def test_musyc_no_gamma():
import numpy as np
from synergy.combination import MuSyC
from synergy.utils.dose_tools import grid
E0, E1, E2, E3 = 1, 0.2, 0.1, 0
h1, h2 = 2.3, 0.8
C1, C2 = 1e-2, 1e-1
alpha12, alpha21 = 3.2, 1.1
model = MuSyC(E0=E0,
E1=E1,
E2=E2,
E3=E3,
h1=h1,
h2=h2,
C1=C1,
C2=C2,
alpha12=alpha12,
alpha21=alpha21,
variant="no_gamma")
npoints = 8
npoints2 = 12
D1, D2 = grid(1e-3, 1e0, 1e-2, 1e1, npoints, npoints2)
np.random.seed(0)
E = model.E(D1, D2)
Efit = E * (1 + (np.random.rand(len(D1)) - 0.5) / 10.)
model.fit(D1, D2, Efit)
assert model.r_squared > 0.9
def test_parametric_model_heatmap_replicates():
from synergy.utils.dose_tools import grid
from synergy.combination import MuSyC
d1, d2 = grid(0.01, 10, 0.01, 10, 5, 5, include_zero=True, replicates=3)
model = MuSyC(E0=1,
E1=0.2,
E2=0.5,
E3=0.1,
h1=1.4,
h2=0.9,
C1=0.2,
C2=0.2,
alpha12=2,
alpha21=0.5,
gamma12=1,
gamma21=1)
model.plot_heatmap(d1, d2, fname="parametric.pdf")
assert 1 == 1
def create_data(i):
if i == 1:
E0, E1, E2, E3 = 1., 0., 0., 0.
h1, h2 = 1., 1.
C1, C2 = 1e-2, 1e-1
alpha12, alpha21 = 10., 1.
gamma12, gamma21 = 1, 1
elif i == 2:
E0, E1, E2, E3 = 1., 0., 0., 0.
h1, h2 = 1., 3.
C1, C2 = 1e-2, 1e-1
alpha12, alpha21 = 0.1, 1.
gamma12, gamma21 = 1, 1
elif i == 3:
E0, E1, E2, E3 = 1., 0.4, 0.2, 0.
h1, h2 = 2., 1.
C1, C2 = 1e-2, 1e-1
alpha12, alpha21 = 1., 1.
gamma12, gamma21 = 1, 1
musyc = MuSyC(E0=E0,
E1=E1,
E2=E2,
E3=E3,
h1=h1,
h2=h2,
C1=C1,
C2=C2,
alpha12=alpha12,
alpha21=alpha21,
gamma12=gamma12,
gamma21=gamma21)
npoints1 = 8
npoints2 = 10
D1, D2 = grid(1e-4, 10, 1e-4, 10, npoints1, npoints2, include_zero=True)
E = musyc.E(D1, D2)
return D1, D2, E
def test_musyc_higher():
import numpy as np
from synergy.utils import dose_tools
from synergy.higher import MuSyC
from synergy.utils import plots
E_params = [2, 1, 1, 1, 1, 0, 0, 0]
h_params = [2, 1, 0.8]
C_params = [0.1, 0.01, 0.1]
alpha_params = [2, 3, 1, 1, 0.7, 0.5, 2, 1, 1]
gamma_params = [0.4, 2, 1, 2, 0.7, 3, 2, 0.5, 2]
params = E_params + h_params + C_params + alpha_params + gamma_params
truemodel = MuSyC()
truemodel.parameters = params
d = dose_tools.grid_multi((1e-3, 1e-3, 1e-3), (1, 1, 1), (6, 6, 6),
include_zero=True)
d1 = d[:, 0]
d2 = d[:, 1]
d3 = d[:, 2]
np.random.seed(0)
E = truemodel.E(d)
noise = 0.05
E_fit = E + noise * (E_params[0] -
E_params[-1]) * (2 * np.random.rand(len(E)) - 1)
model = MuSyC(E_bounds=(0, 2),
h_bounds=(1e-3, 1e3),
alpha_bounds=(1e-5, 1e5),
gamma_bounds=(1e-5, 1e5))
model.fit(d, E_fit)
assert model.r_squared > 0.9
C2 = 0.1
alpha21 = 0.2
alpha12 = 10
gamma21 = 3
gamma12 = 0.7
r1r = 1
r2r = 1
d1, d2 = grid(1e-3, 1e2, 1e-3, 1e2, 20, 20)
model1 = MuSyC(E0=E0,
E1=E1,
E2=E2,
E3=E3,
h1=h1,
h2=h2,
C1=C1,
C2=C2,
alpha21=alpha21,
alpha12=alpha12,
gamma21=gamma21,
gamma12=gamma12)
model2 = MuSyC2(E0=E0,
E1=E1,
E2=E2,
E3=E3,
h1=h1,
h2=h2,
C1=C1,
C2=C2,
alpha21=alpha21,
from synergy.utils.dose_tools import grid
from matplotlib import pyplot as plt
E0, E1, E2, E3 = 1, 0.5, 0.2, 0.1
h1, h2 = 1., 1.
