我们从Python开源项目中，提取了以下13个代码示例，用于说明如何使用sklearn.__version__()。
def get_scipy_status(): """ Returns a dictionary containing a boolean specifying whether SciPy is up-to-date, along with the version string (empty string if not installed). """ scipy_status = {} try: import scipy scipy_version = scipy.__version__ scipy_status['up_to_date'] = parse_version( scipy_version) >= parse_version(scipy_min_version) scipy_status['version'] = scipy_version except ImportError: scipy_status['up_to_date'] = False scipy_status['version'] = "" return scipy_status
def get_numpy_status(): """ Returns a dictionary containing a boolean specifying whether NumPy is up-to-date, along with the version string (empty string if not installed). """ numpy_status = {} try: import numpy numpy_version = numpy.__version__ numpy_status['up_to_date'] = parse_version( numpy_version) >= parse_version(numpy_min_version) numpy_status['version'] = numpy_version except ImportError: numpy_status['up_to_date'] = False numpy_status['version'] = "" return numpy_status
def get_pandas_status(): try: import pandas as pd return _check_version(pd.__version__, pandas_min_version) except ImportError: traceback.print_exc() return default_status
def get_sklearn_status(): try: import sklearn as sk return _check_version(sk.__version__, sklearn_min_version) except ImportError: traceback.print_exc() return default_status
def get_numpy_status(): try: import numpy as np return _check_version(np.__version__, numpy_min_version) except ImportError: traceback.print_exc() return default_status
def get_scipy_status(): try: import scipy as sc return _check_version(sc.__version__, scipy_min_version) except ImportError: traceback.print_exc() return default_status
def get_h2o_status(): try: import h2o return _check_version(h2o.__version__, h2o_min_version) except ImportError: traceback.print_exc() return default_status
def test_valid_estimator(): """Test whether ovk.ONORMA is a valid sklearn estimator.""" from sklearn import __version__ # Adding patch revision number cause crash if LooseVersion(__version__) >= LooseVersion('0.18'): check_estimator(ovk.ONORMA) else: warn('sklearn\'s check_estimator seems to be broken in __version__ <=' ' 0.17.x... skipping')
def test_valid_estimator(): """Test whether ovk.OVKRidge is a valid sklearn estimator.""" from sklearn import __version__ # Adding patch revision number causes crash if LooseVersion(__version__) >= LooseVersion('0.18'): check_estimator(ovk.OVKRidge) else: warn('sklearn\'s check_estimator seems to be broken in __version__ <=' ' 0.17.x... skipping')
def check_version(library, min_version): """Check minimum library version required Parameters ---------- library : str The library name to import. Must have a ``__version__`` property. min_version : str The minimum version string. Anything that matches ``'(\\d+ | [a-z]+ | \\.)'`` Returns ------- ok : bool True if the library exists with at least the specified version. """ ok = True try: library = __import__(library) except ImportError: ok = False else: this_version = LooseVersion(library.__version__) if this_version < min_version: ok = False return ok
def _fit_multiclass(self, X, y, verbose=False): """Fit the calibrated model in multiclass setting Parameters ---------- X : array-like, shape (n_samples, n_features) Training data. y : array-like, shape (n_samples,) Target values. Returns ------- self : object Returns an instance of self. """ class_list = np.unique(y) num_classes = len(class_list) y_mod = np.zeros(len(y)) for i in range(num_classes): y_mod[y==class_list[i]]=i y_mod = y_mod.astype(int) if ((type(self.cv)==str) and (self.cv=='prefit')): self.uncalibrated_classifier = self.base_estimator y_pred = self.uncalibrated_classifier.predict_proba(X) else: y_pred = np.zeros((len(y_mod),num_classes)) if sklearn.__version__ < '0.18': skf = StratifiedKFold(y_mod, n_folds=self.cv,shuffle=True) else: skf = StratifiedKFold(n_splits=self.cv, shuffle=True).split(X, y) for idx, (train_idx, test_idx) in enumerate(skf): if verbose: print("training fold {} of {}".format(idx+1, self.cv)) X_train = np.array(X)[train_idx,:] X_test = np.array(X)[test_idx,:] y_train = np.array(y_mod)[train_idx] # We could also copy the model first and then fit it this_estimator = clone(self.base_estimator) this_estimator.fit(X_train,y_train) y_pred[test_idx,:] = this_estimator.predict_proba(X_test) if verbose: print("Training Full Model") self.uncalibrated_classifier = clone(self.base_estimator) self.uncalibrated_classifier.fit(X, y_mod) # calibrating function if verbose: print("Determining Calibration Function") if self.method=='logistic': self.calib_func, self.cf_list = prob_calibration_function_multiclass(y_mod, self.pre_transform(y_pred), verbose=verbose, **self.calib_kwargs) if self.method=='ridge': self.calib_func, self.cf_list = prob_calibration_function_multiclass(y_mod, self.pre_transform(y_pred), verbose=verbose, method='ridge', **self.calib_kwargs) # training full model return self
def fit(self, X, y, verbose=False): """Fit the calibrated model Parameters ---------- X : array-like, shape (n_samples, n_features) Training data. y : array-like, shape (n_samples,) Target values. Returns ------- self : object Returns an instance of self. """ class_list = np.unique(y) num_classes = len(class_list) y_mod = np.zeros(len(y)) for i in range(num_classes): y_mod[np.where(y==class_list[i])]=i y_mod = y_mod.astype(int) if ((type(self.cv)==str) and (self.cv=='prefit')): self.uncalibrated_classifier = self.base_estimator y_pred = self.uncalibrated_classifier.predict_proba(X)[:,1] else: y_pred = np.zeros((len(y_mod),num_classes)) if sklearn.__version__ < '0.18': skf = StratifiedKFold(y_mod, n_folds=self.cv,shuffle=True) else: skf = StratifiedKFold(n_splits=self.cv, shuffle=True).split(X, y) for idx, (train_idx, test_idx) in enumerate(skf): if verbose: print("training fold {} of {}".format(idx+1, self.cv)) X_train = np.array(X)[train_idx,:] X_test = np.array(X)[test_idx,:] y_train = np.array(y_mod)[train_idx] # We could also copy the model first and then fit it this_estimator = clone(self.base_estimator) this_estimator.fit(X_train,y_train) y_pred[test_idx,:] = this_estimator.predict_proba(X_test) if verbose: print("Training Full Model") self.uncalibrated_classifier = clone(self.base_estimator) self.uncalibrated_classifier.fit(X, y_mod) # calibrating function if verbose: print("Determining Calibration Function") if self.method=='logistic': self.calib_func = prob_calibration_function_multiclass(y_mod, y_pred, verbose=verbose, **self.calib_kwargs) if self.method=='ridge': self.calib_func = prob_calibration_function_multiclass(y_mod, y_pred, verbose=verbose, method='ridge', **self.calib_kwargs) # training full model return self
def test_dump(): Xs, y = load_svmlight_file(datafile) Xd = Xs.toarray() # slicing a csr_matrix can unsort its .indices, so test that we sort # those correctly Xsliced = Xs[np.arange(Xs.shape[0])] for X in (Xs, Xd, Xsliced): for zero_based in (True, False): for dtype in [np.float32, np.float64, np.int32]: f = BytesIO() # we need to pass a comment to get the version info in; # LibSVM doesn't grok comments so they're not put in by # default anymore. dump_svmlight_file(X.astype(dtype), y, f, comment="test", zero_based=zero_based) f.seek(0) comment = f.readline() try: comment = str(comment, "utf-8") except TypeError: # fails in Python 2.x pass assert_in("scikit-learn %s" % sklearn.__version__, comment) comment = f.readline() try: comment = str(comment, "utf-8") except TypeError: # fails in Python 2.x pass assert_in(["one", "zero"][zero_based] + "-based", comment) X2, y2 = load_svmlight_file(f, dtype=dtype, zero_based=zero_based) assert_equal(X2.dtype, dtype) assert_array_equal(X2.sorted_indices().indices, X2.indices) if dtype == np.float32: assert_array_almost_equal( # allow a rounding error at the last decimal place Xd.astype(dtype), X2.toarray(), 4) else: assert_array_almost_equal( # allow a rounding error at the last decimal place Xd.astype(dtype), X2.toarray(), 15) assert_array_equal(y, y2)
