Bug Fix: Update uplift.pyx with isinstance(v, (int, float, np.number)):

[00helloworld]

Final predictions deviate from training outcomes despite identical input data.
During prediction, the branching logic fails to recognize np.number-type values (e.g., np.float32, np.int64) as numerical features. This occurs because the type check isinstance(v, int) or isinstance(v, float) ignores NumPy numeric types.
isinstance(v, (int, float, np.number)) recognizes:
Python natives: int, float
NumPy types: np.int16/32/64, np.float16/32/64
Pandas-backed: pd.Int8Dtype, etc.

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Pull Request Overview

This PR fixes a bug where uplift tree classification fails during prediction when dealing with NumPy numeric types. The issue occurs because the type checking logic only recognizes Python's native int and float types, causing incorrect branching behavior for NumPy arrays with types like np.float32 or np.int64.

• Updated isinstance checks to include np.number in addition to int and float
• Fixed type recognition in both classifyWithoutMissingData and classifyWithMissingData functions

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[jeongyoonlee]

Thanks for catching this, @00helloworld.

In this case, it'd be cleaner to use isinstance(v, numbers.Number). Can you update the PR?

In [1]: import numpy as np

In [2]: a = np.array([1.], dtype=np.float32)

In [3]: isinstance(a[0], int), isinstance(a[0], float), isinstance(a[0], np.number)
Out[3]: (False, False, True)

In [4]: import numbers

In [5]: isinstance(a[0], numbers.Number)
Out[5]: True

[00helloworld]

Thanks for catching this, @00helloworld.

In this case, it'd be cleaner to use isinstance(v, numbers.Number). Can you update the PR?

In [1]: import numpy as np

In [2]: a = np.array([1.], dtype=np.float32)

In [3]: isinstance(a[0], int), isinstance(a[0], float), isinstance(a[0], np.number)
Out[3]: (False, False, True)

In [4]: import numbers

In [5]: isinstance(a[0], numbers.Number)
Out[5]: True

Thanks for reminding. Updated in a new PR #849