def test_igf_intervention_on_mek(self):
bel_file_path = "data/igf.json"
config_file_path = "data/COVID-19-config.json"
data_file_path = "data/observational_igf.csv"
output_pickle_object_file = "igf_scm.pkl"
scm = SCM(bel_file_path, config_file_path, data_file_path)
exogenous_noise = scm.exogenous_dist_dict
condition_data = scm.model(exogenous_noise)
print(condition_data)
# target = "a(p(Erk))"
intervention_data = {"a(p(Mek))": 40.0}
do_model = scm.intervention(intervention_data)
samples = [do_model(exogenous_noise) for _ in range(5000)]
df = pd.DataFrame(samples)
for col in df.columns:
for i in range(len(df)):
if torch.is_tensor(df[col][i]):
df[col][i] = df[col][i].item()
df2 = pd.read_csv("data/bel2scm_samples_igf.csv")
erk_diff = df["a(p(Erk))"] - df2["a(p(Erk))"]
erk_diff.to_csv("data/erk_do_mek_40_minus_erk.csv")
df.to_csv("data/intervention_mek_40_samples_igf.csv")
self.assertTrue(True, True)
def test_covid_direct_simulation_causal_effect(self):
torch.manual_seed(23)
time1 = time.time()
bel_file_path = os.path.join(DATA, "covid_input.json")
config_file_path = os.path.join(DATA, "COVID-19-config.json")
data_file_path = os.path.join(
DATA, "observational_samples_from_sigmoid_known_parameters.csv")
scm = SCM(bel_file_path, config_file_path, data_file_path)
exogenous_noise = scm.exogenous_dist_dict
condition_data = scm.model(exogenous_noise)
intervention_data = {"a(TOCI)": 0.0}
do_model = scm.intervention(intervention_data)
samples = [do_model(exogenous_noise) for _ in range(5000)]
df = pd.DataFrame(samples)
for col in df.columns:
for i in range(len(df)):
if torch.is_tensor(df[col][i]):
df[col][i] = df[col][i].item()
df2 = pd.read_csv(os.path.join(DATA, "bel2scm_samples_covid.csv"))
cytokine_diff = df["a(cytokine)"] - df2["a(cytokine)"]
cytokine_diff.to_csv(
os.path.join(DATA, "cytokine_do_toci_0_minus_cytokine.csv"))
df.to_csv(os.path.join(DATA, "intervention_toci_0_samples_covid.csv"))
def test_igf_intervention_on_ras(self):
"""
Description: This experiment gets causal effect on erk by
intervening on mek for igf graph using bel2scm algorithm
"""
bel_file_path = "data/igf.json"
config_file_path = "data/COVID-19-config.json"
data_file_path = "data/observational_igf.csv"
scm = SCM(bel_file_path, config_file_path, data_file_path)
exogenous_noise = scm.exogenous_dist_dict
condition_data = scm.model(exogenous_noise)
print(condition_data)
intervention_data = {"a(p(Ras))": 30.0}
do_model = scm.intervention(intervention_data)
samples = [do_model(exogenous_noise) for _ in range(5000)]
df = pd.DataFrame(samples)
for col in df.columns:
for i in range(len(df)):
if torch.is_tensor(df[col][i]):
df[col][i] = df[col][i].item()
df2 = pd.read_csv("data/bel2scm_samples_igf.csv")
erk_diff = df["a(p(Erk))"] - df2["a(p(Erk))"]
erk_diff.to_csv("data/erk_do_ras_30_minus_erk.csv")
df.to_csv("data/intervention_samples_igf.csv")
self.assertTrue(True, True)
def test_igf_intervention_on_mek(self):
torch.manual_seed(23)
bel_file_path = os.path.join(DATA, "igf.json")
config_file_path = os.path.join(DATA, "COVID-19-config.json")
data_file_path = os.path.join(DATA, "observational_igf.csv")
output_pickle_object_file = "../../../igf_scm.pkl"
scm = SCM(bel_file_path, config_file_path, data_file_path)
exogenous_noise = scm.exogenous_dist_dict
condition_data = scm.model(exogenous_noise)
print(condition_data)
# target = "a(p(Erk))"
intervention_data = {"a(p(Mek))": 40.0}
do_model = scm.intervention(intervention_data)
