def ada():
pipeline = Pipeline([('count_vectorizer',
CountVectorizer(binary=True,
ngram_range=(1, 2),
max_features=15000,
stop_words=stopwords)),
('clf', AdaBoostClassifier())])
train_report(pipeline)
def check_classifiers(n_samples=10000):
"""
This function is not tested by default, it should be called manually
"""
testX, testY = generate_sample(n_samples, 10, 0.6)
trainX, trainY = generate_sample(n_samples, 10, 0.6)
uniform_features = ['column0']
ada = AdaBoostClassifier(n_estimators=50)
ideal_bayes = GaussianNB()
uBoost_SAMME = uBoostClassifier(
uniform_features=uniform_features,
uniform_label=1,
n_neighbors=50,
efficiency_steps=5,
n_estimators=50,
algorithm="SAMME")
uBoost_SAMME_R = uBoostClassifier(
uniform_features=uniform_features,
uniform_label=1,
n_neighbors=50,
efficiency_steps=5,
n_estimators=50,
algorithm="SAMME.R")
uBoost_SAMME_R_threaded = uBoostClassifier(
uniform_features=uniform_features,
uniform_label=1,
n_neighbors=50,
efficiency_steps=5,
n_estimators=50,
n_threads=3,
subsample=0.9,
algorithm="SAMME.R")
clf_dict = OrderedDict({
"Ada": ada,
"uBOOST": uBoost_SAMME,
"uBOOST.R": uBoost_SAMME_R,
"uBOOST.R2": uBoost_SAMME_R_threaded
})
cvms = {}
for clf_name, clf in clf_dict.items():
clf.fit(trainX, trainY)
p = clf.predict_proba(testX)
metric = KnnBasedCvM(uniform_features=uniform_features)
metric.fit(testX, testY)
cvms[clf_name] = metric(testY, p, sample_weight=np.ones(len(testY)))
assert cvms['uBOOST'] < cvms['ada']
print(cvms)
def get_feature_ranking(X_train, y_train):
print("feature ranking running....-> LogisticRegression")
model1 = LogisticRegression(max_iter=500)
rfe = RFECV(estimator=model1,
step=1,
cv=StratifiedKFold(2),
scoring='accuracy')
rfe = rfe.fit(X_train, y_train)
logr_ranking = []
for x, d in zip(rfe.ranking_, X_train.columns):
logr_ranking.append([d, x])
logr_ranking = pd.DataFrame(logr_ranking, columns=['features1', 'logr'])
logr_ranking.sort_values('features1', inplace=True)
print("feature ranking running....-> GradientBoostingClassifier")
model2 = GradientBoostingClassifier()
rfe = RFECV(estimator=model2,
step=1,
cv=StratifiedKFold(2),
scoring='accuracy')
rfe = rfe.fit(X_train, y_train)
gboost_ranking = []
for x, d in zip(rfe.ranking_, X_train.columns):
gboost_ranking.append([d, x])
gboost_ranking = pd.DataFrame(gboost_ranking,
columns=['features2', 'gboost'])
gboost_ranking.sort_values('features2', inplace=True)
print("feature ranking running....-> AdaBoostClassifier")
model3 = AdaBoostClassifier()
rfe = RFECV(estimator=model3,
step=1,
cv=StratifiedKFold(2),
scoring='accuracy')
rfe = rfe.fit(X_train, y_train)
adaboost_ranking = []
for x, d in zip(rfe.ranking_, X_train.columns):
adaboost_ranking.append([d, x])
adaboost_ranking = pd.DataFrame(adaboost_ranking,
columns=['features3', 'adaboost'])
adaboost_ranking.sort_values('features3', inplace=True)
feature_sum = logr_ranking['logr'] + gboost_ranking[
'gboost'] + adaboost_ranking['adaboost']
df_ranked = pd.concat([logr_ranking['features1'], feature_sum], axis=1)
df_ranked.sort_values(0, inplace=True)
return df_ranked
def check_classifiers(n_samples=10000, output_name_pattern=None):
"""
This function is not tested by default, it should be called manually
"""
testX, testY = generate_sample(n_samples, 10, 0.6)
trainX, trainY = generate_sample(n_samples, 10, 0.6)
uniform_variables = ['column0']
ada = AdaBoostClassifier(n_estimators=50)
ideal_bayes = HidingClassifier(train_variables=trainX.columns[1:],
base_estimator=GaussianNB())
uBoost_SAMME = uBoostClassifier(uniform_variables=uniform_variables,
n_neighbors=50,
efficiency_steps=5,
n_estimators=50,
algorithm="SAMME")
uBoost_SAMME_R = uBoostClassifier(uniform_variables=uniform_variables,
n_neighbors=50,
efficiency_steps=5,
n_estimators=50,
algorithm="SAMME.R")
clf_dict = ClassifiersDict({
"Ada": ada,
"Ideal": ideal_bayes,
"uBOOST": uBoost_SAMME,
"uBOOST.R": uBoost_SAMME_R
})
clf_dict.fit(trainX, trainY)
predictions = Predictions(clf_dict, testX, testY)
# predictions.print_mse(uniform_variables, in_html=False)
print(predictions.compute_metrics())
predictions.sde_curves(uniform_variables)
if output_name_pattern is not None:
pl.savefig(output_name_pattern % "mse_curves", bbox="tight")
_ = pl.figure()
predictions.learning_curves()
if output_name_pattern is not None:
pl.savefig(output_name_pattern % "learning_curves", bbox="tight")
predictions.efficiency(uniform_variables)
if output_name_pattern is not None:
pl.savefig(output_name_pattern % "efficiency_curves", bbox="tight")
def defaultModels(df_xmat, df_ymat_cat):
#### representitive common classifiers in sklearn ####
classifiers = [
GaussianNB(),
LogisticRegression(max_iter=500),
DecisionTreeClassifier(),
KNeighborsClassifier(),
SVC(kernel='rbf'),
AdaBoostClassifier(),
BaggingClassifier(),
ExtraTreesClassifier(),
GradientBoostingClassifier(),
RandomForestClassifier(),
]
cv = StratifiedKFold(n_splits=10)
res = []
for clf in classifiers:
print('processing...' + str(clf)[:10])
metrics_cv = []
for train_index, test_index in cv.split(df_xmat.values, df_ymat_cat):
X_train = df_xmat.iloc[train_index, :].values
X_test = df_xmat.iloc[test_index, :].values
y_train = [df_ymat_cat[i] for i in train_index]
y_test = [df_ymat_cat[i] for i in test_index]
clf.fit(X_train, y_train)
metrics_cv.append(clf.score(X_test, y_test))
res.append([
str(clf)[:10],
np.array(metrics_cv).mean(axis=0),
np.array(metrics_cv).std(axis=0)
])
return res
def __init__(self):
self.random_rate=33
clf1=SVC(C=1.0,random_state=33)
clf2=XGBClassifier(n_estimators=220,learning_rate=0.2,min_child_weight=2.3)
clf3=RandomForestClassifier(n_estimators=80,random_state=330,n_jobs=-1)
clf4=BaggingClassifier(n_estimators=40,random_state=101)
clf5=AdaBoostClassifier(n_estimators=70,learning_rate=1.5,random_state=33)
clf6=GradientBoostingClassifier(n_estimators=250,learning_rate=0.23,random_state=33)
clf7=XGBClassifier(n_estimators=100,learning_rate=0.12,min_child_weight=1)
base_model=[
['svc',clf1],
['xgbc',clf2],
['rfc',clf3],
['bgc',clf4],
['adbc',clf5],
['gdbc',clf6]
]
self.base_models=base_model
self.XGB=clf7
# NOTE: Adjust Trainingset / Testset division ratio:
divratio = 0.3
# Normalization (L1 & L2):
# NOTE: Change 'normtype' value to 'l1' / 'l2' to change normalization type:
normtype = 'l2'#'l1'
# model_selection is used for manually enabling the individual models.
# NOTE: Setting boolean value, eanbles/disables model.
model_selection = {
'ExtraTrees': ( True, ExtraTreesClassifier(n_estimators='warn', criterion='gini', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=False, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None) ),
'RandomForest': ( True, RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1) ),
'AdaBoost': ( True, AdaBoostClassifier(base_estimator=None, n_estimators=50, learning_rate=1.0, algorithm='SAMME.R', random_state=None) ),
'DecisionTree': ( True, DecisionTreeClassifier(criterion='gini', splitter='best', max_depth=5, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, class_weight=None, presort=False) ),
'GradientBoosting': (True, GradientBoostingClassifier(loss='deviance', learning_rate=0.1, n_estimators=100, subsample=1.0, criterion='friedman_mse', min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_depth=3, min_impurity_decrease=0.0, min_impurity_split=None, init=None, random_state=None, max_features=None, verbose=0, max_leaf_nodes=None, warm_start=False, presort='auto', validation_fraction=0.1, n_iter_no_change=None, tol=0.0001) ),
'BernoulliNB': (True, BernoulliNB(alpha=1.0, binarize=0.0, fit_prior=True, class_prior=None) ),
'BaggingClassifier': (True, BaggingClassifier(base_estimator=None, n_estimators=10, max_samples=1.0, max_features=1.0, bootstrap=True, bootstrap_features=False, oob_score=False, warm_start=False, n_jobs=None, random_state=None, verbose=0) ),
'NearestNeighbors': (True, KNeighborsClassifier(n_neighbors=5, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', metric_params=None, n_jobs=None) ), # (n_neighbors=4) ),
'LogisticRegressionCV': (True, LogisticRegressionCV(Cs=10, fit_intercept=True, cv='warn', dual=False, penalty='l2', scoring=None, solver='lbfgs', tol=0.0001, max_iter=100, class_weight=None, n_jobs=None, verbose=0, refit=True, intercept_scaling=1.0, multi_class='warn', random_state=None, l1_ratios=None) ),
'LDA': (True, LinearDiscriminantAnalysis(solver='svd', shrinkage=None, priors=None, n_components=None, store_covariance=False, tol=0.0001) ),
'LogisticRegression': (True, LogisticRegression(penalty='l2', dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=None, random_state=None, solver='warn', max_iter=100, multi_class='warn', verbose=0, warm_start=False, n_jobs=None, l1_ratio=None) ),
'CalibratedClassifierCV': (True, CalibratedClassifierCV(base_estimator=None, method='sigmoid', cv='warn') ),
'LinearSVC': (True, LinearSVC(penalty='l2', loss='squared_hinge', dual=True, tol=0.0001, C=1.0, multi_class='ovr', fit_intercept=True, intercept_scaling=1, class_weight=None, verbose=0, random_state=None, max_iter=1000) ),
'LinearSVM': ( True, SVC(kernel='linear', C=0.025) ), # (C=0.01, penalty='l1', dual=False) ),
'RBF_SVM': (True, SVC(gamma='auto') ),#gamma=2, C=1) ), #
'Nu_SVM': (True, NuSVC(gamma='auto') ),
'GaussianProcess': (False, GaussianProcessClassifier() ), #(1.0 * RBF(1.0)) ),
'NeuralNet': (True, MLPClassifier(alpha=1, max_iter=1000) ),
from sklearn.svm.classes import SVC
import os
import warnings
from sklearn.preprocessing import StandardScaler
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from xgboost import XGBClassifier
import pickle
from sklearn.model_selection import train_test_split
import shutil
from statistics import mean
warnings.filterwarnings('ignore')
classifiers = [
AdaBoostClassifier(),
BaggingClassifier(),
BernoulliNB(),
CalibratedClassifierCV(),
DecisionTreeClassifier(),
ExtraTreeClassifier(),
ExtraTreesClassifier(),
GaussianNB(),
GaussianProcessClassifier(),
GradientBoostingClassifier(),
KNeighborsClassifier(),
LabelPropagation(),
LabelSpreading(),
LinearDiscriminantAnalysis(),
LogisticRegression(),
LogisticRegressionCV(),
from sklearn.cluster.bicluster import SpectralBiclustering
from sklearn.cluster.spectral import SpectralClustering
from sklearn.cluster.bicluster import SpectralCoclustering
from sklearn.manifold.spectral_embedding_ import SpectralEmbedding
from sklearn.preprocessing.data import StandardScaler
from sklearn.manifold.t_sne import TSNE
from sklearn.linear_model.theil_sen import TheilSenRegressor
from sklearn.mixture.dpgmm import VBGMM
from sklearn.feature_selection.variance_threshold import VarianceThreshold
import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning)
clf_dict = {'ARDRegression':ARDRegression(),
'AdaBoostClassifier':AdaBoostClassifier(),
'AdaBoostRegressor':AdaBoostRegressor(),
'AdditiveChi2Sampler':AdditiveChi2Sampler(),
'AffinityPropagation':AffinityPropagation(),
'AgglomerativeClustering':AgglomerativeClustering(),
'BaggingClassifier':BaggingClassifier(),
'BaggingRegressor':BaggingRegressor(),
'BayesianGaussianMixture':BayesianGaussianMixture(),
'BayesianRidge':BayesianRidge(),
'BernoulliNB':BernoulliNB(),
'BernoulliRBM':BernoulliRBM(),
'Binarizer':Binarizer(),
'Birch':Birch(),
'CCA':CCA(),
'CalibratedClassifierCV':CalibratedClassifierCV(),
'DBSCAN':DBSCAN(),
from sklearn.tree import DecisionTreeClassifier
# classification models
classifiers = {
'K-Nearest Neighbors (Braycurtis norm)':
KNeighborsClassifier(n_neighbors=3, algorithm='auto', metric='braycurtis'),
'Random Forest':
RandomForestClassifier(n_estimators=80, n_jobs=1),
'SVM':
SVC(gamma=2, C=1),
'Linear Support Vector Machine':
SVC(kernel="linear", C=0.025),
'Decision Tree':
DecisionTreeClassifier(max_depth=5),
'Ada Boost':
AdaBoostClassifier(n_estimators=80, learning_rate=0.4),
'Naive Bayes':
GaussianNB(),
}
vc = VotingClassifier(estimators=list(classifiers.items()), voting='hard')
def evaluate_model(model_name, model, x, y):
"""Evaluate model accuracy via cross validation."""
print('%s:' % model_name)
model.fit(x, y.values.ravel())
print('CV f1_micro (not reusing data): %s' % np.mean(
cross_val_score(model, x, y.values.ravel(), cv=5, scoring='f1_micro')))
def predict(x, y, signal_matrix, verbose=1):
# Train and test random forests.
# load_path = "../homesite_data/resources/oversampled_normalized_data_ratio_2.5.bin"
load_path = "../homesite_data/resources/oversampled_normalized_data_ratio_2.bin"
homesite = Data()
homesite.load_sliptted_data(load_path)
del homesite.test_x # Deleted to save memory.
clf_ann = NeuralNetwork(path = "../homesite_data/ann_weights.bin", lr = 0.00005, \
lamb = 0)
train_output_ann = clf_ann.get_hidden_output(homesite.train_x)
validation_output_ann = clf_ann.get_hidden_output(homesite.validation_x)
# train_output_ann = np.hstack((train_output_ann, homesite.train_x))
# validation_output_ann = np.hstack((validation_output_ann, homesite.validation_x))
for c in range(1, 10):
# Train classifier.
print "Training classifier."
clf = AdaBoostClassifier(n_estimators=1 + 100 * c)
clf.fit(train_output_ann, homesite.train_y)
# Test classifier.
print 'Testing classifier.'
predicted_labels = clf.predict_proba(validation_output_ann)[:, 1]
# Show final results.
results = confusion_matrix(homesite.validation_y,
np.round(predicted_labels))
accuracy, precision, recall = compute_performance_metrics(results)
auc = compute_auc(homesite.validation_y, predicted_labels)
for f in field:
print("field", f)
temp = groups[f].median()
for i in range(0, 100945):
if (isnull(dataset.loc[i, f])):
condition = dataset.loc[i, '_conds']
dataset.loc[i, f] = temp[condition]
print("values: ", dataset.loc[i, f], " ; ", temp[condition])
dataset['_heatindexm'].fillna(dataset['_heatindexm'].median(), inplace=True)
dataset['_hum'].fillna(dataset['_hum'].median(), inplace=True)
dataset['_tempm'].fillna(dataset['_tempm'].median(), inplace=True)
dataset['_vism'].fillna(dataset['_vism'].median(), inplace=True)
dataset = dataset.values
X = dataset[:, 1:len(dataset[0])]
Y = dataset[:, 0]
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3)
for dept in range(5, 8):
for feats in range(5, 8):
classifier = AdaBoostClassifier(DecisionTreeClassifier(
max_depth=dept,
max_features=feats,
splitter="best",
criterion="entropy"),
learning_rate=1.0)
classifier.fit(X_train, Y_train)
print("depth: ", dept, "features: ", feats)
print("Score", classifier.score(X_train, Y_train))
# homesite.train_x = homesite.train_x[reduced_range]
# homesite.train_y = homesite.train_y[reduced_range]
C = [256, 512]
for c in C:
# Creating classifier.
mean_acc = 0.0
mean_recall = 0.0
mean_precision = 0.0
mean_tpr = 0.0
mean_fpr = np.linspace(0, 1, 100)
all_tpr = []
cvs = StratifiedKFold(homesite.train_y, n_folds=5)
clf = AdaBoostClassifier(n_estimators=c, random_state=0)
# Train classifier.
print "\nTraining classifier param %d" % c
for i, (train, test) in enumerate(cvs):
sm = OverSampler(verbose=False, ratio=2.5)
train_oversampled_x, train_oversampled_train_y = sm.fit_transform(
homesite.train_x[train], homesite.train_y[train])
probas_ = clf.fit(train_oversampled_x,
train_oversampled_train_y).predict_proba(
homesite.train_x[test])
fpr, tpr, thresholds = roc_curve(homesite.train_y[test],
probas_[:, 1])
def select_algorithm(X_all, y_all):
print("\n\n")
algos = [
{
"Name": "NB",
"Classifier": GaussianNB(),
"ParamGrid": {},
},
{
"Name": "SVM",
"Classifier": SVC(),
"ParamGrid": {
# 'kernel': ['linear', 'rbf'],
# 'C': [1, 1e3, 5e3, 1e4, 5e4, 1e5],
# 'gamma': ['auto', 0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1],
# 'max_iter': [-1, 1, 2, 3, 4, 5],
},
},
{
"Name": "KNN",
"Classifier": KNeighborsClassifier(),
"ParamGrid": {
"n_neighbors": [3, 4, 5, 6, 7, 8, 9],
"p": [1, 2, 3],
'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],
},
},
{
"Name": "ADA",
"Classifier": AdaBoostClassifier(),
"ParamGrid": {
"n_estimators": [10, 20, 30, 40, 50, 60, 70, 80],
"learning_rate": [0.5, 1.0, 1.5, 2.0],
'algorithm': ['SAMME', 'SAMME.R'],
},
},
{
"Name": "CART",
"Classifier": DecisionTreeClassifier(),
"ParamGrid": {
"min_samples_split": [2, 3, 4, 5],
"max_depth": [None, 4, 5, 6, 7, 8],
'criterion': ['gini', 'entropy'],
},
},
]
results = []
names = [d['Name'] for d in algos]
best_clf = None
best_score = 0
print("Selecting algorithm using cross validation")
print("With GridSearchCV for parameter tuning")
print("Candidates are: {0}".format(names))
for algo in algos:
# name = algo["Classifier"].__doc__[:24].strip()
name = algo["Name"]
print("\n\n")
print("{0}".format(name))
t0 = time()
cv_score, cv_clf = tune_and_test_classifier(algo["Classifier"],
algo["ParamGrid"],
my_dataset, features_list)
print("Algorithm tuned and tested in %0.3fs" % (time() - t0))
print("Score: {0}".format(cv_score))
if cv_score > best_score:
best_score = cv_score
best_clf = cv_clf
print("\n\n")
print("Best classifier:")
print(best_clf)
print("Best score: {0}".format(best_score))
assert best_score > 0, "THRESHOLD NOT MET"
print("\n\n")
return best_clf
def result():
if request.method == 'POST':
path = request.files.get('myFile')
df = pd.read_csv(path, encoding="ISO-8859-1")
filename = request.form['filename']
str1 = request.form['feature']
str2 = request.form['label']
if str1 in list(df) and str2 in list(df):
y = df[str2]
X = df[str1]
else:
return render_template('nameError.html')
x = []
for subject in X:
result = re.sub(r"http\S+", "", subject)
replaced = re.sub(r'[^a-zA-Z0-9 ]+', '', result)
x.append(replaced)
X = pd.Series(x)
X = X.str.lower()
"""
texts = []
for doc in X:
doc = nlp(doc, disable=['parser', 'ner'])
tokens = [tok.lemma_.lower().strip() for tok in doc if tok.lemma_ != '-PRON-']
tokens = [tok for tok in tokens if tok not in stopwords]
tokens = ' '.join(tokens)
texts.append(tokens)
X = pd.Series(texts)
"""
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.33)
tfidfvect = TfidfVectorizer(ngram_range=(1, 1))
X_train_tfidf = tfidfvect.fit_transform(X_train)
start = time()
clf1 = LinearSVC()
clf1.fit(X_train_tfidf, y_train)
pred_SVC = clf1.predict(tfidfvect.transform(X_test))
a1 = accuracy_score(y_test, pred_SVC)
end = time()
print("accuracy SVC: {} and time: {} s".format(a1, (end - start)))
start = time()
clf2 = LogisticRegression(n_jobs=-1,
multi_class='multinomial',
solver='newton-cg')
clf2.fit(X_train_tfidf, y_train)
pred_LR = clf2.predict(tfidfvect.transform(X_test))
a2 = accuracy_score(y_test, pred_LR)
end = time()
print("accuracy LR: {} and time: {}".format(a2, (end - start)))
start = time()
clf3 = RandomForestClassifier(n_jobs=-1)
clf3.fit(X_train_tfidf, y_train)
pred = clf3.predict(tfidfvect.transform(X_test))
a3 = accuracy_score(y_test, pred)
end = time()
print("accuracy RFC: {} and time: {}".format(a3, (end - start)))
start = time()
clf4 = MultinomialNB()
clf4.fit(X_train_tfidf, y_train)
pred = clf4.predict(tfidfvect.transform(X_test))
a4 = accuracy_score(y_test, pred)
end = time()
print("accuracy MNB: {} and time: {}".format(a4, (end - start)))
start = time()
clf5 = GaussianNB()
clf5.fit(X_train_tfidf.toarray(), y_train)
pred = clf5.predict(tfidfvect.transform(X_test).toarray())
a5 = accuracy_score(y_test, pred)
end = time()
print("accuracy GNB: {} and time: {}".format(a5, (end - start)))
start = time()
clf6 = LogisticRegressionCV(n_jobs=-1)
clf6.fit(X_train_tfidf, y_train)
pred_LR = clf6.predict(tfidfvect.transform(X_test))
a6 = accuracy_score(y_test, pred_LR)
end = time()
print("accuracy LRCV: {} and time: {}".format(a6, (end - start)))
start = time()
clf7 = AdaBoostClassifier()
clf7.fit(X_train_tfidf, y_train)
pred_LR = clf7.predict(tfidfvect.transform(X_test))
a7 = accuracy_score(y_test, pred_LR)
end = time()
print("accuracy ABC: {} and time: {}".format(a7, (end - start)))
start = time()
clf8 = BernoulliNB()
clf8.fit(X_train_tfidf.toarray(), y_train)
pred = clf8.predict(tfidfvect.transform(X_test).toarray())
a8 = accuracy_score(y_test, pred)
end = time()
print("accuracy BNB: {} and time: {}".format(a8, (end - start)))
start = time()
clf9 = Perceptron(n_jobs=-1)
clf9.fit(X_train_tfidf.toarray(), y_train)
pred = clf9.predict(tfidfvect.transform(X_test).toarray())
a9 = accuracy_score(y_test, pred)
end = time()
print("accuracy Per: {} and time: {}".format(a9, (end - start)))
start = time()
clf10 = RidgeClassifierCV()
clf10.fit(X_train_tfidf.toarray(), y_train)
pred = clf10.predict(tfidfvect.transform(X_test).toarray())
a10 = accuracy_score(y_test, pred)
end = time()
print("accuracy RidCV: {} and time: {}".format(a10, (end - start)))
start = time()
clf11 = SGDClassifier(n_jobs=-1)
clf11.fit(X_train_tfidf.toarray(), y_train)
pred = clf11.predict(tfidfvect.transform(X_test).toarray())
a11 = accuracy_score(y_test, pred)
end = time()
print("accuracy SGDC: {} and time: {}".format(a11, (end - start)))
start = time()
clf12 = SGDClassifier(n_jobs=-1)
clf12.fit(X_train_tfidf.toarray(), y_train)
pred = clf12.predict(tfidfvect.transform(X_test).toarray())
a12 = accuracy_score(y_test, pred)
end = time()
print("accuracy XGBC: {} and time: {}".format(a12, (end - start)))
acu_list = [a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12]
max_list = max(acu_list)
if max_list == a1:
pickle.dump(clf1, open(filename + '_model', 'wb'))
elif max_list == a2:
pickle.dump(clf2, open(filename + '_model', 'wb'))
elif max_list == a3:
pickle.dump(clf3, open(filename + '_model', 'wb'))
elif max_list == a4:
pickle.dump(clf4, open(filename + '_model', 'wb'))
elif max_list == a5:
pickle.dump(clf5, open(filename + '_model', 'wb'))
elif max_list == a6:
pickle.dump(clf6, open(filename + '_model', 'wb'))
elif max_list == a7:
pickle.dump(clf7, open(filename + '_model', 'wb'))
elif max_list == a8:
pickle.dump(clf8, open(filename + '_model', 'wb'))
elif max_list == a9:
pickle.dump(clf9, open(filename + '_model', 'wb'))
elif max_list == a10:
pickle.dump(clf10, open(filename + '_model', 'wb'))
elif max_list == a11:
pickle.dump(clf11, open(filename + '_model', 'wb'))
elif max_list == a12:
pickle.dump(clf12, open(filename + '_model', 'wb'))
pickle.dump(tfidfvect, open(filename + '_tfidfVect', 'wb'))
return render_template("result.html",
ac1=a1,
ac2=a2,
ac3=a3,
ac4=a4,
ac5=a5,
ac6=a6,
ac7=a7,
ac8=a8,
ac9=a9,
ac10=a10,
ac11=a11,
ac12=a12)
# Parameters
n_classes = 3
n_estimators = 30
plot_colors = "bry"
plot_step = 0.02
# Load data
iris = load_iris()
plot_idx = 1
for pair in ([0, 1], [0, 2], [2, 3]):
for model in (DecisionTreeClassifier(),
RandomForestClassifier(n_estimators=n_estimators),
ExtraTreesClassifier(n_estimators=n_estimators),
AdaBoostClassifier(DecisionTreeClassifier(),
n_estimators=n_estimators)):
# We only take the two corresponding features
X = iris.data[:, pair]
y = iris.target
# Shuffle
idx = np.arange(X.shape[0])
np.random.seed(13)
np.random.shuffle(idx)
X = X[idx]
y = y[idx]
# Standardize
mean = X.mean(axis=0)
std = X.std(axis=0)
X = (X - mean) / std
# Train
clf = model.fit(X, y)
print("LEARNING STEP")
#default
classifier = "not_init"
if alg == 0:
classifier = DecisionTreeClassifier(max_depth=tree_depth)
if alg == 1:
classifier = RandomForestClassifier(n_estimators=random_forest_size,
random_state=seed,
n_jobs=10)
if alg == 2:
classifier = create_ensemble(seed)
if alg == 3:
classifier = AdaBoostClassifier(DecisionTreeClassifier(),
n_estimators=boosting_size,
random_state=seed)
if alg == 4:
scaler = StandardScaler()
svr = SVR(kernel='rbf',
cache_size=4000,
C=1e3,
gamma=0.0001,
max_iter=200000,
epsilon=0.0001)
classifier = Pipeline([('standardize', scaler), ('svr', svr)])
if alg == 5:
classifier = GaussianNB()
if classifier == "not_init":
print("Classifier not init, exit")
from matplotlib import pyplot as plt
from custom_models import LoanPytorchModel
#Pulling in all data from 2007-2014
wayne_all = WayneLoanApprovalLoader(savename='wayneall_indicator',
csvfile='wayne_county_2007_2014.tsv')
