Mercurial > hg > plosone_underreview
diff notebooks/results_for_30_seconds.ipynb @ 79:98fc06ba2938 branch-tests
sorted classification results
| author | mpanteli <m.x.panteli@gmail.com> |
|---|---|
| date | Tue, 26 Sep 2017 14:37:28 +0100 |
| parents | 9e526f7c9715 |
| children | 4395037087b6 |
line wrap: on
line diff
--- a/notebooks/results_for_30_seconds.ipynb Tue Sep 26 12:40:07 2017 +0100 +++ b/notebooks/results_for_30_seconds.ipynb Tue Sep 26 14:37:28 2017 +0100 @@ -3,9 +3,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stderr", @@ -89,10 +87,8 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, + "execution_count": 2, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -127,10 +123,8 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, + "execution_count": 3, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -151,9 +145,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -212,9 +204,8 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 5, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -223,110 +214,222 @@ "output_type": "stream", "text": [ "/import/c4dm-04/mariap/lda_data_melodia_8_30sec.pickle\n", - "KNN LDA 0.133638538664\n", - "LDA LDA 0.315981704242\n", - "SVM LDA 0.0286516818584\n", - "RF LDA 0.0649346198079\n", - "KNN 0.047906648979\n", - "LDA 0.146811708362\n", - "SVM 0.0721574739754\n", - "RF 0.0285408784052\n", - "KNN 0.0225982700973\n", - "LDA 0.0748199642972\n", - "SVM 0.0498984844477\n", - "RF 0.0184757141783\n", - "KNN 0.272541771809\n", - "LDA 0.189776850025\n", - "SVM 0.291219340444\n", - "RF 0.12107835911\n", - "KNN 0.0638995544919\n", - "LDA 0.0904155775207\n", - "SVM 0.079580312683\n", - "RF 0.0507212741212\n", - "/import/c4dm-04/mariap/pca_data_melodia_8_30sec.pickle\n" - ] - }, - { - "ename": "ValueError", - "evalue": "need at least one array to concatenate", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m<ipython-input-29-78a92fadfdf1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf_results\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclassification\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclassify_for_filenames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_list\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOUTPUT_FILES\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0mdf_results\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_latex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/homes/mp305/code/pythoncode/plosone_underreview/scripts/classification.py\u001b[0m in \u001b[0;36mclassify_for_filenames\u001b[0;34m(file_list)\u001b[0m\n\u001b[1;32m 61\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtransform_label\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTRANSFORM_LABELS\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mYaudio\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_data_from_pickle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 64\u001b[0m \u001b[0;31m#X_train, Y_train, X_test, Y_test = get_train_test_sets(X, Y, traininds, testinds)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 65\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_val_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_val_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mRANDOM_STATE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstratify\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/homes/mp305/code/pythoncode/plosone_underreview/scripts/classification.py\u001b[0m in \u001b[0;36mload_data_from_pickle\u001b[0;34m(filename)\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mload_data_from_pickle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[0mX_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mYaudio\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 24\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mYaudio\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: need at least one array to concatenate" + "KNN LDA 0.151978449974\n", + "LDA LDA 0.320669835863\n", + "SVM LDA 0.0231101788399\n", + "RF LDA 0.0707681464082\n", + "KNN LDA 0.0547390436205\n", + "LDA LDA 0.150312531138\n", + "SVM LDA 0.0787628988868\n", + "RF LDA 0.0311176089141\n", + "KNN LDA 0.0232330458268\n", + "LDA LDA 0.0702474072041\n", + "SVM LDA 0.050068706152\n", + "RF LDA 0.0168479682586\n", + "KNN LDA 0.281733731607\n", + "LDA LDA 0.198582742899\n", + "SVM LDA 0.296355560166\n", + "RF LDA 0.150028594255\n", + "KNN LDA 0.0857923493684\n", + "LDA LDA 0.107355289483\n", + "SVM LDA 0.0896098014444\n", + "RF LDA 0.0507616592735\n", + "/import/c4dm-04/mariap/pca_data_melodia_8_30sec.pickle\n", + "KNN PCA 0.140643930221\n", + "LDA PCA 0.175099072208\n", + "SVM PCA 0.0149273059799\n", + "RF PCA 0.0456009299668\n", + "KNN PCA 0.052516908106\n", + "LDA PCA 0.055028942176\n", + "SVM PCA 0.0479512645907\n", + "RF PCA 0.0263226873341\n", + "KNN PCA 0.0268729640269\n", + "LDA PCA 0.0459303318699\n", + "SVM PCA 0.0386730267598\n", + "RF PCA 0.0186964838343\n", + "KNN PCA 0.220850433533\n", + "LDA PCA 0.161502657527\n", + "SVM PCA 0.245790916558\n", + "RF PCA 0.139792959142\n", + "KNN PCA 0.0814272808267\n", + "LDA PCA 0.0839732813486\n", + "SVM PCA 0.0918638232782\n", + "RF PCA 0.0445219060784\n", + "/import/c4dm-04/mariap/nmf_data_melodia_8_30sec.pickle\n", + "KNN NMF 0.114298949339\n", + "LDA NMF 0.178244078869\n", + "SVM NMF 0.0164055663008\n", + "RF NMF 0.0629920627191\n", + "KNN NMF 0.043057794756\n", + "LDA NMF 0.0586662842996\n", + "SVM NMF 0.00781273342686\n", + "RF NMF 0.0322137767606\n", + "KNN NMF 0.0285281454673\n", + "LDA NMF 0.0463659955869\n", + "SVM NMF 0.00768887594564\n", + "RF NMF 0.020364413503\n", + "KNN NMF 0.177819886656\n", + "LDA NMF 0.166221515627\n", + "SVM NMF 0.010788613595\n", + "RF NMF 0.125503851218\n", + "KNN NMF 0.0795454671166\n", + "LDA NMF 0.0856428557896\n", + "SVM NMF 0.0116920633048\n", + "RF NMF 0.0415516315034\n", + "/import/c4dm-04/mariap/ssnmf_data_melodia_8_30sec.pickle\n", + "KNN SSNMF 0.14322692821\n", + "LDA SSNMF 0.18320247367\n", + "SVM SSNMF 0.0205784326384\n", + "RF SSNMF 0.0453718187022\n", + "KNN SSNMF 0.0431300683181\n", + "LDA SSNMF 0.0533449581285\n", + "SVM SSNMF 0.0106542141335\n", + "RF SSNMF 0.031229373819\n", + "KNN SSNMF 0.0152235481009\n", + "LDA SSNMF 0.038872838043\n", + "SVM SSNMF 0.00536127803533\n", + "RF SSNMF 0.0181117619327\n", + "KNN SSNMF 0.227101074174\n", + "LDA SSNMF 0.165382484171\n", + "SVM SSNMF 0.0184921176111\n", + "RF SSNMF 0.115557523856\n", + "KNN SSNMF 0.0715413500709\n", + "LDA SSNMF 0.0819764377219\n", + "SVM SSNMF 0.0138822224913\n", + "RF SSNMF 0.0350133428793\n", + "/import/c4dm-04/mariap/na_data_melodia_8_30sec.pickle\n", + "KNN NA 0.140075287804\n", + "LDA NA 0.176953549195\n", + "SVM NA 0.0149485545637\n", + "RF NA 0.0826528174744\n", + "KNN NA 0.0515315452955\n", + "LDA NA 0.0599453579616\n", + "SVM NA 