multivariate-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "207e8f67-6369-43dc-a0f6-0858822a8725",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "from itertools import product\n",
    "from pathlib import Path\n",
    "\n",
    "\n",
    "def warn(*args, **kwargs):\n",
    "    pass\n",
    "\n",
    "\n",
    "import warnings\n",
    "\n",
    "warnings.warn = warn\n",
    "\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from joblib import Parallel, delayed\n",
    "from scipy.io import savemat\n",
    "\n",
    "from hyppo.time_series import CCAX, MGCX, DcorrX, HsicX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "e000f79e-2645-4766-be0b-5b28c0bb7138",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_phi(phi=0.5, d=1):\n",
    "    out = np.eye(d)\n",
    "    denom = 1 / np.arange(1, d + 1)\n",
    "    out = out * denom * phi\n",
    "\n",
    "    return out\n",
    "\n",
    "\n",
    "def generate_var(n, d, sigma=1, lag=1, phi=0.5):\n",
    "    epsilons = np.random.normal(0, sigma, (n, d))\n",
    "    etas = np.random.normal(0, sigma, (n, d))\n",
    "\n",
    "    x = epsilons\n",
    "    y = etas\n",
    "\n",
    "    phis = generate_phi(phi, d)\n",
    "\n",
    "    for t in range(lag, n):\n",
    "        x[t] = phis @ y[t - lag] + epsilons[t]\n",
    "        y[t] = phis @ x[t - lag] + etas[t]\n",
    "\n",
    "    return x, y\n",
    "\n",
    "\n",
    "def generate_indep_var(n, d, sigma=1, lag=1, phi=0.5):\n",
    "    epsilons = np.random.normal(0, sigma, (n, d))\n",
    "    etas = np.random.normal(0, sigma, (n, d))\n",
    "\n",
    "    x = epsilons\n",
    "    y = etas\n",
    "\n",
    "    phis = generate_phi(phi, d)\n",
    "\n",
    "    for t in range(lag, n):\n",
    "        x[t] = phis @ x[t - lag] + epsilons[t]\n",
    "        y[t] = phis @ y[t - lag] + etas[t]\n",
    "\n",
    "    return x, y\n",
    "\n",
    "\n",
    "def generate_nonlinear_var(n, d, sigma=1, lag=1, phi=0.5):\n",
    "    epsilons = np.random.normal(0, sigma, (n, d))\n",
    "    etas = np.random.normal(0, sigma, (n, d))\n",
    "\n",
    "    x = epsilons\n",
    "    y = etas\n",
    "\n",
    "    phis = generate_phi(phi, d)\n",
    "\n",
    "    for t in range(lag, n):\n",
    "        x[t] = phis @ y[t - lag] * epsilons[t]\n",
    "\n",
    "    return x, y\n",
    "\n",
    "\n",
    "def generate_data(\n",
    "    n, d, lag, phi, fname, output_dir=\"./data/\", reps=300, generate_func=generate_var\n",
    "):\n",
    "    X_full = np.zeros((reps, n, d))\n",
    "    Y_full = np.zeros((reps, n, d))\n",
    "    for s in range(reps):\n",
    "        np.random.seed(s)\n",
    "        X_full[s], Y_full[s] = generate_func(n, d, lag=lag, phi=phi)\n",
    "\n",
    "    # Save simulated output.\n",
    "    output = {\"X\": X_full, \"Y\": Y_full}\n",
    "    p = Path(output_dir)\n",
    "    if not p.is_dir():\n",
    "        p.mkdir(parents=True)\n",
    "\n",
    "    filename = p / f\"{fname}_data.pkl\"\n",
    "    file = open(filename, \"wb\")\n",
    "    pickle.dump(output, file)\n",
    "    file.close()\n",
    "\n",
    "    # Save to MATLAB format as well.\n",
    "    savemat(p / f\"{fname}_data.mat\", {\"X_full\": X_full, \"Y_full\": Y_full})\n",
    "\n",
    "\n",
    "def run_experiment(test, n, d, reps=300, file=\"\"):\n",
    "    with open(file, \"rb\") as f:\n",
    "        dat = pickle.load(f)\n",
    "    # X = dat[\"X\"]\n",
    "    # Y = dat[\"Y\"]\n",
    "\n",
    "    pvals = []\n",
    "\n",
    "    for seed in range(reps):\n",
    "        np.random.seed(seed)\n",
    "\n",
    "        x = dat[\"X\"][seed, :n, :d]\n",
    "        y = dat[\"Y\"][seed, :n, :d]\n",
    "\n",
    "        res = test.test(x, y, reps=1000, workers=1)\n",
    "        pvals.append(res[1])\n",
    "\n",
    "    rejects = np.array(pvals) <= 0.05\n",
    "\n",
    "    return np.mean(rejects), np.std(rejects)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "96aa78a5-b90e-4b8c-8274-1edc838a232a",
   "metadata": {},
   "outputs": [],
   "source": [
