comparison_plots_omim_vs_disgenet.ipynb

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   "source": [
    "# Comparison Plots - Metrics on OMIM vs DisGeNET"
   ]
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  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import pandas as pd\n",
    "import plotly.io as pio\n",
    "import plotly.express as px\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_METRICS"
   ]
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   "cell_type": "markdown",
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   "source": [
    "## Load Dictionaries \n",
    "containing metrics obtained on the OMIM dataset and on the DisGeNET dataset"
   ]
  },
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   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
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   "source": [
    "disease_method_metric_diamond_d = {}\n",
    "\n",
    "with open(PATH_TO_METRICS + 'diamond_disease_method_metrics.pickle', 'rb') as fin:\n",
    "    disease_method_metric_diamond_d = pickle.load(fin)\n",
    "    \n",
    "with open(PATH_TO_METRICS + 'disease_method_metrics.pickle', 'rb') as fin:\n",
    "    disease_method_metric_d = pickle.load(fin)"
   ]
  },
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   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LinePlots"
   ]
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   "execution_count": 6,
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   "outputs": [
    {
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      "C0006142_Malignant_neoplasm_of_breast\n"
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k genes\"}},\"yaxis\":{\"title\":{\"text\":\"F1 score\"}}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('c25329e1-fc19-4a03-aae6-f89373c4d8dd');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
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     "metadata": {},
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    }
   ],
   "source": [
    "# LINEPLOTS\n",
    "ratios_to_validate = [25, 50, 100, 200, 500, 750, 1000]\n",
    "\n",
    "format = 'pdf'\n",
    "metric = 'F1'\n",
    "\n",
    "metric_name_d = {\n",
    "    'F1': 'F1 score',\n",
    "    'P': 'Precision',\n",
    "    'R': 'Recall'\n",
    "}\n",
    "\n",
    "no_label = 3\n",
    "\n",
    "DISEASE_NAMES = ['C0006142_Malignant_neoplasm_of_breast', 'C0009402_Colorectal_Carcinoma',\\\n",
    "                 'C0023893_Liver_Cirrhosis_Experimental']\n",
    "\n",
    "COMPARE_METHODS = [\"XGDAG - GNNExplainer\",  \"XGDAG - GraphSVX\", \"DIAMOnD\", 'NIAPU']\n",
    "\n",
    "DISEASE_NAMES.append(DISEASE_NAMES[0])\n",
    "\n",
    "colors  = ['skyblue', 'tomato', 'seagreen', 'orange']\n",
    "markers = ['circle', 'square', 'cross', 'diamond', 'x', 'triangle-up', 'triangle-down', 'star']\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.replace('- ', '-<br>'),\n",
    "            name = METHOD,\n",
    "            line = dict(\n",
    "                color   = colors[color_idx]\n",
    "            ),\n",
    "            marker = dict(\n",
    "                symbol = markers[marker_idx]\n",
    "            )\n",
    "        ))\n",
    "        marker_idx += 1\n",
    "        fig.add_trace(go.Scatter(\n",
    "            y = disease_method_metric_diamond_d[DISEASE_NAME][METHOD][metric],\n",
    "            x = [str(x) for x in ratios_to_validate],\n",
    "            # name = METHOD.replace('- ', '-<br>'), #uncomment to save part of name in newline\n",
    "            name = METHOD, #comment if above in uncommented\n",
    "            line = dict(\n",
    "                dash    ='dash',\n",
    "                color = colors[color_idx]\n",
    "            ),\n",
    "            marker = dict(\n",
    "                symbol = markers[marker_idx]\n",
    "            )\n",
    "        ))\n",
    "        color_idx   += 1\n",
    "        marker_idx  += 1\n",
    "        \n",
    "    # if no_label != 1:\n",
    "    #     no_label -= 1\n",
    "    #     fig.update_layout(showlegend=False)\n",
    "    # else:\n",
    "    #     no_label -= 1\n",
    "    #     fig.update_layout(showlegend=True)\n",
    "        \n",
    "    fig.update_layout(xaxis_title='Top k genes',\n",
    "                   yaxis_title=metric_name_d[metric],\n",
    "                   template='plotly_white',\n",
    "                   font=dict( size=22)\n",
    "                   )\n",
    "    \n",
    "                   \n",
    "    fig.show()\n",
    "    # uncomment to save images\n",
    "    # pio.write_image(fig, 'Images/'+format.upper()+'/'+DISEASE_NAME+'_'+metric+'_omim_dataset_vs_disgenet_v2.'+format, scale=1)\n",
    "    pio.write_image(fig, 'Images/OMIM vs DisGeNET/'+DISEASE_NAME+'_'+metric+'.'+format, width=1050, height=600)"
   ]
  }
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