def test_predictproba_hardvoting():
eclf = VotingClassifier(estimators=[('lr1', LogisticRegression()),
('lr2', LogisticRegression())],
voting='hard')
msg = "predict_proba is not available when voting='hard'"
assert_raise_message(AttributeError, msg, eclf.predict_proba, X)
})).tolist()))
newArr = []
for row in s:
t = row.split(',')
t = np.array(t)
t = t.astype(float)
newArr.append(t)
accelArr = np.array(newArr)
actionArr = np.array(dfs['Action'])
# Create classifiers
knn = KNeighborsClassifier(n_neighbors=3)
clf2 = RandomForestClassifier(n_estimators=50, random_state=1)
eclf1 = VotingClassifier(estimators=[('rf', clf2), ('knn', knn)],
voting='hard')
eclf1 = eclf1.fit(accelArr, actionArr)
def detection_callback(device, advertisement_data):
"""Asynch Callback
Args:
device : Bleak device object
advertisement_data : Advertisement Data Read
"""
global aidenBool
global georgeBool
try:
if (device.address == aidenThingy or device.address
random_search = RandomizedSearchCV(clf,
param_distributions=param_dist,
cv=3,
n_iter=10,
verbose=10)
random_search.fit(X_to_file, y_to_file[0])
print(random_search.best_params_)
clf4 = SVC(**random_search.best_params_, probability=True)
scores = cross_val_score(clf4, X_to_file, y, cv=9)
print(scores)
print(np.mean(scores))
from sklearn.ensemble import VotingClassifier
eclf = VotingClassifier(estimators=[('rf', clf1), ('kn', clf2), ('nb', clf3),
('svm', clf4)],
voting='soft')
scores = cross_val_score(eclf, X_to_file, y, cv=9)
print(scores)
print(np.mean(scores))
'''
clf_list = [
RandomForestClassifier(n_estimators = 100, min_samples_leaf=2, min_samples_split=6),
#SVC(kernel='rbf', degree=2, gamma='auto'),
KNeighborsClassifier(n_neighbors=10, p=4),
#GaussianNB(),
MultinomialNB(),
from sklearn.preprocessing import FunctionTransformer
# NOTE: Make sure that the class is labeled 'class' in the data file
tpot_data = np.recfromcsv('PATH/TO/DATA/FILE',
delimiter='COLUMN_SEPARATOR',
dtype=np.float64)
features = np.delete(tpot_data.view(np.float64).reshape(tpot_data.size, -1),
tpot_data.dtype.names.index('class'),
axis=1)
training_features, testing_features, training_classes, testing_classes = \
train_test_split(features, tpot_data['class'], random_state=42)
exported_pipeline = make_pipeline(
Nystroem(gamma=10.0, kernel="polynomial", n_components=10),
make_union(
VotingClassifier([("est",
KNeighborsClassifier(n_neighbors=4,
weights="distance"))]),
FunctionTransformer(lambda X: X)),
make_union(
VotingClassifier([("est",
ExtraTreesClassifier(criterion="entropy",
max_features=1.0,
n_estimators=500))]),
FunctionTransformer(lambda X: X)),
FeatureAgglomeration(affinity="precomputed", linkage="average"),
GaussianNB())
exported_pipeline.fit(training_features, training_classes)
results = exported_pipeline.predict(testing_features)
# 2. 소프트 보팅: 확률
# 2. bagging
# 여러개의 데이터셋 + 같은 ML
# 3. boosting
# 여러번 학습과 예측
# 첫번째 학습과 예측에서 잘못된 것을 두번째 학습에서 가중치 이용해서 수정해서 재학습/예측 반복
cancer = load_breast_cancer()
df = pd.DataFrame(cancer.data, columns=cancer.feature_names)
print(df)
# 개별 모델
lc_r = LogisticRegression(max_iter=10000) # iter -> 최대 반복 횟수
knn_clf = KNeighborsClassifier(n_neighbors=4)
# 개별 모델을 묶어 주는 보팅
vo_clf = VotingClassifier(estimators=[("LR", lc_r), ("KNN", knn_clf)], voting="soft")
# 학습 데이터와 테스트 데이터로 분리
x_train, x_test, y_train, y_test = train_test_split(cancer.data, cancer.target, test_size=0.2, random_state=11)
# 학습과 예측
vo_clf.fit(x_train, y_train)
prediction = vo_clf.predict(x_test)
# 정확도
print("VotingClassifier 정확도:", accuracy_score(y_test, prediction))
# 개별 ML 정확도
models = [lc_r, knn_clf, vo_clf]
for m in models:
m.fit(x_train, y_train)
clf.fit(X_train, y_train)
# Predict y_pred
y_pred = clf.predict(X_test)
# Calculate accuracy
accuracy = accuracy_score(y_pred, y_test)
# Evaluate clf's accuracy on the test set
print('{:s} : {:.3f}'.format(clf_name, accuracy))
# Import VotingClassifier from sklearn.ensemble
from sklearn.ensemble import VotingClassifier
