def try_linear_svc():
"""
Linear SVC
"""
print("LinearSVC")
from sklearn import svm
from sklearn.svm import LinearSVC
from sklearn.svm import SVC
clf_lr = SVC(kernel='rbf', C=1000)
clf_lr.fit(features_train,labels_train)
pred = clf_lr.predict(features_test)
accuracy = accuracy_score(labels_test, pred)
precision = precision_score(labels_test, pred)
recall = recall_score(labels_test, pred)
f1 = f1_score(labels_test, pred)
print("accuracy SVC: ", accuracy)
print("precision: ", precision)
print("recall: ", recall)
print("f1 score: ", f1)
print_separator_line()
dict_results = { "classifier": "linear svc, rbf", "accuracy": accuracy, "precision": precision, "recall": recall }
return dict_results, clf_lr
def try_ada_boost_decision_tree():
"""
AdaBoost appied to Decision Tree
"""
print("AdaBoost to Decision Tree")
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.grid_search import GridSearchCV
param_grid = {"base_estimator__criterion" : ["gini", "entropy"],
"base_estimator__splitter" : ["best", "random"],
"n_estimators": [10, 30]
}
DTC = DecisionTreeClassifier(random_state = 11, max_features = "auto", class_weight = "balanced",max_depth = None)
ABC = AdaBoostClassifier(base_estimator = DTC)
grid_search_ABC = GridSearchCV(ABC, param_grid=param_grid, scoring = 'roc_auc')
grid_search_ABC.fit(features_train,labels_train)
pred = grid_search_ABC.predict(features_test)
accuracy = accuracy_score(labels_test, pred)
precision = precision_score(labels_test, pred)
recall = recall_score(labels_test, pred)
print("DecisionTree after applying AdaBoost and GridSearchCV:")
print("accuracy AdaBoost: ", accuracy)
print("precision: ", precision)
print("recall: ", recall)
print_separator_line()
dict_results = { "classifier": "AdaBoost decision tree", "accuracy": accuracy, "precision": precision, "recall": recall }
return dict_results, grid_search_ABC
def try_k_neighbors():
"""
K Nearest Neighbors classifier:
"""
print "KNeighborsClassifier"
from sklearn.neighbors import NearestNeighbors
from sklearn.neighbors import KNeighborsClassifier
pipe = Pipeline([
('classify', KNeighborsClassifier())
])
clf_knn = KNeighborsClassifier(n_neighbors=5)
params_knn = {}
KNC = KNeighborsClassifier(n_neighbors=2)
ABC = AdaBoostClassifier(base_estimator = KNC)
KNC.fit(features_train,labels_train)
pred = KNC.predict(features_test)
accuracy = accuracy_score(labels_test, pred)
precision = precision_score(labels_test, pred)
recall = recall_score(labels_test, pred)
#f1 = f1_score(labels_test, pred)
print("KNeighborsClassifier:")
print("accuracy KNC: ", accuracy)
print("precision: ", precision)
print("recall: ", recall)
print_separator_line()
dict_results = { "classifier": "K Nearest Neighbors", "accuracy": accuracy, "precision": precision, "recall": recall }
return dict_results, KNC
def try_classifier_GaussianNB():
"""
GaussanNB
"""
print "Classifier: GaussianNB:"
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
clf_GuassianNB = GaussianNB()
clf_GuassianNB.fit(features_train,labels_train)
pred = clf_GuassianNB.predict(features_test)
accuracy = accuracy_score(labels_test, pred)
precision = precision_score(labels_test, pred)
recall = recall_score(labels_test, pred)
f1 = f1_score(labels_test, pred)
print("GaussianNB accuracy: ", accuracy)
print("precision: ", precision)
print("recall: ", recall)
print("f1 score: ", f1)
print_separator_line()
dict_results = { "classifier": "GaussianNB", "accuracy": accuracy, "precision": precision, "recall": recall }
return dict_results, clf_GuassianNB
def try_logistic_regression_pipeline():
"""
Logistic Regression with pipeline:
"""
print("Logistic Regression with pipeline and PCA:")
from sklearn import linear_model, decomposition, datasets
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV
logistic = linear_model.LogisticRegression()
pca = decomposition.PCA()
pipe_lr = Pipeline(steps=[('pca', pca), ('logistic', logistic)])
pipe_lr.fit(features_train,labels_train)
pred_lr = pipe_lr.predict(features_test)
accuracy = accuracy_score(labels_test, pred_lr)
precision = precision_score(labels_test,pred_lr)
recall = recall_score(labels_test,pred_lr)
f1 = f1_score(labels_test,pred_lr)
print("accuracy SVC: ", accuracy)
print("precision: ", precision)
print("recall: ", recall)
print_separator_line()
dict_results = { "classifier": "Logistic regression, pca and pipeline", "accuracy": accuracy, "precision": precision, "recall": recall }
return dict_results, pipe_lr
def try_k_neighbors_pipeline():
"""
K Nearest Neighbors with pipeline:
"""
print "KNeighborsClassifier with Pipeline"
from sklearn.neighbors import NearestNeighbors
from sklearn.neighbors import KNeighborsClassifier
KNC2 = KNeighborsClassifier()
pca = PCA()
pipe = Pipeline([('pca', pca), ('knn', KNC2)])
pipe.fit(features_train, labels_train)
pred = pipe.predict(features_test)
accuracy = accuracy_score(labels_test, pred)
precision = precision_score(labels_test, pred)
recall = recall_score(labels_test, pred)
print("KNeighborsClassifier - with Pipe:")
print("accuracy KNC2: ", accuracy)
print("precision: ", precision)
print("recall: ", recall)
print_separator_line()
dict_results = { "classifier": "K nearest neighbors, pipeline", "accuracy": accuracy, "precision": precision, "recall": recall }
return dict_results, pipe
def try_classifier_Decision_Tree_Pipeline():
"""
Decision Tree Classifier, optimized with Pipeline
"""
print "Decision Tree classifier with pipeline:"
scaler = preprocessing.MinMaxScaler()
skb = SelectKBest(k = 15)
dt3 = tree.DecisionTreeClassifier(criterion='entropy',splitter='best')
clf_DT3 = Pipeline(steps=[('scaling',scaler),("SKB", skb), ("DecisionTree", dt3)])
clf_DT3.fit(features_train,labels_train)
pred = clf_DT3.predict(features_test)
accuracy = accuracy_score(labels_test, pred)
precision = precision_score(labels_test, pred)
recall = recall_score(labels_test, pred)
f1 = f1_score(labels_test, pred)
print("accuracy:",accuracy)
print("precision: ", precision)
print("recall: ", recall)
print("f1 score: ", f1)
print_separator_line()
print_separator_line()
dict_results = { "classifier": "Decision Tree, pipeline", "accuracy": accuracy, "precision": precision, "recall": recall }
return dict_results, clf_DT3
def try_classifier_Decision_Tree2():
"""
Decision Tree Classifier
"""
print "DecisionTree with criterion = entropy:"
from sklearn import tree
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
scaler = preprocessing.MinMaxScaler()
skb = SelectKBest(k = 'all')
### Use entropy as criterion
dt = tree.DecisionTreeClassifier(criterion='entropy',splitter='best')
clf_DT2 = Pipeline(steps=[('scaling',scaler),("SKB", skb), ("DecisionTree", dt)])
clf_DT2.fit(features_train,labels_train)
pred = clf_DT2.predict(features_test)
accuracy = accuracy_score(labels_test, pred)
precision = precision_score(labels_test, pred)
recall = recall_score(labels_test, pred)
f1 = f1_score(labels_test, pred)
print("DT-entropy with pipeline accuracy: ", accuracy)
print("precision: ", precision)
print("recall: ", recall)
print("f1 score: ", f1)
print_separator_line()
dict_results = { "classifier": "Decision Tree, entropy", "accuracy": accuracy, "precision": precision, "recall": recall }
return dict_results, clf_DT2
def try_classifier_GaussianNB_pipeline():
"""
GaussanNB improved with Pipeline
"""
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
print "GaussianNB with pipeline:"
gnb = GaussianNB()
skb = SelectKBest(k = 'all')
pipe_GuassianNB = Pipeline(steps=[('scaling',scaler),("SKB", skb), ("NaiveBayes", gnb)])
pipe_GuassianNB.fit(features_train,labels_train)
pred = pipe_GuassianNB.predict(features_test)
accuracy = pipe_GuassianNB.score(features_test,labels_test)
