5b-analysis-sbm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from graph_tool.all import *\n",
    "from collections import Counter,defaultdict\n",
    "from tqdm import tqdm\n",
    "import numpy as np\n",
    "\n",
    "# Import necessary libraries\n",
    "import pandas as pd\n",
    "from scipy.sparse import csr_matrix, coo_matrix\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import bz2\n",
    "import pickle\n",
    "\n",
    "# Ignore FutureWarning\n",
    "import warnings\n",
    "warnings.simplefilter('ignore')\n",
    "\n",
    "\n",
    "# ADAPTATION FROM https://github.com/martingerlach/hSBM_Topicmodel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import distinctipy\n",
    "# change the default options of visualization\n",
    "text_color = \"#404040\"\n",
    "\n",
    "custom_params = {\"axes.spines.right\": False, \"axes.spines.top\": False, \"axes.spines.left\": True, \"axes.spines.bottom\": True,\n",
    "                \"lines.linewidth\": 2, \"grid.color\": \"lightgray\", \"legend.frameon\": True,\n",
    "                 \"xtick.labelcolor\": text_color, \"ytick.labelcolor\": text_color, \"xtick.color\": text_color, \"ytick.color\": text_color,\"text.color\": text_color,\n",
    "                \"axes.labelcolor\": text_color, \"axes.titlecolor\":text_color,\"figure.dpi\": 200,\n",
    "                \"axes.titlelocation\":\"center\",\"xaxis.labellocation\":\"center\",\"yaxis.labellocation\":\"center\"}\n",
    "\n",
    "\n",
    "#palette = distinctipy.get_colors(21, pastel_factor=0.8)\n",
    "wong = ['#332288', '#117733','#44AA99', '#88CCEE','#DDCC77', '#CC6677','#AA4499','#882255'  ]\n",
    "\n",
    "palette = ['#264653', '#2a9d8f', '#f4a261','#e76f51']\n",
    "sns.set_theme(context='paper', style='white', palette=palette, font_scale=1.1, color_codes=True,\n",
    "rc=custom_params)\n",
    "titles = ['U NP\\nS NP',  'U NP\\nS  P', 'U  P\\nS NP','U  P\\nS  P']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def edgelist(mat):\n",
    "\n",
    "    # Assuming 'sparse_matrix' is your sparse matrix\n",
    "    dense_matrix = mat.toarray()\n",
    "\n",
    "    # Get the indices of the non-zero elements\n",
    "    rows, cols = np.nonzero(dense_matrix)\n",
    "\n",
    "    # Create a DataFrame from these indices\n",
    "    df = pd.DataFrame({\n",
    "        'user_id': [f'user_{row}' for row in rows], \n",
    "        'story_id': [f'story_{col}' for col in cols]\n",
    "    })\n",
    "\n",
    "    # If you want to include the weights (values of the non-zero elements)\n",
    "    df['vote'] = dense_matrix[rows, cols]\n",
    "\n",
    "\n",
    "    # Concatenate 'story_id' and 'username_vote' into a single series\n",
    "    all_ids = pd.concat([df['story_id'], df['user_id']])\n",
    "    # Apply LabelEncoder to the concatenated series\n",
    "    encoder = LabelEncoder().fit(all_ids)\n",
    "\n",
    "    # Transform 'story_id' and 'username_vote' using the fitted encoder\n",
    "    df['story_index'] = encoder.transform(df['story_id'])\n",
    "    df['user_index'] = encoder.transform(df['user_id'])\n",
    "\n",
    "\n",
    "    edge_list = list(df[['story_index','user_index', 'vote']].itertuples(index=False, name=None))\n",
    "    return edge_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def graph(edge_list, n_users, labels):\n",
    "    g = Graph(directed=False)\n",
    "    g.add_edge_list(edge_list,  eprops=[('weight', 'int')])\n",
    "    is_bip, part = is_bipartite(g, partition=True)\n",
    "    if is_bip == False:\n",
    "        print('Not bipartite!')\n",
    "    signs = [0 if w <0 else 1 for w in g.ep.weight.a]\n",
    "    size_a = len([v for v in g.vertices() if part[v] == 0])\n",
    "    size_b = len([v for v in g.vertices() if part[v] == 1])\n",
    "    users_real = min(range(len([size_a, size_b])), key=lambda index: abs([size_a, size_b][index] - n_users))\n",
    "    if users_real == 0:\n",
    "        kinds = [part[v] for v in g.vertices()]\n",
    "        x = 0\n",
    "        labels_ =[]\n",
    "        for v in range(g.num_vertices()):\n",
