Clean code and set default font size
Aurélien Geron
parent
4a2d0ea1ae
commit
f57e80968d
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@ -27,7 +27,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Code example 1-1"
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"# Setup"
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]
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},
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{
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@ -78,6 +78,7 @@
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"%matplotlib inline\n",
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"import matplotlib as mpl\n",
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"\n",
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"mpl.rc('font', size=12)\n",
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"mpl.rc('axes', labelsize=14)\n",
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"mpl.rc('xtick', labelsize=12)\n",
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"mpl.rc('ytick', labelsize=12)"
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@ -104,13 +105,19 @@
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" urllib.request.urlretrieve(url, datapath / filename)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Code example 1-1"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Code example\n",
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"from pathlib import Path\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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@ -124,10 +131,9 @@
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"y = lifesat[[\"Life satisfaction\"]].values\n",
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"\n",
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"# Visualize the data\n",
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"lifesat.plot(kind='scatter',\n",
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" x=\"GDP per capita (USD)\", y='Life satisfaction')\n",
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"lifesat.plot(kind='scatter', grid=True,\n",
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" x=\"GDP per capita (USD)\", y=\"Life satisfaction\")\n",
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"plt.axis([23_500, 62_500, 4, 9])\n",
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"plt.grid(True)\n",
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"plt.show()\n",
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"\n",
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"# Select a linear model\n",
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@ -149,15 +155,17 @@
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"lines:\n",
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"\n",
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"```python\n",
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"import sklearn.linear_model\n",
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"model = sklearn.linear_model.LinearRegression()\n",
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"from sklearn.linear_model import LinearRegression\n",
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"\n",
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"model = LinearRegression()\n",
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"```\n",
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"\n",
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"with these two:\n",
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"\n",
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"```python\n",
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"import sklearn.neighbors\n",
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"model = sklearn.neighbors.KNeighborsRegressor(n_neighbors=3)\n",
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"from sklearn.neighbors import KNeighborsRegressor\n",
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"\n",
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"model = KNeighborsRegressor(n_neighbors=3)\n",
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"```"
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]
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},
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@ -168,9 +176,9 @@
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"outputs": [],
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"source": [
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"# Select a 3-Nearest Neighbors regression model\n",
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"import sklearn.neighbors\n",
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"from sklearn.neighbors import KNeighborsRegressor\n",
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"\n",
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"model = sklearn.neighbors.KNeighborsRegressor(n_neighbors=3)\n",
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"model = KNeighborsRegressor(n_neighbors=3)\n",
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"\n",
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"# Train the model\n",
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"model.fit(X,y)\n",
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@ -274,7 +282,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This function just merges the OECD's life satisfaction data and the World Bank's GDP per capita data:"
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"Preprocess the GDP per capita data to keep only the year 2020:"
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]
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},
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{
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@ -283,19 +291,23 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"def prepare_country_stats(oecd_bli, gdp_per_capita):\n",
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"gdp_year = 2020\n",
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" gdppc = \"GDP per capita (USD)\"\n",
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" oecd_bli = oecd_bli[oecd_bli[\"INEQUALITY\"]==\"TOT\"]\n",
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" oecd_bli = oecd_bli.pivot(index=\"Country\", columns=\"Indicator\", values=\"Value\")\n",
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"gdppc_col = \"GDP per capita (USD)\"\n",
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"lifesat_col = \"Life satisfaction\"\n",
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"\n",
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"gdp_per_capita = gdp_per_capita[gdp_per_capita[\"Year\"] == gdp_year]\n",
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"gdp_per_capita = gdp_per_capita.drop([\"Code\", \"Year\"], axis=1)\n",
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" gdp_per_capita.columns = [\"Country\", gdppc]\n",
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"gdp_per_capita.columns = [\"Country\", gdppc_col]\n",
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"gdp_per_capita.set_index(\"Country\", inplace=True)\n",
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" full_country_stats = pd.merge(left=oecd_bli, right=gdp_per_capita,\n",
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" left_index=True, right_index=True)\n",
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" full_country_stats.sort_values(by=gdppc, inplace=True)\n",
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" return full_country_stats[[gdppc, 'Life satisfaction']]"
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"\n",
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"gdp_per_capita.head()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Preprocess the OECD BLI data to keep only the `Life satisfaction` column:"
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]
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},
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{
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@ -304,8 +316,31 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"full_country_stats = prepare_country_stats(oecd_bli, gdp_per_capita)\n",
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"full_country_stats.to_csv(datapath / \"lifesat_full.csv\")"
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"oecd_bli = oecd_bli[oecd_bli[\"INEQUALITY\"]==\"TOT\"]\n",
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"oecd_bli = oecd_bli.pivot(index=\"Country\", columns=\"Indicator\", values=\"Value\")\n",
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"\n",
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"oecd_bli.head()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Now let's merge the life satisfaction data and the GDP per capita data, keeping only the GDP per capita and Life satisfaction columns:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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"full_country_stats = pd.merge(left=oecd_bli, right=gdp_per_capita,\n",
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" left_index=True, right_index=True)\n",
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"full_country_stats.sort_values(by=gdppc_col, inplace=True)\n",
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"full_country_stats = full_country_stats[[gdppc_col, lifesat_col]]\n",
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"\n",
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"full_country_stats.head()"
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]
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},
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{
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@ -315,56 +350,18 @@
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"To illustrate the risk of overfitting, I use only part of the data in most figures (all countries with a GDP per capita between `min_gdp` and `max_gdp`). Later in the chapter I reveal the missing countries, and show that they don't follow the same linear trend at all."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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"gdppc = \"GDP per capita (USD)\"\n",
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"min_gdp = 23_500\n",
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"max_gdp = 62_500\n",
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"country_stats = full_country_stats[(full_country_stats[gdppc] >= min_gdp) &\n",
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" (full_country_stats[gdppc] <= max_gdp)]\n",
