我们从Python开源项目中，提取了以下8个代码示例，用于说明如何使用sklearn.utils.validation.NotFittedError()。
def _validate_X_predict(self, X, check_input): """Validate X whenever one tries to predict, apply, predict_proba""" if self.tree_ is None: raise NotFittedError("Estimator not fitted, " "call `fit` before exploiting the model.") if check_input: X = check_array(X, dtype='f') n_features = X.shape[1] if self._n_features != n_features: raise ValueError("Number of features of the model must " " match the input. Model n_features is %s and " " input n_features is %s " % (self._n_features, n_features)) return X
def _validate_X_predict(self, X, check_input): """Validate X whenever one tries to predict, apply, predict_proba""" if self._tree is None: raise NotFittedError("Estimator not fitted, " "call `fit` before exploiting the model.") if check_input: X = check_array(X, dtype='f') n_features = X.shape[1] if self._n_features != n_features: raise ValueError("Number of features of the model must " " match the input. Model n_features is %s and " " input n_features is %s " % (self._n_features, n_features)) return X
def transform(self, X): """Use the model to transform new data to Shared Response space Parameters ---------- X : list of 2D arrays, element i has shape=[voxels_i, timepoints_i] Each element in the list contains the fMRI data of one subject. Returns ------- r : list of 2D arrays, element i has shape=[features_i, timepoints_i] Shared responses from input data (X) s : list of 2D arrays, element i has shape=[voxels_i, timepoints_i] Individual data obtained from fitting model to input data (X) """ # Check if the model exist if hasattr(self, 'w_') is False: raise NotFittedError("The model fit has not been run yet.") # Check the number of subjects if len(X) != len(self.w_): raise ValueError("The number of subjects does not match the one" " in the model.") r = [None] * len(X) s = [None] * len(X) for subject in range(len(X)): if X[subject] is not None: r[subject], s[subject] = self._transform_new_data(X[subject], subject) return r, s
def transform_subject(self, X): """Transform a new subject using the existing model Parameters ---------- X : 2D array, shape=[voxels, timepoints] The fMRI data of the new subject. Returns ------- w : 2D array, shape=[voxels, features] Orthogonal mapping `W_{new}` for new subject s : 2D array, shape=[voxels, timepoints] Individual term `S_{new}` for new subject """ # Check if the model exist if hasattr(self, 'w_') is False: raise NotFittedError("The model fit has not been run yet.") # Check the number of TRs in the subject if X.shape[1] != self.r_.shape[1]: raise ValueError("The number of timepoints(TRs) does not match the" "one in the model.") s = np.zeros_like(X) for i in range(self.n_iter): w = self._update_transform_subject(X, s, self.r_) s = self._shrink(X - w.dot(self.r_), self.lam) return w, s
def transform(self, X, y=None): """Use the model to transform matrix to Shared Response space Parameters ---------- X : list of 2D arrays, element i has shape=[voxels_i, samples_i] Each element in the list contains the fMRI data of one subject note that number of voxels and samples can vary across subjects. y : not used as it only applies the mappings Returns ------- s : list of 2D arrays, element i has shape=[features_i, samples_i] Shared responses from input data (X) """ # Check if the model exist if hasattr(self, 'w_') is False: raise NotFittedError("The model fit has not been run yet.") # Check the number of subjects if len(X) != len(self.w_): raise ValueError("The number of subjects does not match the one" " in the model.") s = [None] * len(X) for subject in range(len(X)): s[subject] = self.w_[subject].T.dot(X[subject]) return s
def predict(self, X): """Classify the output for given data Parameters ---------- X : list of 2D arrays, element i has shape=[voxels_i, samples_i] Each element in the list contains the fMRI data of one subject The number of voxels should be according to each subject at the moment of training the model. Returns ------- p: list of arrays, element i has shape=[samples_i] Predictions for each data sample. """ # Check if the model exist if hasattr(self, 'w_') is False: raise NotFittedError("The model fit has not been run yet.") # Check the number of subjects if len(X) != len(self.w_): raise ValueError("The number of subjects does not match the one" " in the model.") X_shared = self.transform(X) p = [None] * len(X_shared) for subject in range(len(X_shared)): sumexp, _, exponents = utils.sumexp_stable( self.theta_.T.dot(X_shared[subject]) + self.bias_) p[subject] = self.classes_[ (exponents / sumexp[np.newaxis, :]).argmax(axis=0)] return p
def transform(self, X, y=None): """Use the model to transform matrix to Shared Response space Parameters ---------- X : list of 2D arrays, element i has shape=[voxels_i, samples_i] Each element in the list contains the fMRI data of one subject note that number of voxels and samples can vary across subjects y : not used (as it is unsupervised learning) Returns ------- s : list of 2D arrays, element i has shape=[features_i, samples_i] Shared responses from input data (X) """ # Check if the model exist if hasattr(self, 'w_') is False: raise NotFittedError("The model fit has not been run yet.") # Check the number of subjects if len(X) != len(self.w_): raise ValueError("The number of subjects does not match the one" " in the model.") s = [None] * len(X) for subject in range(len(X)): s[subject] = self.w_[subject].T.dot(X[subject]) return s
def transform(self, X, y=None): """Use the model to transform data to the Shared Response subspace Parameters ---------- X : list of 2D arrays, element i has shape=[voxels_i, samples_i] Each element in the list contains the fMRI data of one subject. y : not used Returns ------- s : list of 2D arrays, element i has shape=[features_i, samples_i] Shared responses from input data (X) """ # Check if the model exist if hasattr(self, 'w_') is False: raise NotFittedError("The model fit has not been run yet.") # Check the number of subjects if len(X) != len(self.w_): raise ValueError("The number of subjects does not match the one" " in the model.") s = [None] * len(X) for subject in range(len(X)): s[subject] = self.w_[subject].T.dot(X[subject]) return s
