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import marimo |
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__generated_with = "0.10.16" |
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app = marimo.App() |
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@app.cell |
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def _(): |
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import marimo as mo |
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import polars as pl |
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from sklearn import datasets |
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from sklearn.model_selection import train_test_split |
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from sklearn.tree import DecisionTreeClassifier |
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from sklearn.metrics import accuracy_score, classification_report, confusion_matrix |
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mo.md("# Iris Dataset Showcase") |
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return ( |
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DecisionTreeClassifier, |
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accuracy_score, |
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classification_report, |
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confusion_matrix, |
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datasets, |
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mo, |
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pl, |
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train_test_split, |
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) |
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@app.cell(hide_code=True) |
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def _(datasets): |
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iris = datasets.load_iris() |
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X = iris.data |
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y = iris.target |
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return X, iris, y |
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@app.cell(hide_code=True) |
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def _(X, train_test_split, y): |
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) |
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return X_test, X_train, y_test, y_train |
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@app.cell |
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def _(DecisionTreeClassifier, X_train, mo, y_train): |
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classifier = DecisionTreeClassifier() |
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classifier.fit(X_train, y_train) |
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mo.md(f""" |
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## Decision Tree Classifier |
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""") |
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return (classifier,) |
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@app.cell |
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def _(X_test, classifier): |
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y_pred = classifier.predict(X_test) |
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return (y_pred,) |
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@app.cell |
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def _( |
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accuracy_score, |
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classification_report, |
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confusion_matrix, |
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mo, |
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y_pred, |
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y_test, |
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): |
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accuracy = accuracy_score(y_test, y_pred) |
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conf_matrix = confusion_matrix(y_test, y_pred) |
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class_report = classification_report(y_test, y_pred) |
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mo.md(f""" |
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Accuracy: {accuracy} |
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Confusion Matrix: |
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``` |
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{conf_matrix} |
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``` |
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Classification Report: |
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``` |
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{class_report} |
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``` |
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""") |
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return accuracy, class_report, conf_matrix |
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@app.cell |
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def _(X_test, pl, y_pred, y_test): |
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import seaborn as sns |
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import matplotlib.pyplot as plt |
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df = pl.DataFrame({ |
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"sepal length (cm)": X_test[:, 0], |
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"sepal width (cm)": X_test[:, 1], |
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"Predicted": y_pred, |
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"Actual": y_test |
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}) |
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return df, plt, sns |
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@app.cell |
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def _(df, mo, plt, sns): |
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plt.figure(figsize=(10, 6)) |
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sns.scatterplot(data=df, x='sepal length (cm)', y='sepal width (cm)', hue='Predicted', style='Actual', palette='Set1', markers=['o', 's', 'D']) |
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plt.title('Iris Dataset: Sepal Length vs Sepal Width') |
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plt.xlabel('Sepal Length (cm)') |
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plt.ylabel('Sepal Width (cm)') |
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plt.legend(title='Class') |
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mo.vstack( |
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[ |
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mo.md("## Iris Dataset"), |
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plt.gcf() |
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] |
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) |
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return |
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@app.cell |
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def _(conf_matrix, iris, mo, plt, sns): |
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plt.figure(figsize=(8, 6)) |
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sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=iris.target_names, yticklabels=iris.target_names) |
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plt.xlabel('Predicted') |
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plt.ylabel('Actual') |
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mo.vstack([ |
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mo.md("## Confusion Matrix"), |
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plt.gcf() |
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]) |
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return |
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@app.cell(hide_code=True) |
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def _(iris, pl): |
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iris_df = pl.DataFrame(data=iris.data, schema=iris.feature_names) |
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iris_df = iris_df.with_columns(pl.Series("species", iris.target)) |
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return (iris_df,) |
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@app.cell |
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def _(iris_df, mo, plt, sns): |
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sns.pairplot(iris_df.to_pandas(), hue='species', palette='Set1', markers=["o", "s", "D"]) |
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mo.vstack([ |
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mo.md("## Pair Plot"), |
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plt.gcf() |
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]) |
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return |
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@app.cell |
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def _(classifier, iris, mo, plt): |
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from sklearn.tree import plot_tree |
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plt.figure(figsize=(12, 8)) |
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plot_tree(classifier, filled=True, feature_names=iris.feature_names, class_names=iris.target_names) |
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mo.vstack([ |
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mo.md("## Classifier Decision Tree Visualization"), |
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plt.gcf() |
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]) |
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return (plot_tree,) |
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@app.cell(hide_code=True) |
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def _(): |
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tips = { |
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"Saving": ( |
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""" |
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**Saving** |
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- _Name_ your app using the box at the top of the screen, or |
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with `Ctrl/Cmd+s`. You can also create a named app at the |
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command line, e.g., `marimo edit app_name.py`. |
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- _Save_ by clicking the save icon on the bottom right, or by |
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inputting `Ctrl/Cmd+s`. By default marimo is configured |
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to autosave. |
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""" |
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), |
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"Running": ( |
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""" |
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1. _Run a cell_ by clicking the play ( ▷ ) button on the top |
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right of a cell, or by inputting `Ctrl/Cmd+Enter`. |
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2. _Run a stale cell_ by clicking the yellow run button on the |
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right of the cell, or by inputting `Ctrl/Cmd+Enter`. A cell is |
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stale when its code has been modified but not run. |
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3. _Run all stale cells_ by clicking the play ( ▷ ) button on |
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the bottom right of the screen, or input `Ctrl/Cmd+Shift+r`. |
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""" |
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), |
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"Console Output": ( |
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""" |
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Console output (e.g., `print()` statements) is shown below a |
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cell. |
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""" |
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), |
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"Creating, Moving, and Deleting Cells": ( |
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""" |
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1. _Create_ a new cell above or below a given one by clicking |
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the plus button to the left of the cell, which appears on |
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mouse hover. |
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2. _Move_ a cell up or down by dragging on the handle to the |
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right of the cell, which appears on mouse hover. |
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3. _Delete_ a cell by clicking the trash bin icon. Bring it |
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back by clicking the undo button on the bottom right of the |
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screen, or with `Ctrl/Cmd+Shift+z`. |
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""" |
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), |
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"Disabling Automatic Execution": ( |
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""" |
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Via the notebook settings (gear icon) or footer panel, you |
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can disable automatic execution. This is helpful when |
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working with expensive notebooks or notebooks that have |
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side-effects like database transactions. |
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""" |
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), |
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"Disabling Cells": ( |
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""" |
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You can disable a cell via the cell context menu. |
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marimo will never run a disabled cell or any cells that depend on it. |
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This can help prevent accidental execution of expensive computations |
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when editing a notebook. |
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""" |
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), |
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"Code Folding": ( |
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""" |
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You can collapse or fold the code in a cell by clicking the arrow |
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icons in the line number column to the left, or by using keyboard |
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shortcuts. |
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Use the command palette (`Ctrl/Cmd+k`) or a keyboard shortcut to |
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quickly fold or unfold all cells. |
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""" |
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), |
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"Code Formatting": ( |
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""" |
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If you have [ruff](https://github.com/astral-sh/ruff) installed, |
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you can format a cell with the keyboard shortcut `Ctrl/Cmd+b`. |
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""" |
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), |
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"Command Palette": ( |
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""" |
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Use `Ctrl/Cmd+k` to open the command palette. |
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""" |
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), |
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"Keyboard Shortcuts": ( |
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""" |
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Open the notebook menu (top-right) or input `Ctrl/Cmd+Shift+h` to |
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view a list of all keyboard shortcuts. |
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""" |
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), |
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"Configuration": ( |
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""" |
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Configure the editor by clicking the gears icon near the top-right |
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of the screen. |
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""" |
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), |
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} |
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return (tips,) |
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if __name__ == "__main__": |
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app.run() |
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