From 58bbce9714b03eae71ba726de05503e6cf31883a Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus Date: Wed, 22 Dec 2021 19:07:23 +0100 Subject: [PATCH 01/72] adds loaders --- .../loaders/__init__.py | 1 + .../loaders/csv_data_loader.py | 26 ++++++ tests/conftest.py | 81 +++++++++++++++++++ tests/test_loaders.py | 10 +++ 4 files changed, 118 insertions(+) create mode 100644 src/tensorflow_time_series_dataset/loaders/__init__.py create mode 100644 src/tensorflow_time_series_dataset/loaders/csv_data_loader.py create mode 100644 tests/conftest.py create mode 100644 tests/test_loaders.py diff --git a/src/tensorflow_time_series_dataset/loaders/__init__.py b/src/tensorflow_time_series_dataset/loaders/__init__.py new file mode 100644 index 0000000..35fedc3 --- /dev/null +++ b/src/tensorflow_time_series_dataset/loaders/__init__.py @@ -0,0 +1 @@ +from .csv_data_loader import CSVDataLoader diff --git a/src/tensorflow_time_series_dataset/loaders/csv_data_loader.py b/src/tensorflow_time_series_dataset/loaders/csv_data_loader.py new file mode 100644 index 0000000..f1ad29f --- /dev/null +++ b/src/tensorflow_time_series_dataset/loaders/csv_data_loader.py @@ -0,0 +1,26 @@ +import pandas as pd + + +def _read_csv_file(file_path, date_time_col="date_time", **kwds): + file_path = file_path + load_data = pd.read_csv( + file_path, + parse_dates=[date_time_col], + infer_datetime_format=True, + index_col=[date_time_col], + **kwds + ) + + if load_data.isnull().any().sum() != 0: + raise ValueError("Data contains NaNs") + + return load_data + + +class CSVDataLoader: + def __init__(self, file_path, **kwds): + self.file_path = file_path + self.kwds = kwds + + def __call__(self): + return _read_csv_file(self.file_path, **self.kwds) diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..15c2348 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,81 @@ +import pytest +import pandas as pd +import numpy as np + +# ref.: https://www.kaggle.com/avanwyk/encoding-cyclical-features-for-deep-learning +def encode(data, cycl_name, cycl_max, cycl_min=0, cycl=None): + if cycl is None: + cycl = getattr(data.index, cycl_name) + data[cycl_name + "_sin"] = np.float32( + np.sin(2 * np.pi * (cycl - cycl_min) / (cycl_max - cycl_min + 1)) + ) + data[cycl_name + "_cos"] = np.float32( + np.cos(2 * np.pi * (cycl - cycl_min) / (cycl_max - cycl_min + 1)) + ) + return data + + +def get_value_generator(date_range, columns): + drf = date_range.to_frame() + drf_weekday = encode(drf, "weekday", 6, cycl=drf.index.weekday) + drf_dayofyear = encode(drf, "dayofyear", 366, 1) + drf_time = encode( + drf, "time", 24 * 60 - 1, cycl=drf.index.hour * 60 + drf.index.minute + ) + + def gen_value(id, column, line): + if column == "weekday": + return date_range[line].weekday() + elif "weekday" in column: + return drf_weekday.loc[date_range[line], column] + elif "dayofyear" in column: + return drf_dayofyear.loc[date_range[line], column] + elif "time" in column: + return drf_time.loc[date_range[line], column] + else: + col_num = columns.index(column) + return int(f"{id:02d}{col_num:02d}{line:04d}") + + return gen_value + + +def gen_df(ids, columns, date_range): + gen_value = get_value_generator(date_range, columns) + periods = date_range.size + dfs = [] + for i in ids: + df = pd.DataFrame( + { + "date_time": date_range, + "id": i, + **{ + c: [gen_value(int(i), c, l) for l in range(periods)] + for n, c in enumerate(columns) + if c != "weekday" + }, + } + ) + dfs.append(df) + df = pd.concat(dfs) + df["weekday"] = df.date_time.dt.weekday + + return df + + +@pytest.fixture(scope="function", params=[48 * 30, 48 * 30 * 6]) +def time_series_df(request): + df = gen_df( + ids=list(range(10)), + columns=["load", "weekday", "is_holiday"], + date_range=pd.date_range("1/1/1", periods=request.param, freq="30T"), + ) + return df + + +@pytest.fixture(scope="function") +def tmpdf(tmpdir_factory, time_series_df): + file_path = tmpdir_factory.mktemp("csv_data") / "test.csv" + + time_series_df.to_csv(file_path, index=False) + + return file_path, time_series_df diff --git a/tests/test_loaders.py b/tests/test_loaders.py new file mode 100644 index 0000000..868076d --- /dev/null +++ b/tests/test_loaders.py @@ -0,0 +1,10 @@ +import pytest + +from tensorflow_time_series_dataset.loaders import CSVDataLoader + + +def test_csv_loader(tmpdf): + test_data_path, test_df = tmpdf + csv_loader = CSVDataLoader(test_data_path) + loaded_df = csv_loader() + assert loaded_df.equals(test_df.set_index("date_time")), "Error: CSVLoader failed" From 216a7f554eb15121f48c804e9e5ae63773ff7053 Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus Date: Wed, 22 Dec 2021 19:07:45 +0100 Subject: [PATCH 02/72] adds preprocessors --- .../preprocessors/__init__.py | 4 ++ .../preprocessors/cyclical_feature_encoder.py | 33 +++++++++ .../groupby_dataset_generator.py | 33 +++++++++ .../preprocessors/time_series_k_fold.py | 46 ++++++++++++ .../preprocessors/time_series_split.py | 52 ++++++++++++++ tests/test_preprocessors.py | 70 +++++++++++++++++++ 6 files changed, 238 insertions(+) create mode 100644 src/tensorflow_time_series_dataset/preprocessors/__init__.py create mode 100644 src/tensorflow_time_series_dataset/preprocessors/cyclical_feature_encoder.py create mode 100644 src/tensorflow_time_series_dataset/preprocessors/groupby_dataset_generator.py create mode 100644 src/tensorflow_time_series_dataset/preprocessors/time_series_k_fold.py create mode 100644 src/tensorflow_time_series_dataset/preprocessors/time_series_split.py create mode 100644 tests/test_preprocessors.py diff --git a/src/tensorflow_time_series_dataset/preprocessors/__init__.py b/src/tensorflow_time_series_dataset/preprocessors/__init__.py new file mode 100644 index 0000000..1ebfcc6 --- /dev/null +++ b/src/tensorflow_time_series_dataset/preprocessors/__init__.py @@ -0,0 +1,4 @@ +from .time_series_k_fold import TimeSeriesKFold +from .time_series_split import TimeSeriesSplit +from .cyclical_feature_encoder import CyclicalFeatureEncoder +from .groupby_dataset_generator import GroupbyDatasetGenerator diff --git a/src/tensorflow_time_series_dataset/preprocessors/cyclical_feature_encoder.py b/src/tensorflow_time_series_dataset/preprocessors/cyclical_feature_encoder.py new file mode 100644 index 0000000..0e8eaa4 --- /dev/null +++ b/src/tensorflow_time_series_dataset/preprocessors/cyclical_feature_encoder.py @@ -0,0 +1,33 @@ +import tensorflow as tf +import numpy as np + + +class CyclicalFeatureEncoder: + def __init__( + self, cycl_max, cycl_min=0, name="CyclicalFeatureEncoder", dtype=tf.float32 + ): + self.cycl_max = cycl_max + self.cycl_min = cycl_min + self.name = name + self.dtype = dtype + + def encode(self, cycl): + with tf.name_scope(self.name + "::decode"): + cycl = tf.convert_to_tensor(cycl, name="cycl", dtype=self.dtype) + sin = tf.sin( + 2 * np.pi * (cycl - self.cycl_min) / (self.cycl_max - self.cycl_min + 1) + ) + cos = tf.cos( + 2 * np.pi * (cycl - self.cycl_min) / (self.cycl_max - self.cycl_min + 1) + ) + assert np.allclose( + cycl, self.decode(sin, cos) + ), f'Decoding failed. is "cycl_min/max" ({self.cycl_min}/{self.cycl_max}) correct?' + return tf.stack([sin, cos], axis=0) + + def decode(self, sin, cos): + with tf.name_scope(self.name + "::encode"): + angle = (tf.math.atan2(sin, cos) + 2 * np.pi) % (2 * np.pi) + return (angle * (self.cycl_max - self.cycl_min + 1)) / ( + 2 * np.pi + ) + self.cycl_min diff --git a/src/tensorflow_time_series_dataset/preprocessors/groupby_dataset_generator.py b/src/tensorflow_time_series_dataset/preprocessors/groupby_dataset_generator.py new file mode 100644 index 0000000..7d2cd90 --- /dev/null +++ b/src/tensorflow_time_series_dataset/preprocessors/groupby_dataset_generator.py @@ -0,0 +1,33 @@ +import tensorflow as tf + + +class GroupbyDatasetGenerator: + def __init__(self, groupby, columns, dtype=tf.float32, test_mode=False): + self.groupby = groupby + self.columns = sorted(list(set(columns))) + self.dtype = dtype + self.test_mode = test_mode + + def get_generator(self, df): + df.sort_index(inplace=True) + if self.test_mode: + ids = df[self.groupby].unique() + ids = ids[:2] + df = df[df[self.groupby].isin(ids)] + + grpd = df.groupby(self.groupby) + + def generator(): + for _, d in grpd: + yield tf.cast(d[self.columns].values, self.dtype) + + return generator + + def __call__(self, df): + ds = tf.data.Dataset.from_generator( + self.get_generator(df), + output_signature=( + tf.TensorSpec(shape=[None, len(self.columns)], dtype=self.dtype) + ), + ) + return ds diff --git a/src/tensorflow_time_series_dataset/preprocessors/time_series_k_fold.py b/src/tensorflow_time_series_dataset/preprocessors/time_series_k_fold.py new file mode 100644 index 0000000..9107cf0 --- /dev/null +++ b/src/tensorflow_time_series_dataset/preprocessors/time_series_k_fold.py @@ -0,0 +1,46 @@ +#!/usr/bin/env python3 +# -*- time-stamp-pattern: "changed[\s]+:[\s]+%%$"; -*- +# AUTHOR INFORMATION ########################################################## +# file : time_series_k_fold.py +# author : Marcel Arpogaus +# +# created : 2021-07-29 17:57:39 (Marcel Arpogaus) +# changed : 2021-07-29 18:02:20 (Marcel Arpogaus) +# DESCRIPTION ################################################################# +# Probabilistic Short-Term Low-Voltage Load Forecasting using +# Bernstein-Polynomial Normalizing Flows +# LICENSE ##################################################################### +# Copyright (C) 2021 Marcel Arpogaus +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with this program. If not, see . +############################################################################### + + +# REQUIRED PYTHON MODULES ##################################################### +import numpy as np +import pandas as pd + + +class TimeSeriesKFold(): + def __init__(self, fold, n_folds=20): + self.n_folds = n_folds + self.fold = fold + + def __call__(self, data): + days = pd.date_range(data.index.min(), data.index.max(), freq='D') + fold_idx = np.array_split(days.to_numpy(), self.n_folds) + folds = {f: (idx[[0, -1]].astype('datetime64[m]') + [0, 60 * 24 - 1] + ).astype(str).tolist() for f, idx in enumerate(fold_idx)} + + return data[folds[self.fold][0]:folds[self.fold][1]] diff --git a/src/tensorflow_time_series_dataset/preprocessors/time_series_split.py b/src/tensorflow_time_series_dataset/preprocessors/time_series_split.py new file mode 100644 index 0000000..fae2193 --- /dev/null +++ b/src/tensorflow_time_series_dataset/preprocessors/time_series_split.py @@ -0,0 +1,52 @@ +#!/usr/bin/env python3 +# -*- time-stamp-pattern: "changed[\s]+:[\s]+%%$"; -*- +# AUTHOR INFORMATION ########################################################## +# file : time_series_split.py +# author : Marcel Arpogaus +# +# created : 2021-07-29 17:57:39 (Marcel Arpogaus) +# changed : 2021-07-29 18:02:20 (Marcel Arpogaus) +# DESCRIPTION ################################################################# +# Probabilistic Short-Term Low-Voltage Load Forecasting using +# Bernstein-Polynomial Normalizing Flows +# LICENSE ##################################################################### +# Copyright (C) 2021 Marcel Arpogaus +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with this program. If not, see . +############################################################################### + + +# REQUIRED PYTHON MODULES ##################################################### +import numpy as np +import pandas as pd + + +class TimeSeriesSplit(): + RIGHT = 0 + LEFT = 1 + + def __init__(self, split_size, split): + self.split_size = split_size + self.split = split + + def __call__(self, data): + days = pd.date_range(data.index.min(), data.index.max(), freq='D') + days = days.to_numpy().astype('datetime64[m]') + right = days[int(len(days) * self.split_size)] + left = right - 1 + if self.split == self.LEFT: + return data.loc[:str(left)] + else: + return data.loc[str(right):] + diff --git a/tests/test_preprocessors.py b/tests/test_preprocessors.py new file mode 100644 index 0000000..6d3f06a --- /dev/null +++ b/tests/test_preprocessors.py @@ -0,0 +1,70 @@ +import pandas as pd +import numpy as np + +from tensorflow_time_series_dataset.preprocessors import ( + CyclicalFeatureEncoder, + TimeSeriesSplit, + GroupbyDatasetGenerator, +) + +periods = 48 * 10 +test_df = pd.DataFrame( + index=pd.date_range(start="1/1/1992", periods=periods, freq="30T"), + data=np.stack( + [np.random.randint(0, 10, periods), np.random.randint(20, 100, periods)], + axis=1, + ), + columns=["x1", "x2"], +) + + +def test_cyclical_data_encoder(): + encs = { + "x1": [9], + "x2": [99, 20], + "weekday": [6], + "dayofyear": [366, 1], + "month": [12, 1], + "time": [24 * 60 - 1], + } + for name in encs.keys(): + if name == "time": + cycl = test_df.index.hour * 60 + test_df.index.minute + else: + try: + cycl = getattr(test_df, name) + except AttributeError: + cycl = getattr(test_df.index, name) + enc = CyclicalFeatureEncoder(*encs[name]) + enc_dat = enc.encode(cycl) + + +def test_time_series_split(): + df = test_df.assign(id=1) + df = df.combine_first(df.assign(id=2)) + l_splitter = TimeSeriesSplit(0.5, TimeSeriesSplit.LEFT) + r_splitter = TimeSeriesSplit(0.5, TimeSeriesSplit.RIGHT) + l_split = l_splitter(df) + r_split = r_splitter(df) + assert not l_split.index.isin(r_split.index).any(), "Splits overlap" + assert l_split.index.max() < r_split.index.min(), "wrong split" + assert np.unique(l_split.index.date).size == np.floor( + np.unique(df.index.date).size / 2 + ) and np.unique(r_split.index.date).size == np.ceil( + np.unique(df.index.date).size / 2 + ), "Unexpected split size" + assert (r_split.groupby([r_split.index.date, "id"]).x1.count() == 48).all() and ( + l_split.groupby([l_split.index.date, "id"]).x1.count() == 48 + ).all(), "Incomplete Days in Split" + + +def test_groupby_dataset_generator(time_series_df): + time_series_df.set_index("date_time", inplace=True) + columns = list(sorted(time_series_df.columns)) + idx_from_column = {c: i for i, c in enumerate(columns)} + gen = GroupbyDatasetGenerator("id", columns=columns) + for d in gen(time_series_df): + id = int(d[0, idx_from_column["load"]] // 1e6) + assert np.allclose( + time_series_df[time_series_df.id == id][columns].values, d.numpy() + ), "Error: DatasetGenerator failed" From 942a9bb00096006a69f5a3aa8bf3e9486f1a58be Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus Date: Thu, 23 Dec 2021 15:58:40 +0100 Subject: [PATCH 03/72] reimplememts cyclical feature encoder --- .../preprocessors/cyclical_feature_encoder.py | 56 ++++++---- tests/conftest.py | 63 +++++++---- tests/test_loaders.py | 6 +- tests/test_preprocessors.py | 105 ++++++++++++------ 4 files changed, 146 insertions(+), 84 deletions(-) diff --git a/src/tensorflow_time_series_dataset/preprocessors/cyclical_feature_encoder.py b/src/tensorflow_time_series_dataset/preprocessors/cyclical_feature_encoder.py index 0e8eaa4..3f8c628 100644 --- a/src/tensorflow_time_series_dataset/preprocessors/cyclical_feature_encoder.py +++ b/src/tensorflow_time_series_dataset/preprocessors/cyclical_feature_encoder.py @@ -1,33 +1,45 @@ -import tensorflow as tf import numpy as np +def default_cycl_getter(df, k): + try: + cycl = getattr(df, k) + except AttributeError: + cycl = getattr(df.index, k) + return cycl + + class CyclicalFeatureEncoder: def __init__( - self, cycl_max, cycl_min=0, name="CyclicalFeatureEncoder", dtype=tf.float32 + self, cycl_name, cycl_max, cycl_min=0, cycl_getter=default_cycl_getter ): self.cycl_max = cycl_max self.cycl_min = cycl_min - self.name = name - self.dtype = dtype + self.cycl_getter = cycl_getter + self.cycl_name = cycl_name + + def encode(self, data): + cycl = self.cycl_getter(data, self.cycl_name) + sin = np.sin( + 2 * np.pi * (cycl - self.cycl_min) / (self.cycl_max - self.cycl_min + 1) + ) + cos = np.cos( + 2 * np.pi * (cycl - self.cycl_min) / (self.cycl_max - self.cycl_min + 1) + ) + assert np.allclose( + cycl, self.decode(sin, cos) + ), f'Decoding failed. Is "cycl_min/max" ({self.cycl_min}/{self.cycl_max}) correct?' + return sin, cos - def encode(self, cycl): - with tf.name_scope(self.name + "::decode"): - cycl = tf.convert_to_tensor(cycl, name="cycl", dtype=self.dtype) - sin = tf.sin( - 2 * np.pi * (cycl - self.cycl_min) / (self.cycl_max - self.cycl_min + 1) - ) - cos = tf.cos( - 2 * np.pi * (cycl - self.cycl_min) / (self.cycl_max - self.cycl_min + 1) - ) - assert np.allclose( - cycl, self.decode(sin, cos) - ), f'Decoding failed. is "cycl_min/max" ({self.cycl_min}/{self.cycl_max}) correct?' - return tf.stack([sin, cos], axis=0) + def __call__(self, data): + data = data.copy() + sin, cos = self.encode(data) + data[self.cycl_name + "_sin"] = sin + data[self.cycl_name + "_cos"] = cos + return data def decode(self, sin, cos): - with tf.name_scope(self.name + "::encode"): - angle = (tf.math.atan2(sin, cos) + 2 * np.pi) % (2 * np.pi) - return (angle * (self.cycl_max - self.cycl_min + 1)) / ( - 2 * np.pi - ) + self.cycl_min + angle = (np.arctan2(sin, cos) + 2 * np.pi) % (2 * np.pi) + return (angle * (self.cycl_max - self.cycl_min + 1)) / ( + 2 * np.pi + ) + self.cycl_min diff --git a/tests/conftest.py b/tests/conftest.py index 15c2348..f5612a2 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -23,7 +23,7 @@ def get_value_generator(date_range, columns): drf, "time", 24 * 60 - 1, cycl=drf.index.hour * 60 + drf.index.minute ) - def gen_value(id, column, line): + def gen_value(column, line): if column == "weekday": return date_range[line].weekday() elif "weekday" in column: @@ -34,48 +34,67 @@ def gen_value(id, column, line): return drf_time.loc[date_range[line], column] else: col_num = columns.index(column) - return int(f"{id:02d}{col_num:02d}{line:04d}") + return int(f"{col_num:02d}{line:04d}") return gen_value -def gen_df(ids, columns, date_range): +def gen_df(columns, date_range): gen_value = get_value_generator(date_range, columns) periods = date_range.size + df = pd.DataFrame( + { + "date_time": date_range, + **{c: [gen_value(c, l) for l in range(periods)] for c in columns}, + } + ) + return df + + +def gen_df_with_id(ids, columns, date_range): dfs = [] for i in ids: - df = pd.DataFrame( - { - "date_time": date_range, - "id": i, - **{ - c: [gen_value(int(i), c, l) for l in range(periods)] - for n, c in enumerate(columns) - if c != "weekday" - }, - } - ) + df = gen_df(columns, date_range) + df["id"] = i + df[columns] += i * 1e6 dfs.append(df) df = pd.concat(dfs) - df["weekday"] = df.date_time.dt.weekday return df -@pytest.fixture(scope="function", params=[48 * 30, 48 * 30 * 6]) +@pytest.fixture( + scope="function", params=[(["x1", "x2"], 48 * 30), (["x1", "x2"], 48 * 30 * 6)] +) def time_series_df(request): df = gen_df( - ids=list(range(10)), - columns=["load", "weekday", "is_holiday"], - date_range=pd.date_range("1/1/1", periods=request.param, freq="30T"), + columns=request.param[0], + date_range=pd.date_range("1/1/1", periods=request.param[1], freq="30T"), + ) + return df + + +@pytest.fixture( + scope="function", + params=[ + (list(range(10)), ["x1", "x2"], 48 * 30), + (list(range(10)), ["x1", "x2"], 48 * 30 * 6), + ], +) +def time_series_df_with_id(request): + ids, columns, periods = request.param + df = gen_df_with_id( + ids=ids, + columns=columns, + date_range=pd.date_range("1/1/1", periods=periods, freq="30T"), ) return df @pytest.fixture(scope="function") -def tmpdf(tmpdir_factory, time_series_df): +def tmp_csv_with_id(tmpdir_factory, time_series_df_with_id): file_path = tmpdir_factory.mktemp("csv_data") / "test.csv" - time_series_df.to_csv(file_path, index=False) + time_series_df_with_id.to_csv(file_path, index=False) - return file_path, time_series_df + return file_path, time_series_df_with_id diff --git a/tests/test_loaders.py b/tests/test_loaders.py index 868076d..5e7ec7e 100644 --- a/tests/test_loaders.py +++ b/tests/test_loaders.py @@ -1,10 +1,8 @@ -import pytest - from tensorflow_time_series_dataset.loaders import CSVDataLoader -def test_csv_loader(tmpdf): - test_data_path, test_df = tmpdf +def test_csv_loader(tmp_csv_with_id): + test_data_path, test_df = tmp_csv_with_id csv_loader = CSVDataLoader(test_data_path) loaded_df = csv_loader() assert loaded_df.equals(test_df.set_index("date_time")), "Error: CSVLoader failed" diff --git a/tests/test_preprocessors.py b/tests/test_preprocessors.py index 6d3f06a..335e0e3 100644 --- a/tests/test_preprocessors.py +++ b/tests/test_preprocessors.py @@ -1,3 +1,4 @@ +import pytest import pandas as pd import numpy as np @@ -7,41 +8,64 @@ GroupbyDatasetGenerator, ) -periods = 48 * 10 -test_df = pd.DataFrame( - index=pd.date_range(start="1/1/1992", periods=periods, freq="30T"), - data=np.stack( - [np.random.randint(0, 10, periods), np.random.randint(20, 100, periods)], - axis=1, - ), - columns=["x1", "x2"], -) + +@pytest.fixture +def cycl_df(): + date_range = pd.date_range(start="1/1/1992", end="31/12/1992", freq="30T") + test_df = pd.DataFrame( + index=date_range, + data=np.stack( + [ + np.random.randint(0, 10, date_range.size), + np.random.randint(20, 100, date_range.size), + ], + axis=1, + ), + columns=["x1", "x2"], + ) + return test_df -def test_cyclical_data_encoder(): +def test_cyclical_data_encoder(cycl_df): encs = { - "x1": [9], - "x2": [99, 20], - "weekday": [6], - "dayofyear": [366, 1], - "month": [12, 1], - "time": [24 * 60 - 1], + "x1": dict(cycl_max=9), + "x2": dict(cycl_max=99, cycl_min=20), + "weekday": dict(cycl_max=6), + "dayofyear": dict(cycl_max=366, cycl_min=1), + "month": dict(cycl_max=12, cycl_min=1), + "time": dict( + cycl_max=24 * 60 - 1, + cycl_getter=lambda df, k: df.index.hour * 60 + df.index.minute, + ), } - for name in encs.keys(): - if name == "time": - cycl = test_df.index.hour * 60 + test_df.index.minute - else: - try: - cycl = getattr(test_df, name) - except AttributeError: - cycl = getattr(test_df.index, name) - enc = CyclicalFeatureEncoder(*encs[name]) - enc_dat = enc.encode(cycl) + for name, kwds in encs.items(): + enc = CyclicalFeatureEncoder(name, **kwds) + enc_dat = enc(cycl_df) + cycl = enc.cycl_getter(cycl_df, name) + assert np.isclose( + enc.decode(enc_dat[name + "_sin"], enc_dat[name + "_cos"]), cycl + ).all(), "Decoding failed" + +def test_cyclical_data_encoder_except(cycl_df): + encs = { + "x1": dict(cycl_max=cycl_df.x1.max() - 1), + "weekday": dict(cycl_max=cycl_df.index.weekday.max() - 1), + "dayofyear": dict(cycl_max=cycl_df.index.dayofyear.max() - 1), + "month": dict(cycl_max=cycl_df.index.month.max() - 1), + "time": dict( + cycl_max=(cycl_df.index.hour * 60 + cycl_df.index.minute).max() - 1, + cycl_getter=lambda df, k: df.index.hour * 60 + df.index.minute, + ), + } + for name, kwds in encs.items(): + enc = CyclicalFeatureEncoder(name, **kwds) + with pytest.raises(AssertionError): + enc(cycl_df) -def test_time_series_split(): - df = test_df.assign(id=1) - df = df.combine_first(df.assign(id=2)) + +def test_time_series_split(time_series_df_with_id): + df = time_series_df_with_id.set_index("date_time") l_splitter = TimeSeriesSplit(0.5, TimeSeriesSplit.LEFT) r_splitter = TimeSeriesSplit(0.5, TimeSeriesSplit.RIGHT) l_split = l_splitter(df) @@ -58,13 +82,22 @@ def test_time_series_split(): ).all(), "Incomplete Days in Split" -def test_groupby_dataset_generator(time_series_df): - time_series_df.set_index("date_time", inplace=True) - columns = list(sorted(time_series_df.columns)) +def test_groupby_dataset_generator(time_series_df_with_id): + time_series_df_with_id.set_index("date_time", inplace=True) + + columns = list(sorted(time_series_df_with_id.columns)) + records_per_id = time_series_df_with_id.groupby("id").x1.size().max() + expected_shape = (records_per_id, len(columns)) + idx_from_column = {c: i for i, c in enumerate(columns)} + gen = GroupbyDatasetGenerator("id", columns=columns) - for d in gen(time_series_df): - id = int(d[0, idx_from_column["load"]] // 1e6) - assert np.allclose( - time_series_df[time_series_df.id == id][columns].values, d.numpy() + for d in gen(time_series_df_with_id): + assert d.shape == expected_shape, "Wrong shape" + id = int(d[0, idx_from_column["x1"]] // 1e6) + expected_values = time_series_df_with_id[time_series_df_with_id.id == id][ + columns + ].values + assert np.all( + d.numpy() == expected_values, ), "Error: DatasetGenerator failed" From 504380413a65b9ec968e50f8ba2315085b861cd9 Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus Date: Thu, 23 Dec 2021 15:59:45 +0100 Subject: [PATCH 04/72] removes unneeded cast --- .../preprocessors/groupby_dataset_generator.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/tensorflow_time_series_dataset/preprocessors/groupby_dataset_generator.py b/src/tensorflow_time_series_dataset/preprocessors/groupby_dataset_generator.py index 7d2cd90..dbe7e87 100644 --- a/src/tensorflow_time_series_dataset/preprocessors/groupby_dataset_generator.py +++ b/src/tensorflow_time_series_dataset/preprocessors/groupby_dataset_generator.py @@ -19,7 +19,7 @@ def get_generator(self, df): def generator(): for _, d in grpd: - yield tf.cast(d[self.columns].values, self.dtype) + yield d[self.columns].values return generator From 498f4c47d24983706e3cd88a26060d21cf9797c3 Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus Date: Thu, 23 Dec 2021 16:00:43 +0100 Subject: [PATCH 05/72] adds missing __init__.py --- src/tensorflow_time_series_dataset/__init__.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 src/tensorflow_time_series_dataset/__init__.py diff --git a/src/tensorflow_time_series_dataset/__init__.py b/src/tensorflow_time_series_dataset/__init__.py new file mode 100644 index 0000000..e69de29 From 0d1386f578a750a76421dc4c51e5dc745af356f3 Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus Date: Thu, 23 Dec 2021 16:01:01 +0100 Subject: [PATCH 06/72] adds patch generator --- .../pipeline/__init__.py | 29 +++++++ .../pipeline/patch_generator.py | 23 ++++++ tests/test_pipleine.py | 81 +++++++++++++++++++ 3 files changed, 133 insertions(+) create mode 100644 src/tensorflow_time_series_dataset/pipeline/__init__.py create mode 100644 src/tensorflow_time_series_dataset/pipeline/patch_generator.py create mode 100644 tests/test_pipleine.py diff --git a/src/tensorflow_time_series_dataset/pipeline/__init__.py b/src/tensorflow_time_series_dataset/pipeline/__init__.py new file mode 100644 index 0000000..eb012c8 --- /dev/null +++ b/src/tensorflow_time_series_dataset/pipeline/__init__.py @@ -0,0 +1,29 @@ +#!/usr/bin/env python3 +# -*- time-stamp-pattern: "changed[\s]+:[\s]+%%$"; -*- +# AUTHOR INFORMATION ########################################################## +# file : __init__.py +# author : Marcel Arpogaus +# +# created : 2021-07-29 17:57:39 (Marcel Arpogaus) +# changed : 2021-07-29 18:02:20 (Marcel Arpogaus) +# DESCRIPTION ################################################################# +# Probabilistic Short-Term Low-Voltage Load Forecasting using +# Bernstein-Polynomial Normalizing Flows +# LICENSE ##################################################################### +# Copyright (C) 2021 Marcel Arpogaus +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with this program. If not, see . +############################################################################### + +from .windowed_time_series_pipeline import WindowedTimeSeriesPipeline \ No newline at end of file diff --git a/src/tensorflow_time_series_dataset/pipeline/patch_generator.py b/src/tensorflow_time_series_dataset/pipeline/patch_generator.py new file mode 100644 index 0000000..14a7b6f --- /dev/null +++ b/src/tensorflow_time_series_dataset/pipeline/patch_generator.py @@ -0,0 +1,23 @@ +import tensorflow as tf + + +class PatchGenerator: + def __init__(self, window_size, shift): + self.window_size = window_size + self.shift = shift + + def __call__(self, load_data): + def sub_to_patch(sub): + return sub.batch(self.window_size, drop_remainder=True) + + # Why is this needed? + data_set = tf.data.Dataset.from_tensor_slices(load_data) + + data_set = data_set.window( + size=self.window_size, + shift=self.shift, + drop_remainder=True, + ) + data_set = data_set.flat_map(sub_to_patch) + + return data_set diff --git a/tests/test_pipleine.py b/tests/test_pipleine.py new file mode 100644 index 0000000..09061d3 --- /dev/null +++ b/tests/test_pipleine.py @@ -0,0 +1,81 @@ +import pytest +import tensorflow as tf +import numpy as np + +from tensorflow_time_series_dataset.pipeline.patch_generator import PatchGenerator +from tensorflow_time_series_dataset.preprocessors.groupby_dataset_generator import ( + GroupbyDatasetGenerator, +) + + +def get_value(l, c): + return int(f"{c:02d}{l:04d}") + + +def sorted_ds(): + patch = [ + [gen_value(int(id), c, l) for c in patch_columns] + for l in range(line, line + patch_size) + ] + return np.float32(patch) + + +@pytest.mark.parametrize("window_size,shift", [(2 * 48, 48), (48 + 1, 1)]) +def test_patch_generator(time_series_df, window_size, shift): + df = time_series_df.set_index("date_time") + + initial_size = window_size - shift + data_size = df.index.size - initial_size + patches = data_size // shift + + expected_shape = (window_size, len(df.columns)) + + ds = tf.data.Dataset.from_tensors(df) + ds_patched = ds.interleave( + PatchGenerator(window_size=window_size, shift=shift), + num_parallel_calls=tf.data.experimental.AUTOTUNE, + ) + for i, patch in enumerate(ds_patched.as_numpy_iterator()): + assert patch.shape == expected_shape, "Wrong shape" + x1 = patch[0, 0] + idx = int(x1 % 1e4) + expected_values = df.iloc[idx : idx + window_size] + assert np.all(patch == expected_values), "Patch contains wrong data" + assert i + 1 == patches, "Not enough patches" + + +@pytest.fixture +def groupby_dataset(time_series_df_with_id): + time_series_df_with_id.set_index("date_time", inplace=True) + columns = list(sorted([c for c in time_series_df_with_id.columns if c != "id"])) + gen = GroupbyDatasetGenerator("id", columns=columns) + return gen(time_series_df_with_id), time_series_df_with_id + + +@pytest.mark.parametrize("window_size,shift", [(2 * 48, 48), (48 + 1, 1)]) +def test_patch_generator_groupby(groupby_dataset, window_size, shift): + ds, df = groupby_dataset + records_per_id = df.groupby("id").x1.size().max() + ids = df.id.unique() + columns = sorted([c for c in df.columns if c != "id"]) + + initial_size = window_size - shift + data_size = records_per_id - initial_size + patches = data_size / shift * len(ids) + expected_shape = (window_size, len(columns)) + + ds_patched = ds.interleave( + PatchGenerator(window_size=window_size, shift=shift), + num_parallel_calls=tf.data.experimental.AUTOTUNE, + ) + + for i, patch in enumerate(ds_patched.as_numpy_iterator()): + assert patch.shape == expected_shape, "Wrong shape" + x1 = patch[0, 0] + id = int(x1 // 1e6) + idx = int(x1 % 1e4) + expected_values = df[df.id == id].iloc[idx : idx + window_size] + assert np.all( + patch == expected_values[columns].values + ), "Patch contains wrong data" + assert i + 1 == patches, "Not enough patches" From a5a9c52db2df2e7128e8a5a0c259f5d8811cf648 Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus Date: Thu, 23 Dec 2021 17:16:08 +0100 Subject: [PATCH 07/72] adds batch processor --- .../pipeline/batch_processor.py | 46 ++++++++ tests/test_pipleine.py | 109 ++++++++++++++++-- 2 files changed, 147 insertions(+), 8 deletions(-) create mode 100644 src/tensorflow_time_series_dataset/pipeline/batch_processor.py diff --git a/src/tensorflow_time_series_dataset/pipeline/batch_processor.py b/src/tensorflow_time_series_dataset/pipeline/batch_processor.py new file mode 100644 index 0000000..65d3450 --- /dev/null +++ b/src/tensorflow_time_series_dataset/pipeline/batch_processor.py @@ -0,0 +1,46 @@ +import tensorflow as tf + + +class BatchPreprocessor: + def __init__( + self, + history_size, + history_columns, + meta_columns, + prediction_columns, + ): + self.history_size = history_size + self.history_columns = history_columns + self.meta_columns = meta_columns + self.prediction_columns = prediction_columns + + columns = sorted(list(set(history_columns + prediction_columns + meta_columns))) + self.column_idx = {c: i for i, c in enumerate(columns)} + + def __call__(self, batch): + y = [] + x_hist = [] + x_meta = [] + + x_columns = sorted(set(self.history_columns + self.meta_columns)) + y_columns = sorted(self.prediction_columns) + + for c in y_columns: + column = batch[:, self.history_size :, self.column_idx[c]] + y.append(column) + + if len(x_columns) == 0: + ValueError("No feature columns provided") + + for c in x_columns: + column = batch[:, :, self.column_idx[c], None] + if c in self.history_columns: + x_hist.append(column[:, : self.history_size, 0]) + if c in self.meta_columns: + x_meta.append(column[:, self.history_size, None, ...]) + + y = tf.stack(y, axis=2) + x_hist = tf.stack(x_hist, axis=2) + x_meta = tf.concat(x_meta, axis=2) + + return (x_hist, x_meta), y diff --git a/tests/test_pipleine.py b/tests/test_pipleine.py index 09061d3..2e91ddb 100644 --- a/tests/test_pipleine.py +++ b/tests/test_pipleine.py @@ -3,6 +3,7 @@ import numpy as np from tensorflow_time_series_dataset.pipeline.patch_generator import PatchGenerator +from tensorflow_time_series_dataset.pipeline.batch_processor import BatchPreprocessor from tensorflow_time_series_dataset.preprocessors.groupby_dataset_generator import ( GroupbyDatasetGenerator, ) @@ -20,6 +21,27 @@ def sorted_ds(): return np.float32(patch) +@pytest.fixture +def groupby_dataset(time_series_df_with_id): + time_series_df_with_id.set_index("date_time", inplace=True) + columns = list(sorted([c for c in time_series_df_with_id.columns if c != "id"])) + gen = GroupbyDatasetGenerator("id", columns=columns) + return gen(time_series_df_with_id), time_series_df_with_id + + +@pytest.fixture +def patched_dataset(request, time_series_df): + window_size, shift = request.param + df = time_series_df.set_index("date_time") + + ds = tf.data.Dataset.from_tensors(df) + ds = ds.interleave( + PatchGenerator(window_size=window_size, shift=shift), + num_parallel_calls=tf.data.experimental.AUTOTUNE, + ) + return ds, df + + @pytest.mark.parametrize("window_size,shift", [(2 * 48, 48), (48 + 1, 1)]) def test_patch_generator(time_series_df, window_size, shift): df = time_series_df.set_index("date_time") @@ -44,14 +66,6 @@ def test_patch_generator(time_series_df, window_size, shift): assert i + 1 == patches, "Not enough patches" -@pytest.fixture -def groupby_dataset(time_series_df_with_id): - time_series_df_with_id.set_index("date_time", inplace=True) - columns = list(sorted([c for c in time_series_df_with_id.columns if c != "id"])) - gen = GroupbyDatasetGenerator("id", columns=columns) - return gen(time_series_df_with_id), time_series_df_with_id - - @pytest.mark.parametrize("window_size,shift", [(2 * 48, 48), (48 + 1, 1)]) def test_patch_generator_groupby(groupby_dataset, window_size, shift): ds, df = groupby_dataset @@ -79,3 +93,82 @@ def test_patch_generator_groupby(groupby_dataset, window_size, shift): patch == expected_values[columns].values ), "Patch contains wrong data" assert i + 1 == patches, "Not enough patches" + + +def get_id_and_idx(val): + id = val // 1e6 + idx = val % 1e4 + return id, idx + + +def gen_patch(df, idx, size): + return df.values[np.int(idx) : np.int(idx + size)] + + +def gen_batch(df, columns, size, ids, lines): + batch = [] + print(columns) + for id, line in zip(ids, lines): + if "id" in df.columns: + p = gen_patch(df[df.id == id][columns], line, size) + else: + p = gen_patch(df[columns], line, size) + batch.append(p) + return np.float32(batch) + + +def validate_batch( + df, + ds, + history_size, + history_columns, + meta_columns, + prediction_columns, +): + for b, ((x1, x2), y) in enumerate(ds.as_numpy_iterator()): + prediction_size = y.shape[1] + + first_val = x1[:, 0] + ids, lines = get_id_and_idx(first_val) + + assert np.all( + x1 == gen_batch(df, history_columns, history_size, ids, lines) + ), f"Wrong data: history ({b})" + assert np.all( + x2 == gen_batch(df, meta_columns, 1, ids, lines + history_size) + ), f"Wrong data: meta ({b})" + + print(x1.shape) + y_test = x1[:, -prediction_size:] + prediction_size + assert np.all(y == y_test), f"Wrong data: prediction ({b})" + + first_val = y[:, 0] + ids, lines = get_id_and_idx(first_val) + + assert np.all( + y == gen_batch(df, prediction_columns, prediction_size, ids, lines) + ), f"Wrong data: prediction ({b})" + + +@pytest.mark.parametrize( + "patched_dataset", [(2 * 48, 48), (48 + 1, 1)], indirect=["patched_dataset"] +) +def test_batch_processor(patched_dataset): + ds, df = patched_dataset + + columns = list(sorted(df.columns)) + + batch_kwds = dict( + history_size=48, + history_columns=columns[:1], + meta_columns=columns[1:], + prediction_columns=columns[:1], + ) + print(batch_kwds) + ds_batched = ds.batch(4, drop_remainder=True) + ds_batched = ds_batched.map( + BatchPreprocessor(**batch_kwds), + num_parallel_calls=tf.data.experimental.AUTOTUNE, + ) + + validate_batch(df, ds_batched, **batch_kwds) From f7aed8086b8ad96ccc20eed185cc103332a58474 Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus Date: Mon, 3 Jan 2022 16:49:35 +0100 Subject: [PATCH 08/72] extends test for multiple batch and windows sizes --- tests/test_pipleine.py | 158 ++++++++++++++++++++++------------------- 1 file changed, 86 insertions(+), 72 deletions(-) diff --git a/tests/test_pipleine.py b/tests/test_pipleine.py index 2e91ddb..55e42ae 100644 --- a/tests/test_pipleine.py +++ b/tests/test_pipleine.py @@ -1,26 +1,75 @@ +import numpy as np import pytest import tensorflow as tf -import numpy as np - -from tensorflow_time_series_dataset.pipeline.patch_generator import PatchGenerator from tensorflow_time_series_dataset.pipeline.batch_processor import BatchPreprocessor +from tensorflow_time_series_dataset.pipeline.patch_generator import PatchGenerator from tensorflow_time_series_dataset.preprocessors.groupby_dataset_generator import ( GroupbyDatasetGenerator, ) -def get_value(l, c): - return int(f"{c:02d}{l:04d}") +def get_id_and_idx(val): + id = val // 1e6 + idx = val % 1e4 + return id, idx -def sorted_ds(): - patch = [ - [gen_value(int(id), c, l) for c in patch_columns] - for l in range(line, line + patch_size) - ] - return np.float32(patch) +def gen_patch(df, idx, size): + return df.values[np.int(idx) : np.int(idx + size)] +def gen_batch(df, columns, size, ids, lines): + batch = [] + print(columns) + for id, line in zip(ids, lines): + if "id" in df.columns: + p = gen_patch(df[df.id == id][columns], line, size) + else: + p = gen_patch(df[columns], line, size) + batch.append(p) + return np.float32(batch) + + +def validate_batch( + df, + ds, + batch_size, + history_size, + prediction_size, + history_columns, + meta_columns, + prediction_columns, +): + x1_shape = (batch_size, history_size, len(history_columns)) + x2_shape = (batch_size, 1, len(meta_columns)) + y_shape = (batch_size, prediction_size, len(meta_columns)) + for b, ((x1, x2), y) in enumerate(ds.as_numpy_iterator()): + assert x1.shape == x1_shape, f"Wrong shape: history ({b})" + assert x2.shape == x2_shape, f"Wrong shape: meta ({b})" + assert y.shape == y_shape, f"Wrong shape: prediction ({b})" + first_val = x1[:, 0] + ids, lines = get_id_and_idx(first_val) + + assert np.all( + x1 == gen_batch(df, history_columns, history_size, ids, lines) + ), f"Wrong data: history ({b})" + assert np.all( + x2 == gen_batch(df, meta_columns, 1, ids, lines + history_size) + ), f"Wrong data: meta ({b})" + + print(x1.shape) + y_test = x1[:, -prediction_size:] + prediction_size + assert np.all(y == y_test), f"Wrong data: prediction ({b})" + + first_val = y[:, 0] + ids, lines = get_id_and_idx(first_val) + + assert np.all( + y == gen_batch(df, prediction_columns, prediction_size, ids, lines) + ), f"Wrong data: prediction ({b})" + + +# FIXTURES #################################################################### @pytest.fixture def groupby_dataset(time_series_df_with_id): time_series_df_with_id.set_index("date_time", inplace=True) @@ -39,9 +88,20 @@ def patched_dataset(request, time_series_df): PatchGenerator(window_size=window_size, shift=shift), num_parallel_calls=tf.data.experimental.AUTOTUNE, ) - return ds, df + return ds, df, window_size, shift + + +@pytest.fixture(params=list(range(0, 48 * 4, 48))) +def history_size(request): + return request.param + +@pytest.fixture(params=[4, 32]) +def batch_size(request): + return request.param + +# TESTS ####################################################################### @pytest.mark.parametrize("window_size,shift", [(2 * 48, 48), (48 + 1, 1)]) def test_patch_generator(time_series_df, window_size, shift): df = time_series_df.set_index("date_time") @@ -95,80 +155,34 @@ def test_patch_generator_groupby(groupby_dataset, window_size, shift): assert i + 1 == patches, "Not enough patches" -def get_id_and_idx(val): - id = val // 1e6 - idx = val % 1e4 - return id, idx - - -def gen_patch(df, idx, size): - return df.values[np.int(idx) : np.int(idx + size)] - - -def gen_batch(df, columns, size, ids, lines): - batch = [] - print(columns) - for id, line in zip(ids, lines): - if "id" in df.columns: - p = gen_patch(df[df.id == id][columns], line, size) - else: - p = gen_patch(df[columns], line, size) - batch.append(p) - return np.float32(batch) - - -def validate_batch( - df, - ds, - history_size, - history_columns, - meta_columns, - prediction_columns, -): - for b, ((x1, x2), y) in enumerate(ds.as_numpy_iterator()): - prediction_size = y.shape[1] - - first_val = x1[:, 0] - ids, lines = get_id_and_idx(first_val) - - assert np.all( - x1 == gen_batch(df, history_columns, history_size, ids, lines) - ), f"Wrong data: history ({b})" - assert np.all( - x2 == gen_batch(df, meta_columns, 1, ids, lines + history_size) - ), f"Wrong data: meta ({b})" - - print(x1.shape) - y_test = x1[:, -prediction_size:] + prediction_size - assert np.all(y == y_test), f"Wrong data: prediction ({b})" - - first_val = y[:, 0] - ids, lines = get_id_and_idx(first_val) - - assert np.all( - y == gen_batch(df, prediction_columns, prediction_size, ids, lines) - ), f"Wrong data: prediction ({b})" - - @pytest.mark.parametrize( "patched_dataset", [(2 * 48, 48), (48 + 1, 1)], indirect=["patched_dataset"] ) -def test_batch_processor(patched_dataset): - ds, df = patched_dataset +def test_batch_processor(patched_dataset, history_size, batch_size): + history_size = 48 + batch_size = 4 + ds, df, window_size, shift = patched_dataset + predcition_size = window_size - history_size columns = list(sorted(df.columns)) batch_kwds = dict( - history_size=48, + history_size=history_size, history_columns=columns[:1], meta_columns=columns[1:], prediction_columns=columns[:1], ) print(batch_kwds) - ds_batched = ds.batch(4, drop_remainder=True) + ds_batched = ds.batch(batch_size, drop_remainder=True) ds_batched = ds_batched.map( BatchPreprocessor(**batch_kwds), num_parallel_calls=tf.data.experimental.AUTOTUNE, ) - validate_batch(df, ds_batched, **batch_kwds) + validate_batch( + df, + ds_batched, + batch_size=batch_size, + prediction_size=predcition_size, + **batch_kwds, + ) From aa46c64e5f7833eb3993fb4dbbd30e8a5343b1db Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus Date: Mon, 3 Jan 2022 17:58:10 +0100 Subject: [PATCH 09/72] adds pipeline --- .../pipeline/batch_processor.py | 26 +++-- .../pipeline/windowed_time_series_pipeline.py | 91 +++++++++++++++++ tests/test_pipleine.py | 98 ++++++++++++++----- 3 files changed, 184 insertions(+), 31 deletions(-) create mode 100644 src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py diff --git a/src/tensorflow_time_series_dataset/pipeline/batch_processor.py b/src/tensorflow_time_series_dataset/pipeline/batch_processor.py index 65d3450..c659607 100644 --- a/src/tensorflow_time_series_dataset/pipeline/batch_processor.py +++ b/src/tensorflow_time_series_dataset/pipeline/batch_processor.py @@ -14,6 +14,13 @@ def __init__( self.meta_columns = meta_columns self.prediction_columns = prediction_columns + assert ( + len(set(history_columns + meta_columns)) != 0 + ), "No feature columns provided" + if len(history_columns): + assert history_size >= 0, "history_size must be a positive integer" + assert len(prediction_columns) != 0, "No prediction columns provided" + columns = sorted(list(set(history_columns + prediction_columns + meta_columns))) self.column_idx = {c: i for i, c in enumerate(columns)} @@ -29,18 +36,21 @@ def __call__(self, batch): column = batch[:, self.history_size :, self.column_idx[c]] y.append(column) - if len(x_columns) == 0: - ValueError("No feature columns provided") - for c in x_columns: column = batch[:, :, self.column_idx[c], None] - if c in self.history_columns: + if c in self.history_columns and self.history_size: x_hist.append(column[:, : self.history_size, 0]) if c in self.meta_columns: x_meta.append(column[:, self.history_size, None, ...]) y = tf.stack(y, axis=2) - x_hist = tf.stack(x_hist, axis=2) - x_meta = tf.concat(x_meta, axis=2) - - return (x_hist, x_meta), y + x = [] + if len(x_hist): + x.append(tf.stack(x_hist, axis=2)) + if len(x_meta): + x.append(tf.concat(x_meta, axis=2)) + + if len(x) > 1: + return tuple(x), y + else: + return x[0], y diff --git a/src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py b/src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py new file mode 100644 index 0000000..cae7ccb --- /dev/null +++ b/src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py @@ -0,0 +1,91 @@ +#!