Scorecardbundle

V1.2.2 fixed some non-critical bugs in previous versions.

1) Corrected the use of deprecated parameters
- When using `plt.annotate()` in previous versions, parameter `s` is used to pass in the text. However, this parameter has been renamed as `text` and from Python3.9 continuing using `s` may cause in TypeError `annotate() missing 1 required positional argument: 'text'`. In V1.2.2 parameter `text` is used when using `plt.annotate()`
2) Change default parameter values: Change the default value of parameter `min_intervals` in ChiMerge from 1 to 2.

3) Adjust the naming of private variables in classes:
- Several classes in ScorecardBundle are inherited from the `BaseEstimator` and `TransformerMixin` classess in Scikit-learn, and for each parameter Scikit-learn checks whether it is existed inside the class as an property with the exact same name. The previous codes set such parameters as private variables with two underscores as prefix. This resulted in errors like `cannot found __xx in class xxxx` when users try to print the instance or access these private variables. Note that this problem won't stop you from getting the correct results.
- V1.2.2 adjusted the use of OOP in `ChiMerge`, `WOE` and`LogisticRegressionScoreCard`to avoid such problem.

- feature_discretization:
- [Add] Add parameter `decimal` to class `ChiMerge.ChiMerge()`, which allows users to control the number of decimals of the feature interval boundaries.
- [Add] Add data table to the feature visualization `FeatureIntervalAdjustment.plot_event_dist()`.
- [Add] Add function `FeatureIntervalAdjustment.feature_stat()` that computes the input feature's sample distribution, including the sample sizes, event sizes and event proportions of each feature value.

- feature_selection.FeatureSelection:
- [Add] Add function `identify_colinear_features()` that identifies the highly-correlated features pair that may cause colinearity problem.
- [Add] Add function `unstacked_corr_table()` that returns the unstacked correlation table to help analyze the colinearity problem.

- model_training.LogisticRegressionScoreCard:
- [Fix] Alter the `LogisticRegressionScoreCard` class so that it now accepts all parameters of `sklearn.linear_model.LogisticRegression` and its `fit()` fucntion accepts all parameters of the `fit()` of `sklearn.linear_model.LogisticRegression` (including `sample_weight`)
- [Add] Add parameter `baseOdds` for `LogisticRegressionScoreCard`. This allows users to pass user-defined base odds ( of y=1 / of y=0) to the Scorecard model.

- model_evaluation.ModelEvaluation:
- [Add] Add function `pref_table`, which evaluates the classification performance on differet levels of model scores . This function is useful for setting classification threshold based on precision and recall.

- model_interpretation:
- [Add] Add function`ScorecardExplainer.important_features()`to help interpret the result of a individual instance. This function indentifies features who contribute the most in pusing the total score of a particular instance above a threshold.