The fandual odds is now pulling yesterday data, it was working this morning

Version 2: Deep Learning Bot leads to Sorry, the file you have requested does not exist.

(https://colab.research.google.com/drive/1DbPgAVf0D_Q_bmYM20R5zxgKsqftWjgt?usp=sharing)

`## Add Twitter Sentiment Analysis
import tweepy
from textblob import TextBlob

Authenticate with Twitter API

consumer_key = 'x1ImslwHENof8RIstPS6itXtFQ'
consumer_secret = 'OJgsR3xnITHPTMUx7qEfvp3d1Zwoa9tpW6lb6YBBC5Q1eAEoO5'
access_token = '944784851915304960-0aZ9wT0h2r3UZl1nC6C727a1LueSYoT'
access_token_secret = 'E22wQCMRkZ2NXgk6358KChra8R5njzAou0blkB8QLOVkR'

auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)

api = tweepy.API(auth)

Search for tweets about the Warriors

warriors_tweets = tweepy.Cursor(api.search, q='#Brooklyn Nets').items(100)

Perform sentiment analysis on tweets

positive_tweets = 0
negative_tweets = 0
neutral_tweets = 0

for tweet in warriors_tweets:
analysis = TextBlob(tweet.text)
if analysis.sentiment.polarity > 0:
positive_tweets += 1
elif analysis.sentiment.polarity < 0:
negative_tweets += 1
else:
neutral_tweets += 1

Print results

print("Positive tweets: ", positive_tweets)
print("Negative tweets: ", negative_tweets)
print("Neutral tweets: ", neutral_tweets)

##if positive_tweets > negative_tweets:

continue

#else:
##say the opposing team will win

`---------------------------------------------------------------------------
TweepError Traceback (most recent call last)
in
23 neutral_tweets = 0
24
---> 25 for tweet in warriors_tweets:
26 analysis = TextBlob(tweet.text)
27 if analysis.sentiment.polarity > 0:

4 frames
/usr/local/lib/python3.8/dist-packages/tweepy/cursor.py in next(self)
49
50 def next(self):
---> 51 return self.next()
52
53 def next(self):

/usr/local/lib/python3.8/dist-packages/tweepy/cursor.py in next(self)
241 if self.current_page is None or self.page_index == len(self.current_page) - 1:
242 # Reached end of current page, get the next page...
--> 243 self.current_page = self.page_iterator.next()
244 while len(self.current_page) == 0:
245 self.current_page = self.page_iterator.next()

/usr/local/lib/python3.8/dist-packages/tweepy/cursor.py in next(self)
130
131 if self.index >= len(self.results) - 1:
--> 132 data = self.method(max_id=self.max_id, parser=RawParser(), *self.args, **self.kwargs)
133
134 if hasattr(self.method, 'self'):

/usr/local/lib/python3.8/dist-packages/tweepy/binder.py in _call(*args, **kwargs)
251 return method
252 else:
--> 253 return method.execute()
254 finally:
255 method.session.close()

/usr/local/lib/python3.8/dist-packages/tweepy/binder.py in execute(self)
232 raise RateLimitError(error_msg, resp)
233 else:
--> 234 raise TweepError(error_msg, resp, api_code=api_error_code)
235
236 # Parse the response payload

TweepError: Twitter error response: status code = 401`

In the setup instructions it tells you how to set up the Firebase config for android and ios but doesn't have instructions for setting up a web version

2023-02-22 01:38:33.013251: E tensorflow/stream_executor/cuda/cuda_driver.cc:271] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected
2023-02-22 01:38:33.013315: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (5a210547b290): /proc/driver/nvidia/version does not exist
2023-02-22 01:38:33.013714: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
Traceback (most recent call last):
File "main.py", line 5, in
from src.Predict import NN_Runner, XGBoost_Runner
File "/content/src/Predict/NN_Runner.py", line 10, in
model = load_model('Models/NN_Models/Trained-Model-ML')
File "/usr/local/lib/python3.8/dist-packages/keras/saving/save.py", line 205, in load_model
return saved_model_load.load(filepath, compile, options)
File "/usr/local/lib/python3.8/dist-packages/keras/saving/saved_model/load.py", line 155, in load
model.compile(**saving_utils.compile_args_from_training_config(
File "/usr/local/lib/python3.8/dist-packages/keras/saving/saving_utils.py", line 202, in compile_args_from_training_config
optimizer = optimizers.deserialize(optimizer_config)
File "/usr/local/lib/python3.8/dist-packages/keras/optimizers.py", line 95, in deserialize
return deserialize_keras_object(
File "/usr/local/lib/python3.8/dist-packages/keras/utils/generic_utils.py", line 659, in deserialize_keras_object
(cls, cls_config) = class_and_config_for_serialized_keras_object(
File "/usr/local/lib/python3.8/dist-packages/keras/utils/generic_utils.py", line 556, in class_and_config_for_serialized_keras_object
raise ValueError(
ValueError: Unknown optimizer: Custom>Adam. Please ensure this object is passed to the custom_objects argument. See https://www.tensorflow.org/guide/keras/save_and_serialize#registering_the_custom_object for details.
WARNING:tensorflow:Unresolved object in checkpoint: (root).call
WARNING:tensorflow:Unresolved object in checkpoint: (root).call_and_return_all_conditional_losses
WARNING:tensorflow:Unresolved object in checkpoint: (root)._default_save_signature
WARNING:tensorflow:Unresolved object in checkpoint: (root).layer-0.call
WARNING:tensorflow:Unresolved object in checkpoint: (root).layer-0.call_and_return_all_conditional_losses
WARNING:tensorflow:Unresolved object in checkpoint: (root).layer_with_weights-0.call
WARNING:tensorflow:Unresolved object in checkpoint: (root).layer_with_weights-0.call_and_return_all_conditional_losses
WARNING:tensorflow:Unresolved object in checkpoint: (root).layer_with_weights-1.call
WARNING:tensorflow:Unresolved object in checkpoint: (root).layer_with_weights-1.call_and_return_all_conditional_losses
WARNING:tensorflow:Unresolved object in checkpoint: (root).layer_with_weights-2.call
WARNING:tensorflow:Unresolved object in checkpoint: (root).layer_with_weights-2.call_and_return_all_conditional_losses
WARNING:tensorflow:Unresolved object in checkpoint: (root).layer_with_weights-3.call
WARNING:tensorflow:Unresolved object in checkpoint: (root).layer_with_weights-3.call_and_return_all_conditional_losses
WARNING:tensorflow:Unresolved object in checkpoint: (root).call.trace_0
WARNING:tensorflow:Unresolved object in checkpoint: (root).call.trace_1
WARNING:tensorflow:Unresolved object in checkpoint: (root).call.trace_2
WARNING:tensorflow:Unresolved object in checkpoint: (root).call.trace_3
WARNING:tensorflow:Unresolved object in checkpoint: (root).call_and_return_all_conditional_losses.trace_0
WARNING:tensorflow:Unresolved object in checkpoint: (root).call_and_return_all_conditional_losses.trace_1
WARNING:tensorflow:Unresolved object in checkpoint: (root).call_and_return_all_conditional_losses.trace_2
WARNING:tensorflow:Unresolved object in checkpoint: (root).call_and_return_all_conditional_losses.trace_3
WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer._update_step_xla
WARNING:tensorflow:Unresolved object in checkpoint: (root).signatures.serving_default
WARNING:tensorflow:Unresolved object in checkpoint: (root).layer-0.call.trace_0
WARNING:tensorflow:Unresolved object in checkpoint: (root).layer-0.call_and_return_all_conditional_losses.trace_0
WARNING:tensorflow:Unresolved object in checkpoint: (root).layer_with_weights-0.call.trace_0
WARNING:tensorflow:Unresolved object in checkpoint: (root).layer_with_weights-0.call_and_return_all_conditional_losses.trace_0
WARNING:tensorflow:Unresolved object in checkpoint: (root).layer_with_weights-1.call.trace_0
WARNING:tensorflow:Unresolved object in checkpoint: (root).layer_with_weights-1.call_and_return_all_conditional_losses.trace_0
WARNING:tensorflow:Unresolved object in checkpoint: (root).layer_with_weights-2.call.trace_0
WARNING:tensorflow:Unresolved object in checkpoint: (root).layer_with_weights-2.call_and_return_all_conditional_losses.trace_0
WARNING:tensorflow:Unresolved object in checkpoint: (root).layer_with_weights-3.call.trace_0
WARNING:tensorflow:Unresolved object in checkpoint: (root).layer_with_weights-3.call_and_return_all_conditional_losses.trace_0
WARNING:tensorflow:A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/guide/checkpoint#loading_mechanics for details.

As the title suggests, the data pulled from the arbitrage bot is for games that have already commenced. I am wondering if this is a timezone issue due to me residing in Australia. Any ideas on how to fix this? Thank you in advance.

Hey, will you fix the link for the sports bettor?

/barematthias

I am able to get everything working the collab with my api keys

But how do i have this built on firebase

Keep getting: You tried to access openai.Completion, but this is no longer supported.

Anybody got this updated for new version?

I'm using Colab, and I'm able to load games occurring on the day of, but I get this series of text when I run !python3 main.py -A -odds=fanduel

Traceback (most recent call last):
File "/content/main.py", line 106, in
main()
File "/content/main.py", line 79, in main
data, todays_games_uo, frame_ml, home_team_odds, away_team_odds = createTodaysGames(games, df, odds)
File "/content/main.py", line 52, in createTodaysGames
frame_ml = games_data_frame.drop(columns=['TEAM_ID', 'CFID', 'CFPARAMS', 'TEAM_NAME'])
File "/usr/local/lib/python3.8/dist-packages/pandas/util/_decorators.py", line 311, in wrapper
return func(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py", line 4906, in drop
return super().drop(
File "/usr/local/lib/python3.8/dist-packages/pandas/core/generic.py", line 4150, in drop
obj = obj._drop_axis(labels, axis, level=level, errors=errors)
File "/usr/local/lib/python3.8/dist-packages/pandas/core/generic.py", line 4214, in _drop_axis
raise KeyError(f"{labels} not found in axis")
KeyError: "['TEAM_ID' 'CFID' 'CFPARAMS' 'TEAM_NAME'] not found in axis"

Anyidea of this issue?

Creating Dataframe and Writing to Excel File

Got this error

ValueError Traceback (most recent call last)
in
----> 1 MAX_OUTCOMES = max([event.num_outcomes for event in arbitrage_events])
2 ARBITRAGE_EVENTS_COUNT = len(arbitrage_events)
3
4 my_columns = ['ID', 'Sport Key', 'Expected Earnings'] + list(np.array([[f'Bookmaker #{outcome}', f'Name #{outcome}', f'Odds #{outcome}', f'Amount to Buy #{outcome}'] for outcome in range(1, MAX_OUTCOMES + 1)]).flatten())
5 dataframe = pd.DataFrame(columns=my_columns)

ValueError: max() arg is an empty sequence

Hi, I can't seem to figure out what the problem is here:

TypeError Traceback (most recent call last)
in <cell line: 2>()
1 events = []
2 for data in odds_response:
----> 3 events.append(Event(data))
4 print(data)
5 print()

in init(self, data)
7 def init(self, data):
8 self.data = data
----> 9 self.sport_key = data['sport_key']
10 self.id = data['id']
11

 Not sure why this is causing issues. Anyone able to help?

Hello I have a problem at this step :

Creating Dataframe and Writing to Excel File

Error : ValueError Traceback (most recent call last)
in
----> 1 MAX_OUTCOMES = max([event.num_outcomes for event in arbitrage_events])
2 ARBITRAGE_EVENTS_COUNT = len(arbitrage_events)
3
4 my_columns = ['ID', 'Sport Key', 'Expected Earnings'] + list(np.array([[f'Bookmaker #{outcome}', f'Name #{outcome}', f'Odds #{outcome}', f'Amount to Buy #{outcome}'] for outcome in range(1, MAX_OUTCOMES + 1)]).flatten())
5 dataframe = pd.DataFrame(columns=my_columns)

ValueError: max() arg is an empty sequence

What I should do ?