)
self.compare_reference(
raw_data, pct_raw_data, pct_mapping, pct_lower, pct_upper, lengths)
@given(
N=st.integers(min_value=20, max_value=100),
lengths_in=st.lists(
elements=st.integers(min_value=2, max_value=10),
min_size=2,
max_size=5,
),
max_value=st.integers(min_value=100, max_value=1000),
discrete=st.booleans(),
p=st.floats(min_value=0, max_value=0.9),
**hu.gcs_cpu_only
)
def test_bisect_percentil_op_large(
self, N: int, lengths_in: List[int], max_value: int, discrete: bool, p: float, gc, dc
):
lengths = np.array(lengths_in, dtype=np.int32)
D = len(lengths)
if discrete:
raw_data = np.random.randint(0, max_value, size=(N, D))
else:
raw_data = np.random.randn(N, D)
# To generate valid pct_lower and pct_upper
pct_lower = []
pct_upper = []
pct_raw_data = []
for i in range(D):
pct_lower_val = 0.
pct_upper_val = 0.
pct_lower_cur = []
pct_upper_cur = []
# There is no duplicated values in pct_raw_data
if discrete:
pct_raw_data_cur = np.random.choice(
np.arange(max_value), size=lengths[i], replace=False)
else:
pct_raw_data_cur = np.random.randn(lengths[i])
while len(set(pct_raw_data_cur)) < lengths[i]:
pct_raw_data_cur = np.random.randn(lengths[i])
pct_raw_data_cur = np.sort(pct_raw_data_cur)
for _ in range(lengths[i]):
pct_lower_val = pct_upper_val + 0.01
pct_lower_cur.append(pct_lower_val)
pct_upper_val = pct_lower_val + \
0.01 * np.random.randint(1, 20) * (np.random.uniform() < p)
pct_upper_cur.append(pct_upper_val)
# normalization
pct_lower_cur = np.array(pct_lower_cur, np.float32) / pct_upper_val
pct_upper_cur = np.array(pct_upper_cur, np.float32) / pct_upper_val
pct_lower.extend(pct_lower_cur)
pct_upper.extend(pct_upper_cur)
pct_raw_data.extend(pct_raw_data_cur)
pct_lower = np.array(pct_lower, dtype=np.float32)
pct_upper = np.array(pct_upper, dtype=np.float32)
pct_mapping = (pct_lower + pct_upper) / 2.
raw_data = np.array(raw_data, dtype=np.float32)
pct_raw_data = np.array(pct_raw_data, dtype=np.float32)
self.compare_reference(
raw_data, pct_raw_data, pct_mapping, pct_lower, pct_upper, lengths)
if __name__ == "__main__":
import unittest
unittest.main()
|
