parser.add_argument('-n','--n_rounds', help='Number of Boost iterations', type=int, default=2000)
parser.add_argument('-e','--eta', help='Learning rate', type=float, default=0.01)
parser.add_argument('-r','--r_seed', help='Set random seed', type=int, default=3)
parser.add_argument('-b','--minbin', help='Minimum categorical bin size', type=int, default=1)
parser.add_argument('-ct','--cat_trans', help='Category transformation method', type=str, default='std')
parser.add_argument('-cv','--cv', action='store_true')
parser.add_argument('-codetest','--codetest', action='store_true')
parser.add_argument('-getcached', '--getcached', action='store_true')
parser.add_argument('-extra', '--extra', action='store_true')
m_params = vars(parser.parse_args())
# Load data
X, y, X_sub, ids = data.load(m_params)
print("BNP Parabas: AdaBoost...\n")
clf = AdaBoostClassifier(n_estimators=30, learning_rate=0.001, algorithm='SAMME', random_state=1)
if m_params['cv']:
# do cross validation scoring
kf = KFold(X.shape[0], n_folds=4, shuffle=True, random_state=1)
scr = np.zeros([len(kf)])
oob_pred = np.zeros(X.shape[0])
for i, (tr_ix, val_ix) in enumerate(kf):
clf.fit(X[tr_ix], y[tr_ix])
pred = clf.predict_proba(X[val_ix])
oob_pred[val_ix] = np.array(pred[:,1])
scr[i] = log_loss(y[val_ix], np.array(pred[:,1]))
print('Train score is:', scr[i])
print(log_loss(y, oob_pred))
print oob_pred[1:10]
X_spatial_norm = norm_scaller.fit_transform(X_spatial, Y)
# combine features
X_norm = np.concatenate([X_norm, X_spatial_norm], -1)
# save features to csv
np.savetxt("features.csv", X_norm, delimiter=",")
np.savetxt("labels.csv", Y, delimiter=",")
# find the optimal value for the Adaboost and Decision Tree
parameters = {'n_estimators': [15, 25, 50, 75], "learning_rate": [0.5, 0.75], "base_estimator__max_depth":[1, 3, 5, 7], "base_estimator__max_features":[.5], "base_estimator__max_leaf_nodes":[3, 5, 7]}
# base estimator for AdaBoost
base_estimator = DecisionTreeClassifier(criterion="entropy",
class_weight="balanced",
random_state=0)
base_model = AdaBoostClassifier(base_estimator=base_estimator,
random_state=0)
clf = GridSearchCV(base_model, parameters)
clf.fit(X_norm, Y)
best_params = clf.best_params_
acc = []
roc_auc_values = []
mcc = []
# perform k-fold cross validation
kf = StratifiedKFold(n_splits=3, shuffle=True, random_state=0)
fold = 0
for train_index, test_index in kf.split(X_norm, Y):
X_train = X_norm[train_index]
X_test = X_norm[test_index]
y_logistic_regression=logistic_regression.predict(standard_scale.fit_transform(x_test))
print(accuracy_score(y_test, y_logistic_regression))
print(precision_score(y_test, y_logistic_regression))
print(recall_score(y_test, y_logistic_regression))
print(f1_score(y_test, y_logistic_regression))
# # Adaboost
# In[40]:
adaboost_classifier=AdaBoostClassifier(
RandomForestClassifier(), n_estimators=200, algorithm="SAMME.R", learning_rate=0.5
)
adaboost_classifier.fit(x_train_sm, y_train_sm)
# In[41]:
# adaboost metrics
y_train_pred_adaboost=cross_val_predict(adaboost_classifier, x_train_sm, y_train_sm, cv=5)
# In[42]:
if (data[i, -1] == 0):
data[i, -1] = -1
train_data = data[0:5000, :]
test_data = data[5000:, :]
train_x = train_data[:, 0:-1]
train_y = train_data[:, -1]
test_x = test_data[:, 0:-1]
test_y = test_data[:, -1]
# train_index_list=[]
# test_index_list=[]
# kf=KFold(n_splits=5,shuffle=False)
# for train_index,test_index in kf.split(train_x):
# train_index_list.append(train_index)
# test_index_list.append(test_index)
#
# print(test_index_list[3])
#
# train_data=train_x[test_index_list[3],:]
w = np.ones(train_y.shape)
