#!/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", "rb") )
# ## there's an outlier--remove it!
data_dict.pop("TOTAL", 0)

stock = []
salary = []
for x,y in data_dict.items():
    stock.append([x,y][1]['exercised_stock_options'])
    salary.append([x,y][1]['salary'])
import numpy as np
maxsalary = np.nanmax(np.array(salary, dtype = np.float64))
minsalary = np.nanmin(np.array(salary, dtype = np.float64))
maxstock = np.nanmax(np.array(stock, dtype = np.float64))
minstock = np.nanmin(np.array(stock, dtype = np.float64))

print("MAX exercised_stock_options",maxstock)
print("MIN exercised_stock_options",minstock)
print("MAX salary",maxsalary)
print("MIN salary",minsalary)
# Rescaling II
salaryScaled = ((200000. - minsalary)/(maxsalary-minsalary))
stockScaled = ((1000000. - minstock)/(maxstock-minstock))
print("Scaled Salary 200000:", salaryScaled)
print("Scaled Stock 1000000:", stockScaled)
# Rescaling
# from sklearn.preprocessing import MinMaxScaler
# scaler = MinMaxScaler()
# salaryData = np.array(salary, dtype = np.float64)
# stockData = np.array(stock, dtype = np.float64)
# rescaled_salary = scaler.fit_transform(salaryData)
# rescaled_stock = scaler.fit_transform(stockData)





### 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"
# Rescaling




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()

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