def scale_features(self):
"""return dataframe of scaled feature data (via z-score)"""
df = stats.scale_features(self,
metadata_string=self.metadata_string,
prefix=self.prefix)
return DataFrame(df,
metadata_string=self.metadata_string,
prefix=self.prefix)
def test_scale_features_with_metadata():
x = np.random.normal(loc=5, scale=1, size=1000)
y = np.random.normal(loc=10, scale=5, size=1000)
metadata_plate = ["A"] * 1000
df = pd.DataFrame(list(zip(x, y, metadata_plate)))
df.columns = ["x", "y", "Metadata_plate"]
out = stats.scale_features(df)
assert out.shape == df.shape
assert out.columns.tolist() == df.columns.tolist()
def test_scale_features_no_metadata():
x = np.random.normal(loc=5, scale=1, size=1000)
y = np.random.normal(loc=10, scale=5, size=1000)
df = pd.DataFrame(list(zip(x, y)))
df.columns = ["x", "y"]
out = stats.scale_features(df)
assert abs(out["x"].mean() - 0.0) < 1e-6
assert abs(np.std(out["x"]) - 1.0) < 1e-6
assert abs(out["y"].mean() - 0.0) < 1e-6
assert abs(np.std(out["y"]) - 1.0) < 1e-6
def scale_features(self):
"""return dataframe of scaled feature data (via z-score)"""
df = stats.scale_features(self, metadata_string=self.metadata_string,
prefix=self.prefix)
return DataFrame(df, metadata_string=self.metadata_string,
prefix=self.prefix)