def test_pairwise_parallel(): rng = np.random.RandomState(0) for func in (np.array, csr_matrix): X = func(rng.random_sample((5, 4))) Y = func(rng.random_sample((3, 4))) S = euclidean_distances(X) S2 = _parallel_pairwise(X, None, euclidean_distances, n_jobs=-1) assert_array_almost_equal(S, S2) S = euclidean_distances(X, Y) S2 = _parallel_pairwise(X, Y, euclidean_distances, n_jobs=-1) assert_array_almost_equal(S, S2)
def daal_pairwise_distances(X, Y=None, metric="euclidean", n_jobs=None, **kwds): """ Compute the distance matrix from a vector array X and optional Y. This method takes either a vector array or a distance matrix, and returns a distance matrix. If the input is a vector array, the distances are computed. If the input is a distances matrix, it is returned instead. This method provides a safe way to take a distance matrix as input, while preserving compatibility with many other algorithms that take a vector array. If Y is given (default is None), then the returned matrix is the pairwise distance between the arrays from both X and Y. Valid values for metric are: - From scikit-learn: ['cityblock', 'cosine', 'euclidean', 'l1', 'l2', 'manhattan']. These metrics support sparse matrix inputs. - From scipy.spatial.distance: ['braycurtis', 'canberra', 'chebyshev', 'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski', 'mahalanobis', 'matching', 'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'yule'] See the documentation for scipy.spatial.distance for details on these metrics. These metrics do not support sparse matrix inputs. Note that in the case of 'cityblock', 'cosine' and 'euclidean' (which are valid scipy.spatial.distance metrics), the scikit-learn implementation will be used, which is faster and has support for sparse matrices (except for 'cityblock'). For a verbose description of the metrics from scikit-learn, see the __doc__ of the sklearn.pairwise.distance_metrics function. Read more in the :ref:`User Guide <metrics>`. Parameters ---------- X : array [n_samples_a, n_samples_a] if metric == "precomputed", or, \ [n_samples_a, n_features] otherwise Array of pairwise distances between samples, or a feature array. Y : array [n_samples_b, n_features], optional An optional second feature array. Only allowed if metric != "precomputed". metric : string, or callable The metric to use when calculating distance between instances in a feature array. If metric is a string, it must be one of the options allowed by scipy.spatial.distance.pdist for its metric parameter, or a metric listed in pairwise.PAIRWISE_DISTANCE_FUNCTIONS. If metric is "precomputed", X is assumed to be a distance matrix. Alternatively, if metric is a callable function, it is called on each pair of instances (rows) and the resulting value recorded. The callable should take two arrays from X as input and return a value indicating the distance between them. n_jobs : int The number of jobs to use for the computation. This works by breaking down the pairwise matrix into n_jobs even slices and computing them in parallel. If -1 all CPUs are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one are used. `**kwds` : optional keyword parameters Any further parameters are passed directly to the distance function. If using a scipy.spatial.distance metric, the parameters are still metric dependent. See the scipy docs for usage examples. Returns ------- D : array [n_samples_a, n_samples_a] or [n_samples_a, n_samples_b] A distance matrix D such that D_{i, j} is the distance between the ith and jth vectors of the given matrix X, if Y is None. If Y is not None, then D_{i, j} is the distance between the ith array from X and the jth array from Y. """ if (metric not in _VALID_METRICS and not callable(metric) and metric != "precomputed"): raise ValueError("Unknown metric %s. " "Valid metrics are %s, or 'precomputed', or a " "callable" % (metric, _VALID_METRICS)) if metric == "precomputed": X, _ = check_pairwise_arrays(X, Y, precomputed=True) whom = ("`pairwise_distances`. Precomputed distance " " need to have non-negative values.") check_non_negative(X, whom=whom) return X elif ((metric == 'cosine') and (Y is None) and (not issparse(X)) and X.dtype == np.float64): return _daal4py_cosine_distance_dense(X) elif ((metric == 'correlation') and (Y is None) and (not