def main():
path = sys.argv[1]
files = os.listdir(path)
files = [os.path.join(path,f) for f in files if f.endswith('.txt')]
res = []
for file in files:
confusion_matrix.main(file)
file_name = file.split('/')[-1].split('.')[0]
input_file = '{}_confusion_matrix.txt'.format(file_name)
with open(input_file) as f:
first = 0
starting = 0
m = re.search('starting_(\d+)', input_file)
if m is not None:
starting = int(m.group(1))
m = re.search('first_(\d+)', input_file)
if m is not None:
first = int(m.group(1))
data = f.readlines()[-1]
res.append((first, starting, data))
res = sorted(res, key=lambda x: x[0], reverse=False)
output_file = 'aggregated_confusion_matrix.txt'
try:
os.remove(output_file)
except:
pass
with open(output_file, 'a') as f:
for l in res:
f.write('{} {} {}'.format(l[0], l[1], l[2]))
generate_gnuplot(output_file)
def main(params, inventory_txt=None, constant_vars=None):
inputs, df_var = stem.read_params(params)
for i in inputs:
exec ("{0} = str({1})").format(i, inputs[i])
df_var.data_band = [int(b) for b in df_var.data_band]#sometimes read as float
try:
n_tiles = [int(i) for i in n_tiles.split(',')]
support_size = [int(i) for i in support_size.split(',')]
nodata = int(nodata)
str_check = model_dir, mosaic_path, out_dir, train_params
except NameError as e:
missing_var = str(e).split("'")[1]
msg = "Variable '%s' not specified in param file:\n%s" % (missing_var, params)
raise NameError(msg)
# Check that all the variables given were used in training and vice versa
try:
train_inputs, train_vars = stem.read_params(train_params)
except:
raise NameError('train_params not specified or does not exist')
train_vars = sorted(train_vars.index)
pred_vars = sorted(df_var.index)
# Make sure vars are sorted alphabetically since they were for training
df_var = df_var.reindex(pred_vars)
# If constants were given, make a dict and make sure they match the training
# constants
if 'constant_vars' in inputs:
constant_vars = parse_constant_vars(constant_vars)
pred_constants = sorted(constant_vars.keys())
train_constants = [i.replace(' ', '') for i in train_inputs['constant_vars'].strip('"').split(',')]
train_constants = sorted(train_constants)
unmatched_vars = [v for v in pred_vars if v not in train_vars]
if 'constant_vars' in inputs:
unmatched_vars += [v for v in pred_constants if v not in train_constants]
if len(unmatched_vars) != 0:
unmatched_str = '\n'.join(unmatched_vars)
msg = 'Columns not in train params but specified in predict params:\n' + unmatched_str
raise NameError(msg)
unmatched_vars = [v for v in train_vars if v not in pred_vars]
if 'constant_vars' in inputs:
unmatched_vars += [v for v in train_constants if v not in pred_constants]
pred_vars += pred_constants # Add here because it would screw with stuff upstream
if len(unmatched_vars) != 0:
unmatched_str = '\n'.join(unmatched_vars)
msg = 'Columns not in predict params but specified in train params:\n' + unmatched_str
raise NameError(msg)
if not os.path.exists(out_dir): os.mkdir(out_dir)
else: print ('WARNING: out_dir already exists:\n%s\nAny existing files ' + \
'will be overwritten...\n') % out_dir
if not os.path.exists(os.path.join(out_dir, os.path.basename(params))):
shutil.copy2(params, out_dir) #Copy the params for reference
if 'confusion_params' in inputs:
conf_bn = os.path.basename(confusion_params)
new_conf_path = os.path.join(out_dir, conf_bn)
if not os.path.exists(new_conf_path):
shutil.copy2(confusion_params, out_dir)
confusion_params = new_conf_path
if not os.path.exists(model_dir):
sys.exit('model_dir does not exist:\n%s' % model_dir)
if not os.path.exists(mosaic_path):
sys.exit('mosaic_path does not exist:\n%s' % mosaic_path)
mosaic_ds = gdal.Open(mosaic_path)
mosaic_tx = mosaic_ds.GetGeoTransform()
xsize = mosaic_ds.RasterXSize
ysize = mosaic_ds.RasterYSize
prj = mosaic_ds.GetProjection()
driver = mosaic_ds.GetDriver()
m_ulx, x_res, x_rot, m_uly, y_rot, y_res = mosaic_tx
predict_dir = os.path.join(out_dir, 'decisiontree_predictions')
if not os.path.exists(predict_dir):
os.mkdir(predict_dir)
set_txt = glob.glob(os.path.join(model_dir, 'decisiontree_models/*support_sets.txt'))[0]
df_sets = pd.read_csv(set_txt, sep='\t', index_col='set_id')
total_sets = len(df_sets)
t0 = time.time()
if 'n_jobs' in inputs:
# Predict in parallel
n_jobs = int(n_jobs)
args = []
t1 = time.time()
print 'Predicting in parallel with %s jobs...' % n_jobs
print 'Building args and making rasters of TSA arrays...'
for c, (set_id, row) in enumerate(df_sets.iterrows()):
# Save rasters of tsa arrays ahead of time to avoid needing to pickle or fork mosaic_ds
coords = row[['ul_x', 'ul_y', 'lr_x', 'lr_y']]
tsa_ar, tsa_off = mosaic.extract_kernel(mosaic_ds, 1, coords, mosaic_tx,
xsize, ysize, nodata=nodata)
tsa_raster = os.path.join(predict_dir, 'tsa_%s.bsq' % set_id)
tx_out = row.ul_x, mosaic_tx[1], mosaic_tx[2], row.ul_y, mosaic_tx[4], mosaic_tx[5]
dtype_code = mosaic_ds.GetRasterBand(1).DataType
mosaic.array_to_raster(tsa_ar, tx_out, prj, driver, tsa_raster, stem.get_gdal_dtype(dtype_code), silent=True)
# Build list of args to pass to the Pool
tsa_raster = os.path.join(predict_dir, 'tsa_%s.bsq' % set_id)
ds = gdal.Open(tsa_raster)
tsa_tx = ds.GetGeoTransform()
ds = None
tsa_off = stem.calc_offset((mosaic_tx[0], mosaic_tx[3]), (tsa_tx[0], tsa_tx[3]), tsa_tx)
args.append([c, total_sets, set_id, df_var, tsa_raster, tsa_off, coords,
mosaic_tx, xsize, ysize, row.dt_file, nodata, np.uint8,
constant_vars, predict_dir])
print '%.1f minutes\n' % ((time.time() - t1)/60)
p = Pool(n_jobs)
p.map(stem.par_predict, args, 1)
else:
# Loop through each set and generate predictions
for c, (set_id, row) in enumerate(df_sets.ix[1043:].iterrows()):
t1 = time.time()
with open(row.dt_file, 'rb') as f:
dt_model = pickle.load(f)
print '\nPredicting for set %s of %s' % (c + 1, total_sets)
coords = row[['ul_x', 'ul_y', 'lr_x', 'lr_y']]
ar_predict = stem.predict_set(set_id, df_var, mosaic_ds, coords,
mosaic_tx, xsize, ysize, dt_model, nodata,
np.int16, constant_vars)
tx = coords.ul_x, x_res, x_rot, coords.ul_y, y_rot, y_res
out_path = os.path.join(predict_dir, 'prediction_%s.bsq' % set_id)
mosaic.array_to_raster(ar_predict, tx, prj, driver, out_path, gdal.GDT_Byte, nodata=nodata)
print 'Total time for this set: %.1f minutes' % ((time.time() - t1)/60)
#mosaic_ds = None
print '\nTotal time for predicting: %.1f hours\n' % ((time.time() - t0)/3600)#'''
#Aggregate predictions by tile and stitch them back together
if not 'file_stamp' in inputs: file_stamp = os.path.basename(model_dir)
ar_vote, pct_importance, df_sets = stem.aggregate_predictions(ysize, xsize, nodata, n_tiles, mosaic_ds, support_size, predict_dir, df_sets, out_dir, file_stamp, prj, driver, 0)
#df_sets.to_csv(set_txt, sep='\t')'''
mosaic_ds = None
# Save the importance values
importance = pd.DataFrame({'variable': pred_vars,
'pct_importance': pct_importance,
'index': range(len(pred_vars))
})
importance.set_index('index', inplace=True)
importance['rank'] = [int(r) for r in importance.pct_importance.rank(method='first', ascending=False)]
out_txt = os.path.join(out_dir, '%s_importance.txt' % file_stamp)
importance.to_csv(out_txt, sep='\t')#'''
'''ds = gdal.Open(os.path.join(model_dir, '%s_vote.bsq' % file_stamp))
ar_vote = ds.ReadAsArray()
ds = None
ds = gdal.Open(os.path.join(model_dir, '%s_mean.bsq' % file_stamp))
ar_mean = ds.ReadAsArray()
ds = None#'''
if 'confusion_params' in locals():
import confusion_matrix as confusion
vote_dir = os.path.join(model_dir, 'evaluation_vote')
mean_dir = os.path.join(model_dir, 'evaluation_mean')
print '\nComputing confusion matrix for vote...'
out_txt = os.path.join(vote_dir, 'confusion.txt')
print confusion_params
df_v = confusion.main(confusion_params, ar_vote, out_txt, match=True)
try:
out_txt = os.path.join(vote_dir, 'confusion_avg_kernel.txt')
df_v_off = confusion.main(confusion_params, ar_vote, out_txt)
except Exception as e:
print e
'''print '\nGetting confusion matrix for mean...'
out_txt = os.path.join(mean_dir, 'confusion.txt')
df_m = confusion.main(confusion_params, ar_mean, out_txt, match=True)
try:
out_txt = os.path.join(mean_dir, 'confusion_avg_kernel.txt')
df_m_off = confusion.main(confusion_params, ar_mean, out_txt)
except Exception as e:
print e#'''
vote_acc = df_v.ix['producer', 'user']
vote_kap = df_v.ix['producer', 'kappa']
#mean_acc = df_m.ix['user','producer']
#mean_kap = df_m.ix['user', 'kappa']
if 'inventory_txt' in inputs:
df_inv = pd.read_csv(inventory_txt, sep='\t', index_col='stamp')
cols = ['vote_accuracy', 'vote_kappa']#, 'vote_mask', 'mean_accuracy', 'mean_kappa', 'vote_mask']
df_inv.ix[file_stamp, cols] = vote_acc, vote_kap#, False, mean_acc, mean_kap, False
df_inv.to_csv(inventory_txt, sep='\t')
else:
print '\n"inventory_txt" was not specified.' +\
' Model evaluation scores will not be recorded...'
print ''
print 'Vote accuracy .............. ', vote_acc
print 'Vote kappa ................. ', vote_kap
#print 'Mean accuracy .............. ', mean_acc
#print 'Mean kappa ................. ', mean_kap
else:
print '\n"confusion_params" was not specified.' +\
' This model will not be evaluated...' #'''
print '\nTotal prediction runtime: %.1f\n' % ((time.time() - t0)/60)
def main(params, inventory_txt=None):
inputs, df_var = stem.read_params(params)
for i in inputs:
exec("{0} = str({1})").format(i, inputs[i])
df_var.data_band = [int(b)
for b in df_var.data_band] #sometimes read as float
try:
n_tiles = [int(i) for i in n_tiles.split(',')]
support_size = [int(i) for i in support_size.split(',')]
nodata = int(nodata)
str_check = model_dir, mosaic_path, out_dir, train_params
except NameError as e:
missing_var = str(e).split("'")[1]
msg = "Variable '%s' not specified in param file:\n%s" % (missing_var,
params)
raise NameError(msg)
# Check that all the variables given were used in training and vice versa
try:
_, train_vars = stem.read_params(train_params)
except:
raise NameError('train_params not specified or does not exist')
train_vars = sorted(train_vars.index)
pred_vars = sorted(df_var.index)
unmatched_vars = [v for v in pred_vars if v not in train_vars]
if len(unmatched_vars) != 0:
unmatched_str = '\n'.join(unmatched_vars)
msg = 'Columns not in train params but specified in predict params:\n' + unmatched_str
raise NameError(msg)
unmatched_vars = [v for v in train_vars if v not in pred_vars]
if len(unmatched_vars) != 0:
unmatched_str = '\n'.join(unmatched_vars)
msg = 'Columns not in predict params but specified in train params:\n' + unmatched_str
raise NameError(msg)
# Make sure vars are sorted alphabetically since they were for training
df_var = df_var.reindex(pred_vars)
if not os.path.exists(out_dir): os.mkdir(out_dir)
else: print ('WARNING: out_dir already exists:\n%s\nAny existing files ' + \
'will be overwritten...\n') % out_dir
shutil.copy2(params, out_dir) #Copy the params for reference
if 'confusion_params' in inputs:
#shutil.copy2(confusion_params, out_dir)
conf_bn = os.path.basename(confusion_params)
confusion_params = os.path.join(out_dir, conf_bn)
if not os.path.exists(model_dir):
sys.exit('model_dir does not exist:\n%s' % model_dir)
if not os.path.exists(mosaic_path):
sys.exit('mosaic_path does not exist:\n%s' % mosaic_path)
mosaic_ds = gdal.Open(mosaic_path)
mosaic_tx = mosaic_ds.GetGeoTransform()
xsize = mosaic_ds.RasterXSize
ysize = mosaic_ds.RasterYSize
prj = mosaic_ds.GetProjection()
driver = mosaic_ds.GetDriver()
m_ulx, x_res, x_rot, m_uly, y_rot, y_res = mosaic_tx
predict_dir = os.path.join(out_dir, 'decisiontree_predictions')
if not os.path.exists(predict_dir):
os.mkdir(predict_dir)
set_txt = glob.glob(
os.path.join('/vol/v2/stem/imperv/imperv_bdt',
'decisiontree_models/*support_sets.txt'))[0]
df_sets = pd.read_csv(set_txt, sep='\t', index_col='set_id')
total_sets = len(df_sets)
'''# Loop through each set and generate predictions
t0 = time.time()
for c, (set_id, row) in enumerate(df_sets.iterrows()):
t1 = time.time()
with open(row.dt_file, 'rb') as f:
dt_model = pickle.load(f)
print '\nPredicting for set %s of %s' % (c + 1, total_sets)
ar_coords = row[['ul_x', 'ul_y', 'lr_x', 'lr_y']]
ar_predict = stem.predict_set_in_pieces(set_id, df_var, mosaic_ds, ar_coords,
mosaic_tx, xsize, ysize, dt_model, nodata)
tx = ar_coords.ul_x, x_res, x_rot, ar_coords.ul_y, y_rot, y_res
out_path = os.path.join(predict_dir, 'prediction_%s.bsq' % set_id)
array_to_raster(ar_predict, tx, prj, driver, out_path, gdal.GDT_Byte, nodata=nodata)
print 'Total time for this set: %.1f minutes' % ((time.time() - t1)/60)
#mosaic_ds = None
print '\nTotal time for predicting: %.1f hours\n' % ((time.time() - t0)/3600)#'''
#Aggregate predictions by tile and stitch them back together
if not 'file_stamp' in inputs: file_stamp = os.path.basename(model_dir)
ar_mean, ar_vote, pct_importance, df_sets = stem.aggregate_predictions(
ysize, xsize, nodata, n_tiles, mosaic_ds, support_size, predict_dir,
df_sets, out_dir, file_stamp, prj, driver, 0)
#df_sets.to_csv(set_txt, sep='\t')'''
mosaic_ds = None
ds = gdal.Open('/vol/v2/stem/canopy/canopy_bdt/canopy_bdt_vote.bsq')
ar_vote = ds.ReadAsArray()
ds = None
if 'confusion_params' in locals():
import confusion_matrix as confusion
vote_dir = os.path.join(model_dir, 'evaluation_vote')
mean_dir = os.path.join(model_dir, 'evaluation_mean')
print '\nGetting confusion matrix for vote...'
out_txt = os.path.join(vote_dir, 'confusion.txt')
df_v = confusion.main(confusion_params, ar_vote, out_txt, match=True)
try:
out_txt = os.path.join(vote_dir, 'confusion_avg_kernel.txt')
df_v_off = confusion.main(confusion_params, ar_vote, out_txt)
except Exception as e:
print e
print '\nGetting confusion matrix for mean...'
out_txt = os.path.join(mean_dir, 'confusion.txt')
df_m = confusion.main(confusion_params, ar_mean, out_txt, match=True)
try:
out_txt = os.path.join(mean_dir, 'confusion_avg_kernel.txt')
df_m_off = confusion.main(confusion_params, ar_mean, out_txt)
except Exception as e:
print e
vote_acc = df_v.ix['user', 'producer']
vote_kap = df_v.ix['user', 'kappa']
mean_acc = df_m.ix['user', 'producer']
mean_kap = df_m.ix['user', 'kappa']
if 'inventory_txt':
df_inv = pd.read_csv(inventory_txt, sep='\t', index_col='stamp')
cols = [
'vote_accuracy', 'vote_kappa', 'vote_mask', 'mean_accuracy',
'mean_kappa', 'vote_mask'
]
df_inv.ix[file_stamp, cols] = vote_acc, vote_kap, False, \
mean_acc, mean_kap, False
df_inv.to_csv(inventory_txt, sep='\t')
else:
print '\n"inventory_txt" was not specified.' +\
' Model evaluation scores will not be recorded...'
print ''
print 'Vote accuracy .............. ', vote_acc
print 'Vote kappa ................. ', vote_kap
print 'Mean accuracy .............. ', mean_acc
print 'Mean kappa ................. ', mean_kap
else:
print '\n"confusion_params" was not specified.' +\
' This model will not be evaluated...' #'''
def main(params,
inventory_txt=None,
constant_vars=None,
mosaic_shp=None,
resolution=30,
n_jobs_pred=0,
n_jobs_agg=0,
mosaic_nodata=0):
inputs, df_var = stem_conus.read_params(params)
for i in inputs:
exec("{0} = str({1})").format(i, inputs[i])
df_var.data_band = [int(b)
for b in df_var.data_band] #sometimes read as float
try:
support_size = [int(i) for i in support_size.split(',')]
nodata = int(nodata)
str_check = model_dir, mosaic_path, out_dir, train_params
except NameError as e:
missing_var = str(e).split("'")[1]
msg = "Variable '%s' not specified in param file:\n%s" % (missing_var,
params)
raise NameError(msg)
# Check that all the variables given were used in training and vice versa
try:
train_inputs, train_vars = stem_conus.read_params(train_params)
except:
raise NameError('train_params not specified or does not exist')
train_vars = sorted(train_vars.index)
pred_vars = sorted(df_var.index)
# Make sure vars are sorted alphabetically since they were for training
df_var = df_var.reindex(pred_vars)
unmatched_vars = [v for v in pred_vars if v not in train_vars]
if len(unmatched_vars) != 0:
unmatched_str = '\n'.join(unmatched_vars)
msg = 'Columns not in predict params but specified in train params:\n' + unmatched_str
raise NameError(msg)
if not os.path.exists(out_dir): os.mkdir(out_dir)
else: print ('WARNING: out_dir already exists:\n%s\nAny existing files ' + \
'will be overwritten...\n') % out_dir
if not os.path.exists(os.path.join(out_dir, os.path.basename(params))):
shutil.copy2(params, out_dir) #Copy the params for reference
if 'confusion_params' in inputs:
conf_bn = os.path.basename(confusion_params)
new_conf_path = os.path.join(out_dir, conf_bn)
if not os.path.exists(new_conf_path):
shutil.copy2(confusion_params, out_dir)
confusion_params = new_conf_path
if not os.path.exists(model_dir):
sys.exit('model_dir does not exist:\n%s' % model_dir)
if not os.path.exists(mosaic_path):
sys.exit('mosaic_path does not exist:\n%s' % mosaic_path)
if mosaic_path.endswith('.shp'):
mosaic_type = 'vector'
if 'resolution' not in inputs:
warnings.warn('Resolution not specified. Using default of 30...\n')
mosaic_dataset = ogr.Open(mosaic_path)
mosaic_ds = mosaic_dataset.GetLayer()
min_x, max_x, min_y, max_y = mosaic_ds.GetExtent()
xsize = int((max_x - min_x) / resolution)
ysize = int((max_y - min_y) / resolution)
prj = mosaic_ds.GetSpatialRef().ExportToWkt()
x_res = resolution
y_res = -resolution
x_rot = 0
y_rot = 0
mosaic_tx, extent = stem_conus.tx_from_shp(mosaic_path, x_res, y_res)
#df_tiles = attributes_to_df(mosaic_path)
else:
mosaic_type = 'raster'
mosaic_ds = gdal.Open(mosaic_path)
mosaic_tx = mosaic_ds.GetGeoTransform()
xsize = mosaic_ds.RasterXSize
ysize = mosaic_ds.RasterYSize
prj = mosaic_ds.GetProjection()
driver = mosaic_ds.GetDriver()
m_ulx, x_res, x_rot, m_uly, y_rot, y_res = mosaic_tx
driver = gdal.GetDriverByName('gtiff')
# If number of tiles not given, need to set it
if 'n_tiles' not in inputs:
print 'n_tiles not specified. Using default: 25 x 15 ...\n'
n_tiles = 25, 15
else:
n_tiles = [int(i) for i in n_tiles.split(',')]
df_tiles, df_tiles_rc, tile_size = stem_conus.get_tiles(
n_tiles, xsize, ysize, mosaic_tx)
predict_dir = os.path.join(out_dir, 'decisiontree_predictions')
if not os.path.exists(predict_dir):
os.mkdir(predict_dir)
set_txt = glob.glob(
os.path.join(model_dir, 'decisiontree_models/*support_sets.txt'))[0]
df_sets = pd.read_csv(set_txt, sep='\t', index_col='set_id')
total_sets = len(df_sets)
t0 = time.time()
if 'n_jobs_pred' in inputs:
n_jobs = int(n_jobs_pred)
# Predict in parallel
args = []
t1 = time.time()
print 'Predicting in parallel with %s jobs...' % n_jobs
print 'Building args and making rasters of tile arrays...'
for c, (set_id, row) in enumerate(df_sets.iterrows()):
# Save rasters of tsa arrays ahead of time to avoid needing to pickle or fork mosaic
coords = row[['ul_x', 'ul_y', 'lr_x', 'lr_y']]
'''if mosaic_type == 'vector':
tsa_ar, tsa_off = mosaic.kernel_from_shp(mosaic_ds, coords, mosaic_tx, nodata=0)
else:
tsa_ar, tsa_off = mosaic.extract_kernel(mosaic_ds, 1, coords,
mosaic_tx, xsize, ysize,
nodata=nodata)
set_mosaic_path = os.path.join(predict_dir, 'tsa_%s.tif' % set_id)
tx_out = row.ul_x, mosaic_tx[1], mosaic_tx[2], row.ul_y, mosaic_tx[4], mosaic_tx[5]
np_dtype = get_min_numpy_dtype(tsa_ar)
gdal_dtype = gdal_array.NumericTypeCodeToGDALTypeCode(np_dtype)
mosaic.array_to_raster(tsa_ar, tx_out, prj, driver, set_mosaic_path, gdal_dtype, silent=True)
pct_progress = float(c + 1)/total_sets * 100
sys.stdout.write('\rRetreived points for feature %s of %s (%%%.1f)' % (c + 1, total_sets, pct_progress))
sys.stdout.flush()'''
# Build list of args to pass to the Pool
#tsa_off = stem_conus.calc_offset((mosaic_tx[0], mosaic_tx[3]), (tx_out[0], tx_out[3]), tx_out)
args.append([
coords, mosaic_type, mosaic_path, mosaic_tx, prj, nodata, c,
total_sets, set_id, df_var, xsize, ysize, row.dt_file, nodata,
np.uint8, constant_vars, predict_dir
])
#args.append([c, total_sets, set_id, df_var, set_mosaic_path, tsa_off, coords,
#mosaic_tx, xsize, ysize, row.dt_file, nodata, np.uint8,
#constant_vars, predict_dir])
print '%.1f minutes\n' % ((time.time() - t1) / 60)
p = Pool(n_jobs)
p.map(stem_conus.par_predict, args, 1)
else:
# Loop through each set and generate predictions
for c, (set_id, row) in enumerate(df_sets.iterrows()):
t1 = time.time()
with open(row.dt_file, 'rb') as f:
dt_model = pickle.load(f)
print '\nPredicting for set %s of %s' % (c + 1, total_sets)
coords = row[['ul_x', 'ul_y', 'lr_x', 'lr_y']]
ar_predict = stem_conus.predict_set(set_id, df_var, mosaic_ds,
coords, mosaic_tx, xsize,
ysize, dt_model, nodata,
np.int16, constant_vars)
tx = coords.ul_x, x_res, x_rot, coords.ul_y, y_rot, y_res
out_path = os.path.join(predict_dir, 'prediction_%s.tif' % set_id)
np_dtype = get_min_numpy_dtype(ar_predict)
gdal_dtype = gdal_array.NumericTypeCodeToGDALTypeCode(np_dtype)
mosaic.array_to_raster(ar_predict,
tx,
prj,
driver,
out_path,
gdal.GDT_Byte,
nodata=nodata)
print 'Total time for this set: %.1f minutes' % (
(time.time() - t1) / 60)
#mosaic = None
print '\nTotal time for predicting: %.1f hours\n' % (
(time.time() - t0) / 3600) #''' """
#Aggregate predictions by tile and stitch them back together
if not 'file_stamp' in inputs: file_stamp = os.path.basename(model_dir)
t1 = time.time()
agg_stats = [s.strip().lower() for s in agg_stats.split(',')]
if 'n_jobs_agg' in inputs:
n_jobs_agg = int(n_jobs_agg)
if mosaic_type == 'vector':
nodata_mask = mosaic_ds
else:
if 'mosaic_nodata' in inputs: mosaic_nodata = int(mosaic_nodata)
nodata_mask = mosaic_ds.ReadAsArray() != mosaic_nodata
########################################################################################################################################
# jdb 6/22/17 check for sets that errored - if there are any, remove them from the df_sets DF so that the aggregation step doesn't expect them
setErrorLog = os.path.dirname(predict_dir) + '/predication_errors.txt'
if os.path.isfile(setErrorLog):
with open(setErrorLog) as f:
lines = f.readlines()
badSets = [
int(line.split(':')[1].rstrip().strip()) for line in lines
if 'set_id' in line
]
for thisSet in badSets:
df_sets = df_sets[df_sets.index != thisSet]
########################################################################################################################################
pct_importance, df_sets = stem_conus.aggregate_predictions(
n_tiles, ysize, xsize, nodata, nodata_mask, mosaic_tx, support_size,
agg_stats, predict_dir, df_sets, out_dir, file_stamp, prj, driver,
n_jobs_agg)
#print 'Total aggregation time: %.1f hours\n' % ((time.time() - t0)/3600)
mosaic_ds = None
mosaic_dataset = None
# Save the importance values
importance = pd.DataFrame({
'variable': pred_vars,
'pct_importance': pct_importance,
'index': range(len(pred_vars))
})
importance.set_index('index', inplace=True)
importance['rank'] = [
int(r) for r in importance.pct_importance.rank(method='first',
ascending=False)
]
out_txt = os.path.join(out_dir, '%s_importance.txt' % file_stamp)
importance.to_csv(out_txt, sep='\t') #'''
if 'confusion_params' in locals():
import confusion_matrix as confusion
'''
Read the mean or vote back in '''
if 'vote' in agg_stats:
vote_path = os.path.join(out_dir, '%s_vote.tif' % file_stamp)
ar_vote = gdal.Open(vote_path)
print '\nComputing confusion matrix for vote...'
vote_dir = os.path.join(model_dir, 'evaluation_vote')
out_txt = os.path.join(vote_dir, 'confusion.txt')
df_v = confusion.main(confusion_params,
ar_vote,
out_txt,
match=True)
vote_acc = df_v.ix['producer', 'user']
vote_kap = df_v.ix['producer', 'kappa']
'''try:
out_txt = os.path.join(vote_dir, 'confusion_avg_kernel.txt')
df_v_off = confusion.main(confusion_params, ar_vote, out_txt)
except Exception as e:
print e'''
if 'mean' in agg_stats:
mean_path = os.path.join(out_dir, '%s_mean.tif' % file_stamp)
ar_mean = gdal.Open(mean_path)
print '\nGetting confusion matrix for mean...'
mean_dir = os.path.join(model_dir, 'evaluation_mean')
out_txt = os.path.join(mean_dir, 'confusion.txt')
df_m = confusion.main(confusion_params,
ar_mean,
out_txt,
match=True)
mean_acc = df_m.ix['user', 'producer']
mean_kap = df_m.ix['user', 'kappa']
'''try:
out_txt = os.path.join(mean_dir, 'confusion_avg_kernel.txt')
df_m_off = confusion.main(confusion_params, ar_mean, out_txt)
except Exception as e:
print e#'''
if 'inventory_txt' in inputs:
df_inv = pd.read_csv(inventory_txt, sep='\t', index_col='stamp')
cols = [
'vote_accuracy', 'vote_kappa'
] #, 'vote_mask', 'mean_accuracy', 'mean_kappa', 'vote_mask']
df_inv.ix[
file_stamp,
cols] = vote_acc, vote_kap #, False, mean_acc, mean_kap, False
df_inv.to_csv(inventory_txt, sep='\t')
else:
print '\n"inventory_txt" was not specified.' +\
' Model evaluation scores will not be recorded...'
print ''
if 'vote' in agg_stats:
print 'Vote accuracy .............. ', vote_acc
print 'Vote kappa ................. ', vote_kap
if 'mean' in agg_stats:
print 'Mean accuracy .............. ', mean_acc
print 'Mean kappa ................. ', mean_kap
else:
print '\n"confusion_params" was not specified.' +\
' This model will not be evaluated...' #'''
print '\nTotal prediction runtime: %.1f\n' % ((time.time() - t0) / 60)
def main(params, inventory_txt=None, constant_vars=None, mosaic_shp=None, resolution=30, n_jobs=0, n_jobs_agg=0, mosaic_nodata=0, snap_coord=None, overwrite_tiles=False, tile_id_field='name'):
inputs, df_var = stem.read_params(params)
for i in inputs:
exec ("{0} = str({1})").format(i, inputs[i])
df_var.data_band = [int(b) for b in df_var.data_band]#sometimes read as float
try:
support_size = [int(i) for i in support_size.split(',')]
nodata = int(nodata)
str_check = model_dir, mosaic_path, out_dir, train_params
except NameError as e:
missing_var = str(e).split("'")[1]
msg = "Variable '%s' not specified in param file:\n%s" % (missing_var, params)
raise NameError(msg)
# Check that all the variables given were used in training and vice versa
try:
train_inputs, train_vars = stem.read_params(train_params)
except:
raise NameError('train_params not specified or does not exist')
train_vars = sorted(train_vars.index)
pred_vars = sorted(df_var.index)
# Make sure vars are sorted alphabetically since they were for training
df_var = df_var.reindex(pred_vars)
unmatched_vars = [v for v in pred_vars if v not in train_vars]
if len(unmatched_vars) != 0:
unmatched_str = '\n'.join(unmatched_vars)
msg = 'Columns not in predict params but specified in train params:\n' + unmatched_str
raise NameError(msg)
if not os.path.exists(out_dir): os.mkdir(out_dir)
else: print ('WARNING: out_dir already exists:\n%s\nAny existing files ' + \
'will be overwritten...\n') % out_dir
if not os.path.exists(os.path.join(out_dir, os.path.basename(params))):
shutil.copy2(params, out_dir) #Copy the params for reference
if 'confusion_params' in inputs:
conf_bn = os.path.basename(confusion_params)
new_conf_path = os.path.join(out_dir, conf_bn)
if not os.path.exists(new_conf_path):
shutil.copy2(confusion_params, out_dir)
confusion_params = new_conf_path
if not os.path.exists(model_dir):
sys.exit('model_dir does not exist:\n%s' % model_dir)
if not os.path.exists(mosaic_path):
sys.exit('mosaic_path does not exist:\n%s' % mosaic_path)
predict_dir = os.path.join(out_dir, 'decisiontree_predictions')
if not os.path.exists(predict_dir):
os.mkdir(predict_dir)
if not 'file_stamp' in inputs: file_stamp = os.path.basename(model_dir)
db_path = os.path.join(model_dir, file_stamp + '.db')
try:
engine = sqlalchemy.create_engine('sqlite:///%s' % db_path)
with engine.connect() as con, con.begin():
df_sets = pd.read_sql_table('support_sets', con, index_col='set_id')#'''
except:
set_txt = glob.glob(os.path.join(model_dir, 'decisiontree_models/*support_sets.txt'))[0]
if not os.path.isfile(set_txt):
raise IOError('No database or support set txt file found')
df_sets = pd.read_csv(set_txt, sep='\t', index_col='set_id')
if mosaic_path.endswith('.shp'):
mosaic_type = 'vector'
# if subset specified, clip the mosaic and set mosaic path to clipped shp
if 'subset_shp' in inputs:
out_shp_bn = os.path.basename(mosaic_path).replace('.shp', '_clipped.shp')
out_shp = os.path.join(out_dir, out_shp_bn)
cmd = 'ogr2ogr -clipsrc {clip_shp} {out_shp} {in_shp}'.format(clip_shp=subset_shp, out_shp=out_shp, in_shp=mosaic_path)
subprocess.call(cmd, shell=True)#'''
mosaic_path = out_shp
mosaic_dataset = ogr.Open(mosaic_path)
mosaic_ds = mosaic_dataset.GetLayer()
min_x, max_x, min_y, max_y = mosaic_ds.GetExtent()
if 'resolution' not in inputs:
warnings.warn('Resolution not specified. Using default of 30...\n')
# If subset specified, just get sets that overlap the subset
if 'subset_shp' in inputs:
mosaic_geom = ogr.Geometry(ogr.wkbMultiPolygon)
for feature in mosaic_ds:
mosaic_geom.AddGeometry(feature.GetGeometryRef())
df_sets = stem.get_overlapping_sets(df_sets, mosaic_geom)
xsize = int((max_x - min_x)/resolution)
ysize = int((max_y - min_y)/resolution)
prj = mosaic_ds.GetSpatialRef().ExportToWkt()
x_res = resolution
y_res = -resolution
x_rot = 0
y_rot = 0
if 'snap_coord' in train_inputs:
snap_coord = train_inputs['snap_coord'].replace('"','')
snap_coord = [float(c) for c in snap_coord.split(',')]#'''
mosaic_tx, extent = stem.tx_from_shp(mosaic_path, x_res, y_res, snap_coord=snap_coord)
tiles = stem.attributes_to_df(mosaic_path) # Change to accept arbittary geometry
else:
mosaic_type = 'raster'
mosaic_ds = gdal.Open(mosaic_path)
mosaic_tx = mosaic_ds.GetGeoTransform()
xsize = mosaic_ds.RasterXSize
ysize = mosaic_ds.RasterYSize
prj = mosaic_ds.GetProjection()
driver = mosaic_ds.GetDriver()
m_ulx, x_res, x_rot, m_uly, y_rot, y_res = mosaic_tx
#driver = gdal.GetDriverByName('gtiff')
# If number of tiles not given, need to set it
if 'n_tiles' not in inputs:
print 'n_tiles not specified. Using default: 25 x 15 ...\n'
n_tiles = 90, 40
else:
n_tiles = [int(i) for i in n_tiles.split(',')]
#df_tiles, df_tiles_rc, tile_size = stem.get_tiles(n_tiles, xsize, ysize, mosaic_tx)
total_sets = len(df_sets)
t0 = time.time()
last_dts = pd.Series()
agg_stats = [s.strip().lower() for s in agg_stats.split(',')]
n_jobs = int(n_jobs)
tile_dir = os.path.join(model_dir, 'temp_tiles')
#tile_dir = '/home/server/pi/homes/shooper/delete_test'
if not os.path.isdir(tile_dir):
os.mkdir(tile_dir)
tile_path_template = os.path.join(tile_dir, 'tile_{tile_id}_%(stat)s.tif')
n_tiles = len(tiles)
if not overwrite_tiles:
files = os.listdir(tile_dir)
tile_files = pd.DataFrame(columns=agg_stats, index=tiles[tile_id_field])
for stat in agg_stats:
stat_match = [f.split('_')[1] for f in fnmatch.filter(files, 'tile*%s.tif' % stat)]
tile_files[stat] = pd.Series(np.ones(len(stat_match)), index=stat_match)
index_field = tiles.index.name
tiles[index_field] = tiles.index
tiles = tiles.set_index(tile_id_field, drop=False)[tile_files.isnull().any(axis=1)]
tiles.set_index(index_field, inplace=True)
tiles['ul_x'] = [stem.get_ul_coord(xmin, xmax, x_res)
for i, (xmin, xmax) in tiles[['xmin','xmax']].iterrows()]
tiles['ul_y'] = [stem.get_ul_coord(ymin, ymax, y_res)
for i, (ymin, ymax) in tiles[['ymin','ymax']].iterrows()]
tiles['lr_x'] = [xmax if ulx == xmin else xmin for i, (ulx, xmin, xmax)
in tiles[['ul_x', 'xmin','xmin']].iterrows()]
tiles['lr_y'] = [ymax if uly == ymin else ymin for i, (uly, ymin, ymax)
in tiles[['ul_y', 'ymin','ymin']].iterrows()]
support_nrows = int(support_size[0]/abs(y_res))
support_ncols = int(support_size[1]/abs(x_res))
t1 = time.time()
args = [(tile_info, mosaic_path, mosaic_tx, df_sets, df_var, (support_nrows, support_ncols), agg_stats, tile_path_template, prj, nodata, snap_coord) for i, (t_ind, tile_info) in enumerate(tiles[tiles['name'].isin(['1771', '3224', '0333', '0558'])].iterrows())]
#args = [(i + 1, n_tiles, t1, tile_info, mosaic_path, mosaic_tx, df_sets, df_var, (support_nrows, support_ncols), agg_stats, tile_path_template, prj, nodata, snap_coord) for i, (t_ind, tile_info) in enumerate(tiles.iterrows())]
if n_jobs > 1:
print 'Predicting with %s jobs...\n' % n_jobs
pool = Pool(n_jobs)
pool.map(stem.predict_tile, args, 1)
pool.close()
pool.join()
else:
for arg in args:
print 'Predicting with 1 job ...\n'
stem.predict_tile(*arg)#'''
print '\n\nFinished predicting in %.1f hours. \n\nStitching tiles...' % ((time.time() - t1)/3600)
t1 = time.time()
mosaic_ul = mosaic_tx[0], mosaic_tx[3]
driver = gdal.GetDriverByName('gtiff')
for stat in agg_stats:
if stat == 'stdv':
this_nodata = -9999
ar = np.full((ysize, xsize), this_nodata, dtype=np.int16)
