added computational resources

app.py

@@ -1,6 +1,6 @@
 import streamlit as st
 
-from components import buildings_view, models_view, performance_view
+from components import buildings_view, models_view, performance_view, computation_view
 import utils
 
 st.set_page_config(page_title="Electricity Demand Dashboard", layout="wide")
@@ -9,6 +9,7 @@ PAGES = [
     "Buildings",
     "Models",
     "Performance",
+    "Computational Resources",
 ]
 
 
@@ -27,7 +28,6 @@ models = sorted(data["model"].unique().tolist())
 models_to_plot = set()
 model_groups: dict[str, list[str]] = {}
 
-
 for model in models:
     group, model_name = model.split(".", maxsplit=1)
     if group not in model_groups:
@@ -47,10 +47,22 @@ with st.sidebar:
     view = st.selectbox("View", PAGES, index=0)
 
     st.header("Models to include")
+    left, right = st.columns(2)
+    with left:
+        select_none = st.button("Select None", use_container_width=True)
+        if select_none:
+            for model in models:
+                st.session_state[model] = False
+    with right:
+        select_all = st.button("Select All", use_container_width=True)
+        if select_all:
+            for model in models:
+                st.session_state[model] = True
+
     for model_group, models in model_groups.items():
         st.text(model_group)
         for model_name in models:
-            to_plot = st.checkbox(model_name, value=True)
+            to_plot = st.checkbox(model_name, value=True, key=f"{model_group}.{model_name}")
             if to_plot:
                 models_to_plot.add(f"{model_group}.{model_name}")
 
@@ -64,5 +76,7 @@ elif view == "Models":
     models_view(data)
 elif view == "Performance":
     performance_view(data, models_to_plot)
+elif view == "Computational Resources":
+    computation_view(data, models_to_plot)
 else:
     st.write("Not implemented yet")

components.py

@@ -237,3 +237,58 @@ def performance_view(data: pd.DataFrame, models_to_plot: set[str]):
     st.markdown(f"#### {aggregation.capitalize()} {metric} stats per building")
     styled_table = metrics_table.style.pipe(custom_table)
     st.dataframe(styled_table, use_container_width=True)
+
+
+def computation_view(data, models_to_plot: set[str]):
+    data_to_plot = data[data["model"].isin(models_to_plot)].sort_values(
+        by="model", ascending=True
+    )
+
+    st.markdown("#### Computational Resources")
+    fig = px.parallel_coordinates(
+        data_to_plot.groupby("model").mean(numeric_only=True).reset_index(),
+        dimensions=[
+            "model",
+            "resource_usage.CPU",
+            "resource_usage.memory",
+            "MAE.mean",
+            "RMSE.mean",
+            "MBE.mean",
+            "rMAE.mean",
+        ],
+        color="rMAE.mean",
+        color_continuous_scale=px.colors.diverging.Portland,
+    )
+    st.plotly_chart(fig, use_container_width=True)
+
+    st.divider()
+
+    left, center, right = st.columns(3, gap="small")
+    with left:
+        metric = st.selectbox("Metric", ["MAE", "RMSE", "MBE", "rMAE"], index=0)
+    with center:
+        aggregation_per_building = st.selectbox(
+            "Aggregation per building", ["min", "mean", "median", "max", "std"], index=1
+        )
+    with right:
+        aggregation_per_model = st.selectbox(
+            "Aggregation per model", ["min", "mean", "median", "max", "std"], index=1
+        )
+
+    st.markdown(
+        f"#### {aggregation_per_model.capitalize()} {aggregation_per_building.capitalize()} {metric} vs CPU usage"
+    )
+    aggregated_data = (
+        data_to_plot.groupby("model")
+        .agg(aggregation_per_building, numeric_only=True)
+        .reset_index()
+    )
+    fig = px.scatter(
+        aggregated_data,
+        x="resource_usage.CPU",
+        y=f"{metric}.{aggregation_per_model}",
+        color="model",
+        log_x=True,
+    )
+    fig.update_layout(height=600)
+    st.plotly_chart(fig, use_container_width=True)