C1, C2 = 1e-2, 1e-1
alpha12, alpha21 = 0., 0.
gamma12, gamma21 = 1, 1
model = MuSyC(E0=E0,
E1=E1,
E2=E2,
E3=E3,
h1=h1,
h2=h2,
C1=C1,
C2=C2,
alpha12=alpha12,
alpha21=alpha21,
gamma12=gamma12,
gamma21=gamma21)
model2 = MuSyC(E0=E0,
E1=E1,
E2=E2,
E3=E3,
h1=h1,
h2=h2,
C1=C1,
C2=C2,
alpha12=alpha12,
from synergy.combination import MuSyC, CombinationIndex, BRAID, Zimmer
from synergy.utils.dose_tools import grid
from synergy.utils import plots
from matplotlib import pyplot as plt
import numpy as np
mtrue = MuSyC(E0=1,
E1=0.05,
E2=0.75,
E3=0.1,
h1=0.8,
h2=1,
C1=5e1,
C2=1e3,
alpha12=1,
alpha21=20,
gamma21=0.5,
gamma12=1)
model = CombinationIndex()
d1, d2 = grid(1, 1e4, 1., 1e4, 10, 10, include_zero=True)
E = mtrue.E(d1, d2) * (1 + 0.05 * (2 * (np.random.rand(len(d1))) - 1))
model.fit(d1, d2, E)
fig, (ax1, ax2, ax3) = plt.subplots(nrows=1, ncols=3)
fig.set_size_inches(14, 4)
fig.tight_layout(pad=3.0)
# Plot raw data heatmap
plots.plot_heatmap(d1, d2, E, ax=ax1, cmap="RdYlGn", vmin=-1, vmax=1)
from synergy.utils import plots
from synergy.utils.dose_tools import grid
from synergy.utils.data_exchange import to_synergyfinder
E0, E1, E2, E3 = 1., 0., 0., 0.
h1, h2 = 1., 1.
C1, C2 = 1e-2, 1e-1
alpha12, alpha21 = 10., 1.
gamma12, gamma21 = 1, 1
musyc = MuSyC(E0=E0,
E1=E1,
E2=E2,
E3=E3,
h1=h1,
h2=h2,
C1=C1,
C2=C2,
alpha12=alpha12,
alpha21=alpha21,
gamma12=gamma12,
gamma21=gamma21)
npoints1 = 8
npoints2 = 10
D1, D2 = grid(1e-4, 10, 1e-4, 10, npoints1, npoints2, include_zero=True)
E = musyc.E(D1, D2)
# Build ZIP model
model = ZIP()
from synergy.combination import MuSyC
from synergy.utils import plots
from synergy.utils import dose_tools
E0, E1, E2, E3 = 1, 0.7, 0.6, 0.4
h1, h2 = 2.3, 0.8
C1, C2 = 1e-2, 1e-1
alpha12, alpha21 = 10.2, 1.1
gamma12, gamma21 = 1, 1
model = MuSyC(E0=E0,
E1=E1,
E2=E2,
E3=E3,
h1=h1,
h2=h2,
C1=C1,
C2=C2,
alpha12=alpha12,
alpha21=alpha21,
gamma12=gamma12,
gamma21=gamma21)
replicates = 2
npoints = 6
npoints2 = 8
D1, D2 = dose_tools.grid(1e-3,
1,
1e-2,
10,
npoints,
npoints2,
from synergy.combination import MuSyC
from synergy.utils.dose_tools import grid
E0, E1, E2, E3 = 1, 0.6, 0.4, 0.
h1, h2 = 2.3, 0.8
C1, C2 = 1e-2, 1e-1
alpha12, alpha21 = 2., 1.
gamma12, gamma21 = 1.3, 0.5
truemodel = MuSyC(E0=E0,
E1=E1,
E2=E2,
E3=E3,
h1=h1,
h2=h2,
C1=C1,
C2=C2,
alpha12=alpha12,
alpha21=alpha21,
gamma12=gamma12,
gamma21=gamma21)
npoints = 8
npoints2 = 8
d1, d2 = grid(C1 / 1e2,
C1 * 1e2,
C2 / 1e2,
C2 * 1e2,
npoints,
npoints2,
include_zero=True)
from synergy.utils import plots
from synergy.utils.dose_tools import grid
from synergy.utils.data_exchange import to_synergyfinder
E0, E1, E2, E3 = 1., 0., 0., 0.
h1, h2 = 1., 1.
