We Keep Coding

How do i remove single element from counter and return is as a list?

Given a list and a number, create a new list that contains each number of list at most N times, without reordering.
For example if the input number is 2, and the input list is [1,2,3,1,2,1,2,3], you take [1,2,3,1,2], drop the next [1,2] since this would lead to 1 and 2 being in the result 3 times, and then take 3, which leads to [1,2,3,1,2,3].
With list [20,37,20,21] and number 1, the result would be [20,37,21]
For example i had this like my attempt
from collections import Counter
def delete_nth(order, max_e):
c = Counter(order)
result = []
for key, value in c.items():
if value > max_e:
value = value - 1
return [x for x in c.items()]
and output is
dict_items([(20, 2), (37, 1), (21, 1)])
dict_items([(20, 2), (37, 1), (21, 1)])
From list [20, 37, 20, 21], 1 you get: [(20, 2), (37, 1), (21, 1)] should equal [20, 37, 21]
Log
dict_items([(1, 2), (3, 2), (7, 1), (2, 4)])
dict_items([(1, 2), (3, 2), (7, 1), (2, 4)])
From list [1, 1, 3, 3, 7, 2, 2, 2, 2], 3 you get: [(1, 2), (3, 2), (7, 1), (2, 4)] should equal [1, 1, 3, 3, 7, 2, 2, 2]
Log
dict_items([(1, 5), (2, 4), (3, 4), (4, 1), (5, 1)])
dict_items([(1, 5), (2, 4), (3, 4), (4, 1), (5, 1)])
dict_items([(1, 5), (2, 4), (3, 4), (4, 1), (5, 1)])
dict_items([(1, 5), (2, 4), (3, 4), (4, 1), (5, 1)])
dict_items([(1, 5), (2, 4), (3, 4), (4, 1), (5, 1)])
dict_items([(1, 5), (2, 4), (3, 4), (4, 1), (5, 1)])
From list [1, 2, 3, 1, 1, 2, 1, 2, 3, 3, 2, 4, 5, 3, 1], 3 you get: [(1, 5), (2, 4), (3, 4), (4, 1), (5, 1)] should equal [1, 2, 3, 1, 1, 2, 2, 3, 3, 4, 5]
From list [1, 1, 1, 1, 1], 5 you get: [(1, 5)] should equal [1, 1, 1, 1, 1]
Test Passed
Random tests
Testing for delete_nth([21, 45, 29, 29, 29, 6, 24, 45, 45, 45, 29, 21, 29, 24, 6, 45, 21, 38, 45, 21, 24, 45, 24, 6, 6, 29, 45, 29, 6, 45, 21, 29], 4)
Log
dict_items([(21, 5), (45, 9), (29, 8), (6, 5), (24, 4), (38, 1)])
dict_items([(21, 5), (45, 9), (29, 8), (6, 5), (24, 4), (38, 1)])
dict_items([(21, 5), (45, 9), (29, 8), (6, 5), (24, 4), (38, 1)])
dict_items([(21, 5), (45, 9), (29, 8), (6, 5), (24, 4), (38, 1)])
dict_items([(21, 5), (45, 9), (29, 8), (6, 5), (24, 4), (38, 1)])
dict_items([(21, 5), (45, 9), (29, 8), (6, 5), (24, 4), (38, 1)])
dict_items([(21, 5), (45, 9), (29, 8), (6, 5), (24, 4), (38, 1)])
dict_items([(21, 5), (45, 9), (29, 8), (6, 5), (24, 4), (38, 1)])
It should work on random inputs too!: [(21, 5), (45, 9), (29, 8), (6, 5), (24, 4), (38, 1)] should equal [21, 45, 29, 29, 29, 6, 24, 45, 45, 45, 29, 21, 24, 6, 21, 38, 21, 24, 24, 6, 6]
Testing for delete_nth([18, 18, 18, 18, 6, 23, 18, 46, 18, 31, 18, 31, 23, 18, 18, 31], 7)
Log
dict_items([(18, 9), (6, 1), (23, 2), (46, 1), (31, 3)])
dict_items([(18, 9), (6, 1), (23, 2), (46, 1), (31, 3)])
It should work on random inputs too!: [(18, 9), (6, 1), (23, 2), (46, 1), (31, 3)] should equal [18, 18, 18, 18, 6, 23, 18, 46, 18, 31, 18, 31, 23, 31]

