def compute_log_pys(drug_channel):
print drug_channel
seed = drugs_channels.index(drug_channel)
print "seed =", seed
npr.seed(seed)
drug, channel = drug_channel
num_expts, experiment_numbers, experiments = dr.load_crumb_data(
drug, channel)
concs = np.array([])
responses = np.array([])
for i in xrange(num_expts):
concs = np.concatenate((concs, experiments[i][:, 0]))
responses = np.concatenate((responses, experiments[i][:, 1]))
print "responses:", responses
num_pts = len(responses)
pi_bit = dr.compute_pi_bit_of_log_likelihood(responses)
n = 40
c = 3
temperatures = (np.arange(n + 1.) / n)**c
num_models = 2
for model in xrange(1, num_models + 1):
print "model", model
log_pys = np.zeros((n + 1, 2))
log_pys[:, 0] = temperatures
dr.define_model(model)
log_py_file = dr.define_log_py_file(model, drug, channel)
with open(log_py_file, "w") as outfile:
outfile.write("# temperature, log(p(y|t))\n")
for i, temperature in enumerate(temperatures):
log_py = approx_log_py(responses, concs, num_pts, temperature,
pi_bit, model, drug, channel)
log_pys[i, 1] = log_py
np.savetxt(log_py_file, log_pys)
print "\n", log_pys
return None
action='store_true',
help='run hierarchical MCMC on all drugs and channels',
default=False)
parser.add_argument("--plot-data",
action='store_true',
help='plot data points on top of predicted curves',
default=False)
parser.add_argument(
"--save-pdf",
action='store_true',
help=
'save dose-response curves figure as pdf (in addition to png), but the file will probably be massive',
default=False)
args = parser.parse_args()
dr.define_model(args.model)
temperature = 1
num_params = dr.num_params
dr.setup(args.data_file)
drugs_to_run, channels_to_run = dr.list_drug_channel_options(args.all)
num_x_pts = 50
alpha = 0.002 # this is the lowest value I've found that actually shows anything
for drug, channel in it.product(drugs_to_run, channels_to_run):
try:
num_expts, experiment_numbers, experiments = dr.load_crumb_data(
drug, channel)
except:
def do_plot(drug_channel):
global concs, responses
fig = plt.figure(figsize=(5, 8))
axes = {}
axes[1] = fig.add_subplot(211)
axes[2] = fig.add_subplot(212)#, sharey=axes[1])
fsize = 14
for model in xrange(1, num_models+1):
dr.define_model(model)
drug, channel = drug_channel
num_expts, experiment_numbers, experiments = dr.load_crumb_data(drug, channel)
figs_dir = dr.drug_channel_figs_dir(drug, channel)
concs = np.array([])
responses = np.array([])
for i in xrange(num_expts):
concs = np.concatenate((concs, experiments[i][:, 0]))
responses = np.concatenate((responses, experiments[i][:, 1]))
if model == 1:
x0 = np.ones(2)
sigma0 = 0.1
elif model == 2:
x0 = np.copy([pic50, hill])
sigma0 = 0.01
#x0[0] = 6.9
opts = cma.CMAOptions()
es = cma.CMAEvolutionStrategy(x0, sigma0, opts)
while not es.stop():
X = es.ask()
f_vals = [sum_of_square_diffs(x, model) for x in X]
es.tell(X, f_vals)
es.disp()
res = es.result()
ss = res[1]
pic50, hill = res[0]
if model == 1:
hill = 1
conc_min = np.min(concs)
conc_max = np.max(concs)
num_pts = 501
x_range = np.logspace(int(np.log10(conc_min))-1, int(np.log10(conc_max))+2, num_pts)
predicted = dr.dose_response_model(x_range, hill, dr.pic50_to_ic50(pic50))
#fig = plt.figure(figsize=(5,4))
#ax = fig.add_subplot(111)
axes[model].grid()
axes[model].set_xscale('log')
axes[model].set_ylim(0,100)
axes[model].set_ylabel(r"% {} block".format(channel),fontsize=fsize)
