{"action":"created","comment":{"url":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/comments/29472754","id":29472754,"diff_hunk":"@@ -0,0 +1,629 @@\n+\"\"\"Extreme Learning Machines\n+\"\"\"\n+\n+# Author: Issam H. Laradji <issam.laradji@gmail.com>\n+# Licence: BSD 3 clause\n+\n+\n+from abc import ABCMeta, abstractmethod\n+\n+import numpy as np\n+from scipy import linalg\n+\n+from ..base import BaseEstimator, ClassifierMixin, RegressorMixin\n+from .base import logistic, softmax, ACTIVATIONS\n+from ..externals import six\n+from ..preprocessing import LabelBinarizer\n+from ..metrics import mean_squared_error\n+from ..linear_model.ridge import ridge_regression\n+from ..utils import gen_batches, check_random_state\n+from ..utils import check_array, check_X_y, column_or_1d\n+from ..utils.extmath import safe_sparse_dot\n+from ..utils.class_weight import compute_sample_weight\n+\n+\n+def _multiply_weights(X, sample_weight):\n+    \"\"\"Return W*X if sample_weight is not None.\"\"\"\n+    if sample_weight is None:\n+        return X\n+    else:\n+        return X * sample_weight[:, np.newaxis]\n+\n+\n+class BaseELM(six.with_metaclass(ABCMeta, BaseEstimator)):\n+    \"\"\"Base class for ELM classification and regression.\n+\n+    Warning: This class should not be used directly.\n+    Use derived classes instead.\n+    \"\"\"\n+    @abstractmethod\n+    def __init__(self, n_hidden, activation, C, class_weight,\n+                 weight_scale, batch_size, verbose, warm_start,\n+                 random_state):\n+        self.C = C\n+        self.activation = activation\n+        self.class_weight = class_weight\n+        self.weight_scale = weight_scale\n+        self.batch_size = batch_size\n+        self.n_hidden = n_hidden\n+        self.verbose = verbose\n+        self.warm_start = warm_start\n+        self.random_state = random_state\n+\n+    def _init_weights(self, n_features):\n+        \"\"\"Initialize the parameter weights.\"\"\"\n+        rng = check_random_state(self.random_state)\n+\n+        # Use the initialization method recommended by Glorot et al.\n+        weight_init_bound = np.sqrt(6. / (n_features + self.n_hidden))\n+\n+        self.coef_hidden_ = rng.uniform(-weight_init_bound,\n+                                        weight_init_bound, (n_features,\n+                                                            self.n_hidden))\n+        self.intercept_hidden_ = rng.uniform(-weight_init_bound,\n+                                             weight_init_bound,\n+                                             self.n_hidden)\n+        if self.weight_scale != 1:\n+            self.coef_hidden_ *= self.weight_scale\n+            self.intercept_hidden_ *= self.weight_scale\n+\n+    def _compute_hidden_activations(self, X):\n+        \"\"\"Compute the hidden activations.\"\"\"\n+\n+        hidden_activations = safe_sparse_dot(X, self.coef_hidden_)\n+        hidden_activations += self.intercept_hidden_\n+\n+        # Apply the activation method\n+        activation = ACTIVATIONS[self.activation]\n+        hidden_activations = activation(hidden_activations)\n+\n+        return hidden_activations\n+\n+    def _fit(self, X, y, sample_weight=None, incremental=False):\n+        \"\"\"Fit the model to the data X and target y.\"\"\"\n+        # Validate input params\n+        if self.n_hidden <= 0:\n+            raise ValueError(\"n_hidden must be > 0, got %s.\" % self.n_hidden)\n+        if self.C <= 0.0:\n+            raise ValueError(\"C must be > 0, got %s.\" % self.C)\n+        if self.activation not in ACTIVATIONS:\n+            raise ValueError(\"The activation %s is not supported. Supported \"\n+                             \"activation are %s.\" % (self.activation,\n+                                                     ACTIVATIONS))\n+\n+        # Initialize public attributes\n+        if not hasattr(self, 'classes_'):\n+            self.classes_ = None\n+        if not hasattr(self, 'coef_hidden_'):\n+            self.coef_hidden_ = None\n+\n+        # Initialize private attributes\n+        if not hasattr(self, '_HT_H_accumulated'):\n+            self._HT_H_accumulated = None\n+\n+        X, y = check_X_y(X, y, accept_sparse=['csr', 'csc', 'coo'],\n+                         dtype=np.float64, order=\"C\", multi_output=True)\n+\n+        # This outputs a warning when a 1d array is expected\n+        if y.ndim == 2 and y.shape[1] == 1:\n+            y = column_or_1d(y, warn=True)\n+\n+        # Classification\n+        if isinstance(self, ClassifierMixin):\n+            self.label_binarizer_.fit(y)\n+\n+            if self.classes_ is None or not incremental:\n+                self.classes_ = self.label_binarizer_.classes_\n+                if sample_weight is None:\n+                    sample_weight = compute_sample_weight(self.class_weight,\n+                                                          self.classes_, y)\n+            else:\n+                classes = self.label_binarizer_.classes_\n+                if not np.all(np.in1d(classes, self.classes_)):\n+                    raise ValueError(\"`y` has classes not in `self.classes_`.\"\n+                                     \" `self.classes_` has %s. 'y' has %s.\" %\n+                                     (self.classes_, classes))\n+\n+            y = self.label_binarizer_.transform(y)\n+\n+        # Ensure y is 2D\n+        if y.ndim == 1:\n+            y = np.reshape(y, (-1, 1))\n+\n+        n_samples, n_features = X.shape\n+        self.n_outputs_ = y.shape[1]\n+\n+        # Step (1/2): Compute the hidden layer coefficients\n+        if (self.coef_hidden_ is None or (not incremental and\n+                                          not self.warm_start)):\n+            # Randomize and scale the input-to-hidden coefficients\n+            self._init_weights(n_features)\n+\n+        # Step (2/2): Compute hidden-to-output coefficients\n+        if self.batch_size is None:\n+            # Run the least-square algorithm on the whole dataset\n+            batch_size = n_samples\n+        else:\n+            # Run the recursive least-square algorithm on mini-batches\n+            batch_size = self.batch_size\n+\n+        batches = gen_batches(n_samples, batch_size)\n+\n+        # (First time call) Run the least-square algorithm on batch 0\n+        if not incremental or self._HT_H_accumulated is None:\n+            batch_slice = next(batches)\n+            H_batch = self._compute_hidden_activations(X[batch_slice])\n+\n+            # Get sample weights for the batch\n+            if sample_weight is None:\n+                sw = None\n+            else:\n+                sw = sample_weight[batch_slice]\n+\n+            # beta_{0} = inv(H_{0}^T H_{0} + (1. / C) * I) * H_{0}.T y_{0}\n+            self.coef_output_ = ridge_regression(H_batch, y[batch_slice],\n+                                                 1. / self.C,\n+                                                 sample_weight=sw).T\n+\n+            # Initialize K if this is batch based or partial_fit\n+            if self.batch_size is not None or incremental:\n+                # K_{0} = H_{0}^T * W * H_{0}\n+                weighted_H_batch = _multiply_weights(H_batch, sw)\n+                self._HT_H_accumulated = safe_sparse_dot(H_batch.T,\n+                                                         weighted_H_batch)\n+\n+            if self.verbose:\n+                y_scores = self._decision_scores(X[batch_slice])\n+\n+                if self.batch_size is None:\n+                    verbose_string = \"Training mean squared error =\"\n+                else:\n+                    verbose_string = \"Batch 0, Training mean squared error =\"\n+\n+                print(\"%s %f\" % (verbose_string,\n+                                 mean_squared_error(y[batch_slice], y_scores,\n+                                                    sample_weight=sw)))\n+\n+        # Run the least-square algorithm on batch 1, 2, ..., n\n+        for batch, batch_slice in enumerate(batches):\n+            # Compute hidden activations H_{i} for batch i\n+            H_batch = self._compute_hidden_activations(X[batch_slice])\n+\n+            # Get sample weights (sw) for the batch\n+            if sample_weight is None:\n+                sw = None\n+            else:\n+                sw = sample_weight[batch_slice]\n+\n+            weighted_H_batch = _multiply_weights(H_batch, sw)\n+\n+            # Update K_{i+1} by H_{i}^T * W * H_{i}\n+            self._HT_H_accumulated += safe_sparse_dot(H_batch.T,\n+                                                      weighted_H_batch)\n+\n+            # Update beta_{i+1} by\n+            # K_{i+1}^{-1} * H_{i+1}^T * W * (y_{i+1} - H_{i+1} * beta_{i})\n+            y_batch = y[batch_slice] - safe_sparse_dot(H_batch,\n+                                                       self.coef_output_)\n+\n+            weighted_y_batch = _multiply_weights(y_batch, sw)\n+            Hy_batch = safe_sparse_dot(H_batch.T, weighted_y_batch)\n+\n+            # Update hidden-to-output coefficients\n+            regularized_HT_H = self._HT_H_accumulated.copy()\n+            regularized_HT_H.flat[::self.n_hidden + 1] += 1. / self.C","path":"sklearn/neural_network/extreme_learning_machines.py","position":214,"original_position":214,"commit_id":"8ceefafe8ca72952e2a61d60138cd1610cebca86","original_commit_id":"8ceefafe8ca72952e2a61d60138cd1610cebca86","user":{"login":"amueller","id":449558,"avatar_url":"https://avatars.githubusercontent.com/u/449558?v=3","gravatar_id":"","url":"https://api.github.com/users/amueller","html_url":"https://github.com/amueller","followers_url":"https://api.github.com/users/amueller/followers","following_url":"https://api.github.com/users/amueller/following{/other_user}","gists_url":"https://api.github.com/users/amueller/gists{/gist_id}","starred_url":"https://api.github.com/users/amueller/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/amueller/subscriptions","organizations_url":"https://api.github.com/users/amueller/orgs","repos_url":"https://api.github.com/users/amueller/repos","events_url":"https://api.github.com/users/amueller/events{/privacy}","received_events_url":"https://api.github.com/users/amueller/received_events","type":"User","site_admin":false},"body":"same remark about C here.","created_at":"2015-04-30T21:32:47Z","updated_at":"2015-04-30T21:32:47Z","html_url":"https://github.com/scikit-learn/scikit-learn/pull/3306#discussion_r29472754","pull_request_url":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/3306","_links":{"self":{"href":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/comments/29472754"},"html":{"href":"https://github.com/scikit-learn/scikit-learn/pull/3306#discussion_r29472754"},"pull_request":{"href":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/3306"}}},"pull_request":{"url":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/3306","id":17446637,"html_url":"https://github.com/scikit-learn/scikit-learn/pull/3306","diff_url":"https://github.com/scikit-learn/scikit-learn/pull/3306.diff","patch_url":"https://github.com/scikit-learn/scikit-learn/pull/3306.patch","issue_url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/3306","number":3306,"state":"open","locked":false,"title":"[MRG] GSoC 2014: Standard Extreme Learning Machines","user":{"login":"IssamLaradji","id":3382128,"avatar_url":"https://avatars.githubusercontent.com/u/3382128?v=3","gravatar_id":"","url":"https://api.github.com/users/IssamLaradji","html_url":"https://github.com/IssamLaradji","followers_url":"https://api.github.com/users/IssamLaradji/followers","following_url":"https://api.github.com/users/IssamLaradji/following{/other_user}","gists_url":"https://api.github.com/users/IssamLaradji/gists{/gist_id}","starred_url":"https://api.github.com/users/IssamLaradji/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/IssamLaradji/subscriptions","organizations_url":"https://api.github.com/users/IssamLaradji/orgs","repos_url":"https://api.github.com/users/IssamLaradji/repos","events_url":"https://api.github.com/users/IssamLaradji/events{/privacy}","received_events_url":"https://api.github.com/users/IssamLaradji/received_events","type":"User","site_admin":false},"body":"Finished implementing the standard extreme learning machines (ELMs). I am getting the following results with 550 hidden neurons against the digits datasets,\r\n\r\nTraining accuracy using the logistic activation function: 0.999444\r\nTraining accuracy using the tanh activation function: 1.000000\r\n\r\nFortunately, this algorithm is much easier to implement and debug than multi-layer perceptron :).\r\nI will push a test file soon.\r\n\r\n@ogrisel , @larsmans","created_at":"2014-06-22T08:34:21Z","updated_at":"2015-04-30T21:32:47Z","closed_at":null,"merged_at":null,"merge_commit_sha":"b03b37b2058373885668c09da3abe2299b6262bc","assignee":null,"milestone":null,"commits_url":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/3306/commits","review_comments_url":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/3306/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/3306/comments","statuses_url":"https://api.github.com/repos/scikit-learn/scikit-learn/statuses/8ceefafe8ca72952e2a61d60138cd1610cebca86","head":{"label":"IssamLaradji:Extreme-Learning-Machines","ref":"Extreme-Learning-Machines","sha":"8ceefafe8ca72952e2a61d60138cd1610cebca86","user":{"login":"IssamLaradji","id":3382128,"avatar_url":"https://avatars.githubusercontent.com/u/3382128?v=3","gravatar_id":"","url":"https://api.github.com/users/IssamLaradji","html_url":"https://github.com/IssamLaradji","followers_url":"https://api.github.com/users/IssamLaradji/followers","following_url":"https://api.github.com/users/IssamLaradji/following{/other_user}","gists_url":"https://api.github.com/users/IssamLaradji/gists{/gist_id}","starred_url":"https://api.github.com/users/IssamLaradji/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/IssamLaradji/subscriptions","organizations_url":"https://api.github.com/users/IssamLaradji/orgs","repos_url":"https://api.github.com/users/IssamLaradji/repos","events_url":"https://api.github.com/users/IssamLaradji/events{/privacy}","received_events_url":"https://api.github.com/users/IssamLaradji/received_events","type":"User","site_admin":false},"repo":{"id":10518207,"name":"scikit-learn","full_name":"IssamLaradji/scikit-learn","owner":{"login":"IssamLaradji","id":3382128,"avatar_url":"https://avatars.githubusercontent.com/u/3382128?v=3","gravatar_id":"","url":"https://api.github.com/users/IssamLaradji","html_url":"https://github.com/IssamLaradji","followers_url":"https://api.github.com/users/IssamLaradji/followers","following_url":"https://api.github.com/users/IssamLaradji/following{/other_user}","gists_url":"https://api.github.com/users/IssamLaradji/gists{/gist_id}","starred_url":"https://api.github.com/users/IssamLaradji/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/IssamLaradji/subscriptions","organizations_url":"https://api.github.com/users/IssamLaradji/orgs","repos_url":"https://api.github.com/users/IssamLaradji/repos","events_url":"https://api.github.com/users/IssamLaradji/events{/privacy}","received_events_url":"https://api.github.com/users/IssamLaradji/received_events","type":"User","site_admin":false},"private":false,"html_url":"https://github.com/IssamLaradji/scikit-learn","description":"scikit-learn: 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