Predictions #15
Predictions #15
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| import os | ||
| import sys |
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We shouldn't be interacting with the system or calling any lower level functions since our packages only serve high level abstract functions. I would think about another way to suppress the logs. Perhaps try overriding the functions from fbprophet?
predictions_FB.py
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| import os | ||
| import sys | ||
| import pandas as pd | ||
| pd.options.mode.chained_assignment = None |
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Does this need to be added at the root? Perhaps we could move this inside a function using this?
predictions_FB.py
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| ''' | ||
| This function takes in: | ||
| dfi - A dataframe with just the column of close price named as Close and date as index | ||
| plot - Whether you want to plot the resulyts or not( Default is False) | ||
| days - Number of days into the future you want to predict (Dafault is 1) | ||
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| This function returns: | ||
| mean_err - The mean percentage error in calculations | ||
| pred - A pandas datafrmae with predictions, upper and lower bounds | ||
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| ''' |
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Good job documenting the method! If you use PyCharm and initialize a multiline comment (doc string), it'll create the fields for documentation for you!
The benefit of doing that is that you can access the documentation of a function using the __doc__ property. So for fb_predict, you can access the documentation for this method by fb_predict.__doc__
Typically, you'd want to add @:param for each parameter and @:return to describe what is being returned.
This article should help you get started.
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Done, have a look and let me know if it looks good now.
| def __init__(self): | ||
| self.null_fds = [os.open(os.devnull, os.O_RDWR) for x in range(2)] | ||
| self.save_fds = (os.dup(1), os.dup(2)) | ||
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| def __enter__(self): | ||
| os.dup2(self.null_fds[0], 1) | ||
| os.dup2(self.null_fds[1], 2) | ||
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| def __exit__(self, *_): | ||
| os.dup2(self.save_fds[0], 1) | ||
| os.dup2(self.save_fds[1], 2) | ||
| os.close(self.null_fds[0]) | ||
| os.close(self.null_fds[1]) |
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Again, we shouldn't be interacting with any os methods.
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The only way to supress the output seems to be this. This is on the official fbprophet repo issue
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What about this? This is from the same issue thread. Can you try this out?
predictions_FB.py
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| test_predict = prediction[-train_length:] | ||
| test['predict'] = test_predict['trend'] | ||
| test['error'] =((test['y'] - test['predict'])/test['y'])*100 | ||
| mean_err = round(test['error'].mean(),3) |
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Do we want to keep the test models in production?
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This has been implemented to calculate the error by splitting the data into test and train as the main model trains on the entire historic data. If we have some other workaround then I would love to remove the extra model.
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If we don't need this in the final thing, I'd say we remove this. If you want it in development, I'd recommend having the test model in another file which runs within the fb_predict method. Then you could add that file to the .gitignore so that it isn't committed.
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Or we could even leave it be (in another file or method) and have another flag (in the method signature) to take a boolean param for calculating the error so that the test model isn't run every time.
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Ok yes I can do that
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I will make it False by default so if nothing is mentioned, it doesn't run
predictions_FB.py
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| mn = Prophet(daily_seasonality = True) | ||
| with suppress_stdout_stderr(): | ||
| mn.fit(df) | ||
| future = mn.make_future_dataframe(periods=days) |
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Let's add more options for different types of models as we discussed.
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I believe that we can make like different functions corresponding to different models so the user can simply call different functions to use the different models
predictions_FB.py
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| m.plot(prediction) | ||
| plt.title("Prediction of the AZPN Stock Price using the Prophet") | ||
| plt.xlabel("Date") | ||
| plt.ylabel("Close Stock Price") | ||
| plt.show() |
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I think this is a good candidate to factor into a helper function so that other functions can also use down the line.
flag to prevent running it everytime.
Added the implementation of a FB Prophet predictive model to predict the stock price n days into the future.
This is how the output looks: