def test_imputer(self):
try:
model = Imputer(missing_values='NaN', strategy='mean', axis=0)
except TypeError:
model = Imputer(missing_values=np.nan, strategy='mean')
model.axis = 0
data = [[1, 2], [np.nan, 3], [7, 6]]
model.fit(data)
from onnxmltools.convert.coreml.convert import convert
import coremltools # noqa
try:
model_coreml = coremltools.converters.sklearn.convert(model)
except ValueError as e:
if 'not supported' in str(e):
# Python 2.7 + scikit-learn 0.22
return
model_onnx = convert(model_coreml.get_spec())
self.assertTrue(model_onnx is not None)
dump_data_and_model(np.array(data, dtype=np.float32),
model, model_onnx, basename="CmlImputerMeanFloat32")
# 王哥的编程
# 卡鲁帅的一
# 时间： 2020/12/3 21:51
import pandas as pd
from sklearn.impute import SimpleImputer as Imputer
df = pd.DataFrame([["XXL", 8, "black", "class 1", 22],
["L", np.nan, "gray", "class 2", 20],
["XL", 10, "blue", "class 2", 19],
["M", np.nan, "orange", "class 1", 17],
["M", 11, "green", "class 3", np.nan],
["M", 7, "red", "class 1", 22]])
df.columns = ["size", "price", "color", "class", "boh"]
imr = Imputer(
missing_values='NaN', strategy='mean'
) #allowed_strategies = ["mean", "median", "most_frequent", "constant"]
imr.axis = 0
df["price"] = imr.fit_transform(df[["price"]])
df["boh"] = imr.fit_transform(df[["boh"]])
df
