NameError using LinearRegression () with python API
I am trying to run some regressions on Earth Engine using Cloud Datalab. When I replicate the code in this regression tutorial (modifying it for python) I get this error:
NameErrorTraceback (most recent call last)
<ipython-input-45-21c9bd377408> in <module>()
14 linearRegression = collection.reduce(
15 ee.Reducer.linearRegression({
---> 16 numX: 2,
17 numY: 2
18 }))
NameError: name 'numX' is not defined
This doesn't seem to be a problem for other functions, and the same code works in the Javascript API. Is linear regression used in different ways in python API?
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1 answer
You can pass arguments as python keyword arguments, for example:
import ee
ee.Initialize()
# This function adds a time band to the image.
def createTimeBand(image):
# Scale milliseconds by a large constant.
return image.addBands(image.metadata('system:time_start').divide(1e18))
# This function adds a constant band to the image.
def createConstantBand(image):
return ee.Image(1).addBands(image)
# Load the input image collection: projected climate data.
collection = (ee.ImageCollection('NASA/NEX-DCP30_ENSEMBLE_STATS')
.filterDate(ee.Date('2006-01-01'), ee.Date('2099-01-01'))
.filter(ee.Filter.eq('scenario', 'rcp85'))
# Map the functions over the collection, to get constant and time bands.
.map(createTimeBand)
.map(createConstantBand)
# Select the predictors and the responses.
.select(['constant', 'system:time_start', 'pr_mean', 'tasmax_mean']))
# Compute ordinary least squares regression coefficients.
linearRegression = (collection.reduce(
ee.Reducer.linearRegression(
numX= 2,
numY= 2
)))
# Compute robust linear regression coefficients.
robustLinearRegression = (collection.reduce(
ee.Reducer.robustLinearRegression(
numX= 2,
numY= 2
)))
# The results are array images that must be flattened for display.
# These lists label the information along each axis of the arrays.
bandNames = [['constant', 'time'], # 0-axis variation.
['precip', 'temp']] # 1-axis variation.
# Flatten the array images to get multi-band images according to the labels.
lrImage = linearRegression.select(['coefficients']).arrayFlatten(bandNames)
rlrImage = robustLinearRegression.select(['coefficients']).arrayFlatten(bandNames)
# Print to check it works
print rlrImage.getInfo(), lrImage.getInfo()
I haven't checked the results, but there are no errors, images are being generated.
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