dazer 0.1.23

DAtaset siZe Effect estimatoR

DAZER (DAtaset siZe Effect estimatoR)

Class Subsampler

The 'Subsampler' class serves to subsample proportions of the data. While doing so, it is able to preserve the distribution of values in selected features (columns_keep_ratio).
Additionally, it offers the functionality to extract a test dataset. Samples in this dataset will be excluded from following subsamples.

setup & generate test data

import dazer
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split

X, y = make_classification(
            n_samples=100, n_features=10, random_state=444)
df = pd.DataFrame(X)
df.join(pd.Series(y, name='label'))

subsample

subsampler = dazer.Subsampler(df, columns_keep_ratio=['label'], allowed_deviation=.2)

df_test = subsampler.extract_test()

df_test = subsampler.extract_test(test_size=.2, random_state=101)

df_train_1 = subsampler.subsample(subsample_factor=.1, random_state=101)
df_train_2 = subsampler.subsample(subsample_factor=.2, random_state=101)
df_train_3 = subsampler.subsample(subsample_factor=.3, random_state=101)

Class Classifier

prepare data for training and testing

y_test = df_test[target_column] == target_value
X_test = df_test.drop([target_column], axis=1)

y_train = df_train_1[target_column] == target_value
X_train = df_train_1.drop([target_column], axis=1)

model training and evaluation (Random Forest)

classifier = dazer.Classifier(X_train, y_train, X_test, y_test)
model, evaluation = classifier.train_test('rf', scoring='f1')

model training and evaluation (Multi-layer Perceptron)

classifier = dazer.Classifier(X_train, y_train, X_test, y_test)
model, evaluation = classifier.train_test('mlp', scoring='f1', param_model={'solver': 'lbfgs', 'hidden_layer_sizes': (10, 5), 'random_state': 101, 'alpha': 1e-5, 'C': 1})

model training and evaluation (Support Vector Classification with rbf kernel)

classifier = dazer.Classifier(X_train, y_train, X_test, y_test)
model, evaluation = classifier.train_test('svc', scoring='f1', param_model={'kernel': 'rbf', 'C': 1, 'gamma': 2, 'random_state': 101})

available models:

• 'rf' (Random Forest)
• 'xgb' (XGBoost)
• 'mlp' (Multi-layer Perceptron)
• 'gnb' (Gaussian Naive Bayes)
• 'svc' (Support Vector Classification)

save model immediately as .joblib object

classifier = dazer.Classifier(X_train, y_train, X_test, y_test)
model, evaluation = classifier.train_test('rf', model_path='models/model_1.joblib', scoring='f1')

Utils

Useful high level wrappers incorporating the dazer functionalities.

test_dict, train_dict = dazer.subsample_iterative(df, columns_keep_ratio=[], allowed_deviation=.2, test_size=.2, random_states=[101, 102, 103, 104, 105], attempts=10000, ratios=[.2, .4, .6, .8, 1]):

Run unittests

python3 -m unittest discover tests