-
Notifications
You must be signed in to change notification settings - Fork 9
/
Copy pathtrain_test.py
111 lines (94 loc) · 2.65 KB
/
train_test.py
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
import os, sys
sys.path.append("../../python")
sys.path.append("../../../")
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from data.data_loader import matches_data_loader
from data.data_loader import encode_data
data_df = matches_data_loader(
path_to_data="../../submodules/tennis_atp",
path_to_cache="../../cache",
flush_cache=False,
keep_values_from_year=2022,
get_match_statistics=True,
get_reversed_match_data=True,
)
forgotten_columns = ["Versus_1", "Best_Rank_1", "Last_Tournament_Date"]
columns_m = ["tournament_level", "round", "best_of", "Winner"]
columns_1 = [
"ID_1",
"Ranking_1",
"Ranking_Points_1",
"Hand_1",
"Height_1",
"Versus_1",
"Victories_Percentage_1",
"Clay_Victories_Percentage_1",
"Grass_Victories_Percentage_1",
"Carpet_Victories_Percentage_1",
"Hard_Victories_Percentage_1",
"Aces_Percentage_1",
"Doublefaults_Percentage_1",
"First_Serve_Success_Percentage_1",
"Winning_on_1st_Serve_Percentage_1",
"Winning_on_2nd_Serve_Percentage_1",
"Overall_Win_on_Serve_Percentage_1",
"BreakPoint_Face_Percentage_1",
"BreakPoint_Saved_Percentage_1",
"Fatigue_1",
]
columns_2 = [
"ID_2",
"Ranking_2",
"Ranking_Points_2",
"Hand_2",
"Height_2",
"Versus_2",
"Victories_Percentage_2",
"Clay_Victories_Percentage_2",
"Grass_Victories_Percentage_2",
"Carpet_Victories_Percentage_2",
"Hard_Victories_Percentage_2",
"Aces_Percentage_2",
"Doublefaults_Percentage_2",
"First_Serve_Success_Percentage_2",
"Winning_on_1st_Serve_Percentage_2",
"Winning_on_2nd_Serve_Percentage_2",
"Overall_Win_on_Serve_Percentage_2",
"BreakPoint_Face_Percentage_2",
"BreakPoint_Saved_Percentage_2",
"Fatigue_2",
]
data_df = data_df[columns_m + columns_1 + columns_2]
print(data_df.head())
print(data_df.shape)
data_df = data_df[columns_m + columns_1 + columns_2]
data_df = data_df.dropna(axis=0)
fdf = encode_data(data_df)
fdf.to_csv("../cache/test.csv")
fdf = fdf.drop(["ID_1", "Versus_1", "ID_2", "Versus_2"], axis=1)
fdf["diff_ranking"] = fdf["Ranking_2"] - fdf["Ranking_1"]
y = fdf.Winner
fdf = fdf[["diff_ranking"]]
X = fdf.values
print(X)
model = RandomForestClassifier(n_estimators=1000, max_depth=None)
print("FIT")
print(X.shape, y.shape)
model.fit(X, y)
y_pred = model.predict(X)
print(len(y), np.sum(y == y_pred))
print(y_pred)
print(y)
print(np.sum(y_pred))
plt.figure()
plt.scatter(X, y)
plt.show()
"""
z = model.predict(np.expand_dims(list(range(-10000, 10001)), axis=1))
plt.figure()
plt.plot(list(range(-10000, 10001)), z)
plt.show()
"""