From cad74c09f35a0582f66da46dddd534c479f3c597 Mon Sep 17 00:00:00 2001 From: BillMills Date: Thu, 24 Dec 2015 12:08:36 -0800 Subject: [PATCH 1/7] began some translations of qc tests from CSIRO work --- qctests/CSIRO_bottle.py | 34 ++++++++++++++++++++++++++++ qctests/CSIRO_constant_bottom.py | 36 +++++++++++++++++++++++++++++ qctests/CSIRO_surface_spikes.py | 39 ++++++++++++++++++++++++++++++++ qctests/CSIRO_wire_break.py | 7 +++--- 4 files changed, 112 insertions(+), 4 deletions(-) create mode 100644 qctests/CSIRO_bottle.py create mode 100644 qctests/CSIRO_constant_bottom.py create mode 100644 qctests/CSIRO_surface_spikes.py diff --git a/qctests/CSIRO_bottle.py b/qctests/CSIRO_bottle.py new file mode 100644 index 0000000..3ef13f9 --- /dev/null +++ b/qctests/CSIRO_bottle.py @@ -0,0 +1,34 @@ +""" +Implements CSIRO's bottle check +All questionable features result in a flag, in order to minimize false negatives +""" + +import numpy + +def test(p): + """ + Runs the quality control check on profile p and returns a numpy array + of quality control decisions with False where the data value has + passed the check and True where it failed. + """ + + # Get temperature values from the profile. + t = p.t() + # what probe type is this? + isBottle = p.probe_type() == 7 + isUnderway = p.probe_type() == 8 + isCTD = p.probe_type() == 4 + + # initialize qc as a bunch of falses; + qc = numpy.zeros(len(t.data), dtype=bool) + + # check for gaps in data + isTemperature = (t.mask==False) + + # flag any level that is colder than -20 for any of the relevant probe types + for i in range(p.n_levels()): + if isTemperature[i]: + if t.data[i] < -20 and (isBottle or isUnderway or isCTD): + qc[i] = True + + return qc \ No newline at end of file diff --git a/qctests/CSIRO_constant_bottom.py b/qctests/CSIRO_constant_bottom.py new file mode 100644 index 0000000..d3a92bd --- /dev/null +++ b/qctests/CSIRO_constant_bottom.py @@ -0,0 +1,36 @@ +""" +Implements CSIRO's constant-bottom check +All questionable features result in a flag, in order to minimize false negatives +""" + +import numpy + +def test(p): + """ + Runs the quality control check on profile p and returns a numpy array + of quality control decisions with False where the data value has + passed the check and True where it failed. + """ + + # Get temperature values from the profile. + t = p.t() + # depths + d = p.z() + # is this an xbt? + isXBT = p.probe_type() == 2 + latitude = p.latitude() + + # initialize qc as a bunch of falses; + qc = numpy.zeros(len(t.data), dtype=bool) + + # check for gaps in data + isTemperature = (t.mask==False) + isDepth = (d.mask==False) + isData = isTemperature & isData + + # constant temperature at bottom of profile, for latitude > -40 and bottom two depths at least 30m apart: + if isData[-2] and isData[-2] and isXBT: + if t.data[-1] == t.data[-2] and latitude > -40 and d.data[-1] - d.data[-2] > 30: + qc[-1] = True + + return qc \ No newline at end of file diff --git a/qctests/CSIRO_surface_spikes.py b/qctests/CSIRO_surface_spikes.py new file mode 100644 index 0000000..dc1f79d --- /dev/null +++ b/qctests/CSIRO_surface_spikes.py @@ -0,0 +1,39 @@ +""" +Implements CSIRO's surface spike check +All questionable features result in a flag, in order to minimize false negatives +""" + +import numpy + +def test(p): + """ + Runs the quality control check on profile p and returns a numpy array + of quality control decisions with False where the data value has + passed the check and True where it failed. + """ + + # Get temperature values from the profile. + t = p.t() + # depths + d = p.z() + # is this an xbt? + isXBT = p.probe_type() == 2 + + # initialize qc as a bunch of falses; + qc = numpy.zeros(len(t.data), dtype=bool) + + # check for gaps in data + isTemperature = (t.mask==False) + isDepth = (d.mask==False) + isData = isTemperature & isData + + # flag any level that is shallower than 4m and is followed by a level shallower than 8m. + for i in range(p.n_levels()): + if isData[i]: + if d.data[i] < 4 and i < p.n_levels()-1: #only interested in depths less than 4m and not at the bottom of the profile. + if d.data[i+1] < 8: + qc[i] = True + else: + break + + return qc \ No newline at end of file diff --git a/qctests/CSIRO_wire_break.py b/qctests/CSIRO_wire_break.py index 0191d15..68fc2e7 100644 --- a/qctests/CSIRO_wire_break.py +++ b/qctests/CSIRO_wire_break.py @@ -24,9 +24,8 @@ def test(p): isTemperature = (t.mask==False) # wire breaks at bottom of profile: - i = len(t.data)-1 - if isTemperature[i] and isXBT: - if t.data[i] < -2.8 or t.data[i] > 36: - qc[i] = True + if isTemperature[-1] and isXBT: + if t.data[-1] < -2.8 or t.data[-1] > 36: + qc[-1] = True return qc \ No newline at end of file From 91f350029754b0b142b7b1e1579ebbc0592ed8db Mon Sep 17 00:00:00 2001 From: BillMills Date: Sat, 2 Jan 2016 15:23:47 -0800 Subject: [PATCH 2/7] surface spikes --- qctests/CSIRO_surface_spikes.py | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/qctests/CSIRO_surface_spikes.py b/qctests/CSIRO_surface_spikes.py index dc1f79d..5e19851 100644 --- a/qctests/CSIRO_surface_spikes.py +++ b/qctests/CSIRO_surface_spikes.py @@ -12,8 +12,6 @@ def test(p): passed the check and True where it failed. """ - # Get temperature values from the profile. - t = p.t() # depths d = p.z() # is this an xbt? @@ -23,13 +21,11 @@ def test(p): qc = numpy.zeros(len(t.data), dtype=bool) # check for gaps in data - isTemperature = (t.mask==False) isDepth = (d.mask==False) - isData = isTemperature & isData # flag any level that is shallower than 4m and is followed by a level shallower than 8m. for i in range(p.n_levels()): - if isData[i]: + if isDepth[i]: if d.data[i] < 4 and i < p.n_levels()-1: #only interested in depths less than 4m and not at the bottom of the profile. if d.data[i+1] < 8: qc[i] = True From cd4145ab8020efdf9f3cc8b689aa419a0990247b Mon Sep 17 00:00:00 2001 From: BillMills Date: Sun, 20 Mar 2016 12:48:37 -0700 Subject: [PATCH 3/7] polishing CSIRO tests --- qctests/CSIRO_bottle.py | 34 ------------------ qctests/CSIRO_constant_bottom.py | 4 +-- qctests/CSIRO_short_gradient.py | 37 +++++++++++++++++++ qctests/CSIRO_surface_spikes.py | 5 ++- tests/CSIRO_constant_bottom_validation.py | 43 ++++++++++++++++++++++ tests/CSIRO_short_gradient_validation.py | 30 ++++++++++++++++ tests/CSIRO_surface_spikes.py | 33 +++++++++++++++++ util/splitData.py | 44 +++++++++++++---------- 8 files changed, 174 insertions(+), 56 deletions(-) delete mode 100644 qctests/CSIRO_bottle.py create mode 100644 qctests/CSIRO_short_gradient.py create mode 100644 tests/CSIRO_constant_bottom_validation.py create mode 100644 tests/CSIRO_short_gradient_validation.py create mode 100644 tests/CSIRO_surface_spikes.py diff --git a/qctests/CSIRO_bottle.py b/qctests/CSIRO_bottle.py deleted file mode 100644 index 3ef13f9..0000000 --- a/qctests/CSIRO_bottle.py +++ /dev/null @@ -1,34 +0,0 @@ -""" -Implements