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Issue 491: Get rid of Error terms in Plots #539

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merged 2 commits into from
Jun 9, 2022
Merged

Issue 491: Get rid of Error terms in Plots #539

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2c3e541
explicitly render historgram to prevent rounding errors
adnanhemani Jun 5, 2022
6e2990e
new version of code
adnanhemani Jun 6, 2022
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138 changes: 73 additions & 65 deletions datascience/tables.py
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Original file line number Diff line number Diff line change
Expand Up @@ -32,6 +32,10 @@

_INTERACTIVE_PLOTS = False

# Set numpy printoptions to legacy to get around error terms, as described in
# https://github.com/data-8/datascience/issues/491
np.set_printoptions(legacy='1.13')

class Table(collections.abc.MutableMapping):
"""A sequence of string-labeled columns."""
plots = collections.deque(maxlen=10)
Expand Down Expand Up @@ -5393,79 +5397,83 @@ def prepare_hist_with_group(group):
right_end = max([max(self.column(k)) for k in self.labels if np.issubdtype(self.column(k).dtype, np.number)])

def draw_hist(values_dict):
with np.printoptions(legacy='1.13'):
# This code is factored as a function for clarity only.
n = len(values_dict)
colors = [rgb_color + (self.default_alpha,) for rgb_color in
itertools.islice(itertools.cycle(self.chart_colors), n)]
hist_names = list(values_dict.keys())
values = [v[0] for v in values_dict.values()]
weights = [v[1] for v in values_dict.values() if len(v) > 1]
if n > len(weights) > 0:
raise ValueError("Weights were provided for some columns, but not "
" all, and that's not supported.")
if rug and overlay and n > 1:
warnings.warn("Cannot plot overlaid rug plots; rug=True ignored", UserWarning)
# Check if np.printoptions is set to legacy. Throw UserWarning if not
if np.get_printoptions()['legacy'] != '1.13':
warnings.warn("We've detected you're not using the '1.13' legacy setting for `np.printoptions`. "
"This may cause excessive error terms in your plots. We recommend solving this by running the "
"following code: `np.set_printoptions(legacy='1.13')`", UserWarning)
# This code is factored as a function for clarity only.
n = len(values_dict)
colors = [rgb_color + (self.default_alpha,) for rgb_color in
itertools.islice(itertools.cycle(self.chart_colors), n)]
hist_names = list(values_dict.keys())
values = [v[0] for v in values_dict.values()]
weights = [v[1] for v in values_dict.values() if len(v) > 1]
if n > len(weights) > 0:
raise ValueError("Weights were provided for some columns, but not "
" all, and that's not supported.")
if rug and overlay and n > 1:
warnings.warn("Cannot plot overlaid rug plots; rug=True ignored", UserWarning)
if vargs['density']:
y_label = 'Percent per ' + (unit if unit else 'unit')
percentage = plt.FuncFormatter(lambda x, _: "{:g}".format(100*x))
else:
y_label = 'Count'

if overlay and n > 1:
# Reverse because legend prints bottom-to-top
values = values[::-1]
weights = weights[::-1]
colors = list(colors)[::-1]
if len(weights) == n:
vargs['weights'] = weights
if not side_by_side:
vargs.setdefault('histtype', 'stepfilled')
figure = plt.figure(figsize=(width, height))
plt.hist(values, color=colors, **vargs)
# if rug:
# plt.scatter(values, np.zeros_like(values), marker="|", color=colors)
axis = figure.get_axes()[0]
_vertical_x(axis)
axis.set_ylabel(y_label)
if vargs['density']:
y_label = 'Percent per ' + (unit if unit else 'unit')
percentage = plt.FuncFormatter(lambda x, _: "{:g}".format(100*x))
axis.yaxis.set_major_formatter(percentage)
x_unit = ' (' + unit + ')' if unit else ''
if group is not None and len(self.labels) == 2:
#There's a grouping in place but we're only plotting one column's values
label_not_grouped = [l for l in self.labels if l != group][0]
axis.set_xlabel(label_not_grouped + x_unit, fontsize=16)
else:
y_label = 'Count'

if overlay and n > 1:
# Reverse because legend prints bottom-to-top
values = values[::-1]
weights = weights[::-1]
colors = list(colors)[::-1]
if len(weights) == n:
vargs['weights'] = weights
if not side_by_side:
axis.set_xlabel(x_unit, fontsize=16)
plt.legend(hist_names, loc=2, bbox_to_anchor=(1.05, 1))
type(self).plots.append(axis)
else:
_, axes = plt.subplots(n, 1, figsize=(width, height * n))
if 'bins' in vargs:
bins = vargs['bins']
if isinstance(bins, numbers.Integral) and bins > 76 or hasattr(bins, '__len__') and len(bins) > 76:
# Use stepfilled when there are too many bins
vargs.setdefault('histtype', 'stepfilled')
figure = plt.figure(figsize=(width, height))
plt.hist(values, color=colors, **vargs)
# if rug:
# plt.scatter(values, np.zeros_like(values), marker="|", color=colors)
axis = figure.get_axes()[0]
_vertical_x(axis)
if n == 1:
axes = [axes]
for i, (axis, hist_name, values_for_hist, color) in enumerate(zip(axes, hist_names, values, colors)):
axis.set_ylabel(y_label)
if vargs['density']:
axis.yaxis.set_major_formatter(percentage)
x_unit = ' (' + unit + ')' if unit else ''
if group is not None and len(self.labels) == 2:
#There's a grouping in place but we're only plotting one column's values
label_not_grouped = [l for l in self.labels if l != group][0]
axis.set_xlabel(label_not_grouped + x_unit, fontsize=16)
else:
axis.set_xlabel(x_unit, fontsize=16)
plt.legend(hist_names, loc=2, bbox_to_anchor=(1.05, 1))
if len(weights) == n:
vargs['weights'] = weights[i]
axis.set_xlabel(hist_name + x_unit, fontsize=16)
heights, bins, patches = axis.hist(values_for_hist, color=color, **vargs)
if left_end is not None and right_end is not None:
x_shade, height_shade, width_shade = _compute_shading(heights, bins.copy(), left_end, right_end)
axis.bar(x_shade, height_shade, width=width_shade,
color=self.chart_colors[1], align="edge")
_vertical_x(axis)
if rug:
axis.scatter(values_for_hist, np.zeros_like(values_for_hist), marker="|",
color="black", s=100, zorder=10)
type(self).plots.append(axis)
else:
_, axes = plt.subplots(n, 1, figsize=(width, height * n))
if 'bins' in vargs:
bins = vargs['bins']
if isinstance(bins, numbers.Integral) and bins > 76 or hasattr(bins, '__len__') and len(bins) > 76:
# Use stepfilled when there are too many bins
vargs.setdefault('histtype', 'stepfilled')
if n == 1:
axes = [axes]
for i, (axis, hist_name, values_for_hist, color) in enumerate(zip(axes, hist_names, values, colors)):
axis.set_ylabel(y_label)
if vargs['density']:
axis.yaxis.set_major_formatter(percentage)
x_unit = ' (' + unit + ')' if unit else ''
if len(weights) == n:
vargs['weights'] = weights[i]
axis.set_xlabel(hist_name + x_unit, fontsize=16)
heights, bins, patches = axis.hist(values_for_hist, color=color, **vargs)
if left_end is not None and right_end is not None:
x_shade, height_shade, width_shade = _compute_shading(heights, bins.copy(), left_end, right_end)
axis.bar(x_shade, height_shade, width=width_shade,
color=self.chart_colors[1], align="edge")
_vertical_x(axis)
if rug:
axis.scatter(values_for_hist, np.zeros_like(values_for_hist), marker="|",
color="black", s=100, zorder=10)
type(self).plots.append(axis)

draw_hist(values_dict)

Expand Down