IIabm/imputation_robustness_check.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Missing-data Imputation Robustness Check\n",
        "\n",
        "This notebook audits where regression-based imputation is applied and evaluates robustness from two angles:\n",
        "\n",
        "1. Firm size distribution: all firms (with imputed values) vs firms with original observed revenue only.\n",
        "2. Network structure robustness: simulated firm network from all firms vs from observed-only firms.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "f139debd",
      "metadata": {},
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import networkx as nx\n",
        "from scipy import stats\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "sns.set_theme(style='whitegrid')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "f7109525",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "All firms (with imputation): 171\n",
            "Observed-only firms: 111\n",
            "Imputed-count firms: 60\n"
          ]
        }
      ],
      "source": [
        "# Data loading\n",
        "firm_amended = pd.read_csv('Firm_amended.csv')\n",
        "firm_original = pd.read_csv('Firm.csv')\n",
        "\n",
        "# The notebook `AmendFirm_20230216.ipynb` uses OLS:\n",
        "# Revenue_Log ~ Num_Employ_Log\n",
        "# and fills missing Revenue_Log by predicted values from Num_Employ_Log.\n",
        "\n",
        "observed_mask = firm_original['Revenue'].notna()\n",
        "firm_observed_only = firm_amended.loc[observed_mask].copy()\n",
        "\n",
        "print(f'All firms (with imputation): {len(firm_amended)}')\n",
        "print(f'Observed-only firms: {len(firm_observed_only)}')\n",
        "print(f'Imputed-count firms: {len(firm_amended) - len(firm_observed_only)}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "88e2fd2d",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     all_firms  observed_only  delta_all_minus_observed\n",
            "Q10  18.266132      19.364587                 -1.098455\n",
            "Q25  19.126847      20.995426                 -1.868579\n",
            "Q50  21.273558      22.654014                 -1.380456\n",
            "Q75  24.023677      25.818061                 -1.794384\n",
            "Q90  26.562162      26.958298                 -0.396136\n",
            "\n",
            "Mann-Whitney U: MannwhitneyuResult(statistic=6881.5, pvalue=9.665367898714309e-05)\n",
            "Kolmogorov-Smirnov: KstestResult(statistic=0.23502449818239293, pvalue=0.0009254559883309539, statistic_location=20.22851328801836, statistic_sign=1)\n"
          ]
        }
      ],
      "source": [
        "# Size distribution comparison: Revenue_Log\n",
        "size_all = firm_amended['Revenue_Log'].dropna()\n",
        "size_obs = firm_observed_only['Revenue_Log'].dropna()\n",
        "\n",
        "quantiles = [0.1, 0.25, 0.5, 0.75, 0.9]\n",
        "summary = pd.DataFrame({\n",
        "    'all_firms': size_all.quantile(quantiles),\n",
        "    'observed_only': size_obs.quantile(quantiles)\n",
        "})\n",
        "summary.index = [f'Q{int(q*100)}' for q in quantiles]\n",
        "summary['delta_all_minus_observed'] = summary['all_firms'] - summary['observed_only']\n",
        "print(summary)\n",
        "\n",
        "mw = stats.mannwhitneyu(size_all, size_obs, alternative='two-sided')\n",
        "ks = stats.ks_2samp(size_all, size_obs)\n",
        "print('\\nMann-Whitney U:', mw)\n",
        "print('Kolmogorov-Smirnov:', ks)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "471274ce",
      "metadata": {},
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# ECDF plot for distribution-shape comparison\n",
        "plt.figure(figsize=(8, 5))\n",
        "sns.ecdfplot(size_all, label='All firms (with imputation)')\n",
        "sns.ecdfplot(size_obs, label='Observed-only firms')\n",
        "plt.xlabel('Revenue_Log')\n",
        "plt.ylabel('ECDF')\n",
        "plt.title('Revenue_Log Distribution Comparison')\n",
        "plt.legend()\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5cf2b910",
      "metadata": {},
