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{
"cells": [
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import os\n",
"import urllib\n",
"import tarfile\n",
"from sklearn.model_selection import StratifiedShuffleSplit as SSS\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.impute import SimpleImputer\n",
"from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
"from sklearn.compose import ColumnTransformer\n",
"from sklearn.svm import SVR\n",
"from sklearn.ensemble import RandomForestRegressor\n",
"from sklearn.metrics import mean_squared_error\n",
"from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n",
"import joblib\n",
"from scipy.stats import expon, reciprocal"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"DOWNLOAD_ROOT = \"https://raw.githubusercontent.com/ageron/handson-ml2/master/\"\n",
"HOUSING_PATH = os.path.join(\"datasets\", \"housing\")\n",
"HOUSING_URL = DOWNLOAD_ROOT + \"datasets/housing/housing.tgz\""
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def fetch_housing_data(housing_url=HOUSING_URL, housing_path=HOUSING_PATH):\n",
" os.makedirs(housing_path, exist_ok = True) # Create directory if not already there\n",
" tgz_path = os.path.join(housing_path, 'housing.tgz') # Make path for our tgz file\n",
" urllib.request.urlretrieve(housing_url, tgz_path) # Download the file\n",
" housing_tgz = tarfile.open(tgz_path) # Open the file\n",
" housing_tgz.extractall(path=housing_path) # Extract from tarfile\n",
" housing_tgz.close()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"fetch_housing_data()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def load_housing_data(housing_path=HOUSING_PATH):\n",
" csv_path = os.path.join(housing_path, 'housing.csv')\n",
" return pd.read_csv(csv_path)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>longitude</th>\n",
" <th>latitude</th>\n",
" <th>housing_median_age</th>\n",
" <th>total_rooms</th>\n",
" <th>total_bedrooms</th>\n",
" <th>population</th>\n",
" <th>households</th>\n",
" <th>median_income</th>\n",
" <th>median_house_value</th>\n",
" <th>ocean_proximity</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>-122.23</td>\n",
" <td>37.88</td>\n",
" <td>41.0</td>\n",
" <td>880.0</td>\n",
" <td>129.0</td>\n",
" <td>322.0</td>\n",
" <td>126.0</td>\n",
" <td>8.3252</td>\n",
" <td>452600.0</td>\n",
" <td>NEAR BAY</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>-122.22</td>\n",
" <td>37.86</td>\n",
" <td>21.0</td>\n",
" <td>7099.0</td>\n",
" <td>1106.0</td>\n",
" <td>2401.0</td>\n",
" <td>1138.0</td>\n",
" <td>8.3014</td>\n",
" <td>358500.0</td>\n",
" <td>NEAR BAY</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>-122.24</td>\n",
" <td>37.85</td>\n",
" <td>52.0</td>\n",
" <td>1467.0</td>\n",
" <td>190.0</td>\n",
" <td>496.0</td>\n",
" <td>177.0</td>\n",
" <td>7.2574</td>\n",
" <td>352100.0</td>\n",
" <td>NEAR BAY</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>-122.25</td>\n",
" <td>37.85</td>\n",
" <td>52.0</td>\n",
" <td>1274.0</td>\n",
" <td>235.0</td>\n",
" <td>558.0</td>\n",
" <td>219.0</td>\n",
" <td>5.6431</td>\n",
" <td>341300.0</td>\n",
" <td>NEAR BAY</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>-122.25</td>\n",
" <td>37.85</td>\n",
" <td>52.0</td>\n",
" <td>1627.0</td>\n",
" <td>280.0</td>\n",
" <td>565.0</td>\n",
" <td>259.0</td>\n",
" <td>3.8462</td>\n",
" <td>342200.0</td>\n",
" <td>NEAR BAY</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" longitude latitude housing_median_age total_rooms total_bedrooms \\\n",
"0 -122.23 37.88 41.0 880.0 129.0 \n",
"1 -122.22 37.86 21.0 7099.0 1106.0 \n",
"2 -122.24 37.85 52.0 1467.0 190.0 \n",
"3 -122.25 37.85 52.0 1274.0 235.0 \n",
"4 -122.25 37.85 52.0 1627.0 280.0 \n",
"\n",
" population households median_income median_house_value ocean_proximity \n",
"0 322.0 126.0 8.3252 452600.0 NEAR BAY \n",
"1 2401.0 1138.0 8.3014 358500.0 NEAR BAY \n",
"2 496.0 177.0 7.2574 352100.0 NEAR BAY \n",
"3 558.0 219.0 5.6431 341300.0 NEAR BAY \n",
"4 565.0 259.0 3.8462 342200.0 NEAR BAY "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"housing = load_housing_data()\n",
"housing.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# Now we want to do some stratified sampling based on median_income which seems to be fairly important\n",
"\n",
"housing['income_cat'] = pd.cut(housing['median_income'], # Chopping up median income because it's important\n",
" bins=[0., 1.5, 3.0, 4.5, 6., np.inf], # Chop into categories of 0 to 1.5, 1.5 to 3, etc\n",
" labels=[1,2,3,4,5]) # Generic labels\n",
"\n",
"split = SSS(n_splits=1, test_size=0.2, random_state=42) # Initialize our split with proper test size/random state to match book\n",
"for train_index, test_index in split.split(housing, housing['income_cat']):\n",
" strat_train_set = housing.loc[train_index]\n",
" strat_test_set = housing.loc[test_index]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# Now that we finished with our income_cat feature we can drop it\n",
"\n",
"for set_ in (strat_train_set, strat_test_set):\n",
" set_.drop('income_cat', axis=1, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"housing = strat_train_set.drop('median_house_value', axis=1)\n",
"housing_labels = strat_train_set['median_house_value'].copy()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.base import BaseEstimator, TransformerMixin\n",
"\n",
"# column index\n",
"rooms_ix, bedrooms_ix, population_ix, households_ix = 3, 4, 5, 6\n",
"\n",
"class CombinedAttributesAdder(BaseEstimator, TransformerMixin):\n",
" def __init__(self, add_bedrooms_per_room = True): # no *args or **kargs\n",
" self.add_bedrooms_per_room = add_bedrooms_per_room\n",
" def fit(self, X, y=None):\n",
" return self # nothing else to do\n",
" def transform(self, X):\n",
" rooms_per_household = X[:, rooms_ix] / X[:, households_ix]\n",
" population_per_household = X[:, population_ix] / X[:, households_ix]\n",
" if self.add_bedrooms_per_room:\n",
" bedrooms_per_room = X[:, bedrooms_ix] / X[:, rooms_ix]\n",
" return np.c_[X, rooms_per_household, population_per_household,\n",
" bedrooms_per_room]\n",
" else:\n",
" return np.c_[X, rooms_per_household, population_per_household]\n",
"\n",
"attr_adder = CombinedAttributesAdder(add_bedrooms_per_room=False)\n",
"housing_extra_attribs = attr_adder.transform(housing.values)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# Setup a pipeline to do all we've done already for numerical features\n",
"# Note that all estimators but the last must be transformers(specifically they must have a\n",
"# fit_transform() method)\n",
"\n",
"num_pipeline = Pipeline([\n",
" ('imputer', SimpleImputer(strategy='median')),\n",
" ('attribs_adder', CombinedAttributesAdder()), \n",
" ('std_scaler', StandardScaler())\n",
"]) "
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"housing_num = housing.drop('ocean_proximity', axis=1) # Drop non numerical feature for imputation\n",
"housing_cat = housing[['ocean_proximity']] # doubles brackets to get a dataframe instead of a series"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"# Our previous pipeline applies to all columns in a dataframe. To deal with both our categorical and\n",
"# numerical features at once we use ColumnTransformer to perform our pipeline only on numerical features\n",
"# and OneHotEncoder only on categorical features\n",
"\n",
"num_attribs = list(housing_num)\n",
"cat_attribs = ['ocean_proximity']\n",
"\n",
"full_pipeline = ColumnTransformer([\n",
" ('num', num_pipeline, num_attribs), \n",
" ('cat', OneHotEncoder(), cat_attribs)\n",
"])\n",
"\n",
"housing_prepared = full_pipeline.fit_transform(housing)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Exercise One**\n",
"\n",
"Try with an SVM!"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\users\\tsb\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
" \"avoid this warning.\", FutureWarning)\n"
]
},
{
"data": {
"text/plain": [
"SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1,\n",
" gamma='auto_deprecated', kernel='rbf', max_iter=-1, shrinking=True,\n",
" tol=0.001, verbose=False)"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Simple Support Vector Machine\n",
