F = (a**2 + 1)*P*R / a**2(P+R)

#!/usr/bin/python

import sys
import pickle
sys.path.append("../tools/")

from feature_format import featureFormat, targetFeatureSplit
from tester import dump_classifier_and_data
from time import time

### 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','bonus','total_payments','deferral_payments','exercised_stock_options',\
                     'restricted_stock','restricted_stock_deferred','total_stock_value','expenses',\
                     'other','director_fees','loan_advances','deferred_income','long_term_incentive',\
                     'from_poi_to_this_person','from_this_person_to_poi','to_messages','from_messages',\
                     'shared_receipt_with_poi','fraction_from_poi','fraction_to_poi'] 
                      # You will need to use more features

### Load the dictionary containing the dataset
with open("final_project_dataset.pkl", "r") as data_file:
    data_dict = pickle.load(data_file)

### add by @lj 
import matplotlib.pyplot as plt

print "\n","######### total numbers of data point:"
print len(data_dict)

print "\n","######### total numbers of features:"
for features in data_dict.values():
    print len(features)
    print features
    break

print "\n","######### total number of poi:"
pois = [x for x, y in data_dict.items() if y['poi']]
print len(pois)

print ""
print "######### check the nan data:"
for key,value in data_dict.items():
	nan_num = 0
	for i in value.values():
		if i == "NaN":
			nan_num = nan_num + 1

	if nan_num > 15:
		print key,":",nan_num


print "\n","######### find the outliers:"
salary_list = []
bonus_list = []

for features in data_dict.values():
	#plt.scatter(features["salary"], features["bonus"])

	# 
	if features["salary"] == "NaN" or features["bonus"] == "NaN":
		continue
	salary_list.append(features["salary"])
	bonus_list.append(features["bonus"])

# 
bonus_list.sort()
salary_list.sort()

print "\n","######### the top five:"
print salary_list[-5:]
print bonus_list[-5:]

print "\n","######### the bottom five:"
print salary_list[0:5]
print bonus_list[0:5]

# 
#plt.title('The original dataset:')
#plt.xlabel("salary")
#plt.ylabel("bonus")

# 
print "\n","######### top of the salary and bonus:"
print "######### show the problem data point:"
for key,value in data_dict.items():
	if value["salary"] == salary_list[-1]:
		print ""
		print key,"'s salary : ",value["salary"]

	if value["bonus"] == bonus_list[-1]:
		print ""
		print key,"'s bonus : ",value["bonus"]

	if key == "THE TRAVEL AGENCY IN THE PARK" or key == "LOCKHART EUGENE E":
		print ""
		print key,":"
		print value


# 

### end by @lj

### Task 2: Remove outliers

### Store to my_dataset for easy export below.
my_dataset = data_dict

### add by lj
my_dataset.pop("TOTAL")
my_dataset.pop("THE TRAVEL AGENCY IN THE PARK")
my_dataset.pop("LOCKHART EUGENE E")
print "\n","after(remove outliers), the total number of the dataset:"
print len(my_dataset)

#
#for features in my_dataset.values():
#	plt.scatter(features["salary"], features["bonus"])

#plt.title('After removing the outliers:')
#plt.xlabel("salary")
#plt.ylabel("bonus")
#plt.show()

#
import numpy as np
import pandas as pd
df = pd.DataFrame.from_dict(data_dict, orient='index')
df.replace('NaN', np.nan, inplace = True)

df.info()

# 
#for features in my_dataset.values():
#	colors = features["poi"]
#	if colors == True:
#		colors = "red"
#	else:
#		colors = "blue"
#	plt.scatter(features["from_poi_to_this_person"], features["from_this_person_to_poi"], c=colors,alpha=0.5)

#plt.xlabel("from_poi_to_this_person")
#plt.ylabel("from_this_person_to_poi")
#plt.show()

