Baseline error detection#

TSCC.detection.baseline.BASE_always_false_series(df_fea, df_tar, config)[source]#

Generates a Pandas Series filled with False values, matching the length of the input DataFrame.

Parameters:

df_feapandas dataframe: The input DataFrame for which the series of False values is created.
df_tarNone: Not used yet.
configNone: Not used for now.

Returns:

pandas series: A Pandas Series of the same length as df_fea, filled entirely with False values.

Examples

>>> data = {
>>>     "val_raw": [1, None, np.nan, 5, None],
>>> }
>>> df_fea = pd.DataFrame(data)
>>> TSCC.detection.BASE_always_false_series(df_fea, None, None)
0    False
1    False
2    False
3    False
4    False
dtype: bool

TSCC.detection.baseline.BASE_always_true_series(df_fea, df_tar, config)[source]#

Generates a Pandas Series filled with True values, matching the length of the input DataFrame.

Parameters:

df_feapandas dataframe: The input dataframe for which the series of True values is created.
df_tarNone: Not used for now.
configNone: Not used yet.

Returns:

pandas series: A Pandas Series of the same length as df_fea, filled entirely with True values.

Examples

>>> data = {
>>>     "val_raw": [1, None, np.nan, 5, None],
>>> }
>>> df_fea = pd.DataFrame(data)
>>> TSCC.detection.BASE_always_true_series(df_fea, None, None)
0    True
1    True
2    True
3    True
4    True
dtype: bool

TSCC.detection.baseline.BASE_det_perfect(df_fea, df_tar, config)[source]#

Compares two columns from two DataFrames and returns a boolean Series indicating if the values differ.

Parameters:

df_feapandas dataframe: The DataFrame containing the feature data.
df_tarpandas dataframe: The DataFrame containing the target or corrected data.
configobject: A configuration object containing: - colname_raw: the name of the column in df_fea to be compared. - colname_target_corr: the name of the column in df_tar to compare against.

Returns:

pandas series: A boolean Series where True indicates that the values in df_fea[colname_raw] and df_tar[colname_target_corr] are different, and False indicates they are the same.

Examples

>>> data_fea = {"val_raw": [1, 2, 3, 4, 5]
>>> }
>>> data_tar = {"val_tar": [1, 2, 0, 4, 0]
>>> }
>>> df_fea = pd.DataFrame(data_fea)
>>> df_tar = pd.DataFrame(data_tar)
>>> config = TSCC.preprocessing.Config(colname_raw='val_raw', colname_target_corr='val_tar')
>>> TSCC.detection.BASE_det_perfect(df_fea, df_tar, config)
0    False
1    False
2     True
3    False
4     True
dtype: bool