"\n",
"SVR_model = SVR()\n",
"SVR_model.fit(housing_prepared, housing_labels)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"118577.43356412371"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"SVR_pred = SVR_model.predict(housing_prepared)\n",
"SVR_mse = mean_squared_error(SVR_pred, housing_labels)\n",
"SVR_rmse = np.sqrt (SVR_mse)\n",
"SVR_rmse"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting 5 folds for each of 72 candidates, totalling 360 fits\n",
"[CV] C=10.0, gamma=0.01, kernel=linear ...............................\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV] ................ C=10.0, gamma=0.01, kernel=linear, total= 4.4s\n",
"[CV] C=10.0, gamma=0.01, kernel=linear ...............................\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 6.1s remaining: 0.0s\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV] ................ C=10.0, gamma=0.01, kernel=linear, total= 4.3s\n",
"[CV] C=10.0, gamma=0.01, kernel=linear ...............................\n",
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"[CV] C=10.0, gamma=0.03, kernel=rbf ..................................\n",
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"[CV] C=10.0, gamma=0.03, kernel=rbf ..................................\n",
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"[CV] C=10.0, gamma=0.03, kernel=rbf ..................................\n",
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"[CV] C=10.0, gamma=0.1, kernel=linear ................................\n",
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"[CV] C=10.0, gamma=0.3, kernel=linear ................................\n",
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"[CV] C=10.0, gamma=1.0, kernel=linear ................................\n",
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"[CV] ................. C=10.0, gamma=3.0, kernel=linear, total= 4.3s\n",
"[CV] C=10.0, gamma=3.0, kernel=linear ................................\n",
"[CV] ................. C=10.0, gamma=3.0, kernel=linear, total= 4.3s\n",
"[CV] C=10.0, gamma=3.0, kernel=rbf ...................................\n",
"[CV] .................... C=10.0, gamma=3.0, kernel=rbf, total= 7.6s\n",
"[CV] C=10.0, gamma=3.0, kernel=rbf ...................................\n",
"[CV] .................... C=10.0, gamma=3.0, kernel=rbf, total= 7.6s\n",
"[CV] C=10.0, gamma=3.0, kernel=rbf ...................................\n",
"[CV] .................... C=10.0, gamma=3.0, kernel=rbf, total= 7.6s\n",
"[CV] C=10.0, gamma=3.0, kernel=rbf ...................................\n",
"[CV] .................... C=10.0, gamma=3.0, kernel=rbf, total= 7.6s\n",
"[CV] C=10.0, gamma=3.0, kernel=rbf ...................................\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV] .................... C=10.0, gamma=3.0, kernel=rbf, total= 7.6s\n",
"[CV] C=30.0, gamma=0.01, kernel=linear ...............................\n",
"[CV] ................ C=30.0, gamma=0.01, kernel=linear, total= 4.3s\n",
"[CV] C=30.0, gamma=0.01, kernel=linear ...............................\n",
"[CV] ................ C=30.0, gamma=0.01, kernel=linear, total= 4.3s\n",
"[CV] C=30.0, gamma=0.01, kernel=linear ...............................\n",
"[CV] ................ C=30.0, gamma=0.01, kernel=linear, total= 4.4s\n",
"[CV] C=30.0, gamma=0.01, kernel=linear ...............................\n",
"[CV] ................ C=30.0, gamma=0.01, kernel=linear, total= 4.4s\n",
"[CV] C=30.0, gamma=0.01, kernel=linear ...............................\n",
"[CV] ................ C=30.0, gamma=0.01, kernel=linear, total= 4.2s\n",
"[CV] C=30.0, gamma=0.01, kernel=rbf ..................................\n",
"[CV] ................... C=30.0, gamma=0.01, kernel=rbf, total= 7.4s\n",
"[CV] C=30.0, gamma=0.01, kernel=rbf ..................................\n",
"[CV] ................... C=30.0, gamma=0.01, kernel=rbf, total= 7.4s\n",
"[CV] C=30.0, gamma=0.01, kernel=rbf ..................................\n",
"[CV] ................... C=30.0, gamma=0.01, kernel=rbf, total= 7.4s\n",
"[CV] C=30.0, gamma=0.01, kernel=rbf ..................................\n",
"[CV] ................... C=30.0, gamma=0.01, kernel=rbf, total= 7.3s\n",
"[CV] C=30.0, gamma=0.01, kernel=rbf ..................................\n",
"[CV] ................... C=30.0, gamma=0.01, kernel=rbf, total= 7.4s\n",
"[CV] C=30.0, gamma=0.03, kernel=linear ...............................\n",
"[CV] ................ C=30.0, gamma=0.03, kernel=linear, total= 4.3s\n",
"[CV] C=30.0, gamma=0.03, kernel=linear ...............................\n",
"[CV] ................ C=30.0, gamma=0.03, kernel=linear, total= 4.3s\n",
"[CV] C=30.0, gamma=0.03, kernel=linear ...............................\n",
"[CV] ................ C=30.0, gamma=0.03, kernel=linear, total= 4.4s\n",
"[CV] C=30.0, gamma=0.03, kernel=linear ...............................\n",
"[CV] ................ C=30.0, gamma=0.03, kernel=linear, total= 4.3s\n",
"[CV] C=30.0, gamma=0.03, kernel=linear ...............................\n",
"[CV] ................ C=30.0, gamma=0.03, kernel=linear, total= 4.3s\n",
"[CV] C=30.0, gamma=0.03, kernel=rbf ..................................\n",
"[CV] ................... C=30.0, gamma=0.03, kernel=rbf, total= 7.4s\n",
"[CV] C=30.0, gamma=0.03, kernel=rbf ..................................\n",
"[CV] ................... C=30.0, gamma=0.03, kernel=rbf, total= 7.3s\n",
"[CV] C=30.0, gamma=0.03, kernel=rbf ..................................\n",
"[CV] ................... C=30.0, gamma=0.03, kernel=rbf, total= 7.6s\n",
"[CV] C=30.0, gamma=0.03, kernel=rbf ..................................\n",
"[CV] ................... C=30.0, gamma=0.03, kernel=rbf, total= 7.3s\n",
"[CV] C=30.0, gamma=0.03, kernel=rbf ..................................\n",
"[CV] ................... C=30.0, gamma=0.03, kernel=rbf, total= 7.4s\n",
"[CV] C=30.0, gamma=0.1, kernel=linear ................................\n",
"[CV] ................. C=30.0, gamma=0.1, kernel=linear, total= 4.2s\n",
"[CV] C=30.0, gamma=0.1, kernel=linear ................................\n",
"[CV] ................. C=30.0, gamma=0.1, kernel=linear, total= 4.4s\n",
"[CV] C=30.0, gamma=0.1, kernel=linear ................................\n",
"[CV] ................. C=30.0, gamma=0.1, kernel=linear, total= 4.4s\n",
"[CV] C=30.0, gamma=0.1, kernel=linear ................................\n",
"[CV] ................. C=30.0, gamma=0.1, kernel=linear, total= 4.3s\n",
"[CV] C=30.0, gamma=0.1, kernel=linear ................................\n",
"[CV] ................. C=30.0, gamma=0.1, kernel=linear, total= 4.3s\n",
"[CV] C=30.0, gamma=0.1, kernel=rbf ...................................\n",
"[CV] .................... C=30.0, gamma=0.1, kernel=rbf, total= 7.2s\n",
"[CV] C=30.0, gamma=0.1, kernel=rbf ...................................\n",
"[CV] .................... C=30.0, gamma=0.1, kernel=rbf, total= 7.3s\n",
"[CV] C=30.0, gamma=0.1, kernel=rbf ...................................\n",
"[CV] .................... C=30.0, gamma=0.1, kernel=rbf, total= 7.2s\n",
"[CV] C=30.0, gamma=0.1, kernel=rbf ...................................\n",
"[CV] .................... C=30.0, gamma=0.1, kernel=rbf, total= 7.3s\n",
"[CV] C=30.0, gamma=0.1, kernel=rbf ...................................\n",
"[CV] .................... C=30.0, gamma=0.1, kernel=rbf, total= 7.2s\n",
"[CV] C=30.0, gamma=0.3, kernel=linear ................................\n",
"[CV] ................. C=30.0, gamma=0.3, kernel=linear, total= 4.2s\n",
"[CV] C=30.0, gamma=0.3, kernel=linear ................................\n",
"[CV] ................. C=30.0, gamma=0.3, kernel=linear, total= 4.3s\n",
"[CV] C=30.0, gamma=0.3, kernel=linear ................................\n",
"[CV] ................. C=30.0, gamma=0.3, kernel=linear, total= 4.4s\n",
"[CV] C=30.0, gamma=0.3, kernel=linear ................................\n",
"[CV] ................. C=30.0, gamma=0.3, kernel=linear, total= 4.4s\n",
"[CV] C=30.0, gamma=0.3, kernel=linear ................................\n",
"[CV] ................. C=30.0, gamma=0.3, kernel=linear, total= 4.3s\n",
"[CV] C=30.0, gamma=0.3, kernel=rbf ...................................\n",
"[CV] .................... C=30.0, gamma=0.3, kernel=rbf, total= 7.1s\n",
"[CV] C=30.0, gamma=0.3, kernel=rbf ...................................\n",
"[CV] .................... C=30.0, gamma=0.3, kernel=rbf, total= 7.1s\n",
"[CV] C=30.0, gamma=0.3, kernel=rbf ...................................\n",
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"[CV] C=30.0, gamma=0.3, kernel=rbf ...................................\n",
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"[CV] C=30.0, gamma=0.3, kernel=rbf ...................................\n",
"[CV] .................... C=30.0, gamma=0.3, kernel=rbf, total= 7.0s\n",
"[CV] C=30.0, gamma=1.0, kernel=linear ................................\n",
"[CV] ................. C=30.0, gamma=1.0, kernel=linear, total= 4.3s\n",
"[CV] C=30.0, gamma=1.0, kernel=linear ................................\n",
"[CV] ................. C=30.0, gamma=1.0, kernel=linear, total= 4.2s\n",
"[CV] C=30.0, gamma=1.0, kernel=linear ................................\n",
"[CV] ................. C=30.0, gamma=1.0, kernel=linear, total= 4.4s\n",
"[CV] C=30.0, gamma=1.0, kernel=linear ................................\n",
"[CV] ................. C=30.0, gamma=1.0, kernel=linear, total= 4.3s\n",
"[CV] C=30.0, gamma=1.0, kernel=linear ................................\n",
"[CV] ................. C=30.0, gamma=1.0, kernel=linear, total= 4.2s\n",
"[CV] C=30.0, gamma=1.0, kernel=rbf ...................................\n",
"[CV] .................... C=30.0, gamma=1.0, kernel=rbf, total= 6.9s\n",
"[CV] C=30.0, gamma=1.0, kernel=rbf ...................................\n",
"[CV] .................... C=30.0, gamma=1.0, kernel=rbf, total= 6.9s\n",
"[CV] C=30.0, gamma=1.0, kernel=rbf ...................................\n",
"[CV] .................... C=30.0, gamma=1.0, kernel=rbf, total= 6.9s\n",
"[CV] C=30.0, gamma=1.0, kernel=rbf ...................................\n",
"[CV] .................... C=30.0, gamma=1.0, kernel=rbf, total= 6.9s\n",
"[CV] C=30.0, gamma=1.0, kernel=rbf ...................................\n",
"[CV] .................... C=30.0, gamma=1.0, kernel=rbf, total= 7.0s\n",
"[CV] C=30.0, gamma=3.0, kernel=linear ................................\n",
"[CV] ................. C=30.0, gamma=3.0, kernel=linear, total= 4.3s\n",
"[CV] C=30.0, gamma=3.0, kernel=linear ................................\n",
"[CV] ................. C=30.0, gamma=3.0, kernel=linear, total= 4.3s\n",
"[CV] C=30.0, gamma=3.0, kernel=linear ................................\n",
"[CV] ................. C=30.0, gamma=3.0, kernel=linear, total= 4.5s\n",
"[CV] C=30.0, gamma=3.0, kernel=linear ................................\n",
"[CV] ................. C=30.0, gamma=3.0, kernel=linear, total= 4.3s\n",
"[CV] C=30.0, gamma=3.0, kernel=linear ................................\n",
"[CV] ................. C=30.0, gamma=3.0, kernel=linear, total= 4.3s\n",
"[CV] C=30.0, gamma=3.0, kernel=rbf ...................................\n",
"[CV] .................... C=30.0, gamma=3.0, kernel=rbf, total= 7.6s\n",
"[CV] C=30.0, gamma=3.0, kernel=rbf ...................................\n",
"[CV] .................... C=30.0, gamma=3.0, kernel=rbf, total= 7.6s\n",
"[CV] C=30.0, gamma=3.0, kernel=rbf ...................................\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV] .................... C=30.0, gamma=3.0, kernel=rbf, total= 7.6s\n",
"[CV] C=30.0, gamma=3.0, kernel=rbf ...................................\n",
"[CV] .................... C=30.0, gamma=3.0, kernel=rbf, total= 7.6s\n",
"[CV] C=30.0, gamma=3.0, kernel=rbf ...................................\n",
"[CV] .................... C=30.0, gamma=3.0, kernel=rbf, total= 7.7s\n",
"[CV] C=100.0, gamma=0.01, kernel=linear ..............................\n",
"[CV] ............... C=100.0, gamma=0.01, kernel=linear, total= 4.3s\n",
"[CV] C=100.0, gamma=0.01, kernel=linear ..............................\n",
"[CV] ............... C=100.0, gamma=0.01, kernel=linear, total= 4.2s\n",
"[CV] C=100.0, gamma=0.01, kernel=linear ..............................\n",
"[CV] ............... C=100.0, gamma=0.01, kernel=linear, total= 4.4s\n",
"[CV] C=100.0, gamma=0.01, kernel=linear ..............................\n",
"[CV] ............... C=100.0, gamma=0.01, kernel=linear, total= 4.2s\n",
"[CV] C=100.0, gamma=0.01, kernel=linear ..............................\n",
"[CV] ............... C=100.0, gamma=0.01, kernel=linear, total= 4.3s\n",
"[CV] C=100.0, gamma=0.01, kernel=rbf .................................\n",
"[CV] .................. C=100.0, gamma=0.01, kernel=rbf, total= 7.3s\n",
"[CV] C=100.0, gamma=0.01, kernel=rbf .................................\n",
"[CV] .................. C=100.0, gamma=0.01, kernel=rbf, total= 7.4s\n",
"[CV] C=100.0, gamma=0.01, kernel=rbf .................................\n",
"[CV] .................. C=100.0, gamma=0.01, kernel=rbf, total= 7.3s\n",
"[CV] C=100.0, gamma=0.01, kernel=rbf .................................\n",
"[CV] .................. C=100.0, gamma=0.01, kernel=rbf, total= 7.3s\n",
"[CV] C=100.0, gamma=0.01, kernel=rbf .................................\n",
"[CV] .................. C=100.0, gamma=0.01, kernel=rbf, total= 7.4s\n",
"[CV] C=100.0, gamma=0.03, kernel=linear ..............................\n",
"[CV] ............... C=100.0, gamma=0.03, kernel=linear, total= 4.3s\n",
"[CV] C=100.0, gamma=0.03, kernel=linear ..............................\n",
"[CV] ............... C=100.0, gamma=0.03, kernel=linear, total= 4.3s\n",
"[CV] C=100.0, gamma=0.03, kernel=linear ..............................\n",
"[CV] ............... C=100.0, gamma=0.03, kernel=linear, total= 4.3s\n",
"[CV] C=100.0, gamma=0.03, kernel=linear ..............................\n",
"[CV] ............... C=100.0, gamma=0.03, kernel=linear, total= 4.3s\n",
"[CV] C=100.0, gamma=0.03, kernel=linear ..............................\n",
"[CV] ............... C=100.0, gamma=0.03, kernel=linear, total= 4.2s\n",
"[CV] C=100.0, gamma=0.03, kernel=rbf .................................\n",
"[CV] .................. C=100.0, gamma=0.03, kernel=rbf, total= 7.2s\n",
"[CV] C=100.0, gamma=0.03, kernel=rbf .................................\n",
"[CV] .................. C=100.0, gamma=0.03, kernel=rbf, total= 7.3s\n",
"[CV] C=100.0, gamma=0.03, kernel=rbf .................................\n",
"[CV] .................. C=100.0, gamma=0.03, kernel=rbf, total= 7.2s\n",
"[CV] C=100.0, gamma=0.03, kernel=rbf .................................\n",
"[CV] .................. C=100.0, gamma=0.03, kernel=rbf, total= 7.3s\n",
"[CV] C=100.0, gamma=0.03, kernel=rbf .................................\n",
"[CV] .................. C=100.0, gamma=0.03, kernel=rbf, total= 7.2s\n",
"[CV] C=100.0, gamma=0.1, kernel=linear ...............................\n",
"[CV] ................ C=100.0, gamma=0.1, kernel=linear, total= 4.3s\n",
"[CV] C=100.0, gamma=0.1, kernel=linear ...............................\n",
"[CV] ................ C=100.0, gamma=0.1, kernel=linear, total= 4.2s\n",
"[CV] C=100.0, gamma=0.1, kernel=linear ...............................\n",
"[CV] ................ C=100.0, gamma=0.1, kernel=linear, total= 4.3s\n",
"[CV] C=100.0, gamma=0.1, kernel=linear ...............................\n",
"[CV] ................ C=100.0, gamma=0.1, kernel=linear, total= 4.3s\n",
"[CV] C=100.0, gamma=0.1, kernel=linear ...............................\n",
"[CV] ................ C=100.0, gamma=0.1, kernel=linear, total= 4.2s\n",
"[CV] C=100.0, gamma=0.1, kernel=rbf ..................................\n",
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"[CV] C=100.0, gamma=0.1, kernel=rbf ..................................\n",
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"[CV] C=100.0, gamma=0.1, kernel=rbf ..................................\n",
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"[CV] C=100.0, gamma=0.1, kernel=rbf ..................................\n",
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"[CV] C=100.0, gamma=0.1, kernel=rbf ..................................\n",
