Fmt
This commit is contained in:
parent
479ef92236
commit
c38d654ac7
44 changed files with 2526 additions and 701 deletions
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@ -54,11 +54,15 @@
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}
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],
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"source": [
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"r5_bank = pl.read_parquet(\"../property-data/travel-times/transit/000000-bank-tube-station.parquet\")\n",
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"r5_bank = pl.read_parquet(\n",
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" \"../property-data/travel-times/transit/000000-bank-tube-station.parquet\"\n",
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")\n",
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"manual_bank = pl.read_parquet(\"../manual-data/journey_times_bank.parquet\")\n",
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"\n",
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"print(f\"R5 Bank: {r5_bank.shape[0]:,} postcodes\")\n",
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"print(f\"Manual Bank: {manual_bank.shape[0]:,} postcodes ({manual_bank['public_transport_easy_minutes'].null_count():,} null easy)\")"
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"print(\n",
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" f\"Manual Bank: {manual_bank.shape[0]:,} postcodes ({manual_bank['public_transport_easy_minutes'].null_count():,} null easy)\"\n",
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")"
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]
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},
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{
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@ -116,25 +120,49 @@
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"source": [
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"# Join on postcode, keep only rows where both sources have values\n",
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"bank = (\n",
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" r5_bank\n",
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" .join(manual_bank, left_on=\"pcds\", right_on=\"postcode\", how=\"inner\")\n",
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" r5_bank.join(manual_bank, left_on=\"pcds\", right_on=\"postcode\", how=\"inner\")\n",
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" .filter(\n",
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" pl.col(\"public_transport_easy_minutes\").is_not_null()\n",
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" & pl.col(\"public_transport_quick_minutes\").is_not_null()\n",
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" )\n",
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" .with_columns([\n",
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" # Signed error: R5 - Manual (positive = R5 is slower)\n",
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" (pl.col(\"travel_minutes\").cast(pl.Float64) - pl.col(\"public_transport_easy_minutes\").cast(pl.Float64)).alias(\"error_easy\"),\n",
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" (pl.col(\"best_minutes\").cast(pl.Float64) - pl.col(\"public_transport_quick_minutes\").cast(pl.Float64)).alias(\"error_quick\"),\n",
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" # Absolute error\n",
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" (pl.col(\"travel_minutes\").cast(pl.Float64) - pl.col(\"public_transport_easy_minutes\").cast(pl.Float64)).abs().alias(\"abs_error_easy\"),\n",
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" (pl.col(\"best_minutes\").cast(pl.Float64) - pl.col(\"public_transport_quick_minutes\").cast(pl.Float64)).abs().alias(\"abs_error_quick\"),\n",
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" ])\n",
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" .with_columns(\n",
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" [\n",
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" # Signed error: R5 - Manual (positive = R5 is slower)\n",
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" (\n",
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" pl.col(\"travel_minutes\").cast(pl.Float64)\n",
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" - pl.col(\"public_transport_easy_minutes\").cast(pl.Float64)\n",
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" ).alias(\"error_easy\"),\n",
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" (\n",
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" pl.col(\"best_minutes\").cast(pl.Float64)\n",
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" - pl.col(\"public_transport_quick_minutes\").cast(pl.Float64)\n",
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" ).alias(\"error_quick\"),\n",
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" # Absolute error\n",
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" (\n",
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" pl.col(\"travel_minutes\").cast(pl.Float64)\n",
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" - pl.col(\"public_transport_easy_minutes\").cast(pl.Float64)\n",
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" )\n",
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" .abs()\n",
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" .alias(\"abs_error_easy\"),\n",
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" (\n",
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" pl.col(\"best_minutes\").cast(pl.Float64)\n",
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" - pl.col(\"public_transport_quick_minutes\").cast(pl.Float64)\n",
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" )\n",
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" .abs()\n",
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" .alias(\"abs_error_quick\"),\n",
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" ]\n",
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" )\n",
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")\n",
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"\n",
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"print(f\"Joined (non-null): {bank.shape[0]:,} postcodes\")\n",
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"bank.select(\"pcds\", \"travel_minutes\", \"public_transport_easy_minutes\", \"error_easy\",\n",
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" \"best_minutes\", \"public_transport_quick_minutes\", \"error_quick\").head(10)"
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"bank.select(\n",
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" \"pcds\",\n",
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" \"travel_minutes\",\n",
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" \"public_transport_easy_minutes\",\n",
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" \"error_easy\",\n",
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" \"best_minutes\",\n",
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" \"public_transport_quick_minutes\",\n",
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" \"error_quick\",\n",
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").head(10)"
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]
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},
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{
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@ -196,18 +224,23 @@
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" percentiles = [5, 25, 50, 80, 90, 95, 99]\n",
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" rows = []\n",
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" for p in percentiles:\n",
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" rows.append({\n",
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" \"percentile\": f\"p{p}\",\n",
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" f\"{label} signed error\": round(float(np.percentile(col, p)), 1),\n",
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" f\"{label} absolute error\": round(float(np.percentile(abs_col, p)), 1),\n",
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" })\n",
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" rows.append({\n",
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" \"percentile\": \"mean\",\n",
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" f\"{label} signed error\": round(float(np.mean(col)), 1),\n",
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" f\"{label} absolute error\": round(float(np.mean(abs_col)), 1),\n",
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" })\n",
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" rows.append(\n",
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" {\n",
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" \"percentile\": f\"p{p}\",\n",
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" f\"{label} signed error\": round(float(np.percentile(col, p)), 1),\n",
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" f\"{label} absolute error\": round(float(np.percentile(abs_col, p)), 1),\n",
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" }\n",
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" )\n",
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" rows.append(\n",
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" {\n",
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" \"percentile\": \"mean\",\n",
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" f\"{label} signed error\": round(float(np.mean(col)), 1),\n",
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" f\"{label} absolute error\": round(float(np.mean(abs_col)), 1),\n",
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" }\n",
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" )\n",
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" return pl.DataFrame(rows)\n",
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"\n",
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"\n",
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"stats_easy = percentile_stats(\"error_easy\", \"Median (easy)\")\n",
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"stats_quick = percentile_stats(\"error_quick\", \"Best (quick)\")\n",
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"\n",
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@ -1120,24 +1153,42 @@
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}
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],
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"source": [
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"fig = make_subplots(rows=1, cols=2, subplot_titles=[\n",
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" \"Median transit time error (R5 − TfL)\",\n",
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" \"Best transit time error (R5 − TfL)\"\n",
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"])\n",
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"fig = make_subplots(\n",
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" rows=1,\n",
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" cols=2,\n",
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" subplot_titles=[\n",
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" \"Median transit time error (R5 − TfL)\",\n",
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" \"Best transit time error (R5 − TfL)\",\n",
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" ],\n",
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")\n",
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"\n",
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"# Clip for readability\n",
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"easy_clipped = bank[\"error_easy\"].clip(-60, 60).to_numpy()\n",
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"quick_clipped = bank[\"error_quick\"].clip(-60, 60).to_numpy()\n",
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"\n",
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"fig.add_trace(go.Histogram(x=easy_clipped, nbinsx=120, name=\"Median (easy)\",\n",
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" marker_color=\"#0d9488\"), row=1, col=1)\n",
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"fig.add_trace(go.Histogram(x=quick_clipped, nbinsx=120, name=\"Best (quick)\",\n",
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" marker_color=\"#f59e0b\"), row=1, col=2)\n",
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"fig.add_trace(\n",
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" go.Histogram(\n",
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" x=easy_clipped, nbinsx=120, name=\"Median (easy)\", marker_color=\"#0d9488\"\n",
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" ),\n",
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" row=1,\n",
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" col=1,\n",
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")\n",
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"fig.add_trace(\n",
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" go.Histogram(\n",
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" x=quick_clipped, nbinsx=120, name=\"Best (quick)\", marker_color=\"#f59e0b\"\n",
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" ),\n",
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" row=1,\n",
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" col=2,\n",
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")\n",
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"\n",
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"fig.update_xaxes(title_text=\"Error (minutes)\", row=1, col=1)\n",
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"fig.update_xaxes(title_text=\"Error (minutes)\", row=1, col=2)\n",
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"fig.update_yaxes(title_text=\"Count\", row=1, col=1)\n",
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"fig.update_layout(height=400, showlegend=False, title_text=\"Bank: Error Distribution (clipped ±60 min)\")\n",
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"fig.update_layout(\n",
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" height=400,\n",
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" showlegend=False,\n",
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" title_text=\"Bank: Error Distribution (clipped ±60 min)\",\n",
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")\n",
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"fig.show()"
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]
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},
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@ -2104,34 +2155,55 @@
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"# Sample for scatter plot performance\n",
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"sample = bank.sample(n=min(20_000, bank.shape[0]), seed=42)\n",
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"\n",
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"fig = make_subplots(rows=1, cols=2, subplot_titles=[\n",
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" \"Median: R5 vs TfL (easy)\",\n",
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" \"Best: R5 vs TfL (quick)\"\n",
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"])\n",
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"fig = make_subplots(\n",
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" rows=1,\n",
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" cols=2,\n",
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" subplot_titles=[\"Median: R5 vs TfL (easy)\", \"Best: R5 vs TfL (quick)\"],\n",
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")\n",
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"\n",
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"fig.add_trace(go.Scattergl(\n",
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" x=sample[\"public_transport_easy_minutes\"].to_numpy(),\n",
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" y=sample[\"travel_minutes\"].cast(pl.Float64).to_numpy(),\n",
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" mode=\"markers\", marker=dict(size=2, opacity=0.3, color=\"#0d9488\"),\n",
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" name=\"Median\"\n",
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"), row=1, col=1)\n",
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"fig.add_trace(\n",
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" go.Scattergl(\n",
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" x=sample[\"public_transport_easy_minutes\"].to_numpy(),\n",
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" y=sample[\"travel_minutes\"].cast(pl.Float64).to_numpy(),\n",
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" mode=\"markers\",\n",
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" marker=dict(size=2, opacity=0.3, color=\"#0d9488\"),\n",
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" name=\"Median\",\n",
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" ),\n",
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" row=1,\n",
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" col=1,\n",
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")\n",
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"\n",
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"fig.add_trace(go.Scattergl(\n",
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" x=sample[\"public_transport_quick_minutes\"].to_numpy(),\n",
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" y=sample[\"best_minutes\"].cast(pl.Float64).to_numpy(),\n",
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" mode=\"markers\", marker=dict(size=2, opacity=0.3, color=\"#f59e0b\"),\n",
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" name=\"Best\"\n",
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"), row=1, col=2)\n",
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"fig.add_trace(\n",
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" go.Scattergl(\n",
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" x=sample[\"public_transport_quick_minutes\"].to_numpy(),\n",
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" y=sample[\"best_minutes\"].cast(pl.Float64).to_numpy(),\n",
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" mode=\"markers\",\n",
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" marker=dict(size=2, opacity=0.3, color=\"#f59e0b\"),\n",
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" name=\"Best\",\n",
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" ),\n",
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" row=1,\n",
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" col=2,\n",
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")\n",
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"\n",
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"# Perfect agreement line\n",
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"for col in [1, 2]:\n",
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" fig.add_trace(go.Scatter(x=[0, 200], y=[0, 200], mode=\"lines\",\n",
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" line=dict(color=\"red\", dash=\"dash\", width=1),\n",
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" showlegend=False), row=1, col=col)\n",
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" fig.add_trace(\n",
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" go.Scatter(\n",
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" x=[0, 200],\n",
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" y=[0, 200],\n",
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" mode=\"lines\",\n",
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" line=dict(color=\"red\", dash=\"dash\", width=1),\n",
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" showlegend=False,\n",
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" ),\n",
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" row=1,\n",
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" col=col,\n",
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" )\n",
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" fig.update_xaxes(title_text=\"TfL API (minutes)\", row=1, col=col)\n",
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" fig.update_yaxes(title_text=\"R5 (minutes)\", row=1, col=col)\n",
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"\n",
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"fig.update_layout(height=500, showlegend=False, title_text=\"Bank: R5 vs TfL API (20k sample)\")\n",
