Results_overview.ipynb

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    "# dCAM: Dimension-wise Class Activation Map for Explaining Multivariate Data Series Classification\n",
    "\n",
    "### Paul Boniol, Mohammed Meftah, Emmanuel Remy, Themis Palpanas\n",
    "\n",
    "Data series classification is an important and challenging problem in data science. Explaining the classification decisions by finding the discriminant parts of the input that led the algorithm to some decision is a real need in many applications. Convolutional neural networks perform well for the data series classification task; though, the explanations provided by this type of algorithms are poor for the specific case of multivariate data series. Addressing this important limitation is a significant challenge. In this paper, we\n",
    "propose a novel method that solves this problem by highlighting\n",
    "both the temporal and dimensional discriminant information. Our\n",
    "contribution is two-fold: we first describe a convolutional architecture that enables the comparison of dimensions; then, we propose a method that returns dCAM, a Dimension-wise Class Activation Map specifically designed for multivariate time series (and CNNbased models). Experiments with several synthetic and real datasets demonstrate that dCAM is not only more accurate than previous approaches, but the only viable solution for discriminant feature discovery and classification explanation in multivariate time series. This Notebook aims to reproduce and illustrate the results depicted in the paper.\n",
    "\n",
    "The paper can be found here: https://helios2.mi.parisdescartes.fr/~themisp/publications/sigmod22-dcam.pdf\n",
    "\n",
    "\n"
   ]
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   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import interpolate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "# Experimental Evaluation\n",
    "\n",
    "We implemented our algorithms in Python 3.5 using the PyTorch\n",
    "library. The evaluation was conducted on a server with Intel\n",
    "Core i7-8750H CPU 2.20GHz x 12, with 31.3GB RAM, and Quadro\n",
    "P1000/PCle/SSE2 GPU with 4.2GB RAM, and on Jean Zay cluster\n",
    "with Nvidia Tesla V100 SXM2 GPU with 32 GB RAM. We provide both a way to run our code on a single node and on a cluster.\n",
    "\n",
    "## Reproduce the experiments\n",
    "\n",
    "This notebook is using results pre-computed. You can reproduce and run the experiments on your own. To do follow the guidlines in each section.\n",
    "\n",
    "## Set your device\n",
    "\n",
    "Our experiment can be run on servers with both CPU and GPU. We strongly suggest to run our code on GPU servers (for instance, SLURM clusters). The default setting is for GPU. If you want to change it, please do the following:\n",
    "\n",
    "- In each script folder (for instance, \"dCAM/experiments/classification/scripts/\" or \"dCAM/experiments/explanation/scripts_classification_eval/\"), there is a setting.py file. Open this file and do the following changes:\n",
    "\n",
    "```python\n",
    "DEVICE = 'cuda'\n",
    "```\n",
    "to:\n",
    "```python\n",
    "DEVICE = 'cpu'\n",
    "```\n",
    "\n",
    "- This change will impact all the script files in the folder.\n"
   ]
  },
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   "source": [
    "---\n",
    "\n",
    "# Classification Accuracy evaluation (Section 5.3)\n",
    "\n",
    "We first evaluate the classification performance of our proposed\n",
    "approaches (denoted as c-Baselines and d-Baselines in Table 2) and\n",
    "the different baselines (denoted as Baselines in Table 2) over the\n",
    "UCR/UEA multivariate data series. We run each method five times\n",
    "and report the average classification accuracy. Be aware that, as any neural network methods, the training and the prediction is undterministic. Therefore, the results reported in the table below might not be exactly the same. For execution time reasons, we repeated each training only 3 times instaead of 10 as reported in the paper. However, the trends and conclusions still holds.\n",
    "\n",
    "You can modify the training parameters in script_exp.py file for single node execution and script_exp_gpu_parallel.py file for parallel execution:\n",
    "\n",
    "```python\n",
    "parameters = {\n",
    "    'train_test_r': 0.80,    # Dataset training and testing split ratio\n",
    "    'batch_size': 8,         # Number of instances in one batch\n",
    "    'nb_epoch': 1000,        # Number of epoches\n",
    "    'nb_repeat_iteration': 3 # Number of training iteration\n",
    "}\n",
    "```\n",
    "\n",
    "## Reproduce this experiments\n",
    "\n",
    "To reproduce this experiment, go to the folder experiments/classification/scripts/ and run the following steps:\n",
    "\n",
    "### Single node execution\n",
    "\n",
    "You can either run the following script:\n",
    "\n",
    "```bash\n",
    "python script_exp.py\n",
    "```\n",
    "\n",
    "This script will run the entire experiment sequencially. Therefore, it might take a very long time. The results will be stored in results/log folder. An aggregated tables summarizing the results will be created at the end of the execution and sorted in results/merged_results.csv. \n",
    "\n",
    "\n",
    "### Parallel execution\n",
    "\n",
    "We suggest you to use a dedicated infrastruture on which you can this experiment in parallel. To do so, we provide a SLURM version of our code. to run this version, follow these steps:\n",
    "\n",
    "- Modify submit_generic.py such that the template of the bash scripts is compatible with your infrastruture.\n",
    "\n",
    "\n",
    "- Run the following command:\n",
    "```bash\n",
    "python script_exp_gpu_parallel.py\n",
    "```\n",
    "- This script will generate one bash file for each dataset and model that will be then run in parallel. These batch files will be stored in auto_script folder.\n",
    "\n",
    "\n",
    "\n",
    "- Then, run the following command:\n",
    "```bash\n",
    "chmod 777 submit_all.sh\n",
    "./submit_all.sh\n",
    "```\n",
    "- All the SLURM jobs will be laucnhed using sbatch command. The log files for each jobs will be stored in auto_script/output_log folder.\n",
    "\n",
    "\n",
    "\n",
    "- The results will be stored in results/log folder. At the end of the execution, please run the following command:\n",
    "```bash\n",
    "python script_generate_merged_results.py\n",
    "```\n",
    "- An aggregated tables summarizing the results will be created  and sorted in results/merged_results.csv. \n",
    "\n",
    "\n",
    "---\n",
    "\n",
    "## Experimental Results\n"
   ]
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       "      <td>0.705128</td>\n",
       "      <td>0.589744</td>\n",
       "      <td>0.564103</td>\n",
       "      <td>0.596154</td>\n",
       "      <td>0.538462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MotorImagery</th>\n",
       "      <td>0.574561</td>\n",
       "      <td>0.618421</td>\n",
       "      <td>0.587719</td>\n",
       "      <td>0.578947</td>\n",
       "      <td>0.570175</td>\n",
       "      <td>0.521930</td>\n",
       "      <td>0.543860</td>\n",
       "      <td>0.557018</td>\n",
       "      <td>0.622807</td>\n",
       "      <td>0.651316</td>\n",
       "      <td>0.592105</td>\n",
       "      <td>0.677632</td>\n",
       "      <td>0.671053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PEMS-SF</th>\n",
       "      <td>0.901515</td>\n",
       "      <td>0.920455</td>\n",
       "      <td>0.924242</td>\n",
       "      <td>0.935606</td>\n",
       "      <td>0.757576</td>\n",
       "      <td>0.780303</td>\n",
       "      <td>0.708333</td>\n",
       "      <td>0.837121</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.795455</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FaceDetection</th>\n",
       "      <td>0.708798</td>\n",
       "      <td>0.571783</td>\n",
       "      <td>0.591255</td>\n",
       "      <td>0.733227</td>\n",
       "      <td>0.600991</td>\n",
       "      <td>0.643123</td>\n",
       "      <td>0.588777</td>\n",
       "      <td>0.555851</td>\n",
       "      <td>0.561604</td>\n",
       "      <td>0.610196</td>\n",
       "      <td>0.570366</td>\n",
       "      <td>0.592937</td>\n",
       "      <td>0.629049</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               mtex       cnn    resnet  inception      lstm  \\\n",
       "datasets                                                                       \n",
       "AtrialFibrillation         0.500000  0.500000  0.444444   0.500000  0.666667   \n",
       "Libras                     0.828704  0.976852  0.990741   0.861111  0.476852   \n",
       "BasicMotions               0.937500  1.000000  1.000000   1.000000  0.812500   \n",
       "RacketSports               0.748634  0.956284  0.983607   0.923497  0.737705   \n",
       "Epilepsy                   0.993939  1.000000  1.000000   0.975758  0.769697   \n",
       "StandWalkJum               0.611111  0.500000  0.388889   0.611111  0.500000   \n",
       "UWaveGestureLibrary        0.958333  0.946970  0.958333   0.939394  0.886364   \n",
       "Handwriting                0.351667  0.875000  0.940000   0.648333  0.375000   \n",
       "NATOPS                     0.870370  0.986111  0.986111   0.930556  0.796296   \n",
       "FingerMovements            0.611111  0.698413  0.702381   0.670635  0.575397   \n",
       "ArticularyWordRecognition  0.988406  0.997101  1.000000   0.962319  0.915942   \n",
       "HandMovementDirection      0.390071  0.475177  0.439716   0.489362  0.347518   \n",
       "Cricket                    0.990741  1.000000  1.000000   1.000000  0.833333   \n",
       "LSST                       0.480203  0.588156  0.617936   0.405753  0.516751   \n",
       "EthanolConcentration       0.466667  0.358730  0.374603   0.380952  0.307937   \n",
       "SelfRegulationSCP1         0.887906  0.831858  0.820059   0.890855  0.861357   \n",
       "SelfRegulationSCP2         0.561404  0.583333  0.565789   0.609649  0.570175   \n",
       "Heartbeat                  0.756098  0.792683  0.804878   0.764228  0.723577   \n",
       "PhonemeSpectra             0.154173  0.307846  0.358571   0.277361  0.118441   \n",
       "EigenWorms                 0.653846  0.525641  0.660256   0.608974  0.673077   \n",
       "MotorImagery               0.574561  0.618421  0.587719   0.578947  0.570175   \n",
       "PEMS-SF                    0.901515  0.920455  0.924242   0.935606  0.757576   \n",
       "FaceDetection              0.708798  0.571783  0.591255   0.733227  0.600991   \n",
       "\n",
       "                                rnn       gru      ccnn   cresnet  cinception  \\\n",
       "datasets                                                                        \n",
       "AtrialFibrillation         0.722222  0.666667  0.666667  0.555556    0.833333   \n",
       "Libras                     0.763889  0.601852  0.837963  0.847222    0.699074   \n",
       "BasicMotions               0.770833  0.812500  1.000000  1.000000    1.000000   \n",
       "RacketSports               0.732240  0.721311  0.950820  1.000000    0.879781   \n",
       "Epilepsy                   0.600000  0.830303  1.000000  1.000000    1.000000   \n",
       "StandWalkJum               0.444444  0.555556  0.888889  1.000000    0.944444   \n",
       "UWaveGestureLibrary        0.946970  0.905303  0.753788  0.776515    0.647727   \n",
       "Handwriting                0.430000  0.418333  0.425000  0.720000    0.428333   \n",
