# Evaluation Framework¶

SDV contains a Synthetic Data Evaluation Framework that facilitates the task of evaluating the quality of your Synthetic Dataset by applying multiple Synthetic Data Metrics on it and reporting results in a comprehensive way.

## Using the SDV Evaluation Framework¶

To evaluate the quality of synthetic data we essentially need two things: real data and synthetic data that pretends to resemble it.

Let us start by loading a demo table and generate a synthetic replica of it using the GaussianCopula model.

In [1]: from sdv.demo import load_tabular_demo

In [2]: from sdv.tabular import GaussianCopula

In [3]: real_data = load_tabular_demo('student_placements')

In [4]: model = GaussianCopula()

In [5]: model.fit(real_data)

In [6]: synthetic_data = model.sample()


After the previous steps we will have two tables:

• real_data: A table containing data about student placements

In [7]: real_data.head()
Out[7]:
student_id gender  second_perc  high_perc high_spec  degree_perc degree_type  work_experience  experience_years  employability_perc mba_spec  mba_perc   salary  placed start_date   end_date duration
0       17264      M        67.00      91.00  Commerce        58.00    Sci&Tech            False                 0                55.0   Mkt&HR     58.80  27000.0    True 2020-07-23 2020-10-12      3.0
1       17265      M        79.33      78.33   Science        77.48    Sci&Tech             True                 1                86.5  Mkt&Fin     66.28  20000.0    True 2020-01-11 2020-04-09      3.0
2       17266      M        65.00      68.00      Arts        64.00   Comm&Mgmt            False                 0                75.0  Mkt&Fin     57.80  25000.0    True 2020-01-26 2020-07-13      6.0
3       17267      M        56.00      52.00   Science        52.00    Sci&Tech            False                 0                66.0   Mkt&HR     59.43      NaN   False        NaT        NaT      NaN
4       17268      M        85.80      73.60  Commerce        73.30   Comm&Mgmt            False                 0                96.8  Mkt&Fin     55.50  42500.0    True 2020-07-04 2020-09-27      3.0

• synthetic_data: A synthetically generated table that contains data in the same format and with similar statistical properties as the real_data.

In [8]: synthetic_data.head()
Out[8]:
student_id gender  second_perc  high_perc high_spec  degree_perc degree_type  work_experience  experience_years  employability_perc mba_spec   mba_perc        salary  placed start_date   end_date duration
0       17306      M    56.199618  57.892111  Commerce    61.591230    Sci&Tech            False                 0           52.534918  Mkt&Fin  58.340510           NaN   False        NaT        NaT      NaN
1       17319      M    77.482574  68.507005  Commerce    60.842343   Comm&Mgmt            False                 1           80.469771   Mkt&HR  56.546865  26604.236226    True 2020-01-30 2020-06-23      3.0
2       17377      F    66.261257  77.328159  Commerce    68.059109   Comm&Mgmt            False                 1           79.750220  Mkt&Fin  62.749491  21513.379475    True 2020-02-06 2020-12-08      3.0
3       17381      F    82.646843  91.749992  Commerce    74.522105   Comm&Mgmt            False                 0           53.803237   Mkt&HR  72.071654  27790.671438    True 2020-01-30 2020-10-12     12.0
4       17283      M    59.788533  60.037129   Science    66.389419    Sci&Tech            False                 0           51.110733  Mkt&Fin  53.813831  22306.492187    True 2020-02-22 2020-08-28      3.0


Note

### Computing an overall score¶

The simplest way to see how similar the two tables are is to import the sdv.evaluation.evaluate function and run it passing both the synthetic_data and the real_data tables.

In [9]: from sdv.evaluation import evaluate

In [10]: evaluate(synthetic_data, real_data)
Out[10]: 0.48293646303892107


The output of this function call will be a number between 0 and 1 that will indicate us how similar the two tables are, being 0 the worst and 1 the best possible score.

### How was the obtained score computed?¶

The evaluate function applies a collection of pre-configured metric functions and returns the average of the scores that the data obtained on each one of them. In most scenarios this can be enough to get an idea about the similarity of the two tables, but you might want to explore the metrics in more detail.

In order to see the different metrics that were applied you can pass and additional argument aggregate=False, which will make the evaluate function return a dictionary with the scores that each one of the metrics functions returned:

In [11]: evaluate(synthetic_data, real_data, aggregate=False)
Out[11]:
metric                                     name      score  min_value  max_value      goal
1        LogisticDetection             LogisticRegression Detection   0.395440        0.0        1.0  MAXIMIZE
2             SVCDetection                            SVC Detection   0.405284        0.0        1.0  MAXIMIZE
11         GMLogLikelihood           GaussianMixture Log Likelihood -35.313872       -inf        inf  MAXIMIZE
12                  CSTest                              Chi-Squared   0.865763        0.0        1.0  MAXIMIZE
13                  KSTest  Inverted Kolmogorov-Smirnov D statistic   0.913372        0.0        1.0  MAXIMIZE
14          KSTestExtended  Inverted Kolmogorov-Smirnov D statistic   0.899767        0.0        1.0  MAXIMIZE
15  ContinuousKLDivergence   Continuous Kullback–Leibler Divergence   0.580561        0.0        1.0  MAXIMIZE
16    DiscreteKLDivergence     Discrete Kullback–Leibler Divergence   0.818773        0.0        1.0  MAXIMIZE


### Can I control which metrics are applied?¶

By default, the evaluate function will apply all the metrics that are included within the SDV Evaluation framework. However, the list of metrics that are applied can be controlled by passing a list with the names of the metrics that you want to apply.

For example, if you were interested on obtaining only the CSTest and KSTest metrics you can call the evaluate function as follows:

In [12]: evaluate(synthetic_data, real_data, metrics=['CSTest', 'KSTest'])
Out[12]: 0.8895677339539922


Or, if we want to see the scores separately:

In [13]: evaluate(synthetic_data, real_data, metrics=['CSTest', 'KSTest'], aggregate=False)
Out[13]:
metric                                     name     score  min_value  max_value      goal
0  CSTest                              Chi-Squared  0.865763        0.0        1.0  MAXIMIZE
1  KSTest  Inverted Kolmogorov-Smirnov D statistic  0.913372        0.0        1.0  MAXIMIZE


For more details about all the metrix that exist for the different data modalities please check the corresponding guides.