# Load packages.
import pandas as pd
from revoscalepy import RxInSqlServer, RxSqlServerData, RxComputeContext, rx_import
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from scipy.spatial.distance import cdist, pdist
import numpy as np


def perform_clustering():
    ##########################################################################################################################################

    ##	Connect to DB and select data

    ##########################################################################################################################################

    # Connection string to connect to SQL Server named instance
    conn_str = 'Driver=SQL Server;Server=localhost;Database=tpcxbb_1gb;Trusted_Connection=True;'

    input_query = '''SELECT
    ss_customer_sk AS customer,
    ROUND(COALESCE(returns_count / NULLIF(1.0*orders_count, 0), 0), 7) AS orderRatio,
    ROUND(COALESCE(returns_items / NULLIF(1.0*orders_items, 0), 0), 7) AS itemsRatio,
    ROUND(COALESCE(returns_money / NULLIF(1.0*orders_money, 0), 0), 7) AS monetaryRatio,
    COALESCE(returns_count, 0) AS frequency 
    FROM
    (
    SELECT
      ss_customer_sk,
      -- return order ratio
      COUNT(distinct(ss_ticket_number)) AS orders_count,
      -- return ss_item_sk ratio
      COUNT(ss_item_sk) AS orders_items,
      -- return monetary amount ratio
      SUM( ss_net_paid ) AS orders_money
    FROM store_sales s
    GROUP BY ss_customer_sk
  ) orders
  LEFT OUTER JOIN
  (
    SELECT
      sr_customer_sk,
      -- return order ratio
      count(distinct(sr_ticket_number)) as returns_count,
      -- return ss_item_sk ratio
      COUNT(sr_item_sk) as returns_items,
      -- return monetary amount ratio
      SUM( sr_return_amt ) AS returns_money
    FROM store_returns
    GROUP BY sr_customer_sk ) returned ON ss_customer_sk=sr_customer_sk'''


 # Define the columns we wish to import
    column_info = {
        "customer": {"type": "integer"},
        "orderRatio": {"type": "integer"},
        "itemsRatio": {"type": "integer"},
        "frequency": {"type": "integer"}
    }

    data_source = RxSqlServerData(sql_query=input_query, column_Info=column_info, connection_string=conn_str)
    RxInSqlServer(connection_string=conn_str, num_tasks=1, auto_cleanup=False)
    # import data source and convert to pandas dataframe
    customer_data = pd.DataFrame(rx_import(data_source))
    print("Data frame:", customer_data.head(n=20))

    ##########################################################################################################################################

    ##	Determine number of clusters using the Elbow method

    ##########################################################################################################################################

    cdata = customer_data
    K = range(1, 20)
    KM = [KMeans(n_clusters=k).fit(cdata) for k in K]
    centroids = [k.cluster_centers_ for k in KM]

    D_k = [cdist(cdata, cent, 'euclidean') for cent in centroids]
    dist = [np.min(D, axis=1) for D in D_k]
    avgWithinSS = [sum(d) / cdata.shape[0] for d in dist]
    plt.plot(K, avgWithinSS, 'b*-')
    plt.grid(True)
    plt.xlabel('Number of clusters')
    plt.ylabel('Average within-cluster sum of squares')
    plt.title('Elbow for KMeans clustering')
    plt.show()


    ##########################################################################################################################################

    ##	Perform clustering using Kmeans

    ##########################################################################################################################################

    #It looks like k=4 is a good number to use based on the elbow graph
    n_clusters = 4

    est = KMeans(n_clusters=n_clusters, random_state=111).fit(customer_data[["orderRatio", "itemsRatio", "monetaryRatio", "frequency"]])
    clusters = est.labels_
    customer_data['cluster'] = clusters

    #Print some data about the clusters:

    #For each cluster, count the members
    for c in range(n_clusters):
        cluster_members=customer_data[customer_data['cluster']== c][:]
        print('Cluster{0}(n={1}):'.format(c,len(cluster_members)))
        print('-------------------')

    #Print mean values per cluster
    print(customer_data.groupby(['cluster']).mean())


perform_clustering()