cli/streamingphish/streamingphish/predictor.py
def _score(self, samples):
        """
        Takes a list of FQDNs, computes features, transforms to feature vector from training,
        and predicts them as phishing or not phishing (< or > 0.5).

        Args:
            samples (list): FQDN/Hosts to evaluate.

        Return:
            result (list): Scores from classifier i.e. [1.000, 0.981, 0.001].
        """
        sample_features = self._features.compute_features(samples, values_only=False)
        scores = self._classifier.predict_proba(sample_features['values'])[:, 1]

        result = []
        for score in scores:
            result.append(score)
        return result

        Takes a list of FQDNs, computes features, transforms to feature vector from training,
        and predicts them as phishing or not phishing (< or > 0.5).

        Args:
            samples (list): FQDN/Hosts to evaluate.

        Return:
            result (list): Scores from classifier i.e. [1.000, 0.981, 0.001].
        
Takes a list of FQDNs, computes features, transforms to feature vector from training,
and predicts them as phishing or not phishing (< or > 0.5).
Takes a list of FQDNs computes features transforms to feature vector from training and predicts them as phishing or not phishing ( < or > 0 . 5 ) .
def _score(self, samples):
        sample_features = self._features.compute_features(samples, values_only=False)
        scores = self._classifier.predict_proba(sample_features['values'])[:, 1]
        result = []
        for score in scores:
            result.append(score)
        return result
def _score ( self , samples ) : sample_features = self . _features . compute_features ( samples , values_only = False ) scores = self . _classifier . predict_proba ( sample_features [ 'values' ] ) [ : , 1 ] result = [ ] for score in scores : result . append ( score ) return result
Takes a list of FQDNs, computes features, transforms to feature vector from training,
and predicts them as phishing or not phishing (< or > 0.5).
Takes a list of FQDNs computes features transforms to feature vector from training and predicts them as phishing or not phishing ( < or > 0 . 5 ) .
"""
        Takes a list of FQDNs, computes features, transforms to feature vector from training,
        and predicts them as phishing or not phishing (< or > 0.5).

        Args:
            samples (list): FQDN/Hosts to evaluate.

        Return:
            result (list): Scores from classifier i.e. [1.000, 0.981, 0.001].
        """
self
samples
FQDN/Hosts to evaluate.
FQDN / Hosts to evaluate .