In [1]:
import sys
from time import time
import pickle
sys.path.append("ud120-projects/tools/")
sys.path.append("ud120-projects/final_project/")
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import seaborn as sns
%matplotlib inline
In [2]:
from feature_format import featureFormat, targetFeatureSplit
from tester import test_classifier, dump_classifier_and_data
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import Imputer
from sklearn.preprocessing import MinMaxScaler
from sklearn.decomposition import RandomizedPCA
from sklearn.feature_selection import SelectKBest
from sklearn.pipeline import Pipeline, FeatureUnion
from sklearn.grid_search import GridSearchCV
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.cluster import KMeans
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.metrics import classification_report
In [3]:
### Load the dictionary containing the dataset
data_dict = pickle.load(open("ud120-projects/final_project/final_project_dataset.pkl", "r") )
In [4]:
### Task 1: 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".
features_list = ['poi','salary'] # You will need to use more features
### Task 2: Remove outliers
### Task 3: Create new feature(s)
### Store to my_dataset for easy export below.
my_dataset = data_dict
### Extract features and labels from dataset for local testing
data = featureFormat(my_dataset, features_list, sort_keys = True)
labels, features = targetFeatureSplit(data)
### 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
clf = GaussianNB() # Provided to give you a starting point. Try a varity of classifiers.
### Task 5: Tune your classifier to achieve better than .3 precision and recall
### using our testing script.
### Because of the small size of the dataset, the script uses stratified
### shuffle split cross validation. For more info:
### http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.StratifiedShuffleSplit.html
test_classifier(clf, my_dataset, features_list)
### Dump your classifier, dataset, and features_list so
### anyone can run/check your results.
dump_classifier_and_data(clf, my_dataset, features_list)
GaussianNB()
Accuracy: 0.25560 Precision: 0.18481 Recall: 0.79800 F1: 0.30011 F2: 0.47968
Total predictions: 10000 True positives: 1596 False positives: 7040 False negatives: 404 True negatives: 960
In [5]:
### Task 1: 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".
features_list = my_dataset.itervalues().next().keys()
features_list.sort()
features_list.remove('poi')
features_list.insert(0, 'poi')
features_list.remove('email_address')
print features_list
['poi', 'bonus', 'deferral_payments', 'deferred_income', 'director_fees', 'exercised_stock_options', 'expenses', 'from_messages', 'from_poi_to_this_person', 'from_this_person_to_poi', 'loan_advances', 'long_term_incentive', 'other', 'restricted_stock', 'restricted_stock_deferred', 'salary', 'shared_receipt_with_poi', 'to_messages', 'total_payments', 'total_stock_value']
In [9]:
df = pd.DataFrame([entry for entry in my_dataset.itervalues()])
df = df.drop('email_address', axis=1)
#df.dtypes
#df.describe()
#df.count()
df = df.convert_objects(convert_numeric=True)
print "POI Count:\n", df.poi.value_counts()
df.head()
POI Count:
False 128
True 18
dtype: int64
Out[9]:
bonus
deferral_payments
deferred_income
director_fees
exercised_stock_options
expenses
from_messages
from_poi_to_this_person
from_this_person_to_poi
loan_advances
long_term_incentive
other
poi
restricted_stock
restricted_stock_deferred
salary
shared_receipt_with_poi
to_messages
total_payments
total_stock_value
0
600000
NaN
NaN
NaN
NaN
94299
29
38
1
NaN
NaN
1740
False
585062
NaN
365788
702
807
1061827
585062
1
1200000
1295738
-1386055
NaN
6680544
11200
NaN
NaN
NaN
NaN
1586055
2660303
False
3942714
NaN
267102
NaN
NaN
5634343
10623258
2
350000
NaN
-400729
NaN
4890344
78552
NaN
NaN
NaN
NaN
NaN
12961
False
1788391
NaN
170941
NaN
NaN
211725
6678735
3
NaN
NaN
NaN
NaN
651850
NaN
12
10
0
NaN
NaN
NaN
False
386335
NaN
NaN
58
764
NaN
1038185
4
1500000
NaN
-3117011
NaN
5538001
34039
32
32
21
NaN
1617011
11350
True
853064
NaN
243293
1035
1045
288682
6391065
In [222]:
y = df.poi.values
print y.shape
y[:5]
(146,)
Out[222]:
array([False, False, False, False, True], dtype=bool)
In [223]:
X = df.drop('poi', axis=1).values
print X.shape
X
(146, 19)
Out[223]:
array([[ 6.00000000e+05, nan, nan, ...,
8.07000000e+02, 1.06182700e+06, 5.85062000e+05],
[ 1.20000000e+06, 1.29573800e+06, -1.38605500e+06, ...,
nan, 5.63434300e+06, 1.06232580e+07],
[ 3.50000000e+05, nan, -4.00729000e+05, ...,
nan, 2.11725000e+05, 6.67873500e+06],
...,
[ 6.00000000e+05, 2.27449000e+05, nan, ...,
5.33000000e+02, 1.09266300e+06, 3.74504800e+06],
[ 8.00000000e+05, nan, -3.00000000e+05, ...,
8.65000000e+02, 8.75760000e+05, 1.08098800e+06],
[ 6.00000000e+05, nan, nan, ...,
8.73000000e+02, 1.27228400e+06, 7.78546000e+05]])
In [224]:
imp = Imputer(missing_values='NaN', strategy='mean', axis=0)
imp.fit(X)
X = imp.transform(X)
X
Out[224]:
array([[ 6.00000000e+05, 1.64267415e+06, -1.14047514e+06, ...,
8.07000000e+02, 1.06182700e+06, 5.85062000e+05],
[ 1.20000000e+06, 1.29573800e+06, -1.38605500e+06, ...,
2.07386047e+03, 5.63434300e+06, 1.06232580e+07],
[ 3.50000000e+05, 1.64267415e+06, -4.00729000e+05, ...,
2.07386047e+03, 2.11725000e+05, 6.67873500e+06],
...,
[ 6.00000000e+05, 2.27449000e+05, -1.14047514e+06, ...,
5.33000000e+02, 1.09266300e+06, 3.74504800e+06],
[ 8.00000000e+05, 1.64267415e+06, -3.00000000e+05, ...,
8.65000000e+02, 8.75760000e+05, 1.08098800e+06],
[ 6.00000000e+05, 1.64267415e+06, -1.14047514e+06, ...,
8.73000000e+02, 1.27228400e+06, 7.78546000e+05]])
In [225]:
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)
In [226]:
print X_train.shape, X_test.shape, y_train.shape, y_test.shape
(116, 19) (30, 19) (116,) (30,)
In [227]:
scaler = MinMaxScaler()
scaler = scaler.fit()
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-227-43a9e59e25fc> in <module>()
1 scaler = MinMaxScaler()
----> 2 scaler = scaler.fit()
3 X_train = scaler.transform(X_train)
4 X_test = scaler.transform(X_test)
TypeError: fit() takes at least 2 arguments (1 given)
In [ ]:
pca = RandomizedPCA().fit(X_train)
print [round(x, 4) for x in pca.explained_variance_ratio_]
X_train = pca.transform(X_train)
print X_train.shape
X_test = pca.transform(X_test)
print X_test.shape
In [ ]:
selector = SelectKBest().fit(X_train, y_train)
print selector.scores_
X_train = selector.transform(X_train)
print X_train.shape
X_test = selector.transform(X_test)
print X_test.shape
GaussianNB()
Accuracy: 0.25560 Precision: 0.18481 Recall: 0.79800 F1: 0.30011 F2: 0.47968
Total predictions: 10000 True positives: 1596 False positives: 7040 False negatives: 404
True negatives: 960
In [228]:
#print("Fitting the classifier to the training set")
#t0 = time()
#param_grid = {'C': [1e3, 5e3, 1e4, 5e4, 1e5],
