mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-27 18:57:55 +00:00
Mark Aron Szulyovszky
6440ced32c
* feat(Tests): added basic unit tests for walk_forward_train_test() * fix(Tests): inherit from BaseEstimator, fix index problems in walk_forward_train_test * fix(WalkForward): predictions were mistakenly removed, oops * fix(WalkForward): mistakenly re-assiging model
106 lines
3.7 KiB
Python
106 lines
3.7 KiB
Python
from typing import Literal
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from sklearnex import patch_sklearn
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patch_sklearn()
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from load_data import get_all_assets, load_data
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from utils.evaluate import evaluate_predictions
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import pandas as pd
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from sklearn.linear_model import LinearRegression, Lasso, BayesianRidge, LogisticRegression
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from sklearn.tree import DecisionTreeClassifier
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from sklearn.neighbors import KNeighborsRegressor, KNeighborsClassifier
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from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
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from sklearn.svm import SVR
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from sklearn.naive_bayes import GaussianNB
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from sklearn.neural_network import MLPRegressor, MLPClassifier
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from sklearn.ensemble import AdaBoostRegressor, RandomForestRegressor, ExtraTreesRegressor, AdaBoostClassifier, GradientBoostingClassifier, RandomForestClassifier, ExtraTreesClassifier
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from sklearn.preprocessing import MinMaxScaler
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from utils.walk_forward import walk_forward_train_test
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regression_models = [
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('LR', LinearRegression(n_jobs=-1)),
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('BayesianRidge', BayesianRidge()),
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('KNN', KNeighborsRegressor(n_neighbors=15)),
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# ('MLP', MLPRegressor(hidden_layer_sizes=(100,20), max_iter=1000)),
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('AB', AdaBoostRegressor()),
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# ('RF', RandomForestRegressor(n_jobs=-1)),
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# ('SVR', SVR(kernel='rbf', C=1e3, gamma=0.1))
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]
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classification_models = [
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('LR', LogisticRegression(n_jobs=-1)),
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('LDA', LinearDiscriminantAnalysis()),
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('KNN', KNeighborsClassifier()),
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('CART', DecisionTreeClassifier()),
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('NB', GaussianNB()),
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('AB', AdaBoostClassifier()),
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('RF', RandomForestClassifier(n_jobs=-1))
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]
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def run_whole_pipeline(
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ticker_to_predict: str,
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models,
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method: Literal['regression', 'classification'],
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sliding_window_size: int,
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retrain_every: int,
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scaling: bool,
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):
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print('--------\nPredicting: ', ticker_to_predict)
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X, y = load_data(path='data/',
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target_asset=ticker_to_predict,
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target_asset_lags=[1,2,3,4,5,6,8,10,15],
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load_other_assets=False,
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other_asset_lags=[],
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log_returns=True,
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add_date_features=True,
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own_technical_features='level2',
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other_technical_features='none',
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exogenous_features='none',
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index_column='int',
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method=method,
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)
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if scaling:
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# TODO: should move scaling to an expanding window compomenent, probably worth not turning it on for now
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feature_scaler = MinMaxScaler(feature_range= (-1, 1))
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X = pd.DataFrame(feature_scaler.fit_transform(X), columns=X.columns, index=X.index)
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# TODO: should scale y as well probably
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results = pd.DataFrame()
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for model_name, model in models:
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model_over_time, preds = walk_forward_train_test(
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model_name=model_name,
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model = model,
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X = X,
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y = y,
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window_size = sliding_window_size,
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retrain_every = retrain_every
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)
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result = evaluate_predictions(model_name, y, preds, sliding_window_size, method)
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column_name = ticker_to_predict + "_" + model_name
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results[column_name] = result
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return results
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results = pd.DataFrame()
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all_assets = get_all_assets('data/')
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for asset in all_assets:
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for method in ['regression']:
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current_result = run_whole_pipeline(
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ticker_to_predict = asset,
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models = regression_models if method == 'regression' else classification_models,
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method = method,
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sliding_window_size = 120,
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retrain_every = 50,
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scaling = False
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)
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results = pd.concat([results, current_result], axis=1)
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results.to_csv('results.csv') |