#!/usr/bin/python 

""" 
    Skeleton code for k-means clustering mini-project.
"""




import pickle
import numpy as np
import matplotlib.pyplot as plt
import sys
sys.path.append("../tools/")
from feature_format import featureFormat, targetFeatureSplit




def Draw(pred, features, poi, mark_poi=False, name="image.png", f1_name="feature 1", f2_name="feature 2"):
    """ some plotting code designed to help you visualize your clusters """

    ### plot each cluster with a different color--add more colors for
    ### drawing more than five clusters
    colors = ["b", "c", "k", "m", "g"]
    for ii, pp in enumerate(pred):
        plt.scatter(features[ii][0], features[ii][1], color = colors[pred[ii]])

    ### if you like, place red stars over points that are POIs (just for funsies)
    if mark_poi:
        for ii, pp in enumerate(pred):
            if poi[ii]:
                plt.scatter(features[ii][0], features[ii][1], color="r", marker="*")
    plt.xlabel(f1_name)
    plt.ylabel(f2_name)
    plt.savefig(name)
    plt.show()



### load in the dict of dicts containing all the data on each person in the dataset
data_dict = pickle.load( open("../final_project/final_project_dataset.pkl", "r") )
### there's an outlier--remove it! 
data_dict.pop("TOTAL", 0)


# maxy = 0
# miny = 99999999999
# for item in data_dict:
#     #print data_dict['METTS MARK']['exercised_stock_options']
#     if data_dict[item]['salary'] != 'NaN' and data_dict[item]['salary'] > maxy:
#         maxy = data_dict[item]['salary']
#     if data_dict[item]['salary'] != 'NaN' and data_dict[item]['salary'] < miny:
#         miny = data_dict[item]['salary']
# print maxy,miny


### the input features we want to use 
### can be any key in the person-level dictionary (salary, director_fees, etc.) 
feature_1 = "salary"
feature_2 = "exercised_stock_options"
poi  = "poi"
features_list = [poi, feature_1, feature_2]
data = featureFormat(data_dict, features_list )
poi, finance_features = targetFeatureSplit( data )

from sklearn import preprocessing
sal = []
sto = []
id1,id2 = 0,0
for item in finance_features:
    if item[0] != 0.0:
        sal.append(item[0])
    if item[0] == 200000.:
        id1 = sal.index(item[0])
    if item[1] != 0.0:
        sto.append(item[1])
    if item[1] == 1000000.:
        id2 = sto.index(item[1])

sal_train = np.array(sal)
#print sal
sto_train = np.array(sto)
min_max_scaler = preprocessing.MinMaxScaler()
sal_min_max = min_max_scaler.fit_transform(sal_train)
sto_min_max = min_max_scaler.fit_transform(sto_train)
print sal_min_max[id1] , sto_min_max[id2]
quit()
### in the "clustering with 3 features" part of the mini-project,
### you'll want to change this line to 
### for f1, f2, _ in finance_features:
### (as it's currently written, the line below assumes 2 features)
for f1, f2  in finance_features:
    plt.scatter( f1, f2 )
plt.show()

### cluster here; create predictions of the cluster labels
### for the data and store them to a list called pred
from sklearn.cluster import KMeans
kmeans = KMeans(n_clusters=2).fit(data)
pred = kmeans.predict(data)




### rename the "name" parameter when you change the number of features
### so that the figure gets saved to a different file
try:
    Draw(pred, finance_features, poi, mark_poi=False, name="clusters_with_scaling.pdf", f1_name=feature_1, f2_name=feature_2)
except NameError:
    print "no predictions object named pred found, no clusters to plot"