axes[model].set_xlabel(r"{} concentration ($\mu$M)".format(drug),fontsize=fsize)
axes[model].plot(x_range, predicted, color='blue', lw=2, label="Best fit")
axes[model].plot(concs, responses, 'o', color='orange', ms=10, label="Expt data")
axes[model].legend(loc=2)
axes[model].set_title("$M_{}, pIC50 = {}, Hill = {}, SS = {}$".format(model, round(pic50,2), round(hill,2), round(ss,2)),fontsize=fsize)
#axes[2].set_yticklabels([])
fig.tight_layout()
#fig.savefig(figs_dir+"{}_{}_model_{}_best_fit.png".format(drug,channel,model))
fig.savefig(all_figs_dir+"{}_{}_best_fits.png".format(drug, channel))
fig.savefig(figs_dir+"{}_{}_best_fit.pdf".format(drug,channel))
plt.close()
def do_plot(drug_channel):
global concs, responses
fig = plt.figure(figsize=(5, 8))
axes = {}
axes[1] = fig.add_subplot(211)
axes[2] = fig.add_subplot(212) #, sharey=axes[1])
fsize = 14
for model in xrange(1, num_models + 1):
dr.define_model(model)
drug, channel = drug_channel
num_expts, experiment_numbers, experiments = dr.load_crumb_data(
drug, channel)
figs_dir = dr.drug_channel_figs_dir(drug, channel)
concs = np.array([])
responses = np.array([])
for i in xrange(num_expts):
concs = np.concatenate((concs, experiments[i][:, 0]))
responses = np.concatenate((responses, experiments[i][:, 1]))
if model == 1:
x0 = np.ones(2)
sigma0 = 0.1
elif model == 2:
x0 = np.copy([pic50, hill])
sigma0 = 0.01
#x0[0] = 6.9
opts = cma.CMAOptions()
es = cma.CMAEvolutionStrategy(x0, sigma0, opts)
while not es.stop():
X = es.ask()
f_vals = [sum_of_square_diffs(x, model) for x in X]
es.tell(X, f_vals)
es.disp()
res = es.result()
ss = res[1]
pic50, hill = res[0]
if model == 1:
hill = 1
conc_min = np.min(concs)
conc_max = np.max(concs)
num_pts = 501
x_range = np.logspace(
int(np.log10(conc_min)) - 1,
int(np.log10(conc_max)) + 2, num_pts)
predicted = dr.dose_response_model(x_range, hill,
dr.pic50_to_ic50(pic50))
#fig = plt.figure(figsize=(5,4))
#ax = fig.add_subplot(111)
axes[model].grid()
axes[model].set_xscale('log')
axes[model].set_ylim(0, 100)
axes[model].set_ylabel(r"% {} block".format(channel), fontsize=fsize)
axes[model].set_xlabel(r"{} concentration ($\mu$M)".format(drug),
fontsize=fsize)
axes[model].plot(x_range,
predicted,
color='blue',
lw=2,
label="Best fit")
axes[model].plot(concs,
responses,
'o',
color='orange',
ms=10,
label="Expt data")
axes[model].legend(loc=2)
axes[model].set_title("$M_{}, pIC50 = {}, Hill = {}, SS = {}$".format(
model, round(pic50, 2), round(hill, 2), round(ss, 2)),
fontsize=fsize)
#axes[2].set_yticklabels([])
fig.tight_layout()
#fig.savefig(figs_dir+"{}_{}_model_{}_best_fit.png".format(drug,channel,model))
fig.savefig(all_figs_dir + "{}_{}_best_fits.png".format(drug, channel))
fig.savefig(figs_dir + "{}_{}_best_fit.pdf".format(drug, channel))
plt.close()
#drug = 'Amiodarone'
#channel = 'hERG'
num_expts, experiment_numbers, experiments = dr.load_crumb_data(drug, channel)
concs = np.array([])
responses = np.array([])
for i in xrange(num_expts):
concs = np.concatenate((concs, experiments[i][:, 0]))
responses = np.concatenate((responses, experiments[i][:, 1]))
pi_bit = dr.compute_pi_bit_of_log_likelihood(responses)
num_pts = len(responses)
for m in xrange(1, num_models + 1):
dr.define_model(m)
chain_dir = dr.chains_dir(m, drug, channel)
chains = np.array(glob(chain_dir + "*_chain.txt"))
temps = np.array([float(c.split("_")[-2]) for c in chains])
ordered_indices = np.argsort(temps)
temps = temps[ordered_indices]