CSIRO's bottle check -All questionable features result in a flag, in order to minimize false negatives -""" - -import numpy - -def test(p): - """ - Runs the quality control check on profile p and returns a numpy array - of quality control decisions with False where the data value has - passed the check and True where it failed. - """ - - # Get temperature values from the profile. - t = p.t() - # what probe type is this? - isBottle = p.probe_type() == 7 - isUnderway = p.probe_type() == 8 - isCTD = p.probe_type() == 4 - - # initialize qc as a bunch of falses; - qc = numpy.zeros(len(t.data), dtype=bool) - - # check for gaps in data - isTemperature = (t.mask==False) - - # flag any level that is colder than -20 for any of the relevant probe types - for i in range(p.n_levels()): - if isTemperature[i]: - if t.data[i] < -20 and (isBottle or isUnderway or isCTD): - qc[i] = True - - return qc \ No newline at end of file diff --git a/qctests/CSIRO_constant_bottom.py b/qctests/CSIRO_constant_bottom.py index d3a92bd..64539eb 100644 --- a/qctests/CSIRO_constant_bottom.py +++ b/qctests/CSIRO_constant_bottom.py @@ -26,10 +26,10 @@ def test(p): # check for gaps in data isTemperature = (t.mask==False) isDepth = (d.mask==False) - isData = isTemperature & isData + isData = isTemperature & isDepth # constant temperature at bottom of profile, for latitude > -40 and bottom two depths at least 30m apart: - if isData[-2] and isData[-2] and isXBT: + if isData[-1] and isData[-2] and isXBT: if t.data[-1] == t.data[-2] and latitude > -40 and d.data[-1] - d.data[-2] > 30: qc[-1] = True diff --git a/qctests/CSIRO_short_gradient.py b/qctests/CSIRO_short_gradient.py new file mode 100644 index 0000000..bc4414a --- /dev/null +++ b/qctests/CSIRO_short_gradient.py @@ -0,0 +1,37 @@ +""" +Implements CSIRO's short gradient check +All questionable features result in a flag, in order to minimize false negatives +""" + +import numpy + +def test(p): + """ + Runs the quality control check on profile p and returns a numpy array + of quality control decisions with False where the data value has + passed the check and True where it failed. + """ + + # depths + d = p.z() + # temperatures + t = p.t() + + # initialize qc as a bunch of falses; + qc = numpy.zeros(len(t.data), dtype=bool) + + # check for gaps in data + isDepth = (d.mask==False) + isTemperature = (t.mask==False) + isData = isTemperature & isDepth + + for i in range(0, p.n_levels()-1 ): + if isData[i] and isData[i-1]: + deltaD = (d.data[i+1] - d.data[i]) + deltaT = (t.data[i+1] - t.data[i]) + gradshort = deltaD / deltaT + if (deltaT > 0.5 and deltaD < 30) or (gradshort > 0 and gradshort < 12.5): + if abs(deltaT) > 0.4: + qc[i] = True + + return qc \ No newline at end of file diff --git a/qctests/CSIRO_surface_spikes.py b/qctests/CSIRO_surface_spikes.py index 5e19851..f88c601 100644 --- a/qctests/CSIRO_surface_spikes.py +++ b/qctests/CSIRO_surface_spikes.py @@ -18,11 +18,14 @@ def test(p): isXBT = p.probe_type() == 2 # initialize qc as a bunch of falses; - qc = numpy.zeros(len(t.data), dtype=bool) + qc = numpy.zeros(len(d.data), dtype=bool) # check for gaps in data isDepth = (d.mask==False) + if not isXBT: + return qc + # flag any level that is shallower than 4m and is followed by a level shallower than 8m. for i in range(p.n_levels()): if isDepth[i]: diff --git a/tests/CSIRO_constant_bottom_validation.py b/tests/CSIRO_constant_bottom_validation.py new file mode 100644 index 0000000..e142ec5 --- /dev/null +++ b/tests/CSIRO_constant_bottom_validation.py @@ -0,0 +1,43 @@ +import qctests.CSIRO_constant_bottom +import util.testingProfile +import numpy + +##### CSIRO_constant_bottom --------------------------------------------------- + +def test_CSIRO_constant_bottom(): + ''' + Spot-check the nominal behavior of the CSIRO constant bottom test. + ''' + + # nominal + p = util.testingProfile.fakeProfile([0,0,0], [0,100,200], latitude=0, longitude=0, probe_type=2) + qc = qctests.CSIRO_constant_bottom.test(p) + truth = numpy.zeros(3, dtype=bool) + truth[2] = True + + assert numpy.array_equal(qc, truth), 'failed to flag a constant temperature at bottom of profile' + + # inappropriate probe type + p = util.testingProfile.fakeProfile([0,0,0], [0,100,200], latitude=0, longitude=0, probe_type=1) + qc = qctests.CSIRO_constant_bottom.test(p) + truth = numpy.zeros(3, dtype=bool) + + assert numpy.array_equal(qc, truth), 'flagged a constant temperature for an inappropriate probe type' + + # inappropriate latitude + p = util.testingProfile.fakeProfile([0,0,0], [0,100,200], latitude=-41, longitude=0, probe_type=2) + qc = qctests.CSIRO_constant_bottom.test(p) + + assert numpy.array_equal(qc, truth), 'flagged a constant temperature for an inappropriate latitude' + + # inappropriate depth difference + p = util.testingProfile.fakeProfile([0,0,0], [0,100,100], latitude=0, longitude=0, probe_type=2) + qc = qctests.CSIRO_constant_bottom.test(p) + + assert numpy.array_equal(qc, truth), 'flagged a constant temperature for an inappropriate depth distance' + + # not at bottom of profile + p = util.testingProfile.fakeProfile([0,0, -1], [100,200,300], latitude=0, longitude=0, probe_type=2) + qc = qctests.CSIRO_constant_bottom.test(p) + + assert numpy.array_equal(qc, truth), 'flagged a constant temperature not at the bottom of the profile' \ No newline at end of file diff --git a/tests/CSIRO_short_gradient_validation.py b/tests/CSIRO_short_gradient_validation.py new file mode 100644 index 0000000..9f6563c --- /dev/null +++ b/tests/CSIRO_short_gradient_validation.py @@ -0,0 +1,30 @@ +import qctests.CSIRO_short_gradient +import util.testingProfile +import numpy + +##### CSIRO_short_gradient --------------------------------------------------- + +def test_CSIRO_short_gradient(): + ''' + Spot-check the nominal behavior of the CSIRO short gradient test. + ''' + + # nominal + p = util.testingProfile.fakeProfile([0,1], [0,10]) + qc = qctests.CSIRO_short_gradient.test(p) + truth = numpy.zeros(2, dtype=bool) + truth[0] = True + + assert numpy.array_equal(qc, truth), 'failed to flag a gradient at small delta-temp and delta-depth' + + p = util.testingProfile.fakeProfile([0,10], [0,100]) + qc = qctests.CSIRO_short_gradient.test(p) + + assert numpy.array_equal(qc, truth), 'failed to flag a gradient outside of delta temp and depth ranges, but inside gradshort ranges' + + # temperature change too small + p = util.testingProfile.fakeProfile([0,0.1], [0,0.2]) + qc = qctests.CSIRO_short_gradient.test(p) + truth = numpy.zeros(2, dtype=bool) + + assert numpy.array_equal(qc, truth), 'flagged a gradient even though its temperature change was too small to consider' \ No newline at end of file diff --git a/tests/CSIRO_surface_spikes.py b/tests/CSIRO_surface_spikes.py new file mode 100644 index 0000000..03c4250 --- /dev/null +++ b/tests/CSIRO_surface_spikes.py @@ -0,0 +1,33 @@ +import qctests.CSIRO_surface_spikes +import util.testingProfile +import numpy + +##### CSIRO_surface_spikes --------------------------------------------------- + +def test_CSIRO_surface_spikes(): + ''' + Spot-check the nominal behavior of the CSIRO CSIRO_surface_spikes test. + ''' + + # nominal + p = util.testingProfile.fakeProfile([0,0,0], [1,2,3], probe_type=2) + qc = qctests.CSIRO_surface_spikes.test(p) + truth = numpy.zeros(3, dtype=bool) + truth[0] = True + truth[1] = True + + assert numpy.array_equal(qc, truth), 'failed to flag a collection of shallow profiles' + + # inappropriate probe type + p = util.testingProfile.fakeProfile([0,0,0], [1,2,3], probe_type=1) + qc = qctests.CSIRO_surface_spikes.test(p) + truth = numpy.zeros(3, dtype=bool) + + assert numpy.array_equal(qc, truth), 'flagged shallow profiles for an inappropriate probe type' + + # no cluster + p = util.testingProfile.fakeProfile([0,0,0], [1,20,30], probe_type=1) + qc = qctests.CSIRO_surface_spikes.test(p) + truth = numpy.zeros(3, dtype=bool) + + assert numpy.array_equal(qc, truth), 'flagged shallow profile without a cluster' \ No newline at end of file diff --git a/util/splitData.py b/util/splitData.py index 9480ba6..52d728e 100644 --- a/util/splitData.py +++ b/util/splitData.py @@ -1,37 +1,43 @@ -# utility to split a raw datafile up into n roughly equal parts. +# utility to split a raw datafile up into n roughly equal parts, keeping all profiles for a given cruise in the same file -import main import math from wodpy import wod -#filename = '../../AutoQC_raw/quota/quota_all.dat' -#filename = '../data/quota_subset.dat' -filename = '../../AutoQC_raw/quota/test/chunk.dat' +filename = '../quota_all.dat' n = 30 fid = open(filename) fid.read() fileSize = fid.tell() -chunkSize = int(math.ceil(fileSize / n)); +chunkSize = int(math.ceil(fileSize / n)); # final files should be about this big -fileNo = 0 -start = 0 -end = 0 - -target = open('split-' + str(fileNo) + '.dat', 'w') +# identify cruise numbers, profile start and profile end positions for all profiles +markers = [] fid.seek(0) -while not (fid.read(1) == ''): - #write next chunk to open target - fid.seek(end) +while True: start = fid.tell() profile = wod.WodProfile(fid) end = fid.tell() - fid.seek(start) - extract = fid.read(end-start) - target.write(extract) + markers.append( (profile.cruise(), start, end) ) + if profile.is_last_profile_in_file(fid) == True: + break +# sort by cruise number +markers = sorted(markers, key=lambda x: x[0]) - #wrap the file and start a new one once we've crossed the max size - if target.tell() > chunkSize: +# write subfiles +fileNo = 0 +currentCruise = None +target = open('split-' + str(fileNo) + '.dat', 'w') +for i in range(len(markers)): + lastCruise = currentCruise + currentCruise = markers[i][0] + # switch out to the next file when we pass chunksize AND are finished the current cruise + if target.tell() > chunkSize and currentCruise != lastCruise and None not in [lastCruise, currentCruise]: target.close() fileNo += 1 target = open('split-' + str(fileNo) + '.dat', 'w') + fid.seek(markers[i][1]) + extract = fid.read(markers[i][2]-markers[i][1]) + target.write(extract) + +target.close() \ No newline at end of file From e7ac6ee564b6303dafc70d1df028831a8a8fb780 Mon Sep 17 00:00:00 2001 From: BillMills Date: Fri, 1 Apr 2016 17:22:34 -0700 Subject: [PATCH 4/7] exploring new qc tests --- .../production-combination.ipynb | 1870 +++++++++++++++++ 1 file changed, 1870 insertions(+) create mode 100644 learningAnalysis/03-production/production-combination.ipynb diff --git a/learningAnalysis/03-production/production-combination.ipynb b/learningAnalysis/03-production/production-combination.ipynb new file mode 100644 index 0000000..adf02fb --- /dev/null +++ b/learningAnalysis/03-production/production-combination.ipynb @@ -0,0 +1,1870 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Combining QC Tests\n", + "\n", + "As deadlines for v1 of the IQuOD flags approach, we need to decide how best to combine the results of our existing QC tests. In this notebook, we build on previous machine learning trials to measure our current best performance, and propose some additional new QC tests to improve these results. \n", + "\n", + "The trials below were conducted on 12787 profiles from the *quota* dataset; final results should be validated against the entire thing.\n", + "\n", + "## 1. Machine Learning Strategy\n", + "\n", + "We saw previously that support vector machines and quadradic discriminant analysis made useful decisions on our QC flags; here we emply both and poll the results, flagging a profile if it is flagged by either algorithm." + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import pandas, numpy\n", + "from sklearn import svm\n", + "from sklearn.qda import QDA\n", + "from wodpy import wod\n", + "import matplotlib.pyplot as plt\n", + "from collections import Counter\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 157, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "results = pandas.read_csv('results-most.csv')\n", + "\n", + "# slice up for machine learning\n", + "truth = results.ix[:,1].tolist()\n", + "qctests = results.ix[:,2:].values.tolist()\n", + "\n", + "trainingSize = 1000" + ] + }, + { + "cell_type": "code", + "execution_count": 158, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def performance(truth, qcresult):\n", + " # check the performance of a single column qcresult\n", + "\n", + " TT = 0.\n", + " FF = 0.\n", + " TF = 0.\n", + " FT = 0.\n", + " T = 0.\n", + " F = 0.\n", + " \n", + " for i in range(len(qcresult)):\n", + " if truth[i]:\n", + " T += 1.\n", + " else:\n", + " F += 1.\n", + " \n", + " if qcresult[i] and truth[i]:\n", + " TT += 1.\n", + " elif qcresult[i] and not truth[i]:\n", + " TF += 1.\n", + " elif not qcresult[i] and truth[i]:\n", + " FT += 1.\n", + " elif not qcresult[i] and not truth[i]:\n", + " FF += 1.\n", + " \n", + " return (TT/T, FT/T, FF/F, TF/F)" + ] + }, + { + "cell_type": "code", + "execution_count": 159, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Argo_global_range_check (0.1596244131455399, 0.8403755868544601, 1.0, 0.0)\n", + "Argo_gradient_test (0.27386541471048514, 0.7261345852895149, 0.9990945011524531, 0.0009054988475469213)\n", + "Argo_impossible_date_test (0.0, 1.0, 1.0, 0.0)\n", + "Argo_impossible_location_test (0.0, 1.0, 1.0, 0.0)\n", + "Argo_pressure_increasing_test (0.11580594679186229, 0.8841940532081377, 0.866480079025354, 0.13351992097464604)\n", + "Argo_regional_range_test (0.006259780907668232, 0.9937402190923318, 0.9999176819229503, 8.231807704972011e-05)\n", + "Argo_spike_test (0.0297339593114241, 0.9702660406885759, 1.0, 0.0)\n", + "CSIRO_depth (0.837245696400626, 0.162754303599374, 0.2642410273296016, 0.7357589726703985)\n", + "CSIRO_gradient (0.4381846635367762, 0.5618153364632238, 0.8554494567006915, 0.14455054329930853)\n", + "CSIRO_wire_break (0.2519561815336463, 0.7480438184663537, 1.0, 0.0)\n", + "CoTeDe_Argo_density_inversion (0.3489827856025039, 0.651017214397496, 0.5221435627263747, 0.4778564372736253)\n", + "CoTeDe_GTSPP_WOA_normbias (0.8497652582159625, 0.15023474178403756, 0.8016134343101745, 0.1983865656898255)\n", + "CoTeDe_GTSPP_global_range (0.16118935837245696, 0.838810641627543, 1.0, 0.0)\n", + "CoTeDe_GTSPP_gradient (0.1752738654147105, 0.8247261345852895, 0.9990945011524531, 0.0009054988475469213)\n", + "CoTeDe_GTSPP_spike_check (0.03286384976525822, 0.9671361502347418, 0.9997530457688508, 0.0002469542311491604)\n", + "CoTeDe_RoC (0.24882629107981222, 0.7511737089201878, 0.9987652288442542, 0.0012347711557458018)\n", + "CoTeDe_WOA_normbias (0.6118935837245696, 0.38810641627543035, 0.9228679618044122, 0.07713203819558775)\n", + "CoTeDe_anomaly_detection (0.513302034428795, 0.486697965571205, 0.8475469213039183, 0.15245307869608166)\n", + "CoTeDe_digit_roll_over (0.2863849765258216, 0.7136150234741784, 0.990862693447481, 0.009137306552518933)\n", + "CoTeDe_fuzzy_logic (0.7449139280125195, 0.25508607198748046, 0.8888705959828779, 0.11112940401712217)\n", + "CoTeDe_gradient (0.18779342723004694, 0.812206572769953, 0.9990945011524531, 0.0009054988475469213)\n", + "CoTeDe_location_at_sea_test (0.004694835680751174, 0.9953051643192489, 0.9997530457688508, 0.0002469542311491604)\n", + "CoTeDe_profile_envelop (0.2895148669796557, 0.7104851330203443, 1.0, 0.0)\n", + "CoTeDe_spike (0.02190923317683881, 0.9780907668231612, 1.0, 0.0)\n", + "CoTeDe_tukey53H_norm (0.06572769953051644, 0.9342723004694836, 0.9978597299967072, 0.002140270003292723)\n", + "EN_background_available_check (0.018779342723004695, 0.9812206572769953, 0.9997530457688508, 0.0002469542311491604)\n", + "EN_background_check (0.4381846635367762, 0.5618153364632238, 0.9464109318406322, 0.0535890681593678)\n", + "EN_constant_value_check (0.001564945226917058, 0.9984350547730829, 1.0, 0.0)\n", + "EN_range_check (0.1596244131455399, 0.8403755868544601, 1.0, 0.0)\n", + "EN_spike_and_step_check (0.025039123630672927, 0.974960876369327, 0.9999176819229503, 8.231807704972011e-05)\n", + "EN_spike_and_step_suspect (0.29107981220657275, 0.7089201877934272, 0.9980243661508067, 0.001975633849193283)\n", + "EN_stability_check (0.012519561815336464, 0.9874804381846636, 0.9983536384590056, 0.0016463615409944023)\n", + "ICDC_aqc_01_level_order (0.0, 1.0, 1.0, 0.0)\n", + "ICDC_aqc_02_crude_range (0.297339593114241, 0.702660406885759, 0.9995884096147514, 0.0004115903852486006)\n", + "ICDC_aqc_04_max_obs_depth (0.0, 1.0, 1.0, 0.0)\n", + "ICDC_aqc_05_stuck_value (0.006259780907668232, 0.9937402190923318, 1.0, 0.0)\n", + "ICDC_aqc_06_n_temperature_extrema (0.017214397496087636, 0.9827856025039123, 1.0, 0.0)\n", + "ICDC_aqc_07_spike_check (0.026604068857589983, 0.97339593114241, 1.0, 0.0)\n", + "ICDC_aqc_08_gradient_check (0.28012519561815336, 0.7198748043818466, 0.9970365492262101, 0.0029634507737899243)\n", + "ICDC_aqc_09_local_climatology_check (0.7762128325508607, 0.2237871674491393, 0.7737076061903194, 0.2262923938096806)\n", + "WOD_gradient_check (0.3004694835680751, 0.6995305164319249, 0.9939084622983207, 0.006091537701679289)\n" + ] + } + ], + "source": [ + "for i in range(2,len(qctests[0])):\n", + " print results.columns[i], performance(truth, results.ix[:,i].tolist())" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=1.0, cache_size=200, class_weight={0: 1, 1: 100}, coef0=0.0, degree=3,\n", + " gamma=0.0, kernel='linear', max_iter=-1, probability=False,\n", + " random_state=None, shrinking=True, tol=0.001, verbose=False)" + ] + }, + "execution_count": 160, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# create and train an svm\n", + "\n", + "svm_classifier = svm.SVC(kernel='linear', class_weight = {0: 1, 1: 100})\n", + "svm_classifier.fit(qctests[0:trainingSize], truth[0:trainingSize])" + ] + }, + { + "cell_type": "code", + "execution_count": 161, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "QDA(priors=None, reg_param=0.0)" + ] + }, + "execution_count": 161, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# create and train a qda\n", + "\n", + "qda_classifier = QDA()\n", + "qda_classifier.fit(qctests[0:trainingSize], truth[0:trainingSize])" + ] + }, + { + "cell_type": "code", + "execution_count": 162, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.9069767441860465,\n", + " 0.09302325581395349,\n", + " 0.6792132320071524,\n", + " 0.32078676799284755)" + ] + }, + "execution_count": 162, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "svm_prediction = svm_classifier.predict(qctests[trainingSize:])\n", + "qda_prediction = qda_classifier.predict(qctests[trainingSize:])\n", + "\n", + "final_prediction = [ svm_prediction[i] or qda_prediction[i] for i in range(len(svm_prediction))]\n", + "performance(truth[trainingSize:], final_prediction)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So we see with this strategy that about 90% of bad profiles are flagged at the cost of flagging about 1/3 of good profiles.\n", + "\n", + "## 2. New QC Tests\n", + "\n", + "In order to tag the remaining 10% of bad profiles in this sample, we explore the nature of the missed profiles, and argue for some new QC tests to catch these edge cases." + ] + }, + { + "cell_type": "code", + "execution_count": 163, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# who did we miss?\n", + "\n", + "missed = []\n", + "for i in range(len(final_prediction)):\n", + " if not final_prediction[i] and truth[trainingSize:][i]:\n", + " missed.append(results.ix[trainingSize + i,0])" + ] + }, + { + "cell_type": "code", + "execution_count": 164, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# extract the missed profiles into a new file\n", + "fid = open('../../../AutoQC_raw/quota/split-cruise-sorted/split-0.dat')\n", + "target = open('skim.dat', 'w')\n", + "start = 0\n", + "end = 0\n", + "\n", + "while not (fid.read(1) == ''):\n", + " fid.seek(end)\n", + " start = fid.tell()\n", + " profile = wod.WodProfile(fid)\n", + " end = fid.tell()\n", + " if profile.uid() in missed:\n", + " fid.seek(start)\n", + " extract = fid.read(end-start)\n", + " target.write(extract)\n", + "\n", + "target.close()\n", + "fid.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 165, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2.599 2.596 2.596 2.597 2.597 2.597 2.593 2.594 2.586 2.572 2.57 2.559\n", + " 2.551 2.557 2.552 2.557 2.55 2.551 2.548 2.551 2.548 2.545 2.545 2.547\n", + " 2.548 2.551 2.551 2.549 2.546 2.546 2.548 2.544 2.546 2.545 2.536 2.536\n", + " 2.527 2.521 2.509 2.501 2.5 2.504 2.5 2.479 2.473 2.473 2.474 2.474 2.474\n", + " 2.475 2.474 2.475 2.479 2.481 2.479 2.481 2.482 2.483 2.485 2.485 2.485\n", + " 2.485]\n" + ] + } + ], + "source": [ + "fid = open('skim.dat')\n", + "\n", + "while True:\n", + " profile = wod.WodProfile(fid)\n", + " \n", + " plt.plot(profile.z(), profile.t(), marker='o')\n", + " plt.title(profile.uid())\n", + " plt.show()\n", + " print profile.t()\n", + " \n", + " if profile.is_last_profile_in_file(fid):\n", + " break\n", + "\n", + "fid.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A few of these seem simple to identify in autoqc; we introduce new qc tests that flag:\n", + "\n", + " - profiles with 0 or 1 levels in them\n", + " - profiles where all depths are the same\n", + " - profiles where all temperatures are the same\n", + " - profiles that have 'flat tops' - profiles with at least 4 consecutive levels of the same temperature, spread over at least 50 m.\n", + " \n", + "Running these tests on the same set of profiles, we obtain:\n", + "\n", + "```\n", + "Number of profiles tested was 12787\n", + "\n", + " NAME OF TEST FAILS TPR FPR TNR FNR\n", + " IQUOD_const_depth 1699 11.9% 13.4% 86.6% 88.1%\n", + " IQUOD_const_temp 12 0.9% 0.0% 100.0% 99.1%\n", + " IQUOD_flattop 726 16.4% 5.1% 94.9% 83.6%\n", + " IQUOD_nosolo 5 0.3% 0.0% 100.0% 99.7%\n", + " RESULT OF OR OF ALL: 2431 29.0% 18.5% 81.5% 71.0%\n", + "```\n", + "\n", + "The poor performance of `IQUOD_const_depth` is the most surprising - how could a profile with only one depth recorded *not* be flagged? To investigate, these are the first 20 profiles that compose the 13.4% false positive rate for `IQUOD_const_depth`:" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def const_depth(p):\n", + " \n", + " d = p.z()\n", + "\n", + " qc = numpy.zeros(p.n_levels(), dtype=bool)\n", + "\n", + " # no profiles with all the same depth\n", + " depth = Counter(d.data) \n", + " popular_d = depth.most_common(1)\n", + " if popular_d[0][1] == p.n_levels():\n", + " qc[0] = True\n", + " return qc \n", + " \n", + " return qc" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "False\n" + ] + } + ], + "source": [ + "fid = open('../../../AutoQC_raw/quota/split-cruise-sorted/split-0.dat')\n", + "\n", + "i = 0\n", + "while i<20:\n", + " profile = wod.WodProfile(fid)\n", + " qc = const_depth(profile)\n", + " if qc.any():\n", + " plt.plot(profile.z(), profile.t(), marker='o')\n", + " plt.title(profile.uid())\n", + " plt.show()\n", + " print (profile.t_level_qc(originator=True) >= 3).any()\n", + " i += 1\n", + " \n", + " if profile.is_last_profile_in_file(fid):\n", + " break\n", + "\n", + "fid.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are many profiles in *quota* that don't report depth variations, but are not flagged by the temperature qc, producing the large false positive rate reported for `const_depth` above; more investigation needed.\n", + "\n", + "For now, consider only the more obviously useful new qc corrections:" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "new_results = pandas.read_csv('results-newqc.csv')\n", + "\n", + "results['IQUOD_nosolo'] = new_results['IQUOD_nosolo']\n", + "results['IQUOD_const_temp'] = new_results['IQUOD_const_temp']\n", + "\n", + "truth = results.ix[:,1].tolist()\n", + "qctests = results.ix[:,2:].values.tolist()" + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=1.0, cache_size=200, class_weight={0: 1, 1: 100}, coef0=0.0, degree=3,\n", + " gamma=0.0, kernel='linear', max_iter=-1, probability=False,\n", + " random_state=None, shrinking=True, tol=0.001, verbose=False)" + ] + }, + "execution_count": 153, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# redo the svm\n", + "\n", + "svm_classifier = svm.SVC(kernel='linear', class_weight = {0: 1, 1: 100})\n", + "svm_classifier.fit(qctests[0:trainingSize], truth[0:trainingSize])" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "QDA(priors=None, reg_param=0.0)" + ] + }, + "execution_count": 154, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# redo the qda\n", + "\n", + "qda_classifier = QDA()\n", + "qda_classifier.fit(qctests[0:trainingSize], truth[0:trainingSize])" + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.9136212624584718,\n", + " 0.08637873754152824,\n", + " 0.6770675011175682,\n", + " 0.3229324988824318)" + ] + }, + "execution_count": 155, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "svm_prediction = svm_classifier.predict(qctests[trainingSize:])\n", + "qda_prediction = qda_classifier.predict(qctests[trainingSize:])\n", + "\n", + "final_prediction = [ svm_prediction[i] or qda_prediction[i] for i in range(len(svm_prediction))]\n", + "performance(truth[trainingSize:], final_prediction)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Giving us another 0.7% in true positives." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} From cd95a292c209ca50a492f10a8090e7aad86c34a1 Mon Sep 17 00:00:00 2001 From: BillMills Date: Fri, 1 Apr 2016 18:16:02 -0700 Subject: [PATCH 5/7] added long inversoin --- qctests/CSIRO_long_gradient.py | 52 ++++++++++++++++++++++++++++++++++ 1 file changed, 52 insertions(+) create mode 100644 qctests/CSIRO_long_gradient.py diff --git a/qctests/CSIRO_long_gradient.py b/qctests/CSIRO_long_gradient.py new file mode 100644 index 0000000..01c86b6 --- /dev/null +++ b/qctests/CSIRO_long_gradient.py @@ -0,0 +1,52 @@ +""" +Implements CSIRO's long gradient check +All questionable features result in a flag, in order to minimize false negatives +""" + +import numpy + +def test(p): + """ + Runs the quality control check on profile p and returns a numpy array + of quality control decisions with False where the data value has + passed the check and True where it failed. + """ + + # depths + d = p.z() + # temperatures + t = p.t() + + # initialize qc as a bunch of falses; + qc = numpy.zeros(len(t.data), dtype=bool) + + # check for gaps in data + isDepth = (d.mask==False) + isTemperature = (t.mask==False) + isData = isTemperature & isDepth + + on_inv = False # are we currently in an inversion? + + for i in range(0, p.n_levels()-1 ): + if isData[i] and isData[i-1]: + # not interested below 5m: + if d.data[i] < 5: continue + + if t.data[i+1] > t.data[1] and not on_inv: + # entering an inversion + start_inv_temp = t.data[i] + start_inv_depth = d.data[i] + potential_flag = i + on_inv = True + + if t.data[i+1] < t.data[i] and on_inv: + # exiting the inversion + end_inv_temp = t.data[i] + end_inv_depth = d.data[i] + on_inv = False + gradlong = (end_inv_depth - start_inv_depth) / (end_inv_temp - start_inv_depth) + + if abs(gradlong) < 4: + qc[potential_flag] = True + + return qc \ No newline at end of file From 75b1b92918f203fe253e0bda3c5be1434c9aa7a4 Mon Sep 17 00:00:00 2001 From: BillMills Date: Fri, 1 Apr 2016 18:19:17 -0700 Subject: [PATCH 6/7] Revert "exploring new qc tests" This reverts commit e7ac6ee564b6303dafc70d1df028831a8a8fb780. --- .../production-combination.ipynb | 1870 ----------------- 1 file changed, 1870 deletions(-) delete mode 100644 learningAnalysis/03-production/production-combination.ipynb diff --git a/learningAnalysis/03-production/production-combination.ipynb b/learningAnalysis/03-production/production-combination.ipynb deleted file mode 100644 index adf02fb..0000000 --- a/learningAnalysis/03-production/production-combination.ipynb +++ /dev/null @@ -1,1870 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Combining QC Tests\n", - "\n", - "As deadlines for v1 of the IQuOD flags approach, we need to decide how best to combine the results of our existing QC tests. In this notebook, we build on previous machine learning trials to measure our current best performance, and propose some additional new QC tests to improve these results. \n", - "\n", - "The trials below were conducted on 12787 profiles from the *quota* dataset; final results should be validated against the entire thing.