diff --git a/Final_project/cbrown_btripp.ipynb b/Final_project/cbrown_btripp.ipynb
index ca4ecbb333c0850fa80586f4f7ee371943255aff..914b4d530a61ff02f4a49d4c379011778029ebad 100644
--- a/Final_project/cbrown_btripp.ipynb
+++ b/Final_project/cbrown_btripp.ipynb
@@ -16,13 +16,18 @@
"from sklearn.svm import LinearSVC\n",
"from sklearn import preprocessing\n",
"from sklearn.decomposition import PCA, NMF\n",
- "from sklearn.metrics import average_precision_score"
+ "from sklearn.metrics import average_precision_score\n",
+ "from sklearn.feature_selection import SelectKBest, chi2\n",
+ "from sklearn.metrics import classification_report\n",
+ "from sklearn.model_selection import train_test_split\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
- "metadata": {},
+ "metadata": {
+ "collapsed": true
+ },
"outputs": [],
"source": [
"soma_data = pd.read_csv('preop_soma_all.csv', header=None).T\n",
@@ -32,43 +37,887 @@
"#y = soma_data.iloc[13:len(soma_data),1]\n",
"y = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1]\n",
"X = soma_data.iloc[13:soma_data.shape[0],2:soma_data.shape[1]]\n",
- "X = preprocessing.normalize(X, norm='l2', axis=0)\n"
+ "X = preprocessing.normalize(X, norm='l2', axis=0)\n",
+ "#X = np.log(X)\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
- " \"This module will be removed in 0.20.\", DeprecationWarning)\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"#partition data into test and train\n",
- "from sklearn.cross_validation import train_test_split\n",
"X_train, X_test, y_train, y_test = train_test_split(\n",
- " X, y, test_size=0.25, random_state=0)\n"
+ " X, y, test_size=0.30, random_state=0)\n"
]
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "[1 0 0 1 1 1 0 0 0]\n",
- "[1, 1, 0, 1, 1, 0, 0, 0, 0]\n",
- "Score: 0.78\n",
- "Average precision-recall score: 0.59\n"
+ "# Tuning hyper-parameters for precision\n",
+ "\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
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+ " 'precision', 'predicted', average, warn_for)\n",
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+ " 'precision', 'predicted', average, warn_for)\n",
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+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
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+ " 'precision', 'predicted', average, warn_for)\n",
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+ " 'precision', 'predicted', average, warn_for)\n",
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+ " 'precision', 'predicted', average, warn_for)\n",
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+ " 'precision', 'predicted', average, warn_for)\n",
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+ " 'precision', 'predicted', average, warn_for)\n",
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+ " 'precision', 'predicted', average, warn_for)\n",
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+ " 'precision', 'predicted', average, warn_for)\n",
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+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
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+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
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+ " 'precision', 'predicted', average, warn_for)\n",
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+ " 'precision', 'predicted', average, warn_for)\n",
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+ " 'precision', 'predicted', average, warn_for)\n",
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+ " 'precision', 'predicted', average, warn_for)\n",
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+ " 'precision', 'predicted', average, warn_for)\n",
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+ " 'precision', 'predicted', average, warn_for)\n",
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+ " 'precision', 'predicted', average, warn_for)\n",
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+ " 'precision', 'predicted', average, warn_for)\n",
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+ " 'precision', 'predicted', average, warn_for)\n",
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+ " 'precision', 'predicted', average, warn_for)\n",
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+ " 'precision', 'predicted', average, warn_for)\n",
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+ " 'precision', 'predicted', average, warn_for)\n",
