hulaba / geostatistics

Basic implementation of simple kriging predictions and stochastic simulations using Numpy, along with methods for cross-validation and visualization.

Geostatistics

Basic implementation of simple kriging predictions and stochastic simulations using Numpy, along with methods for cross-validation and visualization.
Civil & Environmental Engineering 263n: Scalable Spatial Analytics at UC-Berkeley, Fall 2016
By Paul Sohn, October 26, 2016

This Python module includes a basic implementation of a geostatistical predictive model (simple kriging, equivalent to Gaussian process regression) and methods for stochastic simulation and visualization. The model is tested using rainfall measurements from 827 locations; 414 observations are used as training data and the other 413 observations are used as test data.

The SimpleKriging class provides methods for prediction and simulation, explored below:

Prediction

The SimpleKriging class is instantiated with a training dataset. Predictions for a test dataset are made according to:

$$m(f) = K(x_{test}, x) [K + \sigma_n^2 I]^{-1} y$$

where $K$ is a selected covariance function, $\sigma_n^2$ is a noise covariance value, and $x$, $x_{test}$, and $y$ are the vectors of training and test coordinates, and training rainfall values, respectively. For my predictions, I use the simple squared exponential covariance function, defined as:

$$ K_{SE}(x,x') = exp(- \frac{d^2}{2l^2}) $$

where $l$ is the characteristic length-scale of the Gaussian process. The basic usage of the module to predict values is as follows. First, we load the training and test data:

train = np.genfromtxt('data/train_data.csv', delimiter=',',skip_header=True)
test_raw = np.genfromtxt('data/test_data.csv', delimiter=',',skip_header=True)
test = test_raw[:,1:]

We then instantiate a model using the SimpleKriging class and predict values using the SimpleKriging.predict method, with some arbitrary values:

kriging = gaussian.SimpleKriging(training_data=train)
predict = kriging.predict(test_data=test, l=.5, sigma=.2)
predict[:5]

array([[ 70.94340417],
       [ 23.70437824],
       [ 49.57131344],
       [ 71.06713418],
       [ 70.69029366]])

How can we find better values for l and sigma? We can use the cross_validation function to experimentally find values that minimize cross-validation error.

l_to_test = np.arange(0.7, 1.3, 0.05)[1:]
sigma_to_test = np.arange(0.16, 0.2, 0.005)[1:]  

l_opt, sigma_opt, func, rmse_low = gaussian.cross_validate(train,
                                                     l_values=l_to_test,
                                                     sigma_values=sigma_to_test,
                                                     rmse_opt=1000,
                                                     k_folds=5)

We get values of l_opt = 1.05 and sigma_opt = 0.195, and we can substitute these in to the prediction above. This is what I used for my Kaggle submission.

Simulation

We can generate stochastic simulations using these predictive values by adding a Cholesky decomposition to the predictive means and adding them to a self-defined grid. The SimpleKriging.simulate method includes the ability to output an image and a .kml file in order to visualize predictions in Google Earth: