got error: Error: No such keys(s): 'mode.use_inf_as_null'

[zjn-11]

Problem Description

alphalens.tears.create_full_tear_sheet(factor_data)

Cell In[20], line 1
----> 1 alphalens.tears.create_full_tear_sheet(factor_data)

File ~/opt/miniconda3/envs/vnpy/lib/python3.10/site-packages/alphalens/plotting.py:46, in customize.<locals>.call_w_context(*args, **kwargs)
     44     with plotting_context(), axes_style(), color_palette:
     45         sns.despine(left=True)
---> 46         return func(*args, **kwargs)
     47 else:
     48     return func(*args, **kwargs)

File ~/opt/miniconda3/envs/vnpy/lib/python3.10/site-packages/alphalens/tears.py:501, in create_full_tear_sheet(factor_data, long_short, group_neutral, by_group)
    497 plotting.plot_quantile_statistics_table(factor_data)
    498 create_returns_tear_sheet(
    499     factor_data, long_short, group_neutral, by_group, set_context=False
    500 )
--> 501 create_information_tear_sheet(
    502     factor_data, group_neutral, by_group, set_context=False
    503 )
    504 create_turnover_tear_sheet(factor_data, set_context=False)

File ~/opt/miniconda3/envs/vnpy/lib/python3.10/site-packages/alphalens/plotting.py:48, in customize.<locals>.call_w_context(*args, **kwargs)
     46         return func(*args, **kwargs)
     47 else:
---> 48     return func(*args, **kwargs)

File ~/opt/miniconda3/envs/vnpy/lib/python3.10/site-packages/alphalens/tears.py:365, in create_information_tear_sheet(factor_data, group_neutral, by_group)
    362 plotting.plot_ic_ts(ic, ax=ax_ic_ts)
    364 ax_ic_hqq = [gf.next_cell() for _ in range(fr_cols * 2)]
--> 365 plotting.plot_ic_hist(ic, ax=ax_ic_hqq[::2])
    366 plotting.plot_ic_qq(ic, ax=ax_ic_hqq[1::2])
    368 if not by_group:

File ~/opt/miniconda3/envs/vnpy/lib/python3.10/site-packages/alphalens/plotting.py:282, in plot_ic_hist(ic, ax)
    279     ax = ax.flatten()
    281 for a, (period_num, ic) in zip(ax, ic.items()):
--> 282     sns.histplot(ic.replace(np.nan, 0.0), kde=True, ax=a)
    283     a.set(title="%s Period IC" % period_num, xlabel="IC")
    284     a.set_xlim([-1, 1])

File ~/opt/miniconda3/envs/vnpy/lib/python3.10/site-packages/seaborn/distributions.py:1462, in histplot(data, x, y, hue, weights, stat, bins, binwidth, binrange, discrete, cumulative, common_bins, common_norm, multiple, element, fill, shrink, kde, kde_kws, line_kws, thresh, pthresh, pmax, cbar, cbar_ax, cbar_kws, palette, hue_order, hue_norm, color, log_scale, legend, ax, **kwargs)
   1451 estimate_kws = dict(
   1452     stat=stat,
   1453     bins=bins,
   (...)
   1457     cumulative=cumulative,
   1458 )
   1460 if p.univariate:
-> 1462     p.plot_univariate_histogram(
   1463         multiple=multiple,
   1464         element=element,
   1465         fill=fill,
   1466         shrink=shrink,
   1467         common_norm=common_norm,
   1468         common_bins=common_bins,
   1469         kde=kde,
   1470         kde_kws=kde_kws,
   1471         color=color,
   1472         legend=legend,
   1473         estimate_kws=estimate_kws,
   1474         line_kws=line_kws,
   1475         **kwargs,
   1476     )
   1478 else:
   1480     p.plot_bivariate_histogram(
   1481         common_bins=common_bins,
   1482         common_norm=common_norm,
   (...)
   1492         **kwargs,
   1493     )

File ~/opt/miniconda3/envs/vnpy/lib/python3.10/site-packages/seaborn/distributions.py:418, in _DistributionPlotter.plot_univariate_histogram(self, multiple, element, fill, common_norm, common_bins, shrink, kde, kde_kws, color, legend, line_kws, estimate_kws, **plot_kws)
    416     kde_kws["cumulative"] = estimate_kws["cumulative"]
    417     log_scale = self._log_scaled(self.data_variable)
--> 418     densities = self._compute_univariate_density(
    419         self.data_variable,
    420         common_norm,
    421         common_bins,
    422         kde_kws,
    423         log_scale,
    424         warn_singular=False,
    425     )
    427 # First pass through the data to compute the histograms
    428 for sub_vars, sub_data in self.iter_data("hue", from_comp_data=True):
    429 
    430     # Prepare the relevant data

File ~/opt/miniconda3/envs/vnpy/lib/python3.10/site-packages/seaborn/distributions.py:303, in _DistributionPlotter._compute_univariate_density(self, data_variable, common_norm, common_grid, estimate_kws, log_scale, warn_singular)
    299     common_norm = False
    301 densities = {}
--> 303 for sub_vars, sub_data in self.iter_data("hue", from_comp_data=True):
    304 
    305     # Extract the data points from this sub set and remove nulls
    306     sub_data = sub_data.dropna()
    307     observations = sub_data[data_variable]

File ~/opt/miniconda3/envs/vnpy/lib/python3.10/site-packages/seaborn/_core.py:983, in VectorPlotter.iter_data(self, grouping_vars, reverse, from_comp_data)
    978 grouping_vars = [
    979     var for var in grouping_vars if var in self.variables
    980 ]
    982 if from_comp_data:
--> 983     data = self.comp_data
    984 else:
    985     data = self.plot_data

File ~/opt/miniconda3/envs/vnpy/lib/python3.10/site-packages/seaborn/_core.py:1054, in VectorPlotter.comp_data(self)
   1050 axis = getattr(ax, f"{var}axis")
   1052 # Use the converter assigned to the axis to get a float representation
   1053 # of the data, passing np.nan or pd.NA through (pd.NA becomes np.nan)
-> 1054 with pd.option_context('mode.use_inf_as_null', True):
   1055     orig = self.plot_data[var].dropna()
   1056 comp_col = pd.Series(index=orig.index, dtype=float, name=var)

File ~/opt/miniconda3/envs/vnpy/lib/python3.10/site-packages/pandas/_config/config.py:441, in option_context.__enter__(self)
    440 def __enter__(self) -> None:
--> 441     self.undo = [(pat, _get_option(pat, silent=True)) for pat, val in self.ops]
    443     for pat, val in self.ops:
    444         _set_option(pat, val, silent=True)

File ~/opt/miniconda3/envs/vnpy/lib/python3.10/site-packages/pandas/_config/config.py:441, in <listcomp>(.0)
    440 def __enter__(self) -> None:
--> 441     self.undo = [(pat, _get_option(pat, silent=True)) for pat, val in self.ops]
    443     for pat, val in self.ops:
    444         _set_option(pat, val, silent=True)

File ~/opt/miniconda3/envs/vnpy/lib/python3.10/site-packages/pandas/_config/config.py:135, in _get_option(pat, silent)
    134 def _get_option(pat: str, silent: bool = False) -> Any:
--> 135     key = _get_single_key(pat, silent)
    137     # walk the nested dict
    138     root, k = _get_root(key)

File ~/opt/miniconda3/envs/vnpy/lib/python3.10/site-packages/pandas/_config/config.py:121, in _get_single_key(pat, silent)
    119     if not silent:
    120         _warn_if_deprecated(pat)
--> 121     raise OptionError(f"No such keys(s): {repr(pat)}")
    122 if len(keys) > 1:
    123     raise OptionError("Pattern matched multiple keys")

OptionError: No such keys(s): 'mode.use_inf_as_null'

Please provide any additional information below:

Versions

• Alphalens version: 0.4.3
• Python version: 3.10.10
• Pandas version: 2.0.3
• Matplotlib version: 3.5.3