# Instantiate a VotingClassifier vc
vc = VotingClassifier(estimators=classifiers)
# Fit vc to the training set
vc.fit(X_train, y_train)
# Evaluate the test set predictions
y_pred = vc.predict(X_test)
# Calculate accuracy score
accuracy = accuracy_score(y_pred, y_test)
print('Voting Classifier: {:.3f}'.format(
accuracy)) # Better accuracy than the three other classifiers
# After Vorting Classifier, we have a look at bagging classifiers and regression
# Import DecisionTreeClassifier
if acc_score > best_score:
best_score = acc_score
best_model = bag
best_model
pred = best_model.predict(X_test)
accuracy_score(y_test, pred)
# svc, random forest, and logistic regression in a voting classifier
log_clf = LogisticRegression()
rnd_clf = RandomForestClassifier(random_state=181)
svm_clf = SVC(kernel='linear', random_state=181)
voting_clf = VotingClassifier(estimators=[('lr', log_clf), ('rf', rnd_clf),
('svc', svm_clf)],
voting='hard')
for clf in (log_clf, rnd_clf, svm_clf, voting_clf):
clf.fit(X_train, y_train)
pred = clf.predict(X_test)
print(clf.__class__.__name__, accuracy_score(y_test, pred))
# CV in the voting classifier (OBS takes approx. 20 min)
log_clf = LogisticRegression()
rnd_clf = RandomForestClassifier(random_state=181)
svm_clf = SVC(kernel='linear', probability=True, random_state=181)
voting_clf = VotingClassifier(estimators=[('lr', log_clf), ('rf', rnd_clf),
('svc', svm_clf)],
Features_10_folds.append(features)
Labels_10_folds.append(labels)
#for last fold all remaining
features = data[data.columns[1:44]]
labels = data[data.columns[44]]
Features_10_folds.append(features)
Labels_10_folds.append(labels)
print Features_10_folds[0].shape
clf = RandomForestClassifier(max_depth=8, random_state=0)
mlp = MLPClassifier(hidden_layer_sizes=(50, 25))
gbt = GradientBoostingClassifier()
ovr = OneVsRestClassifier(RandomForestClassifier())
eclf = VotingClassifier(estimators=[('gbt', gbt), ('ovr', ovr)], voting='soft')
acc_RF = []
prec_RF = []
rec_RF = []
f1_RF = []
acc_MLP = []
prec_MLP = []
rec_MLP = []
f1_MLP = []
acc_GBT = []
prec_GBT = []
rec_GBT = []
f1_GBT = []
acc_ovr = []
prec_ovr = []
rec_ovr = []
def test_estimator_html_repr_pipeline():
num_trans = Pipeline(
steps=[("pass",
"passthrough"), ("imputer", SimpleImputer(strategy="median"))])
cat_trans = Pipeline(steps=[
("imputer",
SimpleImputer(strategy="constant", missing_values="empty")),
("one-hot", OneHotEncoder(drop="first")),
])
preprocess = ColumnTransformer([
("num", num_trans, ["a", "b", "c", "d", "e"]),
("cat", cat_trans, [0, 1, 2, 3]),
])
feat_u = FeatureUnion([
("pca", PCA(n_components=1)),
(
"tsvd",
Pipeline([
("first", TruncatedSVD(n_components=3)),
("select", SelectPercentile()),
]),
),
])
clf = VotingClassifier([
("lr", LogisticRegression(solver="lbfgs", random_state=1)),
("mlp", MLPClassifier(alpha=0.001)),
])
pipe = Pipeline([("preprocessor", preprocess), ("feat_u", feat_u),
("classifier", clf)])
html_output = estimator_html_repr(pipe)
# top level estimators show estimator with changes
assert html.escape(str(pipe)) in html_output
for _, est in pipe.steps:
assert ('<div class="sk-toggleable__content"><pre>' +
html.escape(str(est))) in html_output
# low level estimators do not show changes
with config_context(print_changed_only=True):
assert html.escape(str(num_trans["pass"])) in html_output
assert "passthrough</label>" in html_output
assert html.escape(str(num_trans["imputer"])) in html_output
for _, _, cols in preprocess.transformers:
assert f"<pre>{html.escape(str(cols))}</pre>" in html_output
# feature union
for name, _ in feat_u.transformer_list:
assert f"<label>{html.escape(name)}</label>" in html_output
pca = feat_u.transformer_list[0][1]
assert f"<pre>{html.escape(str(pca))}</pre>" in html_output
tsvd = feat_u.transformer_list[1][1]
first = tsvd["first"]
select = tsvd["select"]
assert f"<pre>{html.escape(str(first))}</pre>" in html_output
assert f"<pre>{html.escape(str(select))}</pre>" in html_output
