-
Notifications
You must be signed in to change notification settings - Fork 0
/
random_forest.py
115 lines (93 loc) · 3.99 KB
/
random_forest.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
112
113
114
115
#
# random_forest.py
# - Classify data into wins/losses with random forest.
#
# data_matrices/DataMatrices/old_glicko_1_seasons/old_glicko_1_seasons_combined.csv
# Mean validation accuracy: 0.7022396995591357
# Testing accuracy: 0.7043832823649337
# Best estimators found: 170
# data_matrices/DataMatrices/1_seasons/1_seasons_combined.csv
# Mean validation accuracy: 0.6939939040984052
# Testing accuracy: 0.7023445463812437
# Best estimators found: 160
# data_matrices/DataMatrices/2_seasons/2_seasons_combined.csv
# Mean validation accuracy: 0.6928921670764401
# Testing accuracy: 0.7099236641221374
# Best estimators found: 180
# data_matrices/DataMatrices/3_seasons/3_seasons_combined.csv
# Mean validation accuracy: 0.6840403441115396
# Testing accuracy: 0.7080890973036342
# Best estimators found: 160
# data_matrices/DataMatrices/4_seasons/4_seasons_combined.csv
# Mean validation accuracy: 0.680605577689243
# Testing accuracy: 0.6932826362484157
# Best estimators found: 180
import os
import pickle
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
from utils import remove_init_rows, get_data_and_labels, drop_irrelevant_columns, get_tourney_reg_season
def main(filename):
"""
Main entry point.
:param filename: string, the full filepath.
"""
df = pd.read_csv(filename)
df = remove_init_rows(df)
reg_season_df, tourney_df = get_tourney_reg_season(df)
X, y = get_data_and_labels(drop_irrelevant_columns(reg_season_df))
X_test, y_test = get_data_and_labels(drop_irrelevant_columns(tourney_df))
base_name = os.path.basename(filename).split(".")[0]
model_file = os.path.join(".", "models", base_name + "_random_forest.pkl")
if os.path.isfile(model_file):
with open(model_file, 'rb') as fptr:
clf = pickle.load(fptr)
else:
rf = RandomForestClassifier(n_estimators=50)
clf = GridSearchCV(rf,
{
'n_estimators': [i for i in range(90, 201, 10)]
},
n_jobs=-1,
cv=5)
clf.fit(X, y)
with open(model_file, 'wb') as fptr:
pickle.dump(clf, fptr)
print("Mean validation accuracy: {}".format(clf.best_score_))
print("Testing accuracy: {}".format(accuracy_score(y_test, clf.predict(X_test))))
print("Best estimators found: {}".format(clf.best_params_['n_estimators']))
importance_pairs = list(zip(drop_irrelevant_columns(df).columns.values, clf.best_estimator_.feature_importances_))
zeros = importance_pairs[:len(importance_pairs) // 2]
ones = importance_pairs[len(importance_pairs) // 2:]
pairs_unique = []
for (name, val_0), (_, val_1) in zip(zeros, ones):
name = name.replace("opponents", "ops")
pairs_unique.append((name[:-2], val_0 + val_1))
pairs_unique = sorted(pairs_unique, key=lambda x: x[1], reverse=True)
pairs_nonzero = []
for pair in pairs_unique:
if pair[1] > 0:
pairs_nonzero.append(pair)
features, importances = zip(*pairs_nonzero)
indx = [i for i in range(len(features))]
plt.tick_params(axis="x", labelsize=8)
plt.bar(indx, importances)
plt.xticks(indx, features, rotation="vertical")
plt.ylabel("Random Forest Feature Importance")
plt.tight_layout()
plt.savefig("./plots/" + base_name + "_random_forest_feature_importance.png", dpi=300)
plt.close()
if __name__ == '__main__':
filepaths = [
"data_matrices/DataMatrices/old_glicko_1_seasons/old_glicko_1_seasons_combined.csv",
"data_matrices/DataMatrices/1_seasons/1_seasons_combined.csv",
"data_matrices/DataMatrices/2_seasons/2_seasons_combined.csv",
"data_matrices/DataMatrices/3_seasons/3_seasons_combined.csv",
"data_matrices/DataMatrices/4_seasons/4_seasons_combined.csv"
]
for f in filepaths:
print(f)
main(f)