convert_pd_to_np[source]
convert_pd_to_np(*args)
check_treatment_vector[source]
check_treatment_vector(treatment,control_name=None)
check_p_conditions[source]
check_p_conditions(p,t_groups)
check_explain_conditions[source]
check_explain_conditions(method,models,X=None,treatment=None,y=None)
clean_xgboost_objective[source]
clean_xgboost_objective(objective)
Translate objective to be compatible with loaded xgboost version
Args
objective : string The objective to translate.
Returns
The translated objective, or original if no translation was required.
get_xgboost_objective_metric[source]
get_xgboost_objective_metric(objective)
Get the xgboost version-compatible objective and evaluation metric from a potentially version-incompatible input.
Args
objective : string An xgboost objective that may be incompatible with the installed version.
Returns
A tuple with the translated objective and evaluation metric.
ape[source]
ape(y,p)
Absolute Percentage Error (APE). Args: y (float): target p (float): prediction
Returns: e (float): APE
mape[source]
mape(y,p)
Mean Absolute Percentage Error (MAPE). Args: y (numpy.array): target p (numpy.array): prediction
Returns: e (numpy.float64): MAPE
smape[source]
smape(y,p)
Symmetric Mean Absolute Percentage Error (sMAPE). Args: y (numpy.array): target p (numpy.array): prediction
Returns: e (numpy.float64): sMAPE
rmse[source]
rmse(y,p)
Root Mean Squared Error (RMSE). Args: y (numpy.array): target p (numpy.array): prediction
Returns: e (numpy.float64): RMSE
gini[source]
gini(y,p)
Normalized Gini Coefficient.
Args: y (numpy.array): target p (numpy.array): prediction
Returns: e (numpy.float64): normalized Gini coefficient
regression_metrics[source]
regression_metrics(y,p,w=None,metrics={'RMSE': <function rmse at 0x7f2b5b564a60>, 'sMAPE': <function smape at 0x7f2b5b5649d0>, 'Gini': <function gini at 0x7f2b5b564af0>})
Log metrics for regressors.
Args: y (numpy.array): target p (numpy.array): prediction w (numpy.array, optional): a treatment vector (1 or True: treatment, 0 or False: control). If given, log metrics for the treatment and control group separately metrics (dict, optional): a dictionary of the metric names and functions
logloss[source]
logloss(y,p)
Bounded log loss error. Args: y (numpy.array): target p (numpy.array): prediction Returns: bounded log loss error
classification_metrics[source]
classification_metrics(y,p,w=None,metrics={'AUC': <function roc_auc_score at 0x7f2b6c9455e0>, 'Log Loss': <function logloss at 0x7f2b5b564c10>})
Log metrics for classifiers.
Args: y (numpy.array): target p (numpy.array): prediction w (numpy.array, optional): a treatment vector (1 or True: treatment, 0 or False: control). If given, log metrics for the treatment and control group separately metrics (dict, optional): a dictionary of the metric names and functions
smd[source]
smd(feature,treatment)
Calculate the standard mean difference (SMD) of a feature between the treatment and control groups.
The definition is available at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3144483/#s11title
Args: feature (pandas.Series): a column of a feature to calculate SMD for treatment (pandas.Series): a column that indicate whether a row is in the treatment group or not
Returns: (float): The SMD of the feature
create_table_one[source]
create_table_one(data,treatment_col,features)
Report balance in input features between the treatment and control groups.
References: R's tableone at CRAN: https://github.com/kaz-yos/tableone Python's tableone at PyPi: https://github.com/tompollard/tableone
Args: data (pandas.DataFrame): total or matched sample data treatment_col (str): the column name for the treatment features (list of str): the column names of features
Returns: (pandas.DataFrame): A table with the means and standard deviations in the treatment and control groups, and the SMD between two groups for the features.
class NearestNeighborMatch[source]
NearestNeighborMatch(caliper=0.2,replace=False,ratio=1,shuffle=True,random_state=None)
Propensity score matching based on the nearest neighbor algorithm.
Attributes: caliper (float): threshold to be considered as a match. replace (bool): whether to match with replacement or not ratio (int): ratio of control / treatment to be matched. used only if replace=True. shuffle (bool): whether to shuffle the treatment group data before matching random_state (numpy.random.RandomState or int): RandomState or an int seed
class MatchOptimizer[source]
MatchOptimizer(treatment_col='is_treatment',ps_col='pihat',user_col=None,matching_covariates=['pihat'],max_smd=0.1,max_deviation=0.1,caliper_range=(0.01, 0.5),max_pihat_range=(0.95, 0.999),max_iter_per_param=5,min_users_per_group=1000,smd_cols=['pihat'],dev_cols_transformations={'pihat': <function mean at 0x7f2ba076db80>},dev_factor=1.0,verbose=True)