Update app.py

app.py

@@ -109,102 +109,102 @@ def main():
             selected_columns = st.multiselect("Choose columns", all_columns, default=all_columns)
             insurance_claims_reduced = insurance_claims_reduced[selected_columns].copy()
         
-        with st.expander("Inference Description", expanded=True):
-            insurance_claims_reduced.describe().T
-
-        with st.expander("Head Map", expanded=True):
-            cat_col = insurance_claims_reduced.select_dtypes(include=['object']).columns
-            num_col = insurance_claims_reduced.select_dtypes(exclude=['object']).columns
-
-            # insurance_claims[num_col].hist(bins=15, figsize=(20, 15), layout=(5, 4))
-            # Calculate the correlation matrix
-            corr_matrix = insurance_claims_reduced[num_col].corr()
-            # Create a Matplotlib figure
-            fig, ax = plt.subplots(figsize=(12, 8))
-            # Create a heatmap using seaborn
-            #st.header("Heat Map")
-            sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt='.2f', ax=ax)
-            # Set the title for the heatmap
-            ax.set_title('Correlation Heatmap')
-            # Display the heatmap in Streamlit
-            st.pyplot(fig)
-
-        if st.button("Prediction"):
-            #insurance_claims_reduced = insurance_claims_reduced[selected_columns].copy()
-            
-            s = setup(insurance_claims_reduced, session_id = 123, remove_multicollinearity=p_remove_multicollinearity, multicollinearity_threshold=p_multicollinearity_threshold,
-                    # remove_outliers=p_remove_outliers, outliers_method=p_outliers_method, 
-                    transformation=p_transformation, 
-                    normalize=p_normalize, pca=p_pca, pca_method=p_pca_method)
-            exp_clustering = ClusteringExperiment()
-            # init setup on exp
-            exp_clustering.setup(insurance_claims_reduced, session_id = 123)
-
-            with st.spinner("Analyzing..."):
-                #with col2:
-                #st.markdown("<br><br><br><br>", unsafe_allow_html=True)
-                # train kmeans model
-                cluster_model = create_model(selected_model, num_clusters = selected_clusters)
-
-                cluster_model_2 = assign_model(cluster_model)
-                # Calculate summary statistics for each cluster
-                cluster_summary = cluster_model_2.groupby('Cluster').agg(['count', 'mean', 'median', 'min', 'max', 
-                                                                            'std', 'var', 'sum', ('quantile_25', lambda x: x.quantile(0.25)), 
-                                                                            ('quantile_75', lambda x: x.quantile(0.75)), 'skew'])
+            with st.expander("Inference Description", expanded=True):
+                insurance_claims_reduced.describe().T
+
+            with st.expander("Head Map", expanded=True):
+                cat_col = insurance_claims_reduced.select_dtypes(include=['object']).columns
+                num_col = insurance_claims_reduced.select_dtypes(exclude=['object']).columns
+
+                # insurance_claims[num_col].hist(bins=15, figsize=(20, 15), layout=(5, 4))
+                # Calculate the correlation matrix
+                corr_matrix = insurance_claims_reduced[num_col].corr()
+                # Create a Matplotlib figure
+                fig, ax = plt.subplots(figsize=(12, 8))
+                # Create a heatmap using seaborn
+                #st.header("Heat Map")
+                sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt='.2f', ax=ax)
+                # Set the title for the heatmap
+                ax.set_title('Correlation Heatmap')
+                # Display the heatmap in Streamlit
+                st.pyplot(fig)
+
+            if st.button("Prediction"):
+                #insurance_claims_reduced = insurance_claims_reduced[selected_columns].copy()
                 
-                with st.expander("Cluster Summary", expanded=False):
-                    #st.header("Cluster Summary")
-                    cluster_summary
-
-                with st.expander("Model Assign", expanded=False):
-                    #st.header("Assign Model")
-                    cluster_model_2
-
-                # all_metrics = get_metrics()
-                # all_metrics
-
-                with st.expander("Clustering Metrics", expanded=False):
-                    #st.header("Clustering Metrics")
-                    cluster_results = pull()
-                    cluster_results
-
-                with st.expander("Clustering Plots", expanded=False):
-                    if graph_select:
-                        #st.header("Clustering Plots")
-                        # plot pca cluster plot 
-                        plot_model(cluster_model, plot = 'cluster', display_format = 'streamlit')
-                        
-                        if selected_model != 'ap':
-                            plot_model(cluster_model, plot = 'tsne', display_format = 'streamlit')
-                        
-                        if selected_model not in ('ap', 'meanshift', 'dbscan', 'optics'):
-                            plot_model(cluster_model, plot = 'elbow', display_format = 'streamlit')
-                        
-                        if selected_model not in ('ap', 'meanshift', 'sc', 'hclust', 'dbscan', 'optics'):
-                            plot_model(cluster_model, plot = 'silhouette', display_format = 'streamlit')
-                        
-                        if selected_model not in ('ap', 'sc', 'hclust', 'dbscan', 'optics', 'birch'):
-                            plot_model(cluster_model, plot = 'distance', display_format = 'streamlit')
-                        
-                        if selected_model != 'ap':
-                            plot_model(cluster_model, plot = 'distribution', display_format = 'streamlit')  
-
-                with st.expander("Feature Importance", expanded=False):
-                    # Create a Classification Model to extract feature importance
-                    if graph_select and feat_imp_select:
-                        #st.header("Feature Importance")
-                        from pycaret.classification import setup, create_model, get_config
-                        s = setup(cluster_model_2, target = 'Cluster')
-                        lr = create_model('lr')
-                        
-                        # this is how you can recreate the table
-                        feat_imp = pd.DataFrame({'Feature': get_config('X_train').columns, 'Value' : abs(lr.coef_[0])}).sort_values(by='Value', ascending=False)
-                        # sort by feature importance value and filter top 10
-                        feat_imp = feat_imp.sort_values(by='Value', ascending=False).head(10)
-                        # Display the filtered table in Streamlit
-                        # st.dataframe(feat_imp)
-                        # Display the filtered table as a bar chart in Streamlit
-                        st.bar_chart(feat_imp.set_index('Feature'))
+                s = setup(insurance_claims_reduced, session_id = 123, remove_multicollinearity=p_remove_multicollinearity, multicollinearity_threshold=p_multicollinearity_threshold,
+                        # remove_outliers=p_remove_outliers, outliers_method=p_outliers_method, 
+                        transformation=p_transformation, 
+                        normalize=p_normalize, pca=p_pca, pca_method=p_pca_method)
+                exp_clustering = ClusteringExperiment()
+                # init setup on exp
+                exp_clustering.setup(insurance_claims_reduced, session_id = 123)
+
+                with st.spinner("Analyzing..."):
+                    #with col2:
+                    #st.markdown("<br><br><br><br>", unsafe_allow_html=True)
+                    # train kmeans model
+                    cluster_model = create_model(selected_model, num_clusters = selected_clusters)
+
+                    cluster_model_2 = assign_model(cluster_model)
+                    # Calculate summary statistics for each cluster
+                    cluster_summary = cluster_model_2.groupby('Cluster').agg(['count', 'mean', 'median', 'min', 'max', 
+                                                                                'std', 'var', 'sum', ('quantile_25', lambda x: x.quantile(0.25)), 
+                                                                                ('quantile_75', lambda x: x.quantile(0.75)), 'skew'])
+                    
+                    with st.expander("Cluster Summary", expanded=False):
+                        #st.header("Cluster Summary")
+                        cluster_summary
+
+                    with st.expander("Model Assign", expanded=False):
+                        #st.header("Assign Model")
+                        cluster_model_2
+
+                    # all_metrics = get_metrics()
+                    # all_metrics
+
+                    with st.expander("Clustering Metrics", expanded=False):
+                        #st.header("Clustering Metrics")
+                        cluster_results = pull()
+                        cluster_results
+
+                    with st.expander("Clustering Plots", expanded=False):
+                        if graph_select:
+                            #st.header("Clustering Plots")
+                            # plot pca cluster plot 
+                            plot_model(cluster_model, plot = 'cluster', display_format = 'streamlit')
+                            
+                            if selected_model != 'ap':
+                                plot_model(cluster_model, plot = 'tsne', display_format = 'streamlit')
+                            
+                            if selected_model not in ('ap', 'meanshift', 'dbscan', 'optics'):
+                                plot_model(cluster_model, plot = 'elbow', display_format = 'streamlit')
+                            
+                            if selected_model not in ('ap', 'meanshift', 'sc', 'hclust', 'dbscan', 'optics'):
+                                plot_model(cluster_model, plot = 'silhouette', display_format = 'streamlit')
+                            
+                            if selected_model not in ('ap', 'sc', 'hclust', 'dbscan', 'optics', 'birch'):
+                                plot_model(cluster_model, plot = 'distance', display_format = 'streamlit')
+                            
+                            if selected_model != 'ap':
+                                plot_model(cluster_model, plot = 'distribution', display_format = 'streamlit')  
+
+                    with st.expander("Feature Importance", expanded=False):
+                        # Create a Classification Model to extract feature importance
+                        if graph_select and feat_imp_select:
+                            #st.header("Feature Importance")
+                            from pycaret.classification import setup, create_model, get_config
+                            s = setup(cluster_model_2, target = 'Cluster')
+                            lr = create_model('lr')
+                            
+                            # this is how you can recreate the table
+                            feat_imp = pd.DataFrame({'Feature': get_config('X_train').columns, 'Value' : abs(lr.coef_[0])}).sort_values(by='Value', ascending=False)
+                            # sort by feature importance value and filter top 10
+                            feat_imp = feat_imp.sort_values(by='Value', ascending=False).head(10)
+                            # Display the filtered table in Streamlit
+                            # st.dataframe(feat_imp)
+                            # Display the filtered table as a bar chart in Streamlit
+                            st.bar_chart(feat_imp.set_index('Feature'))
 
     elif page == "Anomaly Detection":
         #with col1: