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
D1, D2 = grid(1e-3, 1, 1e-2, 10, npoints, npoints) E = model.E(D1, D2) E_2 = model2.E(D1, D2) schindler = Schindler() schindler2 = Schindler() schindler.fit(D1, D2, E, drug1_model=drug1, drug2_model=drug2) schindler2.fit(D1, D2, E_2, drug1_model=drug1, drug2_model=drug2) fig = plt.figure(figsize=(7, 6)) ax = fig.add_subplot(2, 2, 1) model.plot_heatmap(D1, D2, ax=ax, title="Dose Surface", vmin=E3, vmax=E0) ax.set_xticks([]) ax = fig.add_subplot(2, 2, 2) schindler.plot_heatmap(ax=ax, title="Schindler", vmin=-0.01, vmax=0.01) ax.set_xticks([]) ax.set_yticks([]) ax = fig.add_subplot(2, 2, 3) model2.plot_heatmap(D1, D2, ax=ax, title="Dose Surface", vmin=E3, Vmax=E0) ax = fig.add_subplot(2, 2, 4) schindler2.plot_heatmap(ax=ax, title="Schindler", center_on_zero=True) ax.set_yticks([]) plt.tight_layout()
#Efit = E*(1+(np.random.rand(len(D1))-0.5)/10.)
Efit = E
# Output data to test in synergyfinder R package
df = to_synergyfinder(D1, D2, Efit * 100)
df.to_csv("synergyfinder_comparison/zip_test_data.csv", index=None)
synergy = model.fit(D1, D2, Efit, use_jacobian=True)
print(model.drug1_model, model.drug2_model)
fig = plt.figure(figsize=(7, 3))
ax = fig.add_subplot(121)
musyc.plot_heatmap(D1, D2, ax=ax, title="Data")
ax = fig.add_subplot(122)
model.plot_heatmap(ax=ax, title="ZIP")
plt.tight_layout()
plt.show()
if False:
sfdf = pd.read_csv("synergyfinder_comparison/synergyfinder_output.csv",
index_col=0)
sfdf.sort_values(by=["d2", "d1"], inplace=True)
print("Correlation between this package and synergyfinder = %f" %
np.corrcoef(model.synergy, sfdf['synergy'])[0, 1])
fig = plt.figure(figsize=(4, 4))
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([])
ax = fig.add_subplot(1, 4, 3)
schindler.plot_heatmap(ax=ax,
title="Schindler",
vmin=-0.01,
vmax=0.01,
# 21.5 ms ± 422 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
# 29.6 ms ± 101 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
# 30.3 ms ± 284 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
# 20 ms ± 137 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
# 25.2 ms ± 343 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
# 25.8 ms ± 302 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
#%timeit model.fit(D1, D2, Efit, use_jacobian=False)
print(model)
fig = plt.figure(figsize=(7, 7))
ax = fig.add_subplot(221)
plots.plot_heatmap(D1, D2, E, ax=ax, title="True", cmap="viridis")
ax = fig.add_subplot(222)
plots.plot_heatmap(D1, D2, Efit, ax=ax, title="Noisy", cmap="viridis")
ax = fig.add_subplot(223)
model.plot_heatmap(D1, D2, ax=ax, title="Fit")
ax = fig.add_subplot(224)
model.plot_residual_heatmap(D1,
D2,
Efit,
ax=ax,
title="Residuals",
center_on_zero=True)
plt.tight_layout()
plt.show()