Interactive ipynb

dashboard.py

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+import streamlit as st
+from mlos_analyzer.core.storage import storage
+from mlos_analyzer.visualization.plots import plot_whisker_plots
+from mlos_analyzer.visualization.correlation import plot_heatmap, plot_correlation_table_target
+from mlos_analyzer.visualization.failure_metrics import (
+    plot_success_failure_distribution,
+    plot_failure_rate_by_config,
+)
+from mlos_analyzer.visualization.statistical import (
+    run_pairwise_stat_tests,
+    compare_score_distributions,
+)
+from mlos_analyzer.visualization.timeseries import plot_metric_over_time, plot_moving_average
+from mlos_analyzer.visualization.distributions import (
+    plot_metric_distribution,
+    plot_violin_comparison,
+)
+from mlos_analyzer.visualization.performance import (
+    plot_parallel_coordinates,
+    plot_performance_radar,
+)
+
+
+def main():
+    st.set_page_config(page_title="MLOS Analyzer Dashboard", layout="wide")
+    st.title("MLOS Experiment Analysis Dashboard")
+
+    st.sidebar.header("Settings")
+    experiment_ids = list(storage.experiments.keys())
+    selected_experiment = st.sidebar.selectbox("Select Experiment", experiment_ids)
+
+    if selected_experiment:
+        df = storage.experiments[selected_experiment].results_df
+        metrics = [col for col in df.columns if col.startswith("result")]
+
+        tab1, tab2, tab3, tab4, tab5, tab6 = st.tabs(
+            ["Overview", "Performance", "Time Series", "Distributions", "Failures", "Statistics"]
+        )
+
+        with tab1:
+            st.header("Experiment Overview")
+            col1, col2 = st.columns(2)
+            with col1:
+                st.subheader("Dataset Info")
+                st.write(df.describe())
+            with col2:
+                st.subheader("Configuration Distribution")
+                config_counts = df["tunable_config_id"].value_counts()
+                st.bar_chart(config_counts)
+
+        with tab2:
+            st.header("Performance Analysis")
+            selected_metric = st.selectbox("Select Metric", metrics, key="perf_metric")
+
+            col1, col2 = st.columns(2)
+            with col1:
+                fig_whisker = plot_whisker_plots(df, selected_metric)
+                st.plotly_chart(fig_whisker)
+            with col2:
+                fig_heatmap = plot_heatmap(df)
+                st.plotly_chart(fig_heatmap)
+
+            selected_metrics = st.multiselect(
+                "Select Metrics for Advanced Analysis", metrics, default=metrics[:3]
+            )
+            if selected_metrics:
+                col3, col4 = st.columns(2)
+                with col3:
+                    fig = plot_parallel_coordinates(df, selected_metrics)
+                    st.plotly_chart(fig)
+                with col4:
+                    fig = plot_performance_radar(df, selected_metrics)
+                    st.plotly_chart(fig)
+
+        with tab3:
+            st.header("Time Series Analysis")
+            metric = st.selectbox("Select Metric", metrics, key="ts_metric")
+            window = st.slider("Moving Average Window", 2, 20, 5)
+
+            col1, col2 = st.columns(2)
+            with col1:
+                fig = plot_metric_over_time(df, metric)
+                st.plotly_chart(fig)
+            with col2:
+                fig = plot_moving_average(df, metric, window)
+                st.plotly_chart(fig)
+
+        with tab4:
+            st.header("Distribution Analysis")
+            metric = st.selectbox("Select Metric", metrics, key="dist_metric")
+
+            col1, col2 = st.columns(2)
+            with col1:
+                fig = plot_metric_distribution(df, metric)
+                st.plotly_chart(fig)
+            with col2:
+                fig = plot_violin_comparison(df, metric)
+                st.plotly_chart(fig)
+
+        with tab5:
+            st.header("Failure Analysis")
+            col1, col2 = st.columns(2)
+            with col1:
+                fig_dist = plot_success_failure_distribution(df)
+                st.plotly_chart(fig_dist)
+            with col2:
+                fig_rate = plot_failure_rate_by_config(df)
+                st.plotly_chart(fig_rate)
+
+        with tab6:
+            st.header("Statistical Analysis")
+            test_metric = st.selectbox("Select Test Metric", metrics)
+            alpha = st.slider("Significance Level (α)", 0.01, 0.10, 0.05)
+
+            results = run_pairwise_stat_tests(df, test_metric, alpha=alpha)
+            st.dataframe(results)
+
+            st.subheader("Configuration Comparison")
+            config1, config2 = st.columns(2)
+            with config1:
+                cfg1 = st.selectbox("First Configuration", df["tunable_config_id"].unique())
+            with config2:
+                cfg2 = st.selectbox("Second Configuration", df["tunable_config_id"].unique())
+
+            if cfg1 != cfg2:
+                fig_compare = compare_score_distributions(df, test_metric, cfg1, cfg2)
+                st.plotly_chart(fig_compare)
+
+
+if __name__ == "__main__":
+    main()