Commit
Β·
8fa82fa
1
Parent(s):
a5f640f
Showcase Decision Tree Classifier using Iris dataset.
Browse files- app.py +116 -313
- pyproject.toml +5 -0
- uv.lock +383 -1
app.py
CHANGED
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@@ -7,361 +7,164 @@ 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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This means that unlike traditional notebooks, marimo notebooks **run
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automatically** when you modify them or
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interact with UI elements, like this slider: {slider}.
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{"##" + "π" * slider.value}
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"""
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return
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@app.cell(hide_code=True)
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def _(
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marimo lets you disable automatic execution: just go into the
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notebook settings and set
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"Runtime > On Cell Change" to "lazy".
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When the runtime is lazy, after running a cell, marimo marks its
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descendants as stale instead of automatically running them. The
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lazy runtime puts you in control over when cells are run, while
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still giving guarantees about the notebook state.
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"""
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)
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}
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)
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return
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@app.cell(hide_code=True)
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def _(mo):
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mo.md(
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"""
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Tip: This is a tutorial notebook. You can create your own notebooks
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by entering `marimo edit` at the command line.
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"""
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).callout()
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return
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@app.cell(hide_code=True)
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def _(
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A marimo notebook is made up of small blocks of Python code called
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cells.
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marimo reads your cells and models the dependencies among them: whenever
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a cell that defines a global variable is run, marimo
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**automatically runs** all cells that reference that variable.
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Reactivity keeps your program state and outputs in sync with your code,
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making for a dynamic programming environment that prevents bugs before they
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happen.
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"""
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)
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return
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@app.cell(hide_code=True)
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def _(changed, mo):
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(
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mo.md(
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f"""
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**β¨ Nice!** The value of `changed` is now {changed}.
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When you updated the value of the variable `changed`, marimo
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**reacted** by running this cell automatically, because this cell
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references the global variable `changed`.
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Reactivity ensures that your notebook state is always
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consistent, which is crucial for doing good science; it's also what
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enables marimo notebooks to double as tools and apps.
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"""
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)
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if changed
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else mo.md(
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"""
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**π See it in action.** In the next cell, change the value of the
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variable `changed` to `True`, then click the run button.
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"""
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)
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)
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return
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@app.cell
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def _():
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return (changed,)
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"""
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The order of cells on the page has no bearing on
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the order in which cells are executed: marimo knows that a cell
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reading a variable must run after the cell that defines it. This
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frees you to organize your code in the way that makes the most
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sense for you.
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"""
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)
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}
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return
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@app.cell
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def _(
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**Global names must be unique.** To enable reactivity, marimo imposes a
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constraint on how names appear in cells: no two cells may define the same
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variable.
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"""
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)
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return
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@app.cell(hide_code=True)
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def _(mo):
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mo.accordion(
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{
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"Tip: encapsulation": (
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"""
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By encapsulating logic in functions, classes, or Python modules,
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you can minimize the number of global variables in your notebook.
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"""
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)
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}
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return
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@app.cell(hide_code=True)
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def _(mo):
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mo.accordion(
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{
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"Tip: private variables": (
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"""
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Variables prefixed with an underscore are "private" to a cell, so
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they can be defined by multiple cells.
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"""
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)
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return
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@app.cell(hide_code=True)
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def _(mo):
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mo.md(
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"""
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## 2. UI elements
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Cells can output interactive UI elements. Interacting with a UI
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element **automatically triggers notebook execution**: when
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you interact with a UI element, its value is sent back to Python, and
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every cell that references that element is re-run.
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marimo provides a library of UI elements to choose from under
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`marimo.ui`.
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"""
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)
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return
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@app.cell
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def _(
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icon = mo.ui.dropdown(["π", "π", "β¨"], value="π")
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return (icon,)
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@app.cell
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def _(
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""
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marimo cells parse Python (and only Python), and marimo notebooks are
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stored as pure Python files β outputs are _not_ included. There's no
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magical syntax.
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The Python files generated by marimo are:
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- easily versioned with git, yielding minimal diffs
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- legible for both humans and machines
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- formattable using your tool of choice,
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- usable as Python scripts, with UI elements taking their default
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values, and
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- importable by other modules (more on that in the future).
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"""
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)
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return
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@app.cell
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def _(mo):
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return
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@app.cell
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def _(mo):
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```
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in a terminal to start the marimo notebook server. From here
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you can create a new notebook or edit existing ones.
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**Running as apps.** Use
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```
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marimo run notebook.py
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```
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to start a webserver that serves your notebook as an app in read-only mode,
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with code cells hidden.
