def test_syntax10_multi_output1(self):
in_df = pandas.DataFrame(
data=dict(Sepal_Length=[2.5, 1, 2.1, 1.0],
Sepal_Width=[.75, .9, .8, .76],
Petal_Length=[0, 2.5, 2.6, 2.4],
Species=["setosa", "viginica", "setosa", 'versicolor']))
# generate two new Columns - Petal_Normed and Sepal_Normed
normed = LogMeanVarianceScaler() << {
'Petal_Normed': 'Petal_Length',
'Sepal_Normed': 'Sepal_Width'
}
out_df = normed.fit_transform(in_df)
self.assertEqual(sorted(list(out_df.columns)), [
'Petal_Length', 'Petal_Normed', 'Sepal_Length', 'Sepal_Normed',
'Sepal_Width', 'Species'
])
from nimbusml import FileDataStream
from nimbusml.datasets import get_dataset
from nimbusml.preprocessing.normalization import LogMeanVarianceScaler
# data input (as a FileDataStream)
path = get_dataset('infert').as_filepath()
data = FileDataStream.read_csv(
path, sep=',', numeric_dtype=numpy.float32) # Error with integer input
print(data.head())
# age case education induced parity pooled.stratum row_num ...
# 0 26.0 1.0 0-5yrs 1.0 6.0 3.0 1.0 ...
# 1 42.0 1.0 0-5yrs 1.0 1.0 1.0 2.0 ...
# 2 39.0 1.0 0-5yrs 2.0 6.0 4.0 3.0 ...
# 3 34.0 1.0 0-5yrs 2.0 4.0 2.0 4.0 ...
# 4 35.0 1.0 6-11yrs 1.0 3.0 32.0 5.0 ...
# transform usage
xf = LogMeanVarianceScaler(columns={'in': 'induced', 'sp': 'spontaneous'})
# fit and transform
features = xf.fit_transform(data)
# print features
print(features.head())
# age case education in ... row_num sp spontaneous stratum
# 0 26.0 1.0 0-5yrs 0.230397 ... 1.0 0.919880 2.0 1.0
# 1 42.0 1.0 0-5yrs 0.230397 ... 2.0 0.000000 0.0 2.0
# 2 39.0 1.0 0-5yrs 0.912377 ... 3.0 0.000000 0.0 3.0
# 3 34.0 1.0 0-5yrs 0.912377 ... 4.0 0.000000 0.0 4.0
# 4 35.0 1.0 6-11yrs 0.230397 ... 5.0 0.238241 1.0 5.0
###############################################################################
# LogMeanVarianceScaler
import pandas as pd
from nimbusml.preprocessing.normalization import LogMeanVarianceScaler
in_df = pd.DataFrame(
data=dict(Sepal_Length=[2.5, 1, 2.1, 1.0],
Sepal_Width=[.75, .9, .8, .76],
Petal_Length=[0, 2.5, 2.6, 2.4],
Species=["setosa", "viginica", "setosa", 'versicolor']))
# generate two new Columns - Petal_Normed and Sepal_Normed
normed = LogMeanVarianceScaler() << {
'Petal_Normed': 'Petal_Length',
'Sepal_Normed': 'Sepal_Width'
}
out_df = normed.fit_transform(in_df)
print('LogMeanVarianceScaler\n', (out_df))