Type stubs for Python machine learning libraries
Project description
Mypy type stubs for numpy, pandas and matplotlib
This is a PEP-561-compliant stub-only package which provides type information for matplotlib, numpy and pandas. The mypy type checker can recognize the types in these packages by installing this package:
pip install data-science-types
To get the most up-to-date version, install it from GitHub directly:
pip install git+https://github.com/predictive-analytics-lab/data-science-types
Or clone the repository somewhere and do pip install -e .
.
There is also minor support for Tensorflow and Tensorflow Probability.
Examples
These are the kinds of things that can be checked:
Array creation
import numpy as np
arr1: np.ndarray[np.int64] = np.array([3, 7, 39, -3]) # OK
arr2: np.ndarray[np.int32] = np.array([3, 7, 39, -3]) # Type error
arr3: np.ndarray[np.int32] = np.array([3, 7, 39, -3], dtype=np.int32) # OK
arr4: np.ndarray[float] = np.array([3, 7, 39, -3], dtype=float) # Type error: the type of ndarray can not be just "float"
arr5: np.ndarray[np.float64] = np.array([3, 7, 39, -3], dtype=float) # OK
Operations
import numpy as np
arr1: np.ndarray[np.int64] = np.array([3, 7, 39, -3])
arr2: np.ndarray[np.int64] = np.array([4, 12, 9, -1])
result1: np.ndarray[np.int64] = np.divide(arr1, arr2) # Type error
result2: np.ndarray[np.float64] = np.divide(arr1, arr2) # OK
compare: np.ndarray[np.bool_] = (arr1 == arr2)
Reductions
import numpy as np
arr: np.ndarray[np.float64] = np.array([[1.3, 0.7], [-43.0, 5.6]])
sum1: int = np.sum(arr) # Type error
sum2: np.float64 = np.sum(arr) # OK
sum3: float = np.sum(arr) # Also OK: np.float64 is a subclass of float
sum4: np.ndarray[np.float64] = np.sum(arr, axis=0) # OK
# the same works with np.max, np.min and np.prod
Philosophy
The goal is not to recreate the class hierarchy exactly. The goal is to have useful checks on our code. Often the actual API in the libraries is more permissive than the type signatures in our stubs; but this is (usually) a feature and not a bug.
Contributing
We always welcome contributions. All pull requests are subject to CI checks. We check for compliance with Mypy and that the file formatting conforms to our Black specification.
You can install these dev dependencies via
pip install -e .[dev]
Checking compliance with Mypy
The settings for Mypy are specified in the mypy.ini
file in the repository.
Just running
mypy tests
from the base directory should take these settings into account. We enforce 0 mypy errors.
Formatting with black
We use Black to format the stub files.
First install black
and then run
black -l 100 -t py36 -S .
from the base directory.
License
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