def test_text_label(self):
X, y = get_iris()
ap = FastLinearClassifier(
feature=[
'Sepal_Width',
'Sepal_Length',
'Petal_Width',
'Petal_Length'])
ap.fit(X, y)
scores = ap.predict(X)
assert str(scores.dtype) == "object"
###############################################################################
# FastLinearClassifier
import numpy as np
from nimbusml.datasets import get_dataset
from nimbusml.linear_model import FastLinearClassifier
from sklearn.model_selection import train_test_split
# use 'iris' data set to create test and train data
# Sepal_Length Sepal_Width Petal_Length Petal_Width Label Species Setosa
# 0 5.1 3.5 1.4 0.2 0 setosa 1.0
# 1 4.9 3.0 1.4 0.2 0 setosa 1.0
np.random.seed(0)
df = get_dataset("iris").as_df()
df.drop(['Species'], inplace=True, axis=1)
X_train, X_test, y_train, y_test = \
train_test_split(df.loc[:, df.columns != 'Label'], df['Label'])
lr = FastLinearClassifier().fit(X_train, y_train)
scores = lr.predict(X_test)
# evaluate the model
print('Accuracy:', np.mean(y_test == [i for i in scores]))
