Data-Analyst-Nanodegree/P5 - Identifying Fraud from Enron Email Dataset/poi_id.py

# Ignored usage of deprecated modules for sklearn 0.18.
# This would be updated in future when sklearn 0.20 releases.
import warnings
warnings.filterwarnings("ignore")

import pickle

from feature_format import featureFormat, targetFeatureSplit
from tester import dump_classifier_and_data

import pandas as pd
import numpy as np

from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
from sklearn.grid_search import GridSearchCV

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', 'long_term_incentive',
                    'bonus-to-salary_ratio', 'deferral_payments', 'expenses',
                    'restricted_stock_deferred', 'restricted_stock',
                    'deferred_income', 'fraction_mail_from_poi',
                    'total_payments', 'other', 'fraction_mail_to_poi',
                    'from_poi_to_this_person', 'from_this_person_to_poi',
                    'to_messages','from_messages', 'shared_receipt_with_poi',
                    'loan_advances', 'director_fees',
                    'exercised_stock_options', 'total_stock_value']

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

# Converting the given pickled Enron data to a pandas dataframe
enron_df = pd.DataFrame.from_records(list(data_dict.values()))

# Set the index of df to be the employees series:
employees = pd.Series(list(data_dict.keys()))
enron_df.set_index(employees, inplace=True)

# Coerce numeric values into floats or ints; also change NaN to zero:
enron_df_new = enron_df.apply(lambda x : pd.to_numeric(x, errors = 'coerce')).copy().fillna(0)

# Dropping column 'email_address' as not required in analysis
enron_df_new.drop('email_address', axis = 1, inplace = True)

### Task 2: Remove outliers
enron_df_new.drop(['TOTAL', 'THE TRAVEL AGENCY IN THE PARK', 'FREVERT MARK A',
    'MARTIN AMANDA K', 'BHATNAGAR SANJAY'], axis = 0, inplace = True)

### Task 3: Create new feature(s)
enron_df_new['bonus-to-salary_ratio'] = enron_df_new['bonus']/enron_df_new['salary']
enron_df_new['fraction_mail_from_poi'] = enron_df_new['from_poi_to_this_person']/enron_df_new['from_messages']
enron_df_new['fraction_mail_to_poi'] = enron_df_new['from_this_person_to_poi']/enron_df_new['to_messages']

# Clean all 'inf' values which we got if the person's from_messages = 0
enron_df_new = enron_df_new.replace('inf', 0)
enron_df_new = enron_df_new.fillna(0)

# Converting the above modified dataframe to a dictionary
enron_dict = enron_df_new.to_dict('index')

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

### split data into training and testing datasets
from sklearn import cross_validation
features_train, features_test, \
    labels_train, labels_test = cross_validation.train_test_split(features, labels,
                                    test_size=0.3,  random_state=42)

# Stratified ShuffleSplit cross-validator
from sklearn.model_selection import StratifiedShuffleSplit
sss = StratifiedShuffleSplit(n_splits=1000, test_size=0.3,random_state = 42)

# Importing modules for feature scaling and selection
from sklearn.preprocessing import MinMaxScaler
from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.decomposition import PCA
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV

# Defining functions to be used via the pipeline
scaler = MinMaxScaler()
skb = SelectKBest(f_classif)
# pca = PCA()

### 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.

from sklearn.naive_bayes import GaussianNB
clf_gnb = GaussianNB()

pipeline = Pipeline(steps = [("SKB", skb), ("NaiveBayes",clf_gnb)])
param_grid = {"SKB__k":[3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19]}

grid = GridSearchCV(pipeline, param_grid, verbose = 0, cv = sss, scoring = 'f1')

t0 = time()
grid.fit(features, labels)
print("Training time: ", round(time()-t0, 3), "s")

# Best algorithm
clf = grid.best_estimator_

t0 = time()
# Refit the best algorithm:
clf.fit(features_train, labels_train)
prediction = clf.predict(features_test)
print("Testing time: ", round(time()-t0, 3), "s")

print("Accuracy of GaussianNB classifer is  : ", accuracy_score(labels_test, prediction))
print("Precision of GaussianNB classifer is : ", precision_score(prediction, labels_test))
print("Recall of GaussianNB classifer is    : ", recall_score(prediction, labels_test))
print("f1-score of GaussianNB classifer is  : ", f1_score(prediction, labels_test))

### 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.
dump_classifier_and_data(clf, my_dataset, features_list)