def main():
p_path = '/vol/v2/stem/imperv/models/imperv_20161012_0958/imperv_20161012_0958_vote.bsq'
t_path = '/vol/v2/stem/imperv/truth_map/imperv2001_CAORWA.bsq'
nodata_p = 255
nodata_t = 255
out_dir = '/vol/v2/stem/imperv/models/imperv_20161012_0958/evaluation_vote/'
ds_p = gdal.Open(p_path)
ar_p = ds_p.ReadAsArray()
ds_p = None
ds_t = gdal.Open(t_path)
ar_t = ds_t.ReadAsArray()
ds_t = None
sample_txt = '/vol/v2/stem/imperv/samples/imperv_sample1454542_20161007_0843/imperv_sample1454542_20161007_0843_test.txt'
#sample_txt = '/vol/v2/stem/canopy/samples/canopy_sample1454542_20161017_1919/canopy_sample1454542_20161017_1919_test.txt'
df = pd.read_csv(sample_txt, sep='\t', index_col='obs_id')
p_samples, t_samples = get_samples(ar_p, ar_t, df, nodata_p, nodata_t, match=False)
#t_mask = (t_samples > 0) & (p_samples > 0)
#t_samples = t_samples[t_mask]
#p_samples = p_samples[t_mask]
out_png = os.path.join(out_dir, 'imperv_20161012_0958_2dhistogram_average_hex_gray.png')
#out_png = os.path.join(out_dir, 'canopy_20161018_2254_2dhistogram_bestmatch_hex_gray.png')
#import pdb; pdb.set_trace()
sns.set_context(context='paper', font_scale=1.4)
histogram_2d(t_samples, p_samples, out_png, bins=50, hexplot=True, vmax=4000)
print out_png
ar_p = None
ar_t = None
p_samples = None
t_samples = None
def main(p_path, t_path, nodata_p, nodata_t, sample_txt, out_png):
ds_p = gdal.Open(p_path)
ar_p = ds_p.ReadAsArray()
ds_p = None
ds_t = gdal.Open(t_path)
ar_t = ds_t.ReadAsArray()
ds_t = None
df = pd.read_csv(sample_txt, sep='\t', index_col='obs_id')
p_samples, t_samples = get_samples(ar_p,
ar_t,
df,
nodata_p,
nodata_t,
match=False)
if not os.path.isdir(os.path.dirname(out_png)):
os.makedirs(os.path.dirname(out_png))
sns.set_context(context='paper', font_scale=1.4)
histogram_2d(t_samples,
p_samples,
out_png,
bins=50,
hexplot=True,
vmax=4000)
print(out_png)
ar_p = None
ar_t = None
p_samples = None
t_samples = None
def par_get_match(args):
t0 = time.time()
tile_ind, this_in, this_match, in_nodata, match_nodata, count, total_tiles = args
matched_vals, _ = get_samples(this_in,
this_match,
in_nodata,
match_nodata,
match=True)
print 'Time for getting array %s of %s: %.1f seconds' % (
count, total_tiles, time.time() - t0)
return tile_ind, matched_vals
def get_samples(self, model_name, zero_inflated=True, num_samples=1000):
return get_samples(
self.model_dict[model_name],
self.guide_dict[model_name],
self.p_data_train,
self.t_data,
self.s_data,
self.r_data,
None,
self.p_types_train,
self.p_stories_train,
self.p_subreddits_train,
zero_inflated,
num_samples=num_samples,
)
def main(sample_txt, ref_raster, pred_raster, p_nodata, t_nodata, target_col, bins, out_txt, match=None, predict_col=None):
p_nodata = int(p_nodata)
t_nodata = int(t_nodata)
ds_p = gdal.Open(pred_raster)
ar_p = ds_p.ReadAsArray()
ds_r = gdal.Open(ref_raster)
ar_r = ds_r.ReadAsArray()
r_xsize = ds_r.RasterXSize
r_ysize = ds_r.RasterYSize
p_xsize = ds_p.RasterXSize
p_ysize = ds_p.RasterYSize
tx_r = ds_r.GetGeoTransform()
tx_p = ds_p.GetGeoTransform()
# If two arrays are different sizes, make prediction array match reference
if not r_xsize == p_xsize or r_ysize == p_ysize or tx_r != tx_p:
warnings.warn('Prediction and reference rasters do not share the same extent. Snapping prediction raster to reference....')
offset = mosaic.calc_offset((tx_r[0], tx_r[3]), tx_p)
t_inds, p_inds = mosaic.get_offset_array_indices((r_ysize, r_xsize), (p_ysize, p_xsize), offset)
ar_buf = np.full(ar_r.shape, p_nodata, dtype=ar_p.dtype)
ar_buf[t_inds[0]:t_inds[1], t_inds[2]:t_inds[3]] = ar_p[p_inds[0]:p_inds[1], p_inds[2]:p_inds[3]]
ar_p = ar_buf.copy()
del ar_buf
bins = parse_bins(bins)
sample = pd.read_csv(sample_txt, sep='\t')
if target_col in sample.columns:
t_sample = sample[target_col]
else:
raise IndexError('target_col "%s" not in sample' % target_col)
if match:
t_sample, p_sample = get_samples(ar_p, ar_r, p_nodata, t_nodata, sample, match=match)
elif predict_col:
p_sample = sample[predict_col]
else:
p_sample = ar_p[sample.row, sample.col]
t_sample = ar_r[sample.row, sample.col]
rmse = area_weighted_rmse(ar_p, ar_r, p_sample, t_sample, bins, p_nodata, out_txt=out_txt)
return rmse
def main(search_dir, models, t_path, inventory_txt, t_nodata=255):
df_inv = pd.read_csv(inventory_txt, sep='\t', index_col='stamp')
columns = df_inv.columns
if 'vote_rmse' not in columns:
df_inv['vote_rmse'] = None
if 'mean_rmse' not in columns:
df_inv['mean_rmse'] = None
df_inv = df_inv.ix[models]
ds = gdal.Open(t_path)
ar_t = ds.ReadAsArray()
nodata_mask = ar_t == t_nodata
ds = None
for model in models:
print '\nCalculating RMSE for ', model
model_dir = os.path.join(search_dir, model)
if not os.path.exists(model_dir):
print 'Model dir does not exist: %s. Skipping...\n' % model_dir
continue
confusion_params = os.path.join(model_dir,
'confusion_matrix_params.txt')
if not os.path.exists(confusion_params):
print 'Could not find confusion params: ', confusion_params
predict_params = os.path.join(model_dir, 'predict_stem_params.txt')
inputs, _ = stem.read_params(predict_params)
p_nodata = int(inputs['nodata'].replace('"', ''))
this_srch_str = os.path.join(model_dir, 'train_stem*_params.txt')
train_params = glob.glob(this_srch_str)
if len(train_params) == 0:
print 'Can not find test data for ', model, '\n'
continue
train_params = train_params[0]
inputs, _ = stem.read_params(train_params)
test_txt = inputs['sample_txt'].replace('predictors',
'test').replace('"', '')
train_txt = inputs['sample_txt'].replace('"', '')
else:
inputs = read_params(confusion_params)
for k, v in inputs.iteritems():
inputs[k] = v.replace('"', '')
test_txt = inputs['sample_txt']
p_nodata = int(inputs['p_nodata'])
train_txt = inputs['sample_txt'].replace('_test',
'').replace('"', '')
#df = pd.read_csv(test_txt, sep='\t', index_col='obs_id')
train_sample = pd.read_csv(train_txt, sep='\t', index_col='obs_id')
# Set any pixels used for training to -1 so they can be avoided for testing
n_rows, n_cols = ar_t.shape
n_pixels = ar_t.size
pixel_ids = np.arange(n_pixels,
dtype=np.uint32).reshape(n_rows, n_cols)
pixel_ids[
train_sample.row,
train_sample.col] = n_pixels #will always be 1 more than last id
pixel_ids[nodata_mask] = n_pixels
n_samples = int(
int(
os.path.basename(train_txt).split('_')[1].replace(
'sample', '')) * 0.2)
test_ids = np.array(random.sample(pixel_ids[pixel_ids != n_pixels],
n_samples),
dtype=np.uint32)
test_rows = test_ids / n_cols
test_cols = test_ids % n_cols
#test_cols = random.sample(ar_col[ar_col != -1], n_samples)
df = pd.DataFrame({'row': test_rows, 'col': test_cols})
for agg_method in ['vote', 'mean']:
p_path = os.path.join(model_dir, '%s_%s.bsq' % (model, agg_method))
ds = gdal.Open(p_path)
ar_p = ds.ReadAsArray()
t_samples, p_samples = get_samples(ar_p,
ar_t,
p_nodata,
255,
samples=df,
match='best')
rmse = np.round(calc_rmse(t_samples, p_samples), 1)
print agg_method, ': ', rmse
df_inv.ix[model, '%s_rmse' % agg_method] = rmse
out_txt = os.path.join(
model_dir, '%s_random_test_sample%s.txt' % (model, n_samples))
df.to_csv(out_txt, sep='\t', index=False)
out_txt = inventory_txt.replace('.txt', '_randomRMSE.txt')
df_inv.to_csv(out_txt, sep='\t')