init

frontend/pages/inclusion_dependency_view.py

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+import random
+import time
+
+import streamlit as st
+
+from silkmoth.silkmoth_engine import SilkMothEngine
+from silkmoth.utils import jaccard_similarity, contain
+import os
+import json
+from utils import *
+
+
+# Streamlit app
+st.title("SilkMoth Engine Input Interface")
+st.divider()
+st.subheader("Inclusion Dependency Experiment")
+
+
+# Input fields for SilkMothEngine parameters
+# Allow the user to select the number of thresholds (up to 4)
+num_thresholds = st.number_input("Number of Thresholds", min_value=1, max_value=4, value=1, step=1)
+
+# Dynamically create sliders for the selected number of thresholds
+thresholds = []
+for i in range(num_thresholds):
+    threshold = st.slider(f"Threshold {i + 1}", 0.0, 1.0, 0.5, 0.05)
+    thresholds.append(threshold)
+
+
+# sim_thresh = st.slider("Similarity Threshold", 0.0, 1.0, 0.0, 0.05)
+check_filter = st.checkbox("Enable Check Filter", value=False)
+nn_filter = st.checkbox("Enable Nearest Neighbor Filter", value=False)
+
+# Directory containing the JSON files
+data_folder = "../experiments/data/webtables/"
+
+# JSON files to be used
+reference_file = "reference_sets_inclusion_dependency.json"
+source_file = "source_sets_inclusion_dependency.json"
+
+# Full paths to the selected files
+reference_file_path = os.path.join(data_folder, reference_file)
+source_file_path = os.path.join(data_folder, source_file)
+
+# Run the SilkMothEngine with progress animation and loading mask
+if st.button("Run SilkMoth Engine"):
+    if reference_file and source_file:
+        try:
+            # Create a placeholder for the loading animation
+            loading_placeholder = st.empty()
+            loading_placeholder.markdown("<div style='text-align: center; font-size: 20px;'>SilkMothEngine is running...</div>", unsafe_allow_html=True)
+
+            # Open and load reference and source sets from selected files
+            with open(reference_file_path, 'r', encoding='utf-8') as ref_file:
+                reference_sets = json.load(ref_file)
+            with open(source_file_path, 'r', encoding='utf-8') as src_file:
+                source_sets = json.load(src_file)
+
+
+
+            st.write(f"Create Inverted Index ...")
+            in_index_time_start = time.time()
+            # Initialize and run the SilkMothEngine
+            silk_moth_engine = SilkMothEngine(
+                related_thresh=0,
+                source_sets=source_sets,
+                sim_metric=contain,
+                sim_func=jaccard_similarity,
+                sim_thresh=0,
+                is_check_filter=False,
+                is_nn_filter=False,
+            )
+            in_index_time_end = time.time()
+            in_index_elapsed_time = in_index_time_end - in_index_time_start
+            st.write(f"Inverted Index created in {in_index_elapsed_time:.2f} seconds.")
+
+
+            elapsed_times_final = []
+            labels = ["NO FILTER"]
+            if check_filter:
+                labels.append("CHECK FILTER")
+
+            if nn_filter:
+                labels.append("NN FILTER")
+
+
+            for label in labels:
+                elapsed_times = []
+                for idx, related_thresh in enumerate(thresholds):
+
+                    if label == "CHECK FILTER":
+                        silk_moth_engine.is_check_filter = True
+                        silk_moth_engine.is_nn_filter = False
+                    elif label == "NN FILTER":
+                        silk_moth_engine.is_check_filter = False
+                        silk_moth_engine.is_nn_filter = True
+
+
+                    st.write(f"Processing Threshold {idx + 1}: {related_thresh} with {label} ...")
+                    silk_moth_engine.set_related_threshold(related_thresh)
+                    # Measure the time taken to search for related sets
+                    time_start = time.time()
+
+
+                    for ref_set in reference_sets:
+                        related_sets = silk_moth_engine.search_sets(ref_set)
+                        del related_sets
+
+                    time_end = time.time()
+
+
+                    elapsed_time = time_end - time_start
+                    elapsed_times.append(elapsed_time)
+
+                elapsed_times_final.append(elapsed_times)
+
+            # Remove the loading animation
+            loading_placeholder.empty()
+
+            # Display results
+            st.success("SilkMoth Engine ran successfully!")
+            fig = plot_elapsed_times(
+                related_thresholds=thresholds,
+                elapsed_times_list=elapsed_times_final,
+                fig_text="Inclusion Dependency (α = 0.0)",
+                legend_labels=labels,
+                file_name="webtable_inclusion_dependency_experiment_demo.png"
+            )
+            st.pyplot(fig)
+
+        except Exception as e:
+            st.error(f"An error occurred: {e}")
+    else:
+        st.warning("Please upload both reference and source set files.")