🌐 Mailman 3 March 2019 - scikit-learn - python.org

Using sklearn-crfsuite on Production Systems
by Astha Agarwal Aug. 5, 2019

Aug. 5, 2019

Hi, I'm wondering if anyone is using sklearn-crfsuite on production systems? Is this library suitable for usage in industry on production systems (and not academia) for non-big data problems? Thanks, Astha

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Can cluster based on the continuous access duration of an item?
by lampahome March 31, 2019

March 31, 2019

I have data which contain access duration of each items. EX: t0~t4 is the access time duration. 1 means the item was accessed in the time duration, 0 means not. ID,t0,t1,t2,t3,t4 0,1,0,0,1 1,1,0,0,1 2,0,0,1,1 3,0,1,1,1 Can cluster the group which item will access for a continuous duration? Like above, ID=2,ID=3 are what I want. I try KMeans, DBSCAN but it seems doesn't well Is there any algo recommended? thx

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GridsearchCV returns worse scoring the broader parameter space gets
by Andreas Tosstorff March 31, 2019

March 31, 2019

Dear all, I am new to scikit learn so please excuse my ignorance. Using GridsearchCV I am trying to optimize a DecisionTreeRegressor. The broader I make the parameter space, the worse the scoring gets. Setting min_samples_split to range(2,10) gives me a neg_mean_squared_error of -0.04. When setting it to range(2,5) The score is -0.004. simple_tree =GridSearchCV(tree.DecisionTreeRegressor(random_state=42), n_jobs=4, param_grid={'min_samples_split': range(2, 10)}, scoring='neg_mean_squared_error', cv=10, refit='neg_mean_squared_error') simple_tree.fit(x_tr,y_tr).score(x_tr,y_tr) I expect an equal or more positive score for a more extensive grid search compared to the less extensive one. I would really appreciate your help! Kind regards, Andreas

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F1 score weirdness
by Max Halford March 29, 2019

March 29, 2019

Hey everyone, I've stumbled upon an inconsistency with the F1 score and I can't seem to get around it. I have two lists y_true = [0, 1, 2, 2, 2] and y_pred = [0, 0, 2, 2, 1]. sklearn tells me that the macro-averaged F1 score is 0.488888... If I understand correctly the macro-average F1 score is the harmonic mean of the macro-average precision score and the macro-average recall score. sklearn tells me that the macro-average precision is 0.5 whilst the macro-average recall is 0.555555... If use the statistics.harmonic_mean function from Python's standard library this gives me around 0.526315. So which is correct: 0.488888 or 0.526315? I apologize in advance if I've overlooked something silly. Best regards. -- Max Halford +336 28 25 13 38

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How to improve mse when training regression model with month-base data?
by lampahome March 25, 2019

March 25, 2019

I want to predict sold number of item in every day in month. But data is too huge, so I train it with incrementall learning ex: sklearn.neural_network.MLPRegressor I train data per 3 months ex: 1st training with data containing from Jan. to Mar. Then train from Apr. to Jun. Then I evaluate with Feb. data, and I found mse will grow up when train incrementally till Oct. to Dec. It seems catastrophic forgetting happens that prediction of Feb. is very mess but prediction of near month ex: Dec. is good because the training of Dec. is near. Tune parameters doesn't improve well because catastrophic forgetting still happen frequently. How should I improve that? Change another way or training period? Or I should split model into 12 model and one model is reponsible for a month?

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How to write the cross-project prediction algorithm
by 阿布迪兰 March 19, 2019

March 19, 2019

I am new in machine learning and now I am facing an issue. I have 7 projects I would like to predict whether a pull request would be rejected or not (Yes or No). And I would like to build a prediction model by using data from 6 projects as source project and predict the rejection of the pull request in the seventh project as a target project. Can someone tell me how can I structure my algorithm in Scikit-lear? Hope that my question is clear. Thanks Best regards, Abdillah

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Any model can predict multiple trend from hierarchical data?
by lampahome March 18, 2019

March 18, 2019

My hierarchical data are about sell numbers of 3 hot drinks and 3 cold drinks each month. Generally, cluster them into two group which one contain hot and another contains cold is better. But I don't want to cluster. When I study about sklearn.linear_model, I found they can only predict one trend for both hot and cold pattern. The trend of hot and cold is the same. But that make sense because it's "linear" model which suitable for linear separable data. Now, I want to predict different trend for hot and cold drink with only one model. If I have many features as I can, is there any model able to predict multiple patterns from hierarchical data? PS: under the condition that has no noise, data only conatin each trend of each kind of drink. thx

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What theory cause SGDRegressor can partial_fit but RandomForestRegressor can't?
by lampahome March 14, 2019

March 14, 2019

As title, I'm confused why some algo can partial_fit and some algo can't. For regression model, I found SGD can but RF can't. Is about the difference of algo? I thought it's able to partial_fit because gradient descent, or just another reason? thx

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Difference in prediction accuracy using SGDClassifier and Cross validation scores.
by Rajnish kamboj March 12, 2019

March 12, 2019

Hi I have recently started machine learning and it is my first query regarding prediction accuracy. There is difference in prediction accuracy using SGDClassifier and Cross validation scores. import numpy as np from sklearn.datasets import fetch_openml from sklearn.linear_model import SGDClassifier mnist = fetch_openml('mnist_784', version=1, cache=True) X, y = mnist['data'], mnist['target'] X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:] shuffled_index = np.random.permutation(60000) # shuffle the 0 - 60000 range X_train, y_train = X_train[shuffled_index], y_train[shuffled_index] y_train_5 = (y_train == '5') y_test_5 = (y_test == '5') sgd_clf = SGDClassifier(random_state=42, tol=1e-3, max_iter=1000) sgd_clf.fit(X_train, y_train_5) # Predicting for all 5s print("####### PREDICTION STATS ##############") y_train_5_pred = sgd_clf.predict(X_train) print("Total y_train_5 [False|True both]]:", len(y_train_5)) print("Total y_train_5 [Only 5s]:", sum(y_train_5)) # some other digit may be predicted as 5 and some 5s may be predicted as not 5 print("Predicted 5s:", sum(y_train_5_pred)) correctly_predicted = sum(np.logical_and(y_train_5_pred, y_train_5)) print("Correct Predicted", correctly_predicted) print("Accuracy:", correctly_predicted/sum(y_train_5) * 100) from sklearn.model_selection import cross_val_score cross_val_score(sgd_clf, X_train, y_train_5, cv=3, scoring='accuracy') *MY Output* ####### PREDICTION STATS ############## Total y_train_5 [False|True both]]: 60000 Total y_train_5 [Only 5s]: 5421 Predicted 5s: 3863 Correct Predicted 3574*Accuracy: 65.9287954251983* array([*0.9323 , 0.96805, 0.9641* ]) ####################################### So as per my observation there is a difference, why? SGDCLassifier is *~65.92%* accurate cross_val_score are *~95%* Am I comparing it in wrong way? OR I am missing something? Thanks Rajnish

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[WiMLDS scikit-learn] open source sprint in Nairobi, Kenya
by Reshama Shaikh March 12, 2019

March 12, 2019

I am an organizer of the New York City chapter of WiMLDS (Women in Machine Learning & Data Science) (http://wimlds.org). We would like to organize a scikit-learn sprint for our Nairobi chapter, ( https://www.meetup.com/topics/wimlds/all/), which is also our 4th largest of 51 chapters, with 2100+ members. Reference: Impact Report for WiMLDS Scikit-learn Sprints ( https://reshamas.github.io/impact-report-for-wimlds-scikit-learn-sprints/) Would anyone be available to facilitate this event? It would be on a Saturday in June 2019. I can be reached at reshama(a)wimlds.org for more information. Best, Reshama ---------------------------------------------- Reshama Shaikh <https://reshamas.github.io> NYC WiMLDS <https://meet.meetup.com/wf/click?upn=XTL-2BADyqX-2FD82kkVyMmL1UTawfruRN9Xsi…> NYC PyLadies <https://meet.meetup.com/wf/click?upn=pEEcc35imY7Cq0tG1vyTt6zEs68RbcMfjPcajN…> ----------------------------------------------

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