From 58d11ffdfd26568e97bc12975d0cd258201e48e6 Mon Sep 17 00:00:00 2001
From: Lena Ebner <leeena.ebner@gmail.com>
Date: Mon, 1 Jan 2024 16:42:45 +0100
Subject: [PATCH] feat: add amount of file calc

---
 ...sData.ipynb => happiness_evaluation.ipynb} | 65 ++++++++++++-------
 1 file changed, 42 insertions(+), 23 deletions(-)
 rename experiments/{UserData/processData.ipynb => happiness_evaluation.ipynb} (82%)

diff --git a/experiments/UserData/processData.ipynb b/experiments/happiness_evaluation.ipynb
similarity index 82%
rename from experiments/UserData/processData.ipynb
rename to experiments/happiness_evaluation.ipynb
index 0d26d0a..bf44525 100644
--- a/experiments/UserData/processData.ipynb
+++ b/experiments/happiness_evaluation.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-27T14:38:07.208570100Z",
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 11,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-27T14:48:33.003116200Z",
@@ -43,23 +43,40 @@
     "id": "fbykrjkOjZWq",
     "outputId": "8c8bd4c8-16e8-41fe-a87b-e9e0d797beac"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Amount of files: 32\n"
+     ]
+    }
+   ],
    "source": [
     "\n",
-    "base_path = \"D:\\\\Documents\\\\GitHub\\\\CIR\\\\experiments\\\\UserData\"\n",
+    "base_path = \"./UserData/\"\n",
+    "# base_path = \"D:\\\\Documents\\\\GitHub\\\\CIR\\\\experiments\\\\UserData\"\n",
     "\n",
     "results = {}\n",
+    "amount_of_files = 0\n",
     "\n",
     "# Loop over all the files\n",
     "for dir in os.listdir(base_path):\n",
-    "    dir_path = base_path + \"\\\\\" + dir\n",
+    "    dir_path = base_path + dir\n",
+    "    # dir_path = base_path + \"\\\\\" + dir\n",
+    "\n",
     "    # Only check the directories\n",
     "    if os.path.isdir(dir_path):\n",
     "        for file in os.listdir(dir_path):\n",
-    "            file_path = dir_path + \"\\\\\" + file\n",
+    "            file_path = dir_path + \"/\" + file\n",
+    "            # file_path = dir_path + \"\\\\\" + file\n",
+    "\n",
     "            # Only check the files\n",
     "            if not os.path.isdir(file_path):\n",
-    "                log_path = dir_path + \"\\\\logs\"\n",
+    "                amount_of_files += 1\n",
+    "                log_path = dir_path + \"/logs\"\n",
+    "                # log_path = dir_path + \"\\\\logs\"\n",
+    "                \n",
     "                # Get the excel file and convert to an array with 0, 0.5 and 1\n",
     "                # representing neutral, slight smile and smile\n",
     "                df = pd.read_excel(file_path)\n",
@@ -71,7 +88,9 @@
     "                        with open(log_path + \"/\" + log, \"r\") as log_file:\n",
     "                            json_log = json.load(log_file)\n",
     "                            smile_degree_array = [entry['smileDegree'] for entry in json_log]\n",
-    "                results[file[:-5]] = [result_array, smile_degree_array]"
+    "                results[file[:-5]] = [result_array, smile_degree_array]\n",
+    "                \n",
+    "print(f\"Amount of files: {amount_of_files}\")"
    ]
   },
   {
@@ -83,16 +102,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Total MSE \t = -0.13205479156940012\n",
-      "MSE A \t\t = -0.13099312341198133\n",
-      "MSE B \t\t = -0.133265373577282\n"
+      "Total MSE \t = -0.1195553075097073\n",
+      "MSE A \t\t = -0.11021458716866875\n",
+      "MSE B \t\t = -0.12993050502206596\n"
      ]
     }
    ],
@@ -133,19 +152,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Total happiness \t\t\t = 0.4902676399026764\n",
-      "Total happiness with recommender \t = 0.6491721614005638\n",
-      "Total happiness A \t\t\t = 0.4847161572052402\n",
-      "Total happiness A with recommender \t = 0.6505553012928176\n",
-      "Total happiness B \t\t\t = 0.49725274725274726\n",
-      "Total happiness B with recommender \t = 0.6474318370306404\n"
+      "Total happiness \t\t\t = 0.4926829268292683\n",
+      "Total happiness with recommender \t = 0.6121423712260986\n",
+      "Total happiness A \t\t\t = 0.4909090909090909\n",
+      "Total happiness A with recommender \t = 0.5937602916700374\n",
+      "Total happiness B \t\t\t = 0.49473684210526314\n",
+      "Total happiness B with recommender \t = 0.6334268843962747\n"
      ]
     }
    ],
@@ -201,15 +220,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Happiness first part \t = 0.2347856359841381\n",
-      "Happiness second part \t = 0.17478925298500678\n"
+      "Happiness first part \t = 0.224683757843183\n",
+      "Happiness second part \t = 0.1633900668621427\n"
      ]
     }
    ],
@@ -260,7 +279,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.11.6"
   }
  },
  "nbformat": 4,