'batch_size': BATCH_SIZE,
'epochs': EPOCHS,
'verbose': 1,
'callbacks': [es],
'use_multiprocessing': True
}
# ==================================================================================
# Training and prediction
training2(img_list, pctls, model_func, feat_list_new, uncertainty, data_path,
batch, DROPOUT_RATE, HOLDOUT, **model_params)
prediction(img_list, pctls, feat_list_new, data_path, batch, remove_perm=True)
viz_params = {
'img_list': img_list,
'pctls': pctls,
'data_path': data_path,
'uncertainty': uncertainty,
'batch': batch,
'feat_list_new': feat_list_new
}
viz = VizFuncs(viz_params)
viz.metric_plots()
viz.time_plot()
# viz.metric_plots_multi()
viz.false_map()
# viz.time_size()
tf.config.set_soft_device_placement(True)
# tf.config.experimental.set_visible_devices(NUM_PARALLEL_EXEC_UNITS, 'CPU')
os.environ["OMP_NUM_THREADS"] = str(NUM_PARALLEL_EXEC_UNITS)
os.environ["KMP_BLOCKTIME"] = "30"
os.environ["KMP_SETTINGS"] = "1"
os.environ["KMP_AFFINITY"] = "granularity=fine,verbose,compact,1,0"
# ==================================================================================
# Training and prediction with random batches of clouds
cloud_dir = data_path / 'clouds'
training6(img_list, pctls, model_func, feat_list_new, data_path, batch, T,
DROPOUT_RATE, **model_params)
prediction(img_list,
pctls,
feat_list_new,
data_path,
batch,
remove_perm=True,
**model_params)
viz = VizFuncs(viz_params)
viz.metric_plots()
viz.cir_image()
viz.time_plot()
viz.false_map()
viz.metric_plots_multi()
viz.time_size()
accuracy.append(accuracy_score(y_test, preds))
precision.append(precision_score(y_test, preds))
recall.append(recall_score(y_test, preds))
f1.append(f1_score(y_test, preds))
del preds, p_hat, aleatoric, epistemic, X_test, y_test, model, data_test, data_vector_test, data_ind_test
metrics = pd.DataFrame(
np.column_stack([pctls, accuracy, precision, recall, f1]),
columns=['cloud_cover', 'accuracy', 'precision', 'recall', 'f1'])
metrics.to_csv(metrics_path / 'metrics.csv', index=False)
times = [float(i) for i in times]
times_df = pd.DataFrame(np.column_stack([pctls, times]),
columns=['cloud_cover', 'testing_time'])
times_df.to_csv(metrics_path / 'testing_times.csv', index=False)
# ======================================================================================================================
training_BNN_gen_model(img_list_train, feat_list_new, model_func, data_path,
batch, dropout_rate, **model_params)
prediction_BNN_gen_model(img_list_test, pctls, feat_list_new, data_path, batch,
MC_passes, **model_params)
viz = VizFuncs(viz_params)
viz.metric_plots()
viz.metric_plots_multi()
viz.time_plot()
viz.false_map(probs=False, save=False)
viz.false_map_borders()
viz.fpfn_map()
viz.uncertainty_map_NN()
drop_intermediate=True)
fpr = np.array(fpr)
tpr = np.array(tpr)
roc_vals = pd.DataFrame(np.column_stack([fpr, tpr, thresh]),
columns=['fpr', 'tpr', 'thresh'])
roc_vals.to_csv(metrics_path /
'{}'.format('roc_curve_' + str(pctl) + '.csv'),
index=False)
# Delete ROC data to save space
# for batch in batches:
# for img in img_list:
# metrics_path = data_path / batch / 'metrics' / 'testing' / img
# for pctl in pctls:
# Path(metrics_path / '{}'.format('roc_curve_' + str(pctl) + '.csv')).unlink()
# ======================================================================================================================
# Plot ROC curves
for batch in batches:
viz_params = {
'img_list': img_list,
'pctls': pctls,
'data_path': data_path,
'batch': batch,
'feat_list_new': feat_list_new
}
viz = VizFuncs(viz_params)
'batch': batch,
'feat_list_new': feat_list_new
}
NUM_PARALLEL_EXEC_UNITS = os.cpu_count()
config = tf.compat.v1.ConfigProto(
intra_op_parallelism_threads=NUM_PARALLEL_EXEC_UNITS,
inter_op_parallelism_threads=4,
allow_soft_placement=True,
device_count={'CPU': NUM_PARALLEL_EXEC_UNITS})
session = tf.compat.v1.Session(config=config)
tf.compat.v1.keras.backend.set_session(session)
os.environ["KMP_BLOCKTIME"] = "30"
os.environ["KMP_SETTINGS"] = "1"
os.environ["KMP_AFFINITY"] = "granularity=fine,verbose,compact,1,0"
os.environ['MKL_NUM_THREADS'] = str(NUM_PARALLEL_EXEC_UNITS)
os.environ['GOTO_NUM_THREADS'] = str(NUM_PARALLEL_EXEC_UNITS)
os.environ['OMP_NUM_THREADS'] = str(NUM_PARALLEL_EXEC_UNITS)
# ==================================================================================
training_bnn(img_list, pctls, feat_list_new, data_path, batch, **model_params)
prediction_bnn(img_list, pctls, feat_list_new, data_path, batch, MC_passes)
viz = VizFuncs(viz_params)
viz.metric_plots()
viz.cir_image()
viz.time_plot()
viz.false_map(probs=False, save=False)
viz.false_map_borders()
viz.metric_plots_multi()
viz.median_highlight()
'feat_list_new': feat_list_new } # Set some optimized config parameters tf.config.threading.set_intra_op_parallelism_threads(NUM_PARALLEL_EXEC_UNITS) tf.config.threading.set_inter_op_parallelism_threads(2) tf.config.set_soft_device_placement(True) # tf.config.experimental.set_visible_devices(NUM_PARALLEL_EXEC_UNITS, 'CPU') os.environ["OMP_NUM_THREADS"] = str(NUM_PARALLEL_EXEC_UNITS) os.environ["KMP_BLOCKTIME"] = "30" os.environ["KMP_SETTINGS"] = "1" os.environ["KMP_AFFINITY"] = "granularity=fine,verbose,compact,1,0" # ================================================================================== # Training and prediction with random batches of clouds cloud_dir = data_path / 'clouds' # training3(img_list, pctls, model_func, feat_list_new, uncertainty, # data_path, batch, DROPOUT_RATE, HOLDOUT, **model_params) # # prediction_with_uncertainty(img_list, pctls, feat_list_new, data_path, batch, DROPOUT_RATE, MC_PASSES=MC_PASSES, # weight_decay=0.005, length_scale=0.00001, remove_perm=True, **model_params) viz = VizFuncs(viz_params) viz.metric_plots() viz.time_plot() # viz.false_map() viz.metric_plots_multi() # viz.time_size()
# Set some optimized config parameters
NUM_PARALLEL_EXEC_UNITS = os.cpu_count()
config = tf.compat.v1.ConfigProto(
intra_op_parallelism_threads=NUM_PARALLEL_EXEC_UNITS,
inter_op_parallelism_threads=4,
allow_soft_placement=True,
device_count={'CPU': NUM_PARALLEL_EXEC_UNITS})
session = tf.compat.v1.Session(config=config)
tf.compat.v1.keras.backend.set_session(session)
# os.environ["OMP_NUM_THREADS"] = "4"
os.environ["KMP_BLOCKTIME"] = "30"
os.environ["KMP_SETTINGS"] = "1"
os.environ["KMP_AFFINITY"] = "granularity=fine,verbose,compact,1,0"
os.environ['MKL_NUM_THREADS'] = str(NUM_PARALLEL_EXEC_UNITS)
os.environ['GOTO_NUM_THREADS'] = str(NUM_PARALLEL_EXEC_UNITS)
os.environ['OMP_NUM_THREADS'] = str(NUM_PARALLEL_EXEC_UNITS)
# ==================================================================================
# training4(img_list, pctls, model_func, feat_list_new, data_path, batch, **model_params)
# prediction(img_list, pctls, feat_list_new, data_path, batch, remove_perm=True, **model_params)
viz = VizFuncs(viz_params)
# viz.metric_plots()
# viz.cir_image()
# viz.time_plot()
# viz.false_map(probs=False, save=False)
viz.false_map_borders()
# viz.metric_plots_multi()
# viz.median_highlight()
'glacial_outwash_coarse', 'hydric', 'eolian_sed_coarse', 'eolian_sed_fine',
'saline_lake_sed', 'alluv_coastal_sed_fine', 'coastal_sed_coarse',
'GSW_distSeasonal', 'aspect', 'curve', 'elevation', 'hand', 'slope', 'spi',
'twi', 'sti', 'GSW_perm', 'flooded'
]
img_list = ['4337_LC08_026038_20160325_1']
viz_params = {
'img_list': img_list,
'pctls': pctls,
'data_path': data_path,
'batch': batch,
'feat_list_new': feat_list_new
}
viz = VizFuncs(viz_params)
viz.cir_image(overwrite=True)
# Doesn't work very well for CIR images - water and dry earth are both turqoise.
def hist_equalize_cir(img,
view_hist=False,
view_img=False,
std_low=1.75,
std_high=1.75,
save=False):
spectra_stack_path = data_path / 'images' / img / 'stack' / 'spectra_stack.tif'
band_combo_dir = data_path / 'band_combos'
cir_file = band_combo_dir / '{}'.format(img + '_cir_img' + '.png')
with rasterio.open(spectra_stack_path, 'r') as f:
print('Evaluating predictions')
accuracy.append(accuracy_score(y_test, preds))
precision.append(precision_score(y_test, preds))
recall.append(recall_score(y_test, preds))
f1.append(f1_score(y_test, preds))
del preds, pred_probs, X_test, y_test, trained_model, data_test, data_vector_test, data_ind_test
metrics = pd.DataFrame(np.column_stack([pctls, accuracy, precision, recall, f1]),
columns=['cloud_cover', 'accuracy', 'precision', 'recall', 'f1'])
metrics.to_csv(metrics_path / 'metrics.csv', index=False)
times = [float(i) for i in times] # Convert time objects to float, otherwise valMetrics will be non-numeric
times_df = pd.DataFrame(np.column_stack([pctls, times]),
columns=['cloud_cover', 'testing_time'])
times_df.to_csv(metrics_path / 'testing_times.csv', index=False)
# ======================================================================================================================
# rf_training(img_list, pctls, feat_list_new, data_path, batch, n_jobs=None)
# prediction_rf(img_list, pctls, feat_list_new, data_path, batch)
viz = VizFuncs(viz_params)
# viz.metric_plots()
viz.cir_image()
viz.rgb_image(percent=1.25, overwrite=True)
# viz.time_plot()
# viz.false_map(probs=True, save=False)
# viz.false_map_borders()
# viz.metric_plots_multi()
# viz.median_highlight()
recall.append(recall_score(y_test, preds))
f1.append(f1_score(y_test, preds))
roc_auc.append(roc_auc_score(y_test, pred_probs[:, 1]))
del preds, pred_probs, X_test, y_test, trained_model, data_test, data_vector_test, data_ind_test
metrics = pd.DataFrame(
np.column_stack([pctls, accuracy, precision, recall, f1, roc_auc]),
columns=[
'cloud_cover', 'accuracy', 'precision', 'recall', 'f1', 'auc'
])
metrics.to_csv(metrics_path / 'metrics.csv', index=False)
times = [
float(i) for i in times
] # Convert time objects to float, otherwise valMetrics will be non-numeric
times_df = pd.DataFrame(np.column_stack([pctls, times]),
columns=['cloud_cover', 'testing_time'])
times_df.to_csv(metrics_path / 'testing_times.csv', index=False)
# ======================================================================================================================
# NN_training(img_list, pctls, model_func, feat_list_new, feat_list_all, data_path, batch, **model_params)
NN_prediction(img_list, pctls, feat_list_all, data_path, batch, **model_params)
viz = VizFuncs(viz_params)
viz.metric_plots()
# viz.metric_plots_multi()
# viz.median_highlight()
# viz.time_plot()
# viz.false_map(probs=False, save=False)
# viz.false_map_borders()