Doing value = value - 1 only decrements the local variable value inside the loop; it doesn't decrement the actual value inside the Counter.
I might try building the list inside the loop as you decrement the counter:
from collections import Counter
def delete_nth(order, max_e):
# Build counter with maximum count max_e.
counts = {
i: min(max_e, count)
for i, count in Counter(order).items()
}
# Build result by iterating over original list.
result = []
for i in order:
if not counts[i]:
continue
counts[i] -= 1
result.append(i)
return result
assert delete_nth([1,2,3,1,2,1,2,3], 2) == [1,2,3,1,2,3]

Perhaps something like this?
def keep_at_most_n(arr, n):
count = {}
output = []
for element in arr:
if count.get(element, 0) < n:
output.append(element)
count[element] = count.get(element, 0) + 1
return output

lda model corpus TypeError: '<' not supported between instances of 'int' and 'tuple'

I am trying to find the dominant topic in each article through following:
lda_model is the model, corpus consist of id2word.doc2bow(text), texts is text data on which model is applied.
def format_topics_sentences(ldamodel=lda_model, corpus=corpus, texts=data):
# Init output
sent_topics_df = pd.DataFrame()
# Get main topic in each document
for i, row in enumerate(ldamodel[corpus]):
row = sorted(row, key=lambda x: (x[1]), reverse=True)
# Get the Dominant topic, Perc Contribution and Keywords for each document
for j, (topic_num, prop_topic) in enumerate(row):
if j == 0: # => dominant topic
wp = ldamodel.show_topic(topic_num)
topic_keywords = ", ".join([word for word, prop in wp])
sent_topics_df = sent_topics_df.append(pd.Series([int(topic_num), round(prop_topic,4), topic_keywords]), ignore_index=True)
else:
break
sent_topics_df.columns = ['Dominant_Topic', 'Perc_Contribution', 'Topic_Keywords']
# Add original text to the end of the output
contents = pd.Series(texts)
sent_topics_df = pd.concat([sent_topics_df, contents], axis=1)
return(sent_topics_df)
df_topic_sents_keywords = format_topics_sentences(ldamodel=optimal_model, corpus=corpus, texts=data)
# Format
df_dominant_topic = df_topic_sents_keywords.reset_index()
df_dominant_topic.columns = ['Document_No', 'Dominant_Topic', 'Topic_Perc_Contrib', 'Keywords', 'Text']
# Show
df_dominant_topic.head(10)
For the following block it seems I am getting the error:
for i, row in enumerate(model_gr[corpus][0:1]):
print(row)
row = sorted(row, key=lambda x: (x[1]), reverse=True)
Getting the following error:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Input In [186], in <cell line: 1>()
1 for i, row in enumerate(lda_model_gr[corpus]):
2 print(row)
----> 3 row = sorted(row, key=lambda x: (x[1]), reverse=True)
TypeError: '<' not supported between instances of 'int' and 'tuple'
It will be helpful, if the error can be resolved.
print(row) produces:
([(5, 0.28596485), (6, 0.16911359), (8, 0.030976538), (10, 0.011954311), (11, 0.068368636), (13, 0.34729925), (15, 0.016343366), (17, 0.05741114)], [(0, [6, 11]), (1, [5]), (2, [5]), (3, [6, 5]), (4, [13, 6, 5, 11, 10]), (5, [13, 5, 17, 10, 15, 19]), (6, [5, 13, 11, 17]), (7, [5, 13, 11]), (8, [5, 13, 17, 10]), (9, [5, 13, 8, 6, 17]), (10, [13, 6, 5, 11, 17, 10]), (11, [13, 6, 5, 11, 10, 17, 15, 16, 19]), (12, [5, 13, 11, 19, 10]), (13, [5, 13]), (14, [8]), (15, [13, 5, 11, 10]), (16, [6]), (17, [13, 5, 19, 11]), (18, [11, 13, 19]), (19, [5]), (20, [5, 13]), (21, [13, 6, 11, 15, 19]), (22, [13, 17, 6, 15, 11, 10, 19]), (23, [5, 11, 13, 16, 15]), (24, [5, 13, 6, 15, 19]), (25, [6, 5, 13]), (26, [6, 5, 13, 15]), (27, [11, 6]), (28, [13, 5, 17, 15, 11, 19]), (29, [17, 10, 13]), (30, [13, 6, 5, 10, 15]), (31, [6, 5, 13, 11]), (32, [5, 11]), (33, [5]), (34, [13, 10, 5, 11, 17, 6, 16]), (35, [5, 17, 13, 11]), (36, [5]), (37, []), (38, [6, 13, 5, 11]), (39, [13, 8]), (40, [13, 8]), (41, [10, 5, 13]), (42, [13, 5, 11, 6]), (43, []), (44, [6, 16, 19]), (45, [5, 6, 11]), (46, [5, 13]), (47, [5, 19, 11]), (48, [13, 15]), (49, [13, 15]), (50, [5]), (51, [5, 17]), (52, []), (53, [13, 6, 5, 2]), (54, []), (55, [6, 13, 11, 10]), (56, [13, 5]), (57, [11]), (58, [15]), (59, [5, 15, 16]), (60, [6]), (61, [6, 13, 5, 11, 17, 10]), (62, [17, 13, 8, 15, 10]), (63, [8]), (64, [13, 6, 15, 19, 11]), (65, [6]), (66, [5, 13]), (67, [13, 17, 5]), (68, [6, 13, 5, 11, 19, 10]), (69, [5, 13, 17]), (70, [13, 5, 11, 15, 10, 17]), (71, [13, 6, 11, 15, 19, 10]), (72, [17, 15, 19, 11, 10, 13, 16]), (73, [6, 5]), (74, [5, 17, 13, 11]), (75, []), (76, [11, 13, 5, 17, 10, 6, 19]), (77, [13]), (78, [5, 13, 17, 6, 15, 10]), (79, [13, 5, 6, 11, 19]), (80, [6]), (81, [5, 13, 11, 17, 19]), (82, [6, 13, 11, 8]), (83, [13, 6, 11, 5]), (84, [11, 13, 17, 15]), (85, [6, 8, 13, 17, 10]), (86, [13, 17, 19, 10]), (87, [5]), (88, [17, 13, 6, 11]), (89, [5, 13]), (90, [13, 5, 8, 19]), (91, []), (92, [6, 17]), (93, [6, 5, 11, 10]), (94, [13]), (95, [5, 13, 11, 15, 10, 17]), (96, [5]), (97, [6, 13, 11, 5]), (98, [5, 13]), (99, [5, 15, 17, 11]), (100, [11, 17, 13, 15]), (101, [13, 11, 10, 17]), (102, [13, 5]), (103, []), (104, [13, 6, 11, 5, 15, 19]), (105, [13, 11, 17, 10, 8, 15]), (106, [6, 5, 8, 13, 11]), (107, [5, 13, 15, 10, 17]), (108, [13, 19]), (109, []), (110, [6, 5, 11, 13]), (111, [6, 13, 5, 17, 15]), (112, [5]), (113, [5, 13, 11, 6, 15]), (114, [5, 13, 11]), (115, [6]), (116, [13, 17, 11, 10, 5, 19]), (117, [5, 11, 13, 10]), (118, [6, 17, 15]), (119, [5]), (120, [5, 19]), (121, []), (122, [17, 13, 6, 19]), (123, [6, 5, 13, 11]), (124, [13, 15]), (125, [6, 13]), (126, [5, 6, 17]), (127, [6, 5, 11, 13]), (128, [13, 5, 6, 17, 10, 15, 11]), (129, [5, 6, 11, 13, 19]), (130, [5, 6, 10]), (131, [13, 6, 5, 17, 11, 10, 15]), (132, [8, 5]), (133, [16]), (134, [6, 10]), (135, [13, 5, 6, 17, 19, 11]), (136, [13, 11, 17]), (137, [13, 19, 15, 2]), (138, [17, 13, 11, 5]), (139, [13, 6, 15, 11, 19]), (140, [13, 10]), (141, [13]), (142, [13, 11, 17, 5]), (143, [10]), (144, [6, 5, 11, 10, 13]), (145, [5]), (146, [13, 5]), (147, [6, 5, 13, 10]), (148, [13, 15]), (149, [5, 6, 15, 13, 11]), (150, [13]), (151, [5]), (152, []), (153, [5, 8, 6, 13]), (154, [5, 13, 6, 11, 15]), (155, [13, 6, 5, 11, 10]), (156, [6, 5, 13, 11]), (157, [6, 5]), (158, [6, 13, 5, 11]), (159, [17, 