### Task 3: Create new feature(s)
def computeFraction( poi_messages, all_messages ):
    fraction = 0.
    
    if poi_messages == "NaN" or all_messages == "NaN":
        return 0

    fraction = float(poi_messages)/all_messages
    
    return fraction

for i in data_dict:
    my_dataset[i]['fraction_from_poi'] = computeFraction(my_dataset[i]['from_poi_to_this_person'],my_dataset[i]['to_messages'])
    my_dataset[i]['fraction_to_poi'] = computeFraction(my_dataset[i]['from_this_person_to_poi'],my_dataset[i]['from_messages'])

#for features in my_dataset.values():
#	colors = features["poi"]
#	if colors == True:
#		colors = "red"
#	else:
#		colors = "blue"
#	plt.scatter(features["fraction_from_poi"], features["fraction_to_poi"], c=colors,alpha=0.5)

#plt.xlabel("fraction_from_poi")
#plt.ylabel("fraction_to_poi")
#plt.show()

##
### 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".
from sklearn.feature_selection import SelectPercentile,SelectKBest

def Select_K_Best(data_dict, features_list, k):

    data_array = featureFormat(data_dict, features_list)
    labels, features = targetFeatureSplit(data_array)

    k_best = SelectKBest(k=k)
    k_best.fit(features, labels)
    scores = k_best.scores_
    tuples = zip(features_list[1:], scores)
    k_best_features = sorted(tuples, key=lambda x: x[1], reverse=True)

    return k_best_features[:k]

print "\n",Select_K_Best(my_dataset,features_list,5)

features_list = ["poi"] + [x[0] for x in Select_K_Best(my_dataset,features_list,5)] 

print "\n",features_list

### end by @lj

### Extract features and labels from dataset for local testing
data = featureFormat(my_dataset, features_list, sort_keys = True)
labels, features = targetFeatureSplit(data)

## 
from sklearn.preprocessing import MinMaxScaler

print "\n",features[0:3]

scaler = MinMaxScaler()
features_new = scaler.fit_transform(features)

print "\n",features_new[0:3]

### 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

# Provided to give you a starting point. Try a variety of classifiers.

labels = np.asarray(labels)

from sklearn.cross_validation import train_test_split
features_train, features_test, labels_train, labels_test = \
    train_test_split(features, labels, test_size=0.3, random_state=42)

## 1. GaussianNB
from sklearn.naive_bayes import GaussianNB
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV

t1 = time()

clf_NB = GaussianNB()
parm = {}

clf_NB = Pipeline([('scaler',scaler),('gnb',clf_NB)])
gs = GridSearchCV(clf_NB,parm)
gs.fit(features_train,labels_train)

clf_NB = gs.best_estimator_

print "\nGaussianNB score:\n",clf_NB.score(features_train,labels_train)
print "GaussianNB score time:", round(time()-t1, 3), "s"

##  Test Point
from tester import dump_classifier_and_data,test_classifier

print "\nGaussianNB:\n",test_classifier(clf_NB,my_dataset,features_list)

## 2. Decision Tree Classifier
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree

t2 = time()

parms = {'criterion': ['gini', 'entropy'], \
			'min_samples_split': [2, 5, 10, 20], \
			'max_depth': [None, 2, 5, 10], \
			'splitter': ['random', 'best'], \
			'max_leaf_nodes': [None, 5, 10, 20]   }

clf_DT = tree.DecisionTreeClassifier()

gs = GridSearchCV(clf_DT,parms)
gs.fit(features_train,labels_train)

clf_DT = gs.best_estimator_

print "\nDecision Tree Classifier\n",clf_DT.score(features_train,labels_train)
print "Decision Tree Classifier:", round(time()-t2, 3), "s"

##  Test Point
print "\nDecision Tree Classifier Test Point:\n",test_classifier(clf_DT,my_dataset,features_list)

## 3. SVM

from sklearn.svm import SVC
parms = {'svc__kernel':('linear','rbf'),'svc__C':[1.0,2.0]}

t3 = time()
clf_SVC = SVC()

pipeline2 = Pipeline([('scaler',scaler),('svc',clf_SVC)])
# a = pipeline.fit(features_train,labels_train)
gs = GridSearchCV(pipeline2,parms)
gs.fit(features_train,labels_train)

clf_SVC = gs.best_estimator_

print "\nSVM \n",clf_SVC.score(features_train,labels_train)
print "Decision Tree Classifier:", round(time()-t3, 3), "s"