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+ " 'precision', 'predicted', average, warn_for)\n",
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+ " 'precision', 'predicted', average, warn_for)\n",
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+ " 'precision', 'predicted', average, warn_for)\n",
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+ " 'precision', 'predicted', average, warn_for)\n",
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+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
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+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
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+ " 'precision', 'predicted', average, warn_for)\n",
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+ " 'precision', 'predicted', average, warn_for)\n",
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+ " 'precision', 'predicted', average, warn_for)\n",
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+ " 'precision', 'predicted', average, warn_for)\n",
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+ " 'precision', 'predicted', average, warn_for)\n",
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+ " 'precision', 'predicted', average, warn_for)\n",
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+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
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+ " 'precision', 'predicted', average, warn_for)\n",
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+ " 'precision', 'predicted', average, warn_for)\n",
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+ " 'precision', 'predicted', average, warn_for)\n",
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+ " 'precision', 'predicted', average, warn_for)\n",
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+ " 'precision', 'predicted', average, warn_for)\n",
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+ " 'precision', 'predicted', average, warn_for)\n",
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+ "output_type": "stream",
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+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n",
+ "/home/otu/btripp2/.conda/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+ " 'precision', 'predicted', average, warn_for)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Best parameters found on training set:\n",
+ "\n",
+ "{'classify__C': 1, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 25}\n",
+ "\n",
+ "Grid scores on training set:\n",
+ "\n",
+ "0.180 (+/-0.480) for {'classify__C': 1, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 2}\n",
+ "0.080 (+/-0.367) for {'classify__C': 1, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 3}\n",
+ "0.080 (+/-0.367) for {'classify__C': 1, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 5}\n",
+ "0.140 (+/-0.376) for {'classify__C': 1, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 10}\n",
+ "0.460 (+/-0.627) for {'classify__C': 1, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 25}\n",
+ "0.460 (+/-0.627) for {'classify__C': 1, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 26}\n",
+ "0.460 (+/-0.627) for {'classify__C': 1, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 50}\n",
+ "0.460 (+/-0.627) for {'classify__C': 1, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 75}\n",
+ "0.153 (+/-0.421) for {'classify__C': 2, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 2}\n",
+ "0.080 (+/-0.367) for {'classify__C': 2, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 3}\n",
+ "0.080 (+/-0.367) for {'classify__C': 2, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 5}\n",
+ "0.296 (+/-0.777) for {'classify__C': 2, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 10}\n",
+ "0.436 (+/-0.627) for {'classify__C': 2, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 25}\n",
+ "0.436 (+/-0.627) for {'classify__C': 2, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 26}\n",
+ "0.436 (+/-0.627) for {'classify__C': 2, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 50}\n",
+ "0.436 (+/-0.627) for {'classify__C': 2, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 75}\n",
+ "0.153 (+/-0.421) for {'classify__C': 3, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 2}\n",
+ "0.080 (+/-0.367) for {'classify__C': 3, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 3}\n",