chains = chains[ordered_indices]
temps = temps[::2]
chains = chains[::2]
num_temps = len(temps)
log_p_ys = np.zeros(num_temps)
for i in xrange(num_temps):
print i + 1, "/", num_temps
chain = np.loadtxt(chains[i], usecols=range(dr.num_params))
responses = np.concatenate((responses, experiments[i][:, 1]))
num_pts = len(responses)
pi_bit = dr.compute_pi_bit_of_log_likelihood(responses)
#model = 2 #int(sys.argv[1])
n = 40
c = 3
temperatures = (np.arange(n+1.)/n)**c
num_models = 2
model = int(sys.argv[1])
dr.define_model(model)
#prior_x = np.linspace(0,2,1001)
#prior_pdf = st.logistic.pdf(prior_x, loc=dr.mu, scale=dr.s)
#prior_pdf = st.fisk.pdf(prior_x, c=dr.beta, scale=dr.alpha, loc=0)
for temperature in temperatures:
chain_file = dr.define_chain_file(model, drug, channel, temperature)
chain = do_mcmc(temperature)
np.savetxt(chain_file, chain)
"""fig = plt.figure()
ax = fig.add_subplot(111)
ax.hist(chain[:,1],color='blue',edgecolor='blue',normed=True,bins=40,label="t = {}".format(temperature))
ax.plot(prior_x,prior_pdf,color='red',label='Prior',lw=2)
ax.legend()
#drug = 'Amiodarone'
#channel = 'hERG'
num_expts, experiment_numbers, experiments = dr.load_crumb_data(drug, channel)
concs = np.array([])
responses = np.array([])
for i in xrange(num_expts):
concs = np.concatenate((concs, experiments[i][:, 0]))
responses = np.concatenate((responses, experiments[i][:, 1]))
pi_bit = dr.compute_pi_bit_of_log_likelihood(responses)
num_pts = len(responses)
for m in xrange(1, num_models+1):
dr.define_model(m)
chain_dir = dr.chains_dir(m, drug, channel)
chains = np.array(glob(chain_dir+"*_chain.txt"))
temps = np.array([float(c.split("_")[-2]) for c in chains])
ordered_indices = np.argsort(temps)
temps = temps[ordered_indices]
chains = chains[ordered_indices]
temps = temps[::2]
chains = chains[::2]
num_temps = len(temps)
log_p_ys = np.zeros(num_temps)
for i in xrange(num_temps):
print i+1, "/", num_temps
chain = np.loadtxt(chains[i], usecols=range(dr.num_params))
import doseresponse as dr
import matplotlib.pyplot as plt
import os
model = 2
dr.define_model(model)
fsize = 14
priors_fig_dir = '../output/priors/'
if not os.path.exists(priors_fig_dir):
os.makedirs(priors_fig_dir)
for i in xrange(dr.num_params):
fig = plt.figure(figsize=(5, 4))
ax = fig.add_subplot(111)
ax.plot(dr.prior_xs[i], dr.prior_pdfs[i], lw=2)
xlower, xupper = dr.prior_xs[i][[0, -1]]
ax.set_xlim(xlower, xupper)
ax.grid()
if i == 0:
ax.set_xticks(range(int(xlower), int(xupper) + 1, 3))
ax.set_ylabel('Prior pdf', size=fsize)
ax.set_xlabel(dr.labels[i], size=fsize)
fig.tight_layout()
fig.savefig(priors_fig_dir + '{}_prior.pdf'.format(dr.labels[i]))
plt.close()
def plot_mcmc_samples(drug_channel):
drug, channel = drug_channel
fig = plt.figure(figsize=(5, 8))
axes = {}
axes[1] = fig.add_subplot(211)
axes[2] = fig.add_subplot(212)
#drug = "Amiodarone"
#channel = "hERG"
# drug = "Lopinavir"
# channel = "Kir2.1"
num_models = 2
for model in xrange(1, num_models+1):
dr.define_model(model)
chain_file = dr.define_chain_file(model, drug, channel, temperature)
num_expts, experiment_numbers, experiments = dr.load_crumb_data(drug, channel)
figs_dir = dr.drug_channel_figs_dir(drug, channel)
concs = np.array([])
responses = np.array([])
for i in xrange(num_expts):
concs = np.concatenate((concs, experiments[i][:, 0]))
responses = np.concatenate((responses, experiments[i][:, 1]))
how_many_samples_to_plot = 1200