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"country_stats.to_csv(datapath / \"lifesat.csv\")\n",
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"country_stats.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [],
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"source": [
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"country_stats.plot(kind='scatter', figsize=(5,3),\n",
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" x=\"GDP per capita (USD)\", y='Life satisfaction')\n",
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"min_gdp = 23_500\n",
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"max_gdp = 62_500\n",
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"\n",
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"min_life_sat = 4\n",
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"max_life_sat = 9\n",
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"\n",
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"plt.axis([min_gdp, max_gdp, min_life_sat, max_life_sat])\n",
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"position_text = {\n",
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" \"Hungary\": (28_000, 4.2),\n",
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" \"France\": (40_000, 5),\n",
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" \"New Zealand\": (30_000, 8),\n",
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" \"Australia\": (50_000, 5.5),\n",
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" \"United States\": (59_000, 5.5),\n",
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" \"Denmark\": (46_000, 8.5)\n",
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"}\n",
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"\n",
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"for country, pos_text in position_text.items():\n",
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" pos_data_x, pos_data_y = country_stats[[\"GDP per capita (USD)\",\n",
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" \"Life satisfaction\"]].loc[country]\n",
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" country = \"U.S.\" if country == \"United States\" else country\n",
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" plt.annotate(country, xy=(pos_data_x, pos_data_y), xytext=pos_text,\n",
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" arrowprops=dict(facecolor='black', width=0.5, shrink=0.2,\n",
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" headwidth=5))\n",
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" plt.plot(pos_data_x, pos_data_y, \"ro\")\n",
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"\n",
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"plt.grid(True)\n",
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"\n",
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"save_fig('money_happy_scatterplot')\n",
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"plt.show()"
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"country_stats = full_country_stats[(full_country_stats[gdppc_col] >= min_gdp) &\n",
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" (full_country_stats[gdppc_col] <= max_gdp)]\n",
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"country_stats.head()"
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]
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},
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{
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@ -373,8 +370,8 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"highlighted_countries = country_stats.loc[list(position_text.keys())]\n",
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"highlighted_countries[[gdppc, \"Life satisfaction\"]].sort_values(by=gdppc)"
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"country_stats.to_csv(datapath / \"lifesat.csv\")\n",
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"full_country_stats.to_csv(datapath / \"lifesat_full.csv\")"
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]
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},
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{
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@ -383,37 +380,34 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"country_stats.plot(kind='scatter', figsize=(5,3), grid=True,\n",
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" x=gdppc_col, y=lifesat_col)\n",
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"\n",
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"min_life_sat = 4\n",
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"max_life_sat = 9\n",
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"\n",
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"position_text = {\n",
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" \"Hungary\": (28_000, 4.2),\n",
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" \"France\": (40_000, 5),\n",
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" \"New Zealand\": (28_000, 8.2),\n",
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" \"Australia\": (50_000, 5.5),\n",
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" \"United States\": (59_000, 5.5),\n",
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" \"Denmark\": (46_000, 8.5)\n",
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"}\n",
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"\n",
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"for country, pos_text in position_text.items():\n",
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" pos_data_x = country_stats[gdppc_col].loc[country]\n",
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" pos_data_y = country_stats[lifesat_col].loc[country]\n",
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" country = \"U.S.\" if country == \"United States\" else country\n",
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" plt.annotate(country, xy=(pos_data_x, pos_data_y),\n",
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" xytext=pos_text,\n",
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" arrowprops=dict(facecolor='black', width=0.5,\n",
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" shrink=0.15, headwidth=5))\n",
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" plt.plot(pos_data_x, pos_data_y, \"ro\")\n",
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"\n",
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"country_stats.plot(kind='scatter', figsize=(5,3),\n",
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" x=\"GDP per capita (USD)\", y='Life satisfaction')\n",
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"plt.axis([min_gdp, max_gdp, min_life_sat, max_life_sat])\n",
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"\n",
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"X = np.linspace(min_gdp, max_gdp, 1000)\n",
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"\n",
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"w1, w2 = 4.2, 0\n",
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"plt.plot(X, w1 + w2 * 1e-5 * X, \"r\")\n",
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"plt.text(40_000, 4.9, fr\"$\\theta_0 = {w1}$\",\n",
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" fontsize=14, color=\"r\")\n",
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"plt.text(40_000, 4.4, fr\"$\\theta_1 = {w2}$\",\n",
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" fontsize=14, color=\"r\")\n",
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"\n",
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"w1, w2 = 10, -9\n",
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"plt.plot(X, w1 + w2 * 1e-5 * X, \"g\")\n",
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"plt.text(26_000, 8.5, fr\"$\\theta_0 = {w1}$\",\n",
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" fontsize=14, color=\"g\")\n",
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"plt.text(26_000, 8.0, fr\"$\\theta_1 = {w2} \\times 10^{{-5}}$\",\n",
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" fontsize=14, color=\"g\")\n",
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"\n",
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"w1, w2 = 3, 8\n",
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"plt.plot(X, w1 + w2 * 1e-5 * X, \"b\")\n",
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"plt.text(48_000, 8.5, fr\"$\\theta_0 = {w1}$\",\n",
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" fontsize=14, color=\"b\")\n",
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"plt.text(48_000, 8.0, fr\"$\\theta_1 = {w2} \\times 10^{{-5}}$\",\n",
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" fontsize=14, color=\"b\")\n",
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"plt.grid(True)\n",
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"\n",
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"save_fig('tweaking_model_params_plot')\n",
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"save_fig('money_happy_scatterplot')\n",
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"plt.show()"
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]
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},
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@ -423,16 +417,8 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn import linear_model\n",
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"\n",
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"X_sample = country_stats[[\"GDP per capita (USD)\"]].values\n",
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"y_sample = country_stats[[\"Life satisfaction\"]].values\n",
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"\n",
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"lin1 = linear_model.LinearRegression()\n",
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"lin1.fit(X_sample, y_sample)\n",
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"\n",
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"t0, t1 = lin1.intercept_[0], lin1.coef_[0][0]\n",
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"t0, t1"
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"highlighted_countries = country_stats.loc[list(position_text.keys())]\n",
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"highlighted_countries[[gdppc_col, lifesat_col]].sort_values(by=gdppc_col)"
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]
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},
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{
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@ -441,23 +427,29 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"country_stats.plot(kind='scatter', figsize=(5,3),\n",
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" x=\"GDP per capita (USD)\", y='Life satisfaction')\n",
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"plt.axis([min_gdp, max_gdp, min_life_sat, max_life_sat])\n",
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"country_stats.plot(kind='scatter', figsize=(5,3), grid=True,\n",
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" x=gdppc_col, y=lifesat_col)\n",
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"\n",
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"X = np.linspace(min_gdp, max_gdp, 1000)\n",
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"plt.plot(X, t0 + t1 * X, \"b\")\n",
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"\n",
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"plt.text(max_gdp - 20_000, min_life_sat + 1.5,\n",
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" fr\"$\\theta_0 = {t0:.2f}$\",\n",
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" fontsize=14, color=\"b\")\n",
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"plt.text(max_gdp - 20_000, min_life_sat + 1,\n",
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" fr\"$\\theta_1 = {t1 * 1e5:.2f} \\times 10^{{-5}}$\",\n",
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" fontsize=14, color=\"b\")\n",
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"w1, w2 = 4.2, 0\n",
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"plt.plot(X, w1 + w2 * 1e-5 * X, \"r\")\n",
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"plt.text(40_000, 4.9, fr\"$\\theta_0 = {w1}$\", color=\"r\")\n",
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"plt.text(40_000, 4.4, fr\"$\\theta_1 = {w2}$\", color=\"r\")\n",
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"\n",
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"w1, w2 = 10, -9\n",
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"plt.plot(X, w1 + w2 * 1e-5 * X, \"g\")\n",
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"plt.text(26_000, 8.5, fr\"$\\theta_0 = {w1}$\", color=\"g\")\n",
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"plt.text(26_000, 8.0, fr\"$\\theta_1 = {w2} \\times 10^{{-5}}$\", color=\"g\")\n",
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"\n",
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"w1, w2 = 3, 8\n",
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"plt.plot(X, w1 + w2 * 1e-5 * X, \"b\")\n",
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"plt.text(48_000, 8.5, fr\"$\\theta_0 = {w1}$\", color=\"b\")\n",
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"plt.text(48_000, 8.0, fr\"$\\theta_1 = {w2} \\times 10^{{-5}}$\", color=\"b\")\n",
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"\n",
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"plt.axis([min_gdp, max_gdp, min_life_sat, max_life_sat])\n",
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"plt.grid(True)\n",
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"\n",
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"save_fig('best_fit_model_plot')\n",
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"save_fig('tweaking_model_params_plot')\n",
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"plt.show()"
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]
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},
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@ -467,11 +459,16 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"gdp_year = 2020\n",
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"gdp_per_capita_clean = gdp_per_capita[gdp_per_capita[\"Year\"] == gdp_year]\n",
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"gdp_per_capita_clean = gdp_per_capita_clean.drop([\"Code\", \"Year\"], axis=1)\n",
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"gdp_per_capita_clean.columns = [\"Country\", \"GDP per capita (USD)\"]\n",
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"gdp_per_capita_clean.set_index(\"Country\", inplace=True)"
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"from sklearn import linear_model\n",
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"\n",
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"X_sample = country_stats[[gdppc_col]].values\n",
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"y_sample = country_stats[[lifesat_col]].values\n",
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"\n",
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"lin1 = linear_model.LinearRegression()\n",
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"lin1.fit(X_sample, y_sample)\n",
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"\n",
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"t0, t1 = lin1.intercept_[0], lin1.coef_[0][0]\n",
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"print(f\"θ0={t0:.2f}, θ1={t1:.2e}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -480,10 +477,21 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"cyprus_gdp_per_capita = gdp_per_capita_clean.loc[\"Cyprus\"][\"GDP per capita (USD)\"]\n",
|
||||
"print(cyprus_gdp_per_capita)\n",
|
||||
"cyprus_predicted_life_satisfaction = lin1.predict([[cyprus_gdp_per_capita]])[0, 0]\n",
|
||||
"cyprus_predicted_life_satisfaction"
|
||||
"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",
|
||||
"plt.plot(X, t0 + t1 * X, \"b\")\n",
|
||||
"\n",
|
||||
"plt.text(max_gdp - 20_000, min_life_sat + 1.5,\n",
|
||||
" fr\"$\\theta_0 = {t0:.2f}$\", color=\"b\")\n",
|
||||
"plt.text(max_gdp - 20_000, min_life_sat + 1,\n",
|
||||
" fr\"$\\theta_1 = {t1 * 1e5:.2f} \\times 10^{{-5}}$\", color=\"b\")\n",
|
||||
"\n",
|
||||
"plt.axis([min_gdp, max_gdp, min_life_sat, max_life_sat])\n",
|
||||
"\n",
|
||||
"save_fig('best_fit_model_plot')\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -492,31 +500,8 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"country_stats.plot(kind='scatter', figsize=(5,3),\n",
|
||||
" x=\"GDP per capita (USD)\", y='Life satisfaction')\n",
|
||||
"\n",
|
||||
"X = np.linspace(min_gdp, max_gdp, 1000)\n",
|
||||
"plt.plot(X, t0 + t1 * X, \"b\")\n",
|
||||
"\n",
|
||||
"plt.text(min_gdp + 15_000, max_life_sat - 1.5,\n",
|
||||
" fr\"$\\theta_0 = {t0:.2f}$\",\n",
|
||||
" fontsize=14, color=\"b\")\n",
|
||||
"plt.text(min_gdp + 15_000, max_life_sat - 1,\n",
|
||||
" fr\"$\\theta_1 = {t1 * 1e5:.2f} \\times 10^{{-5}}$\",\n",
|
||||
" fontsize=14, color=\"b\")\n",
|
||||
"\n",
|
||||
"plt.plot([cyprus_gdp_per_capita, cyprus_gdp_per_capita],\n",
|
||||
" [min_life_sat, cyprus_predicted_life_satisfaction], \"r--\")\n",
|
||||
"plt.text(cyprus_gdp_per_capita + 1000, 5.0,\n",
|
||||
" fr\"Prediction = {cyprus_predicted_life_satisfaction:.2f}\",\n",
|
||||
" fontsize=14, color=\"r\")\n",
|
||||
"plt.plot(cyprus_gdp_per_capita, cyprus_predicted_life_satisfaction, \"ro\")\n",
|
||||
"\n",
|
||||
"plt.axis([min_gdp, max_gdp, min_life_sat, max_life_sat])\n",
|
||||
"plt.grid(True)\n",
|
||||
"\n",
|
||||
"save_fig('cyprus_prediction_plot')\n",
|
||||
"plt.show()"
|
||||
"cyprus_gdp_per_capita = gdp_per_capita[gdppc_col].loc[\"Cyprus\"]\n",
|
||||
"cyprus_gdp_per_capita"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -525,9 +510,8 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"missing_data = full_country_stats[(full_country_stats[gdppc] < min_gdp) |\n",
|
||||
" (full_country_stats[gdppc] > max_gdp)]\n",
|
||||
"missing_data"
|
||||
"cyprus_predicted_life_satisfaction = lin1.predict([[cyprus_gdp_per_capita]])[0, 0]\n",
|
||||
"cyprus_predicted_life_satisfaction"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -536,17 +520,27 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"position_text2 = {\n",
|
||||
" \"South Africa\": (20_000, 4.2),\n",
|
||||
" \"Colombia\": (6_000, 8.2),\n",
|
||||
" \"Brazil\": (18_000, 7.8),\n",
|
||||
" \"Mexico\": (24_000, 7.4),\n",
|
||||
" \"Chile\": (30_000, 7.0),\n",
|
||||
" \"Norway\": (60_000, 6.2),\n",
|
||||
" \"Switzerland\": (65_000, 5.7),\n",
|
||||
" \"Ireland\": (80_000, 5.5),\n",
|
||||
" \"Luxembourg\": (100_000, 5.0),\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",
|
||||
"plt.plot(X, t0 + t1 * X, \"b\")\n",
|
||||
"\n",
|
||||
"plt.text(min_gdp + 15_000, max_life_sat - 1.5,\n",
|
||||
" fr\"$\\theta_0 = {t0:.2f}$\", color=\"b\")\n",
|
||||
"plt.text(min_gdp + 15_000, max_life_sat - 1,\n",
|
||||
" fr\"$\\theta_1 = {t1 * 1e5:.2f} \\times 10^{{-5}}$\", color=\"b\")\n",
|
||||
"\n",
|
||||
"plt.plot([cyprus_gdp_per_capita, cyprus_gdp_per_capita],\n",
|
||||
" [min_life_sat, cyprus_predicted_life_satisfaction], \"r--\")\n",
|
||||
"plt.text(cyprus_gdp_per_capita + 1000, 5.0,\n",
|
||||
" fr\"Prediction = {cyprus_predicted_life_satisfaction:.2f}\", color=\"r\")\n",
|
||||
"plt.plot(cyprus_gdp_per_capita, cyprus_predicted_life_satisfaction, \"ro\")\n",
|
||||
"\n",
|
||||
"plt.axis([min_gdp, max_gdp, min_life_sat, max_life_sat])\n",
|
||||
"\n",
|
||||
"save_fig('cyprus_prediction_plot')\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -555,33 +549,9 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"full_country_stats.plot(kind='scatter', figsize=(8,3),\n",
|
||||
" x=\"GDP per capita (USD)\", y='Life satisfaction')\n",
|
||||
"\n",
|
||||
"for country, pos_text in position_text2.items():\n",
|
||||
" pos_data_x, pos_data_y = missing_data.loc[country]\n",
|
||||
" plt.annotate(country, xy=(pos_data_x, pos_data_y), xytext=pos_text,\n",
|
||||
" arrowprops=dict(facecolor='black', width=0.5, shrink=0.1,\n",
|
||||
" headwidth=5))\n",
|
||||
" plt.plot(pos_data_x, pos_data_y, \"rs\")\n",
|
||||
"\n",
|
||||
"X = np.linspace(0, 115_000, 1000)\n",
|
||||
"plt.plot(X, t0 + t1 * X, \"b:\")\n",
|
||||
"\n",
|
||||
"lin_reg_full = linear_model.LinearRegression()\n",
|
||||
"Xfull = np.c_[full_country_stats[\"GDP per capita (USD)\"]]\n",
|
||||
"yfull = np.c_[full_country_stats[\"Life satisfaction\"]]\n",
|
||||
"lin_reg_full.fit(Xfull, yfull)\n",
|
||||
"\n",
|
||||
"t0full, t1full = lin_reg_full.intercept_[0], lin_reg_full.coef_[0][0]\n",
|
||||
"X = np.linspace(0, 115_000, 1000)\n",
|
||||
"plt.plot(X, t0full + t1full * X, \"k\")\n",
|
||||
"\n",
|
||||
"plt.axis([0, 115_000, min_life_sat, max_life_sat])\n",
|
||||
"plt.grid(True)\n",
|
||||
"\n",
|
||||
"save_fig('representative_training_data_scatterplot')\n",
|
||||
"plt.show()"
|
||||
"missing_data = full_country_stats[(full_country_stats[gdppc_col] < min_gdp) |\n",
|
||||
" (full_country_stats[gdppc_col] > max_gdp)]\n",
|
||||
"missing_data"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -589,13 +559,66 @@
|
