"""Base classes for all estimators.""" # Author: Gael Varoquaux # License: BSD 3 clause import copy import warnings from collections import defaultdict import numpy as np from scipy import sparse from .externals import six from .utils.fixes import signature from . import __version__ ############################################################################## def _first_and_last_element(arr): """Returns first and last element of numpy array or sparse matrix.""" if isinstance(arr, np.ndarray) or hasattr(arr, 'data'): # numpy array or sparse matrix with .data attribute data = arr.data if sparse.issparse(arr) else arr return data.flat[0], data.flat[-1] else: # Sparse matrices without .data attribute. Only dok_matrix at # the time of writing, in this case indexing is fast return arr[0, 0], arr[-1, -1] def clone(estimator, safe=True): """Constructs a new estimator with the same parameters. Clone does a deep copy of the model in an estimator without actually copying attached data. It yields a new estimator with the same parameters that has not been fit on any data. Parameters ---------- estimator : estimator object, or list, tuple or set of objects The estimator or group of estimators to be cloned safe : boolean, optional If safe is false, clone will fall back to a deep copy on objects that are not estimators. """ estimator_type = type(estimator) # XXX: not handling dictionaries if estimator_type in (list, tuple, set, frozenset): return estimator_type([clone(e, safe=safe) for e in estimator]) elif not hasattr(estimator, 'get_params'): if not safe: return copy.deepcopy(estimator) else: raise TypeError("Cannot clone object '%s' (type %s): " "it does not seem to be a scikit-learn estimator " "as it does not implement a 'get_params' methods." % (repr(estimator), type(estimator))) klass = estimator.__class__ new_object_params = estimator.get_params(deep=False) for name, param in six.iteritems(new_object_params): new_object_params[name] = clone(param, safe=False) new_object = klass(**new_object_params) params_set = new_object.get_params(deep=False) # quick sanity check of the parameters of the clone for name in new_object_params: param1 = new_object_params[name] param2 = params_set[name] if param1 is not param2: raise RuntimeError('Cannot clone object %s, as the constructor ' 'either does not set or modifies parameter %s' % (estimator, name)) return new_object ############################################################################### def _pprint(params, offset=0, printer=repr): """Pretty print the dictionary 'params' Parameters ---------- params : dict The dictionary to pretty print offset : int The offset in characters to add at the begin of each line. printer : callable The function to convert entries to strings, typically the builtin str or repr """ # Do a multi-line justified repr: options = np.get_printoptions() np.set_printoptions(precision=5, threshold=64, edgeitems=2) params_list = list() this_line_length = offset line_sep = ',\n' + (1 + offset // 2) * ' ' for i, (k, v) in enumerate(sorted(six.iteritems(params))): if type(v) is float: # use str for representing floating point numbers # this way we get consistent representation across # architectures and versions. this_repr = '%s=%s' % (k, str(v)) else: # use repr of the rest this_repr = '%s=%s' % (k, printer(v)) if len(this_repr) > 500: this_repr = this_repr[:300] + '...' + this_repr[-100:] if i > 0: if (this_line_length + len(this_repr) >= 75 or '\n' in this_repr): params_list.append(line_sep) this_line_length = len(line_sep) else: params_list.append(', ') this_line_length += 2 params_list.append(this_repr) this_line_length += len(this_repr) np.set_printoptions(**options) lines = ''.join(params_list) # Strip trailing space to avoid nightmare in doctests lines = '\n'.join(l.rstrip(' ') for l in lines.split('\n')) return lines ############################################################################### class BaseEstimator(object): """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``). """ @classmethod def _get_param_names(cls): """Get parameter names for the estimator""" # fetch the constructor or the original constructor before # deprecation wrapping if any init = getattr(cls.__init__, 'deprecated_original', cls.__init__) if init is object.__init__: # No explicit constructor to introspect return [] # introspect the constructor arguments to find the model parameters # to represent init_signature = signature(init) # Consider the constructor parameters excluding 'self' parameters = [p for p in init_signature.parameters.values() if p.name != 'self' and p.kind != p.VAR_KEYWORD] for p in parameters: if p.kind == p.VAR_POSITIONAL: raise RuntimeError("scikit-learn estimators should always " "specify their parameters in the signature" " of their __init__ (no varargs)." " %s with constructor %s doesn't " " follow this convention." % (cls, init_signature)) # Extract and sort argument names excluding 'self' return sorted([p.name for p in parameters]) def get_params(self, deep=True): """Get parameters for this estimator. Parameters ---------- deep : boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns ------- params : mapping of string to any Parameter names mapped to their values. """ out = dict() for key in self._get_param_names(): value = getattr(self, key, None) if deep and hasattr(value, 'get_params'): deep_items = value.get_params().items() out.update((key + '__' + k, val) for k, val in deep_items) out[key] = value return out def set_params(self, **params): """Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form ``__`` so that it's possible to update each component of a nested object. Returns ------- self """ if not params: # Simple optimization to gain speed (inspect is slow) return self valid_params = self.get_params(deep=True) nested_params = defaultdict(dict) # grouped by prefix for key, value in params.items(): key, delim, sub_key = key.partition('__') if key not in valid_params: raise ValueError('Invalid parameter %s for estimator %s. ' 'Check the list of available parameters ' 'with `estimator.get_params().keys()`.' % (key, self)) if delim: nested_params[key][sub_key] = value else: setattr(self, key, value) valid_params[key] = value for key, sub_params in nested_params.items(): valid_params[key].set_params(**sub_params) return self def __repr__(self): class_name = self.__class__.__name__ return '%s(%s)' % (class_name, _pprint(self.get_params(deep=False), offset=len(class_name),),) def __getstate__(self): try: state = super(BaseEstimator, self).__getstate__() except AttributeError: state = self.__dict__.copy() if type(self).__module__.startswith('sklearn.'): return dict(state.items(), _sklearn_version=__version__) else: return state def __setstate__(self, state): if type(self).__module__.startswith('sklearn.'): pickle_version = state.pop("_sklearn_version", "pre-0.18") if pickle_version != __version__: warnings.warn( "Trying to unpickle estimator {0} from version {1} when " "using version {2}. This might lead to breaking code or " "invalid results. Use at your own risk.".format( self.__class__.__name__, pickle_version, __version__), UserWarning) try: super(BaseEstimator, self).__setstate__(state) except AttributeError: self.__dict__.update(state) ############################################################################### class ClassifierMixin(object): """Mixin class for all classifiers in scikit-learn.""" _estimator_type = "classifier" def score(self, X, y, sample_weight=None): """Returns the mean accuracy on the given test data and labels. In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted. Parameters ---------- X : array-like, shape = (n_samples, n_features) Test samples. y : array-like, shape = (n_samples) or (n_samples, n_outputs) True labels for X. sample_weight : array-like, shape = [n_samples], optional Sample weights. Returns ------- score : float Mean accuracy of self.predict(X) wrt. y. """ from .metrics import accuracy_score return accuracy_score(y, self.predict(X), sample_weight=sample_weight) ############################################################################### class RegressorMixin(object): """Mixin class for all regression estimators in scikit-learn.""" _estimator_type = "regressor" def score(self, X, y, sample_weight=None): """Returns the coefficient of determination R^2 of the prediction. The coefficient R^2 is defined as (1 - u/v), where u is the residual sum of squares ((y_true - y_pred) ** 2).sum() and v is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R^2 score of 0.0. Parameters ---------- X : array-like, shape = (n_samples, n_features) Test samples. For some estimators this may be a precomputed kernel matrix instead, shape = (n_samples, n_samples_fitted], where n_samples_fitted is the number of samples used in the fitting for the estimator. y : array-like, shape = (n_samples) or (n_samples, n_outputs) True values for X. sample_weight : array-like, shape = [n_samples], optional Sample weights. Returns ------- score : float R^2 of self.predict(X) wrt. y. """ from .metrics import r2_score return r2_score(y, self.predict(X), sample_weight=sample_weight, multioutput='variance_weighted') ############################################################################### class ClusterMixin(object): """Mixin class for all cluster estimators in scikit-learn.""" _estimator_type = "clusterer" def fit_predict(self, X, y=None): """Performs clustering on X and returns cluster labels. Parameters ---------- X : ndarray, shape (n_samples, n_features) Input data. y : Ignored not used, present for API consistency by convention. Returns ------- labels : ndarray, shape (n_samples,) cluster labels """ # non-optimized default implementation; override when a better # method is possible for a given clustering algorithm self.fit(X) return self.labels_ class BiclusterMixin(object): """Mixin class for all bicluster estimators in scikit-learn""" @property def biclusters_(self): """Convenient way to get row and column indicators together. Returns the ``rows_`` and ``columns_`` members. """ return self.rows_, self.columns_ def get_indices(self, i): """Row and column indices of the i'th bicluster. Only works if ``rows_`` and ``columns_`` attributes exist. Parameters ---------- i : int The index of the cluster. Returns ------- row_ind : np.array, dtype=np.intp Indices of rows in the dataset that belong to the bicluster. col_ind : np.array, dtype=np.intp Indices of columns in the dataset that belong to the bicluster. """ rows = self.rows_[i] columns = self.columns_[i] return np.nonzero(rows)[0], np.nonzero(columns)[0] def get_shape(self, i): """Shape of the i'th bicluster. Parameters ---------- i : int The index of the cluster. Returns ------- shape : (int, int) Number of rows and columns (resp.) in the bicluster. """ indices = self.get_indices(i) return tuple(len(i) for i in indices) def get_submatrix(self, i, data): """Returns the submatrix corresponding to bicluster `i`. Parameters ---------- i : int The index of the cluster. data : array The data. Returns ------- submatrix : array The submatrix corresponding to bicluster i. Notes ----- Works with sparse matrices. Only works if ``rows_`` and ``columns_`` attributes exist. """ from .utils.validation import check_array data = check_array(data, accept_sparse='csr') row_ind, col_ind = self.get_indices(i) return data[row_ind[:, np.newaxis], col_ind] ############################################################################### class TransformerMixin(object): """Mixin class for all transformers in scikit-learn.""" def fit_transform(self, X, y=None, **fit_params): """Fit to data, then transform it. Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X. Parameters ---------- X : numpy array of shape [n_samples, n_features] Training set. y : numpy array of shape [n_samples] Target values. Returns ------- X_new : numpy array of shape [n_samples, n_features_new] Transformed array. """ # non-optimized default implementation; override when a better # method is possible for a given clustering algorithm if y is None: # fit method of arity 1 (unsupervised transformation) return self.fit(X, **fit_params).transform(X) else: # fit method of arity 2 (supervised transformation) return self.fit(X, y, **fit_params).transform(X) class DensityMixin(object): """Mixin class for all density estimators in scikit-learn.""" _estimator_type = "DensityEstimator" def score(self, X, y=None): """Returns the score of the model on the data X Parameters ---------- X : array-like, shape = (n_samples, n_features) Returns ------- score : float """ pass class OutlierMixin(object): """Mixin class for all outlier detection estimators in scikit-learn.""" _estimator_type = "outlier_detector" def fit_predict(self, X, y=None): """Performs outlier detection on X. Returns -1 for outliers and 1 for inliers. Parameters ---------- X : ndarray, shape (n_samples, n_features) Input data. y : Ignored not used, present for API consistency by convention. Returns ------- y : ndarray, shape (n_samples,) 1 for inliers, -1 for outliers. """ # override for transductive outlier detectors like LocalOulierFactor return self.fit(X).predict(X) ############################################################################### class MetaEstimatorMixin(object): """Mixin class for all meta estimators in scikit-learn.""" # this is just a tag for the moment ############################################################################### def is_classifier(estimator): """Returns True if the given estimator is (probably) a classifier. Parameters ---------- estimator : object Estimator object to test. Returns ------- out : bool True if estimator is a classifier and False otherwise. """ return getattr(estimator, "_estimator_type", None) == "classifier" def is_regressor(estimator): """Returns True if the given estimator is (probably) a regressor. Parameters ---------- estimator : object Estimator object to test. Returns ------- out : bool True if estimator is a regressor and False otherwise. """ return getattr(estimator, "_estimator_type", None) == "regressor" def is_outlier_detector(estimator): """Returns True if the given estimator is (probably) an outlier detector. Parameters ---------- estimator : object Estimator object to test. Returns ------- out : bool True if estimator is an outlier detector and False otherwise. """ return getattr(estimator, "_estimator_type", None) == "outlier_detector"