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# Benchmark Models

Benchmark models are a set of standard models that the Numerai team built. Their predictions are then given out every round so that anyone can easily submit them and stake on them if they want.

The list of models and their recent performance is here:

`> pip install numerapi`

from numerapi import NumerAPI

napi = NumerAPI()

napi.download_dataset("v4.2/train_benchmark_models.parquet", "train_benchmark_models.parquet")

napi.download_dataset("v4.2/validation_benchmark_models.parquet", "validation_benchmark_models.parquet")

napi.download_dataset("v4.2/live_benchmark_models.parquet", "live_benchmark_models.parquet")

**Walk Forward Cross Validation**

All predictions are made using a Walk-Forward framework. This means all predictions are made using models which were trained only on data which was available prior to the date of the prediction being made.

Specifically, the data is split up into chunks of 156 eras. Then for each chunk of eras, the predictions are given by a model which is trained up to first_era_of_chunk - purge_eras. The number of purge_eras is always 8 for 20D targets, and 16 for 60D targets.

So eras 157 to 313 are predicted using a model trained up to era148, and then eras 314-470 are predicted using a model trained up to 306, and so on.

**Standard Large LGBM params**

All of the models use the following LGBM parameters:

standard_large_lgbm_params = {

"n_estimators": 20000,

"learning_rate": 0.001,

"max_depth": 6,

"num_leaves": 2**6,

"colsample_bytree": 0.1,

}

We've found that having more trees can be helpful, and we've found that having less trees with more depth can also achieve similar results with lower compute requirements. See more here: https://forum.numer.ai/t/super-massive-lgbm-grid-search/6463

**Ensembles**

All of the ensembles use the following steps:

- 1.gaussianize each of the predictions on a per-era basis
- 2.standardize to standard deviation 1
- 3.dot-product the predictions with a weights vector representing the desired weight on each model
- 4.gaussianize the resulting predictions vector, and neutralize if there are any features to neutralize to

Steps 1,2, and 3 look something like this:

def rank_gauss_pow1(s: pd.Series) -> pd.Series:

# do rank-normalize

s_rank = rank_keep_ties_keep_na(s)

# gaussianize

s_rank_norm = pd.Series(stats.norm.ppf(s_rank), index=s_rank.index)

# Standardize to 1 std

result_series = s_rank_norm / s_rank_norm.std()

return result_series

ensemble_cols = ["V4_LGBM_NOMI20", "V42_RAIN_ENSEMBLE"]

weight_vector = [0.1, 0.9]

for col in X[ensemble_cols]:

if "era" in X.columns:

X[col] = X.groupby("era", group_keys=False)[col].transform(lambda s1: rank_gauss_pow1(s1))

else:

# check X contains only a single era

assert 1800 < X.shape[0] < 6000

X[col] = rank_gauss_pow1(X[col])

return X[ensemble_cols].dot(weight_vector)

**Neutralization**

A couple of the models have some neutralization involved. This is basically doing a regression to find out your predictions' exposures to each feature, and then subtracting those exposures from your predictions vector such that the result is a vector which is orthogonal to all of those features.

Here's the code to neutralize some list of prediction columns (columns) by some list of features (neutralizers)

def neutralize(

df, columns, neutralizers=None, proportion=1.0, era_col="era"

):

if neutralizers is None:

neutralizers = []

unique_eras = df[era_col].unique()

computed = []

for u in unique_eras:

df_era = df[df[era_col] == u]

scores = df_era[columns].values

scores2 = []

for x in scores.T:

x = pd.Series(x)

x = (x.rank(method="first") - 0.5) / len(x.dropna())

x = stats.norm.ppf(x)

scores2.append(x)

scores = np.array(scores2).T

exposures = (

df_era[neutralizers]

.fillna(df_era[neutralizers].median())

.fillna(0.5)

.values

)

scores -= proportion * exposures.dot(

np.linalg.pinv(exposures.astype(np.float32), rcond=1e-6).dot(

scores.astype(np.float32)

)

)

scores /= pd.DataFrame(scores).std(ddof=0, axis=0, skipna=True).values

computed.append(scores)

return pd.DataFrame(np.concatenate(computed), columns=columns, index=df.index)

**{data_version}_LGBM_{target}**

There are many models that have some combination of a data version (V2, V3, V4, V41, V42) and a target (Nomi20, Sam60).

These are models trained in the standard walk-forward way, with standard LGBM parameters, using the specified data version and target. That's all!

**V42_RAIN_ENSEMBLE and V42_RAIN_ENSEMBLE2**

The V42_RAIN_ENSEMBLE and V42_RAIN_ENSEMBLE2 models are models we built which take advantage of 22 different standard models, all ensembled together.

V42_RAIN_ENSEMBLE uses the 11 best targets that we had made as of the release of the Rain dataset, with an emphasis on Cyrus, the scoring target, and twice as much weight on 20D targets as on 60D targets.

The different targets are:

Cyrus20: 33%

Ralph20: 3.3%

Jeremy20: 3.3%

Waldo20: 3.3%

Tyler20: 3.3%

Victor20: 3.3%

Alpha20: 3.3%

Bravo20: 3.3%

Charlie20: 3.3%

Delta20: 3.3%

Echo20: 3.3%

Cyrus60: 17%

Ralph60: 1.7%

Jeremy60: 1.7%

Waldo60: 1.7%

Tyler60: 1.7%

Victor60: 1.7%

Alpha60: 1.7%

Bravo60: 1.7%

Charlie60: 1.7%

Delta60: 1.7%

Echo60: 1.7%

V42_RAIN_ENSEMBLE2 uses the same set of 11 targets, but with no weight on the 60D versions. It is also neutral to the serenity set of features.

**V42_LGBM_CT_BLEND**

This is a simple 50/50 blend of V42_LGBM_TEAGER20 and V42_LGBM_CYRUS20

**V42_EXAMPLE_PREDS**

This is a standard model using V42 data and Cyrus20 target.

**V41_EXAMPLE_PREDS**

This is what was once the Sunshine Example Model.

It uses standard models built on 6 different targets: Nomi20, Jerome60, Ralph20, Victor20, Waldo20, Tyler20.

These models are equal weight in the ensemble.

The final predictions are then neutralized at a proportion of 50% to the V4.1 "medium" feature set.

**V3_EXAMPLE_PREDS**

This is a standard model trained on the v3 equivalent feature set and the Ralph20 target.

**V2_EXAMPLE_PREDS**

This is a standard model trained on the v2 equivalent feature set and the Nomi20 target.

**Other Models**

There are several other models which you'll find on the Benchmark Models page but whose predictions aren't present in the predictions files.

V4_EXAMPLE_PREDS - This is a standard model on V4 data and Nomi20, and is neutral to the 50 "riskiest features" calculated by which features have the biggest change in mean correlation with Nomi between half1 and half2 of the training data.

INTEGRATION_TEST - This used to submit V2 example predictions for years, but with V2 deleted now, it is just submitting my latest favorite model. So right now this slot is submitting RAIN_ENSEMBLE predictions.

NB_HELLO_NUMERAI - This submits the model created by the default Hello Numerai tutorial notebook.

NB_FEATURE_NEUTRAL - This submits the model created by the feature neutralization tutorial notebook.

NB_TARGET_ENSEMBLE - This submits the model created by the target ensemble tutorial notebook.

NB_EXAMPLE_MODEL - This submits the model created by the barebones example_model notebook.

Last modified 6d ago