jnothman

#!/bin/bash

# requires curl, jq, python

# set these variables
repo=scikit-learn/scikit-learn
remote=upstream
token=  # GitHub personal access token goes here

list_open_prs() {

jnothman

from sklearn.externals import joblib
from sklearn.linear_model import LogisticRegression
from sklearn.feature_selection import RFE
from sklearn.model_selection import GridSearchCV
from sklearn.datasets import make_classification


memory = joblib.Memory('/tmp')

jnothman

jnothman

jnothman

from django.views import View
from django.http import HttpResponse

MIMETYPES = {
    'ps': 'application/postscript',
    'eps': 'application/postscript',
    'pdf': 'application/pdf',
    'svg': 'image/svg+xml',
    'png': 'image/png',
    'jpeg': 'image/jpeg',

jnothman

SELECT
?subj
?subjLabel
?prop
?position
?positionLabel
?start
?end
?district
?districtLabel

jnothman

import warnings
from sklearn.utils.testing import assert_warns_message, assert_no_warnings


class DeprecationDict(dict):
    """A dict which raises a warning when some keys are looked up

    Note, this does not raise a warning for __contains__ and iteration.

    It also will raise a warning even after the key has been manually set by

jnothman

import warnings
import sklearn
warnings.simplefilter('ignore')
from sklearn import *

X, y = datasets.make_multilabel_classification()
for clf in [tree.DecisionTreeClassifier(),
            neighbors.KNeighborsClassifier(),
            neural_network.MLPClassifier(),
            multioutput.MultiOutputClassifier(linear_model.LogisticRegression()),

jnothman

import warnings
import sklearn
warnings.simplefilter('ignore')
from sklearn import *

X, y = datasets.make_multilabel_classification()
for clf in [tree.DecisionTreeClassifier(),
            neighbors.KNeighborsClassifier(),
            neural_network.MLPClassifier(),
            multioutput.MultiOutputClassifier(linear_model.LogisticRegression()),

jnothman

from sklearn.feature_extraction import DictVectorizer

class PandasVectorizer(DictVectorizer):
    def fit(self, x, y=None):
        return super(PandasVectorizer, self).fit(x.to_dict('records'))
    
    def fit_transform(self, x, y=None):
        return super(PandasVectorizer, self).fit_transform(x.to_dict('records'))
    
    def transform(self, x):