organization string | repo_name string | base_commit string | iss_html_url string | iss_label string | title string | body string | code null | pr_html_url string | commit_html_url string | file_loc string | own_code_loc list | ass_file_loc list | other_rep_loc list | analysis dict | loctype dict | iss_has_pr int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
xtekky | gpt4free | c5691c5993f8595d90052e4a81b582d63fe81919 | https://github.com/xtekky/gpt4free/issues/913 | bug
stale | TypeError: unhashable type: 'Model' | import g4f, asyncio
async def run_async():
_providers = [
g4f.Provider.ChatgptAi,
g4f.Provider.ChatgptLogin,
g4f.Provider.DeepAi,
g4f.Provider.Opchatgpts,
g4f.Provider.Vercel,
g4f.Provider.Wewordle,
g4f.Provider.You,
g4f.Provider.Yqcloud,
]
responses = [
provider.create_async(
model=g4f.models.default,
messages=[{"role": "user", "content": "Hello"}],
)
for provider in _providers
]
responses = await asyncio.gather(*responses)
for idx, provider in enumerate(_providers):
print(f"{provider.__name__}:", responses[idx])
asyncio.run(run_async())
TypeError: unhashable type: 'Model'
Version 0.0.3.0 | null | https://github.com/xtekky/gpt4free/pull/924 | null | {'base_commit': 'c5691c5993f8595d90052e4a81b582d63fe81919', 'files': [{'path': 'README.md', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [241, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 277]}}}, {'path': 'g4f/Provider/Aivvm.py', 'status': 'modified', 'Loc': {"('Aivvm', 'create_async_generator', 31)": {'mod': [44]}}}, {'path': 'g4f/Provider/Bard.py', 'status': 'modified', 'Loc': {"('Bard', None, 12)": {'add': [15]}, "('Bard', 'create_async', 18)": {'mod': [34, 45, 46, 48, 49, 50, 51, 60]}}}, {'path': 'g4f/Provider/ChatgptLogin.py', 'status': 'modified', 'Loc': {"('ChatgptLogin', 'create_async', 16)": {'mod': [55]}}}, {'path': 'g4f/Provider/CodeLinkAva.py', 'status': 'modified', 'Loc': {"('CodeLinkAva', 'create_async_generator', 16)": {'mod': [43, 46, 47]}}}, {'path': 'g4f/Provider/H2o.py', 'status': 'modified', 'Loc': {"('H2o', None, 12)": {'add': [85]}, "('H2o', 'create_async_generator', 18)": {'mod': [26, 39, 46, 74]}}}, {'path': 'g4f/Provider/HuggingChat.py', 'status': 'modified', 'Loc': {"('HuggingChat', 'create_async_generator', 18)": {'add': [31], 'mod': [28, 29, 40, 65, 79, 80, 92]}}}, {'path': 'g4f/Provider/Vitalentum.py', 'status': 'modified', 'Loc': {"('Vitalentum', 'create_async_generator', 16)": {'mod': [49]}}}, {'path': 'g4f/Provider/__init__.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [26, 43, 69], 'mod': [40]}}}, {'path': 'g4f/Provider/base_provider.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [3, 103], 'mod': [8]}, "('BaseProvider', 'create_completion', 21)": {'mod': [24]}, "('AsyncProvider', 'create_completion', 42)": {'mod': [49]}, "('AsyncProvider', 'create_async', 53)": {'mod': [55]}, "('AsyncGeneratorProvider', 'create_completion', 63)": {'mod': [70, 72, 73, 81]}, "('AsyncGeneratorProvider', 'create_async', 86)": {'mod': [92]}}}, {'path': 'g4f/__init__.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [8], 'mod': [3]}, "('ChatCompletion', 'create', 11)": {'add': [54], 'mod': [16, 18, 19, 20, 21, 22, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 36, 37, 46, 47, 48, 50, 51]}}}, {'path': 'g4f/models.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [4, 7, 14, 17, 27, 33, 34, 35]}}}, {'path': 'testing/test_chat_completion.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [6, 8, 10, 11, 12]}}}, {'path': 'testing/test_providers.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [3]}, "('Styles', None, 11)": {'mod': [11, 12, 13, 14]}, "(None, 'main', 16)": {'mod': [32, 36]}, "(None, 'get_providers', 39)": {'mod': [48, 49, 53]}, "(None, 'create_response', 56)": {'mod': [57, 58, 59, 60, 61, 62]}}}]} | [] | [] | [] | {
"iss_type": "1",
"iss_reason": "2",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"g4f/Provider/CodeLinkAva.py",
"g4f/Provider/H2o.py",
"g4f/Provider/ChatgptLogin.py",
"g4f/Provider/Aivvm.py",
"g4f/Provider/HuggingChat.py",
"g4f/Provider/__init__.py",
"g4f/__init__.py",
"g4f/models.py",
"g4f/Provider/Vitalentum.py",
"g4f/Provider/Bard.py",
"g4f/Provider/base_provider.py"
],
"doc": [
"README.md"
],
"test": [
"testing/test_chat_completion.py",
"testing/test_providers.py"
],
"config": [],
"asset": []
} | 1 |
xtekky | gpt4free | 2dcdce5422cd01cd058490d4daef5f69300cca89 | https://github.com/xtekky/gpt4free/issues/2006 | bug
stale | CORS not enabled for API | **Bug description**
Run docker image
Try to access the Completion API via Javascript console in Browser
`fetch("http://localhost:1337/v1/chat/completions", {
"headers": {
"accept-language": "de-DE,de;q=0.9,en-DE;q=0.8,en;q=0.7,en-US;q=0.6",
"cache-control": "no-cache",
"content-type": "application/json",
"pragma": "no-cache",
"sec-ch-ua": "\"Google Chrome\";v=\"125\", \"Chromium\";v=\"125\", \"Not.A/Brand\";v=\"24\"",
"sec-ch-ua-mobile": "?0",
"sec-ch-ua-platform": "\"Windows\"",
"sec-fetch-dest": "empty",
"sec-fetch-mode": "cors",
"sec-fetch-site": "same-origin",
},
"referrerPolicy": "no-referrer",
"body": JSON.stringify({model: "gpt-3.5-turbo",
messages: [{"role": "user", "content": "Hello"}]}),
"method": "POST",
"mode": "cors"
})`
`Promise {<pending>}
Access to fetch at 'http://localhost:1337/v1/chat/completions' from origin 'null' has been blocked by CORS policy: Response to preflight request doesn't pass access control check: No 'Access-Control-Allow-Origin' header is present on the requested resource. If an opaque response serves your needs, set the request's mode to 'no-cors' to fetch the resource with CORS disabled.`
It is no option to set "mode": "no-cors" because for some reason Chrome reset the request "content-type": "application/json", to "content-type": "text/plain"
**Screenshots**
**Environment**
Docker
**Additional context**
Solution might be to just add a CORSMiddleware to the FastAPI
https://fastapi.tiangolo.com/tutorial/cors/
If i have some time, i might create a PR
| null | https://github.com/xtekky/gpt4free/pull/2281 | null | {'base_commit': '2dcdce5422cd01cd058490d4daef5f69300cca89', 'files': [{'path': 'g4f/api/__init__.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [14]}, "(None, 'create_app', 24)": {'add': [26]}}}]} | [] | [] | [] | {
"iss_type": "1",
"iss_reason": "2",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"g4f/api/__init__.py"
],
"doc": [],
"test": [],
"config": [],
"asset": []
} | 1 |
xtekky | gpt4free | 5d8e603095156303a016cc16e2811a8f2bc74f15 | https://github.com/xtekky/gpt4free/issues/1338 | bug | How to use providers via HTTP Request ? | I am trying to use the api version of this project, but, the providers option in my request is not working, am i doing something wrong?
```js
const response = await axios.post(
`${API_BASE}`,
{
provider: 'g4f.Provider.ChatgptAi',
temperature:0.75,
top_p: 0.6,
model: 'gpt-3.5-long',
messages: [
{role: 'user', content: msg }
],
},
{
headers: {
'Content-Type': 'application/json',
}
}
);
return response.data.choices[0].message.content
```
this is the code, i already tried with `provider: 'ChatgptAi'` and others providers, but the api always ignore the option providers. | null | https://github.com/xtekky/gpt4free/pull/1344 | null | {'base_commit': '5d8e603095156303a016cc16e2811a8f2bc74f15', 'files': [{'path': 'g4f/api/__init__.py', 'status': 'modified', 'Loc': {"('Api', 'chat_completions', 71)": {'add': [86, 94, 100]}}}]} | [] | [] | [] | {
"iss_type": "2",
"iss_reason": "2",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"g4f/api/__init__.py"
],
"doc": [],
"test": [],
"config": [],
"asset": []
} | 1 |
xtekky | gpt4free | 0d8e4ffa2c0706b0381f53c3985d04255b7170f5 | https://github.com/xtekky/gpt4free/issues/2334 | bug | Model "command-r+" returning 401 error: "You have to be logged in" | **Bug description**
I'm experiencing an issue with the model "command-r+" not working. When attempting to use this model through the g4f API (running "g4f api"), I receive the following error:
```
ERROR:root:Request failed with status code: 401, response: {"error":"You have to be logged in."}
Traceback (most recent call last):
File "/usr/lib/python3.11/site-packages/g4f/api/__init__.py", line 177, in chat_completions
response = self.client.chat.completions.create(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.11/site-packages/g4f/client/client.py", line 241, in create
return next(response)
^^^^^^^^^^^^^^
File "/usr/lib/python3.11/site-packages/g4f/client/client.py", line 119, in iter_append_model_and_provider
for chunk in response:
File "/usr/lib/python3.11/site-packages/g4f/client/client.py", line 79, in iter_response
for chunk in response:
File "/usr/lib/python3.11/site-packages/g4f/Provider/HuggingChat.py", line 83, in create_completion
raise RuntimeError(f"Request failed with status code: {response.status_code}, response: {response.text}")
RuntimeError: Request failed with status code: 401, response: {"error":"You have to be logged in."}
```
The model "command-r+" was working perfectly until yesterday. I am using g4f with Docker.
**Steps to Reproduce**
1. Run "g4f api" with the "command-r+" model.
2. Observe the error message in the terminal output.
**Environment**
- Python version: 3.11
- Location: Not in a Cloudflare-flagged country
- g4f version: [g4f v-0.3.3.3]
**Additional context**
No recent changes were made on my side. The model was working without issues until yesterday. | null | https://github.com/xtekky/gpt4free/pull/2313 | null | {'base_commit': '0d8e4ffa2c0706b0381f53c3985d04255b7170f5', 'files': [{'path': 'README.md', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [72], 'mod': [31, 169, 186, 197, 198, 199, 200, 293, 299, 305, 773, 776]}}}, {'path': 'docs/async_client.md', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [264], 'mod': [60, 102, 233, 309]}}}, {'path': 'docs/client.md', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [65, 107]}}}, {'path': 'docs/docker.md', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [74]}}}, {'path': 'docs/git.md', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [98]}}}, {'path': 'docs/interference-api.md', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [71, 111, 138]}}}, {'path': 'docs/providers-and-models.md', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [11, 133, 192], 'mod': [19, 21, 22, 23, 24, 27, 28, 31, 35, 36, 37, 38, 39, 40, 41, 44, 46, 47, 48, 49, 50, 51, 54, 56, 60, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 92, 107, 108, 109, 110, 111, 112, 113, 114, 143, 150, 173, 178, 202, 225]}}}, {'path': 'g4f/Provider/AIUncensored.py', 'status': 'modified', 'Loc': {"('AIUncensored', None, 11)": {'add': [27], 'mod': [12, 18, 19, 20, 21, 23, 24, 25, 105, 106, 107, 108, 110, 111, 112]}, "('AIUncensored', 'get_model', 29)": {'add': [31]}, '(None, None, None)': {'mod': [4, 8]}, "('AIUncensored', 'create_async_generator', 36)": {'mod': [41, 46, 47, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103]}}}, {'path': 'g4f/Provider/Ai4Chat.py', 'status': 'modified', 'Loc': {"('Ai4Chat', None, 13)": {'mod': [17]}}}, {'path': 'g4f/Provider/AiMathGPT.py', 'status': 'removed', 'Loc': {}}, {'path': 'g4f/Provider/Airforce.py', 'status': 'modified', 'Loc': {"('Airforce', None, 13)": {'add': [17], 'mod': [15, 16, 19, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 86, 87, 88, 89, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124]}, "('Airforce', 'create_async_generator', 115)": {'add': [125], 'mod': [127, 128, 129, 130, 131, 132, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 152, 153, 155, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166]}, '(None, None, None)': {'mod': [2, 3, 4, 8, 10, 11]}, "('Airforce', 'get_model', 106)": {'mod': [110]}, "('Airforce', '_generate_image', 135)": {'mod': [168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196]}, "('Airforce', '_generate_text', 182)": {'mod': [198, 199, 200, 201, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 228, 229, 230, 231, 232, 234, 235, 236, 237, 238, 240, 241, 243, 244, 245]}}}, {'path': 'g4f/Provider/Allyfy.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [5], 'mod': [3, 7]}, "('Allyfy', None, 11)": {'add': [14], 'mod': [11]}, "('Allyfy', 'create_async_generator', 17)": {'add': [23, 42], 'mod': [25, 26, 27, 28, 29, 30, 31, 32, 33, 36, 37, 38, 39, 44, 45, 47, 51, 55, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70]}}}, {'path': 'g4f/Provider/Bing.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [20]}}}, {'path': 'g4f/Provider/Blackbox.py', 'status': 'modified', 'Loc': {"('Blackbox', None, 19)": {'add': [27, 114, 115, 121], 'mod': [29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 57, 61, 65, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 82, 83, 84, 85, 86, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 109, 110, 111, 112, 113, 119, 131, 132, 133, 134, 136, 137, 138, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 161, 162, 163, 164, 165]}, "('Blackbox', 'create_async_generator', 168)": {'add': [229, 276], 'mod': [172, 175, 177, 178, 179, 181, 182, 183, 184, 185, 186, 187, 188, 190, 191, 192, 195, 200, 202, 204, 205, 206, 208, 209, 211, 213, 214, 215, 216, 217, 218, 220, 221, 223, 224, 226, 233, 239, 240, 241, 242, 244, 245, 246, 248, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 263, 264, 278, 281, 282, 283, 284, 285, 286, 287, 288, 289, 291, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 351, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372]}, '(None, None, None)': {'mod': [3, 4, 8, 10, 12]}}}, {'path': 'g4f/Provider/ChatGot.py', 'status': 'removed', 'Loc': {}}, {'path': 'g4f/Provider/ChatGpt.py', 'status': 'modified', 'Loc': {"('ChatGpt', None, 73)": {'add': [74, 78, 79, 85]}, "('ChatGpt', 'create_completion', 88)": {'add': [94], 'mod': [96, 97, 98, 99, 100, 101, 102, 103, 105, 106, 107, 108, 110, 111, 113, 116, 117, 118, 140, 141, 146, 149, 151, 154, 157, 158, 159, 160, 161, 162, 163, 169, 192, 210, 212, 213, 216, 217, 220, 221, 223, 225]}, '(None, None, None)': {'mod': [6]}}}, {'path': 'g4f/Provider/ChatGptEs.py', 'status': 'modified', 'Loc': {"('ChatGptEs', 'create_async_generator', 37)": {'mod': [60]}}}, {'path': 'g4f/Provider/ChatHub.py', 'status': 'removed', 'Loc': {}}, {'path': 'g4f/Provider/ChatifyAI.py', 'status': 'removed', 'Loc': {}}, {'path': 'g4f/Provider/Cloudflare.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [2]}, "('Cloudflare', None, 12)": {'add': [12], 'mod': [20, 21, 22, 25, 28, 31, 39, 42, 44, 46, 48, 51, 53, 55, 56, 57, 58, 60, 62, 64, 68, 71, 79, 84, 86, 88, 91, 92, 95, 98, 101, 102, 103, 107, 110, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212]}, "('Cloudflare', 'create_async_generator', 123)": {'add': [169, 189], 'mod': [128, 129, 157, 160, 161, 166, 192, 193]}}}, {'path': 'g4f/Provider/DarkAI.py', 'status': 'modified', 'Loc': {"('DarkAI', None, 11)": {'mod': [12, 19, 21, 24]}, "('DarkAI', 'create_async_generator', 42)": {'mod': [54, 55, 80, 81, 82]}}}, {'path': 'g4f/Provider/DeepInfraChat.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [9]}, "('DeepInfraChat', None, 12)": {'mod': [20, 22, 23, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55]}, "('DeepInfraChat', 'create_async_generator', 69)": {'mod': [100, 103, 104, 105, 106, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]}}}, {'path': 'g4f/Provider/Editee.py', 'status': 'removed', 'Loc': {}}, {'path': 'g4f/Provider/Free2GPT.py', 'status': 'modified', 'Loc': {"('Free2GPT', None, 15)": {'mod': [19]}, "('Free2GPT', 'create_async_generator', 22)": {'mod': [52, 53, 54, 55, 57]}}}, {'path': 'g4f/Provider/FreeChatgpt.py', 'status': 'removed', 'Loc': {}}, {'path': 'g4f/Provider/FreeGpt.py', 'status': 'modified', 'Loc': {"('FreeGpt', None, 22)": {'mod': [27]}}}, {'path': 'g4f/Provider/GizAI.py', 'status': 'modified', 'Loc': {"('GizAI', None, 11)": {'add': [15], 'mod': [12, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 70, 71, 72]}, "('GizAI', 'create_async_generator', 75)": {'add': [89], 'mod': [101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 151]}, '(None, None, None)': {'mod': [3, 7]}}}, {'path': 'g4f/Provider/HuggingChat.py', 'status': 'modified', 'Loc': {"('HuggingChat', None, 11)": {'add': [21, 32]}, "('HuggingChat', 'create_completion', 49)": {'add': [85, 86, 144], 'mod': [88, 89, 90, 91, 151]}}}]} | [] | [] | [] | {
"iss_type": "1",
"iss_reason": "2",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"g4f/Provider/Ai4Chat.py",
"g4f/Provider/ChatifyAI.py",
"g4f/Provider/ChatGptEs.py",
"g4f/Provider/DeepInfraChat.py",
"g4f/Provider/AiMathGPT.py",
"g4f/Provider/Allyfy.py",
"g4f/Provider/ChatGpt.py",
"g4f/Provider/Bing.py",
"g4f/Provider/AIUncensored.py",
"g4f/Provider/HuggingChat.py",
"g4f/Provider/ChatHub.py",
"g4f/Provider/FreeChatgpt.py",
"g4f/Provider/Airforce.py",
"g4f/Provider/DarkAI.py",
"g4f/Provider/Blackbox.py",
"g4f/Provider/ChatGot.py",
"g4f/Provider/Editee.py",
"g4f/Provider/GizAI.py",
"g4f/Provider/FreeGpt.py",
"g4f/Provider/Cloudflare.py",
"g4f/Provider/Free2GPT.py"
],
"doc": [
"docs/async_client.md",
"docs/docker.md",
"README.md",
"docs/providers-and-models.md",
"docs/git.md",
"docs/interference-api.md",
"docs/client.md"
],
"test": [],
"config": [],
"asset": []
} | 1 |
xtekky | gpt4free | b2bfc88218d3ffb367c6a4bcb14c0748666d348f | https://github.com/xtekky/gpt4free/issues/1206 | bug
stale | OpenaiChat:\lib\asyncio\base_events.py", line 498, in _make_subprocess_transport raise NotImplementedError | 
this problem happens today after I update to the latest version! | null | https://github.com/xtekky/gpt4free/pull/1207 | null | {'base_commit': 'b2bfc88218d3ffb367c6a4bcb14c0748666d348f', 'files': [{'path': 'g4f/Provider/needs_auth/OpenaiChat.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [4]}, "(None, 'get_arkose_token', 146)": {'mod': [147, 148, 149, 150, 151, 177, 178, 179, 180, 182, 186, 187]}}}]} | [] | [] | [] | {
"iss_type": "1",
"iss_reason": "1",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"g4f/Provider/needs_auth/OpenaiChat.py"
],
"doc": [],
"test": [],
"config": [],
"asset": []
} | 1 |
Z4nzu | hackingtool | b2cf73c8f414cd9c30d920beb2e7a000934c1f92 | https://github.com/Z4nzu/hackingtool/issues/354 | target not found yay and python-pip.19.1.1-1 | i have a problem when i try to run bash install.sh it says error target not found yay, python-pip.19.1.1-1 , i have installed the yay and i have no idea how to install python-pip so i need help.
OS: Arch linux 64x_86X
shell: bash 5.1.6
| null | https://github.com/Z4nzu/hackingtool/pull/355 | null | {'base_commit': 'b2cf73c8f414cd9c30d920beb2e7a000934c1f92', 'files': [{'path': 'install.sh', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [74, 96, 111]}}}]} | [] | [] | [] | {
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Z4nzu | hackingtool | 1e088ad35b66dda0ee9139a5220627f86cb54365 | https://github.com/Z4nzu/hackingtool/issues/347 | enhancement | Typos found by codespell | ./tools/xss_attack.py:107: vulnerabilites ==> vulnerabilities
./tools/information_gathering_tools.py:87: Scaning ==> Scanning
./tools/information_gathering_tools.py:117: informations ==> information
./tools/information_gathering_tools.py:168: informations ==> information
./tools/forensic_tools.py:60: Aquire ==> Acquire
./tools/wireless_attack_tools.py:51: bluetooh ==> bluetooth
./tools/webattack.py:89: analizing ==> analyzing
./tools/phising_attack.py:40: enginee ==> engine
./tools/phising_attack.py:95: Engagment ==> Engagement
./tools/payload_creator.py:89: writen ==> written
./tools/others/socialmedia_finder.py:53: Usege ==> Usage | null | https://github.com/Z4nzu/hackingtool/pull/350 | null | {'base_commit': '1e088ad35b66dda0ee9139a5220627f86cb54365', 'files': [{'path': '.github/workflows/lint_python.yml', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [25]}}}, {'path': 'tools/forensic_tools.py', 'status': 'modified', 'Loc': {"('Guymager', None, 59)": {'mod': [60]}}}, {'path': 'tools/information_gathering_tools.py', 'status': 'modified', 'Loc': {"('ReconSpider', None, 86)": {'mod': [87]}, "('Infoga', None, 115)": {'mod': [117]}, "('Shodan', None, 166)": {'mod': [168]}}}, {'path': 'tools/others/socialmedia_finder.py', 'status': 'modified', 'Loc': {"('Sherlock', None, 50)": {'mod': [53]}}}, {'path': 'tools/payload_creator.py', 'status': 'modified', 'Loc': {"('Venom', None, 85)": {'mod': [89]}}}, {'path': 'tools/phising_attack.py', 'status': 'modified', 'Loc': {"('Setoolkit', None, 37)": {'mod': [40]}, "('ISeeYou', None, 92)": {'mod': [95]}}}, {'path': 'tools/webattack.py', 'status': 'modified', 'Loc': {"('Dirb', None, 84)": {'mod': [89]}}}, {'path': 'tools/wireless_attack_tools.py', 'status': 'modified', 'Loc': {"('BluePot', None, 49)": {'mod': [51]}}}, {'path': 'tools/xss_attack.py', 'status': 'modified', 'Loc': {"('XSSStrike', None, 105)": {'mod': [107]}}}]} | [] | [] | [] | {
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Z4nzu | hackingtool | 0a4faeac9c4f93a61c937b0e57023b693beeca6f | https://github.com/Z4nzu/hackingtool/issues/174 | SyntaxError: invalid syntax | Traceback (most recent call last):
File "/home/kali/hackingtool/hackingtool.py", line 11, in <module>
from tools.ddos import DDOSTools
File "/home/kali/hackingtool/tools/ddos.py", line 29
"sudo", "python3 ddos", method, url, socks_type5.4.1, threads, proxylist, multiple, timer])
I'm getting this error someone can help? also in sudo in python3 | null | https://github.com/Z4nzu/hackingtool/pull/176 | null | {'base_commit': '0a4faeac9c4f93a61c937b0e57023b693beeca6f', 'files': [{'path': 'tools/ddos.py', 'status': 'modified', 'Loc': {"('ddos', 'run', 20)": {'mod': [29]}}}]} | [] | [] | [] | {
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scikit-learn | scikit-learn | 0e8e38e3b2f4b79f03fe8a3e655b9f506ab0f2a6 | https://github.com/scikit-learn/scikit-learn/issues/768 | Arpack wrappers fail with new scipy | I have scipy 0.11.0.dev-c1ea274. This does not seem to play well with the current arpack wrappers.
I'm a bit out of my depth there, though.
| null | https://github.com/scikit-learn/scikit-learn/pull/802 | null | {'base_commit': '0e8e38e3b2f4b79f03fe8a3e655b9f506ab0f2a6', 'files': [{'path': 'sklearn/utils/arpack.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [55]}, "(None, 'svds', 1540)": {'add': [1598], 'mod': [1540]}, "(None, 'eigs', 1048)": {'mod': [1048]}, "(None, 'eigsh', 1264)": {'mod': [1264]}}}]} | [] | [] | [] | {
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scikit-learn | scikit-learn | bb7e34bc52461749e6014787a05a9507eda11011 | https://github.com/scikit-learn/scikit-learn/issues/21668 | Build / CI
cython | CI with boundscheck=False | I really dislike segmentation faults! Unfortunately, there are many issues reporting them.
Findings in #21654, #21283 were easier with setting `boundscheck = True`.
**Proposition**
Set up one CI configuration that runs with `boundscheck = True` globally which should be easier now that #21512 is merged. | null | https://github.com/scikit-learn/scikit-learn/pull/21779 | null | {'base_commit': 'c9e5067cb14de578ab48b64f399743b994e3ca94', 'files': [{'path': 'azure-pipelines.yml', 'status': 'modified', 'Loc': {'(None, None, 202)': {'add': [202]}}}, {'path': 'doc/computing/parallelism.rst', 'status': 'modified', 'Loc': {'(None, None, 216)': {'add': [216]}}}, {'path': 'sklearn/_build_utils/__init__.py', 'status': 'modified', 'Loc': {"(None, 'cythonize_extensions', 40)": {'add': [72], 'mod': [81]}}}]} | [] | [] | [] | {
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scikit-learn | scikit-learn | 64ab789905077ba8990522688c11177442e5e91f | https://github.com/scikit-learn/scikit-learn/issues/29358 | Documentation | Sprints page | ### Describe the issue linked to the documentation
The following sprints are listed:
https://scikit-learn.org/stable/about.html#sprints
But, that is a small subset, given the list here:
https://blog.scikit-learn.org/sprints/
Are the sprints posted on the "About Us" page of a certain criteria, such as Dev sprints only?
### Suggest a potential alternative/fix
_No response_ | null | https://github.com/scikit-learn/scikit-learn/pull/29418 | null | {'base_commit': '64ab789905077ba8990522688c11177442e5e91f', 'files': [{'path': 'doc/about.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [548, 549, 551, 552, 553, 554, 555, 557, 558, 559, 560, 561, 563, 564, 565]}}}]} | [] | [] | [] | {
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scikit-learn | scikit-learn | 41e129f1a6eb17a39ff0b25f682d903d0ae3c5af | https://github.com/scikit-learn/scikit-learn/issues/5991 | Easy
Enhancement | PERF : StratifiedShuffleSplit is slow when using large number of classes | When using large number of classes (e.g. > 10000, e.g for recommender systems), `StratifiedShuffleSplit` is very slow when compared to `ShuffleSplit`. Looking at the code, I believe that the following part:
``` python
for i, class_i in enumerate(classes):
permutation = rng.permutation(class_counts[i])
perm_indices_class_i = np.where((y == class_i))[0][permutation]
```
`l. 1070` in `sklearn.model_selection._split` is suboptimal : we should build an index matrix holding the indices for each class in the dataset (implying to do a single pass over data, maybe along with a `bincount(classes)`). Indeed np.where does a pass over `y` at each call, leading to a `O(n_classes * len(y))` complexity, whereas it could be `O(len(y))` only.
I obtain a significant gain in perf doing:
``` python
class_indices = np.zeros((n_classes, class_counts.max()), dtype='int')
count = np.zeros(n_classes, dtype='int')
for i in range(len(y_indices)):
class_indices[y_indices[i], count[y_indices[i]]] = i
count[y_indices[i]] += 1
```
and subsequently replacing
``` python
perm_indices_class_i = np.where((y == class_i))[0][permutation]
```
by
``` python
perm_indices_class_i = class_indices[class_i,:class_counts[i]][permutation]
```
This is suboptimal given we iterate over y values using within a Python loop. I believe that the proper way to do this would be to create a `bincount_with_ref` cython function that would both count the occurence of classes and accumulate class index in a `class_indices` array - in `arrayfuncs.pyx`. Memory usage goes up of `len(y) * sizeof('int')`, which is typically small when compared to `X` size.
Would this be useful ? I'll have to provide benchmarks !
| null | https://github.com/scikit-learn/scikit-learn/pull/9197 | null | {'base_commit': '41e129f1a6eb17a39ff0b25f682d903d0ae3c5af', 'files': [{'path': 'doc/whats_new.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [219]}}}, {'path': 'sklearn/model_selection/_split.py', 'status': 'modified', 'Loc': {"('StratifiedShuffleSplit', '_iter_indices', 1495)": {'add': [1523], 'mod': [1536, 1538]}}}]} | [] | [] | [] | {
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scikit-learn | scikit-learn | 4143356c3c51831300789e4fdf795d83716dbab6 | https://github.com/scikit-learn/scikit-learn/issues/10336 | help wanted | Should mixture models have a clusterer-compatible interface | Mixture models are currently a bit different. They are basically clusterers, except they are probabilistic, and are applied to inductive problems unlike many clusterers. But they are unlike clusterers in API:
* they have an `n_components` parameter, with identical purpose to `n_clusters`
* they do not store the `labels_` of the training data
* they do not have a `fit_predict` method
And they are almost entirely documented separately.
Should we make the MMs more like clusterers? | null | https://github.com/scikit-learn/scikit-learn/pull/11281 | null | {'base_commit': '4143356c3c51831300789e4fdf795d83716dbab6', 'files': [{'path': 'doc/whats_new/v0.20.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [583]}}}, {'path': 'sklearn/mixture/base.py', 'status': 'modified', 'Loc': {"('BaseMixture', 'fit', 172)": {'add': [190], 'mod': [175, 243]}}}, {'path': 'sklearn/mixture/tests/test_bayesian_mixture.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [3, 9], 'mod': [17]}, "(None, 'test_invariant_translation', 400)": {'add': [421]}}}, {'path': 'sklearn/mixture/tests/test_gaussian_mixture.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [5, 571]}}}]} | [] | [] | [] | {
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scikit-learn | scikit-learn | d7795a431e30d23f7e8499bdbe89dbdc6e9a068e | https://github.com/scikit-learn/scikit-learn/issues/16001 | Bug
Easy
good first issue
help wanted | Possible infinite loop iterations in synthetic data sets generation module | Hello,
I found two code snippets in https://github.com/scikit-learn/scikit-learn/blob/7e85a6d1f/sklearn/datasets/_samples_generator.py are susceptible to infinite loop iterations when using make_multilabel_classification():
1) https://github.com/scikit-learn/scikit-learn/blob/7e85a6d1f/sklearn/datasets/_samples_generator.py#L357
2) https://github.com/scikit-learn/scikit-learn/blob/7e85a6d1f/sklearn/datasets/_samples_generator.py#L371
These happen when the parameters of make_multilabel_classification functions are EITHER (allowed_unlabeled = False and n_classes = 0) OR length = 0.
I am using the version 0.20.3 of scikit-learn.
Please let me know if you have any questions about this.
Thank You
| null | https://github.com/scikit-learn/scikit-learn/pull/16006 | null | {'base_commit': 'd7795a431e30d23f7e8499bdbe89dbdc6e9a068e', 'files': [{'path': 'doc/whats_new/v0.23.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [65]}}}, {'path': 'sklearn/datasets/_samples_generator.py', 'status': 'modified', 'Loc': {"(None, 'make_multilabel_classification', 263)": {'add': [344]}}}, {'path': 'sklearn/datasets/tests/test_samples_generator.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [224]}}}]} | [] | [] | [] | {
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scikit-learn | scikit-learn | 0e3cbbdcdfeec1c6b10aea11524add6350a8f4e0 | https://github.com/scikit-learn/scikit-learn/issues/933 | Speed up tree construction | CC: @pprett @amueller @bdholt1
Hi folks,
Everyone will agree that tree-based methods have shown to perform quite well (e.g., the recent achievement of Peter!) and are increasingly used by our users. However, the tree module still has a major drawback: it is slow as hell in comparison to other machine learning packages.
For that reason, I think we should put some more effort into accelerating the tree module. In particular, I would like to suggest to move the whole `Tree` class (not the estimators, but only our struct-of-arrays representation) from tree.py into Cython in _tree.pyx. First the code would be a lot faster. But second, it could also actually be more readable and maintainable if the whole tree construction process was packaged into a single file, in a single class. Currently, the construction process is indeed split across 2 files, estimator classes, the Tree class and all the Cython routines. (imo, this is a mess.)
To show that indeed the construction process could be a lot faster, I profiled `recursive_partition` using line-profiler (see link below). Insignicant Python instructions do actually take quite some time in comparison to the important parts of the algorithm. E.g., line 314 vs line 320. A mere Python if-statement is only twice faster than finding the best threshold!!!
I let you examine the rest of the profiling report by yourself, but as far as I am concerned, I am convinced that we could indeed significantly speed up the tree module (and be 5-10x faster at least).
http://pastebin.com/0rC1QmPy (toggle text warping)
What's your opinion about this? Since I am increasingly using the module myself, I can actually work on that in the days to come.
| null | https://github.com/scikit-learn/scikit-learn/pull/946 | null | {'base_commit': '0e3cbbdcdfeec1c6b10aea11524add6350a8f4e0', 'files': [{'path': 'doc/whats_new.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [11]}}}, {'path': 'sklearn/ensemble/_gradient_boosting.c', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [381, 637, 673, 746, 931, 973, 4913, 5993], 'mod': [1, 608, 760, 975, 996, 1026, 1027, 1031, 1033, 1056, 1058, 1073, 1075, 1088, 1092, 1093, 1309, 1628, 3821, 3823, 3838, 3840]}, "(None, 'PyInit__gradient_boosting', 4052)": {'add': [4118], 'mod': [4135, 4142, 4144, 4147, 4150, 4157, 4159, 4162, 4169, 4171]}, "(None, '__pyx_f_7sklearn_8ensemble_18_gradient_boosting__predict_regression_tree_inplace_fast', 1096)": {'mod': [1096, 1100, 1110, 1113, 1114, 1115, 1116, 1117, 1119, 1120, 1121, 1130, 1131, 1135, 1139, 1142, 1143, 1147, 1150, 1152, 1153, 1157, 1164, 1165, 1166, 1169, 1173, 1174, 1177, 1183, 1186, 1188, 1190, 1195, 1196, 1198, 1202, 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349, 350, 362, 363, 365, 366, 368, 369, 371, 372, 374, 375, 377, 378, 402, 420, 430, 431, 432, 433, 434, 511, 512, 513, 514, 515, 516, 563, 567, 571, 573, 599, 600, 601, 604, 605, 607, 608, 609, 610, 611, 612, 613, 614, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 627, 628, 629, 630, 631, 632, 634, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 671, 672, 673, 674, 675, 676, 677, 678, 680, 681, 682, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 696, 698, 701, 702, 703, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 747, 748, 749, 750, 752, 753, 755, 756, 757, 759, 760, 761, 763, 764, 765, 767, 768, 770, 771, 773, 774, 776, 777, 779, 780, 781, 782, 783, 785, 786, 788, 789, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 808, 809, 810, 811, 812, 813, 814, 815, 816, 818, 819, 820, 821, 823, 824, 826, 828, 829, 830, 831, 832, 833, 835, 836, 837, 838, 839, 840, 842, 843, 845, 846, 847, 848, 850, 851, 852, 853, 854, 855, 856, 858, 859, 861, 862, 863, 864, 866, 868, 869, 870, 871, 872, 873, 874, 875, 877, 878, 880, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 895, 896, 898, 899, 901, 902, 903, 905, 906, 907, 909, 910, 911, 913, 914, 916, 917, 919, 920, 922, 923, 927, 928, 929, 931, 932, 934, 935, 937, 938, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 953, 954, 955, 956, 957, 958, 959, 960, 961, 963, 964, 965, 966, 968, 969, 971, 973, 974, 975, 976, 977, 978, 980, 981, 982, 983, 984, 985, 987, 988, 990, 991, 992, 993, 995, 996, 997, 999, 1000, 1002, 1003, 1004, 1005, 1006, 1008, 1009, 1011, 1012, 1013, 1014, 1016, 1018, 1019, 1020, 1022, 1023, 1025, 1026, 1027, 1028, 1030]}}}, {'path': 'sklearn/tree/tests/test_tree.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [182]}, "(None, 'test_numerical_stability', 183)": {'add': [201, 202]}, "(None, 'test_min_samples_leaf', 316)": {'add': [317], 'mod': [319, 321, 322, 323, 324, 325, 326, 328, 329]}}}, {'path': 'sklearn/tree/tree.py', 'status': 'modified', 'Loc': {"(None, 'node_to_str', 81)": {'add': [96], 'mod': [82, 83, 84, 85, 91]}, "('BaseDecisionTree', 'fit', 465)": {'add': [501, 554], 'mod': [487, 492, 512, 557, 558, 559, 560, 561, 562]}, "(None, 'recurse', 104)": {'mod': [105, 106, 107, 109, 113, 114, 117]}, "('Tree', None, 133)": {'mod': [133, 134, 136, 137, 138, 139, 140, 141, 143, 144, 145, 146, 148, 149, 150, 151, 153, 154, 156, 157, 159, 160, 162, 163, 164, 166, 167, 168, 170, 171, 172, 174, 175, 177, 178, 179, 180, 182, 184, 185, 187, 188, 190, 191, 192, 194, 195, 196, 198, 199, 200, 201, 203, 204, 206, 207, 208, 209, 210, 211, 212, 213, 215, 216, 217, 219, 220, 221, 222, 223, 224, 225, 227, 228, 230, 231, 232, 234, 236, 237, 238, 239, 240, 241, 243, 245, 247, 248, 249, 250, 251, 252, 254, 255, 256, 257, 259, 260, 261, 262, 264, 265, 267, 269, 270, 271, 272, 273, 274, 275, 276, 278, 279, 280, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 293, 295, 296, 297, 298, 300, 301, 302, 303, 304, 305, 306, 307, 308, 310, 311, 313, 314, 315, 316, 318, 319, 320, 321, 323, 324, 325, 326, 327, 329, 330, 331, 332, 334, 335, 337, 338, 339, 340, 341, 342, 343, 345, 346, 347, 348, 349, 350, 351, 353, 354, 355, 356, 357, 358, 359, 361, 363, 364, 366, 367, 369, 371, 372, 373, 375, 376, 377, 378, 379, 380, 382, 384, 385, 387, 389, 390, 391, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 411, 413, 414, 415, 416, 417, 418, 419, 421, 423, 424, 425, 427]}, "('BaseDecisionTree', '__init__', 439)": {'mod': [460]}, "('BaseDecisionTree', 'predict', 570)": {'mod': [587]}, "('DecisionTreeClassifier', 'predict_proba', 727)": {'mod': [742]}, "('ExtraTreeClassifier', '__init__', 932)": {'mod': [949]}, "('ExtraTreeRegressor', '__init__', 978)": {'mod': [995]}}}]} | [] | [] | [] | {
"iss_type": "4",
"iss_reason": "2",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"sklearn/tree/_tree.pyx",
"sklearn/ensemble/_gradient_boosting.pyx",
"sklearn/ensemble/_gradient_boosting.c",
"sklearn/ensemble/gradient_boosting.py",
"sklearn/ensemble/forest.py",
"sklearn/tree/tree.py"
],
"doc": [
"doc/whats_new.rst"
],
"test": [
"sklearn/tree/tests/test_tree.py",
"sklearn/ensemble/tests/test_gradient_boosting.py",
"sklearn/ensemble/tests/test_forest.py"
],
"config": [],
"asset": []
} | 1 | |
scikit-learn | scikit-learn | 77aeb825b6494de1e3a2c1e7233b182e05d55ab0 | https://github.com/scikit-learn/scikit-learn/issues/27982 | Documentation
good first issue
help wanted | Ensure that we have an example in the docstring of each public function or class | We should make sure that we have a small example for all public functions or classes. Most of the missing examples are linked to functions.
I could list the following classes and functions for which `numpydoc` did not find any example:
- [x] sklearn.base.BaseEstimator
- [x] sklearn.base.BiclusterMixin
- [x] sklearn.base.ClassNamePrefixFeaturesOutMixin
- [x] sklearn.base.ClassifierMixin
- [x] sklearn.base.ClusterMixin
- [x] sklearn.base.DensityMixin
- [x] sklearn.base.MetaEstimatorMixin
- [x] sklearn.base.OneToOneFeatureMixin
- [x] sklearn.base.OutlierMixin
- [x] sklearn.base.RegressorMixin
- [x] sklearn.base.TransformerMixin
- [x] sklearn.base.clone
- [x] sklearn.base.is_classifier
- [x] sklearn.base.is_regressor
- [x] sklearn.cluster.affinity_propagation
- [x] sklearn.cluster.cluster_optics_dbscan
- [x] sklearn.cluster.cluster_optics_xi
- [x] sklearn.cluster.compute_optics_graph
- [x] sklearn.cluster.estimate_bandwidth
- [x] sklearn.cluster.k_means
- [x] sklearn.cluster.mean_shift
- [x] sklearn.cluster.spectral_clustering
- [x] sklearn.cluster.ward_tree
- [x] sklearn.covariance.graphical_lasso
- [x] sklearn.covariance.ledoit_wolf
- [x] sklearn.covariance.ledoit_wolf_shrinkage
- [x] sklearn.covariance.shrunk_covariance
- [x] sklearn.datasets.clear_data_home
- [x] sklearn.datasets.dump_svmlight_file
- [x] sklearn.datasets.fetch_20newsgroups
- [x] sklearn.datasets.fetch_20newsgroups_vectorized
- [x] sklearn.datasets.fetch_california_housing
- [x] sklearn.datasets.fetch_covtype
- [x] sklearn.datasets.fetch_kddcup99
- [x] sklearn.datasets.fetch_lfw_pairs
- [x] sklearn.datasets.fetch_lfw_people
- [x] sklearn.datasets.fetch_olivetti_faces
- [x] sklearn.datasets.fetch_openml
- [x] sklearn.datasets.fetch_rcv1
- [x] sklearn.datasets.fetch_species_distributions
- [x] sklearn.datasets.get_data_home
- [x] sklearn.datasets.load_diabetes
- [x] sklearn.datasets.load_files
- [x] sklearn.datasets.load_linnerud
- [x] sklearn.datasets.load_svmlight_files
- [x] sklearn.datasets.make_biclusters
- [x] sklearn.datasets.make_checkerboard
- [x] sklearn.datasets.make_circles
- [x] sklearn.datasets.make_classification
- [x] sklearn.datasets.make_friedman1
- [x] sklearn.datasets.make_friedman2
- [x] sklearn.datasets.make_friedman3
- [x] sklearn.datasets.make_gaussian_quantiles
- [x] sklearn.datasets.make_hastie_10_2
- [x] sklearn.datasets.make_low_rank_matrix
- [x] sklearn.datasets.make_moons
- [x] sklearn.datasets.make_multilabel_classification
- [x] sklearn.datasets.make_s_curve
- [x] sklearn.datasets.make_sparse_coded_signal
- [x] sklearn.datasets.make_sparse_spd_matrix
- [x] sklearn.datasets.make_sparse_uncorrelated
- [x] sklearn.datasets.make_spd_matrix
- [x] sklearn.datasets.make_swiss_roll
- [x] sklearn.decomposition.dict_learning
- [x] sklearn.decomposition.dict_learning_online
- [x] sklearn.decomposition.sparse_encode
- [x] sklearn.feature_extraction.image.grid_to_graph
- [x] sklearn.feature_extraction.image.img_to_graph
- [x] sklearn.feature_extraction.image.reconstruct_from_patches_2d
- [x] sklearn.feature_selection.SelectorMixin
- [x] sklearn.feature_selection.chi2
- [x] sklearn.feature_selection.f_classif
- [x] sklearn.feature_selection.f_regression
- [x] sklearn.feature_selection.mutual_info_classif
- [x] sklearn.feature_selection.mutual_info_regression
- [x] sklearn.feature_selection.r_regression
- [x] sklearn.gaussian_process.kernels.Kernel
- [x] sklearn.get_config
- [x] sklearn.isotonic.check_increasing
- [x] sklearn.isotonic.isotonic_regression
- [x] sklearn.linear_model.enet_path
- [x] sklearn.linear_model.lars_path
- [x] sklearn.linear_model.lars_path_gram
- [x] sklearn.linear_model.orthogonal_mp
- [x] sklearn.linear_model.orthogonal_mp_gram
- [x] sklearn.linear_model.ridge_regression
- [x] sklearn.manifold.locally_linear_embedding
- [x] sklearn.manifold.smacof
- [x] sklearn.manifold.spectral_embedding
- [x] sklearn.manifold.trustworthiness
- [x] sklearn.metrics.calinski_harabasz_score
- [x] sklearn.metrics.check_scoring
- [x] sklearn.metrics.cohen_kappa_score
- [x] sklearn.metrics.consensus_score
- [x] sklearn.metrics.coverage_error
- [x] sklearn.metrics.davies_bouldin_score
- [x] sklearn.metrics.get_scorer
- [x] sklearn.metrics.get_scorer_names
- [x] sklearn.metrics.homogeneity_completeness_v_measure
- [x] sklearn.metrics.label_ranking_loss
- [x] sklearn.metrics.mutual_info_score
- [x] sklearn.metrics.pairwise.additive_chi2_kernel
- [x] sklearn.metrics.pairwise.chi2_kernel
- [x] sklearn.metrics.pairwise.cosine_distances
- [x] sklearn.metrics.pairwise.cosine_similarity
- [x] sklearn.metrics.pairwise.distance_metrics
- [x] sklearn.metrics.pairwise.kernel_metrics
- [x] sklearn.metrics.pairwise.laplacian_kernel
- [x] sklearn.metrics.pairwise.linear_kernel
- [x] sklearn.metrics.pairwise.paired_cosine_distances
- [x] sklearn.metrics.pairwise.paired_euclidean_distances
- [x] sklearn.metrics.pairwise.pairwise_kernels
- [x] sklearn.metrics.pairwise.polynomial_kernel
- [x] sklearn.metrics.pairwise.rbf_kernel
- [x] sklearn.metrics.pairwise.sigmoid_kernel
- [x] sklearn.metrics.pairwise_distances
- [x] sklearn.metrics.pairwise_distances_argmin
- [x] sklearn.metrics.pairwise_distances_argmin_min
- [x] sklearn.metrics.silhouette_samples
- [x] sklearn.metrics.silhouette_score
- [x] sklearn.model_selection.check_cv
- [x] sklearn.model_selection.permutation_test_score
- [x] sklearn.model_selection.validation_curve
- [x] sklearn.neighbors.sort_graph_by_row_values
- [x] sklearn.preprocessing.binarize
- [x] sklearn.preprocessing.maxabs_scale
- [x] sklearn.preprocessing.minmax_scale
- [x] sklearn.preprocessing.normalize
- [x] sklearn.preprocessing.robust_scale
- [x] sklearn.preprocessing.scale
- [x] sklearn.set_config
- [x] sklearn.show_versions
- [x] sklearn.svm.l1_min_c
- [x] sklearn.utils._safe_indexing
- [x] sklearn.utils.arrayfuncs.min_pos
- [x] sklearn.utils.as_float_array
- [x] sklearn.utils.assert_all_finite
- [x] sklearn.utils.check_X_y
- [x] sklearn.utils.check_array
- [x] sklearn.utils.check_consistent_length
- [x] sklearn.utils.check_random_state
- [x] sklearn.utils.check_scalar
- [x] sklearn.utils.class_weight.compute_class_weight
- [x] sklearn.utils.class_weight.compute_sample_weight
- [x] sklearn.utils.deprecated
- [x] sklearn.utils.discovery.all_displays
- [x] sklearn.utils.discovery.all_estimators
- [x] sklearn.utils.discovery.all_functions
- [x] sklearn.utils.estimator_checks.check_estimator
- [x] sklearn.utils.estimator_html_repr
- [x] sklearn.utils.extmath.density
- [x] sklearn.utils.extmath.randomized_range_finder
- [x] sklearn.utils.extmath.safe_sparse_dot
- [x] sklearn.utils.indexable
- [x] sklearn.utils.metadata_routing.MetadataRequest
- [x] sklearn.utils.metadata_routing.MetadataRouter
- [x] sklearn.utils.metadata_routing.MethodMapping
- [x] sklearn.utils.metadata_routing.get_routing_for_object
- [x] sklearn.utils.metadata_routing.process_routing
- [x] sklearn.utils.murmurhash3_32
- [x] sklearn.utils.parallel.Parallel
- [x] sklearn.utils.parallel.delayed
- [x] sklearn.utils.parallel_backend
- [x] sklearn.utils.random.sample_without_replacement
- [x] sklearn.utils.register_parallel_backend
- [x] sklearn.utils.safe_mask
- [x] sklearn.utils.safe_sqr
- [x] sklearn.utils.sparsefuncs.incr_mean_variance_axis
- [x] sklearn.utils.sparsefuncs.inplace_column_scale
- [x] sklearn.utils.sparsefuncs.inplace_csr_column_scale
- [x] sklearn.utils.sparsefuncs.inplace_row_scale
- [x] sklearn.utils.sparsefuncs.inplace_swap_column
- [x] sklearn.utils.sparsefuncs.inplace_swap_row
- [x] sklearn.utils.sparsefuncs.mean_variance_axis
- [x] sklearn.utils.sparsefuncs_fast.inplace_csr_row_normalize_l1
- [x] sklearn.utils.sparsefuncs_fast.inplace_csr_row_normalize_l2
- [x] sklearn.utils.validation.check_is_fitted
- [x] sklearn.utils.validation.check_memory
- [x] sklearn.utils.validation.check_symmetric
- [x] sklearn.utils.validation.column_or_1d
The code used to find the list above is detailed below:
<details>
```python
import importlib
import inspect
from pathlib import Path
from numpydoc.docscrape import NumpyDocString
path_sklearn_doc = Path(
"/{path_to_git_repo}/scikit-learn/doc/_build/html/stable/"
"modules/generated"
)
missing_examples_name = []
for document in path_sklearn_doc.glob("*.html"):
extracted_doc = []
full_name = document.stem
try:
module_name, class_or_function_name = full_name.rsplit(".", maxsplit=1)
module = importlib.import_module(module_name)
class_or_function = getattr(module, class_or_function_name)
except (ValueError, AttributeError, ImportError):
# This is due to the experimental module and function with
# module name
continue
is_class = inspect.isclass(class_or_function)
docstring = NumpyDocString(class_or_function.__doc__)
if not docstring["Examples"]:
missing_examples_name.append(full_name)
for full_name in sorted(missing_examples_name):
print(f"- [ ] {full_name}")
```
</details> | null | https://github.com/scikit-learn/scikit-learn/pull/28564 | null | {'base_commit': 'd967cfe8124902181892411b18b50dce9921a32d', 'files': [{'path': 'sklearn/datasets/_samples_generator.py', 'status': 'modified', 'Loc': {"(None, 'make_low_rank_matrix', 1359)": {'add': [1413]}}}]} | [] | [] | [] | {
"iss_type": "4",
"iss_reason": "2",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"sklearn/datasets/_samples_generator.py"
],
"doc": [],
"test": [],
"config": [],
"asset": []
} | 1 |
scikit-learn | scikit-learn | e11c4d21a4579f0d49f414a4b76e386f80f0f074 | https://github.com/scikit-learn/scikit-learn/issues/19269 | New Feature
module:datasets | sklearn.datasets.load_files select file extension | <!--
If you want to propose a new algorithm, please refer first to the scikit-learn
inclusion criterion:
https://scikit-learn.org/stable/faq.html#what-are-the-inclusion-criteria-for-new-algorithms
-->
#### Describe the workflow you want to enable
When using load_files in a directory where there are different kinds of files (.txt, .png, ...), the user might want to load only certain files (*.txt for example). This feature would put load_files closer to the function `index_directory` from tensorflow.python.keras.preprocessing.dataset_utils.py.
For MacOs users, .DStore files also gets loaded which is an undesired behaviour.
#### Describe your proposed solution
Add an argument to select the types of files to load.
| null | https://github.com/scikit-learn/scikit-learn/pull/22498 | null | {'base_commit': 'e11c4d21a4579f0d49f414a4b76e386f80f0f074', 'files': [{'path': 'doc/whats_new/v1.1.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [175]}}}, {'path': 'sklearn/datasets/_base.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [13]}, "(None, 'load_files', 99)": {'add': [108, 145, 186, 214], 'mod': [218]}}}, {'path': 'sklearn/datasets/tests/test_base.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [131]}}}]} | [] | [] | [] | {
"iss_type": "4",
"iss_reason": "2",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"sklearn/datasets/_base.py"
],
"doc": [
"doc/whats_new/v1.1.rst"
],
"test": [
"sklearn/datasets/tests/test_base.py"
],
"config": [],
"asset": []
} | 1 |
scikit-learn | scikit-learn | cdd693bf955acd2a97cce48011d168c6b1ef316d | https://github.com/scikit-learn/scikit-learn/issues/8364 | Easy
Documentation
Sprint | Matplotlib update on CI makes example look different | The examples look different on the current dev website, in particular the classifier comparison that's on the landing pages looks a bit odd now:
http://scikit-learn.org/dev/auto_examples/classification/plot_classifier_comparison.html
I suspect the culprit is the CI upgrading to matplotlib v2. I think we should go through the examples and see how they are holding up with the new styles. | null | https://github.com/scikit-learn/scikit-learn/pull/8516 https://github.com/scikit-learn/scikit-learn/pull/8369 | null | {'base_commit': '676e8630243b894aa2976ef6fb6048f9880b8a23', 'files': [{'path': 'examples/svm/plot_separating_hyperplane.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [15, 20], 'mod': [18, 19, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 38, 39, 40, 41, 43, 44, 45]}}}]} | [] | [] | [] | {
"iss_type": "4",
"iss_reason": "2",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"examples/svm/plot_separating_hyperplane.py"
],
"doc": [],
"test": [],
"config": [],
"asset": []
} | 1 |
scikit-learn | scikit-learn | 839b356f45fac7724eab739dcc129a0c8f650a23 | https://github.com/scikit-learn/scikit-learn/issues/15005 | API | Implement SLEP009: keyword-only arguments | [SLEP009](https://scikit-learn-enhancement-proposals.readthedocs.io/en/latest/slep009/proposal.html) is all but accepted.
It proposes to make most parameters keyword-only.
We should do this by first:
* [x] Merging #13311
* [x] Perhaps getting some stats on usage of positional arguments as per https://github.com/scikit-learn/enhancement_proposals/pull/19#issuecomment-514671933
* [ ] applying the deprecation to each subpackage. Checked means PR opened at least.
* [x] base
* [x] calibration
* [x] cluster
* [x] compose
* [x] covariance
* [x] cross_decomposition
* [x] datasets
* [x] decomposition
* [x] discriminant_analysis
* [x] dummy
* [x] ensemble
* [x] feature_extraction
* [x] feature_selection
* [x] gaussian_process
* [x] impute
* [x] inspection
* [x] isotonic
* [x] kernel_approximation
* [x] kernel_ridge
* [x] linear_model
* [x] manifold
* [x] metrics
* [x] metrics.pairwise
* [x] mixture
* [x] model_selection
* [x] multiclass
* [x] multioutput
* [x] naive_bayes
* [x] neighbors
* [x] neural_network
* [x] pipeline
* [x] preprocessing
* [x] random_projection
* [x] semi_supervised
* [x] svm
* [x] tree
* [x] utils
We might along the way establish rules of thumb and principles like "are the semantics reasonably clear when the argument is passed positionally?" As I noted on the mailing list, I think they are clear for PCA's components, for Pipeline's steps, and for GridSearchCV's estimator and parameter grid. Other parameters of those estimators seem more suitable for keyword-only. Trickier is whether n_components in TSNE should follow PCA in being positional... It's not as commonly set by users. | null | https://github.com/scikit-learn/scikit-learn/pull/17007 https://github.com/scikit-learn/scikit-learn/pull/17046 https://github.com/scikit-learn/scikit-learn/pull/17006 https://github.com/scikit-learn/scikit-learn/pull/17005 https://github.com/scikit-learn/scikit-learn/pull/13311 https://github.com/scikit-learn/scikit-learn/pull/16719 | null | {'base_commit': '839b356f45fac7724eab739dcc129a0c8f650a23', 'files': [{'path': 'sklearn/datasets/_base.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [19]}, "(None, 'load_files', 83)": {'mod': [83]}, "(None, 'load_wine', 270)": {'mod': [270]}, "(None, 'load_iris', 384)": {'mod': [384]}, "(None, 'load_breast_cancer', 498)": {'mod': [498]}, "(None, 'load_digits', 622)": {'mod': [622]}, "(None, 'load_diabetes', 745)": {'mod': [745]}, "(None, 'load_linnerud', 837)": {'mod': [837]}, "(None, 'load_boston', 940)": {'mod': [940]}}}, {'path': 'sklearn/datasets/_california_housing.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [38]}, "(None, 'fetch_california_housing', 51)": {'mod': [51]}}}, {'path': 'sklearn/datasets/_covtype.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [30]}, "(None, 'fetch_covtype', 43)": {'mod': [43]}}}, {'path': 'sklearn/datasets/_kddcup99.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [25]}, "(None, 'fetch_kddcup99', 46)": {'mod': [46]}}}, {'path': 'sklearn/datasets/_lfw.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [22]}, "(None, 'fetch_lfw_people', 218)": {'mod': [218]}, "(None, 'fetch_lfw_pairs', 388)": {'mod': [388]}}}, {'path': 'sklearn/datasets/_olivetti_faces.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [27]}, "(None, 'fetch_olivetti_faces', 38)": {'mod': [38]}}}, {'path': 'sklearn/datasets/_openml.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [25]}, "(None, 'fetch_openml', 611)": {'mod': [611]}}}, {'path': 'sklearn/datasets/_rcv1.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [27]}, "(None, 'fetch_rcv1', 78)": {'mod': [78]}}}, {'path': 'sklearn/datasets/_samples_generator.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [20]}, "(None, 'make_classification', 36)": {'mod': [36]}, "(None, 'make_multilabel_classification', 264)": {'mod': [264]}, "(None, 'make_hastie_10_2', 425)": {'mod': [425]}, "(None, 'make_regression', 473)": {'mod': [473]}, "(None, 'make_circles', 595)": {'mod': [595]}, "(None, 'make_moons', 671)": {'mod': [671]}, "(None, 'make_blobs', 734)": {'mod': [734]}, "(None, 'make_friedman1', 892)": {'mod': [892]}, "(None, 'make_friedman2', 954)": {'mod': [954]}, "(None, 'make_friedman3', 1019)": {'mod': [1019]}, "(None, 'make_low_rank_matrix', 1083)": {'mod': [1083]}, "(None, 'make_sparse_coded_signal', 1152)": {'mod': [1152]}, "(None, 'make_sparse_uncorrelated', 1214)": {'mod': [1214]}, "(None, 'make_spd_matrix', 1265)": {'mod': [1265]}, "(None, 'make_sparse_spd_matrix', 1298)": {'mod': [1298]}, "(None, 'make_swiss_roll', 1372)": {'mod': [1372]}, "(None, 'make_s_curve', 1424)": {'mod': [1424]}, "(None, 'make_gaussian_quantiles', 1466)": {'mod': [1466]}, "(None, 'make_biclusters', 1561)": {'mod': [1561]}, "(None, 'make_checkerboard', 1652)": {'mod': [1652]}}}, {'path': 'sklearn/datasets/_species_distributions.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [52]}, "(None, 'fetch_species_distributions', 140)": {'mod': [140]}}}, {'path': 'sklearn/datasets/_svmlight_format_io.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [27]}, "(None, 'load_svmlight_file', 40)": {'mod': [40, 154, 155]}, "(None, 'load_svmlight_files', 199)": {'mod': [199]}, "(None, 'dump_svmlight_file', 383)": {'mod': [383]}}}, {'path': 'sklearn/datasets/_twenty_newsgroups.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [47]}, "(None, 'fetch_20newsgroups', 149)": {'mod': [149]}, "(None, 'fetch_20newsgroups_vectorized', 325)": {'mod': [325]}}}, {'path': 'sklearn/datasets/tests/test_base.py', 'status': 'modified', 'Loc': {"(None, 'test_load_digits_n_class_lt_10', 154)": {'mod': [155]}}}, {'path': 'sklearn/linear_model/tests/test_omp.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [21, 22]}}}]} | [] | [] | [] | {
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scikit-learn | scikit-learn | 62d205980446a1abc1065f4332fd74eee57fcf73 | https://github.com/scikit-learn/scikit-learn/issues/12779 | Easy
good first issue | Remove "from __future__ import XXX" | Given #12746, I think we should remove ``from __future__ import XXX``, right? @adrinjalali
```
$ git grep "from __future__ import" | wc -l
147
``` | null | https://github.com/scikit-learn/scikit-learn/pull/13079 | null | {'base_commit': '62d205980446a1abc1065f4332fd74eee57fcf73', 'files': [{'path': 'sklearn/utils/_random.pyx', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [0], 'mod': [16]}}}]} | [] | [] | [] | {
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scikit-learn | scikit-learn | 45019594938f92f3344c80bb0d351793dd91334b | https://github.com/scikit-learn/scikit-learn/issues/12306 | module:impute | SimpleImputer to Crash on Constant Imputation with string value when dataset is encoded Numerically | #### Description
The title kind of describes it. It might be pretty logical, but just putting it out here as it took a while for me to realize and debug what exactly happened.
The SimpleImputer has the ability to impute missing values with a constant. If the data is categorical, it is possible to impute with a string value. However, when fetching a dataset from OpenML (or many other datasets from different sources) the data is encoded numerically automatically as numeric. When applying the SimpleImputer and a string value, scikit-learn crashes. I assume there's not a lot that can be done about this, as everything behaves exactly as you would expect when you dive deep into the code, but maybe the documentation can be extended a little bit (probably on SimpleImputer side, or maybe on the side of the data sources).
What do you think?
#### Steps/Code to Reproduce
```
import numpy as np
import sklearn.datasets
import sklearn.compose
import sklearn.tree
import sklearn.impute
X, y = sklearn.datasets.fetch_openml('Australian', 4, return_X_y=True)
numeric_imputer = sklearn.impute.SimpleImputer(strategy='mean')
numeric_scaler = sklearn.preprocessing.StandardScaler()
nominal_imputer = sklearn.impute.SimpleImputer(strategy='constant', fill_value='missing')
nominal_encoder = sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore')
numeric_idx = [1, 2, 7, 10, 13]
nominal_idx = [0, 3, 4, 5, 6, 8, 9, 11, 12]
print('missing numeric vals:', np.count_nonzero(~np.isnan(X[:, numeric_idx])))
print('missing nominal vals:', np.count_nonzero(~np.isnan(X[:, nominal_idx])))
clf_nom = sklearn.pipeline.make_pipeline(nominal_imputer, nominal_encoder)
clf_nom.fit(X[:, nominal_idx], y)
```
#### Expected Results
A fitted classifier? Depending on how you write the documentation, the current error could also be the expected result.
#### Actual Results
```
missing numeric vals: 3450
missing nominal vals: 6210
Traceback (most recent call last):
File "/home/janvanrijn/projects/sklearn-bot/testjan.py", line 23, in <module>
clf_nom.fit(X[:, nominal_idx], y)
File "/home/janvanrijn/anaconda3/envs/sklearn-bot/lib/python3.6/site-packages/sklearn/pipeline.py", line 265, in fit
Xt, fit_params = self._fit(X, y, **fit_params)
File "/home/janvanrijn/anaconda3/envs/sklearn-bot/lib/python3.6/site-packages/sklearn/pipeline.py", line 230, in _fit
**fit_params_steps[name])
File "/home/janvanrijn/anaconda3/envs/sklearn-bot/lib/python3.6/site-packages/sklearn/externals/joblib/memory.py", line 329, in __call__
return self.func(*args, **kwargs)
File "/home/janvanrijn/anaconda3/envs/sklearn-bot/lib/python3.6/site-packages/sklearn/pipeline.py", line 614, in _fit_transform_one
res = transformer.fit_transform(X, y, **fit_params)
File "/home/janvanrijn/anaconda3/envs/sklearn-bot/lib/python3.6/site-packages/sklearn/base.py", line 465, in fit_transform
return self.fit(X, y, **fit_params).transform(X)
File "/home/janvanrijn/anaconda3/envs/sklearn-bot/lib/python3.6/site-packages/sklearn/impute.py", line 241, in fit
"data".format(fill_value))
ValueError: 'fill_value'=missing is invalid. Expected a numerical value when imputing numerical data
```
#### Versions
```
Python=3.6.0
numpy==1.15.2
scikit-learn==0.20.0
scipy==1.1.0
```
| null | https://github.com/scikit-learn/scikit-learn/pull/25081 | null | {'base_commit': '45019594938f92f3344c80bb0d351793dd91334b', 'files': [{'path': 'sklearn/impute/_base.py', 'status': 'modified', 'Loc': {"('SimpleImputer', None, 142)": {'mod': [179, 180, 181]}}}]} | [] | [] | [] | {
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scikit-learn | scikit-learn | 5ad3421a5b5759ecfaaab93406592d988f5d487f | https://github.com/scikit-learn/scikit-learn/issues/16556 | New Feature
module:ensemble | Add Pre-fit Model to Stacking Model | <!--
If you want to propose a new algorithm, please refer first to the scikit-learn
inclusion criterion:
https://scikit-learn.org/stable/faq.html#what-are-the-inclusion-criteria-for-new-algorithms
-->
#### Describe the workflow you want to enable
Allow pre-fit models to stacking model such as `StackingClassifier` and `StackingRegressor` so that the final estimator can use their predictions directly without fitting the model on the given training data.
The motivation for this functionality originates from situation in which it is not possible to fit model on the entire dataset (due to compliance or other non-technical restrictions) or simply a research question to test with different models trained on different data. I feel this added flexibility could be beneficial in the long term.
#### Describe your proposed solution
One possible solution I have in mind is to exclude fitted estimators during fitting. We can iterate through the list of estimators and see if they have been fitted (which sklearn already has helper functions). If yes, we skip them when fitting the estimators.
#### Describe alternatives you've considered, if relevant
Another option I thought about was to ask users to specify if they want to exclude fitting certain estimators. But in this case, I feel it is safer to check the estimators' status regardless, which makes the manual input somewhat redundant.
| null | https://github.com/scikit-learn/scikit-learn/pull/22215 | null | {'base_commit': '5ad3421a5b5759ecfaaab93406592d988f5d487f', 'files': [{'path': 'doc/whats_new/v1.1.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [372]}}}, {'path': 'sklearn/ensemble/_stacking.py', 'status': 'modified', 'Loc': {"('StackingClassifier', None, 281)": {'add': [328], 'mod': [309, 317, 366, 368]}, "('StackingRegressor', None, 579)": {'add': [614, 625], 'mod': [606, 649, 651]}, "('_BaseStacking', 'fit', 123)": {'mod': [155, 156, 157, 158, 159, 160, 161, 162, 176, 177, 179, 180, 181, 182, 183, 184, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 204, 205, 206]}}}, {'path': 'sklearn/ensemble/tests/test_stacking.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [45, 532]}}}]} | [] | [] | [] | {
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scikit-learn | scikit-learn | 9b2aac9e5c8749243c73f2377519d2f2c407b095 | https://github.com/scikit-learn/scikit-learn/issues/7603 | When min_samples_split and min_samples_leaf are greater than or equal to 1.0 and 0.5, no error is thrown. | <!-- Instructions For Filing a Bug: https://github.com/scikit-learn/scikit-learn/blob/master/CONTRIBUTING.md#filing-bugs -->
#### Description
This is a silent bug in version 0.18.0, as a result of the following change: "Random forest, extra trees, decision trees and gradient boosting estimator accept the parameter min_samples_split and min_samples_leaf provided as a percentage of the training samples. By yelite and Arnaud Joly."
The bug is that no error is thrown when large float values are passed. In theory, it would be useless to set `min_samples_split` to 1.0 or more, or `min_samples_leaf` to 0.5 or more. For example, `min_samples_split=2` gives a very different result compared with `min_samples_split=2.0`. In this case, accidentally setting `min_samples_split=2.0` in 0.18.0 would produce a tree with no splits. In this example, the error would be completely silent, and difficult to debug. This would probably be an unexpected outcome, especially for users coming from version 0.17.1, where these two values (`2.0` and `2`) would behave identically.
#### Steps/Code to Reproduce
Example:
```
from sklearn.ensemble import RandomForestClassifier
from sklearn.cross_validation import train_test_split
from sklearn.datasets import load_iris
iris = load_iris()
X, y = iris.data[:, [0,1,2]], iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.33)
rf = RandomForestClassifier(n_estimators=5, min_samples_leaf=3.0)
rf.fit(X_train, y_train)
print "rf score %s" % rf.score(X_test, y_test)
```
#### Expected Results
The RandomForestClassifier scores in the ~0.9 range in 0.17.1, and I believe an error should be thrown in 0.18.0.
#### Actual Results
The RandomForestClassifier scores in the ~0.3 range in 0.18.0, with no error thrown.
#### Versions
```
Darwin-15.6.0-x86_64-i386-64bit
('Python', '2.7.11 (default, Jan 22 2016, 08:29:18) \n[GCC 4.2.1 Compatible Apple LLVM 7.0.2 (clang-700.1.81)]')
('NumPy', '1.11.2')
('SciPy', '0.17.0')
('Scikit-Learn', '0.18')
```
| null | https://github.com/scikit-learn/scikit-learn/pull/7604 | null | {'base_commit': '9b2aac9e5c8749243c73f2377519d2f2c407b095', 'files': [{'path': 'sklearn/tree/tests/test_tree.py', 'status': 'modified', 'Loc': {"(None, 'test_error', 496)": {'add': [511, 523]}}}, {'path': 'sklearn/tree/tree.py', 'status': 'modified', 'Loc': {"('BaseDecisionTree', 'fit', 117)": {'add': [218, 220, 223, 225], 'mod': [261, 262, 263, 264, 265, 266, 267, 268, 312, 313, 376]}}}]} | [] | [] | [] | {
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scikit-learn | scikit-learn | 86476582a3759b82fd163d27522bd2de6ad95b6c | https://github.com/scikit-learn/scikit-learn/issues/11568 | TST: optics function is not tested | Related to https://github.com/scikit-learn/scikit-learn/pull/1984 that was merged: it seems that the `optics` function (that @amueller added to the `cluster/__init__.py` in https://github.com/scikit-learn/scikit-learn/pull/11567) is not tested (at least not in `test_optics.py`)
(so the function `optics` that wraps the `OPTICS` class) | null | https://github.com/scikit-learn/scikit-learn/pull/13271 | null | {'base_commit': '86476582a3759b82fd163d27522bd2de6ad95b6c', 'files': [{'path': 'doc/modules/classes.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [117]}}}, {'path': 'sklearn/cluster/__init__.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [14, 35]}}}, {'path': 'sklearn/cluster/dbscan_.py', 'status': 'modified', 'Loc': {"(None, 'dbscan', 23)": {'mod': [98, 99, 100]}}}, {'path': 'sklearn/cluster/optics_.py', 'status': 'modified', 'Loc': {"(None, 'optics', 24)": {'mod': [24, 25, 26, 27, 28, 29, 30, 32, 33, 34, 35, 36, 38, 39, 40, 41, 42, 44, 46, 47, 48, 49, 51, 52, 53, 55, 56, 57, 58, 59, 61, 62, 63, 65, 66, 67, 68, 69, 71, 73, 75, 76, 78, 79, 80, 81, 82, 84, 85, 87, 88, 89, 90, 91, 93, 94, 96, 97, 98, 99, 100, 102, 103, 104, 105, 106, 107, 109, 110, 111, 112, 113, 114, 115, 116, 117, 119, 120, 122, 123, 124, 125, 127, 128, 129, 130, 132, 133, 135, 136, 137, 138, 139, 141, 142, 144, 145, 146, 147, 148, 150, 151, 152, 153, 154, 156, 157, 158, 159, 161, 162, 164, 165, 166, 167, 168, 169, 170, 172, 173, 174, 175, 176, 177, 179, 180, 181, 182, 183, 184, 185]}}}]} | [] | [] | [] | {
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scikit-learn | scikit-learn | ebf2bf81075ae1f4eb47ea0f54981c512bda5ceb | https://github.com/scikit-learn/scikit-learn/issues/5022 | Deprecate n_iter in SGDClassifier and implement max_iter. | We should implement a stopping condition based on the scaled norm of the parameter update as done in the new SAG solver for LogisticRegression / Ridge. The convergence check should be done at the end of the each epoch to avoid introducing too much overhead.
Other classes sharing the same underlying implementation should be updated as well, e.g.:
- SGDRegressor
- PassiveAggressiveClassifier
- Perceptron
maybe others.
We should store the effective number of iterations in a new `n_iter_` attribute on the estimator at the end of `fit` as done in many other scikit-learn model that accept a `max_iter` hyperparam.
| null | https://github.com/scikit-learn/scikit-learn/pull/5036 | null | {'base_commit': 'ebf2bf81075ae1f4eb47ea0f54981c512bda5ceb', 'files': [{'path': 'benchmarks/bench_covertype.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [105]}}}, {'path': 'benchmarks/bench_sgd_regression.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [23, 27], 'mod': [1, 2, 4, 5, 6, 8, 78, 80, 81, 90, 92, 94, 95, 96]}}}, {'path': 'benchmarks/bench_sparsify.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [66, 75, 76, 80]}}}, {'path': 'doc/modules/kernel_approximation.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [66, 67, 68]}}}, {'path': 'doc/modules/linear_model.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [1268]}}}, {'path': 'doc/modules/sgd.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [66, 67, 68]}}}, {'path': 'doc/tutorial/text_analytics/working_with_text_data.rst', 'status': 'modified', 'Loc': 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scikit-learn | scikit-learn | dc1cad2b3fddb8b9069d7cfd89cb1039260baf8e | https://github.com/scikit-learn/scikit-learn/issues/28976 | Documentation
help wanted | `min_samples` in HDSCAN | ### Describe the issue linked to the documentation
I find the description of the `min_samples` argument in sklearn.cluster.HDBSCAN confusing.
It says "The number of samples in a neighborhood for a point to be considered as a core point. This includes the point itself."
But if I understand everything correctly `min_samples` corresponds to the $k$ used to compute the core distance $\text{core}_k\left(x\right)$ for every sample $x$ where the $k$'th core distance for some sample $x$ is defined as the distance to the $k$'th nearest-neighbor of $x$ (counting itself). (-> which exactly what is happening in the code here: https://github.com/scikit-learn-contrib/hdbscan/blob/fc94241a4ecf5d3668cbe33b36ef03e6160d7ab7/hdbscan/_hdbscan_reachability.pyx#L45-L47, where it is called `min_points`)
I don't understand how both of these descriptions are equivalent. I would assume that other people might find that confusing as well.
Link in Code: https://github.com/scikit-learn/scikit-learn/blob/8721245511de2f225ff5f9aa5f5fadce663cd4a3/sklearn/cluster/_hdbscan/hdbscan.py#L441-L444
Link in Documentation: https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html#sklearn.cluster.DBSCAN
### Suggest a potential alternative/fix
_No response_ | null | https://github.com/scikit-learn/scikit-learn/pull/29263 | null | {'base_commit': 'dc1cad2b3fddb8b9069d7cfd89cb1039260baf8e', 'files': [{'path': 'sklearn/cluster/_hdbscan/_reachability.pyx', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [65, 66]}}}, {'path': 'sklearn/cluster/_hdbscan/hdbscan.py', 'status': 'modified', 'Loc': {"('HDBSCAN', None, 419)": {'mod': [444, 445]}}}]} | [] | [] | [] | {
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scikit-learn | scikit-learn | 127415b209ca1df3f8502bdf74de56c33aff2565 | https://github.com/scikit-learn/scikit-learn/issues/901 | add predict and fit_predict to more clustering algorithms | We should add `predict` and `fit_predict` to other clustering algorithms than `KMeans`: they are useful to retrieve cluster labels independently of the underlying attribute names...
| null | https://github.com/scikit-learn/scikit-learn/pull/907 | null | {'base_commit': '127415b209ca1df3f8502bdf74de56c33aff2565', 'files': [{'path': 'sklearn/base.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [365]}}}, {'path': 'sklearn/cluster/affinity_propagation_.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [12]}, "('AffinityPropagation', None, 168)": {'mod': [168]}}}, {'path': 'sklearn/cluster/dbscan_.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [13]}, "('DBSCAN', None, 112)": {'mod': [112]}}}, {'path': 'sklearn/cluster/hierarchical.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [18]}, "('Ward', None, 257)": {'mod': [257]}}}, {'path': 'sklearn/cluster/k_means_.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [19]}, "('KMeans', None, 599)": {'mod': [599, 759, 760, 762, 763, 764, 765]}, "('MiniBatchKMeans', None, 963)": {'mod': [963]}}}, {'path': 'sklearn/cluster/mean_shift_.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [12]}, "('MeanShift', None, 202)": {'mod': [202]}}}, {'path': 'sklearn/cluster/spectral.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [10]}, "('SpectralClustering', None, 227)": {'mod': [227]}}}, {'path': 'sklearn/cluster/tests/test_k_means.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [419]}}}]} | [] | [] | [] | {
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scikit-learn | scikit-learn | 9385c45c0379ceab913daa811b1e7d4128faee35 | https://github.com/scikit-learn/scikit-learn/issues/4700 | Bug | cross_val_predict AttributeError with lists | When calling the cross_val_predict with an X parameter that is a list type, an AttributeError is raised on line 1209. This is because it is checking for the shape of the X parameter, but a list does not have the shape attribute.
The documentation says that this function supports lists so I am supposing that it isn't intended behavior. Commenting out that line also makes the rest of the function work perfectly fine.
Also not that the cross_val_score function, that takes the same arguments, works fine.
I can provide the dataset I used if necessary.
| null | https://github.com/scikit-learn/scikit-learn/pull/4705 | null | {'base_commit': '9385c45c0379ceab913daa811b1e7d4128faee35', 'files': [{'path': 'sklearn/cross_validation.py', 'status': 'modified', 'Loc': {"(None, 'cross_val_predict', 958)": {'mod': [1027]}}}, {'path': 'sklearn/tests/test_cross_validation.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [1037]}, "('MockClassifier', 'predict', 95)": {'mod': [98]}}}, {'path': 'sklearn/utils/mocking.py', 'status': 'modified', 'Loc': {"('CheckingClassifier', 'fit', 46)": {'add': [51]}, '(None, None, None)': {'mod': [1, 2]}, "('CheckingClassifier', None, 33)": {'mod': [33]}, "('CheckingClassifier', 'predict', 55)": {'mod': [58]}}}]} | [] | [] | [] | {
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scikit-learn | scikit-learn | 053d2d1af477d9dc17e69162b9f2298c0fda5905 | https://github.com/scikit-learn/scikit-learn/issues/19705 | [RFC] Minimal scipy version for 1.0 (or 0.26) release | #### Proposal
I'd like to propose to increase the minimal scipy version to 1.0.
```python
SCIPY_MIN_VERSION = '1.0.0'
```
#### Reasoning
1. In case we should release scikit-learn 1.0, it would be a good fit:smirk:
2. Linear quantile regression #9978 could make it into the next release. It uses `scipy.optimize.linprog` under the hood. Scipy 1.0.0 has introduced a new solver `method="interior-point"` which is set as default method. Having it available would help us to avoid to support the `"simplex"` method in scikit-learn. Note, that scipy v1.3.0 introduced the `"revised simplex"` method and version 1.5 the `"highs**"` solvers which are much preferred.
I think we should avoid the legacy simplex method.
3. *Your reason for scipy 1.0.0.*
| null | https://github.com/scikit-learn/scikit-learn/pull/20069 | null | {'base_commit': '053d2d1af477d9dc17e69162b9f2298c0fda5905', 'files': [{'path': '.circleci/config.yml', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [6, 11, 50, 99, 133]}}}, {'path': '.travis.yml', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [43, 48, 49, 50, 51, 52, 53, 54]}}}, {'path': 'azure-pipelines.yml', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [121, 125], 'mod': [14, 41, 60, 86, 108, 124, 135, 136, 137, 139, 143, 144, 146, 147, 149, 154, 155, 157, 158, 159, 160, 174, 183, 184, 185, 189, 190, 234, 235]}}}, {'path': 'build_tools/azure/install.sh', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [73, 75]}}}, {'path': 'build_tools/azure/posix-32.yml', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [48, 66]}}}, {'path': 'build_tools/azure/test_script.sh', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [7]}}}, {'path': 'doc/conftest.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [9]}, "(None, 'setup_preprocessing', 80)": {'add': [82]}}}, {'path': 'doc/modules/sgd.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [133]}}}, {'path': 'doc/tutorial/statistical_inference/supervised_learning.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [176]}}}, {'path': 'doc/whats_new/v1.0.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [14]}}}, {'path': 'pyproject.toml', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [14]}}}, {'path': 'sklearn/_min_dependencies.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [13], 'mod': [8, 11, 12, 29, 30, 31]}}}, {'path': 'sklearn/decomposition/_truncated_svd.py', 'status': 'modified', 'Loc': {"('TruncatedSVD', None, 24)": {'mod': [90, 91, 92, 97, 99, 101]}}}, {'path': 'sklearn/ensemble/_hist_gradient_boosting/tests/test_loss.py', 'status': 'modified', 'Loc': {"(None, 'test_derivatives', 66)": {'mod': [101]}}}, {'path': 'sklearn/utils/tests/test_validation.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [27, 52, 348, 371, 372]}, "(None, 'test_check_array_dtype_numeric_errors', 374)": {'mod': [376, 377, 378]}}}]} | [] | [] | [] | {
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scikit-learn | scikit-learn | a0ba256dbe9380b5d2cf9cee133482fc87768267 | https://github.com/scikit-learn/scikit-learn/issues/19304 | New Feature
Easy
module:ensemble | Poisson criterion in RandomForestRegressor | #### Describe the workflow you want to enable
I want to officially use the Poisson splitting criterion in `RandomForestRegressor`.
#### Describe your proposed solution
#17386 implemented the poisson splitting criterion for `DecisionTreeRegressor` and `ExtraTreeRegressor`. This also enabled—somewhat silently—to do:
```
import numpy as np
from sklearn.ensemble import RandomForestRegressor
y = [0, 1, 2]
X = np.arange(6).reshape(3, 2)
rf = RandomForestRegressor(criterion="poisson")
rf.fit(X, y)
```
Note: The same is true for `ensemble.ExtraTreesRegressor`.
Tasks:
- [ ] Add the poisson splitting criterion to the docstring of `RandomForestRegressor`.
- [ ] Add input validation (non-negative `y`) to `RandomForestRegressor`.
- [ ] Expand the tests for `RandomForestRegressor`. | null | https://github.com/scikit-learn/scikit-learn/pull/19464 | null | {'base_commit': 'a0ba256dbe9380b5d2cf9cee133482fc87768267', 'files': [{'path': 'sklearn/ensemble/_forest.py', 'status': 'modified', 'Loc': {"('BaseForest', 'fit', 274)": {'add': [317]}, "('RandomForestRegressor', None, 1279)": {'mod': [1301, 1304, 1305, 1307, 1308]}}}]} | [] | [] | [] | {
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scikit-learn | scikit-learn | 8453daa6b983ee2fd73d537e81e58b3f6b0e3147 | https://github.com/scikit-learn/scikit-learn/issues/4846 | Bug | RidgeClassifier triggers data copy | RidgeClassifier always triggers a data copy even when not using sample weights.
Regression introduced in #4838.
See:
https://github.com/scikit-learn/scikit-learn/pull/4838#discussion_r32090535
| null | https://github.com/scikit-learn/scikit-learn/pull/4851 | null | {'base_commit': '99d08b571e4813e8d91d809b851b46e8cd5dd88f', 'files': [{'path': 'sklearn/linear_model/ridge.py', 'status': 'modified', 'Loc': {"('RidgeClassifier', 'fit', 575)": {'mod': [593, 594, 601, 602, 603]}, "('RidgeClassifierCV', 'fit', 1053)": {'mod': [1073, 1074, 1080, 1081, 1082]}}}]} | [] | [] | [] | {
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} | 1 |
scikit-learn | scikit-learn | c9e227b70d64f73b953d8d60629d6ac63e02a91c | https://github.com/scikit-learn/scikit-learn/issues/7467 | Bug | float numbers can't be set to RFECV's parameter "step" | #### Description
When I use RFECV with parameter 'step' as a float number will cause warnings/errors "rfe.py:203: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future". And the analysis can't be finished until integer or 1/2.
I read description of RFECV and learned that parameter 'step' can accept float. (introduction online: If greater than or equal to 1, then step corresponds to the (integer) number of features to remove at each iteration. If within (0.0, 1.0), then step corresponds to the percentage (rounded down) of features to remove at each iteration.)
And I didn't read any bugs from source script. Please tell.
| null | https://github.com/scikit-learn/scikit-learn/pull/7469 | null | {'base_commit': 'c9e227b70d64f73b953d8d60629d6ac63e02a91c', 'files': [{'path': 'sklearn/feature_selection/rfe.py', 'status': 'modified', 'Loc': {"('RFECV', 'fit', 378)": {'add': [398], 'mod': [427]}}}, {'path': 'sklearn/feature_selection/tests/test_rfe.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [184]}}}]} | [] | [] | [] | {
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scikit-learn | scikit-learn | 9b42b0cc7d5cf6978805619bc2433e3888c38d0c | https://github.com/scikit-learn/scikit-learn/issues/17814 | Bug | l1_ratio in sklearn.linear_model's ElasticNet greater than 1? | I accidentally ran ElasticNet (from sklearn.linear_model) for l1_ratio >1, and no error or warning was raised. From the docsstring, it says that ``0 < l1_ratio < 1``. Should we raise a ValueError or something? Found this with @mathurinm.
If this turns out to be something to be done, I could help out if someone could point me towards the right direction. Thanks !
p/s: Not sure if this should be under bugs/documentations/others, so I listed it under others. Sklearn version is 0.22.1. | null | https://github.com/scikit-learn/scikit-learn/pull/17846 | null | {'base_commit': '9b42b0cc7d5cf6978805619bc2433e3888c38d0c', 'files': [{'path': 'sklearn/linear_model/_coordinate_descent.py', 'status': 'modified', 'Loc': {"('ElasticNet', 'fit', 719)": {'add': [757]}}}, {'path': 'sklearn/linear_model/tests/test_coordinate_descent.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [60]}}}]} | [] | [] | [] | {
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} | 1 |
scikit-learn | scikit-learn | 38c7e93b1edcbfb85060cf7c14cca3ab47b9267c | https://github.com/scikit-learn/scikit-learn/issues/8499 | Bug | Memory leak in LogisticRegression | Dear all,
while running many logistic regressions, I encountered a continuous memory increase on several (Debian) machines. The problem is isolated in this code:
```python
import sklearn
from sklearn.linear_model import LogisticRegression
import numpy as np
import time
import psutil
import os
if __name__ == "__main__":
print("Sklearn version: %s" % sklearn.__version__)
n_samples = 2
n_features = 2
data = np.arange(n_samples*n_features).reshape((n_samples,n_features))
labels = np.arange(n_samples)
last_output_time = 0
process = psutil.Process(os.getpid())
for i in range(10000000):
clf = LogisticRegression()
clf.fit(X=data, y=labels)
del clf
if time.time()-last_output_time >= 5:
print(process.get_memory_info()[0] / float(2 ** 20))
last_output_time = time.time()
```
This was Python 2.7 under Linux 3.16.0-4-amd64 #1 SMP Debian 3.16.39-1+deb8u1 (2017-02-22) x86_64 GNU/Linux, with scikit-learn 0.18.1. Is this reproducable? | null | https://github.com/scikit-learn/scikit-learn/pull/9024 | null | {'base_commit': '38c7e93b1edcbfb85060cf7c14cca3ab47b9267c', 'files': [{'path': 'sklearn/svm/src/liblinear/liblinear_helper.c', 'status': 'modified', 'Loc': {"(None, 'free_problem', 217)": {'add': [221]}}}, {'path': 'sklearn/svm/src/liblinear/linear.cpp', 'status': 'modified', 'Loc': {"(None, 'free_model_content', 2907)": {'add': [2912]}}}]} | [] | [] | [] | {
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} | 1 |
scikit-learn | scikit-learn | e25e8e2119ab6c5aa5072b05c0eb60b10aee4b05 | https://github.com/scikit-learn/scikit-learn/issues/29906 | Bug | Incorrect sample weight handling in `KBinsDiscretizer` | ### Describe the bug
Sample weights are not properly passed through when specifying subsample within KBinsDiscretizer.
### Steps/Code to Reproduce
```python
from sklearn.datasets import make_blobs
from sklearn.preprocessing import KBinsDiscretizer
import numpy as np
rng = np.random.RandomState(42)
# Four centres
centres = np.array([[0, 0], [0, 5], [3, 1], [2, 4], [8, 8]])
X, _ = make_blobs(
n_samples=100,
cluster_std=0.5,
centers=centres,
random_state=10,
)
# Randomly generate sample weights
sample_weight = rng.randint(0, 10, size=X.shape[0])
est = KBinsDiscretizer(n_bins=4, strategy='quantile', subsample=20,
random_state=10).fit(X, sample_weight=sample_weight)
```
### Expected Results
No error is thrown
### Actual Results
```
[253](https://file+.vscode-resource.vscode-cdn.net/Users/shrutinath/sklearn-dev/~/sklearn-dev/scikit-learn/sklearn/preprocessing/_discretization.py:253) if sample_weight is not None:
--> [254](https://file+.vscode-resource.vscode-cdn.net/Users/shrutinath/sklearn-dev/~/sklearn-dev/scikit-learn/sklearn/preprocessing/_discretization.py:254) sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype)
[256](https://file+.vscode-resource.vscode-cdn.net/Users/shrutinath/sklearn-dev/~/sklearn-dev/scikit-learn/sklearn/preprocessing/_discretization.py:256) bin_edges = np.zeros(n_features, dtype=object)
[257](https://file+.vscode-resource.vscode-cdn.net/Users/shrutinath/sklearn-dev/~/sklearn-dev/scikit-learn/sklearn/preprocessing/_discretization.py:257) for jj in range(n_features):
File ~/sklearn-dev/scikit-learn/sklearn/utils/validation.py:2133, in _check_sample_weight(sample_weight, X, dtype, copy, ensure_non_negative)
[2130](https://file+.vscode-resource.vscode-cdn.net/Users/shrutinath/sklearn-dev/~/sklearn-dev/scikit-learn/sklearn/utils/validation.py:2130) raise ValueError("Sample weights must be 1D array or scalar")
[2132](https://file+.vscode-resource.vscode-cdn.net/Users/shrutinath/sklearn-dev/~/sklearn-dev/scikit-learn/sklearn/utils/validation.py:2132) if sample_weight.shape != (n_samples,):
-> [2133](https://file+.vscode-resource.vscode-cdn.net/Users/shrutinath/sklearn-dev/~/sklearn-dev/scikit-learn/sklearn/utils/validation.py:2133) raise ValueError(
[2134](https://file+.vscode-resource.vscode-cdn.net/Users/shrutinath/sklearn-dev/~/sklearn-dev/scikit-learn/sklearn/utils/validation.py:2134) "sample_weight.shape == {}, expected {}!".format(
[2135](https://file+.vscode-resource.vscode-cdn.net/Users/shrutinath/sklearn-dev/~/sklearn-dev/scikit-learn/sklearn/utils/validation.py:2135) sample_weight.shape, (n_samples,)
[2136](https://file+.vscode-resource.vscode-cdn.net/Users/shrutinath/sklearn-dev/~/sklearn-dev/scikit-learn/sklearn/utils/validation.py:2136) )
[2137](https://file+.vscode-resource.vscode-cdn.net/Users/shrutinath/sklearn-dev/~/sklearn-dev/scikit-learn/sklearn/utils/validation.py:2137) )
[2139](https://file+.vscode-resource.vscode-cdn.net/Users/shrutinath/sklearn-dev/~/sklearn-dev/scikit-learn/sklearn/utils/validation.py:2139) if ensure_non_negative:
[2140](https://file+.vscode-resource.vscode-cdn.net/Users/shrutinath/sklearn-dev/~/sklearn-dev/scikit-learn/sklearn/utils/validation.py:2140) check_non_negative(sample_weight, "`sample_weight`")
ValueError: sample_weight.shape == (100,), expected (20,)!
```
### Versions
```shell
System:
python: 3.12.4 | packaged by conda-forge | (main, Jun 17 2024, 10:13:44) [Clang 16.0.6 ]
executable: /Users/shrutinath/micromamba/envs/scikit-learn/bin/python
machine: macOS-14.3-arm64-arm-64bit
Python dependencies:
sklearn: 1.6.dev0
pip: 24.0
setuptools: 70.1.1
numpy: 2.0.0
scipy: 1.14.0
Cython: 3.0.10
pandas: 2.2.2
matplotlib: 3.9.0
joblib: 1.4.2
threadpoolctl: 3.5.0
Built with OpenMP: True
threadpoolctl info:
user_api: blas
internal_api: openblas
num_threads: 8
prefix: libopenblas
...
num_threads: 8
prefix: libomp
filepath: /Users/shrutinath/micromamba/envs/scikit-learn/lib/libomp.dylib
version: None
Output is truncated. View as a scrollable element or open in a text editor. Adjust cell output settings...
```
| null | https://github.com/scikit-learn/scikit-learn/pull/29907 | null | {'base_commit': 'e25e8e2119ab6c5aa5072b05c0eb60b10aee4b05', 'files': [{'path': 'sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py', 'status': 'modified', 'Loc': {"(None, 'make_missing_value_data', 564)": {'mod': [571]}}}, {'path': 'sklearn/inspection/tests/test_permutation_importance.py', 'status': 'modified', 'Loc': {"(None, 'test_permutation_importance_equivalence_array_dataframe', 303)": {'mod': [314]}}}, {'path': 'sklearn/preprocessing/_discretization.py', 'status': 'modified', 'Loc': {"('KBinsDiscretizer', None, 25)": {'add': [59, 177]}, "('KBinsDiscretizer', '__init__', 183)": {'add': [188, 195]}, "('KBinsDiscretizer', 'fit', 201)": {'add': [219, 242, 247, 248, 256, 276], 'mod': [216, 234, 235, 236, 237, 238, 239, 245, 253, 254, 259, 273, 275, 279, 280]}, '(None, None, None)': {'mod': [14]}}}, {'path': 'sklearn/preprocessing/tests/test_discretization.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [13, 14, 26, 27, 29, 31, 41, 46, 121, 126, 144, 286, 295, 304, 482], 'mod': [20, 22, 23, 24, 37, 115, 117, 118, 119, 123, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 248, 250, 251, 252, 277, 343]}, "(None, 'test_KBD_inverse_transform_Xt_deprecation', 484)": {'add': [500], 'mod': [484, 486]}, "(None, 'test_fit_transform', 52)": {'mod': [52, 53, 54, 55]}, "(None, 'test_valid_n_bins', 58)": {'mod': [59, 60, 61, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73]}, "(None, 'test_invalid_n_bins_array', 76)": {'mod': [79, 86, 93, 104]}, "(None, 'test_fit_transform_n_bins_array', 150)": {'mod': [150, 152, 154]}, "(None, 'test_kbinsdiscretizer_effect_sample_weight', 164)": {'mod': [169, 171, 172]}, "(None, 'test_kbinsdiscretizer_no_mutating_sample_weight', 176)": {'mod': [178]}, "(None, 'test_same_min_max', 186)": {'mod': [189]}, "(None, 'test_transform_1d_behavior', 199)": {'mod': [201, 205]}, "(None, 'test_numeric_stability', 212)": {'mod': [218]}, "(None, 'test_encode_options', 222)": {'mod': [223, 225, 234]}, "(None, 'test_nonuniform_strategies', 255)": {'mod': [256, 261, 266, 271]}, "(None, 'test_inverse_transform', 309)": {'mod': [309, 310]}, "(None, 'test_transform_outside_fit_range', 317)": {'mod': [319]}, "(None, 'test_overwrite', 328)": {'mod': [332]}, "(None, 'test_redundant_bins', 345)": {'mod': [345, 347]}, "(None, 'test_percentile_numeric_stability', 354)": {'mod': [358]}, "(None, 'test_consistent_dtype', 370)": {'mod': [372]}, "(None, 'test_32_equal_64', 389)": {'mod': [395, 400]}, "(None, 'test_kbinsdiscretizer_subsample_default', 407)": {'mod': [410]}, "(None, 'test_kbinsdiscrtizer_get_feature_names_out', 446)": {'mod': [452]}, "(None, 'test_kbinsdiscretizer_subsample', 463)": {'mod': [467, 468, 469]}}}, {'path': 'sklearn/preprocessing/tests/test_polynomial.py', 'status': 'modified', 'Loc': {"(None, 'test_spline_transformer_kbindiscretizer', 377)": {'mod': [389]}}}, {'path': 'sklearn/preprocessing/tests/test_target_encoder.py', 'status': 'modified', 'Loc': {"(None, 'test_invariance_of_encoding_under_label_permutation', 554)": {'mod': [564, 565, 566]}}}, {'path': 'sklearn/tests/test_docstring_parameters.py', 'status': 'modified', 'Loc': {"(None, 'test_fit_docstring_attributes', 181)": {'add': [226]}}}, {'path': 'sklearn/utils/_indexing.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [16, 416]}, "(None, 'resample', 420)": {'add': [453, 523], 'mod': [420, 434, 526]}}}, {'path': 'sklearn/utils/_test_common/instance_generator.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [565], 'mod': [962, 963, 964, 965, 966, 967, 968, 969, 970]}}}, {'path': 'sklearn/utils/stats.py', 'status': 'modified', 'Loc': {"(None, '_weighted_percentile', 9)": {'add': [72]}}}, {'path': 'sklearn/utils/tests/test_indexing.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [6, 497, 548]}}}, {'path': 'sklearn/utils/tests/test_stats.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [1], 'mod': [5]}}}]} | [] | [] | [] | {
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scikit-learn | scikit-learn | dcfb3df9a3df5aa2a608248316d537cd6b3643ee | https://github.com/scikit-learn/scikit-learn/issues/6656 | New Feature
module:ensemble | var.monotone option in GradientBoosting | Hi, is it possible to add the equivalent of the var.monotone option in R GBM package to the GradientBoostingClassifier/Regressor? Sometimes it is really useful when we know/want some factors to have monotonic effect to avoid overfitting and non-intuitive results.
Thanks!
| null | https://github.com/scikit-learn/scikit-learn/pull/15582 | null | {'base_commit': 'dcfb3df9a3df5aa2a608248316d537cd6b3643ee', 'files': [{'path': 'doc/modules/ensemble.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [1052], 'mod': [900]}}}, {'path': 'doc/whats_new/v0.23.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [186]}}}, {'path': 'sklearn/ensemble/_hist_gradient_boosting/common.pxd', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [32]}}}, {'path': 'sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py', 'status': 'modified', 'Loc': {"('BaseHistGradientBoosting', '__init__', 30)": {'add': [41], 'mod': [32, 33]}, "('BaseHistGradientBoosting', None, 26)": {'add': [84]}, "('BaseHistGradientBoosting', 'fit', 85)": {'add': [360]}, "('HistGradientBoostingRegressor', None, 725)": {'add': [792]}, "('HistGradientBoostingClassifier', None, 910)": {'add': [980]}, "('HistGradientBoostingRegressor', '__init__', 867)": {'mod': [870, 871, 878, 879]}, "('HistGradientBoostingClassifier', '__init__', 1059)": {'mod': [1061, 1062, 1070, 1071]}}}, {'path': 'sklearn/ensemble/_hist_gradient_boosting/grower.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [19]}, "('TreeNode', '__init__', 90)": {'add': [97], 'mod': [91]}, "('TreeGrower', '__init__', 167)": {'add': [191, 202], 'mod': [170, 171, 198]}, "('TreeGrower', None, 116)": {'add': [254]}, "('TreeNode', None, 25)": {'mod': [74]}, "('TreeGrower', '_intilialize_root', 255)": {'mod': [268]}, "('TreeGrower', '_compute_best_split_and_push', 286)": {'mod': [297]}, "('TreeGrower', 'split_next', 304)": {'mod': [332, 337, 375, 377, 378]}, "('TreeGrower', '_finalize_leaf', 414)": {'mod': [415, 417, 418, 420, 421, 422, 423, 424, 425]}, "(None, '_fill_predictor_node_array', 455)": {'mod': [467, 470]}}}, {'path': 'sklearn/ensemble/_hist_gradient_boosting/splitting.pyx', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [21, 26, 41, 83, 128, 143, 154, 368, 388, 432, 458, 485, 492, 544, 570, 668], 'mod': [73, 353, 381, 407, 415, 484, 489, 490, 491, 522, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 568, 574, 575, 576, 607, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 628, 641, 642, 643, 644, 645, 648, 649, 650, 651, 652]}}}, {'path': 'sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py', 'status': 'modified', 'Loc': {"(None, 'test_early_stopping_on_test_set_with_warm_start', 650)": {'add': [661]}}}, {'path': 'sklearn/ensemble/_hist_gradient_boosting/tests/test_grower.py', 'status': 'modified', 'Loc': {"(None, 'test_grow_tree', 77)": {'add': [136]}, "(None, 'test_split_on_nan_with_infinite_values', 358)": {'mod': [396, 397]}}}, {'path': 'sklearn/ensemble/_hist_gradient_boosting/tests/test_splitting.py', 'status': 'modified', 'Loc': {"(None, 'test_histogram_split', 13)": {'add': [45, 50, 56], 'mod': [59]}, "(None, 'test_gradient_and_hessian_sanity', 72)": {'add': [108, 115, 121, 122], 'mod': [111, 117, 125, 128]}, "(None, 'test_split_indices', 172)": {'add': [208, 217], 'mod': [211, 219]}, "(None, 'test_min_gain_to_split', 239)": {'add': [265, 272], 'mod': [268, 274]}, "(None, 'test_splitting_missing_values', 368)": {'add': [402, 405, 410], 'mod': [412]}, '(None, None, None)': {'mod': [7]}}}]} | [] | [] | [] | {
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"sklearn/ensemble/_hist_gradient_boosting/grower.py",
"sklearn/ensemble/_hist_gradient_boosting/splitting.pyx",
"sklearn/ensemble/_hist_gradient_boosting/common.pxd",
"sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py"
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"doc/modules/ensemble.rst",
"doc/whats_new/v0.23.rst"
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"sklearn/ensemble/_hist_gradient_boosting/tests/test_grower.py",
"sklearn/ensemble/_hist_gradient_boosting/tests/test_splitting.py",
"sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py"
],
"config": [],
"asset": []
} | 1 |
scikit-learn | scikit-learn | 417788c6a54c39614b82acf1a04b1f97f8a32199 | https://github.com/scikit-learn/scikit-learn/issues/6783 | "scoring must return a number" error with custom scorer | #### Description
I'm encountering the same error (`ValueError: scoring must return a number, got [...] (<class 'numpy.core.memmap.memmap'>) instead.`) as #6147, despite running v0.17.1. This is because I'm creating my own scorer, following the example in this [article](http://bigdataexaminer.com/data-science/dealing-with-unbalanced-classes-svm-random-forests-and-decision-trees-in-python/).
#### Steps/Code to Reproduce
``` python
import pandas as pd
import numpy as np
from sklearn.cross_validation import cross_val_score
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import make_classification
from functools import partial
def cutoff_predict(clf, X, cutoff):
return (clf.predict_proba(X)[:, 1] > cutoff).astype(int)
def perc_diff_score(y, ypred, X=None):
values = X[:,0]
actual_value = np.sum(np.multiply(y, values))
predict_value = np.sum(np.multiply(ypred, values))
difference = predict_value - actual_value
percent_diff = abs(difference * 100 / actual_value )
return -1*percent_diff
def perc_diff_cutoff(clf, X, y, cutoff=None):
ypred = cutoff_predict(clf, X, cutoff)
return perc_diff_score(y, ypred, X)
def perc_diff_score_cutoff(cutoff):
return partial(perc_diff_cutoff, cutoff=cutoff)
clf = RandomForestClassifier()
X_train, y_train = make_classification(n_samples=int(1e6), n_features=5, random_state=0)
values = abs(100000 * np.random.randn(len(X_train))).reshape((X_train.shape[0], 1))
X_train = np.append(values, X_train, 1)
cutoff = 0.1
validated = cross_val_score(clf, X_train, y_train, scoring=perc_diff_score_cutoff(cutoff),
verbose=3,
n_jobs=-1,
)
```
#### Expected Results
No error.
#### Actual Results
Same error as in #6147 :
```
/home/gillesa/anaconda2/lib/python2.7/site-packages/sklearn/cross_validation.pyc in _score(estimator=ExtraTreesClassifier(bootstrap=False, class_weig..., random_state=None, verbose=0, warm_start=False), X_test=memmap([[ 0., 9., 56., ..., 1., 0., 0.... [ 0., 6., 57., ..., 1., 0., 0.]]), y_test=memmap([0, 0, 0, ..., 0, 0, 0]), scorer=make_scorer(roc_auc_score, needs_threshold=True))
1604 score = scorer(estimator, X_test)
1605 else:
1606 score = scorer(estimator, X_test, y_test)
1607 if not isinstance(score, numbers.Number):
1608 raise ValueError("scoring must return a number, got %s (%s) instead."
-> 1609 % (str(score), type(score)))
1610 return score
1611
1612
1613 def _permutation_test_score(estimator, X, y, cv, scorer):
ValueError: scoring must return a number, got 0.671095795498 (<class 'numpy.core.memmap.memmap'>) instead.
```
#### Workaround
Updated `perc_diff_score()` as follows to add cast to `float`.:
``` python
def perc_diff_score(y, ypred, X=None):
values = X[:,0]
actual_value = np.sum(np.multiply(y, values))
predict_value = np.sum(np.multiply(ypred, values))
difference = predict_value - actual_value
percent_diff = np.float(abs(difference * 100 / actual_value ))
return -1*percent_diff
```
#### Versions
Darwin-15.4.0-x86_64-i386-64bit
Python 3.5.1 |Anaconda 4.0.0 (x86_64)| (default, Dec 7 2015, 11:24:55)
[GCC 4.2.1 (Apple Inc. build 5577)]import numpy; print("NumPy", numpy.**version**)
NumPy 1.11.0
SciPy 0.17.0
Scikit-Learn 0.17.1
| null | https://github.com/scikit-learn/scikit-learn/pull/6789 | null | {'base_commit': '417788c6a54c39614b82acf1a04b1f97f8a32199', 'files': [{'path': 'sklearn/cross_validation.py', 'status': 'modified', 'Loc': {"(None, '_score', 1645)": {'add': [1650]}}}, {'path': 'sklearn/model_selection/_validation.py', 'status': 'modified', 'Loc': {"(None, '_score', 298)": {'add': [303]}}}, {'path': 'sklearn/model_selection/tests/test_validation.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [5]}, "(None, 'test_cross_val_predict_with_method', 746)": {'add': [771]}}}]} | [] | [] | [] | {
"iss_type": "4",
"iss_reason": "2",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"sklearn/model_selection/_validation.py",
"sklearn/cross_validation.py"
],
"doc": [],
"test": [
"sklearn/model_selection/tests/test_validation.py"
],
"config": [],
"asset": []
} | 1 | |
scikit-learn | scikit-learn | 3f49cee020a91a0be5d0d5602d29b3eefce9d758 | https://github.com/scikit-learn/scikit-learn/issues/3722 | Bug
Easy | preprocessing.scale provides consistent results on arrays with zero variance | I'm using Python 2.7, NumPy 1.8.2 and scikit-learn 0.14.1 on x64 linux (all installed through Anaconda) and getting very inconsistent results for preprocessing.scale function:
> print preprocessing.scale(np.zeros(6) + np.log(1e-5))
> [ 0. 0. 0. 0. 0. 0.]
>
> print preprocessing.scale(np.zeros(8) + np.log(1e-5))
> [-1. -1. -1. -1. -1. -1. -1. -1.]
>
> print preprocessing.scale(np.zeros(22) + np.log(1e-5))
> [ 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
I would guess this is not is supposed to be happening. Quick investigation, points to the fact that np.std() of second and third array is not exactly zero, but very close to machine zero. sklearn still uses it to divide data (it doesn't go into the "std == 0.0" case in the code).
Note that in the case of the array, this can be easily fixed by passing with_std=False, but when that happens for one of the many features in 2D matrix this is not an option.
| null | https://github.com/scikit-learn/scikit-learn/pull/4436 | null | {'base_commit': 'ad26ae47057885415f74893d6329a481b0ce01bd', 'files': [{'path': 'doc/whats_new.rst', 'status': 'modified', 'Loc': {'(None, None, 231)': {'add': [231]}, '(None, None, 3378)': {'add': [3378]}}}, {'path': 'sklearn/preprocessing/_weights.py', 'status': 'modified', 'Loc': {}}, {'path': 'sklearn/preprocessing/data.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [9, 20]}, "(None, 'scale', 69)": {'add': [143, 145]}, "(None, '_mean_and_std', 44)": {'mod': [60]}}}, {'path': 'sklearn/preprocessing/tests/test_data.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [4, 16, 101]}, "(None, 'test_one_hot_encoder_unknown_transform', 816)": {'mod': [831]}}}]} | [] | [] | [] | {
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"iss_reason": "1",
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} | {
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"sklearn/preprocessing/_weights.py",
"sklearn/preprocessing/data.py"
],
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"doc/whats_new.rst"
],
"test": [
"sklearn/preprocessing/tests/test_data.py"
],
"config": [],
"asset": []
} | 1 |
scikit-learn | scikit-learn | eda99f3cec70ba90303de0ef3ab7f988657fadb9 | https://github.com/scikit-learn/scikit-learn/issues/13362 | Bug
Blocker | return_intercept==True in ridge_regression raises an exception | <!--
If your issue is a usage question, submit it here instead:
- StackOverflow with the scikit-learn tag: https://stackoverflow.com/questions/tagged/scikit-learn
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For more information, see User Questions: http://scikit-learn.org/stable/support.html#user-questions
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<!-- Instructions For Filing a Bug: https://github.com/scikit-learn/scikit-learn/blob/master/CONTRIBUTING.md#filing-bugs -->
#### Description
<!-- Example: Joblib Error thrown when calling fit on LatentDirichletAllocation with evaluate_every > 0-->
#### Steps/Code to Reproduce
```python
from sklearn.linear_model import ridge_regression
ridge_regression([[0], [1], [3]], [0, 1, 3], 1, solver='auto', return_intercept=True)
```
#### Expected Results
<!-- Example: No error is thrown. Please paste or describe the expected results.-->
`(array([1]), 0)` (the values can differ, but at least no exception should be raised)
#### Actual Results
<!-- Please paste or specifically describe the actual output or traceback. -->
```
---------------------------------------------------------------------------
UnboundLocalError Traceback (most recent call last)
<ipython-input-5-84df44249e86> in <module>
----> 1 ridge_regression([[0], [1], [3]], [1, 2, 3], 1, solver='auto', return_intercept=True)
~/.pyenv/versions/3.7.2/envs/kaggle-3.7.2/lib/python3.7/site-packages/sklearn/linear_model/ridge.py in ridge_regression(X, y, alpha, sample_weight, solver, max_iter, tol, verbose, random_state, return_n_iter, return_intercept)
450 return coef, n_iter, intercept
451 elif return_intercept:
--> 452 return coef, intercept
453 elif return_n_iter:
454 return coef, n_iter
UnboundLocalError: local variable 'intercept' referenced before assignment
```
#### Versions
<!--
Please run the following snippet and paste the output below.
For scikit-learn >= 0.20:
import sklearn; sklearn.show_versions()
For scikit-learn < 0.20:
import platform; print(platform.platform())
import sys; print("Python", sys.version)
import numpy; print("NumPy", numpy.__version__)
import scipy; print("SciPy", scipy.__version__)
import sklearn; print("Scikit-Learn", sklearn.__version__)
-->
```
Linux-4.20.8-arch1-1-ARCH-x86_64-with-arch
Python 3.7.2 (default, Feb 22 2019, 18:13:04)
[GCC 8.2.1 20181127]
NumPy 1.16.1
SciPy 1.2.1
Scikit-Learn 0.21.dev0
```
<!-- Thanks for contributing! -->
| null | https://github.com/scikit-learn/scikit-learn/pull/13363 | null | {'base_commit': 'eda99f3cec70ba90303de0ef3ab7f988657fadb9', 'files': [{'path': 'doc/whats_new/v0.21.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [342]}}}, {'path': 'sklearn/linear_model/ridge.py', 'status': 'modified', 'Loc': {"(None, '_ridge_regression', 366)": {'mod': [371, 372, 373, 374, 375, 376, 407, 409, 410, 411, 412, 413, 414, 435, 436]}, "('_BaseRidge', 'fit', 527)": {'mod': [558]}}}, {'path': 'sklearn/linear_model/tests/test_ridge.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [9]}, "(None, 'test_raises_value_error_if_solver_not_supported', 774)": {'mod': [781]}, "(None, 'test_ridge_fit_intercept_sparse', 816)": {'mod': [835, 836, 837]}}}]} | [] | [] | [] | {
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"sklearn/linear_model/ridge.py"
],
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"doc/whats_new/v0.21.rst"
],
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"sklearn/linear_model/tests/test_ridge.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | df2fb490a58f272067b33aad372bb4fe2393bb93 | https://github.com/pandas-dev/pandas/issues/7261 | Bug
Missing-data
Dtype Conversions | API: Should Index.min and max use nanmin and nanmax? | Index and Series `min` and `max` handles `nan` and `NaT` differently. Even though `min` and `max` are defined in `IndexOpsMixin`, `Series` doesn't use them and use `NDFrame` definitions.
```
pd.Index([np.nan, 1.0]).min()
# nan
pd.Index([np.nan, 1.0]).max()
# nan
pd.DatetimeIndex([pd.NaT, '2011-01-01']).min()
# NaT
pd.DatetimeIndex([pd.NaT, '2011-01-01']).max()
#2011-01-01 00:00:00
# Series excludes nan and NaT
pd.Series([np.nan, 1.0]).min()
#1.0
pd.Series([np.nan, 1.0]).max()
#1.0
pd.Series([pd.NaT, pd.Timestamp('2011-01-01')]).min()
#2011-01-01 00:00:00
pd.Series([pd.NaT, pd.Timestamp('2011-01-01')]).max()
#2011-01-01 00:00:00
```
| null | https://github.com/pandas-dev/pandas/pull/7279 | null | {'base_commit': 'df2fb490a58f272067b33aad372bb4fe2393bb93', 'files': [{'path': 'doc/source/v0.14.1.txt', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [67]}}}, {'path': 'pandas/core/base.py', 'status': 'modified', 'Loc': {"('IndexOpsMixin', 'max', 237)": {'mod': [239]}, "('IndexOpsMixin', 'min', 241)": {'mod': [243]}}}, {'path': 'pandas/tests/test_base.py', 'status': 'modified', 'Loc': {"('TestIndexOps', None, 192)": {'add': [212]}, '(None, None, None)': {'mod': [2]}}}, {'path': 'pandas/tseries/index.py', 'status': 'modified', 'Loc': {"('DatetimeIndex', 'min', 1757)": {'mod': [1761, 1762, 1764]}, "('DatetimeIndex', 'max', 1767)": {'mod': [1771, 1772, 1774]}}}]} | [] | [] | [] | {
"iss_type": "2",
"iss_reason": "1",
"loc_way": "pr",
"loc_scope": null,
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} | {
"code": [
"pandas/core/base.py",
"pandas/tseries/index.py"
],
"doc": [
"doc/source/v0.14.1.txt"
],
"test": [
"pandas/tests/test_base.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | abd5333e7a3332921707888de9621c52dd3408e6 | https://github.com/pandas-dev/pandas/issues/7943 | Enhancement
API Design
Timezones | tz_localize should support is_dst input array | When storing datetimes with timezone information in mysql I split out the is_dst flag into a separate column. Then when reconstructing the Timestamps I am either forced to iterate through each row and call pytz.timezone.localize on every Timestamp which is very slow or do some magic with localizing what I can and then manually dealing with the fall transition time (note that infer_dst won't work because there could be many rows that have transitions in them). I would much rather create the DatetimeIndex from the column of dates and then call tz_localize with the is_dst column. This would then appropriately set the offset.
```
di = DatetimeIndex(frame['DateColumn'])
di = di.tz_localize(TimeZone, is_dst_flat=frame['IsDstColumn'])
```
Thoughts?
| null | https://github.com/pandas-dev/pandas/pull/7963 | null | {'base_commit': 'abd5333e7a3332921707888de9621c52dd3408e6', 'files': [{'path': 'doc/source/timeseries.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [1359, 1490, 1509], 'mod': [1492, 1493, 1494, 1503, 1507, 1511, 1512, 1513, 1514, 1516, 1517]}}}, {'path': 'doc/source/v0.15.0.txt', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [468, 547]}}}, {'path': 'pandas/core/generic.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [26]}, "('NDFrame', None, 68)": {'mod': [3562]}, "('NDFrame', 'tz_localize', 3562)": {'mod': [3575, 3576, 3584, 3600, 3605]}, "('NDFrame', '_tz_localize', 3584)": {'mod': [3593]}}}, {'path': 'pandas/tseries/index.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [8], 'mod': [21]}, "('DatetimeIndex', None, 122)": {'add': [147, 182], 'mod': [1648]}, "('DatetimeIndex', '__new__', 183)": {'mod': [187, 191, 217, 243, 312]}, "('DatetimeIndex', '_generate', 335)": {'mod': [336, 450]}, "('DatetimeIndex', 'tz_localize', 1648)": {'mod': [1659, 1674]}}}, {'path': 'pandas/tseries/tests/test_timezones.py', 'status': 'modified', 'Loc': {"('TestTimeZoneSupportPytz', 'test_infer_dst', 426)": {'add': [443, 449], 'mod': [432, 433, 438, 439, 440, 441, 448]}, "('TestTimeZoneSupportPytz', None, 58)": {'add': [450], 'mod': [426]}}}, {'path': 'pandas/tseries/tests/test_tslib.py', 'status': 'modified', 'Loc': {"('TestTimestamp', 'test_tz', 216)": {'add': [234]}}}, {'path': 'pandas/tslib.pyx', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [378, 381, 2201, 2222, 2309], 'mod': [362, 372, 373, 383, 1768, 1769, 2186, 2313]}}}]} | [] | [] | [] | {
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"pandas/core/generic.py",
"pandas/tslib.pyx",
"pandas/tseries/index.py"
],
"doc": [
"doc/source/timeseries.rst",
"doc/source/v0.15.0.txt"
],
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"pandas/tseries/tests/test_timezones.py",
"pandas/tseries/tests/test_tslib.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | a9421af1aac906cc38d025ed5db4a2b55cb8b9bc | https://github.com/pandas-dev/pandas/issues/16773 | Performance
Sparse | SparseDataFrame constructor has horrible performance for df with many columns | #### Code Sample
This is an example taken directly from the [docs](https://pandas.pydata.org/pandas-docs/stable/sparse.html#sparsedataframe), only that I've changed the sparsity of the arrays from 90% to 99%.
```python
import pandas as pd
from scipy.sparse import csr_matrix
import numpy as np
arr = np.random.random(size=(1000, 5))
arr[arr < .99] = 0
sp_arr = csr_matrix(arr)
%timeit sdf = pd.SparseDataFrame(sp_arr)
```
```
4.78 ms ± 381 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
```
Now, here's what happens when I increase the number of columns from 5 to 2000:
```python
import pandas as pd
from scipy.sparse import csr_matrix
import numpy as np
arr = np.random.random(size=(1000, 2000))
arr[arr < .99] = 0
sp_arr = csr_matrix(arr)
%timeit sdf = pd.SparseDataFrame(sp_arr)
```
```
8.69 s ± 208 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
```
Note that initializing a the `scipy.sparse.csr_matrix` object itself is way (!!!) faster:
```python
import pandas as pd
from scipy.sparse import csr_matrix
import numpy as np
arr = np.random.random(size=(1000, 2000))
arr[arr < .99] = 0
%timeit sp_arr = csr_matrix(arr)
```
```
13 ms ± 248 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
```
#### Problem description
The construction of a SparseDataFrame with many columns is ridiculously slow. I've traced the problem to [this line](https://github.com/pandas-dev/pandas/blob/1c0b63281db0486aa8182d550e9bceb641e5f9a4/pandas/core/sparse/frame.py#L162) in the `SparseDataFrame._init_dict()` function. I don't know why the data frame is constructed by assigning individual columns of a `DataFrame` object. I think the `DataFrame._init_dict` method uses a much more efficient method.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.10.0-24-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.20.2
pytest: 3.1.2
pip: 9.0.1
setuptools: 36.0.1
Cython: 0.25.2
numpy: 1.12.1
scipy: 0.19.0
xarray: None
IPython: 6.1.0
sphinx: 1.6.1
patsy: None
dateutil: 2.6.0
pytz: 2017.2
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: 0.9.6
lxml: None
bs4: 4.6.0
html5lib: 0.999999999
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
pandas_gbq: None
pandas_datareader: None
</details>
| null | https://github.com/pandas-dev/pandas/pull/16883 | null | {'base_commit': 'a9421af1aac906cc38d025ed5db4a2b55cb8b9bc', 'files': [{'path': 'asv_bench/benchmarks/sparse.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [0, 29]}}}, {'path': 'doc/source/whatsnew/v0.21.0.txt', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [137]}}}, {'path': 'pandas/core/sparse/frame.py', 'status': 'modified', 'Loc': {"('SparseDataFrame', '_init_dict', 131)": {'mod': [146, 166, 167, 168, 169, 170]}}}, {'path': 'pandas/tests/reshape/test_reshape.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [645]}}}, {'path': 'pandas/tests/sparse/test_frame.py', 'status': 'modified', 'Loc': {"('TestSparseDataFrame', None, 29)": {'add': [1097]}}}]} | [] | [] | [] | {
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],
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"pandas/tests/reshape/test_reshape.py",
"pandas/tests/sparse/test_frame.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | ba48fc4a033f11513fa2dd44c946e18b7bc27ad2 | https://github.com/pandas-dev/pandas/issues/26058 | Docs
CI | DOC: test new sphinx 2 release | The docs are currently being built with sphinx 1.8.5 (see eg https://travis-ci.org/pandas-dev/pandas/jobs/518832177 for a recent build on master).
Sphinx has released 2.0.0 (http://www.sphinx-doc.org/en/master/changes.html#release-2-0-0-released-mar-29-2019), and it would be good to test our docs with this new release, and see if we need to make changes / report regressions to sphinx.
For somebody wanting to tackle this:
- test it locally to see if there are big problems with building the docs
- make a PR that ensures sphinx 2 is installed in the doc environment, so we can check the build log on travis (I am actually not fully sure why it is not yet picking up sphinx 2 on travis, since we don't pin the version in the [travis-36-doc.yaml file](https://github.com/pandas-dev/pandas/blob/a07ed594ec6a5befc967fb1b18244bbeb3bc2bf1/ci/deps/travis-36-doc.yaml#L36) | null | https://github.com/pandas-dev/pandas/pull/26519 | null | {'base_commit': 'ba48fc4a033f11513fa2dd44c946e18b7bc27ad2', 'files': [{'path': 'pandas/core/indexes/base.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [54]}, "('Index', None, 165)": {'add': [2790]}}}, {'path': 'pandas/core/indexes/interval.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [11]}, "('IntervalIndex', None, 127)": {'mod': [808]}}}]} | [] | [] | [] | {
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],
"doc": [],
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"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | 45d8d77f27cf0dbc8cefe932f8fb64f6982b9527 | https://github.com/pandas-dev/pandas/issues/10078 | good first issue
Needs Tests | Pandas attempts to convert some strings to timestamps when grouping by a timestamp and aggregating? | I am working through logs of web requests, and when I want to find the most common, say, user agent string for a (disguised) user, I run something like the following:
```
from pandas import Series, DataFrame, Timestamp
tdf = DataFrame({'day': {0: Timestamp('2015-02-24 00:00:00'), 1: Timestamp('2015-02-24 00:00:00'),
2: Timestamp('2015-02-24 00:00:00'), 3: Timestamp('2015-02-24 00:00:00'),
4: Timestamp('2015-02-24 00:00:00')},
'userAgent': {0: 'some UA string', 1: 'some UA string', 2: 'some UA string',
3: 'another UA string', 4: 'some UA string'},
'userId': {0: '17661101', 1: '17661101', 2: '17661101', 3: '17661101', 4: '17661101'}})
def most_common_values(df):
return Series({c: s.value_counts().index[0] for c,s in df.iteritems()})
tdf.groupby('day').apply(most_common_values)
```
Note that in this (admittedly unusual) example, all of the lines are identical. I'm not sure if that is necessary to recreate the issue. And, I'm obscuring the exact purpose of this code, but it reproduces the bug: The 'userId' comes back as a Timestamp, not a string. This happens after the function most_common_values returns, since that userId string is not returned as a timestamp. if we change the value of the userId to an int:
```
tdf['userId'] = tdf.userId.astype(int)
```
or if the value of the associated integer is small enough:
```
tdf['userId'] = '15320104`
```
then the results are what we'd expect (the most common value as its original type is returned.)
I imagine that for some reason something like a dateutil parser is being called on strings by default but that probably shoulnd't be happening...
| null | https://github.com/pandas-dev/pandas/pull/30646 | null | {'base_commit': '45d8d77f27cf0dbc8cefe932f8fb64f6982b9527', 'files': [{'path': 'pandas/tests/frame/test_constructors.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [2427], 'mod': [2]}}}, {'path': 'pandas/tests/frame/test_missing.py', 'status': 'modified', 'Loc': {"('TestDataFrameInterpolate', 'test_interp_ignore_all_good', 948)": {'add': [972]}}}, {'path': 'pandas/tests/groupby/test_apply.py', 'status': 'modified', 'Loc': {"(None, 'test_apply_datetime_issue', 704)": {'add': [716]}}}, {'path': 'pandas/tests/groupby/test_categorical.py', 'status': 'modified', 'Loc': {"(None, 'test_series_groupby_on_2_categoricals_unobserved_zeroes_or_nans', 1309)": {'add': [1332]}}}, {'path': 'pandas/tests/groupby/test_groupby.py', 'status': 'modified', 'Loc': {"(None, 'test_groupby_crash_on_nunique', 2011)": {'add': [2025]}}}, {'path': 'pandas/tests/indexing/multiindex/test_loc.py', 'status': 'modified', 'Loc': {"(None, 'test_loc_nan_multiindex', 416)": {'add': [439]}}}, {'path': 'pandas/tests/indexing/test_loc.py', 'status': 'modified', 'Loc': {"(None, 'test_loc_setitem_float_intindex', 974)": {'add': [985]}}}, {'path': 'pandas/tests/io/parser/test_index_col.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [7]}, "(None, 'test_multi_index_naming_not_all_at_beginning', 163)": {'add': [174]}}}, {'path': 'pandas/tests/reshape/test_concat.py', 'status': 'modified', 'Loc': {"(None, 'test_concat_datetimeindex_freq', 2719)": {'add': [2732]}}}, {'path': 'pandas/tests/reshape/test_pivot.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [1967]}}}]} | [] | [] | [] | {
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"pandas/tests/reshape/test_pivot.py",
"pandas/tests/groupby/test_apply.py",
"pandas/tests/indexing/test_loc.py",
"pandas/tests/frame/test_constructors.py",
"pandas/tests/indexing/multiindex/test_loc.py",
"pandas/tests/groupby/test_groupby.py",
"pandas/tests/reshape/test_concat.py",
"pandas/tests/frame/test_missing.py",
"pandas/tests/io/parser/test_index_col.py",
"pandas/tests/groupby/test_categorical.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | 636dd01fdacba0c8f0e7b5aaa726165983fc861d | https://github.com/pandas-dev/pandas/issues/21356 | IO JSON
good first issue | JSON nested_to_record Silently Drops Top-Level None Values | xref https://github.com/pandas-dev/pandas/pull/21164#issuecomment-394510095
`nested_to_record` is silently dropping `None` values that appear at the top of the JSON. This is IMO unexpected and undesirable.
#### Code Sample, a copy-pastable example if possible
```python
In [3]: data = {
...: "id": None,
...: "location": {
...: "country": None
...: }
...: }
In [5]: nested_to_record(data)
Out[5]: {'location.country': None}
```
#### Problem description
The top level `None` value should not be dropped but rather preserved along with lower levels for consistency.
#### Expected Output
```python
In [5]: nested_to_record(data)
Out[5]: {'id': None, 'location.country': None}
```
Note this will break a few tests in `pandas/test_normalize.py`
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: ab6aaf73a848a8725a23bb880be5221dd5ef5b3d
python: 3.6.4.final.0
python-bits: 64
OS: Darwin
OS-release: 17.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.0.dev0+67.gab6aaf73a
pytest: 3.4.1
pip: 10.0.1
setuptools: 38.5.1
Cython: 0.27.3
numpy: 1.14.1
scipy: 1.0.0
pyarrow: 0.8.0
xarray: 0.10.0
IPython: 6.2.1
sphinx: 1.7.0
patsy: 0.5.0
dateutil: 2.6.1
pytz: 2018.3
blosc: None
bottleneck: 1.2.1
tables: 3.4.2
numexpr: 2.6.4
feather: 0.4.0
matplotlib: 2.1.2
openpyxl: 2.5.0
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.0.2
lxml: 4.1.1
bs4: 4.6.0
html5lib: 1.0.1
sqlalchemy: 1.2.5
pymysql: 0.8.0
psycopg2: 2.7.4 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: 0.1.3
fastparquet: 0.1.4
pandas_gbq: 0.4.1
pandas_datareader: None
</details>
| null | https://github.com/pandas-dev/pandas/pull/21363 | null | {'base_commit': '636dd01fdacba0c8f0e7b5aaa726165983fc861d', 'files': [{'path': 'doc/source/whatsnew/v0.23.1.txt', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [33]}}}, {'path': 'pandas/io/json/normalize.py', 'status': 'modified', 'Loc': {"(None, 'nested_to_record', 24)": {'mod': [83, 84]}}}, {'path': 'pandas/tests/io/json/test_normalize.py', 'status': 'modified', 'Loc': {"('TestNestedToRecord', 'test_nonetype_top_level_bottom_level', 379)": {'add': [397]}, "('TestNestedToRecord', 'test_nonetype_multiple_levels', 406)": {'add': [425]}, "('TestJSONNormalize', 'test_missing_field', 240)": {'mod': [241, 242, 245, 249]}, "('TestNestedToRecord', None, 258)": {'mod': [354, 355, 356]}, "('TestNestedToRecord', 'test_nonetype_dropping', 354)": {'mod': [370]}}}]} | [] | [] | [] | {
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],
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"pandas/tests/io/json/test_normalize.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | 19f715c51d16995fc6cd0c102fdba2f213a83a0f | https://github.com/pandas-dev/pandas/issues/24607 | Missing-data
Complex | DES: Should util.is_nan check for complex('nan')? | It doesn't at the moment. A handful of functions in libs.missing _do_ check for complex nan, and could be simplified/de-duplicated if we make util.is_nan also catch the complex case. | null | https://github.com/pandas-dev/pandas/pull/24628 | null | {'base_commit': 'd106e9975100cd0f2080d7b1a6111f20fb64f906', 'files': [{'path': 'pandas/_libs/missing.pyx', 'status': 'modified', 'Loc': {'(None, None, 15)': {'mod': [15]}, '(None, None, 23)': {'mod': [23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39]}, '(None, None, 65)': {'mod': [65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76]}, '(None, None, 104)': {'mod': [104, 105, 106, 107, 108]}, '(None, None, 110)': {'mod': [110, 111, 112, 113, 114, 115]}, '(None, None, 131)': {'mod': [131]}, '(None, None, 157)': {'mod': [157]}, '(None, None, 192)': {'mod': [192]}, '(None, None, 302)': {'mod': [302]}, '(None, None, 312)': {'mod': [312]}}}, {'path': 'pandas/_libs/tslibs/nattype.pxd', 'status': 'modified', 'Loc': {'(None, None, 20)': {'mod': [20]}}}, {'path': 'pandas/_libs/tslibs/nattype.pyx', 'status': 'modified', 'Loc': {'(None, None, 16)': {'add': [16]}, '(None, None, 695)': {'add': [695]}, '(None, None, 704)': {'add': [704]}, '(None, None, 689)': {'mod': [689]}, '(None, None, 701)': {'mod': [701]}, '(None, None, 706)': {'mod': [706]}, '(None, None, 708)': {'mod': [708, 709]}}}, {'path': 'pandas/_libs/tslibs/util.pxd', 'status': 'modified', 'Loc': {'(None, None, 218)': {'mod': [218]}, '(None, None, 228)': {'mod': [228]}}}, {'path': 'pandas/tests/dtypes/test_missing.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [3], 'mod': [10]}, "('TestNAObj', 'test_empty_like', 389)": {'add': [394]}}}]} | [] | [] | [] | {
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"pandas/_libs/tslibs/nattype.pyx",
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"doc": [],
"test": [
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"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | a797b28c87d90a439dfa2c12b4a11e62bf0d6db2 | https://github.com/pandas-dev/pandas/issues/7778 | Bug
Datetime
Dtype Conversions
Timedelta | BUG: df.apply handles np.timedelta64 as timestamp, should be timedelta | I think there may be a bug with the row-wise handling of `numpy.timedelta64` data types when using `DataFrame.apply`. As a check, the problem does not appear when using `DataFrame.applymap`. The problem may be related to #4532, but I'm unsure. I've included an example below.
This is only a minor problem for my use-case, which is cross-checking timestamps from a counter/timer card. I can easily work around the issue with `DataFrame.itertuples` etc.
Thank you for your time and for making such a useful package!
#### Example
##### Version
Import and check versions.
```
$ date
Thu Jul 17 16:28:38 CDT 2014
$ conda update pandas
Fetching package metadata: ..
# All requested packages already installed.
# packages in environment at /Users/harrold/anaconda:
#
pandas 0.14.1 np18py27_0
$ ipython
Python 2.7.8 |Anaconda 2.0.1 (x86_64)| (default, Jul 2 2014, 15:36:00)
Type "copyright", "credits" or "license" for more information.
IPython 2.1.0 -- An enhanced Interactive Python.
Anaconda is brought to you by Continuum Analytics.
Please check out: http://continuum.io/thanks and https://binstar.org
? -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help -> Python's own help system.
object? -> Details about 'object', use 'object??' for extra details.
In [1]: from __future__ import print_function
In [2]: import numpy as np
In [3]: import pandas as pd
In [4]: pd.util.print_versions.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.8.final.0
python-bits: 64
OS: Darwin
OS-release: 11.4.2
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.14.1
nose: 1.3.3
Cython: 0.20.1
numpy: 1.8.1
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.1.0
sphinx: 1.2.2
patsy: 0.2.1
scikits.timeseries: None
dateutil: 1.5
pytz: 2014.4
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.3.1
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.5.5
lxml: 3.3.5
bs4: 4.3.1
html5lib: 0.999
httplib2: 0.8
apiclient: 1.2
rpy2: None
sqlalchemy: 0.9.4
pymysql: None
psycopg2: None
```
##### Create test data
Using subset of original raw data as example.
```
In [5]: datetime_start = np.datetime64(u'2014-05-31T01:23:19.9600345Z')
In [6]: timedeltas_elapsed = [30053400, 40053249, 50053098]
```
Compute datetimes from elapsed timedeltas, then create differential timedeltas from datetimes. All elements are either type `numpy.datetime64` or `numpy.timedelta64`.