# We have some data, now lets choose a model and some metrics, before putting them into experiment objects.
lr1 = LogisticRegression()
lr2 = LogisticRegression()
lrb1 = LogisticRegression(class_weight='balanced')
lrb2 = LogisticRegression(class_weight='balanced')
ada1 = AdaBoostClassifier()
ada2 = AdaBoostClassifier()
timemodels = [lr1, lr2]
criterion = accuracy_score # Thankfully this task has a pretty easy evaluation... you either get it right or wrong
# Getting temporally contiguous cuts of data, putting them into different experiments
data_time1 = wayne_all.get_dates([2007, 2008, 2009, 2010])
expmt_time1 = StratifiedExperiment(timemodels[0],
criterion,
data_time1[:, :-1],
data_time1[:, -1],
test_size=0.8)
data_time2 = wayne_all.get_dates([2011, 2012, 2013, 2014])
temp=groups[f].median()
for i in range(0,768):
if (dataset.loc[i,f]==0) & (dataset.loc[i,'outcome']==0):
dataset.loc[i,f]=temp[0]
if (dataset.loc[i,f]==0) & (dataset.loc[i,'outcome']==1):
dataset.loc[i,f]=temp[1]
dataset = dataset.values
X = dataset[:,0:len(dataset[0]) -1]
Y = dataset[:, (len(dataset[0])-1)]
#this is for decision tree
data=[[0,0,0,0,0]]
df=pd.DataFrame(data,columns=['feats','depth','split','max_leaf','acc'])
for feats in range(2, 7):
for dept in range(2, 6):
acc = 0
for split in range(5,40,5):
for leaf in range(7,10):
for i in range(20):
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3)
classifier=AdaBoostClassifier(DecisionTreeClassifier(max_depth=dept, max_features=feats,min_samples_split=split,splitter="best",criterion="entropy",max_leaf_nodes=leaf),learning_rate=1.0)
classifier.fit(X_train, Y_train)
res = classifier.score(X_test, Y_test)
acc = acc + res
acc = acc / 20
print('feats:', feats, 'Depth:', dept,'split:',split,'max_leaf',leaf, 'acc:', acc*100)
df=df.append({'feats':feats,'depth':dept,'split':split,'max_leaf':leaf,'acc':acc},ignore_index=True)
df.to_csv('Adaboost_result.csv', sep=',')
g_train = g.iloc[train_ind, :]
g_test = g.iloc[test_ind, :]
clf = tree.DecisionTreeClassifier(criterion='gini',
max_depth=6,
min_samples_leaf=3)
####################
clf = RandomForestClassifier(criterion='gini',
max_depth=6,
min_samples_leaf=3,
n_estimators=50)
####################
clf = AdaBoostClassifier(DecisionTreeClassifier(criterion='gini',
max_depth=6,
min_samples_leaf=3),
n_estimators=200,
learning_rate=0.1)
####################
clf = neighbors.KNeighborsClassifier(100, weights='uniform')
clf = neighbors.KNeighborsClassifier(100, weights='distance')
####################
clf = GaussianNB()
##############################
t0 = time()
param_grid = {
'C': [150, 500, 750, 1000],
'gamma': [0.0005, 0.001, 0.05, .01],
}
clf = GridSearchCV(SVC(kernel='rbf', class_weight='auto'), param_grid)
clf = clf.fit(X_train, y_train)
plt.title("Variance VS Components")
plt.show()
# Selecting the ideal number of components and fitting the data
pca = PCA(n_components=35)
X = pca.fit_transform(X)
### Training the models ###
models = [
("Gaussian NB", GaussianNB()),
("KNN", KNeighborsClassifier()),
("Decision Tree", DecisionTreeClassifier()),
("Random Forest", RandomForestClassifier()),
("Logistic Regression", LogisticRegression()),
("LDA", LinearDiscriminantAnalysis()),
("AdaBoost", AdaBoostClassifier()),
("QDA", QuadraticDiscriminantAnalysis()),
("Neural Net", MLPClassifier()),
("Gradient Boosting", GradientBoostingClassifier()),
("Extra Trees", ExtraTreesClassifier()),
# ("SVM", SVC(kernel="linear")),
("XGBOOST Classifer", XGBClassifier()),
]
## Model comparison ###
start = timeit.default_timer()
accuracies = []
for name, model in models:
# kfold = model_selection.KFold(n_splits=10)
class SkClassifier(MultiClassifier):
type_map = dict(
MultiClassifier.type_map,
c=float,
gamma=float,
cache=int,
n_estimators=int,
n_neighbors=int,
radius=float,
# probability=str_to_bool, # TODO
# class_weights # TODO
)
base_param_grid = {'svc__C': np.logspace(-2, 3, 5)}
classifiers = {
'svm': {
'build':
lambda self: SVC(kernel='linear',
C=self.c,
probability=self.probability,
cache_size=self.cache,
class_weight=SVM_CLASS_WEIGHTS
if SVM_CLASS_WEIGHTS else None),
'param_grid':
dict(base_param_grid),
'test_params': {
'probability': False
},
'roc_params': {
'probability': True
},
},
'svm-rbf': {
'build':
lambda self: SVC(kernel='rbf',
C=self.c,
probability=self.probability,
cache_size=self.cache,
gamma=self.gamma,
class_weight=SVM_CLASS_WEIGHTS
if SVM_CLASS_WEIGHTS else None),
'param_grid':
dict(base_param_grid, svc__gamma=np.logspace(-9, 3, 5)),
'test_params': {
'probability': False
},
'roc_params': {
'probability': True
},
},
'mlp': {
'build':
lambda self: MLPClassifier(solver='lbfgs',
alpha=1e-5,
hidden_layer_sizes=(5, 2),
random_state=42),
'param_grid':
dict(),
},
'knn': {
'build':
lambda self: KNeighborsClassifier(n_neighbors=self.n_neighbors,
n_jobs=self.n_jobs),
'param_grid':
dict(),
},
'rnn': {
'build':
lambda self: RadiusNeighborsClassifier(
radius=self.radius, n_jobs=self.n_jobs, outlier_label=0),
'param_grid':
dict(),
},
'ada': {
'build': lambda self: AdaBoostClassifier(),
'param_grid': dict(),
},
'ada-svm': {
'build':
lambda self: AdaBoostClassifier(base_estimator=SVC(
probability=True,
kernel='rbf',
C=self.c,
gamma=self.gamma,
cache_size=self.cache)),
'param_grid':
dict(),
},
'ada-sgd': {
'build':
lambda self: AdaBoostClassifier(
base_estimator=SGDClassifier(loss='hinge'), algorithm='SAMME'),
'param_grid':
dict(),
},
'rf': {
'build':
lambda self: RandomForestClassifier(n_estimators=self.n_estimators,
n_jobs=self.n_jobs),
'param_grid':
dict(),
},
'et': {
'build':
lambda self: ExtraTreesClassifier(n_estimators=self.n_estimators,
n_jobs=self.n_jobs),
'param_grid':
dict(),
},
'gnb': {
'build': lambda self: GaussianNB(),
'param_grid': dict(),
},
'bnb': {
'build': lambda self: BernoulliNB(),
'param_grid': dict(),
},
}
def __init__(self,
classifier_name='svm',
c=1000,
gamma=0.02,
cache=2000,
n_estimators=200,
n_neighbors=16,
radius=1.0,
probability=True,
**kwargs):
super().__init__(classifier_name, **kwargs)
logger.info(
'Initializing Scikit-learn classifier {}'.format(classifier_name))
self.c = c
self.gamma = gamma
self.cache = cache
self.probability = probability
self.n_estimators = n_estimators
self.n_neighbors = n_neighbors
self.radius = radius
def load_model(self, model_dir):
super().load_model(model_dir=model_dir)
self.clf = joblib.load(os.path.join(model_dir, 'clf.pkl'))
def save_model(self, output_dir):
super().save_model(output_dir=output_dir)
joblib.dump(self.clf, os.path.join(output_dir, 'clf.pkl'))
def _build_classifier(self, *args, **kwargs):
return self.classifier_dict['build'](self)
def _get_param_grid(self):
return self.classifier_dict['param_grid']
def _get_test_params(self):
return self.classifier_dict.get('test_params', {})
def _get_cv_params(self):
return {
**self._get_test_params(),
**self.classifier_dict.get('cv_params', {})
}
def _get_roc_params(self):
return self.classifier_dict.get('roc_params', {})
tuned_parameters = [{'n_estimators':[5, 10, 100, 200],
'criterion':['gini', 'entropy'],
'max_features':['log2', 'sqrt'],
'max_depth':[10, 100]
}]
algo = RandomForestClassifier()
elif choice=='i' or choice=='I':
print("\n**********************************\n")
print(" \t AdaBoost Classifier")
tuned_parameters = [{'n_estimators':[5, 10, 50, 100, 200],
'learning_rate':[0.1, 0.2, 0.5, 1],
'algorithm':['SAMME', 'SAMME.R'],
'random_state':[1, 2, 3, 5]
}]
algo = AdaBoostClassifier()
elif choice=='j' or choice=='J':
print("\n**********************************\n")
print(" \t Gradient Boosting Classifier")
tuned_parameters = [{'n_estimators':[5, 10, 50, 100, 200],
'learning_rate':[0.1, 0.2, 0.5, 1],
'min_impurity_decrease': [0.0001],
'max_depth':[10, 100]
}]
algo = GradientBoostingClassifier()
elif choice=='k' or choice=='K':
print("\n**********************************\n")
print(" \t XG Boost")
tuned_parameters = [{'n_estimators':[5, 10, 50, 100, 200],
train_data = train_data.dropna()
train_data = preprocess_data(train_data)
X = train_data[['is_1', 'is_2', 'is_3', 'Fare', 'is_male', 'is_female']]
Y = train_data['Survived']
XTrain, XTest, YTrain, YTest = train_test_split(X, Y, test_size=0.2)
n_estimators = 100
models = [
DecisionTreeClassifier(max_depth=3),
BaggingClassifier(n_estimators=n_estimators),
RandomForestClassifier(n_estimators=n_estimators),
ExtraTreesClassifier(n_estimators=n_estimators),
AdaBoostClassifier(n_estimators=n_estimators)
]
model_title = [
'DecisionTree', 'Bagging', 'RandomForest', 'ExtraTrees', 'AdaBoost'
]
surv_preds, surv_probs, scores, fprs, tprs, thres = ([] for i in range(6))
for i, model in enumerate(models):
print('Fitting {0}'.format(model_title[i]))
clf = model.fit(XTrain, YTrain)
surv_preds.append(model.predict(XTest))
surv_probs.append(model.predict_proba(XTest))
scores.append(model.score(XTest, YTest))
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm.classes import SVR
from sklearn.tree import DecisionTreeClassifier
DECISION_TREE = DecisionTreeClassifier()
LOGISTIC_REGRESSION = LogisticRegression()
NAIVE_BAYS = GaussianNB()
K_N_N = KNeighborsClassifier()
SUPPORT_VECTOR = svm.SVC(kernel="linear")
# Ensemble classifiers
RANDOM_FOREST = RandomForestClassifier(n_estimators=100)
GRADIENT_BOOST_CL = GradientBoostingClassifier(n_estimators=100)
ADA_BOOST = AdaBoostClassifier(n_estimators=100)
EXTRA_TREE = ExtraTreesClassifier(n_estimators=100)
# Regressors
GRADIENT_BOOST_RG = GradientBoostingRegressor(n_estimators=100, learning_rate=0.1)