       "NATOPS                     0.898148  0.800926  0.875000  0.921296    0.842593   \n",
       "FingerMovements            0.571429  0.567460  0.599206  0.611111    0.587302   \n",
       "ArticularyWordRecognition  0.976812  0.968116  0.892754  0.973913    0.797101   \n",
       "HandMovementDirection      0.404255  0.446809  0.354610  0.382979    0.297872   \n",
       "Cricket                    0.916667  0.925926  0.972222  1.000000    0.935185   \n",
       "LSST                       0.532995  0.518782  0.526904  0.525212    0.501523   \n",
       "EthanolConcentration       0.317460  0.349206  0.403175  0.749206    0.409524   \n",
       "SelfRegulationSCP1         0.882006  0.864307  0.837758  0.831858    0.884956   \n",
       "SelfRegulationSCP2         0.557018  0.592105  0.583333  0.561404    0.596491   \n",
       "Heartbeat                  0.735772  0.723577  0.719512  0.764228    0.719512   \n",
       "PhonemeSpectra             0.106197  0.116692  0.297101  0.332834    0.280110   \n",
       "EigenWorms                 0.576923  0.608974  0.519231  0.705128    0.589744   \n",
       "MotorImagery               0.521930  0.543860  0.557018  0.622807    0.651316   \n",
       "PEMS-SF                    0.780303  0.708333  0.837121  1.000000    0.795455   \n",
       "FaceDetection              0.643123  0.588777  0.555851  0.561604    0.610196   \n",
       "\n",
       "                               dcnn   dresnet  dinception  \n",
       "datasets                                                   \n",
       "AtrialFibrillation         0.500000  0.500000    0.555556  \n",
       "Libras                     0.912037  0.967593    0.722222  \n",
       "BasicMotions               1.000000  1.000000    1.000000  \n",
       "RacketSports               0.939891  1.000000    0.934426  \n",
       "Epilepsy                   1.000000  1.000000    1.000000  \n",
       "StandWalkJum               1.000000  1.000000    0.888889  \n",
       "UWaveGestureLibrary        0.886364  0.954545    0.875000  \n",
       "Handwriting                0.788333  0.931667    0.543333  \n",
       "NATOPS                     0.967593  0.986111    0.921296  \n",
       "FingerMovements            0.718254  0.714286    0.626984  \n",
       "ArticularyWordRecognition  0.968116  1.000000    0.910145  \n",
       "HandMovementDirection      0.446809  0.418440    0.347518  \n",
       "Cricket                    1.000000  1.000000    1.000000  \n",
       "LSST                       0.559391  0.694078    0.594247  \n",
       "EthanolConcentration       0.352381  0.380952    0.380952  \n",
       "SelfRegulationSCP1         0.831858  0.852507    0.896755  \n",
       "SelfRegulationSCP2         0.561404  0.574561    0.587719  \n",
       "Heartbeat                  0.752033  0.821138    0.788618  \n",
       "PhonemeSpectra             0.313843  0.390305    0.304098  \n",
       "EigenWorms                 0.564103  0.596154    0.538462  \n",
       "MotorImagery               0.592105  0.677632    0.671053  \n",
       "PEMS-SF                         NaN       NaN         NaN  \n",
       "FaceDetection              0.570366  0.592937    0.629049  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mtex</th>\n",
       "      <th>cnn</th>\n",
       "      <th>resnet</th>\n",
       "      <th>inception</th>\n",
       "      <th>lstm</th>\n",
       "      <th>rnn</th>\n",
       "      <th>gru</th>\n",
       "      <th>ccnn</th>\n",
       "      <th>cresnet</th>\n",
       "      <th>cinception</th>\n",
       "      <th>dcnn</th>\n",
       "      <th>dresnet</th>\n",
       "      <th>dinception</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean_accuracy</th>\n",
       "      <td>0.692424</td>\n",
       "      <td>0.739601</td>\n",
       "      <td>0.745197</td>\n",
       "      <td>0.725984</td>\n",
       "      <td>0.625797</td>\n",
       "      <td>0.644854</td>\n",
       "      <td>0.645029</td>\n",
       "      <td>0.697997</td>\n",
       "      <td>0.758386</td>\n",
       "      <td>0.692677</td>\n",
       "      <td>0.737495</td>\n",
       "      <td>0.775132</td>\n",
       "      <td>0.714378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_rank</th>\n",
       "      <td>7.260870</td>\n",
       "      <td>4.826087</td>\n",
       "      <td>4.434783</td>\n",
       "      <td>5.608696</td>\n",
       "      <td>9.739130</td>\n",
       "      <td>9.043478</td>\n",
       "      <td>9.130435</td>\n",
       "      <td>7.826087</td>\n",
       "      <td>5.043478</td>\n",
       "      <td>7.347826</td>\n",
       "      <td>5.590909</td>\n",
       "      <td>3.136364</td>\n",
       "      <td>5.545455</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   mtex       cnn    resnet  inception      lstm       rnn  \\\n",
       "mean_accuracy  0.692424  0.739601  0.745197   0.725984  0.625797  0.644854   \n",
       "mean_rank      7.260870  4.826087  4.434783   5.608696  9.739130  9.043478   \n",
       "\n",
       "                    gru      ccnn   cresnet  cinception      dcnn   dresnet  \\\n",
       "mean_accuracy  0.645029  0.697997  0.758386    0.692677  0.737495  0.775132   \n",
       "mean_rank      9.130435  7.826087  5.043478    7.347826  5.590909  3.136364   \n",
       "\n",
       "               dinception  \n",
       "mean_accuracy    0.714378  \n",
       "mean_rank        5.545455  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "methods = ['mtex','cnn','resnet','inception','lstm','rnn','gru','ccnn','cresnet','cinception','dcnn','dresnet','dinception']\n",
    "\n",
    "df_classification = pd.read_csv('experiments/classification/results/merged_results.csv')\n",
    "df_classification.rename(columns={'Unnamed: 0':'datasets'}, inplace=True)\n",
    "df_classification.set_index('datasets', inplace=True)\n",
    "\n",
    "df_classification_average = pd.DataFrame()\n",
    "df_classification_average['mean_accuracy'] = df_classification.mean()\n",
    "df_classification_average['mean_rank'] = df_classification[methods].rank(1,method='min',ascending=False).mean()\n",
    "display(df_classification)\n",
    "display(df_classification_average.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-25T09:11:10.574701Z",
     "start_time": "2022-08-25T09:11:10.423003Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Average rank per method')"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_classification_average.sort_values('mean_rank', ascending=False).plot.bar(y='mean_rank',figsize=(20,4))\n",
    "plt.xticks(rotation=0)\n",
    "plt.title(\"Average rank per method\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-25T09:11:22.842753Z",
     "start_time": "2022-08-25T09:11:22.524202Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Inception Time/cInception Time/MTEX')"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "\n",
    "plt.subplot(1,3,1)\n",
    "plt.scatter(df_classification['cnn'],df_classification['dcnn'],c='blue',marker='o')\n",
    "plt.scatter(df_classification['ccnn'],df_classification['dcnn'],c='red',marker='x')\n",
    "plt.scatter(df_classification['mtex'],df_classification['dcnn'],c='black',marker='s')\n",
    "plt.plot([0,1],[0,1],color='black',linestyle='--')\n",
    "plt.xlim(0,1.01)\n",
    "plt.ylim(0,1.01)\n",
    "plt.ylabel('dCNN')\n",
    "plt.xlabel('CNN/cCNN/MTEX')\n",
    "\n",
    "plt.subplot(1,3,2)\n",
    "plt.scatter(df_classification['resnet'],df_classification['dresnet'],c='blue',marker='o')\n",
    "plt.scatter(df_classification['cresnet'],df_classification['dresnet'],c='red',marker='x')\n",
    "plt.scatter(df_classification['mtex'],df_classification['dresnet'],c='black',marker='s')\n",
    "plt.plot([0,1],[0,1],color='black',linestyle='--')\n",
    "plt.xlim(0,1.01)\n",
    "plt.ylim(0,1.01)\n",
    "plt.ylabel('dResnet')\n",
    "plt.xlabel('Resnet/cResnet/MTEX')\n",
    "\n",
    "plt.subplot(1,3,3)\n",
    "plt.scatter(df_classification['inception'],df_classification['dinception'],c='blue',marker='o')\n",
    "plt.scatter(df_classification['cinception'],df_classification['dinception'],c='red',marker='x')\n",
    "plt.scatter(df_classification['mtex'],df_classification['dinception'],c='black',marker='s')\n",
    "plt.plot([0,1],[0,1],color='black',linestyle='--')\n",
    "plt.xlim(0,1.01)\n",
    "plt.ylim(0,1.01)\n",
    "plt.ylabel('dInception Time')\n",
    "plt.xlabel('Inception Time/cInception Time/MTEX')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "# Discriminant Features Identification (Section 5.4)\n",
    "\n",
    "\n",
    "\n",
    "We now evaluate the classification accuracy (C-acc) and the discriminant features identification accuracy (Dr-acc) on synthetically\n",
    "built datasets. Table 3 depicts both C-acc and Dr-acc on Type 1 and\n",
    "2 datasets, when varying the number of dimensions from 10 to 100.\n",
    "In this experiment, we keep as baselines only ResNet and cResNet,\n",
    "which are the most accurate methods among all other baselines.\n",
    "\n",
    "the prediction is undterministic. Therefore, the results reported in the table below might not be exactly the same. For execution time reasons, we repeated each training only 3 times instaead of 10 as reported in the paper. However, the trends and conclusions still holds.\n",
    "\n",
    "You can modify the training parameters in script_exp.py file for single node execution and script_exp_gpu_parallel.py file for parallel execution:\n",
    "\n",
    "```python\n",
    "parameters = {\n",
    "    'train_test_r': 0.80,    # Dataset training and testing split ratio\n",
    "    'batch_size': 8,         # Number of instances in one batch\n",
    "    'nb_epoch': 1000,        # Number of epoches\n",
    "    'nb_repeat_iteration': 3 # Number of training iteration\n",
    "}\n",
    "```\n",
    "\n",
    "## Reproduce this experiments: Classification Evaluation\n",
    "\n",
    "To reproduce this experiment, go to the folder experiments/explanation/scripts_classification_eval/ and run the following steps:\n",
    "\n",
    "### Single node execution\n",
    "\n",
    "You can either run the following script:\n",
    "\n",
    "```bash\n",
    "python script_exp.py\n",
    "```\n",
    "\n",
    "This script will run the entire experiment sequencially. Therefore, it might take a very long time. The results will be stored in results_classification/log folder. An aggregated tables summarizing the results will be created at the end of the execution and sorted in results_classification/merged_results_classification.csv. \n",
    "\n",
    "\n",
    "### Parallel execution\n",
    "\n",
    "We suggest you to use a dedicated infrastruture on which you can this experiment in parallel. To do so, we provide a SLURM version of our code. to run this version, follow these steps:\n",
    "\n",
    "- Modify submit_generic.py such that the template of the bash scripts is compatible with your infrastruture.\n",
    "\n",
    "\n",
    "- Run the following command:\n",
    "```bash\n",
    "python script_exp_gpu_parallel.py\n",
    "```\n",
    "- This script will generate one bash file for each dataset and model that will be then run in parallel. These batch files will be stored in auto_script folder.\n",
    "\n",
    "\n",
    "\n",
    "- Then, run the following command:\n",
    "```bash\n",
    "chmod 777 submit_all.sh\n",
    "./submit_all.sh\n",
    "```\n",
    "- All the SLURM jobs will be laucnhed using sbatch command. The log files for each jobs will be stored in auto_script/output_log folder.\n",
    "\n",
    "\n",
    "\n",
    "- The results will be stored in results/log folder. At the end of the execution, please run the following command:\n",
    "```bash\n",
    "python script_generate_merged_results.py\n",
    "```\n",
    "- An aggregated tables summarizing the results will be created  and sorted in results_classification/merged_results_classification.csv. \n",
    "\n",
    "\n",
    "---\n",
    "## Reproduce this experiments: Discriminant Features Identification Evaluation\n",
    "\n",
    "To reproduce this experiment, go to the folder explanation/scripts_explanation_eval/ and run the following steps:\n",
    "\n",
    "### Single node execution\n",
    "\n",
    "You can either run the following script:\n",
    "\n",
    "```bash\n",
    "python script_exp.py\n",
    "```\n",
    "\n",
    "This script will run the entire experiment sequencially. Therefore, it might take a very long time. The results will be stored in results_classification/log folder. An aggregated tables summarizing the results will be created at the end of the execution and sorted in results_explanation/merged_results_explanation.csv. \n",
    "\n",
    "### Parallel execution\n",
    "\n",
    "We suggest you to use a dedicated infrastruture on which you can this experiment in parallel. To do so, we provide a SLURM version of our code. to run this version, follow these steps:\n",
    "\n",
    "- Modify submit_generic.py such that the template of the bash scripts is compatible with your infrastruture.\n",
    "\n",
    "\n",
    "- Run the following command:\n",
    "```bash\n",
    "python script_exp_gpu_parallel.py\n",
    "```\n",
    "- This script will generate one bash file for each dataset and model that will be then run in parallel. These batch files will be stored in auto_script folder.\n",
    "\n",
    "\n",
    "\n",
    "- Then, run the following command:\n",
    "```bash\n",
    "chmod 777 submit_all.sh\n",
    "./submit_all.sh\n",
    "```\n",
    "- All the SLURM jobs will be laucnhed using sbatch command. The log files for each jobs will be stored in auto_script/output_log folder.\n",
    "\n",
    "\n",
    "\n",
    "- The results will be stored in results/log folder. At the end of the execution, please run the following command:\n",
    "```bash\n",
    "python script_generate_merged_results.py\n",
    "```\n",
    "- An aggregated tables summarizing the results will be created  and sorted in results_explanation/merged_results_explanation.csv. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Experimental Results: Classification Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-25T09:14:06.778889Z",
     "start_time": "2022-08-25T09:14:06.754262Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mtex</th>\n",
       "      <th>resnet</th>\n",
       "      <th>cresnet</th>\n",
       "      <th>dcnn</th>\n",
       "      <th>dresnet</th>\n",
       "      <th>dinception</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datasets</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>synth_ShapesAll_type1_nbdim_10</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>synth_ShapesAll_type1_nbdim_20</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.868333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>synth_ShapesAll_type1_nbdim_40</th>\n",
       "      <td>0.996667</td>\n",
       "      <td>0.680000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>synth_ShapesAll_type1_nbdim_60</th>\n",
       "      <td>0.913333</td>\n",
       "      <td>0.651667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>synth_ShapesAll_type1_nbdim_100</th>\n",
       "      <td>0.570000</td>\n",
       "      <td>0.588333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.981667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>synth_ShapesAll_type2_nbdim_10</th>\n",
       "      <td>0.596667</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>0.596667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.966667</td>\n",
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       "    <tr>\n",
       "      <th>synth_ShapesAll_type2_nbdim_20</th>\n",
       "      <td>0.538333</td>\n",
       "      <td>0.603333</td>\n",
       "      <td>0.581667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>synth_ShapesAll_type2_nbdim_40</th>\n",
       "      <td>0.563333</td>\n",
       "      <td>0.605000</td>\n",
       "      <td>0.561667</td>\n",
       "      <td>0.851667</td>\n",
       "      <td>0.716667</td>\n",
       "      <td>0.711667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>synth_ShapesAll_type2_nbdim_60</th>\n",
       "      <td>0.573333</td>\n",
       "      <td>0.611667</td>\n",
       "      <td>0.573333</td>\n",
       "      <td>0.715000</td>\n",
       "      <td>0.593333</td>\n",
       "      <td>0.665000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>synth_ShapesAll_type2_nbdim_100</th>\n",
       "      <td>0.553333</td>\n",
       "      <td>0.605000</td>\n",
       "      <td>0.565000</td>\n",
       "      <td>0.555000</td>\n",
       "      <td>0.595833</td>\n",
       "      <td>0.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>synth_starlight_type1_nbdim_10</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.996667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>synth_starlight_type1_nbdim_20</th>\n",
       "      <td>0.923333</td>\n",
       "      <td>0.796667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.991667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>synth_starlight_type1_nbdim_40</th>\n",
       "      <td>0.715000</td>\n",
       "      <td>0.623333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.993333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.956667</td>\n",
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       "    <tr>\n",
       "      <th>synth_starlight_type1_nbdim_60</th>\n",
       "      <td>0.608333</td>\n",
       "      <td>0.615000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.980000</td>\n",
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       "      <th>synth_starlight_type1_nbdim_100</th>\n",
       "      <td>0.568333</td>\n",
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       "      <td>0.825000</td>\n",
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       "    <tr>\n",
       "      <th>synth_starlight_type2_nbdim_10</th>\n",
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       "      <td>0.576667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.955000</td>\n",
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       "    <tr>\n",
       "      <th>synth_starlight_type2_nbdim_20</th>\n",
       "      <td>0.548333</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>0.575000</td>\n",
       "      <td>0.993333</td>\n",
       "      <td>0.996667</td>\n",
       "      <td>0.700000</td>\n",
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       "    <tr>\n",
       "      <th>synth_starlight_type2_nbdim_40</th>\n",
       "      <td>0.565000</td>\n",
       "      <td>0.556667</td>\n",
       "      <td>0.566667</td>\n",
       "      <td>0.840000</td>\n",
       "      <td>0.571667</td>\n",
       "      <td>0.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>synth_starlight_type2_nbdim_60</th>\n",
       "      <td>0.553333</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.573333</td>\n",
       "      <td>0.663333</td>\n",
       "      <td>0.545000</td>\n",
       "      <td>0.546667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>synth_starlight_type2_nbdim_100</th>\n",
       "      <td>0.536667</td>\n",
       "      <td>0.596667</td>\n",
       "      <td>0.605000</td>\n",
       "      <td>0.586667</td>\n",
       "      <td>0.585000</td>\n",
       "      <td>0.601667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     mtex    resnet   cresnet      dcnn  \\\n",
       "datasets                                                                  \n",
       "synth_ShapesAll_type1_nbdim_10   1.000000  1.000000  1.000000  1.000000   \n",
       "synth_ShapesAll_type1_nbdim_20   1.000000  0.868333  1.000000  1.000000   \n",
       "synth_ShapesAll_type1_nbdim_40   0.996667  0.680000  1.000000  1.000000   \n",
       "synth_ShapesAll_type1_nbdim_60   0.913333  0.651667  1.000000  1.000000   \n",
       "synth_ShapesAll_type1_nbdim_100  0.570000  0.588333  1.000000  1.000000   \n",
       "synth_ShapesAll_type2_nbdim_10   0.596667  0.766667  0.596667  1.000000   \n",
       "synth_ShapesAll_type2_nbdim_20   0.538333  0.603333  0.581667  1.000000   \n",
       "synth_ShapesAll_type2_nbdim_40   0.563333  0.605000  0.561667  0.851667   \n",
       "synth_ShapesAll_type2_nbdim_60   0.573333  0.611667  0.573333  0.715000   \n",
       "synth_ShapesAll_type2_nbdim_100  0.553333  0.605000  0.565000  0.555000   \n",
       "synth_starlight_type1_nbdim_10   1.000000  0.996667  1.000000  1.000000   \n",
       "synth_starlight_type1_nbdim_20   0.923333  0.796667  1.000000  1.000000   \n",
       "synth_starlight_type1_nbdim_40   0.715000  0.623333  1.000000  0.993333   \n",
       "synth_starlight_type1_nbdim_60   0.608333  0.615000  1.000000  0.980000   \n",
       "synth_starlight_type1_nbdim_100  0.568333  0.593333  1.000000  0.950000   \n",
       "synth_starlight_type2_nbdim_10   0.601667  0.686667  0.576667  1.000000   \n",
       "synth_starlight_type2_nbdim_20   0.548333  0.560000  0.575000  0.993333   \n",
       "synth_starlight_type2_nbdim_40   0.565000  0.556667  0.566667  0.840000   \n",
       "synth_starlight_type2_nbdim_60   0.553333  0.583333  0.573333  0.663333   \n",
       "synth_starlight_type2_nbdim_100  0.536667  0.596667  0.605000  0.586667   \n",
       "\n",
       "                                  dresnet  dinception  \n",
       "datasets                                               \n",
       "synth_ShapesAll_type1_nbdim_10   1.000000    1.000000  \n",
       "synth_ShapesAll_type1_nbdim_20   1.000000    1.000000  \n",
       "synth_ShapesAll_type1_nbdim_40   1.000000    1.000000  \n",
       "synth_ShapesAll_type1_nbdim_60   1.000000    1.000000  \n",
       "synth_ShapesAll_type1_nbdim_100  1.000000    0.981667  \n",
       "synth_ShapesAll_type2_nbdim_10   1.000000    0.966667  \n",
       "synth_ShapesAll_type2_nbdim_20   1.000000    0.875000  \n",
       "synth_ShapesAll_type2_nbdim_40   0.716667    0.711667  \n",
       "synth_ShapesAll_type2_nbdim_60   0.593333    0.665000  \n",
       "synth_ShapesAll_type2_nbdim_100  0.595833    0.583333  \n",
       "synth_starlight_type1_nbdim_10   1.000000    1.000000  \n",
       "synth_starlight_type1_nbdim_20   1.000000    0.991667  \n",
       "synth_starlight_type1_nbdim_40   1.000000    0.956667  \n",
       "synth_starlight_type1_nbdim_60   0.990000    0.931667  \n",
       "synth_starlight_type1_nbdim_100  0.966667    0.825000  \n",
       "synth_starlight_type2_nbdim_10   1.000000    0.955000  \n",
       "synth_starlight_type2_nbdim_20   0.996667    0.700000  \n",
       "synth_starlight_type2_nbdim_40   0.571667    0.550000  \n",
       "synth_starlight_type2_nbdim_60   0.545000    0.546667  \n",
       "synth_starlight_type2_nbdim_100  0.585000    0.601667  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>mtex</th>\n",
       "      <th>resnet</th>\n",
       "      <th>cresnet</th>\n",
       "      <th>dcnn</th>\n",
       "      <th>dresnet</th>\n",
       "      <th>dinception</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean_accuracy</th>\n",
       "      <td>0.69625</td>\n",
       "      <td>0.679417</td>\n",
       "      <td>0.78875</td>\n",
       "      <td>0.906417</td>\n",
       "      <td>0.878042</td>\n",
