From 47eee77a813309be6c9fcd965e1075486b461b0d Mon Sep 17 00:00:00 2001 From: Rahul Shrestha Date: Sun, 21 Apr 2024 20:08:44 +0200 Subject: [PATCH] fix Signed-off-by: Rahul Shrestha --- .../test_causal_inference_discovery_book.py | 48 ------------------- 1 file changed, 48 deletions(-) diff --git a/tests/causal-inference-discovery-book/test_causal_inference_discovery_book.py b/tests/causal-inference-discovery-book/test_causal_inference_discovery_book.py index 46b9fda55..3bcc3444d 100644 --- a/tests/causal-inference-discovery-book/test_causal_inference_discovery_book.py +++ b/tests/causal-inference-discovery-book/test_causal_inference_discovery_book.py @@ -44,54 +44,6 @@ def intervene(self, treatment_value, sample_size=100): @mark.usefixtures("fixed_seed") class TestCausalInferenceDiscoveryBook(object): - def test_dowhy_chapter_6(self): - # Instantiate the SCM - scm = GPSMemorySCM() - - # Generate observational data - gps_obs, hippocampus_obs, memory_obs = scm.sample(600) - - # Run an experiment - treatments = [] - experiment_results = [] - - # Sample over various treatments - for treatment in np.arange(1, 21): - gps_hours, hippocampus, memory = scm.intervene(treatment_value=treatment, sample_size=30) - experiment_results.append(memory) - treatments.append(gps_hours) - - # Naive model 1 - - lr_naive = LinearRegression() - lr_naive.fit(X=gps_obs.reshape(-1, 1), y=memory_obs) - - # Experimental model - - treatments_unpack = np.array(treatments).flatten() - results_unpack = np.array(experiment_results).flatten() - - lr_experiment = LinearRegression() - lr_experiment.fit(X=treatments_unpack.reshape(-1, 1), y=results_unpack) - - X_test = np.arange(1, 21).reshape(-1, 1) - preds_naive = lr_naive.predict(X_test) - preds_experiment = lr_experiment.predict(X_test) - - # Get coefficients - print(f"Naive model:\n{lr_naive.coef_[0]}\n") - print(f"Experiemntal model:\n{lr_experiment.coef_[0]}") - - # Model E(Z|X) - lr_zx = LinearRegression() - lr_zx.fit(X=gps_obs.reshape(-1, 1), y=hippocampus_obs) - - # Model E(Y|X, Z)E(X) - lr_yxz = LinearRegression() - lr_yxz.fit(X=np.array([gps_obs, hippocampus_obs]).T, y=memory_obs) - - # Compute the expected causal effect - lr_zx.coef_[0] * lr_yxz.coef_[1] def test_dowhy_chapter_7(self): # Instantiate the SCM