      "source": [
        "## Direct Comparison Using `count_with_gfirm.csv`\n",
        "\n",
        "This section extracts full experimental firm networks from `g_firm` in the large `count_with_gfirm.csv` file and compares them against non-imputed networks generated with the same logic in `model.py`.\n",
        "\n",
        "To control runtime and memory, it uses the first `N` unique `s_id` networks."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "0124641f",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "count_with_gfirm.csv exists; probe rows = 1\n",
            "One experimental graph loaded: nodes = 171 edges = 818\n"
          ]
        }
      ],
      "source": [
        "# Prepare network-comparison dependencies\n",
        "# 1) lightweight check for count_with_gfirm.csv\n",
        "# 2) build_firm_graph() matching model.py logic for observed-only simulation\n",
        "\n",
        "import os\n",
        "import json\n",
        "\n",
        "if os.path.exists('count_with_gfirm.csv'):\n",
        "    probe = pd.read_csv('count_with_gfirm.csv', usecols=['s_id', 'g_firm'], nrows=1)\n",
        "    print('count_with_gfirm.csv exists; probe rows =', len(probe))\n",
        "    if len(probe) > 0 and pd.notna(probe.loc[0, 'g_firm']):\n",
        "        G_probe = nx.adjacency_graph(json.loads(probe.loc[0, 'g_firm']))\n",
        "        print('One experimental graph loaded: nodes =', G_probe.number_of_nodes(), 'edges =', G_probe.number_of_edges())\n",
        "    else:\n",
        "        print('g_firm is empty in probe row; verify extraction in analysis_firm_risk_component.ipynb.')\n",
        "else:\n",
        "    print('count_with_gfirm.csv not found. Please extract Sample.g_firm first.')\n",
        "\n",
        "# Build BOM graph once (for simulated network generation)\n",
        "bom = pd.read_csv('BomCateNet.csv', index_col=0).fillna(0)\n",
        "G_bom = nx.from_pandas_adjacency(bom.T, create_using=nx.MultiDiGraph())\n",
        "\n",
        "def build_firm_graph(firm_df, seed=0, netw_prf_n=2, prf_size=True):\n",
        "    rng = np.random.default_rng(seed)\n",
        "    F = firm_df.copy().fillna(0)\n",
        "    F['Code'] = F['Code'].astype(str)\n",
        "\n",
        "    attrs = F[['Code', 'Name', 'Type_Region', 'Revenue_Log']].copy()\n",
        "    products = []\n",
        "    for _, row in F.loc[:, '1':].iterrows():\n",
        "        products.append(row[row == 1].index.to_list())\n",
        "    attrs['Product_Code'] = products\n",
        "    attrs.set_index('Code', inplace=True)\n",
        "\n",
        "    G = nx.MultiDiGraph()\n",
        "    G.add_nodes_from(F['Code'].tolist())\n",
        "    nx.set_node_attributes(G, {code: attrs.loc[code].to_dict() for code in G.nodes()})\n",
        "\n",
        "    for node in list(G.nodes()):\n",
        "        pred_products = []\n",
        "        for p in G.nodes[node].get('Product_Code', []):\n",
        "            if p in G_bom:\n",
        "                pred_products += list(G_bom.predecessors(p))\n",
        "        pred_products = sorted(set(pred_products))\n",
        "\n",
        "        for pred_p in pred_products:\n",
        "            pred_firms = F['Code'][F[pred_p] == 1].to_list() if pred_p in F.columns else []\n",
        "            n = min(netw_prf_n, len(pred_firms))\n",
        "            if n <= 0:\n",
        "                continue\n",
        "\n",
        "            if prf_size:\n",
        "                sizes = np.array([max(1e-9, float(G.nodes[f].get('Revenue_Log', 0))) for f in pred_firms], dtype=float)\n",
        "                probs = sizes / sizes.sum() if sizes.sum() > 0 else None\n",
        "                chosen = rng.choice(pred_firms, size=n, replace=False, p=probs)\n",
        "            else:\n",
        "                chosen = rng.choice(pred_firms, size=n, replace=False)\n",
        "\n",
        "            G.add_edges_from([(str(f), node, {'Product': pred_p}) for f in chosen])\n",
        "\n",
        "    for node in list(G.nodes()):\n",
        "        if G.degree(node) == 0:\n",
        "            for p in G.nodes[node].get('Product_Code', []):\n",
        "                if p not in G_bom:\n",
        "                    continue\n",
        "                for succ_p in G_bom.successors(p):\n",
        "                    succ_firms = F['Code'][F[succ_p] == 1].to_list() if succ_p in F.columns else []\n",
        "                    n = min(netw_prf_n, len(succ_firms))\n",
        "                    if n <= 0:\n",
        "                        continue\n",
        "                    if prf_size:\n",
        "                        sizes = np.array([max(1e-9, float(G.nodes[f].get('Revenue_Log', 0))) for f in succ_firms], dtype=float)\n",
        "                        probs = sizes / sizes.sum() if sizes.sum() > 0 else None\n",
        "                        chosen = rng.choice(succ_firms, size=n, replace=False, p=probs)\n",
        "                    else:\n",
        "                        chosen = rng.choice(succ_firms, size=n, replace=False)\n",
        "                    G.add_edges_from([(node, str(f), {'Product': p}) for f in chosen])\n",
        "\n",
        "    return G"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "acb844b1",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "c:\\Users\\ASUS\\Documents\\Project\\IIabm\\venv\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
            "  from .autonotebook import tqdm as notebook_tqdm\n",
            "Reading chunks from count_with_gfirm.csv: 1chunk [00:07,  7.41s/chunk]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Extracted experimental networks: 12881\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Building metrics for experimental networks: 100%|██████████| 12881/12881 [03:17<00:00, 65.11network/s]\n",
            "Building metrics for observed-only simulated networks: 100%|██████████| 12881/12881 [12:03<00:00, 17.81network/s]\n"
          ]
        },
        {
          "data": {
            "text/html": [
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>metric</th>\n",
              "      <th>exp_q10</th>\n",
              "      <th>exp_q25</th>\n",
              "      <th>exp_q50</th>\n",
              "      <th>exp_q75</th>\n",
              "      <th>exp_q90</th>\n",
              "      <th>sim_q10</th>\n",
              "      <th>sim_q25</th>\n",
              "      <th>sim_q50</th>\n",
              "      <th>sim_q75</th>\n",
              "      <th>sim_q90</th>\n",
              "      <th>mw_u</th>\n",
              "      <th>mw_pvalue</th>\n",
              "      <th>ks_statistic</th>\n",
              "      <th>ks_pvalue</th>\n",
              "      <th>cliffs_delta</th>\n",
              "      <th>wasserstein_distance</th>\n",
              "      <th>cohen_d</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>firm_num_supplier_firms</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>1.018947e+12</td>\n",
              "      <td>4.438885e-08</td>\n",
              "      <td>0.030905</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.003134</td>\n",
              "      <td>0.580167</td>\n",
              "      <td>0.011394</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>firm_num_customer_firms</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>8.561743e+11</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.140991</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.162380</td>\n",
              "      <td>1.595933</td>\n",
              "      <td>-0.226000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>firm_weighted_betweenness</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.001357</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000128</td>\n",
              "      <td>0.002363</td>\n",
              "      <td>9.882519e+11</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.063961</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.033165</td>\n",
              "      <td>0.000443</td>\n",
              "      <td>-0.145224</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>firm_weighted_closeness</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.029412</td>\n",
              "      <td>0.079619</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.024242</td>\n",
              "      <td>0.111364</td>\n",
              "      <td>1.024913e+12</td>\n",
              "      <td>2.397071e-06</td>\n",
              "      <td>0.034881</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.002702</td>\n",
              "      <td>0.005074</td>\n",
              "      <td>-0.045781</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>firm_pagerank</td>\n",
              "      <td>0.001826</td>\n",
              "      <td>0.001927</td>\n",
              "      <td>0.002224</td>\n",
              "      <td>0.005013</td>\n",
              "      <td>0.014727</td>\n",
              "      <td>0.002991</td>\n",
              "      <td>0.003069</td>\n",
              "      <td>0.003179</td>\n",
              "      <td>0.005520</td>\n",
              "      <td>0.016487</td>\n",
              "      <td>5.323516e+11</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.670247</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.479185</td>\n",
              "      <td>0.001240</td>\n",
              "      <td>-0.064583</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      metric   exp_q10   exp_q25   exp_q50   exp_q75  \\\n",
              "0    firm_num_supplier_firms  0.000000  0.000000  0.000000  6.000000   \n",
              "1    firm_num_customer_firms  1.000000  1.000000  2.000000  3.000000   \n",
              "2  firm_weighted_betweenness  0.000000  0.000000  0.000000  0.000000   \n",
              "3    firm_weighted_closeness  0.000000  0.000000  0.000000  0.029412   \n",
              "4              firm_pagerank  0.001826  0.001927  0.002224  0.005013   \n",
              "\n",
              "     exp_q90   sim_q10   sim_q25   sim_q50   sim_q75    sim_q90          mw_u  \\\n",
              "0  16.000000  0.000000  0.000000  0.000000  6.000000  14.000000  1.018947e+12   \n",
              "1   7.000000  1.000000  1.000000  2.000000  4.000000  11.000000  8.561743e+11   \n",
              "2   0.001357  0.000000  0.000000  0.000000  0.000128   0.002363  9.882519e+11   \n",
              "3   0.079619  0.000000  0.000000  0.000000  0.024242   0.111364  1.024913e+12   \n",
              "4   0.014727  0.002991  0.003069  0.003179  0.005520   0.016487  5.323516e+11   \n",
              "\n",
              "      mw_pvalue  ks_statistic  ks_pvalue  cliffs_delta  wasserstein_distance  \\\n",
              "0  4.438885e-08      0.030905        0.0     -0.003134              0.580167   \n",
              "1  0.000000e+00      0.140991        0.0     -0.162380              1.595933   \n",
              "2  0.000000e+00      0.063961        0.0     -0.033165              0.000443   \n",
              "3  2.397071e-06      0.034881        0.0      0.002702              0.005074   \n",
              "4  0.000000e+00      0.670247        0.0     -0.479185              0.001240   \n",
              "\n",
              "    cohen_d  \n",
              "0  0.011394  \n",
              "1 -0.226000  \n",
              "2 -0.145224  \n",
              "3 -0.045781  \n",
              "4 -0.064583  "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
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              "        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>metric</th>\n",
              "      <th>pearson_corr</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>firm_num_supplier_firms</td>\n",
              "      <td>0.995273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>firm_num_customer_firms</td>\n",
              "      <td>0.972093</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>firm_weighted_betweenness</td>\n",
              "      <td>0.983254</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>firm_weighted_closeness</td>\n",
              "      <td>0.991394</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>firm_pagerank</td>\n",
              "      <td>0.994468</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      metric  pearson_corr\n",
              "0    firm_num_supplier_firms      0.995273\n",
              "1    firm_num_customer_firms      0.972093\n",
              "2  firm_weighted_betweenness      0.983254\n",
              "3    firm_weighted_closeness      0.991394\n",
              "4              firm_pagerank      0.994468"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Top10 Jaccard (firm_num_supplier_firms): 1.0000\n",
            "Top10 Jaccard (firm_num_customer_firms): 0.5385\n",
            "Top10 Jaccard (firm_weighted_betweenness): 0.6667\n",
            "Top10 Jaccard (firm_weighted_closeness): 1.0000\n",
            "Top10 Jaccard (firm_pagerank): 1.0000\n"
          ]
        }
      ],
      "source": [
        "import json\n",
        "from scipy import stats\n",
        "from tqdm.auto import tqdm\n",
        "\n",
        "N = None  # None means use all available s_id; set an integer to cap for faster testing\n",
        "\n",
        "# 1) Extract one g_firm per s_id from large file\n",
        "seen = set()\n",
        "exp_graph_pairs = []\n",
        "for chunk in tqdm(\n",
        "    pd.read_csv('count_with_gfirm.csv', usecols=['s_id', 'g_firm'], chunksize=200000),\n",
        "    desc='Reading chunks from count_with_gfirm.csv',\n",
        "    unit='chunk'\n",
        "):\n",
        "    chunk = chunk.dropna(subset=['s_id', 'g_firm'])\n",
        "    for sid, g in zip(chunk['s_id'].tolist(), chunk['g_firm'].tolist()):\n",
        "        sid = int(sid)\n",
        "        if sid in seen:\n",
        "            continue\n",
        "        seen.add(sid)\n",
        "        exp_graph_pairs.append((sid, g))\n",
        "        if N is not None and len(exp_graph_pairs) >= N:\n",
        "            break\n",
        "    if N is not None and len(exp_graph_pairs) >= N:\n",
        "        break\n",
        "\n",
        "print('Extracted experimental networks:', len(exp_graph_pairs))\n",
        "\n",
        "# 2) Build experimental metrics from full g_firm\n",
        "# (computed with graph_metrics_with_closeness below)\n",
        "def graph_metrics_with_closeness(G):\n",
        "    Gw = nx.DiGraph()\n",
        "    Gw.add_nodes_from(G.nodes())\n",
        "    for u, v in G.edges():\n",
        "        if Gw.has_edge(u, v):\n",
        "            Gw[u][v]['weight'] += 1\n",
        "        else:\n",
        "            Gw.add_edge(u, v, weight=1)\n",
        "\n",
        "    indeg = dict(G.in_degree())\n",
        "    outdeg = dict(G.out_degree())\n",
        "    pr = nx.pagerank(Gw, weight='weight') if Gw.number_of_edges() > 0 else {n: 0 for n in Gw.nodes()}\n",
        "    bt = nx.betweenness_centrality(Gw, weight='weight') if Gw.number_of_edges() > 0 else {n: 0 for n in Gw.nodes()}\n",
        "    cl = nx.closeness_centrality(Gw, distance='weight') if Gw.number_of_edges() > 0 else {n: 0 for n in Gw.nodes()}\n",
        "\n",
        "    return pd.DataFrame({\n",
        "        'id_firm': [int(str(n)) for n in G.nodes()],\n",
        "        'firm_num_supplier_firms': [indeg[n] for n in G.nodes()],\n",
        "        'firm_num_customer_firms': [outdeg[n] for n in G.nodes()],\n",
        "        'firm_weighted_betweenness': [bt.get(n, 0) for n in G.nodes()],\n",
        "        'firm_weighted_closeness': [cl.get(n, 0) for n in G.nodes()],\n",
        "        'firm_pagerank': [pr.get(n, 0) for n in G.nodes()]\n",
        "    })\n",
        "\n",
        "exp_full_parts = []\n",
        "for sid, g in tqdm(exp_graph_pairs, desc='Building metrics for experimental networks', unit='network'):\n",
        "    G_exp = nx.adjacency_graph(json.loads(g))\n",
        "    exp_full_parts.append(graph_metrics_with_closeness(G_exp).assign(s_id=sid))\n",
        "exp_full = pd.concat(exp_full_parts, ignore_index=True)\n",
        "\n",
        "# 3) Build non-imputed comparison set with same N\n",
        "sim_parts = []\n",
        "for seed in tqdm(range(len(exp_graph_pairs)), desc='Building metrics for observed-only simulated networks', unit='network'):\n",
        "    G_sim = build_firm_graph(firm_observed_only, seed=seed, netw_prf_n=2, prf_size=True)\n",
        "    sim_parts.append(graph_metrics_with_closeness(G_sim).assign(s_id=seed + 1))\n",
        "sim_full = pd.concat(sim_parts, ignore_index=True)\n",
        "\n",
        "# 4) Restrict both to observed-only firm ids\n",
        "obs_ids = set(firm_observed_only['Code'].astype(int).tolist())\n",
        "exp_full = exp_full[exp_full['id_firm'].isin(obs_ids)].copy()\n",
        "sim_full = sim_full[sim_full['id_firm'].isin(obs_ids)].copy()\n",
        "\n",
        "metrics_cmp = [\n",
        "    'firm_num_supplier_firms',\n",
        "    'firm_num_customer_firms',\n",
        "    'firm_weighted_betweenness',\n",
        "    'firm_weighted_closeness',\n",
        "    'firm_pagerank'\n",
        "]\n",
        "\n",
        "rows = []\n",
        "for m in metrics_cmp:\n",
        "    e = exp_full[m].to_numpy()\n",
        "    s = sim_full[m].to_numpy()\n",
        "\n",
        "    # Significance tests\n",
        "    mw_res = stats.mannwhitneyu(e, s, alternative='two-sided')\n",
        "    mw_u = float(mw_res.statistic)\n",
        "    mw_p = float(mw_res.pvalue)\n",
        "\n",
        "    ks_res = stats.ks_2samp(e, s)\n",
        "    ks_stat = float(ks_res.statistic)\n",
        "    ks_p = float(ks_res.pvalue)\n",
        "\n",
        "    # Effect sizes\n",
        "    n_e = len(e)\n",
        "    n_s = len(s)\n",
        "    cliffs_delta = (2.0 * mw_u) / (n_e * n_s) - 1.0\n",
        "    wasserstein = float(stats.wasserstein_distance(e, s))\n",
        "\n",
        "    # Cohen's d (pooled SD)\n",
        "    e_mean, s_mean = float(np.mean(e)), float(np.mean(s))\n",
        "    e_std, s_std = float(np.std(e, ddof=1)), float(np.std(s, ddof=1))\n",
        "    pooled_sd = np.sqrt(((n_e - 1) * e_std**2 + (n_s - 1) * s_std**2) / (n_e + n_s - 2))\n",
        "    cohen_d = (e_mean - s_mean) / pooled_sd if pooled_sd > 0 else np.nan\n",
        "\n",
        "    q = [0.1, 0.25, 0.5, 0.75, 0.9]\n",
        "    eq = np.quantile(e, q)\n",
        "    sq = np.quantile(s, q)\n",
        "    rows.append({\n",
        "        'metric': m,\n",
        "        'exp_q10': eq[0], 'exp_q25': eq[1], 'exp_q50': eq[2], 'exp_q75': eq[3], 'exp_q90': eq[4],\n",
        "        'sim_q10': sq[0], 'sim_q25': sq[1], 'sim_q50': sq[2], 'sim_q75': sq[3], 'sim_q90': sq[4],\n",
        "        'mw_u': mw_u,\n",
        "        'mw_pvalue': mw_p,\n",
        "        'ks_statistic': ks_stat,\n",
        "        'ks_pvalue': ks_p,\n",
        "        'cliffs_delta': cliffs_delta,\n",
        "        'wasserstein_distance': wasserstein,\n",
        "        'cohen_d': cohen_d\n",
        "    })\n",
        "\n",
        "dist_compare = pd.DataFrame(rows)\n",
        "display(dist_compare)\n",
        "\n",
        "# 5) Node-level mean consistency and top-node overlap\n",
        "exp_node_mean = exp_full.groupby('id_firm')[metrics_cmp].mean()\n",
        "sim_node_mean = sim_full.groupby('id_firm')[metrics_cmp].mean()\n",
        "common_nodes = sorted(set(exp_node_mean.index) & set(sim_node_mean.index))\n",
        "\n",
        "node_corr = pd.DataFrame({\n",
        "    'metric': metrics_cmp,\n",
        "    'pearson_corr': [\n",
        "        np.corrcoef(exp_node_mean.loc[common_nodes, m], sim_node_mean.loc[common_nodes, m])[0, 1]\n",
        "        for m in metrics_cmp\n",
        "    ]\n",
        "})\n",
        "display(node_corr)\n",
        "\n",
        "for m in ['firm_num_supplier_firms', 'firm_num_customer_firms', 'firm_weighted_betweenness', 'firm_weighted_closeness', 'firm_pagerank']:\n",
        "    top_exp = set(exp_node_mean.loc[common_nodes, m].sort_values(ascending=False).head(10).index)\n",
        "    top_sim = set(sim_node_mean.loc[common_nodes, m].sort_values(ascending=False).head(10).index)\n",
        "    j = len(top_exp & top_sim) / len(top_exp | top_sim)\n",
        "    print(f'Top10 Jaccard ({m}): {j:.4f}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "c1b7d79c",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 3600x2400 with 6 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# 五个网络指标的 ECDF 对比图（多子图，x 轴对数坐标）\n",