0.0468615478392\n", + "RF NA 0.0398757075984\n", + "KNN NA 0.0273364752119\n", + "LDA NA 0.0378819151174\n", + "SVM NA 0.038290667129\n", + "RF NA 0.0317183273408\n", + "KNN NA 0.221769305159\n", + "LDA NA 0.191217962613\n", + "SVM NA 0.250268813953\n", + "RF NA 0.113553333212\n", + "KNN NA 0.0814734970192\n", + "LDA NA 0.0839348156722\n", + "SVM NA 0.0881235182136\n", + "RF NA 0.0586010122134\n", + "\\begin{tabular}{lllrrrrr}\n", + "\\toprule\n", + "{} & 0 & 1 & 2 & 3 & 4 & 5 & 6 \\\\\n", + "\\midrule\n", + "0 & LDA & KNN & 0.151978 & 0.054739 & 0.023233 & 0.281734 & 0.085792 \\\\\n", + "1 & LDA & LDA & 0.320670 & 0.150313 & 0.070247 & 0.198583 & 0.107355 \\\\\n", + "2 & LDA & SVM & 0.023110 & 0.078763 & 0.050069 & 0.296356 & 0.089610 \\\\\n", + "3 & LDA & RF & 0.070768 & 0.031118 & 0.016848 & 0.150029 & 0.050762 \\\\\n", + "4 & PCA & KNN & 0.140644 & 0.052517 & 0.026873 & 0.220850 & 0.081427 \\\\\n", + "5 & PCA & LDA & 0.175099 & 0.055029 & 0.045930 & 0.161503 & 0.083973 \\\\\n", + "6 & PCA & SVM & 0.014927 & 0.047951 & 0.038673 & 0.245791 & 0.091864 \\\\\n", + "7 & PCA & RF & 0.045601 & 0.026323 & 0.018696 & 0.139793 & 0.044522 \\\\\n", + "8 & NMF & KNN & 0.114299 & 0.043058 & 0.028528 & 0.177820 & 0.079545 \\\\\n", + "9 & NMF & LDA & 0.178244 & 0.058666 & 0.046366 & 0.166222 & 0.085643 \\\\\n", + "10 & NMF & SVM & 0.016406 & 0.007813 & 0.007689 & 0.010789 & 0.011692 \\\\\n", + "11 & NMF & RF & 0.062992 & 0.032214 & 0.020364 & 0.125504 & 0.041552 \\\\\n", + "12 & SSNMF & KNN & 0.143227 & 0.043130 & 0.015224 & 0.227101 & 0.071541 \\\\\n", + "13 & SSNMF & LDA & 0.183202 & 0.053345 & 0.038873 & 0.165382 & 0.081976 \\\\\n", + "14 & SSNMF & SVM & 0.020578 & 0.010654 & 0.005361 & 0.018492 & 0.013882 \\\\\n", + "15 & SSNMF & RF & 0.045372 & 0.031229 & 0.018112 & 0.115558 & 0.035013 \\\\\n", + "16 & NA & KNN & 0.140075 & 0.051532 & 0.027336 & 0.221769 & 0.081473 \\\\\n", + "17 & NA & LDA & 0.176954 & 0.059945 & 0.037882 & 0.191218 & 0.083935 \\\\\n", + "18 & NA & SVM & 0.014949 & 0.046862 & 0.038291 & 0.250269 & 0.088124 \\\\\n", + "19 & NA & RF & 0.082653 & 0.039876 & 0.031718 & 0.113553 & 0.058601 \\\\\n", + "\\bottomrule\n", + "\\end{tabular}\n", + "\n" ] } ], "source": [ - "df_results = classification.classify_for_filenames(file_list=mapper.OUTPUT_FILES)\n", - "print df_results.to_latex()" + "df_results = classification.classify_for_filenames(file_list=mapper.OUTPUT_FILES)" ] }, { "cell_type": "code", - "execution_count": 63, - "metadata": { - "collapsed": false, - "scrolled": true - }, + "execution_count": 12, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "/import/c4dm-04/mariap/lda_data_melodia_8_30sec.pickle\n", - "KNN LDA 0.150711701486\n", - "LDA LDA 0.310672996086\n", - "SVM LDA 0.031779420956\n", - "RF LDA 0.0698577462255\n", - "KNN LDA 0.0479291909939\n", - "LDA LDA 0.152723754358\n", - "SVM LDA 0.0743307824034\n", - "RF LDA 0.0375455360373\n", - "KNN LDA 0.0236333678307\n", - "LDA LDA 0.0678370978359\n", - "SVM LDA 0.0453560519328\n", - "RF LDA 0.0145428300454\n", - "KNN LDA 0.260115390181\n", - "LDA LDA 0.189620718522\n", - "SVM LDA 0.300264733852\n", - "RF LDA 0.130466596212\n", - "KNN LDA 0.0816262047653\n", - "LDA LDA 0.0841375171139\n", - "SVM LDA 0.0915018547681\n", - "RF LDA 0.0497879065996\n", - "/import/c4dm-04/mariap/pca_data_melodia_8_30sec.pickle\n" - ] - }, - { - "ename": "ValueError", - "evalue": "need at least one array to concatenate", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m<ipython-input-63-fa2902ad1eeb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mclassification\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mRANDOM_STATE\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m55\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf_results\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclassification\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclassify_for_filenames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_list\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOUTPUT_FILES\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0mdf_results\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_latex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/homes/mp305/code/pythoncode/plosone_underreview/scripts/classification.py\u001b[0m in \u001b[0;36mclassify_for_filenames\u001b[0;34m(file_list)\u001b[0m\n\u001b[1;32m 61\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtransform_label\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTRANSFORM_LABELS\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mYaudio\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_data_from_pickle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 64\u001b[0m \u001b[0;31m#X_train, Y_train, X_test, Y_test = get_train_test_sets(X, Y, traininds, testinds)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 65\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_val_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_val_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mRANDOM_STATE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstratify\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/homes/mp305/code/pythoncode/plosone_underreview/scripts/classification.py\u001b[0m in \u001b[0;36mload_data_from_pickle\u001b[0;34m(filename)\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mload_data_from_pickle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[0mX_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mYaudio\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 24\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mYaudio\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: need at least one array to concatenate" + "\\begin{tabular}{llrrrrr}\n", + "\\toprule\n", + " 0 & 1 & 2 & 3 & 4 & 5 & 6 \\\\\n", + "\\midrule\n", + " LDA & KNN & 0.151978 & 0.054739 & 0.023233 & 0.281734 & 0.085792 \\\\\n", + " LDA & LDA & 0.320670 & 0.150313 & 0.070247 & 0.198583 & 0.107355 \\\\\n", + " LDA & SVM & 0.023110 & 0.078763 & 0.050069 & 0.296356 & 0.089610 \\\\\n", + " LDA & RF & 0.070768 & 0.031118 & 0.016848 & 0.150029 & 0.050762 \\\\\n", + " PCA & KNN & 0.140644 & 0.052517 & 0.026873 & 0.220850 & 0.081427 \\\\\n", + " PCA & LDA & 0.175099 & 0.055029 & 0.045930 & 0.161503 & 0.083973 \\\\\n", + " PCA & SVM & 0.014927 & 0.047951 & 0.038673 & 0.245791 & 0.091864 \\\\\n", + " PCA & RF & 0.045601 & 0.026323 & 0.018696 & 0.139793 & 0.044522 \\\\\n", + " NMF & KNN & 0.114299 & 0.043058 & 0.028528 & 0.177820 & 0.079545 \\\\\n", + " NMF & LDA & 0.178244 & 0.058666 & 0.046366 & 0.166222 & 0.085643 \\\\\n", + " NMF & SVM & 0.016406 & 0.007813 & 0.007689 & 0.010789 & 0.011692 \\\\\n", + " NMF & RF & 0.062992 & 0.032214 & 0.020364 & 0.125504 & 0.041552 \\\\\n", + " SSNMF & KNN & 0.143227 & 0.043130 & 0.015224 & 0.227101 & 0.071541 \\\\\n", + " SSNMF & LDA & 0.183202 & 0.053345 & 0.038873 & 0.165382 & 0.081976 \\\\\n", + " SSNMF & SVM & 0.020578 & 0.010654 & 0.005361 & 0.018492 & 0.013882 \\\\\n", + " SSNMF & RF & 0.045372 & 0.031229 & 0.018112 & 0.115558 & 0.035013 \\\\\n", + " NA & KNN & 0.140075 & 0.051532 & 0.027336 & 0.221769 & 0.081473 \\\\\n", + " NA & LDA & 0.176954 & 0.059945 & 0.037882 & 0.191218 & 0.083935 \\\\\n", + " NA & SVM & 0.014949 & 0.046862 & 0.038291 & 0.250269 & 0.088124 \\\\\n", + " NA & RF & 0.082653 & 0.039876 & 0.031718 & 0.113553 & 0.058601 \\\\\n", + "\\bottomrule\n", + "\\end{tabular}\n", + "\n", + "\\begin{tabular}{llrrrrr}\n", + "\\toprule\n", + " 0 & 1 & 2 & 3 & 4 & 5 & 6 \\\\\n", + "\\midrule\n", + " LDA & LDA & 0.320670 & 0.150313 & 0.070247 & 0.198583 & 0.107355 \\\\\n", + " SSNMF & LDA & 0.183202 & 0.053345 & 0.038873 & 0.165382 & 0.081976 \\\\\n", + " NMF & LDA & 0.178244 & 0.058666 & 0.046366 & 0.166222 & 0.085643 \\\\\n", + " NA & LDA & 0.176954 & 0.059945 & 0.037882 & 0.191218 & 0.083935 \\\\\n", + " PCA & LDA & 0.175099 & 0.055029 & 0.045930 & 0.161503 & 0.083973 \\\\\n", + " LDA & KNN & 0.151978 & 0.054739 & 0.023233 & 0.281734 & 0.085792 \\\\\n", + " SSNMF & KNN & 0.143227 & 0.043130 & 0.015224 & 0.227101 & 0.071541 \\\\\n", + " PCA & KNN & 0.140644 & 0.052517 & 0.026873 & 0.220850 & 0.081427 \\\\\n", + " NA & KNN & 0.140075 & 0.051532 & 0.027336 & 0.221769 & 0.081473 \\\\\n", + " NMF & KNN & 0.114299 & 0.043058 & 0.028528 & 0.177820 & 0.079545 \\\\\n", + " NA & RF & 0.082653 & 0.039876 & 0.031718 & 0.113553 & 0.058601 \\\\\n", + " LDA & RF & 0.070768 & 0.031118 & 0.016848 & 0.150029 & 0.050762 \\\\\n", + " NMF & RF & 0.062992 & 0.032214 & 0.020364 & 0.125504 & 0.041552 \\\\\n", + " PCA & RF & 0.045601 & 0.026323 & 0.018696 & 0.139793 & 0.044522 \\\\\n", + " SSNMF & RF & 0.045372 & 0.031229 & 0.018112 & 0.115558 & 0.035013 \\\\\n", + " LDA & SVM & 0.023110 & 0.078763 & 0.050069 & 0.296356 & 0.089610 \\\\\n", + " SSNMF & SVM & 0.020578 & 0.010654 & 0.005361 & 0.018492 & 0.013882 \\\\\n", + " NMF & SVM & 0.016406 & 0.007813 & 0.007689 & 0.010789 & 0.011692 \\\\\n", + " NA & SVM & 0.014949 & 0.046862 & 0.038291 & 0.250269 & 0.088124 \\\\\n", + " PCA & SVM & 0.014927 & 0.047951 & 0.038673 & 0.245791 & 0.091864 \\\\\n", + "\\bottomrule\n", + "\\end{tabular}\n", + "\n" ] } ], "source": [ - "classification.RANDOM_STATE = 55\n", - "df_results = classification.classify_for_filenames(file_list=mapper.OUTPUT_FILES)\n", - "print df_results.to_latex()" + "print df_results.to_latex(index=False)\n", + "# sort by 'all'\n", + "df_results_sorted = df_results.sort_values(2, ascending=False, inplace=False)\n", + "df_results_sorted.head()\n", + "print df_results_sorted.to_latex(index=False)" ] }, { "cell_type": "code", "execution_count": 43, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -373,9 +476,7 @@ { "cell_type": "code", "execution_count": 46, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -454,9 +555,7 @@ { "cell_type": "code", "execution_count": 48, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stderr", @@ -668,9 +767,7 @@ { "cell_type": "code", "execution_count": 53, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -726,9 +823,7 @@ { "cell_type": "code", "execution_count": 55, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -748,9 +843,7 @@ { "cell_type": "code", "execution_count": 62, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": {