Fig1_alter.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.stats import multivariate_normal\n",
    "from scipy.stats import entropy\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import roc_auc_score\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import warnings\n",
    "import string\n",
    "import pickle\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "sns.set(color_codes=True, style=\"white\", context=\"talk\", font_scale=1.5)\n",
    "PALETTE = sns.color_palette(\"Set1\")\n",
    "sns.set_palette(PALETTE[1:5] + PALETTE[6:], n_colors=9)\n",
    "\n",
    "def truncate(n, dicimals=2):\n",
    "    return round(n, dicimals)\n",
    "\n",
    "# helper function to compare two numbers\n",
    "def compare(A, B):\n",
    "    if A > B:\n",
    "        return \">\"\n",
    "    elif A < B:\n",
    "        return \"<\"\n",
    "    else:\n",
    "        return \"=\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For generating Variable C\n",
    "def generate_gauss_mix(\n",
    "    N=5000,\n",
    "    prior=(0.5, 0.5),\n",
    "    p=1,\n",
    "    mu_class0=0,\n",
    "    mu_class0_0=0,\n",
    "    mu_class0_1=5,\n",
    "    mu_class1=1,\n",
    "    mu_class1_0=0,\n",
    "    mu_class1_1=5,\n",
    "    sig_class0=1,\n",
    "    sig_class0_0=1,\n",
    "    sig_class0_1=1,\n",
    "    sig_class1=1,\n",
    "    sig_class1_0=1,\n",
    "    sig_class1_1=1,\n",
    "    split_class0=None,\n",
    "    split_class1=None,\n",
    "):\n",
    "    class0_mix = True if split_class0 is not None else False\n",
    "    class1_mix = True if split_class1 is not None else False\n",
    "    \n",
    "    prior_0, prior_1 = prior\n",
    "    p_class0, p_class1 = prior\n",
    "    n0 = int(N * prior_0)  # number of samples from class 0\n",
    "    n1 = N - n0  # total number of samples from class 1\n",
    "\n",
    "    if class1_mix:\n",
    "        p_class0, p_class1_0, p_class1_1 = (\n",
    "            prior_0,\n",
    "            prior_1 * split_class1[0],\n",
    "            prior_1 * split_class1[1],\n",
    "        )\n",
    "        n0 = int(N * p_class0)  # number of samples from class 0\n",
    "        n1 = N - n0  # total number of samples from class 1\n",
    "        n1_0 = int(N * p_class1_0)  # number of samples from class 1_0\n",
    "        n1_1 = n1 - n1_0  # number of samples from class 1_1\n",
    "        mu_class1_0 = np.array([mu_class1_0] * p)\n",
    "        mu_class1_1 = np.array([mu_class1_1] * p)\n",
    "        sig_class0 = np.identity(p) * sig_class0\n",
    "        sig_class1_0 = np.identity(p) * sig_class1_0\n",
    "        sig_class1_1 = np.identity(p) * sig_class1_1\n",
    "        x_1_0 = np.random.multivariate_normal(mu_class1_0, sig_class1_0, size=n1_0)\n",
    "        x_1_1 = np.random.multivariate_normal(mu_class1_1, sig_class1_1, size=n1_1)\n",
    "        x_1 = np.vstack((x_1_0, x_1_1))\n",
    "    else:\n",
    "        # p_class0, p_class1 = prior\n",
    "        mu_class1 = np.array([mu_class1] * p)\n",
    "        sig_class0 = np.identity(p) * sig_class0\n",
    "        sig_class1 = np.identity(p) * sig_class1\n",
    "        x_1 = np.random.multivariate_normal(mu_class1, sig_class1, size=n1)\n",
    "\n",
    "    if class0_mix:\n",
    "        p_class0_0, p_class0_1 = split_class0\n",
    "        n0 = int(N * p_class0)\n",
    "        n0_0 = int(n0 * p_class0_0)\n",
    "        n0_1 = n0 - n0_0\n",
    "        mu_class0_0 = np.array([mu_class0_0] * p)\n",
    "        mu_class0_1 = np.array([mu_class0_1] * p)\n",
    "        sig_class0_0 = np.identity(p) * sig_class0_0\n",
    "        sig_class0_1 = np.identity(p) * sig_class0_1\n",
    "        x_0_0 = np.random.multivariate_normal(mu_class0_0, sig_class0_0, size=n0_0)\n",
    "        x_0_1 = np.random.multivariate_normal(mu_class0_1, sig_class0_1, size=n0_1)\n",
    "        x_0 = np.vstack((x_0_0, x_0_1))\n",
    "    else:\n",
    "        mu_class0 = np.array([mu_class0] * p)\n",
    "        x_0 = np.random.multivariate_normal(mu_class0, sig_class0, size=n0)\n",
    "\n",
    "    x = np.vstack((x_0, x_1))\n",
    "    y = np.array([0] * n0 + [1] * n1).reshape(-1, 1)\n",
    "\n",
    "    # Create the probability density functions (PDFs) for the two Gaussian distributions\n",
    "    if class0_mix:\n",
    "        pdf_class0_0 = multivariate_normal(mu_class1_0, sig_class1_0)\n",
    "        pdf_class0_1 = multivariate_normal(mu_class1_1, sig_class1_1)\n",
    "        p_x_given_class0_0 = pdf_class0_0.pdf(x)\n",
    "        p_x_given_class0_1 = pdf_class0_1.pdf(x)\n",
    "        p_x_given_class0 = (\n",
    "            split_class1[0] * p_x_given_class0_0 + split_class0[1] * p_x_given_class0_1\n",
    "        )\n",
    "    else:\n",
    "        pdf_class0 = multivariate_normal(mu_class0, sig_class0)\n",
    "        p_x_given_class0 = pdf_class0.pdf(x)\n",
    "\n",
    "    if class1_mix:\n",
    "        pdf_class1_0 = multivariate_normal(mu_class1_0, sig_class1_0)\n",
    "        pdf_class1_1 = multivariate_normal(mu_class1_1, sig_class1_1)\n",
    "        p_x_given_class1_0 = pdf_class1_0.pdf(x)\n",
    "        p_x_given_class1_1 = pdf_class1_1.pdf(x)\n",
