Forest Hypothesis Testing angle (2).ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from proglearn.progressive_learner import ClassificationProgressiveLearner\n",
        "from proglearn.transformers import TreeClassificationTransformer\n",
        "from proglearn.voters import TreeClassificationVoter\n",
        "from proglearn.deciders import SimpleArgmaxAverage\n",
        "\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from joblib import Parallel, delayed\n",
        "from collections import defaultdict\n",
        "import joblib\n",
        "from sklearn.utils.fixes import _joblib_parallel_args\n",
        "import threading\n",
        "from sklearn.ensemble._base import _partition_estimators\n",
        "from sklearn.utils.validation import check_is_fitted\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.utils.validation import check_X_y, check_array\n",
        "\n",
        "\n",
        "from hyppo.tools import multimodal_independence, indep_sim, rot_ksamp\n",
        "from hyppo.tools import SIMULATIONS\n",
        "from hyppo.ksample._utils import k_sample_transform\n",
        "from tqdm import tqdm\n",
        "from hyppo.tools import * \n",
        "\n",
        "from scipy.stats import entropy, multivariate_normal\n",
        "from math import ceil\n",
        "\n",
        "\n",
        "import matplotlib.pyplot as plt"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": "2023-02-10 17:29:06.771090: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n2023-02-10 17:29:07.718652: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n2023-02-10 17:29:07.718750: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n2023-02-10 17:29:07.718762: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n2023-02-10 17:29:09.221020: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:267] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n2023-02-10 17:29:09.221087: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (ybai312): /proc/driver/nvidia/version does not exist\n2023-02-10 17:29:09.224085: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
        }
      ],
      "execution_count": 1,
      "metadata": {
        "gather": {
          "logged": 1676050150223
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class UncertaintyForest:\n",
        "    \"\"\"\n",
        "    A class used to represent an Uncertainty Forest.\n",
        "    Parameters\n",
        "    ----------\n",
        "    n_estimators : int, default=100\n",
        "        The number of trees in the UncertaintyForest\n",
        "    kappa : float, default=np.inf\n",
        "        The coefficient for finite sample correction. If set to the default\n",
        "        value, finite sample correction is not performed.\n",
        "    max_depth : int, default=None\n",
        "        The maximum depth of a tree in the UncertaintyForest.\n",
        "    tree_construction_proportion : float, default=0.5\n",
        "        The proportions of the input data set aside to train each decision\n",
        "        tree. The remainder of the data is used to fill in voting posteriors.\n",
        "    max_features : {\"auto\", \"sqrt\", \"log2\"}, int or float, default=\"auto\"\n",
        "        The number of features to consider when looking for the best split:\n",
        "        - If int, then consider `max_features` features at each split.\n",
        "        - If float, then `max_features` is a fraction and\n",
        "          `round(max_features * n_features)` features are considered at each\n",
        "          split.\n",
        "        - If \"auto\", then `max_features=sqrt(n_features)`.\n",
        "        - If \"sqrt\", then `max_features=sqrt(n_features)` (same as \"auto\").\n",
        "        - If \"log2\", then `max_features=log2(n_features)`.\n",
        "        - If None, then `max_features=n_features`.\n",
        "        Note: the search for a split does not stop until at least one\n",
        "        valid partition of the node samples is found, even if it requires to\n",
        "        effectively inspect more than ``max_features`` features.\n",
        "    poisson_sampler : boolean, default=False\n",
        "        To match the GRF theory [#1grf]_, if True, the number of features\n",
        "        considered at each tree are drawn from a poisson distribution with\n",
        "        mean equal to `max_features`.\n",
        "    n_jobs : int, default=None\n",
        "        The number of jobs to run in parallel. ``-1`` means use all\n",
        "        processors. None equates to 1.\n",
        "    max_samples : int or float, default=None\n",
        "        The number of samples to draw from X (without replacement) to train\n",
        "        each tree.\n",
        "        - If None, then draw `X.shape[0]` samples.\n",
        "        - If int, then draw `max_samples` samples.\n",
