Lecture 13: Jul 5, Functions. Background Asserting Input Debugging Errors. James Balamuta STAT UIUC

Transcription

1 Lecture 13: Jul 5, 2018 Functions Background Asserting Input Debugging Errors James Balamuta STAT UIUC

2 Announcements hw04 will be due Tuesday, July 10th, 2018 Project Proposal is due on Friday, July 13th, 11:59 PM Overview of the Proposal stage Visit OHs in IH 104 for help drafting or reviewing a proposal Quiz 04 covers Week 3 CBTF. Window: Jul 6th Sign up: Demo of CBTF Environment: v=6oapvo4tifk&t=8s

3 Today's Lecture Objectives Describe a function Understand why functions are useful Differentiate between input parameter types Explain the "fail fast" principle of function design Apply the different modes of debugging code

4 Background

5 Definition: Functions are a piece of code that performs a specified task that may or may not depend on parameters and it may or may not return one or more values. Source

6 Previously Addition Function adding values together Function Name Actual name of the function that can be called e.g. add(1, 2) Parameters Variables that receive expressions that can be used in the function s body add = function(a, b) { Body Statements in between {} that are run when the function is called } summed = a + b return(summed) Return Value Result made available from running the body statements

7 Motivation repeated use of a definition Consider: message("hello World!") Translates to: Say "Hello World!" in the console

8 What if we wanted to repeat the phrase elsewhere?

9 Options related to recreating a piece of code 1. Retype 2. Copy and paste 3. Writing a function say_hello_world = function() { } message("hello World!") say_hello_world() # Hello World!

10 Functions are Powerful expressive pieces of code 1. The logic flow is chunked instead of a series of long statements 2. Decrease the probability of an error by applying a common definition 3. Easier to share code with people that you collaborate with.

11 Clarity of Routine Naming Matters Redux What's happening here? set.seed(1115) sample(6, size = 1) # [1] 3 sample(6, size = 1) # [1] 5 sample(6, size = 1) # [1] 5

12 Die Roll! Adding Documentation via Code # Perform a single dice roll roll_die = function(num_sides) { sample(num_sides, size = 1) } # Set seed for reproducibility set.seed(1115) roll_die(6) # [1] 3 Source

13 Default Parameters Function Name Actual name of the function that can be called e.g. roll_die() being proactive roll_die = function(num_sides = 6) { Body Statements in between {} that are run when the function is called } Parameters Variables that receive a specific data type that can be used in the function s body Default Values The initial values used if the parameters are not supplied on function call roll = sample(num_sides, size = 1) return(roll) Return Value Result made available from running the body statements set.seed(1115) roll_die() # [1] 3 set.seed(1115) roll_die(25) # [1] 11 # Anticipate need of user # Allow function to still be dynamic

14 Function Name Actual name of the function that can be called e.g. roll_n_die(2, 10) Generalizing enabling multiple rolls roll_n_die = function(num_rolls, num_sides = 6) { Body Statements in between {} that are run when the function is called } Positional Parameter Variables that must be specified on function call in the order that they appear. Default Parameter Variables that will use prespecified values if not supplied on function call Default Values The initial values used if the parameters are not supplied on function call rolls = sample(num_sides, size = num_rolls, replace = TRUE) return(rolls) Return Value Result made available from running the body statements set.seed(1115) roll_n_die(3, 10) # [1] # Roll the die three times

15 Your Turn Clean up the following code by implementing a function that: 1. Generates data from a normal distribution 2. Applies the mean normalization set.seed(325) x = rnorm(10) y = rnorm(10) x = x_nmu = (x - mean(x)) / (max(x) - min(x)) y_nmu = (y - mean(y)) / (max(y) - min(y)) max x x x ( ) min( x)

16 Asserting Input

17 Logit Function Transforming probabilities to log scale # Define logit function logit = function(p) { log(p / (1 - p)) } logit(.5) # [1] 0 logit(.75) # [1] What is going wrong with 1.5, -.5, and "a"? # The beginning of the end logit(1.5) # [1] NaN logit(-.5) # [1] NaN logit("a") # [1] Error in 1 - p : non-numeric argument to binary operator

18 Types of Input what path to walk on??? Developer Envisioned Path User Preferred Path

19 User Input does it work with xyz??? User Path 2.0

20 Image Source Programming today is a race between software engineers striving to build bigger and better idiot-proof programs, and the universe trying to build bigger and better idiots. So far, the universe is winning. Rick Cook

21 Definition Defensive Programming means creating a situation where the code guards against flaws that might occur when a user acts with a program. # No data x = numeric(0) summed = 0 # Unprotected Loop for(index in 1:length(x)) { summed = summed + x[index] } # Protected loop for(index in seq_along(x)) { summed = summed + x[index] }

22 Fail Fast control the user path Bad User Input Error occurs Bad User Input Statement 1 Statement 1 Specific Error Continuing error Statement 2 Terminal error Statement 3 Generic Error

23 Protecting Input fixing the algorithm logit_safe = function(p) { if(!is.numeric(p)) { stop("`p` must be numeric") } else if (any(p >= 1)) { stop("all `p` values must be less than 1") } else if (any(p <= 0)) { stop("all `p` values must be greater than 0") } log(p/(1 - p)) } * Recall that is.*() functions can be used to verify input types and any() reduces a vector of logical values to just one to work with an if(), which is nonvectorized.

24 Levels of Messaging signal conditions to terminate routine ## Output (STDOUT) cat("working on `x` data set.") # "Working on `x` data set." print("working on `x` data set.") # [1] "Working on `x` data set." ## Diagnostic (STDERR) message("working on `x` data set.") # "Working on `x` data set." # Show contents # Show Vector "as-is" # Status update warning("`x` contains multiple columns only using one.") # Recover code run # "Warning Message: `x` contains multiple columns only using one." stop("`x` must be numeric") # "Error: `x` must be numeric" # End code run

25 Your Turn 1. Identify what is wrong with the following function 2. Propose a fix for it z_score = function(x, mu, std) { } (x - mu) / std

26 Debugging Errors

27 my_function() # Error in my_function() : could not find function "my_function" message "in a bottle" # Error: unexpected string constant in "message "in a bottle"" 3 + "toad" # Error in 3 + "toad" : non-numeric argument to binary operator

28 Errors Happen code will error in predictable ways Syntax Errors relate to ill-formatted commands x # Missing assignment operator y = (4 + 6 # Missing closing parentheses Semantic Errors involve improper use of R commands b = a * c(4, 5) # Use of undefined variable `a` 3 + "toad" # Not supported use of addition Logical Errors lead to unexpected results or bugs add = function(a, b) { a - b } # Not adding but subtracting values! while(true) { x = x + 1} # Never-ending loops

29 What's a Bug? a logical error or flaw associated with program execution * The term bug was popularized by Rear Admiral Grace M. Hopper in 1947, who recounted the tale of the moth being in the Harvard Mark II. Source: Origins of 'bug'

30 Bugs, Bugs, Everywhere at some point errors appear within code 1. This is natural as humans are prone to err. Computers, not so much. 2. It's both a painful and time intensive process to locate and patch bugs. 3. The best offense to lowering bug counts comes from writing clear, concise, and documented code. XKCD: 1403

31 Finding your bug is a process of confirming the many things you believe are true, until you find one which is not true. Norm Matloff in Guide to Faster, Less Frustrating Debugging

32 Steps to Isolate and Fix Bugs 1. Create a reproducible example that captures the bug. Use reprex to generate the issue. 2. Determine the severity of the bug. Show stopping, livable, harmless 3. Try to locate the bug in the code. Breakpoints, tracebacks, and more! 4. Write a patch to prevent this bug from reoccurring. Unit tests and coverage checks with continuous integration

33 Nested Function Error running into an error f = function(x) { g(x + 2) } g = function(x) { h(x - 2) } h = function(x) { val = log(x) if(val < 0) { return(val^2) } val } f(-4) # Error in if (val < 0) { : missing value where TRUE/FALSE needed # In addition: Warning message: # In log(x) :

34 Traceback following the bread crumbs back f(-4) # Error in if (val < 0) { : missing value where TRUE/FALSE needed # In addition: Warning message: # In log(x) : # Where's the error? traceback() # 3: h(x) at #2 # 2: g(x) at #2 # 1: f(-4)

35 Examining the State ways to figure out what is happening 1. Print out the contents of variables. 2. Enter into an interactive "debug" mode Set debug(func) and then call func(). Remove debug with undebug(func) to return function to normal. 3. Insert a browser() statement at fixed points in the function. 4. Use breakpoints.

36 Print Statements manually looking at output f = function(x) { message(x) g(x + 2) } g = function(x) { message(x) h(x - 2) } h = function(x) { message(x) val = log(x) message(val) if(val < 0) { return(val^2) } val } # Call function again f(-4) # -4 # -2 # -4 # NaN

37 Interactive Debug Mode surfin' through code without modify it debug(f). # Enter debug mode f(-4) # Call function, escape browser with Q undebug(f) # Remove debug from function Command Shortcut Description n or Enter F10 Execute next statement s Shift+F4 Step into function f Shift+F6 Finish function/loop c Shift+F5 Continue running Q Shift+F8 Stop debugging

38 Breakpoints require the use of an R Scripts instead of RMarkdown

39 Previously Create an R Script keeping a record of your R commands Click the White Plus in top-left Select R Script Opens a new R Script in the top-left Script Editor [Cmd / Cntrl + Shift + N]

40 Breakpoints stopping code in its tracks

41 Breakpoints matrix-style stopping of code Image Source

42 Recap Background Functions allow for dynamic input and output Provide a way to standardize and share code Asserting Input Users will supply odd data Catch their errors as quickly as possible Debugging Errors Types of Programming Errors Use breakpoints, tracebacks, and more!

43 Acknowledgements Bob Rudis' minimalist gestalt C++ function diagram

44 This work is licensed under the Creative Commons Attribution-NonCommercial- ShareAlike 4.0 International License