diff --git a/README.md b/README.md
index 137589b..b20b831 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-# SDNist v2.3: Deidentified Data Report Tool
+# SDNist v2.4: Deidentified Data Report Tool
## [SDNist is the offical software package for engaging in the NIST Collaborative Research Cycle](https://pages.nist.gov/privacy_collaborative_research_cycle)
@@ -37,7 +37,7 @@ Setting Up the SDNIST Report Tool
### Brief Setup Instructions
-SDNist requires Python version 3.7 or greater. If you have installed a previous version of the SDNist library, we recommend installing v2.3 in a virtual environment. v2.3 can be installed via [Release 2.3](https://github.com/usnistgov/SDNist/releases/tag/v2.3.0) or via the Pypi server: `pip install sdnist` or, if you already have a version installed, `pip install --upgrade sdnist`.
+SDNist requires Python version 3.7 or greater. If you have installed a previous version of the SDNist library, we recommend installing v2.4 in a virtual environment. v2.4 can be installed via [Release 2.4](https://github.com/usnistgov/SDNist/releases/tag/v2.4) or via the Pypi server: `pip install sdnist` or, if you already have a version installed, `pip install --upgrade sdnist`.
The NIST Diverse Community Exceprt data will download on the fly.
diff --git a/sdnist/metrics/kmarginal.py b/sdnist/metrics/kmarginal.py
index f28a467..0cf8f28 100644
--- a/sdnist/metrics/kmarginal.py
+++ b/sdnist/metrics/kmarginal.py
@@ -58,10 +58,13 @@ def __init__(self,
self.features = self.td.columns.tolist()
marg_cols = list(set(self.features).difference(['PUMA', 'INDP']))
marg_cols = sorted(marg_cols)
- self.marginals = [(f1, f2)
- for i, f1 in enumerate(marg_cols)
- for j, f2 in enumerate(marg_cols)
- if i < j]
+ if len(marg_cols) == 1:
+ self.marginals = [(marg_cols[0], marg_cols[0])]
+ else:
+ self.marginals = [(f1, f2)
+ for i, f1 in enumerate(marg_cols)
+ for j, f2 in enumerate(marg_cols)
+ if i < j]
def marginal_pairs(self):
for _ in self.marginals:
diff --git a/sdnist/metrics/pca.py b/sdnist/metrics/pca.py
index 2dcf53d..f880ced 100644
--- a/sdnist/metrics/pca.py
+++ b/sdnist/metrics/pca.py
@@ -44,7 +44,7 @@ def __init__(self, target: pd.DataFrame, synthetic: pd.DataFrame,):
self.comp_df = None
def compute_pca(self):
- cc = 5
+ cc = 5 if self.tar.shape[1] > 5 else self.tar.shape[1]
t_pca = PCA(n_components=cc)
tdf_v = self.tar.values
@@ -185,10 +185,12 @@ def plot_all_components_pairs(title: str,
plt.close(fig)
fig, ax = plt.subplots(cc, cc, figsize=(6, 6))
-
for i, pc_i in enumerate(d.columns):
for j, pc_j in enumerate(d.columns):
- ax_t = ax[i, j]
+ if cc == 1:
+ ax_t = ax
+ else:
+ ax_t = ax[i, j]
if pc_i == pc_j:
ax_t.text(0.5, 0.5, pc_i,
ha='center', va='center', color='black', fontsize=30)
diff --git a/sdnist/metrics/unique_exact_matches.py b/sdnist/metrics/unique_exact_matches.py
index ef22520..7e63dbc 100644
--- a/sdnist/metrics/unique_exact_matches.py
+++ b/sdnist/metrics/unique_exact_matches.py
@@ -26,10 +26,13 @@ def unique_exact_matches(target_data: pd.DataFrame, deidentified_data: pd.DataFr
# number of unique target records that exactly match in deidentified data
t_rec_matched = merged.shape[0]
- # percent of unique target records that exactly match in deidentified data
- perc_t_rec_matched = t_rec_matched/t_unique_records * 100
+ if t_unique_records > 0:
+ # percent of unique target records that exactly match in deidentified data
+ perc_t_rec_matched = t_rec_matched/t_unique_records * 100
- perc_t_rec_matched = round(perc_t_rec_matched, 2)
+ perc_t_rec_matched = round(perc_t_rec_matched, 2)
+ else:
+ perc_t_rec_matched = 0
return t_rec_matched, perc_t_rec_matched, t_unique_records, perc_t_unique_records
diff --git a/sdnist/report/dataset/__init__.py b/sdnist/report/dataset/__init__.py
index df33c1d..24eb4d3 100644
--- a/sdnist/report/dataset/__init__.py
+++ b/sdnist/report/dataset/__init__.py
@@ -66,7 +66,7 @@ def feature_space_size(target_df: pd.DataFrame, data_dict: Dict):
'DEAR']:
size = size * len(data_dict[col]['values'])
elif col in ['PUMA', 'DENSITY']:
- size = size * len(target_df['PUMA'].unique())
+ size = size * len(target_df[col].unique())
elif col in ['NOC', 'NPF', 'INDP']:
size = size * len(target_df[col].unique())
diff --git a/sdnist/report/dataset/binning.py b/sdnist/report/dataset/binning.py
index 801dde7..5bee3c1 100644
--- a/sdnist/report/dataset/binning.py
+++ b/sdnist/report/dataset/binning.py
@@ -32,6 +32,7 @@ def percentile_rank_synthetic(synthetic: pd.DataFrame,
if f not in to.columns.tolist():
continue
nna_mask = s[~s[f].isin(['N'])].index # not na mask
+
st = pd.DataFrame(pd.to_numeric(s.loc[nna_mask, f]).astype(int), columns=[f])
final_st = st.copy()
max_b = 0
@@ -50,7 +51,7 @@ def percentile_rank_synthetic(synthetic: pd.DataFrame,
else:
min_b = max_b
final_st.loc[(st[f] > min_b), f] = b
- s.loc[nna_mask, f] = final_st
+ s.loc[nna_mask, f] = final_st[f]
return s
diff --git a/sdnist/report/dataset/validate.py b/sdnist/report/dataset/validate.py
index 8048a16..3929ec0 100644
--- a/sdnist/report/dataset/validate.py
+++ b/sdnist/report/dataset/validate.py
@@ -26,9 +26,9 @@ def console_out(text: str):
"nan_records": len(nan_df),
"nan_features": nan_features
}
- sd = sd.dropna()
- console_out(f'Found {len(nan_df)} records with NaN values. '
- f'Removed records with NaN values.')
+ # sd = sd.dropna()
+ # console_out(f'Found {len(nan_df)} records with NaN values. '
+ # f'Removed records with NaN values.')
for f in features:
# check feature has out of bound value
@@ -43,7 +43,8 @@ def console_out(text: str):
fd.loc[mask, f] = pd.to_numeric(fd.loc[mask, f], errors="coerce")
nans = fd[fd.isna().any(axis=1)]
if len(nans):
- vob_vals = list(set(synth_data.loc[nans.index, f].values.tolist()))
+ vob_vals = list(set([str(v)
+ for v in synth_data.loc[nans.index, f].values.tolist()]))
vob_features.append((f, vob_vals))
console_out(f'Value out of bound for feature {f}, '
f'out of bound values: {vob_vals}. '
@@ -62,7 +63,8 @@ def console_out(text: str):
f_unique = sd['PUMA'].unique().tolist()
v_intersect = set(f_unique).intersection(set(f_vals))
if len(v_intersect) < len(f_unique):
- vob_vals = list(set(f_unique).difference(v_intersect))
+ vob_vals = list(set([str(v) for v in
+ list(set(f_unique).difference(v_intersect))]))
vob_features.append((f, vob_vals))
console_out(f'Value out of bound for feature {f}, '
f'out of bound values: {vob_vals}. Dropping feature from evaluation.')
@@ -73,7 +75,8 @@ def console_out(text: str):
nans = fd[fd.isna().any(axis=1)]
vob_vals = []
if len(nans):
- vob_vals.extend(list(set(synth_data.loc[nans.index, f].values.tolist())))
+ vob_vals.extend(list(set([str(v)
+ for v in synth_data.loc[nans.index, f].values.tolist()])))
fd = fd.dropna()
mask = fd[fd[f] != 'N'].index if has_N else fd.index
@@ -87,7 +90,8 @@ def console_out(text: str):
real_vals = [int(v) for v in real_vals]
v_intersect = set(f_unique).intersection(set(real_vals))
if len(v_intersect) < len(f_unique):
- vob_vals.extend(list(set(f_unique).difference(v_intersect)))
+ vob_vals.extend(set([str(v)
+ for v in list(set(f_unique).difference(v_intersect))]))
if len(vob_vals):
vob_features.append((f, vob_vals))
@@ -102,11 +106,10 @@ def console_out(text: str):
if len(vob_features):
last_vob_f, vob_vals = vob_features[-1]
-
if last_vob_f == f:
sd = sd.loc[:, sd.columns != f]
if has_nan:
- vob_features = (last_vob_f, ['nan'] + vob_vals)
+ vob_features[-1] = (last_vob_f, ['nan'] + list(set(vob_vals)))
validation_log['values_out_of_bound'] = dict(vob_features)
return sd, validation_log
\ No newline at end of file
diff --git a/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/k_marginal_synopsys/subsample_error_comparison.csv b/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/k_marginal_synopsys/subsample_error_comparison.csv
deleted file mode 100644
index 9a0d4a0..0000000
--- a/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/k_marginal_synopsys/subsample_error_comparison.csv
+++ /dev/null
@@ -1,11 +0,0 @@
-,Sub-Sample Size,Sub-Sample K-Marginal Score,Deidentified Data K-marginal score,Absolute Diff. From Deidentified Data K-marginal Score
-0,10%,840,121,719
-1,20%,893,121,772
-2,30%,915,121,794
-3,40%,932,121,811
-4,50%,946,121,825
-5,60%,955,121,834
-6,70%,963,121,842
-7,80%,972,121,851
-8,90%,981,121,860
-9,100%,1000,121,879
diff --git a/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/linear_regression/black_women/density_plot.svg b/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/linear_regression/black_women/density_plot.svg
deleted file mode 100644
index dc469dc..0000000
--- a/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/linear_regression/black_women/density_plot.svg
+++ /dev/null
@@ -1,3524 +0,0 @@
-
-
-
diff --git a/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/pca/components_eigenvector.csv b/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/pca/components_eigenvector.csv
deleted file mode 100644
index 8dfb7ea..0000000
--- a/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/pca/components_eigenvector.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-,AGEP,DEAR,DENSITY,DEYE,DPHY,DREM,DVET,EDU,HISP,HOUSING_TYPE,INDP,INDP_CAT,MSP,NOC,NPF,OWN_RENT,PINCP,PINCP_DECILE,POVPIP,PUMA,PWGTP,RAC1P,SEX,WGTP
-0,-0.2588955096118093,0.01802261993377851,-0.04667386419715261,0.0003811614740298996,-0.2759244856359121,-0.2862683902295275,-0.023410513883852496,-0.35712298502231526,0.03958105852260524,-0.0795985637961921,-0.28318382274934495,-0.2800271219731493,-0.2485332884223704,0.2700905002592898,0.24270602288367818,0.06808963436010954,0.3826015296606076,-0.3161611763121064,-0.12610029459660485,0.038108588371959275,0.07421476129273,0.060990534463411725,-0.0037300730348088524,0.08119736351629667
-1,-0.03706176136684479,0.0754708042780653,0.11289508891260977,0.03912986137035291,0.10364032642777438,0.09876053699523768,-0.01644834395297575,0.07589467754514373,0.09795080624846302,-0.4003101956669859,0.23550271690131214,0.2328147890670006,0.009120748880659682,0.19582891279027803,0.15071075004261056,0.4332145143243806,-0.04060512241411092,0.10335376540915228,-0.3152746939146745,0.013035049168211807,0.35869076562538016,0.08188496940135402,0.05093171204224089,0.41706592401612136
-2,0.015550101961189958,-0.137052124502603,0.2594752358338638,-0.17303898724947242,-0.1360410573291881,-0.12137219608365353,-0.008982996172376963,-0.12330522693464233,0.13110082420757188,0.23806425584770813,-0.17102850342850554,-0.17424922986551614,0.3444036558582083,-0.32995075262403195,-0.3865848191844678,0.07670359771314218,-0.08597518211551228,-0.17150377100546413,-0.06751826100445822,-0.18439663028602588,0.3714190011587209,0.11172070960094165,-0.012890662598524154,0.30484044735855303
-3,0.43306701078423787,-0.4119753871067327,-0.2664229328918346,-0.3242787409247648,-0.030533626477405137,0.025811113159965662,0.19100581540528513,0.03326400273099771,-0.16640357119280824,-0.30087836223373915,-0.25953699871892055,-0.25661191881183565,-0.10649822526287565,-0.04851872812751665,-0.035700639350146765,0.11873415623313101,-0.12206204326546893,0.09698171785945736,-0.2691021035420778,0.11061050253798962,-0.052711003424215266,-0.18418687211523357,-0.03159039059588755,0.015854398574435883
-4,0.004796826391542582,-0.10820794575740209,0.011981889211874883,-0.09460537396035387,-0.5491368022888661,-0.5491399130088699,0.11967652026429706,0.030885813327467485,-0.12204024167238489,-0.044175381917005846,0.33330215505417127,0.33250436138898487,-0.09186671053948907,-0.09119973734824198,-0.09182378118621291,0.015077508993621412,0.03249181140492273,0.2300354734429578,0.1260637493318684,0.06530849533461289,0.0231090639979183,-0.14447521231709926,-0.03203825445380767,0.017848205149850617
diff --git a/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/pca/deidentified.png b/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/pca/deidentified.png
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diff --git a/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/apparent_match_distribution/apparent_match_distribution.jpg b/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/apparent_match_distribution/apparent_match_distribution.jpg
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rename from sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/apparent_match_distribution/apparent_match_distribution.jpg
rename to sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/apparent_match_distribution/apparent_match_distribution.jpg
diff --git a/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/apparent_match_distribution/unique_matched_percents.csv b/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/apparent_match_distribution/unique_matched_percents.csv
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diff --git a/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/correlation_difference/corr_diff.jpg b/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/correlation_difference/corr_diff.jpg
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diff --git a/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/correlation_difference/correlation_difference.csv b/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/correlation_difference/correlation_difference.csv
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diff --git a/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/inconsistencies/age/child_DVET_example.csv b/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/inconsistencies/age/child_DVET_example.csv
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diff --git a/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/inconsistencies/age/child_INDP_CAT_example.csv b/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/inconsistencies/age/child_INDP_CAT_example.csv
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diff --git a/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/inconsistencies/age/child_INDP_example.csv b/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/inconsistencies/age/child_INDP_example.csv
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diff --git a/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/inconsistencies/age/child_MSP_example.csv b/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/inconsistencies/age/child_MSP_example.csv
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diff --git a/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/k_marginal_breakdown/univariate/indp_indp_cat_6.jpg b/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/k_marginal_breakdown/univariate/indp_indp_cat_6.jpg
similarity index 100%
rename from sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/k_marginal_breakdown/univariate/indp_indp_cat_6.jpg
rename to sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/k_marginal_breakdown/univariate/indp_indp_cat_6.jpg
diff --git a/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/k_marginal_breakdown/univariate/indp_indp_cat_7.jpg b/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/k_marginal_breakdown/univariate/indp_indp_cat_7.jpg
similarity index 100%
rename from sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/k_marginal_breakdown/univariate/indp_indp_cat_7.jpg
rename to sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/k_marginal_breakdown/univariate/indp_indp_cat_7.jpg
diff --git a/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/k_marginal_breakdown/univariate/indp_indp_cat_8.jpg b/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/k_marginal_breakdown/univariate/indp_indp_cat_8.jpg
similarity index 100%
rename from sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/k_marginal_breakdown/univariate/indp_indp_cat_8.jpg
rename to sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/k_marginal_breakdown/univariate/indp_indp_cat_8.jpg
diff --git a/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/k_marginal_breakdown/univariate/indp_indp_cat_9.jpg b/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/k_marginal_breakdown/univariate/indp_indp_cat_9.jpg
similarity index 100%
rename from sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/k_marginal_breakdown/univariate/indp_indp_cat_9.jpg
rename to sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/k_marginal_breakdown/univariate/indp_indp_cat_9.jpg
diff --git a/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/k_marginal_breakdown/worst_5_puma_k_marginal_scores.csv b/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/k_marginal_breakdown/worst_5_puma_k_marginal_scores.csv
similarity index 53%
rename from sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/k_marginal_breakdown/worst_5_puma_k_marginal_scores.csv
rename to sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/k_marginal_breakdown/worst_5_puma_k_marginal_scores.csv
index 17db10d..1148568 100644
--- a/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/k_marginal_breakdown/worst_5_puma_k_marginal_scores.csv
+++ b/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/k_marginal_breakdown/worst_5_puma_k_marginal_scores.csv
@@ -1,6 +1,6 @@
,PUMA,40% Target Subsample Baseline,Deidentified Data Score
-0,01-01301,811,0
-1,30-00600,885,0
-2,28-01100,856,0
+0,01-01301,813,0
+1,30-00600,886,0
+2,28-01100,851,0
3,51-51255,879,0
-4,08-00803,856,0
+4,08-00803,861,0
diff --git a/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/k_marginal_synopsys/score_in_each_puma.csv b/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/k_marginal_synopsys/score_in_each_puma.csv
similarity index 83%
rename from sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/k_marginal_synopsys/score_in_each_puma.csv
rename to sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/k_marginal_synopsys/score_in_each_puma.csv
index ab94bee..4a37012 100644
--- a/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/k_marginal_synopsys/score_in_each_puma.csv
+++ b/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/k_marginal_synopsys/score_in_each_puma.csv
@@ -1,21 +1,21 @@
,PUMA,40% Target Subsample Baseline,Deidentified Data Score
-0,"['01-01301', 'Birmingham City (West)', 'https://censusreporter.org/profiles/79500US0101301-birmingham-city-west-puma-al/']",811,0
-1,"['30-00600', 'East Montana (Outside Billings City)', 'https://censusreporter.org/profiles/79500US3000600-east-montana-outside-billings-city-puma-mt/']",885,0
-2,"['28-01100', 'Central Region--Jackson City (East & Central)', 'https://censusreporter.org/profiles/79500US2801100-central-region-jackson-city-east-central-puma-ms/']",856,0
+0,"['01-01301', 'Birmingham City (West)', 'https://censusreporter.org/profiles/79500US0101301-birmingham-city-west-puma-al/']",813,0
+1,"['30-00600', 'East Montana (Outside Billings City)', 'https://censusreporter.org/profiles/79500US3000600-east-montana-outside-billings-city-puma-mt/']",886,0
+2,"['28-01100', 'Central Region--Jackson City (East & Central)', 'https://censusreporter.org/profiles/79500US2801100-central-region-jackson-city-east-central-puma-ms/']",851,0
3,"['51-51255', 'Alexandria City', 'https://censusreporter.org/profiles/79500US5151255-alexandria-city-puma-va/']",879,0
-4,"['08-00803', 'Boulder County (Central)--Boulder City', 'https://censusreporter.org/profiles/79500US0800803-boulder-county-central-boulder-city-puma-co/']",856,0
-5,"['06-08507', 'Santa Clara County (Southwest)--Cupertino, Saratoga Cities & Los Gatos Town', 'https://censusreporter.org/profiles/79500US0608507-santa-clara-county-southwest-cupertino-saratoga-cities-los-gatos-town-puma-ca/']",879,2
-6,"['32-00405', 'Las Vegas City (Southeast)', 'https://censusreporter.org/profiles/79500US3200405-las-vegas-city-southeast-puma-nv/']",857,5
-7,"['06-07502', 'San Francisco County (North & East)--North Beach & Chinatown', 'https://censusreporter.org/profiles/79500US0607502-san-francisco-county-north-east-north-beach-chinatown-puma-ca/']",854,6
-8,"['19-01700', 'Des Moines City', 'https://censusreporter.org/profiles/79500US1901700-des-moines-city-puma-ia/']",861,7
-9,"['29-01901', 'St. Louis City (North)', 'https://censusreporter.org/profiles/79500US2901901-st-louis-city-north-puma-mo/']",841,7
-10,"['17-03529', 'Chicago City (South)--South Shore, Hyde Park, Woodlawn, Grand Boulevard & Douglas', 'https://censusreporter.org/profiles/79500US1703529-chicago-city-south-south-shore-hyde-park-woodlawn-grand-boulevard-douglas-puma-il/']",866,8
-11,"['36-04010', 'NYC-Brooklyn Community District 17--East Flatbush, Farragut & Rugby', 'https://censusreporter.org/profiles/79500US3604010-nyc-brooklyn-community-district-17-east-flatbush-farragut-rugby-puma-ny/']",861,9
-12,"['40-00200', 'Cherokee, Sequoyah & Adair Counties', 'https://censusreporter.org/profiles/79500US4000200-cherokee-sequoyah-adair-counties-puma-ok/']",854,9
-13,"['24-01004', 'Montgomery County (South)--Bethesda, Potomac & North Bethesda', 'https://censusreporter.org/profiles/79500US2401004-montgomery-county-south-bethesda-potomac-north-bethesda-puma-md/']",902,9
-14,"['13-04600', 'Atlanta Regional Commission--Fulton County (Central)--Atlanta City (Central)', 'https://censusreporter.org/profiles/79500US1304600-atlanta-regional-commission-fulton-county-central-atlanta-city-central-puma-ga/']",857,10
-15,"['26-02702', 'Washtenaw County (East Central)--Ann Arbor City Area', 'https://censusreporter.org/profiles/79500US2602702-washtenaw-county-east-central-ann-arbor-city-area-puma-mi/']",844,14
-16,"['36-03710', 'NYC-Bronx Community District 1 & 2--Hunts Point, Longwood & Melrose', 'https://censusreporter.org/profiles/79500US3603710-nyc-bronx-community-district-1-2-hunts-point-longwood-melrose-puma-ny/']",834,15
-17,"['38-00100', 'West North Dakota--Minot City', 'https://censusreporter.org/profiles/79500US3800100-west-north-dakota-minot-city-puma-nd/']",885,25
+4,"['08-00803', 'Boulder County (Central)--Boulder City', 'https://censusreporter.org/profiles/79500US0800803-boulder-county-central-boulder-city-puma-co/']",861,0
+5,"['06-08507', 'Santa Clara County (Southwest)--Cupertino, Saratoga Cities & Los Gatos Town', 'https://censusreporter.org/profiles/79500US0608507-santa-clara-county-southwest-cupertino-saratoga-cities-los-gatos-town-puma-ca/']",887,2
+6,"['32-00405', 'Las Vegas City (Southeast)', 'https://censusreporter.org/profiles/79500US3200405-las-vegas-city-southeast-puma-nv/']",853,5
+7,"['06-07502', 'San Francisco County (North & East)--North Beach & Chinatown', 'https://censusreporter.org/profiles/79500US0607502-san-francisco-county-north-east-north-beach-chinatown-puma-ca/']",850,6
+8,"['19-01700', 'Des Moines City', 'https://censusreporter.org/profiles/79500US1901700-des-moines-city-puma-ia/']",862,7
+9,"['29-01901', 'St. Louis City (North)', 'https://censusreporter.org/profiles/79500US2901901-st-louis-city-north-puma-mo/']",840,7
+10,"['17-03529', 'Chicago City (South)--South Shore, Hyde Park, Woodlawn, Grand Boulevard & Douglas', 'https://censusreporter.org/profiles/79500US1703529-chicago-city-south-south-shore-hyde-park-woodlawn-grand-boulevard-douglas-puma-il/']",873,8
+11,"['36-04010', 'NYC-Brooklyn Community District 17--East Flatbush, Farragut & Rugby', 'https://censusreporter.org/profiles/79500US3604010-nyc-brooklyn-community-district-17-east-flatbush-farragut-rugby-puma-ny/']",849,9
+12,"['40-00200', 'Cherokee, Sequoyah & Adair Counties', 'https://censusreporter.org/profiles/79500US4000200-cherokee-sequoyah-adair-counties-puma-ok/']",852,9
+13,"['24-01004', 'Montgomery County (South)--Bethesda, Potomac & North Bethesda', 'https://censusreporter.org/profiles/79500US2401004-montgomery-county-south-bethesda-potomac-north-bethesda-puma-md/']",901,9
+14,"['13-04600', 'Atlanta Regional Commission--Fulton County (Central)--Atlanta City (Central)', 'https://censusreporter.org/profiles/79500US1304600-atlanta-regional-commission-fulton-county-central-atlanta-city-central-puma-ga/']",853,10
+15,"['26-02702', 'Washtenaw County (East Central)--Ann Arbor City Area', 'https://censusreporter.org/profiles/79500US2602702-washtenaw-county-east-central-ann-arbor-city-area-puma-mi/']",852,14
+16,"['36-03710', 'NYC-Bronx Community District 1 & 2--Hunts Point, Longwood & Melrose', 'https://censusreporter.org/profiles/79500US3603710-nyc-bronx-community-district-1-2-hunts-point-longwood-melrose-puma-ny/']",833,15
+17,"['38-00100', 'West North Dakota--Minot City', 'https://censusreporter.org/profiles/79500US3800100-west-north-dakota-minot-city-puma-nd/']",889,25
18,"['51-01301', 'Arlington County (North)', 'https://censusreporter.org/profiles/79500US5101301-arlington-county-north-puma-va/']",872,85
-19,"['17-03531', 'Chicago City (South)--Auburn Gresham, Roseland, Chatham, Avalon Park & Burnside', 'https://censusreporter.org/profiles/79500US1703531-chicago-city-south-auburn-gresham-roseland-chatham-avalon-park-burnside-puma-il/']",841,116
+19,"['17-03531', 'Chicago City (South)--Auburn Gresham, Roseland, Chatham, Avalon Park & Burnside', 'https://censusreporter.org/profiles/79500US1703531-chicago-city-south-auburn-gresham-roseland-chatham-avalon-park-burnside-puma-il/']",828,116
diff --git a/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/k_marginal_synopsys/subsample_error_comparison.csv b/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/k_marginal_synopsys/subsample_error_comparison.csv
new file mode 100644
index 0000000..02ed53f
--- /dev/null
+++ b/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/k_marginal_synopsys/subsample_error_comparison.csv
@@ -0,0 +1,12 @@
+,Sub-Sample Size,Sub-Sample K-Marginal Score,Deidentified Data K-marginal score,Absolute Diff. From Deidentified Data K-marginal Score
+0,1%,565,121,444
+1,5%,774,121,653
+2,10%,842,121,721
+3,20%,891,121,770
+4,30%,918,121,797
+5,40%,932,121,811
+6,50%,946,121,825
+7,60%,955,121,834
+8,70%,964,121,843
+9,80%,973,121,852
+10,90%,981,121,860
diff --git a/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/linear_regression/aiannh_men/density_plot.svg b/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/linear_regression/aiannh_men/density_plot.svg
similarity index 81%
rename from sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/linear_regression/aiannh_men/density_plot.svg
rename to sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/linear_regression/aiannh_men/density_plot.svg
index 3df78e8..c20e471 100644
--- a/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/linear_regression/aiannh_men/density_plot.svg
+++ b/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/linear_regression/aiannh_men/density_plot.svg
@@ -6,7 +6,7 @@
- 2023-05-19T18:02:31.816979
+ 2023-06-19T20:40:31.525884
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@@ -44,921 +44,921 @@ L 57.895881 174.202125
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L 40.704688 191.078125
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rename to sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/linear_regression/aiannh_men/target_counts.csv
diff --git a/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/linear_regression/aiannh_men/target_deidentified_counts_difference.csv b/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/linear_regression/aiannh_men/target_deidentified_counts_difference.csv
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rename to sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/linear_regression/aiannh_men/target_deidentified_counts_difference.csv
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diff --git a/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/linear_regression/aiannh_women/target_counts.csv b/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/linear_regression/aiannh_women/target_counts.csv
similarity index 100%
rename from sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/linear_regression/aiannh_women/target_counts.csv
rename to sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/linear_regression/aiannh_women/target_counts.csv
diff --git a/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/linear_regression/aiannh_women/target_deidentified_counts_difference.csv b/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/linear_regression/aiannh_women/target_deidentified_counts_difference.csv
similarity index 100%
rename from sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/linear_regression/aiannh_women/target_deidentified_counts_difference.csv
rename to sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/linear_regression/aiannh_women/target_deidentified_counts_difference.csv
diff --git a/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/linear_regression/asian_men/density_plot.svg b/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/linear_regression/asian_men/density_plot.svg
similarity index 81%
rename from sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/linear_regression/asian_men/density_plot.svg
rename to sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/linear_regression/asian_men/density_plot.svg
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rename from sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/linear_regression/asian_men/target_counts.csv
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diff --git a/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/linear_regression/asian_men/target_deidentified_counts_difference.csv b/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/linear_regression/asian_men/target_deidentified_counts_difference.csv
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rename from sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/linear_regression/asian_men/target_deidentified_counts_difference.csv
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