AutoDataPrep¶

In [ ]:

class AutoDataPrep(name: str = "",
                   cursor=None,
                   cat_method: str = "ooe",
                   num_method: str = "none",
                   nbins: int = 20,
                   outliers_threshold: float = 4.0,
                   na_method: str = "auto",
                   cat_topk: int = 10,
                   normalize: bool = True,
                   normalize_min_cat: int = 6,
                   id_method: int = "drop",
                   apply_pca: bool = False,
                   rule: (str, datetime.timedelta) = "auto",
                   identify_ts: bool = True,
                   save: bool = True,)

Automatically find relations between the different features to preprocess the data according to each column type.

Parameters¶

Name	Type	Optional	Description
name	str	❌	Name of the model.
cursor	DBcursor	✓	Vertica database cursor.
cat_method	str	✓	Method for encoding categorical features. This can be set to 'label' for label encoding and 'ooe' for One-Hot Encoding.
num_method	str	✓	[Only used for non-time series datasets] Method for encoding numerical features. This can be set to 'same_freq' to encode using frequencies, 'same_width' to encode using regular bins, or 'none' to not encode numerical features.
nbins	int	✓	[Only used for non-time series datasets] Number of bins used to discretize numerical features.
outliers_threshold	float	✓	[Only used for non-time series datasets] How to deal with outliers. If a number is used, all elements with an absolute z-score greater than the threshold will be converted to NULL values. Otherwise, outliers are treated as regular values.
na_method	str	✓	Method for handling missing values. auto: Mean for the numerical features and creates a new category for the categorical vcolumns. For time series datasets, 'constant' interpolation is used for categorical features and 'linear' for the others. drop: Drops the missing values.
cat_topk	int	✓	Keeps the top-k most frequent categories and merges the others into one unique category. If unspecified, all categories are kept.
normalize	bool	✓	If True, the data will be normalized using the z-score. The 'num_method' parameter must be set to 'none'.
normalize_min_cat	int	✓	Minimum feature cardinality before using normalization.
id_method	str	✓	Method for handling ID features. drop : Drops any feature detected as ID. none : Does not change ID features.
apply_pca	bool	✓	[Only used for non-time series datasets] If True, a PCA is applied at the end of the preprocessing.
rule	str / time	✓	[Only used for time series datasets] Interval to use to slice the time. For example, '5 minutes' will create records separated by '5 minutes' time interval. If set to auto, the rule will be detected using aggregations.
identify_ts	bool	✓	If True and parameter 'ts' is undefined when fitting the model, the function will try to automatically detect the parameter 'ts'.
print_info	bool	✓	If True, prints the model information at each step.

Attributes¶

Name	Type	Description
X_in	list	Variables used to fit the model.
X_out	list	Variables created by the model.
ts	str	TS component.
by	list	vcolumns used in the partition.
sql_	str	SQL needed to deploy the model.
final_relation_	vDataFrame	Relation created after fitting the model.
model_grid_	tablesample	Grid containing the different models information.

Main Methods¶

Name	Description
fit	Trains the model.

AutoDataPrep also inherits the vModel methods.

Example¶

In [4]:

from verticapy.learn.delphi import AutoDataPrep

model = AutoDataPrep("titanic_autodataprep")
model.fit("public.titanic",)

Out[4]:

	123 pclass Int	123 survived Int	123 age Numeric(45,29)	123 sibsp Numeric(33,15)	123 parch Numeric(33,15)	123 fare Numeric(48,30)	123 body Numeric(60,29)	123 sex_female Int	123 sex_male Int	123 ticket_113781 Int	123 ticket_1601 Int	123 ticket_19950 Int	123 ticket_3101295 Int	123 ticket_347077 Int	123 ticket_347082 Int	123 ticket_347088 Int	123 ticket_CA_2144 Int	123 ticket_CA._2343 Int	123 ticket_Others Int	123 ticket_S.O.C._14879 Int	123 cabin_A34 Int	123 cabin_B57_B59_B63_B66 Int	123 cabin_B96_B98 Int	123 cabin_C22_C26 Int	123 cabin_C23_C25_C27 Int	...	123 embarked_C Int	123 embarked_Q Int	123 embarked_S Int	123 boat_10 Int	123 boat_13 Int	123 boat_14 Int	123 boat_15 Int	123 boat_3 Int	123 boat_4 Int	123 boat_5 Int	123 boat_8 Int	123 boat_C Int	123 boat_NULL Int	123 boat_Others Int	123 home.dest_Cornwall___Akron__OH Int	123 home.dest_London Int	123 home.dest_Montreal__PQ Int	123 home.dest_NULL Int	123 home.dest_New_York__NY Int	123 home.dest_Others Int	123 home.dest_Paris__France Int	123 home.dest_Philadelphia__PA Int	123 home.dest_Sweden_Winnipeg__MN Int	123 Int	123 home.dest_Winnipeg__MB Int
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