{"action":"created","comment":{"url":"https://api.github.com/repos/ReactionMechanismGenerator/RMG-database/pulls/comments/72469925","id":72469925,"diff_hunk":"@@ -0,0 +1,424 @@\n+#!/usr/bin/env python\n+# encoding: utf-8\n+\n+\"\"\"\n+This script is meant to use statistical tests to evaluate the rate rules of RMG.\n+\"\"\"\n+\n+import os.path\n+import math\n+import numpy\n+import matplotlib\n+matplotlib.use('Agg')\n+from matplotlib import pyplot as plt\n+import re\n+import copy\n+import csv\n+\n+from rmgpy.thermo import *\n+from rmgpy.kinetics import *\n+from rmgpy.data.reference import *\n+from rmgpy.data.base import Entry\n+from rmgpy.data.thermo import ThermoDatabase\n+from rmgpy.data.kinetics import saveEntry\n+from rmgpy.molecule import Molecule\n+from rmgpy.species import Species\n+from rmgpy.reaction import Reaction\n+from rmgpy.data.rmg import RMGDatabase\n+from rmgpy.data.kinetics.common import UndeterminableKineticsError\n+\n+def getKineticsDepository(FullDatabase, family, depositoryLabel):\n+    \"\"\"\n+    Retrieve the NIST exact and approximated kinetics for those same reactions from RMG.\n+    Note: does NOT average up the database or create any rate rules from training data.  \n+    If that is desired it must be done prior to entering this function.\n+    \"\"\"\n+    \n+    depository = None\n+    for tempDepository in family.depositories:\n+        if re.search(depositoryLabel, tempDepository.label):\n+            depository=tempDepository\n+            break\n+    else:\n+        print 'Depository {} not found in {} family.'.format(depositoryLabel, family.label)\n+        return\n+            \n+    \n+    exactKinetics={}\n+    approxKinetics={}\n+    \n+    for key, entry in depository.entries.iteritems():\n+        try:\n+            reaction=entry.item\n+            template=family.getReactionTemplate(reaction)\n+            exactKinetics[key]=entry.data\n+            approxKinetics[key]=family.rules.estimateKinetics(template)[0]\n+        except UndeterminableKineticsError:\n+            # See if the reaction was written in the reverse direction\n+            reaction = Reaction(reactants = copy.deepcopy(entry.item.products),\n+                                products = copy.deepcopy(entry.item.reactants),\n+                                kinetics = copy.deepcopy(entry.data)\n+                                )\n+            \n+            template=family.getReactionTemplate(reaction)\n+\n+            # Getting thermo data erases the atomLabels, so do this after finding the template\n+            # But we need it for setting the reverse kinetics\n+            for spec in reaction.reactants + reaction.products:\n+                spec.getThermoData()\n+\n+            reverseKinetics = reaction.generateReverseRateCoefficient()\n+            reaction.kinetics = reverseKinetics\n+            \n+            exactKinetics[key]=reaction.kinetics\n+            approxKinetics[key]=family.rules.estimateKinetics(template)[0]\n+\n+    return exactKinetics, approxKinetics\n+        \n+def getKineticsLeaveOneOut(family, averaging=True):\n+    \"\"\"\n+    Performs the leave one out test on a family. It returns a dictionary of \n+    the original exact nodes and a dictionary of the new averaged nodes. \n+    The returned dictionary entries will be of a KineticModel class\n+    It deletes a single entry in the family, and then re-averages the tree\n+    and then tries to re-estimate that original deleted entry.\n+    \n+    The original family should not contained averaged nodes when starting out.  The\n+    leave one out test should be performed only for original exact matches.