    "        p_x_given_class1 = (\n",
    "            split_class1[0] * p_x_given_class1_0 + split_class1[1] * p_x_given_class1_1\n",
    "        )\n",
    "    else:\n",
    "        pdf_class1 = multivariate_normal(mu_class1, sig_class1)\n",
    "        p_x_given_class1 = pdf_class1.pdf(x)\n",
    "\n",
    "    p_x = p_x_given_class0 * p_class0 + p_x_given_class1 * p_class1\n",
    "\n",
    "    pos_class0 = p_x_given_class0 * p_class0 / p_x\n",
    "    pos_class1 = p_x_given_class1 * (1 - p_class0) / p_x\n",
    "\n",
    "    posterior = np.hstack((pos_class0.reshape(-1, 1), pos_class1.reshape(-1, 1)))\n",
    "    stats_conen = np.mean(entropy(posterior, base=np.exp(1), axis=1))\n",
    "\n",
    "    # if class0_mix:\n",
    "    #     prior_y_0 = np.array([p_class0_0, p_class0_1])\n",
    "    # else:\n",
    "    #     prior_y_0 = np.array(p_class0)\n",
    "\n",
    "    # if class1_mix:\n",
    "    #     prior_y_1 = np.array([p_class1_0, p_class1_1])\n",
    "    # else:\n",
    "    #     prior_y_1 = np.array(p_class1)\n",
    "        \n",
    "    entropy_y = entropy(prior, base=np.exp(1))\n",
    "    correlation = np.corrcoef(x_0.T, x_1.T)\n",
    "    MI = entropy_y - stats_conen\n",
    "    auc = roc_auc_score(y, posterior[:, 1])\n",
    "    pauc_90 = roc_auc_score(y, posterior[:, 1], max_fpr=0.1)\n",
    "    pauc_98 = roc_auc_score(y, posterior[:, 1], max_fpr=0.02)\n",
    "    if MI == 0.0:\n",
    "        # replace the posterior with random numbers ~ U(0,1) to calculate the ROC curve\n",
    "        posterior_ = np.random.uniform(0, 1, size=(N, 2))\n",
    "        fpr, tpr, thresholds = metrics.roc_curve(\n",
    "            y, posterior_[:, 1], pos_label=1, drop_intermediate=False\n",
    "        )\n",
    "    else: \n",
    "        fpr, tpr, thresholds = metrics.roc_curve(\n",
    "        y, posterior[:, 1], pos_label=1, drop_intermediate=False\n",
    "    )\n",
    "    tpr_s = np.max(tpr[fpr <= 0.02])\n",
    "    y_pred = np.argmax(posterior, axis=1)\n",
    "    accuracy = metrics.accuracy_score(y, y_pred)\n",
    "    tn, fp, fn, tp = metrics.confusion_matrix(y, y_pred).ravel()\n",
    "    sensitivity = tp / (tp + fn)\n",
    "    specificity = tn / (tn + fp)\n",
    "    f1 = metrics.f1_score(y, y_pred)\n",
    "\n",
    "    statistics = {\n",
    "        # 'Correlation': correlation[0,1],\n",
    "        \"Accuracy\": accuracy,\n",
    "        # \"F1\": f1,\n",
    "        \"MI\": MI,\n",
    "        \"AUC\": auc,\n",
    "        # \"pAUC_90\": pauc_90,\n",
    "        # \"pAUC_98\": pauc_98,\n",
    "        \"S@98\": tpr_s,\n",
    "        # 'Sensitivity': sensitivity,\n",
    "        # 'Specificity': specificity,\n",
    "        # 'TN': tn,\n",
    "        # 'FP': fp,\n",
    "        # 'FN': fn,\n",
    "        # 'TP': tp\n",
    "        # \"tpr\": tpr,\n",
    "        # \"fpr\": fpr,\n",
    "    }\n",
    "    x_min, x_max = np.min(x), np.max(x)\n",
    "    # print(x_min, x_max)\n",
    "    xs = np.linspace(x_min - 1, x_max + 1, 1000)\n",
    "    pdf = pd.DataFrame()\n",
    "    pdf[\"x\"] = xs\n",
    "    if class0_mix:\n",
    "        pdf[\"pdf_class0\"] =  split_class0[0] * pdf_class0_0.pdf(xs) + split_class0[1] * pdf_class0_1.pdf(xs)\n",
    "    else:\n",
    "        pdf[\"pdf_class0\"] = pdf_class0.pdf(xs)\n",
    "    if class1_mix:\n",
    "        pdf[\"pdf_class1\"] =  split_class1[0] * pdf_class1_0.pdf(xs) + split_class1[1] * pdf_class1_1.pdf(xs)\n",
    "    else:\n",
    "        pdf[\"pdf_class1\"] = pdf_class1.pdf(xs)\n",
    "\n",
    "    return x, y, posterior[:, 1], statistics, pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 50000\n",
    "x_A, y_A, posterior_A, statistics_A, pdf_A = generate_gauss_mix(\n",
    "    N=N,\n",
    "    prior=(0.5, 0.5),\n",
    "    p=1,\n",
    "    mu_class0=0,\n",
    "    mu_class1=1,\n",
    "    sig_class0=1,\n",
    "    sig_class1=1,\n",
    "    split_class1=None,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate the data\n",
    "x_B, y_B, posterior_B, statistics_B, pdf_B = generate_gauss_mix(\n",
    "    N=N,\n",
    "    prior=(0.5, 0.5),\n",
    "    p=1,\n",
    "    mu_class0=0,\n",
    "    mu_class1_0=0,\n",
    "    mu_class1_1=5,\n",
    "    sig_class0=1,\n",
    "    sig_class1_0=1,\n",
    "    sig_class1_1=1,\n",
    "    split_class1=(0.75, 0.25),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate the data\n",
    "x_C, y_C, posterior_C, statistics_C, pdf_C = generate_gauss_mix(\n",
    "    N=N,\n",
    "    prior=(0.5, 0.5),\n",
    "    p=1,\n",
    "    mu_class0_0=0,\n",
    "    mu_class0_1=5,\n",
    "    mu_class1_0=0,\n",
    "    mu_class1_1=5,\n",
    "    sig_class0_0=1,\n",
    "    sig_class0_1=1,\n",
    "    sig_class1_0=1,\n",
    "    sig_class1_1=1,\n",
    "    split_class0=(0.75, 0.25),\n",
    "    split_class1=(0.75, 0.25),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_trunk_classification(\n",
    "    n_samples,\n",
    "    n_dim=4096,\n",
    "    n_informative=1,\n",
    "    simulation: str = \"trunk\",\n",
    "    mu_0: float = 0,\n",
    "    mu_1: float = 1,\n",
    "    rho: int = 0,\n",
    "    band_type: str = \"ma\",\n",
