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[Minor] Split prep_or_copy_df into copy and check_multiple_series_id (#…
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…1647)

* separate copy from check multiple ID

* fix remaining references

* clean up

* add copy to tests

* fix tests

* update tests

* fixes

* finish fixes
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@ourownstory
ourownstory authored Sep 13, 2024
1 parent fe309be commit 4904808
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23 changes: 1 addition & 22 deletions docs/source/how-to-guides/feature-guides/global_local_trend.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -1263,28 +1263,7 @@
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "Invalid frequency: NaT",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"Input \u001b[0;32mIn [27]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m future \u001b[38;5;241m=\u001b[39m m\u001b[38;5;241m.\u001b[39mmake_future_dataframe(df_test)\n\u001b[0;32m----> 2\u001b[0m forecast \u001b[38;5;241m=\u001b[39m \u001b[43mm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfuture\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m metrics \u001b[38;5;241m=\u001b[39m m\u001b[38;5;241m.\u001b[39mtest(df_test)\n\u001b[1;32m 4\u001b[0m forecast_trend \u001b[38;5;241m=\u001b[39m m\u001b[38;5;241m.\u001b[39mpredict_trend(df_test)\n",
"File \u001b[0;32m~/Desktop/code/neural_prophet/neuralprophet/forecaster.py:831\u001b[0m, in \u001b[0;36mNeuralProphet.predict\u001b[0;34m(self, df, decompose, raw)\u001b[0m\n\u001b[1;32m 829\u001b[0m df, received_ID_col, received_single_time_series, _ \u001b[38;5;241m=\u001b[39m df_utils\u001b[38;5;241m.\u001b[39mprep_or_copy_df(df)\n\u001b[1;32m 830\u001b[0m \u001b[38;5;66;03m# to get all forecasteable values with df given, maybe extend into future:\u001b[39;00m\n\u001b[0;32m--> 831\u001b[0m df, periods_added \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_maybe_extend_df\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 832\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prepare_dataframe_to_predict(df)\n\u001b[1;32m 833\u001b[0m \u001b[38;5;66;03m# normalize\u001b[39;00m\n",
"File \u001b[0;32m~/Desktop/code/neural_prophet/neuralprophet/forecaster.py:2773\u001b[0m, in \u001b[0;36mNeuralProphet._maybe_extend_df\u001b[0;34m(self, df)\u001b[0m\n\u001b[1;32m 2771\u001b[0m extended_df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame()\n\u001b[1;32m 2772\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m df_name, df_i \u001b[38;5;129;01min\u001b[39;00m df\u001b[38;5;241m.\u001b[39mgroupby(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mID\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m-> 2773\u001b[0m _ \u001b[38;5;241m=\u001b[39m \u001b[43mdf_utils\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minfer_frequency\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_i\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_lags\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmax_lags\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfreq\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata_freq\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2774\u001b[0m \u001b[38;5;66;03m# to get all forecasteable values with df given, maybe extend into future:\u001b[39;00m\n\u001b[1;32m 2775\u001b[0m periods_add[df_name] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_maybe_extend_periods(df_i)\n",
"File \u001b[0;32m~/Desktop/code/neural_prophet/neuralprophet/df_utils.py:1324\u001b[0m, in \u001b[0;36minfer_frequency\u001b[0;34m(df, freq, n_lags, min_freq_percentage)\u001b[0m\n\u001b[1;32m 1322\u001b[0m freq_df \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m()\n\u001b[1;32m 1323\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m df_name, df_i \u001b[38;5;129;01min\u001b[39;00m df\u001b[38;5;241m.\u001b[39mgroupby(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mID\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m-> 1324\u001b[0m freq_df\u001b[38;5;241m.\u001b[39mappend(\u001b[43m_infer_frequency\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_i\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfreq\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmin_freq_percentage\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 1325\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mset\u001b[39m(freq_df)) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m n_lags \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 1326\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 1327\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOne or more dataframes present different major frequencies, please make sure all dataframes present the same major frequency for auto-regression\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1328\u001b[0m )\n",
"File \u001b[0;32m~/Desktop/code/neural_prophet/neuralprophet/df_utils.py:1252\u001b[0m, in \u001b[0;36m_infer_frequency\u001b[0;34m(df, freq, min_freq_percentage)\u001b[0m\n\u001b[1;32m 1250\u001b[0m dominant_freq_percentage \u001b[38;5;241m=\u001b[39m distribution\u001b[38;5;241m.\u001b[39mmax() \u001b[38;5;241m/\u001b[39m \u001b[38;5;28mlen\u001b[39m(df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mds\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m 1251\u001b[0m num_freq \u001b[38;5;241m=\u001b[39m frequencies[np\u001b[38;5;241m.\u001b[39margmax(distribution)] \u001b[38;5;66;03m# get value of most common diff\u001b[39;00m\n\u001b[0;32m-> 1252\u001b[0m inferred_freq \u001b[38;5;241m=\u001b[39m \u001b[43mconvert_num_to_str_freq\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnum_freq\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mds\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miloc\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1254\u001b[0m log\u001b[38;5;241m.\u001b[39minfo(\n\u001b[1;32m 1255\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMajor frequency \u001b[39m\u001b[38;5;132;01m{\u001b[39;00minferred_freq\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m corresponds to \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnp\u001b[38;5;241m.\u001b[39mround(dominant_freq_percentage \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m100\u001b[39m, \u001b[38;5;241m3\u001b[39m)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m% of the data.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1256\u001b[0m )\n\u001b[1;32m 1257\u001b[0m ideal_freq_exists \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m \u001b[38;5;28;01mif\u001b[39;00m dominant_freq_percentage \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m min_freq_percentage \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
