def test_staged_predict():
"""Check staged predictions."""
# AdaBoost classification
for alg in ['SAMME', 'SAMME.R']:
clf = AdaBoostClassifier(algorithm=alg, n_estimators=10)
clf.fit(iris.data, iris.target)
predictions = clf.predict(iris.data)
staged_predictions = [p for p in clf.staged_predict(iris.data)]
proba = clf.predict_proba(iris.data)
staged_probas = [p for p in clf.staged_predict_proba(iris.data)]
score = clf.score(iris.data, iris.target)
staged_scores = [s for s in clf.staged_score(iris.data, iris.target)]
assert_equal(len(staged_predictions), 10)
assert_array_almost_equal(predictions, staged_predictions[-1])
assert_equal(len(staged_probas), 10)
assert_array_almost_equal(proba, staged_probas[-1])
assert_equal(len(staged_scores), 10)
assert_array_almost_equal(score, staged_scores[-1])
# AdaBoost regression
clf = AdaBoostRegressor(n_estimators=10)
clf.fit(boston.data, boston.target)
predictions = clf.predict(boston.data)
staged_predictions = [p for p in clf.staged_predict(boston.data)]
score = clf.score(boston.data, boston.target)
staged_scores = [s for s in clf.staged_score(boston.data, boston.target)]
assert_equal(len(staged_predictions), 10)
assert_array_almost_equal(predictions, staged_predictions[-1])
assert_equal(len(staged_scores), 10)
assert_array_almost_equal(score, staged_scores[-1])
def cook():
x, y, weights = load_data()
n_components = 200
svd = TruncatedSVD(n_components, random_state=42)
x_unweighted = svd.fit_transform(x)
x_weighted = svd.fit_transform(weighted(x, weights))
for i in range(9):
frac = 1 - (i * 0.01 + 0.01)
print frac
x_train, x_test, y_train, y_test = train_test_split(x_unweighted, y, test_size=frac)
classifier = AdaBoostClassifier(n_estimators=100)
classifier.fit(x_train, y_train)
print "Unweighted: ", classifier.score(x_test, y_test)
x_train, x_test, y_train, y_test = train_test_split(x_weighted, y, test_size=frac)
classifier = AdaBoostClassifier(n_estimators=100)
classifier.fit(x_train, y_train)
print "Weighted: ", classifier.score(x_test, y_test)
print '--------------------------'
'''
class AdaBoost:
def __init__(self, data, n_estimators=50, learning_rate=1.0):
features, weights, labels = data
self.clf = AdaBoostClassifier(n_estimators=n_estimators, learning_rate=learning_rate)
self.predictions, self.trnaccuracy, self.tstaccuracy = None, None, None
self.dataset = split_dataset(features, weights, labels)
def train(self):
"""
Train Ada Boost on the higgs dataset
"""
self.clf = self.clf.fit(self.dataset['training']['features'], self.dataset['training']['labels'])
def predict(self):
"""
Predict label using Ada Boost
:return:
"""
self.predictions = self.clf.predict(self.dataset['test']['features'])
def evaluate(self):
self.trnaccuracy = self.clf.score(self.dataset['training']['features'],
self.dataset['training']['labels'],
sample_weight=self.dataset['training']['weights'])
self.tstaccuracy = self.clf.score(self.dataset['test']['features'],
self.dataset['test']['labels'],
sample_weight=self.dataset['test']['weights'])
def cvalidate():
targetset = np.genfromtxt(open('trainLabels.csv','r'), dtype='f16')
y = [x for x in targetset]
trainset = np.genfromtxt(open('train.csv','r'), delimiter=',', dtype='f16')
X = np.array([x for x in trainset])
X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size = 0.3, random_state = 0)
X_train, X_test = decomposition_pca(X_train, X_test)
#SVM
c_range = 10.0 ** np.arange(6.5,7.5,.25)
gamma_range = 10.0 ** np.arange(-2.5,0.5,.25)
parameters = {'kernel':['rbf'], 'C':c_range, 'gamma':gamma_range}
svr = SVC()
clf = grid_search.GridSearchCV(svr, parameters)
clf.fit(X_train, y_train)
bdt = AdaBoostClassifier(base_estimator = clf.best_estimator_,
algorithm="SAMME",
n_estimators=100)
#bdt = AdaBoostClassifier(base_estimator = KNeighborsClassifier(n_neighbors=10))
bdt.fit(X_train, y_train)
print bdt.score(X_test, y_test)
def test_pickle():
# Check pickability.
import pickle
# Adaboost classifier
for alg in ['SAMME', 'SAMME.R']:
obj = AdaBoostClassifier(algorithm=alg)
obj.fit(iris.data, iris.target)
score = obj.score(iris.data, iris.target)
s = pickle.dumps(obj)
obj2 = pickle.loads(s)
assert_equal(type(obj2), obj.__class__)
score2 = obj2.score(iris.data, iris.target)
assert_equal(score, score2)
# Adaboost regressor
obj = AdaBoostRegressor(random_state=0)
obj.fit(boston.data, boston.target)
score = obj.score(boston.data, boston.target)
s = pickle.dumps(obj)
obj2 = pickle.loads(s)
assert_equal(type(obj2), obj.__class__)
score2 = obj2.score(boston.data, boston.target)
assert_equal(score, score2)
def cvalidate():
from sklearn import cross_validation
trainset = np.genfromtxt(open('train.csv','r'), delimiter=',')[1:]
X = np.array([x[1:8] for x in trainset])
y = np.array([x[8] for x in trainset])
#print X,y
import math
for i, x in enumerate(X):
for j, xx in enumerate(x):
if(math.isnan(xx)):
X[i][j] = 26.6
#print X[0:3]
#print y[0:3]
X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size = 0.3, random_state = 0)
X_train, X_test = decomposition_pca(X_train, X_test)
bdt = AdaBoostClassifier(base_estimator = KNeighborsClassifier(n_neighbors=20, algorithm = 'auto'), algorithm="SAMME", n_estimators = 200)
bdt.fit(X_train, y_train)
print bdt.score(X_test, y_test)
class Model_Adaboost(object):
def __init__(self,model,parameter = {"n_estimators" : 50, "CV_size": 0}):
self.train = model.train
self.test = model.test
self.CVsize = float(parameter["CV_size"].get())
train = np.array(self.train)
self.X_train = train[:, :-1]
self.y_train = train[:, -1]
self.X_train,self.X_CV,self.y_train,self.y_CV = train_test_split(self.X_train, self.y_train, test_size=self.CVsize)
if self.CVsize == 0:
self.clf = AdaBoostClassifier(n_estimators = int(parameter["n_estimators"].get()))
self.model = model
def fit(self):
self.clf.fit(self.X_train,self.y_train)
def score(self):
pre = self.clf.predict(self.X_train)
truth = self.y_train
print ("score: " + str(self.clf.score(self.X_train,truth)))
print ("f1: " + str(f1_score(truth,pre, average=None)))
print ("AUC score: " + str(roc_auc_score(truth,pre)))
def save_results(self):
pre = self.model.clf.predict(self.model.test)
df = pd.DataFrame({"predict":pre})
fileName = tkFileDialog.asksaveasfilename()
df.to_csv(fileName)
def crossValidation(self):
estimatorList = [3,5,7,10,13,15,20,25,30,50]
bestScore = [0,0] #score,n_estimator
bestF1ScoreNeg = [0,0]
bestF1ScorePos = [0,0]
#bestAUCScore = [0,0]
for e in estimatorList:
self.clf = AdaBoostClassifier(n_estimators = e)
self.clf.fit(self.X_train,self.y_train)
pre = self.clf.predict(self.X_CV)
truth = self.y_CV
score = self.clf.score(self.X_CV,truth)
if score > bestScore[0]:
bestScore[0] = score
bestScore[1] = e
f1pos = f1_score(truth,pre, average=None)[1]
if f1pos > bestF1ScorePos[0]:
bestF1ScorePos[0] = f1pos
bestF1ScorePos[1] = e
f1neg = f1_score(truth,pre, average=None)[0]
if f1neg > bestF1ScoreNeg[0]:
bestF1ScoreNeg[0] = f1neg
bestF1ScoreNeg[1] = e
print ("Adaboost:")
print ("Best [score,n_estimators] on Cross Validation set: " + str(bestScore))
print ("Best [f1(pos),n_estimators] on Cross Validation set: " + str(bestF1ScorePos))
print ("Best [f1(neg),n_estimators] on Cross Validation set" + str(bestF1ScoreNeg))
def adaboost(df,label_name,feature_names,features_len,ifeat,n_estimators=100):
# TODO: just copied from RF, needs real code
from sklearn.ensemble import AdaBoostClassifier
print('---------------------------------------------------')
print(ifeat,features_len,'Adaboost, features:',feature_names)
df_train_Y = df[label_name]
train_Y = df_train_Y.values.ravel() # turn from 2D to 1D
df_train_X = df[feature_names]
train_X = df_train_X.values
clf =AdaBoostClassifier(n_estimators=n_estimators)
clf = clf.fit(train_X,train_Y)
# output = clf.predict(train_X)
E_in = round(1.-clf.score(train_X, train_Y),5) # 'in sample' error
#print('\tE_in :',E_in)
# -----
# Kfold as estimator for 'out of sample' error
kf=skl.cross_validation.KFold(n=len(train_X), n_folds=5)
cv_scores=skl.cross_validation.cross_val_score(clf, train_X, y=train_Y, cv=kf)
E_out = round(1.-np.mean(cv_scores),5)
#print("\tE_out:",E_out)
return E_in,E_out
def test_iris():
# Check consistency on dataset iris.
classes = np.unique(iris.target)
clf_samme = prob_samme = None
for alg in ['SAMME', 'SAMME.R']:
clf = AdaBoostClassifier(algorithm=alg)
clf.fit(iris.data, iris.target)
assert_array_equal(classes, clf.classes_)
proba = clf.predict_proba(iris.data)
if alg == "SAMME":
clf_samme = clf
prob_samme = proba
assert_equal(proba.shape[1], len(classes))
assert_equal(clf.decision_function(iris.data).shape[1], len(classes))
score = clf.score(iris.data, iris.target)
assert score > 0.9, "Failed with algorithm %s and score = %f" % \
(alg, score)
# Somewhat hacky regression test: prior to
# ae7adc880d624615a34bafdb1d75ef67051b8200,
# predict_proba returned SAMME.R values for SAMME.
clf_samme.algorithm = "SAMME.R"
assert_array_less(0,
np.abs(clf_samme.predict_proba(iris.data) - prob_samme))
def prediction(feat,label):
x_train, x_test, y_train, y_test = cross_validation.train_test_split(feat, label, test_size = 0.25, random_state = 0)
num_leaves = []
accuracy_score = []
auc_score = []
# for depth in range(1,10):
# clf = tree.DecisionTreeClassifier(max_depth = depth)
# clf.fit(x_train,y_train)
# predictions = clf.predict(x_test)
# accuracy = clf.score(x_test,y_test)
# auc = metrics.roc_auc_score(y_test,predictions)
# num_leaves.append(depth)
# accuracy_score.append(accuracy)
# auc_score.append(auc)
for depth in range(1,10):
clf = AdaBoostClassifier(tree.DecisionTreeClassifier(max_depth = depth), n_estimators = 100)
clf.fit(x_train,y_train)
predictions = clf.predict(x_test)
accuracy = clf.score(x_test,y_test)
auc = metrics.roc_auc_score(y_test,predictions)
num_leaves.append(depth)
accuracy_score.append(accuracy)
auc_score.append(auc)
return num_leaves,accuracy_score,auc_score
def ADA_Classifier(X_train, X_cv, X_test, Y_train,Y_cv,Y_test, Actual_DS):
print("***************Starting AdaBoost Classifier***************")
t0 = time()
clf = AdaBoostClassifier(n_estimators=300)
clf.fit(X_train, Y_train)
preds = clf.predict(X_cv)
score = clf.score(X_cv,Y_cv)
print("AdaBoost Classifier - {0:.2f}%".format(100 * score))
Summary = pd.crosstab(label_enc.inverse_transform(Y_cv), label_enc.inverse_transform(preds),
rownames=['actual'], colnames=['preds'])
Summary['pct'] = (Summary.divide(Summary.sum(axis=1), axis=1)).max(axis=1)*100
print(Summary)
#Check with log loss function
epsilon = 1e-15
#ll_output = log_loss_func(Y_cv, preds, epsilon)
preds2 = clf.predict_proba(X_cv)
ll_output2= log_loss(Y_cv, preds2, eps=1e-15, normalize=True)
print(ll_output2)
print("done in %0.3fs" % (time() - t0))
preds3 = clf.predict_proba(X_test)
#preds4 = clf.predict_proba((Actual_DS.ix[:,'feat_1':]))
preds4 = clf.predict_proba(Actual_DS)
print("***************Ending AdaBoost Classifier***************")
return pd.DataFrame(preds2) , pd.DataFrame(preds3),pd.DataFrame(preds4)
def adaboost_skin(X_train, y_train, X_test, y_test):
"""Learn the skin data sets with AdaBoost.
X_*: Samples.
y_*: labels.
"""
print 'AdaBoost'
min_iter = 1
max_iter = 200
steps = 30
diff = (max_iter - min_iter) / steps
iterations = [min_iter + diff * step for step in xrange(steps+1)]
scores = []
for T in iterations:
clf = AdaBoostClassifier(
base_estimator=DecisionTreeClassifier(max_depth=1),
algorithm="SAMME",
n_estimators=T)
clf.fit(X_train.toarray(), y_train)
scores.append(100 * clf.score(X_test.toarray(), y_test))
print '\t%d Iterations: %.2f%%' % (T, scores[-1])
return iterations, scores
def boost_report():
svm_train_features = list()
svm_train_classes = list()
svm_test_features = list()
svm_test_classes = list()
for record in mit_records:
svm_train_features.append(list(record.features.values()))
svm_train_classes.append(record.my_class)
for record in mim_records:
svm_test_features.append(list(record.features.values()))
svm_test_classes.append(record.my_class)
svm_classifier = svm.SVC(kernel="linear", C=0.1)
svm_classifier.fit(svm_train_features, svm_train_classes)
print("linear kernel svm accuracy: " +
str(svm_classifier.score(svm_test_features, svm_test_classes)))
classifier = AdaBoostClassifier(
base_estimator=svm_classifier,
n_estimators=100,
algorithm='SAMME')
classifier.fit(svm_train_features, svm_train_classes)
print("adaboost accuracy: " +
str(classifier.score(svm_test_features, svm_test_classes)))
class AdaBoostcls(object):
"""docstring for ClassName"""
def __init__(self):
self.adaboost_cls = AdaBoostClassifier()
self.prediction = None
self.train_x = None
self.train_y = None
def train_model(self, train_x, train_y):
try:
self.train_x = train_x
self.train_y = train_y
self.adaboost_cls.fit(train_x, train_y)
except:
print(traceback.format_exc())
def predict(self, test_x):
try:
self.test_x = test_x
self.prediction = self.adaboost_cls.predict(test_x)
return self.prediction
except:
print(traceback.format_exc())
def accuracy_score(self, test_y):
try:
# return r2_score(test_y, self.prediction)
return self.adaboost_cls.score(self.test_x, test_y)
except:
print(traceback.format_exc())
def AB_results(): # AdaBoostClassifier
print "--------------AdaBoostClassifier-----------------"
rang = [60, 80]
# print "--------------With HOG-----------------"
# ans = []
# print "n_estimators Accuracy"
# for i in rang:
# clf = AdaBoostClassifier(n_estimators=i)
# clf.fit(X_train_hog, y_train)
# mean_accuracy = clf.score(X_test_hog, y_test)
# print i, " ", mean_accuracy
# ans.append('('+str(i)+", "+str(mean_accuracy)+')')
# print ans
# plt.plot(rang, ans, linewidth=2.0)
# plt.xlabel("n_estimators")
# plt.ylabel("mean_accuracy")
# plt.savefig("temp_hog.png")
print "\n--------------Without HOG-----------------"
ans = []
print "n_estimators Accuracy"
for i in rang:
clf = AdaBoostClassifier(n_estimators=i)
clf.fit(X_train, y_train)
mean_accuracy = clf.score(X_test, y_test)
print i, " ", mean_accuracy
ans.append('('+str(i)+", "+str(mean_accuracy)+')')
print ans
plt.plot(rang, ans, linewidth=2.0)
plt.xlabel("n_estimators")
plt.ylabel("mean_accuracy")
plt.savefig("temp_plain.png")
def boost_report(test_split_size):
scd_count = 0
for record in records:
if (record.my_class == "SCD"):
scd_count += 1
print(scd_count)
shuffle(records)
split = int(len(records) * (1 / test_split_size))
print(len(records))
train_set = records[:(len(records) - split)]
test_set = records[split:]
print("split:", test_split_size, "train:", len(train_set), "test:", split)
svm_train_features = list()
svm_train_classes = list()
svm_test_features = list()
svm_test_classes = list()
for record in train_set:
svm_train_features.append(list(record.features.values()))
svm_train_classes.append(record.my_class)
for record in test_set:
svm_test_features.append(list(record.features.values()))
svm_test_classes.append(record.my_class)
svm_classifier = svm.SVC(kernel="linear", C=0.1)
svm_classifier.fit(svm_train_features, svm_train_classes)
print("linear kernel svm accuracy: " +
str(svm_classifier.score(svm_test_features, svm_test_classes)))
classifier = AdaBoostClassifier(
base_estimator=svm_classifier,
n_estimators=50,
algorithm='SAMME'
)
classifier.fit(svm_train_features, svm_train_classes)
print("adaboost accuracy: " +
str(classifier.score(svm_test_features, svm_test_classes)))
classifier2 = AdaBoostClassifier(
n_estimators=50,
algorithm='SAMME'
)
classifier2.fit(svm_train_features, svm_train_classes)
print("adaboost2 accuracy: " +
str(classifier2.score(svm_test_features, svm_test_classes)))
def test_iris():
"""Check consistency on dataset iris."""
for alg in ['SAMME', 'SAMME.R']:
clf = AdaBoostClassifier(algorithm=alg)
clf.fit(iris.data, iris.target)
score = clf.score(iris.data, iris.target)
assert score > 0.9, "Failed with algorithm %s and score = %f" % \
(alg, score)
def run_cv_model(self, max_depth=3, criterion='entropy', learning_rate=1., n_estimators=300, do_plot=True):
# use k-fold cross validation
# Supported criteria are gini for the Gini impurity and entropy for the information gain.
tree = DecisionTreeClassifier(criterion=criterion, max_depth=max_depth, random_state=0)
clf = AdaBoostClassifier(base_estimator=tree, n_estimators=n_estimators,
learning_rate=learning_rate,
random_state=0)
# resample the test data without replacement. This means that each data point is part of a test a
# training set only once. (paraphrased from Raschka p.176). In Stratified KFold, the features are
# evenly disributed such that each test and training set is an accurate representation of the whole
# this is the 0.17 version
#kfold = StratifiedKFold(y=self.y_train, n_folds=self.cv, random_state=0)
# this is the 0.18dev version
skf = StratifiedKFold(n_folds=self.cv, random_state=0)
# do the cross validation
train_scores = []
test_scores = []
#for k, (train, test) in enumerate(kfold):
for k, (train, test) in enumerate(skf.split(X=self.x_train, y=self.y_train)):
# run the learning algorithm
clf.fit(self.x_train[train], self.y_train[train])
train_score = clf.score(self.x_train[test], self.y_train[test])
train_scores.append(train_score)
test_score = clf.score(self.x_test, self.y_test)
test_scores.append(test_score)
print('Fold:', k+1, ', Training score:', train_score, ', Test score:', test_score)
train_score = np.mean(train_scores)
print('Training score is', train_score)
test_score = np.mean(test_scores)
print('Test score is', test_score)
if do_plot:
self.__plot_learning_curve(clf)
return train_score, test_score
def model_design(run_as_main=False):
from skimage.data import imread
from skimage.filters import threshold_adaptive
from skimage.restoration import denoise_tv_bregman
from sklearn.cross_validation import train_test_split, StratifiedKFold
from sklearn.svm import SVC
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier
from sklearn.ensemble import AdaBoostClassifier, BaggingClassifier
labels = pd.read_csv('../data/trainLabels.csv', sep=',')
X, y = [], np.array(labels.Class)
for ID in labels.ID:
original = imread('../data/trainResized/' + str(ID) +'.Bmp', as_grey=True)
denoised = denoise_tv_bregman(original, 3)
binarilized = threshold_adaptive(denoised, block_size=13, method='gaussian')
feature = binarilized.reshape(1,400)[0]
X.append(feature)
X = np.array(X)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)
clf = AdaBoostClassifier(base_estimator=
ExtraTreesClassifier(
n_estimators=500,
criterion='entropy',
class_weight='auto',
n_jobs=-1
), n_estimators=50)
# clf = AdaBoostClassifier(base_estimator=
# RandomForestClassifier(
# n_estimators=500,
# criterion='entropy',
# class_weight='auto',
# n_jobs=-1
# ), n_estimators=20)
clf.fit(X_train, y_train)
print clf.score(X_test, y_test)
def AdaBoost(X, y, tst_size, n_est): X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size = tst_size, random_state = 0) clf = AdaBoostClassifier(n_estimators = n_est) score = 0 for i in range(100): clf.fit(X_train, y_train) score += clf.score(X_test, y_test) score = score/100 return score
def test_sample_weight_elm():
"""Smoke test - AdaBoostClassifier should work with ELMClassifer."""
