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docs: adjust notebooks to new API (#770)
### Summary of Changes The notebooks are now being executed again and work with the new API.
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‎.github/README.md

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@@ -12,8 +12,9 @@ common tasks on small to moderately sized datasets. As such, a major focus is to
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Instead of implementing DS methods from scratch, we use established DS libraries under the hood such as:
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* [pandas](https://pandas.pydata.org) for manipulation of tabular data,
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* [scikit-learn](https://scikit-learn.org) for machine learning, and
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* [polars](https://docs.pola.rs/) for manipulation of tabular data,
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* [scikit-learn](https://scikit-learn.org) for classical machine learning,
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* [torch](https://pytorch.org) for deep learning, and
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* [seaborn](https://seaborn.pydata.org) for visualization.
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For more specialized tasks, we recommend using these or other DS libraries directly.

‎docs/README.md

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@@ -12,8 +12,9 @@ common tasks on small to moderately sized datasets. As such, a major focus is to
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Instead of implementing DS methods from scratch, we use established DS libraries under the hood such as:
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* [pandas](https://pandas.pydata.org) for manipulation of tabular data,
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* [scikit-learn](https://scikit-learn.org) for machine learning, and
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* [polars](https://docs.pola.rs/) for manipulation of tabular data,
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* [scikit-learn](https://scikit-learn.org) for classical machine learning,
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* [torch](https://pytorch.org) for deep learning, and
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* [seaborn](https://seaborn.pydata.org) for visualization.
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For more specialized tasks, we recommend using these or other DS libraries directly.

‎docs/tutorials/classification.ipynb

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},
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{
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"cell_type": "code",
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"execution_count": null,
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"source": [
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"from safeds.data.tabular.containers import Table\n",
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"\n",
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"metadata": {
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"collapsed": false
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},
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"outputs": []
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"outputs": [],
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"execution_count": null
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},
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{
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"cell_type": "markdown",
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"source": [
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"train_table, testing_table = titanic.split_rows(0.6)\n",
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"\n",
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"metadata": {
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"collapsed": false
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},
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"outputs": []
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"outputs": [],
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"execution_count": null
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},
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{
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"cell_type": "markdown",
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"source": [
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"from safeds.data.tabular.transformation import OneHotEncoder\n",
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"\n",
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"metadata": {
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"collapsed": false
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},
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"outputs": []
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"outputs": [],
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"execution_count": null
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},
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"cell_type": "markdown",
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"source": "transformed_table = encoder.transform(train_table)",
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"metadata": {
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"collapsed": false
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},
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"outputs": []
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"outputs": [],
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"execution_count": null
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},
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"cell_type": "markdown",
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},
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"cell_type": "code",
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"execution_count": null,
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"source": [
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"extra_names = [\"id\", \"name\", \"ticket\", \"cabin\", \"port_embarked\", \"age\", \"fare\"]\n",
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"\n",
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"metadata": {
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"collapsed": false
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},
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"outputs": []
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"outputs": [],
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"execution_count": null
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},
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"cell_type": "markdown",
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"source": [
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"from safeds.ml.classical.classification import RandomForestClassifier\n",
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"\n",
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"metadata": {
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"collapsed": false
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"outputs": []
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"outputs": [],
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"execution_count": null
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"cell_type": "markdown",
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"cell_type": "code",
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"execution_count": null,
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"source": [
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"encoder = OneHotEncoder().fit(test_table, [\"sex\"])\n",
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"transformed_test_table = encoder.transform(test_table)\n",
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" transformed_test_table\n",
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")\n",
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"#For visualisation purposes we only print out the first 15 rows.\n",
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"prediction.to_table().slice_rows(start=0, end=15)"
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"prediction.to_table().slice_rows(start=0, length=15)"
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],
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"metadata": {
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"collapsed": false
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"outputs": [],
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"execution_count": null
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"cell_type": "markdown",
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"cell_type": "code",
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"source": [
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"encoder = OneHotEncoder().fit(test_table, [\"sex\"])\n",
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"testing_table = encoder.transform(testing_table)\n",
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"metadata": {
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"collapsed": false
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},
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"outputs": []
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"outputs": [],
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"execution_count": null
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}
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],
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"metadata": {

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