paper_tables.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Dict, List\n",
    "import wandb\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "api = wandb.Api()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiments_list = pd.read_csv(\"../../Paper/experiments_paper.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiments_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_camel_case(snake_str):\n",
    "    components = snake_str.split('_')\n",
    "    return components[0] + ''.join(x.title() for x in components[1:])\n",
    "\n",
    "def number_to_word(number: int):\n",
    "    num_to_word = {\n",
    "        1: \"one\",\n",
    "        5: \"five\",\n",
    "        10: \"ten\",\n",
    "        15: \"fiveteen\",\n",
    "        20: \"twenty\",\n",
    "        50: \"fifty\"\n",
    "    }\n",
    "\n",
    "    return num_to_word.get(number, number)\n",
    "\n",
    "def retrieve_table(experiments_list: pd.DataFrame, output_file_name: str, columns: List[str], iterations: List[int]) -> None:\n",
    "    experiments = experiments_list[\"Strategie\"].unique()\n",
    "\n",
    "    metrics = {\"Strategy\": [], \"Interpolation\": [], \"Block Size\": []}\n",
    "\n",
    "    for column in columns:\n",
    "        for iteration in iterations:\n",
    "            metrics[f\"{column}_mean_{number_to_word(iteration)}\"] = []\n",
    "            metrics[f\"{column}_std_{number_to_word(iteration)}\"] = []\n",
    "\n",
    "    for experiment in experiments:\n",
    "        print(\"experiment:\", experiment)\n",
    "        runs = experiments_list[experiments_list[\"Strategie\"] == experiment]\n",
    "\n",
    "        assert len(runs) == 3\n",
    "\n",
    "        experiment_metrics = {column: {iteration: [] for iteration in iterations} for column in columns}\n",
    "\n",
    "        for _, runs in runs.iterrows():\n",
    "            run_url = runs[\"Wandb Link\"].replace(\"https://wandb.ai\", \"\")\n",
    "\n",
    "            run = api.run(run_url)\n",
    "\n",
    "            for column in columns:\n",
    "                full_column_name = f\"best_model_val/{column}\"\n",
    "\n",
    "                if full_column_name not in run.summary:\n",
    "                    print(f\"Column {full_column_name} does not existing for dataset.\")\n",
    "\n",
    "                metric_history = [item for item in run.scan_history(keys=[full_column_name, \"trainer/iteration\"])]\n",
    "\n",
    "                metric_history = pd.DataFrame(metric_history)\n",
    "\n",
    "                for iteration in iterations:\n",
    "                    metric_value = metric_history.loc[metric_history[\"trainer/iteration\"] == iteration, full_column_name]\n",
    "                    assert len(metric_value) == 1\n",
    "                    experiment_metrics[column][iteration].append(metric_value.iloc[0])\n",
    "\n",
    "\n",
    "        descriptions = runs[\"Description\"].split(\", \")\n",
    "\n",
    "        metrics[\"Strategy\"].append(descriptions[0])\n",
    "\n",
    "        if len(descriptions) > 1:\n",
    "            metrics[\"Interpolation\"].append(descriptions[1].capitalize())\n",
    "            metrics[\"Block Size\"].append(descriptions[2])\n",
    "        else:\n",
    "            metrics[\"Interpolation\"].append(float(\"nan\"))\n",
    "            metrics[\"Block Size\"].append(float(\"nan\"))\n",
    "\n",
    "        for column in columns:\n",
    "            for iteration in iterations:\n",
    "                current_metrics = np.array(experiment_metrics[column][iteration])\n",
    "                metrics[f\"{column}_mean_{number_to_word(iteration)}\"].append(current_metrics.mean().round(decimals=3))\n",
    "                metrics[f\"{column}_std_{number_to_word(iteration)}\"].append(current_metrics.std(ddof=1).round(decimals=3))\n",
    "\n",
    "    metrics = pd.DataFrame(metrics)\n",
    "    metrics = metrics.rename({column: to_camel_case(column.replace(\" \", \"_\")) for column in metrics.columns}, axis=\"columns\")\n",
    "    metrics.to_csv(output_file_name, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\"mean_dice_score\"]\n",
    "iterations = [5, 10, 15, 20, 50]\n",
    "\n",
    "experiments = [\n",
    "                \"Random Sampling\",\n",
    "                \"Uncertainty Sampling (Entropy)\",\n",
    "                \"Uncertainty Sampling (Distance)\",\n",
    "                \"Representativeness Sampling (Uniform Sampling of Slices from all Scans)\",\n",
    "                \"Representativeness Sampling (Mean-Shift Clustering of Feature Vectors)\",\n",
    "                \"Representativeness Sampling (Maximum Distance of Feature Vectors)\"\n",
    "            ]\n",
    "\n",
    "current_experiment_list = experiments_list.copy()\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Dataset\"] == \"Decathlon, Hippocampus\"]\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Strategie\"].isin(experiments)]\n",
    "\n",
    "retrieve_table(current_experiment_list,\n",
    "                \"/Users/josafat/Desktop/Semi-Supervised-Active-Learning-for-3D-Medical-Image-Segmentation---MDPI-Submission/data/hippocampus/baseline_strategies.csv\",\n",
    "                columns,\n",
    "                iterations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\"mean_dice_score\"]\n",
    "iterations = [5, 10, 15, 20, 50]\n",
    "\n",
    "experiments = [\n",
    "                \"Random Sampling\",\n",
    "                \"Block Selection ohne Interpolation, Random, Block Size 5\",\n",
    "                \"Signed Distance Interpolation, Random, Block Size 5\",\n",
    "                \"ITK Interpolation, Random, Block Size 5\",\n",
    "                \"Block Selection ohne Interpolation, Random, Block Size 10\",\n",
