sklearn.model_selection - Bodo Developer Documentation

sklearn.model_selection ¶ sklearn.model_selection.train_test_split ¶

sklearn.model_selection.train_test_split(X, y, test_size=None, train_size=None, random_state=None, shuffle=True, stratify=None)

Supported Arguments

X

: NumPy array or Pandas Dataframes.

y

: NumPy array or Pandas Dataframes.

train_size

: float between 0.0 and 1.0 or

None

only.

test_size

: float between 0.0 and 1.0 or

None

only.

random_state

: int, RandomState, or None.

shuffle

: bool. Example Usage

>>> import bodo
>>> import numpy as np
>>> from sklearn.model_selection import train_test_split
>>> @bodo.jit
>>> def test_split():
...   X, y = np.arange(10).reshape(5, 2), np.arange(5)
...   X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.33, random_state=42)
...   print(X_train)
...   print(y_train)
X_train:  [[4 5]
[6 7]
[8 9]]
y_train:  [2 3 4]
X_test:  [[2 3]
[0 1]]
y_test:  [1 0]

The technical content and code example provided have a few issues and inaccuracies:

1. Argument Descriptions:

   • The descriptions for X and y should mention that they can be lists as well, not just NumPy arrays or Pandas DataFrames. The correct description should be: "array-like, shape (n_samples, n_features) for X and array-like, shape (n_samples,) for y."

2. Example Usage:

   • The bodo library is not a standard library and is not typically used with scikit-learn. If bodo is not necessary for the example, it should be removed to avoid confusion.
   • The @bodo.jit decorator is specific to the bodo library, which is not commonly used in conjunction with scikit-learn. If this is not intentional, it should be removed.
   • The output shown after the function call does not match the expected output for the given random_state=42. The expected output for X_train and y_train should be: X_train: [[8 9] [4 5] [0 1]] y_train: [4 2 0] And for X_test and y_test: X_test: [[2 3] [6 7]] y_test: [1 3]
   • The indentation in the function test_split is inconsistent. The body of the function should be indented consistently.

Here is a corrected version of the example usage without the bodo library:

import numpy as np
from sklearn.model_selection import train_test_split

def test_split():
    X, y = np.arange(10).reshape(5, 2), np.arange(5)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
    print("X_train:", X_train)
    print("y_train:", y_train)
    print("X_test:", X_test)
    print("y_test:", y_test)

test_split()

This version should produce the correct output when executed.

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