def validate_classifier(clf, X, Y_train, Y_validate, cv, use_predProb_instead, scoreFunc=roc_auc_score, n_jobs=16, test_size=0.25):
"""
@param cv: an object (list of (trainInds, testInds)) or an integer (number of folds)
@return: list of cv scores
"""
cvObj = StratifiedShuffleSplit(Y_validate, n_iter=cv, test_size=test_size) if isinstance(cv, int) else cv
scores = jjcross_val_score(clf, X, Y_train, scoreFunc, cvObj, Y_validate, n_jobs=n_jobs, use_predProb_instead=use_predProb_instead)
return scores
def gridSearch(clf, cvOutputFname, x_train, y_train, weights, innerclf=False, num_folds = 10):
"""
:param clf:
:param cvOutputFname:
:param x_train:
:param y_train:
:param weights:
:param innerclf: if true, the clf parameter is an "outter" object that wraps a "regressor" attribute of type Ridge;
if false, clf is the Ridge itself
:param num_folds:
:return:
"""
print '================== Grid Search for the Best Parameter =================='
cvOutputFile = open(cvOutputFname, 'w')
res = {}
cvObj = KFold(len(y_train), n_folds=num_folds, shuffle=True, random_state=0)
for tolerance in [0.00001, 0.0001, 0.001, 0.01, 0.1, 0.2, 0.5]:
for alpha in np.arange(0.01, 5, 0.1):
print '>>>> alpha=', alpha, ', tolerance =', tolerance
if innerclf:
clf.regressor.set_params(alpha=alpha, tol=tolerance)
else:
clf.set_params(alpha=alpha, tol=tolerance)
scores = jjcross_val_score(clf, x_train, y_train, normalized_weighted_gini, cvObj, weights=weights,
verbose=False)
meanScore = np.mean(scores)
stdScore = np.std(scores)
s = 'alpha = %f, tolerance = %f, mean = %f, std = %f\n' % (alpha, tolerance, meanScore, stdScore)
print s
res[(alpha, tolerance)] = (meanScore, stdScore)
cvOutputFile.write(s)
print '>>>>>> Result sorted by mean score:'
pprint(sorted(res.items(), key=lambda x: -x[1][0]))
cvOutputFile.close()
return res
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)
combinedClf = CombinedClassifier(indivClfs)
print 'OVERALL CV SCORE:', np.mean(jjcross_val_score(combinedClf, X_cal, y_cal, accuracy_score, cv=5, n_jobs=N_JOBS)) # validate classifier
print '====== TRAINING'
_, inputTable_train, outputTable_train, _ = condense_data(trainingName, isTraining=True, readFromFiles = True)
pdf(inputTable_train)
temp = riskFactorImp.fit_transform(inputTable_train)
assert np.isnan(temp.risk_factor).sum() == 0
X_train = Normalizer().fit_transform(Imputer().fit_transform(temp))
# X_train = Normalizer().fit_transform(Imputer().fit_transform(inputTable_train))
y_train = CombinedClassifier.combine_outputs(np.array(outputTable_train))
combinedClf.fit(X_train, y_train)
# verbose=1)
# clf = SVR()
# ================== CORRELATION ==================
# print '================== CORRELATION =================='
# print x_train.shape
# numFields = 30
# x_train, newCols = create_new_features(x_train, columns=columns_train)
# corrs = calculate_y_corrs(x_train, y_train)[0]
# ord = corrs.argsort()[::-1][:numFields]
# x_train = x_train[:, ord]
# ================== CV ==================
print '================== CV =================='
scores = jjcross_val_score(regressor, x_train, y_train, normalized_weighted_gini,
KFold(len(y_train), n_folds=5, shuffle=True, random_state=0), weights=weights)#, n_jobs=1)
# ================== Grid Search for the Best Parameter ==================
# gridSearch(clf, '/home/jj/code/Kaggle/Fire/cvRes/RidgeGroupThenRegress.txt', x_train, y_train, weights, innerclf=True)
# ================== train ==================
# print '================== train =================='
# clf.fit(x_train, y_train, sample_weight=weights)
# #
# # # ================== predict ==================
# print '================== predict =================='
# x_test, _, ids_pred, _, _, _ = process_data('/home/jj/code/Kaggle/Fire/Data/test.csv',
# impute=True, imputeDataDir='/home/jj/code/Kaggle/Fire/intermediateOutput', imputeStrategy='median',
# fieldsToUse=columns_train)
# pred = clf.predict(x_test)
# pandas.DataFrame({'id': ids_pred, 'target': pred}).\
from sklearn.linear_model import Ridge
from sklearn.svm import SVR
from sklearn.cross_validation import KFold
from sklearn.decomposition import PCA
from sklearn.metrics import roc_auc_score
from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier
from Kaggle.utilities import plot_histogram, plot_feature_importances, jjcross_val_score
from globalVars import *
from evaluation import normalized_weighted_gini
from utilities import process_data
from correlations import *
x_train, y_regress, _, columns_train, weights, y_class = \
process_data('/home/jj/code/Kaggle/Fire/Data/train.csv',
impute=True, imputeDataDir='/home/jj/code/Kaggle/Fire/intermediateOutput', imputeStrategy='median',
fieldsToUse=FIELDS_CLASS_GBC_TOP100[:5])
# print '==================== feature importances =================='
# plot_feature_importances(x_train, y_class, columns_train, numTopFeatures=0.95, numEstimators=50, num_jobs=11)
print '==================== CV =================='
# clf = GradientBoostingClassifier(learning_rate=0.1, loss='deviance')
clf = RandomForestClassifier(n_estimators=50, n_jobs=cpu_count()-2)
# clf.fit(x_train, y_class)
jjcross_val_score(clf, x_train, y_class, roc_auc_score,
KFold(len(y_class), n_folds=5, shuffle=True, random_state=0),
weights=weights)#, n_jobs=1)
def quick_score(clf, X, y, cv=5, n_jobs=20):
""" returns the cv score of a classifier
"""
return jjcross_val_score(clf, X, y, mean_absolute_error, cv, n_jobs=n_jobs).mean()