def _plot_contours(
ax: plt.Axes,
clf: Union[sklearn.svm.SVC, sklearn.svm.LinearSVC],
xx: np.ndarray,
yy: np.ndarray,
**kwargs: Optional
) -> None:
"""
Plot the decision boundaries for a classifier.
:param ax: plt.Axes, axis to plot onto
:param clf: Union[sklearn.svm.SVC, sklearn.svm.LinearSVC], sklearn classifiers
:param xx: np.ndarray, x values to plot onto
:param yy: np.ndarray, y values to plot onto
:param kwargs: Optional, kwargs
:return: None
"""
z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
z = z.reshape(xx.shape)
ax.contourf(xx, yy, z, **kwargs)
return None
def plot_chm_contour(chm: np.ndarray,
ax: plt.Axes = None,
**kwargs) -> plt.Axes:
"Plot chm as contour function"
if ax is None: fig, ax = plt.subplots(figsize=figsize)
xs = range(chm.shape[-2])
ys = range(chm.shape[-1], 0, -1)
X, Y = np.meshgrid(xs, ys)
top = np.amax(chm)
bot = np.amin(chm)
cs = ax.contourf(X,
Y,
chm,
levels=np.linspace(bot, top, num=100, endpoint=True))
return ax
def kde_plot(axes: plt.Axes, x: np.ndarray, y: np.ndarray, **kwargs): kde_results = utils.kde_2d(x, y, **kwargs) clevels = axes.contourf(*kde_results, 10, cmap='YlGn_r') # Delete outer levels for level in clevels.collections: for kp, path in reversed(list(enumerate(level.get_paths()))): verts = path.vertices # (N,2)-shape array of contour line coordinates diameter = np.max(verts.max(axis=0) - verts.min(axis=0)) dataset_diameter = max([(x.max()-x.min()), (y.max()-y.min())]) if diameter > 0.75*dataset_diameter: del (level.get_paths()[kp]) # Identify points within contours inside = np.full_like(x, False, dtype=bool) for level in clevels.collections: for kp, path in reversed(list(enumerate(level.get_paths()))): inside |= path.contains_points(tuple(zip(*(x,y)))) ax.scatter(x[~inside], y[~inside], marker='.', color='g', s=2, alpha=0.1)
def class_contour(x: np.array,
y: np.array,
clf: nn.Module,
prec: float = 0.05,
title: str = "Class Contour",
ax: plt.Axes = None):
""" Plots the class contour IF the dimnesions is 2 or lower.
Otherwise the embeddings are visualized using TSNE.
Paramters:
x: np.array
Feature matrix.
y: np.array
Labels for the x.
clf: nn.Module
Downstream classifier.
prec: float
Precision of the mesh grid (if it is created, i.e. when input dim is <=2)
title: str
Title of the plot.
ax: matplotlib.Axes
Axes for the plot. If None a new axis is created.
Return:
matplotlib.Figure: Figure of the plot IF the axis paramter is None.
Otherwise nothing is returned.
"""
if isinstance(x, torch.Tensor):
x = x.detach().cpu().numpy()
if isinstance(y, torch.Tensor):
y = y.detach().cpu().numpy()
if len(x.shape) != 2:
x = np.array(list(x))
if len(y.shape) != 2:
y = np.array(list(y))
fig = None
if ax is None:
fig, ax = plt.subplots(figsize=(5, 5))
clf_device = clf.device
mode = ""
try:
h = prec # step size in the mesh
x_min, x_max = x[:, 0].min() - 0, x[:, 0].max() + min(h, 0)
y_min, y_max = x[:, 1].min() - 0, x[:, 1].max() + min(h, 0)
# np.linspace(2.0, 3.0, num=5)
xx, yy = np.meshgrid(
np.linspace(x_min, x_max, num=int((x_max - x_min) // h)),
np.linspace(y_min, y_max, num=int((y_max - y_min) // h)))
clf = clf.cpu()
Z = clf(
torch.tensor(
np.c_[xx.ravel(), yy.ravel()],
dtype=torch.float32).detach().cpu()).detach().cpu().numpy()
Z = np.argmax(Z, axis=1)
Z = Z.reshape(xx.shape)
Z = ndimage.gaussian_filter(Z, sigma=1.0, order=0)
ax.contourf(xx,
yy,
Z,
alpha=0.8,
cmap=sns.color_palette("Spectral", as_cmap=True))
except:
# mode = " - TSNE"
# x = TSNE(n_components=2).fit_transform(x)
mode = " - PCA"
x = PCA(n_components=2).fit_transform(x)
ax.scatter(x[:, 0],
x[:, 1],
c=y.astype(int),
cmap=sns.color_palette("Spectral", as_cmap=True),
edgecolors='w')
ax.set_title(f"{title} (Sample Shape: {x.shape}) {mode}")
clf = clf.to(clf_device)
if fig is not None:
return fig
