mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-27 18:57:55 +00:00
Mark Aron Szulyovszky
7aedb91069
* feat(Eval): added format_data_for_backtest() * feat(Data): added many configurable parameters to load_files to reduce boilerplate and prepare for HPO * feat(Core): added walk forward method of training/testing * fix(Model): remove the unnecessary softmax activation from the keras models * feat(Core): added walk_forward_train_test()
96 lines
3.2 KiB
Python
96 lines
3.2 KiB
Python
#%% Import all the stuff, load data, define constants
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from load_data import load_files, create_target_classes
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import pandas as pd
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import numpy as np
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from utils.sktime import from_df_to_sktime_data
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from sktime.utils.plotting import plot_series
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from sktime.forecasting.model_selection import temporal_train_test_split
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from sklearn.metrics import accuracy_score
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from sklearn.pipeline import Pipeline
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from sktime.classification.interval_based import (
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TimeSeriesForestClassifier,
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SupervisedTimeSeriesForest,
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)
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from sktime.forecasting.model_selection import SlidingWindowSplitter
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from sktime.forecasting.model_selection import ForecastingRandomizedSearchCV
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from sktime.forecasting.compose import make_reduction
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from sklearn.tree import DecisionTreeClassifier
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from sklearn.metrics import confusion_matrix
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from sklearn.preprocessing import StandardScaler
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from sktime.forecasting.model_selection import (
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SlidingWindowSplitter,
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ForecastingGridSearchCV,
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)
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from utils.evaluate import print_classification_metrics, format_data_for_backtest
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from sklearn.ensemble import RandomForestRegressor
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from sklearnex import patch_sklearn
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patch_sklearn()
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ticket_to_predict = 'BTC_USD'
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print('Predicting: ', ticket_to_predict)
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data = load_files(path='data/',
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own_asset=ticket_to_predict,
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load_other_assets=True,
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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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)
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target_col = 'target'
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returns_col = ticket_to_predict + '_returns'
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data = create_target_classes(data, returns_col, 1, 'two')
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X = data.drop(columns=[target_col])
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y = data[target_col]
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X_train, X_test, y_train, y_test = temporal_train_test_split(X, y, test_size=0.2)
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X_train = from_df_to_sktime_data(X_train)
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X_test = from_df_to_sktime_data(X_test)
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#%%
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# pipe = RecursiveTabularRegressionForecaster(steps=[
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# # ("deseasonalizer", OptionalPassthrough(Deseasonalizer())),
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# ("scaler", StandardScaler()),
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# ("classifier", TimeSeriesForestClassifier(n_estimators=200, random_state=1)),
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# ])
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# pipe.fit(X_train, y_train)
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# regressor = RandomForestRegressor(n_estimators=20)
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# model = DecisionTreeClassifier(random_state=1)
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model = TimeSeriesForestClassifier(n_estimators=50, random_state=1)
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model.fit(y = y_train, X = X_train)
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# forecaster = make_reduction(model, scitype="tabular-regressor")
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# nested_params = {"window_length": list(range(2,30)),
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# "estimator__max_depth": list(range(5,16))}
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# # "estimator__n_estimators": list(range(10,200))}
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#%%
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# cv = SlidingWindowSplitter(initial_window=40, window_length=30)
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# nrcv = ForecastingRandomizedSearchCV(forecaster, strategy="refit", cv=cv,
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# param_distributions=nested_params,
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# n_iter=5, random_state=42)
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# nrcv.fit(y = y_train, X = X_train, fh=np.array([1]))
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# print(nrcv.best_params_)
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# print(nrcv.best_score_)
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# model = DecisionTreeClassifier(random_state=1)
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# model.fit(X_train, y_train)
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# preds = nrcv.best_forecaster_.predict( X=X_test)
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# # print(preds)
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preds = model.predict(X_test)
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print(print_classification_metrics(y_test, preds))
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# backtest_data = format_data_for_backtest(data, returns_col, X_test, preds)
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# print(backtest_data)
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# %%
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