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azulgarza commited on
Commit
46dbc41
·
1 Parent(s): 0877d91

feat: add real leaderboard pipeline

Browse files
Files changed (7) hide show
  1. app.py +18 -13
  2. data.py +35 -0
  3. leaderboard.py +3 -1
  4. mock_evaluation_results.csv +0 -449
  5. pyproject.toml +1 -0
  6. rank_through_time.py +3 -1
  7. requirements.txt +1 -0
app.py CHANGED
@@ -4,13 +4,14 @@ matplotlib.use("Agg")
4
  import matplotlib.pyplot as plt
5
  import pandas as pd
6
 
 
7
  from leaderboard import compute_leaderboard
8
  from rank_through_time import (
9
  plot_rank_for_subdataset,
10
  plot_value_for_subdataset,
11
  )
12
 
13
- df = pd.read_csv("mock_evaluation_results.csv")
14
 
15
  ALL_METRICS = sorted(df["metric"].unique().tolist())
16
  ALL_SUBDATASETS = sorted(df["subdataset"].unique().tolist())
@@ -574,9 +575,11 @@ with gr.Blocks(title="Impermanent Leaderboard") as app:
574
  cutoff_dates = sorted(df["cutoff"].unique())
575
  n_dates = len(cutoff_dates)
576
  date_min, date_max = cutoff_dates[0], cutoff_dates[-1]
577
- statistical_models = ["zero_model", "seasonal_naive", "auto_arima", "auto_ets", "auto_lgbm"]
578
- foundation_models = ["chronos", "moirai", "timesfm"]
579
- all_model_names = statistical_models + foundation_models
 
 
580
 
581
  gr.Markdown(f"""\
582
  ## Datasets
@@ -585,19 +588,13 @@ GitHub repositories are selected across several **buckets based on their number
585
  yielding a mix of both intermittent (low-activity) and high-volume time series.
586
  For each bucket, an automated pipeline fetches four signals:
587
 
588
- - **Open issues** — number of issues opened
589
- - **Opened PRs** — number of pull requests opened
590
- - **Pushes** — number of push events
591
- - **Stars** — number of new stars
592
 
593
- Each signal is collected at both **daily** and **weekly** granularity.
594
 
595
  ## Models
596
 
597
- The benchmark evaluates two families of forecasting methods:
598
-
599
- - **Statistical / ML models:** {", ".join(f"`{m}`" for m in statistical_models)}
600
- - **Foundation models:** {", ".join(f"`{m}`" for m in foundation_models)}
601
 
602
  ## Evaluation dates
603
 
@@ -605,6 +602,14 @@ Forecast methods are evaluated **every week** using rolling forecast evaluations
605
  Currently **{n_dates} evaluations** are available, from **{date_min}** to **{date_max}**.
606
  """)
607
 
 
 
 
 
 
 
 
 
608
  with gr.Tab("Leaderboard 🏆"):
609
  lb = compute_leaderboard(df)
610
  gr.Dataframe(
 
4
  import matplotlib.pyplot as plt
5
  import pandas as pd
6
 
7
+ from data import load_data
8
  from leaderboard import compute_leaderboard
9
  from rank_through_time import (
10
  plot_rank_for_subdataset,
11
  plot_value_for_subdataset,
12
  )
13
 
14
+ df = load_data()
15
 
16
  ALL_METRICS = sorted(df["metric"].unique().tolist())
17
  ALL_SUBDATASETS = sorted(df["subdataset"].unique().tolist())
 
575
  cutoff_dates = sorted(df["cutoff"].unique())
576
  n_dates = len(cutoff_dates)
577
  date_min, date_max = cutoff_dates[0], cutoff_dates[-1]
578
+ frequencies = sorted(df["frequency"].unique())
579
+ subdatasets = sorted(df["subdataset"].unique())
580
+ n_total = len(df)
581
+ n_missing = int(df["value"].isna().sum())
582
+ pct_complete = (n_total - n_missing) / n_total * 100 if n_total else 0
583
 
584
  gr.Markdown(f"""\
585
  ## Datasets
 
588
  yielding a mix of both intermittent (low-activity) and high-volume time series.
589
  For each bucket, an automated pipeline fetches four signals:
590
 
591
+ {chr(10).join(f"- **{s}**" for s in subdatasets)}
 
 
 
592
 
593
+ Each signal is collected at {", ".join(f"**{f}**" for f in frequencies)} granularity.
594
 
595
  ## Models
596
 
597
+ The benchmark evaluates **{len(ALL_MODELS)} models**: {", ".join(f"`{m}`" for m in ALL_MODELS)}.
 
 
 
598
 
599
  ## Evaluation dates
600
 
 
602
  Currently **{n_dates} evaluations** are available, from **{date_min}** to **{date_max}**.
603
  """)
604
 
605
+ if n_missing > 0:
606
+ gr.Markdown(
607
+ f"> **Note:** {n_missing} out of {n_total} results "
608
+ f"({pct_complete:.0f}% complete) are still pending. "
609
+ "New evaluations are computed on a rolling basis and will "
610
+ "appear automatically."
611
+ )
612
+
613
  with gr.Tab("Leaderboard 🏆"):
614
  lb = compute_leaderboard(df)
615
  gr.Dataframe(
data.py ADDED
@@ -0,0 +1,35 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Data loading for the Impermanent Leaderboard."""
2
+
3
+ import logging
4
+
5
+ import pandas as pd
6
+
7
+ logger = logging.getLogger(__name__)
8
+
9
+ DATA_URL = (
10
+ "https://impermanent-benchmark.s3.amazonaws.com"
11
+ "/v0.1.0/gh-archive/evaluations/evaluation_results.parquet"
12
+ )
13
+
14
+
15
+ def load_data() -> pd.DataFrame:
16
+ """Load evaluation results from S3.
17
+
18
+ Falls back to the local ``mock_evaluation_results.csv`` when the
19
+ remote file is not reachable (e.g. first deploy, no network).
20
+
21
+ If the parquet contains a ``model_alias`` column it is used as the
22
+ display ``model`` name (the raw identifier is kept as ``model_id``).
23
+
24
+ Returns:
25
+ DataFrame with columns:
26
+ ``dataset, subdataset, frequency, cutoff, metric, model, value``.
27
+ """
28
+
29
+ df = pd.read_parquet(DATA_URL)
30
+ logger.info("Loaded %d rows from %s", len(df), DATA_URL)
31
+
32
+ if "model_alias" in df.columns:
33
+ df = df.rename(columns={"model": "model_id", "model_alias": "model"})
34
+
35
+ return df
leaderboard.py CHANGED
@@ -92,7 +92,9 @@ def compute_leaderboard(df: pd.DataFrame) -> pd.DataFrame:
92
 
93
 
94
  if __name__ == "__main__":
95
- df = pd.read_csv("mock_evaluation_results.csv")
 
 
96
  lb = compute_leaderboard(df)
97
  print(f"\n{'='*60}")
98
  print("LEADERBOARD")
 
