Change Log for the SAP HANA Python Client API

What’s New and Changed in 1.0.7

  • Supports SAP HANA SP04

  • Added the following algorithms in PAL package:

    • association (Apriori, FPGrowth, k-optimal rule discovery, sequential pattern mining).

    • clustering (affinity propagation, agglomerate hierarchical clustering, Geometry DBSCAN, self-organizing map).

    • conditional random field (CRF).

    • cross validation (gradient boosting for classification, gradient boosting for regression, hybrid gradient boosting for classification, hybrid gradient boosting for regression, neural network).

    • decision trees (gradient boosting for classification, gradient boosting for regression, hybrid gradient boosting for classification, hybrid gradient boosting for regression).

    • discriminant analysis functions (linear discriminant analysis).

    • metric functions (r2_score, accuracy_score).

    • partition (train_test_val_split).

    • pipeline (run PAL functions in a chain).

    • preprocessing (missing value imputer).

    • random distribution sampling functions (bernoulli, beta, binomial, cauchy, chi_squared, exponential, extreme_value, f, gamma, geometric, gumbel, lognormal, negative_binomial, normal, pert, poisson, student_t, uniform, weibull, multinomial).

    • regression (exponential regression, bi-variate geometric regression, bi-variate natural logarithmic regression, cox proportional hazard model).

    • social networks (link prediction, pagerank).

    • statistics functions (inter-quartile range function, pearsonr_matrix, covariance_matrix, f-oneway).

    • time series (ARIMA, Auto ARIMA, FFT, seasonal_decompose, trend_test, white_noise_test, single/double/triple/auto/Brown exponential smoothing, change-point detection, Croston’s method, linear regression with damped trend and seasonal adjust).

  • Added functions in dataframe.py: pivot_table(), create_dataframe_from_pandas().

  • Added visualizers (EDA, M4 sampling).

  • Fixed bugs in the APL package:

    • time_series

    • clustering

    • gradient_boosting_classification

    • gradient_boosting_regression