9 ms·

Python, Machine Learning, and Language Wars (2015)

178 points by dekhtiar 11y ago

faaef 11y ago
Buddha about Language Wars: Then the Buddha gave advice of extreme importance to the group of Brahmins: 'It is not proper for a wise man who maintains (lit. protects) truth to come to the conclusion: "This alone is Truth, and everything else is false'.' Asked by the young Brahmin to explain the idea of maintaining or protecting truth, the Buddha said: ' A man has a faith. If he says, "This is my faith", so far he maintains truth. But by 9 that he cannot proceed to the absolute conclusion: "This alone is Truth, and everything else is false". In other words, a man may believe what he likes, and he may say 'I believe this'. So far he respects truth. But because of his belief or faith, he should not say that what he believes is alone the Truth, and everything else is false. (from "What the Buddha taught, a really great book!)
- platz 11y ago
  > 'I believe this'. So far he respects truth What if he believes is objectively wrong?
  - faaef 11y ago
    He only talks about wise men who protect truth, so the truth being true is a given I'd say. EDIT: Of course he's talking to people who swear by the truth they protect. Instead of telling them they're wrong, telling them others might be right is far more likely to get them to consider his point of view -- that other "truths" are just as equal.
    - deleted 11y ago
      [deleted]
danso 11y ago
As someone who's switched from Ruby to Python (for now, because the latter is far easier to teach, IMO) and also put significant time into learning R, because of how strong ggplot2 is...I was really surprised at the lack of Google results for "switching from python to r" -- or similarly phrased queries to find guides on how to go from Python to R...in fact, that particular query will bring up more results for R -> Python than the other way around (e.g. "Python Displacing R as The Programming Language For Data")...Talk of R is so ubiquitous in academia (and in the wild, ggplot2 tends to wow nearly on the same level as D3) that I had just assumed there was a fair number of developers who have tried jumping into R...but there aren't...I think minimaxir's guides are the most and only comprehensive how-to-do-R-as-written-by-an-outsider things I've seen on the web [1]. But by and far the common scenario is that of the author's: "Well, I guess it’s no big secret that I was an R person once" That said, one of the things I've appreciated about R is how it "just works"...I usually go through Homebrew, but RStudio works just as well. I can see why that's a huge appeal for both beginners and people who want to do computation but not necessarily become developers. Also, I used to hate how `<-` was used for assignment...but now, that's one of the things I miss most about using R...I've grown up with single-equals-sign assignment in every other language I've learned, but after having to teach some programming...the difference between `==` and `=` is a common and often hugely stumping error for beginners. Not only that, they have trouble remembering how assignment even works, even for basic variable assignment...I've come to realize that I've programmed so long that I immediately recognize the pattern, but that can't possibly be the case for novices, who if they've taken general math classes, have never seen the equals sign that way. The `<-` operator makes a lot more sense...though I would've never thought that if hadn't read Hadley Wickham's style guide [2] [1] http://minimaxir.com/2015/02/ggplot-tutorial/ http://minimaxir.com/2015/02/ggplot-tutorial/ [2] http://adv-r.had.co.nz/Style.html http://adv-r.had.co.nz/Style.html
- CalRobert 11y ago
  You know, I remember when I was trying my first language other than BASIC (VB6, perhaps? or maybe 1995 era JS?) and it bugged me that "x = y" wasn't the same as "y = x". Remembering it as "LET x = y" was helpful.
  - gedrap 11y ago
    I've also seen this confused with folks who are just learning their first language and = is a common assignment operator in typical 'first languages' these days.
  - ktRolster 11y ago
    People have been upset about that since at least the 1950s. A lot of people who make programming languages use the := for the assignment operator instead.
    - RodgerTheGreat 11y ago
      Helps when you prounounce the assignment operator as "becomes" rather than "equals", too.
  - SixSigma 11y ago
    > it bugged me that "x = y" wasn't the same as "y = x" it is in Prolog
  - x1798DE 11y ago
    Heh, that brings me back to when I was a kid, thinking, "x = x + 1? No it doesn't."
  - anon4 11y ago
    I bet that '=' is now used more in programming than in math. In that case, isn't its original meaning the wrong one today?
- p4wnc6 11y ago
  As a predominantly Python-focused engineer, I've spent considerable time teaching myself R and there is a lot to like about R. For boutique statistical libraries especially. For instance, the enjoyment of using PySTAN is nowhere near as high as simply using STAN directly from R. However, when you dig into the R internals, and you learn about its generic function model of OO, and about the mangled history of S3 and S4 classes, it becomes very frustrating. R mostly "just works" if you stick to the libraries. But if you want to really understand e.g. polymorphic dispatching and how you can design your own tools to use it, it's a deal-breaker pain in the ass in R. It just simply is not suited for real computer science situations when you need to design the software, rather than just making scripts that treat libraries as APIs. Since the times when you need "just scripting" are about 0.00001% of real-world cases, it unfortunately means that as nice as R is, it's just not a good enough tool to standardize into a real-world workflow. You're way better off using Python, even if you have to give up easy access to certain libraries, re-write your own implementations, or kludge them on with tools like rpy2.
akshayB 11y ago
There are lot of options when it comes to machine learning frameworks open source and commercial as well. Also many of these frameworks are designed to solve a specific problem. Some of the machine learning frameworks are optimized to run on certain types of hardware. In my opinion selecting a machine learning framework depends on the technology stack of your company because it makes lot of sense to leverage existing system rather then developing everything on an entirely new infrastructure and language.
stared 11y ago
Previous submission: https://news.ycombinator.com/item?id=10113413 https://news.ycombinator.com/item?id=10113413
gtrubetskoy 11y ago
I'm surprised the article doesn't mention Anaconda, which is Python with all the things he lists pre-installed for you. I've been a fan for some time now: https://www.continuum.io/why-anaconda https://www.continuum.io/why-anaconda
- alceufc 11y ago
  I also think that Anaconda is great. However, I hope that in the future we could install numpy, matplotlib, jupyter, etc. just using pip.
  - stared 11y ago
    I like Anaconda (and I recommend it as the easiest installation for data sci), but on OS X it is easy to install Python and relevant numerical packages with Homebrew and pip.
  - zo1 11y ago
    I did that earlier today: pip install jupyter pip install numpy pip install scipy pip install scikit-learn pip install matplotlib The only problem I had was with OpenCV, which requires manual make installation if you want the contrib package. The other problem was when trying to install scikit-learn, it requires manual pip installation of scipy.
    - yeukhon 11y ago
      The reason you can't is because of the C libraries levaaged must be installed prior to install numpy and scipy. For example, you can't get through PyYaml unless python-dev is installed on Ubuntu. I am not sure if wheel would fix it but I don't think so.
      - takeda 11y ago
        Wheel is a binary distribution, so files would already be compiled and therefore python-dev would not be needed anymore.
knite 11y ago
Can we get "(2015)" on the title?
p4wnc6 11y ago
I too am a Python-preferring machine learning engineer, but my reasons are almost precisely the opposite of the post's. 1. No matter how much you ever think, as a scientist, that you "only do an analysis one time" it is false 99.99999% of the time. You will always want to run it multiple times. Other people will want help modifying and running variations of it. Employers will need you, the scientist, to "productionize" it and make it suitable for automated deployment, probably cross-platform. 2. Your analysis will have to adapt to changing data inputs, which means you invariably have to create a (well-designed, unit-tested, and best-practices compliant) tool kit for custom data cleaning, pre-processing, database I/O, file system I/O, and visualization. 3. You will inevitably need to be concerned with raw-metal performance, but generally in isolated pockets of your code, so you'll need a language like Python that supports targeted performance optimization with tools like Cython. 4. Code is read (especially by newbies who need your help) much more than it is written, so you need a language that is easy to explain and reason about, with very few syntactical tricks and complicated conceptual nuances. Overall, Python suits this niche very well. It is a full-service object-oriented language with a huge and well-maintained standard library. The third party tools for machine learning and general numeric computing are by far the best in the open source world (apart from a handful of boutique R libraries, which can use via rpy2 anyway), and Python is a simple language that is easy to teach and explain but also supports lots of targeted optimization in the CPython layer. From the very first line of code you write, when you still naively believe "I will only run this once and I just need to crank it out," you need to be obsessed with writing well-designed, extensible, unit-tested code that is only a short distance from already being "production ready" -- and Python is a great language choice for this.
spot 11y ago
why pick just one language? with the polyglot Beaker Notebook, you can work with many languages, even in the same notebook, and your data is automatically translated between them. each of these languages has its strong point. there is always some library you want to use in some other language. or you want to collaborate with someone. or next year you change your mind and Julia is finally good enough. http://BeakerNotebook.com http://BeakerNotebook.com
- stuartaxelowen 11y ago
  Knowing other languages is great, but there is very real overhead in learning them. Python is mature enough that you have mature libraries available for pretty much everything.
  - spot 11y ago
    believe it or not, there are some people who know R and face overhead to learn Python. and they love ggplot2. and frankly, Python has no libraries for interactive visualization in your browser because only JavaScript runs there. etc.
    - evanpw 11y ago
      http://bokeh.pydata.org/en/latest/ http://bokeh.pydata.org/en/latest/ "Bokeh is a Python interactive visualization library that targets modern web browsers for presentation." (Of course, it works by generating JavaScript)
      - spot 11y ago
        right, and in order to really customize what bokeh does you need to write JS in strings in your python code, so this just proves my point. http://bokeh.pydata.org/en/latest/docs/user_guide/interaction.html#userguide-interaction http://bokeh.pydata.org/en/latest/docs/user_guide/interactio... see the CustomJS function.
        11y ago
gavinh 11y ago
I know what you want to ask next: “Okay, what about turning my model into a nice and shiny web application? I bet this is something that you can’t do in R!” Sorry, but you lose this bet; have a look at Shiny by RStudio A web application framework for R. True, but it is a dumpster fire.

