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WP 11 : tableaux ajustés, PDF final exporté

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Simon-Pierre Boucher committed 7 days ago (Oct 10, 2026) parent 518511c

14 changed files +56 −59

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789 789 ) [3]
790 790 [4]) (./sections/literature.tex [5] [6]) (./sections/data.tex [7] [8]
791 791 (./tables/exclusions.tex) [9] (./tables/descriptives.tex)
792 −Overfull \hbox (60.22191pt too wide) in paragraph at lines 1--36
793 − [][]
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797 793 File: figures/f7_indice.pdf Graphic file (type pdf)
798 794 <use figures/f7_indice.pdf>
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806 802 (Font) Font shape `OT1/ptm/b/n' tried instead on input line 41.
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808 −(./sections/methodology.tex [11] [12] [13] [14]) (./sections/results.tex
803 + [10 <./figures/f7_indice.pdf>] (./tables/density_dist.tex)) (./sections/method
804 +ology.tex [11]
805 +[12] [13] [14]) (./sections/results.tex
809 806 <figures/f1_mdape_vs_k.pdf, id=438, 547.99101pt x 244.40283pt>
810 807 File: figures/f1_mdape_vs_k.pdf Graphic file (type pdf)
811 808 <use figures/f1_mdape_vs_k.pdf>
812 809 Package pdftex.def Info: figures/f1_mdape_vs_k.pdf used on input line 15.
813 810 (pdftex.def) Requested size: 469.75502pt x 209.51479pt.
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817 813 File: figures/f2_surface_d_T.pdf Graphic file (type pdf)
818 814 <use figures/f2_surface_d_T.pdf>
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820 816 (pdftex.def) Requested size: 422.77664pt x 186.08864pt.
821 − (./tables/surface_geo_hedo.tex) [16 <./figures/f1_mdape_vs_k.pdf> <./figures/f
822 −2_surface_d_T.pdf>] (./tables/h2.tex)
817 + (./tables/surface_geo_hedo.tex)
818 +[16 <./figures/f1_mdape_vs_k.pdf> <./figures/f2_surface_d_T.pdf>]
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834 − (./tables/h3.tex) [19 <./figures/f3_ajustements.pdf>] (./tables/grid.tex)
835 −[20] (./tables/selection.tex) [21] (./tables/weighting.tex) (./tables/h4.tex)
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831 +[19 <./figures/f3_ajustements.pdf>] (./tables/grid.tex) [20]
832 +(./tables/selection.tex) [21] (./tables/weighting.tex) (./tables/h4.tex)
836 833 [22] (./tables/marginal.tex) [23] (./tables/competition.tex)
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838 835 File: figures/f4_competition.pdf Graphic file (type pdf)
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885 +Output written on main.pdf (38 pages, 385249 bytes).
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modified paper/make_paper_tables.py +3 −3
@@ -52,9 +52,9 @@ ex["Rule"] = EN[:len(ex)]; ex["Removed"] = ex["Removed"].map(n_)
52 52 rows = [["Single-family transactions, raw", n_(D["brut"])]] + ex.values.tolist() + [["Final sample", n_(D["propre"])]]
53 53 write("exclusions", tabular(pd.DataFrame(rows, columns=["Step", "n"]), ["Step", "n"], "p{0.8\\textwidth}r"))
54 54 ds = R.get("descriptives") or {p: dict(n=0, prix_med=0, prix_moy=0, aire_med=0, terrain_med=0, age_med=0, detache=0, ratio_role_med=0) for p in ["train", "val", "test"]}
55 −dd = pd.DataFrame({"Statistic": ["Sales", "Median price (\\$)", "Mean price (\\$)", "Median living area (m$^2$)", "Median lot area (m$^2$)", "Median age (years)", "Detached (\\%)", "Median price/assessment ratio", "Median sales within 2 km, 12 months", "Median candidate pool (25 km, 24 months)"],
56 − **{{"train": "Development 2021--2023", "val": "Validation 2024", "test": "Test 2025--2026"}[p]: [n_(ds[p]["n"]), n_(ds[p]["prix_med"]), n_(ds[p]["prix_moy"]), f1(ds[p]["aire_med"]), n_(ds[p]["terrain_med"]), f1(ds[p]["age_med"]), f1(ds[p]["detache"]), f2(ds[p]["ratio_role_med"]), (n_(ds[p]["dens_med"]) if "dens_med" in ds[p] else "--"), (n_(ds[p]["n_cand_med"]) if "n_cand_med" in ds[p] else "--")] for p in ["train", "val", "test"]}})
57 −write("descriptives", tabular(dd, list(dd.columns), "lrrr"))
55 +dd = pd.DataFrame({"Statistic": ["Sales", "Median price (\\$)", "Mean price (\\$)", "Median living area (m$^2$)", "Median lot area (m$^2$)", "Median age (years)", "Detached (\\%)", "Median price/assessment ratio", "Median sales within 2 km, 12 mo.", "Median candidate pool (25 km, 24 mo.)"],
56 + **{{"train": "2021--23 (dev.)", "val": "2024 (val.)", "test": "2025--26 (test)"}[p]: [n_(ds[p]["n"]), n_(ds[p]["prix_med"]), n_(ds[p]["prix_moy"]), f1(ds[p]["aire_med"]), n_(ds[p]["terrain_med"]), f1(ds[p]["age_med"]), f1(ds[p]["detache"]), f2(ds[p]["ratio_role_med"]), (n_(ds[p]["dens_med"]) if "dens_med" in ds[p] else "--"), (n_(ds[p]["n_cand_med"]) if "n_cand_med" in ds[p] else "--")] for p in ["train", "val", "test"]}})
57 +write("descriptives", tabular(dd, list(dd.columns), "p{0.46\\textwidth}rrr"))
58 58 dist = R["densite_distribution"]; tot = sum(dist.values())
59 59 dd = pd.DataFrame({"Density (sales within 2 km, 12 months)": [DENS_EN[d] for d in DENS], "Test targets": [n_(dist[d]) for d in DENS], "Share (\\%)": [f1(dist[d] / tot * 100) for d in DENS]})
60 60 write("density_dist", tabular(dd, list(dd.columns), "lrr"))
modified paper/tables/descriptives.tex +4 −4
@@ -1,6 +1,6 @@
1 −\begin{tabular}{lrrr}
1 +\begin{tabular}{p{0.46\textwidth}rrr}
2 2 \toprule
3 −Statistic & Development 2021--2023 & Validation 2024 & Test 2025--2026 \\
3 +Statistic & 2021--23 (dev.) & 2024 (val.) & 2025--26 (test) \\
4 4 \midrule
5 5 Sales & 201,642 & 64,795 & 124,559 \\
6 6 Median price (\$) & 350,000 & 415,000 & 470,000 \\
@@ -10,7 +10,7 @@ Median lot area (m$^2$) & 650 & 645 & 650 \\
10 10 Median age (years) & 41.0 & 42.0 & 43.0 \\
11 11 Detached (\%) & 81.6 & 80.9 & 81.3 \\
12 12 Median price/assessment ratio & 1.39 & 1.35 & 1.20 \\
13 −Median sales within 2 km, 12 months & -- & 92 & 108 \\
14 −Median candidate pool (25 km, 24 months) & -- & 6,188 & 6,788 \\
13 +Median sales within 2 km, 12 mo. & -- & 92 & 108 \\
14 +Median candidate pool (25 km, 24 mo.) & -- & 6,188 & 6,788 \\
15 15 \bottomrule
16 16 \end{tabular}
17 17