Value error array with 0 features in linear regression scikit












1












$begingroup$


My input and output data are written in an 6xn row-column excel file,thatI read them using pandas



using this code :



from sklearn import linear_model

import os

import pandas as pd

import numpy as np

import openpyxl as pyx

import numpy as np

import matplotlib.pyplot as plt



path = r"E:"

os.chdir( path )



data1 = pd.read_excel("input.xlsx", header=None,index=False)

data2 = pd.read_excel("target.xlsx", header=None,index=False)



# print df.head()



le= 20;lg=len(data1)

x_train=;x_t=;y_train=;y_t=;x_test=;x_ts=;y_test=;y_ts=;

for i in range(le):    

    x_t = data1.iloc[i,:]

    x_train.append(x_t)

    y_t = data2.iloc[i,:]

    y_train.append(y_t)

    if i > le :

        x_ts = data1.iloc[lg-i,:]

        x_test.append(x_ts)

        y_ts = data2.iloc[lg-i,:]

        y_test.append(y_ts)







ols = linear_model.LinearRegression()

model = ols.fit(x_train, y_train)



print (model.predict(x_test)[0:5])


I get this error :




File
"C:ProgramDataAnaconda3libsite-packagesspyderutilssitesitecustomize.py",
line 880, in runfile
execfile(filename, namespace)



File
"C:ProgramDataAnaconda3libsite-packagesspyderutilssitesitecustomize.py",
line 102, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)



File "E:/Scikit-Learn.py", line 46, in
print (model.predict(x_test)[0:5])



File
"C:ProgramDataAnaconda3libsite-packagessklearnlinear_modelbase.py",
line 268, in predict
return self._decision_function(X)



File
"C:ProgramDataAnaconda3libsite-packagessklearnlinear_modelbase.py",
line 251, in _decision_function
X = check_array(X, accept_sparse=['csr', 'csc', 'coo'])



File
"C:ProgramDataAnaconda3libsite-packagessklearnutilsvalidation.py",
line 424, in check_array
context))



ValueError: Found array with 0 feature(s) (shape=(1, 0)) while a
minimum of 1 is required.







machine-learning scikit-learn linear-regression







share|improve this question









$endgroup$




bumped to the homepage by Community♦ yesterday


This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.















  • $begingroup$
    You should spend more time reading your error messages and do some more debugging work by printing out the values of your variables for instance, because this is an error you should be able to catch by yourself.
    $endgroup$
    – Valentin Calomme
    Jul 12 '18 at 12:05










  • $begingroup$
    I actually did it, that's a pity I didn't delete the question before your negative vote
    $endgroup$
    – FabioSpaghetti
    Jul 12 '18 at 12:06
















1












$begingroup$


My input and output data are written in an 6xn row-column excel file,thatI read them using pandas



using this code :



from sklearn import linear_model

import os

import pandas as pd

import numpy as np

import openpyxl as pyx

import numpy as np

import matplotlib.pyplot as plt



path = r"E:"

os.chdir( path )



data1 = pd.read_excel("input.xlsx", header=None,index=False)

data2 = pd.read_excel("target.xlsx", header=None,index=False)



# print df.head()



le= 20;lg=len(data1)

x_train=;x_t=;y_train=;y_t=;x_test=;x_ts=;y_test=;y_ts=;

for i in range(le):    

    x_t = data1.iloc[i,:]

    x_train.append(x_t)

    y_t = data2.iloc[i,:]

    y_train.append(y_t)

    if i > le :

        x_ts = data1.iloc[lg-i,:]

        x_test.append(x_ts)

        y_ts = data2.iloc[lg-i,:]

        y_test.append(y_ts)







ols = linear_model.LinearRegression()

model = ols.fit(x_train, y_train)



print (model.predict(x_test)[0:5])


I get this error :




File
"C:ProgramDataAnaconda3libsite-packagesspyderutilssitesitecustomize.py",
line 880, in runfile
execfile(filename, namespace)



File
"C:ProgramDataAnaconda3libsite-packagesspyderutilssitesitecustomize.py",
line 102, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)



File "E:/Scikit-Learn.py", line 46, in
print (model.predict(x_test)[0:5])



File
"C:ProgramDataAnaconda3libsite-packagessklearnlinear_modelbase.py",
line 268, in predict
return self._decision_function(X)



File
"C:ProgramDataAnaconda3libsite-packagessklearnlinear_modelbase.py",
line 251, in _decision_function
X = check_array(X, accept_sparse=['csr', 'csc', 'coo'])



File
"C:ProgramDataAnaconda3libsite-packagessklearnutilsvalidation.py",
line 424, in check_array
context))



ValueError: Found array with 0 feature(s) (shape=(1, 0)) while a
minimum of 1 is required.







machine-learning scikit-learn linear-regression







share|improve this question









$endgroup$




bumped to the homepage by Community♦ yesterday


This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.















  • $begingroup$
    You should spend more time reading your error messages and do some more debugging work by printing out the values of your variables for instance, because this is an error you should be able to catch by yourself.
    $endgroup$
    – Valentin Calomme
    Jul 12 '18 at 12:05










  • $begingroup$
    I actually did it, that's a pity I didn't delete the question before your negative vote
    $endgroup$
    – FabioSpaghetti
    Jul 12 '18 at 12:06














1












1








1





$begingroup$


My input and output data are written in an 6xn row-column excel file,thatI read them using pandas



using this code :



from sklearn import linear_model

import os

import pandas as pd

import numpy as np

import openpyxl as pyx

import numpy as np

import matplotlib.pyplot as plt



path = r"E:"

os.chdir( path )



data1 = pd.read_excel("input.xlsx", header=None,index=False)

data2 = pd.read_excel("target.xlsx", header=None,index=False)



# print df.head()



le= 20;lg=len(data1)

x_train=;x_t=;y_train=;y_t=;x_test=;x_ts=;y_test=;y_ts=;

for i in range(le):    

    x_t = data1.iloc[i,:]

    x_train.append(x_t)

    y_t = data2.iloc[i,:]

    y_train.append(y_t)

    if i > le :

        x_ts = data1.iloc[lg-i,:]

        x_test.append(x_ts)

        y_ts = data2.iloc[lg-i,:]

        y_test.append(y_ts)







ols = linear_model.LinearRegression()

model = ols.fit(x_train, y_train)



print (model.predict(x_test)[0:5])


I get this error :




File
"C:ProgramDataAnaconda3libsite-packagesspyderutilssitesitecustomize.py",
line 880, in runfile
execfile(filename, namespace)



File
"C:ProgramDataAnaconda3libsite-packagesspyderutilssitesitecustomize.py",
line 102, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)



File "E:/Scikit-Learn.py", line 46, in
print (model.predict(x_test)[0:5])



File
"C:ProgramDataAnaconda3libsite-packagessklearnlinear_modelbase.py",
line 268, in predict
return self._decision_function(X)



File
"C:ProgramDataAnaconda3libsite-packagessklearnlinear_modelbase.py",
line 251, in _decision_function
X = check_array(X, accept_sparse=['csr', 'csc', 'coo'])



File
"C:ProgramDataAnaconda3libsite-packagessklearnutilsvalidation.py",
line 424, in check_array
context))



ValueError: Found array with 0 feature(s) (shape=(1, 0)) while a
minimum of 1 is required.







machine-learning scikit-learn linear-regression







share|improve this question









$endgroup$




My input and output data are written in an 6xn row-column excel file,thatI read them using pandas



using this code :



from sklearn import linear_model

import os

import pandas as pd

import numpy as np

import openpyxl as pyx

import numpy as np

import matplotlib.pyplot as plt



path = r"E:"

os.chdir( path )



data1 = pd.read_excel("input.xlsx", header=None,index=False)

data2 = pd.read_excel("target.xlsx", header=None,index=False)



# print df.head()



le= 20;lg=len(data1)

x_train=;x_t=;y_train=;y_t=;x_test=;x_ts=;y_test=;y_ts=;

for i in range(le):    

    x_t = data1.iloc[i,:]

    x_train.append(x_t)

    y_t = data2.iloc[i,:]

    y_train.append(y_t)

    if i > le :

        x_ts = data1.iloc[lg-i,:]

        x_test.append(x_ts)

        y_ts = data2.iloc[lg-i,:]

        y_test.append(y_ts)







ols = linear_model.LinearRegression()

model = ols.fit(x_train, y_train)



print (model.predict(x_test)[0:5])


I get this error :




File
"C:ProgramDataAnaconda3libsite-packagesspyderutilssitesitecustomize.py",
line 880, in runfile
execfile(filename, namespace)



File
"C:ProgramDataAnaconda3libsite-packagesspyderutilssitesitecustomize.py",
line 102, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)



File "E:/Scikit-Learn.py", line 46, in
print (model.predict(x_test)[0:5])



File
"C:ProgramDataAnaconda3libsite-packagessklearnlinear_modelbase.py",
line 268, in predict
return self._decision_function(X)



File
"C:ProgramDataAnaconda3libsite-packagessklearnlinear_modelbase.py",
line 251, in _decision_function
X = check_array(X, accept_sparse=['csr', 'csc', 'coo'])



File
"C:ProgramDataAnaconda3libsite-packagessklearnutilsvalidation.py",
line 424, in check_array
context))



ValueError: Found array with 0 feature(s) (shape=(1, 0)) while a
minimum of 1 is required.







machine-learning scikit-learn linear-regression




machine-learning scikit-learn linear-regression






share|improve this question













share|improve this question











share|improve this question




share|improve this question










asked Jul 11 '18 at 19:25









FabioSpaghettiFabioSpaghetti

1244




1244





bumped to the homepage by Community♦ yesterday


This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.







bumped to the homepage by Community♦ yesterday


This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.














  • $begingroup$
    You should spend more time reading your error messages and do some more debugging work by printing out the values of your variables for instance, because this is an error you should be able to catch by yourself.
    $endgroup$
    – Valentin Calomme
    Jul 12 '18 at 12:05










  • $begingroup$
    I actually did it, that's a pity I didn't delete the question before your negative vote
    $endgroup$
    – FabioSpaghetti
    Jul 12 '18 at 12:06


















  • $begingroup$
    You should spend more time reading your error messages and do some more debugging work by printing out the values of your variables for instance, because this is an error you should be able to catch by yourself.
    $endgroup$
    – Valentin Calomme
    Jul 12 '18 at 12:05










  • $begingroup$
    I actually did it, that's a pity I didn't delete the question before your negative vote
    $endgroup$
    – FabioSpaghetti
    Jul 12 '18 at 12:06
















$begingroup$
You should spend more time reading your error messages and do some more debugging work by printing out the values of your variables for instance, because this is an error you should be able to catch by yourself.
$endgroup$
– Valentin Calomme
Jul 12 '18 at 12:05




$begingroup$
You should spend more time reading your error messages and do some more debugging work by printing out the values of your variables for instance, because this is an error you should be able to catch by yourself.
$endgroup$
– Valentin Calomme
Jul 12 '18 at 12:05












$begingroup$
I actually did it, that's a pity I didn't delete the question before your negative vote
$endgroup$
– FabioSpaghetti
Jul 12 '18 at 12:06




$begingroup$
I actually did it, that's a pity I didn't delete the question before your negative vote
$endgroup$
– FabioSpaghetti
Jul 12 '18 at 12:06










1 Answer
1






active

oldest

votes


















1












$begingroup$

The error says that the array you feed into predict has shape=(1,0) meaning that it must be an empty iterable.



And by looking at your code, it's easy to see why. x_test starts of as empty, and in your code, the only way to add things to it is if i > le. But since your loop is defined as for i in range(le), by definition i will never be greater than le since the last value of range is le-1






share|improve this answer









$endgroup$














    Your Answer





    StackExchange.ifUsing("editor", function () {
    return StackExchange.using("mathjaxEditing", function () {
    StackExchange.MarkdownEditor.creationCallbacks.add(function (editor, postfix) {
    StackExchange.mathjaxEditing.prepareWmdForMathJax(editor, postfix, [["$", "$"], ["\\(","\\)"]]);
    });
    });
    }, "mathjax-editing");

    StackExchange.ready(function() {
    var channelOptions = {
    tags: "".split(" "),
    id: "557"
    };
    initTagRenderer("".split(" "), "".split(" "), channelOptions);

    StackExchange.using("externalEditor", function() {
    // Have to fire editor after snippets, if snippets enabled
    if (StackExchange.settings.snippets.snippetsEnabled) {
    StackExchange.using("snippets", function() {
    createEditor();
    });
    }
    else {
    createEditor();
    }
    });

    function createEditor() {
    StackExchange.prepareEditor({
    heartbeatType: 'answer',
    autoActivateHeartbeat: false,
    convertImagesToLinks: false,
    noModals: true,
    showLowRepImageUploadWarning: true,
    reputationToPostImages: null,
    bindNavPrevention: true,
    postfix: "",
    imageUploader: {
    brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
    contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
    allowUrls: true
    },
    onDemand: true,
    discardSelector: ".discard-answer"
    ,immediatelyShowMarkdownHelp:true
    });


    }
    });














    draft saved

    draft discarded


















    StackExchange.ready(
    function () {
    StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fdatascience.stackexchange.com%2fquestions%2f34325%2fvalue-error-array-with-0-features-in-linear-regression-scikit%23new-answer', 'question_page');
    }
    );

    Post as a guest















    Required, but never shown

























    1 Answer
    1






    active

    oldest

    votes








    1 Answer
    1






    active

    oldest

    votes









    active

    oldest

    votes






    active

    oldest

    votes









    1












    $begingroup$

    The error says that the array you feed into predict has shape=(1,0) meaning that it must be an empty iterable.



    And by looking at your code, it's easy to see why. x_test starts of as empty, and in your code, the only way to add things to it is if i > le. But since your loop is defined as for i in range(le), by definition i will never be greater than le since the last value of range is le-1






    share|improve this answer









    $endgroup$


















      1












      $begingroup$

      The error says that the array you feed into predict has shape=(1,0) meaning that it must be an empty iterable.



      And by looking at your code, it's easy to see why. x_test starts of as empty, and in your code, the only way to add things to it is if i > le. But since your loop is defined as for i in range(le), by definition i will never be greater than le since the last value of range is le-1






      share|improve this answer









      $endgroup$
















        1












        1








        1





        $begingroup$

        The error says that the array you feed into predict has shape=(1,0) meaning that it must be an empty iterable.



        And by looking at your code, it's easy to see why. x_test starts of as empty, and in your code, the only way to add things to it is if i > le. But since your loop is defined as for i in range(le), by definition i will never be greater than le since the last value of range is le-1






        share|improve this answer









        $endgroup$



        The error says that the array you feed into predict has shape=(1,0) meaning that it must be an empty iterable.



        And by looking at your code, it's easy to see why. x_test starts of as empty, and in your code, the only way to add things to it is if i > le. But since your loop is defined as for i in range(le), by definition i will never be greater than le since the last value of range is le-1







        share|improve this answer












        share|improve this answer



        share|improve this answer










        answered Jul 12 '18 at 12:04









        Valentin CalommeValentin Calomme

        1,315423




        1,315423






























            draft saved

            draft discarded




















































            Thanks for contributing an answer to Data Science Stack Exchange!


            • Please be sure to answer the question. Provide details and share your research!

            But avoid …



            • Asking for help, clarification, or responding to other answers.

            • Making statements based on opinion; back them up with references or personal experience.


            Use MathJax to format equations. MathJax reference.


            To learn more, see our tips on writing great answers.




            draft saved


            draft discarded














            StackExchange.ready(
            function () {
            StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fdatascience.stackexchange.com%2fquestions%2f34325%2fvalue-error-array-with-0-features-in-linear-regression-scikit%23new-answer', 'question_page');
            }
            );

            Post as a guest















            Required, but never shown





















































            Required, but never shown














            Required, but never shown












            Required, but never shown







            Required, but never shown

































            Required, but never shown














            Required, but never shown












            Required, but never shown







            Required, but never shown







            -

            Popular posts from this blog

            How to label and detect the document text images

            Image
            1 $begingroup$ This is what I mean as document text image: I want to label the texts in image as separate blocks and my model should detect these labels as classes. NOTE: This is how the end result should be like: The labels like Block 1, Block 2, Block 3,.. should be Logo, Title, Date,.. Others, etc. Work done: First approach : I tried to implement this method via Object Detection, it didn't work. It didn't even detect any text. Second approach : Then I tried it using PixelLink. As this model is build for scene text detection, it detected each and every text in the image. But this method can detect multiple lines of text if the threshold values are increased. But I have no idea how do I add labels to the text blocks. PIXEL_CLS_WEIGHT_all_ones = 'PIXEL_CLS_WEIGHT_all_ones' PIXEL_C...
            Read more

            Vallis Paradisi

            Image
            Coordinata: .mw-parser-output .geo-default,.mw-parser-output .geo-dms,.mw-parser-output .geo-dec{display:inline}.mw-parser-output .geo-nondefault,.mw-parser-output .geo-multi-punct{display:none}.mw-parser-output .longitude,.mw-parser-output .latitude{white-space:nowrap} 33°03′S 71°37′W  /  33.05°S 71.617°W  / -33.05; -71.617 Vallis Paradisi Res apud Vicidata repertae: Civitas : Chilia Numerus incolarum : 296 655 Zona horaria : UTC-4 Situs interretialis Nomen officiale : Valparaíso Gubernium Consilium : municipal council of Valparaíso Geographia Superficies : 401 600 000 Metrum quadratum Coniunctiones urbium Urbes gemellae : Ovetum , Badalona , Santa Fe , Ora longa , Sciamhaevum , Quantunia , Callao , Bat Yam , Melaka , Novorossiysk , Busan , Corduba , Odessa , Rosario , Malacca , Barcino , Soteropolis , Franciscopolis (California) , Guanajuato, Silao Tabula aut despectus Despectus in portum sinumque et urbem Vallis Paradisi Vallis Para...
            Read more

            Tabula Rosettana

            Image
            Tabula multilinguis Rosettana in Museo Britannico ostenditur. Tabula Rosettana, [1] etiam titulo OGIS 90 agnita, est stela decreto de rebus sacris in Aegypto anno 196 a.C.n. lato inscripta. Tabula iuxta Rosettam Aegypti, urbem in delta Nili et ad oram maris Mediterranei iacentem, anno 1799 a milite Francico reperta est. Inventio stelae, linguis duabus scripturisque tribus inscriptae, eruditis Instituti Aegypti statim nuntiata est; ibi enim iussu imperatoris Napoleonis eruditi omnium scientiarum (sub aegide Commissionis Scientiarum et Artium) properaverant cum expeditione Francica. Qua a Britannis mox debellata, tabula Rosettana Londinium missa hodie apud Museum Britannicum iacet. Textus Graecus cito lectus interpretationi textuum Aegyptiorum (in formis hieroglyphica et demotica expressorum) gradatim adiuvit. Denique textum plene interpretatus est Ioannes Franciscus Champollion. Ab opere eruditorum cumulativo coepit hodiernus scripturae hieroglyphicae linguaeque Aegyptiae a...
            Read more