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45 lines
1.8 KiB
Python
45 lines
1.8 KiB
Python
import pandas as pd
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import numpy as np
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from pandas.tseries.frequencies import to_offset
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def align_outputs(y_predicted, X_trans, X_test, y_test, target_column_name,
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predicted_column_name='predicted',
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horizon_colname='horizon_origin'):
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"""
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Demonstrates how to get the output aligned to the inputs
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using pandas indexes. Helps understand what happened if
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the output's shape differs from the input shape, or if
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the data got re-sorted by time and grain during forecasting.
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Typical causes of misalignment are:
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* we predicted some periods that were missing in actuals -> drop from eval
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* model was asked to predict past max_horizon -> increase max horizon
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* data at start of X_test was needed for lags -> provide previous periods
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"""
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if (horizon_colname in X_trans):
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df_fcst = pd.DataFrame({predicted_column_name: y_predicted,
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horizon_colname: X_trans[horizon_colname]})
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else:
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df_fcst = pd.DataFrame({predicted_column_name: y_predicted})
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# y and X outputs are aligned by forecast() function contract
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df_fcst.index = X_trans.index
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# align original X_test to y_test
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X_test_full = X_test.copy()
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X_test_full[target_column_name] = y_test
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# X_test_full's index does not include origin, so reset for merge
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df_fcst.reset_index(inplace=True)
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X_test_full = X_test_full.reset_index().drop(columns='index')
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together = df_fcst.merge(X_test_full, how='right')
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# drop rows where prediction or actuals are nan
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# happens because of missing actuals
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# or at edges of time due to lags/rolling windows
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clean = together[together[[target_column_name,
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predicted_column_name]].notnull().all(axis=1)]
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return(clean)
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