#!/usr/bin/python

import sys

import pickle

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

import pandas as pd

import numpy as np

import copy

from preprocess_data import FeatureSel,add_features

from sklearn.svm import LinearSVC

from sklearn.grid_search import GridSearchCV

from sklearn.pipeline import Pipeline

from sklearn.preprocessing import StandardScaler

from feature_format import featureFormat, targetFeatureSplit

from tester import test_classifier, dump_classifier_and_data

### 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

features_list = ['poi', 'bonus', 'deferral_payments', 'deferred_income', 'director_fees',

'to_messages', 'total_payments', 'total_stock_value']

### Load the dictionary containing the dataset

data_dict = pickle.load(open("./data/final_project_dataset.pkl", "r"))

### Task 2: Remove outliers

del data_dict["TOTAL"]

del data_dict["LOCKHART EUGENE E"]

### Task 3: Create new feature(s)

### Store to my_dataset for easy export below.

my_dataset = data_dict

my_dataset, new_feature_names, financial_features = add_features(data_dict)

features_list.extend(new_feature_names)

for f in financial_features:

del features_list[features_list.index(f)]

### Extract features and labels from dataset for local testing

data = featureFormat(my_dataset, features_list, sort_keys=True)

labels, features = targetFeatureSplit(data)

from preprocess_data import linearsvc_outlier_rm

# features,labels,_=linearsvc_outlier_rm(np.array(features),np.array(labels))

### 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

sd = StandardScaler()

fsl = FeatureSel(k_best=5, pca_comp=5)

# clf=Pipeline([("fsl",fsl),("sd",sd),("lvc",LinearSVC(C=0.000001))])

clf = Pipeline([("fsl", fsl), ("sd", sd), ("lvc", LinearSVC())])

gscv=GridSearchCV(clf,{"lvc__C":np.logspace(-6,-1,5),

"fsl__k_best":[1,5,10],

"fsl__pca_comp":[0,5,10]},

scoring="recall",verbose=0)

gscv.fit(np.array(features),np.array(labels))

### 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(gscv.best_estimator_, my_dataset, features_list)

### Dump your classifier, dataset, and features_list so

### anyone can run/check your results.

dump_classifier_and_data(gscv.best_estimator_, my_dataset, features_list)