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<div>Hi Steve<br>
<br>
</div>
Great news! I gave it a quick try (on Ubuntu 14.04, GRASS 7
master). Size input raster layers: rows: 1578, columns: 1436 <br>
<br>
<b>1st try - input full map, classes 1/0, </b><br>
I had to stop as it took too much time. Stopping it did not stop
the python processes however, I had to kill the processes.<br>
<br>
<b>2nd try - input random sample of 100 points, 1 (12) and 0 (88),
with b flag</b><br>
r.randomforest -b igroup=predictors@SampleSize roi=test2
output=test2_output ntrees=500 mfeatures=-1 minsplit=2 randst=1
lines=100<br>
Group <predictors> references the following raster maps:<br>
Traceback (most recent call last):<br>
File "/home/paulo/.grass7/addons/scripts/r.randomforest",<br>
line 335, in <module><br>
main()<br>
File "/home/paulo/.grass7/addons/scripts/r.randomforest",<br>
line 243, in main<br>
class_weight = "balanced", max_features = mfeatures,<br>
min_samples_split = minsplit, random_state = randst)<br>
TypeError: __init__() got an unexpected keyword argument<br>
'class_weight'<br>
Removing raster <tmp_jNyNcqZa><br>
<br>
<b>3rd try</b><b> - input random sample of 100 points, 1 (#12) and
0 (#88), with b flag</b><br>
r.randomforest igroup=predictors@SampleSize roi=test2
output=test2_output ntrees=500 mfeatures=-1 minsplit=2 randst=1
lines=100<br>
Group <predictors> references the following raster maps:<br>
Our OOB prediction of accuracy is: 89.0%<br>
Raster Importance<br>
0 bio1_wc30s@SampleSize 0.183670<br>
1 bio2_wc30s@SampleSize 0.139914<br>
2 bio3_wc30s@SampleSize 0.105035<br>
3 bio4_wc30s@SampleSize 0.106413<br>
4 bio13_wc30s@SampleSize 0.087399<br>
5 bio14_wc30s@SampleSize 0.146495<br>
6 dm_wc30s@SampleSize 0.104575<br>
7 llds_wc30s@SampleSize 0.126499<br>
Removing raster <tmp_RhTllKlA><br>
<br>
<b>Questions</b><br>
* I am using it for species distribution modeling
(presence/absence input map), but I prefer to use the regression
mode. Is there a way to force it to use the regression mode?<br>
* Are you planning to implement other classification methods?
Seems if this works it shouldn't be too hard to replace the
randomforest method by any of the other methods in scipy? I have
for som time been thinking about using scipy, but my programming
skills are not up to standards. But perhaps it is easier using
your addon as template?<br>
<br>
Cheers,<br>
<br>
Paulo<br>
<br>
<br>
<div><br>
<div>
<div class="gmail_extra"><br>
<div class="gmail_quote">On Sat, Mar 26, 2016 at 5:40 PM,
Steven Pawley <span dir="ltr"><<a
href="mailto:dr.stevenpawley@gmail.com"
target="_blank"><a class="moz-txt-link-abbreviated" href="mailto:dr.stevenpawley@gmail.com">dr.stevenpawley@gmail.com</a></a>></span>
wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0
.8ex;border-left:1px #ccc solid;padding-left:1ex">
<div>Hello developers,</div>
<div><br>
</div>
<div>I would like to draw your attention to a new GRASS
add-on, r.randomforest, which uses the scikit-learn
and pandas Python packages to classify GRASS rasters.
Similar to existing GRASS classification methods, it
uses an imagery group and a raster of labelled pixels
as the inputs for the classification. It also reads
the rasters row-by-row, and then bundles these rows
based on a user specified row increment to the
classifier to keep memory requirements low, but also
allow efficient classification because the
scikit-learn implementation is multithreaded by
default, and row-by-row results in too much stop-start
behaviour. The feature importance scores and
out-of-bag error are displayed in the command window.</div>
<div><br>
</div>
<div>I would appreciate testing - you need to have
scikit-learn and pandas installed in your Python
environment which is easy on Linux and OS X, and
instructions are provided in the tool for Windows.</div>
<div><br>
</div>
<div>I have another add-on that I will upload soon,
r.roc, which generates ROC and AUROC for prediction
models.</div>
<div><br>
</div>
<div>Steve</div>
<div><br>
<div>Sent from <a href="https://aka.ms/sdimjr"
target="_blank">Outlook Mobile</a></div>
<br>
</div>
<br>
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</blockquote>
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