\n+    \"\"\"   \n+    exactKinetics={}\n+    approxKinetics={}\n+\n+    rootTemplate = family.getRootTemplate()\n+\n+    for entryKey in family.rules.entries.keys():\n+        template = family.retrieveTemplate(entryKey.split(';'))\n+        exactKineticsEntry=family.rules.getRule(template)  # fetch the best matched rule\n+        if exactKineticsEntry.rank == 0:\n+            # Skip rank zero entries, because they are replaced by averaged values\n+            # during model generation\n+            continue\n+\n+        exactKineticsData = exactKineticsEntry.data\n+        if exactKineticsData.comment:\n+            if 'training' not in exactKineticsData.comment:\n+                # Averaged nodes have kinetics data comments, so skip them unless \n+                # They were created from training reactions\n+                continue\n+\n+        exactKinetics[entryKey] = exactKineticsData\n+\n+        if averaging:\n+            # In this scheme, we remove the data fully and try to pretend the database \n+            # wants this kinetic value when we know nothing about it\n+            familyCopy=copy.deepcopy(family)\n+            familyCopy.rules.entries.pop(entryKey)\n+            familyCopy.fillKineticsRulesByAveragingUp()\n+            approxKinetics[entryKey], approxKineticsEntry=familyCopy.rules.estimateKinetics(template)\n+        else:\n+            # In this scheme, do not re-average the tree, just try to see if the nearest\n+            # best node provides a good estimate for the original kinetics.  \n+            # This takes significanty less time to run, but is not a true validation of the data, \n+            # it is just testing our kinetics selection algorithm\n+\n+            # Skip the top node in this scheme, because nothing can predict it if removed\n+\n+            if template == rootTemplate:\n+                continue\n+\n+            originalEntry = family.rules.entries[entryKey]\n+            family.rules.entries.pop(entryKey)\n+            approxKinetics[entryKey], approxKineticsEntry=family.rules.estimateKinetics(template)\n+            # Re-add the data back into the original family\n+            family.rules.entries[entryKey] = originalEntry\n+\n+\n+        \n+        \n+\n+    return exactKinetics, approxKinetics\n+\n+        \n+\n+def calculateParity(exactKineticModel, approxKineticModel, T):\n+    '''\n+    Calculates the parity values between two sets of kinetics evaluated at temperature T\n+    '''\n+    exact = exactKineticModel.getRateCoefficient(T)\n+    approx = approxKineticModel.getRateCoefficient(T)\n+    \n+    return float(approx)/float(exact)\n+\n+\n+def analyzeForParity(exactKinetics, approxKinetics, T=1000.0, cutoff=0.0):\n+    \"\"\"\n+    Creates a parity plot from the exactKinetics and approxKinetics (dictionarys with \n+    kineticModels are entries). Uses the median temperature of the exactKinetics to \n+    give the best comparison. Returns the parityData in a dictionary with\n+    {key: [exactCoefficient(T), approxCoefficient(T)}\n+    \"\"\"\n+    parityData={}\n+    for key in approxKinetics:\n+            \n+        exact=exactKinetics[key].getRateCoefficient(T)\n+        approx=approxKinetics[key].getRateCoefficient(T)\n+        dataPoint=[exact, approx]\n+        if cutoff!=0 and math.log10((float(exact)/float(approx)))**2 > cutoff**2:\n+            continue\n+        parityData[key]=dataPoint\n+\n+    return parityData\n+\n+\n+def calculateQ(parityData):\n+    \"\"\"\n+    Calculates the predicted root mean square error\n+    \"\"\"\n+    Q=0.0\n+    for key, value in parityData.iteritems():\n+        Q+=(math.log10(value[0]/value[1]))**2\n+    return (Q/len(parityData))**0.5\n+\n+def createParityPlot(parityData):\n+    \n+    keyList=parityData.keys()\n+    xAxis=[]\n+    yAxis=[]\n+    for key in keyList:\n+        xAxis.append(parityData[key][0])\n+        yAxis.append(parityData[key][1])\n+    \n+    plt.loglog(xAxis,yAxis, 'ks')\n+    \n+    minimum=min(min(xAxis), min(yAxis))\n+    maximum=max(max(xAxis), max(yAxis))\n+    plt.loglog([minimum/10,maximum*10], [minimum/10,maximum*10], 'k')\n+    plt.loglog([minimum*10,maximum*10], [minimum/10,maximum/10], 'k--')\n+    plt.loglog([minimum/10,maximum/10],[minimum*10,maximum*10], 'k--')\n+    plt.xlabel('Actual rate coefficient (cm^3/mol-s)')\n+    plt.ylabel('Estimated rate coefficient (cm^3/mol-s)')\n+    plt.axis([minimum/10, maximum*10, minimum/10, maximum*10])\n+\n+def countNodes(family):\n+    \"\"\"\n+    Count the number of groups under each tree in the Family's Groups.\n+    Returns a list containing the following information\n+    [Family Label, Number of Rules, Top Node Label 1, Number of Children, ..., Top Node Label N, Number of Children]\n+    \"\"\"\n+    countList=[family.label]\n+    \n+    #get top nodes\n+    forwardTemplate = family.groups.top[:]\n+\n+    temporary = []\n+    for entry in forwardTemplate:\n+        if entry not in temporary:\n+            temporary.append(entry)\n+        else:\n+            # Duplicate node found at top of tree\n+            # eg. R_recombination: ['Y_rad', 'Y_rad']\n+            assert len(forwardTemplate)==2 , 'Can currently only do symmetric trees with nothing else in them'\n+            \n+    forwardTemplate = temporary\n+    \n+    countList.append(len(family.rules.entries))\n+    for group in forwardTemplate:\n+        checkList=[group]\n+        childrenList=[group]\n+        while len(checkList)>0:\n+            childrenList.extend(checkList[0].children)\n+            checkList.extend(checkList[0].children)\n+            del checkList[0]\n+        \n+        countList.extend([group.label, len(childrenList)])\n+    \n+    return countList\n+\n+\n+###########################################################################################################\n+# Functions for the full Database\n+\n+def obtainKineticsFamilyStatistics(FullDatabase, trialDir):\n+    \"\"\"\n+    Obtains statistics for the kinetics families by creating\n+    a FamilyStatistics.csv file that gives information about each family: the total number of\n+    rules, and the top node names and the number of groups under each.\n+    Note: does NOT average up the database or create any rate rules from training data.  \n+    If that is desired it must be done prior to entering this function.  (averaging may not be desired\n+    as it would add non-exact rules to the rule count)\n+    \"\"\"\n+    allFamilyNames=FullDatabase.kinetics.families.keys()\n+    allFamilyNames.sort(key=str.lower)  # Perform test on families alphabetically\n+    \n+    with open(os.path.join(trialDir, 'FamilyStatistics.csv'), 'wb') as csvfile:\n+        csvwriter=csv.writer(csvfile)\n+        csvwriter.writerow(['Family','Number of Rules', 'Top Node 1', 'Number of Groups', 'Top Node 2', 'Number of Groups', 'Top Node 3', 'Number of Groups'])\n+        \n+        for familyName in allFamilyNames: \n+            family=FullDatabase.kinetics.families[familyName]\n+            print \"Processing\", familyName + '...', '(' + str(len(family.rules.entries)) + ' rate rules)'\n+            # Save to csv file\n+            csvwriter.writerow(countNodes(family))\n+\n+\n+def compareNIST(FullDatabase, trialDir):\n+    \"\"\"\n+    Compare NIST reaction kinetics with estimates from RMG.  Creates parity plot and\n+    calculates the predicted root mean square error from the families.\n+    Note: does NOT average up the database or create any rate rules from training data.  \n+    If that is desired it must be done prior to entering this function.\n+    \"\"\"\n+    trialDir=os.path.join(trialDir, 'compareNIST')\n+    if not os.path.exists(trialDir):\n+        os.makedirs(trialDir)\n+    \n+\n+    allFamilyNames=FullDatabase.kinetics.families.keys()\n+    allFamilyNames.sort(key=str.lower)  # Perform test on families alphabetically\n+    \n+    QDict={}\n+    \n+    with open(os.path.join(trialDir, 'QDict_LOO.csv'), 'wb') as csvfile:\n+        csvwriter=csv.writer(csvfile)\n+        for familyName in allFamilyNames: \n+            family=FullDatabase.kinetics.families[familyName]\n+            print \"Processing\", familyName + '...', '(' + str(len(family.rules.entries)) + ' rate rules)'\n+            if len(family.rules.entries) < 2:\n+                print '    Skipping', familyName, ': only has one rate rule...'\n+            else:\n+                exactKinetics, approxKinetics = getKineticsDepository(FullDatabase, family, 'NIST')\n+                \n+                #prune for exact matches only\n+                keysToRemove=[]\n+                for key, kinetics in