|||
"execution_count": 25,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"position_text_missing_countries = {\n",
|
||||
" \"South Africa\": (20_000, 4.2),\n",
|
||||
" \"Colombia\": (6_000, 8.2),\n",
|
||||
" \"Brazil\": (18_000, 7.8),\n",
|
||||
" \"Mexico\": (24_000, 7.4),\n",
|
||||
" \"Chile\": (30_000, 7.0),\n",
|
||||
" \"Norway\": (51_000, 6.2),\n",
|
||||
" \"Switzerland\": (62_000, 5.7),\n",
|
||||
" \"Ireland\": (81_000, 5.2),\n",
|
||||
" \"Luxembourg\": (92_000, 4.7),\n",
|
||||
"}"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"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",
|
||||
" pos_data_x, pos_data_y = missing_data.loc[country]\n",
|
||||
" plt.annotate(country, xy=(pos_data_x, pos_data_y),\n",
|
||||
" xytext=pos_text,\n",
|
||||
" arrowprops=dict(facecolor='black', width=0.5,\n",
|
||||
" shrink=0.1, headwidth=5))\n",
|
||||
" plt.plot(pos_data_x, pos_data_y, \"rs\")\n",
|
||||
"\n",
|
||||
"X = np.linspace(0, 115_000, 1000)\n",
|
||||
"plt.plot(X, t0 + t1 * X, \"b:\")\n",
|
||||
"\n",
|
||||
"lin_reg_full = linear_model.LinearRegression()\n",
|
||||
"Xfull = np.c_[full_country_stats[gdppc_col]]\n",
|
||||
"yfull = np.c_[full_country_stats[lifesat_col]]\n",
|
||||
"lin_reg_full.fit(Xfull, yfull)\n",
|
||||
"\n",
|
||||
"t0full, t1full = lin_reg_full.intercept_[0], lin_reg_full.coef_[0][0]\n",
|
||||
"X = np.linspace(0, 115_000, 1000)\n",
|
||||
"plt.plot(X, t0full + t1full * X, \"k\")\n",
|
||||
"\n",
|
||||
"plt.axis([0, 115_000, min_life_sat, max_life_sat])\n",
|
||||
"\n",
|
||||
"save_fig('representative_training_data_scatterplot')\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 27,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from sklearn import preprocessing\n",
|
||||
"from sklearn import pipeline\n",
|
||||
"\n",
|
||||
"full_country_stats.plot(kind='scatter', figsize=(8,3),\n",
|
||||
" x=\"GDP per capita (USD)\", y='Life satisfaction')\n",
|
||||
"plt.axis([0, 115_000, min_life_sat, max_life_sat])\n",
|
||||
" x=gdppc_col, y=lifesat_col, grid=True)\n",
|
||||
"\n",
|
||||
"poly = preprocessing.PolynomialFeatures(degree=10, include_bias=False)\n",
|
||||
"scaler = preprocessing.StandardScaler()\n",
|
||||
|
|
@ -608,64 +631,57 @@
|
|||
"pipeline_reg.fit(Xfull, yfull)\n",
|
||||
"curve = pipeline_reg.predict(X[:, np.newaxis])\n",
|
||||
"plt.plot(X, curve)\n",
|
||||
"plt.grid(True)\n",
|
||||
"\n",
|
||||
"plt.axis([0, 115_000, min_life_sat, max_life_sat])\n",
|
||||
"\n",
|
||||
"save_fig('overfitting_model_plot')\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"w_countries = [c for c in full_country_stats.index if \"W\" in c.upper()]\n",
|
||||
"full_country_stats.loc[w_countries][\"Life satisfaction\"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 27,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"all_w_countries = [c for c in gdp_per_capita_clean.index if \"W\" in c.upper()]\n",
|
||||
"gdp_per_capita_clean.loc[all_w_countries].sort_values(by=gdppc)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 28,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"plt.figure(figsize=(8,3))\n",
|
||||
"\n",
|
||||
"plt.xlabel(\"GDP per capita (USD)\")\n",
|
||||
"plt.ylabel('Life satisfaction')\n",
|
||||
"\n",
|
||||
"country_stats.plot(ax=plt.gca(), kind='scatter',\n",
|
||||
" x=gdppc, y='Life satisfaction')\n",
|
||||
"missing_data.plot(ax=plt.gca(), kind='scatter',\n",
|
||||
" x=gdppc, y='Life satisfaction', marker=\"s\", color=\"r\")\n",
|
||||
"w_countries = [c for c in full_country_stats.index if \"W\" in c.upper()]\n",
|
||||
"full_country_stats.loc[w_countries][lifesat_col]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 29,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"all_w_countries = [c for c in gdp_per_capita.index if \"W\" in c.upper()]\n",
|
||||
"gdp_per_capita.loc[all_w_countries].sort_values(by=gdppc_col)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 30,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"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",
|
||||
"X = np.linspace(0, 115_000, 1000)\n",
|
||||
"plt.plot(X, t0full + t1full * X, \"k-\", label=\"Linear model on all data\")\n",
|
||||
"plt.plot(X, t0 + t1*X, \"b:\", label=\"Linear model on partial data\")\n",
|
||||
"plt.plot(X, t0full + t1full * X, \"k-\", label=\"Linear model on all data\")\n",
|
||||
"\n",
|
||||
"ridge = linear_model.Ridge(alpha=10**9.5)\n",
|
||||
"Xsample = country_stats[[\"GDP per capita (USD)\"]]\n",
|
||||
"ysample = country_stats[[\"Life satisfaction\"]]\n",
|
||||
"ridge.fit(Xsample, ysample)\n",
|
||||
"X_sample = country_stats[[gdppc_col]]\n",
|
||||
"y_sample = country_stats[[lifesat_col]]\n",
|
||||
"ridge.fit(X_sample, y_sample)\n",
|
||||
"t0ridge, t1ridge = ridge.intercept_[0], ridge.coef_[0][0]\n",
|
||||
"plt.plot(X, t0ridge + t1ridge * X, \"b--\", label=\"Regularized linear model on partial data\")\n",
|
||||
"\n",
|
||||
"plt.plot(X, t0ridge + t1ridge * X, \"b--\",\n",
|
||||
" label=\"Regularized linear model on partial data\")\n",
|
||||
"plt.legend(loc=\"lower right\")\n",
|
||||
"plt.axis([0, 115_000, 0, 10])\n",
|
||||
"\n",
|
||||
"plt.axis([0, 115_000, min_life_sat, max_life_sat])\n",
|
||||
"plt.grid(True)\n",
|
||||
"\n",
|
||||
"save_fig('ridge_model_plot')\n",
|
||||
"plt.show()"
|
||||
|
|
|
|||
Loading…
Reference in New Issue