        "import matplotlib\n",
        "from matplotlib.ticker import LogLocator, FuncFormatter, NullFormatter\n",
        "\n",
        "# 设置中文字体（含 fallback），并处理负号/数学文本显示\n",
        "matplotlib.rcParams['font.family'] = 'sans-serif'\n",
        "matplotlib.rcParams['font.sans-serif'] = ['SimSun', 'Microsoft YaHei', 'DejaVu Sans']\n",
        "matplotlib.rcParams['axes.unicode_minus'] = False\n",
        "matplotlib.rcParams['mathtext.fontset'] = 'dejavusans'\n",
        "matplotlib.rcParams['mathtext.default'] = 'regular'\n",
        "\n",
        "metric_name_map = {\n",
        "    'firm_num_supplier_firms': '入度中心性',\n",
        "    'firm_num_customer_firms': '出度中心性',\n",
        "    'firm_weighted_betweenness': '加权介数中心性',\n",
        "    'firm_weighted_closeness': '加权接近中心性',\n",
        "    'firm_pagerank': 'PageRank值'\n",
        "}\n",
        "\n",
        "plot_metrics = list(metric_name_map.keys())\n",
        "epsilon = 1e-6\n",
        "\n",
        "fig, axes = plt.subplots(2, 3, figsize=(12, 8), dpi=300, sharey=True)\n",
        "axes = axes.flatten()\n",
        "\n",
        "for i, metric in enumerate(plot_metrics):\n",
        "    ax = axes[i]\n",
        "\n",
        "    exp_vals = exp_full[metric].to_numpy() + epsilon\n",
        "    sim_vals = sim_full[metric].to_numpy() + epsilon\n",
        "\n",
        "    sns.ecdfplot(\n",
        "        exp_vals,\n",
        "        ax=ax,\n",
        "        label='实验网络（含插值）',\n",
        "        linewidth=1.8,\n",
        "        alpha=0.85\n",
        "    )\n",
        "    sns.ecdfplot(\n",
        "        sim_vals,\n",
        "        ax=ax,\n",
        "        label='对照网络（仅原始样本）',\n",
        "        linewidth=1.8,\n",
        "        alpha=0.85\n",
        "    )\n",
        "\n",
        "    ax.set_xscale('log')\n",
        "\n",
        "    # 自定义对数坐标刻度格式，避免 U+2212 字形问题\n",
        "    ax.xaxis.set_major_locator(LogLocator(base=10.0))\n",
        "    ax.xaxis.set_minor_formatter(NullFormatter())\n",
        "    ax.xaxis.set_major_formatter(FuncFormatter(lambda x, pos: f\"1e{int(np.log10(x))}\" if x > 0 else \"\"))\n",
        "\n",
        "    # 子图边框统一为黑色\n",
        "    for spine in ax.spines.values():\n",
        "        spine.set_color('black')\n",
        "        spine.set_linewidth(1.0)\n",
        "\n",
        "    ax.set_title(metric_name_map[metric], fontsize=12)\n",
        "    ax.set_xlabel('指标值（对数坐标）', fontsize=11)\n",
        "    if i % 3 == 0:\n",
        "        ax.set_ylabel('经验累积分布函数', fontsize=11)\n",
        "    else:\n",
        "        ax.set_ylabel('')\n",
        "    ax.grid(True, alpha=0.3)\n",
        "\n",
        "# 每个子图显示各自图例\n",
        "for i in range(len(plot_metrics)):\n",
        "    axes[i].legend(loc='best', fontsize=9, frameon=False)\n",
        "\n",
        "# 隐藏第 6 个空子图\n",
        "axes[-1].axis('off')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cbc08952",
      "metadata": {},
      "source": [
        "**1) 规模分布层面：插值样本与原始样本存在统计差异，但方向清晰且可解释。**  \n",
        "`Revenue_Log` 在含插值样本中的 Q10/Q25/Q50/Q75 仍低于仅原始观测样本（约下降 0.4-1.9），与补充样本中中小企业占比更高这一设定一致。该差异提示：插值主要改变了样本的边际规模分布，而非引入无方向的随机噪声。\n",
        "\n",
        "**2) 网络指标分布层面：全量样本下“统计显著”与“实质效应”仍需区分。**  \n",
        "在直接比较（`count_with_gfirm.csv` 全部 `s_id` vs 无插值生成网络）中，5 个指标的 MW/KS 检验均显著，但效应量显示明显分化：\n",
        "- `firm_num_supplier_firms` 与 `firm_weighted_closeness` 的 Cliff's delta 接近 0（约 -0.003 与 0.003），`firm_weighted_betweenness` 也为小效应（约 -0.033）；\n",
        "- `firm_num_customer_firms` 为小到中等效应（Cliff's delta 约 -0.162）；\n",
        "- `firm_pagerank` 仍为相对最强差异（Cliff's delta 约 -0.479，KS statistic 约 0.670）。\n",
        "因此，更稳妥的解读是：多数指标体现为“超大样本下显著但效应有限”，而 `pagerank` 分布存在较明显偏移。\n",
        "\n",
        "**3) 节点相对排序层面：核心结构关系仍高度稳健。**  \n",
        "尽管总体分布存在差异，节点均值相关性仍维持在高水平（Pearson: 0.972-0.995；`pagerank`=0.994）。关键节点识别方面，Top10 Jaccard 分别为：supplier=1.0000、customer=0.5385、betweenness=0.6667、closeness=1.0000、pagerank=1.0000。说明核心节点集合在主要指标上总体保持稳定，尤其是 supplier、closeness 与 pagerank 的关键节点识别几乎完全一致。"
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