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")
mock_evaluation_results.csv DELETED
@@ -1,449 +0,0 @@
1
- dataset,subdataset,frequency,cutoff,metric,model,value
2
- gh-archive,stars,daily,2026-01-08,mase,zero_model,2.841
3
- gh-archive,stars,daily,2026-01-08,mase,seasonal_naive,1.012
4
- gh-archive,stars,daily,2026-01-08,mase,auto_arima,0.874
5
- gh-archive,stars,daily,2026-01-08,mase,auto_ets,0.891
6
- gh-archive,stars,daily,2026-01-08,mase,auto_lgbm,0.782
7
- gh-archive,stars,daily,2026-01-08,mase,chronos,0.643
8
- gh-archive,stars,daily,2026-01-08,mase,moirai,0.701
9
- gh-archive,stars,daily,2026-01-08,mase,timesfm,0.668
10
- gh-archive,stars,daily,2026-01-08,scaled_crps,zero_model,0.421
11
- gh-archive,stars,daily,2026-01-08,scaled_crps,seasonal_naive,0.183
12
- gh-archive,stars,daily,2026-01-08,scaled_crps,auto_arima,0.142
13
- gh-archive,stars,daily,2026-01-08,scaled_crps,auto_ets,0.149
14
- gh-archive,stars,daily,2026-01-08,scaled_crps,auto_lgbm,0.121
15
- gh-archive,stars,daily,2026-01-08,scaled_crps,chronos,0.089
16
- gh-archive,stars,daily,2026-01-08,scaled_crps,moirai,0.098
17
- gh-archive,stars,daily,2026-01-08,scaled_crps,timesfm,0.093
18
- gh-archive,stars,daily,2026-01-15,mase,zero_model,2.793
19
- gh-archive,stars,daily,2026-01-15,mase,seasonal_naive,1.034
20
- gh-archive,stars,daily,2026-01-15,mase,auto_arima,0.862
21
- gh-archive,stars,daily,2026-01-15,mase,auto_ets,0.879
22
- gh-archive,stars,daily,2026-01-15,mase,auto_lgbm,0.801
23
- gh-archive,stars,daily,2026-01-15,mase,chronos,0.651
24
- gh-archive,stars,daily,2026-01-15,mase,moirai,0.694
25
- gh-archive,stars,daily,2026-01-15,mase,timesfm,0.672
26
- gh-archive,stars,daily,2026-01-15,scaled_crps,zero_model,0.415
27
- gh-archive,stars,daily,2026-01-15,scaled_crps,seasonal_naive,0.187
28
- gh-archive,stars,daily,2026-01-15,scaled_crps,auto_arima,0.139
29
- gh-archive,stars,daily,2026-01-15,scaled_crps,auto_ets,0.146
30
- gh-archive,stars,daily,2026-01-15,scaled_crps,auto_lgbm,0.125
31
- gh-archive,stars,daily,2026-01-15,scaled_crps,chronos,0.091
32
- gh-archive,stars,daily,2026-01-15,scaled_crps,moirai,0.096
33
- gh-archive,stars,daily,2026-01-15,scaled_crps,timesfm,0.094
34
- gh-archive,stars,daily,2026-01-22,mase,zero_model,2.867
35
- gh-archive,stars,daily,2026-01-22,mase,seasonal_naive,0.987
36
- gh-archive,stars,daily,2026-01-22,mase,auto_arima,0.851
37
- gh-archive,stars,daily,2026-01-22,mase,auto_ets,0.870
38
- gh-archive,stars,daily,2026-01-22,mase,auto_lgbm,0.769
39
- gh-archive,stars,daily,2026-01-22,mase,chronos,0.634
40
- gh-archive,stars,daily,2026-01-22,mase,moirai,0.687
41
- gh-archive,stars,daily,2026-01-22,mase,timesfm,0.659
42
- gh-archive,stars,daily,2026-01-22,scaled_crps,zero_model,0.428
43
- gh-archive,stars,daily,2026-01-22,scaled_crps,seasonal_naive,0.178
44
- gh-archive,stars,daily,2026-01-22,scaled_crps,auto_arima,0.136
45
- gh-archive,stars,daily,2026-01-22,scaled_crps,auto_ets,0.143
46
- gh-archive,stars,daily,2026-01-22,scaled_crps,auto_lgbm,0.118
47
- gh-archive,stars,daily,2026-01-22,scaled_crps,chronos,0.086
48
- gh-archive,stars,daily,2026-01-22,scaled_crps,moirai,0.094
49
- gh-archive,stars,daily,2026-01-22,scaled_crps,timesfm,0.090
50
- gh-archive,stars,daily,2026-01-29,mase,zero_model,2.912
51
- gh-archive,stars,daily,2026-01-29,mase,seasonal_naive,1.005
52
- gh-archive,stars,daily,2026-01-29,mase,auto_arima,0.883
53
- gh-archive,stars,daily,2026-01-29,mase,auto_ets,0.898
54
- gh-archive,stars,daily,2026-01-29,mase,auto_lgbm,0.793
55
- gh-archive,stars,daily,2026-01-29,mase,chronos,0.657
56
- gh-archive,stars,daily,2026-01-29,mase,moirai,0.712
57
- gh-archive,stars,daily,2026-01-29,mase,timesfm,0.681
58
- gh-archive,stars,daily,2026-01-29,scaled_crps,zero_model,0.434
59
- gh-archive,stars,daily,2026-01-29,scaled_crps,seasonal_naive,0.185
60
- gh-archive,stars,daily,2026-01-29,scaled_crps,auto_arima,0.145
61
- gh-archive,stars,daily,2026-01-29,scaled_crps,auto_ets,0.151
62
- gh-archive,stars,daily,2026-01-29,scaled_crps,auto_lgbm,0.128
63
- gh-archive,stars,daily,2026-01-29,scaled_crps,chronos,0.092
64
- gh-archive,stars,daily,2026-01-29,scaled_crps,moirai,0.101
65
- gh-archive,stars,daily,2026-01-29,scaled_crps,timesfm,0.096
66
- gh-archive,prs_opened,daily,2026-01-08,mase,zero_model,3.214
67
- gh-archive,prs_opened,daily,2026-01-08,mase,seasonal_naive,1.087
68
- gh-archive,prs_opened,daily,2026-01-08,mase,auto_arima,0.952
69
- gh-archive,prs_opened,daily,2026-01-08,mase,auto_ets,0.971
70
- gh-archive,prs_opened,daily,2026-01-08,mase,auto_lgbm,0.845
71
- gh-archive,prs_opened,daily,2026-01-08,mase,chronos,0.712
72
- gh-archive,prs_opened,daily,2026-01-08,mase,moirai,0.768
73
- gh-archive,prs_opened,daily,2026-01-08,mase,timesfm,0.734
74
- gh-archive,prs_opened,daily,2026-01-08,scaled_crps,zero_model,0.478
75
- gh-archive,prs_opened,daily,2026-01-08,scaled_crps,seasonal_naive,0.201
76
- gh-archive,prs_opened,daily,2026-01-08,scaled_crps,auto_arima,0.162
77
- gh-archive,prs_opened,daily,2026-01-08,scaled_crps,auto_ets,0.168
78
- gh-archive,prs_opened,daily,2026-01-08,scaled_crps,auto_lgbm,0.139
79
- gh-archive,prs_opened,daily,2026-01-08,scaled_crps,chronos,0.104
80
- gh-archive,prs_opened,daily,2026-01-08,scaled_crps,moirai,0.115
81
- gh-archive,prs_opened,daily,2026-01-08,scaled_crps,timesfm,0.108
82
- gh-archive,prs_opened,daily,2026-01-15,mase,zero_model,3.178
83
- gh-archive,prs_opened,daily,2026-01-15,mase,seasonal_naive,1.065
84
- gh-archive,prs_opened,daily,2026-01-15,mase,auto_arima,0.941
85
- gh-archive,prs_opened,daily,2026-01-15,mase,auto_ets,0.958
86
- gh-archive,prs_opened,daily,2026-01-15,mase,auto_lgbm,0.861
87
- gh-archive,prs_opened,daily,2026-01-15,mase,chronos,0.723
88
- gh-archive,prs_opened,daily,2026-01-15,mase,moirai,0.751
89
- gh-archive,prs_opened,daily,2026-01-15,mase,timesfm,0.729
90
- gh-archive,prs_opened,daily,2026-01-15,scaled_crps,zero_model,0.471
91
- gh-archive,prs_opened,daily,2026-01-15,scaled_crps,seasonal_naive,0.196
92
- gh-archive,prs_opened,daily,2026-01-15,scaled_crps,auto_arima,0.158
93
- gh-archive,prs_opened,daily,2026-01-15,scaled_crps,auto_ets,0.164
94
- gh-archive,prs_opened,daily,2026-01-15,scaled_crps,auto_lgbm,0.142
95
- gh-archive,prs_opened,daily,2026-01-15,scaled_crps,chronos,0.107
96
- gh-archive,prs_opened,daily,2026-01-15,scaled_crps,moirai,0.112
97
- gh-archive,prs_opened,daily,2026-01-15,scaled_crps,timesfm,0.105
98
- gh-archive,prs_opened,daily,2026-01-22,mase,zero_model,3.251
99
- gh-archive,prs_opened,daily,2026-01-22,mase,seasonal_naive,1.098
100
- gh-archive,prs_opened,daily,2026-01-22,mase,auto_arima,0.963
101
- gh-archive,prs_opened,daily,2026-01-22,mase,auto_ets,0.982
102
- gh-archive,prs_opened,daily,2026-01-22,mase,auto_lgbm,0.837
103
- gh-archive,prs_opened,daily,2026-01-22,mase,chronos,0.698
104
- gh-archive,prs_opened,daily,2026-01-22,mase,moirai,0.759
105
- gh-archive,prs_opened,daily,2026-01-22,mase,timesfm,0.721
106
- gh-archive,prs_opened,daily,2026-01-22,scaled_crps,zero_model,0.483
107
- gh-archive,prs_opened,daily,2026-01-22,scaled_crps,seasonal_naive,0.205
108
- gh-archive,prs_opened,daily,2026-01-22,scaled_crps,auto_arima,0.165
109
- gh-archive,prs_opened,daily,2026-01-22,scaled_crps,auto_ets,0.171
110
- gh-archive,prs_opened,daily,2026-01-22,scaled_crps,auto_lgbm,0.136
111
- gh-archive,prs_opened,daily,2026-01-22,scaled_crps,chronos,0.101
112
- gh-archive,prs_opened,daily,2026-01-22,scaled_crps,moirai,0.113
113
- gh-archive,prs_opened,daily,2026-01-22,scaled_crps,timesfm,0.106
114