mcmc_samples = np.loadtxt(chain_file, usecols=range(dr.num_params))
saved_its = mcmc_samples.shape[0]
sample_indices = npr.randint(0, saved_its, how_many_samples_to_plot)
mcmc_samples = mcmc_samples[sample_indices]
conc_min = np.min(concs)
conc_max = np.max(concs)
fsize = 14
num_pts = 101
x_range = np.logspace(int(np.log10(conc_min))-1, int(np.log10(conc_max))+2, num_pts)
axes[model].set_xscale('log')
axes[model].grid()
axes[model].set_ylabel(r"% {} block".format(channel), fontsize=fsize)
axes[model].set_xlabel(r"{} concentration ($\mu$M)".format(drug), fontsize=fsize)
axes[model].set_ylim(0,100)
for i in xrange(how_many_samples_to_plot):
if model == 1:
pic50 = mcmc_samples[i,0]
hill = 1
title = "$M_1$, fixed $Hill=1$, varying $pIC50$"
elif model == 2:
pic50, hill = mcmc_samples[i,:2]
title = "$M_2$, varying $pIC50$ and $Hill$"
axes[model].plot(x_range, dr.dose_response_model(x_range, hill, dr.pic50_to_ic50(pic50)),color='black',alpha=0.01)
axes[model].plot(concs, responses, 'o', color='orange', ms=10, label="Expt data")
axes[model].set_title(title, fontsize = fsize)
axes[model].legend(loc=2)
#axes[2].set_yticklabels([])
fig.tight_layout()
fig.savefig(all_figs_dir+'{}_{}_mcmc_samples.png'.format(drug, channel))
fig.savefig(figs_dir+'{}_{}_mcmc_samples.pdf'.format(drug,channel,model))
plt.close()
return None
def plot_mcmc_samples(drug_channel):
drug, channel = drug_channel
fig = plt.figure(figsize=(5, 8))
axes = {}
axes[1] = fig.add_subplot(211)
axes[2] = fig.add_subplot(212)
#drug = "Amiodarone"
#channel = "hERG"
# drug = "Lopinavir"
# channel = "Kir2.1"
num_models = 2
for model in xrange(1, num_models + 1):
dr.define_model(model)
chain_file = dr.define_chain_file(model, drug, channel, temperature)
num_expts, experiment_numbers, experiments = dr.load_crumb_data(
drug, channel)
figs_dir = dr.drug_channel_figs_dir(drug, channel)
concs = np.array([])
responses = np.array([])
for i in xrange(num_expts):
concs = np.concatenate((concs, experiments[i][:, 0]))
responses = np.concatenate((responses, experiments[i][:, 1]))
how_many_samples_to_plot = 1200
mcmc_samples = np.loadtxt(chain_file, usecols=range(dr.num_params))
saved_its = mcmc_samples.shape[0]
sample_indices = npr.randint(0, saved_its, how_many_samples_to_plot)
mcmc_samples = mcmc_samples[sample_indices]
conc_min = np.min(concs)
conc_max = np.max(concs)
fsize = 14
num_pts = 101
x_range = np.logspace(
int(np.log10(conc_min)) - 1,
int(np.log10(conc_max)) + 2, num_pts)
axes[model].set_xscale('log')
axes[model].grid()
axes[model].set_ylabel(r"% {} block".format(channel), fontsize=fsize)
axes[model].set_xlabel(r"{} concentration ($\mu$M)".format(drug),
fontsize=fsize)
axes[model].set_ylim(0, 100)
for i in xrange(how_many_samples_to_plot):
if model == 1:
pic50 = mcmc_samples[i, 0]
hill = 1
title = "$M_1$, fixed $Hill=1$, varying $pIC50$"
elif model == 2:
pic50, hill = mcmc_samples[i, :2]
title = "$M_2$, varying $pIC50$ and $Hill$"
axes[model].plot(x_range,
dr.dose_response_model(x_range, hill,
dr.pic50_to_ic50(pic50)),
color='black',
alpha=0.01)
axes[model].plot(concs,
responses,
'o',
color='orange',
ms=10,
label="Expt data")
axes[model].set_title(title, fontsize=fsize)
axes[model].legend(loc=2)
#axes[2].set_yticklabels([])
fig.tight_layout()
fig.savefig(all_figs_dir + '{}_{}_mcmc_samples.png'.format(drug, channel))
fig.savefig(figs_dir +
'{}_{}_mcmc_samples.pdf'.format(drug, channel, model))
plt.close()
return None