From helmrp at yahoo.com  Sun Nov  1 16:53:29 2020
From: helmrp at yahoo.com (The Helmbolds)
Date: Sun, 1 Nov 2020 21:53:29 +0000 (UTC)
Subject: [scikit-learn] Issue with Sklearn.Logistic Regression
References: <431768126.745150.1604267609819.ref@mail.yahoo.com>
Message-ID: <431768126.745150.1604267609819@mail.yahoo.com>

Here's my ynp and Xnp arrays:

   Print ynp
[0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
 0 1 0 0 0 0 0 0 1 1 0 0 1 1 0 0 1 0 0 0 0 0 0 1 0 0 0 1 1 0 1 1 0 1 0 0 1
 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0
 1 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1
 1 1 1 1 1 1 1 1 1 1 1 1]
Shape of ynp = 160

    Print Xnp
[-3.00000000e+00 -2.95000000e+00 -2.90000000e+00 -2.85000000e+00
 -2.80000000e+00 -2.75000000e+00 -2.70000000e+00 -2.65000000e+00
 -2.60000000e+00 -2.55000000e+00 -2.50000000e+00 -2.45000000e+00
 -2.40000000e+00 -2.35000000e+00 -2.30000000e+00 -2.25000000e+00
 -2.20000000e+00 -2.15000000e+00 -2.10000000e+00 -2.05000000e+00
 -2.00000000e+00 -1.95000000e+00 -1.90000000e+00 -1.85000000e+00
 -1.80000000e+00 -1.75000000e+00 -1.70000000e+00 -1.65000000e+00
 -1.60000000e+00 -1.55000000e+00 -1.50000000e+00 -1.45000000e+00
 -1.40000000e+00 -1.35000000e+00 -1.30000000e+00 -1.25000000e+00
 -1.20000000e+00 -1.15000000e+00 -1.10000000e+00 -1.05000000e+00
 -1.00000000e+00 -9.50000000e-01 -9.00000000e-01 -8.50000000e-01
 -8.00000000e-01 -7.50000000e-01 -7.00000000e-01 -6.50000000e-01
 -6.00000000e-01 -5.50000000e-01 -5.00000000e-01 -4.50000000e-01
 -4.00000000e-01 -3.50000000e-01 -3.00000000e-01 -2.50000000e-01
 -2.00000000e-01 -1.50000000e-01 -1.00000000e-01 -5.00000000e-02
 -2.28983499e-15  5.00000000e-02  1.00000000e-01  1.50000000e-01
  2.00000000e-01  2.50000000e-01  3.00000000e-01  3.50000000e-01
  4.00000000e-01  4.50000000e-01  5.00000000e-01  5.50000000e-01
  6.00000000e-01  6.50000000e-01  7.00000000e-01  7.50000000e-01
  8.00000000e-01  8.50000000e-01  9.00000000e-01  9.50000000e-01
  1.00000000e+00  1.05000000e+00  1.10000000e+00  1.15000000e+00
  1.20000000e+00  1.25000000e+00  1.30000000e+00  1.35000000e+00
  1.40000000e+00  1.45000000e+00  1.50000000e+00  1.55000000e+00
  1.60000000e+00  1.65000000e+00  1.70000000e+00  1.75000000e+00
  1.80000000e+00  1.85000000e+00  1.90000000e+00  1.95000000e+00
  2.00000000e+00  2.05000000e+00  2.10000000e+00  2.15000000e+00
  2.20000000e+00  2.25000000e+00  2.30000000e+00  2.35000000e+00
  2.40000000e+00  2.45000000e+00  2.50000000e+00  2.55000000e+00
  2.60000000e+00  2.65000000e+00  2.70000000e+00  2.75000000e+00
  2.80000000e+00  2.85000000e+00  2.90000000e+00  2.95000000e+00
  3.00000000e+00  3.05000000e+00  3.10000000e+00  3.15000000e+00
  3.20000000e+00  3.25000000e+00  3.30000000e+00  3.35000000e+00
  3.40000000e+00  3.45000000e+00  3.50000000e+00  3.55000000e+00
  3.60000000e+00  3.65000000e+00  3.70000000e+00  3.75000000e+00
  3.80000000e+00  3.85000000e+00  3.90000000e+00  3.95000000e+00
  4.00000000e+00  4.05000000e+00  4.10000000e+00  4.15000000e+00
  4.20000000e+00  4.25000000e+00  4.30000000e+00  4.35000000e+00
  4.40000000e+00  4.45000000e+00  4.50000000e+00  4.55000000e+00
  4.60000000e+00  4.65000000e+00  4.70000000e+00  4.75000000e+00
  4.80000000e+00  4.85000000e+00  4.90000000e+00  4.95000000e+00]
Shape of Xnp = 160
   Press ENTER to continue = 

    Now Call Logistic Regression
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-3-300811cacc0b> in <module>
    103 
    104 aprint("Now Call Logistic Regression")
--> 105 logreg = LogisticRegression.fit(Xnp, ynp)
    106 aprint("Print logreg output")
    107 print(logreg)

TypeError: fit() missing 1 required positional argument: 'y'

Eh!?!?
What happened????




"You won't find the right answers if you don't ask the right questions!" (Robert Helmbold, 2013)
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From g.lemaitre58 at gmail.com  Sun Nov  1 16:58:28 2020
From: g.lemaitre58 at gmail.com (=?UTF-8?Q?Guillaume_Lema=C3=AEtre?=)
Date: Sun, 1 Nov 2020 22:58:28 +0100
Subject: [scikit-learn] Issue with Sklearn.Logistic Regression
In-Reply-To: <431768126.745150.1604267609819@mail.yahoo.com>
References: <431768126.745150.1604267609819.ref@mail.yahoo.com>
 <431768126.745150.1604267609819@mail.yahoo.com>
Message-ID: <CACDxx9gExyRR_3ceoup6U85NN2ix1++JpmVVegadvWmTY9wi0Q@mail.gmail.com>

You forgot the parentheses to instantiate the object LogisticRegression

On Sun, 1 Nov 2020 at 22:55, The Helmbolds via scikit-learn <
scikit-learn at python.org> wrote:

> Here's my ynp and Xnp arrays:
>
>    Print ynp
> [0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
>  0 1 0 0 0 0 0 0 1 1 0 0 1 1 0 0 1 0 0 0 0 0 0 1 0 0 0 1 1 0 1 1 0 1 0 0 1
>  1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0
>  1 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1
>  1 1 1 1 1 1 1 1 1 1 1 1]
> Shape of ynp = 160
>
>     Print Xnp
> [-3.00000000e+00 -2.95000000e+00 -2.90000000e+00 -2.85000000e+00
>  -2.80000000e+00 -2.75000000e+00 -2.70000000e+00 -2.65000000e+00
>  -2.60000000e+00 -2.55000000e+00 -2.50000000e+00 -2.45000000e+00
>  -2.40000000e+00 -2.35000000e+00 -2.30000000e+00 -2.25000000e+00
>  -2.20000000e+00 -2.15000000e+00 -2.10000000e+00 -2.05000000e+00
>  -2.00000000e+00 -1.95000000e+00 -1.90000000e+00 -1.85000000e+00
>  -1.80000000e+00 -1.75000000e+00 -1.70000000e+00 -1.65000000e+00
>  -1.60000000e+00 -1.55000000e+00 -1.50000000e+00 -1.45000000e+00
>  -1.40000000e+00 -1.35000000e+00 -1.30000000e+00 -1.25000000e+00
>  -1.20000000e+00 -1.15000000e+00 -1.10000000e+00 -1.05000000e+00
>  -1.00000000e+00 -9.50000000e-01 -9.00000000e-01 -8.50000000e-01
>  -8.00000000e-01 -7.50000000e-01 -7.00000000e-01 -6.50000000e-01
>  -6.00000000e-01 -5.50000000e-01 -5.00000000e-01 -4.50000000e-01
>  -4.00000000e-01 -3.50000000e-01 -3.00000000e-01 -2.50000000e-01
>  -2.00000000e-01 -1.50000000e-01 -1.00000000e-01 -5.00000000e-02
>  -2.28983499e-15  5.00000000e-02  1.00000000e-01  1.50000000e-01
>   2.00000000e-01  2.50000000e-01  3.00000000e-01  3.50000000e-01
>   4.00000000e-01  4.50000000e-01  5.00000000e-01  5.50000000e-01
>   6.00000000e-01  6.50000000e-01  7.00000000e-01  7.50000000e-01
>   8.00000000e-01  8.50000000e-01  9.00000000e-01  9.50000000e-01
>   1.00000000e+00  1.05000000e+00  1.10000000e+00  1.15000000e+00
>   1.20000000e+00  1.25000000e+00  1.30000000e+00  1.35000000e+00
>   1.40000000e+00  1.45000000e+00  1.50000000e+00  1.55000000e+00
>   1.60000000e+00  1.65000000e+00  1.70000000e+00  1.75000000e+00
>   1.80000000e+00  1.85000000e+00  1.90000000e+00  1.95000000e+00
>   2.00000000e+00  2.05000000e+00  2.10000000e+00  2.15000000e+00
>   2.20000000e+00  2.25000000e+00  2.30000000e+00  2.35000000e+00
>   2.40000000e+00  2.45000000e+00  2.50000000e+00  2.55000000e+00
>   2.60000000e+00  2.65000000e+00  2.70000000e+00  2.75000000e+00
>   2.80000000e+00  2.85000000e+00  2.90000000e+00  2.95000000e+00
>   3.00000000e+00  3.05000000e+00  3.10000000e+00  3.15000000e+00
>   3.20000000e+00  3.25000000e+00  3.30000000e+00  3.35000000e+00
>   3.40000000e+00  3.45000000e+00  3.50000000e+00  3.55000000e+00
>   3.60000000e+00  3.65000000e+00  3.70000000e+00  3.75000000e+00
>   3.80000000e+00  3.85000000e+00  3.90000000e+00  3.95000000e+00
>   4.00000000e+00  4.05000000e+00  4.10000000e+00  4.15000000e+00
>   4.20000000e+00  4.25000000e+00  4.30000000e+00  4.35000000e+00
>   4.40000000e+00  4.45000000e+00  4.50000000e+00  4.55000000e+00
>   4.60000000e+00  4.65000000e+00  4.70000000e+00  4.75000000e+00
>   4.80000000e+00  4.85000000e+00  4.90000000e+00  4.95000000e+00]
> Shape of Xnp = 160
>    Press ENTER to continue =
>
>     Now Call Logistic Regression
>
> ---------------------------------------------------------------------------TypeError                                 Traceback (most recent call last)<ipython-input-3-300811cacc0b> in <module>    103     104 aprint("Now Call Logistic Regression")--> 105 logreg = LogisticRegression.fit(Xnp, ynp)    106 aprint("Print logreg output")    107 print(logreg)
> TypeError: fit() missing 1 required positional argument: 'y'
>
>
> Eh!?!?
>
> What happened*???*
>
>
>
>
>
> "You won't find the right answers if you don't ask the right questions!"
> (Robert Helmbold, 2013)
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>


-- 
Guillaume Lemaitre
Scikit-learn @ Inria Foundation
https://glemaitre.github.io/
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From helmrp at yahoo.com  Sun Nov  1 17:38:48 2020
From: helmrp at yahoo.com (The Helmbolds)
Date: Sun, 1 Nov 2020 22:38:48 +0000 (UTC)
Subject: [scikit-learn] Issue with Sklearn.Logistic Regression
In-Reply-To: <CACDxx9gExyRR_3ceoup6U85NN2ix1++JpmVVegadvWmTY9wi0Q@mail.gmail.com>
References: <431768126.745150.1604267609819.ref@mail.yahoo.com>
 <431768126.745150.1604267609819@mail.yahoo.com>
 <CACDxx9gExyRR_3ceoup6U85NN2ix1++JpmVVegadvWmTY9wi0Q@mail.gmail.com>
Message-ID: <1298535362.749438.1604270328791@mail.yahoo.com>