    "generate_data(100, 100, 1, 0.65, \"linear_var1\", generate_func=generate_var)\n",
    "generate_data(100, 100, 1, 1, \"nonlinear_var1\", generate_func=generate_nonlinear_var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "0346e97b-d313-45e3-822b-8ad146bb9cc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "tests = [\n",
    "    [\"dcorr\", DcorrX(max_lag=1)],\n",
    "    # [\"cca\", CCAX(max_lag=1)],\n",
    "    [\"hsic\", HsicX(max_lag=1)],\n",
    "    [\"mgc\", MGCX(max_lag=1)],\n",
    "]\n",
    "\n",
    "sigma = 1\n",
    "n = 100\n",
    "ds = np.arange(0, 101, 10)\n",
    "ds[0] = 1\n",
    "# ds = [10]\n",
    "lag = 1\n",
    "reps = 50\n",
    "\n",
    "args = list(product(tests, ds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "529a3ad8-75cf-4ec1-bc74-da96c6a87ee8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=33)]: Using backend LokyBackend with 33 concurrent workers.\n",
      "[Parallel(n_jobs=33)]: Done   4 out of  33 | elapsed:  3.4min remaining: 24.7min\n",
      "[Parallel(n_jobs=33)]: Done   8 out of  33 | elapsed:  3.9min remaining: 12.1min\n",
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      "[Parallel(n_jobs=33)]: Done  16 out of  33 | elapsed:  6.5min remaining:  6.9min\n",
      "[Parallel(n_jobs=33)]: Done  20 out of  33 | elapsed:  7.0min remaining:  4.6min\n",
      "[Parallel(n_jobs=33)]: Done  24 out of  33 | elapsed: 54.7min remaining: 20.5min\n",
      "[Parallel(n_jobs=33)]: Done  33 out of  33 | elapsed: 57.1min finished\n"
     ]
    }
   ],
   "source": [
    "n_jobs = len(args)\n",
    "\n",
    "res = Parallel(n_jobs, verbose=10)(\n",
    "    delayed(run_experiment)(test, n, d, reps=reps, file=\"./data/linear_var1_data.pkl\")\n",
    "    for (_, test), d in args\n",
    ")\n",
    "\n",
    "df = [[a[0][0]] + [a[1]] + [*b] for a, b in zip(args, res)]\n",
    "df = pd.DataFrame(df, columns=[\"test\", \"d\", \"power\", \"std\"])\n",
    "df.to_csv(\"./linear_var.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "778340ee-66b4-455f-8129-225b801c27ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=33)]: Using backend LokyBackend with 33 concurrent workers.\n",
      "[Parallel(n_jobs=33)]: Done   5 out of  30 | elapsed:  3.8min remaining: 18.8min\n",
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      "[Parallel(n_jobs=33)]: Done  30 out of  30 | elapsed: 57.1min finished\n"
     ]
    }
   ],
   "source": [
    "n_jobs = len(args)\n",
    "\n",
    "ds = np.arange(5, 100, 10)\n",
    "args = list(product(tests, ds))\n",
    "\n",
    "res = Parallel(n_jobs, verbose=10)(\n",
    "    delayed(run_experiment)(test, n, d, reps=reps, file=\"./data/linear_var1_data.pkl\")\n",
    "    for (_, test), d in args\n",
    ")\n",
    "\n",
    "df = [[a[0][0]] + [a[1]] + [*b] for a, b in zip(args, res)]\n",
    "df = pd.DataFrame(df, columns=[\"test\", \"d\", \"power\", \"std\"])\n",
    "df.to_csv(\"./linear_var2.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73c3eea3-6672-468a-8e6f-c40b5005d988",
   "metadata": {},
   "outputs": [],
   "source": [
    "# res = Parallel(n_jobs, verbose=10)(\n",
    "#     delayed(run_experiment)(test, n, d, reps=reps, file=\"./data/indep_var1_data.pkl\")\n",
    "#     for (_, test), d in args\n",
    "# )\n",
    "\n",
    "# df = [[a[0][0]] + [a[1]] + [*b] for a, b in zip(args, res)]\n",
    "# df = pd.DataFrame(df, columns=[\"test\", \"d\", \"power\", \"std\"])\n",
    "# df.to_csv(\"./indep_var.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7468dae6-a2e5-45b7-b0e7-65cc167f8878",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = np.arange(0, 21, 2)\n",
    "ds[0] = 1\n",
    "\n",
    "args = list(product(tests, ds))\n",
    "\n",
    "res = Parallel(len(args), verbose=10)(\n",
    "    delayed(run_experiment)(\n",
    "        test, n, d, reps=reps, file=\"./data/nonlinear_var1_data.pkl\"\n",
    "    )\n",
    "    for (_, test), d in args\n",
    ")\n",
    "\n",
    "df = [[a[0][0]] + [a[1]] + [*b] for a, b in zip(args, res)]\n",
    "df = pd.DataFrame(df, columns=[\"test\", \"d\", \"power\", \"std\"])\n",
