The order of the output CSV seems to be wrong

Hello, I would like to report a issue I found with the talos package output of the CSV with my current solution. My python version is python 3.5.2 (default, Oct 8 2019, 13:06:37) on the WSL and the keras is together with TF2.0 in which the model itself works. My talos.__version__ prints ‘0.6.3’ and it is installed via pip. I ran the code converted from the example:

import tensorflow as tf
from tensorflow import keras
import talos

x, y = talos.templates.datasets.iris()
train_num = int(len(x)*0.8)
train_fea = x[:train_num]
train_label = y[:train_num]
val_fea = x[train_num:]
val_label = y[train_num:]

def iris_model(x_train, y_train, x_val, y_val, params):
    model = tf.keras.models.Sequential()                            
    model.add(tf.keras.layers.Dense(params['first_neuron'], input_dim=x_train.shape[1], activation='relu'))
    
    model.add(tf.keras.layers.Dropout(params['dropout']))
    model.add(tf.keras.layers.Dense(y_train.shape[1], activation=params['last_activation']))

    model.compile(optimizer=params['optimizer'](lr=lr_normalizer(params['lr'], params['optimizer'])), loss=params['loss'],
                  metrics=['acc'])

    out = model.fit(x_train, y_train,
                    batch_size=params['batch_size'],
                    epochs=params['epochs'],
                    verbose=0,
                    validation_data=[x_val, y_val])
    
    return out, model

from talos.utils import lr_normalizer
from tensorflow.keras.optimizers import Adam, Nadam
from tensorflow.keras.activations import softmax
from tensorflow.keras.losses import categorical_crossentropy, logcosh

p = {
     'lr': (0.1, 10, 10),
     'first_neuron':[4, 8, 16, 32, 64, 128],
     'batch_size': [2, 3, 4],
     'epochs': [200],
     'dropout': (0, 0.40, 10),
     'optimizer': [Adam, Nadam],
     'loss': ['categorical_crossentropy'],
     'last_activation': ['softmax'],
     'weight_regulizer': [None]
     }


scan_object = talos.Scan(x=train_fea,
                         y=train_label,
                         x_val=val_fea,
                         y_val=val_label,
                         params=p,
                         model=iris_model,
                         fraction_limit=.001,
                         experiment_name='iris',
                         print_params=True)

scan_object.details

The output CSV looks like

round_epochs	loss	val_loss	acc	val_acc	batch_size	dropout	epochs	first_neuron	last_activation	loss	lr	optimizer	weight_regulizer
200	1.916683	2.028089	0.35	0.266667	7.03	categorical_crossentropy	<class ‘tensorflow.python.keras.optimizer_v2.nadam.Nadam’>	200	None	32	softmax	0.28	3
200	3.511631	1.842899	0.291667	0.266667	6.04	categorical_crossentropy	<class ‘tensorflow.python.keras.optimizer_v2.adam.Adam’>	200	None	16	softmax	0.04	2
200	1.131929	1.112004	0.308333	0.266667	0.1	categorical_crossentropy	<class ‘tensorflow.python.keras.optimizer_v2.adam.Adam’>	200	None	16	softmax	0.36	2

for an example. During differenc trials, the titles always keep the same and the values of the parameters part can go wrong in arbitrary orders. After I investigate the code, I have found that in the talos/logging/results.py L21, the looping takes the key of self.round_params and this is a dictionary defined in talos/parameters/ParamSpace.py L187. Changing the looping item into the sorted fixed order list object self._param_dict_keys can solve the problem. According to some brief searching, it seems keep the default order of dictionary is a feature added in python 3.6 and I tested in my python 3.5.2 that it was really the case that the dictionary entries order is not fixed to the default. I hope this change do not cause any potential risks to the packages. Thanks a lot.

[hep-beginner]

Dear mikkokotila, Many thanks for the reply. Sorry for my too simple thinking. I decide to move to python 3.6 and that version works well with talos. This is a very nice and easy to use package.

[denis-sumin]

It it helps, there is the following observation:

If I print a params object from my one-train (one-round) function, the order of params matches the order of the columns in the resulted scan object.

The order of columns, on the other hand, is alphabetical.

The root of this problem is, naturally, the fact that in python <=3.5 the order of dicts is arbitrary while starting from 3.6 the insertion order is preserved (starting from 3.7 it is in the standard).