[MRG] Refactor ratio to pick up any class

circle.yml

@@ -23,7 +23,7 @@ dependencies:
     - sudo apt-get install build-essential python-dev python-setuptools
     # install numpy first as it is a compile time dependency for other packages
     - pip install --upgrade numpy
-    - pip install --upgrade scipy matplotlib setuptools nose coverage sphinx pillow sphinx-gallery sphinx_rtd_theme
+    - pip install --upgrade scipy matplotlib setuptools nose coverage pillow sphinx-gallery sphinx_rtd_theme sphinx==1.5.6
     # Installing required packages for `make -C doc check command` to work.
     - sudo -E apt-get -yq update
     - sudo -E apt-get -yq --no-install-suggests --no-install-recommends --force-yes install dvipng texlive-latex-base texlive-latex-extra

doc/api.rst

@@ -174,3 +174,6 @@ Utilities
    :toctree: generated/
 
    utils.estimator_checks.check_estimator
+   utils.check_neighbors_object
+   utils.check_ratio
+   utils.hash_X_y

doc/whats_new.rst

@@ -14,6 +14,8 @@ Bug fixes
 
 - Fixed a bug in :class:`under_sampling.NearMiss` version 3. The
   indices returned were wrong. By `Guillaume Lemaitre`_.
+- fixed bug for :class:`ensemble.BalanceCascade` and :class:`combine.SMOTEENN`
+  and :class:`SMOTETomek. By `Guillaume Lemaitre`_.`
 
 New features
 ~~~~~~~~~~~~
@@ -32,6 +34,7 @@ Enhancement
   `Guillaume Lemaitre`_
 - Remove seaborn dependence and improve the examples. By `Guillaume
   Lemaitre`_.
+- adapt all classes to multi-class resampling. By `Guillaume Lemaitre`_
 
 API changes summary
 ~~~~~~~~~~~~~~~~~~~
@@ -45,7 +48,14 @@ API changes summary
 - move the under-sampling methods in `prototype_selection` and
   `prototype_generation` submodule to make a clearer dinstinction. By
   `Guillaume Lemaitre`_.
+- change `ratio` such that it can adapt to multiple class problems. By
+  `Guillaume Lemaitre`_.
+
+Deprecation
+~~~~~~~~~~~
 
+- deprecate the use of float as ratio in favor of dictionary, string, or
+  callable. By `Guillaume Lemaitre`_.
 
 .. _changes_0_2:
 

imblearn/base.py

@@ -8,97 +8,67 @@
 
 import logging
 import warnings
-from numbers import Real
 from abc import ABCMeta, abstractmethod
-from collections import Counter
 
-import numpy as np
 from sklearn.base import BaseEstimator
 from sklearn.externals import six
 from sklearn.utils import check_X_y
-from sklearn.utils.multiclass import type_of_target
 from sklearn.utils.validation import check_is_fitted
 
+from .utils import hash_X_y
+
 
 class SamplerMixin(six.with_metaclass(ABCMeta, BaseEstimator)):
-    """Mixin class for samplers with abstact method.
+    """Mixin class for samplers with abstract method.
 
     Warning: This class should not be used directly. Use the derive classes
     instead.
     """
 
     _estimator_type = 'sampler'
 
-    def fit(self, X, y):
-        """Find the classes statistics before to perform sampling.
-
-        Parameters
-        ----------
-        X : ndarray, shape (n_samples, n_features)
-            Matrix containing the data which have to be sampled.
-
-        y : ndarray, shape (n_samples, )
-            Corresponding label for each sample in X.
-
-        Returns
-        -------
-        self : object,
-            Return self.
+    def _validate_size_ngh_deprecation(self):
+        "Private function to warn about the deprecation about size_ngh."
 
-        """
+        # Announce deprecation if necessary
+        if self.size_ngh is not None:
+            warnings.warn('`size_ngh` will be replaced in version 0.4. Use'
+                          ' `n_neighbors` instead.', DeprecationWarning)
+            self.n_neighbors = self.size_ngh
 
-        # Check the consistency of X and y
-        X, y = check_X_y(X, y)
+    def _validate_k_deprecation(self):
+        """Private function to warn about deprecation of k in ADASYN"""
+        if self.k is not None:
+            warnings.warn('`k` will be replaced in version 0.4. Use'
+                          ' `n_neighbors` instead.', DeprecationWarning)
+            self.n_neighbors = self.k
 
-        self.min_c_ = None
-        self.maj_c_ = None
-        self.stats_c_ = {}
-        self.X_shape_ = None
+    def _validate_k_m_deprecation(self):
+        """Private function to warn about deprecation of k in ADASYN"""
+        if self.k is not None:
+            warnings.warn('`k` will be replaced in version 0.4. Use'
+                          ' `k_neighbors` instead.', DeprecationWarning)
+            self.k_neighbors = self.k
 
-        if hasattr(self, 'ratio'):
-            self._validate_ratio()
+        if self.m is not None:
+            warnings.warn('`m` will be replaced in version 0.4. Use'
+                          ' `m_neighbors` instead.', DeprecationWarning)
+            self.m_neighbors = self.m
 
+    def _validate_deprecation(self):
         if hasattr(self, 'size_ngh'):
             self._validate_size_ngh_deprecation()
         elif hasattr(self, 'k') and not hasattr(self, 'm'):
             self._validate_k_deprecation()
         elif hasattr(self, 'k') and hasattr(self, 'm'):
             self._validate_k_m_deprecation()
 
-        self.logger.info('Compute classes statistics ...')
-
-        # Raise an error if there is only one class
-        if np.unique(y).size <= 1:
-            raise ValueError("Sampler can't balance when only one class is"
-                             " present.")
-
-        # Store the size of X to check at sampling time if we have the
-        # same data
-        self.X_shape_ = X.shape
-
-        # Create a dictionary containing the class statistics
-        self.stats_c_ = Counter(y)
-
-        # Find the minority and majority classes
-        self.min_c_ = min(self.stats_c_, key=self.stats_c_.get)
-        self.maj_c_ = max(self.stats_c_, key=self.stats_c_.get)
-
-        self.logger.info('%s classes detected: %s',
-                         np.unique(y).size, self.stats_c_)
-
-        # Check if the ratio provided at initialisation make sense
-        if isinstance(self.ratio, Real):
-            if self.ratio < (self.stats_c_[self.min_c_] /
-                             self.stats_c_[self.maj_c_]):
-                raise RuntimeError('The ratio requested at initialisation'
-                                   ' should be greater or equal than the'
-                                   ' balancing ratio of the current data.'
-                                   ' Got {} < {}.'.format(
-                                       self.ratio,
-                                       self.stats_c_[self.min_c_] /
-                                       self.stats_c_[self.maj_c_]))
-
-        return self
+    def _check_hash_X_y(self, X, y):
+        """Private function to check that the X and y in fitting are the same
+        than in sampling."""
+        X_hash, y_hash = hash_X_y(X, y)
+        if self.X_hash_ != X_hash or self.y_hash_ != y_hash:
+            raise RuntimeError("X and y need to be same array earlier fitted.")
 
     def sample(self, X, y):
         """Resample the dataset.
@@ -124,25 +94,9 @@ def sample(self, X, y):
         # Check the consistency of X and y
         X, y = check_X_y(X, y)
 
-        # Check that the data have been fitted
-        check_is_fitted(self, 'stats_c_')
-
-        # Check if the size of the data is identical than at fitting
-        if X.shape != self.X_shape_:
-            raise RuntimeError('The data that you attempt to resample do not'
-                               ' seem to be the one earlier fitted. Use the'
-                               ' fitted data. Shape of data is {}, got {}'
-                               ' instead.'.format(X.shape, self.X_shape_))
-
-        if hasattr(self, 'ratio'):
-            self._validate_ratio()
-
-        if hasattr(self, 'size_ngh'):
-            self._validate_size_ngh_deprecation()
-        elif hasattr(self, 'k') and not hasattr(self, 'm'):
-            self._validate_k_deprecation()
-        elif hasattr(self, 'k') and hasattr(self, 'm'):
-            self._validate_k_m_deprecation()
+        self._validate_deprecation()
+        check_is_fitted(self, 'ratio_')
+        self._check_hash_X_y(X, y)
 
         return self._sample(X, y)
 
@@ -169,56 +123,6 @@ def fit_sample(self, X, y):
 
         return self.fit(X, y).sample(X, y)
 
-    def _validate_ratio(self):
-        # The ratio correspond to the number of samples in the minority class
-        # over the number of samples in the majority class. Thus, the ratio
-        # cannot be greater than 1.0
-        if isinstance(self.ratio, Real):
-            if self.ratio > 1:
-                raise ValueError('Ratio cannot be greater than one.'
-                                 ' Got {}.'.format(self.ratio))
-            elif self.ratio <= 0:
-                raise ValueError('Ratio cannot be negative.'
-                                 ' Got {}.'.format(self.ratio))
-
-        elif isinstance(self.ratio, six.string_types):
-            if self.ratio != 'auto':
-                raise ValueError("Unknown string for the parameter ratio."
-                                 " Got {} instead of 'auto'".format(
-                                     self.ratio))
-        else:
-            raise ValueError('Unknown parameter type for ratio.'
-                             ' Got {} instead of float or str'.format(
-                                 type(self.ratio)))
-
-    def _validate_size_ngh_deprecation(self):
-        "Private function to warn about the deprecation about size_ngh."
-
-        # Announce deprecation if necessary
-        if self.size_ngh is not None:
-            warnings.warn('`size_ngh` will be replaced in version 0.4. Use'
-                          ' `n_neighbors` instead.', DeprecationWarning)
-            self.n_neighbors = self.size_ngh
-
-    def _validate_k_deprecation(self):
-        """Private function to warn about deprecation of k in ADASYN"""
-        if self.k is not None:
-            warnings.warn('`k` will be replaced in version 0.4. Use'
-                          ' `n_neighbors` instead.', DeprecationWarning)
-            self.n_neighbors = self.k
-
-    def _validate_k_m_deprecation(self):
-        """Private function to warn about deprecation of k in ADASYN"""
-        if self.k is not None:
-            warnings.warn('`k` will be replaced in version 0.4. Use'
-                          ' `k_neighbors` instead.', DeprecationWarning)
-            self.k_neighbors = self.k
-
-        if self.m is not None:
-            warnings.warn('`m` will be replaced in version 0.4. Use'
-                          ' `m_neighbors` instead.', DeprecationWarning)
-            self.m_neighbors = self.m
-
     @abstractmethod
     def _sample(self, X, y):
         """Resample the dataset.
@@ -252,133 +156,3 @@ def __setstate__(self, dict):
         logger = logging.getLogger(__name__)
         self.__dict__.update(dict)
         self.logger = logger
-
-
-class BaseBinarySampler(six.with_metaclass(ABCMeta, SamplerMixin)):
-    """Base class for all binary class sampler.
-
-    Warning: This class should not be used directly. Use derived classes
-    instead.
-
-    """
-
-    def __init__(self, ratio='auto', random_state=None):
-        """Initialize this object and its instance variables.
-
-        Parameters
-        ----------
-        ratio : str or float, optional (default='auto')
-            If 'auto', the ratio will be defined automatically to balanced
-            the dataset. Otherwise, the ratio will corresponds to the number
-            of samples in the minority class over the the number of samples
-            in the majority class.
-
-        random_state : int, RandomState or None, optional (default=None)
-
-            - If int, random_state is the seed used by the random number
-            generator;
-            - If RandomState instance, random_state is the random number
-            generator;
-            - If None, the random number generator is the RandomState instance
-            used by np.random.
-
-        Returns
-        -------
-        None
-
-        """
-        self.ratio = ratio
-        self.random_state = random_state
-        self.logger = logging.getLogger(__name__)
-
-    def fit(self, X, y):
-        """Find the classes statistics before to perform sampling.
-
-        Parameters
-        ----------
-        X : ndarray, shape (n_samples, n_features)
-            Matrix containing the data which have to be sampled.
-
-        y : ndarray, shape (n_samples, )
-            Corresponding label for each sample in X.
-
-        Returns
-        -------
-        self : object,
-            Return self.
-
-        """
-
-        super(BaseBinarySampler, self).fit(X, y)
-
-        # Check that the target type is binary
-        if not type_of_target(y) == 'binary':
-            warnings.simplefilter('always', UserWarning)
-            warnings.warn('The target type should be binary.')
-
-        return self
-
-
-class BaseMulticlassSampler(six.with_metaclass(ABCMeta, SamplerMixin)):
-    """Base class for all multiclass sampler.
-
-    Warning: This class should not be used directly. Use derived classes
-    instead.
-
-    """
-    def __init__(self, ratio='auto', random_state=None):
-        """Initialize this object and its instance variables.
-
-        Parameters
-        ----------
-        ratio : str or float, optional (default='auto')
-            If 'auto', the ratio will be defined automatically to balanced
-            the dataset. Otherwise, the ratio will corresponds to the number
-            of samples in the minority class over the the number of samples
-            in the majority class.
-
-        random_state : int, RandomState or None, optional (default=None)
-
-            - If int, random_state is the seed used by the random number
-            generator;
-            - If RandomState instance, random_state is the random number
-            generator;
-            - If None, the random number generator is the RandomState instance
-            used by np.random.
-
-        Returns
-        -------
-        None
-
-        """
-        self.ratio = ratio
-        self.random_state = random_state
-        self.logger = logging.getLogger(__name__)
-
-    def fit(self, X, y):
-        """Find the classes statistics before to perform sampling.
-
-        Parameters
-        ----------
-        X : ndarray, shape (n_samples, n_features)
-            Matrix containing the data which have to be sampled.
-
-        y : ndarray, shape (n_samples, )
-            Corresponding label for each sample in X.
-
-        Returns
-        -------
-        self : object,
-            Return self.
-
-        """
-
-        super(BaseMulticlassSampler, self).fit(X, y)
-
-        # Check that the target type is either binary or multiclass
-        if not (type_of_target(y) == 'binary' or
-                type_of_target(y) == 'multiclass'):
-            warnings.simplefilter('always', UserWarning)
-            warnings.warn('The target type should be binary or multiclass.')
-
-        return self