microsoftml.rx_fast_forest: Random Forest

Straipsnis
03/03/2023

Usage

microsoftml.rx_fast_forest(formula: str,
    data: [revoscalepy.datasource.RxDataSource.RxDataSource,
    pandas.core.frame.DataFrame], method: ['binary',
    'regression'] = 'binary', num_trees: int = 100,
    num_leaves: int = 20, min_split: int = 10,
    example_fraction: float = 0.7, feature_fraction: float = 1,
    split_fraction: float = 1, num_bins: int = 255,
    first_use_penalty: float = 0, gain_conf_level: float = 0,
    train_threads: int = 8, random_seed: int = None,
    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,
    ensemble: microsoftml.modules.ensemble.EnsembleControl = None,
    compute_context: revoscalepy.computecontext.RxComputeContext.RxComputeContext = None)

Description

Machine Learning Fast Forest

Details

Decision trees are non-parametric models that perform a sequence of simple tests on inputs. This decision procedure maps them to outputs found in the training dataset whose inputs were similar to the instance being processed. A decision is made at each node of the binary tree data structure based on a measure of similarity that maps each instance recursively through the branches of the tree until the appropriate leaf node is reached and the output decision returned.

Decision trees have several advantages:

They are efficient in both computation and memory usage during training and prediction.
They can represent non-linear decision boundaries.
They perform integrated feature selection and classification.
They are resilient in the presence of noisy features.

Fast forest regression is a random forest and quantile regression forest implementation using the regression tree learner in rx_fast_trees. The model consists of an ensemble of decision trees. Each tree in a decision forest outputs a Gaussian distribution by way of prediction. An aggregation is performed over the ensemble of trees to find a Gaussian distribution closest to the combined distribution for all trees in the model.

This decision forest classifier consists of an ensemble of decision trees. Generally, ensemble models provide better coverage and accuracy than single decision trees. Each tree in a decision forest outputs a Gaussian distribution.

Arguments

formula

The formula as described in revoscalepy.rx_formula. Interaction terms and F() are not currently supported in microsoftml.

data

A data source object or a character string specifying a .xdf file or a data frame object.

method

A character string denoting Fast Tree type:

"binary" for the default Fast Tree Binary Classification or
"regression" for Fast Tree Regression.

num_trees

Specifies the total number of decision trees to create in the ensemble.By creating more decision trees, you can potentially get better coverage, but the training time increases. The default value is 100.

num_leaves

The maximum number of leaves (terminal nodes) that can be created in any tree. Higher values potentially increase the size of the tree and get better precision, but risk overfitting and requiring longer training times. The default value is 20.

min_split

Minimum number of training instances required to form a leaf. That is, the minimal number of documents allowed in a leaf of a regression tree, out of the sub-sampled data. A 'split' means that features in each level of the tree (node) are randomly divided. The default value is 10.

example_fraction

The fraction of randomly chosen instances to use for each tree. The default value is 0.7.

feature_fraction

The fraction of randomly chosen features to use for each tree. The default value is 0.7.

split_fraction

The fraction of randomly chosen features to use on each split. The default value is 0.7.

num_bins

Maximum number of distinct values (bins) per feature. The default value is 255.

first_use_penalty

The feature first use penalty coefficient. The default value is 0.

gain_conf_level

Tree fitting gain confidence requirement (should be in the range [0,1]). The default value is 0.

train_threads

The number of threads to use in training. If None is specified, the number of threads to use is determined internally. The default value is None.

random_seed

Specifies the random seed. The default value is None.

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.

transform_objects

NOT SUPPORTED. A named list that contains objects that can be referenced by transforms, transform_function, and row_selection.

transform_function

The variable transformation function.

transform_variables

A character vector of input data set variables needed for the transformation function.

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.

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.

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.

compute_context

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

ensemble

Control parameters for ensembling.

Returns

A FastForest object with the trained model.

Note

This algorithm is multi-threaded and will always attempt to load the entire dataset into memory.

References

Wikipedia: Random forest

Quantile regression forest

From Stumps to Trees to Forests

Binary classification example

'''
Binary Classification.
'''
import numpy
import pandas
from microsoftml import rx_fast_forest, rx_predict
from revoscalepy.etl.RxDataStep import rx_data_step
from microsoftml.datasets.datasets import get_dataset

infert = get_dataset("infert")

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

infertdf = infert.as_df()
infertdf["isCase"] = infertdf.case == 1
data_train, data_test, y_train, y_test = train_test_split(infertdf, infertdf.isCase)

forest_model = rx_fast_forest(
    formula=" isCase ~ age + parity + education + spontaneous + induced ",
    data=data_train)
    
# RuntimeError: The type (RxTextData) for file is not supported.
score_ds = rx_predict(forest_model, data=data_test,
                     extra_vars_to_write=["isCase", "Score"])
                     
# Print the first five rows
print(rx_data_step(score_ds, number_rows_read=5))

Output:

Not adding a normalizer.
Making per-feature arrays
Changing data from row-wise to column-wise
Beginning processing data.
Rows Read: 186, Read Time: 0, Transform Time: 0
Beginning processing data.
Processed 186 instances
Binning and forming Feature objects
Reserved memory for tree learner: 7176 bytes
Starting to train ...
Not training a calibrator because a valid calibrator trainer was not provided.
Elapsed time: 00:00:00.2704185
Elapsed time: 00:00:00.0443884
Beginning processing data.
Rows Read: 62, Read Time: 0, Transform Time: 0
Beginning processing data.
Elapsed time: 00:00:00.0253862
Finished writing 62 rows.
Writing completed.
Rows Read: 5, Total Rows Processed: 5, Total Chunk Time: Less than .001 seconds 
  isCase PredictedLabel      Score
0  False          False -36.205067
1   True          False -40.396084
2  False          False -33.242531
3  False          False -87.212494
4   True          False -13.100666

Regression example

'''
Regression.
'''
import numpy
import pandas
from microsoftml import rx_fast_forest, rx_predict
from revoscalepy.etl.RxDataStep import rx_data_step
from microsoftml.datasets.datasets import get_dataset

airquality = get_dataset("airquality")

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

airquality = airquality.as_df()


######################################################################
# Estimate a regression fast forest
# Use the built-in data set 'airquality' to create test and train data

df = airquality[airquality.Ozone.notnull()]
df["Ozone"] = df.Ozone.astype(float)

data_train, data_test, y_train, y_test = train_test_split(df, df.Ozone)

airFormula = " Ozone ~ Solar_R + Wind + Temp "

# Regression Fast Forest for train data
ff_reg = rx_fast_forest(airFormula, method="regression", data=data_train)

# Put score and model variables in data frame
score_df = rx_predict(ff_reg, data=data_test, write_model_vars=True)
print(score_df.head())

# Plot actual versus predicted values with smoothed line
# Supported in the next version.
# rx_line_plot(" Score ~ Ozone ", type=["p", "smooth"], data=score_df)