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"fig.update_layout(\n",
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" height=500, showlegend=False, title_text=\"Bank: R5 vs TfL API (20k sample)\"\n",
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")\n",
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"fig.show()"
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]
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},
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@ -403063,7 +403135,8 @@
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"\n",
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"fig = px.scatter_map(\n",
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" map_sample.to_pandas(),\n",
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" lat=\"lat\", lon=\"long\",\n",
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" lat=\"lat\",\n",
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" lon=\"long\",\n",
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" color=\"error_easy\",\n",
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" color_continuous_scale=\"RdBu_r\", # red=positive (R5 slower), blue=negative (R5 faster)\n",
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" range_color=[-30, 30],\n",
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@ -403071,8 +403144,14 @@
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" center={\"lat\": 51.5, \"lon\": -0.1},\n",
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" opacity=0.5,\n",
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" title=\"Bank — Median transit error (R5 − TfL easy), minutes\",\n",
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" hover_data={\"pcds\": True, \"travel_minutes\": True, \"public_transport_easy_minutes\": True,\n",
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" \"error_easy\": \":.0f\", \"lat\": False, \"long\": False},\n",
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" hover_data={\n",
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" \"pcds\": True,\n",
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" \"travel_minutes\": True,\n",
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" \"public_transport_easy_minutes\": True,\n",
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" \"error_easy\": \":.0f\",\n",
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" \"lat\": False,\n",
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" \"long\": False,\n",
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" },\n",
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" height=700,\n",
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")\n",
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"fig.update_layout(map_style=\"carto-positron\")\n",
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@ -803994,7 +804073,8 @@
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"source": [
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"fig = px.scatter_map(\n",
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" map_sample.to_pandas(),\n",
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" lat=\"lat\", lon=\"long\",\n",
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" lat=\"lat\",\n",
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" lon=\"long\",\n",
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" color=\"error_quick\",\n",
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" color_continuous_scale=\"RdBu_r\",\n",
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" range_color=[-30, 30],\n",
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@ -804002,8 +804082,14 @@
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" center={\"lat\": 51.5, \"lon\": -0.1},\n",
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" opacity=0.5,\n",
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" title=\"Bank — Best transit error (R5 − TfL quick), minutes\",\n",
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" hover_data={\"pcds\": True, \"best_minutes\": True, \"public_transport_quick_minutes\": True,\n",
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" \"error_quick\": \":.0f\", \"lat\": False, \"long\": False},\n",
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" hover_data={\n",
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" \"pcds\": True,\n",
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" \"best_minutes\": True,\n",
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" \"public_transport_quick_minutes\": True,\n",
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" \"error_quick\": \":.0f\",\n",
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" \"lat\": False,\n",
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" \"long\": False,\n",
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" },\n",
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" height=700,\n",
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")\n",
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"fig.update_layout(map_style=\"carto-positron\")\n",
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@ -1204925,7 +1205011,8 @@
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"source": [
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"fig = px.scatter_map(\n",
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" map_sample.to_pandas(),\n",
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" lat=\"lat\", lon=\"long\",\n",
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" lat=\"lat\",\n",
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" lon=\"long\",\n",
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" color=\"abs_error_easy\",\n",
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" color_continuous_scale=\"YlOrRd\",\n",
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" range_color=[0, 30],\n",
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@ -1204933,8 +1205020,14 @@
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" center={\"lat\": 51.5, \"lon\": -0.1},\n",
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" opacity=0.5,\n",
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" title=\"Bank — Absolute median transit error |R5 − TfL easy|, minutes\",\n",
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" hover_data={\"pcds\": True, \"travel_minutes\": True, \"public_transport_easy_minutes\": True,\n",
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" \"abs_error_easy\": \":.0f\", \"lat\": False, \"long\": False},\n",
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" hover_data={\n",
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" \"pcds\": True,\n",
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" \"travel_minutes\": True,\n",
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" \"public_transport_easy_minutes\": True,\n",
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" \"abs_error_easy\": \":.0f\",\n",
