Merge pull request #32 from Baukebrenninkmeijer/feature/upgrade-dythi…

…ng-version

.github/workflows/run-tests.yml

@@ -7,7 +7,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ['3.7',  '3.8',  '3.9', '3.10']
+        python-version: ['3.8',  '3.9', '3.10']
     name: Python ${{ matrix.python-version }} sample
     steps:
     - name: Checkout

data/tests/fake_associations.csv

@@ -1,9 +1,9 @@
 ,trans_id,account_id,trans_amount,balance_after_trans,trans_type,trans_operation,trans_k_symbol,trans_date
-trans_id,1.0,0.4746435587895609,-0.21334695910231155,0.014054082101829615,0.4929328532971835,0.8514759788482767,0.8714152457284374,-0.029289257805795953
-account_id,0.4746435587895609,1.0,0.024214269962367203,0.06713217170734949,0.05505763017128429,0.10541730588406732,0.20035158555476978,-0.026417221145424448
-trans_amount,-0.21334695910231155,0.024214269962367203,1.0,0.4104949508716057,0.14384781147688352,0.47075620299312654,0.44913033115620205,-0.051358949898267885
-balance_after_trans,0.014054082101829615,0.06713217170734949,0.4104949508716057,1.0,0.10800536670088424,0.20073482566116083,0.13085896079274767,0.03224007887636768
+trans_id,1.0,0.474643558789561,-0.2133469591023116,0.0140540821018296,0.4929328532971835,0.8514759788482767,0.8714152457284374,-0.02928925780579595
+account_id,0.474643558789561,1.0,0.024214269962367224,0.0671321717073495,0.05505763017128429,0.10541730588406732,0.20035158555476978,-0.026417221145424455
+trans_amount,-0.2133469591023116,0.024214269962367224,1.0,0.41049495087160576,0.14384781147688352,0.47075620299312654,0.44913033115620205,-0.05135894989826789
+balance_after_trans,0.0140540821018296,0.0671321717073495,0.41049495087160576,1.0,0.10800536670088424,0.20073482566116083,0.13085896079274767,0.03224007887636768
 trans_type,0.4929328532971835,0.05505763017128429,0.14384781147688352,0.10800536670088424,1.0,0.6997816191537789,0.5221930686175845,0.056104192679759315
 trans_operation,0.8514759788482767,0.10541730588406732,0.47075620299312654,0.20073482566116083,0.6997816191537789,1.0,0.6677368630738517,0.09200128175029092
 trans_k_symbol,0.8714152457284374,0.20035158555476978,0.44913033115620205,0.13085896079274767,0.5221930686175845,0.6677368630738517,1.0,0.14896840029863861
-trans_date,-0.029289257805795953,-0.026417221145424448,-0.051358949898267885,0.03224007887636768,0.056104192679759315,0.09200128175029092,0.14896840029863861,1.0
+trans_date,-0.02928925780579595,-0.026417221145424455,-0.05135894989826789,0.03224007887636768,0.056104192679759315,0.09200128175029092,0.14896840029863861,1.0

