WOW World of Walkover-weight “My God, it’s full of cows!” (David Bowman, 2001)

Can walkover-weight suggest a cow needs attention?

Join with breeding information …

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

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)

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

About a quarter of the data are zeroes … instances 0 weights λ weights BBYG ,93557, BBYG ,54539,7261,193 JJVX20077, QBH20057, QBH ,36580,36272 QBH ,30067,1092,118 QBH200848,53410,535224

“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”

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 - cow’s 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)

Does [change in] milk order correlate to WOW?

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

∆∆ 3-day ∆-order and 3-day ∆-weight

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

Re-running took too long … but … you’ve 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 cow’s index as decile and as ratio to herdsize.

Cow’s position (index) as ratio to herdsize

Cow index vs. herd size

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/trends is needed this segued into another area of the project.. this segued into another area of the project..

Visualization tools (alpha and beta)

In the meantime … health data is obtained …

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

Health data becomes available farmyear Qty > 50 BBYG BBYG BBYG QBH QBH QBH QBH

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

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

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 <1% positive)  Random selection of 1% as negatives (about 120)

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

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-)

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)

=== Summary === Correctly Classified Instances % Incorrectly Classified Instances % Kappa statistic Mean absolute error Root mean squared error Relative absolute error % Root relative squared error % Total Number of Instances 128 === Detailed Accuracy By Class === TP Rate FP Rate Precision Recall F-Measure ROC Area Class UDDER DISORDER NONE === Confusion Matrix === a b <-- classified as | a = UDDER DISORDER 7 58 | b = NONE

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