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Set random seeds where applicable

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Alexander Hess 2024-07-15 11:26:43 +02:00
parent 3125c82096
commit f0d92ed229
Signed by: alexander
GPG key ID: 344EA5AB10D868E0
3 changed files with 82 additions and 48 deletions
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34
00_python_in_a_nutshell/05_content_functions.ipynb
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@ -418,21 +418,39 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"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`." "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",
"\n",
"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."
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 16, "execution_count": 16,
"metadata": {}, "metadata": {},
"outputs": [],
"source": [
"random.seed(42)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"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`."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"0.7021021034327006" "0.6394267984578837"
] ]
}, },
"execution_count": 16, "execution_count": 17,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@ -450,16 +468,16 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 17, "execution_count": 18,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"False" "True"
] ]
}, },
"execution_count": 17, "execution_count": 18,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@ -477,7 +495,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 18, "execution_count": 19,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -486,7 +504,7 @@
"3" "3"
] ]
}, },
"execution_count": 18, "execution_count": 19,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }

20
01_scientific_stack/00_content_numpy.ipynb
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@ -1274,13 +1274,29 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"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." "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."
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 44, "execution_count": 44,
"metadata": {}, "metadata": {},
"outputs": [],
"source": [
"np.random.seed(42)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"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."
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
@ -1288,7 +1304,7 @@
"<matplotlib.collections.PathCollection at 0x7f008b235d00>" "<matplotlib.collections.PathCollection at 0x7f008b235d00>"
] ]
}, },
"execution_count": 44, "execution_count": 45,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
}, },

76
03_classification/00_content.ipynb
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@ -761,9 +761,9 @@
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"array([1, 0, 2, 2, 1, 0, 1, 1, 1, 0, 0, 2, 2, 0, 0, 2, 0, 1, 0, 0, 2, 2,\n", "array([2, 1, 2, 1, 2, 2, 1, 1, 0, 2, 0, 0, 2, 2, 0, 2, 1, 0, 0, 0, 1, 0,\n",
" 0, 2, 1, 0, 2, 2, 2, 1, 0, 1, 1, 2, 0, 1, 2, 1, 2, 1, 2, 1, 0, 1,\n", " 1, 2, 2, 1, 1, 1, 1, 0, 2, 2, 1, 0, 2, 0, 0, 0, 0, 1, 1, 0, 2, 2,\n",
" 0])" " 1])"
] ]
}, },
"execution_count": 19, "execution_count": 19,
@ -772,7 +772,7 @@
} }
], ],
"source": [ "source": [
"X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7, test_size=0.3, stratify=y)\n", "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",
"\n", "\n",
"y_test" "y_test"
] ]
@ -869,9 +869,9 @@
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"array([1, 0, 2, 2, 1, 0, 1, 1, 1, 0, 0, 2, 1, 0, 0, 2, 0, 2, 0, 0, 2, 2,\n", "array([2, 1, 2, 1, 2, 2, 1, 1, 0, 2, 0, 0, 2, 2, 0, 2, 1, 0, 0, 0, 1, 0,\n",
" 0, 2, 1, 0, 2, 1, 2, 1, 0, 1, 1, 2, 0, 1, 2, 1, 2, 1, 2, 1, 0, 1,\n", " 1, 2, 2, 1, 1, 1, 1, 0, 2, 2, 1, 0, 2, 0, 0, 0, 0, 1, 1, 0, 1, 2,\n",
" 0])" " 1])"
] ]
}, },
"execution_count": 23, "execution_count": 23,
@ -898,9 +898,9 @@
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"array([1, 0, 2, 2, 1, 0, 1, 1, 1, 0, 0, 2, 2, 0, 0, 2, 0, 1, 0, 0, 2, 2,\n", "array([2, 1, 2, 1, 2, 2, 1, 1, 0, 2, 0, 0, 2, 2, 0, 2, 1, 0, 0, 0, 1, 0,\n",
" 0, 2, 1, 0, 2, 2, 2, 1, 0, 1, 1, 2, 0, 1, 2, 1, 2, 1, 2, 1, 0, 1,\n", " 1, 2, 2, 1, 1, 1, 1, 0, 2, 2, 1, 0, 2, 0, 0, 0, 0, 1, 1, 0, 2, 2,\n",
" 0])" " 1])"
] ]
}, },
"execution_count": 24, "execution_count": 24,
@ -927,7 +927,7 @@
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"(array([12, 17, 27]),)" "(array([42]),)"
] ]
}, },
"execution_count": 25, "execution_count": 25,
@ -954,7 +954,7 @@
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"np.float64(0.9333333333333333)" "np.float64(0.9777777777777777)"
] ]
}, },
"execution_count": 26, "execution_count": 26,
@ -981,7 +981,7 @@
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"np.float64(0.9523809523809523)" "np.float64(0.9714285714285714)"
] ]
}, },
"execution_count": 27, "execution_count": 27,
@ -1066,35 +1066,35 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
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"14 0.9111111111111111\n", "14 0.9333333333333333\n",
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] ]
} }