LINEAR_RG = LinearRegression()
RIDGE_RG = Ridge()
LASSO_RG = Lasso()
SVR_RG = SVR()
def getClassifierMap():
CLASSIFIER_MAP = {
"DECISION_TREE": DECISION_TREE,
"LOGISTIC_REGRESSION": LOGISTIC_REGRESSION,
"NAIVE_BAYS": NAIVE_BAYS,
y,
test_size=0.2,
random_state=42)
#:# preprocessing
transform_pipeline = Pipeline([('scaler', StandardScaler())])
X_train = pd.DataFrame(transform_pipeline.fit_transform(X_train),
columns=X_train.columns)
#:# model
params = {'learning_rate': 0.5, 'n_estimators': 300}
classifier = AdaBoostClassifier(**params)
classifier.fit(X_train, y_train)
#:# hash
#:# e595f5d5683f3e3692608020cd5bde18
md5 = hashlib.md5(str(classifier).encode('utf-8')).hexdigest()
print(f'md5: {md5}')
#:# audit
y_pred = classifier.predict(transform_pipeline.transform(X_test))
y_pred_proba = classifier.predict_proba(
transform_pipeline.transform(X_test))[:, 1]
tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel()
print(f'acc: {accuracy_score(y_test, y_pred)}')
def all_classifier_models():
models = []
metrix = []
c_report = []
train_accuracy = []
test_accuracy = []
models.append(('LogisticRegression', LogisticRegression(solver='liblinear', multi_class='ovr')))
models.append(('LinearDiscriminantAnalysis', LinearDiscriminantAnalysis()))
models.append(('KNeighborsClassifier', KNeighborsClassifier()))
models.append(('DecisionTreeClassifier', DecisionTreeClassifier()))
models.append(('GaussianNB', GaussianNB()))
models.append(('RandomForestClassifier', RandomForestClassifier(n_estimators=100)))
models.append(('SVM', SVC(gamma='auto')))
models.append(('Linear_SVM', LinearSVC()))
models.append(('XGB', XGBClassifier()))
models.append(('SGD', SGDClassifier()))
models.append(('Perceptron', Perceptron()))
models.append(('ExtraTreeClassifier', ExtraTreeClassifier()))
models.append(('OneClassSVM', OneClassSVM(gamma = 'auto')))
models.append(('NuSVC', NuSVC()))
models.append(('MLPClassifier', MLPClassifier(solver='lbfgs', alpha=1e-5, random_state=1)))
models.append(('RadiusNeighborsClassifier', RadiusNeighborsClassifier(radius=2.0)))
models.append(('OutputCodeClassifier', OutputCodeClassifier(estimator=RandomForestClassifier(random_state=0),random_state=0)))
models.append(('OneVsOneClassifier', OneVsOneClassifier(estimator = RandomForestClassifier(random_state=1))))
models.append(('OneVsRestClassifier', OneVsRestClassifier(estimator = RandomForestClassifier(random_state=1))))
models.append(('LogisticRegressionCV', LogisticRegressionCV()))
models.append(('RidgeClassifierCV', RidgeClassifierCV()))
models.append(('RidgeClassifier', RidgeClassifier()))
models.append(('PassiveAggressiveClassifier', PassiveAggressiveClassifier()))
models.append(('GaussianProcessClassifier', GaussianProcessClassifier()))
models.append(('HistGradientBoostingClassifier', HistGradientBoostingClassifier()))
estimators = [('rf', RandomForestClassifier(n_estimators=10, random_state=42)),('svr', make_pipeline(StandardScaler(),LinearSVC(random_state=42)))]
models.append(('StackingClassifier', StackingClassifier(estimators=estimators, final_estimator=LogisticRegression())))
clf1 = LogisticRegression(multi_class='multinomial', random_state=1)
clf2 = RandomForestClassifier(n_estimators=50, random_state=1)
clf3 = GaussianNB()
models.append(('VotingClassifier', VotingClassifier(estimators=[('lr', clf1), ('rf', clf2), ('gnb', clf3)], voting='hard')))
models.append(('AdaBoostClassifier', AdaBoostClassifier()))
models.append(('GradientBoostingClassifier', GradientBoostingClassifier()))
models.append(('BaggingClassifier', BaggingClassifier()))
models.append(('ExtraTreesClassifier', ExtraTreesClassifier()))
models.append(('CategoricalNB', CategoricalNB()))
models.append(('ComplementNB', ComplementNB()))
models.append(('BernoulliNB', BernoulliNB()))
models.append(('MultinomialNB', MultinomialNB()))
models.append(('CalibratedClassifierCV', CalibratedClassifierCV()))
models.append(('LabelPropagation', LabelPropagation()))
models.append(('LabelSpreading', LabelSpreading()))
models.append(('NearestCentroid', NearestCentroid()))
models.append(('QuadraticDiscriminantAnalysis', QuadraticDiscriminantAnalysis()))
models.append(('GaussianMixture', GaussianMixture()))
models.append(('BayesianGaussianMixture', BayesianGaussianMixture()))
test_accuracy= []
names = []
for name, model in models:
try:
m = model
m.fit(X_train, y_train)
y_pred = m.predict(X_test)
train_acc = round(m.score(X_train, y_train) * 100, 2)
test_acc = metrics.accuracy_score(y_test,y_pred) *100
c_report.append(classification_report(y_test, y_pred))
test_accuracy.append(test_acc)
names.append(name)
metrix.append([name, train_acc, test_acc])
except:
print("Exception Occurred :",name)
return metrix,test_accuracy,names
