Feb-17-2021, 06:04 PM
import csv
import pickle
import logging
import numpy as np
from sklearn.svm import SVR
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class Model(object):
def __init__(self):
pass
def _get_data(self, filename):
training_data = csv.reader(open('data/%s' % filename, 'rb'))
logging.info('Training Finish Position')
y = [] # Target to train on
X = [] # Features
for i, row in enumerate(training_data):
# Skip the first row since it's the headers
if i == 0:
continue
# Get the target
y.append(float(row[-1]))
# Get the features
data = np.array(
[float(_ if len(str(_)) > 0 else 0) for _ in row[5:-1]]
)
X.append(data)
return X, y
def train(self):
clf = SVR(C=1.0, epsilon=0.1, cache_size=1000)
X, y, = self._get_data('training-2016-12-01-2017-02-28.csv')
# Fit the model
clf.fit(X, y)
# Pickle the model so we can save and reuse it
s = pickle.dumps(clf)
# Save the model to a file
f = open('finish_pos.model', 'wb')
f.write(s)
f.close()
def predict(self):
f = open('finish_pos.model', 'rb')
clf = pickle.loads(f.read())
f.close()
validation_data = csv.reader(
open('data/validation-2017-03-01-2017-03-31.csv', 'rb')
)
races = {}
for i, row in enumerate(validation_data):
if i == 0:
continue
race_id = row[1]
finish_pos = float(row[-1])
if race_id not in races:
races[race_id] = []
if finish_pos < 1:
continue
data = np.array([
float(_ if len(str(_)) > 0 else 0)
for _ in row[5:-1]
])
data = data.reshape(1, -1)
morning_line = row[0].split('-')
morning_line = float(morning_line[0]) / float(morning_line[1])
races[race_id].append(
{
'entry_id': row[0],
'data': data,
'prediction': None,
'finish_pos': finish_pos,
'odds': morning_line
}
)
for race_id, horses in races.iteritems():
for horse in horses:
horse['prediction'] = clf.predict(
horse['data']
)
tests = [
'SVR - Baseline',
'SVR - All Races',
'SVR - 1 Offset',
'SVR - 2 Offset',
'SVR - Std',
'SVR - 2 Offset + Std'
]
print('Test Name\tNum Races\tWins\tWPS')
for test in tests:
num_races = 0
num_correct_pred_win = 0
num_correct_pred_wps = 0
for race_id, horses in races.iteritems():
if len(horses) < 2:
continue
diff = horses[1]['prediction'] - horses[0]['prediction']
std = np.std([x['prediction'] for x in horses])
if test == 'SVR - Baseline':
horses.sort(key=lambda x: x['odds'])
else:
horses.sort(key=lambda x: x['prediction'])
if test == 'SVR - 1 Offset' and diff < 1.0:
continue
if test == 'SVR - 2 Offset' and diff < 2.0:
continue
if test == 'SVR - Std' and std < 1.4:
continue
if test == 'SVR - 2 Offset + Std' and (
std < 1.4 or diff < 2.0
):
continue
num_races += 1
if horses[0]['finish_pos'] == 1:
num_correct_pred_win += 1
if horses[0]['finish_pos'] in [1, 2, 3]:
num_correct_pred_wps += 1
print('%s\t%s\t%s\t%s' % (
test,
num_races,
num_correct_pred_win,
num_correct_pred_wps
))
if __name__ == '__main__':
trn = Model()
trn.train()
trn.predict()Error:Error Traceback (most recent call last)
<ipython-input-5-ace27f6cf126> in <module>
158
159 trn = Model()
--> 160 trn.train()
161 trn.predict()
<ipython-input-5-ace27f6cf126> in train(self)
42
43 clf = SVR(C=1.0, epsilon=0.1, cache_size=1000)
---> 44 X, y, = self._get_data('training-2016-12-01-2017-02-28.csv')
45
46 # Fit the model
<ipython-input-5-ace27f6cf126> in _get_data(self, filename)
23 X = [] # Features
24
---> 25 for i, row in enumerate(training_data):
26 # Skip the first row since it's the headers
27 if i == 0:
Error: iterator should return strings, not bytes (did you open the file in text mode?)I am attaching the file training-2016-12-01-2017-02-28.csv to this post. I am not sure what I did wrong. It defaults to text mode and then still gives me an error?
I put the file horseracingpredcition.ipynb in the subdirectory
horseracingprediction
That subdirectory itself has a data subdirectory with the csv files in it. All six of them are in it.
I just attached the first one.
I think that what is shown in the csv read command is general enough. I usually put the csv in the same directory as the *.ipynb file, but that is only a quick and dirty way to do it. I can also put in my complete path like
C:\users\namdownloads\csvfilename
Bu that mikes it too specific to only my computer. If I give it to someone else they will obviously have a different path, so again it is wrong.
I think what is presented in the python code is general enough, but it does not work. why doesn't it work?
Thanks in advance.
Respectfully,
LZ
I am using python 3.8.5 on Windows 10 home version, 64 bit. I do not know how to attach the file so no files are attached for now.