[scikit-learn] R user trying to learn Python

[C W]

Hi Sebastian,

I looked through your book. I think it is great if you already know Python,
and looking to learn machine learning.

For me, I have some sense of machine learning, but none of Python.

Unlike R, which is specifically for statistics analysis. Python is broad!

Maybe some expert here with R can tell me how to go about this. :)

On Sun, Jun 18, 2017 at 12:53 PM, Sebastian Raschka <se.raschka at gmail.com>
wrote:

> Hi,
>
> > I am extremely frustrated using this thing. Everything comes after a
> dot! Why would you type the sam thing at the beginning of every line. It's
> not efficient.
> >
> > code 1:
> > y_sin = np.sin(x)
> > y_cos = np.cos(x)
> >
> > I know you can import the entire package without the "as np", but I see
> np.something as the standard. Why?
>
> Because it makes it clear where this function is coming from. Sure, you
> could do
>
> from numpy import *
>
> but this is NOT!!! recommended. The reason why this is not recommended is
> that it would clutter up your main name space. For instance, numpy has its
> own sum function. If you do from numpy import *, Python's in-built `sum`
> will be gone from your main name space and replaced by NumPy's sum. This is
> confusing and should be avoided.
>
> > In the code above, sklearn > linear_model > Ridge, one lives inside the
> other, it feels that there are multiple layer, how deep do I have to dig in?
> >
> > Can someone explain the mentality behind this setup?
>
> This is one way to organize your code and package. Sklearn contains many
> things, and organizing it by subpackages (linear_model, svm, ...) makes
> only sense; otherwise, you would end up with code files > 100,000 lines or
> so, which would make life really hard for package developers.
>
> Here, scikit-learn tries to follow the core principles of good object
> oriented program design, for instance, Abstraction, encapsulation,
> modularity, hierarchy, ...
>
> > What are some good ways and resources to learn Python for data analysis?
>
> I think baed on your questions, a good resource would be an introduction
> to programming book or course. I think that sections on objected oriented
> programming would make the rationale/design/API of scikit-learn and Python
> classes as a whole more accessible and address your concerns and questions.
>
> Best,
> Sebastian
>
> > On Jun 18, 2017, at 12:02 PM, C W <tmrsg11 at gmail.com> wrote:
> >
> > Dear Scikit-learn,
> >
> > What are some good ways and resources to learn Python for data analysis?
> >
> > I am extremely frustrated using this thing. Everything comes after a
> dot! Why would you type the sam thing at the beginning of every line. It's
> not efficient.
> >
> > code 1:
> > y_sin = np.sin(x)
> > y_cos = np.cos(x)
> >
> > I know you can import the entire package without the "as np", but I see
> np.something as the standard. Why?
> >
> > Code 2:
> > model = LogisticRegression()
> > model.fit(X_train, y_train)
> > model.score(X_test, y_test)
> >
> > In R, everything is saved to a variable. In the code above, what if I
> accidentally ran model.fit(), I would not know.
> >
> > Code 3:
> > from sklearn import linear_model
> > reg = linear_model.Ridge (alpha = .5)
> > reg.fit ([[0, 0], [0, 0], [1, 1]], [0, .1, 1])
> >
> > In the code above, sklearn > linear_model > Ridge, one lives inside the
> other, it feels that there are multiple layer, how deep do I have to dig in?
> >
> > Can someone explain the mentality behind this setup?
> >
> > Thank you very much!
> >
> > M
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> > scikit-learn at python.org
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>
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