forestatrisk.validate package

forestatrisk.validate.diffproj module

mat_diffproj(input_raster, blk_rows=128)

Compute a confusion matrix from a raster of differences.

This function computes a confusion matrix from a raster of differences. The raster of differences can be obtained using function .r_diffproj().

Parameters:

input_raster – Raster of differences obtain with forestatrisk.r_projdiff.

Returns:

A confusion matrix. [[np00, np01], [np10, np11]].

r_diffproj(inputA, inputB, output_file='diffproj.tif', blk_rows=128)

Compute a raster of differences for comparison.

This function compute a raster of differences between two rasters of future forest cover. Rasters must have the same extent and resolution.

Parameters:

• inputA – Path to first raster (predictions).

• inputB – Path to second raster of (sd. predictions or observations).

• output_file – Name of the output raster file for differences.

• blk_rows – If > 0, number of rows for computation by block.

forestatrisk.validate.map_accuracy module

map_accuracy(mat)

Compute accuracy indices from a confusion matrix.

Compute Overall Accuracy, Expected Accuracy, Figure of Merit, Specificity, Sensitivity, True Skill Statistics and Cohen’s Kappa from a confusion matrix.

Parameters:

mat – Confusion matrix. Format: [[n00, n01], [n10, n11]] with pred on lines and obs on columns.

Returns:

A dictionnary of accuracy indices.

map_confmat(r_obs0, r_obs1, r_pred0, r_pred1, blk_rows=0)

Compute a confusion matrix.

This function computes a confusion matrix at a given resolution. Number of pixels in each category (0, 1) and in each spatial cell are given by r_obs* and r_pred* rasters.

Parameters:

• r_obs0 – Raster counting the number of 0 for observations.

• r_obs1 – Raster counting the number of 1 for observations.

• r_pred0 – Raster counting the number of 0 for predictions.

• r_pred1 – Raster counting the number of 1 for predictions.

• blk_rows – If > 0, number of lines per block.

Returns:

A numpy array of shape (2,2).

forestatrisk.validate.map_validation module

map_validation(pred, obs, blk_rows=128)

Compute accuracy indices based on predicted and observed forest-cover change (fcc) maps.

Compute the Overall Accuracy, the Figure of Merit, the Specificity, the Sensitivity, the True Skill Statistics and the Cohen’s Kappa from a confusion matrix built on predictions vs. observations.

Parameters:

• pred – Raster of predicted fcc.

• obs – Raster of observed fcc.

• blk_rows – If > 0, number of rows for block (else 256x256).

Returns:

A dictionnary of accuracy indices.

forestatrisk.validate.model_validation module

accuracy_indices(pred, obs)

Compute accuracy indices.

Compute the Overall Accuracy, the Figure of Merit, the Specificity, the Sensitivity, the True Skill Statistics and the Cohen’s Kappa from a confusion matrix built on predictions vs. observations.

Parameters:

• pred – List of predictions.

• obs – List of observations.

Returns:

A dictionnary of accuracy indices.

computeAUC(pos_scores, neg_scores, n_sample=100000)

Compute the AUC index.

Compute the Area Under the ROC Curve (AUC). See Liu et al. 2011.

Parameters:

• pos_scores – Scores of positive observations.

• neg_scores – Scores of negative observations.

• n_samples – Number of samples to approximate AUC.

Returns:

AUC value.

cross_validation(data, formula, mod_type='icar', ratio=30, nrep=5, seed=1234, icar_args={'beta_start': 0, 'burnin': 1000, 'mcmc': 1000, 'n_neighbors': None, 'neighbors': None, 'thin': 1}, rf_args={'n_estimators': 100, 'n_jobs': None})

Model cross-validation

Performs model cross-validation.

Parameters:

• data – Full dataset.

• formula – Model formula.

• mod_type – Model type, can be either “icar”, “glm”, or “rf”.

• ratio – Percentage of data used for testing.

• nrep – Number of repetitions for cross-validation.

• seed – Seed for reproducibility.

• icar_args – Dictionnary of arguments for the binomial iCAR model.

• rf_args – Dictionnary of arguments for the random forest model.

Returns:

A Pandas data frame with cross-validation results.

forestatrisk.validate.resample_sum module

coarsen_sum(a, c)

Resample to coarser resolution using sum

This is an internal function used by resample_sum.

Parameters:

• a – 2D numpy array

• c – Coarseness, in number of cells

resample_sum(input_raster, output_raster, val=0, window_size=2)

Resample to coarser resolution with counts.

This function resamples to coarser resolution counting pixel number having a given value. Window’s size is limited to 1000 pixels.

Parameters:

• input_raster – Path to input raster.

• val – Pixel value to consider.

• window_size – Size of the window in number of pixels.

• output_raster – Path to output raster file.