Opening_Weekend/nn2.py at master · SwammyD/Opening_Weekend · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
import sklearn
from sklearn.neighbors import NearestNeighbors
from sklearn.neighbors import KNeighborsRegressor
import numpy as np
from sklearn.neighbors import DistanceMetric
from sklearn.neighbors.ball_tree import BallTree
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
import random
import pandas as pd

prejoined = pd.read_csv("joined_25.csv")
actors = pd.read_csv("title.principals.tsv", delimiter='\t')

# def cleanHumans(commanstr):
# 	return commanstr.split(",")

def cleanHumans0(commanstr):
	try:
		return str(commanstr.split(",")[0])
	except:
		return "nm0000000"

def cleanHumans1(commanstr):
	try:
		return str(commanstr.split(",")[1])
	except:
		return "nm0000000"
def cleanHumans2(commanstr):
	try:
		return str(commanstr.split(",")[2])
	except:
		return "nm0000000"
def cleanHumans3(commanstr):
	try:
		return str(commanstr.split(",")[3])
	except:
		return "nm0000000"
def cleanHumans4(commanstr):
	try:
		return str(commanstr.split(",")[4])
	except:
		return "nm0000000"

actors['humans0'] = actors['principalCast'].apply(cleanHumans0)
actors['humans1'] = actors['principalCast'].apply(cleanHumans1)
actors['humans2'] = actors['principalCast'].apply(cleanHumans2)
actors['humans3'] = actors['principalCast'].apply(cleanHumans3)
actors['humans4'] = actors['principalCast'].apply(cleanHumans4)

del actors['principalCast']

#print(data)
joined = pd.merge(prejoined, actors,  how='left', on='tconst')
#joined['humans'] = joined['principalCast'].split(",")

floatdf = joined.copy()
del floatdf['endYear']
del floatdf['originalTitle']
del floatdf['primaryTitle']
del floatdf['titleType']

def cleantconst(tconst):
	return int(tconst[2:])
def uncleartconsts(unclean):
	return "tt" + str(unclean)
def cleanhuman(human):
	return int(human[2:])
def uncleanhuman(unclean):
	return "nm" + str(unclean)

floatdf['tconst'] = floatdf['tconst'].apply(cleantconst)
floatdf['humans0'] = floatdf['humans0'].apply(cleanhuman)
floatdf['humans1'] = floatdf['humans1'].apply(cleanhuman)
floatdf['humans2'] = floatdf['humans2'].apply(cleanhuman)
floatdf['humans3'] = floatdf['humans3'].apply(cleanhuman)
floatdf['humans4'] = floatdf['humans4'].apply(cleanhuman)
floatdf['budget'] = floatdf['budget'].astype("int")
floatdf['openingweekend'] = floatdf['openingweekend'].astype("int")


#floatdf = joined.apply(LabelEncoder().fit_transform)

# nparray0 = floatdf.as_matrix()

# nparray = np.squeeze(np.asarray(nparray0))

x2 = floatdf.as_matrix()

x_0 = floatdf.copy()

del x_0['openingweekend']

x = x_0.as_matrix()

# print(x[38])

y = floatdf['openingweekend'].as_matrix()

X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.1)

# print(X_train[38])

print("data properly formatted")


#index,tconst,isAdult,startYear,runtimeMinutes,Action,Animation,Biography,Comedy,Crime,Documentary,Drama,Family,Fantasy,History,Horror,Music,Musical,Mystery,Romance,Sci-Fi,Sport,Thriller,War,Western,budget,human0-4

def movieDistance(x,y):

	year = 1*abs(x[4]-y[4])
	budget = abs(x[26]-y[26])/100000
	genre = 0
	for i in range(20):
		index = i + 5
		genre += abs(x[index]-y[index])
	genre = genre*10

	xhumans = [x[27],x[28],x[29],x[30],x[31]]
	yhumans = [y[27],y[28],y[29],y[30],y[31]]

	humans = 0

	for hum in xhumans:
		if hum in yhumans:
			humans += -20

	#print("********")
	# print(x)
	# print(y)
	# print(x[26])
	# print(y[26])
	# print(len(x))
	# print("**")
	# print(year)
	# print(budget)
	# print(genre)
	# print(humans)
	# print(year + budget + genre + humans)

	return (year + budget + genre + humans)

# nbrs = NearestNeighbors(n_neighbors=4, algorithm='auto', metric=movieDistance)

# test2 = []

# for i in range(9):
# 	random = randint(0,len(x2))
# 	test2.append(x2[random])

# nbrs.fit(x2)

# print(nbrs.kneighbors([[1., 1., 1.]]))

nbrsreg = KNeighborsRegressor(n_neighbors=5, metric=movieDistance)

print("START FIT")

nbrsreg.fit(X_train,y_train)

print("START PERDICTION")

ow_perdiction = nbrsreg.predict(X_test)

print("regression perdiction:")
print(ow_perdiction)
print("actual restults:")
print(y_test)

results = []

it = np.nditer(ow_perdiction, flags=["c_index"])

# for x,y in np.ndenumerate(ow_perdiction):
# 	percentdiff = (ow_perdiction[x,y] - ytest[x,y])/ytest[x,y]
# 	results.append(percentdiff)

while not it.finished:
	percentdiff = (ow_perdiction[it.index] - y_test[it.index])/y_test[it.index]
	if (abs(percentdiff)) < 1:
		results.append(abs(percentdiff))
	it.iternext()

print(results)

print(sum(results)/len(results))