        "        - If float, then draw `max_samples * X.shape[0]` samples. Thus,\n",
        "          `max_samples` should be in the interval `(0, 1)`.\n",
        "        Note: The number of samples used to learn the tree will be further\n",
        "        reduced per the `tree_construction_proportion` value.\n",
        "    honest_prior : {\"ignore\", \"uniform\", \"empirical\"}, default=\"ignore\"\n",
        "        Method for dealing with empty leaves during evaluation of a test\n",
        "        sample. If \"ignore\", trees in which the leaf is empty are not used in\n",
        "        the prediction. If \"uniform\", the prior tree posterior is 1/(number of\n",
        "        classes). If \"empirical\", the prior tree posterior is the relative\n",
        "        class frequency in the voting subsample. If all tree leaves are empty,\n",
        "        \"ignore\" will use the empirical prior and the others will use their\n",
        "        respective priors.\n",
        "    tree_kwargs : dict, default={}\n",
        "        Named arguments to be passed to each\n",
        "        sklearn.tree.DecisionTreeClassifier tree used in the construction\n",
        "        of the forest in addition to the above parameters.\n",
        "    Attributes\n",
        "    ----------\n",
        "    transformers_ : list of sklearn.tree.DecisionTreeClassifier\n",
        "        The collection of fitted trees.\n",
        "    voters_ : list of proglearn.voter.TreeClassificationVoter\n",
        "        The collection of honest voters for leaves in matching trees in\n",
        "        `self.estimators_` at the same index.\n",
        "    n_features_ : int\n",
        "        The number of features when `fit` is performed.\n",
        "    tree_kwargs_ : dict\n",
        "        Full set of keyword arguments passed to the Forest transformer.\n",
        "    References\n",
        "    ----------\n",
        "    .. [#1grf] Athey, Susan, Julie Tibshirani and Stefan Wager.\n",
        "        \"Generalized Random Forests\", Annals of Statistics, 2019.\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(\n",
        "        self,\n",
        "        n_estimators=100,\n",
        "        kappa=np.inf,\n",
        "        max_depth=None,\n",
        "        tree_construction_proportion=0.63,\n",
        "        max_features=\"auto\",\n",
        "        poisson_sampler=False,\n",
        "        max_samples=None,\n",
        "        n_jobs=None,\n",
        "        honest_prior=\"ignore\",\n",
        "        weighted_trees=False,\n",
        "        min_samples_leaf=1,\n",
        "        min_samples_split=2,\n",
        "        min_impurity_decrease=0.0,\n",
        "    ):\n",
        "        self.n_estimators = n_estimators\n",
        "        self.kappa = kappa\n",
        "        self.max_depth = max_depth\n",
        "        self.tree_construction_proportion = tree_construction_proportion\n",
        "        self.max_features = max_features\n",
        "        self.poisson_sampler = poisson_sampler\n",
        "        self.max_samples = max_samples\n",
        "        self.n_jobs = n_jobs\n",
        "        self.honest_prior = honest_prior\n",
        "        self.verbose = 0\n",
        "        self.weighted_trees = weighted_trees\n",
        "        self.min_samples_leaf = min_samples_leaf\n",
        "        self.min_samples_split = min_samples_split\n",
        "        self.min_impurity_decrease = min_impurity_decrease\n",
        "        \n",
        "\n",
        "    def fit(self, X, y):\n",
        "        \"\"\"\n",
        "        fits forest to data X with labels y\n",
        "        Parameters\n",
        "        ----------\n",
        "        X : array of shape [n_samples, n_features]\n",
        "            The data that will be trained on\n",
        "        y : array of shape [n_samples]\n",
        "            The label for cluster membership of the given data\n",
        "        Returns\n",
        "        -------\n",
        "        self : UncertaintyForest\n",
        "            The object itself.\n",
        "        \"\"\"\n",
        "        X, y = check_X_y(X, y)\n",
        "\n",
        "        self.n_samples_, self.n_features_ = X.shape\n",
        "        self.n_classes_ = len(np.unique(y))\n",
        "        self.empirical_prior_ = np.bincount(y, minlength=self.n_classes_) / len(y)\n",
        "\n",
        "        self.transformers_ = [\n",
        "            DecisionTreeClassifier(\n",
        "                max_features = self.max_features,\n",
        "                max_depth = self.max_depth,\n",
        "                min_samples_leaf = self.min_samples_leaf,\n",
        "                min_samples_split = self.min_samples_split,\n",
        "                min_impurity_decrease = self.min_impurity_decrease,\n",
        "                )\n",
        "            for _ in range(self.n_estimators)\n",
        "        ]\n",
        "\n",
        "        # Source https://github.com/scikit-learn/scikit-learn/blob/95119c13a/sklearn/ensemble/_forest.py#L387\n",
        "        self.transformers_, self.transformer_indices_ = list(zip(\n",
        "            *Parallel(n_jobs=self.n_jobs, verbose=self.verbose, **_joblib_parallel_args(prefer=\"threads\"))(\n",
        "                delayed(_parallel_build_trees)(\n",
        "                    X, y, tree, self.n_samples_, self.n_classes_,\n",
        "                    self.honest_prior, self.tree_construction_proportion,\n",
        "                    self.kappa, self.weighted_trees)\n",
        "                for tree in self.transformers_)\n",
        "                ))\n",
        "\n",
        "        return self\n",
        "\n",
        "    def predict_proba(self, X):\n",
        "        \"\"\"\n",
        "        estimates class posteriors for each example in input data X.\n",
        "        Parameters\n",
        "        ----------\n",
        "        X : array of shape [n_samples, n_features]\n",
        "            The data whose posteriors we are estimating.\n",
        "        Returns\n",
        "        -------\n",
        "        y_proba_hat : ndarray of shape [n_samples, n_classes]\n",
        "            posteriors per example\n",
        "        \"\"\"\n",
        "        X = self._validate_X_predict(X)\n",
        "        n_jobs, _, _ = _partition_estimators(self.n_estimators, self.n_jobs)\n",
        "\n",
        "        # avoid storing the output of every estimator by summing them here\n",
        "        posteriors = np.zeros((X.shape[0], self.n_classes_), dtype=np.float64)\n",
        "        lock = threading.Lock()\n",
        "\n",
        "        Parallel(n_jobs=n_jobs, verbose=self.verbose, **_joblib_parallel_args(require=\"sharedmem\"))(\n",
        "            delayed(_accumulate_prediction)(tree.tree_.predict, X, posteriors, lock, None, self.weighted_trees)\n",
        "            for tree in self.transformers_)\n",
        "\n",
        "        # Normalize to unit length, due to prior weighting\n",
        "        zero_mask = posteriors.sum(1) == 0\n",
        "        posteriors[~zero_mask] /= posteriors[~zero_mask].sum(1, keepdims=True)\n",
        "        posteriors[zero_mask] = self.empirical_prior_\n",
        "\n",
        "        return posteriors\n",
        "\n",
        "    def _validate_X_predict(self, X):\n",
        "        \"\"\"\n",
        "        From sklearn\n",
        "        Validate X whenever one tries to predict, apply, predict_proba.\"\"\"\n",
        "        check_is_fitted(self)\n",
        "\n",
        "        return self.transformers_[0]._validate_X_predict(X, check_input=True)\n",
        "\n",
        "    def predict(self, X):\n",
        "        \"\"\"\n",
        "        predicts class labels for each example in input data X.\n",
        "        Parameters\n",
        "        ----------\n",
        "        X : array of shape [n_samples, n_features]\n",
        "            The data on which we are performing inference.\n",
        "        Returns\n",
        "        -------\n",
        "        y_hat : ndarray of shape [n_samples]\n",
        "            predicted class label per example\n",
        "        \"\"\"\n",
        "        return self.predict_proba(X).argmax(1)\n",
        "\n",
        "    def predict_proba_honest(self, X, voters=True):\n",
        "        \"\"\"\n",
        "        Returns predicted posteriors of the training data, excluding trees a\n",
        "        sample was used in construction of.\n",
        "        Parameters\n",
        "        ----------\n",
        "        X : array of shape [n_samples, n_features]\n",
        "            The data on which the forest was fit to.\n",
        "        Returns\n",
        "        -------\n",
        "        y_proba_hat : ndarray of shape [n_samples, n_classes]\n",
        "            posteriors per example\n",
        "        \"\"\"\n",
        "        X = self._validate_X_predict(X)\n",
        "        n_jobs, _, _ = _partition_estimators(self.n_estimators, self.n_jobs)\n",
        "\n",
        "        # avoid storing the output of every estimator by summing them here\n",
        "        posteriors = np.zeros((X.shape[0], self.n_classes_), dtype=np.float64)\n",
        "        lock = threading.Lock()\n",
        "\n",
        "        if voters:\n",
        "            Parallel(n_jobs=n_jobs, verbose=self.verbose, **_joblib_parallel_args(require=\"sharedmem\"))(\n",
        "                delayed(_accumulate_prediction)(tree.tree_.predict, X, posteriors, lock, honest_indices)\n",
        "                for tree, honest_indices in zip(self.transformers_, self.voter_indices_))\n",
        "        else:\n",
        "            Parallel(n_jobs=n_jobs, verbose=self.verbose, **_joblib_parallel_args(require=\"sharedmem\"))(\n",
        "                delayed(_accumulate_prediction)(tree.tree_.predict, X, posteriors, lock, indices)\n",
        "                for tree, indices in zip(self.transformers_, self.transformer_indices_))\n",
        "\n",
        "        # Normalize to unit length, due to prior weighting\n",
        "        zero_mask = posteriors.sum(1) == 0\n",
        "        posteriors[~zero_mask] /= posteriors[~zero_mask].sum(1, keepdims=True)\n",
        "        posteriors[zero_mask] = self.empirical_prior_\n",
        "\n",
        "        return posteriors\n",
        "\n",
        "\n",
        "    @property\n",
        "    def voter_indices_(self):\n",
        "        if not hasattr(self, \"transformer_indices_\"):\n",
        "            raise AttributeError(\"Model has not been fitted. Please fit first.\")\n",
        "        return [\n",
        "            np.setdiff1d(np.arange(self.n_samples_), transformer_indices)\n",
        "            for transformer_indices in self.transformer_indices_]\n",
        "\n",
        "\n",
        "def _parallel_build_trees(\n",
        "    X, y, tree, n_samples_, n_classes_,\n",
        "    honest_prior, tree_construction_proportion,\n",
        "    kappa, weighted_trees):\n",
        "        # https://github.com/scikit-learn/scikit-learn/blob/95119c13a/sklearn/ensemble/_forest.py#L140\n",
        "        \"\"\"\n",
        "        Private function used to fit a single tree in parallel.\"\"\"\n",
        "\n",
        "        transformer_indices = np.random.choice(\n",
        "            n_samples_,\n",
        "            int(tree_construction_proportion * n_samples_),\n",
        "            replace=False)  # No bootstrapping\n",
        "        voter_indices = np.setdiff1d(\n",
        "            np.arange(n_samples_), transformer_indices)\n",
        "\n",
        "        sample_weight = np.ones((n_samples_,), dtype=np.float64)\n",
        "        sample_counts = np.bincount(\n",
        "            transformer_indices, minlength=n_samples_)\n",
        "        sample_weight *= sample_counts\n",
        "\n",
        "        #tree = tree.fit(X, y, sample_weight=sample_weight, check_input=False) ### Why using y, rather than X[transformer_indices,:],y[transformer_indices]\n",
        "        tree = tree.fit(X[transformer_indices,:],y[transformer_indices], sample_weight=sample_weight[transformer_indices], check_input=False)\n",
        "        \n",
        "        honest_leaves = tree.apply(X[voter_indices])\n",
        "        # empirical_prior = np.bincount(\n",
        "        #     y[voter_indices], minlength=n_classes_)\n",
        "\n",
        "        # if honest_prior == 'ignore':\n",
        "        #     posterior_dict = defaultdict(lambda: np.zeros(n_classes_))\n",
        "        # elif honest_prior == 'empirical':\n",
        "        #     posterior_dict = defaultdict(lambda: empirical_prior)\n",
        "        # elif honest_prior == 'uniform':\n",
        "        #     posterior_dict = defaultdict(lambda: np.ones(n_classes_) / n_classes_)\n",
        "\n",
        "        tree.tree_.value[:, :, :] = 0 # empirical_prior\n",
        "\n",
        "        for leaf_id, yval in zip(honest_leaves, y[voter_indices]):\n",
        "            # posterior_dict[leaf_id][yval] += 1\n",
        "            tree.tree_.value[leaf_id][0, yval] += 1\n",
        "\n",
        "        # for leaf_id in np.unique(honest_leaves):\n",
        "        #     leaf_id_indices = np.where(honest_leaves == leaf_id)[0]\n",
        "        #     correction = 1 / (kappa * len(leaf_id_indices))\n",
        "        #     posterior = correction * empirical_prior + \\\n",
        "        #         (1 - correction) * np.bincount(\n",
        "        #             y[voter_indices[leaf_id_indices]],\n",
        "        #             minlength=n_classes_) / len(leaf_id_indices)\n",
        "        #     posterior_dict[leaf_id] = posterior * (len(leaf_id_indices) if weighted_trees else 1)\n",
        "\n",
        "        return tree, transformer_indices\n",
        "\n",
        "\n",
        "def _accumulate_prediction(predict, X, out, lock, honest_indices=None, weighted_trees=False):\n",
        "    \"\"\"\n",
        "    See https://github.com/scikit-learn/scikit-learn/blob/95119c13af77c76e150b753485c662b7c52a41a2/sklearn/ensemble/_forest.py#L460\n",
        "    This is a utility function for joblib's Parallel.\n",
        "    It can't go locally in ForestClassifier or ForestRegressor, because joblib\n",
        "    complains that it cannot pickle it when placed there.\n",
        "    \"\"\"\n",
        "\n",
        "    # predict = DecisionTreeClassifier.tree_.predict\n",
        "    if honest_indices is not None:\n",
        "        proba = predict(X[honest_indices])\n",
        "    else:\n",
        "        proba = predict(X)\n",
        "    if not weighted_trees:\n",