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**Convert a Jupyter notebook.** Convert a Jupyter notebook to a marimo
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notebook using `marimo convert`:
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```
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marimo convert your_notebook.ipynb > your_app.py
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```
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**Tutorials.** marimo comes packaged with tutorials:
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- `dataflow`: more on marimo's automatic execution
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- `ui`: how to use UI elements
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- `markdown`: how to write markdown, with interpolated values and
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LaTeX
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- `plots`: how plotting works in marimo
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- `sql`: how to use SQL
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- `layout`: layout elements in marimo
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- `fileformat`: how marimo's file format works
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- `markdown-format`: for using `.md` files in marimo
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- `for-jupyter-users`: if you are coming from Jupyter
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Start a tutorial with `marimo tutorial`; for example,
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```
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marimo tutorial dataflow
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```
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In addition to tutorials, we have examples in our
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[our GitHub repo](https://www.github.com/marimo-team/marimo/tree/main/examples).
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"""
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)
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return
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@app.cell(hide_code=True)
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def _(
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Here are some tips to help you get started with the marimo editor.
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"""
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)
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return
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@app.cell
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def _(mo,
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return
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@app.cell
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def _(mo):
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the right conditions, clumps together to form a small sphere
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called a "marimo moss ball". Made of just strands of algae, these
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beloved assemblages are greater than the sum of their parts.
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"""
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)
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return
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@app.cell(hide_code=True)
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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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@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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# Split the dataset into training and testing sets
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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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| 66 |
+
mo,
|
| 67 |
+
y_pred,
|
| 68 |
+
y_test,
|
| 69 |
+
):
|
| 70 |
+
# Calculate accuracy
|
| 71 |
+
accuracy = accuracy_score(y_test, y_pred)
|
| 72 |
|
| 73 |
+
# Confusion matrix
|