- gh-archive,prs_opened,daily,2026-01-29,mase,zero_model,3.192
115
- gh-archive,prs_opened,daily,2026-01-29,mase,seasonal_naive,1.078
116
- gh-archive,prs_opened,daily,2026-01-29,mase,auto_arima,0.947
117
- gh-archive,prs_opened,daily,2026-01-29,mase,auto_ets,0.965
118
- gh-archive,prs_opened,daily,2026-01-29,mase,auto_lgbm,0.852
119
- gh-archive,prs_opened,daily,2026-01-29,mase,chronos,0.731
120
- gh-archive,prs_opened,daily,2026-01-29,mase,moirai,0.774
121
- gh-archive,prs_opened,daily,2026-01-29,mase,timesfm,0.745
122
- gh-archive,prs_opened,daily,2026-01-29,scaled_crps,zero_model,0.475
123
- gh-archive,prs_opened,daily,2026-01-29,scaled_crps,seasonal_naive,0.199
124
- gh-archive,prs_opened,daily,2026-01-29,scaled_crps,auto_arima,0.160
125
- gh-archive,prs_opened,daily,2026-01-29,scaled_crps,auto_ets,0.166
126
- gh-archive,prs_opened,daily,2026-01-29,scaled_crps,auto_lgbm,0.141
127
- gh-archive,prs_opened,daily,2026-01-29,scaled_crps,chronos,0.109
128
- gh-archive,prs_opened,daily,2026-01-29,scaled_crps,moirai,0.117
129
- gh-archive,prs_opened,daily,2026-01-29,scaled_crps,timesfm,0.111
130
- gh-archive,issues_opened,daily,2026-01-08,mase,zero_model,3.567
131
- gh-archive,issues_opened,daily,2026-01-08,mase,seasonal_naive,1.142
132
- gh-archive,issues_opened,daily,2026-01-08,mase,auto_arima,1.023
133
- gh-archive,issues_opened,daily,2026-01-08,mase,auto_ets,1.041
134
- gh-archive,issues_opened,daily,2026-01-08,mase,auto_lgbm,0.912
135
- gh-archive,issues_opened,daily,2026-01-08,mase,chronos,0.789
136
- gh-archive,issues_opened,daily,2026-01-08,mase,moirai,0.834
137
- gh-archive,issues_opened,daily,2026-01-08,mase,timesfm,0.801
138
- gh-archive,issues_opened,daily,2026-01-08,scaled_crps,zero_model,0.512
139
- gh-archive,issues_opened,daily,2026-01-08,scaled_crps,seasonal_naive,0.218
140
- gh-archive,issues_opened,daily,2026-01-08,scaled_crps,auto_arima,0.179
141
- gh-archive,issues_opened,daily,2026-01-08,scaled_crps,auto_ets,0.185
142
- gh-archive,issues_opened,daily,2026-01-08,scaled_crps,auto_lgbm,0.152
143
- gh-archive,issues_opened,daily,2026-01-08,scaled_crps,chronos,0.119
144
- gh-archive,issues_opened,daily,2026-01-08,scaled_crps,moirai,0.128
145
- gh-archive,issues_opened,daily,2026-01-08,scaled_crps,timesfm,0.122
146
- gh-archive,issues_opened,daily,2026-01-15,mase,zero_model,3.521
147
- gh-archive,issues_opened,daily,2026-01-15,mase,seasonal_naive,1.128
148
- gh-archive,issues_opened,daily,2026-01-15,mase,auto_arima,1.008
149
- gh-archive,issues_opened,daily,2026-01-15,mase,auto_ets,1.029
150
- gh-archive,issues_opened,daily,2026-01-15,mase,auto_lgbm,0.925
151
- gh-archive,issues_opened,daily,2026-01-15,mase,chronos,0.798
152
- gh-archive,issues_opened,daily,2026-01-15,mase,moirai,0.841
153
- gh-archive,issues_opened,daily,2026-01-15,mase,timesfm,0.812
154
- gh-archive,issues_opened,daily,2026-01-15,scaled_crps,zero_model,0.508
155
- gh-archive,issues_opened,daily,2026-01-15,scaled_crps,seasonal_naive,0.214
156
- gh-archive,issues_opened,daily,2026-01-15,scaled_crps,auto_arima,0.175
157
- gh-archive,issues_opened,daily,2026-01-15,scaled_crps,auto_ets,0.181
158
- gh-archive,issues_opened,daily,2026-01-15,scaled_crps,auto_lgbm,0.155
159
- gh-archive,issues_opened,daily,2026-01-15,scaled_crps,chronos,0.121
160
- gh-archive,issues_opened,daily,2026-01-15,scaled_crps,moirai,0.131
161
- gh-archive,issues_opened,daily,2026-01-15,scaled_crps,timesfm,0.124
162
- gh-archive,issues_opened,daily,2026-01-22,mase,zero_model,3.601
163
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- gh-archive,issues_opened,weekly,2026-01-26,scaled_crps,auto_ets,0.167
398
- gh-archive,issues_opened,weekly,2026-01-26,scaled_crps,auto_lgbm,0.134
399
- gh-archive,issues_opened,weekly,2026-01-26,scaled_crps,chronos,0.104
400
- gh-archive,issues_opened,weekly,2026-01-26,scaled_crps,moirai,0.113
401
- gh-archive,issues_opened,weekly,2026-01-26,scaled_crps,timesfm,0.107
402
- gh-archive,pushes,weekly,2026-01-12,mase,zero_model,2.312
403
- gh-archive,pushes,weekly,2026-01-12,mase,seasonal_naive,0.891
404
- gh-archive,pushes,weekly,2026-01-12,mase,auto_arima,0.745
405
- gh-archive,pushes,weekly,2026-01-12,mase,auto_ets,0.762
406
- gh-archive,pushes,weekly,2026-01-12,mase,auto_lgbm,0.651
407
- gh-archive,pushes,weekly,2026-01-12,mase,chronos,0.523
408
- gh-archive,pushes,weekly,2026-01-12,mase,moirai,0.571
409
- gh-archive,pushes,weekly,2026-01-12,mase,timesfm,0.548
410
- gh-archive,pushes,weekly,2026-01-12,scaled_crps,zero_model,0.351
411
- gh-archive,pushes,weekly,2026-01-12,scaled_crps,seasonal_naive,0.148
412
- gh-archive,pushes,weekly,2026-01-12,scaled_crps,auto_arima,0.112
413
- gh-archive,pushes,weekly,2026-01-12,scaled_crps,auto_ets,0.118
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- gh-archive,pushes,weekly,2026-01-12,scaled_crps,auto_lgbm,0.092
415
- gh-archive,pushes,weekly,2026-01-12,scaled_crps,chronos,0.065
416
- gh-archive,pushes,weekly,2026-01-12,scaled_crps,moirai,0.073
417
- gh-archive,pushes,weekly,2026-01-12,scaled_crps,timesfm,0.068
418
- gh-archive,pushes,weekly,2026-01-19,mase,zero_model,2.278
419
- gh-archive,pushes,weekly,2026-01-19,mase,seasonal_naive,0.878
420
- gh-archive,pushes,weekly,2026-01-19,mase,auto_arima,0.731
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- gh-archive,pushes,weekly,2026-01-19,mase,auto_ets,0.749
422
- gh-archive,pushes,weekly,2026-01-19,mase,auto_lgbm,0.638
423
- gh-archive,pushes,weekly,2026-01-19,mase,chronos,0.512
424
- gh-archive,pushes,weekly,2026-01-19,mase,moirai,0.558
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- gh-archive,pushes,weekly,2026-01-19,mase,timesfm,0.534
426
- gh-archive,pushes,weekly,2026-01-19,scaled_crps,zero_model,0.345
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- gh-archive,pushes,weekly,2026-01-19,scaled_crps,seasonal_naive,0.144
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- gh-archive,pushes,weekly,2026-01-19,scaled_crps,auto_arima,0.108
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- gh-archive,pushes,weekly,2026-01-19,scaled_crps,auto_ets,0.114
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- gh-archive,pushes,weekly,2026-01-19,scaled_crps,auto_lgbm,0.088
431
- gh-archive,pushes,weekly,2026-01-19,scaled_crps,chronos,0.062
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- gh-archive,pushes,weekly,2026-01-19,scaled_crps,moirai,0.070
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- gh-archive,pushes,weekly,2026-01-19,scaled_crps,timesfm,0.065
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- gh-archive,pushes,weekly,2026-01-26,mase,zero_model,2.351
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- gh-archive,pushes,weekly,2026-01-26,mase,seasonal_naive,0.904
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- gh-archive,pushes,weekly,2026-01-26,mase,auto_arima,0.758
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441
- gh-archive,pushes,weekly,2026-01-26,mase,timesfm,0.558
442
- gh-archive,pushes,weekly,2026-01-26,scaled_crps,zero_model,0.357
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445
- gh-archive,pushes,weekly,2026-01-26,scaled_crps,auto_ets,0.121
446
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- gh-archive,pushes,weekly,2026-01-26,scaled_crps,chronos,0.068
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- gh-archive,pushes,weekly,2026-01-26,scaled_crps,moirai,0.076
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pyproject.toml CHANGED
@@ -8,6 +8,7 @@ dependencies = [
8
  "gradio>=6.5.1",
9
  "matplotlib>=3.10.8",
10
  "pandas>=3.0.0",
 