"[CV] ................... C=100.0, gamma=0.1, kernel=rbf, total= 7.1s\n",
"[CV] C=100.0, gamma=0.3, kernel=linear ...............................\n",
"[CV] ................ C=100.0, gamma=0.3, kernel=linear, total= 4.4s\n",
"[CV] C=100.0, gamma=0.3, kernel=linear ...............................\n",
"[CV] ................ C=100.0, gamma=0.3, kernel=linear, total= 4.2s\n",
"[CV] C=100.0, gamma=0.3, kernel=linear ...............................\n",
"[CV] ................ C=100.0, gamma=0.3, kernel=linear, total= 4.4s\n",
"[CV] C=100.0, gamma=0.3, kernel=linear ...............................\n",
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"[CV] C=100.0, gamma=0.3, kernel=linear ...............................\n",
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"[CV] C=100.0, gamma=0.3, kernel=rbf ..................................\n",
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"[CV] C=100.0, gamma=0.3, kernel=rbf ..................................\n",
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"[CV] C=100.0, gamma=0.3, kernel=rbf ..................................\n",
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"[CV] C=100.0, gamma=0.3, kernel=rbf ..................................\n",
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"[CV] C=100.0, gamma=0.3, kernel=rbf ..................................\n",
"[CV] ................... C=100.0, gamma=0.3, kernel=rbf, total= 7.0s\n",
"[CV] C=100.0, gamma=1.0, kernel=linear ...............................\n",
"[CV] ................ C=100.0, gamma=1.0, kernel=linear, total= 4.3s\n",
"[CV] C=100.0, gamma=1.0, kernel=linear ...............................\n",
"[CV] ................ C=100.0, gamma=1.0, kernel=linear, total= 4.2s\n",
"[CV] C=100.0, gamma=1.0, kernel=linear ...............................\n",
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"[CV] C=100.0, gamma=1.0, kernel=linear ...............................\n",
"[CV] ................ C=100.0, gamma=1.0, kernel=linear, total= 4.3s\n",
"[CV] C=100.0, gamma=1.0, kernel=linear ...............................\n",
"[CV] ................ C=100.0, gamma=1.0, kernel=linear, total= 4.3s\n",
"[CV] C=100.0, gamma=1.0, kernel=rbf ..................................\n",
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"[CV] C=100.0, gamma=1.0, kernel=rbf ..................................\n",
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"[CV] C=100.0, gamma=1.0, kernel=rbf ..................................\n",
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"[CV] C=100.0, gamma=1.0, kernel=rbf ..................................\n",
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"[CV] C=100.0, gamma=1.0, kernel=rbf ..................................\n",
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"[CV] C=100.0, gamma=3.0, kernel=linear ...............................\n",
"[CV] ................ C=100.0, gamma=3.0, kernel=linear, total= 4.3s\n",
"[CV] C=100.0, gamma=3.0, kernel=linear ...............................\n",
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"[CV] C=100.0, gamma=3.0, kernel=linear ...............................\n",
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"[CV] C=100.0, gamma=3.0, kernel=linear ...............................\n",
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"[CV] C=100.0, gamma=3.0, kernel=linear ...............................\n",
"[CV] ................ C=100.0, gamma=3.0, kernel=linear, total= 4.3s\n",
"[CV] C=100.0, gamma=3.0, kernel=rbf ..................................\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV] ................... C=100.0, gamma=3.0, kernel=rbf, total= 7.6s\n",
"[CV] C=100.0, gamma=3.0, kernel=rbf ..................................\n",
"[CV] ................... C=100.0, gamma=3.0, kernel=rbf, total= 7.6s\n",
"[CV] C=100.0, gamma=3.0, kernel=rbf ..................................\n",
"[CV] ................... C=100.0, gamma=3.0, kernel=rbf, total= 7.6s\n",
"[CV] C=100.0, gamma=3.0, kernel=rbf ..................................\n",
"[CV] ................... C=100.0, gamma=3.0, kernel=rbf, total= 7.6s\n",
"[CV] C=100.0, gamma=3.0, kernel=rbf ..................................\n",
"[CV] ................... C=100.0, gamma=3.0, kernel=rbf, total= 7.7s\n",
"[CV] C=300.0, gamma=0.01, kernel=linear ..............................\n",
"[CV] ............... C=300.0, gamma=0.01, kernel=linear, total= 4.4s\n",
"[CV] C=300.0, gamma=0.01, kernel=linear ..............................\n",
"[CV] ............... C=300.0, gamma=0.01, kernel=linear, total= 4.4s\n",
"[CV] C=300.0, gamma=0.01, kernel=linear ..............................\n",
"[CV] ............... C=300.0, gamma=0.01, kernel=linear, total= 4.4s\n",
"[CV] C=300.0, gamma=0.01, kernel=linear ..............................\n",
"[CV] ............... C=300.0, gamma=0.01, kernel=linear, total= 4.4s\n",
"[CV] C=300.0, gamma=0.01, kernel=linear ..............................\n",
"[CV] ............... C=300.0, gamma=0.01, kernel=linear, total= 4.3s\n",
"[CV] C=300.0, gamma=0.01, kernel=rbf .................................\n",
"[CV] .................. C=300.0, gamma=0.01, kernel=rbf, total= 7.2s\n",
"[CV] C=300.0, gamma=0.01, kernel=rbf .................................\n",
"[CV] .................. C=300.0, gamma=0.01, kernel=rbf, total= 7.3s\n",
"[CV] C=300.0, gamma=0.01, kernel=rbf .................................\n",
"[CV] .................. C=300.0, gamma=0.01, kernel=rbf, total= 7.2s\n",
"[CV] C=300.0, gamma=0.01, kernel=rbf .................................\n",
"[CV] .................. C=300.0, gamma=0.01, kernel=rbf, total= 7.3s\n",
"[CV] C=300.0, gamma=0.01, kernel=rbf .................................\n",
"[CV] .................. C=300.0, gamma=0.01, kernel=rbf, total= 7.2s\n",
"[CV] C=300.0, gamma=0.03, kernel=linear ..............................\n",
"[CV] ............... C=300.0, gamma=0.03, kernel=linear, total= 4.4s\n",
"[CV] C=300.0, gamma=0.03, kernel=linear ..............................\n",
"[CV] ............... C=300.0, gamma=0.03, kernel=linear, total= 4.3s\n",
"[CV] C=300.0, gamma=0.03, kernel=linear ..............................\n",
"[CV] ............... C=300.0, gamma=0.03, kernel=linear, total= 4.4s\n",
"[CV] C=300.0, gamma=0.03, kernel=linear ..............................\n",
"[CV] ............... C=300.0, gamma=0.03, kernel=linear, total= 4.4s\n",
"[CV] C=300.0, gamma=0.03, kernel=linear ..............................\n",
"[CV] ............... C=300.0, gamma=0.03, kernel=linear, total= 4.3s\n",
"[CV] C=300.0, gamma=0.03, kernel=rbf .................................\n",
"[CV] .................. C=300.0, gamma=0.03, kernel=rbf, total= 7.1s\n",
"[CV] C=300.0, gamma=0.03, kernel=rbf .................................\n",
"[CV] .................. C=300.0, gamma=0.03, kernel=rbf, total= 7.0s\n",
"[CV] C=300.0, gamma=0.03, kernel=rbf .................................\n",
"[CV] .................. C=300.0, gamma=0.03, kernel=rbf, total= 7.0s\n",
"[CV] C=300.0, gamma=0.03, kernel=rbf .................................\n",
"[CV] .................. C=300.0, gamma=0.03, kernel=rbf, total= 7.1s\n",
"[CV] C=300.0, gamma=0.03, kernel=rbf .................................\n",
"[CV] .................. C=300.0, gamma=0.03, kernel=rbf, total= 7.0s\n",
"[CV] C=300.0, gamma=0.1, kernel=linear ...............................\n",
"[CV] ................ C=300.0, gamma=0.1, kernel=linear, total= 4.3s\n",
"[CV] C=300.0, gamma=0.1, kernel=linear ...............................\n",
"[CV] ................ C=300.0, gamma=0.1, kernel=linear, total= 4.3s\n",
"[CV] C=300.0, gamma=0.1, kernel=linear ...............................\n",
"[CV] ................ C=300.0, gamma=0.1, kernel=linear, total= 4.4s\n",
"[CV] C=300.0, gamma=0.1, kernel=linear ...............................\n",
"[CV] ................ C=300.0, gamma=0.1, kernel=linear, total= 4.3s\n",
"[CV] C=300.0, gamma=0.1, kernel=linear ...............................\n",
"[CV] ................ C=300.0, gamma=0.1, kernel=linear, total= 4.3s\n",
"[CV] C=300.0, gamma=0.1, kernel=rbf ..................................\n",
