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Vectorize #3

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54d5dfc
VAE model updated
kaushalpaneri Oct 12, 2018
6cf0151
Debugging iarange
kaushalpaneri Oct 19, 2018
78361b1
Experiment: condition on iid data added
kaushalpaneri Oct 23, 2018
49668d9
gitignore updated
kaushalpaneri Oct 23, 2018
2ae197d
VAE removed
kaushalpaneri Oct 23, 2018
ba9fa5c
Deleted unnecessary files
kaushalpaneri Oct 23, 2018
757bef6
added HMC
robertness Oct 27, 2018
630d9f0
variational inference experiment
kaushalpaneri Nov 2, 2018
7f4eb63
variational inference experiment
kaushalpaneri Nov 2, 2018
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39 changes: 39 additions & 0 deletions .gitignore
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Original file line number Diff line number Diff line change
Expand Up @@ -95,3 +95,42 @@ venv.bak/
*.aux
*.out
*.pdf


# Compiled source #
###################
*.com
*.class
*.dll
*.exe
*.o
*.so

# Packages #
############
# it's better to unpack these files and commit the raw source
# git has its own built in compression methods
*.7z
*.dmg
*.gz
*.iso
*.jar
*.rar
*.tar
*.zip

# Logs and databases #
######################
*.log
*.sql
*.sqlite

# OS generated files #
######################
.DS_Store
.DS_Store?
._*
.Spotlight-V100
.Trashes
ehthumbs.db
Thumbs.db
115 changes: 90 additions & 25 deletions notebooks/biochemical_model.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -32,17 +32,20 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 79,
"metadata": {},
"outputs": [],
"source": [
"from math import exp\n",
"\n",
"from pyro.distributions import LogNormal\n",
"from pyro.distributions import LogNormal,Normal\n",
"from pyro.primitives import iarange\n",
"from pyro import condition, do, infer, sample\n",
"from pyro.infer import EmpiricalMarginal\n",
"from torch import tensor\n",
"\n",
"import sys\n",
"sys.path.append('../')\n",
"from causal_demon.inference import infer_dist\n",
"from causal_demon.transmitters import cancer_signaling\n",
"\n",
Expand All @@ -65,27 +68,58 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 82,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor(0.1904)\n"
]
}
],
"source": [
"noise_vars = ['N_egf', 'N_igf', 'N_sos', 'N_ras', 'N_pi3k', 'N_akt', 'N_raf', 'N_mek', 'N_erk']\n",
"noise_prior = {N: LogNormal(0, 10) for N in noise_vars}"
"noise_prior = {N: LogNormal(0, 10) for N in noise_vars}\n",
"\n",
"print(noise_prior['N_egf'].sample())"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 83,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"{'akt': tensor(405643.),\n",
" 'egf': tensor(44953.8320),\n",
" 'erk': tensor(3115.5735),\n",
" 'igf': tensor(0.1029),\n",
" 'mek': tensor(162.8303),\n",
" 'pi3k': tensor(119770.7422),\n",
" 'raf': tensor(4.5652),\n",
" 'ras': tensor(176.5171),\n",
" 'sos': tensor(28.5478)}"
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cancer_signaling(noise_prior)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"execution_count": 84,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Experimental use only, please ignore\n",
Expand Down Expand Up @@ -127,12 +161,19 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 213,
"metadata": {},
"outputs": [],
"source": [
"evidence = {'egf': tensor(800.), 'igf': tensor(0.), 'erk': tensor(800.)}\n",
"cancer_obs = condition(cancer_signaling, data=evidence)"
"evidence = {'egf': tensor([800.]), 'igf': tensor([0.,0.]), 'erk': tensor([800.])}\n",
"\n",
"cancer_obs = condition(cancer_signaling, data= evidence)\n",
"\n",
"#evidence = [{'egf': tensor([800.]), 'igf': tensor([0.]), 'erk': tensor([800.])},{'egf': tensor([800.]), 'igf': tensor([0.]), 'erk': tensor([800.])},{'egf': tensor([600.]), 'igf': tensor([0.]), 'erk': tensor([600.])},{'egf': tensor([800.]), 'igf': tensor([0.]), 'erk': tensor([800.])},{'egf': tensor([600.]), 'igf': tensor([0.]), 'erk': tensor([600.])}]\n",
"\n",
"#with iarange('data',len(evidence)) as ind:\n",
"# print(ind)\n",
"# cancer_obs = condition(cancer_signaling, data= evidence)"
]
},
{
Expand All @@ -144,7 +185,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 180,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -160,8 +201,10 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"execution_count": 181,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"noise_marginals = {\n",
Expand All @@ -179,11 +222,11 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 204,
"metadata": {},
"outputs": [],
"source": [
"cancer_do = do(cancer_signaling, data={'igf': tensor(800.)})"
"cancer_do = do(cancer_signaling, data={'igf': tensor([800.,600.])})"
]
},
{
Expand All @@ -195,21 +238,43 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 207,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<pyro.infer.importance.Importance object at 0x152ad7df98>\n"
]
}
],
"source": [
"cancer_do_dist = infer_dist(cancer_do, noise_marginals)\n",
"print(cancer_do_dist)\n",
"erk_cf_marginal = EmpiricalMarginal(cancer_do_dist, sites = 'erk')"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 201,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[tensor([90.9036, 90.9029])]\n"
]
}
],
"source": [
"hist(erk_cf_marginal, 'Erk')"
"#hist(erk_cf_marginal, 'Erk')\n",
"\n",
"print(erk_cf_marginal())\n",
"#\n",
"#plt.hist([erk_cf_marginal().item() for _ in range(100)])\n",
"#plt.title(\"Erk\")"
]
},
{
Expand All @@ -226,9 +291,9 @@
],
"metadata": {
"kernelspec": {
"display_name": "venv",
"display_name": "Python 3",
"language": "python",
"name": "venv"
"name": "python3"
},
"language_info": {
"codemirror_mode": {
Expand All @@ -240,7 +305,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
"version": "3.6.3"
}
},
"nbformat": 4,
Expand Down
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