Skip to content

Instantly share code, notes, and snippets.

@ngopal

ngopal/Galvanize.ipynb

Created May 1, 2017 22:39
Show Gist options
  • Save ngopal/3faf46669d13ff46c8d370800b5f894e to your computer and use it in GitHub Desktop.
Save ngopal/3faf46669d13ff46c8d370800b5f894e to your computer and use it in GitHub Desktop.
Download ZIP
for Miles
Display the source blob
Display the rendered blob
Raw
Galvanize.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 6.5 3. 5.8 2.2]\n",
" [ 5.2 3.4 1.4 0.2]\n",
" [ 6.4 2.8 5.6 2.1]\n",
" [ 5.8 2.6 4. 1.2]\n",
" [ 6.7 3.1 4.4 1.4]\n",
" [ 5.4 3. 4.5 1.5]\n",
" [ 5.5 2.6 4.4 1.2]\n",
" [ 5.5 4.2 1.4 0.2]\n",
" [ 6.1 2.8 4. 1.3]\n",
" [ 4.6 3.2 1.4 0.2]\n",
" [ 6.3 3.3 6. 2.5]\n",
" [ 5. 3.6 1.4 0.2]\n",
" [ 5.7 2.8 4.5 1.3]\n",
" [ 4.7 3.2 1.3 0.2]\n",
" [ 6.3 2.7 4.9 1.8]\n",
" [ 5.1 3.8 1.5 0.3]\n",
" [ 5.3 3.7 1.5 0.2]\n",
" [ 6.3 2.5 5. 1.9]\n",
" [ 5.6 2.8 4.9 2. ]\n",
" [ 5. 2. 3.5 1. ]\n",
" [ 7.7 3.8 6.7 2.2]\n",
" [ 6.4 3.1 5.5 1.8]\n",
" [ 5.8 2.7 4.1 1. ]\n",
" [ 5.1 3.4 1.5 0.2]\n",
" [ 6.4 3.2 4.5 1.5]\n",
" [ 4.3 3. 1.1 0.1]\n",
" [ 5.7 2.6 3.5 1. ]\n",
" [ 5. 3. 1.6 0.2]\n",
" [ 6.1 3. 4.9 1.8]\n",
" [ 5.7 2.8 4.1 1.3]\n",
" [ 6. 2.9 4.5 1.5]\n",
" [ 4.9 3.1 1.5 0.1]\n",
" [ 6.2 2.2 4.5 1.5]\n",
" [ 6.5 2.8 4.6 1.5]\n",
" [ 6.9 3.1 4.9 1.5]\n",
" [ 7.7 3. 6.1 2.3]\n",
" [ 4.4 2.9 1.4 0.2]\n",
" [ 5.5 2.5 4. 1.3]\n",
" [ 4.8 3. 1.4 0.3]\n",
" [ 6.9 3.1 5.4 2.1]\n",
" [ 6.3 3.4 5.6 2.4]\n",
" [ 5.4 3.4 1.7 0.2]\n",
" [ 6.9 3.1 5.1 2.3]\n",
" [ 5. 3.5 1.3 0.3]\n",
" [ 6.5 3. 5.2 2. ]\n",
" [ 7.2 3.6 6.1 2.5]\n",
" [ 6.8 3.2 5.9 2.3]\n",
" [ 6.7 3.3 5.7 2.1]\n",
" [ 5. 2.3 3.3 1. ]\n",
" [ 7.2 3.2 6. 1.8]\n",
" [ 5.7 3. 4.2 1.2]\n",
" [ 4.9 3.1 1.5 0.1]\n",
" [ 5. 3.3 1.4 0.2]\n",
" [ 5.6 3. 4.5 1.5]\n",
" [ 4.6 3.4 1.4 0.3]\n",
" [ 7.7 2.8 6.7 2. ]\n",
" [ 6.9 3.2 5.7 2.3]\n",
" [ 5.2 4.1 1.5 0.1]\n",
" [ 4.8 3.1 1.6 0.2]\n",
" [ 7.9 3.8 6.4 2. ]\n",
" [ 5. 3.4 1.6 0.4]\n",
" [ 7.1 3. 5.9 2.1]\n",
" [ 6.7 3.1 4.7 1.5]\n",
" [ 5.1 3.8 1.9 0.4]\n",
" [ 5.1 3.5 1.4 0.2]\n",
" [ 6.1 2.9 4.7 1.4]\n",
" [ 6.4 2.8 5.6 2.2]\n",
" [ 5.1 3.7 1.5 0.4]\n",
" [ 7.4 2.8 6.1 1.9]\n",
" [ 6.6 2.9 4.6 1.3]\n",
" [ 5.1 3.8 1.6 0.2]\n",
" [ 4.9 2.4 3.3 1. ]\n",
" [ 5.8 2.7 3.9 1.2]\n",
" [ 7. 3.2 4.7 1.4]\n",
" [ 5.7 3.8 1.7 0.3]\n",
" [ 4.5 2.3 1.3 0.3]\n",
" [ 6. 2.2 4. 1. ]\n",
" [ 6.3 2.9 5.6 1.8]\n",
" [ 4.6 3.6 1. 0.2]\n",
" [ 6.8 2.8 4.8 1.4]\n",
" [ 6.4 2.9 4.3 1.3]\n",
" [ 4.9 2.5 4.5 1.7]\n",
" [ 5.7 2.9 4.2 1.3]\n",
" [ 5.6 3. 4.1 1.3]\n",
" [ 4.4 3. 1.3 0.2]\n",
" [ 5.2 3.5 1.5 0.2]\n",
" [ 5.5 2.4 3.8 1.1]\n",
" [ 6.5 3. 5.5 1.8]\n",
" [ 4.8 3. 1.4 0.1]\n",
" [ 6. 2.7 5.1 1.6]\n",
" [ 6.7 3.1 5.6 2.4]\n",
