para2value - 'NoneType' object is not subscriptable about hyperactive HOT 12 CLOSED

  def run(self):

    hyper = hyperactive.Hyperactive(distribution="multiprocessing",
                                    verbosity=["progress_bar", "print_results", "print_times"])

    self.search_space = self.get_search_space()

    # Later use actual search space combinations to determin n_iter
    # keys, values = zip(*self.search_space.items())
    # combinations = [dict(zip(keys, v)) for v in itertools.product(*values)]
    # combinations_count = len(combinations)

    mem = None

    if jh.file_exists(self.path):
      with open(self.path, "r") as f:
        mem = pd.read_csv(f, sep=";", na_values='nan')
      if not mem.empty:
        if not click.confirm('Previous optimization results for {} exists. Continue?'.format(self.study_name),
                             default=True):
          mem = None

    if self.optimizer == "RandomSearchOptimizer":
      optimizer = hyperactive.RandomSearchOptimizer()
    elif self.optimizer == "RandomRestartHillClimbingOptimizer":
      optimizer = hyperactive.RandomRestartHillClimbingOptimizer(
        epsilon=0.1,
        distribution="laplace",
        n_neighbours=4,
        rand_rest_p=0.1,
        n_iter_restart=20,
      )
    elif self.optimizer == "RandomAnnealingOptimizer":
      optimizer = hyperactive.RandomAnnealingOptimizer(
        epsilon=0.1,
        distribution="laplace",
        n_neighbours=4,
        rand_rest_p=0.1,
        annealing_rate=0.999,
        start_temp=0.8,
      )
    elif self.optimizer == "HillClimbingOptimizer":
      optimizer = hyperactive.HillClimbingOptimizer(
        epsilon=0.1, distribution="laplace", n_neighbours=4, rand_rest_p=0.1
      )
    elif self.optimizer == "RepulsingHillClimbingOptimizer":
      optimizer = hyperactive.RepulsingHillClimbingOptimizer(
        epsilon=0.1,
        distribution="laplace",
        n_neighbours=4,
        repulsion_factor=5,
        rand_rest_p=0.1,
      )
    elif self.optimizer == "SimulatedAnnealingOptimizer":
      optimizer = hyperactive.SimulatedAnnealingOptimizer(
        epsilon=0.1,
        distribution="laplace",
        n_neighbours=4,
        rand_rest_p=0.1,
        p_accept=0.15,
        norm_factor="adaptive",
        annealing_rate=0.999,
        start_temp=0.8,
      )
    elif self.optimizer == "ParallelTemperingOptimizer":
      optimizer = hyperactive.ParallelTemperingOptimizer(n_iter_swap=5, rand_rest_p=0.05)
    elif self.optimizer == "ParticleSwarmOptimizer":
      optimizer = hyperactive.ParticleSwarmOptimizer(
        inertia=0.4,
        cognitive_weight=0.7,
        social_weight=0.7,
        temp_weight=0.3,
        rand_rest_p=0.05,
      )
    elif self.optimizer == "EvolutionStrategyOptimizer":
      optimizer = hyperactive.EvolutionStrategyOptimizer(
        mutation_rate=0.5, crossover_rate=0.5, rand_rest_p=0.05
      )
    else:
      raise ValueError('Entered optimizer which is {} is not known.'.format(
        self.optimizer
      ))

    if mem is None or mem.empty:
      # init empty pandas dataframe
      search_data = pd.DataFrame(columns=list(self.search_space.keys()) + ["score", "daily_balance"])
      with open(self.path, "w") as f:
        search_data.to_csv(f, sep=";", index=False, na_rep='nan')

      hyper.add_search(self.objective_function, self.search_space, optimizer=optimizer,
                       n_iter=self.iterations,
                       n_jobs=self.cpu_cores)
    else:
      mem.drop('daily_balance', 1, inplace=True)
      hyper.add_search(self.objective_function, self.search_space, optimizer=optimizer, memory_warm_start=mem,
                       n_iter=self.iterations,
                       n_jobs=self.cpu_cores)
    hyper.run()

import random
import numpy as np
import pandas as pd

from hyperactive import Hyperactive

# set a path to save the dataframe
path = "./search_data.csv"
search_space = {
    "n_neighbors": list(range(1, 50)),
}

# get para names from search space + the score
para_names = list(search_space.keys()) + ["score"]

# init empty pandas dataframe
search_data = pd.DataFrame(columns=para_names)
search_data.to_csv(path, index=False)


def objective_function(para):
    #score = random.choice([1.2, 2.3, np.nan])
    score = np.nan

