import streamlit as st st.title('Numerai Example Script') # content below from # https://github.com/numerai/example-scripts/blob/master/example_model.py # import pandas as pd from lightgbm import LGBMRegressor import gc import json from pathlib import Path from numerapi import NumerAPI from utils import ( save_model, load_model, neutralize, get_biggest_change_features, validation_metrics, ERA_COL, DATA_TYPE_COL, TARGET_COL, EXAMPLE_PREDS_COL ) # download all the things napi = NumerAPI() current_round = napi.get_current_round() # Tournament data changes every week so we specify the round in their name. Training # and validation data only change periodically, so no need to download them every time. print('Downloading dataset files...') Path("./v4").mkdir(parents=False, exist_ok=True) napi.download_dataset("v4/train.parquet") napi.download_dataset("v4/validation.parquet") napi.download_dataset("v4/live.parquet", f"v4/live_{current_round}.parquet") napi.download_dataset("v4/validation_example_preds.parquet") napi.download_dataset("v4/features.json") print('Reading minimal training data') # read the feature metadata and get a feature set (or all the features) with open("v4/features.json", "r") as f: feature_metadata = json.load(f) # features = list(feature_metadata["feature_stats"].keys()) # get all the features # features = feature_metadata["feature_sets"]["small"] # get the small feature set features = feature_metadata["feature_sets"]["medium"] # get the medium feature set # read in just those features along with era and target columns read_columns = features + [ERA_COL, DATA_TYPE_COL, TARGET_COL] # note: sometimes when trying to read the downloaded data you get an error about invalid magic parquet bytes... # if so, delete the file and rerun the napi.download_dataset to fix the corrupted file training_data = pd.read_parquet('v4/train.parquet', columns=read_columns) validation_data = pd.read_parquet('v4/validation.parquet', columns=read_columns) live_data = pd.read_parquet(f'v4/live_{current_round}.parquet', columns=read_columns) # pare down the number of eras to every 4th era # every_4th_era = training_data[ERA_COL].unique()[::4] # training_data = training_data[training_data[ERA_COL].isin(every_4th_era)] # getting the per era correlation of each feature vs the target all_feature_corrs = training_data.groupby(ERA_COL).apply( lambda era: era[features].corrwith(era[TARGET_COL]) ) # find the riskiest features by comparing their correlation vs # the target in each half of training data; we'll use these later riskiest_features = get_biggest_change_features(all_feature_corrs, 50) # "garbage collection" (gc) gets rid of unused data and frees up memory gc.collect() model_name = f"model_target" print(f"Checking for existing model '{model_name}'") model = load_model(model_name) if not model: print(f"model not found, creating new one") params = {"n_estimators": 2000, "learning_rate": 0.01, "max_depth": 5, "num_leaves": 2 ** 5, "colsample_bytree": 0.1} model = LGBMRegressor(**params) # train on all of train and save the model so we don't have to train next time model.fit(training_data.filter(like='feature_', axis='columns'), training_data[TARGET_COL]) print(f"saving new model: {model_name}") save_model(model, model_name) gc.collect() nans_per_col = live_data[live_data["data_type"] == "live"][features].isna().sum() # check for nans and fill nans if nans_per_col.any(): total_rows = len(live_data[live_data["data_type"] == "live"]) print(f"Number of nans per column this week: {nans_per_col[nans_per_col > 0]}") print(f"out of {total_rows} total rows") print(f"filling nans with 0.5") live_data.loc[:, features] = live_data.loc[:, features].fillna(0.5) else: print("No nans in the features this week!") # double check the feature that the model expects vs what is available to prevent our # pipeline from failing if Numerai adds more data and we don't have time to retrain! model_expected_features = model.booster_.feature_name() if set(model_expected_features) != set(features): print(f"New features are available! Might want to retrain model {model_name}.") validation_data.loc[:, f"preds_{model_name}"] = model.predict( validation_data.loc[:, model_expected_features]) live_data.loc[:, f"preds_{model_name}"] = model.predict( live_data.loc[:, model_expected_features]) gc.collect() # neutralize our predictions to the riskiest features validation_data[f"preds_{model_name}_neutral_riskiest_50"] = neutralize( df=validation_data, columns=[f"preds_{model_name}"], neutralizers=riskiest_features, proportion=1.0, normalize=True, era_col=ERA_COL ) live_data[f"preds_{model_name}_neutral_riskiest_50"] = neutralize( df=live_data, columns=[f"preds_{model_name}"], neutralizers=riskiest_features, proportion=1.0, normalize=True, era_col=ERA_COL ) model_to_submit = f"preds_{model_name}_neutral_riskiest_50" # rename best model to "prediction" and rank from 0 to 1 to meet upload requirements validation_data["prediction"] = validation_data[model_to_submit].rank(pct=True) live_data["prediction"] = live_data[model_to_submit].rank(pct=True) validation_data["prediction"].to_csv(f"validation_predictions_{current_round}.csv") live_data["prediction"].to_csv(f"live_predictions_{current_round}.csv") validation_preds = pd.read_parquet('v4/validation_example_preds.parquet') validation_data[EXAMPLE_PREDS_COL] = validation_preds["prediction"] # get some stats about each of our models to compare... # fast_mode=True so that we skip some of the stats that are slower to calculate validation_stats = validation_metrics(validation_data, [model_to_submit, f"preds_{model_name}"], example_col=EXAMPLE_PREDS_COL, fast_mode=True, target_col=TARGET_COL) print(validation_stats[["mean", "sharpe"]].to_markdown()) print(f''' Done! Next steps: 1. Go to numer.ai/tournament (make sure you have an account) 2. Submit validation_predictions_{current_round}.csv to the diagnostics tool 3. Submit tournament_predictions_{current_round}.csv to the "Upload Predictions" button ''')