Set random seeds where applicable
This commit is contained in:
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3125c82096
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344EA5AB10D868E0
3 changed files with 82 additions and 48 deletions
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@ -418,21 +418,39 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"To access a function inside the [random <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/random.html) module, for example, the [random() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/random.html#random.random) function, we use the `.` operator, formally called the attribute access operator. The [random() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/random.html#random.random) function simply returns a random decimal number between `0` and `1`."
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"To access a function inside the [random <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/random.html) module, for example, the [seed() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/random.html#random.seed) function, we use the `.` operator, formally called the attribute access operator. \n",
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"\n",
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"We use [random.seed() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/random.html#random.seed) to make the random numbers *replicable* on separate runs of this notebook."
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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": 16,
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"metadata": {},
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"outputs": [],
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"source": [
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"random.seed(42)"
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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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"The [random() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/random.html#random.random) function simply returns a random decimal number between `0` and `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": 17,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"0.7021021034327006"
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"0.6394267984578837"
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]
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},
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"execution_count": 16,
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"execution_count": 17,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -450,16 +468,16 @@
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"execution_count": 18,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"False"
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"True"
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]
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},
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"execution_count": 17,
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"execution_count": 18,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -477,7 +495,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"execution_count": 19,
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"metadata": {},
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"outputs": [
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{
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@ -486,7 +504,7 @@
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"3"
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]
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},
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"execution_count": 18,
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"execution_count": 19,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -1274,13 +1274,29 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Let us quickly generate some random data points and draw a scatter plot with [matplotlib <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_plt.png\">](https://matplotlib.org/)'s [plt.scatter() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_plt.png\">](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.scatter.html#matplotlib.pyplot.scatter) function."
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"First, let's set the [np.random.seed() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_np.png\">](https://docs.python.org/3/library/random.html#random.seed) to make the random numbers *replicable* on separate runs of this notebook."
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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": 44,
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"metadata": {},
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"outputs": [],
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"source": [
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"np.random.seed(42)"
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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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"Then, let us quickly generate some random data points and draw a scatter plot with [matplotlib <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_plt.png\">](https://matplotlib.org/)'s [plt.scatter() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_plt.png\">](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.scatter.html#matplotlib.pyplot.scatter) function."
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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": 45,
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"metadata": {},
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"outputs": [
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{
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"data": {
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@ -1288,7 +1304,7 @@
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"<matplotlib.collections.PathCollection at 0x7f008b235d00>"
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]
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},
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"execution_count": 44,
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"execution_count": 45,
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"metadata": {},
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"output_type": "execute_result"
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},
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@ -761,9 +761,9 @@
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{
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"data": {
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"text/plain": [
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"array([1, 0, 2, 2, 1, 0, 1, 1, 1, 0, 0, 2, 2, 0, 0, 2, 0, 1, 0, 0, 2, 2,\n",
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" 0, 2, 1, 0, 2, 2, 2, 1, 0, 1, 1, 2, 0, 1, 2, 1, 2, 1, 2, 1, 0, 1,\n",
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" 0])"
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"array([2, 1, 2, 1, 2, 2, 1, 1, 0, 2, 0, 0, 2, 2, 0, 2, 1, 0, 0, 0, 1, 0,\n",
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" 1, 2, 2, 1, 1, 1, 1, 0, 2, 2, 1, 0, 2, 0, 0, 0, 0, 1, 1, 0, 2, 2,\n",
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" 1])"
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]
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},
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"execution_count": 19,
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@ -772,7 +772,7 @@
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}
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],
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"source": [
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"X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7, test_size=0.3, stratify=y)\n",
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"X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7, test_size=0.3, random_state=42, stratify=y)\n",
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"\n",
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"y_test"
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]
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@ -869,9 +869,9 @@
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{
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"data": {
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"text/plain": [
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"array([1, 0, 2, 2, 1, 0, 1, 1, 1, 0, 0, 2, 1, 0, 0, 2, 0, 2, 0, 0, 2, 2,\n",
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" 0, 2, 1, 0, 2, 1, 2, 1, 0, 1, 1, 2, 0, 1, 2, 1, 2, 1, 2, 1, 0, 1,\n",
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" 0])"
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"array([2, 1, 2, 1, 2, 2, 1, 1, 0, 2, 0, 0, 2, 2, 0, 2, 1, 0, 0, 0, 1, 0,\n",
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" 1, 2, 2, 1, 1, 1, 1, 0, 2, 2, 1, 0, 2, 0, 0, 0, 0, 1, 1, 0, 1, 2,\n",
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" 1])"
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]
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},
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"execution_count": 23,
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@ -898,9 +898,9 @@
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{
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"data": {
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"text/plain": [
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"array([1, 0, 2, 2, 1, 0, 1, 1, 1, 0, 0, 2, 2, 0, 0, 2, 0, 1, 0, 0, 2, 2,\n",
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" 0, 2, 1, 0, 2, 2, 2, 1, 0, 1, 1, 2, 0, 1, 2, 1, 2, 1, 2, 1, 0, 1,\n",
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" 0])"
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"array([2, 1, 2, 1, 2, 2, 1, 1, 0, 2, 0, 0, 2, 2, 0, 2, 1, 0, 0, 0, 1, 0,\n",
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" 1, 2, 2, 1, 1, 1, 1, 0, 2, 2, 1, 0, 2, 0, 0, 0, 0, 1, 1, 0, 2, 2,\n",
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" 1])"
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]
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},
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"execution_count": 24,
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{
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"data": {
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"text/plain": [
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"(array([12, 17, 27]),)"
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"(array([42]),)"
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},
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"execution_count": 25,
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{
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"data": {
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"text/plain": [
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"np.float64(0.9333333333333333)"
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"np.float64(0.9777777777777777)"
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"execution_count": 26,
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{
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"data": {
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"text/plain": [
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"np.float64(0.9523809523809523)"
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"np.float64(0.9714285714285714)"
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]
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},
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"execution_count": 27,
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"name": "stdout",
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"output_type": "stream",
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"26 0.9333333333333333\n",
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"27 0.9111111111111111\n",
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"26 0.9555555555555556\n",
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"27 0.9333333333333333\n",
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"28 0.9111111111111111\n",
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"29 0.9111111111111111\n",
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"30 0.9111111111111111\n"
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]
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}
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