    "            if part[v] == 0:\n",
    "                labels_.append(labels[x])\n",
    "                x+=1\n",
    "            else:\n",
    "                labels_.append(999)\n",
    "    else:    \n",
    "        kinds = [part[v]+1  if part[v] == 0 else 0 for v in g.vertices()]\n",
    "        x = 0\n",
    "        labels_ =[]\n",
    "        for v in range(g.num_vertices()):\n",
    "            if part[v] == 1:\n",
    "                labels_.append(labels[x])\n",
    "                x+=1\n",
    "            else:\n",
    "                labels_.append(999)\n",
    "\n",
    " \n",
    "    #docs - 0, words - 1\n",
    "    #docs are users, words are stories\n",
    "    \n",
    "\n",
    "    color = ['red' if s == 0 else 'blue' for s in signs]\n",
    "    g.vp['kind'] = g.new_vertex_property(\"int\", vals=kinds)\n",
    "    g.ep['sign'] = g.new_edge_property(\"int\", vals=signs)\n",
    "    g.ep['color'] = g.new_edge_property(\"string\", vals=color)\n",
    "    g.vp['label'] = g.new_vertex_property(\"int\", vals=labels_)\n",
    "    clabel = g.vp['kind']\n",
    "\n",
    "\n",
    "    state_args_ = {'clabel': clabel, 'pclabel': clabel}\n",
    "\n",
    "    return g, state_args_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def membership(g, state, bipartite=True):\n",
    "    levels = state.get_levels()\n",
    "    df_ = pd.DataFrame(columns=[f'level_{level}' for level in range(len(levels))])\n",
    "    for i, level in enumerate(levels):\n",
    "        for v in g.vertices():\n",
    "            if bipartite ==True:\n",
    "                #only look at the users\n",
    "                if g.vp.kind[v] == 0:\n",
    "                    membership = level.get_blocks()[v]\n",
    "                    df_.loc[g.vertex_index[v], f'level_{i}'] = membership\n",
    "            else:\n",
    "                membership = level.get_blocks()[v]\n",
    "                df_.loc[g.vertex_index[v], f'level_{i}'] = membership\n",
    "\n",
    "    return df_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "scenarios = ['u_np_s_np',  'u_np_s_p', 'u_p_s_np','u_p_s_p']\n",
    "\n",
    "def load_matrices(path, scenarios=scenarios):\n",
    "    return [pickle.load(open(f'{path}{scenario}.pkl', 'rb')) for scenario in scenarios]\n",
    "\n",
    "def load_data(path, scenarios=scenarios):\n",
    "    path_adj = path+'/adj/'\n",
    "    path_labels = path+'/label/'\n",
    "    file_names = [f'{scenario}.pkl' for scenario in scenarios]\n",
    "    \n",
    "    mats = [pickle.load(open(path_adj + f, 'rb')) for f in file_names]\n",
    "    labels = [pickle.load(open(path_labels + f, 'rb')) for f in file_names]\n",
    "    return mats, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics.cluster import rand_score\n",
    "\n",
    "def rand_score_df(mats, labels, scenarios, membership_dict):\n",
    "    # Create a dictionary to store the Rand scores\n",
    "    rand_scores_dict = {}\n",
    "\n",
    "    for i, mat in enumerate(mats):\n",
    "        membership_df = membership_dict[scenarios[i]]\n",
    "\n",
    "        # Compute Rand score with labels\n",
    "        for level in range(len(state.levels)):\n",
    "            RI = rand_score(labels[i], membership_df[f'level_{level}'])\n",
    "\n",
    "            # Store the Rand score in the dictionary\n",
    "            rand_scores_dict.setdefault(scenarios[i], {})[f'level_{level}'] = RI\n",
    "\n",
    "    # Convert the dictionary to a DataFrame\n",
    "    df = pd.DataFrame(rand_scores_dict).T\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "mats = load_matrices('./data/meneame/synth/uniform_new/bipartite/', scenarios=scenarios)\n",
    "adj, labels = load_data('./data/meneame/synth/uniform_new/')\n",
    "uniform_scenarios = [f'uniform/{scenario}' for scenario in scenarios]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bipartite network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mat shape (1000, 800)\n",
      "mat shape (1000, 800)\n",
      "mat shape (1000, 800)\n",
      "mat shape (1000, 800)\n"
     ]
    }
   ],
   "source": [
    "states = {}\n",
    "gs = {}\n",
    "new_labels = []\n",
    "\n",
    "for i, mat in enumerate(mats):\n",
    "    edge_list = edgelist(mat)\n",
    "    print(f'mat shape {mat.shape}')\n",
    "    g, state_args = graph(edge_list, n_users=mat.shape[0], labels=labels[i])\n",
    "    g, state_args = graph(edge_list, n_users=mat.shape[0], labels=labels[i])\n",
    "    new_labels.append([x for x in list(g.vp.label) if x != 999])\n",
    "\n",
    "    state = minimize_nested_blockmodel_dl(g, state_args=dict(recs=[g.ep.sign], rec_types=[\"discrete-binomial\"],\n",
    "                                                                deg_corr=True, **state_args), multilevel_mcmc_args=dict(verbose=False))\n",
    "    states[scenarios[i]] = state\n",
    "    gs[scenarios[i]] = g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "membership_dict = {}\n",
    "for i,mat in enumerate(mats):\n",
    "    g = gs[scenarios[i]]\n",
    "    state = states[scenarios[i]]\n",
    "    \n",
    "    membership_df = membership(g, state)\n",
    "    membership_df.to_csv(f'./results/meneame/synth/uniform_new/bipartite/{scenarios[i]}_membership.csv', index=False)\n",
    "    membership_dict[scenarios[i]] = membership_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>level_0</th>\n",
       "      <th>level_1</th>\n",
       "      <th>level_2</th>\n",
       "      <th>level_3</th>\n",
       "      <th>level_4</th>\n",
       "      <th>level_5</th>\n",
       "      <th>level_6</th>\n",
       "      <th>level_7</th>\n",
       "      <th>level_8</th>\n",
       "      <th>level_9</th>\n",
       "      <th>level_10</th>\n",
       "      <th>level_11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>u_np_s_np</th>\n",
       "      <td>0.026176</td>\n",
       "      <td>0.044677</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",
       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>u_np_s_p</th>\n",
       "      <td>0.045776</td>\n",
       "      <td>0.070957</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",
       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>u_p_s_np</th>\n",
       "      <td>0.514256</td>\n",
       "      <td>0.500172</td>\n",
       "      <td>0.499518</td>\n",
       "      <td>0.499518</td>\n",
       "      <td>0.499518</td>\n",
       "      <td>0.499518</td>\n",
       "      <td>0.499518</td>\n",
       "      <td>0.499518</td>\n",
       "      <td>0.499518</td>\n",
       "      <td>0.499518</td>\n",
       "      <td>0.499518</td>\n",
       "      <td>0.499518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>u_p_s_p</th>\n",
       "      <td>0.513088</td>\n",
       "      <td>0.499363</td>\n",
       "      <td>0.500582</td>\n",
       "      <td>0.500582</td>\n",
       "      <td>0.500582</td>\n",
       "      <td>0.500582</td>\n",
       "      <td>0.500582</td>\n",
       "      <td>0.500582</td>\n",
       "      <td>0.500582</td>\n",
       "      <td>0.500582</td>\n",
       "      <td>0.500582</td>\n",
       "      <td>0.500582</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            level_0   level_1   level_2   level_3   level_4   level_5  \\\n",
       "u_np_s_np  0.026176  0.044677  1.000000  1.000000  1.000000  1.000000   \n",
       "u_np_s_p   0.045776  0.070957  1.000000  1.000000  1.000000  1.000000   \n",
       "u_p_s_np   0.514256  0.500172  0.499518  0.499518  0.499518  0.499518   \n",
       "u_p_s_p    0.513088  0.499363  0.500582  0.500582  0.500582  0.500582   \n",
       "\n",
       "            level_6   level_7   level_8   level_9  level_10  level_11  \n",
       "u_np_s_np  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  \n",
       "u_np_s_p   1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  \n",
       "u_p_s_np   0.499518  0.499518  0.499518  0.499518  0.499518  0.499518  \n",
       "u_p_s_p    0.500582  0.500582  0.500582  0.500582  0.500582  0.500582  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bip=rand_score_df(mats, new_labels, scenarios, membership_dict=membership_dict)\n",
    "df_bip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Unipartite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.signet import *\n",
    "import os\n",
    "def sbm_uni(mat, degree_corrected=False):\n",
    "\n",
    "    \"\"\"Apply Stochastic Block Model community detection algorithm.\"\"\"\n",
    "\n",
    "    graph_build = SignedNetwork()\n",
    "    g = graph_build.graph_construction(repre=mat, repre_type='adj', is_directed=check_if_directed(mat))\n",
    "    state = minimize_nested_blockmodel_dl(g, state_args=dict(recs=[g.ep.sign],\n",
    "                                                        rec_types=[\"discrete-binomial\"],\n",
    "                                                        deg_corr=degree_corrected))\n",
    "    return g,state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "warning: no node IDs assigned, default indexes are used\n",
      "    level_0 level_1 level_2 level_3 level_4 level_5 level_6 level_7 level_8  \\\n",
      "0       989       0       1       0       0       0       0       0       0   \n",
      "1       989       0       0       0       0       0       0       0       0   \n",
      "2       989       0       0       0       0       0       0       0       0   \n",
      "3       989       0       0       0       0       0       0       0       0   \n",
      "4       989       0       0       0       0       0       0       0       0   \n",
      "..      ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
      "995     989       0       0       0       0       0       0       0       0   \n",
      "996     989       0       0       0       0       0       0       0       0   \n",
      "997     989       8       0       0       0       0       0       0       0   \n",
      "998     989       8       0       0       0       0       0       0       0   \n",
      "999     654       0       0       0       0       0       0       0       0   \n",
      "\n",
      "    level_9 level_10  \n",
      "0         0        0  \n",
      "1         0        0  \n",
      "2         0        0  \n",
      "3         0        0  \n",
      "4         0        0  \n",
      "..      ...      ...  \n",
      "995       0        0  \n",
      "996       0        0  \n",
      "997       0        0  \n",
      "998       0        0  \n",
      "999       0        0  \n",
      "\n",
      "[1000 rows x 11 columns]\n",
      "warning: no node IDs assigned, default indexes are used\n",
      "    level_0 level_1 level_2 level_3 level_4 level_5 level_6 level_7 level_8  \\\n",
      "0       229       7       0       0       0       0       0       0       0   \n",
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      "4       277       2       0       0       0       0       0       0       0   \n",
      "..      ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
      "995      28       7       0       0       0       0       0       0       0   \n",
      "996     229       6       0       0       0       0       0       0       0   \n",
      "997      28       2       0       0       0       0       0       0       0   \n",
      "998     465       7       0       0       0       0       0       0       0   \n",
      "999     733       2       0       0       0       0       0       0       0   \n",
      "\n",
      "    level_9 level_10  \n",
      "0         0        0  \n",
      "1         0        0  \n",
      "2         0        0  \n",
      "3         0        0  \n",
      "4         0        0  \n",
      "..      ...      ...  \n",
      "995       0        0  \n",
      "996       0        0  \n",
      "997       0        0  \n",
      "998       0        0  \n",
      "999       0        0  \n",
      "\n",
      "[1000 rows x 11 columns]\n",
      "warning: no node IDs assigned, default indexes are used\n",
      "    level_0 level_1 level_2 level_3 level_4 level_5 level_6 level_7 level_8  \\\n",
      "0       327       1       6       6       0       0       0       0       0   \n",
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      "4        60      30       4       6       0       0       0       0       0   \n",
      "..      ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
      "995     858       2       0       0       0       0       0       0       0   \n",
      "996     420       5       0       0       0       0       0       0       0   \n",
      "997     839       4       0       0       0       0       0       0       0   \n",
      "998     420      30       0       0       0       0       0       0       0   \n",
      "999     874       2       0       0       0       0       0       0       0   \n",
      "\n",
      "    level_9 level_10  \n",
      "0         0        0  \n",
      "1         0        0  \n",
      "2         0        0  \n",
      "3         0        0  \n",
      "4         0        0  \n",
      "..      ...      ...  \n",
      "995       0        0  \n",
      "996       0        0  \n",
      "997       0        0  \n",
      "998       0        0  \n",
      "999       0        0  \n",
      "\n",
      "[1000 rows x 11 columns]\n",
      "warning: no node IDs assigned, default indexes are used\n",
      "    level_0 level_1 level_2 level_3 level_4 level_5 level_6 level_7 level_8  \\\n",
      "0       472      12       1       0       0       0       0       0       0   \n",
      "1       472       0       2       0       0       0       0       0       0   \n",
      "2        82      19       2       0       0       0       0       0       0   \n",
      "3       127      19       2       0       0       0       0       0       0   \n",
      "4       367      19       1       0       0       0       0       0       0   \n",
      "..      ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
      "995     662      19       0       0       0       0       0       0       0   \n",
      "996     639      19       0       0       0       0       0       0       0   \n",
      "997     655      19       0       0       0       0       0       0       0   \n",
      "998     639      22       0       0       0       0       0       0       0   \n",
      "999     662      12       0       0       0       0       0       0       0   \n",
      "\n",
      "    level_9 level_10  \n",
      "0         0        0  \n",
      "1         0        0  \n",
      "2         0        0  \n",
      "3         0        0  \n",
      "4         0        0  \n",
      "..      ...      ...  \n",
      "995       0        0  \n",
      "996       0        0  \n",
      "997       0        0  \n",
      "998       0        0  \n",
      "999       0        0  \n",
      "\n",
      "[1000 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "membership_dict_uni = {}\n",
    "for i, A in enumerate(adj):\n",
    "    g,state = sbm_uni(A, degree_corrected=True)\n",
    "    membership_df = membership(g, state, bipartite=False)\n",
    "    membership_df.to_csv(f'./results/meneame/synth/uniform_new/unipartite/{scenarios[i]}_membership.csv', index=False)\n",
    "    membership_dict_uni[scenarios[i]] = membership_df\n",
    "    print(membership_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "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>level_0</th>\n",
       "      <th>level_1</th>\n",
       "      <th>level_2</th>\n",
       "      <th>level_3</th>\n",
       "      <th>level_4</th>\n",
       "      <th>level_5</th>\n",
       "      <th>level_6</th>\n",
       "      <th>level_7</th>\n",
       "      <th>level_8</th>\n",
       "      <th>level_9</th>\n",
       "      <th>level_10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>u_np_s_np</th>\n",
       "      <td>0.624757</td>\n",
       "      <td>0.644757</td>\n",
       "      <td>0.998000</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",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>u_np_s_p</th>\n",
       "      <td>0.103035</td>\n",
       "      <td>0.184034</td>\n",
       "      <td>0.990040</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",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>u_p_s_np</th>\n",
