diff --git a/bayes_opt_run.py b/bayes_opt_run.py
new file mode 100644
index 0000000000000000000000000000000000000000..96f820643eb1a174a211056226a5e3fe125fdf3a
--- /dev/null
+++ b/bayes_opt_run.py
@@ -0,0 +1,59 @@
+from bayes_opt import BayesianOptimization
+from bayes_opt.logger import JSONLogger
+from bayes_opt.event import Events
+
+
+from pipeline_test_command_params_just_tls import RunCommand
+from pipeline_test_command_params_just_tls import main as pipeline_main
+
+def bayes_opt_main(
+ n_tiles,
+ slice_thickness,
+ find_stems_height,
+ find_stems_thickness,
+ graph_maximum_cumulative_gap,
+ add_leaves_voxel_length,
+ find_stems_min_points,
+ graph_edge_length,
+ add_leaves_edge_length
+):
+ n_tiles = int(n_tiles)
+ find_stems_min_points = int(find_stems_min_points)
+ return pipeline_main(
+ n_tiles,
+ slice_thickness,
+ find_stems_height,
+ find_stems_thickness,
+ graph_maximum_cumulative_gap,
+ add_leaves_voxel_length,
+ find_stems_min_points,
+ graph_edge_length,
+ add_leaves_edge_length
+ )
+
+pbounds = {
+ 'n_tiles': (3, 3),
+ 'slice_thickness': (0.25, 0.75),
+ 'find_stems_height': (0.5, 2.0),
+ 'find_stems_thickness': (0.1, 1.0),
+ 'graph_maximum_cumulative_gap': (5, 20),
+ 'add_leaves_voxel_length': (0.1, 0.5),
+ 'find_stems_min_points': (50, 500),
+ 'graph_edge_length': (0.1, 2.0),
+ 'add_leaves_edge_length': (0.2, 1.5)
+}
+
+optimizer = BayesianOptimization(
+ f=bayes_opt_main,
+ pbounds=pbounds,
+ random_state=1,
+ allow_duplicate_points=True
+)
+
+logger = JSONLogger(path="./bayes_opt_run_logs.json")
+optimizer.subscribe(Events.OPTIMIZATION_STEP, logger)
+
+optimizer.maximize(
+ init_points=5,
+ n_iter=100
+)
diff --git a/helpers/find_param_importance.py b/helpers/find_param_importance.py
new file mode 100644
index 0000000000000000000000000000000000000000..518a542cbee94603af825aee7f7901151e2be340
--- /dev/null
+++ b/helpers/find_param_importance.py
@@ -0,0 +1,105 @@
+# partially based on : https://medium.com/analytics-vidhya/feature-importance-explained-bfc8d874bcf
+import json
+import matplotlib.pyplot as plt
+import pandas as pd
+import argparse
+from sklearn.linear_model import LinearRegression
+from sklearn.preprocessing import StandardScaler
+
+class FindParamImportance:
+ def __init__(
+ self,
+ logs_json_file,
+ plot_file_path='feature_importance.png',
+ verbose=False
+ ) -> None:
+ self.logs_json_file = logs_json_file
+ self.plot_file_path = plot_file_path
+ self.verbose = verbose
+
+ def get_data(self):
+ runs = []
+ for line in open('logs.json', 'r'):
+ runs.append(json.loads(line))
+
+ # get header of the logs
+ header = ['target']
+
+ for key in runs[0]['params']:
+ header.append(key)
+
+ # create a dictionary from the header with empty lists
+ data = {}
+ data = data.fromkeys(header)
+ data = {key: [] for key in header}
+
+ # fill the dictionary with the data
+ for run in runs:
+ data['target'].append(run['target'])
+ for key in run['params']:
+ data[key].append(run['params'][key])
+
+ # create a dataframe from the dictionary
+ df = pd.DataFrame(data)
+
+ return df
+
+ def get_feature_importance(self, df):
+ # get the features
+ X = df.drop('target', axis=1)
+ # get the target
+ y = df['target']
+
+ ss = StandardScaler()
+ X_scaled = ss.fit_transform(X)
+
+ model=LinearRegression()
+ model.fit(X_scaled,y)
+ importance=model.coef_
+
+ # combine importance with the feature names
+ feature_importance = pd.DataFrame({'feature': X.columns, 'importance': abs(importance)})
+ # sort the values
+ feature_importance.sort_values(by='importance', ascending=True, inplace=True)
+
+ if self.verbose:
+ print(feature_importance)
+
+ return feature_importance
+
+ def gen_plot_of_feature_importance(self, feature_importance):
+ plt.figure(figsize=(10, 6))
+ plt.barh(feature_importance['feature'], feature_importance['importance'])
+ plt.title('Feature Importance')
+
+ # save the plot
+ plt.savefig(self.plot_file_path)
+
+
+ def main(self):
+ df = self.get_data()
+ feature_importance = self.get_feature_importance(df)
+ self.gen_plot_of_feature_importance(feature_importance)
+ if self.verbose:
+ print('Done')
+ print('Plot saved to: ', self.plot_file_path)
+
+if __name__ == '__main__':
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--logs_json_file', type=str, default='logs.json')
+ parser.add_argument('--plot_file_path', type=str, default='feature_importance.png')
+ parser.add_argument('--verbose', help="Print more information.", action="store_true")
+
+
+ args = parser.parse_args()
+ logs_json_file = args.logs_json_file
+ plot_file_path = args.plot_file_path
+ verbose = args.verbose
+
+ find_param_importance = FindParamImportance(
+ logs_json_file=logs_json_file,
+ plot_file_path=plot_file_path,
+ verbose=verbose
+ )
+
+ find_param_importance.main()
\ No newline at end of file
diff --git a/pipeline_test_command_params_just_tls.py b/pipeline_test_command_params_just_tls.py
index 5242fb987278809a3bfd3d54cfa32b963e4e3eed..7fcd28cdb9f8c7d5e282bdb2ca8e55259faa115e 100644
--- a/pipeline_test_command_params_just_tls.py
+++ b/pipeline_test_command_params_just_tls.py
@@ -5,9 +5,6 @@ import wandb
from metrics.instance_segmentation_metrics_in_folder import \
InstanceSegmentationMetricsInFolder
-# wandb.login()
-
-# wandb.init(project="instance_segmentation_classic", entity="smart_forest")
# define a class to run the command with arguments
class RunCommand:
@@ -30,9 +27,12 @@ def main(
graph_edge_length,
add_leaves_edge_length
):
+
+ USE_WANDB = False
# initialize the sweep
- run = wandb.init(project="paper-sweep-nibio-model-just-tls", entity="smart_forest")
+ if USE_WANDB:
+ run = wandb.init(project="paper-sweep-nibio-model-just-tls", entity="smart_forest")
# get files for the sweep
print("Getting files for the sweep")
@@ -92,7 +92,10 @@ def main(
# log the metric
print("Logging the metric")
- wandb.log({"f1_score": f1_score})
+ if USE_WANDB:
+ wandb.log({"f1_score": f1_score})
+
+ return f1_score
if __name__ == "__main__":
# use argparse to get the arguments