Python matplotlib.pyplot.figlegend() Examples
The following are 5
code examples of matplotlib.pyplot.figlegend().
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Example #1
| Source File: test_predict_functional.py From vnpy_crypto with MIT License | 5 votes |
|
def test_lm_contrast(self):
np.random.seed(542)
n = 200
x1 = np.random.normal(size=n)
x2 = np.random.normal(size=n)
x3 = np.random.normal(size=n)
y = x1 + 2*x2 + x3 - x1*x2 + x2*x3 + np.random.normal(size=n)
df = pd.DataFrame({"y": y, "x1": x1, "x2": x2, "x3": x3})
fml = "y ~ x1 + x2 + x3 + x1*x2 + x2*x3"
model = sm.OLS.from_formula(fml, data=df)
result = model.fit()
values = {"x2": 1, "x3": 1} # y = 4
values2 = {"x2": 0, "x3": 0} # y = x1
pr, cb, fvals = predict_functional(result, "x1", values=values,
values2=values2, ci_method='scheffe')
plt.clf()
fig = plt.figure()
ax = plt.axes([0.1, 0.1, 0.67, 0.8])
plt.plot(fvals, pr, '-', label="Estimate", color='orange', lw=4)
plt.plot(fvals, 4 - fvals, '-', label="Truth", color='lime', lw=4)
plt.fill_between(fvals, cb[:, 0], cb[:, 1], color='grey')
ha, lb = ax.get_legend_handles_labels()
leg = plt.figlegend(ha, lb, "center right")
leg.draw_frame(False)
plt.xlabel("Focus variable", size=15)
plt.ylabel("Mean contrast", size=15)
plt.title("Linear model contrast")
self.close_or_save(fig)
Example #2
| Source File: test_predict_functional.py From vnpy_crypto with MIT License | 5 votes |
|
def test_glm_formula_contrast(self):
np.random.seed(542)
n = 50
x1 = np.random.normal(size=n)
x2 = np.random.normal(size=n)
x3 = np.random.normal(size=n)
mn = 5 + 0.1*x1 + 0.1*x2 + 0.1*x3 - 0.1*x1*x2
y = np.random.poisson(np.exp(mn), size=len(mn))
df = pd.DataFrame({"y": y, "x1": x1, "x2": x2, "x3": x3})
fml = "y ~ x1 + x2 + x3 + x1*x2"
model = sm.GLM.from_formula(fml, data=df, family=sm.families.Poisson())
result = model.fit()
values = {"x2": 1, "x3": 1} # y = 5.2
values2 = {"x2": 0, "x3": 0} # y = 5 + 0.1*x1
pr, cb, fvals = predict_functional(result, "x1", values=values,
values2=values2, ci_method='simultaneous')
plt.clf()
fig = plt.figure()
ax = plt.axes([0.1, 0.1, 0.67, 0.8])
plt.plot(fvals, pr, '-', label="Estimate", color='orange', lw=4)
plt.plot(fvals, 0.2 - 0.1*fvals, '-', label="Truth", color='lime', lw=4)
plt.fill_between(fvals, cb[:, 0], cb[:, 1], color='grey')
ha, lb = ax.get_legend_handles_labels()
leg = plt.figlegend(ha, lb, "center right")
leg.draw_frame(False)
plt.xlabel("Focus variable", size=15)
plt.ylabel("Linear predictor contrast", size=15)
plt.title("Poisson regression contrast")
self.close_or_save(fig)
Example #3
| Source File: test_predict_functional.py From vnpy_crypto with MIT License | 4 votes |
|
def test_formula(self):
np.random.seed(542)
n = 500
x1 = np.random.normal(size=n)
x2 = np.random.normal(size=n)
x3 = np.random.normal(size=n)
x4 = np.random.randint(0, 5, size=n)
x4 = np.asarray(["ABCDE"[i] for i in x4])
x5 = np.random.normal(size=n)
y = 0.3*x2**2 + (x4 == "B") + 0.1*(x4 == "B")*x2**2 + x5 + np.random.normal(size=n)
df = pd.DataFrame({"y": y, "x1": x1, "x2": x2, "x3": x3, "x4": x4, "x5": x5})
fml = "y ~ x1 + bs(x2, df=4) + x3 + x2*x3 + I(x1**2) + C(x4) + C(x4)*bs(x2, df=4) + x5"
model = sm.OLS.from_formula(fml, data=df)
result = model.fit()
summaries = {"x1": np.mean, "x3": pctl(0.75), "x5": np.mean}
values = {"x4": "B"}
pr1, ci1, fvals1 = predict_functional(result, "x2", summaries, values)
values = {"x4": "C"}
pr2, ci2, fvals2 = predict_functional(result, "x2", summaries, values)
plt.clf()
fig = plt.figure()
ax = plt.axes([0.1, 0.1, 0.7, 0.8])
plt.plot(fvals1, pr1, '-', label='x4=B')
plt.plot(fvals2, pr2, '-', label='x4=C')
ha, lb = ax.get_legend_handles_labels()
plt.figlegend(ha, lb, "center right")
plt.xlabel("Focus variable", size=15)
plt.ylabel("Fitted mean", size=15)
plt.title("Linear model prediction")
self.close_or_save(fig)
plt.clf()
fig = plt.figure()
ax = plt.axes([0.1, 0.1, 0.7, 0.8])
plt.plot(fvals1, pr1, '-', label='x4=B')