##  Test Point
print "\nSVM:\n",test_classifier(clf_SVC,my_dataset,features_list)


### 4. RandomForest
from sklearn.ensemble import RandomForestClassifier

t4 = time()

print '\nRandomForest\n'
clf_RF = RandomForestClassifier()
parameters = {'criterion': ['gini', 'entropy'], \
			'max_depth': [None, 2, 5, 10], \
			'max_leaf_nodes': [None, 5, 10, 20], \
			'n_estimators': [1, 5, 10, 50, 100]}
gs = GridSearchCV(clf_RF, parameters)
gs.fit(features_train,labels_train)

clf_RF = gs.best_estimator_

print "\nRandomForest:\n",clf_RF.score(features_train,labels_train)
print "Decision Tree Classifier:", round(time()-t4, 3), "s"

##  Test Point
print "\nRandomForest:\n",test_classifier(clf_RF,my_dataset,features_list)

### 5. AdaBoost
from sklearn.ensemble import AdaBoostClassifier

t5 = time()

clf_AB = AdaBoostClassifier(algorithm='SAMME')
parameters = {'learning_rate': [0.1, 0.5, 1.0, 5.0], \
			'algorithm': ['SAMME', 'SAMME.R'], \
			'n_estimators': [1, 5, 10, 50, 100]}
gs = GridSearchCV(clf_AB, parameters)
gs.fit(features_train,labels_train)

clf_AB = gs.best_estimator_

print "\nAdaBoost:\n",clf_AB.score(features_train,labels_train)
print "Decision Tree Classifier:", round(time()-t5, 3), "s"

##  Test Point
print "\nAdaBoost:\n",test_classifier(clf_AB,my_dataset,features_list)


### Task 5: Tune your classifier to achieve better than .3 precision and recall 
### using our testing script. Check the tester.py script in the final project
### folder for details on the evaluation method, especially the test_classifier
### function. 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

# Example starting point. Try investigating other evaluation techniques!
from sklearn.cross_validation import train_test_split
features_train, features_test, labels_train, labels_test = \
    train_test_split(features, labels, test_size=0.3, random_state=42)

### Task 6: Dump your classifier, dataset, and features_list so anyone can
### check your results. You do not need to change anything below, but make sure
### that the version of poi_id.py that you submit can be run on its own and
### generates the necessary .pkl files for validating your results.

clf = clf_NB

dump_classifier_and_data(clf, my_dataset, features_list)

#!/usr/bin/pickle

""" a basic script for importing student's POI identifier,
    and checking the results that they get from it 
 
    requires that the algorithm, dataset, and features list
    be written to my_classifier.pkl, my_dataset.pkl, and
    my_feature_list.pkl, respectively

    that process should happen at the end of poi_id.py
"""

import pickle
import sys
from sklearn.cross_validation import StratifiedShuffleSplit
sys.path.append("../tools/")
from feature_format import featureFormat, targetFeatureSplit

PERF_FORMAT_STRING = "\
\tAccuracy: {:>0.{display_precision}f}\tPrecision: {:>0.{display_precision}f}\t\
Recall: {:>0.{display_precision}f}\tF1: {:>0.{display_precision}f}\tF2: {:>0.{display_precision}f}"
RESULTS_FORMAT_STRING = "\tTotal predictions: {:4d}\tTrue positives: {:4d}\tFalse positives: {:4d}\
\tFalse negatives: {:4d}\tTrue negatives: {:4d}"

def test_classifier(clf, dataset, feature_list, folds = 1000):
    data = featureFormat(dataset, feature_list, sort_keys = True)
    labels, features = targetFeatureSplit(data)
    cv = StratifiedShuffleSplit(labels, folds, random_state = 42)
    true_negatives = 0
    false_negatives = 0
    true_positives = 0
    false_positives = 0
    for train_idx, test_idx in cv: 
        features_train = []
        features_test  = []
        labels_train   = []
        labels_test    = []
        for ii in train_idx:
            features_train.append( features[ii] )
            labels_train.append( labels[ii] )
        for jj in test_idx:
            features_test.append( features[jj] )
            labels_test.append( labels[jj] )
        
        ### fit the classifier using training set, and test on test set
        clf.fit(features_train, labels_train)
        predictions = clf.predict(features_test)
        for prediction, truth in zip(predictions, labels_test):
            if prediction == 0 and truth == 0:
                true_negatives += 1
            elif prediction == 0 and truth == 1:
                false_negatives += 1
            elif prediction == 1 and truth == 0:
                false_positives += 1
            elif prediction == 1 and truth == 1:
                true_positives += 1
            else:
                print "Warning: Found a predicted label not == 0 or 1."
                print "All predictions should take value 0 or 1."
                print "Evaluating performance for processed predictions:"
                break
    try:
        total_predictions = true_negatives + false_negatives + false_positives + true_positives
        accuracy = 1.0*(true_positives + true_negatives)/total_predictions
        precision = 1.0*true_positives/(true_positives+false_positives)
        recall = 1.0*true_positives/(true_positives+false_negatives)
        f1 = 2.0 * true_positives/(2*true_positives + false_positives+false_negatives)
        f2 = (1+2.0*2.0) * precision*recall/(4*precision + recall)
        print clf
        print PERF_FORMAT_STRING.format(accuracy, precision, recall, f1, f2, display_precision = 5)
        print RESULTS_FORMAT_STRING.format(total_predictions, true_positives, false_positives, false_negatives, true_negatives)
        print ""
    except:
        print "Got a divide by zero when trying out:", clf
        print "Precision or recall may be undefined due to a lack of true positive predicitons."

CLF_PICKLE_FILENAME = "my_classifier.pkl"
DATASET_PICKLE_FILENAME = "my_dataset.pkl"
FEATURE_LIST_FILENAME = "my_feature_list.pkl"

def dump_classifier_and_data(clf, dataset, feature_list):
    with open(CLF_PICKLE_FILENAME, "w") as clf_outfile:
        pickle.dump(clf, clf_outfile)
    with open(DATASET_PICKLE_FILENAME, "w") as dataset_outfile:
        pickle.dump(dataset, dataset_outfile)
    with open(FEATURE_LIST_FILENAME, "w") as featurelist_outfile:
        pickle.dump(feature_list, featurelist_outfile)

def load_classifier_and_data():
    with open(CLF_PICKLE_FILENAME, "r") as clf_infile:
        clf = pickle.load(clf_infile)
    with open(DATASET_PICKLE_FILENAME, "r") as dataset_infile:
        dataset = pickle.load(dataset_infile)
    with open(FEATURE_LIST_FILENAME, "r") as featurelist_infile:
        feature_list = pickle.load(featurelist_infile)
    return clf, dataset, feature_list

def main():
    ### load up student's classifier, dataset, and feature_list
    clf, dataset, feature_list = load_classifier_and_data()
    ### Run testing script
    test_classifier(clf, dataset, feature_list)

if __name__ == '__main__':
    main()

import matplotlib.pyplot as plt

print "\n","######### total numbers of data point:"
print len(data_dict)

# 获取所有的feature总数以及名称，注意第一个是label，所以最后feature数是len(features)-1
print "\n","######### total numbers of features:"
for features in data_dict.values():
    print len(features)
    print features
    break

# 检查数据中poi的数目
print "\n","######### total number of poi:"
pois = [x for x, y in data_dict.items() if y['poi']]
print len(pois)

print "######### check the nan data:"
for key,value in data_dict.items():
	nan_num = 0
	for i in value.values():
		if i == "NaN":
			nan_num = nan_num + 1

	if nan_num > 15:
		print key,":",nan_num

print "\n","######### find the outliers:"
salary_list = []
bonus_list = []

for features in data_dict.values():
	plt.scatter(features["salary"], features["bonus"])

	# 只有当非无效值时才加入list
	if features["salary"] == "NaN" or features["bonus"] == "NaN":
		continue
	salary_list.append(features["salary"])
	bonus_list.append(features["bonus"])

# 排序
bonus_list.sort()
salary_list.sort()

# 输出最大的前5位
print "\n","######### the top five:"
print salary_list[-5:]
print bonus_list[-5:]

print "\n","######### the bottom five:"
print salary_list[0:5]
print bonus_list[0:5]


# 描画salary与bonus的点阵图，这是未处理异常值时的原始数据
plt.title('The original dataset:')
plt.xlabel("salary")
plt.ylabel("bonus")
plt.show()

# 打印出异常数据
print "\n","######### top of the salary and bonus:"
print "######### show the problem data point:"
for key,value in data_dict.items():
	if value["salary"] == salary_list[-1]:
		print ""
		print key,"'s salary : ",value["salary"]

	if value["bonus"] == bonus_list[-1]:
		print ""
		print key,"'s bonus : ",value["bonus"]

	# 这部分数据是通过输出无效值时，目视得到的异常数据
	if key == "THE TRAVEL AGENCY IN THE PARK" or key == "LOCKHART EUGENE E":
		print ""
		print key,":"
		print value

my_dataset = data_dict

### add by lj
# 将异常数据删除
my_dataset.pop("TOTAL")
my_dataset.pop("THE TRAVEL AGENCY IN THE PARK")
my_dataset.pop("LOCKHART EUGENE E")
print "\n","after(remove outliers), the total number of the dataset:"
print len(my_dataset)

# 重新绘图
for features in my_dataset.values():
	plt.scatter(features["salary"], features["bonus"])

plt.title('After removing the outliers:')
plt.xlabel("salary")
plt.ylabel("bonus")
plt.show()

import numpy as np
import pandas as pd

# 输出每个feature的信息，仅供自己参考用，非要求
df = pd.DataFrame.from_dict(data_dict, orient='index')
df.replace('NaN', np.nan, inplace = True)

df.info()

for features in my_dataset.values():
	colors = features["poi"]
	# 根据是否是POI，决定点的颜色
	if colors == True:
		colors = "red"
	else:
		colors = "blue"
	plt.scatter(features["from_poi_to_this_person"], features["from_this_person_to_poi"], c=colors,alpha=0.5)

plt.xlabel("from_poi_to_this_person")
plt.ylabel("from_this_person_to_poi")
plt.show()

def computeFraction( poi_messages, all_messages ):
    fraction = 0.
    if poi_messages == "NaN" or all_messages == "NaN":
        return 0

    fraction = float(poi_messages)/all_messages
    
    return fraction

# 添加新的特征
for i in data_dict:
    my_dataset[i]['fraction_from_poi'] = computeFraction(my_dataset[i]['from_poi_to_this_person'],my_dataset[i]['to_messages'])
    my_dataset[i]['fraction_to_poi'] = computeFraction(my_dataset[i]['from_this_person_to_poi'],my_dataset[i]['from_messages'])

for features in my_dataset.values():
	colors = features["poi"]
	if colors == True:
		colors = "red"
	else:
		colors = "blue"
	plt.scatter(features["fraction_from_poi"], features["fraction_to_poi"], c=colors,alpha=0.5)

plt.xlabel("fraction_from_poi")
plt.ylabel("fraction_to_poi")
plt.show()

from sklearn.feature_selection import SelectPercentile,SelectKBest

def Select_K_Best(data_dict, features_list, k):

    data_array = featureFormat(data_dict, features_list)
    labels, features = targetFeatureSplit(data_array)

    k_best = SelectKBest(k=k)
    k_best.fit(features, labels)
    scores = k_best.scores_
    tuples = zip(features_list[1:], scores)
    k_best_features = sorted(tuples, key=lambda x: x[1], reverse=True)

    return k_best_features[:k]

print "\n",Select_K_Best(my_dataset,features_list,5)

# 选取最高得分的5个特征，以及POI label
features_list = ["poi"] + [x[0] for x in Select_K_Best(my_dataset,features_list,5)] 

print "\n",features_list

from sklearn.preprocessing import MinMaxScaler

print "\n",features[0:3]

scaler = MinMaxScaler()
features_new = scaler.fit_transform(features)

print "\n",features_new[0:3]

labels = np.asarray(labels)

from sklearn.cross_validation import train_test_split
features_train, features_test, labels_train, labels_test = \
    train_test_split(features, labels, test_size=0.3, random_state=42)

