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enable multi-valued treatedment ID #22

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enable multi-valued treatedment ID #22

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36 changes: 26 additions & 10 deletions SyntheticControlMethods/main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,6 +11,9 @@
# limitations under the License.

from __future__ import absolute_import, division, print_function
from typing import Union
from collections.abc import Iterable
from numbers import Number

import pandas as pd
import numpy as np
Expand Down Expand Up @@ -143,7 +146,7 @@ class DataProcessor(object):

def _process_input_data(self, dataset,
outcome_var, id_var, time_var,
treatment_period, treated_unit,
treatment_period, treated_unit: Union(Iterable, str, Number),
pen, exclude_columns, random_seed,
**kwargs):
'''
Expand All @@ -161,20 +164,30 @@ def _process_input_data(self, dataset,
n_controls = dataset[id_var].nunique() - 1
n_covariates = len(covariates)

#Create treated unit list
if isinstance(treated_unit, Iterable):
treated_unit_list = treated_unit
elif isinstance(treated_unit, str) or isinstance(treated_unit, Number):
treated_unit_list = [treated_unit]
elif isinstance(treated_unit, dict):
raise NotImplementedError('Input for treated_unit as a dict is not implemented.')
else:
raise ValueError('Treated Unit must be iterable of values, string, or int')

#All units that are not the treated unit are controls
control_units = dataset.loc[dataset[id_var] != treated_unit][id_var].unique()
control_units = dataset.loc[~dataset[id_var].isin(treated_unit_list)][id_var].unique()

###Get treated unit matrices first###
treated_outcome_all, treated_outcome, unscaled_treated_covariates = self._process_treated_data(
dataset, outcome_var, id_var, time_var,
treatment_period, treated_unit, periods_all,
treatment_period, treated_unit_list, periods_all,
periods_pre_treatment, covariates, n_covariates
)

### Now for control unit matrices ###
control_outcome_all, control_outcome, unscaled_control_covariates = self._process_control_data(
dataset, outcome_var, id_var, time_var,
treatment_period, treated_unit, n_controls,
treatment_period, treated_unit_list, n_controls,
periods_all, periods_pre_treatment, covariates
)

Expand Down Expand Up @@ -213,13 +226,13 @@ def _process_input_data(self, dataset,
'random_seed':random_seed,
}

def _process_treated_data(self, dataset, outcome_var, id_var, time_var, treatment_period, treated_unit,
def _process_treated_data(self, dataset, outcome_var, id_var, time_var, treatment_period, treated_unit_list,
periods_all, periods_pre_treatment, covariates, n_covariates):
'''
Extracts and formats outcome and covariate matrices for the treated unit
'''

treated_data_all = dataset.loc[dataset[id_var] == treated_unit]
treated_data_all = dataset.loc[dataset[id_var].isin(treated_unit_list)]
treated_outcome_all = np.array(treated_data_all[outcome_var]).reshape(periods_all,1) #All outcomes

#Only pre-treatment
Expand All @@ -232,14 +245,14 @@ def _process_treated_data(self, dataset, outcome_var, id_var, time_var, treatmen
return treated_outcome_all, treated_outcome, treated_covariates


def _process_control_data(self, dataset, outcome_var, id_var, time_var, treatment_period, treated_unit, n_controls,
def _process_control_data(self, dataset, outcome_var, id_var, time_var, treatment_period, treated_unit_list, n_controls,
periods_all, periods_pre_treatment, covariates):
'''
Extracts and formats outcome and covariate matrices for the control group
'''

#Every unit that is not the treated unit is control
control_data_all = dataset.loc[dataset[id_var] != treated_unit]
control_data_all = dataset.loc[~dataset[id_var].isin(treated_unit_list)]
control_outcome_all = np.array(control_data_all[outcome_var]).reshape(n_controls, periods_all).T #All outcomes

#Only pre-treatment
Expand Down Expand Up @@ -312,9 +325,12 @@ def __init__(self, dataset,
E.g. 1990 for german reunification.

treated_unit:
Type: str.
Type: Iterable, str, or Number
Name of the unit that recieved treatment, e.g. "West Germany"
data["id_var"] == treated_unit
if isinstance(treated_unit, str) or isinstance(treated_unit, Number):
data["id_var"].isiin([treated_unit])
elif isinstance(treated_unit, Iterable):
data["id_var"].isin(treated_unit)

n_optim:
Type: int. Default: 10.
Expand Down