From 81407ede2bd850c720787ba63363de9001ca3069 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Aur=C3=A9lien=20Geron?= <ageron@users.noreply.github.com>
Date: Mon, 1 Nov 2021 14:42:42 +1300
Subject: [PATCH] Replace np.round(a) with a.round(), and other similar changes

---
 02_end_to_end_machine_learning_project.ipynb | 22 ++++++++++----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/02_end_to_end_machine_learning_project.ipynb b/02_end_to_end_machine_learning_project.ipynb
index 88161b5..02c3bf9 100644
--- a/02_end_to_end_machine_learning_project.ipynb
+++ b/02_end_to_end_machine_learning_project.ipynb
@@ -350,7 +350,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "np.sum(test_set[\"total_bedrooms\"].isnull())"
+    "test_set[\"total_bedrooms\"].isnull().sum()"
    ]
   },
   {
@@ -1428,8 +1428,8 @@
     "\n",
     "    def fit(self, X, y=None):  # y is required even though we don't use it\n",
     "        X = check_array(X)  # checks that X is an array with finite float values\n",
-    "        self.mean_ = np.mean(X, axis=0)\n",
-    "        self.scale_ = np.std(X, axis=0)\n",
+    "        self.mean_ = X.mean(axis=0)\n",
+    "        self.scale_ = X.std(axis=0)\n",
     "        self.n_features_in_ = X.shape[1]  # every estimator stores this in fit()\n",
     "        return self  # always return self!\n",
     "\n",
@@ -1529,7 +1529,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "np.round(similarities[:3], 2)"
+    "similarities[:3].round(2)"
    ]
   },
   {
@@ -1618,7 +1618,7 @@
    "outputs": [],
    "source": [
     "housing_num_prepared = num_pipeline.fit_transform(housing_num)\n",
-    "np.round(housing_num_prepared[:2], 2)"
+    "housing_num_prepared[:2].round(2)"
    ]
   },
   {
@@ -1881,7 +1881,7 @@
    "outputs": [],
    "source": [
     "housing_predictions = lin_reg.predict(housing)\n",
-    "np.round(housing_predictions[:5], -2)"
+    "housing_predictions[:5].round(-2)"
    ]
   },
   {
@@ -1907,7 +1907,7 @@
    "outputs": [],
    "source": [
     "# Not in the book\n",
-    "error_ratios = np.round(housing_predictions[:5], -2) / housing_labels.iloc[:5].values - 1\n",
+    "error_ratios = housing_predictions[:5].round(-2) / housing_labels.iloc[:5].values - 1\n",
     "print(\", \".join([f\"{100 * ratio:.1f}%\" for ratio in error_ratios]))"
    ]
   },
@@ -2151,7 +2151,7 @@
     "                 \"split1_test_score\", \"split2_test_score\", \"mean_test_score\"]]\n",
     "score_cols = [\"split0\", \"split1\", \"split2\", \"mean_test_rmse\"]\n",
     "cv_res.columns = [\"n_clusters\", \"max_features\"] + score_cols\n",
-    "cv_res[score_cols] = -np.round(cv_res[score_cols]).astype(np.int64)\n",
+    "cv_res[score_cols] = -cv_res[score_cols].round().astype(np.int64)\n",
     "cv_res.head()"
    ]
   },
@@ -2212,7 +2212,7 @@
     "                 \"param_random_forest__max_features\", \"split0_test_score\",\n",
     "                 \"split1_test_score\", \"split2_test_score\", \"mean_test_score\"]]\n",
     "cv_res.columns = [\"n_clusters\", \"max_features\"] + score_cols\n",
-    "cv_res[score_cols] = -np.round(cv_res[score_cols]).astype(np.int64)\n",
+    "cv_res[score_cols] = -cv_res[score_cols].round().astype(np.int64)\n",
     "cv_res.head()"
    ]
   },
@@ -2368,7 +2368,7 @@
    "source": [
     "final_model = rnd_search.best_estimator_\n",
     "feature_importances = final_model[\"random_forest\"].feature_importances_\n",
-    "np.round(feature_importances, 2)"
+    "feature_importances.round(2)"
    ]
   },
   {
@@ -2733,7 +2733,7 @@
     "np.random.seed(42)\n",
     "\n",
     "s = expon(scale=1).rvs(100_000)  # get 100,000 samples\n",
-    "np.sum((s > 0.105) & (s < 2.29)) / 100_000"
+    "((s > 0.105) & (s < 2.29)).sum() / 100_000"
    ]
   },
   {