I'm using a predict function for a linear SVM classifier:

def predict(self, train_file: str, test_file: str, lower_case: bool) -> pd.DataFrame:
"Train model using sklearn pipeline"
train_df = self.read_data(train_file, lower_case)
dev_df = self.read_data(dev_file, lower_case)
learner = self.pipeline.fit(train_df['text'], train_df['truth'])
# Fit the learner to the test data
test_df = self.read_data(test_file, lower_case)
# assuming dev_df is validset
dev_df['pred'] = learner.predict(dev_df['text'])
test_df['pred'] = learner.predict(test_df['text'])
return test_df, dev_df

And the predict function refers also to:

    def run_classifier(files: Tuple[str, str, str],
               method: str,
               method_class: Base,
               model_file: str,
               lower_case: bool) -> None:
"Inherit classes from classifiers.py and apply the predict/accuracy methods"
train, dev, test = files  # Unpack train, dev and test filenames
result = method_class.predict(train, test, lower_case)
# Plot confusion matrix
fig, ax = plot_confusion_matrix(result['truth'], result['pred'], normalize=True)
ax.set_title("Normalized Confusion Matrix: {}".format(method.title()))

Where the dev set is not really taken into consideration. When I run predict, I receive this error: predict() missing 1 required positional argument: 'lower_case but I don't understand how to solve it.

  • $\begingroup$ is method_class the name of your class ? If so, no self is needed in the function declaration and that would explain why he thinks lower_case is missing. Otherwise try method_class.predict(train=train, test=test, lower_case=lower_case) and pls reupload the error $\endgroup$
    – mprouveur
    Sep 15, 2020 at 14:07
  • $\begingroup$ hi, method_class helps me because I have many classifiers and specifying it when running the code changes the outcome. What should I do? $\endgroup$
    – Anna
    Sep 15, 2020 at 14:16
  • $\begingroup$ I am not sure I quite understood but this seems fine. Have you tried declaring your function as def predict(train_file: str, test_file: str, lower_case: bool) instead of def predict(self, train_file: str, test_file: str, lower_case: bool)? And have you tried calling predict with method_class.predict(train=train, test=test, lower_case=lower_case)? $\endgroup$
    – mprouveur
    Sep 15, 2020 at 14:56
  • $\begingroup$ As for the second one, it says it got an unexpected argument (train) or I don't understand what you meant at all... I am basing on this repo (github.com/prrao87/fine-grained-sentiment) $\endgroup$
    – Anna
    Sep 15, 2020 at 15:06
  • $\begingroup$ Yes sorry that's a typo, I meant method_class.predict(train_file=train, test_file=test, lower_case=lower_case) $\endgroup$
    – mprouveur
    Sep 15, 2020 at 15:09

1 Answer 1


Based on the repo you linked in the comment section (github.com/prrao87/fine-grained-sentiment), method_class is meant to be a Class not an instance. Therefore method_class.predict is meant to be a static method not an instance method, Static method are declared without specifying a self (as they are not linked to an instance but a class) declaring your function as def predict(train_file: str, test_file: str, lower_case: bool) should fix your problem.

Some reference on the topic : https://www.makeuseof.com/tag/python-instance-static-class-methods/


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