166 lines
4.8 KiB
Plaintext
166 lines
4.8 KiB
Plaintext
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "8fb02733",
|
|
"metadata": {},
|
|
"source": [
|
|
"Imports"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "96f3b128",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"import yaml\n",
|
|
"import owncloud\n",
|
|
"import pandas as pd\n",
|
|
"import numpy as np"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "c20cee7c",
|
|
"metadata": {},
|
|
"source": [
|
|
"Connection to Owncloud"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "c4c94558",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Load credentials\n",
|
|
"with open(\"../login.yaml\") as f:\n",
|
|
" cfg = yaml.safe_load(f)\n",
|
|
" \n",
|
|
"url, password = cfg[0][\"url\"], cfg[1][\"password\"]\n",
|
|
"\n",
|
|
"# Connect once\n",
|
|
"oc = owncloud.Client.from_public_link(url, folder_password=password)\n",
|
|
"# File pattern\n",
|
|
"base = \"adabase-public-{num:04d}-v_0_0_2.h5py\""
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "07c03d07",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"num_files = 2 # number of files to process (min: 1, max: 30)\n",
|
|
"performance_data = []\n",
|
|
"\n",
|
|
"for i in range(num_files):\n",
|
|
" file_name = base.format(num=i)\n",
|
|
" local_tmp = f\"tmp_{i:04d}.h5\"\n",
|
|
"\n",
|
|
" oc.get_file(file_name, local_tmp)\n",
|
|
" print(f\"{file_name} geöffnet\")\n",
|
|
"\n",
|
|
" # check SIGNALS table for AUs\n",
|
|
" with pd.HDFStore(local_tmp, mode=\"r\") as store:\n",
|
|
" cols = store.select(\"SIGNALS\", start=0, stop=1).columns\n",
|
|
" au_cols = [c for c in cols if c.startswith(\"AU\")]\n",
|
|
" if not au_cols:\n",
|
|
" print(f\"Subject {i} enthält keine AUs\")\n",
|
|
" continue\n",
|
|
"\n",
|
|
" # load performance table\n",
|
|
" with pd.HDFStore(local_tmp, mode=\"r\") as store:\n",
|
|
" perf_df = store.select(\"PERFORMANCE\")\n",
|
|
"\n",
|
|
" f1_cols = [c for c in [\"AUDITIVE F1\", \"VISUAL F1\", \"F1\"] if c in perf_df.columns]\n",
|
|
" if not f1_cols:\n",
|
|
" print(f\"Subject {i}: keine F1-Spalten gefunden\")\n",
|
|
" continue\n",
|
|
"\n",
|
|
" subject_entry = {\"subjectID\": i}\n",
|
|
" valid_scores = []\n",
|
|
"\n",
|
|
" # iterate rows: each (study, level, phase)\n",
|
|
" for _, row in perf_df.iterrows():\n",
|
|
" study, level, phase = row[\"STUDY\"], row[\"LEVEL\"], row[\"PHASE\"]\n",
|
|
" col_name = f\"STUDY_{study}_LEVEL_{level}_PHASE_{phase}\"\n",
|
|
"\n",
|
|
" # collect valid F1 values among the three columns\n",
|
|
" scores = [row[c] for c in f1_cols if pd.notna(row[c])]\n",
|
|
" if scores:\n",
|
|
" mean_score = float(np.mean(scores))\n",
|
|
" subject_entry[col_name] = mean_score\n",
|
|
" valid_scores.extend(scores)\n",
|
|
"\n",
|
|
" # compute overall average across all valid combinations\n",
|
|
" if valid_scores:\n",
|
|
" subject_entry[\"overall_score\"] = float(np.mean(valid_scores))\n",
|
|
" performance_data.append(subject_entry)\n",
|
|
" print(f\"Subject {i}: {len(valid_scores)} gültige Scores, Overall = {subject_entry['overall_score']:.3f}\")\n",
|
|
" else:\n",
|
|
" print(f\"Subject {i}: keine gültigen F1-Scores\")\n",
|
|
"\n",
|
|
"# build dataframe\n",
|
|
"if performance_data:\n",
|
|
" performance_df = pd.DataFrame(performance_data)\n",
|
|
" combination_cols = sorted([c for c in performance_df.columns if c.startswith(\"STUDY_\")])\n",
|
|
" final_cols = [\"subjectID\", \"overall_score\"] + combination_cols\n",
|
|
" performance_df = performance_df[final_cols]\n",
|
|
" performance_df.to_csv(\"au_performance.csv\", index=False)\n",
|
|
"\n",
|
|
" print(f\"\\nGesamt Subjects mit Action Units: {len(performance_df)}\")\n",
|
|
"else:\n",
|
|
" print(\"Keine gültigen Daten gefunden.\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "0bcaf065",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"performance_df.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "db95eea7",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"with pd.HDFStore(local_tmp, mode=\"r\") as store:\n",
|
|
" performance = store.select(\"PERFORMANCE\")\n",
|
|
"performance"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "base",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.11.5"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|