# voting classifier
for name, est in clf.estimators:
assert f"<label>{html.escape(name)}</label>" in html_output
assert f"<pre>{html.escape(str(est))}</pre>" in html_output
# 决策树
dt_clf = DecisionTreeClassifier(random_state=666)
dt_clf.fit(X_train, y_train)
dt_score = dt_clf.score(X_test, y_test)
y_predict1 = log_clf.predict(X_test)
y_predict2 = svm_clf.predict(X_test)
y_predict3 = dt_clf.predict(X_test)
y_predict = np.array((y_predict1 + y_predict2 + y_predict3) >= 2, dtype='int')
score1 = accuracy_score(y_test, y_predict)
print(score1)
# 使用Voting Classifier 少数服从多数
from sklearn.ensemble import VotingClassifier
voting_clf = VotingClassifier(estimators=[
('log_clf', LogisticRegression()), ('svm_clf', SVC()),
('dt_clf', DecisionTreeClassifier(random_state=666))
],
voting='hard')
voting_clf.fit(X_train, y_train)
score2 = voting_clf.score(X_test, y_test)
print(score2)
# 更合理的投票，应该有权值
# Hard Voting Classifier
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import VotingClassifier
voting_clf = VotingClassifier(estimators=[
('log_clf', LogisticRegression()), ('svm_clf', SVC()),
('dt_clf', DecisionTreeClassifier(random_state=666))
# 1.0
# <h4> Voting Classifier </h4>
from sklearn.linear_model import LogisticRegression#importing logistc regression
from sklearn.svm import SVC#importing Svm
estimators = []
log_reg = LogisticRegression(solver='liblinear')
estimators.append(('Logistic', log_reg))
tree = DecisionTreeClassifier()
estimators.append(('Tree', tree))
svm_clf = SVC(gamma='scale')
estimators.append(('svm', svm_clf))
voting = VotingClassifier(estimators=estimators)
voting.fit(x_train, y_train)
voting.fit(x_train,y_train)
voting.score(x_test,y_test)
# 0.8051948051948052
voting.score(x_train,y_train)
#0.8110749185667753
df['carrier'] = pd.factorize(df['carrier'])[0]
df['dest'] = pd.factorize(df['dest'])[0]
test_x = enc.transform(df)
print train_x.shape
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import VotingClassifier
clf1 = LogisticRegression(random_state=1)
clf2 = RandomForestClassifier(random_state=1)
clf3 = GaussianNB()
eclf = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2), ('gnb', clf3)],
voting='hard')
eclf.fit(train_x.toarray(), train_y)
# Evaluate on test set
pr = eclf.predict(test_x.toarray())
# print results
cm = confusion_matrix(test_y, pr)
print "<------- VotingClassifier -------->"
print "Confusion matrix:"
print pd.DataFrame(cm)
report_svm = precision_recall_fscore_support(list(test_y),
list(pr),
average='binary')
print "\n[-] Precision = %0.2f\n[-] Recall = %0.2f\n[-] F1 score = %0.2f\n[-] Accuracy = %0.2f" % \
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
# -------------------------------------------------------------------------------- #
print_dividing_line('VotingClassifier')
from sklearn.linear_model import LogisticRegression
log_clf = LogisticRegression()
from sklearn.svm import SVC
svc_clf = SVC()
from sklearn.ensemble import RandomForestClassifier
rf_clf = RandomForestClassifier()
from sklearn.ensemble import VotingClassifier
voting_clf = VotingClassifier( estimators=[("log", log_clf), ("svc", svc_clf), ("rf", rf_clf)], voting="hard" ) # soft
from sklearn.metrics import accuracy_score
for clf in ( log_clf, svc_clf, rf_clf, voting_clf ):
clf.fit( X_train, y_train )
y_pred = clf.predict( X_test )
print( clf.__class__.__name__, accuracy_score(y_test, y_pred) )
# -------------------------------------------------------------------------------- #
print_dividing_line('BaggingClassifier')
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import BaggingClassifier
bag_clf = BaggingClassifier( DecisionTreeClassifier(), n_estimators=500, max_samples=100, bootstrap=True, n_jobs=-1 )
from sklearn.metrics import accuracy_score
tprRF,
'',
label="SuperStrength with Random Forest, auc= %0.2f" % aucRF)
plt.title('Receiver Operating Characteristic')
plt.xlabel('False Positive')
plt.ylabel('True Positive')
plt.legend(loc=4)
plt.show()
'''
####################################### VOTING_CLASSIFIER ########################################
'''
from sklearn.ensemble import VotingClassifier
votingClf = VotingClassifier(estimators=[('tr', classificadorTREE),
('rf', classificadorRF),
('nb', classificadorNB)],
voting='soft',
weights=[1.1, 2, 1])
for clf, label in zip(
[classificadorTREE, classificadorRF, classificadorNB, votingClf],
['Decision Tree', 'Random Forest', 'Naive Bayes', 'Ensemble']):
scores = cross_val_score(clf, previsores, classe, cv=5, scoring='accuracy')
print("Accuracy: %0.2f [%s]" % (scores.mean(), label))
'''
#################################################################################################
############################################ ENSEMBLE ###########################################
#################################################################################################
'''
"slotprice",
"creative",
"keypage",
"advertiser",
"usertag",
]
"""
estimators = []
num_estimators = 11
models_already_created = False
do_LR = True
if models_already_created:
for i in range(num_estimators):
model = load("svm" + str(i))
estimators.append(("svm" + str(i), model))
v = VotingClassifier(estimators, n_jobs=-1)
voting_bidder = Bidder(("voting", v), None)
voting_bidder.train()
voting_bidder.test()
elif do_LR:
"""
kernel = C(1.0, (1e-3, 1e3)) * RBF(10, (1e-2, 1e2))
clf3 = GaussianProcessClassifier(
kernel=kernel,
max_iter_predict=100,
multi_class="one_vs_one",
n_jobs=-1,
n_restarts_optimizer=5,
)
b5 = Bidder(("gp", clf3), None)
model5 = b5.train()
Xtr = scaler.fit_transform(Xtr)
Xte = scaler.transform(Xte)
for name, clf in Classifiers:
try:
clone_clf = clone(clf)
clone_clf.fit(Xtr,ytr)
y_pred = clone_clf.predict(Xte)
df_sim[name] = [score(yte,y_pred)]
except:
print("Classifier %s failed to process dataset %s" % (name,Name))
df = pd.concat([df,df_sim])
df.to_csv("CSVs/%s.csv" % Name)
return df
VotingSVC = VotingClassifier([("RBF SVC",SVC(gamma="scale")),
("Linear SVC",SVC(kernel="linear")),
("Poly SVC",SVC(kernel="poly"))])
BaggingSVC = BaggingClassifier(base_estimator=SVC(gamma="scale"),n_estimators=10, random_state=0)
Classifiers = [("Linear SVC",SVC(kernel="linear",gamma="scale")),
("RBF SVC",SVC(gamma="scale")),
("Poly SVC",SVC(kernel="poly",gamma="scale")),
("SVC Ensemble",VotingSVC),
("Bagging SVC",BaggingSVC),
("DEP",DEP()),
("r-DEP (Ensemble)",make_pipeline(EnsembleTransform(VotingSVC),StandardScaler(),DEP())),
("r-DEP (Bagging)",make_pipeline(EnsembleTransform(BaggingSVC),StandardScaler(),DEP())),
]
AllDataSets = [
("Breast Cancer Wisconsin","wdbc",1),
("Diabetes","diabetes",1),
m3 = RandomForestClassifier(n_estimators=80)
models.append(('r_forest', m3))
m4 = RandomForestClassifier(n_estimators=90)
models.append(('r_forest', m4))
m5 = KNeighborsClassifier(n_neighbors=1)
models.append(('knn', m5))
m6 = KNeighborsClassifier(n_neighbors=2)
models.append(('knn', m6))
m7 = KNeighborsClassifier(n_neighbors=3)
models.append(('knn', m7))
m8 = KNeighborsClassifier(n_neighbors=4)
models.append(('knn', m8))
m9 = KNeighborsClassifier(n_neighbors=5)
models.append(('knn', m9))
# create voting ensemble
e = VotingClassifier(models, weights=[0.8, 0.9, 1, 1.1, 1.1, 1.1, 1, 0.9, 0.8])
e.fit(train_X.values, np.ravel(train_Y.values))
preds = e.predict(test_X.values)
print(accuracy_score(np.ravel(test_Y.values), preds))
'''
df_pred = pd.DataFrame(preds, columns=['coverType_1to7'])
df_pred.insert(loc=0, column='id', value=np.ravel(df_test_X_ids.values))
print(df_pred[:10])
df_pred.to_csv('voting_4forst_5knn_weights.csv', index=False)
'''
# get cv result
# result = model_selection.cross_val_score(e, df_X.values, np.ravel(df_Y.values)
# , cv=k)
# print(result.mean())
# predictions = clf.predict(df_test_X.values[:20000])
print("After Standardization\nMean ", np.mean(X_train), "Standard Deviation ",
np.std(X_train), "\n")
#Voting ensemble method. Combining all tree based algorithms.
models = []
models.append(("XGB", XGBClassifier()))
models.append(("RF", RandomForestClassifier()))
models.append(("DT", DecisionTreeClassifier()))
models.append(("ADB", AdaBoostClassifier()))
models.append(("GB", GradientBoostingClassifier()))
#############################################################################
# test and train the upsampled data against classifiers
# to find the optimum prediction
#############################################################################
ensemble = VotingClassifier(estimators=models)
ensemble.fit(X_train, y_train)
y_pred = ensemble.predict(X_test)
print(classification_report(y_pred, y_test))
print("Voting Ensemble:>", accuracy_score(y_pred, y_test))
SVM = SVC(kernel="linear", class_weight="balanced", probability=True)
SVM.fit(X_train, y_train)
y_pred = SVM.predict(X_test)
print(classification_report(y_pred, y_test))
print("SVM: ", accuracy_score(y_pred, y_test))
XGBC = XGBClassifier(learning_rate=0.1,
n_estimators=10000,
max_depth=4,
min_child_weight=6,
def perform():
try:
filename = session['filename']
data = pd.read_csv(filename, header=0)
array = data.values
X = array[:,0:8]
y = array[:,8]
X_train, X_validation, Y_train, Y_validation = train_test_split(X, y, test_size=0.33,random_state=1)
# Spot Check Algorithms
models = []
models.append(('LR', LogisticRegression(solver='liblinear', multi_class='ovr')))
#models.append(('LDA', LinearDiscriminantAnalysis()))
models.append(('KNN', KNeighborsClassifier()))
models.append(('CART', DecisionTreeClassifier(criterion="entropy")))
models.append(('NB', GaussianNB()))
#models.append(('SVM', SVC(gamma='auto')))
#models.append(('ANN', MLPClassifier(hidden_layer_sizes=(13,13,13),max_iter=500, random_state=42)))
models.append(('RF', RandomForestClassifier()))
models.append(('BG',BaggingClassifier(DecisionTreeClassifier(), max_samples= 0.5, max_features =1.0, n_estimators =20)))
models.append(('ADA',AdaBoostClassifier(DecisionTreeClassifier(),n_estimators = 5, learning_rate = 1)))
LR=LogisticRegression(solver='liblinear', multi_class='ovr')
LDA=LinearDiscriminantAnalysis()
KNN=KNeighborsClassifier()
CART=DecisionTreeClassifier(criterion="entropy")
RF=RandomForestClassifier()
NB=GaussianNB()
SVM=SVC(gamma='auto')
ANN=MLPClassifier(hidden_layer_sizes=(13,13,13),max_iter=500, random_state=42)
BG=BaggingClassifier(DecisionTreeClassifier(), max_samples= 0.5, max_features = 1.0, n_estimators =20)
ADA=AdaBoostClassifier(DecisionTreeClassifier(),n_estimators = 5, learning_rate = 1)
models.append(('VOTE',VotingClassifier( estimators= [('RF',RF),('BG',BG)], voting = 'hard')))
# evaluate each model in turn
results = []
names = []
for name, model in models:
kfold = StratifiedKFold(n_splits=5, random_state=1, shuffle=True)
cv_results = cross_val_score(model, X_train, Y_train, cv=kfold, scoring='accuracy')
results.append(cv_results)
names.append(name)
#print('%s: %f (%f)' % (name, cv_results.mean(), cv_results.std()))
# Compare Algorithms
plt.boxplot(results, labels=names)
plt.title('Algorithm Comparison')
plt.plot()
session['strFile']="./static/images/perf.png"
strFile = "./static/images/perf.png"
if os.path.isfile(strFile):
os.remove(strFile)
plt.savefig(strFile)
plt.close()
except KeyError:
flash('Dataset not uploaded!')
#plt.savefig('/home/saurabh/Desktop/DPS/static/images/perf.png')
return render_template('dashboard.html', name = 'Plot Showing Accuracy of Different Algorithms:', url ='/static/images/perf.png')
for clf, label in zip([rfc, gbc, lgr], clf_labels):
scores = cross_val_score(estimator=clf,
X=X_train,
y=y_train,
cv=10,
scoring="roc_auc")
print("ROC AUC: %0.2F (+/- %0.2f) [%s]" %
(scores.mean(), scores.std(), label))
print(
"------------------------VotingClassifier集成模型-------------------------------"
)
ensemble_clf = VotingClassifier(estimators=[
('RandomForestClassifier', rfc), ('GradientBoostingClassifier', gbc),
('LogisticRegression', lgr)
],
voting='soft',
weights=[1, 1, 1],
flatten_transform=True)
ensemble_clf.fit(X_train, y_train)
preds = ensemble_clf.predict(X_test)
print("VotingClassifier ROC AUC:%.3f" %
roc_auc_score(y_true=y_test, y_score=preds))
print("VotingClassifier accuracy_scorer:%.3f" %
accuracy_score(y_true=y_test, y_pred=preds))
clf_labels = [
"RandomForestClassifier", "GradientBoostingClassifier",
"LogisticRegression", "VotingClassifier"
]
class HogFaceClassifier:
svc_pipeline = Pipeline([
# ('preprocess', FunctionTransformer(get_face_hog)),
('classifier',
SVC(C=10,
kernel='poly',
gamma=1,
shrinking=False,
class_weight='balanced',
probability=True,
tol=0.001,
cache_size=10000,
max_iter=-1,
verbose=0))
])
et_pipeline = Pipeline([
# ('preprocess', FunctionTransformer(get_face_hog)),
('classifier',
ExtraTreesClassifier(n_estimators=10000,
criterion='entropy',
max_features=0.2,
verbose=0,
n_jobs=2))
])
ens = VotingClassifier(estimators=[
('svc', svc_pipeline),
('et', et_pipeline),
],
voting='soft',
weights=[10, 1],
n_jobs=2)
def __init__(self,
binary_classification: bool = False,
params: dict = None):
self.binary_classification = binary_classification
#self.ens = self.et_pipeline
print(str(self.ens.get_params().keys()))
if params is not None:
self.ens.set_params(**params)
def fit(self, x, y):
self.ens.fit(x, y)
def save(self, file):
joblib.dump(self.ens, file)
def load(self, file):
self.ens = joblib.load(file)
def cv_test(self, x, y):
score = cross_val_score(self.ens, x, y, cv=3, verbose=3, n_jobs=3)
return score.mean()
def prediction(self, X):
return self.ens.predict(X)
def evaluate(self, x_test, y_test) -> ClassificationResults:
preds = self.prediction(x_test)
pred_probs = self.ens.predict_proba(x_test)
acc = accuracy_score(from_hot_one(y_test), preds)
results = ClassificationResults(labels=y_test,
preds=preds,
pred_probs=pred_probs,
acc=acc,
binary=self.binary_classification)
return results
def file_output(
self,
Y_optimization_pred: np.ndarray,
Y_valid_pred: np.ndarray,
Y_test_pred: np.ndarray,
) -> Tuple[Optional[float], Dict[str, Union[str, int, float, List, Dict,
Tuple]]]:
# Abort if self.Y_optimization is None
# self.Y_optimization can be None if we use partial-cv, then,
# obviously no output should be saved.
if self.Y_optimization is None:
return None, {}
# Abort in case of shape misalignment
if np.shape(self.Y_optimization)[0] != Y_optimization_pred.shape[0]:
return (
1.0,
{
'error':
"Targets %s and prediction %s don't have "
"the same length. Probably training didn't "
"finish" %
(np.shape(self.Y_optimization), Y_optimization_pred.shape)
},
)
# Abort if predictions contain NaNs
for y, s in [
# Y_train_pred deleted here. Fix unittest accordingly.
[Y_optimization_pred, 'optimization'],
[Y_valid_pred, 'validation'],
[Y_test_pred, 'test']
]:
if y is not None and not np.all(np.isfinite(y)):
return (
1.0,
{
'error':
'Model predictions for %s set contains NaNs.' % s
},
)
# Abort if we don't want to output anything.
# Since disable_file_output can also be a list, we have to explicitly
# compare it with True.
if self.disable_file_output is True:
return None, {}
# Notice that disable_file_output==False and disable_file_output==[]
# means the same thing here.
if self.disable_file_output is False:
self.disable_file_output = []
# Here onwards, the self.disable_file_output can be treated as a list
self.disable_file_output = cast(List, self.disable_file_output)
# This file can be written independently of the others down bellow
if ('y_optimization' not in self.disable_file_output):
if self.output_y_hat_optimization:
self.backend.save_targets_ensemble(self.Y_optimization)
models: Optional[BaseEstimator] = None
if hasattr(self, 'models'):
if len(self.models) > 0 and self.models[
0] is not None: # type: ignore[attr-defined]
if ('models' not in self.disable_file_output):
if self.task_type in CLASSIFICATION_TASKS:
models = VotingClassifier(
estimators=None,
voting='soft',
)
else:
models = VotingRegressor(estimators=None)
# Mypy cannot understand hasattr yet
models.estimators_ = self.models # type: ignore[attr-defined]
self.backend.save_numrun_to_dir(
seed=self.seed,
idx=self.num_run,
budget=self.budget,
model=self.model
if 'model' not in self.disable_file_output else None,
cv_model=models
if 'cv_model' not in self.disable_file_output else None,
ensemble_predictions=(Y_optimization_pred if 'y_optimization'
not in self.disable_file_output else None),
valid_predictions=(Y_valid_pred if 'y_valid'
not in self.disable_file_output else None),
test_predictions=(Y_test_pred if 'y_test'
not in self.disable_file_output else None),
)
return None, {}
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import VotingClassifier
# In[42]:
clf1=LogisticRegression(random_state=101)
clf2=RandomForestClassifier(random_state=101)
clf3=GaussianNB()
# In[43]:
X=df.drop(['Observed Attendance'],axis=1)
y=df['Observed Attendance']
eclf1=VotingClassifier(estimators=[('lr',clf1),('rf',clf2),('gnb',clf3)],weights=(1,2,3),voting='hard')
eclf1=eclf1.fit(X_train,y_train)
eclf1=eclf1.fit(X,y)
print(eclf1.predict(X))
# In[44]:
eclf2=VotingClassifier(estimators=[('lr',clf1),('rf',clf2),('gnb',clf3)],weights=(1,2,3),voting='hard')
eclf2=eclf2.fit(X_train,y_train)
predict=eclf2.predict(X_test)
print(classification_report(y_test,predict))
# In[45]:
print('Sensitivity_std:%f' % r_s)
print('Specificity_std:%f' % s_s)
print('f1_std:%f' % f_s)
if __name__ == '__main__':
# clf_lr = MLPClassifier(activation='relu',alpha=0.005)
# clf_lr.fit(data.iloc[:, :-1], data.iloc[:, -1])
clf_lr = LogisticRegression(C=0.07)
ada_lr = AdaBoostClassifier(clf_lr, n_estimators=20)
clf_svm = SVC(gamma=30, probability=True)
ada_svm = AdaBoostClassifier(clf_svm, n_estimators=20, algorithm="SAMME")
clf_nb = MultinomialNB(alpha=10)
ada_nb = AdaBoostClassifier(clf_nb, n_estimators=20)
# clf_dt=DecisionTreeClassifier()
# ada_dt=AdaBoostClassifier(clf_dt,n_estimators=20, learning_rate=0.5)
voting_clf = VotingClassifier(estimators=[("ada_lr", ada_lr),
("ada_svm", ada_svm),
("ada_nb", ada_nb)],
voting='soft') #,weights=[1.2,1,1]
for clf in [("lr", clf_lr), ("svm", clf_svm), ("NB", clf_nb),
('ensemble', voting_clf)]:
print(clf[0])
result(data, clf[1])
for clf in [("lr", clf_lr), ("svm", clf_svm), ("NB", clf_nb),
('ensemble', voting_clf)]:
print(clf[0])
std(data, clf[1])
print('************Stats of '+col+'****************')
print(encoded_ds[col].describe())
plt.hist(encoded_ds[col])
plt.show()
print('************End of stats for '+col+'*************')
print('\n')
#build X and y
X = encoded_ds.iloc[:,0:-1] #all columns except last column
y = integer_encoded_churn
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.3)
clf_knn = KNeighborsClassifier(n_neighbors=5)
clf_rf = RandomForestClassifier(random_state=1, n_estimators=100, max_depth=100, max_leaf_nodes=100)
clf_lr = LogisticRegression(class_weight="balanced")
estimators = [('knn', clf_knn), ('lr', clf_lr), ('dt', clf_rf)]
clf_avg = VotingClassifier(estimators,voting='soft')
clf_avg.fit(X_train,y_train)
print(accuracy_score(y_test, clf_avg.predict(X_test)))
y_pred = [0, 2, 1, 3]
y_true = [0, 1, 2, 3]
print(accuracy_score(y_true, y_pred))
plt.figure(figsize=(12,10))
cor = encoded_ds.corr()
sns.heatmap(cor, annot=True, cmap=plt.cm.Reds)
plt.show()
# The prediction seems to be quite similar for the 5 classifiers except when Adaboost is compared to the others classifiers.
#
# The 5 classifiers give more or less the same prediction but there is some differences. Theses differences between the 5 classifier predictions are sufficient to consider an ensembling vote.
# ### 6.2 Ensemble modeling
# #### 6.2.1 Combining models
#
# I choosed a voting classifier to combine the predictions coming from the 5 classifiers.
#
# I preferred to pass the argument "soft" to the voting parameter to take into account the probability of each vote.
# In[75]:
votingC = VotingClassifier(estimators=[('rfc', RFC_best), ('extc', ExtC_best),
('svc', SVMC_best), ('adac',ada_best),('gbc',GBC_best)], voting='soft', n_jobs=4)
votingC = votingC.fit(X_train, Y_train)
# ### 6.3 Prediction
# #### 6.3.1 Predict and Submit results
# In[76]:
test_Survived = pd.Series(votingC.predict(test), name="Survived")
results = pd.concat([IDtest,test_Survived],axis=1)
results.to_csv("ensemble_python_voting.csv",index=False)
# k fold cross validation
kfolds = KFold(n_splits=10, random_state=0)
# create the sub models
estimators = []
model1 = LogisticRegression()
estimators.append(('logistic', model1))
model2 = DecisionTreeClassifier()
estimators.append(('cart', model2))
model3 = SVC()
estimators.append(('svm', model3))
estimators
ensemble = VotingClassifier(estimators)
results = cross_val_score(ensemble, X_train, y_train, cv=kfolds)
print(results.mean())
modelfit = ensemble.fit(X_train, y_train)
y_pred = modelfit.predict(X_test)
y_pred
from sklearn.metrics import confusion_matrix, accuracy_score, classification_report
accuracy_score(y_test, y_pred)
cm = confusion_matrix(y_test, y_pred)
cm
import numpy as np
from sklearn.cross_validation import train_test_split
from sklearn.ensemble import AdaBoostClassifier, VotingClassifier
from sklearn.kernel_approximation import Nystroem
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import make_pipeline, make_union
from sklearn.preprocessing import FunctionTransformer
# NOTE: Make sure that the class is labeled 'class' in the data file
tpot_data = np.recfromcsv('PATH/TO/DATA/FILE', delimiter='COLUMN_SEPARATOR', dtype=np.float64)
features = np.delete(tpot_data.view(np.float64).reshape(tpot_data.size, -1), tpot_data.dtype.names.index('class'), axis=1)
training_features, testing_features, training_classes, testing_classes = \
train_test_split(features, tpot_data['class'], random_state=42)
exported_pipeline = make_pipeline(
make_union(
make_union(VotingClassifier([('branch',
AdaBoostClassifier(learning_rate=1.0, n_estimators=500)
)]), FunctionTransformer(lambda X: X)),
FunctionTransformer(lambda X: X)
),
Nystroem(gamma=8.0, kernel="polynomial", n_components=27),
LogisticRegression(C=0.0001, dual=False, penalty="l1")
)
exported_pipeline.fit(training_features, training_classes)
results = exported_pipeline.predict(testing_features)
print("-------------------------------------------------")
#define a decision tree model using entropy based information gain
#decTreeModel2 = tree.DecisionTreeClassifier(criterion='entropy')
#decTreeModel2 = AdaBoostClassifier()
#decTreeModel2 = GaussianNB()
#decTreeModel2 = GradientBoostingClassifier()
#decTreeModel2 = BaggingClassifier()
clf1 = LogisticRegression(random_state=1)
clf2 = RandomForestClassifier(random_state=1)
clf3 = GradientBoostingClassifier()
clf4 = SVC()
decTreeModel2 = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2),
('gnb', clf3), ('bc', clf4)],
voting='hard')
#decTreeModel2 = LogisticRegression(random_state=1)
#decTreeModel2 = LogisticRegression(random_state=1)
#train_dfs = preprocessing.normalize(train_dfs)
#Split the data: 60% training : 40% test set
instances_train, instances_test, target_train, target_test = cross_validation.train_test_split(
train_dfs, targetLabels, test_size=0.4, random_state=0)
#fit the model using just the test set
decTreeModel2.fit(instances_train, target_train)
#Use the model to make predictions for the test set queries
predictions = decTreeModel2.predict(instances_test)
#Output the accuracy score of the model on the test set
print("Accuracy= " +
assert_array_equal(eclf2.transform(X).shape, (4, 6))
assert_array_equal(eclf3.transform(X).shape, (3, 4, 2))
assert_array_almost_equal(eclf1.transform(X),
eclf2.transform(X))
assert_array_almost_equal(
eclf3.transform(X).swapaxes(0, 1).reshape((4, 6)),
eclf2.transform(X)
)
@pytest.mark.filterwarnings('ignore: Default solver will be changed') # 0.22
@pytest.mark.filterwarnings('ignore: Default multi_class will') # 0.22
@pytest.mark.parametrize(
"X, y, voter",
[(X, y, VotingClassifier(
[('lr', LogisticRegression()),
('rf', RandomForestClassifier(n_estimators=5))])),
(X_r, y_r, VotingRegressor(
[('lr', LinearRegression()),
('rf', RandomForestRegressor(n_estimators=5))]))]
)
@pytest.mark.parametrize("drop", [None, 'drop'])
def test_none_estimator_with_weights(X, y, voter, drop):
# check that an estimator can be set to None and passing some weight
# regression test for
# https://github.com/scikit-learn/scikit-learn/issues/13777
voter.fit(X, y, sample_weight=np.ones(y.shape))
voter.set_params(lr=drop)
voter.fit(X, y, sample_weight=np.ones(y.shape))
y_pred = voter.predict(X)
assert y_pred.shape == y.shape