    "                \"Signed Distance Interpolation, Random, Block Size 10\",\n",
    "                \"ITK Interpolation, Random, Block Size 10\",\n",
    "            ]\n",
    "\n",
    "current_experiment_list = experiments_list.copy()\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Dataset\"] == \"Decathlon, Hippocampus\"]\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Strategie\"].isin(experiments)]\n",
    "\n",
    "retrieve_table(current_experiment_list,\n",
    "                \"/Users/josafat/1-Documents/4_Studium/IT-Systems-Engineering/2_Skripte/2-Master/3._Semester/Masterprojekt/Paper/Latex/data/hippocampus/interpolation_strategies_random.csv\",\n",
    "                columns, iterations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\"mean_dice_score\"]\n",
    "iterations = [5, 10, 15, 20, 50]\n",
    "\n",
    "experiments = [\n",
    "                \"Random Sampling\",\n",
    "                \"Block Selection ohne Interpolation, Uncertainty (Entropy), Block Size 5\",\n",
    "                \"Signed Distance Interpolation, Uncertainty (Entropy), Block Size 5\",\n",
    "                \"ITK Interpolation, Uncertainty (Entropy), Block Size 5\",\n",
    "                \"Block Selection ohne Interpolation, Uncertainty (Entropy), Block Size 10\",\n",
    "                \"Signed Distance Interpolation, Uncertainty (Entropy), Block Size 10\",\n",
    "                \"ITK Interpolation, Uncertainty (Entropy), Block Size 10\",\n",
    "            ]\n",
    "\n",
    "current_experiment_list = experiments_list.copy()\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Dataset\"] == \"Decathlon, Hippocampus\"]\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Strategie\"].isin(experiments)]\n",
    "\n",
    "retrieve_table(current_experiment_list,\n",
    "                \"/Users/josafat/1-Documents/4_Studium/IT-Systems-Engineering/2_Skripte/2-Master/3._Semester/Masterprojekt/Paper/Latex/data/hippocampus/interpolation_strategies_uncertainty.csv\",\n",
    "                columns, iterations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\"mean_dice_score\"]\n",
    "iterations = [5, 10, 15, 20, 50]\n",
    "\n",
    "experiments = [\n",
    "                \"Random Sampling\",\n",
    "                \"Uncertainty Sampling (Entropy)\",\n",
    "                \"Uncertainty Sampling (Distance)\",\n",
    "                \"Representativeness Sampling (Uniform Sampling of Slices from all Scans)\",\n",
    "                \"Representativeness Sampling (Mean-Shift Clustering of Feature Vectors)\",\n",
    "                \"Representativeness Sampling (Maximum Distance of Feature Vectors)\"\n",
    "            ]\n",
    "\n",
    "current_experiment_list = experiments_list.copy()\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Dataset\"] == \"Decathlon, Heart\"]\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Strategie\"].isin(experiments)]\n",
    "\n",
    "retrieve_table(current_experiment_list,\n",
    "                \"/Users/josafat/Desktop/Semi-Supervised-Active-Learning-for-3D-Medical-Image-Segmentation---MDPI-Submission/data/heart/baseline_strategies.csv\",\n",
    "                columns,\n",
    "                iterations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\"mean_dice_score\"]\n",
    "iterations = [5, 10, 15, 20, 50]\n",
    "\n",
    "experiments = [\n",
    "                \"Random Sampling\",\n",
    "                \"Block Selection ohne Interpolation, Random, Block Size 5\",\n",
    "                \"Signed Distance Interpolation, Random, Block Size 5\",\n",
    "                \"ITK Interpolation, Random, Block Size 5\",\n",
    "                \"Block Selection ohne Interpolation, Random, Block Size 10\",\n",
    "                \"Signed Distance Interpolation, Random, Block Size 10\",\n",
    "                \"ITK Interpolation, Random, Block Size 10\",\n",
    "                \"Block Selection ohne Interpolation, Random, Block Size 15\",\n",
    "                \"Signed Distance Interpolation, Random, Block Size 15\",\n",
    "                \"ITK Interpolation, Random, Block Size 15\",\n",
    "            ]\n",
    "\n",
    "current_experiment_list = experiments_list.copy()\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Dataset\"] == \"Decathlon, Heart\"]\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Strategie\"].isin(experiments)]\n",
    "\n",
    "retrieve_table(current_experiment_list,\n",
    "                \"/Users/josafat/Desktop/Semi-Supervised-Active-Learning-for-3D-Medical-Image-Segmentation---MDPI-Submission/data/heart/interpolation_strategies_random.csv\",\n",
    "                columns, iterations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\"mean_dice_score\"]\n",
    "iterations = [5, 10, 15, 20, 50]\n",
    "\n",
    "experiments = [\n",
    "                \"Random Sampling\",\n",
    "                \"Block Selection ohne Interpolation, Uncertainty (Entropy), Block Size 5\",\n",
    "                \"Signed Distance Interpolation, Uncertainty (Entropy), Block Size 5\",\n",
    "                \"ITK Interpolation, Uncertainty (Entropy), Block Size 5\",\n",
    "                \"Block Selection ohne Interpolation, Uncertainty (Entropy), Block Size 10\",\n",
    "                \"Signed Distance Interpolation, Uncertainty (Entropy), Block Size 10\",\n",
    "                \"ITK Interpolation, Uncertainty (Entropy), Block Size 10\",\n",
    "                \"Block Selection ohne Interpolation, Uncertainty (Entropy), Block Size 15\",\n",
    "                \"Signed Distance Interpolation, Uncertainty (Entropy), Block Size 15\",\n",
    "                \"ITK Interpolation, Uncertainty (Entropy), Block Size 15\",\n",
    "            ]\n",
    "\n",
