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1 Parent(s): a5f640f

Showcase Decision Tree Classifier using Iris dataset.

Browse files
Files changed (3) hide show
  1. app.py +116 -313
  2. pyproject.toml +5 -0
  3. uv.lock +383 -1
app.py CHANGED
@@ -7,361 +7,164 @@ app = marimo.App()
7
  @app.cell
8
  def _():
9
  import marimo as mo
10
-
11
- mo.md("# Welcome to marimo! πŸŒŠπŸƒ")
12
- return (mo,)
13
-
14
-
15
- @app.cell
16
- def _(mo):
17
- slider = mo.ui.slider(1, 22)
18
- return (slider,)
19
-
20
-
21
- @app.cell
22
- def _(mo, slider):
23
- mo.md(
24
- f"""
25
- marimo is a **reactive** Python notebook.
26
-
27
- This means that unlike traditional notebooks, marimo notebooks **run
28
- automatically** when you modify them or
29
- interact with UI elements, like this slider: {slider}.
30
-
31
- {"##" + "πŸƒ" * slider.value}
32
- """
33
  )
34
- return
35
 
36
 
37
  @app.cell(hide_code=True)
38
- def _(mo):
39
- mo.accordion(
40
- {
41
- "Tip: disabling automatic execution": mo.md(
42
- rf"""
43
- marimo lets you disable automatic execution: just go into the
44
- notebook settings and set
45
-
46
- "Runtime > On Cell Change" to "lazy".
47
-
48
- When the runtime is lazy, after running a cell, marimo marks its
49
- descendants as stale instead of automatically running them. The
50
- lazy runtime puts you in control over when cells are run, while
51
- still giving guarantees about the notebook state.
52
- """
53
- )
54
- }
55
- )
56
- return
57
-
58
-
59
- @app.cell(hide_code=True)
60
- def _(mo):
61
- mo.md(
62
- """
63
- Tip: This is a tutorial notebook. You can create your own notebooks
64
- by entering `marimo edit` at the command line.
65
- """
66
- ).callout()
67
- return
68
 
69
 
70
  @app.cell(hide_code=True)
71
- def _(mo):
72
- mo.md(
73
- """
74
- ## 1. Reactive execution
75
-
76
- A marimo notebook is made up of small blocks of Python code called
77
- cells.
78
-
79
- marimo reads your cells and models the dependencies among them: whenever
80
- a cell that defines a global variable is run, marimo
81
- **automatically runs** all cells that reference that variable.
82
-
83
- Reactivity keeps your program state and outputs in sync with your code,
84
- making for a dynamic programming environment that prevents bugs before they
85
- happen.
86
- """
87
- )
88
- return
89
-
90
-
91
- @app.cell(hide_code=True)
92
- def _(changed, mo):
93
- (
94
- mo.md(
95
- f"""
96
- **✨ Nice!** The value of `changed` is now {changed}.
97
-
98
- When you updated the value of the variable `changed`, marimo
99
- **reacted** by running this cell automatically, because this cell
100
- references the global variable `changed`.
101
-
102
- Reactivity ensures that your notebook state is always
103
- consistent, which is crucial for doing good science; it's also what
104
- enables marimo notebooks to double as tools and apps.
105
- """
106
- )
107
- if changed
108
- else mo.md(
109
- """
110
- **🌊 See it in action.** In the next cell, change the value of the
111
- variable `changed` to `True`, then click the run button.
112
- """
113
- )
114
- )
115
- return
116
 
117
 
118
  @app.cell
119
- def _():
120
- changed = False
121
- return (changed,)
122
-
123
 
124
- @app.cell(hide_code=True)
125
- def _(mo):
126
- mo.accordion(
127
- {
128
- "Tip: execution order": (
129
- """
130
- The order of cells on the page has no bearing on
131
- the order in which cells are executed: marimo knows that a cell
132
- reading a variable must run after the cell that defines it. This
133
- frees you to organize your code in the way that makes the most
134
- sense for you.
135
- """
136
- )
137
- }
138
- )
139
- return
140
 
141
 
142
- @app.cell(hide_code=True)
143
- def _(mo):
144
- mo.md(
145
- """
146
- **Global names must be unique.** To enable reactivity, marimo imposes a
147
- constraint on how names appear in cells: no two cells may define the same
148
- variable.
149
- """
150
- )
151
- return
152
-
153
-
154
- @app.cell(hide_code=True)
155
- def _(mo):
156
- mo.accordion(
157
- {
158
- "Tip: encapsulation": (
159
- """
160
- By encapsulating logic in functions, classes, or Python modules,
161
- you can minimize the number of global variables in your notebook.
162
- """
163
- )
164
- }
165
- )
166
- return
167
-
168
-
169
- @app.cell(hide_code=True)
170
- def _(mo):
171
- mo.accordion(
172
- {
173
- "Tip: private variables": (
174
- """
175
- Variables prefixed with an underscore are "private" to a cell, so
176
- they can be defined by multiple cells.
177
- """
178
- )
179
- }
180
- )
181
- return
182
-
183
-
184
- @app.cell(hide_code=True)
185
- def _(mo):
186
- mo.md(
187
- """
188
- ## 2. UI elements
189
-
190
- Cells can output interactive UI elements. Interacting with a UI
191
- element **automatically triggers notebook execution**: when
192
- you interact with a UI element, its value is sent back to Python, and
193
- every cell that references that element is re-run.
194
-
195
- marimo provides a library of UI elements to choose from under
196
- `marimo.ui`.
197
- """
198
- )
199
- return
200
 
201
 
202
  @app.cell
203
- def _(mo):
204
- mo.md("""**🌊 Some UI elements.** Try interacting with the below elements.""")
205
- return
 
 
 
 
 
 
 
206
 
 
 
207
 
208
- @app.cell
209
- def _(mo):
210
- icon = mo.ui.dropdown(["πŸƒ", "🌊", "✨"], value="πŸƒ")
211
- return (icon,)
212
 
 
 
213
 
214
- @app.cell
215
- def _(icon, mo):
216
- repetitions = mo.ui.slider(1, 16, label=f"number of {icon.value}: ")
217
- return (repetitions,)
218
 
219
 
220
- @app.cell
221
- def _(icon, repetitions):
222
- icon, repetitions
223
- return
 
 
224
 
225
 
226
  @app.cell
227
- def _(icon, mo, repetitions):
228
- mo.md("# " + icon.value * repetitions.value)
229
- return
230
 
231
-
232
- @app.cell(hide_code=True)
233
- def _(mo):
234
- mo.md(
235
- """
236
- ## 3. marimo is just Python
237
-
238
- marimo cells parse Python (and only Python), and marimo notebooks are
239
- stored as pure Python files β€” outputs are _not_ included. There's no
240
- magical syntax.
241
-
242
- The Python files generated by marimo are:
243
-
244
- - easily versioned with git, yielding minimal diffs
245
- - legible for both humans and machines
246
- - formattable using your tool of choice,
247
- - usable as Python scripts, with UI elements taking their default
248
- values, and
249
- - importable by other modules (more on that in the future).
250
- """
251
- )
252
- return
253
 
254
 
255
- @app.cell(hide_code=True)
256
- def _(mo):
257
- mo.md(
258
- """
259
- ## 4. Running notebooks as apps
260
-
261
- marimo notebooks can double as apps. Click the app window icon in the
262
- bottom-right to see this notebook in "app view."
263
-
264
- Serve a notebook as an app with `marimo run` at the command-line.
265
- Of course, you can use marimo just to level-up your
266
- notebooking, without ever making apps.
267
- """
268
  )
269
  return
270
 
271
 
272
- @app.cell(hide_code=True)
273
- def _(mo):
274
- mo.md(
275
- """
276
- ## 5. The `marimo` command-line tool
277
-
278
- **Creating and editing notebooks.** Use
279
-
280
- ```
281
- marimo edit
282
- ```
283
-
284
- in a terminal to start the marimo notebook server. From here
285
- you can create a new notebook or edit existing ones.
286
-
287
-
288
- **Running as apps.** Use
289
-
290
- ```
291
- marimo run notebook.py
292
- ```
293
-
294
- to start a webserver that serves your notebook as an app in read-only mode,
295
- with code cells hidden.
296
-
297
- **Convert a Jupyter notebook.** Convert a Jupyter notebook to a marimo
298
- notebook using `marimo convert`:
299
-
300
- ```
301
- marimo convert your_notebook.ipynb > your_app.py
302
- ```
303
-
304
- **Tutorials.** marimo comes packaged with tutorials:
305
-
306
- - `dataflow`: more on marimo's automatic execution
307
- - `ui`: how to use UI elements
308
- - `markdown`: how to write markdown, with interpolated values and
309
- LaTeX
310
- - `plots`: how plotting works in marimo
311
- - `sql`: how to use SQL
312
- - `layout`: layout elements in marimo
313
- - `fileformat`: how marimo's file format works
314
- - `markdown-format`: for using `.md` files in marimo
315
- - `for-jupyter-users`: if you are coming from Jupyter
316
-
317
- Start a tutorial with `marimo tutorial`; for example,
318
-
319
- ```
320
- marimo tutorial dataflow
321
- ```
322
-
323
- In addition to tutorials, we have examples in our
324
- [our GitHub repo](https://www.github.com/marimo-team/marimo/tree/main/examples).
325
- """
326
- )
327
  return
328
 
329
 
330
  @app.cell(hide_code=True)
331
- def _(mo):
332
- mo.md(
333
- """
334
- ## 6. The marimo editor
335
-
336
- Here are some tips to help you get started with the marimo editor.
337
- """
338
- )
339
- return
340
 
341
 
342
  @app.cell
343
- def _(mo, tips):
344
- mo.accordion(tips)
 
 
 
 
345
  return
346
 
347
 
348
- @app.cell(hide_code=True)
349
- def _(mo):
350
- mo.md("""## Finally, a fun fact""")
351
- return
352
-
353
-
354
- @app.cell(hide_code=True)
355
- def _(mo):
356
- mo.md(
357
- """
358
- The name "marimo" is a reference to a type of algae that, under
359
- the right conditions, clumps together to form a small sphere
360
- called a "marimo moss ball". Made of just strands of algae, these
361
- beloved assemblages are greater than the sum of their parts.
362
- """
363
- )
364
- return
365
 
366
 
367
  @app.cell(hide_code=True)
 
7
  @app.cell
8
  def _():
9
  import marimo as mo
10
+ import polars as pl
11
+ from sklearn import datasets
12
+ from sklearn.model_selection import train_test_split
13
+ from sklearn.tree import DecisionTreeClassifier
14
+ from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
15
+
16
+ mo.md("# Iris Dataset Showcase")
17
+ return (
18
+ DecisionTreeClassifier,
19
+ accuracy_score,
20
+ classification_report,
21
+ confusion_matrix,
22
+ datasets,
23
+ mo,
24
+ pl,
25
+ train_test_split,
 
 
 
 
 
 
 
26
  )
 
27
 
28
 
29
  @app.cell(hide_code=True)
30
+ def _(datasets):
31
+ iris = datasets.load_iris()
32
+ X = iris.data
33
+ y = iris.target
34
+ return X, iris, y
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
35
 
36
 
37
  @app.cell(hide_code=True)
38
+ def _(X, train_test_split, y):
39
+ # Split the dataset into training and testing sets
40
+ X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
41
+ return X_test, X_train, y_test, y_train
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
42
 
43
 
44
  @app.cell
45
+ def _(DecisionTreeClassifier, X_train, mo, y_train):
46
+ classifier = DecisionTreeClassifier()
 
 
47
 
48
+ classifier.fit(X_train, y_train)
49
+ mo.md(f"""
50
+ ## Decision Tree Classifier
51
+ """)
52
+ return (classifier,)
 
 
 
 
 
 
 
 
 
 
 
53
 
54
 
55
+ @app.cell
56
+ def _(X_test, classifier):
57
+ y_pred = classifier.predict(X_test)
58
+ return (y_pred,)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
59
 
60
 
61
  @app.cell
62
+ def _(
63
+ accuracy_score,
64
+ classification_report,
65
+ confusion_matrix,
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 = [{ name = "marimo", specifier = ">=0.10.16" }]
 
 
 
 
 
 
 
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
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
152
  [[package]]
153
  name = "parso"
154
  version = "0.8.4"
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158
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