"File \u001b[0;32m~/Desktop/code/neural_prophet/neuralprophet/df_utils.py:1159\u001b[0m, in \u001b[0;36mconvert_num_to_str_freq\u001b[0;34m(freq_num, initial_time_stamp)\u001b[0m\n\u001b[1;32m 1144\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mconvert_num_to_str_freq\u001b[39m(freq_num, initial_time_stamp):\n\u001b[1;32m 1145\u001b[0m \u001b[38;5;124;03m\"\"\"Convert numeric frequencies into frequency tags\u001b[39;00m\n\u001b[1;32m 1146\u001b[0m \n\u001b[1;32m 1147\u001b[0m \u001b[38;5;124;03m Parameters\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1157\u001b[0m \u001b[38;5;124;03m frequency tag\u001b[39;00m\n\u001b[1;32m 1158\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1159\u001b[0m aux_ts \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdate_range\u001b[49m\u001b[43m(\u001b[49m\u001b[43minitial_time_stamp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mperiods\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m100\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfreq\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_timedelta\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfreq_num\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1160\u001b[0m freq_str \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39minfer_freq(aux_ts)\n\u001b[1;32m 1161\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m freq_str\n",
"File \u001b[0;32m~/Desktop/code/neural_prophet/env/lib/python3.8/site-packages/pandas/core/indexes/datetimes.py:1070\u001b[0m, in \u001b[0;36mdate_range\u001b[0;34m(start, end, periods, freq, tz, normalize, name, closed, inclusive, **kwargs)\u001b[0m\n\u001b[1;32m 1067\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m freq \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m com\u001b[38;5;241m.\u001b[39many_none(periods, start, end):\n\u001b[1;32m 1068\u001b[0m freq \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mD\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m-> 1070\u001b[0m dtarr \u001b[38;5;241m=\u001b[39m \u001b[43mDatetimeArray\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_generate_range\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1071\u001b[0m \u001b[43m \u001b[49m\u001b[43mstart\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstart\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1072\u001b[0m \u001b[43m \u001b[49m\u001b[43mend\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mend\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1073\u001b[0m \u001b[43m \u001b[49m\u001b[43mperiods\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mperiods\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1074\u001b[0m \u001b[43m \u001b[49m\u001b[43mfreq\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfreq\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1075\u001b[0m \u001b[43m \u001b[49m\u001b[43mtz\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtz\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1076\u001b[0m \u001b[43m \u001b[49m\u001b[43mnormalize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnormalize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1077\u001b[0m \u001b[43m \u001b[49m\u001b[43minclusive\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minclusive\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1078\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1079\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1080\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m DatetimeIndex\u001b[38;5;241m.\u001b[39m_simple_new(dtarr, name\u001b[38;5;241m=\u001b[39mname)\n",
"File \u001b[0;32m~/Desktop/code/neural_prophet/env/lib/python3.8/site-packages/pandas/core/arrays/datetimes.py:409\u001b[0m, in \u001b[0;36mDatetimeArray._generate_range\u001b[0;34m(cls, start, end, periods, freq, tz, normalize, ambiguous, nonexistent, inclusive)\u001b[0m\n\u001b[1;32m 404\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m com\u001b[38;5;241m.\u001b[39mcount_not_none(start, end, periods, freq) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m3\u001b[39m:\n\u001b[1;32m 405\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 406\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOf the four parameters: start, end, periods, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 407\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mand freq, exactly three must be specified\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 408\u001b[0m )\n\u001b[0;32m--> 409\u001b[0m freq \u001b[38;5;241m=\u001b[39m \u001b[43mto_offset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfreq\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 411\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m start \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 412\u001b[0m start \u001b[38;5;241m=\u001b[39m Timestamp(start)\n",
"File \u001b[0;32mpandas/_libs/tslibs/offsets.pyx:3580\u001b[0m, in \u001b[0;36mpandas._libs.tslibs.offsets.to_offset\u001b[0;34m()\u001b[0m\n",
"File \u001b[0;32mpandas/_libs/tslibs/offsets.pyx:3682\u001b[0m, in \u001b[0;36mpandas._libs.tslibs.offsets.to_offset\u001b[0;34m()\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: Invalid frequency: NaT"
]
}
],
"outputs": [],
"source": [
"future = m.make_future_dataframe(df_test)\n",
"forecast = m.predict(future)\n",
Expand Down
6 changes: 4 additions & 2 deletions neuralprophet/data/process.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -399,7 +399,8 @@ def _check_dataframe(
"Dataframe has less than n_forecasts + n_lags rows. "
"Forecasting not possible. Please either use a larger dataset, or adjust the model parameters."
)
df, _, _, _ = df_utils.prep_or_copy_df(df)
# df = df.copy(deep=True)
# df, _, _, _ = df_utils.check_multiple_series_id(df)
df, regressors_to_remove, lag_regressors_to_remove = df_utils.check_dataframe(
df=df,
check_y=check_y,
Expand Down Expand Up @@ -474,7 +475,8 @@ def _handle_missing_data(
The pre-processed DataFrame, including imputed missing data, if applicable.
"""
df, _, _, _ = df_utils.prep_or_copy_df(df)
# df = df.copy(deep=True)
# df, _, _, _ = df_utils.check_multiple_series_id(df)

if n_lags == 0 and not predicting:
# drop rows with NaNs in y and count them
Expand Down
3 changes: 2 additions & 1 deletion neuralprophet/data/transform.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -24,7 +24,8 @@ def _normalize(df: pd.DataFrame, config_normalization: Normalization) -> pd.Data
-------
df: pd.DataFrame, normalized
"""
df, _, _, _ = df_utils.prep_or_copy_df(df)
# df = df.copy(deep=True)
# df, _, _, _ = df_utils.check_multiple_series_id(df)
df_norm = pd.DataFrame()
for df_name, df_i in df.groupby("ID"):
data_params = config_normalization.get_data_params(df_name)
Expand Down
47 changes: 27 additions & 20 deletions neuralprophet/df_utils.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -22,7 +22,7 @@ class ShiftScale:
scale: float = 1.0


def prep_or_copy_df(df: pd.DataFrame) -> tuple[pd.DataFrame, bool, bool, list[str]]:
def check_multiple_series_id(df: pd.DataFrame) -> tuple[pd.DataFrame, bool, bool, list[str]]:
"""Copy df if it contains the ID column. Creates ID column with '__df__' if it is a df with a single time series.
Parameters
----------
Expand All @@ -42,26 +42,23 @@ def prep_or_copy_df(df: pd.DataFrame) -> tuple[pd.DataFrame, bool, bool, list[st
if not isinstance(df, pd.DataFrame):
raise ValueError("Provided DataFrame (df) must be of pd.DataFrame type.")

# Create a copy of the dataframe
df_copy = df.copy(deep=True)

df_has_id_column = "ID" in df_copy.columns
df_has_id_column = "ID" in df.columns

# If there is no ID column, then add one with a single value
if not df_has_id_column:
log.debug("Provided DataFrame (df) contains a single time series.")
df_copy["ID"] = "__df__"
return df_copy, df_has_id_column, True, ["__df__"]
df["ID"] = "__df__"
return df, df_has_id_column, True, ["__df__"]

# Create a list of unique ID values
unique_id_values = list(df_copy["ID"].unique())
unique_id_values = list(df["ID"].unique())
# Check if there is only one unique ID value
df_has_single_time_series = len(unique_id_values) == 1
num_time_series_id = len(unique_id_values)

single_or_multiple_message = "a single" if df_has_single_time_series else "multiple"
log.debug(f"Provided DataFrame (df) has an ID column and contains {single_or_multiple_message} time series.")
log.debug(f"Provided DataFrame (df) has an ID column and contains {num_time_series_id} time series.")

return df_copy, df_has_id_column, df_has_single_time_series, unique_id_values
return df, df_has_id_column, df_has_single_time_series, unique_id_values


def return_df_in_original_format(df, received_ID_col=False, received_single_time_series=True):
Expand Down Expand Up @@ -285,7 +282,8 @@ def init_data_params(
ShiftScale entries containing ``shift`` and ``scale`` parameters for each column
"""
# Compute Global data params
df, _, _, _ = prep_or_copy_df(df)
# df = df.copy(deep=True)
# df, _, _, _ = check_multiple_series_id(df)
df_merged = df.copy(deep=True).drop("ID", axis=1)
global_data_params = data_params_definition(
df_merged, normalize, config_lagged_regressors, config_regressors, config_events, config_seasonality
Expand Down Expand Up @@ -382,6 +380,8 @@ def normalize(df, data_params):
"""
df = df.copy(deep=True)
for name in df.columns:
if name == "ID":
continue
if name not in data_params.keys():
raise ValueError(f"Unexpected column {name} in data")
new_name = name
Expand Down Expand Up @@ -428,7 +428,8 @@ def check_dataframe(
pd.DataFrame or dict
checked dataframe
"""
df, _, _, _ = prep_or_copy_df(df)
# df = df.copy(deep=True)
# df, _, _, _ = check_multiple_series_id(df)
if df.groupby("ID").size().min() < 1:
raise ValueError("Dataframe has no rows.")
if "ds" not in df:
Expand Down Expand Up @@ -642,7 +643,9 @@ def _crossvalidation_with_time_threshold(df, n_lags, n_forecasts, k, fold_pct, f
min_train = total_samples - samples_fold - (k - 1) * (samples_fold - samples_overlap)
assert min_train >= samples_fold
folds = []
df_fold, _, _, _ = prep_or_copy_df(df)
df_fold = df
# df_fold = df.copy(deep=True)
# df_fold, _, _, _ = check_multiple_series_id(df_fold)
for i in range(k, 0, -1):
threshold_time_stamp = find_time_threshold(df_fold, n_lags, n_forecasts, samples_fold, inputs_overbleed=True)
df_train, df_val = split_considering_timestamp(
Expand Down Expand Up @@ -704,7 +707,8 @@ def crossvalidation_split_df(
validation data
"""
df, _, _, _ = prep_or_copy_df(df)
# df = df.copy(deep=True)
df, _, _, _ = check_multiple_series_id(df)
folds = []
if len(df["ID"].unique()) == 1:
for df_name, df_i in df.groupby("ID"):
Expand Down Expand Up @@ -764,7 +768,8 @@ def double_crossvalidation_split_df(df, n_lags, n_forecasts, k, valid_pct, test_
tuple of k tuples [(folds_val, folds_test), …]
elements same as :meth:`crossvalidation_split_df` returns
"""
df, _, _, _ = prep_or_copy_df(df)
# df = df.copy(deep=True)
# df, _, _, _ = check_multiple_series_id(df)
if len(df["ID"].unique()) > 1:
raise NotImplementedError("double_crossvalidation_split_df not implemented for df with many time series")
fold_pct_test = float(test_pct) / k
Expand Down Expand Up @@ -885,7 +890,8 @@ def split_df(
pd.DataFrame, dict
validation data
"""
df, _, _, _ = prep_or_copy_df(df)
# df = df.copy(deep=True)
# df, _, _, _ = check_multiple_series_id(df)
df_train = pd.DataFrame()
df_val = pd.DataFrame()
if local_split:
Expand Down Expand Up @@ -1367,7 +1373,8 @@ def infer_frequency(df, freq, n_lags, min_freq_percentage=0.7):
Valid frequency tag according to major frequency.
"""
df, _, _, _ = prep_or_copy_df(df)
# df = df.copy(deep=True)
# df, _, _, _ = check_multiple_series_id(df)
freq_df = list()
for df_name, df_i in df.groupby("ID"):
freq_df.append(_infer_frequency(df_i, freq, min_freq_percentage))
Expand Down Expand Up @@ -1410,8 +1417,8 @@ def create_dict_for_events_or_regressors(
if other_df is None:
# if other_df is None, create dictionary with None for each ID
return {df_name: None for df_name in df_names}

other_df, received_ID_col, _, _ = prep_or_copy_df(other_df)
other_df = other_df.copy(deep=True)
other_df, received_ID_col, _, _ = check_multiple_series_id(other_df)
# if other_df does not contain ID, create dictionary with original ID with the same other_df for each ID
if not received_ID_col:
other_df = other_df.drop("ID", axis=1)
Expand Down
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