通过

microsoftml.concat：将多个列串联为一个向量

  • 项目

使用情况

microsoftml.concat(cols: [dict, list], **kargs)

说明

将多个列合并为一个向量值列。

详细信息

concat 从多个列创建单个向量值列。 在训练模型之前，可以对数据执行此方法。 当列数高达数百到数千列时，串联会显著提高数据处理速度。

参数

cols

要转换的字符 dict 或变量名称列表。 如果是 dict，则键表示要创建的新变量的名称。 请注意，所有输入变量的类型必须相同。 可以使用串联转换生成多个输出列。 在这种情况下，需要使用向量列表来定义输入和输出变量之间的一对一映射。 例如，若要将 InNameA 和 InNameB 列连接到 OutName1 列，并将 InNameC 和 InNameD 列连接到 OutName2 列，请使用 dict(OutName1 = [InNameA, InNameB], outName2 = [InNameC, InNameD])

kargs

发送到计算引擎的其他参数。

返回

一个定义串联转换的对象。

请参阅

drop_columns, select_columns.

示例

'''
Example on logistic regression and concat.
'''
import numpy
import pandas
import sklearn
from microsoftml import rx_logistic_regression, concat, rx_predict
from microsoftml.datasets.datasets import get_dataset

iris = get_dataset("iris")

if sklearn.__version__ < "0.18":
    from sklearn.cross_validation import train_test_split
else:
    from sklearn.model_selection import train_test_split

# We use iris dataset.
irisdf = iris.as_df()

# The training features.
features = ["Sepal_Length", "Sepal_Width", "Petal_Length", "Petal_Width"]

# The label.
label = "Label"

# microsoftml needs a single dataframe with features and label.
cols = features + [label]

# We split into train/test. y_train, y_test are not used.
data_train, data_test, y_train, y_test = train_test_split(irisdf[cols], irisdf[label])

# We train a logistic regression.
# A concat transform is added to group features in a single vector column.
multi_logit_out = rx_logistic_regression(
                        formula="Label ~ Features",
                        method="multiClass",
                        data=data_train,
                        ml_transforms=[concat(cols={'Features': features})])
                        
# We show the coefficients.
print(multi_logit_out.coef_)

# We predict.
prediction = rx_predict(multi_logit_out, data=data_test)

print(prediction.head())

输出：

Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
Beginning processing data.
Rows Read: 112, Read Time: 0, Transform Time: 0
Beginning processing data.
Beginning processing data.
Rows Read: 112, Read Time: 0, Transform Time: 0
Beginning processing data.
Beginning processing data.
Rows Read: 112, Read Time: 0.001, Transform Time: 0
Beginning processing data.
LBFGS multi-threading will attempt to load dataset into memory. In case of out-of-memory issues, turn off multi-threading by setting trainThreads to 1.
Beginning optimization
num vars: 15
improvement criterion: Mean Improvement
L1 regularization selected 9 of 15 weights.
Not training a calibrator because it is not needed.
Elapsed time: 00:00:00.2348578
Elapsed time: 00:00:00.0197433
OrderedDict([('0+(Bias)', 1.943994402885437), ('1+(Bias)', 0.6346845030784607), ('2+(Bias)', -2.57867693901062), ('0+Petal_Width', -2.7277402877807617), ('0+Petal_Length', -2.5394322872161865), ('0+Sepal_Width', 0.4810805320739746), ('1+Sepal_Width', -0.5790582299232483), ('2+Petal_Width', 2.547518491744995), ('2+Petal_Length', 1.6753791570663452)])
Beginning processing data.
Rows Read: 38, Read Time: 0, Transform Time: 0
Beginning processing data.
Elapsed time: 00:00:00.0662932
Finished writing 38 rows.
Writing completed.
    Score.0   Score.1   Score.2
0  0.320061  0.504115  0.175825
1  0.761624  0.216213  0.022163
2  0.754765  0.215548  0.029687
3  0.182810  0.517855  0.299335
4  0.018770  0.290014  0.691216