issparse(X)) and X.dtype == np.float64): return _daal4py_correlation_distance_dense(X) elif metric in PAIRWISE_DISTANCE_FUNCTIONS: func = PAIRWISE_DISTANCE_FUNCTIONS[metric] elif callable(metric): func = partial(_pairwise_callable, metric=metric, **kwds) else: if issparse(X) or issparse(Y): raise TypeError("scipy distance metrics do not" " support sparse matrices.") dtype = bool if metric in PAIRWISE_BOOLEAN_FUNCTIONS else None if (dtype == bool and (X.dtype != bool or (Y is not None and Y.dtype != bool))): msg = "Data was converted to boolean for metric %s" % metric warnings.warn(msg, DataConversionWarning) X, Y = check_pairwise_arrays(X, Y, dtype=dtype) # precompute data-derived metric params params = _precompute_metric_params(X, Y, metric=metric, **kwds) kwds.update(**params) if effective_n_jobs(n_jobs) == 1 and X is Y: return distance.squareform(distance.pdist(X, metric=metric, **kwds)) func = partial(distance.cdist, metric=metric, **kwds) return _parallel_pairwise(X, Y, func, n_jobs, **kwds)
def pairwise_distances(X, Y=None, metric="euclidean", n_jobs=1, **kwds): """ Compute the distance matrix from a vector array X and optional Y. This method takes either a vector array or a distance matrix, and returns a distance matrix. If the input is a vector array, the distances are computed. If the input is a distances matrix, it is returned instead. This method provides a safe way to take a distance matrix as input, while preserving compatibility with many other algorithms that take a vector array. If Y is given (default is None), then the returned matrix is the pairwise distance between the arrays from both X and Y. Valid values for metric are: - From scikit-learn: ['cityblock', 'cosine', 'euclidean', 'l1', 'l2', 'manhattan']. These metrics support sparse matrix inputs. - From scipy.spatial.distance: ['braycurtis', 'canberra', 'chebyshev', 'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski', 'mahalanobis', 'matching', 'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'yule'] See the documentation for scipy.spatial.distance for details on these metrics. These metrics do not support sparse matrix inputs. Note that in the case of 'cityblock', 'cosine' and 'euclidean' (which are valid scipy.spatial.distance metrics), the scikit-learn implementation will be used, which is faster and has support for sparse matrices (except for 'cityblock'). For a verbose description of the metrics from scikit-learn, see the __doc__ of the sklearn.pairwise.distance_metrics function. Read more in the :ref:`User Guide <metrics>`. Parameters ---------- X : array [n_samples_a, n_samples_a] if metric == "precomputed", or, \ [n_samples_a, n_features] otherwise Array of pairwise distances between samples, or a feature array. Y : array [n_samples_b, n_features], optional An optional second feature array. Only allowed if metric != "precomputed". metric : string, or callable The metric to use when calculating distance between instances in a feature array. If metric is a string, it must be one of the options allowed by scipy.spatial.distance.pdist for its metric parameter, or a metric listed in pairwise.PAIRWISE_DISTANCE_FUNCTIONS. If metric is "precomputed", X is assumed to be a distance matrix. Alternatively, if metric is a callable function, it is called on each pair of instances (rows) and the resulting value recorded. The callable should take two arrays from X as input and return a value indicating the distance between them. n_jobs : int The number of jobs to use for the computation. This works by breaking down the pairwise matrix into n_jobs even slices and computing them in parallel. If -1 all CPUs are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one are used. `**kwds` : optional keyword parameters Any further parameters are passed directly to the distance function. If using a scipy.spatial.distance metric, the parameters are still metric dependent. See the scipy docs for usage examples. Returns ------- D : array [n_samples_a, n_samples_a] or [n_samples_a, n_samples_b] A distance matrix D such that D_{i, j} is the distance between the ith and jth vectors of the given matrix X, if Y is None. If Y is not None, then D_{i, j} is the distance between the ith array from X and the jth array from Y. """ if (metric not in _VALID_METRICS and not callable(metric) and metric != "precomputed"): raise ValueError("Unknown metric %s. " "Valid metrics are %s, or 'precomputed', or a " "callable" % (metric, _VALID_METRICS)) if metric == "precomputed": X, _ = check_pairwise_arrays(X, Y, precomputed=True) return X elif metric in PAIRWISE_DISTANCE_FUNCTIONS: func = PAIRWISE_DISTANCE_FUNCTIONS[metric] elif callable(metric): func = partial(_pairwise_callable, metric=metric, **kwds) else: if issparse(X) or issparse(Y): raise TypeError("scipy distance metrics do not" " support sparse matrices.") dtype = bool if metric in PAIRWISE_BOOLEAN_FUNCTIONS else None X, Y = check_pairwise_arrays(X, Y, dtype=dtype) if n_jobs == 1 and X is Y: return distance.squareform(distance.pdist(X, metric=metric, **kwds)) func = partial(distance.cdist, metric=metric, **kwds) return _parallel_pairwise(X, Y, func, n_jobs, **kwds)
def pairwise_distances(X, Y=None, metric="euclidean", n_jobs=1, **kwds): """ Compute the distance matrix from a vector array X and optional Y. This method takes either a vector array or a distance matrix, and returns a distance matrix. If the input is a vector array, the distances are computed. If the input is a distances matrix, it is returned instead. This method provides a safe way to take a distance matrix as input, while preserving compatibility with many other algorithms that take a vector array. If Y is given (default is None), then the returned matrix is the pairwise distance between the arrays from both X and Y. Valid values for metric are: - From scikit-learn: ['cityblock', 'cosine', 'euclidean', 'l1', 'l2', 'manhattan']. These metrics support sparse matrix inputs. Also, ['masked_euclidean'] but it does not yet support sparse matrices. - From scipy.spatial.distance: ['braycurtis', 'canberra', 'chebyshev', 'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski', 'mahalanobis', 'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'yule'] See the documentation for scipy.spatial.distance for details on these metrics. These metrics do not support sparse matrix inputs. Note that in the case of 'cityblock', 'cosine' and 'euclidean' (which are valid scipy.spatial.distance metrics), the scikit-learn implementation will be used, which is faster and has support for sparse matrices (except for 'cityblock'). For a verbose description of the metrics from scikit-learn, see the __doc__ of the sklearn.pairwise.distance_metrics function. Read more in the :ref:`User Guide <metrics>`. Parameters ---------- X : array [n_samples_a, n_samples_a] if metric == "precomputed", or, \ [n_samples_a, n_features] otherwise Array of pairwise distances between samples, or a feature array. Y : array [n_samples_b, n_features], optional An optional second feature array. Only allowed if metric != "precomputed". metric : string, or callable The metric to use when calculating distance between instances in a feature array. If metric is a string, it must be one of the options allowed by scipy.spatial.distance.pdist for its metric parameter, or a metric listed in pairwise.PAIRWISE_DISTANCE_FUNCTIONS. If metric is "precomputed", X is assumed to be a distance matrix. Alternatively, if metric is a callable function, it is called on each pair of instances (rows) and the resulting value recorded. The callable should take two arrays from X as input and return a value indicating the distance between them. n_jobs : int The number of jobs to use for the computation. This works by breaking down the pairwise matrix into n_jobs even slices and computing them in parallel. If -1 all CPUs are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one are used. **kwds : optional keyword parameters Any further parameters are passed directly to the distance function. If using a scipy.spatial.distance metric, the parameters are still metric dependent. See the scipy docs for usage examples. Returns ------- D : array [n_samples_a, n_samples_a] or [n_samples_a, n_samples_b] A distance matrix D such that D_{i, j} is the distance between the ith and jth vectors of the given matrix X, if Y is None. If Y is not None, then D_{i, j} is the distance between the ith array from X and the jth array from Y. See also -------- pairwise_distances_chunked : performs the same calculation as this funtion, but returns a generator of chunks of the distance matrix, in order to limit memory usage. paired_distances : Computes the distances between corresponding elements of two arrays """ if (metric not in _VALID_METRICS and not callable(metric) and metric != "precomputed"): raise ValueError("Unknown metric %s. " "Valid metrics are %s, or 'precomputed', or a " "callable" % (metric, _VALID_METRICS)) if metric in _MASKED_METRICS or callable(metric): missing_values = kwds.get("missing_values") if kwds.get( "missing_values") is not None else np.nan if np.all(_get_mask(X.data if issparse(X) else X, missing_values)): raise ValueError( "One or more samples(s) only have missing values.") if metric == "precomputed": X, _ = check_pairwise_arrays(X, Y, precomputed=True) return X elif metric in PAIRWISE_DISTANCE_FUNCTIONS: func = PAIRWISE_DISTANCE_FUNCTIONS[metric] elif callable(metric): func = partial(_pairwise_callable, metric=metric, **kwds) else: if issparse(X) or issparse(Y): raise TypeError("scipy distance metrics do not" " support sparse matrices.") dtype = bool if metric in PAIRWISE_BOOLEAN_FUNCTIONS else None X, Y = check_pairwise_arrays(X, Y, dtype=dtype) if n_jobs == 1 and X is Y: return distance.squareform(distance.pdist(X, metric=metric, **kwds)) func = partial(distance.cdist, metric=metric, **kwds) return _parallel_pairwise(X, Y, func, n_jobs, **kwds)
cs112 = laplacian_kernel(x1.values.reshape(1, -1), [df2]) cs111[0][0] = 1 print(cs111) ### [[1.]] print(cs112) ### [[0.10699213]] print(np.argmax(cs111)) ## 0 print(np.argmax(cs112)) ### 0 r = [0, 0] cs_55 = [cs111[0][0], cs112[0][0]] print(cs_55) ############# [1.0, 0.10699212985311443] np.argmax(cs_55) ### 0 from sklearn.metrics.pairwise import distance_metrics cs113 = distance_metrics() ##cs114 = distance_metrics(x1.values.reshape(1,-1),[df2]) print(np.argmax(cs113)) ## 0 print(cs113) from sklearn.metrics.pairwise import _parallel_pairwise cs114 = _parallel_pairwise(x.values.reshape(1, -1), [df1], n_jobs=1, func=euclidean_distances) cs115 = _parallel_pairwise(x1.values.reshape(1, -1), [df2], n_jobs=2, func=euclidean_distances) cs114[0][0] = 1 print(cs114) ### [[1.]] print(cs115) ### [[28.53037718]] print(np.argmax(cs114)) ## 0 print(np.argmax(cs115)) ### 0 r = [0, 0] cs_56 = [cs114[0][0], cs115[0][0]] print(cs_56) ############# [1.0, 28.530377177229887] np.argmax(cs_56) ### 1
def daal_pairwise_distances(X, Y=None, metric="euclidean", n_jobs=None, force_all_finite=True, **kwds): """ Compute the distance matrix from a vector array X and optional Y. This method takes either a vector array or a distance matrix, and returns a distance matrix. If the input is a vector array, the distances are computed. If the input is a distances matrix, it is returned instead. This method provides a safe way to take a distance matrix as input, while preserving compatibility with many other algorithms that take a vector array. If Y is given (default is None), then the returned matrix is the pairwise distance between the arrays from both X and Y. Valid values for metric are: - From scikit-learn: ['cityblock', 'cosine', 'euclidean', 'l1', 'l2', 'manhattan']. These metrics support sparse matrix inputs. ['nan_euclidean'] but it does not yet support sparse matrices. - From scipy.spatial.distance: ['braycurtis', 'canberra', 'chebyshev', 'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski', 'mahalanobis', 'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'yule'] See the documentation for scipy.spatial.distance for details on these metrics. These metrics do not support sparse matrix inputs. Note that in the case of 'cityblock', 'cosine' and 'euclidean' (which are valid scipy.spatial.distance metrics), the scikit-learn implementation will be used, which is faster and has support for sparse matrices (except for 'cityblock'). For a verbose description of the metrics from scikit-learn, see the __doc__ of the sklearn.pairwise.distance_metrics function. Read more in the :ref:`User Guide <metrics>`. Parameters ---------- X : array [n_samples_a, n_samples_a] if metric == "precomputed", or, \ [n_samples_a, n_features] otherwise Array of pairwise distances between samples, or a feature array. Y : array [n_samples_b, n_features], optional An optional second feature array. Only allowed if metric != "precomputed". metric : string, or callable The metric to use when calculating distance between instances in a feature array. If metric is a string, it must be one of the options allowed by scipy.spatial.distance.pdist for its metric parameter, or a metric listed in pairwise.PAIRWISE_DISTANCE_FUNCTIONS. If metric is "precomputed", X is assumed to be a distance matrix. Alternatively, if metric is a callable function, it is called on each pair of instances (rows) and the resulting value recorded. The callable should take two arrays from X as input and return a value indicating the distance between them. n_jobs : int or None, optional (default=None) The number of jobs to use for the computation. This works by breaking down the pairwise matrix into n_jobs even slices and computing them in parallel. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary <n_jobs>` for more details. force_all_finite : boolean or 'allow-nan', (default=True) Whether to raise an error on np.inf and np.nan in array. The possibilities are: - True: Force all values of array to be finite. - False: accept both np.inf and np.nan in array. - 'allow-nan': accept only np.nan values in array. Values cannot be infinite. .. versionadded:: 0.22 **kwds : optional keyword parameters Any further parameters are passed directly to the distance function. If using a scipy.spatial.distance metric, the parameters are still metric dependent. See the scipy docs for usage examples. Returns ------- D : array [n_samples_a, n_samples_a] or [n_samples_a, n_samples_b] A distance matrix D such that D_{i, j} is the distance between the ith and jth vectors of the given matrix X, if Y is None. If Y is not None, then D_{i, j} is the distance between the ith array from X and the jth array from Y. See also -------- pairwise_distances_chunked : performs the same calculation as this function, but returns a generator of chunks of the distance matrix, in order to limit memory usage. paired_distances : Computes the distances between corresponding elements of two arrays """ if metric not in _VALID_METRICS and not callable(metric) and metric != "precomputed": raise ValueError("Unknown metric %s. Valid metrics are %s, or 'precomputed', " "or a callable" % (metric, _VALID_METRICS)) X = _daal_check_array(X, accept_sparse=['csr', 'csc', 'coo'], force_all_finite=force_all_finite) _patching_status = PatchingConditionsChain( "sklearn.metrics.pairwise_distances") _dal_ready = _patching_status.and_conditions([ (metric == 'cosine' or metric == 'correlation', f"'{metric}' metric is not supported. " "Only 'cosine' and 'correlation' metrics are supported."), (Y is None, "Second feature array is not supported."), (not issparse(X), "X is sparse. Sparse input is not supported."), (X.dtype == np.float64, f"{X.dtype} X data type is not supported. Only np.float64 is supported.") ]) _patching_status.write_log() if _dal_ready: if metric == 'cosine': return _daal4py_cosine_distance_dense(X) if metric == 'correlation': return _daal4py_correlation_distance_dense(X) raise ValueError(f"'{metric}' distance is wrong for daal4py.") if metric == "precomputed": X, _ = check_pairwise_arrays(X, Y, precomputed=True, force_all_finite=force_all_finite) whom = ("`pairwise_distances`. Precomputed distance " " need to have non-negative values.") check_non_negative(X, whom=whom) return X if metric in PAIRWISE_DISTANCE_FUNCTIONS: func = PAIRWISE_DISTANCE_FUNCTIONS[metric] elif callable(metric): func = partial(_pairwise_callable, metric=metric, force_all_finite=force_all_finite, **kwds) else: if issparse(X) or issparse(Y): raise TypeError("scipy distance metrics do not" " support sparse matrices.") dtype = bool if metric in PAIRWISE_BOOLEAN_FUNCTIONS else None if dtype == bool and (X.dtype != bool or (Y is not None and Y.dtype != bool)): msg = "Data was converted to boolean for metric %s" % metric warnings.warn(msg, DataConversionWarning) X, Y = check_pairwise_arrays(X, Y, dtype=dtype, force_all_finite=force_all_finite) # precompute data-derived metric params params = _precompute_metric_params(X, Y, metric=metric, **kwds) kwds.update(**params) if effective_n_jobs(n_jobs) == 1 and X is Y: return distance.squareform(distance.pdist(X, metric=metric, **kwds)) func = partial(distance.cdist, metric=metric, **kwds) return _parallel_pairwise(X, Y, func, n_jobs, **kwds)