X = Xdigits_binary[:50]
y = ydigits_binary[:50]
elm = ELMClassifier(n_hidden=20)
clf = AdaBoostClassifier(n_estimators=3, base_estimator=elm)
clf.fit(X, y)
assert_greater(clf.score(X, y), 0.9)
def performAdaBoostClass(X_train, y_train, X_test, y_test, parameters, fout, savemodel): """ Ada boosting binary classification """ clf = AdaBoostClassifier() clf.fit(X_train, y_train) accuracy = clf.score(X_test, y_test) return accuracy
def ab_classify(self): print "Adaboost" clf = AdaBoostClassifier() clf.fit(self.descr, self.target) mean = clf.score(self.test_descr, self.test_target) pred = clf.predict(self.test_descr) print "Pred ", pred print "Mean : %3f" % mean print "Feature Importances ", clf.feature_importances_
def test_staged_predict():
# Check staged predictions.
rng = np.random.RandomState(0)
iris_weights = rng.randint(10, size=iris.target.shape)
boston_weights = rng.randint(10, size=boston.target.shape)
# AdaBoost classification
for alg in ['SAMME', 'SAMME.R']:
clf = AdaBoostClassifier(algorithm=alg, n_estimators=10)
clf.fit(iris.data, iris.target, sample_weight=iris_weights)
predictions = clf.predict(iris.data)
staged_predictions = [p for p in clf.staged_predict(iris.data)]
proba = clf.predict_proba(iris.data)
staged_probas = [p for p in clf.staged_predict_proba(iris.data)]
score = clf.score(iris.data, iris.target, sample_weight=iris_weights)
staged_scores = [
s for s in clf.staged_score(
iris.data, iris.target, sample_weight=iris_weights)]
assert_equal(len(staged_predictions), 10)
assert_array_almost_equal(predictions, staged_predictions[-1])
assert_equal(len(staged_probas), 10)
assert_array_almost_equal(proba, staged_probas[-1])
assert_equal(len(staged_scores), 10)
assert_array_almost_equal(score, staged_scores[-1])
# AdaBoost regression
clf = AdaBoostRegressor(n_estimators=10, random_state=0)
clf.fit(boston.data, boston.target, sample_weight=boston_weights)
predictions = clf.predict(boston.data)
staged_predictions = [p for p in clf.staged_predict(boston.data)]
score = clf.score(boston.data, boston.target, sample_weight=boston_weights)
staged_scores = [
s for s in clf.staged_score(
boston.data, boston.target, sample_weight=boston_weights)]
assert_equal(len(staged_predictions), 10)
assert_array_almost_equal(predictions, staged_predictions[-1])
assert_equal(len(staged_scores), 10)
assert_array_almost_equal(score, staged_scores[-1])
def classify(features_train, labels_train, features_test, labels_test):
adaBoost = AdaBoostClassifier()
adaBoost.fit(features_train, labels_train)
aBScore = adaBoost.score(features_test, labels_test)
#aBScore = 0
#print("Ada Boost: ", aBScore)
#%timeit adaBoost.fit(features_train, labels_train)
adaBoostCust = AdaBoostCustom()
adaBoostCust.fit(features_train, labels_train)
aBCScore = adaBoostCust.score(features_test, labels_test)
#aBCScore = 0
#print("AdaBoost Custom: ", aBCScore)
#%timeit adaBoostCust.fit(features_train, labels_train)
decisionTree = DecisionTreeClassifier(random_state=0)
decisionTree.fit(features_train, labels_train)
dTScore = decisionTree.score(features_test, labels_test)
#dTScore = 0
#print("decision Tree: ", dTScore)
#%timeit decisionTree.fit(features_train, labels_train)
logReg = LogisticRegression()
logReg.fit(features_train, labels_train)
logRegScore = logReg.score(features_test, labels_test)
#logRegScore = 0
#print("logReg Score: ", logRegScore)
#%timeit logReg.fit(features_train, labels_train)
logRegCust = LogisticRegressionCustom()
logRegCust.fitMulticlassOneVsOne(addOnesCol(features_train), labels_train, alpha = 0.1, nrIt = 800)
logRegCustScore = logRegCust.scoreMulticlassOneVsOne(addOnesCol(features_test), labels_test)
#logRegCustScore = 0
#print("LogRegCust Score: ", logRegCustScore)
#%timeit logRegCust.fitMulticlass(features_train, labels_train)
linReg = LinearRegression()
linReg.fit(features_train, labels_train)
pred = linReg.predict(features_test)
linRegScore = scoreForLinReg(pred, labels_test)
#linRegScore = linReg.score(features_test, labels_test)
#linRegScore = 0
linRegCust = LinearLeastSquares(features_train, number_iteration=800, feature_normalizer=True)
linRegCust.fit(labels_train)
linRegCustScore = linRegCust.score(features_test, labels_test)
#linRegCustScore = 0
locWeigRegCust = LocalWeightedRegressionCustom()
locWeigRegCustScore = locWeigRegCust.score(features_train, labels_train, features_test, labels_test, 1)
#locWeigRegCustScore = 0
return aBScore, aBCScore, dTScore, logRegScore, logRegCustScore, linRegScore, linRegCustScore, locWeigRegCustScore
def performAdaBoostClass(X_train, y_train, X_test, y_test, parameters):
"""
Ada Boosting binary Classification
"""
n = parameters[0]
l = parameters[1]
clf = AdaBoostClassifier(n_estimators = n, learning_rate = l)
clf.fit(X_train, y_train)
accuracy = clf.score(X_test, y_test)
#auc = roc_auc_score(y_test, clf.predict(X_test))
return accuracy
def voting():
X, y = preprocess()
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)
clfs = []
clfs.append(RandomForestClassifier(n_estimators=500, criterion="entropy", class_weight="auto", n_jobs=-1))
clfs.append(RandomForestClassifier(n_estimators=200, criterion="entropy", class_weight="auto", n_jobs=-1))
clfs.append(RandomForestClassifier(n_estimators=500, criterion="gini", class_weight="auto", n_jobs=-1))
clfs.append(RandomForestClassifier(n_estimators=200, criterion="gini", class_weight="auto", n_jobs=-1))
clfs.append(ExtraTreesClassifier(n_estimators=500, criterion="entropy", class_weight="auto", n_jobs=-1))
clfs.append(ExtraTreesClassifier(n_estimators=200, criterion="entropy", class_weight="auto", n_jobs=-1))
clfs.append(ExtraTreesClassifier(n_estimators=500, criterion="gini", class_weight="auto", n_jobs=-1))
clfs.append(ExtraTreesClassifier(n_estimators=200, criterion="gini", class_weight="auto", n_jobs=-1))
ab = AdaBoostClassifier(
base_estimator=ExtraTreesClassifier(n_estimators=500, criterion="entropy", class_weight="auto", n_jobs=-1),
n_estimators=10,
)
ab.fit(X_train, y_train)
print ab.score(X_test, y_test)
sys.exit()
for clf in clfs:
clf.fit(X_train, y_train)
for clf in clfs:
print clf.score(X_train, y_train), clf.score(X_test, y_test)
y_pred = []
test_num = len(y_test) / 10
pbar_cnt = 0
widgets = ["Predicting...", Percentage(), " ", Bar(marker=RotatingMarker()), " ", ETA()]
pbar = ProgressBar(widgets=widgets, maxval=test_num).start()
for i in xrange(test_num):
pbar_cnt += 1
pbar.update(pbar_cnt)
prediction = [clf.predict(X_test[i])[0] for clf in clfs]
y_pred.append(most_freq_term(prediction))
pbar.finish()
print accuracy_score(y_test[:test_num], y_pred)
def run_model(self, max_depth=3, criterion='entropy', learning_rate=1., n_estimators=300, do_plot=True):
# Supported criteria for tree are gini for the Gini impurity and entropy for the information gain.
tree = DecisionTreeClassifier(criterion='entropy', max_depth=max_depth, random_state=0)
clf = AdaBoostClassifier(base_estimator=tree, n_estimators=n_estimators,
learning_rate=learning_rate,
random_state=0)
clf.fit(self.x_train, self.y_train)
# check model accuracy
train_score = clf.score(self.x_train, self.y_train)
print('Training score is', train_score)
test_score = clf.score(self.x_test, self.y_test)
print('Test score is', test_score)
if do_plot:
self.__plot_learning_curve(clf)
return train_score, test_score
def test_classifiers2(data, ind):
from sklearn.ensemble import AdaBoostClassifier
clf = AdaBoostClassifier(n_estimators=100)
clf.fit(data[ind[:1000], :-1], data[ind[:1000], -1])
print clf.score(data[ind[1000:], :-1], data[ind[1000:], -1])
out = clf.predict(data[ind[1000:], :-1])
print(confusion_matrix(data[ind[1000:], -1], out))
from sklearn.ensemble import GradientBoostingClassifier
clf = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0, max_depth=1, random_state=0)
clf.fit(data[ind[:1000], :-1], data[ind[:1000], -1])
print clf.score(data[ind[1000:], :-1], data[ind[1000:], -1])
out = clf.predict(data[ind[1000:], :-1])
print(confusion_matrix(data[ind[1000:], -1], out))
from sklearn.neural_network import MLPClassifier
clf = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(10, 10), random_state=1)
clf.fit(data[ind[:1000], :-1], data[ind[:1000], -1])
print clf.score(data[ind[1000:], :-1], data[ind[1000:], -1])
out = clf.predict(data[ind[1000:], :-1])
print(confusion_matrix(data[ind[1000:], -1], out))
import xgboost as xgb
xgb_model = xgb.XGBClassifier().fit(data[ind[:1000], :-1], data[ind[:1000], -1])
out = xgb_model.predict(data[ind[1000:], :-1])
a = confusion_matrix(data[ind[1000:], -1], out)
print float(a[0, 0] + a[1, 1]) / np.sum(a)
print a
def boosting(train_x, train_y, test_x, test_y, n_estimators, iterations):
name = (
"Results/adaboost_"
+ str(n_estimators)
+ "_results"
+ datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
+ ".csv"
)
file = open(name, "w")
file.write(
"AdaBoost w/ n_estimators = "
+ str(n_estimators)
+ " Analysis Started on "
+ datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
)
