GitHub PullRequestReviewCommentEvent at 2014-10-02 07:32:05
Repository: scikit-learn
Actor: arjoly
Payload: {"action":"created","comment":{"url":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/comments/18325443","id":18325443,"diff_hunk":"@@ -0,0 +1,342 @@\n+# -*- coding: utf-8 -*-\n+\"\"\"\n+A Theil-Sen Estimator for Multiple Linear Regression Model\n+\"\"\"\n+\n+# Author: Florian Wilhelm <florian.wilhelm@gmail.com>\n+#\n+# License: BSD 3 clause\n+\n+from __future__ import division, print_function, absolute_import\n+\n+import warnings\n+from itertools import combinations\n+\n+import numpy as np\n+from scipy import linalg\n+from scipy.special import binom\n+from scipy.linalg.lapack import get_lapack_funcs\n+\n+from .base import LinearModel\n+from ..base import RegressorMixin\n+from ..utils import check_array, check_random_state, ConvergenceWarning\n+from ..utils import check_consistent_length, _get_n_jobs\n+from ..externals.joblib import Parallel, delayed\n+from ..externals.six.moves import xrange\n+\n+\n+def _modified_weiszfeld_step(X, y):\n+    \"\"\"Modified Weiszfeld step.\n+\n+    Parameters\n+    ----------\n+    X : array, shape = [n_samples, n_features]\n+        Training vector, where n_samples is the number of samples and\n+        n_features is the number of features.\n+\n+    y : array, shape = [n_features]\n+        Current start vector.\n+\n+    Returns\n+    -------\n+    new_y : array, shape = [n_features]\n+        New iteration step.\n+\n+    Notes\n+    -----\n+    This function defines one iteration step in order to approximate the\n+    spatial median (L1 median). It is a form of an iteratively re-weighted\n+    least squares method.\n+    \"\"\"\n+    X = X.T\n+    diff = X.T - y\n+    normdiff = np.sqrt(np.sum(diff ** 2, axis=1))\n+    mask = normdiff >= 1e-6\n+    if mask.sum() < X.shape[1]:\n+        eta = 1.\n+    else:\n+        eta = 0.\n+    diff = diff[mask, :]\n+    normdiff = normdiff[mask][:, np.newaxis]\n+    res = np.sum(diff / normdiff, axis=0)\n+    T_denom = np.sum(1 / normdiff, axis=0)\n+    T_nom = np.sum(X.T[mask, :] / normdiff, axis=0)\n+    if T_denom != 0.:\n+        T = T_nom / T_denom\n+    else:\n+        T = T_nom\n+    r = linalg.norm(res)\n+    if r < 1.e-6:\n+        r = 1.\n+    return max(0., 1. - eta / r) * T + min(1., eta / r) * y\n+\n+\n+def _spatial_median(X, max_iter=300, tol=1.e-3):\n+    \"\"\"Spatial median (L1 median).\n+\n+    Parameters\n+    ----------\n+    X : array, shape = [n_samples, n_features]\n+        Training vector, where n_samples in the number of samples and\n+        n_features is the number of features.\n+\n+    max_iter : int, optional\n+        Maximum number of iterations.  Default is 300.\n+\n+    tol : float, optional\n+        Stop the algorithm if spmed has converged. Default is 1.e-3.\n+\n+    Returns\n+    -------\n+    spmed : array, shape = [n_features]\n+        Spatial median.\n+\n+    n_iter: int\n+        Number of iterations needed.\n+\n+    References\n+    ----------\n+    - On Computation of Spatial Median for Robust Data Mining, 2005\n+      T. Kärkkäinen and S. Äyrämö\n+      http://users.jyu.fi/~samiayr/pdf/ayramo_eurogen05.pdf\n+    \"\"\"\n+    if X.shape[1] == 1:\n+        return 1, np.median(X.ravel())\n+    # We are computing the tol on the squared norm\n+    tol **= 2\n+    spmed_old = np.mean(X, axis=0)\n+    for n_iter in xrange(max_iter):\n+        spmed = _modified_weiszfeld_step(X, spmed_old)\n+        if np.sum((spmed_old - spmed) ** 2) < tol:\n+            return n_iter, spmed\n+        else:\n+            spmed_old = spmed\n+    warnings.warn(\"Maximum number of iterations {max_iter} reached in spatial \"\n+                  \"median for TheilSen regressor.\".format(max_iter=max_iter),\n+                  ConvergenceWarning)\n+    return n_iter, spmed\n+\n+\n+def _breakdown_point(n_samples, n_subsamples):\n+    \"\"\"Approximation of the breakdown point.\n+\n+    Parameters\n+    ----------\n+    n_samples : int\n+        Number of samples.