        "        zero_mask = proba.sum(1) == 0\n",
        "        proba[~zero_mask] /= proba[~zero_mask].sum(1, keepdims=True)\n",
        "    with lock:\n",
        "        if honest_indices is not None:\n",
        "            out[honest_indices] += proba\n",
        "        else:\n",
        "            out += proba"
      ],
      "outputs": [],
      "execution_count": 2,
      "metadata": {
        "collapsed": false,
        "gather": {
          "logged": 1676050150503
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class UFTest_permute_tree:\n",
        "    def __init__(self, uf):\n",
        "        self.name = 'UF'\n",
        "        self.uf = uf\n",
        "        self.fit_ = False\n",
        "        \n",
        "    def statistic(self, X, y):\n",
        "        y = y.ravel()\n",
        "        if not self.fit_:\n",
        "            self.uf = self.uf.fit(X, y)\n",
        "            self.y_ = y\n",
        "#             if hasattr(self, \"honest_leaf_indices_\"):\n",
        "#                 assert hasattr(self, \"transformer_leaf_indices_\")\n",
        "#                 del self.honest_leaf_indices_\n",
        "#                 del self.transformer_leaf_indices_\n",
        "            self.fit_ = True\n",
        "            H_YX = np.mean(entropy(self.uf.predict_proba(X), base=np.exp(1), axis=1))\n",
        "\n",
        "        # if not hasattr(self, \"honest_leaf_indices_\"):\n",
        "            self.honest_leaf_indices_ = []\n",
        "            self.transformer_leaf_indices_ = []\n",
        "            for tree, voter_indices, transformer_indices in zip(self.uf.transformers_, self.uf.voter_indices_, self.uf.transformer_indices_):\n",
        "                self.honest_leaf_indices_.append(tree.apply(X[voter_indices]))\n",
        "                self.transformer_leaf_indices_.append(tree.apply(X[transformer_indices]))\n",
        "        else:\n",
        "            H_YX = self._refit_apply_oob(X, self.y_) # changed from y to self.y_ as does the permutation internally per tree\n",
        "            ### y has been permuted\n",
        "\n",
        "        _, counts = np.unique(y, return_counts=True)\n",
        "        H_Y = entropy(counts, base=np.exp(1))\n",
        "        return max(H_Y - H_YX, 0)\n",
        "\n",
        "    def _refit_apply_oob(self, X, y):\n",
        "        posteriors = np.zeros((X.shape[0], self.uf.n_classes_), dtype=np.float64)\n",
        "        # posteriors = np.zeros((X.shape[0], self.uf.n_estimators, self.uf.n_classes_), dtype=np.float64)\n",
        "\n",
        "        # np.random.shuffle(y)\n",
        "        for tree, honest_leaf_indices, voter_indices, tranformer_leaf_indices, transformer_indices in zip(\n",
        "            self.uf.transformers_, self.honest_leaf_indices_, self.uf.voter_indices_, self.transformer_leaf_indices_, self.uf.transformer_indices_):\n",
        "        # for tree_idx, (honest_leaf_indices, voter_indices) in enumerate(zip(self.honest_leaf_indices_, self.uf.voter_indices_)):\n",
        "            tree.tree_.value[:, :, :] = 0\n",
        "\n",
        "            y_voters = y[voter_indices]\n",
        "            np.random.shuffle(y_voters)\n",
        "            for leaf_id, yval in zip(honest_leaf_indices, y_voters):\n",
        "                tree.tree_.value[leaf_id][0, yval] += 1\n",
        "\n",
        "            leaf_indices = np.hstack((honest_leaf_indices, tranformer_leaf_indices))\n",
        "            sample_indices = np.hstack((voter_indices, transformer_indices))\n",
        "\n",
        "            proba = tree.tree_.value[leaf_indices][:, 0, :]\n",
        "            normalizer = proba.sum(axis=1)[:, np.newaxis]\n",
        "            normalizer[normalizer == 0.0] = 1.0 # solves divide by 0 error\n",
        "            proba /= normalizer\n",
        "            posteriors[sample_indices] += proba\n",
        "\n",
        "        # Normalize to unit length, due to prior weighting\n",
        "        zero_mask = posteriors.sum(1) == 0\n",
        "        posteriors[~zero_mask] /= posteriors[~zero_mask].sum(1, keepdims=True)\n",
        "        posteriors[zero_mask] = self.uf.empirical_prior_\n",
        "\n",
        "        return np.mean(entropy(posteriors, base=np.exp(1), axis=1))\n",
        "    "
      ],
      "outputs": [],
      "execution_count": 3,
      "metadata": {
        "collapsed": false,
        "gather": {
          "logged": 1676050152642
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def _perm_stat(calc_stat,sim,sample_size = 100,angle = 90,dim = 2): \n",
        "    \n",
        "    if hasattr(calc_stat, \"fit_\"):\n",
        "        calc_stat.fit_ = False\n",
        "    \n",
        "    x, y = rot_ksamp(sim, n=sample_size, p=dim,degree = angle, noise=False)\n",
        "    #print(np.var(y))\n",
        "    if np.var(y) == 0:\n",
        "        observe_stat = 0\n",
        "        null_dist = 0\n",
        "    else:\n",
        "        X, y = k_sample_transform([x, y])\n",
        "        #print(X.shape)\n",
        "        y = y.astype(int)\n",
        "\n",
        "        observe_stat = calc_stat.statistic(X, y)\n",
        "        # Refit Forests at each permutation\n",
        "        permy = np.random.permutation(y)\n",
        "        \n",
        "        if hasattr(calc_stat, \"fit_\"):\n",
        "            calc_stat.fit_ = False\n",
        "        \n",
        "        null_dist = calc_stat.statistic(X, permy)\n",
        "    #print(observe_stat,null_dist)\n",
        "    \n",
        "    return observe_stat,null_dist\n",
        "\n",
        "\n",
        "def estimate_power(calc_stat,sim,sample_size_power = 100,angle_power = 90 ,dim_power = 2,reps = 1000): \n",
        "    alt_dist, null_dist = map(np.float64,\n",
        "                              zip(*[_perm_stat(calc_stat,sim,sample_size_power,angle_power,dim_power) for _ in range(reps)]),)\n",
        "    \n",
        "    # Obtain power by cutoff\n",
        "    cutoff = np.sort(null_dist)[ceil(reps * (1 - 0.05))]\n",
        "    print(\"cutoff \" + str(cutoff))\n",
        "    empirical_power = (1 + (alt_dist >= cutoff).sum()) / (1 + reps)\n",
        "    print('power ' + str(empirical_power))\n",
        "    return empirical_power"
      ],
      "outputs": [],
      "execution_count": 4,
      "metadata": {
        "collapsed": false,
        "gather": {
          "logged": 1676050155293
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "POWER_REPS = 1000\n",
        "#sample_size = 100\n",
        "\n",
        "test = UFTest_permute_tree(\n",
        "    uf = UncertaintyForest(\n",
        "        n_estimators=100,\n",
        "        tree_construction_proportion=0.8,\n",
        "        kappa=np.inf,\n",
        "        # min_samples_leaf=5,\n",
        "        honest_prior=\"ignore\",\n",
        "        max_features=1.0,\n",
        "        n_jobs=-1,\n",
        "    )\n",
        ")\n",
        "\n",
        "simulations = SIMULATIONS.keys()\n",
        "#simulations = 'linear'"
      ],
      "outputs": [],
      "execution_count": 5,
      "metadata": {
        "collapsed": false,
        "gather": {
          "logged": 1676050158623
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "SIMULATIONS.keys()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 6,
          "data": {
            "text/plain": "dict_keys(['linear', 'exponential', 'cubic', 'joint_normal', 'step', 'quadratic', 'w_shaped', 'spiral', 'uncorrelated_bernoulli', 'logarithmic', 'fourth_root', 'sin_four_pi', 'sin_sixteen_pi', 'square', 'two_parabolas', 'circle', 'ellipse', 'diamond', 'multiplicative_noise', 'multimodal_independence'])"
          },
          "metadata": {}
        }
      ],
      "execution_count": 6,
      "metadata": {
        "collapsed": false,
        "gather": {
          "logged": 1676050159827
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "power_reps =5\n",
        "sample_size = 100\n",
        "angle = np.arange(0,95,5)\n",
        "POWER = []\n",
        "for angle_i in angle:\n",
        "    print('multimodal_independence',angle_i)\n",
        "            # Calculate the mean power over 5 times\n",
        "    power= np.mean([estimate_power(test,'multimodal_independence',100,angle_i,1) for _ in range(power_reps)])\n",
        "    print(power)\n",
        "    POWER.append(power)\n",
        "print(POWER)\n",
        "\n",
        "sample_size = range(5,105,5)\n",
        "alpha = 0.05*np.ones((len(POWER)))\n",
        "\n"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "multimodal_independence 0\ncutoff 0.06305890184525209\npower 0.07492507492507493\ncutoff 0.0697506295611483\npower 0.03396603396603397\ncutoff 0.06771611119825693\npower 0.05094905094905095\ncutoff 0.06717170447382437\npower 0.054945054945054944\ncutoff 0.07013893571392749\npower 0.03696303696303696\n0.05034965034965035\nmultimodal_independence 10\ncutoff 0.06609959638906626\npower 0.054945054945054944\ncutoff 0.06921844673214295\npower 0.029970029970029972\ncutoff 0.0655375562770486\npower 0.07392607392607392\ncutoff 0.06951541583479293\npower 0.04095904095904096\ncutoff 0.06726155591372629\npower 0.04895104895104895\n0.049750249750249755\nmultimodal_independence 20\ncutoff 0.06670273975103436\npower 0.04595404595404595\ncutoff 0.0673420350933096\npower 0.057942057942057944\ncutoff 0.06644750330918348\npower 0.04795204795204795\ncutoff 0.0672730212202668\npower 0.05194805194805195\ncutoff 0.0667189130976048\npower 0.059940059940059943\n0.05274725274725276\nmultimodal_independence 30\ncutoff 0.06450569949173746\npower 0.07092907092907093\ncutoff 0.0657021263806955\npower 0.055944055944055944\ncutoff 0.06415499036240768\npower 0.07692307692307693\ncutoff 0.06555891795106394\npower 0.056943056943056944\ncutoff 0.06829705670156738\npower 0.03896103896103896\n0.059940059940059943\nmultimodal_independence 40\ncutoff 0.06578936748583242\npower 0.055944055944055944\ncutoff 0.06618461660755359\npower 0.06593406593406594\ncutoff 0.06509024187548595\npower 0.06493506493506493\ncutoff 0.06848545369301373\npower 0.04595404595404595\ncutoff 0.06591110144109946\npower 0.053946053946053944\n0.057342657342657345\nmultimodal_independence 50\ncutoff 0.06734438367144302\npower 0.06893106893106893\ncutoff 0.06771058406776098\npower 0.052947052947052944\ncutoff 0.06621541807593145\npower 0.055944055944055944\ncutoff 0.06642748225843065\npower 0.061938061938061936\ncutoff 0.06647779776177842\npower 0.04195804195804196\n0.056343656343656345\nmultimodal_independence 60\ncutoff 0.06623476344161672\npower 0.06993006993006994\ncutoff 0.06592110121004435\npower 0.057942057942057944\ncutoff 0.06606113484010123\npower 0.04595404595404595\ncutoff 0.06827806060173014\npower 0.04895104895104895\ncutoff 0.06672824103331265\npower 0.04495504495504495\n0.05354645354645354\nmultimodal_independence 70\ncutoff 0.06757828627832485\npower 0.04995004995004995\ncutoff 0.06501693445535395\npower 0.04395604395604396\ncutoff 0.06546352618099183\npower 0.06893106893106893\ncutoff 0.06737472887506335\npower 0.04095904095904096\ncutoff 0.0670612747989171\npower 0.04795204795204795\n0.05034965034965035\nmultimodal_independence 80\ncutoff 0.06920209306396863\npower 0.04395604395604396\ncutoff 0.06734148520343308\npower 0.04995004995004995\ncutoff 0.0668276696257285\npower 0.052947052947052944\ncutoff 0.06555740966185064\npower 0.07792207792207792\ncutoff 0.0693166827782622\npower 0.03896103896103896\n0.05274725274725275\nmultimodal_independence 90\ncutoff 0.0658986355333191\npower 0.057942057942057944\ncutoff 0.06722589663262035\npower 0.054945054945054944\ncutoff 0.06809135492462992\npower 0.04595404595404595\ncutoff 0.06608703680532335\npower 0.055944055944055944\ncutoff 0.06849691901957311\npower 0.03296703296703297\n0.04955044955044955\n[0.05034965034965035, 0.049750249750249755, 0.05274725274725276, 0.059940059940059943, 0.057342657342657345, 0.056343656343656345, 0.05354645354645354, 0.05034965034965035, 0.05274725274725275, 0.04955044955044955]\n"
        }
      ],
      "execution_count": 23,
      "metadata": {
        "collapsed": false,
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot (angle,alpha,c = 'r',linestyle='--',label = \"alpha = 0.05\")\n",
        "plt.plot(angle,POWER,label = \"power\")\n",
        "plt.yticks(np.arange(0,1.1,0.1))\n",
        "plt.xticks(np.arange(0,110,10))\n",
        "plt.title(\"Statistical Power for MutiModal Independent Simulation with 100 trees (Increasing Angle)\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 26,
      "metadata": {
        "collapsed": false,
        "gather": {
          "logged": 1675185076173
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "power_reps =5\n",
        "sample_size = 100\n",
        "angle = np.arange(0,95,5)"
      ],
      "outputs": [],
      "execution_count": 7,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1676050350400
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(nrows=4, ncols=5, figsize=(25, 20))\n",
        "plt.suptitle(\n",
        "    \"Multivariate Independence Testing (Increasing Dimension)\",\n",
        "    y=0.93,\n",
        "    va=\"baseline\",\n",
        "    )\n",
        "## Interupt once\n",
        "for i, row in enumerate(ax):\n",
        "    for j, col in enumerate(row):\n",
        "        count = 5 * i + j\n",
        "        sim = list(SIMULATIONS.keys())[count]\n",
        "        # if sim in list(SIMULATIONS.keys())[0:8]:\n",
        "        #     print(sim)\n",
        "        #     POWER = np.load(\n",
        "        #         \"POWER_1D_multi_cutoff_0130{}.npy\".format(sim))\n",
        "        # #sim = ['linear','exponential']\n",
        "        # else: \n",
        "        POWER = []\n",
        "        angle = np.arange(0,95,5)\n",
        "        #dim = range(1,20,1)\n",
        "        for angle_i in angle:\n",
        "            print(sim,angle_i)\n",
        "                # Calculate the mean power over 5 times\n",
        "            power= np.mean([estimate_power(test,sim,100,angle_i,1) for _ in range(power_reps)])\n",
        "            print(power)\n",
        "            POWER.append(power)\n",
        "        np.save(\n",
        "            \"POWER_1D_multi_cutoff_angle_0209{}.npy\".format(sim),\n",
        "            POWER)\n",
        "           \n",
        "        #est_power = np.genfromtxt( \"{}_{}.csv\".format(sim, 'uf'),delimiter=\",\",)\n",
        "        # col.plot(sample_size,POWER)\n",
        "        # col.set_yticks(np.arange(0,1.1,0.1))\n",
        "        # col.set_xticks(range(0,120,20))\n",
        "        # col.set_title(list(SIMULATIONS.keys())[count])\n",