plt.fill_between(fvals1, ci1[:, 0], ci1[:, 1], color='grey')
plt.plot(fvals2, pr2, '-', label='x4=C')
plt.fill_between(fvals2, ci2[:, 0], ci2[:, 1], color='grey')
ha, lb = ax.get_legend_handles_labels()
plt.figlegend(ha, lb, "center right")
plt.xlabel("Focus variable", size=15)
plt.ylabel("Fitted mean", size=15)
plt.title("Linear model prediction")
self.close_or_save(fig)
Example #4
| Source File: test_predict_functional.py From vnpy_crypto with MIT License | 4 votes |
|
def test_noformula_prediction(self):
np.random.seed(6434)
n = 200
x1 = np.random.normal(size=n)
x2 = np.random.normal(size=n)
x3 = np.random.normal(size=n)
y = x1 - x2 + np.random.normal(size=n)
exog = np.vstack((x1, x2, x3)).T
model = sm.OLS(y, exog)
result = model.fit()
summaries = {"x3": pctl(0.75)}
values = {"x2": 1}
pr1, ci1, fvals1 = predict_functional(result, "x1", summaries, values)
values = {"x2": -1}
pr2, ci2, fvals2 = predict_functional(result, "x1", summaries, values)
plt.clf()
fig = plt.figure()
ax = plt.axes([0.1, 0.1, 0.7, 0.8])
plt.plot(fvals1, pr1, '-', label='x2=1', lw=4, alpha=0.6, color='orange')
plt.plot(fvals2, pr2, '-', label='x2=-1', lw=4, alpha=0.6, color='lime')
ha, lb = ax.get_legend_handles_labels()
leg = plt.figlegend(ha, lb, "center right")
leg.draw_frame(False)
plt.xlabel("Focus variable", size=15)
plt.ylabel("Fitted mean", size=15)
plt.title("Linear model prediction")
self.close_or_save(fig)
plt.clf()
fig = plt.figure()
ax = plt.axes([0.1, 0.1, 0.7, 0.8])
plt.plot(fvals1, pr1, '-', label='x2=1', lw=4, alpha=0.6, color='orange')
plt.fill_between(fvals1, ci1[:, 0], ci1[:, 1], color='grey')
plt.plot(fvals1, pr2, '-', label='x2=1', lw=4, alpha=0.6, color='lime')
plt.fill_between(fvals2, ci2[:, 0], ci2[:, 1], color='grey')
ha, lb = ax.get_legend_handles_labels()
plt.figlegend(ha, lb, "center right")
plt.xlabel("Focus variable", size=15)
plt.ylabel("Fitted mean", size=15)
plt.title("Linear model prediction")
self.close_or_save(fig)
Example #5
| Source File: summary.py From neleval with Apache License 2.0 | 4 votes |
|
def _generate_plots(self, all_results, primary_regroup, secondary_regroup):
for figure_name, figure_data in self._regroup(all_results, **primary_regroup):
figure_data = self._regroup(figure_data, **secondary_regroup)
n_secondary = len(figure_data)
colors = plt.get_cmap(self.cmap)(np.linspace(0, 1.0, n_secondary))
fig = plt.figure(figure_name, figsize=self.figsize)
ax = fig.add_subplot(1, 1, 1)
if self.secondary == 'markers':
markers = self._marker_cycle()
patches = []
for (secondary_name, results), color, marker in zip(figure_data, colors, markers):
# recall-precision
data = np.array([result.data for result in results])
patches.append(self._plot(ax, data[..., 1], data[..., 0],
marker=self._marker(secondary_name) or marker,
color=self._color(secondary_name) or color,
label=self._t(secondary_name)))
plt.xlabel(self._t('recall'))
plt.ylabel(self._t('precision'))
self._set_lim(plt.ylim)
self._set_lim(plt.xlim)
fig.tight_layout()
else:
secondary_names, figure_data = zip(*figure_data)
scores = np.array([result.data for results in figure_data for result in results])
if tuple(self.metrics) == ('fscore',):
axis_label = 'fscore'
else:
axis_label = 'score'
axis_label = '{} {}'.format(self._t(figure_name), self._t(axis_label))
self._plot1d(ax, [(scores[..., c], kwargs) for c, kwargs in self._metric_data()],
[len(group) for group in figure_data], secondary_names, axis_label, secondary_regroup.get('label'))
plt.grid(axis='x' if self.secondary == 'rows' else 'y')
yield figure_name, fig, {}
if self.secondary == 'markers' and n_secondary > 1:
# XXX: this uses `ax` defined above
fig = plt.figure()
legend = plt.figlegend(*ax.get_axes().get_legend_handles_labels(), loc='center',
ncol=self._ncol(n_secondary),
prop=make_small_font())
fig.canvas.draw()
# FIXME: need some padding
bbox = legend.get_window_extent().transformed(fig.dpi_scale_trans.inverted())
yield '_legend_', fig, {'bbox_inches': bbox}