       "      <td>0.842083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_rank</th>\n",
       "      <td>4.70000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>2.65000</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>1.850000</td>\n",
       "      <td>2.900000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  mtex    resnet  cresnet      dcnn   dresnet  dinception\n",
       "mean_accuracy  0.69625  0.679417  0.78875  0.906417  0.878042    0.842083\n",
       "mean_rank      4.70000  4.350000  2.65000  1.700000  1.850000    2.900000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "methods = ['mtex','resnet','cresnet','dcnn','dresnet','dinception']\n",
    "\n",
    "df_classification_synth = pd.read_csv('experiments/explanation/results_classification/merged_results_classification.csv')\n",
    "df_classification_synth.rename(columns={'Unnamed: 0':'datasets'}, inplace=True)\n",
    "df_classification_synth.set_index('datasets', inplace=True)\n",
    "\n",
    "df_classification_synth_average = pd.DataFrame()\n",
    "df_classification_synth_average['mean_accuracy'] = df_classification_synth.mean()\n",
    "df_classification_synth_average['mean_rank'] = df_classification_synth[methods].rank(1,method='min',ascending=False).mean()\n",
    "display(df_classification_synth)\n",
    "display(df_classification_synth_average.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-25T09:14:07.512833Z",
     "start_time": "2022-08-25T09:14:07.397563Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Average rank per method')"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_classification_synth_average.sort_values('mean_rank', ascending=False).plot.bar(y='mean_rank',figsize=(20,4))\n",
    "plt.xticks(rotation=0)\n",
    "plt.title(\"Average rank per method\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-25T09:14:08.376337Z",
     "start_time": "2022-08-25T09:14:08.114559Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_name = ['ShapesAll','starlight']\n",
    "types = [1,2]\n",
    "dims = [10,20,40,60,100]\n",
    "pattern_name = 'synth_{}_type{}_nbdim_{}'\n",
    "\n",
    "methods_color = ['green','royalblue','darkorange','grey','gold','blue']\n",
    "methods_style = ['-','--','--','-','-','-']\n",
    "\n",
    "\n",
    "list_type_1 = {method:[] for method in methods}\n",
    "list_type_2 = {method:[] for method in methods}\n",
    "list_type_F = {method:[] for method in methods}\n",
    "\n",
    "for dim in dims:\n",
    "    for method in methods:\n",
    "        tmp_1 = []\n",
    "        tmp_2 = []\n",
    "        for dataset in dataset_name:\n",
    "            val_1 = df_classification_synth.loc[pattern_name.format(dataset,1,dim)][method]\n",
    "            val_2 = df_classification_synth.loc[pattern_name.format(dataset,2,dim)][method]\n",
    "            tmp_1.append(val_1)\n",
    "            tmp_2.append(val_2)\n",
    "        list_type_1[method].append(np.mean(tmp_1))\n",
    "        list_type_2[method].append(np.mean(tmp_2))\n",
    "        list_type_F[method].append((2*np.mean(tmp_2)*np.mean(tmp_1))/(np.mean(tmp_2)+np.mean(tmp_1)))\n",
    "            \n",
    "            \n",
    "\n",
    "plt.figure(figsize=(20,4))\n",
    "plt.subplot(1,3,1)\n",
    "for style,color,method in zip(methods_style,methods_color,methods):\n",
    "    plt.plot(dims,list_type_1[method],marker='o',linestyle=style,color=color)\n",
    "plt.xlabel('number of dimensions')\n",
    "plt.ylabel('C-acc Type 1')\n",
    "    \n",
    "plt.subplot(1,3,2)\n",
    "for style,color,method in zip(methods_style,methods_color,methods):\n",
    "    plt.plot(dims,list_type_2[method],marker='o',linestyle=style,color=color)\n",
    "plt.xlabel('number of dimensions')\n",
    "plt.ylabel('C-acc Type 2')\n",
    "\n",
    "plt.subplot(1,3,3)\n",
    "plt.xlabel('number of dimensions')\n",
    "plt.ylabel('C-acc F(Type 1,Type 2)')\n",
    "for style,color,method in zip(methods_style,methods_color,methods):\n",
    "    plt.plot(dims,list_type_F[method],marker='o',linestyle=style,color=color)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Experimental Results: Discriminant Features Identification Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-25T09:14:10.472884Z",
     "start_time": "2022-08-25T09:14:10.448632Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>mtex</th>\n",
       "      <th>resnet</th>\n",
       "      <th>cresnet</th>\n",
       "      <th>dcnn</th>\n",
       "      <th>dresnet</th>\n",
       "      <th>dinception</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datasets</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>synth_ShapesAll_type1_nbdim_10</th>\n",
       "      <td>0.542385</td>\n",
       "      <td>0.092734</td>\n",
       "      <td>0.779917</td>\n",
       "      <td>0.556526</td>\n",
       "      <td>0.754868</td>\n",
       "      <td>0.704127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>synth_ShapesAll_type1_nbdim_20</th>\n",
       "      <td>0.549572</td>\n",
       "      <td>0.031800</td>\n",
       "      <td>0.792017</td>\n",
       "      <td>0.511180</td>\n",
       "      <td>0.770258</td>\n",
       "      <td>0.349503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>synth_ShapesAll_type1_nbdim_40</th>\n",
       "      <td>0.349617</td>\n",
       "      <td>0.008465</td>\n",
       "      <td>0.799459</td>\n",
       "      <td>0.414699</td>\n",
       "      <td>0.631102</td>\n",
       "      <td>0.564770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>synth_ShapesAll_type1_nbdim_60</th>\n",
       "      <td>0.022645</td>\n",
       "      <td>0.005705</td>\n",
       "      <td>0.843239</td>\n",
       "      <td>0.438962</td>\n",
       "      <td>0.555048</td>\n",
       "      <td>0.505367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>synth_ShapesAll_type1_nbdim_100</th>\n",
       "      <td>0.012102</td>\n",
       "      <td>0.002646</td>\n",
       "      <td>0.908953</td>\n",
       "      <td>0.435013</td>\n",
       "      <td>0.446992</td>\n",
       "      <td>0.438184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>synth_ShapesAll_type2_nbdim_10</th>\n",
       "      <td>0.030361</td>\n",
       "      <td>0.045034</td>\n",
       "      <td>0.144853</td>\n",
       "      <td>0.533635</td>\n",
       "      <td>0.670685</td>\n",
       "      <td>0.321777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>synth_ShapesAll_type2_nbdim_20</th>\n",
       "      <td>0.024542</td>\n",
       "      <td>0.021929</td>\n",
       "      <td>0.136291</td>\n",
       "      <td>0.326249</td>\n",
       "      <td>0.617281</td>\n",
       "      <td>0.458521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>synth_ShapesAll_type2_nbdim_40</th>\n",
       "      <td>0.005521</td>\n",
       "      <td>0.006323</td>\n",
       "      <td>0.004864</td>\n",
       "      <td>0.349042</td>\n",
       "      <td>0.370021</td>\n",
       "      <td>0.461074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>synth_ShapesAll_type2_nbdim_60</th>\n",
       "      <td>0.016446</td>\n",
       "      <td>0.003458</td>\n",
       "      <td>0.004708</td>\n",
       "      <td>0.277558</td>\n",
       "      <td>0.384835</td>\n",
       "      <td>0.282510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>synth_ShapesAll_type2_nbdim_100</th>\n",
       "      <td>0.017074</td>\n",
       "      <td>0.002075</td>\n",
       "      <td>0.001422</td>\n",
       "      <td>0.239137</td>\n",
       "      <td>0.024611</td>\n",
       "      <td>0.002305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>synth_starlight_type1_nbdim_10</th>\n",
       "      <td>0.044365</td>\n",
       "      <td>0.078951</td>\n",
       "      <td>0.807954</td>\n",
       "      <td>0.495670</td>\n",
       "      <td>0.614726</td>\n",
       "      <td>0.399697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>synth_starlight_type1_nbdim_20</th>\n",
       "      <td>0.005868</td>\n",
       "      <td>0.023777</td>\n",
       "      <td>0.928122</td>\n",
       "      <td>0.331045</td>\n",
       "      <td>0.484865</td>\n",
       "      <td>0.300216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>synth_starlight_type1_nbdim_40</th>\n",
       "      <td>0.026670</td>\n",
       "      <td>0.007201</td>\n",
       "      <td>0.929862</td>\n",
       "      <td>0.297168</td>\n",
       "      <td>0.351059</td>\n",
       "      <td>0.142703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>synth_starlight_type1_nbdim_60</th>\n",
       "      <td>0.122499</td>\n",
       "      <td>0.004479</td>\n",
       "      <td>0.902565</td>\n",
       "      <td>0.275630</td>\n",
       "      <td>0.281982</td>\n",
       "      <td>0.063739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>synth_starlight_type1_nbdim_100</th>\n",
       "      <td>0.076962</td>\n",
       "      <td>0.002732</td>\n",
       "      <td>0.950565</td>\n",
       "      <td>0.196857</td>\n",
       "      <td>0.241068</td>\n",
       "      <td>0.027641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>synth_starlight_type2_nbdim_10</th>\n",
       "      <td>0.082958</td>\n",
       "      <td>0.025770</td>\n",
       "      <td>0.020603</td>\n",
       "      <td>0.230320</td>\n",
       "      <td>0.511495</td>\n",
       "      <td>0.108313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>synth_starlight_type2_nbdim_20</th>\n",
       "      <td>0.035240</td>\n",
       "      <td>0.013333</td>\n",
       "      <td>0.066844</td>\n",
       "      <td>0.267511</td>\n",
       "      <td>0.321861</td>\n",
       "      <td>0.029591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>synth_starlight_type2_nbdim_40</th>\n",
       "      <td>0.065346</td>\n",
       "      <td>0.005525</td>\n",
       "      <td>0.006991</td>\n",
       "      <td>0.088435</td>\n",
       "      <td>0.080863</td>\n",
       "      <td>0.016723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>synth_starlight_type2_nbdim_60</th>\n",
       "      <td>0.071611</td>\n",
       "      <td>0.003761</td>\n",
       "      <td>0.003544</td>\n",
       "      <td>0.003351</td>\n",
       "      <td>0.003927</td>\n",
       "      <td>0.011369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>synth_starlight_type2_nbdim_100</th>\n",
       "      <td>0.067173</td>\n",
       "      <td>0.003037</td>\n",
       "      <td>0.007468</td>\n",
       "      <td>0.002629</td>\n",
       "      <td>0.006973</td>\n",
       "      <td>0.018260</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     mtex    resnet   cresnet      dcnn  \\\n",
       "datasets                                                                  \n",
       "synth_ShapesAll_type1_nbdim_10   0.542385  0.092734  0.779917  0.556526   \n",
       "synth_ShapesAll_type1_nbdim_20   0.549572  0.031800  0.792017  0.511180   \n",
       "synth_ShapesAll_type1_nbdim_40   0.349617  0.008465  0.799459  0.414699   \n",
       "synth_ShapesAll_type1_nbdim_60   0.022645  0.005705  0.843239  0.438962   \n",
       "synth_ShapesAll_type1_nbdim_100  0.012102  0.002646  0.908953  0.435013   \n",
       "synth_ShapesAll_type2_nbdim_10   0.030361  0.045034  0.144853  0.533635   \n",
       "synth_ShapesAll_type2_nbdim_20   0.024542  0.021929  0.136291  0.326249   \n",
       "synth_ShapesAll_type2_nbdim_40   0.005521  0.006323  0.004864  0.349042   \n",
       "synth_ShapesAll_type2_nbdim_60   0.016446  0.003458  0.004708  0.277558   \n",
       "synth_ShapesAll_type2_nbdim_100  0.017074  0.002075  0.001422  0.239137   \n",
       "synth_starlight_type1_nbdim_10   0.044365  0.078951  0.807954  0.495670   \n",
       "synth_starlight_type1_nbdim_20   0.005868  0.023777  0.928122  0.331045   \n",
       "synth_starlight_type1_nbdim_40   0.026670  0.007201  0.929862  0.297168   \n",
       "synth_starlight_type1_nbdim_60   0.122499  0.004479  0.902565  0.275630   \n",
       "synth_starlight_type1_nbdim_100  0.076962  0.002732  0.950565  0.196857   \n",
       "synth_starlight_type2_nbdim_10   0.082958  0.025770  0.020603  0.230320   \n",
       "synth_starlight_type2_nbdim_20   0.035240  0.013333  0.066844  0.267511   \n",
       "synth_starlight_type2_nbdim_40   0.065346  0.005525  0.006991  0.088435   \n",
       "synth_starlight_type2_nbdim_60   0.071611  0.003761  0.003544  0.003351   \n",
       "synth_starlight_type2_nbdim_100  0.067173  0.003037  0.007468  0.002629   \n",
       "\n",
       "                                  dresnet  dinception  \n",
       "datasets                                               \n",
       "synth_ShapesAll_type1_nbdim_10   0.754868    0.704127  \n",
       "synth_ShapesAll_type1_nbdim_20   0.770258    0.349503  \n",
       "synth_ShapesAll_type1_nbdim_40   0.631102    0.564770  \n",
       "synth_ShapesAll_type1_nbdim_60   0.555048    0.505367  \n",
       "synth_ShapesAll_type1_nbdim_100  0.446992    0.438184  \n",
       "synth_ShapesAll_type2_nbdim_10   0.670685    0.321777  \n",
       "synth_ShapesAll_type2_nbdim_20   0.617281    0.458521  \n",
       "synth_ShapesAll_type2_nbdim_40   0.370021    0.461074  \n",
       "synth_ShapesAll_type2_nbdim_60   0.384835    0.282510  \n",
       "synth_ShapesAll_type2_nbdim_100  0.024611    0.002305  \n",
       "synth_starlight_type1_nbdim_10   0.614726    0.399697  \n",
       "synth_starlight_type1_nbdim_20   0.484865    0.300216  \n",
       "synth_starlight_type1_nbdim_40   0.351059    0.142703  \n",
       "synth_starlight_type1_nbdim_60   0.281982    0.063739  \n",
       "synth_starlight_type1_nbdim_100  0.241068    0.027641  \n",
       "synth_starlight_type2_nbdim_10   0.511495    0.108313  \n",
       "synth_starlight_type2_nbdim_20   0.321861    0.029591  \n",
       "synth_starlight_type2_nbdim_40   0.080863    0.016723  \n",
       "synth_starlight_type2_nbdim_60   0.003927    0.011369  \n",
       "synth_starlight_type2_nbdim_100  0.006973    0.018260  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>mtex</th>\n",
       "      <th>resnet</th>\n",
       "      <th>cresnet</th>\n",
       "      <th>dcnn</th>\n",
       "      <th>dresnet</th>\n",
       "      <th>dinception</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean_accuracy</th>\n",
       "      <td>0.108448</td>\n",
       "      <td>0.019437</td>\n",
       "      <td>0.452012</td>\n",
       "      <td>0.313531</td>\n",
       "      <td>0.406226</td>\n",
       "      <td>0.26032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_rank</th>\n",
       "      <td>4.200000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>2.850000</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>3.35000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   mtex    resnet   cresnet      dcnn   dresnet  dinception\n",
       "mean_accuracy  0.108448  0.019437  0.452012  0.313531  0.406226     0.26032\n",
       "mean_rank      4.200000  5.500000  2.850000  3.200000  1.900000     3.35000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_explanation_synth = pd.read_csv('experiments/explanation/results_explanation/merged_results_explanation.csv')\n",
    "df_explanation_synth.rename(columns={'Unnamed: 0':'datasets'}, inplace=True)\n",
    "df_explanation_synth.set_index('datasets', inplace=True)\n",
    "\n",
    "df_explanation_synth_average = pd.DataFrame()\n",
    "df_explanation_synth_average['mean_accuracy'] = df_explanation_synth.mean()\n",
    "df_explanation_synth_average['mean_rank'] = df_explanation_synth[methods].rank(1,method='min',ascending=False).mean()\n",
    "display(df_explanation_synth)\n",
    "display(df_explanation_synth_average.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-25T09:14:18.069571Z",
     "start_time": "2022-08-25T09:14:17.948593Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Average rank per method')"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_explanation_synth_average.sort_values('mean_rank', ascending=False).plot.bar(y='mean_rank',figsize=(20,4))\n",
    "plt.xticks(rotation=0)\n",
    "plt.title(\"Average rank per method\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-25T09:14:25.583222Z",
     "start_time": "2022-08-25T09:14:25.314550Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "list_type_1 = {method:[] for method in methods}\n",
    "list_type_2 = {method:[] for method in methods}\n",
    "list_type_F = {method:[] for method in methods}\n",
    "\n",
    "for dim in dims:\n",
    "    for method in methods:\n",
    "        tmp_1 = []\n",
    "        tmp_2 = []\n",
    "        for dataset in dataset_name:\n",
    "            val_1 = df_explanation_synth.loc[pattern_name.format(dataset,1,dim)][method]\n",
    "            val_2 = df_explanation_synth.loc[pattern_name.format(dataset,2,dim)][method]\n",
    "            tmp_1.append(val_1)\n",
    "            tmp_2.append(val_2)\n",
    "        list_type_1[method].append(np.mean(tmp_1))\n",
    "        list_type_2[method].append(np.mean(tmp_2))\n",
    "        list_type_F[method].append((2*np.mean(tmp_2)*np.mean(tmp_1))/(np.mean(tmp_2)+np.mean(tmp_1)))\n",
    "            \n",
    "            \n",
    "\n",
    "plt.figure(figsize=(20,4))\n",
    "plt.subplot(1,3,1)\n",
    "for style,color,method in zip(methods_style,methods_color,methods):\n",
    "    plt.plot(dims,list_type_1[method],marker='o',linestyle=style,color=color)\n",
    "plt.xlabel('number of dimensions')\n",
    "plt.ylabel('Dr-acc Type 1')\n",
    "    \n",
    "plt.subplot(1,3,2)\n",
    "for style,color,method in zip(methods_style,methods_color,methods):\n",
    "    plt.plot(dims,list_type_2[method],marker='o',linestyle=style,color=color)\n",
    "plt.xlabel('number of dimensions')\n",
    "plt.ylabel('Dr-acc Type 2')\n",
    "\n",
    "plt.subplot(1,3,3)\n",
    "plt.xlabel('number of dimensions')\n",
    "plt.ylabel('Dr-acc F(Type 1,Type 2)')\n",
    "for style,color,method in zip(methods_style,methods_color,methods):\n",
    "    plt.plot(dims,list_type_F[method],marker='o',linestyle=style,color=color)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Parameters influence: C-acc versus Dr-acc (Section 5.6)\n",
    "\n",
    "In this section, we first analyze the relation between C-acc and\n",
    "Dr-acc. We then evaluate the impact that C-acc has on the number\n",
    "of permutations that have been correctly classified ng. We finally\n",
    "evaluate the impact that ng has on Dr-acc.\n",
    "\n",
    "### Reproduce this experiment\n",
    "\n",
    "The results for this experiments are computed while the previous script is running. Please run first the previous script (Discriminant Features Identification Evaluation).\n",
    "\n",
    "### Experimental Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-25T13:37:16.542199Z",
     "start_time": "2022-08-25T13:37:16.535787Z"
    }
   },
   "outputs": [],
   "source": [
    "df_success_permutation = pd.read_csv('experiments/explanation/results_parameters/merged_results_parameters.csv')\n",
    "df_success_permutation.rename(columns={'Unnamed: 0':'datasets'}, inplace=True)\n",
    "df_success_permutation.set_index('datasets', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-25T13:37:17.952358Z",
     "start_time": "2022-08-25T13:37:17.197500Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,12))\n",
    "\n",
    "plt.subplot(3,3,1)\n",
    "plt.scatter(df_explanation_synth['dcnn'],df_classification_synth['dcnn'],s=60)\n",
    "plt.xlabel('Dr-acc')\n",
    "plt.ylabel('C-acc')\n",
    "plt.title('dCNN')\n",
    "\n",
    "plt.subplot(3,3,2)\n",
    "plt.scatter(df_explanation_synth['dresnet'],df_classification_synth['dresnet'],s=60)\n",
    "plt.xlabel('Dr-acc')\n",
    "plt.ylabel('C-acc')\n",
    "plt.title('dResnet')\n",
    "\n",
    "plt.subplot(3,3,3)\n",
    "plt.scatter(df_explanation_synth['dinception'],df_classification_synth['dinception'],s=60)\n",
    "plt.xlabel('Dr-acc')\n",
    "plt.ylabel('C-acc')\n",
    "plt.title('dInception Time')\n",
    "\n",
    "plt.subplot(3,3,4)\n",
    "plt.scatter(df_explanation_synth['dcnn'],df_success_permutation['dcnn'],s=60)\n",
    "plt.xlabel('Dr-acc')\n",
    "plt.ylabel('ng/k')\n",
    "plt.title('dCNN')\n",
    "\n",
    "plt.subplot(3,3,5)\n",
    "plt.scatter(df_explanation_synth['dresnet'],df_success_permutation['dresnet'],s=60)\n",
    "plt.xlabel('Dr-acc')\n",
    "plt.ylabel('ng/k')\n",
    "plt.title('dResnet')\n",
    "\n",
    "plt.subplot(3,3,6)\n",
    "plt.scatter(df_explanation_synth['dinception'],df_success_permutation['dinception'],s=60)\n",
    "plt.xlabel('Dr-acc')\n",
    "plt.ylabel('ng/k')\n",
    "plt.title('dInception Time')\n",
    "\n",
    "\n",
    "plt.subplot(3,3,7)\n",
    "plt.scatter(df_classification_synth['dcnn'],df_success_permutation['dcnn'],s=60)\n",
    "plt.xlabel('C-acc')\n",
    "plt.ylabel('ng/k')\n",
    "plt.title('dCNN')\n",
    "plt.plot([0.5,1],[0,100],color='red',linestyle='--')\n",
    "\n",
    "plt.subplot(3,3,8)\n",
    "plt.scatter(df_classification_synth['dresnet'],df_success_permutation['dresnet'],s=60)\n",
    "plt.xlabel('C-acc')\n",
    "plt.ylabel('ng/k')\n",
    "plt.title('dResnet')\n",
    "plt.plot([0.5,1],[0,100],color='red',linestyle='--')\n",
    "\n",
    "plt.subplot(3,3,9)\n",
    "plt.scatter(df_classification_synth['dinception'],df_success_permutation['dinception'],s=60)\n",
    "plt.xlabel('C-acc')\n",
    "plt.ylabel('ng/k')\n",
    "plt.title('dInception Time')\n",
    "plt.plot([0.5,1],[0,100],color='red',linestyle='--')\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "### Influence of k (section 5.5)\n",
    "\n",
    "This section analyzes the influence of the number of permutations\n",
    "k on the discriminative features identification accuracy (Dr-acc).\n",
    "We compute the Dr-acc for 20 different instances for which dCAM\n",
    "is computed using a value of k between 1 and 400. \n",
    "\n",
    "#### Reproduce this experiment\n",
    "\n",
    "To reproduce this experiment, go to the folder experiments/explanation/scripts_explanation_eval, please run the following code:\n",
    "\n",
    "- Modify submit_generic.py such that the template of the bash scripts is compatible with your infrastruture.\n",
    "\n",
    "\n",
    "- Run the following command:\n",
    "```bash\n",
    "python script_exp_influence_k_gpu_parallel.py\n",
    "```\n",
    "- This script will generate one bash file for each dataset and model that will be then run in parallel. These batch files will be stored in auto_script folder.\n",
    "\n",
    "\n",
    "\n",
    "- Then, run the following command:\n",
    "```bash\n",
    "chmod 777 submit_all_influence_k.sh\n",
    "./submit_all_influence_k.sh\n",
    "```\n",
    "- All the SLURM jobs will be laucnhed using sbatch command. The log files for each jobs will be stored in auto_script/output_log folder.\n",
    "\n",
    "\n",
    "\n",
    "- The results will be stored in results_parameters/log_k_exp folder. At the end of the execution, please run the following command:\n",
    "```bash\n",
    "python script_generate_merged_param_influence_k_results.py\n",
    "```\n",
    "- An aggregated tables summarizing the results will be created  and sorted in results_parameters/merged_results_influence_k.csv. \n",
    "\n",
    "#### Experimental Results\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-27T12:06:01.139972Z",
     "start_time": "2022-08-27T12:06:01.117206Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>dcnn</th>\n",
       "      <th>dresnet</th>\n",
       "      <th>dinception</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datasets</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>synth_ShapesAll_type1_nbdim_10</th>\n",
       "      <td>0.08317471481135356;0.13045553797123274;0.1710...</td>\n",
       "      <td>0.0829406135560105;0.11549970766814883;0.16129...</td>\n",
       "      <td>0.0710893368869797;0.23904447912131344;0.39759...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>synth_ShapesAll_type1_nbdim_20</th>\n",
       "      <td>0.03889996665711023;0.07960735947870629;0.1471...</td>\n",
       "      <td>0.04609806506852345;0.0747954954516058;0.11521...</td>\n",
       "      <td>0.019815070601948318;0.07020703591231486;0.120...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>synth_ShapesAll_type1_nbdim_40</th>\n",
       "      <td>0.015716731001695586;0.04336228291612712;0.089...</td>\n",
       "      <td>0.019446938550095067;0.041303077773683104;0.07...</td>\n",
       "      <td>0.00932314634374279;0.041634171011652336;0.079...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>synth_ShapesAll_type1_nbdim_60</th>\n",
       "      <td>0.009514300457745798;0.038344691542619;0.08439...</td>\n",
       "      <td>0.012013057697953322;0.038547695218483044;0.08...</td>\n",
       "      <td>0.005674773759352031;0.04462948013601706;0.101...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>synth_ShapesAll_type1_nbdim_100</th>\n",
       "      <td>0.005095461002783425;0.024848648881647024;0.05...</td>\n",
       "      <td>0.006338664334846117;0.03129470550096957;0.093...</td>\n",
       "      <td>0.002928272431916062;0.009455695787195493;0.03...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>synth_ShapesAll_type2_nbdim_10</th>\n",
       "      <td>0.1403815242777618;0.17300988466388503;0.19964...</td>\n",
       "      <td>0.18833356065490053;0.22007402247218724;0.2463...</td>\n",
       "      <td>0.06932624435353828;0.15730690149640733;0.2035...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>synth_ShapesAll_type2_nbdim_20</th>\n",
       "      <td>0.02994870438158927;0.0356888845541881;0.04814...</td>\n",
       "      <td>0.08615201544804453;0.10881007402266434;0.1226...</td>\n",
       "      <td>0.0329583632277996;0.06792210167338272;0.12104...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>synth_ShapesAll_type2_nbdim_40</th>\n",
       "      <td>0.010099046468934387;0.011948591592911948;0.01...</td>\n",
       "      <td>0.01644836847861092;0.016217118948302935;0.022...</td>\n",
       "      <td>0.007525361667547846;0.019945958050994517;0.03...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>synth_ShapesAll_type2_nbdim_60</th>\n",
       "      <td>0.005690681374650816;0.006306898202240342;0.00...</td>\n",
       "      <td>0.006484346138532906;0.007926536986828237;0.00...</td>\n",
       "      <td>0.0035460930606960156;0.00427709455887442;0.01...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                              dcnn  \\\n",
       "datasets                                                                             \n",
       "synth_ShapesAll_type1_nbdim_10   0.08317471481135356;0.13045553797123274;0.1710...   \n",
       "synth_ShapesAll_type1_nbdim_20   0.03889996665711023;0.07960735947870629;0.1471...   \n",
       "synth_ShapesAll_type1_nbdim_40   0.015716731001695586;0.04336228291612712;0.089...   \n",
       "synth_ShapesAll_type1_nbdim_60   0.009514300457745798;0.038344691542619;0.08439...   \n",
       "synth_ShapesAll_type1_nbdim_100  0.005095461002783425;0.024848648881647024;0.05...   \n",
       "synth_ShapesAll_type2_nbdim_10   0.1403815242777618;0.17300988466388503;0.19964...   \n",
       "synth_ShapesAll_type2_nbdim_20   0.02994870438158927;0.0356888845541881;0.04814...   \n",
       "synth_ShapesAll_type2_nbdim_40   0.010099046468934387;0.011948591592911948;0.01...   \n",
       "synth_ShapesAll_type2_nbdim_60   0.005690681374650816;0.006306898202240342;0.00...   \n",
       "\n",
       "                                                                           dresnet  \\\n",
       "datasets                                                                             \n",
       "synth_ShapesAll_type1_nbdim_10   0.0829406135560105;0.11549970766814883;0.16129...   \n",
       "synth_ShapesAll_type1_nbdim_20   0.04609806506852345;0.0747954954516058;0.11521...   \n",
       "synth_ShapesAll_type1_nbdim_40   0.019446938550095067;0.041303077773683104;0.07...   \n",
       "synth_ShapesAll_type1_nbdim_60   0.012013057697953322;0.038547695218483044;0.08...   \n",
       "synth_ShapesAll_type1_nbdim_100  0.006338664334846117;0.03129470550096957;0.093...   \n",
       "synth_ShapesAll_type2_nbdim_10   0.18833356065490053;0.22007402247218724;0.2463...   \n",
       "synth_ShapesAll_type2_nbdim_20   0.08615201544804453;0.10881007402266434;0.1226...   \n",
       "synth_ShapesAll_type2_nbdim_40   0.01644836847861092;0.016217118948302935;0.022...   \n",
       "synth_ShapesAll_type2_nbdim_60   0.006484346138532906;0.007926536986828237;0.00...   \n",
       "\n",
       "                                                                        dinception  \n",
       "datasets                                                                            \n",
       "synth_ShapesAll_type1_nbdim_10   0.0710893368869797;0.23904447912131344;0.39759...  \n",
       "synth_ShapesAll_type1_nbdim_20   0.019815070601948318;0.07020703591231486;0.120...  \n",
       "synth_ShapesAll_type1_nbdim_40   0.00932314634374279;0.041634171011652336;0.079...  \n",
       "synth_ShapesAll_type1_nbdim_60   0.005674773759352031;0.04462948013601706;0.101...  \n",
       "synth_ShapesAll_type1_nbdim_100  0.002928272431916062;0.009455695787195493;0.03...  \n",
       "synth_ShapesAll_type2_nbdim_10   0.06932624435353828;0.15730690149640733;0.2035...  \n",
       "synth_ShapesAll_type2_nbdim_20   0.0329583632277996;0.06792210167338272;0.12104...  \n",
       "synth_ShapesAll_type2_nbdim_40   0.007525361667547846;0.019945958050994517;0.03...  \n",
       "synth_ShapesAll_type2_nbdim_60   0.0035460930606960156;0.00427709455887442;0.01...  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_influence_k = pd.read_csv('experiments/explanation/results_parameters/merged_results_influence_k.csv')\n",
    "df_influence_k.rename(columns={'Unnamed: 0':'datasets'}, inplace=True)\n",
    "df_influence_k.set_index('datasets', inplace=True)\n",
    "\n",
    "def generate_curve(string,dims):\n",
    "    if type(string) is not str:\n",
    "        return [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],1\n",
    "    result = []\n",
    "    string_vals = string.split(';')[:-1]\n",
    "    for val in string_vals:\n",
    "        result.append(float(val))\n",
    "        \n",
    "    result = (np.array(result) - min(result))/(max(result) - min(result))\n",
    "    f = interpolate.interp1d(dims, result)\n",
    "    x_new = np.arange(min(dims),max(dims),1)\n",
    "    y_new = f(x_new)\n",
    "    for i,val in enumerate(y_new):\n",
    "        if val > 0.9:\n",
    "            reach = i\n",
    "            break\n",
    "    return result,reach\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def generate_plot_convergence_k(method,type_d,dims):\n",
    "    plt.title(method)\n",
    "    #plt.xticks(dims[],dims)\n",
    "    plt.xlim(dims[0],dims[-1])\n",
    "    plt.ylim(0,1)\n",
    "    plt.hlines(0.9,xmin=dims[0],xmax=dims[-1],linestyles='--',color='black')\n",
    "    plt.ylabel('norm(Dr-acc)')\n",
    "    plt.xlabel('Number of permutations k')\n",
    "    plt.yticks([0,0.25,0.50,0.75,0.9,1],[\"a=0\",\"a=0.25\",\"a=0.5\",\"a=0.75\",\"a=0.9\",\"a=1\"])\n",
    "    curve,reach = generate_curve(df_influence_k[method]['synth_ShapesAll_type{}_nbdim_10'.format(type_d)],dims)\n",
    "    plt.plot(dims,curve,label='D=10',color='darkblue')\n",
    "    plt.vlines(reach,0,0.9,linestyles='--',color='darkblue')\n",
    "    curve,reach = generate_curve(df_influence_k[method]['synth_ShapesAll_type{}_nbdim_20'.format(type_d)],dims)\n",
    "    plt.plot(dims,curve,label='D=20',color='lightblue')\n",
    "    plt.vlines(reach,0,0.9,linestyles='--',color='lightblue')\n",
    "    curve,reach = generate_curve(df_influence_k[method]['synth_ShapesAll_type{}_nbdim_40'.format(type_d)],dims)\n",
    "    plt.plot(dims,curve,label='D=40',color='lightgreen')\n",
    "    plt.vlines(reach,0,0.9,linestyles='--',color='lightgreen')\n",
    "    curve,reach = generate_curve(df_influence_k[method]['synth_ShapesAll_type{}_nbdim_60'.format(type_d)],dims)\n",
    "    plt.plot(dims,curve,label='D=60',color='darkgreen')\n",
    "    plt.vlines(reach,0,0.9,linestyles='--',color='darkgreen')\n",
    "    if type_d == 1:\n",
    "        curve,reach = generate_curve(df_influence_k[method]['synth_ShapesAll_type{}_nbdim_100'.format(type_d)],dims)\n",
    "        plt.plot(dims,curve,label='D=100',color='darkred')\n",
    "        plt.vlines(reach,0,0.9,linestyles='--',color='darkred')\n",
    "        \n",
    "\n",
    "df_influence_k    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-27T12:06:07.517694Z",
     "start_time": "2022-08-27T12:06:06.985199Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dims = [1,2,3,4,5,6,7,8,9,10,20,40,60,80,100,150,200,400]\n",
    "\n",
    "plt.figure(figsize=(20,20))\n",
    "\n",
    "plt.subplot(3,2,1)\n",
    "generate_plot_convergence_k('dcnn',1,dims)\n",
    "\n",
    "plt.subplot(3,2,2)\n",
    "generate_plot_convergence_k('dcnn',2,dims)\n",
    "\n",
    "plt.subplot(3,2,3)\n",
    "generate_plot_convergence_k('dresnet',1,dims)\n",
    "\n",
    "plt.subplot(3,2,4)\n",
    "generate_plot_convergence_k('dresnet',2,dims)\n",
    "\n",
    "plt.subplot(3,2,5)\n",
    "generate_plot_convergence_k('dinception',1,dims)\n",
    "\n",
    "plt.subplot(3,2,6)\n",
    "generate_plot_convergence_k('dinception',2,dims)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "# Execution time evaluation (Section 5.7)\n",
    "\n",
    "In this section, we evaluate the execution time of our proposed\n",
    "approaches and the baselines. We first evaluate the execution time required to train the baselines and our proposed approaches. We then measure the execution time needed to compute the dCAM when we change the network architecture.\n",
    "\n",
    "\n",
    "## Reproduce this experiment: Training Execution Time Evaluation\n",
    "\n",
    "To reproduce this experiment, go to the folder experiments/execution_time/scripts_training, please run the following code:\n",
    "\n",
    "- Modify submit_generic.py such that the template of the bash scripts is compatible with your infrastruture.\n",
    "\n",
    "\n",
    "- Run the following command:\n",
    "```bash\n",
    "python script_exp_gpu_parallel_dim.py\n",
    "python script_exp_gpu_parallel_length.py\n",
    "```\n",
    "- This script will generate one bash file for each dataset and model that will be then run in parallel. These batch files will be stored in auto_script folder.\n",
    "\n",
    "\n",
    "\n",
    "- Then, run the following command:\n",
    "```bash\n",
    "chmod 777 submit_all_epoch_exp_dim.sh\n",
    "chmod 777 submit_all_epoch_exp_length.sh\n",
    "./submit_all_epoch_exp_dim.sh\n",
    "./submit_all_epoch_exp_length.sh\n",
    "```\n",
    "- All the SLURM jobs will be laucnhed using sbatch command. The log files for each jobs will be stored in auto_script/output_log folder.\n",
    "\n",
    "\n",
    "\n",
    "- The results will be stored in results_epoch/log_dim and results_epoch/log_length folders. At the end of the execution, please run the following command:\n",
    "```bash\n",
    "python script_generate_merged_results_dim.py\n",
    "python script_generate_merged_results_length.py\n",
    "```\n",
    "- An aggregated tables summarizing the results will be created  and sorted in results_epoch/merged_results_dim.csv and results_epoch/merged_results_length.csv. \n",
    "\n",
    "\n",
    "\n",
    "## Experimental Results: Training execution time Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-26T22:14:50.532426Z",
     "start_time": "2022-08-26T22:14:50.504048Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mtex</th>\n",
       "      <th>cnn</th>\n",
       "      <th>inception</th>\n",
       "      <th>resnet</th>\n",
       "      <th>cresnet</th>\n",
       "      <th>ccnn</th>\n",
       "      <th>cinception</th>\n",
       "      <th>dcnn</th>\n",
       "      <th>dresnet</th>\n",
       "      <th>dinception</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datasets</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.290488</td>\n",
       "      <td>0.334677</td>\n",
       "      <td>0.466892</td>\n",
       "      <td>0.687256</td>\n",
       "      <td>0.695033</td>\n",
       "      <td>0.330577</td>\n",
       "      <td>0.438817</td>\n",
       "      <td>0.332090</td>\n",
       "      <td>0.685729</td>\n",
       "      <td>0.440101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.289525</td>\n",
       "      <td>0.338360</td>\n",
       "      <td>0.467544</td>\n",
       "      <td>0.689692</td>\n",
       "      <td>0.683266</td>\n",
       "      <td>0.333087</td>\n",
       "      <td>0.438456</td>\n",
       "      <td>0.336767</td>\n",
       "      <td>0.693787</td>\n",
       "      <td>0.442722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>0.290776</td>\n",
       "      <td>0.341567</td>\n",
       "      <td>0.467773</td>\n",
       "      <td>0.693586</td>\n",
       "      <td>0.689009</td>\n",
       "      <td>0.333371</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.378320</td>\n",
       "      <td>0.866815</td>\n",
       "      <td>0.502142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>0.291258</td>\n",
       "      <td>0.339923</td>\n",
       "      <td>0.464597</td>\n",
       "      <td>0.701244</td>\n",
       "      <td>0.873869</td>\n",
       "      <td>0.349944</td>\n",
       "      <td>0.444758</td>\n",
       "      <td>0.383687</td>\n",
       "      <td>0.955132</td>\n",
       "      <td>0.504626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>0.293570</td>\n",
       "      <td>0.340946</td>\n",
       "      <td>0.467651</td>\n",
       "      <td>0.698335</td>\n",
       "      <td>1.343804</td>\n",
       "      <td>0.537106</td>\n",
       "      <td>0.457573</td>\n",
       "      <td>0.575750</td>\n",
       "      <td>1.404490</td>\n",
       "      <td>0.517514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>0.339628</td>\n",
       "      <td>0.383589</td>\n",
       "      <td>0.937229</td>\n",
       "      <td>0.800598</td>\n",
       "      <td>2.868599</td>\n",
       "      <td>1.071892</td>\n",
       "      <td>1.358494</td>\n",
       "      <td>1.107074</td>\n",
       "      <td>3.048783</td>\n",
       "      <td>1.389244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>0.346004</td>\n",
       "      <td>0.388914</td>\n",
       "      <td>0.543191</td>\n",
       "      <td>0.871854</td>\n",
       "      <td>5.387030</td>\n",
       "      <td>2.056006</td>\n",
       "      <td>1.440987</td>\n",
       "      <td>2.109863</td>\n",
       "      <td>5.697705</td>\n",
       "      <td>1.506632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5000</th>\n",
       "      <td>0.665258</td>\n",
       "      <td>1.081526</td>\n",
       "      <td>0.678648</td>\n",
       "      <td>1.284613</td>\n",
       "      <td>26.045379</td>\n",
       "      <td>10.094087</td>\n",
       "      <td>6.558964</td>\n",
       "      <td>10.364157</td>\n",
       "      <td>27.155610</td>\n",
       "      <td>6.875988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10000</th>\n",
       "      <td>1.142691</td>\n",
       "      <td>2.050993</td>\n",
       "      <td>1.073804</td>\n",
       "      <td>2.180178</td>\n",
       "      <td>54.507705</td>\n",
       "      <td>21.541470</td>\n",
       "      <td>12.943290</td>\n",
       "      <td>22.401181</td>\n",
       "      <td>56.274368</td>\n",
       "      <td>13.945950</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              mtex       cnn  inception    resnet    cresnet       ccnn  \\\n",
       "datasets                                                                  \n",
       "10        0.290488  0.334677   0.466892  0.687256   0.695033   0.330577   \n",
       "20        0.289525  0.338360   0.467544  0.689692   0.683266   0.333087   \n",
       "50        0.290776  0.341567   0.467773  0.693586   0.689009   0.333371   \n",
       "100       0.291258  0.339923   0.464597  0.701244   0.873869   0.349944   \n",
       "200       0.293570  0.340946   0.467651  0.698335   1.343804   0.537106   \n",
       "500       0.339628  0.383589   0.937229  0.800598   2.868599   1.071892   \n",
       "1000      0.346004  0.388914   0.543191  0.871854   5.387030   2.056006   \n",
       "5000      0.665258  1.081526   0.678648  1.284613  26.045379  10.094087   \n",
       "10000     1.142691  2.050993   1.073804  2.180178  54.507705  21.541470   \n",
       "\n",
       "          cinception       dcnn    dresnet  dinception  \n",
       "datasets                                                \n",
       "10          0.438817   0.332090   0.685729    0.440101  \n",
       "20          0.438456   0.336767   0.693787    0.442722  \n",
       "50          0.448154   0.378320   0.866815    0.502142  \n",
       "100         0.444758   0.383687   0.955132    0.504626  \n",
       "200         0.457573   0.575750   1.404490    0.517514  \n",
       "500         1.358494   1.107074   3.048783    1.389244  \n",
       "1000        1.440987   2.109863   5.697705    1.506632  \n",
       "5000        6.558964  10.364157  27.155610    6.875988  \n",
       "10000      12.943290  22.401181  56.274368   13.945950  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mtex</th>\n",
       "      <th>cnn</th>\n",
       "      <th>inception</th>\n",
       "      <th>resnet</th>\n",
       "      <th>cresnet</th>\n",
       "      <th>ccnn</th>\n",
       "      <th>cinception</th>\n",
       "      <th>dcnn</th>\n",
       "      <th>dresnet</th>\n",
       "      <th>dinception</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dims</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.297229</td>\n",
       "      <td>0.342486</td>\n",
       "      <td>0.463674</td>\n",
       "      <td>0.702691</td>\n",
       "      <td>0.874322</td>\n",
       "      <td>0.348722</td>\n",
       "      <td>0.440359</td>\n",
       "      <td>0.380487</td>\n",
       "      <td>0.927132</td>\n",
       "      <td>0.502411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.293651</td>\n",
       "      <td>0.337282</td>\n",
       "      <td>0.471502</td>\n",
       "      <td>0.706168</td>\n",
       "      <td>1.340403</td>\n",
       "      <td>0.536276</td>\n",
       "      <td>0.477533</td>\n",
       "      <td>0.588952</td>\n",
       "      <td>1.422947</td>\n",
       "      <td>0.545268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.290482</td>\n",
       "      <td>0.351566</td>\n",
       "      <td>0.471656</td>\n",
       "      <td>0.707481</td>\n",
       "      <td>2.319057</td>\n",
       "      <td>0.921733</td>\n",
       "      <td>0.744856</td>\n",
       "      <td>1.050996</td>\n",
       "      <td>2.509720</td>\n",
       "      <td>0.870724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>0.336461</td>\n",
       "      <td>0.387645</td>\n",
       "      <td>0.517749</td>\n",
       "      <td>0.768306</td>\n",
       "      <td>3.363327</td>\n",
       "      <td>1.259592</td>\n",
       "      <td>1.006288</td>\n",
       "      <td>1.493108</td>\n",
       "      <td>3.688971</td>\n",
       "      <td>1.231074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>0.342849</td>\n",
       "      <td>0.401547</td>\n",
       "      <td>0.530411</td>\n",
       "      <td>0.950496</td>\n",
       "      <td>5.445929</td>\n",
       "      <td>1.986127</td>\n",
       "      <td>1.550226</td>\n",
       "      <td>2.995375</td>\n",
       "      <td>6.446001</td>\n",
       "      <td>2.474738</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          mtex       cnn  inception    resnet   cresnet      ccnn  cinception  \\\n",
       "dims                                                                            \n",
       "10    0.297229  0.342486   0.463674  0.702691  0.874322  0.348722    0.440359   \n",
       "20    0.293651  0.337282   0.471502  0.706168  1.340403  0.536276    0.477533   \n",
       "40    0.290482  0.351566   0.471656  0.707481  2.319057  0.921733    0.744856   \n",
       "60    0.336461  0.387645   0.517749  0.768306  3.363327  1.259592    1.006288   \n",
       "100   0.342849  0.401547   0.530411  0.950496  5.445929  1.986127    1.550226   \n",
       "\n",
       "          dcnn   dresnet  dinception  \n",
       "dims                                  \n",
       "10    0.380487  0.927132    0.502411  \n",
       "20    0.588952  1.422947    0.545268  \n",
       "40    1.050996  2.509720    0.870724  \n",
       "60    1.493108  3.688971    1.231074  \n",
       "100   2.995375  6.446001    2.474738  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_exec_dim = pd.read_csv('experiments/execution_time/results_epoch/merged_results_dim.csv')\n",
    "df_exec_dim.rename(columns={'Unnamed: 0':'dims'}, inplace=True)\n",
    "df_exec_dim['dims'] = [10,20,40,60,100]\n",
    "df_exec_dim.set_index('dims', inplace=True)\n",
    "\n",
    "df_exec_length = pd.read_csv('experiments/execution_time/results_epoch/merged_results_length.csv')\n",
    "df_exec_length.rename(columns={'Unnamed: 0':'datasets'}, inplace=True)\n",
    "df_exec_length.set_index('datasets', inplace=True)\n",
    "\n",
    "display(df_exec_length)\n",
    "display(df_exec_dim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-26T22:15:43.523250Z",
     "start_time": "2022-08-26T22:15:42.334408Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10.0, 100.0)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "\n",
    "method_color = {\n",
    "    'resnet': 'royalblue',\n",
    "    'cresnet': 'darkorange',\n",
    "    'dcnn': 'grey',\n",
    "    'dresnet': 'gold',\n",
    "    'dinception': 'royalblue',\n",
    "    'cnn': 'darkgreen',\n",
    "    'inception': 'darkblue',\n",
    "    'ccnn': 'darkred',\n",
    "    'cinception': 'black',\n",
    "    'mtex': 'brown',\n",
    "}\n",
    "\n",
    "method_style = {\n",
    "    'resnet': '--',\n",
    "    'cresnet': '--',\n",
    "    'dcnn': '-',\n",
    "    'dresnet': '-',\n",
    "    'dinception': '-',\n",
    "    'cnn': '--',\n",
    "    'inception': '--',\n",
    "    'ccnn': '--',\n",
    "    'cinception': '--',\n",
    "    'mtex': '-',\n",
    "}\n",
    "\n",
    "method_scatter = {\n",
    "    'resnet': 'x',\n",
    "    'cresnet': 's',\n",
    "    'dcnn': 'o',\n",
    "    'dresnet': 'o',\n",
    "    'dinception': 'o',\n",
    "    'cnn': 'x',\n",
    "    'inception': 'x',\n",
    "    'ccnn': 's',\n",
    "    'cinception': 's',\n",
    "    'mtex': '^',\n",
    "}\n",
    "\n",
    "def generate_subplot(df_exec):\n",
    "    for method in df_exec.columns:\n",
    "        plt.plot(df_exec.index,df_exec[method],color=method_color[method],linestyle=method_style[method],label=method)\n",
    "        plt.scatter(df_exec.index,df_exec[method],c=method_color[method],marker=method_scatter[method])\n",
    "    plt.yscale('log')\n",
    "    plt.ylabel('time (second)')\n",
    "    \n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "plt.title('Execution time per epoch versus the data series length')\n",
    "generate_subplot(df_exec_length)\n",
    "plt.xlabel('Data series length')\n",
    "plt.xscale('log')\n",
    "plt.legend()\n",
    "plt.xlim(10,10000)\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.title('Execution time per epoch versus the Number of dimension')\n",
    "generate_subplot(df_exec_dim)\n",
    "plt.xlabel('number of dimension')\n",
    "plt.xlim(10,100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reproduce this experiment: dCAM Execution Time\n",
    "\n",
    "To reproduce this experiment, go in the folder experiments/execution_time/scripts_explanation, and please run the following code:\n",
    "\n",
    "- Modify submit_generic.py such that the template of the bash scripts is compatible with your infrastruture.\n",
    "\n",
    "\n",
    "- Run the following command:\n",
    "```bash\n",
    "python script_exp_gpu_parallel_dim.py\n",
    "python script_exp_gpu_parallel_length.py\n",
    "python script_exp_gpu_parallel_k.py\n",
    "```\n",
    "- This script will generate one bash file for each dataset and model that will be then run in parallel. These batch files will be stored in auto_script folder.\n",
    "\n",
    "\n",
    "\n",
    "- Then, run the following command:\n",
    "```bash\n",
    "chmod 777 submit_all_epoch_exp_dim.sh\n",
    "chmod 777 submit_all_epoch_exp_length.sh\n",
    "chmod 777 submit_all_epoch_exp_k.sh\n",
    "./submit_all_epoch_exp_dim.sh\n",
    "./submit_all_epoch_exp_length.sh\n",
    "./submit_all_epoch_exp_k.sh\n",
    "```\n",
    "- All the SLURM jobs will be laucnhed using sbatch command. The log files for each jobs will be stored in auto_script/output_log folder.\n",
    "\n",
    "\n",
    "\n",
    "- The results will be stored in results_explanation/log_dim, results_explanation/log_k and results_explanation/log_length folders. At the end of the execution, please run the following command:\n",
    "```bash\n",
    "python script_generate_merged_results_dim.py\n",
    "python script_generate_merged_results_length.py\n",
    "python script_generate_merged_results_k.py\n",
    "```\n",
    "- An aggregated tables summarizing the results will be created  and sorted in results_explanation/merged_results_dim.csv, results_explanation/merged_results_k.csv and results_explanation/merged_results_length.csv. \n",
    "\n",
    "\n",
    "\n",
    "## Experimental Results: dCAM Execution Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-15T16:10:50.704276Z",
     "start_time": "2022-09-15T16:10:50.679568Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>dcnn</th>\n",
       "      <th>dresnet</th>\n",
       "      <th>dinception</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datasets</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.640818</td>\n",
       "      <td>0.915698</td>\n",
       "      <td>0.452846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.591694</td>\n",
       "      <td>0.869245</td>\n",
       "      <td>0.751069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>0.851729</td>\n",
       "      <td>1.136353</td>\n",
       "      <td>1.012098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>1.248197</td>\n",
       "      <td>1.541939</td>\n",
       "      <td>1.379090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>2.079021</td>\n",
       "      <td>2.387546</td>\n",
       "      <td>2.197674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>11.886593</td>\n",
       "      <td>12.157275</td>\n",
       "      <td>11.914519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>16.114142</td>\n",
       "      <td>15.515659</td>\n",
       "      <td>16.006720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5000</th>\n",
       "      <td>48.587935</td>\n",
       "      <td>50.234158</td>\n",
       "      <td>48.963026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10000</th>\n",
       "      <td>92.492629</td>\n",
       "      <td>93.419359</td>\n",
       "      <td>88.183068</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               dcnn    dresnet  dinception\n",
       "datasets                                  \n",
       "10         0.640818   0.915698    0.452846\n",
       "20         0.591694   0.869245    0.751069\n",
       "50         0.851729   1.136353    1.012098\n",
       "100        1.248197   1.541939    1.379090\n",
       "200        2.079021   2.387546    2.197674\n",
       "500       11.886593  12.157275   11.914519\n",
       "1000      16.114142  15.515659   16.006720\n",
       "5000      48.587935  50.234158   48.963026\n",
       "10000     92.492629  93.419359   88.183068"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dcnn</th>\n",
       "      <th>dresnet</th>\n",
       "      <th>dinception</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dims</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>12.037824</td>\n",
       "      <td>12.760549</td>\n",
       "      <td>12.178625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>24.128774</td>\n",
       "      <td>24.109189</td>\n",
       "      <td>24.676765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>72.769336</td>\n",
       "      <td>72.200794</td>\n",
       "      <td>75.559556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>157.154231</td>\n",
       "      <td>158.960655</td>\n",
       "      <td>155.842409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>436.156633</td>\n",
       "      <td>435.525187</td>\n",
       "      <td>422.279505</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            dcnn     dresnet  dinception\n",
       "dims                                    \n",
       "10     12.037824   12.760549   12.178625\n",
       "20     24.128774   24.109189   24.676765\n",
       "40     72.769336   72.200794   75.559556\n",
       "60    157.154231  158.960655  155.842409\n",
       "100   436.156633  435.525187  422.279505"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dcnn</th>\n",
       "      <th>dresnet</th>\n",
       "      <th>dinception</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datasets</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.808729</td>\n",
       "      <td>0.748329</td>\n",
       "      <td>0.787623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.683872</td>\n",
       "      <td>0.750682</td>\n",
       "      <td>0.728515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.862084</td>\n",
       "      <td>1.078303</td>\n",
       "      <td>1.023756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.226898</td>\n",
       "      <td>1.619883</td>\n",
       "      <td>1.564688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2.494377</td>\n",
       "      <td>2.852471</td>\n",
       "      <td>2.676503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>6.117644</td>\n",
       "      <td>6.407637</td>\n",
       "      <td>6.343607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>12.029003</td>\n",
       "      <td>12.232168</td>\n",
       "      <td>11.824625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>23.432897</td>\n",
       "      <td>24.347931</td>\n",
       "      <td>23.481931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>57.264550</td>\n",
       "      <td>60.772728</td>\n",
       "      <td>57.838810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>115.927501</td>\n",
       "      <td>120.830903</td>\n",
       "      <td>115.482876</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                dcnn     dresnet  dinception\n",
       "datasets                                    \n",
       "1           0.808729    0.748329    0.787623\n",
       "2           0.683872    0.750682    0.728515\n",
       "5           0.862084    1.078303    1.023756\n",
       "10          1.226898    1.619883    1.564688\n",
       "20          2.494377    2.852471    2.676503\n",
       "50          6.117644    6.407637    6.343607\n",
       "100        12.029003   12.232168   11.824625\n",
       "200        23.432897   24.347931   23.481931\n",
       "500        57.264550   60.772728   57.838810\n",
       "1000      115.927501  120.830903  115.482876"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_exec_dim = pd.read_csv('experiments/execution_time/results_explanation/merged_results_dim.csv')\n",
    "df_exec_dim.rename(columns={'Unnamed: 0':'dims'}, inplace=True)\n",
    "df_exec_dim['dims'] = [10,20,40,60,100]\n",
    "df_exec_dim.set_index('dims', inplace=True)\n",
    "\n",
    "df_exec_length = pd.read_csv('experiments/execution_time/results_explanation/merged_results_length.csv')\n",
    "df_exec_length.rename(columns={'Unnamed: 0':'datasets'}, inplace=True)\n",
    "df_exec_length.set_index('datasets', inplace=True)\n",
    "\n",
    "df_exec_k = pd.read_csv('experiments/execution_time/results_explanation/merged_results_k.csv')\n",
    "df_exec_k.rename(columns={'Unnamed: 0':'datasets'}, inplace=True)\n",
    "df_exec_k.set_index('datasets', inplace=True)\n",
    "\n",
    "display(df_exec_length)\n",
    "display(df_exec_dim)\n",
    "display(df_exec_k)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-15T16:18:26.213119Z",
     "start_time": "2022-09-15T16:18:25.625785Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fe3da8d3f40>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,6))\n",
    "\n",
    "plt.subplot(1,3,1)\n",
    "plt.plot(df_exec_dim.index,df_exec_dim['dcnn'],label='dcnn')\n",
    "plt.scatter(df_exec_dim.index,df_exec_dim['dcnn'])\n",
    "plt.plot(df_exec_dim.index,df_exec_dim['dresnet'],label='dresnet')\n",
    "plt.scatter(df_exec_dim.index,df_exec_dim['dresnet'])\n",
    "plt.plot(df_exec_dim.index,df_exec_dim['dinception'],label='dinception')\n",
    "plt.scatter(df_exec_dim.index,df_exec_dim['dinception'])\n",
    "plt.xlabel('number of dimension')\n",
    "plt.ylabel('Execution time (seconds)')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1,3,2)\n",
    "plt.plot(df_exec_length.index,df_exec_length['dcnn'],label='dcnn')\n",
    "plt.scatter(df_exec_length.index,df_exec_length['dcnn'])\n",
    "plt.plot(df_exec_length.index,df_exec_length['dresnet'],label='dresnet')\n",
    "plt.scatter(df_exec_length.index,df_exec_length['dresnet'])\n",
    "plt.plot(df_exec_length.index,df_exec_length['dinception'],label='dinception')\n",
    "plt.scatter(df_exec_length.index,df_exec_length['dinception'])\n",
    "plt.xlabel('time series length')\n",
    "plt.ylabel('Execution time (seconds)')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1,3,3)\n",
    "plt.plot(df_exec_k.index,df_exec_k['dcnn'],label='dcnn')\n",
    "plt.scatter(df_exec_k.index,df_exec_k['dcnn'])\n",
    "plt.plot(df_exec_k.index,df_exec_k['dresnet'],label='dresnet')\n",
    "plt.scatter(df_exec_k.index,df_exec_k['dresnet'])\n",
    "plt.plot(df_exec_k.index,df_exec_k['dinception'],label='dinception')\n",
    "plt.scatter(df_exec_k.index,df_exec_k['dinception'])\n",
    "plt.xlabel('number of of permutation k')\n",
    "plt.ylabel('Execution time (seconds)')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}