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azulgarza commited on
Commit
7901bf8
·
1 Parent(s): 7898153

feat: add style changes and cleaning

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Files changed (4) hide show
  1. app.py +23 -8
  2. data.py +2 -0
  3. leaderboard.py +12 -21
  4. uv.lock +46 -1
app.py CHANGED
@@ -16,25 +16,35 @@ df = load_data()
16
  ALL_METRICS = sorted(df["metric"].unique().tolist())
17
  ALL_SUBDATASETS = sorted(df["subdataset"].unique().tolist())
18
  ALL_MODELS = sorted(df["model"].unique().tolist())
 
19
 
20
 
21
- def build_table(metric, subdataset, models):
22
- sub = df[df["metric"] == metric]
 
23
  if subdataset != "All":
24
  sub = sub[sub["subdataset"] == subdataset]
 
 
25
  if models:
26
  sub = sub[sub["model"].isin(models)]
27
  pivot = sub.pivot_table(
28
- index=["subdataset", "cutoff"], columns="model", values="value"
 
 
29
  )
30
  pivot = pivot.sort_index()
31
  pivot = pivot.reset_index()
 
 
 
32
  return pivot
33
 
34
 
35
  def build_plots(metric, subdataset):
36
- fig_rank = plot_rank_for_subdataset(df, metric, subdataset)
37
- fig_value = plot_value_for_subdataset(df, metric, subdataset)
 
38
  return fig_rank, fig_value
39
 
40
 
@@ -664,6 +674,11 @@ Currently **{n_dates} evaluations** are available, from **{date_min}** to **{dat
664
  value="All",
665
  label="Subdataset",
666
  )
 
 
 
 
 
667
  models_dd = gr.Dropdown(
668
  choices=ALL_MODELS,
669
  value=ALL_MODELS,
@@ -672,15 +687,15 @@ Currently **{n_dates} evaluations** are available, from **{date_min}** to **{dat
672
  )
673
 
674
  results_table = gr.Dataframe(
675
- value=build_table(ALL_METRICS[0], "All", ALL_MODELS),
676
  label="Results",
677
  interactive=False,
678
  )
679
 
680
- for control in [metric_dd, subdataset_dd, models_dd]:
681
  control.change(
682
  fn=build_table,
683
- inputs=[metric_dd, subdataset_dd, models_dd],
684
  outputs=results_table,
685
  )
686
 
 
16
  ALL_METRICS = sorted(df["metric"].unique().tolist())
17
  ALL_SUBDATASETS = sorted(df["subdataset"].unique().tolist())
18
  ALL_MODELS = sorted(df["model"].unique().tolist())
19
+ ALL_FREQUENCIES = sorted(df["frequency"].unique().tolist())
20
 
21
 
22
+ def build_table(metric, subdataset, frequency, models):
23
+ data = load_data()
24
+ sub = data[data["metric"] == metric]
25
  if subdataset != "All":
26
  sub = sub[sub["subdataset"] == subdataset]
27
+ if frequency != "All":
28
+ sub = sub[sub["frequency"] == frequency]
29
  if models:
30
  sub = sub[sub["model"].isin(models)]
31
  pivot = sub.pivot_table(
32
+ index=["subdataset", "frequency", "cutoff"],
33
+ columns="model",
34
+ values="value",
35
  )
36
  pivot = pivot.sort_index()
37
  pivot = pivot.reset_index()
38
+ # Format numeric columns to 3 decimal places
39
+ num_cols = pivot.select_dtypes(include="number").columns
40
+ pivot[num_cols] = pivot[num_cols].round(3)
41
  return pivot
42
 
43
 
44
  def build_plots(metric, subdataset):
45
+ data = load_data()
46
+ fig_rank = plot_rank_for_subdataset(data, metric, subdataset)
47
+ fig_value = plot_value_for_subdataset(data, metric, subdataset)
48
  return fig_rank, fig_value
49
 
50
 
 
674
  value="All",
675
  label="Subdataset",
676
  )
677
+ frequency_dd = gr.Dropdown(
678
+ choices=["All"] + ALL_FREQUENCIES,
679
+ value="All",
680
+ label="Frequency",
681
+ )
682
  models_dd = gr.Dropdown(
683
  choices=ALL_MODELS,
684
  value=ALL_MODELS,
 
687
  )
688
 
689
  results_table = gr.Dataframe(
690
+ value=build_table(ALL_METRICS[0], "All", "All", ALL_MODELS),
691
  label="Results",
692
  interactive=False,
693
  )
694
 
695
+ for control in [metric_dd, subdataset_dd, frequency_dd, models_dd]:
696
  control.change(
697
  fn=build_table,
698
+ inputs=[metric_dd, subdataset_dd, frequency_dd, models_dd],
699
  outputs=results_table,
700
  )
701
 
data.py CHANGED
@@ -29,4 +29,6 @@ def load_data() -> pd.DataFrame:
29
  if "model_alias" in df.columns:
30
  df = df.rename(columns={"model": "model_id", "model_alias": "model"})
31
 
 
 
32
  return df
 
29
  if "model_alias" in df.columns:
30
  df = df.rename(columns={"model": "model_id", "model_alias": "model"})
31
 
32
+ print(df)
33
+
34
  return df
leaderboard.py CHANGED
@@ -14,9 +14,9 @@ def compute_leaderboard(df: pd.DataFrame) -> pd.DataFrame:
14
  Returns a dataframe with columns: model, rank CRPS, rank MASE
15
  """
16
  ranked = df.copy()
17
- ranked["rank"] = ranked.groupby(
18
- ["metric", "subdataset", "frequency", "cutoff"]
19
- )["value"].rank(method="min")
20
 
21
  # Step 2: average ranks across cutoffs per (metric, subdataset, frequency, model)
22
  per_subdataset = (
@@ -27,9 +27,9 @@ def compute_leaderboard(df: pd.DataFrame) -> pd.DataFrame:
27
 
28
  # Print per-subdataset ranks for manual inspection
29
  for metric in sorted(per_subdataset["metric"].unique()):
30
- print(f"\n{'='*60}")
31
  print(f"Metric: {metric}")
32
- print(f"{'='*60}")
33
  sub = per_subdataset[per_subdataset["metric"] == metric]
34
  pivot = sub.pivot_table(
35
  index=["subdataset", "frequency"], columns="model", values="rank"
@@ -37,11 +37,7 @@ def compute_leaderboard(df: pd.DataFrame) -> pd.DataFrame:
37
  print(pivot.to_string())
38
 
39
  # Step 3: average across all (subdataset, frequency) combos
40
- overall = (
41
- per_subdataset.groupby(["metric", "model"])["rank"]
42
- .mean()
43
- .reset_index()
44
- )
45
 
46
  # Pivot so each metric becomes a column
47
  leaderboard = overall.pivot(index="model", columns="metric", values="rank")
@@ -52,7 +48,7 @@ def compute_leaderboard(df: pd.DataFrame) -> pd.DataFrame:
52
  # Average metric values: mean across all (subdataset, frequency, cutoff) per (metric, model)
53
  avg_values = (
54
  df.groupby(["metric", "model"])["value"]
55
- .mean()
56
  .reset_index()
57
  .pivot(index="model", columns="metric", values="value")
58
  )
@@ -69,16 +65,13 @@ def compute_leaderboard(df: pd.DataFrame) -> pd.DataFrame:
69
 
70
  # Round for display
71
  for col in leaderboard.columns:
72
- if col.startswith("rank "):
73
- leaderboard[col] = leaderboard[col].round(2)
74
- elif col.startswith("avg "):
75
- leaderboard[col] = leaderboard[col].round(4)
76
 
77
  # Add medals to model names
78
  leaderboard = leaderboard.reset_index(drop=True)
79
  leaderboard["model"] = [
80
- f"{MEDALS.get(i, '')} {m}".strip()
81
- for i, m in enumerate(leaderboard["model"])
82
  ]
83
 
84
  # Reorder: model, avg columns, rank columns
@@ -89,14 +82,12 @@ def compute_leaderboard(df: pd.DataFrame) -> pd.DataFrame:
89
  return leaderboard
90
 
91
 
92
-
93
-
94
  if __name__ == "__main__":
95
  from data import load_data
96
 
97
  df = load_data()
98
  lb = compute_leaderboard(df)
99
- print(f"\n{'='*60}")
100
  print("LEADERBOARD")
101
- print(f"{'='*60}")
102
  print(lb.to_string(index=False))
 
14
  Returns a dataframe with columns: model, rank CRPS, rank MASE
15
  """
16
  ranked = df.copy()
17
+ ranked["rank"] = ranked.groupby(["metric", "subdataset", "frequency", "cutoff"])[
18
+ "value"
19
+ ].rank(method="min")
20
 