178 points by dekhtiar 11y ago

faaef 11y ago

Buddha about Language Wars: Then the Buddha gave advice of extreme importance to the group of Brahmins: 'It is not proper for a wise man who maintains (lit. protects) truth to come to the conclusion: "This alone is Truth, and everything else is false'.' Asked by the young Brahmin to explain the idea of maintaining or protecting truth, the Buddha said: ' A man has a faith. If he says, "This is my faith", so far he maintains truth. But by 9 that he cannot proceed to the absolute conclusion: "This alone is Truth, and everything else is false". In other words, a man may believe what he likes, and he may say 'I believe this'. So far he respects truth. But because of his belief or faith, he should not say that what he believes is alone the Truth, and everything else is false. (from "What the Buddha taught, a really great book!)

platz 11y ago
> 'I believe this'. So far he respects truth What if he believes is objectively wrong?
- faaef 11y ago
  He only talks about wise men who protect truth, so the truth being true is a given I'd say. EDIT: Of course he's talking to people who swear by the truth they protect. Instead of telling them they're wrong, telling them others might be right is far more likely to get them to consider his point of view -- that other "truths" are just as equal.
  - deleted 11y ago
    [deleted]

danso 11y ago

As someone who's switched from Ruby to Python (for now, because the latter is far easier to teach, IMO) and also put significant time into learning R, because of how strong ggplot2 is...I was really surprised at the lack of Google results for "switching from python to r" -- or similarly phrased queries to find guides on how to go from Python to R...in fact, that particular query will bring up more results for R -> Python than the other way around (e.g. "Python Displacing R as The Programming Language For Data")...Talk of R is so ubiquitous in academia (and in the wild, ggplot2 tends to wow nearly on the same level as D3) that I had just assumed there was a fair number of developers who have tried jumping into R...but there aren't...I think minimaxir's guides are the most and only comprehensive how-to-do-R-as-written-by-an-outsider things I've seen on the web [1]. But by and far the common scenario is that of the author's: "Well, I guess it’s no big secret that I was an R person once" That said, one of the things I've appreciated about R is how it "just works"...I usually go through Homebrew, but RStudio works just as well. I can see why that's a huge appeal for both beginners and people who want to do computation but not necessarily become developers. Also, I used to hate how `<-` was used for assignment...but now, that's one of the things I miss most about using R...I've grown up with single-equals-sign assignment in every other language I've learned, but after having to teach some programming...the difference between `==` and `=` is a common and often hugely stumping error for beginners. Not only that, they have trouble remembering how assignment even works, even for basic variable assignment...I've come to realize that I've programmed so long that I immediately recognize the pattern, but that can't possibly be the case for novices, who if they've taken general math classes, have never seen the equals sign that way. The `<-` operator makes a lot more sense...though I would've never thought that if hadn't read Hadley Wickham's style guide [2] [1] http://minimaxir.com/2015/02/ggplot-tutorial/ http://minimaxir.com/2015/02/ggplot-tutorial/ [2] http://adv-r.had.co.nz/Style.html http://adv-r.had.co.nz/Style.html

CalRobert 11y ago
You know, I remember when I was trying my first language other than BASIC (VB6, perhaps? or maybe 1995 era JS?) and it bugged me that "x = y" wasn't the same as "y = x". Remembering it as "LET x = y" was helpful.
- gedrap 11y ago
  I've also seen this confused with folks who are just learning their first language and = is a common assignment operator in typical 'first languages' these days.
- ktRolster 11y ago
  People have been upset about that since at least the 1950s. A lot of people who make programming languages use the := for the assignment operator instead.
  - RodgerTheGreat 11y ago
    Helps when you prounounce the assignment operator as "becomes" rather than "equals", too.
- SixSigma 11y ago
  > it bugged me that "x = y" wasn't the same as "y = x" it is in Prolog
- x1798DE 11y ago
  Heh, that brings me back to when I was a kid, thinking, "x = x + 1? No it doesn't."
- anon4 11y ago
  I bet that '=' is now used more in programming than in math. In that case, isn't its original meaning the wrong one today?
p4wnc6 11y ago
As a predominantly Python-focused engineer, I've spent considerable time teaching myself R and there is a lot to like about R. For boutique statistical libraries especially. For instance, the enjoyment of using PySTAN is nowhere near as high as simply using STAN directly from R. However, when you dig into the R internals, and you learn about its generic function model of OO, and about the mangled history of S3 and S4 classes, it becomes very frustrating. R mostly "just works" if you stick to the libraries. But if you want to really understand e.g. polymorphic dispatching and how you can design your own tools to use it, it's a deal-breaker pain in the ass in R. It just simply is not suited for real computer science situations when you need to design the software, rather than just making scripts that treat libraries as APIs. Since the times when you need "just scripting" are about 0.00001% of real-world cases, it unfortunately means that as nice as R is, it's just not a good enough tool to standardize into a real-world workflow. You're way better off using Python, even if you have to give up easy access to certain libraries, re-write your own implementations, or kludge them on with tools like rpy2.

akshayB 11y ago

There are lot of options when it comes to machine learning frameworks open source and commercial as well. Also many of these frameworks are designed to solve a specific problem. Some of the machine learning frameworks are optimized to run on certain types of hardware. In my opinion selecting a machine learning framework depends on the technology stack of your company because it makes lot of sense to leverage existing system rather then developing everything on an entirely new infrastructure and language.

stared 11y ago

Previous submission: https://news.ycombinator.com/item?id=10113413 https://news.ycombinator.com/item?id=10113413

gtrubetskoy 11y ago

I'm surprised the article doesn't mention Anaconda, which is Python with all the things he lists pre-installed for you. I've been a fan for some time now: https://www.continuum.io/why-anaconda https://www.continuum.io/why-anaconda

alceufc 11y ago
I also think that Anaconda is great. However, I hope that in the future we could install numpy, matplotlib, jupyter, etc. just using pip.
- stared 11y ago
  I like Anaconda (and I recommend it as the easiest installation for data sci), but on OS X it is easy to install Python and relevant numerical packages with Homebrew and pip.
- zo1 11y ago
  I did that earlier today: pip install jupyter pip install numpy pip install scipy pip install scikit-learn pip install matplotlib The only problem I had was with OpenCV, which requires manual make installation if you want the contrib package. The other problem was when trying to install scikit-learn, it requires manual pip installation of scipy.
  - yeukhon 11y ago
    The reason you can't is because of the C libraries levaaged must be installed prior to install numpy and scipy. For example, you can't get through PyYaml unless python-dev is installed on Ubuntu. I am not sure if wheel would fix it but I don't think so.
    - takeda 11y ago
      Wheel is a binary distribution, so files would already be compiled and therefore python-dev would not be needed anymore.

knite 11y ago

Can we get "(2015)" on the title?

p4wnc6 11y ago

I too am a Python-preferring machine learning engineer, but my reasons are almost precisely the opposite of the post's. 1. No matter how much you ever think, as a scientist, that you "only do an analysis one time" it is false 99.99999% of the time. You will always want to run it multiple times. Other people will want help modifying and running variations of it. Employers will need you, the scientist, to "productionize" it and make it suitable for automated deployment, probably cross-platform. 2. Your analysis will have to adapt to changing data inputs, which means you invariably have to create a (well-designed, unit-tested, and best-practices compliant) tool kit for custom data cleaning, pre-processing, database I/O, file system I/O, and visualization. 3. You will inevitably need to be concerned with raw-metal performance, but generally in isolated pockets of your code, so you'll need a language like Python that supports targeted performance optimization with tools like Cython. 4. Code is read (especially by newbies who need your help) much more than it is written, so you need a language that is easy to explain and reason about, with very few syntactical tricks and complicated conceptual nuances. Overall, Python suits this niche very well. It is a full-service object-oriented language with a huge and well-maintained standard library. The third party tools for machine learning and general numeric computing are by far the best in the open source world (apart from a handful of boutique R libraries, which can use via rpy2 anyway), and Python is a simple language that is easy to teach and explain but also supports lots of targeted optimization in the CPython layer. From the very first line of code you write, when you still naively believe "I will only run this once and I just need to crank it out," you need to be obsessed with writing well-designed, extensible, unit-tested code that is only a short distance from already being "production ready" -- and Python is a great language choice for this.

spot 11y ago

why pick just one language? with the polyglot Beaker Notebook, you can work with many languages, even in the same notebook, and your data is automatically translated between them. each of these languages has its strong point. there is always some library you want to use in some other language. or you want to collaborate with someone. or next year you change your mind and Julia is finally good enough. http://BeakerNotebook.com http://BeakerNotebook.com

stuartaxelowen 11y ago
Knowing other languages is great, but there is very real overhead in learning them. Python is mature enough that you have mature libraries available for pretty much everything.
- spot 11y ago
  believe it or not, there are some people who know R and face overhead to learn Python. and they love ggplot2. and frankly, Python has no libraries for interactive visualization in your browser because only JavaScript runs there. etc.
  - evanpw 11y ago
    http://bokeh.pydata.org/en/latest/ http://bokeh.pydata.org/en/latest/ "Bokeh is a Python interactive visualization library that targets modern web browsers for presentation." (Of course, it works by generating JavaScript)
    - spot 11y ago
      right, and in order to really customize what bokeh does you need to write JS in strings in your python code, so this just proves my point. http://bokeh.pydata.org/en/latest/docs/user_guide/interaction.html#userguide-interaction http://bokeh.pydata.org/en/latest/docs/user_guide/interactio... see the CustomJS function.
      - 11y ago

gavinh 11y ago

I know what you want to ask next: “Okay, what about turning my model into a nice and shiny web application? I bet this is something that you can’t do in R!” Sorry, but you lose this bet; have a look at Shiny by RStudio A web application framework for R. True, but it is a dumpster fire.