Create DecisionBoundaries_LearningCurves_Algorithms.py

DecisionBoundaries_LearningCurves_Algorithms.py

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+import streamlit as st
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.datasets import make_classification, make_moons, make_circles, make_blobs
+from sklearn.model_selection import train_test_split, learning_curve
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.naive_bayes import GaussianNB
+from sklearn.linear_model import LogisticRegression
+from sklearn.svm import SVC
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score
+from mlxtend.plotting import plot_decision_regions
+
+# Image
+st.image("https://huggingface.co/spaces/varshitha22/KNN/resolve/main/logo.png")
+st.markdown("<br>", unsafe_allow_html=True)
+
+# Sidebar for dataset selection
+st.sidebar.header("Dataset Options")
+data_type = st.sidebar.selectbox("Select Data Type:", ["Blobs", "Circles", "Moons", "Classification"])
+noise = st.sidebar.slider("Add Noise:", 0.0, 1.0, 0.2, step=0.05)
+
+# Sidebar for model selection
+st.sidebar.header("Model Selection")
+model_name = st.sidebar.radio("Choose a Model:", ["KNN", "Decision Tree", "Naive Bayes", "Logistic Regression", "SVC"])
+
+# Display number of neighbors selector only if KNN is selected
+neighbors = None
+if model_name == "KNN":
+    neighbors = st.sidebar.number_input("Neighbors", min_value=1, max_value=25, value=5, step=1)
+    knn_weights = st.sidebar.radio("KNN Weights:", ["uniform", "distance"])
+
+# KNN Algorithm
+st.sidebar.subheader("KNN Algorithm")
+algorithms_selected = []
+if st.sidebar.checkbox("auto", value=True):
+    algorithms_selected.append("auto")
+if st.sidebar.checkbox("ball_tree"):
+    algorithms_selected.append("ball_tree")
+if st.sidebar.checkbox("kd_tree"):
+    algorithms_selected.append("kd_tree")
+if st.sidebar.checkbox("brute"):
+    algorithms_selected.append("brute")
+
+# KNN Metric
+st.sidebar.subheader("KNN Metric")
+metrics_selected = []
+if st.sidebar.checkbox("euclidean", value=True):
+    metrics_selected.append("euclidean")
+if st.sidebar.checkbox("manhattan"):
+    metrics_selected.append("manhattan")
+if st.sidebar.checkbox("minkowski"):
+    metrics_selected.append("minkowski")
+
+# Generate dataset
+if data_type == "Blobs":
+    X, y = make_blobs(n_samples=5000, centers=2, cluster_std=noise, random_state=42)
+elif data_type == "Circles":
+    X, y = make_circles(n_samples=5000, noise=noise, factor=0.5, random_state=42)
+elif data_type == "Moons":
+    X, y = make_moons(n_samples=5000, noise=noise, random_state=42)
+else:
+    X, y = make_classification(n_samples=5000, n_features=2, n_classes=2, n_informative=2, n_redundant=0, random_state=42)
+
+# Split dataset
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Model selection
+if model_name == "KNN":
+    model = KNeighborsClassifier(n_neighbors=neighbors, weights=knn_weights, algorithm=algorithms_selected[0] if algorithms_selected else 'auto', metric=metrics_selected[0] if metrics_selected else 'minkowski')
+elif model_name == "Decision Tree":
+    model = DecisionTreeClassifier(random_state=42)
+elif model_name == "Naive Bayes":
+    model = GaussianNB()
+elif model_name == "Logistic Regression":
+    model = LogisticRegression(max_iter=200, random_state=42)
+else:
+    model = SVC(probability=True, kernel='linear', random_state=42)
+
+# Fit the model
+model.fit(X_train, y_train)
+
+# Predict and calculate metrics
+y_pred = model.predict(X_test)
+accuracy = accuracy_score(y_test, y_pred)
+precision = precision_score(y_test, y_pred)
+recall = recall_score(y_test, y_pred)
+f1 = f1_score(y_test, y_pred)
+auc = roc_auc_score(y_test, model.predict_proba(X_test)[:, 1]) if hasattr(model, "predict_proba") else "N/A"
+
+# --- Display model performance under the radio button ---
+with st.sidebar:
+    st.subheader(f"{model_name} Model Evaluation Metrics")
+    st.write(f" Accuracy: {accuracy:.2f}")
+    st.write(f" Precision: {precision:.2f}")
+    st.write(f" Recall: {recall:.2f}")
+    st.write(f" F1 Score: {f1:.2f}")
+    st.write(f" AUC Score: {auc:.2f}")
+
+# Plot dataset
+st.subheader("Dataset Visualization")
+fig, ax = plt.subplots()
+sns.scatterplot(x=X[:, 0], y=X[:, 1], hue=y, palette="coolwarm", s=50, edgecolor="k")
+st.pyplot(fig)
+
+# Decision Boundary
+st.subheader("Decision Boundary")
+fig, ax = plt.subplots()
+plot_decision_regions(X_train, y_train, clf=model, legend=2)
+st.pyplot(fig)
+
+# Learning Curve
+st.subheader("Learning Curve")
+train_sizes, train_scores, test_scores = learning_curve(model, X_train, y_train, cv=5, train_sizes=np.linspace(0.1, 1.0, 10))
+train_mean = np.mean(train_scores, axis=1)
+test_mean = np.mean(test_scores, axis=1)
+
+fig, ax = plt.subplots()
+ax.plot(train_sizes, train_mean, label='Train Accuracy', marker='o')
+ax.plot(train_sizes, test_mean, label='Test Accuracy', marker='s')
+ax.set_xlabel("Training Size")
+ax.set_ylabel("Accuracy")
+ax.legend()
+st.pyplot(fig)