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comparecast-0.3.0
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Comparing Sequential Forecasters
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | comparecast-0.3.0 |
| نام | comparecast |
| نسخه کتابخانه | 0.3.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Yo Joong Choe, Aaditya Ramdas |
| ایمیل نویسنده | yjchoe@cmu.edu |
| آدرس صفحه اصلی | https://github.com/yjchoe/ComparingForecasters |
| آدرس اینترنتی | https://pypi.org/project/comparecast/ |
| مجوز | MIT |
# ComparingForecasters
[](https://pypi.org/project/comparecast/)
[](https://pypi.org/project/comparecast/)
Code accompanying our paper, [_Comparing Sequential Forecasters_](https://arxiv.org/abs/2110.00115) _(under revision)_,
where we derive anytime-valid, distribution-free, and non-asymptotic
confidence sequences (CS) and e-processes for comparing probability forecasts on sequential data.
## Installation
Requires Python 3.7+.
From `pip`:
```shell
pip install --upgrade pip
pip install --upgrade pandas seaborn tqdm confseq
pip install --upgrade comparecast
```
From source:
```shell
git clone https://github.com/yjchoe/ComparingForecasters
cd ComparingForecasters
pip install --upgrade pip
pip install -r requirements.txt
pip install -e .
```
## Data Sources
See [`data/README.md`](data/README.md).
## Sample Usage
Also see experiment notebooks below.
### Python
```python
import comparecast as cc
# Generate/retrieve synthetic data
data = cc.data_utils.synthetic.get_data("default", size=1000)
# Calculate, save, and plot the forecasts
forecasters = ["k29_poly3", "laplace", "constant_0.5"]
data = cc.forecast(data, forecasters, out_file="data/test.csv")
cc.plot_forecasts(data, forecasters, plots_dir="plots/test")
# Compare forecasts using confidence sequences & e-values
results = cc.compare_forecasts(
data,
"k29_poly3",
"laplace",
scoring_rule="brier",
alpha=0.05,
compute_cs=True,
compute_e=True,
)
# returns a pandas DataFrame
results.tail(5)
# time lcb ucb e_pq e_qp
# 995 996 0.012868 0.072742 2025.725774 0.021684
# 996 997 0.013050 0.072879 2157.262456 0.021672
# 997 998 0.012635 0.072492 1886.687861 0.021596
# 998 999 0.012824 0.072637 2013.209084 0.021583
# 999 1000 0.012447 0.072275 1783.204679 0.021519
# Draw a comparison plot and save in plots/test/*.pdf
results, axes = cc.plot_comparison(
data,
"k29_poly3",
"laplace",
scoring_rule="brier",
alpha=0.05,
baselines=("h", "acs"),
plot_e=True,
plot_width=True,
plots_dir="plots/test",
)
```
### Command Line Interface
```shell
# Generate synthetic data and forecasts
python3 forecast.py -d default -n 1000 -f all \
-o forecasts/test.csv -p plots/test
# Compare forecasts and plot results
python3 plot_comparisons.py -d forecasts/test.csv \
-p k29_poly3 -q laplace --baselines h acs -o plots/test
# Compare 538 and vegas forecasters
python3 plot_comparisons.py -d forecasts/mlb_2010_2019.csv \
-p fivethirtyeight -q vegas --baselines acs -o plots/test/mlb_2010_2019 \
--ylim-scale 0.01
```
## Experiments
Main experiments:
- [**`nb_comparecast_synthetic.ipynb`**](nb_comparecast_synthetic.ipynb):
Experiments on synthetic data and forecasts.
Includes comparison with a fixed-time CI.
Section 5.1 in our paper.
- [**`nb_comparecast_scoringrules.ipynb`**](nb_comparecast_scoringrules.ipynb):
Experiments on synthetic data and forecasts using different scoring rules.
Section 5.1 (Figure 4) in our paper.
- [**`nb_comparecast_baseball.ipynb`**](nb_comparecast_baseball.ipynb):
Experiments on Major League Baseball forecasts,
leading up to the 2019 World Series.
Section 5.2 in our paper.
- [**`nb_comparecast_weather.ipynb`**](nb_comparecast_weather.ipynb):
Experiments on postprocessing methods for ensemble weather forecasts.
Includes e-value comparison with
[Henzi & Ziegel (2021)](https://arxiv.org/abs/2103.08402).
Section 5.3 in our paper.
Additional experiments:
- [**`nb_comparecast_weather_eda.ipynb`**](nb_comparecast_weather_eda.ipynb):
Exploratory plots on the ensemble weather forecast dataset.
Appendix I.3 in our paper.
- [**`nb_iid_mean.ipynb`**](nb_iid_mean.ipynb):
Comparison of uniform boundaries on the mean of IID data.
Partly reproduces Figure 1 from
[Howard et al. (2021)](https://doi.org/10.1214/20-AOS1991).
Appendix I.4 in our paper.
- [**`nb_cgf_convexity.ipynb`**](nb_cgf_convexity.ipynb):
Illustration of the Exponential CGF-like function.
Appendix F.2 (Figure 6) in our paper.
- [**`nb_comparecast_comparison_with_dm.ipynb`**](nb_comparecast_comparison_with_dm.ipynb):
Validity comparison with classical comparison methods (DM & GW).
Appendix H.2 (Figure 7) in our paper.
- [**`nb_comparecast_comparison_with_dm_power.ipynb`**](nb_comparecast_comparison_with_dm_power.ipynb):
"Power" comparison with classical comparison methods (DM & GW).
Appendix H.2 (Figure 8) in our paper.
- [**`nb_eprocess_ville.ipynb`**](nb_eprocess_ville.ipynb):
Illustrating some properties of (sub-exponential) e-/p-processes
in the context of game-theoretic statistical inference.
Not used in our paper.
## License
MIT
## Authors
[YJ Choe](http://yjchoe.github.io/) and
[Aaditya Ramdas](https://www.stat.cmu.edu/~aramdas/)
نیازمندی
| مقدار | نام |
|---|---|
| >=1.20 | numpy |
| - | scipy |
| >=1.0 | pandas |
| >=0.11 | seaborn |
| - | tqdm |
| - | openpyxl |
| >=0.0.9 | confseq |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7 | Python |
نحوه نصب
نصب پکیج whl comparecast-0.3.0:
pip install comparecast-0.3.0.whl
نصب پکیج tar.gz comparecast-0.3.0:
pip install comparecast-0.3.0.tar.gz