    "current_experiment_list = experiments_list.copy()\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Dataset\"] == \"Decathlon, Heart\"]\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Strategie\"].isin(experiments)]\n",
    "\n",
    "retrieve_table(current_experiment_list,\n",
    "                \"/Users/josafat/Desktop/Semi-Supervised-Active-Learning-for-3D-Medical-Image-Segmentation---MDPI-Submission/data/heart/interpolation_strategies_uncertainty.csv\",\n",
    "                columns, iterations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\"mean_dice_score\"]\n",
    "iterations = [5, 10, 15, 20, 50]\n",
    "\n",
    "experiments = [\n",
    "                \"Random Sampling\",\n",
    "                \"Uncertainty Sampling (Entropy)\",\n",
    "                \"Uncertainty Sampling (Distance)\",\n",
    "                \"Representativeness Sampling (Uniform Sampling of Slices from all Scans)\",\n",
    "                \"Representativeness Sampling (Mean-Shift Clustering of Feature Vectors)\",\n",
    "                \"Representativeness Sampling (Maximum Distance of Feature Vectors)\"\n",
    "            ]\n",
    "\n",
    "current_experiment_list = experiments_list.copy()\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Dataset\"] == \"Decathlon, Prostate\"]\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Strategie\"].isin(experiments)]\n",
    "\n",
    "retrieve_table(current_experiment_list,\n",
    "                \"/Users/josafat/Desktop/Semi-Supervised-Active-Learning-for-3D-Medical-Image-Segmentation---MDPI-Submission/data/prostate/baseline_strategies.csv\",\n",
    "                columns,\n",
    "                iterations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\"mean_dice_score\"]\n",
    "iterations = [5, 10, 15, 20, 50]\n",
    "\n",
    "experiments = [\n",
    "                \"Random Sampling\",\n",
    "                \"Block Selection ohne Interpolation, Random, Block Size 5\",\n",
    "                \"Signed Distance Interpolation, Random, Block Size 5\",\n",
    "                \"ITK Interpolation, Random, Block Size 5\",\n",
    "                \"Block Selection ohne Interpolation, Random, Block Size 10\",\n",
    "                \"Signed Distance Interpolation, Random, Block Size 10\",\n",
    "                \"ITK Interpolation, Random, Block Size 10\",\n",
    "                \"Block Selection ohne Interpolation, Random, Block Size 15\",\n",
    "                \"Signed Distance Interpolation, Random, Block Size 15\",\n",
    "                \"ITK Interpolation, Random, Block Size 15\",\n",
    "            ]\n",
    "\n",
    "current_experiment_list = experiments_list.copy()\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Dataset\"] == \"Decathlon, Prostate\"]\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Strategie\"].isin(experiments)]\n",
    "\n",
    "retrieve_table(current_experiment_list,\n",
    "                \"/Users/josafat/Desktop/Semi-Supervised-Active-Learning-for-3D-Medical-Image-Segmentation---MDPI-Submission/data/prostate/interpolation_strategies_random.csv\",\n",
    "                columns, iterations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\"mean_dice_score\"]\n",
    "iterations = [5, 10, 15, 20, 50]\n",
    "\n",
    "experiments = [\n",
    "                \"Random Sampling\",\n",
    "                \"Block Selection ohne Interpolation, Uncertainty (Entropy), Block Size 5\",\n",
    "                \"Signed Distance Interpolation, Uncertainty (Entropy), Block Size 5\",\n",
    "                \"ITK Interpolation, Uncertainty (Entropy), Block Size 5\",\n",
    "                \"Block Selection ohne Interpolation, Uncertainty (Entropy), Block Size 10\",\n",
    "                \"Signed Distance Interpolation, Uncertainty (Entropy), Block Size 10\",\n",
    "                \"ITK Interpolation, Uncertainty (Entropy), Block Size 10\",\n",
    "                \"Block Selection ohne Interpolation, Uncertainty (Entropy), Block Size 15\",\n",
    "                \"Signed Distance Interpolation, Uncertainty (Entropy), Block Size 15\",\n",
    "                \"ITK Interpolation, Uncertainty (Entropy), Block Size 15\",\n",
    "            ]\n",
    "\n",
    "current_experiment_list = experiments_list.copy()\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Dataset\"] == \"Decathlon, Prostate\"]\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Strategie\"].isin(experiments)]\n",
    "\n",
    "retrieve_table(current_experiment_list,\n",
    "                \"/Users/josafat/Desktop/Semi-Supervised-Active-Learning-for-3D-Medical-Image-Segmentation---MDPI-Submission/data/prostate/interpolation_strategies_uncertainty.csv\",\n",
    "                columns, iterations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Diagrams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_diagram_data(experiment_list, output_file_path: str, columns, iterations: int = 50):\n",
    "    experiment_data = {}\n",
    "    experiment_data[\"iteration\"] = list(range(1, iterations+2))\n",
    "\n",
    "    experiments = experiment_list[\"Strategie\"].unique()\n",
    "\n",
    "    for experiment in experiments:\n",
    "        current_experiments = experiment_list[experiment_list[\"Strategie\"] == experiment]\n",
    "\n",
    "        current_metrics = {column: np.zeros((iterations + 1, len(current_experiments))) for column in columns}\n",
    "        \n",
    "        for idx, (_, experiment) in enumerate(current_experiments.iterrows()):\n",
    "            run_url = experiment[\"Wandb Link\"].replace(\"https://wandb.ai\", \"\")\n",
    "            experiment_name = experiment[\"Strategie\"]\n",
    "\n",
    "            run = api.run(run_url)\n",
    "\n",
    "            for column in columns:\n",
    "                full_column_name = f\"best_model_val/{column}\"\n",
    "\n",
    "                if full_column_name not in run.summary:\n",
    "                    print(f\"Column {full_column_name} does not existing for dataset.\")\n",
    "\n",
    "                metric_history = [item for item in run.scan_history(keys=[full_column_name, \"trainer/iteration\"])]\n",
    "                metric_history = pd.DataFrame(metric_history)\n",
    "\n",
    "                if len(metric_history) == iterations + 1:\n",
    "                    current_metrics[column][:, idx] = metric_history[full_column_name].dropna().values\n",
    "                else:\n",
    "                    print(f\"{run_url} is incomplete.\")\n",
    "\n",
    "        for column in columns:\n",
    "            experiment_data[f\"{experiment_name.replace(', ', '_')}_{column.replace('bestModelVal', '')}_mean\"] = current_metrics[column].mean(axis=1)\n",
    "            experiment_data[f\"{experiment_name.replace(', ', '_')}_{column.replace('bestModelVal', '')}_min\"] = current_metrics[column].min(axis=1)\n",
    "            experiment_data[f\"{experiment_name.replace(', ', '_')}_{column.replace('bestModelVal', '')}_max\"] = current_metrics[column].max(axis=1)\n",
    "\n",
    "    experiment_data = pd.DataFrame(experiment_data)\n",
    "    experiment_data.to_csv(output_file_path, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\"mean_dice_score\"]\n",
    "\n",
    "experiments = [\n",
    "                \"Random Sampling\",\n",
    "                \"Uncertainty Sampling (Entropy)\",\n",
    "                \"Uncertainty Sampling (Distance)\",\n",
    "            ]\n",
    "\n",
    "current_experiment_list = experiments_list.copy()\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Dataset\"] == \"Decathlon, Heart\"]\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Strategie\"].isin(experiments)]\n",
    "\n",
    "get_diagram_data(current_experiment_list,\n",
    "                \"/Users/josafat/Desktop/Semi-Supervised-Active-Learning-for-3D-Medical-Image-Segmentation---MDPI-Submission/data/heart/diagram_uncertainty_sampling.csv\",\n",
    "                columns,\n",
    "                50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\"mean_dice_score\"]\n",
    "\n",
    "experiments = [\n",
    "                \"Random Sampling\",\n",
    "                \"Uncertainty Sampling (Entropy)\",\n",
    "                \"Uncertainty Sampling (Distance)\",\n",
    "            ]\n",
    "\n",
    "current_experiment_list = experiments_list.copy()\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Dataset\"] == \"Decathlon, Hippocampus\"]\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Strategie\"].isin(experiments)]\n",
    "\n",
    "get_diagram_data(current_experiment_list,\n",
    "                \"/Users/josafat/Desktop/Semi-Supervised-Active-Learning-for-3D-Medical-Image-Segmentation---MDPI-Submission/data/hippocampus/diagram_uncertainty_sampling.csv\",\n",
    "                columns,\n",
    "                50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\"mean_dice_score\"]\n",
    "\n",
    "experiments = [\n",
    "                \"Random Sampling\",\n",
    "                \"Uncertainty Sampling (Entropy)\",\n",
    "                \"Uncertainty Sampling (Distance)\",\n",
    "            ]\n",
    "\n",
    "current_experiment_list = experiments_list.copy()\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Dataset\"] == \"Decathlon, Prostate\"]\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Strategie\"].isin(experiments)]\n",
    "\n",
    "get_diagram_data(current_experiment_list,\n",
    "                \"/Users/josafat/Desktop/Semi-Supervised-Active-Learning-for-3D-Medical-Image-Segmentation---MDPI-Submission/data/prostate/diagram_uncertainty_sampling.csv\",\n",
    "                columns,\n",
    "                50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Representativeness Sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\"mean_dice_score\"]\n",
    "\n",
    "experiments = [\n",
    "                \"Random Sampling\",\n",
    "                \"Representativeness Sampling (Uniform Sampling of Slices from all Scans)\",\n",
    "                \"Representativeness Sampling (Mean-Shift Clustering of Feature Vectors)\",\n",
    "                \"Representativeness Sampling (Maximum Distance of Feature Vectors)\",\n",
    "            ]\n",
    "\n",
    "current_experiment_list = experiments_list.copy()\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Dataset\"] == \"Decathlon, Heart\"]\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Strategie\"].isin(experiments)]\n",
    "\n",
    "get_diagram_data(current_experiment_list,\n",
    "                \"/Users/josafat/Desktop/Semi-Supervised-Active-Learning-for-3D-Medical-Image-Segmentation---MDPI-Submission/data/heart/diagram_representativeness_sampling.csv\",\n",
    "                columns,\n",
    "                50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\"mean_dice_score\"]\n",
    "\n",
    "experiments = [\n",
    "                \"Random Sampling\",\n",
    "                \"Representativeness Sampling (Uniform Sampling of Slices from all Scans)\",\n",
    "                \"Representativeness Sampling (Mean-Shift Clustering of Feature Vectors)\",\n",
    "                \"Representativeness Sampling (Maximum Distance of Feature Vectors)\",\n",
    "            ]\n",
    "\n",
    "current_experiment_list = experiments_list.copy()\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Dataset\"] == \"Decathlon, Hippocampus\"]\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Strategie\"].isin(experiments)]\n",
    "\n",
    "get_diagram_data(current_experiment_list,\n",
    "                \"/Users/josafat/Desktop/Semi-Supervised-Active-Learning-for-3D-Medical-Image-Segmentation---MDPI-Submission/data/hippocampus/diagram_representativeness_sampling.csv\",\n",
    "                columns,\n",
    "                50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\"mean_dice_score\"]\n",
    "\n",
    "experiments = [\n",
    "                \"Random Sampling\",\n",
    "                \"Representativeness Sampling (Uniform Sampling of Slices from all Scans)\",\n",
    "                \"Representativeness Sampling (Mean-Shift Clustering of Feature Vectors)\",\n",
    "                \"Representativeness Sampling (Maximum Distance of Feature Vectors)\",\n",
    "            ]\n",
    "\n",
    "current_experiment_list = experiments_list.copy()\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Dataset\"] == \"Decathlon, Prostate\"]\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Strategie\"].isin(experiments)]\n",
    "\n",
    "get_diagram_data(current_experiment_list,\n",
    "                \"/Users/josafat/Desktop/Semi-Supervised-Active-Learning-for-3D-Medical-Image-Segmentation---MDPI-Submission/data/prostate/diagram_representativeness_sampling.csv\",\n",
    "                columns,\n",
    "                50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Block Query Strategies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\"mean_dice_score\"]\n",
    "\n",
    "experiments = [\n",
    "                \"Random Sampling\",\n",
    "                \"Block Selection ohne Interpolation, Random, Block Size 5\",\n",
    "                \"Signed Distance Interpolation, Random, Block Size 5\",\n",
    "                \"ITK Interpolation, Random, Block Size 5\",\n",
    "            ]\n",
    "\n",
    "current_experiment_list = experiments_list.copy()\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Dataset\"] == \"Decathlon, Heart\"]\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Strategie\"].isin(experiments)]\n",
    "\n",
    "get_diagram_data(current_experiment_list,\n",
    "                \"/Users/josafat/1-Documents/4_Studium/IT-Systems-Engineering/2_Skripte/2-Master/3._Semester/Masterprojekt/Paper/Latex/data/heart/diagram_interpolation_random_block_size_5.csv\",\n",
    "                columns,\n",
    "                50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\"mean_dice_score\"]\n",
    "\n",
    "experiments = [\n",
    "                \"Random Sampling\",\n",
    "                \"Block Selection ohne Interpolation, Random, Block Size 5\",\n",
    "                \"Signed Distance Interpolation, Random, Block Size 5\",\n",
    "                \"ITK Interpolation, Random, Block Size 5\",\n",
    "            ]\n",
    "\n",
    "current_experiment_list = experiments_list.copy()\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Dataset\"] == \"Decathlon, Hippocampus\"]\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Strategie\"].isin(experiments)]\n",
    "\n",
    "get_diagram_data(current_experiment_list,\n",
    "                \"/Users/josafat/1-Documents/4_Studium/IT-Systems-Engineering/2_Skripte/2-Master/3._Semester/Masterprojekt/Paper/Latex/data/hippocampus/diagram_interpolation_random_block_size_5.csv\",\n",
    "                columns,\n",
    "                50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\"mean_dice_score\"]\n",
    "\n",
    "experiments = [\n",
    "                \"Random Sampling\",\n",
    "                \"Block Selection ohne Interpolation, Random, Block Size 5\",\n",
    "                \"Signed Distance Interpolation, Random, Block Size 5\",\n",
    "                \"ITK Interpolation, Random, Block Size 5\",\n",
    "            ]\n",
    "\n",
    "current_experiment_list = experiments_list.copy()\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Dataset\"] == \"Decathlon, Prostate\"]\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Strategie\"].isin(experiments)]\n",
    "\n",
    "get_diagram_data(current_experiment_list,\n",
    "                \"/Users/josafat/1-Documents/4_Studium/IT-Systems-Engineering/2_Skripte/2-Master/3._Semester/Masterprojekt/Paper/Latex/data/prostate/diagram_interpolation_random_block_size_5.csv\",\n",
    "                columns,\n",
    "                50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\"mean_dice_score\"]\n",
    "\n",
    "experiments = [\n",
    "                \"Random Sampling\",\n",
    "                \"Block Selection ohne Interpolation, Random, Block Size 10\",\n",
    "                \"Signed Distance Interpolation, Random, Block Size 10\",\n",
    "                \"ITK Interpolation, Random, Block Size 10\",\n",
    "            ]\n",
    "\n",
    "current_experiment_list = experiments_list.copy()\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Dataset\"] == \"Decathlon, Heart\"]\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Strategie\"].isin(experiments)]\n",
    "\n",
    "get_diagram_data(current_experiment_list,\n",
    "                \"/Users/josafat/Desktop/Semi-Supervised-Active-Learning-for-3D-Medical-Image-Segmentation---MDPI-Submission/data/heart/diagram_interpolation_random.csv\",\n",
    "                columns,\n",
    "                50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\"mean_dice_score\"]\n",
    "\n",
    "experiments = [\n",
    "                \"Random Sampling\",\n",
    "                \"Block Selection ohne Interpolation, Random, Block Size 10\",\n",
    "                \"Signed Distance Interpolation, Random, Block Size 10\",\n",
    "                \"ITK Interpolation, Random, Block Size 10\",\n",
    "            ]\n",
    "\n",
    "current_experiment_list = experiments_list.copy()\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Dataset\"] == \"Decathlon, Hippocampus\"]\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Strategie\"].isin(experiments)]\n",
    "\n",
    "get_diagram_data(current_experiment_list,\n",
    "                \"/Users/josafat/Desktop/Semi-Supervised-Active-Learning-for-3D-Medical-Image-Segmentation---MDPI-Submission/data/hippocampus/diagram_interpolation_random.csv\",\n",
    "                columns,\n",
    "                50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\"mean_dice_score\"]\n",
    "\n",
    "experiments = [\n",
    "                \"Random Sampling\",\n",
    "                \"Block Selection ohne Interpolation, Random, Block Size 10\",\n",
    "                \"Signed Distance Interpolation, Random, Block Size 10\",\n",
    "                \"ITK Interpolation, Random, Block Size 10\",\n",
    "            ]\n",
    "\n",
    "current_experiment_list = experiments_list.copy()\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Dataset\"] == \"Decathlon, Prostate\"]\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Strategie\"].isin(experiments)]\n",
    "\n",
    "get_diagram_data(current_experiment_list,\n",
    "                \"/Users/josafat/Desktop/Semi-Supervised-Active-Learning-for-3D-Medical-Image-Segmentation---MDPI-Submission/data/prostate/diagram_interpolation_random.csv\",\n",
    "                columns,\n",
    "                50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\"mean_dice_score\"]\n",
    "\n",
    "experiments = [\n",
    "                \"Random Sampling\",\n",
    "                \"Block Selection ohne Interpolation, Random, Block Size 15\",\n",
    "                \"Signed Distance Interpolation, Random, Block Size 15\",\n",
    "                \"ITK Interpolation, Random, Block Size 15\",\n",
    "            ]\n",
    "\n",
    "current_experiment_list = experiments_list.copy()\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Dataset\"] == \"Decathlon, Heart\"]\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Strategie\"].isin(experiments)]\n",
    "\n",
    "get_diagram_data(current_experiment_list,\n",
    "                \"/Users/josafat/1-Documents/4_Studium/IT-Systems-Engineering/2_Skripte/2-Master/3._Semester/Masterprojekt/Paper/Latex/data/heart/diagram_interpolation_random_block_size_15.csv\",\n",
    "                columns,\n",
    "                50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\"mean_dice_score\"]\n",
    "\n",
    "experiments = [\n",
    "                \"Random Sampling\",\n",
    "                \"Block Selection ohne Interpolation, Random, Block Size 15\",\n",
    "                \"Signed Distance Interpolation, Random, Block Size 15\",\n",
    "                \"ITK Interpolation, Random, Block Size 15\",\n",
    "            ]\n",
    "\n",
    "current_experiment_list = experiments_list.copy()\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Dataset\"] == \"Decathlon, Hippocampus\"]\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Strategie\"].isin(experiments)]\n",
    "\n",
    "get_diagram_data(current_experiment_list,\n",
    "                \"/Users/josafat/1-Documents/4_Studium/IT-Systems-Engineering/2_Skripte/2-Master/3._Semester/Masterprojekt/Paper/Latex/data/hippocampus/diagram_interpolation_random_block_size_15.csv\",\n",
    "                columns,\n",
    "                50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\"mean_dice_score\"]\n",
    "\n",
    "experiments = [\n",
    "                \"Random Sampling\",\n",
    "                \"Block Selection ohne Interpolation, Random, Block Size 15\",\n",
    "                \"Signed Distance Interpolation, Random, Block Size 15\",\n",
    "                \"ITK Interpolation, Random, Block Size 15\",\n",
    "            ]\n",
    "\n",
    "current_experiment_list = experiments_list.copy()\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Dataset\"] == \"Decathlon, Prostate\"]\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Strategie\"].isin(experiments)]\n",
    "\n",
    "get_diagram_data(current_experiment_list,\n",
    "                \"/Users/josafat/1-Documents/4_Studium/IT-Systems-Engineering/2_Skripte/2-Master/3._Semester/Masterprojekt/Paper/Latex/data/prostate/diagram_interpolation_random_block_size_15.csv\",\n",
    "                columns,\n",
    "                50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\"mean_dice_score\"]\n",
    "\n",
    "experiments = [\n",
    "                \"Random Sampling\",\n",
    "                \"Block Selection ohne Interpolation, Random, Block Size 10\",\n",
    "                \"Signed Distance Interpolation, Random, Block Size 10\",\n",
    "                \"ITK Interpolation, Random, Block Size 10\",\n",
    "            ]\n",
    "\n",
    "current_experiment_list = experiments_list.copy()\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Dataset\"] == \"Decathlon, Prostate\"]\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Strategie\"].isin(experiments)]\n",
    "\n",
    "get_diagram_data(current_experiment_list,\n",
    "                \"/Users/josafat/Desktop/Semi-Supervised-Active-Learning-for-3D-Medical-Image-Segmentation---MDPI-Submission/data/prostate/diagram_interpolation_random.csv\",\n",
    "                columns,\n",
    "                50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\"mean_dice_score\"]\n",
    "\n",
    "experiments = [\n",
    "                \"Random Sampling\",\n",
    "                \"Block Selection ohne Interpolation, Uncertainty (Entropy), Block Size 10\",\n",
    "                \"Signed Distance Interpolation, Uncertainty (Entropy), Block Size 10\",\n",
    "                \"ITK Interpolation, Uncertainty (Entropy), Block Size 10\",\n",
    "            ]\n",
    "\n",
    "current_experiment_list = experiments_list.copy()\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Dataset\"] == \"Decathlon, Heart\"]\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Strategie\"].isin(experiments)]\n",
    "\n",
    "get_diagram_data(current_experiment_list,\n",
    "                \"/Users/josafat/Desktop/Semi-Supervised-Active-Learning-for-3D-Medical-Image-Segmentation---MDPI-Submission/data/heart/diagram_interpolation_uncertainty.csv\",\n",
    "                columns,\n",
    "                50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\"mean_dice_score\"]\n",
    "\n",
    "experiments = [\n",
    "                \"Random Sampling\",\n",
    "                \"Block Selection ohne Interpolation, Uncertainty (Entropy), Block Size 10\",\n",
    "                \"Signed Distance Interpolation, Uncertainty (Entropy), Block Size 10\",\n",
    "                \"ITK Interpolation, Uncertainty (Entropy), Block Size 10\",\n",
    "            ]\n",
    "\n",
    "current_experiment_list = experiments_list.copy()\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Dataset\"] == \"Decathlon, Hippocampus\"]\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Strategie\"].isin(experiments)]\n",
    "\n",
    "get_diagram_data(current_experiment_list,\n",
    "                \"/Users/josafat/Desktop/Semi-Supervised-Active-Learning-for-3D-Medical-Image-Segmentation---MDPI-Submission/data/hippocampus/diagram_interpolation_uncertainty.csv\",\n",
    "                columns,\n",
    "                50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\"mean_dice_score\"]\n",
    "\n",
    "experiments = [\n",
    "                \"Random Sampling\",\n",
    "                \"Block Selection ohne Interpolation, Random, Block Size 5\",\n",
    "                \"Block Selection ohne Interpolation, Random, Block Size 10\",\n",
    "                \"Block Selection ohne Interpolation, Random, Block Size 15\",\n",
    "            ]\n",
    "\n",
    "current_experiment_list = experiments_list.copy()\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Dataset\"] == \"Decathlon, Hippocampus\"]\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Strategie\"].isin(experiments)]\n",
    "\n",
    "get_diagram_data(current_experiment_list,\n",
    "                \"/Users/josafat/1-Documents/4_Studium/IT-Systems-Engineering/2_Skripte/2-Master/3._Semester/Masterprojekt/Paper/Latex/data/hippocampus/diagram_block_selection.csv\",\n",
    "                columns,\n",
    "                50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\"mean_dice_score\"]\n",
    "\n",
    "experiments = [\n",
    "                \"Random Sampling\",\n",
    "                \"Block Selection ohne Interpolation, Random, Block Size 5\",\n",
    "                \"Block Selection ohne Interpolation, Random, Block Size 10\",\n",
    "                \"Block Selection ohne Interpolation, Random, Block Size 15\",\n",
    "            ]\n",
    "\n",
    "current_experiment_list = experiments_list.copy()\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Dataset\"] == \"Decathlon, Heart\"]\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Strategie\"].isin(experiments)]\n",
    "\n",
    "get_diagram_data(current_experiment_list,\n",
    "                \"/Users/josafat/1-Documents/4_Studium/IT-Systems-Engineering/2_Skripte/2-Master/3._Semester/Masterprojekt/Paper/Latex/data/heart/diagram_block_selection.csv\",\n",
    "                columns,\n",
    "                50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\"mean_dice_score\"]\n",
    "\n",
    "experiments = [\n",
    "                \"Random Sampling\",\n",
    "                \"Block Selection ohne Interpolation, Random, Block Size 5\",\n",
    "                \"Block Selection ohne Interpolation, Random, Block Size 10\",\n",
    "                \"Block Selection ohne Interpolation, Random, Block Size 15\",\n",
    "            ]\n",
    "\n",
    "current_experiment_list = experiments_list.copy()\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Dataset\"] == \"Decathlon, Prostate\"]\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Strategie\"].isin(experiments)]\n",
    "\n",
    "get_diagram_data(current_experiment_list,\n",
    "                \"/Users/josafat/1-Documents/4_Studium/IT-Systems-Engineering/2_Skripte/2-Master/3._Semester/Masterprojekt/Paper/Latex/data/prostate/diagram_block_selection.csv\",\n",
    "                columns,\n",
    "                50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpolation Quality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def retrieve_interpolation_metrics(experiments_list: pd.DataFrame, block_sizes: List[int], datasets: List[str]) -> Dict[str, np.ndarray]:\n",
    "    interpolation_quality_metrics = {}\n",
    "    interpolation_thickness = {}\n",
    "\n",
    "    interpolation_names = {\n",
    "        \"ITK\": \"morphological contour interpolation\",\n",
    "        \"Signed Distance\": \"signed distance interpolation\"\n",
    "    }\n",
    "\n",
    "    experiments_list = experiments_list[experiments_list[\"Strategie\"].str.contains(\"Random\").fillna(False)]\n",
    "    experiments_list = experiments_list[~experiments_list[\"Strategie\"].str.contains(\"Block Selection ohne Interpolation\").fillna(False)]\n",
    "\n",
    "    for block_size in block_sizes:\n",
    "        for dataset in datasets:\n",
    "            current_experiments_list = experiments_list[experiments_list[\"Dataset\"] == f\"Decathlon, {dataset}\"]\n",
    "            current_experiments_list = current_experiments_list[current_experiments_list[\"Strategie\"].str.contains(f\"Block Size {block_size}\").fillna(False)]\n",
    "\n",
    "            for interpolation_method in [\"ITK\", \"Signed Distance\"]:\n",
    "                current_experiments = current_experiments_list[current_experiments_list[\"Strategie\"].str.contains(interpolation_method).fillna(False)]\n",
    "\n",
    "                assert len(current_experiments) == 3\n",
    "\n",
    "                if len(current_experiments) == 0:\n",
    "                    print(f\"{dataset} dataset, {interpolation_names[interpolation_method]}, block size {block_size}\")\n",
    "\n",
    "                current_metrics = []\n",
    "                current_thickness = []\n",
    "\n",
    "                for idx, (_, experiment) in enumerate(current_experiments.iterrows()):\n",
    "                    run_url = experiment[\"Wandb Link\"].replace(\"https://wandb.ai\", \"\")\n",
    "\n",
    "                    run = api.run(run_url)\n",
    "\n",
    "                    for item in run.scan_history(keys=[\"val/mean_dice_score_interpolation\", \"val/interpolation_thickness\"]):\n",
    "                        if not type(item[\"val/mean_dice_score_interpolation\"]) == str:\n",
    "                            current_metrics.append(item[\"val/mean_dice_score_interpolation\"])\n",
    "                            current_thickness.append(item[\"val/interpolation_thickness\"])\n",
    "\n",
    "                current_metrics = np.array(current_metrics)\n",
    "                current_thickness = np.array(current_thickness)\n",
    "\n",
    "                if len(block_sizes) == 1:\n",
    "                    key = f\"{dataset} dataset, {interpolation_names[interpolation_method]}\"\n",
    "                else:\n",
    "                    key = f\"{dataset} dataset, {interpolation_names[interpolation_method]}, block size {block_size}\"\n",
    "        \n",
    "                interpolation_quality_metrics[key] = current_metrics\n",
    "                interpolation_thickness[key] = current_thickness\n",
    "\n",
    "    return interpolation_quality_metrics, interpolation_thickness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dice_scores, thickness = retrieve_interpolation_metrics(experiments_list, block_sizes=[10], datasets=[\"Heart\", \"Hippocampus\", \"Prostate\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "metrics_to_plot = [\n",
    "                    \"Hippocampus dataset, signed distance interpolation\",\n",
    "                    \"Hippocampus dataset, morphological contour interpolation\",\n",
    "                    \"Heart dataset, signed distance interpolation\",\n",
    "                    \"Heart dataset, morphological contour interpolation\",\n",
    "                    \"Prostate dataset, signed distance interpolation\",\n",
    "                    \"Prostate dataset, morphological contour interpolation\",\n",
    "                    ]\n",
    "\n",
    "curent_metrics = {key: value for key, value in dice_scores.items() if key in metrics_to_plot}\n",
    "\n",
    "plt.boxplot(list(curent_metrics.values()), labels=list(curent_metrics.keys()), vert=False)\n",
    "\n",
    "ax = plt.gca()\n",
    "ax.set_xlabel(\"Mean interpolation Dice score\")\n",
    "\n",
    "plt.savefig(\"/Users/josafat/Desktop/Semi-Supervised-Active-Learning-for-3D-Medical-Image-Segmentation---MDPI-Submission/images/interpolation_quality_datasets.pdf\", bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(thickness[\"Hippocampus dataset, signed distance interpolation\"], dice_scores[\"Hippocampus dataset, signed distance interpolation\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "thickness_labels = []\n",
    "thickness_values = []\n",
    "column = \"Prostate dataset, signed distance interpolation\"\n",
    "\n",
    "for block_thickness in np.unique(thickness[column]):\n",
    "    thickness_labels.append(f\"Block size {int(block_thickness)}\")\n",
    "    thickness_values.append(dice_scores[column][thickness[column] == block_thickness])\n",
    "\n",
    "plt.boxplot(thickness_values, labels=thickness_labels, vert=False)\n",
    "\n",
    "ax = plt.gca()\n",
    "ax.set_xlabel(\"Mean interpolation Dice score\")\n",
    "\n",
    "plt.savefig(\"/Users/josafat/Desktop/Semi-Supervised-Active-Learning-for-3D-Medical-Image-Segmentation---MDPI-Submission/images/interpolation_quality_thickness_prostate.pdf\", bbox_inches = 'tight')\n",
    "\n",
    "# plt.scatter(thickness[\"Heart dataset, signed distance interpolation\"], dice_scores[\"Heart dataset, signed distance interpolation\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiments = [\n",
    "                  \"Signed Distance Interpolation, Random, Block Size 5\",\n",
    "                  \"ITK Interpolation, Random, Block Size 5\",\n",
    "                  \"Signed Distance Interpolation, Random, Block Size 10\",\n",
    "                  \"ITK Interpolation, Random, Block Size 10\",\n",
    "                  \"Signed Distance Interpolation, Random, Block Size 15\",\n",
    "                  \"ITK Interpolation, Random, Block Size 15\",\n",
    "            ]\n",
    "\n",
    "current_experiment_list = experiments_list.copy()\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Dataset\"] == \"Decathlon, Heart\"]\n",
    "current_experiment_list = current_experiment_list[current_experiment_list[\"Strategie\"].isin(experiments)]\n",
    "\n",
    "metrics = retrieve_interpolation_table(\n",
    "      current_experiment_list,\n",
    "      \"/Users/josafat/Desktop/Semi-Supervised-Active-Learning-for-3D-Medical-Image-Segmentation---MDPI-Submission/data/interpolation_quality.csv\"\n",
    "      )\n",
    "metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiments_list[experiments_list[\"Strategie\"].str.contains(\"Block Size 5\").fillna(False)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics"
   ]
  }
 ],
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