scikit-learn

问题 I am trying to find the fastest way to perform the following pairwise distance calculation in Python. I want to use the distances to rank a list_of_objects by their similarity. Each item in the list_of_objects is characterised by four measurements a, b, c, d, which are made on very different scales e.g.: object_1 = [0.2, 4.5, 198, 0.003] object_2 = [0.3, 2.0, 999, 0.001] object_3 = [0.1, 9.2, 321, 0.023] list_of_objects = [object_1, object_2, object_3] The aim is to get a pairwise distance

问题 I am trying to find the fastest way to perform the following pairwise distance calculation in Python. I want to use the distances to rank a list_of_objects by their similarity. Each item in the list_of_objects is characterised by four measurements a, b, c, d, which are made on very different scales e.g.: object_1 = [0.2, 4.5, 198, 0.003] object_2 = [0.3, 2.0, 999, 0.001] object_3 = [0.1, 9.2, 321, 0.023] list_of_objects = [object_1, object_2, object_3] The aim is to get a pairwise distance

问题 I am trying to find the fastest way to perform the following pairwise distance calculation in Python. I want to use the distances to rank a list_of_objects by their similarity. Each item in the list_of_objects is characterised by four measurements a, b, c, d, which are made on very different scales e.g.: object_1 = [0.2, 4.5, 198, 0.003] object_2 = [0.3, 2.0, 999, 0.001] object_3 = [0.1, 9.2, 321, 0.023] list_of_objects = [object_1, object_2, object_3] The aim is to get a pairwise distance

问题 Running locally on a Jupyter notebook and using the MNIST dataset (28k entries, 28x28 pixels per image, the following takes 27 seconds . from sklearn.neighbors import KNeighborsClassifier knn_clf = KNeighborsClassifier(n_jobs=1) knn_clf.fit(pixels, labels) However, the following takes 1722 seconds , in other words ~64 times longer : from sklearn.model_selection import cross_val_predict y_train_pred = cross_val_predict(knn_clf, pixels, labels, cv = 3, n_jobs=1) My naive understanding is that

问题 I am interested in calculating similarity between vectors, however this similarity has to be a number between 0 and 1. There are many questions concerning tf-idf and cosine similarity, all indicating that the value lies between 0 and 1. From Wikipedia: In the case of information retrieval, the cosine similarity of two documents will range from 0 to 1, since the term frequencies (using tf–idf weights) cannot be negative. The angle between two term frequency vectors cannot be greater than 90°.

问题 I want to score different classifiers with different parameters. For speedup on LogisticRegression I use LogisticRegressionCV (which at least 2x faster) and plan use GridSearchCV for others. But problem while it give me equal C parameters, but not the AUC ROC scoring. I'll try fix many parameters like scorer , random_state , solver , max_iter , tol ... Please look at example (real data have no mater): Test data and common part: from sklearn import datasets boston = datasets.load_boston() X =

问题 I want to score different classifiers with different parameters. For speedup on LogisticRegression I use LogisticRegressionCV (which at least 2x faster) and plan use GridSearchCV for others. But problem while it give me equal C parameters, but not the AUC ROC scoring. I'll try fix many parameters like scorer , random_state , solver , max_iter , tol ... Please look at example (real data have no mater): Test data and common part: from sklearn import datasets boston = datasets.load_boston() X =

问题 In a multilabel classification setting, sklearn.metrics.accuracy_score only computes the subset accuracy (3): i.e. the set of labels predicted for a sample must exactly match the corresponding set of labels in y_true. This way of computing the accuracy is sometime named, perhaps less ambiguously, exact match ratio (1): Is there any way to get the other typical way to compute the accuracy in scikit-learn, namely (as defined in (1) and (2), and less ambiguously referred to as the Hamming score

问题 In a multilabel classification setting, sklearn.metrics.accuracy_score only computes the subset accuracy (3): i.e. the set of labels predicted for a sample must exactly match the corresponding set of labels in y_true. This way of computing the accuracy is sometime named, perhaps less ambiguously, exact match ratio (1): Is there any way to get the other typical way to compute the accuracy in scikit-learn, namely (as defined in (1) and (2), and less ambiguously referred to as the Hamming score

问题 I have a dataset of 22 GB. I would like to process it on my laptop. Of course I can't load it in memory. I use a lot sklearn but for much smaller datasets. In this situations the classical approach should be something like. Read only part of the data -> Partial train your estimator -> delete the data -> read other part of the data -> continue to train your estimator. I have seen that some sklearn algorithm have the partial fit method that should allow us to train the estimator with various