What parentheses?Enclosing what?
"You won't find the right answers if you don't ask the right questions!" (Robert Helmbold, 2013) 

    On Sunday, November 1, 2020, 02:58:46 PM MST, Guillaume Lema?tre <g.lemaitre58 at gmail.com> wrote:  
 
 You forgot the parentheses to instantiate the object LogisticRegression

On Sun, 1 Nov 2020 at 22:55, The Helmbolds via scikit-learn <scikit-learn at python.org> wrote:

Here's my ynp and Xnp arrays:

   Print ynp
[0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
 0 1 0 0 0 0 0 0 1 1 0 0 1 1 0 0 1 0 0 0 0 0 0 1 0 0 0 1 1 0 1 1 0 1 0 0 1
 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0
 1 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1
 1 1 1 1 1 1 1 1 1 1 1 1]
Shape of ynp = 160

    Print Xnp
[-3.00000000e+00 -2.95000000e+00 -2.90000000e+00 -2.85000000e+00
 -2.80000000e+00 -2.75000000e+00 -2.70000000e+00 -2.65000000e+00
 -2.60000000e+00 -2.55000000e+00 -2.50000000e+00 -2.45000000e+00
 -2.40000000e+00 -2.35000000e+00 -2.30000000e+00 -2.25000000e+00
 -2.20000000e+00 -2.15000000e+00 -2.10000000e+00 -2.05000000e+00
 -2.00000000e+00 -1.95000000e+00 -1.90000000e+00 -1.85000000e+00
 -1.80000000e+00 -1.75000000e+00 -1.70000000e+00 -1.65000000e+00
 -1.60000000e+00 -1.55000000e+00 -1.50000000e+00 -1.45000000e+00
 -1.40000000e+00 -1.35000000e+00 -1.30000000e+00 -1.25000000e+00
 -1.20000000e+00 -1.15000000e+00 -1.10000000e+00 -1.05000000e+00
 -1.00000000e+00 -9.50000000e-01 -9.00000000e-01 -8.50000000e-01
 -8.00000000e-01 -7.50000000e-01 -7.00000000e-01 -6.50000000e-01
 -6.00000000e-01 -5.50000000e-01 -5.00000000e-01 -4.50000000e-01
 -4.00000000e-01 -3.50000000e-01 -3.00000000e-01 -2.50000000e-01
 -2.00000000e-01 -1.50000000e-01 -1.00000000e-01 -5.00000000e-02
 -2.28983499e-15  5.00000000e-02  1.00000000e-01  1.50000000e-01
  2.00000000e-01  2.50000000e-01  3.00000000e-01  3.50000000e-01
  4.00000000e-01  4.50000000e-01  5.00000000e-01  5.50000000e-01
  6.00000000e-01  6.50000000e-01  7.00000000e-01  7.50000000e-01
  8.00000000e-01  8.50000000e-01  9.00000000e-01  9.50000000e-01
  1.00000000e+00  1.05000000e+00  1.10000000e+00  1.15000000e+00
  1.20000000e+00  1.25000000e+00  1.30000000e+00  1.35000000e+00
  1.40000000e+00  1.45000000e+00  1.50000000e+00  1.55000000e+00
  1.60000000e+00  1.65000000e+00  1.70000000e+00  1.75000000e+00
  1.80000000e+00  1.85000000e+00  1.90000000e+00  1.95000000e+00
  2.00000000e+00  2.05000000e+00  2.10000000e+00  2.15000000e+00
  2.20000000e+00  2.25000000e+00  2.30000000e+00  2.35000000e+00
  2.40000000e+00  2.45000000e+00  2.50000000e+00  2.55000000e+00
  2.60000000e+00  2.65000000e+00  2.70000000e+00  2.75000000e+00
  2.80000000e+00  2.85000000e+00  2.90000000e+00  2.95000000e+00
  3.00000000e+00  3.05000000e+00  3.10000000e+00  3.15000000e+00
  3.20000000e+00  3.25000000e+00  3.30000000e+00  3.35000000e+00
  3.40000000e+00  3.45000000e+00  3.50000000e+00  3.55000000e+00
  3.60000000e+00  3.65000000e+00  3.70000000e+00  3.75000000e+00
  3.80000000e+00  3.85000000e+00  3.90000000e+00  3.95000000e+00
  4.00000000e+00  4.05000000e+00  4.10000000e+00  4.15000000e+00
  4.20000000e+00  4.25000000e+00  4.30000000e+00  4.35000000e+00
  4.40000000e+00  4.45000000e+00  4.50000000e+00  4.55000000e+00
  4.60000000e+00  4.65000000e+00  4.70000000e+00  4.75000000e+00
  4.80000000e+00  4.85000000e+00  4.90000000e+00  4.95000000e+00]
Shape of Xnp = 160
   Press ENTER to continue = 

    Now Call Logistic Regression
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-3-300811cacc0b> in <module>
    103 
    104 aprint("Now Call Logistic Regression")
--> 105 logreg = LogisticRegression.fit(Xnp, ynp)
    106 aprint("Print logreg output")
    107 print(logreg)

TypeError: fit() missing 1 required positional argument: 'y'

Eh!?!?
What happened????




"You won't find the right answers if you don't ask the right questions!" (Robert Helmbold, 2013)_______________________________________________
scikit-learn mailing list
scikit-learn at python.org
https://mail.python.org/mailman/listinfo/scikit-learn



-- 
Guillaume Lemaitre
Scikit-learn @ Inria Foundation
https://glemaitre.github.io/_______________________________________________
scikit-learn mailing list
scikit-learn at python.org
https://mail.python.org/mailman/listinfo/scikit-learn
  
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From ivomarbsoares at gmail.com  Sun Nov  1 17:42:57 2020
From: ivomarbsoares at gmail.com (Ivomar Brito Soares)
Date: Sun, 1 Nov 2020 19:42:57 -0300
Subject: [scikit-learn] Issue with Sklearn.Logistic Regression
In-Reply-To: <1298535362.749438.1604270328791@mail.yahoo.com>
References: <431768126.745150.1604267609819.ref@mail.yahoo.com>
 <431768126.745150.1604267609819@mail.yahoo.com>
 <CACDxx9gExyRR_3ceoup6U85NN2ix1++JpmVVegadvWmTY9wi0Q@mail.gmail.com>
 <1298535362.749438.1604270328791@mail.yahoo.com>
Message-ID: <CADkk+J0n5LLHecVX4KCEm8vBChGTCwzivGJJoui-K5bjXZEzoA@mail.gmail.com>

logreg = LogisticRegression*()*.fit(Xnp, ynp)


On Sun, Nov 1, 2020 at 7:39 PM The Helmbolds via scikit-learn <
scikit-learn at python.org> wrote:

> What parentheses?
> Enclosing what?
>
> "You won't find the right answers if you don't ask the right questions!"
> (Robert Helmbold, 2013)
>
>
> On Sunday, November 1, 2020, 02:58:46 PM MST, Guillaume Lema?tre <
> g.lemaitre58 at gmail.com> wrote:
>
>
> You forgot the parentheses to instantiate the object LogisticRegression
>
> On Sun, 1 Nov 2020 at 22:55, The Helmbolds via scikit-learn <
> scikit-learn at python.org> wrote:
>
> Here's my ynp and Xnp arrays:
>
>    Print ynp
> [0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
>  0 1 0 0 0 0 0 0 1 1 0 0 1 1 0 0 1 0 0 0 0 0 0 1 0 0 0 1 1 0 1 1 0 1 0 0 1
>  1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0
>  1 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1
>  1 1 1 1 1 1 1 1 1 1 1 1]
> Shape of ynp = 160
>
>     Print Xnp
> [-3.00000000e+00 -2.95000000e+00 -2.90000000e+00 -2.85000000e+00
>  -2.80000000e+00 -2.75000000e+00 -2.70000000e+00 -2.65000000e+00
>  -2.60000000e+00 -2.55000000e+00 -2.50000000e+00 -2.45000000e+00
>  -2.40000000e+00 -2.35000000e+00 -2.30000000e+00 -2.25000000e+00
>  -2.20000000e+00 -2.15000000e+00 -2.10000000e+00 -2.05000000e+00
>  -2.00000000e+00 -1.95000000e+00 -1.90000000e+00 -1.85000000e+00
>  -1.80000000e+00 -1.75000000e+00 -1.70000000e+00 -1.65000000e+00
>  -1.60000000e+00 -1.55000000e+00 -1.50000000e+00 -1.45000000e+00
>  -1.40000000e+00 -1.35000000e+00 -1.30000000e+00 -1.25000000e+00
>  -1.20000000e+00 -1.15000000e+00 -1.10000000e+00 -1.05000000e+00
>  -1.00000000e+00 -9.50000000e-01 -9.00000000e-01 -8.50000000e-01
>  -8.00000000e-01 -7.50000000e-01 -7.00000000e-01 -6.50000000e-01
>  -6.00000000e-01 -5.50000000e-01 -5.00000000e-01 -4.50000000e-01
>  -4.00000000e-01 -3.50000000e-01 -3.00000000e-01 -2.50000000e-01
>  -2.00000000e-01 -1.50000000e-01 -1.00000000e-01 -5.00000000e-02
>  -2.28983499e-15  5.00000000e-02  1.00000000e-01  1.50000000e-01
>   2.00000000e-01  2.50000000e-01  3.00000000e-01  3.50000000e-01
>   4.00000000e-01  4.50000000e-01  5.00000000e-01  5.50000000e-01
>   6.00000000e-01  6.50000000e-01  7.00000000e-01  7.50000000e-01
>   8.00000000e-01  8.50000000e-01  9.00000000e-01  9.50000000e-01
>   1.00000000e+00  1.05000000e+00  1.10000000e+00  1.15000000e+00
>   1.20000000e+00  1.25000000e+00  1.30000000e+00  1.35000000e+00
>   1.40000000e+00  1.45000000e+00  1.50000000e+00  1.55000000e+00
>   1.60000000e+00  1.65000000e+00  1.70000000e+00  1.75000000e+00
>   1.80000000e+00  1.85000000e+00  1.90000000e+00  1.95000000e+00
>   2.00000000e+00  2.05000000e+00  2.10000000e+00  2.15000000e+00
>   2.20000000e+00  2.25000000e+00  2.30000000e+00  2.35000000e+00
>   2.40000000e+00  2.45000000e+00  2.50000000e+00  2.55000000e+00
>   2.60000000e+00  2.65000000e+00  2.70000000e+00  2.75000000e+00
>   2.80000000e+00  2.85000000e+00  2.90000000e+00  2.95000000e+00
>   3.00000000e+00  3.05000000e+00  3.10000000e+00  3.15000000e+00
>   3.20000000e+00  3.25000000e+00  3.30000000e+00  3.35000000e+00
>   3.40000000e+00  3.45000000e+00  3.50000000e+00  3.55000000e+00
>   3.60000000e+00  3.65000000e+00  3.70000000e+00  3.75000000e+00
>   3.80000000e+00  3.85000000e+00  3.90000000e+00  3.95000000e+00
>   4.00000000e+00  4.05000000e+00  4.10000000e+00  4.15000000e+00
>   4.20000000e+00  4.25000000e+00  4.30000000e+00  4.35000000e+00
>   4.40000000e+00  4.45000000e+00  4.50000000e+00  4.55000000e+00
>   4.60000000e+00  4.65000000e+00  4.70000000e+00  4.75000000e+00
>   4.80000000e+00  4.85000000e+00  4.90000000e+00  4.95000000e+00]
> Shape of Xnp = 160
>    Press ENTER to continue =
>
>     Now Call Logistic Regression
>
> ---------------------------------------------------------------------------TypeError                                 Traceback (most recent call last)<ipython-input-3-300811cacc0b> in <module>    103     104 aprint("Now Call Logistic Regression")--> 105 logreg = LogisticRegression.fit(Xnp, ynp)    106 aprint("Print logreg output")    107 print(logreg)
> TypeError: fit() missing 1 required positional argument: 'y'
>
>
> Eh!?!?
>
> What happened*???*
>
>
>
>
>
> "You won't find the right answers if you don't ask the right questions!"
> (Robert Helmbold, 2013)
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>
>
>
> --
> Guillaume Lemaitre
> Scikit-learn @ Inria Foundation
> https://glemaitre.github.io/
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>


