comparison notebooks/results_for_30_seconds.ipynb @ 71:04fc6e809a42 branch-tests

notebooks

 {
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/homes/mp305/anaconda/lib/python2.7/site-packages/librosa/core/audio.py:33: UserWarning: Could not import scikits.samplerate. Falling back to scipy.signal\n",
+      "  warnings.warn('Could not import scikits.samplerate. '\n"
      ]
     }
    ],
    "source": [
     "import numpy as np\n",

     "print np.array_equal(np.unique(testset[1]), np.unique(trainset[1]))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",

     "        pickle.dump(testset, f)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['/import/c4dm-04/mariap/train_data_melodia_8_30sec.pickle', '/import/c4dm-04/mariap/val_data_melodia_8_30sec.pickle', '/import/c4dm-04/mariap/test_data_melodia_8_30sec.pickle'] ['/import/c4dm-04/mariap/lda_data_melodia_8_30sec_30sec.pickle', '/import/c4dm-04/mariap/pca_data_melodia_8_30sec_30sec.pickle', '/import/c4dm-04/mariap/nmf_data_melodia_8_30sec_30sec.pickle', '/import/c4dm-04/mariap/ssnmf_data_melodia_8_30sec_30sec.pickle', '/import/c4dm-04/mariap/na_data_melodia_8_30sec_30sec.pickle']\n"
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['/import/c4dm-04/mariap/train_data_melodia_8_30sec.pickle', '/import/c4dm-04/mariap/val_data_melodia_8_30sec.pickle', '/import/c4dm-04/mariap/test_data_melodia_8_30sec.pickle'] ['/import/c4dm-04/mariap/lda_data_melodia_8_30sec.pickle', '/import/c4dm-04/mariap/pca_data_melodia_8_30sec.pickle', '/import/c4dm-04/mariap/nmf_data_melodia_8_30sec.pickle', '/import/c4dm-04/mariap/ssnmf_data_melodia_8_30sec.pickle', '/import/c4dm-04/mariap/na_data_melodia_8_30sec.pickle']\n"
      ]
     }
    ],
    "source": [
     "mapper.INPUT_FILES = OUTPUT_FILES\n",

     "print mapper.INPUT_FILES, mapper.OUTPUT_FILES"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",

       "mapping rhy\n",
       "training with PCA transform...\n",
       "variance explained 1.0\n",
       "138 400\n",
       "training with PCA transform...\n",
-      "variance explained 0.989999211296\n",
+      "variance explained 0.989994197011\n",
       "training with LDA transform...\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "/homes/mp305/anaconda/lib/python2.7/site-packages/sklearn/utils/validation.py:526: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+      "  y = column_or_1d(y, warn=True)\n",
       "/homes/mp305/anaconda/lib/python2.7/site-packages/sklearn/discriminant_analysis.py:455: UserWarning: The priors do not sum to 1. Renormalizing\n",
       "  UserWarning)\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "variance explained 1.0\n",
+      "training with NMF transform...\n",
+      "reconstruction error 6.59195506061\n",
+      "training with SSNMF transform...\n",
+      "reconstruction error 25.0727210368\n",
       "transform test data...\n",
       "mapping mel\n",
       "training with PCA transform...\n",
       "variance explained 1.0\n",
       "214 240\n",
       "training with PCA transform...\n",
       "variance explained 0.990347897477\n",
       "training with LDA transform...\n",
       "variance explained 1.0\n",
-      "transform test data...\n",
-      "mapping mfc\n",
-      "training with PCA transform...\n",
-      "variance explained 1.0\n",
-      "39 80\n",
-      "training with PCA transform...\n",
-      "variance explained 0.991458741216\n",
-      "training with LDA transform...\n",
-      "variance explained 0.942657629903\n",
-      "transform test data...\n",
-      "mapping chr\n",
-      "training with PCA transform...\n",
-      "variance explained 1.0\n",
-      "70 120\n",
-      "training with PCA transform...\n",
-      "variance explained 0.990503308525\n",
-      "training with LDA transform...\n",
-      "variance explained 0.954607427999\n",
-      "transform test data...\n"
+      "training with NMF transform...\n"
      ]
     }
    ],
    "source": [
     "print \"mapping...\"\n",
-    "_, _, ldadata_list, _, _, Y, Yaudio = mapper.lda_map_and_average_frames(min_variance=0.99)\n",
-    "mapper.write_output([], [], ldadata_list, [], [], Y, Yaudio)"
+    "#_, _, ldadata_list, _, _, Y, Yaudio = mapper.lda_map_and_average_frames(min_variance=0.99)\n",
+    "#mapper.write_output([], [], ldadata_list, [], [], Y, Yaudio)\n",
+    "data_list, pcadata_list, ldadata_list, nmfdata_list, ssnmfdata_list, classlabs, audiolabs = mapper.map_and_average_frames(min_variance=0.99)\n",
+    "mapper.write_output(data_list, pcadata_list, ldadata_list, nmfdata_list, ssnmfdata_list, classlabs, audiolabs)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 29,