Temporal Features¶
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flood_forecast.preprocessing.temporal_feats.
make_temporal_features
(features_list: Dict, dt_column: str, df: pandas.core.frame.DataFrame) → pandas.core.frame.DataFrame[source]¶ A function that creates temporal features
- Parameters
features_list (Dict) – A list of features in the form of a dictionary
dt_column (str) – [description]
df (pd.DataFrame) – [description]
- Returns
The DF with several new columns added
- Return type
pd.DataFrame
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flood_forecast.preprocessing.temporal_feats.
create_feature
(key: str, value: str, df: pandas.core.frame.DataFrame, dt_column: str)[source]¶ - Function to create temporal features
Uses dict to make val.
- Parameters
key (str s) – The datetime feature you would like to create
value (str) – The type of feature you would like to create (cyclical or numerical)
df (pd.DataFrame) – The Pandas dataframe with the datetime
dt_column (str) – The name of the datetime column
- Returns
The dataframe with the newly added column
- Return type
pd.DataFrame
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flood_forecast.preprocessing.temporal_feats.
feature_fix
(preprocess_params: Dict, dt_column: str, df: pandas.core.frame.DataFrame)[source]¶ Adds temporal features
- Parameters
preprocess_params (Dict) – Dictionary of temporal parameters e.g. {“day”:”numerical”}
dt_column – The column name of the data
df (pd.DataFrame) – The dataframe to add the temporal features to
- Returns
Returns the new data-frame and a list of the new column names
- Return type
Tuple(pd.Dataframe, List[str])
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flood_forecast.preprocessing.temporal_feats.
cyclical
(df: pandas.core.frame.DataFrame, feature_column: str) → pandas.core.frame.DataFrame[source]¶ A function to create cyclical encodings for Pandas data-frames.
- Parameters
df (pd.DataFrame) – A Pandas Dataframe where you want the dt encoded
feature_column (str) – The name of the feature column. Should be either (day_of_week, hour, month, year)
- Returns
The dataframe with three new columns: norm_feature, cos_feature, sin_feature
- Return type
pd.DataFrame