Refactor code.
This commit is contained in:
alberto
@@ -35,6 +35,7 @@ Studiamo i grafi di Erdos-Reiny e di tipo Sensors. Dal plot possiamo
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Figura (dida: Grafi di ER e sensor colorati in base al segnale (non filtrato, sopra) e filtrato
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attraverso la valutazione $g(\mathcal{L})s$.
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test
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\printbibliography
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@@ -0,0 +1,177 @@
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import matplotlib.pyplot as plt
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import matplotlib.ticker as ticker
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import numpy as np
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import numpy.linalg as LA
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import scienceplots # noqa: F401
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import scipy
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# Requires latex installed
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from pygsp import graphs
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from afgl.util.build_T_matrix import build_T_matrix
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from afgl.util.lanczos import lanczos
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from afgl.util.plot import latex_log_formatter
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def plot_graphs(G_ER, G_Sensor, s: np.ndarray, N: int, p: float) -> None:
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fig, axs = plt.subplots(2, 2, figsize=(6.6, 5))
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# Set coordinates
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G_ER.set_coordinates()
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G_Sensor.set_coordinates()
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signal_ER = filter_signal_with_fourier(G_ER, s)
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signal_S = filter_signal_with_fourier(G_Sensor, s)
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# TOP LEFT
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G_ER.plot(s, ax=axs[0, 0], vertex_size=15, edge_width=0.5, edge_color="gray")
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axs[0, 0].set_title(rf"Erdős-Rényi Graph $(N = {N}, p = {p})$", pad=20)
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axs[0, 0].set_axis_off()
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# BOTTOM LEFT
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G_ER.plot(
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signal_ER, ax=axs[1, 0], vertex_size=15, edge_width=0.5, edge_color="gray"
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)
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axs[1, 0].set_title("", pad=20)
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axs[1, 0].set_axis_off()
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# TOP RIGHT
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G_Sensor.plot(s, ax=axs[0, 1], vertex_size=15, edge_width=0.5, edge_color="gray")
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axs[0, 1].set_title(rf"Sensor Network $(N = {N})$", pad=20)
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axs[0, 1].set_axis_off()
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# BOTTOM RIGHT
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G_Sensor.plot(
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signal_S, ax=axs[1, 1], vertex_size=15, edge_width=0.5, edge_color="gray"
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)
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axs[1, 1].set_title("", pad=20)
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axs[1, 1].set_axis_off()
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# Prevent label/title overlap
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plt.savefig("./out/printed_graphs.pdf", bbox_inches="tight")
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def g_extended(t: np.ndarray) -> np.ndarray:
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return np.sin(0.5 * np.pi * np.cos(np.pi * t) ** 2)
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"""
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Evaluates the function sin(0.5*pi*cos(pi*t)^2)chi_[-1/2,1/2] where chi_I is the
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characteristic function of I, as defined in example 1 (see [1]).
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"""
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def g(T: np.ndarray) -> np.ndarray:
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if scipy.sparse.issparse(T):
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# Operator & not supporting sparse matrix
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T = T.toarray()
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Chi = ((T >= -1 / 2) & (T <= 1 / 2)).astype(int)
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# Apply g_extended where Chi is True, else output 0
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return np.where(Chi, g_extended(T), 0)
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"""
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Computes the approximation g_M (see [1]) using Lanczos
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"""
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def compute_g_M(
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V: np.ndarray, alp: np.ndarray, beta: np.ndarray, s: np.ndarray
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) -> np.ndarray:
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M = len(alp)
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e_1 = np.zeros(M)
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e_1[0] = 1
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T = build_T_matrix(alp, beta)
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y = LA.norm(s) * (g(T) @ e_1)
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return V @ y
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def filter_signal_with_fourier(G, s: np.ndarray) -> np.ndarray:
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G.compute_fourier_basis()
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U = G.U
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return (U @ np.diag(g(G.e)) @ U.T) @ s
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def plot_error_comparison(
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l_err_ER: np.ndarray, t_err_ER: np.ndarray, l_err_S: np.ndarray, t_err_S: np.ndarray
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) -> None:
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fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(6.6, 2.5))
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# Left plot (Erdos-Renyi)
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ax1.plot(l_err_ER, label=r"$\left\lVert g_{M+3} - g_M \right\rVert_2$")
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ax1.plot(t_err_ER, label=r"$\left\lVert e_M \right\rVert_2$")
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ax1.set_title("Erdős-Rényi graph")
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# Right plot (Sensor)
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ax2.plot(l_err_S, label=r"$\left\lVert g_{M+3} - g_M \right\rVert_2$")
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ax2.plot(t_err_S, label=r"$\left\lVert e_M \right\rVert_2$")
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ax2.set_title("Sensor graph")
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# Apply identical formatting to both subplots
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for ax in (ax1, ax2):
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ax.xaxis.set_major_locator(ticker.MultipleLocator(50))
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ax.set_yscale("log")
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ax.yaxis.set_major_formatter(ticker.FuncFormatter(latex_log_formatter))
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ax.legend()
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# Prevents overlapping of labels between the subplots
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plt.tight_layout()
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plt.savefig("./out/ex1_estimate.pdf", bbox_inches="tight")
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def run_comparison_1_for_graph(
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G, s: np.ndarray, M_MAX: int
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) -> tuple[np.ndarray, np.ndarray]:
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"""Compares the error with error generated by Lanczos.
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Args:
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G: Graph
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s: Signal vector
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Returns:
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[lanczos_err, true_err]: Vectors of errors norm(g_{M+3} - g_M) and norm(e_M)
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as defined in [1]
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"""
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G.compute_laplacian("combinatorial")
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L = G.L
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j = 3
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V, alp, beta = lanczos(L, s, M_MAX + j)
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lanczos_err = np.zeros(M_MAX + j)
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true_err = np.zeros(M_MAX + j)
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GLs = filter_signal_with_fourier(G, s)
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for M in range(2, M_MAX + j):
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g_M = compute_g_M(V[:, 0:M], alp[0:M], beta[0 : M - 1], s)
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g_Mj = compute_g_M(V[:, 0 : M + j], alp[0 : M + j], beta[0 : M + j - 1], s)
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lanczos_err[M - 1] = LA.norm(g_Mj - g_M)
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true_err[M - 1] = LA.norm(GLs - g_M)
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return lanczos_err, true_err
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def run() -> None:
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"""Ripete il test corrispondente ad Example 1 dell'articolo limitandosi al
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metodo di Lanczos (no Chebyshev) e utilizzando come funzione g(t) = sin(0.5π
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cos(πt)2) * \chi_{[-0.5, 0.5]}.
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"""
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N = 500
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M_MAX = 200
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p = 0.04
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s = np.random.randint(1, 10000, N).astype(float)
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# Normalize s as in request
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s /= LA.norm(s)
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G_ER = graphs.ErdosRenyi(N, p)
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G_S = graphs.Sensor(N)
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l_err_ER, t_err_ER = run_comparison_1_for_graph(G_ER, s, M_MAX)
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l_err_S, t_err_S = run_comparison_1_for_graph(G_S, s, M_MAX)
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plot_error_comparison(l_err_ER, t_err_ER, l_err_S, t_err_S)
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plot_graphs(G_ER, G_S, s, N, p)
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@@ -0,0 +1,13 @@
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def run() -> None:
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"""Genera i grafi di di Erdos-Reny di grandezza crescente (ad esempio 250,
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500,1000, 2000, 4000) e parametro p = 0.04 e misura il tempo
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computazionale del metodo di Lanczos utilizzando come soglia per il criterio
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d'arresto epsilon = 10^-2 (o una soglia a scelta).
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Args:
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None
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Returns:
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None
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"""
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pass
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+7
-180
@@ -1,191 +1,18 @@
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# src/afgl/main.py
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import sys
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import matplotlib.pyplot as plt
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import matplotlib.ticker as ticker
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import numpy as np
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import numpy.linalg as LA
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import scienceplots # noqa: F401
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import scipy
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# Requires latex installed
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from pygsp import graphs, plotting
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from afgl.util.build_T_matrix import build_T_matrix
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from afgl.util.lanczos import lanczos
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import afgl.example_1 as ex1
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import afgl.example_2 as ex2
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from afgl.util.plot import plot_setup
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def plot_setup():
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plotting.BACKEND = "matplotlib"
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plt.style.use(["science"])
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# TODO match font with document
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def latex_sci(val, decimals=2):
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"""Converts a value to LaTeX scientific notation A x 10^{B}."""
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if val == 0:
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return "0"
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exponent = int(np.floor(np.log10(abs(val))))
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mantissa = val / 10**exponent
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return rf"{mantissa:.{decimals}f} \times 10^{{{exponent}}}"
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def plot_graphs(G_ER, G_Sensor, s, N, p):
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fig, axs = plt.subplots(2, 2, figsize=(6.6, 5))
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# Set coordinates
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G_ER.set_coordinates()
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G_Sensor.set_coordinates()
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signal_ER = filter_signal_with_fourier(G_ER, s)
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signal_S = filter_signal_with_fourier(G_Sensor, s)
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# TOP LEFT
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G_ER.plot(s, ax=axs[0, 0], vertex_size=15, edge_width=0.5, edge_color="gray")
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axs[0, 0].set_title(rf"Erdős-Rényi Graph $(N = {N}, p = {p})$", pad=20)
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axs[0, 0].set_axis_off()
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# BOTTOM LEFT
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G_ER.plot(
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signal_ER, ax=axs[1, 0], vertex_size=15, edge_width=0.5, edge_color="gray"
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)
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axs[1, 0].set_title("", pad=20)
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axs[1, 0].set_axis_off()
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# TOP RIGHT
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G_Sensor.plot(s, ax=axs[0, 1], vertex_size=15, edge_width=0.5, edge_color="gray")
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axs[0, 1].set_title(rf"Sensor Network $(N = {N})$", pad=20)
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axs[0, 1].set_axis_off()
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# BOTTOM RIGHT
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G_Sensor.plot(
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signal_S, ax=axs[1, 1], vertex_size=15, edge_width=0.5, edge_color="gray"
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)
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axs[1, 1].set_title("", pad=20)
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axs[1, 1].set_axis_off()
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# Prevent label/title overlap
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plt.savefig("./out/printed_graphs.pdf", bbox_inches="tight")
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def g_extended(t):
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return np.sin(0.5 * np.pi * np.cos(np.pi * t) ** 2)
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"""
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Evaluates the function sin(0.5*pi*cos(pi*t)^2)chi_[-1/2,1/2] where chi_I is the
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characteristic function of I, as defined in example 1 (see [1]).
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"""
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def g(T):
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if scipy.sparse.issparse(T):
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# Operator & not supporting sparse matrix
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T = T.toarray()
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Chi = ((T >= -1 / 2) & (T <= 1 / 2)).astype(int)
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# Apply g_extended where Chi is True, else output 0
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return np.where(Chi, g_extended(T), 0)
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"""
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Computes the approximation g_M (see [1]) using Lanczos
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"""
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def compute_g_M(V, alp, beta, s):
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M = len(alp)
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e_1 = np.zeros(M)
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e_1[0] = 1
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T = build_T_matrix(alp, beta)
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y = LA.norm(s) * (g(T) @ e_1)
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return V @ y
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def latex_log_formatter(y, pos):
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"""Custom formatter to render tick labels as LaTeX 10^{n}."""
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if y <= 0:
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return ""
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# Extract the exponent using log10
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n = int(np.round(np.log10(y)))
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return f"$10^{{{n}}}$"
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def filter_signal_with_fourier(G, s):
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G.compute_fourier_basis()
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U = G.U
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GLs = (U @ np.diag(g(G.e)) @ U.T) @ s
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# GLs = g(L) @ s
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return GLs
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def run_comparison_1_for_graph(G, s, M_MAX):
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G.compute_laplacian("combinatorial")
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L = G.L
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j = 3
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[V, alp, beta] = lanczos(L, s, M_MAX + j)
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lanczos_err = np.zeros(M_MAX + j)
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true_err = np.zeros(M_MAX + j)
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GLs = filter_signal_with_fourier(G, s)
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# GLs = g(L) @ s
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for M in range(2, M_MAX + j):
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g_M = compute_g_M(V[:, 0:M], alp[0:M], beta[0 : M - 1], s)
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g_Mj = compute_g_M(V[:, 0 : M + j], alp[0 : M + j], beta[0 : M + j - 1], s)
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lanczos_err[M - 1] = LA.norm(g_Mj - g_M)
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true_err[M - 1] = LA.norm(GLs - g_M)
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return [lanczos_err, true_err]
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def example_1():
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N = 500
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M_MAX = 200
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p = 0.04
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s = np.random.randint(1, 10000, N)
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# Normalize s as in request
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s = s / LA.norm(s)
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G_ER = graphs.ErdosRenyi(N, p)
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G_S = graphs.Sensor(N)
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[l_err_ER, t_err_ER] = run_comparison_1_for_graph(G_ER, s, M_MAX)
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[l_err_S, t_err_S] = run_comparison_1_for_graph(G_S, s, M_MAX)
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fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(6.6, 2.5))
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# Left plot (Erdos-Renyi)
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ax1.plot(l_err_ER, label=r"$\left\lVert g_{M+3} - g_M \right\rVert_2$")
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ax1.plot(t_err_ER, label=r"$\left\lVert e_M \right\rVert_2$")
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ax1.set_title("Erdős-Rényi graph")
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# Right plot (Sensor)
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ax2.plot(l_err_S, label=r"$\left\lVert g_{M+3} - g_M \right\rVert_2$")
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ax2.plot(t_err_S, label=r"$\left\lVert e_M \right\rVert_2$")
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ax2.set_title("Sensor graph")
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# Apply identical formatting to both subplots
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for ax in (ax1, ax2):
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ax.xaxis.set_major_locator(ticker.MultipleLocator(50))
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ax.set_yscale("log")
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ax.yaxis.set_major_formatter(ticker.FuncFormatter(latex_log_formatter))
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ax.legend()
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# Prevents overlapping of labels between the subplots
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plt.tight_layout()
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plt.savefig("./out/ex1_estimate.pdf", bbox_inches="tight")
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plot_graphs(G_ER, G_S, s, N, p)
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def run():
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def run() -> None:
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plot_setup()
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example_1()
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# example_1()
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ex1.run()
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ex2.run()
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if __name__ == "__main__":
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@@ -0,0 +1,28 @@
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import matplotlib.pyplot as plt
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import numpy as np
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import scienceplots # noqa: F401
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from pygsp import plotting
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def latex_sci(val: float, decimals: int = 2) -> str:
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"""Converts a value to LaTeX scientific notation A x 10^{B}."""
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if val == 0:
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return "0"
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exponent = int(np.floor(np.log10(abs(val))))
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mantissa = val / 10**exponent
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return rf"{mantissa:.{decimals}f} \times 10^{{{exponent}}}"
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def latex_log_formatter(y: float, pos: int) -> str:
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"""Custom formatter to render tick labels as LaTeX 10^{n}."""
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if y <= 0:
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return ""
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# Extract the exponent using log10
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n = int(np.round(np.log10(y)))
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return f"$10^{{{n}}}$"
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def plot_setup() -> None:
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plotting.BACKEND = "matplotlib"
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plt.style.use(["science"])
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# TODO match font with document
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