Redigera

Dela via

microsoftml.rx_featurize: Data transformation for data sources

  • Artikel

Usage

microsoftml.rx_featurize(data: typing.Union[revoscalepy.datasource.RxDataSource.RxDataSource,
    pandas.core.frame.DataFrame],
    output_data: typing.Union[revoscalepy.datasource.RxDataSource.RxDataSource,
    str] = None, overwrite: bool = False,
    data_threads: int = None, random_seed: int = None,
    max_slots: int = 5000, ml_transforms: list = None,
    ml_transform_vars: list = None, row_selection: str = None,
    transforms: dict = None, transform_objects: dict = None,
    transform_function: str = None,
    transform_variables: list = None,
    transform_packages: list = None,
    transform_environment: dict = None, blocks_per_read: int = None,
    report_progress: int = None, verbose: int = 1,
    compute_context: revoscalepy.computecontext.RxComputeContext.RxComputeContext = None)

Description

Transforms data from an input data set to an output data set.

Arguments

data

A revoscalepy data source object, a data frame, or the path to a .xdf file.

output_data

Output text or xdf file name or an RxDataSource with write capabilities in which to store transformed data. If None, a data frame is returned. The default value is None.

overwrite

If True, an existing output_data is overwritten; if False an existing output_data is not overwritten. The default value is False.

data_threads

An integer specifying the desired degree of parallelism in the data pipeline. If None, the number of threads used is determined internally. The default value is None.

random_seed

Specifies the random seed. The default value is None.

max_slots

Max slots to return for vector valued columns (<=0 to return all).

ml_transforms

Specifies a list of MicrosoftML transforms to be performed on the data before training or None if no transforms are to be performed. See featurize_text, categorical, and categorical_hash, for transformations that are supported. These transformations are performed after any specified Python transformations. The default value is None.

ml_transform_vars

Specifies a character vector of variable names to be used in ml_transforms or None if none are to be used. The default value is None.

row_selection

NOT SUPPORTED. Specifies the rows (observations) from the data set that are to be used by the model with the name of a logical variable from the data set (in quotes) or with a logical expression using variables in the data set. For example:

  • row_selection = "old" will only use observations in which the value of the variable old is True.

  • row_selection = (age > 20) & (age < 65) & (log(income) > 10) only uses observations in which the value of the age variable is between 20 and 65 and the value of the log of the income variable is greater than 10.

The row selection is performed after processing any data transformations (see the arguments transforms or transform_function). As with all expressions, row_selection can be defined outside of the function call using the expression function.

transforms

NOT SUPPORTED. An expression of the form that represents the first round of variable transformations. As with all expressions, transforms (or row_selection) can be defined outside of the function call using the expression function. The default value is None.

transform_objects

NOT SUPPORTED. A named list that contains objects that can be referenced by transforms, transform_function, and row_selection. The default value is None.

transform_function

The variable transformation function. The default value is None.

transform_variables

A character vector of input data set variables needed for the transformation function. The default value is None.

transform_packages

NOT SUPPORTED. A character vector specifying additional Python packages (outside of those specified in RxOptions.get_option("transform_packages")) to be made available and preloaded for use in variable transformation functions. For example, those explicitly defined in revoscalepy functions via their transforms and transform_function arguments or those defined implicitly via their formula or row_selection arguments. The transform_packages argument may also be None, indicating that no packages outside RxOptions.get_option("transform_packages") are preloaded.

transform_environment

NOT SUPPORTED. A user-defined environment to serve as a parent to all environments developed internally and used for variable data transformation. If transform_environment = None, a new "hash" environment with parent revoscalepy.baseenv is used instead The default value is None.

blocks_per_read

Specifies the number of blocks to read for each chunk of data read from the data source.

report_progress

An integer value that specifies the level of reporting on the row processing progress:

  • 0: no progress is reported.

  • 1: the number of processed rows is printed and updated.

  • 2: rows processed and timings are reported.

  • 3: rows processed and all timings are reported.

The default value is 1.

verbose

An integer value that specifies the amount of output wanted. If 0, no verbose output is printed during calculations. Integer values from 1 to 4 provide increasing amounts of information. The default value is 1.

compute_context

Sets the context in which computations are executed, specified with a valid revoscalepy.RxComputeContext. Currently local and revoscalepy.RxInSqlServer compute contexts are supported.

Returns

A data frame or an revoscalepy.RxDataSource object representing the created output data.

See also

rx_predict, revoscalepy.rx_data_step, revoscalepy.rx_import.

Example

'''
Example with rx_featurize.
'''
import numpy
import pandas
from microsoftml import rx_featurize, categorical

# rx_featurize basically allows you to access data from the MicrosoftML transforms
# In this example we'll look at getting the output of the categorical transform
# Create the data
categorical_data = pandas.DataFrame(data=dict(places_visited=[
                "London", "Brunei", "London", "Paris", "Seria"]),
                dtype="category")
                
print(categorical_data)

# Invoke the categorical transform
categorized = rx_featurize(data=categorical_data,
                           ml_transforms=[categorical(cols=dict(xdatacat="places_visited"))])

# Now let's look at the data
print(categorized)

Output:

  places_visited
0         London
1         Brunei
2         London
3          Paris
4          Seria
Beginning processing data.
Rows Read: 5, Read Time: 0, Transform Time: 0
Beginning processing data.
Beginning processing data.
Rows Read: 5, Read Time: 0, Transform Time: 0
Beginning processing data.
Elapsed time: 00:00:00.0521300
Finished writing 5 rows.
Writing completed.
  places_visited  xdatacat.London  xdatacat.Brunei  xdatacat.Paris  \
0         London              1.0              0.0             0.0   
1         Brunei              0.0              1.0             0.0   
2         London              1.0              0.0             0.0   
3          Paris              0.0              0.0             1.0   
4          Seria              0.0              0.0             0.0   

   xdatacat.Seria  
0             0.0  
1             0.0  
2             0.0  
3             0.0  
4             1.0