🌐 Mailman 3 March 2018 - scikit-learn - python.org

One-hot encoding
by Sarah Wait Zaranek Aug. 3, 2018

Aug. 3, 2018

Hello - I was just wondering if there was a way to improve performance on the one-hot encoder. Or, is there any plans to do so in the future? I am working with a matrix that will ultimately have 20 million categorical variables, and my bottleneck is the one-hot encoder. Let me know if this isn't the place to inquire. My code is very simple when using the encoder, but I cut and pasted it here for completeness. enc = OneHotEncoder(sparse=True) Xtrain = enc.fit_transform(tiledata) Thanks, Sarah

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Get parameters of classes in a Pipeline within cross_validate
by Roberto Guidotti April 6, 2018

April 6, 2018

Hi scikit-learners, I have a simple Pipeline with Feature Selection and SVC classifier and I use it in a cross validation schema with cross_validate / cross_validation_score functions. I need to extract the selected features for each fold of the CV and in general get information about the fitted elements of the pipeline in each of the CV fold. Is there a way to get these information (e.g. fs.get_support() or fs.scores_) or I need to build my own cross_validate function? Thank you, Roberto -- Ing. Roberto Guidotti, PhD. PostDoc Fellow Institute for Advanced Biomedical Technologies - ITAB Department of Neuroscience and Imaging University of Chieti "G. D'Annunzio" Via dei Vestini, 33 66013 Chieti, Italy tel: +39 0871 3556919 e-mail: r.guidotti(a)unich.it; rguidotti(a)acm.org linkedin: http://it.linkedin.com/in/robertogui/ twitter: @robbisg github: https://github.com/robbisg

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(no subject)
by PARK Jinwoo April 6, 2018

April 6, 2018

Dear scikit-learn experts Hello, I am a graduate school student majoring in doping control analysis in Korea. Now I'm in a research institute that carries out doping control analyses. I received a project by my advising doctor. It's about operating an AI project. A workshop is scheduled in April, so it needs to be done in a month. However, I haven't learn computer science at all and I'm totally ignorant of it. So I desperately need your advice. To be specific, the 3 xml files shown in the picture are analysis results named positive, negative, and unknown from top to bottom. We'd like to let AI learn positive and negative data, input unknown datum, and then see what result will turn out. I came to know that there's a module called 'iris calssification' in scikit-learn and I'm thinking of utilizing that as it seems similar with my assignment However, while the database of iris is a csv file with 150 data and labels inside, what I have are 3 xml files each one of which represents one data, which are stored in C:\Users\Jinwoo\Documents\Python Scripts\mzdata The training process is not shuffling randomly the 150 data and dividing into training set and test set. The data are already assigned into training ones and testing one. Also, when training the program, training labels naming positive and negative should be inserted on my own. What I know all is that it will be appropriate to use fit() function and predict() function to train and test. But I have no idea on what to import, how to write codes correctly, and so on It will be thankful to give me some help

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Error random_state parameter changed by estimator
by Manoj Karthick April 1, 2018

April 1, 2018

I am working on adding a new estimator to the scikit-learn library, but the make command always exits with the below error message: AssertionError: Estimator XYZ should not change or mutate the parameter random_state from 0 to <mtrand.RandomState object at 0x1a25da9e10> during fit. Can you help me understand what the issue is? Error log: self = <sklearn.utils._unittest_backport.TestCase testMethod=__init__> msg = ‘Estimator XYZ should not change or mutate the parameter random_state from 0 to <mtrand.RandomState object at 0x1a25da9e10> during fit.' def fail(self, msg=None): """Fail immediately, with the given message.""" > raise self.failureException(msg) E AssertionError: Estimator XYZ should not change or mutate the parameter random_state from 0 to <mtrand.RandomState object at 0x1a25da9e10> during fit. msg = 'Estimator XYZ should not change or mutate the parameter random_state from 0 to <mtrand.RandomState object at 0x1a25da9e10> during fit.' self = <sklearn.utils._unittest_backport.TestCase testMethod=__init__> Thanks in advance, Manoj Karthick Selva Kumar

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I’m in trouble and I need your advice on operating scikit-learn
by PARK Jinwoo March 29, 2018

March 29, 2018

Dear scikit-learn experts Hello, I am a graduate school student majoring in doping control analysis in Korea. Now I'm in a research institute that carries out doping control analyses. I received a project by my advising doctor. It's about operating an AI project. A workshop is scheduled in April, so it needs to be done in a month. However, I haven't learn computer science at all and I'm totally ignorant of it. So I desperately need your advice. To be specific, the 3 xml files shown in the picture are analysis results named positive, negative, and unknown from top to bottom. We'd like to let AI learn positive and negative data, input unknown datum, and then see what result will turn out. I came to know that there's a module called 'iris calssification' in scikit-learn and I'm thinking of utilizing that as it seems similar with my assignment However, while the database of iris is a csv file with 150 data and labels inside, what I have are 3 xml files each one of which represents one data, which are stored in C:\Users\Jinwoo\Documents\Python Scripts\mzdata The training process is not shuffling randomly the 150 data and dividing into training set and test set. The data are already assigned into training ones and testing one. Also, when training the program, training labels naming positive and negative should be inserted on my own. What I know all is that it will be appropriate to use fit() function and predict() function to train and test. But I have no idea on what to import, how to write codes correctly, and so on It will be thankful to give me some help. <https://mail.python.org/mailman/listinfo/scikit-learn>

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problem with check_estimator for distance metric learning
by wdevazel March 29, 2018

March 29, 2018

(Sorry, I sent this mail as a reply instead of starting a new thread... Here is the new thread.) Hi all, We are currently trying to add to the metric-learn package (https://github.com/metric-learn/metric-learn) a feature that would allow to do cross-validation with Weakly Supervised Metric Learners using scikit-learn's cross-validation routines. Distance Metric Learning algorithms learn distance metrics between samples, using some supervised information about similarity between training samples. Some Metric Learning algorithms are weakly supervised (Weakly Supervised Metric Learners), i.e. they do not train on labeled samples, but for instance on labeled *pairs* of samples (the label telling whether the pair is of similar or dissimilar samples). To cross-validate these algorithms, we make a train and a test by splitting on the pairs. Indeed a use case of metric learning is to classify at test time unseen pairs as similar or dissimilar (those pairs can involve already seen samples). For that, we made a dataset representation that allows to easily slice on pairs of samples: we mock a 3D array containing pairs of samples, that would be of shape (n_constraints, 2, n_features) (each line is a pair of samples). We do so with an object that we called ConstrainedDataset, which is more memory efficient than the described array (because samples would be duplicated through pairs). Now we have a problem when running scikit-learn's *check_estimator* on these algorithms, because it launches a series of tests where the estimator takes as input regular arrays, whereas Weakly Supervised Metric Learners always learn on ConstrainedDatasets (or more generally on pairs, or tuples for some other algorithms). We therefore thought of two main possibilities (that could be combined) to solve this problem: - taking the maximum number of tests yielded by check_estimator that pass in our setting, and modifying the others by replacing array inputs with ConstrainedDatasets - wrapping a Weakly Supervised Metric Learner into a MockSklearnEstimator that would transform any array as input into a ConstrainedDataset before passing it to the underlying Weakly Supervised Metric Learner However these options are not really satisfying: the first one will create a lot of code and after that one cannot see at a glance if the estimator passes scikit-learn's check_estimator, and the second adds so much wrapping that we are not even really testing the Weakly Supervised Metric Learner) For more information, see this PR where the new feature is being implemented, including the constraints.ConstrainedDataset object, as well as a comment on what is problematic when using scikit-learn's check_estimator: https://github.com/metric-learn/metric-learn/pull/85#issuecomment-375659820 Any advice about how to design the weakly supervised algorithms, the data structure containing the pairs of samples, or how to use anyway scikit-learn's check_estimator would be appreciated! Thanks! Best regards, William

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Validation curve - Learning curve
by S Hamidizade March 28, 2018

March 28, 2018

Dear Mr. / Ms. I would appreciate if you could let me know in the following example code: from collections import Counter from sklearn.datasets import make_classification from sklearn.model_selection import train_test_split,StratifiedKFold,learning_curve,validation_curve,GridSearchCV from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LogisticRegression from sklearn.pipeline import Pipeline from sklearn.metrics import classification_report import numpy as np import matplotlib.pyplot as plt def plot_learning_curve(train_sizes, train_scores, test_scores, title, alpha=0.1): train_mean = np.mean(train_scores, axis=1) train_std = np.std(train_scores, axis=1) test_mean = np.mean(test_scores, axis=1) test_std = np.std(test_scores, axis=1) plt.plot(train_sizes, train_mean, label='train score', color='blue', marker='o') plt.fill_between(train_sizes, train_mean + train_std, train_mean - train_std, color='blue', alpha=alpha) plt.plot(train_sizes, test_mean, label='test score', color='red', marker='o') plt.fill_between(train_sizes, test_mean + test_std, test_mean - test_std, color='red', alpha=alpha) plt.title(title) plt.xlabel('Number of training points') plt.ylabel('F-measure') plt.grid(ls='--') plt.legend(loc='best') plt.show() def plot_validation_curve(param_range, train_scores, test_scores, title, alpha=0.1): train_mean = np.mean(train_scores, axis=1) train_std = np.std(train_scores, axis=1) test_mean = np.mean(test_scores, axis=1) test_std = np.std(test_scores, axis=1) plt.plot(param_range, train_mean, label='train score', color='blue', marker='o') plt.fill_between(param_range, train_mean + train_std, train_mean - train_std, color='blue', alpha=alpha) plt.plot(param_range, test_mean, label='test score', color='red', marker='o') plt.fill_between(param_range, test_mean + test_std, test_mean - test_std, color='red', alpha=alpha) plt.title(title) plt.grid(ls='--') plt.xlabel('Parameter value') plt.ylabel('F-measure') plt.legend(loc='best') plt.show() X, y = make_classification(n_classes=2, class_sep=2,weights=[0.9, 0.1], n_informative=3, n_redundant=1, flip_y=0, n_features=20, n_clusters_per_class=1, n_samples=1000, random_state=10) print('Original dataset shape {}'.format(Counter(y))) ln = X.shape names = ["x%s" % i for i in range(1, ln[1] + 1)] X_train, X_test, y_train, y_test = train_test_split(X, y,random_state=0) st=StandardScaler() rg = LogisticRegression(class_weight = { 0:1, 1:6.5 }, random_state = 42, solver = 'saga',max_iter=100,n_jobs=-1) param_grid = {'clf__C': [0.001,0.01,0.1,0.002,0.02,0.005,0.0007,.0006,0.0005], 'clf__class_weight':[{ 0:1, 1:6 },{ 0:1, 1:4 },{ 0:1, 1:5.5 },{ 0:1, 1:4.5 },{ 0:1, 1:5 }] } pipeline = Pipeline(steps=[('scaler', st), ('clf', rg )]) cv=StratifiedKFold(n_splits=5,random_state=42) rg_cv = GridSearchCV(pipeline, param_grid, cv=cv, scoring = 'f1') rg_cv.fit(X_train, y_train) print("Tuned rg best params: {}".format(rg_cv.best_params_)) ypred = rg_cv.predict(X_train) print(classification_report(y_train, ypred)) print('######################') ypred2 = rg_cv.predict(X_test) print(classification_report(y_test, ypred2)) plt.figure(figsize=(9,6)) param_range1=[i / 10000.0 for i in range(1, 11)] param_range2=[{ 0:1, 1:6 },{ 0:1, 1:4 },{ 0:1, 1:5.5 },{ 0:1, 1:4.5 },{ 0:1, 1:5 }] if __name__ == '__main__': train_sizes, train_scores, test_scores = learning_curve( estimator= rg_cv.best_estimator_ , X= X_train, y = y_train, train_sizes=np.arange(0.1,1.1,0.1), cv= cv, scoring='f1', n_jobs= - 1) plot_learning_curve(train_sizes, train_scores, test_scores, title='Learning curve for Logistic Regression') train_scores, test_scores = validation_curve( estimator=rg_cv.best_estimator_, X=X_train, y=y_train, param_name="clf__C", param_range=param_range1, cv=cv, scoring="f1", n_jobs=-1) plot_validation_curve(param_range1, train_scores, test_scores, title="Validation Curve for C", alpha=0.1) train_scores, test_scores = validation_curve( estimator=rg_cv.best_estimator_, X=X_train, y=y_train, param_name="clf__class_weight", param_range=param_range2, cv=cv, scoring="f1", n_jobs=-1) plot_validation_curve(param_range2, train_scores, test_scores, title="Validation Curve for class_weight", alpha=0.1) 1- Why when the best estimator of GridSearchCv is passed into the learning curve function, it prints all the previous print lines several times (run on windows)? 2- How to plot validation curve for class weight? TypeError: float() argument must be a string or a number, not 'dict' Thanks in advance. Best regards,

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How to apply LR in gridded (for multiple location datasets) time series datasets?
by Vishal Singh March 28, 2018

March 28, 2018

Hello, This code is written for multivariate (multiple independent variables x1,x2,x3..xn and a dependent variable y) time series analysis using logistic regression (correlation and prediction). #Import Libraries import numpy as np import matplotlib.pyplot as plt import pandas as pd #Import Dataset dataset = pd.read_csv(‘precipitation.csv’) x = dataset.iloc[:,[2,3]].values y =dataset.iloc[:,4].values #Split Training Set and Testing Set from sklearn.cross_validation import train_test_split x_train, x_test, y_train, y_test =train_test_split(x,y,test_size=0.25) #Feature Scaling from sklearn.preprocessing import StandardScaler sc_X=StandardScaler() x_train=sc_X.fit_transform(x_train) x_test=sc_X.transform(x_test) #Training the Logistic Model from sklearn.linear_model import LogisticRegression classifier = LogisticRegression() classifier.fit(x_train, y_train) #Predicting the Test Set Result y_pred = classifier.predict(x_test) This code is based on one point location (one lat/long) datasets. Suppose, I am having gridded datasets (which has many points/locations, lat/long, varying in space and time) then How I will implement this code. I am not expertise in python. If somebody can help me in this? If somebody can give me an example or idea so I can implement this code as per my requirement. Thank you in advance. Vishu

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Add MAPE as a new metric
by Mohamed Ali Jamaoui March 25, 2018

March 25, 2018

Dear all, Following @amuller suggestion in issue 10708 <https://github.com/scikit-learn/scikit-learn/issues/10708>, I submitted a PR to add MAPE as a new metric. @jnothman <https://github.com/jnothman> has already approved it. It would be great if someone else could review it as well to prepare for a potential merge. - PR link: https://github.com/scikit-learn/scikit-learn/pull/10711 Thanks & regards, Mohamed Ali JAMAOUI

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Run time complexity of algorithms
by Shubham Ashok Gandhi March 21, 2018

March 21, 2018

Hello, Hope you guys are doing well. I needed information on the time complexity of the models under supervised learning <http://scikit-learn.org/stable/supervised_learning.html#supervised-learning> title. I am looking for this information because- we (my team) are building a platform that allows a user to run multiple models. The models we select to build are based on a couple of user requirements such as time, accuracy and model interpretability. This information will help us in understanding what models not to select for large datasets and so on. More specifically, I am looking for information on these algorithms OLS Elastic Net LARS Bayesian regression Linear Discriminant Analysis SVM (all kernels) Nearest Neighbors regression Decision Trees Random Forest AdaBoost Gradient Tree Boosting Let me know if there's additional information or details you need me to provide -- Regards, Shubham Ashok Gandhi Ph: (+91) 8987419771 <089874%2019771>

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