    "    return_params: bool = False,\n",
    "    mix: float = 0.5,\n",
    "    seed=None,\n",
    "):\n",
    "    if n_dim < n_informative:\n",
    "        raise ValueError(\n",
    "            f\"Number of informative dimensions {n_informative} must be less than number \"\n",
    "            f\"of dimensions, {n_dim}\"\n",
    "        )\n",
    "    rng = np.random.default_rng(seed=seed)\n",
    "    rng1 = np.random.default_rng(seed=seed)\n",
    "    mu_0 = np.array([mu_0 / np.sqrt(i) for i in range(1, n_informative + 1)])\n",
    "    mu_1 = np.array([mu_1 / np.sqrt(i) for i in range(1, n_informative + 1)])\n",
    "    if rho != 0:\n",
    "        if band_type == \"ma\":\n",
    "            cov = _moving_avg_cov(n_informative, rho)\n",
    "        elif band_type == \"ar\":\n",
    "            cov = _autoregressive_cov(n_informative, rho)\n",
    "        else:\n",
    "            raise ValueError(f'Band type {band_type} must be one of \"ma\", or \"ar\".')\n",
    "    else:\n",
    "        cov = np.identity(n_informative)\n",
    "    if mix < 0 or mix > 1:\n",
    "        raise ValueError(\"Mix must be between 0 and 1.\")\n",
    "    # speed up computations for large multivariate normal matrix with SVD approximation\n",
    "    if n_informative > 1000:\n",
    "        method = \"cholesky\"\n",
    "    else:\n",
    "        method = \"svd\"\n",
    "    if simulation == \"trunk\":\n",
    "        X = np.vstack(\n",
    "            (\n",
    "                rng.multivariate_normal(mu_0, cov, n_samples // 2, method=method),\n",
    "                rng1.multivariate_normal(mu_1, cov, n_samples // 2, method=method),\n",
    "            )\n",
    "        )\n",
    "    elif simulation == \"trunk_overlap\":\n",
    "        mixture_idx = rng.choice(\n",
    "            2, n_samples // 2, replace=True, shuffle=True, p=[mix, 1 - mix]\n",
    "        )\n",
    "        norm_params = [[mu_0, cov], [mu_1, cov]]\n",
    "        X_mixture = np.fromiter(\n",
    "            (\n",
    "                rng.multivariate_normal(*(norm_params[i]), size=1, method=method)\n",
    "                for i in mixture_idx\n",
    "            ),\n",
    "            dtype=np.dtype((float, n_informative)),\n",
    "        )\n",
    "        X_mixture_2 = np.fromiter(\n",
    "            (\n",
    "                rng1.multivariate_normal(*(norm_params[i]), size=1, method=method)\n",
    "                for i in mixture_idx\n",
    "            ),\n",
    "            dtype=np.dtype((float, n_informative)),\n",
    "        )\n",
    "        X = np.vstack(\n",
    "            (\n",
    "                X_mixture.reshape(n_samples // 2, n_informative),\n",
    "                X_mixture_2.reshape(n_samples // 2, n_informative),\n",
    "            )\n",
    "        )\n",
    "    elif simulation == \"trunk_mix\":\n",
    "        mixture_idx = rng.choice(\n",
    "            2, n_samples // 2, replace=True, shuffle=True, p=[mix, 1 - mix]\n",
    "        )\n",
    "        norm_params = [[mu_0, cov], [mu_1, cov]]\n",
    "        X_mixture = np.fromiter(\n",
    "            (\n",
    "                rng1.multivariate_normal(*(norm_params[i]), size=1, method=method)\n",
    "                for i in mixture_idx\n",
    "            ),\n",
    "            dtype=np.dtype((float, n_informative)),\n",
    "        )\n",
    "        X = np.vstack(\n",
    "            (\n",
    "                rng.multivariate_normal(\n",
    "                    np.zeros(n_informative), cov, n_samples // 2, method=method\n",
    "                ),\n",
    "                X_mixture.reshape(n_samples // 2, n_informative),\n",
    "            )\n",
    "        )\n",
    "    else:\n",
    "        raise ValueError(f\"Simulation must be: trunk, trunk_overlap, trunk_mix\")\n",
    "    if n_dim > n_informative:\n",
    "        X = np.hstack(\n",
    "            (X, rng.normal(loc=0, scale=1, size=(X.shape[0], n_dim - n_informative)))\n",
    "        )\n",
    "    y = np.concatenate((np.zeros(n_samples // 2), np.ones(n_samples // 2)))\n",
    "    if return_params:\n",
    "        returns = [X, y]\n",
    "        if simulation == \"trunk\":\n",
    "            returns += [[mu_0, mu_1], [cov, cov]]\n",
    "        elif simulation == \"trunk-overlap\":\n",
    "            returns += [[np.zeros(n_informative), np.zeros(n_informative)], [cov, cov]]\n",
    "        elif simulation == \"trunk-mix\":\n",
    "            returns += [*list(zip(*norm_params)), X_mixture]\n",
    "        return returns\n",
    "    return X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "from sktree.stats import build_oob_forest\n",
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegressionCV, LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sktree.ensemble import HonestForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "from sklearn.metrics import roc_auc_score, roc_curve\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "class NaivelyCalibratedLinearSVC(LinearSVC):\n",
    "    \"\"\"LinearSVC with `predict_proba` method that naively scales\n",
    "    `decision_function` output.\"\"\"\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        super().fit(X, y)\n",
    "        df = self.decision_function(X)\n",
    "        self.df_min_ = df.min()\n",
    "        self.df_max_ = df.max()\n",
    "\n",
    "    def predict_proba(self, X):\n",
    "        \"\"\"Min-max scale output of `decision_function` to [0,1].\"\"\"\n",
    "        df = self.decision_function(X)\n",
    "        calibrated_df = (df - self.df_min_) / (self.df_max_ - self.df_min_)\n",
    "        proba_pos_class = np.clip(calibrated_df, 0, 1)\n",
    "        proba_neg_class = 1 - proba_pos_class\n",
    "        proba = np.c_[proba_neg_class, proba_pos_class]\n",
    "        return proba\n",
    "\n",
    "\n",
    "def make_simulation(name=\"linear\", seed=23, sample_size=256):\n",
    "    if name == \"linear\":\n",
    "        X, y = make_trunk_classification(\n",
    "            n_samples=sample_size,\n",
    "            n_dim=4096,\n",
    "            simulation=\"trunk\",\n",
    "            n_informative=1,\n",
    "            seed=seed,\n",
    "        )\n",
    "    elif name == \"nonlinear\":\n",
    "        X, y = make_trunk_classification(\n",
    "            n_samples=sample_size,\n",
    "            n_dim=4096,\n",
    "            simulation=\"trunk_mix\",\n",
    "            mu_0=0,\n",
    "            mu_1=5,\n",
    "            mix=0.75,\n",
    "            n_informative=1,\n",
    "            seed=seed,\n",
    "        )\n",
    "    else:\n",
    "        X, y = make_trunk_classification(\n",
    "            n_samples=sample_size,\n",
    "            n_dim=4096,\n",
    "            simulation=\"trunk_overlap\",\n",
    "            mu_0=0,\n",
    "            mu_1=5,\n",
    "            mix=0.75,\n",
    "            n_informative=1,\n",
    "            seed=seed,\n",
    "        )\n",
    "\n",
    "    #     train_samples = 256  # Samples used for training the models\n",
    "    #     X_train, X_test, y_train, y_test = train_test_split(\n",
    "    #         X,\n",
    "    #         y,\n",
    "    #         shuffle=True,\n",
    "    #         stratify=y,\n",
    "    #         test_size=100000 - train_samples,\n",
    "    #     )\n",
    "\n",
    "    #     lr = LogisticRegressionCV(\n",
    "    #         Cs=np.logspace(-6, 6, 101), cv=10, scoring=\"neg_log_loss\", max_iter=1_000\n",
    "    #     )\n",
    "    lr = LogisticRegression(penalty=\"l1\", solver=\"liblinear\", n_jobs=n_jobs,random_state=seed)\n",
    "    gnb = GaussianNB()\n",
    "    #     svc = NaivelyCalibratedLinearSVC(C=1.0)\n",
    "    svc = SVC(probability=True, kernel=\"rbf\", random_state=seed)\n",
    "    rfc = RandomForestClassifier(n_estimators=6000, max_features=0.3, n_jobs=n_jobs,random_state=seed)\n",
    "    might = HonestForestClassifier(\n",
    "        n_estimators=6000,\n",
    "        max_samples=1.6,\n",
    "        max_features=0.3,\n",
    "        bootstrap=True,\n",
    "        stratify=True,\n",
    "        random_state=seed,\n",
    "        n_jobs=n_jobs,\n",
    "    )\n",
    "\n",
    "    clf_list = [\n",
    "        (might, \"MIGHT\"),\n",
    "        (rfc, \"Random Forest\"),\n",
    "        (svc, \"SVM\"),\n",
    "        (gnb, \"Naive Bayes\"),\n",
    "        (lr, \"Logistic Regression\"),\n",
    "    ]\n",
    "\n",
    "    fpr_s = []\n",
    "    tpr_s = []\n",
    "    for i, (clf, name) in enumerate(clf_list):\n",
    "        if name == \"MIGHT\":\n",
    "            _, observe_proba = build_hyppo_oob_forest(clf, X, y)\n",
    "            observe_proba = np.nanmean(observe_proba, axis=0)\n",
    "            fpr, tpr, thresholds = roc_curve(\n",
    "                y, observe_proba[:, 1], pos_label=1, drop_intermediate=False\n",
    "            )\n",
    "        else:\n",
    "            X_train, X_test, y_train, y_test = train_test_split(\n",
    "                X, y, shuffle=True, stratify=y, test_size=0.2\n",
    "            )\n",
    "\n",
    "            X_fit, X_cal, y_fit, y_cal = train_test_split(\n",
    "                X_train, y_train, test_size=0.5, stratify=y_train\n",
    "            )\n",
    "            clf.fit(X_fit, X_fit)\n",
    "\n",
    "            calibrated_model = CalibratedClassifierCV(\n",
    "                clf, cv=\"prefit\", method=\"isotonic\"\n",
    "            )\n",
    "            calibrated_model.fit(X_cal, y_cal)\n",
    "            posterior = calibrated_model.predict_proba(X_val)\n",
    "\n",
    "            fpr, tpr, thresholds = roc_curve(\n",
    "                y_test,\n",
    "                posterior(X_test)[:, 1],\n",
    "                pos_label=1,\n",
    "                drop_intermediate=False,\n",
    "            )\n",
    "        fpr_s.append(fpr)\n",
    "        tpr_s.append(tpr)\n",
    "\n",
    "    return clf_list, fpr_s, tpr_s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# clf_linear_s = []\n",
    "# fpr_linear_s = []\n",
    "# tpr_linear_s = []\n",
    "# N_ITR = 10\n",
    "\n",
    "# for i in range(N_ITR):\n",
    "#     #     print(i)\n",
    "#     clf_linear, fpr_linear, tpr_linear = make_simulation(\"linear\", seed=i)\n",
    "#     clf_linear_s.append(clf_linear)\n",
    "#     fpr_linear_s.append(fpr_linear)\n",
    "#     tpr_linear_s.append(tpr_linear)\n",
    "\n",
    "#     #     print(\"non\")\n",
    "#     clf_nonlinear, fpr_nonlinear, tpr_nonlinear = make_simulation(\"nonlinear\", seed=i)\n",
    "#     clf_nonlinear_s.append(clf_nonlinear)\n",
    "#     fpr_nonlinear_s.append(fpr_nonlinear)\n",
    "#     tpr_nonlinear_s.append(tpr_nonlinear)\n",
    "\n",
    "#     #     print(\"null\")\n",
    "#     clf_null, fpr_null, tpr_null = make_simulation(\"null\", seed=i)\n",
    "#     clf_null_s.append(clf_null)\n",
    "#     fpr_null_s.append(fpr_null)\n",
    "#     tpr_null_s.append(tpr_null)\n",
    "\n",
    "#     with open(DIRECTORY + \"fpr_linear.pkl\", \"wb\") as f:\n",
    "#         pickle.dump(fpr_linear, f)\n",
    "\n",
    "#     with open(DIRECTORY + \"tpr_linear.pkl\", \"wb\") as f:\n",
    "#         pickle.dump(tpr_linear, f)\n",
    "\n",
    "#     with open(DIRECTORY + \"fpr_nonlinear.pkl\", \"wb\") as f:\n",
    "#         pickle.dump(fpr_nonlinear, f)\n",
    "\n",
    "#     with open(DIRECTORY + \"tpr_nonlinear.pkl\", \"wb\") as f:\n",
    "#         pickle.dump(tpr_nonlinear, f)\n",
    "\n",
    "#     with open(DIRECTORY + \"fpr_null.pkl\", \"wb\") as f:\n",
    "#         pickle.dump(fpr_null, f)\n",
    "\n",
    "#     with open(DIRECTORY + \"tpr_null.pkl\", \"wb\") as f:\n",
    "#         pickle.dump(tpr_null, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_name = [\n",
    "    \"MIGHT\",\n",
    "    \"Random Forest\",\n",
    "    \"SVM\",\n",
    "    \"Naive Bayes\",\n",
    "    \"Logistic Regression\",\n",
    "]\n",
    "\n",
    "DIRECTORY = \"fig1_pkl_v2/\"\n",
    "SAMPLE = \".\" #\"_256.\"\n",
    "\n",
    "with open(DIRECTORY + \"fpr_linear\" + str(SAMPLE) + \"pkl\", \"rb\") as f:\n",
    "    fpr_linear_s = pickle.load(f)\n",
    "\n",
    "with open(DIRECTORY + \"tpr_linear\" + str(SAMPLE) + \"pkl\", \"rb\") as f:\n",
    "    tpr_linear_s = pickle.load(f)\n",
    "\n",
    "with open(DIRECTORY + \"fpr_nonlinear\" + str(SAMPLE) + \"pkl\", \"rb\") as f:\n",
    "    fpr_nonlinear_s = pickle.load(f)\n",
    "\n",
    "with open(DIRECTORY + \"tpr_nonlinear\" + str(SAMPLE) + \"pkl\", \"rb\") as f:\n",
    "    tpr_nonlinear_s = pickle.load(f)\n",
    "\n",
    "with open(DIRECTORY + \"fpr_null\" + str(SAMPLE) + \"pkl\", \"rb\") as f:\n",
    "    fpr_null_s = pickle.load(f)\n",
    "\n",
    "with open(DIRECTORY + \"tpr_null\" + str(SAMPLE) + \"pkl\", \"rb\") as f:\n",
    "    tpr_null_s = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_fpr = np.linspace(0, 1, 1001)\n",
    "N_ITR = 1 #len(tpr_null_s)\n",
    "SAMPLE = 256\n",
    "\n",
    "linear_s = []\n",
    "nonlinear_s = []\n",
    "null_s = []\n",
    "for j in range(5):\n",
    "    tpr_s = []\n",
    "    for i in range(N_ITR):\n",
    "        tpr = np.interp(base_fpr, fpr_linear_s[j], tpr_linear_s[j])\n",
    "        tpr_s.append(tpr)\n",
    "        \n",
    "    tpr_s = np.mean(tpr_s, axis=0)\n",
    "    linear_s.append(tpr_s)\n",
    "    \n",
    "    tpr_s = []\n",
    "    for i in range(N_ITR):\n",
    "        tpr = np.interp(base_fpr, fpr_nonlinear_s[j], tpr_nonlinear_s[j])\n",
    "        tpr_s.append(tpr)\n",
    "        \n",
    "    tpr_s = np.mean(tpr_s, axis=0)\n",
    "    nonlinear_s.append(tpr_s)\n",
    "    \n",
    "    tpr_s = []\n",
    "    for i in range(N_ITR):\n",
    "        tpr = np.interp(base_fpr, fpr_null_s[j], tpr_null_s[j])\n",
    "        tpr_s.append(tpr)\n",
    "        \n",
    "    tpr_s = np.mean(tpr_s, axis=0)\n",
    "    null_s.append(tpr_s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2000x1500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the data\n",
    "fontsize = 15\n",
    "tablefontsize = 20\n",
    "labelsize = 20\n",
    "density = True\n",
    "bins = 50\n",
    "fig, ax = plt.subplots(2, 3, figsize=(20, 15))\n",
    "k = 0\n",
    "\n",
    "plt.rcParams[\"axes.axisbelow\"] = True\n",
    "# downsample the data to plot the histograms to 250 points each\n",
    "scale = N // SAMPLE\n",
    "x_A_ = x_A[::scale]\n",
    "y_A_ = y_A[::scale]\n",
    "x_B_ = x_B[::scale]\n",
    "y_B_ = y_B[::scale]\n",
    "x_C_ = x_C[::scale]\n",
    "y_C_ = y_C[::scale]\n",
    "# plot variable A\n",
    "x_0_A, x_1_A = x_A_[y_A_ == 0], x_A_[y_A_ == 1]\n",
    "x_0_B, x_1_B = x_B_[y_B_ == 0], x_B_[y_B_ == 1]\n",
    "x_0_C, x_1_C = x_C_[y_C_ == 0], x_C_[y_C_ == 1]\n",
    "# x_min, x_max = np.min(x_A), np.max(x_A)\n",
    "# xs = np.linspace(x_min-1, x_max+1, 1000)\n",
    "\n",
    "# set the ticks size for all the plots\n",
    "for i in range(2):\n",
    "    for j in range(3):\n",
    "        ax[i, j].tick_params(labelsize=labelsize - 1)\n",
    "\n",
    "ax[0, 0].hist(x_0_A[:SAMPLE // 2], density=density, alpha=0.6, color=PALETTE[1])\n",
    "ax[0, 0].hist(x_1_A[:SAMPLE // 2], density=density, alpha=0.3, color=PALETTE[0])\n",
    "sns.lineplot(\n",
    "    pdf_A, x=\"x\", y=\"pdf_class0\", label=\"Control\", color=PALETTE[1], ax=ax[0, 0]\n",
    ")\n",
    "sns.lineplot(\n",
    "    pdf_A,\n",
    "    x=\"x\",\n",
    "    y=\"pdf_class1\",\n",
    "    label=\"Case\",\n",
    "    color=PALETTE[0],\n",
    "    ax=ax[0, 0],\n",
    "    linestyle=\"--\",\n",
    ")\n",
    "# Set the axis labels\n",
    "ax[0, 0].set_title(\"Linear\")\n",
    "ax[0, 0].set_xlabel(\"Variable A\", fontsize=labelsize)\n",
    "ax[0, 0].set_ylabel(\"Likelihood\", fontsize=labelsize)\n",
    "# remove the legend\n",
    "ax[0, 0].legend().remove()\n",
    "# ax[0, 0].legend(fontsize=fontsize)\n",
    "# set the xticks to just min and max\n",
    "ax[0, 0].set_xticks([-3, 0, 3])\n",
    "ax[0, 0].set_yticks([0.0, 0.3, 0.6])\n",
    "ax[0, 0].spines[\"left\"].set_color(\"#dddddd\")\n",
    "ax[0, 0].spines[\"right\"].set_color(\"#dddddd\")\n",
    "ax[0, 0].spines[\"top\"].set_color(\"#dddddd\")\n",
    "ax[0, 0].spines[\"bottom\"].set_color(\"#dddddd\")\n",
    "\n",
    "ax[0, 1].hist(x_0_B[:N // 2], density=density, alpha=0.6, color=PALETTE[1])\n",
    "ax[0, 1].hist(x_1_B[:N // 2], density=density, alpha=0.3, color=PALETTE[0])\n",
    "sns.lineplot(\n",
    "    pdf_B, x=\"x\", y=\"pdf_class0\", label=\"Control\", color=PALETTE[1], ax=ax[0, 1]\n",
    ")\n",
    "sns.lineplot(\n",
    "    pdf_B,\n",
    "    x=\"x\",\n",
    "    y=\"pdf_class1\",\n",
    "    label=\"Case\",\n",
    "    color=PALETTE[0],\n",
    "    ax=ax[0, 1],\n",
    "    linestyle=\"--\",\n",
    ")\n",
    "ax[0, 1].set_title(\"Non-linear\")\n",
    "ax[0, 1].set_xlabel(\"Variable B\", fontsize=labelsize)\n",
    "ax[0, 1].set_ylabel(\"\")\n",
    "# remove the legend\n",
    "ax[0, 1].legend().remove()\n",
    "# ax[0, 1].legend(fontsize=fontsize)\n",
    "# set the xtiks to integers\n",
    "# ax[0, 1].set_xticks(np.arange(-6, 9, 2))\n",
    "ax[0, 1].set_xticks([-3, 0, 3, 6])\n",
    "ax[0, 1].set_yticks([0.0, 0.3, 0.6])\n",
    "ax[0, 1].spines[\"left\"].set_color(\"#dddddd\")\n",
    "ax[0, 1].spines[\"right\"].set_color(\"#dddddd\")\n",
    "ax[0, 1].spines[\"top\"].set_color(\"#dddddd\")\n",
    "ax[0, 1].spines[\"bottom\"].set_color(\"#dddddd\")\n",
    "\n",
    "ax[0, 2].hist(x_0_C[:N // 2], density=density, alpha=0.6, color=PALETTE[1])\n",
    "ax[0, 2].hist(x_1_C[:N // 2], density=density, alpha=0.3, color=PALETTE[0])\n",
    "sns.lineplot(\n",
    "    pdf_C, x=\"x\", y=\"pdf_class0\", label=\"Control\", color=PALETTE[1], ax=ax[0, 2]\n",
    ")\n",
    "sns.lineplot(\n",
    "    pdf_C,\n",
    "    x=\"x\",\n",
    "    y=\"pdf_class1\",\n",
    "    label=\"Case\",\n",
    "    color=PALETTE[0],\n",
    "    ax=ax[0, 2],\n",
    "    linestyle=\"--\",\n",
    ")\n",
    "ax[0, 2].set_title(\"Null\")\n",
    "ax[0, 2].set_xlabel(\"Variable C\", fontsize=labelsize)\n",
    "ax[0, 2].set_ylabel(\"\")\n",
    "ax[0, 2].legend(fontsize=fontsize, frameon=False, loc=2)\n",
    "ax[0, 2].set_xticks([-3, 0, 3, 6])\n",
    "ax[0, 2].set_yticks([0.0, 0.3, 0.6])\n",
    "ax[0, 2].spines[\"left\"].set_color(\"#dddddd\")\n",
    "ax[0, 2].spines[\"right\"].set_color(\"#dddddd\")\n",
    "ax[0, 2].spines[\"top\"].set_color(\"#dddddd\")\n",
    "ax[0, 2].spines[\"bottom\"].set_color(\"#dddddd\")\n",
    "\n",
    "# plot the ROC curve\n",
    "# convert the fpr, tpr, thresholds in a dataframe\n",
    "fpr_A, tpr_A, thresholds_A = metrics.roc_curve(\n",
    "    y_A, posterior_A, pos_label=1, drop_intermediate=False\n",
    ")\n",
    "fpr_B, tpr_B, thresholds_B = metrics.roc_curve(\n",
    "    y_B, posterior_B, pos_label=1, drop_intermediate=False\n",
    ")\n",
    "fpr_C, tpr_C, thresholds_C = metrics.roc_curve(\n",
    "    y_C, posterior_C, pos_label=1, drop_intermediate=False\n",
    ")\n",
    "df_roc_A = pd.DataFrame({\"fpr\": fpr_A, \"tpr\": tpr_A, \"thresholds\": thresholds_A})\n",
    "df_roc_B = pd.DataFrame({\"fpr\": fpr_B, \"tpr\": tpr_B, \"thresholds\": thresholds_B})\n",
    "df_roc_C = pd.DataFrame({\"fpr\": fpr_C, \"tpr\": tpr_C, \"thresholds\": thresholds_C})\n",
    "\n",
    "# plot the ROC curve\n",
    "# remove the padding from the subplots\n",
    "\n",
    "ax[1, 0].set_xlabel(\"False Positive Rate\", fontsize=labelsize)\n",
    "ax[1, 0].set_ylabel(\"True Positive Rate\", fontsize=labelsize)\n",
    "# ax[1, 0].set_title(\"ROC Curve: Variable A\")\n",
    "ax[1, 0].plot(df_roc_A[\"fpr\"], df_roc_A[\"tpr\"], color=\"k\")\n",
    " \n",
    "for i, name in enumerate(clf_name):\n",
    "    if name == \"MIGHT\":\n",
    "        ax[1, 0].plot(base_fpr, linear_s[0], color=PALETTE[0])\n",
    "    else:\n",
    "        ax[1, 0].plot(base_fpr, linear_s[i], label=name)\n",
    "        continue\n",
    "\n",
    "ax[1, 0].plot([0, 1], [0, 1], \"k--\", label=\"Random\", alpha=0.5)\n",
    "\n",
    "\n",
    "# sa98 = max(\n",
    "#     [\n",
    "#         tpr_A\n",
    "#         for (fpr_A, tpr_A) in zip(fpr_A, tpr_A)\n",
    "#         if fpr_A <= 0.02\n",
    "#     ]\n",
    "# )\n",
    "# ax[1, 0].axvline(\n",
    "#     x=0.02,\n",
    "#     color=PALETTE[-2],\n",
    "#     ymin=0,\n",
    "#     ymax=sa98,\n",
    "#     #     label=\"S@\" + str(spec) + \" = \" + str(round(sa98, 2)),\n",
    "#     linestyle=\"--\",\n",
    "# )\n",
    "# ax[1, 0].axhline(y=sa98, xmin=0, xmax=0.02, color=PALETTE[-2], linestyle=\"--\")\n",
    "\n",
    "# set the ticks to 0, 1\n",
    "ax[1, 0].set_xticks([0, 1])\n",
    "ax[1, 0].set_yticks([0, 1])\n",
    "\n",
    "ax[1, 0].spines[\"left\"].set_color(\"#dddddd\")\n",
