Mailman 3 January 2018 - scikit-learn

Position at BIDS (UC Berkeley) to work on NumPy
by Stefan van der Walt Jan. 4, 2018

Jan. 4, 2018

Hi everyone, The Berkeley Institute for Data Science (BIDS) is hiring scientific Python Developers to contribute to NumPy. You can read more about the new positions here: https://bids.berkeley.edu/news/bids-receives-sloan-foundation-grant-contrib… If you enjoy collaborative work as well as the technical challenges posed by numerical computing, this is an excellent opportunity to play a fundamental role in the development of one of the most impactful libraries in the entire Python ecosystem. Best regards Stéfan Job link: https://jobsprod.is.berkeley.edu/psc/jobsprod/EMPLOYEE/HRMS/c/HRS_HRAM.HRS_…

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A necessary feature for Decision trees
by 李扬 Jan. 4, 2018

Jan. 4, 2018

Hi, I`m a graduate student utilizing sklean for some data work. And when I`m handling the data using the Decision Trees library, I found there are some inconvenience: Neither the classificationTree nor the regressionTree supports categorical feature. That means the Decision trees model can only accept continuous feature. For example, the categorical feature like app name such as google, facebook can`t be input into the model, because they can`t be transformed to continuous value properly. And there don`t exist a corresponding algorithm to divide discrete feature in the Decision Trees library. However, the CART algorithm itself has considered the use of categorical feature. So I have made some modification of Decision Trees library based on CART and apply the new model on my own work. And it proves that the support for categorical feature indeed improves the performance, which is very necessary for decision tree, I think. I`m very willing to contribute this to sklearn community, but I`m new to this community, not so familiar about the procedure. Could u give some suggestions or comments on this new feature? Or has anyone already processed on this feature? Thank you so much. Best wishes! -- 顺颂时祺！李扬上海交通大学电子信息与电气工程学院电话：18818212371 地址：上海市闵行区东川路800号邮编：200240 Yang Li +86 188 1821 2371 Shanghai Jiao Tong University School of Electronic，Information and Electrical Engineering F1203026 800 Dongchuan Road, Minhang District, Shanghai 200240

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Re: [scikit-learn] Any plans on generalizing Pipeline and transformers?
by Manuel Castejón Limas Jan. 4, 2018

Jan. 4, 2018

Thank you all for your interest! In order to clarify the case allow me to try to synthesize the spirit of what I'd like to put into the pipeline using this sequence of steps: #%% import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.cluster import DBSCAN from sklearn.mixture import GaussianMixture from sklearn.model_selection import train_test_split np.random.seed(seed=42) """ Data preparation """ URL = "https://raw.githubusercontent.com/mcasl/PAELLA/master/data/sin_60_ percent_noise.csv" data = pd.read_csv(URL, usecols=['V1','V2']) X, y = data[['V1']], data[['V2']] (data_train, data_test, X_train, X_test, y_train, y_test) = train_test_split(data, X, y) """ Parameters setup """ dbscan__eps = 0.06 mclust__n_components = 3 paella__noise_label = -1 paella__max_it = 20, paella__regular_size = 400, paella__minimum_size = 100, paella__width_r = 0.99, paella__n_neighbors = 5, paella__power = 30, paella__random_state = None #%% """ DBSCAN clustering to detect noise suspects (label == -1) """ dbscan_input = data_train dbscan_clustering = DBSCAN(eps = dbscan__eps) dbscan_output = dbscan_clustering.fit_predict(dbscan_input) plt.scatter(data_train['V1'], data_train['V2'], cmap='cool', alpha=0.1, c=np.int64(dbscan_output == -1)) #%% """ GaussianMixture fitted with filtered data_train in order to help locate the ellipsoids but predict is applied to the whole data_train set. """ mclust_input = data_train[ dbscan_output != 1] mclust_clustering = GaussianMixture(n_components = mclust__n_components) mclust_clustering.fit(mclust_input) mclust_output = mclust_clustering.predict(data_train) plt.scatter(data_train['V1'], data_train['V2'], cmap='cool', alpha=0.1, c=mclust_output) #%% """ mclust and dbscan results are combined. """ clustering_output = mclust_output.copy() clustering_output[dbscan_output == -1] = -1 plt.scatter(data_train['V1'], data_train['V2'], cmap='cool', alpha=0.1, c=clustering_output) #%% """ Old-good Paella paper: https://link.springer.com/article/10.1023/B:DAMI. 0000031630.50685.7c The Paella algorithm calculates sample_weight to be used by the final step regressor (Yes, it is an outlier detection algorithm but we are focusing now on this interesting collateral result). I am currently aggressively changing the code in order to make it fit somehow with the pipeline """ from paella import Paella paella_input = pd.concat([data, clustering_output], axis=1, inplace=False) paella_run = Paella(noise_label = paella__noise_label, max_it = paella__max_it, regular_size = paella__regular_size, minimum_size = paella__minimum_size, width_r = paella__width_r, n_neighbors = paella__n_neighbors, power = paella__power, random_state = paella__random_state) paella_output = paella_run.fit_predict(paella_input, y_train) # paella_output is a vector with sample_weight #%% """ Here we fit a regressor using sample_weight=paella_output """ from sklearn.linear_model import LinearRegression regressor_input=X_train lm = LinearRegression() lm.fit(X=regressor_input, y=y_train, sample_weight=paella_output) regressor_output = lm.predict(X_train) #... In this example we can see that: - A particular step might need results produced not necessarily from the immediately previous step. - The X parameter is not sequentially transformed. Sometimes we might need to skip to a previous step - y sometimes is the target, sometimes is not. For the regressor it is indeed, but for the paella algorithm the prediction is expressed as a vector representing sample_weights. All in all the conclusion is that the chain of processes is not as linear as imposed by the current API. I guess that all these difficulties could be solved by: - Passing a dictionary through the different steps containing the partial results that the following steps will need. - As a christmas gift :-) , a reference to the pipeline itself inserted in that dictionary could provide access to the internal status of the previous steps should it be needed. Another interesting study case with similar needs would be a regressor using a previous clustering step in order to fit one model per cluster. In such case, the clustering results would be needed during the fitting. Thanks for your interest! Manolo

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pomegranate v0.9.0 released: probabilistic modeling for Python
by Jacob Schreiber Jan. 3, 2018

Jan. 3, 2018

Howdy all! I'm pleased to announced the release of pomegranate v0.9.0. The focus of this release is on missing value support across all model fitting / structure learning / inference methods and models. This enables you to do everything from fitting a multivariate Gaussian distribution to an incomplete data set (using a GPU if desired!) to learning the structure of a Bayesian network on an incomplete data set, to running Viterbi decoding using a hidden Markov model on a sequence with some missing values. Read more about it here: http://bit.ly/2CyrXtX Thanks! Jacob

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MLPClassifier as a feature selector
by Thomas Evangelidis Jan. 3, 2018

Jan. 3, 2018

Greetings, I want to train a MLPClassifier with one hidden layer and use it as a feature selector for an MLPRegressor. Is it possible to get the values of the neurons from the last hidden layer of the MLPClassifier to pass them as input to the MLPRegressor? If it is not possible with scikit-learn, is anyone aware of any scikit-compatible NN library that offers this functionality? For example this one: http://scikit-neuralnetwork.readthedocs.io/en/latest/index.html I wouldn't like to do this in Tensorflow because the MLP there is much slower than scikit-learn's implementation. Thomas -- ====================================================================== Dr Thomas Evangelidis Post-doctoral Researcher CEITEC - Central European Institute of Technology Masaryk University Kamenice 5/A35/2S049, 62500 Brno, Czech Republic email: tevang(a)pharm.uoa.gr tevang3(a)gmail.com website: https://sites.google.com/site/thomasevangelidishomepage/

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