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Added "id" parameter for make_forecaseting_frame so that it will not … #1080

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Added "id" parameter for make_forecaseting_frame so that it will not … #1080

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8 changes: 5 additions & 3 deletions tsfresh/utilities/dataframe_functions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -579,7 +579,7 @@ def roll_time_series(
return df_shift.sort_values(by=["id", column_sort or "sort"])


def make_forecasting_frame(x, kind, max_timeshift, rolling_direction):
def make_forecasting_frame(x, id, kind, max_timeshift, rolling_direction):
"""
Takes a singular time series x and constructs a DataFrame df and target vector y that can be used for a time series
forecasting task.
Expand All @@ -597,6 +597,8 @@ def make_forecasting_frame(x, kind, max_timeshift, rolling_direction):

:param x: the singular time series
:type x: np.array or pd.Series
:param id: the id for the time series
:type id: str
:param kind: the kind of the time series
:type kind: str
:param rolling_direction: The sign decides, if to roll backwards (if sign is positive) or forwards in "time"
Expand All @@ -614,7 +616,7 @@ def make_forecasting_frame(x, kind, max_timeshift, rolling_direction):
else:
t = range(n)

df = pd.DataFrame({"id": ["id"] * n, "time": t, "value": x, "kind": kind})
df = pd.DataFrame({"id": id, "time": t, "value": x, "kind": kind})

df_shift = roll_time_series(
df,
Expand Down Expand Up @@ -644,7 +646,7 @@ def mask_first(x):

# make sure that the format is the same as the
# df_shift index
y.index = map(lambda x: ("id", x), y.index)
y.index = map(lambda x: (id, x), y.index)

return df_shift, y

Expand Down