approxKinetics.iteritems():","path":"testing/evaluateKinetics.py","position":293,"original_position":293,"commit_id":"cb2f649b2c67310bb1fb2ed0f64fcc00f1d702a2","original_commit_id":"cb2f649b2c67310bb1fb2ed0f64fcc00f1d702a2","user":{"login":"connie","id":583115,"avatar_url":"https://avatars.githubusercontent.com/u/583115?v=3","gravatar_id":"","url":"https://api.github.com/users/connie","html_url":"https://github.com/connie","followers_url":"https://api.github.com/users/connie/followers","following_url":"https://api.github.com/users/connie/following{/other_user}","gists_url":"https://api.github.com/users/connie/gists{/gist_id}","starred_url":"https://api.github.com/users/connie/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/connie/subscriptions","organizations_url":"https://api.github.com/users/connie/orgs","repos_url":"https://api.github.com/users/connie/repos","events_url":"https://api.github.com/users/connie/events{/privacy}","received_events_url":"https://api.github.com/users/connie/received_events","type":"User","site_admin":false},"body":"Added and set default to false for now.","created_at":"2016-07-27T16:07:01Z","updated_at":"2016-07-27T16:07:01Z","html_url":"https://github.com/ReactionMechanismGenerator/RMG-database/pull/117#discussion_r72469925","pull_request_url":"https://api.github.com/repos/ReactionMechanismGenerator/RMG-database/pulls/117","_links":{"self":{"href":"https://api.github.com/repos/ReactionMechanismGenerator/RMG-database/pulls/comments/72469925"},"html":{"href":"https://github.com/ReactionMechanismGenerator/RMG-database/pull/117#discussion_r72469925"},"pull_request":{"href":"https://api.github.com/repos/ReactionMechanismGenerator/RMG-database/pulls/117"}}},"pull_request":{"url":"https://api.github.com/repos/ReactionMechanismGenerator/RMG-database/pulls/117","id":78547011,"html_url":"https://github.com/ReactionMechanismGenerator/RMG-database/pull/117","diff_url":"https://github.com/ReactionMechanismGenerator/RMG-database/pull/117.diff","patch_url":"https://github.com/ReactionMechanismGenerator/RMG-database/pull/117.patch","issue_url":"https://api.github.com/repos/ReactionMechanismGenerator/RMG-database/issues/117","number":117,"state":"open","locked":false,"title":"Add evaluateKinetics.py script to testing folder","user":{"login":"connie","id":583115,"avatar_url":"https://avatars.githubusercontent.com/u/583115?v=3","gravatar_id":"","url":"https://api.github.com/users/connie","html_url":"https://github.com/connie","followers_url":"https://api.github.com/users/connie/followers","following_url":"https://api.github.com/users/connie/following{/other_user}","gists_url":"https://api.github.com/users/connie/gists{/gist_id}","starred_url":"https://api.github.com/users/connie/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/connie/subscriptions","organizations_url":"https://api.github.com/users/connie/orgs","repos_url":"https://api.github.com/users/connie/repos","events_url":"https://api.github.com/users/connie/events{/privacy}","received_events_url":"https://api.github.com/users/connie/received_events","type":"User","site_admin":false},"body":"This is a revised and updated script of Nathan's old kinetics evaluation script containing 3 main functionalities: \r\n\r\n- `obtainKineticsFamilyStatistics`: gathers and saves data about families into a .csv files containing info on the number of **exact** rate rules, number of groups per tree in the groups\r\n- `leaveOneOut`: performs a leave one out test by deleting an **exact** node (excludes averaged ones), averaging the tree and then trying to estimate that tree.  Creates parity plots for each family and creates a .csv of the root mean square error.\r\n- `compareNIST`: compares the family estimated kinetics against the exact kinetics from the NIST depository within each family.  Also creates parity plots and .csv of error data.","created_at":"2016-07-22T22:07:37Z","updated_at":"2016-07-27T16:07:01Z","closed_at":null,"merged_at":null,"merge_commit_sha":"20d7a545e3114087a280a2726f0025fc46e70ca3","assignee":{"login":"nyee","id":1444635,"avatar_url":"https://avatars.githubusercontent.com/u/1444635?v=3","gravatar_id":"","url":"https://api.github.com/users/nyee","html_url":"https://github.com/nyee","followers_url":"https://api.github.com/users/nyee/followers","following_url":"https://api.github.com/users/nyee/following{/other_user}","gists_url":"https://api.github.com/users/nyee/gists{/gist_id}","starred_url":"https://api.github.com/users/nyee/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/nyee/subscriptions","organizations_url":"https://api.github.com/users/nyee/orgs","repos_url":"https://api.github.com/users/nyee/repos","events_url":"https://api.github.com/users/nyee/events{/privacy}","received_events_url":"https://api.github.com/users/nyee/received_events","type":"User","site_admin":false},"assignees":[{"login":"nyee","id":1444635,"avatar_url":"https://avatars.githubusercontent.com/u/1444635?v=3","gravatar_id":"","url":"https://api.github.com/users/nyee","html_url":"https://github.com/nyee","followers_url":"https://api.github.com/users/nyee/followers","following_url":"https://api.github.com/users/nyee/following{/other_user}","gists_url":"https://api.github.com/users/nyee/gists{/gist_id}","starred_url":"https://api.github.com/users/nyee/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/nyee/subscriptions","organizations_url":"https://api.github.com/users/nyee/orgs","repos_url":"https://api.github.com/users/nyee/repos","events_url":"https://api.github.com/users/nyee/events{/privacy}","received_events_url":"https://api.github.com/users/nyee/received_events","type":"User","site_admin":false}],"milestone":null,"commits_url":"https://api.github.com/repos/ReactionMechanismGenerator/RMG-database/pulls/117/commits","review_comments_url":"https://api.github.com/repos/ReactionMechanismGenerator/RMG-database/pulls/117/comments","review_comment_url":"https://api.github.com/repos/ReactionMechanismGenerator/RMG-database/pulls/comments{/number}","comments_url":"https://api.github.com/repos/ReactionMechanismGenerator/RMG-database/issues/117/comments","statuses_url":"https://api.github.com/repos/ReactionMechanismGenerator/RMG-database/statuses/cb2f649b2c67310bb1fb2ed0f64fcc00f1d702a2","head":{"label":"ReactionMechanismGenerator:leaveOneOut","ref":"leaveOneOut","sha":"cb2f649b2c67310bb1fb2ed0f64fcc00f1d702a2","user":{"login":"ReactionMechanismGenerator","id":12617462,"avatar_url":"https://avatars.githubusercontent.com/u/12617462?v=3","gravatar_id":"","url":"https://api.github.com/users/ReactionMechanismGenerator","html_url":"https://github.com/ReactionMechanismGenerator","followers_url":"https://api.github.com/users/ReactionMechanismGenerator/followers","following_url":"https://api.github.com/users/ReactionMechanismGenerator/following{/other_user}","gists_url":"https://api.github.com/users/ReactionMechanismGenerator/gists{/gist_id}","starred_url":"https://api.github.com/users/ReactionMechanismGenerator/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/ReactionMechanismGenerator/subscriptions","organizations_url":"https://api.github.com/users/ReactionMechanismGenerator/orgs","repos_url":"https://api.github.com/users/ReactionMechanismGenerator/repos","events_url":"https://api.github.com/users/ReactionMechanismGenerator/events{/privacy}","received_events_url":"https://api.github.com/users/ReactionMechanismGenerator/received_events","type":"Organization","site_admin":false},"repo":{"id":1838004,"name":"RMG-database","full_name":"ReactionMechanismGenerator/RMG-database","owner":{"login":"ReactionMechanismGenerator","id":12617462,"avatar_url":"https://avatars.githubusercontent.com/u/12617462?v=3","gravatar_id":"","url":"https://api.github.com/users/ReactionMechanismGenerator","html_url":"https://github.com/ReactionMechanismGenerator","followers_url":"https://api.github.com/users/ReactionMechanismGenerator/followers","following_url":"https://api.github.com/users/ReactionMechanismGenerator/following{/other_user}","gists_url":"https://api.github.com/users/ReactionMechanismGenerator/gists{/gist_id}","starred_url":"https://api.github.com/users/ReactionMechanismGenerator/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/ReactionMechanismGenerator/subscriptions","organizations_url":"https://api.github.com/users/ReactionMechanismGenerator/orgs","repos_url":"https://api.github.com/users/ReactionMechanismGenerator/repos","events_url":"https://api.github.com/users/ReactionMechanismGenerator/events{/privacy}","received_events_url":"https://api.github.com/users/ReactionMechanismGenerator/received_events","type":"Organization","site_admin":false},"private":false,"html_url":"https://github.com/ReactionMechanismGenerator/RMG-database","description":"The 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