file.write("Iteration, Instances, Train Time, Test Time, Training Accuracy, Testing Accuracy")
logging.info("Starting Boosting Analysis")
outer_time = datetime.datetime.now()
boost = AdaBoostClassifier(tree.DecisionTreeClassifier(max_depth=19), n_estimators=n_estimators)
for i in range(iterations):
sample_size = int(random.uniform(0.001, 1.0) * train_y.shape[0])
index = random.sample(xrange(0, train_y.shape[0]), sample_size)
start = datetime.datetime.now()
boost.fit(train_x[index], train_y[index])
end = datetime.datetime.now()
train_time = end - start
train_score = boost.score(train_x, train_y)
start = datetime.datetime.now()
test_score = boost.score(test_x, test_y)
test_time = datetime.datetime.now() - start
file.write(
"%4d, %4d, %s, %s, %2.6f, %2.6f \n" % (i, len(index), train_time, test_time, train_score, test_score)
)
logging.info("Analysis completed in %s" % (datetime.datetime.now() - outer_time))
file.close()
bagging_clf3.fit(X, y)
# print(bagging_clf3.oob_score_)# 0.856
from sklearn.ensemble import RandomForestClassifier
tf_clf = RandomForestClassifier(n_estimators=500,
max_leaf_nodes=16,
oob_score=True,
random_state=666,
n_jobs=-1)
tf_clf.fit(X, y)
# print('tf',tf_clf.oob_score_) #0.92
from sklearn.ensemble import ExtraTreesClassifier
et_clf = ExtraTreesClassifier(n_estimators=500,
bootstrap=True,
oob_score=True,
random_state=666,
n_jobs=-1)
et_clf.fit(X, y)
# print('et',et_clf.oob_score_) #0.892
from sklearn.ensemble import AdaBoostClassifier
ada_clf = AdaBoostClassifier(DecisionTreeClassifier(max_depth=2),
n_estimators=500)
ada_clf.fit(X_train, y_train)
print(ada_clf.score(X_test, y_test)) # 0.832
from sklearn.ensemble import GradientBoostingClassifier
gb_clf = GradientBoostingClassifier(max_depth=2, n_estimators=30)
gb_clf.fit(X_train, y_train)
print(gb_clf.score(X_test, y_test)) # 0.912
training_classes = input_data.loc[training_indices, 'class'].values
testing_features = input_data.loc[testing_indices].drop('class',
axis=1).values
testing_classes = input_data.loc[testing_indices, 'class'].values
ss = StandardScaler()
training_features = ss.fit_transform(training_features.astype(float))
testing_features = ss.transform(testing_features.astype(float))
# Create and fit the model on the training data
try:
clf = AdaBoostClassifier(learning_rate=learning_rate,
n_estimators=n_estimators)
clf.fit(training_features, training_classes)
testing_score = clf.score(testing_features, testing_classes)
except KeyboardInterrupt:
sys.exit(1)
except:
continue
param_string = ''
param_string += 'learning_rate={},'.format(learning_rate)
param_string += 'n_estimators={}'.format(n_estimators)
out_text = '\t'.join([
dataset.split('/')[-1][:-7], 'AdaBoostClassifier', param_string,
str(testing_score)
])
print(out_text)
if data < 10000.0:
data = 1
elif data < 50000.0:
data = 2
elif data < 250000.0:
data = 3
elif data < 1000000.0:
data = 4
else:
data = 5
testTarget[x] = data
x += 1
viewPredict = AdaBoostClassifier(n_estimators=400, learning_rate=.7)
viewPredict.fit(trainData, trainTarget)
prediction = viewPredict.predict(testData)
sampleTest = []
print(viewPredict.score(testData, testTarget))
disp = plot_confusion_matrix(viewPredict,
testData,
testTarget,
normalize='true')
print("Confusion Matrix")
print(disp.confusion_matrix)
dump(viewPredict, 'Prediction.joblib')
estimators = bags.estimators_
tree.export_graphviz(estimators[0], out_file='tree_balanced_1.dot')
tree.export_graphviz(estimators[9], out_file='tree_balanced_10.dot')
## se puede visualizar copiando y pegando el contenido del archivo .dot en http://www.webgraphviz.com/
## predicciones y probabilidades en la muestra de entrenamiento
pred_train = bags.predict(X_train) #predicción
probs_train = bags.predict_proba(X_train) #probabilidad
#calculemos el ajuste accuracy del modelo en train y en test
probs2 = bags.predict_proba(X_test)
#una vez que ya tengo las predicciones puedo calcular matriz de confusión, accuracy, recall, precision, auc, etc etc
score_train = bags.score(X_train, y_train)
score_testing = bags.score(X_test, y_test)
#para calcular otras métricas precision, recall, etc necesito también predecir en testing
pred_testing = bags.predict(X_test) #predigo en base testing
#calculo recall, precision, auc, fpr, tpr, auc, f1 etc en ambas bases
fpr_train, tpr_train, thresholds_train = metrics.roc_curve(y_train,
pred_train,
pos_label=1)
fpr_test, tpr_test, thresholds_test = metrics.roc_curve(y_test,
pred_testing,
pos_label=1)
auc_train = metrics.auc(fpr_train, tpr_train)
aggClassEst = np.zeros([m, 1])
for i in range(len(self.weakclass)):
classEst = self.stumpClassify(X,self.weakclass[i]['dim'],\
self.weakclass[i]['thresh'],\
self.weakclass[i]['ineq'])
aggClassEst += self.weakclass[i]['alpha'] * classEst
return np.sign(aggClassEst)
def score(self, X_test, y_test):
y_pred = self.predict(X_test)
m = X.shape[0]
y_test = y_test.reshape(-1, 1)
count = np.ones([m, 1])
count[y_pred != y_test] = 0
accuracy = np.sum(count) / m
return accuracy
#%%
X = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]).reshape(-1, 1)
y = np.array([1, 1, 1, -1, -1, -1, 1, 1, 1, -1])
#%%
clf1 = AdaBoost(30, 1)
clf1.fit(X, y)
accuracy1 = clf1.score(X, y)
#%%
from sklearn.ensemble import AdaBoostClassifier
clf2 = AdaBoostClassifier(n_estimators=100, random_state=0)
clf2.fit(X, y)
accuracy2 = clf2.score(X, y)
y = train["class"]
clh = tree.DecisionTreeClassifier(max_depth=7)
clf = AdaBoostClassifier(base_estimator=clh, n_estimators=10)
clf.fit(x, y)
print(np.mean(cross_val_score(clf, x, y, cv=8)))
print(confusion_matrix(y, clf.predict(x)))
#test_x = test[["meanfreq","sd","median","Q25","Q75","IQR","skew","kurt","sp.ent","sfm","mode",
# "centroid","peakf","meanfun","minfun",
# "maxfun","meandom","mindom","maxdom","dfrange","modindx"]]
test_x = test[[
"meanfreq", "sd", "freq.median", "freq.Q25", "freq.Q75", "freq.IQR",
"skew", "kurt", "sp.ent", "sfm", "meanfun", "minfun", "maxfun", "meandom",
"mindom", "maxdom", "dfrange", "modindx", "dfslope", "meanpeakf"
]]
print(clf.score(test_x, test["class"]))
#---------------------------------------------------------------------------#
svcfit = SVC(C=0.01, kernel='linear')
x = preprocessing.scale(x)
svcfit.fit(x, y)
print(np.mean(cross_val_score(svcfit, x, y, cv=8)))
print(confusion_matrix(y, svcfit.predict(x)))
test_x = preprocessing.scale(test_x)
print(svcfit.score(test_x, test["class"]))
#---------------------------------------------------------------------------#
X_train, X_test = X[:n_split], X[n_split:]
y_train, y_test = y[:n_split], y[n_split:]
bdt_real = AdaBoostClassifier(DecisionTreeClassifier(max_depth=2),
n_estimators=600,
learning_rate=1)
bdt_discrete = AdaBoostClassifier(DecisionTreeClassifier(max_depth=2),
n_estimators=600,
learning_rate=1.5,
algorithm="SAMME")
bdt_real.fit(X_train, y_train)
bdt_discrete.fit(X_train, y_train)
print bdt_real.score(X_test, y_test)
real_test_errors = []
discrete_test_errors = []
for real_test_predict, discrete_train_predict in zip(
bdt_real.staged_predict(X_test), bdt_discrete.staged_predict(X_test)):
real_test_errors.append(1. - accuracy_score(real_test_predict, y_test))
discrete_test_errors.append(1. -
accuracy_score(discrete_train_predict, y_test))
n_trees_discrete = len(bdt_discrete)
n_trees_real = len(bdt_real)
# Boosting might terminate early, but the following arrays are always
# n_estimators long. We crop them to the actual number of trees here:
# df_train = pd.read_csv("data/training_set_VU_DM.csv")
df_test = pd.read_csv("data/test_short.csv")
data, df_test = prep_data(df_train, df_test)
predictors = [
c for c in data.columns if c not in
["booking_bool", "click_bool", "gross_bookings_usd", "position"]
]
X = data[predictors]
y = data.booking_bool.astype(int)
clf = AdaBoostClassifier(n_estimators=100)
training = clf.fit(X, y)
score = clf.score(X, y)
print(score, "score")
scores = cross_val_score(clf, X, y, cv=5)
print("mean score: ", scores.mean())
print("ada scores:")
print(scores)
prediction_test_set = clf.predict(df_test)
predictions = pd.DataFrame({
'hotel_id': df_test.prop_id,
'search_id': df_test.srch_id,
'booking_prob': prediction_test_set
})
predictions.to_csv('wattt.csv', index=False)
clf = RandomForestClassifier(n_estimators=10, min_weight_fraction_leaf=0.1)
pickle.dump(training_labels, f)
if not os.path.isfile(DataFile_test):
with open(DataFile_test, "wb") as f:
pickle.dump(test_data, f)
if not os.path.isfile(LabelFile_test):
with open(LabelFile_test, "wb") as f:
pickle.dump(test_labels, f)
else:
if os.path.isfile(DataFile) and os.path.isfile(LabelFile):
with open(DataFile, "rb") as f:
training_data = pickle.load(f)
with open(LabelFile, "rb") as f:
training_labels = pickle.load(f)
with open(DataFile_test, "rb") as f:
test_data = pickle.load(f)
with open(LabelFile_test, "rb") as f:
test_labels = pickle.load(f)
X_train, X_val, y_train, y_val = model_selection.train_test_split(training_data, training_labels, train_size=.99, test_size=.01)
print("training!")
