About

An anomaly detection challenge for data with complex dependency structure.

Accompanying software package.

For further information, please visit our challenge website at http://nicococo.github.io/CropYieldLoss

Data

There are three states from India: Bihar, MP (Madhya Pradesh), UP (Uttar Pradesh) Two sets of data from different sources:

• Yield data per year per district Source: ICRISAT http://vdsa.icrisat.ac.in

• Daily weather data (max temperature, min temperature, percipitation, wind, relative humidity, solar) from different weather grid points. Weather data is available for several spatial locations within each district.
  Source: http://globalweather.tamu.edu

Processed data can be aquired at http://... and should contain the following information:

• WDATA (548, 11284, 6) ['Max Temperature' 'Min Temperature' 'Precipitation' 'Wind' 'Relative Humidity' 'Solar']
• DATES (11284, 3) ['Day' 'Month' 'Year']
• STATIONS (548, 4) ['Id' 'Longitude' 'Latitude' 'Elevation']
• DISTS (93, 5) ['DistCode' 'DistName' 'StateCode' 'StateName' 'StationIds']
• LABEL (93, 31) [1979 - 2009]

Installation

You can conveniently install the software package using: pip install git+https://github.com/nicococo/CropYieldLoss.git

Basic Example

The most basic example for loading, preparing, training, and testing is given in ex1_simple.py:

wdata, dates, stations, dists, label = load_data('cylad1.npz')
F = get_basic_weather_station_feats(wdata, dates)
X, inds, y = get_exms_basic_feats(3, F, stations, dates, dists, label)
X_train, inds_train, y_train, X_test, inds_test, y_test = split_train_test(X, inds, y, perc=0.2)
mySvm = svm.LinearSVC(C=1., fit_intercept=False, class_weight='balanced')
mySvm.fit(X_train, y_train)
fpr, tpr, thresholds = metrics.roc_curve(y_test, mySvm.decision_function(X_test), pos_label=+1)
print 'AUC: ', metrics.auc(fpr, tpr)

Note: This example assumes that the processed data 'cylad1.npz' (see website where to download) is stored in your current path.

References

If you use results, software, or data from this challenge in your own research, please cite our paper: to appear...