samples = [do_model(exogenous_noise) for _ in range(5000)]
df = pd.DataFrame(samples)
for col in df.columns:
for i in range(len(df)):
if torch.is_tensor(df[col][i]):
df[col][i] = df[col][i].item()
df2 = pd.read_csv(os.path.join(DATA, "bel2scm_samples_igf.csv"))
erk_diff = df["a(p(Erk))"] - df2["a(p(Erk))"]
erk_diff.to_csv(os.path.join(DATA, "erk_do_mek_40_minus_erk.csv"))
df.to_csv(os.path.join(DATA, "intervention_mek_40_samples_igf.csv"))
def test_mapk_erk_samples(self):
bel_file_path = os.path.join(DATA, "mapk.json")
config_file_path = os.path.join(DATA, "COVID-19-config.json")
data_file_path = os.path.join(DATA, "mapk3000.csv")
# output_pickle_object_file = os.path.join(HERE, "../mapk_scm.pkl"
scm = SCM(bel_file_path, config_file_path, data_file_path)
exogenous_noise = scm.exogenous_dist_dict
samples = [scm.model(exogenous_noise) for i in range(3000)]
print(samples)
def test_covid_toci0_bel2scm_causal_effect(self):
torch.manual_seed(23)
bel_file_path = os.path.join(DATA, "covid_input.json")
config_file_path = os.path.join(DATA, "COVID-19-config.json")
data_file_path = os.path.join(
DATA, "observational_samples_from_sigmoid_known_parameters.csv")
scm = SCM(bel_file_path, config_file_path, data_file_path)
condition_data = {
# 'a(SARS_COV2)': tensor(67.35032),
# 'a(PRR)': tensor(89.7037),
# 'a(ACE2)': tensor(29.747593),
# 'a(AngII)': tensor(68.251114),
# 'a(AGTR1)': tensor(90.96106999999999),
# 'a(ADAM17)': tensor(86.84893000000001),
# 'a(TOCI)': tensor(40.76684),
# 'a(TNF)': tensor(76.85005),
# 'a(sIL_6_alpha)': tensor(87.99491),
# 'a(EGF)': tensor(84.55391),
# 'a(EGFR)': tensor(79.94534),
# 'a(IL6_STAT3)': tensor(83.39896),
# 'a(NF_xB)': tensor(82.79433399999999),
# 'a(IL6_AMP)': tensor(81.38015),
# 'a(cytokine)': tensor(80.21895)
'a(SARS_COV2)': tensor(61.631156999999995),
'a(PRR)': tensor(87.76389),
'a(ACE2)': tensor(39.719845),
'a(AngII)': tensor(59.212959999999995),
'a(AGTR1)': tensor(84.39899399999999),
'a(ADAM17)': tensor(85.84442),
'a(TOCI)': tensor(67.33063),
'a(TNF)': tensor(77.83915),
'a(sIL_6_alpha)': tensor(57.584044999999996),
'a(EGF)': tensor(86.26822),
'a(EGFR)': tensor(81.4849),
'a(IL6_STAT3)': tensor(69.57323000000001),
'a(NF_xB)': tensor(83.75941),
'a(IL6_AMP)': tensor(77.52906),
'a(cytokine)': tensor(79.07555)
}
target = "a(cytokine)"
intervention_data = {"a(TOCI)": 0.0}
causal_effects1, counterfactual_samples1 = scm.counterfactual_inference(
condition_data, intervention_data, target, True)
samples_df = pd.DataFrame(causal_effects1)
samples_df.to_csv(os.path.join(
DATA,
"causal_effect_bel2scm_hardcoded_sigmoid_moderately_ill.csv"),
index=False)
def test_igf(self):
torch.manual_seed(101)
bel_file_path = os.path.join(DATA, "igf.json")
config_file_path = os.path.join(DATA, "COVID-19-config.json")
data_file_path = os.path.join(DATA, "observational_igf.csv")
# output_pickle_object_file = "../../igf_scm.pkl"
scm = SCM(bel_file_path, config_file_path, data_file_path)
# save_scm_object(output_pickle_object_file, scm)
exogenous_noise = scm.exogenous_dist_dict
samples = [scm.model(exogenous_noise) for i in range(5000)]
df = pd.DataFrame(samples)
for col in df.columns:
for i in range(len(df)):
if torch.is_tensor(df[col][i]):
df[col][i] = df[col][i].item()
df.to_csv(os.path.join(DATA, "bel2scm_samples_igf.csv"))
def test_covid_noisy_model_samples(self):
torch.manual_seed(23)
time1 = time.time()
bel_file_path = os.path.join(DATA, "covid_input.json")
config_file_path = os.path.join(DATA, "COVID-19-config.json")
