load hyperotp results with vp api

Author: tgiani

n3fit/src/n3fit/model_trainer.py

@@ -113,6 +113,7 @@ def __init__(
         theoryid=None,
         lux_params=None,
         replicas=None,
+        trials=None,
     ):
         """
         Parameters
@@ -153,6 +154,8 @@ def __init__(
                 if not give, the photon is not generated
             replicas: list
                 list with the replicas ids to be fitted
+            trials: str
+                name of the file containing the trials defining the methodology
         """
         # Save all input information
         self.exp_info = list(exp_info)
@@ -169,6 +172,7 @@ def __init__(
         self.lux_params = lux_params
         self.replicas = replicas
         self.experiments_data = experiments_data
+        self.trials=trials
 
         # Initialise internal variables which define behaviour
         if debug:
@@ -853,7 +857,6 @@ def hyperparametrizable(self, params):
         # Reset the internal state of the backend every time this function is called
         print("")
         clear_backend_state()
-
         # When doing hyperopt some entries in the params dictionary
         # can bring with them overriding arguments
         if self.mode_hyperopt:
@@ -867,7 +870,7 @@ def hyperparametrizable(self, params):
         else:
             with open(params['hyperopt_res'], 'r') as file:
                 hyperopt_params = json.load(file)
-
+        import pdb; pdb.set_trace()
         # Preprocess some hyperparameters
         if self.mode_hyperopt:
             epochs = int(params["epochs"])

n3fit/src/n3fit/performfit.py

@@ -36,6 +36,7 @@ def performfit(
     load=None,
     hyperscanner=None,
     hyperopt=None,
+    trials,
     kfold_parameters,
     tensorboard=None,
     debug=False,
@@ -115,6 +116,8 @@ def performfit(
             dictionary containing the details of the hyperscanner
         hyperopt: int
             if given, number of hyperopt iterations to run
+        trials: str 
+            file containing trials defining the methodology
         kfold_parameters: None, dict
             dictionary with kfold settings used in hyperopt.
         tensorboard: None, dict
@@ -197,6 +200,7 @@ def performfit(
             theoryid=theoryid,
             lux_params=fiatlux,
             replicas=replica_idxs,
+            trials=trials,
         )
 
         # This is just to give a descriptive name to the fit function

n3fit/src/n3fit/scripts/n3fit_exec.py

@@ -9,6 +9,7 @@
 import re
 import shutil
 import sys
+import pandas as pd
 
 from ruamel.yaml import error
 
@@ -18,6 +19,8 @@
 from validphys.config import Config, ConfigError, Environment, EnvironmentError_
 from validphys.core import FitSpec
 from validphys.utils import yaml_safe
+from validphys.api import API
+from validphys.hyperoptplot import generate_dictionary
 
 N3FIT_FIXED_CONFIG = dict(use_cuts='internal', use_t0=True, actions_=[], allow_legacy_names=False)
 
@@ -234,7 +237,16 @@ def produce_hyperscanner(self, parameters, hyperscan_config=None, hyperopt=None)
                 "db_path": db_path,
             }
         return HyperScanner(parameters, hyperscan_config, **extra_args)
-
+    
+    def produce_trials(self, trials_file):
+        hyperscan = API.hyperscan(hyperscan=trials_file)
+        dict_trials = generate_dictionary(hyperscan.tries_files[1].parent,"average")
+        hyperopt_dataframe = pd.DataFrame(dict_trials)
+        n_termalization = 400
+        n_best = 10
+        best = hyperopt_dataframe[n_termalization:].sort_values('loss')[:n_best]
+        return best.to_dict(orient='list')
+        
 
 class N3FitApp(App):
     """The class which parsers and performs the fit"""