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Replace infs with nans to avoid crash when creating a heatmap #442

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merged 4 commits into from
Jan 13, 2022
Merged

Replace infs with nans to avoid crash when creating a heatmap #442

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ac88f82
FIX-#440: replace infs with nans
cgarciae Jan 6, 2022
255095d
Change test name
cgarciae Jan 13, 2022
b55ac55
remove ignore format option on PandasExecutor.py
cgarciae Jan 13, 2022
7cab120
Merge branch 'fix-infs-in-heatmap' of github.com:lux-org/lux into fix…
cgarciae Jan 13, 2022
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6 changes: 6 additions & 0 deletions lux/executor/PandasExecutor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -281,6 +281,8 @@ def execute_binning(ldf: LuxDataFrame, vis: Vis):
"""
import numpy as np

vis._vis_data = vis._vis_data.replace([np.inf, -np.inf], np.nan)

bin_attribute = [x for x in vis._inferred_intent if x.bin_size != 0][0]
bin_attr = bin_attribute.attribute
series = vis.data[bin_attr]
Expand Down Expand Up @@ -379,6 +381,10 @@ def execute_2D_binning(vis: Vis) -> None:
----------
vis : Vis
"""
import numpy as np

vis._vis_data = vis._vis_data.replace([np.inf, -np.inf], np.nan)

pd.reset_option("mode.chained_assignment")
with pd.option_context("mode.chained_assignment", None):
x_attr = vis.get_attr_by_channel("x")[0].attribute
Expand Down
19 changes: 19 additions & 0 deletions tests/test_pandas.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,6 +15,7 @@
from .context import lux
import pytest
import pandas as pd
import numpy as np


def test_head_tail(global_var):
Expand Down Expand Up @@ -54,3 +55,21 @@ def test_convert_dtype(global_var):
cdf = df.convert_dtypes()
cdf._ipython_display_()
assert list(cdf.recommendation.keys()) == ["Correlation", "Distribution", "Occurrence"]


def test_infs():
nrows = 100_000

# continuous
c1 = np.random.uniform(0, 1, size=nrows)
c1[2] = np.inf
c2 = np.random.uniform(0, 1, size=nrows)
c2[3] = np.inf

# discrete
d1 = np.random.randint(0, 2, size=nrows)
d2 = np.random.randint(0, 2, size=nrows)

df = pd.DataFrame({"c1": c1, "c2": c2, "d1": d1, "d2": d2})

df._ipython_display_()