C1, C2 = 1e-2, 1e-1
alpha12, alpha21 = 10., 1.
gamma12, gamma21 = 1, 1
musyc = MuSyC(E0=E0,
E1=E1,
E2=E2,
E3=E3,
h1=h1,
h2=h2,
C1=C1,
C2=C2,
alpha12=alpha12,
alpha21=alpha21,
gamma12=gamma12,
gamma21=gamma21)
npoints1 = 8
npoints2 = 10
D1, D2 = grid(1e-4, 10, 1e-4, 10, npoints1, npoints2, include_zero=True)
E = musyc.E(D1, D2)
# Build ZIP model
model = ZIP()
C1, C2 = 1e-2, 1e-1
#alpha12, alpha21 = 3.2, 1.1
#gamma12, gamma21 = 1, 1
npoints = 50
npoints2 = 50
d1, d2 = grid(1e-3 / 3, 1 / 3, 1e-3, 10, npoints, npoints2, include_zero=False)
MuSyC(E0=E0,
E1=E1,
E2=E2,
E3=E3,
h1=h1,
h2=h2,
C1=C1,
C2=C2,
alpha12=1,
alpha21=1,
gamma12=1,
gamma21=1).plot_surface_plotly(d1,
d2,
fname="1_1_1_1.html",
auto_open=True)
MuSyC(E0=E0,
E1=E1,
E2=E2,
E3=E3,
h1=h1,
h2=h2,
C1=C1,
schindler = Schindler()
s_synergy = schindler.fit(d1,
d2,
E,
drug1_model=loewe.drug1_model,
drug2_model=loewe.drug2_model)
bliss = Bliss()
bsynergy = bliss.fit(d1,
d2,
E,
drug1_model=loewe.drug1_model,
drug2_model=loewe.drug2_model)
musyc = MuSyC()
musyc.fit(d1, d2, E)
fig = plt.figure(figsize=(12, 3))
ax = fig.add_subplot(1, 4, 1)
musyc.plot_heatmap(d1, d2, ax=ax, logscale=False, title="Sham Surface")
ax = fig.add_subplot(1, 4, 2)
loewe.plot_heatmap(ax=ax,
title="-log(Loewe)",
vmin=-0.01,
vmax=0.01,
logscale=False,
neglog=True)
ax.set_yticks([])
from synergy.combination import MuSyC
from synergy.utils import plots
from synergy.utils.dose_tools import grid
E0, E1, E2, E3 = 1, 0.2, 0.1, 0.4
h1, h2 = 2.3, 0.8
C1, C2 = 1e-2, 1e-1
alpha12, alpha21 = 3.2, 1.1
gamma12, gamma21 = 1, 1
model = MuSyC(E0=E0,
E1=E1,
E2=E2,
E3=E3,
h1=h1,
h2=h2,
C1=C1,
C2=C2,
alpha12=alpha12,
alpha21=alpha21,
gamma12=gamma12,
gamma21=gamma21)
npoints = 8
npoints2 = 12
D1, D2 = grid(1e-3 / 3, 1 / 3, 1e-2, 10, npoints, npoints2, include_zero=True)
E = model.E(D1, D2)
Efit = E * (1 + (np.random.rand(len(D1)) - 0.5) / 5.)
model.fit(D1, D2, Efit)
from synergy.combination import MuSyC
from synergy.utils.dose_tools import grid
E0, E1, E2, E3 = 1, 0.6, 0.4, 0.
h1, h2 = 2.3, 0.8
C1, C2 = 1e-2, 1e-1
alpha12, alpha21 = 2., 1.
gamma12, gamma21 = 1, 1
truemodel = MuSyC(E0=E0,
E1=E1,
E2=E2,
E3=E3,
h1=h1,
h2=h2,
C1=C1,
C2=C2,
alpha12=alpha12,
alpha21=alpha21,
gamma12=gamma12,
gamma21=gamma21)
npoints = 8
npoints2 = 8
d1, d2 = grid(C1 / 1e2,
C1 * 1e2,
C2 / 1e2,
C2 * 1e2,
npoints,
npoints2,
include_zero=True)
