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Remove repeated DF joins. #90

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Remove repeated DF joins. #90

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19 changes: 9 additions & 10 deletions loglizer/preprocessing.py
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Original file line number Diff line number Diff line change
Expand Up @@ -43,7 +43,7 @@ def transform(self, x, window_y, y):
y = y
data_dict = {"SessionId": x["SessionId"].values, "window_y": window_y.values, "y": y.values, "x": np.array(x["EventSequence"].tolist())}
return data_dict


class FeatureExtractor(object):

Expand Down Expand Up @@ -91,12 +91,12 @@ def fit_transform(self, X_seq, term_weighting=None, normalization=None, oov=Fals
X = X[:, idx]
self.events = np.array(X_df.columns)[idx].tolist()
X = np.hstack([X, oov_vec.reshape(X.shape[0], 1)])

num_instance, num_event = X.shape
if self.term_weighting == 'tf-idf':
df_vec = np.sum(X > 0, axis=0)
self.idf_vec = np.log(num_instance / (df_vec + 1e-8))
idf_matrix = X * np.tile(self.idf_vec, (num_instance, 1))
idf_matrix = X * np.tile(self.idf_vec, (num_instance, 1))
X = idf_matrix
if self.normalization == 'zero-mean':
mean_vec = X.mean(axis=0)
Expand All @@ -105,8 +105,8 @@ def fit_transform(self, X_seq, term_weighting=None, normalization=None, oov=Fals
elif self.normalization == 'sigmoid':
X[X != 0] = expit(X[X != 0])
X_new = X
print('Train data shape: {}-by-{}\n'.format(X_new.shape[0], X_new.shape[1]))

print('Train data shape: {}-by-{}\n'.format(X_new.shape[0], X_new.shape[1]))
return X_new

def transform(self, X_seq):
Expand All @@ -129,23 +129,22 @@ def transform(self, X_seq):
X_df = pd.DataFrame(X_counts)
X_df = X_df.fillna(0)
empty_events = set(self.events) - set(X_df.columns)
for event in empty_events:
X_df[event] = [0] * len(X_df)
X_df = pd.concat([X_df, pd.DataFrame(np.zeros((len(X_df),len(empty_events))), columns=empty_events)], axis=1)
X = X_df[self.events].values
if self.oov:
oov_vec = np.sum(X_df[X_df.columns.difference(self.events)].values > 0, axis=1)
X = np.hstack([X, oov_vec.reshape(X.shape[0], 1)])

num_instance, num_event = X.shape
if self.term_weighting == 'tf-idf':
idf_matrix = X * np.tile(self.idf_vec, (num_instance, 1))
idf_matrix = X * np.tile(self.idf_vec, (num_instance, 1))
X = idf_matrix
if self.normalization == 'zero-mean':
X = X - np.tile(self.mean_vec, (num_instance, 1))
elif self.normalization == 'sigmoid':
X[X != 0] = expit(X[X != 0])
X_new = X

print('Test data shape: {}-by-{}\n'.format(X_new.shape[0], X_new.shape[1]))
print('Test data shape: {}-by-{}\n'.format(X_new.shape[0], X_new.shape[1]))

return X_new