From 4ba9496a87953f34f14d3b45c9a1fe1e474b32f5 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Aur=C3=A9lien=20Geron?= <ageron@users.noreply.github.com>
Date: Sat, 19 Feb 2022 18:18:08 +1300
Subject: [PATCH] Update plot options

---
 01_the_machine_learning_landscape.ipynb | 30 ++++++++++++-------------
 1 file changed, 15 insertions(+), 15 deletions(-)

diff --git a/01_the_machine_learning_landscape.ipynb b/01_the_machine_learning_landscape.ipynb
index e5644b5..90d6586 100644
--- a/01_the_machine_learning_landscape.ipynb
+++ b/01_the_machine_learning_landscape.ipynb
@@ -104,11 +104,11 @@
    "source": [
     "import matplotlib.pyplot as plt\n",
     "\n",
-    "plt.rc('font', size=14)\n",
+    "plt.rc('font', size=12)\n",
     "plt.rc('axes', labelsize=14, titlesize=14)\n",
-    "plt.rc('legend', fontsize=14)\n",
-    "plt.rc('xtick',labelsize=10)\n",
-    "plt.rc('ytick',labelsize=10)"
+    "plt.rc('legend', fontsize=12)\n",
+    "plt.rc('xtick', labelsize=10)\n",
+    "plt.rc('ytick', labelsize=10)"
    ]
   },
   {
@@ -214,7 +214,7 @@
     "model = KNeighborsRegressor(n_neighbors=3)\n",
     "\n",
     "# Train the model\n",
-    "model.fit(X,y)\n",
+    "model.fit(X, y)\n",
     "\n",
     "# Make a prediction for Cyprus\n",
     "print(model.predict(X_new)) # outputs [[6.33333333]]\n"
@@ -399,7 +399,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "country_stats.plot(kind='scatter', figsize=(5,3), grid=True,\n",
+    "country_stats.plot(kind='scatter', figsize=(5, 3), grid=True,\n",
     "                   x=gdppc_col, y=lifesat_col)\n",
     "\n",
     "min_life_sat = 4\n",
@@ -422,7 +422,7 @@
     "    plt.annotate(country, xy=(pos_data_x, pos_data_y),\n",
     "                 xytext=pos_text, fontsize=12,\n",
     "                 arrowprops=dict(facecolor='black', width=0.5,\n",
-    "                                 shrink=0.15, headwidth=5))\n",
+    "                                 shrink=0.08, headwidth=5))\n",
     "    plt.plot(pos_data_x, pos_data_y, \"ro\")\n",
     "\n",
     "plt.axis([min_gdp, max_gdp, min_life_sat, max_life_sat])\n",
@@ -447,7 +447,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "country_stats.plot(kind='scatter', figsize=(5,3), grid=True,\n",
+    "country_stats.plot(kind='scatter', figsize=(5, 3), grid=True,\n",
     "                   x=gdppc_col, y=lifesat_col)\n",
     "\n",
     "X = np.linspace(min_gdp, max_gdp, 1000)\n",
@@ -497,7 +497,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "country_stats.plot(kind='scatter', figsize=(5,3), grid=True,\n",
+    "country_stats.plot(kind='scatter', figsize=(5, 3), grid=True,\n",
     "                   x=gdppc_col, y=lifesat_col)\n",
     "\n",
     "X = np.linspace(min_gdp, max_gdp, 1000)\n",
@@ -540,7 +540,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "country_stats.plot(kind='scatter', figsize=(5,3), grid=True,\n",
+    "country_stats.plot(kind='scatter', figsize=(5, 3), grid=True,\n",
     "                   x=gdppc_col, y=lifesat_col)\n",
     "\n",
     "X = np.linspace(min_gdp, max_gdp, 1000)\n",
@@ -598,7 +598,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "full_country_stats.plot(kind='scatter', figsize=(8,3),\n",
+    "full_country_stats.plot(kind='scatter', figsize=(8, 3),\n",
     "                        x=gdppc_col, y=lifesat_col, grid=True)\n",
     "\n",
     "for country, pos_text in position_text_missing_countries.items():\n",
@@ -606,7 +606,7 @@
     "    plt.annotate(country, xy=(pos_data_x, pos_data_y),\n",
     "                 xytext=pos_text, fontsize=12,\n",
     "                 arrowprops=dict(facecolor='black', width=0.5,\n",
-    "                                 shrink=0.1, headwidth=5))\n",
+    "                                 shrink=0.08, headwidth=5))\n",
     "    plt.plot(pos_data_x, pos_data_y, \"rs\")\n",
     "\n",
     "X = np.linspace(0, 115_000, 1000)\n",
@@ -636,7 +636,7 @@
     "from sklearn import preprocessing\n",
     "from sklearn import pipeline\n",
     "\n",
-    "full_country_stats.plot(kind='scatter', figsize=(8,3),\n",
+    "full_country_stats.plot(kind='scatter', figsize=(8, 3),\n",
     "                        x=gdppc_col, y=lifesat_col, grid=True)\n",
     "\n",
     "poly = preprocessing.PolynomialFeatures(degree=10, include_bias=False)\n",
@@ -683,7 +683,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "country_stats.plot(kind='scatter', x=gdppc_col, y=lifesat_col, figsize=(8,3))\n",
+    "country_stats.plot(kind='scatter', x=gdppc_col, y=lifesat_col, figsize=(8, 3))\n",
     "missing_data.plot(kind='scatter', x=gdppc_col, y=lifesat_col,\n",
     "                  marker=\"s\", color=\"r\", grid=True, ax=plt.gca())\n",
     "\n",
@@ -698,7 +698,7 @@
     "t0ridge, t1ridge = ridge.intercept_[0], ridge.coef_[0][0]\n",
     "plt.plot(X, t0ridge + t1ridge * X, \"b--\",\n",
     "         label=\"Regularized linear model on partial data\")\n",
-    "plt.legend(loc=\"lower right\", fontsize=13)\n",
+    "plt.legend(loc=\"lower right\")\n",
     "\n",
     "plt.axis([0, 115_000, min_life_sat, max_life_sat])\n",
     "\n",