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update readme and add scripts for volcano dataset
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,105 @@ | ||
| import datetime | ||
| import pickle | ||
| import warnings | ||
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| import numpy as np | ||
| import pandas as pd | ||
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| warnings.filterwarnings('ignore') | ||
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| def make_datetime(year, month, day): | ||
| try: | ||
| date = datetime.datetime(int(year), int(month), int(day)) | ||
| except ValueError as e: | ||
| if e.args[0] == 'day is out of range for month': | ||
| date = datetime.datetime(int(year), int(month), int(day)-1) | ||
| return datetime.datetime.timestamp(date) + 61851630000 # make sure the timestamp is positive | ||
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| def clean_csv(): | ||
| source_dir = 'events.csv' | ||
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| df = pd.read_csv(source_dir, header=0) | ||
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| df = df[~df['event_date_year'].isna()] | ||
| df = df[df['event_date_year'] > 0] | ||
| df['event_date_month'].fillna(1, inplace=True) | ||
| df['event_date_day'].fillna(1, inplace=True) | ||
| df.drop_duplicates(inplace=True) | ||
| norm_const = 1000000 | ||
| df['event_timestamp'] = df.apply( | ||
| lambda x: make_datetime(x['event_date_year'], x['event_date_month'], x['event_date_day']), | ||
| axis=1)/norm_const | ||
| df.sort_values(by=['event_date_year', 'event_date_month', 'event_date_day'], inplace=True) | ||
| df['event_type'] = [0] * len(df) | ||
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| df.to_csv('volcano.csv', index=False, header=True) | ||
| return | ||
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| def make_seq(df): | ||
| seq = [] | ||
| df['time_diff'] = df['event_timestamp'].diff() | ||
| df.index = np.arange(len(df)) | ||
| for index, row in df.iterrows(): | ||
| if index == 0: | ||
| event_dict = {"time_since_last_event": 0.0, | ||
| "time_since_start": 0.0, | ||
| "type_event": row['event_type'] | ||
| } | ||
| start_event_time = row['event_timestamp'] | ||
| else: | ||
| event_dict = {"time_since_last_event": row['time_diff'], | ||
| "time_since_start": row['event_timestamp'] - start_event_time, | ||
| "type_event": row['event_type'] | ||
| } | ||
| seq.append(event_dict) | ||
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| return seq | ||
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| def make_pkl(target_dir, dim_process, split, seqs): | ||
| with open(target_dir, "wb") as f_out: | ||
| pickle.dump( | ||
| { | ||
| "dim_process": dim_process, | ||
| split: seqs | ||
| }, f_out | ||
| ) | ||
| return | ||
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| def make_dataset(source_dir): | ||
| df = pd.read_csv(source_dir, header=0) | ||
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| vols = np.unique(df['volcano_name']) | ||
| total_seq = [] | ||
| for vol in vols: | ||
| df_ = df[df['volcano_name'] == vol] | ||
| df_.sort_values('event_timestamp', inplace=True) | ||
| total_seq.append(make_seq(df_)) | ||
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| print(len(total_seq)) | ||
| make_pkl('train.pkl', 1, 'train', total_seq[:400]) | ||
| count_seq(total_seq[:400]) | ||
| make_pkl('dev.pkl', 1, 'dev', total_seq[400:450]) | ||
| count_seq(total_seq[400:450]) | ||
| make_pkl('test.pkl', 1, 'test', total_seq[450:]) | ||
| count_seq(total_seq[450:]) | ||
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| return | ||
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| def count_seq(seqs): | ||
| total_len = [len(seq) for seq in seqs] | ||
| print(np.mean(total_len)) | ||
| print(np.sum(total_len)) | ||
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| return | ||
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| if __name__ == '__main__': | ||
| # clean_csv() | ||
| make_dataset('volcano.csv') |