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add ensure_sorted and ProcessedDF named tuple #93

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Jun 25, 2024
Merged

add ensure_sorted and ProcessedDF named tuple #93

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41 changes: 36 additions & 5 deletions nbs/processing.ipynb
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -33,7 +33,7 @@
"import re\n",
"import reprlib\n",
"import warnings\n",
"from typing import Any, Dict, Generator, List, Optional, Tuple, Union\n",
"from typing import Any, Dict, Generator, List, NamedTuple, Optional, Tuple, Union\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
Expand Down Expand Up @@ -242,7 +242,7 @@
"source": [
"for engine in engines:\n",
" series = generate_series(2, engine=engine)\n",
" x = np.random.rand(series.shape[0]) \n",
" x = np.random.rand(series.shape[0])\n",
" series = assign_columns(series, 'x', x)\n",
" series = assign_columns(series, ['y', 'z'], np.vstack([x, x]).T)\n",
" series = assign_columns(series, 'ones', 1)\n",
Expand Down Expand Up @@ -1607,7 +1607,38 @@
{
"cell_type": "code",
"execution_count": null,
"id": "d3e059e1-8c6f-41b1-b3c0-4ca40adf09e4",
"id": "033b0a8a-98b1-486b-9414-1f5ef698f80f",
"metadata": {},
"outputs": [],
"source": [
"#| export\n",
"def ensure_sorted(df: DataFrame, id_col: str, time_col: str) -> DataFrame:\n",
" sort_idxs = maybe_compute_sort_indices(df=df, id_col=id_col, time_col=time_col)\n",
" if sort_idxs is not None:\n",
" df = take_rows(df=df, idxs=sort_idxs)\n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4de12264-0bd1-4eed-935b-7b7fb1cbebc0",
"metadata": {},
"outputs": [],
"source": [
"#| exporti\n",
"class _ProcessedDF(NamedTuple):\n",
" uids: Series\n",
" times: np.ndarray\n",
" data: np.ndarray\n",
" indptr: np.ndarray\n",
" sort_idxs: Optional[np.ndarray]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "62293bd2-b921-40b2-b1af-25f0b8e55006",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -1617,7 +1648,7 @@
" id_col: str,\n",
" time_col: str,\n",
" target_col: Optional[str],\n",
") -> Tuple[Series, np.ndarray, np.ndarray, np.ndarray, Optional[np.ndarray]]:\n",
") -> _ProcessedDF:\n",
" \"\"\"Extract components from dataframe\n",
" \n",
" Parameters\n",
Expand Down Expand Up @@ -1660,7 +1691,7 @@
" data = data[sort_idxs]\n",
" last_idxs = sort_idxs[last_idxs]\n",
" times = df[time_col].to_numpy()[last_idxs]\n",
" return uids, times, data, indptr, sort_idxs "
" return _ProcessedDF(uids, times, data, indptr, sort_idxs)"
]
},
{
Expand Down
4 changes: 4 additions & 0 deletions utilsforecast/_modidx.py
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Original file line number Diff line number Diff line change
Expand Up @@ -84,6 +84,8 @@
'utilsforecast/processing.py'),
'utilsforecast.processing.DataFrameProcessor.process': ( 'processing.html#dataframeprocessor.process',
'utilsforecast/processing.py'),
'utilsforecast.processing._ProcessedDF': ( 'processing.html#_processeddf',
'utilsforecast/processing.py'),
'utilsforecast.processing._ensure_month_ends': ( 'processing.html#_ensure_month_ends',
'utilsforecast/processing.py'),
'utilsforecast.processing._multiply_pl_freq': ( 'processing.html#_multiply_pl_freq',
Expand Down Expand Up @@ -111,6 +113,8 @@
'utilsforecast/processing.py'),
'utilsforecast.processing.drop_index_if_pandas': ( 'processing.html#drop_index_if_pandas',
'utilsforecast/processing.py'),
'utilsforecast.processing.ensure_sorted': ( 'processing.html#ensure_sorted',
'utilsforecast/processing.py'),
'utilsforecast.processing.fill_null': ( 'processing.html#fill_null',
'utilsforecast/processing.py'),
'utilsforecast.processing.filter_with_mask': ( 'processing.html#filter_with_mask',
Expand Down
33 changes: 24 additions & 9 deletions utilsforecast/processing.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,14 +5,14 @@
'filter_with_mask', 'is_nan', 'is_none', 'is_nan_or_none', 'match_if_categorical', 'vertical_concat',
'horizontal_concat', 'copy_if_pandas', 'join', 'drop_index_if_pandas', 'rename', 'sort', 'offset_times',
'offset_dates', 'time_ranges', 'repeat', 'cv_times', 'group_by', 'group_by_agg', 'is_in', 'between',
'fill_null', 'cast', 'value_cols_to_numpy', 'make_future_dataframe', 'anti_join', 'process_df',
'DataFrameProcessor', 'backtest_splits', 'add_insample_levels']
'fill_null', 'cast', 'value_cols_to_numpy', 'make_future_dataframe', 'anti_join', 'ensure_sorted',
'process_df', 'DataFrameProcessor', 'backtest_splits', 'add_insample_levels']

# %% ../nbs/processing.ipynb 2
import re
import reprlib
import warnings
from typing import Any, Dict, Generator, List, Optional, Tuple, Union
from typing import Any, Dict, Generator, List, NamedTuple, Optional, Tuple, Union

import numpy as np
import pandas as pd
Expand Down Expand Up @@ -626,12 +626,27 @@ def anti_join(df1: DataFrame, df2: DataFrame, on: Union[str, List[str]]) -> Data
return out

# %% ../nbs/processing.ipynb 74
def ensure_sorted(df: DataFrame, id_col: str, time_col: str) -> DataFrame:
sort_idxs = maybe_compute_sort_indices(df=df, id_col=id_col, time_col=time_col)
if sort_idxs is not None:
df = take_rows(df=df, idxs=sort_idxs)
return df

# %% ../nbs/processing.ipynb 75
class _ProcessedDF(NamedTuple):
uids: Series
times: np.ndarray
data: np.ndarray
indptr: np.ndarray
sort_idxs: Optional[np.ndarray]

# %% ../nbs/processing.ipynb 76
def process_df(
df: DataFrame,
id_col: str,
time_col: str,
target_col: Optional[str],
) -> Tuple[Series, np.ndarray, np.ndarray, np.ndarray, Optional[np.ndarray]]:
) -> _ProcessedDF:
"""Extract components from dataframe

Parameters
Expand Down Expand Up @@ -674,9 +689,9 @@ def process_df(
data = data[sort_idxs]
last_idxs = sort_idxs[last_idxs]
times = df[time_col].to_numpy()[last_idxs]
return uids, times, data, indptr, sort_idxs
return _ProcessedDF(uids, times, data, indptr, sort_idxs)

# %% ../nbs/processing.ipynb 76
# %% ../nbs/processing.ipynb 78
class DataFrameProcessor:
def __init__(
self,
Expand All @@ -693,7 +708,7 @@ def process(
) -> Tuple[Series, np.ndarray, np.ndarray, np.ndarray, Optional[np.ndarray]]:
return process_df(df, self.id_col, self.time_col, self.target_col)

# %% ../nbs/processing.ipynb 81
# %% ../nbs/processing.ipynb 83
def _single_split(
df: DataFrame,
i_window: int,
Expand Down Expand Up @@ -758,7 +773,7 @@ def _single_split(
)
return cutoffs, train_mask, valid_mask

# %% ../nbs/processing.ipynb 82
# %% ../nbs/processing.ipynb 84
def backtest_splits(
df: DataFrame,
n_windows: int,
Expand Down Expand Up @@ -790,7 +805,7 @@ def backtest_splits(
valid = filter_with_mask(df, valid_mask)
yield cutoffs, train, valid

# %% ../nbs/processing.ipynb 86
# %% ../nbs/processing.ipynb 88
def add_insample_levels(
df: DataFrame,
models: List[str],
Expand Down
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