data/tests/fake_associations_theil.csv

@@ -1,9 +1,9 @@
 ,trans_id,account_id,trans_amount,balance_after_trans,trans_type,trans_operation,trans_k_symbol,trans_date
-trans_id,1.0,0.4746435587895609,-0.21334695910231155,0.014054082101829615,0.4929328532971835,0.8514759788482767,0.8714152457284374,-0.029289257805795953
-account_id,0.4746435587895609,1.0,0.024214269962367203,0.06713217170734949,0.05505763017128429,0.10541730588406732,0.20035158555476978,-0.026417221145424448
-trans_amount,-0.21334695910231155,0.024214269962367203,1.0,0.4104949508716057,0.14384781147688352,0.47075620299312654,0.44913033115620205,-0.051358949898267885
-balance_after_trans,0.014054082101829615,0.06713217170734949,0.4104949508716057,1.0,0.10800536670088424,0.20073482566116083,0.13085896079274767,0.03224007887636768
-trans_type,0.4929328532971835,0.05505763017128429,0.14384781147688352,0.10800536670088424,1.0,0.4315720065489207,0.2502006364307283,0.056104192679759315
-trans_operation,0.8514759788482767,0.10541730588406732,0.47075620299312654,0.20073482566116083,0.8307948936127061,1.0,0.6143594912151971,0.09200128175029092
-trans_k_symbol,0.8714152457284374,0.20035158555476978,0.44913033115620205,0.13085896079274767,0.4751127792537729,0.6060246958534777,1.0,0.14896840029863861
-trans_date,-0.029289257805795953,-0.026417221145424448,-0.051358949898267885,0.03224007887636768,0.056104192679759315,0.09200128175029092,0.14896840029863861,1.0
+trans_id,1.0,0.474643558789561,-0.2133469591023116,0.0140540821018296,0.4929328532971835,0.8514759788482767,0.8714152457284374,-0.02928925780579595
+account_id,0.474643558789561,1.0,0.024214269962367224,0.0671321717073495,0.05505763017128429,0.10541730588406732,0.20035158555476978,-0.026417221145424455
+trans_amount,-0.2133469591023116,0.024214269962367224,1.0,0.41049495087160576,0.14384781147688352,0.47075620299312654,0.44913033115620205,-0.05135894989826789
+balance_after_trans,0.0140540821018296,0.0671321717073495,0.41049495087160576,1.0,0.10800536670088424,0.20073482566116083,0.13085896079274767,0.03224007887636768
+trans_type,0.4929328532971835,0.05505763017128429,0.14384781147688352,0.10800536670088424,1.0,0.8307948936127061,0.4751127792537729,0.056104192679759315
+trans_operation,0.8514759788482767,0.10541730588406732,0.47075620299312654,0.20073482566116083,0.4315720065489207,1.0,0.6060246958534777,0.09200128175029092
+trans_k_symbol,0.8714152457284374,0.20035158555476978,0.44913033115620205,0.13085896079274767,0.2502006364307283,0.6143594912151971,1.0,0.14896840029863861
+trans_date,-0.02928925780579595,-0.026417221145424455,-0.05135894989826789,0.03224007887636768,0.056104192679759315,0.09200128175029092,0.14896840029863861,1.0

data/tests/real_associations.csv

@@ -1,9 +1,9 @@
 ,trans_id,account_id,trans_amount,balance_after_trans,trans_type,trans_operation,trans_k_symbol,trans_date
-trans_id,1.0,0.5228300496676785,-0.22033993565166976,0.028875713717958756,0.47428712569977377,0.8352915293991163,0.8414511312320141,0.05457374320401556
-account_id,0.5228300496676785,1.0,0.025829382873738007,0.10896692701953434,0.04827916714843414,0.08816937558698971,0.16462397499373396,0.05213217137068295
-trans_amount,-0.22033993565166976,0.025829382873738007,1.0,0.39474608564158525,0.19389971128773834,0.5311580367062264,0.4949401219382614,0.016639052876948952
-balance_after_trans,0.028875713717958756,0.10896692701953434,0.39474608564158525,1.0,0.12419170529475135,0.23006188182617215,0.1781853887887315,0.12393532301006092
+trans_id,1.0,0.5228300496676787,-0.22033993565166987,0.028875713717958783,0.47428712569977377,0.8352915293991163,0.8414511312320141,0.054573743204015576
+account_id,0.5228300496676787,1.0,0.025829382873738,0.10896692701953438,0.04827916714843414,0.08816937558698971,0.16462397499373396,0.05213217137068296
+trans_amount,-0.22033993565166987,0.025829382873738,1.0,0.39474608564158536,0.19389971128773834,0.5311580367062264,0.4949401219382614,0.01663905287694896
+balance_after_trans,0.028875713717958783,0.10896692701953438,0.39474608564158536,1.0,0.12419170529475135,0.23006188182617215,0.1781853887887315,0.12393532301006098
 trans_type,0.47428712569977377,0.04827916714843414,0.19389971128773834,0.12419170529475135,1.0,0.7092204023979536,0.5164395860852621,0.031496012385482906
 trans_operation,0.8352915293991163,0.08816937558698971,0.5311580367062264,0.23006188182617215,0.7092204023979536,1.0,0.6631455360238371,0.0930897099137863
 trans_k_symbol,0.8414511312320141,0.16462397499373396,0.4949401219382614,0.1781853887887315,0.5164395860852621,0.6631455360238371,1.0,0.10794487843160071
-trans_date,0.05457374320401556,0.05213217137068295,0.016639052876948952,0.12393532301006092,0.031496012385482906,0.0930897099137863,0.10794487843160071,1.0
+trans_date,0.054573743204015576,0.05213217137068296,0.01663905287694896,0.12393532301006098,0.031496012385482906,0.0930897099137863,0.10794487843160071,1.0

data/tests/real_associations_theil.csv

@@ -1,9 +1,9 @@
 ,trans_id,account_id,trans_amount,balance_after_trans,trans_type,trans_operation,trans_k_symbol,trans_date
-trans_id,1.0,0.5228300496676785,-0.22033993565166976,0.028875713717958756,0.47428712569977377,0.8352915293991163,0.8414511312320141,0.05457374320401556
-account_id,0.5228300496676785,1.0,0.025829382873738007,0.10896692701953434,0.04827916714843414,0.08816937558698971,0.16462397499373396,0.05213217137068295
-trans_amount,-0.22033993565166976,0.025829382873738007,1.0,0.39474608564158525,0.19389971128773834,0.5311580367062264,0.4949401219382614,0.016639052876948952
-balance_after_trans,0.028875713717958756,0.10896692701953434,0.39474608564158525,1.0,0.12419170529475135,0.23006188182617215,0.1781853887887315,0.12393532301006092
-trans_type,0.47428712569977377,0.04827916714843414,0.19389971128773834,0.12419170529475135,1.0,0.4591498022313355,0.25020452459692694,0.031496012385482906
-trans_operation,0.8352915293991163,0.08816937558698971,0.5311580367062264,0.23006188182617215,0.9044741348299755,1.0,0.6177492339768428,0.0930897099137863
-trans_k_symbol,0.8414511312320141,0.16462397499373396,0.4949401219382614,0.1781853887887315,0.4820483704707144,0.6041794163417048,1.0,0.10794487843160071
-trans_date,0.05457374320401556,0.05213217137068295,0.016639052876948952,0.12393532301006092,0.031496012385482906,0.0930897099137863,0.10794487843160071,1.0
+trans_id,1.0,0.5228300496676787,-0.22033993565166987,0.028875713717958783,0.47428712569977377,0.8352915293991163,0.8414511312320141,0.054573743204015576
+account_id,0.5228300496676787,1.0,0.025829382873738,0.10896692701953438,0.04827916714843414,0.08816937558698971,0.16462397499373396,0.05213217137068296
+trans_amount,-0.22033993565166987,0.025829382873738,1.0,0.39474608564158536,0.19389971128773834,0.5311580367062264,0.4949401219382614,0.01663905287694896
+balance_after_trans,0.028875713717958783,0.10896692701953438,0.39474608564158536,1.0,0.12419170529475135,0.23006188182617215,0.1781853887887315,0.12393532301006098
+trans_type,0.47428712569977377,0.04827916714843414,0.19389971128773834,0.12419170529475135,1.0,0.9044741348299755,0.4820483704707144,0.031496012385482906
+trans_operation,0.8352915293991163,0.08816937558698971,0.5311580367062264,0.23006188182617215,0.4591498022313355,1.0,0.6041794163417048,0.0930897099137863
+trans_k_symbol,0.8414511312320141,0.16462397499373396,0.4949401219382614,0.1781853887887315,0.25020452459692694,0.6177492339768428,1.0,0.10794487843160071
+trans_date,0.054573743204015576,0.05213217137068296,0.01663905287694896,0.12393532301006098,0.031496012385482906,0.0930897099137863,0.10794487843160071,1.0

requirements.txt

@@ -1,7 +1,7 @@
-dython==0.5.1
+dython==0.7.3
 scipy
-seaborn<=0.11.1
-pandas
+pandas==1.5.*
+seaborn
 numpy
 matplotlib
 tqdm

setup.py

@@ -5,7 +5,7 @@
 
 setuptools.setup(
     name="table-evaluator",
-    version="v1.4.2",
+    version="v1.5.0",
     author="Bauke Brenninkmeijer",
     author_email="[email protected]",
     description="A package to evaluate how close a synthetic data set is to real data.",
@@ -14,20 +14,19 @@
     url="https://github.com/Baukebrenninkmeijer/Table-Evaluator",
     packages=setuptools.find_packages(),
     install_requires=[
-        'pandas',
+        'pandas==1.5.*',
         'numpy',
         'tqdm',
         'psutil',
-        'dython==0.5.1',
-        'seaborn<=0.11.1',
+        'dython==0.7.3',
+        'seaborn',
         'matplotlib',
         'scikit-learn',
         'scipy'
     ],
     classifiers=[
         'Programming Language :: Python',
         'Programming Language :: Python :: 3',
-        'Programming Language :: Python :: 3.7',
         'Programming Language :: Python :: 3.8',
         'Programming Language :: Python :: 3.9',
         'Programming Language :: Python :: 3.10',

table_evaluator/table_evaluator.py

@@ -18,7 +18,7 @@
 from sklearn.exceptions import ConvergenceWarning
 from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
 from sklearn.linear_model import Lasso, Ridge, ElasticNet, LogisticRegression
-from dython.nominal import compute_associations, numerical_encoding
+from dython.nominal import associations, numerical_encoding
 from .viz import *
 from .metrics import *
 from .notebook import visualize_notebook, isnotebook, EvaluationResult
@@ -92,15 +92,15 @@ def __init__(self, real: pd.DataFrame, fake: pd.DataFrame, cat_cols=None, unique
     def plot_mean_std(self, fname=None):
         """
         Class wrapper function for plotting the mean and std using `viz.plot_mean_std`.
-        :param fname: If not none, saves the plot with this file name. 
+        :param fname: If not none, saves the plot with this file name.
         """
         plot_mean_std(self.real, self.fake, fname=fname)
 
     def plot_cumsums(self, nr_cols=4, fname=None):
         """
         Plot the cumulative sums for all columns in the real and fake dataset. Height of each row scales with the length of the labels. Each plot contains the
         values of a real columns and the corresponding fake column.
-        :param fname: If not none, saves the plot with this file name. 
+        :param fname: If not none, saves the plot with this file name.
         """
         nr_charts = len(self.real.columns)
         nr_rows = max(1, nr_charts // nr_cols)
@@ -124,7 +124,7 @@ def plot_cumsums(self, nr_cols=4, fname=None):
             cdf(r, f, col, 'Cumsum', ax=axes[i])
         plt.tight_layout(rect=[0, 0.02, 1, 0.98])
 
-        if fname is not None: 
+        if fname is not None:
             plt.savefig(fname)
 
         plt.show()
@@ -133,7 +133,7 @@ def plot_distributions(self, nr_cols=3, fname=None):
         """
         Plot the distribution plots for all columns in the real and fake dataset. Height of each row of plots scales with the length of the labels. Each plot
         contains the values of a real columns and the corresponding fake column.
-        :param fname: If not none, saves the plot with this file name. 
+        :param fname: If not none, saves the plot with this file name.
         """
         nr_charts = len(self.real.columns)
         nr_rows = max(1, nr_charts // nr_cols)
@@ -175,7 +175,7 @@ def plot_distributions(self, nr_cols=3, fname=None):
                 ax.set_xticklabels(axes[i].get_xticklabels(), rotation='vertical')
         plt.tight_layout(rect=[0, 0.02, 1, 0.98])
 
-        if fname is not None: 
+        if fname is not None:
             plt.savefig(fname)
 
         plt.show()
@@ -213,8 +213,8 @@ def custom_cosine(a, b):
         else:
             raise ValueError(f'`how` parameter must be in [euclidean, mae, rmse]')
 
-        real_corr = compute_associations(self.real, nominal_columns=self.categorical_columns, theil_u=True)
-        fake_corr = compute_associations(self.fake, nominal_columns=self.categorical_columns, theil_u=True)
+        real_corr = associations(self.real, nominal_columns=self.categorical_columns, nom_nom_assoc='theil', compute_only=True)
+        fake_corr = associations(self.fake, nominal_columns=self.categorical_columns, nom_nom_assoc='theil', compute_only=True)
 
         return distance_func(
             real_corr.values,
@@ -241,7 +241,7 @@ def plot_pca(self, fname=None):
         ax[0].set_title('Real data')
         ax[1].set_title('Fake data')
 
-        if fname is not None: 
+        if fname is not None:
             plt.savefig(fname)
 
         plt.show()
@@ -367,25 +367,25 @@ def score_estimators(self):
     def visual_evaluation(self, save_dir=None, **kwargs):
         """
         Plot all visual evaluation metrics. Includes plotting the mean and standard deviation, cumulative sums, correlation differences and the PCA transform.
-        :save_dir: directory path to save images 
+        :save_dir: directory path to save images
         :param kwargs: any kwargs for matplotlib.
         """
-        if save_dir is None: 
+        if save_dir is None:
             self.plot_mean_std()
             self.plot_cumsums()
             self.plot_distributions()
             self.plot_correlation_difference(**kwargs)
-            self.plot_pca()    
-        else: 
+            self.plot_pca()
+        else:
             save_dir = Path(save_dir)
             save_dir.mkdir(parents=True, exist_ok=True)
 
             self.plot_mean_std(fname=save_dir/'mean_std.png')
             self.plot_cumsums(fname=save_dir/'cumsums.png')
             self.plot_distributions(fname=save_dir/'distributions.png')
             self.plot_correlation_difference(fname=save_dir/'correlation_difference.png', **kwargs)
-            self.plot_pca(fname=save_dir/'pca.png') 
-        
+            self.plot_pca(fname=save_dir/'pca.png')
+
 
     def basic_statistical_evaluation(self) -> float:
         """
@@ -428,7 +428,7 @@ def correlation_correlation(self) -> float:
         total_metrics = pd.DataFrame()
         for ds_name in ['real', 'fake']:
             ds = getattr(self, ds_name)
-            corr_df = compute_associations(ds, nominal_columns=self.categorical_columns, theil_u=True)
+            corr_df = associations(ds, nominal_columns=self.categorical_columns, nom_nom_assoc='theil', compute_only=True)
             values = corr_df.values
             values = values[~np.eye(values.shape[0], dtype=bool)].reshape(values.shape[0], -1)
             total_metrics[ds_name] = values.flatten()
@@ -621,7 +621,7 @@ def evaluate(self, target_col: str, target_type: str = 'class', metric: str = No
         :param kfold: Use a 5-fold CV for the ML estimators if set to True. Train/Test on 80%/20% of the data if set to False.
         :param notebook: Better visualization of the results in a python notebook
         :param verbose: whether to print verbose logging.
-        :param return_outputs: Will omit printing and instead return a dictionairy with all results. 
+        :param return_outputs: Will omit printing and instead return a dictionairy with all results.
         """
         self.verbose = verbose if verbose is not None else self.verbose
         self.comparison_metric = metric if metric is not None else self.comparison_metric

tests/__init__.py

@@ -0,0 +1,2 @@
+import sys
+sys.path.append('..')