def add_label(self,
graph: PGM,
text: str,
x: float,
y: float,
label_id: str = None,
color: str = None,
size: str = None,
weight: str = None):
if label_id is None:
label_id = str(uuid.uuid4())
graph.add_node(
Node(label_id,
text,
x,
y,
plot_params={
'fill': False,
'linewidth': 0.0
},
label_params={
'color': color or 'black',
'size': size or 'small',
'weight': weight or 'normal'
}))
def layer(self,
graph: PGM,
node_texts: List[str],
x: float,
y: float,
spacing: float = 1,
spacing_pow: float = 1,
**other_node_params) -> List[Node]:
nodes = [
Node(str(uuid.uuid4()), node_texts[i], x, y - (spacing * float(i)),
**other_node_params) for i in range(len(node_texts))
]
for node in nodes:
graph.add_node(node)
return nodes
def convoluted_hierarchical_p():
G = PGM()
G.add_node("likes",
content="$l_{j, i}$",
x=1,
y=1,
scale=1.2,
observed=True)
G.add_node("p_shop", content="$p_{j, i}$", x=1, y=2, scale=1.2)
G.add_node("alpha_owner", content=r"$\alpha_{j}$", x=0, y=3, scale=1.2)
G.add_node("beta_owner", content=r"$\beta_{j}$", x=2, y=3, scale=1.2)
G.add_node("lambda_a_pop",
content=r"$\lambda_{\alpha}$",
x=0,
y=4,
scale=1.2)
G.add_node("lambda_b_pop",
content=r"$\lambda_{\beta}$",
x=2,
y=4,
scale=1.2)
G.add_node(
"tau_lambda_a",
content=r"$\tau_{\lambda_{\alpha}}$",
x=0,
y=5,
fixed=True,
)
G.add_node(
"tau_lambda_b",
content=r"$\tau_{\lambda_{\beta}}$",
x=2,
y=5,
fixed=True,
)
G.add_edge("alpha_owner", "p_shop")
G.add_edge("beta_owner", "p_shop")
G.add_edge("p_shop", "likes")
G.add_edge("lambda_a_pop", "alpha_owner")
G.add_edge("lambda_b_pop", "beta_owner")
G.add_edge("tau_lambda_a", "lambda_a_pop")
G.add_edge("tau_lambda_b", "lambda_b_pop")
G.add_plate(plate=[0.5, 0.2, 1, 2.3], label=r"shop $i$")
G.add_plate(plate=[-0.5, 0, 3, 3.5], label=r"owner $j$")
G.render()
def hierarchical_pgm():
G = PGM()
tfm_plot_params = {"ec": "red"}
G.add_node("likes",
content=r"$l_{j,i}$",
x=0,
y=0,
scale=1.2,
observed=True)
G.add_node(
"p_shop",
content=r"$p_{j,i}$",
x=0,
y=1,
scale=1.2,
plot_params=tfm_plot_params,
)
G.add_node("mu_shop", content=r"$\mu_{j,i}$", x=1, y=1, scale=1.2)
G.add_node("mu_owner", content=r"$\mu_{j}$", x=1, y=2, scale=1.2)
G.add_node("sigma_owner", content=r"$\sigma_{j}$", x=2, y=2, scale=1.2)
G.add_node(
"p_owner",
content=r"$p_{j}$",
x=0,
y=2,
scale=1.2,
plot_params=tfm_plot_params,
)
G.add_node("mu_population", content=r"$\mu$", x=1, y=3, scale=1.2)
G.add_node(
"sigma_population",
content=r"$\sigma$",
x=2,
y=3,
scale=1.2,
fixed=True,
)
G.add_node(
"p_population",
content="p",
x=0,
y=3,
scale=1.2,
plot_params=tfm_plot_params,
)
G.add_node("lambda", content=r"$\lambda$", x=3, y=2, scale=1.2, fixed=True)
G.add_node("mean_population",
content="mean",
x=1,
y=4,
scale=1.2,
fixed=True)
G.add_node(
"variance_population",
content="variance",
x=2,
y=4,
scale=1.2,
fixed=True,
)
G.add_edge("mu_shop", "p_shop")
G.add_edge("p_shop", "likes")
G.add_edge("mu_owner", "mu_shop")
G.add_edge("sigma_owner", "mu_shop")
G.add_edge("mu_owner", "p_owner")
G.add_edge("mu_population", "mu_owner")
G.add_edge("sigma_population", "mu_owner")
G.add_edge("mu_population", "p_population")
G.add_edge("lambda", "sigma_owner")
G.add_edge("mean_population", "mu_population")
G.add_edge("variance_population", "mu_population")
G.add_plate([-0.5, -0.5, 2, 2], label="shop $i$", position="bottom right")
G.add_plate([-0.7, -0.7, 3.2, 3.2],
label="owner $j$",
position="bottom right")
G.render()
def pgm():
from daft import PGM, Node, Plate
from matplotlib import rc
rc("font", family="serif", size=8)
rc("text", usetex=True)
pgm = PGM([9.5, 8.5], origin=[0., 0.2], observed_style='inner')
#pgm.add_node(Node('dispersion',r"\center{$\sigma_{Ia}$ \newline $\sigma_{!Ia}$}", 1,6,scale=1.2,aspect=1.8))
pgm.add_node(Node('Rate_Ia', r"{SNIa Rate}", 1, 8, fixed=1))
pgm.add_node(Node('Rate_II', r"{SNII Rate}", 2, 8, scale=1.6, aspect=1.2))
pgm.add_node(Node('L_Ia', r"{SNIa L, $\sigma_L$}", 3, 8, scale=1.6, aspect=1.2))
pgm.add_node(Node('L_II', r"{SNII L, $\sigma_L$}", 4, 8, scale=1.6, aspect=1.2))
pgm.add_node(Node('Cosmology', r"Cosmology", 7, 8, scale=1.6, aspect=1.2))
pgm.add_node(Node('Calibration', r"Calibration", 8, 8, scale=1.6, aspect=1.2))
# pgm.add_node(Node('Neighbors',r"\centering{Neighbor \newline Redshifts}", 5,7, scale=1.6,aspect=1.2))
pgm.add_node(Node('Redshift', r"{Redshift}", 6, 7, scale=1.6, aspect=1.2))
pgm.add_node(Node('Type_prob', r"Type prob", 1, 6, fixed=1, offset=(20, -10)))
pgm.add_node(Node('Distance', r"$L_D$", 7, 6, fixed=1, offset=(10, 10)))
pgm.add_node(Node('Type', r"Type", 1, 5, scale=1.6, aspect=1.2))
pgm.add_node(Node('Luminosity', r"Luminosity", 4, 4, scale=1.6, aspect=1.2))
pgm.add_node(Node('Flux', r"Flux", 7, 3, scale=1.2, fixed=True, offset=(-20, -20)))
pgm.add_node(Node('Obs_Type', r"Obs type", 1, 1, scale=1.6, aspect=1.2, observed=1))
pgm.add_node(Node('Obs_Redshift', r"Obs redshift", 6, 1, scale=1.6, aspect=1.2, observed=1))
pgm.add_node(Node('Counts', r"Counts", 8, 1, scale=1.2, observed=1))
pgm.add_edge("Rate_Ia","Type_prob")
pgm.add_edge("Rate_II","Type_prob")
pgm.add_edge("Cosmology","Distance")
pgm.add_edge("Redshift","Distance")
pgm.add_edge("Type_prob", "Type")
pgm.add_edge("Type","Luminosity")
pgm.add_edge("L_Ia", "Luminosity")
pgm.add_edge("L_II", "Luminosity")
pgm.add_edge("Luminosity","Flux")
pgm.add_edge("Redshift","Flux")
pgm.add_edge("Distance","Flux")
pgm.add_edge("Type","Obs_Type")
# pgm.add_edge("Neighbors","Obs_Redshift")
pgm.add_edge("Redshift","Obs_Redshift")
pgm.add_edge("Flux","Counts")
pgm.add_edge("Calibration","Counts")
# Big Plate: Galaxy
pgm.add_plate(Plate([0.4, 0.5, 8.2, 7.],
label=r"SNe $i = 1, \cdots, N_{SN}$",
shift=-0.2,label_offset=[20,2]))
pgm.add_plate(Plate([0.5, 3.5, 4., 2.],
label=r"Type $\in \{Ia, II\}$",
shift=-0.2,label_offset=[20,2]))
# Render and save.
pgm.render()
# pgm.figure.text(0.01,0.9,r'\underline{UNIVERSAL}',size='large')
# pgm.figure.text(0.01,0.55,r'{\centering \underline{INDIVIDUAL} \newline \underline{SN}}',size='large')
# pgm.figure.text(0.01,0.2,r'\underline{OBSERVATORY}',size='large')
# pgm.figure.text(0.01,0.1,r'\underline{DATA}',size='large')
pgm.figure.savefig("../results/nodes_pgm.pdf")
def korea_pgm():
G = PGM()
G.add_node("s_mean", r"$\mu_{s}$", x=0, y=1)
G.add_node("s_scale", r"$\sigma_{s}$", x=1, y=1)
G.add_node("s_height", r"$h_s$", x=0.5, y=0)
G.add_edge("s_mean", "s_height")
G.add_edge("s_scale", "s_height")
G.add_node("n_mean", r"$\mu_{n}$", x=2, y=1)
G.add_node("n_scale", r"$\sigma_{n}$", x=3, y=1)
G.add_node("n_height", r"$h_n$", x=2.5, y=0)
G.add_edge("n_mean", "n_height")
G.add_edge("n_scale", "n_height")
G.show()
def coin_flip_pgm():
G = PGM()
G.add_node("alpha", content=r"$\alpha$", x=-1, y=1, scale=1.2, fixed=True)
G.add_node("beta", content=r"$\beta$", x=1, y=1, scale=1.2, fixed=True)
G.add_node("p", content="p", x=0, y=1, scale=1.2)
G.add_node("result", content="result", x=0, y=0, scale=1.2, observed=True)
G.add_edge("alpha", "p")
G.add_edge("beta", "p")
G.add_edge("p", "result")
G.show()
def car_crash_pgm():
G = PGM()
G.add_node("crashes", content="crashes", x=0, y=0, scale=1.5)
G.add_node("rate", content="rate", x=0, y=1, scale=1.5)
G.add_edge("rate", "crashes")
G.show()
#!/usr/bin/env python
from matplotlib import rc
from daft import PGM, Node, Plate
rc("font", family="serif", size=12)
rc("text", usetex=True)
pgm = PGM([6, 4.2], origin=[0., 0.2], observed_style='inner')
# x_1 and c distributions on top line
pgm.add_node(Node("sigdist", r"$\sigma_{\mathrm{int}}^{\mathrm{dist}}$", 3, 4))
pgm.add_node(Node("x1dist", r"$x_1^{\mathrm{dist}}$", 4, 4))
pgm.add_node(Node("cdist", r"$c^{\mathrm{dist}}$", 5, 4))
# Per-SN parameters: top line in the plate
pgm.add_node(Node("x1itrue", r"$x_{1,i}^\mathrm{true}$", 4, 3))
pgm.add_node(Node("citrue", r"$c_i^\mathrm{true}$", 5, 3))
# Per-SN parameters: second line in the plate
pgm.add_node(Node("x0itrue", r"$x_{0,i}^\mathrm{true}$", 3, 2))
#pgm.add_node(Node("mui", r"$\mu_i$", 2, 2))
# Per-SN parameters: third line in the plate
pgm.add_node(Node("zi", r"$z_i$", 2, 1, observed=True))
# Observed photometry
pgm.add_node(Node("fij", r"$f_{i,j}$", 4, 1, observed=True))
pgm.add_node(Node("t0true", r"$t_0^{\mathrm{true}}$", 5, 1))
#!/usr/bin/env python
from matplotlib import rc
import matplotlib
from daft import PGM, Node, Plate
rc("font", family="serif", size=8)
rc("text", usetex=True)
pgm = PGM([9.5, 8.5], origin=[0.0, 0.2], observed_style="inner")
# pgm.add_node(Node('dispersion',r"\center{$\sigma_{Ia}$ \newline $\sigma_{!Ia}$}", 1,6,scale=1.2,aspect=1.8))
# pgm.add_node(Node('rate',r"{\center Relative \newline Rates}", 2,8,scale=1.6,aspect=1.2))
# pgm.add_node(Node('theta_T',r"\center{$\alpha_{Ia}$, $\alpha_{!Ia}$ \newline $\beta_{Ia}$, $\beta_{!Ia}$}", 4,6,scale=1.4,aspect=1.8))
pgm.add_node(Node("theta_T", r"\center{SNe~Ia, Non-Ia Populations \newline Rates}", 3, 8, scale=1.8, aspect=3))
pgm.add_node(Node("Global Transmission", r"\centering{Global \newline Throughput}", 8, 8, scale=1.6, aspect=1.2))
pgm.add_node(Node("Transmission", r"Throughput", 8, 4, scale=1.6, aspect=1.2))
# pgm.add_node(Node('theta_T2',r"{Non-Ia}", 4,8,scale=1.6,aspect=1.2))
pgm.add_node(Node("mu", r"{\center Cosmology}", 7, 8, scale=1.6, aspect=1.2))
# pgm.add_node(Node('G_Ni',r"$g_{Ni}$", 8,5))
# pgm.add_node(Node('Flux_g',r"$f_{gi}(\lambda)$", 8, 3))
# pgm.add_node(Node('z',r"$z_i$", 3, 3, fixed=True,offset=(-10,-5)))
# pgm.add_node(Node('Counts',r"$\overline{n}_i$", 7, 2,scale=1.2,fixed=True,offset=(-15,0)))
# pgm.add_node(Node('Counts_g',r"$\overline{\mathit{ADU}}_{gi}$", 8, 2,scale=1.2,fixed=True,offset=(20,0)))
def ice_cream_n_group_pgm():
G = PGM()
G.add_node("alpha", content=r"$\alpha$", x=-1, y=1, scale=1.2, fixed=True)
G.add_node("beta", content=r"$\beta$", x=1, y=1, scale=1.2, fixed=True)
G.add_node("p", content=r"$p_{i}$", x=0, y=1, scale=1.2)
G.add_node("likes", content=r"$l_{i}$", x=0, y=0, scale=1.2, observed=True)
G.add_edge("alpha", "p")
G.add_edge("beta", "p")
G.add_edge("p", "likes")
G.add_plate([-0.5, -0.8, 1, 2.3], label=r"shop $i$")
G.show()
def ice_cream_one_group_pgm():
G = PGM()
G.add_node("alpha", content=r"$\alpha$", x=-1, y=1, scale=1.2, fixed=True)
G.add_node("beta", content=r"$\beta$", x=1, y=1, scale=1.2, fixed=True)
G.add_node("p", content="p", x=0, y=1, scale=1.2)
G.add_node("likes", content="l", x=0, y=0, scale=1.2, observed=True)
G.add_edge("alpha", "p")
G.add_edge("beta", "p")
G.add_edge("p", "likes")
G.show()
def fully_connect(self, graph: PGM, layer1: List[Node],
layer2: List[Node]):
for l1_node in layer1:
for l2_node in layer2:
graph.add_edge(l1_node.name, l2_node.name)
#!/usr/bin/env python
from matplotlib import rc
from daft import PGM, Node, Plate
rc("font", family="serif", size=10)
rc("text", usetex=True)
