The oneR algorithm returns a rule list that splits on only one (usually continuous) feature It works by building a greedy rule list using only one feature at a time, and then returning the rule list with the highest accuracy

Expand source code
'''The oneR algorithm returns a rule list that splits on only one (usually continuous) feature
It works by building a greedy rule list using only one feature at a time, and then returning
the rule list with the highest accuracy
'''

import math
import numpy as np
from copy import deepcopy
from sklearn.base import BaseEstimator
from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin
from sklearn.utils.multiclass import check_classification_targets, unique_labels
from sklearn.utils.validation import check_X_y, check_array, check_is_fitted

from imodels import GreedyRuleListClassifier
from imodels.rule_list.rule_list import RuleList


class OneRClassifier(GreedyRuleListClassifier):
    def __init__(self, max_depth=5, class_weight=None, criterion='gini'):
        self.max_depth = max_depth
        self.feature_names = None
        self.class_weight = class_weight
        self.criterion = criterion
        self._estimator_type = 'classifier'

    def fit(self, X, y, depth=0, feature_names=None, verbose=False):
        """Fit oneR
        """

        self.classes_ = unique_labels(y)

        # set self.feature_names and make sure x, y are not pandas type
        if 'pandas' in str(type(X)):
            self.feature_names = X.columns
            X = X.values
        else:
            if feature_names is None:
                self.feature_names = ['feat ' + str(i) for i in range(X.shape[1])]
        if feature_names is not None:
            self.feature_names = feature_names
        if 'pandas' in str(type(y)):
            y = y.values

        ms = []
        accs = np.zeros(X.shape[1])
        for col_idx in range(X.shape[1]):
            x = X[:, col_idx].reshape(-1, 1)
            m = GreedyRuleListClassifier(max_depth=self.max_depth, class_weight=self.class_weight,
                                         criterion=self.criterion)
            feat_names_single = [self.feature_names[col_idx]]
            m.fit(x, y, feature_names=feat_names_single)
            accs[col_idx] = np.mean(m.predict(x) == y)
            ms.append(m)
            # print('acc', feat_names_single[0], f'{accs[col_idx]:0.2f}')
        col_idx_best = np.argmax(accs)
        self.rules_ = ms[col_idx_best].rules_
        self.complexity_ = len(self.rules_)

        # need to adjust index_col since was fitted with only 1 col
        for rule in self.rules_:
            if 'index_col' in rule:
                rule['index_col'] += col_idx_best
        self.depth = len(self.rules_)
        return self

Classes

class OneRClassifier (max_depth=5, class_weight=None, criterion='gini')

Base class for all estimators in scikit-learn.

Notes

All estimators should specify all the parameters that can be set at the class level in their __init__ as explicit keyword arguments (no *args or **kwargs).

Params

max_depth Maximum depth the list can achieve criterion: str Criterion used to split 'gini', 'entropy', or 'neg_corr' strategy: str How to select which side of split becomes leaf node Currently only supports 'max' - (higher risk side of split becomes leaf node)

Expand source code
class OneRClassifier(GreedyRuleListClassifier):
    def __init__(self, max_depth=5, class_weight=None, criterion='gini'):
        self.max_depth = max_depth
        self.feature_names = None
        self.class_weight = class_weight
        self.criterion = criterion
        self._estimator_type = 'classifier'

    def fit(self, X, y, depth=0, feature_names=None, verbose=False):
        """Fit oneR
        """

        self.classes_ = unique_labels(y)

        # set self.feature_names and make sure x, y are not pandas type
        if 'pandas' in str(type(X)):
            self.feature_names = X.columns
            X = X.values
        else:
            if feature_names is None:
                self.feature_names = ['feat ' + str(i) for i in range(X.shape[1])]
        if feature_names is not None:
            self.feature_names = feature_names
        if 'pandas' in str(type(y)):
            y = y.values

        ms = []
        accs = np.zeros(X.shape[1])
        for col_idx in range(X.shape[1]):
            x = X[:, col_idx].reshape(-1, 1)
            m = GreedyRuleListClassifier(max_depth=self.max_depth, class_weight=self.class_weight,
                                         criterion=self.criterion)
            feat_names_single = [self.feature_names[col_idx]]
            m.fit(x, y, feature_names=feat_names_single)
            accs[col_idx] = np.mean(m.predict(x) == y)
            ms.append(m)
            # print('acc', feat_names_single[0], f'{accs[col_idx]:0.2f}')
        col_idx_best = np.argmax(accs)
        self.rules_ = ms[col_idx_best].rules_
        self.complexity_ = len(self.rules_)

        # need to adjust index_col since was fitted with only 1 col
        for rule in self.rules_:
            if 'index_col' in rule:
                rule['index_col'] += col_idx_best
        self.depth = len(self.rules_)
        return self

Ancestors

Methods

def fit(self, X, y, depth=0, feature_names=None, verbose=False)

Fit oneR

Expand source code
def fit(self, X, y, depth=0, feature_names=None, verbose=False):
    """Fit oneR
    """

    self.classes_ = unique_labels(y)

    # set self.feature_names and make sure x, y are not pandas type
    if 'pandas' in str(type(X)):
        self.feature_names = X.columns
        X = X.values
    else:
        if feature_names is None:
            self.feature_names = ['feat ' + str(i) for i in range(X.shape[1])]
    if feature_names is not None:
        self.feature_names = feature_names
    if 'pandas' in str(type(y)):
        y = y.values

    ms = []
    accs = np.zeros(X.shape[1])
    for col_idx in range(X.shape[1]):
        x = X[:, col_idx].reshape(-1, 1)
        m = GreedyRuleListClassifier(max_depth=self.max_depth, class_weight=self.class_weight,
                                     criterion=self.criterion)
        feat_names_single = [self.feature_names[col_idx]]
        m.fit(x, y, feature_names=feat_names_single)
        accs[col_idx] = np.mean(m.predict(x) == y)
        ms.append(m)
        # print('acc', feat_names_single[0], f'{accs[col_idx]:0.2f}')
    col_idx_best = np.argmax(accs)
    self.rules_ = ms[col_idx_best].rules_
    self.complexity_ = len(self.rules_)

    # need to adjust index_col since was fitted with only 1 col
    for rule in self.rules_:
        if 'index_col' in rule:
            rule['index_col'] += col_idx_best
    self.depth = len(self.rules_)
    return self