| <?xml version="1.0" encoding="UTF-8"?><PMML xmlns="http://www.dmg.org/PMML-4_3" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" version="4.3"> | |
| <Header description="Model kind: logistic"> | |
| <Application name="BigML"/> | |
| </Header> | |
| <DataDictionary> | |
| <DataField name="sepal length" displayName="" optype="continuous" dataType="double"> | |
| <Extension name="BigML-Field_ID" value="000000"/> | |
| </DataField> | |
| <DataField name="sepal width" displayName="" optype="continuous" dataType="double"> | |
| <Extension name="BigML-Field_ID" value="000001"/> |
| #!/usr/bin/python | |
| import functools | |
| import numpy as np | |
| from matplotlib import pyplot as pp | |
| from matplotlib.collections import PolyCollection | |
| from matplotlib.cm import get_cmap | |
| from bigml.api import BigML |
Dynamic scatterplot of the iris dataset.
Controls:
| #!/usr/bin/env python | |
| from __future__ import print_function | |
| from scipy.stats import pearsonr,spearmanr | |
| """ | |
| Edward Tufte uses this example from Anscombe to show 4 datasets of x | |
| and y that have the same mean, standard deviation, and regression | |
| line, but which are qualitatively different. | |
| matplotlib fun for a rainy day |
| Number of map-reduce blocks on columns, and population size on rows [200,500,1000,5000,10000] | |
| 1 3 5 10 20 30 50 | |
| ================================================================================ | |
| uncorrelated data | |
| 0.020074 0.067028 0.150469 0.112782 0.260606 0.485714 0.400000 | |
| -0.018003 -0.095463 -0.102538 0.050660 0.031538 0.358824 0.333333 | |
| -0.011529 -0.000860 -0.019766 -0.164176 -0.203649 -0.167781 -0.183459 | |
| -0.004745 -0.021326 -0.014286 -0.075185 -0.091779 -0.063402 -0.051401 | |
| 0.005333 0.000626 0.007783 0.024034 -0.040481 -0.017537 -0.115995 |
| KIVA_SNAPSHOT_URL = 'http://s3.kiva.org/snapshots/kiva_ds_json.zip' | |
| PRUNE_FIELDS = ['terms', 'payments', 'basket_amount', 'description', 'name', | |
| 'borrowers', 'translator', 'video', 'image', | |
| 'funded_date', 'paid_date', 'paid_amount', 'funded_amount', | |
| 'planned_expiration_date', 'bonus_credit_eligibility', | |
| 'partner_id'] | |
| KEYS = ['sector', 'use', 'posted_date', 'location.country', | |
| 'journal_totals.entries', 'activity', | |
| 'loan_amount', 'status', 'lender_count'] |
| // check if a loan ID is at the end of the url | |
| var re = /lend\/(\d+)/ ; | |
| var result = re.exec(window.location.href) ; | |
| var predict_url = "" ; | |
| var model = "" | |
| var global = null ; | |
| // get stored bigml auth parameters and build prediction url | |
| chrome.storage.sync.get(['model','username','apikey'],function(items){ | |
| predict_url = "https://bigml.io/andromeda/prediction?username=" + items.username + ";api_key=" + items.apikey ; |
| function fetchModel(){ | |
| chrome.storage.sync.get({ | |
| username: "BigML Username", | |
| apikey: "BigML API Key" | |
| }, | |
| function(items){ | |
| var models_url = "https://bigml.io/andromeda/model?name=kiva-model;username=" + items.username + ";api_key=" + items.apikey ; | |
| var xmlHttp = new XMLHttpRequest() ; | |
| xmlHttp.open("GET",models_url,false) ; | |
| xmlHttp.send(null) ; |
| // Saves options to chrome.storage | |
| function save_options() { | |
| var username = document.getElementById('username').value; | |
| var apikey = document.getElementById('apikey').value; | |
| chrome.storage.sync.set({ | |
| username: username, | |
| apikey: apikey | |
| }, function() { | |
| // Update status to let user know options were saved. | |
| chrome.runtime.sendMessage({greeting:"fetchmodel"}) ; |
| <!DOCTYPE html> | |
| <html> | |
| <head><title>Kiva Predictor Options</title></head> | |
| <body> | |
| <label> | |
| BigML Username: | |
| <input type="text" id="username"> | |
| </label> | |
| <br> | |
| <label> |