```
In [7]: df = pd.DataFrame(dict(datetimes = timedeltas_elapsed))
In [8]: df = df.applymap(lambda elt: np.timedelta64(elt, 'us'))
In [9]: df = df.applymap(lambda elt: np.datetime64(datetime_start + elt))
In [10]: df['differential_timedeltas'] = df['datetimes'] - df['datetimes'].shift()
In [11]: print(df)
datetimes differential_timedeltas
0 2014-05-31 01:23:50.013434500 NaT
1 2014-05-31 01:24:00.013283500 00:00:09.999849
2 2014-05-31 01:24:10.013132500 00:00:09.999849
```
##### Expected behavior
With element-wise handling using `DataFrame.applymap`, all elements are correctly identified as datetimes (timestamps) or timedeltas.
```
In [12]: print(df.applymap(lambda elt: type(elt)))
datetimes differential_timedeltas
0 <class 'pandas.tslib.Timestamp'> <type 'numpy.timedelta64'>
1 <class 'pandas.tslib.Timestamp'> <type 'numpy.timedelta64'>
2 <class 'pandas.tslib.Timestamp'> <type 'numpy.timedelta64'>
```
##### Bug
With row-wise handling using `DataFrame.apply`, all elements are type `pandas.tslib.Timestamp`. I expected 'differential_timedeltas' to be type `numpy.timedelta64` or another type of timedelta, not a type of datetime (timestamp).
```
In [13]: # For 'datetimes':
In [14]: print(df.apply(lambda row: type(row['datetimes']), axis=1))
0 <class 'pandas.tslib.Timestamp'>
1 <class 'pandas.tslib.Timestamp'>
2 <class 'pandas.tslib.Timestamp'>
dtype: object
In [15]: # For 'differential_timedeltas':
In [16]: print(df.apply(lambda row: type(row['differential_timedeltas']), axis=1))
0 <class 'pandas.tslib.NaTType'>
1 <class 'pandas.tslib.Timestamp'>
2 <class 'pandas.tslib.Timestamp'>
dtype: object
```
| null | https://github.com/pandas-dev/pandas/pull/7779 | null | {'base_commit': 'a797b28c87d90a439dfa2c12b4a11e62bf0d6db2', 'files': [{'path': 'doc/source/v0.15.0.txt', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [189]}}}, {'path': 'pandas/core/frame.py', 'status': 'modified', 'Loc': {"('DataFrame', '_apply_standard', 3516)": {'add': [3541], 'mod': [3550]}}}, {'path': 'pandas/core/internals.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [1292], 'mod': [28]}, "('BlockManager', 'as_matrix', 2589)": {'mod': [2598]}, "(None, '_interleaved_dtype', 3620)": {'mod': [3650, 3652, 3673, 3674, 3675, 3676]}}}, {'path': 'pandas/core/series.py', 'status': 'modified', 'Loc': {"('Series', None, 89)": {'mod': [240]}, "('Series', 'from_array', 240)": {'mod': [247]}}}, {'path': 'pandas/tests/test_frame.py', 'status': 'modified', 'Loc': {"('TestDataFrame', None, 1921)": {'add': [9637]}}}, {'path': 'pandas/tests/test_internals.py', 'status': 'modified', 'Loc': {"(None, 'create_block', 34)": {'add': [46, 71], 'mod': [44, 70]}, '(None, None, None)': {'mod': [6]}, "('TestBlockManager', 'test_interleave', 558)": {'mod': [559]}}}]} | [] | [] | [] | {
"iss_type": "2",
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} | {
"code": [
"pandas/core/internals.py",
"pandas/core/frame.py",
"pandas/core/series.py"
],
"doc": [
"doc/source/v0.15.0.txt"
],
"test": [
"pandas/tests/test_frame.py",
"pandas/tests/test_internals.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | fcb0263762a31724ba6db39bf1564569dda068a0 | https://github.com/pandas-dev/pandas/issues/16991 | Bug
Indexing | ValueError on df.columns.isin(pd.Series()) | #### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame(columns=list('ab'))
s1 = pd.Series(['a'])
s2 = pd.Series()
df.columns.isin(s1)
df.columns.isin(s2)
```
#### Problem description
The second call to `df.columns.isin(s2)` fails with
D:\Anaconda\envs\py3k\lib\site-packages\pandas\core\algorithms.py in <lambda>(x, y)
402 # work-around for numpy < 1.8 and comparisions on py3
403 # faster for larger cases to use np.in1d
--> 404 f = lambda x, y: htable.ismember_object(x, values)
405 if (_np_version_under1p8 and compat.PY3) or len(comps) > 1000000:
406 f = lambda x, y: np.in1d(x, y)
pandas\_libs\hashtable_func_helper.pxi in pandas._libs.hashtable.ismember_object (pandas\_libs\hashtable.c:30162)()
ValueError: Buffer dtype mismatch, expected 'Python object' but got 'double'
#### Expected Output
array([ False, False], dtype=bool)
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.3.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
pandas: 0.20.3
numpy: 1.13.1
Might be linked to [#16394](https://github.com/pandas-dev/pandas/issues/16394)
| null | https://github.com/pandas-dev/pandas/pull/17006 | null | {'base_commit': 'fcb0263762a31724ba6db39bf1564569dda068a0', 'files': [{'path': 'doc/source/whatsnew/v0.21.0.txt', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [206]}}}, {'path': 'pandas/core/algorithms.py', 'status': 'modified', 'Loc': {"(None, '_ensure_data', 41)": {'add': [67]}}}, {'path': 'pandas/tests/frame/test_analytics.py', 'status': 'modified', 'Loc': {"('TestDataFrameAnalytics', None, 27)": {'mod': [1154]}, "('TestDataFrameAnalytics', 'test_isin_empty', 1154)": {'mod': [1156, 1157]}}}, {'path': 'pandas/tests/indexes/test_base.py', 'status': 'modified', 'Loc': {"('TestIndex', None, 32)": {'add': [1409]}}}, {'path': 'pandas/tests/series/test_analytics.py', 'status': 'modified', 'Loc': {"('TestSeriesAnalytics', None, 35)": {'add': [1137]}}}, {'path': 'pandas/tests/test_algos.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [599]}}}]} | [] | [] | [] | {
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"pandas/core/algorithms.py"
],
"doc": [
"doc/source/whatsnew/v0.21.0.txt"
],
"test": [
"pandas/tests/test_algos.py",
"pandas/tests/indexes/test_base.py",
"pandas/tests/series/test_analytics.py",
"pandas/tests/frame/test_analytics.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | 0e8331f85cde8db2841aad92054d8e896e88fcef | https://github.com/pandas-dev/pandas/issues/51236 | Docs
good first issue | DOC fix EX02 errors in docstrings | pandas has a script for validating docstrings
https://github.com/pandas-dev/pandas/blob/ced983358b06576af1a73c3e936171cc6dc98a6d/ci/code_checks.sh#L560-L568
which can be run with
```
./ci/code_checks.sh docstrings
```
Currently, many functions fail the EX02 check, and so are excluded from the check.
The task here is:
1. pick 2-3 functions
2. run `./ci/code_checks.sh docstrings`
3. fixup the docstrings according to whatever error is reported
4. stage, commit, push, open pull request 🚀
**Please don't comment `take` as multiple people can work on this simultaneously**. You also don't need to ask for permission to work on this, feel free to just start 😄 Though if you're working on some set of functions you can comment that
If you're new here, please check the contributing guide https://pandas.pydata.org/docs/dev/development/contributing.html
TIP: `./ci/code_checks.sh docstrings` may take a while to run - you may want to comment-out the `docstrings` check which checks `EX01` and the part which checks all the other codes (these are currently lines 86 - 577) | null | https://github.com/pandas-dev/pandas/pull/51724 | null | {'base_commit': 'ce3260110f8f5e17c604e7e1a67ed7f8fb07f5fc', 'files': [{'path': 'ci/code_checks.sh', 'status': 'modified', 'Loc': {'(None, None, 82)': {'mod': [82, 83]}, '(None, None, 560)': {'mod': [560, 561, 562, 563, 564, 565, 566, 567, 568]}}}, {'path': 'pandas/core/dtypes/common.py', 'status': 'modified', 'Loc': {"(None, 'is_datetime64tz_dtype', 309)": {'add': [324, 333]}, "(None, 'is_datetime64_any_dtype', 873)": {'add': [888]}, "(None, 'is_datetime64_ns_dtype', 915)": {'add': [930]}}}, {'path': 'pandas/plotting/_core.py', 'status': 'modified', 'Loc': {"('PlotAccessor', None, 613)": {'mod': [992, 993]}}}, {'path': 'pandas/plotting/_misc.py', 'status': 'modified', 'Loc': {"(None, 'parallel_coordinates', 391)": {'mod': [450, 451]}}}]} | [] | [] | [] | {
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"loc_scope": null,
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} | {
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"pandas/plotting/_core.py",
"pandas/plotting/_misc.py",
"pandas/core/dtypes/common.py"
],
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"test": [],
"config": [],
"asset": [
"ci/code_checks.sh"
]
} | 1 |
pandas-dev | pandas | 2e087c7841aec84030fb489cec9bfeb38fe8086f | https://github.com/pandas-dev/pandas/issues/10043 | Indexing | iloc breaks on read-only dataframe | This is picking up #9928 again. I don't know if the behavior is expected, but it is a bit odd to me. Maybe I'm doing something wrong, I'm not that familiar with the pandas internals.
We call `df.iloc[indices]` and that breaks with a read-only dataframe. I feel that it shouldn't though, as it is not writing.
Minimal reproducing example:
``` python
import pandas as pd
import numpy as np
array = np.eye(10)
array.setflags(write=False)
X = pd.DataFrame(array)
X.iloc[[1, 2, 3]]
```
> ValueError buffer source array is read-only
Is there a way to slice the rows of the dataframe in another way that doesn't need a writeable array?
| null | https://github.com/pandas-dev/pandas/pull/10070 | null | {'base_commit': '2e087c7841aec84030fb489cec9bfeb38fe8086f', 'files': [{'path': 'pandas/src/generate_code.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [148, 170], 'mod': [96, 97, 98, 99, 100, 101, 143, 145]}}}, {'path': 'pandas/tests/test_common.py', 'status': 'modified', 'Loc': {"('TestTake', '_test_dtype', 631)": {'add': [632]}, "('TestTake', 'test_2d_with_out', 630)": {'mod': [631, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674]}}}]} | [] | [] | [] | {
"iss_type": "1",
"iss_reason": "1",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"pandas/src/generate_code.py"
],
"doc": [],
"test": [
"pandas/tests/test_common.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | 89b3d6b201b5d429a202b5239054d5a70c8b5071 | https://github.com/pandas-dev/pandas/issues/38495 | Performance
Regression | Major Performance regression of df.groupby(..).indices | I'm experiencing major performance regressions with pandas=1.1.5 versus 1.1.3
Version 1.1.3:
```
Python 3.7.9 | packaged by conda-forge | (default, Dec 9 2020, 20:36:16) [MSC v.1916 64 bit (AMD64)]
Type 'copyright', 'credits' or 'license' for more information
IPython 7.19.0 -- An enhanced Interactive Python. Type '?' for help.
PyDev console: using IPython 7.19.0
Python 3.7.9 | packaged by conda-forge | (default, Dec 9 2020, 20:36:16) [MSC v.1916 64 bit (AMD64)] on win32
In[2]: import time
... : import numpy as np
... : import pandas as pd
... : pd.__version__
Out[2]: '1.1.3'
In[3]: numel = 10000000
... : df = pd.DataFrame(dict(a=np.random.rand(numel), b=np.random.randint(0,4000, numel)))
... : start = time.time()
... : groupby_indices = df.groupby('b').indices
... : time.time() - start
Out[3]: 0.46085023880004883
```
Version 1.1.5:
```
Python 3.7.9 | packaged by conda-forge | (default, Dec 9 2020, 20:36:16) [MSC v.1916 64 bit (AMD64)]
Type 'copyright', 'credits' or 'license' for more information
IPython 7.19.0 -- An enhanced Interactive Python. Type '?' for help.
PyDev console: using IPython 7.19.0
Python 3.7.9 | packaged by conda-forge | (default, Dec 9 2020, 20:36:16) [MSC v.1916 64 bit (AMD64)] on win32
In[2]: import time
... : import numpy as np
... : import pandas as pd
... : pd.__version__
Out[2]: '1.1.5'
In[3]: numel = 10000000
... : df = pd.DataFrame(dict(a=np.random.rand(numel), b=np.random.randint(0,4000, numel)))
... : start = time.time()
... : groupby_indices = df.groupby('b').indices
... : time.time() - start
Out[3]: 57.36550998687744
``` | null | https://github.com/pandas-dev/pandas/pull/38892 | null | {'base_commit': '89b3d6b201b5d429a202b5239054d5a70c8b5071', 'files': [{'path': 'asv_bench/benchmarks/groupby.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [128]}}}]} | [] | [] | [] | {
"iss_type": "2",
"iss_reason": "2",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"asv_bench/benchmarks/groupby.py"
],
"doc": [],
"test": [],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | 03e58585036c83ca3d4c86d7d3d7ede955c15130 | https://github.com/pandas-dev/pandas/issues/37748 | Bug
Indexing | BUG: ValueError is mistakenly raised if a numpy array is assigned to a pd.Series of dtype=object and both have the same length | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
pd.__version__ # '1.1.3'
pdseries = pd.Series(index=[1,2,3,4], dtype=object)
pdseries.loc[1] = np.zeros(100) # this works fine
pdseries.loc[3] = np.zeros(4) # this raises a value error because len(pdseries)==len(np.zeros(4))
```
TypeError: only size-1 arrays can be converted to Python scalars
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/Users/daniel/.conda/envs/production_system/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 2878, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-40-460230264bf1>", line 1, in <module>
pdseries.loc[3] = np.zeros(4)
File "/Users/daniel/.conda/envs/production_system/lib/python3.7/site-packages/pandas/core/indexing.py", line 670, in __setitem__
iloc._setitem_with_indexer(indexer, value)
File "/Users/daniel/.conda/envs/production_system/lib/python3.7/site-packages/pandas/core/indexing.py", line 1802, in _setitem_with_indexer
self.obj._mgr = self.obj._mgr.setitem(indexer=indexer, value=value)
File "/Users/daniel/.conda/envs/production_system/lib/python3.7/site-packages/pandas/core/internals/managers.py", line 534, in setitem
return self.apply("setitem", indexer=indexer, value=value)
File "/Users/daniel/.conda/envs/production_system/lib/python3.7/site-packages/pandas/core/internals/managers.py", line 406, in apply
applied = getattr(b, f)(**kwargs)
File "/Users/daniel/.conda/envs/production_system/lib/python3.7/site-packages/pandas/core/internals/blocks.py", line 887, in setitem
values = values.astype(arr_value.dtype, copy=False)
ValueError: setting an array element with a sequence.
#### Problem description
It is possible to assign (numpy) arrays to elements of pandas.Series ofd type=object. Unfortunately, in case the array is of the same size as the Series a ValueError is raised.
How can one avoid this error?
#### Expected Output
The interesting thing is that the assignment takes place as expected:
In[42]: pdseries
Out[42]:
1 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...
2 NaN
3 [0.0, 0.0, 0.0, 0.0]
4 NaN
One might argue that a warning could be useful but an error is misleading and tricky to debug.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.7.8.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Mon Aug 31 22:12:52 PDT 2020; root:xnu-6153.141.2~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.1.3
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 49.6.0.post20201009
Cython : 0.29.21
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 5.8.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.2.1
sqlalchemy : 1.3.20
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
| null | https://github.com/pandas-dev/pandas/pull/38266 | null | {'base_commit': '03e58585036c83ca3d4c86d7d3d7ede955c15130', 'files': [{'path': 'doc/source/whatsnew/v1.2.0.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [680]}}}, {'path': 'pandas/core/indexers.py', 'status': 'modified', 'Loc': {"(None, 'is_scalar_indexer', 68)": {'add': [81]}}}, {'path': 'pandas/tests/indexing/test_indexers.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [30]}}}, {'path': 'pandas/tests/indexing/test_loc.py', 'status': 'modified', 'Loc': {"('TestLocSeries', 'test_loc_setitem_dt64tz_values', 2054)": {'add': [2074]}}}]} | [] | [] | [] | {
"iss_type": "1",
"iss_reason": "1",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"pandas/core/indexers.py"
],
"doc": [
"doc/source/whatsnew/v1.2.0.rst"
],
"test": [
"pandas/tests/indexing/test_indexers.py",
"pandas/tests/indexing/test_loc.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | f09d514cf0b09e65baf210a836de04e69b208cef | https://github.com/pandas-dev/pandas/issues/49247 | Bug
Reshaping
Warnings | BUG: Getting FutureWarning for Groupby.mean when using .pivot_table | ### Pandas version checks
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the main branch of pandas.
### Reproducible Example
```python
import pandas as pd
df = pd.DataFrame({"C1": ["a", "b", "c"],
"C2": [1, 2, 3]})
table = pd.pivot_table(df, columns=['C2'])
```
### Issue Description
Getting FutureWarning:
"<stdin>:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function."
### Expected Behavior
pivot_table is internally using DataFrameGroupBy.mean, but does not allow a user to pass a numeric_only argument as suggested in the FutureWarning
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 91111fd99898d9dcaa6bf6bedb662db4108da6e6
python : 3.9.13.final.0
python-bits : 64
OS : Linux
OS-release : 4.4.0-19041-Microsoft
Version : #1237-Microsoft Sat Sep 11 14:32:00 PST 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.5.1
numpy : 1.23.4
pytz : 2022.5
dateutil : 2.8.2
setuptools : 65.5.0
pip : 22.3
Cython : None
pytest : 7.1.3
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : 8.5.0
pandas_datareader: None
bs4 : 4.11.1
bottleneck : None
brotli :
fastparquet : None
fsspec : 2022.10.0
gcsfs : None
matplotlib : 3.6.1
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.9.2
snappy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
zstandard : None
tzdata : None
</details>
| null | https://github.com/pandas-dev/pandas/pull/49615 | null | {'base_commit': 'f09d514cf0b09e65baf210a836de04e69b208cef', 'files': [{'path': 'pandas/core/reshape/pivot.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [23]}, "(None, '__internal_pivot_table', 113)": {'mod': [167]}}}, {'path': 'pandas/tests/reshape/test_pivot.py', 'status': 'modified', 'Loc': {"('TestPivotTable', 'test_pivot_table_nocols', 146)": {'mod': [150]}, "('TestPivotTable', 'test_no_col', 909)": {'mod': [914]}, "('TestPivotTable', 'test_margin_with_only_columns_defined', 954)": {'mod': [978]}, "('TestPivotTable', 'test_pivot_string_func_vs_func', 2003)": {'mod': [2007]}}}, {'path': 'pandas/util/_exceptions.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [5, 6]}, "(None, 'find_stack_level', 28)": {'add': [49]}}}]} | [] | [] | [] | {
"iss_type": "2",
"iss_reason": "2",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"pandas/core/reshape/pivot.py",
"pandas/util/_exceptions.py"
],
"doc": [],
"test": [
"pandas/tests/reshape/test_pivot.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | e226bacd9e0d69ce3a81abfa09ae850f4610f888 | https://github.com/pandas-dev/pandas/issues/8169 | Bug
Groupby
Dtype Conversions | BUG: groupby.count() on different dtypes seems buggy | from [SO](http://stackoverflow.com/questions/25648923/groupby-count-returns-different-values-for-pandas-dataframe-count-vs-describ)
something odd going on here:
```
vals = np.hstack((np.random.randint(0,5,(100,2)), np.random.randint(0,2,(100,2))))
df = pd.DataFrame(vals, columns=['a', 'b', 'c', 'd'])
df[df==2] = np.nan
df2 = df.copy()
df2['a'] = df2['a'].astype('float32')
df2['b'] = df2['b'].astype('float32')
```
```
df.groupby(['c', 'd']).count()
df2.groupby(['c','d']).count()
```
| null | https://github.com/pandas-dev/pandas/pull/8171 | null | {'base_commit': 'e226bacd9e0d69ce3a81abfa09ae850f4610f888', 'files': [{'path': 'doc/source/v0.15.0.txt', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [671]}}}, {'path': 'pandas/core/groupby.py', 'status': 'modified', 'Loc': {"(None, '_count_compat', 149)": {'mod': [150, 151, 152, 153]}, "('BaseGrouper', 'aggregate', 1491)": {'mod': [1530, 1537]}, "('NDFrameGroupBy', '_cython_agg_blocks', 2467)": {'mod': [2480]}}}, {'path': 'pandas/tests/test_groupby.py', 'status': 'modified', 'Loc': {"('TestGroupBy', None, 62)": {'add': [2216]}}}]} | [] | [] | [] | {
"iss_type": "2",
"iss_reason": "1",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"pandas/core/groupby.py"
],
"doc": [
"doc/source/v0.15.0.txt"
],
"test": [
"pandas/tests/test_groupby.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | 9ea0d4485e77c95ff0d8766990ab55d43472b66e | https://github.com/pandas-dev/pandas/issues/4312 | Indexing
Dtype Conversions | BUG: astype assignment via iloc/loc not working | http://stackoverflow.com/questions/17778139/pandas-unable-to-change-column-data-type/17778560#17778560
This might be trying to coerce `object` dtype to a real dtype (int/float) and is failing
Should prob raise for now (or work). Not working with iloc/loc.
```
In [66]: df = DataFrame([['1','2','3','.4',5,6.,'foo']],columns=list('ABCDEFG'))
In [67]: df.dtypes
Out[67]:
A object
B object
C object
D object
E int64
F float64
G object
dtype: object
In [68]: df.iloc[:,0:3] = df.iloc[:,0:3].astype(int)
In [69]: df.dtypes
Out[69]:
A object
B object
C object
D object
E int64
F float64
G object
dtype: object
In [70]: df.iloc[:,0:3] = df.iloc[:,0:3].convert_objects(convert_numeric=True)
In [71]: df.dtypes
Out[71]:
A object
B object
C object
D object
E int64
F float64
G object
dtype: object
```
| null | https://github.com/pandas-dev/pandas/pull/4624 | null | {'base_commit': '9ea0d4485e77c95ff0d8766990ab55d43472b66e', 'files': [{'path': 'doc/source/release.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [267]}}}, {'path': 'pandas/core/common.py', 'status': 'modified', 'Loc': {"(None, '_possibly_downcast_to_dtype', 960)": {'add': [989]}, "(None, '_maybe_upcast_indexer', 895)": {'mod': [895, 896, 897, 898, 900, 901, 903, 904, 905, 906, 907, 908, 909, 911, 912, 914, 915, 916, 918, 919, 920, 922, 923, 924, 925, 927]}}}, {'path': 'pandas/core/groupby.py', 'status': 'modified', 'Loc': {"('SeriesGroupBy', 'transform', 1521)": {'mod': [1560]}}}, {'path': 'pandas/core/indexing.py', 'status': 'modified', 'Loc': {"('_NDFrameIndexer', 'setter', 126)": {'mod': [127, 128, 129, 130, 131]}}}, {'path': 'pandas/core/internals.py', 'status': 'modified', 'Loc': {"('Block', None, 29)": {'add': [41], 'mod': [456]}, "('DatetimeBlock', None, 1106)": {'add': [1106]}, "('DatetimeBlock', '_try_coerce_args', 1131)": {'add': [1139], 'mod': [1136, 1137, 1138]}, '(None, None, None)': {'add': [1440]}, "('Block', '_try_cast_result', 456)": {'mod': [459]}, "('Block', 'setitem', 512)": {'mod': [516, 517, 518, 520, 521, 522, 523, 524, 525, 526, 527, 528, 530, 531, 532, 533, 534, 536]}, "('Block', 'create_block', 565)": {'mod': [588]}, "('NumericBlock', None, 841)": {'mod': [845, 846]}, "('DatetimeBlock', '_can_hold_element', 1119)": {'mod': [1122, 1123]}}}, {'path': 'pandas/tests/test_common.py', 'status': 'modified', 'Loc': {"(None, 'test_nan_to_nat_conversions', 121)": {'mod': [130, 131, 132, 137]}}}, {'path': 'pandas/tests/test_frame.py', 'status': 'modified', 'Loc': {"('TestDataFrame', 'test_where', 7642)": {'mod': [7675]}}}, {'path': 'pandas/tests/test_indexing.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [1199]}, "('TestIndexing', 'test_ix_assign_column_mixed', 955)": {'mod': [967, 968, 969, 970, 971]}}}]} | [] | [] | [] | {
"iss_type": "2",
"iss_reason": "1",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"pandas/core/internals.py",
"pandas/core/common.py",
"pandas/core/indexing.py",
"pandas/core/groupby.py"
],
"doc": [
"doc/source/release.rst"
],
"test": [
"pandas/tests/test_common.py",
"pandas/tests/test_indexing.py",
"pandas/tests/test_frame.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | 70435eba769c6bcf57332306455eb70db9fa1111 | https://github.com/pandas-dev/pandas/issues/40730 | Bug
cut
NA - MaskedArrays | BUG: qcut fails with Float64Dtype | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
series = pd.Series([1.0, 2.0, 3.0, 4.4], dtype=pd.Float64Dtype())
pd.qcut(series, 2)
```
#### Problem description
`pd.qcut` currently accepts the nullable `Int64Dtype` as well as `'float64'`, so I would expect it to work with the `Float64Dtype` as well. Instead the following error is produced:
```python-traceback
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-29-1db98f70db38> in <module>
1 series = pd.Series([1.0,2.0,3.0,4.0], dtype=pd.Float64Dtype())
----> 2 pd.qcut(series, 2)
~/.pyenv/versions/3.8.2/envs/woodwork/lib/python3.8/site-packages/pandas/core/reshape/tile.py in qcut(x, q, labels, retbins, precision, duplicates)
356 quantiles = q
357 bins = algos.quantile(x, quantiles)
--> 358 fac, bins = _bins_to_cuts(
359 x,
360 bins,
~/.pyenv/versions/3.8.2/envs/woodwork/lib/python3.8/site-packages/pandas/core/reshape/tile.py in _bins_to_cuts(x, bins, right, labels, precision, include_lowest, dtype, duplicates, ordered)
408
409 if include_lowest:
--> 410 ids[x == bins[0]] = 1
411
412 na_mask = isna(x) | (ids == len(bins)) | (ids == 0)
IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices
```
#### Expected Output
Should match that of `float64`
```
0 (0.999, 2.5]
1 (0.999, 2.5]
2 (2.5, 4.0]
3 (2.5, 4.0]
dtype: category
Categories (2, interval[float64]): [(0.999, 2.5] < (2.5, 4.0]]
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.8.2.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Sun Jul 5 00:43:10 PDT 2020; root:xnu-6153.141.1~9/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.3
numpy : 1.19.5
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 41.2.0
Cython : None
pytest : 6.0.1
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.7
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
| null | https://github.com/pandas-dev/pandas/pull/40969 | null | {'base_commit': '70435eba769c6bcf57332306455eb70db9fa1111', 'files': [{'path': 'doc/source/whatsnew/v1.3.0.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [698]}}}, {'path': 'pandas/core/reshape/tile.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [28], 'mod': [27]}, "(None, '_coerce_to_type', 468)": {'mod': [491]}}}, {'path': 'pandas/tests/reshape/test_qcut.py', 'status': 'modified', 'Loc': {"(None, 'test_qcut_nullable_integer', 296)": {'mod': [296, 297]}}}]} | [] | [] | [] | {
"iss_type": "1",
"iss_reason": "1",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"pandas/core/reshape/tile.py"
],
"doc": [
"doc/source/whatsnew/v1.3.0.rst"
],
"test": [
"pandas/tests/reshape/test_qcut.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | 38afa9310040f1bd4fb122008e96fe6d719b12a2 | https://github.com/pandas-dev/pandas/issues/19787 | Missing-data
Categorical
Clean
good first issue | Clean: Categorical.fillna NaN in categories checking | We don't allow NaN in the categories anymore, so this block should be unreachable.
https://github.com/pandas-dev/pandas/blob/8bfcddc7728deaf8e840416d83c8feda86630d27/pandas/core/arrays/categorical.py#L1622-L1628
If anyone wants to remove it and test things out. | null | https://github.com/pandas-dev/pandas/pull/19880 | null | {'base_commit': '38afa9310040f1bd4fb122008e96fe6d719b12a2', 'files': [{'path': '.gitignore', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [63], 'mod': [93]}}}, {'path': 'pandas/core/arrays/categorical.py', 'status': 'modified', 'Loc': {"('Categorical', 'fillna', 1590)": {'mod': [1630, 1631, 1632, 1633, 1634, 1635, 1636]}}}]} | [] | [] | [] | {
"iss_type": "4",
"iss_reason": "2",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"pandas/core/arrays/categorical.py"
],
"doc": [],
"test": [],
"config": [
".gitignore"
],
"asset": []
} | 1 |
pandas-dev | pandas | 2dad23f766790510d09e66f1e02b57a395d479b1 | https://github.com/pandas-dev/pandas/issues/9570 | Enhancement
Timedelta | timedelta string conversion requires two-digit hour value | `Timedelta('00:00:00')` works fine whereas `Timedelta('0:00:00')` raises and error. Unsure whether to call this a bug, but under some circumstances the `datetime` module in pure python will produce time delta strings without the leading 0.
| null | https://github.com/pandas-dev/pandas/pull/9868 | null | {'base_commit': '2dad23f766790510d09e66f1e02b57a395d479b1', 'files': [{'path': 'doc/source/whatsnew/v0.16.1.txt', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [52]}}}, {'path': 'pandas/tseries/tests/test_timedeltas.py', 'status': 'modified', 'Loc': {"('TestTimedeltas', 'test_construction', 35)": {'add': [66]}}}, {'path': 'pandas/tseries/timedeltas.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [122]}, "(None, 'convert', 190)": {'mod': [212, 216]}}}]} | [] | [] | [] | {
"iss_type": "1",
"iss_reason": "2",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"pandas/tseries/timedeltas.py"
],
"doc": [
"doc/source/whatsnew/v0.16.1.txt"
],
"test": [
"pandas/tseries/tests/test_timedeltas.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | b03df731095154e94d23db51d11df5dd736622f8 | https://github.com/pandas-dev/pandas/issues/3925 | Datetime | Access DateTimeIndexed dataframe by timestamp | Hello,
I am new to pandas and thanks for this great library!
I have a data frame like this:
```
Gold_2012.head()
open high low close volume
date_time
2012-01-02 18:01:00 1571.0 1571.0 1569.1 1569.8 351
2012-01-02 18:02:00 1569.8 1570.0 1569.7 1569.8 54
2012-01-02 18:03:00 1570.0 1570.0 1569.1 1569.9 247
2012-01-02 18:04:00 1570.0 1570.0 1569.8 1569.9 55
2012-01-02 18:05:00 1569.8 1569.9 1568.5 1568.5 48
```
I am trying to access the first element of this dataframe. If I use loc function, everything works out:
```
Gold_2012.loc[Gold_2012.index[0]]
open 1571.0
high 1571.0
low 1569.1
close 1569.8
volume 351.0
Name: 2012-01-02 18:01:00-06:00, dtype: float64
```
But if I do something like this, an error is thrown. Is this expected?
```
Gold_2012[Gold_2012.index[0]]
```
---
KeyError Traceback (most recent call last)
<ipython-input-30-bb7117766fdd> in <module>()
----> 1 Gold_2012[Gold_2012.index[0]]
/Users/chen/Virtualenvs/python3Env/lib/python3.3/site-packages/pandas/core/frame.py in **getitem**(self, key)
1926 else:
1927 # get column
-> 1928 return self._get_item_cache(key)
1929
1930 def _getitem_slice(self, key):
/Users/chen/Virtualenvs/python3Env/lib/python3.3/site-packages/pandas/core/generic.py in _get_item_cache(self, item)
568 return cache[item]
569 except Exception:
--> 570 values = self._data.get(item)
571 res = self._box_item_values(item, values)
572 cache[item] = res
/Users/chen/Virtualenvs/python3Env/lib/python3.3/site-packages/pandas/core/internals.py in get(self, item)
1382
1383 def get(self, item):
-> 1384 _, block = self._find_block(item)
1385 return block.get(item)
1386
/Users/chen/Virtualenvs/python3Env/lib/python3.3/site-packages/pandas/core/internals.py in _find_block(self, item)
1524
1525 def _find_block(self, item):
-> 1526 self._check_have(item)
1527 for i, block in enumerate(self.blocks):
1528 if item in block:
/Users/chen/Virtualenvs/python3Env/lib/python3.3/site-packages/pandas/core/internals.py in _check_have(self, item)
1531 def _check_have(self, item):
1532 if item not in self.items:
-> 1533 raise KeyError('no item named %s' % com.pprint_thing(item))
1534
1535 def reindex_axis(self, new_axis, method=None, axis=0, copy=True):
KeyError: 'no item named 2012-01-02 18:01:00-06:00'
| null | https://github.com/pandas-dev/pandas/pull/3931 | null | {'base_commit': 'b03df731095154e94d23db51d11df5dd736622f8', 'files': [{'path': 'RELEASE.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [256, 357, 360]}}}, {'path': 'pandas/core/indexing.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [2]}}}, {'path': 'pandas/tseries/index.py', 'status': 'modified', 'Loc': {"('DatetimeIndex', '_partial_date_slice', 1070)": {'add': [1104, 1112]}}}, {'path': 'pandas/tseries/tests/test_timeseries.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [253]}}}]} | [] | [] | [] | {
"iss_type": "1",
"iss_reason": "3",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"pandas/tseries/index.py",
"pandas/core/indexing.py"
],
"doc": [
"RELEASE.rst"
],
"test": [
"pandas/tseries/tests/test_timeseries.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | f231c9a74a544ec94cd12e813cb2543fb5a18556 | https://github.com/pandas-dev/pandas/issues/35331 | good first issue
Needs Tests | BUG: np.argwhere on pandas series | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
numpy/numpy#15555 reports an issue with `np.argwhere` on pandas Series. Reporting here for visibility.
MRE:
```python
>>> import numpy as np
>>> import pandas as pd
>>> s = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e'])
>>> np.argwhere(s < 0)
```
which, with `numpy.__version__ ==1.20.0.dev0+046a736` gives:
**pd.__version__ == 0.25.3:**
```
FutureWarning: Series.nonzero() is deprecated and will be removed in a future version.Use Series.to_numpy().nonzero() instead
array([[3]])
```
**pd.__version__ == 1.0.5:**
```
ValueError: Length of passed values is 1, index implies 5.
```
| null | https://github.com/pandas-dev/pandas/pull/53381 | null | {'base_commit': 'f231c9a74a544ec94cd12e813cb2543fb5a18556', 'files': [{'path': 'pandas/tests/series/test_npfuncs.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [5, 7]}, "(None, 'test_numpy_unique', 19)": {'add': [21]}}}]} | [] | [] | [] | {
"iss_type": "1",
"iss_reason": "2",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [],
"doc": [],
"test": [
"pandas/tests/series/test_npfuncs.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | 5de6b84f5117b005a8f010d4510a758b50f3d14e | https://github.com/pandas-dev/pandas/issues/12081 | Reshaping
Error Reporting | DataFrame.merge with Series should give nice error message | Right now trying this results in "IndexError: list index out of range". It should say can't merge DataFrame with a Series...
I know this for quite a while now, but still get trapped on it every once in a while. This would be very helpful for beginners.
Other people also get confused: http://stackoverflow.com/questions/27281734/pandas-merge-on-index-not-working
| null | https://github.com/pandas-dev/pandas/pull/12112 | null | {'base_commit': '5de6b84f5117b005a8f010d4510a758b50f3d14e', 'files': [{'path': 'doc/source/whatsnew/v0.18.0.txt', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [205]}}}, {'path': 'pandas/tools/merge.py', 'status': 'modified', 'Loc': {"('_MergeOperation', '__init__', 157)": {'add': [186]}}}, {'path': 'pandas/tools/tests/test_merge.py', 'status': 'modified', 'Loc': {"('TestMerge', None, 45)": {'add': [263]}}}]} | [] | [] | [] | {
"iss_type": "4",
"iss_reason": "2",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"pandas/tools/merge.py"
],
"doc": [
"doc/source/whatsnew/v0.18.0.txt"
],
"test": [
"pandas/tools/tests/test_merge.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | a3c0e7bcfb8bbe9ca45df7e571a305d403e0f066 | https://github.com/pandas-dev/pandas/issues/44597 | API Design
Deprecate | API/DEPR: int downcasting in DataFrame.where |
`Block.where` has special downcasting logic that splits blocks differently from any other Block methods. I would like to deprecate and eventually remove this bespoke logic.
The relevant logic is only reached AFAICT when we have integer dtype (non-int64) and an integer `other` too big for this dtype, AND the passed `cond` has all-`True` columns.
(Identifying the affected behavior is difficult in part because it relies on `can_hold_element` incorrectly returning `True` in these cases)
```
import numpy as np
import pandas as pd
arr = np.arange(6).astype(np.int16).reshape(3, 2)
df = pd.DataFrame(arr)
mask = np.zeros(arr.shape, dtype=bool)
mask[:, 0] = True
res = df.where(mask, 2**17)
>>> res.dtypes
0 int16
1 int32
dtype: object
```
The simplest thing to do would be to not do any downcasting in these cases, in which case we would end up with all-int32. The next simplest would be to downcast column-wise, which would give the same end result but with less consolidation.
We do not have any test cases that fail if I disable this downcasting (after I fix a problem with an expressions.where call that the downcasting somehow makes irrelevant). This makes me think the current behavior is not intentional, or at least not a priority.
Any objection to deprecating the integer downcasting entirely?
| null | https://github.com/pandas-dev/pandas/pull/45009 | null | {'base_commit': 'a3c0e7bcfb8bbe9ca45df7e571a305d403e0f066', 'files': [{'path': 'doc/source/whatsnew/v1.4.0.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [547]}}}, {'path': 'pandas/core/internals/blocks.py', 'status': 'modified', 'Loc': {"('Block', 'where', 1138)": {'add': [1229]}}}, {'path': 'pandas/tests/frame/indexing/test_where.py', 'status': 'modified', 'Loc': {"('TestDataFrameIndexingWhere', 'test_where_axis', 464)": {'add': [501], 'mod': [503]}, '(None, None, None)': {'add': [719]}, "('TestDataFrameIndexingWhere', None, 50)": {'mod': [101, 464]}, "('TestDataFrameIndexingWhere', 'test_where_alignment', 101)": {'mod': [144]}}}, {'path': 'pandas/tests/frame/methods/test_clip.py', 'status': 'modified', 'Loc': {"('TestDataFrameClip', None, 11)": {'mod': [139]}, "('TestDataFrameClip', 'test_clip_with_na_args', 139)": {'mod': [154]}}}]} | [] | [] | [] | {
"iss_type": "4",
"iss_reason": "2",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"pandas/core/internals/blocks.py"
],
"doc": [
"doc/source/whatsnew/v1.4.0.rst"
],
"test": [
"pandas/tests/frame/methods/test_clip.py",
"pandas/tests/frame/indexing/test_where.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | 32f789fbc5d5a72d9d1ac14935635289eeac9009 | https://github.com/pandas-dev/pandas/issues/52151 | Bug
Groupby
Categorical | BUG: Inconsistent behavior with `groupby/min` and `observed=False` on categoricals between 2.0 and 2.1 | ### Pandas version checks
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the [main branch](https://pandas.pydata.org/docs/dev/getting_started/install.html#installing-the-development-version-of-pandas) of pandas.
### Reproducible Example
```python
import pandas as pd
import numpy as np
df = pd.DataFrame({
"cat_1": pd.Categorical(list("AB"), categories=list("ABCDE"), ordered=True),
"cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=True),
"value_1": np.random.uniform(size=2),
})
chunk1 = df[df.cat_1 == "A"]
chunk2 = df[df.cat_1 == "B"]
df1 = chunk1.groupby("cat_1", observed=False).min()
df2 = chunk2.groupby("cat_1", observed=False).min()
df3 = pd.concat([df1, df2], ignore_index=False)
res3 = df3.groupby(level=0, observed=False).min()
print(f"\n{res3}")
```
### Issue Description
When performing a `groupby/min` with a categorical dtype and `observed=False`, the results differ between `1.5.3` (and `2.0`) and 2.1.
Output with 1.5.3 or 2.0:
```python
cat_2 value_1
cat_1
A 1 0.384993
B 2 0.955231
C NaN NaN
D NaN NaN
E NaN NaN
```
Output with the latest `main`:
```python
cat_2 value_1
cat_1
A 1 0.297557
B 1 0.081856
C 1 NaN
D 1 NaN
E 1 NaN
```
The change can be traced to this PR:
* https://github.com/pandas-dev/pandas/pull/52120
### Expected Behavior
I'm not sure if the changed behavior is intended. Please advise.
### Installed Versions
<details>
commit : d22d1f2db0bc7846f679b2b0a572216f23fa83cc
python : 3.8.16.final.0
python-bits : 64
OS : Darwin
OS-release : 22.3.0
Version : Darwin Kernel Version 22.3.0: Thu Jan 5 20:50:36 PST 2023; root:xnu-8792.81.2~2/RELEASE_ARM64_T6020
machine : arm64
processor : arm
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 2.1.0.dev0+293.gd22d1f2db0
numpy : 1.23.5
pytz : 2022.7.1
dateutil : 2.8.2
setuptools : 67.4.0
pip : 23.0.1
Cython : 0.29.33
pytest : 7.2.1
hypothesis : 6.68.2
sphinx : 4.5.0
blosc : None
feather : None
xlsxwriter : 3.0.8
lxml.etree : 4.9.2
html5lib : 1.1
pymysql : 1.0.2
psycopg2 : 2.9.3
jinja2 : 3.1.2
IPython : 8.11.0
pandas_datareader: None
bs4 : 4.11.2
bottleneck : 1.3.6
brotli :
fastparquet : 2023.2.0
fsspec : 2023.1.0
gcsfs : 2023.1.0
matplotlib : 3.6.3
numba : 0.56.4
numexpr : 2.8.3
odfpy : None
openpyxl : 3.1.0
pandas_gbq : None
pyarrow : 11.0.0
pyreadstat : 1.2.1
pyxlsb : 1.0.10
s3fs : 2023.1.0
scipy : 1.10.1
snappy :
sqlalchemy : 2.0.4
tables : 3.7.0
tabulate : 0.9.0
xarray : 2023.1.0
xlrd : 2.0.1
zstandard : 0.19.0
tzdata : None
qtpy : None
pyqt5 : None
</details>
| null | https://github.com/pandas-dev/pandas/pull/52236 | null | {'base_commit': '32f789fbc5d5a72d9d1ac14935635289eeac9009', 'files': [{'path': 'pandas/core/groupby/ops.py', 'status': 'modified', 'Loc': {"('WrappedCythonOp', '_ea_wrap_cython_operation', 358)": {'add': [404]}}}, {'path': 'pandas/tests/groupby/test_min_max.py', 'status': 'modified', 'Loc': {"(None, 'test_min_max_nullable_uint64_empty_group', 235)": {'add': [249]}}}]} | [] | [] | [] | {
"iss_type": "2",
"iss_reason": "1",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"pandas/core/groupby/ops.py"
],
"doc": [],
"test": [
"pandas/tests/groupby/test_min_max.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | 8924277fa3dbe775f46e679ab8bd97b293e465ea | https://github.com/pandas-dev/pandas/issues/41556 | Bug
Groupby
Algos | BUG: groupby.shift return keys filled with `fill_value` when `fill_value` is specified | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
In [2]: df = pd.DataFrame({'a': [2, 1, 2, 1], 'b': ['x', 'x', 'y', 'y']})
In [3]: df.groupby('a').shift(1)
Out[3]:
b
0 NaN
1 NaN
2 x
3 x
In [4]: df.groupby('a').shift(1, fill_value='fill')
Out[4]:
a b
0 fill fill
1 fill fill
2 2 x
3 1 x
```
#### Problem description
When specifying `fill_value` in `groupby.shift`, the returned result includes the key column with keys filled with `fill_value`. When `fill_value` is unspecified (None), the key column is not included.
#### Expected Output
It seems pretty strange that keys are to be filled with `fill_value`. This makes more sense to me:
```python
In [4]: df.groupby('a').shift(1, fill_value='fill')
Out[4]:
b
0 fill
1 fill
2 x
3 x
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.7.10.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-76-generic
Version : #86-Ubuntu SMP Fri Jan 17 17:24:28 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.1
setuptools : 52.0.0.post20210125
Cython : 0.29.23
pytest : 6.2.4
hypothesis : 6.12.0
sphinx : 3.5.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.23.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 2021.04.0
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 1.0.1
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.53.1
</details>
| null | https://github.com/pandas-dev/pandas/pull/41858 | null | {'base_commit': '8924277fa3dbe775f46e679ab8bd97b293e465ea', 'files': [{'path': 'asv_bench/benchmarks/groupby.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [371]}}}, {'path': 'doc/source/whatsnew/v1.4.0.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [170, 264]}}}, {'path': 'pandas/core/groupby/groupby.py', 'status': 'modified', 'Loc': {"('GroupBy', '_get_cythonized_result', 2809)": {'add': [2824, 2874]}, "('GroupBy', 'shift', 2997)": {'add': [3034], 'mod': [3025]}, "('GroupBy', 'blk_func', 2908)": {'mod': [2949]}}}, {'path': 'pandas/tests/groupby/test_groupby_shift_diff.py', 'status': 'modified', 'Loc': {"(None, 'test_group_shift_with_fill_value', 41)": {'mod': [58]}}}]} | [] | [] | [] | {
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"loc_scope": null,
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} | {
"code": [
"asv_bench/benchmarks/groupby.py",
"pandas/core/groupby/groupby.py"
],
"doc": [
"doc/source/whatsnew/v1.4.0.rst"
],
"test": [
"pandas/tests/groupby/test_groupby_shift_diff.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | 92093457ca13ba037257d0b8d41735268535c84f | https://github.com/pandas-dev/pandas/issues/3573 | Bug
Output-Formatting | Unintuitive default behavior with wide DataFrames in the IPython notebook | In the IPython notebook, HTML output it the default and whether summary view is displayed should not be governed by hypothetical line width. I ran into this problem in a demo recently and it took me a minute to figure out what was wrong, definitely a bad change in 0.11.
| null | https://github.com/pandas-dev/pandas/pull/3663 | null | {'base_commit': '0ed4549ac857fbf2c7e975acdf1d987bacc3ea32', 'files': [{'path': 'RELEASE.rst', 'status': 'modified', 'Loc': {'(None, None, 65)': {'add': [65]}, '(None, None, 87)': {'add': [87]}, '(None, None, 141)': {'add': [141]}}}, {'path': 'doc/source/faq.rst', 'status': 'modified', 'Loc': {'(None, None, 38)': {'mod': [38, 39]}, '(None, None, 48)': {'mod': [48, 49, 50, 51]}}}, {'path': 'pandas/core/common.py', 'status': 'modified', 'Loc': {"(None, 'in_qtconsole', 1895)": {'add': [1906]}, "(None, 'in_ipnb_frontend', 1908)": {'mod': [1908]}}}, {'path': 'pandas/core/config_init.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [129, 250], 'mod': [123, 141, 142, 143, 144]}}}, {'path': 'pandas/core/format.py', 'status': 'modified', 'Loc': {"(None, 'get_console_size', 1702)": {'mod': [1705, 1721]}}}, {'path': 'pandas/core/frame.py', 'status': 'modified', 'Loc': {"('DataFrame', '_repr_fits_vertical_', 606)": {'add': [613], 'mod': [608, 609, 610, 612, 615, 616, 618, 619, 620, 621, 622, 624]}, "('DataFrame', '_repr_fits_horizontal_', 624)": {'add': [628, 642], 'mod': [636, 639, 640, 648, 649, 651, 652, 653, 658]}, "('DataFrame', '_repr_html_', 729)": {'add': [733], 'mod': [739]}, "('DataFrame', '__unicode__', 682)": {'mod': [700, 701, 702, 703, 704, 705, 706, 707]}}}, {'path': 'pandas/tests/test_format.py', 'status': 'modified', 'Loc': {"('TestDataFrameFormatting', 'test_repr_max_columns_max_rows', 203)": {'add': [241], 'mod': [236, 238]}, "('TestDataFrameFormatting', 'test_wide_repr_multiindex_cols', 854)": {'add': [855], 'mod': [859, 860, 861]}, "('TestDataFrameFormatting', 'test_expand_frame_repr', 167)": {'mod': [173, 174]}, "('TestDataFrameFormatting', 'test_wide_repr', 787)": {'mod': [790]}, "('TestDataFrameFormatting', 'test_wide_repr_named', 810)": {'mod': [813]}, "('TestDataFrameFormatting', 'test_wide_repr_multiindex', 831)": {'mod': [836]}, "('TestDataFrameFormatting', 'test_wide_repr_unicode', 876)": {'mod': [879]}}}]} | [] | [] | [] | {
"iss_type": "4",
"iss_reason": "2",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"pandas/core/common.py",
"pandas/core/frame.py",
"pandas/core/config_init.py",
"pandas/core/format.py"
],
"doc": [
"RELEASE.rst",
"doc/source/faq.rst"
],
"test": [
"pandas/tests/test_format.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | a214915e241ea15f3d072d54930d0e0c8f42ee10 | https://github.com/pandas-dev/pandas/issues/19482 | Dtype Conversions
Error Reporting
Numeric Operations | Rank With 'method=first' Broken for Objects | Came across this working on #15779
```python
In []: df = pd.DataFrame({'key': ['a'] * 5, 'val': ['bar', 'bar', 'foo', 'bar', 'baz']})
In []: df.groupby('key').rank(method='first')
Out []:
Empty DataFrame
Columns: []
Index: []
```
#### Expected Output
```python
Out[]:
val
0 1.0
1 2.0
2 5.0
3 3.0
4 4.0
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: d3f7d2a666aa824e2df98083aa5c1fd9bb63252e
python: 3.6.3.final.0
python-bits: 64
OS: Darwin
OS-release: 17.4.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.23.0.dev0+169.gd3f7d2a66.dirty
pytest: 3.2.1
pip: 9.0.1
setuptools: 36.5.0.post20170921
Cython: 0.26.1
numpy: 1.13.3
scipy: 1.0.0
pyarrow: 0.8.0
xarray: 0.10.0
IPython: 6.2.1
sphinx: 1.6.3
patsy: 0.4.1
dateutil: 2.6.1
pytz: 2017.2
blosc: None
bottleneck: 1.2.1
tables: 3.4.2
numexpr: 2.6.4
feather: 0.4.0
matplotlib: 2.1.1
openpyxl: 2.5.0b1
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.0.2
lxml: 4.1.1
bs4: 4.6.0
html5lib: 1.0.1
sqlalchemy: 1.1.13
pymysql: 0.7.11.None
psycopg2: None
jinja2: 2.10
s3fs: 0.1.2
fastparquet: 0.1.3
pandas_gbq: None
pandas_datareader: None
</details>
| null | https://github.com/pandas-dev/pandas/pull/19481 | null | {'base_commit': 'a214915e241ea15f3d072d54930d0e0c8f42ee10', 'files': [{'path': 'doc/source/whatsnew/v0.23.0.txt', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [583]}}}, {'path': 'pandas/_libs/algos.pxd', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [13]}}}, {'path': 'pandas/_libs/algos.pyx', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [34, 35, 36, 37, 38, 39, 40]}}}, {'path': 'pandas/_libs/groupby.pyx', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [19, 20]}}}, {'path': 'pandas/_libs/groupby_helper.pxi.in', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [446]}}}, {'path': 'pandas/core/groupby.py', 'status': 'modified', 'Loc': {"('GroupBy', None, 1147)": {'add': [1770]}, "('BaseGrouper', None, 1926)": {'add': [2185], 'mod': [2245, 2376, 2377]}, "('_GroupBy', None, 551)": {'mod': [997]}, "('_GroupBy', '_cython_transform', 997)": {'mod': [1005, 1010]}, "('BaseGrouper', '_cython_operation', 2245)": {'mod': [2317, 2318, 2320, 2337]}, "('BaseGrouper', '_transform', 2396)": {'mod': [2397, 2409, 2411]}}}, {'path': 'pandas/tests/groupby/test_groupby.py', 'status': 'modified', 'Loc': {"('TestGroupBy', None, 37)": {'add': [1897]}}}]} | [] | [] | [] | {
"iss_type": "2",
"iss_reason": "2",
"loc_way": "pr",
"loc_scope": null,
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} | {
"code": [
"pandas/_libs/groupby.pyx",
"pandas/_libs/groupby_helper.pxi.in",
"pandas/_libs/algos.pyx",
"pandas/core/groupby.py",
"pandas/_libs/algos.pxd"
],
"doc": [
"doc/source/whatsnew/v0.23.0.txt"
],
"test": [
"pandas/tests/groupby/test_groupby.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | 679dbd021eccc238e422057009365e2ee1c04b25 | https://github.com/pandas-dev/pandas/issues/21687 | Docs
Usage Question
Algos
Window | "on" argument of DataFrame.rolling only works for datetime columns | the `on=` argument of `DataFrame.rolling` only works for datetime columns.
```
df = pd.DataFrame([
[18, 0],
[2, 0],
[1, 0],
[9, 1],
[8, 1],
], columns=['value', 'roll'])
```
```
df.roll = pd.to_datetime(df.roll, unit='s')
df.rolling('1s', on='roll').value.max()
```
returns:
```
0 18.0
1 18.0
2 18.0
3 9.0
4 9.0
Name: value, dtype: float64
```
as expected.
But
```df.rolling(1, on='roll').value.max()```
returns:
```
0 18.0
1 2.0
2 1.0
3 9.0
4 8.0
Name: value, dtype: float64
```
If this is intentional behavior, I'd be happy to change the docs to note this (the docs currently imply that `on=` can be used for any column).
| null | https://github.com/pandas-dev/pandas/pull/27265 | null | {'base_commit': '679dbd021eccc238e422057009365e2ee1c04b25', 'files': [{'path': 'pandas/core/window.py', 'status': 'modified', 'Loc': {"('Window', None, 489)": {'mod': [516, 517]}}}]} | [] | [] | [] | {
"iss_type": "2",
"iss_reason": "2",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"pandas/core/window.py"
],
"doc": [],
"test": [],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | 940104efc9e708bc93744dfaa36c9492b03b1ca4 | https://github.com/pandas-dev/pandas/issues/20452 | Reshaping
API Design | BUG: New feature allowing merging on combination of columns and index levels drops levels of index | #### Code Sample, a copy-pastable example if possible
```python
In [1]: import pandas as pd
In [2]: pd.__version__
Out[2]: '0.23.0.dev0+657.g01882ba5b'
In [3]: df1 = pd.DataFrame({'v1' : range(12)}, index=pd.MultiIndex.from_product([list('abc'),list('xy'),[1,2]], names=['abc','xy','num']))
...: df1
...:
Out[3]:
v1
abc xy num
a x 1 0
2 1
y 1 2
2 3
b x 1 4
2 5
y 1 6
2 7
c x 1 8
2 9
y 1 10
2 11
In [4]: df2 = pd.DataFrame({'v2': [100*i for i in range(1,7)]}, index=pd.MultiIndex.from_product([list('abc'), list('xy')],names=['abc','xy']))
In [5]: df2
Out[5]:
v2
abc xy
a x 100
y 200
b x 300
y 400
c x 500
y 600
In [6]: df1.merge(df2, on=['abc','xy']) # 'num' disappears
Out[6]:
v1 v2
abc xy
a x 0 100
x 1 100
y 2 200
y 3 200
b x 4 300
x 5 300
y 6 400
y 7 400
c x 8 500
x 9 500
y 10 600
y 11 600
In [7]: df1.reset_index().merge(df2, on=['abc','xy']) # This preserves 'num'
Out[7]:
abc xy num v1 v2
0 a x 1 0 100
1 a x 2 1 100
2 a y 1 2 200
3 a y 2 3 200
4 b x 1 4 300
5 b x 2 5 300
6 b y 1 6 400
7 b y 2 7 400
8 c x 1 8 500
9 c x 2 9 500
10 c y 1 10 600
11 c y 2 11 600
In [8]: df1.merge(df2, on='xy') # 'abc' and 'num' disappear
Out[8]:
v1 v2
xy
x 0 100
x 0 300
x 0 500
x 1 100
x 1 300
x 1 500
x 4 100
x 4 300
x 4 500
x 5 100
x 5 300
x 5 500
x 8 100
x 8 300
x 8 500
x 9 100
x 9 300
x 9 500
y 2 200
y 2 400
y 2 600
y 3 200
y 3 400
y 3 600
y 6 200
y 6 400
y 6 600
y 7 200
y 7 400
y 7 600
y 10 200
y 10 400
y 10 600
y 11 200
y 11 400
y 11 600
```
#### Problem description
It seems that the new feature implemented in #17484 that allows merging on a combination of columns and index levels can drop index levels, which is really non-intuitive. In the first example, the index level named "num" gets dropped, while in the last example, both "abc" and "xy" are dropped.
If this is the desired behavior, then it needs to be carefully documented.
N.B. There is also an error in the docs of merging.rst that says this feature was introduced in v.0.22, but it will be introduced in v0.23
I'm guessing @jmmease will need to look at this.
#### Expected Output
```python
In [6]: df1.merge(df2, on=['abc','xy'])
Out[6]:
v1 v2
abc xy num
a x 1 0 100
2 1 100
y 1 2 200
2 3 200
b x 1 4 300
2 5 300
y 1 6 400
2 7 400
c x 1 8 500
2 9 500
y 1 10 600
2 11 600
In [8]: df1.merge(df2, on='xy')
Out[8]:
abc_x num v1 abc_y v2
xy
x a 1 0 a 100
x a 1 0 b 300
x a 1 0 c 500
x a 2 1 a 100
x a 2 1 b 300
x a 2 1 c 500
x b 1 4 a 100
x b 1 4 b 300
x b 1 4 c 500
x b 2 5 a 100
x b 2 5 b 300
x b 2 5 c 500
x c 1 8 a 100
x c 1 8 b 300
x c 1 8 c 500
x c 2 9 a 100
x c 2 9 b 300
x c 2 9 c 500
y a 1 2 a 200
y a 1 2 b 400
y a 1 2 c 600
y a 2 3 a 200
y a 2 3 b 400
y a 2 3 c 600
y b 1 6 a 200
y b 1 6 b 400
y b 1 6 c 600
y b 2 7 a 200
y b 2 7 b 400
y b 2 7 c 600
y c 1 10 a 200
y c 1 10 b 400
y c 1 10 c 600
y c 2 11 a 200
y c 2 11 b 400
y c 2 11 c 600
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.4.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.23.0.dev0+657.g01882ba5b
pytest: 3.4.0
pip: 9.0.1
setuptools: 38.5.1
Cython: 0.25.1
numpy: 1.14.1
scipy: 1.0.0
pyarrow: 0.8.0
xarray: None
IPython: 6.2.1
sphinx: 1.7.1
patsy: 0.5.0
dateutil: 2.6.1
pytz: 2018.3
blosc: 1.5.1
bottleneck: 1.2.1
tables: 3.4.2
numexpr: 2.6.4
feather: None
matplotlib: 2.2.0
openpyxl: 2.5.0
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.0.2
lxml: 4.1.1
bs4: 4.6.0
html5lib: 1.0.1
sqlalchemy: 1.2.5
pymysql: 0.8.0
psycopg2: None
jinja2: 2.10
s3fs: 0.1.3
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
| null | https://github.com/pandas-dev/pandas/pull/20475 | null | {'base_commit': '940104efc9e708bc93744dfaa36c9492b03b1ca4', 'files': [{'path': 'doc/source/merging.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [1202], 'mod': [1136, 1137, 1141, 1142, 1143, 1146, 1164]}}}, {'path': 'doc/source/whatsnew/v0.24.0.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [1543]}}}, {'path': 'pandas/core/reshape/merge.py', 'status': 'modified', 'Loc': {"('_MergeOperation', '_maybe_add_join_keys', 646)": {'add': [717]}}}, {'path': 'pandas/tests/reshape/merge/test_join.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [732]}}}, {'path': 'pandas/tests/reshape/merge/test_merge.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [1400, 1401, 1402, 1403, 1404, 1405, 1406, 1407, 1408, 1409]}, "(None, 'test_merge_series', 1409)": {'mod': [1419]}}}]} | [] | [] | [] | {
"iss_type": "2",
"iss_reason": "2",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"pandas/core/reshape/merge.py"
],
"doc": [
"doc/source/whatsnew/v0.24.0.rst",
"doc/source/merging.rst"
],
"test": [
"pandas/tests/reshape/merge/test_merge.py",
"pandas/tests/reshape/merge/test_join.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | 13940c7f3c0371d6799bbd88b9c6546392b418a1 | https://github.com/pandas-dev/pandas/issues/35650 | good first issue
Needs Tests | BUG: pd.factorize with read-only datetime64 numpy array raises ValueError | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
In [1]: pandas.__version__
[PYFLYBY] import pandas
Out[1]: u'0.24.2'
In [2]: arr = numpy.array([numpy.datetime64('2015-11-20T15:06:58.000')])
In [3]: arr.dtype
Out[3]: dtype('<M8[ms]')
In [4]: arr.flags.writeable = False
[PYFLYBY] import pandas as pd
In [5]: pd.factorize(arr)
```
#### Problem description
[Construction with non-mutable datetime64 strings]
#### Expected Output
(array([0]), array(['2015-11-20T15:06:58.000000000'], dtype='datetime64[ns]'))
#### Output of ``pd.show_versions()``
<details>
pandas/_libs/tslibs/conversion.pyx in pandas._libs.tslibs.conversion.ensure_datetime64ns()
/usr/local/python/python-2.7/std/lib/python2.7/site-packages/pandas/_libs/tslibs/conversion.so in View.MemoryView.memoryview_cwrapper()
/usr/local/python/python-2.7/std/lib/python2.7/site-packages/pandas/_libs/tslibs/conversion.so in View.MemoryView.memoryview.__cinit__()
ValueError: buffer source array is read-only
</details>
| null | https://github.com/pandas-dev/pandas/pull/35775 | null | {'base_commit': '13940c7f3c0371d6799bbd88b9c6546392b418a1', 'files': [{'path': 'pandas/tests/test_algos.py', 'status': 'modified', 'Loc': {"('TestFactorize', 'test_object_factorize', 245)": {'add': [253]}}}]} | [] | [] | [] | {
"iss_type": "1",
"iss_reason": "1",
"loc_way": "pr",
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"info_type": null
} | {
"code": [],
"doc": [],
"test": [
"pandas/tests/test_algos.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | 816f94575c9ec1af2169a28536217c4d16dd6b4b | https://github.com/pandas-dev/pandas/issues/16033 | Docs | DOC: styler warnings in doc-build | https://travis-ci.org/pandas-dev/pandas/jobs/222779268
```
/tmp/doc/source/generated/pandas.io.formats.style.Styler.rst:74: WARNING: failed to import template:
/tmp/doc/source/generated/pandas.io.formats.style.Styler.rst:74: WARNING: toctree references unknown document 'generated/template:'
```
cc @TomAugspurger @jorisvandenbossche
I just pushed a change to fix the path of the imports (after ``pandas.formats`` change), but I think it still needs something. | null | https://github.com/pandas-dev/pandas/pull/16094 | null | {'base_commit': 'f0bd908336a260cafa9d83c8244dd1a0a056f72d', 'files': [{'path': 'pandas/tests/io/formats/test_css.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [2]}, "(None, 'test_css_parse_strings', 46)": {'mod': [48, 49, 50, 51]}, "(None, 'test_css_parse_invalid', 79)": {'mod': [80]}, "(None, 'test_css_side_shorthands', 99)": {'mod': [118]}}}]} | [] | [] | [] | {
"iss_type": "4",
"iss_reason": "2",
"loc_way": "pr",
"loc_scope": null,
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} | {
"code": [],
"doc": [],
"test": [
"pandas/tests/io/formats/test_css.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | 2067d7e306ae720d455f356e4da21f282a8a762e | https://github.com/pandas-dev/pandas/issues/35811 | Bug
Usage Question
API Design
Series | BUG/QST: Series.transform with a dictionary | What is the expected output of passing a dictionary to `Series.transform`? For example:
s = pd.Series([1, 2, 3])
result1 = s.transform({'a': lambda x: x + 1})
result2 = s.transform({'a': lambda x: x + 1, 'b': lambda x: x + 2})
The docs say that `dict of axis labels -> functions` is acceptable, but I can't find any example in the docs where the output is described/shown. Under the hood, `Series.transform` is just calling `Series.aggregate` which produces the following outputs for `result1` and `result2`.
````
# result1
a 0 2
1 3
2 4
dtype: int64
# result2
a 0 2
1 3
2 4
b 0 3
1 4
2 5
dtype: int64
````
`result1` is deemed acceptable (the length of the result equals the length of the input) and is returned, but `result2` raises; it is not a transformation.
I am wondering if a better return would be a DataFrame where the keys are the column names ('a' and 'b' in this example). | null | https://github.com/pandas-dev/pandas/pull/35964 | null | {'base_commit': '2067d7e306ae720d455f356e4da21f282a8a762e', 'files': [{'path': 'doc/source/whatsnew/v1.2.0.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [344]}}}, {'path': 'pandas/core/aggregation.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [23], 'mod': [21]}, "(None, 'validate_func_kwargs', 353)": {'add': [386]}}}, {'path': 'pandas/core/base.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [7]}, "('SelectionMixin', None, 132)": {'mod': [563]}}}, {'path': 'pandas/core/frame.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [47], 'mod': [119]}, "('DataFrame', None, 341)": {'mod': [7464, 7468, 7469, 7470, 7471, 7472]}}}, {'path': 'pandas/core/generic.py', 'status': 'modified', 'Loc': {"('NDFrame', None, 168)": {'mod': [10651, 10652, 10653, 10654, 10656, 10658, 10659, 10660, 10661, 10662, 10664, 10666, 10667, 10668, 10669, 10670, 10671, 10672, 10673, 10674, 10676, 10677, 10678, 10679, 10681, 10682, 10683, 10685, 10686, 10687, 10688, 10690, 10691, 10692, 10693, 10694, 10695, 10696, 10697, 10698, 10699, 10700, 10701, 10702, 10704, 10705, 10707, 10708, 10709, 10710, 10711, 10712, 10713, 10714, 10715, 10716, 10717, 10718, 10719, 10720, 10721, 10723]}}}, {'path': 'pandas/core/series.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [27, 91]}, "('Series', None, 141)": {'mod': [4084, 4088, 4089, 4090, 4091]}}}, {'path': 'pandas/core/shared_docs.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [259]}}}, {'path': 'pandas/tests/frame/apply/test_frame_transform.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [1, 7], 'mod': [6]}, "(None, 'test_agg_transform', 11)": {'add': [13, 14], 'mod': [11, 12, 16, 17, 19, 20, 21, 22, 24, 25, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51]}, "(None, 'test_transform_method_name', 67)": {'add': [72], 'mod': [69]}, "(None, 'test_transform_and_agg_err', 54)": {'mod': [54, 55, 56, 57, 58, 60, 61, 62, 63]}}}, {'path': 'pandas/tests/series/apply/test_series_apply.py', 'status': 'modified', 'Loc': {"('TestSeriesAggregate', 'test_transform', 203)": {'add': [213, 221], 'mod': [212]}}}, {'path': 'pandas/tests/series/apply/test_series_transform.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [5], 'mod': [4]}, "(None, 'test_transform_none_to_type', 53)": {'add': [59], 'mod': [55, 57, 58]}, "(None, 'test_transform', 8)": {'mod': [8, 9, 13, 20, 21, 22, 23, 24, 26, 27, 29, 30, 32, 33, 34, 35, 36, 37]}, "(None, 'test_transform_and_agg_error', 41)": {'mod': [41, 43, 47]}}}]} | [] | [] | [] | {
"iss_type": "4",
"iss_reason": "1",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"pandas/core/aggregation.py",
"pandas/core/base.py",
"pandas/core/generic.py",
"pandas/core/frame.py",
"pandas/core/series.py",
"pandas/core/shared_docs.py"
],
"doc": [
"doc/source/whatsnew/v1.2.0.rst"
],
"test": [
"pandas/tests/series/apply/test_series_apply.py",
"pandas/tests/frame/apply/test_frame_transform.py",
"pandas/tests/series/apply/test_series_transform.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | 889c2ff67af14213e8ed065df2957b07e34ac95b | https://github.com/pandas-dev/pandas/issues/33810 | Testing
IO Parquet | TST: add Feather V2 round-trip test | no that pyarrow 0.17 has landed, we should have a round-trip Feather V2 test to ensure we have dtype preservation (we can likely re-use some of our test frames from the parquet tests). | null | https://github.com/pandas-dev/pandas/pull/33422 | null | {'base_commit': '889c2ff67af14213e8ed065df2957b07e34ac95b', 'files': [{'path': 'doc/source/conf.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [418]}}}, {'path': 'doc/source/user_guide/io.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [4586, 4588, 4589, 4595, 4596]}}}, {'path': 'doc/source/whatsnew/v1.1.0.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [91]}}}, {'path': 'pandas/core/frame.py', 'status': 'modified', 'Loc': {"('DataFrame', 'to_feather', 2061)": {'add': [2068], 'mod': [2063, 2072]}, "('DataFrame', None, 324)": {'mod': [2061]}}}, {'path': 'pandas/io/feather_format.py', 'status': 'modified', 'Loc': {"(None, 'to_feather', 10)": {'add': [17, 18], 'mod': [10, 12, 61]}}}, {'path': 'pandas/tests/io/test_feather.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [6]}, "('TestFeather', 'test_basic', 52)": {'add': [73]}, "('TestFeather', 'test_path_localpath', 147)": {'add': [150]}, "('TestFeather', None, 21)": {'mod': [30]}, "('TestFeather', 'check_round_trip', 30)": {'mod': [36, 38]}, "('TestFeather', 'test_unsupported_other', 103)": {'mod': [105, 106]}}}]} | [] | [] | [] | {
"iss_type": "4",
"iss_reason": "2",
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"loc_scope": null,
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} | {
"code": [
"pandas/io/feather_format.py",
"pandas/core/frame.py",
"doc/source/conf.py"
],
"doc": [
"doc/source/user_guide/io.rst",
"doc/source/whatsnew/v1.1.0.rst"
],
"test": [
"pandas/tests/io/test_feather.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | b6691127523f965003dbf877a358c81af5012989 | https://github.com/pandas-dev/pandas/issues/15630 | Numeric Operations
Algos | Pandas (0.18) Rank: unexpected behavior for method = 'dense' and pct = True | I find the behavior of rank function with method = 'dense' and pct = True unexpected as it looks like, in order to calculate percentile ranks, the function is using the total number of observations instead of the number of _distinct_ observations.
#### Code Sample, a copy-pastable example if possible
```
import pandas as pd
n_rep = 2
ts = pd.Series([1,2,3,4] * n_rep )
output = ts.rank(method = 'dense', pct = True)
```
#### Problem description
```
ts.rank(method = 'dense', pct = True)
Out[116]:
0 0.125
1 0.250
2 0.375
3 0.500
4 0.125
5 0.250
6 0.375
7 0.500
```
#### Expected Output
Something similar to:
```
pd.Series([1,2,3,4] * 2).rank(method = 'dense', pct = True) * n_rep
Out[118]:
0 0.25
1 0.50
2 0.75
3 1.00
4 0.25
5 0.50
6 0.75
7 1.00
```
Also, I would expected the result above to be invariant to n_rep.
i.e. I would expect a "mapping" {value -> pct_rank} that would not depend on how many times the value is repeated, while it is not the case here.
| null | https://github.com/pandas-dev/pandas/pull/15639 | null | {'base_commit': 'b6691127523f965003dbf877a358c81af5012989', 'files': [{'path': 'doc/source/whatsnew/v0.23.0.txt', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [909]}}}, {'path': 'pandas/_libs/algos_rank_helper.pxi.in', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [216, 388]}}}, {'path': 'pandas/tests/frame/test_rank.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [6, 13], 'mod': [3, 4, 5, 8, 10, 12]}, "('TestRank', 'test_rank_2d_tie_methods', 247)": {'add': [268]}}}, {'path': 'pandas/tests/series/test_rank.py', 'status': 'modified', 'Loc': {"('TestSeriesRank', 'test_rank_modify_inplace', 370)": {'add': [378]}}}]} | [] | [] | [] | {
"iss_type": "2",
"iss_reason": "2",
"loc_way": "pr",
"loc_scope": null,
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} | {
"code": [
"pandas/_libs/algos_rank_helper.pxi.in"
],
"doc": [
"doc/source/whatsnew/v0.23.0.txt"
],
"test": [
"pandas/tests/series/test_rank.py",
"pandas/tests/frame/test_rank.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | 95be01dbc060f405b7928cc6e4ba4d6d6181c22a | https://github.com/pandas-dev/pandas/issues/13420 | Groupby
Categorical | DataFrame.groupby(grp, axis=1) with categorical grp breaks | While attempting to use `pd.qcut` (which returned a Categorical) to bin some data in groups for plotting, I encountered the following error. The idea is to group a DataFrame by columns (`axis=1`) using a Categorical.
#### Minimal breaking example
```
>>> import pandas
>>> df = pandas.DataFrame({'a':[1,2,3,4], 'b':[-1,-2,-3,-4], 'c':[5,6,7,8]})
>>> df
a b c
0 1 -1 5
1 2 -2 6
2 3 -3 7
3 4 -4 8
>>> grp = pandas.Categorical([1,0,1])
>>> df.groupby(grp, axis=1).mean()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/ntawolf/anaconda3/lib/python3.5/site-packages/pandas/core/generic.py", line 3778, in groupby
**kwargs)
File "/home/ntawolf/anaconda3/lib/python3.5/site-packages/pandas/core/groupby.py", line 1427, in groupby
return klass(obj, by, **kwds)
File "/home/ntawolf/anaconda3/lib/python3.5/site-packages/pandas/core/groupby.py", line 354, in __init__
mutated=self.mutated)
File "/home/ntawolf/anaconda3/lib/python3.5/site-packages/pandas/core/groupby.py", line 2390, in _get_grouper
raise ValueError("Categorical dtype grouper must "
ValueError: Categorical dtype grouper must have len(grouper) == len(data)
```
#### Expected behaviour
Same as
```
>>> df.T.groupby(grp, axis=0).mean().T
0 1
0 -1 3
1 -2 4
2 -3 5
3 -4 6
```
So, it works as expected when doubly transposed. This makes it appear as a bug to me.
#### Proposed solution
In [`if is_categorical_dtype(gpr) and len(gpr) != len(obj):`](https://github.com/pydata/pandas/blob/master/pandas/core/groupby.py#L2406), change `len(obj)` to `obj.shape[axis]`. This assumes that `len(obj) == obj.shape[0]` for all `obj`.
So, supposing you agree that this is a bug, should a test be put in [`test_groupby_categorical`](https://github.com/pydata/pandas/blob/master/pandas/tests/test_groupby.py#L3968)?
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 3.19.0-59-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 22.0.5
Cython: 0.24
numpy: 1.10.4
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.2.0
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.1
xlsxwriter: 0.8.9
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.40.0
pandas_datareader: None
```
| null | https://github.com/pandas-dev/pandas/pull/27788 | null | {'base_commit': '54e58039fddc79492e598e85279c42e85d06967c', 'files': [{'path': 'pandas/tests/groupby/test_categorical.py', 'status': 'modified', 'Loc': {"(None, 'test_seriesgroupby_observed_apply_dict', 1159)": {'add': [1165]}}}]} | [] | [] | [] | {
"iss_type": "1",
"iss_reason": "1",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [],
"doc": [],
"test": [
"pandas/tests/groupby/test_categorical.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | be61825986ba565bc038beb2f5df2750fc1aca30 | https://github.com/pandas-dev/pandas/issues/13565 | Docs
Usage Question
Timezones | Call unique() on a timezone aware datetime series returns non timezone aware result | Call unique() on a timezone aware datetime series returns non timezone aware result.
#### Code Sample
import pandas as pd
import pytz
import datetime
In [242]: ts = pd.Series([datetime.datetime(2011,2,11,20,0,0,0,pytz.utc), datetime.datetime(2011,2,11,20,0,0,0,pytz.utc), datetime.datetime(2011,2,11,21,0,0,0,pytz.utc)])
In [243]: ts
Out[243]:
0 2011-02-11 20:00:00+00:00
1 2011-02-11 20:00:00+00:00
2 2011-02-11 21:00:00+00:00
dtype: datetime64[ns, UTC]
In [244]: ts.unique()
Out[244]: array(['2011-02-11T20:00:00.000000000', '2011-02-11T21:00:00.000000000'], dtype='datetime64[ns]')
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Linux
OS-release: 3.16.0-4-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: de_AT.UTF-8
pandas: 0.18.1
nose: 1.3.4
pip: 8.1.2
setuptools: 22.0.5
Cython: 0.21.1
numpy: 1.11.0
scipy: 0.14.0
statsmodels: None
xarray: None
IPython: 4.2.0
sphinx: 1.2.3
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: 3.1.1
numexpr: 2.4
matplotlib: 1.4.2
openpyxl: 2.3.5
xlrd: 0.9.2
xlwt: 0.7.4
xlsxwriter: None
lxml: 3.6.0
bs4: None
html5lib: 1.0b3
httplib2: 0.9
apiclient: None
sqlalchemy: 0.9.8
pymysql: None
psycopg2: None
jinja2: 2.7.3
boto: None
pandas_datareader: None
| null | https://github.com/pandas-dev/pandas/pull/13979 | null | {'base_commit': 'be61825986ba565bc038beb2f5df2750fc1aca30', 'files': [{'path': 'doc/source/whatsnew/v0.19.0.txt', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [460, 906]}}}, {'path': 'pandas/core/base.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [10, 11, 24]}, "('IndexOpsMixin', None, 799)": {'mod': [955]}, "('IndexOpsMixin', 'unique', 955)": {'mod': [957, 958, 962, 963, 964, 965, 966, 967, 969]}}}, {'path': 'pandas/core/series.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [20], 'mod': [80]}, "('Series', None, 99)": {'add': [1233]}}}, {'path': 'pandas/indexes/base.py', 'status': 'modified', 'Loc': {"('Index', None, 89)": {'add': [3219]}, '(None, None, None)': {'mod': [63]}}}, {'path': 'pandas/indexes/category.py', 'status': 'modified', 'Loc': {"('CategoricalIndex', None, 23)": {'add': [285]}}}, {'path': 'pandas/tests/indexes/test_category.py', 'status': 'modified', 'Loc': {"('TestCategoricalIndex', 'test_duplicates', 390)": {'add': [397]}}}, {'path': 'pandas/tests/indexes/test_multi.py', 'status': 'modified', 'Loc': {"('TestMultiIndex', None, 28)": {'add': [1929]}}}, {'path': 'pandas/tests/test_base.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [19]}, "('TestIndexOps', 'test_value_counts_unique_nunique', 450)": {'add': [505, 510, 533, 579], 'mod': [455, 457, 458, 459, 461, 462, 463, 465, 466, 467, 469, 471, 472, 473, 475, 476, 477, 479, 480, 483, 490, 491, 502, 504, 507, 528, 529, 530, 573, 575, 577, 578]}, "('TestIndexOps', 'test_value_counts_inferred', 585)": {'mod': [593, 594]}, "('TestIndexOps', 'test_value_counts_bins', 612)": {'mod': [630, 631, 655, 656, 664, 665]}, "('TestIndexOps', 'test_value_counts_datetime64', 668)": {'mod': [694, 695, 697, 717, 718, 720, 721, 722, 736, 737, 739]}}}, {'path': 'pandas/tests/test_categorical.py', 'status': 'modified', 'Loc': {"('TestCategorical', None, 28)": {'add': [1304]}}}, {'path': 'pandas/tseries/base.py', 'status': 'modified', 'Loc': {"('DatetimeIndexOpsMixin', None, 108)": {'mod': [746, 747, 748, 750, 751, 752, 753, 754, 755, 756, 757]}}}, {'path': 'pandas/util/testing.py', 'status': 'modified', 'Loc': {"(None, 'makeUnicodeIndex', 1536)": {'mod': [1537]}}}]} | [] | [] | [] | {
"iss_type": "2",
"iss_reason": "2",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"pandas/indexes/base.py",
"pandas/core/base.py",
"pandas/core/series.py",
"pandas/tseries/base.py",
"pandas/indexes/category.py"
],
"doc": [
"doc/source/whatsnew/v0.19.0.txt"
],
"test": [
"pandas/util/testing.py",
"pandas/tests/test_base.py",
"pandas/tests/indexes/test_category.py",
"pandas/tests/indexes/test_multi.py",
"pandas/tests/test_categorical.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | c4a996adfc91f023b46ce3cb67e33fc8b2ca3627 | https://github.com/pandas-dev/pandas/issues/9400 | Visualization
Error Reporting | Improve error message in plotting.py's _plot | This a minor enhancement proposal. At the moment I cannot submit a pull request. I will probably have time to create one during the next week.
This is a snippet from `tools/plotting.py`: https://github.com/pydata/pandas/blob/master/pandas/tools/plotting.py#L2269-2283
``` python
def _plot(data, x=None, y=None, subplots=False,
ax=None, kind='line', **kwds):
kind = _get_standard_kind(kind.lower().strip())
if kind in _all_kinds:
klass = _plot_klass[kind]
else:
raise ValueError('Invalid chart type given %s' % kind)
from pandas import DataFrame
if kind in _dataframe_kinds:
if isinstance(data, DataFrame):
plot_obj = klass(data, x=x, y=y, subplots=subplots, ax=ax,
kind=kind, **kwds)
else:
raise ValueError('Invalid chart type given %s' % kind)
```
Which results in following error message:
```
C:\Anaconda3\lib\site-packages\pandas\tools\plotting.py in plot_series(series, label, kind, use_index, rot, xticks, yticks, xlim, ylim, ax, style, grid, legend, logx, logy, secondary_y, **kwds)
2231 klass = _plot_klass[kind]
2232 else:
-> 2233 raise ValueError('Invalid chart type given %s' % kind)
2234
2235 """
ValueError: Invalid chart type given hist
```
I would suggest using the format string `"Invalid chart type given: '%s'"` instead.
| null | https://github.com/pandas-dev/pandas/pull/9417 | null | {'base_commit': 'c4a996adfc91f023b46ce3cb67e33fc8b2ca3627', 'files': [{'path': 'pandas/tools/plotting.py', 'status': 'modified', 'Loc': {"(None, '_plot', 2269)": {'mod': [2269, 2277]}}}]} | [] | [] | [] | {
"iss_type": "4",
"iss_reason": "2",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"pandas/tools/plotting.py"
],
"doc": [],
"test": [],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | 53243e8ec73ecf5035a63f426a9c703d6835e9a7 | https://github.com/pandas-dev/pandas/issues/54889 | Build | BUILD: Race condition between .pxi.in and .pyx compiles in parallel build of 2.1.0 | ### Installation check
- [X] I have read the [installation guide](https://pandas.pydata.org/pandas-docs/stable/getting_started/install.html#installing-pandas).
### Platform
Linux-6.4.7-gentoo-dist-x86_64-AMD_Ryzen_5_3600_6-Core_Processor-with-glibc2.38
### Installation Method
Built from source
### pandas Version
2.1.0
### Python Version
3.11.5
### Installation Logs
<details>
<summary>Build log excerpt</summary>
```
gpep517 build-wheel --backend mesonpy --output-fd 3 --wheel-dir /tmp/portage/dev-python/pandas-2.1.0/work/pandas-2.1.0-python3_10/wheel --config-json {"builddir": "/tmp/portage/dev-python/pandas-2.1.0/work/pandas-2.1.0-python3_10", "setup-args": [], "compile-args": ["-v", "-j12", "-l0"]}
2023-08-31 07:02:26,275 gpep517 INFO Building wheel via backend mesonpy
+ meson setup /tmp/portage/dev-python/pandas-2.1.0/work/pandas-2.1.0 /tmp/portage/dev-python/pandas-2.1.0/work/pandas-2.1.0-python3_10 -Dbuildtype=release -Db_ndebug=if-release -Db_vscrt=md --vsenv --native-file=/tmp/portage/dev-python/pandas-2.1.0/work/pandas-2.1.0-python3_10/meson-python-native-file.ini
The Meson build system
Version: 1.2.1
Source dir: /tmp/portage/dev-python/pandas-2.1.0/work/pandas-2.1.0
Build dir: /tmp/portage/dev-python/pandas-2.1.0/work/pandas-2.1.0-python3_10
Build type: native build
Project name: pandas
Project version: 2.1.0
C compiler for the host machine: x86_64-pc-linux-gnu-gcc (gcc 13.2.1 "x86_64-pc-linux-gnu-gcc (Gentoo 13.2.1_p20230826 p7) 13.2.1 20230826")
C linker for the host machine: x86_64-pc-linux-gnu-gcc ld.bfd 2.41
C++ compiler for the host machine: x86_64-pc-linux-gnu-g++ (gcc 13.2.1 "x86_64-pc-linux-gnu-g++ (Gentoo 13.2.1_p20230826 p7) 13.2.1 20230826")
C++ linker for the host machine: x86_64-pc-linux-gnu-g++ ld.bfd 2.41
Cython compiler for the host machine: cython (cython 0.29.36)
Host machine cpu family: x86_64
Host machine cpu: x86_64
Program python found: YES (/usr/bin/python3.10)
Found pkg-config: /usr/bin/pkg-config (1.8.1)
Run-time dependency python found: YES 3.10
Build targets in project: 53
pandas 2.1.0
User defined options
Native files: /tmp/portage/dev-python/pandas-2.1.0/work/pandas-2.1.0-python3_10/meson-python-native-file.ini
buildtype : release
vsenv : True
b_ndebug : if-release
b_vscrt : md
Found samurai-1.9 at /usr/bin/samu
Visual Studio environment is needed to run Ninja. It is recommended to use Meson wrapper:
/usr/lib/python-exec/python3.10/meson compile -C .
Generating targets: 0%| | 0/53 eta ?
Generating targets: 98%|█████████▊| 52/53 eta 00:00
Writing build.ninja: 0%| | 0/225 eta ?
+ /usr/bin/samu -v -j12 -l0
[…]
samu: job failed: cython -M --fast-fail -3 --include-dir /tmp/portage/dev-python/pandas-2.1.0/work/pandas-2.1.0-python3_10/pandas/_libs '-X always_allow_keywords=true' /tmp/portage/dev-python/pandas-2.1.0/work/pandas-2.1.0/pandas/_libs/interval.pyx -o pandas/_libs/interval.cpython-310-x86_64-linux-gnu.so.p/pandas/_libs/interval.pyx.c
[33mWARNING [0m [34mOverriding pythran description with argspec information for: numpy.random.binomial[0m
[33mWARNING [0m [34mOverriding pythran description with argspec information for: numpy.random.bytes[0m
[33mWARNING [0m [34mOverriding pythran description with argspec information for: numpy.random.chisquare[0m
[33mWARNING [0m [34mOverriding pythran description with argspec information for: numpy.random.choice[0m
[33mWARNING [0m [34mOverriding pythran description with argspec information for: numpy.random.dirichlet[0m
[33mWARNING [0m [34mOverriding pythran description with argspec information for: numpy.random.exponential[0m
[33mWARNING [0m [34mOverriding pythran description with argspec information for: numpy.random.f[0m
[33mWARNING [0m [34mOverriding pythran description with argspec information for: numpy.random.gamma[0m
[33mWARNING [0m [34mOverriding pythran description with argspec information for: numpy.random.geometric[0m
[33mWARNING [0m [34mOverriding pythran description with argspec information for: numpy.random.pareto[0m
[33mWARNING [0m [34mOverriding pythran description with argspec information for: numpy.random.gumbel[0m
[33mWARNING [0m [34mOverriding pythran description with argspec information for: numpy.random.poisson[0m
[33mWARNING [0m [34mOverriding pythran description with argspec information for: numpy.random.negative_binomial[0m
[33mWARNING [0m [34mOverriding pythran description with argspec information for: numpy.random.normal[0m
[33mWARNING [0m [34mOverriding pythran description with argspec information for: numpy.random.laplace[0m
[33mWARNING [0m [34mOverriding pythran description with argspec information for: numpy.random.logistic[0m
[33mWARNING [0m [34mOverriding pythran description with argspec information for: numpy.random.lognormal[0m
[33mWARNING [0m [34mOverriding pythran description with argspec information for: numpy.random.logseries[0m
[33mWARNING [0m [34mOverriding pythran description with argspec information for: numpy.random.power[0m
[33mWARNING [0m [34mOverriding pythran description with argspec information for: numpy.random.ranf[0m
[33mWARNING [0m [34mOverriding pythran description with argspec information for: numpy.random.randint[0m
[33mWARNING [0m [34mOverriding pythran description with argspec information for: numpy.random.random[0m
[33mWARNING [0m [34mOverriding pythran description with argspec information for: numpy.random.random_integers[0m
[33mWARNING [0m [34mOverriding pythran description with argspec information for: numpy.random.random_sample[0m
[33mWARNING [0m [34mOverriding pythran description with argspec information for: numpy.random.rayleigh[0m
[33mWARNING [0m [34mOverriding pythran description with argspec information for: numpy.random.sample[0m
[33mWARNING [0m [34mOverriding pythran description with argspec information for: numpy.random.standard_exponential[0m
[33mWARNING [0m [34mOverriding pythran description with argspec information for: numpy.random.standard_gamma[0m
[33mWARNING [0m [34mOverriding pythran description with argspec information for: numpy.random.standard_normal[0m
[33mWARNING [0m [34mOverriding pythran description with argspec information for: numpy.random.uniform[0m
[33mWARNING [0m [34mOverriding pythran description with argspec information for: numpy.random.weibull[0m
Error compiling Cython file:
------------------------------------------------------------
...
bint kh_exist_strbox(kh_strbox_t*, khiter_t) nogil
khuint_t kh_needed_n_buckets(khuint_t element_n) nogil
include "khash_for_primitive_helper.pxi"
^
------------------------------------------------------------
/tmp/portage/dev-python/pandas-2.1.0/work/pandas-2.1.0/pandas/_libs/khash.pxd:129:0: 'khash_for_primitive_helper.pxi' not found
```
</details>
Full build log: [dev-python:pandas-2.1.0:20230831-050223.log](https://github.com/pandas-dev/pandas/files/12482393/dev-python.pandas-2.1.0.20230831-050223.log)
```
$ find /tmp/portage/dev-python/pandas-2.1.0/work/pandas-2.1.0-python3_10/ -name '*.pxi'
/tmp/portage/dev-python/pandas-2.1.0/work/pandas-2.1.0-python3_10/pandas/_libs/intervaltree.pxi
/tmp/portage/dev-python/pandas-2.1.0/work/pandas-2.1.0-python3_10/pandas/_libs/sparse_op_helper.pxi
```
It looks that meson files do not declare dependencies between `khash_for_primitive_helper.pxi` and `khash.pxd` files, so the former isn't necessarily created before the latter is attempt to be compiled. | null | https://github.com/pandas-dev/pandas/pull/54958 | null | {'base_commit': '53243e8ec73ecf5035a63f426a9c703d6835e9a7', 'files': [{'path': 'pandas/_libs/meson.build', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [72]}}}]} | [] | [] | [] | {
"iss_type": "2",
"iss_reason": "1",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [],
"doc": [],
"test": [],
"config": [],
"asset": [
"pandas/_libs/meson.build"
]
} | 1 |
meta-llama | llama | 7565eb6fee2175b2d4fe2cfb45067a61b35d7f5e | https://github.com/meta-llama/llama/issues/658 | documentation | Confusion about the default max_seq_len = 2048 | When reading the class Transformer, I found that the code use max_seq_len * 2 to prepare the rotary positional encoding, which confused me for a while. Then I realized that the default max_seq_len was set to 2048, and the 'max_seq_len * 2' aims to generate 4096 positional embeddings, corresponding to the 4K context length in the paper. I understand it can achieve the purpose but why not setting max_seq_len directly to 4096? which is more clear and less likely to cause misconception.
self.freqs_cis = precompute_freqs_cis(
self.params.dim // self.params.n_heads, self.params.max_seq_len * 2
) | null | https://github.com/meta-llama/llama/pull/754 | null | {'base_commit': '7565eb6fee2175b2d4fe2cfb45067a61b35d7f5e', 'files': [{'path': 'llama/model.py', 'status': 'modified', 'Loc': {"('Transformer', '__init__', 414)": {'add': [450]}}}]} | [] | [] | [] | {
"iss_type": "3",
"iss_reason": "2",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"llama/model.py"
],
"doc": [],
"test": [],
"config": [],
"asset": []
} | 1 |
pallets | flask | f88765d504ce2fa9bc3926c76910b11510522892 | https://github.com/pallets/flask/issues/1224 | Starting up a public server. | I ran into this problem today with one of my applications trying to make it public to my local network.
C:\Users\Savion\Documents\GitHub\Example-Flask-Website>flask\Scripts\python run.
py
- Running on http://127.0.0.1:5000/
- Restarting with reloader
10.101.37.124 - - [26/Oct/2014 15:51:23] "GET / HTTP/1.1" 404 -
- Running on http://0.0.0.0:5000/
10.101.37.124 - - [26/Oct/2014 15:51:38] "GET / HTTP/1.1" 404 -
The problem that i run into is the fact that this app continuously attempts to default to localhost. It is not until 2 Ctrl + C, that it goes to 0.0.0.0, then I still receive a 404 error in my browser. I do have routes that are valid when running locally. I have tried to create a new virtualenv and i still recieve the same error, I reset the firewall rule on this application. All effort that did not return rewarded.
Any Ideas onto why my app makes an attempt to startup on the localhost first then moves over, but then returns a 404?
| null | null | null | {'base_commit': 'f88765d504ce2fa9bc3926c76910b11510522892', 'files': [{'path': 'flask/views.py', 'Loc': {}}]} | [] | [] | [] | {
"iss_type": "1\n404 error",
"iss_reason": "5",
"loc_way": "comment",
"loc_scope": "0",
"info_type": "Code"
} | {
"code": [
"flask/views.py"
],
"doc": [],
"test": [],
"config": [],
"asset": []
} | null | |
pallets | flask | 2d8a21c7321a9ead8e27208b49a18f4b8b27e2c1 | https://github.com/pallets/flask/issues/834 | How to get the serialized version of the session cookie in 0.10? | In version 0.9 I could simply get the value of the `session` like this:
```
flask.session.serialize()
```
But after upgrading to 0.10 this is not working anymore.. what's the alternative? How can I get the session value?
(`flask.request.cookies.get('session')` is not good for me, because I would like to get the session right after login, so it's not part of the request yet)
| null | null | null | {'base_commit': '2d8a21c7321a9ead8e27208b49a18f4b8b27e2c1', 'files': [{'path': 'flask/sessions.py', 'Loc': {"('SecureCookieSessionInterface', 'get_signing_serializer', 308)": {'mod': []}, "('TaggedJSONSerializer', 'dumps', 60)": {'mod': []}}, 'status': 'modified'}]} | [] | [] | [] | {
"iss_type": "3\nhow to do …",
"iss_reason": "5",
"loc_way": "comment",
"loc_scope": "0",
"info_type": "Code"
} | {
"code": [
"flask/sessions.py"
],
"doc": [],
"test": [],
"config": [],
"asset": []
} | null | |
pallets | flask | 22d82e70b3647ed16c7d959a939daf533377382b | https://github.com/pallets/flask/issues/4015 | 2.0.0: build requires ContextVar module | Simple I cannot find it.
```console
+ /usr/bin/python3 setup.py build '--executable=/usr/bin/python3 -s'
Traceback (most recent call last):
File "setup.py", line 4, in <module>
setup(
File "/usr/lib/python3.8/site-packages/setuptools/__init__.py", line 144, in setup
return distutils.core.setup(**attrs)
File "/usr/lib64/python3.8/distutils/core.py", line 121, in setup
dist.parse_config_files()
File "/usr/lib/python3.8/site-packages/setuptools/dist.py", line 689, in parse_config_files
parse_configuration(self, self.command_options,
File "/usr/lib/python3.8/site-packages/setuptools/config.py", line 121, in parse_configuration
meta.parse()
File "/usr/lib/python3.8/site-packages/setuptools/config.py", line 426, in parse
section_parser_method(section_options)
File "/usr/lib/python3.8/site-packages/setuptools/config.py", line 399, in parse_section
self[name] = value
File "/usr/lib/python3.8/site-packages/setuptools/config.py", line 184, in __setitem__
value = parser(value)
File "/usr/lib/python3.8/site-packages/setuptools/config.py", line 515, in _parse_version
version = self._parse_attr(value, self.package_dir)
File "/usr/lib/python3.8/site-packages/setuptools/config.py", line 349, in _parse_attr
module = import_module(module_name)
File "/usr/lib64/python3.8/importlib/__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "<frozen importlib._bootstrap>", line 1014, in _gcd_import
File "<frozen importlib._bootstrap>", line 991, in _find_and_load
File "<frozen importlib._bootstrap>", line 975, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 671, in _load_unlocked
File "<frozen importlib._bootstrap_external>", line 783, in exec_module
File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
File "/home/tkloczko/rpmbuild/BUILD/Flask-2.0.0/src/flask/__init__.py", line 7, in <module>
from .app import Flask
File "/home/tkloczko/rpmbuild/BUILD/Flask-2.0.0/src/flask/app.py", line 19, in <module>
from werkzeug.local import ContextVar
ImportError: cannot import name 'ContextVar' from 'werkzeug.local' (/usr/lib/python3.8/site-packages/werkzeug/local.py)
``` | null | null | null | {'base_commit': '22d82e70b3647ed16c7d959a939daf533377382b', 'files': [{'path': 'setup.py', 'Loc': {'(None, None, None)': {'mod': [7]}}, 'status': 'modified'}]} | [] | [] | [] | {
"iss_type": "5",
"iss_reason": "5",
"loc_way": "comment",
"loc_scope": "0",
"info_type": "Code"
} | {
"code": [
"setup.py"
],
"doc": [],
"test": [],
"config": [],
"asset": []
} | null | |
pallets | flask | 43e2d7518d2e89dc7ed0b4ac49b2d20211ad1bfa | https://github.com/pallets/flask/issues/2977 | Serial port access problem in DEBUG mode. | ### Expected Behavior
Sending commands through the serial port.
```python
app = Flask(__name__)
serialPort = serial.Serial(port = "COM5", baudrate=1000000,
bytesize=8, timeout=2, stopbits=serial.STOPBITS_ONE)
lamp = {
1 : {'name' : 'n1', 'state' : True},
2 : {'name' : 'n2', 'state' : True}
}
@app.route("/")
def hello():
templateData = {
'lamp': lamp
}
return render_template('main.html', **templateData)
@app.route("/setPin/<action>")
def action(action):
if action == "on":
serialPort.write(b"n2c1111\r\n")
lamp[1]["state"] = True
if action == "off":
serialPort.write(b"n2c0000\r\n")
lamp[1]["state"] = False
templateData = {
'lamp': lamp
}
return render_template('main.html', **templateData)
if __name__ == "__main__":
app.run(host='0.0.0.0', port=5000, debug=True)
```
### Actual Behavior
I can not access the serial port with FLASK_ENV = development and FLASK_DEBUG = 1. Everything works fine with DEBUG mode disabled.
```pytb
FLASK_APP = app.py
FLASK_ENV = development
FLASK_DEBUG = 1
In folder C:/Users/user/PycharmProjects/Ho_server
C:\Users\user\Anaconda3\python.exe -m flask run
* Serving Flask app "app.py" (lazy loading)
* Environment: development
* Debug mode: on
* Restarting with stat
* Debugger is active!
* Debugger PIN: 138-068-963
* Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)
127.0.0.1 - - [30/Oct/2018 10:49:27] "GET /setPin/on HTTP/1.1" 500 -
Traceback (most recent call last):
File "C:\Users\user\Anaconda3\lib\site-packages\flask\_compat.py", line 35, in reraise
raise value
File "C:\Users\user\PycharmProjects\H_server\app.py", line 8, in <module>
bytesize=8, timeout=2, stopbits=serial.STOPBITS_ONE)
File "C:\Users\user\Anaconda3\lib\site-packages\serial\serialwin32.py", line 31, in __init__
super(Serial, self).__init__(*args, **kwargs)
File "C:\Users\user\Anaconda3\lib\site-packages\serial\serialutil.py", line 240, in __init__
self.open()
File "C:\Users\user\Anaconda3\lib\site-packages\serial\serialwin32.py", line 62, in open
raise SerialException("could not open port {!r}: {!r}".format(self.portstr, ctypes.WinError()))
serial.serialutil.SerialException: could not open port 'COM5': PermissionError(13, 'Access is denied.', None, 5)
```
### Environment
* Python version: 3.6.5
* Flask version: 1.0.2
| null | null | null | {} | [
{
"Loc": [
7
],
"path": null
}
] | [] | [] | {
"iss_type": "1",
"iss_reason": "3",
"loc_way": "comment",
"loc_scope": "3",
"info_type": "Code"
} | {
"code": [
null
],
"doc": [],
"test": [],
"config": [],
"asset": []
} | null | |
pallets | flask | 1a7fd980f8579bd7d7d53c812a77c1dc64be52ba | https://github.com/pallets/flask/issues/1749 | JSONEncoder and aware datetimes | I was surprised to see that though flask.json.JSONEncoder accepts datetime objects, it ignores the timezone. I checked werkzeug.http.http_date and it can handle timezone aware dates just fine if they are passed in, but the JSONEncoder insists on transforming the datetime to a timetuple, like this
`return http_date(o.timetuple())`
This means i have to convert all my dates to utc before encoding them, otherwise I should overwrite the dafault() method in the encoder. Can you help me understand why the encoder was made to function with naive dates only?
Thx
| null | null | null | {'base_commit': '1a7fd980f8579bd7d7d53c812a77c1dc64be52ba', 'files': [{'path': 'flask/json.py', 'Loc': {"('JSONEncoder', 'default', 60)": {'mod': [78]}}, 'status': 'modified'}]} | [] | [] | [] | {
"iss_type": "1",
"iss_reason": "5",
"loc_way": "comment",
"loc_scope": "0",
"info_type": "Code"
} | {
"code": [
"flask/json.py"
],
"doc": [],
"test": [],
"config": [],
"asset": []
} | null | |
sherlock-project | sherlock | 144d43830f663808c5fbca75b797350060acf7dd | https://github.com/sherlock-project/sherlock/issues/559 | Results files saved to specific folder | Having just installed Sherlock I was surprised to see the results files are just jumbled in with everything else instead of being in their own Results folder.
Having a separate folder would keep things cleaner especially as you use it more and the number of files increases. | null | null | null | {'base_commit': '144d43830f663808c5fbca75b797350060acf7dd', 'files': [{'path': 'README.md', 'Loc': {'(None, None, 65)': {'mod': [65]}}, 'status': 'modified'}, {'path': 'sherlock/sherlock.py', 'Loc': {"(None, 'main', 462)": {'mod': [478]}}, 'status': 'modified'}]} | [] | [] | [] | {
"iss_type": "3",
"iss_reason": "5",
"loc_way": "comment",
"loc_scope": "0",
"info_type": "Code\n+\nDoc"
} | {
"code": [
"sherlock/sherlock.py"
],
"doc": [
"README.md"
],
"test": [],
"config": [],
"asset": []
} | null | |
sherlock-project | sherlock | 7ec56895a37ada47edd6573249c553379254d14a | https://github.com/sherlock-project/sherlock/issues/1911 | question | How do you search for usernames? New to this. | <!--
######################################################################
WARNING!
IGNORING THE FOLLOWING TEMPLATE WILL RESULT IN ISSUE CLOSED AS INCOMPLETE.
######################################################################
-->
## Checklist
<!--
Put x into all boxes (like this [x]) once you have completed what they say.
Make sure complete everything in the checklist.
-->
- [ ] I'm asking a question regarding Sherlock
- [ ] My question is not a tech support question.
**We are not your tech support**.
If you have questions related to `pip`, `git`, or something that is not related to Sherlock, please ask them on [Stack Overflow](https://stackoverflow.com/) or [r/learnpython](https://www.reddit.com/r/learnpython/)
## Question
ASK YOUR QUESTION HERE
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sherlock-project | sherlock | 65ce128b7fd8c8915c40495191d9c136f1d2322b | https://github.com/sherlock-project/sherlock/issues/1297 | bug | name 'requests' is not defined | <!--
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## Description
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| null | null | null | {'base_commit': '65ce128b7fd8c8915c40495191d9c136f1d2322b', 'files': [{'path': 'sherlock/sites.py', 'Loc': {}}]} | [] | [] | [] | {
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"info_type": "Code"
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"code": [
"sherlock/sites.py"
],
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"test": [],
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sherlock-project | sherlock | f63e17066dc4881ee5a164aed60b6e8f1e9ab129 | https://github.com/sherlock-project/sherlock/issues/462 | environment | File "sherlock.py", line 24, in <module> from requests_futures.sessions import FuturesSession ModuleNotFoundError: No module named 'requests_futures' | File "sherlock.py", line 24, in <module>
from requests_futures.sessions import FuturesSession
ModuleNotFoundError: No module named 'requests_futures' | null | null | null | {'base_commit': 'f63e17066dc4881ee5a164aed60b6e8f1e9ab129', 'files': [{'path': 'requirements.txt', 'Loc': {}}]} | [] | [] | [] | {
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"info_type": "Code"
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"code": [],
"doc": [],
"test": [],
"config": [
"requirements.txt"
],
"asset": []
} | null |
sherlock-project | sherlock | 6c6faff416896a41701aa3e24e5b5a584bd5cb44 | https://github.com/sherlock-project/sherlock/issues/236 | question | No module named 'torrequest' | Hi,
similar problem to module "requests_futures"
Traceback (most recent call last):
File "sherlock.py", line 25, in <module>
from torrequest import TorRequest
ModuleNotFoundError: No module named 'torrequest'
| null | null | null | {'base_commit': '6c6faff416896a41701aa3e24e5b5a584bd5cb44', 'files': [{'path': 'requirements.txt', 'Loc': {}}]} | [] | [] | [] | {
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"loc_way": "comment",
"loc_scope": "0",
"info_type": "Doc\n依赖声明"
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"doc": [],
"test": [],
"config": [
"requirements.txt"
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keras-team | keras | c0d95fd6c2cd8ffc0738819825c3065e3c89977c | https://github.com/keras-team/keras/issues/4954 | TimeDistributed Wrapper not working with LSTM/GRU | Please make sure that the boxes below are checked before you submit your issue. If your issue is an implementation question, please ask your question on [StackOverflow](http://stackoverflow.com/questions/tagged/keras) or [join the Keras Slack channel](https://keras-slack-autojoin.herokuapp.com/) and ask there instead of filing a GitHub issue.
Thank you!
- [x] Check that you are up-to-date with the master branch of Keras. You can update with:
pip install git+git://github.com/fchollet/keras.git --upgrade --no-deps
- [x] If running on TensorFlow, check that you are up-to-date with the latest version. The installation instructions can be found [here](https://www.tensorflow.org/get_started/os_setup).
- [x] If running on Theano, check that you are up-to-date with the master branch of Theano. You can update with:
pip install git+git://github.com/Theano/Theano.git --upgrade --no-deps
- [x] Provide a link to a GitHub Gist of a Python script that can reproduce your issue (or just copy the script here if it is short).
I am trying to apply word level attention to words in a document after passing the sentences through a GRU. However, the TimeDistributed Wrapper isn't working with GRU/LSTM.
I get the following error
```python
File "/home/##/.local/lib/python2.7/site-packages/keras/engine/topology.py", line 569, in __call__
self.add_inbound_node(inbound_layers, node_indices, tensor_indices)
File "/home/##/.local/lib/python2.7/site-packages/keras/engine/topology.py", line 632, in add_inbound_node
Node.create_node(self, inbound_layers, node_indices, tensor_indices)
File "/home/##/.local/lib/python2.7/site-packages/keras/engine/topology.py", line 164, in create_node
output_tensors = to_list(outbound_layer.call(input_tensors[0], mask=input_masks[0]))
File "/home/##/.local/lib/python2.7/site-packages/keras/layers/wrappers.py", line 129, in call
y = self.layer.call(X) # (nb_samples * timesteps, ...)
File "/home/##/.local/lib/python2.7/site-packages/keras/layers/recurrent.py", line 201, in call
input_shape = K.int_shape(x)
File "/home/##/.local/lib/python2.7/site-packages/keras/backend/theano_backend.py", line 128, in int_shape
raise Exception('Not a Keras tensor:', x)
Exception: ('Not a Keras tensor:', Reshape{3}.0)
```
The code snipped is written below
```python
input_layer = Input(shape=(document_size, img_h,), dtype='int32', name='input_layer')
embedding_layer = TimeDistributed(Embedding(len(W2V), img_w, input_length=img_h, weights=[W2V], trainable=True, mask_zero=True))(input_layer)
gru_word = TimeDistributed(GRU(GRU_layers[0], return_sequences=True, activation=conv_non_linear))(embedding_layer)
```
| null | null | null | {'base_commit': 'c0d95fd6c2cd8ffc0738819825c3065e3c89977c', 'files': [{'path': 'Version', 'Loc': {}}, {'path': 'Version', 'Loc': {}}]} | [] | [] | [] | {
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keras-team | keras | 980a6be629610ee58c1eae5a65a4724ce650597b | https://github.com/keras-team/keras/issues/16234 | type:support | Compiling model in callback causes TypeError | **System information**.
- Have I written custom code (as opposed to using a stock example script provided in Keras): yes
- TensorFlow version (use command below): 2.8.0 (2.4 too)
- Python version: 3.7
**Describe the problem**.
In a fine-tuning case I would like to do transfer-learning phase first (with fine-tuned layers frozen) and after that, all layers should be unfrozen. I wrote a callback that unfreeze the layers after few epochs. Unfortunately, after changing the layers' `trainable` attribute, the model should be recompiled - and the recompilation causes the `TypeError` (see colab).
I am aware that I can workaround this by compiling and fitting model twice - for both phases separately - but the usage of callback seems more elegant to me.
**Standalone code to reproduce the issue**.
https://colab.research.google.com/drive/1u6VlH6EIQGXSp7vEIngTasp3v2EE42Wi?usp=sharing
**Source code / logs**.
```
TypeError: 'NoneType' object is not callable
```
| null | null | null | {'base_commit': '980a6be629610ee58c1eae5a65a4724ce650597b', 'files': [{'path': 'keras/engine/training.py', 'Loc': {"('Model', 'make_train_function', 998)": {'mod': []}}, 'status': 'modified'}]} | [] | [] | [] | {
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keras-team | keras | 90f441a6a0ed4334cac53760289061818a68b7c1 | https://github.com/keras-team/keras/issues/2893 | Is the cifar10_cnn.py example actually performing data augmentation? | When `datagen.fit(X_train)` is called in the [`cifar10_cnn.py` example](https://github.com/fchollet/keras/blob/master/examples/cifar10_cnn.py#L103), shouldn't it be (when `data_augmentation=True`):
``` python
datagen.fit(X_train, augment=True)
```
as the [default value for `augment` is `False`](https://github.com/fchollet/keras/blob/master/keras/preprocessing/image.py#L410)?
Also, I am right in thinking when using `augment=True` the original (i.e. non-augmented - ignoring any normalisation/standardisation) data is not necessarily trained on? If so, I thought data augmentation is a method of artificially increasing the size of your dataset, so shouldn't we additionally be training on the non-augmented data? Thanks
- [x] Check that you are up-to-date with the master branch of Keras. You can update with:
pip install git+git://github.com/fchollet/keras.git --upgrade --no-deps
- [x] If running on Theano, check that you are up-to-date with the master branch of Theano. You can update with:
pip install git+git://github.com/Theano/Theano.git --upgrade --no-deps
- [x] Provide a link to a GitHub Gist of a Python script that can reproduce your issue (or just copy the script here if it is short).
| null | null | null | {'base_commit': '90f441a6a0ed4334cac53760289061818a68b7c1', 'files': [{'path': 'keras/preprocessing/image.py', 'Loc': {"('ImageDataGenerator', 'fit', 404)": {'mod': [419, 420, 421, 422, 423]}}, 'status': 'modified'}]} | [] | [] | [] | {
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"keras/preprocessing/image.py"
],
"doc": [],
"test": [],
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} | null | |
keras-team | keras | 654404c2ed8db47a5361a3bff9126a16507c9c4c | https://github.com/keras-team/keras/issues/1787 | What happened to WordContextProduct? | ``` python
In [1]: import keras
In [2]: keras.__version__
Out[2]: '0.3.2'
In [3]: from keras.layers.embeddings import WordContextProduct
Using Theano backend.
/usr/local/lib/python3.5/site-packages/theano/tensor/signal/downsample.py:5: UserWarning: downsample module has been moved to the pool module.
warnings.warn("downsample module has been moved to the pool module.")
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
<ipython-input-3-65e83b407b3e> in <module>()
----> 1 from keras.layers.embeddings import WordContextProduct
ImportError: cannot import name 'WordContextProduct'
```
This page now returns a 404: https://github.com/fchollet/keras/blob/master/examples/skipgram_word_embeddings.py
Was this code taken out of keras, or just moved somewhere else?
Thanks,
Zach
---
Please make sure that the boxes below are checked before you submit your issue. Thank you!
- [x] Check that you are up-to-date with the master branch of Keras. You can update with:
pip install git+git://github.com/fchollet/keras.git --upgrade --no-deps
- [x] If running on Theano, check that you are up-to-date with the master branch of Theano. You can update with:
pip install git+git://github.com/Theano/Theano.git --upgrade --no-deps
- [x] Provide a link to a GitHub Gist of a Python script that can reproduce your issue (or just copy the script here if it is short).
I'm trying something like this:
``` python
models = []
# Word vectors
model_word = Sequential()
model_word.add(Embedding(1e4, 300, input_length=1))
model_word.add(Reshape(dims=(300,)))
models.append(model_word)
# Context vectors
model_context = Sequential()
model_context.add(Embedding(1e4, 300, input_length=1))
model_context.add(Reshape(dims=(300,)))
models.append(model_context)
# Combined model
model = Sequential()
model.add(Merge(models, mode='dot'))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='mean_squared_error', optimizer=Adam(lr=0.001))
```
Does that look reasonable? And then as input, I need to provide 2 lists of indexes?
" | null | null | null | {} | [
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keras-team | keras | 8778add0d66aed64a8970c34576bf5800bc19170 | https://github.com/keras-team/keras/issues/3335 | Masking the output of a conv layer | Hi,
I am trying to apply a given mask in the output of a conv layer. The simplest form of my problem can be seen in the img
The mask should be considered as an input when training/predicting. I have already tried to use the Merge layer (mode='mul') to apply the input mask as follows:
``` python
main_input= Input(shape=(3, 64, 64))
mask1_input = Input(shape=(1, 64, 64))
mask2_input = Input(shape=(1, 64, 64))
conv1 = Convolution2D(1,7,7, border_mode='same')(main_input)
merged_model1 = Sequential()
merged_model1.add(Merge([conv1, mask1_input], mode='mul'))
conv2 = Convolution2D(1, 7,7, border_mode='same')(main_input)
merged_model2 = Sequential()
merged_model2.add(Merge([conv2, mask2_input], mode='mul'))
model = Sequential()
model.add(Merge([merged_model1,merged_model2],mode='sum'))
```
But it is not working, maybe because I'm trying to merge a layer with a Tensor. But even if I could do that, I don't feel this is the right way to do that. Can someone help?
Please make sure that the boxes below are checked before you submit your issue. Thank you!
- [X] Check that you are up-to-date with the master branch of Keras. You can update with:
pip install git+git://github.com/fchollet/keras.git --upgrade --no-deps
- [X] If running on Theano, check that you are up-to-date with the master branch of Theano. You can update with:
pip install git+git://github.com/Theano/Theano.git --upgrade --no-deps
| null | null | null | {'base_commit': '8778add0d66aed64a8970c34576bf5800bc19170', 'files': [{'path': 'keras/src/models/model.py', 'Loc': {}}]} | [] | [] | [] | {
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"keras/src/models/model.py"
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keras-team | keras | ed07472bc5fc985982db355135d37059a1f887a9 | https://github.com/keras-team/keras/issues/13101 | type:support | model.fit : AttributeError: 'Model' object has no attribute '_compile_metrics' | **System information**
- Have I written custom code (as opposed to using example directory): Yes
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Linux Mint 19.3
- TensorFlow backend (yes / no): yes
- TensorFlow version: 2.0.0b1
- Keras version: 2.2.4-tf
- Python version: 3.6
- CUDA/cuDNN version: /
- GPU model and memory: GTX 940MX, 430.26
**Describe the current behavior**
The model.fit() function throws a `AttributeError: 'Model' object has no attribute '_compile_metrics'` exception.
**Describe the expected behavior**
It should work ?
**Code to reproduce the issue**
```python
import numpy as np
import tensorflow as tf
input_3D = tf.keras.Input(shape=(None, None, None, 1)) # unknown width, length and depth, 1 gray channel
network_3D = tf.keras.layers.Conv3D(
filters = 128, # dimensionality of output space
kernel_size = 5, # shape of 2D convolution window (5x5)
strides = 1, # stride of convolution along all spatial dimensions
padding = "same", data_format = "channels_last", # input with shape (batch, height, width, channels)
activation = tf.keras.layers.LeakyReLU(alpha = 0.2), # activation function to use
use_bias = True,
kernel_initializer = tf.keras.initializers.TruncatedNormal(stddev = 1e-2),
# initializer for the kernel weights matrix
bias_initializer = 'zeros', # initializer for the bias vector
input_shape = (None, None, None, 1)
)(input_3D)
network_3D = tf.keras.layers.BatchNormalization(
momentum = 0.1, # momentum + decay = 1.0
epsilon = 1e-5,
scale = True
)(network_3D)
model = tf.keras.Model(inputs = input_3D, outputs = network_3D)
model.loss = tf.losses.mean_squared_error
model.optimizer = tf.keras.optimizers.Adam(learning_rate = 0.002)
v = np.zeros((100,100,100,100))
l = np.zeros((100,100,100))
model.fit(v, l, epochs = 20, batch_size = 1)
```
**Other info / logs**
```python
Traceback (most recent call last):
File ".../venv/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 3296, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-14-a0cacfaacdab>", line 1, in <module>
history = model.fit(v, l, epochs = 20, batch_size = 1)
File ".../venv/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py", line 643, in fit
use_multiprocessing=use_multiprocessing)
File ".../venv/lib/python3.6/site-packages/tensorflow/python/keras/engine/training_arrays.py", line 632, in fit
shuffle=shuffle)
File ".../venv/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py", line 2385, in _standardize_user_data
metrics=self._compile_metrics,
AttributeError: 'Model' object has no attribute '_compile_metrics'
```
| null | null | null | {'base_commit': 'ed07472bc5fc985982db355135d37059a1f887a9', 'files': [{'path': 'keras/engine/training.py', 'Loc': {"('Model', 'compile', 40)": {'mod': []}}, 'status': 'modified'}]} | [] | [] | [] | {
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keras-team | keras | a3d160b9467c99cbb27f9aa0382c759f45c8ee66 | https://github.com/keras-team/keras/issues/9741 | Improve Keras Documentation User Experience for Long Code Snippets By Removing The Need For Horizontal Slide Bars | **Category**: documentation user-experience
**Comment**: modify highlight.js <code></code> to wrap long documentation code snippets
**Why**: eliminates the need for a user to manually click and slide a horizontal slider just to get a quick sense of what available parameters and their default values are
**Context**
While reading the documentation, and coming from a scikit-learn background, I really like how their documentation shows all the class and method parameters ([example page](http://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html)). It's very helpful to quickly be able to see the default parameters.
Take [Dense](https://keras.io/layers/core/#dense) for example. If the documentation looked like this (imagine this a code block, not individually highlighted lines):
`keras.layers.Dense(units, activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None)`
Benefits:
- easy to read
- no scrolling a horizontal slider
- immediately tells me the available parameters and their default values
Compare that experience to the current Keras experience:
```
keras.layers.Dense(units, activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None)
```
Disadvantages:
- requires scrolling horizontally to see the rest
- easy to lose track of where you are while scrolling
- requires physical action to see everything
The Keras team no-doubt is busy with much bigger concerns than documentation formatting. One could say that the "Arguments" are all listed below or by clicking the "Source". True, however the key point I'm trying to make is usability, and quick readability. Reading through an "Argument"'s verbose description, or having to scroll horizontally is not quick nor an optimal experience.
I'm not going to make a case for why making documentation easy-to-read is important. I think the Keras documentation **content** itself is outstanding. | null | null | null | {'base_commit': 'a3d160b9467c99cbb27f9aa0382c759f45c8ee66', 'files': [{'path': 'docs/autogen.py', 'Loc': {}}]} | [] | [] | [] | {
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keras-team | keras | 7a12fd0f8597760cf8e1238a9b021e247693517b | https://github.com/keras-team/keras/issues/2372 | problem of save/load model | HI,
Thanks for making such a wonderful tool!
I'm using Keras 1.0. I want to save and load the model both the arch and the parameters. So I use the method in FAQ. Here is the code.
```
def save_model(self, model, options):
json_string = model.to_json()
open(options['file_arch'], 'w').write(json_string)
model.save_weights(options['file_weight'])
def load_model(self, options):
self.model = model_from_json(open(options['file_arch']).read())
self.model.load_weights(options['file_weight'])
return self.model
```
When I load model and use model.predict(), there is a error:
AttributeError: 'NoneType' object has no attribute 'predict'
Don't know why. If I don't load the model from file, just train a model and use it, everything seems ok.
I checked the issues, most people just need to load the parameters. Is it possible when I load the architecture, I overwrite the old model and loose the model.predict()?
Thanks again for making Keras!
Ben
| null | null | null | {'base_commit': '7a12fd0f8597760cf8e1238a9b021e247693517b', 'files': [{'path': 'keras/src/trainers/trainer.py', 'Loc': {"('Trainer', 'compile', 40)": {'mod': []}}, 'status': 'modified'}]} | [] | [] | [] | {
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"code": [
"keras/src/trainers/trainer.py"
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"doc": [],
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keras-team | keras | 284ef7b495a61238dccc6149996c4cb88fef1c5a | https://github.com/keras-team/keras/issues/933 | Same model but graph gives bad performance | Hello,
I am learning to use Graph as it seems more powerful so I implemented one of my previous model which uses Sequential. Here is the model using sequential (number of dimension set in random):
```
def build_generation_embedding_model(self, dim):
print "Build model ..."
input_model = Sequential()
input_model.add(TimeDistributedDense(dim, input_shape=(10,10)))
input_model.add(LSTM(dim, return_sequences=False))
input_model.add(Dense(dim))
canonical_model = Sequential()
canonical_model.add(TimeDistributedDense(dim, input_shape=(15,15)))
canonical_model.add(LSTM(dim, return_sequences=False))
canonical_model.add(Dense(dim))
self.model = Sequential()
self.model.add(Merge([input_model, canonical_model], mode='concat'))
self.model.add(Dense(15))
self.model.add(Activation('softmax'))
self.model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
```
The model works fine and below is my reimplementation using Graph:
```
def build_generation_embedding_model_graph(self, dim):
self.model = Graph()
self.model.add_input(name='input1', input_shape=(10,10))
self.model.add_input(name='canonical', input_shape=(15,15))
self.model.add_node(TimeDistributedDense(dim), name='Embed_input1', input='input1')
self.model.add_node(TimeDistributedDense(dim), name='Embed_canonical', input='canonical')
self.model.add_node(LSTM(dim, return_sequences=False), name='Hidden_input1', input='Embed_input1')
self.model.add_node(LSTM(dim, return_sequences=False), name='Hidden_canonical', input='Embed_canonical')
self.model.add_node(Dense(15), name='merge', inputs=['Hidden_input1','Hidden_canonical'], merge_mode='concat')
self.model.add_node(Activation('softmax'), name='activation', input='merge')
self.model.add_output(name='output', input='merge')
self.model.compile('rmsprop', {'output':'categorical_crossentropy'})
```
My impression is that they are exactly the same model (grateful if somebody spotted something wrong there). But the model based on Graph gives a loss of 3.6 while the loss for the other one is around 0.002.
Is there a reason for this please ?
Thank you for your help
| null | null | null | {} | [
{
"Loc": [
36
],
"path": null
}
] | [] | [] | {
"iss_type": "1",
"iss_reason": "3",
"loc_way": "comment",
"loc_scope": "3",
"info_type": "Code"
} | {
"code": [
null
],
"doc": [],
"test": [],
"config": [],
"asset": []
} | null | |
keras-team | keras | c2b844ba2fe8d0d597da9ef6a9af3b20d18d0bec | https://github.com/keras-team/keras/issues/7603 | Loss Increases after some epochs | I have tried different convolutional neural network codes and I am running into a similar issue. The network starts out training well and decreases the loss but after sometime the loss just starts to increase. I have shown an example below:
Epoch 15/800
1562/1562 [==============================] - 49s - loss: 0.9050 - acc: 0.6827 - val_loss: 0.7667 - val_acc: 0.7323
Epoch 16/800
1562/1562 [==============================] - 49s - loss: 0.8906 - acc: 0.6864 - val_loss: 0.7404 - val_acc: 0.7434
Epoch 380/800
1562/1562 [==============================] - 49s - loss: 1.5519 - acc: 0.4880 - val_loss: 1.4250 - val_acc: 0.5233
Epoch 381/800
1562/1562 [==============================] - 48s - loss: 1.5416 - acc: 0.4897 - val_loss: 1.5032 - val_acc: 0.4868
Epoch 800/800
1562/1562 [==============================] - 49s - loss: 1.8483 - acc: 0.3402 - val_loss: 1.9454 - val_acc: 0.2398
I have tried this on different cifar10 architectures I have found on githubs. I am training this on a GPU Titan-X Pascal. This only happens when I train the network in batches and with data augmentation. I have changed the optimizer, the initial learning rate etc. I have also attached a link to the code. I just want a cifar10 model with good enough accuracy for my tests, so any help will be appreciated. The code is from this:
https://github.com/fchollet/keras/blob/master/examples/cifar10_cnn.py
| null | null | null | {'base_commit': 'c2b844ba2fe8d0d597da9ef6a9af3b20d18d0bec', 'files': [{'path': 'examples/cifar10_cnn.py', 'Loc': {'(None, None, None)': {'mod': [65]}}, 'status': 'modified'}]} | [] | [] | [] | {
"iss_type": "2",
"iss_reason": "5",
"loc_way": "comment",
"loc_scope": "0",
"info_type": "Code"
} | {
"code": [
"examples/cifar10_cnn.py"
],
"doc": [],
"test": [],
"config": [],
"asset": []
} | null | |
keras-team | keras | 530eff62e5463e00d73e72c51cc830b9ac3a14ab | https://github.com/keras-team/keras/issues/3997 | Using keras for Distributed training raise RuntimeError("Graph is finalized and cannot be modified.") | I'm using keras for distributed training with following code:
``` python
#!/usr/bin/env python
# -*- coding:utf-8 -*-
# Created by Enigma on 2016/9/26
import numpy as np
import tensorflow as tf
# Define Hyperparameters
FLAGS = tf.app.flags.FLAGS
# For missions
tf.app.flags.DEFINE_string("ps_hosts", "",
"Comma-separated list of hostname:port pairs")
tf.app.flags.DEFINE_string("worker_hosts", "",
"Comma-separated list of hostname:port pairs")
tf.app.flags.DEFINE_string("job_name", "", "One of 'ps', 'worker'")
tf.app.flags.DEFINE_integer("task_index", 0, "Index of task within the job")
# Hyperparameters
from keras import backend as K
from keras.layers import Input, Dense
from keras.models import Model
def main(_):
ps_hosts = FLAGS.ps_hosts.split(",")
worker_hosts = FLAGS.worker_hosts.split(",")
cluster = tf.train.ClusterSpec({"ps": ps_hosts, "worker": worker_hosts})
server_config = tf.ConfigProto(
gpu_options=tf.GPUOptions(allow_growth=True),
log_device_placement=True)
server = tf.train.Server(cluster, config=server_config,
job_name=FLAGS.job_name, task_index=FLAGS.task_index)
if FLAGS.job_name == "ps":
server.join()
elif FLAGS.job_name == "worker":
with tf.device(tf.train.replica_device_setter(
worker_device="/job:worker/task:%d/cpu:0" % FLAGS.task_index,
cluster=cluster)):
global_step = tf.Variable(0, name='global_step', trainable=False)
inputs = Input(shape=[1, ])
hidden = Dense(10, activation='relu')(inputs)
output = Dense(1, activation='sigmoid')(hidden)
model = Model(input=inputs, output=output)
saver = tf.train.Saver()
summary_op = tf.merge_all_summaries()
sv = tf.train.Supervisor(is_chief=(FLAGS.task_index == 0),
logdir="./checkpoint/",
# init_op=init_op,
summary_op=summary_op,
saver=saver,
global_step=global_step,
save_model_secs=60)
with sv.managed_session(server.target) as sess:
step = 0
K.set_session(sess)
model.compile(optimizer='sgd', loss='mse')
while step < 1000000:
train_x = np.random.randn(1)
train_y = 2 * train_x + np.random.randn(1) * 0.33 + 10
model.fit(train_x, train_y)
sv.stop()
if __name__ == "__main__":
tf.app.run()
```
then I run it with:
```
/opt/anaconda3/bin/python /cache/allenwoods/keras_dis_test.py --ps_hosts=0.0.0.0:48636 --worker_hosts=0.0.0.0:46261 --job_name=ps --task_index=0
/opt/anaconda3/bin/python /cache/allenwoods/keras_dis_test.py --ps_hosts=0.0.0.0:48636 --worker_hosts=0.0.0.0:46261 --job_name=worker --task_index=0
```
it doesn't work and return
```
Traceback (most recent call last):
File "/cache/allenwoods/keras_dis_test.py", line 73, in <module>
tf.app.run()
File "/opt/anaconda3/lib/python3.5/site-packages/tensorflow/python/platform/app.py", line 30, in run
sys.exit(main(sys.argv[:1] + flags_passthrough))
File "/cache/allenwoods/keras_dis_test.py", line 69, in main
model.fit(train_x, train_y)
File "/opt/anaconda3/lib/python3.5/contextlib.py", line 77, in __exit__
self.gen.throw(type, value, traceback)
File "/opt/anaconda3/lib/python3.5/site-packages/tensorflow/python/training/supervisor.py", line 969, in managed_session
self.stop(close_summary_writer=close_summary_writer)
File "/opt/anaconda3/lib/python3.5/site-packages/tensorflow/python/training/supervisor.py", line 797, in stop
stop_grace_period_secs=self._stop_grace_secs)
File "/opt/anaconda3/lib/python3.5/site-packages/tensorflow/python/training/coordinator.py", line 386, in join
six.reraise(*self._exc_info_to_raise)
File "/opt/anaconda3/lib/python3.5/site-packages/six.py", line 686, in reraise
raise value
File "/opt/anaconda3/lib/python3.5/site-packages/tensorflow/python/training/supervisor.py", line 959, in managed_session
yield sess
File "/cache/allenwoods/VRLforTraffic/src/missions/keras_dis_test.py", line 65, in main
model.compile(optimizer='sgd', loss='mse')
File "/opt/anaconda3/lib/python3.5/site-packages/keras/engine/training.py", line 484, in compile
self.optimizer = optimizers.get(optimizer)
File "/opt/anaconda3/lib/python3.5/site-packages/keras/optimizers.py", line 580, in get
instantiate=True, kwargs=kwargs)
File "/opt/anaconda3/lib/python3.5/site-packages/keras/utils/generic_utils.py", line 18, in get_from_module
return res()
File "/opt/anaconda3/lib/python3.5/site-packages/keras/optimizers.py", line 134, in __init__
self.iterations = K.variable(0.)
File "/opt/anaconda3/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py", line 149, in variable
v = tf.Variable(value, dtype=_convert_string_dtype(dtype), name=name)
File "/opt/anaconda3/lib/python3.5/site-packages/tensorflow/python/ops/variables.py", line 215, in __init__
dtype=dtype)
File "/opt/anaconda3/lib/python3.5/site-packages/tensorflow/python/ops/variables.py", line 327, in _init_from_args
self._snapshot = array_ops.identity(self._variable, name="read")
File "/opt/anaconda3/lib/python3.5/contextlib.py", line 77, in __exit__
self.gen.throw(type, value, traceback)
File "/opt/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 4150, in name_scope
yield scope
File "/opt/anaconda3/lib/python3.5/contextlib.py", line 77, in __exit__
self.gen.throw(type, value, traceback)
File "/opt/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 3645, in get_controller
yield default
File "/opt/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 4150, in name_scope
yield scope
File "/opt/anaconda3/lib/python3.5/contextlib.py", line 77, in __exit__
self.gen.throw(type, value, traceback)
File "/opt/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 2891, in name_scope
yield "" if new_stack is None else new_stack + "/"
File "/opt/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 4150, in name_scope
yield scope
File "/opt/anaconda3/lib/python3.5/site-packages/tensorflow/python/ops/variables.py", line 293, in _init_from_args
initial_value, name="initial_value", dtype=dtype)
File "/opt/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 657, in convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "/opt/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/constant_op.py", line 180, in _constant_tensor_conversion_function
return constant(v, dtype=dtype, name=name)
File "/opt/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/constant_op.py", line 167, in constant
attrs={"value": tensor_value, "dtype": dtype_value}, name=name).outputs[0]
File "/opt/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 2339, in create_op
self._check_not_finalized()
File "/opt/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 2080, in _check_not_finalized
raise RuntimeError("Graph is finalized and cannot be modified.")
RuntimeError: Graph is finalized and cannot be modified.
```
I wondering if it happens because keras' model wasn't created as part of the graph used in tf.train.Supervisor, but I have not a clue on how to prove it or fix it. Any idea？
| null | null | null | {'base_commit': '530eff62e5463e00d73e72c51cc830b9ac3a14ab', 'files': [{'path': 'keras/engine/training.py', 'Loc': {"('Model', '_make_train_function', 685)": {'mod': []}, "('Model', '_make_test_function', 705)": {'mod': []}, "('Model', '_make_predict_function', 720)": {'mod': []}}, 'status': 'modified'}, {'path': 'keras/backend/tensorflow_backend.py', 'Loc': {"(None, 'manual_variable_initialization', 31)": {'mod': []}}, 'status': 'modified'}]} | [] | [] | [] | {
"iss_type": "1",
"iss_reason": "5",
"loc_way": "comment",
"loc_scope": "0",
"info_type": "Code"
} | {
"code": [
"keras/engine/training.py",
"keras/backend/tensorflow_backend.py"
],
"doc": [],
"test": [],
"config": [],
"asset": []
} | null | |
keras-team | keras | c2e36f369b411ad1d0a40ac096fe35f73b9dffd3 | https://github.com/keras-team/keras/issues/4810 | Parent module '' not loaded, cannot perform relative import with vgg16.py | just set up my ubuntu and have the python 3.5 installed, together with Keras...the following occurs:
RESTART: /usr/local/lib/python3.5/dist-packages/keras/applications/vgg16.py
Traceback (most recent call last):
File "/usr/local/lib/python3.5/dist-packages/keras/applications/vgg16.py", line 14, in <module>
from ..models import Model
SystemError: Parent module '' not loaded, cannot perform relative import
| null | null | null | {'base_commit': 'c2e36f369b411ad1d0a40ac096fe35f73b9dffd3', 'files': [{'path': 'keras/applications/vgg16.py', 'Loc': {'(None, None, None)': {'mod': [14, 15, 16, 17, 18, 19, 20, 21]}}, 'status': 'modified'}]} | [] | [] | [] | {
"iss_type": "1",
"iss_reason": "5",
"loc_way": "comment",
"loc_scope": "0",
"info_type": "Code"
} | {
"code": [
"keras/applications/vgg16.py"
],
"doc": [],
"test": [],
"config": [],
"asset": []
} | null |
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