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" \"lat\": False,\n",
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" \"long\": False,\n",
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" },\n",
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" height=700,\n",
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")\n",
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"fig.update_layout(map_style=\"carto-positron\")\n",
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@ -1204998,9 +1205091,15 @@
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],
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"source": [
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"bank.sort(\"abs_error_easy\", descending=True).select(\n",
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" \"pcds\", \"lat\", \"long\",\n",
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" \"travel_minutes\", \"public_transport_easy_minutes\", \"error_easy\",\n",
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" \"best_minutes\", \"public_transport_quick_minutes\", \"error_quick\",\n",
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" \"pcds\",\n",
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" \"lat\",\n",
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" \"long\",\n",
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" \"travel_minutes\",\n",
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" \"public_transport_easy_minutes\",\n",
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" \"error_easy\",\n",
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" \"best_minutes\",\n",
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" \"public_transport_quick_minutes\",\n",
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" \"error_quick\",\n",
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").head(30)"
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]
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},
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@ -1205945,45 +1206044,75 @@
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"\n",
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"dist_df = bank.with_columns(\n",
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" # Rough km distance using Haversine approximation\n",
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" ((((pl.col(\"lat\") - BANK_LAT) * 111.32) ** 2 +\n",
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" ((pl.col(\"long\") - BANK_LON) * 111.32 * np.cos(np.radians(BANK_LAT))) ** 2) ** 0.5\n",
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" (\n",
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" (\n",
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" ((pl.col(\"lat\") - BANK_LAT) * 111.32) ** 2\n",
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" + ((pl.col(\"long\") - BANK_LON) * 111.32 * np.cos(np.radians(BANK_LAT))) ** 2\n",
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" )\n",
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" ** 0.5\n",
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" ).alias(\"dist_km\")\n",
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")\n",
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"\n",
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"# Bin by 5km\n",
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"binned = (\n",
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" dist_df\n",
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" .with_columns((pl.col(\"dist_km\") / 5).floor() * 5)\n",
|
||||
" dist_df.with_columns((pl.col(\"dist_km\") / 5).floor() * 5)\n",
|
||||
" .group_by(\"dist_km\")\n",
|
||||
" .agg([\n",
|
||||
" pl.col(\"error_easy\").median().alias(\"median_error_easy\"),\n",
|
||||
" pl.col(\"error_quick\").median().alias(\"median_error_quick\"),\n",
|
||||
" pl.col(\"abs_error_easy\").median().alias(\"median_abs_error_easy\"),\n",
|
||||
" pl.len().alias(\"count\"),\n",
|
||||
" ])\n",
|
||||
" .agg(\n",
|
||||
" [\n",
|
||||
" pl.col(\"error_easy\").median().alias(\"median_error_easy\"),\n",
|
||||
" pl.col(\"error_quick\").median().alias(\"median_error_quick\"),\n",
|
||||
" pl.col(\"abs_error_easy\").median().alias(\"median_abs_error_easy\"),\n",
|
||||
" pl.len().alias(\"count\"),\n",
|
||||
" ]\n",
|
||||
" )\n",
|
||||
" .sort(\"dist_km\")\n",
|
||||
" .filter(pl.col(\"count\") > 50)\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"fig = make_subplots(rows=1, cols=2, subplot_titles=[\n",
|
||||
" \"Median signed error by distance\",\n",
|
||||
" \"Median absolute error by distance\"\n",
|
||||
"])\n",
|
||||
"fig = make_subplots(\n",
|
||||
" rows=1,\n",
|
||||
" cols=2,\n",
|
||||
" subplot_titles=[\n",
|
||||
" \"Median signed error by distance\",\n",
|
||||
" \"Median absolute error by distance\",\n",
|
||||
" ],\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"fig.add_trace(go.Scatter(\n",
|
||||
" x=binned[\"dist_km\"].to_numpy(), y=binned[\"median_error_easy\"].to_numpy(),\n",
|
||||
" mode=\"lines+markers\", name=\"Easy\", line=dict(color=\"#0d9488\")\n",
|
||||
"), row=1, col=1)\n",
|
||||
"fig.add_trace(go.Scatter(\n",
|
||||
" x=binned[\"dist_km\"].to_numpy(), y=binned[\"median_error_quick\"].to_numpy(),\n",
|
||||
" mode=\"lines+markers\", name=\"Quick\", line=dict(color=\"#f59e0b\")\n",
|
||||
"), row=1, col=1)\n",
|
||||
"fig.add_trace(\n",
|
||||
" go.Scatter(\n",
|
||||
" x=binned[\"dist_km\"].to_numpy(),\n",
|
||||
" y=binned[\"median_error_easy\"].to_numpy(),\n",
|
||||
" mode=\"lines+markers\",\n",
|
||||
" name=\"Easy\",\n",
|
||||
" line=dict(color=\"#0d9488\"),\n",
|
||||
" ),\n",
|
||||
" row=1,\n",
|
||||
" col=1,\n",
|
||||
")\n",
|
||||
"fig.add_trace(\n",
|
||||
" go.Scatter(\n",
|
||||
" x=binned[\"dist_km\"].to_numpy(),\n",
|
||||
" y=binned[\"median_error_quick\"].to_numpy(),\n",
|
||||
" mode=\"lines+markers\",\n",
|
||||
" name=\"Quick\",\n",
|
||||
" line=dict(color=\"#f59e0b\"),\n",
|
||||
" ),\n",
|
||||
" row=1,\n",
|
||||
" col=1,\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"fig.add_trace(go.Scatter(\n",
|
||||
" x=binned[\"dist_km\"].to_numpy(), y=binned[\"median_abs_error_easy\"].to_numpy(),\n",
|
||||
" mode=\"lines+markers\", name=\"|Easy|\", line=dict(color=\"#0d9488\"),\n",
|
||||
" showlegend=False\n",
|
||||
"), row=1, col=2)\n",
|
||||
"fig.add_trace(\n",
|
||||
" go.Scatter(\n",
|
||||
" x=binned[\"dist_km\"].to_numpy(),\n",
|
||||
" y=binned[\"median_abs_error_easy\"].to_numpy(),\n",
|
||||
" mode=\"lines+markers\",\n",
|
||||
" name=\"|Easy|\",\n",
|
||||
" line=dict(color=\"#0d9488\"),\n",
|
||||
" showlegend=False,\n",
|
||||
" ),\n",
|
||||
" row=1,\n",
|
||||
" col=2,\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"for col in [1, 2]:\n",
|
||||
" fig.update_xaxes(title_text=\"Distance from Bank (km)\", row=1, col=col)\n",
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue