Allow user to change optimizer and objective function. Also added r-squared evaluator. Changed temp file location to user temp folder. Added a prediction dictionary to access the values predicted by model.

tests/test_reeps.py

@@ -1,22 +1,53 @@
 import json
+import numpy as np
+import pyswarms as ps
 import sys
-
 sys.path.append('../')
 from reeps import REEPS
 
-
 with open('one_ree_settings.txt') as file:
     testing_params = json.load(file)
 
 beaker = REEPS(**testing_params)
 
+
+# def new_obj(predicted_dict, meas_df, epsilon):
+#     meas_cols = list(meas_df)
+#     pred_keys = list(predicted_dict.keys())
+#     meas = meas_df[meas_cols[2]]
+#     pred = (predicted_dict['re_org'] + epsilon) / (predicted_dict['re_aq'] + epsilon)
+#     log_pred = np.log10(pred)
+#     log_meas = np.log10(meas)
+#     obj = np.sum((log_pred - log_meas) ** 2)
+#     return obj
+# #
+# #
+# # def new_obj(ping):
+# #     print(ping)
+# beaker.set_objective_function(new_obj)
+# objective_kwargs = {"epsilon": 1e-14}
+# beaker.set
+# noinspection PyUnusedLocal
+def optimizer(func, x_guess):
+    lb = np.array([1e-1])
+    ub = np.array([1e1])
+    bounds = (lb, ub)
+    options = {'c1': 1e-3, 'c2': 1e-3, 'w': 0.9}
+    mini_optimizer = ps.single.global_best.GlobalBestPSO(n_particles=100, dimensions=1,
+                                                         options=options, bounds=bounds)
+    f_opt, x_opt = mini_optimizer.optimize(func, iters=100)
+
+    return x_opt
+
+
 minimizer_kwargs = {"method": 'SLSQP',
                     "bounds": [(1e-1, 1e1)],
                     "constraints": (),
                     "options": {'disp': True, 'maxiter': 1000, 'ftol': 1e-6}}
-est_enthalpy = beaker.fit(minimizer_kwargs)
+# est_enthalpy = beaker.fit(optimizer=optimizer)
+est_enthalpy = beaker.fit()
 print(est_enthalpy)
-# info_dict = {"Nd(H(A)2)3(org)": {"h0": est_enthalpy}}
-#
-beaker.update_xml(est_enthalpy)
-beaker.parity_plot()
+
+# beaker.update_xml(est_enthalpy)
+# beaker.parity_plot()
+# print(beaker.r_squared())