21
  # Step 2: average ranks across cutoffs per (metric, subdataset, frequency, model)
22
  per_subdataset = (
 
27
 
28
  # Print per-subdataset ranks for manual inspection
29
  for metric in sorted(per_subdataset["metric"].unique()):
30
+ print(f"\n{'=' * 60}")
31
  print(f"Metric: {metric}")
32
+ print(f"{'=' * 60}")
33
  sub = per_subdataset[per_subdataset["metric"] == metric]
34
  pivot = sub.pivot_table(
35
  index=["subdataset", "frequency"], columns="model", values="rank"
 
37
  print(pivot.to_string())
38
 
39
  # Step 3: average across all (subdataset, frequency) combos
40
+ overall = per_subdataset.groupby(["metric", "model"])["rank"].mean().reset_index()
 
 
 
 
41
 
42
  # Pivot so each metric becomes a column
43
  leaderboard = overall.pivot(index="model", columns="metric", values="rank")
 
48
  # Average metric values: mean across all (subdataset, frequency, cutoff) per (metric, model)
49
  avg_values = (
50
  df.groupby(["metric", "model"])["value"]
51
+ .median() # using median instead of mean to avoid outliers
52
  .reset_index()
53
  .pivot(index="model", columns="metric", values="value")
54
  )
 
65
 
66
  # Round for display
67
  for col in leaderboard.columns:
68
+ if col.startswith("rank ") or col.startswith("avg "):
69
+ leaderboard[col] = leaderboard[col].round(3)
 
 
70
 
71
  # Add medals to model names
72
  leaderboard = leaderboard.reset_index(drop=True)
73
  leaderboard["model"] = [
74
+ f"{MEDALS.get(i, '')} {m}".strip() for i, m in enumerate(leaderboard["model"])
 
75
  ]
76
 
77
  # Reorder: model, avg columns, rank columns
 
82
  return leaderboard
83
 
84
 
 
 
85
  if __name__ == "__main__":
86
  from data import load_data
87
 
88
  df = load_data()
89
  lb = compute_leaderboard(df)
90
+ print(f"\n{'=' * 60}")
91
  print("LEADERBOARD")
92
+ print(f"{'=' * 60}")
93
  print(lb.to_string(index=False))
uv.lock CHANGED
@@ -1,5 +1,5 @@
1
  version = 1
2
- revision = 3
3
  requires-python = ">=3.12"
4
  resolution-markers = [
5
  "python_full_version >= '3.14' and sys_platform == 'win32'",
@@ -532,6 +532,7 @@ dependencies = [
532
  { name = "gradio" },
533
  { name = "matplotlib" },
534
  { name = "pandas" },
 
535
  ]
536
 
537
  [package.dev-dependencies]
@@ -544,6 +545,7 @@ requires-dist = [
544
  { name = "gradio", specifier = ">=6.5.1" },
545
  { name = "matplotlib", specifier = ">=3.10.8" },
546
  { name = "pandas", specifier = ">=3.0.0" },
 
547
  ]
548
 
549
  [package.metadata.requires-dev]
@@ -1123,6 +1125,49 @@ wheels = [
1123
  { url = "https://files.pythonhosted.org/packages/8e/37/efad0257dc6e593a18957422533ff0f87ede7c9c6ea010a2177d738fb82f/pure_eval-0.2.3-py3-none-any.whl", hash = "sha256:1db8e35b67b3d218d818ae653e27f06c3aa420901fa7b081ca98cbedc874e0d0", size = 11842, upload-time = "2024-07-21T12:58:20.04Z" },
1124
  ]
1125
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1126
  [[package]]
1127
  name = "pydantic"
1128
  version = "2.12.5"
 
1
  version = 1
2
+ revision = 2
3
  requires-python = ">=3.12"
4
  resolution-markers = [
5
  "python_full_version >= '3.14' and sys_platform == 'win32'",
 
532
  { name = "gradio" },
533
  { name = "matplotlib" },
534
  { name = "pandas" },
535
+ { name = "pyarrow" },
536
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537
 
538
  [package.dev-dependencies]
 
545
  { name = "gradio", specifier = ">=6.5.1" },
546
  { name = "matplotlib", specifier = ">=3.10.8" },
547
  { name = "pandas", specifier = ">=3.0.0" },
548
+ { name = "pyarrow", specifier = ">=19.0.0" },
549
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550
 
551
  [package.metadata.requires-dev]
 
1125
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1126
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1127
 
1128
+ [[package]]
1129
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1130
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1131
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1132
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