MLP Regressor: ValueError: shapes (110,4) and (2,110) not aligned: 4 (dim 1) != 2 (dim 0)

[raul-parada]

I'm trying to use the MLPRegressor model to learn a batch (learn_many, pred_many) of dataframes with variable dimensions in the number of rows, however the number of columns is 2 (x) and target y of only one.
If I configure the model as:

model = (
    pp.StandardScaler() |
    nn.MLPRegressor(
        hidden_dims=(110,),
        activations=(
            nn.activations.ReLU,
            nn.activations.ReLU,
            nn.activations.ReLU
        ),
        optimizer=optim.SGD(1e-4),
        seed=42
    )
)

I got this error:

File "<__array_function__ internals>", line 200, in dot
ValueError: shapes (110,4) and (2,110) not aligned: 4 (dim 1) != 2 (dim 0)

How should I modify the above model to suite my data? Should I put in inside the loop to control each time the number of rows?

[MaxHalford]

Hey there. We'll need a minimal reproducible example to debug this.

[raul-parada]

Here the model.learn_many(x, y)
And below data:
x = {'latitude(m)': {2687426: 41.39042133911506, 2687427: 41.38954084417816, 2687428: 41.39004263366844, 2687429: 41.39026115897981, 2687430: 41.3903534209147, 2687431: 41.39168720075243, 2687432: 41.389906005812335, 2687433: 41.3896067998804, 2687434: 41.38962954069514, 2687435: 41.38966463228027, 2687436: 41.38997196128503, 2687437: 41.38970996702829, 2687438: 41.389857918626056, 2687439: 41.39035987920118, 2687440: 41.39059462971834, 2687441: 41.38980863933558, 2687442: 41.39039964788776, 2687443: 41.38975909163339, 2687444: 41.39020360204856, 2687445: 41.389946461758434, 2687446: 41.39120275118262, 2687447: 41.39005686132916, 2687448: 41.38984822987522, 2687449: 41.39114608699223, 2687450: 41.39081869581249, 2687451: 41.390114637310354, 2687452: 41.3896941581337, 2687453: 41.39140800627263, 2687454: 41.39083467671437, 2687455: 41.39183585479083, 2687456: 41.39045462873719, 2687457: 41.39069555407563, 2687458: 41.390779516537926, 2687459: 41.39050044746961, 2687460: 41.39062791908504, 2687461: 41.39013500822785, 2687462: 41.39084700910927, 2687463: 41.39033056040013, 2687464: 41.39130939788125, 2687465: 41.39042995908144, 2687466: 41.38992355946002, 2687467: 41.39072435675788, 2687468: 41.390874790073525, 2687469: 41.38940380953638, 2687470: 41.390639313834114, 2687471: 41.39017354847877, 2687472: 41.389994704308, 2687473: 41.39127694632597, 2687474: 41.39049730502261, 2687475: 41.3904772642558, 2687476: 41.38974230585069, 2687477: 41.39076890573515, 2687478: 41.39048219358096, 2687479: 41.38957127695049, 2687480: 41.39024996924423, 2687481: 41.3905056274347, 2687482: 41.39131419104382, 2687483: 41.3897904535093, 2687484: 41.39054944857269, 2687485: 41.39014415611732, 2687486: 41.39029699654753, 2687487: 41.38985319841511, 2687488: 41.39027932244997, 2687489: 41.39000063648752, 2687490: 41.39090635134317, 2687491: 41.39012566949913, 2687492: 41.39045088858986, 2687493: 41.39193497121499, 2687494: 41.38991058086917, 2687495: 41.38996378190495, 2687496: 41.38955852648304, 2687497: 41.39093580497119, 2687498: 41.391344362822366, 2687499: 41.3896609948518, 2687500: 41.39148154265635, 2687501: 41.39057872597164, 2687502: 41.38949408892791, 2687503: 41.39088983692174, 2687504: 41.38951031516973, 2687505: 41.39063055699967, 2687506: 41.39125762058165, 2687507: 41.39066176128346, 2687508: 41.391454391375255, 2687509: 41.39240696408756, 2687510: 41.39064978508938, 2687511: 41.391251168564594, 2687512: 41.39058486834821, 2687513: 41.39021028676462, 2687514: 41.39072150750746, 2687515: 41.38948338317169, 2687516: 41.38962991360429, 2687517: 41.39069871702501, 2687518: 41.3900297569778, 2687519: 41.39079750542168, 2687520: 41.39074822072524, 2687521: 41.389761936109146, 2687522: 41.391325253181954, 2687523: 41.39039120218556, 2687524: 41.38978565904199, 2687525: 41.390070230226335, 2687526: 41.38978120289239, 2687527: 41.392132444031, 2687528: 41.389824580179415, 2687529: 41.39136679996845, 2687530: 41.38982296365283, 2687531: 41.39286353759682, 2687532: 41.39139614270374, 2687533: 41.38926616242303, 2687534: 41.38844039121088, 2687535: 41.39310210021111}, 'longitude(m)': {2687426: 2.1645387830545704, 2687427: 2.165901386822197, 2687428: 2.1642446040808894, 2687429: 2.164761949403105, 2687430: 2.1642810366173086, 2687431: 2.1654142681625914, 2687432: 2.1652276434184423, 2687433: 2.1656185975582214, 2687434: 2.1636992086118414, 2687435: 2.1655431510168746, 2687436: 2.1664681005496824, 2687437: 2.1661237136298235, 2687438: 2.166318192942456, 2687439: 2.166867792789403, 2687440: 2.1642935081041355, 2687441: 2.1662534161899254, 2687442: 2.164215604931173, 2687443: 2.1661882867612774, 2687444: 2.1648378567619373, 2687445: 2.1641179093469374, 2687446: 2.163714544294265, 2687447: 2.165030453232293, 2687448: 2.165303164641005, 2687449: 2.163637554435297, 2687450: 2.163662631646569, 2687451: 2.16495493122646, 2687452: 2.1655045569523703, 2687453: 2.1638761925863275, 2687454: 2.1639502254304595, 2687455: 2.1643708848142955, 2687456: 2.1641364855078304, 2687457: 2.1641491802611053, 2687458: 2.1640291083946472, 2687459: 2.164070550537502, 2687460: 2.1665435107831303, 2687461: 2.166682425007083, 2687462: 2.1664541129023367, 2687463: 2.1669340502512395, 2687464: 2.1638538120976465, 2687465: 2.1669692177670843, 2687466: 2.1664044768788124, 2687467: 2.1641079905788363, 2687468: 2.1635849955542765, 2687469: 2.1635036615691305, 2687470: 2.166710358074336, 2687471: 2.164877510009361, 2687472: 2.164181053060099, 2687473: 2.1637577488216104, 2687474: 2.166885560854316, 2687475: 2.1644608669167185, 2687476: 2.16544162161937, 2687477: 2.1637232627628653, 2687478: 2.166719783654896, 2687479: 2.165941346624584, 2687480: 2.1644227487649648, 2687481: 2.164420786079659, 2687482: 2.163751930705867, 2687483: 2.165378686223096, 2687484: 2.164358119602697, 2687485: 2.164916343237991, 2687486: 2.1643583523337457, 2687487: 2.1639958392041976, 2687488: 2.166872126714503, 2687489: 2.164296960805789, 2687490: 2.166380898665088, 2687491: 2.164474271308847, 2687492: 2.166757650734248, 2687493: 2.164964247316191, 2687494: 2.1641246230905247, 2687495: 2.165152121791671, 2687496: 2.1656813616654937, 2687497: 2.1638056042977727, 2687498: 2.163791894329425, 2687499: 2.1660593410056923, 2687500: 2.164027791066545, 2687501: 2.1666030160139265, 2687502: 2.1658366697012057, 2687503: 2.1638713422089086, 2687504: 2.165744044913534, 2687505: 2.1638917341516, 2687506: 2.165889931463341, 2687507: 2.16385373585268, 2687508: 2.163937631636369, 2687509: 2.164356900787214, 2687510: 2.164214632726351, 2687511: 2.1637780702917246, 2687512: 2.166777530096569, 2687513: 2.166781378833949, 2687514: 2.1637809811166275, 2687515: 2.163556482620449, 2687516: 2.166018475962697, 2687517: 2.166637069306896, 2687518: 2.166544072540592, 2687519: 2.1665151885599965, 2687520: 2.1665759939017475, 2687521: 2.1620764678200546, 2687522: 2.163820809919434, 2687523: 2.166829915167404, 2687524: 2.163907438868404, 2687525: 2.1643956820129606, 2687526: 2.162122615855465, 2687527: 2.1648019090650377, 2687528: 2.1619646474072742, 2687529: 2.1639298434809486, 2687530: 2.163956265763196, 2687531: 2.1636626454841603, 2687532: 2.163968709196234, 2687533: 2.163208704625358, 2687534: 2.162120857635756, 2687535: 2.166216303869489}}

y= {2687426: 14215280005135267334, 2687427: 14215280004681986824, 2687428: 14215279982014841758, 2687429: 14215280004977614736, 2687430: 14215279982216274169, 2687431: 14215280017101476860, 2687432: 14215280005017827787, 2687433: 14215280004315897140, 2687434: 14215279981902924563, 2687435: 14215280005030779204, 2687436: 14215280005593751519, 2687437: 14215280005430167992, 2687438: 14215280005452569596, 2687439: 14215280005830354591, 2687440: 14215279982236608587, 2687441: 14215280005449636502, 2687442: 14215279982217458002, 2687443: 14215280005431922240, 2687444: 14215280004980597014, 2687445: 14215279981960776565, 2687446: 14215279993732558229, 2687447: 14215280005059108555, 2687448: 14215280005016534805, 2687449: 14215279993718879831, 2687450: 14215279982243479269, 2687451: 14215280004970584696, 2687452: 14215280005025342287, 2687453: 14215279993790856001, 2687454: 14215279982265711690, 2687455: 14215280016939085801, 2687456: 14215279982217877330, 2687457: 14215279982280744323, 2687458: 14215279982277169680, 2687459: 14215279982223342251, 2687460: 14215280005823598253, 2687461: 14215280005619421060, 2687462: 14215280005879186037, 2687463: 14215280005835669080, 2687464: 14215279993744387170, 2687465: 14215280005841504034, 2687466: 14215280005546685808, 2687467: 14215279982275301032, 2687468: 14215279982165960380, 2687469: 14215279981077496421, 2687470: 14215280005848827175, 2687471: 14215280004980134601, 2687472: 14215279982012074358, 2687473: 14215279993737211019, 2687474: 14215280005851733438, 2687475: 14215280005141367274, 2687476: 14215280005027850549, 2687477: 14215279982244377109, 2687478: 14215280005843282550, 2687479: 14215280004683877565, 2687480: 14215280004944002243, 2687481: 14215280005141129043, 2687482: 14215279993738234220, 2687483: 14215280005038123832, 2687484: 14215280005140478253, 2687485: 14215280004981401816, 2687486: 14215280004943395013, 2687487: 14215279981953799259, 2687488: 14215280005650039995, 2687489: 14215279982014350565, 2687490: 14215280005881039261, 2687491: 14215280004928876975, 2687492: 14215280005832330887, 2687493: 14215280016995769654, 2687494: 14215279981959811125, 2687495: 14215280005057490296, 2687496: 14215280004673312534, 2687497: 14215279982267390265, 2687498: 14215279993788847180, 2687499: 14215280005425039533, 2687500: 14215279993819972871, 2687501: 14215280005823021169, 2687502: 14215280004672665011, 2687503: 14215279982264896121, 2687504: 14215280004670554080, 2687505: 14215279982203411590, 2687506: 14215280017230983570, 2687507: 14215279982246738021, 2687508: 14215279993796011239, 2687509: 14215280017739183931, 2687510: 14215279982280347257, 2687511: 14215279993737062374, 2687512: 14215280005848370216, 2687513: 14215280005643970356, 2687514: 14215279982241829200, 2687515: 14215279981091034521, 2687516: 14215280005401518460, 2687517: 14215280005871618010, 2687518: 14215280005601309734, 2687519: 14215280005868027302, 2687520: 14215280005872857420, 2687521: 14215279981278336594, 2687522: 14215279993744205454, 2687523: 14215280005832934191, 2687524: 14215279981918383142, 2687525: 14215280004927065192, 2687526: 14215279981279121258, 2687527: 14215280017040872308, 2687528: 14215279981282308749, 2687529: 14215279993791785473, 2687530: 14215279981930449370, 2687531: 14215279994859874981, 2687532: 14215279993815131476, 2687533: 14215279980995520310, 2687534: 14215279980216847709, 2687535: 14215280018586660863}

import pandas as pd
from river import metrics
from river import neural_net as nn
from river import optim
from river import preprocessing as pp
model.learn_many(pd.DataFrame(x), pd.DataFrame(y))

please, let me know if you require more data. Note the number of rows varies. Thanks

[raul-parada]

Did you have the change to replicate the issue? thanks

[MaxHalford]

I haven't had time yet. I'll let you know when I do.