pyodide web: add demo scatter

pyodide_web_demo/examples/examples.json

@@ -35,6 +35,10 @@
       {
         "label": "Fiatlight: Word Count",
         "filename": "fl_word_count.py"
+      },
+      {
+        "label": "Fiatlight: Draw Scatter",
+        "filename": "scatter_fiatlight.py"
       }
     ]
   }

pyodide_web_demo/examples/scatter_fiatlight.py

@@ -0,0 +1,67 @@
+import matplotlib
+matplotlib.use('Agg')
+
+from matplotlib.figure import Figure
+from matplotlib import pyplot as plt
+from sklearn.linear_model import LogisticRegression
+from sklearn.inspection import DecisionBoundaryDisplay
+from sklearn.tree import DecisionTreeClassifier
+import pandas as pd
+import numpy as np
+from enum import Enum
+import fiatlight as fl
+
+
+from scatter_widget_bundle import ScatterData
+
+class DecisionStrategy(Enum):
+    logistic_regression = LogisticRegression
+    decision_tree = DecisionTreeClassifier
+
+
+def plot_boundary(df: pd.DataFrame, strategy: DecisionStrategy, eps: float=1.0) -> Figure | None:
+    if len(df) and (df['color'].nunique() > 1):
+        X = df[['x', 'y']].values
+        y = df['color']
+        fig, ax = plt.subplots()
+        if strategy == DecisionStrategy.logistic_regression:
+            classifier = LogisticRegression().fit(X, y)
+        else:
+            classifier = DecisionTreeClassifier().fit(X, y)
+        disp = DecisionBoundaryDisplay.from_estimator(
+            classifier, X,
+            response_method="predict_proba" if len(np.unique(df['color'])) == 2 else "predict",
+            xlabel="x", ylabel="y",
+            #alpha=0.5,
+            eps=eps,
+            ax=ax
+        )
+        disp.ax_.scatter(X[:, 0], X[:, 1], c=y, edgecolor="k")
+        ax.set_title(f"{classifier.__class__.__name__}")
+        return fig
+    else:
+        return None
+
+
+
+def scatter_source(scatter_data: ScatterData) -> ScatterData:
+    return scatter_data
+
+@fl.with_fiat_attributes(eps__range=(0.1, 10.0))
+def scatter_to_figure(
+        scatter_data: ScatterData,
+        strategy: DecisionStrategy = DecisionStrategy.logistic_regression,
+        eps: float=1.0) -> Figure:
+    return plot_boundary(scatter_data.data_as_pandas(), strategy, eps)
+
+
+def scatter_to_df(scatter_data: ScatterData) -> pd.DataFrame:
+    return scatter_data.data_as_pandas()
+
+graph = fl.FunctionsGraph()
+graph.add_function(scatter_source)
+graph.add_function(scatter_to_figure)
+graph.add_function(scatter_to_df)
+graph.add_link(scatter_source, scatter_to_df)
+graph.add_link(scatter_source, scatter_to_figure)
+fl.run(graph)

pyodide_web_demo/js/pyodide_loader.js

@@ -56,8 +56,11 @@ async function loadPyodideAndPackages() {
             // 'ipython',
             // 'sqlite3',
 
-            "pyodide_dist/fiatlight-0.1.0-py3-none-any.whl"
+            "pyodide_dist/fiatlight-0.1.0-py3-none-any.whl",
 
+            "scikit-learn",
+            "scipy",
+            "pyodide_dist/scatter_widget_bundle-0.1.0-py3-none-any.whl",
         ];
 
         const totalSteps = packages.length;

pyodide_web_demo/justfile

@@ -42,3 +42,5 @@ build_dist:
 srv:
     python3 -m http.server 8005
 
+rsync_tq:
+    rsync -avz --delete . [email protected]:HTML/probabl/