\n", - "\n", - "## 1. Machine Learning Strategy\n", - "\n", - "We saw previously that support vector machines and quadradic discriminant analysis made useful decisions on our QC flags; here we emply both and poll the results, flagging a profile if it is flagged by either algorithm." - ] - }, - { - "cell_type": "code", - "execution_count": 156, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import pandas, numpy\n", - "from sklearn import svm\n", - "from sklearn.qda import QDA\n", - "from wodpy import wod\n", - "import matplotlib.pyplot as plt\n", - "from collections import Counter\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 157, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "results = pandas.read_csv('results-most.csv')\n", - "\n", - "# slice up for machine learning\n", - "truth = results.ix[:,1].tolist()\n", - "qctests = results.ix[:,2:].values.tolist()\n", - "\n", - "trainingSize = 1000" - ] - }, - { - "cell_type": "code", - "execution_count": 158, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def performance(truth, qcresult):\n", - " # check the performance of a single column qcresult\n", - "\n", - " TT = 0.\n", - " FF = 0.\n", - " TF = 0.\n", - " FT = 0.\n", - " T = 0.\n", - " F = 0.\n", - " \n", - " for i in range(len(qcresult)):\n", - " if truth[i]:\n", - " T += 1.\n", - " else:\n", - " F += 1.\n", - " \n", - " if qcresult[i] and truth[i]:\n", - " TT += 1.\n", - " elif qcresult[i] and not truth[i]:\n", - " TF += 1.\n", - " elif not qcresult[i] and truth[i]:\n", - " FT += 1.\n", - " elif not qcresult[i] and not truth[i]:\n", - " FF += 1.\n", - " \n", - " return (TT/T, FT/T, FF/F, TF/F)" - ] - }, - { - "cell_type": "code", - "execution_count": 159, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Argo_global_range_check (0.1596244131455399, 0.8403755868544601, 1.0, 0.0)\n", - "Argo_gradient_test (0.27386541471048514, 0.7261345852895149, 0.9990945011524531, 0.0009054988475469213)\n", - "Argo_impossible_date_test (0.0, 1.0, 1.0, 0.0)\n", - "Argo_impossible_location_test (0.0, 1.0, 1.0, 0.0)\n", - "Argo_pressure_increasing_test (0.11580594679186229, 0.8841940532081377, 0.866480079025354, 0.13351992097464604)\n", - "Argo_regional_range_test (0.006259780907668232, 0.9937402190923318, 0.9999176819229503, 8.231807704972011e-05)\n", - "Argo_spike_test (0.0297339593114241, 0.9702660406885759, 1.0, 0.0)\n", - "CSIRO_depth (0.837245696400626, 0.162754303599374, 0.2642410273296016, 0.7357589726703985)\n", - "CSIRO_gradient (0.4381846635367762, 0.5618153364632238, 0.8554494567006915, 0.14455054329930853)\n", - "CSIRO_wire_break (0.2519561815336463, 0.7480438184663537, 1.0, 0.0)\n", - "CoTeDe_Argo_density_inversion (0.3489827856025039, 0.651017214397496, 0.5221435627263747, 0.4778564372736253)\n", - "CoTeDe_GTSPP_WOA_normbias (0.8497652582159625, 0.15023474178403756, 0.8016134343101745, 0.1983865656898255)\n", - "CoTeDe_GTSPP_global_range (0.16118935837245696, 0.838810641627543, 1.0, 0.0)\n", - "CoTeDe_GTSPP_gradient (0.1752738654147105, 0.8247261345852895, 0.9990945011524531, 0.0009054988475469213)\n", - "CoTeDe_GTSPP_spike_check (0.03286384976525822, 0.9671361502347418, 0.9997530457688508, 0.0002469542311491604)\n", - "CoTeDe_RoC (0.24882629107981222, 0.7511737089201878, 0.9987652288442542, 0.0012347711557458018)\n", - "CoTeDe_WOA_normbias (0.6118935837245696, 0.38810641627543035, 0.9228679618044122, 0.07713203819558775)\n", - "CoTeDe_anomaly_detection (0.513302034428795, 0.486697965571205, 0.8475469213039183, 0.15245307869608166)\n", - "CoTeDe_digit_roll_over (0.2863849765258216, 0.7136150234741784, 0.990862693447481, 0.009137306552518933)\n", - "CoTeDe_fuzzy_logic (0.7449139280125195, 0.25508607198748046, 0.8888705959828779, 0.11112940401712217)\n", - "CoTeDe_gradient (0.18779342723004694, 0.812206572769953, 0.9990945011524531, 0.0009054988475469213)\n", - "CoTeDe_location_at_sea_test (0.004694835680751174, 0.9953051643192489, 0.9997530457688508, 0.0002469542311491604)\n", - "CoTeDe_profile_envelop (0.2895148669796557, 0.7104851330203443, 1.0, 0.0)\n", - "CoTeDe_spike (0.02190923317683881, 0.9780907668231612, 1.0, 0.0)\n", - "CoTeDe_tukey53H_norm (0.06572769953051644, 0.9342723004694836, 0.9978597299967072, 0.002140270003292723)\n", - "EN_background_available_check (0.018779342723004695, 0.9812206572769953, 0.9997530457688508, 0.0002469542311491604)\n", - "EN_background_check (0.4381846635367762, 0.5618153364632238, 0.9464109318406322, 0.0535890681593678)\n", - "EN_constant_value_check (0.001564945226917058, 0.9984350547730829, 1.0, 0.0)\n", - "EN_range_check (0.1596244131455399, 0.8403755868544601, 1.0, 0.0)\n", - "EN_spike_and_step_check (0.025039123630672927, 0.974960876369327, 0.9999176819229503, 8.231807704972011e-05)\n", - "EN_spike_and_step_suspect (0.29107981220657275, 0.7089201877934272, 0.9980243661508067, 0.001975633849193283)\n", - "EN_stability_check (0.012519561815336464, 0.9874804381846636, 0.9983536384590056, 0.0016463615409944023)\n", - "ICDC_aqc_01_level_order (0.0, 1.0, 1.0, 0.0)\n", - "ICDC_aqc_02_crude_range (0.297339593114241, 0.702660406885759, 0.9995884096147514, 0.0004115903852486006)\n", - "ICDC_aqc_04_max_obs_depth (0.0, 1.0, 1.0, 0.0)\n", - "ICDC_aqc_05_stuck_value (0.006259780907668232, 0.9937402190923318, 1.0, 0.0)\n", - "ICDC_aqc_06_n_temperature_extrema (0.017214397496087636, 0.9827856025039123, 1.0, 0.0)\n", - "ICDC_aqc_07_spike_check (0.026604068857589983, 0.97339593114241, 1.0, 0.0)\n", - "ICDC_aqc_08_gradient_check (0.28012519561815336, 0.7198748043818466, 0.9970365492262101, 0.0029634507737899243)\n", - "ICDC_aqc_09_local_climatology_check (0.7762128325508607, 0.2237871674491393, 0.7737076061903194, 0.2262923938096806)\n", - "WOD_gradient_check (0.3004694835680751, 0.6995305164319249, 0.9939084622983207, 0.006091537701679289)\n" - ] - } - ], - "source": [ - "for i in range(2,len(qctests[0])):\n", - " print results.columns[i], performance(truth, results.ix[:,i].tolist())" - ] - }, - { - "cell_type": "code", - "execution_count": 160, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "SVC(C=1.0, cache_size=200, class_weight={0: 1, 1: 100}, coef0=0.0, degree=3,\n", - " gamma=0.0, kernel='linear', max_iter=-1, probability=False,\n", - " random_state=None, shrinking=True, tol=0.001, verbose=False)" - ] - }, - "execution_count": 160, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# create and train an svm\n", - "\n", - "svm_classifier = svm.SVC(kernel='linear', class_weight = {0: 1, 1: 100})\n", - "svm_classifier.fit(qctests[0:trainingSize], truth[0:trainingSize])" - ] - }, - { - "cell_type": "code", - "execution_count": 161, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "QDA(priors=None, reg_param=0.0)" - ] - }, - "execution_count": 161, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# create and train a qda\n", - "\n", - "qda_classifier = QDA()\n", - "qda_classifier.fit(qctests[0:trainingSize], truth[0:trainingSize])" - ] - }, - { - "cell_type": "code", - "execution_count": 162, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.9069767441860465,\n", - " 0.09302325581395349,\n", - " 0.6792132320071524,\n", - " 0.32078676799284755)" - ] - }, - "execution_count": 162, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "svm_prediction = svm_classifier.predict(qctests[trainingSize:])\n", - "qda_prediction = qda_classifier.predict(qctests[trainingSize:])\n", - "\n", - "final_prediction = [ svm_prediction[i] or qda_prediction[i] for i in range(len(svm_prediction))]\n", - "performance(truth[trainingSize:], final_prediction)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So we see with this strategy that about 90% of bad profiles are flagged at the cost of flagging about 1/3 of good profiles.\n", - "\n", - "## 2. New QC Tests\n", - "\n", - "In order to tag the remaining 10% of bad profiles in this sample, we explore the nature of the missed profiles, and argue for some new QC tests to catch these edge cases." - ] - }, - { - "cell_type": "code", - "execution_count": 163, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# who did we miss?\n", - "\n", - "missed = []\n", - "for i in range(len(final_prediction)):\n", - " if not final_prediction[i] and truth[trainingSize:][i]:\n", - " missed.append(results.ix[trainingSize + i,0])" - ] - }, - { - "cell_type": "code", - "execution_count": 164, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# extract the missed profiles into a new file\n", - "fid = open('../../../AutoQC_raw/quota/split-cruise-sorted/split-0.dat')\n", - "target = open('skim.dat', 'w')\n", - "start = 0\n", - "end = 0\n", - "\n", - "while not (fid.read(1) == ''):\n", - " fid.seek(end)\n", - " start = fid.tell()\n", - " profile = wod.WodProfile(fid)\n", - " end = fid.tell()\n", - " if profile.uid() in missed:\n", - " fid.seek(start)\n", - " extract = fid.read(end-start)\n", - " target.write(extract)\n", - "\n", - "target.close()\n", - "fid.close()" - ] - }, - { - "cell_type": "code", - "execution_count": 165, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2.599 2.596 2.596 2.597 2.597 2.597 2.593 2.594 2.586 2.572 2.57 2.559\n", - " 2.551 2.557 2.552 2.557 2.55 2.551 2.548 2.551 2.548 2.545 2.545 2.547\n", - " 2.548 2.551 2.551 2.549 2.546 2.546 2.548 2.544 2.546 2.545 2.536 2.536\n", - " 2.527 2.521 2.509 2.501 2.5 2.504 2.5 2.479 2.473 2.473 2.474 2.474 2.474\n", - " 2.475 2.474 2.475 2.479 2.481 2.479 2.481 2.482 2.483 2.485 2.485 2.485\n", - " 2.485]\n" - ] - } - ], - "source": [ - "fid = open('skim.dat')\n", - "\n", - "while True:\n", - " profile = wod.WodProfile(fid)\n", - " \n", - " plt.plot(profile.z(), profile.t(), marker='o')\n", - " plt.title(profile.uid())\n", - " plt.show()\n", - " print profile.t()\n", - " \n", - " if profile.is_last_profile_in_file(fid):\n", - " break\n", - "\n", - "fid.close()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A few of these seem simple to identify in autoqc; we introduce new qc tests that flag:\n", - "\n", - " - profiles with 0 or 1 levels in them\n", - " - profiles where all depths are the same\n", - " - profiles where all temperatures are the same\n", - " - profiles that have 'flat tops' - profiles with at least 4 consecutive levels of the same temperature, spread over at least 50 m.\n", - " \n", - "Running these tests on the same set of profiles, we obtain:\n", - "\n", - "```\n", - "Number of profiles tested was 12787\n", - "\n", - " NAME OF TEST FAILS TPR FPR TNR FNR\n", - " IQUOD_const_depth 1699 11.9% 13.4% 86.6% 88.1%\n", - " IQUOD_const_temp 12 0.9% 0.0% 100.0% 99.1%\n", - " IQUOD_flattop 726 16.4% 5.1% 94.9% 83.6%\n", - " IQUOD_nosolo 5 0.3% 0.0% 100.0% 99.7%\n", - " RESULT OF OR OF ALL: 2431 29.0% 18.5% 81.5% 71.0%\n", - "```\n", - "\n", - "The poor performance of `IQUOD_const_depth` is the most surprising - how could a profile with only one depth recorded *not* be flagged? To investigate, these are the first 20 profiles that compose the 13.4% false positive rate for `IQUOD_const_depth`:" - ] - }, - { - "cell_type": "code", - "execution_count": 147, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def const_depth(p):\n", - " \n", - " d = p.z()\n", - "\n", - " qc = numpy.zeros(p.n_levels(), dtype=bool)\n", - "\n", - " # no profiles with all the same depth\n", - " depth = Counter(d.data) \n", - " popular_d = depth.most_common(1)\n", - " if popular_d[0][1] == p.n_levels():\n", - " qc[0] = True\n", - " return qc \n", - " \n", - " return qc" - ] - }, - { - "cell_type": "code", - "execution_count": 150, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "False\n" - ] - } - ], - "source": [ - "fid = open('../../../AutoQC_raw/quota/split-cruise-sorted/split-0.dat')\n", - "\n", - "i = 0\n", - "while i<20:\n", - " profile = wod.WodProfile(fid)\n", - " qc = const_depth(profile)\n", - " if qc.any():\n", - " plt.plot(profile.z(), profile.t(), marker='o')\n", - " plt.title(profile.uid())\n", - " plt.show()\n", - " print (profile.t_level_qc(originator=True) >= 3).any()\n", - " i += 1\n", - " \n", - " if profile.is_last_profile_in_file(fid):\n", - " break\n", - "\n", - "fid.close()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There are many profiles in *quota* that don't report depth variations, but are not flagged by the temperature qc, producing the large false positive rate reported for `const_depth` above; more investigation needed.