#boosting = GradientBoostingClassifier(n_estimators=250, loss='exponential', learning_rate=0.2)
#boosting = AdaBoostClassifier(RandomForestClassifier(n_estimators=600, max_depth=15, n_jobs=7), n_estimators=25, learning_rate=.1)
boosting = AdaBoostClassifier(SVC(C=50, gamma=2), n_estimators=25, learning_rate=.1)
boosting.fit(X_train, y_train[:,1])
print("Classifier has a score of %0.4f"
% (boosting.score(test_data, test_labels[:,1])))
def main():
df = pd.read_csv("Dataset/winequality-white.csv", delimiter=";")
n, bins, patches = plt.hist(x=np.array(df.iloc[:, -1]),
bins='auto',
color='#0504aa',
alpha=0.7)
plt.grid(axis='y', alpha=0.75)
plt.xlabel('Quality')
plt.ylabel('Count')
plt.xticks(np.arange(10),
('0', '1', '2', '3', '4', '5', '6', '7', '8', '9'))
plt.title('Class Distribution')
plt.ylim(ymax=2200)
plt.savefig('WinePlots/wineClassDistributionOriginal.png')
plt.close()
lowquality = df.loc[df['quality'] <= 6].index
highquality = df.loc[df['quality'] > 6].index
df.iloc[lowquality, df.columns.get_loc('quality')] = 0
df.iloc[highquality, df.columns.get_loc('quality')] = 1
seed = 200
np.random.seed(seed)
X = np.array(df.iloc[:, 0:-1])
Y = np.array(df.iloc[:, -1])
n, bins, patches = plt.hist(x=Y, bins='auto', color='#0504aa', alpha=0.7)
plt.grid(axis='y', alpha=0.75)
plt.xlabel('Quality')
plt.ylabel('Count')
plt.xticks(np.arange(2), ('Low', 'High'))
plt.title('Class Distribution')
plt.ylim(ymax=4000)
plt.savefig('WinePlots/wineClassDistribution.png')
plt.close()
training_x1, testing_x1, training_y, testing_y = train_test_split(
X, Y, test_size=0.3, random_state=seed, shuffle=True, stratify=Y)
standardScalerX = StandardScaler()
training_x = standardScalerX.fit_transform(training_x1)
testing_x = standardScalerX.fit_transform(testing_x1)
# DT Max Depth Gini
max_depth_array = []
training_depth_array = []
testing_depth_array = []
cross_val_score_array = []
print('DT Max Depth Gini')
for i in range(1, 50):
max_depth_array.append(i)
learner = DecisionTreeClassifier(criterion='gini',
max_depth=i + 1,
random_state=seed)
cross_val_score_array.append(
cross_val_score(learner, training_x, training_y, cv=10).mean())
learner.fit(training_x, training_y)
training_depth_array.append(learner.score(training_x, training_y))
testing_depth_array.append(learner.score(testing_x, testing_y))
plot_validation_curve(max_depth_array, training_depth_array,
cross_val_score_array,
"Cross Validation Score vs Max Depth Gini", 'Score',
'Max Depth', [1, 50],
'WinePlots/winemaxdepthGini.png')
# DT Max Depth Entropy
max_depth_array = []
training_depth_array = []
testing_depth_array = []
cross_val_score_array = []
print('DT Max Depth Entropy')
for i in range(1, 50):
max_depth_array.append(i)
learner = DecisionTreeClassifier(criterion='entropy',
max_depth=i + 1,
random_state=seed)
cross_val_score_array.append(
cross_val_score(learner, training_x, training_y, cv=10).mean())
learner.fit(training_x, training_y)
training_depth_array.append(learner.score(training_x, training_y))
testing_depth_array.append(learner.score(testing_x, testing_y))
plot_validation_curve(max_depth_array, training_depth_array,
cross_val_score_array,
"Cross Validation Score vs Max Depth Entropy",
'Score', 'Max Depth', [1, 50],
'WinePlots/winemaxdepthEntropy.png')
# DT Random Search & Learning Curve
max_depths = np.arange(1, 20, 1)
params = {'criterion': ['gini', 'entropy'], 'max_depth': max_depths}
learner = DecisionTreeClassifier(random_state=seed)
start = time.clock()
dt_cv = RandomizedSearchCV(learner,
n_jobs=1,
param_distributions=params,
refit=True,
n_iter=40)
dt_cv.fit(training_x, training_y)
print(dt_cv.best_params_)
dt_train_time = time.clock() - start
print('Time to Train: ' + str(dt_train_time))
print('Training Accuracy: ' + str(dt_cv.score(training_x, training_y)))
print('Testing Accuracy: ' + str(dt_cv.score(testing_x, testing_y)))
print(dt_cv.best_params_) # entropy, max depth 11
start = time.clock()
test_y_predicted = dt_cv.predict(testing_x)
dt_query_time = time.clock() - start
print('Time to Query: ' + str(dt_query_time))
y_true = pd.Series(testing_y)
y_pred = pd.Series(test_y_predicted)
print(
pd.crosstab(y_true,
y_pred,
rownames=['True'],
colnames=['Predicted'],
margins=True))
train_sizes, train_scores, test_scores = learning_curve(
dt_cv,
training_x,
training_y,
n_jobs=-1,
cv=10,
train_sizes=np.linspace(.1, 1.0, 10),
random_state=seed)
plot_learning_curve(train_scores, test_scores, train_sizes,
'WinePlots/wineDTLearningCurve.png',
"Learning Curve DT")
# Adaboost Max Depth
max_depth_array = []
training_depth_array = []
testing_depth_array = []
cross_val_score_array = []
print('Adaboost Max Depth')
for i in range(1, 20, 2):
max_depth_array.append(i)
learner2 = DecisionTreeClassifier(max_depth=i,
criterion='gini',
random_state=seed)
boosted_learner2 = AdaBoostClassifier(base_estimator=learner2,
random_state=seed,
algorithm='SAMME')
cross_val_score_array.append(
cross_val_score(boosted_learner2, training_x, training_y,
cv=10).mean())
boosted_learner2.fit(training_x, training_y)
training_depth_array.append(
boosted_learner2.score(training_x, training_y))
plot_validation_curve(
max_depth_array, training_depth_array, cross_val_score_array,
"Cross Validation Score vs Max Depth of Base Estimator", 'Score',
'Max Depth', [1, 20], 'WinePlots/wineboostedmaxdepth.png')
# Adaboost Estimators
estimator_array = []
training_depth_array = []
testing_depth_array = []
cross_val_score_array = []
n_estimators = range(1, 55, 5)
print('Adaboost Estimators')
for i in n_estimators:
estimator_array.append(i)
boosted_learner2 = AdaBoostClassifier(
base_estimator=DecisionTreeClassifier(criterion='gini',
max_depth=1),
algorithm='SAMME',
random_state=seed,
n_estimators=i)
cross_val_score_array.append(
cross_val_score(boosted_learner2, training_x, training_y,
cv=10).mean())
boosted_learner2.fit(training_x, training_y)
training_depth_array.append(
boosted_learner2.score(training_x, training_y))
plot_validation_curve(estimator_array, training_depth_array,
cross_val_score_array,
"Cross Validation Score vs Number of Estimators",
'Score', 'Number of Estimators', [1, 50],
'WinePlots/wineboostedestimators.png')
# Adaboost Learning Rate
learning_rate_array = []
training_depth_array = []
testing_depth_array = []
cross_val_score_array = []
learning_rates = [0.01, 0.02, 0.04, 0.08, 0.16, 0.32, 0.64, 1, 1.5, 2]
print('Adaboost Learning Rate')
for i in learning_rates:
learning_rate_array.append(i)
boosted_learner2 = AdaBoostClassifier(
base_estimator=DecisionTreeClassifier(criterion='gini',
max_depth=1),
algorithm='SAMME',
random_state=seed,
learning_rate=i)
cross_val_score_array.append(
cross_val_score(boosted_learner2, training_x, training_y,
cv=10).mean())
boosted_learner2.fit(training_x, training_y)
training_depth_array.append(
boosted_learner2.score(training_x, training_y))
plot_validation_curve(learning_rate_array, training_depth_array,
cross_val_score_array,
"Cross Validation Score vs Learning Rates", 'Score',
'Learning Rate', [0, 2],
'WinePlots/wineboostedLearningRate.png')
# Adaboost Random Search & Learning Curve
max_depths = np.arange(1, 20, 1)
params = {
'n_estimators': [10, 15, 20, 25, 30, 40, 50],
'learning_rate': [0.01, 0.02, 0.04, 0.08, 0.16, 0.32, 0.64, 1],
'base_estimator__max_depth': max_depths
}
learner = AdaBoostClassifier(
base_estimator=DecisionTreeClassifier(criterion='gini'),
random_state=seed)
print('starting grid search')
boost_cv = RandomizedSearchCV(learner,
n_jobs=1,
param_distributions=params,
refit=True,
n_iter=50)
start = time.clock()
boost_cv.fit(training_x, training_y)
dt_train_time = time.clock() - start
print('Time to Train: ' + str(dt_train_time))
print('Training Accuracy: ' + str(boost_cv.score(training_x, training_y)))
print('Testing Accuracy: ' + str(boost_cv.score(testing_x, testing_y)))
print(boost_cv.best_params_)
start = time.clock()
test_y_predicted = boost_cv.predict(testing_x)
dt_query_time = time.clock() - start
print('Time to Query: ' + str(dt_query_time))
y_true = pd.Series(testing_y)
y_pred = pd.Series(test_y_predicted)
print(
pd.crosstab(y_true,
y_pred,
rownames=['True'],
colnames=['Predicted'],
margins=True))
train_sizes, train_scores, test_scores = learning_curve(
boost_cv,
training_x,
training_y,
n_jobs=-1,
cv=10,
train_sizes=np.linspace(.1, 1.0, 10),
random_state=seed)
plot_learning_curve(train_scores, test_scores, train_sizes,
'WinePlots/wineboostedLearningCurve.png')
# KNN Number of Neighbors
knn_array = []
training_depth_array = []
testing_depth_array = []
cross_val_score_array = []
print('KNN Number of Neighbors with Manhattan Distance')
for i in range(1, 50, 2):
knn_array.append(i)
learner = KNeighborsClassifier(n_neighbors=i, p=1)
cross_val_score_array.append(
cross_val_score(learner, training_x, training_y, cv=10).mean())
learner.fit(training_x, training_y)
training_depth_array.append(learner.score(training_x, training_y))
plot_validation_curve(knn_array, training_depth_array,
cross_val_score_array,
"Cross Validation Score vs k Neighbors Manhattan",
'Score', 'k Neighbors', [1, 50],
'WinePlots/wineManhattanKNN.png')
knn_array = []
training_depth_array = []
testing_depth_array = []
cross_val_score_array = []
print('KNN Number of Neighbors with Euclidean Distance')
for i in range(1, 50, 2):
knn_array.append(i)
learner = KNeighborsClassifier(n_neighbors=i, p=2)
cross_val_score_array.append(
cross_val_score(learner, training_x, training_y, cv=10).mean())
learner.fit(training_x, training_y)
training_depth_array.append(learner.score(training_x, training_y))
plot_validation_curve(knn_array, training_depth_array,
cross_val_score_array,
"Cross Validation Score vs k Neighbors Euclidean",
'Score', 'k Neighbors', [1, 50],
'WinePlots/wineEuclideanKNN.png')
params = {'p': [1, 2], 'n_neighbors': np.arange(2, 50, 1)}
learner = KNeighborsClassifier()
print('starting random search')
knn_cv = RandomizedSearchCV(learner,
n_jobs=1,
param_distributions=params,
refit=True,
n_iter=100)
start = time.clock()
knn_cv.fit(training_x, training_y)
dt_train_time = time.clock() - start
print('Time to Train: ' + str(dt_train_time))
print('Training Accuracy: ' + str(knn_cv.score(training_x, training_y)))
