Skip to content

Instantly share code, notes, and snippets.

@PhilipMay

PhilipMay/SVC.ipynb

Last active January 14, 2019 18:17
Show Gist options

  • Select an option

    Learn more about clone URLs
  • Save PhilipMay/02a4c7ebc8982dceaf6242dae75e9a8b to your computer and use it in GitHub Desktop.

Select an option

Learn more about clone URLs
Save PhilipMay/02a4c7ebc8982dceaf6242dae75e9a8b to your computer and use it in GitHub Desktop.
Download ZIP
Display the source blob
Display the rendered blob
Raw
SVC.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from sklearn.datasets import load_breast_cancer\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.svm import SVC\n",
"\n",
"rng = np.random.RandomState(42)\n",
"data = load_breast_cancer()\n",
"X = data.data\n",
"y = data.target\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=rng)\n",
"X_train, X_dsel, y_train, y_dsel = train_test_split(X_train, y_train, test_size=0.5, random_state=rng)\n",
"\n",
"model_svc = SVC(probability=True, gamma='auto').fit(X_train, y_train)\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model_svc.predict(X_test)\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 1., 1., 1., 1., 1., 1.,\n",
" 1., 1., 1., 1., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
" 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
" 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 1., 1., 1.,\n",
" 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
" 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
" 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 1., 1., 1., 1., 1., 1., 1.,\n",
" 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
" 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
" 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
" 1., 0., 1., 1., 1., 1., 1., 1., 1., 0., 1., 1., 1., 1., 1., 1., 1.,\n",
" 1.])"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.rint(model_svc.predict_proba(X_test)[:,1])\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[0.37961521, 0.62038479],\n",
" [0.37769948, 0.62230052],\n",
" [0.37769948, 0.62230052],\n",
" [0.37903112, 0.62096888],\n",
" [0.4117856 , 0.5882144 ],\n",
" [0.37769948, 0.62230052],\n",
" [0.37769948, 0.62230052],\n",
" [0.37769948, 0.62230052],\n",
" [0.37801983, 0.62198017],\n",
" [0.37915486, 0.62084514],\n",
" [0.61645183, 0.38354817],\n",
" [0.37769948, 0.62230052],\n",
" [0.38738933, 0.61261067],\n",
" [0.37769948, 0.62230052],\n",
" [0.37770591, 0.62229409],\n",
" [0.37769948, 0.62230052],\n",
" [0.37769948, 0.62230052],\n",
" [0.37790093, 0.62209907],\n",
" [0.37769948, 0.62230052],\n",
" [0.37769948, 0.62230052],\n",
" [0.37769948, 0.62230052],\n",
" [0.57075499, 0.42924501],\n",
" [0.37769948, 0.62230052],\n",
" [0.38820738, 0.61179262],\n",
" [0.37803164, 0.62196836],\n",
" [0.37769948, 0.62230052],\n",
" [0.3776995 , 0.6223005 ],\n",
" [0.37770703, 0.62229297],\n",
" [0.40888522, 0.59111478],\n",
" [0.37769948, 0.62230052],\n",
" [0.37929526, 0.62070474],\n",
" [0.37778254, 0.62221746],\n",
" [0.37910163, 0.62089837],\n",
" [0.37769948, 0.62230052],\n",
" [0.40440997, 0.59559003],\n",
" [0.38115184, 0.61884816],\n",
" [0.37769948, 0.62230052],\n",
" [0.3777521 , 0.6222479 ],\n",
" [0.37769948, 0.62230052],\n",
" [0.37769948, 0.62230052],\n",
" [0.38158163, 0.61841837],\n",
" [0.37769948, 0.62230052],\n",
" [0.3796811 , 0.6203189 ],\n",
" [0.37865105, 0.62134895],\n",
" [0.37769948, 0.62230052],\n",
" [0.3777815 , 0.6222185 ],\n",
" [0.37769948, 0.62230052],\n",
" [0.37769948, 0.62230052],\n",
" [0.37793951, 0.62206049],\n",
" [0.41730172, 0.58269828],\n",
" [0.37769948, 0.62230052],\n",
" [0.37769948, 0.62230052],\n",
" [0.37774549, 0.62225451],\n",
" [0.37769948, 0.62230052],\n",
" [0.37810188, 0.62189812],\n",
" [0.3809238 , 0.6190762 ],\n",
" [0.38851049, 0.61148951],\n",
" [0.37769948, 0.62230052],\n",
" [0.37777633, 0.62222367],\n",
" [0.37937256, 0.62062744],\n",
" [0.37772105, 0.62227895],\n",
" [0.37769948, 0.62230052],\n",
" [0.37769948, 0.62230052],\n",
" [0.37769136, 0.62230864],\n",
" [0.70977645, 0.29022355],\n",
