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Merged
BaptisteDE merged 1 commit into from
Mar 26, 2025
Merged

✨ AddFourierPairs #30

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77 changes: 77 additions & 0 deletions tests/test_processing.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -36,6 +36,7 @@
FillOtherColumns,
DropColumns,
ReplaceTag,
AddFourierPairs,
)

RESOURCES_PATH = Path(__file__).parent / "resources"
Expand Down Expand Up @@ -988,3 +989,79 @@ def test_replace_tag(self):
rep = ReplaceTag({"Whr": "Wh"})
res = rep.fit_transform(df)
check_feature_names_out(rep, res)

def test_add_fourier_pairs(self):
test_df = pd.DataFrame(
data=np.arange(24).astype("float64"),
index=pd.date_range("2009-01-01 00:00:00", freq="H", periods=24, tz="UTC"),
columns=["feat_1"],
)

signal = AddFourierPairs(period="24h", order=2)
res = signal.fit_transform(test_df)

ref_df = pd.DataFrame(
data=np.array(
[
[0.0, 0.000000e00, 1.000000e00, 0.000000e00, 1.000000e00],
[1.0, 2.588190e-01, 9.659258e-01, 5.000000e-01, 8.660254e-01],
[2.0, 5.000000e-01, 8.660254e-01, 8.660254e-01, 5.000000e-01],
[3.0, 7.071068e-01, 7.071068e-01, 1.000000e00, 6.123234e-17],
[4.0, 8.660254e-01, 5.000000e-01, 8.660254e-01, -5.000000e-01],
[5.0, 9.659258e-01, 2.588190e-01, 5.000000e-01, -8.660254e-01],
[6.0, 1.000000e00, 6.123234e-17, 1.224647e-16, -1.000000e00],
[7.0, 9.659258e-01, -2.588190e-01, -5.000000e-01, -8.660254e-01],
[8.0, 8.660254e-01, -5.000000e-01, -8.660254e-01, -5.000000e-01],
[9.0, 7.071068e-01, -7.071068e-01, -1.000000e00, -1.836970e-16],
[10.0, 5.000000e-01, -8.660254e-01, -8.660254e-01, 5.000000e-01],
[11.0, 2.588190e-01, -9.659258e-01, -5.000000e-01, 8.660254e-01],
[12.0, 1.224647e-16, -1.000000e00, -2.449294e-16, 1.000000e00],
[13.0, -2.588190e-01, -9.659258e-01, 5.000000e-01, 8.660254e-01],
[14.0, -5.000000e-01, -8.660254e-01, 8.660254e-01, 5.000000e-01],
[15.0, -7.071068e-01, -7.071068e-01, 1.000000e00, 3.061617e-16],
[16.0, -8.660254e-01, -5.000000e-01, 8.660254e-01, -5.000000e-01],
[17.0, -9.659258e-01, -2.588190e-01, 5.000000e-01, -8.660254e-01],
[18.0, -1.000000e00, -1.836970e-16, 3.673940e-16, -1.000000e00],
[19.0, -9.659258e-01, 2.588190e-01, -5.000000e-01, -8.660254e-01],
[20.0, -8.660254e-01, 5.000000e-01, -8.660254e-01, -5.000000e-01],
[21.0, -7.071068e-01, 7.071068e-01, -1.000000e00, -4.286264e-16],
[22.0, -5.000000e-01, 8.660254e-01, -8.660254e-01, 5.000000e-01],
[23.0, -2.588190e-01, 9.659258e-01, -5.000000e-01, 8.660254e-01],
]
),
columns=[
"feat_1",
"1 days 00:00:00_order_1_Sine",
"1 days 00:00:00_order_1_Cosine",
"1 days 00:00:00_order_2_Sine",
"1 days 00:00:00_order_2_Cosine",
],
index=pd.date_range("2009-01-01 00:00:00", freq="H", periods=24, tz="UTC"),
)

pd.testing.assert_frame_equal(res, ref_df)

test_df_phi = pd.DataFrame(
data=np.arange(24),
index=pd.date_range("2009-01-01 06:00:00", freq="H", periods=24),
columns=["feat_1"],
)
test_df_phi = test_df_phi.tz_localize("UTC")
res = signal.transform(test_df_phi)

assert res.iloc[0, 1] == 1.0

test_df = pd.DataFrame(
data=np.arange(24).astype("float64"),
index=pd.date_range("2009-01-01 00:00:00", freq="H", periods=24, tz="UTC"),
columns=["feat_1__°C__building__room"],
)

signal = AddFourierPairs(period=dt.timedelta(hours=24), unit="W")
res = signal.fit_transform(test_df)

assert list(res.columns) == [
"feat_1__°C__building__room",
"1 day, 0:00:00_order_1_Sine__W__BLOCK__SUB_BLOCK",
"1 day, 0:00:00_order_1_Cosine__W__BLOCK__SUB_BLOCK",
]
135 changes: 135 additions & 0 deletions tide/processing.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -19,6 +19,7 @@
from tide.regressors import SkSTLForecast, SkProphet
from tide.classifiers import STLEDetector
from tide.meteo import sun_position, beam_component, sky_diffuse, ground_diffuse
from tide.utils import get_tags_max_level

FUNCTION_MAP = {"mean": np.mean, "average": np.average, "sum": np.sum, "dot": np.dot}

Expand Down Expand Up @@ -2798,3 +2799,137 @@ def _transform_implementation(self, X: pd.Series | pd.DataFrame):
check_is_fitted(self, attributes=["feature_names_in_", "feature_names_out_"])
X.columns = self.feature_names_out_
return X


class AddFourierPairs(BaseProcessing):
"""
A transformer that adds a pair of new columns with sine and cosine
signal of given period.
Based on time series index, phase shift is computed from the beginning
of the year.

Parameters
----------
period: str | pd.Timedelta | dt.timedelta = "24h"
Period of thte signal. Will automaticaly convert a string to pandas TimeDelta
order: int = 1,
Sinus and Cosinus order. Will add a pair of feature for order "n", with a
pulsation n * 2 * pi * f
amplitude: float | int = 1.0,
Amplitude of the signal
unit: str = "-",
Unit of the signal
block: str = "BLOCK",
block tag according to tide taging system. Will only be used if level 2 tags
or more are already used
sub_block: str = "SUB_BLOCK",
sub_block tag according to tide taging system. Will only be used if level 3 tags
or more are already used

Attributes
----------
feature_names_in_ : list[str]
Names of input columns (set during fit).
feature_names_out_ : list[str]
Names of output columns with replaced tag components.

Examples
--------
>>> import pandas as pd
>>> # Create DataFrame with DateTimeIndex
>>> data = pd.DataFrame(
... data=np.arange(24).astype("float64"),
... index=pd.date_range("2009-01-01 00:00:00", freq="H", periods=24, tz="UTC"),
... columns=["feat_1"],
... )

>>> signal = AddFourierPairs(period="24h", order=2)
>>> result = signal.fit_transform(data)

>>> print(result.head())
feat_1 1 days 00:00:00_order_1_Sine 1 days 00:00:00_order_1_Cosine 1 days 00:00:00_order_2_Sine 1 days 00:00:00_order_2_Cosine
2009-01-01 00:00:00+00:00 0.0 0.000000 1.000000 0.000000 1.000000e+00
2009-01-01 01:00:00+00:00 1.0 0.258819 0.965926 0.500000 8.660254e-01
2009-01-01 02:00:00+00:00 2.0 0.500000 0.866025 0.866025 5.000000e-01
2009-01-01 03:00:00+00:00 3.0 0.707107 0.707107 1.000000 6.123234e-17
2009-01-01 04:00:00+00:00 4.0 0.866025 0.500000 0.866025 -5.000000e-01


Notes
-----
- Tide tags follow the format "name__unit__block__sub_block"
- If unit, block or sub_block is given, but data have a lower level tag, it will be
ignored.
- The transformer preserves the order of tag components

Returns
-------
pd.DataFrame
The DataFrame with new columns corresponding to the Fourier pairs
"""

def __init__(
self,
period: str | pd.Timedelta | dt.timedelta = "24h",
order: int = 1,
amplitude: float | int = 1.0,
unit: str = "-",
block: str = "BLOCK",
sub_block: str = "SUB_BLOCK",
):
BaseProcessing.__init__(self)

self.period = pd.to_timedelta(period) if isinstance(period, str) else period
self.order = order
self.amplitude = amplitude
self.unit = unit
self.block = block
self.sub_block = sub_block

def _fit_implementation(self, X: pd.Series | pd.DataFrame, y=None):
self.fit_check_features(X)
max_level = get_tags_max_level(X.columns)
self.new_columns_ = []
for od in range(1, self.order + 1):
for trig in ["Sine", "Cosine"]:
name = f"{self.period}_order_{od}_{trig}"
if max_level > 0:
name += f"__{self.unit}"
if max_level > 1:
name += f"__{self.block}"
if max_level > 2:
name += f"__{self.sub_block}"
self.new_columns_.append(name)
self.feature_names_out_.append(self.new_columns_)

return self

def _transform_implementation(self, X: pd.Series | pd.DataFrame):
check_is_fitted(
self, attributes=["feature_names_in_", "feature_names_out_", "new_columns_"]
)
begin = X.index[0]
frequency = 1 / self.period.total_seconds()
year_start = pd.Timestamp(begin.year, 1, 1)
if begin.tz:
year_start = year_start.tz_localize(begin.tz)
seconds_from_start_of_year = (begin - year_start).total_seconds()
phi = 2 * np.pi * frequency * seconds_from_start_of_year

new_index = X.index.to_frame().diff().squeeze()
sec_dt = [element.total_seconds() for element in new_index]
increasing_seconds = pd.Series(sec_dt).cumsum().to_numpy()
increasing_seconds[0] = 0

omega = 2 * np.pi * frequency
for od, idx in zip(
range(1, self.order + 1), range(0, len(self.new_columns_), 2)
):
X[self.new_columns_[idx]] = self.amplitude * np.sin(
od * omega * increasing_seconds + phi
)
X[self.new_columns_[idx + 1]] = self.amplitude * np.cos(
od * omega * increasing_seconds + phi
)

return X