def factor_score_dynamics_plot(
self,
factor,
agg_method="mean",
group=None,
figsize=None,
write_tex=False,
dpi=200,
save_path=None,
width=None,
height=None,
):
if figsize is None:
figsize = (12, 8)
to_concat = []
for period in self.periods:
df = self.data_proc.score_df(
periods=period,
factors=factor,
other_vars=[group, "__period__"],
agg_method=agg_method,
)
to_concat.append(df)
data = pd.concat(to_concat, axis=0, sort=True)
fig, ax = plt.subplots(figsize=figsize)
sns.pointplot(
x="__period__",
y=factor,
hue=group,
data=data,
ax=ax,
kind="bar",
dodge=0.15,
join=True,
capsize=0.05,
)
sns.despine(fig=fig, ax=ax)
base_title = "Factor Score Dynamics"
title = title_text(base_title, periods="all", factors=factor)
if write_tex is True:
write_figure_tex_snippet(save_path, title, width=width, height=height)
if save_path is not None:
fig.savefig(save_path, dpi=dpi, bbox_inches="tight")
return fig, ax
def score_pairplot(
self,
periods="all",
factors="all",
agg_method="means",
group=None,
pair_kws=None,
save_path=None,
dpi=200,
write_tex=False,
width=None,
height=None,
):
pair_kws = {} if pair_kws is None else pair_kws
if write_tex is True:
assert (
save_path is not None
), "To write a tex file, please provide a save_path"
if group is None:
other_vars = []
else:
other_vars = [group]
if width is None and height is None:
width = 1
df = self.data_proc.score_df(
periods=periods,
factors=factors,
other_vars=other_vars,
agg_method=agg_method,
)
kwargs = self._basic_pairplot_args()
kwargs.update(pair_kws)
variables = [col for col in df.columns if col != group]
grid = sns.pairplot(data=df, hue=group, vars=variables, **kwargs)
base_title = "Joint Distribution of Factor Scores"
title = title_text(base_title, periods=periods, factors=factors)
if write_tex is True:
write_figure_tex_snippet(save_path, title, width=width, height=height)
if save_path is not None:
grid.savefig(save_path, dpi=dpi, bbox_inches="tight")
return grid
def score_heatmap(
self,
periods="all",
factors="all",
agg_method="means",
figsize=None,
heatmap_kws=None,
save_path=None,
dpi=200,
write_tex=False,
width=None,
height=None,
):
heatmap_kws = {} if heatmap_kws is None else heatmap_kws
if write_tex is True:
assert (
save_path is not None
), "To write a tex file, please provide a save_path"
df = self.data_proc.score_df(
periods=periods, factors=factors, order="by_factor", agg_method=agg_method
)
corr = df.corr()
if width is None and height is None:
if len(corr) <= 5:
width = 0.5
elif len(corr) <= 9:
width = 0.8
else:
width = 1
if figsize is None:
figsize = (len(corr), 0.8 * len(corr))
kwargs = self._basic_heatmap_args()
kwargs.update(heatmap_kws)
fig, ax = plt.subplots(figsize=figsize)
ax = sns.heatmap(data=corr, ax=ax, **kwargs)
base_title = "Correlations of Factor Scores"
title = title_text(basic_name=base_title, periods=periods, factors=factors)
if save_path is not None:
fig.savefig(save_path, dpi=dpi, bbox_inches="tight")
if write_tex is True:
write_figure_tex_snippet(save_path, title, width=width, height=height)
return fig, ax
def autoregression_plot(
self,
period,
factor,
agg_method="mean",
reg_kws=None,
figsize=(10, 5),
save_path=None,
dpi=200,
write_tex=False,
width=None,
height=None,
):
reg_kws = {} if reg_kws is None else reg_kws
if write_tex is True:
assert (
save_path is not None
), "To write a tex file, please provide a save_path"
if width is None and height is None:
width = 0.8
kwargs = self._basic_regplot_args()
kwargs.update(reg_kws)
df = self.data_proc.score_df(
factors=factor, periods=[period, period + 1], agg_method=agg_method
)
fig, ax = plt.subplots(figsize=figsize)
x = f"{factor}_{period}"
y = "{}_{}".format(factor, period + 1)
sns.regplot(x=x, y=y, data=df, ax=ax, **kwargs)
base_title = "Autoregression Plot"
title = title_text(base_title, periods=period, factors=factor)
if write_tex is True:
write_figure_tex_snippet(save_path, title, width=width, height=height)
if save_path is not None:
fig.savefig(save_path, dpi=dpi, bbox_inches="tight")
return fig, ax
def score_regression_table(
self,
periods=None,
stages=None,
controls=None,
agg_method="mean",
write_tex=False,
save_path=None,
):
controls = [] if controls is None else controls
assert (
periods is None or stages is None
), "You cannot specify periods and stages for a score regression."