"[CV] ................... C=300.0, gamma=0.1, kernel=rbf, total= 7.0s\n",
"[CV] C=300.0, gamma=0.1, kernel=rbf ..................................\n",
"[CV] ................... C=300.0, gamma=0.1, kernel=rbf, total= 6.9s\n",
"[CV] C=300.0, gamma=0.1, kernel=rbf ..................................\n",
"[CV] ................... C=300.0, gamma=0.1, kernel=rbf, total= 7.0s\n",
"[CV] C=300.0, gamma=0.1, kernel=rbf ..................................\n",
"[CV] ................... C=300.0, gamma=0.1, kernel=rbf, total= 6.9s\n",
"[CV] C=300.0, gamma=0.1, kernel=rbf ..................................\n",
"[CV] ................... C=300.0, gamma=0.1, kernel=rbf, total= 6.9s\n",
"[CV] C=300.0, gamma=0.3, kernel=linear ...............................\n",
"[CV] ................ C=300.0, gamma=0.3, kernel=linear, total= 4.4s\n",
"[CV] C=300.0, gamma=0.3, kernel=linear ...............................\n",
"[CV] ................ C=300.0, gamma=0.3, kernel=linear, total= 4.3s\n",
"[CV] C=300.0, gamma=0.3, kernel=linear ...............................\n",
"[CV] ................ C=300.0, gamma=0.3, kernel=linear, total= 4.4s\n",
"[CV] C=300.0, gamma=0.3, kernel=linear ...............................\n",
"[CV] ................ C=300.0, gamma=0.3, kernel=linear, total= 4.3s\n",
"[CV] C=300.0, gamma=0.3, kernel=linear ...............................\n",
"[CV] ................ C=300.0, gamma=0.3, kernel=linear, total= 4.3s\n",
"[CV] C=300.0, gamma=0.3, kernel=rbf ..................................\n",
"[CV] ................... C=300.0, gamma=0.3, kernel=rbf, total= 6.9s\n",
"[CV] C=300.0, gamma=0.3, kernel=rbf ..................................\n",
"[CV] ................... C=300.0, gamma=0.3, kernel=rbf, total= 6.9s\n",
"[CV] C=300.0, gamma=0.3, kernel=rbf ..................................\n",
"[CV] ................... C=300.0, gamma=0.3, kernel=rbf, total= 7.0s\n",
"[CV] C=300.0, gamma=0.3, kernel=rbf ..................................\n",
"[CV] ................... C=300.0, gamma=0.3, kernel=rbf, total= 6.9s\n",
"[CV] C=300.0, gamma=0.3, kernel=rbf ..................................\n",
"[CV] ................... C=300.0, gamma=0.3, kernel=rbf, total= 6.9s\n",
"[CV] C=300.0, gamma=1.0, kernel=linear ...............................\n",
"[CV] ................ C=300.0, gamma=1.0, kernel=linear, total= 4.3s\n",
"[CV] C=300.0, gamma=1.0, kernel=linear ...............................\n",
"[CV] ................ C=300.0, gamma=1.0, kernel=linear, total= 4.3s\n",
"[CV] C=300.0, gamma=1.0, kernel=linear ...............................\n",
"[CV] ................ C=300.0, gamma=1.0, kernel=linear, total= 4.4s\n",
"[CV] C=300.0, gamma=1.0, kernel=linear ...............................\n",
"[CV] ................ C=300.0, gamma=1.0, kernel=linear, total= 4.4s\n",
"[CV] C=300.0, gamma=1.0, kernel=linear ...............................\n",
"[CV] ................ C=300.0, gamma=1.0, kernel=linear, total= 4.3s\n",
"[CV] C=300.0, gamma=1.0, kernel=rbf ..................................\n",
"[CV] ................... C=300.0, gamma=1.0, kernel=rbf, total= 6.9s\n",
"[CV] C=300.0, gamma=1.0, kernel=rbf ..................................\n",
"[CV] ................... C=300.0, gamma=1.0, kernel=rbf, total= 6.9s\n",
"[CV] C=300.0, gamma=1.0, kernel=rbf ..................................\n",
"[CV] ................... C=300.0, gamma=1.0, kernel=rbf, total= 6.8s\n",
"[CV] C=300.0, gamma=1.0, kernel=rbf ..................................\n",
"[CV] ................... C=300.0, gamma=1.0, kernel=rbf, total= 6.8s\n",
"[CV] C=300.0, gamma=1.0, kernel=rbf ..................................\n",
"[CV] ................... C=300.0, gamma=1.0, kernel=rbf, total= 6.9s\n",
"[CV] C=300.0, gamma=3.0, kernel=linear ...............................\n",
"[CV] ................ C=300.0, gamma=3.0, kernel=linear, total= 4.3s\n",
"[CV] C=300.0, gamma=3.0, kernel=linear ...............................\n",
"[CV] ................ C=300.0, gamma=3.0, kernel=linear, total= 4.3s\n",
"[CV] C=300.0, gamma=3.0, kernel=linear ...............................\n",
"[CV] ................ C=300.0, gamma=3.0, kernel=linear, total= 4.3s\n",
"[CV] C=300.0, gamma=3.0, kernel=linear ...............................\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV] ................ C=300.0, gamma=3.0, kernel=linear, total= 4.4s\n",
"[CV] C=300.0, gamma=3.0, kernel=linear ...............................\n",
"[CV] ................ C=300.0, gamma=3.0, kernel=linear, total= 4.3s\n",
"[CV] C=300.0, gamma=3.0, kernel=rbf ..................................\n",
"[CV] ................... C=300.0, gamma=3.0, kernel=rbf, total= 7.6s\n",
"[CV] C=300.0, gamma=3.0, kernel=rbf ..................................\n",
"[CV] ................... C=300.0, gamma=3.0, kernel=rbf, total= 7.6s\n",
"[CV] C=300.0, gamma=3.0, kernel=rbf ..................................\n",
"[CV] ................... C=300.0, gamma=3.0, kernel=rbf, total= 7.6s\n",
"[CV] C=300.0, gamma=3.0, kernel=rbf ..................................\n",
"[CV] ................... C=300.0, gamma=3.0, kernel=rbf, total= 7.7s\n",
"[CV] C=300.0, gamma=3.0, kernel=rbf ..................................\n",
"[CV] ................... C=300.0, gamma=3.0, kernel=rbf, total= 7.6s\n",
"[CV] C=1000.0, gamma=0.01, kernel=linear .............................\n",
"[CV] .............. C=1000.0, gamma=0.01, kernel=linear, total= 4.4s\n",
"[CV] C=1000.0, gamma=0.01, kernel=linear .............................\n",
"[CV] .............. C=1000.0, gamma=0.01, kernel=linear, total= 4.5s\n",
"[CV] C=1000.0, gamma=0.01, kernel=linear .............................\n",
"[CV] .............. C=1000.0, gamma=0.01, kernel=linear, total= 4.5s\n",
"[CV] C=1000.0, gamma=0.01, kernel=linear .............................\n",
"[CV] .............. C=1000.0, gamma=0.01, kernel=linear, total= 4.6s\n",
"[CV] C=1000.0, gamma=0.01, kernel=linear .............................\n",
"[CV] .............. C=1000.0, gamma=0.01, kernel=linear, total= 4.4s\n",
"[CV] C=1000.0, gamma=0.01, kernel=rbf ................................\n",
"[CV] ................. C=1000.0, gamma=0.01, kernel=rbf, total= 7.0s\n",
"[CV] C=1000.0, gamma=0.01, kernel=rbf ................................\n",
"[CV] ................. C=1000.0, gamma=0.01, kernel=rbf, total= 7.0s\n",
"[CV] C=1000.0, gamma=0.01, kernel=rbf ................................\n",
"[CV] ................. C=1000.0, gamma=0.01, kernel=rbf, total= 7.0s\n",
"[CV] C=1000.0, gamma=0.01, kernel=rbf ................................\n",
"[CV] ................. C=1000.0, gamma=0.01, kernel=rbf, total= 7.0s\n",
"[CV] C=1000.0, gamma=0.01, kernel=rbf ................................\n",
"[CV] ................. C=1000.0, gamma=0.01, kernel=rbf, total= 7.0s\n",
"[CV] C=1000.0, gamma=0.03, kernel=linear .............................\n",
"[CV] .............. C=1000.0, gamma=0.03, kernel=linear, total= 4.5s\n",
"[CV] C=1000.0, gamma=0.03, kernel=linear .............................\n",
"[CV] .............. C=1000.0, gamma=0.03, kernel=linear, total= 4.5s\n",
"[CV] C=1000.0, gamma=0.03, kernel=linear .............................\n",
"[CV] .............. C=1000.0, gamma=0.03, kernel=linear, total= 4.5s\n",
"[CV] C=1000.0, gamma=0.03, kernel=linear .............................\n",
"[CV] .............. C=1000.0, gamma=0.03, kernel=linear, total= 4.6s\n",
"[CV] C=1000.0, gamma=0.03, kernel=linear .............................\n",
"[CV] .............. C=1000.0, gamma=0.03, kernel=linear, total= 4.4s\n",
"[CV] C=1000.0, gamma=0.03, kernel=rbf ................................\n",
"[CV] ................. C=1000.0, gamma=0.03, kernel=rbf, total= 6.9s\n",
"[CV] C=1000.0, gamma=0.03, kernel=rbf ................................\n",
"[CV] ................. C=1000.0, gamma=0.03, kernel=rbf, total= 6.9s\n",
"[CV] C=1000.0, gamma=0.03, kernel=rbf ................................\n",
"[CV] ................. C=1000.0, gamma=0.03, kernel=rbf, total= 6.9s\n",
"[CV] C=1000.0, gamma=0.03, kernel=rbf ................................\n",
"[CV] ................. C=1000.0, gamma=0.03, kernel=rbf, total= 6.9s\n",
"[CV] C=1000.0, gamma=0.03, kernel=rbf ................................\n",
"[CV] ................. C=1000.0, gamma=0.03, kernel=rbf, total= 6.9s\n",
"[CV] C=1000.0, gamma=0.1, kernel=linear ..............................\n",
"[CV] ............... C=1000.0, gamma=0.1, kernel=linear, total= 4.5s\n",
"[CV] C=1000.0, gamma=0.1, kernel=linear ..............................\n",
"[CV] ............... C=1000.0, gamma=0.1, kernel=linear, total= 4.5s\n",
"[CV] C=1000.0, gamma=0.1, kernel=linear ..............................\n",
"[CV] ............... C=1000.0, gamma=0.1, kernel=linear, total= 4.5s\n",
"[CV] C=1000.0, gamma=0.1, kernel=linear ..............................\n",
"[CV] ............... C=1000.0, gamma=0.1, kernel=linear, total= 4.5s\n",
"[CV] C=1000.0, gamma=0.1, kernel=linear ..............................\n",
"[CV] ............... C=1000.0, gamma=0.1, kernel=linear, total= 4.4s\n",
"[CV] C=1000.0, gamma=0.1, kernel=rbf .................................\n",
"[CV] .................. C=1000.0, gamma=0.1, kernel=rbf, total= 6.9s\n",
"[CV] C=1000.0, gamma=0.1, kernel=rbf .................................\n",
"[CV] .................. C=1000.0, gamma=0.1, kernel=rbf, total= 6.8s\n",
"[CV] C=1000.0, gamma=0.1, kernel=rbf .................................\n",
"[CV] .................. C=1000.0, gamma=0.1, kernel=rbf, total= 6.9s\n",
"[CV] C=1000.0, gamma=0.1, kernel=rbf .................................\n",
"[CV] .................. C=1000.0, gamma=0.1, kernel=rbf, total= 6.8s\n",
"[CV] C=1000.0, gamma=0.1, kernel=rbf .................................\n",
"[CV] .................. C=1000.0, gamma=0.1, kernel=rbf, total= 6.8s\n",
"[CV] C=1000.0, gamma=0.3, kernel=linear ..............................\n",
"[CV] ............... C=1000.0, gamma=0.3, kernel=linear, total= 4.5s\n",
"[CV] C=1000.0, gamma=0.3, kernel=linear ..............................\n",
"[CV] ............... C=1000.0, gamma=0.3, kernel=linear, total= 4.5s\n",
"[CV] C=1000.0, gamma=0.3, kernel=linear ..............................\n",
"[CV] ............... C=1000.0, gamma=0.3, kernel=linear, total= 4.5s\n",
"[CV] C=1000.0, gamma=0.3, kernel=linear ..............................\n",
"[CV] ............... C=1000.0, gamma=0.3, kernel=linear, total= 4.5s\n",
"[CV] C=1000.0, gamma=0.3, kernel=linear ..............................\n",
"[CV] ............... C=1000.0, gamma=0.3, kernel=linear, total= 4.5s\n",
"[CV] C=1000.0, gamma=0.3, kernel=rbf .................................\n",
"[CV] .................. C=1000.0, gamma=0.3, kernel=rbf, total= 6.8s\n",
"[CV] C=1000.0, gamma=0.3, kernel=rbf .................................\n",
"[CV] .................. C=1000.0, gamma=0.3, kernel=rbf, total= 6.8s\n",
"[CV] C=1000.0, gamma=0.3, kernel=rbf .................................\n",
"[CV] .................. C=1000.0, gamma=0.3, kernel=rbf, total= 6.8s\n",
"[CV] C=1000.0, gamma=0.3, kernel=rbf .................................\n",
"[CV] .................. C=1000.0, gamma=0.3, kernel=rbf, total= 6.8s\n",
"[CV] C=1000.0, gamma=0.3, kernel=rbf .................................\n",
"[CV] .................. C=1000.0, gamma=0.3, kernel=rbf, total= 6.8s\n",
"[CV] C=1000.0, gamma=1.0, kernel=linear ..............................\n",
"[CV] ............... C=1000.0, gamma=1.0, kernel=linear, total= 4.4s\n",
"[CV] C=1000.0, gamma=1.0, kernel=linear ..............................\n",
"[CV] ............... C=1000.0, gamma=1.0, kernel=linear, total= 4.5s\n",
"[CV] C=1000.0, gamma=1.0, kernel=linear ..............................\n",
"[CV] ............... C=1000.0, gamma=1.0, kernel=linear, total= 4.5s\n",
"[CV] C=1000.0, gamma=1.0, kernel=linear ..............................\n",
"[CV] ............... C=1000.0, gamma=1.0, kernel=linear, total= 4.5s\n",
"[CV] C=1000.0, gamma=1.0, kernel=linear ..............................\n",
"[CV] ............... C=1000.0, gamma=1.0, kernel=linear, total= 4.5s\n",
"[CV] C=1000.0, gamma=1.0, kernel=rbf .................................\n",
"[CV] .................. C=1000.0, gamma=1.0, kernel=rbf, total= 6.8s\n",
"[CV] C=1000.0, gamma=1.0, kernel=rbf .................................\n",
"[CV] .................. C=1000.0, gamma=1.0, kernel=rbf, total= 6.9s\n",
"[CV] C=1000.0, gamma=1.0, kernel=rbf .................................\n",
"[CV] .................. C=1000.0, gamma=1.0, kernel=rbf, total= 6.8s\n",
"[CV] C=1000.0, gamma=1.0, kernel=rbf .................................\n",
"[CV] .................. C=1000.0, gamma=1.0, kernel=rbf, total= 6.8s\n",
"[CV] C=1000.0, gamma=1.0, kernel=rbf .................................\n",
"[CV] .................. C=1000.0, gamma=1.0, kernel=rbf, total= 6.9s\n",
"[CV] C=1000.0, gamma=3.0, kernel=linear ..............................\n",
"[CV] ............... C=1000.0, gamma=3.0, kernel=linear, total= 4.4s\n",
"[CV] C=1000.0, gamma=3.0, kernel=linear ..............................\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV] ............... C=1000.0, gamma=3.0, kernel=linear, total= 4.6s\n",
"[CV] C=1000.0, gamma=3.0, kernel=linear ..............................\n",
"[CV] ............... C=1000.0, gamma=3.0, kernel=linear, total= 4.5s\n",
"[CV] C=1000.0, gamma=3.0, kernel=linear ..............................\n",
"[CV] ............... C=1000.0, gamma=3.0, kernel=linear, total= 4.6s\n",
"[CV] C=1000.0, gamma=3.0, kernel=linear ..............................\n",
"[CV] ............... C=1000.0, gamma=3.0, kernel=linear, total= 4.5s\n",
"[CV] C=1000.0, gamma=3.0, kernel=rbf .................................\n",
"[CV] .................. C=1000.0, gamma=3.0, kernel=rbf, total= 7.6s\n",
"[CV] C=1000.0, gamma=3.0, kernel=rbf .................................\n",
"[CV] .................. C=1000.0, gamma=3.0, kernel=rbf, total= 7.7s\n",
"[CV] C=1000.0, gamma=3.0, kernel=rbf .................................\n",
"[CV] .................. C=1000.0, gamma=3.0, kernel=rbf, total= 7.6s\n",
"[CV] C=1000.0, gamma=3.0, kernel=rbf .................................\n",
"[CV] .................. C=1000.0, gamma=3.0, kernel=rbf, total= 7.7s\n",
"[CV] C=1000.0, gamma=3.0, kernel=rbf .................................\n",
"[CV] .................. C=1000.0, gamma=3.0, kernel=rbf, total= 7.7s\n",
"[CV] C=3000.0, gamma=0.01, kernel=linear .............................\n",
"[CV] .............. C=3000.0, gamma=0.01, kernel=linear, total= 5.3s\n",
"[CV] C=3000.0, gamma=0.01, kernel=linear .............................\n",
"[CV] .............. C=3000.0, gamma=0.01, kernel=linear, total= 5.0s\n",
"[CV] C=3000.0, gamma=0.01, kernel=linear .............................\n",
"[CV] .............. C=3000.0, gamma=0.01, kernel=linear, total= 4.9s\n",
"[CV] C=3000.0, gamma=0.01, kernel=linear .............................\n",
"[CV] .............. C=3000.0, gamma=0.01, kernel=linear, total= 5.1s\n",
"[CV] C=3000.0, gamma=0.01, kernel=linear .............................\n",
"[CV] .............. C=3000.0, gamma=0.01, kernel=linear, total= 4.9s\n",
"[CV] C=3000.0, gamma=0.01, kernel=rbf ................................\n",
"[CV] ................. C=3000.0, gamma=0.01, kernel=rbf, total= 7.1s\n",
"[CV] C=3000.0, gamma=0.01, kernel=rbf ................................\n",
"[CV] ................. C=3000.0, gamma=0.01, kernel=rbf, total= 6.9s\n",
"[CV] C=3000.0, gamma=0.01, kernel=rbf ................................\n",
"[CV] ................. C=3000.0, gamma=0.01, kernel=rbf, total= 7.3s\n",
"[CV] C=3000.0, gamma=0.01, kernel=rbf ................................\n",
"[CV] ................. C=3000.0, gamma=0.01, kernel=rbf, total= 8.1s\n",
"[CV] C=3000.0, gamma=0.01, kernel=rbf ................................\n",
"[CV] ................. C=3000.0, gamma=0.01, kernel=rbf, total= 7.7s\n",
"[CV] C=3000.0, gamma=0.03, kernel=linear .............................\n",
"[CV] .............. C=3000.0, gamma=0.03, kernel=linear, total= 5.1s\n",
"[CV] C=3000.0, gamma=0.03, kernel=linear .............................\n",
"[CV] .............. C=3000.0, gamma=0.03, kernel=linear, total= 4.8s\n",
"[CV] C=3000.0, gamma=0.03, kernel=linear .............................\n",
"[CV] .............. C=3000.0, gamma=0.03, kernel=linear, total= 5.0s\n",
"[CV] C=3000.0, gamma=0.03, kernel=linear .............................\n",
"[CV] .............. C=3000.0, gamma=0.03, kernel=linear, total= 5.1s\n",
"[CV] C=3000.0, gamma=0.03, kernel=linear .............................\n",
"[CV] .............. C=3000.0, gamma=0.03, kernel=linear, total= 5.2s\n",
"[CV] C=3000.0, gamma=0.03, kernel=rbf ................................\n",
"[CV] ................. C=3000.0, gamma=0.03, kernel=rbf, total= 8.0s\n",
"[CV] C=3000.0, gamma=0.03, kernel=rbf ................................\n",
"[CV] ................. C=3000.0, gamma=0.03, kernel=rbf, total= 7.8s\n",
"[CV] C=3000.0, gamma=0.03, kernel=rbf ................................\n",
"[CV] ................. C=3000.0, gamma=0.03, kernel=rbf, total= 7.3s\n",
"[CV] C=3000.0, gamma=0.03, kernel=rbf ................................\n",
"[CV] ................. C=3000.0, gamma=0.03, kernel=rbf, total= 7.1s\n",
"[CV] C=3000.0, gamma=0.03, kernel=rbf ................................\n",
"[CV] ................. C=3000.0, gamma=0.03, kernel=rbf, total= 7.5s\n",
"[CV] C=3000.0, gamma=0.1, kernel=linear ..............................\n",
"[CV] ............... C=3000.0, gamma=0.1, kernel=linear, total= 5.1s\n",
"[CV] C=3000.0, gamma=0.1, kernel=linear ..............................\n",
"[CV] ............... C=3000.0, gamma=0.1, kernel=linear, total= 4.7s\n",
"[CV] C=3000.0, gamma=0.1, kernel=linear ..............................\n",
"[CV] ............... C=3000.0, gamma=0.1, kernel=linear, total= 4.9s\n",
"[CV] C=3000.0, gamma=0.1, kernel=linear ..............................\n",
"[CV] ............... C=3000.0, gamma=0.1, kernel=linear, total= 4.9s\n",
"[CV] C=3000.0, gamma=0.1, kernel=linear ..............................\n",
"[CV] ............... C=3000.0, gamma=0.1, kernel=linear, total= 4.7s\n",
"[CV] C=3000.0, gamma=0.1, kernel=rbf .................................\n",
"[CV] .................. C=3000.0, gamma=0.1, kernel=rbf, total= 6.9s\n",
"[CV] C=3000.0, gamma=0.1, kernel=rbf .................................\n",
"[CV] .................. C=3000.0, gamma=0.1, kernel=rbf, total= 7.2s\n",
"[CV] C=3000.0, gamma=0.1, kernel=rbf .................................\n",
"[CV] .................. C=3000.0, gamma=0.1, kernel=rbf, total= 7.6s\n",
"[CV] C=3000.0, gamma=0.1, kernel=rbf .................................\n",
"[CV] .................. C=3000.0, gamma=0.1, kernel=rbf, total= 7.2s\n",
"[CV] C=3000.0, gamma=0.1, kernel=rbf .................................\n",
"[CV] .................. C=3000.0, gamma=0.1, kernel=rbf, total= 7.0s\n",
"[CV] C=3000.0, gamma=0.3, kernel=linear ..............................\n",
"[CV] ............... C=3000.0, gamma=0.3, kernel=linear, total= 4.9s\n",
"[CV] C=3000.0, gamma=0.3, kernel=linear ..............................\n",
"[CV] ............... C=3000.0, gamma=0.3, kernel=linear, total= 4.8s\n",
"[CV] C=3000.0, gamma=0.3, kernel=linear ..............................\n",
"[CV] ............... C=3000.0, gamma=0.3, kernel=linear, total= 4.9s\n",
"[CV] C=3000.0, gamma=0.3, kernel=linear ..............................\n",
"[CV] ............... C=3000.0, gamma=0.3, kernel=linear, total= 4.9s\n",
"[CV] C=3000.0, gamma=0.3, kernel=linear ..............................\n",
"[CV] ............... C=3000.0, gamma=0.3, kernel=linear, total= 4.7s\n",
"[CV] C=3000.0, gamma=0.3, kernel=rbf .................................\n",
"[CV] .................. C=3000.0, gamma=0.3, kernel=rbf, total= 6.8s\n",
"[CV] C=3000.0, gamma=0.3, kernel=rbf .................................\n",
"[CV] .................. C=3000.0, gamma=0.3, kernel=rbf, total= 6.8s\n",
"[CV] C=3000.0, gamma=0.3, kernel=rbf .................................\n",
"[CV] .................. C=3000.0, gamma=0.3, kernel=rbf, total= 6.8s\n",
"[CV] C=3000.0, gamma=0.3, kernel=rbf .................................\n",
"[CV] .................. C=3000.0, gamma=0.3, kernel=rbf, total= 6.8s\n",
"[CV] C=3000.0, gamma=0.3, kernel=rbf .................................\n",
"[CV] .................. C=3000.0, gamma=0.3, kernel=rbf, total= 6.8s\n",
"[CV] C=3000.0, gamma=1.0, kernel=linear ..............................\n",
"[CV] ............... C=3000.0, gamma=1.0, kernel=linear, total= 4.9s\n",
"[CV] C=3000.0, gamma=1.0, kernel=linear ..............................\n",
"[CV] ............... C=3000.0, gamma=1.0, kernel=linear, total= 4.8s\n",
"[CV] C=3000.0, gamma=1.0, kernel=linear ..............................\n",
"[CV] ............... C=3000.0, gamma=1.0, kernel=linear, total= 4.9s\n",
"[CV] C=3000.0, gamma=1.0, kernel=linear ..............................\n",
"[CV] ............... C=3000.0, gamma=1.0, kernel=linear, total= 4.9s\n",
"[CV] C=3000.0, gamma=1.0, kernel=linear ..............................\n",
"[CV] ............... C=3000.0, gamma=1.0, kernel=linear, total= 4.8s\n",
"[CV] C=3000.0, gamma=1.0, kernel=rbf .................................\n",
"[CV] .................. C=3000.0, gamma=1.0, kernel=rbf, total= 6.9s\n",
"[CV] C=3000.0, gamma=1.0, kernel=rbf .................................\n",
"[CV] .................. C=3000.0, gamma=1.0, kernel=rbf, total= 6.9s\n",
"[CV] C=3000.0, gamma=1.0, kernel=rbf .................................\n",
"[CV] .................. C=3000.0, gamma=1.0, kernel=rbf, total= 6.9s\n",
"[CV] C=3000.0, gamma=1.0, kernel=rbf .................................\n",
"[CV] .................. C=3000.0, gamma=1.0, kernel=rbf, total= 6.8s\n",
"[CV] C=3000.0, gamma=1.0, kernel=rbf .................................\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV] .................. C=3000.0, gamma=1.0, kernel=rbf, total= 6.9s\n",
"[CV] C=3000.0, gamma=3.0, kernel=linear ..............................\n",
"[CV] ............... C=3000.0, gamma=3.0, kernel=linear, total= 4.8s\n",
"[CV] C=3000.0, gamma=3.0, kernel=linear ..............................\n",
"[CV] ............... C=3000.0, gamma=3.0, kernel=linear, total= 4.8s\n",
"[CV] C=3000.0, gamma=3.0, kernel=linear ..............................\n",
"[CV] ............... C=3000.0, gamma=3.0, kernel=linear, total= 5.0s\n",
"[CV] C=3000.0, gamma=3.0, kernel=linear ..............................\n",
"[CV] ............... C=3000.0, gamma=3.0, kernel=linear, total= 4.9s\n",
"[CV] C=3000.0, gamma=3.0, kernel=linear ..............................\n",
"[CV] ............... C=3000.0, gamma=3.0, kernel=linear, total= 4.8s\n",
"[CV] C=3000.0, gamma=3.0, kernel=rbf .................................\n",
"[CV] .................. C=3000.0, gamma=3.0, kernel=rbf, total= 7.6s\n",
"[CV] C=3000.0, gamma=3.0, kernel=rbf .................................\n",
"[CV] .................. C=3000.0, gamma=3.0, kernel=rbf, total= 7.8s\n",
"[CV] C=3000.0, gamma=3.0, kernel=rbf .................................\n",
"[CV] .................. C=3000.0, gamma=3.0, kernel=rbf, total= 7.7s\n",
"[CV] C=3000.0, gamma=3.0, kernel=rbf .................................\n",
"[CV] .................. C=3000.0, gamma=3.0, kernel=rbf, total= 7.7s\n",
"[CV] C=3000.0, gamma=3.0, kernel=rbf .................................\n",
"[CV] .................. C=3000.0, gamma=3.0, kernel=rbf, total= 7.7s\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=1)]: Done 360 out of 360 | elapsed: 51.4min finished\n"
]
},
{
"data": {
"text/plain": [
"GridSearchCV(cv=5, error_score='raise-deprecating',\n",
" estimator=SVR(C=1.0, cache_size=200, coef0=0.0, degree=3,\n",
" epsilon=0.1, gamma='auto_deprecated', kernel='rbf',\n",
" max_iter=-1, shrinking=True, tol=0.001,\n",
" verbose=False),\n",
" iid='warn', n_jobs=None,\n",
" param_grid=[{'C': [10.0, 30.0, 100.0, 300.0, 1000.0, 3000.0],\n",
" 'gamma': [0.01, 0.03, 0.1, 0.3, 1.0, 3.0],\n",
" 'kernel': ['linear', 'rbf']}],\n",
" pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
" scoring='neg_mean_squared_error', verbose=2)"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Our rmse was terrible, worse than either linear regression or random forests\n",
"# Lets try to fine tune our hyper parameters\n",
"\n",
"param_grid = [\n",
" {'kernel': ['linear', 'rbf'], 'C': [10., 30., 100., 300., 1000., 3000.],\n",
" 'gamma': [0.01, 0.03, 0.1, 0.3, 1.0, 3.0]}\n",
"]\n",
"\n",
"SVR_model = SVR()\n",
"\n",
"grid_search = GridSearchCV(SVR_model, param_grid, cv=5,\n",