/usr/bin/env python3 +# -*- time-stamp-pattern: "changed[\s]+:[\s]+%%$"; -*- +# AUTHOR INFORMATION ########################################################## +# file : windowed_time_series_pipeline.py +# author : Marcel Arpogaus +# +# created : 2021-07-29 17:57:39 (Marcel Arpogaus) +# changed : 2022-01-03 16:51:57 (Marcel Arpogaus) +# DESCRIPTION ################################################################# +# Probabilistic Short-Term Low-Voltage Load Forecasting using +# Bernstein-Polynomial Normalizing Flows +# LICENSE ##################################################################### +# Copyright (C) 2021 Marcel Arpogaus +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with this program. If not, see . +############################################################################### +# REQUIRED PYTHON MODULES ##################################################### +import tensorflow as tf + +from tensorflow_time_series_dataset.pipeline.batch_processor import BatchPreprocessor +from tensorflow_time_series_dataset.pipeline.patch_generator import PatchGenerator + + +class WindowedTimeSeriesPipeline: + def __init__( + self, + history_size, + prediction_size, + history_columns, + meta_columns, + prediction_columns, + shift, + batch_size, + cycle_length, + shuffle_buffer_size, + seed, + ): + self.history_size = history_size + self.prediction_size = prediction_size + self.window_size = history_size + prediction_size + self.history_columns = history_columns + self.meta_columns = meta_columns + self.prediction_columns = prediction_columns + self.shift = shift + self.batch_size = batch_size + self.cycle_length = cycle_length + self.shuffle_buffer_size = shuffle_buffer_size + self.seed = seed + + def __call__(self, ds): + + if self.shuffle_buffer_size > 0: + ds = ds.shuffle( + self.cycle_length * self.shuffle_buffer_size, seed=self.seed + ) + + ds = ds.interleave( + PatchGenerator(self.window_size, self.shift), + cycle_length=self.cycle_length, + num_parallel_calls=tf.data.experimental.AUTOTUNE, + ) + + if self.shuffle_buffer_size > 0: + ds = ds.shuffle(self.batch_size * self.shuffle_buffer_size, seed=self.seed) + + ds = ds.batch(self.batch_size, drop_remainder=True) + + ds = ds.map( + BatchPreprocessor( + self.history_size, + self.history_columns, + self.meta_columns, + self.prediction_columns, + ), + num_parallel_calls=tf.data.experimental.AUTOTUNE, + ) + + ds = ds.prefetch(tf.data.experimental.AUTOTUNE) + + return ds diff --git a/tests/test_pipleine.py b/tests/test_pipleine.py index 55e42ae..57e0476 100644 --- a/tests/test_pipleine.py +++ b/tests/test_pipleine.py @@ -3,6 +3,9 @@ import tensorflow as tf from tensorflow_time_series_dataset.pipeline.batch_processor import BatchPreprocessor from tensorflow_time_series_dataset.pipeline.patch_generator import PatchGenerator +from tensorflow_time_series_dataset.pipeline.windowed_time_series_pipeline import ( + WindowedTimeSeriesPipeline, +) from tensorflow_time_series_dataset.preprocessors.groupby_dataset_generator import ( GroupbyDatasetGenerator, ) @@ -43,27 +46,43 @@ def validate_batch( x1_shape = (batch_size, history_size, len(history_columns)) x2_shape = (batch_size, 1, len(meta_columns)) y_shape = (batch_size, prediction_size, len(meta_columns)) - for b, ((x1, x2), y) in enumerate(ds.as_numpy_iterator()): - assert x1.shape == x1_shape, f"Wrong shape: history ({b})" - assert x2.shape == x2_shape, f"Wrong shape: meta ({b})" - assert y.shape == y_shape, f"Wrong shape: prediction ({b})" - first_val = x1[:, 0] - ids, lines = get_id_and_idx(first_val) - - assert np.all( - x1 == gen_batch(df, history_columns, history_size, ids, lines) - ), f"Wrong data: history ({b})" - assert np.all( - x2 == gen_batch(df, meta_columns, 1, ids, lines + history_size) - ), f"Wrong data: meta ({b})" - - print(x1.shape) - y_test = x1[:, -prediction_size:] + prediction_size - assert np.all(y == y_test), f"Wrong data: prediction ({b})" + for b, (x, y) in enumerate(ds.as_numpy_iterator()): + x1, x2 = None, None + if history_size and len(history_columns) and len(meta_columns): + x1, x2 = x + elif history_size and len(history_columns): + x1 = x + elif len(meta_columns): + x2 = x + + if x1 is not None: + assert x1.shape == x1_shape, f"Wrong shape: history ({b})" + first_val = x1[:, 0] + ids, lines = get_id_and_idx(first_val) + + assert np.all( + x1 == gen_batch(df, history_columns, history_size, ids, lines) + ), f"Wrong data: history ({b})" + if x2 is not None: + assert np.all( + x2 == gen_batch(df, meta_columns, 1, ids, lines + history_size) + ), f"Wrong data: meta ({b})" + + if history_columns == prediction_columns: + y_test = x1[:, -prediction_size:] + prediction_size + assert np.all(y == y_test), f"Wrong data: prediction ({b})" + + if x2 is not None: + first_val = x2[:, 0] + ids, lines = get_id_and_idx(first_val) + assert x2.shape == x2_shape, f"Wrong shape: meta ({b})" + assert np.all( + x2 == gen_batch(df, meta_columns, 1, ids, lines) + ), f"Wrong data: meta ({b})" + assert y.shape == y_shape, f"Wrong shape: prediction ({b})" first_val = y[:, 0] ids, lines = get_id_and_idx(first_val) - assert np.all( y == gen_batch(df, prediction_columns, prediction_size, ids, lines) ), f"Wrong data: prediction ({b})" @@ -159,10 +178,8 @@ def test_patch_generator_groupby(groupby_dataset, window_size, shift): "patched_dataset", [(2 * 48, 48), (48 + 1, 1)], indirect=["patched_dataset"] ) def test_batch_processor(patched_dataset, history_size, batch_size): - history_size = 48 - batch_size = 4 ds, df, window_size, shift = patched_dataset - predcition_size = window_size - history_size + prediction_size = window_size - history_size columns = list(sorted(df.columns)) @@ -172,7 +189,7 @@ def test_batch_processor(patched_dataset, history_size, batch_size): meta_columns=columns[1:], prediction_columns=columns[:1], ) - print(batch_kwds) + ds_batched = ds.batch(batch_size, drop_remainder=True) ds_batched = ds_batched.map( BatchPreprocessor(**batch_kwds), @@ -183,6 +200,41 @@ def test_batch_processor(patched_dataset, history_size, batch_size): df, ds_batched, batch_size=batch_size, - prediction_size=predcition_size, + prediction_size=prediction_size, + **batch_kwds, + ) + + +@pytest.mark.parametrize( + "prediction_size,shift", [(1, 1), (12, 1), (48, 1), (12, 12), (48, 48)] +) +def test_windowed_time_series_pipeline( + time_series_df, batch_size, history_size, prediction_size, shift +): + df = time_series_df.set_index("date_time") + + columns = list(sorted(df.columns)) + + batch_kwds = dict( + history_size=history_size, + prediction_size=prediction_size, + history_columns=columns[:1], + meta_columns=columns[1:], + prediction_columns=columns[:1], + batch_size=batch_size, + ) + pipeline_kwds = dict( + shift=shift, + cycle_length=1, + shuffle_buffer_size=100, + seed=1, + ) + pipeline = WindowedTimeSeriesPipeline(**batch_kwds, **pipeline_kwds) + ds = tf.data.Dataset.from_tensors(df) + ds = pipeline(ds) + ds + validate_batch( + df, + ds, **batch_kwds, ) From 5a25b7ea50f31d8a0065b21ecddd6f94271c3168 Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus Date: Tue, 4 Jan 2022 10:49:56 +0100 Subject: [PATCH 10/72] adds pipline test for groupby dataset --- tests/test_pipleine.py | 42 ++++++++++++++++++++++++++++++++++++------ 1 file changed, 36 insertions(+), 6 deletions(-) diff --git a/tests/test_pipleine.py b/tests/test_pipleine.py index 57e0476..f66c01f 100644 --- a/tests/test_pipleine.py +++ b/tests/test_pipleine.py @@ -23,10 +23,9 @@ def gen_patch(df, idx, size): def gen_batch(df, columns, size, ids, lines): batch = [] - print(columns) for id, line in zip(ids, lines): if "id" in df.columns: - p = gen_patch(df[df.id == id][columns], line, size) + p = gen_patch(df[df.id == id[0]][columns], line, size) else: p = gen_patch(df[columns], line, size) batch.append(p) @@ -110,7 +109,7 @@ def patched_dataset(request, time_series_df): return ds, df, window_size, shift -@pytest.fixture(params=list(range(0, 48 * 4, 48))) +@pytest.fixture(params=list(range(0, 48 * 2, 48))) def history_size(request): return request.param @@ -205,9 +204,7 @@ def test_batch_processor(patched_dataset, history_size, batch_size): ) -@pytest.mark.parametrize( - "prediction_size,shift", [(1, 1), (12, 1), (48, 1), (12, 12), (48, 48)] -) +@pytest.mark.parametrize("prediction_size,shift", [(1, 1), (48, 1), (48, 48)]) def test_windowed_time_series_pipeline( time_series_df, batch_size, history_size, prediction_size, shift ): @@ -238,3 +235,36 @@ def test_windowed_time_series_pipeline( ds, **batch_kwds, ) + + +@pytest.mark.parametrize("prediction_size,shift", [(1, 1), (48, 1), (48, 48)]) +def test_windowed_time_series_pipeline_groupby( + groupby_dataset, batch_size, history_size, prediction_size, shift +): + ds, df = groupby_dataset + + ids = df.id.unique() + columns = sorted([c for c in df.columns if c != "id"]) + + batch_kwds = dict( + history_size=history_size, + prediction_size=prediction_size, + history_columns=columns[:1], + meta_columns=columns[1:], + prediction_columns=columns[:1], + batch_size=batch_size, + ) + pipeline_kwds = dict( + shift=shift, + cycle_length=len(ids), + shuffle_buffer_size=1000, + seed=1, + ) + pipeline = WindowedTimeSeriesPipeline(**batch_kwds, **pipeline_kwds) + ds = pipeline(ds) + ds + validate_batch( + df, + ds, + **batch_kwds, + ) From b5583a60fa9db2299976c826a69318c1baed05b3 Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus Date: Tue, 4 Jan 2022 15:45:08 +0100 Subject: [PATCH 11/72] moves ds validation function into new util module --- .../utils/__init__.py | 0 .../utils/test.py | 93 +++++++++++++ tests/conftest.py | 20 +++ tests/test_pipleine.py | 130 ++++-------------- 4 files changed, 136 insertions(+), 107 deletions(-) create mode 100644 src/tensorflow_time_series_dataset/utils/__init__.py create mode 100644 src/tensorflow_time_series_dataset/utils/test.py diff --git a/src/tensorflow_time_series_dataset/utils/__init__.py b/src/tensorflow_time_series_dataset/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/tensorflow_time_series_dataset/utils/test.py b/src/tensorflow_time_series_dataset/utils/test.py new file mode 100644 index 0000000..84f23d3 --- /dev/null +++ b/src/tensorflow_time_series_dataset/utils/test.py @@ -0,0 +1,93 @@ +from contextlib import nullcontext as does_not_raise + +import numpy as np +import pytest + + +def get_id_and_idx(val): + id = val // 1e6 + idx = val % 1e4 + return id, idx + + +def gen_patch(df, idx, size): + return df.values[np.int(idx) : np.int(idx + size)] + + +def gen_batch(df, columns, size, ids, lines): + batch = [] + for id, line in zip(ids, lines): + if "id" in df.columns: + p = gen_patch(df[df.id == id[0]][columns], line, size) + else: + p = gen_patch(df[columns], line, size) + batch.append(p) + return np.float32(batch) + + +def validate_dataset( + df, + ds, + batch_size, + history_size, + prediction_size, + history_columns, + meta_columns, + prediction_columns, +): + x1_shape = (batch_size, history_size, len(history_columns)) + x2_shape = (batch_size, 1, len(meta_columns)) + y_shape = (batch_size, prediction_size, len(meta_columns)) + for b, (x, y) in enumerate(ds.as_numpy_iterator()): + print(y.shape) + x1, x2 = None, None + if history_size and len(history_columns) and len(meta_columns): + x1, x2 = x + elif history_size and len(history_columns): + x1 = x + elif len(meta_columns): + x2 = x + + if x1 is not None: + assert x1.shape == x1_shape, f"Wrong shape: history ({b})" + first_val = x1[:, 0] + ids, lines = get_id_and_idx(first_val) + + assert np.all( + x1 == gen_batch(df, history_columns, history_size, ids, lines) + ), f"Wrong data: history ({b})" + if x2 is not None: + assert np.all( + x2 == gen_batch(df, meta_columns, 1, ids, lines + history_size) + ), f"wrong data: meta not consecutive ({b})" + + last_val = x1[:, -1] + ids, lines = get_id_and_idx(last_val) + y_test = gen_batch(df, prediction_columns, prediction_size, ids, lines + 1) + assert np.all(y == y_test), f"Wrong data: prediction not consecutive ({b})" + + if x2 is not None: + first_val = x2[:, 0] + ids, lines = get_id_and_idx(first_val) + assert x2.shape == x2_shape, f"Wrong shape: meta ({b})" + assert np.all( + x2 == gen_batch(df, meta_columns, 1, ids, lines) + ), f"Wrong data: meta ({b})" + + assert y.shape == y_shape, f"Wrong shape: prediction ({b})" + first_val = y[:, 0] + ids, lines = get_id_and_idx(first_val) + assert np.all( + y == gen_batch(df, prediction_columns, prediction_size, ids, lines) + ), f"Wrong data: prediction ({b})" + + +def get_ctxmgr(prediction_size): + if prediction_size <= 0: + ctxmgr = pytest.raises( + AssertionError, + match="prediction_size must be a positive integer greater than zero", + ) + else: + ctxmgr = does_not_raise() + return ctxmgr diff --git a/tests/conftest.py b/tests/conftest.py index f5612a2..1795f4d 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -98,3 +98,23 @@ def tmp_csv_with_id(tmpdir_factory, time_series_df_with_id): time_series_df_with_id.to_csv(file_path, index=False) return file_path, time_series_df_with_id + + +@pytest.fixture(params=[1] + list(range(0, 48 * 2, 48))) +def history_size(request): + return request.param + + +@pytest.fixture(params=[0, 1, 48]) +def prediction_size(request): + return request.param + + +@pytest.fixture +def shift(prediction_size): + return prediction_size + + +@pytest.fixture(params=[4, 32]) +def batch_size(request): + return request.param diff --git a/tests/test_pipleine.py b/tests/test_pipleine.py index f66c01f..79b473c 100644 --- a/tests/test_pipleine.py +++ b/tests/test_pipleine.py @@ -9,83 +9,8 @@ from tensorflow_time_series_dataset.preprocessors.groupby_dataset_generator import ( GroupbyDatasetGenerator, ) - - -def get_id_and_idx(val): - id = val // 1e6 - idx = val % 1e4 - return id, idx - - -def gen_patch(df, idx, size): - return df.values[np.int(idx) : np.int(idx + size)] - - -def gen_batch(df, columns, size, ids, lines): - batch = [] - for id, line in zip(ids, lines): - if "id" in df.columns: - p = gen_patch(df[df.id == id[0]][columns], line, size) - else: - p = gen_patch(df[columns], line, size) - batch.append(p) - return np.float32(batch) - - -def validate_batch( - df, - ds, - batch_size, - history_size, - prediction_size, - history_columns, - meta_columns, - prediction_columns, -): - x1_shape = (batch_size, history_size, len(history_columns)) - x2_shape = (batch_size, 1, len(meta_columns)) - y_shape = (batch_size, prediction_size, len(meta_columns)) - for b, (x, y) in enumerate(ds.as_numpy_iterator()): - x1, x2 = None, None - if history_size and len(history_columns) and len(meta_columns): - x1, x2 = x - elif history_size and len(history_columns): - x1 = x - elif len(meta_columns): - x2 = x - - if x1 is not None: - assert x1.shape == x1_shape, f"Wrong shape: history ({b})" - first_val = x1[:, 0] - ids, lines = get_id_and_idx(first_val) - - assert np.all( - x1 == gen_batch(df, history_columns, history_size, ids, lines) - ), f"Wrong data: history ({b})" - if x2 is not None: - assert np.all( - x2 == gen_batch(df, meta_columns, 1, ids, lines + history_size) - ), f"Wrong data: meta ({b})" - - if history_columns == prediction_columns: - y_test = x1[:, -prediction_size:] + prediction_size - assert np.all(y == y_test), f"Wrong data: prediction ({b})" - - if x2 is not None: - first_val = x2[:, 0] - ids, lines = get_id_and_idx(first_val) - assert x2.shape == x2_shape, f"Wrong shape: meta ({b})" - assert np.all( - x2 == gen_batch(df, meta_columns, 1, ids, lines) - ), f"Wrong data: meta ({b})" - - assert y.shape == y_shape, f"Wrong shape: prediction ({b})" - first_val = y[:, 0] - ids, lines = get_id_and_idx(first_val) - assert np.all( - y == gen_batch(df, prediction_columns, prediction_size, ids, lines) - ), f"Wrong data: prediction ({b})" - +from tensorflow_time_series_dataset.utils.test import validate_dataset +from tensorflow_time_series_dataset.utils.test import get_ctxmgr # FIXTURES #################################################################### @pytest.fixture @@ -109,16 +34,6 @@ def patched_dataset(request, time_series_df): return ds, df, window_size, shift -@pytest.fixture(params=list(range(0, 48 * 2, 48))) -def history_size(request): - return request.param - - -@pytest.fixture(params=[4, 32]) -def batch_size(request): - return request.param - - # TESTS ####################################################################### @pytest.mark.parametrize("window_size,shift", [(2 * 48, 48), (48 + 1, 1)]) def test_patch_generator(time_series_df, window_size, shift): @@ -195,7 +110,7 @@ def test_batch_processor(patched_dataset, history_size, batch_size): num_parallel_calls=tf.data.experimental.AUTOTUNE, ) - validate_batch( + validate_dataset( df, ds_batched, batch_size=batch_size, @@ -204,7 +119,6 @@ def test_batch_processor(patched_dataset, history_size, batch_size): ) -@pytest.mark.parametrize("prediction_size,shift", [(1, 1), (48, 1), (48, 48)]) def test_windowed_time_series_pipeline( time_series_df, batch_size, history_size, prediction_size, shift ): @@ -226,18 +140,19 @@ def test_windowed_time_series_pipeline( shuffle_buffer_size=100, seed=1, ) - pipeline = WindowedTimeSeriesPipeline(**batch_kwds, **pipeline_kwds) - ds = tf.data.Dataset.from_tensors(df) - ds = pipeline(ds) - ds - validate_batch( - df, - ds, - **batch_kwds, - ) + + with get_ctxmgr(prediction_size): + pipeline = WindowedTimeSeriesPipeline(**batch_kwds, **pipeline_kwds) + ds = tf.data.Dataset.from_tensors(df) + ds = pipeline(ds) + ds + validate_dataset( + df, + ds, + **batch_kwds, + ) -@pytest.mark.parametrize("prediction_size,shift", [(1, 1), (48, 1), (48, 48)]) def test_windowed_time_series_pipeline_groupby( groupby_dataset, batch_size, history_size, prediction_size, shift ): @@ -260,11 +175,12 @@ def test_windowed_time_series_pipeline_groupby( shuffle_buffer_size=1000, seed=1, ) - pipeline = WindowedTimeSeriesPipeline(**batch_kwds, **pipeline_kwds) - ds = pipeline(ds) - ds - validate_batch( - df, - ds, - **batch_kwds, - ) + with get_ctxmgr(prediction_size): + pipeline = WindowedTimeSeriesPipeline(**batch_kwds, **pipeline_kwds) + ds = pipeline(ds) + ds + validate_dataset( + df, + ds, + **batch_kwds, + ) From fe3bd2c4dbfffc0c316e6178f6305750c798b83e Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus Date: Tue, 4 Jan 2022 15:45:55 +0100 Subject: [PATCH 12/72] assert invalid prediction sizes --- .../pipeline/__init__.py | 30 +------------------ .../pipeline/windowed_time_series_pipeline.py | 5 +++- 2 files changed, 5 insertions(+), 30 deletions(-) diff --git a/src/tensorflow_time_series_dataset/pipeline/__init__.py b/src/tensorflow_time_series_dataset/pipeline/__init__.py index eb012c8..edc747f 100644 --- a/src/tensorflow_time_series_dataset/pipeline/__init__.py +++ b/src/tensorflow_time_series_dataset/pipeline/__init__.py @@ -1,29 +1 @@ -#!/usr/bin/env python3 -# -*- time-stamp-pattern: "changed[\s]+:[\s]+%%$"; -*- -# AUTHOR INFORMATION ########################################################## -# file : __init__.py -# author : Marcel Arpogaus -# -# created : 2021-07-29 17:57:39 (Marcel Arpogaus) -# changed : 2021-07-29 18:02:20 (Marcel Arpogaus) -# DESCRIPTION ################################################################# -# Probabilistic Short-Term Low-Voltage Load Forecasting using -# Bernstein-Polynomial Normalizing Flows -# LICENSE ##################################################################### -# Copyright (C) 2021 Marcel Arpogaus -# -# This program is free software: you can redistribute it and/or modify -# it under the terms of the GNU General Public License as published by -# the Free Software Foundation, either version 3 of the License, or -# (at your option) any later version. -# -# This program is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -# GNU General Public License for more details. -# -# You should have received a copy of the GNU General Public License -# along with this program. If not, see . -############################################################################### - -from .windowed_time_series_pipeline import WindowedTimeSeriesPipeline \ No newline at end of file +from .windowed_time_series_pipeline import WindowedTimeSeriesPipeline diff --git a/src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py b/src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py index cae7ccb..d07a511 100644 --- a/src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py +++ b/src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py @@ -5,7 +5,7 @@ # author : Marcel Arpogaus # # created : 2021-07-29 17:57:39 (Marcel Arpogaus) -# changed : 2022-01-03 16:51:57 (Marcel Arpogaus) +# changed : 2022-01-04 12:11:45 (Marcel Arpogaus) # DESCRIPTION ################################################################# # Probabilistic Short-Term Low-Voltage Load Forecasting using # Bernstein-Polynomial Normalizing Flows @@ -46,6 +46,9 @@ def __init__( shuffle_buffer_size, seed, ): + assert ( + prediction_size > 0 + ), "prediction_size must be a positive integer greater than zero" self.history_size = history_size self.prediction_size = prediction_size self.window_size = history_size + prediction_size From d9ad528cc703e874175522484a0e1e63e5f4a058 Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus Date: Tue, 4 Jan 2022 15:46:22 +0100 Subject: [PATCH 13/72] refactores variable name --- src/tensorflow_time_series_dataset/__init__.py | 1 + .../pipeline/patch_generator.py | 5 ++--- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/src/tensorflow_time_series_dataset/__init__.py b/src/tensorflow_time_series_dataset/__init__.py index e69de29..084822e 100644 --- a/src/tensorflow_time_series_dataset/__init__.py +++ b/src/tensorflow_time_series_dataset/__init__.py @@ -0,0 +1 @@ +from .windowed_time_series_dataset_factory import WindowedTimeSeriesDataSetFactory diff --git a/src/tensorflow_time_series_dataset/pipeline/patch_generator.py b/src/tensorflow_time_series_dataset/pipeline/patch_generator.py index 14a7b6f..4bcafab 100644 --- a/src/tensorflow_time_series_dataset/pipeline/patch_generator.py +++ b/src/tensorflow_time_series_dataset/pipeline/patch_generator.py @@ -6,12 +6,11 @@ def __init__(self, window_size, shift): self.window_size = window_size self.shift = shift - def __call__(self, load_data): + def __call__(self, data): def sub_to_patch(sub): return sub.batch(self.window_size, drop_remainder=True) - # Why is this needed? - data_set = tf.data.Dataset.from_tensor_slices(load_data) + data_set = tf.data.Dataset.from_tensor_slices(data) data_set = data_set.window( size=self.window_size, From 577abae85d69833ea4ed33d39f3fabfa871a9067 Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus Date: Tue, 4 Jan 2022 15:46:47 +0100 Subject: [PATCH 14/72] adds factory class and test --- .../windowed_time_series_dataset_factory.py | 45 +++++++++++++++++++ tests/test_factory.py | 34 ++++++++++++++ 2 files changed, 79 insertions(+) create mode 100644 src/tensorflow_time_series_dataset/windowed_time_series_dataset_factory.py create mode 100644 tests/test_factory.py diff --git a/src/tensorflow_time_series_dataset/windowed_time_series_dataset_factory.py b/src/tensorflow_time_series_dataset/windowed_time_series_dataset_factory.py new file mode 100644 index 0000000..8a73c04 --- /dev/null +++ b/src/tensorflow_time_series_dataset/windowed_time_series_dataset_factory.py @@ -0,0 +1,45 @@ +import tensorflow as tf +from .pipeline import WindowedTimeSeriesPipeline + + +class WindowedTimeSeriesDataSetFactory: + default_pipline_kwds = dict( + shift=None, + batch_size=32, + cycle_length=100, + shuffle_buffer_size=1000, + seed=42, + ) + + def __init__(self, **pipline_kwds): + self.preprocessors = [] + self.data_loader = None + self.data_pipeline = WindowedTimeSeriesPipeline( + **{**self.default_pipline_kwds, **pipline_kwds} + ) + + def add_preprocessor(self, preprocessor): + self.preprocessors.append(preprocessor) + + def set_data_loader(self, data_loader): + self.data_loader = data_loader + + def get_dataset(self, data=None): + if self.data_loader is not None and data is None: + data = self.data_loader() + elif data is not None: + data = data + else: + raise ValueError("No data provided") + + for preprocessor in self.preprocessors: + data = preprocessor(data) + + if not isinstance(data, tf.data.Dataset): + data = tf.data.Dataset.from_tensors(data) + + ds = self.data_pipeline(data) + return ds + + def __call__(self, data=None): + return self.get_dataset(data) diff --git a/tests/test_factory.py b/tests/test_factory.py new file mode 100644 index 0000000..9cabbe1 --- /dev/null +++ b/tests/test_factory.py @@ -0,0 +1,34 @@ +from tensorflow_time_series_dataset import WindowedTimeSeriesDataSetFactory +from tensorflow_time_series_dataset.utils.test import get_ctxmgr, validate_dataset + + +def test_windowed_time_series_dataset_factory( + time_series_df, batch_size, history_size, prediction_size, shift +): + + df = time_series_df.set_index("date_time") + columns = list(sorted(df.columns)) + + batch_kwds = dict( + history_size=history_size, + prediction_size=prediction_size, + history_columns=columns[:1], + meta_columns=columns[1:], + prediction_columns=columns[:1], + batch_size=batch_size, + ) + factory_kwds = dict( + shift=shift, + cycle_length=1, + shuffle_buffer_size=100, + seed=1, + ) + with get_ctxmgr(prediction_size): + factory = WindowedTimeSeriesDataSetFactory(**batch_kwds, **factory_kwds) + ds = factory(df) + ds + validate_dataset( + df, + ds, + **batch_kwds, + ) From a8290bb8b77dde4cd36950bd9cb6537059c11fe3 Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus Date: Tue, 4 Jan 2022 17:12:29 +0100 Subject: [PATCH 15/72] adds more tests for factory --- tests/conftest.py | 21 +++++-- tests/test_factory.py | 136 +++++++++++++++++++++++++++++++++++++++++- 2 files changed, 148 insertions(+), 9 deletions(-) diff --git a/tests/conftest.py b/tests/conftest.py index 1795f4d..a495713 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -77,8 +77,8 @@ def time_series_df(request): @pytest.fixture( scope="function", params=[ - (list(range(10)), ["x1", "x2"], 48 * 30), - (list(range(10)), ["x1", "x2"], 48 * 30 * 6), + (list(range(5)), ["x1", "x2"], 48 * 30), + (list(range(10)), ["x1", "x2"], 48 * 30 * 3), ], ) def time_series_df_with_id(request): @@ -91,6 +91,15 @@ def time_series_df_with_id(request): return df +@pytest.fixture(scope="function") +def tmp_csv(tmpdir_factory, time_series_df): + file_path = tmpdir_factory.mktemp("csv_data") / "test.csv" + + time_series_df.to_csv(file_path, index=False) + + return file_path, time_series_df + + @pytest.fixture(scope="function") def tmp_csv_with_id(tmpdir_factory, time_series_df_with_id): file_path = tmpdir_factory.mktemp("csv_data") / "test.csv" @@ -100,14 +109,14 @@ def tmp_csv_with_id(tmpdir_factory, time_series_df_with_id): return file_path, time_series_df_with_id -@pytest.fixture(params=[1] + list(range(0, 48 * 2, 48))) +@pytest.fixture(params=[0, 1, 48]) def history_size(request): return request.param -@pytest.fixture(params=[0, 1, 48]) -def prediction_size(request): - return request.param +@pytest.fixture +def prediction_size(history_size): + return history_size @pytest.fixture diff --git a/tests/test_factory.py b/tests/test_factory.py index 9cabbe1..16709d6 100644 --- a/tests/test_factory.py +++ b/tests/test_factory.py @@ -1,4 +1,9 @@ from tensorflow_time_series_dataset import WindowedTimeSeriesDataSetFactory +from tensorflow_time_series_dataset.loaders import CSVDataLoader +from tensorflow_time_series_dataset.preprocessors import ( + GroupbyDatasetGenerator, + TimeSeriesSplit, +) from tensorflow_time_series_dataset.utils.test import get_ctxmgr, validate_dataset @@ -9,7 +14,7 @@ def test_windowed_time_series_dataset_factory( df = time_series_df.set_index("date_time") columns = list(sorted(df.columns)) - batch_kwds = dict( + common_kwds = dict( history_size=history_size, prediction_size=prediction_size, history_columns=columns[:1], @@ -24,11 +29,136 @@ def test_windowed_time_series_dataset_factory( seed=1, ) with get_ctxmgr(prediction_size): - factory = WindowedTimeSeriesDataSetFactory(**batch_kwds, **factory_kwds) + factory = WindowedTimeSeriesDataSetFactory(**common_kwds, **factory_kwds) ds = factory(df) ds validate_dataset( df, ds, - **batch_kwds, + **common_kwds, + ) + + +def test_windowed_time_series_dataset_factory_groupby( + time_series_df_with_id, batch_size, history_size, prediction_size, shift +): + + df = time_series_df_with_id.set_index("date_time") + + ids = df.id.unique() + columns = sorted([c for c in df.columns if c != "id"]) + + history_columns = columns[:1] + meta_columns = columns[1:] + prediction_columns = columns[:1] + common_kwds = dict( + history_size=history_size, + prediction_size=prediction_size, + history_columns=history_columns, + meta_columns=meta_columns, + prediction_columns=prediction_columns, + batch_size=batch_size, + ) + factory_kwds = dict( + shift=shift, + cycle_length=len(ids), + shuffle_buffer_size=100, + seed=1, + ) + with get_ctxmgr(prediction_size): + factory = WindowedTimeSeriesDataSetFactory(**common_kwds, **factory_kwds) + factory.add_preprocessor( + GroupbyDatasetGenerator( + "id", columns=history_columns + meta_columns + prediction_columns + ) + ) + + ds = factory(df) + ds + validate_dataset( + df, + ds, + **common_kwds, + ) + + +def test_windowed_time_series_dataset_factory_csv_loader( + tmp_csv, batch_size, history_size, prediction_size, shift +): + + test_data_path, df = tmp_csv + df = df.set_index("date_time") + columns = list(sorted(df.columns)) + + common_kwds = dict( + history_size=history_size, + prediction_size=prediction_size, + history_columns=columns[:1], + meta_columns=columns[1:], + prediction_columns=columns[:1], + batch_size=batch_size, + ) + factory_kwds = dict( + shift=shift, + cycle_length=1, + shuffle_buffer_size=100, + seed=1, + ) + with get_ctxmgr(prediction_size): + factory = WindowedTimeSeriesDataSetFactory(**common_kwds, **factory_kwds) + factory.set_data_loader(CSVDataLoader(file_path=test_data_path)) + ds = factory() + ds + validate_dataset( + df, + ds, + **common_kwds, + ) + + +def test_windowed_time_series_dataset_factory_csv_loader_with_preprocessors( + tmp_csv_with_id, batch_size, history_size, prediction_size, shift +): + + test_data_path, df = tmp_csv_with_id + df = df.set_index("date_time") + + splitter = TimeSeriesSplit(0.5, TimeSeriesSplit.LEFT) + df = splitter(df) + + ids = df.id.unique() + columns = sorted([c for c in df.columns if c != "id"]) + + history_columns = columns[:1] + meta_columns = columns[1:] + prediction_columns = columns[:1] + common_kwds = dict( + history_size=history_size, + prediction_size=prediction_size, + history_columns=history_columns, + meta_columns=meta_columns, + prediction_columns=prediction_columns, + batch_size=batch_size, + ) + factory_kwds = dict( + shift=shift, + cycle_length=len(ids), + shuffle_buffer_size=100, + seed=1, + ) + with get_ctxmgr(prediction_size): + factory = WindowedTimeSeriesDataSetFactory(**common_kwds, **factory_kwds) + factory.set_data_loader(CSVDataLoader(file_path=test_data_path)) + factory.add_preprocessor(splitter) + factory.add_preprocessor( + GroupbyDatasetGenerator( + "id", columns=history_columns + meta_columns + prediction_columns + ) + ) + ds = factory() + ds + validate_dataset( + df, + ds, + **common_kwds, ) From 32e982f99069ef5d3e83fed94d40cc9978bf66df Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus Date: Fri, 7 Jan 2022 09:14:50 +0100 Subject: [PATCH 16/72] removes bp --- .../pipeline/windowed_time_series_pipeline.py | 27 ------------ .../preprocessors/time_series_k_fold.py | 43 ++++--------------- .../preprocessors/time_series_split.py | 42 +++--------------- 3 files changed, 14 insertions(+), 98 deletions(-) diff --git a/src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py b/src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py index d07a511..40e7cf4 100644 --- a/src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py +++ b/src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py @@ -1,30 +1,3 @@ -#!/usr/bin/env python3 -# -*- time-stamp-pattern: "changed[\s]+:[\s]+%%$"; -*- -# AUTHOR INFORMATION ########################################################## -# file : windowed_time_series_pipeline.py -# author : Marcel Arpogaus -# -# created : 2021-07-29 17:57:39 (Marcel Arpogaus) -# changed : 2022-01-04 12:11:45 (Marcel Arpogaus) -# DESCRIPTION ################################################################# -# Probabilistic Short-Term Low-Voltage Load Forecasting using -# Bernstein-Polynomial Normalizing Flows -# LICENSE ##################################################################### -# Copyright (C) 2021 Marcel Arpogaus -# -# This program is free software: you can redistribute it and/or modify -# it under the terms of the GNU General Public License as published by -# the Free Software Foundation, either version 3 of the License, or -# (at your option) any later version. -# -# This program is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -# GNU General Public License for more details. -# -# You should have received a copy of the GNU General Public License -# along with this program. If not, see . -############################################################################### # REQUIRED PYTHON MODULES ##################################################### import tensorflow as tf diff --git a/src/tensorflow_time_series_dataset/preprocessors/time_series_k_fold.py b/src/tensorflow_time_series_dataset/preprocessors/time_series_k_fold.py index 9107cf0..0243671 100644 --- a/src/tensorflow_time_series_dataset/preprocessors/time_series_k_fold.py +++ b/src/tensorflow_time_series_dataset/preprocessors/time_series_k_fold.py @@ -1,46 +1,21 @@ -#!/usr/bin/env python3 -# -*- time-stamp-pattern: "changed[\s]+:[\s]+%%$"; -*- -# AUTHOR INFORMATION ########################################################## -# file : time_series_k_fold.py -# author : Marcel Arpogaus -# -# created : 2021-07-29 17:57:39 (Marcel Arpogaus) -# changed : 2021-07-29 18:02:20 (Marcel Arpogaus) -# DESCRIPTION ################################################################# -# Probabilistic Short-Term Low-Voltage Load Forecasting using -# Bernstein-Polynomial Normalizing Flows -# LICENSE ##################################################################### -# Copyright (C) 2021 Marcel Arpogaus -# -# This program is free software: you can redistribute it and/or modify -# it under the terms of the GNU General Public License as published by -# the Free Software Foundation, either version 3 of the License, or -# (at your option) any later version. -# -# This program is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -# GNU General Public License for more details. -# -# You should have received a copy of the GNU General Public License -# along with this program. If not, see . -############################################################################### - - # REQUIRED PYTHON MODULES ##################################################### import numpy as np import pandas as pd -class TimeSeriesKFold(): +class TimeSeriesKFold: def __init__(self, fold, n_folds=20): self.n_folds = n_folds self.fold = fold def __call__(self, data): - days = pd.date_range(data.index.min(), data.index.max(), freq='D') + days = pd.date_range(data.index.min(), data.index.max(), freq="D") fold_idx = np.array_split(days.to_numpy(), self.n_folds) - folds = {f: (idx[[0, -1]].astype('datetime64[m]') + [0, 60 * 24 - 1] - ).astype(str).tolist() for f, idx in enumerate(fold_idx)} + folds = { + f: (idx[[0, -1]].astype("datetime64[m]") + [0, 60 * 24 - 1]) + .astype(str) + .tolist() + for f, idx in enumerate(fold_idx) + } - return data[folds[self.fold][0]:folds[self.fold][1]] + return data[folds[self.fold][0] : folds[self.fold][1]] diff --git a/src/tensorflow_time_series_dataset/preprocessors/time_series_split.py b/src/tensorflow_time_series_dataset/preprocessors/time_series_split.py index fae2193..d014762 100644 --- a/src/tensorflow_time_series_dataset/preprocessors/time_series_split.py +++ b/src/tensorflow_time_series_dataset/preprocessors/time_series_split.py @@ -1,38 +1,7 @@ -#!/usr/bin/env python3 -# -*- time-stamp-pattern: "changed[\s]+:[\s]+%%$"; -*- -# AUTHOR INFORMATION ########################################################## -# file : time_series_split.py -# author : Marcel Arpogaus -# -# created : 2021-07-29 17:57:39 (Marcel Arpogaus) -# changed : 2021-07-29 18:02:20 (Marcel Arpogaus) -# DESCRIPTION ################################################################# -# Probabilistic Short-Term Low-Voltage Load Forecasting using -# Bernstein-Polynomial Normalizing Flows -# LICENSE ##################################################################### -# Copyright (C) 2021 Marcel Arpogaus -# -# This program is free software: you can redistribute it and/or modify -# it under the terms of the GNU General Public License as published by -# the Free Software Foundation, either version 3 of the License, or -# (at your option) any later version. -# -# This program is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -# GNU General Public License for more details. -# -# You should have received a copy of the GNU General Public License -# along with this program. If not, see . -############################################################################### - - -# REQUIRED PYTHON MODULES ##################################################### -import numpy as np import pandas as pd -class TimeSeriesSplit(): +class TimeSeriesSplit: RIGHT = 0 LEFT = 1 @@ -41,12 +10,11 @@ def __init__(self, split_size, split): self.split = split def __call__(self, data): - days = pd.date_range(data.index.min(), data.index.max(), freq='D') - days = days.to_numpy().astype('datetime64[m]') + days = pd.date_range(data.index.min(), data.index.max(), freq="D") + days = days.to_numpy().astype("datetime64[m]") right = days[int(len(days) * self.split_size)] left = right - 1 if self.split == self.LEFT: - return data.loc[:str(left)] + return data.loc[: str(left)] else: - return data.loc[str(right):] - + return data.loc[str(right) :] From f1c0fab45b06649f2b071953514ba2602ee31c9e Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus Date: Fri, 7 Jan 2022 09:39:41 +0100 Subject: [PATCH 17/72] updates bp --- .../__init__.py | 24 +++++++++++++++++++ .../loaders/__init__.py | 24 +++++++++++++++++++ .../loaders/csv_data_loader.py | 24 +++++++++++++++++++ .../pipeline/__init__.py | 24 +++++++++++++++++++ .../pipeline/batch_processor.py | 24 +++++++++++++++++++ .../pipeline/patch_generator.py | 24 +++++++++++++++++++ .../pipeline/windowed_time_series_pipeline.py | 24 +++++++++++++++++++ .../preprocessors/__init__.py | 24 +++++++++++++++++++ .../preprocessors/cyclical_feature_encoder.py | 24 +++++++++++++++++++ .../groupby_dataset_generator.py | 24 +++++++++++++++++++ .../preprocessors/time_series_k_fold.py | 24 +++++++++++++++++++ .../preprocessors/time_series_split.py | 24 +++++++++++++++++++ .../utils/__init__.py | 24 +++++++++++++++++++ .../utils/test.py | 24 +++++++++++++++++++ .../windowed_time_series_dataset_factory.py | 24 +++++++++++++++++++ 15 files changed, 360 insertions(+) diff --git a/src/tensorflow_time_series_dataset/__init__.py b/src/tensorflow_time_series_dataset/__init__.py index 084822e..72e57c6 100644 --- a/src/tensorflow_time_series_dataset/__init__.py +++ b/src/tensorflow_time_series_dataset/__init__.py @@ -1 +1,25 @@ +# -*- time-stamp-pattern: "changed[\s]+:[\s]+%%$"; -*- +# AUTHOR INFORMATION ########################################################## +# file : __init__.py +# author : Marcel Arpogaus +# +# created : 2022-01-07 09:02:38 (Marcel Arpogaus) +# changed : 2022-01-07 09:02:38 (Marcel Arpogaus) +# DESCRIPTION ################################################################# +# ... +# LICENSE ##################################################################### +# Copyright 2022 Marcel Arpogaus +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +############################################################################### from .windowed_time_series_dataset_factory import WindowedTimeSeriesDataSetFactory diff --git a/src/tensorflow_time_series_dataset/loaders/__init__.py b/src/tensorflow_time_series_dataset/loaders/__init__.py index 35fedc3..c31109c 100644 --- a/src/tensorflow_time_series_dataset/loaders/__init__.py +++ b/src/tensorflow_time_series_dataset/loaders/__init__.py @@ -1 +1,25 @@ +# -*- time-stamp-pattern: "changed[\s]+:[\s]+%%$"; -*- +# AUTHOR INFORMATION ########################################################## +# file : __init__.py +# author : Marcel Arpogaus +# +# created : 2022-01-07 09:02:38 (Marcel Arpogaus) +# changed : 2022-01-07 09:02:38 (Marcel Arpogaus) +# DESCRIPTION ################################################################# +# ... +# LICENSE ##################################################################### +# Copyright 2022 Marcel Arpogaus +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +############################################################################### from .csv_data_loader import CSVDataLoader diff --git a/src/tensorflow_time_series_dataset/loaders/csv_data_loader.py b/src/tensorflow_time_series_dataset/loaders/csv_data_loader.py index f1ad29f..e4715ff 100644 --- a/src/tensorflow_time_series_dataset/loaders/csv_data_loader.py +++ b/src/tensorflow_time_series_dataset/loaders/csv_data_loader.py @@ -1,3 +1,27 @@ +# -*- time-stamp-pattern: "changed[\s]+:[\s]+%%$"; -*- +# AUTHOR INFORMATION ########################################################## +# file : csv_data_loader.py +# author : Marcel Arpogaus +# +# created : 2022-01-07 09:02:38 (Marcel Arpogaus) +# changed : 2022-01-07 09:02:38 (Marcel Arpogaus) +# DESCRIPTION ################################################################# +# ... +# LICENSE ##################################################################### +# Copyright 2022 Marcel Arpogaus +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +############################################################################### import pandas as pd diff --git a/src/tensorflow_time_series_dataset/pipeline/__init__.py b/src/tensorflow_time_series_dataset/pipeline/__init__.py index edc747f..ceeb28b 100644 --- a/src/tensorflow_time_series_dataset/pipeline/__init__.py +++ b/src/tensorflow_time_series_dataset/pipeline/__init__.py @@ -1 +1,25 @@ +# -*- time-stamp-pattern: "changed[\s]+:[\s]+%%$"; -*- +# AUTHOR INFORMATION ########################################################## +# file : __init__.py +# author : Marcel Arpogaus +# +# created : 2022-01-07 09:02:38 (Marcel Arpogaus) +# changed : 2022-01-07 09:02:38 (Marcel Arpogaus) +# DESCRIPTION ################################################################# +# ... +# LICENSE ##################################################################### +# Copyright 2022 Marcel Arpogaus +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +############################################################################### from .windowed_time_series_pipeline import WindowedTimeSeriesPipeline diff --git a/src/tensorflow_time_series_dataset/pipeline/batch_processor.py b/src/tensorflow_time_series_dataset/pipeline/batch_processor.py index c659607..6ffcc53 100644 --- a/src/tensorflow_time_series_dataset/pipeline/batch_processor.py +++ b/src/tensorflow_time_series_dataset/pipeline/batch_processor.py @@ -1,3 +1,27 @@ +# -*- time-stamp-pattern: "changed[\s]+:[\s]+%%$"; -*- +# AUTHOR INFORMATION ########################################################## +# file : batch_processor.py +# author : Marcel Arpogaus +# +# created : 2022-01-07 09:02:38 (Marcel Arpogaus) +# changed : 2022-01-07 09:02:38 (Marcel Arpogaus) +# DESCRIPTION ################################################################# +# ... +# LICENSE ##################################################################### +# Copyright 2022 Marcel Arpogaus +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +############################################################################### import tensorflow as tf diff --git a/src/tensorflow_time_series_dataset/pipeline/patch_generator.py b/src/tensorflow_time_series_dataset/pipeline/patch_generator.py index 4bcafab..713a057 100644 --- a/src/tensorflow_time_series_dataset/pipeline/patch_generator.py +++ b/src/tensorflow_time_series_dataset/pipeline/patch_generator.py @@ -1,3 +1,27 @@ +# -*- time-stamp-pattern: "changed[\s]+:[\s]+%%$"; -*- +# AUTHOR INFORMATION ########################################################## +# file : patch_generator.py +# author : Marcel Arpogaus +# +# created : 2022-01-07 09:02:38 (Marcel Arpogaus) +# changed : 2022-01-07 09:02:38 (Marcel Arpogaus) +# DESCRIPTION ################################################################# +# ... +# LICENSE ##################################################################### +# Copyright 2022 Marcel Arpogaus +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +############################################################################### import tensorflow as tf diff --git a/src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py b/src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py index 40e7cf4..1707e33 100644 --- a/src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py +++ b/src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py @@ -1,3 +1,27 @@ +# -*- time-stamp-pattern: "changed[\s]+:[\s]+%%$"; -*- +# AUTHOR INFORMATION ########################################################## +# file : windowed_time_series_pipeline.py +# author : Marcel Arpogaus +# +# created : 2022-01-07 09:02:38 (Marcel Arpogaus) +# changed : 2022-01-07 09:02:38 (Marcel Arpogaus) +# DESCRIPTION ################################################################# +# ... +# LICENSE ##################################################################### +# Copyright 2022 Marcel Arpogaus +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +############################################################################### # REQUIRED PYTHON MODULES ##################################################### import tensorflow as tf diff --git a/src/tensorflow_time_series_dataset/preprocessors/__init__.py b/src/tensorflow_time_series_dataset/preprocessors/__init__.py index 1ebfcc6..2df83d7 100644 --- a/src/tensorflow_time_series_dataset/preprocessors/__init__.py +++ b/src/tensorflow_time_series_dataset/preprocessors/__init__.py @@ -1,3 +1,27 @@ +# -*- time-stamp-pattern: "changed[\s]+:[\s]+%%$"; -*- +# AUTHOR INFORMATION ########################################################## +# file : __init__.py +# author : Marcel Arpogaus +# +# created : 2022-01-07 09:02:38 (Marcel Arpogaus) +# changed : 2022-01-07 09:02:38 (Marcel Arpogaus) +# DESCRIPTION ################################################################# +# ... +# LICENSE ##################################################################### +# Copyright 2022 Marcel Arpogaus +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +############################################################################### from .time_series_k_fold import TimeSeriesKFold from .time_series_split import TimeSeriesSplit from .cyclical_feature_encoder import CyclicalFeatureEncoder diff --git a/src/tensorflow_time_series_dataset/preprocessors/cyclical_feature_encoder.py b/src/tensorflow_time_series_dataset/preprocessors/cyclical_feature_encoder.py index 3f8c628..f62a391 100644 --- a/src/tensorflow_time_series_dataset/preprocessors/cyclical_feature_encoder.py +++ b/src/tensorflow_time_series_dataset/preprocessors/cyclical_feature_encoder.py @@ -1,3 +1,27 @@ +# -*- time-stamp-pattern: "changed[\s]+:[\s]+%%$"; -*- +# AUTHOR INFORMATION ########################################################## +# file : cyclical_feature_encoder.py +# author : Marcel Arpogaus +# +# created : 2022-01-07 09:02:38 (Marcel Arpogaus) +# changed : 2022-01-07 09:02:38 (Marcel Arpogaus) +# DESCRIPTION ################################################################# +# ... +# LICENSE ##################################################################### +# Copyright 2022 Marcel Arpogaus +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +############################################################################### import numpy as np diff --git a/src/tensorflow_time_series_dataset/preprocessors/groupby_dataset_generator.py b/src/tensorflow_time_series_dataset/preprocessors/groupby_dataset_generator.py index dbe7e87..db92f22 100644 --- a/src/tensorflow_time_series_dataset/preprocessors/groupby_dataset_generator.py +++ b/src/tensorflow_time_series_dataset/preprocessors/groupby_dataset_generator.py @@ -1,3 +1,27 @@ +# -*- time-stamp-pattern: "changed[\s]+:[\s]+%%$"; -*- +# AUTHOR INFORMATION ########################################################## +# file : groupby_dataset_generator.py +# author : Marcel Arpogaus +# +# created : 2022-01-07 09:02:38 (Marcel Arpogaus) +# changed : 2022-01-07 09:02:38 (Marcel Arpogaus) +# DESCRIPTION ################################################################# +# ... +# LICENSE ##################################################################### +# Copyright 2022 Marcel Arpogaus +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +############################################################################### import tensorflow as tf diff --git a/src/tensorflow_time_series_dataset/preprocessors/time_series_k_fold.py b/src/tensorflow_time_series_dataset/preprocessors/time_series_k_fold.py index 0243671..7fbf8c6 100644 --- a/src/tensorflow_time_series_dataset/preprocessors/time_series_k_fold.py +++ b/src/tensorflow_time_series_dataset/preprocessors/time_series_k_fold.py @@ -1,3 +1,27 @@ +# -*- time-stamp-pattern: "changed[\s]+:[\s]+%%$"; -*- +# AUTHOR INFORMATION ########################################################## +# file : time_series_k_fold.py +# author : Marcel Arpogaus +# +# created : 2022-01-07 09:02:38 (Marcel Arpogaus) +# changed : 2022-01-07 09:02:38 (Marcel Arpogaus) +# DESCRIPTION ################################################################# +# ... +# LICENSE ##################################################################### +# Copyright 2022 Marcel Arpogaus +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +############################################################################### # REQUIRED PYTHON MODULES ##################################################### import numpy as np import pandas as pd diff --git a/src/tensorflow_time_series_dataset/preprocessors/time_series_split.py b/src/tensorflow_time_series_dataset/preprocessors/time_series_split.py index d014762..46dc8fa 100644 --- a/src/tensorflow_time_series_dataset/preprocessors/time_series_split.py +++ b/src/tensorflow_time_series_dataset/preprocessors/time_series_split.py @@ -1,3 +1,27 @@ +# -*- time-stamp-pattern: "changed[\s]+:[\s]+%%$"; -*- +# AUTHOR INFORMATION ########################################################## +# file : time_series_split.py +# author : Marcel Arpogaus +# +# created : 2022-01-07 09:02:38 (Marcel Arpogaus) +# changed : 2022-01-07 09:02:38 (Marcel Arpogaus) +# DESCRIPTION ################################################################# +# ... +# LICENSE ##################################################################### +# Copyright 2022 Marcel Arpogaus +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +############################################################################### import pandas as pd diff --git a/src/tensorflow_time_series_dataset/utils/__init__.py b/src/tensorflow_time_series_dataset/utils/__init__.py index e69de29..d7c3685 100644 --- a/src/tensorflow_time_series_dataset/utils/__init__.py +++ b/src/tensorflow_time_series_dataset/utils/__init__.py @@ -0,0 +1,24 @@ +# -*- time-stamp-pattern: "changed[\s]+:[\s]+%%$"; -*- +# AUTHOR INFORMATION ########################################################## +# file : __init__.py +# author : Marcel Arpogaus +# +# created : 2022-01-07 09:02:38 (Marcel Arpogaus) +# changed : 2022-01-07 09:02:38 (Marcel Arpogaus) +# DESCRIPTION ################################################################# +# ... +# LICENSE ##################################################################### +# Copyright 2022 Marcel Arpogaus +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +############################################################################### diff --git a/src/tensorflow_time_series_dataset/utils/test.py b/src/tensorflow_time_series_dataset/utils/test.py index 84f23d3..b70e1d2 100644 --- a/src/tensorflow_time_series_dataset/utils/test.py +++ b/src/tensorflow_time_series_dataset/utils/test.py @@ -1,3 +1,27 @@ +# -*- time-stamp-pattern: "changed[\s]+:[\s]+%%$"; -*- +# AUTHOR INFORMATION ########################################################## +# file : test.py +# author : Marcel Arpogaus +# +# created : 2022-01-07 09:02:38 (Marcel Arpogaus) +# changed : 2022-01-07 09:02:38 (Marcel Arpogaus) +# DESCRIPTION ################################################################# +# ... +# LICENSE ##################################################################### +# Copyright 2022 Marcel Arpogaus +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +############################################################################### from contextlib import nullcontext as does_not_raise import numpy as np diff --git a/src/tensorflow_time_series_dataset/windowed_time_series_dataset_factory.py b/src/tensorflow_time_series_dataset/windowed_time_series_dataset_factory.py index 8a73c04..95ac8ce 100644 --- a/src/tensorflow_time_series_dataset/windowed_time_series_dataset_factory.py +++ b/src/tensorflow_time_series_dataset/windowed_time_series_dataset_factory.py @@ -1,3 +1,27 @@ +# -*- time-stamp-pattern: "changed[\s]+:[\s]+%%$"; -*- +# AUTHOR INFORMATION ########################################################## +# file : windowed_time_series_dataset_factory.py +# author : Marcel Arpogaus +# +# created : 2022-01-07 09:02:38 (Marcel Arpogaus) +# changed : 2022-01-07 09:02:38 (Marcel Arpogaus) +# DESCRIPTION ################################################################# +# ... +# LICENSE ##################################################################### +# Copyright 2022 Marcel Arpogaus +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +############################################################################### import tensorflow as tf from .pipeline import WindowedTimeSeriesPipeline From a44eef2b6c43c6516c1ebb380938a99c7fda5263 Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus Date: Fri, 7 Jan 2022 09:39:58 +0100 Subject: [PATCH 18/72] adds GH pytest workflow --- .github/workflows/pytest.yaml | 34 ++++++++++++++++++++++++++++++++++ 1 file changed, 34 insertions(+) create mode 100644 .github/workflows/pytest.yaml diff --git a/.github/workflows/pytest.yaml b/.github/workflows/pytest.yaml new file mode 100644 index 0000000..b6d940d --- /dev/null +++ b/.github/workflows/pytest.yaml @@ -0,0 +1,34 @@ +name: Lint and test code + +on: [push] + +jobs: + build: + + runs-on: ubuntu-latest + strategy: + matrix: + python-version: [3.6, 3.7, 3.8, 3.9] + + steps: + - uses: actions/checkout@v2 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v2 + with: + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install flake8 pytest + if [ -f requirements.txt ]; then pip install -r requirements.txt; fi + pip install . + - name: Lint with flake8 + run: | + # stop the build if there are Python syntax errors or undefined names + flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics + # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide + flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics + - name: Test with pytest + run: | + pytest + From eab5c3425d84d0ce2c8e66d70f5b9f87b28c99c6 Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus Date: Fri, 7 Jan 2022 18:00:02 +0100 Subject: [PATCH 19/72] adds new pytest fixtures for columns --- .../__init__.py | 4 +- .../pipeline/batch_processor.py | 21 ++- .../utils/test.py | 50 +++++-- .../windowed_time_series_dataset_factory.py | 24 ++- tests/conftest.py | 78 +++++----- tests/test_factory.py | 133 ++++++++++++----- tests/test_pipleine.py | 139 ++++++++++++------ 7 files changed, 309 insertions(+), 140 deletions(-) diff --git a/src/tensorflow_time_series_dataset/__init__.py b/src/tensorflow_time_series_dataset/__init__.py index 72e57c6..44de58d 100644 --- a/src/tensorflow_time_series_dataset/__init__.py +++ b/src/tensorflow_time_series_dataset/__init__.py @@ -4,7 +4,7 @@ # author : Marcel Arpogaus # # created : 2022-01-07 09:02:38 (Marcel Arpogaus) -# changed : 2022-01-07 09:02:38 (Marcel Arpogaus) +# changed : 2022-01-07 16:12:49 (Marcel Arpogaus) # DESCRIPTION ################################################################# # ... # LICENSE ##################################################################### @@ -22,4 +22,4 @@ # See the License for the specific language governing permissions and # limitations under the License. ############################################################################### -from .windowed_time_series_dataset_factory import WindowedTimeSeriesDataSetFactory +from .windowed_time_series_dataset_factory import WindowedTimeSeriesDatasetFactory diff --git a/src/tensorflow_time_series_dataset/pipeline/batch_processor.py b/src/tensorflow_time_series_dataset/pipeline/batch_processor.py index 6ffcc53..dfad12a 100644 --- a/src/tensorflow_time_series_dataset/pipeline/batch_processor.py +++ b/src/tensorflow_time_series_dataset/pipeline/batch_processor.py @@ -4,7 +4,7 @@ # author : Marcel Arpogaus # # created : 2022-01-07 09:02:38 (Marcel Arpogaus) -# changed : 2022-01-07 09:02:38 (Marcel Arpogaus) +# changed : 2022-01-07 14:50:17 (Marcel Arpogaus) # DESCRIPTION ################################################################# # ... # LICENSE ##################################################################### @@ -41,14 +41,19 @@ def __init__( assert ( len(set(history_columns + meta_columns)) != 0 ), "No feature columns provided" - if len(history_columns): + if len(meta_columns) == 0: + assert history_size > 0, ( + "history_size must be a positive integer greater than zero" + ", when no meta date is used" + ) + else: assert history_size >= 0, "history_size must be a positive integer" assert len(prediction_columns) != 0, "No prediction columns provided" columns = sorted(list(set(history_columns + prediction_columns + meta_columns))) self.column_idx = {c: i for i, c in enumerate(columns)} - def __call__(self, batch): + def __call__(self, patch): y = [] x_hist = [] x_meta = [] @@ -56,13 +61,17 @@ def __call__(self, batch): x_columns = sorted(set(self.history_columns + self.meta_columns)) y_columns = sorted(self.prediction_columns) + assert ( + len(set(x_columns + y_columns)) == patch.shape[-1] + ), "Patch shape dos not match column number" + for c in y_columns: - column = batch[:, self.history_size :, self.column_idx[c]] + column = patch[:, self.history_size :, self.column_idx[c]] y.append(column) for c in x_columns: - column = batch[:, :, self.column_idx[c], None] - if c in self.history_columns and self.history_size: + column = patch[:, :, self.column_idx[c], None] + if self.history_size and c in self.history_columns: x_hist.append(column[:, : self.history_size, 0]) if c in self.meta_columns: x_meta.append(column[:, self.history_size, None, ...]) diff --git a/src/tensorflow_time_series_dataset/utils/test.py b/src/tensorflow_time_series_dataset/utils/test.py index b70e1d2..58824f1 100644 --- a/src/tensorflow_time_series_dataset/utils/test.py +++ b/src/tensorflow_time_series_dataset/utils/test.py @@ -4,7 +4,7 @@ # author : Marcel Arpogaus # # created : 2022-01-07 09:02:38 (Marcel Arpogaus) -# changed : 2022-01-07 09:02:38 (Marcel Arpogaus) +# changed : 2022-01-07 14:50:51 (Marcel Arpogaus) # DESCRIPTION ################################################################# # ... # LICENSE ##################################################################### @@ -42,7 +42,7 @@ def gen_batch(df, columns, size, ids, lines): batch = [] for id, line in zip(ids, lines): if "id" in df.columns: - p = gen_patch(df[df.id == id[0]][columns], line, size) + p = gen_patch(df[df.id == id][columns], line, size) else: p = gen_patch(df[columns], line, size) batch.append(p) @@ -58,12 +58,21 @@ def validate_dataset( history_columns, meta_columns, prediction_columns, + history_reference_column="ref", + meta_reference_column="ref", + prediction_reference_column="ref", ): x1_shape = (batch_size, history_size, len(history_columns)) x2_shape = (batch_size, 1, len(meta_columns)) - y_shape = (batch_size, prediction_size, len(meta_columns)) + y_shape = (batch_size, prediction_size, len(prediction_columns)) + history_columns = list(sorted(history_columns)) + meta_columns = list(sorted(meta_columns)) + prediction_columns = list(sorted(prediction_columns)) + history_columns_idx = {c: i for i, c in enumerate(history_columns)} + meta_columns_idx = {c: i for i, c in enumerate(meta_columns)} + prediction_columns_idx = {c: i for i, c in enumerate(prediction_columns)} + for b, (x, y) in enumerate(ds.as_numpy_iterator()): - print(y.shape) x1, x2 = None, None if history_size and len(history_columns) and len(meta_columns): x1, x2 = x @@ -74,9 +83,8 @@ def validate_dataset( if x1 is not None: assert x1.shape == x1_shape, f"Wrong shape: history ({b})" - first_val = x1[:, 0] + first_val = x1[:, 0, history_columns_idx[history_reference_column]] ids, lines = get_id_and_idx(first_val) - assert np.all( x1 == gen_batch(df, history_columns, history_size, ids, lines) ), f"Wrong data: history ({b})" @@ -85,13 +93,13 @@ def validate_dataset( x2 == gen_batch(df, meta_columns, 1, ids, lines + history_size) ), f"wrong data: meta not consecutive ({b})" - last_val = x1[:, -1] + last_val = x1[:, -1, history_columns_idx[history_reference_column]] ids, lines = get_id_and_idx(last_val) y_test = gen_batch(df, prediction_columns, prediction_size, ids, lines + 1) assert np.all(y == y_test), f"Wrong data: prediction not consecutive ({b})" if x2 is not None: - first_val = x2[:, 0] + first_val = x2[:, 0, meta_columns_idx[meta_reference_column]] ids, lines = get_id_and_idx(first_val) assert x2.shape == x2_shape, f"Wrong shape: meta ({b})" assert np.all( @@ -99,19 +107,41 @@ def validate_dataset( ), f"Wrong data: meta ({b})" assert y.shape == y_shape, f"Wrong shape: prediction ({b})" - first_val = y[:, 0] + first_val = y[:, 0, prediction_columns_idx[prediction_reference_column]] ids, lines = get_id_and_idx(first_val) assert np.all( y == gen_batch(df, prediction_columns, prediction_size, ids, lines) ), f"Wrong data: prediction ({b})" -def get_ctxmgr(prediction_size): +def get_ctxmgr( + prediction_size, history_columns, meta_columns, history_size, prediction_columns +): if prediction_size <= 0: ctxmgr = pytest.raises( AssertionError, match="prediction_size must be a positive integer greater than zero", ) + elif len(set(history_columns + meta_columns)) == 0: + ctxmgr = pytest.raises( + AssertionError, + match="No feature columns provided", + ) + elif len(meta_columns) == 0 and history_size <= 0: + ctxmgr = pytest.raises( + AssertionError, + match="history_size must be a positive integer greater than zero, when no meta date is used", + ) + elif history_size < 0: + ctxmgr = pytest.raises( + AssertionError, + match="history_size must be a positive integer", + ) + elif len(prediction_columns) == 0: + ctxmgr = pytest.raises( + AssertionError, + match="No prediction columns provided", + ) else: ctxmgr = does_not_raise() return ctxmgr diff --git a/src/tensorflow_time_series_dataset/windowed_time_series_dataset_factory.py b/src/tensorflow_time_series_dataset/windowed_time_series_dataset_factory.py index 95ac8ce..cc8b806 100644 --- a/src/tensorflow_time_series_dataset/windowed_time_series_dataset_factory.py +++ b/src/tensorflow_time_series_dataset/windowed_time_series_dataset_factory.py @@ -4,7 +4,7 @@ # author : Marcel Arpogaus # # created : 2022-01-07 09:02:38 (Marcel Arpogaus) -# changed : 2022-01-07 09:02:38 (Marcel Arpogaus) +# changed : 2022-01-07 16:12:30 (Marcel Arpogaus) # DESCRIPTION ################################################################# # ... # LICENSE ##################################################################### @@ -26,19 +26,31 @@ from .pipeline import WindowedTimeSeriesPipeline -class WindowedTimeSeriesDataSetFactory: +class WindowedTimeSeriesDatasetFactory: default_pipline_kwds = dict( shift=None, batch_size=32, - cycle_length=100, + cycle_length=1, shuffle_buffer_size=1000, seed=42, ) - def __init__(self, **pipline_kwds): + def __init__( + self, + history_columns, + prediction_columns, + meta_columns=[], + dtype=tf.float32, + **pipline_kwds + ): + self.columns = set(history_columns + meta_columns + prediction_columns) self.preprocessors = [] self.data_loader = None + self.dtype = dtype self.data_pipeline = WindowedTimeSeriesPipeline( + history_columns=history_columns, + meta_columns=meta_columns, + prediction_columns=prediction_columns, **{**self.default_pipline_kwds, **pipline_kwds} ) @@ -60,7 +72,9 @@ def get_dataset(self, data=None): data = preprocessor(data) if not isinstance(data, tf.data.Dataset): - data = tf.data.Dataset.from_tensors(data) + data = data[sorted(self.columns)] + tensors = tf.convert_to_tensor(data, dtype=self.dtype) + data = tf.data.Dataset.from_tensors(tensors) ds = self.data_pipeline(data) return ds diff --git a/tests/conftest.py b/tests/conftest.py index a495713..e00a79e 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,51 +1,29 @@ +import itertools import pytest import pandas as pd import numpy as np -# ref.: https://www.kaggle.com/avanwyk/encoding-cyclical-features-for-deep-learning -def encode(data, cycl_name, cycl_max, cycl_min=0, cycl=None): - if cycl is None: - cycl = getattr(data.index, cycl_name) - data[cycl_name + "_sin"] = np.float32( - np.sin(2 * np.pi * (cycl - cycl_min) / (cycl_max - cycl_min + 1)) - ) - data[cycl_name + "_cos"] = np.float32( - np.cos(2 * np.pi * (cycl - cycl_min) / (cycl_max - cycl_min + 1)) - ) - return data +columns = ["ref", "x1", "x2"] def get_value_generator(date_range, columns): - drf = date_range.to_frame() - drf_weekday = encode(drf, "weekday", 6, cycl=drf.index.weekday) - drf_dayofyear = encode(drf, "dayofyear", 366, 1) - drf_time = encode( - drf, "time", 24 * 60 - 1, cycl=drf.index.hour * 60 + drf.index.minute - ) - - def gen_value(column, line): + def gen_value(column, line, id=0): if column == "weekday": return date_range[line].weekday() - elif "weekday" in column: - return drf_weekday.loc[date_range[line], column] - elif "dayofyear" in column: - return drf_dayofyear.loc[date_range[line], column] - elif "time" in column: - return drf_time.loc[date_range[line], column] else: col_num = columns.index(column) - return int(f"{col_num:02d}{line:04d}") + return int(f"{col_num:02d}{line:04d}") + (id * 1e6) return gen_value -def gen_df(columns, date_range): +def gen_df(columns, date_range, id=0): gen_value = get_value_generator(date_range, columns) periods = date_range.size df = pd.DataFrame( { "date_time": date_range, - **{c: [gen_value(c, l) for l in range(periods)] for c in columns}, + **{c: [gen_value(c, l, id) for l in range(periods)] for c in columns}, } ) return df @@ -54,9 +32,8 @@ def gen_df(columns, date_range): def gen_df_with_id(ids, columns, date_range): dfs = [] for i in ids: - df = gen_df(columns, date_range) + df = gen_df(columns, date_range, i) df["id"] = i - df[columns] += i * 1e6 dfs.append(df) df = pd.concat(dfs) @@ -64,7 +41,8 @@ def gen_df_with_id(ids, columns, date_range): @pytest.fixture( - scope="function", params=[(["x1", "x2"], 48 * 30), (["x1", "x2"], 48 * 30 * 6)] + scope="function", + params=[(columns + ["weekday"], 48 * 30 * 3)], ) def time_series_df(request): df = gen_df( @@ -77,8 +55,7 @@ def time_series_df(request): @pytest.fixture( scope="function", params=[ - (list(range(5)), ["x1", "x2"], 48 * 30), - (list(range(10)), ["x1", "x2"], 48 * 30 * 3), + (list(range(5)), columns + ["weekday"], 48 * 30 * 3), ], ) def time_series_df_with_id(request): @@ -124,6 +101,39 @@ def shift(prediction_size): return prediction_size -@pytest.fixture(params=[4, 32]) +@pytest.fixture(params=[32]) def batch_size(request): return request.param + + +@pytest.fixture(params=[[], ["ref"], ["x2", "ref"], columns]) +def history_columns(request): + return request.param + + +@pytest.fixture(params=[[], ["ref", "weekday"]]) +def meta_columns(request): + return request.param + + +@pytest.fixture( + params=[ + [], + list( + itertools.chain( + ["ref"], + *[ + [c + "_sin", c + "_cos"] + for c in ["weekday", "dayofyear", "time", "month"] + ], + ) + ), + ] +) +def meta_columns_cycle(request): + return request.param + + +@pytest.fixture +def prediction_columns(history_columns): + return history_columns diff --git a/tests/test_factory.py b/tests/test_factory.py index 16709d6..765d142 100644 --- a/tests/test_factory.py +++ b/tests/test_factory.py @@ -1,25 +1,32 @@ -from tensorflow_time_series_dataset import WindowedTimeSeriesDataSetFactory +from tensorflow_time_series_dataset import WindowedTimeSeriesDatasetFactory from tensorflow_time_series_dataset.loaders import CSVDataLoader from tensorflow_time_series_dataset.preprocessors import ( GroupbyDatasetGenerator, TimeSeriesSplit, + CyclicalFeatureEncoder, ) from tensorflow_time_series_dataset.utils.test import get_ctxmgr, validate_dataset def test_windowed_time_series_dataset_factory( - time_series_df, batch_size, history_size, prediction_size, shift + time_series_df, + batch_size, + history_size, + prediction_size, + shift, + history_columns, + meta_columns, + prediction_columns, ): df = time_series_df.set_index("date_time") - columns = list(sorted(df.columns)) common_kwds = dict( history_size=history_size, prediction_size=prediction_size, - history_columns=columns[:1], - meta_columns=columns[1:], - prediction_columns=columns[:1], + history_columns=history_columns, + meta_columns=meta_columns, + prediction_columns=prediction_columns, batch_size=batch_size, ) factory_kwds = dict( @@ -28,8 +35,15 @@ def test_windowed_time_series_dataset_factory( shuffle_buffer_size=100, seed=1, ) - with get_ctxmgr(prediction_size): - factory = WindowedTimeSeriesDataSetFactory(**common_kwds, **factory_kwds) + + with get_ctxmgr( + history_size=history_size, + prediction_size=prediction_size, + history_columns=history_columns, + meta_columns=meta_columns, + prediction_columns=prediction_columns, + ): + factory = WindowedTimeSeriesDatasetFactory(**common_kwds, **factory_kwds) ds = factory(df) ds validate_dataset( @@ -40,17 +54,19 @@ def test_windowed_time_series_dataset_factory( def test_windowed_time_series_dataset_factory_groupby( - time_series_df_with_id, batch_size, history_size, prediction_size, shift + time_series_df_with_id, + batch_size, + history_size, + prediction_size, + shift, + history_columns, + meta_columns, + prediction_columns, ): df = time_series_df_with_id.set_index("date_time") ids = df.id.unique() - columns = sorted([c for c in df.columns if c != "id"]) - - history_columns = columns[:1] - meta_columns = columns[1:] - prediction_columns = columns[:1] common_kwds = dict( history_size=history_size, prediction_size=prediction_size, @@ -65,8 +81,16 @@ def test_windowed_time_series_dataset_factory_groupby( shuffle_buffer_size=100, seed=1, ) - with get_ctxmgr(prediction_size): - factory = WindowedTimeSeriesDataSetFactory(**common_kwds, **factory_kwds) + + with get_ctxmgr( + history_size=history_size, + prediction_size=prediction_size, + history_columns=history_columns, + meta_columns=meta_columns, + prediction_columns=prediction_columns, + ): + + factory = WindowedTimeSeriesDatasetFactory(**common_kwds, **factory_kwds) factory.add_preprocessor( GroupbyDatasetGenerator( "id", columns=history_columns + meta_columns + prediction_columns @@ -83,19 +107,25 @@ def test_windowed_time_series_dataset_factory_groupby( def test_windowed_time_series_dataset_factory_csv_loader( - tmp_csv, batch_size, history_size, prediction_size, shift + tmp_csv, + batch_size, + history_size, + prediction_size, + shift, + history_columns, + meta_columns, + prediction_columns, ): test_data_path, df = tmp_csv df = df.set_index("date_time") - columns = list(sorted(df.columns)) common_kwds = dict( history_size=history_size, prediction_size=prediction_size, - history_columns=columns[:1], - meta_columns=columns[1:], - prediction_columns=columns[:1], + history_columns=history_columns, + meta_columns=meta_columns, + prediction_columns=prediction_columns, batch_size=batch_size, ) factory_kwds = dict( @@ -104,8 +134,14 @@ def test_windowed_time_series_dataset_factory_csv_loader( shuffle_buffer_size=100, seed=1, ) - with get_ctxmgr(prediction_size): - factory = WindowedTimeSeriesDataSetFactory(**common_kwds, **factory_kwds) + with get_ctxmgr( + history_size=history_size, + prediction_size=prediction_size, + history_columns=history_columns, + meta_columns=meta_columns, + prediction_columns=prediction_columns, + ): + factory = WindowedTimeSeriesDatasetFactory(**common_kwds, **factory_kwds) factory.set_data_loader(CSVDataLoader(file_path=test_data_path)) ds = factory() ds @@ -117,26 +153,41 @@ def test_windowed_time_series_dataset_factory_csv_loader( def test_windowed_time_series_dataset_factory_csv_loader_with_preprocessors( - tmp_csv_with_id, batch_size, history_size, prediction_size, shift + tmp_csv_with_id, + batch_size, + history_size, + prediction_size, + shift, + history_columns, + meta_columns_cycle, + prediction_columns, ): - + # define encoder args + # [name: kwds] + encs = { + "weekday": dict(cycl_max=6), + "dayofyear": dict(cycl_max=366, cycl_min=1), + "month": dict(cycl_max=12, cycl_min=1), + "time": dict( + cycl_max=24 * 60 - 1, + cycl_getter=lambda df, k: df.index.hour * 60 + df.index.minute, + ), + } test_data_path, df = tmp_csv_with_id - df = df.set_index("date_time") - + test_df = df.set_index("date_time") + for name, kwds in encs.items(): + enc = CyclicalFeatureEncoder(name, **kwds) + test_df = enc(test_df) splitter = TimeSeriesSplit(0.5, TimeSeriesSplit.LEFT) - df = splitter(df) + test_df = splitter(test_df) ids = df.id.unique() - columns = sorted([c for c in df.columns if c != "id"]) - history_columns = columns[:1] - meta_columns = columns[1:] - prediction_columns = columns[:1] common_kwds = dict( history_size=history_size, prediction_size=prediction_size, history_columns=history_columns, - meta_columns=meta_columns, + meta_columns=meta_columns_cycle, prediction_columns=prediction_columns, batch_size=batch_size, ) @@ -146,19 +197,27 @@ def test_windowed_time_series_dataset_factory_csv_loader_with_preprocessors( shuffle_buffer_size=100, seed=1, ) - with get_ctxmgr(prediction_size): - factory = WindowedTimeSeriesDataSetFactory(**common_kwds, **factory_kwds) + with get_ctxmgr( + history_size=history_size, + prediction_size=prediction_size, + history_columns=history_columns, + meta_columns=meta_columns_cycle, + prediction_columns=prediction_columns, + ): + factory = WindowedTimeSeriesDatasetFactory(**common_kwds, **factory_kwds) factory.set_data_loader(CSVDataLoader(file_path=test_data_path)) factory.add_preprocessor(splitter) + for name, kwds in encs.items(): + factory.add_preprocessor(CyclicalFeatureEncoder(name, **kwds)) factory.add_preprocessor( GroupbyDatasetGenerator( - "id", columns=history_columns + meta_columns + prediction_columns + "id", columns=history_columns + meta_columns_cycle + prediction_columns ) ) ds = factory() ds validate_dataset( - df, + test_df, ds, **common_kwds, ) diff --git a/tests/test_pipleine.py b/tests/test_pipleine.py index 79b473c..f0031f0 100644 --- a/tests/test_pipleine.py +++ b/tests/test_pipleine.py @@ -14,19 +14,24 @@ # FIXTURES #################################################################### @pytest.fixture -def groupby_dataset(time_series_df_with_id): +def groupby_dataset( + time_series_df_with_id, history_columns, meta_columns, prediction_columns +): time_series_df_with_id.set_index("date_time", inplace=True) - columns = list(sorted([c for c in time_series_df_with_id.columns if c != "id"])) - gen = GroupbyDatasetGenerator("id", columns=columns) - return gen(time_series_df_with_id), time_series_df_with_id + used_cols = set(history_columns + meta_columns + prediction_columns) + gen = GroupbyDatasetGenerator("id", columns=used_cols) + return gen(time_series_df_with_id), time_series_df_with_id[list(used_cols) + ["id"]] @pytest.fixture -def patched_dataset(request, time_series_df): +def patched_dataset( + request, time_series_df, history_columns, meta_columns, prediction_columns +): window_size, shift = request.param df = time_series_df.set_index("date_time") + used_cols = set(history_columns + meta_columns + prediction_columns) - ds = tf.data.Dataset.from_tensors(df) + ds = tf.data.Dataset.from_tensors(df[sorted(used_cols)]) ds = ds.interleave( PatchGenerator(window_size=window_size, shift=shift), num_parallel_calls=tf.data.experimental.AUTOTUNE, @@ -62,7 +67,7 @@ def test_patch_generator(time_series_df, window_size, shift): @pytest.mark.parametrize("window_size,shift", [(2 * 48, 48), (48 + 1, 1)]) def test_patch_generator_groupby(groupby_dataset, window_size, shift): ds, df = groupby_dataset - records_per_id = df.groupby("id").x1.size().max() + records_per_id = df.groupby("id").size().max() ids = df.id.unique() columns = sorted([c for c in df.columns if c != "id"]) @@ -78,60 +83,83 @@ def test_patch_generator_groupby(groupby_dataset, window_size, shift): for i, patch in enumerate(ds_patched.as_numpy_iterator()): assert patch.shape == expected_shape, "Wrong shape" - x1 = patch[0, 0] - id = int(x1 // 1e6) - idx = int(x1 % 1e4) - expected_values = df[df.id == id].iloc[idx : idx + window_size] - assert np.all( - patch == expected_values[columns].values - ), "Patch contains wrong data" + if len(columns): + x1 = patch[0, 0] + id = int(x1 // 1e6) + idx = int(x1 % 1e4) + expected_values = df[df.id == id].iloc[idx : idx + window_size] + assert np.all( + patch == expected_values[columns].values + ), "Patch contains wrong data" assert i + 1 == patches, "Not enough patches" @pytest.mark.parametrize( "patched_dataset", [(2 * 48, 48), (48 + 1, 1)], indirect=["patched_dataset"] ) -def test_batch_processor(patched_dataset, history_size, batch_size): +def test_batch_processor( + patched_dataset, + history_size, + batch_size, + history_columns, + meta_columns, + prediction_columns, +): ds, df, window_size, shift = patched_dataset prediction_size = window_size - history_size - columns = list(sorted(df.columns)) - batch_kwds = dict( history_size=history_size, - history_columns=columns[:1], - meta_columns=columns[1:], - prediction_columns=columns[:1], + history_columns=history_columns, + meta_columns=meta_columns, + prediction_columns=prediction_columns, ) - ds_batched = ds.batch(batch_size, drop_remainder=True) - ds_batched = ds_batched.map( - BatchPreprocessor(**batch_kwds), - num_parallel_calls=tf.data.experimental.AUTOTUNE, - ) - - validate_dataset( - df, - ds_batched, - batch_size=batch_size, + with get_ctxmgr( + history_size=history_size, prediction_size=prediction_size, - **batch_kwds, - ) + history_columns=history_columns, + meta_columns=meta_columns, + prediction_columns=prediction_columns, + ): + ds_batched = ds.batch(batch_size, drop_remainder=True) + ds_batched = ds_batched.map( + BatchPreprocessor(**batch_kwds), + num_parallel_calls=tf.data.experimental.AUTOTUNE, + ) + + validate_dataset( + df, + ds_batched, + batch_size=batch_size, + prediction_size=prediction_size, + **batch_kwds, + ) def test_windowed_time_series_pipeline( - time_series_df, batch_size, history_size, prediction_size, shift + time_series_df, + batch_size, + history_size, + prediction_size, + shift, + history_columns, + meta_columns, + prediction_columns, ): df = time_series_df.set_index("date_time") - - columns = list(sorted(df.columns)) + used_cols = sorted( + set( + history_columns + meta_columns + prediction_columns, + ) + ) batch_kwds = dict( history_size=history_size, prediction_size=prediction_size, - history_columns=columns[:1], - meta_columns=columns[1:], - prediction_columns=columns[:1], + history_columns=history_columns, + meta_columns=meta_columns, + prediction_columns=prediction_columns, batch_size=batch_size, ) pipeline_kwds = dict( @@ -141,9 +169,15 @@ def test_windowed_time_series_pipeline( seed=1, ) - with get_ctxmgr(prediction_size): + with get_ctxmgr( + history_size=history_size, + prediction_size=prediction_size, + history_columns=history_columns, + meta_columns=meta_columns, + prediction_columns=prediction_columns, + ): pipeline = WindowedTimeSeriesPipeline(**batch_kwds, **pipeline_kwds) - ds = tf.data.Dataset.from_tensors(df) + ds = tf.data.Dataset.from_tensors(df[used_cols]) ds = pipeline(ds) ds validate_dataset( @@ -154,19 +188,25 @@ def test_windowed_time_series_pipeline( def test_windowed_time_series_pipeline_groupby( - groupby_dataset, batch_size, history_size, prediction_size, shift + groupby_dataset, + batch_size, + history_size, + prediction_size, + shift, + history_columns, + meta_columns, + prediction_columns, ): ds, df = groupby_dataset ids = df.id.unique() - columns = sorted([c for c in df.columns if c != "id"]) batch_kwds = dict( history_size=history_size, prediction_size=prediction_size, - history_columns=columns[:1], - meta_columns=columns[1:], - prediction_columns=columns[:1], + history_columns=history_columns, + meta_columns=meta_columns, + prediction_columns=prediction_columns, batch_size=batch_size, ) pipeline_kwds = dict( @@ -175,7 +215,14 @@ def test_windowed_time_series_pipeline_groupby( shuffle_buffer_size=1000, seed=1, ) - with get_ctxmgr(prediction_size): + + with get_ctxmgr( + history_size=history_size, + prediction_size=prediction_size, + history_columns=history_columns, + meta_columns=meta_columns, + prediction_columns=prediction_columns, + ): pipeline = WindowedTimeSeriesPipeline(**batch_kwds, **pipeline_kwds) ds = pipeline(ds) ds From 2db7f0a246648a22c3a16ac71a34ad63d5840222 Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus Date: Fri, 7 Jan 2022 18:02:51 +0100 Subject: [PATCH 20/72] updates python dependencies --- setup.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/setup.py b/setup.py index 34b2407..2dbfcf5 100644 --- a/setup.py +++ b/setup.py @@ -13,10 +13,8 @@ author_email="marcel.arpogaus@gmail.com", packages=find_packages("src"), package_dir={"": "src"}, - install_requires=[ - "tensorflow==2.6.*", - ], - description="A tensorflow dataset from time series data.", + install_requires=["tensorflow==2.6.*", "pandas==1.3.*"], + description="A TensorFlow dataset from time-series data.", long_description=long_description, long_description_content_type="text/markdown", classifiers=[ From 8c010dd44a6657cd540b75ee2f1ef354faf5321c Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus Date: Fri, 7 Jan 2022 18:10:16 +0100 Subject: [PATCH 21/72] updates readme --- .images/example1.png | Bin 0 -> 76434 bytes .images/example2.png | Bin 0 -> 70761 bytes .images/example3.png | Bin 0 -> 72372 bytes README.md | 0 README.org | 240 +++++++++++++++++++++++++++++++++++++++++++ 5 files changed, 240 insertions(+) create mode 100644 .images/example1.png create mode 100644 .images/example2.png create mode 100644 .images/example3.png delete 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zEY9>&`&@k(Tr`UKMH5)W9e1y~3(3h*zEJwrtFl9bwgtgsFOSwfzX8mszN|Y`H7B)Ty9EB4Jf%c3Qc{1o7}>%R6F80l?sNb~J@@k` z*2^zDcP8Le+pmrCi5)FvghocTL54~|MMZ_2D1f$6zE4x0V?*Qt9@f91LGmJl1VNCD z9|q1vMlrEV5XcgLc3Sp_2#A*B&8P2SUyxlwMi#&%8*KRPHM`raA5IZKM<`z?%wN1D z>VbHC`3fjQ--2qrt6I4zA zc!vLygJV|Mz$x&f$MZ*#2K#2Yl53zdt%Bst$i}@qxP?2MYeXEw3t9AY + +[[https://github.com/MArpogaus/tensorflow_timeseries_dataset/graphs/contributors][https://img.shields.io/github/contributors/MArpogaus/tensorflow_timeseries_dataset.svg?style=flat-square]] +[[https://github.com/MArpogaus/tensorflow_timeseries_dataset/network/members][https://img.shields.io/github/forks/MArpogaus/tensorflow_timeseries_dataset.svg?style=flat-square]] +[[https://github.com/MArpogaus/tensorflow_timeseries_dataset/stargazers][https://img.shields.io/github/stars/MArpogaus/tensorflow_timeseries_dataset.svg?style=flat-square]] +[[https://github.com/MArpogaus/tensorflow_timeseries_dataset/issues][https://img.shields.io/github/issues/MArpogaus/tensorflow_timeseries_dataset.svg?style=flat-square]] +[[https://github.com/MArpogaus/tensorflow_timeseries_dataset/blob/master/LICENSE][https://img.shields.io/github/license/MArpogaus/tensorflow_timeseries_dataset.svg?style=flat-square]] +[[https://linkedin.com/in/MArpogaus][https://img.shields.io/badge/-LinkedIn-black.svg?style=flat-square&logo=linkedin&colorB=555]] + +** About The Project + :PROPERTIES: + :CUSTOM_ID: about-the-project + :END: +This python packages should help you to create TensorFlow datasets for time-series data. + +** Getting Started :noexport: + :PROPERTIES: + :CUSTOM_ID: getting-started + :END: +This is an example of how you may give instructions on setting up your +project locally. To get a local copy up and running follow these simple +example steps. + +*** Prerequisites + :PROPERTIES: + :CUSTOM_ID: prerequisites + :END: + +- A +- B + +*** Installation + :PROPERTIES: + :CUSTOM_ID: installation + :END: + +1. Step +2. Step + +** Usage + :PROPERTIES: + :CUSTOM_ID: usage + :END: +*** Example Data +Suppose you have a dataset in the following form: + +#+begin_src python :session :exports both +import numpy as np +import pandas as pd + +columns=['x1', 'x2', 'x3'] +periods=48 * 14 +test_df=pd.DataFrame( + index=pd.date_range( + start='1/1/1992', + periods=periods, + freq='30T' + ), + data=np.stack( + [ + np.random.normal(0,0.5,periods), + np.random.normal(1,0.5,periods), + np.random.normal(2,0.5,periods) + ], + axis=1 + ), + columns=columns +) +test_df.head() +#+end_src + +#+RESULTS: +: x1 x2 x3 +: 1992-01-01 00:00:00 -0.703962 1.806191 2.180241 +: 1992-01-01 00:30:00 0.022343 0.813200 2.609294 +: 1992-01-01 01:00:00 -0.033567 1.330009 1.791400 +: 1992-01-01 01:30:00 0.170615 0.702265 2.313323 +: 1992-01-01 02:00:00 -0.427118 1.156591 2.623082 + +*** Single-Step Prediction +this module contains the factory class =WindowedTimeSeriesDatasetFactory= to create a TensorFlow dataset from pandas dataframes, or other data sources as we will see later. +We will use it now to create a dataset with =48= historic time-steps as the input to predict a single time-step in the future. + +#+NAME: ds1 +#+begin_src python :session :exports both +from tensorflow_time_series_dataset import WindowedTimeSeriesDatasetFactory as Factory + +factory_kwds=dict( + history_size=48, + prediction_size=1, + history_columns=['x1', 'x2', 'x3'], + prediction_columns=['x3'], + batch_size=4 +) +factory=Factory(**factory_kwds) +ds1=factory(test_df) +ds1 +#+end_src + +This returns the following TensorFlow Dataset: + +#+RESULTS: ds1 +: + +#+NAME: ds1_plot +#+begin_src python :session :results file :exports both +from tensorflow_time_series_dataset.utils.visualisation import plot_patch +fig=plot_patch( + ds1, + figsize=(8,4), + ,**factory_kwds +) + +fname='.images/example1.png' +fig.savefig(fname) +fname +#+end_src + +#+RESULTS: ds1_plot +[[file:.images/example1.png]] + +*** Multi-Step Prediction +Lets now change the prediction size to a whole day consisting of =6= half-hour time-steps. +#+Name: ds2 +#+begin_src python :session :exports both +factory_kwds.update(dict( + prediction_size=6 +)) +factory=Factory(**factory_kwds) +ds2=factory(test_df) +ds2 +#+end_src + + +This returns the following TensorFlow Dataset: +#+RESULTS: ds2 +: + +#+NAME: ds2_plot +#+begin_src python :session :results file :exports both +fig=plot_patch( + ds2, + figsize=(8,4), + **factory_kwds +) + +fname='.images/example2.png' +fig.savefig(fname) +fname +#+end_src + +#+RESULTS: ds2_plot +[[file:.images/example2.png]] + +*** Add Meta data as features +this module contains the factory class =WindowedTimeSeriesDatasetFactory= to create a TensorFlow dataset from pandas dataframes, or other data sources as we will see later. +#+NAME: ds3 +#+begin_src python :session +import itertools +from tensorflow_time_series_dataset.preprocessors import CyclicalFeatureEncoder +encs = { + "weekday": dict(cycl_max=6), + "dayofyear": dict(cycl_max=366, cycl_min=1), + "month": dict(cycl_max=12, cycl_min=1), + "time": dict( + cycl_max=24 * 60 - 1, + cycl_getter=lambda df, k: df.index.hour * 60 + df.index.minute, + ), +} +factory_kwds.update(dict( + meta_columns=list(itertools.chain(*[[c+'_sin', c+'_cos'] for c in encs.keys()])) +)) +factory=Factory(**factory_kwds) +for name, kwds in encs.items(): + factory.add_preprocessor(CyclicalFeatureEncoder(name, **kwds)) +ds3=factory(test_df) +ds3 +#+end_src + +This returns the following TensorFlow Dataset: +#+RESULTS: ds3 +: + +#+NAME: ds3_plot +#+begin_src python :session :results file :exports both +fig=plot_patch( + ds3, + figsize=(8,4), + **factory_kwds +) + +fname='.images/example3.png' +fig.savefig(fname) +fname +#+end_src + +#+RESULTS: ds3_plot +[[file:.images/example3.png]] + +** Contributing + :PROPERTIES: + :CUSTOM_ID: contributing + :END: +Contributions are what make the open source community such an amazing +place to learn, inspire, and create. Any contributions you make are +*greatly appreciated*. + +If you have a suggestion that would make this better, please fork the +repo and create a pull request. You can also simply open an issue with +the tag "enhancement". Don't forget to give the project a star! Thanks +again! + +1. Fork the Project +2. Create your Feature Branch (Symbol's value as variable is void: git) +3. Commit your Changes (Symbol's value as variable is void: git) +4. Push to the Branch (Symbol's value as variable is void: git) +5. Open a Pull Request + +Any contributions are *greatly appreciated*. + +** License + :PROPERTIES: + :CUSTOM_ID: license + :END: +Distributed under the [[file:LICENSE][Apache License 2.0]] + +** Contact + :PROPERTIES: + :CUSTOM_ID: contact + :END: +[[https://github.com/marpogaus][Marcel Arpogaus]] - [[mailto:marcel.arpogaus@gmail.com][marcel.arpogaus@gmail.com]] + +Project Link: +[[https://github.com/MArpogaus/tensorflow_timeseries_dataset]] + +** Acknowledgments + :PROPERTIES: + :CUSTOM_ID: acknowledgments + :END: +Parts of this work have been funded by the Federal Ministry for the Environment, Nature Conservation and Nuclear Safety due to a decision of the German Federal Parliament (AI4Grids: 67KI2012A). 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1992-01-01 00:00:00 0.258083 0.902987 1.617639 + 1992-01-01 00:30:00 0.203509 1.414581 1.771407 + 1992-01-01 01:00:00 0.013242 1.774091 2.686694 + 1992-01-01 01:30:00 0.351366 1.142042 1.304276 + 1992-01-01 02:00:00 0.158146 -0.222801 2.538896 - + ## Single-Step Prediction @@ -109,7 +109,7 @@ We can plot the result with the utility function `plot_path`: ![img](.images/example1.png) - + ## Multi-Step Prediction @@ -141,7 +141,7 @@ Again, lets plot the results to see what changed: ![img](.images/example2.png) - + ## Add Meta data as features @@ -169,6 +169,8 @@ this module contains the factory class `WindowedTimeSeriesDatasetFactory` to cre This returns the following TensorFlow Dataset: + + Again, lets plot the results to see what changed: fig=plot_patch( diff --git a/README.org b/README.org index 24c624a..48eedf5 100644 --- a/README.org +++ b/README.org @@ -160,7 +160,7 @@ fname *** Add Meta data as features this module contains the factory class =WindowedTimeSeriesDatasetFactory= to create a TensorFlow dataset from pandas dataframes, or other data sources as we will see later. #+NAME: ds3 -#+begin_src python :session +#+begin_src python :session :exports both import itertools from tensorflow_time_series_dataset.preprocessors import CyclicalFeatureEncoder encs = { @@ -183,6 +183,7 @@ ds3 #+end_src This returns the following TensorFlow Dataset: + #+RESULTS: ds3 : From 5cdabd75d00f899502aa9c3235e300cdf889ab63 Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus Date: Mon, 10 Jan 2022 11:03:51 +0100 Subject: [PATCH 26/72] plot_patch now uses same color for same columns --- .../utils/visualisation.py | 87 +++++++++++++++++++ 1 file changed, 87 insertions(+) create mode 100644 src/tensorflow_time_series_dataset/utils/visualisation.py diff --git a/src/tensorflow_time_series_dataset/utils/visualisation.py b/src/tensorflow_time_series_dataset/utils/visualisation.py new file mode 100644 index 0000000..138fc8c --- /dev/null +++ b/src/tensorflow_time_series_dataset/utils/visualisation.py @@ -0,0 +1,87 @@ +#!/usr/bin/env python3 +# -*- time-stamp-pattern: "changed[\s]+:[\s]+%%$"; -*- +# AUTHOR INFORMATION ########################################################## +# file : visualisation.py +# author : Marcel Arpogaus +# +# created : 2022-01-07 16:50:58 (Marcel Arpogaus) +# changed : 2022-01-10 10:51:50 (Marcel Arpogaus) +# DESCRIPTION ################################################################# +# ... +# LICENSE ##################################################################### +# ... +############################################################################### +import numpy as np +import matplotlib.pyplot as plt + +prop_cycle = plt.rcParams["axes.prop_cycle"] +colors = prop_cycle.by_key()["color"] + + +def plot_patch( + ds, + history_size, + prediction_size, + history_columns, + prediction_columns, + meta_columns=[], + figsize=(16, 8), + **kwds +): + x, y = next(ds.as_numpy_iterator()) + x1, x2 = None, None + + if history_size and len(history_columns) and len(meta_columns): + x1, x2 = x + elif history_size and len(history_columns): + x1 = x + elif len(meta_columns): + x2 = x + + x1_columns = sorted(history_columns) + x2_columns = sorted(meta_columns) + y_columns = sorted(prediction_columns) + + x1_column_ch = {k: c for c, k in enumerate(x1_columns)} + y_column_ch = {k: c for c, k in enumerate(y_columns)} + + fig = plt.figure(figsize=figsize) + if x1 is not None: + for c in x1_columns: + ch = x1_column_ch[c] + plt.plot( + np.arange(history_size), + x1[0, ..., ch], + color=colors[ch % len(colors)], + label=c, + ) + + if x2 is not None: + plt.table( + cellText=x2[0], + rowLabels=["meta data"], + colLabels=x2_columns, + colLoc="right", + loc="top", + edges="horizontal", + ) + for c in y_columns: + ch = y_column_ch[c] + color_idx = x1_column_ch.get(c, ch) + plt.scatter( + np.arange(prediction_size) + history_size, + y[0, ..., ch], + color=colors[color_idx % len(colors)], + label=c + " (target)", + s=32, + edgecolors="k", + ) + + # Adjust layout to make room for the table: + plt.subplots_adjust(left=0.1, top=1.2) + plt.legend(loc="lower right") + plt.xlabel("Time Steps") + plt.ylabel("Observations") + plt.tight_layout() + + return fig From 5d2b43667d2de790efff9aaca84217b273449dd6 Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus Date: Mon, 10 Jan 2022 11:05:51 +0100 Subject: [PATCH 27/72] updates README --- .images/example1.png | Bin 75981 -> 71720 bytes .images/example2.png | Bin 68504 -> 69540 bytes .images/example3.png | Bin 71896 -> 72471 bytes README.md | 36 +++++++++++++++++++----------------- README.org | 21 +++++++++++---------- 5 files changed, 30 insertions(+), 27 deletions(-) diff --git a/.images/example1.png b/.images/example1.png index 020c12246befd415570bf047d046287d45f43743..80cb93945d7545c6c353692c19e48b18bb5e0522 100644 GIT binary patch literal 71720 zcmcG$WmwhQ7cIOuDczmYC?TD~mTnMGP`W#$8&tZaq?HDh4gu-zMp7E2JEY-V+jIWs 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zBvjUT{N8=;Vq203`Mpo7GGJZt2?+Q+59~f`ajB+=UoXF^vj*_5b`2a1~*Ul$EV?juxdo*;Au3~1rh8B%RgUcJ>2w+|DECV>D7ODpYFU5Z!`r!_yatfqhT=3C=|20MtEt{J5^Q_4c_wTuMyqtXj%d z2#f&zJUZv(XVnj_SDcc70R!IjtcmRPGV(N1Q6hR_-<1_Bq(FE_d=nFH|C3ZTu(IWd zB?3XyKZOk91^3_&suh__WRtKI2$qLLnNZ%GLQq_20Ozs0Wd7zoHQ3CX|BPVQBrY*= zGsG?*x}ILQ=)SbGI!xXAhBpKIa zPJqC5#2yC_JUkSvd=peh_+UbKLsQcf%vEmyU?$+9ZB^f#)Kys4Cjry$Tc}HjdASX# zFB(k2*#t{1>Tv~5f;#a(g9< + ## Example Data @@ -62,18 +62,18 @@ Suppose you have a dataset in the following form: test_df.head() x1 x2 x3 - 1992-01-01 00:00:00 0.258083 0.902987 1.617639 - 1992-01-01 00:30:00 0.203509 1.414581 1.771407 - 1992-01-01 01:00:00 0.013242 1.774091 2.686694 - 1992-01-01 01:30:00 0.351366 1.142042 1.304276 - 1992-01-01 02:00:00 0.158146 -0.222801 2.538896 + 1992-01-01 00:00:00 -0.282495 0.824307 1.986228 + 1992-01-01 00:30:00 0.056311 1.288896 1.875381 + 1992-01-01 01:00:00 -0.272162 0.662390 1.956936 + 1992-01-01 01:30:00 -0.075224 0.765020 1.940887 + 1992-01-01 02:00:00 0.343781 0.652433 1.864976 - + ## Single-Step Prediction -this module contains the factory class `WindowedTimeSeriesDatasetFactory` to create a TensorFlow dataset from pandas dataframes, or other data sources as we will see later. +The factory class `WindowedTimeSeriesDatasetFactory` is used to create a TensorFlow dataset from pandas dataframes, or other data sources as we will see later. We will use it now to create a dataset with `48` historic time-steps as the input to predict a single time-step in the future. from tensorflow_time_series_dataset import WindowedTimeSeriesDatasetFactory as Factory @@ -109,11 +109,11 @@ We can plot the result with the utility function `plot_path`: ![img](.images/example1.png) - + ## Multi-Step Prediction -Lets now change the prediction size to a whole day consisting of `6` half-hour time-steps. +Lets now increase the change the prediction size to `6` half-hour time-steps. factory_kwds.update(dict( prediction_size=6 @@ -141,11 +141,13 @@ Again, lets plot the results to see what changed: ![img](.images/example2.png) - + -## Add Meta data as features +## Preprocessing: Add Metadata features -this module contains the factory class `WindowedTimeSeriesDatasetFactory` to create a TensorFlow dataset from pandas dataframes, or other data sources as we will see later. +Preprocessors can be used to transform the data before it is fed into the model. +A Preprocessor can be any python callable. +In this case we will be using the a class called `CyclicalFeatureEncoder` to encode our one-dimensional cyclical features like the *time* or *weekday* to two-dimensional coordinates using a sine and cosine transformation as suggested in [this blogpost](). import itertools from tensorflow_time_series_dataset.preprocessors import CyclicalFeatureEncoder diff --git a/README.org b/README.org index 48eedf5..46d94ed 100644 --- a/README.org +++ b/README.org @@ -74,14 +74,14 @@ test_df.head() #+RESULTS: : x1 x2 x3 -: 1992-01-01 00:00:00 -0.703962 1.806191 2.180241 -: 1992-01-01 00:30:00 0.022343 0.813200 2.609294 -: 1992-01-01 01:00:00 -0.033567 1.330009 1.791400 -: 1992-01-01 01:30:00 0.170615 0.702265 2.313323 -: 1992-01-01 02:00:00 -0.427118 1.156591 2.623082 +: 1992-01-01 00:00:00 0.164779 1.065385 1.898858 +: 1992-01-01 00:30:00 0.105352 0.508127 1.540083 +: 1992-01-01 01:00:00 -0.766406 1.430624 1.626804 +: 1992-01-01 01:30:00 -0.422850 0.869291 2.327598 +: 1992-01-01 02:00:00 -0.755304 1.832349 2.670264 *** Single-Step Prediction -this module contains the factory class =WindowedTimeSeriesDatasetFactory= to create a TensorFlow dataset from pandas dataframes, or other data sources as we will see later. +The factory class =WindowedTimeSeriesDatasetFactory= is used to create a TensorFlow dataset from pandas dataframes, or other data sources as we will see later. We will use it now to create a dataset with =48= historic time-steps as the input to predict a single time-step in the future. #+NAME: ds1 @@ -124,7 +124,7 @@ fname #+RESULTS: ds1_plot [[file:.images/example1.png]] *** Multi-Step Prediction -Lets now change the prediction size to a whole day consisting of =6= half-hour time-steps. +Lets now increase the change the prediction size to =6= half-hour time-steps. #+Name: ds2 #+begin_src python :session :exports both factory_kwds.update(dict( @@ -157,8 +157,10 @@ fname #+RESULTS: ds2_plot [[file:.images/example2.png]] -*** Add Meta data as features -this module contains the factory class =WindowedTimeSeriesDatasetFactory= to create a TensorFlow dataset from pandas dataframes, or other data sources as we will see later. +*** Preprocessing: Add Metadata features +Preprocessors can be used to transform the data before it is fed into the model. +A Preprocessor can be any python callable. +In this case we will be using the a class called =CyclicalFeatureEncoder= to encode our one-dimensional cyclical features like the /time/ or /weekday/ to two-dimensional coordinates using a sine and cosine transformation as suggested in [this blogpost](https://www.kaggle.com/avanwyk/encoding-cyclical-features-for-deep-learning). #+NAME: ds3 #+begin_src python :session :exports both import itertools @@ -183,7 +185,6 @@ ds3 #+end_src This returns the following TensorFlow Dataset: - #+RESULTS: ds3 : From 8fa5ef004c78b21d239a740a8c3e726eaf90e28f Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus Date: Mon, 10 Jan 2022 11:25:37 +0100 Subject: [PATCH 28/72] adds contribution guidelines --- .images/example1.png | Bin 71720 -> 72861 bytes .images/example2.png | Bin 69540 -> 68534 bytes .images/example3.png | Bin 72471 -> 70693 bytes CONTRIBUTING.md | 17 +++++++++ README.md | 82 ++++++++++++++++--------------------------- README.org | 45 ++++++++---------------- 6 files changed, 63 insertions(+), 81 deletions(-) create mode 100644 CONTRIBUTING.md 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zFrap!pyuSj{tO`@6s}cmhq6AqLQuj@t*ue~EsW(A6aWLn$B@2niRO%kVrv!%FjtTm zdwSv{zw<+<+C+&PD}DBQJr8_lH&|w&Z{M;Y7-VZSC(^wsb()L<%P~`0j4;AM0r%kVA#wTgox6AOVPQisI}7eoW#b;KyX%wDkRbtd zM+lX>G*qG3!$BKyB(At@&Zr?2Gq2^0I0WSkzrw?&;7#}GUS114NEnce0WgvRG8-mw zo0wbvCUBqXe@mkoHz<%@Hl|b%pK?e}VOR@*HTZDianrb;48Tz+4l_#5ATt(eXlcRi z-Ls*igB)Bd&;T~0rUD5$lp#;)?dwB?g8-?ZQ?<+{Yy85aqFUHJ#{yM?38o=T^Du>S^wzuyHC8ivS#$@2d_Zv6iojM`7} Y=6ReJ?l!o`qu`H*n&y>Uv{}&q0b58)$p8QV diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..eacabe4 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,17 @@ +# Contributing + +Contributions are what make the open source community such an amazing +place to learn, inspire, and create. +Thank you for considering a contribution to this project. +Any contributions you make are _greatly appreciated_. + +If you have a suggestion that would make this better, please fork the repo and create a pull request. +You can also simply open an issue with the tag "enhancement". + +1. Fork the project +2. Create a feature branch for your changes +3. Commit your changes +4. Push to the branch +5. Open a pull request + +Thanks again for considering contribution. diff --git a/README.md b/README.md index 48cfbe1..d50f6ed 100644 --- a/README.md +++ b/README.md @@ -6,41 +6,43 @@ [![img](https://img.shields.io/badge/-LinkedIn-black.svg?style=flat-square&logo=linkedin&colorB=555)](https://linkedin.com/in/MArpogaus) -# Table of Contents +# TensorFlow time-series Dataset 1. [About The Project](#about-the-project) 2. [Usage](#usage) - 1. [Example Data](#org172632d) - 2. [Single-Step Prediction](#org660c3b2) - 3. [Multi-Step Prediction](#org487b8c3) - 4. [Preprocessing: Add Metadata features](#orgae97c2b) -3. [Contributing](#contributing) -4. [License](#license) -5. [Contact](#contact) -6. [Acknowledgments](#acknowledgments) + 1. [Example Data](#org705c8ce) + 2. [Single-Step Prediction](#orge605148) + 3. [Multi-Step Prediction](#org46070f1) + 4. [Preprocessing: Add Metadata features](#org6f4b42f) +3. [License](#license) +4. [Contact](#contact) +5. [Acknowledgments](#acknowledgments) -# About The Project +## About The Project -This python packages should help you to create TensorFlow datasets for time-series data. +This python package should help you to create TensorFlow datasets for time-series data. -# Usage +## Usage - + -## Example Data +### Example Data Suppose you have a dataset in the following form: import numpy as np import pandas as pd + # make things determeinisteic + np.random.seed(1) + columns=['x1', 'x2', 'x3'] periods=48 * 14 test_df=pd.DataFrame( @@ -62,16 +64,16 @@ Suppose you have a dataset in the following form: test_df.head() x1 x2 x3 - 1992-01-01 00:00:00 -0.282495 0.824307 1.986228 - 1992-01-01 00:30:00 0.056311 1.288896 1.875381 - 1992-01-01 01:00:00 -0.272162 0.662390 1.956936 - 1992-01-01 01:30:00 -0.075224 0.765020 1.940887 - 1992-01-01 02:00:00 0.343781 0.652433 1.864976 + 1992-01-01 00:00:00 0.812173 1.205133 1.578044 + 1992-01-01 00:30:00 -0.305878 1.429935 1.413295 + 1992-01-01 01:00:00 -0.264086 0.550658 1.602187 + 1992-01-01 01:30:00 -0.536484 1.159828 1.644974 + 1992-01-01 02:00:00 0.432704 1.159077 2.005718 - + -## Single-Step Prediction +### Single-Step Prediction The factory class `WindowedTimeSeriesDatasetFactory` is used to create a TensorFlow dataset from pandas dataframes, or other data sources as we will see later. We will use it now to create a dataset with `48` historic time-steps as the input to predict a single time-step in the future. @@ -109,11 +111,11 @@ We can plot the result with the utility function `plot_path`: ![img](.images/example1.png) - + -## Multi-Step Prediction +### Multi-Step Prediction -Lets now increase the change the prediction size to `6` half-hour time-steps. +Lets now increase the prediction size to `6` half-hour time-steps. factory_kwds.update(dict( prediction_size=6 @@ -141,9 +143,9 @@ Again, lets plot the results to see what changed: ![img](.images/example2.png) - + -## Preprocessing: Add Metadata features +### Preprocessing: Add Metadata features Preprocessors can be used to transform the data before it is fed into the model. A Preprocessor can be any python callable. @@ -188,38 +190,16 @@ Again, lets plot the results to see what changed: ![img](.images/example3.png) - - -# Contributing - -Contributions are what make the open source community such an amazing -place to learn, inspire, and create. Any contributions you make are -**greatly appreciated**. - -If you have a suggestion that would make this better, please fork the -repo and create a pull request. You can also simply open an issue with -the tag “enhancement”. Don’t forget to give the project a star! Thanks -again! - -1. Fork the Project -2. Create your Feature Branch -3. Commit your Changes -4. Push to the Branch -5. Open a Pull Request - -Any contributions are **greatly appreciated**. - - -# License +## License Distributed under the [Apache License 2.0](LICENSE) -# Contact +## Contact [Marcel Arpogaus](https://github.com/marpogaus) - [marcel.arpogaus@gmail.com](mailto:marcel.arpogaus@gmail.com) @@ -229,7 +209,7 @@ Project Link: -# Acknowledgments +## Acknowledgments Parts of this work have been funded by the Federal Ministry for the Environment, Nature Conservation and Nuclear Safety due to a decision of the German Federal Parliament (AI4Grids: 67KI2012A). diff --git a/README.org b/README.org index 46d94ed..9d38830 100644 --- a/README.org +++ b/README.org @@ -8,13 +8,15 @@ [[https://github.com/MArpogaus/tensorflow_timeseries_dataset/blob/master/LICENSE][https://img.shields.io/github/license/MArpogaus/tensorflow_timeseries_dataset.svg?style=flat-square]] [[https://linkedin.com/in/MArpogaus][https://img.shields.io/badge/-LinkedIn-black.svg?style=flat-square&logo=linkedin&colorB=555]] -#+TOC: headlines 2 +* TensorFlow time-series Dataset + +#+TOC: headlines 2 local ** About The Project :PROPERTIES: :CUSTOM_ID: about-the-project :END: -This python packages should help you to create TensorFlow datasets for time-series data. +This python package should help you to create TensorFlow datasets for time-series data. ** Getting Started :noexport: :PROPERTIES: @@ -47,10 +49,14 @@ example steps. *** Example Data Suppose you have a dataset in the following form: +#+NAME: df #+begin_src python :session :exports both import numpy as np import pandas as pd +# make things determeinisteic +np.random.seed(1) + columns=['x1', 'x2', 'x3'] periods=48 * 14 test_df=pd.DataFrame( @@ -72,13 +78,13 @@ test_df=pd.DataFrame( test_df.head() #+end_src -#+RESULTS: +#+RESULTS: df : x1 x2 x3 -: 1992-01-01 00:00:00 0.164779 1.065385 1.898858 -: 1992-01-01 00:30:00 0.105352 0.508127 1.540083 -: 1992-01-01 01:00:00 -0.766406 1.430624 1.626804 -: 1992-01-01 01:30:00 -0.422850 0.869291 2.327598 -: 1992-01-01 02:00:00 -0.755304 1.832349 2.670264 +: 1992-01-01 00:00:00 0.812173 1.205133 1.578044 +: 1992-01-01 00:30:00 -0.305878 1.429935 1.413295 +: 1992-01-01 01:00:00 -0.264086 0.550658 1.602187 +: 1992-01-01 01:30:00 -0.536484 1.159828 1.644974 +: 1992-01-01 02:00:00 0.432704 1.159077 2.005718 *** Single-Step Prediction The factory class =WindowedTimeSeriesDatasetFactory= is used to create a TensorFlow dataset from pandas dataframes, or other data sources as we will see later. @@ -124,7 +130,7 @@ fname #+RESULTS: ds1_plot [[file:.images/example1.png]] *** Multi-Step Prediction -Lets now increase the change the prediction size to =6= half-hour time-steps. +Lets now increase the prediction size to =6= half-hour time-steps. #+Name: ds2 #+begin_src python :session :exports both factory_kwds.update(dict( @@ -205,27 +211,6 @@ fname #+RESULTS: ds3_plot [[file:.images/example3.png]] -** Contributing - :PROPERTIES: - :CUSTOM_ID: contributing - :END: -Contributions are what make the open source community such an amazing -place to learn, inspire, and create. Any contributions you make are -*greatly appreciated*. - -If you have a suggestion that would make this better, please fork the -repo and create a pull request. You can also simply open an issue with -the tag "enhancement". Don't forget to give the project a star! Thanks -again! - -1. Fork the Project -2. Create your Feature Branch -3. Commit your Changes -4. Push to the Branch -5. Open a Pull Request - -Any contributions are *greatly appreciated*. - ** License :PROPERTIES: :CUSTOM_ID: license From ebb24fcf3fb240a43ed6aec2d682924793d995ff Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus Date: Tue, 11 Jan 2022 17:01:34 +0100 Subject: [PATCH 29/72] makes generation of reference values more robust --- .../utils/test.py | 85 ++++++++++++++----- tests/conftest.py | 24 +++--- tests/test_pipleine.py | 31 +++++-- tests/test_preprocessors.py | 16 ++-- 4 files changed, 107 insertions(+), 49 deletions(-) diff --git a/src/tensorflow_time_series_dataset/utils/test.py b/src/tensorflow_time_series_dataset/utils/test.py index 58824f1..75248cb 100644 --- a/src/tensorflow_time_series_dataset/utils/test.py +++ b/src/tensorflow_time_series_dataset/utils/test.py @@ -4,7 +4,7 @@ # author : Marcel Arpogaus # # created : 2022-01-07 09:02:38 (Marcel Arpogaus) -# changed : 2022-01-07 14:50:51 (Marcel Arpogaus) +# changed : 2022-01-11 15:50:28 (Marcel Arpogaus) # DESCRIPTION ################################################################# # ... # LICENSE ##################################################################### @@ -28,23 +28,29 @@ import pytest -def get_id_and_idx(val): - id = val // 1e6 - idx = val % 1e4 - return id, idx +def get_idx(ref, val): + idx = np.where(ref == val)[0] + assert len(idx) == 1, "Could not determine index from refference value" + return int(idx) def gen_patch(df, idx, size): - return df.values[np.int(idx) : np.int(idx + size)] + x = df.values[idx : idx + size] + assert len(x) == size, "Could not generate patch from reference data" + return x -def gen_batch(df, columns, size, ids, lines): +def gen_batch(df, columns, size, refs, ref_col): batch = [] - for id, line in zip(ids, lines): + columns = list(sorted(columns)) + for ref in refs: if "id" in df.columns: - p = gen_patch(df[df.id == id][columns], line, size) + id = ref // 1e5 + idx = get_idx(df[df.id == id][ref_col], ref) + p = gen_patch(df[df.id == id][columns], idx, size) else: - p = gen_patch(df[columns], line, size) + idx = get_idx(df[ref_col], ref) + p = gen_patch(df[columns], idx, size) batch.append(p) return np.float32(batch) @@ -61,17 +67,35 @@ def validate_dataset( history_reference_column="ref", meta_reference_column="ref", prediction_reference_column="ref", + **kwds, ): + df = df.sort_index() x1_shape = (batch_size, history_size, len(history_columns)) x2_shape = (batch_size, 1, len(meta_columns)) y_shape = (batch_size, prediction_size, len(prediction_columns)) + history_columns = list(sorted(history_columns)) meta_columns = list(sorted(meta_columns)) prediction_columns = list(sorted(prediction_columns)) + history_columns_idx = {c: i for i, c in enumerate(history_columns)} meta_columns_idx = {c: i for i, c in enumerate(meta_columns)} prediction_columns_idx = {c: i for i, c in enumerate(prediction_columns)} + assert ( + len(set(history_columns + meta_columns + prediction_columns) - set(df.columns)) + == 0 + ), "Not all columns in test df" + assert ( + history_reference_column in df.columns + ), f"history_reference_column ({history_reference_column}) not in df.columns" + assert ( + meta_reference_column in df.columns + ), f"meta_reference_column ({meta_reference_column}) not in df.columns" + assert ( + prediction_reference_column in df.columns + ), f"prediction_reference_column ({prediction_reference_column}) not in df.columns" + for b, (x, y) in enumerate(ds.as_numpy_iterator()): x1, x2 = None, None if history_size and len(history_columns) and len(meta_columns): @@ -83,36 +107,57 @@ def validate_dataset( if x1 is not None: assert x1.shape == x1_shape, f"Wrong shape: history ({b})" - first_val = x1[:, 0, history_columns_idx[history_reference_column]] - ids, lines = get_id_and_idx(first_val) + ref = x1[:, 0, history_columns_idx[history_reference_column]] assert np.all( - x1 == gen_batch(df, history_columns, history_size, ids, lines) + x1 + == gen_batch( + df, history_columns, history_size, ref, history_reference_column + ) ), f"Wrong data: history ({b})" if x2 is not None: assert np.all( - x2 == gen_batch(df, meta_columns, 1, ids, lines + history_size) + x2 + == gen_batch( + df, + meta_columns, + 1, + ref + history_size, + history_reference_column, + ) ), f"wrong data: meta not consecutive ({b})" last_val = x1[:, -1, history_columns_idx[history_reference_column]] - ids, lines = get_id_and_idx(last_val) - y_test = gen_batch(df, prediction_columns, prediction_size, ids, lines + 1) + y_test = gen_batch( + df, + prediction_columns, + prediction_size, + last_val + 1, + history_reference_column, + ) assert np.all(y == y_test), f"Wrong data: prediction not consecutive ({b})" if x2 is not None: first_val = x2[:, 0, meta_columns_idx[meta_reference_column]] - ids, lines = get_id_and_idx(first_val) assert x2.shape == x2_shape, f"Wrong shape: meta ({b})" assert np.all( - x2 == gen_batch(df, meta_columns, 1, ids, lines) + x2 == gen_batch(df, meta_columns, 1, first_val, meta_reference_column) ), f"Wrong data: meta ({b})" assert y.shape == y_shape, f"Wrong shape: prediction ({b})" first_val = y[:, 0, prediction_columns_idx[prediction_reference_column]] - ids, lines = get_id_and_idx(first_val) assert np.all( - y == gen_batch(df, prediction_columns, prediction_size, ids, lines) + y + == gen_batch( + df, + prediction_columns, + prediction_size, + first_val, + prediction_reference_column, + ) ), f"Wrong data: prediction ({b})" + return b + 1 + def get_ctxmgr( prediction_size, history_columns, meta_columns, history_size, prediction_columns diff --git a/tests/conftest.py b/tests/conftest.py index e00a79e..bf92dd4 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -6,19 +6,15 @@ columns = ["ref", "x1", "x2"] -def get_value_generator(date_range, columns): - def gen_value(column, line, id=0): - if column == "weekday": - return date_range[line].weekday() - else: - col_num = columns.index(column) - return int(f"{col_num:02d}{line:04d}") + (id * 1e6) +def gen_value(column, line, id=0): + if column == "ref": + return id * 1e5 + line + else: + return np.random.randint(0, 1000) - return gen_value - -def gen_df(columns, date_range, id=0): - gen_value = get_value_generator(date_range, columns) +def gen_df(columns, date_range, id=0, seed=1): + np.random.seed(seed) periods = date_range.size df = pd.DataFrame( { @@ -42,7 +38,7 @@ def gen_df_with_id(ids, columns, date_range): @pytest.fixture( scope="function", - params=[(columns + ["weekday"], 48 * 30 * 3)], + params=[(columns, 48 * 30 * 3)], ) def time_series_df(request): df = gen_df( @@ -55,7 +51,7 @@ def time_series_df(request): @pytest.fixture( scope="function", params=[ - (list(range(5)), columns + ["weekday"], 48 * 30 * 3), + (list(range(5)), columns, 48 * 30 * 3), ], ) def time_series_df_with_id(request): @@ -111,7 +107,7 @@ def history_columns(request): return request.param -@pytest.fixture(params=[[], ["ref", "weekday"]]) +@pytest.fixture(params=[[], ["ref", "x1"]]) def meta_columns(request): return request.param diff --git a/tests/test_pipleine.py b/tests/test_pipleine.py index f0031f0..58801ae 100644 --- a/tests/test_pipleine.py +++ b/tests/test_pipleine.py @@ -58,7 +58,7 @@ def test_patch_generator(time_series_df, window_size, shift): for i, patch in enumerate(ds_patched.as_numpy_iterator()): assert patch.shape == expected_shape, "Wrong shape" x1 = patch[0, 0] - idx = int(x1 % 1e4) + idx = int(x1 % 1e5) expected_values = df.iloc[idx : idx + window_size] assert np.all(patch == expected_values), "Patch contains wrong data" assert i + 1 == patches, "Not enough patches" @@ -85,8 +85,8 @@ def test_patch_generator_groupby(groupby_dataset, window_size, shift): assert patch.shape == expected_shape, "Wrong shape" if len(columns): x1 = patch[0, 0] - id = int(x1 // 1e6) - idx = int(x1 % 1e4) + id = int(x1 // 1e5) + idx = int(x1 % 1e5) expected_values = df[df.id == id].iloc[idx : idx + window_size] assert np.all( patch == expected_values[columns].values @@ -128,13 +128,18 @@ def test_batch_processor( num_parallel_calls=tf.data.experimental.AUTOTUNE, ) - validate_dataset( + batches = validate_dataset( df, ds_batched, batch_size=batch_size, prediction_size=prediction_size, **batch_kwds, ) + initial_size = window_size - shift + patch_data = df.index.unique().size - initial_size + patches = patch_data / shift + expected_batches = int(patches // batch_size) + assert batches == expected_batches, "Not enough batches" def test_windowed_time_series_pipeline( @@ -180,12 +185,19 @@ def test_windowed_time_series_pipeline( ds = tf.data.Dataset.from_tensors(df[used_cols]) ds = pipeline(ds) ds - validate_dataset( + batches = validate_dataset( df, ds, **batch_kwds, ) + window_size = history_size + prediction_size + initial_size = window_size - shift + patch_data = df.index.unique().size - initial_size + patches = patch_data / shift + expected_batches = int(patches // batch_size) + assert batches == expected_batches, "Not enough batches" + def test_windowed_time_series_pipeline_groupby( groupby_dataset, @@ -226,8 +238,15 @@ def test_windowed_time_series_pipeline_groupby( pipeline = WindowedTimeSeriesPipeline(**batch_kwds, **pipeline_kwds) ds = pipeline(ds) ds - validate_dataset( + batches = validate_dataset( df, ds, **batch_kwds, ) + + window_size = history_size + prediction_size + initial_size = window_size - shift + patch_data = df.index.unique().size - initial_size + patches = patch_data / shift * len(ids) + expected_batches = int(patches // batch_size) + assert batches == expected_batches, "Not enough batches" diff --git a/tests/test_preprocessors.py b/tests/test_preprocessors.py index 335e0e3..b792c7b 100644 --- a/tests/test_preprocessors.py +++ b/tests/test_preprocessors.py @@ -83,21 +83,19 @@ def test_time_series_split(time_series_df_with_id): def test_groupby_dataset_generator(time_series_df_with_id): - time_series_df_with_id.set_index("date_time", inplace=True) + df = time_series_df_with_id.set_index("date_time") + columns = list(sorted(df.columns)) + df = df[columns] - columns = list(sorted(time_series_df_with_id.columns)) - records_per_id = time_series_df_with_id.groupby("id").x1.size().max() + records_per_id = df.groupby("id").size().max() expected_shape = (records_per_id, len(columns)) idx_from_column = {c: i for i, c in enumerate(columns)} - gen = GroupbyDatasetGenerator("id", columns=columns) - for d in gen(time_series_df_with_id): + for d in gen(df.sample(frac=1)): assert d.shape == expected_shape, "Wrong shape" - id = int(d[0, idx_from_column["x1"]] // 1e6) - expected_values = time_series_df_with_id[time_series_df_with_id.id == id][ - columns - ].values + id = int(d[0, idx_from_column["ref"]] // 1e5) + expected_values = df[df.id == id].sort_index().values assert np.all( d.numpy() == expected_values, ), "Error: DatasetGenerator failed" From 97267742fe74cd63b7a88b3cd79c6ba0e3050280 Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus Date: Wed, 12 Jan 2022 09:31:06 +0100 Subject: [PATCH 30/72] validates number of batches for factory --- .../utils/test.py | 4 ++- tests/test_factory.py | 33 ++++++++++++++++--- 2 files changed, 32 insertions(+), 5 deletions(-) diff --git a/src/tensorflow_time_series_dataset/utils/test.py b/src/tensorflow_time_series_dataset/utils/test.py index 75248cb..90f0584 100644 --- a/src/tensorflow_time_series_dataset/utils/test.py +++ b/src/tensorflow_time_series_dataset/utils/test.py @@ -4,7 +4,7 @@ # author : Marcel Arpogaus # # created : 2022-01-07 09:02:38 (Marcel Arpogaus) -# changed : 2022-01-11 15:50:28 (Marcel Arpogaus) +# changed : 2022-01-12 09:09:21 (Marcel Arpogaus) # DESCRIPTION ################################################################# # ... # LICENSE ##################################################################### @@ -156,6 +156,8 @@ def validate_dataset( ) ), f"Wrong data: prediction ({b})" + assert b != None, "No iteration. Is there enough data?" + return b + 1 diff --git a/tests/test_factory.py b/tests/test_factory.py index 765d142..7c0312a 100644 --- a/tests/test_factory.py +++ b/tests/test_factory.py @@ -46,11 +46,17 @@ def test_windowed_time_series_dataset_factory( factory = WindowedTimeSeriesDatasetFactory(**common_kwds, **factory_kwds) ds = factory(df) ds - validate_dataset( + batches = validate_dataset( df, ds, **common_kwds, ) + window_size = history_size + prediction_size + initial_size = window_size - shift + patch_data = df.index.unique().size - initial_size + patches = patch_data / shift + expected_batches = int(patches // batch_size) + assert batches == expected_batches, "Not enough batches" def test_windowed_time_series_dataset_factory_groupby( @@ -99,11 +105,17 @@ def test_windowed_time_series_dataset_factory_groupby( ds = factory(df) ds - validate_dataset( + batches = validate_dataset( df, ds, **common_kwds, ) + window_size = history_size + prediction_size + initial_size = window_size - shift + patch_data = df.index.unique().size - initial_size + patches = patch_data / shift * len(ids) + expected_batches = int(patches // batch_size) + assert batches == expected_batches, "Not enough batches" def test_windowed_time_series_dataset_factory_csv_loader( @@ -145,11 +157,17 @@ def test_windowed_time_series_dataset_factory_csv_loader( factory.set_data_loader(CSVDataLoader(file_path=test_data_path)) ds = factory() ds - validate_dataset( + batches = validate_dataset( df, ds, **common_kwds, ) + window_size = history_size + prediction_size + initial_size = window_size - shift + patch_data = df.index.unique().size - initial_size + patches = patch_data / shift + expected_batches = int(patches // batch_size) + assert batches == expected_batches, "Not enough batches" def test_windowed_time_series_dataset_factory_csv_loader_with_preprocessors( @@ -216,8 +234,15 @@ def test_windowed_time_series_dataset_factory_csv_loader_with_preprocessors( ) ds = factory() ds - validate_dataset( + batches = validate_dataset( test_df, ds, **common_kwds, ) + + window_size = history_size + prediction_size + initial_size = window_size - shift + patch_data = test_df.index.unique().size - initial_size + patches = patch_data / shift * len(ids) + expected_batches = int(patches // batch_size) + assert batches == expected_batches, "Not enough batches" From 09db65f5ae47d26b38db265c9ac51faf9bb33d45 Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus <38564291+MArpogaus@users.noreply.github.com> Date: Fri, 18 Mar 2022 10:18:29 +0100 Subject: [PATCH 31/72] bumps tf version --- setup.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/setup.py b/setup.py index 963ec3d..a2f977a 100644 --- a/setup.py +++ b/setup.py @@ -1,4 +1,4 @@ -from setuptools import setup, find_packages +from setuptools import find_packages, setup package = "tensorflow_time_series_dataset" version = "0.1" @@ -13,7 +13,7 @@ author_email="marcel.arpogaus@gmail.com", packages=find_packages("src"), package_dir={"": "src"}, - install_requires=["tensorflow==2.6.*", "pandas==1.3.*"], + install_requires=["tensorflow==2.8.*", "pandas==1.3.*"], description="A TensorFlow dataset from time-series data.", long_description=long_description, long_description_content_type="text/markdown", From f5202d2845e2583b21b30d467e301517f5c5f5f1 Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus <38564291+MArpogaus@users.noreply.github.com> Date: Fri, 18 Mar 2022 10:19:44 +0100 Subject: [PATCH 32/72] uses tf impl of atan2 for decodeing --- .../preprocessors/cyclical_feature_encoder.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/src/tensorflow_time_series_dataset/preprocessors/cyclical_feature_encoder.py b/src/tensorflow_time_series_dataset/preprocessors/cyclical_feature_encoder.py index f62a391..62cff3e 100644 --- a/src/tensorflow_time_series_dataset/preprocessors/cyclical_feature_encoder.py +++ b/src/tensorflow_time_series_dataset/preprocessors/cyclical_feature_encoder.py @@ -4,7 +4,7 @@ # author : Marcel Arpogaus # # created : 2022-01-07 09:02:38 (Marcel Arpogaus) -# changed : 2022-01-07 09:02:38 (Marcel Arpogaus) +# changed : 2022-03-17 17:23:19 (Marcel Arpogaus) # DESCRIPTION ################################################################# # ... # LICENSE ##################################################################### @@ -23,6 +23,7 @@ # limitations under the License. ############################################################################### import numpy as np +import tensorflow as tf def default_cycl_getter(df, k): @@ -63,7 +64,7 @@ def __call__(self, data): return data def decode(self, sin, cos): - angle = (np.arctan2(sin, cos) + 2 * np.pi) % (2 * np.pi) + angle = (tf.math.atan2(sin, cos) + 2 * np.pi) % (2 * np.pi) return (angle * (self.cycl_max - self.cycl_min + 1)) / ( 2 * np.pi ) + self.cycl_min From c8f8464424066bd89404e3478d2681cd4f1c3554 Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus <38564291+MArpogaus@users.noreply.github.com> Date: Thu, 1 Sep 2022 11:34:23 +0200 Subject: [PATCH 33/72] uses np arctan2 for decoding --- .../preprocessors/cyclical_feature_encoder.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/src/tensorflow_time_series_dataset/preprocessors/cyclical_feature_encoder.py b/src/tensorflow_time_series_dataset/preprocessors/cyclical_feature_encoder.py index 62cff3e..a33afb2 100644 --- a/src/tensorflow_time_series_dataset/preprocessors/cyclical_feature_encoder.py +++ b/src/tensorflow_time_series_dataset/preprocessors/cyclical_feature_encoder.py @@ -4,7 +4,7 @@ # author : Marcel Arpogaus # # created : 2022-01-07 09:02:38 (Marcel Arpogaus) -# changed : 2022-03-17 17:23:19 (Marcel Arpogaus) +# changed : 2022-09-01 11:13:50 (Marcel Arpogaus) # DESCRIPTION ################################################################# # ... # LICENSE ##################################################################### @@ -23,7 +23,6 @@ # limitations under the License. ############################################################################### import numpy as np -import tensorflow as tf def default_cycl_getter(df, k): @@ -64,7 +63,7 @@ def __call__(self, data): return data def decode(self, sin, cos): - angle = (tf.math.atan2(sin, cos) + 2 * np.pi) % (2 * np.pi) + angle = (np.arctan2(sin, cos) + 2 * np.pi) % (2 * np.pi) return (angle * (self.cycl_max - self.cycl_min + 1)) / ( 2 * np.pi ) + self.cycl_min From c096d933583a5271dd6c384f4f7000600c53c23d Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus <38564291+MArpogaus@users.noreply.github.com> Date: Fri, 2 Sep 2022 15:56:39 +0200 Subject: [PATCH 34/72] removes seed --- .../pipeline/windowed_time_series_pipeline.py | 6 ++---- .../preprocessors/__init__.py | 4 ++-- src/tensorflow_time_series_dataset/utils/visualisation.py | 2 +- .../windowed_time_series_dataset_factory.py | 4 ++-- tests/conftest.py | 8 ++++++-- tests/test_factory.py | 4 ---- tests/test_pipleine.py | 2 -- 7 files changed, 13 insertions(+), 17 deletions(-) diff --git a/src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py b/src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py index 1707e33..3cb5d6d 100644 --- a/src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py +++ b/src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py @@ -4,7 +4,7 @@ # author : Marcel Arpogaus # # created : 2022-01-07 09:02:38 (Marcel Arpogaus) -# changed : 2022-01-07 09:02:38 (Marcel Arpogaus) +# changed : 2022-09-02 15:49:20 (Marcel Arpogaus) # DESCRIPTION ################################################################# # ... # LICENSE ##################################################################### @@ -41,7 +41,6 @@ def __init__( batch_size, cycle_length, shuffle_buffer_size, - seed, ): assert ( prediction_size > 0 @@ -56,7 +55,6 @@ def __init__( self.batch_size = batch_size self.cycle_length = cycle_length self.shuffle_buffer_size = shuffle_buffer_size - self.seed = seed def __call__(self, ds): @@ -72,7 +70,7 @@ def __call__(self, ds): ) if self.shuffle_buffer_size > 0: - ds = ds.shuffle(self.batch_size * self.shuffle_buffer_size, seed=self.seed) + ds = ds.shuffle(self.shuffle_buffer_size) ds = ds.batch(self.batch_size, drop_remainder=True) diff --git a/src/tensorflow_time_series_dataset/preprocessors/__init__.py b/src/tensorflow_time_series_dataset/preprocessors/__init__.py index 2df83d7..27fe13c 100644 --- a/src/tensorflow_time_series_dataset/preprocessors/__init__.py +++ b/src/tensorflow_time_series_dataset/preprocessors/__init__.py @@ -22,7 +22,7 @@ # See the License for the specific language governing permissions and # limitations under the License. ############################################################################### -from .time_series_k_fold import TimeSeriesKFold -from .time_series_split import TimeSeriesSplit from .cyclical_feature_encoder import CyclicalFeatureEncoder from .groupby_dataset_generator import GroupbyDatasetGenerator +from .time_series_k_fold import TimeSeriesKFold +from .time_series_split import TimeSeriesSplit diff --git a/src/tensorflow_time_series_dataset/utils/visualisation.py b/src/tensorflow_time_series_dataset/utils/visualisation.py index 138fc8c..8649d99 100644 --- a/src/tensorflow_time_series_dataset/utils/visualisation.py +++ b/src/tensorflow_time_series_dataset/utils/visualisation.py @@ -11,8 +11,8 @@ # LICENSE ##################################################################### # ... ############################################################################### -import numpy as np import matplotlib.pyplot as plt +import numpy as np prop_cycle = plt.rcParams["axes.prop_cycle"] colors = prop_cycle.by_key()["color"] diff --git a/src/tensorflow_time_series_dataset/windowed_time_series_dataset_factory.py b/src/tensorflow_time_series_dataset/windowed_time_series_dataset_factory.py index cc8b806..454c043 100644 --- a/src/tensorflow_time_series_dataset/windowed_time_series_dataset_factory.py +++ b/src/tensorflow_time_series_dataset/windowed_time_series_dataset_factory.py @@ -4,7 +4,7 @@ # author : Marcel Arpogaus # # created : 2022-01-07 09:02:38 (Marcel Arpogaus) -# changed : 2022-01-07 16:12:30 (Marcel Arpogaus) +# changed : 2022-09-02 12:17:23 (Marcel Arpogaus) # DESCRIPTION ################################################################# # ... # LICENSE ##################################################################### @@ -23,6 +23,7 @@ # limitations under the License. ############################################################################### import tensorflow as tf + from .pipeline import WindowedTimeSeriesPipeline @@ -32,7 +33,6 @@ class WindowedTimeSeriesDatasetFactory: batch_size=32, cycle_length=1, shuffle_buffer_size=1000, - seed=42, ) def __init__( diff --git a/tests/conftest.py b/tests/conftest.py index bf92dd4..76eff58 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -2,9 +2,14 @@ import pytest import pandas as pd import numpy as np +import tensorflow as tf columns = ["ref", "x1", "x2"] +seed = 1 +tf.random.set_seed(seed) +np.random.seed(seed) + def gen_value(column, line, id=0): if column == "ref": @@ -13,8 +18,7 @@ def gen_value(column, line, id=0): return np.random.randint(0, 1000) -def gen_df(columns, date_range, id=0, seed=1): - np.random.seed(seed) +def gen_df(columns, date_range, id=0): periods = date_range.size df = pd.DataFrame( { diff --git a/tests/test_factory.py b/tests/test_factory.py index 7c0312a..3792a08 100644 --- a/tests/test_factory.py +++ b/tests/test_factory.py @@ -33,7 +33,6 @@ def test_windowed_time_series_dataset_factory( shift=shift, cycle_length=1, shuffle_buffer_size=100, - seed=1, ) with get_ctxmgr( @@ -85,7 +84,6 @@ def test_windowed_time_series_dataset_factory_groupby( shift=shift, cycle_length=len(ids), shuffle_buffer_size=100, - seed=1, ) with get_ctxmgr( @@ -144,7 +142,6 @@ def test_windowed_time_series_dataset_factory_csv_loader( shift=shift, cycle_length=1, shuffle_buffer_size=100, - seed=1, ) with get_ctxmgr( history_size=history_size, @@ -213,7 +210,6 @@ def test_windowed_time_series_dataset_factory_csv_loader_with_preprocessors( shift=shift, cycle_length=len(ids), shuffle_buffer_size=100, - seed=1, ) with get_ctxmgr( history_size=history_size, diff --git a/tests/test_pipleine.py b/tests/test_pipleine.py index 58801ae..c123901 100644 --- a/tests/test_pipleine.py +++ b/tests/test_pipleine.py @@ -171,7 +171,6 @@ def test_windowed_time_series_pipeline( shift=shift, cycle_length=1, shuffle_buffer_size=100, - seed=1, ) with get_ctxmgr( @@ -225,7 +224,6 @@ def test_windowed_time_series_pipeline_groupby( shift=shift, cycle_length=len(ids), shuffle_buffer_size=1000, - seed=1, ) with get_ctxmgr( From c241b18041f3813911e739f30e181d178b57e3f8 Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus <38564291+MArpogaus@users.noreply.github.com> Date: Fri, 2 Sep 2022 16:10:26 +0200 Subject: [PATCH 35/72] optimizes pipline with caching --- ...{batch_processor.py => patch_processor.py} | 20 +++++++------- .../pipeline/windowed_time_series_pipeline.py | 26 +++++++++---------- .../groupby_dataset_generator.py | 8 ++++-- tests/test_pipleine.py | 12 ++++----- 4 files changed, 34 insertions(+), 32 deletions(-) rename src/tensorflow_time_series_dataset/pipeline/{batch_processor.py => patch_processor.py} (84%) diff --git a/src/tensorflow_time_series_dataset/pipeline/batch_processor.py b/src/tensorflow_time_series_dataset/pipeline/patch_processor.py similarity index 84% rename from src/tensorflow_time_series_dataset/pipeline/batch_processor.py rename to src/tensorflow_time_series_dataset/pipeline/patch_processor.py index dfad12a..221487c 100644 --- a/src/tensorflow_time_series_dataset/pipeline/batch_processor.py +++ b/src/tensorflow_time_series_dataset/pipeline/patch_processor.py @@ -1,10 +1,10 @@ # -*- time-stamp-pattern: "changed[\s]+:[\s]+%%$"; -*- # AUTHOR INFORMATION ########################################################## -# file : batch_processor.py +# file : patch_processor.py # author : Marcel Arpogaus # # created : 2022-01-07 09:02:38 (Marcel Arpogaus) -# changed : 2022-01-07 14:50:17 (Marcel Arpogaus) +# changed : 2022-09-02 16:00:49 (Marcel Arpogaus) # DESCRIPTION ################################################################# # ... # LICENSE ##################################################################### @@ -25,7 +25,7 @@ import tensorflow as tf -class BatchPreprocessor: +class PatchPreprocessor: def __init__( self, history_size, @@ -66,22 +66,22 @@ def __call__(self, patch): ), "Patch shape dos not match column number" for c in y_columns: - column = patch[:, self.history_size :, self.column_idx[c]] + column = patch[self.history_size :, self.column_idx[c]] y.append(column) for c in x_columns: - column = patch[:, :, self.column_idx[c], None] + column = patch[:, self.column_idx[c], None] if self.history_size and c in self.history_columns: - x_hist.append(column[:, : self.history_size, 0]) + x_hist.append(column[self.history_size, 0]) if c in self.meta_columns: - x_meta.append(column[:, self.history_size, None, ...]) + x_meta.append(column[self.history_size, None, ...]) - y = tf.stack(y, axis=2) + y = tf.stack(y, axis=-1) x = [] if len(x_hist): - x.append(tf.stack(x_hist, axis=2)) + x.append(tf.stack(x_hist, axis=-1)) if len(x_meta): - x.append(tf.concat(x_meta, axis=2)) + x.append(tf.concat(x_meta, axis=-1)) if len(x) > 1: return tuple(x), y diff --git a/src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py b/src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py index 3cb5d6d..25f0e93 100644 --- a/src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py +++ b/src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py @@ -4,7 +4,7 @@ # author : Marcel Arpogaus # # created : 2022-01-07 09:02:38 (Marcel Arpogaus) -# changed : 2022-09-02 15:49:20 (Marcel Arpogaus) +# changed : 2022-09-02 16:01:56 (Marcel Arpogaus) # DESCRIPTION ################################################################# # ... # LICENSE ##################################################################### @@ -25,7 +25,7 @@ # REQUIRED PYTHON MODULES ##################################################### import tensorflow as tf -from tensorflow_time_series_dataset.pipeline.batch_processor import BatchPreprocessor +from tensorflow_time_series_dataset.pipeline.patch_processor import PatchPreprocessor from tensorflow_time_series_dataset.pipeline.patch_generator import PatchGenerator @@ -41,6 +41,7 @@ def __init__( batch_size, cycle_length, shuffle_buffer_size, + cache, ): assert ( prediction_size > 0 @@ -55,27 +56,18 @@ def __init__( self.batch_size = batch_size self.cycle_length = cycle_length self.shuffle_buffer_size = shuffle_buffer_size + self.cache = cache def __call__(self, ds): - if self.shuffle_buffer_size > 0: - ds = ds.shuffle( - self.cycle_length * self.shuffle_buffer_size, seed=self.seed - ) - ds = ds.interleave( PatchGenerator(self.window_size, self.shift), cycle_length=self.cycle_length, num_parallel_calls=tf.data.experimental.AUTOTUNE, ) - if self.shuffle_buffer_size > 0: - ds = ds.shuffle(self.shuffle_buffer_size) - - ds = ds.batch(self.batch_size, drop_remainder=True) - ds = ds.map( - BatchPreprocessor( + PatchPreprocessor( self.history_size, self.history_columns, self.meta_columns, @@ -84,6 +76,14 @@ def __call__(self, ds): num_parallel_calls=tf.data.experimental.AUTOTUNE, ) + if self.cache: + ds = ds.cache() + + if self.shuffle_buffer_size: + ds = ds.shuffle(self.shuffle_buffer_size) + + ds = ds.batch(self.batch_size, drop_remainder=True) + ds = ds.prefetch(tf.data.experimental.AUTOTUNE) return ds diff --git a/src/tensorflow_time_series_dataset/preprocessors/groupby_dataset_generator.py b/src/tensorflow_time_series_dataset/preprocessors/groupby_dataset_generator.py index db92f22..f20a7ec 100644 --- a/src/tensorflow_time_series_dataset/preprocessors/groupby_dataset_generator.py +++ b/src/tensorflow_time_series_dataset/preprocessors/groupby_dataset_generator.py @@ -4,7 +4,7 @@ # author : Marcel Arpogaus # # created : 2022-01-07 09:02:38 (Marcel Arpogaus) -# changed : 2022-01-07 09:02:38 (Marcel Arpogaus) +# changed : 2022-09-02 16:03:22 (Marcel Arpogaus) # DESCRIPTION ################################################################# # ... # LICENSE ##################################################################### @@ -26,10 +26,11 @@ class GroupbyDatasetGenerator: - def __init__(self, groupby, columns, dtype=tf.float32, test_mode=False): + def __init__(self, groupby, columns, dtype=tf.float32, shuffle=False, test_mode=False): self.groupby = groupby self.columns = sorted(list(set(columns))) self.dtype = dtype + self.shuffle = shuffle self.test_mode = test_mode def get_generator(self, df): @@ -54,4 +55,7 @@ def __call__(self, df): tf.TensorSpec(shape=[None, len(self.columns)], dtype=self.dtype) ), ) + if self.shuffle: + len_ids = df[self.groupby].unique().size + ds = ds.shuffle(len_ids) return ds diff --git a/tests/test_pipleine.py b/tests/test_pipleine.py index c123901..336fd37 100644 --- a/tests/test_pipleine.py +++ b/tests/test_pipleine.py @@ -1,7 +1,7 @@ import numpy as np import pytest import tensorflow as tf -from tensorflow_time_series_dataset.pipeline.batch_processor import BatchPreprocessor +from tensorflow_time_series_dataset.pipeline.patch_processor import PatchPreprocessor from tensorflow_time_series_dataset.pipeline.patch_generator import PatchGenerator from tensorflow_time_series_dataset.pipeline.windowed_time_series_pipeline import ( WindowedTimeSeriesPipeline, @@ -122,11 +122,10 @@ def test_batch_processor( meta_columns=meta_columns, prediction_columns=prediction_columns, ): - ds_batched = ds.batch(batch_size, drop_remainder=True) - ds_batched = ds_batched.map( - BatchPreprocessor(**batch_kwds), + ds_batched = ds.map( + PatchPreprocessor(**batch_kwds), num_parallel_calls=tf.data.experimental.AUTOTUNE, - ) + ).batch(batch_size) batches = validate_dataset( df, @@ -171,6 +170,7 @@ def test_windowed_time_series_pipeline( shift=shift, cycle_length=1, shuffle_buffer_size=100, + cache=True ) with get_ctxmgr( @@ -183,7 +183,6 @@ def test_windowed_time_series_pipeline( pipeline = WindowedTimeSeriesPipeline(**batch_kwds, **pipeline_kwds) ds = tf.data.Dataset.from_tensors(df[used_cols]) ds = pipeline(ds) - ds batches = validate_dataset( df, ds, @@ -235,7 +234,6 @@ def test_windowed_time_series_pipeline_groupby( ): pipeline = WindowedTimeSeriesPipeline(**batch_kwds, **pipeline_kwds) ds = pipeline(ds) - ds batches = validate_dataset( df, ds, From 80c4d511c3e1d1781498d0368ebe1179189c4697 Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus <38564291+MArpogaus@users.noreply.github.com> Date: Fri, 2 Sep 2022 17:04:53 +0200 Subject: [PATCH 36/72] fixes history size --- .../pipeline/patch_processor.py | 4 ++-- .../pipeline/windowed_time_series_pipeline.py | 2 +- .../preprocessors/groupby_dataset_generator.py | 4 +++- src/tensorflow_time_series_dataset/utils/test.py | 6 ++++-- .../windowed_time_series_dataset_factory.py | 7 ++----- tests/test_factory.py | 4 ---- tests/test_pipleine.py | 5 +---- 7 files changed, 13 insertions(+), 19 deletions(-) diff --git a/src/tensorflow_time_series_dataset/pipeline/patch_processor.py b/src/tensorflow_time_series_dataset/pipeline/patch_processor.py index 221487c..5c72266 100644 --- a/src/tensorflow_time_series_dataset/pipeline/patch_processor.py +++ b/src/tensorflow_time_series_dataset/pipeline/patch_processor.py @@ -4,7 +4,7 @@ # author : Marcel Arpogaus # # created : 2022-01-07 09:02:38 (Marcel Arpogaus) -# changed : 2022-09-02 16:00:49 (Marcel Arpogaus) +# changed : 2022-09-02 17:04:13 (Marcel Arpogaus) # DESCRIPTION ################################################################# # ... # LICENSE ##################################################################### @@ -72,7 +72,7 @@ def __call__(self, patch): for c in x_columns: column = patch[:, self.column_idx[c], None] if self.history_size and c in self.history_columns: - x_hist.append(column[self.history_size, 0]) + x_hist.append(column[: self.history_size, 0]) if c in self.meta_columns: x_meta.append(column[self.history_size, None, ...]) diff --git a/src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py b/src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py index 25f0e93..fcc5f87 100644 --- a/src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py +++ b/src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py @@ -4,7 +4,7 @@ # author : Marcel Arpogaus # # created : 2022-01-07 09:02:38 (Marcel Arpogaus) -# changed : 2022-09-02 16:01:56 (Marcel Arpogaus) +# changed : 2022-09-02 16:43:49 (Marcel Arpogaus) # DESCRIPTION ################################################################# # ... # LICENSE ##################################################################### diff --git a/src/tensorflow_time_series_dataset/preprocessors/groupby_dataset_generator.py b/src/tensorflow_time_series_dataset/preprocessors/groupby_dataset_generator.py index f20a7ec..8946a1b 100644 --- a/src/tensorflow_time_series_dataset/preprocessors/groupby_dataset_generator.py +++ b/src/tensorflow_time_series_dataset/preprocessors/groupby_dataset_generator.py @@ -26,7 +26,9 @@ class GroupbyDatasetGenerator: - def __init__(self, groupby, columns, dtype=tf.float32, shuffle=False, test_mode=False): + def __init__( + self, groupby, columns, dtype=tf.float32, shuffle=False, test_mode=False + ): self.groupby = groupby self.columns = sorted(list(set(columns))) self.dtype = dtype diff --git a/src/tensorflow_time_series_dataset/utils/test.py b/src/tensorflow_time_series_dataset/utils/test.py index 90f0584..7673ccf 100644 --- a/src/tensorflow_time_series_dataset/utils/test.py +++ b/src/tensorflow_time_series_dataset/utils/test.py @@ -4,7 +4,7 @@ # author : Marcel Arpogaus # # created : 2022-01-07 09:02:38 (Marcel Arpogaus) -# changed : 2022-01-12 09:09:21 (Marcel Arpogaus) +# changed : 2022-09-02 16:59:17 (Marcel Arpogaus) # DESCRIPTION ################################################################# # ... # LICENSE ##################################################################### @@ -106,7 +106,9 @@ def validate_dataset( x2 = x if x1 is not None: - assert x1.shape == x1_shape, f"Wrong shape: history ({b})" + assert ( + x1.shape == x1_shape + ), f"Wrong shape: history ({b}: {x1.shape} != {x1_shape})" ref = x1[:, 0, history_columns_idx[history_reference_column]] assert np.all( x1 diff --git a/src/tensorflow_time_series_dataset/windowed_time_series_dataset_factory.py b/src/tensorflow_time_series_dataset/windowed_time_series_dataset_factory.py index 454c043..7bba589 100644 --- a/src/tensorflow_time_series_dataset/windowed_time_series_dataset_factory.py +++ b/src/tensorflow_time_series_dataset/windowed_time_series_dataset_factory.py @@ -4,7 +4,7 @@ # author : Marcel Arpogaus # # created : 2022-01-07 09:02:38 (Marcel Arpogaus) -# changed : 2022-09-02 12:17:23 (Marcel Arpogaus) +# changed : 2022-09-02 16:36:19 (Marcel Arpogaus) # DESCRIPTION ################################################################# # ... # LICENSE ##################################################################### @@ -29,10 +29,7 @@ class WindowedTimeSeriesDatasetFactory: default_pipline_kwds = dict( - shift=None, - batch_size=32, - cycle_length=1, - shuffle_buffer_size=1000, + shift=None, batch_size=32, cycle_length=1, shuffle_buffer_size=1000, cache=True ) def __init__( diff --git a/tests/test_factory.py b/tests/test_factory.py index 3792a08..b90a54a 100644 --- a/tests/test_factory.py +++ b/tests/test_factory.py @@ -44,7 +44,6 @@ def test_windowed_time_series_dataset_factory( ): factory = WindowedTimeSeriesDatasetFactory(**common_kwds, **factory_kwds) ds = factory(df) - ds batches = validate_dataset( df, ds, @@ -102,7 +101,6 @@ def test_windowed_time_series_dataset_factory_groupby( ) ds = factory(df) - ds batches = validate_dataset( df, ds, @@ -153,7 +151,6 @@ def test_windowed_time_series_dataset_factory_csv_loader( factory = WindowedTimeSeriesDatasetFactory(**common_kwds, **factory_kwds) factory.set_data_loader(CSVDataLoader(file_path=test_data_path)) ds = factory() - ds batches = validate_dataset( df, ds, @@ -229,7 +226,6 @@ def test_windowed_time_series_dataset_factory_csv_loader_with_preprocessors( ) ) ds = factory() - ds batches = validate_dataset( test_df, ds, diff --git a/tests/test_pipleine.py b/tests/test_pipleine.py index 336fd37..cec3387 100644 --- a/tests/test_pipleine.py +++ b/tests/test_pipleine.py @@ -167,10 +167,7 @@ def test_windowed_time_series_pipeline( batch_size=batch_size, ) pipeline_kwds = dict( - shift=shift, - cycle_length=1, - shuffle_buffer_size=100, - cache=True + shift=shift, cycle_length=1, shuffle_buffer_size=100, cache=True ) with get_ctxmgr( From e4b2a3b0f9df6557dcfe4b9125826864e71a9485 Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus <38564291+MArpogaus@users.noreply.github.com> Date: Fri, 2 Sep 2022 17:36:28 +0200 Subject: [PATCH 37/72] fixes pipeline tests --- tests/test_pipleine.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/test_pipleine.py b/tests/test_pipleine.py index cec3387..b917a06 100644 --- a/tests/test_pipleine.py +++ b/tests/test_pipleine.py @@ -220,6 +220,7 @@ def test_windowed_time_series_pipeline_groupby( shift=shift, cycle_length=len(ids), shuffle_buffer_size=1000, + cache=True ) with get_ctxmgr( From fc82e63e026d014a5fdfa8d3e2a689435cacf06c Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus <38564291+MArpogaus@users.noreply.github.com> Date: Mon, 5 Sep 2022 17:18:21 +0200 Subject: [PATCH 38/72] refactors batch shape validation into validate_dataset --- .../pipeline/windowed_time_series_pipeline.py | 4 +- .../utils/test.py | 54 +++++++++++++------ tests/test_factory.py | 41 +++----------- tests/test_pipleine.py | 39 +++----------- 4 files changed, 57 insertions(+), 81 deletions(-) diff --git a/src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py b/src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py index fcc5f87..f7fc2b0 100644 --- a/src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py +++ b/src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py @@ -4,7 +4,7 @@ # author : Marcel Arpogaus # # created : 2022-01-07 09:02:38 (Marcel Arpogaus) -# changed : 2022-09-02 16:43:49 (Marcel Arpogaus) +# changed : 2022-09-05 17:13:33 (Marcel Arpogaus) # DESCRIPTION ################################################################# # ... # LICENSE ##################################################################### @@ -82,7 +82,7 @@ def __call__(self, ds): if self.shuffle_buffer_size: ds = ds.shuffle(self.shuffle_buffer_size) - ds = ds.batch(self.batch_size, drop_remainder=True) + ds = ds.batch(self.batch_size) ds = ds.prefetch(tf.data.experimental.AUTOTUNE) diff --git a/src/tensorflow_time_series_dataset/utils/test.py b/src/tensorflow_time_series_dataset/utils/test.py index 7673ccf..92790c8 100644 --- a/src/tensorflow_time_series_dataset/utils/test.py +++ b/src/tensorflow_time_series_dataset/utils/test.py @@ -4,7 +4,7 @@ # author : Marcel Arpogaus # # created : 2022-01-07 09:02:38 (Marcel Arpogaus) -# changed : 2022-09-02 16:59:17 (Marcel Arpogaus) +# changed : 2022-09-05 17:17:10 (Marcel Arpogaus) # DESCRIPTION ################################################################# # ... # LICENSE ##################################################################### @@ -61,6 +61,7 @@ def validate_dataset( batch_size, history_size, prediction_size, + shift, history_columns, meta_columns, prediction_columns, @@ -70,9 +71,18 @@ def validate_dataset( **kwds, ): df = df.sort_index() - x1_shape = (batch_size, history_size, len(history_columns)) - x2_shape = (batch_size, 1, len(meta_columns)) - y_shape = (batch_size, prediction_size, len(prediction_columns)) + + if "id" in df.columns: + ids = len(df.id.unique()) + else: + ids = 1 + + window_size = history_size + prediction_size + initial_size = window_size - shift + patch_data = df.index.unique().size - initial_size + patches = patch_data / shift * ids + has_remainder = patches % batch_size != 0 + expected_batches = int(patches // batch_size) + int(has_remainder) history_columns = list(sorted(history_columns)) meta_columns = list(sorted(meta_columns)) @@ -96,7 +106,15 @@ def validate_dataset( prediction_reference_column in df.columns ), f"prediction_reference_column ({prediction_reference_column}) not in df.columns" - for b, (x, y) in enumerate(ds.as_numpy_iterator()): + for batch_no, (x, y) in enumerate(ds.as_numpy_iterator(), start=1): + if batch_no == expected_batches and has_remainder: + bs = int(patches % batch_size) + else: + bs = batch_size + x1_shape = (bs, history_size, len(history_columns)) + x2_shape = (bs, 1, len(meta_columns)) + y_shape = (bs, prediction_size, len(prediction_columns)) + x1, x2 = None, None if history_size and len(history_columns) and len(meta_columns): x1, x2 = x @@ -108,14 +126,14 @@ def validate_dataset( if x1 is not None: assert ( x1.shape == x1_shape - ), f"Wrong shape: history ({b}: {x1.shape} != {x1_shape})" + ), f"Wrong shape: history ({batch_no}: {x1.shape} != {x1_shape})" ref = x1[:, 0, history_columns_idx[history_reference_column]] assert np.all( x1 == gen_batch( df, history_columns, history_size, ref, history_reference_column ) - ), f"Wrong data: history ({b})" + ), f"Wrong data: history ({batch_no})" if x2 is not None: assert np.all( x2 @@ -126,7 +144,7 @@ def validate_dataset( ref + history_size, history_reference_column, ) - ), f"wrong data: meta not consecutive ({b})" + ), f"wrong data: meta not consecutive ({batch_no})" last_val = x1[:, -1, history_columns_idx[history_reference_column]] y_test = gen_batch( @@ -136,16 +154,18 @@ def validate_dataset( last_val + 1, history_reference_column, ) - assert np.all(y == y_test), f"Wrong data: prediction not consecutive ({b})" + assert np.all( + y == y_test + ), f"Wrong data: prediction not consecutive ({batch_no})" if x2 is not None: first_val = x2[:, 0, meta_columns_idx[meta_reference_column]] - assert x2.shape == x2_shape, f"Wrong shape: meta ({b})" + assert x2.shape == x2_shape, f"Wrong shape: meta ({batch_no})" assert np.all( x2 == gen_batch(df, meta_columns, 1, first_val, meta_reference_column) - ), f"Wrong data: meta ({b})" + ), f"Wrong data: meta ({batch_no})" - assert y.shape == y_shape, f"Wrong shape: prediction ({b})" + assert y.shape == y_shape, f"Wrong shape: prediction ({batch_no})" first_val = y[:, 0, prediction_columns_idx[prediction_reference_column]] assert np.all( y @@ -156,11 +176,15 @@ def validate_dataset( first_val, prediction_reference_column, ) - ), f"Wrong data: prediction ({b})" + ), f"Wrong data: prediction ({batch_no})" - assert b != None, "No iteration. Is there enough data?" + assert batch_no != None, "No iteration. Is there enough data?" + + assert ( + batch_no == expected_batches + ), f"Not enough batches. {batch_no} != {expected_batches}" - return b + 1 + return batch_no def get_ctxmgr( diff --git a/tests/test_factory.py b/tests/test_factory.py index b90a54a..fbbe0c3 100644 --- a/tests/test_factory.py +++ b/tests/test_factory.py @@ -24,13 +24,13 @@ def test_windowed_time_series_dataset_factory( common_kwds = dict( history_size=history_size, prediction_size=prediction_size, + shift=shift, history_columns=history_columns, meta_columns=meta_columns, prediction_columns=prediction_columns, batch_size=batch_size, ) factory_kwds = dict( - shift=shift, cycle_length=1, shuffle_buffer_size=100, ) @@ -44,17 +44,11 @@ def test_windowed_time_series_dataset_factory( ): factory = WindowedTimeSeriesDatasetFactory(**common_kwds, **factory_kwds) ds = factory(df) - batches = validate_dataset( + validate_dataset( df, ds, **common_kwds, ) - window_size = history_size + prediction_size - initial_size = window_size - shift - patch_data = df.index.unique().size - initial_size - patches = patch_data / shift - expected_batches = int(patches // batch_size) - assert batches == expected_batches, "Not enough batches" def test_windowed_time_series_dataset_factory_groupby( @@ -74,13 +68,13 @@ def test_windowed_time_series_dataset_factory_groupby( common_kwds = dict( history_size=history_size, prediction_size=prediction_size, + shift=shift, history_columns=history_columns, meta_columns=meta_columns, prediction_columns=prediction_columns, batch_size=batch_size, ) factory_kwds = dict( - shift=shift, cycle_length=len(ids), shuffle_buffer_size=100, ) @@ -101,17 +95,11 @@ def test_windowed_time_series_dataset_factory_groupby( ) ds = factory(df) - batches = validate_dataset( + validate_dataset( df, ds, **common_kwds, ) - window_size = history_size + prediction_size - initial_size = window_size - shift - patch_data = df.index.unique().size - initial_size - patches = patch_data / shift * len(ids) - expected_batches = int(patches // batch_size) - assert batches == expected_batches, "Not enough batches" def test_windowed_time_series_dataset_factory_csv_loader( @@ -131,13 +119,13 @@ def test_windowed_time_series_dataset_factory_csv_loader( common_kwds = dict( history_size=history_size, prediction_size=prediction_size, + shift=shift, history_columns=history_columns, meta_columns=meta_columns, prediction_columns=prediction_columns, batch_size=batch_size, ) factory_kwds = dict( - shift=shift, cycle_length=1, shuffle_buffer_size=100, ) @@ -151,17 +139,11 @@ def test_windowed_time_series_dataset_factory_csv_loader( factory = WindowedTimeSeriesDatasetFactory(**common_kwds, **factory_kwds) factory.set_data_loader(CSVDataLoader(file_path=test_data_path)) ds = factory() - batches = validate_dataset( + validate_dataset( df, ds, **common_kwds, ) - window_size = history_size + prediction_size - initial_size = window_size - shift - patch_data = df.index.unique().size - initial_size - patches = patch_data / shift - expected_batches = int(patches // batch_size) - assert batches == expected_batches, "Not enough batches" def test_windowed_time_series_dataset_factory_csv_loader_with_preprocessors( @@ -198,13 +180,13 @@ def test_windowed_time_series_dataset_factory_csv_loader_with_preprocessors( common_kwds = dict( history_size=history_size, prediction_size=prediction_size, + shift=shift, history_columns=history_columns, meta_columns=meta_columns_cycle, prediction_columns=prediction_columns, batch_size=batch_size, ) factory_kwds = dict( - shift=shift, cycle_length=len(ids), shuffle_buffer_size=100, ) @@ -226,15 +208,8 @@ def test_windowed_time_series_dataset_factory_csv_loader_with_preprocessors( ) ) ds = factory() - batches = validate_dataset( + validate_dataset( test_df, ds, **common_kwds, ) - - window_size = history_size + prediction_size - initial_size = window_size - shift - patch_data = test_df.index.unique().size - initial_size - patches = patch_data / shift * len(ids) - expected_batches = int(patches // batch_size) - assert batches == expected_batches, "Not enough batches" diff --git a/tests/test_pipleine.py b/tests/test_pipleine.py index b917a06..0af3381 100644 --- a/tests/test_pipleine.py +++ b/tests/test_pipleine.py @@ -127,18 +127,14 @@ def test_batch_processor( num_parallel_calls=tf.data.experimental.AUTOTUNE, ).batch(batch_size) - batches = validate_dataset( + validate_dataset( df, ds_batched, batch_size=batch_size, prediction_size=prediction_size, + shift=shift, **batch_kwds, ) - initial_size = window_size - shift - patch_data = df.index.unique().size - initial_size - patches = patch_data / shift - expected_batches = int(patches // batch_size) - assert batches == expected_batches, "Not enough batches" def test_windowed_time_series_pipeline( @@ -161,14 +157,13 @@ def test_windowed_time_series_pipeline( batch_kwds = dict( history_size=history_size, prediction_size=prediction_size, + shift=shift, history_columns=history_columns, meta_columns=meta_columns, prediction_columns=prediction_columns, batch_size=batch_size, ) - pipeline_kwds = dict( - shift=shift, cycle_length=1, shuffle_buffer_size=100, cache=True - ) + pipeline_kwds = dict(cycle_length=1, shuffle_buffer_size=100, cache=True) with get_ctxmgr( history_size=history_size, @@ -180,19 +175,12 @@ def test_windowed_time_series_pipeline( pipeline = WindowedTimeSeriesPipeline(**batch_kwds, **pipeline_kwds) ds = tf.data.Dataset.from_tensors(df[used_cols]) ds = pipeline(ds) - batches = validate_dataset( + validate_dataset( df, ds, **batch_kwds, ) - window_size = history_size + prediction_size - initial_size = window_size - shift - patch_data = df.index.unique().size - initial_size - patches = patch_data / shift - expected_batches = int(patches // batch_size) - assert batches == expected_batches, "Not enough batches" - def test_windowed_time_series_pipeline_groupby( groupby_dataset, @@ -211,17 +199,13 @@ def test_windowed_time_series_pipeline_groupby( batch_kwds = dict( history_size=history_size, prediction_size=prediction_size, + shift=shift, history_columns=history_columns, meta_columns=meta_columns, prediction_columns=prediction_columns, batch_size=batch_size, ) - pipeline_kwds = dict( - shift=shift, - cycle_length=len(ids), - shuffle_buffer_size=1000, - cache=True - ) + pipeline_kwds = dict(cycle_length=len(ids), shuffle_buffer_size=1000, cache=True) with get_ctxmgr( history_size=history_size, @@ -232,15 +216,8 @@ def test_windowed_time_series_pipeline_groupby( ): pipeline = WindowedTimeSeriesPipeline(**batch_kwds, **pipeline_kwds) ds = pipeline(ds) - batches = validate_dataset( + validate_dataset( df, ds, **batch_kwds, ) - - window_size = history_size + prediction_size - initial_size = window_size - shift - patch_data = df.index.unique().size - initial_size - patches = patch_data / shift * len(ids) - expected_batches = int(patches // batch_size) - assert batches == expected_batches, "Not enough batches" From 7dbff34d78ab22d0db09540e02eba3969de1fcfd Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus <38564291+MArpogaus@users.noreply.github.com> Date: Wed, 9 Nov 2022 17:23:33 +0100 Subject: [PATCH 39/72] switches to pyproject.toml --- pyproject.toml | 49 +++++++++++++++++++ setup.py | 42 +++------------- .../__init__.py | 4 +- 3 files changed, 57 insertions(+), 38 deletions(-) create mode 100644 pyproject.toml diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..c605097 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,49 @@ +[build-system] +requires = ["setuptools >= 61.0.0"] +build-backend = "setuptools.build_meta" + +[project] +name = "tensorflow_time_series_dataset" +readme = "README.md" +requires-python = ">=3.7" +license = {text = "Apache Software License (Apache 2.0)"} +classifiers = [ + "Operating System :: OS Independent", + "Intended Audience :: Developers", + "Intended Audience :: Education", + "Intended Audience :: Science/Research", + "License :: OSI Approved :: Apache Software License", + "Programming Language :: Python :: 3 :: Only", + "Programming Language :: Python :: 3.7", + "Programming Language :: Python :: 3.8", + "Programming Language :: Python :: 3.9", + "Topic :: Scientific/Engineering :: Artificial Intelligence", + "Topic :: Scientific/Engineering :: Mathematics", + "Topic :: Scientific/Engineering", + "Topic :: Software Development :: Libraries :: Python Modules", + "Topic :: Software Development :: Libraries", + "Topic :: Software Development", +] +dependencies = [ + "tensorflow>=2.5.*", + "pandas>=1.3.*", + "matplotlib>=3.6.2" +] +dynamic = ["version"] + +[project.optional-dependencies] +train = [ + 'pandas', + 'PyYAML' +] +develop = [ + 'tensorflow_time_series_dataset[train]', + 'pytest', + 'black', + 'isort', + 'python-lsp-server[all]', + 'python-lsp-black', +] + +[tool.setuptools.dynamic] +version = {attr = "tensorflow_time_series_dataset.__version__"} diff --git a/setup.py b/setup.py index a2f977a..7f4ecfe 100644 --- a/setup.py +++ b/setup.py @@ -1,38 +1,8 @@ -from setuptools import find_packages, setup +# Only provided for legacy compatibility +# https://setuptools.pypa.io/en/latest/userguide/pyproject_config.html +# +# Metadate is specified pyproject.toml -package = "tensorflow_time_series_dataset" -version = "0.1" +from setuptools import setup -with open("README.md", "r", encoding="utf-8") as fh: - long_description = fh.read() - -setup( - name=package, - version=version, - author="Marcel Arpogaus", - author_email="marcel.arpogaus@gmail.com", - packages=find_packages("src"), - package_dir={"": "src"}, - install_requires=["tensorflow==2.8.*", "pandas==1.3.