" [ 6.7 2.5 5.8 1.8]\n",
" [ 5.1 2.5 3. 1.1]\n",
" [ 6.8 3. 5.5 2.1]\n",
" [ 6.3 2.5 4.9 1.5]\n",
" [ 5.5 2.3 4. 1.3]\n",
" [ 6.7 3. 5.2 2.3]\n",
" [ 6. 2.2 5. 1.5]\n",
" [ 7.3 2.9 6.3 1.8]\n",
" [ 6.3 2.8 5.1 1.5]\n",
" [ 6.4 3.2 5.3 2.3]\n",
" [ 5.8 4. 1.2 0.2]\n",
" [ 6.6 3. 4.4 1.4]\n",
" [ 5.7 4.4 1.5 0.4]\n",
" [ 5.4 3.9 1.7 0.4]\n",
" [ 4.6 3.1 1.5 0.2]\n",
" [ 6.4 2.7 5.3 1.9]\n",
" [ 5.6 2.9 3.6 1.3]\n",
" [ 5.5 3.5 1.3 0.2]\n",
" [ 6.3 2.3 4.4 1.3]\n",
" [ 6. 3. 4.8 1.8]\n",
" [ 6.2 2.9 4.3 1.3]\n",
" [ 5.9 3. 4.2 1.5]\n",
" [ 5.4 3.4 1.5 0.4]\n",
" [ 6.7 3.3 5.7 2.5]\n",
" [ 5.1 3.3 1.7 0.5]\n",
" [ 5. 3.5 1.6 0.6]\n",
" [ 5. 3.4 1.5 0.2]\n",
" [ 5.5 2.4 3.7 1. ]\n",
" [ 6.7 3. 5. 1.7]\n",
" [ 4.8 3.4 1.6 0.2]\n",
" [ 5.8 2.8 5.1 2.4]\n",
" [ 5.8 2.7 5.1 1.9]\n",
" [ 6.1 3. 4.6 1.4]\n",
" [ 7.6 3. 6.6 2.1]\n",
" [ 5.2 2.7 3.9 1.4]\n",
" [ 5.7 2.5 5. 2. ]\n",
" [ 7.7 2.6 6.9 2.3]\n",
" [ 4.9 3. 1.4 0.2]\n",
" [ 5.9 3. 5.1 1.8]\n",
" [ 4.9 3.1 1.5 0.1]\n",
" [ 5.8 2.7 5.1 1.9]\n",
" [ 6.2 2.8 4.8 1.8]\n",
" [ 7.2 3. 5.8 1.6]\n",
" [ 4.7 3.2 1.6 0.2]]\n",
"[2 0 2 1 1 1 1 0 1 0 2 0 1 0 2 0 0 2 2 1 2 2 1 0 1 0 1 0 2 1 1 0 1 1 1 2 0\n",
" 1 0 2 2 0 2 0 2 2 2 2 1 2 1 0 0 1 0 2 2 0 0 2 0 2 1 0 0 1 2 0 2 1 0 1 1 1\n",
" 0 0 1 2 0 1 1 2 1 1 0 0 1 2 0 1 2 2 1 2 1 1 2 2 2 2 2 0 1 0 0 0 2 1 0 1 2\n",
" 1 1 0 2 0 0 0 1 1 0 2 2 1 2 1 2 2 0 2 0 2 2 2 0]\n",
"[[ 5.6 2.5 3.9 1.1]\n",
" [ 4.4 3.2 1.3 0.2]\n",
" [ 6.3 3.3 4.7 1.6]\n",
" [ 5. 3.2 1.2 0.2]\n",
" [ 5.4 3.9 1.3 0.4]\n",
" [ 4.8 3.4 1.9 0.2]\n",
" [ 6.1 2.8 4.7 1.2]\n",
" [ 5.4 3.7 1.5 0.2]\n",
" [ 5.6 2.7 4.2 1.3]\n",
" [ 6.1 2.6 5.6 1.4]\n",
" [ 5.1 3.5 1.4 0.3]\n",
" [ 6.2 3.4 5.4 2.3]\n",
" [ 6. 3.4 4.5 1.6]\n",
" [ 5.9 3.2 4.8 1.8]\n",
" [ 6.5 3.2 5.1 2. ]]\n",
"[1 0 1 0 0 0 1 0 1 2 0 2 1 1 2]\n",
"Training set accuracy: 0.962962962963\n",
"Validation set accuracy: 0.8\n"
]
}
],
"source": [
"from sklearn import datasets\n",
"from sklearn.svm import SVC\n",
"from sklearn.multiclass import OneVsRestClassifier\n",
"from sklearn.cross_validation import train_test_split\n",
"\n",
"iris = datasets.load_iris()\n",
"\n",
"X, y = iris.data, iris.target\n",
"\n",
"X_train, X_test, y_train, y_test = \\\n",
" train_test_split(X, y, test_size=0.1, random_state=12345)\n",
"\n",
"print X_train\n",
"print y_train\n",
"print X_test\n",
"print y_test\n",
" \n",
" \n",
"svc_model = SVC(kernel='linear')\n",
"multiclass_model = OneVsRestClassifier(svc_model)\n",
"\n",
"multiclass_model.fit(X_train, y_train)\n",
"\n",
"print 'Training set accuracy: ', multiclass_model.score(X_train, y_train)\n",
"print 'Validation set accuracy:', multiclass_model.score(X_test, y_test)\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment