added core project structure

Author: pxxthik

.gitignore

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+__pycache__/
+
+.taipy/
+
+.vscode/
+.idea/
+
+user_data/
+
+*.toml

algos.py

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+import numpy as np
+
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.svm import SVC
+from sklearn.linear_model import LogisticRegression
+from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import StandardScaler
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.naive_bayes import GaussianNB
+
+import plotly.express as px
+
+
+def fit(X, y, model_name):
+
+    if model_name == "RandomForestClassifier":
+        model = RandomForestClassifier()
+    elif model_name == "SVC":
+        model = SVC()
+    elif model_name == "KNeighborsClassifier":
+        model = KNeighborsClassifier()
+    elif model_name == "LogisticRegression":
+        model = LogisticRegression()
+    elif model_name == "AdaBoostClassifier":
+        model = AdaBoostClassifier()
+    elif model_name == "GradientBoostingClassifier":
+        model = GradientBoostingClassifier()
+    elif model_name == "GaussianNB":
+        model = GaussianNB()
+    elif model_name == "DecisionTreeClassifier":
+        model = DecisionTreeClassifier()
+
+    return make_pipeline(
+        StandardScaler(),
+        model,
+    ).fit(X, y)
+
+
+def plot(X, y, model):
+    x1, x2 = X[:, 0], X[:, 1]
+
+    # Create a mesh grid
+    x1_range = np.arange(x1.min()-1, x1.max()+1, 0.01)
+    x2_range = np.arange(x2.min()-1, x2.max()+1, 0.01)
+    xx1, xx2 = np.meshgrid(x1_range, x2_range)
+
+    # Predict on the mesh grid
+    y_pred_mesh = model.predict(np.c_[xx1.ravel(), xx2.ravel()]).reshape(xx1.shape)
+
+    # Plot the decision boundaries
+    fig = px.scatter(x=x1, y=x2, color=y)
+    fig.update_traces(marker={"size": 15})
+    # Add decision boundaries
+    fig.add_contour(x=x1_range, y=x2_range, z=y_pred_mesh, showscale=False, opacity=0.2)
+
+    return fig

config.py

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+from algos import fit, plot
+from taipy import Config
+
+
+def configure():
+    X = Config.configure_data_node("X")
+    y = Config.configure_data_node("y")
+    model_name = Config.configure_data_node("model_name", default_data="MLPClassifier")
+
+    model = Config.configure_data_node("model")
+    fit_task = Config.configure_task(
+        id="fit", function=fit, input=[X, y, model_name], output=model, skippable=True
+    )
+
+    fig = Config.configure_data_node("fig")
+    plot_task = Config.configure_task(
+        id="plot", function=plot, input=[X, y, model], output=fig, skippable=True
+    )
+
+    scenario = Config.configure_scenario(id="scenario", task_configs=[fit_task, plot_task])
+    Config.export("scenario.toml")
+    return scenario

interface.py

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+from taipy.gui import Markdown
+
+selected_scenario = None
+figure = None
+    
+def on_change(state, var_name):
+    if var_name == "selected_scenario":
+        state.figure = state.selected_scenario.fig.read()
+
+root_page = """
+<|container|
+
+# Decision region plots from Sklearn models 
+*Dataset used: make_moon from sklearn.datasets*
+
+<br/>
+
+### Select a model:
+
+<layout_scenario|layout|columns=1 2|
+
+<|{selected_scenario}|scenario_selector|show_add_button=False|>
+
+<scenario|part|render={selected_scenario}|
+
+<|chart|figure={figure}|>
+
+|scenario>
+
+|layout_scenario>
+
+|>
+"""
+
+interface = Markdown(root_page)

main.py

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+import taipy as tp
+
+from time import time
+from config import configure
+from interface import interface
+from taipy import Core, Gui
+from sklearn.datasets import make_moons
+
+if __name__ == "__main__":
+    core = Core()
+    my_scenario = configure()
+    core.run()
+
+    start = time()
+
+    dataset = make_moons(noise=0.3, random_state=42)
+    for model_name in ["RandomForestClassifier", "SVC", 
+                       "KNeighborsClassifier", "LogisticRegression",
+                       "AdaBoostClassifier", "GradientBoostingClassifier",
+                       "DecisionTreeClassifier", "GaussianNB"]:
+        scenario = tp.create_scenario(my_scenario, name=model_name)
+
+        scenario.X.write(dataset[0])
+        scenario.y.write(dataset[1])
+        scenario.model_name.write(model_name)
+
+        tp.submit(scenario)
+    
+    print(f"Total time {time()-start}")
+
+    # Instantiate, configure and run the GUI
+    gui = Gui(pages={"/": interface})
+
+    gui.run(dark_mode=False, port=3559, title="Taipy Scikit Demo App")