*"], - description="A TensorFlow dataset from time-series data.", - long_description=long_description, - long_description_content_type="text/markdown", - classifiers=[ - "Operating System :: OS Independent", - "Intended Audience :: Developers", - "Intended Audience :: Education", - "Intended Audience :: Science/Research", - "License :: OSI Approved :: Apache Software License", - "Programming Language :: Python :: 3 :: Only", - "Programming Language :: Python :: 3.7", - "Programming Language :: Python :: 3.8", - "Programming Language :: Python :: 3.9", - "Topic :: Scientific/Engineering :: Artificial Intelligence", - "Topic :: Scientific/Engineering :: Mathematics", - "Topic :: Scientific/Engineering", - "Topic :: Software Development :: Libraries :: Python Modules", - "Topic :: Software Development :: Libraries", - "Topic :: Software Development", - ], - python_requires=">=3.7", -) +setup() diff --git a/src/tensorflow_time_series_dataset/__init__.py b/src/tensorflow_time_series_dataset/__init__.py index 44de58d..23a755e 100644 --- a/src/tensorflow_time_series_dataset/__init__.py +++ b/src/tensorflow_time_series_dataset/__init__.py @@ -4,7 +4,7 @@ # author : Marcel Arpogaus # # created : 2022-01-07 09:02:38 (Marcel Arpogaus) -# changed : 2022-01-07 16:12:49 (Marcel Arpogaus) +# changed : 2022-11-09 17:02:21 (Marcel Arpogaus) # DESCRIPTION ################################################################# # ... # LICENSE ##################################################################### @@ -22,4 +22,4 @@ # See the License for the specific language governing permissions and # limitations under the License. ############################################################################### -from .windowed_time_series_dataset_factory import WindowedTimeSeriesDatasetFactory +__version__ = "0.1.0" From b5e1f9d8e98eb47ebfe2539c4e280116bef0afe5 Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus <38564291+MArpogaus@users.noreply.github.com> Date: Wed, 9 Nov 2022 17:25:14 +0100 Subject: [PATCH 40/72] adds drop_remainder argument to factory --- .../{windowed_time_series_dataset_factory.py => factory.py} | 0 .../pipeline/windowed_time_series_pipeline.py | 6 ++++-- src/tensorflow_time_series_dataset/utils/test.py | 5 +++-- tests/test_factory.py | 5 ++++- 4 files changed, 11 insertions(+), 5 deletions(-) rename src/tensorflow_time_series_dataset/{windowed_time_series_dataset_factory.py => factory.py} (100%) diff --git a/src/tensorflow_time_series_dataset/windowed_time_series_dataset_factory.py b/src/tensorflow_time_series_dataset/factory.py similarity index 100% rename from src/tensorflow_time_series_dataset/windowed_time_series_dataset_factory.py rename to src/tensorflow_time_series_dataset/factory.py diff --git a/src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py b/src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py index f7fc2b0..183da39 100644 --- a/src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py +++ b/src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py @@ -4,7 +4,7 @@ # author : Marcel Arpogaus # # created : 2022-01-07 09:02:38 (Marcel Arpogaus) -# changed : 2022-09-05 17:13:33 (Marcel Arpogaus) +# changed : 2022-11-09 16:08:45 (Marcel Arpogaus) # DESCRIPTION ################################################################# # ... # LICENSE ##################################################################### @@ -42,6 +42,7 @@ def __init__( cycle_length, shuffle_buffer_size, cache, + drop_remainder ): assert ( prediction_size > 0 @@ -57,6 +58,7 @@ def __init__( self.cycle_length = cycle_length self.shuffle_buffer_size = shuffle_buffer_size self.cache = cache + self.drop_remainder = drop_remainder def __call__(self, ds): @@ -82,7 +84,7 @@ def __call__(self, ds): if self.shuffle_buffer_size: ds = ds.shuffle(self.shuffle_buffer_size) - ds = ds.batch(self.batch_size) + ds = ds.batch(self.batch_size, drop_remainder=self.drop_remainder) ds = ds.prefetch(tf.data.experimental.AUTOTUNE) diff --git a/src/tensorflow_time_series_dataset/utils/test.py b/src/tensorflow_time_series_dataset/utils/test.py index 92790c8..5f04b43 100644 --- a/src/tensorflow_time_series_dataset/utils/test.py +++ b/src/tensorflow_time_series_dataset/utils/test.py @@ -4,7 +4,7 @@ # author : Marcel Arpogaus # # created : 2022-01-07 09:02:38 (Marcel Arpogaus) -# changed : 2022-09-05 17:17:10 (Marcel Arpogaus) +# changed : 2022-11-09 17:07:26 (Marcel Arpogaus) # DESCRIPTION ################################################################# # ... # LICENSE ##################################################################### @@ -65,6 +65,7 @@ def validate_dataset( history_columns, meta_columns, prediction_columns, + drop_remainder, history_reference_column="ref", meta_reference_column="ref", prediction_reference_column="ref", @@ -81,7 +82,7 @@ def validate_dataset( initial_size = window_size - shift patch_data = df.index.unique().size - initial_size patches = patch_data / shift * ids - has_remainder = patches % batch_size != 0 + has_remainder = (patches % batch_size != 0) and not drop_remainder expected_batches = int(patches // batch_size) + int(has_remainder) history_columns = list(sorted(history_columns)) diff --git a/tests/test_factory.py b/tests/test_factory.py index fbbe0c3..a3f07f4 100644 --- a/tests/test_factory.py +++ b/tests/test_factory.py @@ -1,4 +1,4 @@ -from tensorflow_time_series_dataset import WindowedTimeSeriesDatasetFactory +from tensorflow_time_series_dataset.factory import WindowedTimeSeriesDatasetFactory from tensorflow_time_series_dataset.loaders import CSVDataLoader from tensorflow_time_series_dataset.preprocessors import ( GroupbyDatasetGenerator, @@ -29,6 +29,7 @@ def test_windowed_time_series_dataset_factory( meta_columns=meta_columns, prediction_columns=prediction_columns, batch_size=batch_size, + drop_remainder=True, ) factory_kwds = dict( cycle_length=1, @@ -73,6 +74,7 @@ def test_windowed_time_series_dataset_factory_groupby( meta_columns=meta_columns, prediction_columns=prediction_columns, batch_size=batch_size, + drop_remainder=True, ) factory_kwds = dict( cycle_length=len(ids), @@ -124,6 +126,7 @@ def test_windowed_time_series_dataset_factory_csv_loader( meta_columns=meta_columns, prediction_columns=prediction_columns, batch_size=batch_size, + drop_remainder=False, ) factory_kwds = dict( cycle_length=1, From 7fd41c36a3aa545c66af16b6d7e0484ac306ac8b Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus <38564291+MArpogaus@users.noreply.github.com> Date: Wed, 9 Nov 2022 17:25:41 +0100 Subject: [PATCH 41/72] updates readme --- .images/example1.png | Bin 72861 -> 69360 bytes .images/example2.png | Bin 68534 -> 74733 bytes .images/example3.png | Bin 70693 -> 71832 bytes README.md | 27 ++++++++++++++------------- README.org | 20 +++++++++++--------- 5 files changed, 25 insertions(+), 22 deletions(-) diff --git a/.images/example1.png b/.images/example1.png index 9349269bcd2385844c82b8702305ef957f980999..c9f7b41abf3453067d597485eb24a6fe0341cda3 100644 GIT binary patch literal 69360 zcmcG0bx@Vx_wG4_NSAaSr9`?Lj(`%v^6Ec1Oma9m61|~K#*Y&2sGy51MrFl zJqi!_Cg}7+%Sp}F)XCMr!33gU;ACfI>ttnN2y-!UaI~ra-2p*z;po2?1M91^!BY`E1qSN8MeMp5>{|nPIKU=#@h4{tix1Udb6+{;N 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[About The Project](#about-the-project) 2. [Usage](#usage) - 1. [Example Data](#org705c8ce) - 2. [Single-Step Prediction](#orge605148) - 3. [Multi-Step Prediction](#org46070f1) - 4. [Preprocessing: Add Metadata features](#org6f4b42f) + 1. [Example Data](#org801d13b) + 2. [Single-Step Prediction](#org0c7c414) + 3. [Multi-Step Prediction](#org23f37ef) + 4. [Preprocessing: Add Metadata features](#org92ad4f6) 3. [License](#license) 4. [Contact](#contact) 5. [Acknowledgments](#acknowledgments) @@ -31,7 +31,7 @@ This python package should help you to create TensorFlow datasets for time-serie ## Usage - + ### Example Data @@ -71,21 +71,22 @@ Suppose you have a dataset in the following form: 1992-01-01 02:00:00 0.432704 1.159077 2.005718 - + ### Single-Step Prediction The factory class `WindowedTimeSeriesDatasetFactory` is used to create a TensorFlow dataset from pandas dataframes, or other data sources as we will see later. We will use it now to create a dataset with `48` historic time-steps as the input to predict a single time-step in the future. - from tensorflow_time_series_dataset import WindowedTimeSeriesDatasetFactory as Factory + from tensorflow_time_series_dataset.factory import WindowedTimeSeriesDatasetFactory as Factory factory_kwds=dict( history_size=48, prediction_size=1, history_columns=['x1', 'x2', 'x3'], prediction_columns=['x3'], - batch_size=4 + batch_size=4, + drop_remainder=True, ) factory=Factory(**factory_kwds) ds1=factory(test_df) @@ -93,7 +94,7 @@ We will use it now to create a dataset with `48` historic time-steps as the inpu This returns the following TensorFlow Dataset: - + We can plot the result with the utility function `plot_path`: @@ -111,7 +112,7 @@ We can plot the result with the utility function `plot_path`: ![img](.images/example1.png) - + ### Multi-Step Prediction @@ -126,7 +127,7 @@ Lets now increase the prediction size to `6` half-hour time-steps. This returns the following TensorFlow Dataset: - + Again, lets plot the results to see what changed: @@ -143,7 +144,7 @@ Again, lets plot the results to see what changed: ![img](.images/example2.png) - + ### Preprocessing: Add Metadata features @@ -173,7 +174,7 @@ In this case we will be using the a class called `CyclicalFeatureEncoder` to enc This returns the following TensorFlow Dataset: - + Again, lets plot the results to see what changed: diff --git a/README.org b/README.org index 9d38830..36f3fca 100644 --- a/README.org +++ b/README.org @@ -31,16 +31,16 @@ example steps. :CUSTOM_ID: prerequisites :END: -- A -- B + - tensorflow>=2.5.* + - pandas>=1.3.* + - matplotlib>=3.6.2 *** Installation :PROPERTIES: :CUSTOM_ID: installation :END: -1. Step -2. Step + pip install git+https://github.com/MArpogaus/tensorflow_timeseries_dataset.git ** Usage :PROPERTIES: @@ -86,20 +86,22 @@ test_df.head() : 1992-01-01 01:30:00 -0.536484 1.159828 1.644974 : 1992-01-01 02:00:00 0.432704 1.159077 2.005718 + *** Single-Step Prediction The factory class =WindowedTimeSeriesDatasetFactory= is used to create a TensorFlow dataset from pandas dataframes, or other data sources as we will see later. We will use it now to create a dataset with =48= historic time-steps as the input to predict a single time-step in the future. #+NAME: ds1 #+begin_src python :session :exports both -from tensorflow_time_series_dataset import WindowedTimeSeriesDatasetFactory as Factory +from tensorflow_time_series_dataset.factory import WindowedTimeSeriesDatasetFactory as Factory factory_kwds=dict( history_size=48, prediction_size=1, history_columns=['x1', 'x2', 'x3'], prediction_columns=['x3'], - batch_size=4 + batch_size=4, + drop_remainder=True, ) factory=Factory(**factory_kwds) ds1=factory(test_df) @@ -109,7 +111,7 @@ ds1 This returns the following TensorFlow Dataset: #+RESULTS: ds1 -: +: We can plot the result with the utility function =plot_path=: @@ -144,7 +146,7 @@ ds2 This returns the following TensorFlow Dataset: #+RESULTS: ds2 -: +: Again, lets plot the results to see what changed: #+NAME: ds2_plot @@ -192,7 +194,7 @@ ds3 This returns the following TensorFlow Dataset: #+RESULTS: ds3 -: +: Again, lets plot the results to see what changed: #+NAME: ds3_plot From 5cd59c897edd82c7be84bfbc0daccacfeb5379df Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus <38564291+MArpogaus@users.noreply.github.com> Date: Wed, 9 Nov 2022 17:26:51 +0100 Subject: [PATCH 42/72] applies formatters --- pyproject.toml | 6 +++--- .../pipeline/windowed_time_series_pipeline.py | 4 ++-- src/tensorflow_time_series_dataset/utils/test.py | 4 ++-- tests/conftest.py | 5 +++-- tests/test_factory.py | 2 +- tests/test_pipleine.py | 8 +++++--- tests/test_preprocessors.py | 6 +++--- 7 files changed, 19 insertions(+), 16 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index c605097..f1d7f5b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -25,9 +25,9 @@ classifiers = [ "Topic :: Software Development", ] dependencies = [ - "tensorflow>=2.5.*", - "pandas>=1.3.*", - "matplotlib>=3.6.2" + "tensorflow~=2.5", + "pandas~=1.3", + "matplotlib~=3.6" ] dynamic = ["version"] diff --git a/src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py b/src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py index 183da39..b1bbf52 100644 --- a/src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py +++ b/src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py @@ -25,8 +25,8 @@ # REQUIRED PYTHON MODULES ##################################################### import tensorflow as tf -from tensorflow_time_series_dataset.pipeline.patch_processor import PatchPreprocessor from tensorflow_time_series_dataset.pipeline.patch_generator import PatchGenerator +from tensorflow_time_series_dataset.pipeline.patch_processor import PatchPreprocessor class WindowedTimeSeriesPipeline: @@ -42,7 +42,7 @@ def __init__( cycle_length, shuffle_buffer_size, cache, - drop_remainder + drop_remainder, ): assert ( prediction_size > 0 diff --git a/src/tensorflow_time_series_dataset/utils/test.py b/src/tensorflow_time_series_dataset/utils/test.py index 5f04b43..5805851 100644 --- a/src/tensorflow_time_series_dataset/utils/test.py +++ b/src/tensorflow_time_series_dataset/utils/test.py @@ -65,7 +65,7 @@ def validate_dataset( history_columns, meta_columns, prediction_columns, - drop_remainder, + drop_remainder, history_reference_column="ref", meta_reference_column="ref", prediction_reference_column="ref", @@ -179,7 +179,7 @@ def validate_dataset( ) ), f"Wrong data: prediction ({batch_no})" - assert batch_no != None, "No iteration. Is there enough data?" + assert batch_no is not None, "No iteration. Is there enough data?" assert ( batch_no == expected_batches diff --git a/tests/conftest.py b/tests/conftest.py index 76eff58..18d933d 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,7 +1,8 @@ import itertools -import pytest -import pandas as pd + import numpy as np +import pandas as pd +import pytest import tensorflow as tf columns = ["ref", "x1", "x2"] diff --git a/tests/test_factory.py b/tests/test_factory.py index a3f07f4..c2d2545 100644 --- a/tests/test_factory.py +++ b/tests/test_factory.py @@ -1,9 +1,9 @@ from tensorflow_time_series_dataset.factory import WindowedTimeSeriesDatasetFactory from tensorflow_time_series_dataset.loaders import CSVDataLoader from tensorflow_time_series_dataset.preprocessors import ( + CyclicalFeatureEncoder, GroupbyDatasetGenerator, TimeSeriesSplit, - CyclicalFeatureEncoder, ) from tensorflow_time_series_dataset.utils.test import get_ctxmgr, validate_dataset diff --git a/tests/test_pipleine.py b/tests/test_pipleine.py index 0af3381..4261cd2 100644 --- a/tests/test_pipleine.py +++ b/tests/test_pipleine.py @@ -1,18 +1,20 @@ import numpy as np import pytest import tensorflow as tf -from tensorflow_time_series_dataset.pipeline.patch_processor import PatchPreprocessor + from tensorflow_time_series_dataset.pipeline.patch_generator import PatchGenerator +from tensorflow_time_series_dataset.pipeline.patch_processor import PatchPreprocessor from tensorflow_time_series_dataset.pipeline.windowed_time_series_pipeline import ( WindowedTimeSeriesPipeline, ) from tensorflow_time_series_dataset.preprocessors.groupby_dataset_generator import ( GroupbyDatasetGenerator, ) -from tensorflow_time_series_dataset.utils.test import validate_dataset -from tensorflow_time_series_dataset.utils.test import get_ctxmgr +from tensorflow_time_series_dataset.utils.test import get_ctxmgr, validate_dataset # FIXTURES #################################################################### + + @pytest.fixture def groupby_dataset( time_series_df_with_id, history_columns, meta_columns, prediction_columns diff --git a/tests/test_preprocessors.py b/tests/test_preprocessors.py index b792c7b..b22b042 100644 --- a/tests/test_preprocessors.py +++ b/tests/test_preprocessors.py @@ -1,11 +1,11 @@ -import pytest -import pandas as pd import numpy as np +import pandas as pd +import pytest from tensorflow_time_series_dataset.preprocessors import ( CyclicalFeatureEncoder, - TimeSeriesSplit, GroupbyDatasetGenerator, + TimeSeriesSplit, ) From 9d6cb7c713f63dfdaa86ce4880b0f21d23d59fd5 Mon Sep 17 00:00:00 2001 From: MArpogaus <38564291+MArpogaus@users.noreply.github.com> Date: Thu, 10 Nov 2022 08:33:18 +0000 Subject: [PATCH 43/72] updates compatible python versions --- .github/workflows/pytest.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/pytest.yaml b/.github/workflows/pytest.yaml index 96a16d2..5a2d204 100644 --- a/.github/workflows/pytest.yaml +++ b/.github/workflows/pytest.yaml @@ -7,7 +7,7 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - python-version: [3.7, 3.8, 3.9] + python-version: [3.8, 3.9, 3.10] steps: - uses: actions/checkout@v2 From 0cc968020f40da99485a7cd6a4e025bb3d685024 Mon Sep 17 00:00:00 2001 From: MArpogaus <38564291+MArpogaus@users.noreply.github.com> Date: Thu, 10 Nov 2022 08:35:15 +0000 Subject: [PATCH 44/72] removes python version 3.10 --- .github/workflows/pytest.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/pytest.yaml b/.github/workflows/pytest.yaml index 5a2d204..f159d01 100644 --- a/.github/workflows/pytest.yaml +++ b/.github/workflows/pytest.yaml @@ -7,7 +7,7 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - python-version: [3.8, 3.9, 3.10] + python-version: [3.8, 3.9] steps: - uses: actions/checkout@v2 From b21c88822e7c98c7f520326e7dce6f3802c8ff2e Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus <38564291+MArpogaus@users.noreply.github.com> Date: Thu, 10 Nov 2022 11:12:36 +0100 Subject: [PATCH 45/72] adds missing new drop_remainder kwd to tests --- tests/test_factory.py | 3 ++- tests/test_pipleine.py | 3 +++ 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/tests/test_factory.py b/tests/test_factory.py index c2d2545..119192d 100644 --- a/tests/test_factory.py +++ b/tests/test_factory.py @@ -29,7 +29,7 @@ def test_windowed_time_series_dataset_factory( meta_columns=meta_columns, prediction_columns=prediction_columns, batch_size=batch_size, - drop_remainder=True, + drop_remainder=False, ) factory_kwds = dict( cycle_length=1, @@ -188,6 +188,7 @@ def test_windowed_time_series_dataset_factory_csv_loader_with_preprocessors( meta_columns=meta_columns_cycle, prediction_columns=prediction_columns, batch_size=batch_size, + drop_remainder=True, ) factory_kwds = dict( cycle_length=len(ids), diff --git a/tests/test_pipleine.py b/tests/test_pipleine.py index 4261cd2..fa6dd3b 100644 --- a/tests/test_pipleine.py +++ b/tests/test_pipleine.py @@ -135,6 +135,7 @@ def test_batch_processor( batch_size=batch_size, prediction_size=prediction_size, shift=shift, + drop_remainder=False, **batch_kwds, ) @@ -164,6 +165,7 @@ def test_windowed_time_series_pipeline( meta_columns=meta_columns, prediction_columns=prediction_columns, batch_size=batch_size, + drop_remainder=True, ) pipeline_kwds = dict(cycle_length=1, shuffle_buffer_size=100, cache=True) @@ -206,6 +208,7 @@ def test_windowed_time_series_pipeline_groupby( meta_columns=meta_columns, prediction_columns=prediction_columns, batch_size=batch_size, + drop_remainder=False, ) pipeline_kwds = dict(cycle_length=len(ids), shuffle_buffer_size=1000, cache=True) From 9f7d150c7cbf8953e28412ec446f4fa48350d98a Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus <38564291+MArpogaus@users.noreply.github.com> Date: Thu, 10 Nov 2022 11:13:33 +0100 Subject: [PATCH 46/72] adds pre-commit hooks --- .pre-commit-config.yaml | 24 ++++++++++++++++++++++++ README.md | 13 ++++++------- pyproject.toml | 16 ++++++++++++++-- 3 files changed, 44 insertions(+), 9 deletions(-) create mode 100644 .pre-commit-config.yaml diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..c6039ef --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,24 @@ +# See https://pre-commit.com for more information +# See https://pre-commit.com/hooks.html for more hooks +repos: +- repo: https://github.com/PyCQA/autoflake + rev: v1.7.7 + hooks: + - id: autoflake +- repo: https://github.com/timothycrosley/isort + rev: 5.10.1 + hooks: + - id: isort + args: ["--profile", "black"] +- repo: https://github.com/psf/black + rev: 22.10.0 + hooks: + - id: black +- repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.3.0 + hooks: + - id: trailing-whitespace + - id: end-of-file-fixer + - id: check-yaml + - id: check-toml + - id: check-added-large-files diff --git a/README.md b/README.md index 0d94ca6..391b4d2 100644 --- a/README.md +++ b/README.md @@ -39,10 +39,10 @@ Suppose you have a dataset in the following form: import numpy as np import pandas as pd - + # make things determeinisteic np.random.seed(1) - + columns=['x1', 'x2', 'x3'] periods=48 * 14 test_df=pd.DataFrame( @@ -79,7 +79,7 @@ The factory class `WindowedTimeSeriesDatasetFactory` is used to create a TensorF We will use it now to create a dataset with `48` historic time-steps as the input to predict a single time-step in the future. from tensorflow_time_series_dataset.factory import WindowedTimeSeriesDatasetFactory as Factory - + factory_kwds=dict( history_size=48, prediction_size=1, @@ -104,7 +104,7 @@ We can plot the result with the utility function `plot_path`: figsize=(8,4), **factory_kwds ) - + fname='.images/example1.png' fig.savefig(fname) fname @@ -136,7 +136,7 @@ Again, lets plot the results to see what changed: figsize=(8,4), **factory_kwds ) - + fname='.images/example2.png' fig.savefig(fname) fname @@ -183,7 +183,7 @@ Again, lets plot the results to see what changed: figsize=(8,4), **factory_kwds ) - + fname='.images/example3.png' fig.savefig(fname) fname @@ -213,4 +213,3 @@ Project Link: ## Acknowledgments Parts of this work have been funded by the Federal Ministry for the Environment, Nature Conservation and Nuclear Safety due to a decision of the German Federal Parliament (AI4Grids: 67KI2012A). - diff --git a/pyproject.toml b/pyproject.toml index f1d7f5b..e9a43b7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -38,12 +38,24 @@ train = [ ] develop = [ 'tensorflow_time_series_dataset[train]', - 'pytest', + 'autoflake', 'black', 'isort', - 'python-lsp-server[all]', + 'pre-commit', + 'pytest', 'python-lsp-black', + 'python-lsp-server[all]', ] [tool.setuptools.dynamic] version = {attr = "tensorflow_time_series_dataset.__version__"} + +[tool.autoflake] +check = true +expand-star-imports = true +remove-unused-variables = true +ignore-init-module-imports = true +remove-all-unused-imports = true + +[tool.isort] +profile = "black" From 554820bbecdacb9ccbe1717bfb2c29a6cea4f589 Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus <38564291+MArpogaus@users.noreply.github.com> Date: Fri, 11 Nov 2022 12:45:06 +0100 Subject: [PATCH 47/72] sets minimal supported pandas version to 1.1.0 --- pyproject.toml | 7 +------ 1 file changed, 1 insertion(+), 6 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index e9a43b7..04d4106 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -26,18 +26,13 @@ classifiers = [ ] dependencies = [ "tensorflow~=2.5", - "pandas~=1.3", + "pandas>=1.1.0", "matplotlib~=3.6" ] dynamic = ["version"] [project.optional-dependencies] -train = [ - 'pandas', - 'PyYAML' -] develop = [ - 'tensorflow_time_series_dataset[train]', 'autoflake', 'black', 'isort', From 5043a7f71460b08ca0b8394a792c1565b59c52f5 Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus <38564291+MArpogaus@users.noreply.github.com> Date: Fri, 11 Nov 2022 12:46:18 +0100 Subject: [PATCH 48/72] adds pre-commit workflow --- .github/workflows/pre-commit.yml | 11 +++++++++++ 1 file changed, 11 insertions(+) create mode 100644 .github/workflows/pre-commit.yml diff --git a/.github/workflows/pre-commit.yml b/.github/workflows/pre-commit.yml new file mode 100644 index 0000000..a91667d --- /dev/null +++ b/.github/workflows/pre-commit.yml @@ -0,0 +1,11 @@ +name: run pre-commit hooks + +on: [push] + +jobs: + pre-commit: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + - uses: actions/setup-python@v3 + - uses: pre-commit/action@v3.0.0 From 8b593d171d0cb88a4df70a385d26fa92ebed5d3e Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus <38564291+MArpogaus@users.noreply.github.com> Date: Fri, 11 Nov 2022 12:47:41 +0100 Subject: [PATCH 49/72] adds pytest-xdist --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index 04d4106..70d2fe8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -38,6 +38,7 @@ develop = [ 'isort', 'pre-commit', 'pytest', + 'pytest-xdist', 'python-lsp-black', 'python-lsp-server[all]', ] From 80715a127082694696f38a9dd342b882ab60ce8c Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus <38564291+MArpogaus@users.noreply.github.com> Date: Thu, 15 Feb 2024 16:50:15 +0100 Subject: [PATCH 50/72] ops: migrates to ruf for formatting and linting --- .pre-commit-config.yaml | 50 +++++++++++++----------- pyproject.toml | 84 +++++++++++++++++++++-------------------- 2 files changed, 70 insertions(+), 64 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index c6039ef..fdfb27c 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,24 +1,28 @@ -# See https://pre-commit.com for more information -# See https://pre-commit.com/hooks.html for more hooks repos: -- repo: https://github.com/PyCQA/autoflake - rev: v1.7.7 - hooks: - - id: autoflake -- repo: https://github.com/timothycrosley/isort - rev: 5.10.1 - hooks: - - id: isort - args: ["--profile", "black"] -- repo: https://github.com/psf/black - rev: 22.10.0 - hooks: - - id: black -- repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.3.0 - hooks: - - id: trailing-whitespace - - id: end-of-file-fixer - - id: check-yaml - - id: check-toml - - id: check-added-large-files +- repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.2.1 + hooks: + - id: ruff + args: [--fix] + - id: ruff-format +- repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.5.0 + hooks: + - id: check-added-large-files + - id: check-executables-have-shebangs + - id: check-merge-conflict + - id: check-shebang-scripts-are-executable + - id: check-toml + - id: check-yaml + - id: end-of-file-fixer + - id: mixed-line-ending + - id: trailing-whitespace +- repo: https://github.com/macisamuele/language-formatters-pre-commit-hooks + rev: v2.12.0 + hooks: + - id: pretty-format-yaml + args: [--autofix, --indent, '2'] + - id: pretty-format-toml + args: [--autofix] + - id: pretty-format-ini + args: [--autofix] diff --git a/pyproject.toml b/pyproject.toml index 70d2fe8..aaf0483 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,57 +1,59 @@ [build-system] -requires = ["setuptools >= 61.0.0"] build-backend = "setuptools.build_meta" +requires = ["setuptools >= 61.0.0"] [project] -name = "tensorflow_time_series_dataset" -readme = "README.md" -requires-python = ">=3.7" -license = {text = "Apache Software License (Apache 2.0)"} classifiers = [ - "Operating System :: OS Independent", - "Intended Audience :: Developers", - "Intended Audience :: Education", - "Intended Audience :: Science/Research", - "License :: OSI Approved :: Apache Software License", - "Programming Language :: Python :: 3 :: Only", - "Programming Language :: Python :: 3.7", - "Programming Language :: Python :: 3.8", - "Programming Language :: Python :: 3.9", - "Topic :: Scientific/Engineering :: Artificial Intelligence", - "Topic :: Scientific/Engineering :: Mathematics", - "Topic :: Scientific/Engineering", - "Topic :: Software Development :: Libraries :: Python Modules", - "Topic :: Software Development :: Libraries", - "Topic :: Software Development", + "Operating System :: OS Independent", + "Intended Audience :: Developers", + "Intended Audience :: Education", + "Intended Audience :: Science/Research", + "License :: OSI Approved :: Apache Software License", + "Programming Language :: Python :: 3 :: Only", + "Programming Language :: Python :: 3.7", + "Programming Language :: Python :: 3.8", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Topic :: Scientific/Engineering :: Artificial Intelligence", + "Topic :: Scientific/Engineering :: Mathematics", + "Topic :: Scientific/Engineering", + "Topic :: Software Development :: Libraries :: Python Modules", + "Topic :: Software Development :: Libraries", + "Topic :: Software Development" ] dependencies = [ - "tensorflow~=2.5", - "pandas>=1.1.0", - "matplotlib~=3.6" + "tensorflow>=2.5", + "pandas>=1.1.0", + "matplotlib>=3.6" ] dynamic = ["version"] +license = {text = "Apache Software License (Apache 2.0)"} +name = "tensorflow_time_series_dataset" +readme = "README.md" +requires-python = ">=3.7" [project.optional-dependencies] develop = [ - 'autoflake', - 'black', - 'isort', - 'pre-commit', - 'pytest', - 'pytest-xdist', - 'python-lsp-black', - 'python-lsp-server[all]', + 'python_project_skeleton[test]', + 'ipykernel', + 'pre-commit', + 'python-lsp-ruff', + 'python-lsp-server[all]', + 'ruff' +] +test = [ + 'pytest', + 'pytest-xdist' ] -[tool.setuptools.dynamic] -version = {attr = "tensorflow_time_series_dataset.__version__"} +[tool.ruff] +indent-width = 4 +line-length = 88 +target-version = "py37" -[tool.autoflake] -check = true -expand-star-imports = true -remove-unused-variables = true -ignore-init-module-imports = true -remove-all-unused-imports = true +[tool.ruff.lint] +select = ["I", "E", "F"] -[tool.isort] -profile = "black" +[tool.setuptools.dynamic] +version = {attr = "tensorflow_time_series_dataset.__version__"} From c02805baf1bbdf8d6e4bd40a40e28be98a494019 Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus <38564291+MArpogaus@users.noreply.github.com> Date: Thu, 15 Feb 2024 17:20:45 +0100 Subject: [PATCH 51/72] style: applies ruff formatter --- src/tensorflow_time_series_dataset/factory.py | 4 ++-- src/tensorflow_time_series_dataset/loaders/__init__.py | 4 ++-- .../loaders/csv_data_loader.py | 2 +- .../pipeline/__init__.py | 4 ++-- .../pipeline/windowed_time_series_pipeline.py | 1 - .../preprocessors/__init__.py | 10 +++++----- .../preprocessors/cyclical_feature_encoder.py | 9 +++++---- src/tensorflow_time_series_dataset/utils/test.py | 5 +++-- .../utils/visualisation.py | 5 ++--- tests/conftest.py | 5 ++++- tests/test_factory.py | 4 ---- tests/test_pipleine.py | 1 - tests/test_preprocessors.py | 1 - 13 files changed, 26 insertions(+), 29 deletions(-) diff --git a/src/tensorflow_time_series_dataset/factory.py b/src/tensorflow_time_series_dataset/factory.py index 7bba589..62319d7 100644 --- a/src/tensorflow_time_series_dataset/factory.py +++ b/src/tensorflow_time_series_dataset/factory.py @@ -38,7 +38,7 @@ def __init__( prediction_columns, meta_columns=[], dtype=tf.float32, - **pipline_kwds + **pipline_kwds, ): self.columns = set(history_columns + meta_columns + prediction_columns) self.preprocessors = [] @@ -48,7 +48,7 @@ def __init__( history_columns=history_columns, meta_columns=meta_columns, prediction_columns=prediction_columns, - **{**self.default_pipline_kwds, **pipline_kwds} + **{**self.default_pipline_kwds, **pipline_kwds}, ) def add_preprocessor(self, preprocessor): diff --git a/src/tensorflow_time_series_dataset/loaders/__init__.py b/src/tensorflow_time_series_dataset/loaders/__init__.py index c31109c..fd970f4 100644 --- a/src/tensorflow_time_series_dataset/loaders/__init__.py +++ b/src/tensorflow_time_series_dataset/loaders/__init__.py @@ -4,7 +4,7 @@ # author : Marcel Arpogaus # # created : 2022-01-07 09:02:38 (Marcel Arpogaus) -# changed : 2022-01-07 09:02:38 (Marcel Arpogaus) +# changed : 2024-02-15 17:17:28 (Marcel Arpogaus) # DESCRIPTION ################################################################# # ... # LICENSE ##################################################################### @@ -22,4 +22,4 @@ # See the License for the specific language governing permissions and # limitations under the License. ############################################################################### -from .csv_data_loader import CSVDataLoader +from .csv_data_loader import CSVDataLoader # noqa: F401 diff --git a/src/tensorflow_time_series_dataset/loaders/csv_data_loader.py b/src/tensorflow_time_series_dataset/loaders/csv_data_loader.py index e4715ff..0c03d7d 100644 --- a/src/tensorflow_time_series_dataset/loaders/csv_data_loader.py +++ b/src/tensorflow_time_series_dataset/loaders/csv_data_loader.py @@ -32,7 +32,7 @@ def _read_csv_file(file_path, date_time_col="date_time", **kwds): parse_dates=[date_time_col], infer_datetime_format=True, index_col=[date_time_col], - **kwds + **kwds, ) if load_data.isnull().any().sum() != 0: diff --git a/src/tensorflow_time_series_dataset/pipeline/__init__.py b/src/tensorflow_time_series_dataset/pipeline/__init__.py index ceeb28b..368492d 100644 --- a/src/tensorflow_time_series_dataset/pipeline/__init__.py +++ b/src/tensorflow_time_series_dataset/pipeline/__init__.py @@ -4,7 +4,7 @@ # author : Marcel Arpogaus # # created : 2022-01-07 09:02:38 (Marcel Arpogaus) -# changed : 2022-01-07 09:02:38 (Marcel Arpogaus) +# changed : 2024-02-15 17:17:46 (Marcel Arpogaus) # DESCRIPTION ################################################################# # ... # LICENSE ##################################################################### @@ -22,4 +22,4 @@ # See the License for the specific language governing permissions and # limitations under the License. ############################################################################### -from .windowed_time_series_pipeline import WindowedTimeSeriesPipeline +from .windowed_time_series_pipeline import WindowedTimeSeriesPipeline # noqa: F401 diff --git a/src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py b/src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py index b1bbf52..f5ce9a7 100644 --- a/src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py +++ b/src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py @@ -61,7 +61,6 @@ def __init__( self.drop_remainder = drop_remainder def __call__(self, ds): - ds = ds.interleave( PatchGenerator(self.window_size, self.shift), cycle_length=self.cycle_length, diff --git a/src/tensorflow_time_series_dataset/preprocessors/__init__.py b/src/tensorflow_time_series_dataset/preprocessors/__init__.py index 27fe13c..a52d1ef 100644 --- a/src/tensorflow_time_series_dataset/preprocessors/__init__.py +++ b/src/tensorflow_time_series_dataset/preprocessors/__init__.py @@ -4,7 +4,7 @@ # author : Marcel Arpogaus # # created : 2022-01-07 09:02:38 (Marcel Arpogaus) -# changed : 2022-01-07 09:02:38 (Marcel Arpogaus) +# changed : 2024-02-15 17:18:03 (Marcel Arpogaus) # DESCRIPTION ################################################################# # ... # LICENSE ##################################################################### @@ -22,7 +22,7 @@ # See the License for the specific language governing permissions and # limitations under the License. ############################################################################### -from .cyclical_feature_encoder import CyclicalFeatureEncoder -from .groupby_dataset_generator import GroupbyDatasetGenerator -from .time_series_k_fold import TimeSeriesKFold -from .time_series_split import TimeSeriesSplit +from .cyclical_feature_encoder import CyclicalFeatureEncoder # noqa: F401 +from .groupby_dataset_generator import GroupbyDatasetGenerator # noqa: F401 +from .time_series_k_fold import TimeSeriesKFold # noqa: F401 +from .time_series_split import TimeSeriesSplit # noqa: F401 diff --git a/src/tensorflow_time_series_dataset/preprocessors/cyclical_feature_encoder.py b/src/tensorflow_time_series_dataset/preprocessors/cyclical_feature_encoder.py index a33afb2..8243e13 100644 --- a/src/tensorflow_time_series_dataset/preprocessors/cyclical_feature_encoder.py +++ b/src/tensorflow_time_series_dataset/preprocessors/cyclical_feature_encoder.py @@ -4,7 +4,7 @@ # author : Marcel Arpogaus # # created : 2022-01-07 09:02:38 (Marcel Arpogaus) -# changed : 2022-09-01 11:13:50 (Marcel Arpogaus) +# changed : 2024-02-15 17:02:59 (Marcel Arpogaus) # DESCRIPTION ################################################################# # ... # LICENSE ##################################################################### @@ -50,9 +50,10 @@ def encode(self, data): cos = np.cos( 2 * np.pi * (cycl - self.cycl_min) / (self.cycl_max - self.cycl_min + 1) ) - assert np.allclose( - cycl, self.decode(sin, cos) - ), f'Decoding failed. Is "cycl_min/max" ({self.cycl_min}/{self.cycl_max}) correct?' + assert np.allclose(cycl, self.decode(sin, cos)), ( + 'Decoding failed. Is "cycl_min/max"' + f"({self.cycl_min}/{self.cycl_max}) correct?" + ) return sin, cos def __call__(self, data): diff --git a/src/tensorflow_time_series_dataset/utils/test.py b/src/tensorflow_time_series_dataset/utils/test.py index 5805851..2ca76c5 100644 --- a/src/tensorflow_time_series_dataset/utils/test.py +++ b/src/tensorflow_time_series_dataset/utils/test.py @@ -4,7 +4,7 @@ # author : Marcel Arpogaus # # created : 2022-01-07 09:02:38 (Marcel Arpogaus) -# changed : 2022-11-09 17:07:26 (Marcel Arpogaus) +# changed : 2024-02-15 17:03:39 (Marcel Arpogaus) # DESCRIPTION ################################################################# # ... # LICENSE ##################################################################### @@ -204,7 +204,8 @@ def get_ctxmgr( elif len(meta_columns) == 0 and history_size <= 0: ctxmgr = pytest.raises( AssertionError, - match="history_size must be a positive integer greater than zero, when no meta date is used", + match="history_size must be a positive integer greater than zero," + " when no meta date is used", ) elif history_size < 0: ctxmgr = pytest.raises( diff --git a/src/tensorflow_time_series_dataset/utils/visualisation.py b/src/tensorflow_time_series_dataset/utils/visualisation.py index 8649d99..4749828 100644 --- a/src/tensorflow_time_series_dataset/utils/visualisation.py +++ b/src/tensorflow_time_series_dataset/utils/visualisation.py @@ -1,11 +1,10 @@ -#!/usr/bin/env python3 # -*- time-stamp-pattern: "changed[\s]+:[\s]+%%$"; -*- # AUTHOR INFORMATION ########################################################## # file : visualisation.py # author : Marcel Arpogaus # # created : 2022-01-07 16:50:58 (Marcel Arpogaus) -# changed : 2022-01-10 10:51:50 (Marcel Arpogaus) +# changed : 2024-02-15 17:01:25 (Marcel Arpogaus) # DESCRIPTION ################################################################# # ... # LICENSE ##################################################################### @@ -26,7 +25,7 @@ def plot_patch( prediction_columns, meta_columns=[], figsize=(16, 8), - **kwds + **kwds, ): x, y = next(ds.as_numpy_iterator()) x1, x2 = None, None diff --git a/tests/conftest.py b/tests/conftest.py index 18d933d..9fd5d4f 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -24,7 +24,10 @@ def gen_df(columns, date_range, id=0): df = pd.DataFrame( { "date_time": date_range, - **{c: [gen_value(c, l, id) for l in range(periods)] for c in columns}, + **{ + col: [gen_value(col, line, id) for line in range(periods)] + for col in columns + }, } ) return df diff --git a/tests/test_factory.py b/tests/test_factory.py index 119192d..0bb5c5b 100644 --- a/tests/test_factory.py +++ b/tests/test_factory.py @@ -18,7 +18,6 @@ def test_windowed_time_series_dataset_factory( meta_columns, prediction_columns, ): - df = time_series_df.set_index("date_time") common_kwds = dict( @@ -62,7 +61,6 @@ def test_windowed_time_series_dataset_factory_groupby( meta_columns, prediction_columns, ): - df = time_series_df_with_id.set_index("date_time") ids = df.id.unique() @@ -88,7 +86,6 @@ def test_windowed_time_series_dataset_factory_groupby( meta_columns=meta_columns, prediction_columns=prediction_columns, ): - factory = WindowedTimeSeriesDatasetFactory(**common_kwds, **factory_kwds) factory.add_preprocessor( GroupbyDatasetGenerator( @@ -114,7 +111,6 @@ def test_windowed_time_series_dataset_factory_csv_loader( meta_columns, prediction_columns, ): - test_data_path, df = tmp_csv df = df.set_index("date_time") diff --git a/tests/test_pipleine.py b/tests/test_pipleine.py index fa6dd3b..ddca097 100644 --- a/tests/test_pipleine.py +++ b/tests/test_pipleine.py @@ -1,7 +1,6 @@ import numpy as np import pytest import tensorflow as tf - from tensorflow_time_series_dataset.pipeline.patch_generator import PatchGenerator from tensorflow_time_series_dataset.pipeline.patch_processor import PatchPreprocessor from tensorflow_time_series_dataset.pipeline.windowed_time_series_pipeline import ( diff --git a/tests/test_preprocessors.py b/tests/test_preprocessors.py index b22b042..b6f3ea5 100644 --- a/tests/test_preprocessors.py +++ b/tests/test_preprocessors.py @@ -1,7 +1,6 @@ import numpy as np import pandas as pd import pytest - from tensorflow_time_series_dataset.preprocessors import ( CyclicalFeatureEncoder, GroupbyDatasetGenerator, From 7555dd39fac321523b47c99246a2e6fd7b5e2252 Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus <38564291+MArpogaus@users.noreply.github.com> Date: Thu, 15 Feb 2024 16:50:53 +0100 Subject: [PATCH 52/72] ci: update all github workflow actions --- .github/workflows/pre-commit.yml | 4 ++-- .github/workflows/pytest.yaml | 32 -------------------------------- .github/workflows/test.yaml | 24 ++++++++++++++++++++++++ 3 files changed, 26 insertions(+), 34 deletions(-) delete mode 100644 .github/workflows/pytest.yaml create mode 100644 .github/workflows/test.yaml diff --git a/.github/workflows/pre-commit.yml b/.github/workflows/pre-commit.yml index a91667d..b6dcc07 100644 --- a/.github/workflows/pre-commit.yml +++ b/.github/workflows/pre-commit.yml @@ -7,5 +7,5 @@ jobs: runs-on: ubuntu-latest steps: - uses: actions/checkout@v3 - - uses: actions/setup-python@v3 - - uses: pre-commit/action@v3.0.0 + - uses: actions/setup-python@v4 + - uses: pre-commit/action@v3.0.1 diff --git a/.github/workflows/pytest.yaml b/.github/workflows/pytest.yaml deleted file mode 100644 index f159d01..0000000 --- a/.github/workflows/pytest.yaml +++ /dev/null @@ -1,32 +0,0 @@ -name: Lint and test code - -on: [push] - -jobs: - build: - runs-on: ubuntu-latest - strategy: - matrix: - python-version: [3.8, 3.9] - - steps: - - uses: actions/checkout@v2 - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v2 - with: - python-version: ${{ matrix.python-version }} - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install flake8 pytest - if [ -f requirements.txt ]; then pip install -r requirements.txt; fi - pip install . - - name: Lint with flake8 - run: | - # stop the build if there are Python syntax errors or undefined names - flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics - # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide - flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics - - name: Test with pytest - run: | - pytest diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml new file mode 100644 index 0000000..70251ee --- /dev/null +++ b/.github/workflows/test.yaml @@ -0,0 +1,24 @@ +name: Lint and test code + +on: [push] + +jobs: + build: + + runs-on: ubuntu-latest + strategy: + matrix: + python-version: [3.8, 3.9, '3.10', '3.11'] + + steps: + - uses: actions/checkout@v3 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v4 + with: + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install .