pgm = PGM([4.5, 4.5], origin=[0., 0.2], observed_style='inner')
pgm.add_node(Node('Pars',r"$\theta_G$", 1, 3))
pgm.add_node(Node('mu',r"$\mu_G$", 1, 1))
pgm.add_node(Node('SNpars',r"$\theta^{dist}$", 1, 4))
pgm.add_node(Node('^z',r"$\hat{z_i}$", 3,1))
pgm.add_node(Node('Spars',r"$\hat{T}_{Si}$,$\hat{z_{Si}},\hat{\theta}_{Si}$", 3, 2, scale=1.8,observed=True))
pgm.add_node(Node('^Counts',r"$\hat{f_i}$", 2, 2, observed=True))
pgm.add_node(Node('^Host',r"$\hat{z_{Hi}},\hat{\theta}_{Hi}$", 3, 3, observed=True,scale=1.5))
pgm.add_node(Node('^mu',r"$\hat{\mu_i}$", 2, 1))
pgm.add_node(Node('SNpars_i',r"$\theta^{true}_i$", 2, 3))
pgm.add_node(Node('^Type',r"$\hat{T_i}$", 2, 4))
def hierarchical_p():
"""A naive representation of the hierarchical p that we desire."""
G = PGM()
G.add_node("p_shop", content=r"$p_{j, i}$", x=1, y=2, scale=1.2)
G.add_node("likes",
content="$l_{j, i}$",
x=1,
y=1,
scale=1.2,
observed=True)
G.add_node("p_owner", content=r"$p_{j}$", x=1, y=3, scale=1.2)
G.add_node("p_pop", content=r"$p$", x=1, y=4, scale=1.2)
G.add_edge("p_pop", "p_owner")
G.add_edge("p_owner", "p_shop")
G.add_edge("p_shop", "likes")
G.add_plate(plate=[0.3, 0.3, 1.5, 2.2], label=r"shop $i$")
G.add_plate(plate=[0, -0.1, 2.1, 3.6], label=r"owner $j$")
G.render()
#!/usr/bin/env python
from matplotlib import rc
from daft import PGM, Node, Plate
rc("font", family="serif", size=12)
rc("text", usetex=True)
pgm = PGM([8.5, 6.5], origin=[0., 0.2], observed_style='inner')
#pgm.add_node(Node('dispersion',r"\center{$\sigma_{Ia}$ \newline $\sigma_{!Ia}$}", 1,6,scale=1.2,aspect=1.8))
pgm.add_node(Node('rate',r"$r$",2,6))
#pgm.add_node(Node('theta_T',r"\center{$\alpha_{Ia}$, $\alpha_{!Ia}$ \newline $\beta_{Ia}$, $\beta_{!Ia}$}", 4,6,scale=1.4,aspect=1.8))
pgm.add_node(Node('theta_T',r"\center{$\Theta_{Ia}$ \newline $\Theta_{!Ia}$}", 4,6,scale=1.5,aspect=1.2))
pgm.add_node(Node('mu',r"$\Omega_M$, $w$", 5,6, scale=1.4))
pgm.add_node(Node('Transmission',r"$Z$", 7, 6))
pgm.add_node(Node('G_i',r"$g_i$", 3,5))
#pgm.add_node(Node('G_Ni',r"$g_{Ni}$", 8,5))
pgm.add_node(Node('theta_Ti',r"\center{$\theta_{Ia,i}$ \newline $\theta_{!Ia,i}$}", 1,4,scale=1.5,aspect=1.2))
pgm.add_node(Node('Type',r"$T_i$", 2, 4))
pgm.add_node(Node('Gals',r"$G$", 8,4, observed=True))
pgm.add_node(Node('Luminosity',r"$L_i(t,\lambda)$", 4, 3, scale=1.4))
pgm.add_node(Node('HD',r"$\mu_i$", 5,3,fixed=True,offset=(10,-5)))
#pgm.add_node(Node('Flux_g',r"$f_{gi}(\lambda)$", 8, 3))
#pgm.add_node(Node('z',r"$z_i$", 3, 3, fixed=True,offset=(-10,-5)))
#!/usr/bin/env python
from matplotlib import rc
from daft import PGM, Node, Plate
rc("font", family="serif", size=12)
rc("text", usetex=True)
pgm = PGM([6.5, 4.5], origin=[0., 0.2], observed_style='inner')
#pgm.add_node(Node('G',r"$G$", 3,1))
#pgm.add_node(Node('Coords',r"${RA}_i$/${Dec}_i$", 2,1,fixed=True))
pgm.add_node(Node('G_i',r"$z_i, z_{Ni}$", 2,2, scale=1.2))
pgm.add_node(Node('mu',r"$\Omega_M$, $w$", 3,4, scale=1.4))
pgm.add_node(Node('rate',r"$\theta_{r1}$, $\theta_{r2}$", 5,4,scale=1.4))
pgm.add_node(Node('HD',r"$\mu_i$", 3,2,fixed=True,offset=(0,-22)))
pgm.add_node(Node('theta_T',r"\center{$\alpha_{Ia}$, $\alpha_{non-Ia}$ \newline $\sigma_{Ia}$, $\sigma_{non-Ia}$}", 4,4,scale=1.65,aspect=1.5))
#pgm.add_node(Node('theta_Ti',r"$\theta_{Ti}^{Ia}$, $\theta_{Ti}^{non-Ia}$", 2,6,scale=1.5,aspect=1.5))
#pgm.add_node(Node('Host',r"$\theta_{gi}$", 2, 3, fixed=True,offset=(-10,-5)))
#pgm.add_node(Node('z',r"$z_i$", 2, 2, fixed=True,offset=(-10,-5)))
pgm.add_node(Node('Type',r"$T_i$", 5,3))
pgm.add_node(Node('Luminosity',r"$L_i(t,\lambda)$", 4,3, scale=1.4))
#pgm.add_node(Node('Flux',r"$n_i(t,\lambda)$", 5, 5, scale=1.2,fixed=True,offset=(15,0)))
#pgm.add_node(Node('Flux_g',r"$n_{gi}(\lambda)$", 5, 2,fixed=True,offset=(0,-20)))
#pgm.add_node(Node('Transmission',r"$\phi(\lambda)$", 7, 7))
#pgm.add_node(Node('Counts',r"$\overline{f}_i$", 8, 5,scale=1.2,fixed=True,offset=(15,0)))
#pgm.add_node(Node('Counts_g',r"$\overline{f}_{gi}$", 8, 4,scale=1.2,fixed=True,offset=(10,-25)))
#pgm.add_node(Node('Zeropoints',r"${Z}$", 9, 7, observed=True))
from daft import PGM
pgm = PGM(directed=True)
pgm.add_node('G', content='G', observed=False, x=0, y=0)
pgm.add_node('rho', content='rho', observed=False, x=1, y=0, alternate=True)
pgm.add_node('R', content='R', observed=True, x=.25 + .75 / 2, y=1.2)
pgm.add_node('tau', content='tau', observed=True, x=1.5, y=.8)
pgm.add_node('f', content='f', observed=False, x=1.75, y=2, shape='rectangle')
pgm.add_node('beta',
content='beta',
observed=False,
x=1.75,
y=3,
alternate=True)
pgm.add_node('ITI', content='ITI', observed=True, x=2.5, y=2)
pgm.add_node('k', content='k', observed=False, x=1, y=2, alternate=True)
pgm.add_node('B', content='B', observed=False, x=-0.5, y=2)
pgm.add_node('p_omega',
content='Pom',
observed=False,
x=.25,
y=3,
alternate=True)
pgm.add_node('omega',
content='om',
observed=False,
x=.25,
y=2.25,
shape='rectangle')
pgm.add_node('a', content='a', observed=False, x=-1.25, y=2.5)