\n", - "\n", - "For now, consider only the more obviously useful new qc corrections:" - ] - }, - { - "cell_type": "code", - "execution_count": 152, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "new_results = pandas.read_csv('results-newqc.csv')\n", - "\n", - "results['IQUOD_nosolo'] = new_results['IQUOD_nosolo']\n", - "results['IQUOD_const_temp'] = new_results['IQUOD_const_temp']\n", - "\n", - "truth = results.ix[:,1].tolist()\n", - "qctests = results.ix[:,2:].values.tolist()" - ] - }, - { - "cell_type": "code", - "execution_count": 153, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "SVC(C=1.0, cache_size=200, class_weight={0: 1, 1: 100}, coef0=0.0, degree=3,\n", - " gamma=0.0, kernel='linear', max_iter=-1, probability=False,\n", - " random_state=None, shrinking=True, tol=0.001, verbose=False)" - ] - }, - "execution_count": 153, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# redo the svm\n", - "\n", - "svm_classifier = svm.SVC(kernel='linear', class_weight = {0: 1, 1: 100})\n", - "svm_classifier.fit(qctests[0:trainingSize], truth[0:trainingSize])" - ] - }, - { - "cell_type": "code", - "execution_count": 154, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "QDA(priors=None, reg_param=0.0)" - ] - }, - "execution_count": 154, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# redo the qda\n", - "\n", - "qda_classifier = QDA()\n", - "qda_classifier.fit(qctests[0:trainingSize], truth[0:trainingSize])" - ] - }, - { - "cell_type": "code", - "execution_count": 155, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.9136212624584718,\n", - " 0.08637873754152824,\n", - " 0.6770675011175682,\n", - " 0.3229324988824318)" - ] - }, - "execution_count": 155, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "svm_prediction = svm_classifier.predict(qctests[trainingSize:])\n", - "qda_prediction = qda_classifier.predict(qctests[trainingSize:])\n", - "\n", - "final_prediction = [ svm_prediction[i] or qda_prediction[i] for i in range(len(svm_prediction))]\n", - "performance(truth[trainingSize:], final_prediction)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Giving us another 0.7% in true positives." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} From 95343df628658f729cab060ecd827b01da8ebbc5 Mon Sep 17 00:00:00 2001 From: BillMills Date: Wed, 6 Apr 2016 18:57:48 -0700 Subject: [PATCH 7/7] completes CSIRO implementation --- qctests/CSIRO_gradient.py | 42 ------------------------- qctests/CSIRO_long_gradient.py | 4 +-- qctests/CSIRO_short_gradient.py | 4 +-- tests/CSIRO_gradient_validation.py | 22 ------------- tests/CSIRO_long_gradient_validation.py | 34 ++++++++++++++++++++ 5 files changed, 38 insertions(+), 68 deletions(-) delete mode 100644 qctests/CSIRO_gradient.py delete mode 100644 tests/CSIRO_gradient_validation.py create mode 100644 tests/CSIRO_long_gradient_validation.py diff --git a/qctests/CSIRO_gradient.py b/qctests/CSIRO_gradient.py deleted file mode 100644 index a9606c8..0000000 --- a/qctests/CSIRO_gradient.py +++ /dev/null @@ -1,42 +0,0 @@ -""" -Implements the gradient test of DOI: 10.1175/JTECHO539.1 -All questionable features result in a flag, in order to minimize false negatives -""" - -import numpy - -def test(p): - """ - Runs the quality control check on profile p and returns a numpy array - of quality control decisions with False where the data value has - passed the check and True where it failed. - """ - - # Get temperature values from the profile. - t = p.t() - # Get depth values (m) from the profile. - d = p.z() - # is this an xbt? - isXBT = p.probe_type() == 2 - - assert len(t.data) == len(d.data), 'Number of temperature measurements does not equal number of depth measurements.' - - # initialize qc as a bunch of falses; - # implies all measurements pass when a gradient can't be calculated, such as at edges & gaps in data: - qc = numpy.zeros(len(t.data), dtype=bool) - - # check for gaps in data - isTemperature = (t.mask==False) - isDepth = (d.mask==False) - isData = isTemperature & isDepth - - for i in range(0,len(t.data)-1): - if isData[i] & isData[i+1]: - - gradient = (d.data[i+1] - d.data[i]) / (t.data[i+1] - t.data[i]) - - # gradient flag - if gradient > -0.4 and gradient < 12.5: - qc[i] = True - - return qc diff --git a/qctests/CSIRO_long_gradient.py b/qctests/CSIRO_long_gradient.py index 01c86b6..ad3308d 100644 --- a/qctests/CSIRO_long_gradient.py +++ b/qctests/CSIRO_long_gradient.py @@ -28,11 +28,11 @@ def test(p): on_inv = False # are we currently in an inversion? for i in range(0, p.n_levels()-1 ): - if isData[i] and isData[i-1]: + if isData[i] and isData[i+1]: # not interested below 5m: if d.data[i] < 5: continue - if t.data[i+1] > t.data[1] and not on_inv: + if t.data[i+1] > t.data[i] and not on_inv: # entering an inversion start_inv_temp = t.data[i] start_inv_depth = d.data[i] diff --git a/qctests/CSIRO_short_gradient.py b/qctests/CSIRO_short_gradient.py index bc4414a..959756a 100644 --- a/qctests/CSIRO_short_gradient.py +++ b/qctests/CSIRO_short_gradient.py @@ -26,11 +26,11 @@ def test(p): isData = isTemperature & isDepth for i in range(0, p.n_levels()-1 ): - if isData[i] and isData[i-1]: + if isData[i] and isData[i+1]: deltaD = (d.data[i+1] - d.data[i]) deltaT = (t.data[i+1] - t.data[i]) gradshort = deltaD / deltaT - if (deltaT > 0.5 and deltaD < 30) or (gradshort > 0 and gradshort < 12.5): + if (deltaT > 0.5 and deltaD < 30) or abs(gradshort) < 0.4 or (gradshort > 0 and gradshort < 12.5): if abs(deltaT) > 0.4: qc[i] = True diff --git a/tests/CSIRO_gradient_validation.py b/tests/CSIRO_gradient_validation.py deleted file mode 100644 index 34090d5..0000000 --- a/tests/CSIRO_gradient_validation.py +++ /dev/null @@ -1,22 +0,0 @@ -import qctests.CSIRO_gradient -import util.testingProfile -import numpy - -##### CSIRO_gradient_test --------------------------------------------------- - -def test_CSIRO_gradient(): - ''' - Spot-check some values in the CSIRO gradient test. - ''' - - p = util.testingProfile.fakeProfile([0,10,0,0.1,-24.9,-24.1], [10,20,30,40,50,60]) - qc = qctests.CSIRO_gradient.test(p) - truth = numpy.zeros(6, dtype=bool) - truth[0] = True; # in range - truth[1] = False; # too low - truth[2] = False; # too high - truth[3] = False; # right on the low margin - truth[4] = False; # right on the high margin - truth[5] = False; # can't caluclate gradient at endpoint - - assert numpy.array_equal(qc, truth), 'nominal beahvior failure in CSIRO_gradient' \ No newline at end of file diff --git a/tests/CSIRO_long_gradient_validation.py b/tests/CSIRO_long_gradient_validation.py new file mode 100644 index 0000000..6e69f68 --- /dev/null +++ b/tests/CSIRO_long_gradient_validation.py @@ -0,0 +1,34 @@ +import qctests.CSIRO_long_gradient +import util.testingProfile +import numpy + +##### CSIRO_long_gradient --------------------------------------------------- + +def test_CSIRO_long_gradient(): + ''' + Spot-check the nominal behavior of the CSIRO long gradient test. + ''' + + # nominal + p = util.testingProfile.fakeProfile([20,10,15,15,15,10], [0,5,10,15,20,25]) + qc = qctests.CSIRO_long_gradient.test(p) + truth = numpy.zeros(6, dtype=bool) + truth[1] = True + + assert numpy.array_equal(qc, truth), 'failed to flag a nominal long inversion' + + # gradlong too large + p = util.testingProfile.fakeProfile([20,10,15,15,15,10], [0,10,20,30,40,50]) + qc = qctests.CSIRO_long_gradient.test(p) + truth = numpy.zeros(6, dtype=bool) + + assert numpy.array_equal(qc, truth), 'should not flag a long inversion with such a large gradient' + + # too shallow + p = util.testingProfile.fakeProfile([20,10,15,15,15,10], [0,1,6,11,16,21]) + qc = qctests.CSIRO_long_gradient.test(p) + truth = numpy.zeros(6, dtype=bool) + + print truth, qc + + assert numpy.array_equal(qc, truth), 'should not flag a long inversion that begins at < 5m' \ No newline at end of file