From 55e6a573c6cce2f4d9a40a7e578a95a1208621a5 Mon Sep 17 00:00:00 2001 From: Raunak Dey <39820997+RaunakDey@users.noreply.github.com> Date: Fri, 7 Jun 2024 11:18:47 -0400 Subject: [PATCH] figures+results --- assets/llmtime_top_fig.png | Bin 0 -> 188931 bytes data/__pycache__/metrics.cpython-311.pyc | Bin 0 -> 10748 bytes data/__pycache__/metrics.cpython-39.pyc | Bin 0 -> 5562 bytes data/__pycache__/serialize.cpython-311.pyc | Bin 0 -> 13892 bytes data/__pycache__/serialize.cpython-39.pyc | Bin 0 -> 7970 bytes .../__pycache__/small_context.cpython-311.pyc | Bin 0 -> 5236 bytes data/__pycache__/small_context.cpython-39.pyc | Bin 0 -> 2759 bytes data/__pycache__/synthetic.cpython-39.pyc | Bin 0 -> 1305 bytes data/autoformer_dataset.py | 588 ++++ data/last_val_mae.csv | 25 + data/last_value_results.csv | 22 + data/metrics.py | 137 + data/monash.py | 140 + data/paper_mae.csv | 31 + data/paper_mae_normalized.csv | 31 + data/paper_mae_raw.csv | 31 + data/serialize.py | 226 ++ data/small_context.py | 114 + data/synthetic.py | 21 + datasets/memorization/IstanbulTraffic.csv | 267 ++ datasets/memorization/TSMCStock.csv | 247 ++ datasets/memorization/TurkeyPower.csv | 365 +++ ...istanbul_traffic_last_two_weeks_hourly.csv | 336 ++ .../memorization/tsmc_stock_2022_daily.csv | 247 ++ .../memorization/turkey_power_2022_daily.csv | 366 +++ datasets/synthetic/beat.npy | Bin 0 -> 1728 bytes datasets/synthetic/exp.npy | Bin 0 -> 1728 bytes datasets/synthetic/gaussian_wave.npy | Bin 0 -> 1728 bytes datasets/synthetic/linear.npy | Bin 0 -> 1728 bytes datasets/synthetic/linear_cos.npy | Bin 0 -> 1728 bytes datasets/synthetic/log.npy | Bin 0 -> 1728 bytes datasets/synthetic/sigmoid.npy | Bin 0 -> 1728 bytes datasets/synthetic/sinc.npy | Bin 0 -> 1728 bytes datasets/synthetic/sine.npy | Bin 0 -> 1728 bytes datasets/synthetic/square.npy | Bin 0 -> 1728 bytes datasets/synthetic/x_times_sine.npy | Bin 0 -> 1728 bytes datasets/synthetic/xsin.npy | Bin 0 -> 1728 bytes demo.py | 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bytes install.sh | 19 + 77 files changed, 15885 insertions(+) create mode 100644 assets/llmtime_top_fig.png create mode 100644 data/__pycache__/metrics.cpython-311.pyc create mode 100644 data/__pycache__/metrics.cpython-39.pyc create mode 100644 data/__pycache__/serialize.cpython-311.pyc create mode 100644 data/__pycache__/serialize.cpython-39.pyc create mode 100644 data/__pycache__/small_context.cpython-311.pyc create mode 100644 data/__pycache__/small_context.cpython-39.pyc create mode 100644 data/__pycache__/synthetic.cpython-39.pyc create mode 100644 data/autoformer_dataset.py create mode 100644 data/last_val_mae.csv create mode 100644 data/last_value_results.csv create mode 100644 data/metrics.py create mode 100644 data/monash.py create mode 100644 data/paper_mae.csv create mode 100644 data/paper_mae_normalized.csv create mode 100644 data/paper_mae_raw.csv create mode 100644 data/serialize.py create mode 100644 data/small_context.py create mode 100644 data/synthetic.py create mode 100644 datasets/memorization/IstanbulTraffic.csv create mode 100644 datasets/memorization/TSMCStock.csv create mode 100644 datasets/memorization/TurkeyPower.csv create mode 100644 datasets/memorization/istanbul_traffic_last_two_weeks_hourly.csv create mode 100644 datasets/memorization/tsmc_stock_2022_daily.csv create mode 100644 datasets/memorization/turkey_power_2022_daily.csv create mode 100644 datasets/synthetic/beat.npy create mode 100644 datasets/synthetic/exp.npy create mode 100644 datasets/synthetic/gaussian_wave.npy create mode 100644 datasets/synthetic/linear.npy create mode 100644 datasets/synthetic/linear_cos.npy create mode 100644 datasets/synthetic/log.npy create mode 100644 datasets/synthetic/sigmoid.npy create mode 100644 datasets/synthetic/sinc.npy create mode 100644 datasets/synthetic/sine.npy create mode 100644 datasets/synthetic/square.npy create mode 100644 datasets/synthetic/x_times_sine.npy create mode 100644 datasets/synthetic/xsin.npy create mode 100644 demo.py create mode 100644 figures/figure_1.ipynb create mode 100644 figures/figure_2.ipynb create mode 100644 figures/figure_3.ipynb create mode 100644 figures/figure_4.ipynb create mode 100644 figures/figure_5.ipynb create mode 100644 figures/figure_6.ipynb create mode 100644 figures/figure_7.ipynb create mode 100644 figures/figure_8.ipynb create mode 100644 figures/figure_appendix.ipynb create mode 100644 figures/outputs/alignment_effects.pdf create mode 100644 figures/outputs/autoformer_aggregated_192.pdf create mode 100644 figures/outputs/autoformer_aggregated_96.pdf create mode 100644 figures/outputs/autoformer_by_dataset_192.pdf create mode 100644 figures/outputs/autoformer_by_dataset_96.pdf create mode 100644 figures/outputs/darts_legend_1.svg create mode 100644 figures/outputs/darts_legend_2.svg create mode 100644 figures/outputs/darts_legend_3.svg create mode 100644 figures/outputs/darts_qualitative.pdf create mode 100644 figures/outputs/darts_qualitative_legend_1.pdf create mode 100644 figures/outputs/darts_qualitative_legend_2.pdf create mode 100644 figures/outputs/darts_results.svg create mode 100644 figures/outputs/encoding1.svg create mode 100644 figures/outputs/encoding2.svg create mode 100644 figures/outputs/histograms.pdf create mode 100644 figures/outputs/informer_examples.pdf create mode 100644 figures/outputs/mae_aggregated.pdf create mode 100644 figures/outputs/mmlu_scaling.pdf create mode 100644 figures/outputs/simplicity_bias_plot.svg create mode 100644 figures/outputs/synthetic_qualitative.pdf create mode 100644 figures/outputs/synthetic_qualitative.png create mode 100644 figures/outputs/synthetic_qualitative_legend_1.pdf create mode 100644 figures/outputs/synthetic_qualitative_legend_2.pdf create mode 100644 fine_tuned_results/artest3_preds.csv create mode 100644 fine_tuned_results/artest3_preds.jpeg create mode 100644 fine_tuned_results/sinetest1_preds.csv create mode 100644 fine_tuned_results/sinetest1_preds.jpeg create mode 100644 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__init__(self): + pass + + def __call__(self, index: pd.DatetimeIndex) -> np.ndarray: + pass + + def __repr__(self): + return self.__class__.__name__ + "()" + + +class SecondOfMinute(TimeFeature): + """Minute of hour encoded as value between [-0.5, 0.5]""" + + def __call__(self, index: pd.DatetimeIndex) -> np.ndarray: + return index.second / 59.0 - 0.5 + + +class MinuteOfHour(TimeFeature): + """Minute of hour encoded as value between [-0.5, 0.5]""" + + def __call__(self, index: pd.DatetimeIndex) -> np.ndarray: + return index.minute / 59.0 - 0.5 + + +class HourOfDay(TimeFeature): + """Hour of day encoded as value between [-0.5, 0.5]""" + + def __call__(self, index: pd.DatetimeIndex) -> np.ndarray: + return index.hour / 23.0 - 0.5 + + +class DayOfWeek(TimeFeature): + """Hour of day encoded as value between [-0.5, 0.5]""" + + def __call__(self, index: pd.DatetimeIndex) -> np.ndarray: + return index.dayofweek / 6.0 - 0.5 + + +class DayOfMonth(TimeFeature): + """Day of month encoded as value between [-0.5, 0.5]""" + + def __call__(self, index: pd.DatetimeIndex) -> np.ndarray: + return (index.day - 1) / 30.0 - 0.5 + + +class DayOfYear(TimeFeature): + """Day of year encoded as value between [-0.5, 0.5]""" + + def __call__(self, index: pd.DatetimeIndex) -> np.ndarray: + return (index.dayofyear - 1) / 365.0 - 0.5 + + +class MonthOfYear(TimeFeature): + """Month of year encoded as value between [-0.5, 0.5]""" + + def __call__(self, index: pd.DatetimeIndex) -> np.ndarray: + return (index.month - 1) / 11.0 - 0.5 + + +class WeekOfYear(TimeFeature): + """Week of year encoded as value between [-0.5, 0.5]""" + + def __call__(self, index: pd.DatetimeIndex) -> np.ndarray: + return (index.isocalendar().week - 1) / 52.0 - 0.5 + + +def time_features_from_frequency_str(freq_str: str) -> List[TimeFeature]: + """ + Returns a list of time features that will be appropriate for the given frequency string. + Parameters + ---------- + freq_str + Frequency string of the form [multiple][granularity] such as "12H", "5min", "1D" etc. + """ + + features_by_offsets = { + offsets.YearEnd: [], + offsets.QuarterEnd: [MonthOfYear], + offsets.MonthEnd: [MonthOfYear], + offsets.Week: [DayOfMonth, WeekOfYear], + offsets.Day: [DayOfWeek, DayOfMonth, DayOfYear], + offsets.BusinessDay: [DayOfWeek, DayOfMonth, DayOfYear], + offsets.Hour: [HourOfDay, DayOfWeek, DayOfMonth, DayOfYear], + offsets.Minute: [ + MinuteOfHour, + HourOfDay, + DayOfWeek, + DayOfMonth, + DayOfYear, + ], + offsets.Second: [ + SecondOfMinute, + MinuteOfHour, + HourOfDay, + DayOfWeek, + DayOfMonth, + DayOfYear, + ], + } + + offset = to_offset(freq_str) + + for offset_type, feature_classes in features_by_offsets.items(): + if isinstance(offset, offset_type): + return [cls() for cls in feature_classes] + + supported_freq_msg = f""" + Unsupported frequency {freq_str} + The following frequencies are supported: + Y - yearly + alias: A + M - monthly + W - weekly + D - daily + B - business days + H - hourly + T - minutely + alias: min + S - secondly + """ + raise RuntimeError(supported_freq_msg) + + +def time_features(dates, freq='h'): + return np.vstack([feat(dates) for feat in time_features_from_frequency_str(freq)]) + + + +class Dataset_ETT_hour(Dataset): + def __init__(self, root_path, flag='train', size=None, + features='S', data_path='ETTh1.csv', + target='OT', scale=True, timeenc=0, freq='h'): + # size [seq_len, label_len, pred_len] + # info + if size == None: + self.seq_len = 24 * 4 * 4 + self.label_len = 24 * 4 + self.pred_len = 24 * 4 + else: + self.seq_len = size[0] + self.label_len = size[1] + self.pred_len = size[2] + # init + assert flag in ['train', 'test', 'val'] + type_map = {'train': 0, 'val': 1, 'test': 2} + self.set_type = type_map[flag] + + self.features = features + self.target = target + self.scale = scale + self.timeenc = timeenc + self.freq = freq + + self.root_path = root_path + self.data_path = data_path + self.__read_data__() + + def __read_data__(self): + self.scaler = StandardScaler() + df_raw = pd.read_csv(os.path.join(self.root_path, + self.data_path)) + + num_train = int(len(df_raw) * 0.85) + num_test = self.pred_len # int(len(df_raw) * 0.2) + num_vali = len(df_raw) - num_train - num_test + border1s = [0, num_train - self.seq_len, len(df_raw) - num_test - self.seq_len] + border2s = [num_train, num_train + num_vali, len(df_raw)] + + # border1s = [0, 12 * 30 * 24 - self.seq_len, 12 * 30 * 24 + 4 * 30 * 24 - self.seq_len] + # border2s = [12 * 30 * 24, 12 * 30 * 24 + 4 * 30 * 24, 12 * 30 * 24 + 8 * 30 * 24] + border1 = border1s[self.set_type] + border2 = border2s[self.set_type] + + if self.features == 'M' or self.features == 'MS': + cols_data = df_raw.columns[1:] + df_data = df_raw[cols_data] + elif self.features == 'S': + df_data = df_raw[[self.target]] + + if self.scale: + train_data = df_data[border1s[0]:border2s[0]] + self.scaler.fit(train_data.values) + data = self.scaler.transform(df_data.values) + else: + data = df_data.values + + df_stamp = df_raw[['date']][border1:border2] + df_stamp['date'] = pd.to_datetime(df_stamp.date) + if self.timeenc == 0: + df_stamp['month'] = df_stamp.date.apply(lambda row: row.month, 1) + df_stamp['day'] = df_stamp.date.apply(lambda row: row.day, 1) + df_stamp['weekday'] = df_stamp.date.apply(lambda row: row.weekday(), 1) + df_stamp['hour'] = df_stamp.date.apply(lambda row: row.hour, 1) + data_stamp = df_stamp.drop(['date'], 1).values + elif self.timeenc == 1: + data_stamp = time_features(pd.to_datetime(df_stamp['date'].values), freq=self.freq) + data_stamp = data_stamp.transpose(1, 0) + + self.data_x = data[border1:border2] + self.data_y = data[border1:border2] + self.data_stamp = data_stamp + + def __getitem__(self, index): + s_begin = index + s_end = s_begin + self.seq_len + r_begin = s_end - self.label_len + r_end = r_begin + self.label_len + self.pred_len + + seq_x = self.data_x[s_begin:s_end] + seq_y = self.data_y[r_begin:r_end] + seq_x_mark = self.data_stamp[s_begin:s_end] + seq_y_mark = self.data_stamp[r_begin:r_end] + + return seq_x, seq_y, seq_x_mark, seq_y_mark + + def __len__(self): + return len(self.data_x) - self.seq_len - self.pred_len + 1 + + def inverse_transform(self, data): + return self.scaler.inverse_transform(data) + + +class Dataset_ETT_minute(Dataset): + def __init__(self, root_path, flag='train', size=None, + features='S', data_path='ETTm1.csv', + target='OT', scale=True, timeenc=0, freq='t'): + # size [seq_len, label_len, pred_len] + # info + if size == None: + self.seq_len = 24 * 4 * 4 + self.label_len = 24 * 4 + self.pred_len = 24 * 4 + else: + self.seq_len = size[0] + self.label_len = size[1] + self.pred_len = size[2] + # init + assert flag in ['train', 'test', 'val'] + type_map = {'train': 0, 'val': 1, 'test': 2} + self.set_type = type_map[flag] + + self.features = features + self.target = target + self.scale = scale + self.timeenc = timeenc + self.freq = freq + + self.root_path = root_path + self.data_path = data_path + self.__read_data__() + + def __read_data__(self): + self.scaler = StandardScaler() + df_raw = pd.read_csv(os.path.join(self.root_path, + self.data_path)) + + num_train = int(len(df_raw) * 0.85) + num_test = self.pred_len # int(len(df_raw) * 0.2) + num_vali = len(df_raw) - num_train - num_test + border1s = [0, num_train - self.seq_len, len(df_raw) - num_test - self.seq_len] + border2s = [num_train, num_train + num_vali, len(df_raw)] + + # border1s = [0, 12 * 30 * 24 * 4 - self.seq_len, 12 * 30 * 24 * 4 + 4 * 30 * 24 * 4 - self.seq_len] + # border2s = [12 * 30 * 24 * 4, 12 * 30 * 24 * 4 + 4 * 30 * 24 * 4, 12 * 30 * 24 * 4 + 8 * 30 * 24 * 4] + border1 = border1s[self.set_type] + border2 = border2s[self.set_type] + + if self.features == 'M' or self.features == 'MS': + cols_data = df_raw.columns[1:] + df_data = df_raw[cols_data] + elif self.features == 'S': + df_data = df_raw[[self.target]] + + if self.scale: + train_data = df_data[border1s[0]:border2s[0]] + self.scaler.fit(train_data.values) + data = self.scaler.transform(df_data.values) + else: + data = df_data.values + + df_stamp = df_raw[['date']][border1:border2] + df_stamp['date'] = pd.to_datetime(df_stamp.date) + if self.timeenc == 0: + df_stamp['month'] = df_stamp.date.apply(lambda row: row.month, 1) + df_stamp['day'] = df_stamp.date.apply(lambda row: row.day, 1) + df_stamp['weekday'] = df_stamp.date.apply(lambda row: row.weekday(), 1) + df_stamp['hour'] = df_stamp.date.apply(lambda row: row.hour, 1) + df_stamp['minute'] = df_stamp.date.apply(lambda row: row.minute, 1) + df_stamp['minute'] = df_stamp.minute.map(lambda x: x // 15) + data_stamp = df_stamp.drop(['date'], 1).values + elif self.timeenc == 1: + data_stamp = time_features(pd.to_datetime(df_stamp['date'].values), freq=self.freq) + data_stamp = data_stamp.transpose(1, 0) + + self.data_x = data[border1:border2] + self.data_y = data[border1:border2] + self.data_stamp = data_stamp + + def __getitem__(self, index): + s_begin = index + s_end = s_begin + self.seq_len + r_begin = s_end - self.label_len + r_end = r_begin + self.label_len + self.pred_len + + seq_x = self.data_x[s_begin:s_end] + seq_y = self.data_y[r_begin:r_end] + seq_x_mark = self.data_stamp[s_begin:s_end] + seq_y_mark = self.data_stamp[r_begin:r_end] + + return seq_x, seq_y, seq_x_mark, seq_y_mark + + def __len__(self): + return len(self.data_x) - self.seq_len - self.pred_len + 1 + + def inverse_transform(self, data): + return self.scaler.inverse_transform(data) + + +class Dataset_Custom(Dataset): + def __init__(self, root_path, flag='train', size=None, + features='S', data_path='ETTh1.csv', + target='OT', scale=True, timeenc=0, freq='h'): + # size [seq_len, label_len, pred_len] + # info + if size == None: + self.seq_len = 24 * 4 * 4 + self.label_len = 24 * 4 + self.pred_len = 24 * 4 + else: + self.seq_len = size[0] + self.label_len = size[1] + self.pred_len = size[2] + # init + assert flag in ['train', 'test', 'val'] + type_map = {'train': 0, 'val': 1, 'test': 2} + self.set_type = type_map[flag] + + self.features = features + self.target = target + self.scale = scale + self.timeenc = timeenc + self.freq = freq + + self.root_path = root_path + self.data_path = data_path + self.__read_data__() + + def __read_data__(self): + self.scaler = StandardScaler() + df_raw = pd.read_csv(os.path.join(self.root_path, + self.data_path)) + + ''' + df_raw.columns: ['date', ...(other features), target feature] + ''' + cols = list(df_raw.columns) + cols.remove(self.target) + cols.remove('date') + df_raw = df_raw[['date'] + cols + [self.target]] + # print(cols) + num_train = int(len(df_raw) * 0.85) + num_test = self.pred_len # int(len(df_raw) * 0.2) + num_vali = len(df_raw) - num_train - num_test + border1s = [0, num_train - self.seq_len, len(df_raw) - num_test - self.seq_len] + border2s = [num_train, num_train + num_vali, len(df_raw)] + border1 = border1s[self.set_type] + border2 = border2s[self.set_type] + + if self.features == 'M' or self.features == 'MS': + cols_data = df_raw.columns[1:] + df_data = df_raw[cols_data] + elif self.features == 'S': + df_data = df_raw[[self.target]] + + if self.scale: + train_data = df_data[border1s[0]:border2s[0]] + self.scaler.fit(train_data.values) + data = self.scaler.transform(df_data.values) + else: + data = df_data.values + + df_stamp = df_raw[['date']][border1:border2] + df_stamp['date'] = pd.to_datetime(df_stamp.date) + if self.timeenc == 0: + df_stamp['month'] = df_stamp.date.apply(lambda row: row.month, 1) + df_stamp['day'] = df_stamp.date.apply(lambda row: row.day, 1) + df_stamp['weekday'] = df_stamp.date.apply(lambda row: row.weekday(), 1) + df_stamp['hour'] = df_stamp.date.apply(lambda row: row.hour, 1) + data_stamp = df_stamp.drop(['date'], 1).values + elif self.timeenc == 1: + data_stamp = time_features(pd.to_datetime(df_stamp['date'].values), freq=self.freq) + data_stamp = data_stamp.transpose(1, 0) + + self.data_x = data[border1:border2] + self.data_y = data[border1:border2] + self.data_stamp = data_stamp + + def __getitem__(self, index): + s_begin = index + s_end = s_begin + self.seq_len + r_begin = s_end - self.label_len + r_end = r_begin + self.label_len + self.pred_len + + seq_x = self.data_x[s_begin:s_end] + seq_y = self.data_y[r_begin:r_end] + seq_x_mark = self.data_stamp[s_begin:s_end] + seq_y_mark = self.data_stamp[r_begin:r_end] + + return seq_x, seq_y, seq_x_mark, seq_y_mark + + def __len__(self): + return len(self.data_x) - self.seq_len - self.pred_len + 1 + + def inverse_transform(self, data): + return self.scaler.inverse_transform(data) + + +class Dataset_Pred(Dataset): + def __init__(self, root_path, flag='pred', size=None, + features='S', data_path='ETTh1.csv', + target='OT', scale=True, inverse=False, timeenc=0, freq='15min', cols=None): + # size [seq_len, label_len, pred_len] + # info + if size == None: + self.seq_len = 24 * 4 * 4 + self.label_len = 24 * 4 + self.pred_len = 24 * 4 + else: + self.seq_len = size[0] + self.label_len = size[1] + self.pred_len = size[2] + # init + assert flag in ['pred'] + + self.features = features + self.target = target + self.scale = scale + self.inverse = inverse + self.timeenc = timeenc + self.freq = freq + self.cols = cols + self.root_path = root_path + self.data_path = data_path + self.__read_data__() + + def __read_data__(self): + self.scaler = StandardScaler() + df_raw = pd.read_csv(os.path.join(self.root_path, + self.data_path)) + ''' + df_raw.columns: ['date', ...(other features), target feature] + ''' + if self.cols: + cols = self.cols.copy() + cols.remove(self.target) + else: + cols = list(df_raw.columns) + cols.remove(self.target) + cols.remove('date') + df_raw = df_raw[['date'] + cols + [self.target]] + border1 = len(df_raw) - self.seq_len + border2 = len(df_raw) + + if self.features == 'M' or self.features == 'MS': + cols_data = df_raw.columns[1:] + df_data = df_raw[cols_data] + elif self.features == 'S': + df_data = df_raw[[self.target]] + + if self.scale: + self.scaler.fit(df_data.values) + data = self.scaler.transform(df_data.values) + else: + data = df_data.values + + tmp_stamp = df_raw[['date']][border1:border2] + tmp_stamp['date'] = pd.to_datetime(tmp_stamp.date) + pred_dates = pd.date_range(tmp_stamp.date.values[-1], periods=self.pred_len + 1, freq=self.freq) + + df_stamp = pd.DataFrame(columns=['date']) + df_stamp.date = list(tmp_stamp.date.values) + list(pred_dates[1:]) + if self.timeenc == 0: + df_stamp['month'] = df_stamp.date.apply(lambda row: row.month, 1) + df_stamp['day'] = df_stamp.date.apply(lambda row: row.day, 1) + df_stamp['weekday'] = df_stamp.date.apply(lambda row: row.weekday(), 1) + df_stamp['hour'] = df_stamp.date.apply(lambda row: row.hour, 1) + df_stamp['minute'] = df_stamp.date.apply(lambda row: row.minute, 1) + df_stamp['minute'] = df_stamp.minute.map(lambda x: x // 15) + data_stamp = df_stamp.drop(['date'], 1).values + elif self.timeenc == 1: + data_stamp = time_features(pd.to_datetime(df_stamp['date'].values), freq=self.freq) + data_stamp = data_stamp.transpose(1, 0) + + self.data_x = data[border1:border2] + if self.inverse: + self.data_y = df_data.values[border1:border2] + else: + self.data_y = data[border1:border2] + self.data_stamp = data_stamp + + def __getitem__(self, index): + s_begin = index + s_end = s_begin + self.seq_len + r_begin = s_end - self.label_len + r_end = r_begin + self.label_len + self.pred_len + + seq_x = self.data_x[s_begin:s_end] + if self.inverse: + seq_y = self.data_x[r_begin:r_begin + self.label_len] + else: + seq_y = self.data_y[r_begin:r_begin + self.label_len] + seq_x_mark = self.data_stamp[s_begin:s_end] + seq_y_mark = self.data_stamp[r_begin:r_end] + + return seq_x, seq_y, seq_x_mark, seq_y_mark + + def __len__(self): + return len(self.data_x) - self.seq_len + 1 + + def inverse_transform(self, data): + return self.scaler.inverse_transform(data) + +from torch.utils.data import DataLoader + +data_dict = { + 'ETTh1': Dataset_ETT_hour, + 'ETTh2': Dataset_ETT_hour, + 'ETTm1': Dataset_ETT_minute, + 'ETTm2': Dataset_ETT_minute, + 'custom': Dataset_Custom, +} + + +def data_provider(args, flag): + Data = data_dict[args.data] + timeenc = 0 if args.embed != 'timeF' else 1 + + if flag == 'test': + shuffle_flag = False + drop_last = False + batch_size = args.batch_size + freq = args.freq + elif flag == 'pred': + shuffle_flag = False + drop_last = False + batch_size = 1 + freq = args.freq + Data = Dataset_Pred + else: + shuffle_flag = True + drop_last = True + batch_size = args.batch_size + freq = args.freq + + data_set = Data( + root_path=args.root_path, + data_path=args.data_path, + flag=flag, + size=[args.seq_len, args.label_len, args.pred_len], + features=args.features, + target=args.target, + timeenc=timeenc, + freq=freq + ) + print(flag, len(data_set)) + data_loader = DataLoader( + data_set, + batch_size=batch_size, + shuffle=shuffle_flag, + num_workers=args.num_workers, + drop_last=drop_last) + return data_set, data_loader diff --git a/data/last_val_mae.csv b/data/last_val_mae.csv new file mode 100644 index 0000000..9282e6c --- /dev/null +++ b/data/last_val_mae.csv @@ -0,0 +1,25 @@ +dataset,mae +bitcoin,7.777284173521224e+17 +wind_4_seconds,0.0 +tourism_quarterly,15845.100306204946 +traffic_weekly,1.1855844384623289 +us_births,1152.6666666666667 +sunspot,3.933333396911621 +covid_deaths,353.70939849624057 +weather,2.362190193902301 +nn5_daily,8.262752532958984 +solar_10_minutes,2.7269221451758905 +fred_md,2825.672461360778 +tourism_yearly,99456.0540551959 +oikolab_weather,120.03774162319314 +cif_2016,386526.36704240675 +solar_4_seconds,0.0 +australian_electricity_demand,659.600688770839 +traffic_hourly,0.026246391982575817 +solar_weekly,1729.4092503457175 +tourism_monthly,5636.83029361023 +nn5_weekly,16.708553516113007 +pedestrian_counts,170.8838383838384 +kaggle_web_traffic_weekly,2081.781183003247 +hospital,24.06573229030856 +saugeenday,21.496667098999023 diff --git a/data/last_value_results.csv b/data/last_value_results.csv new file mode 100644 index 0000000..122c97d --- /dev/null +++ b/data/last_value_results.csv @@ -0,0 +1,22 @@ +dataset,rmse,mse,mae +weather,3.114059003829736,19.799946602304583,2.362190193902301 +tourism_yearly,111078.12328993063,363513954611.7144,99456.0540551959 +tourism_quarterly,19527.771486861628,6201928361.602383,15845.100306204946 +tourism_monthly,7374.891631391369,619111237.8884984,5636.83029361023 +cif_2016,513908.01521842513,4215886809397.5137,386526.3670424068 +australian_electricity_demand,800.8367705189698,885757.1106004094,652.185211690267 +pedestrian_counts,201.73714366476048,151208.0053661616,149.78377525252526 +nn5_weekly,20.207279217372406,551.324271498317,16.708553516113007 +kaggle_web_traffic_weekly,2751.056545255073,745610075.6193119,2081.781183003247 +solar_10_minutes,2.916518075950651,15.696517675162108,2.72692214517589 +solar_weekly,1918.441068452442,4732343.391918798,1729.4092503457173 +fred_md,3128.761597032152,789147915.1258634,2825.672461360779 +traffic_hourly,0.038692458579408555,0.0018708090134443467,0.029967592352576376 +traffic_weekly,1.5367087177120735,4.087448585855902,1.1855844384623289 +hospital,28.901001578961637,5851.362559756627,24.06573229030856 +covid_deaths,403.4146883707207,4435194.603884712,353.7093984962405 +saugeenday,39.79399009437644,1583.56164763133,21.496667098999023 +us_births,1608.8383179590587,2588360.7333333334,1152.6666666666667 +solar_4_seconds,0.0,0.0,0.0 +wind_4_seconds,0.0,0.0,0.0 +oikolab_weather,130.07108228180007,81269.08468305029,120.03774162319314 diff --git a/data/metrics.py b/data/metrics.py new file mode 100644 index 0000000..dd4301b --- /dev/null +++ b/data/metrics.py @@ -0,0 +1,137 @@ +import numpy as np +from jax import vmap +import jax.numpy as jnp + +def quantile_loss(target, pred, q): + q_pred = jnp.quantile(pred, q, axis=0) + return 2 * jnp.sum( + jnp.abs((q_pred - target) * ((target <= q_pred) * 1.0 - q)) + ) + +def calculate_crps(target, pred, num_quantiles=20): + quantiles = jnp.linspace(0, 1.0, num_quantiles+1)[1:] + vec_quantile_loss = vmap(lambda q: quantile_loss(target, pred, q)) + crps = jnp.sum(vec_quantile_loss(quantiles)) + crps = crps / (jnp.sum(np.abs(target)) * len(quantiles)) + return crps + +import jax +from jax import grad,vmap +from .serialize import serialize_arr, SerializerSettings +import openai + +def nll(input_arr, target_arr, model, settings:SerializerSettings, transform, count_seps=True, prompt=None, temp=1): + """ Returns the NLL/dimension (log base e) of the target array (continuous) according to the LM + conditioned on the input array. Applies relevant log determinant for transforms and + converts from discrete NLL of the LLM to continuous by assuming uniform within the bins. + inputs: + input_arr: (n,) context array + target_arr: (n,) ground truth array + Returns: NLL/D + """ + input_str = serialize_arr(vmap(transform)(input_arr), settings) + target_str = serialize_arr(vmap(transform)(target_arr), settings) + if prompt: + input_str = prompt + '\n' + input_str + if not input_str.endswith(settings.time_sep): + print('Appending time separator to input... Are you sure you want this?') + prompt = input_str + settings.time_sep + target_str + else: + prompt = input_str + target_str + response = openai.Completion.create(model=model, prompt=prompt, logprobs=5, max_tokens=0, echo=True, temperature=temp) + #print(response['choices'][0]) + logprobs = np.array(response['choices'][0].logprobs.token_logprobs, dtype=np.float32) + tokens = np.array(response['choices'][0].logprobs.tokens) + top5logprobs = response['choices'][0].logprobs.top_logprobs + seps = tokens==settings.time_sep + target_start = np.argmax(np.cumsum(seps)==len(input_arr)) + 1 + logprobs = logprobs[target_start:] + tokens = tokens[target_start:] + top5logprobs = top5logprobs[target_start:] + seps = tokens==settings.time_sep + assert len(logprobs[seps]) == len(target_arr), f'There should be one separator per target. Got {len(logprobs[seps])} separators and {len(target_arr)} targets.' + #adjust logprobs by removing extraneous and renormalizing (see appendix of paper) + # logp' = logp - log(1-pk*pextra) + allowed_tokens = [settings.bit_sep + str(i) for i in range(settings.base)] + allowed_tokens += [settings.time_sep, settings.plus_sign, settings.minus_sign, settings.bit_sep+settings.decimal_point] + allowed_tokens = {t for t in allowed_tokens if len(t) > 0} + + p_extra = np.array([sum(np.exp(ll) for k,ll in top5logprobs[i].items() if not (k in allowed_tokens)) for i in range(len(top5logprobs))]) + if settings.bit_sep == '': + p_extra = 0 + adjusted_logprobs = logprobs - np.log(1-p_extra) + digits_bits = -adjusted_logprobs[~seps].sum() + seps_bits = -adjusted_logprobs[seps].sum() + BPD = digits_bits/len(target_arr) + if count_seps: + BPD += seps_bits/len(target_arr) + #print("BPD unadjusted:", -logprobs.sum()/len(target_arr), "BPD adjusted:", BPD) + # log p(x) = log p(token) - log bin_width = log p(token) + prec * log base + transformed_nll = BPD - settings.prec*np.log(settings.base) + avg_logdet_dydx = np.log(vmap(grad(transform))(target_arr)).mean() + return transformed_nll-avg_logdet_dydx + +class Evaluator: + + def __init__(self): + self.non_numerical_cols = [ + "serialized_history", + "serialized_target", + "serialized_prediction", + "history_len", + "num_channels", + "example_num", + "sample_num", + ] + + def evaluate_df(self, gt_df, pred_df): + cols = [c for c in gt_df.columns if c not in self.non_numerical_cols] + num_channels = gt_df["num_channels"].iloc[0] + history_len = gt_df["history_len"].iloc[0] + gt_vals = gt_df[cols].to_numpy().reshape(len(gt_df), -1, num_channels) # (num_examples, history_len + target_len, num_channels) + gt_vals = gt_vals[:, history_len:, :] # (num_examples, target_len, num_channels) + + cols = [c for c in pred_df.columns if c not in self.non_numerical_cols] + num_channels = pred_df["num_channels"].iloc[0] + pred_df = pred_df[cols + ["example_num"]] + + all_pred_vals = [] + for example_num in sorted(pred_df["example_num"].unique()): + pred_vals = pred_df[pred_df["example_num"] == example_num][cols].to_numpy() # (num_samples, target_len * num_channels) + pred_vals = pred_vals.reshape(pred_vals.shape[0], -1, num_channels) # (num_samples, target_len, num_channels) + all_pred_vals.append(pred_vals) + + pred_vals = np.stack(all_pred_vals, axis=1) # (num_samples, num_examples, target_len, num_channels) + assert gt_vals.shape == pred_vals.shape[1:] + + diff = (gt_vals[None] - pred_vals) # (num_samples, num_examples, target_len, num_channels) + mse = np.mean(diff**2) + mae = np.mean(np.abs(diff)) + crps = calculate_crps(gt_vals, pred_vals) + + return { + "mse": mse, + "mae": mae, + "crps": crps, + } + + def evaluate(self, gt, pred): + ''' + gt: (batch_size, steps) + pred: (batch_size, num_samples, steps) + ''' + assert gt.shape == (pred.shape[0], pred.shape[2]), f"wrong shapes: gt.shape: {gt.shape}, pred.shape: {pred.shape}" + diff = (gt[:, None, :] - pred) # (batch_size, num_samples, steps) + mse = np.mean(diff**2) + mae = np.mean(np.abs(diff)) + std = np.std(gt, axis=1) + 1e-8 # (batch_size,) + normlized_diff = diff / std[:, None, None] # (batch_size, num_samples, steps) + nmse = np.mean(normlized_diff**2) + nmae = np.mean(np.abs(normlized_diff)) + + return { + "nmse": nmse, + "nmae": nmae, + "mse": mse, + "mae": mae, + } \ No newline at end of file diff --git a/data/monash.py b/data/monash.py new file mode 100644 index 0000000..28636b7 --- /dev/null +++ b/data/monash.py @@ -0,0 +1,140 @@ +import pandas as pd +import numpy as np +from collections import defaultdict +from sklearn.preprocessing import StandardScaler +import datasets +from datasets import load_dataset +import os +import pickle + +fix_pred_len = { + 'australian_electricity_demand': 336, + 'pedestrian_counts': 24, + 'traffic_hourly': 168, +} + +def get_benchmark_test_sets(): + test_set_dir = "datasets/monash" + if not os.path.exists(test_set_dir): + os.makedirs(test_set_dir) + + if len(os.listdir(test_set_dir)) > 0: + print(f'Loading test sets from {test_set_dir}') + test_sets = {} + for file in os.listdir(test_set_dir): + test_sets[file.split(".")[0]] = pickle.load(open(os.path.join(test_set_dir, file), 'rb')) + return test_sets + else: + print(f'No files found in {test_set_dir}. You are not using our preprocessed datasets!') + + benchmarks = { + "monash_tsf": datasets.get_dataset_config_names("monash_tsf"), + } + + test_sets = defaultdict(list) + for path in benchmarks: + pred_lens = [24, 48, 96, 192] if path == "ett" else [None] + for name in benchmarks[path]: + for pred_len in pred_lens: + if pred_len is None: + ds = load_dataset(path, name) + else: + ds = load_dataset(path, name, multivariate=False, prediction_length=pred_len) + + train_example = ds['train'][0]['target'] + val_example = ds['validation'][0]['target'] + + if len(np.array(train_example).shape) > 1: + print(f"Skipping {name} because it is multivariate") + continue + + pred_len = len(val_example) - len(train_example) + if name in fix_pred_len: + print(f"Fixing pred len for {name}: {pred_len} -> {fix_pred_len[name]}") + pred_len = fix_pred_len[name] + + tag = name + print("Processing", tag) + + pairs = [] + for x in ds['test']: + if np.isnan(x['target']).any(): + print(f"Skipping {name} because it has NaNs") + break + history = np.array(x['target'][:-pred_len]) + target = np.array(x['target'][-pred_len:]) + pairs.append((history, target)) + else: + scaler = None + if path == "ett": + trainset = np.array(ds['train'][0]['target']) + scaler = StandardScaler().fit(trainset[:,None]) + test_sets[tag] = (pairs, scaler) + + for name in test_sets: + try: + with open(os.path.join(test_set_dir,f"{name}.pkl"), 'wb') as f: + pickle.dump(test_sets[name], f) + print(f"Saved {name}") + except: + print(f"Failed to save {name}") + + return test_sets + +def get_datasets(): + benchmarks = get_benchmark_test_sets() + # shuffle the benchmarks + for k, v in benchmarks.items(): + x, _scaler = v # scaler is not used + train, test = zip(*x) + np.random.seed(0) + ind = np.arange(len(train)) + ind = np.random.permutation(ind) + train = [train[i] for i in ind] + test = [test[i] for i in ind] + benchmarks[k] = [list(train), list(test)] + + df = pd.read_csv('data/last_val_mae.csv') + df.sort_values(by='mae') + + df_paper = pd.read_csv('data/paper_mae_raw.csv') # pdf text -> csv + datasets = df_paper['Dataset'] + name_map = { + 'Aus. Electricity Demand' :'australian_electricity_demand', + 'Kaggle Weekly': 'kaggle_web_traffic_weekly', + 'FRED-MD': 'fred_md', + 'Saugeen River Flow': 'saugeenday', + + } + datasets = [name_map.get(d, d) for d in datasets] + # lower case and repalce spaces with underscores + datasets = [d.lower().replace(' ', '_') for d in datasets] + df_paper['Dataset'] = datasets + df_paper = df_paper.reset_index(drop=True) + # for each dataset, add last value mae to df_paper + for dataset in df_paper['Dataset']: + if dataset in df['dataset'].values: + df_paper.loc[df_paper['Dataset'] == dataset, 'Last Value'] = df[df['dataset'] == dataset]['mae'].values[0] + # turn '-' into np.nan + df_paper = df_paper.replace('-', np.nan) + # convert all values to float + for method in df_paper.columns[1:]: + df_paper[method] = df_paper[method].astype(float) + df_paper.to_csv('data/paper_mae.csv', index=False) + # normalize each method by dividing by last value mae + for method in df_paper.columns[1:-1]: # skip dataset and last value + df_paper[method] = df_paper[method] / df_paper['Last Value'] + # sort df by minimum mae across methods + df_paper['normalized_min'] = df_paper[df_paper.columns[1:-1]].min(axis=1) + df_paper['normalized_median'] = df_paper[df_paper.columns[1:-1]].median(axis=1) + df_paper = df_paper.sort_values(by='normalized_min') + df_paper = df_paper.reset_index(drop=True) + # save as csv + df_paper.to_csv('data/paper_mae_normalized.csv', index=False) + return benchmarks + +def main(): + get_datasets() + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/data/paper_mae.csv b/data/paper_mae.csv new file mode 100644 index 0000000..c09dfdf --- /dev/null +++ b/data/paper_mae.csv @@ -0,0 +1,31 @@ +Dataset,SES,Theta,TBATS,ETS,(DHR-)ARIMA,PR,CatBoost,FFNN,DeepAR,N-BEATS,WaveNet,Transformer,Last Value +tourism_yearly,95579.23,90653.6,94121.08,94818.89,95033.24,82682.97,79567.22,79593.22,71471.29,70951.8,69905.47,74316.52,99456.0540551959 +tourism_quarterly,15014.19,7656.49,9972.42,8925.52,10475.47,9092.58,10267.97,8981.04,9511.37,8640.56,9137.12,9521.67,15845.100306204946 +tourism_monthly,5302.1,2069.96,2940.08,2004.51,2536.77,2187.28,2537.04,2022.21,1871.69,2003.02,2095.13,2146.98,5636.83029361023 +cif_2016,581875.97,714818.58,855578.4,642421.42,469059.49,563205.57,603551.3,1495923.44,3200418.0,679034.8,5998224.62,4057973.04,386526.3670424068 +australian_electricity_demand,659.6,665.04,370.74,1282.99,1045.92,247.18,241.77,258.76,302.41,213.83,227.5,231.45,659.600688770839 +dominick,5.7,5.86,7.08,5.81,7.1,8.19,8.09,5.85,5.23,8.28,5.1,5.18, +bitcoin,5.33e+18,5.33e+18,9.9e+17,1.1e+18,3.62e+18,6.66e+17,1.93e+18,1.45e+18,1.95e+18,1.06e+18,2.46e+18,2.61e+18,7.777284173521224e+17 +pedestrian_counts,170.87,170.94,222.38,216.5,635.16,44.18,43.41,46.41,44.78,66.84,46.46,47.29,170.8838383838384 +vehicle_trips,29.98,30.76,21.21,30.95,30.07,27.24,22.61,22.93,22.0,28.16,24.15,28.01, +kdd_cup,42.04,42.06,39.2,44.88,52.2,36.85,34.82,37.16,48.98,49.1,37.08,44.46, +weather,2.24,2.51,2.3,2.35,2.45,8.17,2.51,2.09,2.02,2.34,2.29,2.03,2.362190193902301 +nn5_daily,6.63,3.8,3.7,3.72,4.41,5.47,4.22,4.06,3.94,4.92,3.97,4.16,8.262752532958984 +nn5_weekly,15.66,15.3,14.98,15.7,15.38,14.94,15.29,15.02,14.69,14.19,19.34,20.34,16.708553516113007 +kaggle_daily,363.43,358.73,415.4,403.23,340.36,,,,,,,, +kaggle_web_traffic_weekly,2337.11,2373.98,2241.84,2668.28,3115.03,4051.75,10715.36,2025.23,2272.58,2051.3,2025.5,3100.32,2081.781183003247 +solar_10_minutes,3.28,3.29,8.77,3.28,2.37,3.28,5.69,3.28,3.28,3.52,,3.28,2.7269221451758905 +solar_weekly,1202.39,1210.83,908.65,1131.01,839.88,1044.98,1513.49,1050.84,721.59,1172.64,1996.89,576.35,1729.4092503457175 +electricity_hourly,845.97,846.03,574.3,1344.61,868.2,537.38,407.14,354.39,329.75,350.37,286.56,398.8, +electricity_weekly,74149.18,74111.14,24347.24,67737.82,28457.18,44882.52,34518.43,27451.83,50312.05,32991.72,61429.32,76382.47, +carparts,0.55,0.53,0.58,0.56,0.56,0.41,0.53,0.39,0.39,0.98,0.4,0.39, +fred_md,2798.22,3492.84,1989.97,2041.42,2957.11,8921.94,2475.68,2339.57,4264.36,2557.8,2508.4,4666.04,2825.672461360778 +traffic_hourly,0.03,0.03,0.04,0.03,0.04,0.02,0.02,0.01,0.01,0.02,0.02,0.01,0.0262463919825758 +traffic_weekly,1.12,1.13,1.17,1.14,1.22,1.13,1.17,1.15,1.18,1.11,1.2,1.42,1.1855844384623289 +rideshare,6.29,7.62,6.45,6.29,3.37,6.3,6.07,6.59,6.28,5.55,2.75,6.29, +hospital,21.76,18.54,17.43,17.97,19.6,19.24,19.17,22.86,18.25,20.18,19.35,36.19,24.06573229030856 +covid_deaths,353.71,321.32,96.29,85.59,85.77,347.98,475.15,144.14,201.98,158.81,1049.48,408.66,353.70939849624057 +temperature_rain,8.18,8.22,7.14,8.21,7.19,6.13,6.76,5.56,5.37,7.28,5.81,5.24, +sunspot,4.93,4.93,2.57,4.93,2.57,3.83,2.27,7.97,0.77,14.47,0.17,0.13,3.933333396911621 +saugeenday,21.5,21.49,22.26,30.69,22.38,25.24,21.28,22.98,23.51,27.92,22.17,28.06,21.496667098999023 +us_births,1192.2,586.93,399.0,419.73,526.33,574.93,441.7,557.87,424.93,422.0,504.4,452.87,1152.6666666666667 diff --git a/data/paper_mae_normalized.csv b/data/paper_mae_normalized.csv new file mode 100644 index 0000000..ee6b341 --- /dev/null +++ b/data/paper_mae_normalized.csv @@ -0,0 +1,31 @@ +Dataset,SES,Theta,TBATS,ETS,(DHR-)ARIMA,PR,CatBoost,FFNN,DeepAR,N-BEATS,WaveNet,Transformer,Last Value,normalized_min,normalized_median +sunspot,1.2533898102487173,1.2533898102487173,0.6533898199471001,1.2533898102487173,0.6533898199471001,0.9737287978199975,0.5771186347392675,2.026271153688089,0.19576270870010395,3.6788134998577977,0.04322033828443854,0.033050846923394175,3.933333396911621,0.033050846923394175,0.9737287978199975 +covid_deaths,1.000001700559165,0.9084293529266092,0.2722291248391112,0.24197830299075218,0.24248719532091148,0.9838019613824273,1.34333439263998,0.4075096692731279,0.5710337380309863,0.44898439418111175,2.96706845919774,1.1553552202383548,353.70939849624057,0.24197830299075218,0.9084293529266092 +pedestrian_counts,0.9999190187675484,1.0003286537608984,1.3013518545884437,1.266942515147037,3.7169108910891087,0.25853820008866557,0.2540322151618147,0.27158800059110383,0.26204935717452343,0.3911428993645633,0.27188059701492534,0.27673769765036205,170.8838383838384,0.2540322151618147,0.3911428993645633 +australian_electricity_demand,0.9999989557760464,1.0082463698443023,0.5620673330267002,1.9451010616602633,1.58568664012323,0.3747418767263844,0.36653994472100476,0.3922979529966794,0.458474354481859,0.32418098349543967,0.34490564347945807,0.35089411509151897,659.600688770839,0.32418098349543967,0.458474354481859 +tourism_monthly,0.9406172837969468,0.3672205640724105,0.5215839127413151,0.3556094286308854,0.4500348365775033,0.3880336795804277,0.4500827358375371,0.3587494912331008,0.33204654078759493,0.3553450956773656,0.37168583953556084,0.38088427150871706,5636.83029361023,0.33204654078759493,0.38088427150871706 +solar_weekly,0.6952605346361114,0.7001408138403036,0.5254106278304898,0.6539863249684281,0.48564560403045365,0.6042410145493922,0.8751485512740526,0.6076294548499331,0.4172465249944457,0.6780581286734667,1.1546659644620334,0.3332640899687479,1729.4092503457175,0.3332640899687479,0.6539863249684281 +us_births,1.0342972816657028,0.5091931752458068,0.34615384615384615,0.3641382301908618,0.45661943319838055,0.4987825332562174,0.3831983805668016,0.4839820705610179,0.36864950838635047,0.3661075766338924,0.437593984962406,0.39288895315211103,1152.6666666666667,0.34615384615384615,0.437593984962406 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+tourism_yearly,0.961019727838344,0.9114940348396415,0.9463584785674776,0.9533747432547206,0.9555299665041875,0.8313518044272377,0.800023897548177,0.800285319542514,0.7186218142169105,0.7133985022231359,0.702877976248726,0.7472297257918155,99456.0540551959,0.702877976248726,0.8313518044272377 +fred_md,0.9902846272042592,1.2361092970831906,0.7042465208588531,0.7224545760045026,1.046515489829959,3.1574572502658014,0.876138347191086,0.8279692823538782,1.5091487277143167,0.9052004558122859,0.8877178916879889,1.651302500132285,2825.672461360778,0.7042465208588531,0.9902846272042592 +hospital,0.9041902293894837,0.7703900208125471,0.7242663464273299,0.746704890722841,0.8144360522074393,0.7994770226770985,0.7965683224906435,0.9498983751766358,0.7583396914686615,0.8385367108952104,0.8040478372558139,1.5037979963973074,24.06573229030856,0.7242663464273299,0.8040478372558139 +nn5_weekly,0.937244506826888,0.9156986560952354,0.8965467887782109,0.939638490241516,0.9204866229244916,0.8941528053635828,0.9151001602415783,0.8989407721928389,0.8791904090221573,0.8492656163393065,1.1574909809726701,1.2173405663383716,16.708553516113007,0.8492656163393065,0.9156986560952354 +weather,0.9482724997260088,1.0625732028179828,0.9736726559686696,0.9948394528375538,1.0371730465753222,3.4586546083756655,1.0625732028179828,0.8847721091193562,0.8551385935029185,0.9906060934637768,0.9694392965948928,0.8593719528766953,2.362190193902301,0.8551385935029185,0.9906060934637768 +bitcoin,6.853292076103736,6.853292076103736,1.2729379278316508,1.414375475368501,4.654581109849067,0.8563400605412924,2.48158606132837,1.864404035713024,2.5073019790623428,1.3629436399005554,3.1630578812786476,3.355927264283443,7.777284173521224e+17,0.8563400605412924,2.5073019790623428 +solar_10_minutes,1.2028212854564042,1.20648842352182,3.216080083369715,1.2028212854564042,0.8691117215035604,1.2028212854564042,2.086601559221628,1.2028212854564042,1.2028212854564042,1.2908325990263851,,1.2028212854564042,2.7269221451758905,0.8691117215035604,1.2028212854564042 +traffic_weekly,0.9446817650985787,0.9531164237155302,0.9868550581833365,0.9615510823324818,1.0290283512680944,0.9531164237155302,0.9868550581833365,0.9699857409494334,0.9952897168002881,0.9362471064816271,1.0121590340341913,1.1977215236071264,1.1855844384623289,0.9362471064816271,0.9868550581833365 +kaggle_web_traffic_weekly,1.1226492097639231,1.1403600048758329,1.0768855143391425,1.2817293295689465,1.4963292133835864,1.9462900486759183,5.147207635214411,0.9728351935040241,1.0916517156339651,0.9853581234895814,0.9729648901321829,1.489263148938341,2081.781183003247,0.9728351935040241,1.1403600048758329 +saugeenday,1.0001550426857162,0.9996898542937693,1.0355093604736765,1.4276631748848667,1.0410916211770385,1.1741355012738361,0.9899208980628857,1.0690029246938493,1.0936579094670322,1.2988059903155906,1.0313226649461549,1.3053186278028464,21.496667098999023,0.9899208980628857,1.0690029246938493 +cif_2016,1.5053978709197886,1.8493397629496653,2.2135059156420556,1.6620377670885214,1.2135252080967047,1.4570948272157827,1.5614751061310725,3.870171785294736,8.279947431500512,1.7567619130249434,15.51828059207645,10.498567202673625,386526.3670424068,1.2135252080967047,1.8493397629496653 +dominick,,,,,,,,,,,,,,, +vehicle_trips,,,,,,,,,,,,,,, +kdd_cup,,,,,,,,,,,,,,, +kaggle_daily,,,,,,,,,,,,,,, +electricity_hourly,,,,,,,,,,,,,,, +electricity_weekly,,,,,,,,,,,,,,, +carparts,,,,,,,,,,,,,,, +rideshare,,,,,,,,,,,,,,, +temperature_rain,,,,,,,,,,,,,,, diff --git a/data/paper_mae_raw.csv b/data/paper_mae_raw.csv new file mode 100644 index 0000000..6f6e0be --- /dev/null +++ b/data/paper_mae_raw.csv @@ -0,0 +1,31 @@ +Dataset,SES,Theta,TBATS,ETS,(DHR-)ARIMA,PR,CatBoost,FFNN,DeepAR,N-BEATS,WaveNet,Transformer +Tourism Yearly,95579.23,90653.60,94121.08,94818.89,95033.24,82682.97,79567.22,79593.22,71471.29,70951.80,69905.47,74316.52 +Tourism Quarterly,15014.19,7656.49,9972.42,8925.52,10475.47,9092.58,10267.97,8981.04,9511.37,8640.56,9137.12,9521.67 +Tourism Monthly,5302.10,2069.96,2940.08,2004.51,2536.77,2187.28,2537.04,2022.21,1871.69,2003.02,2095.13,2146.98 +CIF 2016,581875.97,714818.58,855578.40,642421.42,469059.49,563205.57,603551.30,1495923.44,3200418.00,679034.80,5998224.62,4057973.04 +Aus. Electricity Demand,659.60,665.04,370.74,1282.99,1045.92,247.18,241.77,258.76,302.41,213.83,227.50,231.45 +Dominick,5.70,5.86,7.08,5.81,7.10,8.19,8.09,5.85,5.23,8.28,5.10,5.18 +Bitcoin,5.33e18,5.33e18,9.9e17,1.10e18,3.62e18,6.66e17,1.93e18,1.45e18,1.95e18,1.06e18,2.46e18,2.61e18 +Pedestrian Counts,170.87,170.94,222.38,216.50,635.16,44.18,43.41,46.41,44.78,66.84,46.46,47.29 +Vehicle Trips,29.98,30.76,21.21,30.95,30.07,27.24,22.61,22.93,22.00,28.16,24.15,28.01 +KDD Cup,42.04,42.06,39.20,44.88,52.20,36.85,34.82,37.16,48.98,49.10,37.08,44.46 +Weather,2.24,2.51,2.30,2.35,2.45,8.17,2.51,2.09,2.02,2.34,2.29,2.03 +NN5 Daily,6.63,3.80,3.70,3.72,4.41,5.47,4.22,4.06,3.94,4.92,3.97,4.16 +NN5 Weekly,15.66,15.30,14.98,15.70,15.38,14.94,15.29,15.02,14.69,14.19,19.34,20.34 +Kaggle Daily,363.43,358.73,415.40,403.23,340.36,-,-,-,-,-,-, +Kaggle Weekly,2337.11,2373.98,2241.84,2668.28,3115.03,4051.75,10715.36,2025.23,2272.58,2051.30,2025.50,3100.32 +Solar 10 Minutes,3.28,3.29,8.77,3.28,2.37,3.28,5.69,3.28,3.28,3.52,-,3.28 +Solar Weekly,1202.39,1210.83,908.65,1131.01,839.88,1044.98,1513.49,1050.84,721.59,1172.64,1996.89,576.35 +Electricity Hourly,845.97,846.03,574.30,1344.61,868.20,537.38,407.14,354.39,329.75,350.37,286.56,398.80 +Electricity Weekly,74149.18,74111.14,24347.24,67737.82,28457.18,44882.52,34518.43,27451.83,50312.05,32991.72,61429.32,76382.47 +Carparts,0.55,0.53,0.58,0.56,0.56,0.41,0.53,0.39,0.39,0.98,0.40,0.39 +FRED-MD,2798.22,3492.84,1989.97,2041.42,2957.11,8921.94,2475.68,2339.57,4264.36,2557.80,2508.40,4666.04 +Traffic Hourly,0.03,0.03,0.04,0.03,0.04,0.02,0.02,0.01,0.01,0.02,0.02,0.01 +Traffic Weekly,1.12,1.13,1.17,1.14,1.22,1.13,1.17,1.15,1.18,1.11,1.20,1.42 +Rideshare,6.29,7.62,6.45,6.29,3.37,6.30,6.07,6.59,6.28,5.55,2.75,6.29 +Hospital,21.76,18.54,17.43,17.97,19.60,19.24,19.17,22.86,18.25,20.18,19.35,36.19 +COVID Deaths,353.71,321.32,96.29,85.59,85.77,347.98,475.15,144.14,201.98,158.81,1049.48,408.66 +Temperature Rain,8.18,8.22,7.14,8.21,7.19,6.13,6.76,5.56,5.37,7.28,5.81,5.24 +Sunspot,4.93,4.93,2.57,4.93,2.57,3.83,2.27,7.97,0.77,14.47,0.17,0.13 +Saugeen River Flow,21.50,21.49,22.26,30.69,22.38,25.24,21.28,22.98,23.51,27.92,22.17,28.06 +US Births,1192.20,586.93,399.00,419.73,526.33,574.93,441.70,557.87,424.93,422.00,504.40,452.87 \ No newline at end of file diff --git a/data/serialize.py b/data/serialize.py new file mode 100644 index 0000000..23f64ef --- /dev/null +++ b/data/serialize.py @@ -0,0 +1,226 @@ +from functools import partial +import numpy as np +from dataclasses import dataclass + +def vec_num2repr(val, base, prec, max_val): + """ + Convert numbers to a representation in a specified base with precision. + + Parameters: + - val (np.array): The numbers to represent. + - base (int): The base of the representation. + - prec (int): The precision after the 'decimal' point in the base representation. + - max_val (float): The maximum absolute value of the number. + + Returns: + - tuple: Sign and digits in the specified base representation. + + Examples: + With base=10, prec=2: + 0.5 -> 50 + 3.52 -> 352 + 12.5 -> 1250 + """ + base = float(base) + bs = val.shape[0] + sign = 1 * (val >= 0) - 1 * (val < 0) + val = np.abs(val) + max_bit_pos = int(np.ceil(np.log(max_val) / np.log(base)).item()) + + before_decimals = [] + for i in range(max_bit_pos): + digit = (val / base**(max_bit_pos - i - 1)).astype(int) + before_decimals.append(digit) + val -= digit * base**(max_bit_pos - i - 1) + + before_decimals = np.stack(before_decimals, axis=-1) + + if prec > 0: + after_decimals = [] + for i in range(prec): + digit = (val / base**(-i - 1)).astype(int) + after_decimals.append(digit) + val -= digit * base**(-i - 1) + + after_decimals = np.stack(after_decimals, axis=-1) + digits = np.concatenate([before_decimals, after_decimals], axis=-1) + else: + digits = before_decimals + return sign, digits + +def vec_repr2num(sign, digits, base, prec, half_bin_correction=True): + """ + Convert a string representation in a specified base back to numbers. + + Parameters: + - sign (np.array): The sign of the numbers. + - digits (np.array): Digits of the numbers in the specified base. + - base (int): The base of the representation. + - prec (int): The precision after the 'decimal' point in the base representation. + - half_bin_correction (bool): If True, adds 0.5 of the smallest bin size to the number. + + Returns: + - np.array: Numbers corresponding to the given base representation. + """ + base = float(base) + bs, D = digits.shape + digits_flipped = np.flip(digits, axis=-1) + powers = -np.arange(-prec, -prec + D) + val = np.sum(digits_flipped/base**powers, axis=-1) + + if half_bin_correction: + val += 0.5/base**prec + + return sign * val + +@dataclass +class SerializerSettings: + """ + Settings for serialization of numbers. + + Attributes: + - base (int): The base for number representation. + - prec (int): The precision after the 'decimal' point in the base representation. + - signed (bool): If True, allows negative numbers. Default is False. + - fixed_length (bool): If True, ensures fixed length of serialized string. Default is False. + - max_val (float): Maximum absolute value of number for serialization. + - time_sep (str): Separator for different time steps. + - bit_sep (str): Separator for individual digits. + - plus_sign (str): String representation for positive sign. + - minus_sign (str): String representation for negative sign. + - half_bin_correction (bool): If True, applies half bin correction during deserialization. Default is True. + - decimal_point (str): String representation for the decimal point. + """ + base: int = 10 + prec: int = 3 + signed: bool = True + fixed_length: bool = False + max_val: float = 1e7 + time_sep: str = ' ,' + bit_sep: str = ' ' + plus_sign: str = '' + minus_sign: str = ' -' + half_bin_correction: bool = True + decimal_point: str = '' + missing_str: str = ' Nan' + +def serialize_arr(arr, settings: SerializerSettings): + """ + Serialize an array of numbers (a time series) into a string based on the provided settings. + + Parameters: + - arr (np.array): Array of numbers to serialize. + - settings (SerializerSettings): Settings for serialization. + + Returns: + - str: String representation of the array. + """ + # max_val is only for fixing the number of bits in nunm2repr so it can be vmapped + assert np.all(np.abs(arr[~np.isnan(arr)]) <= settings.max_val), f"abs(arr) must be <= max_val,\ + but abs(arr)={np.abs(arr)}, max_val={settings.max_val}" + + if not settings.signed: + assert np.all(arr[~np.isnan(arr)] >= 0), f"unsigned arr must be >= 0" + plus_sign = minus_sign = '' + else: + plus_sign = settings.plus_sign + minus_sign = settings.minus_sign + + vnum2repr = partial(vec_num2repr,base=settings.base,prec=settings.prec,max_val=settings.max_val) + sign_arr, digits_arr = vnum2repr(np.where(np.isnan(arr),np.zeros_like(arr),arr)) + ismissing = np.isnan(arr) + + def tokenize(arr): + return ''.join([settings.bit_sep+str(b) for b in arr]) + + bit_strs = [] + for sign, digits,missing in zip(sign_arr, digits_arr, ismissing): + if not settings.fixed_length: + # remove leading zeros + nonzero_indices = np.where(digits != 0)[0] + if len(nonzero_indices) == 0: + digits = np.array([0]) + else: + digits = digits[nonzero_indices[0]:] + # add a decimal point + prec = settings.prec + if len(settings.decimal_point): + digits = np.concatenate([digits[:-prec], np.array([settings.decimal_point]), digits[-prec:]]) + digits = tokenize(digits) + sign_sep = plus_sign if sign==1 else minus_sign + if missing: + bit_strs.append(settings.missing_str) + else: + bit_strs.append(sign_sep + digits) + bit_str = settings.time_sep.join(bit_strs) + bit_str += settings.time_sep # otherwise there is ambiguity in number of digits in the last time step + return bit_str + +def deserialize_str(bit_str, settings: SerializerSettings, ignore_last=False, steps=None): + """ + Deserialize a string into an array of numbers (a time series) based on the provided settings. + + Parameters: + - bit_str (str): String representation of an array of numbers. + - settings (SerializerSettings): Settings for deserialization. + - ignore_last (bool): If True, ignores the last time step in the string (which may be incomplete due to token limit etc.). Default is False. + - steps (int, optional): Number of steps or entries to deserialize. + + Returns: + - None if deserialization failed for the very first number, otherwise + - np.array: Array of numbers corresponding to the string. + """ + # ignore_last is for ignoring the last time step in the prediction, which is often a partially generated due to token limit + orig_bitstring = bit_str + bit_strs = bit_str.split(settings.time_sep) + # remove empty strings + bit_strs = [a for a in bit_strs if len(a) > 0] + if ignore_last: + bit_strs = bit_strs[:-1] + if steps is not None: + bit_strs = bit_strs[:steps] + vrepr2num = partial(vec_repr2num,base=settings.base,prec=settings.prec,half_bin_correction=settings.half_bin_correction) + max_bit_pos = int(np.ceil(np.log(settings.max_val)/np.log(settings.base)).item()) + sign_arr = [] + digits_arr = [] + try: + for i, bit_str in enumerate(bit_strs): + if bit_str.startswith(settings.minus_sign): + sign = -1 + elif bit_str.startswith(settings.plus_sign): + sign = 1 + else: + assert settings.signed == False, f"signed bit_str must start with {settings.minus_sign} or {settings.plus_sign}" + bit_str = bit_str[len(settings.plus_sign):] if sign==1 else bit_str[len(settings.minus_sign):] + if settings.bit_sep=='': + bits = [b for b in bit_str.lstrip()] + else: + bits = [b[:1] for b in bit_str.lstrip().split(settings.bit_sep)] + if settings.fixed_length: + assert len(bits) == max_bit_pos+settings.prec, f"fixed length bit_str must have {max_bit_pos+settings.prec} bits, but has {len(bits)}: '{bit_str}'" + digits = [] + for b in bits: + if b==settings.decimal_point: + continue + # check if is a digit + if b.isdigit(): + digits.append(int(b)) + else: + break + #digits = [int(b) for b in bits] + sign_arr.append(sign) + digits_arr.append(digits) + except Exception as e: + print(f"Error deserializing {settings.time_sep.join(bit_strs[i-2:i+5])}{settings.time_sep}\n\t{e}") + print(f'Got {orig_bitstring}') + print(f"Bitstr {bit_str}, separator {settings.bit_sep}") + # At this point, we have already deserialized some of the bit_strs, so we return those below + if digits_arr: + # add leading zeros to get to equal lengths + max_len = max([len(d) for d in digits_arr]) + for i in range(len(digits_arr)): + digits_arr[i] = [0]*(max_len-len(digits_arr[i])) + digits_arr[i] + return vrepr2num(np.array(sign_arr), np.array(digits_arr)) + else: + # errored at first step + return None diff --git a/data/small_context.py b/data/small_context.py new file mode 100644 index 0000000..853ce7d --- /dev/null +++ b/data/small_context.py @@ -0,0 +1,114 @@ +import darts.datasets +import pandas as pd + +dataset_names = [ + 'AirPassengersDataset', + 'AusBeerDataset', + 'AustralianTourismDataset', + 'ETTh1Dataset', + 'ETTh2Dataset', + 'ETTm1Dataset', + 'ETTm2Dataset', + 'ElectricityDataset', + 'EnergyDataset', + 'ExchangeRateDataset', + 'GasRateCO2Dataset', + 'HeartRateDataset', + 'ILINetDataset', + 'IceCreamHeaterDataset', + 'MonthlyMilkDataset', + 'MonthlyMilkIncompleteDataset', + 'SunspotsDataset', + 'TaylorDataset', + 'TemperatureDataset', + 'TrafficDataset', + 'USGasolineDataset', + 'UberTLCDataset', + 'WeatherDataset', + 'WineDataset', + 'WoolyDataset', +] + +def get_descriptions(w_references=False): + descriptions = [] + for dsname in dataset_names: + d = getattr(darts.datasets,dsname)().__doc__ + + if w_references: + descriptions.append(d) + continue + + lines = [] + for l in d.split("\n"): + if l.strip().startswith("References"): + break + if l.strip().startswith("Source"): + break + if l.strip().startswith("Obtained"): + break + lines.append(l) + + d = " ".join([x.strip() for x in lines]).strip() + + descriptions.append(d) + + return dict(zip(dataset_names,descriptions)) + +def get_dataset(dsname): + darts_ds = getattr(darts.datasets,dsname)().load() + if dsname=='GasRateCO2Dataset': + darts_ds = darts_ds[darts_ds.columns[1]] + series = darts_ds.pd_series() + + if dsname == 'SunspotsDataset': + series = series.iloc[::4] + if dsname =='HeartRateDataset': + series = series.iloc[::2] + return series + +def get_datasets(n=-1,testfrac=0.2): + datasets = [ + 'AirPassengersDataset', + 'AusBeerDataset', + 'GasRateCO2Dataset', # multivariate + 'MonthlyMilkDataset', + 'SunspotsDataset', #very big, need to subsample? + 'WineDataset', + 'WoolyDataset', + 'HeartRateDataset', + ] + datas = [] + for i,dsname in enumerate(datasets): + series = get_dataset(dsname) + splitpoint = int(len(series)*(1-testfrac)) + + train = series.iloc[:splitpoint] + test = series.iloc[splitpoint:] + datas.append((train,test)) + if i+1==n: + break + return dict(zip(datasets,datas)) + +def get_memorization_datasets(n=-1,testfrac=0.15, predict_steps=30): + datasets = [ + 'IstanbulTraffic', + 'TSMCStock', + 'TurkeyPower' + ] + datas = [] + for i,dsname in enumerate(datasets): + with open(f"datasets/memorization/{dsname}.csv") as f: + series = pd.read_csv(f, index_col=0, parse_dates=True).values.reshape(-1) + # treat as float + series = series.astype(float) + series = pd.Series(series) + if predict_steps is not None: + splitpoint = len(series)-predict_steps + else: + splitpoint = int(len(series)*(1-testfrac)) + train = series.iloc[:splitpoint] + test = series.iloc[splitpoint:] + datas.append((train,test)) + if i+1==n: + break + return dict(zip(datasets,datas)) \ No newline at end of file diff --git a/data/synthetic.py b/data/synthetic.py new file mode 100644 index 0000000..4e21c49 --- /dev/null +++ b/data/synthetic.py @@ -0,0 +1,21 @@ +import os +from glob import glob +import numpy as np +import pandas as pd + +def get_synthetic_datasets(): + dss = [] + dir_path = os.path.dirname(os.path.realpath(__file__)) + dss += glob(f'{dir_path}/../datasets/synthetic/*.npy') + x = [] + labels = [] + for ds in dss: + s = np.load(ds)[:3] + x.append(s) + labels += [ds.split('/')[-1].split('.')[0]+(f'_{i}' if len(s)>1 else '') for i in range(len(s))] + x = np.concatenate(x) + # subtract mean + # x -= np.mean(x, axis=1, keepdims=True) + data = [pd.Series(x[i],index=pd.RangeIndex(len(x[i]))) for i in range(len(x))] + synthetic_datasets = {dsname:(dat[:140],dat[140:]) for dsname,dat in zip(labels,data)} + return synthetic_datasets \ No newline at end of file diff --git a/datasets/memorization/IstanbulTraffic.csv b/datasets/memorization/IstanbulTraffic.csv new file mode 100644 index 0000000..fb71c5c --- /dev/null +++ b/datasets/memorization/IstanbulTraffic.csv @@ -0,0 +1,267 @@ +2023-05-05 23:57:00,22 +2023-05-06 00:58:00,8 +2023-05-06 01:58:00,1 +2023-05-06 02:59:00,1 +2023-05-06 03:59:00,1 +2023-05-06 04:59:00,1 +2023-05-06 05:59:00,6 +2023-05-06 06:59:00,15 +2023-05-06 07:59:00,17 +2023-05-06 08:59:00,35 +2023-05-06 09:59:00,42 +2023-05-06 10:59:00,40 +2023-05-06 11:59:00,49 +2023-05-06 12:59:00,56 +2023-05-06 13:59:00,62 +2023-05-06 15:00:00,67 +2023-05-06 16:00:00,65 +2023-05-06 17:00:00,60 +2023-05-06 18:00:00,54 +2023-05-06 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v&HOpW6%Q)QTs=!%FsxsbdaPfBBT4m1&5c6TP)a50q&9eg8r;|Ez{I}+%%ts* literal 0 HcmV?d00001 diff --git a/demo.py b/demo.py new file mode 100644 index 0000000..c5b8b06 --- /dev/null +++ b/demo.py @@ -0,0 +1,123 @@ +import os +os.environ['OMP_NUM_THREADS'] = '4' +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +import openai +#openai.api_key = os.environ['OPENAI_API_KEY'] +openai.api_key = '' #chandra2 +openai.api_base = os.environ.get("OPENAI_API_BASE", "https://api.openai.com/v1") +from data.serialize import SerializerSettings +from models.utils import grid_iter +from models.promptcast import get_promptcast_predictions_data +from models.darts import get_arima_predictions_data +from models.llmtime import get_llmtime_predictions_data +from data.small_context import get_datasets +from models.validation_likelihood_tuning import get_autotuned_predictions_data +import logging +import pickle + +logging.basicConfig(level=logging.DEBUG) + + + +def plot_preds(train, test, pred_dict, model_name, show_samples=False): + pred = pred_dict['median'] + pred = pd.Series(pred, index=test.index) + plt.figure(figsize=(8, 6), dpi=100) + plt.plot(train) + plt.plot(test, label='Truth', color='black') + plt.plot(pred, label=model_name, color='purple') + # shade 90% confidence interval + samples = pred_dict['samples'] + lower = np.quantile(samples, 0.05, axis=0) + upper = np.quantile(samples, 0.95, axis=0) + plt.fill_between(pred.index, lower, upper, alpha=0.3, color='purple') + if show_samples: + samples = pred_dict['samples'] + # convert df to numpy array + samples = samples.values if isinstance(samples, pd.DataFrame) else samples + for i in range(min(10, samples.shape[0])): + plt.plot(pred.index, samples[i], color='purple', alpha=0.3, linewidth=1) + plt.legend(loc='upper left') + if 'NLL/D' in pred_dict: + nll = pred_dict['NLL/D'] + if nll is not None: + plt.text(0.03, 0.85, f'NLL/D: {nll:.2f}', transform=plt.gca().transAxes, bbox=dict(facecolor='white', alpha=0.5)) + plt.show() + + + +gpt4_hypers = dict( + alpha=0.3, + basic=True, + temp=1.0, + top_p=0.8, + settings=SerializerSettings(base=10, prec=3, signed=True, time_sep=', ', bit_sep='', minus_sign='-') +) + +gpt3_hypers = dict( + temp=0.7, + alpha=0.95, + beta=0.3, + basic=False, + settings=SerializerSettings(base=10, prec=3, signed=True, half_bin_correction=True) +) + + +promptcast_hypers = dict( + temp=0.7, + settings=SerializerSettings(base=10, prec=0, signed=True, + time_sep=', ', + bit_sep='', + plus_sign='', + minus_sign='-', + half_bin_correction=False, + decimal_point='') +) + +arima_hypers = dict(p=[12,30], d=[1,2], q=[0]) + +model_hypers = { + #'LLMTime GPT-3.5': {'model': 'gpt-3.5-turbo-instruct', **gpt3_hypers}, + 'LLMTime GPT-4': {'model': 'gpt-4', **gpt4_hypers}, + #'LLMTime GPT-3': {'model': 'text-davinci-003', **gpt3_hypers}, + #'PromptCast GPT-3': {'model': 'text-davinci-003', **promptcast_hypers}, + 'ARIMA': arima_hypers, + +} + +model_predict_fns = { + + #'LLMTime GPT-3': get_llmtime_predictions_data, + #'LLMTime GPT-3.5': get_llmtime_predictions_data, + 'LLMTime GPT-4': get_llmtime_predictions_data, + #'PromptCast GPT-3': get_promptcast_predictions_data, + 'ARIMA': get_arima_predictions_data, +} + +model_names = list(model_predict_fns.keys()) + + + +datasets = get_datasets() +ds_name = 'AirPassengersDataset' + +data = datasets[ds_name] +train, test = data # or change to your own data +logging.debug("train and test are {} and {}".format(train, test)) +out = {} +print('\n') +print(train) + + +for model in model_names: # GPT-4 takes a about a minute to run + model_hypers[model].update({'dataset_name': ds_name}) # for promptcast + hypers = list(grid_iter(model_hypers[model])) + num_samples = 10 + pred_dict = get_autotuned_predictions_data(train, test, hypers, num_samples, model_predict_fns[model], verbose=False, parallel=False) + out[model] = pred_dict + plot_preds(train, test, pred_dict, model, show_samples=True) + + + diff --git a/figures/figure_1.ipynb b/figures/figure_1.ipynb new file mode 100644 index 0000000..a77fba1 --- /dev/null +++ b/figures/figure_1.ipynb @@ -0,0 +1,483 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/nategruver/opt/anaconda3/lib/python3.8/site-packages/pandas/core/computation/expressions.py:20: UserWarning: Pandas requires version '2.7.3' or newer of 'numexpr' (version '2.7.1' currently installed).\n", + " from pandas.core.computation.check import NUMEXPR_INSTALLED\n" + ] + }, + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: 'eval/small_context_tuned/AirPassengersDataset.pkl'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/Users/nategruver/Desktop/shell_dir/time-series-lm/figures/figure_1.ipynb Cell 1\u001b[0m line \u001b[0;36m5\n\u001b[1;32m 47\u001b[0m datasets \u001b[39m=\u001b[39m get_datasets()\n\u001b[1;32m 48\u001b[0m \u001b[39mfor\u001b[39;00m dsname,(train,test) \u001b[39min\u001b[39;00m datasets\u001b[39m.\u001b[39mitems():\n\u001b[1;32m 49\u001b[0m \u001b[39m# if dsname == \"SunspotsDataset\":\u001b[39;00m\n\u001b[1;32m 50\u001b[0m \u001b[39m# continue\u001b[39;00m\n\u001b[1;32m 51\u001b[0m \n\u001b[1;32m 52\u001b[0m \u001b[39m# print(dsname)\u001b[39;00m\n\u001b[0;32m---> 53\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mopen\u001b[39;49m(\u001b[39mf\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39meval/small_context_tuned/\u001b[39;49m\u001b[39m{\u001b[39;49;00mdsname\u001b[39m}\u001b[39;49;00m\u001b[39m.pkl\u001b[39;49m\u001b[39m'\u001b[39;49m,\u001b[39m'\u001b[39;49m\u001b[39mrb\u001b[39;49m\u001b[39m'\u001b[39;49m) \u001b[39mas\u001b[39;00m f:\n\u001b[1;32m 54\u001b[0m data_dict \u001b[39m=\u001b[39m pickle\u001b[39m.\u001b[39mload(f)\n\u001b[1;32m 55\u001b[0m \u001b[39mfor\u001b[39;00m model_name,preds \u001b[39min\u001b[39;00m data_dict\u001b[39m.\u001b[39mitems():\n\u001b[1;32m 56\u001b[0m \u001b[39m# print(f\"\\t{model_name}\")\u001b[39;00m\n", + "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/IPython/core/interactiveshell.py:284\u001b[0m, in \u001b[0;36m_modified_open\u001b[0;34m(file, *args, **kwargs)\u001b[0m\n\u001b[1;32m 277\u001b[0m \u001b[39mif\u001b[39;00m file \u001b[39min\u001b[39;00m {\u001b[39m0\u001b[39m, \u001b[39m1\u001b[39m, \u001b[39m2\u001b[39m}:\n\u001b[1;32m 278\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m 279\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mIPython won\u001b[39m\u001b[39m'\u001b[39m\u001b[39mt let you open fd=\u001b[39m\u001b[39m{\u001b[39;00mfile\u001b[39m}\u001b[39;00m\u001b[39m by default \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 280\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mas it is likely to crash IPython. If you know what you are doing, \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 281\u001b[0m \u001b[39m\"\u001b[39m\u001b[39myou can use builtins\u001b[39m\u001b[39m'\u001b[39m\u001b[39m open.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 282\u001b[0m )\n\u001b[0;32m--> 284\u001b[0m \u001b[39mreturn\u001b[39;00m io_open(file, \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'eval/small_context_tuned/AirPassengersDataset.pkl'" + ] + } + ], + "source": [ + "import pickle\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from collections import defaultdict\n", + "\n", + "import sys\n", + "sys.path.append(\"..\")\n", + "from data.small_context import get_datasets\n", + "from data.metrics import calculate_crps\n", + "\n", + "sns.set(style=\"whitegrid\", font_scale=1)\n", + "\n", + "name_map = {\n", + " \"gp\": \"SM-GP\",\n", + " \"arima\": \"ARIMA\",\n", + " \"TCN\": \"TCN\",\n", + " \"N-BEATS\": \"N-BEATS\",\n", + " \"N-HiTS\": \"N-HiTS\",\n", + " 'text-davinci-003':'GPT-3',\n", + " 'LLaMA7B': 'LLaMA7B',\n", + " 'LLaMA13B': 'LLaMA13B',\n", + " 'LLaMA30B': 'LLaMA30B', \n", + " 'LLaMA70B': 'LLaMA70B',\n", + " \"llama1_7B\": \"LLaMA 7B\",\n", + " \"llama1_13B\": \"LLaMA 13B\",\n", + " \"llama1_30B\": \"LLaMA 30B\",\n", + " \"llama1_70B\": \"LLaMA 70B\",\n", + " \"llama2_7B\": \"LLaMA-2 7B\",\n", + " \"llama2_13B\": \"LLaMA-2 13B\",\n", + " \"llama2_70B\": \"LLaMA-2 70B\",\n", + " \"llama2_7B_chat\": \"LLaMA-2 7B (chat)\",\n", + " \"llama2_13B_chat\": \"LLaMA-2 13B (chat)\",\n", + " \"llama2_70B_chat\": \"LLaMA-2 70B (chat)\",\n", + "}\n", + "\n", + "hue_order = ['N-BEATS','SM-GP','TCN','N-HiTS','ARIMA']#, 'LLaMA70B']\n", + "hue_order += [\"LLaMA-2 70B\", 'GPT-3']\n", + "# hue_order += [\n", + "# \"LLaMA 7B\", \"LLaMA-2 7B\", \"LLaMA-2 7B (chat)\",\n", + "# \"LLaMA 13B\", \"LLaMA-2 13B\", \"LLaMA-2 13B (chat)\",\n", + "# \"LLaMA 30B\", \"LLaMA 70B\", \"LLaMA-2 70B\", \"LLaMA-2 70B (chat)\"\n", + "# ]\n", + "nlls = defaultdict(list)\n", + "crps = defaultdict(list)\n", + "mae = defaultdict(list)\n", + "datasets = get_datasets()\n", + "for dsname,(train,test) in datasets.items():\n", + " # if dsname == \"SunspotsDataset\":\n", + " # continue\n", + "\n", + " # print(dsname)\n", + " with open(f'eval/small_context_tuned/{dsname}.pkl','rb') as f:\n", + " data_dict = pickle.load(f)\n", + " for model_name,preds in data_dict.items():\n", + " # print(f\"\\t{model_name}\")\n", + " if model_name in ['ada','babbage','curie']:\n", + " continue\n", + " if 'NLL/D' not in preds:\n", + " continue\n", + " nll = preds['NLL/D']\n", + " if model_name=='text-davinci-003-tuned':\n", + " model_name='GPT3'\n", + "\n", + " if type(preds['samples']) == np.ndarray:\n", + " nlls[model_name].append(nll)\n", + " crps[model_name].append(calculate_crps(test.values,preds['samples'][:10],10))\n", + " tmae = np.abs(test.values-preds['median']).mean()/np.abs(test.values).mean()\n", + " mae[model_name].append(tmae)\n", + " else:\n", + " nlls[model_name].append(nll)\n", + " crps[model_name].append(calculate_crps(test.values,preds['samples'].values[:10],10))\n", + " tmae = np.abs(test.values-preds['median']).mean()/np.abs(test.values).mean()\n", + " mae[model_name].append(tmae)\n", + "\n", + "llama_models = [\n", + " \"llama1_7B\",\n", + " \"llama2_7B\",\n", + " \"llama2_7B_chat\",\n", + " \"llama1_13B\",\n", + " \"llama2_13B\",\n", + " \"llama2_13B_chat\",\n", + " \"llama1_30B\",\n", + " \"llama1_70B\",\n", + " \"llama2_70B\",\n", + " \"llama2_70B_chat\",\n", + "]\n", + "for dsname,(train,test) in datasets.items():\n", + " # if dsname == \"SunspotsDataset\":\n", + " # continue\n", + "\n", + " for model_name in llama_models:\n", + " if model_name in ['llama2_70B', 'llama2_70B_chat']:\n", + " fn = f'eval/llama_70B_sweep_sample/{model_name}/darts-{dsname}/1.0_0.9_0.99_0.3_3_,_.pkl'\n", + " else:\n", + " fn = f'eval/llama_2_results/{model_name}/darts-{dsname}/0.4_0.9_0.99_0.3_3_,_.pkl'\n", + " with open(fn,'rb') as f:\n", + " data_dict = pickle.load(f)\n", + "\n", + " preds = data_dict#[model_name]\n", + "\n", + " if 'NLL/D' not in preds:\n", + " continue\n", + " nll = preds['NLL/D']\n", + "\n", + " if type(preds['samples']) == np.ndarray:\n", + " nlls[model_name].append(nll)\n", + " crps[model_name].append(calculate_crps(test.values,preds['samples'][:10],10))\n", + " tmae = np.abs(test.values-preds['median']).mean()/np.abs(test.values).mean()\n", + " mae[model_name].append(tmae)\n", + " else:\n", + " nlls[model_name].append(nll)\n", + " crps[model_name].append(calculate_crps(test.values,preds['samples'].values[:10],10))\n", + " tmae = np.abs(test.values-preds['median']).mean()/np.abs(test.values).mean()\n", + " mae[model_name].append(tmae)\n", + "\n", + "\n", + "nlls = {k:np.array(v) for k,v in nlls.items()}\n", + "crps = {k:np.array(v) for k,v in crps.items()}\n", + "mae = {k:np.array(v) for k,v in mae.items()}\n", + "\n", + "print({k: len(v) for k,v in nlls.items()})\n", + "\n", + "# Update dataset keys by removing 'Dataset' substring\n", + "dataset_keys = [key.replace('Dataset', '') for key in datasets.keys()]\n", + "\n", + "fig, ax = plt.subplots(1, 1, figsize=(3.5, 2.2))\n", + "dfs = [pd.DataFrame({'Dataset':dataset_keys,'CRPS':v,'Type':k}) for k,v in crps.items()]\n", + "df = pd.concat(dfs)\n", + "\n", + "df['Type'] = df['Type'].apply(lambda x: name_map[x])\n", + "\n", + "palette = sns.color_palette('Dark2', len(hue_order))\n", + "palette = palette[:2] + palette[3:-1] + ['#a60355'] + palette[2:3]\n", + "\n", + "sns.barplot(\n", + " # x='Dataset',\n", + " order=hue_order,#['SM-GP','N-BEATS','TCN','N-HiTS','ARIMA','GPT-3', 'LLaMA70B'],\n", + " y='Type',\n", + " x='CRPS',\n", + " # hue='Type',\n", + " data=df,\n", + " ax=ax, \n", + " palette=palette,\n", + " errwidth=1,\n", + " errorbar='se',\n", + ")\n", + "\n", + "#color the first 5 bars grey\n", + "for i in range(5):\n", + " ax.patches[i].set_facecolor('#D3D3D3')\n", + " ax.patches[i].set_edgecolor('grey')\n", + " ax.patches[i].set_linewidth(0.5)\n", + "\n", + "# ax.set_title('Aggregated', pad=15)\n", + "ax.set_ylabel('')\n", + "ax.legend(loc='upper right', frameon=True, framealpha=0.7)\n", + "# plt.setp(ax.get_xticklabels(), rotation=45)\n", + "plt.setp(ax.get_yticklabels())\n", + "#remove space between x tick labels and x axis\n", + "ax.tick_params(axis='x', which='major', pad=0)\n", + "ax.set_xticklabels(ax.get_xticklabels(), fontsize=9)\n", + "\n", + "ax.set_xlabel('CRPS', fontsize=14) # Remove x-axis label\n", + "ax.set_xlim((0, 0.2))\n", + "\n", + "ax.get_legend().remove()\n", + "handles, labels = ax.get_legend_handles_labels()\n", + "ax.legend(\n", + " handles=handles,\n", + " labels=labels,\n", + " markerscale=1.5,\n", + " bbox_to_anchor=(1.05, 1),\n", + " loc='upper left',\n", + " borderaxespad=0.3,\n", + ")\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig('small_DARTS_CRPS.png', dpi=300, bbox_inches='tight')\n", + "plt.savefig('small_DARTS_CRPS.pdf', bbox_inches='tight')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df.to_csv(\"crps_top_fig.csv\", index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sns.set(style=\"whitegrid\", font_scale=1)\n", + "\n", + "old_names = [\n", + " 'ada',\n", + " 'babbage',\n", + " 'curie',\n", + " 'text-davinci-003'\n", + "]\n", + "name_map = {\n", + " # \"arima\": \"ARIMA\",\n", + " 'ada': \"Ada\",\n", + " 'babbage': \"Babbage\",\n", + " 'curie': \"Curie\",\n", + " 'text-davinci-003':'Davinci',\n", + " # 'LLaMA7B':'LLaMA7B',\n", + " # 'LLaMA13B':'LLaMA13B',\n", + " # 'LLaMA30B':'LLaMA30B',\n", + " # 'LLaMA70B':'LLaMA70B',\n", + " \"llama1_7B\": \"LLaMA 7B\",\n", + " \"llama1_13B\": \"LLaMA 13B\",\n", + " \"llama1_30B\": \"LLaMA 30B\",\n", + " \"llama1_70B\": \"LLaMA 70B\",\n", + " \"llama2_7B\": \"LLaMA-2 7B\",\n", + " \"llama2_13B\": \"LLaMA-2 13B\",\n", + " \"llama2_70B\": \"LLaMA-2 70B\",\n", + " \"llama2_7B_chat\": \"LLaMA-2 7B (chat)\",\n", + " \"llama2_13B_chat\": \"LLaMA-2 13B (chat)\",\n", + " \"llama2_70B_chat\": \"LLaMA-2 70B (chat)\",\n", + "}\n", + "hue_order = ['SM-GP','N-BEATS','TCN','N-HiTS','ARIMA','GPT-3']#, 'LLaMA7B', 'LLaMA13B', 'LLaMA30B', 'LLaMA70B']\n", + "nlls = defaultdict(list)\n", + "crps = defaultdict(list)\n", + "mae = defaultdict(list)\n", + "datasets = get_datasets()\n", + "for dsname,(train,test) in datasets.items():\n", + " with open(f'eval/small_context_tuned/{dsname}.pkl','rb') as f:\n", + " data_dict = pickle.load(f)\n", + "\n", + " for model_name in old_names:\n", + "\n", + " preds = data_dict[model_name]\n", + "\n", + " if 'NLL/D' not in preds:\n", + " continue\n", + " nll = preds['NLL/D']\n", + " if model_name=='text-davinci-003-tuned':\n", + " model_name='GPT3'\n", + "\n", + " if type(preds['samples']) == np.ndarray:\n", + " nlls[model_name].append(nll)\n", + " crps[model_name].append(calculate_crps(test.values,preds['samples'][:10],10))\n", + " tmae = np.abs(test.values-preds['median']).mean()/np.abs(test.values).mean()\n", + " mae[model_name].append(tmae)\n", + " else:\n", + " nlls[model_name].append(nll)\n", + " crps[model_name].append(calculate_crps(test.values,preds['samples'].values[:10],10))\n", + " tmae = np.abs(test.values-preds['median']).mean()/np.abs(test.values).mean()\n", + " mae[model_name].append(tmae)\n", + "\n", + "llama_models = [\n", + " \"llama1_7B\",\n", + " \"llama2_7B\",\n", + " \"llama2_7B_chat\",\n", + " \"llama1_13B\",\n", + " \"llama2_13B\",\n", + " \"llama2_13B_chat\",\n", + " \"llama1_30B\",\n", + " \"llama1_70B\",\n", + " \"llama2_70B\",\n", + " \"llama2_70B_chat\",\n", + "]\n", + "for dsname,(train,test) in datasets.items():\n", + " for model_name in llama_models:\n", + " if model_name in ['llama2_70B', 'llama2_70B_chat']:\n", + " fn = f'eval/llama_70B_sweep_sample/{model_name}/darts-{dsname}/1.0_0.9_0.99_0.3_3_,_.pkl'\n", + " else:\n", + " fn = f'eval/llama_2_results/{model_name}/darts-{dsname}/0.4_0.9_0.99_0.3_3_,_.pkl'\n", + " with open(fn,'rb') as f:\n", + " data_dict = pickle.load(f)\n", + "\n", + " preds = data_dict#[model_name]\n", + "\n", + " if 'NLL/D' not in preds:\n", + " continue\n", + " nll = preds['NLL/D']\n", + "\n", + " if type(preds['samples']) == np.ndarray:\n", + " nlls[model_name].append(nll)\n", + " crps[model_name].append(calculate_crps(test.values,preds['samples'][:10],10))\n", + " tmae = np.abs(test.values-preds['median']).mean()/np.abs(test.values).mean()\n", + " mae[model_name].append(tmae)\n", + " else:\n", + " nlls[model_name].append(nll)\n", + " crps[model_name].append(calculate_crps(test.values,preds['samples'].values[:10],10))\n", + " tmae = np.abs(test.values-preds['median']).mean()/np.abs(test.values).mean()\n", + " mae[model_name].append(tmae)\n", + "\n", + "nlls = {k:np.array(v) for k,v in nlls.items()}\n", + "crps = {k:np.array(v) for k,v in crps.items()}\n", + "mae = {k:np.array(v) for k,v in mae.items()}\n", + "\n", + "# Update dataset keys by removing 'Dataset' substring\n", + "dataset_keys = [key.replace('Dataset', '') for key in datasets.keys()]\n", + "\n", + "# fig, ax = plt.subplots(1, 1, figsize=(3, 3))\n", + "# dfs = [pd.DataFrame({'Dataset':dataset_keys,'NLL/D':v,'Type':k}) for k,v in nlls.items()]\n", + "# df = pd.concat(dfs)\n", + "\n", + "# df['Type'] = df['Type'].apply(lambda x: name_map[x])\n", + "\n", + "palette = sns.color_palette('Dark2', len(hue_order))\n", + "palette = palette[:2] + palette[3:] + palette[2:3]\n", + "\n", + "mmlu_numbers = {\n", + " 'ada': 0.238,\n", + " 'babbage': 0.235,\n", + " 'curie': 0.237,\n", + " 'text-davinci-003': 0.569, \n", + " 'cohere-medium': 0.279,\n", + " 'cohere-base-light': 0.264, \n", + " 'cohere-base': 0.324,\n", + " 'cohere-command-nightly': 0.452,\n", + " 'forefront-gpt-j-6b-vanilla': 0.249,\n", + " \"alephalpha-luminous-extended\": 0.321, \n", + " \"alephalpha-luminous-supreme\": 0.452, \n", + " # 'ARIMA': 0.,\n", + " 'cohere-command-light': 0.264,\n", + " 'cohere-command-light-nightly': 0.264,\n", + " 'forefront-gpt-neox-20b-vanilla': 0.24,\n", + " # 'LLaMA7B': 0.351, \n", + " # 'LLaMA13B': 0.469, \n", + " # 'LLaMA30B': 0.578, \n", + " # 'LLaMA70B': 0.634,\n", + " 'llama1_7B': 0.351,\n", + " 'llama2_7B': 0.453,\n", + " # 'llama2_7B_chat': 0.469,\n", + " 'llama1_13B': 0.469,\n", + " 'llama2_13B': 0.548,\n", + " # 'llama2_13B_chat': 0.469,\n", + " 'llama1_30B': 0.578,\n", + " 'llama1_70B': 0.634,\n", + " 'llama2_70B': 0.689,\n", + " # 'llama2_70B_chat': 0.578,\n", + "}\n", + "\n", + "df = []\n", + "for k,nll in nlls.items():\n", + " if \"chat\" in k:\n", + " continue\n", + " print(k)\n", + " _crps, _mae = crps[k], mae[k]\n", + " # print(k,nll)\n", + " # print(list(datasets.keys()))\n", + " # for i in range(len(nll)):\n", + " d = {\n", + " # 'Dataset':list(datasets.keys()),\n", + " 'MAE': np.mean(_mae),\n", + " 'CRPS': np.mean(_crps),\n", + " 'NLL/D': np.mean(nll), #nll[i],#np.mean(nll),\n", + " 'Type':k,\n", + " 'MMLU Accuracy': mmlu_numbers[k],\n", + " }\n", + " df.append(d)\n", + "df = pd.DataFrame(df)\n", + "\n", + "\n", + "host_name_map = {\n", + " 'openai': 'OpenAI',\n", + " 'cohere': 'Cohere',\n", + " 'forefront': 'Forefront',\n", + " 'alephalpha': 'Aleph Alpha',\n", + " 'LLaMA': 'LLaMA'\n", + "}\n", + "\n", + "def hostify(x):\n", + " if x in ['Ada','Babbage','Curie','Davinci']:\n", + " return 'openai-'+x\n", + " if 'LLaMA' in x:\n", + " return x.replace('LLaMA','LLaMA-')\n", + " return x\n", + "\n", + "df['Type'] = df['Type'].apply(lambda x: name_map[x])\n", + "df['Type'] = df['Type'].apply(hostify)\n", + "df['host'] = df['Type'].apply(lambda x: host_name_map[x.split(\"-\")[0]])\n", + "\n", + "# fig, ax = plt.subplots(1, 1, figsize=(3.5, 2.2))\n", + "\n", + "fig, ax = plt.subplots(1, 1, figsize=(3.5, 2), gridspec_kw = {'wspace': 0.6})\n", + "\n", + "sns.regplot(\n", + " data=df,\n", + " x='MMLU Accuracy',\n", + " # x='Dataset',\n", + " y='CRPS',\n", + " order=1,\n", + " # errorbar='se',\n", + " ax=ax, \n", + " scatter_kws={'color':'white'},\n", + ")\n", + "\n", + "sns.scatterplot(\n", + " data=df,\n", + " x='MMLU Accuracy',\n", + " y='CRPS',\n", + " color='black',\n", + " # hue='host',\n", + " # palette='Dark2',\n", + " ax=ax,\n", + ")\n", + "\n", + "#remove pad between yticklabels and axis\n", + "ax.tick_params(axis='y', which='major', pad=0)\n", + "ax.tick_params(axis='x', which='major', pad=0)\n", + "\n", + "ax.set_xlabel(ax.get_xlabel(), fontsize=16)\n", + "ax.set_ylabel(ax.get_ylabel(), fontsize=16)\n", + "\n", + "plt.savefig('small_crps_vs_mmlu.png', bbox_inches='tight', dpi=300)\n", + "plt.savefig('small_crps_vs_mmlu.svg', bbox_inches='tight')\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df.to_csv(\"mmlu_top_fig.csv\", index=False)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/figures/figure_2.ipynb b/figures/figure_2.ipynb new file mode 100644 index 0000000..22bb239 --- /dev/null +++ b/figures/figure_2.ipynb @@ -0,0 +1,268 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/nategruver/opt/anaconda3/lib/python3.8/site-packages/pandas/core/computation/expressions.py:20: UserWarning: Pandas requires version '2.7.3' or newer of 'numexpr' (version '2.7.1' currently installed).\n", + " from pandas.core.computation.check import NUMEXPR_INSTALLED\n", + "/var/folders/4z/0371zfqj5mx0rlct4r3qclfm0000gn/T/ipykernel_84216/1313326255.py:120: UserWarning: The figure layout has changed to tight\n", + " plt.tight_layout()\n" + ] + }, + { + "data": { + "image/png": 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", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pickle\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from collections import defaultdict\n", + "\n", + "import sys\n", + "sys.path.append(\"..\")\n", + "from data.small_context import get_datasets\n", + "from data.metrics import calculate_crps\n", + "\n", + "from matplotlib.patches import FancyBboxPatch\n", + "\n", + "dsname = 'WineDataset'\n", + "train, test = get_datasets()[dsname]\n", + "\n", + "pkl_fn = \"../precomputed_outputs/tokenization/gpt.pkl\"\n", + "with open(pkl_fn,'rb') as f:\n", + " (df1,df2,data_list) = pickle.load(f)\n", + "\n", + "sns.set(style='whitegrid', font_scale=1.)\n", + "# Define colors\n", + "color_base = sns.color_palette(\"Dark2\")[2]\n", + "color_binary = sns.color_palette(\"Dark2\")[0]\n", + "color_direct_encoding = sns.color_palette(\"Dark2\")[3]\n", + "\n", + "# Define data and labels\n", + "\n", + "data_list = data_list[:1] + data_list[2:]\n", + "\n", + "llama_fns = [\n", + " \"../precomputed_outputs/tokenization/llama_spaces.pkl\",\n", + " \"../precomputed_outputs/tokenization/llama_no_spaces.pkl\",\n", + "]\n", + "for fn in llama_fns:\n", + " with open(fn,'rb') as f:\n", + " results = pickle.load(f)\n", + " data_list.append(results)\n", + "\n", + "# 151, 167, 267\n", + "examples = [\n", + " '\" 1 5 1 , 1 6 7 , ... , 2 6 7\"',#\\n\\n[\" 1\", \" 5\", \" 1\", \" ,\", ...]',\n", + " '\"151,167,...,267\"'#\\n\\n[\"151\", \",\", \"167\", ... \"267\"]',\n", + "]\n", + "labels = ['GPT-3 spaces ', 'GPT-3 no spaces ',]\n", + "colors = [color_base, color_direct_encoding]\n", + "\n", + "# Initialize a figure\n", + "fig, axs = plt.subplots(1, 2, figsize=(5, 1.8), sharex=True, sharey=True, constrained_layout = True)\n", + "axs = axs.flatten()\n", + "\n", + "lines_labels = []\n", + "# Loop through data, labels, colors, and axes simultaneously\n", + "for dat, example, label, color, ax in zip(data_list[:2], examples, labels, colors, axs):\n", + " combined = pd.concat([train, test]).iloc[110:]\n", + "\n", + " # Plot data\n", + " l1, = ax.plot(combined, label='Ground Truth', color='k')\n", + " \n", + " if label == 'Base 10 & spaces':\n", + " l2, = ax.plot(dat['median'], color=color)\n", + " else:\n", + " l2, = ax.plot(dat['median'], color=color, alpha=0.5)\n", + "\n", + " # Fill between\n", + " lower = dat['samples'].quantile(0.1, axis=0)\n", + " upper = dat['samples'].quantile(0.9, axis=0)\n", + " if label == 'Base 10 & spaces':\n", + " ax.fill_between(lower.index, lower, upper, alpha=0.5, color=color, label=label)\n", + " elif label == 'Base 2 & spaces':\n", + " ax.fill_between(lower.index, lower, upper, alpha=0.3, color=color, label=label)\n", + " else:\n", + " ax.fill_between(lower.index, lower, upper, alpha=0.3, color=color, label=label)\n", + "\n", + " lines_labels.extend([l1,l2])\n", + " # remove y ticks\n", + " ax.set_yticks([])\n", + " ax.set_ylim([10000, 42000])\n", + " ax.set_xticklabels([])\n", + " ax.grid(False)\n", + " ax.set_title(example, fontsize=14, pad=15)\n", + "\n", + "all_handles = []\n", + "all_labels = []\n", + "for ax in axs:\n", + " handles, labels = ax.get_legend_handles_labels()\n", + " all_handles.append(handles[-1])\n", + " all_labels.append(labels[-1])\n", + "\n", + "lgd = fig.legend(\n", + " all_handles, \n", + " all_labels, \n", + " loc=\"lower center\", \n", + " bbox_to_anchor=(0.52,-0.19),\n", + " ncol=4,\n", + " fontsize=14,\n", + " # columnspacing=0.0,\n", + " handletextpad=0.5\n", + " # frameon=True\n", + ")\n", + "\n", + "# Calculate bounds to encompass both subplots\n", + "x0 = axs[0].get_position().x0\n", + "y0 = axs[1].get_position().y0\n", + "width = axs[0].get_position().width\n", + "height = (axs[0].get_position().y1 - axs[1].get_position().y0) + axs[1].get_position().height\n", + "\n", + "# Create the FancyBboxPatch with rounded edges\n", + "fancy_box = FancyBboxPatch((x0, y0), width, height,\n", + " boxstyle=\"round,pad=0.02,rounding_size=0.15\",\n", + " facecolor='none', edgecolor='black', lw=2)\n", + "\n", + "# Add the box to the figure\n", + "fig.patches.append(fancy_box)\n", + "\n", + "\n", + "# plt.subplots_adjust(right=0.85)\n", + "# Improve layout\n", + "plt.tight_layout()\n", + "\n", + "plt.savefig(f'outputs/encoding1.svg', bbox_inches='tight', dpi=300)\n", + "plt.show()\n", + "\n", + "# 151, 167, 267\n", + "examples = [\n", + " '\"1 5 1, 1 6 7 , ... , 2 6 7\"',#\\n\\n[\"1\", \" \", \"5\", \" \", \"1\", \",\", ...]',\n", + " '\"151,167,...,267\"'#\\n\\n[\"1\", \"5\", \"1\", \",\", ...]',\n", + "]\n", + "labels = ['LLaMA spaces ', 'LLaMA no spaces ']\n", + "colors = [color_base, color_direct_encoding]\n", + "\n", + "# Initialize a figure\n", + "fig, axs = plt.subplots(1, 2, figsize=(5, 1.8), sharex=True, sharey=True, constrained_layout = True)\n", + "axs = axs.flatten()\n", + "\n", + "lines_labels = []\n", + "# Loop through data, labels, colors, and axes simultaneously\n", + "for dat, example, label, color, ax in zip(data_list[2:], examples, labels, colors, axs):\n", + " combined = pd.concat([train, test]).iloc[110:]\n", + "\n", + " # Plot data\n", + " l1, = ax.plot(combined, label='Ground Truth', color='k')\n", + " # if not len(lines_labels):\n", + " # lines_labels.extend([l1])\n", + "\n", + " if label == 'Base 10 & spaces':\n", + " l2, = ax.plot(dat['median'], color=color)\n", + " else:\n", + " # l2, = ax.plot(dat['median'], color='grey')\n", + " l2, = ax.plot(dat['median'], color=color, alpha=0.5)\n", + " # l2, = ax.plot(dat['median'], color=color)\n", + "\n", + " # Fill between\n", + " lower = dat['samples'].quantile(0.1, axis=0)\n", + " upper = dat['samples'].quantile(0.9, axis=0)\n", + " if label == 'Base 10 & spaces':\n", + " ax.fill_between(lower.index, lower, upper, alpha=0.5, color=color, label=label)\n", + " elif label == 'Base 2 & spaces':\n", + " ax.fill_between(lower.index, lower, upper, alpha=0.3, color=color, label=label)\n", + " else:\n", + " ax.fill_between(lower.index, lower, upper, alpha=0.3, color=color, label=label)\n", + "\n", + " lines_labels.extend([l1,l2])\n", + " # remove y ticks\n", + " ax.set_yticks([])\n", + " ax.set_ylim([10000, 42000])\n", + " ax.set_xticklabels([])\n", + " ax.grid(False)\n", + " ax.set_title(example, fontsize=14, pad=15)\n", + "\n", + "all_handles = []\n", + "all_labels = []\n", + "for ax in axs:\n", + " handles, labels = ax.get_legend_handles_labels()\n", + " all_handles.append(handles[-1])\n", + " all_labels.append(labels[-1])\n", + "\n", + "lgd = fig.legend(\n", + " all_handles, \n", + " all_labels, \n", + " loc=\"lower center\", \n", + " bbox_to_anchor=(0.52,-0.19),\n", + " ncol=4,\n", + " fontsize=14,\n", + " # columnspacing=0.0,\n", + " handletextpad=0.5\n", + " # frameon=True\n", + ")\n", + "# plt.subplots_adjust(right=0.85)\n", + "# Improve layout\n", + "plt.tight_layout()\n", + "\n", + "plt.savefig(f'outputs/encoding2.svg', bbox_inches='tight', dpi=300)\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/figures/figure_3.ipynb b/figures/figure_3.ipynb new file mode 100644 index 0000000..5bf950e --- /dev/null +++ b/figures/figure_3.ipynb @@ -0,0 +1,403 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " dataset method KS statistic p-value \\\n", + "0 Exponential Fixed Bins 0.096510 8.295925e-08 \n", + "1 Exponential GMM 0.042184 7.686409e-02 \n", + "2 Exponential Laplace 0.194000 2.265926e-30 \n", + "3 Exponential Decimal AR 0.037694 1.478059e-01 \n", + "4 Square + Student t Fixed Bins 0.063306 1.323141e-03 \n", + ".. ... ... ... ... \n", + "115 Square + Student t Decimal AR 0.067020 5.475213e-04 \n", + "116 ARIMA Residuals Fixed Bins 0.130438 9.325848e-12 \n", + "117 ARIMA Residuals GMM 0.105188 8.587462e-08 \n", + "118 ARIMA Residuals Laplace 0.124500 9.526585e-11 \n", + "119 ARIMA Residuals Decimal AR 0.100312 3.987595e-07 \n", + "\n", + " Wasserstein Distance \n", + "0 0.115748 \n", + "1 0.050113 \n", + "2 0.328035 \n", + "3 0.046562 \n", + "4 0.082194 \n", + ".. ... \n", + "115 0.085739 \n", + "116 0.279867 \n", + "117 0.231522 \n", + "118 0.172670 \n", + "119 0.237887 \n", + "\n", + "[120 rows x 5 columns]\n", + "Exponential\n", + "square_student_t_mixture\n", + "Density\n", + "ARIMA Residuals\n" + ] + }, + { + "data": { + "image/png": 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3x9raGltbW6ytrTl48GCefQIZRHDPJPPomciw14S+iJHHxpuYmJBUWbTJC0J5Mnv2bBYuXMioUaMwNDRkypQpdO/eHYC2bdvi4eGBm5sbR48eJTExkUGDBqmdP2DAAJYtW1bs5ZIkiVu3bnH+/HkuXLhAQEAAqampaGlp4ejoSNu2bfPNQwT3QiptbYyCIGjOwMCA5cuX5/i057179+TffX1933hZQkJCOHfuHBcuXODixYtER0ejUCgwMzOjb9++tGvXjnfffZcqVaoUKD8R3AupZs2aaot0GxgpsHMWb6cgCJrx8PDg/PnzPHr0CIVCQaVKlXB1daVVq1a88847NGrUqFD5imhUQFk7XO3t7Tl2+Cz+V9Pv7k4tGvCZs1j9SRAEzXh7eyNJEs2aNWPo0KH06dMnx6GbmhLBvYCydrjqJNugkl7Iqz/ZRovpCwRB0NyUKVM4f/48N2/eZPbs2Xz11Vc0bdqUFi1a0KJFC5ycnNDT09M4XxHcNZC1wzUzE5MqahORWVlZMWTIkLdeRkEoS6KjowvchlxeTZw4kYkTJ8ozQF64cIELFy6wdu1aJElCR0cHR0dHWrRoQfPmzXFxcaFy5cr55iuCezHJOhFZvRp2hWqTF+34QkURHBzMqFGj2LJlC1ZWViVdnBJnaGhI165d6dq1KwDPnj3j4sWLnD9/nsuXL+Pv74+npydaWlo0bNiQPXv25JmfiBzFKHPN3tGlPglWEq+jNVsFKiH233MUimIvoiCUGnFxcahUKuLi4kq6KKWSjY0NQ4YMYciQIahUKgICAuRafX4PMIEI7m+MQpF9zonME5MB1KlTh88//zzXPPQqK0RNXig3pDQVCq3852IpaLry5NKlS5w8eRITExOGDx8uT39w9+5dGjZsiFKpxNnZGWdnZyZPnlygG6KIFG+IYeXK+Pv7c+7UdQDadXIlTKX+EFTt2rXZsWMHL1++BKB169YEnEuQR+B06+dKo3rOGtf+BaE0Umgp+WfJQRIDXwEQmJj+7z9LDiLpp09vq1/LjNpz3yuxMpYEX19fpk+fLm8fOnSI/fv3k5KSwoABA+jYsSOrVq1SmyHS0NAw33wr1u3xLYuJjSE4OJjg4GDiXsfKQykzfuzt7UkOt+TZDQOe3TAgPswYlaSSzymOhQAEoTRJDHxFwoNQEh6EonwWi0IC5bNYeV9G4K9INm7cSI0aNfjll19YsGABT5484dChQyiVSoYPH86ZM2fYsmWLxvmKmvtbknUoJaQPp3wd/SLXEThZm3ZyaoMXzTZCaVSQUTBmVGIhnTGk6GO6y7KnT58yadIkXF1dcXV15dChQ1y8eJH+/fszd+5cEhIS8PX1Zfz48RrlKyLBW5S5wxWyB/OssjbtdO/XHH1DZzmYm1go1DpgBaE00GQUjCaBvbwOm9TV1VWbYrhhw4bcvHlT3m7WrBmHDx/WOF/RLFPKZW7akSSJi2f/ZNV/f2TVf3/E78KfJV08QcjmTYyCCQ4OZuDAgfJDg+WJlZWV2uiXevXq8fz5c3k7KSmpQLNAZiWCexmj1o4fl3fNXxDeNPNKRkiqN79+ankeNtmvXz/279+Pv78/kL4q1qtXr3j1Kr3/4ezZs4WaP140y5RhOY3IuRn0SB5u2apVK1QqlTwaB6BGjRrFsqivIAAY6RmgUGpxx+tzXr94CMCrBAkF8PinqUQbpHcUmTXpiJ3bzHzz0zatjEoloVQW7CEPTdKWVnXr1kVLS4sRI0bQsmVLzM3NUSgUzJw5k9jYWG7evJltquGCEMG9jMuoyQPEvW6AsfRv+3uVKlUIeaDHsxvR8r5apnXVpkkQwV4oDq9fPCQu8DYAesD0Wkr0wlRk1LMrWdYtUD5ahnoolQp2+VwmLPTf0WJxryMBBfv3BmBYORCAatWNGDSkVTG+ipIxYcIE+Xc/Pz/594sXLwLg5OSkNlSyoERwL0dymtws82gcgHfauxAbZCpPk2BTxU7t4So7OzsmT5789gsvlCuVtFRFOj8sNJYXwVGZ9iho4TyK2GgFsdFRuZxVNk2aNIn27dtjZGQkr+UKoFAoMDQ0xMTEpFD5iuBezuQ1uVlOaRrY18e4zr+1/Xr16rH3p2vc/espAC6tGqMwfiZq+kKJ09HJe0HosupNVaZEcK/gcqrtvwx6IS8v2KBRLYj9t6aftVmncePG3Lt3j6dPnwKi5i8IhXH58mU8PT2ZP38+derUITExka1bt/Ly5Uv69u2Lk5OTxnmK4C7kW9vPfDxrs04t06qYm5vLwT2/mj+AtbU1gwcPfoOvSBAKJikpiUWLFnHs2DH09fUZO3YsY8eOzTHtnTt3WLBgAffv36devXosWrSIJk2aFLkM165dY9y4cSgUCl69ekWdOnWYNm0ap0+fRqFQ8Ouvv7Jx40batGmjUb4iuAsayxrsdRPrFbjmD9C0rgNHfP7C/+rfAHTs4Ux0RKK8nfWGIG4GwpvyzTffcPv2bbZs2UJwcDDu7u5YWVnRs2dPtXTx8fFMmDCBvn37smzZMnbs2MEnn3zC8ePHqVSpUpHK4OXlRbVq1dixYweWlpY8evSIM2fO0Lt3b+bNm8fEiRPx9PQUwV14+zSp+WekCX0Ro3YDyLqd+Yag6c2gdevWvLirI397AGjoUAe3Mc2L+ZULZVl8fDy7du3Cy8sLBwcHHBwcePDgAdu2bcsW3A8fPoyenh4zZ85EoVAwd+5czp49i6+vL25ubkUqx99//82HH36IpaUlAKdPn0aSJIYPH07VqlV57733WLduncb5iuAulEpZbxia3QyM1b49AFStaqrWV5D1BpDfDaIw3yZ27tzJixcv5G0HBwciIiLkfTl1TmeeJVR8Y3mz7t69S2pqKs7OzvI+V1dXNmzYgEqlQqn89xnPgIAAXF1dUfxvgieFQoGLiws3btwocnCPiopSe0jJz88PIyMjuZ1dT0+vUA9vlcrgLv1vrHbr9o6kpUpY1aqKoVEl2nZ2AijydnHkURbLUJ5fd0xkIhY10j8gBgZ62DWwVLtm0xa10dZRUsMw/QZgpGtDdOUotLTTP6zGJrpo69Qv8PHGde0I8Asm6FkoAPXsrUiIT1XbtrNsCbH/PkZuZWaHiW6yvM/OsiZXr14lMDB93HbDhg2pWdUZRVxoga+Rebu6hTEKw38/PwWRkTY5ORktLS15v5aWFg3MLNFVauV2KgC1qpiRlpZGJetGoJ37Op/61eqQlpaGrq05kk7uD8brWFchLS2N6hZGaOV9aczMjUhLSyMt7d8nZDN+j42NVRtWqKurm23R6bCwMExNTdX2m5ubk5SURFRUFFWrVlVLW69ePfXrm5mpTeFdWObm5jx58gRI/3+4fv06bdq0kW8u58+fp2bNmhrnq5A0+Ut4S5KTkwu00oggCNk5OjpmC2S5Ka+ftUmTJhEZGSlvT548mSlTpqil2b9/PytXruTUqVPyvmfPntG1a1fOnDkjN5MAjBo1CldXV7XFdVauXIm/vz8///xzkcq6aNEifvvtN2bNmsWdO3fYuXMnixcvZtCgQUyfPp0jR47w2WefMXXqVI3yLZU1d21tbRwdHVEqlfLXIEEQ8iZJEiqVSq3Gmp/y9lmTJInExESOHTum1qyS081OT08v24RcGdv6+voFSps1XWF89tlnnDlzhvnz56NQKKhduzZ9+/YF4OXLl/Tu3ZtPP/1U43xLZXBXKpUFrnkIglB45fGzVpBVigAsLCyIjIwkNTVVviGGhYWhr6+vNgVvRtrw8HC1feHh4VSvXr3I5a1evTq//fYbR44cISUlhb59+8o3DW9vb7UVmDQhZoUUBKFCatSoEdra2ty4cUPed/36dfmbTGbNmjXD399f7qOQJIk///yTZs2aFUtZjIyMeP/99/nwww/VbiyFDewggrsgCBWUgYEB/fv3Z+HChdy8eZMTJ06wefNmRo4cCaTX4hMTEwHo2bMnMTExLFmyhIcPH7JkyRISEhLo1atXsZRlw4YNvPPOO9y7l75+8pMnT/j444/p27dvodv0RXAXBKHCmj17Ng4ODowaNYpFixYxZcoUunfvDkDbtm3lFZAMDQ3x9PTk+vXruLm5ERAQwMaNG4v8ABPA3r17WblyJTY2NnLz0KRJk7h48SKSJLFs2TK2bdumcb6lcrSMIAhCRTFkyBAAtm3bhra2NteuXWPEiBFMmjSJyZMnM2PGDJ48ecKePXs0ylfU3AVBEErQP//8Q+/eveVa+7lz55AkSW7yadmypTx3kyZEcBfeurLyZbEg5Swrr0UovRISEjAw+HfupUuXLmFhYUHduukLnKSkpBTq70wE9wK4f/8+06dP591336VJkya0bduWadOmcffu3ZIuWqlx8uRJRo0aRfPmzXF0dKRbt24sWbJEXgcyw65du1i+fHmxXHPWrFl07ty5WPLKLDk5maVLl/L777/nma44X4tQcdWqVYvr19OXyoyIiOCvv/7inXfeASAxMZG9e/dSv359jfMVwT0fDx48YMiQIURFRTFv3jw2b97MzJkzCQ4OZvDgwWrDqCqqffv2MXHiRGxtbfn222/x8vJi1KhRHD16lCFDhhAd/e8yf+vXrycqKqrkClsAoaGhbNmyhdTU1DzTlYXXIpR+7733Hr/99hszZszg008/JTU1lR49egAwaNAg/vrrr0ItkFMqH2IqTX766SdMTU3x8vJSe/Kva9eu9OzZk3Xr1rFx48YSLGHx27t3L7Nnz5aHZeVn7dq19OnTh4ULF8r7WrduTfPmzXn//ffZtWsX48ePf0OlFYSybcyYMQQEBHDo0CEAunfvTseOHYH0icyGDRvGgAEDNM5X1NzzER4eLj/WnVmlSpWYM2dOtnGu27dvp0ePHjRt2pRhw4bh5+eHvb09ly9fBmD16tXY29tnu469vT2rV6+Wt58/f87MmTNp27YtDg4OtGnThpkzZ6rNl9G5c2eWLl3KqFGjaNq0KXPnzgXSZ5mbP38+77zzDo6OjgwePFht4d3ilvEeZdWwYUNmz54tL2jQuXNngoKC2LdvH/b29jx//rzA70d0dDSzZ8+mZcuWtGjRgm+//Tbb/wnAiRMncHNzw9HRkXfffZevv/6a+Ph4+fjq1avp1q0bp0+fpm/fvjRp0oQePXqwf/9+IP1979KlC5A+TC63Zp+cXosgFIauri7r1q3j8OHD/P7776xatUo+tnDhwkIvaylq7vno2LEjZ86cYejQoXzwwQe0bt0aOzs7FApFtjmft27dytdff82IESPo0KED58+fZ9q0aRpfMyEhgZEjR2JqasqCBQswMjLC39+fNWvWoK+vz1dffSWn3bZtG2PGjOHjjz+mcuXKJCUlMWrUKMLDw5k+fTrVq1dnz549jB8/nh9//DHXCf9VKpUcLDP+zdwsoaWllevcIx07duTQoUMkJSXRq1cvWrRogYWFBQCjR4+W061Zs4YJEybQuHFjJk6cWOBHt1UqFePHjycoKAh3d3dMTEz48ccfuXXrlloev//+OzNmzKBv375MmzaNoKAgvv/+ex4+fMhPP/0klz8sLIyvvvqKzz77DGtrazZt2oS7uzuOjo7Y2NiwZs0aJk+ezGeffSaPec6qsK9FEHJja2vLq1evCAgIQKFQUKdOnWzTIGhCBPd8fPTRR4SFhbFp0yY5qJqamtK2bVtGjhxJ06ZNgfQAtH79enr06MG8efMAaNeuHa9fv2bXrl0aXfPp06dYWlqyfPlybGxsgPRmjoCAAK5cuaKW1srKihkzZsjbv/76K3fv3uXXX3+VH41u3749I0aM4Lvvvst1rOycOXPYt2+f2j4HBwf5d29vb1q1apXjuYsXL0alUnHs2DFOnDgBpHcSdenShTFjxsiBvnHjxujq6lK1alWN1oQ8e/YsN2/exMvLi/bt2wPQpk0btVq1JEl89913tGvXju+++07eX6dOHUaPHs2ZM2fkr7oJCQksWbJEvtHVqVOHTp06cebMGcaOHUujRo3k19C4ceMcy1TY1yIIObl//z6LFi2SO1Yhfd6fd999lwULFhRqyl/RLFMAU6dO5dy5c6xYsYKBAwdiaGjI77//zuDBg/H29gbSHxd+9eqV/JU+Q79+/TS+XqNGjdi+fTvW1tY8ffqUM2fOsGnTJh4/fpxtZrqMQJTBz8+PatWq4eDgQGpqKqmpqaSlpdGpUydu376t1rmZ2eTJk9m9eze7d++WF7jO2N69e7daoM/KyMiIVatWceLECebPn0+PHj2IiYnhp59+omfPnvj7+2v8HmR27do1dHR0aNeunbyvUqVKdOjQQd5+/PgxL1++pHPnzvLrTk1NpUWLFhgaGnLhwgW1PDMH5IypXTM33wjC2/L8+XNGjBiBv78/7dq1o1u3bigUCtq3b8+NGzcYOnQoISEhGucrau4FVKVKFd577z3ee+89IH2x3P/85z98++239O3bVx41kXmCf0CutWrqp59+YsOGDURFRWFubk6TJk0wMDAgNjZWLV3Wx5+joqIICwvLNRiHhYVRpUqVbPtr1qwp1w4yFiBwdHTUqMw1a9Zk2LBhDBs2DJVKxYkTJ5g1axaLFy9m7969GuWVWXR0NCYmJtmahTKvXpPx/i9atIhFixZlyyM0NFRtO/O44oxJosSYdaEkrFu3jvj4eLy9vWnevDl+fn4cP34cDw8PXr9+zQcffMCaNWtYvHixRvmK4J6HkJAQPvjgA6ZOncqgQYPUjjVu3Jjp06czadIknj17hqmpKUC2cd1Zh8plBKi0tDR55ZvXr9XXHf39999ZtmwZ//nPf3Bzc5NvGFOnTs13YQUjIyPq1Kmj1jSRWWG+3uXl6NGjLFiwgB07dmBrayvvVyqVdO/enatXr/Lrr7/men5B3g9TU1MiIyPV0oD6e5vRNjlz5kxatmyZ7To53dAEoTQ4f/48Q4YMoXnz7Gv81qxZk6FDh8od/poQzTJ5MDc3R1tbm+3bt5OUlJTt+OPHj9HT06N27drY2tpSo0YNfH191dKcPHlSbTtjrumMdTIBtXa2jG1jY2PGjx8vB/bXr19z/fr1HEeIZNayZUtevHiBmZkZjo6O8s+FCxf48ccf1YJjcahfvz5RUVFs2bIlx+NPnz6lQYMG8nbWqVQL8n60adOG1NRUuT0f0h80ytzUYmdnh5mZGc+fP1d73RYWFqxYsUJe+7QgCvoeZX0tglAYkZGRap+RrKytrbNVGgtC1NzzoKWlxcKFC5k0aRIffPABw4YNo27duiQkJHDhwgW2bdvG1KlT5VrhzJkz+eKLL5g7dy69evXi5s2bbN68WS3PDh064OHhwfz58xk3bhwvXrxg7dq1VK5cWU7TtGlTduzYwbJly+jUqROhoaFs2rSJ8PDwfGugbm5u/PLLL4wZM4ZPP/2UGjVqcPHiRby8vBg+fHiB5od2c3Mr8KK/dnZ2TJgwAU9PT4KDg+nXrx+Wlpa8evWK3377DT8/P3766Sc5vbGxMXfu3OHKlSs0bdq0QO9HmzZtaNu2LfPmzePVq1dYW1vj7e1NREQEZmZmQPr/1fTp05k/fz5aWlp06tSJmJgY1q1bR0hISJ59BlkZGRkB6f0XdevWzXXO7qyvpThW5REqnmrVqvHo0aNcj9+9e7dwo7EkIV+3b9+Wpk+fLrVv315q0qSJ5OLiIg0fPlw6evRotrS+vr5Snz59JAcHB2ngwIHSTz/9JDVo0EC6dOmSnGbfvn1S9+7dJQcHB6lfv37S+fPnpR49ekirVq2SJEmSVCqVtHLlSql9+/aSo6Oj1LVrV2nx4sWSj4+P1KBBA+nhw4eSJElSp06dJHd392xlCA8Pl2bPni21adNGatKkidSjRw/Jy8tLSktLe0PvkCQdPnxYGjNmjNSqVSvJwcFBatOmjTRlyhTp77//Vkv3+++/y+W6evVqgd4PSZKk+Ph46auvvpJatWolOTk5SXPmzJG+/vprqVOnTmr5Hzp0SBowYIDUpEkTqWXLltKnn34q3b17Vz6+atUqqUGDBtnK36BBA7XreXh4SE5OTlKLFi2k5OTkHF9zTq9FEDQ1a9YsqVmzZtLp06clSZKkixcvSvb29tKrV6+kixcvSg4ODtLixYs1zldM+fuGXb58mZEjR+Y5lFAQhIrr2bNnDBkyhIiICNauXUulSpUYO3YsNWvW5NmzZ9SuXRsfHx9MTEw0ylc0GgqCIJQgGxsbtm3bRseOHeVmV4VCQUJCAh999FGhAjuINndBEIQSZ2try4YNG4D0ByJv3LhR5IXLRbOMIAhCOSRq7oIgCCWooGsSZB1WnR8R3AVBEEqQJEnZno5OS0sjOjqalJQUDAwMcHFx0ThfEdwFQRBK0KlTp3Lcr1KpuHbtGjNnzqRFixYa51ukNnd3d3cGDhxYqAvnRaVSkZqailKpzHWaWUEQ1En/W3dAW1u7wE/PlrfPmqbvQVJSEosWLeLYsWPo6+szduxYxo4dm2Pa06dP8/333xMYGEjNmjWZNm1atokC34RffvmFlStXcvXqVY3OK1LN/eDBgxw4cABra2vc3Nzo378/VlZWRckSSJ9HPL85VARByJmjo2OBR1qU189aQd+Db775htu3b7NlyxaCg4Nxd3fHysoq21oNd+/eZfLkycycOVNeq2Hq1Kns3r2bhg0bvqmXAaTPoZTftCM5KVJwP3v2LAcOHGD//v2sWrWKNWvW0Lp1a9zc3OjWrRt6enqFyjfjjuvo6Fjsc6EIQnmVlpbGrVu3NJrzRtPPWkxMTJEWkHjTNHkP4uPj2bVrF15eXjg4OODg4MCDBw/Ytm1btuB+8OBBWrduzciRIwGoXbs2J0+e5MiRI288uPfq1UuemFATRQruZmZmjBkzhjFjxnD37l327t3LoUOHmDFjBkZGRvTu3Rs3N7dc5+bITcbXQy0tLRHcy4EffviBwMBAebtWrVqFWqFKKBhNmlc0+awFBwczatQotmzZUizf0N+kgrwHd+/eJTU1FWdnZ3mfq6srGzZsQKVSqd0gBgwYQEpKSrY8sk7BXVgHDhxg7dq1rFmzhvr16/Pq1StWr17Ny5cvcXNzY9iwYRrnWWwdqg0bNmTOnDm4u7tz9epV9uzZw6+//sqvv/5K3bp1+fDDDxk4cGCha/NC2RUYGCjPES+UTmlpafmmiYmJQaVSERMTU+h1Ct60jNcRFxendrPS1dXN1kwTFhaGqamp2n5zc3OSkpKIiopSW5uhbt26auc+ePAAPz+/Qq9vmtkff/yBu7s7ZmZmJCYmAjBp0iQCAgKoVKkSZ86cYcWKFfTu3VujfIt1tExMTAyHDx/G19eXq1evIkkSTZs2RaFQsHjxYjZt2sSGDRvynN6yPHvsn0pC7L/91wZGCuycy9+Apcw19eLubBfejIK0u2csAn7v3r1sc+6XNh06dCAhIUHenjx5MlOmTFFLk5CQkC3gZ2xnXfEss4iICKZMmYKLi0uxdKhu2bIFOzs7fHx8MDQ05K+//iIgIIBhw4Yxb948JkyYwNatW99+cE9NTeX06dMcOHCA06dPk5ycjKmpKSNGjOCDDz6gfv36APz5559MmDCB2bNn57qOZ3mXECvxOrr8PxCcuaaesQasULoVpM09Yxpme3t7+XNd2mS0uZ85cyZbzT0rPT29bEE8Yzu36ZvDw8MZM2YMkiSxatWqYpnT/9GjR4wdO1Ze2+D06dNIkiRPu925c2e+//57jfMtUnD/6quvOHz4MNHR0SiVStq2bcsHH3xA586d0dZWz9rFxYWOHTvyxx9/FOWSQhmQOaBbWFgQHBxcgqURCiJrm3uaSoVWlsCVEciUSmW+aUuaoaFhvjcrCwsLIiMjSU1NleNVWFgY+vr6OXYah4SEyB2q3t7e2ZbULKzY2Fi16126dImqVavKi7MrFIo8v0nkpkjBffv27dSqVYsxY8bQv3//fNvh6tatS506dYpySaEMMNVqxrNX6WuU6ibWw8oqhLt375ZwqQRNaCmVuB/ZyuOIf9eeTQ1PX1zd/cgvaF9Nn73Qrmp1lvcaUSJlLKpGjRqhra3NjRs35CXurl+/jqOjY7YaeXx8POPHj0epVOLt7a22fm9R1ahRg7///htIX3EtICCArl27Aunj9n19faldu7bG+RYpuG/dujXfNtXExET5K85nn31WlMsJZURwYAQP/g4CoIF9fSzqWKh9jRdNNWXD44hQ/g59Lm9rR8djBjyJDCFVVTyjREqSgYEB/fv3Z+HChSxdupTQ0FA2b96Mh4cHkF6LNzIyQl9fH09PTwIDA9m6dat8DNKbbzJW7iqsbt268fPPP2NkZMSDBw9ITk6Wg/vIkSO5evUq7u7uGudbpOA+a9Ys5s6dm+vENwcOHGDJkiVcvny5KJcRSrnMHag9evRQO2ZuYQSJRvAqRt5nWqdidqiXdZK2FtL//i0vZs+ezcKFCxk1ahSGhoZMmTKF7t27A9C2bVs8PDxwc3Pj6NGjJCYmMmjQILXzBwwYwLJly4pUhk8//RQ/Pz82btwIgJOTk/w5MjIy4uOPP2bMmDEa56tRcI+IiFBb6y8oKIibN2/meOdSqVT88ccfOS4sLZQvmTtQ07/eqndehQbHyDV5gOoWhVgPUihxaZX1COveGEm3/IzwMjAwYPny5SxfvjzbsXv37sm/Z134vjgZGhqya9cuLl68SFpaGm3btpX7C9atW1fofDX6X9LR0WHKlClER6e3vSkUCjw9PfH09MwxvSRJtG/fvtCFE8onY2NjduzYIc+VIR5qKjvKU2AvTTIGpOTk/v37HDt2jMmTJ2uUp0b/U0ZGRixfvhx/f38kScLT05MuXbrkOCxKS0sLMzMz+vXrp1GBhIrh5cuX4sEmQfifR48eER4enuMcMpcuXcLT0xMXFxcUCgU2NjbUrFkz3zw1vg136NCBDh06AOlj14cPH06bNm00zUYQBEEAli9fzk8//ZRnGoVCIc9WWa9ePQ4cOJDvGPsij5YRhKIS8wcJFdmePXto2LAhXbp0yXFOnJyaZYKCgvIddaZRcF+5ciU9e/bE3t5e3s6PQqHg888/1+QyQgVjZWWlNuJGtMELFUlKSgrDhw9n4MCBOR4/ePAgx48ff7Nt7uvXr8fOzk4O7uvXr8/3HBHchYIQk4sJFdX69evlp1Fz0rVr10I92a9RcPf29labHc3b21vjCwr/ymlW0syTi5XXicUEQfhX69atAfDz8+Po0aM8f/4cpVJJnTp1eO+992jatGmhpljWKHK0bNkyz21BM3qVFWrB3MRCUWEmFxME4V/z5s1j9+7dcpu7JEmcO3cOb29vxo0bx3/+8x+N8yxytTA5OZmHDx/KXytu3rzJpk2b0NbWZuTIkRov1FHRZA7m+oZlYw3LzO3jrVq1UuvYqVq1KoHK+JIqmiCUObt372bPnj20a9eOKVOmEBYWxqRJk9i6dSu+vr5s2rSJhg0b0rdvX43yLVJwf/HiBSNGjMDQ0JD9+/cTGhrKqFGj5HmUjx8/zrZt23B0dCzKZSq0zA/71KlTp1T0X2R9IjXzRGE6yTYYG78oyeIJQpny66+/0rhxYzZs2ICWlhZ+fn5A+kSL8+bN48mTJ/zyyy9vN7ivXr2akJAQhg8fDsD+/ftJSEhg+fLlNGvWjPHjx7Nx40ZWr15dlMtUKP7+/pw7dR2Adp1cCVO9VJsbvTSMKslaU79xI0qeXiAyrHQv4iAIpc2DBw+YMmVKrkOCO3XqxIoVKzTOt0jB/eLFiwwbNozRo0cD6Qtmm5ub8/777wMwePDgfAfnVyRnz53lxtX78nb3fs1pWM9JLU1MbIw8/3lMbH2orJ5HaRhV8qZr6mLcu1CRaGtr57iYSIbCzs9VpOD+6tUreeqBhIQEbty4Ic+oBum1uvj4itv+mnW2xMjIWLWFKyRJUqup9x9aNubhyTyl75uoqYtx70JFUqdOHX777TeGDh2abZGjtLQ0Dh06hIODg8b5Fmn5lGrVqvHq1SsgfRhPamoq77zzjnz8wYMHxTqpfVkjSZLa7zk9LpxRUw8ODi7161K+TRnfUB48eCAHeUEojz788ENu377NyJEj1f7Wjx07xtixY7lz5w7jxo3TON8i1dybNm3Kjh07qFWrFl5eXmhra9OpUydSU1M5fvw4u3btolevXkW5RJlWVjoaS9vYeqVSqdauLxb3EMozNzc37ty5w7Zt23j8+DF6enooFAoWLFiArq4uc+bMoVOnThrnW6RP8ZdffsnIkSPlr8wTJ07EzMwMPz8/pk+fjqWlZYVafSlrM0xwYGyZ6Gh822PrlUoFFhb/rs6UdZ3VatWqkRxuKd8YxeIeQnk3b948eanS+Ph4Pv74Y6ytrenYsWO+y5fmpkjB3cbGhgMHDnDx4kVq1KhB06ZNAahfvz7Tpk1j0KBBmJmZFeUSZUp+i1YI6bKuzpTTOquZ2/XNq5mXSDkF4W1q0qSJ/PsXX3xR5PyK/P3byMgo+9Jq5uZ8+umnRc1aKAE5TYnwJmRenale/XrZRgVlllNfRWlrShKEwtq3b1+B0g0YMACA58+fc/XqVXk7N0X+REiSxKVLlwgLC8txonmA/v37F/UywluSdUoEA2MFD0L+4M6dO0D6PBj5zSNd3LKu3NSiRQuaWg8U0zSUYtHR0VSpUqWki1EmzJkzB0mSUCgUaoMwMmTszwjm/v7+zJ49+80G9ydPnjB+/Hi19tLMMgosgvub8abGg2edEiE2yJRnN9Lbv5vWNcbY+O33H2ReucnGxoam1m+9CEIBBQcHM2rUKLZs2VKoCa9yU15vGIsXL9YovZOTE19//XW+6YoU3FesWEFwcDD9+/enSZMmeQ7EFzSXX8fj2xoPnrkJ5U10DGd9nWJ+mrLDvJIRkioNhfLfikZcXBwqlYq4uDi1tFnTaeJN3TBKg9zmcc+NjY1NgUaQFSm4X7p0iQ8//JD58+cXJRshFwXpeCwNT6wWVdbXmXXYaE43uczeVj+BkJ2RngEKpRZ3vD7n9YuHAATFpjfP3vH6nGij9Ca8yjXq0fjjVdnOL2htPLcbhpC7IgX3tLQ0GjVqVFxlEXKgScdjcSmJp2bz+naQNfgbKdUXZM/WTyA6WN+61y8eEhd4GwBVijYK6qAKvU9cZGqu5+RWG1epJJTKgt2xNUlbWjVq1CjHtvasMlfqCqJIn4AmTZrw119/MWjQoKJkU26Ul6lvM89vU1qems0c/B1d6mOX5Zu5mAe/9DDVSWV6rcdU0sp5gEWG3GrjSqWCXT6XCQuNlfdFRr0EYJfPFUxN0pshq1U3YtCQVsVc+rfP1dU1W3BXqVSEh4fz/PlzrKysaNeuncb5Fim4T506lY8//piWLVvSo0ePCj/hU1l5IlUQ3rSsgV3XuBpSmgqFVsFGWoWFxvIiOEreTkxMBhRERSSTGB+V22ll0i+//JLrsYCAAMaMGcO7776rcb5FCu6enp4YGxvz5ZdfMnv2bKpUqZJtmJxCoeDUqVNFuUyZ8aYn1CouWVd/KmsUirc7FFMoOu1Kxii0lPyz5CCJgenzUYWnxKJAQdiyY0g66XOYG7e0pca47E2B+vrGtHAehY6OwVstd0lr1qwZQ4cOxdPTk27duml0bpGC+6NHj9DS0qJGjRpFyUYopMLOwVIWV3/KzLByZbV+gW79XGlUz7mESyUURGLgKxIehALp3UcL6UTlpwkkkL7Aj55N1VzPrWiBPYOtrS3btm3T+LwiBfeTJ08W5XRBQ1lHjdjb2xPyQK9CzsGSuV8gNrbivO7yxlBM0ZGvS5cuYWCg+Y2tWIcUJCcno62t/dafYKwochoy+Dr6hdwUVN2y4k6vLAhl1Q8//JDj/uTkZG7fvs2VK1fkBZA0UeTgHhUVxapVqzh27BgRERFs2rQJHR0dfvzxR9zd3bG1tS3qJYRM8hoyaGFlkm3qADsnMSQwgxguKZRGnp6eeR53dXVlxowZGudbpL/uqKgohgwZwj///IONjY08nCc6OprTp09z8+ZNfHx8xHzcb1HW9vTMAQ1y7kAti6tBFYYYLimURt7e3jnuVyqVVKtWjVq1ahUq3yIF9zVr1hAUFMRPP/2Evb29vApTly5d8PT0ZOrUqaxbtw4PD4+iXEYogqwBLacO1NI4rl0QKooWLVq8kXyL1Dh+8uRJBg8eTJs2bVBkeQa8Q4cODBkyhMuXLxepgIKQn6xDIwsyHYGYskAo74pUcw8NDaVhw4a5Hq9bty7bt28vyiWEN6C8NcPkNDRS39Alz+YoMWWBUN4V6a/ZzMyMoKCgXI/fv38fU1PTolxCeAPKYzNM1qGRBWmOEm3wQnlWpODevn17du7cyaBBg6hcWX1Gq2vXruHj40Pfvn2LVEChaDLXaKF81NTzI55gFYQiBvfJkydz6tQpBgwYgKurKwqFgu3bt7N582YuXLiAkZEREydOLK6yCvnIKahlrtFC+amp5yVrMw2Ip1iFiqdIwd3CwoIdO3bw1VdfcfbsWSRJ4vjx40D62MwFCxZQs2bNYimokL+sQa0i1NJzk/WmJp5iFSqaIvcg2djY4OXlRUxMDE+fPkWSJGrWrImZmVlxlE/QUHlsTy8OoqlGKK3WrFlTqPMmT56c5/FCBXeVSsWBAwf4448/+Ouvv4iIiEChUFC1alWaNGlCz5496dGjh5iGQCg1cvpW8yrktbzt0soeh/adS7KIQgW1du1aeb3pgpIkqfiDe2BgIJ988olcS9fX16dKlSqkpKQQEhJCUFAQx44do169eqxZs4batWtreglBeCOyfqvJvN26UgMxNFIoMYMHD8bFxaVAaf39/fHx8ck3nUZ/vVFRUXz00UdERkYyduxYBg4cqDZ3TFpaGn///Te//fYbO3bsYNSoUfz222/lcsXynIhvKmWXQqEUQyMroKSkJBYtWsSxY8fQ19dn7NixjB07Nse0d+7cYcGCBdy/f5969eqxaNEimjRpUizlaN68Of369StQWi0treIP7ps2bSIiIgIvL68cVwbR0tKiSZMmNGnShG7dujF27Fi8vb2ZMmWKJpcpNfKryWVd9MLY2Pitl1EoHlmbbTp0ac7zhAju3LkDQOPGjbl37x5Pnz4FoE6dOnz++eclVVyhmHzzzTfcvn2bLVu2EBwcjLu7O1ZWVvTs2VMtXXx8PBMmTKBv374sW7aMHTt28Mknn3D8+HEqVapUQqXPm0bB/fTp0/Tq1atASz61bNmS7t27c/r06TIb3DPX5HJqDjt2+Cz+V+8BFXtkSnmRuZlGt5JEbJApz26kz6NtU8WUtLQ0Hjx4kL5tY8MPP/xAYGD6ep7FFezFrJ5vT3x8PLt27cLLywsHBwccHBx48OAB27ZtyxbcDx8+jJ6eHjNnzkShUDB37lzOnj2Lr68vbm5uRSqHQqFApcp7vdnM0tLSCtRKoNFfTlBQEIMHDy5weicnJ86ePavJJUpUXsvPZX1c3cRCgUpSiZEp5Vjm6ZUb2NfHos6/C6XUqFGDkJAQOW316tXZuXMnL16kr5vr4OBARESEvA1gbW2d7+cnr1k9TSwUJMUjgn8xuXv3LqmpqTg7//v8g6urKxs2bEClUqkF0ICAAPlZHkgPyC4uLty4caPIwb1SpUrExMQUOH1sbCz6+vr5ptPoLyMxMREjI6MCpzc2NiY+Pl6TSwDIUwenpaUV+JwzZ84QGRkJgKmpKR06dNDonIYNGxL8SEng0/QPrINTLapVtUYivSy6lSQePwzi4d30mppTy7rYNbCkbWcnAKxqVcXQqFKBtwtzTlG3S+KapaEMxZFH0xa10daxRdko/bmNJvXtsLNMxqLSc3k7PjYZYtO3rczsMNH9dxugcV07AvyCCXqWvsxcPXsrEuJT1bbDY+J4eO9/f2NV6hIXkyL/zWX9m9TRVxDgFyqfX93CGIXhv5+fgshIm5ycrLbAvZaWFg3MLNFV5r3ofa0qZqSlpVHJuhFo6+WaTr9aHdLS0tC1NUfSyb3WqWNdhbS0NKpbGKGV96UxMzciLS1NLU5k/B4bG4u29r/hTVdXF11d9VWfwsLCMDU1Vdtvbm5OUlISUVFRVK1aVS1tvXr11K9vZiZ/kyuK2rVrc+PGDUaOHFmg9Ddu3MDa2jrfdApJg7+Ehg0b8u233xZ4SoEDBw7g7u7O33//XdBLAOl/aLdu3dLoHEEQ0jk6OmYLZLkpr5+1SZMmyRU3SB8TnrV5eP/+/axcuZJTp07J+549e0bXrl05c+YMlpaW8v5Ro0bh6uqq1vS2cuVK/P39+fnnn4tU1tWrV+Pp6cnmzZtp2bJlnmkvXLjAxx9/zLhx4/jyyy/zTFsqv9Npa2vj6OiIUqnUaOynIFRkkiShUqnUaqz5KW+fNUmSSExM5NixY2rNKjnd7PT09EhOTlbbl7Gdtdkjt7QFaR7Jz8iRI9mzZw9jx47lww8/5P3336dRo0Zq36SePHnC7t27+fnnn6latSrjxo3LN1+Ng/u1a9cK3Fzy559/apo9kD6ksKA1D0EQCq88ftYMDQ0LlM7CwoLIyEhSU1PlG2JYWBj6+vrZRr5ZWFgQHh6uti88PJzq1asXubxVqlRhy5YtTJ48mV9++YWtW7eira2NsbExOjo6REdHk5iYiEKhoE6dOnh6emJiYpJvvhoH919//ZVff/21QGk1fepKEAThbWnUqBHa2trcuHGD5s2bA3D9+nX5m0xmzZo1w8vLS45pkiTx559/8umnnxZLWWrXrs1vv/3G0aNH8fX15fbt20RERBAfH0/VqlVxcHCgS5cu9OvXr8DP02jU5v6m5kAQBEEoCfPnz+fPP/9k6dKlhIaG4u7ujoeHB927dycsLAwjIyP09fWJi4ujW7du9OnTh6FDh7Jz5058fX05duxYqR3nrlFwFwRBKE8SEhJYuHAhx44dw9DQkHHjxjF69GgA7O3t8fDwkIc63rx5kwULFvDo0SPs7e1ZtGgRjRs3LsHS500Ed0EQhBI0YsSIAqXbunUrAH5+fqxbt07ezk2pHC0jCIJQUVy/fj3fZxMyHw8PD+fatWv55itq7oIgCCWooFMPZO5IzfoEbY7pi1QqAYAvv/wSe3t7Nm/enO3YrFmzsLe3z/bj7OxM3759+emnn9TSr169Gnt7e3n78uXL8jnnz5/P8foZbYD29vY8f/482/ELFy5gb29f4IfPMsqQ9cfJyYkePXrw/fffk5qaWqC8CirjdV6+fDnPdPb29qxevbpYrw3pX40L+vVYEIqTUqks0E/Wc/IjmmWKKDY2lhMnTtCgQQN8fHwYM2ZMtuGf1apVUxtpJEkS4eHh7Ny5k2XLlqGnp8dHH32U53WUSiW+vr60bds227HDhw/nee6ePXto0KAB9+/f5/r167i6uhbotWWdVjQyMpKDBw+yYcMGUlNT+c9//lOgfArCwcEBHx+fbI94C0JFcODAAdauXcuaNWuoX78+r169YvXq1bx8+RI3Nze6d++ucZ6i5l5EBw8eBGDu3Lk8ffqUS5cuZUujq6uLk5OT/OPs7Ey3bt3YsGEDlpaW7N27N9/ruLi4cPz48RxrzIcPH6ZRo0Y5nhcTE8OJEycYO3Ystra27Ny5s8CvLXOZnZyc6NSpEytWrMDJyalAZdaEoaEhTk5OBX4ARRDKiz/++AN3d3dev35NYmIikD59go+PD1evXmXq1Kn5VuByIoJ7Ee3Zs4c2bdrQunVrateurVHw1NHRwcDAoEAPevXu3ZuoqKhsN4+7d+/y9OlTevXqleN5v//+O6mpqbRr145+/fpx9OhRoqKiClzGnBgaGmYr84kTJ3Bzc8PR0ZF3332Xr7/+Wm3SuMTERBYuXEj79u3lpRg3bdokH8+pWebKlSsMGTKEZs2a0aNHDy5evKh2zdyacrI2sSQmJrJixQq6d+9OkyZNcHFxYcyYMXnOeXThwgUGDx6Ms7MzLVq04LPPPuPRo0eavVGCUABbtmzBzs4OX19fHB0d+euvvwgICGDYsGFcv36ddu3a5TsyJiciuBfBgwcPuHXrFv379wegf//+/PHHH9keUwZITU2Vf5KTk3n+/DkeHh48efJEPj8v9erVo379+vj6+qrtP3ToEC1btqRatWo5nrdnzx7atWuHubk5/fv3JyUlhX379hXo9WUtc0hICF5eXly4cIH3339fTvf7778zadIk7OzsWLt2LZMnT+bAgQNMnDhR7uVfunQpZ8+exd3dnU2bNtGlSxe++eYb9uzZk+O1//rrL8aOHYuRkRGrVq1i5MiRfPHFFwUqd1YzZ85kz549TJgwgc2bNzN79mwePHjAl19+meMohWfPnjFx4kSaNGnC+vXrWbJkCU+ePGHChAkazbstCAXx6NEj3Nzc5G+tp0+fRpIkeXx9586defz4scb5ijb3ItizZw8mJiZ07py+sPKAAQNYvXo1u3fvVnssOSgoCAcHh2zn16lThwULFvDhhx8W6Hq9evXC29ubhQsXynNhHD58ONdHoO/du8dff/3FqlWrALCysqJ169Zy30B+ciqzlZUVU6ZMYcKECUB6/8F3331Hu3bt+O6779Re2+jRozlz5gwdO3bkypUrvPvuu/Tp0weAVq1aUalSJczMzHK8tqenJ2ZmZqxfvx4dHR0gfSrn6dOn51vuzJKTk3n9+jXz5s2jd+/eQPpCMnFxcSxbtozw8PBsN8abN2+SmJjIJ598goWFBQCWlpb88ccfxMfHi6YjoVjFxsaqzWVz6dIlqlatKj8gpVAosk1aVhAiuBdSSkoKBw4coGvXriQmJpKYmEjlypVxdXXl119/ZcKECXKPdrVq1Vi/fj2Q3ga+bt06AgMDWbZsmdpCAfnp3bs3q1at4tKlS7Rt25aAgABCQkLo3r07f/zxR7b0e/bswdjYmObNm8uLAfTo0YMFCxZw6dIlWrdunef1du/eDaQ/xffzzz9z+fJl5s2bR5cuXeQ0jx8/5uXLl3zyySdq/QEtWrTA0NCQCxcu0LFjR1q1asXOnTt5+fIlHTp0oEOHDkyaNCnXa1+/fp1OnTrJgR2ge/fuajPlFYSurq7c/BMSEsKTJ094+vSpPM1rTh+aZs2aoaenx8CBA+nZsyft27enVatWNG3aVKNrC0JB1KhRQ24ifP36NQEBAXTt2hVIrzz5+vpSu3ZtjfMVwb2QTp8+zatXr9i9e7ccBDM7d+6cvGCIrq4ujo6O8jEXFxc++OADPv74Y3bt2qW2yHhebG1tadSokTxq5vDhw7Rt2zbHBcgzbj4xMTG888472Y7v3Lkz3+CeuczNmzdn9OjRTJ06lZ9//lmeaCmj/X7RokUsWrQoWx6hoekLScydOxdLS0sOHDjA4sWLWbx4Mc7OzixcuJCGDRtmOy86OhpTU1O1fdra2tn2FcS5c+dYunQpjx8/pnLlyjRs2FCeDySnZpmaNWvyyy+/sHHjRnbv3o23tzfGxsZ89NFHTJs2TUyGJxSrbt268fPPP2NkZMSDBw9ITk6Wg/vIkSO5evUq7u7uGucrgnsh7dmzBxsbG5YsWaK2X5IkJk+ezM6dO3NdDcrAwIBly5YxZMgQZs+ezY4dOwocMHr37s2mTZtYsGABvr6+zJgxI8d0p06dIjIyksWLF2e76+/YsYMTJ07w6tWrXJtFslIqlXh4eNCnTx9mzZrFoUOH0NPTk79Ozpw5M8eFBjJuPLq6unz22Wd89tlnBAcHc+rUKdatW8eXX37JoUOHsp1nYmKSre9CkiSio6Pl7Yz3LGs7+OvXr6lcuTIAgYGBTJo0ia5du+Lp6YmNjQ0KhYJt27Zx7ty5XF9v06ZNWbNmDcnJyVy/fh0fHx82bNhAw4YNc+28FoTC+PTTT/Hz82Pjxo0A8vMkAEZGRnz88ccFakbNSnSoFkJYWBjnzp2jT58+tGrVSu2ndevW9OzZkzNnzqitsZlV06ZNGTx4MP7+/uzfv7/A1+7VqxdRUVFs2LCB6OhotSaSzPbs2YOlpSWDBg3KVsYRI0aQkpKSa2dmbqytrZk4cSLPnj3Dy8sLADs7O8zMzHj+/DmOjo7yj4WFBStWrODOnTskJibSo0cP+SEvKysrhg0bRp8+feQ1aLNq06YNZ8+eJSEhQd537tw5UlJS5O2Mtu+XL1/K+6Kjo9VGtdy+fZukpCQmTJhArVq15BtCRmDPqeb+888/06lTJ5KTk9HV1aVNmzYsXrwYINfyCkJhGRoaygt1b9iwgW3btsnNjxkVoMIQwb0Q9u/fT2pqqtw5mFX//v1JS0vLd977adOmUaVKFVasWEFcXFyBrm1jY4OjoyOenp506tQpx+lGQ0NDOXfuHL17987xG4Grqyu1atXCx8dHo/U2AUaPHk3t2rXx8vIiKCgILS0tpk+fzs6dO/n666+5cOECR44cYdy4cdy5cwcHBwf09fVxcHBgzZo1bN26lStXruDj48O+ffvkGkpWkyZNIj4+nnHjxnHy5El2797NnDlz1Nrg7e3tqVGjBmvXruX48eOcOHGC8ePHY2BgIKdxcHBAW1ubb7/9lgsXLnDq1CmmTJnC6dOnAXJc47d169aEhYUxadIkzpw5w/nz55k9eza6urp06tRJo/dLEApCqVTStm1bOnTokK1f6f79+4Wabl0E90LYu3cv9evXp0GDBjked3V1pWbNmuzatSvPVatMTU2ZOnUqYWFhrF27tsDX7927NykpKbneXPbv309aWpo8OiQn77//Ps+fP8+zaSInurq6zJkzh8TERJYvXw7AoEGDWLFihbx4wcKFC6lZsyZbt27FxsYGgK+++go3Nzc2b97M2LFjWbduHQMHDmThwoU5XqdOnTr88ssv8s1j3bp1uLu7q/UvaGlpsWrVKszNzfniiy9YsmQJffr0UXuar3bt2qxYsYKQkBA+++wz5s+fD6TPsKdQKHKcgKlhw4Zs2LCBuLg4vvjiCyZPnkxUVBSbN2/Gzs5Oo/dLEAri0aNHXL58GT8/v2w/hw4dYs2aNVy8eBE/P78cpxjJiZg4TBAEoQQtX7482xxTWWWs/gTpz7wcOHAg3/llRIeqIAhCCdqzZw/29vZ07do1x2bU+/fvc+zYMbUV7YKCguRvxbkRwV0QBKEEpaSkMGLECAYOHJjj8YMHD3L8+HGNlysVzTKCIAgl6NKlSzRq1CjH51UgfW6kiIgIrKysNMpXBHdBEIQStnfvXv744w9MTEz49NNP5SaXkydP5jiCpiBEcBcEQShB27dvl5+jgPTpSo4ePUpKSgqtWrWiUaNGeHt7azynkRgKKQiCUIK2b99O/fr1OX36NBs3biQyMpK9e/diYGCAu7s7Dx8+5Mcff9Q431LZoapSqUhNTUWpVIp5PAShgCRJQqVSoa2tXaBl2KD8fdYK8x6UtOfPn/Pll19iYWGBhYUFLVu25Nq1awwbNozRo0cTHBzMyZMnmTZtmkb5lsrgnpqayq1bt0q6GIJQJjk6OqKrq1ugtOX1s6bJe1DSKleurFbWBg0acOXKFbXtXbt2aZxvqQzuGXdcR0fHQnUkCEJFlJaWxq1btzSqsZa3z1ph3oOSZmNjI686BlC/fn21ZSxfv35dqAXpS2Vwz/h6qKWlVS7+4AThbdKkeaWkP2vR0dG5DgEsirLUxDR48GDmzZtH9+7d6dGjB82aNSM6OpoXL15QvXp1jhw5gqWlpcb5lsrgLghF9dg/lYTYfweCGRgpsHMWf+6lSXBwMKNGjWLLli0aj+EuT9LS0tDT02Pq1KnUrl2batWqoVAo+OSTT4iOjiYkJKRQU/6Kv3ahXEqIlXgdLUb5lmZxcXGoVKoCz4haXmVMZqdQKAgMDCQwMBBIX6PZwMCAPn36MGXKFI3zFcFdEAShBHl4eNCuXTuMjIzktZGBIvcbiOAuCIJQgvr37/9G8hXBXRCEt0JKU6HQyr82WtB05cW+ffsKlG7AgAEa5SuCuyAIb4VCS8k/Sw6SGPgKgMDE9H//WXIQST99LV/9WmbUnvteiZWxJMyZMwdJktTmbM+JCO6CIJRaiYGvSHgQCkAS0aCApGcRJJD7imXlXeZ5ZTJIkkRYWBi+vr68fv2aJUuWaJyvCO6CIAglKLd53AEmTJjAgAEDOHv2LK1bt9Yo34rTsCUIwlsTHR2dbxoDdFBI6f8KOdPW1uaDDz7gyJEjGp8rgrsgCMUqODiYgQMHEhwcnGc6MyqxkM6YUektlaxs0tbWJjw8XPPz3kBZBKHEnT13lhtX78vbTi0a4NC+SwmWqOLQ5OEkQ8rG5F4lJSgoiO3bt1OzZk2NzxXBXSiXIiMj1WqOtSOrlWBphOLwpuahKWkdOnTIcX9ycjKRkZEAzJo1S+N8RbOMIAhFIqne/EiXgjb1lEU6Ojpoa2tn+zEyMsLFxYWvv/6a0aNHa5yvqLkLglAkCqUWd7w+5/WLhwAExaoAuOP1OdFG6fVHsyYdsXObWehrlOd5aE6cOPFG8hXBXRCEInv94iFxgbcBiE/SA2oR//IRcZFJAFSyrFuCpauYRHAXBKFY6SvTUCChr9S8uUbbtDIqlYRSWbD52DVJW5rFxcXh5eXF8ePHef78Odra2tjY2NC9e3fGjRuHvr6+xnmK4C4IQoEVpFPTVCeV6bUeU0lLpXH+WoZ6KJUKdvlcJiw0Vt4fGfUSgF0+VzA1SZ8St1p1IwYNaaXxNTJLSkpi0aJFHDt2DH19fcaOHcvYsWNzTHv69Gm+//57AgMDqVmzJtOmTaNLl6KPwIqLi2Po0KE8evQIY2NjGjZsiCRJBAYGsmbNGnx9ffHx8aFSJc2GjIrgLpRJmRfjEAtxvB2aLK5RmMCeWVhoLC+Co+TtuNfpgT48LJbEeM1rsbn55ptvuH37Nlu2bCE4OBh3d3esrKzo2bOnWrq7d+8yefJkZs6cSYcOHTh//jxTp05l9+7dNGzYsEhlWLt2LU+ePGHp0qVq88dIksSuXbtYsGABGzZs4IsvvtAoX/GJEMoksRjH21feOjXj4+PZtWsXXl5eODg44ODgwIMHD9i2bVu24H7w4EFat27NyJEjAahduzYnT57kyJEjRQ7ux44do3///tkmBlMoFAwePJjr169z9OhREdwFQSh/tLX0AMX//i0ed+/eJTU1FWdnZ3mfq6srGzZsQKVSqS2WMWDAAFJSUrLlERsbm22fpsLCwmjatGmux11cXPD19dU4XxHcBUEgLS3/zk+VSiX/mzn921hYW1/fmBbOo9DRMch2LHNZMn6Pi4tTK5euri66uupPw4aFhWFqaqq239zcnKSkJKKioqhataq8v25d9dE+Dx48wM/Pj6FDhxbthQGWlpY8efIk1+PPnj3DzMxM43xFcBcEgVu3bqltZzxYk9mzZ88A+Oeff+RAr6+vj52d3VspY06BHeDevXskJCSo7evQoYPavsmTJ2dbhzQhISFbwM/YTk5OzrUcERERTJkyBRcXl2LpUO3atSvbt2+nadOm9OzZU/7GkJqaysGDB/nll1/o16+fxvmK4C4IAo6Ojuo1cIUCrSxreGYEe1tbWxo0aPA2i5cne3t7+fe0tDRu3brFmTNnstXcs9LT08sWxDO2cxt6GB4ezpgxY5AkiVWrVhV5nVOAzz77jEuXLvHFF19w/Phxvv/+ewD+/vtvZs+eTe3atcUC2UL5lXl0jIlF9nHNO3bs4OrVqwD06NHjrZatPNDS0srWvOJ+ZCuPI0Ll7dTw6P/t/wXtq+nDIdvWacjUd/u8vYLmIKdmIUNDw3ybiywsLIiMjCQ1NVW+cYWFhaGvr4+xsXG29CEhIXKHqre3t1qzTVEYGRmxfft2tm/fjp7ev30Ktra2zJkzhwEDBmBoaKhxviK4C2VC5tEx+obZg/vLly958OABAM2bNwcx22CRPY4I5e/Q5/K2dnQ8ZsCTyBBSVekdibam1UuodEXXqFEjtLW1uXHjxv/+ZuD69es4Ojpmq5HHx8czfvx4lEol3t7eVKtWvBPRZYyxf/XqFQEBASgUCurUqcOIESMKnacI7oIgFIikrYX0v3/LAwMDA/r378/ChQtZunQpoaGhbN68GQ8PDyC9Fm9kZIS+vj6enp4EBgaydetW+RikB2UjI6Mil+X+/fssWrSI69evy/uUSiXvvvsuCxYsKNSUv2JWSKFCKI620YourbIeYd0bk1a5+IYjlrTZs2fj4ODAqFGjWLRoEVOmTKF79+4AtG3blsOHDwNw9OhREhMTGTRoEG3btpV/CrO2aVbPnz9nxIgR+Pv7065dO7p164ZCoaB9+/bcuHGDoUOHEhISonG+ouYulEn+/v6cO5Vey2nXyTXf9MbGxmrt8i1atCAkJITAwPRH2WvVqsW0adPeWHnLC0m3fIUMAwMDli9fzvLly7Mdu3fvnvx7YcaZF9S6deuIj4/H29ub5s2b4+fnx/Hjx/Hw8OD169d88MEHrFmzJseFtPNSvv6nhAojJjZGnts7JrY+VM47vVKpQJL+faJVkiSCgoLkdnpBKCnnz59nyJAhcrt/ZjVr1mTo0KHs379f43xFcBfKhMzL5vUf2l7j880tjCDRCF7FAKCbWA8rqxDu3r1brOUUBE1FRkbmObTU2tqaV69eaZyvCO5CqfTDDz/ITSY9evQgMjJWrqm/fv1aLa1SqcDCwoL69esDULVqVQKV8dnyDA2O4cHfQQDUq18v39q+ILwN1apV49GjR7kev3v3LtWraz4qSQR3oVTIHMxbtWqVrQklrw7RrLVynWQbjI1fvNkCC0IxadWqFT4+Przzzjtq66lKkoSfnx+//vproaY5EMFdKBUCAwPVxqmbajXj2av0x80LEqwz18ojw17nmRay1/Zr165dlOILQqFNnDiRM2fO8Mknn7B27VoqVaqEQqFgyJAhPHv2jNq1azN58mSN8xXBXSiVggMjNArWmspa27du4qI2mkaMnhHeFhsbG7Zt28by5cupUqUKKSkpKBQKEhIS+Oijj/j8888xMTHROF8R3IUiyzo1QFI8ZWIhjaxt8EmVX4rRM0KJsLW1ZcOGDUD6rJs3btzIcT4cTZTOT51QpmSdGiAx7t9tRS7LW+7cuZMXL9KbWho3bvxWpo3VRGkrj1BxKJXKIgd2EMFdeMP0KivUavaQXrvXjrfj2Y0oAGyqmGJlZVWqhiVaWVmpdfKKZhqhrBHBXdBYfjM0ZpV1STx9Q0WZGJaYuZNXEMoaEdwFjeU3Q2NWmacKgMI9hPS2KZVKbGxs5G0xmkYoa0RwF964zFMFQPaHkEqjatWqkRxuKQ/HrNuqudo3ltLcUSwIIIK7UAoU9AnTty3zcMx32rtw8eyf8jcQpxYN+My56EusCcKbIoK7UGSZm136D23Pq5DXatv5KStPmGb+BlI7MvtiDaIDVihNRHAXiixz0Hv9+nW27YLQ9AnTkpbTdAiiA1YoTURwF/K156er3PvrHwA69nDm8Z1XGtXMy6Oc5ocXhNJEBHdBrTmhTp06fP7552rH7/31D35nAwBo0KhWoWrm5U1O88NnfvBJPAQllDQR3AW1RStsbGyyTbcrZJe1n0AvuT7OzpCWlgaAs7Mzq1at4unTp/I5oh1eeJtEcC/nMgdqSG8+aNVwkNqQvsxPhyqVSlQqlZw+v+l2K7LM/QTvtHdBN7Ge2mIgaWkhog1eKDEiuJcxmo61ztx0AKTPNherPvdL5mGI9vb2hDzQ02i6XSFd5mDfwL4+FnX+fV8BtYeiBOFNE8G9jMn6KH9WWZtUMs+LDlCtkmO2oYuZa5w6yTa8jn5RpkauFIfiHmuftdkGwLRO7kupCUJxE8G9GOU59a2xAjun7G+3Wk08lzSZnTt3Dv+r6auyu7ZqiEP7TmrHFZmmYZQkiZfPo+RADenBOmuHaOiLsjUM8U14E2PtM9fkAapbaL5UmiAUlgjuxSivqW/1DXOeHTHrOVlvEDdv3MP/6t9A+jBElaSSA3O36q4c8flL7bi1URv+eaUHiCYVTb3psfZZh0+2atUKlUrFy5cvAbC0tOTDDz8s9usKuUtKSmLRokUcO3YMfX19xo4dy9ixY3NMe+fOHRYsWMD9+/epV68eixYtokmTJm+5xAUngvtblNPsiPk93fnk4Qu1YYhZPXmgflzUwku3ly9fqi0nmBxuybMb0QCYtbAsyaJVSN988w23b99my5YtBAcH4+7ujpWVFT179lRLFx8fz4QJE+jbty/Lli1jx44dfPLJJxw/fpxKlSqVUOnzVm6Cu6bNG0W9RtZml5ymvs0cuLv3a07Dek7Z0hTH051C2ZBTu/6NG/82mzV1tX8rf8dCuvj4eHbt2oWXlxcODg44ODjw4MEDtm3bli24Hz58GD09PWbOnIlCoWDu3LmcPXsWX19f3NzcSugV5K3c/OXk17yRORDntC+nD1LWPDJPHJVTLTvrduZALUlSmZz6Vig++bXrG1aunO1v7N495GY3l1aNURg/486dOwBYW1szePDgt/wqyo+7d++SmpqKs7OzvM/V1ZUNGzagUqnUhgAHBATg6uoq92kpFApcXFy4ceOGCO6ayBi+9/RWEgqFFoYmClKSISk+fX9O2/qGEhLp27qVJB4/DOLh3fRRI04t6xIXkyJv57TPqWVdAvy0CHoWCkA9eyteBMWpHTevUQm7humdYlXM9FAopTy37ZSWtO3sBIBVrarERCbKx3NLY2hU6Y1tv41rlMYylKbXHROZiEWN9EnHDAz0sGuQ+99IFTM9YiIT0dJODyjGJrpo69SnhmH66KfGde0I8AuW/2arWxijMMw+/DUvGWmTk5OzPWHbwMwSXWXeT9rWqmJGWloalawbgbZerun0q9UhLS0NXVtzJJ3cn5vQsa5CWloa1S2MyO8hXzNzI9LS0uQHx+Dfh8hiY2PR1v43vOnq6mZbui4sLAxTU1O1/ebm5iQlJREVFUXVqlXV0tarV0/9+mZmpfo5BoWkyV/CW5KcnMytW7dKuhiCUCY5OjoWeA3O8vpZmzRpEpGRkfL25MmTmTJlilqa/fv3s3LlSk6dOiXve/bsGV27duXMmTNYWv7bBzJq1ChcXV3VpuZYuXIl/v7+/Pzzz2/uhRRBqay5a2tr4+joiFKpVBvaJwhC7iRJQqVSqdVY81PePmuSJJGYmMixY8fUmlVyutnp6emRnJysti9jW19fv0Bps6YrTUplcC+u1b8FQchbefysGRoaFiidhYUFkZGRpKamyjfEsLAw9PX1MTY2zpY2PDxcbV94eDjVq5feZxfEpCGCIFRIjRo1Qltbmxs3bsj7rl+/Ln+TyaxZs2b4+/vLfRSSJPHnn3/SrFmzt1lkjYjgLghChWRgYED//v1ZuHAhN2/e5MSJE2zevJmRI0cC6bX4xMREAHr27ElMTAxLlizh4cOHLFmyhISEBHr16lWSLyFPpbJDVRAE4W1ISEhg4cKFHDt2DENDQ8aNG8fo0aOB9En0PDw85KGON2/eZMGCBTx69Ah7e3sWLVpE48aNS7D0eRPBXRAEoRwqlR2qxSktLY2UlJSSLoYgCG+ZlpYWOjo6JV2MElNug7skSbx8+ZLo6GiNHuoQBKH80NPTw9zcPNvol4qg3Ab36OhooqKiqFatGpUrVy4XY3gFQSgYSZJISUkhOjqaoKD0uXsqWoAvl8FdkiRCQ0MxNjbG3Ny8pIsjCEIJMDAwwMjIiOfPnxMeHl7hgnu5HAqZMd9ERfvPFARBnUKhoEqVKiQlJVW4vrdyGdxTU1MBNHoMWxCE8imjUzXzBGMVQbkM7hlEO7sgCBU1DpTr4C4IglBRVbjgnqZSleprjBgxghEjRhRjaXJ2+fJl7O3tuXz58hu/VkmQ0t78/3NhrzFr1izs7e1z/fH19QXe3t8CQOfOnZk1a1auxzP+XjL/NGnShC5duvDNN9+QkJCQLW15/dsqKypco7SWUon7ka08jgh9I/nbVa3O8l5v5wMp5E6hpeSfJQdJDHz1RvLXr2VG7bnvFfr8atWqsWbNmhyP1alTB4AFCxYUOv83Zf78+Tg4OADpj+7fvXuXVatWERYWxrfffguAg4MDPj4+2Ra3EN6uChfcAR5HhPJ36POSLobwhiUGviLhwZu5iReVrq4uTk5OeaYpjcGxXr16auVu06YNsbGxrF+/ngULFmBoaIihoWG+r0148ypcs0x5sGvXLtzc3HBycqJp06a8//77HDlyRD6+d+9e7O3tCQgIYMCAATRt2pS+ffvKX/dzc+LECT766COcnZ1p0qQJPXv2ZNu2bWppQkNDcXd3p02bNjg7OzN8+HD8/f3l4yqVio0bN9KtWzeaNGlCjx492Lp1a/G+ARVE5mYZb29v7O3t2bt3r3z80qVLNGzYkLVr18r7rl27xvDhw2nWrBktW7bE3d2diIgItXzv3r3LmDFjcHZ2plOnThw4cKBI5cw65Dhrs8zq1avp1q0bp0+fpm/fvvLfxf79+9XO27JlCz179sTR0ZF27dqxcOFC4uLiilS2ikwE9zJm27ZtzJ8/n65du+Lp6cl3332Hrq4uM2bM4OXLl2ppP/nkE7p06cKaNWuwtbVl2rRpnDlzJsd8T58+zaRJk3BwcGDdunWsXr0aGxsbvvrqKwICAgB4/fo1H374IZcvX+Y///kPa9asQU9Pj7Fjx/L06VMAFi5cyKpVq+jXrx8bNmygZ8+eLF26VC0ACelSU1Oz/eQ2VcaIESNo0aIFy5cvJyIigri4OObMmYOTkxOffvopAFevXmX06NHo6+vzww8/MGfOHK5cucLIkSPlqWtDQkIYPnw4sbGxfPvtt0ydOpXvvvuOkJCQApVZpVLJZU1ISODPP//E29ub/v3757lIRlhYGF999RUjR45k48aN1KxZE3d3dx49egTAwYMH+fbbbxk2bBibNm1i0qRJ/PbbbyxevFiTt1TIpEI2y5Rlz549Y9y4cUycOFHeZ21tjZubG9evX6dPnz7y/hEjRjBp0iQA2rVrx4ABA1i7di0dOnTIlu/Dhw8ZMGAAc+fOlfc5OzvTqlUrLl++TLNmzdi3bx9BQUHs27ePRo0aAeDi4kL//v25evUqkiTx66+/8sUXXzBhwgQA2rZti0KhwNPTk48++ghTU9M38r6UNUFBQXLbdWZffvml/N5lplAo8PDwoF+/fnz77bdoaWkRFRXFli1b5IWtV6xYga2tLZ6envK+Zs2a0adPH/bs2cOwYcP4+eefSUtLY+PGjfIC0La2tgwePLhA5c6YDjezmjVrMm3atDzPS0hIYMmSJbRp0wZI71fo1KkTZ86coW7duly5coWaNWsybNgwlEolLVu2pFKlSkRHRxeoXEJ2IriXMRkjGmJiYnj8+DH//POP/PU36xqPAwYMkH9XKBR069aN1atXy7W4zMaPHw+k186fPHlCYGCgvHByRr7Xr1+nZs2acmCH9Ee8jx49CsCOHTuQJInOnTvLD5JB+kiM9evXc/36dbp27Vrk96A8qFatGuvXr8+2P/OizFnZ2NgwY8YMFi9ejCRJeHh4YGNjA6QHz4CAAMaNG4ckSfL7b2NjQ926dblw4QLDhg3j+vXrODk5yYEd0m8AVlZWBSr3okWL5JtScnIyz549Y+PGjQwcOBAfH58888ncDp/xOuPj4wFo3bo1Pj4+uLm50bVrVzp06EDfvn0r7Bj14iCCexkTGBjI/Pnz8fPzQ0dHBzs7Oxo2bAiQ7St91vUdzczMkCSJmJiYbPlGRESwYMECTpw4gUKhoHbt2jRv3lwt36ioKMzMzHItW1RUFIDat4fMCvrVvyLQ1dXF0dFR4/N69+7NsmXLAHj33Xfl/TExMahUKry8vPDy8sp2np6eHpA+oV7NmjWzHa9WrVqBrm9ra6tWbldXV1q2bEnXrl3ZvHkz8+bNy/VcAwMD+feMZewy/rZ69+6NSqVi+/btcrOgtbU1M2bMoHfv3gUqm6BOBPcyRKVSMWHCBHR0dNi9e7e8BuTDhw/57bffsqWPiopSmzgtPDwcLS0tTExMsqWdMWMGjx8/5ueff8bZ2RldXV0SEhL49ddf5TQZkzBl9eeff1KlShW5Y23Lli1Urlw5W7qC1g6F3H399ddUrlwZXV1d5s+fj6enJ4A88+no0aNzvLlmBFZTU9NsCz3DvzfmwrCysqJq1apyv0thvffee7z33nvExsZy/vx5vLy8+M9//oOrqysWFhZFyrsiEh2qZUhkZCRPnjxh4MCBODo6ynPnnD17FkgP/pmdOHFC/l2SJI4dO4arq2uOq91fv36d7t2706pVK/l41nybN2/Os2fPePDggXxeUlISU6ZMYffu3XJNPzIyEkdHR/knIiKClStXFimACHDs2DEOHjzI7NmzmT9/PqdPn2bPnj0AGBoa0rhxYx4/fqz23tevX5/Vq1fLTXetW7fG399f7VvUw4cPefbsWaHL9fz5cyIiIuTx+YUxbdo0uX/IyMiIXr16MXHiRFJTUwkNLZ3DWUs7UXMvhV6+fMnPP/+cbX+DBg2wtrZm27ZtWFpaYmxszLlz5/D29gZQe0oQ4JtvviEpKQlbW1t27drFo0eP2LJlS47XbNq0Kb///jsODg5YWlry559/snHjRhQKhZyvm5sbW7du5bPPPuPzzz/H1NQUb29vUlJS+Oijj7CxsaFfv3783//9H0FBQTRp0oQnT57w/fffU7NmzSJ9+Cu6iIgIFi5cSNu2bXn//fcB6Nq1Kx4eHrz77rtYWlrKHdlffvkl/fr1Iy0tjc2bNxMQECB3wI8aNYrdu3czbtw4pkyZQlpaGt9//32BVyx6+PCh3MQjSRLBwcGsXbsWPT09hg8fXujX17p1axYsWMDy5ctp3749MTExrFmzhjp16sjNjoJmKmRwt6taPf9EJZh3YGAgHh4e2fYPHDiQdevWsWTJEmbNmoWuri716tVj/fr1LF26lGvXrqk9rr5w4UI8PT159uwZjRs3ZvPmzXLtOqtly5axePFieehZnTp1WLRoEQcOHODatWtAeu3wl19+4ZtvvmHx4sWoVCqcnJzw9vaWO/Y8PDzw9PRk586dvHz5EjMzM3r37s20adPkERxvi36t3PsHSnPeOVm0aBEJCQksWrRI3jd//nx69+7N3Llz2bRpE23btmXTpk2sWbOGzz//HB0dHRwcHPjpp5/kzkxTU1N27Ngh/w1VrlyZ8ePHc/jw4QKV46uvvpJ/VyqVmJiY4OTkxLffflukm/fQoUNJSUlh586dbN++HX19fdq0acN//vOfCr1UXlGUywWyExMTefLkCba2tujr66sdS1Op0FK+2daot3GNvOzdu5fZs2fzxx9/5Nh5VhFIaSoUWm/2/+BtXEMourziQXlW4f4y30bQLcnALqR7G0FXBHahNBN/nYIgCOWQCO7lkJubG/fu3auwTTKCIIjgLgiCUC6J4C4IglAOieAuCIJQDongLgiCUA6J4C4IglAOieAuCIJQDongLgiCUA6J4C4IglAOVbjgLqnSysQ1Hj16xOLFi+nRowfNmjXD1dWVoUOHsn379myrHNnb2/Pll1/mmtfgwYOxt7dn9erV8j57e3vs7e3573//m+M5KpWKdu3aZVuUuaxQqd78lEmFvcaIESPk99/e3p6GDRvi7OyMm5sb3t7eav+/xSVj0fSc5uMvbqtXr8be3r5AaYcOHYq9vb28mldWWd+rjPfLxcUFNze3HNcxENJVuFkhFUot7nh9zusXD99I/pVr1KPxx6uKlMfhw4eZPXs2devWZcyYMdja2pKYmMiZM2dYunQp586dY926dfISZEqlklOnTpGUlCRPx5rh+fPn8gLXWSmVSnx9ffniiy+yHbt69WqZnkdbqVSwy+cyYaGxbyT/atWNGDSkVaHPb9y4MQsWLAAgLS2N6Ohozp49i4eHB9euXeOHH36QVysqDh07dsTHxyfb6lwl6fHjx/j7+9OgQQN27txJjx49ckyX+b2C9PcrY1rsmTNnYmJikuO6wBVdhQvuAK9fPCQu8HZJFyNHjx49Yvbs2bRr144ffvhBXpADoEOHDrRq1YrPP/+cI0eOyMuPubi4cO3aNc6ePUu3bt3U8jt8+DCNGjXi77//znatjPPu3LlD48aN1Y4dOnQo1/PKirDQWF4ER5V0MXJkaGiotqYopH8Ls7OzY8mSJRw8eJB+/foV2/WqVq2qtm5qabB3716sra355JNPmDFjBv/88w+1a9fOli6n9wqgffv2tGnThr1794rgnoMK1yxT2v34448olUoWLVqkFtgz9OjRg/79+6vts7GxoUmTJvj6+mZLf/jw4VzXNG3RogXm5ubZzktNTeXYsWO5nie8OcOHD8fCwoKdO3eq7d+1axd9+vShSZMmdOzYkdWrV5OWpt78d+bMGYYOHYqTkxNt27Zl/vz58nq5WZtlZs2axbhx4/Dx8aFr1640bdqUoUOH8uTJE06dOkXfvn1p1qwZgwYNynaD37VrF25ubjg5OdG0aVPef/99jhw5otHrTEtLY//+/XTq1ImuXbtSqVIlfHx8NMpDT08PXV1dsYh2LkRwL2X++OMPWrdunedC1MuXL8+2aHDv3r3lppkMjx8/5u7du7kGaS0tLXr06JEtuPv5+ZGUlETnzp2L8EqEwlAqlbRp04abN2/Kbe+enp783//9H23atGHDhg0MGzYMLy8v/u///k8+79SpU3zyySeYmZnxww8/MGPGDE6cOMH06dNzvZa/vz+//PILs2bNwsPDg0ePHjFhwgQ8PDz45JNP+O9//8uLFy+YMWOGfM62bduYP38+Xbt2xdPTk++++w5dXV1mzJjBy5cvC/w6z549S1hYGP3790dfX59evXqxb98+kpOTs6WVJInU1FT5JykpicePHzN79mxev34tr0wlqKuQzTKlVXR0NNHR0TmuaJO1k02hUKitbNSrVy++/fZbtaaZw4cP4+zsnOfC1L1792bbtm1qTTOHDx+mS5cu2drvhbfD3NyclJQUoqKi0NPTY926dQwZMoR58+YB0LZtW0xMTJg3bx5jxoyR10lt1KgRa9askWuyurq6rFy5MscFsQFev37NDz/8QN26dQG4cuUKO3fu5Oeff6ZNmzYA/PPPPyxfvpyYmBiMjY159uwZ48aNk5ftA7C2tsbNzY3r168X+Nve3r17adCgAY6OjkD6TKa7d+/m6NGj9O3bVy3t1atXcXBwUNunUCho0KABK1eupFOnTgW6ZkUjgnspknWB6wz//PMP3bt3V9tnbW3NyZMn5W0rKyucnJzw9fVVC+7Dhg3L85oZK8v7+vrSuHFjkpOTOXHiBN9++20RX41QWBmLoykUCvz9/UlMTKRz587ZRkkBXLhwARsbG+7cucOUKVPUmih69+6d7RteZlWqVJEDO6TfVACaNWsm7zMxMQGQg/usWbPk7cePH/PPP//Ii2/nVOvOSUREBKdOneLTTz+Vm43q16+PtbU1Pj4+2YK7g4ODvLxgaGgoP/zwAykpKfzwww/Y2dkV6JoVkQjupYipqSmVKlUiKChIbX+NGjXYvXu3vL127Vru37+f7fxevXqxcuVKkpKSePLkCU+fPqVnz555XlOhUNCzZ0951My5c+dQKpW8++67hISEFM8LEzQSEhKCvr4+JiYmREVFATBhwoQc04aGhhIdHY0kSXk25eXE0NAwx/2VKlXK9ZzAwEDmz5+Pn58fOjo62NnZyQtYF3TFzgMHDpCSksLq1avVhucCBAUF8ejRI7WbTuXKleUaPqTffPr168fYsWPZu3dvqesoLi1EcC9lOnfuzKlTp4iLi5M/fLq6ump/3Bm1qax69uzJsmXLOHfuHLdu3cq37T5D79692bJlC3///TeHDx+me/fuYlHiEpKamsrly5dxcXFBS0sLY2NjAL777rscm+vMzc0xNDREoVAQERGhdiwpKYlLly6p1cSLQqVSMWHCBHR0dNi9ezeNGjVCW1ubhw8fajTefM+ePTg7O2frD4iPj2fixIns2LFDboLKibm5OfPnz2fq1KksWbKEFStWFPo1lWeiQ7WUmTBhAqmpqcybNy/Hr7mJiYk8e/Ysx3MtLCxwdXXF19eXI0eOFLj908nJCWtra3777TdOnjwpRsmUIB8fH8LCwvjwww+B9Fqqjo4OISEhODo6yj/a2tr897//5fnz51SuXJlGjRpx6tQptbzOnj3LhAkTiu15hcjISJ48ecLAgQPlMmRcB3JvVszs1q1b3L9/Hzc3N1q1aqX206lTJ1q3bs1vv/1GYmJinvn07NmTdu3acfDgQa5cuVL0F1cOVciae+Ua9Upt3vb29nz77bfMnj0bNzc3Bg4ciL29Pampqfj7+7N7927Cw8MZP358juf36tULDw8PFApFtnb6vPTs2RNvb29MTExo2bJlkV5DaVGtulGpzTsuLo4bN24A6UExMjKS8+fP4+PjQ79+/eT/O1NTU8aPH8/KlSuJi4ujVatWhISEsHLlShQKhdwk8vnnn/PZZ5/xxRdf0L9/f8LDw/nvf/9L165dadCgAbdvF/25DjMzM6ytrdm2bRuWlpYYGxtz7tw5vL29AUhISMg3jz179qCjo5Pr3+b777/PxYsXOXz4MG5ubnnmNWfOHPr168fXX3/Nvn371AYYCBUwuEuqtCI/QVqQayiUhf9D69GjB02aNGHHjh3s3r2boKAgJEnCxsaG3r17M3To0By/okN6kF6yZAkdO3bEyKjgAah3795s2rSJXr16FeuTkSVFpZKK9ARpQa+hVBZujPWdO3cYMmQIkN7vUblyZRo0aMDChQsZNGiQWtpp06ZRrVo1tm/fzo8//kiVKlVo06YNX3zxhfx/3KlTJzZs2MCaNWuYNGkSVatWpW/fvkyZMqVoLzKLdevWsWTJEmbNmoWuri716tVj/fr1LF26lGvXrjFixIhcz01KSuLQoUO8++67uTYtdu/enUWLFrFz5858g7udnR0jRoxg8+bN7Nixg+HDhxflpZU7CqmgvSBlSGJiIk+ePMHW1hZ9ff2SLo4gCCWoosaDsl9FEwRBELIRwV0QBKEcEsFdEAShHBLBXRAEoRwSwV0QBKEcKtfBvRwOBBIEQUMVNQ6Uy+Ce8eh8fHx8CZdEEISS9vr1axQKRYWbUqNcPsSkpaWFiYmJ/Nh1pUqVxIT+glCBZMwBHxMTQ0xMDCYmJhXuCdZy+RATpP/nvnz5Up5VTxCEikdLS4vq1atTpUqVClfBK7fBPUNaWhopKSklXQxBEN4ybW1ttLS0KlxQz1Dug7sgCEJFVC47VAVBECo6EdwFQRDKIRHcBUEQyiER3AVBEMohEdwFQRDKIRHcBUEQyiER3AVBEMqh/wdZdfpG8TIZygAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pickle\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "sns.set_style(\"whitegrid\")\n", + "import matplotlib \n", + "# matplotlib.rcParams.update({'font.size': 18})\n", + "\n", + "ds_map = {\n", + " \"Exponential\": \"Exponential\",\n", + " \"square_student_t_mixture\": \"Square + Student t\",\n", + " \"ARIMA Residuals\": \"ARIMA Residuals\",\n", + "}\n", + "\n", + "#increase vertical spacing\n", + "fig, ax = plt.subplots(\n", + " 3, 2, \n", + " figsize=(3.5, 3),\n", + " gridspec_kw={'width_ratios': [6, 1.5], 'hspace': 0.5, 'wspace': 0.1},\n", + ")\n", + "\n", + "w_dist_map = {\n", + " \"Exponent.\": \"Exponential\",\n", + " \"Square +\\nStudent-t\": \"Square + Student t\",\n", + " \"ARIMA\\nResiduals\": \"ARIMA Residuals\",\n", + "}\n", + "\n", + "csv_fn = \"../precomputed_outputs/uncertainty_quantification/wasserstein.csv\"\n", + "df = pd.read_csv(csv_fn)\n", + "df[\"dataset\"] = df[\"dataset\"].apply(lambda x: w_dist_map[x])\n", + "print(df)\n", + "\n", + "samples_fn = \"../precomputed_outputs/uncertainty_quantification/samples.pkl\"\n", + "with open(samples_fn,'rb') as f:\n", + " out_all = pickle.load(f)\n", + "\n", + "for i,(ds, datadict) in enumerate(out_all.items()):\n", + " print(ds)\n", + "\n", + " samples = datadict['data']\n", + " rnn_samples = datadict['RNN-8']\n", + " # make seaborn histograms\n", + "\n", + " bins = np.quantile(samples,.005),np.quantile(samples,.99)\n", + " ax[i,0].set_xlim(*bins)\n", + "\n", + " sns.histplot(\n", + " samples,\n", + " ax=ax[i,0],\n", + " kde=False,\n", + " bins=50,\n", + " stat='density',\n", + " alpha=.7, \n", + " binrange=bins,\n", + " label='Ground Truth',\n", + " color='k',\n", + " edgecolor=None\n", + " )\n", + "\n", + " ar_color = '#4f01ecff'# sns.color_palette('Dark2')[3]\n", + " sns.histplot(\n", + " rnn_samples,\n", + " ax=ax[i,0],\n", + " kde=False,\n", + " bins=50,\n", + " stat='density',\n", + " alpha=.3, \n", + " binrange=bins,\n", + " label='Decimal AR',\n", + " color=ar_color,\n", + " edgecolor=None\n", + " )\n", + " \n", + " if i == 1:\n", + " y_label = ax[i,0].yaxis.get_label().get_text()\n", + " print(y_label)\n", + " ax[i,0].set_ylabel(y_label, labelpad=7, fontsize=14)\n", + " else:\n", + " ax[i,0].set_ylabel(\"\")\n", + "\n", + " if i!=2:\n", + " ax[i,0].set_xticks([])\n", + "\n", + " ax[i,0].set_title(ds_map[ds])#, y=0.65, x=0.27, fontsize=14)\n", + " ax[i,0].grid(False)\n", + " ax[i,0].set_yticks([])\n", + " ax[i,0].set_xticklabels([])\n", + "\n", + " _df = df[df[\"dataset\"] == ds_map[ds]]\n", + " sns.barplot(\n", + " x='dataset',\n", + " y='Wasserstein Distance',\n", + " hue='method',\n", + " hue_order=[\"Laplace\",\"GMM\",\"Fixed Bins\",\"Decimal AR\"],\n", + " errwidth=1,\n", + " data=_df,\n", + " ax=ax[i,1], \n", + " palette=sns.color_palette('Dark2')[:2] + sns.color_palette('Dark2')[3:4] + sns.color_palette('Dark2')[2:3]\n", + " )\n", + " ax[i,1].get_legend().remove()\n", + " ax[i,1].set_title(\"\")\n", + " ax[i,1].set_xlabel(\"\")\n", + " ax[i,1].set_xticklabels([])\n", + "\n", + " #flip y axis labels to be on the right\n", + " ax[i,1].yaxis.tick_right()\n", + " ax[i,1].yaxis.set_label_position(\"right\")\n", + "\n", + " if i == 1:\n", + " y_label = ax[i,1].yaxis.get_label().get_text()\n", + " #flipped horizontally \n", + " ax[i,1].set_ylabel(y_label, labelpad=20, fontsize=14, rotation=270)\n", + " else:\n", + " ax[i,1].set_ylabel(\"\")\n", + "\n", + "# plt.subplots_adjust(wspace=0, hspace=0)\n", + "ax[0,1].legend(\n", + " # loc='upper right', \n", + " ncol=2, \n", + " labelspacing=0.5,\n", + " handlelength=1.5, \n", + " columnspacing=1.5,\n", + " fontsize=12,\n", + " bbox_to_anchor=(1.25, -3),\n", + " frameon=True,\n", + ")\n", + "\n", + "# plt.tight_layout()\n", + "plt.subplots_adjust(hspace=0.1) # adjust space between axes\n", + "plt.savefig('outputs/histograms.pdf', bbox_inches='tight')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# import pickle\n", + "# import matplotlib \n", + "# import matplotlib.pyplot as plt\n", + "# import seaborn as sns\n", + "# import numpy as np\n", + "\n", + "# sns.set_style(\"whitegrid\")\n", + "# matplotlib.rcParams.update({'font.size': 18})\n", + "\n", + "# with open(\"uncertainty_results.pkl\", 'rb') as f:\n", + "# out_alls = pickle.load(f)\n", + "\n", + "# samples = out_alls[0][\"square_student_t_mixture\"]['data']\n", + "\n", + "# fig, ax = plt.subplots(1, 1, figsize=(5, 3))\n", + "\n", + "# bins = np.quantile(samples,.009),np.quantile(samples,.991)\n", + "\n", + "# # samples = (samples + 7) / 2\n", + "# # bins = (bins[0] + 7) / 2, (bins[1] + 7) / 2\n", + "\n", + "# sns.histplot(samples,ax=ax,kde=False,bins=100,stat='density',alpha=.7, binrange=bins,label='GT',color='k')\n", + "\n", + "# ax.set_ylim(0,0.33)\n", + "# ax.set_xlabel(\"Value\")\n", + "\n", + "# y_ticks = ax.get_yticks()\n", + "# y_tick_labels = ax.get_yticklabels()\n", + "\n", + "# y_ticks = y_ticks[:3]\n", + "# y_tick_labels = y_tick_labels[:3]\n", + "\n", + "# ax.set_yticks(y_ticks)\n", + "# ax.set_yticklabels(y_tick_labels)\n", + "# ax.set_xlabel(\"\")\n", + "\n", + "# ax.set_xlim(*bins)\n", + "\n", + "# ax.spines[['right', 'top']].set_visible(False)\n", + "# ax.spines['left'].set_bounds(0, 0.12)\n", + "\n", + "# plt.grid(False)\n", + "\n", + "# color1 = '#a60355'\n", + "# color2 = '#5400c2'\n", + "\n", + "# h1, h2, h3, h4 = np.linspace(0.3, 0.12, num=4, endpoint=True)\n", + "# x1, x2 = ax.get_xlim()\n", + "# y1, y2 = ax.get_xlim()\n", + "# x = (x1 + x2) / 2\n", + "\n", + "# plt.text(\n", + "# x-14, h1 + 0.05, \n", + "# \"0.195 $\\\\rightarrow$ ['1','9','5']\", \n", + "# color=\"black\", fontsize=12,\n", + "# bbox=dict(\n", + "# facecolor='none', edgecolor=color1, boxstyle='round', linewidth=0.5\n", + "# )\n", + "# )\n", + "# plt.text(\n", + "# x+3, h1 + 0.05, \n", + "# \"0.537 $\\\\rightarrow$ ['5','3','7']\", \n", + "# color=\"black\", fontsize=12,\n", + "# bbox=dict(\n", + "# facecolor='none', edgecolor=color2, boxstyle='round', linewidth=0.5\n", + "# )\n", + "# )\n", + "\n", + "# for i in range(10):\n", + "# bin_size = (x2 - x1) / 10\n", + "# y = x - 4.5 * bin_size + bin_size * i\n", + "\n", + "# if i == 1:\n", + "# plt.plot([x, y], [h1, h2], linewidth=1.5, color=color1)\n", + "# plt.text(\n", + "# (x+y)/2 - 6, (h1+h2)/2 + 0.01, \n", + "# \"P$\\,$('1')\", \n", + "# fontsize=11, color='black',\n", + "# bbox=dict(\n", + "# facecolor='white', edgecolor=color1, boxstyle='round', linewidth=0.5\n", + "# )\n", + "# )\n", + "# elif i == 5:\n", + "# plt.plot([x, y], [h1, h2], linewidth=1.5, color=color2)\n", + "# plt.text(\n", + "# (x+y)/2 + 1, (h1+h2)/2 + 0.01, \n", + "# \"P$\\,$('5')\", \n", + "# fontsize=11, color='black',\n", + "# bbox=dict(\n", + "# facecolor='white', edgecolor=color2, boxstyle='round', linewidth=0.5\n", + "# )\n", + "# )\n", + "# else:\n", + "# plt.plot([x, y], [h1, h2], linewidth=0.25, color='black')\n", + " \n", + "# for j in range(10):\n", + "# bin_size = (x2 - x1) / 100\n", + "# z = y - 4.5 * bin_size + bin_size * j\n", + "# # plt.scatter(z, 0.15, s=5, linewidth=0.5, edgecolor='black',color='grey')\n", + "# if i == 1 and j == 9:\n", + "# plt.plot([y, z], [h2, h3], linewidth=1.5, color=color1)\n", + "# plt.text(\n", + "# (y+z)/2 + 1.5, (h2+h3)/2 - 0.01, \n", + "# \"P$\\,$('9' | '1')\", \n", + "# fontsize=11, color='black',\n", + "# bbox=dict(\n", + "# facecolor='white', edgecolor=color1, boxstyle='round', linewidth=0.5\n", + "# )\n", + "# )\n", + "# elif i == 5 and j == 2:\n", + "# plt.plot([y, z], [h2, h3], linewidth=1.5, color=color2)\n", + "# plt.text(\n", + "# (y+z)/2 + 1, (h2+h3)/2 - 0.01, \n", + "# \"P$\\,$('3' | '5')\", \n", + "# fontsize=11, color='black',\n", + "# bbox=dict(\n", + "# facecolor='white', edgecolor=color2, boxstyle='round', linewidth=0.5\n", + "# )\n", + "# )\n", + "# else:\n", + "# plt.plot([y, z], [h2, h3], linewidth=0.1, alpha=0.5, color='black')\n", + "\n", + "# for k in range(10):\n", + "# bin_size = (x2 - x1) / 1000\n", + "# zz = z - 4.5 * bin_size + bin_size * k\n", + "# # plt.scatter(z, 0.15, s=5, linewidth=0.5, edgecolor='black',color='grey')\n", + "# if i == 1 and j == 9 and k == 4:\n", + "# plt.plot([z, zz], [h3, h4], linewidth=1.5, color=color1)\n", + "# # plt.plot([z, zz], [h3, h4], linewidth=0.01, alpha=0.1, color='red')\n", + "# plt.text(\n", + "# (z+zz)/2 - 7, (h3+h4)/2 - 0.01, \n", + "# \"P$\\,$('5' | '19')\", \n", + "# fontsize=11, color='black',\n", + "# bbox=dict(\n", + "# facecolor='white', edgecolor=color1, boxstyle='round', linewidth=0.5\n", + "# )\n", + "# )\n", + "# elif i == 5 and j == 2 and k == 9:\n", + "# plt.plot([z, zz], [h3, h4], linewidth=1.5, color=color2)\n", + "# plt.text(\n", + "# (z+zz)/2 + 1, (h3+h4)/2 - 0.01, \n", + "# \"P$\\,$('7' | '53')\", \n", + "# fontsize=11, color='black',\n", + "# bbox=dict(\n", + "# facecolor='white', edgecolor=color2, boxstyle='round', linewidth=0.5\n", + "# )\n", + "# )\n", + "\n", + "# if i == 1 and j == 9:\n", + "# plt.scatter(z, h3, s=1, linewidth=0.1, edgecolor=color1,color=color1, zorder=10)\n", + "# elif i == 5 and j == 2:\n", + "# plt.scatter(z, h3, s=1, linewidth=0.1, edgecolor=color2,color=color2, zorder=10)\n", + "# else:\n", + "# plt.scatter(z, h3, s=1, linewidth=0.1, edgecolor='black',color='grey', zorder=10)\n", + "\n", + "# if i == 1:\n", + "# plt.scatter(y, h2, s=10, linewidth=0.5, edgecolor=color1,color=color1, zorder=10)\n", + "# elif i == 5:\n", + "# plt.scatter(y, h2, s=10, linewidth=0.5, edgecolor=color2,color=color2, zorder=10)\n", + "# else:\n", + "# plt.scatter(y, h2, s=10, linewidth=0.5, edgecolor='black',color='grey', zorder=10)\n", + "\n", + "# plt.scatter(x, h1, s=20, linewidth=0.5, edgecolor='black',color='grey', zorder=10)\n", + "\n", + "# # ycoords = ax.get_label_coords()\n", + "# # print(ycoords)\n", + "# ax.set_yticks([])\n", + "\n", + "# #set xticks to split xlim into 10 equal parts\n", + "# ax.set_xticks(np.linspace(*ax.get_xlim(), num=6, endpoint=True))\n", + "# ax.set_xticklabels([\n", + "# '0.0', '0.2', '0.4', '0.6', '0.8', '1.0'\n", + "# ])\n", + "\n", + "# ax.yaxis.set_label_coords(-0.04,0.2)\n", + "\n", + "# # ax.xaxis.set_label_coords(0.5,-0.13)\n", + "# plt.show()\n", + "\n", + "# fig.savefig(\"hierarchical.pdf\", dpi=300, bbox_inches='tight')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/figures/figure_4.ipynb b/figures/figure_4.ipynb new file mode 100644 index 0000000..b505c7a --- /dev/null +++ b/figures/figure_4.ipynb @@ -0,0 +1,484 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/4z/0371zfqj5mx0rlct4r3qclfm0000gn/T/ipykernel_82335/1589168628.py:148: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n", + " plt.tight_layout()\n" + ] + }, + { + "data": { + "image/png": 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zHdbrO0kMwzAMwzAMwzDVxTpJDMMwDMMwDMMwpbBOUimZmZk1nQWGqXEhISFIS0ur8riXL19iypQp1Uo7IyMDixYtQq9evaCjo4Phw4fj9u3bn5vVGsfKDIZhmPqLlfHfNtZJ+v/evXuHwYMH4927dzWdFYapUYsXL8abN2+434VCIXR0dPD06VOx4zIzM3H27Nlqpe3p6YmoqCj4+PggLCwMqqqqGDt2LOLi4qSS96+JlRkMwzD1FyvjGdZJ+v9ycnIgFAqRk5NT01lhmBr1adQXIkJeXh5KSkq+KN1Xr17h6tWrWLx4MfT09NCxY0csXLgQrVu3xvHjx78o7ZrAygyGYZj6i5XxDOskMQzzVbRo0QI7d+4UiyokJycHOTk5ZGVl1WDOGIZhGIZhxNXrEOAMw9QezZo1g6mpqdhjZ86cwatXrzBv3rzPSlN0l6sqBQUF3E9JjpeELNJkGKb+IiLIycnVdDYYhpEQ6yQxDFMj7t69i7lz58La2hpmZmaflUZRURGePHlS5XGiNVbx8fFS21BWFmkyDFO/KSoq1nQWGIaREOskMQzz1f3333+YOXMmdHR0sH79+s9Op2HDhujSpYtExwFAx44dJTpe0teWdpoMw9RfsbGxNZ0FppbIzMzE999/X9PZYKrAOkkMw5Rx8eJFLuKcUCiEnJwcLly4gJiYGO6YhISEz0o7KCgIK1asQN++fbFmzZovGlmVk5ND48aNqzxOSUmJ+ynJ8ZKQRZoMw9RfbKodA3yMmjdq1Cjs378fP//8c01nh6kE6yQxDFPG1q1byzzm6+tb5rHqVvp//fUXli1bhhEjRmD+/Pms0cAwDMN8U1jUvLqDdZIYhhFT3b2PJBUfH4+VK1eid+/ecHNzQ0pKCveckpISvvvuO5m8LsMwDFN3REdHw9vbGy9fvoSamhpWr16N9u3bV3j8rFmzIC8vj9WrV3/FXDLfAtZJYhhGzC+//CLxsVeuXJH4+DNnzqCoqAgRERGIiIgQe87BweGLKrj8/HwIBIIKnxeN2OXk5FS4g7qioiKUlZU/Ow8MwzDMlyksLIS7uzvmzJkDa2tr7Ny5E9OnT8fhw4fLPf6///7DiRMnYG9v/5VzWvuwdU7SxzpJDMNUS1paGsLCwhAaGoo3b95IFF0OACZOnIiJEydKPT/5+fk4deoU8vPzKzxGdNfq8uXLFeZXWVkZ/fr1Yx0lhmGYGnLjxg00b94cfD4fADBp0iTs378fsbGxZQLkpKWlYd26dRg4cOAXb3Ze17F1TrLBOkkMw0jk1q1bCA4ORkREBIqKitC+fXu4u7vXdLYgEAiQn5+Pdu3aVRhAoWnTpgCAX3/9tdw7X3l5eXj9+jUEAgHrJDEMw9SQ+Ph4dOrUiftdXl4e7dq1Q1xcXJlO0pIlSzBu3DgkJibi7du3XzurtQpb5yQbrJPEMEyFsrKycPjwYYSGhiI+Ph4A0LdvX4wYMQI6Ojo1nDtxjRs3rnBdk6jzVNkxDMMwTM3Ky8vjIoeKKCsrl5kpcPLkSeTm5mLIkCHlBhWqTE1uQi6rdNnm5pKrzqbOrJPEMEwZUVFRCA4OxpkzZyAQCGBoaIiRI0di8eLFcHJyqnUdJIZhGKbuU1ZW5hr8Ivn5+WjSpAn3e0pKCnx8fBAYGPhZryHahLygoADFxcUVHvf+/XsAwOPHj5Genl7hcQoKCmU6dpVhm5vXPEm3HmGdJIZhxNjZ2SEmJga//vor3NzcMGDAAPzvf/9DdnY2Fi9eXNPZYxiGYeqpTp064ciRI9zvJSUlSEhIQMeOHbnHrl69ipSUFPTv3x/Ax2APRIRHjx7h+PHjVb5Gw4YN8csvvyAiIqLStaypqakAPnZAKjtOWVkZvXv3lniqNtvcvGZVZ1Nn1kliGEbM8+fPwePxMGbMGPTq1QstW7as6SwxDMMw34Du3bsjNTUV//zzD2xsbLBz5060b98enTt35o6xt7cXi2bn6+uLt2/fShwhVU5ODgoKCigpKUHnzp0rXMv69u1bnDp1Cr/99luFUVxF61kVFBQk3lScbW5es6qzP2MDGeaD+f8qCjnMMLXR3r170aVLF3h7e6NXr16YOHEi/vvvP3YLn2EYhpEpJSUl+Pn5ITAwEN27d8e1a9ewadMmAICtrS2OHTsm1dcTrVMt79+na1krO4apn9idJBljYRmZuqZHjx7o0aMHsrOzcezYMRw+fBhTpkzB999/Dzk5Obx69Qr6+vo1nU2GYRimHtLQ0EBYWFiZx8PDw8s93sPDQ9ZZqha2b1/9wTpJMiarsIxs0zBG1r777js4OzvD2dkZz549Q1hYGI4fP46FCxdi69atsLGxgY2NDdTV1Ws6q/UOu74ZhmHqHrZvX/3COkl1ELs7xXxtPB4P8+bNg5eXF86dO4ewsDDs27cPe/fulXgzWUYy7PpmGIapm9i+ffUL6yTVQWzTMKamKCgowNraGtbW1khKSsLRo0drOkv1Dru+GYZh6rb6tG+fLGY21JXZEqyTxDCMmHfv3kl8rK2trQxzwjAMwzBMTZHFzIa6NFuCdZIYhhFjaWlZrePZdDuGYRiGqX9kMbOhLs2WYJ0khmHEEBEAQE1NDX379sWPP/5YwzliGIZhmG+XNCLmASxqXnWxThIjpq7ME2Vk5+TJk9y/zZs3w8DAALa2tujTp0+dmD/NMAzDMPWFtCLmASxqXnWxThLDqUvzRBnZ6dSpE6ZMmYIpU6bg6dOnOHnyJHbs2IGlS5fC2NgY/fv3h4WFBbfDN8MwDMMwsiGNiHkAi5r3OVgnieHUpXmizNehoqICFRUVeHp6Ijo6GidPnsTatWsxf/58WFhYwNbWFhYWFjWdTYZhGIap1+pTxDxZkfZsKNZJYhhGIlpaWtDS0sKsWbOwY8cO7NixAydPnqzXgRsqmwfO5oAzDMMwTO0gi9lQNd5Jio6Ohre3N16+fAk1NTWsXr0a7du3FzsmOzsbS5cuxeXLlyEvL49+/frBy8sLioqKNZRrpjrYOqe6TygU4saNGzh9+jQiIiKQnp4OTU1N2NjY1HTWZKaqeeBsDjjDMEz91bhxY8jJyVU4xa0+kEZAiNoyECiL2VBS7yQVFBTg5cuXUFFRqfLYwsJCuLu7Y86cObC2tsbOnTsxffp0HD58WOy4tWvXorCwEGfPnkVhYSEmTZqEvXv3YuLEidXKGxsV/vrYOqe6q7yOkaqqKsaMGYN+/fqhXbt2NZ1FmapqHjibA84wDFO16rQLa5NWrVphzZo1XFlf30grIMSnA4GZmZnIy8urMs2UlJRKpwY2bty4xgfYJe4kmZiYYNeuXVBVVeUe8/f3h729PVq2bMk99uzZMwwbNkyiKTg3btxA8+bNwefzAQCTJk3C/v37ERsbiy5dunDHEREmT56MJk2aoEmTJuDz+bh69aqkWQfARoVrClvnVPdcu3aN6xhlZGSgS5cuGDFiBGxsbPDrr7/WdPa+uormeLM54AzDfMtk0S6sbWTRQaotd6ikERDi04HAzMxMbPnTF8XCkgpfNzs7GwBw+PDhSutOhQbymDLVo0Y7ShJ3klJSUlBUVMT9XlJSgrVr18LAwEDsYqiO+Ph4dOrUiftdXl4e7dq1Q1xcnFgnafny5WJ/d/HiRaipqVXrtdioMMNIxtXVFfLy8tDR0UG/fv3w22+/AQCSk5ORnJxc5nh9ff2vnUWGYRimhsmiXfgtqG13qKQZECIvLw/FwhI0+e8N5NMLyz2GFATAD0DT/96gWXH5y2ZKWjRCrlVb5OXl1Y1OUnlEm05+rry8vDJhhJWVlSu99bdu3TrExcVh3bp1EucxLy8PeXl5EAgEkJeXL3ctk4KCAvezorVOhYWFEAgEyMvLQ8OGDSV6/YKCAu5nZbcfq0MWacoqXVnlFajZtU5EBDk5uRp57a+hpKQEkZGRuH37ttjjomteTk6O+wzq4uggwzAMI31f2i78VtSWDpKsyKcXQiGloPznlIqBHwD5DAEUCoRfOWfVU6OBG5SVlblGtEh+fj6aNGlS5tji4mJ4e3vj5s2b2LdvH1q0aCHRaxQVFeHJkyfIyclBYmIimjZtWm76SUlJ3M+KGr+5ublITEzE8+fPJT7B37x5A+DjXbPSIy5fQhZpyipdWeU1NTUVa9euhZeXF3744QeppVsd9TVwSEBAQE1ngZEBFkCFYRiGqaukFWSiOmq0k9SpUyccOXKE+72kpAQJCQno2LGj2HECgQDu7u7IyMhASEhItRrFDRs2RJcuXZCZmYm4uDj8/PPP5d4yFI1+tG7dGm3bti03rezsbOTk5OD333+XuLEhuuPUsWNHsSmEX0IWacoqXVnlNTY2FkSEn376SarpVuf16ysDA4OazgIjZSyACsMwDFNXSTPIRJcuXSSeCVSjnaTu3bsjNTUV//zzD2xsbLBz5060b98enTt3Fjtu2bJlyMrKQkBAQLXXAokWxxUVFUFRURGNGjVCo0aNyhwn6l2KjimPQCCAoqIiGjduLPGCO9F0QiUlJakt0pNFmrJKty7ltTrq81Q7pv5hAVQYhmGYukqaQSaqs1ziiztJX9JYVFJSgp+fH7y9vbFkyRKoqqpi06ZNAABbW1u4ubnB3Nwchw4dgoKCAnr06MH9ra6uLnbv3v2l2WcYMWxKEsMwDMN8PjaIyMiKNINMSKJanSR3d/cy8/kmTpwoFsSgsvmC5dHQ0EBYWFiZx8PDw7n/s4XhzNfApiQxDMMwjORk0S5kqq+2hBWvbyTuJDk4OMgyH3VWfdqt+FvHpiQxTPWxu68M821i7cLaQ1Zhxb/1zpfEnaRVq1bJMh91kqx2KxalXVHnS5KOF8A6XwxTG9WngRV295Vhvl2sXVi7yCKseG3b0+lrk2rgBoFAgJMnTyI4OBjBwcHSTLpWksVuxUDVnS9JOl5A+Z0vhqnK3Llzq3U8qyglJ8uBlZrA7r4yDFOZb61dWB9Ju4PUsEQOoP//s5aTSicpLi4OwcHBOHr0KDIzM8vdh6g+k/ZCsqo6X1V1vIDyO18MI4mbN2+K/Z6UlITi4mL8/PPP+PHHH5GRkYHXr19DUVERKioqNZTLuklWAysMwzC1ybfeLmQq1qRIHv2etUCjkgZSTVcWUwM/u5NUXFyMM2fOIDg4GLdv34acnBwMDQ1hb28Pa2trqWXwW1ZRx0oWETwYRuTcuXPc/48fP47169fD19cXXbt25R6PjY3F5MmT0a9fv89+HT8/P1y5cgWBgYFflN+66GtH6GEYhpE11i5kJCXtDhIgm6mB1e4kvX79GiEhIThy5AjS0tK4eejbt2+Hqamp1DLGMEzN27hxIzw9PcU6SADQpUsXTJ8+HatWrcKoUaOqne6BAwewadMm6OnpSSur3zy2jrF+YQExmLqCtQuZ2kLaUwMl7iRFREQgODgY165dQ+PGjdGvXz8MHDgQXbp0gYGBwTcb+YJh6rP09HQ0a9as3OcUFBSQl5dXrfQ+fPgAb29v3Lx5E7/++qsUcsgA9W8do6w6CHWl41HXAmLUlc+VkS7WLmTqO4k7SR4eHuDxeNiwYQMsLS3RqFEjAEB2drbMMsdIV32KqsV8Hd26dcP27duho6Mj1ghKSkqCr68vunfvXq30Hj16hIYNG+LYsWPYunUr3r59K+0sf5Pq0zpGWXUQ6lLHoy4FxKhLnysjXaxdyNR3EneSunXrhnv37sHHxwdRUVFwcHCAmpqaLPPGSFF9i6rFfB2zZ8/GiBEjYG5uDm1tbTRv3hypqamIiorC999/j+3bt1crPQsLC1hYWMgot4y01zHKamClsnQTExMhFAqRmJhY6WLv6g7YyKrj8a3fRalLHTpGuli7kKnvJO4kBQcHIz4+HmFhYTh69CiCgoLw22+/oW/fvpCTq/1h/L51LKoW8zlUVFRw4sQJ7Nu3D3fv3sWbN2/QokULuLq6YtSoUWjevHmN5o+IkJeXB4FAgMLCwjI7v4uIGuSi4z5VWFgIgUCAvLw8bqf4qtKtKs3y0q1Lec3Pz0dERESlAyupqakAPgb7iI6OLvcYZWVl9O7dW2yLg8rSlSTN8tKtSkFBAfezutNEK5KYmAg3Nzf4+fnhp59+kkqagGzyCsimQyervAL1rwNKRPWqvcTahUx9V63ADR07dsTMmTPh6emJixcv4vDhw9i2bRuICBs3bsTAgQNhbW1d4RoGpuaxqFpMdbVp0wazZ8+u6WyUq6ioCM+fP0diYiKaNm1a4Z2HpKQk7md5lXdubi4SExPx/PlzbrAgJyen0nSrSrO8dKtKs7blNT4+Hr/88kuFHRFRR6dx48blLpjNz89HfHw8Hj58KJbXytKtKs2K0q3KmzdvAADx8fEoKiqS6G8kSVMoFOLBgwdIT0+XSpqidAHp5jU1NRVr166Fl5cXfvjhB6mkCcgmr4Ds8lvTKhocqatk0S6Mjo6Gt7c3Xr58CTU1NaxevRrt27cXOyY7OxtLly7F5cuXIS8vj379+sHLy6vefb5MzfqsEOANGjSAubk5zM3NkZ6ejqNHj+Lw4cNYsGABlixZAhMTk2pPw2HqrszMzApHEEVT+FJSUirteDVu3LhejRjWJwKBAIcOHcK1a9eQnJyMlStX4tatW1BXVy8T9e5ra9iwIX7//XfExcXh559/rvAcIyIAQOvWrdG2bdsyz2dnZyMnJwe///47dx5mZmZWmm5VaZaXblVp1sa8du7cucqBlQ4dOqBdu3blpllSUlJuXitK94cffoCcnBxUVFTQqlWrCvP6abpVTQ0U3UH78ccfK107U51pfKI7eR07dkSXLl0k+puaSjc2NhZEhJ9++qnW5xWQXX5rUmxsbE1nQWak1S4sLCyEu7s75syZA2tra+zcuRPTp0/H4cOHxY5bu3YtCgsLcfbsWRQWFmLSpEnYu3cvJk6cKKu3yHyDvngz2RYtWmD06NEYPXo0Hj58iEOHDuHUqVPSyBtTB2RmZmLLn74oFpaU+7xoAefhw4cr7SQpNJDHlKkerKNUy6SlpWHUqFGIi4tDp06dEBsbi4KCAly4cAGrV6/Gvn37oK2tXWP5E20cp6ioiEaNGnELhz8lGl0UHfcpgUAARUVFNG7cmGv4FxUVVZpuVWmWl25VabK8Ar/88kuVe118mm5+fj4uXrwo0ZrLyMhIxMfHV3jcp+suK+t8FRcXcz8ru4vyacerqg7d56ZbGSUlJe6nNKOO1bV0a9K3MgXtS9qFN27cQPPmzcHn8wEAkyZNwv79+xEbGyvWWSYiTJ48GU2aNEGTJk3A5/Nx9epVmbwf5tslcSdp7ty5EidqYmLyWZlh6p68vDwUC0vQ5L83kE8vu9aBFATAD0DT/96gWXH5t8FLWjRCrlVb5OXliY02Vza/XZI7VOzu1Jdbu3YtcnNzcfLkSfzyyy/Q0NAAAPz5558YO3Ys/vzzT/j7+9dwLpn6prp7XUhjzSVQdt2lLEKrSyuIzqfpAuyuPvN1yaJdGB8fj06dOnG/y8vLo127doiLixPrJC1fvlzs7y5evChx0AhJ17JK4nPWndZEml8rr6I1itIiWusozbyKZlZIQuJO0pEjRyAnJ4c2bdqgQYPKd8r9VkZLmP8jn14IhZSyF4e8UjHwAyCfIYBCgVCitDIzM7HFdwuKS4orPEaSO1QK8gqY4jGFVfhf4Pz585g3bx46dOiAkpL/u1vYqFEjuLq6Ys6cOTWYO4YRJ+01l7IIrS6rDl1V5abEd/U/KTelEeEQqP7dNLYlRe0ni3ZhXl4edxdRRDS4UJF169YhLi4O69atk+g1JF3LKonPWXdaE2l+rbxWVgZ8jvj4eKSlpUk1r0VFRZCXl5fobyTuJPXr1w8XLlyAQCBA3759YWtrC11d3c/KKMNUJi8vD8UlxSgR/AKi8qf5lBSlAohCSVFbFBeWXdQrJ1cIKL4VuzvFVF9hYWGFEezk5eW/aKH26tWrP/tvmepp3LgxNzWRqT5ph1av6m8+J92qys2qykygbLkpq7teNbklRX2LmFeTZNEuVFZWLnM3Ij8/v9yGcXFxMbc5+b59+9CiRQuJXkPStayS+Jx1pzWR5tfK64cPH3D58uXPzuOnOnbsiDZt2kg1r6J1lJKQuJO0ceNG5Ofn4/z58zh58iTGjBmDVq1awcbGBra2tlBVVf2sTDNMRYgaAVRB5SdqBFRwTEU3UysbvazOiOi3QlNTE3/99RdMTU3LPHf8+HFu+h1Tu7Vq1arKdT5M9dXGzmeF5WYVZSZQttyU1V2vmtqSQpYb336LnS9ZtAs7deqEI0eOcL+XlJQgISEBHTt2FDtOIBDA3d0dGRkZCAkJqVYEREnXskric9Zy1kSaXyuvn94F/FKiNYnSzGt1ZrtVK3CDsrIybGxsYGNjg5ycHERERODkyZPYt28f2rZtCz6fD1tb2zInM8PUBtJcX9ClS5dvYlrptGnTMHr0aNjb28PU1BRycnI4ceIEfH19ceXKFezevbumsyiR2tiY/dpk0UGqS5+rLPL6rXQ+ZbV1xNfekkJWG9/KsvNV20m7Xdi9e3ekpqbin3/+gY2NDXbu3In27dujc+fOYsctW7YMWVlZCAgIYFMuGZn57Oh2TZs2hYODAxwcHJCRkYGIiAicOnUKO3bswO+//14mXCPD1DRpri+ob5sCVkRPTw/+/v7YsGEDdu/eDSLCvn37oKamBj8/PxgaGtZ0FiUii8ZsXeogyEpd6iTIKq914b0zsiWrzlddI412oZKSEvz8/ODt7Y0lS5ZAVVUVmzZtAgDY2trCzc0N5ubmOHToEBQUFNCjRw/ub3V1devMwB1TN3xxCHDg47qF/Px8FBQUoKSkBG/fvpVGsgwjE7JYX1Cf6evrIzg4GAUFBcjMzPzixaM1RdqN2brUQZCluvT+61JepU1BvhEAuf//s36SVZAJpvq+pF2ooaGBsLCwMo+Hh4dz/69stgfDSMtnd5I+fPiA06dP4/Tp04iOjkbjxo1hZWUFNzc3GBsbSzOPDMN8Re/evav0+czMTLEGxrc2vaS0b7nRzciOLO5SKik1g772KDRsWDca/9X9DGoqtHrpdKvakqK+Y+1Cpr6pViep9AVw7949KCsrw9zcHOPGjUPPnj2/qQXtDFNfWVhYVGsqIRvRYxjpktVdyrrSQQKq/xnINLR6JRumAxJuSdFAHny7/lWGyq5rWLuQqc8k7iQNHz4c0dHRaNSoEUxNTbF582aYmpp+UaSN2oStL2CYj1auXPlNrLdimNqM3aX8vM9AJqHVK9kwHah603TRhunV2cSyLqjv7UKGkbiTFBUVBXl5eXTp0gVpaWkICgpCUFBQucfKyclh//79Usvk1yCrkTtZdL5Yh46RpYEDB9Z0Fr5p7PpmmNqnog3Tgc/bNL0+qO/tQoaRuJOkr6/P/b+q0ZC6Oloii5E7WXS+2IJx5mu6f/8+bt68CYFAwF3bRIS8vDzcuXMHoaGhNZzD+oVd30xdUpc69XUpr3XBt9AuZL5tEneSAgMDZZmPek0WjR3WgGK+hgMHDmD58uXlVnANGjSAiYlJDeSq/mPXN1NX1KVOvczWepXIAfT/f35DWLuQqe/q1wpChmGkKigoCL169cLNmzfh6uqKoUOH4t69e9i8eTMaNWoEOzu7ms4iwzA1rC50kERkkdcmRfLo96wFmhTJSz1thmFqDuskMQxToTdv3sDJyQnff/89NDQ0cOfOHSgpKaFPnz6YMGECAgICajqLDMMwNa5RCWtOMUx9w65qhsPmazOfatiwIZSUlAAAHTp0wKtXr1BUVATg4+7mL1++rMHcMQzDMAzDyAbrJDEc0XztVq1a1XRWmFpCVVUV58+fBwB07NgRQqEQ0dHRAIDExMSazBrDMAzDMIzMVGszWab+q0tzyxnZGzNmDKZMmYKsrCysXLkSlpaW8PLygrW1NY4fPw5dXd2aziLDMAzDMIzUsTtJjEx9q1F/6gsrKyvs2LEDnTt3BgAsXboUv/76K4KDg9GpUycsWrSohnPIMAzDMAwjfexOEiNToqg/bFFr3WVmZoaePXsCAFq0aIGtW7eiuLhY4h3rGYZhGIZh6hrWcmVkjnWQ6q6ioiJ4e3tj6NCh3GNRUVEwMjLCmjVrIBR+O7vLMwzDMAzz7WCtV4ZhKuTr64tjx46Bz+dzj6mpqWHmzJkIDQ3F7t27azB3DMMwDMMwssGm2zF1koJ8IwBy//+ndLAQ6GUdP34cs2fPxrBhw7jHmjdvjtGjR0NBQQEBAQGYMGFCDeaQYRiGYRhG+lgniamTlJSaQV97FBo2VJZamqIQ6CzC3/9JT09Hu3btyn2uU6dOLAw4wzAMwzD1Eptux9RZ0uwgibAOkrhOnTrhzJkz5T537tw5dOjQ4SvniGEYhmEYRvbYnSSGYSo0cuRIzJkzBxkZGbCyssIPP/yAtLQ0nD9/HqdOncKqVatqOosMwzAMwzBSxzpJDMNUaMCAAcjNzcW2bdvw77//co+3aNECCxcuxIABA2oucwzDMAzDMDLCOkkMw1TK2dkZTk5OiI+PR0ZGBpo1a4ZOnTqhQQM2W5dhGIZhmPqJtXIYhqmSnJwcOnXqhM6dOyM/Px+5ubk1nSWGYRiGYRiZYZ0khmHKuH//PiZOnIh//vmHeywoKAi9evXC0KFD0bNnT+zZs6fmMsgwDMMwDCNDrJPEMIyYp0+fYsSIEXjy5Am3Z9SDBw+wYsUKtGvXDr6+vpg8eTI2btyI//77r4ZzyzAMwzAMI31sTRLDMGL8/PygoqKCffv2QVn5Y5j1gIAAAMD69euhoqICAEhJSUFgYCCsrKxqLK8MwzAMwzCywO4kMQwjJjIyEiNGjOA6SABw5coVtGvXjusgAYCJiQkeP35cE1lkGIZhGIaRKdZJYhhGTEZGBn766Sfu9xcvXiA9PR3du3cXO05ZWRkCgeBrZ49hGIZhGEbmWCeJYRgxzZs3R2pqKvf7jRs3ICcnByMjI7HjXrx4gZYtW37t7DEMwzAMw8gc6yQxDCPGwMAAoaGhICIUFxcjLCwMjRo1Qs+ePbljBAIBDhw4AB0dnWqlLRQK8eeff6Jnz57o1q0bxo8fj9evX0v7LTAMwzAMw3yRGu8kRUdHY8CAAejWrRucnJyQkJBQ5hihUIgVK1age/fuMDIyws6dO2sgpwzzbZg0aRKioqJgZWUFa2trPH78GGPHjsV3330HAAgLC8OwYcMQHx+PcePGVSvtbdu24a+//sKyZcsQHBwMoVCIcePGsWl7DMMwDADWLmRqjxrtJBUWFsLd3R3jxo3DrVu30KNHD0yfPr3McYGBgYiOjsaZM2cQHByM4OBgnDt37utnmGG+Ab/99htCQ0PRvXt3/Pbbb/D29oaHhwf3/KZNm5Ceno6tW7dCVVVV4nQFAgH27t2LqVOnwszMDCoqKti4cSMSExPx77//yuKtMAzDMHUIaxcytUmNdpJu3LiB5s2bg8/nQ1FREZMmTcLr168RGxsrdtyJEyfg6uqK5s2bo0OHDnBxccHRo0drKNcMU/916dIFK1euhJ+fH4YPHy723KFDh3D27FmYmppWK82nT58iNzdXbG1Ts2bNoKamhsjISKnkm2EYhqm7WLuQqU1qtJMUHx+PTp06cb/Ly8ujXbt2iIuLEzsuLi5O7LiOHTuWOYZhmK+jTZs2aNCg+kVHYmIiAOB///uf2OOtW7fmnmMYhmG+XaxdyNQmckRENfXi27Ztw8uXL7F27VruMWdnZwwdOhT29vbcY2pqajhz5gzatWsHALh+/ToWLVqEiIiIStO/e/cuiAgNGzaEUChEXl4eFBQUICcn91n5FS1kb9y4MRo0aCCTNAHUqbyWlJQgOzsbDfJLAOFnnkoN5CBUlsd3330HeXl5Ls2Pex1/Xl4BAlDMpQlI93Nt1KgR5OTkqh244Ft29OhReHl54cmTJ2KdLC8vLyQlJWHfvn3VSk90fcvLy3/z1yHL65enKat0v1ZeZVFuss+1gVTruCZNmrB6owpfq10ojXoDqNvntizyKpXrBSjTLqyp9pvCZ72SlCgrK6OgoEDssfz8fDRp0kTsMSUlJbHjCgoK0Lhx4yrTF32QcnJykJeX5xaeS4ss0pRVurLKq4KCAlq0aAG0kEGaUibNz6CoqOiLCqtvkZKSEoCPa5NE/wc+zkEvvXGtpESfP7sOWV5ZXmVTbrLPVbp1HKs3qva12oXs3JZNurJoEwI1136r0U5Sp06dcOTIEe73kpISJCQkoGPHjmWOe/nyJX777TcAH2/HfnpMebS1taWbYYZhPptoml1SUhLat2/PPZ6UlAQej1ft9Nj1zTAMU7+wdiFTm9TomqTu3bsjNTUV//zzDwQCAbZv34727dujc+fOYsfZ2tpi586dSE1NRUJCAoKCgtC/f/8ayjXDMJ9DRUUFTZs2xc2bN7nHsrKy8PjxY+jr69dgzhiGYZjagLULmdqkRjtJSkpK8PPzQ2BgILp3745r165h06ZNAD5eAMeOHQMAjBgxAnp6erCzs8OwYcMwfPhwWFpa1mDOGYapLkVFRbi4uGD9+vU4e/Ysnj59ij/++AM//fQTrK2tazp7DMMwTA1j7UKmNqnRwA0Mw3xbSkpK4OPjg8OHD6OgoAD6+vpYtGgR2rZtW9NZYxiGYRiG4bBOEsMwDMMwDMMwTCk1Ot2OYRiGYRiGYRimtmGdJIZhGIZhGIZhmFJYJ4lhGIZhGIZhGKYU1kliGIZhGIZhGIYphXWSGIZhGIZhGIZhSmGdJIZhGIZhGIZhmFJYJ4lhGIZhGIZhGKYU1kliGIZhGIZhGIYphXWSapGSkpKazkKNYvsaM5J69+4d93923tR+ou9IFt/Vt/7916X3LxQKazoLDFNnSetar0tlRk37JjpJ0joh3r17h4sXL2LXrl14+fKlVNIUCAQYNWoUEhISIC8vL9NKRFYXhrTSlZOTk0o6IqLPkhUI9YtAIMCJEydw5coVAMCzZ89qOEfVI43z8Wuc29JKWygUcp1aaV/jskpTlqRdxkv7/efm5iI/P1+qaQLAixcv8PTpU6mnK1IXrgWmdpD1gLS0zhdRPl+9eoW8vDypXeuidG7evCk24Cgt9el6kaP69G5k6MOHDxg7dizy8/ORmpqKBg0aYNWqVejTp88Xpfv8+XN4eHggNTUVvr6+MDIyAvDxJPuSC6KoqAhpaWlIT0+HvLw8fvvtty/KZ+n8vnjxAoqKivjf//4HNTW1L04zKioKDx8+RF5eHlRUVGBqavrFaaalpaFly5bc76LP80s/V6bmpaenY+TIkVBSUoKysjIaNmyIPXv21MrvNiUlBa9fv4aSkhJatmyJNm3afHGasjq3Hz9+jEePHkFZWRna2tr45Zdfvjive/fuxbVr1xAVFYWWLVsiMDAQP/300xenGxUVhefPnyMqKgq9evWChYUFlJSUvjhdoPyy90s+2wcPHiA5ORm5ubno378/gI+NH3l5+S/KpyzKTSKCnZ0dNDU1MXPmTLHz7Evt27cPu3fvhq+vL8LDw2FtbQ0DAwOppS8tsqjjmNpDKBSiQYMGKCoqwt69ezFixAg0btz4i9KUxvX8KVE+nz59imnTpmHx4sUwNDT84jpOVJadPHkSK1asQEBAADp37vzZ6cn6ehF9tklJSSAiqdSh1aHwVV/tKzl58iQiIyMhEAigqamJYcOGfXGazs7OMDQ0xIQJE1BQUAA/Pz8sWrQI6urqaNu27Wen+/vvv8PX1xc7duzAmDFjsHDhQjg7O39xo2fevHmIi4vDo0ePoKWlhY0bN+Lnn3/+7HwCwIkTJ7B582bIyckhPz8fbdq0wciRI2FnZ/fZaV65cgVz5sxBx44dkZKSgqZNm6Jdu3bo1KkTgM9rnIwaNQo3b97EsGHD8NNPP8HR0ZFrUIvSqo0NakYyLVq0wD///AMbGxs8fPgQ48ePR25uLpo0aVLTWRPz+vVrTJ8+HW/evEFmZiZ69OgBd3d36Orqfnaasjq3vby8EBcXh2fPnuH777/HihUrxDpJn5Pmli1bcPjwYQwdOhR9+/ZFTk4Ofvjhh2qlUZ4zZ85g48aNaNy4MbKyspCWlgZLS0uxYz73+hY1TAAgOzsbRUVFaNmyJeTk5MSek1R4eDg2bNiA9PR0FBQUICAgAPv37//iRpksyk3g4wizi4sL1qxZg4yMDMydOxft2rX7oryKDBw4ELdu3cLo0aPRqFEjTJky5YvSk0U9L4s6jqk9Sl/Df/zxB4gIBQUFX3Q9EhHXQZo6dSq6d+8OJyenL2pfEBEaNGiAwsJChISEwNzcnBtA/xKi95+SkoLTp09j/Pjx6Ny582eVbYDsrxehUAh5eXmkpaVh1KhRmDFjBlq1aiX1DmmlqJ7x8vIiOzs7cnFxIT6fT6GhoWLPC4XCaqfp7OxMLi4uYo9FRESQoaEhPXv27LPzWlxczP3/zZs3tGbNGuLxeLRs2TLu8ZKSkmqnO2rUKHJxcaGIiAi6fPkyXbp0qcwx1f0cYmJiSF9fn/7++2/KysqiR48ekYuLC40aNYoEAkG180hElJqaSj179iR/f38iIrp37x4ZGBjQ1atX6eXLl5SVlVWt9ETvacSIEdStWzdycXEhKysr6tatG7m5uZG/vz+9efOGsrOzy/07pm4oLCykvLw8MjU1pREjRpCZmRmtX7+eXr58yR3zOdeNNAkEAjI1NSVPT086e/YshYWFkY6ODi1atOiz0pPlue3t7U3W1tZ0+/ZtysvLo8ePH1eaB0lcuHCBdHV16ebNm2Wee/LkCUVGRlJsbKzE6ZX+W01NTQoJCaGUlBQiIu49FxcXU3FxMZfPLzkH/P39aeDAgTR48GDasGEDFRYWVjvN27dvk7q6Ou3fv59u3rxJ4eHhZGRkRPPnzyehUPjZ5Y60y83yREZGUs+ePcnBwYHu378vtTLy4MGDxOPxSEVFhbZv307p6emflY4s6nlZ1HFM7RQSEkJTpkyhBw8eSC3NiIgIcnBwoLS0NKmluWHDBjI0NKRt27YREYmVb5+roKCAJk6cSDo6OrR69Wru8eqWl7K+Xkq/zzVr1tDUqVPLPP411KtO0ubNm8nExIRiYmKIiKioqEjs+dKVh6Qf9OLFi8nKyopyc3OJiCg/P5+IiO7evUu6uroVNig+R1JSEu3fv580NTVpzJgx3OOlO1NV2bJlC/Xp04dSU1PFHs/IyKCIiAjat28fPXnypNp527VrF7m5uYk9dv78eeLxeBQVFVXt9IiIoqKiaPDgwVxjh4ioT58+1K9fP9LX1ycjI6MylV9lRN/p2bNnaezYsXTgwAF6/PgxrVu3jnr37k3dunUjY2NjGj58OIWGhtLTp0+poKDgs/LOfH2fFuKignjDhg3Uo0cPmjNnjlQqvepcbxUZPHgwubq6ij22YcMG6t27N+Xk5HCPSVoxyercvnjxIpmbm5cpE3Jzc2nbtm00f/58WrZsGdcRkaTcLC4upoULF9L69evF8h8XF0czZswgDQ0N4vF41L9/fwoKCpL4MxAIBDR27Fixil0kLCyMJk+eTGPHjqX169dTcnKyRGmWJspHaGgodevWjdauXUuzZ88mMzMzmj17Nr1//17itJKTk0lbW5tr3Ijy7+7uTpMnTy7zmtWp+KVdboqIzntRnl6/fk2DBg0iExMTOnv2bJn6VFKl39urV68oNjaWQkJCSFVVtdqfK5Fs6nki2dRxTO0TFxdHfD6feDweHT58mIi+vMyPiIggW1tb8vLyIqFQKJVBuuzsbNqxYwdZW1uToaEhNwj4JQMsIocOHSJ7e3syMTGhQ4cOcY9XJ99f63rZt28fWVtb05IlS7jHvuYgaL0J3BAbG4uIiAisW7cOXbp0AQA0aNAAaWlpmD9/PgYOHAh7e3scOHAAgGSLXSMjI3Hw4EEMGzYMjRs3RklJCTfvffbs2eDz+VBVVa1WPl+9eoV///0X586dw4sXL/Dy5Uvk5eUBAH788UeMHDkSa9euRXJyMvh8PtLS0iQK6EBEyMnJwePHj+Hq6srNJc/JycH169fh7OyMKVOmYO3atRg4cCD++eefauVbQUEBb968QXZ2Nvd6nTp1goKCArKysqqVlohQKMTz588RGxsLANi2bRtevXoFNzc3rFq1CjY2Nli4cCGOHz8uUXqi79TQ0BACgQChoaHo0qULZs6ciWPHjmHGjBlISUlBYmIiFi5ciEGDBmHYsGEoKCj4rPwzX09JSQkaNGgAoVCIe/fu4dixYzh58iQAwNPTE56enrh+/Tq2bNmCv//+G25uboiNja1yAanounr37h1iYmKQk5PDXW9V/W1FvLy8kJubi61btwIACgsLAQC//vor2rVrJ1b2SDrFQdrntui9xcfHg8fj4ddffwUAFBQU4Pz58xgxYgQ2b96MCxcu4Pjx4xg4cCBycnIkKjcbNGiA169fiy3+/+eff+Dp6YmzZ8+iX79+3PTf48ePS7yIWigUori4GPr6+txj9+/fx/z58zFv3jxER0cjLS0NZ86cQWBgoERpfprvFy9eYPPmzVixYgVmzZqFfv36IS0tDU+ePMH8+fMlDhIyfPhw9O3bF5MmTQLwMdhIw4YNoaKiIja1R/T9V2dqjrTLzaKiIgDgprCI8tS2bVsEBgZCW1sbU6dORUhICHJyciTOJwAUFxdDTk4OOTk5ePHiBVJSUtChQwcMHToU27Ztw9mzZzF79mzExsbizZs3uHz5cqXpyaKeF5FFHcfUPm3atMHMmTOhpaWF1atX4969e5CXlwd9vGlQ7fRE67/z8/Nx9epVfPjwAQ0aNPji4BBNmzaFq6sr5syZg9atW8PV1RVXr16FnJwcN/1XEuW9p0GDBmHFihXo1KkTDhw4gH379kEgEHB1rCS+1vVSXFwMJSUlhIaG4r///gOAauXzS9WbNUl5eXkoKSlB8+bNAXxsVJ08eRJ79uxBTEwMNDQ0oKGhgWXLlkFRURFDhgypMk0NDQ04ODjA19cXxcXFGD16NOTl5TFx4kS0adMG8+fPByD5/O+ioiJ4eXkhOjoaAPDzzz8jLS0NzZs3R6NGjdC+fXt06NAB8vLyUFFRwfHjx8Hn87F161Zoa2tXmracnByaNm2K9PR0ZGZmcp+Bn58fQkJC0LJlS8yZMwfdunXD+fPnsXfvXvTo0QOtW7euMt8A0Lp1a8TGxiI+Ph6ampqQk5PDTz/9BGVlZSQmJood+/DhQ2hoaFSZZpcuXeDh4YFWrVoBAPr27QsTExN07doVAGBpack1CK2srKCkpFTu5yxa2EdEKCkpQePGjbFkyRIMGzYMixYtwqpVq6CkpISdO3fCxcUFzs7OyM7OxrFjx2BkZCS1Bd+MbIjmJQPAlClTkJSUhPz8fKSkpMDf3x+7d+/GoEGD8PPPP2PFihXYvn07OnTowDWiKku3QYMGuHXrFmbOnAkigqKiIjZs2IBu3boB+L9z68GDB/jw4QOsrKwqTXPPnj04duwYTp8+DSUlJRQXF6NRo0YQCoUICAhAbm4uDh06hPfv30NRURFqamooLCxEr169uLKrNFmd26IySyAQID09HYWFhVBSUsLWrVsRGhqKpk2bYvny5TA0NERRURGmTp2KixcvwtbWtsrvS05ODnp6eggPD0dgYCBiYmJw/PhxNGnSBFu2bIGWlhaaNm2Kbt26gc/n48mTJ9w1X5n8/HzExsYiKioKFhYWOHbsGHbt2oWYmBhMmTIF/fr1Q+fOnbFlyxacOHEC48ePR9OmTatMF/jYiVFUVMTTp0/x888/w8bGBklJSfjrr7/g6emJ77//Ht7e3pgxYwb4fD4mTpxYYVpz5sxBeno6Vq5cCeBjx1NJSQnPnz+Hn58fVFVVsWjRIrx79w6//fYb2rRpAzk5OfD5fInWbEmr3ASAhIQErF+/Hk2aNEHHjh3RsGFDtG/fHu3atYO8vDw6dOiA5cuXIygoCCtXrkRubi4cHBzw448/VpnPkpISKCgoQCAQYMqUKYiJiYGmpiZWrlyJli1bwszMDIGBgfDw8ICzszMAYOTIkejZs2eFacqinheRRR3H1LzSQRWKi4vRuHFjmJqaom3btlixYgWcnJywfv162NjYlDm+IqXbfA0bNoStrS1at26NVatWwdHREf7+/ujUqROEQiHXqamKqD5KSEjA27dv8fr1a3Tp0gXm5ub44YcfsGPHDsyZMwdTp07FkCFDJBpgK53P8+fP48WLF4iKikK/fv3Qo0cPrF27FitWrEBYWBjS09Ph4uIi0bUNfL3rZezYsdDQ0MDWrVuxadMmpKSkYNiwYVxHVNbrk+p8J6moqAgNGzYE8DF06du3b9G0aVPs2bMH//zzD1q2bInNmzfD0NAQTZs2xaJFi3D37t0qC0+hUAhlZWWsWrUK7dq1w6ZNm5CdnY3s7GzEx8cjMDCQe11JR6saNmyIQYMGQVFREe/fv4eOjg7s7e3x+PFjvHv3Dmlpabhx4wbk5OTw5s0b/O9//8P79+9x6tSpSjtJqampiI+Ph56eHv73v/9h//79+PDhA65du4a4uDgYGBhg7dq1XGWcmZmJo0ePVmvUxMbGBsrKylwawMcCR1lZWWx0ccmSJYiJiUFQUFCVaTZr1gyOjo747rvvAACdOnUqM/rSrl07PHjwAAoKCuV+zsXFxZg8eTJGjRoFY2NjKCgooLi4GP/73/9gZWWF69ev4/nz51iwYAE6dOiAGTNmQFlZGQAkapgxNU9UGSxatAgvX77E7t270apVK4wcORLfffcdCgsLkZaWBiMjI+zfvx+JiYlQV1eXKN2srCx4eXlhwIAB+Pnnn3H58mUMHz4cq1atwoABAyAvL4+cnBz4+vpCT0+v0vRKh7q+evUqmjdvzjXmxo8fj9evX6NTp044cuQIEhISQETcXeSzZ8+W6STJ6twuKSlBYGAghg0bBl1dXfj4+HB3nUTlko+PDxeFLjMzE/Ly8twdsYqEhYXB0tISzZs3R8+ePfHgwQNs2LABQqEQtra2GDNmDH7//XcuDykpKVBVVa2yI7Nt2zbo6+tDX18fEyZMwMaNG3Ho0CGkp6ejQ4cOWLduHfr378+NLGpoaODGjRtQVFSsNN27d+/iyZMncHZ25o4VVbyvX7/GxYsX0apVK1hZWeGXX37B7t270axZM6SlpZWbHhEhPT0dWVlZaN26NdatW4eJEyfiu+++Q0pKCsaPH48ff/wRv/zyC54/fw6hUIjDhw8jMzMTnTp1wqhRoyrNr4g0yk2Rf//9F//++y8AQE1NjRtky8vLg7KyMho3boymTZuiS5cuKCkpgY+PDx49eoQVK1ZU+b2JGi+jR49GkyZNsGfPHigoKEBRURHXr19Ho0aNoKOjg+PHj2Pnzp2Ql5eHu7t7uWnJqp4vTRZ1HFOzRI3o/Px8+Pv74/r162jRogV+/vlneHl5Yfny5di+fTs8PT0RHx8Pd3f3KhvdpQeu0tLS8O7dOzRv3hxmZmb46aefsGzZMgwYMABbtmxBr169AFQ9kC5KMzo6Gl5eXmjWrBkyMjKQlZUFPp+PhQsX4o8//kBgYCDWr1+PmJgYzJs3r8r3L3rdkJAQ+Pn54ffff4eSkhJmzpyJMWPGYPbs2Vi1ahU2btyIY8eOITMzE/Pnz+eutcrI4noRfQ7v37/Hu3fvcPfuXXTv3h2amprw9vbGtm3bEBQUhOTkZEyZMuXrBHD4ahP7ZGTevHn06NEjIiLy8PAgHo9H2trapK6uTm5ubpSYmCh2/KpVq2jBggVEVPl85eTkZMrPz+eOCQ8PJ11dXeLxePTvv/9WK4+fvs7Fixdp0qRJ5ODgQP/88w/3uGieZVFRESUkJNDr16/p2rVrlaZdVFREPXv2pKCgICL6OO99ypQpZGpqSsOGDSN/f39ubYJo7vaNGzdo5MiR9PbtW4nyX978T6FQSOnp6dSjRw9u3r2/vz+pq6tTQkKCROmW9xpCoZBbJE30cT7qiBEjyixIF9m+fTvxeDwyMjKigwcPij2XlJREPXv2JA0NDeLz+WLrtGp6YT9TPUlJSeTk5MQFAli3bh1ZWlrS27dvKSAggNzd3SkzM1OitErPP8/KyiJvb2/usVevXtGSJUuIx+ORj48Pd5xo/YMkduzYQTwejxYvXkxERAsXLiQ+n09Pnjzh5pMXFhZSSkoKRUZGcsFfPj0nZXVu29vbc4tgiT6ucXFzc6Pp06fTkSNHuLUuos8kMTGRnJ2dKy33li9fTlpaWmLXaVJSEiUkJNDbt2/FykBR/hYvXkxDhw6tdD3AoUOHiMfjcWV8UlIShYeH08KFCyk0NFQscI4o3Y0bN9KYMWMoLy+v0s8hOjqaW8s2bNgw+vfff+nNmzd05MgRysrKIg8PD9q0aRMREWVmZtKYMWPo9OnTFaZXUFBAhYWFlJiYSKtWraLBgwfTrFmz6MOHDzRo0CDy9PSkjIwMsb9JTU2ltLS0Csu3qnxuuSmSlJREQUFBZGZmRo6OjpSTk0PJycl048YNOn/+PG3bto3Wr19Pbm5u5OHhQRYWFuTt7S1x/i5cuECDBg3i1vSGh4fThAkTSENDg9TV1bl6qyqyqudFvkYdx3xdpb/3oUOH0qhRo2jHjh1c+X7s2DEiIsrJyaFt27YRj8ejgICAStMsfZ7MmDGDhg0bRtra2sTn82nevHlERPTixQvy9PQkVVVV2rp1q8T5zcjIIGNjY9q4cSMRET148IBUVVXp9OnTFBMTQ4WFhfT27VtasWJFmfqgMk+ePCFdXV3677//iOhjuaehoUEPHz6kCxcucOXv5s2bKTIyUqI0ZXG9iPLx7t074vP5NHjwYLKysiJ9fX2uvnrz5g0tW7aMbGxsaOrUqVRQUCDzQA51upM0atQocnBwEKtkDx8+TNu3b6dbt25xDQdR5yAnJ4fGjBlDmzdvrjDNvXv3kpubG2lqatLQoUPpw4cP3HPR0dFkZWVFdnZ2dPfuXYm/nPT0dIqJiaELFy5wHZPo6GiaOXMm9evXj1avXs0tQhcKhdVaRDh06FAaMWIE97ciCQkJ5eYvNzeXBg0aRHPmzJH4NUQ+TS8vL4+srKzI19eXoqOjicfj0eXLl8v92+fPn3OFUmmfvtfHjx/Txo0b6eDBg7Rv3z7S1tam8PDwCvN079494vP5NGDAALKwsKDly5eL5XfXrl2kq6tLO3bsICLWOaoL7t+/z31fIunp6WRmZkY3btygI0eOkL6+Pt29e5eIiLZu3UoWFhYSBSoo/f3v3r2bpk6dSt27d+caYEREHz58ID8/Py6K3Oc4ceIEde3alczNzcnIyEgsMELpxmxlZHFue3p6ko2NjVhEuMr+ViAQ0PTp02ngwIEVphkaGkpaWlr08OFD7m8+lZaWRo8fP6aSkhJ6+fIlbd26lbS1tenVq1cVphsVFUUqKip09uzZCvNYVFTEBdMpLCykc+fOkYaGBl29erXCdEVSU1MpODiY9PX1SUVFhfuOiouLKSMjg3r37k3Hjh2jwsJC8vHxIT6fX+F3Fx0dTe7u7mRkZESurq60b98+2rx5Mw0cOJAMDQ3J1taWO1b0mUtSf8iq3Ny6dSvdunWLiD4uED9x4gT16NGD7O3tKw168WlAoKqcP3+ejI2N6dmzZ1yQEQ8PD7pw4QK5u7vTrFmzqkxDFvV8Rb6kjmNqp23bttGAAQO4a3f8+PHk4eFBCQkJtHfvXsrOzqasrCw6d+6cxGnOnTuXbGxsKDo6muLi4qhnz540adIkSktLo6KiIkpLS6MlS5aQnZ2dxGkeO3aMhgwZQkQfr8k+ffrQ4sWLKTk5mcaPH88NUlV3QOXMmTNcEKF79+6Rjo4OBQUFUUFBAVlbW3NRMj+HLK4XPp9PCxcupKKiIiouLiZVVVUKDQ2luLg4ys3NpczMTFq1ahX5+fl9dr6ro852klasWEGWlpZcAVlex6L0yZSVlUULFy4kKyurCiun+fPnk4ODA61fv57Onz/PRQhKT0/nwtW+f/+ehgwZQkZGRhQREVFlqMOIiAgaP3486ejoEI/Ho+nTp3N/8+bNG1q+fDnx+XyaPXs2VzlJ2vlatmwZmZubc78XFxeXaUg8ePCArl69StnZ2XThwgXy8PCo1oVbOm2ij6MSospXKBSSm5sb8fl86tatG+3Zs6fcv83Pz6du3bpxI+vlpenl5UVEHy9oBwcHLrRxYGBgpfkSCAQ0b948MjU1pW3btlG/fv3Iw8ODG0VOSEggS0tLcnZ2rvZ7ZmpGYGAg8Xg8WrhwIVexZWdn04QJE8jNzY309PTo+PHj3PEnTpygoUOHViui2YIFC0hXV5ecnJyIx+PRokWLxEKIZ2VlUUhICDk6OkoczvTTMkjUyTExMaFHjx5Ve8RL2uf2X3/9RTwej5KSkoiIyg1r/fr1a/Lx8aFbt25RYGAgzZ49m8zNzcvcARG5ffs2qaio0MmTJ4lIPNJY6fI3ODiYbGxsyMTEhKytrcnOzq7Su+RpaWnUo0cPmjRpEveY6C5c6c/x6NGj5O3tTX5+fjRnzhwyMzOjffv2SfR5EBFdvnyZevToQXw+n2xtbbmOAxHRhAkTiMfjkYODA+np6VW43cPbt2/JwMCAZs+eTVu3bqXBgweTqakpPXr0iMLDw8nR0ZEGDBjAdfaIJOvQyqrczM3NJRsbGzIwMKAzZ84Q0cfv7dq1azRgwADS09PjBiCIPp4n1Tl3Sx+bnZ1NAwcOJB0dHdLV1aXQ0FAumt3q1atpxowZJBAIKkxfFvV8Rb6kjmNqH9H3v3TpUlq6dCkRfdzuoHfv3pSUlMSFuS9vm4LKPHnyhGxsbLhBFV9fX7K0tKQ3b97Qjh07uLtRaWlpVd7NLi08PJyGDh1KREROTk7k5ubGlRM2Njbce6iuv/76iywtLSkzM5MsLS3FooM6Ojpyd66qSxbXy+3bt8nOzo6bFeLo6Ehubm6UlZVFbm5uXNn+NcOA18lO0tGjR4nH44mFOf208MzMzKQlS5bQ3LlzydvbmyZMmEBWVlb05s2bctPcv38/GRkZUUxMjFgFJhQKaejQoWRjY8NVcgKBgGbOnFkmD5+KiooiHR0d2r17N126dIliY2MpLi6OSkpKuNcQhXm0t7cnNzc3bjS2Knv27CEej0dWVlZiYchL510gEFBYWBhpaWmRjo4OWVhY0MSJE6u9j4ao4fP+/Xvq3r272HvesGED8Xg8WrduXYV/P2DAAPLw8BB7TPR95eXlkYWFBc2dO5d7Lisri1JSUqq8MyC6UAoKCmj8+PG0evVq2rdvH/Xr148cHR0pLi6OiIjOnTtHPB6vyg4XUzsUFhbSyZMnycDAgEaMGMF1fiIiIkhdXZ369u1Lz58/p9TUVIqJiSELCwuxcNMVEZ0vjx8/JldXV66SE5UnkyZNEuvMFBQUVHkOXrx4kW7fvl3ha717946GDh1KPXr0kGhQ5dO/l9a5HRERQTwej3R0dGjOnDnc3ZdPy82YmBiuc2BqakozZ86scD8jUZjrLVu2ENHHskeUb19fX/L09BS7Q+7v7087duyg06dPc/mviLW1NamqqpKdnR0FBARUeAcjNDSUhgwZQgYGBuTl5cWF9K2KqJxMSEigFy9e0K1bt8jV1ZV69eollsbu3bspJCRE7E7jp8zMzLipXSKGhoa0YsUKIvo45WzChAlkZ2cnNr2sqopeVuWm6O+9vLxIXV2d/P39uQ7z48ePyc3NjVRUVOjIkSNlXrcyomMKCgooJyeH+45TUlLo5s2bYoMYDx48IAMDA7HX+JQs6vmKfGkdx9Q+ouvL29ublixZQocOHSIDAwNum4jExEQyNzenCxcuVCvdhIQE6t27N2VnZ1NoaKjYrIbJkyeTo6PjZ+X3zp07pK6uTgMGDKCBAweKbRXh6uoq0dS98gZfUlJSyN7envT19WncuHFE9H+fzeDBgz/rjoysrpfIyEhu4H/27Nk0aNAgrr06ceJEcnNzY/skVeXGjRvE4/GIz+eThYVFhQVoVlYWhYaGEp/Pp4EDB9LGjRsrXFfw6tUrcnBw4EZDS6czevRo6tevH40fP57s7e3FKjlfX98KRxezs7PJzs6OfH19K3wvJSUlXOUfGhpKjo6OZGtrW+W+Ef/++y+pqqrS9u3bafLkydSrVy86evRouZ9DWloa3blzh/7++296/vy5xOs2yrvYevfuLVYpE33cc6Cy27WLFi0iQ0NDsQ3WSp/kNjY25OHhQUVFRdWaCidKQzQKeeDAAZo2bRolJyfThQsXaOjQoWRtbU3Xr1+nkpIS6t+/P5smUYeUlJRQZGQk9enTh6ysrLhG6uXLl8nAwIDMzMzI0NCQ7OzsyN3dXeJ0X7x4QX/88Qc5OjqKDRbcvn2b9PT0aOjQoXTlyhWJpsQdP36ceDweGRsbk6OjI+3fv5+rLEuf4/n5+TRt2jTq1q0b+fn5cR2Uikj73H7+/DlpamrSnj17KDQ0lPr27UtOTk5cQ/LTuzM5OTkUGxtLaWlpleaVz+dze/6UlJRwHaKjR4+Sjo4Od1emuvvriDYkLCgooOnTp5OZmRlt2rSJXr9+zR1TuqzIzs6u9nq0goICys7OpocPH3Lv/fnz5zR79mwyMTGhP//8k5KTk+nUqVOVdmyHDx8utqedqIMyduxYsY2DHzx4QDNnzqT+/fuTt7d3lWWdrMrNT/3555/cBuaizzAhIYEWL15MKioqtGbNGonSEX2uubm55OnpSf369SMTExMaNmwYt2fK69ev6Y8//qBhw4aRtbW12PTRT8minv+UtOo4pvYorzN/9uxZ4vF4xOPx6MqVK9zjkZGRZGxsTPfv36/Wa8TExFDPnj1p7dq1pK+vLzarYdu2beTh4VFlGS86996+fSvWLvPx8SF9fX1uOcTr168pNDSUtLW16cWLF5WmWbp8uHLlCp0/f54rg3fu3EkmJibk7u5O9+7do1u3btHChQvJ1NRU4unfX+N6SU1NJTMzM+rfvz+ZmJiIDY7NmjWL5s+f/9WXTNSpTtK7d+9IW1ub/P396cWLFzR37lwyNjbmFtcSlX+RVFVJR0dHk62tbZkNFUULrvPy8uj+/fvk4eFBlpaWYlMyKnL79m0aNWpUpQvWAgMDacqUKdxJGh4eLraxV3liYmKIx+NRWFgYEX3c1Hb27NlkbGzMjegSVb9hEhoaSufOnaPU1NRyP8OAgACaOHFilRd/aT4+PsTj8UhDQ4OCgoLERhKFQiElJCTQ1KlTJd6hurCwkNasWUMxMTFl5uUWFhaKrbW6efMmTZo0iYyNjSk8PFzigoCpWaUL+qKiIoqNjSUXFxfS1tbm5mRnZmbSgQMHKCgoSKK1J6JrITs7m/bs2UODBw8mFRUVsaApRB/XIllbW5OWlpZEC01PnjxJ3bp1I19fX5o1axbp6uqSqqoqjR49mjZs2EAxMTGUnp7OHb9mzRrS0tIq93yX1bldWFhIqqqqXIM0Pz+fjh07RkOHDqW+ffuKlWXllRkVjdqdPXuWDA0Nafz48WJ3su/du0fa2trl3iG4fPmy2OhoeUJCQsjU1FSscly9ejUZGxvTokWLxF6rumVc6fVXHh4eZG9vT5qammRjY0O7d+8mIqKXL1/SmjVrqHv37sTj8Wj27NkVprdixQrS0dEpM30xMTGR1NXVywR5ePPmDc2ZM4c8PT0rzae0y83SRA2MT9f3aGpq0oQJE7iOaGpqKvn5+RGPx6OLFy9KnP7gwYNp/Pjx9Ndff9Hhw4dp2LBhpKenRxcvXqSCggL666+/aPny5ZWul5JVPS+LOo6pPUTfaXZ2Nvn7+9Pq1au5KdT79+8nXV1dmjx5Mh05coT27NlD5ubmVQ4CiM6pkpISsSnHvr6+pKqqyq31KSgooKSkJLKwsBBrh5VHdA3evHmTevbsSSYmJmRhYUEPHjygoqIi2rhxI2lra5OBgQFZWlqSpaVlpQFjPuXv7086Ojqko6NDfD6f67AcPHiQBg4cSDwej8zNzcnR0ZHi4+MrTEfW10vpwEDx8fHcjAV/f3/q2bMnjRkzhvLz8ykpKYkiIiKoW7duVQYyk4U61UlydHQUm37w8uVL2rBhA/Xq1YtmzZrFfeilT2zRz8pu0f3zzz/UvXt37vfi4mIqLi6mFStWiHWcTpw4QTwer0xnqjxHjhwhQ0PDSkc47927R+rq6pVWGJ+6e/dumduuL168oDVr1lDPnj3FevWSNiJu3rzJjbQMHz6cJk2aRJcvXxYbuUhKSqrWgsGjR4+ShoYGxcTEcJX+ypUryzQ+qxOkYsqUKcTj8cjAwIAWLlxIvr6+lJ+fz73PFy9ekKurK12/fp2IPnZ+PTw8xObgMrXPw4cP6c6dO5SYmMgFHCnd8E9NTaVly5aRiopKldGHPiUqAwQCAbm4uFBkZCRdu3aNxo4dW+Gi1b///lvi9P/44w+ytramgoICevz4Me3evZuGDx9OPB6PtLS0aMSIEbRu3Tp68uQJpaamco3/T0fDZHVuCwQC7i5z6TLw0qVLNGHCBOrVq1e1p1WJHDt2jBwdHWnIkCF08+ZNKi4uJhMTE7GogCKzZ8+uMghGQEAAeXh4cJHKSt/BOXDgAJmYmNCUKVPEKsrPGVUcOXIkubq60t27dyk5OZl4PB7t2rWL69CmpKTQ7du3Ky2XhUIheXl5kampKQUFBdG7d++45/r16ydWDpe+U5ednV3pdDhZlJtERFevXqXo6Gh6+/Yt5eTklCnLo6KiyNLSkoYMGcKNrGdnZ3NTkyRx5MgRGjBggNjAQHZ2Ns2YMYN0dHQqbZCVJot6XhZ1HFM72dvbk5mZGRkZGZGWlhaFh4dTTk4OnTlzhuzt7cnIyIiGDx9ebjlV2qeR7AYPHkxDhgyh2NhYysnJ4a5PR0dHcnJyInt7exo/frxEeczMzCRTU1PasGEDHTx4kCZOnEgqKipcZyg+Pp527txJ//33n9jAUFXev39PvXv3prNnz9KtW7fI29ub+vTpQ6tWraLCwkLKzc2lhw8fUmxsbIXrTIlkf72IPtuXL1+SpaUlGRsbU58+fbi6aNeuXWRtbU0aGhpkbm5OFhYWNXY3V47oM7eVrwFv377FL7/8IvZYamoqjh8/juDgYG5vijZt2nB7AEmyh9HTp08xbNgwrFq1Cv369SvzPBFBKBTCz88P169fx9atW9GsWbNy0/r7778xZMgQ/Pvvv1i0aBGOHj3K5efTvOTl5aFPnz6YOHEit5leZTZu3IhBgwahffv2AD7GpFdQ+LjVVXJyMo4ePYrQ0FC0bdsW69atww8//MBtUFaZrKwszJ49G9999x0aNWqEJ0+e4OHDh2jdujW0tLRgb2+Pdu3agcfjcX9TWbqvXr3CgAEDMG/ePG6fitDQUCxatAjW1taYPHkyVFRUqny/IkSE1NRU7NixAzdu3EBycjLMzMzw/v17PHv2DI6OjtDU1ETv3r3h7e2NjIwMbN68GQDw4cMHtGnTRuLXYr6uuLg4bhM/JSUltGrVitsYuX379mjWrBl4PB4aNGiAEydO4O7duxgzZgz++OOPKvfBKX3N+fj4IDo6Gjt27ICysjKePn0Kf39/PHr0CL169cL06dOrTK800X4ODx8+xLRp02BtbQ1PT09ufwldXV2oqakhMTER79+/R3FxMXbv3g0TE5MyeZT1uV36cyh93d6/fx8HDx7E5cuX4ejoCA8PjzLHfCo5ORnv379Hfn4+unbtiidPnmD79u1ITk5GcnIyevbsidWrV6OkpAQNGjSAnJwc/P39sXnzZoSHh5cpv0WuX7+OMWPGwNfXF7179+YeL52Xy5cvY+nSpWjdujVGjhwJc3Pzan1nojRWrFiBv/76Cy1btsS8efPw9OlTbN++HZs3b4adnR0MDQ0lTi8gIAC7d++Gg4MDRo4ciXnz5qGwsBDbtm1D48aNxY6tqiyWdrkpsnHjRvj5+QEAfv31V3z48AHt2rVD8+bN8f3336NTp05o3bo1FBQU4O3tDU1NTYwfPx7W1tbVep39+/dzdbGCggL3fuPj4+Hi4oJZs2ZhwIABVaYji3pe2nUcU3uULt9OnjyJkJAQ+Pj4IDc3F0FBQQgMDMT06dPh5uYGAHj9+jV++uknifYCAoC5c+fi7t27sLCwwO3bt/Hy5UusX78exsbGiIqKQmhoKJo1a4bff/8djo6OFaZTeuPT7OxsbNiwAQsXLoS8vDwSEhKwd+9eBAcHY9KkSZg2bZrE71/UDiwuLsbLly+xdetWLFmyhNvTbf/+/Th37hw6d+6MqVOnolOnTlWm+TWuF6FQiMGDB0NTUxMaGhqIjo7GuXPn4OzsDHd3d7x58wY3b96EsrIyOnToINHehzJRI12zz1R6xKi0vLw8OnLkCA0ePJhsbW25OdCS+vDhA/Xr14/c3NzEetelX6e4uJjGjx8vNs/8U0FBQcTj8ej9+/eUlJRElpaW5OXlJZZm6ZGu5ORkcnR0pFOnTlWav5KSErp16xYX6vvT50RycnIoLCyMHBwcyMHBQSxCUWWKi4tp/fr1ZGdnx83RP336NDc/X0tLi4veFBQUVOWi68ePH3OjsKVHg2/dukXa2trcyLOki9hFEhMTyd/fn4YNG0aDBg2i58+f05EjR2j8+PFkbGxM48ePp4CAAOratSuLQlRHvHr1ihYvXkz9+vUjIyMjOnz4MG3ZsoXmzp1LLi4uZG9vT7169SJ1dXUyNDTkRrckPbeJPk4zGDp0KG3YsEHs8Xfv3tHKlStpwIAB5ObmVu3wxiILFy6kbt26ceGs3dzcyN7engQCAWVmZtLz58+rXBws7XM7Nja2zN2H8qZaxcfH07p168jc3JymTJlS6R33u3fv0siRI8nIyIgLdEH08a6Wp6cnGRoalrmzJVoPUNm0yLdv31LXrl1p7969YvkUKSoq4vL14sULcnR0pH79+pG/v3+1y5DLly+Tvb09EX3cT8nc3JybYjZ48OAqp8JVlKZoJNTc3LzCu4VVkVW5ef36de5O5YIFCygiIoLWrFlD48ePJ1dXVzI3NydDQ0PS09MjDQ0N4vF4NGTIkGovkA4JCSFzc3NuPzCR7OxsGjRoUKVBGkqTRT0v7TqOqX2SkpLo7NmzYmVQVlYW7d27l9TV1cnLy+uzpt3PmTOHnj59SkQf22xz5swhVVXVakXRrM7WE1paWhJvPSEqywUCAXl5edGYMWPKbK0gEAho//79NHToUOLz+RLvWSSL6+XTtaSLFy/mpiu/fv2aNm3aRD179qQZM2bUmmmvtb6TdOPGDQoPDy9zW09UCJeu8C9cuEBubm7UrVs3LrSppK5evUpdu3al6dOnU0pKitiXmZubS2vWrCETE5MKQzpeu3aNVFVV6fz580T0cX766tWrqXv37rR169Yym90RfVyD1LNnzwqjR31KdNIcOHCA/vrrLy4vQqFQrGI5f/48DRo0iJydnausqEt/fg4ODjRkyBCxTp2VlRU5OjrSlClTyNzcnHg8Hu3fv7/C9LKyssoEnigdze/9+/dkbW1N5ubmdObMGW6TwYpcv36djI2NuQs7OzubwsLCuAte9NnFxcWRh4cHF5lr4MCBteYiYyonqhz4fD5NnDiRO69F05JSU1MpPT2d7t+/T5cuXeKuMUmtWbOGDA0NydDQUGzhLtHHgYXNmzeTi4tLtdd4lI5QaW1tTR4eHjR//nzq0aNHhZVG6etNVuf29evXSVVVlaytrWnBggV04cKFMuVP6am4KSkptH37duLz+eWWU0QfO5T6+vrk4+NDd+7cocjISLHKNjExkZYvX0729vbk5eVFubm5lJiYSF27dq0y8t6n0eFEn9GjR4/E1jCV/rydnZ0lnn5RusF+//59MjExoYULF5Kuri7du3ePe+5Lpua+evWKevXqRcOHDxebDihpR0Pa5SbRx6l7orL6wYMH5OXlRTwer8w0wqysLCosLKRnz57Rs2fP6MSJE1WG0y+vXklISCADAwMaN26cWJ129+5d6tGjR6UbVcqynpd2HcfUHqLz48iRI9S3b1/i8XhkYWEhtiSioKCAzpw5Q5qamuTi4lLlMgTR+XL//n0KCQkhPp8vti4vIyODtmzZQhoaGjR79uxqDVpIc+uJ0mXLqFGjyMbGhmbOnElGRkbk6OhYpjw5dOiQ2Lq+isjqeimd35CQEFq4cCFpaWmJlZepqakUEBBAfD6f+vbtW2F99DXV6k7Sq1eviMfjkb6+PhkZGdGyZcvo0KFDVFRUVOGIwO3bt8nLy0uidUMiJSUlVFRURCEhIWRoaEjW1ta0e/duunr1KgUEBNC8efPI2tq6wqg5SUlJpKenV27Iwz/++IPU1dXJ09OT26zsxYsXFB4eTkZGRhQcHFxp3m7evElDhw7lTpacnBxycHAgS0tL8vf3p5SUFO7Y0id3VFRUpRXd8+fPuf+LPsvjx4+Tvr4+16iZNWsWWVtbU3Z2NhUWFlJBQUGlI8Jr1qwhBwcH4vF45OrqSnfu3CGi/6tQSxdOY8aMIT09PfLz86t0XmtkZCTx+XzS09PjLiahUEjnzp3jQvaWjuwnGrWXJLgGU3uIwqn279+f+vfvT9HR0dxz1R05L69heubMGeLz+eTs7FzundvS11F1CQQC2rRpE/F4POrevbtY3isjq3P78OHDpK6uTkuXLiV7e3vS0tIiIyMjWr58OYWFhVFeXl6ZhkJ2drbYxtmfcnJyKhPFiOjjusqDBw9ScHAw3b59mzZu3EgODg7k6upK+vr65O3tXWlehw8fzi1+Jvq/7/rWrVtkaWlZJvKUpGtxSm/KLXqvoo7ljBkzSFNTU2xD7RcvXpCRkZFYpKrqysrKoiFDhtDQoUPp2LFjEoXiJpJNuSnav6p0JyIhIYFWrFhBPB5PLLJcddc3ifKTm5tL586do3Xr1lFYWBi9evWKYmJiyNzcnCwtLWn+/Pm0ePFiMjU1rbTzKat6XhZ1HFN7iK6PuLg40tLSIl9fX1q/fj0ZGBiQh4eH2P5HRUVFFBkZSTdu3Kg0TVHdcf/+fdLT0yMrKyvi8Xg0Z84csfZfXl4ehYWFEY/Hq7TzXzpNaW49Udrdu3dpwoQJlJ2dTfn5+RQdHU18Pp/Mzc0lrouIvt714ufnR2pqajRixAhSUVGhSZMmcWuRiT6+/8OHD5Orq+sX1cvSUqs7SS9fvqS+ffvSpEmTKCgoiExNTUlDQ4NMTEzIy8uLjh8/TsnJyWVOqKoWk306ClY6ykZUVBRNmjSJTE1NSV9fn8zNzWnTpk2V7pNhbW1NPB6P/vjjD3r8+HGZRtq2bdvIxsaGtLS0qHfv3qSjo0OOjo60ffv2SvMpFAq5EZIBAwZwo57FxcXk5eVFvXr1onXr1omNREgSrGHZsmVlNuQk+thAGTFiBNnb29Py5ctJX1+fa6RUNSK6evVqMjc3p3379lFQUBBZW1vToEGDyjRwS+dv1qxZ1K9fvyrvdj158oSmTp1KPB5PLAT77du3uch+f/75Z5Xvm6k9yjufSkpK6OLFizRixAhuxFxE0iAkouMyMjLo2bNndPjwYW7qwf3798nJyYkGDx5MBw4cqHZ0NFGeExMT6d69exQbG8sNXhQWFpKzszP16tWrWvuQyercdnFxIWdnZyouLqbTp0/TnDlzyNjYmHg8HllbW9OiRYvo8OHD3A7mlYmLiyMXFxexRkJCQgLt2rWL1NTUuCmQtra2dP/+fTp+/DiZm5vT9OnTK03Xx8eHVFVV6e3bt0T0fxXzhw8fyNDQsMooUZIoLCykefPmkaurK3l7e1NcXBx9+PCB3NzcyMLCggYOHEgTJkwgc3NzWrhw4Re/XlFREc2cOZP4fD7t2LGjynNBFuWmaP8qUYCf0p2gpKQk2rt3L3Xt2pXc3NzKTb8ypV9TdMfTysqKDAwMqH///hQaGkrp6ek0f/58cnFxIQ8PjypHmWVRz8uijmNqj9KBUPz9/WnlypXcc+fOnSNLS0saMWIE/fvvv9U+twUCAXl6epKPjw+lpqbSjh07qEePHuTl5cUNYBB9LFsk3Y9LmltPlHbkyBEaMmSI2CyAkpISiouLo3HjxpGenl6ZKK7lkfX1UnpfurFjx9K1a9coLy+PC8M+ePBgevjwYZnw/rVBre4kEX0Ms6uiokLXrl3jIpTMnz+f9PX1icfjUd++fcnd3Z0iIiK4XnN5X15eXp7YuoBPR+kePHhAM2bM4OaTJyQkUFpaWpUdruHDh9PEiRPp7t27pKurS4MGDaLr16+XqeRevHhB165do71791J4eLjEkX6EQiFduHCBXF1dyczMTGxet4+PDxkbG9OcOXPERlwrO3kzMzPJxcWFeDwe2dnZ0ebNm8Wef/jwIenp6ZGmpqbEUxZDQ0NJQ0NDLA9Xr14ldXX1ckduSl8IFYUEFgqFYmtEXr16RatXr+b29BB5/vw5rV27lkxNTbk9W5i649GjR3T69GkKCgqi+/fvk1AopIcPH5KnpycZGxtz61QkITqvcnJyaMiQIWRtbU3q6upcxLjExER6+/Ytubu7k729Pa1atUriKZmi8uL8+fNkY2ND+vr6ZGhoSMOGDeMqltOnT5OWllaVc9VleW6LPoM7d+6QsbEx+fn5ic1b19fXp169elHXrl25zk1ERESlacbFxZG+vj5XHty7d48mTZrEDQxFRERQdnY2DR8+nKZNm0ZEJNGI7YYNG8ja2ppWrVolNj3Lxsam3LtWp06dKnfj3tL8/f3F7s4PHDiQHB0duX3uROt68vPzKTQ0lDw9PWnJkiUSb0IrKR8fH3J0dKy00SOLcpPo43SYWbNmcb+Xd9fwxIkTZGJiQnw+nxutrU6jZ+HCheTk5MRN6RHVnzY2NtyMCUmmBIpIq54nkk0dx9QepTvq27dvJwMDAxo5cqTYMU+fPuXWZvv7+1frzsyff/7JRb8UOXbsGJmamtK4cePowoULEl0rstp6QlQOvH37lm7fvs2lu3PnTrHIkklJSTRv3jzi8XiVTlv7WtfL+/fvafny5TR48GCxgf2XL1+Sg4MDt7FvbduupdZ2kkThPLOzs8nd3Z2GDBki9kW7uLiQg4MDjRo1inr16kU8Hq/MwuzSRowYQQYGBhQQEMB9CaKfGRkZZGxsTKtWrSrzd59utFja6tWryczMjBuJTUxMpD59+pCpqSmdPHmyWpVEeUq/bmRkJC1cuJC6d+8utnA7NDSUTE1Nyc3Njf777z+J0j106BB17dqVJk6cSE5OTrRgwQLuPeTk5ND06dNJV1eXCz1Z2Z2eqKgoUlFR4fawERUMr169oh49elS4yaUozfI+2+3bt9OIESPI2tpabJpGUlIS+fv7U9euXbmdo4k+XnybN28us+s9UzuJvvsjR45Q7969aeDAgWRhYUG6urrcuR0fH08rV64kbW1tWrp0abXSF41gP3z4kPLy8mjLli1kbW1Nnp6elJeXR2lpadVafyLK77t378jAwIB27NhBz58/pxMnTpCHhweZm5vTkSNHSCgU0tChQ8nS0rLCtYuyOrc/vY7y8vLIw8ODTE1NuXJzw4YNZGxsTB8+fKCYmBiKiIio8m420cd54qNHjyZHR0duLUjXrl1p69atYo2PXbt2iU2dk8SxY8fI1taWZsyYQTExMeTm5kajRo3iOgGi93Xy5Mkqt1/YuHEj9e/fnxwdHSkoKIiOHz9Oo0eP5r6Ly5cv04QJE8jW1rZaYd4/V2UdGVmUm0QfNz8vvbmtaLDuzp07NGbMGO73wsJCunbtGg0aNIh4PJ7Eg3YlJSWUmZlJTk5OZQIxvHv3jhwdHWnUqFESpSVKT5r1vIg06zim9ih93p84cYIOHDhAnp6exOPxygxOZWZmkqOjI82cObPSNEt/95cuXaJRo0aRqqpqmfP77t27ZGdnRxYWFlXuHSarrSdKb3ytrq5ODx48oIKCApo0aRKZm5vTtm3bxLYjEAUOqoqsrhdRuVZcXEzh4eFkY2NDPB6vzOyIrKwscnd3Jx6PV61tB76GWtdJio6OLtOTjIiIIB6PxzWgtm7dSjo6Otzo45s3byqNdpWYmEhDhw4lNTU1GjlyJK1du1Zsvc6gQYNo+vTp1SoohUIhhYWFcbdbS+fZ1dWVtLS0KCAgQGzEuDpzv8urDCMiIkhdXZ309fXFRlqvX79O+vr61Vp0PHfuXHJ0dKSlS5eSg4MDubu7c5/nu3fvyNjYuMpoV+np6aSiokLz588vk+/Vq1eThYWFxPkRWbduHfXq1YtmzpzJTUPy8vLins/JyaHw8HDq1asX9e3bl1tHkZOTw6ZL1CGPHz+mbt26cWtuIiIiSEtLix48eMAVxO/evaM///yz0g3kli9fLnYn5M6dO8Tn88s0+o4cOUIaGhoUEhLyWfktKiqi8+fPk7u7u9i1/uTJE/Lw8OAiT16+fJl27NhRbhpf49wuvRYnMTGRunfvTkuXLqXg4GDS0ND47M34bt26RWPHjqUBAwbQ3Llz6ezZs9xzomt+8+bNNHv27GqPBN69e5cGDBhA5ubmZGxszI2Gihr0jx49oq5du1JoaGiFaQQGBpK6ujpduHCBpk6dSkOGDKFx48aVaSA9fPiQZs2aRf3796clS5ZUe72bNMii3CT6eKeNx+PRsWPHiOj/6pvU1FTq1asXrVixQux4oVBI0dHRYuuTJJGRkUE9evTgvo+ioiLutQ4ePEgWFhZVbhosi3r+U9Ko45jaycfHhwYPHkwZGRn0/PlzWrRoERkbG4tNuxOR9Brfs2cP7d+/nyIiIsjV1ZW6detWpuPy7t07mj59eqXnd+nzacOGDTRy5EhuoObJkyfk5eVFtra2tGbNmmqVlaJ0i4qKyM/Pj1asWCH23pYsWULGxsa0YsUKevbsmcTpikj7eil9nIuLC126dIni4+PJzc2NbGxsaMuWLWJTvYuLi8XW4NYWtaqT5OvrS05OTuWegN7e3mRubk7r1q0jNTW1KqeIfCo8PJx4PB4NGjSIHB0daf78+fT8+XPKzMyklStXVjkvXxKlpzUsW7aMeDwerVmzhptzL4mzZ8+KHS+6iLKzs8nc3Jy8vb1p165dZGVlJbaw7d27d5V2wj6NEhQdHU2zZs2iq1ev0qFDh8jBwYFcXFy4RtTmzZtJRUWl0lGIxMRE0tLSIj6fT+fOneOmJh49epS6devGTYuQtHMomn4imlJTWFhIPj4+1LNnT7FOrUAgoGvXrtHQoUOrHQ6aqVmi89DPz49bE/Ho0SMyMDCgffv2UXx8PDk4OHDnTmWVyL///ks8Hk9sutK9e/dIV1eXO19K//3IkSM/K8QzEXGjlWZmZmUCHERGRhKPx6OoqCixgZbS/5fVuX3v3j0KDAyk5cuXi3VcBAIBFRUV0apVq4jH45GGhoZEc9Mrk5+fX+EGhFFRUaSrq/vZ0zHev39Pw4YNowEDBlBYWBj32SUlJVV4l1/k2rVrpKKiwt1Jz8jIoIULF5Kenh6ZmZmJhcMl+jhNRRR2/tPnvgZpl5tEH8/Bffv20bRp08jU1FRstoGdnV2ZzmLpBkxFg4OJiYl06NAhWrhwIZ06dUpsiszUqVPJ2tq6TP1w4MABGj58eKX1qazqeVnUcUztEx0dTaNHj6aTJ09yj71584Z8fX3J3Nycpk2bJvEsHtE5c+PGDTIyMuLuVD969Ijmzp1LxsbGEkWEK4+stp4YM2YMqampkaenZ5mptHv27KFevXqRq6trlVGTv9b1EhoaSg4ODtxUwtTUVJozZw7x+XxatmyZRFMMa1Kt6ST9+++/3Jzk8ly7do10dXWJx+NRWFiYxOmWrgy2bt1KixYtotWrV5ONjQ25u7uLTd+Qxi330hVbUFAQqaqqkoeHh0TR9uLi4qhPnz40cuTIMrccBw8ezO1CLooENmjQILK0tKw0qARRxQvg5s+fT0OGDOFGyUeMGEEDBw6kiIgIyszMlGgqTn5+Prm4uHANpOvXr5O2trbEe2KIREZGkoqKCheeVnTxX7p0ifr371+mICkpKaFHjx6Rq6urWBQbpnb6tMG3adMmmjJlChERmZubc6PcxcXFZGRkJBbEoCK+vr5kZWVFRB/Lh9OnT1NiYiIZGhrS+vXruXNI1FHy9PQsd6RREoWFhdw6nIMHD4qtZUpMTBTr2H1KVuf2nTt3yMTEhJydnblOla+vr9gxSUlJ1K9fP+rdu7dUpxSdPXuWbt++TZGRkXTo0CGuYfslcnJyaNq0aTRgwADy8/OjjIwMbopfRSqLLLpnzx6ysrKiKVOmlIlAlZ2dXaONY2mVm0Qf714aGBhQbGwsxcbG0tKlS8nY2JjWr19PHh4e5OLiUmHntrL89e3bl5vu1q1bN5o7d67Y1MXBgwfT4MGD6fz585SamkqRkZHUs2dP8vPzqzBdWdXzsqzjmNrj/fv3NHbsWNLU1CyzXjUjI4P++usv6t27N9nb21N+fr5Edz1ev35N06dPpz/++EPs8ZcvX9KGDRvI3Nyc/vjjj2pHgZTV1hO3b98mc3Nz0tbWpn///bfMQOLx48dp4sSJlabxta6Xv//+m1xcXGjGjBlijxcXF9OGDRvI3t6eJk6cWGUbtibVik7S8+fPSU1NjZsKU9HJ6OfnRzweT2zEtCqiOc9ERBcvXiRnZ2d6/PgxXblyhezt7WnkyJFi+0ZIoyFROo2rV68Sj8eTeBQ3ODiYXFxcaOjQodwCWE9PTxo4cKDY6FxRURGdOXOGnJycxPb6+NTq1auJz+fTiRMnKD4+vsz7GzduHHfiX7t2jaZOnUoGBgaVbn755s0bunz5MoWEhHCNj+nTpxOPxyN1dXWxaX+SFFJpaWnUrVs3bp6uQCAQu+PQu3dvevLkCV29epVevHjBjTwIhcJat8iPqVxoaChlZ2fThQsXuOiR7u7uRPTxnM7Ozqb+/ftL1Fh88uQJGRsbU//+/UlFRYUuXbpEJSUltHz5cuLz+bRhwwbKy8uj4uJiunPnDunq6lb7DjSR+N2oBQsWkLq6Ovn7+1NCQgIX/cjY2Ljchqiszm1RBDNfX18qKiqinJwcCggIIEtLyzKLdA8dOkTq6uqfPdXwU7m5ubR48WJSU1MjdXV1cnR0JB8fH6mkXVxcTOvWraMBAwZQjx49yNbWttLjq4osKhoRHTNmTI0v0pd2uUn08a5Yt27dxKZ4vnv3jrZs2ULm5uakqqrKRSUsLi6WOF17e3uaNGkSV+ecPXuW1NTUxDo3ojUWampqpKOjQ3369Kl07Zys6nlZ1HFM7SW6M6Gnp1dmelZhYSEdOXKEW+sniV27dpG5uTmpq6uXGehKSUmhvXv3koGBgdidq/J8ja0nROd2Wloa2dvbk6GhIR0/frzMXdnKOnRf83oJDQ0lAwMD0tTUpOPHj5f5jPbv3092dnZid6lrmxrvJOXl5Ynt1F56Y9TAwECxHnhcXBzZ2NiQm5tbhQujiT6Ogr148aLc53x8fMja2poyMjLo9u3b5OLiQo6OjhQQEFDtkMCVKf0+JBkpKH2inj9/nsaPH89V7ubm5mJTfEofW9leSNeuXeMiWKmoqJCbmxtNmTKFHjx4wM37P3nyJI0aNYqrDB88eEAbNmyosIF2/PhxcnJyIjMzM+ratavYovq///6beDwezZ07V2wzzaoq5mXLlpW7UFI0RVJdXZ3MzMy4aUNdu3alrl270okTJypNl6k9hEIh3b59m3g8HndNz507l3r06EFr1qwhoo/BGtatW0fm5uZVRp0TnVNXrlwhHo9HOjo63Fqm1NRUmj9/PllbW1P37t3Jzs6OzMzMJL7TIapgHj58SHv27CFPT09as2YNV6GJGnG6urrk7OxMgwYN4qbFfVo5yerctrKyKhMBLi4ujiwtLSkoKIhmzJhB+/bto5iYGMrKyiI+n0+DBg2S6qDClStXKDIyssymhdIQFBREgwYNqjS6qKSRRa9cuUJOTk40fPhw2r17d42sQZFFuUkkvhFvSUkJd/7l5eVRcHAw8fl8cnR0FJt6U1W6U6dOJT6fzwXlENWLrq6utH79elq4cCEtX76c3r9/T8XFxXT//n0KDw+vcB9BUX6kXc8TyaaOY2qPihr7Dx48oPHjxxOfz5d4U2mR8s7/Y8eOkY2NDfH5/DKd89zc3EoHoolkt/WE6P0/efKEDh06RMuWLROrS6ZMmUIaGhrk7+9f5QbQRLK/Xsq7yXD//n2ytbUlOzs7+ueff8rU7ZXtz1cbyBERoQYNGDAAv/32G9atWwciQklJCRQUFHDp0iVMmDABW7duhaWlJXf8nj17sG7dOvz999/Q1NQsN82RI0ciISEBfD4f/fv3R6tWrfDDDz8AAAoKCrBo0SJoaWnB2dkZz58/x59//ono6GisXr0axsbGUn1/QqEQDRo0kOhYIoKcnBwA4P79+wgICMDly5dhYmKCDRs2AABKSkogLy8vdmxFkpOT4e/vjwcPHoCIoKKigoKCAvzzzz8wNTVF165dMXLkSEycOBFNmzbFxo0boaioyL3Gp27evIkJEyZg3rx56NGjB37++Wfk5+ejadOm2LJlC2xsbJCeno5Ro0ZBT08PM2bMgLq6epXv//bt2wgODsazZ89gbm4OT09PvHjxAkOGDMGECROgpaWFFi1aIDk5Ga9fv0ZGRgYKCgrg6ekp0efK1B5z5szBo0ePsH//fmRkZCAgIAD//fcfcnJy0KZNG8jJycHX1xe//fabROkFBgbi3r17yM/Px9WrV7F48WI4ODigpKQEV65cwYsXL7hzX5JrW3S9Pn78GJMmTYKqqip+/fVX7Nu3Dzo6Oti2bRuaN2+OixcvYtq0aWjfvj1mzJgBU1NTAChzXcri3F68eDGOHj2KqKgoAEB+fj6UlZVx//59DB06FNra2igsLMSzZ8/Qu3dvbNq0CcePH8eFCxe4cqQuKCgogJKSUrnPrVmzBqdPn8bRo0fRrFkzfPjwAaNGjUJBQQFmz54NU1NTNG7cmDs+NjYWixYtQtu2bbF27dqv9RYAyK7cdHJygrKyMvbs2QMAKCoqQsOGDXH//n28f/8evXv3xpkzZxAUFITk5GQsWrQIJiYmlaYZEBCAlStX4tKlS2jdujUEAgEUFRVRWFgIXV1daGpqoqCgAFlZWcjLy8O+ffvA4/Gq/AxkUc8D0q/jmNqjdNvpr7/+wuPHj/HhwwcYGhrCzs4OJSUl2LRpE+7fvw8zMzNMmzYNjRo1kjjNvLw8vH79Gh07doSioiLu3r2LLVu2IDU1FcOHD8ewYcMkyqfoXMrNzcWYMWOQmZmJt2/fokmTJujTpw/c3d1RUlKClStX4s2bNzA0NMT06dMrLNtEaQKAvLw8Hj58iMmTJ6Nly5YAgKdPn0JfXx9Lly5Fx44dsWbNGvj7+2Po0KGYO3culJWVK0xXltdLcXExFBQUUFhYiLi4ODx69AgqKirQ0NBAamoqZsyYgffv38PJyQn9+/fn3k+tV0OdMyL6GHGKx+Nxo6aiXrNoXw7RLr9E/9f7j4mJod27d1eZpq6uLqmqqpKjoyO5urpScHAwpaenk1AopNOnT1OvXr24CFipqal08OBBGb3LqpUeLSk9wvbmzRtasWIF2dra0pw5c6q1n0Xp3Yv9/PxoxIgRNGrUKHr48CHFxsbS2rVrqU+fPmRpaUmzZs3ibqdWlHZqair17t2b++xL53njxo3E4/HI2NiYLl++TImJiWRubk7W1tZ07ty5Cqcwlk7j5cuXtGTJEuLz+TRjxgwyMjKqdG47U/uV/t5Fo/t3794le3t7LjJWamoqPX78mA4cOEC3bt2qchHnp+dSUlISEX0cwRMFS/nchbYiovUYomABiYmJZGBgQKGhoXTjxg3uLnVsbCyZmJhwe8OUHvWW1bm9fft2mjx5Mtna2tL69eu5Oy3JycnUo0cPWrduHXfnOigoiAsoUZ9GzT8nsijRx3PkS7dlqC5ZlJtEFW/EK1qTV3p0XTRlRk1NrdJpSEVFRRQQEEAqKiq0cOFCsXULw4cPJ1dXV260+vLly6Snp1flnmBEsqnnSx8rrTqOqZ3mz59PFhYWNH36dBo5ciS3UeyTJ08oMzOT1q5dSyYmJtyMhIqU/t63bdtGTk5OpKKiQtbW1rRkyRJKTU2l+Ph4+uOPP8jOzo5WrVpVrXNFGltPfPp6OTk51Lt3b9q4cSNlZ2dTcXExRUdHk5mZGdnb23N1zr59+6pcLySr66V0m5XoYxlsbW1Nenp6pKKiQtOnT6f4+HgqLi6mWbNmkbm5OS1durTaAStqSo12kp48eUILFiwoEzfdwsJCbFNFkY0bN9L06dOrXEDn7+9PdnZ2NGPGDFq8eDFt2bKF29V48eLF9Pr1a1q0aBF5eHhUe9GcNN26dYv7v+gkK32yJSQkUEFBAW3dupUGDBhAEydOrFYM+dJpHTp0iFuEW3pqw759+7jIXZ/utlxaaGgoubm5cZ+XKG3RnNOwsDDy9vYmPT09+vvvvyk/P5/MzMxo165dEuc3PT2dNm/eTH369CETExN6+vQp91x15tMzNU/0XaWnp1NmZqbYFINVq1aRgYHBF0UWO3LkCHl4eNCMGTO4NTi5ubm0e/duUlNTo9mzZ1c5Vacir169IkdHR8rOziahUEjW1tbclKaFCxfS2LFjubRzc3PJ0dGReDxehYEbiKRzbgcGBhKPx6ObN2/S2rVric/n0+zZsykpKYkGDRpEnp6eJBAIuGvz+fPnZGFh8U0ENpFGZFFZkEW5WdlGvKIBNdFxIvfv36e1a9dWGMBBdGx+fj6dOXOGjIyMyNHRkQQCAS1YsIDs7e3F6src3FxycHDg1kJVdv7Kqp4nkm4dx9S8ly9fipXbERERZGZmJralw/Hjx2nIkCE0YcIEbh2rv78/N2BWlZCQENLT06PAwEC6du0aLVu2jAsg8OHDB3r37h3NnTuXHBwcKlwzJKutJ0aOHCkWkOLu3bvk6OjI1XGi6+z169dkZGRUrW1fiGR/vXh6etKwYcPo0aNHlJqaSuHh4WRkZEQjRozgyo/FixdXGViiNqnxNUlv374lX19fUlFRoblz59KIESPIw8ODu1BEJ8WhQ4eIx+NVGP89KSmJoqOj6ebNm1RQUEDh4eE0ZMgQmjhxIl24cIGys7Np79695ObmRqqqqjRgwADS0NCosQZEdHQ09erVixYsWMCN2BUVFXHvd9WqVVw0O6FQSKGhocTn82ncuHFVjgqXrlxKFziXLl0iNze3MvN4MzIyqlxXsHr1anJychK7yF6+fEmmpqZc4IsPHz6Qh4cH9ezZs9I8RkZG0vDhw+nIkSNlohwVFRXRgQMHaPDgweTk5CS2qSLb8K/ucXNzIx6PRwsWLKADBw4Q0cfIOtOnT6fVq1dTfn6+xHO0RddGaGgo6evr0+LFi2n79u1UWFhIAoGAm+t86tQp0tDQIBcXl89aZ/jixQvS19enyMhImjBhAo0ZM4a7jpYsWUIuLi5EJN4wF416y+rcFoW5Fs2Xz87OJj8/Pxo4cCAZGhqKBTgQ5Ss8PLzcENj11ZdEFpUVaZabn6pqI16i8u/mfkooFFJkZCTXEMvPz6c7d+6Qra0tde3alQwMDLi7dqL04uPjyczMTOJAKNKq50VkUccxNevOnTukra1NcXFx3Hm2Z88ecnV1pcLCQrFr47///iMej1dlMIFnz57RqVOnuPRSUlJo0KBBdPjwYbHjQkJCyNTUlOv0p6WlVTirQVZbT+zdu5eMjY25a0EoFNLNmzeJx+PRw4cPueNEaw9HjhxJ3t7eVaZLJJvrZceOHWJ3r169ekUDBgwoE0n08ePHpKurKzYgUhP7030uha89vS83NxcCgQBpaWno3Lkzfv75Zzg5OaFNmzbw8/PDmzdvcOHCBSgrK3PzoSMjI7Fo0SJs2rQJv//+e5k0o6KisGnTJsTExCAtLQ2WlpbYunUrmjRpgh07dsDX1xcjR47EmDFjMGbMGNy+fRvh4eEoKCgQm7f+NbVt2xZ8Ph/Xr1/H3Llz4enpiU6dOgEAgoKCcPjwYezevRsAICcnhyFDhqB58+bo1KkTFBUVK0xXKBRyc0e3bduGZ8+eoUmTJliwYAF69uyJli1bIjAwEH///Tc+fPiAadOm4fvvv8f3339fbnqiuajZ2dmQk5MTmyf/yy+/YP369dDT0wMAtG7dGkKhEL///nuFeSQi7NixA3fv3kVycjIaNGiA77//Hnw+H6qqqtDX14eTkxM6duwIPz8/bN68GW/fvoWjo6PEa7uYmkOl1uSUlJRgwYIFMDExwc2bN7F161YEBQXB3NwcBQUFePjwIZKTk9GuXTuJ0paTk8OrV6+wefNmLF68GDY2NkhOTsapU6cQHBwMZWVleHh4oG/fvmjVqhW37qG6fvrpJ3Tr1g0zZsxA48aNceDAAW6et+h8Fc2/Fv0cO3aszM7t5ORkTJ06FWPHjoWFhQUAoGnTppgwYQJat26N4OBgFBYW4ty5c7CwsICCggKePn2K+fPnY/bs2Wjfvn21P4O6SF5enltz4OzsjI4dO8LV1RWWlpZQUVH5qnmRdrlZnv79+6Nt27ZYunQpJkyYAIFAgBMnTqBJkybc51D6dRs2bFhuOnJycjh58iQiIiKwbds2/P333xg9ejT+/PNPbN++HadOncKTJ0/wyy+/oEGDBsjPz4e7uzsMDAxgZWVVbpqyqOdFpF3HMTWvqKgIu3fvhp6eHjp27Ijc3Fw0adIE6enpiI+P564L0blibGyM9u3bQyAQVJhmXl4eHB0dMXLkSPTt2xcA0KhRI2RnZ6NZs2Zi6Q0dOhQPHjxAaGgonJ2d0aJFC7Ro0aLcdJ89e4Z27dpBU1MT169fR1ZWFrp164aGDRti//79mDZtGhQVFbm0W7VqhVatWlX5GSgoKCA3NxevX7/G5cuX0bJlS6ioqKBLly44ceIEWrVqhTZt2nDX9A8//FDp+iMRWVwv9+7dg6+vL3x9fbnHGjZsiJcvX3K/l5SUgIigqqoKW1tbPH78WOzYOuNr9siys7Np0qRJ1LdvX+rduzeNHz+e61Hm5uZSREQE2djYkKmpKRcpJyEhgfT09Mrs/yHy7t070tfXJx8fH7pz5w5FRkaKjZy+ePGCJk6cSA4ODuTr68tF8CgsLJTKBrKfQzRSIBAIKCAggIYMGUIuLi509+5dSkpKIkNDQ27XdKLPu4Miihw2efJkMjExoSFDhnBrKd69e0dr1qwhY2PjKufxioSHh5OBgQFdvHixwveUkZFB48ePp82bN1c6/eLevXs0btw4WrNmDR08eJCcnJxITU2NVFVVacSIEbRp0yZKSEigy5cv04wZM8jKyopu3LhR7c+A+bpKf+cxMTEUGRnJPZaZmUlJSUm0bt068vLyIlNTU+LxeDR79uxqvcbVq1fJ0dGRiD6OXI0bN4769u1LY8eOpcGDB3MjdpJOzRRdWzk5OfT69Wt69+4dFRYW0qNHj0hbW5scHBzo+PHjdOPGDfLz8yMNDQ1uymt5ryGLc7uqMNcXLlygCRMmUP/+/engwYNUUFBAFhYWtHjxYok+g/qmupFFZUma5WZFPt2IV6Q69UZ8fDyNGTOG1NXVydramns8MTGRFi5cSCoqKrRz504iIho/fjwNGjSowrRkUc+XR9p1HFNzCgsLycPDg6ytren+/fvk4OBADx8+pCdPnlD37t1p0aJFYndnEhMTqXfv3pWGiT979iz17NmTYmNjqaSkhC5fvkz5+fnUvXt3sc2pRen6+/vTuHHjqpx9IKutJx49ekSTJk0iQ0ND4vF43FQ/Hx8fUlNTo8WLF9OtW7fo9evXFBAQQF27dhWbtl0VaV4vR44cIX19fcrNzaXExEQ6fPgwpaWlkYODA82dO7dMpD0fHx/y8PAQ2wKjrviqnaT+/fuTh4cHnTx5kg4ePEh9+/al2bNncyelQCCge/fukbOzM2lpadGZM2fIwcGh0luVTk5OZULhEn3cAf7gwYN06NAhun79Os2cOZP69+9Py5YtqzA8+Ncguu1Z+kI8fvw4jRw5kgYOHEirVq2iqKgosWOrKzQ0lAwNDbn5sUuWLCEej0cGBgZ06dIlIvpYWe3fv1/iebwJCQlkbm5Orq6u9OrVK+5EL10RHz16lAwNDavcpDEtLY3Gjx9Po0aN4haeX7x4kQ4cOEA8Ho9rVIoqWkkWBzM1q/R+ZIGBgWRubk6amppkbm4uNlWA6ON0u4SEBNq0aVOZPX2q8vLlS24DVV1dXXJ1deX2n9i0aRO5u7uL5aUyouvrwYMHNGrUKDIzMyMLCwtycHCgR48eUUxMDA0YMIAL2zx8+HBur4yKGqDSPrclDXN9//59rozT1NSk0aNHV/n+67vaMD1XmuVmZUQb8To4OND27dsrDZ1eEdFaLhUVFbHz8v+1d/dxNZ//H8DfuZvN/b6+s+++37Gx7aQaUYpOUUTk6FZySKlYmsXKXSGTJLkplPsKKTUPE8MmNxMqI/c3Y4m5TSiWovvz+v3hdz47R7c4J8n7+c9Gp8+5znFdn+tz3b3f8q2d8q13NW0LVEc//yJ19HHszcrNzYVEIoGuri4cHBwAPN8atmLFCmEiLD09Hbt374aTkxNcXFyqvd5ff/0FXV1djBs3Dvb29kJC0/Xr18PY2Fgpt5tMJoO7uzumTZtW7TXVmXoC+Ge7qb6+vlJwn5iYGIjFYojFYhgYGMDS0lIpv2dNVN1e7ty5gxEjRsDAwEApUXpoaKiQWFz+nH3lyhWYmJi8tc9xdTZI+uabb2Bra6sUI33t2rWQSqUoKCjAn3/+KeyVvHHjBmbMmAGRSFTtjNX169fh5OSklJ/h1q1bWL9+PbS0tIR48MOGDcO5c+ewbt06GBgYIDY2Vn0ftAZVddzp6emYOHEiHBwclPZ5vuyZitLSUsyYMUOY9Tt+/Djs7e1x6NAhTJo0CT179kR0dDSysrJeuuzHjx8XcsOkpqYKq3L37t3D3r17YWBggG3bttXqWpmZmdDV1VVKPhgREQFDQ0PExMRg1apVGDJkCMaNG/fS5WR15/r160pRag4dOgQdHR1s3LgR6enpGDRoEMRisdJM2suu8sjJBwY7d+6Er68vIiIilKKVeXp61nplSl6G3Nxc9O7dG0FBQcjOzkZUVBREIhEOHDgAmUyG0tJSXL16FZcvX651NB5V1e2FCxfC1NRUWPHOzs6GhYUF+vXrh19++aVCpLY7d+7A19cXHh4etSonqxuqvG9WR56I19raGr6+vrXKm6Lo7NmzuHbtGtauXQtNTU3MmTNHmEgoKyvDzp07MXz48GrPuKmjn3+ROvs49mbI7/VeXl7C4EOeKDY/Px8xMTFCgByJRILvvvuu2uvJ7+/Z2dlC7jn5GdF79+5h3rx56N+/P+zs7DBjxgw4OTmhf//+tT4TGBMTAx8fH3h6eqJbt27CGaeysjIkJycjKioKkZGRSgERqiN/zouMjERERARCQ0MhFouV+rOMjAykpqbi6NGjL3XOVNXtRf7dnj9/HpqamtDU1FRKlBscHAwzMzOYmppi4MCBsLCwwNSpU2td3vqmTgZJy5Ytg6GhoVAB5TfJM2fOQEdHB8OGDYNIJMLw4cORmpoK4PkNNCEhodpBgjyEqDyT+tmzZ+Hp6SlsTdm/fz/y8/MxcuRIeHt7A8BLjb5VaeHChRg4cCDc3d0xc+ZMLFiwAPHx8fjpp59w5coV5OTk4Pjx4/Dx8cHgwYOxaNGiVwpXW15ejhEjRmDWrFkoKirCnDlzEB4ejuLiYmzdulUYOL5qtvG0tDQMGTIE+vr6wuzfwIED4ezsXOukbvKZ/kWLFqF37964fv069u/fDy0tLaWH6YKCggYVurihKS0thaOjI6ysrFBYWIiSkhJYW1sLQQwyMjLQt29fuLq6QlNTE7GxsUKCypcRExMjDIAU60dZWRl+++03JCQkwN/fHyYmJjW2GZlMpjRICw0Nhbu7u3C9oUOHYuHChcjMzMTMmTNfeqVLVXX7VcNcFxQUvNJ3zNRLFffN2tq8eTNcXV1rrAfyAdDDhw+RmZkptJ3y8nJs374dPXr0wLhx4/D48WNkZGQgLS1NKSz4i9TVz79I3X0ce3N2796N7OxsBAQEQFNTEytWrBDqRnFxMf7880/k5eXV+rlAvgJvY2MDQ0NDYSdAXl4edu7cCR8fH4wdOxYrVqyo9l6vrtQTVe0WysvLQ2RkJPr37w8XF5fXSriqyvZSWFgorFLv2rULkyZNgr+/P0QiEZYsWSK8Ljk5GVu3bkVERISwUvW2Uvsg6fLly/j666/h6elZoRLa2NjA2dkZu3fvRlpaGvr27StEjgJq3i6Rm5uLsWPHwtHRER4eHtDV1UW3bt2wcuVKpQ5i/fr1cHV1Ve0HewkymQy+vr7CMqqJiQns7e2hp6eHnj17QltbGyKRCJaWltDR0YGOjg5EIhECAgJeaf/msWPHkJSUhMzMTLi5uQmzGYcPH8a8efNeO9pVWVkZoqOjERAQAB8fH2zevLnabOtVuXnzJvr06QOpVIqvv/5aKfRlfdgqw6pXXl6OxMRESCQSREVF4datW7CwsMDly5dRWlqKmTNnYvHixSgoKICbmxtEIhGkUimuX79e47Xl9T42Nha6urrw8vKCtbU1hg4dKpxbyMrKwvTp02FsbAxPT89abaN9sV4tXLhQOLsjv48Az+umpqamkNvlZamjbtfXMNesdlR136yNmsLfy+vSjRs34ODgAAMDA0gkEuzfv19YBUpLS4OZmRl69+4NbW1tYTKyMurs5yuj7j6O1Q35ICE7Oxu3bt1SupetX78empqaQpqD2lJ8ZiooKBAmkr7//ntoa2sjKipKqc69zABdlaknFMuwadMmzJgxA3Z2dli9ejUyMjIgk8mwc+dO2NjYwMbGBqdPn651OV/0uu0lKSkJAQEBGDRoEOzs7LB06VKcOnUKRUVFKCwsxMqVK6GlpSVEZG5I6mwlycbGBl5eXsI/tI+PD2xtbZUO1sbFxUFfX1+YPa2NEydOwN3dHTY2NvDz81M6yCevhMuXL8eMGTPe+KpEfHw8jI2NER4ejmvXriE/Px8PHjxAWloaUlNTsWHDBoSFhQnLv5cuXar2elU17pKSEpSWlmLPnj3Q1dUVZv/c3Nwwffp0lX+uVyH/t4mPj4eOjg48PDzeugN97HkdnDVrFszMzHDq1Cl4eHhg165dSE5OxqRJk4QtDnPnzoWHhwcWLFhQ4zXldePevXtwdnYWHs5u3ryJOXPmQCKRYO7cuXj27BlKSkqQm5tb4xmM1NRUhIeHY+TIkZg9e7aw2rVu3TpYWFjA19cXdnZ2whalR48ewcbGptqDwTWVXx11uz6GuWZvF3n9LCoqgomJCby8vJCSkgIHBwcMGjQIP/74o9Bn3L59G+Hh4bXaoq6Ofv5t7uNY9eT3sitXrkAikUBPTw+Ojo7YuHGj8Ky2b98+9OrVC05OTjWGhQf+qS/5+fnIzMzEoUOHcPLkSeHnK1asgEgkQmBgYK23o6o79cS8efNgZmYGHx8f+Pj4wMjICC4uLkJAn0OHDsHe3h6jR4+ucUJBHe1lz549MDIywrRp0zBv3jz4+fmhV69eMDMzQ1xcnLB7Yfv27TA0NISDg8MrnYmsr9Q6SFL8B926dauQENXLywtmZmYVEmQlJibCxsbmpbe4FBYWVpkk78yZM9DT06t2FqwuHT58GMbGxpg2bdpLJYZVpPjgVl1wh4sXL2Lo0KEYMWIEpFIpLCwsXun9qvLi1qVXcfPmTVhaWmL48OHCmRMeLNVvld2IR40ahWHDhgk5EsLDwzF+/Hjh5z4+PoiJian1ezx69Ahr1qyBk5MTbty4Ifx9bm4uwsLCYG1tjYkTJ9aq40xKSkK/fv3g5OQEHx8fWFlZwcDAALa2tjh69ChcXV0hEonw22+/CZ9PfsO/fft2rcv8InXVbcX7ampqKkQiEXbs2PHa12V1QxX3TVXw8/NTaqNOTk4wMzODSCTC8uXLa31GQR39fH3p45jqKQbWefbsGczMzODv748dO3YIUTpDQ0OF1aNLly5BV1cXmzdvrva68npSVFQEqVQKOzs7iMVimJqawsvLS6n+de/eHVKpVDgfWJMbN25ALBYLxzUePHiAHTt2YOTIkXB1dRUmBdLT018qEu/Ro0dhYmKitKKckpICKysrODk5CVtgT58+Xe2gTl3t5cKFC9DS0sK2bduEfr+srAxZWVlwdnaGtra2sF342bNnSE5Ohrm5OfT09PD06dN6cZ97XWpfSVK8gR45cgRSqRTdunWrMKNcUFCAIUOGvHQG4RcdPHgQJ0+eRHp6OrZt2yZE2qhPbty4ATMzM3h4eCjtA5VX7upmC65evQpDQ0OMHTtWWEau7vVr166Fv78/AgICXvogb105dOgQRCKRyvfmM9XbsWMH/Pz8sHPnTqXzCZcvX4aTkxMWLVqEoqIiBAYGYtSoUTh58iRWr14NIyOjl9pXvX37dhgbG0MkEimFNQaez4pt3rwZAwYMqLHjvHz5MrS1tbFlyxZhduvRo0fYuXMnrK2t0b17dwQGBuL7779H9+7dMXHiRLi6uqJ3795Ch/g6Wz/VVbfrU5hr9nZ4cTLx22+/FSJORUZGCofhg4ODoaWlBTc3N+EMR01U2c83xD6OPffiv+Ovv/4Kb29v4QH82bNnCAgIgKWlJfz9/YXBQ00rE4oP405OTnB3d0dubi5u374NfX19hISE4M6dO0KflZaWphRYpyaqSD1RWloqPOPJX/fTTz9hzJgxQrJc+d9fvnwZIpEIW7durbFs6moveXl5QnodxWsqXnv69Ono1q0bjh8/DuD5M+zJkydfaQdGfaW2QZLiaFaxQ8/MzISnpyesra0RHh4uVNoxY8ZAKpW+1ns+ffoUc+fOhZaWFrS1teHo6KgU5rE+ycvLg4ODA0aMGIFdu3bV+rB1fn4+EhISYG9vDysrK6UteZU10Nu3b7/xbYbVkclkePLkCYYNG1Zj+E32Zj1+/FjI4SAPYb17925hxi8hIQGDBw/G2bNnkZaWhgEDBkAsFsPMzKzG2bXK6u6hQ4dgZ2cHGxubSqN/vZjZ+0X5+fkwMDCoEOoVeH6jl+c0MjIyQkJCAjZu3AgXFxcsW7ZMmLx4nZmwuqjbfHaP1SQlJQUhISEYOHAgxGIxtm/fjuLiYjg6OiI8PBx37tyBj48P4uPjIZPJsHXrVojFYkyaNKnGmXZ19PMNrY9jz23YsAGenp5Cf7F48WLY2dkpnU+TW7VqFaytreHs7FwhhURVysrKkJ6eDolEIgwWJk2aBKlUitzcXAQFBSEmJuaV7pmqSD1x7tw5Ib2LYm4mXV1d4c/ygVRRUREkEgkSExNrLJu62svo0aMhkUiEPyu2dfn/5+fnQyqVwsnJSSmiZUOi8kHSiRMnhP+vbOSZlZWF/Px8zJw5ExKJBAsXLoSXlxf69eunsjKkpKQgPT0d9+7dU9k11aG0tBRTp06FRCLBmjVrqo0cBCg/4O3fvx9jx46FmZkZ9u3bp3RN+X+3bt0KQ0PDWm1JetNOnz79StH8WN1KSEhA165dsWLFCoSHh0NHRwfOzs4IDw9HaWkpfvjhBwwaNAiFhYXIzMzExYsXkZmZWe01FW/kmZmZSE1NVQoz6ubmBltbW6xdu/al6siIESMwcuRI4c/y+5Di+x09ehTm5uYVZssqK9ur4rrN3pTjx49DLBZj9uzZCA4ORnh4uNAes7OzhaTPVlZWQn8ZHx+PgICAKrewA+rr5xtyH/cuk8lk2LRpE6ysrODh4YGbN28iLS0N/fv3h66uLnbs2FEh91tCQgLc3NyqXEW6cuUKDh48iMTERKGuXrhwAQMHDoRMJkNwcDDMzc2Fej127Fi4ubnVWFZ1pZ5Yvnw5unfvjr1798LT0xMXL17EzZs3MWjQIPj6+ipt7c7Ly4OFhQV27dpV7TXV1V5u3bqFcePGwcTERGkXxIuTIgAQHR2N3r17C8lvGxqVDpLOnTuHvn37Yvbs2cIDf2lpqfBlBgcHC9EvZDIZli9fjt69e6NXr171fkCjTqGhoXB0dKwxJLDif4HnM+ne3t4Qi8VK0bNKSkrw+++/w8DAAP7+/uorOHtnKJ5Dmj9/Pjw9PfH48WNcu3YNQUFBsLW1hbGxMWJiYiCRSBAQEFDrw5tVJaGVn9m7e/cufHx8YGNjg7lz59bqzOLff/8NXV1duLu748iRIxW2OSi2o+DgYJiamuLJkycVOmrG3lYPHz6EsbGxUjQveTtWfBCMi4uDnp4esrKykJ6ejl69egl5Xyqjrn6e+7iGLyUlBY6OjnB0dMTFixfx8OFDjBgxApaWlti6dWuFPqOgoKDS68hz8PXs2RPdu3eHWCzGpUuXkJ2dDQMDA3h5ecHIyAjnzp0TfmfBggXw9fWt9T1elakngOdtZPr06dDW1saAAQNQVFSE8vJyREREYOjQoXB3d8cvv/yCXbt2wdnZWdjeVxV1tRd5hMGMjAwEBQVBLBYjICBA6fMrvu/evXthZ2fXoII1KFLpICk3NxeLFi2Cra0tJk6cqBSSd/PmzejVq5dSpQWeH/TkyExV3wwUVdYoMjIyEBgYCLFYjHnz5gF4nlTS3Nxc6VAuY6/qxZnilJQU2NraCjNMxcXFuH//PtauXQtLS0sMGDAAIpFIKapQTdeuKgmtfEuD/JyTubl5rUJ9A8/blJOTE+zs7JCYmFihI1OcCaupQ2LsbRMVFQUvLy/hYQyouDK6YcMGxMbGwsXFBVpaWjAzM6vxXLA6+3nu4xq+jIwMuLm5CSHni4uL4eXlBVNTU6xbt67GSbC7d++iR48eCA0Nxblz57Bnzx4hkA/wPHR49+7dhTN2jx49wrlz56Cnp4eff/652murK/WEXHh4uJCXKDg4WAgXHh8fD1dXV2hpaUEikcDLy6tWgzlVt5ctW7Zg9OjRwvPovXv3sGrVKvTv3x8eHh7CpEh5eblS9Gh3d/dahz5/26hskCT/RyopKUFMTAwcHBzg5OSE06dP48GDB+jdu7dSBa0uAgf7R2xsLLZs2VKhAio2iqysLKEiu7m5wdHREUOGDKnrorIGaN26dRg5ciQuX76sNPscExODrl27VkhAd+vWLSQmJmLOnDm1fo+8vDzY2NggKioKQMUktJs2bRJeW9PWvReVlZVh1qxZGDRoEKKjo5W2BMjvQaGhoZg8eTInYWUNyty5c2vMW/LDDz/AxMQEaWlpOHjwoJA/pSrq6Oe5j3v35OTkwMfHBxYWFsJk25IlS9CnTx/MnDmz2mA0pqamFYIuBAYGwsrKCuXl5bh//z5WrlwJbW1tmJubY/DgwTA3N0dwcHC1ZVJH6gngn3qcnZ2N7OxsFBYWYteuXUJ6CPmgsLi4GNnZ2Xj69Gm1u4rU1V5OnjwJTU1Nof3Kr5efn4/4+HgMGzYMdnZ2SpH4Dh8+DB0dHSE5dEOkkkGS/EaouCVHvmRoZ2eH4OBg4cAaD45qTyaTwdvbG+bm5tiwYUO1ez7lh/esrKzQs2fPBrs/lNUdmUyG6Oho2NnZQUtLC3PmzMGBAweEnwcGBsLZ2VmYIVYcRNV0lkfxPvDo0aMak9COGDECt27deuXPsmHDBvTt2xeLFy/GX3/9Jfz90aNH0a1bN+Em3xBClrJ3m7xtffvtt8Ls+osUt8ro6+vXKvmqOvp57uPeXYWFhQgJCYGFhQWCgoJQVlaGjRs3IigoqMrfkUqlSueK5BNb27dvr3DeKDMzE2vWrEF8fHyFybyqqDL1hKKzZ8/CysoKiYmJQoCD33//HUZGRrCzs8PFixdx/Phx4SxRVf2QutrLw4cPoauri9WrVwNAhSAUxcXF2Lt3L5ycnDBgwAAcO3YMT548QY8ePYScgw1VE1IBDQ0NIiJq0uSfy0kkEvr4449p48aNdPr0aWrbti3p6upS48aNqaysTOm1rHIaGhoUGhpKMTExFBUVRTk5OTR8+HD67LPPiIgIgPDdt2zZkoqLi6lfv34klUrpX//61xssOWsINDQ0yNXVlUaMGEH79u2jdevW0aVLl+jHH3+kmTNnkkQiobCwMDp69ChpamqShoYGyWQyatSokVAvK1NSUkLNmjUjIqKkpCTq1asXtW3blv766y+6f/8+FRQUkEQioRYtWlDHjh2padOm1KlTJ/r0009f+bOMHTuWvvjiC/L19aXc3FxydXWl1q1bk7e3N02cOJGMjIyU2hNjb6vGjRsTEZGRkRGtWrWKTpw4QQYGBkqvkdfzVq1aUdu2balp06Y1Xlcd/Tz3ce+u5s2b05QpU+iTTz6hbdu2kaurK61atYpatmxZ6evDwsLo7NmzdODAASJ63o+899579OjRI1q6dCk1b96cAgIC6Pbt29S5c2fq0KEDtWvXjoyMjOh///tfrcqUnJxMsbGx9PDhQzp16hR16tSJiIg+/PBDmjhxIrVv3542btxIJ06coK+++qrWn7VDhw7UuHFjioyMpL///puGDRtGhoaGlJCQQN999x2NHj2aysrKaP78+UREVfZD6movUqmUBg4cSBMmTBCu06hRI9q/fz+1bt2aDA0NycLCgtq1a0dxcXE0bdo0ysnJIRsbG3J3d6/19/A20gCAV/3lkJAQOnjwIHXs2JE6dOhALVu2pM8//5yaNWtG2tra1L59e7p27Rr9+OOP9Mcff1D//v1p4sSJ9MEHH6jyM7wTUlJSyNfXl0xMTEgqlVK3bt2En5WWltLOnTvJ39+fQkNDaciQIW+wpKyhysnJocOHD9Mvv/xC6enp5OXlRX/++ScdOHCAVq9eTX369Kn297dt20bDhw8X/jx79mzKzMyk5cuXU0FBAf3nP/+h6OhounDhAq1du5aIiKZMmUK6uro0ZswYlXyGmzdvkpubG3Xu3JkuXrxIffv2pZCQECIiHiSxBuXq1as0ZswY6tatG/n7+wuTDIr1PCYmhnbs2EGRkZH04YcfVnqduurnuY97d+3du5d27txJYWFh1Lx58wo/B0BhYWGUlJREZmZm5ODgQF26dCEiIisrKyooKKCvvvqKZDIZ5eTkUFZWFuXl5VGbNm1o37591Lp160rft7J7fnJyMoWHh5NMJiMnJyeyt7dX+vnJkydJX1+/2s8jnyxUVFZWRr6+vpSenk42NjZkb29PHTt2JCKiLVu2UKtWrWjYsGHVf1EKVNVeXF1dSUNDg6Kjo4Xfbdq0KV24cIGkUiktWLCArKyshNdfuXKF1q9fT02bNqWFCxfWurxvrVddgpLJZPD19YVIJIK+vj5MTExgb28PPT099OzZE9ra2hCJRLC0tISOjg50dHQgEokQEBDAW1pekWIS2sOHDwt/f+TIEejq6ta7pLms4dq9ezfc3d3h5+cHkUgEU1PTag/cxsbGQiQSCdt6UlJS4OPjU+GA9+smoa2NJ0+eYNCgQUrbMzjfEGuIUlJSoK+vj9GjRyM1NVXIe/TkyROkpaVBX1+/2jDDdd3Pcx/37qpN7p6ff/4ZQ4cOxZQpU3D16lV4eHjAxcUFDx48UNoOWlJSguzsbCFfUmXUlXpCkTxCq6LQ0FD07NkTvr6+OH/+/CtdV+5128uvv/4KkUgknEOSf4e5ubkQi8VYvny58Fp5H1lSUtJgI9lV5rVWkoiIEhISaOXKleTo6EiWlpb00UcfUWFhIWVmZhIAysjIoL///puys7Pp7t275OfnR1paWqoa471znjx5QuPGjaNGjRrR6NGj6dNPP6WpU6dS165dKTw8/E0Xj71DHjx4QBkZGZSYmEhNmjQRVmRedOzYMXJ3d6dVq1aRqakp3b59m8aNG0d37tyhxYsXk6WlpbA1JykpiUJDQ+np06fUrFkzCg4OJkNDQ5WXvby8XNgWWF5eLmxRYqyhOXbsGAUGBlJOTg516dKFRCIRZWZmUl5eHllYWNB3331X4zXqsp/nPo5V58yZMzRv3jzKy8ujkpIS2r17N7Vt21ZYvant/Rz/v4oUGxtL0dHRlJOTQ+3bt6cVK1aQjo4OZWVl0dKlS+n69eukq6tLEyZMoA4dOtR4PblVq1ZRYmIiTZ48mQYOHEjvvfee8LPIyEhatmwZicVicnNze60+7lXby8mTJ+nSpUt05swZOnv2LDk7O5ObmxsREVlbW9OXX35JixcvJg0NDeGzxcXFUUZGBs2ZM+ed6TNfe5BERHTkyBGaNWsW9enTh5ydnUlHR0cVZWNVKCsrIz8/Pzp//jwVFBTQRx99RImJiW+6WOwd8mKHUJWHDx+SpaUlOTo60tSpU4mI6NGjR3Tw4EGKjIykpk2b0oYNG+jf//638Dvnz5+nJk2a0HvvvSdsqVCX2n4Oxt5mMpmMIiMj6dq1a3Tjxg3S19cnfX19MjMzq/U16rKf5z6OVSc7O5u8vb2pqKiIxowZQ3Z2dkRU+Ta3yshfl5ycTF5eXjR16lTS1tamWbNm0dOnT2n27Nk0ePBgKi4upsWLF9Phw4dp7dq11Llz51qX8datW+Tr60tPnjwhBwcHGjZsmLCl9eTJkxQUFESff/45BQQEUKtWrV7ti/h/L9teUlJSaMqUKbRlyxYier7dLykpiWxtbenmzZv0+PFjioiIoDZt2gh95KFDh8jT05M2btxIvXv3fq3yvlVUtSSluOynGElEvnzH21lULzw8HKampu/U0ierX2raUjNo0CCIRCJ4e3vjjz/+EF5fWFiI5ORkSCQSGBkZca40xt4Cdd3Pcx/HqlJQUIDJkyfD1tYWq1evfuk6oqrUE+np6ZBKpUhMTERaWprSz4qLi+Ht7Q0TExOEhoYKEVqTkpJqDHX+KmrTXu7evQtdXV2sWbNG+LusrCxERETAzMwMXbt2FcJ8y3M1ZWRkoHv37oiNjVVped8GKllJkpMv+2loaNCYMWMqLDEy1Xv27BkHwmD10qhRo6hNmzb0zTff0Pjx4+mzzz6jKVOmkJ6eHjVr1ozKy8vp0qVLtHz5cjp+/DgtWbKEBg8e/KaLzViDJe/uX2f1tK77ee7jWFXKy8spLCyMUlJSqGvXrjRlyhRq3759ta+XbxN7/PgxSaVSWrZsGX3xxRf0ww8/ULt27cjT05MmTZpEqamp1L17d1qyZEmVkVUB0Pjx4yklJYU+/fRTatSoEbVp04YkEglpamoKUSXXr19PGzZsoC5dulD79u0pOTmZpk+fTlKpVOXfSU3txczMjIyNjSkwMJBkMhkBoMaNG1NhYSH9/PPPFBsbSy1atKCgoCDq0qUL/f3332RjY0OmpqY0d+5clZe3vlPpIInon2W/K1eukEQioVGjRr32UiJj7O0SEhIiRCxq3bo13b9/n1xcXKioqIhmzJhBJiYmQqjXa9eu0aZNm2jr1q0UFBRUIZoQY6x+4X6e1SexsbH022+/0erVq6scsFeWeuLbb78lFxcX+uCDD2j79u00cuRI6tOnDwUEBNC9e/eoU6dO5OfnV+17nzt3jiIiIujLL7+kjh070q5du+js2bMEgPT09MjQ0JAcHBzo0KFDlJaWRs2bNyctLS0aO3asqr+GGo0aNYref/99ioqKIqJ/ItmdP3+e7t27RwMHDqSkpCSKjY2lnJwc8vf3p+joaAJAGzZsqPPy1gcqHyTJhYWF0fHjxykmJkaomIyxhg8AJSYmkqGhIf33v/9V6pzc3d3p1KlTNGXKFLK0tBRyN2RlZdG+ffvIxsaG2rZt+wZLzxirLe7nWX1RWFhI77//foW/V3fqicePH9OMGTOopKSEIiIiqGXLlnTkyBG6c+cOzZs3Tzgj1b9/fyorK6MVK1a8kbYSFhZG69evpwMHDtAnn3wi9Mv3798nGxsb8vDwEAZux44do/j4eNq3bx917NiR9u3bV+flrS/UNkgiInr69Cm1aNFCXZdnjL0lFBNLzp8/n2JjY8nNzY1GjRolJPqr7aFbxlj9wf08q6/i4uIoMDBQeNhPTU2l7du3k4uLi1Jeofnz59Ply5fJx8eH0tPTafPmzZSYmEgfffRRrd7n2rVrNHz4cJJIJBQYGEhERCtXrqTNmzfTxIkTqaCggHbv3k2tWrWihIQEtXzW6qCaPFMSiYS+/vprCg4OVgpkdP78edq/fz+NGzeO2rRpU+dlri/UOkhijDE5xf3gcXFxFBQURObm5uTp6Uldu3Z9w6VjjDHWUNRV6gmZTEYaGhq0ZMkS2r59O23ZsoWuXbtGkydPpuXLl5O5uTkRPT8r1KRJkze64rpr1y5au3YtaWpq0oQJE2jJkiVUVFREK1euFCY6FCcr5dvx3mU8SGKM1RnFG3BaWhq5ublRSEgIWVtbv+GSMcYYawjeROqJW7du0ciRI+mzzz6jixcvkre3N7m6uhJR/dolUVOeKaaMB0mMsToFgABQo0aN6PHjx9SuXbs3XSTGGGMNhIWFBd28eZMsLS1p/PjxpKmpSRoaGlRUVCREUn306BFFRUWRpqbma7+ffICRkJBAQUFBJBaLafXq1fU2B9/r5pl6l/C3wRirUxoaGtSoUSOSyWQ8QGKMMaYyo0aNos6dO1N8fDwdOXKE/P396ffff6eSkhJq3rw5GRsbU1BQEGlqatLw4cNp7969r/2e8oGFkZERdezYkXJzc6msrIyI/gm7X598/PHHFBkZSZ06daLY2Fhas2YNFRQU8ACpEvyNMMbeCL4hM8YYU5WQkBC6d+8ehYSEUI8ePWjPnj1UUFBAfn5+dPDgQSooKKDGjRtTt27daObMmWRnZ0fff/89/fTTTyp5/44dO9K0adPowoULFBcXR0Svl5NMnVq0aEFLly4lIyMj2rt3LwUFBVFOTs6bLla9w08pjDHGGGPsrQWAvvzyS4qNjaXWrVtTSUkJdejQgfbu3UtdunQhPz8/SkxMpNzcXCIi6tKlC02YMIF8fX1pwIABKiuDnp4effXVV/THH3+o5Jrq1LhxY5o6dSoNHz6c7t+/z7nOKsFnkhhjjDHGWIPyplJPnDlzhkQiEX3wwQcqva46VZVn6l3HgyTGGGOMMdbgcOoJ9jp4ux1jjDHGGGtwGjduTDKZjIiIRo8eTZGRkbRv3z7KyMh4wyVjbwNeSWKMMcYYYw0Wp55gr4IHSYwxxhhjrMHjXEDsZfAgiTHGGGOMMcYU8HCaMcYYY4wxxhTwIIkxxhhjjDHGFPAgiTHGGGOMMcYU8CCJMcYYY4wxxhTwIIkxxhhjjDHGFPAgiTHGGGOMMcYU8CCJMcYYY4wxxhTwIIkxxhhjjDHGFPAgiTHGGGOMMcYU8CCJMcYYY4wxxhTwIIkxxhhjjDHGFPAgiTHGGGOMMcYU/B8TvmmlhMtR5QAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pickle\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from matplotlib.ticker import FormatStrFormatter\n", + "import matplotlib.gridspec as gridspec\n", + "\n", + "sns.set(style=\"whitegrid\", font_scale=1)\n", + "\n", + "gpt_color = sns.color_palette('Dark2',3)[2]\n", + "\n", + "name_order = ['N-BEATS','SM-GP','TCN','N-HiTS', \"LLaMA-2\", 'GPT-3', 'ARIMA']\n", + "def color_map(x):\n", + " if x == 'GPT-3':\n", + " return gpt_color\n", + " elif x == 'LLaMA-2':\n", + " return '#a60355'\n", + " else:\n", + " return 'grey'\n", + "\n", + "palette = [color_map(x) for x in name_order]\n", + "\n", + "csv_fn = \"../precomputed_outputs/deterministic_csvs/darts_results_agg.csv\"\n", + "df = pd.read_csv(csv_fn)\n", + "df['Type'] = df['Type'].apply(lambda x: x.replace(\" 70B\", \"\"))\n", + "\n", + "fig, ax = plt.subplots(\n", + " 1, 3, \n", + " figsize=(10, 1.25), \n", + " gridspec_kw={'width_ratios': [7, 14, 6], 'wspace': 0.5},\n", + ")\n", + "\n", + "c = sns.barplot(\n", + " data=df,\n", + " order=name_order,#['SM-GP','N-BEATS','TCN','N-HiTS','ARIMA','GPT-3','LLaMA70B'],\n", + " x='Type',\n", + " y='MAE',\n", + " ax=ax[0], \n", + " color=\"grey\",\n", + " palette=palette,\n", + " alpha=0.3,\n", + " edgecolor='black',\n", + " errwidth=1.,\n", + " errorbar=('ci', 68)\n", + ")\n", + "for bar,name in zip(c.containers[0], name_order):\n", + " if name != \"GPT-3\" and name != 'LLaMA-2':\n", + " continue\n", + " bar.set_alpha(1.0)\n", + " bar.set_edgecolor('grey')\n", + "\n", + "ax[0].set_ylabel('MAE')\n", + "ax[0].yaxis.set_major_formatter(FormatStrFormatter('%.1f'))\n", + "ax[0].tick_params(axis='y', which='major', pad=0, labelsize=9)\n", + "ax[0].set(xlabel=None)\n", + "ax[0].set_xticklabels(ax[0].get_xticklabels(), rotation=50, horizontalalignment='right')\n", + "ax[0].set_xlabel('') # Remove x-axis label\n", + "ax[0].set_title(\"Darts\")\n", + "\n", + "csv_fn = \"../precomputed_outputs/deterministic_csvs/monash_results_agg.csv\"\n", + "df = pd.read_csv(csv_fn)\n", + "sorted_vals = df.groupby([\"method\"])['v'].aggregate(np.mean).reset_index().sort_values('v')\n", + "name_order = list(sorted_vals.values[:,0])[::-1]\n", + "\n", + "def color_map(x):\n", + " if x == 'GPT-3':\n", + " return gpt_color\n", + " elif x == 'LLaMA-2':\n", + " return '#a60355'\n", + " else:\n", + " return 'grey'\n", + "\n", + "palette = [color_map(x) for x in name_order]\n", + "\n", + "c = sns.barplot(\n", + " data=df,\n", + " x='method',\n", + " y=\"v\",\n", + " order=name_order,\n", + " color=\"grey\",\n", + " palette=palette,\n", + " alpha=0.3,\n", + " edgecolor='black',\n", + " errwidth=1.,\n", + " ax=ax[1],\n", + " errorbar=('ci', 68)\n", + ")\n", + "for bar,name in zip(c.containers[0], name_order):\n", + " if name != \"GPT-3\" and name != 'LLaMA-2':\n", + " continue\n", + " bar.set_alpha(1.0)\n", + " bar.set_edgecolor('grey')\n", + "\n", + "ax[1].set_ylabel('')\n", + "ax[1].set_ylabel('Scaled MAE')#, fontsize=16)\n", + "ax[1].set_ylim(0, 2.2)\n", + "ax[1].set_xlabel(\"\")\n", + "ax[1].set_xticklabels(ax[1].get_xticklabels(), rotation=50, horizontalalignment='right')\n", + "ax[1].set_title(\"Monash\")\n", + "\n", + "csv_fn = \"../precomputed_outputs/deterministic_csvs/autoformer_comb_results.csv\"\n", + "df = pd.read_csv(csv_fn)\n", + "df['method'] = df['method'].apply(lambda x: x.replace(\" 70B\", \"\"))\n", + "\n", + "name_order = ['Informer', 'Reformer', 'Transformer', 'Autoformer', 'LLaMA-2', 'FEDformer']\n", + "\n", + "def color_map(x):\n", + " if x == 'GPT-3':\n", + " return gpt_color\n", + " elif x == 'LLaMA-2':\n", + " return '#a60355'\n", + " else:\n", + " return 'grey'\n", + "\n", + "palette = [color_map(x) for x in name_order]\n", + "\n", + "c = sns.barplot(\n", + " data=df[df['pred_len'] == 96],\n", + " x='method',\n", + " y='mae',\n", + " order=name_order,\n", + " errwidth=1.,\n", + " color=\"grey\",\n", + " palette=palette,\n", + " alpha=0.3,\n", + " edgecolor='black',\n", + " ax=ax[2],\n", + " errorbar=('ci', 68)\n", + ")\n", + "ax[2].set_xlabel(\"\")\n", + "# ax.set_xticklabels([])\n", + "ax[2].tick_params(axis='y', which='major', pad=0, labelsize=9)\n", + "ax[2].set_xticklabels(ax[2].get_xticklabels(), rotation=50, horizontalalignment='right')\n", + "\n", + "for bar,name in zip(c.containers[0], name_order):\n", + " if name != \"GPT-3\" and name != 'LLaMA-2':\n", + " continue\n", + " bar.set_alpha(1.0)\n", + " bar.set_edgecolor('grey')\n", + "\n", + "ax[2].set_ylabel(\"MAE\")#, fontsize=17)\n", + "ax[2].set_title(\"Informer\")\n", + "# ax.set_ylim(0, 0.7)\n", + "# ax.set_title(\"Aggregated (horizon = 96)\", pad=15)\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig('outputs/mae_aggregated.pdf', bbox_inches='tight')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Darts" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# from matplotlib.ticker import FormatStrFormatter\n", + "# import matplotlib.gridspec as gridspec\n", + "\n", + "# sns.set(style=\"whitegrid\", font_scale=1)\n", + "\n", + "# name_map = {\n", + "# \"gp\": \"SM-GP\",\n", + "# \"arima\": \"ARIMA\",\n", + "# \"TCN\": \"TCN\",\n", + "# \"N-BEATS\": \"N-BEATS\",\n", + "# \"N-HiTS\": \"N-HiTS\",\n", + "# 'text-davinci-003':'GPT-3',\n", + "# 'LLaMA7B': 'LLaMA7B',\n", + "# 'LLaMA13B': 'LLaMA13B',\n", + "# 'LLaMA30B': 'LLaMA30B', \n", + "# 'LLaMA70B': 'LLaMA70B',\n", + "# \"llama1_7B\": \"LLaMA 7B\",\n", + "# \"llama1_13B\": \"LLaMA 13B\",\n", + "# \"llama1_30B\": \"LLaMA 30B\",\n", + "# \"llama1_70B\": \"LLaMA 70B\",\n", + "# \"llama2_7B\": \"LLaMA-2 7B\",\n", + "# \"llama2_13B\": \"LLaMA-2 13B\",\n", + "# \"llama2_70B\": \"LLaMA-2 70B\",\n", + "# \"llama2_7B_chat\": \"LLaMA-2 7B (chat)\",\n", + "# \"llama2_13B_chat\": \"LLaMA-2 13B (chat)\",\n", + "# \"llama2_70B_chat\": \"LLaMA-2 70B (chat)\",\n", + "# }\n", + "\n", + "# hue_order = ['SM-GP','N-BEATS','TCN','N-HiTS','ARIMA']#, 'LLaMA70B']\n", + "# hue_order += [\"LLaMA-2 70B\", 'GPT-3']\n", + "# # hue_order += [\n", + "# # \"LLaMA 7B\", \"LLaMA-2 7B\", \"LLaMA-2 7B (chat)\",\n", + "# # \"LLaMA 13B\", \"LLaMA-2 13B\", \"LLaMA-2 13B (chat)\",\n", + "# # \"LLaMA 30B\", \"LLaMA 70B\", \"LLaMA-2 70B\", \"LLaMA-2 70B (chat)\"\n", + "# # ]\n", + "# nlls = defaultdict(list)\n", + "# crps = defaultdict(list)\n", + "# mae = defaultdict(list)\n", + "# datasets = get_datasets()\n", + "# for dsname,(train,test) in datasets.items():\n", + "# # if dsname == \"SunspotsDataset\":\n", + "# # continue\n", + "\n", + "# # print(dsname)\n", + "# with open(f'eval/small_context_tuned/{dsname}.pkl','rb') as f:\n", + "# data_dict = pickle.load(f)\n", + "# for model_name,preds in data_dict.items():\n", + "# # print(f\"\\t{model_name}\")\n", + "# if model_name in ['ada','babbage','curie']:\n", + "# continue\n", + "# if 'NLL/D' not in preds:\n", + "# continue\n", + "# nll = preds['NLL/D']\n", + "# if model_name=='text-davinci-003-tuned':\n", + "# model_name='GPT3'\n", + "\n", + "# if type(preds['samples']) == np.ndarray:\n", + "# nlls[model_name].append(nll)\n", + "# crps[model_name].append(calculate_crps(test.values,preds['samples'][:10],10))\n", + "# tmae = np.abs(test.values-preds['median']).mean()/np.abs(test.values).mean()\n", + "# mae[model_name].append(tmae)\n", + "# else:\n", + "# nlls[model_name].append(nll)\n", + "# crps[model_name].append(calculate_crps(test.values,preds['samples'].values[:10],10))\n", + "# tmae = np.abs(test.values-preds['median']).mean()/np.abs(test.values).mean()\n", + "# mae[model_name].append(tmae)\n", + "\n", + "# llama_models = [\n", + "# \"llama1_7B\",\n", + "# \"llama2_7B\",\n", + "# \"llama2_7B_chat\",\n", + "# \"llama1_13B\",\n", + "# \"llama2_13B\",\n", + "# \"llama2_13B_chat\",\n", + "# \"llama1_30B\",\n", + "# \"llama1_70B\",\n", + "# \"llama2_70B\",\n", + "# \"llama2_70B_chat\",\n", + "# ]\n", + "# for dsname,(train,test) in datasets.items():\n", + "# # if dsname == \"SunspotsDataset\":\n", + "# # continue\n", + "\n", + "# for model_name in llama_models:\n", + "# if model_name in ['llama2_70B', 'llama2_70B_chat']:\n", + "# fn = f'eval/llama_70B_sweep_sample/{model_name}/darts-{dsname}/1.0_0.9_0.99_0.3_3_,_.pkl'\n", + "# else:\n", + "# fn = f'eval/llama_2_results/{model_name}/darts-{dsname}/0.4_0.9_0.99_0.3_3_,_.pkl'\n", + "# with open(fn,'rb') as f:\n", + "# data_dict = pickle.load(f)\n", + "\n", + "# preds = data_dict#[model_name]\n", + "\n", + "# if 'NLL/D' not in preds:\n", + "# continue\n", + "# nll = preds['NLL/D']\n", + "\n", + "# if type(preds['samples']) == np.ndarray:\n", + "# nlls[model_name].append(nll)\n", + "# crps[model_name].append(calculate_crps(test.values,preds['samples'][:10],10))\n", + "# tmae = np.abs(test.values-preds['median']).mean()/np.abs(test.values).mean()\n", + "# mae[model_name].append(tmae)\n", + "# else:\n", + "# nlls[model_name].append(nll)\n", + "# crps[model_name].append(calculate_crps(test.values,preds['samples'].values[:10],10))\n", + "# tmae = np.abs(test.values-preds['median']).mean()/np.abs(test.values).mean()\n", + "# mae[model_name].append(tmae)\n", + "\n", + "\n", + "# nlls = {k:np.array(v) for k,v in nlls.items()}\n", + "# crps = {k:np.array(v) for k,v in crps.items()}\n", + "# mae = {k:np.array(v) for k,v in mae.items()}\n", + "\n", + "# dfs = [pd.DataFrame({'Dataset':dataset_keys,'NLL/D':v,'Type':k, 'CRPS':crps[k],'MAE':mae[k]}) for k,v in nlls.items()]\n", + "# df = pd.concat(dfs)\n", + "\n", + "# df['Type'] = df['Type'].apply(lambda x: name_map[x])\n", + "\n", + "# df.to_csv(\"darts_results_agg.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Monash" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# import itertools\n", + "# import pickle \n", + "# from data.serialize import SerializerSettings\n", + "# from types import SimpleNamespace\n", + "# import pickle\n", + "\n", + "# # load results into {ds_name: pred_dict} dict\n", + "# gpt_results = {}\n", + "# for ds_name in datasets:\n", + "# if ds_name == \"kaggle_web_traffic_weekly\":\n", + "# continue\n", + "\n", + "# path = f'eval/{ds_name}.pkl'\n", + "# with open(path, 'rb') as f:\n", + "# pred_dict = pickle.load(f)\n", + "# gpt_results[ds_name] = pred_dict\n", + "# gpt_results[ds_name]['test_frac'] = len(pred_dict['preds']) / len(datasets[ds_name])\n", + "\n", + "# # add GPT MAE as a column in df_paper\n", + "# df_paper = pd.read_csv('eval/paper_mae_normalized.csv')\n", + "# for ds_name in gpt_results:\n", + "# df_paper.loc[df_paper['Dataset'] == ds_name, 'normalized_davinci'] = gpt_results[ds_name]['mae'] / df_paper[df_paper['Dataset'] == ds_name]['Last Value'].values[0]\n", + "# # test frac\n", + "# df_paper.loc[df_paper['Dataset'] == ds_name, 'test_frac'] = gpt_results[ds_name]['test_frac']\n", + "# # save df_paper\n", + "# df_paper.to_csv(f'eval/davinci_tuned_mae.csv', index=False)\n", + "# df_paper = df_paper[df_paper['Dataset'].isin(predictable_datasets)]\n", + "# df_paper" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# import pandas as pd\n", + "# import numpy as np\n", + "# import matplotlib.pyplot as plt\n", + "# import seaborn as sns\n", + "\n", + "# tractable_datasets = [\n", + "# \"covid_deaths\", \"solar_weekly\", \"tourism_monthly\", \"australian_electricity_demand\", \"pedestrian_counts\",\n", + "# \"traffic_hourly\", \"hospital\", \"fred_md\", \"tourism_yearly\", \"tourism_quarterly\", \"us_births\",\n", + "# \"nn5_weekly\",\"solar_10_minutes\", \"traffic_weekly\", \"saugeenday\", \"cif_2016\",\n", + "# # \"weather\"\n", + "# ]\n", + "\n", + "# df = pd.read_csv(f'eval/davinci_tuned_mae.csv')\n", + "# df['normalized_llama'] = pd.read_csv(f'eval/llama_tuned_mae.csv')['normalized_llama']\n", + "# df = df[df['Dataset'].isin(tractable_datasets)]\n", + "# df = df.drop(\"Dataset\", axis=1)\n", + "# # print(df)\n", + "\n", + "# new_df = []\n", + "# for i, c in enumerate(df.columns):\n", + "# if c == 'test_frac':\n", + "# continue\n", + "\n", + "# for v in df.values[:,i]:\n", + "# new_df.append({\"method\": c, \"v\": v})\n", + "# df = pd.DataFrame(new_df)\n", + "# df = df[df['method'] != 'Last Value']\n", + "# df = df[df['method'] != 'normalized_min']\n", + "# df = df[df['method'] != 'normalized_median']\n", + "# print(np.unique(df['method']))\n", + "\n", + "# gpt_color = sns.color_palette('Dark2',3)[2]\n", + "# name_map = {\n", + "# '(DHR-)ARIMA': 'ARIMA',\n", + "# 'CatBoost': 'CatBoost',\n", + "# 'DeepAR': 'DeepAR',\n", + "# 'ETS': 'ETS',\n", + "# 'FFNN': 'FFNN',\n", + "# 'N-BEATS': 'N-BEATS',\n", + "# 'PR': 'PR',\n", + "# 'SES': 'SES',\n", + "# 'TBATS': 'TBATS',\n", + "# 'Theta': 'Theta',\n", + "# 'Transformer': 'Transform.',\n", + "# 'WaveNet': 'WaveNet',\n", + "# 'normalized_davinci': \"GPT-3\",\n", + "# 'normalized_llama': 'LLaMA-2',\n", + "# }\n", + "# df['method'] = df['method'].apply(lambda s: name_map[s])\n", + "\n", + "# sorted_vals = df.groupby([\"method\"])['v'].aggregate(np.mean).reset_index().sort_values('v')\n", + "# name_order = list(sorted_vals.values[:,0])[::-1]\n", + "\n", + "# def color_map(x):\n", + "# if x == 'GPT-3':\n", + "# return gpt_color\n", + "# elif x == 'LLaMA-2':\n", + "# return '#a60355'\n", + "# else:\n", + "# return 'grey'\n", + "\n", + "# palette = [color_map(x) for x in name_order]\n", + "\n", + "# fig, ax = plt.subplots(figsize=(4, 3))\n", + "# c = sns.barplot(\n", + "# data=df,\n", + "# x='method',\n", + "# y=\"v\",\n", + "# order=name_order,\n", + "# color=\"grey\",\n", + "# palette=palette,\n", + "# alpha=0.3,\n", + "# edgecolor='black',\n", + "# errwidth=1.\n", + "# )\n", + "# for bar,name in zip(c.containers[0], name_order):\n", + "# if name != \"GPT-3\" and name != 'LLaMA-2':\n", + "# continue\n", + "# bar.set_alpha(1.0)\n", + "# bar.set_edgecolor('grey')\n", + "\n", + "# plt.ylabel('')\n", + "# plt.ylabel('Normalized MAE', fontsize=16)\n", + "# # plt.title('Monash', fontsize=16)\n", + "# plt.ylim(0, 2.2)\n", + "# plt.xticks(rotation=60)\n", + "# plt.margins(x=0.02)\n", + "\n", + "# #change fontsize of y tick labels\n", + "# ax.tick_params(axis='y', which='major', labelsize=13)\n", + "\n", + "# plt.tight_layout()\n", + "# plt.savefig('monash_bars.pdf', dpi=300, bbox_inches='tight')\n", + "# plt.show()\n", + "\n", + "# df.to_csv(\"monash_results_agg.csv\", index=False)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/figures/figure_5.ipynb b/figures/figure_5.ipynb new file mode 100644 index 0000000..d4c6ed6 --- /dev/null +++ b/figures/figure_5.ipynb @@ -0,0 +1,438 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# Darts context size scaling\n", + "import os\n", + "\n", + "# compare the NLL/D for the predictions of the different models\n", + "from collections import defaultdict\n", + "import seaborn as sns\n", + "from data.metrics import calculate_crps\n", + "import numpy as np\n", + "import pickle\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "from data.small_context import get_datasets\n", + "sns.set_style(\"whitegrid\")\n", + "import matplotlib \n", + "matplotlib.rcParams.update({'font.size': 18})\n", + "sns.set_context(\"paper\", font_scale=1.5, rc={\"lines.linewidth\": 2})\n", + "\n", + "hue_order = ['SM-GP','N-BEATS','TCN','N-HiTS','ARIMA','GPT-3']\n", + "palette = sns.color_palette('Dark2', len(hue_order))\n", + "palette = palette[:2] + palette[3:] + palette[2:3]\n", + "\n", + "avg_nlls = defaultdict(list)\n", + "crps = defaultdict(list)\n", + "mae = defaultdict(list)\n", + "datasets = get_datasets()\n", + "for train_frac in [0.1, 0.2, 0.5, 1]:\n", + " if train_frac == 1:\n", + " output_dir = '../precomputed_outputs/subsample/small_context_tuned'\n", + " else:\n", + " output_dir = f'../precomputed_outputs/subsample/small_context_tuned_{train_frac}'\n", + "\n", + " nlls = defaultdict(dict)\n", + " for dsname,(train,test) in datasets.items():\n", + " with open(f'{output_dir}/{dsname}.pkl','rb') as f:\n", + " data_dict = pickle.load(f)\n", + " for model_name,preds in data_dict.items():\n", + " if model_name in ['ada', 'babbage', 'curie']:\n", + " continue\n", + " if 'NLL/D' not in preds:\n", + " continue\n", + " nll = preds['NLL/D']\n", + " if model_name == 'text-davinci-003':\n", + " model_name = 'GPT-3'\n", + " nlls[model_name][dsname] = nll\n", + " # update with new runs\n", + " output_dir = f'eval/small_context_tuned_new_{train_frac}' if train_frac < 1 else 'eval/small_context_tuned'\n", + " for dsname,(train,test) in datasets.items():\n", + " if os.path.exists(f'{output_dir}/{dsname}.pkl'):\n", + " with open(f'{output_dir}/{dsname}.pkl','rb') as f:\n", + " data_dict = pickle.load(f)\n", + " for model_name,preds in data_dict.items():\n", + " if model_name not in ['gp']:\n", + " continue\n", + " if model_name in ['ada', 'babbage', 'curie']:\n", + " continue\n", + " if 'NLL/D' not in preds:\n", + " continue\n", + " nll = preds['NLL/D']\n", + " if model_name == 'text-davinci-003':\n", + " model_name = 'GPT-3'\n", + " print(f'Updated {model_name} {dsname} [{train_frac}]: {nlls[model_name][dsname]} --> {nll}')\n", + " nlls[model_name][dsname] = nll\n", + " avg_nlls[train_frac] = {k: np.array(list(v.values())).mean() for k,v in nlls.items()}\n", + " \n", + "df = pd.DataFrame(avg_nlls)\n", + "df = df.reset_index().rename(columns={'index':'Model'})\n", + "# drop arima model\n", + "# plot a line at arima\n", + "arima_nll = df[df['Model'] == 'arima'][1].values[0]\n", + "\n", + "df = df[df['Model'] != 'arima']\n", + "# change gp to GP\n", + "df['Model'] = df['Model'].apply(lambda x: 'SM-GP' if x == 'gp' else x)\n", + "\n", + "df = df.melt(id_vars=['Model'], var_name='Train Fraction', value_name='NLL/D')" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pickle\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import matplotlib.patches as mpatches\n", + "from matplotlib.gridspec import GridSpec\n", + "\n", + "import sys\n", + "sys.path.append(\"..\")\n", + "from data.small_context import get_datasets\n", + "\n", + "sns.set(style=\"whitegrid\", font_scale=1.)\n", + "\n", + "datasets = get_datasets()\n", + "\n", + "fig = plt.figure(figsize=(10, 2.25))\n", + "gs = GridSpec(\n", + " 2, 8, figure=fig, \n", + " width_ratios=[2, 8, 6, 7/1.5, 4, 9/1.5, 6, 10], \n", + " wspace=0.0, hspace=0.5\n", + ")\n", + "\n", + "ax = [\n", + " fig.add_subplot(gs[0, 0]),\n", + " fig.add_subplot(gs[0, 1]),\n", + " fig.add_subplot(gs[1, 0]),\n", + " fig.add_subplot(gs[1, 1]),\n", + " fig.add_subplot(gs[:, 3]),\n", + " fig.add_subplot(gs[:, 5]),\n", + " fig.add_subplot(gs[:, 7]),\n", + "]\n", + "\n", + "name_order = ['SM-GP','N-BEATS','TCN','N-HiTS','ARIMA','LLaMA-2 70B','GPT-3']\n", + "palette = sns.color_palette('Dark2', len(name_order))\n", + "palette = palette[:2] + palette[3:6] + ['#a60355'] + palette[2:3]\n", + "\n", + "datasets_to_plot = [\n", + " 'AirPassengersDataset', \n", + " 'GasRateCO2Dataset',\n", + "]\n", + "\n", + "csv_fn = \"../precomputed_outputs/deterministic_csvs/darts_results_agg.csv\"\n", + "df = pd.read_csv(csv_fn)\n", + "\n", + "ax_idx = 0\n", + "# Iterate through the datasets and plot the samples\n", + "for idx, dsname in enumerate(datasets_to_plot):\n", + " train,test = datasets[dsname]\n", + "\n", + " pkl_fn = f\"../precomputed_outputs/darts/{dsname}.pkl\"\n", + " with open(pkl_fn,'rb') as f:\n", + " data_dict = pickle.load(f)\n", + "\n", + " if not 'text-davinci-003' in data_dict:\n", + " continue\n", + " samples = data_dict['text-davinci-003']['samples']\n", + " lower = samples.quantile(0.1,axis=0)\n", + " upper = samples.quantile(0.9,axis=0)\n", + " \n", + " pred_color = sns.color_palette('Dark2')[2]\n", + " train_test = pd.concat([train,test])\n", + "\n", + " ax[2*idx+1].plot(train_test, color='k',label='Ground Truth', linewidth=1)\n", + " ax[2*idx+1].fill_between(samples.iloc[0].index, lower, upper, alpha=0.5)\n", + " ax[2*idx+1].plot(samples.iloc[0].index, data_dict['text-davinci-003']['median'], color=pred_color,label='GPT3 Median',linewidth=1)\n", + " # ax[2*idx+1].set_xlabel(dsname.replace(\"Dataset\",\"\"))\n", + " ax[2*idx+1].get_yaxis().set_visible(False)\n", + " # ax[2*idx+1].get_xaxis().set_visible(False)\n", + " ax[2*idx+1].set_title(dsname.replace(\"Dataset\",\"\"))\n", + "\n", + "\n", + " import matplotlib.dates as mdates\n", + " myFmt = mdates.DateFormatter('%d')\n", + " # ax[2*idx+1].xaxis.set_major_formatter(myFmt)\n", + " # ax[2*idx+1].xaxis.set_major_locator(plt.MaxNLocator(2))\n", + " # ax.set_xticklabels([0, 0, len(train_test) // 2, len(train_test)])\n", + " \n", + " # palette = sns.color_palette('Dark2', len(hue_order))\n", + " # palette = palette[:2] + palette[3:-1] + ['#a60355'] + palette[2:3]\n", + "\n", + " sns.barplot(\n", + " x='Dataset',\n", + " # x='data_type',\n", + " # order=['Trend', 'Periodic', 'Trend + Periodic'],\n", + " y='NLL/D',\n", + " hue='Type', \n", + " hue_order=name_order,\n", + " data=df[df['Dataset'] == dsname.replace(\"Dataset\",\"\")], \n", + " ax=ax[2*idx], \n", + " palette=palette,\n", + " )\n", + "\n", + " ax[2*idx].get_legend().remove()\n", + "\n", + "\n", + "gpt_color = sns.color_palette('Dark2',3)[2]\n", + "\n", + "name_order = ['SM-GP','N-BEATS','TCN','N-HiTS','ARIMA','LLaMA-2 70B','GPT-3']\n", + "palette = sns.color_palette('Dark2', len(name_order))\n", + "palette = palette[:2] + palette[3:6] + ['#a60355'] + palette[2:3]\n", + "\n", + "csv_fn = \"../precomputed_outputs/deterministic_csvs/promptcast_comparison.csv\"\n", + "promptcast_df = pd.read_csv(csv_fn)\n", + "promptcast_df['Type'] = promptcast_df['model_name']\n", + "promptcast_df['CRPS'] = promptcast_df['crps']\n", + "\n", + "promptcast_palette = sns.color_palette('colorblind', 5)\n", + "promptcast_palette = [\n", + " promptcast_palette[0], \n", + " promptcast_palette[-1], \n", + " '#a60355', \n", + " gpt_color\n", + "]\n", + "model_names = [\n", + " 'PromptCast\\n(LLaMA)', 'PromptCast\\n(GPT-3)', \n", + " 'LLMTime\\n(LLaMA)', 'LLMTime\\n(GPT-3)',\n", + "]\n", + "\n", + "\n", + "c = sns.barplot(\n", + " data=df,\n", + " order=name_order,#['SM-GP','N-BEATS','TCN','N-HiTS','ARIMA','GPT-3','LLaMA70B'],\n", + " x='Type',\n", + " y='NLL/D',\n", + " ax=ax[4], \n", + " color=\"grey\",\n", + " palette=palette,\n", + " # edgecolor='black',\n", + " errwidth=1.,\n", + " errorbar=('ci', 68)\n", + ")\n", + "\n", + "name_order = [\n", + " 'PromptCast\\n(GPT-3)', 'PromptCast\\n(LLaMA)',\n", + " 'N-BEATS', 'SM-GP','TCN','N-HiTS','ARIMA',\n", + " 'LLaMA-2 70B','GPT-3'\n", + "]\n", + "c = sns.barplot(\n", + " data=pd.concat([promptcast_df, df]),\n", + " order= name_order,#['SM-GP','N-BEATS','TCN','N-HiTS','ARIMA','GPT-3','LLaMA70B'],\n", + " x='Type',\n", + " y='CRPS',\n", + " ax=ax[5], \n", + " color=\"grey\",\n", + " palette=promptcast_palette[:2] + palette,\n", + " # edgecolor='black',\n", + " errwidth=1.,\n", + " errorbar=('ci', 68)\n", + ")\n", + "\n", + "for a in ax:\n", + " a.set_xlabel('')\n", + " a.set_xticklabels([])\n", + " a.set_xticks([])\n", + "\n", + "ax[4].set_ylabel(\"NLL/D\")\n", + "ax[5].set_ylabel(\"CRPS\")\n", + "\n", + "name_order = ['SM-GP','N-BEATS','TCN','N-HiTS','ARIMA','LLaMA-2 70B','GPT-3']\n", + "csv_fn = \"../precomputed_outputs/deterministic_csvs/subsampling_results.csv\"\n", + "df = pd.read_csv(csv_fn)\n", + "\n", + "sns.pointplot(\n", + " x='Train Fraction', \n", + " y='NLL/D', \n", + " hue='Model',\n", + " data=df, \n", + " palette=palette, \n", + " hue_order=name_order,\n", + " ax=ax[6],\n", + ")\n", + "ax[6].axhline(arima_nll, linestyle='--', label='ARIMA', color=palette[-3], linewidth=4)\n", + "ax[6].set_ylim(3.5,10.5)\n", + "# plt.ylabel('NLL/Dimension')\n", + "\n", + "#change all linewidths to 2\n", + "for l in ax[6].lines:\n", + " l.set_linewidth(2)\n", + "\n", + "#change point sizes to 100\n", + "for c in ax[6].collections:\n", + " c.set_sizes([20])\n", + "\n", + "#turn legend off\n", + "ax[6].get_legend().remove()\n", + "\n", + "fig.savefig('outputs/darts_results.svg', bbox_inches='tight')\n", + "plt.show()\n", + "\n", + "\n", + "combined_palette = palette + promptcast_palette[:2]\n", + "combined_names = name_order[:-2] + model_names[2:] + model_names[:2]\n", + "print(combined_names)\n", + "#make legend using palette and model names\n", + "handles = []\n", + "for i, m in enumerate(combined_names):\n", + " handles.append(\n", + " mpatches.Patch(color=combined_palette[i], label=m)\n", + " )\n", + "labels = combined_names\n", + "\n", + "fig, ax = plt.subplots(\n", + " 1, 1, \n", + " figsize=(10, 2), \n", + ")\n", + "ax.axis('off')\n", + "\n", + "leg = ax.legend(\n", + " handles=handles[:5],\n", + " labels=labels[:5],\n", + " loc='upper left',\n", + " # fontsize=9,\n", + " handlelength=1.5,\n", + " # bbox_to_anchor=(0.2, -0.28),\n", + " columnspacing=3, \n", + " handletextpad=1.,\n", + " frameon=True,\n", + " ncols=5\n", + ")\n", + "\n", + "plt.tight_layout()\n", + "fig.savefig('outputs/darts_legend_1.svg', bbox_inches='tight')\n", + "plt.show()\n", + "\n", + "fig, ax = plt.subplots(\n", + " 1, 1, \n", + " figsize=(10, 2), \n", + ")\n", + "ax.axis('off')\n", + "\n", + "leg = ax.legend(\n", + " handles=handles[5:7],\n", + " labels=labels[5:7],\n", + " loc='upper left',\n", + " # fontsize=9,\n", + " handlelength=1.5,\n", + " # bbox_to_anchor=(0.2, -0.28),\n", + " columnspacing=3, \n", + " handletextpad=1.,\n", + " frameon=True,\n", + " ncols=5\n", + ")\n", + "\n", + "fig.savefig('outputs/darts_legend_2.svg', bbox_inches='tight')\n", + "plt.show()\n", + "\n", + "fig, ax = plt.subplots(\n", + " 1, 1, \n", + " figsize=(10, 2), \n", + ")\n", + "ax.axis('off')\n", + "\n", + "leg = ax.legend(\n", + " handles=handles[7:9],\n", + " labels=labels[7:9],\n", + " loc='upper left',\n", + " # fontsize=9,\n", + " handlelength=1.5,\n", + " # bbox_to_anchor=(0.2, -0.28),\n", + " columnspacing=3, \n", + " handletextpad=1.,\n", + " frameon=True,\n", + " ncols=5\n", + ")\n", + "\n", + "#put (a) above the first 3 subplots\n", + "# ax[0].text(1, -0.2, '(a)', transform=ax[0].transAxes, size=14, weight='bold')\n", + "# ax[0].text(4.5, -0.2, '(b)', transform=ax[0].transAxes, size=14, weight='bold')\n", + "\n", + "fig.savefig('outputs/darts_legend_3.svg', bbox_inches='tight')\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/figures/figure_6.ipynb b/figures/figure_6.ipynb new file mode 100644 index 0000000..40cd385 --- /dev/null +++ b/figures/figure_6.ipynb @@ -0,0 +1,497 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dict_keys(['test_loss', 'train_loss', 'complexity', 'equation', 'test_preds', 'train_preds', 'nll', 'train_x', 'train_y', 'test_x', 'test_y'])\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/4z/0371zfqj5mx0rlct4r3qclfm0000gn/T/ipykernel_88427/3016424578.py:64: MatplotlibDeprecationWarning: The legendHandles attribute was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use legend_handles instead.\n", + " for legobj in leg.legendHandles:\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pickle\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from collections import defaultdict\n", + "\n", + "sns.set(style=\"whitegrid\", font_scale=1.3)\n", + "\n", + "pkl_fn = \"../precomputed_outputs/simplicity_bias/simplicity_bias_results.pkl\"\n", + "with open(pkl_fn, 'rb') as f:\n", + " results = pickle.load(f)\n", + "\n", + "print(results.keys())\n", + "test_losses = results['test_loss']\n", + "nlls = results['nll']\n", + "complexities = results['complexity']\n", + "train_losses = results['train_loss']\n", + "equations = results['equation']\n", + "train_preds = results['train_preds']\n", + "test_preds = results['test_preds']\n", + "\n", + "train_x = results['train_x']\n", + "train_y = results['train_y']\n", + "test_x = results['test_x']\n", + "test_y = results['test_y']\n", + "\n", + "test_losses = np.array(test_losses)\n", + "test_losses = (test_losses - test_losses.min()) / (test_losses.max() - test_losses.min())\n", + "nlls = np.array(nlls)\n", + "nlls = (nlls - nlls.min()) / (nlls.max() - nlls.min())\n", + "complexities = np.array(complexities)\n", + "complexities = (complexities - complexities.min()) / (complexities.max() - complexities.min())\n", + "train_losses = np.array(train_losses)\n", + "train_losses = (train_losses - train_losses.min()) / (train_losses.max() - train_losses.min())\n", + "\n", + "fig, ax = plt.subplots(figsize=(2, 2))\n", + "ax.plot(train_losses, label='Train Loss', color='black', linewidth=1.)\n", + "ax.plot(complexities, label='Complexity', color='tab:brown', linewidth=1.)\n", + "ax.plot(test_losses, label='Test Loss', color='blue', linewidth=1.)\n", + "ax.plot(nlls, label='LLM NLL', color='tab:cyan', linewidth=2.)\n", + "# ax.set_ylim(0, 500)\n", + "\n", + "ax.set_ylabel(\"Normalized Value\", rotation=270, labelpad=18, fontsize=14)\n", + "#reduce y tick label pad\n", + "ax.tick_params(axis='y', which='major', pad=0)\n", + "\n", + "#move y labels to the right\n", + "ax.yaxis.tick_right()\n", + "ax.yaxis.set_label_position(\"right\")\n", + "\n", + "# add two column legend above the figure\n", + "handles, labels = ax.get_legend_handles_labels()\n", + "leg = fig.legend(\n", + " handles, labels,\n", + " ncol=2,\n", + " bbox_to_anchor=(1.2, -0.1),#1.08),\n", + " columnspacing=0.5,\n", + " handletextpad=0.5,\n", + " handlelength=1.0,\n", + " fontsize=12,\n", + ")\n", + "\n", + "# set the linewidth of each legend object\n", + "for legobj in leg.legendHandles:\n", + " legobj.set_linewidth(1.5)\n", + "\n", + "#add xlabels for [1,2,3,4,5]\n", + "ax.set_xticks([0, 1, 2, 3, 4])\n", + "ax.set_xticklabels([1, 2, 3, 4, 5])\n", + "\n", + "#turn off grid lines\n", + "ax.grid(False)\n", + "\n", + "fig.savefig(\"outputs/simplicity_bias_plot.svg\", bbox_inches='tight')\n", + "plt.show()\n", + "plt.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['(x0 + 0.3652524)',\n", + " 'cos(cos(x0 / -0.031412385) * (-1.5252972 + x0))',\n", + " '(sin(cos(cos(x0 / 0.031470172) * -1.4668792)) + (cos(0.81965065) * x0))',\n", + " '(sin(cos(cos((x0 / sin(-0.03127001)) + 0.07646165) * -1.4539052)) + (sin(sin(cos(cos(exp(cos(-0.03127001) + x0))))) * x0))',\n", + " '(cos((cos((x0 / -0.03127001) + 0.07646165) / -0.957405) / sin(sin(cos(x0 - x0)) * exp(cos(sin(x0 / -0.983214))))) / (cos(sin(sin(sin(sin(x0)) - (x0 * (-0.47036648 - (x0 / 0.5857117)))))) - -0.10476875))']" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "equations" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "arima\n", + "TCN\n", + "text-davinci-003\n", + "gp\n", + "arima\n", + "TCN\n", + "N-BEATS\n", + "N-HiTS\n", + "text-davinci-003\n", + "gp\n", + "arima\n", + "TCN\n", + "N-BEATS\n", + "N-HiTS\n", + "text-davinci-003\n", + "gp\n", + "arima\n", + "TCN\n", + "N-BEATS\n", + "N-HiTS\n", + "text-davinci-003\n", + "gp\n", + "arima\n", + "TCN\n", + "N-BEATS\n", + "N-HiTS\n", + "text-davinci-003\n", + "gp\n", + "arima\n", + "TCN\n", + "N-BEATS\n", + "N-HiTS\n", + "text-davinci-003\n", + "gp\n", + "arima\n", + "TCN\n", + "N-BEATS\n", + "N-HiTS\n", + "text-davinci-003\n", + "gp\n", + "arima\n", + "TCN\n", + "N-BEATS\n", + "N-HiTS\n", + "text-davinci-003\n", + "gp\n", + "arima\n", + "TCN\n", + "N-BEATS\n", + "N-HiTS\n", + "text-davinci-003\n", + "arima\n", + "arima\n", + "TCN\n", + "text-davinci-003\n", + "gp\n", + "arima\n", + "TCN\n", + "N-BEATS\n", + "N-HiTS\n", + "text-davinci-003\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "import sys\n", + "sys.path.append(\"..\")\n", + "from data.synthetic import get_synthetic_datasets\n", + "from data.metrics import calculate_crps\n", + "\n", + "datasets = get_synthetic_datasets()\n", + "nlls = defaultdict(dict)\n", + "crps = defaultdict(dict)\n", + "mae = defaultdict(dict)\n", + "\n", + "for dsname,(train,test) in datasets.items():\n", + " with open(f'../precomputed_outputs/synthetic/{dsname}.pkl','rb') as f:\n", + " data_dict = pickle.load(f)\n", + "\n", + " for model_name,preds in data_dict.items():\n", + " print(model_name)\n", + " try: \n", + " if 'NLL/D' not in preds:\n", + " continue\n", + " nll = preds['NLL/D']\n", + " if model_name=='text-davinci-003-tuned':\n", + " model_name='GPT3'\n", + " nlls[model_name][dsname] = nll\n", + " test_values = test.values if isinstance(test,pd.Series) else test\n", + "\n", + " crps[model_name][dsname] = calculate_crps(test_values,preds['samples'].values[:10],5)\n", + " tmae = np.abs(test_values-preds['median'].values).mean()/np.abs(test_values).mean()\n", + " mae[model_name][dsname] = tmae\n", + " except Exception as e:\n", + " print(e)\n", + " \n", + "data = []\n", + "\n", + "for model_name in nlls:\n", + " for dsname in nlls[model_name]:\n", + " entry = {\n", + " \"model_name\": model_name,\n", + " \"dataset_name\": dsname,\n", + " \"nll\": nlls[model_name][dsname],\n", + " \"mae\": mae[model_name][dsname],\n", + " \"crps\": crps[model_name][dsname],\n", + " }\n", + " data.append(entry)\n", + "\n", + "# Convert the list of dictionaries to a DataFrame\n", + "result_df = pd.DataFrame(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['exp', 'x_times_sine', 'linear', 'log', 'beat', 'square',\n", + " 'gaussian_wave', 'sigmoid', 'sine', 'sinc', 'xsin', 'linear_cos'],\n", + " dtype=object)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result_df = result_df[result_df['model_name'].isin(['arima', 'TCN', 'text-davinci-003'])]\n", + "result_df['dataset_name'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/4z/0371zfqj5mx0rlct4r3qclfm0000gn/T/ipykernel_88427/3788039766.py:108: UserWarning: This figure was using a layout engine that is incompatible with subplots_adjust and/or tight_layout; not calling subplots_adjust.\n", + " plt.subplots_adjust(wspace=0, hspace=.5)\n" + ] + }, + { + "data": { + "image/png": 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", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "n_datasets = len(datasets)\n", + "n_cols = 11\n", + "n_rows = 3#(n_datasets + n_cols - 1) // n_cols\n", + "import seaborn as sns\n", + "sns.set(style=\"whitegrid\", font_scale=1.3)\n", + "fig, axes = plt.subplots(\n", + " n_rows, n_cols, \n", + " figsize=(8, 3.5), constrained_layout=True, #sharex=True,\n", + " gridspec_kw={'width_ratios': [\n", + " 0.5, 0.2, 3, 0.2, \n", + " 0.5, 0.2, 3, 0.2, \n", + " 0.5, 0.2, 3\n", + " ]} \n", + ")\n", + "axes = axes.flatten()\n", + "\n", + "dsname_map = {\n", + " \"linear\": \"Linear\",#\\n$(x_{t+1}=x_t+c)$\",\n", + " \"exp\": \"Exponential\",#\\n($x_{t+1}=x_t \\cdot c$)\",\n", + " \"sigmoid\": \"Sigmoid\",#\\n($x_{t+1}=x_t + c x_t(1 - x_t)$)\",\n", + " \"beat\": \"Beat Interference\",#\\n($x_t = x_{t - T}$)\",\n", + " \"linear_cos\": \"Linear + Cosine\",#\\n($x_t=x_{t-T} + c$)\",\n", + " \"x_times_sine\": \"Linear * Sine\",\n", + " \"square\": \"Quadratic\",\n", + " \"sine\": \"Sine\",#\\n($x_t = x_{t - T}$)\"\n", + " \"xsin\": \"X * Sine\",\n", + "}\n", + "\n", + "result_df['ll'] = -1 * result_df['nll']\n", + "\n", + "ax_idx = 0\n", + "# Iterate through the datasets and plot the samples\n", + "# dsnames = ['linear','exp','sigmoid','square','sine','beat','linear_cos','x_times_sine']\n", + "dsnames = ['linear','exp','sigmoid','sine','beat','x_times_sine', 'linear_cos', 'square', 'xsin']\n", + "for idx, dsname in enumerate(dsnames):\n", + " \n", + " ax = axes[ax_idx]\n", + " ax_idx += 1\n", + "\n", + " sns.barplot(\n", + " x='dataset_name',\n", + " # x='data_type',\n", + " # order=['Trend', 'Periodic', 'Trend + Periodic'],\n", + " y='ll',\n", + " hue='model_name', \n", + " hue_order=['TCN', 'arima', \"text-davinci-003\"],\n", + " data=result_df[result_df['dataset_name'] == dsname], \n", + " ax=ax, \n", + " palette='Dark2',\n", + " )\n", + " ax.get_legend().remove()\n", + " if idx % 3 != 0 or idx // 2 != 1:\n", + " ax.set_ylabel(\"\")\n", + " else:\n", + " ax.set_ylabel(\"Log Likelihood\", fontsize=12)\n", + " ax.yaxis.set_label_position(\"left\")\n", + " # ax.yaxis.tick_right()\n", + " # ax.set_yticks([])\n", + " ax.set_ylim((-0.5,12))\n", + " ax.get_xaxis().set_visible(False)\n", + " ax.tick_params(axis='y', which='major', pad=0)\n", + " \n", + " ax.spines['left'].set_color('black')\n", + " ax.plot(0, 1, '^k', transform=ax.transAxes, clip_on=False, zorder=10)\n", + " ax.margins(x=0.1)\n", + " \n", + " ax = axes[ax_idx]\n", + " ax_idx += 1\n", + " ax.set_axis_off()\n", + "\n", + " train,test = datasets[dsname]\n", + " with open(f'../precomputed_outputs/synthetic/{dsname}.pkl','rb') as f:\n", + " data_dict = pickle.load(f)\n", + " \n", + " ax = axes[ax_idx]\n", + " ax_idx += 1\n", + " if not 'text-davinci-003' in data_dict:\n", + " continue\n", + " samples = data_dict['text-davinci-003']['samples']\n", + " lower = samples.quantile(0.1,axis=0)\n", + " upper = samples.quantile(0.9,axis=0)\n", + " \n", + " # if dsname == \"square\":\n", + " # print(np.min(train.values), np.max(train.values))\n", + "\n", + " pred_color = sns.color_palette('Dark2')[2]\n", + " ax.plot(pd.concat([train,test]),color='k',label='Ground Truth', linewidth=1)\n", + " ax.fill_between(samples.iloc[0].index, lower, upper, alpha=0.5)\n", + " ax.plot(data_dict['text-davinci-003']['median'], color=pred_color,label='GPT-3 Median', linewidth=1)\n", + " #ax.plot(samples.T, alpha=0.5)\n", + " ax.set_title(dsname_map[dsname])\n", + " # turn off y axis\n", + " ax.get_yaxis().set_visible(False)\n", + " ax.get_xaxis().set_visible(False)\n", + " if dsname == \"square\":\n", + " ax.set_ylim((-0.01,1))\n", + "\n", + " if idx % 3 != 2:\n", + " ax = axes[ax_idx]\n", + " ax_idx += 1\n", + " ax.set_axis_off()\n", + "\n", + "handles, labels = axes[0].get_legend_handles_labels()\n", + "handles2, labels2 = axes[2].get_legend_handles_labels()\n", + "handles += handles2\n", + "labels += ['TCN', 'ARIMA', 'GPT-3']\n", + "\n", + "plt.subplots_adjust(wspace=0, hspace=.5)\n", + "\n", + "plt.savefig('outputs/synthetic_qualitative.pdf', dpi=300, bbox_inches='tight')\n", + "plt.savefig('outputs/synthetic_qualitative.png', dpi=300, bbox_inches='tight')\n", + "plt.show()\n", + "\n", + "\n", + "fig, ax = plt.subplots(1, 1, figsize=(2, 2))\n", + "ax.legend(\n", + " handles=handles[:2],\n", + " labels=labels[:2],\n", + " markerscale=1.5,\n", + " loc='upper left',\n", + " fontsize=25,\n", + " ncol=5,\n", + " frameon=True\n", + ")\n", + "\n", + "plt.axis(\"off\")\n", + "plt.savefig('outputs/synthetic_qualitative_legend_1.pdf', bbox_inches='tight')\n", + "plt.show()\n", + "\n", + "fig, ax = plt.subplots(1, 1, figsize=(2, 2))\n", + "ax.legend(\n", + " handles=handles[2:],\n", + " labels=labels[2:],\n", + " markerscale=1.5,\n", + " loc='upper left',\n", + " fontsize=25,\n", + " ncol=5,\n", + " frameon=True\n", + ")\n", + "\n", + "plt.axis(\"off\")\n", + "plt.savefig('outputs/synthetic_qualitative_legend_2.pdf', bbox_inches='tight')\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/figures/figure_7.ipynb b/figures/figure_7.ipynb new file mode 100644 index 0000000..fd3b4c2 --- /dev/null +++ b/figures/figure_7.ipynb @@ -0,0 +1,496 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ada\n", + "babbage\n", + "curie\n", + "text-davinci-003\n", + "llama1_7B\n", + "llama2_7B\n", + "llama1_13B\n", + "llama2_13B\n", + "llama1_30B\n", + "llama1_70B\n", + "llama2_70B\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/4z/0371zfqj5mx0rlct4r3qclfm0000gn/T/ipykernel_82941/3800116730.py:258: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n", + " plt.tight_layout()\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "llama2_7B\n", + "llama2_7B_chat\n", + "llama2_13B\n", + "llama2_13B_chat\n", + "llama2_70B\n", + "llama2_70B_chat\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/4z/0371zfqj5mx0rlct4r3qclfm0000gn/T/ipykernel_82941/3800116730.py:332: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " ax[1].set_yticklabels(\n", + "/var/folders/4z/0371zfqj5mx0rlct4r3qclfm0000gn/T/ipykernel_82941/3800116730.py:356: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " ax[2].set_yticklabels(\n", + "/var/folders/4z/0371zfqj5mx0rlct4r3qclfm0000gn/T/ipykernel_82941/3800116730.py:393: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " ax[0].set_yticklabels(\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pickle\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "from collections import defaultdict\n", + "\n", + "import sys\n", + "sys.path.append(\"..