assert not (
periods is None and stages is None
), "You have to specify periods or stages for a score regression"
if write_tex is True:
assert save_path is not None
if periods == "all":
periods = self.periods[:-1]
if stages == "all":
stages = self.stages
if periods is not None:
time = periods
if isinstance(periods, int) or isinstance(periods, float):
periods = [periods]
if isinstance(stages, int) or isinstance(stages, float):
stages = [stages]
if periods is not None:
time = periods
time_name = "period"
else:
time = stages
time_name = "stage"
results = []
for t in time:
for factor in self.factors:
ind = self.factors.index(factor)
trans_name = self.transition_names[ind]
if trans_name != "constant":
mod_kwargs = {
"factor": factor,
"controls": controls,
"agg_method": agg_method,
time_name: t,
}
mod = self._score_regression_model(**mod_kwargs)
res = mod.fit()
res.name = factor
res.period = t
results.append(res)
df = statsmodels_results_to_df(
res_list=results, decimals=2, period_name=time_name.capitalize()
)
base_title = "OLS Estimation of Transition Equations"
title = title_text(base_title, periods=periods, stages=stages, factors="all")
if write_tex is True:
with open(save_path, "w") as t:
t.write(df_to_tex_table(df, title))
return df
def score_regression_residual_plot(
self,
factor,
period=None,
stage=None,
controls=None,
other_vars=None,
agg_method="mean",
reg_kws=None,
save_path=None,
write_tex=False,
width=None,
height=None,
dpi=200,
):
controls = [] if controls is None else controls
other_vars = [] if other_vars is None else other_vars
reg_kws = {} if reg_kws is None else reg_kws
if write_tex is True:
assert (
save_path is not None
), "To write a tex file, please provide a save_path"
if width is None and height is None:
height = 0.9
mod = self._score_regression_model(
period=period,
stage=stage,
factor=factor,
controls=controls,
agg_method=agg_method,
)
res = mod.fit()
data = self.data_proc.reg_df(
factor=factor,
period=period,
stage=stage,
controls=controls + other_vars,
agg_method=agg_method,
)
data["residuals"] = res.resid
data["fitted"] = res.fittedvalues
y_name = f"{factor}_t_plusone"
to_plot = [col for col in data.columns if col not in ["residuals", y_name]]
figsize = (10, len(to_plot) * 5)
fig, axes = plt.subplots(nrows=len(to_plot), figsize=figsize)
kwargs = self._basic_regplot_args()
kwargs.update(reg_kws)
for ax, var in zip(axes, to_plot):
sns.regplot(y="residuals", x=var, ax=ax, data=data, **kwargs)
base_title = "Residual Plot"
title = title_text(base_title, periods=period, factors=factor, stages=stage)
if write_tex is True:
write_figure_tex_snippet(save_path, title, width=width, height=height)
if save_path is not None:
fig.savefig(save_path, dpi=dpi, bbox_inches="tight")
return fig
def measurement_heatmap(
self,
periods="all",
factors="all",
figsize=None,
heatmap_kws=None,
save_path=None,
dpi=200,
write_tex=False,
width=None,
height=None,
):
"""Heatmap of the correlation matrix of measurements.
Args:
periods: periods to include. Can be the name of one period, a list
like object with periods or 'all'.
factors: factors to include. Can be the name of one factor, a list
like object with factors or 'all'.
figsize (tuple): size of the matplotlib figure. If None provided,
the figure automatically scales with the size of the
correlation matrix.
heatmap_kws (dict): dictionary with arguments for sns.heatmap()
save_path (str): path where the plot will be saved. Needs a valid
file extension (.png, .jpg, .eps, ...); Other documents are
saved in the same directory.
dpi (int): resolution of the plot
write_tex (bool): if True, a tex file with the plot is written.
Returns:
fig, ax
"""
heatmap_kws = {} if heatmap_kws is None else heatmap_kws
if write_tex is True:
assert (
save_path is not None
), "To write a tex file, please provide a save_path"
df = self.data_proc.measurements_df(periods=periods, factors=factors)
corr = df.corr()
if figsize is None:
figsize = (len(corr), 0.8 * len(corr))
if width is None and height is None:
if len(corr) <= 5:
width = 0.5
elif len(corr) <= 9:
width = 0.8
else:
width = 1
kwargs = self._basic_heatmap_args()
kwargs.update(heatmap_kws)
fig, ax = plt.subplots(figsize=figsize)
ax = sns.heatmap(data=corr, ax=ax, **kwargs)
base_title = "Correlations of Measurements"
title = title_text(basic_name=base_title, periods=periods, factors=factors)
if save_path is not None:
fig.savefig(save_path, dpi=dpi, bbox_inches="tight")
if write_tex is True:
write_figure_tex_snippet(save_path, title, width=width, height=height)
return fig, ax