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Upgrade chapter 2 to sklearn 0.18 and ensure python 2 and python 3 both work

main
Aurélien Geron 2016-11-03 23:47:11 +01:00
parent dabda0f806
commit 61f9b54cdb
2 changed files with 54 additions and 40 deletions
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90
02_end_to_end_machine_learning_project.ipynb
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@ -92,13 +92,14 @@
"source": [ "source": [
"import os\n", "import os\n",
"import tarfile\n", "import tarfile\n",
"import urllib.request\n", "from six.moves import urllib\n",
"\n", "\n",
"HOUSING_PATH = \"datasets/housing\"\n", "HOUSING_PATH = \"datasets/housing\"\n",
"HOUSING_URL = DATASETS_URL + \"/housing/housing.tgz\"\n", "HOUSING_URL = DATASETS_URL + \"/housing/housing.tgz\"\n",
"\n", "\n",
"def fetch_housing_data(housing_url=HOUSING_URL, housing_path=HOUSING_PATH):\n", "def fetch_housing_data(housing_url=HOUSING_URL, housing_path=HOUSING_PATH):\n",
" os.makedirs(housing_path, exist_ok=True)\n", " if not os.path.exists(housing_path):\n",
" os.makedirs(housing_path)\n",
" tgz_path = os.path.join(housing_path, \"housing.tgz\")\n", " tgz_path = os.path.join(housing_path, \"housing.tgz\")\n",
" urllib.request.urlretrieve(housing_url, tgz_path)\n", " urllib.request.urlretrieve(housing_url, tgz_path)\n",
" housing_tgz = tarfile.open(tgz_path)\n", " housing_tgz = tarfile.open(tgz_path)\n",
@ -235,7 +236,7 @@
"import hashlib\n", "import hashlib\n",
"\n", "\n",
"def test_set_check(identifier, test_ratio, hash):\n", "def test_set_check(identifier, test_ratio, hash):\n",
" return hash(str(identifier).encode(\"ascii\")).digest()[-1] < 256 * test_ratio\n", " return bytearray(hash(np.int64(identifier)).digest())[-1] < 256 * test_ratio\n",
"\n", "\n",
"def split_train_test_by_id(data, test_ratio, id_column, hash=hashlib.md5):\n", "def split_train_test_by_id(data, test_ratio, id_column, hash=hashlib.md5):\n",
" ids = data[id_column]\n", " ids = data[id_column]\n",
@ -264,7 +265,7 @@
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"from sklearn.cross_validation import train_test_split\n", "from sklearn.model_selection import train_test_split\n",
"\n", "\n",
"train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)\n", "train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)\n",
"test_set.head()" "test_set.head()"
@ -302,10 +303,10 @@
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"from sklearn.cross_validation import StratifiedShuffleSplit\n", "from sklearn.model_selection import StratifiedShuffleSplit\n",
"\n", "\n",
"split = StratifiedShuffleSplit(housing[\"income_cat\"], test_size=0.2, random_state=42)\n", "split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\n",
"train_index, test_index = next(iter(split))\n", "for train_index, test_index in split.split(housing, housing[\"income_cat\"]):\n",
" strat_train_set = housing.loc[train_index]\n", " strat_train_set = housing.loc[train_index]\n",
" strat_test_set = housing.loc[test_index]" " strat_test_set = housing.loc[test_index]"
] ]
@ -953,10 +954,10 @@
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"from sklearn.cross_validation import cross_val_score\n", "from sklearn.model_selection import cross_val_score\n",
"\n", "\n",
"tree_scores = cross_val_score(tree_reg, housing_prepared, housing_labels,\n", "tree_scores = cross_val_score(tree_reg, housing_prepared, housing_labels,\n",
" scoring=\"mean_squared_error\", cv=10)\n", " scoring=\"neg_mean_squared_error\", cv=10)\n",
"tree_rmse_scores = np.sqrt(-tree_scores)" "tree_rmse_scores = np.sqrt(-tree_scores)"
] ]
}, },
@ -985,7 +986,7 @@
"outputs": [], "outputs": [],
"source": [ "source": [
"lin_scores = cross_val_score(lin_reg, housing_prepared, housing_labels,\n", "lin_scores = cross_val_score(lin_reg, housing_prepared, housing_labels,\n",
" scoring=\"mean_squared_error\", cv=10)\n", " scoring=\"neg_mean_squared_error\", cv=10)\n",
"lin_rmse_scores = np.sqrt(-lin_scores)\n", "lin_rmse_scores = np.sqrt(-lin_scores)\n",
"display_scores(lin_rmse_scores)" "display_scores(lin_rmse_scores)"
] ]
@ -1016,10 +1017,10 @@
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"from sklearn.cross_validation import cross_val_score\n", "from sklearn.model_selection import cross_val_score\n",
"\n", "\n",
"forest_scores = cross_val_score(forest_reg, housing_prepared, housing_labels,\n", "forest_scores = cross_val_score(forest_reg, housing_prepared, housing_labels,\n",
" scoring=\"mean_squared_error\", cv=10)\n", " scoring=\"neg_mean_squared_error\", cv=10)\n",
"forest_rmse_scores = np.sqrt(-forest_scores)\n", "forest_rmse_scores = np.sqrt(-forest_scores)\n",
"display_scores(forest_rmse_scores)" "display_scores(forest_rmse_scores)"
] ]
@ -1032,7 +1033,7 @@
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"scores = cross_val_score(lin_reg, housing_prepared, housing_labels, scoring=\"mean_squared_error\", cv=10)\n", "scores = cross_val_score(lin_reg, housing_prepared, housing_labels, scoring=\"neg_mean_squared_error\", cv=10)\n",
"pd.Series(np.sqrt(-scores)).describe()" "pd.Series(np.sqrt(-scores)).describe()"
] ]
}, },
@ -1062,7 +1063,7 @@
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"from sklearn.grid_search import GridSearchCV\n", "from sklearn.model_selection import GridSearchCV\n",
"\n", "\n",
"param_grid = [\n", "param_grid = [\n",
" {'n_estimators': [3, 10, 30], 'max_features': [2, 4, 6, 8]},\n", " {'n_estimators': [3, 10, 30], 'max_features': [2, 4, 6, 8]},\n",
@ -1070,7 +1071,7 @@
" ]\n", " ]\n",
"\n", "\n",
"forest_reg = RandomForestRegressor()\n", "forest_reg = RandomForestRegressor()\n",
"grid_search = GridSearchCV(forest_reg, param_grid, cv=5, scoring='mean_squared_error')\n", "grid_search = GridSearchCV(forest_reg, param_grid, cv=5, scoring='neg_mean_squared_error')\n",
"grid_search.fit(housing_prepared, housing_labels)" "grid_search.fit(housing_prepared, housing_labels)"
] ]
}, },
@ -1104,8 +1105,9 @@
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"for params, mean_score, scores in grid_search.grid_scores_:\n", "cvres = grid_search.cv_results_\n",
" print(np.sqrt(-mean_score), np.sqrt(-scores).std(), params)" "for mean_score, params in zip(cvres[\"mean_test_score\"], cvres[\"params\"]):\n",
" print(np.sqrt(-mean_score), params)"
] ]
}, },
{ {
@ -1116,18 +1118,7 @@
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"from sklearn.grid_search import RandomizedSearchCV\n", "pd.DataFrame(grid_search.cv_results_)"
"from scipy.stats import randint\n",
"\n",
"param_distribs = {\n",
" 'n_estimators': randint(low=1, high=200),\n",
" 'max_features': randint(low=1, high=8),\n",
" }\n",
"\n",
"forest_reg = RandomForestRegressor()\n",
"rnd_search = RandomizedSearchCV(forest_reg, param_distributions=param_distribs,\n",
" n_iter=10, cv=5, scoring='mean_squared_error')\n",
"rnd_search.fit(housing_prepared, housing_labels)"
] ]
}, },
{ {
@ -1138,8 +1129,18 @@
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"for params, mean_score, scores in rnd_search.grid_scores_:\n", "from sklearn.model_selection import RandomizedSearchCV\n",
" print(np.sqrt(-mean_score), np.sqrt(-scores).std(), params)" "from scipy.stats import randint\n",
"\n",
"param_distribs = {\n",
" 'n_estimators': randint(low=1, high=200),\n",
" 'max_features': randint(low=1, high=8),\n",
" }\n",
"\n",
"forest_reg = RandomForestRegressor()\n",
"rnd_search = RandomizedSearchCV(forest_reg, param_distributions=param_distribs,\n",
" n_iter=10, cv=5, scoring='neg_mean_squared_error')\n",
"rnd_search.fit(housing_prepared, housing_labels)"
] ]
}, },
{ {
@ -1149,6 +1150,19 @@
"collapsed": false "collapsed": false
}, },
"outputs": [], "outputs": [],
"source": [
"cvres = rnd_search.cv_results_\n",
"for mean_score, params in zip(cvres[\"mean_test_score\"], cvres[\"params\"]):\n",
" print(np.sqrt(-mean_score), params)"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [ "source": [
"feature_importances = grid_search.best_estimator_.feature_importances_\n", "feature_importances = grid_search.best_estimator_.feature_importances_\n",
"feature_importances" "feature_importances"
@ -1156,7 +1170,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 73, "execution_count": 74,
"metadata": { "metadata": {
"collapsed": false "collapsed": false
}, },
@ -1170,7 +1184,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 74, "execution_count": 75,
"metadata": { "metadata": {
"collapsed": false "collapsed": false
}, },
@ -1208,7 +1222,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 75, "execution_count": 76,
"metadata": { "metadata": {
"collapsed": false "collapsed": false
}, },
@ -1240,7 +1254,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 76, "execution_count": 77,
"metadata": { "metadata": {
"collapsed": true "collapsed": true
}, },
@ -1251,7 +1265,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 77, "execution_count": 78,
"metadata": { "metadata": {
"collapsed": false "collapsed": false
}, },
@ -1262,7 +1276,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 78, "execution_count": 79,
"metadata": { "metadata": {
"collapsed": false "collapsed": false
}, },
@ -1281,7 +1295,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 79, "execution_count": 80,
"metadata": { "metadata": {
"collapsed": false "collapsed": false
}, },

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