brif 1.2.1

Build decision trees and random forests for classification and regression.

Description

Build random forests for classification and regression problems. The same program is available on CRAN for R users.

Installation

For Python:

pip install brif

For R:

install.packages('brif')

Examples

import brif
import pandas as pd

# Create a brif object with default parameters.
bf = brif.brif()  

# Display the current parameter values. 
bf.get_param()  

# To change certain parameter values, e.g.:
bf.set_param({'ntrees':100, 'nthreads':2})

# Or simply:
bf.ntrees = 200

# Load input data frame. Data must be a pandas data frame with appropriate headers.
# Header text and factor variable values must not contain any white space.
df = pd.read_csv("auto.csv")

# Train the model
bf.fit(df, 'origin')  # specify the target column name

# Or equivalently
bf.fit(df, 7)  # specify the target column index

# Make predictions 
# The target variable column must be excluded, and all other columns should appear in the same order as in training
# Here, predict the first 10 rows of df
pred_labels = bf.predict(df.iloc[0:10, 0:7], type='class')  # return a list containing the predicted class labels
pred_scores = bf.predict(df.iloc[0:10, 0:7], type='score')  # return a data frame containing predicted probabilities by class

# Note: for a regression problem (i.e., when the response variable is numeric type), the predict function will always return a list containing the predicted values

Parameters

tmp_preddata a character string specifying a filename to save the temporary scoring data. Default is "tmp_brif_preddata.txt".

n_numeric_cuts an integer value indicating the maximum number of split points to generate for each numeric variable.

n_integer_cuts an integer value indicating the maximum number of split points to generate for each integer variable.

max_integer_classes an integer value. If the target variable is integer and has more than max_integer_classes unique values in the training data, then the target variable will be grouped into max_integer_classes bins. If the target variable is numeric, then the smaller of max_integer_classes and the number of unique values number of bins will be created on the target variables and the regression problem will be solved as a classification problem.

max_depth an integer specifying the maximum depth of each tree. Maximum is 40.

min_node_size an integer specifying the minimum number of training cases a leaf node must contain.

ntrees an integer specifying the number of trees in the forest.

ps an integer indicating the number of predictors to sample at each node split. Default is 0, meaning to use sqrt(p), where p is the number of predictors in the input.

max_factor_levels an integer. If any factor variables has more than max_factor_levels, the program stops and prompts the user to increase the value of this parameter if the too-many-level factor is indeed intended.

seed an integer specifying the seed used by the internal random number generator.

bagging_method an integer indicating the bagging sampling method: 0 for sampling without replacement; 1 for sampling with replacement (bootstrapping).

bagging_proportion a numeric scalar between 0 and 1, indicating the proportion of training observations to be used in each tree.

split_search an integer indicating the choice of the split search method. 0: randomly pick a split point; 1: do a local search; 2: random pick subject to regulation; 3: local search subject to regulation; 4 or above: a mix of options 0 to 3.

search_radius a positive integer indicating the split point search radius. This parameter takes effect only in the self-regulating local search (split_search = 2 or above).

nthreads an integer specifying the number of threads used by the program. This parameter takes effect only on systems supporting OpenMP.

vote_method an integer (0 or 1) specifying the voting method in prediction. 0: each leaf contributes the raw count and an average is taken on the sum over all leaves; 1: each leaf contributes an intra-node fraction which is then averaged over all leaves with equal weight.

na_numeric a numeric value, substitute for 'nan' in numeric variables.

na_integer an integer value, substitute for 'nan' in integer variables.

na_factor a character string, substitute for missing values in factor variables.

type a character string indicating the return content of the predict function. For a classification problem, "score" means the by-class probabilities and "class" means the class labels (i.e., the target variable levels). For regression, the predicted values are returned. This is a parameter for the predict function, not an attribute of the brif object.