{ "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": [ "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_valueocean_proximity
0-122.2337.8841.0880.0129.0322.0126.08.3252452600.0NEAR BAY
1-122.2237.8621.07099.01106.02401.01138.08.3014358500.0NEAR BAY
2-122.2437.8552.01467.0190.0496.0177.07.2574352100.0NEAR BAY
3-122.2537.8552.01274.0235.0558.0219.05.6431341300.0NEAR BAY
4-122.2537.8552.01627.0280.0565.0259.03.8462342200.0NEAR BAY
\n", "
" ], "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] ................ 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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\u001b[0m in \u001b[0;36m\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': ,\n", " 'gamma': ,\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 }