如何在不使用train_test_split（）的情况下拆分数据集？

Question

I need to split my dataset into training and testing. I need the last 20% of the values for testing and the first 80% for training. I have currently used the 'train_test_split()' but it picks the data randomly instead of the last 20%. How can I get the last 20% for testing and the first 80% for training? My code is as follows:

numpy_array = df.as_matrix()
X = numpy_array[:, 1:26]
y = numpy_array[:, 0]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=20) #I do not want the data to be random.

Thanks

Answer 1

train_pct_index = int(0.8 * len(X))
X_train, X_test = X[:train_pct_index], X[train_pct_index:]
y_train, y_test = y[:train_pct_index], y[train_pct_index:]

It's one of those situations where it's just better not to involve sklearn helpers. Very straightforward, readable, and not dependent on knowing internal options of sklearn helpers, which code readers may not have experience with.

Answer 2

I think this Stackoverflow topic answers your question :

How to get a non-shuffled train_test_split in sklearn

And especially this piece of text :

in scikit-learn version 0.19, you can pass the parameter shuffle=False to train_test_split to obtain a non-shuffled split.

From the documentation :

shuffle : boolean, optional (default=True)

Whether or not to shuffle the data before splitting. If shuffle=False then >stratify must be None.

Please tell me if I didn't understand your question correctly