        "        #col.yticks(np.arange(0, 1.2, step=0.2))\n",
        "        #col.show()\n",
        "        print(POWER)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "linear 0\ncutoff 0.12132283949723655\npower 0.000999000999000999\ncutoff 0.12326729987937235\npower 0.000999000999000999\ncutoff 0.1238639949009892\npower 0.000999000999000999\ncutoff 0.12189443558652391\npower 0.000999000999000999\ncutoff 0.12565004302854\npower 0.000999000999000999\n0.000999000999000999\nlinear 5\ncutoff 0.09122126882207138\npower 1.0\ncutoff 0.09089312156956364\npower 1.0\ncutoff 0.09185625104206341\npower 1.0\ncutoff 0.09468498453267882\npower 1.0\ncutoff 0.09456437955210317\npower 0.999000999000999\n0.9998001998001997\nlinear 10\ncutoff 0.08980821953329488\npower 1.0\ncutoff 0.09069041942655165\npower 1.0\n"
        }
      ],
      "execution_count": 9,
      "metadata": {
        "collapsed": false,
        "gather": {
          "logged": 1676050367460
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "POWER = np.load(\"POWER_1D_multi_cutoff_0130exponential.npy\")\n",
        "plt.plot (sample_size,alpha,c = 'r',linestyle='--',label = \"alpha = 0.05\")\n",
        "plt.plot(sample_size,POWER,label = \"power\")\n",
        "plt.yticks(np.arange(0,1.1,0.1))\n",
        "plt.xticks(np.arange(0,110,10))\n",
        "plt.title(\"Statistical Power for Linear Simulation with 100 trees (Incresing Sample Size)\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": "<Figure size 640x480 with 1 Axes>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 13,
      "metadata": {
        "collapsed": false,
        "gather": {
          "logged": 1675273969073
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "SIM_TITLES = [\n",
        "    \"Linear\",\n",
        "    \"Exponential\",\n",
        "    \"Cubic\",\n",
        "    \"Joint Normal\",\n",
        "    \"Step\",\n",
        "    \"Quadratic\",\n",
        "    \"W-Shaped\",\n",
        "    \"Spiral\",\n",
        "    \"Bernoulli\",\n",
        "    \"Logarithmic\",\n",
        "    \"Fourth Root\",\n",
        "    \"Sine 4\\u03C0\",\n",
        "    \"Sine 16\\u03C0\",\n",
        "    \"Square\",\n",
        "    \"Two Parabolas\",\n",
        "    \"Circle\",\n",
        "    \"Ellipse\",\n",
        "    \"Diamond\",\n",
        "    \"Noise\",\n",
        "    \"Independence\",\n",
        "]"
      ],
      "outputs": [],
      "execution_count": 12,
      "metadata": {
        "gather": {
          "logged": 1676681944500
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## PLOT FOR Multivariate Independence Testing (Increasing Sample size)\n",
        "\n",
        "fig, ax = plt.subplots(nrows=4, ncols=5, figsize=(25, 20))\n",
        "plt.suptitle(\n",
        "    \"Multivariate Independence Testing (Increasing Sample size) with Cutoff\",\n",
        "    y=0.93,\n",
        "    va=\"baseline\",\n",
        "    )\n",
        "\n",
        "for i, row in enumerate(ax):\n",
        "    for j, col in enumerate(row):\n",
        "        count = 5 * i + j\n",
        "        sim = list(SIMULATIONS.keys())[count]\n",
        "        \n",
        "        #dim = range(1,20,1)\n",
        "        POWER = np.load(\n",
        "                \"POWER_1D_multi_cutoff_angle_0209{}.npy\".format(sim))\n",
        "        \n",
        "        col.set_xticks([])\n",
        "        col.plot(angle,POWER)\n",
        "        if i == 3:\n",
        "            col.set_xticks(range(0,120,20))\n",
        "            col.set_ylim(-0.05, 1.05)\n",
        "            col.set_yticks([])\n",
        "            if j == 0:\n",
        "                col.set_yticks([0, 1])\n",
        "            col.set_title(SIM_TITLES[count])\n",
        "        #est_power = np.genfromtxt( \"{}_{}.csv\".format(sim, 'uf'),delimiter=\",\",)\n",
        "        \n",
        "        col.set_yticks(np.arange(0,1.1,0.1))\n",
        "        col.set_xticks([5,50,100])\n",
        "        col.set_title(list(SIMULATIONS.keys())[count])\n",
        "        \n",
        "        #col.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 2500x2000 with 20 Axes>",
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "execution_count": 13,
      "metadata": {
        "collapsed": false,
        "gather": {
          "logged": 1676681950066
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "python3"
    },
    "kernelspec": {
      "name": "python3",
      "language": "python",
      "display_name": "Python 3 (ipykernel)"
    },
    "language_info": {
      "name": "python",
      "version": "3.8.5",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "microsoft": {
      "host": {
        "AzureML": {
          "notebookHasBeenCompleted": true
        }
      },
      "ms_spell_check": {
        "ms_spell_check_language": "en"
      }
    },
    "nteract": {
      "version": "nteract-front-end@1.0.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 4
}