README.md

DS Toolbox (Data Scienec Toolbox)

The python library aims to provide high-level functions to help data scientist develop prototype models quickly and easily.

How to use:

1. model selection (classification problem)

import pandas as pd
import numpy as np

from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier

from ds_toolbox.model_selection_functions import model_selection_cv
from ds_toolbox.model_selection_functions import classifier_evaluator
from ds_toolbox.model_selection_functions import eval_barchart

# define model objects
LogisticModel = LogisticRegression(penalty='l1', C=0.1, max_iter=1000)
Boosting = AdaBoostClassifier()
RF = RandomForestClassifier()
SVM = SVC( probability=True)

# package classification algorithms in dictionary
classifiers = {"Logistic Regression (L1: Lasso)": LogisticModel,
               "SVM (Kernel=RDF)": SVM,
               "Boosting Tree": Boosting,
               "Random Forest": RF}

# evaluation model with stratified k-fold cross-validation
# cross-validation results were exported as pandas.DataFrames.
train_reports, test_reports = model_selection_cv(classifiers, xdata, ydata,
                                      k=10, random_state=_RANDOM_STATE,
                                      eval_func = classifier_evaluator )

2. model evaluation

from ds_toolbox.model_selection_functions import classifier_evaluator
eval_classier_report = classifier_evaluator(clf, xx, yy)

3. create balanced sample data from imbalanced data

from ds_toolbox.Sampler import balanced_sample_maker
balanced_xx, balanced_yy = balanced_sample_maker(xx, yy)
# print("positive rate (original) {:.3f}".format( sum(yy)/len(yy) ))
# print("positive rate (balanced) {:.3f}".format( sum(balanced_yy)/len(balanced_yy) ))

4. create model ensembler

from ds_toolbox.Ensembler import WeightingAverageEnsembler

ensemlber = WeightingAverageEnsembler()
ensemlber.load_models([clf01, clf02, clf03], weights=[0.3, 0.5, 0.2])
pred_labels = ensembler.predict(xx)
pred_probs = ensembler.predict_proba(xx)