[test] + - name: Test with pytest + run: pytest From 771623dfd23d42fe2d9097cc02f28edd741a29c2 Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus <38564291+MArpogaus@users.noreply.github.com> Date: Thu, 15 Feb 2024 16:51:14 +0100 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-[![img](https://img.shields.io/github/forks/MArpogaus/tensorflow_timeseries_dataset.svg?style=flat-square)](https://github.com/MArpogaus/tensorflow_timeseries_dataset/network/members) -[![img](https://img.shields.io/github/stars/MArpogaus/tensorflow_timeseries_dataset.svg?style=flat-square)](https://github.com/MArpogaus/tensorflow_timeseries_dataset/stargazers) -[![img](https://img.shields.io/github/issues/MArpogaus/tensorflow_timeseries_dataset.svg?style=flat-square)](https://github.com/MArpogaus/tensorflow_timeseries_dataset/issues) -[![img](https://img.shields.io/github/license/MArpogaus/tensorflow_timeseries_dataset.svg?style=flat-square)](https://github.com/MArpogaus/tensorflow_timeseries_dataset/blob/master/LICENSE) +[![img](https://img.shields.io/github/contributors/MArpogaus/tensorflow-timeseries-dataset.svg?style=flat-square)](https://github.com/MArpogaus/tensorflow-timeseries-dataset/graphs/contributors) +[![img](https://img.shields.io/github/forks/MArpogaus/tensorflow-timeseries-dataset.svg?style=flat-square)](https://github.com/MArpogaus/tensorflow-timeseries-dataset/network/members) +[![img](https://img.shields.io/github/stars/MArpogaus/tensorflow-timeseries-dataset.svg?style=flat-square)](https://github.com/MArpogaus/tensorflow-timeseries-dataset/stargazers) +[![img](https://img.shields.io/github/issues/MArpogaus/tensorflow-timeseries-dataset.svg?style=flat-square)](https://github.com/MArpogaus/tensorflow-timeseries-dataset/issues) +[![img](https://img.shields.io/github/license/MArpogaus/tensorflow-timeseries-dataset.svg?style=flat-square)](https://github.com/MArpogaus/tensorflow-timeseries-dataset/blob/main/LICENSE) +[![img](https://img.shields.io/github/actions/workflow/status/MArpogaus/tensorflow-timeseries-dataset/test.yaml.svg?label=test&style=flat-square)](https://github.com/MArpogaus/tensorflow-timeseries-dataset/actions/workflows/test.yaml) +[![img](https://img.shields.io/badge/pre--commit-enabled-brightgreen.svg?logo=pre-commit&style=flat-square)](https://github.com/MArpogaus/tensorflow-timeseries-dataset/blob/main/.pre-commit-config.yaml) [![img](https://img.shields.io/badge/-LinkedIn-black.svg?style=flat-square&logo=linkedin&colorB=555)](https://linkedin.com/in/MArpogaus) # TensorFlow time-series Dataset 1. [About The Project](#about-the-project) -2. [Usage](#usage) - 1. [Example Data](#org801d13b) - 2. [Single-Step Prediction](#org0c7c414) - 3. [Multi-Step Prediction](#org23f37ef) - 4. [Preprocessing: Add Metadata features](#org92ad4f6) -3. [License](#license) -4. [Contact](#contact) -5. [Acknowledgments](#acknowledgments) +2. [Usage](#org0301ec0) + 1. [Example Data](#org1397d97) + 2. [Single-Step Prediction](#org8e15547) + 3. [Multi-Step Prediction](#org3dd7dc8) + 4. [Preprocessing: Add Metadata features](#org7b66c9a) +3. [Contributing](#org141ef05) +4. [License](#orgd3ceeaa) +5. [Contact](#orgd84778f) +6. [Acknowledgments](#org8e17169) @@ -26,12 +29,12 @@ This python package should help you to create TensorFlow datasets for time-series data. - + ## Usage - + ### Example Data @@ -71,7 +74,7 @@ Suppose you have a dataset in the following form: 1992-01-01 02:00:00 0.432704 1.159077 2.005718 - + ### Single-Step Prediction @@ -94,7 +97,7 @@ We will use it now to create a dataset with `48` historic time-steps as the inpu This returns the following TensorFlow Dataset: - + <_PrefetchDataset element_spec=(TensorSpec(shape=(4, 48, 3), dtype=tf.float32, name=None), TensorSpec(shape=(4, 1, 1), dtype=tf.float32, name=None))> We can plot the result with the utility function `plot_path`: @@ -112,7 +115,7 @@ We can plot the result with the utility function `plot_path`: ![img](.images/example1.png) - + ### Multi-Step Prediction @@ -127,7 +130,7 @@ Lets now increase the prediction size to `6` half-hour time-steps. This returns the following TensorFlow Dataset: - + <_PrefetchDataset element_spec=(TensorSpec(shape=(4, 48, 3), dtype=tf.float32, name=None), TensorSpec(shape=(4, 6, 1), dtype=tf.float32, name=None))> Again, lets plot the results to see what changed: @@ -144,7 +147,7 @@ Again, lets plot the results to see what changed: ![img](.images/example2.png) - + ### Preprocessing: Add Metadata features @@ -174,7 +177,7 @@ In this case we will be using the a class called `CyclicalFeatureEncoder` to enc This returns the following TensorFlow Dataset: - + <_PrefetchDataset element_spec=((TensorSpec(shape=(4, 48, 3), dtype=tf.float32, name=None), TensorSpec(shape=(4, 1, 8), dtype=tf.float32, name=None)), TensorSpec(shape=(4, 6, 1), dtype=tf.float32, name=None))> Again, lets plot the results to see what changed: @@ -191,24 +194,31 @@ Again, lets plot the results to see what changed: ![img](.images/example3.png) - + + +## Contributing + +Any Contributions are greatly appreciated! If you have a question, an issue or would like to contribute, please read our [contributing guidelines](CONTRIBUTING.md). + + + ## License Distributed under the [Apache License 2.0](LICENSE) - + ## Contact [Marcel Arpogaus](https://github.com/marpogaus) - [marcel.arpogaus@gmail.com](mailto:marcel.arpogaus@gmail.com) Project Link: - + - + ## Acknowledgments diff --git a/README.org b/README.org index 36f3fca..35ca5ee 100644 --- a/README.org +++ b/README.org @@ -1,11 +1,13 @@ -# Inspired by: https://github.com/othneildrew/Best-README-Template --> +# Inspired by: https://github.com/othneildrew/Best-README-Template #+OPTIONS: toc:nil -[[https://github.com/MArpogaus/tensorflow_timeseries_dataset/graphs/contributors][https://img.shields.io/github/contributors/MArpogaus/tensorflow_timeseries_dataset.svg?style=flat-square]] -[[https://github.com/MArpogaus/tensorflow_timeseries_dataset/network/members][https://img.shields.io/github/forks/MArpogaus/tensorflow_timeseries_dataset.svg?style=flat-square]] -[[https://github.com/MArpogaus/tensorflow_timeseries_dataset/stargazers][https://img.shields.io/github/stars/MArpogaus/tensorflow_timeseries_dataset.svg?style=flat-square]] -[[https://github.com/MArpogaus/tensorflow_timeseries_dataset/issues][https://img.shields.io/github/issues/MArpogaus/tensorflow_timeseries_dataset.svg?style=flat-square]] -[[https://github.com/MArpogaus/tensorflow_timeseries_dataset/blob/master/LICENSE][https://img.shields.io/github/license/MArpogaus/tensorflow_timeseries_dataset.svg?style=flat-square]] +[[https://github.com/MArpogaus/tensorflow-timeseries-dataset/graphs/contributors][https://img.shields.io/github/contributors/MArpogaus/tensorflow-timeseries-dataset.svg?style=flat-square]] +[[https://github.com/MArpogaus/tensorflow-timeseries-dataset/network/members][https://img.shields.io/github/forks/MArpogaus/tensorflow-timeseries-dataset.svg?style=flat-square]] +[[https://github.com/MArpogaus/tensorflow-timeseries-dataset/stargazers][https://img.shields.io/github/stars/MArpogaus/tensorflow-timeseries-dataset.svg?style=flat-square]] +[[https://github.com/MArpogaus/tensorflow-timeseries-dataset/issues][https://img.shields.io/github/issues/MArpogaus/tensorflow-timeseries-dataset.svg?style=flat-square]] +[[https://github.com/MArpogaus/tensorflow-timeseries-dataset/blob/main/LICENSE][https://img.shields.io/github/license/MArpogaus/tensorflow-timeseries-dataset.svg?style=flat-square]] +[[https://github.com/MArpogaus/tensorflow-timeseries-dataset/actions/workflows/test.yaml][https://img.shields.io/github/actions/workflow/status/MArpogaus/tensorflow-timeseries-dataset/test.yaml.svg?label=test&style=flat-square]] +[[https://github.com/MArpogaus/tensorflow-timeseries-dataset/blob/main/.pre-commit-config.yaml][https://img.shields.io/badge/pre--commit-enabled-brightgreen.svg?logo=pre-commit&style=flat-square]] [[https://linkedin.com/in/MArpogaus][https://img.shields.io/badge/-LinkedIn-black.svg?style=flat-square&logo=linkedin&colorB=555]] * TensorFlow time-series Dataset @@ -27,25 +29,19 @@ project locally. To get a local copy up and running follow these simple example steps. *** Prerequisites - :PROPERTIES: - :CUSTOM_ID: prerequisites - :END: - tensorflow>=2.5.* - pandas>=1.3.* - matplotlib>=3.6.2 *** Installation - :PROPERTIES: - :CUSTOM_ID: installation - :END: - pip install git+https://github.com/MArpogaus/tensorflow_timeseries_dataset.git +#+begin_src bash +pip install git+https://github.com/MArpogaus/tensorflow-timeseries-dataset.git +#+end_src ** Usage - :PROPERTIES: - :CUSTOM_ID: usage - :END: + *** Example Data Suppose you have a dataset in the following form: @@ -131,6 +127,7 @@ fname #+RESULTS: ds1_plot [[file:.images/example1.png]] + *** Multi-Step Prediction Lets now increase the prediction size to =6= half-hour time-steps. #+Name: ds2 @@ -213,23 +210,22 @@ fname #+RESULTS: ds3_plot [[file:.images/example3.png]] +** Contributing + +Any Contributions are greatly appreciated! If you have a question, an issue or would like to contribute, please read our [[file:CONTRIBUTING.md][contributing guidelines]]. + + ** License - :PROPERTIES: - :CUSTOM_ID: license - :END: + Distributed under the [[file:LICENSE][Apache License 2.0]] ** Contact - :PROPERTIES: - :CUSTOM_ID: contact - :END: + [[https://github.com/marpogaus][Marcel Arpogaus]] - [[mailto:marcel.arpogaus@gmail.com][marcel.arpogaus@gmail.com]] Project Link: -[[https://github.com/MArpogaus/tensorflow_timeseries_dataset]] +[[https://github.com/MArpogaus/tensorflow-timeseries-dataset]] ** Acknowledgments - :PROPERTIES: - :CUSTOM_ID: acknowledgments - :END: + Parts of this work have been funded by the Federal Ministry for the Environment, Nature Conservation and Nuclear Safety due to a decision of the German Federal Parliament (AI4Grids: 67KI2012A). From d8ec0a1393e79d8906a448f3295f6ec59b17a509 Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus <38564291+MArpogaus@users.noreply.github.com> Date: Fri, 16 Feb 2024 10:53:45 +0100 Subject: [PATCH 54/72] fix: TimeSeriesSplit now works with newer pandas versions --- .../loaders/csv_data_loader.py | 3 +-- .../preprocessors/time_series_split.py | 8 ++++---- tests/conftest.py | 4 ++-- 3 files changed, 7 insertions(+), 8 deletions(-) diff --git a/src/tensorflow_time_series_dataset/loaders/csv_data_loader.py b/src/tensorflow_time_series_dataset/loaders/csv_data_loader.py index 0c03d7d..00e3bde 100644 --- a/src/tensorflow_time_series_dataset/loaders/csv_data_loader.py +++ b/src/tensorflow_time_series_dataset/loaders/csv_data_loader.py @@ -4,7 +4,7 @@ # author : Marcel Arpogaus # # created : 2022-01-07 09:02:38 (Marcel Arpogaus) -# changed : 2022-01-07 09:02:38 (Marcel Arpogaus) +# changed : 2024-02-16 10:30:32 (Marcel Arpogaus) # DESCRIPTION ################################################################# # ... # LICENSE ##################################################################### @@ -30,7 +30,6 @@ def _read_csv_file(file_path, date_time_col="date_time", **kwds): load_data = pd.read_csv( file_path, parse_dates=[date_time_col], - infer_datetime_format=True, index_col=[date_time_col], **kwds, ) diff --git a/src/tensorflow_time_series_dataset/preprocessors/time_series_split.py b/src/tensorflow_time_series_dataset/preprocessors/time_series_split.py index 46dc8fa..a063bc0 100644 --- a/src/tensorflow_time_series_dataset/preprocessors/time_series_split.py +++ b/src/tensorflow_time_series_dataset/preprocessors/time_series_split.py @@ -4,7 +4,7 @@ # author : Marcel Arpogaus # # created : 2022-01-07 09:02:38 (Marcel Arpogaus) -# changed : 2022-01-07 09:02:38 (Marcel Arpogaus) +# changed : 2024-02-16 10:28:30 (Marcel Arpogaus) # DESCRIPTION ################################################################# # ... # LICENSE ##################################################################### @@ -22,7 +22,6 @@ # See the License for the specific language governing permissions and # limitations under the License. ############################################################################### -import pandas as pd class TimeSeriesSplit: @@ -34,8 +33,9 @@ def __init__(self, split_size, split): self.split = split def __call__(self, data): - days = pd.date_range(data.index.min(), data.index.max(), freq="D") - days = days.to_numpy().astype("datetime64[m]") + data = data.sort_index() + days = data.index.date + days = days.astype("datetime64[m]") right = days[int(len(days) * self.split_size)] left = right - 1 if self.split == self.LEFT: diff --git a/tests/conftest.py b/tests/conftest.py index 9fd5d4f..e7644cc 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -51,7 +51,7 @@ def gen_df_with_id(ids, columns, date_range): def time_series_df(request): df = gen_df( columns=request.param[0], - date_range=pd.date_range("1/1/1", periods=request.param[1], freq="30T"), + date_range=pd.date_range("1/1/1", periods=request.param[1], freq="30min"), ) return df @@ -67,7 +67,7 @@ def time_series_df_with_id(request): df = gen_df_with_id( ids=ids, columns=columns, - date_range=pd.date_range("1/1/1", periods=periods, freq="30T"), + date_range=pd.date_range("1/1/1", periods=periods, freq="30min"), ) return df From 8d87eecd4a5fa9d2ca7d0ab2580c294723fc1082 Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus <38564291+MArpogaus@users.noreply.github.com> Date: Fri, 16 Feb 2024 10:57:09 +0100 Subject: [PATCH 55/72] fix: reduce required matplotlib version for python 3.7 compatibility --- .github/workflows/test.yaml | 2 +- pyproject.toml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml index 70251ee..9087759 100644 --- a/.github/workflows/test.yaml +++ b/.github/workflows/test.yaml @@ -8,7 +8,7 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - python-version: [3.8, 3.9, '3.10', '3.11'] + python-version: [3.7, 3.8, 3.9, '3.10', '3.11'] steps: - uses: actions/checkout@v3 diff --git a/pyproject.toml b/pyproject.toml index aaf0483..7cba744 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -25,7 +25,7 @@ classifiers = [ dependencies = [ "tensorflow>=2.5", "pandas>=1.1.0", - "matplotlib>=3.6" + "matplotlib>=3.5" ] dynamic = ["version"] license = {text = "Apache Software License (Apache 2.0)"} From 9367a9f1479248519ca8a91f0ffd664fe07992fe Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus <38564291+MArpogaus@users.noreply.github.com> Date: Fri, 16 Feb 2024 10:59:59 +0100 Subject: [PATCH 56/72] ci: enables parallelization for pytest --- .github/workflows/test.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml index 9087759..d73ef1b 100644 --- a/.github/workflows/test.yaml +++ b/.github/workflows/test.yaml @@ -21,4 +21,4 @@ jobs: python -m pip install --upgrade pip pip install .[test] - name: Test with pytest - run: pytest + run: pytest -n auto From a0dab0105e19c5081da93daa7b44e5d01f2529dd Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus <38564291+MArpogaus@users.noreply.github.com> Date: Fri, 16 Feb 2024 12:08:02 +0100 Subject: [PATCH 57/72] chore: Add project homepage to pyproject.toml --- pyproject.toml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/pyproject.toml b/pyproject.toml index 7cba744..62d1430 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -47,6 +47,9 @@ test = [ 'pytest-xdist' ] +[project.urls] +Homepage = "https://github.com/MArpogaus/tensorflow_timeseries_dataset" + [tool.ruff] indent-width = 4 line-length = 88 From b5514c82e845b0e8b222df1e7f8f470baf1a36ba Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus <38564291+MArpogaus@users.noreply.github.com> Date: Fri, 16 Feb 2024 13:32:07 +0100 Subject: [PATCH 58/72] docs: add PyPi badge and use svg images in README --- .images/example1.png 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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/README.md b/README.md index 6b4e628..6b32b07 100644 --- a/README.md +++ b/README.md @@ -7,19 +7,21 @@ [![img](https://img.shields.io/badge/pre--commit-enabled-brightgreen.svg?logo=pre-commit&style=flat-square)](https://github.com/MArpogaus/tensorflow-timeseries-dataset/blob/main/.pre-commit-config.yaml) [![img](https://img.shields.io/badge/-LinkedIn-black.svg?style=flat-square&logo=linkedin&colorB=555)](https://linkedin.com/in/MArpogaus) +[![img](https://img.shields.io/pypi/v/tensorflow-time-series-dataset.svg?style=flat-square)](https://pypi.org/project/tensorflow-time-series-dataset) + # TensorFlow time-series Dataset 1. [About The Project](#about-the-project) -2. [Usage](#org0301ec0) - 1. [Example Data](#org1397d97) - 2. [Single-Step Prediction](#org8e15547) - 3. [Multi-Step Prediction](#org3dd7dc8) - 4. [Preprocessing: Add Metadata features](#org7b66c9a) -3. [Contributing](#org141ef05) -4. [License](#orgd3ceeaa) -5. [Contact](#orgd84778f) -6. [Acknowledgments](#org8e17169) +2. [Usage](#org6cce3cf) + 1. [Example Data](#orgdcbe859) + 2. [Single-Step Prediction](#org9146fba) + 3. [Multi-Step Prediction](#org8d2da39) + 4. [Preprocessing: Add Metadata features](#org65236db) +3. [Contributing](#org07d9730) +4. [License](#orgf9b82cf) +5. [Contact](#org36cca87) +6. [Acknowledgments](#orge3ed0f5) @@ -29,12 +31,12 @@ This python package should help you to create TensorFlow datasets for time-series data. - + ## Usage - + ### Example Data @@ -74,7 +76,7 @@ Suppose you have a dataset in the following form: 1992-01-01 02:00:00 0.432704 1.159077 2.005718 - + ### Single-Step Prediction @@ -108,14 +110,14 @@ We can plot the result with the utility function `plot_path`: **factory_kwds ) - fname='.images/example1.png' + fname='.images/example1.svg' fig.savefig(fname) fname -![img](.images/example1.png) +![img](.images/example1.svg) - + ### Multi-Step Prediction @@ -140,14 +142,14 @@ Again, lets plot the results to see what changed: **factory_kwds ) - fname='.images/example2.png' + fname='.images/example2.svg' fig.savefig(fname) fname -![img](.images/example2.png) +![img](.images/example2.svg) - + ### Preprocessing: Add Metadata features @@ -187,28 +189,28 @@ Again, lets plot the results to see what changed: **factory_kwds ) - fname='.images/example3.png' + fname='.images/example3.svg' fig.savefig(fname) fname -![img](.images/example3.png) +![img](.images/example3.svg) - + ## Contributing Any Contributions are greatly appreciated! If you have a question, an issue or would like to contribute, please read our [contributing guidelines](CONTRIBUTING.md). - + ## License Distributed under the [Apache License 2.0](LICENSE) - + ## Contact @@ -218,7 +220,7 @@ Project Link: - + ## Acknowledgments diff --git a/README.org b/README.org index 35ca5ee..22474c8 100644 --- a/README.org +++ b/README.org @@ -10,6 +10,8 @@ [[https://github.com/MArpogaus/tensorflow-timeseries-dataset/blob/main/.pre-commit-config.yaml][https://img.shields.io/badge/pre--commit-enabled-brightgreen.svg?logo=pre-commit&style=flat-square]] [[https://linkedin.com/in/MArpogaus][https://img.shields.io/badge/-LinkedIn-black.svg?style=flat-square&logo=linkedin&colorB=555]] +[[https://pypi.org/project/tensorflow-time-series-dataset][https://img.shields.io/pypi/v/tensorflow-time-series-dataset.svg?style=flat-square]] + * TensorFlow time-series Dataset #+TOC: headlines 2 local @@ -37,7 +39,7 @@ example steps. *** Installation #+begin_src bash -pip install git+https://github.com/MArpogaus/tensorflow-timeseries-dataset.git +pip install tensorflow-time-series-dataset #+end_src ** Usage @@ -107,7 +109,7 @@ ds1 This returns the following TensorFlow Dataset: #+RESULTS: ds1 -: +: <_PrefetchDataset element_spec=(TensorSpec(shape=(4, 48, 3), dtype=tf.float32, name=None), TensorSpec(shape=(4, 1, 1), dtype=tf.float32, name=None))> We can plot the result with the utility function =plot_path=: @@ -120,13 +122,13 @@ fig=plot_patch( ,**factory_kwds ) -fname='.images/example1.png' +fname='.images/example1.svg' fig.savefig(fname) fname #+end_src #+RESULTS: ds1_plot -[[file:.images/example1.png]] +[[file:.images/example1.svg]] *** Multi-Step Prediction Lets now increase the prediction size to =6= half-hour time-steps. @@ -143,7 +145,7 @@ ds2 This returns the following TensorFlow Dataset: #+RESULTS: ds2 -: +: <_PrefetchDataset element_spec=(TensorSpec(shape=(4, 48, 3), dtype=tf.float32, name=None), TensorSpec(shape=(4, 6, 1), dtype=tf.float32, name=None))> Again, lets plot the results to see what changed: #+NAME: ds2_plot @@ -154,13 +156,13 @@ fig=plot_patch( **factory_kwds ) -fname='.images/example2.png' +fname='.images/example2.svg' fig.savefig(fname) fname #+end_src #+RESULTS: ds2_plot -[[file:.images/example2.png]] +[[file:.images/example2.svg]] *** Preprocessing: Add Metadata features Preprocessors can be used to transform the data before it is fed into the model. @@ -202,13 +204,13 @@ fig=plot_patch( **factory_kwds ) -fname='.images/example3.png' +fname='.images/example3.svg' fig.savefig(fname) fname #+end_src #+RESULTS: ds3_plot -[[file:.images/example3.png]] +[[file:.images/example3.svg]] ** Contributing From c3701e54d6339ea35e9693e7d29410359099de00 Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus <38564291+MArpogaus@users.noreply.github.com> Date: Fri, 16 Feb 2024 15:21:49 +0100 Subject: [PATCH 59/72] docs!: update project repository urls --- README.md | 54 +++++++++++++++++++++++++------------------------- README.org | 20 +++++++++---------- pyproject.toml | 2 +- 3 files changed, 38 insertions(+), 38 deletions(-) diff --git a/README.md b/README.md index 6b32b07..c7cc135 100644 --- a/README.md +++ b/README.md @@ -1,27 +1,27 @@ -[![img](https://img.shields.io/github/contributors/MArpogaus/tensorflow-timeseries-dataset.svg?style=flat-square)](https://github.com/MArpogaus/tensorflow-timeseries-dataset/graphs/contributors) -[![img](https://img.shields.io/github/forks/MArpogaus/tensorflow-timeseries-dataset.svg?style=flat-square)](https://github.com/MArpogaus/tensorflow-timeseries-dataset/network/members) -[![img](https://img.shields.io/github/stars/MArpogaus/tensorflow-timeseries-dataset.svg?style=flat-square)](https://github.com/MArpogaus/tensorflow-timeseries-dataset/stargazers) -[![img](https://img.shields.io/github/issues/MArpogaus/tensorflow-timeseries-dataset.svg?style=flat-square)](https://github.com/MArpogaus/tensorflow-timeseries-dataset/issues) -[![img](https://img.shields.io/github/license/MArpogaus/tensorflow-timeseries-dataset.svg?style=flat-square)](https://github.com/MArpogaus/tensorflow-timeseries-dataset/blob/main/LICENSE) -[![img](https://img.shields.io/github/actions/workflow/status/MArpogaus/tensorflow-timeseries-dataset/test.yaml.svg?label=test&style=flat-square)](https://github.com/MArpogaus/tensorflow-timeseries-dataset/actions/workflows/test.yaml) -[![img](https://img.shields.io/badge/pre--commit-enabled-brightgreen.svg?logo=pre-commit&style=flat-square)](https://github.com/MArpogaus/tensorflow-timeseries-dataset/blob/main/.pre-commit-config.yaml) +[![img](https://img.shields.io/github/contributors/MArpogaus/tensorflow_time_series_dataset.svg?style=flat-square)](https://github.com/MArpogaus/tensorflow_time_series_dataset/graphs/contributors) +[![img](https://img.shields.io/github/forks/MArpogaus/tensorflow_time_series_dataset.svg?style=flat-square)](https://github.com/MArpogaus/tensorflow_time_series_dataset/network/members) +[![img](https://img.shields.io/github/stars/MArpogaus/tensorflow_time_series_dataset.svg?style=flat-square)](https://github.com/MArpogaus/tensorflow_time_series_dataset/stargazers) +[![img](https://img.shields.io/github/issues/MArpogaus/tensorflow_time_series_dataset.svg?style=flat-square)](https://github.com/MArpogaus/tensorflow_time_series_dataset/issues) +[![img](https://img.shields.io/github/license/MArpogaus/tensorflow_time_series_dataset.svg?style=flat-square)](https://github.com/MArpogaus/tensorflow_time_series_dataset/blob/main/LICENSE) +[![img](https://img.shields.io/github/actions/workflow/status/MArpogaus/tensorflow_time_series_dataset/test.yaml.svg?label=test&style=flat-square)](https://github.com/MArpogaus/tensorflow_time_series_dataset/actions/workflows/test.yaml) +[![img](https://img.shields.io/badge/pre--commit-enabled-brightgreen.svg?logo=pre-commit&style=flat-square)](https://github.com/MArpogaus/tensorflow_time_series_dataset/blob/main/.pre-commit-config.yaml) [![img](https://img.shields.io/badge/-LinkedIn-black.svg?style=flat-square&logo=linkedin&colorB=555)](https://linkedin.com/in/MArpogaus) -[![img](https://img.shields.io/pypi/v/tensorflow-time-series-dataset.svg?style=flat-square)](https://pypi.org/project/tensorflow-time-series-dataset) +[![img](https://img.shields.io/pypi/v/tensorflow_time_series_dataset.svg?style=flat-square)](https://pypi.org/project/tensorflow_time_series_dataset) # TensorFlow time-series Dataset 1. [About The Project](#about-the-project) -2. [Usage](#org6cce3cf) - 1. [Example Data](#orgdcbe859) - 2. [Single-Step Prediction](#org9146fba) - 3. [Multi-Step Prediction](#org8d2da39) - 4. [Preprocessing: Add Metadata features](#org65236db) -3. [Contributing](#org07d9730) -4. [License](#orgf9b82cf) -5. [Contact](#org36cca87) -6. [Acknowledgments](#orge3ed0f5) +2. [Usage](#org5ceb9c6) + 1. [Example Data](#orgde6aeb6) + 2. [Single-Step Prediction](#orgf4338fa) + 3. [Multi-Step Prediction](#orgd346cb0) + 4. [Preprocessing: Add Metadata features](#org394b79a) +3. [Contributing](#org01c4e9f) +4. [License](#org02adeea) +5. [Contact](#org17ef52d) +6. [Acknowledgments](#org7793597) @@ -31,12 +31,12 @@ This python package should help you to create TensorFlow datasets for time-series data. - + ## Usage - + ### Example Data @@ -76,7 +76,7 @@ Suppose you have a dataset in the following form: 1992-01-01 02:00:00 0.432704 1.159077 2.005718 - + ### Single-Step Prediction @@ -117,7 +117,7 @@ We can plot the result with the utility function `plot_path`: ![img](.images/example1.svg) - + ### Multi-Step Prediction @@ -149,7 +149,7 @@ Again, lets plot the results to see what changed: ![img](.images/example2.svg) - + ### Preprocessing: Add Metadata features @@ -196,31 +196,31 @@ Again, lets plot the results to see what changed: ![img](.images/example3.svg) - + ## Contributing Any Contributions are greatly appreciated! If you have a question, an issue or would like to contribute, please read our [contributing guidelines](CONTRIBUTING.md). - + ## License Distributed under the [Apache License 2.0](LICENSE) - + ## Contact [Marcel Arpogaus](https://github.com/marpogaus) - [marcel.arpogaus@gmail.com](mailto:marcel.arpogaus@gmail.com) Project Link: - + - + ## Acknowledgments diff --git a/README.org b/README.org index 22474c8..a69d29f 100644 --- a/README.org +++ b/README.org @@ -1,16 +1,16 @@ # Inspired by: https://github.com/othneildrew/Best-README-Template #+OPTIONS: toc:nil -[[https://github.com/MArpogaus/tensorflow-timeseries-dataset/graphs/contributors][https://img.shields.io/github/contributors/MArpogaus/tensorflow-timeseries-dataset.svg?style=flat-square]] -[[https://github.com/MArpogaus/tensorflow-timeseries-dataset/network/members][https://img.shields.io/github/forks/MArpogaus/tensorflow-timeseries-dataset.svg?style=flat-square]] -[[https://github.com/MArpogaus/tensorflow-timeseries-dataset/stargazers][https://img.shields.io/github/stars/MArpogaus/tensorflow-timeseries-dataset.svg?style=flat-square]] -[[https://github.com/MArpogaus/tensorflow-timeseries-dataset/issues][https://img.shields.io/github/issues/MArpogaus/tensorflow-timeseries-dataset.svg?style=flat-square]] -[[https://github.com/MArpogaus/tensorflow-timeseries-dataset/blob/main/LICENSE][https://img.shields.io/github/license/MArpogaus/tensorflow-timeseries-dataset.svg?style=flat-square]] -[[https://github.com/MArpogaus/tensorflow-timeseries-dataset/actions/workflows/test.yaml][https://img.shields.io/github/actions/workflow/status/MArpogaus/tensorflow-timeseries-dataset/test.yaml.svg?label=test&style=flat-square]] -[[https://github.com/MArpogaus/tensorflow-timeseries-dataset/blob/main/.pre-commit-config.yaml][https://img.shields.io/badge/pre--commit-enabled-brightgreen.svg?logo=pre-commit&style=flat-square]] +[[https://github.com/MArpogaus/tensorflow_time_series_dataset/graphs/contributors][https://img.shields.io/github/contributors/MArpogaus/tensorflow_time_series_dataset.svg?style=flat-square]] +[[https://github.com/MArpogaus/tensorflow_time_series_dataset/network/members][https://img.shields.io/github/forks/MArpogaus/tensorflow_time_series_dataset.svg?style=flat-square]] +[[https://github.com/MArpogaus/tensorflow_time_series_dataset/stargazers][https://img.shields.io/github/stars/MArpogaus/tensorflow_time_series_dataset.svg?style=flat-square]] +[[https://github.com/MArpogaus/tensorflow_time_series_dataset/issues][https://img.shields.io/github/issues/MArpogaus/tensorflow_time_series_dataset.svg?style=flat-square]] +[[https://github.com/MArpogaus/tensorflow_time_series_dataset/blob/main/LICENSE][https://img.shields.io/github/license/MArpogaus/tensorflow_time_series_dataset.svg?style=flat-square]] +[[https://github.com/MArpogaus/tensorflow_time_series_dataset/actions/workflows/test.yaml][https://img.shields.io/github/actions/workflow/status/MArpogaus/tensorflow_time_series_dataset/test.yaml.svg?label=test&style=flat-square]] +[[https://github.com/MArpogaus/tensorflow_time_series_dataset/blob/main/.pre-commit-config.yaml][https://img.shields.io/badge/pre--commit-enabled-brightgreen.svg?logo=pre-commit&style=flat-square]] [[https://linkedin.com/in/MArpogaus][https://img.shields.io/badge/-LinkedIn-black.svg?style=flat-square&logo=linkedin&colorB=555]] -[[https://pypi.org/project/tensorflow-time-series-dataset][https://img.shields.io/pypi/v/tensorflow-time-series-dataset.svg?style=flat-square]] +[[https://pypi.org/project/tensorflow_time_series_dataset][https://img.shields.io/pypi/v/tensorflow_time_series_dataset.svg?style=flat-square]] * TensorFlow time-series Dataset @@ -39,7 +39,7 @@ example steps. *** Installation #+begin_src bash -pip install tensorflow-time-series-dataset +pip install tensorflow_time_series_dataset #+end_src ** Usage @@ -226,7 +226,7 @@ Distributed under the [[file:LICENSE][Apache License 2.0]] [[https://github.com/marpogaus][Marcel Arpogaus]] - [[mailto:marcel.arpogaus@gmail.com][marcel.arpogaus@gmail.com]] Project Link: -[[https://github.com/MArpogaus/tensorflow-timeseries-dataset]] +[[https://github.com/MArpogaus/tensorflow_time_series_dataset]] ** Acknowledgments diff --git a/pyproject.toml b/pyproject.toml index 62d1430..f632006 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -48,7 +48,7 @@ test = [ ] [project.urls] -Homepage = "https://github.com/MArpogaus/tensorflow_timeseries_dataset" +Homepage = "https://github.com/MArpogaus/tensorflow_time_series_dataset" [tool.ruff] indent-width = 4 From 778fdeff3610f8c94bda8884166608f44990b2e1 Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus <38564291+MArpogaus@users.noreply.github.com> Date: Fri, 16 Feb 2024 15:36:04 +0100 Subject: [PATCH 60/72] docs: adds installation sction --- README.md | 53 ++++++++++++++++++++++++++++++++--------------------- README.org | 25 ++++++------------------- 2 files changed, 38 insertions(+), 40 deletions(-) diff --git a/README.md b/README.md index c7cc135..56870f9 100644 --- a/README.md +++ b/README.md @@ -12,31 +12,42 @@ # TensorFlow time-series Dataset -1. [About The Project](#about-the-project) -2. [Usage](#org5ceb9c6) - 1. [Example Data](#orgde6aeb6) - 2. [Single-Step Prediction](#orgf4338fa) - 3. [Multi-Step Prediction](#orgd346cb0) - 4. [Preprocessing: Add Metadata features](#org394b79a) -3. [Contributing](#org01c4e9f) -4. [License](#org02adeea) -5. [Contact](#org17ef52d) -6. [Acknowledgments](#org7793597) +1. [About The Project](#org48e7e83) +2. [Installation](#org4124adc) +3. [Usage](#orgc2c9462) + 1. [Example Data](#org9a62649) + 2. [Single-Step Prediction](#org2b7f25f) + 3. [Multi-Step Prediction](#org96a3f28) + 4. [Preprocessing: Add Metadata features](#org869ce63) +4. [Contributing](#org8423a52) +5. [License](#orgaa9c5fe) +6. [Contact](#org5611eae) +7. [Acknowledgments](#orgce02d96) - + ## About The Project This python package should help you to create TensorFlow datasets for time-series data. - + + +## Installation + +This package is available on [PyPI](https://pypi.org/project/tensorflow-time-series-dataset/). +You install it and all of its dependencies using pip: + + pip install tensorflow_time_series_dataset + + + ## Usage - + ### Example Data @@ -54,7 +65,7 @@ Suppose you have a dataset in the following form: index=pd.date_range( start='1/1/1992', periods=periods, - freq='30T' + freq='30min' ), data=np.stack( [ @@ -76,7 +87,7 @@ Suppose you have a dataset in the following form: 1992-01-01 02:00:00 0.432704 1.159077 2.005718 - + ### Single-Step Prediction @@ -117,7 +128,7 @@ We can plot the result with the utility function `plot_path`: ![img](.images/example1.svg) - + ### Multi-Step Prediction @@ -149,7 +160,7 @@ Again, lets plot the results to see what changed: ![img](.images/example2.svg) - + ### Preprocessing: Add Metadata features @@ -196,21 +207,21 @@ Again, lets plot the results to see what changed: ![img](.images/example3.svg) - + ## Contributing Any Contributions are greatly appreciated! If you have a question, an issue or would like to contribute, please read our [contributing guidelines](CONTRIBUTING.md). - + ## License Distributed under the [Apache License 2.0](LICENSE) - + ## Contact @@ -220,7 +231,7 @@ Project Link: - + ## Acknowledgments diff --git a/README.org b/README.org index a69d29f..6748e4f 100644 --- a/README.org +++ b/README.org @@ -17,28 +17,15 @@ #+TOC: headlines 2 local ** About The Project - :PROPERTIES: - :CUSTOM_ID: about-the-project - :END: -This python package should help you to create TensorFlow datasets for time-series data. - -** Getting Started :noexport: - :PROPERTIES: - :CUSTOM_ID: getting-started - :END: -This is an example of how you may give instructions on setting up your -project locally. To get a local copy up and running follow these simple -example steps. -*** Prerequisites +This python package should help you to create TensorFlow datasets for time-series data. - - tensorflow>=2.5.* - - pandas>=1.3.* - - matplotlib>=3.6.2 +** Installation -*** Installation +This package is available on [[https://pypi.org/project/tensorflow-time-series-dataset/][PyPI]]. +You install it and all of its dependencies using pip: -#+begin_src bash +#+begin_src bash :exports nil pip install tensorflow_time_series_dataset #+end_src @@ -61,7 +48,7 @@ test_df=pd.DataFrame( index=pd.date_range( start='1/1/1992', periods=periods, - freq='30T' + freq='30min' ), data=np.stack( [ From 3bf6fa6706c492a0a7bc185ed17a4a930e2d312f Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus <38564291+MArpogaus@users.noreply.github.com> Date: Fri, 16 Feb 2024 15:36:56 +0100 Subject: [PATCH 61/72] fix: use `min` instead of deprecated `T` for pandas freq --- tests/test_preprocessors.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_preprocessors.py b/tests/test_preprocessors.py index b6f3ea5..34bde99 100644 --- a/tests/test_preprocessors.py +++ b/tests/test_preprocessors.py @@ -10,7 +10,7 @@ @pytest.fixture def cycl_df(): - date_range = pd.date_range(start="1/1/1992", end="31/12/1992", freq="30T") + date_range = pd.date_range(start="1/1/1992", end="31/12/1992", freq="30min") test_df = pd.DataFrame( index=date_range, data=np.stack( From eb9dec1bcfd45a8640296057d88050f745efbf96 Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus <38564291+MArpogaus@users.noreply.github.com> Date: Fri, 16 Feb 2024 15:39:06 +0100 Subject: [PATCH 62/72] ci: update lint and test workflow definitions --- .github/workflows/{pre-commit.yml => pre-commit.yaml} | 2 +- .github/workflows/test.yaml | 6 ++++-- 2 files changed, 5 insertions(+), 3 deletions(-) rename .github/workflows/{pre-commit.yml => pre-commit.yaml} (84%) diff --git a/.github/workflows/pre-commit.yml b/.github/workflows/pre-commit.yaml similarity index 84% rename from .github/workflows/pre-commit.yml rename to .github/workflows/pre-commit.yaml index b6dcc07..846f557 100644 --- a/.github/workflows/pre-commit.yml +++ b/.github/workflows/pre-commit.yaml @@ -6,6 +6,6 @@ jobs: pre-commit: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - uses: actions/setup-python@v4 - uses: pre-commit/action@v3.0.1 diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml index d73ef1b..718056b 100644 --- a/.github/workflows/test.yaml +++ b/.github/workflows/test.yaml @@ -5,13 +5,15 @@ on: [push] jobs: build: - runs-on: ubuntu-latest strategy: matrix: + platform: [ubuntu-latest, macos-latest, windows-latest] python-version: [3.7, 3.8, 3.9, '3.10', '3.11'] + runs-on: ${{ matrix.platform }} + steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v4 with: From 7526fa5fb987d68c4b22fb67bc30a00011c3da09 Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus <38564291+MArpogaus@users.noreply.github.com> Date: Fri, 16 Feb 2024 15:39:45 +0100 Subject: [PATCH 63/72] ci: add test workflow --- .github/workflows/release.yaml | 118 +++++++++++++++++++++++++++++++++ 1 file changed, 118 insertions(+) create mode 100644 .github/workflows/release.yaml diff --git a/.github/workflows/release.yaml b/.github/workflows/release.yaml new file mode 100644 index 0000000..a3753ba --- /dev/null +++ b/.github/workflows/release.yaml @@ -0,0 +1,118 @@ +# Inspired by: https://packaging.python.org/en/latest/guides/publishing-package-distribution-releases-using-github-actions-ci-cd-workflows/ + +name: Publish Python Distribution to PyPI and TestPyPI + +on: push + +jobs: + build: + name: Build + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v4 + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: 3.x + - name: Install pypa/build + run: >- + python3 -m + pip install + build + --user + - name: Build a binary wheel and a source tarball + run: python3 -m build + - name: Store the distribution packages + uses: actions/upload-artifact@v3 + with: + name: python-package-distributions + path: dist/ + + publish-to-pypi: + name: Publish to PyPi + if: startsWith(github.ref, 'refs/tags/') # only publish to PyPI on tag pushes + needs: + - build + runs-on: ubuntu-latest + environment: + name: pypi + url: https://pypi.org/p/tensorflow-time-series-dataset + permissions: + id-token: write # IMPORTANT: mandatory for trusted publishing + steps: + - name: Download all the dists + uses: actions/download-artifact@v3 + with: + name: python-package-distributions + path: dist/ + - name: Publish distribution to PyPI + uses: pypa/gh-action-pypi-publish@release/v1 + + github-release: + name: >- + Sign the Python distribution with Sigstore + and upload them to GitHub Release + needs: + - publish-to-pypi + runs-on: ubuntu-latest + + permissions: + contents: write # IMPORTANT: mandatory for making GitHub Releases + id-token: write # IMPORTANT: mandatory for sigstore + + steps: + - name: Download all the dists + uses: actions/download-artifact@v3 + with: + name: python-package-distributions + path: dist/ + - name: Sign the dists with Sigstore + uses: sigstore/gh-action-sigstore-python@v1.2.3 + with: + inputs: >- + ./dist/*.tar.gz + ./dist/*.whl + - name: Create GitHub Release + env: + GITHUB_TOKEN: ${{ github.token }} + run: >- + gh release create + '${{ github.ref_name }}' + --repo '${{ github.repository }}' + --notes "" + - name: Upload artifact signatures to GitHub Release + env: + GITHUB_TOKEN: ${{ github.token }} + # Upload to GitHub Release using the `gh` CLI. + # `dist/` contains the built packages, and the + # sigstore-produced signatures and certificates. + run: >- + gh release upload + '${{ github.ref_name }}' dist/** + --repo '${{ github.repository }}' + + publish-to-testpypi: + name: Publish Python distribution to TestPyPI + needs: + - build + runs-on: ubuntu-latest + + environment: + name: testpypi + url: https://test.pypi.org/p/tensorflow-time-series-dataset + + permissions: + id-token: write # IMPORTANT: mandatory for trusted publishing + + steps: + - name: Download all the dists + uses: actions/download-artifact@v3 + with: + name: python-package-distributions + path: dist/ + - name: Publish distribution to TestPyPI + uses: pypa/gh-action-pypi-publish@release/v1 + with: + skip-existing: true + repository-url: https://test.pypi.org/legacy/ From 7f67466440ba37c7723f0e15d89e564b85d7320c Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus <38564291+MArpogaus@users.noreply.github.com> Date: Fri, 16 Feb 2024 15:43:10 +0100 Subject: [PATCH 64/72] bump v0.1.1: version 0.1.0 -> 0.1.1 --- src/tensorflow_time_series_dataset/__init__.