10]), (160, [6, 13, 15, 10]), (161, [5, 17, 13, 10]), (162, [13, 10, 17]), (163, [13, 6, 5, 17, 15, 10]), (164, [5, 11, 17, 13, 6, 15]), (165, [6, 13]), (166, [5, 6, 13]), (167, [5]), (168, [5]), (169, [13, 8]), (170, [13, 17, 15]), (171, [5, 13, 11, 15, 19]), (172, [13, 5, 6, 15]), (173, [5]), (174, [5, 11, 13, 15, 10]), (175, [13, 17, 11, 15, 6]), (176, [13, 5, 11]), (177, [6, 5, 13]), (178, [13, 6]), (179, [17]), (180, [8, 19]), (181, []), (182, [5, 6, 13]), (183, [5]), (184, [13, 5, 6, 8, 11, 17, 10, 15]), (185, [6, 13, 11]), (186, [13, 5, 15, 17, 6, 11]), (187, [5, 6, 11, 13]), (188, [6, 11, 5]), (189, [17, 5, 13, 10]), (190, [11, 15, 19]), (191, [5]), (192, [6, 5]), (193, [13, 17, 11, 5]), (194, [5, 13, 11]), (195, [6, 13, 5, 17, 10, 15, 11]),, (275, [13, 5]), (276, [5, 13, 6]), (277, [13, 11, 17, 10]), (278, [13, 11, 19, 15]), (279, [13, 5, 19, 11, 10]), (280, [11, 5]), (281, [8, 5]), (282, []), (283, [5, 13, 6, 11]), (284, [5, 17, 10]), (285, [5, 6]), (286, [6, 5, 8, 13, 11, 16]), (287, [5, 8]), (288, [6, 13, 17, 5, 11, 10]), (289, [13, 15]), (290, [8]), (291, [5, 6, 13, 10]), (292, [6, 13, 5]), (293, [13, 5, 17]), (294, []), (295, [13, 11]), (296, [6, 5, 13, 11, 15, 10]), (297, [13, 5, 6, 11, 19]), (298, [6, 13, 8, 11]), (299, [5, 8, 13]), (300, [13, 11]), (301, [13, 17]), (302, [5, 6, 13, 11]), (303, [8]), (304, [13, 6, 11]), (305, [5]), (306, [6, 11, 10]), (307, [5, 6, 13, 11]), (308, [13]), (309, [17, 19, 15]), (310, [5, 13]), (311, [13, 5, 11, 17]), (312, [11, 15]), (313, [13, 5, 19]), (314, [6, 13, 5, 11, 10]), (315, [11, 8, 5, 10, 13, 17]), (316, [13, 17]), (317, [6, 5, 13, 11]), (318, [8, 17, 13, 11]), (319, [5, 13, 8, 17, 10]), (320, []), (321, [15]), (322, [6, 5, 11, 10]), (323, [5, 13]), (324, []), (325, [17, 5]), (326, [5, 13, 17, 10, 8]), (327, [6, 13, 5, 11, 15, 10]), (328, [8, 9]), (329, []), (330, [5, 17]), (331, [13, 17, 15]), (332, [5, 17, 13, 10]), (333, [6, 5, 13]), (334, [6, 5]), (335, [6, 5, 10]), (336, [13, 6, 17, 5, 15, 10]), (337, [13, 8, 5]), (338, [11]), (339, [5]), (340, [5]), (341, [6, 5, 13, 11]), (342, [5, 17, 13, 19, 10]), (343, [15]), (344, [8, 13]), (345, [13]), (346, [16]), (347, [5, 13, 17, 6, 19]), (348, [15]), (349, [17, 5]), (350, [19]), (351, [5, 17, 13, 10]), (352, [5, 6, 13, 15]), (353, [6]), (354, [5, 6, 13, 11, 15, 17, 10]), (355, [6]), (356, [8, 11, 13]), (357, [6, 5]), (358, [13, 6, 5, 15]), (359, [13, 5, 6, 17, 15, 2]), (360, [13, 6, 11, 5, 15, 10, 19])], [(0, [(6, 0.5968742), (11, 0.39671573)]), (1, [(5, 0.9998293)]), (2, [(5, 0.99847865)]), (3, [(5, 0.4248721), (6, 0.57491225)]), (4, [(5, 0.11363209), (6, 0.25848317), (10, 0.015054545), (11, 0.049808282), (13, 0.5597861)]), (5, [(5, 0.36136225), (10, 0.05261353), (13, 0.42551985), (15, 0.047734324), (17, 0.091814265), (19, 0.020699687)]), (6, [(5, 0.5205156), (11, 0.14817742), (13, 0.29779604), (17, 0.023321701)]), (7, [(5, 0.53978944), (11, 0.046022266), (13, 0.41354123)]), (8, [(5, 9.711002), (10, 0.044227965), (13, 2.7743065), (17, 0.45317963)]), (9, [(5, 0.5029905), (6, 0.06789804), (8, 0.17515083), (13, 0.23967993), (17, 0.011837956)]), (10, [(5, 0.08292074), (6, 0.2702401), (10, 0.010763487), (11, 0.039147977), (13, 0.56089103), (17, 0.03516239)]), (11, [(5, 1.1803184), (6, 1.2289463), (10, 0.072587386), (11, 0.1286138), (13, 2.2562687), (15, 0.030974178), (16, 0.025578478), (17, 0.062436875), (19, 0.010315418)]), (12, [(5, 2.6865792), (10, 0.013735286), (11, 0.39467436), (13, 0.86165273), (19, 0.04294668)]), (13, [(5, 0.81406885), (13, 0.18492718)]), (14, [(8, 0.99394715)]), (15, [(5, 0.27748784), (10, 0.03525126), (11, 0.13823411), (13, 0.54791397)]), (16, [(6, 0.99653596)]), (17, [(5, 0.29293177), (11, 0.027939592), (13, 0.6416563), (19, 0.037042562)]), (18, [(11, 0.6325371), (13, 0.3222076), (19, 0.043747585)]), (19, [(5, 0.9973073)]), (20, [(5, 1.66975), (13, 0.3300581)]), (21, [(6, 0.23515475), (11, 0.22100899), (13, 0.48247397), (15, 0.028825004), (19, 0.028546877)]), (22, [(6, 0.06745948), (10, 0.04103736), (11, 0.051079858), (13, 0.62590384), (15, 0.059846856), (17, 0.11718548), (19, 0.035339724)]), (23, [(5, 0.45226714), (11, 0.3453865), (13, 0.17784867), (15, 0.012043779), (16, 0.012286034)]), (24, [(5, 1.6629748), (6, 0.40630534), (13, 0.8110439), (15, 0.10047675), (19, 0.014751689)]), (25, [(5, 0.9039499), (6, 2.2460606), (13, 0.84837)]), (26, [(5, 0.27978677), (6, 1.6560999), (13, 0.05006357), (15, 0.011488569)]), (27, [(6, 0.2689592), (11, 0.7294827)]), (28, [(5, 0.32642248), (11, 0.01570714), (13, 0.5786019), (15, 0.031102525), (17, 0.03735402), (19, 0.010725663)]), (29, [(10, 0.2839394), (13, 0.09658243), (17, 0.61893487)]), (30, [(5, 0.033488367), (6, 0.32328427), (10, 0.03210918), (13, 0.5788379), (15, 0.028409567)]), (31, [(5, 0.23556039), (6, 0.6963146), (11, 0.018590989), (13, 0.04946159)]), (32, [(5, 1.55593), (11, 0.44189468)]), (33, [(5, 0.999542)]), (34, [(5, 0.06774567), (6, 0.026741652), (10, 0.10180665), (11, 0.03999391), (13, 0.7012891), (16, 0.011515695), (17, 0.037500005)]), (35, [(5, 0.74752474), (11, 0.031898193), (13, 0.036603693), (17, 0.18352643)]), (36, [(5, 0.9991604)]), (37, []), (38, [(5, 0.10876127), (6, 0.6256257), (11, 0.046303306), (13, 0.21748954)]), (39, [(8, 1.6055592), (13, 14.393496)]), (40, [(8, 0.11110507), (13, 0.8872032)]), (41, [(5, 0.3278241), (10, 0.57416123), (13, 0.09722562)]), (42, [(5, 4.0781856), (6, 0.0147080785), (11, 0.029067446), (13, 13.869116)]), (43, []), (44, [(6, 0.96126324), (16, 0.02429104), (19, 0.011483919)]), (45, [(5, 0.5638497), (6, 0.33563486), (11, 0.093593456)]), (46, [(5, 0.6678608), (13, 0.3220007)]), (47, [(5, 0.8983715), (11, 0.024045162), (19, 0.07151932)]), (48, [(13, 2.2934313), (15, 0.7017795)]), (49, [(13, 0.6742898), (15, 0.3240861)]), (50, [(5, 0.98399025)]), (51, [(5, 0.55043167), (17, 0.44937566)]), (52, []), (53, [(2, 0.018934213), (5, 0.023143088), (6, 0.060607478), (13, 0.8957889)]), (54, []), (55, [(6, 0.9087631), (10, 0.021585837), (11, 0.4411331), (13, 0.61902004)]), (56, [(5, 1.7933416), (13, 5.20598)]), (57, [(11, 0.9943286)]), (58, [(15, 0.79481506)]), (59, [(5, 1.9571952), (15, 0.027285596), (16, 0.01508758)]), (60, [(6, 0.99718803)]), (61, [(5, 0.68004125), (6, 1.5184997), (10, 0.017957926), (11, 0.07278491), (13, 0.68644667), (17, 0.022267561)]), (62, [(8, 0.092339344), (10, 0.032022662), (13, 0.38628605), (15, 0.0624752), (17, 0.42331445)]), (63, [(8, 0.9978272)]), (64, [(6, 0.5092547), (11, 0.033407502), (13, 3.0600002), (15, 0.21058686), (19, 0.18120919)]), (65, [(6, 0.9928961)]), (66, [(5, 0.7713471), (13, 0.22808385)]), (67, [(5, 0.1877071), (13, 0.51865894), (17, 0.29328796)]), (68, [(5, 0.23156661), (6, 0.34173933), (10, 0.020027157), (11, 0.10749307), (13, 0.25148773), (19, 0.039078534)]), (69, [(5, 0.5765264), (13, 0.38200068), (17, 0.041211095)]), (70, [(5, 0.64642644), (10, 0.028461495), (11, 0.15770131), (13, 1.0490456), (15, 0.10653486), (17, 0.01148939)]), (71, [(6, 0.30732286), (10, 0.020098355), (11, 0.12924197), (13, 0.42746255), (15, 0.082123965), (19, 0.0326494)]), (72, [(10, 0.06666289), (11, 0.074706614), (13, 0.02957316), (15, 0.09477477), (16, 0.027797878), (17, 0.62801933), (19, 0.077810526)]), (73, [(5, 0.33678973), (6, 0.6623889)]), (74, [(5, 0.4380288), (11, 0.01636293), (13, 0.13888715), (17, 0.4012021)]), (75, []), (76, [(5, 0.3799037), (6, 0.035008635), (10, 0.14057027), (11, 2.1272829), (13, 0.9193346), (17, 0.36919028), (19, 0.022129333)]), (77, [(13, 0.9991247)]), (78, [(5, 1.474765), (6, 0.18323798), (10, 0.06402798), , (152, []), (153, [(5, 1.5941594), (6, 0.18276767), (8, 0.19326493), (13, 0.020113932)]), 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Your data is borked.
Replacing ), with ),\n, and adding some indentation manually, the first few lines of your input looks like this:
(
[
(5, 0.28596485),
(6, 0.16911359),
(8, 0.030976538),
(10, 0.011954311),
(11, 0.068368636),
(13, 0.34729925),
(15, 0.016343366),
(17, 0.05741114)
],
[
(0, [6, 11]),
(1, [5]),
(2, [5]),
(3, [6, 5]),
a little later you have
[
(0, [(6, 0.5968742), (11, 0.39671573)]),
(1, [(5, 0.9998293)]),
(2, [(5, 0.99847865)]),
(3, [(5, 0.4248721),
so x[1] seems like it could be a tuple, a list, or a list of tuples..?
Maybe try a sanity check:
print({type(x[1]) for x in row})
(hint: it's better if you edit your question with code, since the comments destroy any formatting)
Since all the x[1] are tuples you probably have a situation similar to:
>>> lst = [
... (1, 2),
... (1, (2,3)),
... ]
>>> print({type(x) for x in lst})
{<class 'tuple'>}
>>> sorted(lst)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: '<' not supported between instances of 'tuple' and 'int'
>>>
since tuple comparisons compare each individual item. That will definitely be a bit more challenging to track down... maybe:
def get_tuple_type(tpl):
"Return a tuple of the types of individual tuple elements."
return tuple([type(item) for item in tpl])
types = {get_tuple_type(row[0][1])} # initialize with the type of the first element
for x in row:
xtype = get_tuple_type(x[1])
if xtype not in types:
print(f'offending value: {x!r} has type {xtype!r} - not any of {types!r}')
if you find compatible (but not strictly equal) types you can manually copy them into the types set and run again until you find the problem value.

Unexpected output when applying LDA trained model to given corpus

I have trained a LDA model using the following parameters:
>> model = gensim.models.ldamodel.LdaModel(corpus=corpus,
id2word=id2word,
num_topics=25,
passes=10,
minimum_probability=0)
Then, I applied this model to a given corpus:
>> lda_corpus = model[corpus]
I was expecting lda_corpus to be a list of lists or 2D matrix, where the number of rows is the number of docs and the number of columns is the number of topics and each element matrix, a tuple of the form (topic_index, probability). However I am getting this weird result where some elements are again a list:
>> print(lda_model_1[corpus[0]])
>> ([(0, 0.012841966), (3, 0.073988825), (4, 0.05184835), (8, 0.38537887), (10, 0.022958927), (11, 0.24562633), (13, 0.05168812), (17, 0.06522224), (21, 0.024792604)], [(0, [11]), (1, [8, 3, 17, 13]), (2, [3, 17, 8, 13]), (3, [8, 3]), (4, [11]), (5, [8, 17, 3]), (6, [4]), (7, [4, 8]), (8, [8, 13, 3]), (9, [11]), (10, [8, 0]), (11, [8, 13, 0]), (12, [21]), (13, [11]), (14, [11]), (15, [8]), (16, [8, 11, 13, 0]), (17, [11]), (18, [11, 17]), (19, [8, 13, 17, 3]), (20, [17, 13, 8]), (21, [17, 11, 8]), (22, [11]), (23, [8]), (24, [8, 13]), (25, [8, 3, 13])], [(0, [(11, 1.0)]), (1, [(3, 0.15384258), (8, 0.71774876), (13, 0.011975089), (17, 0.11643356)]), (2, [(3, 0.45133045), (8, 0.21692151), (13, 0.09479065), (17, 0.23232804)]), (3, [(3, 0.24423833), (8, 0.75576156)]), (4, [(11, 1.0)]), (5, [(3, 0.02001735), (8, 1.6895359), (17, 0.2904468)]), (6, [(4, 1.0)]), (7, [(4, 1.2565874), (8, 0.7367453)]), (8, [(3, 0.05150538), (8, 0.8553984), (13, 0.07775658)]), (9, [(11, 2.0)]), (10, [(0, 0.13937186), (8, 0.8588695)]), (11, [(0, 0.023420962), (8, 0.7131521), (13, 0.263427)]), (12, [(21, 1.0)]), (13, [(11, 0.99124163)]), (14, [(11, 2.0)]), (15, [(8, 1.0)]), (16, [(0, 0.011193657), (8, 1.7189965), (11, 0.23104382), (13, 0.029387457)]), (17, [(11, 1.9989293)]), (18, [(11, 0.9135094), (17, 0.08400644)]), (19, [(3, 0.07146881), (8, 2.1837764), (13, 0.38799366), (17, 0.352704)]), (20, [(8, 0.22638415), (13, 0.24114841), (17, 0.52740365)]), (21, [(8, 0.02224951), (11, 0.24574266), (17, 0.7231928)]), (22, [(11, 1.0)]), (23, [(8, 1.0)]), (24, [(8, 0.972818), (13, 0.027181994)]), (25, [(3, 0.16742931), (8, 0.7671518), (13, 0.05224549)])])
I would appreciate any help.

The problem was related to model parameters. I was using the following config:
lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus,
id2word=id2word,
num_topics=ntopics,
random_state=100,
update_every=1,
chunksize=100,
passes=10,
alpha='auto',
per_word_topics=True)
However, there were some of them that wasn't necessary and that were causing the trouble. The config I am using now is the following:
lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus, id2word=id2word, num_topics=ntopics, \
update_every=1, chunksize=10000, passes=1)

Restart nested loop in python after condition is met

I have two ranges:
range_1 (0,10)
range_2 (11, 40)
I want to create a list of tuples from the two ranges above (range_1 and range_2) if the sum of any of the two elements in the two ranges is an even number.
Thus 0 from range_1 and 12 from range_2 = 12 which is even, the same with 1 from range_1 and 13 from range_2 = 14 which is even.
However I don't want to go through all the elements in range_2. Only 5 successful attempts are needed, then immediately I have to go back to the second iteration in range_1.
Thus for the first iteration:
(0, 12, 12), (0, 14, 14), (0, 16, 16), (0, 18, 18), (0, 20, 20)
then we go to the second iteration:
(1, 11, 12), (1, 13, 14), (1, 15, 16), (1, 17, 18), (1, 19, 20)
and so on till 9 in range_1:
(9, 11, 20), (9, 13, 22), (9, 15, 24), (9, 17, 26), (9, 19, 28)
Here is my code, which goes through all the elements, which is obviously wrong, because it goes through all the elements in range_2!
list_1 = []
for i in range(10):
for j in range(11,40):
if (i+j)%2 == 0:
list_1.append((i, j, (i+j)))

Just store a counter so that if you reach five then you break out of your nested for-loop:
list_1 = []
for i in range(10):
counter = 0
for j in range(11,40):
if (i+j)%2 == 0:
list_1.append((i, j, (i+j)))
counter += 1
if counter == 5:
break
which gives list_1 as:
[(0, 12, 12), (0, 14, 14), (0, 16, 16), (0, 18, 18), (0, 20, 20),
(1, 11, 12), (1, 13, 14), (1, 15, 16), (1, 17, 18), (1, 19, 20),
(2, 12, 14), (2, 14, 16), (2, 16, 18), (2, 18, 20), (2, 20, 22),
(3, 11, 14), (3, 13, 16), (3, 15, 18), (3, 17, 20), (3, 19, 22),
(4, 12, 16), (4, 14, 18), (4, 16, 20), (4, 18, 22), (4, 20, 24),
(5, 11, 16), (5, 13, 18), (5, 15, 20), (5, 17, 22), (5, 19, 24),
(6, 12, 18), (6, 14, 20), (6, 16, 22), (6, 18, 24), (6, 20, 26),
(7, 11, 18), (7, 13, 20), (7, 15, 22), (7, 17, 24), (7, 19, 26),
(8, 12, 20), (8, 14, 22), (8, 16, 24), (8, 18, 26), (8, 20, 28),
(9, 11, 20), (9, 13, 22), (9, 15, 24), (9, 17, 26), (9, 19, 28)]
It should be noted that this is not the most efficient way to go about creating your data structure. Clearly only ever other j-value generated in the inner for-loop will be used, which is wasteful.
Therefore you could specify a step for the j for-loop of 2 so that only other j-value is considered. However, you must be careful with the starting value now. If you were to always start at 11 and step in 2s then you would only get odd j-values and these could never combine with the current i-value to give an even number if i was even. Therefore you would have to change the j for-loop to start at 12 if i is even and 11 if i is odd.

As others have commented, you can simplify the problem quite a bit by constructing ranges that accomodate what you're trying to do. Here it is as a nested comprehension:
[(i, j, i+j) for i in range(0, 10) for j in range((11 if i % 2 else 12), 21, 2)]

If you're smart about creating your range, you can simplify the algorithm. Use a step to make the range skip every other j, adjust the start based on whether i is even, and set the end to 21 since it will only ever get that high.
list_1 = []
for i in range(10):
start = 12 if i % 2 == 0 else 11
for j in range(start, 21, 2):
list_1.append((i, j, i+j))
print(list_1[-5:]) # For testing
First two lines of output:
[(0, 12, 12), (0, 14, 14), (0, 16, 16), (0, 18, 18), (0, 20, 20)]
[(1, 11, 12), (1, 13, 14), (1, 15, 16), (1, 17, 18), (1, 19, 20)]

Find Longest Weighted Path from DAG with Networkx in Python?

I need a algorithm for find the longest weighted path in a directed, acyclic graph networkx.MultiDiGraph(). My graph has weighted edges and many edges have a null value as the weighting. In networkx doc I have found nothing for solve this problem. My graph has the following structure:
>>> print graph.nodes()
[0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 15, 16, 17, 20, 21, 22, 25, 26, 'end']
>>> print graph.edges()
[(0, 'end'), (1, 0), (1, 10), (1, 5), (2, 1), (2, 11), (2, 6), (3, 2), (3, 12), (3, 7), (4, 8), (4, 3), (4, 13), (5, 'end'), (6, 5), (6, 15), (7, 16), (7, 6), (8, 17), (8, 7), (10, 'end'), (11, 10), (11, 20), (11, 15), (12, 16), (12, 11), (12, 21), (13, 17), (13, 12), (13, 22), (15, 'end'), (16, 25), (16, 15), (17, 16), (17, 26), (20, 'end'), (21, 25), (21, 20), (22, 26), (22, 21), (25, 'end'), (26, 25)]
>>> print graph.edge[7][16]
{1: {'weight': 100.0, 'time': 2}}
>>> print graph.edge[7][6]
{0: {'weight': 0, 'time': 2}}
I find this her, but I have problems with the implementation:
networkx: efficiently find absolute longest path in digraph this solution is without weightings.
How to find the longest path with Python NetworkX? This solution transformed the weightings into negativ values, but my graph has null values… and the nx.dijkstra_path() does not support negative values.
Have anyone an idea or a solution to a similar problem found?

Take the solution in the link 1 and change the line:
pairs = [[dist[v][0]+1,v] for v in G.pred[node]] # incoming pairs
To something like:
pairs = [[dist[v][0]+edge['weight'],v] for u, edge in G[v][node] for v in G.pred[node]] # incoming pairs