    "df.to_csv(\"./nonlinear_var.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "10035785-1faa-471c-98a8-05f5c0aa76ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_linear_lag(n, d, sigma=1, phi=0.5, **kwargs):\n",
    "    epsilons = np.random.normal(0, sigma, (n, d))\n",
    "    etas = np.random.normal(0, sigma, (n, d))\n",
    "\n",
    "    x = epsilons\n",
    "    y = etas\n",
    "\n",
    "    phis_1 = generate_phi(phi=0.1, d=d)\n",
    "    phis_3 = generate_phi(phi=0.65, d=d)\n",
    "\n",
    "    for t in range(3, n):\n",
    "        x[t] = phis_1 @ y[t - 1] + phis_3 @ y[t - 3] + epsilons[t]\n",
    "        y[t] = phis_1 @ x[t - 1] + phis_3 @ x[t - 3] + etas[t]\n",
    "\n",
    "    return x, y\n",
    "\n",
    "\n",
    "def generate_nonlinear_lag(n, d, sigma=1, phi=0.7, **kwargs):\n",
    "    epsilons = np.random.normal(0, sigma, (n, d))\n",
    "    etas = np.random.normal(0, sigma, (n, d))\n",
    "\n",
    "    x = epsilons\n",
    "    y = etas\n",
    "\n",
    "    phis = generate_phi(phi, d)\n",
    "\n",
    "    for t in range(3, n):\n",
    "        x[t] = phis @ y[t - 3] * epsilons[t]\n",
    "\n",
    "    return x, y\n",
    "\n",
    "\n",
    "generate_data(\n",
    "    100, 100, 1, 0.5, \"linear_lag3\", reps=300, generate_func=generate_linear_lag\n",
    ")\n",
    "generate_data(\n",
    "    100, 20, 1, 0.5, \"nonlinear_lag3\", reps=300, generate_func=generate_nonlinear_lag\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "3a488865-134f-4219-b762-cc326ab07f15",
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_lag_experiment(test, n, d, reps=100, file=\"\"):\n",
    "    with open(file, \"rb\") as f:\n",
    "        dat = pickle.load(f)\n",
    "\n",
    "    opt_lags = []\n",
    "\n",
    "    for seed in range(reps):\n",
    "        np.random.seed(seed)\n",
    "\n",
    "        x = dat[\"X\"][seed, :n, :d]\n",
    "        y = dat[\"Y\"][seed, :n, :d]\n",
    "\n",
    "        res = test.statistic(x, y)\n",
    "        opt_lag = res[1]\n",
    "        opt_lags.append(opt_lag)\n",
    "\n",
    "    # rejects = np.array(pvals) <= 0.05\n",
    "\n",
    "    opt_lags = np.array(opt_lags)\n",
    "    return np.mean(opt_lags == 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "830c674e-c52b-45ae-8bfd-05a85b7eea9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "max_lag = 10\n",
    "tests = [\n",
    "    [\"dcorr\", DcorrX(max_lag=10)],\n",
    "    [\"hsic\", HsicX(max_lag=10)],\n",
    "    [\"mgc\", MGCX(max_lag=10)],\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "67dd22cd-0771-473a-96af-c4c27d5f0e7b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=50)]: Using backend LokyBackend with 50 concurrent workers.\n",
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     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='d', ylabel='mean'>"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds = np.arange(0, 101, 2)\n",
    "ds[0] = 1\n",
    "n = 100\n",
    "\n",
    "args = list(product(tests, ds))\n",
    "\n",
    "res = Parallel(50, verbose=10)(\n",
    "    delayed(run_lag_experiment)(\n",
    "        test, n, d, reps=300, file=\"./data/linear_lag3_data.pkl\"\n",
    "    )\n",
    "    for (_, test), d in args\n",
    ")\n",
    "\n",
    "df = [[a[0][0]] + [a[1]] + [b] for a, b in zip(args, res)]\n",
    "df = pd.DataFrame(df, columns=[\"test\", \"d\", \"mean\"])\n",
    "df.to_csv(\"./linear_lag_estimation.csv\", index=False)\n",
    "\n",
    "sns.lineplot(df, x=\"d\", y=\"mean\", hue=\"test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "5359153c-187d-4027-b32b-54b98167bc14",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=50)]: Using backend LokyBackend with 50 concurrent workers.\n",
      "[Parallel(n_jobs=50)]: Done   6 out of  63 | elapsed:    3.6s remaining:   34.6s\n",
      "[Parallel(n_jobs=50)]: Done  13 out of  63 | elapsed:    4.5s remaining:   17.2s\n",
      "[Parallel(n_jobs=50)]: Done  20 out of  63 | elapsed:    5.2s remaining:   11.1s\n",
      "[Parallel(n_jobs=50)]: Done  27 out of  63 | elapsed:    8.7s remaining:   11.6s\n",
      "[Parallel(n_jobs=50)]: Done  34 out of  63 | elapsed:    9.4s remaining:    8.0s\n",
      "[Parallel(n_jobs=50)]: Done  41 out of  63 | elapsed:   10.2s remaining:    5.5s\n",
      "[Parallel(n_jobs=50)]: Done  48 out of  63 | elapsed:   58.7s remaining:   18.4s\n",
      "[Parallel(n_jobs=50)]: Done  55 out of  63 | elapsed:   59.2s remaining:    8.6s\n",
      "[Parallel(n_jobs=50)]: Done  63 out of  63 | elapsed:  1.0min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='d', ylabel='mean'>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds = np.arange(0, 21)\n",
    "ds[0] = 1\n",
    "n = 100\n",
    "\n",
    "args = list(product(tests, ds))\n",
    "\n",
    "res = Parallel(50, verbose=10)(\n",
    "    delayed(run_lag_experiment)(\n",
    "        test, n, d, reps=300, file=\"./data/nonlinear_lag3_data.pkl\"\n",
    "    )\n",
    "    for (_, test), d in args\n",
    ")\n",
    "\n",
    "df = [[a[0][0]] + [a[1]] + [b] for a, b in zip(args, res)]\n",
    "df = pd.DataFrame(df, columns=[\"test\", \"d\", \"mean\"])\n",
    "df.to_csv(\"./nonlinear_lag_estimation.csv\", index=False)\n",
    "\n",
    "sns.lineplot(df, x=\"d\", y=\"mean\", hue=\"test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "1757cc62-317e-45ab-8020-f3c8fea05224",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"./linear_lag_estimation.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "b35156d1-8746-469a-acf4-47ead94afe2e",
   "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>test</th>\n",
       "      <th>d</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>4</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>8</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>10</td>\n",
       "      <td>0.993333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>12</td>\n",
       "      <td>0.980000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>14</td>\n",
       "      <td>0.976667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>16</td>\n",
       "      <td>0.976667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>18</td>\n",
       "      <td>0.960000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>20</td>\n",
       "      <td>0.920000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>22</td>\n",
       "      <td>0.906667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>24</td>\n",
       "      <td>0.896667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>26</td>\n",
       "      <td>0.890000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>28</td>\n",
       "      <td>0.863333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>30</td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>32</td>\n",
       "      <td>0.816667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>34</td>\n",
       "      <td>0.773333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>36</td>\n",
       "      <td>0.763333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>38</td>\n",
       "      <td>0.743333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>40</td>\n",
       "      <td>0.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>42</td>\n",
       "      <td>0.696667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>44</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>46</td>\n",
       "      <td>0.633333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>48</td>\n",
       "      <td>0.630000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>50</td>\n",
       "      <td>0.590000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>52</td>\n",
       "      <td>0.573333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>54</td>\n",
       "      <td>0.563333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>56</td>\n",
       "      <td>0.553333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>58</td>\n",
       "      <td>0.526667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>60</td>\n",
       "      <td>0.513333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>62</td>\n",
       "      <td>0.510000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>64</td>\n",
       "      <td>0.490000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>66</td>\n",
       "      <td>0.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>68</td>\n",
       "      <td>0.440000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>70</td>\n",
       "      <td>0.433333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>72</td>\n",
       "      <td>0.430000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>74</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>76</td>\n",
       "      <td>0.410000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>78</td>\n",
       "      <td>0.390000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>80</td>\n",
       "      <td>0.396667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>82</td>\n",
       "      <td>0.383333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>84</td>\n",
       "      <td>0.390000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>86</td>\n",
       "      <td>0.383333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>88</td>\n",
       "      <td>0.376667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>90</td>\n",
       "      <td>0.363333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>92</td>\n",
       "      <td>0.353333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>94</td>\n",
       "      <td>0.320000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>96</td>\n",
       "      <td>0.326667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>98</td>\n",
       "      <td>0.323333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>dcorr</td>\n",
       "      <td>100</td>\n",
       "      <td>0.330000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     test    d      mean\n",
       "0   dcorr    1  1.000000\n",
       "1   dcorr    2  1.000000\n",
       "2   dcorr    4  1.000000\n",
       "3   dcorr    6  1.000000\n",
       "4   dcorr    8  1.000000\n",
       "5   dcorr   10  0.993333\n",
       "6   dcorr   12  0.980000\n",
       "7   dcorr   14  0.976667\n",
       "8   dcorr   16  0.976667\n",
       "9   dcorr   18  0.960000\n",
       "10  dcorr   20  0.920000\n",
       "11  dcorr   22  0.906667\n",
       "12  dcorr   24  0.896667\n",
       "13  dcorr   26  0.890000\n",
       "14  dcorr   28  0.863333\n",
       "15  dcorr   30  0.833333\n",
       "16  dcorr   32  0.816667\n",
       "17  dcorr   34  0.773333\n",
       "18  dcorr   36  0.763333\n",
       "19  dcorr   38  0.743333\n",
       "20  dcorr   40  0.700000\n",
       "21  dcorr   42  0.696667\n",
       "22  dcorr   44  0.666667\n",
       "23  dcorr   46  0.633333\n",
       "24  dcorr   48  0.630000\n",
       "25  dcorr   50  0.590000\n",
       "26  dcorr   52  0.573333\n",
       "27  dcorr   54  0.563333\n",
       "28  dcorr   56  0.553333\n",
       "29  dcorr   58  0.526667\n",
       "30  dcorr   60  0.513333\n",
       "31  dcorr   62  0.510000\n",
       "32  dcorr   64  0.490000\n",
       "33  dcorr   66  0.450000\n",
       "34  dcorr   68  0.440000\n",
       "35  dcorr   70  0.433333\n",
       "36  dcorr   72  0.430000\n",
       "37  dcorr   74  0.400000\n",
       "38  dcorr   76  0.410000\n",
       "39  dcorr   78  0.390000\n",
       "40  dcorr   80  0.396667\n",
       "41  dcorr   82  0.383333\n",
       "42  dcorr   84  0.390000\n",
       "43  dcorr   86  0.383333\n",
       "44  dcorr   88  0.376667\n",
       "45  dcorr   90  0.363333\n",
       "46  dcorr   92  0.353333\n",
       "47  dcorr   94  0.320000\n",
       "48  dcorr   96  0.326667\n",
       "49  dcorr   98  0.323333\n",
       "50  dcorr  100  0.330000"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.test == \"dcorr\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "924061e9-04de-48dd-93d8-d0b510ccac9b",
   "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>test</th>\n",
       "      <th>d</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>hsic</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>hsic</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>hsic</td>\n",
       "      <td>4</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>hsic</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>hsic</td>\n",
       "      <td>8</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>hsic</td>\n",
       "      <td>10</td>\n",
       "      <td>0.993333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>hsic</td>\n",
       "      <td>12</td>\n",
       "      <td>0.980000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>hsic</td>\n",
       "      <td>14</td>\n",
       "      <td>0.976667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>hsic</td>\n",
       "      <td>16</td>\n",
       "      <td>0.976667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>hsic</td>\n",
       "      <td>18</td>\n",
       "      <td>0.960000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>hsic</td>\n",
       "      <td>20</td>\n",
       "      <td>0.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>hsic</td>\n",
       "      <td>22</td>\n",
       "      <td>0.906667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>hsic</td>\n",
       "      <td>24</td>\n",
       "      <td>0.890000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>hsic</td>\n",
       "      <td>26</td>\n",
       "      <td>0.886667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>hsic</td>\n",
       "      <td>28</td>\n",
       "      <td>0.870000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>hsic</td>\n",
       "      <td>30</td>\n",
       "      <td>0.836667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>hsic</td>\n",
       "      <td>32</td>\n",
       "      <td>0.816667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>hsic</td>\n",
       "      <td>34</td>\n",
       "      <td>0.776667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>hsic</td>\n",
       "      <td>36</td>\n",
       "      <td>0.766667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>hsic</td>\n",
       "      <td>38</td>\n",
       "      <td>0.743333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>hsic</td>\n",
       "      <td>40</td>\n",
       "      <td>0.696667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>hsic</td>\n",
       "      <td>42</td>\n",
       "      <td>0.696667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>hsic</td>\n",
       "      <td>44</td>\n",
       "      <td>0.673333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>hsic</td>\n",
       "      <td>46</td>\n",
       "      <td>0.630000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>hsic</td>\n",
       "      <td>48</td>\n",
       "      <td>0.623333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>hsic</td>\n",
       "      <td>50</td>\n",
       "      <td>0.586667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>hsic</td>\n",
       "      <td>52</td>\n",
       "      <td>0.570000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>hsic</td>\n",
       "      <td>54</td>\n",
       "      <td>0.563333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>hsic</td>\n",
       "      <td>56</td>\n",
       "      <td>0.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>hsic</td>\n",
       "      <td>58</td>\n",
       "      <td>0.526667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>hsic</td>\n",
       "      <td>60</td>\n",
       "      <td>0.516667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>hsic</td>\n",
       "      <td>62</td>\n",
       "      <td>0.503333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>hsic</td>\n",
       "      <td>64</td>\n",
       "      <td>0.490000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>hsic</td>\n",
       "      <td>66</td>\n",
       "      <td>0.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>hsic</td>\n",
       "      <td>68</td>\n",
       "      <td>0.440000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>hsic</td>\n",
       "      <td>70</td>\n",
       "      <td>0.433333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>hsic</td>\n",
       "      <td>72</td>\n",
       "      <td>0.430000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>hsic</td>\n",
       "      <td>74</td>\n",
       "      <td>0.393333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>hsic</td>\n",
       "      <td>76</td>\n",
       "      <td>0.406667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>hsic</td>\n",
       "      <td>78</td>\n",
       "      <td>0.390000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>hsic</td>\n",
       "      <td>80</td>\n",
       "      <td>0.393333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>hsic</td>\n",
       "      <td>82</td>\n",
       "      <td>0.380000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>hsic</td>\n",
       "      <td>84</td>\n",
       "      <td>0.386667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>hsic</td>\n",
       "      <td>86</td>\n",
       "      <td>0.380000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>hsic</td>\n",
       "      <td>88</td>\n",
       "      <td>0.373333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>hsic</td>\n",
       "      <td>90</td>\n",
       "      <td>0.360000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>hsic</td>\n",
       "      <td>92</td>\n",
       "      <td>0.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>hsic</td>\n",
       "      <td>94</td>\n",
       "      <td>0.320000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>hsic</td>\n",
       "      <td>96</td>\n",
       "      <td>0.326667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>hsic</td>\n",
       "      <td>98</td>\n",
       "      <td>0.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>hsic</td>\n",
       "      <td>100</td>\n",
       "      <td>0.330000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     test    d      mean\n",
       "51   hsic    1  1.000000\n",
       "52   hsic    2  1.000000\n",
       "53   hsic    4  1.000000\n",
       "54   hsic    6  1.000000\n",
       "55   hsic    8  1.000000\n",
       "56   hsic   10  0.993333\n",
       "57   hsic   12  0.980000\n",
       "58   hsic   14  0.976667\n",
       "59   hsic   16  0.976667\n",
       "60   hsic   18  0.960000\n",
       "61   hsic   20  0.916667\n",
       "62   hsic   22  0.906667\n",
       "63   hsic   24  0.890000\n",
       "64   hsic   26  0.886667\n",
       "65   hsic   28  0.870000\n",
       "66   hsic   30  0.836667\n",
       "67   hsic   32  0.816667\n",
       "68   hsic   34  0.776667\n",
       "69   hsic   36  0.766667\n",
       "70   hsic   38  0.743333\n",
       "71   hsic   40  0.696667\n",
       "72   hsic   42  0.696667\n",
       "73   hsic   44  0.673333\n",
       "74   hsic   46  0.630000\n",
       "75   hsic   48  0.623333\n",
       "76   hsic   50  0.586667\n",
       "77   hsic   52  0.570000\n",
       "78   hsic   54  0.563333\n",
       "79   hsic   56  0.550000\n",
       "80   hsic   58  0.526667\n",
       "81   hsic   60  0.516667\n",
       "82   hsic   62  0.503333\n",
       "83   hsic   64  0.490000\n",
       "84   hsic   66  0.450000\n",
       "85   hsic   68  0.440000\n",
       "86   hsic   70  0.433333\n",
       "87   hsic   72  0.430000\n",
       "88   hsic   74  0.393333\n",
       "89   hsic   76  0.406667\n",
       "90   hsic   78  0.390000\n",
       "91   hsic   80  0.393333\n",
       "92   hsic   82  0.380000\n",
       "93   hsic   84  0.386667\n",
       "94   hsic   86  0.380000\n",
       "95   hsic   88  0.373333\n",
       "96   hsic   90  0.360000\n",
       "97   hsic   92  0.350000\n",
       "98   hsic   94  0.320000\n",
       "99   hsic   96  0.326667\n",
       "100  hsic   98  0.330000\n",
       "101  hsic  100  0.330000"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.test == \"hsic\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "680bf59f-6125-4721-915d-5a85f0756aff",
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