    # you can access the entire dictionary from "para"
    parameter_dict = para.para_dict

    # save the score in the copy of the dictionary
    parameter_dict["score"] = score

    # append parameter dictionary to pandas dataframe
    search_data = pd.read_csv(path, na_values='nan')
    search_data_new = pd.DataFrame(parameter_dict, columns=para_names, index=[0])
    search_data = search_data.append(search_data_new)
    search_data.to_csv(path, index=False, na_rep='nan')

    return score


hyper0 = Hyperactive()
hyper0.add_search(objective_function, search_space, n_iter=50)
hyper0.run()


search_data_0 = pd.read_csv(path, na_values='nan')
"""
the second run should be much faster than before, 
because Hyperactive already knows most parameters/scores
"""
hyper1 = Hyperactive()
hyper1.add_search(
    objective_function, search_space, n_iter=50, memory_warm_start=search_data_0
)
hyper1.run()

============================== EXCEPTION TRACEBACK:
  File "/usr/local/bin/jesse", line 33, in <module>
    sys.exit(load_entry_point('jesse', 'console_scripts', 'jesse')())
  File "/usr/local/lib/python3.9/site-packages/click/core.py", line 829, in __call__
    return self.main(*args, **kwargs)
  File "/usr/local/lib/python3.9/site-packages/click/core.py", line 782, in main
    rv = self.invoke(ctx)
  File "/usr/local/lib/python3.9/site-packages/click/core.py", line 1259, in invoke
    return _process_result(sub_ctx.command.invoke(sub_ctx))
  File "/usr/local/lib/python3.9/site-packages/click/core.py", line 1066, in invoke
    return ctx.invoke(self.callback, **ctx.params)
  File "/usr/local/lib/python3.9/site-packages/click/core.py", line 610, in invoke
    return callback(*args, **kwargs)
  File "/home/home/src/jesse/jesse/__init__.py", line 379, in optimize_hyperactive
    optimize_mode_hyperactive(start_date, finish_date, optimal_total, cpu, optimizer, iterations)
  File "/home/home/src/jesse/jesse/modes/optimize_hyperactive_mode/__init__.py", line 309, in optimize_mode_hyperactive
    optimizer.run()
  File "/home/home/src/jesse/jesse/modes/optimize_hyperactive_mode/__init__.py", line 268, in run
    hyper.run()
  File "/usr/local/lib/python3.9/site-packages/hyperactive/hyperactive.py", line 201, in run
    self.results_list = run_search(self.process_infos, self.distribution)
  File "/usr/local/lib/python3.9/site-packages/hyperactive/run_search.py", line 42, in run_search
    results_list = single_process(_process_, process_infos)
  File "/usr/local/lib/python3.9/site-packages/hyperactive/distribution.py", line 10, in single_process
    results = [process_func(**search_processes_infos[0])]
  File "/usr/local/lib/python3.9/site-packages/hyperactive/process.py", line 37, in _process_
    optimizer.print_info(
  File "/usr/local/lib/python3.9/site-packages/hyperactive/optimizers.py", line 128, in print_info
    self.optimizer.print_info(*args)
  File "/usr/local/lib/python3.9/site-packages/gradient_free_optimizers/search.py", line 95, in print_info
    print_info(*args)
  File "/usr/local/lib/python3.9/site-packages/gradient_free_optimizers/print_info.py", line 103, in print_info
    _print_results(objective_function, score_best, para_best)
  File "/usr/local/lib/python3.9/site-packages/gradient_free_optimizers/print_info.py", line 66, in _print_results
    para_names = list(para_best.keys())
=========================================================================

 Uncaught Exception: AttributeError: 'NoneType' object has no attribute 'keys'