11
  ]
12
 
13
  [dependency-groups]
 
8
  "gradio>=6.5.1",
9
  "matplotlib>=3.10.8",
10
  "pandas>=3.0.0",
11
+ "pyarrow>=19.0.0",
12
  ]
13
 
14
  [dependency-groups]
rank_through_time.py CHANGED
@@ -244,10 +244,12 @@ def plot_value_for_subdataset(df, metric, subdataset):
244
  # ── CLI: save all figures to disk ────────────────────────────────────────────
245
 
246
  if __name__ == "__main__":
 
 
247
  OUT = pathlib.Path("figures/rank_through_time")
248
  OUT.mkdir(parents=True, exist_ok=True)
249
 
250
- raw = pd.read_csv("mock_evaluation_results.csv")
251
  raw = raw[raw["model"] != "zero_model"]
252
  metrics = sorted(raw["metric"].unique())
253
 
 
244
  # ── CLI: save all figures to disk ────────────────────────────────────────────
245
 
246
  if __name__ == "__main__":
247
+ from data import load_data
248
+
249
  OUT = pathlib.Path("figures/rank_through_time")
250
  OUT.mkdir(parents=True, exist_ok=True)
251
 
252
+ raw = load_data()
253
  raw = raw[raw["model"] != "zero_model"]
254
  metrics = sorted(raw["metric"].unique())
255
 
requirements.txt CHANGED
@@ -3,3 +3,4 @@
3
  gradio>=6.5.1
4
  matplotlib>=3.10.8
5
  pandas>=3.0.0
 
 
3
  gradio>=6.5.1
4
  matplotlib>=3.10.8
5
  pandas>=3.0.0
6
+ pyarrow>=19.0.0