microsoftml.rx_fast_forest: Random Forest
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)
Machine Learning Fast Forest
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
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.
The formula as described in revoscalepy.rx_formula.
Interaction terms and
F() are not currently supported in
A data source object or a character string specifying a .xdf file or a data frame object.
A character string denoting Fast Tree type:
"binary"for the default Fast Tree Binary Classification or
"regression"for Fast Tree Regression.
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.
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.
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.
The fraction of randomly chosen instances to use for each tree. The default value is 0.7.
The fraction of randomly chosen features to use for each tree. The default value is 0.7.
The fraction of randomly chosen features to use on each split. The default value is 0.7.
Maximum number of distinct values (bins) per feature. The default value is 255.
The feature first use penalty coefficient. The default value is 0.
Tree fitting gain confidence requirement (should be in the range [0,1) ). The default value is 0.
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.
Specifies the random seed. The default value is None.
Specifies a list of MicrosoftML transforms to be
performed on the data before training or None if no transforms are
to be performed. See
for transformations that are supported.
These transformations are performed after any specified Python transformations.
The default value is None.
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.
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
row_selection = (age > 20) & (age < 65) & (log(income) > 10)only uses observations in which the value of the
agevariable is between 20 and 65 and the value of the
incomevariable is greater than 10.
The row selection is performed after processing any data
transformations (see the arguments
transform_function). As with all expressions,
row_selection can be
defined outside of the function call using the
NOT SUPPORTED. An expression of the form that represents the first round
of variable transformations. As with
row_selection) can be defined
outside of the function call using the
NOT SUPPORTED. A named list that contains objects that can be
The variable transformation function.
A character vector of input data set variables needed for the transformation function.
NOT SUPPORTED. A character vector specifying additional Python packages
(outside of those specified in
be made available and preloaded for use in variable transformation functions.
For example, those explicitly defined in revoscalepy functions via
transform_function arguments or those defined
implicitly via their
row_selection arguments. The
transform_packages argument may also be None, indicating that
no packages outside
RxOptions.get_option("transform_packages") are preloaded.
NOT SUPPORTED. A user-defined environment to serve as a parent to all
environments developed internally and used for variable data transformation.
transform_environment = None, a new “hash” environment with parent
revoscalepy.baseenv is used instead.
Specifies the number of blocks to read for each chunk of data read from the data source.
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.
An integer value that specifies the amount of output wanted.
0, no verbose output is printed during calculations. Integer
4 provide increasing amounts of information.
Sets the context in which computations are executed,
specified with a valid
Currently local and
RxInSqlServer compute contexts
Control parameters for ensembling.
FastForest object with the trained model.
This algorithm is multi-threaded and will always attempt to load the entire dataset into memory.
''' 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))
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. ''' 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)
Not adding a normalizer. Making per-feature arrays Changing data from row-wise to column-wise Beginning processing data. Rows Read: 87, Read Time: 0, Transform Time: 0 Beginning processing data. Warning: Skipped 4 instances with missing features during training Processed 83 instances Binning and forming Feature objects Reserved memory for tree learner: 21372 bytes Starting to train ... Not training a calibrator because it is not needed. Elapsed time: 00:00:00.0644269 Elapsed time: 00:00:00.0109290 Beginning processing data. Rows Read: 29, Read Time: 0.001, Transform Time: 0 Beginning processing data. Elapsed time: 00:00:00.0314390 Finished writing 29 rows. Writing completed. Solar_R Wind Temp Score 0 190.0 7.4 67.0 26.296144 1 20.0 16.6 63.0 14.274153 2 320.0 16.6 73.0 23.421144 3 187.0 5.1 87.0 80.662109 4 175.0 7.4 89.0 67.570549