La función make_circles — 3:33 min

• 3:33 min | Ultima modificación: Septiembre 27, 2021 | YouTube

https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_circles.html#sklearn.datasets.make_circles

Esta función de scikit-learn, permite la creacion de círculos concentricos de datos donde cada círculo representa una clase. En este video se discuten los parámetros de la función.

import matplotlib.pyplot as plt
from sklearn.datasets import make_circles

X, y = make_circles(
    # -------------------------------------------------------------------------
    # The number of samples.
    n_samples=100,
    # -------------------------------------------------------------------------
    # Shuffle the samples.
    shuffle=False,
    # -------------------------------------------------------------------------
    # Standard deviation of Gaussian noise added to the data.
    noise=0.0,
    # -------------------------------------------------------------------------
    # Determines random number generation for dataset shuffling and noise. Pass
    # an int for reproducible output across multiple function calls.
    # creation.
    random_state=12346,
    # -------------------------------------------------------------------------
    # Scale factor between inner and outer circle in the range (0, 1).
    factor=0.8,
)


plt.figure(figsize=(7, 7))
plt.scatter(
    X[y == 0, 0],
    X[y == 0, 1],
    color="tab:red",
    edgecolors="k",
    s=120,
    alpha=0.9,
)
plt.scatter(
    X[y == 1, 0],
    X[y == 1, 1],
    color="tab:blue",
    edgecolors="k",
    s=120,
    alpha=0.9,
)


plt.gca().spines["left"].set_color("gray")
plt.gca().spines["bottom"].set_color("gray")
plt.gca().spines["top"].set_visible(False)
plt.gca().spines["right"].set_visible(False)

X, y = make_circles(
    n_samples=100,
    shuffle=False,
    noise=0.05,
    random_state=12346,
    factor=0.5,
)

plt.figure(figsize=(7, 7))
plt.scatter(
    X[y == 0, 0],
    X[y == 0, 1],
    color="tab:red",
    edgecolors="k",
    s=120,
    alpha=0.9,
)
plt.scatter(
    X[y == 1, 0],
    X[y == 1, 1],
    color="tab:blue",
    edgecolors="k",
    s=120,
    alpha=0.9,
)


plt.gca().spines["left"].set_color("gray")
plt.gca().spines["bottom"].set_color("gray")
plt.gca().spines["top"].set_visible(False)
plt.gca().spines["right"].set_visible(False)
plt.axis("equal")
plt.show()