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   "source": [
    "# Comparing Metrics and Stability\n",
    "\n",
    "This notebook briefly demonstrates how to use different metrics (in scikit) and compare them between two classification algorithms. Furthermore, it will be shown how the smearing of the input data (i.e. worsening the feature resolution) effects the classification performance.\n",
    "\n",
    "## Loading the Data and the Models\n",
    "\n",
    "Once more, we load the dataframe and the models we trained earlier."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "\nLoad dataframe and classifier...\n...done!\n \n"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from joblib import load\n",
    "\n",
    "print(\" \")\n",
    "print(\"Load dataframe and classifier...\")\n",
    "\n",
    "data_directory = 'http://hadron.physics.fsu.edu/~dlersch/GlueX_PANDA_EIC_ML_Workshop'\n",
    "data_name = 'hands_on_data_033_033_033.csv' #---> Change this name to analyze imbalanced data\n",
    "dataFrame = pd.read_csv(data_directory + '/' + data_name)\n",
    "\n",
    "my_mlp = load('mlp_classifier.joblib')\n",
    "my_rf = load('random_forest_classifier.joblib')\n",
    "\n",
    "print(\"...done!\")\n",
    "print(\" \")"
   ]
  },
  {
   "source": [
    "## Load Evaluation Metrics and prepare Data\n",
    "\n",
    "Feel free to add more / other metrics that you find useful / interesting. Like done before, the input data is normalzed and the semearing values for the stability checks are defined afterwards."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Prepare data and set semaring values...\n...done!\n  \n"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import matthews_corrcoef\n",
    "\n",
    "print(\"Prepare data and set semaring values...\")\n",
    "\n",
    "used_features = ['var1','var2','var3','var4'] #--> Change the elements here, in order to use different features\n",
    "X = dataFrame[used_features].values\n",
    "Y = dataFrame['label'].values\n",
    "\n",
    "scaler = MinMaxScaler() \n",
    "X = scaler.fit_transform(X)\n",
    "\n",
    "smearing_values = [0.0,0.01,0.025,0.05,0.1,0.25,0.5]\n",
    "\n",
    "print(\"...done!\")\n",
    "print(\"  \")"
   ]
  },
  {
   "source": [
    "## Scan Performances \n",
    "\n",
    "Determine performance scores for each classifier and different smearing parameters:"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Determine classification performances for 7 different feature smearings...\n...done!\n  \n"
    }
   ],
   "source": [
    "import numpy as np\n",
    "scanned_mlp_scores = {\n",
    "    'precision': [],\n",
    "    'f1': [],\n",
    "    'acc': [],\n",
    "    'mcc': []\n",
    "}\n",
    "\n",
    "scanned_rf_scores = {\n",
    "    'precision': [],\n",
    "    'f1': [],\n",
    "    'acc': [],\n",
    "    'mcc': []\n",
    "}\n",
    "\n",
    "n_smearing_vals = len(smearing_values)\n",
    "print(\"Determine classification performances for \" + str(n_smearing_vals) + \" different feature smearings...\")\n",
    "\n",
    "#++++++++++++++++++++++++++++++++++++\n",
    "for s in smearing_values:\n",
    "    if s != 0.0:\n",
    "        smearing = np.random.normal(1.0,s,X.shape)\n",
    "        X = np.multiply(X,smearing)\n",
    "\n",
    "    mlp_prediction = my_mlp.predict(X)\n",
    "    rf_prediction = my_rf.predict(X)\n",
    "\n",
    "    scanned_mlp_scores['precision'].append(precision_score(Y,mlp_prediction,average='macro')) #--> There are differnt options for average that you might want to explore\n",
    "    scanned_mlp_scores['f1'].append(f1_score(Y,mlp_prediction,average='macro'))\n",
    "    scanned_mlp_scores['acc'].append(accuracy_score(Y,mlp_prediction))\n",
    "    scanned_mlp_scores['mcc'].append(matthews_corrcoef(Y,mlp_prediction))\n",
    "\n",
    "    scanned_rf_scores['precision'].append(precision_score(Y,rf_prediction,average='macro'))\n",
    "    scanned_rf_scores['f1'].append(f1_score(Y,rf_prediction,average='macro'))\n",
    "    scanned_rf_scores['acc'].append(accuracy_score(Y,rf_prediction))\n",
    "    scanned_rf_scores['mcc'].append(matthews_corrcoef(Y,rf_prediction))\n",
    "#++++++++++++++++++++++++++++++++++++\n",
    "\n",
    "print(\"...done!\")\n",
    "print(\"  \")\n",
    "\n"
   ]
  },
  {
   "source": [
    "Finally, plot and compare the results:"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig,ax = plt.subplots(1,2,sharex=True,sharey=True)\n",
    "fig.set_size_inches(20,8)\n",
    "plt.rcParams.update({'font.size': 20})\n",
    "\n",
    "ax[0].set_title('MLP Classifier')\n",
    "ax[0].plot(smearing_values,scanned_mlp_scores['precision'],'k-o',linewidth=2.0,markersize=10,label='Precision')\n",
    "ax[0].plot(smearing_values,scanned_mlp_scores['f1'],'m-d',linewidth=2.0,markersize=10,label='F1-Score')\n",
    "ax[0].plot(smearing_values,scanned_mlp_scores['acc'],'r-s',linewidth=2.0,markersize=10,label='Accuracy')\n",
    "ax[0].plot(smearing_values,scanned_mlp_scores['mcc'],'g-o',linewidth=2.0,markersize=10,label='MCC')\n",
    "\n",
    "ax[0].set_xlabel(r'Feature Smearing')\n",
    "ax[0].set_ylabel(r'Score')\n",
    "ax[0].set_ylim(0.0,1.0)\n",
    "ax[0].grid(True)\n",
    "ax[0].legend()\n",
    "\n",
    "ax[1].set_title('Random Forest Classifier')\n",
    "ax[1].plot(smearing_values,scanned_rf_scores['precision'],'k-o',linewidth=2.0,markersize=10,label='Precision')\n",
    "ax[1].plot(smearing_values,scanned_rf_scores['f1'],'m-d',linewidth=2.0,markersize=10,label='F1-Score')\n",
    "ax[1].plot(smearing_values,scanned_rf_scores['acc'],'r-s',linewidth=2.0,markersize=10,label='Accuracy')\n",
    "ax[1].plot(smearing_values,scanned_rf_scores['mcc'],'g-o',linewidth=2.0,markersize=10,label='MCC')\n",
    "\n",
    "ax[1].set_xlabel(r'Feature Smearing')\n",
    "ax[1].legend()\n",
    "ax[1].set_ylim(0.0,1.0)\n",
    "ax[1].grid(True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}