data_file_path = os.path.join(DATA,
"covid_noisy_reparameterized_data.csv")
scm = SCM(bel_file_path, config_file_path, data_file_path)
exogenous_noise = scm.exogenous_dist_dict
samples = torch.tensor([
list(scm.model(exogenous_noise).values()) for _ in range(5500)
]).detach().numpy()
np.savetxt(os.path.join(
DATA, "covid_bel2scm_noisy_reparameterized_samples.csv"),
samples,
delimiter=',')
def test_covid(self):
torch.manual_seed(23)
bel_file_path = os.path.join(DATA, "covid_input.json")
config_file_path = os.path.join(DATA, "COVID-19-config.json")
data_file_path = os.path.join(
DATA, "observational_samples_from_sigmoid_known_parameters.csv")
output_pickle_object_file = "../../../covid_scm.pkl"
scm = SCM(bel_file_path, config_file_path, data_file_path)
exogenous_noise = scm.exogenous_dist_dict
samples = [scm.model(exogenous_noise) for i in range(5000)]
df = pd.DataFrame(samples)
for col in df.columns:
for i in range(len(df)):
if torch.is_tensor(df[col][i]):
df[col][i] = df[col][i].item()
df.to_csv(os.path.join(DATA, "bel2scm_samples_covid.csv"))
save_scm_object(output_pickle_object_file, scm)
def test_covid_causal_effect_with_estimated_parameters_datapoint1(self):
torch.manual_seed(23)
time1 = time.time()
bel_file_path = "data/covid_input.json"
config_file_path = "data/COVID-19-config.json"
data_file_path = "data/observational_samples_from_sigmoid_known_parameters.csv"
scm = SCM(bel_file_path, config_file_path, data_file_path)
condition_data = {
'a(SARS_COV2)': tensor(67.35032),
'a(PRR)': tensor(89.7037),
'a(ACE2)': tensor(29.747593),
'a(AngII)': tensor(68.251114),
'a(AGTR1)': tensor(90.96106999999999),
'a(ADAM17)': tensor(86.84893000000001),
'a(TOCI)': tensor(40.76684),
'a(TNF)': tensor(76.85005),
'a(sIL_6_alpha)': tensor(87.99491),
'a(EGF)': tensor(84.55391),
'a(EGFR)': tensor(79.94534),
'a(IL6_STAT3)': tensor(83.39896),
'a(NF_xB)': tensor(82.79433399999999),
'a(IL6_AMP)': tensor(81.38015),
'a(cytokine)': tensor(80.21895)
}
target = "a(cytokine)"
intervention_data = {"a(TOCI)": 0.0}
causal_effects1, counterfactual_samples1 = scm.counterfactual_inference(
condition_data, intervention_data, target, True)
print("time required for causal effects", time.time() - time1)
samples_df = pd.DataFrame(causal_effects1)
samples_df.to_csv(
"data/causal_effect_sigmoid_with_estimated_parameters_datapoint1.csv",
index=False)
def test_covid_causal_effect_with_estimated_parameters_datapoint2(self):
torch.manual_seed(23)
time1 = time.time()
bel_file_path = "data/covid_input.json"
config_file_path = "data/COVID-19-config.json"
data_file_path = "data/observational_samples_from_sigmoid_known_parameters.csv"
scm = SCM(bel_file_path, config_file_path, data_file_path)
condition_data = {
'a(SARS_COV2)': 61.631156999999995,
'a(PRR)': 87.76389,
'a(ACE2)': 39.719845,
'a(AngII)': 59.212959999999995,
'a(AGTR1)': 84.39899399999999,
'a(ADAM17)': 85.84442,
'a(TOCI)': 67.33063,
'a(TNF)': 77.83915,
'a(sIL_6_alpha)': 57.584044999999996,
'a(EGF)': 86.26822,
'a(EGFR)': 81.4849,
'a(IL6_STAT3)': 69.57323000000001,
'a(NF_xB)': 83.75941,
'a(IL6_AMP)': 77.52906,
'a(cytokine)': 79.07555
}
target = "a(cytokine)"
intervention_data = {"a(TOCI)": 0.0}
causal_effects1, counterfactual_samples1 = scm.counterfactual_inference(
condition_data, intervention_data, target, True)
print("time required for causal effects", time.time() - time1)
samples_df = pd.DataFrame(causal_effects1)
samples_df.to_csv(
"data/causal_effect_sigmoid_with_estimated_parameters_datapoint2.csv",
index=False)