Remove useless code in doctests (#7733)

* refactor: Fix matrix display deprecation

* refactor: Remove useless `print` and `pass` statements

* revert: Replace broken doctests

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* revert: Fix failing doctests

* chore: Satisfy pre-commit

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

backtracking/hamiltonian_cycle.py

@@ -71,7 +71,7 @@ def util_hamilton_cycle(graph: list[list[int]], path: list[int], curr_ind: int)
     >>> curr_ind = 1
     >>> util_hamilton_cycle(graph, path, curr_ind)
     True
-    >>> print(path)
+    >>> path
     [0, 1, 2, 4, 3, 0]
 
     Case 2: Use exact graph as in previous case, but in the properties taken from
@@ -85,7 +85,7 @@ def util_hamilton_cycle(graph: list[list[int]], path: list[int], curr_ind: int)
     >>> curr_ind = 3
     >>> util_hamilton_cycle(graph, path, curr_ind)
     True
-    >>> print(path)
+    >>> path
     [0, 1, 2, 4, 3, 0]
     """
 

computer_vision/flip_augmentation.py

@@ -22,7 +22,6 @@ def main() -> None:
     Get images list and annotations list from input dir.
     Update new images and annotations.
     Save images and annotations in output dir.
-    >>> pass  # A doctest is not possible for this function.
     """
     img_paths, annos = get_dataset(LABEL_DIR, IMAGE_DIR)
     print("Processing...")
@@ -48,7 +47,6 @@ def get_dataset(label_dir: str, img_dir: str) -> tuple[list, list]:
     - label_dir <type: str>: Path to label include annotation of images
     - img_dir <type: str>: Path to folder contain images
     Return <type: list>: List of images path and labels
-    >>> pass  # A doctest is not possible for this function.
     """
     img_paths = []
     labels = []
@@ -88,7 +86,6 @@ def update_image_and_anno(
         - new_imgs_list <type: narray>: image after resize
         - new_annos_lists <type: list>: list of new annotation after scale
         - path_list <type: list>: list the name of image file
-    >>> pass  # A doctest is not possible for this function.
     """
     new_annos_lists = []
     path_list = []

computer_vision/mosaic_augmentation.py

@@ -23,7 +23,6 @@ def main() -> None:
     Get images list and annotations list from input dir.
     Update new images and annotations.
     Save images and annotations in output dir.
-    >>> pass  # A doctest is not possible for this function.
     """
     img_paths, annos = get_dataset(LABEL_DIR, IMG_DIR)
     for index in range(NUMBER_IMAGES):
@@ -60,7 +59,6 @@ def get_dataset(label_dir: str, img_dir: str) -> tuple[list, list]:
     - label_dir <type: str>: Path to label include annotation of images
     - img_dir <type: str>: Path to folder contain images
     Return <type: list>: List of images path and labels
-    >>> pass  # A doctest is not possible for this function.
     """
     img_paths = []
     labels = []
@@ -105,7 +103,6 @@ def update_image_and_anno(
         - output_img <type: narray>: image after resize
         - new_anno <type: list>: list of new annotation after scale
         - path[0] <type: string>: get the name of image file
-    >>> pass  # A doctest is not possible for this function.
     """
     output_img = np.zeros([output_size[0], output_size[1], 3], dtype=np.uint8)
     scale_x = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])

data_structures/heap/binomial_heap.py

@@ -71,7 +71,7 @@ class BinomialHeap:
     ...     first_heap.insert(number)
 
     Size test
-    >>> print(first_heap.size)
+    >>> first_heap.size
     30
 
     Deleting - delete() test
@@ -97,7 +97,7 @@ class BinomialHeap:
                   #    # #   #
 
     preOrder() test
-    >>> print(second_heap.preOrder())
+    >>> second_heap.preOrder()
     [(17, 0), ('#', 1), (31, 1), (20, 2), ('#', 3), ('#', 3), (34, 2), ('#', 3), ('#', 3)]
 
     printing Heap - __str__() test

data_structures/heap/heap.py

@@ -9,20 +9,20 @@ class Heap:
     >>> unsorted = [103, 9, 1, 7, 11, 15, 25, 201, 209, 107, 5]
     >>> h = Heap()
     >>> h.build_max_heap(unsorted)
-    >>> print(h)
+    >>> h
     [209, 201, 25, 103, 107, 15, 1, 9, 7, 11, 5]
     >>>
     >>> h.extract_max()
     209
-    >>> print(h)
+    >>> h
     [201, 107, 25, 103, 11, 15, 1, 9, 7, 5]
     >>>
     >>> h.insert(100)
-    >>> print(h)
+    >>> h
     [201, 107, 25, 103, 100, 15, 1, 9, 7, 5, 11]
     >>>
     >>> h.heap_sort()
-    >>> print(h)
+    >>> h
     [1, 5, 7, 9, 11, 15, 25, 100, 103, 107, 201]
     """
 

data_structures/heap/min_heap.py

@@ -27,7 +27,7 @@ class MinHeap:
     >>> myMinHeap.decrease_key(b, -17)
     >>> print(b)
     Node(B, -17)
-    >>> print(myMinHeap["B"])
+    >>> myMinHeap["B"]
     -17
     """
 

data_structures/linked_list/skip_list.py

@@ -443,4 +443,7 @@ def main():
 
 
 if __name__ == "__main__":
+    import doctest
+
+    doctest.testmod()
     main()

graphs/gale_shapley_bigraph.py

@@ -17,7 +17,7 @@ def stable_matching(
 
     >>> donor_pref = [[0, 1, 3, 2], [0, 2, 3, 1], [1, 0, 2, 3], [0, 3, 1, 2]]
     >>> recipient_pref = [[3, 1, 2, 0], [3, 1, 0, 2], [0, 3, 1, 2], [1, 0, 3, 2]]
-    >>> print(stable_matching(donor_pref, recipient_pref))
+    >>> stable_matching(donor_pref, recipient_pref)
     [1, 2, 3, 0]
     """
     assert len(donor_pref) == len(recipient_pref)

graphs/graph_list.py

@@ -18,7 +18,7 @@ class GraphAdjacencyList(Generic[T]):
 
     Directed graph example:
     >>> d_graph = GraphAdjacencyList()
-    >>> d_graph
+    >>> print(d_graph)
     {}
     >>> d_graph.add_edge(0, 1)
     {0: [1], 1: []}
@@ -26,7 +26,7 @@ class GraphAdjacencyList(Generic[T]):
     {0: [1], 1: [2, 4, 5], 2: [], 4: [], 5: []}
     >>> d_graph.add_edge(2, 0).add_edge(2, 6).add_edge(2, 7)
     {0: [1], 1: [2, 4, 5], 2: [0, 6, 7], 4: [], 5: [], 6: [], 7: []}
-    >>> print(d_graph)
+    >>> d_graph
     {0: [1], 1: [2, 4, 5], 2: [0, 6, 7], 4: [], 5: [], 6: [], 7: []}
     >>> print(repr(d_graph))
     {0: [1], 1: [2, 4, 5], 2: [0, 6, 7], 4: [], 5: [], 6: [], 7: []}
@@ -68,7 +68,7 @@ class GraphAdjacencyList(Generic[T]):
      {'a': ['b'], 'b': ['a']}
      >>> char_graph.add_edge('b', 'c').add_edge('b', 'e').add_edge('b', 'f')
      {'a': ['b'], 'b': ['a', 'c', 'e', 'f'], 'c': ['b'], 'e': ['b'], 'f': ['b']}
-     >>> print(char_graph)
+     >>> char_graph
      {'a': ['b'], 'b': ['a', 'c', 'e', 'f'], 'c': ['b'], 'e': ['b'], 'f': ['b']}
     """
 

graphs/minimum_spanning_tree_prims2.py

@@ -69,16 +69,16 @@ class MinPriorityQueue(Generic[T]):
     >>> queue.push(3, 4000)
     >>> queue.push(4, 3000)
 
-    >>> print(queue.extract_min())
+    >>> queue.extract_min()
     2
 
     >>> queue.update_key(4, 50)
 
-    >>> print(queue.extract_min())
+    >>> queue.extract_min()
     4
-    >>> print(queue.extract_min())
+    >>> queue.extract_min()
     1
-    >>> print(queue.extract_min())
+    >>> queue.extract_min()
     3
     """
 

graphs/random_graph_generator.py

@@ -53,7 +53,7 @@ def complete_graph(vertices_number: int) -> dict:
     @input: vertices_number (number of vertices),
             directed (False if the graph is undirected, True otherwise)
     @example:
-    >>> print(complete_graph(3))
+    >>> complete_graph(3)
     {0: [1, 2], 1: [0, 2], 2: [0, 1]}
     """
     return {

machine_learning/local_weighted_learning/local_weighted_learning.py

@@ -71,7 +71,6 @@ def local_weight_regression(
 def load_data(dataset_name: str, cola_name: str, colb_name: str) -> np.mat:
     """
     Function used for loading data from the seaborn splitting into x and y points
-    >>> pass # this function has no doctest
     """
     import seaborn as sns
 
@@ -112,7 +111,6 @@ def plot_preds(
 ) -> plt.plot:
     """
     This function used to plot predictions and display the graph
-    >>> pass #this function has no doctest
     """
     xsort = training_data_x.copy()
     xsort.sort(axis=0)

maths/polynomial_evaluation.py

@@ -45,7 +45,7 @@ if __name__ == "__main__":
     >>> poly = (0.0, 0.0, 5.0, 9.3, 7.0)  # f(x) = 7.0x^4 + 9.3x^3 + 5.0x^2
     >>> x = -13.0
     >>> # f(-13) = 7.0(-13)^4 + 9.3(-13)^3 + 5.0(-13)^2 = 180339.9
-    >>> print(evaluate_poly(poly, x))
+    >>> evaluate_poly(poly, x)
     180339.9
     """
     poly = (0.0, 0.0, 5.0, 9.3, 7.0)

maths/radix2_fft.py

@@ -39,7 +39,7 @@ class FFT:
     >>> x = FFT(A, B)
 
     Print product
-    >>> print(x.product)  # 2x + 3x^2 + 8x^3 + 4x^4 + 6x^5
+    >>> x.product  # 2x + 3x^2 + 8x^3 + 4x^4 + 6x^5
     [(-0+0j), (2+0j), (3+0j), (8+0j), (6+0j), (8+0j)]
 
     __str__ test

matrix/matrix_class.py

@@ -21,9 +21,9 @@ class Matrix:
      [7. 8. 9.]]
 
     Matrix rows and columns are available as 2D arrays
-    >>> print(matrix.rows)
+    >>> matrix.rows
     [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
-    >>> print(matrix.columns())
+    >>> matrix.columns()
     [[1, 4, 7], [2, 5, 8], [3, 6, 9]]
 
     Order is returned as a tuple
@@ -55,7 +55,7 @@ class Matrix:
     [[-3. 6. -3.]
      [6. -12. 6.]
      [-3. 6. -3.]]
-    >>> print(matrix.inverse())
+    >>> matrix.inverse()
     Traceback (most recent call last):
         ...
     TypeError: Only matrices with a non-zero determinant have an inverse

searches/simple_binary_search.py

@@ -13,25 +13,25 @@ from __future__ import annotations
 def binary_search(a_list: list[int], item: int) -> bool:
     """
     >>> test_list = [0, 1, 2, 8, 13, 17, 19, 32, 42]
-    >>> print(binary_search(test_list, 3))
+    >>> binary_search(test_list, 3)
     False
-    >>> print(binary_search(test_list, 13))
+    >>> binary_search(test_list, 13)
     True
-    >>> print(binary_search([4, 4, 5, 6, 7], 4))
+    >>> binary_search([4, 4, 5, 6, 7], 4)
     True
-    >>> print(binary_search([4, 4, 5, 6, 7], -10))
+    >>> binary_search([4, 4, 5, 6, 7], -10)
     False
-    >>> print(binary_search([-18, 2], -18))
+    >>> binary_search([-18, 2], -18)
     True
-    >>> print(binary_search([5], 5))
+    >>> binary_search([5], 5)
     True
-    >>> print(binary_search(['a', 'c', 'd'], 'c'))
+    >>> binary_search(['a', 'c', 'd'], 'c')
     True
-    >>> print(binary_search(['a', 'c', 'd'], 'f'))
+    >>> binary_search(['a', 'c', 'd'], 'f')
     False
-    >>> print(binary_search([], 1))
+    >>> binary_search([], 1)
     False
-    >>> print(binary_search([-.1, .1 , .8], .1))
+    >>> binary_search([-.1, .1 , .8], .1)
     True
     >>> binary_search(range(-5000, 5000, 10), 80)
     True

sorts/bitonic_sort.py

@@ -16,19 +16,19 @@ def comp_and_swap(array: list[int], index1: int, index2: int, direction: int) ->
 
     >>> arr = [12, 42, -21, 1]
     >>> comp_and_swap(arr, 1, 2, 1)
-    >>> print(arr)
+    >>> arr
     [12, -21, 42, 1]
 
     >>> comp_and_swap(arr, 1, 2, 0)
-    >>> print(arr)
+    >>> arr
     [12, 42, -21, 1]
 
     >>> comp_and_swap(arr, 0, 3, 1)
-    >>> print(arr)
+    >>> arr
     [1, 42, -21, 12]
 
     >>> comp_and_swap(arr, 0, 3, 0)
-    >>> print(arr)
+    >>> arr
     [12, 42, -21, 1]
     """
     if (direction == 1 and array[index1] > array[index2]) or (
@@ -46,11 +46,11 @@ def bitonic_merge(array: list[int], low: int, length: int, direction: int) -> No
 
     >>> arr = [12, 42, -21, 1]
     >>> bitonic_merge(arr, 0, 4, 1)
-    >>> print(arr)
+    >>> arr
     [-21, 1, 12, 42]
 
     >>> bitonic_merge(arr, 0, 4, 0)
-    >>> print(arr)
+    >>> arr
     [42, 12, 1, -21]
     """
     if length > 1:

sorts/normal_distribution_quick_sort.md

@@ -17,8 +17,8 @@ The array elements are taken from a Standard Normal Distribution, having mean =
 >>> mu, sigma = 0, 1 # mean and standard deviation
 >>> X = np.random.normal(mu, sigma, p)
 >>> np.save(outfile, X)
->>> print('The array is')
->>> print(X)
+>>> 'The array is'
+>>> X
 
 ```
 

sorts/recursive_insertion_sort.py

@@ -14,17 +14,17 @@ def rec_insertion_sort(collection: list, n: int):
 
     >>> col = [1, 2, 1]
     >>> rec_insertion_sort(col, len(col))
-    >>> print(col)
+    >>> col
     [1, 1, 2]
 
     >>> col = [2, 1, 0, -1, -2]
     >>> rec_insertion_sort(col, len(col))
-    >>> print(col)
+    >>> col
     [-2, -1, 0, 1, 2]
 
     >>> col = [1]
     >>> rec_insertion_sort(col, len(col))
-    >>> print(col)
+    >>> col
     [1]
     """
     # Checks if the entire collection has been sorted
@@ -41,17 +41,17 @@ def insert_next(collection: list, index: int):
 
     >>> col = [3, 2, 4, 2]
     >>> insert_next(col, 1)
-    >>> print(col)
+    >>> col
     [2, 3, 4, 2]
 
     >>> col = [3, 2, 3]
     >>> insert_next(col, 2)
-    >>> print(col)
+    >>> col
     [3, 2, 3]
 
     >>> col = []
     >>> insert_next(col, 1)
-    >>> print(col)
+    >>> col
     []
     """
     # Checks order between adjacent elements

web_programming/reddit.py

@@ -23,8 +23,6 @@ def get_subreddit_data(
     limit : Number of posts to fetch
     age : ["new", "top", "hot"]
     wanted_data : Get only the required data in the list
-
-    >>> pass
     """
     wanted_data = wanted_data or []
     if invalid_search_terms := ", ".join(sorted(set(wanted_data) - valid_terms)):

web_programming/search_books_by_isbn.py

@@ -19,7 +19,6 @@ def get_openlibrary_data(olid: str = "isbn/0140328726") -> dict:
     {'publishers': ['Puffin'], 'number_of_pages': 96, 'isbn_10': ['0140328726'], ...
     # >>> get_openlibrary_data(olid='/authors/OL7353617A')  # doctest: +ELLIPSIS
     {'name': 'Adrian Brisku', 'created': {'type': '/type/datetime', ...
-    >>> pass  # Placate https://github.com/apps/algorithms-keeper
     """
     new_olid = olid.strip().strip("/")  # Remove leading/trailing whitespace & slashes
     if new_olid.count("/") != 1:
@@ -30,8 +29,6 @@ def get_openlibrary_data(olid: str = "isbn/0140328726") -> dict:
 def summarize_book(ol_book_data: dict) -> dict:
     """
     Given Open Library book data, return a summary as a Python dict.
-
-    >>> pass  # Placate https://github.com/apps/algorithms-keeper
     """
     desired_keys = {
         "title": "Title",