def plot_gram_matrix(x_train, gamma, n_reduce=None):
kmatrix = gram_matrix(x_train, gamma)
if n_reduce is not None:
kmatrix = kmatrix[:n_reduce, :n_reduce]
plt.pcolor(kmatrix, cmap=plt.cm.PuRd)
plt.colorbar()
plt.xlim([0, kmatrix.shape[0]])
plt.ylim([0, kmatrix.shape[0]])
plt.gca().invert_yaxis()
plt.gca().xaxis.tick_top()
plt.show()
def plot_lift_curve(y_test,
y_predicted,
curve_color='blue',
font_size=14,
x_lim=(0, 1),
y_lim=(0, 1)):
fpr, tpr, thr = roc_curve(y_test, y_predicted)
percentage_sample = [(n + 1) / len(tpr) for n in range(len(tpr))]
plt.plot(fpr, percentage_sample, color=curve_color, lw=2)
plt.xlim(x_lim)
plt.ylim(y_lim)
plt.xlabel('Percentage of sample', fontsize=font_size)
plt.ylabel('Recall (TPR)', fontsize=font_size)
plt.title('LIFT curve')
def plot_roc_curve(y_test,
y_predicted,
curve_color='coral',
font_size=14,
x_lim=(0, 1),
y_lim=(0, 1)):
"""
cf :
- https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html
"""
fpr, tpr, thr = roc_curve(y_test, y_predicted)
auroc = auc(fpr, tpr)
plt.plot(fpr, tpr, label=f'AUROC : {auroc:.2f}', color=curve_color, lw=2)
# Random classifier line plot
plt.plot([0, 1], [0, 1], linestyle='--')
plt.xlim(x_lim)
plt.ylim(y_lim)
plt.xlabel('1 - specificity (FPR)', fontsize=font_size)
plt.ylabel('Recall (TPR)', fontsize=font_size)
plt.title('Receiver operating characteristic')
plt.legend(loc="lower right")
def plot_training_curve(history,
value_type='accuracy',
fine_tuning=False,
initial_epochs=None):
"""
https://machinelearningmastery.com/diagnose-overfitting-underfitting-lstm-models/
"""
plt.plot(history.history[value_type])
plt.plot(history.history[f'val_{value_type}'])
if fine_tuning:
plt.plot([initial_epochs - 1, initial_epochs - 1],
plt.ylim(),
label='Start Fine Tuning')
plt.title('Training curve')
plt.ylabel(value_type.capitalize())
plt.xlabel('Epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
def plot_correlation_circle(self,
n_plan=None,
labels=None,
label_rotation=0,
lims=None,
save_as_img=False,
plot_size=(10, 8)):
"""
"""
factorial_plan_nb = self.default_factorial_plan_nb if n_plan is None else n_plan
# Build a list of tuples (example : [(0, 1), (2, 3), ... ])
axis_ranks = [(x, x + 1) for x in range(0, factorial_plan_nb, 2)]
pcs = self.pca.components_
for d1, d2 in axis_ranks:
if d2 < self.n_comp:
fig, ax = plt.subplots(figsize=plot_size)
# Fix factorial plan limits
if lims is not None:
xmin, xmax, ymin, ymax = lims
elif pcs.shape[1] < 30:
xmin, xmax, ymin, ymax = -1, 1, -1, 1
else:
xmin, xmax, ymin, ymax = min(pcs[d1, :]), max(
pcs[d1, :]), min(pcs[d2, :]), max(pcs[d2, :])
# affichage des flèches
# s'il y a plus de 30 flèches, on n'affiche pas le triangle à leur extrémité
if pcs.shape[1] < 30:
plt.quiver(np.zeros(pcs.shape[1]),
np.zeros(pcs.shape[1]),
pcs[d1, :],
pcs[d2, :],
angles='xy',
scale_units='xy',
scale=1,
color="grey")
# (doc : https://matplotlib.org/api/_as_gen/matplotlib.pyplot.quiver.html)
else:
lines = [[[0, 0], [x, y]] for x, y in pcs[[d1, d2]].T]
ax.add_collection(
LineCollection(lines, axes=ax, alpha=.1,
color='black'))
# Display variables labels
if labels is not None:
for i, (x, y) in enumerate(pcs[[d1, d2]].T):
if xmin <= x <= xmax and ymin <= y <= ymax:
plt.text(x,
y,
labels[i],
fontsize='14',
ha='center',
va='center',
rotation=label_rotation,
color="blue",
alpha=0.5) # fontsize : 14
# Plot circle
circle = plt.Circle((0, 0), 1, facecolor='none', edgecolor='b')
plt.gca().add_artist(circle)
# définition des limites du graphique
plt.xlim(xmin, xmax)
plt.ylim(ymin, ymax)
# affichage des lignes horizontales et verticales
plt.plot([-1, 1], [0, 0], color='grey', ls='--')
plt.plot([0, 0], [-1, 1], color='grey', ls='--')
# Axes labels with % explained variance
plt.xlabel('F{} ({}%)'.format(d1 + 1,
round(100 * self.evr[d1], 1)),
labelpad=20)
plt.ylabel('F{} ({}%)'.format(d2 + 1,
round(100 * self.evr[d2], 1)),
labelpad=20)
plt.title("Cercle des corrélations (F{} et F{})".format(
d1 + 1, d2 + 1),
pad=20)
if save_as_img:
plt.tight_layout()
plt.savefig(
'corr_circle_{}.jpg'.format(1 if d1 == 0 else d1))
plt.show(block=False)
def plot_factorial_planes(self,
n_plan=None,
X_projected=None,
labels=None,
alpha=1,
illustrative_var=None,
illustrative_var_title=None,
save_as_img=False,
plot_size=(10, 8)):
"""
:param: axis_nb: the total number of axes to display (default is kaiser criterion divided by 2)
"""
X_projected = self.X_projected if X_projected is None else X_projected
factorial_plan_nb = self.default_factorial_plan_nb if n_plan is None else n_plan
axis_ranks = [(x, x + 1) for x in range(0, factorial_plan_nb, 2)]
for d1, d2 in axis_ranks:
if d2 < self.n_comp:
fig = plt.figure(figsize=plot_size)
# Display data points
if illustrative_var is None:
plt.scatter(X_projected[:, d1],
X_projected[:, d2],
alpha=alpha)
else:
illustrative_var = np.array(illustrative_var)
for value in np.unique(illustrative_var):
selected = np.where(illustrative_var == value)
plt.scatter(X_projected[selected, d1],
X_projected[selected, d2],
alpha=alpha,
label=value)
plt.legend(title=illustrative_var_title
if illustrative_var_title is not None else None)
# Display data points labels
if labels is not None:
for i, (x, y) in enumerate(X_projected[:, [d1, d2]]):
plt.text(x,
y,
labels[i],
fontsize='12',
ha='center',
va='bottom')
# Fix factorial plan limits
boundary = np.max(np.abs(X_projected[:, [d1, d2]])) * 1.1
plt.xlim([-boundary, boundary])
plt.ylim([-boundary, boundary])
# Display horizontal & vertical lines
plt.plot([-100, 100], [0, 0], color='grey', ls='--')
plt.plot([0, 0], [-100, 100], color='grey', ls='--')
# Axes labels with % explained variance
plt.xlabel('F{} ({}%)'.format(d1 + 1,
round(100 * self.evr[d1], 1)),
labelpad=20)
plt.ylabel('F{} ({}%)'.format(d2 + 1,
round(100 * self.evr[d2], 1)),
labelpad=20)
plt.title("Projection des individus (sur F{} et F{})".format(
d1 + 1, d2 + 1),
pad=20)
if save_as_img:
plt.tight_layout()
plt.savefig(
'factorial_plan_{}.jpg'.format(1 if d1 == 0 else d1))
plt.show(block=False)