else:
this_nodata = nodata
ar = np.full((ysize, xsize), this_nodata, dtype=np.uint8)
for tile_id, tile_coords in tiles.iterrows():
tile_file = os.path.join(tile_dir, 'tile_%s_%s.tif' % (tile_coords[tile_id_field], stat))
ds = gdal.Open(tile_file)
tile_tx = ds.GetGeoTransform()
tile_ul = tile_tx[0], tile_tx[3]
row_off, col_off = stem.calc_offset(mosaic_ul, tile_ul, mosaic_tx)
# Make sure the tile doesn't exceed the size of ar
tile_rows = min(ds.RasterYSize + row_off, ysize) - row_off
tile_cols = min(ds.RasterXSize + col_off, xsize) - col_off
ar_tile = ds.ReadAsArray(0, 0, tile_cols, tile_rows)
try:
ar[row_off : row_off + tile_rows, col_off : col_off + tile_cols] = ar_tile
except Exception as e:
import pdb; pdb.set_trace()
out_path = os.path.join(model_dir, '%s_%s.tif' % (file_stamp, stat))
#out_path = os.path.join('/home/server/pi/homes/shooper/delete_test', '%s_%s.tif' % (file_stamp, stat))
gdal_dtype = gdal_array.NumericTypeCodeToGDALTypeCode(ar.dtype)
mosaic.array_to_raster(ar, mosaic_tx, prj, driver, out_path, gdal_dtype, nodata=this_nodata)
# Clean up the tiles
shutil.rmtree(tile_dir)
print 'Time for stitching: %.1f minutes\n' % ((time.time() - t1)/60)
# Get feature importances and max importance per set
t1 = time.time()
print 'Getting importance values...'
importance_cols = sorted([c for c in df_sets.columns if 'importance' in c])
df_sets['max_importance'] = nodata
if len(importance_cols) == 0:
# Loop through and get importance
importance_per_var = []
for s, row in df_sets.iterrows():
with open(row.dt_file, 'rb') as f:
dt_model = pickle.load(f)
max_importance, this_importance = stem.get_max_importance(dt_model)
df_sets.ix[s, 'max_importance'] = max_importance
importance_per_var.append(this_importance)
importance = np.array(importance_per_var).mean(axis=0)
else:
df_sets['max_importance'] = np.argmax(df_sets[importance_cols].values, axis=1)
importance = df_sets[importance_cols].mean(axis=0).values
pct_importance = importance / importance.sum()
print '%.1f minutes\n' % ((time.time() - t1)/60)
# Save the importance values
importance = pd.DataFrame({'variable': pred_vars,
'pct_importance': pct_importance,
'index': range(len(pred_vars))
})
importance.set_index('index', inplace=True)
importance['rank'] = [int(r) for r in importance.pct_importance.rank(method='first', ascending=False)]
out_txt = os.path.join(out_dir, '%s_importance.txt' % file_stamp)
importance.to_csv(out_txt, sep='\t')#'''
if 'confusion_params' in locals():
import confusion_matrix as confusion
'''
Read the mean or vote back in '''
if 'vote' in agg_stats:
vote_path = os.path.join(out_dir, '%s_vote.tif' % file_stamp)
ar_vote = gdal.Open(vote_path)
print '\nComputing confusion matrix for vote...'
vote_dir = os.path.join(model_dir, 'evaluation_vote')
out_txt = os.path.join(vote_dir, 'confusion.txt')
df_v = confusion.main(confusion_params, ar_vote, out_txt, match=True)
vote_acc = df_v.ix['producer', 'user']
vote_kap = df_v.ix['producer', 'kappa']
'''try:
out_txt = os.path.join(vote_dir, 'confusion_avg_kernel.txt')
df_v_off = confusion.main(confusion_params, ar_vote, out_txt)
except Exception as e:
print e'''
if 'mean' in agg_stats:
mean_path = os.path.join(out_dir, '%s_mean.tif' % file_stamp)
ar_mean = gdal.Open(mean_path)
print '\nGetting confusion matrix for mean...'
mean_dir = os.path.join(model_dir, 'evaluation_mean')
out_txt = os.path.join(mean_dir, 'confusion.txt')
df_m = confusion.main(confusion_params, ar_mean, out_txt, match=True)
mean_acc = df_m.ix['user','producer']
mean_kap = df_m.ix['user', 'kappa']
'''try:
out_txt = os.path.join(mean_dir, 'confusion_avg_kernel.txt')
df_m_off = confusion.main(confusion_params, ar_mean, out_txt)
except Exception as e:
print e#'''
if 'inventory_txt' in inputs:
df_inv = pd.read_csv(inventory_txt, sep='\t', index_col='stamp')
cols = ['vote_accuracy', 'vote_kappa']#, 'vote_mask', 'mean_accuracy', 'mean_kappa', 'vote_mask']
df_inv.ix[file_stamp, cols] = vote_acc, vote_kap#, False, mean_acc, mean_kap, False
df_inv.to_csv(inventory_txt, sep='\t')
else:
print '\n"inventory_txt" was not specified.' +\
' Model evaluation scores will not be recorded...'
print ''
if 'vote' in agg_stats:
print 'Vote accuracy .............. ', vote_acc
print 'Vote kappa ................. ', vote_kap
if 'mean' in agg_stats:
print 'Mean accuracy .............. ', mean_acc
print 'Mean kappa ................. ', mean_kap
else:
print '\n"confusion_params" was not specified.' +\
' This model will not be evaluated...' #'''
print '\nTotal prediction runtime: %.1f hours\n' % ((time.time() - t0)/3600)