-- 
Ivomar Brito Soares
https://ivomarb.github.io
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From g.lemaitre58 at gmail.com  Sun Nov  1 17:44:27 2020
From: g.lemaitre58 at gmail.com (=?UTF-8?Q?Guillaume_Lema=C3=AEtre?=)
Date: Sun, 1 Nov 2020 23:44:27 +0100
Subject: [scikit-learn] Issue with Sklearn.Logistic Regression
In-Reply-To: <1298535362.749438.1604270328791@mail.yahoo.com>
References: <431768126.745150.1604267609819.ref@mail.yahoo.com>
 <431768126.745150.1604267609819@mail.yahoo.com>
 <CACDxx9gExyRR_3ceoup6U85NN2ix1++JpmVVegadvWmTY9wi0Q@mail.gmail.com>
 <1298535362.749438.1604270328791@mail.yahoo.com>
Message-ID: <CACDxx9i_rXvD51hEPwPvzL3VixF6g3-XSz=BHzUK8ZPZOun7yQ@mail.gmail.com>

estimator = LogisticRegression*()*.fit(X, y)

On Sun, 1 Nov 2020 at 23:40, The Helmbolds via scikit-learn <
scikit-learn at python.org> wrote:

> What parentheses?
> Enclosing what?
>
> "You won't find the right answers if you don't ask the right questions!"
> (Robert Helmbold, 2013)
>
>
> On Sunday, November 1, 2020, 02:58:46 PM MST, Guillaume Lema?tre <
> g.lemaitre58 at gmail.com> wrote:
>
>
> You forgot the parentheses to instantiate the object LogisticRegression
>
> On Sun, 1 Nov 2020 at 22:55, The Helmbolds via scikit-learn <
> scikit-learn at python.org> wrote:
>
> Here's my ynp and Xnp arrays:
>
>    Print ynp
> [0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
>  0 1 0 0 0 0 0 0 1 1 0 0 1 1 0 0 1 0 0 0 0 0 0 1 0 0 0 1 1 0 1 1 0 1 0 0 1
>  1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0
>  1 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1
>  1 1 1 1 1 1 1 1 1 1 1 1]
> Shape of ynp = 160
>
>     Print Xnp
> [-3.00000000e+00 -2.95000000e+00 -2.90000000e+00 -2.85000000e+00
>  -2.80000000e+00 -2.75000000e+00 -2.70000000e+00 -2.65000000e+00
>  -2.60000000e+00 -2.55000000e+00 -2.50000000e+00 -2.45000000e+00
>  -2.40000000e+00 -2.35000000e+00 -2.30000000e+00 -2.25000000e+00
>  -2.20000000e+00 -2.15000000e+00 -2.10000000e+00 -2.05000000e+00
>  -2.00000000e+00 -1.95000000e+00 -1.90000000e+00 -1.85000000e+00
>  -1.80000000e+00 -1.75000000e+00 -1.70000000e+00 -1.65000000e+00
>  -1.60000000e+00 -1.55000000e+00 -1.50000000e+00 -1.45000000e+00
>  -1.40000000e+00 -1.35000000e+00 -1.30000000e+00 -1.25000000e+00
>  -1.20000000e+00 -1.15000000e+00 -1.10000000e+00 -1.05000000e+00
>  -1.00000000e+00 -9.50000000e-01 -9.00000000e-01 -8.50000000e-01
>  -8.00000000e-01 -7.50000000e-01 -7.00000000e-01 -6.50000000e-01
>  -6.00000000e-01 -5.50000000e-01 -5.00000000e-01 -4.50000000e-01
>  -4.00000000e-01 -3.50000000e-01 -3.00000000e-01 -2.50000000e-01
>  -2.00000000e-01 -1.50000000e-01 -1.00000000e-01 -5.00000000e-02
>  -2.28983499e-15  5.00000000e-02  1.00000000e-01  1.50000000e-01
>   2.00000000e-01  2.50000000e-01  3.00000000e-01  3.50000000e-01
>   4.00000000e-01  4.50000000e-01  5.00000000e-01  5.50000000e-01
>   6.00000000e-01  6.50000000e-01  7.00000000e-01  7.50000000e-01
>   8.00000000e-01  8.50000000e-01  9.00000000e-01  9.50000000e-01
>   1.00000000e+00  1.05000000e+00  1.10000000e+00  1.15000000e+00
>   1.20000000e+00  1.25000000e+00  1.30000000e+00  1.35000000e+00
>   1.40000000e+00  1.45000000e+00  1.50000000e+00  1.55000000e+00
>   1.60000000e+00  1.65000000e+00  1.70000000e+00  1.75000000e+00
>   1.80000000e+00  1.85000000e+00  1.90000000e+00  1.95000000e+00
>   2.00000000e+00  2.05000000e+00  2.10000000e+00  2.15000000e+00
>   2.20000000e+00  2.25000000e+00  2.30000000e+00  2.35000000e+00
>   2.40000000e+00  2.45000000e+00  2.50000000e+00  2.55000000e+00
>   2.60000000e+00  2.65000000e+00  2.70000000e+00  2.75000000e+00
>   2.80000000e+00  2.85000000e+00  2.90000000e+00  2.95000000e+00
>   3.00000000e+00  3.05000000e+00  3.10000000e+00  3.15000000e+00
>   3.20000000e+00  3.25000000e+00  3.30000000e+00  3.35000000e+00
>   3.40000000e+00  3.45000000e+00  3.50000000e+00  3.55000000e+00
>   3.60000000e+00  3.65000000e+00  3.70000000e+00  3.75000000e+00
>   3.80000000e+00  3.85000000e+00  3.90000000e+00  3.95000000e+00
>   4.00000000e+00  4.05000000e+00  4.10000000e+00  4.15000000e+00
>   4.20000000e+00  4.25000000e+00  4.30000000e+00  4.35000000e+00
>   4.40000000e+00  4.45000000e+00  4.50000000e+00  4.55000000e+00
>   4.60000000e+00  4.65000000e+00  4.70000000e+00  4.75000000e+00
>   4.80000000e+00  4.85000000e+00  4.90000000e+00  4.95000000e+00]
> Shape of Xnp = 160
>    Press ENTER to continue =
>
>     Now Call Logistic Regression
>
> ---------------------------------------------------------------------------TypeError                                 Traceback (most recent call last)<ipython-input-3-300811cacc0b> in <module>    103     104 aprint("Now Call Logistic Regression")--> 105 logreg = LogisticRegression.fit(Xnp, ynp)    106 aprint("Print logreg output")    107 print(logreg)
> TypeError: fit() missing 1 required positional argument: 'y'
>
>
> Eh!?!?
>
> What happened*???*
>
>
>
>
>
> "You won't find the right answers if you don't ask the right questions!"
> (Robert Helmbold, 2013)
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>
>
>
> --
> Guillaume Lemaitre
> Scikit-learn @ Inria Foundation
> https://glemaitre.github.io/
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>


-- 
Guillaume Lemaitre
Scikit-learn @ Inria Foundation
https://glemaitre.github.io/
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From maykonschots at gmail.com  Sun Nov  1 17:44:31 2020
From: maykonschots at gmail.com (mrschots)
Date: Sun, 1 Nov 2020 23:44:31 +0100
Subject: [scikit-learn] Issue with Sklearn.Logistic Regression
In-Reply-To: <1298535362.749438.1604270328791@mail.yahoo.com>
References: <431768126.745150.1604267609819.ref@mail.yahoo.com>
 <431768126.745150.1604267609819@mail.yahoo.com>
 <CACDxx9gExyRR_3ceoup6U85NN2ix1++JpmVVegadvWmTY9wi0Q@mail.gmail.com>
 <1298535362.749438.1604270328791@mail.yahoo.com>
Message-ID: <CANThK3LFgO8ugPd38Ms_173k4tPRxHoadZ2CeoEhyYqcAnK1Mg@mail.gmail.com>

You should instantiate LogisticRegression() before fitting.

logreg = LogisticRegression().fit(Xnp,ynp)

[]?s

Maykon Schots

Em dom., 1 de nov. de 2020 ?s 23:41, The Helmbolds via scikit-learn <
scikit-learn at python.org> escreveu:

> What parentheses?
> Enclosing what?
>
> "You won't find the right answers if you don't ask the right questions!"
> (Robert Helmbold, 2013)
>
>
> On Sunday, November 1, 2020, 02:58:46 PM MST, Guillaume Lema?tre <
> g.lemaitre58 at gmail.com> wrote:
>
>
> You forgot the parentheses to instantiate the object LogisticRegression
>
> On Sun, 1 Nov 2020 at 22:55, The Helmbolds via scikit-learn <
> scikit-learn at python.org> wrote:
>
> Here's my ynp and Xnp arrays:
>
>    Print ynp
> [0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
>  0 1 0 0 0 0 0 0 1 1 0 0 1 1 0 0 1 0 0 0 0 0 0 1 0 0 0 1 1 0 1 1 0 1 0 0 1
>  1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0
>  1 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1
>  1 1 1 1 1 1 1 1 1 1 1 1]
> Shape of ynp = 160
>
>     Print Xnp
> [-3.00000000e+00 -2.95000000e+00 -2.90000000e+00 -2.85000000e+00
>  -2.80000000e+00 -2.75000000e+00 -2.70000000e+00 -2.65000000e+00
>  -2.60000000e+00 -2.55000000e+00 -2.50000000e+00 -2.45000000e+00
>  -2.40000000e+00 -2.35000000e+00 -2.30000000e+00 -2.25000000e+00
>  -2.20000000e+00 -2.15000000e+00 -2.10000000e+00 -2.05000000e+00
>  -2.00000000e+00 -1.95000000e+00 -1.90000000e+00 -1.85000000e+00
>  -1.80000000e+00 -1.75000000e+00 -1.70000000e+00 -1.65000000e+00
>  -1.60000000e+00 -1.55000000e+00 -1.50000000e+00 -1.45000000e+00
>  -1.40000000e+00 -1.35000000e+00 -1.30000000e+00 -1.25000000e+00
>  -1.20000000e+00 -1.15000000e+00 -1.10000000e+00 -1.05000000e+00
>  -1.00000000e+00 -9.50000000e-01 -9.00000000e-01 -8.50000000e-01
>  -8.00000000e-01 -7.50000000e-01 -7.00000000e-01 -6.50000000e-01
>  -6.00000000e-01 -5.50000000e-01 -5.00000000e-01 -4.50000000e-01
>  -4.00000000e-01 -3.50000000e-01 -3.00000000e-01 -2.50000000e-01
>  -2.00000000e-01 -1.50000000e-01 -1.00000000e-01 -5.00000000e-02
>  -2.28983499e-15  5.00000000e-02  1.00000000e-01  1.50000000e-01
>   2.00000000e-01  2.50000000e-01  3.00000000e-01  3.50000000e-01
>   4.00000000e-01  4.50000000e-01  5.00000000e-01  5.50000000e-01
>   6.00000000e-01  6.50000000e-01  7.00000000e-01  7.50000000e-01
>   8.00000000e-01  8.50000000e-01  9.00000000e-01  9.50000000e-01
>   1.00000000e+00  1.05000000e+00  1.10000000e+00  1.15000000e+00
>   1.20000000e+00  1.25000000e+00  1.30000000e+00  1.35000000e+00
>   1.40000000e+00  1.45000000e+00  1.50000000e+00  1.55000000e+00
>   1.60000000e+00  1.65000000e+00  1.70000000e+00  1.75000000e+00
>   1.80000000e+00  1.85000000e+00  1.90000000e+00  1.95000000e+00
>   2.00000000e+00  2.05000000e+00  2.10000000e+00  2.15000000e+00
>   2.20000000e+00  2.25000000e+00  2.30000000e+00  2.35000000e+00
>   2.40000000e+00  2.45000000e+00  2.50000000e+00  2.55000000e+00
>   2.60000000e+00  2.65000000e+00  2.70000000e+00  2.75000000e+00
>   2.80000000e+00  2.85000000e+00  2.90000000e+00  2.95000000e+00
>   3.00000000e+00  3.05000000e+00  3.10000000e+00  3.15000000e+00
>   3.20000000e+00  3.25000000e+00  3.30000000e+00  3.35000000e+00
>   3.40000000e+00  3.45000000e+00  3.50000000e+00  3.55000000e+00
>   3.60000000e+00  3.65000000e+00  3.70000000e+00  3.75000000e+00
>   3.80000000e+00  3.85000000e+00  3.90000000e+00  3.95000000e+00
>   4.00000000e+00  4.05000000e+00  4.10000000e+00  4.15000000e+00
>   4.20000000e+00  4.25000000e+00  4.30000000e+00  4.35000000e+00
>   4.40000000e+00  4.45000000e+00  4.50000000e+00  4.55000000e+00
>   4.60000000e+00  4.65000000e+00  4.70000000e+00  4.75000000e+00
>   4.80000000e+00  4.85000000e+00  4.90000000e+00  4.95000000e+00]
> Shape of Xnp = 160
>    Press ENTER to continue =
>
>     Now Call Logistic Regression
>
> ---------------------------------------------------------------------------TypeError                                 Traceback (most recent call last)<ipython-input-3-300811cacc0b> in <module>    103     104 aprint("Now Call Logistic Regression")--> 105 logreg = LogisticRegression.fit(Xnp, ynp)    106 aprint("Print logreg output")    107 print(logreg)
> TypeError: fit() missing 1 required positional argument: 'y'
>
>
> Eh!?!?
>
> What happened*???*
>
>
>
>
>
> "You won't find the right answers if you don't ask the right questions!"
> (Robert Helmbold, 2013)
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>
>
>
> --
> Guillaume Lemaitre
> Scikit-learn @ Inria Foundation
> https://glemaitre.github.io/
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>
-- 
Schots
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From helmrp at yahoo.com  Sun Nov  1 18:27:47 2020
From: helmrp at yahoo.com (The Helmbolds)
Date: Sun, 1 Nov 2020 23:27:47 +0000 (UTC)
Subject: [scikit-learn] Issue with Sklearn.Logistic Regression
In-Reply-To: <CANThK3LFgO8ugPd38Ms_173k4tPRxHoadZ2CeoEhyYqcAnK1Mg@mail.gmail.com>
References: <431768126.745150.1604267609819.ref@mail.yahoo.com>
 <431768126.745150.1604267609819@mail.yahoo.com>
 <CACDxx9gExyRR_3ceoup6U85NN2ix1++JpmVVegadvWmTY9wi0Q@mail.gmail.com>
 <1298535362.749438.1604270328791@mail.yahoo.com>
 <CANThK3LFgO8ugPd38Ms_173k4tPRxHoadZ2CeoEhyYqcAnK1Mg@mail.gmail.com>
Message-ID: <1142810319.749554.1604273267037@mail.yahoo.com>

Aha!
Thanks!!!
"You won't find the right answers if you don't ask the right questions!" (Robert Helmbold, 2013) 

    On Sunday, November 1, 2020, 03:46:32 PM MST, mrschots <maykonschots at gmail.com> wrote:  
 
 You should instantiate LogisticRegression() before fitting.?
logreg = LogisticRegression().fit(Xnp,ynp)
[]?s ?
Maykon Schots?
Em dom., 1 de nov. de 2020 ?s 23:41, The Helmbolds via scikit-learn <scikit-learn at python.org> escreveu:

What parentheses?Enclosing what?
"You won't find the right answers if you don't ask the right questions!" (Robert Helmbold, 2013) 

    On Sunday, November 1, 2020, 02:58:46 PM MST, Guillaume Lema?tre <g.lemaitre58 at gmail.com> wrote:  
 
 You forgot the parentheses to instantiate the object LogisticRegression

On Sun, 1 Nov 2020 at 22:55, The Helmbolds via scikit-learn <scikit-learn at python.org> wrote:

Here's my ynp and Xnp arrays:

   Print ynp
[0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
 0 1 0 0 0 0 0 0 1 1 0 0 1 1 0 0 1 0 0 0 0 0 0 1 0 0 0 1 1 0 1 1 0 1 0 0 1
 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0
 1 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1
 1 1 1 1 1 1 1 1 1 1 1 1]
Shape of ynp = 160

    Print Xnp
[-3.00000000e+00 -2.95000000e+00 -2.90000000e+00 -2.85000000e+00
 -2.80000000e+00 -2.75000000e+00 -2.70000000e+00 -2.65000000e+00
 -2.60000000e+00 -2.55000000e+00 -2.50000000e+00 -2.45000000e+00
 -2.40000000e+00 -2.35000000e+00 -2.30000000e+00 -2.25000000e+00
 -2.20000000e+00 -2.15000000e+00 -2.10000000e+00 -2.05000000e+00
 -2.00000000e+00 -1.95000000e+00 -1.90000000e+00 -1.85000000e+00
 -1.80000000e+00 -1.75000000e+00 -1.70000000e+00 -1.65000000e+00
 -1.60000000e+00 -1.55000000e+00 -1.50000000e+00 -1.45000000e+00
 -1.40000000e+00 -1.35000000e+00 -1.30000000e+00 -1.25000000e+00
 -1.20000000e+00 -1.15000000e+00 -1.10000000e+00 -1.05000000e+00
 -1.00000000e+00 -9.50000000e-01 -9.00000000e-01 -8.50000000e-01
 -8.00000000e-01 -7.50000000e-01 -7.00000000e-01 -6.50000000e-01
 -6.00000000e-01 -5.50000000e-01 -5.00000000e-01 -4.50000000e-01
 -4.00000000e-01 -3.50000000e-01 -3.00000000e-01 -2.50000000e-01
 -2.00000000e-01 -1.50000000e-01 -1.00000000e-01 -5.00000000e-02
 -2.28983499e-15  5.00000000e-02  1.00000000e-01  1.50000000e-01
  2.00000000e-01  2.50000000e-01  3.00000000e-01  3.50000000e-01
  4.00000000e-01  4.50000000e-01  5.00000000e-01  5.50000000e-01
  6.00000000e-01  6.50000000e-01  7.00000000e-01  7.50000000e-01
  8.00000000e-01  8.50000000e-01  9.00000000e-01  9.50000000e-01
  1.00000000e+00  1.05000000e+00  1.10000000e+00  1.15000000e+00
  1.20000000e+00  1.25000000e+00  1.30000000e+00  1.35000000e+00
  1.40000000e+00  1.45000000e+00  1.50000000e+00  1.55000000e+00
  1.60000000e+00  1.65000000e+00  1.70000000e+00  1.75000000e+00
  1.80000000e+00  1.85000000e+00  1.90000000e+00  1.95000000e+00
  2.00000000e+00  2.05000000e+00  2.10000000e+00  2.15000000e+00
  2.20000000e+00  2.25000000e+00  2.30000000e+00  2.35000000e+00
  2.40000000e+00  2.45000000e+00  2.50000000e+00  2.55000000e+00
  2.60000000e+00  2.65000000e+00  2.70000000e+00  2.75000000e+00
  2.80000000e+00  2.85000000e+00  2.90000000e+00  2.95000000e+00
  3.00000000e+00  3.05000000e+00  3.10000000e+00  3.15000000e+00
  3.20000000e+00  3.25000000e+00  3.30000000e+00  3.35000000e+00
  3.40000000e+00  3.45000000e+00  3.50000000e+00  3.55000000e+00
  3.60000000e+00  3.65000000e+00  3.70000000e+00  3.75000000e+00
  3.80000000e+00  3.85000000e+00  3.90000000e+00  3.95000000e+00
  4.00000000e+00  4.05000000e+00  4.10000000e+00  4.15000000e+00
  4.20000000e+00  4.25000000e+00  4.30000000e+00  4.35000000e+00
  4.40000000e+00  4.45000000e+00  4.50000000e+00  4.55000000e+00
  4.60000000e+00  4.65000000e+00  4.70000000e+00  4.75000000e+00
  4.80000000e+00  4.85000000e+00  4.90000000e+00  4.95000000e+00]
Shape of Xnp = 160
   Press ENTER to continue = 

    Now Call Logistic Regression
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-3-300811cacc0b> in <module>
    103 
    104 aprint("Now Call Logistic Regression")
--> 105 logreg = LogisticRegression.fit(Xnp, ynp)
    106 aprint("Print logreg output")
    107 print(logreg)

TypeError: fit() missing 1 required positional argument: 'y'

Eh!?!?
What happened????




"You won't find the right answers if you don't ask the right questions!" (Robert Helmbold, 2013)_______________________________________________
scikit-learn mailing list
scikit-learn at python.org
https://mail.python.org/mailman/listinfo/scikit-learn



-- 
Guillaume Lemaitre
Scikit-learn @ Inria Foundation
https://glemaitre.github.io/_______________________________________________
scikit-learn mailing list
scikit-learn at python.org
https://mail.python.org/mailman/listinfo/scikit-learn
  _______________________________________________
scikit-learn mailing list
scikit-learn at python.org
https://mail.python.org/mailman/listinfo/scikit-learn

-- 
Schots_______________________________________________
scikit-learn mailing list
scikit-learn at python.org
https://mail.python.org/mailman/listinfo/scikit-learn
  
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From gael.varoquaux at normalesup.org  Mon Nov  2 05:50:18 2020
From: gael.varoquaux at normalesup.org (Gael Varoquaux)
Date: Mon, 2 Nov 2020 11:50:18 +0100
Subject: [scikit-learn] Changes in Travis billing
Message-ID: <20201102105018.kfarylwpao6iliju@phare.normalesup.org>

Travis is changing it's billing strategy:
https://blog.travis-ci.com/2020-11-02-travis-ci-new-billing

Open repositories are getting a free initial set of credit. They invite
open source projects to contact them to benefit from a more liberal
policy.

I suggest that we do the latter, as I fear that we might run out of
credits, and I am quite convinced that we could benefit from the liberal
policy.

Cheers,

Ga?l

-- 
    Gael Varoquaux
    Research Director, INRIA		  Visiting professor, McGill 
    http://gael-varoquaux.info            http://twitter.com/GaelVaroquaux

From adrin.jalali at gmail.com  Mon Nov  2 10:57:16 2020
From: adrin.jalali at gmail.com (Adrin)
Date: Mon, 2 Nov 2020 16:57:16 +0100
Subject: [scikit-learn] Changes in Travis billing
In-Reply-To: <20201102105018.kfarylwpao6iliju@phare.normalesup.org>
References: <20201102105018.kfarylwpao6iliju@phare.normalesup.org>
Message-ID: <CAEOrW49QWQLKi0Rd5AdESD=YCwsm356egRdH+=fxtqZeiMZNzw@mail.gmail.com>

Shall I contact them? Any other volunteers?

On Mon, Nov 2, 2020 at 11:51 AM Gael Varoquaux <
gael.varoquaux at normalesup.org> wrote:

> Travis is changing it's billing strategy:
> https://blog.travis-ci.com/2020-11-02-travis-ci-new-billing
>
> Open repositories are getting a free initial set of credit. They invite
> open source projects to contact them to benefit from a more liberal
> policy.
>
> I suggest that we do the latter, as I fear that we might run out of
> credits, and I am quite convinced that we could benefit from the liberal
> policy.
>
> Cheers,
>
> Ga?l
>
> --
>     Gael Varoquaux
>     Research Director, INRIA              Visiting professor, McGill
>     http://gael-varoquaux.info            http://twitter.com/GaelVaroquaux
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>
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From oshimikam at gmail.com  Tue Nov  3 11:38:01 2020
From: oshimikam at gmail.com (Mick Men)
Date: Tue, 3 Nov 2020 11:38:01 -0500
Subject: [scikit-learn] implementing regularized random forest
Message-ID: <CAA9TUtn0bzM4ex_ZBcRG2xxNnLCPOYgUgmYbyaNXPZcZe3vqyg@mail.gmail.com>

 Hello,

I am trying to implement my own regularized random forest (RRF) which grows
trees in series and selects new features only if they are better than the
features used in previous splits.

This is for a research project and I will need to ship the code with the
publication. So far I have a working proof of concept where I modified the
scikit-learn forest, tree, and splitter modules. But this mean that I need
to ship my fork version of scikit-learn.

Ideally, I am looking for a way to build my own RRF that uses scikit-learn
API instead of modifying it.
Is it possible?

Thanks.

Mickael
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From niourf at gmail.com  Tue Nov  3 11:44:00 2020
From: niourf at gmail.com (Nicolas Hug)
Date: Tue, 3 Nov 2020 16:44:00 +0000
Subject: [scikit-learn] implementing regularized random forest
In-Reply-To: <CAA9TUtn0bzM4ex_ZBcRG2xxNnLCPOYgUgmYbyaNXPZcZe3vqyg@mail.gmail.com>
References: <CAA9TUtn0bzM4ex_ZBcRG2xxNnLCPOYgUgmYbyaNXPZcZe3vqyg@mail.gmail.com>
Message-ID: <786eea31-abf1-cfab-6884-18d5119a5a66@gmail.com>

Mickael,

You probably don't need to ship an entire fork, but all the tree 
internals that you are using (splitter etc.) are part of a private API 
so yes, you would need to duplicate these into your own implementation.

Nicolas

On 11/3/20 4:38 PM, Mick Men wrote:
> Hello,
>
> I am trying to implement my own regularized random forest (RRF) which 
> grows trees in series and selects new features only if they are better 
> than the features used in previous splits.
>
> This is for a research project and I will need to ship the code with 
> the publication. So far I have a working proof of concept where I 
> modified the scikit-learn forest, tree, and splitter modules. But this 
> mean that I need to ship my fork version of scikit-learn.
>
> Ideally, I am looking for a way to build my own RRF that uses 
> scikit-learn API instead of modifying it.
> Is it possible?
>
> Thanks.
>
> Mickael
>
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
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From thomasjpfan at gmail.com  Tue Nov  3 18:28:27 2020
From: thomasjpfan at gmail.com (Thomas J. Fan)
Date: Tue, 3 Nov 2020 18:28:27 -0500
Subject: [scikit-learn] Changes in Travis billing
In-Reply-To: <CAEOrW49QWQLKi0Rd5AdESD=YCwsm356egRdH+=fxtqZeiMZNzw@mail.gmail.com>
References: <20201102105018.kfarylwpao6iliju@phare.normalesup.org>
 <CAEOrW49QWQLKi0Rd5AdESD=YCwsm356egRdH+=fxtqZeiMZNzw@mail.gmail.com>
Message-ID: <CAK3g5AZyob1P6n-vaViC8p8TVLHGhKZr+vXr=cP1Zyq1wooCpg@mail.gmail.com>

I took this opportunity to migrate from travis-ci.org to
travis-ci.com. The project url is now:

https://travis-ci.com/github/scikit-learn/scikit-learn

The blog post did mention that we need to ask for a number of build
credits. Currently we use travis-ci to test the intel c compiler and
ARM. Looking forward, we may be doing more ARM on travis because it is
the only platform with native ARM support.

As a data point, our cron job that runs scipy-dev, icc-build and ARM
takes around 70 minutes to run, (ARM takes ~ 12 minutes). This means
with a normal allocation of 1000 minutes we can run our cron job ~ 14
times. So we ask for 3000-4000 minutes?

Thomas

On Mon, Nov 2, 2020 at 10:59 AM Adrin <adrin.jalali at gmail.com> wrote:
>
> Shall I contact them? Any other volunteers?
>
> On Mon, Nov 2, 2020 at 11:51 AM Gael Varoquaux <gael.varoquaux at normalesup.org> wrote:
>>
>> Travis is changing it's billing strategy:
>> https://blog.travis-ci.com/2020-11-02-travis-ci-new-billing
>>
>> Open repositories are getting a free initial set of credit. They invite
>> open source projects to contact them to benefit from a more liberal
>> policy.
>>
>> I suggest that we do the latter, as I fear that we might run out of
>> credits, and I am quite convinced that we could benefit from the liberal
>> policy.
>>
>> Cheers,
>>
>> Ga?l
>>
>> --
>>     Gael Varoquaux
>>     Research Director, INRIA              Visiting professor, McGill
>>     http://gael-varoquaux.info            http://twitter.com/GaelVaroquaux
>> _______________________________________________
>> scikit-learn mailing list
>> scikit-learn at python.org
>> https://mail.python.org/mailman/listinfo/scikit-learn
>
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn

From olivier.grisel at ensta.org  Thu Nov  5 11:17:42 2020
From: olivier.grisel at ensta.org (Olivier Grisel)
Date: Thu, 5 Nov 2020 17:17:42 +0100
Subject: [scikit-learn] Changes in Travis billing
In-Reply-To: <CAEOrW49QWQLKi0Rd5AdESD=YCwsm356egRdH+=fxtqZeiMZNzw@mail.gmail.com>
References: <20201102105018.kfarylwpao6iliju@phare.normalesup.org>
 <CAEOrW49QWQLKi0Rd5AdESD=YCwsm356egRdH+=fxtqZeiMZNzw@mail.gmail.com>
Message-ID: <CAFvE7K6dPVtH4e6=0toWujbLiByg=mVOxayh3rTvEUNZCt8q+g@mail.gmail.com>

> Shall I contact them? Any other volunteers?

+1.

I think we are still dependent on travis for ARM-based release builds
and cron-jobs. The rest we can move it to Azure Pipelines or github
actions I believe.

-- 
Olivier

From mahmood.nt at gmail.com  Sun Nov  8 06:21:52 2020
From: mahmood.nt at gmail.com (Mahmood Naderan)
Date: Sun, 8 Nov 2020 12:21:52 +0100
Subject: [scikit-learn] Creating dataset
Message-ID: <CADa2P2VuAJydgQwiZgc+dfUZZL+pk7wu51CiqZ5FymmNn_FyVg@mail.gmail.com>

Hi,
I have created an input file similar to iris data set. That is something
like this:

0.1,0.2,0.3,0.4,M1
...

I want to know how I can create my own dataset similar to the following
lines?

from sklearn.datasets import load_iris
iris = load_iris()


Regards,
Mahmood
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From niourf at gmail.com  Sun Nov  8 06:34:29 2020
From: niourf at gmail.com (Nicolas Hug)
Date: Sun, 8 Nov 2020 11:34:29 +0000
Subject: [scikit-learn] Creating dataset
In-Reply-To: <CADa2P2VuAJydgQwiZgc+dfUZZL+pk7wu51CiqZ5FymmNn_FyVg@mail.gmail.com>
References: <CADa2P2VuAJydgQwiZgc+dfUZZL+pk7wu51CiqZ5FymmNn_FyVg@mail.gmail.com>
Message-ID: <39d577d5-1f3a-8ad2-0789-ce31275e3fbf@gmail.com>

Mahmood,

 From what I understand your dataset is stored in a csv file. I'd 
recommend just reading that csv file with e.g. pandas 
(https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html), 
and then just use the dataframe as input to scikit-learn utilities (you 
may need to separate the features X from the target y). Then, if you 
need, you can wrap all that into a "load_my_dataset()" function.

HTH,

Nicolas

On 11/8/20 11:21 AM, Mahmood Naderan wrote:
> Hi,
> I have created an input file similar to iris data set. That is 
> something like this:
>
> 0.1,0.2,0.3,0.4,M1
> ...
>
> I want to know how I can create my own dataset similar to the 
> following lines?
>
> from sklearn.datasets import load_iris
> iris = load_iris()
>
>
> Regards,
> Mahmood
>
>
>
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
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From salexln at gmail.com  Sun Nov  8 06:38:35 2020
From: salexln at gmail.com (Alex Levin)
Date: Sun, 8 Nov 2020 13:38:35 +0200
Subject: [scikit-learn] Creating dataset
In-Reply-To: <CADa2P2VuAJydgQwiZgc+dfUZZL+pk7wu51CiqZ5FymmNn_FyVg@mail.gmail.com>
References: <CADa2P2VuAJydgQwiZgc+dfUZZL+pk7wu51CiqZ5FymmNn_FyVg@mail.gmail.com>
Message-ID: <CAK2WwdMaQ-7FxhhPVDem4GKPEWg2+Xqevxi_dADGYZGEwjDajA@mail.gmail.com>

Hi Mahmood
You can add your data set to `datasets/data` and then implement
`load_my_data` function in `datasets/_base.py`
Also register it in `datasets/__init__.py`

On Sun, Nov 8, 2020 at 1:24 PM Mahmood Naderan <mahmood.nt at gmail.com> wrote:

> Hi,
> I have created an input file similar to iris data set. That is something
> like this:
>
> 0.1,0.2,0.3,0.4,M1
> ...
>
> I want to know how I can create my own dataset similar to the following
> lines?
>
> from sklearn.datasets import load_iris
> iris = load_iris()
>
>
> Regards,
> Mahmood
>
>
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>
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From g.lemaitre58 at gmail.com  Sun Nov  8 07:01:20 2020
From: g.lemaitre58 at gmail.com (=?UTF-8?Q?Guillaume_Lema=C3=AEtre?=)
Date: Sun, 8 Nov 2020 13:01:20 +0100
Subject: [scikit-learn] Creating dataset
In-Reply-To: <CAK2WwdMaQ-7FxhhPVDem4GKPEWg2+Xqevxi_dADGYZGEwjDajA@mail.gmail.com>
References: <CADa2P2VuAJydgQwiZgc+dfUZZL+pk7wu51CiqZ5FymmNn_FyVg@mail.gmail.com>
 <CAK2WwdMaQ-7FxhhPVDem4GKPEWg2+Xqevxi_dADGYZGEwjDajA@mail.gmail.com>
Message-ID: <CACDxx9gNGgCgUi5KwJ2Lqt3YbptC1K4jHNEVh42+3mS7q4O2og@mail.gmail.com>

I would not recommend the solution of Alex. Do not modify the scikit-learn
source code.
Write it in your own Python module.

But most probably the solution of Nicolas should be enough for 99% of the
use-cases.

Cheers,

On Sun, 8 Nov 2020 at 12:41, Alex Levin <salexln at gmail.com> wrote:

> Hi Mahmood
> You can add your data set to `datasets/data` and then implement
> `load_my_data` function in `datasets/_base.py`
> Also register it in `datasets/__init__.py`
>
> On Sun, Nov 8, 2020 at 1:24 PM Mahmood Naderan <mahmood.nt at gmail.com>
> wrote:
>
>> Hi,
>> I have created an input file similar to iris data set. That is something
>> like this:
>>
>> 0.1,0.2,0.3,0.4,M1
>> ...
>>
>> I want to know how I can create my own dataset similar to the following
>> lines?
>>
>> from sklearn.datasets import load_iris
>> iris = load_iris()
>>
>>
>> Regards,
>> Mahmood
>>
>>
>> _______________________________________________
>> scikit-learn mailing list
>> scikit-learn at python.org
>> https://mail.python.org/mailman/listinfo/scikit-learn
>>
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>


-- 
Guillaume Lemaitre
Scikit-learn @ Inria Foundation
https://glemaitre.github.io/
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From salexln at gmail.com  Sun Nov  8 07:05:29 2020
From: salexln at gmail.com (Alex Levin)
Date: Sun, 8 Nov 2020 14:05:29 +0200
Subject: [scikit-learn] Creating dataset
In-Reply-To: <CACDxx9gNGgCgUi5KwJ2Lqt3YbptC1K4jHNEVh42+3mS7q4O2og@mail.gmail.com>
References: <CADa2P2VuAJydgQwiZgc+dfUZZL+pk7wu51CiqZ5FymmNn_FyVg@mail.gmail.com>
 <CAK2WwdMaQ-7FxhhPVDem4GKPEWg2+Xqevxi_dADGYZGEwjDajA@mail.gmail.com>
 <CACDxx9gNGgCgUi5KwJ2Lqt3YbptC1K4jHNEVh42+3mS7q4O2og@mail.gmail.com>
Message-ID: <CAK2WwdODhk3sbQjCFZ34MWGX9-DVJiK4aFXFPctbHM-RJpztfw@mail.gmail.com>

Guillaume, I only meant that he can do it locally :)
submitting that would be a bad idea

On Sun, Nov 8, 2020 at 2:03 PM Guillaume Lema?tre <g.lemaitre58 at gmail.com>
wrote:

> I would not recommend the solution of Alex. Do not modify the scikit-learn
> source code.
> Write it in your own Python module.
>
> But most probably the solution of Nicolas should be enough for 99% of the
> use-cases.
>
> Cheers,
>
> On Sun, 8 Nov 2020 at 12:41, Alex Levin <salexln at gmail.com> wrote:
>
>> Hi Mahmood
>> You can add your data set to `datasets/data` and then implement
>> `load_my_data` function in `datasets/_base.py`
>> Also register it in `datasets/__init__.py`
>>
>> On Sun, Nov 8, 2020 at 1:24 PM Mahmood Naderan <mahmood.nt at gmail.com>
>> wrote:
>>
>>> Hi,
>>> I have created an input file similar to iris data set. That is something
>>> like this:
>>>
>>> 0.1,0.2,0.3,0.4,M1
>>> ...
>>>
>>> I want to know how I can create my own dataset similar to the following
>>> lines?
>>>
>>> from sklearn.datasets import load_iris
>>> iris = load_iris()
>>>
>>>
>>> Regards,
>>> Mahmood
>>>
>>>
>>> _______________________________________________
>>> scikit-learn mailing list
>>> scikit-learn at python.org
>>> https://mail.python.org/mailman/listinfo/scikit-learn
>>>
>> _______________________________________________
>> scikit-learn mailing list
>> scikit-learn at python.org
>> https://mail.python.org/mailman/listinfo/scikit-learn
>>
>
>
> --
> Guillaume Lemaitre
> Scikit-learn @ Inria Foundation
> https://glemaitre.github.io/
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>
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From mahmood.nt at gmail.com  Sun Nov  8 07:42:38 2020
From: mahmood.nt at gmail.com (Mahmood Naderan)
Date: Sun, 8 Nov 2020 13:42:38 +0100
Subject: [scikit-learn] Creating dataset
In-Reply-To: <CAK2WwdODhk3sbQjCFZ34MWGX9-DVJiK4aFXFPctbHM-RJpztfw@mail.gmail.com>
References: <CADa2P2VuAJydgQwiZgc+dfUZZL+pk7wu51CiqZ5FymmNn_FyVg@mail.gmail.com>
 <CAK2WwdMaQ-7FxhhPVDem4GKPEWg2+Xqevxi_dADGYZGEwjDajA@mail.gmail.com>
 <CACDxx9gNGgCgUi5KwJ2Lqt3YbptC1K4jHNEVh42+3mS7q4O2og@mail.gmail.com>
 <CAK2WwdODhk3sbQjCFZ34MWGX9-DVJiK4aFXFPctbHM-RJpztfw@mail.gmail.com>
Message-ID: <CADa2P2W86r2kRgywZewLs1TzNn5JkU3-T0BvihFTU6djRPg7Sw@mail.gmail.com>

Thanks for the replies.

>I'd recommend just reading that csv file with e.g. pandas
>(
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html),

>and then just use the dataframe as input to scikit-learn utilities (you
may need to
>separate the features X from the target y).


I am trying to follow the steps as described in
https://towardsdatascience.com/a-step-by-step-introduction-to-pca-c0d78e26a0dd

I changed

iris = load_iris()
colors = ["blue","red","green"]
df = DataFrame(
    data=np.c_[iris["data"],  iris["target"]], columns=
iris["feature_names"] + ["target"])

to

data_file = pd.read_csv("mydata.csv")
colors =
["blue","red","green","skyblue","indigo","plum","coral","orange","gray","lime"]
df = DataFrame(
    data=np.c_[data_file["data"], data_file["target"]],
columns=data_file["feature_names"] + ["target"])


But I get this error:

Traceback (most recent call last):
  File
"/home/mahmood/.local/lib/python3.6/site-packages/pandas/core/indexes/base.py",
line 2895, in get_loc
    return self._engine.get_loc(casted_key)
  File "pandas/_libs/index.pyx", line 70, in
pandas._libs.index.IndexEngine.get_loc
  File "pandas/_libs/index.pyx", line 101, in
pandas._libs.index.IndexEngine.get_loc
  File "pandas/_libs/hashtable_class_helper.pxi", line 1675, in
pandas._libs.hashtable.PyObjectHashTable.get_item
  File "pandas/_libs/hashtable_class_helper.pxi", line 1683, in
pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: 'data'

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "pca_gromacs.py", line 12, in <module>
    data=np.c_[data_file["data"], data_file["target"]],
columns=data_file["feature_names"] + ["target"]
  File
"/home/mahmood/.local/lib/python3.6/site-packages/pandas/core/frame.py",
line 2906, in __getitem__
    indexer = self.columns.get_loc(key)
  File
"/home/mahmood/.local/lib/python3.6/site-packages/pandas/core/indexes/base.py",
line 2897, in get_loc
    raise KeyError(key) from err
KeyError: 'data'



It seems that load_iris() do more than read_csv().

Regards,
Mahmood
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From matthieu.brucher at gmail.com  Sun Nov  8 08:41:53 2020
From: matthieu.brucher at gmail.com (Matthieu Brucher)
Date: Sun, 8 Nov 2020 13:41:53 +0000
Subject: [scikit-learn] Creating dataset
In-Reply-To: <CADa2P2W86r2kRgywZewLs1TzNn5JkU3-T0BvihFTU6djRPg7Sw@mail.gmail.com>
References: <CADa2P2VuAJydgQwiZgc+dfUZZL+pk7wu51CiqZ5FymmNn_FyVg@mail.gmail.com>
 <CAK2WwdMaQ-7FxhhPVDem4GKPEWg2+Xqevxi_dADGYZGEwjDajA@mail.gmail.com>
 <CACDxx9gNGgCgUi5KwJ2Lqt3YbptC1K4jHNEVh42+3mS7q4O2og@mail.gmail.com>
 <CAK2WwdODhk3sbQjCFZ34MWGX9-DVJiK4aFXFPctbHM-RJpztfw@mail.gmail.com>
 <CADa2P2W86r2kRgywZewLs1TzNn5JkU3-T0BvihFTU6djRPg7Sw@mail.gmail.com>
Message-ID: <CAHCaCkJ_2yY=Bw_dFqa+GrfTnuWaJ1+3z3oYwD=AS0bYCyk2BQ@mail.gmail.com>

data_file["data"], this works only if you have such a column as well.
load_csv can perfectly do what you need, but you have to adapt the
script to what you have in the csv (which is something only you
know!).
You need to understand what the different statements are doing; just
as you need to understand what processing you apply on your data
(whether it's preprocessing or learning) to properly use any machine
learning tool.

Matthieu

Le dim. 8 nov. 2020 ? 12:44, Mahmood Naderan <mahmood.nt at gmail.com> a ?crit :
>
> Thanks for the replies.
>
> >I'd recommend just reading that csv file with e.g. pandas
> >(https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html),
> >and then just use the dataframe as input to scikit-learn utilities (you may need to
> >separate the features X from the target y).
>
>
> I am trying to follow the steps as described in https://towardsdatascience.com/a-step-by-step-introduction-to-pca-c0d78e26a0dd
>
> I changed
>
> iris = load_iris()
> colors = ["blue","red","green"]
> df = DataFrame(
>     data=np.c_[iris["data"],  iris["target"]], columns= iris["feature_names"] + ["target"])
>
> to
>
> data_file = pd.read_csv("mydata.csv")
> colors = ["blue","red","green","skyblue","indigo","plum","coral","orange","gray","lime"]
> df = DataFrame(
>     data=np.c_[data_file["data"], data_file["target"]], columns=data_file["feature_names"] + ["target"])
>
>
> But I get this error:
>
> Traceback (most recent call last):
>   File "/home/mahmood/.local/lib/python3.6/site-packages/pandas/core/indexes/base.py", line 2895, in get_loc
>     return self._engine.get_loc(casted_key)
>   File "pandas/_libs/index.pyx", line 70, in pandas._libs.index.IndexEngine.get_loc
>   File "pandas/_libs/index.pyx", line 101, in pandas._libs.index.IndexEngine.get_loc
>   File "pandas/_libs/hashtable_class_helper.pxi", line 1675, in pandas._libs.hashtable.PyObjectHashTable.get_item
>   File "pandas/_libs/hashtable_class_helper.pxi", line 1683, in pandas._libs.hashtable.PyObjectHashTable.get_item
> KeyError: 'data'
>
> The above exception was the direct cause of the following exception:
>
> Traceback (most recent call last):
>   File "pca_gromacs.py", line 12, in <module>
>     data=np.c_[data_file["data"], data_file["target"]], columns=data_file["feature_names"] + ["target"]
>   File "/home/mahmood/.local/lib/python3.6/site-packages/pandas/core/frame.py", line 2906, in __getitem__
>     indexer = self.columns.get_loc(key)
>   File "/home/mahmood/.local/lib/python3.6/site-packages/pandas/core/indexes/base.py", line 2897, in get_loc
>     raise KeyError(key) from err
> KeyError: 'data'
>
>
>
> It seems that load_iris() do more than read_csv().
>
> Regards,
> Mahmood
>
>
>
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn



-- 
Quantitative researcher, Ph.D.
Blog: http://blog.audio-tk.com/
LinkedIn: http://www.linkedin.com/in/matthieubrucher

From mahmood.nt at gmail.com  Sun Nov  8 09:19:18 2020
From: mahmood.nt at gmail.com (Mahmood Naderan)
Date: Sun, 8 Nov 2020 15:19:18 +0100
Subject: [scikit-learn] Creating dataset
In-Reply-To: <CAHCaCkJ_2yY=Bw_dFqa+GrfTnuWaJ1+3z3oYwD=AS0bYCyk2BQ@mail.gmail.com>
References: <CADa2P2VuAJydgQwiZgc+dfUZZL+pk7wu51CiqZ5FymmNn_FyVg@mail.gmail.com>
 <CAK2WwdMaQ-7FxhhPVDem4GKPEWg2+Xqevxi_dADGYZGEwjDajA@mail.gmail.com>
 <CACDxx9gNGgCgUi5KwJ2Lqt3YbptC1K4jHNEVh42+3mS7q4O2og@mail.gmail.com>
 <CAK2WwdODhk3sbQjCFZ34MWGX9-DVJiK4aFXFPctbHM-RJpztfw@mail.gmail.com>
 <CADa2P2W86r2kRgywZewLs1TzNn5JkU3-T0BvihFTU6djRPg7Sw@mail.gmail.com>
 <CAHCaCkJ_2yY=Bw_dFqa+GrfTnuWaJ1+3z3oYwD=AS0bYCyk2BQ@mail.gmail.com>
Message-ID: <CADa2P2UtJ+ARJhS-_NLaFvjoq3jkE-pDQTNO35SpKHXVKvz3-Q@mail.gmail.com>

 >You need to understand what the different statements are doing; just
>as you need to understand what processing you apply on your data
>(whether it's preprocessing or learning) to properly use any machine
>learning tool.

I know, but the problem is that the csv file of the iris doesn't have such
information and as I said, I think there are some additional steps that I
don't know exactly what they are.

For example, if you look at
~/.local/lib/python3.6/site-packages/sklearn/datasets/data/iris.csv you
will see

150,4,setosa,versicolor,virginica
5.1,3.5,1.4,0.2,0
4.9,3.0,1.4,0.2,0
...

So, the first line means 150 instances (rows) with 4 columns and three iris
types.
However, when I use

iris = load_iris()
print(iris)

I see a lot of metadata, such as:

{'data': array([[5.1, 3.5, 1.4, 0.2],
       [4.9, 3. , 1.4, 0.2],
...
       [5.9, 3. , 5.1, 1.8]]), 'target': array([0, 0,...]), 'frame': None,
'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='<U10'),
'DESCR': '.. _iris_dataset:\n\nIris plants
dataset\n--------------------\n\n**Data Set Characteristics:**\n\n
 :Number of Instances: 150 (50 in each of three classes)\n    :Number of
Attributes: 4 numeric, predictive attributes and the class\n    :Attribute
Information:\n        - sepal length in cm\n        - sepal width in cm\n
     - petal length in cm\n        - petal width in cm\n        - class:\n
               - Iris-Setosa\n                - Iris-Versicolour\n
       - Iris-Virginica\n                \n


The question is how these metadata are created and stored in this package?
I mean, what does

from sklearn.datasets import load_iris

do with the csv file? If I know, then I am also able to create a similar
dataset.


Regards,
Mahmood
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From niourf at gmail.com  Sun Nov  8 11:18:49 2020
From: niourf at gmail.com (Nicolas Hug)
Date: Sun, 8 Nov 2020 16:18:49 +0000
Subject: [scikit-learn] Creating dataset
In-Reply-To: <CADa2P2UtJ+ARJhS-_NLaFvjoq3jkE-pDQTNO35SpKHXVKvz3-Q@mail.gmail.com>
References: <CADa2P2VuAJydgQwiZgc+dfUZZL+pk7wu51CiqZ5FymmNn_FyVg@mail.gmail.com>
 <CAK2WwdMaQ-7FxhhPVDem4GKPEWg2+Xqevxi_dADGYZGEwjDajA@mail.gmail.com>
 <CACDxx9gNGgCgUi5KwJ2Lqt3YbptC1K4jHNEVh42+3mS7q4O2og@mail.gmail.com>
 <CAK2WwdODhk3sbQjCFZ34MWGX9-DVJiK4aFXFPctbHM-RJpztfw@mail.gmail.com>
 <CADa2P2W86r2kRgywZewLs1TzNn5JkU3-T0BvihFTU6djRPg7Sw@mail.gmail.com>
 <CAHCaCkJ_2yY=Bw_dFqa+GrfTnuWaJ1+3z3oYwD=AS0bYCyk2BQ@mail.gmail.com>
 <CADa2P2UtJ+ARJhS-_NLaFvjoq3jkE-pDQTNO35SpKHXVKvz3-Q@mail.gmail.com>
Message-ID: <cb8d6a33-17af-ea59-e24a-d381ecea8780@gmail.com>

load_iris() reads a csv file, and then retrieves/sets some other info 
like the feature names and a description of the dataset (which comes 
from another file)

Then it packs everything into a Bunch object which is basically a fancy 
dict: 
https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/__init__.py#L63

You can take inspiration from the source code 
(https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_base.py#L396) 
if you want to replicate what fetch_xxx() does, but you do not need a 
Bunch at all to follow the PCA article that you mentioned. As previously 
noted, you just need to understand what each piece is doing at a high 
level and slightly modify the input to the functions according to your 
needs.



On 11/8/20 2:19 PM, Mahmood Naderan wrote:
> >You need to understand what the different statements are doing; just
> >as you need to understand what processing you apply on your data
> >(whether it's preprocessing or learning) to properly use any machine
> >learning tool.
>
> I know, but the problem is that the csv file of the iris doesn't have 
> such information and as I said, I think there are some additional 
> steps that I don't know exactly what they are.
>
> For example, if you look at 
> ~/.local/lib/python3.6/site-packages/sklearn/datasets/data/iris.csv 
> you will see
>
> 150,4,setosa,versicolor,virginica
> 5.1,3.5,1.4,0.2,0
> 4.9,3.0,1.4,0.2,0
> ...
>
> So, the first line means 150 instances (rows) with 4 columns and three 
> iris types.
> However, when I use
>
> iris = load_iris()
> print(iris)
>
> I see a lot of metadata, such as:
>
> {'data': array([[5.1, 3.5, 1.4, 0.2],
> ? ? ? ?[4.9, 3. , 1.4, 0.2],
> ...
> ? ? ?? [5.9, 3. , 5.1, 1.8]]), 'target': array([0, 0,...]), 'frame': 
> None, 'target_names': array(['setosa', 'versicolor', 'virginica'], 
> dtype='<U10'), 'DESCR': '.. _iris_dataset:\n\nIris plants 
> dataset\n--------------------\n\n**Data Set Characteristics:**\n\n ? 
> ?:Number of Instances: 150 (50 in each of three classes)\n ? ?:Number 
> of Attributes: 4 numeric, predictive attributes and the class\n ? 
> ?:Attribute Information:\n ? ? ? ?- sepal length in cm\n ? ? ? ?- 
> sepal width in cm\n ? ? ? ?- petal length in cm\n ? ? ? ?- petal width 
> in cm\n ? ? ? ?- class:\n ? ? ? ? ? ? ? ?- Iris-Setosa\n ? ? ? ? ? ? ? 
> ?- Iris-Versicolour\n ? ? ?- Iris-Virginica\n ? ? ? ? ? ? ? ?\n
>
>
> The question is how these metadata are created and stored in this package?
> I mean, what does
>
> from sklearn.datasets import load_iris
>
> do with the csv file? If I know, then I am also able to create a 
> similar dataset.
>
>
> Regards,
> Mahmood
>
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
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From fad469 at uregina.ca  Sun Nov 15 12:24:15 2020
From: fad469 at uregina.ca (Farzana Anowar)
Date: Sun, 15 Nov 2020 12:24:15 -0500
Subject: [scikit-learn] Incremental learning in scikit-learn
Message-ID: <269bb25e61be58228450320fda2c8267@uregina.ca>

Hello everyone,

Currently, I am working with incremental learning. I know that 
scikit-learn allows using incremental learning for some classifiers i. 
e. SGD. In incremental learning, data is not available all together 
rather the data become available chunk by chunk over the time.

Now, my question is: does scikit-learn allows to have different data 
chunk or all the chunks has to be of the same size?

Thanks!

-- 
Best Regards,

Farzana Anowar,
PhD Candidate
Department of Computer Science
University of Regina

From dbsullivan23 at gmail.com  Sun Nov 15 14:49:21 2020
From: dbsullivan23 at gmail.com (Danny Sullivan)
Date: Sun, 15 Nov 2020 13:49:21 -0600
Subject: [scikit-learn] Incremental learning in scikit-learn
In-Reply-To: <269bb25e61be58228450320fda2c8267@uregina.ca>
References: <269bb25e61be58228450320fda2c8267@uregina.ca>
Message-ID: <CADe2PANi6LJubzNYL8y6O_0ku_p1gU76yEEQurQSkem3evv-rw@mail.gmail.com>

Hi Farzana,

The chunks do not have to be the same size, you just need to call
partial_fit to update the model. Hope that helps.

Danny

El El dom, nov. 15, 2020 a la(s) 11:39 a. m., Farzana Anowar <
fad469 at uregina.ca> escribi?:

> Hello everyone,
>
> Currently, I am working with incremental learning. I know that
> scikit-learn allows using incremental learning for some classifiers i.
> e. SGD. In incremental learning, data is not available all together
> rather the data become available chunk by chunk over the time.
>
> Now, my question is: does scikit-learn allows to have different data
> chunk or all the chunks has to be of the same size?
>
> Thanks!
>
> --
> Best Regards,
>
> Farzana Anowar,
> PhD Candidate
> Department of Computer Science
> University of Regina
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>
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From solegalli at protonmail.com  Tue Nov 17 03:57:13 2020
From: solegalli at protonmail.com (Sole Galli)
Date: Tue, 17 Nov 2020 08:57:13 +0000
Subject: [scikit-learn] imbalanced datasets return uncalibrated predictions
 - why?
Message-ID: <uXM9gCTGMqCkgF7Q3G-66OOh1QeeJGQ9QgFxQ1g-JLpsPlgJo1_MzSK4VQoKDQmkG7EgOlQekLC_HbIHKiI7xB6wrhvJTL8vYdrMfwjontU=@protonmail.com>

Hello team,

I am trying to understand why does logistic regression return uncalibrated probabilities with values tending to low probabilities for the positive (rare) cases, when trained on an imbalanced dataset.

I've read a number of articles, all seem to agree that this is the case, many show empirical proof, but no mathematical demo. When I test it myself, I can see that this is indeed the case, Logit on imbalanced datasets returns uncalibrated probs.

And I understand that it has to do with the cost function, because if we re-balance the dataset with say class_weight = 'balance'. then the probabilities seem to be calibrated as a result.

I was wondering if any of you knows the mathematical demo that supports this conclusion? Any mathematical demo, or clear explanation of why logit would return uncalibrated probs when trained on an imbalanced dataset?

Any link to a relevant article, video, presentation, etc, will be greatly appreciated.

Thanks a lot!

Sole
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From sean.violante at gmail.com  Tue Nov 17 04:17:42 2020
From: sean.violante at gmail.com (Sean Violante)
Date: Tue, 17 Nov 2020 10:17:42 +0100
Subject: [scikit-learn] imbalanced datasets return uncalibrated
 predictions - why?
In-Reply-To: <uXM9gCTGMqCkgF7Q3G-66OOh1QeeJGQ9QgFxQ1g-JLpsPlgJo1_MzSK4VQoKDQmkG7EgOlQekLC_HbIHKiI7xB6wrhvJTL8vYdrMfwjontU=@protonmail.com>
References: <uXM9gCTGMqCkgF7Q3G-66OOh1QeeJGQ9QgFxQ1g-JLpsPlgJo1_MzSK4VQoKDQmkG7EgOlQekLC_HbIHKiI7xB6wrhvJTL8vYdrMfwjontU=@protonmail.com>
Message-ID: <CAL9=spNYbgx69ZR074BFXsu6Nx_TKeJ4A6iimdQaOvN+g-p2AA@mail.gmail.com>

I am not sure if you are using "calibrated" in the correct sense.
Calibrated means that the predictions align with the real world
probabilities.
so if you have a rare class it should have low probabilities



On Tue, Nov 17, 2020 at 9:58 AM Sole Galli via scikit-learn <
scikit-learn at python.org> wrote:

> Hello team,
>
> I am trying to understand why does logistic regression return uncalibrated
> probabilities with values tending to low probabilities for the positive
> (rare) cases, when trained on an imbalanced dataset.
>
> I've read a number of articles, all seem to agree that this is the case,
> many show empirical proof, but no mathematical demo. When I test it myself,
> I can see that this is indeed the case, Logit on imbalanced datasets
> returns uncalibrated probs.
>
> And I understand that it has to do with the cost function, because if we
> re-balance the dataset with say class_weight = 'balance'. then the
> probabilities seem to be calibrated as a result.
>
> I was wondering if any of you knows the mathematical demo that supports
> this conclusion? Any mathematical demo, or clear explanation of why logit
> would return uncalibrated probs when trained on an imbalanced dataset?
>
> Any link to a relevant article, video, presentation, etc, will be greatly
> appreciated.
>
> Thanks a lot!
>
> Sole
>
>
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>
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From rth.yurchak at gmail.com  Tue Nov 17 04:54:33 2020
From: rth.yurchak at gmail.com (Roman Yurchak)
Date: Tue, 17 Nov 2020 10:54:33 +0100
Subject: [scikit-learn] imbalanced datasets return uncalibrated
 predictions - why?
In-Reply-To: <uXM9gCTGMqCkgF7Q3G-66OOh1QeeJGQ9QgFxQ1g-JLpsPlgJo1_MzSK4VQoKDQmkG7EgOlQekLC_HbIHKiI7xB6wrhvJTL8vYdrMfwjontU=@protonmail.com>
References: <uXM9gCTGMqCkgF7Q3G-66OOh1QeeJGQ9QgFxQ1g-JLpsPlgJo1_MzSK4VQoKDQmkG7EgOlQekLC_HbIHKiI7xB6wrhvJTL8vYdrMfwjontU=@protonmail.com>
Message-ID: <8bc985a6-c691-7198-2e7e-1f9b3d23509e@gmail.com>

On 17/11/2020 09:57, Sole Galli via scikit-learn wrote:
> And I understand that it has to do with the cost function, because if we 
> re-balance the dataset with say class_weight = 'balance'. then the 
> probabilities seem to be calibrated as a result.

As far I know, logistic regression will have well calibrated 
probabilities even in the imbalanced case. However, with the default 
decision threshold at 0.5, some of the infrequent categories may never 
be predicted since their probability is too low.

If you use  class_weight = 'balanced' the probabilities will no longer 
be well calibrated, however you would predict some of those infrequent 
categories.

See discussions in 
https://github.com/scikit-learn/scikit-learn/issues/10613 and linked issues.

-- 
Roman

From solegalli at protonmail.com  Thu Nov 19 02:55:42 2020
From: solegalli at protonmail.com (Sole Galli)
Date: Thu, 19 Nov 2020 07:55:42 +0000
Subject: [scikit-learn] imbalanced datasets return uncalibrated
 predictions - why?
In-Reply-To: <8bc985a6-c691-7198-2e7e-1f9b3d23509e@gmail.com>
References: <uXM9gCTGMqCkgF7Q3G-66OOh1QeeJGQ9QgFxQ1g-JLpsPlgJo1_MzSK4VQoKDQmkG7EgOlQekLC_HbIHKiI7xB6wrhvJTL8vYdrMfwjontU=@protonmail.com>
 <8bc985a6-c691-7198-2e7e-1f9b3d23509e@gmail.com>
Message-ID: <8l6TitkWumzGu1tIuTXHh8ex4gKVikho8E-zbme61eg7F2KuJF7881StA5HXFjOAtV0Ku1zZAvrjdxseDO7jF_DmvMBfM9RQesomch_mBv0=@protonmail.com>

Thank you guys, that was actually very helpful.

Best regards
Sole

Soledad Galli
https://www.trainindata.com/

??????? Original Message ???????

On Tuesday, November 17th, 2020 at 10:54 AM, Roman Yurchak <rth.yurchak at gmail.com> wrote:

> On 17/11/2020 09:57, Sole Galli via scikit-learn wrote:
>
> > And I understand that it has to do with the cost function, because if we
> >
> > re-balance the dataset with say class_weight = 'balance'. then the
> >
> > probabilities seem to be calibrated as a result.
>
> As far I know, logistic regression will have well calibrated
>
> probabilities even in the imbalanced case. However, with the default
>
> decision threshold at 0.5, some of the infrequent categories may never
>
> be predicted since their probability is too low.
>
> If you use class_weight = 'balanced' the probabilities will no longer
>
> be well calibrated, however you would predict some of those infrequent
>
> categories.
>
> See discussions in
>
> https://github.com/scikit-learn/scikit-learn/issues/10613 and linked issues.
>
> -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
>
> Roman
>
> scikit-learn mailing list
>
> scikit-learn at python.org
>
> https://mail.python.org/mailman/listinfo/scikit-learn

From marmochiaskl at gmail.com  Wed Nov 25 12:50:38 2020
From: marmochiaskl at gmail.com (Chiara Marmo)
Date: Wed, 25 Nov 2020 18:50:38 +0100
Subject: [scikit-learn] Monthly meeting November 30th 2020
Message-ID: <CAGfF14-DKQkhf8=L=AuL-dDRn2Lbn_E=46Q0+A=FsvYF6dUVdA@mail.gmail.com>

Dear list,

The next scikit-learn monthly meeting will take place on Monday October
30th at 8PM UTC:
https://www.timeanddate.com/worldclock/meetingdetails.html?year=2020&month=11&day=30&hour=20&min=0&sec=0&p1=179&p2=240&p3=195&p4=224

While these meetings are mainly for core-devs to discuss the current
topics, we are also happy to welcome non-core devs and other project
maintainers.

Feel free to join, using the following link:

https://meet.google.com/xhq-yoga-rtf

If you plan to attend and you would like to discuss something specific
about your contribution please add your name (or github pseudo) in the "
Contributors <https://hackmd.io/1yDGruklTNqtx1Eb_uEP0Q#Contributors>"
section, of the public pad:

https://hackmd.io/1yDGruklTNqtx1Eb_uEP0Q

Best

Chiara
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From adrin.jalali at gmail.com  Thu Nov 26 08:45:33 2020
From: adrin.jalali at gmail.com (Adrin)
Date: Thu, 26 Nov 2020 14:45:33 +0100
Subject: [scikit-learn] Changes in Travis billing
In-Reply-To: <CAFvE7K6dPVtH4e6=0toWujbLiByg=mVOxayh3rTvEUNZCt8q+g@mail.gmail.com>
References: <20201102105018.kfarylwpao6iliju@phare.normalesup.org>
 <CAEOrW49QWQLKi0Rd5AdESD=YCwsm356egRdH+=fxtqZeiMZNzw@mail.gmail.com>
 <CAFvE7K6dPVtH4e6=0toWujbLiByg=mVOxayh3rTvEUNZCt8q+g@mail.gmail.com>
Message-ID: <CAEOrW49SNzEffyWNOLTahokJiGQoFCM7bx+s5GYjMrNnw2ZrFg@mail.gmail.com>

I tried contacting them a few times, but they don't really respond in any
meaningful way, or at all.

At this point I'm at a loss, and reading the NumFocus chat and other
packages' experience with
them on the same topic, seems like we just need to move out of Travis.

Cheers,
Adrin

On Thu, Nov 5, 2020 at 5:18 PM Olivier Grisel <olivier.grisel at ensta.org>
wrote:

> > Shall I contact them? Any other volunteers?
>
> +1.
>
> I think we are still dependent on travis for ARM-based release builds
> and cron-jobs. The rest we can move it to Azure Pipelines or github
> actions I believe.
>
> --
> Olivier
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>
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From gael.varoquaux at normalesup.org  Thu Nov 26 09:06:52 2020
From: gael.varoquaux at normalesup.org (Gael Varoquaux)
Date: Thu, 26 Nov 2020 15:06:52 +0100
Subject: [scikit-learn] Changes in Travis billing
In-Reply-To: <CAEOrW49SNzEffyWNOLTahokJiGQoFCM7bx+s5GYjMrNnw2ZrFg@mail.gmail.com>
References: <20201102105018.kfarylwpao6iliju@phare.normalesup.org>
 <CAEOrW49QWQLKi0Rd5AdESD=YCwsm356egRdH+=fxtqZeiMZNzw@mail.gmail.com>
 <CAFvE7K6dPVtH4e6=0toWujbLiByg=mVOxayh3rTvEUNZCt8q+g@mail.gmail.com>
 <CAEOrW49SNzEffyWNOLTahokJiGQoFCM7bx+s5GYjMrNnw2ZrFg@mail.gmail.com>
Message-ID: <20201126140652.tdrsjzfasb64kqua@phare.normalesup.org>

On Thu, Nov 26, 2020 at 02:45:33PM +0100, Adrin wrote:
> At this point I'm at a loss, and reading the NumFocus chat and other
> packages' experience with them on the same topic, seems like we just
> need to move out of Travis.

Agreed. Do we still need them for something essential?

G

From gael.varoquaux at normalesup.org  Thu Nov 26 09:12:12 2020
From: gael.varoquaux at normalesup.org (Gael Varoquaux)
Date: Thu, 26 Nov 2020 15:12:12 +0100
Subject: [scikit-learn] Changes in Travis billing
In-Reply-To: <20201126140652.tdrsjzfasb64kqua@phare.normalesup.org>
References: <20201102105018.kfarylwpao6iliju@phare.normalesup.org>
 <CAEOrW49QWQLKi0Rd5AdESD=YCwsm356egRdH+=fxtqZeiMZNzw@mail.gmail.com>
 <CAFvE7K6dPVtH4e6=0toWujbLiByg=mVOxayh3rTvEUNZCt8q+g@mail.gmail.com>
 <CAEOrW49SNzEffyWNOLTahokJiGQoFCM7bx+s5GYjMrNnw2ZrFg@mail.gmail.com>
 <20201126140652.tdrsjzfasb64kqua@phare.normalesup.org>
Message-ID: <20201126141212.x72eecggoozdocck@phare.normalesup.org>

On Thu, Nov 26, 2020 at 03:06:52PM +0100, Gael Varoquaux wrote:
> On Thu, Nov 26, 2020 at 02:45:33PM +0100, Adrin wrote:
> > At this point I'm at a loss, and reading the NumFocus chat and other
> > packages' experience with them on the same topic, seems like we just
> > need to move out of Travis.

> Agreed. Do we still need them for something essential?

Sorry, ARM, it was just above in the conversation.

I think that we have no other option than reduce the frequency of the
cron, and wait for other platforms to offer ARM, which will hopefully
happen soonish.

G