limits

Limits for the solver variables

class wip.modules.limits.Limits

Bases: object

Methods

define_limit_by_normalization(scalers, ...)	Define upper and lower bounds by variable normalization.
define_limit_by_quantile(feature, ...)	Define upper and lower bounds by percentiles.
define_limit_by_rolling_mean(feature, ...)	Set lower- and upper-bound for a variable defined inside wip.files.rolling_limits.
parse_limits(limits)	Parse json limits:
read_limits(limits[, feature])	Read and return the limit's values

define_bentonita_limit
define_constant_limits
define_flotcor_limit
define_work_dataset
limits_by_rolling_mean

static define_bentonita_limit(feature, datasets, production_query, scalers)

static define_constant_limits(feature, limits)

static define_flotcor_limit(feature, scalers)

static define_limit_by_normalization(scalers, feature, limits)

Define upper and lower bounds by variable normalization.

Returns both values when lmin and lmax are defined Return a left value when only lmin is defined Return a right value when only lmax is defined

static define_limit_by_quantile(feature, models_features, production_query, limits)

Define upper and lower bounds by percentiles.

Parameters

• feature (str) – Feature name and the array with the feature values

• models_features (dict) – Dictionary with each of the model’s features

• production_query (pd.Series) – A pandas.Series that contains the query to filter values with.

• limits (dict) – Dictionary with the limits for each feature.

static define_limit_by_rolling_mean(feature, production_query, datasets, rolling_limits)

Set lower- and upper-bound for a variable defined inside wip.files.rolling_limits.

Parameters

• feature (str) – The tag name defined as dictionary key inside wip.files.rolling_limits.

• production_query –

• datasets (Dict[str, pd.DataFrame]) – Dictionary of pandas.DataFrame with the input datasets.

• rolling_limits (Dict[str, Dict[str, int | float]]) – Dictionary containing the rolling limits to be used for defining the optimization variable’s lower- and upper-bounds.

Returns

Lower- and upper-bounds calculated from the rolling limit.

Return type

Tuple[float, float]

static define_work_dataset(feature, datasets, production_query)

static limits_by_rolling_mean(df, rolling, quant_one, quant_two)

static parse_limits(limits)

Parse json limits:

# tag limits have tags with the same limits as others

static read_limits(limits, feature=None)

Read and return the limit’s values