Automatic Stattest Selection

Release scope:

1. Stat test auto selection algorithm update: https://docs.evidentlyai.com/reports/data-drift#how-it-works

For small data with <= 1000 observations in the reference dataset:

• For numerical features (n_unique > 5): two-sample Kolmogorov-Smirnov test.
• For categorical features or numerical features with n_unique <= 5: chi-squared test.
• For binary categorical features (n_unique <= 2), we use the proportion difference test for independent samples based on Z-score.
  All tests use a 0.95 confidence level by default.

For larger data with > 1000 observations in the reference dataset:

• For numerical features (n_unique > 5): Wasserstein Distance.
• For categorical features or numerical with n_unique <= 5): Jensen–Shannon divergence.
  All tests use a threshold = 0.1 by default.

2. Added options for setting custom statistical test for Categorical and Numerical Target Drift Dashboard/Profile:
   cat_target_stattest_func: Defines a custom statistical test to detect target drift in CatTargetDrift.
num_target_stattest_func: Defines a custom statistical test to detect target drift in NumTargetDrift.

3. Added options for setting custom threshold for drift detection for Categorical and Numerical Target Drift Dashboard/Profile:
   cat_target_threshold: Optional[float] = None
num_target_threshold: Optional[float] = None
   These thresholds highly depends on selected stattest, generally it is either threshold for p_value or threshold for a distance.

Fixes:
#207