precision = precision_score(labels_test, pred)
recall = recall_score(labels_test, pred)
f1 = f1_score(labels_test, pred)
print("GaussianNB with Pipeline, accuracy: ", accuracy)
print("precision: ", precision)
print("recall: ", recall)
print("f1 score: ", f1)
print_separator_line()
dict_results = { "classifier": "GaussianNB with pipeline", "accuracy": accuracy, "precision": precision, "recall": recall }
return dict_results, pred
def try_random_forest():
"""
Random Forest classifier
"""
print("Random Forest")
from sklearn.ensemble import RandomForestClassifier
rf = RandomForestClassifier()
rf.fit(features_train, labels_train)
score_train = rf.score(features_train,labels_train)
score_test = rf.score(features_test,labels_test)
print("score train: ", score_train)
print("score test: ", score_test)
clf_rf = RandomForestClassifier(n_estimators=30, criterion='entropy', max_depth=None, max_features=10)
clf_rf.fit(features_train,labels_train)
pred = clf_rf.predict(features_test)
accuracy = accuracy_score(labels_test, pred)
precision = precision_score(labels_test, pred)
recall = recall_score(labels_test, pred)
f1 = f1_score(labels_test, pred)
print("accuracy RandomForest: ", accuracy)
print("precision: ", precision)
print("recall: ", recall)
print("f1 score: ", f1)
print_separator_line()
dict_results = { "classifier": "Random Forest", "accuracy": accuracy, "precision": precision, "recall": recall }
return dict_results, clf_rf
def try_linear_svc_gridsearchcv():
"""
Linear SVC model optimized with Pipeline and GridSearchCV
"""
scaler = preprocessing.MinMaxScaler()
skb = SelectKBest(k = 'all')
pipe = Pipeline([
('reduce_dim', PCA()),
('classify', LinearSVC())
])
N_FEATURES_OPTIONS = [2, 4, 8]
C_OPTIONS = [1, 10, 100, 1000]
param_grid = [
{
'reduce_dim': [PCA(iterated_power=7), NMF()],
'reduce_dim__n_components': N_FEATURES_OPTIONS,
'classify__C': C_OPTIONS
},
{
'reduce_dim': [SelectKBest(chi2)],
'reduce_dim__k': N_FEATURES_OPTIONS,
'classify__C': C_OPTIONS
},
]
reducer_labels = ['PCA', 'NMF', 'KBest(chi2)']
grid_linearSVC = GridSearchCV(pipe, cv=3, n_jobs=1, param_grid=param_grid)
grid_linearSVC.fit(features_train,labels_train)
pred = grid_linearSVC.predict(features_test)
accuracy = accuracy_score(labels_test, pred)
precision = precision_score(labels_test, pred)
recall = recall_score(labels_test, pred)
print "SVM after applying PCA and GridSearchCV:"
print("accuracy SVC: ", accuracy)
print("precision: ", precision)
print("recall: ", recall)
print_separator_line()
dict_results = { "classifier": "linear SVC with GridSearchCV", "accuracy": accuracy, "precision": precision, "recall": recall }
return dict_results, grid_linearSVC
def try_logistic_regression_tuned():
"""
Logistic Regression tuned
"""
clf_winner = Pipeline(steps=[("scaler", scaler),
("skb", SelectKBest(k='all')),
("clf_winner", LogisticRegression(tol=0.1, C = 1**19, class_weight='balanced'))])
clf_winner.fit(features_train,labels_train)
pred = clf_winner.predict(features_test)
accuracy = accuracy_score(labels_test, pred)
precision = precision_score(labels_test, pred)
recall = recall_score(labels_test, pred)
print("accuracy LogisticRegression with PCA 2: ", accuracy)
print("precision: ", precision)
print("recall: ", recall)
#return clf_winner
print_separator_line()
dict_results = { "classifier": "Logistic regression tuned", "accuracy": accuracy, "precision": precision, "recall": recall }
return dict_results, clf_winner
def try_svm_classifier():
"""
SVM classifier
"""
print("svm SVC classifier:")
clf_svm = svm.SVC(C=10., cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False)
clf_svm.fit(features_train,labels_train)
pred = clf_svm.predict(features_test)
accuracy = accuracy_score(labels_test, pred)
precision = precision_score(labels_test, pred)
recall = recall_score(labels_test, pred)
