sjstebbins

import numpy as np

class MS(object):
    def __init__(self, board_len=5, mines=10, *args, **kwargs):
        super().__init__(*args, **kwargs) 
        self.board_len = board_len
        self.hidden_board = self.create_hidden_board(self.board_len, mines)
        self.player_board = np.full((self.board_len, self.board_len), False)
        self.score = 0
        

sjstebbins

sjstebbins

sjstebbins

#####   Xgboost Cross Validation using Subtest Accuracy/AMS #####
result_df = data.frame(eta = NULL, max_depth= NULL,  nrounds = NULL, threshold = NULL, accuracy = NULL, ams_sum = NULL)
counter = 1 
# record the elapsed time  
ptm <- proc.time()
for (eta in seq(0.02, 0.4, 0.08)){
  for (depth in seq(5, 10, 1)){
    for (nrounds in seq(25, 90, 10)){
      for (threshold in seq(0.1, 0.3, 0.05)){
        print (counter)

sjstebbins

# subset my own training and testing
set.seed(0)
idx = sample.split(dfTrain$Label, SplitRatio = .7)
idx = which(idx == TRUE)
subTrain = dfTrain[idx,]
mytrain = subTrain[, -c(1,32,33)]
subTest = dfTrain[-idx,]
mytest = subTest[, -c(1,32,33)]

sjstebbins

# function to find optimal hidden nodes and threshold
cv_nnet_models = function(train, validation,  start, end) {
    print(paste('Cross validating neural network on ams score with nodes from',start,'to',end))
    # create data frame to return best model for each hidden node level
    neural_df = data.frame(hidden_nodes= NULL, threshold=NULL, ams_sum = NULL)
    # iterate with increasing number of hidden nodes
    for (hidden_nodes in seq(start,end,1)){
        print(paste('Training with', hidden_nodes,'hidden nodes'))
        # train model
        model = train_neural(train, hidden_nodes)

sjstebbins

# if making request for billboard playlist
if request.args.get('day', None, type=str) != None:
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
driver = webdriver.Chrome(executable_path="./chromedriver")
driver.get("http://www.umdmusic.com/default.asp?Chart=D")
elem = driver.find_element_by_name('ChDay')
elem.clear()
elem.send_keys(request.args.get('day', 0, type=str))
elem = driver.find_element_by_name('ChMonth')

sjstebbins

# API: get recommended tracks data based on seed and target values
recommendations_api_endpoint = SPOTIFY_API_URL  + "/recommendations?seed_artists=" + ",".join(seed_artists) + "&target_energy=" + target_energy + "&target_liveness=" + target_liveness + "&target_tempo=" + target_tempo + "&target_speechiness=" + target_speechiness + "&target_acousticness=" + target_acousticness + "&target_instrumentalness=" + target_instrumentalness + "&target_danceability=" + target_danceability + "&target_loudness=" + target_loudness + "&limit=20"
recommendations_response = requests.get(recommendations_api_endpoint, headers=GLOBAL['authorization_header'])
recommendation_data['data'] = json.loads(recommendations_response.text)['tracks']

# set recommended track title and ids
for track in recommendation_data['data']:
    # Dont add duplicate recommendations or songs already in your playlist
    if track['id'] not in recommendation_data['ids'] and track['id'] not in seed_data['song_ids']:
        recommendation_data['titles'].ap

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# get seed_distances from phantom_average_track
seed_distances = [phantom_average_track.values()] + playlist_tracks_attributes
dist = DistanceMetric.get_metric('euclidean')
distances = dist.pairwise(seed_distances)[0]
seed_data['distances'] = distances[1:len(distances)]

# get attributes of the 5 closest tracks to the phantom_average_track
seed_indexes = seed_data['distances'].argsort()[:5]
seed_songs = [seed_data['song_ids'][i] for i in seed_indexes]
seed_artists = [seed_data['artist_ids'][i] for i in seed_indexes]

sjstebbins

# create phantom_average_track
    phantom_average_track = {}
    target_attributes = ['energy','liveness','tempo','speechiness','acousticness','instrumentalness','danceability','loudness']
    for attribute in target_attributes:
        phantom_average_track[attribute] = sum(track[attribute] for track in library_tracks) / len(library_tracks)