def test_GamBinaryClassifier(self):
np.random.seed(0)
df = get_dataset("infert").as_df()
df.columns = [i.replace(': ', '') for i in df.columns]
df = (OneHotVectorizer() << 'education_str').fit_transform(df)
X_train, X_test, y_train, y_test = \
train_test_split(df.loc[:, df.columns != 'case'], df['case'])
lr = GamBinaryClassifier().fit(X_train, y_train)
scores = lr.predict(X_test)
acc = np.mean(y_test == [i for i in scores])
assert_greater(acc, 0.70, "accuracy should %s" % 0.70)
# GamBinaryClassifier
import numpy as np
from nimbusml.datasets import get_dataset
from nimbusml.ensemble import GamBinaryClassifier
from nimbusml.feature_extraction.categorical import OneHotVectorizer
from sklearn.model_selection import train_test_split
# use the built-in data set 'infert' to create test and train data
# Unnamed: 0 education age parity induced case spontaneous stratum \
# 0 1 0.0 26.0 6.0 1.0 1.0 2.0 1.0
# 1 2 0.0 42.0 1.0 1.0 1.0 0.0 2.0
# pooled.stratum education_str
# 0 3.0 0-5yrs
# 1 1.0 0-5yrs
np.random.seed(0)
df = get_dataset("infert").as_df()
# remove : and ' ' from column names, and encode categorical column
df.columns = [i.replace(': ', '') for i in df.columns]
df = (OneHotVectorizer() << 'education_str').fit_transform(df)
X_train, X_test, y_train, y_test = \
train_test_split(df.loc[:, df.columns != 'case'], df['case'])
ftree = GamBinaryClassifier().fit(X_train, y_train)
scores = ftree.predict(X_test)
# evaluate the model
print('Accuracy:', np.mean(y_test == [i for i in scores]))