## 1. GaussianNB
from sklearn.naive_bayes import GaussianNB
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV

t1 = time()

clf_NB = GaussianNB()
parm = {}

clf_NB = Pipeline([('scaler',scaler),('gnb',clf_NB)])
gs = GridSearchCV(clf_NB,parm)
gs.fit(features_train,labels_train)

clf_NB = gs.best_estimator_

print "\nGaussianNB score:\n",clf_NB.score(features_train,labels_train)
print "GaussianNB score time:", round(time()-t1, 3), "s"

##  Test Point
from tester import dump_classifier_and_data,test_classifier

# 使用的是tester.py中的test_classifier函数评分
print "\nGaussianNB:\n",test_classifier(clf_NB,my_dataset,features_list)

## 2. Decision Tree Classifier
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree

t2 = time()

parms = {'criterion': ['gini', 'entropy'], \
			'min_samples_split': [2, 5, 10, 20], \
			'max_depth': [None, 2, 5, 10], \
			'splitter': ['random', 'best'], \
			'max_leaf_nodes': [None, 5, 10, 20]   }

clf_DT = tree.DecisionTreeClassifier()

gs = GridSearchCV(clf_DT,parms)
gs.fit(features_train,labels_train)

clf_DT = gs.best_estimator_

print "\nDecision Tree Classifier\n",clf_DT.score(features_train,labels_train)
print "Decision Tree Classifier:", round(time()-t2, 3), "s"

##  Test Point
print "\nDecision Tree Classifier Test Point:\n",test_classifier(clf_DT,my_dataset,features_list)

## 3. SVM

from sklearn.svm import SVC
parms = {'svc__kernel':('linear','rbf'),'svc__C':[1.0,2.0]}

t3 = time()
clf_SVC = SVC()

pipeline2 = Pipeline([('scaler',scaler),('svc',clf_SVC)])
# a = pipeline.fit(features_train,labels_train)
gs = GridSearchCV(pipeline2,parms)
gs.fit(features_train,labels_train)

clf_SVC = gs.best_estimator_

print "\nSVM \n",clf_SVC.score(features_train,labels_train)
print "Decision Tree Classifier:", round(time()-t3, 3), "s"

##  Test Point
print "\nSVM:\n",test_classifier(clf_SVC,my_dataset,features_list)


### 4. RandomForest
from sklearn.ensemble import RandomForestClassifier

t4 = time()

print '\nRandomForest\n'
clf_RF = RandomForestClassifier()
parameters = {'criterion': ['gini', 'entropy'], \
			'max_depth': [None, 2, 5, 10], \
			'max_leaf_nodes': [None, 5, 10, 20], \
			'n_estimators': [1, 5, 10, 50, 100]}
gs = GridSearchCV(clf_RF, parameters)
gs.fit(features_train,labels_train)

clf_RF = gs.best_estimator_

print "\nRandomForest:\n",clf_RF.score(features_train,labels_train)
print "Decision Tree Classifier:", round(time()-t4, 3), "s"

##  Test Point
print "\nRandomForest:\n",test_classifier(clf_RF,my_dataset,features_list)


### 5. AdaBoost
from sklearn.ensemble import AdaBoostClassifier

t5 = time()

clf_AB = AdaBoostClassifier(algorithm='SAMME')
parameters = {'learning_rate': [0.1, 0.5, 1.0, 5.0], \
			'algorithm': ['SAMME', 'SAMME.R'], \
			'n_estimators': [1, 5, 10, 50, 100]}
gs = GridSearchCV(clf_AB, parameters)
gs.fit(features_train,labels_train)

clf_AB = gs.best_estimator_

print "\nAdaBoost:\n",clf_AB.score(features_train,labels_train)
print "Decision Tree Classifier:", round(time()-t5, 3), "s"

##  Test Point
print "\nAdaBoost:\n",test_classifier(clf_AB,my_dataset,features_list)