scikit-learn cross validation custom splits for time series data

Question

I\'d like to use scikit-learn\'s GridSearchCV to determine some hyper parameters for a random forest model. My data is time dependent and looks something like

i         


        

Answer 1

You just have to pass an iterable with the splits to GridSearchCV. This split should have the following format:

[
 (split1_train_idxs, split1_test_idxs),
 (split2_train_idxs, split2_test_idxs),
 (split3_train_idxs, split3_test_idxs),
 ...
]

To get the idxs you can do something like this:

groups = df.groupby(df.date.dt.year).groups
# {2012: [0, 1], 2013: [2], 2014: [3], 2015: [4, 5]}
sorted_groups = [value for (key, value) in sorted(groups.items())] 
# [[0, 1], [2], [3], [4, 5]]

cv = [(sorted_groups[i] + sorted_groups[i+1], sorted_groups[i+2])
      for i in range(len(sorted_groups)-2)]

This looks like this:

[([0, 1, 2], [3]),  # idxs of first split as (train, test) tuple
 ([2, 3], [4, 5])]  # idxs of second split as (train, test) tuple

Then you can do:

GridSearchCV(estimator, param_grid, cv=cv, ...)