Cleanup run simulation file PCMCI

hawk/analysis/simulation_pcmci.py

@@ -0,0 +1,58 @@
+import time
+
+from tigramite.pcmci import PCMCI
+
+
+def run(
+    datasets,
+    config,
+    independence_tests,
+):
+    params = config["params"]
+    dataset_name = config["dataset_name"]
+    dataframe = datasets[dataset_name]
+
+    independence_test = independence_tests[params["independencetest"]]
+    algorithm = params["algorithm"]
+    lag = params["lag"]
+
+    # Construct a unique identifier for the configuration
+    # param_str = "_".join(f"{k}{v}" for k, v in params.items())
+    # config_id = f"dataset{dataset_name}_{param_str}"
+
+    print(f"Running experiment with config: {config}")
+
+    pcmci = PCMCI(dataframe=dataframe["full_tigramite"], cond_ind_test=independence_test, verbosity=2)
+
+    start_time = time.time()
+    if algorithm == "pcmci":
+        results = pcmci.run_pcmci(tau_max=lag, pc_alpha=0.05, alpha_level=0.01)
+    elif algorithm == "pcmci_plus":
+        results = pcmci.run_pcmciplus(tau_min=0, tau_max=lag)
+    else:
+        raise ValueError(f"Invalid algorithm {algorithm}")
+    end_time = time.time()
+    execution_time = end_time - start_time
+
+    q_matrix = pcmci.get_corrected_pvalues(
+        p_matrix=results["p_matrix"],
+        tau_max=lag,
+        fdr_method="fdr_bh",
+    )
+
+    graph = pcmci.get_graph_from_pmatrix(
+        p_matrix=q_matrix,
+        alpha_level=0.01,
+        tau_min=0,
+        tau_max=lag,
+        link_assumptions=None,
+    )
+
+    results["graph"] = graph
+
+    return {
+        "results": results,
+        "params": params,
+        "dataset_name": dataset_name,
+        "execution_time": execution_time,
+    }