false_edges_experiment.ipynb

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  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "# import networkx as nx\n",
    "# import plotly.io as pio\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "# import igviz as ig\n",
    "# from node2vec import Node2Vec\n",
    "# from gensim.models import KeyedVectors\n",
    "# import seaborn as sns\n",
    "# import matplotlib.pyplot as plt\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "\n",
    "# import src.preprocess as pre\n",
    "# import src.visualize as vis\n",
    "# pio.renderers.default = \"png\"\n",
    "\n",
    "from src import models, training\n",
    "from torch_geometric.nn import GAE\n",
    "import torch\n",
    "import os\n",
    "from sklearn.metrics import roc_auc_score, auc, precision_recall_curve, average_precision_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.09999999999999998 training edges | 0.9 testing edges\n"
     ]
    }
   ],
   "source": [
    "data_name=\"seqfish\"\n",
    "\n",
    "\n",
    "preprocess_output_path = f\"./out/{data_name}/1_preprocessing_output/\"\n",
    "training_output_path = f\"./out/{data_name}/2_training_output/\"\n",
    "evaluation_output_path = f\"./out/{data_name}/3_evaluation_output/\"\n",
    "\n",
    "(cell_train_data, cell_test_data), genelevel_data = training.create_pyg_data(preprocess_output_path, split=0.9)\n",
    "data = (cell_train_data, genelevel_data)\n",
    "\n",
    "\n",
    "\n",
    "def build_clarifyGAE_pytorch(data, hyperparams = None):\n",
    "    num_cells, num_cellfeatures = data[0].x.shape[0], data[0].x.shape[1]\n",
    "    num_genes, num_genefeatures = data[1].x.shape[0], data[1].x.shape[1]\n",
    "    hidden_dim = hyperparams[\"concat_hidden_dim\"] // 2\n",
    "    num_genespercell = hyperparams[\"num_genespercell\"]\n",
    "\n",
    "    cellEncoder = models.GraphEncoder(num_cellfeatures, hidden_dim)\n",
    "    geneEncoder = models.SubgraphEncoder(num_features=num_genefeatures, hidden_dim=hidden_dim, num_vertices = num_cells, num_subvertices = num_genespercell)\n",
    "    \n",
    "    multiviewEncoder = models.MultiviewEncoder(SubgraphEncoder = geneEncoder, GraphEncoder = cellEncoder)\n",
    "    gae = GAE(multiviewEncoder)\n",
    "\n",
    "    return gae\n",
    "\n",
    "def metrics(model, z, pos_edge_index, neg_edge_index):\n",
    "\n",
    "    # torch.manual_seed(0)\n",
    "\n",
    "    pos_y = z.new_ones(pos_edge_index.size(1))\n",
    "    neg_y = z.new_zeros(neg_edge_index.size(1))\n",
    "    y = torch.cat([pos_y, neg_y], dim=0)\n",
    "\n",
    "    pos_pred = model.decode(z, pos_edge_index, sigmoid=True)\n",
    "    neg_pred = model.decode(z, neg_edge_index, sigmoid=True)\n",
    "    pred = torch.cat([pos_pred, neg_pred], dim=0)\n",
    "    \n",
    "\n",
    "    y, pred = y.detach().cpu().numpy(), pred.detach().cpu().numpy()\n",
    "    \n",
    "    \n",
    "    precision, recall, _ = precision_recall_curve(y, pred)\n",
    "    auprc = auc(recall, precision)\n",
    "    auroc = roc_auc_score(y, pred)\n",
    "    ap = average_precision_score(y,pred)\n",
    "\n",
    "    return auprc,ap, auroc\n",
    "\n",
    "def evaluate_model(studyname, data_name):\n",
    "\n",
    "    hyperparameters = {\n",
    "        \"num_genespercell\": 45,\n",
    "        \"concat_hidden_dim\": 64,\n",
    "        \"optimizer\" : \"adam\",\n",
    "        \"criterion\" : torch.nn.BCELoss(),\n",
    "        \"num_epochs\": 400\n",
    "    }\n",
    "\n",
    "    trained_gae = build_clarifyGAE_pytorch(data, hyperparameters)\n",
    "    trained_gae.load_state_dict(torch.load(os.path.join(training_output_path,f'{studyname}_trained_gae_model.pth')))\n",
    "    trained_gae.eval()\n",
    "    \n",
    "    # cell_level_encoder = trained_gae.encoder.encoder_c\n",
    "    # gene_level_encoder = trained_gae.encoder.encoder_g\n",
    "    z, z_c, z_g, gene_embeddings = trained_gae.encode(cell_test_data.x,data[1].x, cell_test_data.edge_index, data[1].edge_index)\n",
    "    print(cell_train_data, cell_test_data)\n",
    "\n",
    "    posmask = cell_test_data.edge_label == 1\n",
    "    negmask = ~posmask\n",
    "\n",
    "    \n",
    "    return metrics(trained_gae, z, cell_test_data.edge_label_index[:, posmask], cell_test_data.edge_label_index[:,negmask])\n",
    "    \n",
    "    \n",
    "\n",
    "def training_metrics(studyname):\n",
    "    df = pd.read_csv(os.path.join(evaluation_output_path, f\"{studyname}_metrics_0.3.csv\"))\n",
    "    print(df[[\"CLARIFY Test AP\",\"CLARIFY Test AUPRC\", \"CLARIFY Test ROC\" ]].max(axis=0))\n",
    "    \n",
    "    \n",
    "# evaluate_model(studyname, data_name)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Added 10.0% false edges\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(torch.Size([2, 821]), torch.Size([2, 782]), torch.Size([821]))"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def add_fake_edges(num_vertices, num_old_edges, fp):\n",
    "    num_add_edges = int((fp) * num_old_edges)\n",
    "    \n",
    "    add_edges = torch.from_numpy(np.random.randint(0,high=num_vertices, size=(2,num_add_edges)))\n",
    "        \n",
    "    print(f\"Added {fp*100}% false edges\")\n",
    "    # print(f\"New edge index dimension: {new_edge_indices.size()}\")\n",
    "    return add_edges\n",
    "  \n",
    "\n",
    "\n",
    "old_edge_label = cell_train_data.edge_label.clone()\n",
    "old_edge_label_index = cell_train_data.edge_label_index.clone()\n",
    "\n",
    "posmask = cell_train_data.edge_label == 1\n",
    "newedges = add_fake_edges(cell_train_data.x.size()[0], cell_train_data.edge_label_index[:, posmask].shape[1], 0.1)\n",
    "cell_train_data.edge_label  = torch.cat([cell_train_data.edge_label , torch.ones(newedges.shape[1])])\n",
    "cell_train_data.edge_label_index =  torch.cat([cell_train_data.edge_label_index , newedges],dim=1)\n",
    "\n",
    "\n",
    "\n",
    "cell_train_data.edge_label_index.shape, old_edge_label_index.shape, cell_train_data.edge_label.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# studynames = [\"withsplit0.3_withgenefeats_withpenalty_120epochs\"]\n",
    "# false_edge_combinations = [(\"0.0\",\"0.1\"),(\"0.1\",\"0.0\"),(\"0.1\",\"0.1\"),(\"0.2\",\"0.2\")]\n",
    "\n",
    "# for fp,fn in false_edge_combinations:\n",
    "#     studynames.append(f\"fp{fp}_fn{fn}_withsplit0.3_withgenefeats_withpenalty_120epochs\")\n",
    "    \n",
    "studynames = ['withsplit0.3_withgenefeats_withpenalty_120epochs',\n",
    " 'fp0.1_fn0.1_withsplit0.3_withgenefeats_withpenalty_120epochs',\n",
    " 'fp0.2_fn0.2_withsplit0.3_withgenefeats_withpenalty_120epochs']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data(x=[1597, 125], edge_index=[2, 782], y=[1597, 1597], edge_label=[782], edge_label_index=[2, 782]) Data(x=[1597, 125], edge_index=[2, 782], y=[1597, 1597], edge_label=[7032], edge_label_index=[2, 7032])\n",
      "Data(x=[1597, 125], edge_index=[2, 782], y=[1597, 1597], edge_label=[782], edge_label_index=[2, 782]) Data(x=[1597, 125], edge_index=[2, 782], y=[1597, 1597], edge_label=[7032], edge_label_index=[2, 7032])\n",
      "Data(x=[1597, 125], edge_index=[2, 782], y=[1597, 1597], edge_label=[782], edge_label_index=[2, 782]) Data(x=[1597, 125], edge_index=[2, 782], y=[1597, 1597], edge_label=[7032], edge_label_index=[2, 7032])\n",
      "Data(x=[1597, 125], edge_index=[2, 782], y=[1597, 1597], edge_label=[782], edge_label_index=[2, 782]) Data(x=[1597, 125], edge_index=[2, 782], y=[1597, 1597], edge_label=[7032], edge_label_index=[2, 7032])\n"
     ]
    }
   ],
   "source": [
    "evals_thirty = []\n",
    "\n",
    "for name in studynames:\n",
    "    evals_thirty.append(evaluate_model(studyname=name, data_name=\"seqfish\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.7834087923170979, 0.7834949786878911, 0.7953464157351215),\n",
       " (0.7827574891767713, 0.7828389258965371, 0.7988982350794225),\n",
       " (0.759574005087805, 0.7596667198024474, 0.7715084528260862),\n",
       " (0.7829097595709589, 0.7830022986053731, 0.7988867889485544)]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evals_thirty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.9532126126915653, 0.9532425950953799, 0.9591026103973257),\n",
       " (0.9480925042124696, 0.9482083254422257, 0.9544046814756142),\n",
       " (0.8480183725962273, 0.8482407154209896, 0.8814499149669769)]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evals_thirty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CLARIFY Test AP       0.904813\n",
      "CLARIFY Test AUPRC    0.904758\n",
      "CLARIFY Test ROC      0.908586\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "studynames = ['withsplit0.3_withgenefeats_withpenalty_120epochs',\n",
    " 'fp0.1_fn0.1_withsplit0.3_withgenefeats_withpenalty_120epochs',\n",
    " 'fp0.2_fn0.2_withsplit0.3_withgenefeats_withpenalty_120epochs',\n",
    " 'fp0.3_fn0.3_withsplit0.3_withgenefeats_withpenalty_120epochs',\n",
    " 'fp0.5_fn0.5_withsplit0.3_withgenefeats_withpenalty_120epochs']\n",
    "\n",
    "training_metrics(studynames[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Data(x=[1597, 125], edge_index=[2, 782], y=[1597, 1597], edge_label=[782], edge_label_index=[2, 782])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cell_train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_name=\"seqfish\"\n",
    "\n",
    "\n",
    "preprocess_output_path = f\"./out/{data_name}/1_preprocessing_output/\"\n",
    "training_output_path = f\"./out/{data_name}/2_training_output/\"\n",
    "evaluation_output_path = f\"./out/{data_name}/3_evaluation_output/\"\n",
    "metrics_path = f\"./out/{data_name}/3_evaluation_output/false_edge_experiments/\"\n",
    "deeplinc_path = f\"./benchmark/deeplinc/{data_name}/false_edge_experiments/\""
   ]
  },
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   "source": [
    "# fp_rates = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
    "fp_rates = [0.0, 0.1, 0.2, 0.3, 0.4, 0.5]\n",
    "deeplinc_aps = [0.8118579]\n",
    "aps = [0.9088231] # initial value\n",
    "for fp in fp_rates[1:]:\n",
    "    metric_path = f\"{metrics_path}fp{fp}_fn0.0_withsplit0.3_withgenefeats_withpenalty_120epochs_metrics_0.3.csv\"\n",
    "    deeplinc_aps.append(np.load(f\"{deeplinc_path}fp{fp}fn0.0test_ap_scores_0.3.npy\")[-1])\n",
    "    aps.append(pd.read_csv(metric_path)[\"CLARIFY Test AP\"].iloc[-1])\n",
    "    \n",
    "    \n",
    "    \n",
    "fp_figure = go.Figure()\n",
    "fp_figure.add_trace(\n",
    "    go.Scatter(x=fp_rates,y=aps, marker=dict(color = \"#d14078\",line=dict(width=2,color='black')),\n",
    "                name=\"Clarify\",opacity=0.7),\n",
    ") \n",
    "fp_figure.add_trace(\n",
    "    go.Scatter(x=fp_rates,y=deeplinc_aps, marker=dict(color = \"#345c72\",line=dict(width=2,color='black')),\n",
    "                name=\"DeepLinc\",opacity=0.7),\n",
    ") \n",
    "fp_figure.update_xaxes(showline=True, linewidth=1, linecolor='black', mirror=True, ticks='outside',tickfont=dict( size=17, color='black'))\n",
    "fp_figure.update_yaxes(showline=True, linewidth=1, linecolor='black', mirror=True,ticks='outside',tickfont=dict( size=17, color='black'))\n",
    "fp_figure.update_layout(\n",
    "    title=f\"Clarify and DeepLinc Average Precision over different FP edge rates\",\n",
    "    xaxis_title=\"False Positive Edge Rate\",\n",
    "    yaxis_title=\"Average Precision\",\n",
    "    legend_title=\"Model\",\n",
    "    width=800,\n",
    "    height=600,\n",
    "    boxmode='group',\n",
    "    plot_bgcolor='white'\n",
    ")\n",
    "fp_figure.show()"
   ]
  },
  {
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     },
     "metadata": {},
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   ],
   "source": [
    "fn_rates = [0.0,0.1, 0.2, 0.3, 0.4, 0.5]\n",
    "fn_deeplinc_aps = [0.8118579]\n",
    "fn_aps = [0.9088231] # initial value\n",
    "for fn in fn_rates[1:]:\n",
    "    metric_path = f\"{metrics_path}fp0.0_fn{fn}_withsplit0.3_withgenefeats_withpenalty_120epochs_metrics_0.3.csv\"\n",
    "    fn_deeplinc_aps.append(np.load(f\"{deeplinc_path}fp0.0fn{fn}test_ap_scores_0.3.npy\")[-1])\n",
    "    fn_aps.append(pd.read_csv(metric_path)[\"CLARIFY Test AP\"].iloc[-1])\n",
    "    \n",
    "    \n",
    "    \n",
    "fn_figure = go.Figure()\n",
    "fn_figure.add_trace(\n",
    "    go.Scatter(x=fn_rates,y=fn_aps, marker=dict(color = \"#d14078\",line=dict(width=2,color='black')),\n",
    "                name=\"Clarify\",opacity=0.7),\n",
    ") \n",
    "fn_figure.add_trace(\n",
    "    go.Scatter(x=fn_rates,y=fn_deeplinc_aps, marker=dict(color = \"#345c72\",line=dict(width=2,color='black')),\n",
    "                name=\"DeepLinc\",opacity=0.7),\n",
    ") \n",
    "fn_figure.update_xaxes(showline=True, linewidth=1, linecolor='black', mirror=True, ticks='outside',tickfont=dict( size=17, color='black'))\n",
    "fn_figure.update_yaxes(showline=True, linewidth=1, linecolor='black', mirror=True,ticks='outside',tickfont=dict( size=17, color='black'))\n",
    "fn_figure.update_layout(\n",
    "    title=f\"Clarify and DeepLinc Average Precision over different FN edge rates\",\n",
    "    xaxis_title=\"False Negative Edge Rate\",\n",
    "    yaxis_title=\"Average Precision\",\n",
    "    legend_title=\"Model\",\n",
    "    width=800,\n",
    "    height=600,\n",
    "    boxmode='group',\n",
    "    plot_bgcolor='white'\n",
    ")\n",
    "fn_figure.show()"
   ]
  },
  {
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   "source": []
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