comparison_plots_omim.ipynb

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  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Methods Comparisons on the OMIM Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import pandas as pd\n",
    "from tqdm.notebook import tqdm\n",
    "from sklearn.metrics import auc\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "from Paths import PATH_TO_DATASETS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total disease used: 3\n",
      "Total methods used: 4\n"
     ]
    }
   ],
   "source": [
    "DISEASE_NAMES = ['C0006142_Malignant_neoplasm_of_breast', 'C0009402_Colorectal_Carcinoma',\\\n",
    "                 'C0023893_Liver_Cirrhosis_Experimental']\n",
    "\n",
    "DISEASE_CODES = {\"C0006142_Malignant_neoplasm_of_breast\": \"C0006142\", \\\n",
    "                    \"C0009402_Colorectal_Carcinoma\": \"C0009402\", \\\n",
    "                    \"C0023893_Liver_Cirrhosis_Experimental\": \"C0023893\"\n",
    "                }\n",
    "\n",
    "GUILD_METHODS   = []\n",
    "COMPARE_METHODS = [\"XGDAG - GNNExplainer\",  \"XGDAG - GraphSVX\", \"NIAPU\", \"DIAMOnD\",]\n",
    "XAI_METHODS     = [\"GNNExplainer\", \"XGDAG - GNNExplainer\", \"GraphSVX\", \"XGDAG - GraphSVX\", \"SubgraphX\", \"XGDAG - SubgraphX\"]\n",
    "\n",
    "ratios_to_validate = [25, 50, 100, 200, 500, 750, 1000]\n",
    "\n",
    "print('Total disease used:', len(DISEASE_NAMES))\n",
    "print('Total methods used:', len(COMPARE_METHODS))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize Dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dictionary to store, the metric achieved by each method for each disease:\n",
    "# - disease_method_metric_d = {disease: {method: {metric: value(s)}}}\n",
    "# COMPARE_METHODS = XAI_METHODS\n",
    "\n",
    "disease_method_metric_d = {}\n",
    "\n",
    "# Inizialization\n",
    "for disease in DISEASE_NAMES:\n",
    "    disease_method_metric_d[disease] = {}\n",
    "    for method in COMPARE_METHODS:\n",
    "        disease_method_metric_d[disease][method] = {}\n",
    "        for metric in ['P', 'R', 'F1', 'AUC']:\n",
    "            disease_method_metric_d[disease][method][metric] = 0"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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   "source": [
    "auc_scores = {}\n",
    "for METHOD in COMPARE_METHODS:\n",
    "\t\tauc_scores[METHOD] = []\n",
    "\t\t\n",
    "for DISEASE_NAME in tqdm(DISEASE_NAMES):\n",
    "\t\trecall_folds = []\n",
    "\t\tprecision_folds = []\n",
    "\t\tF1_folds = []\n",
    "\n",
    "\t\trecall_folds_compare_methods = {}\n",
    "\t\tprecision_folds_compare_methods = {}\n",
    "\t\tF1_folds_compare_methods = {}\n",
    "\t\t\n",
    "\t\tfor METHOD in COMPARE_METHODS:\n",
    "\t\t\t\trecall_folds_compare_methods[METHOD] = []\n",
    "\t\t\t\tprecision_folds_compare_methods[METHOD] = []\n",
    "\t\t\t\tF1_folds_compare_methods[METHOD] = []\n",
    "\n",
    "\t\tfor ratio_to_validate in ratios_to_validate:\n",
    "\t\t\tGENE_APU_SCORES_PATH = PATH_TO_DATASETS + \"/Diamond_dataset/\" + DISEASE_CODES[DISEASE_NAME] + \"_diamond_ranking\"\n",
    "\t\t\tTRAIN_SEEDS_PATH = PATH_TO_DATASETS + \"/Diamond_dataset/\" + DISEASE_CODES[DISEASE_NAME] + \"_seed_genes_scores_diamond.txt\"\n",
    "\n",
    "\t\t\tAPU_scores_df = pd.read_csv(GENE_APU_SCORES_PATH, header = None, sep = \" \")\n",
    "\t\t\tAPU_scores_df.columns = [\"name\", \"score\", \"label\"]\n",
    "\t\t\tAPU_ranking_df = APU_scores_df.sort_values(by = \"score\", ascending= False)\n",
    "\t\t\t\n",
    "\n",
    "\t\t\t# seed genes used for diffusion that we consider as P class in this scenario \n",
    "\t\t\t# (20% or seed genes were removed to check for robustness)\n",
    "\t\t\ttrain_seeds_df = pd.read_csv(TRAIN_SEEDS_PATH, header = None, sep = \" \")\n",
    "\t\t\ttrain_seeds_df.columns = [\"name\", \"GDA Score\"]\n",
    "\t\t\ttrain_seeds_list = train_seeds_df[\"name\"].values.tolist()\n",
    "\n",
    "\t\t\tAPU_ranking_df_not_seeds = APU_ranking_df[~APU_ranking_df['name'].isin(train_seeds_list)]\n",
    "\n",
    "\t\t\tAPU_ranking_candidate_genes = APU_ranking_df_not_seeds[\"name\"].values.tolist()\n",
    "\t\t\t\n",
    "\t\t\tN = None\n",
    "\t\t\t\n",
    "\t\t\tFILE_NAME_ALL_SEEDS = PATH_TO_DATASETS + \"/all_seed_genes/\" + DISEASE_NAME + \"_all_seed_genes.txt\"\n",
    "\t\t\tall_seed_genes_df = pd.read_csv(FILE_NAME_ALL_SEEDS, sep = \" \", header = None)\n",
    "\t\t\tall_seed_genes = all_seed_genes_df[0].values\n",
    "\n",
    "\t\t\tdiamond_all_genes_df = pd.read_csv(PATH_TO_DATASETS + '/Diamond_dataset/diamond_all_genes.txt', header=None)\n",
    "\t\t\tdiamond_all_genes = diamond_all_genes_df[0].values\n",
    "\t\t\tall_seed_genes = list(set(all_seed_genes).intersection(set(diamond_all_genes)))\n",
    "\n",
    "\t\t\ttest_seeds = list(set(all_seed_genes).difference(set(train_seeds_list)))\n",
    "\t\t\t\n",
    "\t\t\tN = len(all_seed_genes)\n",
    "\t\t\t\n",
    "\t\t\tAPU_ranking_candidate_genes = APU_ranking_candidate_genes[:round(ratio_to_validate)]\n",
    "\t\t\tTP = 0\n",
    "\t\t\tFP = 0\n",
    "\t\t\tP = len(test_seeds) #TP+FP\n",
    "\n",
    "\t\t\tfor gene in APU_ranking_candidate_genes:\n",
    "\t\t\t\t\t\n",
    "\t\t\t\t\tif gene in test_seeds:\n",
    "\t\t\t\t\t\t\tTP += 1\n",
    "\t\t\t\t\t\t\t\n",
    "\t\t\t\t\telse:\n",
    "\t\t\t\t\t\t\tFP += 1\n",
    "\t\t\t\n",
    "\t\t\trecall = TP / P\n",
    "\t\t\tprecision = TP / (TP + FP)\n",
    "\n",
    "\t\t\tF1_score = 0\n",
    "\t\t\tif (precision + recall) != 0:\n",
    "\t\t\t\t\tF1_score = 2*(precision*recall)/(precision+recall)\n",
    "\n",
    "\t\t\trecall_folds.append(recall)\n",
    "\t\t\tprecision_folds.append(precision)\n",
    "\t\t\tF1_folds.append(F1_score)\n",
    "\n",
    "\t\t\trecall_folds_compare_methods[\"NIAPU\"].append(recall)\n",
    "\t\t\tprecision_folds_compare_methods[\"NIAPU\"].append(precision)\n",
    "\t\t\tF1_folds_compare_methods[\"NIAPU\"].append(F1_score)\n",
    "\t\t\t\n",
    "\t\t\tfor METHOD in COMPARE_METHODS:\n",
    "\t\t\t\t\tif METHOD != \"NIAPU\":\n",
    "\t\t\t\t\t\t\tranking_method = []\n",
    "\n",
    "\t\t\t\t\t\t\tif METHOD in XAI_METHODS:\n",
    "\t\t\t\t\t\t\t\t\twith open(\"Rankings/\" + DISEASE_CODES[DISEASE_NAME] + \"_all_positives_diamond_\" + METHOD.lower().replace(\"-\", \"_\").replace(\" \", \"\") + \".txt\", \"r\", encoding=\"utf-8\") as rankingFile:\n",
    "\t\t\t\t\t\t\t\t\t\t\tfor line in rankingFile:\n",
    "\t\t\t\t\t\t\t\t\t\t\t\t\tranking_method.append(line.strip(\"\\n\"))\n",
    "\n",
    "\t\t\t\t\t\t\telif METHOD in GUILD_METHODS:        \n",
    "\t\t\t\t\t\t\t\t\tGUILD_METHOD_PATH = \"Rankings/other_methods/GUILD/\" + METHOD + \"/\" + DISEASE_NAME + \"_\" + METHOD + \".txt\"\n",
    "\n",
    "\t\t\t\t\t\t\t\t\tGUILD_scores_df = pd.read_csv(GUILD_METHOD_PATH, header = None, sep = \"\\t\")\n",
    "\t\t\t\t\t\t\t\t\tGUILD_scores_df.columns = [\"name\", \"score\"]\n",
    "\t\t\t\t\t\t\t\t\tGUILD_scores_df = GUILD_scores_df.sort_values(by = \"score\", ascending= False)\n",
    "\n",
    "\t\t\t\t\t\t\t\t\tranking_method_df_not_seeds = GUILD_scores_df[~GUILD_scores_df['name'].isin(train_seeds_list)]\n",
    "\t\t\t\t\t\t\t\t\tranking_method = ranking_method_df_not_seeds[\"name\"].values.tolist()\n",
    "\t\t\t\t\t\t\t\n",
    "\t\t\t\t\t\t\telse:\n",
    "\t\t\t\t\t\t\t\tif METHOD == 'DIAMOnD':\n",
    "\t\t\t\t\t\t\t\t\t\t\ttry:\n",
    "\t\t\t\t\t\t\t\t\t\t\t\twith open('Rankings/other_methods/' + METHOD + '/diamond_output_' + DISEASE_CODES[DISEASE_NAME] + '_diamond_new.txt', 'r', encoding='utf-8') as rankingFile:\n",
    "\t\t\t\t\t\t\t\t\t\t\t\t\tfor line in rankingFile:\n",
    "\t\t\t\t\t\t\t\t\t\t\t\t\t\tranking_method.append(line.strip(\"\\n\"))\n",
    "\t\t\t\t\t\t\t\t\t\t\texcept:\n",
    "\t\t\t\t\t\t\t\t\t\t\t\tprint('[!] Disease not available in the DIAMOnD dataset')\n",
    "\t\t\t\t\t\t\t\telse:\n",
    "\t\t\t\t\t\t\t\t\twith open(\"Rankings/other_methods/\" + METHOD + \"/\" + METHOD.lower() + \"_output_\" + DISEASE_NAME + \".txt\", \"r\", encoding=\"utf-8\") as rankingFile:\n",
    "\t\t\t\t\t\t\t\t\t\tfor line in rankingFile:\n",
    "\t\t\t\t\t\t\t\t\t\t\tranking_method.append(line.strip(\"\\n\"))\n",
    "\t\t\t\t\t\t\t\t\t\n",
    "\n",
    "\t\t\t\t\t\t\tranking_method = ranking_method[:round(ratio_to_validate)]\n",
    "\t\t\t\t\t\t\tTP = 0\n",
    "\t\t\t\t\t\t\tFP = 0\n",
    "\t\t\t\t\t\t\tP = len(test_seeds) #TP+FP\n",
    "\n",
    "\t\t\t\t\t\t\tfor gene in ranking_method:\n",
    "\t\t\t\t\t\t\t\tgene.replace('-', '_')\n",
    "\t\t\t\t\t\t\t\tgene.replace('.', '_')\n",
    "\t\t\t\t\t\t\t\t\n",
    "\t\t\t\t\t\t\t\tif gene in test_seeds:\n",
    "\t\t\t\t\t\t\t\t\tTP += 1\n",
    "\t\t\t\t\t\t\t\telse:\n",
    "\t\t\t\t\t\t\t\t\tFP += 1\n",
    "\n",
    "\t\t\t\t\t\t\trecall = TP / P\n",
    "\t\t\t\t\t\t\tprecision = TP / (TP + FP)\n",
    "\t\t\t\t\t\t\t\n",
    "\t\t\t\t\t\t\tF1_score = 0\n",
    "\t\t\t\t\t\t\tif (precision + recall) != 0:\n",
    "\t\t\t\t\t\t\t\t\tF1_score = 2*(precision*recall)/(precision+recall)\n",
    "\n",
    "\t\t\t\t\t\t\trecall_folds_compare_methods[METHOD].append(recall)\n",
    "\t\t\t\t\t\t\tprecision_folds_compare_methods[METHOD].append(precision)\n",
    "\t\t\t\t\t\t\tF1_folds_compare_methods[METHOD].append(F1_score)\n",
    "\t\t\t\n",
    "\t\t#compute area under the precision-recall curve (AUC)\n",
    "\t\tfor METHOD in COMPARE_METHODS:\n",
    "\t\t\t\tauc_score = auc(recall_folds_compare_methods[METHOD], precision_folds_compare_methods[METHOD])\n",
    "\t\t\t\tauc_scores[METHOD].append(auc_score)\n",
    "\n",
    "\t\t\t\tdisease_method_metric_d[DISEASE_NAME][METHOD]['P']      = precision_folds_compare_methods[METHOD]\n",
    "\t\t\t\tdisease_method_metric_d[DISEASE_NAME][METHOD]['R']      = recall_folds_compare_methods[METHOD]\n",
    "\t\t\t\tdisease_method_metric_d[DISEASE_NAME][METHOD]['F1']     = F1_folds_compare_methods[METHOD]\n",
    "\t\t\t\tdisease_method_metric_d[DISEASE_NAME][METHOD]['AUC']    = auc_score\n",
    "\n"
   ]
  },
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   "source": [
    "## Line Plots"
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   "cell_type": "code",
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   "metadata": {},
   "outputs": [
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           "align": "left"
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          "hovermode": "closest",
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           "style": "light"
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          "plot_bgcolor": "white",
          "polar": {
           "angularaxis": {
            "gridcolor": "#EBF0F8",
            "linecolor": "#EBF0F8",
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           },
           "bgcolor": "white",
           "radialaxis": {
            "gridcolor": "#EBF0F8",
            "linecolor": "#EBF0F8",
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          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "white",
            "gridcolor": "#DFE8F3",
            "gridwidth": 2,
            "linecolor": "#EBF0F8",
            "showbackground": true,
            "ticks": "",
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           "yaxis": {
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            "gridcolor": "#DFE8F3",
            "gridwidth": 2,
            "linecolor": "#EBF0F8",
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            "ticks": "",
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           "zaxis": {
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            "gridcolor": "#DFE8F3",
            "gridwidth": 2,
            "linecolor": "#EBF0F8",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "#EBF0F8"
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          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
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          },
          "ternary": {
           "aaxis": {
            "gridcolor": "#DFE8F3",
            "linecolor": "#A2B1C6",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "#DFE8F3",
            "linecolor": "#A2B1C6",
            "ticks": ""
           },
           "bgcolor": "white",
           "caxis": {
            "gridcolor": "#DFE8F3",
            "linecolor": "#A2B1C6",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "#EBF0F8",
           "linecolor": "#EBF0F8",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "#EBF0F8",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "#EBF0F8",
           "linecolor": "#EBF0F8",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "#EBF0F8",
           "zerolinewidth": 2
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         }
        },
        "xaxis": {
         "title": {
          "text": "Top K Genes"
         }
        },
        "yaxis": {
         "title": {
          "text": "F1 Score"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LINEPLOTS\n",
    "format = 'eps'\n",
    "metric = 'F1'\n",
    "\n",
    "metric_name_d = {\n",
    "    'F1': 'F1 Score',\n",
    "    'P': 'Precision',\n",
    "    'R': 'Recall'\n",
    "}\n",
    "\n",
    "colors  = ['skyblue', 'tomato', 'brown', 'orange', 'green', 'seagreen']\n",
    "markers = ['circle', 'square', 'cross', 'diamond', 'x', 'triangle-up', 'triangle-down', 'star']\n",
    "styles \t= ['solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot']\n",
    "\n",
    "for DISEASE_NAME in DISEASE_NAMES:\n",
    "    # data = []\n",
    "    print(DISEASE_NAME)\n",
    "\n",
    "    fig = go.Figure()\n",
    "\n",
    "    marker_idx  = 0\n",
    "    color_idx   = 0\n",
    "    for METHOD in COMPARE_METHODS:\n",
    "        fig.add_trace(go.Scatter(\n",
    "            y = disease_method_metric_d[DISEASE_NAME][METHOD][metric],\n",
    "            x = [str(x) for x in ratios_to_validate],\n",
    "            name = METHOD,\n",
    "            line = dict(\n",
    "                color = colors[color_idx],\n",
    "                dash = 'solid'\n",
    "            ),\n",
    "            marker = dict(\n",
    "                symbol = markers[marker_idx]\n",
    "            )\n",
    "        ))\n",
    "        \n",
    "        if 'XGDAG' in METHOD: color_idx += 1\n",
    "        marker_idx  += 1\n",
    "        \n",
    "    fig.update_layout(xaxis_title='Top K Genes',\n",
    "                   yaxis_title=metric_name_d[metric],\n",
    "                   template='plotly_white')\n",
    "                   \n",
    "    fig.show()\n",
    "    # uncomment to save images\n",
    "    # pio.write_image(fig, 'Images/XAI/'+DISEASE_NAME+'_'+metric+'.'+format, scale=1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save Dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save dictionary of metrics using Pickle\n",
    "with open('diamond_disease_method_metrics.pickle', 'wb') as fout:\n",
    "    pickle.dump(disease_method_metric_d, fout, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  }
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