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import os |
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import numpy as np |
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import pandas as pd |
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class StandardScaler: |
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""" |
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This scaler code is borrowed from https://github.com/liuxu77/LargeST/blob/main/data/generate_data_for_training.py |
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""" |
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def __init__(self, mean, std): |
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self.mean = mean |
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self.std = std |
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def transform(self, data): |
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return (data - self.mean) / self.std |
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def inverse_transform(self, data): |
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return (data * self.std) + self.mean |
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def generate_data_and_idx(df: pd.DataFrame, x_offsets, y_offsets, add_time_of_day, add_day_of_week): |
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num_samples, num_nodes = df.shape |
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data = np.expand_dims(df.values, axis=-1) |
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feature_list = [data] |
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if add_time_of_day: |
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time_ind = (df.index.values - df.index.values.astype('datetime64[D]')) / np.timedelta64(1, 'D') |
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time_of_day = np.tile(time_ind, [1, num_nodes, 1]).transpose((2, 1, 0)) |
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feature_list.append(time_of_day) |
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if add_day_of_week: |
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dow = df.index.dayofweek |
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dow_tiled = np.tile(dow, [1, num_nodes, 1]).transpose((2, 1, 0)) |
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day_of_week = dow_tiled / 7 |
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feature_list.append(day_of_week) |
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data = np.concatenate(feature_list, axis=-1) |
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min_t = abs(min(x_offsets)) |
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max_t = abs(num_samples - abs(max(y_offsets))) |
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print('idx min & max:', min_t, max_t) |
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idx = np.arange(min_t, max_t, 1) |
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return data, idx |
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def new_and_dying_sensors(df: pd.DataFrame): |
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df = df.sort_index() |
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isna = df.isna() |
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valid = ~isna |
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has_before = valid.cumsum(axis=0).gt(0) |
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has_after = valid[::-1].cumsum(axis=0)[::-1].gt(0) |
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leading_nan = isna & ~has_before |
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trailing_nan = isna & ~has_after |
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internal_nan = isna & has_before & has_after |
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newborn_cols = leading_nan.any(axis=0) |
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dying_cols = trailing_nan.any(axis=0) |
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invalid_cols = internal_nan.any(axis=0) | isna.all(axis=0) |
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newborn_sensors = df.columns[newborn_cols].tolist() |
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dying_sensors = df.columns[dying_cols].tolist() |
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invalid_sensors = df.columns[invalid_cols].tolist() |
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return newborn_sensors, dying_sensors, invalid_sensors |
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def generate_largest_data(df: pd.DataFrame, output_folder: str, sensors: list = None, seq_len_x: int = 12, seq_len_y: int = 12, |
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splits: dict[str, float] = None): |
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if splits is None: |
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splits = {'train': 0.6, 'val': 0.2} |
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x_offsets = np.sort(np.arange(-(seq_len_x - 1), 1, 1)) |
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y_offsets = np.sort(np.arange(1, seq_len_y + 1, 1)) |
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if sensors is not None: |
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df = df[df['sensor_id'].isin(sensors)] |
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df['traffic_intensity'] = df['traffic_intensity'] / 4 |
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df_pivot = df.pivot(index='entry_date', columns='sensor_id', values='traffic_intensity') |
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print('original data shape:', df_pivot.shape) |
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newborn, dying, invalid = new_and_dying_sensors(df_pivot) |
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if len(invalid) > 0: |
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raise Exception("invalid sensors (with nans inside) found") |
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to_drop = set(newborn) | set(dying) |
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df_clean = df_pivot.drop(columns=to_drop) |
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data, idx = generate_data_and_idx(df_clean, x_offsets, y_offsets, add_time_of_day=True, add_day_of_week=True) |
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print('final data shape:', data.shape, 'idx shape:', idx.shape) |
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num_samples = len(idx) |
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num_train = int(num_samples * splits['train']) |
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num_val = int(num_samples * splits['val']) |
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num_test = num_samples - num_train - num_val |
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idx_train = idx[:num_train] |
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idx_val = idx[num_train:num_train + num_val] |
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idx_test = idx[num_train + num_val:] |
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x_train = data[:idx_val[0] - seq_len_x, :, 0] |
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scaler = StandardScaler(mean=x_train.mean(), std=x_train.std()) |
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data[..., 0] = scaler.transform(data[..., 0]) |
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os.makedirs(output_folder, exist_ok=True) |
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np.savez_compressed(os.path.join(output_folder, 'his.npz'), data=data, mean=scaler.mean, std=scaler.std) |
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np.save(os.path.join(output_folder, 'idx_train.npy'), idx_train) |
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np.save(os.path.join(output_folder, 'idx_val.npy'), idx_val) |
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np.save(os.path.join(output_folder, 'idx_test.npy'), idx_test) |