" scoring='neg_mean_squared_error',\n",
" return_train_score=True, \n",
" verbose=2, \n",
" n_jobs=-1)\n",
"\n",
"grid_search.fit(housing_prepared, housing_labels)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'grid_search' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-16-b1068600e498>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mgrid_search\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_params_\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mNameError\u001b[0m: name 'grid_search' is not defined"
]
}
],
"source": [
"grid_search.best_params_"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"grid_search.best_estimator_"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4237489445.3787646\n",
"65096.0017618499\n"
]
}
],
"source": [
"neg_mse = grid_search.best_score_\n",
"mse = -neg_mse\n",
"rmse = np.sqrt(mse)\n",
"print(mse)\n",
"print(rmse)"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['models/SVR_model.pkl']"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Still not great, but much better than before\n",
"\n",
"SVR_model = grid_search.best_estimator_\n",
"joblib.dump(SVR_model, 'models/SVR_model.pkl')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Exercise 2**\n",
"\n",
"Replace Grid Search with a Random Search"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting 5 folds for each of 5 candidates, totalling 25 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 23 out of 25 | elapsed: 2.6min remaining: 13.7s\n",
"[Parallel(n_jobs=-1)]: Done 25 out of 25 | elapsed: 2.7min finished\n"
]
},
{
"data": {
"text/plain": [
"RandomizedSearchCV(cv=5, error_score='raise-deprecating',\n",
" estimator=SVR(C=1.0, cache_size=200, coef0=0.0, degree=3,\n",
" epsilon=0.1, gamma='auto_deprecated',\n",
" kernel='rbf', max_iter=-1, shrinking=True,\n",
" tol=0.001, verbose=False),\n",
" iid='warn', n_iter=5, n_jobs=-1,\n",
" param_distributions={'C': <scipy.stats._distn_infrastructure.rv_frozen object at 0x00000176D38577C8>,\n",
" 'gamma': <scipy.stats._distn_infrastructure.rv_frozen object at 0x00000176D3857A48>,\n",
" 'kernel': ['linear', 'rbf']},\n",
" pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
" return_train_score=False, scoring='neg_mean_squared_error',\n",
" verbose=2)"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"param_distributions = {\n",
" 'kernel': ['linear', 'rbf'], \n",
" 'C': reciprocal(20,200000),\n",
" 'gamma': expon(scale=1.0)\n",
"}\n",
"\n",
"\n",
"SVR_model = SVR()\n",
"\n",
"rnd_search = RandomizedSearchCV(SVR_model, \n",
" param_distributions=param_distributions, \n",
" n_iter=50,\n",
" cv=5,\n",
" scoring='neg_mean_squared_error',\n",
" verbose=2, \n",
" n_jobs=-1, \n",
" )\n",
"\n",
"rnd_search.fit(housing_prepared, housing_labels)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3776176391.944553\n",
"61450.601233385445\n"
]
}
],
"source": [
"neg_mse = rnd_search.best_score_\n",
"mse = -neg_mse\n",
"rmse = np.sqrt(mse)\n",
"print(mse)\n",
"print(rmse) # Even better than the grid search!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"SVR_model = rnd_search.best_estimator_\n",
"joblib.dump(SVR_model, 'models/SVR_tuned_model.pkl')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Exercise 3**\n",
"\n",
"Add transformer to preperation pipeline which chooses only most important attributes"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\users\\tsb\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
" \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
]
}
],
"source": [
"# Have to find feature importances somehow, so we create a random forest model and inspect that\n",
"\n",
"forest_reg = RandomForestRegressor()\n",
"forest_reg.fit(housing_prepared, housing_labels)\n",
"feature_importances = forest_reg.feature_importances_"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.base import BaseEstimator, TransformerMixin\n",
"\n",
"def indices_of_top_k(arr, k):\n",
" return np.sort(np.argpartition(np.array(arr), -k)[-k:])\n",
"\n",
"class TopFeatureSelector(BaseEstimator, TransformerMixin):\n",
" def __init__(self, feature_importances, k): # Need our array of feature importance rankings and the number we'd like to keep\n",
" self.feature_importances = feature_importances\n",
" self.k = k\n",
" def fit(self, X, y=None):\n",
" self.feature_indices_ = indices_of_top_k(self.feature_importances, self.k)\n",
" return self\n",
" def transform(self, X):\n",
" return X[:, self.feature_indices_]"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"k=5"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[-1.15604281, 0.77194962, -0.61493744, -0.08649871, 0. ],\n",
" [-1.17602483, 0.6596948 , 1.33645936, -0.03353391, 0. ],\n",
" [ 1.18684903, -1.34218285, -0.5320456 , -0.09240499, 0. ],\n",
" ...,\n",
" [ 1.58648943, -0.72478134, -0.3167053 , -0.03055414, 1. ],\n",
" [ 0.78221312, -0.85106801, 0.09812139, 0.06150916, 0. ],\n",
" [-1.43579109, 0.99645926, -0.15779865, -0.09586294, 0. ]])"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Check that it's working\n",
"\n",
"selector = TopFeatureSelector(feature_importances, k)\n",
"selector.fit(housing_prepared)\n",
"selector.transform(housing_prepared)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"prep_and_select_pipeline = Pipeline([\n",
" ('preperation', full_pipeline),\n",
" ('feature_selection', TopFeatureSelector(feature_importances, k))\n",
"])"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"housing_prepared_top_features = prep_and_select_pipeline.fit_transform(housing)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[-1.15604281, 0.77194962, -0.61493744, -0.08649871, 0. ],\n",
" [-1.17602483, 0.6596948 , 1.33645936, -0.03353391, 0. ],\n",
" [ 1.18684903, -1.34218285, -0.5320456 , -0.09240499, 0. ],\n",
" [-0.01706767, 0.31357576, -1.04556555, 0.08973561, 1. ],\n",
" [ 0.49247384, -0.65929936, -0.44143679, -0.00419445, 0. ]])"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"housing_prepared_top_features[0:5]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Exercise 4**\n",
"\n",
"Create pipeline for data prep and prediction"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"prep_select_and_predict_pipeline = Pipeline([\n",
" ('preperation', full_pipeline),\n",
" ('feature_selection', TopFeatureSelector(feature_importances, k)),\n",
" ('svm_reg', SVR(**rnd_search.best_params_))\n",
"])"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Pipeline(memory=None,\n",
" steps=[('preperation',\n",
" ColumnTransformer(n_jobs=None, remainder='drop',\n",
" sparse_threshold=0.3,\n",
" transformer_weights=None,\n",
" transformers=[('num',\n",
" Pipeline(memory=None,\n",
" steps=[('imputer',\n",
" SimpleImputer(add_indicator=False,\n",
" copy=True,\n",
" fill_value=None,\n",
" missing_values=nan,\n",
" strategy='median',\n",
" verbose=0)),\n",
" ('attribs_adder',\n",
" CombinedAttributesAdder(add_...\n",
" 1.19018202e-02, 1.24288759e-02, 1.26283374e-02, 4.73852096e-01,\n",
" 2.78258192e-02, 1.23789304e-01, 2.39439353e-02, 1.04137251e-03,\n",
" 1.38398964e-01, 1.33131544e-04, 7.89651465e-04, 2.16944520e-03]),\n",
" k=5)),\n",
" ('svm_reg',\n",
" SVR(C=24167.809457504085, cache_size=200, coef0=0.0, degree=3,\n",
" epsilon=0.1, gamma=0.6260828385112527, kernel='rbf',\n",
" max_iter=-1, shrinking=True, tol=0.001, verbose=False))],\n",
" verbose=False)"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prep_select_and_predict_pipeline.fit(housing, housing_labels)"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([199258.41244986, 357693.77258301, 170734.84525309, 46270.46533462])"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prep_select_and_predict_pipeline.predict(housing.iloc[:4])"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"17606 286600.0\n",
"18632 340600.0\n",
"14650 196900.0\n",
"3230 46300.0\n",
"Name: median_house_value, dtype: float64"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"housing_labels.iloc[:4]"
]
},
{
"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": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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