import json
from pprint import pprint
from tag import count_words
from sets import Set
with open('data.json') as data_file:
data = json.load(data_file)
# pprint([x for x in data if x['winner'] == True])
# print(len(data))
# Win vector
win_vector = [1 if x['winner'] else 0 for x in data]
print(win_vector)
count_vectors = [count_words(x['description']) for x in data]
for x in count_vectors:
sumo = sum([y[1] for y in x])
for i in range(0,len(x)):
if sumo != 0:
x[i] = (x[i][0], float(x[i][1]) / sumo)
print(count_vectors)
words = Set()
for x in count_vector:
for word_tuple in x:
words.add(word_tuple[0])
def predict(json):
# with open('projects_with_users.json') as data_file:
# data = json.load(data_file)
# pprint([x for x in data if x['winner'] == True])
# print(len(data))
#random.shuffle(data)
# Win vector
#win_vector = [1 if x['winner'] else 0 for x in data]
#print(win_vector)
#data = data[:100]
# data2 = []
# for proj in data:
# if proj['winner']:
# data
# data = [y for y in data if y['winner'] == True]
# print(len(data))
# cv_win = [1 if x['winner'] else 0 for x in data]
data = [json]
count_vectors = [count_words(x['description']) for x in data]
pkl_file = open(os.path.join(os.path.dirname(__file__),'words.pkl'), 'rb')
words = pickle.load(pkl_file, encoding='latin1')
pkl_file.close()
# maxOcc = [ ]
# minOcc = [ ]
# for j in range(0, len(words) + 3):
# maxOcc.append(0)
# minOcc.append(0)
matrix = [ ]
for i in range(0, len(count_vectors)):
row = [ ]
for j in range(0, len(words)):
cnt = 0
for item in count_vectors[i]:
if words[j] == item[0]:
cnt = item[1]
row.append(cnt)
# if cnt > maxOcc[j]:
# maxOcc[j] = cnt
# if cnt < minOcc[j]:
# minOcc[j] = cnt
matrix.append(row)
row = 0
for element in data:
nr_wins = 0
nr_projects = 0
nr_hacks = 0
for author in element["authors"]:
nr_wins = nr_wins + author["wins"]
nr_projects = nr_projects + author["projects"]
nr_hacks = nr_hacks + author["hackathons"]
if nr_projects == 0:
matrix[row].append(0)
else:
matrix[row].append(float(nr_wins) / nr_projects)
matrix[row].append(nr_wins)
matrix[row].append(nr_hacks)
row = row + 1
pkl_file = open(os.path.join(os.path.dirname(__file__),'maxOcc.pkl'), 'rb')
maxOcc = pickle.load(pkl_file, encoding='latin1')
pkl_file.close()
for i in range(0, len(matrix)):
for j in range(0, len(matrix[i])):
matrix[i][j] = float(matrix[i][j]) / maxOcc[j]
win_vector = np.loadtxt(os.path.join(os.path.dirname(__file__),'win_vector.txt')).tolist()
#n_matrix = np.array(matrix, size=[100,len(matrix[0])])
# print(len(matrix[0]) == len(matrix[1]))
# print(len(win_vector))
#print(n_matrix[0])
#n_win_vector = np.int64(win_vector)
# print(win_vector[0])
#print(len(a),len(win_vector))
# x,y = a, win_vector
# clf = svm.SVC(gamma=0.001, C=100)
# clf.fit(x,y)
# joblib.dump(clf, 'clf.pkl')
hits = 0;
hits_0 = 0;
return (clf.predict([matrix[0]])[0] == 1.0)
def predict(json):
# with open('projects_with_users.json') as data_file:
# data = json.load(data_file)
# pprint([x for x in data if x['winner'] == True])
# print(len(data))
#random.shuffle(data)
# Win vector
#win_vector = [1 if x['winner'] else 0 for x in data]
#print(win_vector)
#data = data[:100]
# data2 = []
# for proj in data:
# if proj['winner']:
# data
# data = [y for y in data if y['winner'] == True]
# print(len(data))
# cv_win = [1 if x['winner'] else 0 for x in data]
data = [json]
count_vectors = [count_words(x['description']) for x in data]
pkl_file = open(os.path.join(os.path.dirname(__file__), 'words.pkl'), 'rb')
words = pickle.load(pkl_file, encoding='latin1')
pkl_file.close()
# maxOcc = [ ]
# minOcc = [ ]
# for j in range(0, len(words) + 3):
# maxOcc.append(0)
# minOcc.append(0)
matrix = []
for i in range(0, len(count_vectors)):
row = []
for j in range(0, len(words)):
cnt = 0
for item in count_vectors[i]:
if words[j] == item[0]:
cnt = item[1]
row.append(cnt)
# if cnt > maxOcc[j]:
# maxOcc[j] = cnt
# if cnt < minOcc[j]:
# minOcc[j] = cnt
matrix.append(row)
row = 0
for element in data:
nr_wins = 0
nr_projects = 0
nr_hacks = 0
for author in element["authors"]:
nr_wins = nr_wins + author["wins"]
nr_projects = nr_projects + author["projects"]
nr_hacks = nr_hacks + author["hackathons"]
if nr_projects == 0:
matrix[row].append(0)
else:
matrix[row].append(float(nr_wins) / nr_projects)
matrix[row].append(nr_wins)
matrix[row].append(nr_hacks)
row = row + 1
pkl_file = open(os.path.join(os.path.dirname(__file__), 'maxOcc.pkl'),
'rb')
maxOcc = pickle.load(pkl_file, encoding='latin1')
pkl_file.close()
for i in range(0, len(matrix)):
for j in range(0, len(matrix[i])):
matrix[i][j] = float(matrix[i][j]) / maxOcc[j]
win_vector = np.loadtxt(
os.path.join(os.path.dirname(__file__), 'win_vector.txt')).tolist()
#n_matrix = np.array(matrix, size=[100,len(matrix[0])])
# print(len(matrix[0]) == len(matrix[1]))
# print(len(win_vector))
#print(n_matrix[0])
#n_win_vector = np.int64(win_vector)
# print(win_vector[0])
#print(len(a),len(win_vector))
# x,y = a, win_vector
# clf = svm.SVC(gamma=0.001, C=100)
# clf.fit(x,y)
# joblib.dump(clf, 'clf.pkl')
hits = 0
hits_0 = 0
return (clf.predict([matrix[0]])[0] == 1.0)
from sklearn import svm
import numpy as np
from sklearn.externals import joblib
with open('projects_with_users.json') as data_file:
data = json.load(data_file)
# pprint([x for x in data if x['winner'] == True])
# print(len(data))
#random.shuffle(data)
# Win vector
#win_vector = [1 if x['winner'] else 0 for x in data]
#print(win_vector)
count_vectors = [count_words(x['description']) for x in data]
words = [ ]
for x in count_vectors:
for pair in x:
if words.count(pair[0]) == 0:
words.append(pair[0])
maxOcc = [ ]
minOcc = [ ]
for j in range(0, len(words) + 3):
maxOcc.append(0)
minOcc.append(0)
matrix = [ ]
for i in range(0, len(count_vectors)):
