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WIP: Allowing for importing previously loaded training set #35

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WIP: Allowing for importing previously loaded training set #35

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6 changes: 6 additions & 0 deletions .gitignore
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Original file line number Diff line number Diff line change
@@ -1,6 +1,12 @@
# =================================
# Folders generated by our software
# =================================
features_meow
labels_meow
liwc_df_meow
nrc_df_meow
oxford_df_meow

# Trained models
models/
# Model predictions
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22 changes: 20 additions & 2 deletions src/train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -43,6 +43,25 @@
def main(arguments: argparse.Namespace):
print("\nLoading training data from '{}' ...".format(arguments.data_path))
features, labels = read_train_data(data_path=arguments.data_path)
liwc_df, nrc_df = read_liwc(arguments.data_path), read_nrc(arguments.data_path)
oxford_df = read_oxford(arguments.data_path)

# with open('features_meow', "wb") as file_handler:
# pickle.dump(features, file_handler)
# with open('labels_meow', "wb") as file_handler:
# pickle.dump(labels, file_handler)
# with open('liwc_df_meow', "wb") as file_handler:
# pickle.dump(liwc_df, file_handler)
# with open('nrc_df_meow', "wb") as file_handler:
# pickle.dump(nrc_df, file_handler)
# with open('oxford_df_meow', "wb") as file_handler:
# pickle.dump(oxford_df, file_handler)

# features = pickle.load(open('features_meow', 'rb'))
# labels = pickle.load(open('labels_meow', 'rb'))
# liwc_df = pickle.load(open('liwc_df_meow', 'rb'))
# nrc_df = pickle.load(open('nrc_df_meow', 'rb'))
# oxford_df = pickle.load(open('oxford_df_meow', 'rb'))

print("Initialising estimators ...")
fb_user_estimator = FBUserEstimator(
Expand All @@ -51,8 +70,7 @@ def main(arguments: argparse.Namespace):
gender_estimator=gender_estimators.get(arguments.gender_estimator)(),
personality_estimator=personality_estimators.get(arguments.personality_estimator)()
)
liwc_df, nrc_df = read_liwc(arguments.data_path), read_nrc(arguments.data_path)
oxford_df = read_oxford(arguments.data_path)

print("Fitting estimators on training data ...")
fb_user_estimator.fit(features, liwc_df, nrc_df, oxford_df, labels)

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