#!/usr/bin/python 

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




import pickle
import numpy
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)


### 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"
feature_3 = "total_payments"
poi  = "poi"
features_list = [poi, feature_1, feature_2, feature_3]
data = featureFormat(data_dict, features_list )
poi, finance_features = targetFeatureSplit( data )


### 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, f3 in finance_features:
    plt.scatter( f1, f2, f3 )
plt.show()

eso_list = []
sal_list = []
for array in finance_features:
    if array[1] > 0:
        eso_list.append(array[1] * 1.0)
    if array[0] > 0:
        sal_list.append(array[0] * 1.0)

print "Max value of exercised stock options is", max(eso_list)

print "Min value of exercised stock options is", min(eso_list)  

print "Max value of salary is", max(sal_list)

print "Min value of salary is", min(sal_list)  

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


### perform feature scaling on salary and exercised_stock_options
from sklearn.preprocessing import MinMaxScaler

sal = numpy.array([sal_list]).reshape(94,1)  #len(sal_list) = 94
eso = numpy.array(eso_list).reshape(101,1)  #len(eso_list) = 101 
scaler1 = MinMaxScaler()
rescale_sal = scaler1.fit_transform(sal)
print "Salary of $200,000 would be rescaled to", scaler1.transform([[float(200000)]])

scaler2 = MinMaxScaler()
rescale_eso = scaler2.fit_transform(eso)
print "Exercised stock options of $1 million would be rescaled to $", scaler2.transform([[float(1000000)]])





### 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.pdf", f1_name=feature_1, f2_name=feature_2)
except NameError:
    print "no predictions object named pred found, no clusters to plot"