\")\n", + "from data.small_context import get_datasets\n", + "from data.metrics import calculate_crps\n", + "\n", + "sns.set(style=\"whitegrid\", font_scale=1)\n", + "\n", + "old_names = [\n", + " 'ada',\n", + " 'babbage',\n", + " 'curie',\n", + " 'text-davinci-003'\n", + "]\n", + "name_map = {\n", + " # \"arima\": \"ARIMA\",\n", + " 'ada': \"Ada\",\n", + " 'babbage': \"Babbage\",\n", + " 'curie': \"Curie\",\n", + " 'text-davinci-003':'Davinci',\n", + " \"llama1_7B\": \"LLaMA 7B\",\n", + " \"llama1_13B\": \"LLaMA 13B\",\n", + " \"llama1_30B\": \"LLaMA 30B\",\n", + " \"llama1_70B\": \"LLaMA 70B\",\n", + " \"llama2_7B\": \"LLaMA-2 7B\",\n", + " \"llama2_13B\": \"LLaMA-2 13B\",\n", + " \"llama2_70B\": \"LLaMA-2 70B\",\n", + " \"llama2_7B_chat\": \"LLaMA-2 7B (chat)\",\n", + " \"llama2_13B_chat\": \"LLaMA-2 13B (chat)\",\n", + " \"llama2_70B_chat\": \"LLaMA-2 70B (chat)\",\n", + "}\n", + "hue_order = ['SM-GP','N-BEATS','TCN','N-HiTS','ARIMA','GPT-3']#, 'LLaMA7B', 'LLaMA13B', 'LLaMA30B', 'LLaMA70B']\n", + "nlls = defaultdict(list)\n", + "crps = defaultdict(list)\n", + "mae = defaultdict(list)\n", + "datasets = get_datasets()\n", + "for dsname,(train,test) in datasets.items():\n", + " pkl_fn = f\"../precomputed_outputs/darts/{dsname}.pkl\"\n", + " with open(pkl_fn,'rb') as f:\n", + " data_dict = pickle.load(f)\n", + "\n", + " for model_name in old_names:\n", + "\n", + " preds = data_dict[model_name]\n", + "\n", + " if 'NLL/D' not in preds:\n", + " continue\n", + " nll = preds['NLL/D']\n", + " if model_name=='text-davinci-003-tuned':\n", + " model_name='GPT3'\n", + "\n", + " if type(preds['samples']) == np.ndarray:\n", + " nlls[model_name].append(nll)\n", + " crps[model_name].append(calculate_crps(test.values,preds['samples'][:10],10))\n", + " tmae = np.abs(test.values-preds['median']).mean()/np.abs(test.values).mean()\n", + " mae[model_name].append(tmae)\n", + " else:\n", + " nlls[model_name].append(nll)\n", + " crps[model_name].append(calculate_crps(test.values,preds['samples'].values[:10],10))\n", + " tmae = np.abs(test.values-preds['median']).mean()/np.abs(test.values).mean()\n", + " mae[model_name].append(tmae)\n", + "\n", + "llama_models = [\n", + " \"llama1_7B\",\n", + " \"llama2_7B\",\n", + " \"llama2_7B_chat\",\n", + " \"llama1_13B\",\n", + " \"llama2_13B\",\n", + " \"llama2_13B_chat\",\n", + " \"llama1_30B\",\n", + " \"llama1_70B\",\n", + " \"llama2_70B\",\n", + " \"llama2_70B_chat\",\n", + "]\n", + "for dsname,(train,test) in datasets.items():\n", + " for model_name in llama_models:\n", + " if model_name in ['llama2_70B', 'llama2_70B_chat']:\n", + " fn = f'../precomputed_outputs/darts/llama_darts/{model_name}/darts-{dsname}/1.0_0.9_0.99_0.3_3_,_.pkl'\n", + " else:\n", + " fn = f'../precomputed_outputs/darts/llama_darts/{model_name}/darts-{dsname}/0.4_0.9_0.99_0.3_3_,_.pkl'\n", + " with open(fn,'rb') as f:\n", + " data_dict = pickle.load(f)\n", + "\n", + " preds = data_dict#[model_name]\n", + "\n", + " if 'NLL/D' not in preds:\n", + " continue\n", + " nll = preds['NLL/D']\n", + "\n", + " if type(preds['samples']) == np.ndarray:\n", + " nlls[model_name].append(nll)\n", + " crps[model_name].append(calculate_crps(test.values,preds['samples'][:10],10))\n", + " tmae = np.abs(test.values-preds['median']).mean()/np.abs(test.values).mean()\n", + " mae[model_name].append(tmae)\n", + " else:\n", + " nlls[model_name].append(nll)\n", + " crps[model_name].append(calculate_crps(test.values,preds['samples'].values[:10],10))\n", + " tmae = np.abs(test.values-preds['median']).mean()/np.abs(test.values).mean()\n", + " mae[model_name].append(tmae)\n", + "\n", + "nlls = {k:np.array(v) for k,v in nlls.items()}\n", + "crps = {k:np.array(v) for k,v in crps.items()}\n", + "mae = {k:np.array(v) for k,v in mae.items()}\n", + "\n", + "# Update dataset keys by removing 'Dataset' substring\n", + "dataset_keys = [key.replace('Dataset', '') for key in datasets.keys()]\n", + "\n", + "# fig, ax = plt.subplots(1, 1, figsize=(3, 3))\n", + "# dfs = [pd.DataFrame({'Dataset':dataset_keys,'NLL/D':v,'Type':k}) for k,v in nlls.items()]\n", + "# df = pd.concat(dfs)\n", + "\n", + "# df['Type'] = df['Type'].apply(lambda x: name_map[x])\n", + "\n", + "palette = sns.color_palette('Dark2', len(hue_order))\n", + "palette = palette[:2] + palette[3:] + palette[2:3]\n", + "\n", + "mmlu_numbers = {\n", + " 'ada': 0.238,\n", + " 'babbage': 0.235,\n", + " 'curie': 0.237,\n", + " 'text-davinci-003': 0.569, \n", + " 'llama1_7B': 0.351,\n", + " 'llama2_7B': 0.453,\n", + " # 'llama2_7B_chat': 0.469,\n", + " 'llama1_13B': 0.469,\n", + " 'llama2_13B': 0.548,\n", + " # 'llama2_13B_chat': 0.469,\n", + " 'llama1_30B': 0.578,\n", + " 'llama1_70B': 0.634,\n", + " 'llama2_70B': 0.689,\n", + " # 'llama2_70B_chat': 0.578,\n", + "}\n", + "\n", + "df = []\n", + "for k,nll in nlls.items():\n", + " if \"chat\" in k:\n", + " continue\n", + " print(k)\n", + " _crps, _mae = crps[k], mae[k]\n", + " # print(k,nll)\n", + " # print(list(datasets.keys()))\n", + " # for i in range(len(nll)):\n", + " d = {\n", + " # 'Dataset':list(datasets.keys()),\n", + " 'MAE': np.mean(_mae),\n", + " 'CRPS': np.mean(_crps),\n", + " 'NLL/D': np.mean(nll), #nll[i],#np.mean(nll),\n", + " 'Type':k,\n", + " 'MMLU Accuracy': mmlu_numbers[k],\n", + " }\n", + " df.append(d)\n", + "df = pd.DataFrame(df)\n", + "\n", + "\n", + "host_name_map = {\n", + " 'openai': 'OpenAI',\n", + " 'cohere': 'Cohere',\n", + " 'forefront': 'Forefront',\n", + " 'alephalpha': 'Aleph Alpha',\n", + " 'LLaMA': 'LLaMA'\n", + "}\n", + "\n", + "def hostify(x):\n", + " if x in ['Ada','Babbage','Curie','Davinci']:\n", + " return 'openai-'+x\n", + " if 'LLaMA' in x:\n", + " return x.replace('LLaMA','LLaMA-')\n", + " return x\n", + "\n", + "df['Type'] = df['Type'].apply(lambda x: name_map[x])\n", + "df['Type'] = df['Type'].apply(hostify)\n", + "df['host'] = df['Type'].apply(lambda x: host_name_map[x.split(\"-\")[0]])\n", + "\n", + "fig, ax = plt.subplots(1, 2, figsize=(5, 2), gridspec_kw = {'wspace': 0.6})\n", + "\n", + "sns.regplot(\n", + " data=df,\n", + " x='MMLU Accuracy',\n", + " # x='Dataset',\n", + " y='NLL/D',\n", + " order=1,\n", + " # errorbar='se',\n", + " ax=ax[0], \n", + " scatter_kws={'color':'white'},\n", + ")\n", + "\n", + "sns.scatterplot(\n", + " data=df,\n", + " x='MMLU Accuracy',\n", + " y='NLL/D',\n", + " hue='host',\n", + " palette='Dark2',\n", + " ax=ax[0],\n", + ")\n", + "ax[0].get_legend().remove()\n", + "ax[0].set_ylim(4.2, 5.)\n", + "ax[0].set_xlabel(ax[0].get_xlabel(), fontsize=14)\n", + "ax[0].set_ylabel(ax[0].get_ylabel(), fontsize=14)\n", + "\n", + "sns.regplot(\n", + " data=df,\n", + " x='MMLU Accuracy',\n", + " # x='Dataset',\n", + " y='CRPS',\n", + " order=1,\n", + " # errorbar='se',\n", + " ax=ax[1], \n", + " scatter_kws={'color':'white'},\n", + ")\n", + "\n", + "sns.scatterplot(\n", + " data=df,\n", + " x='MMLU Accuracy',\n", + " y='CRPS',\n", + " hue='host',\n", + " palette='Dark2',\n", + " ax=ax[1],\n", + ")\n", + "\n", + "ax[1].set_xlabel(ax[1].get_xlabel(), fontsize=14)\n", + "ax[1].set_ylabel(ax[1].get_ylabel(), fontsize=14)\n", + "\n", + "handles, labels = ax[1].get_legend_handles_labels()\n", + "ax[1].legend(\n", + " handles=handles,\n", + " labels=labels,\n", + " # markerscale=1.5,\n", + " # bbox_to_anchor=(1.05, 1),\n", + " loc='upper right',\n", + " # borderaxespad=0.0,\n", + " columnspacing=0,\n", + " handletextpad=0.5,\n", + " fontsize=10,\n", + " frameon=True,\n", + ")\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig('outputs/mmlu_scaling.pdf', bbox_inches='tight')\n", + "plt.show()\n", + "\n", + "\n", + "df = []\n", + "for k,nll in nlls.items():\n", + " if not \"llama\" in k:\n", + " continue\n", + " if 'llama1' in k:\n", + " continue\n", + " print(k)\n", + " for _mae, _crps, _nll in zip(mae[k], crps[k], nll):\n", + " df.append({\n", + " # 'Dataset':list(datasets.keys()),\n", + " 'MAE': _mae,\n", + " 'CRPS': _crps,\n", + " 'NLL/D': _nll,\n", + " 'Type':k,\n", + " 'size': k.split('_')[1],\n", + " 'is_chat': 'chat' in k\n", + " })\n", + "df = pd.DataFrame(df)\n", + "\n", + "\n", + "gpt_crps = defaultdict(list)\n", + "datasets = get_datasets()\n", + "for dsname,(train,test) in datasets.items():\n", + " fn = f'../precomputed_outputs/darts/gpt4/{dsname}.pkl'\n", + " with open(fn,'rb') as f:\n", + " data_dict = pickle.load(f)\n", + "\n", + " for model_name in data_dict.keys():\n", + " if model_name not in ['text-davinci-003', 'gpt-4']:\n", + " continue\n", + "\n", + " preds = data_dict[model_name]\n", + "\n", + " if model_name=='text-davinci-003':\n", + " model_name='GPT-3'\n", + " else:\n", + " model_name='GPT-4'\n", + "\n", + " if type(preds['samples']) == np.ndarray:\n", + " gpt_crps[model_name].append(calculate_crps(test.values,preds['samples'][:10],10))\n", + " else:\n", + " gpt_crps[model_name].append(calculate_crps(test.values,preds['samples'].values[:10],10))\n", + "\n", + "gpt_df = []\n", + "for k,crps in gpt_crps.items():\n", + " for _crps in crps:\n", + " gpt_df.append({\n", + " 'CRPS': _crps,\n", + " 'Type':k\n", + " })\n", + "gpt_df = pd.DataFrame(gpt_df)\n", + "gpt_df['CRPS'] = gpt_df['CRPS'].astype('float')\n", + "\n", + "\n", + "fig, ax = plt.subplots(1, 3, figsize=(5, 2), gridspec_kw = {'width_ratios': [1, 3, 3]})# gridspec_kw = {'wspace': 0.6})\n", + "\n", + "sns.barplot(\n", + " data=df,\n", + " x='size',\n", + " y='NLL/D',\n", + " hue='is_chat',\n", + " palette='Dark2',\n", + " errorbar='se',\n", + " errwidth=1,\n", + " ax=ax[1],\n", + ")\n", + "\n", + "ax[1].set_xlabel('LLaMA Size')\n", + "ax[1].get_legend().remove()\n", + "ax[1].set_yticklabels(\n", + " ax[1].get_yticklabels(), \n", + " fontsize=9,\n", + ")\n", + "ax[1].set_xticklabels(\n", + " ax[1].get_xticklabels(), \n", + " fontsize=10,\n", + ")\n", + "ax[1].tick_params(pad=0)\n", + "\n", + "sns.barplot(\n", + " data=df,\n", + " x='size',\n", + " y='CRPS',\n", + " hue='is_chat',\n", + " palette='Dark2',\n", + " errorbar='se',\n", + " errwidth=1,\n", + " ax=ax[2],\n", + ")\n", + "\n", + "ax[2].set_xlabel('LLaMA Size')\n", + "ax[2].set_ylim([0.0, 0.265])\n", + "ax[2].get_legend().remove()\n", + "ax[2].set_yticklabels(\n", + " ax[2].get_yticklabels(), \n", + " fontsize=9,\n", + ")\n", + "ax[2].set_xticklabels(\n", + " ax[2].get_xticklabels(), \n", + " fontsize=10,\n", + ")\n", + "ax[2].tick_params(pad=0)\n", + "\n", + "handles, labels = ax[2].get_legend_handles_labels()\n", + "labels = ['Base', 'Chat']\n", + "ax[2].legend(\n", + " handles, labels, \n", + " loc='upper center', \n", + " ncol=2,\n", + " columnspacing=0.5,\n", + " handletextpad=0.5,\n", + " handlelength=1.0,\n", + " fontsize=10,\n", + " frameon=True,\n", + ")\n", + "\n", + "palette[0] = \"#229c55\" #(0.2, 0.6, 0.2)\n", + "palette[1] = \"#ad3603\" #(0.6, 0.2, 0.2)\n", + "\n", + "sns.barplot(\n", + " data=gpt_df,\n", + " x='Type',\n", + " y='CRPS',\n", + " # hue='is_chat',\n", + " palette=palette,\n", + " errorbar='se',\n", + " errwidth=1,\n", + " ax=ax[0],\n", + ")\n", + "\n", + "ax[0].set_yticklabels(\n", + " ax[0].get_yticklabels(), \n", + " fontsize=9,\n", + ")\n", + "ax[0].set_xticklabels(\n", + " ax[0].get_xticklabels(), \n", + " fontsize=9,\n", + " rotation=45\n", + ")\n", + "ax[0].tick_params(pad=0)\n", + "ax[0].set_xlabel('')\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig('outputs/alignment_effects.pdf', bbox_inches='tight')\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/figures/figure_8.ipynb b/figures/figure_8.ipynb new file mode 100644 index 0000000..2837313 --- /dev/null +++ b/figures/figure_8.ipynb @@ -0,0 +1,359 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "name_map = {\n", + " \"arima\": \"ARIMA\",\n", + " \"TCN\": \"TCN\",\n", + " \"N-HiTS\": \"N-HiTS\",\n", + " \"text-davinci-003\": \"GPT-3\\n(interp.)\",\n", + " \"text-davinci-003-Nan\": \"GPT-3\\n(NaNs)\"\n", + "}\n", + "\n", + "with open('eval/missing_expt/missing.pkl','rb') as f:\n", + " all_output = pickle.load(f)\n", + "\n", + "df = []\n", + "for dataset in all_output:\n", + " print(dataset)\n", + " for method in all_output[dataset]:\n", + " print(method)\n", + " \n", + " if method == 'p':\n", + " continue\n", + "\n", + " gpt_nll = all_output[dataset]['text-davinci-003-Nan'][0]\n", + "\n", + " probs = all_output[dataset]['p']\n", + " nlls = all_output[dataset][method]\n", + " for p, nll in zip(probs, nlls):\n", + " df.append({\n", + " \"dataset\": dataset,\n", + " \"method\": method,\n", + " \"nan_fraction\": p,\n", + " \"nll/d\": nll - gpt_nll\n", + " })\n", + "\n", + "df = pd.DataFrame(df)\n", + "print(df)\n", + "\n", + "df['ll/d'] = -1 * df['nll/d']\n", + "df['method'] = df['method'].apply(lambda x: name_map[x])\n", + "\n", + "print(df)\n", + "\n", + "sns.set(style='whitegrid', font_scale=1.25)\n", + "fig, ax = plt.subplots(1, 1, figsize=(5.5, 3.5))\n", + "\n", + "sns.lineplot(\n", + " df,\n", + " x = \"nan_fraction\",\n", + " y = \"ll/d\",\n", + " hue=\"method\",\n", + " hue_order=['TCN', 'ARIMA', \"N-HiTS\", \"GPT-3\\n(interp.)\", \"GPT-3\\n(NaNs)\"],\n", + " errorbar=\"se\",\n", + " palette='Dark2',\n", + " linewidth=3,\n", + " ax=ax,\n", + ")\n", + "\n", + "ax.get_legend().remove()\n", + "handles, labels = ax.get_legend_handles_labels()\n", + "\n", + "handles = handles[:1] + handles[2:3] + handles[1:2] + handles[3:]\n", + "labels = labels[:1] + labels[2:3] + labels[1:2] + labels[3:]\n", + "# handles = [val for pair in zip(handles[:2], handles[-2:]) for val in pair] + handles[2:3]\n", + "# labels = [val for pair in zip(labels[:2], labels[-2:]) for val in pair] + labels[2:3]\n", + "\n", + "leg = ax.legend(\n", + " handles=handles,\n", + " labels=labels,\n", + " # markerscale=,\n", + " # linewidth=3,\n", + " # bbox_to_anchor=(0.5, -0.6),\n", + " loc='lower left',\n", + " borderaxespad=0.3,\n", + " columnspacing=0.5,\n", + " ncol=2,\n", + " fontsize=15,\n", + " # zorder=10,\n", + ")\n", + "\n", + "for legobj in leg.legendHandles:\n", + " legobj.set_linewidth(4.0)\n", + "\n", + "plt.ylim((-7,1))\n", + "plt.xlabel('NaN Fraction')\n", + "plt.ylabel('Log Likelihood / D')\n", + "\n", + "plt.savefig('missing_ll.pdf', bbox_inches='tight')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import cloudpickle\n", + "from data.metrics import calculate_crps\n", + "from data.small_context import get_datasets\n", + "\n", + "datasets = get_datasets()\n", + "\n", + "with open(f'eval/missing_expt/missing_full.pkl','rb') as f:\n", + " all_output = cloudpickle.load(f)\n", + "\n", + "df = []\n", + "for dsname in ['AirPassengersDataset']:\n", + " print(dsname)\n", + " output_dict = all_output[dsname]\n", + "\n", + " train, test = datasets[dsname]\n", + " ps = output_dict['p']\n", + " tmae, crps = defaultdict(list), defaultdict(list)\n", + " for seed, inner_dict in output_dict.items():\n", + " if seed == 'p':\n", + " continue \n", + " \n", + " # print(seed)\n", + " for model, results in inner_dict.items():\n", + " # print(model)\n", + " if model in ['p','gp-Nan', 'gp']:\n", + " continue\n", + "\n", + " # print([x['samples'].shape for x in results])\n", + "\n", + " nlls = [x['NLL/D'] for x in results]\n", + " \n", + " if 'gp' in model:\n", + " samples = [x['samples'] for x in results]\n", + " else:\n", + " samples = [x['samples'].values for x in results]\n", + " \n", + " crps[model].append([\n", + " calculate_crps(test.values, x, 20) for x in samples\n", + " ])\n", + " tmae[model].append([\n", + " np.abs(test.values-x['median']).mean()/np.abs(test.values).mean() for x in results\n", + " ])\n", + "\n", + " \n", + " # print(crps)\n", + " # print(tmae)\n", + " ps = [0.,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]\n", + " for model in crps:\n", + " # print(crps[model])\n", + " for i, (x, y) in enumerate(zip(crps[model], tmae[model])):\n", + " # tmae[model] = np.mean(np.array(tmae[model]), 0)\n", + " # crps[model] = np.mean(np.array(crps[model]), 0)\n", + " x = np.array(x)\n", + " y = np.array(y)\n", + "\n", + " for c, m, p in zip(x, y, ps):\n", + " df.append({\n", + " \"dataset\": dsname,\n", + " \"method\": model,\n", + " \"nan_fraction\": p,\n", + " \"crps\": c,\n", + " \"mae\": m,\n", + " \"seed\": i,\n", + " })\n", + "\n", + "df = pd.DataFrame(df)\n", + "df['method'] = df['method'].apply(lambda x: name_map[x])\n", + "\n", + "sns.set(style='whitegrid', font_scale=1.25)\n", + "fig, ax = plt.subplots(1, 1, figsize=(5.5, 3.5))\n", + "\n", + "sns.lineplot(\n", + " df,\n", + " x = \"nan_fraction\",\n", + " y = \"crps\",\n", + " hue=\"method\",\n", + " hue_order=['TCN', 'ARIMA', \"N-HiTS\", \"GPT-3\\n(interp.)\", \"GPT-3\\n(NaNs)\"],\n", + " errorbar=\"se\",\n", + " palette='Dark2',\n", + " linewidth=3,\n", + " ax=ax,\n", + ")\n", + "\n", + "ax.get_legend().remove()\n", + "handles, labels = ax.get_legend_handles_labels()\n", + "\n", + "# handles = handles[:1] + handles[2:3] + handles[1:2] + handles[3:]\n", + "# labels = labels[:1] + labels[2:3] + labels[1:2] + labels[3:]\n", + "# handles = [val for pair in zip(handles[:2], handles[-2:]) for val in pair] + handles[2:3]\n", + "# labels = [val for pair in zip(labels[:2], labels[-2:]) for val in pair] + labels[2:3]\n", + "\n", + "leg = ax.legend(\n", + " handles=handles,\n", + " labels=labels,\n", + " # markerscale=,\n", + " # linewidth=3,\n", + " # bbox_to_anchor=(0.5, -0.6),\n", + " loc='upper left',\n", + " borderaxespad=0.3,\n", + " columnspacing=0.5,\n", + " ncol=2,\n", + " fontsize=15,\n", + " # zorder=10,\n", + ")\n", + "\n", + "for legobj in leg.legendHandles:\n", + " legobj.set_linewidth(3.0)\n", + "\n", + "plt.xlabel('NaN Fraction')\n", + "plt.ylabel('CRPS')\n", + "\n", + "plt.savefig('missing_crps.pdf', bbox_inches='tight')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "linear: 0.9333333333333333\n", + "log: 0.17647058823529413\n", + "square: 0.8235294117647058\n", + "linear_cos: 0.29411764705882354\n", + "sinc: 0.0\n", + "sigmoid: 0.05263157894736842\n", + "beat: 0.0\n", + "exp: 0.8125\n", + "gaussian_wave: 0.6\n", + "sine: 0.125\n", + "x_times_sine: 0.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/4z/0371zfqj5mx0rlct4r3qclfm0000gn/T/ipykernel_84564/2487969271.py:59: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " ax.set_xticklabels(\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pickle\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from collections import defaultdict\n", + "\n", + "sns.set(style='whitegrid', font_scale=1.)\n", + "\n", + "with open(f'../gpt4_cot.pkl','rb') as f:\n", + " data_dict = pickle.load(f)\n", + "\n", + "for task in data_dict:\n", + " answers = [b.split(\":\")[-1].strip() for a,b in data_dict[task]]\n", + " acc = np.mean([int(a == task) for a in answers])\n", + " print(f\"{task}: {acc}\")\n", + "\n", + "df = pd.DataFrame([\n", + " {\"method\": \"GPT4 + CoT\", \"Task\": \"Linear\", \"Accuracy\": 0.93},\n", + " {\"method\": \"GPT4 + CoT\", \"Task\": \"Square\", \"Accuracy\": 0.82},\n", + " {\"method\": \"GPT4 + CoT\", \"Task\": \"Exp\", \"Accuracy\": 0.81}, \n", + " {\"method\": \"GPT4 + CoT\", \"Task\": \"Gauss Wave\", \"Accuracy\": 0.60},\n", + " {\"method\": \"GPT4 + CoT\", \"Task\": \"Linear Cos\", \"Accuracy\": 0.29},\n", + " {\"method\": \"GPT4 + CoT\", \"Task\": \"Log\", \"Accuracy\": 0.18},\n", + " {\"method\": \"GPT4 + CoT\", \"Task\": \"Sin\", \"Accuracy\": 0.13},\n", + " {\"method\": \"GPT4 + CoT\", \"Task\": \"Sigmoid\", \"Accuracy\": 0.05},\n", + " # {\"method\": \"GPT4 + CoT\", \"Task\": \"Sinc\", \"Accuracy\": 0.0},\n", + " # {\"method\": \"GPT4 + CoT\", \"Task\": \"Beat\", \"Accuracy\": 0.0},\n", + " # {\"method\": \"GPT4 + CoT\", \"Task\": \"X * Sin\", \"Accuracy\": 0.0},\n", + "])\n", + "\n", + "fig, ax = plt.subplots(1, 1, figsize=(4, 3.5))\n", + "\n", + "# import IsleOfDogs2_6 palette \n", + "# from palettable import colorbrewer\n", + "# palette = colorbrewer.qualitative.Set2_8.mpl_colors\n", + "\n", + "#set palette to sns colorblind\n", + "palette = sns.color_palette(\"colorblind\", 8)[::-1]\n", + "\n", + "sns.barplot(\n", + " y='Task',\n", + " x='Accuracy',\n", + " # hue='Type',\n", + " palette=palette,\n", + " data=df,\n", + " ax=ax, \n", + ")\n", + "\n", + "ax.plot([1./11, 1./11], [-0.5, 7.5], color='black', linestyle='--', linewidth=1, label='Random')\n", + "# ax.set_ylim((-0.5, 7.5))\n", + "\n", + "ax.legend(\n", + " # loc='lower right', \n", + " frameon=True, \n", + " framealpha=0.7, \n", + " fontsize=15\n", + ")\n", + "ax.set_xticklabels(\n", + " ['{:,.0%}'.format(x) for x in ax.get_xticks()], \n", + " fontsize=14\n", + ")\n", + "ax.set_xlabel(ax.get_xlabel(), fontsize=17)\n", + "\n", + "ax.set_yticklabels(ax.get_yticklabels(), fontsize=14)\n", + "ax.set_ylabel(\"\")\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig('gpt4_classification.pdf', dpi=300, bbox_inches='tight')\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/figures/figure_appendix.ipynb b/figures/figure_appendix.ipynb new file mode 100644 index 0000000..6e462a7 --- /dev/null +++ b/figures/figure_appendix.ipynb @@ -0,0 +1,1721 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Memorization" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: 'datasets/memorization/IstanbulTraffic.csv'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/Users/nategruver/Desktop/arxiv_push/llmtime/figures/figure_appendix.ipynb Cell 3\u001b[0m line \u001b[0;36m1\n\u001b[1;32m 11\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mdata\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mmetrics\u001b[39;00m \u001b[39mimport\u001b[39;00m calculate_crps\n\u001b[1;32m 12\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mdata\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39msmall_context\u001b[39;00m \u001b[39mimport\u001b[39;00m get_memorization_datasets\n\u001b[0;32m---> 14\u001b[0m datasets \u001b[39m=\u001b[39m get_memorization_datasets()\n\u001b[1;32m 15\u001b[0m nlls \u001b[39m=\u001b[39m defaultdict(\u001b[39mdict\u001b[39m)\n\u001b[1;32m 16\u001b[0m mae \u001b[39m=\u001b[39m defaultdict(\u001b[39mdict\u001b[39m)\n", + "File \u001b[0;32m~/Desktop/arxiv_push/llmtime/data/small_context.py:100\u001b[0m, in \u001b[0;36mget_memorization_datasets\u001b[0;34m(n, testfrac, predict_steps)\u001b[0m\n\u001b[1;32m 98\u001b[0m datas \u001b[39m=\u001b[39m []\n\u001b[1;32m 99\u001b[0m \u001b[39mfor\u001b[39;00m i,dsname \u001b[39min\u001b[39;00m \u001b[39menumerate\u001b[39m(datasets):\n\u001b[0;32m--> 100\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mopen\u001b[39;49m(\u001b[39mf\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mdatasets/memorization/\u001b[39;49m\u001b[39m{\u001b[39;49;00mdsname\u001b[39m}\u001b[39;49;00m\u001b[39m.csv\u001b[39;49m\u001b[39m\"\u001b[39;49m) \u001b[39mas\u001b[39;00m f:\n\u001b[1;32m 101\u001b[0m series \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mread_csv(f, index_col\u001b[39m=\u001b[39m\u001b[39m0\u001b[39m, parse_dates\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\u001b[39m.\u001b[39mvalues\u001b[39m.\u001b[39mreshape(\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m)\n\u001b[1;32m 102\u001b[0m \u001b[39m# treat as float\u001b[39;00m\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'datasets/memorization/IstanbulTraffic.csv'" + ] + } + ], + "source": [ + "import pickle\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "from collections import defaultdict\n", + "\n", + "import sys\n", + "sys.path.append(\"..\")\n", + "from data.small_context import get_datasets\n", + "from data.metrics import calculate_crps\n", + "from data.small_context import get_memorization_datasets\n", + "\n", + "datasets = get_memorization_datasets()\n", + "nlls = defaultdict(dict)\n", + "mae = defaultdict(dict)\n", + "\n", + "name_map = {\n", + " \"gp\": \"SM-GP\",\n", + " \"arima\": \"ARIMA\",\n", + " \"TCN\": \"TCN\",\n", + " \"N-HiTS\": \"N-HiTS\",\n", + " \"N-BEATS\": \"N-BEATS\",\n", + " \"text-davinci-003\": \"GPT-3\",\n", + "}\n", + "\n", + "hue_order = ['SM-GP','N-BEATS','TCN','N-HiTS','ARIMA','GPT-3']\n", + "palette = sns.color_palette('Dark2', len(hue_order))\n", + "palette = palette[:2] + palette[3:] + palette[2:3]\n", + "\n", + "for dsname,(train,test) in datasets.items():\n", + " with open(f'../eval/memorization/{dsname}.pkl','rb') as f:\n", + " data_dict = pickle.load(f)\n", + "\n", + " # print(dsname)\n", + " # print(type(data_dict['gp']['samples']))\n", + "\n", + " for model_name,preds in data_dict.items():\n", + " print(model_name)\n", + " if isinstance(preds['median'], np.ndarray):\n", + " preds['median'] = pd.Series(preds['median'])\n", + " try: \n", + " if 'NLL/D' not in preds:\n", + " continue\n", + " nll = preds['NLL/D']\n", + " if model_name=='text-davinci-003-tuned':\n", + " model_name='GPT3'\n", + " nlls[model_name][dsname] = nll\n", + " test_values = test.values if isinstance(test,pd.Series) else test\n", + "\n", + " tmae = np.abs(test_values-preds['median'].values).mean()/np.abs(test_values).mean()\n", + " mae[model_name][dsname] = tmae\n", + " except Exception as e:\n", + " print(e)\n", + " \n", + "data = []\n", + "\n", + "for model_name in nlls:\n", + " for dsname in nlls[model_name]:\n", + " entry = {\n", + " \"model_name\": model_name,\n", + " \"dataset_name\": dsname,\n", + " \"nll\": nlls[model_name][dsname],\n", + " \"mae\": mae[model_name][dsname],\n", + " }\n", + " data.append(entry)\n", + "\n", + "# Convert the list of dictionaries to a DataFrame\n", + "result_df = pd.DataFrame(data)\n", + "\n", + "# drop all model_names except for 'gp', 'arima', 'TCN', 'text-davinci-003'\n", + "# result_df = result_df[result_df['model_name'].isin(['arima', 'TCN', 'text-davinci-003'])]\n", + "# result_df = result_df[result_df['model_name'].isin(['gp', 'arima', 'N-HiTS', 'TCN', 'text-davinci-003'])]\n", + "\n", + "result_df['dataset_name'].unique()\n", + "result_df['NLL/D'] = result_df['nll']\n", + "result_df['model_name'] = result_df['model_name'].apply(lambda x: name_map[x])\n", + "\n", + "n_cols = 11\n", + "n_rows = 1\n", + "import seaborn as sns\n", + "sns.set(style=\"whitegrid\", font_scale=1.3)\n", + "fig, axes = plt.subplots(\n", + " n_rows, n_cols, \n", + " figsize=(20, 4), constrained_layout=True, #sharex=True,\n", + " gridspec_kw={'width_ratios': [\n", + " 4, 0.1, 1, 0.4, \n", + " 4, 0.1, 1, 0.4, \n", + " 4, 0.1, 1]} \n", + ")\n", + "axes = axes.flatten()\n", + "\n", + "dsname_map = {\n", + " 'IstanbulTraffic': 'Istanbul Traffic',\n", + " 'TSMCStock': 'TSMC Stock',\n", + " 'TurkeyPower': 'Turkey Power',\n", + "}\n", + "\n", + "result_df['ll'] = -1 * result_df['nll']\n", + "\n", + "ax_idx = 0\n", + "# Iterate through the datasets and plot the samples\n", + "for idx, dsname in enumerate(datasets.keys()):\n", + " train,test = datasets[dsname]\n", + " print(dsname, ax_idx)\n", + " with open(f'eval/memorization/{dsname}.pkl','rb') as f:\n", + " data_dict = pickle.load(f)\n", + " ax = axes[ax_idx]\n", + " ax_idx += 1\n", + " if not 'text-davinci-003' in data_dict:\n", + " print('GPT-3 not available')\n", + " continue\n", + " samples = data_dict['text-davinci-003']['samples']\n", + " lower = samples.quantile(0.1,axis=0)\n", + " upper = samples.quantile(0.9,axis=0)\n", + " \n", + " # if dsname == \"square\":\n", + " # print(np.min(train.values), np.max(train.values))\n", + "\n", + " pred_color = sns.color_palette('Dark2')[2]\n", + " ax.plot(pd.concat([train,test]),color='k',label='Ground Truth', linewidth=2)\n", + " ax.fill_between(samples.iloc[0].index, lower, upper, alpha=0.5)\n", + " ax.plot(data_dict['text-davinci-003']['median'], color=pred_color,label='GPT-3 Median', linewidth=2)\n", + " #ax.plot(samples.T, alpha=0.5)\n", + " ax.set_title(dsname_map[dsname], fontsize=20)\n", + " # turn off y axis\n", + " ax.get_yaxis().set_visible(False)\n", + " ax.get_xaxis().set_visible(False)\n", + "\n", + " ax = axes[ax_idx]\n", + " ax_idx += 1\n", + "\n", + " ax.set_axis_off()\n", + "\n", + " ax = axes[ax_idx]\n", + " ax_idx += 1\n", + " \n", + " sns.barplot(\n", + " x='dataset_name',\n", + " # x='data_type',\n", + " # order=['Trend', 'Periodic', 'Trend + Periodic'],\n", + " y='NLL/D',\n", + " hue='model_name', \n", + " hue_order=hue_order,\n", + " data=result_df[result_df['dataset_name'] == dsname], \n", + " ax=ax, \n", + " palette=palette,\n", + " )\n", + " ax.get_legend().remove()\n", + " if idx % 3 != 2:\n", + " ax.set_ylabel(\"\")\n", + " else:\n", + " ax.set_ylabel(\"NLL/D\", rotation=-90, labelpad=15, fontsize=18)\n", + " ax.yaxis.set_label_position(\"right\")\n", + " ax.yaxis.tick_right()\n", + "\n", + " ax.spines['left'].set_color('black')\n", + " ax.plot(0, 0, 'vk', transform=ax.transAxes, clip_on=False, zorder=10)\n", + " ax.margins(x=0.2)\n", + " # ax.spines['bottom'].set_axisline_style(\"-|>\")\n", + " \n", + " # ax.set_yticks([])\n", + " # ax.set_ylim((-0.5,12))\n", + " # ax.set_ylim(bottom=-3, top=9)\n", + " ax.get_xaxis().set_visible(False)\n", + "\n", + " if idx % 3 != 2:\n", + " ax = axes[ax_idx]\n", + " ax_idx += 1\n", + " ax.set_axis_off()\n", + "\n", + "# axes[0].legend(['Ground Truth','Zero shot GPT3'],loc='upper left',fontsize=8,frameon=True,framealpha=0.7)\n", + "\n", + "handles, labels = axes[0].get_legend_handles_labels()\n", + "handles2, labels2 = axes[2].get_legend_handles_labels()\n", + "handles += handles2\n", + "labels += ['SM-GP','N-BEATS','TCN','N-HiTS','ARIMA','GPT-3']\n", + "\n", + "plt.subplots_adjust(wspace=0, hspace=.5)\n", + "\n", + "# # Remove unused subplots\n", + "# for idx in range(n_datasets, n_rows * n_cols):\n", + "# fig.delaxes(axes[idx])\n", + "\n", + "plt.savefig('memorization.pdf', dpi=300, bbox_inches='tight')\n", + "plt.savefig('memorization.png', dpi=300, bbox_inches='tight')\n", + "plt.show()\n", + "\n", + "\n", + "fig, ax = plt.subplots(1, 1, figsize=(2, 2))\n", + "ax.legend(\n", + " handles=handles,\n", + " labels=labels,\n", + " markerscale=1.5,\n", + " loc='upper left',\n", + " fontsize=20,\n", + " ncol=4\n", + ")\n", + "\n", + "plt.axis(\"off\")\n", + "# plt.tight_layout()\n", + "plt.savefig('memorization_legend.pdf', bbox_inches='tight')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Monash samples" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'real_benchmarks'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/Users/nategruver/Desktop/arxiv_push/llmtime/figures/figure_appendix.ipynb Cell 5\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> 1\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mreal_benchmarks\u001b[39;00m \u001b[39mimport\u001b[39;00m get_benchmark_test_sets\n\u001b[1;32m 3\u001b[0m benchmarks \u001b[39m=\u001b[39m get_benchmark_test_sets()\n\u001b[1;32m 4\u001b[0m \u001b[39m# shuffle the benchmarks\u001b[39;00m\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'real_benchmarks'" + ] + } + ], + "source": [ + "from real_benchmarks import get_benchmark_test_sets\n", + "\n", + "benchmarks = get_benchmark_test_sets()\n", + "# shuffle the benchmarks\n", + "for k, v in benchmarks.items():\n", + " x, scaler = v # scaler is not used\n", + " # seed\n", + " np.random.seed(0)\n", + " x = np.random.permutation(x)\n", + " benchmarks[k] = x\n", + " \n", + "df = pd.read_csv('eval/last_value_results.csv')\n", + "df.sort_values(by='mae')\n", + "\n", + "df_paper = pd.read_csv('eval/paper_mae_raw.csv') # pdf text -> csv\n", + "datasets = df_paper['Dataset']\n", + "name_map = {\n", + " 'Aus. Electricity Demand' :'australian_electricity_demand',\n", + " 'Kaggle Weekly': 'kaggle_web_traffic_weekly',\n", + " 'FRED-MD': 'fred_md',\n", + " 'Saugeen River Flow': 'saugeenday',\n", + " \n", + "}\n", + "datasets = [name_map.get(d, d) for d in datasets]\n", + "# lower case and repalce spaces with underscores\n", + "datasets = [d.lower().replace(' ', '_') for d in datasets]\n", + "df_paper['Dataset'] = datasets\n", + "# remove from df_paper datasets in df_paper but not in df\n", + "df_paper = df_paper[df_paper['Dataset'].isin(df['dataset'])]\n", + "df_paper = df_paper.reset_index(drop=True)\n", + "# for each dataset, add last value mae to df_paper\n", + "for dataset in df_paper['Dataset']:\n", + " df_paper.loc[df_paper['Dataset'] == dataset, 'Last Value'] = df[df['dataset'] == dataset]['mae'].values[0]\n", + "# turn '-' into np.nan\n", + "df_paper = df_paper.replace('-', np.nan)\n", + "# convert all values to float\n", + "for method in df_paper.columns[1:]:\n", + " df_paper[method] = df_paper[method].astype(float)\n", + "df_paper.to_csv('eval/paper_mae.csv', index=False)\n", + "# normalize each method by dividing by last value mae\n", + "for method in df_paper.columns[1:-1]: # skip dataset and last value\n", + " df_paper[method] = df_paper[method] / df_paper['Last Value']\n", + "# sort df by minimum mae across methods\n", + "df_paper['normalized_min'] = df_paper[df_paper.columns[1:-1]].min(axis=1)\n", + "df_paper['normalized_median'] = df_paper[df_paper.columns[1:-1]].median(axis=1)\n", + "df_paper = df_paper.sort_values(by='normalized_min')\n", + "df_paper = df_paper.reset_index(drop=True)\n", + "# save as csv\n", + "df_paper.to_csv('eval/paper_mae_normalized.csv', index=False)\n", + "\n", + "predictable_datasets = df_paper.head(10)['Dataset']\n", + "datasets = {k: benchmarks[k] for k in predictable_datasets}" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_predictions(inputs, targets, medians, ax):\n", + " sns.color_palette('Set1')\n", + " train = pd.Series(inputs, index=range(len(inputs)))\n", + " test = pd.Series(targets, index=range(len(inputs), len(inputs)+len(targets)))\n", + " medians = pd.Series(medians, index=test.index)\n", + " ax.plot(pd.concat([train,test]), color='C1',label='Ground Truth', linewidth=1)\n", + " ax.plot(medians, color='C4',label='GPT-3 Median', linewidth=1.5)\n", + " # ax.legend()\n", + " # remove all ticks\n", + " ax.tick_params(axis='both', which='both', bottom=False, top=False, left=False, right=False, labelbottom=False, labelleft=False)\n", + " for spine in ax.spines.values():\n", + " spine.set_edgecolor('grey')\n", + "\n", + " \n", + "\n", + "def plot_pred_dataset(dataset, pred_dict, max_series=None, max_num_samples=None, show_median=True, title=None):\n", + " hyper = pred_dict['info']['hyper']\n", + " settings = hyper.settings\n", + " preds = pred_dict['preds']\n", + " medians = pred_dict['medians']\n", + " if max_series is None:\n", + " max_series = len(preds)\n", + " max_series = min(max_series, len(preds))\n", + " dataset = dataset[:max_series]\n", + " preds = preds[:max_series]\n", + " # separate inputs and targets\n", + " inputs = [xy[0][-hyper.max_history:] for xy in dataset]\n", + " targets = np.array([xy[1] for xy in dataset])\n", + " plot_predictions(inputs, targets, medians, title)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sns.set_palette(\"Set1\")\n", + "\n", + "D = len(datasets) # number of datasets\n", + "N = 4 # number of series per dataset\n", + "np.random.seed(99) # 2\n", + "\n", + "fig, axes = plt.subplots(D, N, figsize=(5.5*N, 4*D), dpi=100)\n", + "\n", + "for i, (ds_name, dataset) in enumerate(datasets.items()):\n", + " print(ds_name)\n", + " path = f'eval/{ds_name}.pkl'\n", + " with open(path, 'rb') as f:\n", + " pred_dict = pickle.load(f)\n", + "\n", + " hyper = pred_dict['info']['hyper']\n", + " settings = hyper.settings\n", + " medians = pred_dict['medians']\n", + " \n", + "\n", + " inputs = [xy[0][-hyper.max_history:] for xy in dataset]\n", + " targets = [xy[1] for xy in dataset]\n", + " # Select N series randomly\n", + " # sample without replacement\n", + " if N < len(medians):\n", + " indices = np.random.choice(len(medians), N, replace=False)\n", + " else:\n", + " indices = np.arange(len(medians))\n", + " inputs = [inputs[i] for i in indices]\n", + " targets = [targets[i] for i in indices]\n", + " medians = [medians[i] for i in indices]\n", + " \n", + "\n", + " for j in range(len(indices)):\n", + " plot_predictions(inputs[j], targets[j], medians[j], axes[i, j])\n", + " # show title inside plot on the leftmost column\n", + " # put the title on the left\n", + " title = ' ' + ds_name.replace('_', ' ').title()\n", + " title = title.replace('Us', 'US')\n", + " axes[i, 0].set_title(title, fontsize=20, y=0.88, loc='left')\n", + "\n", + "for ax in axes.flat:\n", + " ax.tick_params(axis='both', which='both', bottom=False, top=False, left=False, right=False, labelbottom=False, labelleft=False)\n", + "\n", + "from matplotlib.lines import Line2D\n", + "legend_elements = [Line2D([0], [0], color='C1', lw=2, label='Ground Truth'),\n", + " Line2D([0], [0], color='C4', lw=2, label='GPT-3 Median')]\n", + "fig.legend(handles=legend_elements, loc='lower center', ncol=2, fontsize=24, bbox_to_anchor=(0.5, -0.02))\n", + "plt.tight_layout()\n", + "plt.savefig('monash_examples.pdf', bbox_inches='tight', pad_inches=0.1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Darts complete" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Dataset NLL/D Type CRPS MAE \\\n", + "0 AirPassengersDataset 5.904887 SM-GP 0.063900 0.078750 \n", + "1 AusBeerDataset 4.294716 SM-GP 0.198860 0.237520 \n", + "2 GasRateCO2Dataset 0.828726 SM-GP 0.030061 0.041435 \n", + "3 MonthlyMilkDataset 4.383407 SM-GP 0.027661 0.035353 \n", + "4 SunspotsDataset 5.927820 SM-GP 0.553448 0.678478 \n", + ".. ... ... ... ... ... \n", + "3 MonthlyMilkDataset 4.288150 LLaMA-2 70B (chat) 0.060835 0.079583 \n", + "4 SunspotsDataset 4.753432 LLaMA-2 70B (chat) 0.434370 0.606687 \n", + "5 WineDataset 9.567103 LLaMA-2 70B (chat) 0.126714 0.162982 \n", + "6 WoolyDataset 7.882100 LLaMA-2 70B (chat) 0.146765 0.185450 \n", + "7 HeartRateDataset 2.192415 LLaMA-2 70B (chat) 0.082896 0.100848 \n", + "\n", + " ll \n", + "0 -5.904887 \n", + "1 -4.294716 \n", + "2 -0.828726 \n", + "3 -4.383407 \n", + "4 -5.927820 \n", + ".. ... \n", + "3 -4.288150 \n", + "4 -4.753432 \n", + "5 -9.567103 \n", + "6 -7.882100 \n", + "7 -2.192415 \n", + "\n", + "[128 rows x 6 columns]\n", + " Dataset NLL/D Type CRPS MAE \\\n", + "0 AirPassengersDataset 5.904887 SM-GP 0.063900 0.078750 \n", + "0 AirPassengersDataset 5.251196 ARIMA 0.053818 0.054583 \n", + "0 AirPassengersDataset 6.320345 TCN 0.106276 0.124821 \n", + "0 AirPassengersDataset 5.537925 N-BEATS 0.188708 0.222310 \n", + "0 AirPassengersDataset 5.662678 N-HiTS 0.105241 0.134360 \n", + "0 AirPassengersDataset 4.764466 GPT-3 0.037426 0.078064 \n", + "0 AirPassengersDataset 5.179396 LLaMA 7B 0.098354 0.149670 \n", + "0 AirPassengersDataset 5.110602 LLaMA-2 7B 0.089584 0.115622 \n", + "0 AirPassengersDataset 5.340572 LLaMA-2 7B (chat) 0.096157 0.115865 \n", + "0 AirPassengersDataset 4.995787 LLaMA 13B 0.083882 0.091821 \n", + "0 AirPassengersDataset 5.153290 LLaMA-2 13B 0.142451 0.155730 \n", + "0 AirPassengersDataset 5.367811 LLaMA-2 13B (chat) 0.127912 0.154397 \n", + "0 AirPassengersDataset 4.818391 LLaMA 30B 0.090433 0.092792 \n", + "0 AirPassengersDataset 4.805426 LLaMA 70B 0.121781 0.151716 \n", + "0 AirPassengersDataset 4.601174 LLaMA-2 70B 0.031097 0.043575 \n", + "0 AirPassengersDataset 4.927035 LLaMA-2 70B (chat) 0.104230 0.110127 \n", + "\n", + " ll \n", + "0 -5.904887 \n", + "0 -5.251196 \n", + "0 -6.320345 \n", + "0 -5.537925 \n", + "0 -5.662678 \n", + "0 -4.764466 \n", + "0 -5.179396 \n", + "0 -5.110602 \n", + "0 -5.340572 \n", + "0 -4.995787 \n", + "0 -5.153290 \n", + "0 -5.367811 \n", + "0 -4.818391 \n", + "0 -4.805426 \n", + "0 -4.601174 \n", + "0 -4.927035 \n", + " Dataset NLL/D Type CRPS MAE ll\n", + "1 AusBeerDataset 4.294716 SM-GP 0.198860 0.237520 -4.294716\n", + "1 AusBeerDataset 4.137879 ARIMA 0.038713 0.039878 -4.137879\n", + "1 AusBeerDataset 4.255859 TCN 0.047611 0.071912 -4.255859\n", + "1 AusBeerDataset 4.144485 N-BEATS 0.020708 0.024191 -4.144485\n", + "1 AusBeerDataset 4.415001 N-HiTS 0.054403 0.079667 -4.415001\n", + "1 AusBeerDataset 3.398564 GPT-3 0.033140 0.037535 -3.398564\n", + "1 AusBeerDataset 4.639848 LLaMA 7B 0.059580 0.074886 -4.639848\n", + "1 AusBeerDataset 4.444377 LLaMA-2 7B 0.065862 0.103032 -4.444377\n", + "1 AusBeerDataset 4.647383 LLaMA-2 7B (chat) 0.059869 0.072531 -4.647383\n", + "1 AusBeerDataset 4.091325 LLaMA 13B 0.032189 0.043808 -4.091325\n", + "1 AusBeerDataset 4.074365 LLaMA-2 13B 0.041760 0.041805 -4.074365\n", + "1 AusBeerDataset 4.456329 LLaMA-2 13B (chat) 0.066471 0.074380 -4.456329\n", + "1 AusBeerDataset 3.964110 LLaMA 30B 0.037292 0.034942 -3.964110\n", + "1 AusBeerDataset 4.023011 LLaMA 70B 0.028196 0.037952 -4.023011\n", + "1 AusBeerDataset 4.089225 LLaMA-2 70B 0.027312 0.029953 -4.089225\n", + "1 AusBeerDataset 4.138710 LLaMA-2 70B (chat) 0.033623 0.047719 -4.138710\n", + " Dataset NLL/D Type CRPS MAE \\\n", + "2 GasRateCO2Dataset 0.828726 SM-GP 0.030061 0.041435 \n", + "2 GasRateCO2Dataset 0.800576 ARIMA 0.041966 0.043165 \n", + "2 GasRateCO2Dataset 0.821732 TCN 0.036172 0.048148 \n", + "2 GasRateCO2Dataset 0.832746 N-BEATS 0.031888 0.047906 \n", + "2 GasRateCO2Dataset 1.002150 N-HiTS 0.053347 0.070260 \n", + "2 GasRateCO2Dataset -1.694043 GPT-3 0.044981 0.063721 \n", + "2 GasRateCO2Dataset -0.338933 LLaMA 7B 0.046147 0.061553 \n", + "2 GasRateCO2Dataset -0.473832 LLaMA-2 7B 0.032978 0.041717 \n", + "2 GasRateCO2Dataset -0.179904 LLaMA-2 7B (chat) 0.034265 0.053283 \n", + "2 GasRateCO2Dataset -0.479550 LLaMA 13B 0.033994 0.047144 \n", + "2 GasRateCO2Dataset -0.335951 LLaMA-2 13B 0.041495 0.059065 \n", + "2 GasRateCO2Dataset -0.067092 LLaMA-2 13B (chat) 0.040797 0.054375 \n", + "2 GasRateCO2Dataset -0.564685 LLaMA 30B 0.036333 0.043466 \n", + "2 GasRateCO2Dataset -0.537516 LLaMA 70B 0.032915 0.050402 \n", + "2 GasRateCO2Dataset -0.673155 LLaMA-2 70B 0.036368 0.045710 \n", + "2 GasRateCO2Dataset -0.411312 LLaMA-2 70B (chat) 0.042056 0.053532 \n", + "\n", + " ll \n", + "2 -0.828726 \n", + "2 -0.800576 \n", + "2 -0.821732 \n", + "2 -0.832746 \n", + "2 -1.002150 \n", + "2 1.694043 \n", + "2 0.338933 \n", + "2 0.473832 \n", + "2 0.179904 \n", + "2 0.479550 \n", + "2 0.335951 \n", + "2 0.067092 \n", + "2 0.564685 \n", + "2 0.537516 \n", + "2 0.673155 \n", + "2 0.411312 \n", + " Dataset NLL/D Type CRPS MAE \\\n", + "3 MonthlyMilkDataset 4.383407 SM-GP 0.027661 0.035353 \n", + "3 MonthlyMilkDataset 3.600711 ARIMA 0.066076 0.043353 \n", + "3 MonthlyMilkDataset 4.943495 TCN 0.058058 0.082591 \n", + "3 MonthlyMilkDataset 4.567873 N-BEATS 0.032325 0.039211 \n", + "3 MonthlyMilkDataset 3.985428 N-HiTS 0.029383 0.038144 \n", + "3 MonthlyMilkDataset 3.788047 GPT-3 0.012174 0.011280 \n", + "3 MonthlyMilkDataset 4.343635 LLaMA 7B 0.178903 0.203168 \n", + "3 MonthlyMilkDataset 4.326111 LLaMA-2 7B 0.052789 0.079697 \n", + "3 MonthlyMilkDataset 4.746351 LLaMA-2 7B (chat) 0.100153 0.102238 \n", + "3 MonthlyMilkDataset 4.008629 LLaMA 13B 0.065918 0.085651 \n", + "3 MonthlyMilkDataset 4.480991 LLaMA-2 13B 0.074753 0.079742 \n", + "3 MonthlyMilkDataset 4.641934 LLaMA-2 13B (chat) 0.063125 0.103680 \n", + "3 MonthlyMilkDataset 3.731941 LLaMA 30B 0.016423 0.024159 \n", + "3 MonthlyMilkDataset 3.502900 LLaMA 70B 0.009152 0.011570 \n", + "3 MonthlyMilkDataset 3.612837 LLaMA-2 70B 0.023207 0.030989 \n", + "3 MonthlyMilkDataset 4.288150 LLaMA-2 70B (chat) 0.060835 0.079583 \n", + "\n", + " ll \n", + "3 -4.383407 \n", + "3 -3.600711 \n", + "3 -4.943495 \n", + "3 -4.567873 \n", + "3 -3.985428 \n", + "3 -3.788047 \n", + "3 -4.343635 \n", + "3 -4.326111 \n", + "3 -4.746351 \n", + "3 -4.008629 \n", + "3 -4.480991 \n", + "3 -4.641934 \n", + "3 -3.731941 \n", + "3 -3.502900 \n", + "3 -3.612837 \n", + "3 -4.288150 \n", + " Dataset NLL/D Type CRPS MAE ll\n", + "4 SunspotsDataset 5.927820 SM-GP 0.553448 0.678478 -5.927820\n", + "4 SunspotsDataset 4.738173 ARIMA 0.504343 0.549952 -4.738173\n", + "4 SunspotsDataset 4.687903 TCN 0.598617 0.654143 -4.687903\n", + "4 SunspotsDataset 5.855724 N-BEATS 0.735195 0.923481 -5.855724\n", + "4 SunspotsDataset 5.874763 N-HiTS 0.495743 0.630370 -5.874763\n", + "4 SunspotsDataset 4.604078 GPT-3 0.515913 0.597618 -4.604078\n", + "4 SunspotsDataset 4.726327 LLaMA 7B 0.475830 0.640254 -4.726327\n", + "4 SunspotsDataset 4.683876 LLaMA-2 7B 0.501086 0.612576 -4.683876\n", + "4 SunspotsDataset 4.891910 LLaMA-2 7B (chat) 0.453756 0.673664 -4.891910\n", + "4 SunspotsDataset 4.669124 LLaMA 13B 0.473073 0.610858 -4.669124\n", + "4 SunspotsDataset 4.622865 LLaMA-2 13B 0.529991 0.580468 -4.622865\n", + "4 SunspotsDataset 4.708156 LLaMA-2 13B (chat) 0.447592 0.651160 -4.708156\n", + "4 SunspotsDataset 4.629326 LLaMA 30B 0.505053 0.606545 -4.629326\n", + "4 SunspotsDataset 4.699574 LLaMA 70B 0.494811 0.559848 -4.699574\n", + "4 SunspotsDataset 4.497965 LLaMA-2 70B 0.483847 0.600598 -4.497965\n", + "4 SunspotsDataset 4.753432 LLaMA-2 70B (chat) 0.434370 0.606687 -4.753432\n", + " Dataset NLL/D Type CRPS MAE ll\n", + "5 WineDataset 9.553832 SM-GP 0.157715 0.173866 -9.553832\n", + "5 WineDataset 9.329075 ARIMA 0.068827 0.088104 -9.329075\n", + "5 WineDataset 9.631807 TCN 0.101325 0.125552 -9.631807\n", + "5 WineDataset 9.662293 N-BEATS 0.135679 0.174246 -9.662293\n", + "5 WineDataset 9.513285 N-HiTS 0.126602 0.149324 -9.513285\n", + "5 WineDataset 9.401341 GPT-3 0.043531 0.059940 -9.401341\n", + "5 WineDataset 9.672016 LLaMA 7B 0.126112 0.161291 -9.672016\n", + "5 WineDataset 9.534088 LLaMA-2 7B 0.125335 0.152394 -9.534088\n", + "5 WineDataset 9.690506 LLaMA-2 7B (chat) 0.145414 0.190737 -9.690506\n", + "5 WineDataset 9.566581 LLaMA 13B 0.117110 0.144841 -9.566581\n", + "5 WineDataset 9.564420 LLaMA-2 13B 0.115771 0.156929 -9.564420\n", + "5 WineDataset 9.695887 LLaMA-2 13B (chat) 0.099255 0.177152 -9.695887\n", + "5 WineDataset 9.497645 LLaMA 30B 0.073609 0.115200 -9.497645\n", + "5 WineDataset 9.440490 LLaMA 70B 0.089674 0.135971 -9.440490\n", + "5 WineDataset 9.463498 LLaMA-2 70B 0.123378 0.152081 -9.463498\n", + "5 WineDataset 9.567103 LLaMA-2 70B (chat) 0.126714 0.162982 -9.567103\n", + " Dataset NLL/D Type CRPS MAE ll\n", + "6 WoolyDataset 7.450926 SM-GP 0.106607 0.132700 -7.450926\n", + "6 WoolyDataset 7.578408 ARIMA 0.085482 0.120206 -7.578408\n", + "6 WoolyDataset 7.677636 TCN 0.133309 0.236581 -7.677636\n", + "6 WoolyDataset 8.355280 N-BEATS 0.157211 0.184360 -8.355280\n", + "6 WoolyDataset 7.426534 N-HiTS 0.068369 0.078008 -7.426534\n", + "6 WoolyDataset 7.553988 GPT-3 0.131039 0.165112 -7.553988\n", + "6 WoolyDataset 7.950130 LLaMA 7B 0.122254 0.185997 -7.950130\n", + "6 WoolyDataset 7.907113 LLaMA-2 7B 0.135925 0.179883 -7.907113\n", + "6 WoolyDataset 8.049314 LLaMA-2 7B (chat) 0.166814 0.212964 -8.049314\n", + "6 WoolyDataset 7.669655 LLaMA 13B 0.135720 0.175484 -7.669655\n", + "6 WoolyDataset 7.921911 LLaMA-2 13B 0.131007 0.175608 -7.921911\n", + "6 WoolyDataset 7.913400 LLaMA-2 13B (chat) 0.160318 0.198224 -7.913400\n", + "6 WoolyDataset 7.882434 LLaMA 30B 0.155767 0.190744 -7.882434\n", + "6 WoolyDataset 7.458627 LLaMA 70B 0.132477 0.167628 -7.458627\n", + "6 WoolyDataset 7.836560 LLaMA-2 70B 0.111030 0.173446 -7.836560\n", + "6 WoolyDataset 7.882100 LLaMA-2 70B (chat) 0.146765 0.185450 -7.882100\n", + " Dataset NLL/D Type CRPS MAE \\\n", + "7 HeartRateDataset 3.398187 SM-GP 0.045754 0.060506 \n", + "7 HeartRateDataset 2.505075 ARIMA 0.046754 0.059551 \n", + "7 HeartRateDataset 2.479402 TCN 0.042716 0.058826 \n", + "7 HeartRateDataset 2.647820 N-BEATS 0.055881 0.070317 \n", + "7 HeartRateDataset 2.290613 N-HiTS 0.046430 0.065302 \n", + "7 HeartRateDataset 2.272363 GPT-3 0.049866 0.066514 \n", + "7 HeartRateDataset 2.185677 LLaMA 7B 0.179614 0.307132 \n", + "7 HeartRateDataset 2.105081 LLaMA-2 7B 0.069555 0.074867 \n", + "7 HeartRateDataset 2.146309 LLaMA-2 7B (chat) 0.120713 0.177666 \n", + "7 HeartRateDataset 2.140229 LLaMA 13B 0.060131 0.063574 \n", + "7 HeartRateDataset 2.104173 LLaMA-2 13B 0.054633 0.061866 \n", + "7 HeartRateDataset 2.198830 LLaMA-2 13B (chat) 0.057285 0.112405 \n", + "7 HeartRateDataset 2.113969 LLaMA 30B 0.043327 0.058521 \n", + "7 HeartRateDataset 2.072233 LLaMA 70B 0.044294 0.062873 \n", + "7 HeartRateDataset 2.092934 LLaMA-2 70B 0.043658 0.065507 \n", + "7 HeartRateDataset 2.192415 LLaMA-2 70B (chat) 0.082896 0.100848 \n", + "\n", + " ll \n", + "7 -3.398187 \n", + "7 -2.505075 \n", + "7 -2.479402 \n", + "7 -2.647820 \n", + "7 -2.290613 \n", + "7 -2.272363 \n", + "7 -2.185677 \n", + "7 -2.105081 \n", + "7 -2.146309 \n", + "7 -2.140229 \n", + "7 -2.104173 \n", + "7 -2.198830 \n", + "7 -2.113969 \n", + "7 -2.072233 \n", + "7 -2.092934 \n", + "7 -2.192415 \n" + ] + }, + { + "data": { + "image/png": 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", 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", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pickle\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "from collections import defaultdict\n", + "\n", + "import sys\n", + "sys.path.append(\"..\")\n", + "from data.small_context import get_datasets\n", + "\n", + "datasets = get_datasets()\n", + "\n", + "n_datasets = len(datasets)\n", + "n_cols = 15\n", + "n_rows = 2#(n_datasets + n_cols - 1) // n_cols\n", + "import seaborn as sns\n", + "sns.set(style=\"whitegrid\", font_scale=1.3)\n", + "fig, axes = plt.subplots(\n", + " n_rows, n_cols, \n", + " figsize=(16, 4), constrained_layout=True, #sharex=True,\n", + " gridspec_kw={'width_ratios': [\n", + " 3, 0.2, 1.0, 0.2, \n", + " 3, 0.2, 1.0, 0.2, \n", + " 3, 0.2, 1.0, 0.2,\n", + " 3, 0.2, 1.0]}, \n", + ")\n", + "axes = axes.flatten()\n", + "\n", + "name_map = {\n", + " \"gp\": \"SM-GP\",\n", + " \"arima\": \"ARIMA\",\n", + " \"TCN\": \"TCN\",\n", + " \"N-BEATS\": \"N-BEATS\",\n", + " \"N-HiTS\": \"N-HiTS\",\n", + " 'text-davinci-003':'GPT-3',\n", + " 'LLaMA7B': 'LLaMA7B',\n", + " 'LLaMA13B': 'LLaMA13B',\n", + " 'LLaMA30B': 'LLaMA30B', \n", + " 'LLaMA70B': 'LLaMA70B',\n", + " \"llama1_7B\": \"LLaMA 7B\",\n", + " \"llama1_13B\": \"LLaMA 13B\",\n", + " \"llama1_30B\": \"LLaMA 30B\",\n", + " \"llama1_70B\": \"LLaMA 70B\",\n", + " \"llama2_7B\": \"LLaMA-2 7B\",\n", + " \"llama2_13B\": \"LLaMA-2 13B\",\n", + " \"llama2_70B\": \"LLaMA-2 70B\",\n", + " \"llama2_7B_chat\": \"LLaMA-2 7B (chat)\",\n", + " \"llama2_13B_chat\": \"LLaMA-2 13B (chat)\",\n", + " \"llama2_70B_chat\": \"LLaMA-2 70B (chat)\",\n", + "}\n", + "\n", + "hue_order = ['SM-GP','N-BEATS','TCN','N-HiTS','ARIMA']#, 'LLaMA70B']\n", + "hue_order += [\"LLaMA-2 70B\", 'GPT-3']\n", + "nlls = defaultdict(list)\n", + "crps = defaultdict(list)\n", + "mae = defaultdict(list)\n", + "datasets = get_datasets()\n", + "for dsname,(train,test) in datasets.items():\n", + " # if dsname == \"SunspotsDataset\":\n", + " # continue\n", + "\n", + " # print(dsname)\n", + " with open(f'../precomputed_outputs/darts/{dsname}.pkl','rb') as f:\n", + " data_dict = pickle.load(f)\n", + " for model_name,preds in data_dict.items():\n", + " # print(f\"\\t{model_name}\")\n", + " if model_name in ['ada','babbage','curie']:\n", + " continue\n", + " if 'NLL/D' not in preds:\n", + " continue\n", + " nll = preds['NLL/D']\n", + " if model_name=='text-davinci-003-tuned':\n", + " model_name='GPT3'\n", + "\n", + " if type(preds['samples']) == np.ndarray:\n", + " nlls[model_name].append(nll)\n", + " crps[model_name].append(calculate_crps(test.values,preds['samples'][:10],10))\n", + " tmae = np.abs(test.values-preds['median']).mean()/np.abs(test.values).mean()\n", + " mae[model_name].append(tmae)\n", + " else:\n", + " nlls[model_name].append(nll)\n", + " crps[model_name].append(calculate_crps(test.values,preds['samples'].values[:10],10))\n", + " tmae = np.abs(test.values-preds['median']).mean()/np.abs(test.values).mean()\n", + " mae[model_name].append(tmae)\n", + "\n", + "llama_models = [\n", + " \"llama1_7B\",\n", + " \"llama2_7B\",\n", + " \"llama2_7B_chat\",\n", + " \"llama1_13B\",\n", + " \"llama2_13B\",\n", + " \"llama2_13B_chat\",\n", + " \"llama1_30B\",\n", + " \"llama1_70B\",\n", + " \"llama2_70B\",\n", + " \"llama2_70B_chat\",\n", + "]\n", + "for dsname,(train,test) in datasets.items():\n", + " # if dsname == \"SunspotsDataset\":\n", + " # continue\n", + "\n", + " for model_name in llama_models:\n", + " if model_name in ['llama2_70B', 'llama2_70B_chat']:\n", + " fn = f'../precomputed_outputs/darts/llama_darts/{model_name}/darts-{dsname}/1.0_0.9_0.99_0.3_3_,_.pkl'\n", + " else:\n", + " fn = f'../precomputed_outputs/darts/llama_darts/{model_name}/darts-{dsname}/0.4_0.9_0.99_0.3_3_,_.pkl'\n", + " with open(fn,'rb') as f:\n", + " data_dict = pickle.load(f)\n", + "\n", + " preds = data_dict#[model_name]\n", + "\n", + " if 'NLL/D' not in preds:\n", + " continue\n", + " nll = preds['NLL/D']\n", + "\n", + " if type(preds['samples']) == np.ndarray:\n", + " nlls[model_name].append(nll)\n", + " crps[model_name].append(calculate_crps(test.values,preds['samples'][:10],10))\n", + " tmae = np.abs(test.values-preds['median']).mean()/np.abs(test.values).mean()\n", + " mae[model_name].append(tmae)\n", + " else:\n", + " nlls[model_name].append(nll)\n", + " crps[model_name].append(calculate_crps(test.values,preds['samples'].values[:10],10))\n", + " tmae = np.abs(test.values-preds['median']).mean()/np.abs(test.values).mean()\n", + " mae[model_name].append(tmae)\n", + "\n", + "\n", + "nlls = {k:np.array(v) for k,v in nlls.items()}\n", + "crps = {k:np.array(v) for k,v in crps.items()}\n", + "mae = {k:np.array(v) for k,v in mae.items()}\n", + "\n", + "\n", + "dfs = [pd.DataFrame({'Dataset':datasets.keys(),'NLL/D':v,'Type':k, 'CRPS':crps[k],'MAE':mae[k]}) for k,v in nlls.items()]\n", + "df = pd.concat(dfs)\n", + "\n", + "df = df[df['Type'] != 'promptcast']\n", + "df['Type'] = df['Type'].apply(lambda x: name_map[x])\n", + "\n", + "result_df = df\n", + "result_df['ll'] = -1 * result_df['NLL/D']\n", + "\n", + "print(result_df)\n", + "\n", + "datasets_to_plot = [\n", + " 'AirPassengersDataset', 'AusBeerDataset', \n", + " 'GasRateCO2Dataset', 'MonthlyMilkDataset', \n", + " 'SunspotsDataset', 'WineDataset', \n", + " 'WoolyDataset', 'HeartRateDataset'\n", + "]\n", + "hue_order = ['SM-GP','N-BEATS','TCN','N-HiTS','ARIMA',\"LLaMA-2 70B\",'GPT-3']\n", + "\n", + "ax_idx = 0\n", + "# Iterate through the datasets and plot the samples\n", + "for idx, dsname in enumerate(datasets_to_plot):\n", + " train,test = datasets[dsname]\n", + "\n", + " with open(f'../precomputed_outputs/darts/{dsname}.pkl','rb') as f:\n", + " data_dict = pickle.load(f)\n", + " ax = axes[ax_idx]\n", + " ax_idx += 1\n", + " if not 'text-davinci-003' in data_dict:\n", + " continue\n", + " samples = data_dict['text-davinci-003']['samples']\n", + " lower = samples.quantile(0.1,axis=0)\n", + " upper = samples.quantile(0.9,axis=0)\n", + " \n", + " pred_color = sns.color_palette('Dark2')[2]\n", + " train_test = pd.concat([train,test])\n", + "\n", + " ax.plot(train_test, color='k',label='Ground Truth', linewidth=1)\n", + " ax.fill_between(samples.iloc[0].index, lower, upper, alpha=0.5)\n", + " ax.plot(samples.iloc[0].index, data_dict['text-davinci-003']['median'], color=pred_color,label='GPT3 Median',linewidth=1)\n", + " ax.set_title(dsname.replace(\"Dataset\",\"\"))\n", + " ax.get_yaxis().set_visible(False)\n", + " ax.get_xaxis().set_visible(False)\n", + "\n", + " import matplotlib.dates as mdates\n", + " myFmt = mdates.DateFormatter('%d')\n", + " ax.xaxis.set_major_formatter(myFmt)\n", + " ax.xaxis.set_major_locator(plt.MaxNLocator(2))\n", + " # ax.set_xticklabels([0, 0, len(train_test) // 2, len(train_test)])\n", + "\n", + " ax = axes[ax_idx]\n", + " ax_idx += 1\n", + "\n", + " ax.set_axis_off()\n", + "\n", + " ax = axes[ax_idx]\n", + " ax_idx += 1\n", + " \n", + " palette = sns.color_palette('Dark2', len(hue_order))\n", + " palette = palette[:2] + palette[3:-1] + ['#a60355'] + palette[2:3]\n", + "\n", + " _df = result_df[result_df['Dataset'] == dsname]\n", + " print(_df)\n", + " sns.barplot(\n", + " x='Dataset',\n", + " y='NLL/D',\n", + " hue='Type', \n", + " hue_order=hue_order,\n", + " data=_df, \n", + " ax=ax, \n", + " palette=palette,\n", + " )\n", + " ax.get_legend().remove()\n", + " if idx % 4 != 3:\n", + " ax.set_ylabel(\"\")\n", + " else:\n", + " ax.set_ylabel(\"NLL\", rotation=-90, labelpad=15, fontsize=11)\n", + " ax.yaxis.set_label_position(\"right\")\n", + " ax.yaxis.tick_right()\n", + "\n", + " ax.spines['left'].set_color('black')\n", + " ax.plot(0, 0, 'vk', transform=ax.transAxes, clip_on=False, zorder=10)\n", + " ax.margins(x=0.15)\n", + "\n", + " # ax.set_ylim(bottom=-0.05)\n", + " ax.get_xaxis().set_visible(False)\n", + "\n", + " if idx % 4 != 3:\n", + " ax = axes[ax_idx]\n", + " ax_idx += 1\n", + " ax.set_axis_off()\n", + "\n", + "\n", + "handles, labels = axes[0].get_legend_handles_labels()\n", + "handles2, labels2 = axes[2].get_legend_handles_labels()\n", + "handles += handles2\n", + "labels += ['SM-GP','N-BEATS','TCN','N-HiTS','ARIMA',\"LLaMA-2 70B\",'GPT-3']\n", + "\n", + "plt.subplots_adjust(wspace=0, hspace=.5)\n", + "\n", + "plt.savefig('outputs/darts_qualitative.pdf', dpi=300, bbox_inches='tight')\n", + "plt.show()\n", + "plt.close()\n", + "\n", + "fig, ax = plt.subplots(1, 1, figsize=(2, 2))\n", + "ax.legend(\n", + " handles=handles[:2],\n", + " labels=labels[:2],\n", + " markerscale=1.5,\n", + " labelspacing=0.1,\n", + " loc='upper left',\n", + " fontsize=25,\n", + " ncol=8,\n", + " frameon=True\n", + ")\n", + "\n", + "plt.axis(\"off\")\n", + "plt.tight_layout()\n", + "plt.savefig('outputs/darts_qualitative_legend_1.pdf', bbox_inches='tight')\n", + "plt.show()\n", + "\n", + "fig, ax = plt.subplots(1, 1, figsize=(2, 2))\n", + "ax.legend(\n", + " handles=handles[2:],\n", + " labels=labels[2:],\n", + " markerscale=1.5,\n", + " labelspacing=0.1,\n", + " loc='upper left',\n", + " fontsize=25,\n", + " ncol=8,\n", + " frameon=True,\n", + " handlelength=1., \n", + " columnspacing=1.\n", + ")\n", + "\n", + "plt.axis(\"off\")\n", + "plt.tight_layout()\n", + "plt.savefig('outputs/darts_qualitative_legend_2.pdf', bbox_inches='tight')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Informer" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import glob\n", + "import pickle\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from collections import defaultdict\n", + "\n", + "import sys\n", + "sys.path.append(\"..\")\n", + "from data.autoformer_dataset import data_dict\n", + "\n", + "gt_dir = \"../datasets/autoformer\"\n", + "llama_results_dir = \"../precomputed_outputs/autoformer/llama_2_autoformer/llama2_70B\"\n", + "autoformer_results_dir = \"../precomputed_outputs/autoformer/autoformer_results\"\n", + "fedformer_results_dir = \"../precomputed_outputs/autoformer/fedformer_results\"\n", + "\n", + "results = defaultdict(dict)\n", + "for fn in glob.glob(f\"{llama_results_dir}/*/192/*.pkl\"):\n", + " parts = fn.split(\"/\")[-3].split(\"_\")\n", + " dsname, idx = \"_\".join(parts[:-1]), parts[-1]\n", + "\n", + " # if \"ETTh1\" not in dsname:\n", + " # continue\n", + "\n", + " idx = int(idx)\n", + " results[dsname][idx] = pickle.load(open(fn, \"rb\"))\n", + "\n", + "autoformer_dsname_map = {\n", + " \"exchange_rate\": \"Exchange\",\n", + " \"electricity\": \"ECL\",\n", + " \"traffic\": \"traffic\",\n", + " \"weather\": \"weather\",\n", + " \"national_illness\": \"ILI\",\n", + " \"ETTh1\": \"ETTh1\",\n", + " \"ETTh2\": \"ETTh2\",\n", + " \"ETTm1\": \"ETTm1\",\n", + " \"ETTm2\": \"ETTm2\",\n", + "}\n", + "\n", + "df = []\n", + "for dsname in results:\n", + " a_dsname = autoformer_dsname_map[dsname]\n", + "\n", + " if \"ETT\" in dsname:\n", + " dstype = dsname\n", + " else:\n", + " dstype = \"custom\"\n", + "\n", + " for method in [\"Autoformer\", \"Informer\", \"Reformer\", \"Transformer\"]:\n", + " try:\n", + " feature_type = 'M'\n", + " base_dir = f\"{autoformer_results_dir}/{a_dsname}_96_192_{method}_{dstype}_ft{feature_type}_sl96_ll48_pl192_dm512_nh8_el2_dl1_df2048_fc3_ebtimeF_dtTrue_Exp_0\"\n", + " if not os.path.exists(base_dir):\n", + " feature_type = 'S'\n", + " base_dir = f\"{autoformer_results_dir}/{a_dsname}_96_192_{method}_{dstype}_ft{feature_type}_sl96_ll48_pl192_dm512_nh8_el2_dl1_df2048_fc3_ebtimeF_dtTrue_Exp_0\"\n", + " pred = np.load(f\"{base_dir}/pred.npy\")[0]\n", + " gt = np.load(f\"{base_dir}/true.npy\")[0]\n", + " except Exception as e:\n", + " continue\n", + "\n", + " # print(gt[:10,0])\n", + " # print(pred[:10,0])\n", + " # print(gt.shape)\n", + " # print(pred.shape)\n", + " # print(\"\")\n", + "\n", + " mae = np.mean(np.abs(pred - gt))\n", + " mse = np.mean((pred - gt)**2)\n", + "\n", + " df.append({\n", + " 'dsname': dsname,\n", + " 'mae': mae,\n", + " 'mse': mse,\n", + " 'method': method\n", + " })\n", + "\n", + " for method in [\"FEDformer\"]:\n", + " try:\n", + " feature_type = 'M'\n", + " base_dir = f\"{fedformer_results_dir}/{a_dsname}_{method}_random_modes64_{dstype}_ft{feature_type}_sl96_ll48_pl192_dm512_nh8_el2_dl1_df2048_fc3_ebtimeF_dtTrue_Exp_0\"\n", + " # print(base_dir)\n", + " # print(1/0)\n", + " \n", + " # if not os.path.exists(base_dir):\n", + " # feature_type = 'S'\n", + " # base_dir = f\"{fedformer_results_dir}/{a_dsname}_96_192_{method}_{dstype}_ft{feature_type}_sl96_ll48_pl192_dm512_nh8_el2_dl1_df2048_fc3_ebtimeF_dtTrue_Exp_0\"\n", + " pred = np.load(f\"{base_dir}/pred.npy\")[0]\n", + " gt = np.load(f\"{base_dir}/true.npy\")[0]\n", + " except Exception as e:\n", + " continue\n", + "\n", + " # print(gt[:10,0])\n", + " # print(pred[:10,0])\n", + " # print(gt.shape)\n", + " # print(pred.shape)\n", + " # print(\"\")\n", + "\n", + " mae = np.mean(np.abs(pred - gt))\n", + " mse = np.mean((pred - gt)**2)\n", + "\n", + " df.append({\n", + " 'dsname': dsname,\n", + " 'mae': mae,\n", + " 'mse': mse,\n", + " 'method': method\n", + " })\n", + "\n", + " data = data_dict[\"custom\"](\n", + " root_path = gt_dir,\n", + " data_path = f\"{dsname}.csv\",\n", + " flag = \"test\",\n", + " size = [96, 0, 192],\n", + " features = \"M\",\n", + " )\n", + "\n", + " gt_all = pd.read_csv(f\"{gt_dir}/{dsname}.csv\").values[:,1:]\n", + "\n", + " gt_ordered = []\n", + " pred_ordered = []\n", + " mask = []\n", + " for i in range(gt_all.shape[-1]):\n", + " gt = gt_all[-192:,i]\n", + " gt_ordered.append(gt)\n", + "\n", + " if (i+1) not in results[dsname]:\n", + " pred = gt\n", + " mask.append(0)\n", + " else:\n", + " r = results[dsname][i+1]\n", + " pred = r['median'].values\n", + " mask.append(1)\n", + "\n", + " pred_ordered.append(pred)\n", + "\n", + " # print([x.shape for x in gt_ordered])\n", + " # print([x.shape for x in pred_ordered])\n", + "\n", + " gt_ordered = np.stack(gt_ordered, axis=1)\n", + " pred_ordered = np.stack(pred_ordered, axis=1)\n", + " mask = np.array(mask)\n", + "\n", + " gt = data.scaler.transform(gt_ordered)\n", + " pred = data.scaler.transform(pred_ordered)\n", + "\n", + " # for i in range(min(pred.shape[-1], 20)):\n", + " # plt.plot(gt[:,i], label='gt', color='black')\n", + " # plt.plot(pred[:,i], label='pred', color='red')\n", + " # plt.legend()\n", + " # plt.show()\n", + "\n", + " # print(gt_all[:10,0])\n", + " # print(gt[:10,0])\n", + " # print(pred[:10,0])\n", + "\n", + " # print(gt.shape)\n", + " # print(pred.shape)\n", + "\n", + " mae = np.abs(pred - gt) * mask[None,:]\n", + " mae = np.sum(mae) / (np.sum(mask) * pred.shape[0])\n", + " mse = (pred - gt)**2 * mask[None,:]\n", + " mse = np.sum(mse) / (np.sum(mask) * pred.shape[0])\n", + "\n", + " df.append({\n", + " 'dsname': dsname,\n", + " 'mae': mae,\n", + " 'mse': mse,\n", + " 'method': 'LLaMA-2 70B'\n", + " })\n", + "\n", + "df = pd.DataFrame(df)\n", + "df = df[df['dsname'].isin([ 'ETTm2', 'exchange_rate', 'electricity', 'traffic', 'weather'])]\n", + "df_192 = df" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[Errno 2] No such file or directory: '../precomputed_outputs/autoformer/autoformer_results/ETTh2_96_96_Autoformer_ETTh2_ftS_sl96_ll48_pl96_dm512_nh8_el2_dl1_df2048_fc3_ebtimeF_dtTrue_Exp_0/pred.npy'\n", + "[Errno 2] No such file or directory: '../precomputed_outputs/autoformer/autoformer_results/ETTh2_96_96_Informer_ETTh2_ftS_sl96_ll48_pl96_dm512_nh8_el2_dl1_df2048_fc3_ebtimeF_dtTrue_Exp_0/pred.npy'\n", + "[Errno 2] No such file or directory: '../precomputed_outputs/autoformer/autoformer_results/ETTh2_96_96_Reformer_ETTh2_ftS_sl96_ll48_pl96_dm512_nh8_el2_dl1_df2048_fc3_ebtimeF_dtTrue_Exp_0/pred.npy'\n", + "[Errno 2] No such file or directory: '../precomputed_outputs/autoformer/autoformer_results/ETTh2_96_96_Transformer_ETTh2_ftS_sl96_ll48_pl96_dm512_nh8_el2_dl1_df2048_fc3_ebtimeF_dtTrue_Exp_0/pred.npy'\n", + "[Errno 2] No such file or directory: '../precomputed_outputs/autoformer/autoformer_results/ETTm1_96_96_Autoformer_ETTm1_ftS_sl96_ll48_pl96_dm512_nh8_el2_dl1_df2048_fc3_ebtimeF_dtTrue_Exp_0/pred.npy'\n", + "[Errno 2] No such file or directory: '../precomputed_outputs/autoformer/autoformer_results/ETTm1_96_96_Informer_ETTm1_ftS_sl96_ll48_pl96_dm512_nh8_el2_dl1_df2048_fc3_ebtimeF_dtTrue_Exp_0/pred.npy'\n", + "[Errno 2] No such file or directory: '../precomputed_outputs/autoformer/autoformer_results/ETTm1_96_96_Reformer_ETTm1_ftS_sl96_ll48_pl96_dm512_nh8_el2_dl1_df2048_fc3_ebtimeF_dtTrue_Exp_0/pred.npy'\n", + "[Errno 2] No such file or directory: '../precomputed_outputs/autoformer/autoformer_results/ETTm1_96_96_Transformer_ETTm1_ftS_sl96_ll48_pl96_dm512_nh8_el2_dl1_df2048_fc3_ebtimeF_dtTrue_Exp_0/pred.npy'\n", + "[Errno 2] No such file or directory: '../precomputed_outputs/autoformer/autoformer_results/ETTh1_96_96_Autoformer_ETTh1_ftS_sl96_ll48_pl96_dm512_nh8_el2_dl1_df2048_fc3_ebtimeF_dtTrue_Exp_0/pred.npy'\n", + "[Errno 2] No such file or directory: '../precomputed_outputs/autoformer/autoformer_results/ETTh1_96_96_Informer_ETTh1_ftS_sl96_ll48_pl96_dm512_nh8_el2_dl1_df2048_fc3_ebtimeF_dtTrue_Exp_0/pred.npy'\n", + "[Errno 2] No such file or directory: '../precomputed_outputs/autoformer/autoformer_results/ETTh1_96_96_Reformer_ETTh1_ftS_sl96_ll48_pl96_dm512_nh8_el2_dl1_df2048_fc3_ebtimeF_dtTrue_Exp_0/pred.npy'\n", + "[Errno 2] No such file or directory: '../precomputed_outputs/autoformer/autoformer_results/ETTh1_96_96_Transformer_ETTh1_ftS_sl96_ll48_pl96_dm512_nh8_el2_dl1_df2048_fc3_ebtimeF_dtTrue_Exp_0/pred.npy'\n" + ] + } + ], + "source": [ + "results = defaultdict(dict)\n", + "for fn in glob.glob(f\"{llama_results_dir}/*/96/*.pkl\"):\n", + " parts = fn.split(\"/\")[-3].split(\"_\")\n", + " dsname, idx = \"_\".join(parts[:-1]), parts[-1]\n", + "\n", + " # if \"ETTh1\" not in dsname:\n", + " # continue\n", + "\n", + " idx = int(idx)\n", + " results[dsname][idx] = pickle.load(open(fn, \"rb\"))\n", + "\n", + "autoformer_dsname_map = {\n", + " \"exchange_rate\": \"Exchange\",\n", + " \"electricity\": \"ECL\",\n", + " \"traffic\": \"traffic\",\n", + " \"weather\": \"weather\",\n", + " \"national_illness\": \"ILI\",\n", + " \"ETTh1\": \"ETTh1\",\n", + " \"ETTh2\": \"ETTh2\",\n", + " \"ETTm1\": \"ETTm1\",\n", + " \"ETTm2\": \"ETTm2\",\n", + "}\n", + "\n", + "df = []\n", + "for dsname in results:\n", + " a_dsname = autoformer_dsname_map[dsname]\n", + "\n", + " if \"ETT\" in dsname:\n", + " dstype = dsname\n", + " else:\n", + " dstype = \"custom\"\n", + "\n", + " for method in [\"Autoformer\", \"Informer\", \"Reformer\", \"Transformer\"]:\n", + " try:\n", + " feature_type = 'M'\n", + " base_dir = f\"{autoformer_results_dir}/{a_dsname}_96_96_{method}_{dstype}_ft{feature_type}_sl96_ll48_pl96_dm512_nh8_el2_dl1_df2048_fc3_ebtimeF_dtTrue_Exp_0\"\n", + " if not os.path.exists(base_dir):\n", + " feature_type = 'S'\n", + " base_dir = f\"{autoformer_results_dir}/{a_dsname}_96_96_{method}_{dstype}_ft{feature_type}_sl96_ll48_pl96_dm512_nh8_el2_dl1_df2048_fc3_ebtimeF_dtTrue_Exp_0\"\n", + " pred = np.load(f\"{base_dir}/pred.npy\")[0]\n", + " gt = np.load(f\"{base_dir}/true.npy\")[0]\n", + " except Exception as e:\n", + " print(e)\n", + " continue\n", + "\n", + " mae = np.mean(np.abs(pred - gt))\n", + " mse = np.mean((pred - gt)**2)\n", + "\n", + " df.append({\n", + " 'dsname': dsname,\n", + " 'mae': mae,\n", + " 'mse': mse,\n", + " 'method': method\n", + " })\n", + "\n", + " for method in [\"FEDformer\"]:\n", + " try:\n", + " feature_type = 'M'\n", + " base_dir = f\"{fedformer_results_dir}/{a_dsname}_{method}_random_modes64_{dstype}_ft{feature_type}_sl96_ll48_pl96_dm512_nh8_el2_dl1_df2048_fc3_ebtimeF_dtTrue_Exp_0\"\n", + "\n", + " pred = np.load(f\"{base_dir}/pred.npy\")[0]\n", + " gt = np.load(f\"{base_dir}/true.npy\")[0]\n", + " except Exception as e:\n", + " continue\n", + "\n", + " mae = np.mean(np.abs(pred - gt))\n", + " mse = np.mean((pred - gt)**2)\n", + "\n", + " df.append({\n", + " 'dsname': dsname,\n", + " 'mae': mae,\n", + " 'mse': mse,\n", + " 'method': method\n", + " })\n", + "\n", + " data = data_dict[\"custom\"](\n", + " root_path = gt_dir,\n", + " data_path = f\"{dsname}.csv\",\n", + " flag = \"test\",\n", + " size = [96, 0, 96],\n", + " features = \"M\",\n", + " )\n", + "\n", + " gt_all = pd.read_csv(f\"{gt_dir}/{dsname}.csv\").values[:,1:]\n", + "\n", + " gt_ordered = []\n", + " pred_ordered = []\n", + " mask = []\n", + " for i in range(gt_all.shape[-1]):\n", + " gt = gt_all[-96:,i]\n", + " gt_ordered.append(gt)\n", + "\n", + " if (i+1) not in results[dsname]:\n", + " pred = gt\n", + " mask.append(0)\n", + " else:\n", + " r = results[dsname][i+1]\n", + " pred = r['median'].values\n", + " mask.append(1)\n", + "\n", + " pred_ordered.append(pred)\n", + "\n", + " # print([x.shape for x in gt_ordered])\n", + " # print(set([x.shape for x in pred_ordered]))\n", + "\n", + " gt_ordered = np.stack(gt_ordered, axis=1)\n", + " pred_ordered = np.stack(pred_ordered, axis=1)\n", + " mask = np.array(mask)\n", + "\n", + " gt = data.scaler.transform(gt_ordered)\n", + " pred = data.scaler.transform(pred_ordered)\n", + "\n", + " mae = np.abs(pred - gt) * mask[None,:]\n", + " mae = np.sum(mae) / (np.sum(mask) * pred.shape[0])\n", + " mse = (pred - gt)**2 * mask[None,:]\n", + " mse = np.sum(mse) / (np.sum(mask) * pred.shape[0])\n", + "\n", + " df.append({\n", + " 'dsname': dsname,\n", + " 'mae': mae,\n", + " 'mse': mse,\n", + " 'method': 'LLaMA-2 70B'\n", + " })\n", + "\n", + "df = pd.DataFrame(df)\n", + "df = df[df['dsname'].isin([ 'ETTm2', 'exchange_rate', 'electricity', 'traffic', 'weather'])]\n", + "df_96 = df" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(4, 4))\n", + "ax = sns.barplot(\n", + " data=df_96,\n", + " x='dsname',\n", + " order=['ETTm2', 'exchange_rate', 'electricity', 'traffic', 'weather'],\n", + " y='mae',\n", + " hue='method',\n", + " hue_order=['FEDformer', 'Autoformer', 'Informer', 'Reformer', 'Transformer', 'LLaMA-2 70B'],\n", + " ax=ax\n", + ")\n", + "for item in ax.get_xticklabels():\n", + " item.set_rotation(30)\n", + "\n", + "ax.set_xlabel(\"Dataset\", labelpad=10)\n", + "ax.set_ylabel(\"MAE\")\n", + "ax.set_title(\"By Dataset (horizon = 96)\", pad=15)\n", + "ax.set_ylim(0, 0.9)\n", + "\n", + "handles, labels = ax.get_legend_handles_labels()\n", + "ax.legend(\n", + " handles=handles, labels=labels,\n", + " loc='upper left', ncols=2 #bbox_to_anchor=(1.05, 1),\n", + ")\n", + "\n", + "plt.savefig(\"outputs/autoformer_by_dataset_96.pdf\", bbox_inches='tight')\n", + "plt.show()\n", + "\n", + "fig, ax = plt.subplots(1, 1, figsize=(3, 3))\n", + "ax = sns.barplot(\n", + " data=df_96,\n", + " x='method',\n", + " y='mae',\n", + " order=['FEDformer', 'Autoformer', 'Informer', 'Reformer', 'Transformer', 'LLaMA-2 70B'],\n", + " ax=ax\n", + ")\n", + "ax.set_xlabel(\"Method\", labelpad=10)\n", + "ax.set_ylabel(\"MAE\")\n", + "ax.set_title(\"Aggregated (horizon = 96)\", pad=15)\n", + "\n", + "for item in ax.get_xticklabels():\n", + " item.set_rotation(30)\n", + "\n", + "plt.savefig(\"outputs/autoformer_aggregated_96.pdf\", bbox_inches='tight')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(4, 4))\n", + "ax = sns.barplot(\n", + " data=df_192,\n", + " x='dsname',\n", + " order=['ETTm2', 'exchange_rate', 'electricity', 'traffic', 'weather'],\n", + " y='mae',\n", + " hue='method',\n", + " hue_order=['FEDformer','Autoformer', 'Informer', 'Reformer', 'Transformer', 'LLaMA-2 70B'],\n", + " ax=ax\n", + ")\n", + "for item in ax.get_xticklabels():\n", + " item.set_rotation(30)\n", + "\n", + "ax.set_xlabel(\"Dataset\", labelpad=10)\n", + "ax.set_ylabel(\"MAE\")\n", + "ax.set_title(\"By Dataset (horizon = 192)\", pad=15)\n", + "ax.set_ylim(0.0, 0.9)\n", + "\n", + "handles, labels = ax.get_legend_handles_labels()\n", + "ax.legend(\n", + " handles=handles, labels=labels,\n", + " loc='upper left', ncols=2 #bbox_to_anchor=(1.05, 1),\n", + ")\n", + "\n", + "plt.savefig(\"outputs/autoformer_by_dataset_192.pdf\", bbox_inches='tight')\n", + "plt.show()\n", + "\n", + "fig, ax = plt.subplots(1, 1, figsize=(3, 3))\n", + "ax = sns.barplot(\n", + " data=df_192,\n", + " x='method',\n", + " y='mae',\n", + " order=['FEDformer', 'Autoformer', 'Informer', 'Reformer', 'Transformer', 'LLaMA-2 70B'],\n", + " ax=ax\n", + ")\n", + "ax.set_xlabel(\"Method\", labelpad=10)\n", + "ax.set_ylabel(\"MAE\")\n", + "ax.set_title(\"Aggregated (horizon = 192)\", pad=15)\n", + "\n", + "for item in ax.get_xticklabels():\n", + " item.set_rotation(30)\n", + "\n", + "plt.savefig(\"outputs/autoformer_aggregated_192.pdf\", bbox_inches='tight')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_predictions(inputs, targets, medians, ax):\n", + " sns.color_palette('Set1')\n", + " train = pd.Series(inputs, index=range(len(inputs)))\n", + " test = pd.Series(targets, index=range(len(inputs), len(inputs)+len(targets)))\n", + " medians = pd.Series(medians, index=test.index)\n", + " ax.plot(pd.concat([train,test]), color='C1',label='Ground Truth', linewidth=1)\n", + " ax.plot(medians, color='C4',label='GPT-3 Median', linewidth=1.5)\n", + " # ax.legend()\n", + " # remove all ticks\n", + " ax.tick_params(axis='both', which='both', bottom=False, top=False, left=False, right=False, labelbottom=False, labelleft=False)\n", + " for spine in ax.spines.values():\n", + " spine.set_edgecolor('grey')\n", + "\n", + " \n", + "\n", + "def plot_pred_dataset(dataset, pred_dict, max_series=None, max_num_samples=None, show_median=True, title=None):\n", + " hyper = pred_dict['info']['hyper']\n", + " settings = hyper.settings\n", + " preds = pred_dict['preds']\n", + " medians = pred_dict['medians']\n", + " if max_series is None:\n", + " max_series = len(preds)\n", + " max_series = min(max_series, len(preds))\n", + " dataset = dataset[:max_series]\n", + " preds = preds[:max_series]\n", + " # separate inputs and targets\n", + " inputs = [xy[0][-hyper.max_history:] for xy in dataset]\n", + " targets = np.array([xy[1] for xy in dataset])\n", + " plot_predictions(inputs, targets, medians, title)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sns.set_palette(\"Set1\")\n", + "\n", + "D = len(datasets) # number of datasets\n", + "N = 4 # number of series per dataset\n", + "np.random.seed(99) # 2\n", + "\n", + "fig, axes = plt.subplots(D, N, figsize=(5.5*N, 4*D), dpi=100)\n", + "\n", + "for i, (ds_name, dataset) in enumerate(datasets.items()):\n", + " print(ds_name)\n", + " path = f'eval/{ds_name}.pkl'\n", + " with open(path, 'rb') as f:\n", + " pred_dict = pickle.load(f)\n", + "\n", + " hyper = pred_dict['info']['hyper']\n", + " settings = hyper.settings\n", + " medians = pred_dict['medians']\n", + " \n", + "\n", + " inputs = [xy[0][-hyper.max_history:] for xy in dataset]\n", + " targets = [xy[1] for xy in dataset]\n", + " # Select N series randomly\n", + " # sample without replacement\n", + " if N < len(medians):\n", + " indices = np.random.choice(len(medians), N, replace=False)\n", + " else:\n", + " indices = np.arange(len(medians))\n", + " inputs = [inputs[i] for i in indices]\n", + " targets = [targets[i] for i in indices]\n", + " medians = [medians[i] for i in indices]\n", + " \n", + "\n", + " for j in range(len(indices)):\n", + " plot_predictions(inputs[j], targets[j], medians[j], axes[i, j])\n", + " # show title inside plot on the leftmost column\n", + " # put the title on the left\n", + " title = ' ' + ds_name.replace('_', ' ').title()\n", + " title = title.replace('Us', 'US')\n", + " axes[i, 0].set_title(title, fontsize=20, y=0.88, loc='left')\n", + "\n", + "for ax in axes.flat:\n", + " ax.tick_params(axis='both', which='both', bottom=False, top=False, left=False, right=False, labelbottom=False, labelleft=False)\n", + "\n", + "from matplotlib.lines import Line2D\n", + "legend_elements = [Line2D([0], [0], color='C1', lw=2, label='Ground Truth'),\n", + " Line2D([0], [0], color='C4', lw=2, label='GPT-3 Median')]\n", + "fig.legend(handles=legend_elements, loc='lower center', ncol=2, fontsize=24, bbox_to_anchor=(0.5, -0.02))\n", + "plt.tight_layout()\n", + "plt.savefig('monash_examples.pdf', bbox_inches='tight', pad_inches=0.1)" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from data.autoformer_dataset import data_dict\n", + "\n", + "autoformer_dsname_map = {\n", + " \"exchange_rate\": \"Exchange\",\n", + " \"electricity\": \"ECL\",\n", + " \"traffic\": \"traffic\",\n", + " \"weather\": \"weather\",\n", + " \"ETTm2\": \"ETTm2\",\n", + "}\n", + "\n", + "sns.set(style=\"white\", font_scale=1)\n", + "\n", + "results = defaultdict(dict)\n", + "for fn in glob.glob(f\"{llama_results_dir}/*/96/*.pkl\"):\n", + " parts = fn.split(\"/\")[-3].split(\"_\")\n", + " dsname, idx = \"_\".join(parts[:-1]), parts[-1]\n", + "\n", + " if dsname not in autoformer_dsname_map:\n", + " continue \n", + "\n", + " idx = int(idx)\n", + " results[dsname][idx] = pickle.load(open(fn, \"rb\"))\n", + "\n", + "sns.set_palette(\"Set1\")\n", + "\n", + "D = len(autoformer_dsname_map) # number of datasets\n", + "N = 4 # number of series per dataset\n", + "np.random.seed(99) # 2\n", + "\n", + "fig, axes = plt.subplots(D, N, figsize=(5.5*N, 4*D), dpi=100)\n", + "\n", + "df = []\n", + "for i, dsname in enumerate(results):\n", + " if dsname not in autoformer_dsname_map:\n", + " continue\n", + " \n", + " a_dsname = autoformer_dsname_map[dsname]\n", + "\n", + " if \"ETT\" in dsname:\n", + " dstype = dsname\n", + " else:\n", + " dstype = \"custom\"\n", + "\n", + " for method in [\"Autoformer\"]:\n", + " try:\n", + " feature_type = 'M'\n", + " base_dir = f\"{autoformer_results_dir}/{a_dsname}_96_96_{method}_{dstype}_ft{feature_type}_sl96_ll48_pl96_dm512_nh8_el2_dl1_df2048_fc3_ebtimeF_dtTrue_Exp_0\"\n", + " if not os.path.exists(base_dir):\n", + " feature_type = 'S'\n", + " base_dir = f\"{autoformer_results_dir}/{a_dsname}_96_96_{method}_{dstype}_ft{feature_type}_sl96_ll48_pl96_dm512_nh8_el2_dl1_df2048_fc3_ebtimeF_dtTrue_Exp_0\"\n", + " pred = np.load(f\"{base_dir}/pred.npy\")[0]\n", + " gt = np.load(f\"{base_dir}/true.npy\")[0]\n", + " except Exception as e:\n", + " print(e)\n", + " continue\n", + "\n", + " data = data_dict[\"custom\"](\n", + " root_path = gt_dir,\n", + " data_path = f\"{dsname}.csv\",\n", + " flag = \"test\",\n", + " size = [96, 0, 96],\n", + " features = \"M\",\n", + " )\n", + "\n", + " gt_all = pd.read_csv(f\"{gt_dir}/{dsname}.csv\").values[:,1:]\n", + "\n", + " hist_ordered = []\n", + " gt_ordered = []\n", + " pred_ordered = []\n", + " mask = []\n", + " for k in range(gt_all.shape[-1]):\n", + " hist = gt_all[-(192+96):-96,k]\n", + " gt = gt_all[-96:,k]\n", + "\n", + " hist_ordered.append(hist)\n", + " gt_ordered.append(gt)\n", + "\n", + " if (k+1) not in results[dsname]:\n", + " pred = gt\n", + " mask.append(0)\n", + " else:\n", + " r = results[dsname][k+1]\n", + " pred = r['median'].values\n", + " mask.append(1)\n", + "\n", + " pred_ordered.append(pred)\n", + "\n", + " hist_ordered = np.stack(hist_ordered, axis=1)\n", + " gt_ordered = np.stack(gt_ordered, axis=1)\n", + " pred_ordered = np.stack(pred_ordered, axis=1)\n", + " mask = np.array(mask)\n", + "\n", + " hist = data.scaler.transform(hist_ordered)\n", + " gt = data.scaler.transform(gt_ordered)\n", + " pred = data.scaler.transform(pred_ordered)\n", + " \n", + " #pick 4 random series\n", + " indices = np.arange(gt.shape[-1])[mask.astype(bool)]\n", + " indices = np.random.choice(indices, N, replace=False)\n", + " hist = hist[:,indices]\n", + " gt = gt[:,indices]\n", + " pred = pred[:,indices]\n", + "\n", + " for j in range(N):\n", + " plot_predictions(hist[:,j], gt[:,j], pred[:,j], axes[i, j])\n", + "\n", + " title = ' ' + dsname.replace('_', ' ').title()\n", + " title = title.replace('Us', 'US')\n", + " axes[i, 0].set_title(title, fontsize=20, y=0.88, loc='left')\n", + "\n", + "\n", + "for ax in axes.flat:\n", + " ax.tick_params(axis='both', which='both', bottom=False, top=False, left=False, right=False, labelbottom=False, labelleft=False)\n", + "\n", + "from matplotlib.lines import Line2D\n", + "legend_elements = [Line2D([0], [0], color='C1', lw=2, label='Ground Truth'),\n", + " Line2D([0], [0], color='C4', lw=2, label='GPT-3 Median')]\n", + "fig.legend(handles=legend_elements, loc='lower center', ncol=2, fontsize=24, bbox_to_anchor=(0.5, -0.03))\n", + "plt.tight_layout()\n", + "\n", + "plt.savefig('outputs/informer_examples.pdf', bbox_inches='tight', pad_inches=0.1)\n", + " " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/figures/outputs/alignment_effects.pdf b/figures/outputs/alignment_effects.pdf new file mode 100644 index 0000000000000000000000000000000000000000..9244602584634e7d6d42f4d082ca3413de804a5c GIT binary patch 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