def test_plot_oth(self):
#just test that they run
res = self.res
endog = res.model.endog
exog = res.model.exog
plot_fit(res, 0, y_true=None)
plot_partregress(endog, exog, exog_idx=[0, 1])
plot_regress_exog(res, exog_idx=0)
plot_ccpr(res, exog_idx=[0])
plot_ccpr(res, exog_idx=[0, 1])
plt.close('all')
def test_plot_oth(self):
#just test that they run
res = self.res
endog = res.model.endog
exog = res.model.exog
plot_fit(res, 0, y_true=None)
plot_partregress(endog, exog, exog_idx=[0,1])
plot_regress_exog(res, exog_idx=0)
plot_ccpr(res, exog_idx=[0])
plot_ccpr(res, exog_idx=[0,1])
plt.close('all')
x1beta = x1 * res.params[1]
x2beta = x2 * res.params[2]
plt.plot(x1, x1beta + res.resid, 'o')
plt.plot(x1, x1beta, '-')
ax.set_title('X_i beta_i plus residuals versus exog (CCPR)') # + namestr)
ax = fig6.add_subplot(2, 1, 2)
plt.plot(x2, x2beta + res.resid, 'o')
plt.plot(x2, x2beta, '-')
#print res.summary()
doplots = 1
if doplots:
fig1 = smrp.plot_fit(res, 0, y_true=None)
smrp.plot_fit(res, 1, y_true=None)
smrp.plot_partregress(y, exog0, exog_idx=[0, 1])
smrp.plot_regress_exog(res, exog_idx=0)
smrp.plot_ccpr(res, exog_idx=[0])
smrp.plot_ccpr(res, exog_idx=[0, 1])
from statsmodels.graphics.tests.test_regressionplots import TestPlot
tp = TestPlot()
tp.test_plot_fit()
fig1 = smrp.plot_partregress(y, exog0, exog_idx=[0, 1])
#add lowess
ax = fig1.axes[0]
y0 = ax.get_lines()[0]._y
x0 = ax.get_lines()[0]._x
lres = sm.nonparametric.lowess(y0, x0, frac=0.2)
ax.plot(lres[:, 0], lres[:, 1], 'r', lw=1.5)
x2beta = x2*res.params[2]
plt.plot(x1, x1beta + res.resid, 'o')
plt.plot(x1, x1beta, '-')
ax.set_title('X_i beta_i plus residuals versus exog (CCPR)')# + namestr)
ax = fig6.add_subplot(2,1,2)
plt.plot(x2, x2beta + res.resid, 'o')
plt.plot(x2, x2beta, '-')
#print res.summary()
doplots = 1
if doplots:
fig1 = smrp.plot_fit(res, 0, y_true=None)
smrp.plot_fit(res, 1, y_true=None)
smrp.plot_partregress(y, exog0, exog_idx=[0,1])
smrp.plot_regress_exog(res, exog_idx=0)
smrp.plot_ccpr(res, exog_idx=[0])
smrp.plot_ccpr(res, exog_idx=[0,1])
from statsmodels.graphics.tests.test_regressionplots import TestPlot
tp = TestPlot()
tp.test_plot_fit()
fig1 = smrp.plot_partregress(y, exog0, exog_idx=[0,1])
#add lowess
ax = fig1.axes[0]
y0 = ax.get_lines()[0]._y
x0 = ax.get_lines()[0]._x
lres = sm.nonparametric.lowess(y0, x0, frac=0.2)
ax.plot(lres[:,0], lres[:,1], 'r', lw=1.5)
def feature_regression_summary(df,
feat_idx,
target,
model_fit_results,
display_regress_diagnostics=False):
feat = df.columns[feat_idx]
v = vif(np.matrix(df), feat_idx)
colinear = v > 10
if display_regress_diagnostics:
# ‘endog versus exog’, ‘residuals versus exog’, ‘fitted versus exog’ and ‘fitted plus residual versus exog’
fig = plt.figure(constrained_layout=True,
figsize=(2.25 * plot_edge, 4 * plot_edge))
fig = smgrpu.create_mpl_fig(fig)
gs = GridSpec(3, 2, figure=fig)
ax_fit = fig.add_subplot(gs[0, 0])
ax_partial_residuals = fig.add_subplot(gs[0, 1])
ax_partregress = fig.add_subplot(gs[1, 0])
ax_ccpr = fig.add_subplot(gs[1, 1])
ax_dist = fig.add_subplot(gs[2:4, 0:2])
exog_name, exog_idx = smgrpu.maybe_name_or_idx(feat,
model_fit_results.model)
smresults = smtt.maybe_unwrap_results(model_fit_results)
y_name = smresults.model.endog_names
x1 = smresults.model.exog[:, exog_idx]
prstd, iv_l, iv_u = wls_prediction_std(smresults)
# endog versus exog
# use wrapper since it's availab;e!
sm.graphics.plot_fit(model_fit_results, feat, ax=ax_fit)
# residuals versus exog
ax_partial_residuals.plot(x1, smresults.resid, 'o')
ax_partial_residuals.axhline(y=0, color='black')
ax_partial_residuals.set_title('Residuals versus %s' % exog_name,
fontsize='large')
ax_partial_residuals.set_xlabel(exog_name)
ax_partial_residuals.set_ylabel("resid")
# Partial Regression plot: fitted versus exog
exog_noti = np.ones(smresults.model.exog.shape[1], bool)
exog_noti[exog_idx] = False
exog_others = smresults.model.exog[:, exog_noti]
from pandas import Series
smgrp.plot_partregress(smresults.model.data.orig_endog,
Series(x1,
name=exog_name,
index=smresults.model.data.row_labels),
exog_others,
obs_labels=False,
ax=ax_partregress)
# CCPR: fitted plus residual versus exog
# use wrapper since it's availab;e!
sm.graphics.plot_ccpr(model_fit_results, feat, ax=ax_ccpr)
ax_ccpr.set_title('CCPR Plot', fontsize='large')
sns.distplot(df[feat], ax=ax_dist)
fig.suptitle('Regression Plots for %s' % exog_name, fontsize="large")
#fig.tight_layout()
#fig.subplots_adjust(top=.90)
plt.show()
display(
HTML(
"Variance Inflation Factor (<i>VIF</i>) for <b>{}</b>: <b>{}</b> {}"
.format(
feat, round(v, 2), "$\\le 10 \\iff$ low colinearity" if
not colinear else "$> 10 \\iff$ <b>HIGH COLINEARITY</b>")))
display(
HTML(
"<b><i>p-value</i></b> (<i>VIF</i>) for <b>{}</b>: <b>{}</b><br><br>"
.format(feat, model_fit_results.pvalues[feat_idx + 1])))
return v
def partialplot(ycol, xcol, df, othercols=None):
if othercols is None:
othercols=[col for col in df.columns if col not in (ycol,xcol, 'Intercept')]
f=regressionplots.plot_partregress(ycol, xcol, othercols, data=df, label_kwargs={'visible':False})
