"""Print the best model parameters and loss"""
import sys
import numpy as np
import pickle as pkl
import hyperopt
from hyperopt import hp, fmin, tpe, Trials


# Change the following code to your file
################################################################################
# TODO: Declare a folder to hold all trials objects
TRIALS_FOLDER = "trials2"
################################################################################


def merge_trials(trials1, trials2_slice):
    """Merge two hyperopt trials objects

    :trials1: The primary trials object
    :trials2_slice: A slice of the trials object to be merged,
        obtained with, e.g., trials2.trials[:10]
    :returns: The merged trials object

    """
    max_tid = 0
    if len(trials1.trials) > 0:
        max_tid = max([trial["tid"] for trial in trials1.trials])

    for trial in trials2_slice:
        tid = trial["tid"] + max_tid + 1
        hyperopt_trial = Trials().new_trial_docs(
            tids=[None], specs=[None], results=[None], miscs=[None]
        )
        hyperopt_trial[0] = trial
        hyperopt_trial[0]["tid"] = tid
        hyperopt_trial[0]["misc"]["tid"] = tid
        for key in hyperopt_trial[0]["misc"]["idxs"].keys():
            hyperopt_trial[0]["misc"]["idxs"][key] = [tid]
        trials1.insert_trial_docs(hyperopt_trial)
        trials1.refresh()
    return trials1


np.random.seed()

# Load up all runs:
import glob

path = TRIALS_FOLDER + "/*.pkl"
files = 0
for fname in glob.glob(path):

    trials_obj = pkl.load(open(fname, "rb"))
    n_trials = trials_obj["n"]
    trials_obj = trials_obj["trials"]
    if files == 0:
        trials = trials_obj
    else:
        trials = merge_trials(trials, trials_obj.trials[-n_trials:])
    files += 1


print(files, "trials merged")


best_loss = np.inf
best_trial = None
try:
    trials
except NameError:
    raise NameError("No trials loaded. Be sure to set the right folder")

# for trial in trials:
# if trial['result']['status'] == 'ok':
# loss = trial['result']['loss']
# if loss < best_loss:
# best_loss = loss
# best_trial = trial

# print(best_loss, best_trial['misc']['vals'])
# trials = sorted(trials, key=lambda x: (x['result']['loss'] if trials['result']['status'] == 'ok' else float('inf')))

clean_trials = []
for trial in trials:
    clean_trials.append((trial["result"]["loss"], trial["misc"]["vals"]))

clean_trials = sorted(clean_trials, key=lambda x: x[0])

for trial in clean_trials:
    print(trial)