\n+\n+    n_subsamples : int\n+        Number of subsamples to consider.\n+\n+    Returns\n+    -------\n+    breakdown_point : float\n+        Approximation of breakdown point.\n+    \"\"\"\n+    return 1 - (0.5 ** (1 / n_subsamples) * (n_samples - n_subsamples + 1) +\n+                n_subsamples - 1) / n_samples\n+\n+\n+def _lstsq(X, y, indices, intercept):\n+    \"\"\"Least Squares Estimator for TheilSen class.\n+\n+    Parameters\n+    ----------\n+    X : array, shape = [n_samples, n_features]\n+        Design matrix, where n_samples is the number of samples and\n+        n_features is the number of features.\n+\n+    y : array, shape = [n_samples]\n+        Target vector, where n_samples is the number of samples.\n+\n+    indices : array, shape = [n_subpopulation, n_subsamples]\n+        Indices of all subsamples with respect to the chosen subpopulation.\n+\n+    intercept : bool\n+        Fit intercept or not.\n+\n+    Notes\n+    -----\n+    This function calculates the least squares method on a subset of rows of X\n+    and y defined by the indices array. Optionally, an intercept column is\n+    added if intercept is set to true.\n+    \"\"\"\n+    first_elem = 1 if intercept else 0\n+    n_dim = X.shape[1] + first_elem\n+    n_subsamples = indices.shape[1]\n+    weights = np.empty((indices.shape[0], n_dim))\n+    X_sub = np.ones((n_subsamples, n_dim))\n+    # gelss need to pad y to be of the max dim of X\n+    this_y = np.zeros((max(n_subsamples, n_dim)))\n+    lstsq, = get_lapack_funcs(('gelss',), (X_sub, this_y))\n+    for i, ix in enumerate(indices):","path":"sklearn/linear_model/theil_sen.py","position":172,"original_position":172,"commit_id":"374ac6ddadbb607ffda27726568753d86afe7add","original_commit_id":"374ac6ddadbb607ffda27726568753d86afe7add","user":{"login":"arjoly","id":1274722,"avatar_url":"https://avatars.githubusercontent.com/u/1274722?v=2","gravatar_id":"","url":"https://api.github.com/users/arjoly","html_url":"https://github.com/arjoly","followers_url":"https://api.github.com/users/arjoly/followers","following_url":"https://api.github.com/users/arjoly/following{/other_user}","gists_url":"https://api.github.com/users/arjoly/gists{/gist_id}","starred_url":"https://api.github.com/users/arjoly/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/arjoly/subscriptions","organizations_url":"https://api.github.com/users/arjoly/orgs","repos_url":"https://api.github.com/users/arjoly/repos","events_url":"https://api.github.com/users/arjoly/events{/privacy}","received_events_url":"https://api.github.com/users/arjoly/received_events","type":"User","site_admin":false},"body":"Could you avoid abreviation. You seems to mean subset for ix and iter for i.","created_at":"2014-10-02T07:32:05Z","updated_at":"2014-10-02T07:32:05Z","html_url":"https://github.com/scikit-learn/scikit-learn/pull/2949#discussion_r18325443","pull_request_url":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/2949","_links":{"self":{"href":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/comments/18325443"},"html":{"href":"https://github.com/scikit-learn/scikit-learn/pull/2949#discussion_r18325443"},"pull_request":{"href":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/2949"}}},"pull_request":{"url":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/2949","id":13345030,"html_url":"https://github.com/scikit-learn/scikit-learn/pull/2949","diff_url":"https://github.com/scikit-learn/scikit-learn/pull/2949.diff","patch_url":"https://github.com/scikit-learn/scikit-learn/pull/2949.patch","issue_url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/2949","number":2949,"state":"open","locked":false,"title":" [MRG+1] TheilSen robust linear