| 74 |
+
conf_matrix = confusion_matrix(y_test, y_pred)
|
| 75 |
|
| 76 |
+
# Classification report
|
| 77 |
+
class_report = classification_report(y_test, y_pred)
|
|
|
|
|
|
|
| 78 |
|
| 79 |
+
mo.md(f"""
|
| 80 |
+
Accuracy: {accuracy}
|
| 81 |
|
| 82 |
+
Confusion Matrix:
|
| 83 |
+
```
|
| 84 |
+
{conf_matrix}
|
| 85 |
+
```
|
| 86 |
|
| 87 |
|
| 88 |
+
Classification Report:
|
| 89 |
+
```
|
| 90 |
+
{class_report}
|
| 91 |
+
```
|
| 92 |
+
""")
|
| 93 |
+
return accuracy, class_report, conf_matrix
|
| 94 |
|
| 95 |
|
| 96 |
@app.cell
|
| 97 |
+
def _(X_test, pl, y_pred, y_test):
|
| 98 |
+
import seaborn as sns
|
| 99 |
+
import matplotlib.pyplot as plt
|
| 100 |
|
| 101 |
+
df = pl.DataFrame({
|
| 102 |
+
"sepal length (cm)": X_test[:, 0],
|
| 103 |
+
"sepal width (cm)": X_test[:, 1],
|
| 104 |
+
"Predicted": y_pred,
|
| 105 |
+
"Actual": y_test
|
| 106 |
+
})
|
| 107 |
+
return df, plt, sns
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 108 |
|
| 109 |
|
| 110 |
+
@app.cell
|
| 111 |
+
def _(df, mo, plt, sns):
|
| 112 |
+
plt.figure(figsize=(10, 6))
|
| 113 |
+
sns.scatterplot(data=df, x='sepal length (cm)', y='sepal width (cm)', hue='Predicted', style='Actual', palette='Set1', markers=['o', 's', 'D'])
|
| 114 |
+
plt.title('Iris Dataset: Sepal Length vs Sepal Width')
|
| 115 |
+
plt.xlabel('Sepal Length (cm)')
|
| 116 |
+
plt.ylabel('Sepal Width (cm)')
|
| 117 |
+
plt.legend(title='Class')
|
| 118 |
+
mo.vstack(
|
| 119 |
+
[
|
| 120 |
+
mo.md("## Iris Dataset"),
|
| 121 |
+
plt.gcf()
|
| 122 |
+
]
|
| 123 |
)
|
| 124 |
return
|
| 125 |
|
| 126 |
|
| 127 |
+
@app.cell
|
| 128 |
+
def _(conf_matrix, iris, mo, plt, sns):
|
| 129 |
+
plt.figure(figsize=(8, 6))
|
| 130 |
+
sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=iris.target_names, yticklabels=iris.target_names)
|
| 131 |
+
plt.xlabel('Predicted')
|
| 132 |
+
plt.ylabel('Actual')
|
| 133 |
+
mo.vstack([
|
| 134 |
+
mo.md("## Confusion Matrix"),
|
| 135 |
+
plt.gcf()
|
| 136 |
+
])
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 137 |
return
|
| 138 |
|
| 139 |
|
| 140 |
@app.cell(hide_code=True)
|
| 141 |
+
def _(iris, pl):
|
| 142 |
+
iris_df = pl.DataFrame(data=iris.data, schema=iris.feature_names)
|
| 143 |
+
iris_df = iris_df.with_columns(pl.Series("species", iris.target))
|
| 144 |
+
return (iris_df,)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 145 |
|
| 146 |
|
| 147 |
@app.cell
|
| 148 |
+
def _(iris_df, mo, plt, sns):
|
| 149 |
+
sns.pairplot(iris_df.to_pandas(), hue='species', palette='Set1', markers=["o", "s", "D"])
|
| 150 |
+
mo.vstack([
|
| 151 |
+
mo.md("## Pair Plot"),
|
| 152 |
+
plt.gcf()
|
| 153 |
+
])
|
| 154 |
return
|
| 155 |
|
| 156 |
|
| 157 |
+
@app.cell
|
| 158 |
+
def _(classifier, iris, mo, plt):
|
| 159 |
+
from sklearn.tree import plot_tree
|
| 160 |
+
|
| 161 |
+
plt.figure(figsize=(12, 8))
|
| 162 |
+
plot_tree(classifier, filled=True, feature_names=iris.feature_names, class_names=iris.target_names)
|
| 163 |
+
mo.vstack([
|
| 164 |
+
mo.md("## Classifier Decision Tree Visualization"),
|
| 165 |
+
plt.gcf()
|
| 166 |
+
])
|
| 167 |
+
return (plot_tree,)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 168 |
|
| 169 |
|
| 170 |
@app.cell(hide_code=True)
|
pyproject.toml
CHANGED
|
@@ -6,4 +6,9 @@ readme = "README.md"
|
|
| 6 |
requires-python = ">=3.13"
|
| 7 |
dependencies = [
|
| 8 |
"marimo>=0.10.16",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 9 |
]
|
|
|
|
| 6 |
requires-python = ">=3.13"
|
| 7 |
dependencies = [
|
| 8 |
"marimo>=0.10.16",
|
| 9 |
+
"matplotlib>=3.10.0",
|
| 10 |
+
"polars>=1.20.0",
|
| 11 |
+
"pyarrow>=19.0.0",
|
| 12 |
+
"scikit-learn>=1.6.1",
|
| 13 |
+
"seaborn>=0.13.2",
|
| 14 |
]
|
uv.lock
CHANGED
|
@@ -20,10 +20,22 @@ version = "0.1.0"
|
|
| 20 |
source = { virtual = "." }
|
| 21 |
dependencies = [
|
| 22 |
{ name = "marimo" },
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 23 |
]
|
| 24 |
|
| 25 |
[package.metadata]
|
| 26 |
-
requires-dist = [
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 27 |
|
| 28 |
[[package]]
|
| 29 |
name = "click"
|
|
@@ -46,6 +58,46 @@ wheels = [
|
|
| 46 |
{ url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335 },
|
| 47 |
]
|
| 48 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 49 |
[[package]]
|
| 50 |
name = "docutils"
|
| 51 |
version = "0.21.2"
|
|
@@ -55,6 +107,23 @@ wheels = [
|
|
| 55 |
{ url = "https://files.pythonhosted.org/packages/8f/d7/9322c609343d929e75e7e5e6255e614fcc67572cfd083959cdef3b7aad79/docutils-0.21.2-py3-none-any.whl", hash = "sha256:dafca5b9e384f0e419294eb4d2ff9fa826435bf15f15b7bd45723e8ad76811b2", size = 587408 },
|
| 56 |
]
|
| 57 |
|
|
|
|
|
|
|
|
|
|
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|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 58 |
[[package]]
|
| 59 |
name = "h11"
|
| 60 |
version = "0.14.0"
|
|
@@ -94,6 +163,51 @@ wheels = [
|
|
| 94 |
{ url = "https://files.pythonhosted.org/packages/c0/5a/9cac0c82afec3d09ccd97c8b6502d48f165f9124db81b4bcb90b4af974ee/jedi-0.19.2-py2.py3-none-any.whl", hash = "sha256:a8ef22bde8490f57fe5c7681a3c83cb58874daf72b4784de3cce5b6ef6edb5b9", size = 1572278 },
|
| 95 |
]
|
| 96 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 97 |
[[package]]
|
| 98 |
name = "marimo"
|
| 99 |
version = "0.10.16"
|
|
@@ -131,6 +245,37 @@ wheels = [
|
|
| 131 |
{ url = "https://files.pythonhosted.org/packages/3f/08/83871f3c50fc983b88547c196d11cf8c3340e37c32d2e9d6152abe2c61f7/Markdown-3.7-py3-none-any.whl", hash = "sha256:7eb6df5690b81a1d7942992c97fad2938e956e79df20cbc6186e9c3a77b1c803", size = 106349 },
|
| 132 |
]
|
| 133 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 134 |
[[package]]
|
| 135 |
name = "narwhals"
|
| 136 |
version = "1.23.0"
|
|
@@ -140,6 +285,34 @@ wheels = [
|
|
| 140 |
{ url = "https://files.pythonhosted.org/packages/38/13/23b73a1f300521613f967763ef4ada4737474f9d5b620b7e5ba9e4857ee7/narwhals-1.23.0-py3-none-any.whl", hash = "sha256:8d6e7fa0b13af01784837efc060e2a663e5d888decf31f261ff8fc06a7cefeb4", size = 306212 },
|
| 141 |
]
|
| 142 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 143 |
[[package]]
|
| 144 |
name = "packaging"
|
| 145 |
version = "24.2"
|
|
@@ -149,6 +322,33 @@ wheels = [
|
|
| 149 |
{ url = "https://files.pythonhosted.org/packages/88/ef/eb23f262cca3c0c4eb7ab1933c3b1f03d021f2c48f54763065b6f0e321be/packaging-24.2-py3-none-any.whl", hash = "sha256:09abb1bccd265c01f4a3aa3f7a7db064b36514d2cba19a2f694fe6150451a759", size = 65451 },
|
| 150 |
]
|
| 151 |
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
| 152 |
[[package]]
|
| 153 |
name = "parso"
|
| 154 |
version = "0.8.4"
|
|
@@ -158,6 +358,47 @@ wheels = [
|
|
| 158 |
{ url = "https://files.pythonhosted.org/packages/c6/ac/dac4a63f978e4dcb3c6d3a78c4d8e0192a113d288502a1216950c41b1027/parso-0.8.4-py2.py3-none-any.whl", hash = "sha256:a418670a20291dacd2dddc80c377c5c3791378ee1e8d12bffc35420643d43f18", size = 103650 },
|
| 159 |
]
|
| 160 |
|
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|
| 161 |
[[package]]
|
| 162 |
name = "psutil"
|
| 163 |
version = "6.1.1"
|
|
@@ -173,6 +414,27 @@ wheels = [
|
|
| 173 |
{ url = "https://files.pythonhosted.org/packages/7b/d7/7831438e6c3ebbfa6e01a927127a6cb42ad3ab844247f3c5b96bea25d73d/psutil-6.1.1-cp37-abi3-win_amd64.whl", hash = "sha256:f35cfccb065fff93529d2afb4a2e89e363fe63ca1e4a5da22b603a85833c2649", size = 254444 },
|
| 174 |
]
|
| 175 |
|
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|
| 176 |
[[package]]
|
| 177 |
name = "pycrdt"
|
| 178 |
version = "0.11.1"
|
|
@@ -215,6 +477,36 @@ wheels = [
|
|
| 215 |
{ url = "https://files.pythonhosted.org/packages/09/fb/79a8d27966e90feeeb686395c8b1bff8221727abcbd80d2485841393a955/pymdown_extensions-10.14.1-py3-none-any.whl", hash = "sha256:637951cbfbe9874ba28134fb3ce4b8bcadd6aca89ac4998ec29dcbafd554ae08", size = 264283 },
|
| 216 |
]
|
| 217 |
|
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|
| 218 |
[[package]]
|
| 219 |
name = "pyyaml"
|
| 220 |
version = "6.0.2"
|
|
@@ -257,6 +549,78 @@ wheels = [
|
|
| 257 |
{ url = "https://files.pythonhosted.org/packages/0e/4e/33df635528292bd2d18404e4daabcd74ca8a9853b2e1df85ed3d32d24362/ruff-0.9.2-py3-none-win_arm64.whl", hash = "sha256:a1b63fa24149918f8b37cef2ee6fff81f24f0d74b6f0bdc37bc3e1f2143e41c6", size = 10001738 },
|
| 258 |
]
|
| 259 |
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