class LogisticRegression:
def __init__(self):
self.prfrm_oprtns = Operations()
self.model = ProvideModels()
def perform_logistic_regression(self, features_train, labels_train,
features_test, labels_test, algo_name):
print "------------ performing logistic regression ---------------------"
penalties = ['l2', 'l1']
best_accuracy = None
best_f1Score = None
model = None
if algo_name == Constants.logisticRegression:
clf = self.model.get_logisticRegression()
elif algo_name == Constants.logisticRegressionSGD:
clf = self.model.get_SGDClassifier()
for curr_penalty in penalties:
clf.set_params(penalty=curr_penalty)
accuracy, f1score, pred = self.prfrm_oprtns.perform_operations(
features_train, labels_train, features_test, labels_test, clf)
print "accuracy with penalty", curr_penalty, "is", accuracy
print "f1score with penalty", curr_penalty, "is", f1score
if accuracy > best_accuracy:
best_accuracy = accuracy
if f1score > best_f1Score:
best_f1Score = f1score
model = clf
return (model, accuracy, f1score, pred)
class SVM:
def __init__(self):
self.prfrm_oprtns = Operations()
self.model = ProvideModels()
def perform_svm_SVC(self, features_train, labels_train, features_test,
labels_test):
print "------------ performing SVM --------------------"
clf = self.model.get_LinearSVM()
accuracy, f1score, pred = self.prfrm_oprtns.perform_operations(
features_train, labels_train, features_test, labels_test, clf)
return (clf, accuracy, f1score, pred)
class GradientBoosting:
def __init__(self):
self.prfrm_oprtns = Operations()
self.model = ProvideModels()
def perform_gradient_boosting(self,
features_train,
labels_train,
features_test,
labels_test,
algo_name,
learning_rates=[0.3],
features=[0.7],
depths=[9]):
print "--------------- performing gradient boosting ----------------------"
if algo_name == Constants.gbmClassifier:
clf = self.model.get_GBMClassifier(len(features_train.columns))
elif algo_name == Constants.gbmRegressor:
clf = self.model.get_GBMRegressor(len(features_train.columns))
best_accuracy = None
model = None
best_f1score = None
for depth in depths:
for feature in features:
for curr_learning_rate in learning_rates:
clf.set_params(learning_rate=curr_learning_rate,
max_depth=depth,
max_features=feature)
accuracy, f1score, pred = self.prfrm_oprtns.perform_operations(
features_train, labels_train, features_test,
labels_test, clf)
print " accuracy with learning rate ", curr_learning_rate, "max_fatures ", feature, "and depth ", depth, "is ", accuracy
print " f1score with learning rate ", curr_learning_rate, "max_fatures ", feature, "and depth ", depth, "is ", f1score
if accuracy > best_accuracy:
best_accuracy = accuracy
if f1score > best_f1score:
best_f1score = f1score
model = clf
return (model, accuracy, f1score, pred)
class NaiveBayes:
def __init__(self):
self.prfrm_oprtns = Operations()
self.model = ProvideModels()
def perform_naive_bayes(self, features_train, labels_train, features_test,
labels_test, algo_name):
print "-------------- performing naive bayes ---------------"
if algo_name == Constants.gaussianNB:
clf = self.model.get_GaussianNB()
elif algo_name == Constants.multnomialNB:
clf = self.model.get_MultinomialNB()
elif algo_name == Constants.bernoulliNB:
clf = self.model.get_BernoulliNB()
accuracy, f1score, pred = self.prfrm_oprtns.perform_operations(
features_train, labels_train, features_test, labels_test, clf)
return (clf, accuracy, f1score, pred)
class RandomForest:
def __init__(self):
self.prfrm_oprtns = Operations()
self.model = ProvideModels()
def perform_random_forest(self,
features_train,
labels_train,
features_test,
labels_test,
algo_name,
depths=[6, 10, 14, 18, 22, 26]):
print "-------------- performing random forest --------------------"
if algo_name == Constants.randomForestClassifier:
clf = self.model.get_RandomForestClassifier(
len(features_train.columns))
elif algo_name == Constants.randomForestRegressor:
clf = self.model.get_RandomForestRegressor(
len(features_train.columns))
best_accuracy = None
best_f1Score = None
model = None
for depth in depths:
clf.set_params(max_depth=depth)
accuracy, f1score, pred = self.prfrm_oprtns.perform_operations(
features_train, labels_train, features_test, labels_test, clf)
print "accuracy with depth ", depth, "is", accuracy
print "f1score with depth", depth, "is", f1score
if accuracy > best_accuracy:
best_accuracy = accuracy
if f1score > best_f1Score:
best_f1Score = f1score
model = clf
return (model, accuracy, f1score, pred)
class DecisionTree:
def __init__(self):
self.prfrm_oprtns = Operations()
self.model = ProvideModels()
def perform_decision_tree(self,
features_train,
labels_train,
features_test,
labels_test,
algo_name,
depths=[6, 8, 10, 12]):
print "------------- performing decision tree -------------------------"
if algo_name == Constants.decisionTreeClassifier:
clf = self.model.get_DecisionTreeClassifier()
elif algo_name == Constants.decisionTreeRegressor:
clf = self.model.get_DecisionTreeRegressor()
best_accuracy = None
model = None
best_f1Score = None
for depth in depths:
clf.set_params(max_depth=depth)
accuracy, f1score, pred = self.prfrm_oprtns.perform_operations(
features_train, labels_train, features_test, labels_test, clf)
print "accuracy with depth ", depth, " is", accuracy
print "f1Score with depth ", depth, " is", f1score
if accuracy > best_accuracy:
best_accuracy = accuracy
if f1score > best_f1Score:
best_f1Score = f1score
model = clf
return (model, accuracy, f1score, pred)
def __init__(self):
self.prfrm_oprtns = Operations()
self.model = ProvideModels()