comparison notebooks/sensitivity_experiment.ipynb @ 18:ed109218dd4b branch-tests

rename result scripts and more tests

     "\n",
     "import sys\n",
     "sys.path.append('../')\n",
     "import scripts.load_dataset as load_dataset\n",
     "import scripts.map_and_average as mapper\n",
-    "import scripts.results_classification as results_class\n",
-    "import scripts.results as results"
+    "import scripts.classification\n",
+    "import scripts.outliers as outliers"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,

     "    _, _, ldadata_list, _, _, Y, Yaudio = mapper.lda_map_and_average_frames(min_variance=0.99)\n",
     "    X = np.concatenate(ldadata_list)\n",
     "    \n",
     "    # classification and confusion\n",
     "    print \"classifying...\"\n",
-    "    traininds, testinds = results_class.get_train_test_indices()\n",
-    "    X_train, Y_train, X_test, Y_test = results_class.get_train_test_sets(X, Y, traininds, testinds)\n",
-    "    accuracy, _ = results_class.confusion_matrix(X_train, Y_train, X_test, Y_test, saveCF=False, plots=False)\n",
+    "    traininds, testinds = classification.get_train_test_indices()\n",
+    "    X_train, Y_train, X_test, Y_test = classification.get_train_test_sets(X, Y, traininds, testinds)\n",
+    "    accuracy, _ = classification.confusion_matrix(X_train, Y_train, X_test, Y_test, saveCF=False, plots=False)\n",
     "    print accuracy\n",
     "    \n",
     "    # outliers\n",
     "    print \"detecting outliers...\"\n",
-    "    ddf = results.load_metadata(Yaudio, metadata_file=load_dataset.METADATA_FILE)\n",
+    "    ddf = outliers.load_metadata(Yaudio, metadata_file=load_dataset.METADATA_FILE)\n",
     "    df_global, threshold, MD = get_outliers_df(X, Y, chi2thr=0.999)\n",
     "    print_most_least_outliers_topN(df_global, N=10)\n",
     "    \n",
     "    # write output\n",
     "    print \"writing file\"\n",