Welcom to Tabint

NB: this is on development process, many things we want to develop but have not yet done. If you want to contribute please feel free to do so. We are according to nbdev style. So if you do contribute, please do so accordingly. For more information about nbdev style, please visit nbdev document

Installing

git clone https://github.com/KienVu2368/tabint
cd tabint
conda env create -f environment.yml
conda activate tabint

Pre-processing

import pandas as pd
df = pd.read_csv('df_sample.csv')
df_proc, y, pp_outp = tabular_proc(df, 'TARGET', [fill_na(), app_cat(), dummies()])

Unify class for pre processing class.

class cls(TBPreProc):
    @staticmethod
    def func(df, pp_outp, na_dict = None):
        ...
        return df

For example, fill_na class

class fill_na(TBPreProc):
    @staticmethod
    def func(df, pp_outp, na_dict = None):
        na_dict = {} if na_dict is None else na_dict.copy()
        na_dict_initial = na_dict.copy()
        for n,c in df.items(): na_dict = fix_missing(df, c, n, na_dict)
        if len(na_dict_initial.keys()) > 0:
            df.drop([a + '_na' for a in list(set(na_dict.keys()) - set(na_dict_initial.keys()))], axis=1, inplace=True)
        pp_outp['na_dict'] = na_dict
        return df

Dataset

Dataset class contain training set, validation set and test set.

Dataset can be built by split method of SKlearn

ds = TBDataset.from_SKSplit(df_proc, y, cons, cats, ratio = 0.2)

Or by split method of tabint. This method will try to keep the same distribution of categorie variables between training set and validation set.

ds = TBDataset.from_TBSplit(df_proc, y, cons, cats, ratio = 0.2)

Dataset class have method that can simultaneously edit training set, validation set and test set.

Drop method can drop one or many columns in training set, validation set and test set.

ds.drop('DAYS_LAST_PHONE_CHANGE_na')

Or if we need to keep only importance columns that we found above. Just use keep method from dataset.

mpt_features = impt.top_features(24)
ds.keep(impt_features)

Dataset class in tabint also can simultaneously apply a funciton to training set, validation set and test set

ds.apply('DAYS_BIRTH', lambda df: -df['DAYS_BIRTH']/365)

Or we can pass many transformation function at once.

tfs =  {'drop 1': ['AMT_REQ_CREDIT_BUREAU_HOUR_na', 'AMT_REQ_CREDIT_BUREAU_YEAR_na'],
    
        'apply':{'DAYS_BIRTH': lambda df: -df['DAYS_BIRTH']/365,
                 'DAYS_EMPLOYED': lambda df: -df['DAYS_EMPLOYED']/365,
                 'NEW_EXT_SOURCES_MEAN': lambda df: df[['EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3']].mean(axis=1, skipna=True),
                 'NEW_EXT_SOURCES_GEO': lambda df: (df['EXT_SOURCE_1']*df['EXT_SOURCE_2']*df['EXT_SOURCE_3'])**(1/3),
                 'AMT_CREDIT/AMT_GOODS_PRICE': lambda df: df['AMT_CREDIT']/df['AMT_GOODS_PRICE'],
                 'AMT_CREDIT/AMT_CREDIT': lambda df: df['AMT_CREDIT']/df['AMT_CREDIT'],
                 'DAYS_EMPLOYED/DAYS_BIRTH': lambda df: df['DAYS_EMPLOYED']/df['DAYS_BIRTH'],
                 'DAYS_BIRTH*EXT_SOURCE_1_na': lambda df: df['DAYS_BIRTH']*df['EXT_SOURCE_1_na']},
    
        'drop 2': ['AMT_ANNUITY', 'AMT_CREDIT', 'AMT_GOODS_PRICE']}

ds.transform(tfs)

Learner

Learner class unify training method from sklearn model

learner = LGBLearner()
params = {'task': 'train', 'objective': 'binary', 'metric':'binary_logloss'}
learner.fit(params, *ds.trn, *ds.val)

LGBM model

learner = SKLearner(RandomForestClassifier())
learner.fit(*ds.trn, *ds.val)

and XGB model (WIP)

Feature correlation

tabint use đenogram for easy to see and pick features with high correlation

ddg = Dendogram.from_df(ds.x_trn)

ddg.plot()

Feature importance

tabint use permutation importance. Each column or group of columns in validation set in dataset will be permute to calculate the importance.

group_cols = [['AMT_CREDIT', 'AMT_GOODS_PRICE', 'AMT_ANNUITY'], ['FLAG_OWN_CAR_N', 'OWN_CAR_AGE_na']]

impt = Importance.from_Learner(learner, ds, group_cols)

impt.plot()

We can easily get the most importance feature by method in Importance class

impt.top_features(24)

Model performance

Classification problem

Receiver operating characteristic

roc = ReceiverOperatingCharacteristic.from_learner(learner, ds)
roc.plot()

Probability distribution

kde = KernelDensityEstimation.from_learner(learner, ds)
kde.plot()

Precision and Recall

pr = PrecisionRecall.from_series(y_true, y_pred)
pr.plot()

Regression problem

Actual vs Predict

avp = actual_vs_predict.from_learner(learner, ds)
avp.plot(hue = 'Height')

Interpretation and explaination

Partial dependence

tabint use PDPbox library to visualize partial dependence.

pdp = PartialDependence.from_Learner(learner, ds)

info target plot

pdp.info_target_plot('EXT_SOURCE_3')

We can see result as table

pdp.info_target_data()

isolate plot

pdp.isolate_plot('EXT_SOURCE_3')

Tree interpreter

Tf = Traterfall.from_SKTree(learner, ds.x_trn, 3)

Tf.plot(formatting = "$ {:,.3f}")

We can see and filter result table

Tf.data.pos(5)
Tf.data.neg(5)

SHAP

tabint visual SHAP values from SHAP library. SHAP library use red and blue for default color. tabint change these color to green and blue for easy to see and consistence with pdpbox library.

Shap = SHAP.from_Tree(learner, ds)

force plot

Shap.one_force_plot(3)

And we can see table result also.

Shap.one_force_data.pos(5)

Shap.one_force_data.neg(5)

dependence plot

Shap.dependence_plot('EXT_SOURCE_2')