def __init__(self, calibrationTable, score_func=accuracy_score):
"""
calibrate a classifier
@param calibrationTable: a pandas data frame
"""
print '--------- Calibrating Imputer -----------'
X_cal, y_cal, _, _ = impute_risk_factors(calibrationTable)
bestScore = -1
bestPipe = None
bestParams = None
for name, (pipe, params) in make_pipes().iteritems():
print '>'*10, name, '<'*10
_, cur_bestParams, cur_bestScore = fitClfWithGridSearch(
name + '_risk', pipe, params, DatasetPair(np.array(X_cal), y_cal),
saveToDir='/home/jj/code/Kaggle/allstate/output/gridSearchOutput',
useJJ=True, score_func=score_func, n_jobs=N_JOBS, verbosity=0,
minimize=False, cvSplitNum=5,
maxLearningSteps=10,
numConvergenceSteps=4, convergenceTolerance=0, eliteProportion=0.1,
parentsProportion=0.4, mutationProportion=0.1, mutationProbability=0.1,
mutationStdDev=None, populationSize=6)
if cur_bestScore > bestScore:
bestScore = cur_bestScore
bestPipe = clone(pipe)
bestPipe.set_params(**cur_bestParams)
bestParams = cur_bestParams
print '----> best score:', bestScore
pprint(bestParams)
self._imputer = bestPipe
def fitClassifiers(trainData, useJJ, n_jobs=23, selectedClfs=None, overwriteSavedResult=True, verbose=True,
cvSplitNum=10, test_size=0.25, random_states=[None], **fitArgs):
""" fits a list of classifiers by searching for the best parameters using GridSearchCV
@type trainData DatasetPair
@param selectedClfs: which classifiers to fit. if None, fits all.
@return: (a dictionary of {classifier name: classifier}, the best classifier)
"""
res = {}
bestScore = 0
bestClf = ()
if selectedClfs and not isinstance(selectedClfs, Iterable): selectedClfs = [selectedClfs]
intermediateResdir = os.path.join(rootdir, 'intermediate results')
# ------ fit using gridsearchcv -----------
for name, v in classifiersToTry.iteritems():
if selectedClfs and name not in selectedClfs: continue
pipe, paramsDict = makePipe([fillertoTry, normalizerToTry, (name, classifiersToTry[name])])
try:
newpipe, bestParams, score = fitClfWithGridSearch(name, pipe, paramsDict, trainData, intermediateResdir, useJJ=useJJ,
n_jobs=n_jobs, overwriteSavedResult=overwriteSavedResult, verbose=verbose,
cvSplitNum=cvSplitNum, test_size=test_size, random_states=random_states, **fitArgs)
cleanPipe = pipe.set_params(**bestParams)
res[name] = cleanPipe
# check if it's the best classifier
if score > bestScore:
bestScore = score
bestClf = (name, cleanPipe, score)
except Exception as e:
print 'Fitting', name, 'caused an error:', e
return res, bestClf
trainY = np.array(fullTrainY)
data = DatasetPair(trainX, trainY)
randomStates = [0, 1] # try multiple random states for better calibration
popSize = 8 if simple else 15
cvObjs = [StratifiedShuffleSplit([0 if y == 0 else 1 for y in data.Y], n_iter=5, test_size=0.25,
random_state=randomState) for randomState in randomStates]
initPop = [[np.random.randint(len(v)) for v in params.items()] for _ in range(popSize)]
dt = datetime.now()
_, bestParams, score = fitClfWithGridSearch(name, pipe, params, data,
saveToDir='/home/jj/code/Kaggle/Loan Default Prediction/output/gridSearchOutput',
useJJ=True, score_func=mean_absolute_error, n_jobs=20, verbosity=3,
minimize=True, cvObjs=cvObjs, maxLearningSteps=10,
numConvergenceSteps=5, convergenceTolerance=0, eliteProportion=0.1,
parentsProportion=0.4, mutationProportion=0.1, mutationProbability=0.1,
mutationStdDev=None, populationSize=popSize)
bestPipe = clone(pipe)
bestPipe.set_params(**bestParams)
print 'CV Took', datetime.now() - dt
# bestPipe = loadObject('/home/jj/code/Kaggle/Loan Default Prediction/output/gridSearchOutput/GBC_25fts_simple.pk')['best_estimator']
bestPipe.classification_metrics(trainX, trainY, n_iter=10)
# ---------- learn the full training data
dt = datetime.now()
bestPipe.fit(fullTrainX, fullTrainY)
for col in outputTable_cal.columns:
print '>'*20, col, '<'*20
cur_y = np.array(outputTable_cal[col])
bestScore = -1
bestPipe = None
bestParams = None
for name, (pipe, params) in make_pipes().iteritems():
print '>'*10, name, '<'*10
_, cur_bestParams, cur_bestScore = fitClfWithGridSearch(
'_'.join([name, col, calibrationName]), pipe, params, DatasetPair(X_cal, cur_y),
saveToDir='/home/jj/code/Kaggle/allstate/output/gridSearchOutput',
useJJ=True, score_func=accuracy_score, n_jobs=N_JOBS, verbosity=0,
minimize=False, cvSplitNum=5,
maxLearningSteps=10,
numConvergenceSteps=4, convergenceTolerance=0, eliteProportion=0.1,
parentsProportion=0.4, mutationProportion=0.1, mutationProbability=0.1,
mutationStdDev=None, populationSize=6)
if cur_bestScore > bestScore:
bestScore = cur_bestScore
bestPipe = clone(pipe)
bestPipe.set_params(**cur_bestParams)
bestParams = cur_bestParams
indivClfs.append(bestPipe)
print '---->', col, '<----', bestScore
pprint(bestParams)
