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🔥⚰️ Remove measure and transformer. Moved to Tide project #100

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Merged
BaptisteDE merged 1 commit into from
Jul 4, 2025
Merged

🔥⚰️ Remove measure and transformer. Moved to Tide project #100

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23 changes: 7 additions & 16 deletions corrai/learning/cluster.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,8 +11,6 @@
from sklearn.preprocessing import StandardScaler
from sklearn.utils.validation import check_is_fitted


from corrai.transformers import PdSkTransformer
from corrai.base.utils import (
as_1_column_dataframe,
check_datetime_index,
Expand Down Expand Up @@ -218,8 +216,8 @@ def predict(self, X):
def set_point_identifier(X: pd.DataFrame | pd.Series, estimator: KdeSetPoint):
"""
Identifies set points in a time series data using kernel density estimation.
Uses CorrAI KdeSetPointIdentificator combined with a transformer to scale the data.
If no scaler is provided, the function uses scikit learn StandardScaler
Uses CorrAI KdeSetPointIdentificator combined with a StandardScaler to scale
the data

Parameters
----------
Expand All @@ -229,13 +227,6 @@ def set_point_identifier(X: pd.DataFrame | pd.Series, estimator: KdeSetPoint):
estimator : object, optional
A corrai KdeSetPointIdentificator. Default is None, which will use a
`KdeSetPointIdentificator` object with default parameters values.
sk_scaler : object, optional
A scikit-learn scaler object that can transform data.
Default is None, which will use a `StandardScaler` object wrapped in a
corrai `PdSkTransformer` object.
cols : list, optional
The column names to identify set points in. Default is None, which will
identify set points in all columns.

Returns
-------
Expand All @@ -259,9 +250,9 @@ def set_point_identifier(X: pd.DataFrame | pd.Series, estimator: KdeSetPoint):
X = as_1_column_dataframe(X)
check_datetime_index(X)

model = make_pipeline(PdSkTransformer(StandardScaler()), estimator)
model = make_pipeline(StandardScaler().set_output(transform="pandas"), estimator)

pd_scaler = model.named_steps["pdsktransformer"]
pd_scaler = model.named_steps["standardscaler"]
kde = model.named_steps["kdesetpoint"]

duration = X.index[-1] - X.index[-0]
Expand All @@ -276,17 +267,17 @@ def set_point_identifier(X: pd.DataFrame | pd.Series, estimator: KdeSetPoint):
for col in X.columns:
model.fit(X[[col]])
try:
set_points = pd_scaler.inverse_transform(kde.set_points_)
set_points = pd_scaler.inverse_transform([kde.set_points_])
except ValueError:
set_points = None

if set_points is not None:
index_tuple = [
(period, f"set_point_{i}") for i in range(set_points.shape[0])
(period, f"set_point_{i}") for i in range(set_points.shape[1])
]
multiindex = pd.MultiIndex.from_tuples(index_tuple)
set_points_series = pd.Series(
set_points.to_numpy().flatten(), index=multiindex, name=col
set_points.flatten(), index=multiindex, name=col
)
multi_series_list.append(set_points_series)

Expand Down
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