" [0.37780779, 0.62219221],\n",
" [0.37769948, 0.62230052],\n",
" [0.37769948, 0.62230052],\n",
" [0.37770657, 0.62229343],\n",
" [0.39253625, 0.60746375],\n",
" [0.37769948, 0.62230052],\n",
" [0.37769948, 0.62230052],\n",
" [0.37770043, 0.62229957],\n",
" [0.37769948, 0.62230052],\n",
" [0.37769948, 0.62230052],\n",
" [0.37769948, 0.62230052],\n",
" [0.37770027, 0.62229973],\n",
" [0.37769948, 0.62230052],\n",
" [0.37769962, 0.62230038],\n",
" [0.41293341, 0.58706659],\n",
" [0.37769948, 0.62230052],\n",
" [0.38087911, 0.61912089],\n",
" [0.37769948, 0.62230052],\n",
" [0.37769948, 0.62230052],\n",
" [0.37769948, 0.62230052],\n",
" [0.37769948, 0.62230052],\n",
" [0.37769948, 0.62230052],\n",
" [0.37769948, 0.62230052],\n",
" [0.37770086, 0.62229914],\n",
" [0.427462 , 0.572538 ],\n",
" [0.49147585, 0.50852415],\n",
" [0.37769948, 0.62230052],\n",
" [0.37771638, 0.62228362],\n",
" [0.37769974, 0.62230026],\n",
" [0.39480781, 0.60519219],\n",
" [0.38100516, 0.61899484],\n",
" [0.37769948, 0.62230052],\n",
" [0.37769948, 0.62230052],\n",
" [0.3779329 , 0.6220671 ],\n",
" [0.37769948, 0.62230052],\n",
" [0.37769948, 0.62230052],\n",
" [0.38018602, 0.61981398],\n",
" [0.37769948, 0.62230052],\n",
" [0.37769948, 0.62230052],\n",
" [0.37769939, 0.62230061],\n",
" [0.4469403 , 0.5530597 ],\n",
" [0.39270071, 0.60729929],\n",
" [0.37769948, 0.62230052],\n",
" [0.3776995 , 0.6223005 ],\n",
" [0.40954621, 0.59045379],\n",
" [0.37769948, 0.62230052],\n",
" [0.51250668, 0.48749332],\n",
" [0.37748631, 0.62251369],\n",
" [0.37769948, 0.62230052],\n",
" [0.37769948, 0.62230052],\n",
" [0.37769948, 0.62230052],\n",
" [0.37769948, 0.62230052],\n",
" [0.37769948, 0.62230052],\n",
" [0.37769961, 0.62230039],\n",
" [0.37769947, 0.62230053],\n",
" [0.37769948, 0.62230052],\n",
" [0.43154526, 0.56845474],\n",
" [0.37796545, 0.62203455],\n",
" [0.37769948, 0.62230052],\n",
" [0.37777013, 0.62222987],\n",
" [0.37769948, 0.62230052],\n",
" [0.37769948, 0.62230052],\n",
" [0.37944196, 0.62055804],\n",
" [0.3777021 , 0.6222979 ],\n",
" [0.37769948, 0.62230052],\n",
" [0.37769948, 0.62230052],\n",
" [0.37769947, 0.62230053],\n",
" [0.37769948, 0.62230052],\n",
" [0.37787238, 0.62212762],\n",
" [0.37769948, 0.62230052],\n",
" [0.37769943, 0.62230057],\n",
" [0.37817942, 0.62182058],\n",
" [0.38184795, 0.61815205],\n",
" [0.37769948, 0.62230052],\n",
" [0.37769948, 0.62230052],\n",
" [0.37955757, 0.62044243],\n",
" [0.37769948, 0.62230052],\n",
" [0.40919385, 0.59080615],\n",
" [0.38105568, 0.61894432],\n",
" [0.37770047, 0.62229953],\n",
" [0.38184139, 0.61815861],\n",
" [0.37769948, 0.62230052],\n",
" [0.37769948, 0.62230052],\n",
" [0.37769918, 0.62230082],\n",
" [0.3777295 , 0.6222705 ],\n",
" [0.37769948, 0.62230052],\n",
" [0.37770263, 0.62229737],\n",
" [0.37769948, 0.62230052],\n",
" [0.39277865, 0.60722135],\n",
" [0.37783074, 0.62216926],\n",
" [0.37769948, 0.62230052],\n",
" [0.37769948, 0.62230052],\n",
" [0.37769948, 0.62230052],\n",
" [0.37980937, 0.62019063],\n",
" [0.37772753, 0.62227247],\n",
" [0.37769948, 0.62230052],\n",
" [0.38742641, 0.61257359],\n",
" [0.37769948, 0.62230052],\n",
" [0.37860822, 0.62139178],\n",
" [0.37769948, 0.62230052],\n",
" [0.37769956, 0.62230044],\n",
" [0.38171643, 0.61828357],\n",
" [0.37780231, 0.62219769],\n",
" [0.37799166, 0.62200834],\n",
" [0.37769948, 0.62230052],\n",
" [0.37801311, 0.62198689],\n",
" [0.56222902, 0.43777098],\n",
" [0.37769949, 0.62230051],\n",
" [0.37769948, 0.62230052],\n",
" [0.38450044, 0.61549956],\n",
" [0.37830844, 0.62169156],\n",
" [0.37769948, 0.62230052],\n",
" [0.37769948, 0.62230052],\n",
" [0.37794421, 0.62205579],\n",
" [0.52517252, 0.47482748],\n",
" [0.38988081, 0.61011919],\n",
" [0.37778391, 0.62221609],\n",
" [0.37923049, 0.62076951],\n",
" [0.37769948, 0.62230052],\n",
" [0.37769948, 0.62230052],\n",
" [0.37769948, 0.62230052],\n",
" [0.37769948, 0.62230052],\n",
" [0.37975031, 0.62024969]])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model_svc.predict_proba(X_test)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment