-
Notifications
You must be signed in to change notification settings - Fork 0
/
prediction.py
160 lines (127 loc) · 6.25 KB
/
prediction.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
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
#data preprocessing
import pandas as pd
import numpy as np
#produces a prediction model in the form of an ensemble of weak prediction models, typically decision tree
import xgboost as xgb
# Grid search cross validation
from sklearn.model_selection import GridSearchCV
#the outcome (dependent variable) has only a limited number of possible values.
#Logistic Regression is used when response variable is categorical in nature.
from sklearn.linear_model import LogisticRegression
#A random forest is a meta estimator that fits a number of decision tree classifiers
#on various sub-samples of the dataset and use averaging to improve the predictive
#accuracy and control over-fitting.
from sklearn.ensemble import RandomForestClassifier
# a discriminative classifier formally defined by a separating hyperplane.
from sklearn.svm import SVC
# Standardising the data.
from sklearn.preprocessing import scale
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
#for measuring training time
from time import time
from sklearn.metrics import classification_report
file = 'data/final_dataset.csv'
df = pd.read_csv(file, parse_dates=True)
#print(df['home'].unique())
# dropping redundant column? not sure if this is true
drop_columns = ['Unnamed: 0','date','home','away','round','fthp',
'ftap','hm4','hm5','am4','am5', 'homeaway', 'awayhome',
'rn','htformptsstr','atformptsstr','HTWinStreak3',
'HTWinStreak5', 'HTLossStreak3', 'HTLossStreak5', 'ATWinStreak3',
'ATWinStreak5', 'ATLossStreak3', 'ATLossStreak5']
# keep: FTR HTP ATP HM1 HM2 HM3 AM1 AM2 AM3 HTGD ATGD DiffFormPts DiffLP
data = df.drop(drop_columns, axis=1)
# Separate into feature set and target variable
# ftr = Full Time Result (H = home, NH = not home)
X_all = data.drop(['ftr'],1)
y_all = data['ftr']
#Center to the mean and component wise scale to unit variance.
cols = [['htgd','atgd','htp','atp','diffpts','htps','htpc','atps','atpc','htformpts','atformpts']]
for col in cols:
X_all[col] = scale(X_all[col])
#last 3 wins for both sides
X_all['hm1'] = X_all['hm1'].astype('str')
X_all['hm2'] = X_all['hm2'].astype('str')
X_all['hm3'] = X_all['hm3'].astype('str')
X_all['am1'] = X_all['am1'].astype('str')
X_all['am2'] = X_all['am2'].astype('str')
X_all['am3'] = X_all['am3'].astype('str')
#we want continous vars that are integers for our input data, so lets remove any categorical vars
def preprocess_features(X):
''' Preprocesses the football data and converts catagorical variables into dummy variables. '''
# Initialize new output DataFrame
output = pd.DataFrame(index = X.index)
# Investigate each feature column for the data
for col, col_data in X.iteritems():
# If data type is categorical, convert to dummy variables
if col_data.dtype == object:
col_data = pd.get_dummies(col_data, prefix = col)
# Collect the revised columns
output = output.join(col_data)
return output
X_all = preprocess_features(X_all)
print("Processed feature columns ({} total features):\n{}".format(len(X_all.columns), list(X_all.columns)))
# print(X_all.head(5))
# Shuffle and split the dataset into training and testing set.
X_train, X_test, y_train, y_test = train_test_split(X_all, y_all,
test_size = 50,
random_state = 22,
stratify = y_all)
def train_classifier(clf, X_train, y_train):
''' Fits a classifier to the training data. '''
# Start the clock, train the classifier, then stop the clock
start = time()
clf.fit(X_train, y_train)
end = time()
# Print the results
print("Trained model in {:.4f} seconds".format(end - start))
def predict_labels(clf, features, target):
''' Makes predictions using a fit classifier based on F1 score. '''
# Start the clock, make predictions, then stop the clock
start = time()
y_pred = clf.predict(features)
end = time()
# Print and return results
print("Made predictions in {:.4f} seconds.".format(end - start))
return classification_report(target, y_pred), confusion_matrix(target, y_pred)
def train_predict(clf, X_train, y_train, X_test, y_test):
''' Train and predict using a classifer based on F1 score. '''
# Indicate the classifier and the training set size
print("Training a {} using a training set size of {}. . .".format(clf.__class__.__name__, len(X_train)))
print('')
# Train the classifier
train_classifier(clf, X_train, y_train)
# Print the results of prediction for both training and testing
report, conf = predict_labels(clf, X_train, y_train)
# print(f1, acc, prec, recall)
print('Training data results:')
print(report, conf)
report, conf = predict_labels(clf, X_test, y_test)
print('Test data results:')
print(report, conf)
# Initialize the three models (XGBoost is initialized later)
clf_A = LogisticRegression(random_state = 1,
max_iter=1000,
C=100.0,
penalty='l2',
solver='newton-cg',
n_jobs=1)
clf_B = SVC(random_state = 912, kernel='rbf', gamma='scale')
#Boosting refers to this general problem of producing a very accurate prediction rule
#by combining rough and moderately inaccurate rules-of-thumb
clf_C = xgb.XGBClassifier(seed = 82)
train_predict(clf_A, X_train, y_train, X_test, y_test); print('')
train_predict(clf_B, X_train, y_train, X_test, y_test); print('')
train_predict(clf_C, X_train, y_train, X_test, y_test); print('')
'''
#grid={"C":np.logspace(-3,3,7), "penalty":["l2"], "solver":['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga']}# l1 lasso l2 ridge
rands=list([x for x in range(1,101)])
grid={"max_iter":[1000],"C":np.logspace(-3, 3, 7), "penalty":["l2"], "solver":["newton-cg"], "random_state":[1], "n_jobs":[1], "class_weight":['balanced','None']}
logreg=LogisticRegression()
logreg_cv=GridSearchCV(logreg,grid,cv=10)
logreg_cv.fit(X_train,y_train)
print("tuned hpyerparameters :(best parameters) ",logreg_cv.best_params_)
print("accuracy :",logreg_cv.best_score_)
'''