output error

/home/emma/.local/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.
  FutureWarning)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-13-3ea6c0d78a28> in <module>()
    126         Now, we will apply a genetic algorithm to choose a subset of features that gives a better accuracy than the baseline.
    127     '''
--> 128         hof = getHof()
    129         testAccuracyList, validationAccuracyList, individualList, percentileList = getMetrics(hof)
    130

<ipython-input-13-3ea6c0d78a28> in getHof()
     87
     88         # Launch genetic algorithm
---> 89         pop, log = algorithms.eaSimple(pop, toolbox, cxpb=0.5, mutpb=0.2, ngen=numGen, stats=stats, halloffame=hof, verbose=True)
     90
     91         # Return the hall of fame

/home/emma/.local/lib/python3.6/site-packages/deap/algorithms.py in eaSimple(population, toolbox, cxpb, mutpb, ngen, stats, halloffame, verbose)
    149     invalid_ind = [ind for ind in population if not ind.fitness.valid]
    150     fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
--> 151     for ind, fit in zip(invalid_ind, fitnesses):
    152         ind.fitness.values = fit
    153

<ipython-input-13-3ea6c0d78a28> in getFitness(individual, X_train, X_test, y_train, y_test)
     33         cols = [index for index in range(len(individual)) if individual[index] == 0]
     34         X_trainParsed = X_train.drop(X_train.columns[cols], axis=1)
---> 35         X_trainOhFeatures = pd.get_dummies(X_trainParsed)
     36         X_testParsed = X_test.drop(X_test.columns[cols], axis=1)
     37         X_testOhFeatures = pd.get_dummies(X_testParsed)

/home/emma/.local/lib/python3.6/site-packages/pandas/core/reshape/reshape.py in get_dummies(data, prefix, prefix_sep, dummy_na, columns, sparse, drop_first, dtype)
    859                                     drop_first=drop_first, dtype=dtype)
    860             with_dummies.append(dummy)
--> 861         result = concat(with_dummies, axis=1)
    862     else:
    863         result = _get_dummies_1d(data, prefix, prefix_sep, dummy_na,

/home/emma/.local/lib/python3.6/site-packages/pandas/core/reshape/concat.py in concat(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity, sort, copy)
    226                        keys=keys, levels=levels, names=names,
    227                        verify_integrity=verify_integrity,
--> 228                        copy=copy, sort=sort)
    229     return op.get_result()
    230

/home/emma/.local/lib/python3.6/site-packages/pandas/core/reshape/concat.py in __init__(self, objs, axis, join, join_axes, keys, levels, names, ignore_index, verify_integrity, copy, sort)
    260
    261         if len(objs) == 0:
--> 262             raise ValueError('No objects to concatenate')
    263
    264         if keys is None:

ValueError: No objects to concatenate