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Homework2

Active Shooter Exercise.

  • Watch the video
    • Go go https://moodle-projects.wolfware.ncsu.edu/enrol/index.php?id=827
    • Click on the "Shots Fired On Campus: When Lightning Strikes" button (at bottom). Go to next page.
    • Click on the "Enroll me" button. Go to next page.
    • Find the text Shots Fired - Student Edition Please click here in order to veiw the training video. It will take about 50 seconds to load.
    • Click on the "here" and watch the 25 minute video
  • Answer these questions
    • List two things not to do during an active shooter event.
    • List two things best to do during an active shooter event.

Write Your First Learner: ZeroR

Within ninja.rc there are all these learners that accept a training and test set. E.g.

j48() {
	local learner=weka.classifiers.trees.J48
	$Weka $learner -p 0 -C 0.25 -M 2 -t $1 -T $2
}

which is called like in eg3:

eg3 () {
    echo;
    j48 data/weather.arff data/weather.arff
}

Functions like j48 accept arff files as inputs; e.g.

@relation weather

@attribute outlook {sunny, overcast, rainy}
@attribute temperature real
@attribute humidity real
@attribute windy {TRUE, FALSE}
@attribute play {yes, no}

@data
sunny,85,85,FALSE,no
sunny,80,90,TRUE,no
overcast,83,86,FALSE,yes
rainy,70,96,FALSE,yes
rainy,68,80,FALSE,yes
rainy,65,70,TRUE,no
overcast,64,65,TRUE,yes
sunny,72,95,FALSE,no
sunny,69,70,FALSE,yes
rainy,75,80,FALSE,yes
sunny,75,70,TRUE,yes
overcast,72,90,TRUE,yes
overcast,81,75,FALSE,yes
rainy,71,91,TRUE,no

These functions generate output like what eg5 generates:

=== Predictions on test data ===

 inst#     actual  predicted error prediction
     1       2:no       2:no       1
     2       2:no       2:no       1
     3      1:yes      1:yes       1
     4      1:yes      1:yes       1
     5      1:yes      1:yes       1
     6       2:no       2:no       1
     7      1:yes      1:yes       1
     8       2:no       2:no       1
     9      1:yes      1:yes       1
    10      1:yes      1:yes       1
    11      1:yes      1:yes       1
    12      1:yes      1:yes       1
    13      1:yes      1:yes       1
    14       2:no       2:no       1

So if one were to write a new function, one could add their own function providing that function read arff files and generated something like the above:

zeror() {
  myNewLearner $1 $2
}

then that learner could do anything at all as long as it copied the input output.

ZeroR is the world's dumbest classifier. It finds the majority class and predicts that everything in the test set is that majority.

Your task is to

  1. Implement ZeroR. and add it to the
  2. Copy eg10 to, say, eg11
  3. Add your new learner to the line
local learners="j48 jrip nb rbfnet bnet yourLearnerHere";

What to Hand in

Then run eg11 and hand in the results.

Write a Table Reader

Create a class Table that can read csv files or arff files, with instance variables

  • rows: a list
  • cols: summary objects, one per column

When a row is added to a Table, then the summaries are updated. Summary objects are either Nums or Syms.

Nums

def max(x,y) : return x if x>y else y
def min(x,y) : return x if x<y else y

class Num:
  def __init__(i):
    i.mu,i.n,i.m2,i.up,i.lo = 0,0,0,-10e32,10e32
  def add(i,x):
    i.n += 1
    x = float(x)
    if x > i.up: i.up=x
    if x < i.lo: i.lo=x
    delta = x - i.mu
    i.mu += delta/i.n
    i.m2 += delta*(x - i.mu)
    return x 
  def sub(i,x):
    i.n   = max(0,i.n - 1)
    delta = x - i.mu
    i.mu  = max(0,i.mu - delta/i.n)
    i.m2  = max(0,i.m2 - delta*(x - i.mu))
  def sd(i):
    return 0 if i.n <= 2 else (i.m2/(i.n - 1))**0.5

Syms

class Sym:
  def __init__(i):
     i.counts, i.most, i.mode, i.n = {},0,None,0
  def add(i,x):
    i.n += 1
    new = i.counts[x] = i.counts.get(x,0) + 1
    if new > i.most:
      i.most, i.mode = new,x
    return x
  def sub(i,x):
    i.n -= 1
    i.counts[x] -= 1
    if x == i.mode:
      i.most, i.mode = None,None
  def ent(i):
    tmp = 0
    for val in i.counts.values():
      p = val/i.n
      if p:
        tmp -= p*math.log(p,2)
    return tmp

Implement a CSV reader

Don't confuse:

  1. reading csv files from disk
  2. from converting strings to floats,ints or strings
  3. from storing that lot in your table class

For example, the code rows.py handles tasks 1,2 as seperate things (leaving you to implement 3). Feel free to use that code.

Implement a Table reader

Uses the csv reader to read data, passes it into a table instance, one row at a time.

As a side-effect of reading that row, the Num and Sym objects in cols get updated.

What to hand-in

All your code and the results of reading weather.csv.

  • Print mean and standard deviation of all numeric columns;
  • Print mode and entropy of all symbolic columns.

Hint: add a show method to Num and Sym so all you have to do is:

for col in table.cols:
  print(col.show())