weight = w / train_y.shape[0]
estimator = AdaBoostClassifier()
estimator.fit(train_x, train_y, sample_weight=weight)
predict_y = estimator.predict(test_x)
score = estimator.score(test_x, test_y)
print(score)
encoded_test_data[categorical_variables] = encoded_test_data[
categorical_variables].apply(lambda x: d[x.name].transform(x))
independent_variables = [
x for x in train_data.columns
if x not in ['victim_id', 'datetime', 'criticality']
]
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score
def scorer(estimator, X, y):
y1 = np.array(estimator.predict(X))
score = roc_auc_score(y, y1)
return score
from sklearn.ensemble import AdaBoostClassifier
adam = AdaBoostClassifier(learning_rate=2, n_estimators=48, random_state=0)
adam.fit(encoded_train_data[independent_variables],
encoded_train_data['criticality'])
test_predictions = adam.predict(encoded_test_data[independent_variables])
victim_id = test_data['victim_id']
submission = pd.DataFrame({
'victim_id': victim_id,
'criticality': test_predictions
})
submission.to_csv('dataquest_submission4.csv', index=False)
pipe1 = Pipeline([('pca', PCA()), ('classifier', GaussianNB())])
param = {'pca__n_components': [4, 5, 6]}
gsv = GridSearchCV(pipe1, param_grid=param, n_jobs=2, scoring='f1', cv=2)
gsv.fit(features_train, labels_train)
clf = gsv.best_estimator_
print("GausianNB with PCA fitting time: %rs" % round(time() - t0, 3))
pred = clf.predict(features_test)
t0 = time()
test_classifier(clf, my_dataset, financial_features, folds=1000)
print("GausianNB evaluation time: %rs" % round(time() - t0, 3))
'''
Adaboost tuned for comparision with final algorithm
'''
from sklearn.tree import DecisionTreeClassifier
abc = AdaBoostClassifier(random_state=40)
data = featureFormat(my_dataset, financial_features, sort_keys=True)
labels, features = targetFeatureSplit(data)
dt = []
for i in range(6):
dt.append(DecisionTreeClassifier(max_depth=(i + 1)))
ab_params = {'base_estimator': dt, 'n_estimators': [60, 45, 101, 10]}
t0 = time()
abt = GridSearchCV(
abc,
ab_params,
scoring='f1',
)
abt = abt.fit(features_train, labels_train)
print("AdaBoost fitting time: %rs" % round(time() - t0, 3))
abc = abt.best_estimator_
n_classes = 3
n_estimators = 30
cmap = plt.cm.RdYlBu
plot_step = 0.02 # fine step width for decision surface contours
plot_step_coarser = 0.5 # step widths for coarse classifier guesses
RANDOM_SEED = 13 # fix the seed on each iteration
# Load data
iris = load_iris()
plot_idx = 1
models = [DecisionTreeClassifier(max_depth=None),
RandomForestClassifier(n_estimators=n_estimators),
ExtraTreesClassifier(n_estimators=n_estimators),
AdaBoostClassifier(DecisionTreeClassifier(max_depth=3),
n_estimators=n_estimators)]
for pair in ([0, 1], [0, 2], [2, 3]):
for model in models:
# We only take the two corresponding features
X = iris.data[:, pair]
y = iris.target
# Shuffle
idx = np.arange(X.shape[0])
np.random.seed(RANDOM_SEED)
np.random.shuffle(idx)
X = X[idx]
y = y[idx]
# Standardize
num_folds = 10
seed = 7
scoring = 'accuracy'
validation_size = 0.20
seed = 7
X_train, X_validation, Y_train, Y_validation = train_test_split(
X, Y, test_size=validation_size, random_state=seed)
# Spot Check Algorithms
models = []
models.append(('LR', LogisticRegression()))
models.append(('LDA', LinearDiscriminantAnalysis()))
models.append(('KNN', KNeighborsClassifier()))
models.append(('CART', DecisionTreeClassifier()))
models.append(('ADA', AdaBoostClassifier()))
models.append(('GBC', GradientBoostingClassifier()))
models.append(('RFC', RandomForestClassifier()))
models.append(('ETC', ExtraTreesClassifier()))
models.append(('SVM', SVC()))
results = []
names = []
for name, model in models:
kfold = KFold(n_splits=num_folds, random_state=seed)
cv_results = cross_val_score(model,
X_train,
Y_train,
cv=kfold,
scoring=scoring)
results.append(cv_results)
names.append(name)
rf_train_acc, rf_test_acc))
print("Precision score: ", precision_score(Y_test, predictions))
print("Recall score: ", recall_score(Y_test, predictions))
print("F1 score : ", rf_f1_score)
confusion_matrix(Y_test, predictions)
"""Model#3: Ada Boost Classifier"""
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import AdaBoostClassifier
tree = DecisionTreeClassifier(random_state=11,
max_features="auto",
class_weight="balanced",
max_depth=None)
model_ada = AdaBoostClassifier(base_estimator=tree)
model_ada = model_ada.fit(sequences_matrix, Y_train)
predictions = model_ada.predict(test_sequences_matrix)
ada_train_acc = accuracy_score(Y_train, model_ada.predict(sequences_matrix))
ada_test_acc = accuracy_score(Y_test, predictions)
ada_f1_score = f1_score(predictions, Y_test)
print("Accuracy score: \n a) Train : {}\n b) Test : {}".format(
ada_train_acc, ada_test_acc))
print("Precision score: ", precision_score(Y_test, predictions))
print("Recall score: ", recall_score(Y_test, predictions))
print("F1 score : ", ada_f1_score)
confusion_matrix(Y_test, predictions)
"""Model#4: Recurrent Neural Networks"""
def learn(fname):
data = pd.read_csv(fname, encoding='utf-8')
# shuffle data
data = data.sample(frac=1).reset_index(drop=True)
for idx, row in data.iterrows():
if row["content"] is np.nan:
data.drop(idx, inplace=True)
elif isinstance(row["content"], str):
data.set_value(idx, "content", clean_content(row["content"]))
X = data["label"].as_matrix()
del data["label"]
Y = data.as_matrix()
X_train, X_test, y_train, y_test = train_test_split(Y,
X,
test_size=0.20,
random_state=42)
count_vect = CountVectorizer()
X_train_counts = count_vect.fit_transform(X_train[:, 4])
print(X_train_counts.shape)
X_test_counts = count_vect.transform(X_test[:, 4])
print(X_test_counts.shape)
tfidf_transformer = TfidfTransformer()
X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)
X_test_tfidf = tfidf_transformer.transform(X_test_counts)
print(X_train_tfidf.shape)
print(X_test_tfidf.shape)
# add punctuation features
mnb_clf = MultinomialNB().fit(X_train_tfidf, y_train)
ada_clf = AdaBoostClassifier(n_estimators=100).fit(X_train_tfidf, y_train)
text_clf = SGDClassifier(loss='hinge',
penalty='l2',
alpha=1e-3,
n_iter=5,
random_state=42)
svm_clf = text_clf.fit(X_train_tfidf, y_train)
joblib.dump(tfidf_transformer, 'tfidf_transformer.pkl')
joblib.dump(count_vect, 'count_vect.pkl')
joblib.dump(mnb_clf, 'mnb_clf.pkl')
joblib.dump(svm_clf, 'svm_clf.pkl')
joblib.dump(ada_clf, 'ada_clf.pkl')
predicted = mnb_clf.predict(X_test_tfidf)
ada_predictions = ada_clf.predict(X_test_tfidf)
svm_predictions = svm_clf.predict(X_test_tfidf)
ada_score = np.mean(ada_predictions == y_test)
mnb_score = np.mean(predicted == y_test)
svm_score = np.mean(svm_predictions == y_test)
print("MNB: ", mnb_score)
print("ADA: ", ada_score)
print("SVM: ", svm_score)
sketchy_score = (mnb_score + ada_score + svm_score) / 3.0
print("Sketchy score: ", sketchy_score)
Mem_Ext = memory_usage_psutil()
print("Extra Trees Memory usage: ", Mem_Ext)
Cpu_Ext = psutil.cpu_percent()
print("Extra Trees Cpu Percent: ", Cpu_Ext)
# In[16]:
#Adaboost
from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier
start_time = time.clock()
AdaB_model = AdaBoostClassifier(
RandomForestClassifier(n_estimators=100,
n_jobs=-1,
criterion='gini',
class_weight='balanced'))
AdaB_model = AdaB_model.fit(Train_SVD, Y_train)
pred_Adab = AdaB_model.predict(Test_SVD)
Acc_Adab = accuracy_score(Y_test, pred_Adab)
print("Adaboost accuracy =", Acc_Adab)
F1_Adab = f1_score(Y_test, pred_Adab, average='micro')
print("Adaboost F-1 score(micro) = ", F1_Adab)
F1W_Adab = f1_score(Y_test, pred_Adab, average='weighted')
print("Adaboost F-1 score(weighted) = ", F1W_Adab)
Time_Adab = time.clock() - start_time
X = Z
try:
try:
os.remove('.'.join(args.file) + '.' + args.model + '.predict.xml')
except OSError:
pass
except OSError:
pass
dt = DecisionTreeClassifier(min_samples_split=20, random_state=99)
svr = SVR(kernel='poly', C=1e3, degree=2)
reg = linear_model.Lasso(alpha=0.1)
rf = RandomForestClassifier(max_depth=2, random_state=0)
ab = AdaBoostClassifier(DecisionTreeClassifier(max_depth=2),
n_estimators=600,
learning_rate=1.5,
algorithm="SAMME")
gnb = MultinomialNB() #GaussianNB()
lrg = LogisticRegression()
model = dt
if args.model == 'lrg':
model = lrg
elif args.model == 'svr':
model = svr
elif args.model == 'rf':
model = rf
elif args.model == 'av':
model == ab
elif args.model == 'gnb':
model = gnb
if args.semi == 'svr' and len(args.file) == 2: # one labled and one unlabled
from sklearn.model_selection import KFold
cv = 5
kf = KFold(n_splits=cv, shuffle=True)
for train_index, test_index in kf.split(feature, target):
X_train, X_test = feature[train_index], feature[test_index]
y_train, y_test = target[train_index], target[test_index]
from sklearn.ensemble import AdaBoostClassifier
clf4 = AdaBoostClassifier()
clf4.fit(X_train, y_train)
y_train_pred4 = clf4.predict(X_train)
y_pred4 = clf4.predict(X_test)
prec4, rec4, f14, acc_train4, acc_test4 = getScore(y_test, y_pred4, y_train_pred4)
prec_sum4 = prec_sum4 + prec4
rec_sum4 = rec_sum4 + rec4
f14_sum = f14_sum + f14
sum_acc_train4 = sum_acc_train4 + acc_train4
sum_acc_test4 = sum_acc_test4 + acc_test4
"Standard Scaler", "Normal Scaler", "MinMaxScaler", "MaxAbsScaler",
"Kernel Centerer"
]
for preprocess, name in zip(preprocessors, preprocessors_type):
print "-------------------------------------\n"
print "For Preprocessor : ", preprocess
print "--------------------------------------\n"
data = preprocess.fit_transform(forestFrame.values)
train_data, test_data, train_labels, test_labels = cross_validation.train_test_split(
data, target_labels.values, test_size=0.3)
rf = RandomForestClassifier(n_estimators=101)
ada = AdaBoostClassifier(n_estimators=101)
bagging = BaggingClassifier(n_estimators=101)
gradBoost = GradientBoostingClassifier(n_estimators=101)
classifiers = [rf, ada, bagging, gradBoost]
classifier_names = [
"Random Forests", "Adaboost", "Bagging", "Gradient Boost"
]
for classifier, classifier_name in zip(classifiers, classifier_names):
classifier.fit(train_data, train_labels)
predicted_labels = classifier.predict(test_data)
print "----------------------------------\n"
print "Accuracy for ", classifier_name, " : ", metrics.accuracy_score(
test_labels, predicted_labels)
y_test=y_test[:,0].reshape(-1)
print("BAGGİNG")
from sklearn.ensemble import BaggingClassifier
bg = BaggingClassifier(clf,max_samples=0.5, max_features=1.0, n_estimators=20)
bg.fit(X_train, y_train)
y_pred =bg.predict(X_test)
polat=accuracy_score(y_test, y_pred)
print("Accuracy:",polat)
#ADABOOST
#integer
X_train=X_train.astype ("int")
X_test=X_test.astype("int")
y_train=y_train.astype("int")
y_test=y_test.astype("int")
print("ADABOOST")
clf = AdaBoostClassifier(DecisionTreeClassifier(max_depth=3), n_estimators=10, learning_rate=0.01)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
memati=accuracy_score(y_test, y_pred)
print("Accuracy:",memati)
'''# Create Decision Tree classifer object
clf = DecisionTreeClassifier(criterion="entropy", max_depth=3)
# Train Decision Tree Classifer
clf = clf.fit(X_train,y_train)
#Predict the response for test dataset
y_pred = clf.predict(X_test)
print("Accuracy:",metrics.accuracy_score(y_test, y_pred))
from sklearn.tree import export_graphviz
from sklearn.externals.six import StringIO
# initial visualization
plt.xlim(0.0, 1.0)
plt.ylim(0.0, 1.0)
plt.scatter(bumpy_fast, grade_fast, color="b", label="fast")
plt.scatter(grade_slow, bumpy_slow, color="r", label="slow")
plt.legend()
plt.xlabel("bumpiness")
plt.ylabel("grade")
plt.show()
# your code here! name your classifier object clf if you want the
# visualization code (prettyPicture) to show you the decision boundary
clf = AdaBoostClassifier(n_estimators=50)
clf.fit(features_train, labels_train)
pred = clf.predict(features_test)
acc = accuracy_score(pred, labels_test)
print(clf.score(features_test, labels_test))
print(f'Accuracy: {acc}')
try:
prettyPicture(clf, features_test, labels_test)
except NameError:
pass
# hf.lasso_selection(features, labels, features_list)
### Task 4: Try a varity of classifiers
### Please name your classifier clf for easy export below.
### Note that if you want to do PCA or other multi-stage operations,
### you'll need to use Pipelines. For more info:
### http://scikit-learn.org/stable/modules/pipeline.html
random_seed = 1303
features_train, features_test, labels_train, labels_test = \
train_test_split(features, labels, test_size=0.3, random_state=random_seed)
cv = StratifiedShuffleSplit(labels_train,
n_iter=20,
test_size=0.5,
random_state=random_seed)
ada = AdaBoostClassifier(random_state=random_seed)
selector = RFE(ada, step=1)
pipe_ada = Pipeline(steps=[('RFE', selector), ('ada', ada)])
params_ada_gs = {
"RFE__n_features_to_select": np.arange(11, 15, 2),
"ada__learning_rate": np.arange(0.3, 0.7, 0.2),
"ada__n_estimators": [50, 100]
}
# pipe_ada= Pipeline(steps=[('RFE', selector), ('ada', ada)])
# params_ada_gs = {"RFE__n_features_to_select": [15],
# "ada__learning_rate" : [0.5],
# "ada__n_estimators" : [50]
# }
gs = GridSearchCV(pipe_ada, params_ada_gs, scoring='f1', cv=cv)
#%%
from sklearn.naive_bayes import MultinomialNB
import pandas as pd
import numpy as np
#%%
data = pd.read_csv('spambase.data').as_matrix()
np.random.shuffle(data)
X = data[:, :48]
y = data[:, -1]
#%%
Xtrain = X[:-100, ]
ytrain = y[:-100, ]
Xtest = X[-100:, ]
ytest = y[-100:, ]
#%%
model = MultinomialNB()
model.fit(Xtrain, ytrain)
print("\nAccuracy for NB: ", model.score(Xtest, ytest))
#%%
from sklearn.ensemble import AdaBoostClassifier
#%%
model = AdaBoostClassifier()
model.fit(Xtrain, ytrain)
print("Accuracy for Adaboost is: ", model.score(Xtest, ytest))
kfold = model_selection.KFold(n_splits=10, random_state=7)
cart = DecisionTreeClassifier()
num_trees = 100
model = BaggingClassifier(base_estimator=cart,
n_estimators=num_trees,
random_state=7)
results = model_selection.cross_val_score(model, X, Y, cv=kfold)
print(results.mean())
# AdaBoost Classification
from sklearn.ensemble import AdaBoostClassifier
seed = 7
num_trees = 70
kfold = model_selection.KFold(n_splits=10, random_state=seed)
model = AdaBoostClassifier(n_estimators=num_trees, random_state=seed)
results = model_selection.cross_val_score(model, X, Y, cv=kfold)
print(results.mean())
# Voting Ensemble for Classification
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from sklearn.ensemble import VotingClassifier
kfold = model_selection.KFold(n_splits=10, random_state=seed)
# create the sub models
estimators = []
model1 = LogisticRegression(solver='lbfgs', max_iter=10000)
names = [
"Nearest Neighbors", "Linear SVM", "RBF SVM", "Gaussian Process",
"Decision Tree", "Random Forest", "Neural Net", "AdaBoost", "Naive Bayes",
"QDA"
]
classifiers = [
KNeighborsClassifier(3),
SVC(kernel="linear", C=0.025),
SVC(gamma=2, C=1),
GaussianProcessClassifier(1.0 * RBF(1.0)),
DecisionTreeClassifier(max_depth=5),
RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),
MLPClassifier(alpha=1),
AdaBoostClassifier(),
GaussianNB(),
QuadraticDiscriminantAnalysis()
]
X, y = make_classification(n_features=2,
n_redundant=0,
n_informative=2,
random_state=1,
n_clusters_per_class=1)
rng = np.random.RandomState(2)
X += 2 * rng.uniform(size=X.shape)
linearly_separable = (X, y)
datasets = [
make_moons(noise=0.3, random_state=0),
from sklearn.feature_selection import SelectKBest, chi2
from imblearn.pipeline import Pipeline
from customer_review_API_lib import run_test_predictions
DIRECTORY = 'C:/Users/bergj/Documents/Geroge Mason/Courses/2019-Spring/GMU- CS 584/FinalProject/data/'
run_test_predictions(
toys_file='{}{}'.format(DIRECTORY, 'amazon_reviews_us_Apparel_v1_00.tsv'),
apparel_file='{}{}'.format(DIRECTORY, 'amazon_reviews_us_Toys_v1_00.tsv'),
min_words=12,
n_reviews=200000,
pipeline=Pipeline([('tfidf',
TfidfVectorizer(norm='l2',
max_df=0.6,
min_df=75,
ngram_range=(1, 1),
stop_words='english')),
('feature-extract', SelectKBest(chi2, k=20)),
('clf',
AdaBoostClassifier(base_estimator=LogisticRegression(
C=0.1, class_weight='balanced',
solver='liblinear'),
n_estimators=10,
learning_rate=5))]),
gridsearch_args=dict(param_grid={
'clf__n_estimators': [2, 5, 10, 15],
'clf__learning_rate': [0.1, 1, 5]
},
scoring='f1'))
clf_mlp.fit(X_train, y_train)
end_time = time.time() - start_time
print end_time
print "Evaluation time"
start_time = time.time()
predictions = clf_mlp.predict(X_test)
end_time = time.time() - start_time
print end_time
print(classification_report(y_test, predictions))
joblib.dump(
clf_mlp, 'datasetB_results/' + 'MLP_logistic_sgd_' + method + '-' +
size + '.joblib.pkl')
from sklearn.ensemble import AdaBoostClassifier
print "AdaBoostClassifier"
clf_ada = AdaBoostClassifier()
print "Training classifier"
start_time = time.time()
clf_ada.fit(X_train, y_train)
end_time = time.time() - start_time
print end_time
print "Evaluation time"
start_time = time.time()
predictions = clf_ada.predict(X_test)
end_time = time.time() - start_time
print end_time
print(classification_report(y_test, predictions))
joblib.dump(
clf_ada, 'datasetB_results/' + 'AdaBoostClassifier_' + method +
'-' + size + '.joblib.pkl')
})
# appending our result table
result_tabulation = result_tabulation.append(Bagging_Meta_estimator,
ignore_index=True)
# view the result table
result_tabulation
# In[183]:
# Adaboost
from sklearn.ensemble import AdaBoostClassifier
# build the model
adaboost = AdaBoostClassifier(random_state=10)
# fit the model
adaboost.fit(X_train, y_train)
# In[184]:
# predict the values
y_pred_adaboost = adaboost.predict(X_test)
# In[185]:
adaboost_metrics = pd.Series({
'Model':
"AdaBoost",
'AUC Score':
metrics.roc_auc_score(y_test, y_pred_adaboost),
start_time = time.time()
print("开始训练模型..")
model.fit(x_train, t_train) # 训练模型
end_time = time.time()
print("训练结束! 耗时:", end_time-start_time, "s")
joblib.dump(model, save_file) # 保存模型
if __name__ == "__main__":
# adaboost
n_est = 20
print("弱分类器数为: ", n_est)
model = AdaBoostClassifier(DecisionTreeClassifier(max_depth=2, min_samples_split=20, min_samples_leaf=5),
algorithm="SAMME", n_estimators=n_est, learning_rate=0.5)
dataset_dir = os.path.abspath(os.path.join(os.getcwd(), "..")) + "/model"
save_dir = dataset_dir + '/model.pkl'
if(os.path.isfile(save_dir)): # 读取模型
model = joblib.load(save_dir) # 加载模型
else:
train_mnist(model, save_dir) # 训练模型
(x_train, t_train), (x_test, t_test) = load_mnist() # 加载训练集、测试集
print("测试集识别精确度:", model.score(x_test, t_test))
RandomForestClassifier(criterion='entropy',
min_samples_split=5,
random_state=24)),
('Random Forrest 2',
RandomForestClassifier(criterion='entropy',
max_depth=20,
random_state=24)),
('Random Forrest 3',
RandomForestClassifier(criterion='entropy',
min_samples_split=20,
random_state=24)),
('Random Forrest 4',
RandomForestClassifier(criterion='entropy',
min_samples_split=50,
random_state=24)),
('ADdaBOost', AdaBoostClassifier(n_estimators=100,
random_state=24)),
('PERceptron',
CalibratedClassifierCV(Perceptron(max_iter=50,
tol=-np.infty,
random_state=24),
cv=10,
method='isotonic')),
('PERceptron 2',
CalibratedClassifierCV(Perceptron(max_iter=100,
tol=-np.infty,
random_state=24),
cv=10,
method='isotonic')),
('KNeighbors Classifier', KNeighborsClassifier(n_neighbors=5)),
('KNeighbors Classifier 2', KNeighborsClassifier(n_neighbors=2)),
('Multi-Layer Perceptron', MLPClassifier(random_state=24))]
from sklearn.datasets import load_iris
from sklearn.ensemble import AdaBoostClassifier
# 导包
from sklearn.model_selection import cross_val_score
from __init__ import write_log
log_file = "test_cnn_9_1.log"
# 载入数据，sklearn中自带的iris数据集
iris = load_iris()
write_log(str(iris), file=log_file)
"""
AdaBoostClassifier参数解释
base_estimator:弱分类器，默认是CART分类树：DecisionTressClassifier
algorithm：在scikit-learn实现了两种AdaBoost分类算法，即SAMME和SAMME.R，
SAMME就是原理篇介绍到的AdaBoost算法，指Discrete AdaBoost
SAMME.R指Real AdaBoost，返回值不再是离散的类型，而是一个表示概率的实数值，算法流程见后文
两者的主要区别是弱分类器权重的度量，SAMME使用了分类效果作为弱分类器权重，SAMME.R使用了预测概率作为弱分类器权重。
SAMME.R的迭代一般比SAMME快，默认算法是SAMME.R。因此，base_estimator必须使用支持概率预测的分类器。
loss：这个只在回归中用到，不解释了
n_estimator:最大迭代次数，默认50。在实际调参过程中，常常将n_estimator和学习率learning_rate一起考虑
learning_rate:每个弱分类器的权重缩减系数v。f_k(x)=f_{k-1}*a_k*G_k(x)。较小的v意味着更多的迭代次数，默认是1，也就是v不发挥作用。
另外的弱分类器的调参，弱分类器不同则参数不同，这里不详细叙述
"""
# 构建模型
clf = AdaBoostClassifier(n_estimators=100) # 弱分类器个数设为100
scores = cross_val_score(clf, iris.data, iris.target)
print(scores.mean())
y = df[DEPENDENT_VARIABLE]
X = df.drop(DEPENDENT_VARIABLE,axis=1)
from sklearn.tree import DecisionTreeClassifier
basetree = DecisionTreeClassifier( criterion="entropy")
from sklearn.feature_selection import RFE
rfe = RFE(basetree)
rfe.fit(X,y)
rfe.ranking_
rankdf = pd.DataFrame({"rank" : rfe.ranking_ , "feature":X.columns})
rankdf[rankdf["rank"] == 1]
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import RandomizedSearchCV , cross_val_score
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.ensemble import GradientBoostingClassifier , AdaBoostClassifier
X_train , X_test ,y_train, y_test = train_test_split(X , y , stratify = y)
basetree = DecisionTreeClassifier( criterion="gini" , min_samples_split=0.4)
clf = AdaBoostClassifier(n_estimators=50 , learning_rate=0.5)
cross_val_score(clf , X,y,scoring="roc_auc")
#将数据规定在[-1,1]之间
scaler = MinMaxScaler(feature_range=(-1, 1))
# 数据转换
rescaledX = scaler.fit_transform(traindata)
# 设定数据的打印格式
set_printoptions(precision=3)
print(rescaledX)
# 调参
num_folds = 5
seed = 7
kfold = KFold(n_splits=num_folds, random_state=seed)
from sklearn.model_selection import GridSearchCV
scoring = 'accuracy'
param_grid = {'n_estimators': [10, 30, 50, 70, 90, 100]}
model = AdaBoostClassifier(LogisticRegression(C=1000,
multi_class='multinomial',
solver='lbfgs'),
algorithm='SAMME.R')
#LogisticRegression(),algorithm='SAMME.R'
kfold = KFold(n_splits=num_folds, random_state=seed)
grid = GridSearchCV(estimator=model,
param_grid=param_grid,
scoring=scoring,
cv=kfold)
grid_result = grid.fit(X=rescaledX, y=trainlabel)
print('最优：%s 使用%s' % (grid_result.best_score_, grid_result.best_params_))
cv_results = zip(grid_result.cv_results_['mean_test_score'],
grid_result.cv_results_['std_test_score'],
grid_result.cv_results_['params'])
for mean, std, param in cv_results:
print('%f (%f) with %r' % (mean, std, param))
msg = "Taxa de acerto do vencedor entre os dois algoritmos no mundo real: {0}".format(
taxa_de_acerto)
print(msg)
resultados = {}
from sklearn.naive_bayes import MultinomialNB
modeloMultinomial = MultinomialNB()
resultadoMultinomial = fit_and_predict("MultinomialNB", modeloMultinomial,
treino_dados, treino_marcacoes)
resultados[resultadoMultinomial] = modeloMultinomial
from sklearn.ensemble import AdaBoostClassifier
modeloAdaBoost = AdaBoostClassifier(
random_state=0) # elimina randomizacao (sempre mesmo resultado)
resultadoAdaBoost = fit_and_predict("AdaBoostClassifier", modeloAdaBoost,
treino_dados, treino_marcacoes)
resultados[resultadoAdaBoost] = modeloAdaBoost
# Algoritmo Um Contra Resto usando LinearSVC
from sklearn.multiclass import OneVsRestClassifier
from sklearn.svm import LinearSVC
modeloOneVsRest = OneVsRestClassifier(
LinearSVC(random_state=0)) # elimina randomizacao (sempre mesmo resultado)
resultadoOneVsRest = fit_and_predict("OneVsRest", modeloOneVsRest,
treino_dados, treino_marcacoes)
resultados[resultadoOneVsRest] = modeloOneVsRest
# Algoritmo Um Contra Um (todas as categorias são testadas entre si)
from sklearn.multiclass import OneVsOneClassifier
if ( len(inpgrade1.columns) == 2 ):
grade = load_data("ml_scripts/data/" + course + "/MasterTrainingData1.csv")
X = grade[['Homework 1', 'Quiz 1 ']].values
scaler = StandardScaler().fit(X)
X = scaler.transform(X)
y = grade[["Grade"]].values.ravel()
y1 = []
for label in y:
if label == "Good":
y1.append(0)
if label == "OK":
y1.append(1)
if label == "High-risk":
y1.append(2)
# model = MLPClassifier(random_state=0, hidden_layer_sizes=(7, 20), alpha=0.0001, solver='lbfgs',max_iter=200, learning_rate = 'adaptive')
model = AdaBoostClassifier(random_state=0, n_estimators=1000)
model.fit(X, y1)
chosenModels[0] = model
elif ( len(inpgrade1.columns) == 5 ):
grade = load_data("ml_scripts/data/" + course + "/MasterTrainingData2.csv")
X = grade[['Quiz 1 ', 'Quiz 2 ', 'Quiz 3', 'Homework 1', 'Homework 2']].values
y = grade[["Grade"]].values.ravel()
y1 = []
for label in y:
if label == "Good":
y1.append(0)
if label == "OK":
y1.append(1)
if label == "High-risk":
y1.append(2)