# 'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1], }
#clf = GridSearchCV(SVC(kernel='rbf', class_weight='auto'), param_grid)
#clf = clf.fit(X_train_pca, y_train)
#print("done in %0.3fs" % (time() - t0))
##print("Best estimator found by grid search:")
#print(clf.best_estimator_)
clf = GaussianNB()
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
print clf.score(X_test, y_test)
print precision_score(y_test, y_pred, average='binary')
print recall_score(y_test, y_pred, average='binary')
print classification_report(y_test, y_pred, target_names=['non-poi','poi'])
test_classifier(clf, my_dataset, features_list)
0.233333333333
0.0434782608696
0.5
precision recall f1-score support
non-poi 0.86 0.21 0.34 28
poi 0.04 0.50 0.08 2
avg / total 0.80 0.23 0.33 30
GaussianNB()
Accuracy: 0.33620 Precision: 0.14720 Recall: 0.83000 F1: 0.25006 F2: 0.43056
Total predictions: 15000 True positives: 1660 False positives: 9617 False negatives: 340 True negatives: 3383
In [229]:
clf = SVC()
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
print clf.score(X_test, y_test)
print precision_score(y_test, y_pred, average='binary')
print recall_score(y_test, y_pred, average='binary')
print classification_report(y_test, y_pred, target_names=['non-poi','poi'])
test_classifier(clf, my_dataset, features_list)
0.933333333333
0.0
0.0
precision recall f1-score support
non-poi 0.93 1.00 0.97 28
poi 0.00 0.00 0.00 2
avg / total 0.87 0.93 0.90 30
Got a divide by zero when trying out: SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
kernel='rbf', max_iter=-1, probability=False, random_state=None,
shrinking=True, tol=0.001, verbose=False)
In [230]:
clf = DecisionTreeClassifier()
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
print clf.score(X_test, y_test)
print precision_score(y_test, y_pred, average='binary')
print recall_score(y_test, y_pred, average='binary')
print classification_report(y_test, y_pred, target_names=['non-poi','poi'])
test_classifier(clf, my_dataset, features_list)
0.866666666667
0.0
0.0
precision recall f1-score support
non-poi 0.93 0.93 0.93 28
poi 0.00 0.00 0.00 2
avg / total 0.87 0.87 0.87 30
DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
random_state=None, splitter='best')
Accuracy: 0.78987 Precision: 0.20492 Recall: 0.20000 F1: 0.20243 F2: 0.20096
Total predictions: 15000 True positives: 400 False positives: 1552 False negatives: 1600 True negatives: 11448
In [231]:
clf = KMeans()
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
print clf.score(X_test, y_test)
print precision_score(y_test, y_pred, average='macro')
print recall_score(y_test, y_pred, average='macro')
#print classification_report(y_test, y_pred, target_names=['non-poi','poi'])
test_classifier(clf, my_dataset, features_list)
-2.61145007563e+14
0.25
0.0714285714286
KMeans(copy_x=True, init='k-means++', max_iter=300, n_clusters=8, n_init=10,
n_jobs=1, precompute_distances='auto', random_state=None, tol=0.0001,
verbose=0)
Accuracy: 0.93433 Precision: 0.97002 Recall: 0.90977 F1: 0.93893 F2: 0.92121
Total predictions: 15000 True positives: 7572 False positives: 234 False negatives: 751 True negatives: 6443
GaussianNB()
Accuracy: 0.25560 Precision: 0.18481 Recall: 0.79800 F1: 0.30011 F2: 0.47968
Total predictions: 10000 True positives: 1596 False positives: 7040 False negatives: 404
True negatives: 960
In [ ]:
In [ ]:
###############################################################################
