From bbad0e522d6b4b392f1926fa935f2a7fac093411 Mon Sep 17 00:00:00 2001
From: Luc Giffon <luc.giffon@lis-lab.fr>
Date: Thu, 26 Mar 2020 08:48:31 +0100
Subject: [PATCH] results_to_csv script
---
code/vizualisation/results_to_csv.py | 102 +++++++++++++++++++++++++++
1 file changed, 102 insertions(+)
create mode 100644 code/vizualisation/results_to_csv.py
diff --git a/code/vizualisation/results_to_csv.py b/code/vizualisation/results_to_csv.py
new file mode 100644
index 0000000..d800d64
--- /dev/null
+++ b/code/vizualisation/results_to_csv.py
@@ -0,0 +1,102 @@
+from pathlib import Path
+import os
+import pandas as pd
+from pprint import pprint
+import pickle
+from collections import defaultdict
+
+from dotenv import load_dotenv, find_dotenv
+
+
+dct_experiment_id_subset = dict((str(idx), "train+dev/train+dev") for idx in range(1, 9))
+dct_experiment_id_subset.update(dict((str(idx), "train/dev") for idx in range(9, 17)))
+
+dct_experiment_id_technique = {"1": 'None',
+ "2": 'Random',
+ "3": 'OMP',
+ "4": 'OMP Distillation',
+ "5": 'Kmeans',
+ "6": 'Zhang Similarities',
+ "7": 'Zhang Predictions',
+ "8": 'Ensemble',
+ "9": 'None',
+ "10": 'Random',
+ "11": 'OMP',
+ "12": 'OMP Distillation',
+ "13": 'Kmeans',
+ "14": 'Zhang Similarities',
+ "15": 'Zhang Predictions',
+ "16": 'Ensemble'
+ }
+
+
+dct_dataset_fancy = {
+ "boston": "Boston",
+ "breast_cancer": "Breast Cancer",
+ "california_housing": "California Housing",
+ "diabetes": "Diabetes",
+ "diamonds": "Diamonds",
+ "digits": "Digits",
+ "iris": "Iris",
+ "kin8nm": "Kin8nm",
+ "kr-vs-kp": "KR-VS-KP",
+ "olivetti_faces": "Olivetti Faces",
+ "spambase": "Spambase",
+ "steel-plates": "Steel Plates",
+ "wine": "Wine",
+ "gamma": "Gamma",
+ "lfw_pairs": "LFW Pairs"
+}
+
+skip_attributes = ["datetime", "model_weights"]
+
+
+if __name__ == "__main__":
+
+ load_dotenv(find_dotenv('.env'))
+ dir_name = "results/bolsonaro_models_25-03-20"
+ dir_path = Path(os.environ["project_dir"]) / dir_name
+
+ output_dir_file = dir_path / "results.csv"
+
+ dct_results = defaultdict(lambda: [])
+
+ for root, dirs, files in os.walk(dir_path, topdown=False):
+ for file_str in files:
+ path_dir = Path(root)
+ path_file = path_dir / file_str
+ obj_results = pickle.load(open(path_file, 'rb'))
+
+ path_dir_split = str(path_dir).split("/")
+
+ bool_wo_weights = "no_weights" in str(path_file)
+
+ if bool_wo_weights:
+ forest_size = int(path_dir_split[-1].split("_")[0])
+ else:
+ forest_size = int(path_dir_split[-1])
+
+ seed = int(path_dir_split[-3])
+ id_xp = str(path_dir_split[-5])
+ dataset = str(path_dir_split[-6])
+
+ dct_results["forest_size"].append(forest_size)
+ dct_results["seed"].append(seed)
+ dct_results["dataset"].append(dct_dataset_fancy[dataset])
+ dct_results["subset"].append(dct_experiment_id_subset[id_xp])
+ dct_results["strategy"].append(dct_experiment_id_technique[id_xp])
+ dct_results["wo_weights"].append(bool_wo_weights)
+
+ for key_result, val_result in obj_results.items():
+ if key_result in skip_attributes:
+ continue
+ if val_result == "":
+ val_result = None
+ dct_results[key_result].append(val_result)
+
+ print(path_file)
+
+
+ final_df = pd.DataFrame.from_dict(dct_results)
+ final_df.to_csv(output_dir_file)
+ print(final_df)
--
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