       "      <td>0.552729</td>\n",
       "      <td>0.500895</td>\n",
       "      <td>0.500797</td>\n",
       "      <td>0.499868</td>\n",
       "      <td>0.499518</td>\n",
       "      <td>0.499518</td>\n",
       "      <td>0.499518</td>\n",
       "      <td>0.499518</td>\n",
       "      <td>0.499518</td>\n",
       "      <td>0.499518</td>\n",
       "      <td>0.499518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>u_p_s_p</th>\n",
       "      <td>0.552126</td>\n",
       "      <td>0.499145</td>\n",
       "      <td>0.504859</td>\n",
       "      <td>0.501301</td>\n",
       "      <td>0.501301</td>\n",
       "      <td>0.501301</td>\n",
       "      <td>0.501301</td>\n",
       "      <td>0.501301</td>\n",
       "      <td>0.501301</td>\n",
       "      <td>0.501301</td>\n",
       "      <td>0.501301</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            level_0   level_1   level_2   level_3   level_4   level_5  \\\n",
       "u_np_s_np  0.624757  0.644757  0.998000  1.000000  1.000000  1.000000   \n",
       "u_np_s_p   0.103035  0.184034  0.990040  1.000000  1.000000  1.000000   \n",
       "u_p_s_np   0.552729  0.500895  0.500797  0.499868  0.499518  0.499518   \n",
       "u_p_s_p    0.552126  0.499145  0.504859  0.501301  0.501301  0.501301   \n",
       "\n",
       "            level_6   level_7   level_8   level_9  level_10  \n",
       "u_np_s_np  1.000000  1.000000  1.000000  1.000000  1.000000  \n",
       "u_np_s_p   1.000000  1.000000  1.000000  1.000000  1.000000  \n",
       "u_p_s_np   0.499518  0.499518  0.499518  0.499518  0.499518  \n",
       "u_p_s_p    0.501301  0.501301  0.501301  0.501301  0.501301  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_uni=rand_score_df(adj, labels, scenarios, membership_dict=membership_dict_uni)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "dropbox_folder ='/Users/Cande007/Dropbox/Apps/Overleaf/2023_community_detection_on_signed_networks/'\n",
    "text_size = 30\n",
    "\n",
    "def plot_df(ax,df, titles=titles, text_size=text_size, leg=False):\n",
    "    df.plot(kind='line',ax=ax,marker='s', markersize=3, linestyle='-', linewidth=10, alpha =0.7, legend=False)\n",
    "    ax.set_xlabel('Levels', fontsize=text_size)\n",
    "    ax.set_ylabel('Rand Index', fontsize=text_size)\n",
    "    #plt.legend(loc='best')\n",
    "    ax.tick_params(axis='both', labelsize=text_size)\n",
    "    # set x-ticks text to only show the integer part\n",
    "    ax.set_xticks(range(len(df.index)), range(len(df.index)))\n",
    "    if leg:\n",
    "        patchList = [mpatches.Patch(color=palette[key], label=title) for key, title in enumerate(titles)]\n",
    "        plt.legend(handles=patchList, bbox_to_anchor=(1, 1), fontsize = text_size/1.5)\n",
    "\n",
    "from src.commdet import create_directory  \n",
    "def save_plot(path, filename, dropbox_folder=dropbox_folder):\n",
    "    create_directory(path)\n",
    "    create_directory(path.replace('./', f'{dropbox_folder}'))\n",
    "    plt.savefig(path+filename)\n",
    "    plt.savefig(path.replace('./', f'{dropbox_folder}')+filename)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 3000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1,ax2) = plt.subplots(1, 2, figsize = (15,5),dpi =200, sharey=True, constrained_layout=True)\n",
    "\n",
    "# Define panel letters\n",
    "panel_labels = ['(A)', '(B)']\n",
    "\n",
    "# Add panel letters to subplots\n",
    "for i, ax in enumerate([ax1,ax2]):\n",
    "    ax.text(0, 1.2, panel_labels[i], transform=ax.transAxes, fontsize=text_size, fontweight='bold', va='top', ha='right')\n",
    "\n",
    "fig.suptitle('SBM', fontsize=text_size)\n",
    "\n",
    "ax1.set_title('Bipartite', fontsize=text_size)\n",
    "plot_df(ax1,df_bip.T)\n",
    "\n",
    "ax2.set_title('Unipartite', fontsize=text_size)\n",
    "plot_df(ax2,df_uni.T, leg = True)\n",
    "\n",
    "\n",
    "save_plot(path = './plots/uniform/', filename = 'sbm.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
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