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