Open zjn-11 opened 9 months ago
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'
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