print('Testing Accuracy: ' + str(knn_cv.score(testing_x, testing_y)))
print(knn_cv.best_params_)
start = time.clock()
test_y_predicted = knn_cv.predict(testing_x)
dt_query_time = time.clock() - start
print('Time to Query: ' + str(dt_query_time))
y_true = pd.Series(testing_y)
y_pred = pd.Series(test_y_predicted)
print(
pd.crosstab(y_true,
y_pred,
rownames=['True'],
colnames=['Predicted'],
margins=True))
train_sizes, train_scores, test_scores = learning_curve(
knn_cv,
training_x,
training_y,
n_jobs=-1,
cv=10,
train_sizes=np.linspace(.1, 1.0, 10),
random_state=seed)
plot_learning_curve(train_scores, test_scores, train_sizes,
'WinePlots/wineKNNLearningCurve.png')
# ANN 1 Layer with different number of neurons
ann_array = []
training_depth_array = []
testing_depth_array = []
cross_val_score_array = []
print('ANN Number of Neurons')
for i in [
1, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 70, 80, 90, 100
]:
print('------hey we are on ' + str(i))
ann_array.append(i)
learner = MLPClassifier(hidden_layer_sizes=([i]))
cross_val_score_array.append(
cross_val_score(learner, training_x, training_y, cv=10).mean())
learner.fit(training_x, training_y)
training_depth_array.append(learner.score(training_x, training_y))
plot_validation_curve(
ann_array, training_depth_array, cross_val_score_array,
"Cross Validation Score vs Neurons in One Hidden Layer", 'Score',
'Number of Neurons', [1, 100], 'WinePlots/wineANNNeurons.png')
# ANN Neurons per Layers
ann_array = []
training_depth_array = []
testing_depth_array = []
cross_val_score_array = []
print('ANN Number of Layers')
for i in [1, 3, 5, 8, 10, 11, 13, 15, 17, 20, 23, 25]:
print('------hey we are on ' + str(i))
hidden_layers = []
for x in range(i):
hidden_layers.append(22)
ann_array.append(i)
learner = MLPClassifier(hidden_layer_sizes=(hidden_layers),
activation='relu',
alpha=0.0051)
cross_val_score_array.append(
cross_val_score(learner, training_x, training_y, cv=10).mean())
learner.fit(training_x, training_y)
training_depth_array.append(learner.score(training_x, training_y))
plot_validation_curve(ann_array, training_depth_array,
cross_val_score_array,
"Cross Validation Score vs # of Hidden Layers",
'Score', 'Number of Hidden Layers', [1, 25],
'WinePlots/wineANNLayers.png')
# ANN Learning Curve
params = {
'hidden_layer_sizes': [(11, 11), (5, 5), (11, ), (5, ),
(22, ), (22, 22), (5, 5, 5), (11, 11, 11),
(22, 22, 22)],
'alpha':
np.arange(0.0001, 0.01, 0.005),
'activation': ['relu', 'logistic']
}
learner = MLPClassifier(max_iter=500, random_state=seed)
##### best params {'hidden_layer_sizes': (11,11), 'alpha': 0.0001, 'activation': 'relu'}
# ann_cv = MLPClassifier(max_iter=3000,hidden_layer_sizes=(22,22,22), alpha=0.0051, activation='relu', random_state=seed)
print('starting random search')
ann_cv = RandomizedSearchCV(learner,
n_jobs=1,
param_distributions=params,
refit=True,
n_iter=20,
verbose=1000)
ann_cv.fit(training_x, training_y)
print(ann_cv.best_params_)
final_ann = MLPClassifier(**ann_cv.best_params_)
start = time.clock()
final_ann.fit(training_x, training_y)
dt_train_time = time.clock() - start
# print('refit time: ' + str(final_ann.refit_time_))
# print(final_ann.best_params_)
print('Time to Train: ' + str(dt_train_time))
print('Training Accuracy: ' + str(final_ann.score(training_x, training_y)))
print('Testing Accuracy: ' + str(final_ann.score(testing_x, testing_y)))
# print(final_ann.best_params_)
start = time.clock()
test_y_predicted = final_ann.predict(testing_x)
dt_query_time = time.clock() - start
print('Time to Query: ' + str(dt_query_time))
y_true = pd.Series(testing_y)
y_pred = pd.Series(test_y_predicted)
print(
pd.crosstab(y_true,
y_pred,
rownames=['True'],
colnames=['Predicted'],
margins=True))
train_sizes, train_scores, test_scores = learning_curve(
final_ann,
training_x,
training_y,
n_jobs=-1,
cv=5,
train_sizes=np.linspace(.1, 1.0, 10),
random_state=seed)
plot_learning_curve(train_scores, test_scores, train_sizes,
'WinePlots/wineANNLearningCurve.png')
# ANN over epochs
ann_array = []
training_depth_array = []
cross_val_score_array = []
testing_depth_array = []
learner = MLPClassifier(hidden_layer_sizes=(22, ),
alpha=0.0051,
activation='relu',
max_iter=1,
random_state=seed,
verbose=10,
warm_start=True)
for i in np.arange(3000):
ann_array.append(i)
learner = learner.fit(training_x, training_y)
score = learner.score(training_x, training_y)
print(score)
training_depth_array.append(score)
cross_score = learner.score(testing_x, testing_y)
cross_val_score_array.append(cross_score)
print(cross_score)
plot_validation_curve(ann_array, training_depth_array,
cross_val_score_array,
"Cross Validation Score vs. Epochs", 'Score',
'Epochs', [0, 3000], 'WinePlots/wineANNEpochs.png')
# SVM Kernels Sigmoid vs RBF
svm_array = []
training_depth_array = []
testing_depth_array = []
cross_val_score_array = []
print('SVM Kernels Sigmoid Different Gamma Values')
for i in np.arange(0.01, 2, 0.1):
print('------hey we are on ' + str(i))
svm_array.append(i)
learner = svm.SVC(kernel='sigmoid', gamma=i)
cross_val_score_array.append(
cross_val_score(learner, training_x, training_y, cv=10).mean())
learner.fit(training_x, training_y)
training_depth_array.append(learner.score(training_x, training_y))
plt.plot(svm_array, training_depth_array, label='Training')
plt.plot(svm_array, cross_val_score_array, label='Cross Validation')
plt.legend(loc=4, fontsize=8)
plt.title("Cross Validation Score vs. Gamma Values - Sigmoid Kernel")
plt.ylabel('Score')
plt.xlabel('Gamma Values')
plt.xlim([0.00, 2.0])
plt.savefig('WinePlots/wineGammaSigmoid.png')
plt.close()
svm_array = []
training_depth_array = []
testing_depth_array = []
cross_val_score_array = []
print('SVM Kernels RBF Different Gamma Values')
for i in np.arange(0.01, 2, 0.1):
print('------hey we are on ' + str(i))
svm_array.append(i)
learner = svm.SVC(kernel='rbf', gamma=i)
cross_score = cross_val_score(learner, training_x, training_y,
cv=10).mean()
print(cross_score)
cross_val_score_array.append(cross_score)
learner.fit(training_x, training_y)
training_depth_array.append(learner.score(training_x, training_y))
plt.plot(svm_array, training_depth_array, label='Training')
plt.plot(svm_array, cross_val_score_array, label='Cross Validation')
plt.legend(loc=4, fontsize=8)
plt.title("Cross Validation Score vs. Gamma Values - RBF Kernel")
plt.ylabel('Score')
plt.xlabel('Gamma Values')
plt.xlim([0.00, 2.0])
plt.savefig('WinePlots/wineGammaRBF.png')
plt.close()
# SVM C Values
svm_array = []
training_depth_array = []
testing_depth_array = []
cross_val_score_array = []
print('SVM Kernels Sigmoid Different C Values')
for i in np.arange(0.01, 2, 0.1):
print('------hey we are on ' + str(i))
svm_array.append(i)
learner = svm.SVC(kernel='sigmoid', C=i)
cross_val_score_array.append(
cross_val_score(learner, training_x, training_y, cv=10).mean())
learner.fit(training_x, training_y)
training_depth_array.append(learner.score(training_x, training_y))
plot_validation_curve(
svm_array, training_depth_array, cross_val_score_array,
"Cross Validation Score vs. C Values - Sigmoid Kernel", 'Score',
'C Values', [0.00, 2.0], 'WinePlots/wineCSigmoid.png')
svm_array = []
training_depth_array = []
testing_depth_array = []
cross_val_score_array = []
print('SVM Kernels RBF Different C Values')
for i in np.arange(0.01, 2, 0.1):
print('------hey we are on ' + str(i))
svm_array.append(i)
learner = svm.SVC(kernel='rbf', C=i)
cross_val_score_array.append(
cross_val_score(learner, training_x, training_y, cv=10).mean())
learner.fit(training_x, training_y)
training_depth_array.append(learner.score(training_x, training_y))
plot_validation_curve(svm_array, training_depth_array,
cross_val_score_array,
"Cross Validation Score vs. C Values - RBF Kernel",
'Score', 'C Values', [0.00, 2.0],
'WinePlots/wineCRBF.png')
#Learning Curve Sigmoid
params = {'gamma': np.arange(0.01, 2, 0.1), 'C': np.arange(0.01, 1, 0.1)}
learner = svm.SVC(kernel='sigmoid')
print('starting grid search')
svc_cv = RandomizedSearchCV(learner,
n_jobs=1,
param_distributions=params,
refit=True,
n_iter=50)
svc_cv.fit(training_x, training_y)
best_params = svc_cv.best_params_ #{'gamma': 0.51, 'C': 0.01}
print(best_params)
final_svc = svm.SVC(kernel='sigmoid', **best_params)
final_svc.fit(training_x, training_y)
print(final_svc.score(testing_x, testing_y))
print(final_svc.score(training_x, training_y))
test_y_predicted = final_svc.predict(testing_x)
y_true = pd.Series(testing_y)
y_pred = pd.Series(test_y_predicted)
print(
pd.crosstab(y_true,
y_pred,
rownames=['True'],
colnames=['Predicted'],
margins=True))
train_sizes, train_scores, test_scores = learning_curve(
final_svc,
training_x,
training_y,
n_jobs=-1,
cv=10,
train_sizes=np.linspace(.1, 1.0, 10),
random_state=seed)
plot_learning_curve(train_scores, test_scores, train_sizes,
'WinePlots/wineSVCLearningCurveSigmoid.png')
# Learning Curve RBF
params = {'gamma': np.arange(0.01, 2, 0.1), 'C': np.arange(0.01, 1, 0.1)}
learner = svm.SVC(kernel='rbf')
print('starting grid search')
svc_cv = RandomizedSearchCV(learner,
n_jobs=1,
param_distributions=params,
refit=True,
n_iter=50)
svc_cv.fit(training_x, training_y)
best_params = svc_cv.best_params_ #{'gamma': 1.31, 'C': 0.91}
print(best_params)
final_svc = svm.SVC(kernel='rbf', **best_params)
final_svc.fit(training_x, training_y)
print(final_svc.score(testing_x, testing_y))
print(final_svc.score(training_x, training_y))
test_y_predicted = final_svc.predict(testing_x)
y_true = pd.Series(testing_y)
y_pred = pd.Series(test_y_predicted)
print(
pd.crosstab(y_true,
y_pred,
rownames=['True'],
colnames=['Predicted'],
margins=True))
train_sizes, train_scores, test_scores = learning_curve(
final_svc,
training_x,
training_y,
n_jobs=-1,
cv=10,
train_sizes=np.linspace(.1, 1.0, 10),
random_state=seed)
plot_learning_curve(train_scores, test_scores, train_sizes,
'WinePlots/wineSVCLearningCurveRBF.png')
# SVM over Epochs Sigmoid
svm_array = []
training_depth_array = []
testing_depth_array = []
cross_val_score_array = []
print('SVM Different Epochs Sigmoid')
for i in np.arange(1000):
svm_array.append(i)
learner = svm.SVC(kernel='sigmoid', verbose=100, max_iter=i)
learner = learner.fit(training_x, training_y)
score = learner.score(training_x, training_y)
print(score)
training_depth_array.append(score)
cross_score = learner.score(testing_x, testing_y)
cross_val_score_array.append(cross_score)
plot_validation_curve(svm_array, training_depth_array,
cross_val_score_array,
"Cross Validation Score vs. Epochs", 'Score',
'Epochs', [0, 1000],
'WinePlots/wineSVMEpochsSigmoid.png')
# SVM over Epochs RBF
svm_array = []
training_depth_array = []
cross_val_score_array = []
print('SVM Different Epochs RBF')
for i in np.arange(1000):
svm_array.append(i)
learner = svm.SVC(kernel='rbf', verbose=100, max_iter=i)
learner = learner.fit(training_x, training_y)
score = learner.score(training_x, training_y)
print(score)
training_depth_array.append(score)
cross_score = learner.score(testing_x, testing_y)
cross_val_score_array.append(cross_score)
plot_validation_curve(svm_array, training_depth_array,
cross_val_score_array,
"Cross Validation Score vs. Epochs", 'Score',
'Epochs', [0, 1000],
'WinePlots/wineSVMEpochsRBF.png')
# Timing Wine
# Training Time
dt_clf = DecisionTreeClassifier(max_depth=19, criterion='gini')
ada_clf = AdaBoostClassifier(
base_estimator=DecisionTreeClassifier(max_depth=11),
n_estimators=40,
learning_rate=1)
knn_clf = KNeighborsClassifier(p=1, n_neighbors=2)
ann_clf = MLPClassifier(hidden_layer_sizes=(22, ),
alpha=0.0051,
activation='relu')
svm_rbf_clf = svm.SVC(kernel='rbf', gamma=1.31, C=0.91)
svm_sigmoid_clf = svm.SVC(kernel='sigmoid', gamma=0.51, C=0.01)
labels = [
"Decision Tree", "Adaboost", "KNN", "ANN", "SVM_RBF", "SVM_Sigmoid"
]
count = 0
for clf in [
dt_clf, ada_clf, knn_clf, ann_clf, svm_rbf_clf, svm_sigmoid_clf
]:
iteration_array = []
train_array = []
query_array = []
for i in [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]:
if count == 3:
clf = MLPClassifier(hidden_layer_sizes=(22, ),
alpha=0.0051,
activation='relu')
if count == 4:
clf = svm.SVC(kernel='rbf', gamma=1.31, C=0.91)
if count == 5:
clf = svm.SVC(kernel='sigmoid', gamma=0.51, C=0.01)
X_train = training_x[:int(training_x.shape[0] * i), :]
Y_train = training_y[:int(training_y.shape[0] * i)]
iteration_array.append(X_train.shape[0])
st = time.clock()
clf.fit(X_train, Y_train)
train_time = time.clock() - st
train_array.append(train_time)
plt.plot(iteration_array, train_array, label=labels[count])
plt.legend(loc=4, fontsize=8)
plt.title("Training Times for Learners", fontdict={'size': 16})
plt.ylabel("Time")
plt.xlabel("Iteration Size")
count = count + 1
plt.savefig("WineTrainingTimes.png")
plt.close()
# Query Time
dt_clf = DecisionTreeClassifier(max_depth=19, criterion='gini')
ada_clf = AdaBoostClassifier(
base_estimator=DecisionTreeClassifier(max_depth=11),
n_estimators=40,
learning_rate=1)
knn_clf = KNeighborsClassifier(p=1, n_neighbors=2)
ann_clf = MLPClassifier(hidden_layer_sizes=(22, ),
alpha=0.0051,
activation='relu')
svm_rbf_clf = svm.SVC(kernel='rbf', gamma=1.31, C=0.91)
svm_sigmoid_clf = svm.SVC(kernel='sigmoid', gamma=0.51, C=0.01)
labels = [
"Decision Tree", "Adaboost", "KNN", "ANN", "SVM_RBF", "SVM_Sigmoid"
]
count = 0
for clf in [
dt_clf, ada_clf, knn_clf, ann_clf, svm_rbf_clf, svm_sigmoid_clf
]:
iteration_array = []
query_array = []
clf.fit(training_x, training_y)
for i in [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]:
X_test = testing_x[:int(testing_x.shape[0] * i), :]
iteration_array.append(X_test.shape[0])
st = time.clock()
clf.predict(X_test)
query_time = time.clock() - st
query_array.append(query_time)
plt.plot(iteration_array, query_array, label=labels[count])
plt.legend(loc=4, fontsize=8)
plt.title("Query Times for Learners", fontdict={'size': 16})
plt.ylabel("Time")
plt.xlabel("Iteration Size")
count = count + 1
plt.savefig("WineQueryTimes.png")
plt.close()
random_forest_list = []
for i in range(len(size_list)):
np.random.shuffle(x_y_set)
RandomForestClassifierModel.fit(x_y_set[:size_list[i], :2],
x_y_set[:size_list[i], 2])
random_forest_list.append(
RandomForestClassifierModel.score(X_test, Y_test))
# AdaBoost
AdaBoostClassifierModel = AdaBoostClassifier()
ada_boost_list = []
for i in range(len(size_list)):
np.random.shuffle(x_y_set)
AdaBoostClassifierModel.fit(x_y_set[:size_list[i], :2],
x_y_set[:size_list[i], 2])
ada_boost_list.append(AdaBoostClassifierModel.score(X_test, Y_test))
# LogisticRegression
LogisticRegressionModel = LogisticRegression()
logistic_regression_list = []
for i in range(len(size_list)):
np.random.shuffle(x_y_set)
LogisticRegressionModel.fit(x_y_set[:size_list[i], :2],
x_y_set[:size_list[i], 2])
logistic_regression_list.append(
LogisticRegressionModel.score(X_test, Y_test))
# MLPClassifier
MLPClassifierModel = MLPClassifier()
neural_network_list = []
for i in range(len(size_list)):
for mean_score, params in zip(cvres["mean_test_score"], cvres["params"]):
print(mean_score, params)
"""
0.10030303030303031 {'n_neighbors': 5, 'p': 1}
0.10242424242424245 {'n_neighbors': 5, 'p': 2}
0.12121212121212119 {'n_neighbors': 15, 'p': 1}
0.11787878787878787 {'n_neighbors': 15, 'p': 2}
0.11424242424242426 {'n_neighbors': 30, 'p': 1}
0.11363636363636362 {'n_neighbors': 30, 'p': 2}
"""
# %%
ada_clf = AdaBoostClassifier(n_estimators=100)
ada_clf.fit(X_train, y_train)
pred_train, pred_dev = ada_clf.predict(X_train), ada_clf.predict(X_dev)
train_acc = ada_clf.score(X_train, y_train)
dev_acc = ada_clf.score(X_dev, y_dev)
train_uar = recall_score(y_train, pred_train, average='macro')
dev_uar = recall_score(y_dev, pred_dev, average='macro')
print(f"train_acc = {train_acc:.2f}, dev_acc = {dev_acc:.2f}")
print(f"train_uar = {train_uar:.2f}, dev_uar = {dev_uar:.2f}")
"""train_acc = 0.29, dev_acc = 0.24
train_uar = 0.29, dev_uar = 0.25"""
# %%
gboost_clf = GradientBoostingClassifier(n_estimators=100)
gboost_clf.fit(X_train, y_train)
pred_train, pred_dev = gboost_clf.predict(X_train), gboost_clf.predict(X_dev)
train_acc = gboost_clf.score(X_train, y_train)
model_gnb = GaussianNB()
model_gnb.fit(xtr, ytr)
bayes_score = model_gnb.score(xte, yte)
model_lr = LogisticRegression()
model_lr.fit(xtr, ytr)
logreg_score = model_lr.score(xte, yte)
model_rc = RidgeClassifier()
model_rc.fit(xtr, ytr)
ridge_score = model_rc.score(xte, yte)
#model_sgd = SGDClassifier();model_sgd.fit(xtr,ytr)
#sgd_score = model_sgd.score(xte,yte)
#%%
xtr, xte, ytr, yte = train_test_split(X, y, train_size=0.75)
model_gbc = GradientBoostingClassifier(learning_rate=0.01, n_estimators=256)
model_gbc.fit(xtr, ytr)
gbc_score = model_gbc.score(xte, yte)
model_ada = AdaBoostClassifier(learning_rate=0.01)
model_ada.fit(xtr, ytr)
ada_score = model_ada.score(xte, yte)
model_rf = RandomForestClassifier(n_estimators=256)
model_rf.fit(xtr, ytr)
rf_score = model_rf.score(xte, yte)
avg_guess = (model_gbc.predict(xte) + model_ada.predict(xte) +
model_rf.predict(xte)) / 3
avg_score = np.mean(
[bayes_score, logreg_score, ridge_score, gbc_score, ada_score, rf_score])
for i, line in enumerate(data.splitlines()):
line = line.split(",")
X[i, :], y[i] = line[:4], line[4]
y -= (y == 0)
# In[13]:
X = StandardScaler().fit_transform(X)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=.4,
random_state=42)
clf.fit(X_train, y_train)
score = clf.score(X_test, y_test)
print(f" score = {score} ")
plt.figure(1)
plt.rcParams["figure.figsize"] = [20, 10]
fig, axs = plt.subplots(1, 2)
axs[0].scatter(X_train[:, 0],
X_train[:, 1],
c=y_train,
cmap=plt.cm.viridis,
marker='o')
axs[0].set_title('Données d entraînement', fontsize=14)
axs[1].scatter(X_test[:, 0],
X_test[:, 1],
c=y_test,
cmap=plt.cm.viridis,
'VisitTeamstartupDef', 'VisitTeamstartupPer'
]]
trainY = file2016['WoL']
testX = testfile[[
'HomeTeamBackupDef', 'HomeTeambackupPer', 'HomeTeamstartupDef',
'HomeTeamstartupPer', 'VisitBackupDef', 'VisitTeambackupPer',
'VisitTeamstartupDef', 'VisitTeamstartupPer'
]]
testY = testfile['WoL']
##plt.scatter(X[:,0],Y[:,0],marker='o',c=Y)
#bdt = AdaBoostClassifier(base_estimator=linear_model.LogisticRegression(),algorithm='SAMME',n_estimators=2000,learning_rate=0.02)
bdt = AdaBoostClassifier(DecisionTreeClassifier(max_depth=5,
min_samples_split=5,
min_samples_leaf=5),
algorithm='SAMME.R',
n_estimators=500,
learning_rate=0.2)
bdt.fit(trainX, trainY)
preictions = bdt.predict(testX)
print(bdt.estimator_weights_)
print("score : ", bdt.score(testX, testY))
print("\n", preictions)
'''
for i,row in testfile.iterrows():
if (row['WoL'] == )
'''
confusion_matrix = confusion_matrix(testY, preictions)
print(confusion_matrix)
print('X type is {Xtype}'.format(Xtype=type(X)))
Xdf = pd.DataFrame(X)
print('Xdf head()---------------------------')
print(Xdf.head())
ydf = pd.DataFrame(y)
print('ydf head()---------------------------')
print(ydf.head())
clf = AdaBoostClassifier(n_estimators=100, random_state=0)
clf.fit(X, y)
print clf.feature_importances_
print clf.predict([[0, 0, 0, 0]])
print clf.score(X, y)
dfclf = AdaBoostClassifier(n_estimators=100, random_state=0)
dfclf.fit(Xdf, ydf)
print dfclf.feature_importances_
# print dfclf.predict([[0, 0, 0, 0]])
print dfclf.predict_proba(Xdf)
print dfclf.predict(Xdf)
print dfclf.score(Xdf, ydf)
print ydf.head(10)
yTest = workARR[test_index]
print("start random forrest")
if cnt < 2:
randForrC.fit(trainX, yTrain)
tmpSCR = randForrC.score(testX, yTest)
scores['rand Forest'][label].append(tmpSCR)
else:
randForrR.fit(trainX, yTrain)
tmpSCR = randForrR.score(testX, yTest)
scores['rand Forest'][label].append(tmpSCR)
print("start adaBoost")
if cnt < 2:
adaBoostC.fit(trainX, yTrain)
tmpSCR = adaBoostC.score(testX, yTest)
scores['adaBoost'][label].append(tmpSCR)
else:
adaBoostR.fit(trainX, yTrain)
tmpSCR = adaBoostR.score(testX, yTest)
scores['adaBoost'][label].append(tmpSCR)
print("start bagging withOUT out-of-bag")
if cnt < 2:
bagCoobN.fit(trainX, yTrain)
tmpSCR = bagCoobN.score(testX, yTest)
scores['bagging (NO out of bag)'][label].append(tmpSCR)
else:
bagRoobN.fit(trainX, yTrain)
tmpSCR = bagRoobN.score(testX, yTest)
scores['bagging (NO out of bag)'][label].append(tmpSCR)
placeToTakeForTest = int(random() * len(usedData))
x = usedData.pop(placeToTakeForTest)
y = usedValue.pop(placeToTakeForTest)
testSetX.append(x)
testSetY.append(y)
for n_estimators in [1800]:
usedData = np.array(usedData)
usedValue = np.array(usedValue)
testSetX = np.array(testSetX)
testSetY = np.array(testSetY)
clf = AdaBoostClassifier(n_estimators=n_estimators)
clf = clf.fit(usedData, usedValue)
value1 = clf.score(testSetX, testSetY)
if value1 > best:
best = value1
nbBest = n_estimators
print("best nb : ")
print(nbBest)
print(best)
tot += best
if nbBest in result:
result[nbBest] += 1
else:
result[nbBest] = 1
print("\n")
scaler = StandardScaler()
scaler.fit(x_train)
x_train = scaler.transform(x_train)
x_test = scaler.transform(x_test)
ab = AdaBoostClassifier()
ab.fit(x_train, y_train)
y_pred_train = ab.predict(x_train)
y_pred_test = ab.predict(x_test)
print("classifier", ab)
print ("Accuracy on Train Set")
print (ab.score(x_train, y_train))
print ("MLP Classifier")
print ("Accuracy on Test Set")
print (ab.score(x_test, y_test))
print ("Report")
print (classification_report(y_test,ab.predict(x_test)))
param_grid = {
'n_estimators': [1000,2000,3000],
'learning_rate':[1.0,5.0,10.0],
'algorithm':['SAMME.R','SAMME']
}
if __name__ == '__main__':
np.set_printoptions(edgeitems=5)
trainingdata = sio.loadmat('mnist_dataset/mnist_train.mat')
traininglabeldata = sio.loadmat('mnist_dataset/mnist_train_labels.mat')
testdata = sio.loadmat('mnist_dataset/mnist_test.mat')
testlabeldata = sio.loadmat('mnist_dataset/mnist_test_labels.mat')
trainingImg = trainingdata["mnist_train"]
trainingLabel = traininglabeldata["mnist_train_labels"]
testImg = testdata["mnist_test"]
testLabel = testlabeldata["mnist_test_labels"]
trainingImg /= 255
testImg /= 255
num_TestData = testImg.shape[0]
bdt = AdaBoostClassifier(DecisionTreeClassifier(criterion="entropy"))
bdt.fit(trainingImg, trainingLabel.ravel())
predict = bdt.predict(testImg).reshape(num_TestData, 1)
score = bdt.score(testImg, testLabel)
wrongCount = num_TestData - np.count_nonzero(predict == testLabel)
with open('adaboost_result.csv', 'w') as file:
file.write("Error Rate: %f\n" %(wrongCount / 10000))
file.write("Score: %f\n" %(score))
for i in range(0, num_TestData):
file.write("%d %d\n" %(predict[i], testLabel[i]))
Xb_test = X_basic.iloc[train_inds:, :]
yb_test = y_basic.iloc[train_inds:, :]
# null accuracy rates for basic dataset (dict may not be great for this)
null_rates_basic = list(y_basic.mean())
##########################
# AdaBoost on basic data #
##########################
ADB_Scores = list(np.zeros(y_basic.shape[1]))
for i in range(y_basic.shape[1]):
from sklearn.ensemble import AdaBoostClassifier
adb = AdaBoostClassifier(n_estimators=100)
adb.fit(Xb_train, yb_train.iloc[:, i])
ADB_Scores[i] = adb.score(Xb_test, yb_test.iloc[:, i])
# compare to null accuracy rates; difference in accuracy:
for i in range(len(ADB_Scores)):
print str(list(y_basic.mean())[i]) + '\t' + str(
ADB_Scores[i]) + '\t' + str(ADB_Scores[i] - null_rates_basic[i])
###############################
# Random Forest on basic data #
###############################
RF_Scores = list(np.zeros(y_basic.shape[1]))
for i in range(y_basic.shape[1]):
from sklearn.ensemble import RandomForestClassifier
rf = RandomForestClassifier(n_estimators=20)
rf.fit(Xb_train, yb_train.iloc[:, i])
data = pd.read_csv("spambase.data", sep=",", header=None)
Shuffle_data = shuffle(data)
inputs = Shuffle_data.iloc[:, :57]
labels = Shuffle_data.iloc[:, -1]
x_train, x_test, y_train, y_test = train_test_split(inputs,
labels,
test_size=0.3)
model_01 = MultinomialNB()
model_01.fit(x_train, y_train)
score_01 = model_01.score(x_test, y_test)
model_02 = AdaBoostClassifier()
model_02.fit(x_train, y_train)
score_02 = model_02.score(x_test, y_test)
print("Using NB : ", score_01, " Using AdaBoost : ", score_02)
prediction01 = model_01.predict(x_test)
prediction02 = model_02.predict(x_test)
for i in range(5):
print("NB : ", prediction01[i], " Ada: ", prediction02[i], " Actual",
list(y_test)[i])
# In[5]:
help(AdaBoostClassifier)
# In[6]:
#参数解释：
clf = AdaBoostClassifier(
base_estimator=None,
n_estimators=1000,
learning_rate=0.01,
algorithm='SAMME.R',
random_state=2020,
)
t1 = time.time()
clf = clf.fit(x_train, y_train)
pred = clf.predict(x_test)
print('predict:', pred)
score = clf.score(x_test, y_test)
print('score:', score)
t2 = time.time()
# # 时间成本
# In[7]:
print('time:', t2 - t1)
# In[ ]:
X_train, X_test, y_train, y_test = train_test_split(tfidf,
label,
test_size=0.7,
random_state=75)
classifier_nb = MultinomialNB(class_prior=None,
fit_prior=False).fit(X_train, y_train)
filename = 'finalized_model.sav'
joblib.dump(classifier_nb, filename)
score = classifier_nb.score(X_test, y_test)
classifier_en = AdaBoostClassifier(n_estimators=100)
classifier_en.fit(tfidf, label)
score2 = classifier_en.score(X_test, y_test)
print "Score for Naive--- "
print score
print "For ADAboost is "
print score2
classifier_svm = SVC(probability=True, kernel='sigmoid')
classifier_svm.fit(X_test, y_test)
score3 = classifier_svm.score(X_test, y_test)
print "Score for SVC is--- "
print score3
sentiments = pd.DataFrame(columns=['text', 'class', 'prob'])
i = 0
min_samples_split=20,
min_samples_leaf=5),
algorithm="SAMME",
n_estimators=21,
learning_rate=0.8)
print(Ada)
epoch = 5
scores = []
# Train model
start = datetime.now()
for i in range(epoch):
Ada.fit(feature_train_scaled, y_train_decode)
scores.append(Ada.score(feature_test_scaled, y_test_decode)) #准确率
print("This took ", datetime.now() - start)
print(u'The accuracy of the model is: ')
display_scores(scores) #准确率
params = {
"learning_rate": uniform(0.1, 0.9), # default 0.1
"n_estimators": randint(10, 120) # default 5
# "max_depth": randint(2, 6) # default 3
}
search = RandomizedSearchCV(Ada,
param_distributions=params,
random_state=42,
n_iter=30,
from kullback_leibner_divergence_criterion import KullbackLeibnerCriterion
kldc = KullbackLeibnerCriterion(1, np.array([2], dtype='int64'))
#Create the tree
dt = DecisionTreeClassifier(max_depth=2, criterion=kldc)
# Create and fit an AdaBoosted decision tree
bdt = AdaBoostClassifier(dt, algorithm="SAMME", n_estimators=200)
bdt.fit(X, y, w)
#from sklearn.ensemble import RandomForestClassifier
#bdt = RandomForestClassifier(criterion=kldc, max_depth=2, n_estimators=100)
#bdt.fit(X, y)
print('distance score: ', bdt.score(X, y))
plot_colors = "br"
plot_step = 0.02
class_names = "AB"
plt.figure(figsize=(10, 5))
# Plot the decision boundaries
plt.subplot(121)
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),
np.arange(y_min, y_max, plot_step))
Z = bdt.predict(np.c_[xx.ravel(), yy.ravel()])
print('Accuracy (a decision tree):', dt.score(test_data, test_labels))
#http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
rfc = RandomForestClassifier(n_estimators=1000)
rfc.fit(train_data, train_labels)
print('Accuracy (a random forest):', rfc.score(test_data, test_labels))
#http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html
abc = AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=1), n_estimators=1000, learning_rate=0.1)
abc.fit(train_data, train_labels)
print('Accuracy (adaboost with decision trees):', abc.score(test_data, test_labels))
###Own implementation of Bagging:
np.random.seed(1)
B = 1000
n = train_data.shape[0]
sn = int(n*2.0/3.0) # nr of training data in subset for each tree
nf = train_data.shape[1]
all_preds = np.zeros((B,test_data.shape[0]))
for b in range(B):
bs_sample_index = np.random.choice(range(n), size=sn, replace=True)
subt = train_data[bs_sample_index,]
dtree.fit(x_train, y_train)
dtree.score(x_test, y_test)
# 0.625
dtree.score(x_train, y_train)
# 100%
# model is overfitting
################ Now With BaggingClassifier ############
bg = BaggingClassifier(DecisionTreeClassifier(),
max_samples=0.5,
max_features=1.0,
n_estimators=20)
bg.fit(x_train, y_train) #fitting the model
bg.score(x_test, y_test)
# 66.25
bg.score(x_train, y_train)
# 94.375
# model is Underfitting
################ Now With BoostingClassifier ###########
ada = AdaBoostClassifier(DecisionTreeClassifier(),
n_estimators=10,
learning_rate=1)
ada.fit(x_train, y_train)
ada.score(x_test, y_test)
ada.score(x_train, y_train)
# 100%
# model is overfitting
# By looking at all the above model we can tell that "Bagging model" is gives the good result
score_time = time() - start # get the score time
print("{:<15}| score = {:.3f} | time = {:,.3f}s/{:,.3f}s".format(
name, score, train_time, score_time))
# Fitting AdaBoost Classification to the Training set
classifier = AdaBoostClassifier(DecisionTreeClassifier(max_depth=7,
min_samples_split=20,
min_samples_leaf=5),
algorithm="SAMME",
n_estimators=200,
learning_rate=0.8)
start = time()
classifier.fit(X_train, y_train)
train_time = time() - start
start = time()
score = classifier.score(X_test, y_test)
score_time = time() - start
print("score = {:.3f} | time = {:,.3f}s/{:,.3f}s".format(
score, train_time, score_time))
# Calculating feature inportance
feature_name = cv.get_feature_names()
feature_name = np.array(feature_name)
feature_name = np.insert(feature_name, 0, "avg_star_rating", axis=0)
importances = classifier.feature_importances_
indices = np.argsort(importances)[::-1]
feature_name = np.array(feature_name)
for f in range(100):
print("%2d) %-*s %f" %
(f + 1, 30, feature_name[indices[f]], importances[indices[f]]))
# 1) dryer 0.155159
testSetX1 = np.array(testSetX1)
testSetY1 = np.array(testSetY1)
scaler1 = StandardScaler()
scaler1.fit(usedData)
usedDataScale2 = scaler1.transform(usedData)
testSetXScale2 = scaler1.transform(testSetX)
clf1 = AdaBoostClassifier(n_estimators=200)
clf1 = clf1.fit(usedDataScale2, usedValue)
#print("\n importance 1:")
print(clf1.predict_proba(testSetXScale2))
print(clf1.predict(testSetXScale2))
value1 = clf1.predict(testSetXScale2)
value1Score = clf1.score(testSetXScale2, testSetY)
clf2 = MLPClassifier()
clf2 = clf2.fit(usedData, usedValue)
#print("\n importance 2:")
print(clf2.predict(testSetX))
print(clf2.predict_proba(testSetX))
value2 = clf2.predict(testSetX)
value2Score = clf2.score(testSetX, testSetY)
clf3 = KNeighborsClassifier(n_neighbors=nbNeighbors, weights=weightValue)
clf3 = clf3.fit(usedData1, usedValue1)
#print("\n importance 3:")
#print(clf3.predict_proba(testSetX1))
print(clf3.predict(testSetX1))
value3 = clf3.predict(testSetX1)