|
|
| import pandas as pd |
| from stochatreat import stochatreat |
|
|
| import random |
| from functools import reduce |
| random.seed(13984759) |
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|
| """Assigns treatments in prep for the assignment Survey. If there is a used_cl, the assign_treatment function will use |
| ActualUse data from the used CL |
| - Treatment Assignment will use data from the old_phase |
| - New treatment assignment will take effect in the new_phase""" |
|
|
| class Treatment(): |
|
|
| def __init__(self,seed): |
| self.i = seed |
|
|
| def prepare_strat(self, df,continuous_strat,discrete_strat): |
| |
| for var in continuous_strat: |
| df[var] = df[var].astype(float) |
| median = df[var].median() |
| df.loc[df[var] >= median,var+"Strat"] = f"{var}High" |
| df.loc[df[var] < median, var + "Strat"] = f"{var}Low" |
|
|
| |
| df.loc[df[var].isnull(), var + "Strat"] = f"{var}Low" |
|
|
| |
| for var in discrete_strat: |
| df[var+"Strat"] = df[var].astype(str).apply(lambda x: var+x) |
|
|
| |
| strat_vars = [x+"Strat" for x in discrete_strat+continuous_strat] |
| df["Stratifier"] = df[strat_vars].values.tolist() |
| df["Stratifier"] = df["Stratifier"].apply(lambda x: 'X'.join(x)) |
| return df |
|
|
| """ Used to assign randomly assign treatment to a subset of the data. The varname should already be in the data set. The function only modifies the values for the subset |
| |
| Input: |
| - df: the full dataframe |
| - subset_var: the categorical vairable used to subset |
| - subset_val: the value of subset_var we will keep |
| - inputs to _assign_treat_var |
| |
| Outpu: |
| - the full df with the varname randomly filled""" |
|
|
| def subset_treat_var_wrapper(self,df, subset_var, subset_val, rand_dict: dict, stratum_cols: list, varname): |
| r_varname = "Randomized" + varname |
| df_dl = self.assign_treat_var(df=df.loc[df[subset_var] == subset_val], |
| rand_dict=rand_dict, |
| stratum_cols=stratum_cols, |
| varname=r_varname) |
|
|
| df = df.merge(df_dl[["AppCode", r_varname]], how='left', on="AppCode") |
| df.loc[df[subset_var] == subset_val, varname] = df.loc[ |
| df[subset_var] == subset_val, r_varname] |
| df = df.drop(columns=[r_varname]) |
| return df |
|
|
| def assign_treat_var(self, df: pd.DataFrame, rand_dict: dict, stratum_cols: list, varname: str): |
| treats = stochatreat(data=df, |
| stratum_cols=stratum_cols, |
| treats=len(rand_dict.keys()), |
| probs=list(rand_dict.values()), |
| idx_col='AppCode', |
| random_state= self.i, |
| misfit_strategy='stratum' |
| ) |
|
|
| self.i = self.i * 2 |
|
|
| raw_treat_values = range(len(rand_dict.keys())) |
| treat_labels = list(rand_dict.keys()) |
| translate_dict = dict(zip(raw_treat_values, treat_labels)) |
| treats[varname] = treats["treat"].apply(lambda x: translate_dict[x]) |
|
|
| treat_tab = pd.crosstab(treats["stratum_id"], treats[varname], margins=True).reset_index() |
|
|
| df = df.merge(treats[["AppCode", varname]], on="AppCode") |
| |
| value_len = [len(df[x].unique()) for x in stratum_cols] |
| number_of_strata = reduce(lambda x, y: x * y, value_len) |
|
|
| assert len(treats["stratum_id"].unique()) == number_of_strata |
| return df |
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