🎉 Admin for related charts

apps/cli/__init__.py

@@ -198,6 +198,12 @@ def cli_back() -> None:
                 "anomalist": "apps.anomalist.cli.cli",
             },
         },
+        {
+            "name": "Related Charts",
+            "commands": {
+                "related-charts": "apps.related_charts.cli.cli",
+            },
+        },
     ]
     # Add subgroups (don't modify)
     + subgroups

apps/housekeeper/charts.py

@@ -214,9 +214,7 @@ def _get_main_message_usage(chart, refs):
 def send_extra_messages(chart, refs, **kwargs):
     """Provide more context in the thread"""
     ## 1/ Similar charts
-    similar_messages = (
-        f"🕵️ <{OWID_ENV.wizard_url}similar_charts?chart_search_text={chart['slug']}| → Explore similar charts>"
-    )
+    similar_messages = f"🕵️ <{OWID_ENV.wizard_url}similar_charts?slug={chart['slug']}| → Explore similar charts>"
 
     ## 2/ AI: Chart description, chart edit timeline, suggestion
     log.info("Getting AI summary...")

apps/related_charts/cli.py

@@ -0,0 +1,217 @@
+import datetime as dt
+from typing import Optional
+
+import click
+import pandas as pd
+import structlog
+from rich_click.rich_command import RichCommand
+from sqlalchemy import text
+from tqdm.auto import tqdm
+
+from apps.wizard.app_pages.similar_charts import data
+from etl import config
+from etl.db import get_engine
+
+config.enable_bugsnag()
+log = structlog.get_logger()
+
+
+def load_data(chart_slug: Optional[str]) -> tuple[pd.DataFrame, pd.DataFrame]:
+    """
+    Load chart data and coview sessions DataFrame.
+
+    Returns:
+        charts: DataFrame indexed by slug, containing metadata (like chart_id, views_365d, etc.)
+        coviews_df: DataFrame with MultiIndex (slug1, slug2)
+                    and columns ['coviews', 'pageviews'].
+    """
+    log.info("Loading chart data...")
+    charts = data.get_raw_charts().set_index("slug", drop=False)
+
+    # If chart_slug is provided, verify it's in charts
+    if chart_slug and chart_slug not in charts.index:
+        log.warning("Chart slug not found in data. Exiting.", chart_slug=chart_slug)
+        return pd.DataFrame(), pd.DataFrame()
+
+    log.info("Loading coview sessions...")
+    coviews_df = data.get_coviews_sessions(
+        after_date=str(dt.date.today() - dt.timedelta(days=365)), min_sessions=3
+    ).to_frame(name="coviews")
+
+    # If a single chart slug is given, filter for that slug1 only
+    if chart_slug:
+        coviews_df = coviews_df[coviews_df.index.get_level_values("slug1") == chart_slug]
+
+    # Filter out any coviews rows whose slug1 isn't in our charts
+    coviews_df = coviews_df[coviews_df.index.get_level_values("slug1").isin(charts.index)]
+
+    # Add pageviews of slug2 to coviews dataframe
+    coviews_df["pageviews_1"] = charts["views_365d"].reindex(coviews_df.index.get_level_values("slug1")).values
+    coviews_df["pageviews_2"] = charts["views_365d"].reindex(coviews_df.index.get_level_values("slug2")).values
+
+    return charts, coviews_df
+
+
+def compute_recommendations(
+    charts: pd.DataFrame,
+    coviews_df: pd.DataFrame,
+    chart_slug: Optional[str],
+    top: int,
+    regularization: float,
+    score_method: str,
+) -> pd.DataFrame:
+    """
+    Given charts and coview data, compute a DataFrame of recommended pairs:
+    chosen_chart, related_chart, chartId, relatedChartId, etc.
+
+    The 'score' is computed as:
+        score = coviews - regularization * pageviews
+
+    Args:
+        charts: DataFrame of chart metadata.
+        coviews_df: DataFrame with columns ['coviews', 'pageviews'].
+        chart_slug: Optional single slug to process; otherwise compute for all slugs.
+        top: How many top-related charts to retrieve for each slug.
+        regularization: Factor to penalize high-view charts.
+        score_method: Scoring method to use for recommendations.
+
+    Returns:
+        A DataFrame of recommended chart pairs (chosen_chart, related_chart, score, etc.).
+    """
+    # If we failed to load data (e.g., an invalid slug), return empty
+    if charts.empty or coviews_df.empty:
+        return pd.DataFrame()
+
+    # Compute the score
+    if score_method == "coviews":
+        coviews_df["score"] = coviews_df["coviews"] - regularization * coviews_df["pageviews_2"]
+    elif score_method == "jaccard":
+        coviews_df["score"] = coviews_df["coviews"] / (
+            coviews_df["pageviews_1"] + coviews_df["pageviews_2"] - coviews_df["coviews"]
+        )
+    # If a single chart slug is requested, ensure we only keep those rows
+    if chart_slug:
+        if chart_slug not in coviews_df.index.get_level_values("slug1"):
+            log.info("No coview data for this chart slug.", chart_slug=chart_slug)
+            return pd.DataFrame()
+        coviews_df = coviews_df.loc[[chart_slug]]
+
+    recommended_rows = []
+
+    # Group by 'slug1' so each group has all charts related to that slug1
+    grouped = coviews_df.groupby(level="slug1", sort=False)
+    log.info("Calculating related charts...")
+
+    for slug1, group in tqdm(grouped, desc="Calculating related charts"):
+        top_related = group.sort_values("score", ascending=False).head(top)
+        for related_slug, score in zip(top_related.index.get_level_values("slug2"), top_related["score"]):
+            recommended_rows.append({"chosen_chart": slug1, "related_chart": related_slug, "score": score})
+
+    if not recommended_rows:
+        return pd.DataFrame()
+
+    # Build the recommendations DataFrame
+    recommended_df = pd.DataFrame(recommended_rows)
+    recommended_df["chartId"] = recommended_df["chosen_chart"].map(charts["chart_id"])
+    recommended_df["relatedChartId"] = recommended_df["related_chart"].map(charts["chart_id"])
+    recommended_df["label"] = "good"
+    recommended_df["reviewer"] = "production"
+
+    # Warn if some related_chart slugs can't be mapped to chartIds
+    ix_missing = recommended_df["relatedChartId"].isnull()
+    if ix_missing.any():
+        log.warning("Chart ID not found for some related chart slugs.", n_missing=ix_missing.sum())
+        recommended_df = recommended_df[~ix_missing]
+
+    return recommended_df
+
+
+def write_recommendations(
+    engine, recommended_df: pd.DataFrame, charts: pd.DataFrame, chart_slug: Optional[str]
+) -> None:
+    """
+    Writes the recommended DataFrame to the 'related_charts' table in the database.
+    If 'chart_slug' is specified, only deletes existing rows for that slug before inserting.
+    Otherwise, clears all 'production' rows first.
+    """
+    if recommended_df.empty:
+        log.info("No related charts found. Nothing to write.")
+        return
+
+    with engine.begin() as conn:
+        if chart_slug:
+            log.info("Deleting existing 'production' reviews for this chart.", chart_slug=chart_slug)
+            conn.execute(
+                text("""
+                    DELETE FROM related_charts
+                    WHERE reviewer = 'production' AND chartId = :chartId
+                """),
+                {"chartId": charts.loc[chart_slug, "chart_id"]},
+            )
+        else:
+            log.info("Deleting all existing 'production' reviews.")
+            conn.execute(text("DELETE FROM related_charts WHERE reviewer = 'production'"))
+
+        log.info("Inserting new related chart records.", rows=len(recommended_df))
+        recommended_df[["chartId", "relatedChartId", "label", "reviewer", "score"]].to_sql(
+            "related_charts", con=conn, if_exists="append", index=False
+        )
+
+
+@click.command(name="related-charts", cls=RichCommand, help=__doc__)
+@click.option(
+    "--chart-slug",
+    type=str,
+    help="Get related charts only for the chart with this slug.",
+)
+@click.option(
+    "--top",
+    type=int,
+    default=6,
+    help="Pick the top N related charts.",
+)
+@click.option(
+    "--regularization", type=float, default=0.001, help="Factor by which to penalize charts with high pageviews."
+)
+@click.option(
+    "--score",
+    type=click.Choice(["jaccard", "coviews"]),
+    default="jaccard",
+    help="Scoring method to use for recommendations.",
+)
+@click.option(
+    "--dry-run/--no-dry-run",
+    default=False,
+    help="If set, no changes will be written to the database.",
+)
+def cli(chart_slug: Optional[str], top: int, regularization: float, score: str, dry_run: bool) -> None:
+    """
+    Generates a table of related charts (by coviews) and optionally writes them
+    to the database. If a single chart slug is provided, only that chart’s
+    related charts will be generated.
+    """
+    engine = get_engine()
+
+    # 1. Load data (no score calculated here)
+    charts, coviews_df = load_data(chart_slug)
+
+    # 2. Compute recommendations (score is applied here)
+    recommended_df = compute_recommendations(charts, coviews_df, chart_slug, top, regularization, score)
+
+    if recommended_df.empty:
+        log.info("No recommendations generated. Exiting.")
+        return
+
+    # 3. Dry-run check
+    if dry_run:
+        log.info("Dry run mode enabled. No changes will be written to the database.")
+        log.info("Recommended DataFrame preview:", data=recommended_df.head())
+        return
+
+    # 4. Otherwise, write to DB
+    write_recommendations(engine, recommended_df, charts, chart_slug)
+    log.info("Related charts updated successfully.")
+
+
+if __name__ == "__main__":
+    cli()

apps/wizard/app_pages/insight_search/embeddings.py

@@ -5,14 +5,12 @@
 
 import streamlit as st
 import torch
-from joblib import Memory
 from sentence_transformers import SentenceTransformer, util
 from structlog import get_logger
 
+from etl.config import memory
 from etl.paths import CACHE_DIR
 
-memory = Memory(CACHE_DIR, verbose=0)
-
 # Initialize log.
 log = get_logger()