print("accuracy SVC: ", accuracy)
print("precision: ", precision)
print("recall: ", recall)
print_separator_line()
dict_results = { "classifier": "svm.SVC", "accuracy": accuracy, "precision": precision, "recall": recall }
return dict_results, clf_svm
def try_svc_tuned():
"""
Apply the tuned parameters generated by GridSearchCV
"""
from sklearn.model_selection import GridSearchCV
parameters = {'kernel':['rbf'], 'C':[1, 1000]}
svc = SVC()
clf_svm3 = GridSearchCV(svc, parameters)
clf_svm3.fit(features_train,labels_train)
clf_svm3 = clf_svm3.best_estimator_
pred = clf_svm3.predict(features_test)
accuracy = accuracy_score(labels_test, pred)
precision = precision_score(labels_test, pred)
recall = recall_score(labels_test, pred)
print("accuracy SVC: ", accuracy)
print("precision: ", precision)
print("recall: ", recall)
print_separator_line()
dict_results = { "classifier": "svc tuned", "accuracy": accuracy, "precision": precision, "recall": recall }
return dict_results, clf_svm3
def try_logistic_regression():
"""
Logistic Regression classifier:
"""
print("Logistic Regression")
from sklearn import linear_model, decomposition, datasets
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV
logistic = linear_model.LogisticRegression()
logistic.fit(features_train,labels_train)
pred = logistic.predict(features_test)
accuracy = accuracy_score(labels_test, pred)
precision = precision_score(labels_test, pred)
recall = recall_score(labels_test, pred)
print("accuracy LogisticRegression with PCA: ", accuracy)
print("precision: ", precision)
print("recall: ", recall)
print_separator_line()
dict_results = { "classifier": "Logistic regression", "accuracy": accuracy, "precision": precision, "recall": recall }
return dict_results, logistic
def try_svc_pipeline_gridsearchcv():
"""
SVC with pipeline and GridSearchCV
generate the best parameters
"""
print "results pipeline svm:"
pca = decomposition.PCA()
svm = SVC()
pipe = Pipeline(steps=[('pca', pca), ('svm', svm)])
n_components = [10, 14, 18]
params_grid = {
'svm__C': [1, 10, 100, 1000],
'svm__kernel': ['linear', 'rbf'],
'svm__gamma': [0.001, 0.0001],
'pca__n_components': n_components,
}
estimator = GridSearchCV(pipe, params_grid)
estimator.fit(features_train,labels_train)
print estimator.best_params_, estimator.best_score_
params = estimator.best_params_
clf_svm2 = SVC(C=100, kernel='rbf', decision_function_shape='ovr', degree=3, gamma='auto', coef0=0.0,max_iter=-1, probability=False, random_state=None, shrinking=True)
clf_svm2.fit(features_train,labels_train)
pred = clf_svm2.predict(features_test)
accuracy = accuracy_score(labels_test, pred)
precision = precision_score(labels_test, pred)
recall = recall_score(labels_test, pred)
print("accuracy SVC: ", accuracy)
print("precision: ", precision)
print("recall: ", recall)
print_separator_line()
dict_results = { "classifier": "svc with pipeline", "accuracy": accuracy, "precision": precision, "recall": recall }
return dict_results, clf_svm2
from helpers_enron import make_csv
make_csv(data_dict)
### Task: Remove outliers
data_dict.pop('TOTAL',0)
data_dict.pop('THE TRAVEL AGENCY IN THE PARK',0)
### This record has NaN in every field
data_dict.pop('LOCKHART EUGENE E',0)
### Task: Select what features you'll use.
### features_list is a list of strings, each of which is a feature name.
### The first feature must be "poi".
print_separator_line()
### Combine poi, financial and email features
features_list = poi + features_financial + features_email
### Get the best features
features_list = set_kbest_features_list(data_dict, features_list)
### csv file is written in order to see results as spreadsheet
output_file = "test_results_original.csv"
### *** UNCOMMENT THESE LINES TO RERUN THE CLASSIFIERS WITH ADDED FEATURES ***
### Additional features: log of financial fields and POI email ratio
