X=np.array([0]),
opts=dict(xlabel='Index',
ylabel='Targeted Class',
title="Detected samples in class " +
TARGET_CLASS))
features_line = vis.line(
X=np.column_stack([0] * nb_columns),
Y=np.column_stack(dataframe_features_values[0][:]),
opts=dict(legend=dataframe_features_values_columns,
xlabel='Index',
ylabel='Feature Value',
title='Values of extracted features for each sample'))
if alg.type == 'classification' or alg.type == 'clustering':
statistic_pie = vis.pie(X=classes_stats,
opts=dict(legend=classes,
title='Classification results'))
# -------------------------------------------------------------
count = 0
for x in range(nb_rows):
vis.line(X=np.column_stack([count] * nb_columns),
Y=np.column_stack(dataframe_features_values[x][:]),
win=features_line,
update='append')
if alg.type == 'regression':
match = (int(float(dataframe_predictions_values[x])) == int(
float(TARGET_CLASS)))
if match:
message = "%s\n" % (dataframe_idx_values[x])
vis.text(message, win=list_detected_samples_text, append=True)
# quiver plot
X = np.arange(0, 2.1, .2)
Y = np.arange(0, 2.1, .2)
X = np.broadcast_to(np.expand_dims(X, axis=1), (len(X), len(X)))
Y = np.broadcast_to(np.expand_dims(Y, axis=0), (len(Y), len(Y)))
U = np.multiply(np.cos(X), Y)
V = np.multiply(np.sin(X), Y)
viz.quiver(
X=U,
Y=V,
opts=dict(normalize=0.9),
)
# pie chart
X = np.asarray([19, 26, 55])
viz.pie(X=X,
opts=dict(legend=['Residential', 'Non-Residential', 'Utility']))
# scatter plot example with various type of updates
colors = np.random.randint(0, 255, (
2,
3,
))
win = viz.scatter(
X=np.random.rand(255, 2),
Y=(np.random.rand(255) + 1.5).astype(int),
opts=dict(markersize=10, markercolor=colors, legend=['1', '2']),
)
viz.scatter(X=np.random.rand(255),
Y=np.random.rand(255),
opts=dict(
opts=dict(legend=['Men', 'Women'])
)
# stemplot
Y = np.linspace(0, 2 * math.pi, 70)
X = np.column_stack((np.sin(Y), np.cos(Y)))
viz.stem(
X=X,
Y=Y,
opts=dict(legend=['Sine', 'Cosine'])
)
# pie chart
X = np.asarray([19, 26, 55])
viz.pie(
X=X,
opts=dict(legend=['Residential', 'Non-Residential', 'Utility'])
)
# mesh plot
x = [0, 0, 1, 1, 0, 0, 1, 1]
y = [0, 1, 1, 0, 0, 1, 1, 0]
z = [0, 0, 0, 0, 1, 1, 1, 1]
X = np.c_[x, y, z]
i = [7, 0, 0, 0, 4, 4, 6, 6, 4, 0, 3, 2]
j = [3, 4, 1, 2, 5, 6, 5, 2, 0, 1, 6, 3]
k = [0, 7, 2, 3, 6, 7, 1, 1, 5, 5, 7, 6]
Y = np.c_[i, j, k]
viz.mesh(X=X, Y=Y, opts=dict(opacity=0.5))
# SVG plotting
svgstr = """