# Train a SVM classification model
print("Fitting the classifier to the training set")
t0 = time()
param_grid = {'C': [1e3, 5e3, 1e4, 5e4, 1e5],
'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1], }
clf = GridSearchCV(SVC(kernel='rbf', class_weight='auto'), param_grid)
clf = clf.fit(X_train_pca, y_train)
print("done in %0.3fs" % (time() - t0))
print("Best estimator found by grid search:")
print(clf.best_estimator_)
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [61]:
# http://scikit-learn.org/stable/auto_examples/feature_stacker.html
pca = RandomizedPCA()
selection = SelectKBest()
combined_features = FeatureUnion([("pca", pca), ("univ_select", selection)])
X_train = combined_features.fit(X_train, y_train).transform(X_train)
X_test = combined_features.transform(X_test)
In [72]:
combined_features
Out[72]:
FeatureUnion(n_jobs=1,
transformer_list=[('pca', RandomizedPCA(copy=True, iterated_power=3, n_components=None,
random_state=None, whiten=False)), ('univ_select', SelectKBest(k=10, score_func=<function f_classif at 0x10f010de8>))],
transformer_weights=None)
In [62]:
X_train.shape
Out[62]:
(116, 39)
In [64]:
clf = GaussianNB()
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
print clf.score(X_test, y_test), precision_score(y_test, y_pred), recall_score(y_test, y_pred)
0.866666666667 0.0 0.0
In [65]:
clf = SVC()
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
print clf.score(X_test, y_test), precision_score(y_test, y_pred), recall_score(y_test, y_pred)
0.933333333333 0.0 0.0
/usr/local/lib/python2.7/site-packages/sklearn/metrics/classification.py:958: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.
'precision', 'predicted', average, warn_for)
In [66]:
clf = DecisionTreeClassifier()
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
print clf.score(X_test, y_test), precision_score(y_test, y_pred), recall_score(y_test, y_pred)
0.8 0.0 0.0
In [67]:
clf = KMeans()
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
print clf.score(X_test, y_test), precision_score(y_test, y_pred), recall_score(y_test, y_pred)
-4.67485140732e+14 0.823529411765 0.5
/usr/local/lib/python2.7/site-packages/sklearn/metrics/classification.py:1082: DeprecationWarning: The default `weighted` averaging is deprecated, and from version 0.18, use of precision, recall or F-score with multiclass or multilabel data or pos_label=None will result in an exception. Please set an explicit value for `average`, one of (None, 'micro', 'macro', 'weighted', 'samples'). In cross validation use, for instance, scoring="f1_weighted" instead of scoring="f1".
sample_weight=sample_weight)
/usr/local/lib/python2.7/site-packages/sklearn/metrics/classification.py:958: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/usr/local/lib/python2.7/site-packages/sklearn/metrics/classification.py:1172: DeprecationWarning: The default `weighted` averaging is deprecated, and from version 0.18, use of precision, recall or F-score with multiclass or multilabel data or pos_label=None will result in an exception. Please set an explicit value for `average`, one of (None, 'micro', 'macro', 'weighted', 'samples'). In cross validation use, for instance, scoring="f1_weighted" instead of scoring="f1".
sample_weight=sample_weight)
/usr/local/lib/python2.7/site-packages/sklearn/metrics/classification.py:960: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples.
'recall', 'true', average, warn_for)
In [ ]:
svm = SVC()
#gnb = GaussianNB()
#tree = DecisionTreeClassifier()
#kmeans = KMeans()
# Do grid search over k, n_components and C:
##pipeline = Pipeline([("features", combined_features), ("svm", svm), ("gnb", gnb), ("tree", tree), ("kmeans", kmeans)])
pipeline = Pipeline([("features", combined_features), ("gnb", gnb)])
param_grid = dict(features__pca__n_components=[x+1 for x in xrange(X.shape[1]-1)],
features__univ_select__k=[x+1 for x in xrange(X.shape[1]-1)],
#svm__C=[0.1, 1, 10])
grid_search = GridSearchCV(pipeline, param_grid=param_grid, verbose=10)
grid_search.fit(X, y)
print(grid_search.best_estimator_)
In [ ]:
print "Fitting the classifier to the training set"
t0 = time()
param_grid = {
'C': [1e3, 5e3, 1e4, 5e4, 1e5],
'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1],
}
clf = GridSearchCV(SVC(kernel='rbf', class_weight='auto'), param_grid)
clf = clf.fit(X_train_pca, y_train)
print "training time: ", round(time()-t0,3), "s"
print "Best estimator found by grid search:"
print clf.best_estimator_
In [ ]:
svm = SVC(kernel='rbf', C=10000.0)
gnb = GaussianNB()
tree = DecisionTreeClassifier(min_samples_split=40)
kmeans = KMeans(n_clusters=2)
# Do grid search over k, n_components and C:
pipeline = Pipeline([("features", combined_features), ("svm", svm)])
param_grid = dict(features__pca__n_components=[1, 2, 3],
features__univ_select__k=[1, 2],
svm__C=[0.1, 1, 10])
grid_search = GridSearchCV(pipeline, param_grid=param_grid, verbose=10)
grid_search.fit(X, y)
print(grid_search.best_estimator_)
In [ ]:
In [139]:
col_first_half = list(df.columns[0:(len(df.columns)/6)])
col_first_half.append('poi')
In [128]:
col_second_half = df.columns[(len(df.columns)/2):]
In [140]:
sns.pairplot(df[col_first_half], hue="poi")
Out[140]:
<seaborn.axisgrid.PairGrid at 0x123a7ff50>
In [48]:
### Task 2: Remove outliers
for entry in my_dataset.itervalues():
for feature in features_list:
print entry
{'salary': 365788, 'to_messages': 807, 'deferral_payments': 'NaN', 'total_payments': 1061827, 'exercised_stock_options': 'NaN', 'bonus': 600000, 'restricted_stock': 585062, 'shared_receipt_with_poi': 702, 'restricted_stock_deferred': 'NaN', 'total_stock_value': 585062, 'expenses': 94299, 'loan_advances': 'NaN', 'from_messages': 29, 'other': 1740, 'from_this_person_to_poi': 1, 'poi': False, 'director_fees': 'NaN', 'deferred_income': 'NaN', 'long_term_incentive': 'NaN', 'email_address': 'mark.metts@enron.com', 'from_poi_to_this_person': 38}
{'salary': 365788, 'to_messages': 807, 'deferral_payments': 'NaN', 'total_payments': 1061827, 'exercised_stock_options': 'NaN', 'bonus': 600000, 'restricted_stock': 585062, 'shared_receipt_with_poi': 702, 'restricted_stock_deferred': 'NaN', 'total_stock_value': 585062, 'expenses': 94299, 'loan_advances': 'NaN', 'from_messages': 29, 'other': 1740, 'from_this_person_to_poi': 1, 'poi': False, 'director_fees': 'NaN', 'deferred_income': 'NaN', 'long_term_incentive': 'NaN', 'email_address': 'mark.metts@enron.com', 'from_poi_to_this_person': 38}
{'salary': 365788, 'to_messages': 807, 'deferral_payments': 'NaN', 'total_payments': 1061827, 'exercised_stock_options': 'NaN', 'bonus': 600000, 'restricted_stock': 585062, 'shared_receipt_with_poi': 702, 'restricted_stock_deferred': 'NaN', 'total_stock_value': 585062, 'expenses': 94299, 'loan_advances': 'NaN', 'from_messages': 29, 'other': 1740, 'from_this_person_to_poi': 1, 'poi': False, 'director_fees': 'NaN', 'deferred_income': 'NaN', 'long_term_incentive': 'NaN', 'email_address': 'mark.metts@enron.com', 'from_poi_to_this_person': 38}
{'salary': 365788, 'to_messages': 807, 'deferral_payments': 'NaN', 'total_payments': 1061827, 'exercised_stock_options': 'NaN', 'bonus': 600000, 'restricted_stock': 585062, 'shared_receipt_with_poi': 702, 'restricted_stock_deferred': 'NaN', 'total_stock_value': 585062, 'expenses': 94299, 'loan_advances': 'NaN', 'from_messages': 29, 'other': 1740, 'from_this_person_to_poi': 1, 'poi': False, 'director_fees': 'NaN', 'deferred_income': 'NaN', 'long_term_incentive': 'NaN', 'email_address': 'mark.metts@enron.com', 'from_poi_to_this_person': 38}
{'salary': 365788, 'to_messages': 807, 'deferral_payments': 'NaN', 'total_payments': 1061827, 'exercised_stock_options': 'NaN', 'bonus': 600000, 'restricted_stock': 585062, 'shared_receipt_with_poi': 702, 'restricted_stock_deferred': 'NaN', 'total_stock_value': 585062, 'expenses': 94299, 'loan_advances': 'NaN', 'from_messages': 29, 'other': 1740, 'from_this_person_to_poi': 1, 'poi': False, 'director_fees': 'NaN', 'deferred_income': 'NaN', 'long_term_incentive': 'NaN', 'email_address': 'mark.metts@enron.com', 'from_poi_to_this_person': 38}
{'salary': 365788, 'to_messages': 807, 'deferral_payments': 'NaN', 'total_payments': 1061827, 'exercised_stock_options': 'NaN', 'bonus': 600000, 'restricted_stock': 585062, 'shared_receipt_with_poi': 702, 'restricted_stock_deferred': 'NaN', 'total_stock_value': 585062, 'expenses': 94299, 'loan_advances': 'NaN', 'from_messages': 29, 'other': 1740, 'from_this_person_to_poi': 1, 'poi': False, 'director_fees': 'NaN', 'deferred_income': 'NaN', 'long_term_incentive': 'NaN', 'email_address': 'mark.metts@enron.com', 'from_poi_to_this_person': 38}
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{'salary': 274975, 'to_messages': 873, 'deferral_payments': 'NaN', 'total_payments': 1272284, 'exercised_stock_options': 384728, 'bonus': 600000, 'restricted_stock': 393818, 'shared_receipt_with_poi': 874, 'restricted_stock_deferred': 'NaN', 'total_stock_value': 778546, 'expenses': 125978, 'loan_advances': 'NaN', 'from_messages': 16, 'other': 200308, 'from_this_person_to_poi': 6, 'poi': True, 'director_fees': 'NaN', 'deferred_income': 'NaN', 'long_term_incentive': 71023, 'email_address': 'ben.glisan@enron.com', 'from_poi_to_this_person': 52}
{'salary': 274975, 'to_messages': 873, 'deferral_payments': 'NaN', 'total_payments': 1272284, 'exercised_stock_options': 384728, 'bonus': 600000, 'restricted_stock': 393818, 'shared_receipt_with_poi': 874, 'restricted_stock_deferred': 'NaN', 'total_stock_value': 778546, 'expenses': 125978, 'loan_advances': 'NaN', 'from_messages': 16, 'other': 200308, 'from_this_person_to_poi': 6, 'poi': True, 'director_fees': 'NaN', 'deferred_income': 'NaN', 'long_term_incentive': 71023, 'email_address': 'ben.glisan@enron.com', 'from_poi_to_this_person': 52}
{'salary': 274975, 'to_messages': 873, 'deferral_payments': 'NaN', 'total_payments': 1272284, 'exercised_stock_options': 384728, 'bonus': 600000, 'restricted_stock': 393818, 'shared_receipt_with_poi': 874, 'restricted_stock_deferred': 'NaN', 'total_stock_value': 778546, 'expenses': 125978, 'loan_advances': 'NaN', 'from_messages': 16, 'other': 200308, 'from_this_person_to_poi': 6, 'poi': True, 'director_fees': 'NaN', 'deferred_income': 'NaN', 'long_term_incentive': 71023, 'email_address': 'ben.glisan@enron.com', 'from_poi_to_this_person': 52}
Content source: austinjalexander/sandbox
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