EDA_BASEruns.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "##############################################\n",
    "#Explorative Data Analysis of Baserun results#\n",
    "#Maintainer: Christopher Chan                #\n",
    "#Date: 2022-07-26                            #\n",
    "#Version: 0.1.3                              #\n",
    "##############################################\n",
    "\n",
    "import os, sys\n",
    "import re\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "Base_path = os.path.abspath(\"/home/chris/Dropbox/HOTOSM/HOTOSM_OAM_codeV2\")\n",
    "figure_path = os.path.join(Base_path, \"figure\")\n",
    "BR_KBYpath = os.path.join(Base_path, \"iter_log\", \"BASE_15cmKBY\")\n",
    "BR_ALLpath = os.path.join(Base_path, \"iter_log\", \"BASE_15cmALL\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Experiment</th>\n",
       "      <th>Input_dataset</th>\n",
       "      <th>mean_Precision</th>\n",
       "      <th>mean_Recall(TPR)</th>\n",
       "      <th>mean_Specificity(TNR)</th>\n",
       "      <th>mean_OA</th>\n",
       "      <th>mean_Dice</th>\n",
       "      <th>mean_IoU</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4-layer U-Net</td>\n",
       "      <td>KBY</td>\n",
       "      <td>0.876089</td>\n",
       "      <td>0.957042</td>\n",
       "      <td>0.988150</td>\n",
       "      <td>0.985646</td>\n",
       "      <td>0.914778</td>\n",
       "      <td>0.842941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5-layer U-Net</td>\n",
       "      <td>KBY</td>\n",
       "      <td>0.887338</td>\n",
       "      <td>0.954440</td>\n",
       "      <td>0.989391</td>\n",
       "      <td>0.986578</td>\n",
       "      <td>0.919666</td>\n",
       "      <td>0.851280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EfficientNet B1 U-Net (ImageNet)</td>\n",
       "      <td>KBY</td>\n",
       "      <td>0.906424</td>\n",
       "      <td>0.968574</td>\n",
       "      <td>0.991246</td>\n",
       "      <td>0.989421</td>\n",
       "      <td>0.936469</td>\n",
       "      <td>0.880528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EfficientNet B1 U-Net (Vanilla | No ImageNet)</td>\n",
       "      <td>KBY</td>\n",
       "      <td>0.902977</td>\n",
       "      <td>0.960412</td>\n",
       "      <td>0.990966</td>\n",
       "      <td>0.988506</td>\n",
       "      <td>0.930810</td>\n",
       "      <td>0.870574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EfficientNet B1 U-Net (OCC-transfered-UNtrained)</td>\n",
       "      <td>KBY</td>\n",
       "      <td>0.905312</td>\n",
       "      <td>0.668110</td>\n",
       "      <td>0.993882</td>\n",
       "      <td>0.967658</td>\n",
       "      <td>0.624473</td>\n",
       "      <td>0.531484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>EfficientNet B1 U-Net (OCC-transfered-trained)</td>\n",
       "      <td>KBY</td>\n",
       "      <td>0.905570</td>\n",
       "      <td>0.974020</td>\n",
       "      <td>0.991108</td>\n",
       "      <td>0.989733</td>\n",
       "      <td>0.938549</td>\n",
       "      <td>0.884213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>EfficientNet B2 U-Net (ImageNet)</td>\n",
       "      <td>KBY</td>\n",
       "      <td>0.909726</td>\n",
       "      <td>0.962329</td>\n",
       "      <td>0.991640</td>\n",
       "      <td>0.989280</td>\n",
       "      <td>0.935289</td>\n",
       "      <td>0.878443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>EfficientNet B2 U-Net (Vanilla | No ImageNet)</td>\n",
       "      <td>KBY</td>\n",
       "      <td>0.900405</td>\n",
       "      <td>0.948631</td>\n",
       "      <td>0.990814</td>\n",
       "      <td>0.987418</td>\n",
       "      <td>0.923889</td>\n",
       "      <td>0.858544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4-layer U-Net</td>\n",
       "      <td>ALL</td>\n",
       "      <td>0.810815</td>\n",
       "      <td>0.941240</td>\n",
       "      <td>0.939060</td>\n",
       "      <td>0.939534</td>\n",
       "      <td>0.871173</td>\n",
       "      <td>0.771751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5-layer U-Net</td>\n",
       "      <td>ALL</td>\n",
       "      <td>0.817440</td>\n",
       "      <td>0.939155</td>\n",
       "      <td>0.941800</td>\n",
       "      <td>0.941225</td>\n",
       "      <td>0.874081</td>\n",
       "      <td>0.776326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>EfficientNet B1 U-Net (ImageNet)</td>\n",
       "      <td>ALL</td>\n",
       "      <td>0.787711</td>\n",
       "      <td>0.936536</td>\n",
       "      <td>0.929964</td>\n",
       "      <td>0.931392</td>\n",
       "      <td>0.855701</td>\n",
       "      <td>0.747795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>EfficientNet B1 U-Net (Vanilla | No ImageNet)</td>\n",
       "      <td>ALL</td>\n",
       "      <td>0.778491</td>\n",
       "      <td>0.916876</td>\n",
       "      <td>0.927609</td>\n",
       "      <td>0.925278</td>\n",
       "      <td>0.842036</td>\n",
       "      <td>0.727169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>EfficientNet B1 U-Net (OCC-transfered-UNtrained)</td>\n",
       "      <td>ALL</td>\n",
       "      <td>0.902935</td>\n",
       "      <td>0.281554</td>\n",
       "      <td>0.991601</td>\n",
       "      <td>0.837372</td>\n",
       "      <td>0.429257</td>\n",
       "      <td>0.273283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>EfficientNet B1 U-Net (OCC-transfered-trained)</td>\n",
       "      <td>ALL</td>\n",
       "      <td>0.780991</td>\n",
       "      <td>0.930698</td>\n",
       "      <td>0.927580</td>\n",
       "      <td>0.928257</td>\n",
       "      <td>0.849298</td>\n",
       "      <td>0.738069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>EfficientNet B2 U-Net (ImageNet)</td>\n",
       "      <td>ALL</td>\n",
       "      <td>0.781347</td>\n",
       "      <td>0.934473</td>\n",
       "      <td>0.927437</td>\n",
       "      <td>0.928965</td>\n",
       "      <td>0.851077</td>\n",
       "      <td>0.740761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>EfficientNet B2 U-Net (Vanilla | No ImageNet)</td>\n",
       "      <td>ALL</td>\n",
       "      <td>0.801353</td>\n",
       "      <td>0.922926</td>\n",
       "      <td>0.936516</td>\n",
       "      <td>0.933564</td>\n",
       "      <td>0.857853</td>\n",
       "      <td>0.751089</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          Experiment Input_dataset  \\\n",
       "0                                      4-layer U-Net           KBY   \n",
       "1                                      5-layer U-Net           KBY   \n",
       "2                   EfficientNet B1 U-Net (ImageNet)           KBY   \n",
       "3      EfficientNet B1 U-Net (Vanilla | No ImageNet)           KBY   \n",
       "4   EfficientNet B1 U-Net (OCC-transfered-UNtrained)           KBY   \n",
       "5     EfficientNet B1 U-Net (OCC-transfered-trained)           KBY   \n",
       "6                   EfficientNet B2 U-Net (ImageNet)           KBY   \n",
       "7      EfficientNet B2 U-Net (Vanilla | No ImageNet)           KBY   \n",
       "8                                      4-layer U-Net           ALL   \n",
       "9                                      5-layer U-Net           ALL   \n",
       "10                  EfficientNet B1 U-Net (ImageNet)           ALL   \n",
       "11     EfficientNet B1 U-Net (Vanilla | No ImageNet)           ALL   \n",
       "12  EfficientNet B1 U-Net (OCC-transfered-UNtrained)           ALL   \n",
       "13    EfficientNet B1 U-Net (OCC-transfered-trained)           ALL   \n",
       "14                  EfficientNet B2 U-Net (ImageNet)           ALL   \n",
       "15     EfficientNet B2 U-Net (Vanilla | No ImageNet)           ALL   \n",
       "\n",
       "    mean_Precision  mean_Recall(TPR)  mean_Specificity(TNR)   mean_OA  \\\n",
       "0         0.876089          0.957042               0.988150  0.985646   \n",
       "1         0.887338          0.954440               0.989391  0.986578   \n",
       "2         0.906424          0.968574               0.991246  0.989421   \n",
       "3         0.902977          0.960412               0.990966  0.988506   \n",
       "4         0.905312          0.668110               0.993882  0.967658   \n",
       "5         0.905570          0.974020               0.991108  0.989733   \n",
       "6         0.909726          0.962329               0.991640  0.989280   \n",
       "7         0.900405          0.948631               0.990814  0.987418   \n",
       "8         0.810815          0.941240               0.939060  0.939534   \n",
       "9         0.817440          0.939155               0.941800  0.941225   \n",
       "10        0.787711          0.936536               0.929964  0.931392   \n",
       "11        0.778491          0.916876               0.927609  0.925278   \n",
       "12        0.902935          0.281554               0.991601  0.837372   \n",
       "13        0.780991          0.930698               0.927580  0.928257   \n",
       "14        0.781347          0.934473               0.927437  0.928965   \n",
       "15        0.801353          0.922926               0.936516  0.933564   \n",
       "\n",
       "    mean_Dice  mean_IoU  \n",
       "0    0.914778  0.842941  \n",
       "1    0.919666  0.851280  \n",
       "2    0.936469  0.880528  \n",
       "3    0.930810  0.870574  \n",
       "4    0.624473  0.531484  \n",
       "5    0.938549  0.884213  \n",
       "6    0.935289  0.878443  \n",
       "7    0.923889  0.858544  \n",
       "8    0.871173  0.771751  \n",
       "9    0.874081  0.776326  \n",
       "10   0.855701  0.747795  \n",
       "11   0.842036  0.727169  \n",
       "12   0.429257  0.273283  \n",
       "13   0.849298  0.738069  \n",
       "14   0.851077  0.740761  \n",
       "15   0.857853  0.751089  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GLOB2_csv = pd.read_csv(os.path.join(Base_path, \"GlobXP_STATS2.csv\"))\n",
    "\n",
    "# Rename Experiment\n",
    "GLOB2_csv[\"Experiment\"] = GLOB2_csv[\"Experiment\"].replace({\"4Unet\": \"4-layer U-Net\",\n",
    "                                                           \"5Unet\": \"5-layer U-Net\",\n",
    "                                                           \"EB1-Unet-IMN\": \"EfficientNet B1 U-Net (ImageNet)\",\n",
    "                                                           \"EB1-Unet-qubvel(transfer-Untrained)\": \"EfficientNet B1 U-Net (OCC-transfered-UNtrained)\",\n",
    "                                                           \"EB1-Unet-qubvel(transfer-trained)\": \"EfficientNet B1 U-Net (OCC-transfered-trained)\",\n",
    "                                                           \"EB1-Unet-NoIMN\": \"EfficientNet B1 U-Net (Vanilla | No ImageNet)\",\n",
    "                                                           \"EB2-Unet-IMN\": \"EfficientNet B2 U-Net (ImageNet)\",\n",
    "                                                           \"EB2-Unet-NoIMN\": \"EfficientNet B2 U-Net (Vanilla | No ImageNet)\"})\n",
    "\n",
    "GLOB2_csv = GLOB2_csv.reindex(columns = [\"Experiment\", \"Input_dataset\", \"mean_Precision\", \"mean_Recall(TPR)\", \"mean_Specificity(TNR)\", \"mean_OA\", \"mean_Dice\", \"mean_IoU\"])\n",
    "\n",
    "GLOB2_csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 96 entries, 0 to 95\n",
      "Data columns (total 4 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Experiment     96 non-null     object \n",
      " 1   Input_dataset  96 non-null     object \n",
      " 2   variable       96 non-null     object \n",
      " 3   value          96 non-null     float64\n",
      "dtypes: float64(1), object(3)\n",
      "memory usage: 3.1+ KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Experiment</th>\n",
       "      <th>Input_dataset</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4-layer U-Net</td>\n",
       "      <td>KBY</td>\n",
       "      <td>mean_Precision</td>\n",
       "      <td>0.876089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5-layer U-Net</td>\n",
       "      <td>KBY</td>\n",
       "      <td>mean_Precision</td>\n",
       "      <td>0.887338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EfficientNet B1 U-Net (ImageNet)</td>\n",
       "      <td>KBY</td>\n",
       "      <td>mean_Precision</td>\n",
       "      <td>0.906424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EfficientNet B1 U-Net (Vanilla | No ImageNet)</td>\n",
       "      <td>KBY</td>\n",
       "      <td>mean_Precision</td>\n",
       "      <td>0.902977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EfficientNet B1 U-Net (OCC-transfered-UNtrained)</td>\n",
       "      <td>KBY</td>\n",
       "      <td>mean_Precision</td>\n",
       "      <td>0.905312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>EfficientNet B1 U-Net (Vanilla | No ImageNet)</td>\n",
       "      <td>KBY + DZK + DZKN</td>\n",
       "      <td>mean_IoU</td>\n",
       "      <td>0.727169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>EfficientNet B1 U-Net (OCC-transfered-UNtrained)</td>\n",
       "      <td>KBY + DZK + DZKN</td>\n",
       "      <td>mean_IoU</td>\n",
       "      <td>0.273283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>EfficientNet B1 U-Net (OCC-transfered-trained)</td>\n",
       "      <td>KBY + DZK + DZKN</td>\n",
       "      <td>mean_IoU</td>\n",
       "      <td>0.738069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>EfficientNet B2 U-Net (ImageNet)</td>\n",
       "      <td>KBY + DZK + DZKN</td>\n",
       "      <td>mean_IoU</td>\n",
       "      <td>0.740761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>EfficientNet B2 U-Net (Vanilla | No ImageNet)</td>\n",
       "      <td>KBY + DZK + DZKN</td>\n",
       "      <td>mean_IoU</td>\n",
       "      <td>0.751089</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          Experiment     Input_dataset  \\\n",
       "0                                      4-layer U-Net               KBY   \n",
       "1                                      5-layer U-Net               KBY   \n",
       "2                   EfficientNet B1 U-Net (ImageNet)               KBY   \n",
       "3      EfficientNet B1 U-Net (Vanilla | No ImageNet)               KBY   \n",
       "4   EfficientNet B1 U-Net (OCC-transfered-UNtrained)               KBY   \n",
       "..                                               ...               ...   \n",
       "91     EfficientNet B1 U-Net (Vanilla | No ImageNet)  KBY + DZK + DZKN   \n",
       "92  EfficientNet B1 U-Net (OCC-transfered-UNtrained)  KBY + DZK + DZKN   \n",
       "93    EfficientNet B1 U-Net (OCC-transfered-trained)  KBY + DZK + DZKN   \n",
       "94                  EfficientNet B2 U-Net (ImageNet)  KBY + DZK + DZKN   \n",
       "95     EfficientNet B2 U-Net (Vanilla | No ImageNet)  KBY + DZK + DZKN   \n",
       "\n",
       "          variable     value  \n",
       "0   mean_Precision  0.876089  \n",
       "1   mean_Precision  0.887338  \n",
       "2   mean_Precision  0.906424  \n",
       "3   mean_Precision  0.902977  \n",
       "4   mean_Precision  0.905312  \n",
       "..             ...       ...  \n",
       "91        mean_IoU  0.727169  \n",
       "92        mean_IoU  0.273283  \n",
       "93        mean_IoU  0.738069  \n",
       "94        mean_IoU  0.740761  \n",
       "95        mean_IoU  0.751089  \n",
       "\n",
       "[96 rows x 4 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Try long format\n",
    "#GLOB2_csv_L = pd.melt(GLOB2_csv, id_vars = [\"Experiment\", \"Input_dataset\"],\n",
    "#                      value_vars = [\"mean_Precision\", \"mean_Recall(TPR)\", \"mean_Specificity(TNR)\", \"mean_OA\", \"mean_Dice\", \"mean_IoU\"])\n",
    "GLOB2_csv_L = pd.melt(GLOB2_csv, id_vars = [\"Experiment\", \"Input_dataset\"])\n",
    "print(GLOB2_csv_L.info())\n",
    "GLOB2_csv_L[\"Input_dataset\"] = GLOB2_csv_L[\"Input_dataset\"].replace(\"ALL\", \"KBY + DZK + DZKN\")\n",
    "#GLOB2_csv_L = pd.melt(GLOB2_csv_L, id_vars = [\"Experiment\", \"Input_dataset\", \"variable\"])\n",
    "\n",
    "GLOB2_csv_L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Experiment          EfficientNet B1 U-Net (OCC-transfered-UNtrained)\n",
      "Input_dataset                                                    KBY\n",
      "mean_Precision                                              0.905312\n",
      "mean_Recall(TPR)                                             0.66811\n",
      "Name: 4, dtype: object\n",
      "Experiment          EfficientNet B1 U-Net (OCC-transfered-UNtrained)\n",
      "Input_dataset                                                    ALL\n",
      "mean_Precision                                              0.902935\n",
      "mean_Recall(TPR)                                            0.281554\n",
      "Name: 12, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(GLOB2_csv.iloc[4, 0:4])\n",
    "print(GLOB2_csv.iloc[12, 0:4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x720 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "cpalette = [\"gray\", \"magenta\"]\n",
    "\n",
    "Cat_CAA = sns.catplot(data = GLOB2_csv_L, x = \"value\", y = \"Experiment\",\n",
    "                      hue = \"Input_dataset\", col = \"variable\",\n",
    "                      kind = \"strip\", jitter = False,\n",
    "                      height = 10, aspect = 0.3, orient = \"h\",\n",
    "                      sharey = True, s = 10,\n",
    "                      palette = cpalette, legend = False);\n",
    "\n",
    "# Use semantically meaningful titles for the columns\n",
    "titles = [\"Precision\", \"Recall (TPR)\", \"Specificity (TNR)\", \"OA\", \n",
    "          \"Dice score\", \"IoU\"]\n",
    "\n",
    "Cat_CAA.fig.suptitle(\"Class-based accuracy assessment metrics between different CNN architectures and data-input\", y = 1.05)\n",
    "Cat_CAA.despine(bottom = True)\n",
    "\n",
    "for ax, title in zip(Cat_CAA.axes.flat, titles):\n",
    "\n",
    "    # Set a different title for each axes\n",
    "    ax.set(title = title)\n",
    "    \n",
    "    for label in ax.axes.get_yticklabels(which = 'major'):\n",
    "        label.set(rotation=30, horizontalalignment = 'right')\n",
    "\n",
    "    # Make the grid horizontal instead of vertical\n",
    "    ax.xaxis.grid(True)\n",
    "    ax.yaxis.grid(True)\n",
    "    ax.set_xlabel(\"\")\n",
    "\n",
    "    \n",
    "plt.legend(title = \"Input dataset\", loc = 2, bbox_to_anchor = (1, 1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
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