    "ax[1, 0].spines[\"right\"].set_color(\"#dddddd\")\n",
    "ax[1, 0].spines[\"top\"].set_color(\"#dddddd\")\n",
    "ax[1, 0].spines[\"bottom\"].set_color(\"#dddddd\")\n",
    "\n",
    "ax[1, 0].set_ylim(bottom=0)\n",
    "ax[1, 0].set_xlim(left=0)\n",
    "ax[1, 0].set_ylim(top=1)\n",
    "ax[1, 0].set_xlim(right=1)\n",
    "ax[1, 0].set_axisbelow(True)\n",
    "\n",
    "ax[1, 1].set_xlabel(\"False Positive Rate\", fontsize=labelsize)\n",
    "ax[1, 1].set_ylabel(\"\")\n",
    "# ax[1, 1].set_title(\"ROC Curve: Variable B\")\n",
    "ax[1, 1].plot(df_roc_B[\"fpr\"], df_roc_B[\"tpr\"], color=\"k\")\n",
    "\n",
    "for i, name in enumerate(clf_name):\n",
    "    if name == \"MIGHT\":\n",
    "        ax[1, 1].plot(base_fpr, nonlinear_s[0], color=PALETTE[0])\n",
    "    else:\n",
    "        ax[1, 1].plot(base_fpr, nonlinear_s[i], label=name)\n",
    "        continue\n",
    "\n",
    "ax[1, 1].plot([0, 1], [0, 1], \"k--\", label=\"Random\", alpha=0.5)\n",
    "\n",
    "# sa98 = max(\n",
    "#     [\n",
    "#         tpr_B\n",
    "#         for (fpr_B, tpr_B) in zip(fpr_B, tpr_B)\n",
    "#         if fpr_B <= 0.02\n",
    "#     ]\n",
    "# )\n",
    "# ax[1, 1].axvline(\n",
    "#     x=0.02,\n",
    "#     color=PALETTE[-2],\n",
    "#     ymin=0,\n",
    "#     ymax=sa98,\n",
    "#     #     label=\"S@\" + str(spec) + \" = \" + str(round(sa98, 2)),\n",
    "#     linestyle=\"--\",\n",
    "# )\n",
    "# ax[1, 1].axhline(y=sa98, xmin=0, xmax=0.02, color=PALETTE[-2], linestyle=\"--\")\n",
    "\n",
    "ax[1, 1].set_xticks([0, 1])\n",
    "ax[1, 1].set_yticks([0, 1])\n",
    "ax[1, 1].set_axisbelow(True)\n",
    "ax[1, 1].vlines(\n",
    "    x=0.005, ymin=0, ymax=statistics_B[\"S@98\"] - 0.005, color=\"k\", linestyle=\"-\"\n",
    ")\n",
    "\n",
    "ax[1, 1].spines[\"left\"].set_color(\"#dddddd\")\n",
    "ax[1, 1].spines[\"right\"].set_color(\"#dddddd\")\n",
    "ax[1, 1].spines[\"top\"].set_color(\"#dddddd\")\n",
    "ax[1, 1].spines[\"bottom\"].set_color(\"#dddddd\")\n",
    "\n",
    "ax[1, 1].set_ylim(bottom=0)\n",
    "ax[1, 1].set_xlim(left=0)\n",
    "ax[1, 1].set_ylim(top=1)\n",
    "ax[1, 1].set_xlim(right=1)\n",
    "\n",
    "ax[1, 2].set_xlabel(\"False Positive Rate\", fontsize=labelsize)\n",
    "ax[1, 2].set_ylabel(\"\")\n",
    "# ax[1, 2].set_title(\"ROC Curve: Variable C\")\n",
    "ax[1, 2].plot(df_roc_C[\"fpr\"], df_roc_C[\"tpr\"], label=\"Truth\", color=\"k\")\n",
    "\n",
    "for i, name in enumerate(clf_name):\n",
    "    if name == \"MIGHT\":\n",
    "        ax[1, 2].plot(base_fpr, null_s[0], color=PALETTE[0], label=name)\n",
    "    else:\n",
    "        ax[1, 2].plot(base_fpr, null_s[i], label=name)\n",
    "        continue\n",
    "\n",
    "\n",
    "\n",
    "# ax[1, 2].axvline(\n",
    "#     x=0.02,\n",
    "#     color=PALETTE[-2],\n",
    "#     ymin=0,\n",
    "#     ymax=0.02,\n",
    "#     #     label=\"S@\" + str(spec) + \" = \" + str(round(sa98, 2)),\n",
    "#     linestyle=\"--\",\n",
    "# )\n",
    "# ax[1, 2].axhline(y=0.02, xmin=0, xmax=0.02, color=PALETTE[-2], linestyle=\"--\")\n",
    "        \n",
    "        \n",
    "ax[1, 2].plot([0, 1], [0, 1], \"k--\", label=\"Random\", alpha=0.5)\n",
    "ax[1, 2].legend(fontsize=fontsize, frameon=False, loc=2)\n",
    "ax[1, 2].set_xticks([0, 1])\n",
    "ax[1, 2].set_yticks([0, 1])\n",
    "ax[1, 2].set_ylim(bottom=0)\n",
    "\n",
    "ax[1, 2].spines[\"left\"].set_color(\"#dddddd\")\n",
    "ax[1, 2].spines[\"right\"].set_color(\"#dddddd\")\n",
    "ax[1, 2].spines[\"top\"].set_color(\"#dddddd\")\n",
    "ax[1, 2].spines[\"bottom\"].set_color(\"#dddddd\")\n",
    "\n",
    "ax[1, 2].set_ylim(bottom=0)\n",
    "ax[1, 2].set_xlim(left=0)\n",
    "ax[1, 2].set_ylim(top=1)\n",
    "ax[1, 2].set_xlim(right=1)\n",
    "ax[1, 2].set_axisbelow(True)\n",
    "\n",
    "\n",
    "# ax[1, 0].set_title(string.ascii_uppercase[0], loc=\"left\", weight='bold')\n",
    "# ax[1, 1].set_title(string.ascii_uppercase[1], loc=\"left\", weight='bold')\n",
    "k = 0\n",
    "for i in range(2):\n",
    "    for j in range(3):\n",
    "        ax[i, j].set_title(string.ascii_uppercase[k], loc=\"left\", weight=\"bold\")\n",
    "        k += 1\n",
    "\n",
    "\n",
    "# statistics_A_list = np.array(list(statistics_A.values()))\n",
    "# statistics_B_list = np.array(list(statistics_B.values()))\n",
    "# statistics_C_list = np.array(list(statistics_C.values()))\n",
    "# df = pd.DataFrame(\n",
    "#     np.vstack((statistics_A_list, statistics_B_list, statistics_C_list)).T,\n",
    "#     index=statistics_A.keys(),\n",
    "#     columns=[\"Variable A\", \"Variable B\", \"Variable C\"],\n",
    "# )\n",
    "# winners = df.idxmax(axis=1)\n",
    "# # print the statistics\n",
    "# for i, (key, value) in enumerate(statistics_A.items()):\n",
    "#     if winners[key] == \"Variable A\":\n",
    "#         fontweight = \"bold\"\n",
    "#     else:\n",
    "#         fontweight = \"normal\"\n",
    "#     ax[1, 0].text(\n",
    "#         0.50,\n",
    "#         0.25 - i * 0.05,\n",
    "#         f\"{key}: \" + \"%.2f\" % round(value, 2),\n",
    "#         fontsize=tablefontsize,\n",
    "#         transform=ax[1, 0].transAxes,\n",
    "#         fontweight=fontweight,\n",
    "#     )\n",
    "# for i, (key, value) in enumerate(statistics_B.items()):\n",
    "#     if winners[key] == \"Variable B\":\n",
    "#         fontweight = \"bold\"\n",
    "#     else:\n",
    "#         fontweight = \"normal\"\n",
    "#     ax[1, 1].text(\n",
    "#         0.50,\n",
    "#         0.25 - i * 0.05,\n",
    "#         # bold the value if it is greater than the value in the previous variable\n",
    "#         f\"{key}: \" + \"%.2f\" % round(value, 2),\n",
    "#         fontsize=tablefontsize,\n",
    "#         transform=ax[1, 1].transAxes,\n",
    "#         fontweight=fontweight,\n",
    "#     )\n",
    "\n",
    "# for i, (key, value) in enumerate(statistics_C.items()):\n",
    "#     if winners[key] == \"Variable C\":\n",
    "#         fontweight = \"bold\"\n",
    "#     else:\n",
    "#         fontweight = \"normal\"\n",
    "#     ax[1, 2].text(\n",
    "#         0.50,\n",
    "#         0.25 - i * 0.05,\n",
    "#         f\"{key}: \" + \"%.2f\" % round(value, 2),\n",
    "#         fontsize=tablefontsize,\n",
    "#         transform=ax[1, 2].transAxes,\n",
    "#         fontweight=fontweight,\n",
    "#     )\n",
    "\n",
    "# plt.subplots_adjust(top=1, bottom=0, right=2, left=1, hspace=1, wspace=1)\n",
    "# plt.margins(0, 0)\n",
    "\n",
    "# print(df.to_latex())\n",
    "\n",
    "# save the plot as a svg\n",
    "# plt.savefig(\"figure1_sameparam.svg\", format=\"svg\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "64\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\n",
    "SAMPLE = 256\n",
    "X, y = make_trunk_classification(\n",
    "        n_samples=256,\n",
    "        n_dim=4096,\n",
    "        simulation=\"trunk_overlap\",\n",
    "        mu_0=0,\n",
    "        mu_1=5,\n",
    "        mix=0.75,\n",
    "        n_informative=1,\n",
    "        seed=23,\n",
    "    )\n",
    "    # observe_dim_probas.append([])\n",
    "# null_dim_probas.append([])\n",
    "for j in [64]:\n",
    "    print(j)\n",
    "    X_t = np.concatenate((X[: j // 2], X[SAMPLE // 2 : SAMPLE // 2 + j // 2]))\n",
    "    y_t = np.concatenate((np.zeros(j // 2), np.ones(j // 2)))\n",
    "\n",
    "    cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=23)\n",
    "    all_proba = np.full(\n",
    "        (j, 2), np.nan, dtype=np.float64\n",
    "    )\n",
    "    for k, (train_ix, test_ix) in enumerate(cv.split(X_t, y_t)):\n",
    "        X_train, X_test, y_train, y_test = X_t[train_ix, :], X_t[test_ix, :], y_t[train_ix], y_t[test_ix]\n",
    "        est = HonestForestClassifier(\n",
    "            n_estimators=100,\n",
    "            max_samples=1.6,\n",
    "            max_features=0.3,\n",
    "            bootstrap=True,\n",
    "            stratify=True,\n",
    "            n_jobs=1,\n",
    "            random_state=23,\n",
    "#             honest_prior=\"ignore\"\n",
    "        )\n",
    "        est.fit(X_train, y_train)\n",
    "        all_proba[test_ix, :] = est.predict_proba(X_test)\n",
    "#         print(all_proba)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Calculate_SA98(y_true, y_pred_proba, max_fpr=0.02) -> float:\n",
    "    if y_true.squeeze().ndim != 1:\n",
    "        raise ValueError(f\"y_true must be 1d, not {y_true.shape}\")\n",
    "    if 0 in y_true or -1 in y_true:\n",
    "        fpr, tpr, thresholds = roc_curve(\n",
    "            y_true, y_pred_proba[:, 1], pos_label=1, drop_intermediate=False\n",
    "        )\n",
    "    else:\n",
    "        fpr, tpr, thresholds = roc_curve(\n",
    "            y_true, y_pred_proba[:, 1], pos_label=2, drop_intermediate=False\n",
    "        )\n",
    "    s98 = max([tpr for (fpr, tpr) in zip(fpr, tpr) if fpr <= max_fpr])\n",
    "    return s98"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "ar = np.array([[[np.nan, np.nan], [1,0], [np.nan, np.nan]], [[np.nan, np.nan], [1,0], [0.5, 0.5]]])\n",
    "\n",
    "# ar /= ar.sum(1, keepdims=True)\n",
    "# ar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0. , 0. ],\n",
       "       [2. , 0. ],\n",
       "       [0.5, 0.5]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.nansum([ar[0], ar[1]], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = np.random.RandomState(1)\n",
    "X = rng.normal(0, 1, (100, 2))\n",
    "y = [0] * 75 + [1] * 25\n",
    "clf = HonestForestClassifier(\n",
    "    honest_fraction=0.02, random_state=0, honest_prior=\"ignore\", n_estimators=2\n",
    ")\n",
    "clf = clf.fit(X, y)\n",
    "\n",
    "y_proba = clf.predict_proba(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = rng.normal(0, 1, (100, 2))\n",
    "y = [0] * 75 + [1] * 25\n",
    "clf = HonestForestClassifier(\n",
    "    honest_fraction=0.5, random_state=0, honest_prior=\"ignore\", n_estimators=100\n",
    ")\n",
    "clf = clf.fit(X, y)\n",
    "\n",
    "y_proba = clf.predict_proba(X)\n",
    "\n",
    "assert (\n",
    "    len(np.where(np.isnan(y_proba[:, 0]))[0]) < 10\n",
    "), f\"Failed with, prior {clf.estimators_[0].empirical_prior_}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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