WOW

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WOW

• World of Walkover-weight

My God, its full of cows! (David Bowman,
2001)
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Can walkover-weight suggest a cow needs attention?
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Join with breeding information
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Position at the outset

• Obstacle No health information!!!
• Suggested Milking order (i.e. where a cow is in
  the herd/line-up) is hierarchical and affected by
  health issues
• Proposed goal to predict a drop in milking order
  using WOW and other facts

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Assumptions deck of cards

• Same cows come in for milking each time
• Cows are well-behaved (e.g. arrive in a nice
  queue)
• Data is in good shape (e.g. one reading per cow
  per milking)

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Data problems

• Multiple entries for cows (e.g. four entries for
  22719193 in QBH2005)
• Delete duplicate weights (SQL problem?)
• Cow skipped and recycled back into order
• Use average if more than one value

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About a quarter of the data are zeroes
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zero problems

• Differentiate between a missing cow, a missing
  weight and a zero weight
• Ignore missing cows
• Cow skipped and recycled back into order
• Time-based interpolation
• Can be problematic if cow has been missing for a
  while
• Add flag to indicate weight was guessed

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other issues in data preparation

• Change milking date to milk index
• Change birthdate to age in months
• Change parturition date to days since last calved
• Additional derivatives
• milking index - cows position in milk order
• ?-index change in index for a cow over various
  time periods (1, 3 and 7 days)
• mu-weight average weight over varying-length
  periods (3, 7, 14, 21 and 28 milkings)
• ?-mu-weight change in index for a cow (1, 3,
  and 7 days)

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Does change in milk order correlate to WOW?
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Correlation coefficients QBH2006 (dense)

• WOW to index 0.12
• WOW to 14-day mu-weight 0.93
• Index to 10-day mu-weight 0.14
• 3-day ?-order to ?-weight 0.045

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3-day ?-order and 3-day ?-weight
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Predict change in milking order

• Use M5P to predict how the milking order will
  change for a cow at the next milking
• Approx. 205,000 QBH2006 samples (with fewer than
  5/25 missing attributes)
• 2/3 training 1/3 testing

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Re-running took too long but youve all seen
it before, where accuracy was 51.89
(discrimination 0.527) and the model tree was
hugely ugly (65 nodes, 33 leaves). Also tried
predicting cows index as decile and as ratio to
herdsize.
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Cows position (index) as ratio to herdsize
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Cow index vs. herd size
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Where to? .

• Data must still be scrubbed so that milking order
  makes sense (if milking order is going to be
  relevant)
• Perhaps cow order needs to be described in
  completely different terms (e.g. cow buddies)
• Easy visualization of herds/cows/breeds/dates/tren
  ds is needed
• this segued into another area of the project ..

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Visualization tools (alpha and beta)
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In the meantime health data is obtained
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Can WOW predict onset of illness?

• Combine original attributes and derivatives with
  health judgments
• Cows with unknown health are considered healthy
• Need equal number of positive and negative
  instances

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Health data becomes available
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Not so much health data

• 1613 recorded instances of health
• 913 different cows with health info
• 2540 cows with milking info
• 788 milked cows with health data
• 7 broad categories of illness
• Calving disorder
• Metabolic disorder
• Udder disorder (only one with 50 in herd)
• Reproductive disorder
• Lameness
• Infectious diseases
• Other ailments

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Data sparseness

• QBH2006
• 75 instances out of 324,291 have health
• 63 udder disorder
• 10 metabolic disorder
• 2 lameness
• Only .002 positives ? will never be isolated ?
  must subsample negatives
• Random selection of 75 negatives ? data
  sparseness ? over-fitting likely

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Data sparseness

• QBH2006
• 36 cows have illness at some time, so just learn
  those?
• 11,966 records for those cows, 76 of which have
  illness (still
• Random selection of 1 as negatives (about 120)

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Refinements to approach

• QBH2006
• Restrict target objective to UDDER DISORDER
• Randomly select equal number of negatives from
  cows who have health problem at some point
• goal differentiate between healthy and
  unhealthy state

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Detecting mastitis amidst random normal cows

• QBH2006
• Restrict learning objective to UDDER DISORDER
• Randomly select equal number of negatives from
  all cows that have been milked (63,63-)

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When is a cow sick?

• So far, attempted to predict health label at
  point of milking, but ..
• when was the health label attached? before,
  during or after the current milking?
• Goal predict whether cow needs attention at the
  next milking (i.e. time series)

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Summary Correctly Classified Instances
90 70.3125 Incorrectly
Classified Instances 38
29.6875 Kappa statistic
0.4026 Mean absolute error
0.3446 Root mean squared error
0.4532 Relative absolute error
68.8933 Root relative squared error
90.5974 Total Number of Instances
128 Detailed Accuracy By Class TP
Rate FP Rate Precision Recall F-Measure
ROC Area Class 0.508 0.108 0.821
0.508 0.627 0.707 UDDER DISORDER
0.892 0.492 0.652 0.892 0.753
0.707 NONE Confusion Matrix a b
7 58 b NONE
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Agenda

• Replace quantified attributes with simpler (e.g.
  boolean, nominal) ones
• Characterise exceptions
• Below average weight for cow/herd/breed/age
• Dropped decile/50 in order
• Broad statistical measures
• How many std.devs. from mean
• z-score (probability of variation)
• Choose negative instances more carefully (select
  fewer interpolates)
• Spend more time with people who know cows