public code v1

2026-05-22 10:02:10 +02:00
commit 46a9ecf065
166 changed files with 6982454 additions and 0 deletions
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+import streamlit as st
+import pandas as pd
+
+from pygrex.recommender import GroupRecommender
+from pygrex.utils import AggregationStrategy
+
+st.set_page_config(page_title="Group Recommendation", page_icon="🎯", layout="wide")
+st.title("🎯 Group Recommendation")
+
+#  Session State Checks
+# Ensure data is loaded and a model is trained before proceeding.
+if not st.session_state.get("data_loaded", False):
+    st.warning("⚠️ Please load data on the **📄 Data Preparation** page first.")
+    st.stop()
+if not st.session_state.get("trained_model", False):
+    st.warning("⚠️ Please train a model on the **🧠 Model Training** page first.")
+    st.stop()
+
+#  Retrieve objects from session state
+data_reader = st.session_state.data_reader
+group_handler = st.session_state.group_handler
+model = st.session_state.trained_model
+model_name = st.session_state.model_name
+
+#  Recommendation Setup
+st.header("1. Select a Group and Strategy")
+
+group_filename = st.session_state.group_filename
+
+try:
+    available_groups = group_handler.read_groups(filename=group_filename)
+
+    col1, col2 = st.columns(2)
+    with col1:
+        selected_group_id = st.selectbox(
+            "Choose a group:",
+            options=available_groups,
+            help="These groups were loaded from your group data file.",
+        )
+
+    # Parse and display members of the selected group
+    if selected_group_id:
+        group_members = group_handler.parse_group_members(selected_group_id)
+        st.write("👥 **Group Members:**", ", ".join(map(str, group_members)))
+
+    with col2:
+        # Use the AggregationStrategy Enum to populate the selectbox
+        agg_strategy_enum = st.selectbox(
+            "Choose an aggregation strategy:",
+            options=list(AggregationStrategy),
+            format_func=lambda x: x.name.replace("_", " ").title(),
+            help="Select the method for combining individual member preferences.",
+        )
+
+    # Conditional Input for Most Respected Person
+    mrp_id = None
+    if agg_strategy_enum == AggregationStrategy.MOST_RESPECTED_PERSON:
+        mrp_id = st.selectbox(
+            "Select the Most Respected Person:",
+            options=group_members,  # type: ignore
+            help="This user's preferences will solely determine the group recommendation.",
+        )
+
+except Exception as e:
+    st.error(f"Could not read groups from file '{group_filename}'. Error: {e}")
+    st.stop()
+
+# Top-K Configuration
+st.header("2. Specify Number of Recommendations")
+top_k = st.slider(
+    "Number of items to recommend (Top-K):",
+    min_value=1,
+    max_value=50,
+    value=10,
+    help="Adjust the slider to change the length of the final recommendation list.",
+)
+
+# Generate Recommendations
+st.header("3. Generate and View Recommendations")
+
+if st.button("Generate Group Recommendations", type="primary"):
+    if not selected_group_id:
+        st.warning("Please select a group first.")
+    else:
+        with st.spinner("Generating recommendations..."):
+            try:
+                # 1. Instantiate the GroupRecommender
+                group_recommender = GroupRecommender(data=data_reader)
+
+                # 2. Setup the recommendation process
+                group_recommender.setup_recommendation(
+                    model=model,
+                    members=group_members,  # type: ignore
+                    data=data_reader,
+                    aggregation_strategy=agg_strategy_enum,
+                    most_respected_person=mrp_id,
+                )
+
+                # 3. Get the final recommendation list
+                recommended_items = group_recommender.get_group_recommendations(
+                    top_k=top_k
+                )
+
+                # Store the recommender instance for the explanation page
+                st.session_state.group_recommender = group_recommender
+                st.session_state.recommended_items = recommended_items
+
+                st.success("✅ Recommendations generated successfully!")
+
+            except Exception as e:
+                st.error(f"An error occurred while generating recommendations: {e}")
+
+
+#  Display Results
+if "recommended_items" in st.session_state:
+    st.markdown("")
+    st.subheader(f"Top {top_k} Recommended Items")
+
+    recommender = st.session_state.group_recommender
+    scores = recommender.get_recommendation_scores()
+
+    # Create a DataFrame for nice display
+    rec_data = []
+    for i, item_id in enumerate(st.session_state.recommended_items):  # type: ignore
+        rec_data.append(
+            {
+                "Rank": i + 1,
+                "Item ID": item_id,
+                "Aggregated Score": scores.get(item_id, 0.0),
+            }
+        )
+
+    if not rec_data:
+        st.info("No recommendations were generated for this group.")
+    else:
+        st.dataframe(pd.DataFrame(rec_data), use_container_width=True, hide_index=True)
+
+        #  Show detailed individual predictions
+        with st.expander("🔍 View Individual Predictions"):
+            individual_preds = recommender.get_individual_predictions()
+            if individual_preds:
+                # Convert to a more readable DataFrame
+                df_preds = pd.DataFrame(
+                    individual_preds
+                ).T  # Transpose to have users as rows
+                df_preds.index.name = "User ID"
+                st.write(
+                    "Predicted scores (1-5 scale) for each user on items in the candidate pool:"
+                )
+                st.dataframe(df_preds.head(10))
+            else:
+                st.write("No individual predictions available.")
+
+    st.info(
+        "Navigate to the **💬 Explanation & Evaluation** page to analyze these recommendations."
+    )