regression","user":{"login":"FlorianWilhelm","id":3184072,"avatar_url":"https://avatars.githubusercontent.com/u/3184072?v=2","gravatar_id":"","url":"https://api.github.com/users/FlorianWilhelm","html_url":"https://github.com/FlorianWilhelm","followers_url":"https://api.github.com/users/FlorianWilhelm/followers","following_url":"https://api.github.com/users/FlorianWilhelm/following{/other_user}","gists_url":"https://api.github.com/users/FlorianWilhelm/gists{/gist_id}","starred_url":"https://api.github.com/users/FlorianWilhelm/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/FlorianWilhelm/subscriptions","organizations_url":"https://api.github.com/users/FlorianWilhelm/orgs","repos_url":"https://api.github.com/users/FlorianWilhelm/repos","events_url":"https://api.github.com/users/FlorianWilhelm/events{/privacy}","received_events_url":"https://api.github.com/users/FlorianWilhelm/received_events","type":"User","site_admin":false},"body":"A multiple linear Theil-Sen regression for the Scikit-Learn toolbox. The implementation is based on the algorithm from the paper \"Theil-Sen Estimators in a Multiple Linear Regression Model\" of Xin Dang, Hanxiang Peng, Xueqin Wang and Heping Zhang. It is parallelized with the help of joblib.\r\nOn a personal note, I think that the popular Theil-Sen regression would be a nice addition to Scikit-Learn.\r\nI am looking forward to your feedback. \r\n\r\nFlorian","created_at":"2014-03-08T20:15:37Z","updated_at":"2014-10-02T07:32:05Z","closed_at":null,"merged_at":null,"merge_commit_sha":"c1329fc53794d011911720972d16c28f797b07f3","assignee":null,"milestone":null,"commits_url":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/2949/commits","review_comments_url":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/2949/comments","review_comment_url":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/comments/{number}","comments_url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/2949/comments","statuses_url":"https://api.github.com/repos/scikit-learn/scikit-learn/statuses/374ac6ddadbb607ffda27726568753d86afe7add","head":{"label":"FlorianWilhelm:theilsen","ref":"theilsen","sha":"374ac6ddadbb607ffda27726568753d86afe7add","user":{"login":"FlorianWilhelm","id":3184072,"avatar_url":"https://avatars.githubusercontent.com/u/3184072?v=2","gravatar_id":"","url":"https://api.github.com/users/FlorianWilhelm","html_url":"https://github.com/FlorianWilhelm","followers_url":"https://api.github.com/users/FlorianWilhelm/followers","following_url":"https://api.github.com/users/FlorianWilhelm/following{/other_user}","gists_url":"https://api.github.com/users/FlorianWilhelm/gists{/gist_id}","starred_url":"https://api.github.com/users/FlorianWilhelm/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/FlorianWilhelm/subscriptions","organizations_url":"https://api.github.com/users/FlorianWilhelm/orgs","repos_url":"https://api.github.com/users/FlorianWilhelm/repos","events_url":"https://api.github.com/users/FlorianWilhelm/events{/privacy}","received_events_url":"https://api.github.com/users/FlorianWilhelm/received_events","type":"User","site_admin":false},"repo":{"id":15860508,"name":"scikit-learn","full_name":"FlorianWilhelm/scikit-learn","owner":{"login":"FlorianWilhelm","id":3184072,"avatar_url":"https://avatars.githubusercontent.com/u/3184072?v=2","gravatar_id":"","url":"https://api.github.com/users/FlorianWilhelm","html_url":"https://github.com/FlorianWilhelm","followers_url":"https://api.github.com/users/FlorianWilhelm/followers","following_url":"https://api.github.com/users/FlorianWilhelm/following{/other_user}","gists_url":"https://api.github.com/users/FlorianWilhelm/gists{/gist_id}","starred_url":"https://api.github.com/users/FlorianWilhelm/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/FlorianWilhelm/subscriptions","organizations_url":"https://api.github.com/users/FlorianWilhelm/orgs","repos_url":"https://api.github.com/users/FlorianWilhelm/repos","events_url":"https://api.github.com/users/FlorianWilhelm/events{/privacy}","received_events_url":"https://api.github.com/users/FlorianWilhelm/received_events","type":"User","site_admin":false},"private":false,"html_url":"https://github.com/FlorianWilhelm/scikit-learn","description":"scikit-learn: 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