def test_PE_F(self):
for adaptive_weights in [ 'ridge', 'decisiontree', 'ridgeCV','decisiontreeCV']: #False,
is_correct = 1
try:
model = None
if adaptive_weights == False:
df, true_TE = generate_uniform_given_importance(num_control=100, num_treated=100,
num_cov=7, min_val=0,
max_val=3, covar_importance=[4,3,2,1,0,0,0])
holdout, true_TE = generate_uniform_given_importance(num_control=100, num_treated=100,
num_cov=7, min_val=0,
max_val=3, covar_importance=[4,3,2,1,0,0,0])
covar_importance = np.array([4,3,2,1,0,0,0])
weight_array = covar_importance/covar_importance.sum()
model = matching.DAME(repeats=False, verbose=0,adaptive_weights =adaptive_weights)
model.fit(holdout_data=holdout,weight_array = list(weight_array))
output = model.predict(df)
else:
df, true_TE = generate_uniform_given_importance()
holdout, true_TE = generate_uniform_given_importance()
model = matching.DAME(repeats=False, verbose=0,adaptive_weights =adaptive_weights)
model.fit(holdout_data=holdout)
output = model.predict(df)
if check_statistics(model):
is_correct = 0
break
except (KeyError, ValueError):
is_correct = 0
self.assertEqual(1, is_correct,
msg='DAME-Error when we use PE method: {0} '.format(adaptive_weights))
def test_verbose_F(self):
#Test verbose
df, true_TE = generate_uniform_given_importance()
for verbose in [0,1,2,3]:
is_correct = 1
try:
df, true_TE = generate_uniform_given_importance(num_control=1000, num_treated=1000,
num_cov=7, min_val=0,
max_val=3, covar_importance=[4,3,2,1,0,0,0])
holdout, true_TE = generate_uniform_given_importance(num_control=100, num_treated=100,
num_cov=7, min_val=0,
max_val=3, covar_importance=[4,3,2,1,0,0,0])
covar_importance = np.array([4,3,2,1,0,0,0])
weight_array = covar_importance/covar_importance.sum()
model = matching.DAME(missing_data_replace = 2, want_bf = True, verbose = verbose)
model.fit(holdout_data=holdout)
output = model.predict(df)
model = matching.DAME(verbose=verbose) # repeats = True
model.fit(holdout_data=0.5)
output = model.predict(df)
if check_statistics(model):
is_correct = 0
break
except (KeyError, ValueError):
is_correct = 0
self.assertEqual(1, is_correct, msg='DAME-Error when verbose ={0}'.format(verbose))
def test_miss_data_F(self):
is_correct = 1
try:
for missing_holdout_replace in [0,1,2]:
for missing_data_replace in [0,1,2]:
#Test missig data handling
df, true_TE = generate_uniform_given_importance(num_control=1000, num_treated=1000)
#Create missing df
m,n = df.shape
for i in range(int(m/10)):
for j in [0,int(n/2)]:
df.iloc[i,j] = np.nan
holdout = df.copy()
model = matching.DAME(repeats = False,missing_holdout_replace = missing_holdout_replace,missing_data_replace=missing_data_replace )
model.fit(holdout_data=holdout)
output = model.predict(df)
if check_statistics(model):
is_correct = 0
break
except (KeyError, ValueError):
is_correct = 0
self.assertEqual(1, is_correct, msg='DAME-Error when do missing data'\
'handling with missing_holdout_replace = {0},missing_data_replace{1}'.format(missing_holdout_replace,missing_data_replace))
def test_datasets_F(self):
df_path = os.path.join((os.path.dirname(__file__)), 'basicTestData.csv')
for gen in [generate_uniform_given_importance,generate_binomial_given_importance,generate_binomial_decay_importance,df_path]:
is_correct = 1
try:
df = None
holdout = None
if type(gen) != str:
df, true_TE = gen()
holdout, true_TE = gen()
else:
df = gen
holdout = gen
model = matching.DAME(repeats=False)
model.fit(holdout_data=holdout)
output = model.predict(df)
if check_statistics(model):
is_correct = 0
break
except (KeyError, ValueError):
is_correct = 0
self.assertEqual(1, is_correct,
msg='DAME-Error when we use the dataset generated by {0} '.format(str(gen)))
def test_miss_data_indicator_F(self):
is_correct = 1
try:
df, true_TE = generate_uniform_given_importance(num_control=1000,
num_treated=1000)
#Create missing df
m, n = df.shape
for i in range(int(m / 100)):
for j in [0, int(n / 2)]:
df.iloc[i, j] = 'a'
holdout = df.copy()
model = matching.DAME(missing_indicator='a',
missing_holdout_replace=1,
missing_data_replace=1)
model.fit(holdout_data=holdout)
output = model.predict(df)
if check_statistics(model):
is_correct = 0
except (KeyError, ValueError):
is_correct = 0
self.assertEqual(1,
is_correct,
msg='DAME-Error when missing_indicator')
def test_repeats_F(self):
#Test other parameters
df, true_TE = generate_uniform_given_importance(
num_control=100,
num_treated=100,
num_cov=7,
min_val=0,
max_val=3,
covar_importance=[4, 3, 2, 1, 0, 0, 0])
holdout, true_TE = generate_uniform_given_importance(
num_control=100,
num_treated=100,
num_cov=7,
min_val=0,
max_val=3,
covar_importance=[4, 3, 2, 1, 0, 0, 0])
is_correct = 1
try:
model = matching.DAME(repeats=True)
model.fit(holdout_data=holdout)
output = model.predict(df)
if check_statistics(model):
is_correct = 0
except (KeyError, ValueError):
is_correct = 0
self.assertEqual(1, is_correct, msg='DAME-Error when repeat = True')
def test_other_param_F(self):
is_correct = 1
try:
df, true_TE = generate_uniform_given_importance(num_control=1000, num_treated=1000,
num_cov=7, min_val=0,
max_val=3, covar_importance=[4,3,2,1,0,0,0])
holdout, true_TE = generate_uniform_given_importance(num_control=100, num_treated=100,
num_cov=7, min_val=0,
max_val=3, covar_importance=[4,3,2,1,0,0,0])
model = matching.DAME( early_stop_pe= 1, verbose=0)
model.fit(holdout_data=holdout)
output = model.predict(df)
if check_statistics(model):
is_correct = 0
# model = matching.DAME( stop_unmatched_c= True, verbose=0)
# model.fit(holdout_data=holdout)
# output = model.predict(df)
# if check_statistics(model):
# is_correct = 0
# model = matching.DAME(stop_unmatched_t= True, verbose=0)
# model.fit(holdout_data=holdout)
# output = model.predict(df)
# if check_statistics(model):
# is_correct = 0
model = matching.DAME(early_stop_un_c_frac = 0.5, verbose=0)
model.fit(holdout_data=holdout)
output = model.predict(df)
if check_statistics(model):
is_correct = 0
model = matching.DAME(early_stop_un_t_frac = 0.5, verbose=0)
model.fit(holdout_data=holdout)
output = model.predict(df)
if check_statistics(model):
is_correct = 0
model = matching.DAME(early_stop_iterations= 2, verbose=0)
model.fit(holdout_data=holdout)
output = model.predict(df)
if check_statistics(model):
is_correct = 0
except (KeyError, ValueError):
is_correct = 0
self.assertEqual(1, is_correct, msg='DAME-Error when other parameters')
def test_no_matching_F(self):
#Test data split
df, true_TE = generate_uniform_given_importance(num_control=3000, num_treated=3000)
is_correct = 1
try:
df = pd.DataFrame([[1,2,0,1.0],[3,4,1,2.0],[5,6,0,5.0],[7,8,1,8.0],[9,10,1,10.0]])
df.columns = ['cov1','cov2','treated','outcome']
holdout = df.copy()
model = matching.DAME(repeats=True)
model.fit(holdout_data=holdout)
output = model.predict(df)
except (KeyError, ValueError):
is_correct = 0
self.assertEqual(1, is_correct, msg='DAME-Error when no matching')
def test_data_split_F(self):
#Test data split
df, true_TE = generate_uniform_given_importance(num_control=3000, num_treated=3000)
is_correct = 1
try:
for holdout in [0.3,0.5,0.7]:
model = matching.DAME(repeats=True)
model.fit(holdout_data=holdout)
output = model.predict(df)
if check_statistics(model):
is_correct = 0
break
except (KeyError, ValueError):
is_correct = 0
self.assertEqual(1, is_correct, msg='DAME-Error when holdout = {0}'.format(holdout))
def test_has_unmatched_units_F(self):
#Test data split
is_correct = 1
try:
df = pd.DataFrame([[1,2,0,1.0],[3,4,0,2.0],[5,6,0,5.0],[7,8,0,8.0],[9,10,1,10.0],[9,20,0,10.0]])
df.columns = ['cov1','cov2','treated','outcome']
holdout = df.copy()
model = matching.DAME(repeats=True)
model.fit(holdout_data=holdout)
output = model.predict(df)
if check_statistics(model):
is_correct = 0
except (KeyError, ValueError):
is_correct = 0
self.assertEqual(1, is_correct, msg='DAME-Error when no matching')
def test_want_pebf_F(self):
#Test
df, true_TE = generate_uniform_given_importance(num_control=3000, num_treated=3000,
num_cov=6, min_val=0,
max_val=3, covar_importance=[4,3,2,1,0,0])
holdout, true_TE = generate_uniform_given_importance(num_control=100, num_treated=100,
num_cov=6, min_val=0,
max_val=3, covar_importance=[4,3,2,1,0,0])
is_correct = 1
try:
for want_pe in [False, True]:
for want_bf in [False, True]:
model = matching.DAME(want_pe=want_pe,want_bf=want_bf)
model.fit(holdout_data=holdout)
output = model.predict(df)
if check_statistics(model) or (want_pe and len(model.pe_each_iter)==0) or (want_bf and len(model.bf_each_iter)==0):
is_correct = 0
break
except (KeyError, ValueError):
is_correct = 0
self.assertEqual(1, is_correct, msg='DAME Error when want_pe = {0} want_bf = {1}'.format(str(want_pe),str(want_bf)))
