diff --git a/BubbleML/miniML/README.md b/BubbleML/miniML/README.md index 71089459..f4ede3f1 100644 --- a/BubbleML/miniML/README.md +++ b/BubbleML/miniML/README.md @@ -33,11 +33,9 @@ options: Example call: ```bash -./miniML.py --ham_features_file TESTING_ONLY_Hamiltonian_features.csv --config_file miniML_config.json --solver_uuid bd63d0e0-a681-11ef-b4bd-6f1bb9e0689f --solver_labels_file TESTING_ONLY_solver.ccsdt.labels.csv --verbose +./miniML.py -v --ham_features_file ../../Hamiltonian_features/experimental/fast_double_factorization_features/Hamiltonian_features.csv --config_file miniML_config.json --solver_uuid 16537433-9f4c-4eae-a65d-787dc3b35b59 --solver_labels_file ../../scripts/solver_labels.DMRG_Niagara_cluster_lowest_energy.16537433-9f4c-4eae-a65d-787dc3b35b59.csv --verbose ``` -Note that the `solver_uuid` is contrived for this example. The solver is CCSDT in this example. +Note that the `solver_uuid` is specific to the DMRG algorithm running on a specific compute platform in this example. If the `--verbose` flag is included, then a `plot_.png` plot and the `probs.csv` file will be generated as artifacts. - -TODO: As we are integrating, use the `TESTING_ONLY_Hamiltonian_features.csv` and `TESTING_ONLY_solver.ccsdt.labels.csv` files. As more data is collated, we should point to `../../Hamiltonian_features/experimental/fast_double_factorization/Hamiltonian_features.csv` and an appropriately aligned `solver..labels.csv` files. \ No newline at end of file diff --git a/BubbleML/miniML/miniML.py b/BubbleML/miniML/miniML.py index db795bd2..798a5bc9 100755 --- a/BubbleML/miniML/miniML.py +++ b/BubbleML/miniML/miniML.py @@ -140,15 +140,30 @@ def trainML( ) logging.info('Percent of solvable space: ', str(ml_solvability_ratio)) + timestamp = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M") + + #explain all the predictions in the test set plt.figure() explainer = shap.KernelExplainer(model.predict_proba, X_train) shap_values = explainer.shap_values(X_train) class_index = 1 shap.initjs() - shap.summary_plot(shap_values[1],features=X.columns,plot_type="bar") + + shap.summary_plot( + shap_values[1], + features=X.columns, + plot_type="bar" + ) + #shap.force_plot(explainer.expected_value[class_index], shap_values[class_index], X_train, matplotlib=True, show=False) + plt.savefig( + f"shap_summary_plot_solver={solver_uuid}_{timestamp}.png", + format="png" + ) + + if verbose: # print to file y_pred = model.predict(X_train) @@ -164,7 +179,6 @@ def trainML( "prob_class_1": probs[:,1] } ) - timestamp = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M") probs_file_name = f"probs_solver={solver_uuid}_{timestamp}.csv" df.to_csv(probs_file_name, index=False) logging.info(f"wrote probs to file {probs_file_name}.") diff --git a/BubbleML/miniML/plot_solver=16537433-9f4c-4eae-a65d-787dc3b35b59_2024-12-10-07-22.png b/BubbleML/miniML/plot_solver=16537433-9f4c-4eae-a65d-787dc3b35b59_2024-12-10-07-22.png deleted file mode 100644 index 7deae3b8..00000000 Binary files a/BubbleML/miniML/plot_solver=16537433-9f4c-4eae-a65d-787dc3b35b59_2024-12-10-07-22.png and /dev/null differ diff --git a/BubbleML/miniML/plot_solver=16537433-9f4c-4eae-a65d-787dc3b35b59_2024-12-16-12-35.png b/BubbleML/miniML/plot_solver=16537433-9f4c-4eae-a65d-787dc3b35b59_2024-12-16-12-35.png new file mode 100644 index 00000000..8740675f Binary files /dev/null and b/BubbleML/miniML/plot_solver=16537433-9f4c-4eae-a65d-787dc3b35b59_2024-12-16-12-35.png differ diff --git a/BubbleML/miniML/probs_solver=16537433-9f4c-4eae-a65d-787dc3b35b59_2024-12-10-07-29.csv b/BubbleML/miniML/probs_solver=16537433-9f4c-4eae-a65d-787dc3b35b59_2024-12-10-07-29.csv deleted file mode 100644 index be276508..00000000 --- a/BubbleML/miniML/probs_solver=16537433-9f4c-4eae-a65d-787dc3b35b59_2024-12-10-07-29.csv +++ /dev/null @@ -1,212 +0,0 @@ -max_vertex_degree,min_vertex_degree,y_train,y_pred,prob_class_0,prob_class_1 -49102,8908,True,True,0.009644893681213997,0.9903551063187861 -17934,3906,True,True,0.2976748629578255,0.7023251370421745 -21502,5474,True,True,0.014719173248778783,0.9852808267512212 -6824,1800,True,True,0.22039075411414474,0.7796092458858551 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