+ "0.080 (+/-0.367) for {'classify__C': 3, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 5}\n",
+ "0.376 (+/-0.740) for {'classify__C': 3, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 10}\n",
+ "0.436 (+/-0.627) for {'classify__C': 3, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 25}\n",
+ "0.436 (+/-0.627) for {'classify__C': 3, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 26}\n",
+ "0.436 (+/-0.627) for {'classify__C': 3, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 50}\n",
+ "0.436 (+/-0.627) for {'classify__C': 3, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 75}\n",
+ "0.153 (+/-0.421) for {'classify__C': 4, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 2}\n",
+ "0.080 (+/-0.367) for {'classify__C': 4, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 3}\n",
+ "0.080 (+/-0.367) for {'classify__C': 4, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 5}\n",
+ "0.376 (+/-0.740) for {'classify__C': 4, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 10}\n",
+ "0.436 (+/-0.627) for {'classify__C': 4, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 25}\n",
+ "0.436 (+/-0.627) for {'classify__C': 4, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 26}\n",
+ "0.436 (+/-0.627) for {'classify__C': 4, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 50}\n",
+ "0.436 (+/-0.627) for {'classify__C': 4, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 75}\n",
+ "0.153 (+/-0.421) for {'classify__C': 5, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 2}\n",
+ "0.080 (+/-0.367) for {'classify__C': 5, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 3}\n",
+ "0.080 (+/-0.367) for {'classify__C': 5, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 5}\n",
+ "0.376 (+/-0.740) for {'classify__C': 5, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 10}\n",
+ "0.436 (+/-0.627) for {'classify__C': 5, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 25}\n",
+ "0.436 (+/-0.627) for {'classify__C': 5, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 26}\n",
+ "0.436 (+/-0.627) for {'classify__C': 5, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 50}\n",
+ "0.436 (+/-0.627) for {'classify__C': 5, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 75}\n",
+ "0.153 (+/-0.421) for {'classify__C': 10, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 2}\n",
+ "0.080 (+/-0.367) for {'classify__C': 10, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 3}\n",
+ "0.080 (+/-0.367) for {'classify__C': 10, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 5}\n",
+ "0.376 (+/-0.740) for {'classify__C': 10, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 10}\n",
+ "0.436 (+/-0.627) for {'classify__C': 10, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 25}\n",
+ "0.436 (+/-0.627) for {'classify__C': 10, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 26}\n",
+ "0.436 (+/-0.627) for {'classify__C': 10, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 50}\n",
+ "0.436 (+/-0.627) for {'classify__C': 10, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 75}\n",
+ "0.000 (+/-0.000) for {'classify__C': 1, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 2}\n",
+ "0.000 (+/-0.000) for {'classify__C': 1, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 3}\n",
+ "0.000 (+/-0.000) for {'classify__C': 1, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 5}\n",
+ "0.000 (+/-0.000) for {'classify__C': 1, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 10}\n",
+ "0.000 (+/-0.000) for {'classify__C': 1, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 25}\n",
+ "0.080 (+/-0.285) for {'classify__C': 1, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 26}\n",
+ "0.000 (+/-0.000) for {'classify__C': 1, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 50}\n",
+ "0.000 (+/-0.000) for {'classify__C': 1, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 75}\n",
+ "0.000 (+/-0.000) for {'classify__C': 2, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 2}\n",
+ "0.000 (+/-0.000) for {'classify__C': 2, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 3}\n",
+ "0.000 (+/-0.000) for {'classify__C': 2, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 5}\n",
+ "0.000 (+/-0.000) for {'classify__C': 2, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 10}\n",
+ "0.000 (+/-0.000) for {'classify__C': 2, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 25}\n",
+ "0.060 (+/-0.214) for {'classify__C': 2, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 26}\n",
+ "0.053 (+/-0.244) for {'classify__C': 2, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 50}\n",
+ "0.000 (+/-0.000) for {'classify__C': 2, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 75}\n",
+ "0.000 (+/-0.000) for {'classify__C': 3, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 2}\n",
+ "0.000 (+/-0.000) for {'classify__C': 3, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 3}\n",
+ "0.000 (+/-0.000) for {'classify__C': 3, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 5}\n",
+ "0.000 (+/-0.000) for {'classify__C': 3, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 10}\n",
+ "0.000 (+/-0.000) for {'classify__C': 3, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 25}\n",
+ "0.096 (+/-0.342) for {'classify__C': 3, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 26}\n",
+ "0.053 (+/-0.244) for {'classify__C': 3, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 50}\n",
+ "0.000 (+/-0.000) for {'classify__C': 3, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 75}\n",
+ "0.000 (+/-0.000) for {'classify__C': 4, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 2}\n",
+ "0.000 (+/-0.000) for {'classify__C': 4, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 3}\n",
+ "0.000 (+/-0.000) for {'classify__C': 4, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 5}\n",
+ "0.000 (+/-0.000) for {'classify__C': 4, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 10}\n",
+ "0.000 (+/-0.000) for {'classify__C': 4, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 25}\n",
+ "0.096 (+/-0.342) for {'classify__C': 4, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 26}\n",
+ "0.053 (+/-0.244) for {'classify__C': 4, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 50}\n",
+ "0.000 (+/-0.000) for {'classify__C': 4, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 75}\n",
+ "0.000 (+/-0.000) for {'classify__C': 5, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 2}\n",
+ "0.000 (+/-0.000) for {'classify__C': 5, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 3}\n",
+ "0.000 (+/-0.000) for {'classify__C': 5, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 5}\n",
+ "0.000 (+/-0.000) for {'classify__C': 5, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 10}\n",
+ "0.000 (+/-0.000) for {'classify__C': 5, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 25}\n",
+ "0.096 (+/-0.342) for {'classify__C': 5, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 26}\n",
+ "0.053 (+/-0.244) for {'classify__C': 5, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 50}\n",
+ "0.000 (+/-0.000) for {'classify__C': 5, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 75}\n",
+ "0.000 (+/-0.000) for {'classify__C': 10, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 2}\n",
+ "0.000 (+/-0.000) for {'classify__C': 10, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 3}\n",
+ "0.000 (+/-0.000) for {'classify__C': 10, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 5}\n",
+ "0.000 (+/-0.000) for {'classify__C': 10, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 10}\n",
+ "0.060 (+/-0.214) for {'classify__C': 10, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 25}\n",
+ "0.096 (+/-0.342) for {'classify__C': 10, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 26}\n",
+ "0.080 (+/-0.367) for {'classify__C': 10, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 50}\n",
+ "0.000 (+/-0.000) for {'classify__C': 10, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 75}\n",
+ "\n",
+ "Detailed classification report:\n",
+ "\n",
+ "The scores are computed on the test set.\n",
+ "\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.67 0.80 0.73 5\n",
+ " 1 0.80 0.67 0.73 6\n",
+ "\n",
+ "avg / total 0.74 0.73 0.73 11\n",
+ "\n",
+ "\n",
+ "# Tuning hyper-parameters for recall\n",
+ "\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Best parameters found on training set:\n",
+ "\n",
+ "{'classify__C': 1, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 25}\n",
+ "\n",
+ "Grid scores on training set:\n",
+ "\n",
+ "0.180 (+/-0.480) for {'classify__C': 1, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 2}\n",
+ "0.080 (+/-0.367) for {'classify__C': 1, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 3}\n",
+ "0.080 (+/-0.367) for {'classify__C': 1, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 5}\n",
+ "0.160 (+/-0.405) for {'classify__C': 1, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 10}\n",
+ "0.420 (+/-0.491) for {'classify__C': 1, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 25}\n",
+ "0.420 (+/-0.491) for {'classify__C': 1, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 26}\n",
+ "0.420 (+/-0.491) for {'classify__C': 1, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 50}\n",
+ "0.420 (+/-0.491) for {'classify__C': 1, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 75}\n",
+ "0.180 (+/-0.480) for {'classify__C': 2, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 2}\n",
+ "0.080 (+/-0.367) for {'classify__C': 2, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 3}\n",
+ "0.080 (+/-0.367) for {'classify__C': 2, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 5}\n",
+ "0.260 (+/-0.597) for {'classify__C': 2, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 10}\n",
+ "0.420 (+/-0.491) for {'classify__C': 2, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 25}\n",
+ "0.420 (+/-0.491) for {'classify__C': 2, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 26}\n",
+ "0.420 (+/-0.491) for {'classify__C': 2, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 50}\n",
+ "0.420 (+/-0.491) for {'classify__C': 2, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 75}\n",
+ "0.180 (+/-0.480) for {'classify__C': 3, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 2}\n",
+ "0.080 (+/-0.367) for {'classify__C': 3, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 3}\n",
+ "0.080 (+/-0.367) for {'classify__C': 3, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 5}\n",
+ "0.340 (+/-0.569) for {'classify__C': 3, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 10}\n",
+ "0.420 (+/-0.491) for {'classify__C': 3, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 25}\n",
+ "0.420 (+/-0.491) for {'classify__C': 3, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 26}\n",
+ "0.420 (+/-0.491) for {'classify__C': 3, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 50}\n",
+ "0.420 (+/-0.491) for {'classify__C': 3, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 75}\n",
+ "0.180 (+/-0.480) for {'classify__C': 4, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 2}\n",
+ "0.080 (+/-0.367) for {'classify__C': 4, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 3}\n",
+ "0.080 (+/-0.367) for {'classify__C': 4, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 5}\n",
+ "0.340 (+/-0.569) for {'classify__C': 4, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 10}\n",
+ "0.420 (+/-0.491) for {'classify__C': 4, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 25}\n",
+ "0.420 (+/-0.491) for {'classify__C': 4, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 26}\n",
+ "0.420 (+/-0.491) for {'classify__C': 4, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 50}\n",
+ "0.420 (+/-0.491) for {'classify__C': 4, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 75}\n",
+ "0.180 (+/-0.480) for {'classify__C': 5, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 2}\n",
+ "0.080 (+/-0.367) for {'classify__C': 5, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 3}\n",
+ "0.080 (+/-0.367) for {'classify__C': 5, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 5}\n",
+ "0.340 (+/-0.569) for {'classify__C': 5, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 10}\n",
+ "0.420 (+/-0.491) for {'classify__C': 5, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 25}\n",
+ "0.420 (+/-0.491) for {'classify__C': 5, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 26}\n",
+ "0.420 (+/-0.491) for {'classify__C': 5, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 50}\n",
+ "0.420 (+/-0.491) for {'classify__C': 5, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 75}\n",
+ "0.180 (+/-0.480) for {'classify__C': 10, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 2}\n",
+ "0.080 (+/-0.367) for {'classify__C': 10, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 3}\n",
+ "0.080 (+/-0.367) for {'classify__C': 10, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 5}\n",
+ "0.420 (+/-0.749) for {'classify__C': 10, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 10}\n",
+ "0.420 (+/-0.491) for {'classify__C': 10, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 25}\n",
+ "0.420 (+/-0.491) for {'classify__C': 10, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 26}\n",
+ "0.420 (+/-0.491) for {'classify__C': 10, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 50}\n",
+ "0.420 (+/-0.491) for {'classify__C': 10, 'reduce_dim': PCA(copy=True, iterated_power=7, n_components=25, random_state=None,\n",
+ " svd_solver='auto', tol=0.0, whiten=False), 'reduce_dim__n_components': 75}\n",
+ "0.000 (+/-0.000) for {'classify__C': 1, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 2}\n",
+ "0.000 (+/-0.000) for {'classify__C': 1, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 3}\n",
+ "0.000 (+/-0.000) for {'classify__C': 1, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 5}\n",
+ "0.000 (+/-0.000) for {'classify__C': 1, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 10}\n",
+ "0.000 (+/-0.000) for {'classify__C': 1, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 25}\n",
+ "0.080 (+/-0.285) for {'classify__C': 1, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 26}\n",
+ "0.000 (+/-0.000) for {'classify__C': 1, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 50}\n",
+ "0.000 (+/-0.000) for {'classify__C': 1, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 75}\n",
+ "0.000 (+/-0.000) for {'classify__C': 2, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 2}\n",
+ "0.000 (+/-0.000) for {'classify__C': 2, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 3}\n",
+ "0.000 (+/-0.000) for {'classify__C': 2, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 5}\n",
+ "0.000 (+/-0.000) for {'classify__C': 2, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 10}\n",
+ "0.000 (+/-0.000) for {'classify__C': 2, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 25}\n",
+ "0.080 (+/-0.285) for {'classify__C': 2, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 26}\n",
+ "0.080 (+/-0.367) for {'classify__C': 2, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 50}\n",
+ "0.000 (+/-0.000) for {'classify__C': 2, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 75}\n",
+ "0.000 (+/-0.000) for {'classify__C': 3, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 2}\n",
+ "0.000 (+/-0.000) for {'classify__C': 3, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 3}\n",
+ "0.000 (+/-0.000) for {'classify__C': 3, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 5}\n",
+ "0.000 (+/-0.000) for {'classify__C': 3, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 10}\n",
+ "0.000 (+/-0.000) for {'classify__C': 3, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 25}\n",
+ "0.160 (+/-0.569) for {'classify__C': 3, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 26}\n",
+ "0.080 (+/-0.367) for {'classify__C': 3, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 50}\n",
+ "0.000 (+/-0.000) for {'classify__C': 3, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 75}\n",
+ "0.000 (+/-0.000) for {'classify__C': 4, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 2}\n",
+ "0.000 (+/-0.000) for {'classify__C': 4, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 3}\n",
+ "0.000 (+/-0.000) for {'classify__C': 4, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 5}\n",
+ "0.000 (+/-0.000) for {'classify__C': 4, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 10}\n",
+ "0.000 (+/-0.000) for {'classify__C': 4, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 25}\n",
+ "0.160 (+/-0.569) for {'classify__C': 4, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 26}\n",
+ "0.080 (+/-0.367) for {'classify__C': 4, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 50}\n",
+ "0.000 (+/-0.000) for {'classify__C': 4, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 75}\n",
+ "0.000 (+/-0.000) for {'classify__C': 5, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 2}\n",
+ "0.000 (+/-0.000) for {'classify__C': 5, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 3}\n",
+ "0.000 (+/-0.000) for {'classify__C': 5, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 5}\n",
+ "0.000 (+/-0.000) for {'classify__C': 5, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 10}\n",
+ "0.000 (+/-0.000) for {'classify__C': 5, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 25}\n",
+ "0.160 (+/-0.569) for {'classify__C': 5, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 26}\n",
+ "0.080 (+/-0.367) for {'classify__C': 5, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 50}\n",
+ "0.000 (+/-0.000) for {'classify__C': 5, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 75}\n",
+ "0.000 (+/-0.000) for {'classify__C': 10, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 2}\n",
+ "0.000 (+/-0.000) for {'classify__C': 10, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 3}\n",
+ "0.000 (+/-0.000) for {'classify__C': 10, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 5}\n",
+ "0.000 (+/-0.000) for {'classify__C': 10, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 10}\n",
+ "0.080 (+/-0.285) for {'classify__C': 10, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 25}\n",
+ "0.160 (+/-0.569) for {'classify__C': 10, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 26}\n",
+ "0.160 (+/-0.733) for {'classify__C': 10, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 50}\n",
+ "0.000 (+/-0.000) for {'classify__C': 10, 'reduce_dim': SelectKBest(k=75, score_func=<function chi2 at 0x2b0717038ae8>), 'reduce_dim__k': 75}\n",
+ "\n",
+ "Detailed classification report:\n",
+ "\n",
+ "The scores are computed on the test set.\n",
+ "\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.67 0.80 0.73 5\n",
+ " 1 0.80 0.67 0.73 6\n",
+ "\n",
+ "avg / total 0.74 0.73 0.73 11\n",
+ "\n",
+ "\n",
+ "[0 0 0 1 1 1 0 0 0 1 1]\n",
+ "[1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1]\n",
+ "Score: 0.67\n"
]
}
],
@@ -78,57 +927,63 @@
" ('classify', LinearSVC())\n",
"])\n",
"\n",
- "N_FEATURES_OPTIONS = [26] #[25, 50, 75, 100]\n",
- "C_OPTIONS = [(n) for n in range(1,6)]\n",
- "LOSS_OPTIONS = ['squared_hinge','hinge']\n",
- "\n",
+ "N_FEATURES_OPTIONS = [2, 3, 5, 10, 25, 26, 50, 75] #[26, 50, 75, 100]\n",
+ "C_OPTIONS = [1, 2, 3, 4, 5, 10] #[(n) for n in range(1,6)]\n",
+ "#LOSS_OPTIONS = ['squared_hinge','hinge']\n",
"\n",
- "param_grid = [\n",
+ "tuned_parameters = [\n",
" {\n",
- " 'reduce_dim': [PCA(iterated_power=\"auto\")],\n",
+ " 'reduce_dim': [PCA(iterated_power=7)],\n",
" 'reduce_dim__n_components': N_FEATURES_OPTIONS,\n",
- " 'classify__C': C_OPTIONS,\n",
- " 'classify__loss': LOSS_OPTIONS\n",
+ " 'classify__C': C_OPTIONS\n",
+ " },\n",
+ " {\n",
+ " 'reduce_dim': [SelectKBest(chi2)],\n",
+ " 'reduce_dim__k': N_FEATURES_OPTIONS,\n",
+ " 'classify__C': C_OPTIONS\n",
" },\n",
"]\n",
- "reducer_labels = ['PCA']\n",
"\n",
- "grid = GridSearchCV(pipe, cv=5, n_jobs=1, param_grid=param_grid, refit='AUC')\n",
- "grid.fit(X_train, y_train)\n",
+ "scores = ['precision', 'recall']\n",
+ "\n",
+ "for score in scores:\n",
+ " print(\"# Tuning hyper-parameters for %s\" % score)\n",
+ " print()\n",
+ "\n",
+ " grid = GridSearchCV(pipe, cv=5, param_grid=tuned_parameters, scoring= score)\n",
+ " grid.fit(X_train, y_train)\n",
+ " \n",
+ " print(\"Best parameters found on training set:\")\n",
+ " print()\n",
+ " print(grid.best_params_)\n",
+ " print()\n",
+ " print(\"Grid scores on training set:\")\n",
+ " print()\n",
+ " means = grid.cv_results_['mean_test_score']\n",
+ " stds = grid.cv_results_['std_test_score']\n",
+ " for mean, std, params in zip(means, stds, grid.cv_results_['params']):\n",
+ " print(\"%0.3f (+/-%0.03f) for %r\"\n",
+ " % (mean, std * 2, params))\n",
+ " print()\n",
+ "\n",
+ " print(\"Detailed classification report:\")\n",
+ " print()\n",
+ " #print(\"The model is trained on the full development set.\")\n",
+ " print(\"The scores are computed on the test set.\")\n",
+ " print()\n",
+ " y_true, y_pred = y_test, grid.predict(X_test)\n",
+ " print(classification_report(y_true, y_pred))\n",
+ " print()\n",
"\n",
"print(grid.predict(X_test))\n",
"print(y_test)\n",
"print('Score: {0:0.2f}'.format(grid.score(X_test,y_test)))\n",
"\n",
"y_score = grid.decision_function(X_test)\n",
- "average_precision = average_precision_score(y_test, y_score)\n",
- "print('Average precision-recall score: {0:0.2f}'.format(\n",
- " average_precision))\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Pipeline(memory=None,\n",
- " steps=[('reduce_dim', PCA(copy=True, iterated_power='auto', n_components=26, random_state=None,\n",
- " svd_solver='auto', tol=0.0, whiten=False)), ('classify', LinearSVC(C=1, class_weight=None, dual=True, fit_intercept=True,\n",
- " intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
- " multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
- " verbose=0))])\n"
- ]
- }
- ],
- "source": [
- "#grid.get_params().keys()\n",
- "print(grid.best_estimator_)\n",
- "#cv_dict = grid.cv_results_\n",
- "\n"
+ "\n",
+ "#average_precision = average_precision_score(y_test, y_score)\n",
+ "#print('Average precision-recall score: {0:0.2f}'.format(\n",
+ " # average_precision))\n"
]
}
],