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/tensorflow_time_series_dataset/__init__.py b/src/tensorflow_time_series_dataset/__init__.py index 23a755e..da91966 100644 --- a/src/tensorflow_time_series_dataset/__init__.py +++ b/src/tensorflow_time_series_dataset/__init__.py @@ -4,7 +4,7 @@ # author : Marcel Arpogaus # # created : 2022-01-07 09:02:38 (Marcel Arpogaus) -# changed : 2022-11-09 17:02:21 (Marcel Arpogaus) +# changed : 2024-02-16 15:43:05 (Marcel Arpogaus) # DESCRIPTION ################################################################# # ... # LICENSE ##################################################################### @@ -22,4 +22,4 @@ # See the License for the specific language governing permissions and # limitations under the License. ############################################################################### -__version__ = "0.1.0" +__version__ = "0.1.1" From d13efb49b2297074caa697a6bc1c9bdd898b2dd6 Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus <38564291+MArpogaus@users.noreply.github.com> Date: Mon, 19 Feb 2024 13:27:05 +0100 Subject: [PATCH 65/72] docs: add docstrings in numpy format --- .../__init__.py | 3 +- src/tensorflow_time_series_dataset/factory.py | 106 ++++++++--- .../loaders/csv_data_loader.py | 60 ++++++- .../pipeline/patch_generator.py | 50 ++++-- .../pipeline/patch_processor.py | 78 ++++++-- .../pipeline/windowed_time_series_pipeline.py | 83 +++++++-- .../preprocessors/cyclical_feature_encoder.py | 92 +++++++++- .../groupby_dataset_generator.py | 81 +++++++-- .../preprocessors/time_series_k_fold.py | 48 +++-- .../preprocessors/time_series_split.py | 44 ++++- .../utils/test.py | 170 +++++++++++++++--- .../utils/visualisation.py | 54 ++++-- tests/test_factory.py | 52 +++--- tests/test_pipleine.py | 30 ++-- tests/test_preprocessors.py | 8 +- 15 files changed, 767 insertions(+), 192 deletions(-) diff --git a/src/tensorflow_time_series_dataset/__init__.py b/src/tensorflow_time_series_dataset/__init__.py index da91966..794f943 100644 --- a/src/tensorflow_time_series_dataset/__init__.py +++ b/src/tensorflow_time_series_dataset/__init__.py @@ -4,7 +4,7 @@ # author : Marcel Arpogaus # # created : 2022-01-07 09:02:38 (Marcel Arpogaus) -# changed : 2024-02-16 15:43:05 (Marcel Arpogaus) +# changed : 2024-02-19 13:26:55 (Marcel Arpogaus) # DESCRIPTION ################################################################# # ... # LICENSE ##################################################################### @@ -22,4 +22,5 @@ # See the License for the specific language governing permissions and # limitations under the License. ############################################################################### +""".. include:: ../../README.md""" __version__ = "0.1.1" diff --git a/src/tensorflow_time_series_dataset/factory.py b/src/tensorflow_time_series_dataset/factory.py index 62319d7..ff47474 100644 --- a/src/tensorflow_time_series_dataset/factory.py +++ b/src/tensorflow_time_series_dataset/factory.py @@ -4,7 +4,7 @@ # author : Marcel Arpogaus # # created : 2022-01-07 09:02:38 (Marcel Arpogaus) -# changed : 2022-09-02 16:36:19 (Marcel Arpogaus) +# changed : 2024-02-19 12:57:42 (Marcel Arpogaus) # DESCRIPTION ################################################################# # ... # LICENSE ##################################################################### @@ -22,42 +22,95 @@ # See the License for the specific language governing permissions and # limitations under the License. ############################################################################### +from typing import Any, Dict, List + import tensorflow as tf from .pipeline import WindowedTimeSeriesPipeline class WindowedTimeSeriesDatasetFactory: - default_pipline_kwds = dict( - shift=None, batch_size=32, cycle_length=1, shuffle_buffer_size=1000, cache=True - ) + """A factory to initialize a new WindowedTimeSeriesPipeline. + + Parameters + ---------- + history_columns : List[str] + List of columns representing the history data. + prediction_columns : List[str] + List of columns representing the prediction data. + meta_columns : List[str], optional + List of columns representing the meta data, by default []. + dtype : tf.DType, optional + Data type of the dataset, by default tf.float32. + **pipeline_kwargs : Any + Keyword arguments for the data pipeline. + + """ + + default_pipeline_kwargs: Dict[str, Any] = { + "shift": None, + "batch_size": 32, + "cycle_length": 1, + "shuffle_buffer_size": 1000, + "cache": True, + } def __init__( self, - history_columns, - prediction_columns, - meta_columns=[], - dtype=tf.float32, - **pipline_kwds, - ): - self.columns = set(history_columns + meta_columns + prediction_columns) - self.preprocessors = [] - self.data_loader = None - self.dtype = dtype - self.data_pipeline = WindowedTimeSeriesPipeline( + history_columns: List[str], + prediction_columns: List[str], + meta_columns: List[str] = [], + dtype: tf.DType = tf.float32, + **pipeline_kwargs: Any, + ) -> None: + self.columns: set = set(history_columns + meta_columns + prediction_columns) + self.preprocessors: List[Any] = [] + self.data_loader: Any = None + self.dtype: tf.DType = dtype + self.data_pipeline: WindowedTimeSeriesPipeline = WindowedTimeSeriesPipeline( history_columns=history_columns, meta_columns=meta_columns, prediction_columns=prediction_columns, - **{**self.default_pipline_kwds, **pipline_kwds}, + **{**self.default_pipeline_kwargs, **pipeline_kwargs}, ) - def add_preprocessor(self, preprocessor): + def add_preprocessor(self, preprocessor: Any) -> None: + """Add a preprocessor to the list of preprocessors. + + Parameters + ---------- + preprocessor : Any + The preprocessor function to be added. + + """ self.preprocessors.append(preprocessor) - def set_data_loader(self, data_loader): + def set_data_loader(self, data_loader: Any) -> None: + """Set a data loader for the dataset. + bk. + + Parameters + ---------- + data_loader : Any + The data loader function. + + """ self.data_loader = data_loader - def get_dataset(self, data=None): + def get_dataset(self, data: Any = None) -> tf.data.Dataset: + """Get the dataset for the given data. + + Parameters + ---------- + data : Any, optional + The data to create the dataset from, by default None. + + Returns + ------- + tf.data.Dataset + The created TensorFlow dataset. + + """ if self.data_loader is not None and data is None: data = self.data_loader() elif data is not None: @@ -76,5 +129,18 @@ def get_dataset(self, data=None): ds = self.data_pipeline(data) return ds - def __call__(self, data=None): + def __call__(self, data: Any = None) -> tf.data.Dataset: + """Call method to get the dataset. + + Parameters + ---------- + data : Any, optional + The data to create the dataset from, by default None. + + Returns + ------- + tf.data.Dataset + The created TensorFlow dataset. + + """ return self.get_dataset(data) diff --git a/src/tensorflow_time_series_dataset/loaders/csv_data_loader.py b/src/tensorflow_time_series_dataset/loaders/csv_data_loader.py index 00e3bde..d30e494 100644 --- a/src/tensorflow_time_series_dataset/loaders/csv_data_loader.py +++ b/src/tensorflow_time_series_dataset/loaders/csv_data_loader.py @@ -4,7 +4,7 @@ # author : Marcel Arpogaus # # created : 2022-01-07 09:02:38 (Marcel Arpogaus) -# changed : 2024-02-16 10:30:32 (Marcel Arpogaus) +# changed : 2024-02-19 13:01:07 (Marcel Arpogaus) # DESCRIPTION ################################################################# # ... # LICENSE ##################################################################### @@ -22,16 +22,41 @@ # See the License for the specific language governing permissions and # limitations under the License. ############################################################################### +from typing import Union + import pandas as pd -def _read_csv_file(file_path, date_time_col="date_time", **kwds): - file_path = file_path +def _read_csv_file( + file_path: str, date_time_col: str = "date_time", **kwargs: Union[str, bool] +) -> pd.DataFrame: + """Read CSV file into a pandas DataFrame. + + Parameters + ---------- + file_path : str + File path to the CSV file. + date_time_col : str, optional + Name of the datetime column in the CSV file. + **kwargs + Additional keyword arguments for pd.read_csv. + + Returns + ------- + pd.DataFrame + DataFrame containing the data from the CSV file. + + Raises + ------ + ValueError + If the data contains NaN values. + + """ load_data = pd.read_csv( file_path, parse_dates=[date_time_col], index_col=[date_time_col], - **kwds, + **kwargs, ) if load_data.isnull().any().sum() != 0: @@ -41,9 +66,28 @@ def _read_csv_file(file_path, date_time_col="date_time", **kwds): class CSVDataLoader: - def __init__(self, file_path, **kwds): + """Load data from a CSV file. + + Parameters + ---------- + file_path : str + File path to the CSV file. + **kwargs + Additional keyword arguments for pd.read_csv. + + """ + + def __init__(self, file_path: str, **kwargs: Union[str, bool]): self.file_path = file_path - self.kwds = kwds + self.kwargs = kwargs + + def __call__(self) -> pd.DataFrame: + """Load data from the CSV file using _read_csv_file. + + Returns + ------- + pd.DataFrame + DataFrame containing the data from the CSV file. - def __call__(self): - return _read_csv_file(self.file_path, **self.kwds) + """ + return _read_csv_file(self.file_path, **self.kwargs) diff --git a/src/tensorflow_time_series_dataset/pipeline/patch_generator.py b/src/tensorflow_time_series_dataset/pipeline/patch_generator.py index 713a057..447373e 100644 --- a/src/tensorflow_time_series_dataset/pipeline/patch_generator.py +++ b/src/tensorflow_time_series_dataset/pipeline/patch_generator.py @@ -4,7 +4,7 @@ # author : Marcel Arpogaus # # created : 2022-01-07 09:02:38 (Marcel Arpogaus) -# changed : 2022-01-07 09:02:38 (Marcel Arpogaus) +# changed : 2024-02-19 12:52:06 (Marcel Arpogaus) # DESCRIPTION ################################################################# # ... # LICENSE ##################################################################### @@ -22,25 +22,55 @@ # See the License for the specific language governing permissions and # limitations under the License. ############################################################################### + import tensorflow as tf class PatchGenerator: - def __init__(self, window_size, shift): - self.window_size = window_size - self.shift = shift + """A generator class that creates windows from provided time-series data. + + Attributes + ---------- + window_size : int + The size of each patch. + shift : int + The shift between patches. + + """ + + def __init__(self, window_size: int, shift: int) -> None: + """Parameters + ---------- + window_size : int + The size of each patch. + shift : int + The shift between patches. + + """ + self.window_size: int = window_size + self.shift: int = shift + + def __call__(self, data: tf.Tensor) -> tf.data.Dataset: + """Converts input data into patches of provided window size. + + Parameters + ---------- + data : tf.Tensor + The input data to generate patches from. + + Returns + ------- + tf.data.Dataset + A dataset of patches generated from the input data. - def __call__(self, data): - def sub_to_patch(sub): - return sub.batch(self.window_size, drop_remainder=True) + """ - data_set = tf.data.Dataset.from_tensor_slices(data) + data_set: tf.data.Dataset = tf.data.Dataset.from_tensor_slices(data) data_set = data_set.window( size=self.window_size, shift=self.shift, drop_remainder=True, - ) - data_set = data_set.flat_map(sub_to_patch) + ).flat_map(lambda sub: sub.batch(self.window_size, drop_remainder=True)) return data_set diff --git a/src/tensorflow_time_series_dataset/pipeline/patch_processor.py b/src/tensorflow_time_series_dataset/pipeline/patch_processor.py index 5c72266..2b2d673 100644 --- a/src/tensorflow_time_series_dataset/pipeline/patch_processor.py +++ b/src/tensorflow_time_series_dataset/pipeline/patch_processor.py @@ -4,7 +4,7 @@ # author : Marcel Arpogaus # # created : 2022-01-07 09:02:38 (Marcel Arpogaus) -# changed : 2022-09-02 17:04:13 (Marcel Arpogaus) +# changed : 2024-02-19 12:41:38 (Marcel Arpogaus) # DESCRIPTION ################################################################# # ... # LICENSE ##################################################################### @@ -22,21 +22,51 @@ # See the License for the specific language governing permissions and # limitations under the License. ############################################################################### +from typing import Dict, List, Tuple + import tensorflow as tf class PatchPreprocessor: + """A class for preprocessing patches of time series data. + + Parameters + ---------- + history_size : int + The number of past time steps to include for each sample. + history_columns : List[str] + The names of columns in the dataset that are part of the historical data. + meta_columns : List[str] + The names of columns in the dataset that are part of the meta data. + prediction_columns : List[str] + The names of columns in the dataset that are to be predicted. + + Attributes + ---------- + history_size : int + The number of past time steps to include for each sample. + history_columns : List[str] + The names of columns in the dataset that are part of the historical data. + meta_columns : List[str] + The names of columns in the dataset that are part of the meta data. + prediction_columns : List[str] + The names of columns in the dataset that are to be predicted. + column_idx : Dict[str, int] + A mapping from column names to their indices in the input dataset. + + """ + def __init__( self, - history_size, - history_columns, - meta_columns, - prediction_columns, - ): - self.history_size = history_size - self.history_columns = history_columns - self.meta_columns = meta_columns - self.prediction_columns = prediction_columns + history_size: int, + history_columns: List[str], + meta_columns: List[str], + prediction_columns: List[str], + ) -> None: + self.history_size: int = history_size + self.history_columns: List[str] = history_columns + self.meta_columns: List[str] = meta_columns + self.prediction_columns: List[str] = prediction_columns assert ( len(set(history_columns + meta_columns)) != 0 @@ -51,19 +81,33 @@ def __init__( assert len(prediction_columns) != 0, "No prediction columns provided" columns = sorted(list(set(history_columns + prediction_columns + meta_columns))) - self.column_idx = {c: i for i, c in enumerate(columns)} + self.column_idx: Dict[str, int] = {c: i for i, c in enumerate(columns)} + + def __call__(self, patch: tf.Tensor) -> Tuple[tf.Tensor, tf.Tensor]: + """Process a single patch of data. + + Parameters + ---------- + patch : tf.Tensor + A tensor representing a patch of time series data. + + Returns + ------- + Tuple[tf.Tensor, tf.Tensor] + A tuple containing the input features (as one or more tensors) and + the target values as tensors. - def __call__(self, patch): - y = [] - x_hist = [] - x_meta = [] + """ + y: List[tf.Tensor] = [] + x_hist: List[tf.Tensor] = [] + x_meta: List[tf.Tensor] = [] x_columns = sorted(set(self.history_columns + self.meta_columns)) y_columns = sorted(self.prediction_columns) assert ( len(set(x_columns + y_columns)) == patch.shape[-1] - ), "Patch shape dos not match column number" + ), "Patch shape does not match column number" for c in y_columns: column = patch[self.history_size :, self.column_idx[c]] @@ -77,7 +121,7 @@ def __call__(self, patch): x_meta.append(column[self.history_size, None, ...]) y = tf.stack(y, axis=-1) - x = [] + x: List[tf.Tensor] = [] if len(x_hist): x.append(tf.stack(x_hist, axis=-1)) if len(x_meta): diff --git a/src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py b/src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py index f5ce9a7..36ea891 100644 --- a/src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py +++ b/src/tensorflow_time_series_dataset/pipeline/windowed_time_series_pipeline.py @@ -4,7 +4,7 @@ # author : Marcel Arpogaus # # created : 2022-01-07 09:02:38 (Marcel Arpogaus) -# changed : 2022-11-09 16:08:45 (Marcel Arpogaus) +# changed : 2024-02-19 12:53:06 (Marcel Arpogaus) # DESCRIPTION ################################################################# # ... # LICENSE ##################################################################### @@ -23,27 +23,69 @@ # limitations under the License. ############################################################################### # REQUIRED PYTHON MODULES ##################################################### +from typing import List, Union + import tensorflow as tf +from tensorflow.data import Dataset from tensorflow_time_series_dataset.pipeline.patch_generator import PatchGenerator from tensorflow_time_series_dataset.pipeline.patch_processor import PatchPreprocessor class WindowedTimeSeriesPipeline: + """A Pipeline to process time-series data in a rolling window for TensorFlow models. + + This class provides functionality to generate windowed datasets from time series + data for training, validating, and testing machine learning models. It allows for + the generation of datasets that include historical data windows along with + corresponding future values (for either regression or classification tasks). + + Parameters + ---------- + history_size : int + The size of the history window. + prediction_size : int + The size of the prediction window. + history_columns : List[str] + The names of the columns to be used for the history window. + meta_columns : List[str] + The names of the columns to be used for metadata in each window. + prediction_columns : List[str] + The names of the columns from which the future values are predicted. + shift : int + The shift (in time units) for the sliding window. + batch_size : int + The size of the batch for each dataset iteration. + cycle_length : int + The number of data files that the dataset can process in parallel. + shuffle_buffer_size : int + The buffer size for data shuffling. + cache : Union[str, bool] + Whether to cache the dataset in memory or to a specific file. + drop_remainder : bool + Whether to drop the remainder of batches that are not equal to the batch size. + + Raises + ------ + AssertionError + If the prediction_size is not greater than zero. + + """ + def __init__( self, - history_size, - prediction_size, - history_columns, - meta_columns, - prediction_columns, - shift, - batch_size, - cycle_length, - shuffle_buffer_size, - cache, - drop_remainder, - ): + history_size: int, + prediction_size: int, + history_columns: List[str], + meta_columns: List[str], + prediction_columns: List[str], + shift: int, + batch_size: int, + cycle_length: int, + shuffle_buffer_size: int, + cache: Union[str, bool], + drop_remainder: bool, + ) -> None: assert ( prediction_size > 0 ), "prediction_size must be a positive integer greater than zero" @@ -60,7 +102,20 @@ def __init__( self.cache = cache self.drop_remainder = drop_remainder - def __call__(self, ds): + def __call__(self, ds: Dataset) -> Dataset: + """Applies the pipeline operations to the given dataset. + + Parameters + ---------- + ds : Dataset + The input dataset to transform. + + Returns + ------- + Dataset + The transformed dataset, ready for model training or evaluation. + + """ ds = ds.interleave( PatchGenerator(self.window_size, self.shift), cycle_length=self.cycle_length, diff --git a/src/tensorflow_time_series_dataset/preprocessors/cyclical_feature_encoder.py b/src/tensorflow_time_series_dataset/preprocessors/cyclical_feature_encoder.py index 8243e13..5801c72 100644 --- a/src/tensorflow_time_series_dataset/preprocessors/cyclical_feature_encoder.py +++ b/src/tensorflow_time_series_dataset/preprocessors/cyclical_feature_encoder.py @@ -4,7 +4,7 @@ # author : Marcel Arpogaus # # created : 2022-01-07 09:02:38 (Marcel Arpogaus) -# changed : 2024-02-15 17:02:59 (Marcel Arpogaus) +# changed : 2024-02-19 12:53:30 (Marcel Arpogaus) # DESCRIPTION ################################################################# # ... # LICENSE ##################################################################### @@ -22,10 +22,28 @@ # See the License for the specific language governing permissions and # limitations under the License. ############################################################################### +from typing import Any, Callable, Tuple + import numpy as np +import pandas as pd + + +def default_cycl_getter(df: pd.DataFrame, k: str) -> Any: + """Get the cyclical feature 'k' from a DataFrame or its index. + + Parameters + ---------- + df : pd.DataFrame + DataFrame object. + k : str + Key to get the cyclical feature. + Returns + ------- + Any + Cyclical feature corresponding to 'k'. -def default_cycl_getter(df, k): + """ try: cycl = getattr(df, k) except AttributeError: @@ -35,14 +53,45 @@ def default_cycl_getter(df, k): class CyclicalFeatureEncoder: def __init__( - self, cycl_name, cycl_max, cycl_min=0, cycl_getter=default_cycl_getter - ): + self, + cycl_name: str, + cycl_max: int, + cycl_min: int = 0, + cycl_getter: Callable = default_cycl_getter, + ) -> None: + """Initialize the CyclicalFeatureEncoder object. + + Parameters + ---------- + cycl_name : str + Name of the cyclical feature. + cycl_max : int + Maximum value of the cyclical feature. + cycl_min : int, optional + Minimum value of the cyclical feature, by default 0. + cycl_getter : Callable, optional + Function to get the cyclical feature, by default default_cycl_getter. + + """ self.cycl_max = cycl_max self.cycl_min = cycl_min self.cycl_getter = cycl_getter self.cycl_name = cycl_name - def encode(self, data): + def encode(self, data: pd.DataFrame) -> Tuple[np.ndarray, np.ndarray]: + """Encode the cyclical feature into sine and cosine components. + + Parameters + ---------- + data : pd.DataFrame + Data containing the cyclical feature. + + Returns + ------- + Tuple[np.ndarray, np.ndarray] + Sine and cosine components of the encoded cyclical feature. + + """ cycl = self.cycl_getter(data, self.cycl_name) sin = np.sin( 2 * np.pi * (cycl - self.cycl_min) / (self.cycl_max - self.cycl_min + 1) @@ -56,14 +105,43 @@ def encode(self, data): ) return sin, cos - def __call__(self, data): + def __call__(self, data: pd.DataFrame) -> pd.DataFrame: + """Call method to encode the cyclical feature and add the sine and cosine + components as new columns in the data. + + Parameters + ---------- + data : pd.DataFrame + Data containing the cyclical feature. + + Returns + ------- + pd.DataFrame + Updated data with sine and cosine components. + + """ data = data.copy() sin, cos = self.encode(data) data[self.cycl_name + "_sin"] = sin data[self.cycl_name + "_cos"] = cos return data - def decode(self, sin, cos): + def decode(self, sin: np.ndarray, cos: np.ndarray) -> np.ndarray: + """Decode the encoded sine and cosine components back to the cyclical feature. + + Parameters + ---------- + sin : np.ndarray + Sine component of the encoded cyclical feature. + cos : np.ndarray + Cosine component of the encoded cyclical feature. + + Returns + ------- + np.ndarray + Decoded cyclical feature. + + """ angle = (np.arctan2(sin, cos) + 2 * np.pi) % (2 * np.pi) return (angle * (self.cycl_max - self.cycl_min + 1)) / ( 2 * np.pi diff --git a/src/tensorflow_time_series_dataset/preprocessors/groupby_dataset_generator.py b/src/tensorflow_time_series_dataset/preprocessors/groupby_dataset_generator.py index 8946a1b..877cb0c 100644 --- a/src/tensorflow_time_series_dataset/preprocessors/groupby_dataset_generator.py +++ b/src/tensorflow_time_series_dataset/preprocessors/groupby_dataset_generator.py @@ -4,7 +4,7 @@ # author : Marcel Arpogaus # # created : 2022-01-07 09:02:38 (Marcel Arpogaus) -# changed : 2022-09-02 16:03:22 (Marcel Arpogaus) +# changed : 2024-02-19 12:57:02 (Marcel Arpogaus) # DESCRIPTION ################################################################# # ... # LICENSE ##################################################################### @@ -22,20 +22,60 @@ # See the License for the specific language governing permissions and # limitations under the License. ############################################################################### + +from typing import Callable, List + +import pandas as pd import tensorflow as tf class GroupbyDatasetGenerator: + """Creates a generator that yields the data for each group in the given column. + + Parameters + ---------- + groupby : str + The column name to group the dataframe by. + columns : List[str] + The list of columns to keep in the dataset. + dtype : tf.dtypes.DType, optional + The dtype of the output tensors, by default tf.float32. + shuffle : bool, optional + Whether to shuffle the dataset or not, by default False. + test_mode : bool, optional + Whether the generator is in test mode or not, by default False. + + """ + def __init__( - self, groupby, columns, dtype=tf.float32, shuffle=False, test_mode=False - ): - self.groupby = groupby - self.columns = sorted(list(set(columns))) - self.dtype = dtype - self.shuffle = shuffle - self.test_mode = test_mode - - def get_generator(self, df): + self, + groupby: str, + columns: List[str], + dtype: tf.dtypes.DType = tf.float32, + shuffle: bool = False, + test_mode: bool = False, + ) -> None: + self.groupby: str = groupby + self.columns: List[str] = sorted(list(set(columns))) + self.dtype: tf.dtypes.DType = dtype + self.shuffle: bool = shuffle + self.test_mode: bool = test_mode + + def get_generator(self, df: pd.DataFrame) -> Callable[[], tf.Tensor]: + """Returns the dataset generator with the given parameters. + + Parameters + ---------- + df : pd.DataFrame + The source DataFrame to generate data from. + + Returns + ------- + Callable[[], tf.Tensor] + A function that when called returns a generator yielding batches of + data as tensors. + + """ df.sort_index(inplace=True) if self.test_mode: ids = df[self.groupby].unique() @@ -44,14 +84,27 @@ def get_generator(self, df): grpd = df.groupby(self.groupby) - def generator(): + def generator() -> tf.Tensor: for _, d in grpd: - yield d[self.columns].values + yield d[self.columns].values.astype(self.dtype.as_numpy_dtype) return generator - def __call__(self, df): - ds = tf.data.Dataset.from_generator( + def __call__(self, df: pd.DataFrame) -> tf.data.Dataset: + """Makes the class instance callable and returns a TensorFlow dataset. + + Parameters + ---------- + df : pd.DataFrame + The DataFrame to create the dataset from. + + Returns + ------- + tf.data.Dataset + A TensorFlow dataset created from the DataFrame. + + """ + ds: tf.data.Dataset = tf.data.Dataset.from_generator( self.get_generator(df), output_signature=( tf.TensorSpec(shape=[None, len(self.columns)], dtype=self.dtype) diff --git a/src/tensorflow_time_series_dataset/preprocessors/time_series_k_fold.py b/src/tensorflow_time_series_dataset/preprocessors/time_series_k_fold.py index 7fbf8c6..e2d07b0 100644 --- a/src/tensorflow_time_series_dataset/preprocessors/time_series_k_fold.py +++ b/src/tensorflow_time_series_dataset/preprocessors/time_series_k_fold.py @@ -4,7 +4,7 @@ # author : Marcel Arpogaus # # created : 2022-01-07 09:02:38 (Marcel Arpogaus) -# changed : 2022-01-07 09:02:38 (Marcel Arpogaus) +# changed : 2024-02-19 12:35:49 (Marcel Arpogaus) # DESCRIPTION ################################################################# # ... # LICENSE ##################################################################### @@ -23,20 +23,48 @@ # limitations under the License. ############################################################################### # REQUIRED PYTHON MODULES ##################################################### +from typing import Dict, List + import numpy as np import pandas as pd class TimeSeriesKFold: - def __init__(self, fold, n_folds=20): - self.n_folds = n_folds - self.fold = fold - - def __call__(self, data): - days = pd.date_range(data.index.min(), data.index.max(), freq="D") - fold_idx = np.array_split(days.to_numpy(), self.n_folds) - folds = { - f: (idx[[0, -1]].astype("datetime64[m]") + [0, 60 * 24 - 1]) + """Time series cross-validation using a sliding window. + + Parameters + ---------- + fold : int + The current fold to be used. + n_folds : int, optional + The total number of folds (the default is 20). + + """ + + def __init__(self, fold: int, n_folds: int = 20) -> None: + self.n_folds: int = n_folds + self.fold: int = fold + + def __call__(self, data: pd.DataFrame) -> pd.DataFrame: + """Divide the data into train/test sets for the current fold. + + Parameters + ---------- + data : pd.DataFrame + The data to be folded, requiring a datetime index. + + Returns + ------- + pd.DataFrame + The subset of data corresponding to the current fold. + + """ + days: pd.DatetimeIndex = pd.date_range( + data.index.min(), data.index.max(), freq="D" + ) + fold_idx: List[np.ndarray] = np.array_split(days.to_numpy(), self.n_folds) + folds: Dict[int, List[str]] = { + f: (idx[[0, -1]].astype("datetime64[m]") + np.array([0, 60 * 24 - 1])) .astype(str) .tolist() for f, idx in enumerate(fold_idx) diff --git a/src/tensorflow_time_series_dataset/preprocessors/time_series_split.py b/src/tensorflow_time_series_dataset/preprocessors/time_series_split.py index a063bc0..516e373 100644 --- a/src/tensorflow_time_series_dataset/preprocessors/time_series_split.py +++ b/src/tensorflow_time_series_dataset/preprocessors/time_series_split.py @@ -4,7 +4,7 @@ # author : Marcel Arpogaus # # created : 2022-01-07 09:02:38 (Marcel Arpogaus) -# changed : 2024-02-16 10:28:30 (Marcel Arpogaus) +# changed : 2024-02-19 12:09:44 (Marcel Arpogaus) # DESCRIPTION ################################################################# # ... # LICENSE ##################################################################### @@ -24,20 +24,48 @@ ############################################################################### +from typing import Any + +import numpy as np + + class TimeSeriesSplit: - RIGHT = 0 - LEFT = 1 + RIGHT: int = 0 + LEFT: int = 1 + + def __init__(self, split_size: float, split: int) -> None: + """Initialize the TimeSeriesSplit object. + + Parameters + ---------- + split_size : float + Proportion of split size for the time series data. + split : int + Split position indicator (LEFT or RIGHT). + + """ + self.split_size: float = split_size + self.split: int = split + + def __call__(self, data: Any) -> Any: + """Splits the time series data based on the split position. + + Parameters + ---------- + data : Any + Input time series data to split. - def __init__(self, split_size, split): - self.split_size = split_size - self.split = split + Returns + ------- + Any + Left or right split of the time series data based on the split position. - def __call__(self, data): + """ data = data.sort_index() days = data.index.date days = days.astype("datetime64[m]") right = days[int(len(days) * self.split_size)] - left = right - 1 + left = right - np.timedelta64(1, "m") if self.split == self.LEFT: return data.loc[: str(left)] else: diff --git a/src/tensorflow_time_series_dataset/utils/test.py b/src/tensorflow_time_series_dataset/utils/test.py index 2ca76c5..a05b062 100644 --- a/src/tensorflow_time_series_dataset/utils/test.py +++ b/src/tensorflow_time_series_dataset/utils/test.py @@ -4,7 +4,7 @@ # author : Marcel Arpogaus # # created : 2022-01-07 09:02:38 (Marcel Arpogaus) -# changed : 2024-02-15 17:03:39 (Marcel Arpogaus) +# changed : 2024-02-19 13:09:50 (Marcel Arpogaus) # DESCRIPTION ################################################################# # ... # LICENSE ##################################################################### @@ -22,25 +22,83 @@ # See the License for the specific language governing permissions and # limitations under the License. ############################################################################### -from contextlib import nullcontext as does_not_raise +from contextlib import nullcontext +from typing import ContextManager, List, Union import numpy as np import pytest +from numpy.typing import ArrayLike +from pandas import DataFrame -def get_idx(ref, val): +def get_idx(ref: ArrayLike, val: float) -> int: + """Get the index of a value in a reference array. + + Parameters + ---------- + ref : ArrayLike + Reference array to search. + val : float + Value to find in the reference array. + + Returns + ------- + int + Index of the value in the reference array. + + """ idx = np.where(ref == val)[0] - assert len(idx) == 1, "Could not determine index from refference value" + assert len(idx) == 1, "Could not determine index from reference value" return int(idx) -def gen_patch(df, idx, size): +def gen_patch(df: DataFrame, idx: int, size: int) -> np.ndarray: + """Generate a patch of data from a DataFrame. + + Parameters + ---------- + df : DataFrame + DataFrame containing the data. + idx : int + Starting index of the patch. + size : int + Size of the patch to generate. + + Returns + ------- + np.ndarray + Patch of data from the DataFrame. + + """ x = df.values[idx : idx + size] assert len(x) == size, "Could not generate patch from reference data" return x -def gen_batch(df, columns, size, refs, ref_col): +def gen_batch( + df: DataFrame, columns: list, size: int, refs: ArrayLike, ref_col: str +) -> np.ndarray: + """Generate a batch of data from a DataFrame based on reference values. + + Parameters + ---------- + df : DataFrame + DataFrame containing the data. + columns : list + List of columns to include in the batch. + size : int + Size of each patch in the batch. + refs : ArrayLike + Array of reference values. + ref_col : str + Name of the column containing reference values. + + Returns + ------- + np.ndarray + Batch of data based on reference values. + + """ batch = [] columns = list(sorted(columns)) for ref in refs: @@ -56,21 +114,58 @@ def gen_batch(df, columns, size, refs, ref_col): def validate_dataset( - df, - ds, - batch_size, - history_size, - prediction_size, - shift, - history_columns, - meta_columns, - prediction_columns, - drop_remainder, - history_reference_column="ref", - meta_reference_column="ref", - prediction_reference_column="ref", - **kwds, -): + df: DataFrame, + ds: object, + batch_size: int, + history_size: int, + prediction_size: int, + shift: int, + history_columns: list, + meta_columns: list, + prediction_columns: list, + drop_remainder: bool, + history_reference_column: str = "ref", + meta_reference_column: str = "ref", + prediction_reference_column: str = "ref", + **kwargs, +) -> int: + """Validate the dataset generator with the given parameters. + + Parameters + ---------- + df : DataFrame + DataFrame containing the data. + ds : object + Dataset object used for validation. + batch_size : int + Size of each batch. + history_size : int + Size of the history window. + prediction_size : int + Size of the prediction window. + shift : int + Shift between consecutive windows. + history_columns : list + List of columns for the history window. + meta_columns : list + List of columns for the meta data. + prediction_columns : list + List of columns for the prediction. + drop_remainder : bool + Whether to drop remainder batches. + history_reference_column : str, optional + Name of the column containing history reference values. + meta_reference_column : str, optional + Name of the column containing meta reference values. + prediction_reference_column : str, optional + Name of the column containing prediction reference values. + + Returns + ------- + int + Number of batches validated. + + """ df = df.sort_index() if "id" in df.columns: @@ -188,9 +283,34 @@ def validate_dataset( return batch_no -def get_ctxmgr( - prediction_size, history_columns, meta_columns, history_size, prediction_columns -): +def validate_args( + prediction_size: int, + history_columns: List[str], + meta_columns: List[str], + history_size: int, + prediction_columns: List[str], +) -> ContextManager[Union[None]]: + """Validate input arguments of the PatchPreprocessor class. + + Parameters + ---------- + prediction_size : int + The size of the prediction. + history_columns : List[str] + List of history columns. + meta_columns : List[str] + List of meta columns. + history_size : int + The size of the history. + prediction_columns : List[str] + List of prediction columns. + + Returns + ------- + ctxmgr : contextlib.ContextManager + Context manager indicating if input is valid or raises an AssertionError. + + """ if prediction_size <= 0: ctxmgr = pytest.raises( AssertionError, @@ -218,5 +338,5 @@ def get_ctxmgr( match="No prediction columns provided", ) else: - ctxmgr = does_not_raise() + ctxmgr = nullcontext() return ctxmgr diff --git a/src/tensorflow_time_series_dataset/utils/visualisation.py b/src/tensorflow_time_series_dataset/utils/visualisation.py index 4749828..3b1a45c 100644 --- a/src/tensorflow_time_series_dataset/utils/visualisation.py +++ b/src/tensorflow_time_series_dataset/utils/visualisation.py @@ -4,29 +4,57 @@ # author : Marcel Arpogaus # # created : 2022-01-07 16:50:58 (Marcel Arpogaus) -# changed : 2024-02-15 17:01:25 (Marcel Arpogaus) +# changed : 2024-02-19 12:07:12 (Marcel Arpogaus) # DESCRIPTION ################################################################# # ... # LICENSE ##################################################################### # ... ############################################################################### +from typing import Any, List, Optional, Tuple + import matplotlib.pyplot as plt import numpy as np - -prop_cycle = plt.rcParams["axes.prop_cycle"] -colors = prop_cycle.by_key()["color"] +from tensorflow.data import Dataset def plot_patch( - ds, - history_size, - prediction_size, - history_columns, - prediction_columns, - meta_columns=[], - figsize=(16, 8), - **kwds, -): + ds: Dataset, + history_size: int, + prediction_size: int, + history_columns: List[str], + prediction_columns: List[str], + meta_columns: Optional[List[str]] = None, + figsize: Tuple[int, int] = (16, 8), + **kwargs: Any, +) -> plt.Figure: + """Plot history and prediction data from a dataset. + + Parameters + ---------- + ds : Dataset + Dataset to extract data from. + history_size : int + Number of timesteps in the history. + prediction_size : int + Number of timesteps in the prediction. + history_columns : List[str] + Columns to use for history data. + prediction_columns : List[str] + Columns to use for prediction data. + meta_columns : List[str], optional + Columns for additional meta data. Defaults to []. + figsize : Tuple[int, int], optional + Size of the figure. Defaults to (16, 8). + + Returns + ------- + plt.Figure + The generated plot figure. + + """ + prop_cycle = plt.rcParams["axes.prop_cycle"] + colors = prop_cycle.by_key()["color"] + x, y = next(ds.as_numpy_iterator()) x1, x2 = None, None diff --git a/tests/test_factory.py b/tests/test_factory.py index 0bb5c5b..340daf8 100644 --- a/tests/test_factory.py +++ b/tests/test_factory.py @@ -5,7 +5,7 @@ GroupbyDatasetGenerator, TimeSeriesSplit, ) -from tensorflow_time_series_dataset.utils.test import get_ctxmgr, validate_dataset +from tensorflow_time_series_dataset.utils.test import validate_args, validate_dataset def test_windowed_time_series_dataset_factory( @@ -20,7 +20,7 @@ def test_windowed_time_series_dataset_factory( ): df = time_series_df.set_index("date_time") - common_kwds = dict( + common_kwargs = dict( history_size=history_size, prediction_size=prediction_size, shift=shift, @@ -30,24 +30,24 @@ def test_windowed_time_series_dataset_factory( batch_size=batch_size, drop_remainder=False, ) - factory_kwds = dict( + factory_kwargs = dict( cycle_length=1, shuffle_buffer_size=100, ) - with get_ctxmgr( + with validate_args( history_size=history_size, prediction_size=prediction_size, history_columns=history_columns, meta_columns=meta_columns, prediction_columns=prediction_columns, ): - factory = WindowedTimeSeriesDatasetFactory(**common_kwds, **factory_kwds) + factory = WindowedTimeSeriesDatasetFactory(**common_kwargs, **factory_kwargs) ds = factory(df) validate_dataset( df, ds, - **common_kwds, + **common_kwargs, ) @@ -64,7 +64,7 @@ def test_windowed_time_series_dataset_factory_groupby( df = time_series_df_with_id.set_index("date_time") ids = df.id.unique() - common_kwds = dict( + common_kwargs = dict( history_size=history_size, prediction_size=prediction_size, shift=shift, @@ -74,19 +74,19 @@ def test_windowed_time_series_dataset_factory_groupby( batch_size=batch_size, drop_remainder=True, ) - factory_kwds = dict( + factory_kwargs = dict( cycle_length=len(ids), shuffle_buffer_size=100, ) - with get_ctxmgr( + with validate_args( history_size=history_size, prediction_size=prediction_size, history_columns=history_columns, meta_columns=meta_columns, prediction_columns=prediction_columns, ): - factory = WindowedTimeSeriesDatasetFactory(**common_kwds, **factory_kwds) + factory = WindowedTimeSeriesDatasetFactory(**common_kwargs, **factory_kwargs) factory.add_preprocessor( GroupbyDatasetGenerator( "id", columns=history_columns + meta_columns + prediction_columns @@ -97,7 +97,7 @@ def test_windowed_time_series_dataset_factory_groupby( validate_dataset( df, ds, - **common_kwds, + **common_kwargs, ) @@ -114,7 +114,7 @@ def test_windowed_time_series_dataset_factory_csv_loader( test_data_path, df = tmp_csv df = df.set_index("date_time") - common_kwds = dict( + common_kwargs = dict( history_size=history_size, prediction_size=prediction_size, shift=shift, @@ -124,24 +124,24 @@ def test_windowed_time_series_dataset_factory_csv_loader( batch_size=batch_size, drop_remainder=False, ) - factory_kwds = dict( + factory_kwargs = dict( cycle_length=1, shuffle_buffer_size=100, ) - with get_ctxmgr( + with validate_args( history_size=history_size, prediction_size=prediction_size, history_columns=history_columns, meta_columns=meta_columns, prediction_columns=prediction_columns, ): - factory = WindowedTimeSeriesDatasetFactory(**common_kwds, **factory_kwds) + factory = WindowedTimeSeriesDatasetFactory(**common_kwargs, **factory_kwargs) factory.set_data_loader(CSVDataLoader(file_path=test_data_path)) ds = factory() validate_dataset( df, ds, - **common_kwds, + **common_kwargs, ) @@ -156,7 +156,7 @@ def test_windowed_time_series_dataset_factory_csv_loader_with_preprocessors( prediction_columns, ): # define encoder args - # [name: kwds] + # [name: kwargs] encs = { "weekday": dict(cycl_max=6), "dayofyear": dict(cycl_max=366, cycl_min=1), @@ -168,15 +168,15 @@ def test_windowed_time_series_dataset_factory_csv_loader_with_preprocessors( } test_data_path, df = tmp_csv_with_id test_df = df.set_index("date_time") - for name, kwds in encs.items(): - enc = CyclicalFeatureEncoder(name, **kwds) + for name, kwargs in encs.items(): + enc = CyclicalFeatureEncoder(name, **kwargs) test_df = enc(test_df) splitter = TimeSeriesSplit(0.5, TimeSeriesSplit.LEFT) test_df = splitter(test_df) ids = df.id.unique() - common_kwds = dict( + common_kwargs = dict( history_size=history_size, prediction_size=prediction_size, shift=shift, @@ -186,22 +186,22 @@ def test_windowed_time_series_dataset_factory_csv_loader_with_preprocessors( batch_size=batch_size, drop_remainder=True, ) - factory_kwds = dict( + factory_kwargs = dict( cycle_length=len(ids), shuffle_buffer_size=100, ) - with get_ctxmgr( + with validate_args( history_size=history_size, prediction_size=prediction_size, history_columns=history_columns, meta_columns=meta_columns_cycle, prediction_columns=prediction_columns, ): - factory = WindowedTimeSeriesDatasetFactory(**common_kwds, **factory_kwds) + factory = WindowedTimeSeriesDatasetFactory(**common_kwargs, **factory_kwargs) factory.set_data_loader(CSVDataLoader(file_path=test_data_path)) factory.add_preprocessor(splitter) - for name, kwds in encs.items(): - factory.add_preprocessor(CyclicalFeatureEncoder(name, **kwds)) + for name, kwargs in encs.items(): + factory.add_preprocessor(CyclicalFeatureEncoder(name, **kwargs)) factory.add_preprocessor( GroupbyDatasetGenerator( "id", columns=history_columns + meta_columns_cycle + prediction_columns @@ -211,5 +211,5 @@ def test_windowed_time_series_dataset_factory_csv_loader_with_preprocessors( validate_dataset( test_df, ds, - **common_kwds, + **common_kwargs, ) diff --git a/tests/test_pipleine.py b/tests/test_pipleine.py index ddca097..14e8100 100644 --- a/tests/test_pipleine.py +++ b/tests/test_pipleine.py @@ -9,7 +9,7 @@ from tensorflow_time_series_dataset.preprocessors.groupby_dataset_generator import ( GroupbyDatasetGenerator, ) -from tensorflow_time_series_dataset.utils.test import get_ctxmgr, validate_dataset +from tensorflow_time_series_dataset.utils.test import validate_args, validate_dataset # FIXTURES #################################################################### @@ -109,14 +109,14 @@ def test_batch_processor( ds, df, window_size, shift = patched_dataset prediction_size = window_size - history_size - batch_kwds = dict( + batch_kwargs = dict( history_size=history_size, history_columns=history_columns, meta_columns=meta_columns, prediction_columns=prediction_columns, ) - with get_ctxmgr( + with validate_args( history_size=history_size, prediction_size=prediction_size, history_columns=history_columns, @@ -124,7 +124,7 @@ def test_batch_processor( prediction_columns=prediction_columns, ): ds_batched = ds.map( - PatchPreprocessor(**batch_kwds), + PatchPreprocessor(**batch_kwargs), num_parallel_calls=tf.data.experimental.AUTOTUNE, ).batch(batch_size) @@ -135,7 +135,7 @@ def test_batch_processor( prediction_size=prediction_size, shift=shift, drop_remainder=False, - **batch_kwds, + **batch_kwargs, ) @@ -156,7 +156,7 @@ def test_windowed_time_series_pipeline( ) ) - batch_kwds = dict( + batch_kwargs = dict( history_size=history_size, prediction_size=prediction_size, shift=shift, @@ -166,22 +166,22 @@ def test_windowed_time_series_pipeline( batch_size=batch_size, drop_remainder=True, ) - pipeline_kwds = dict(cycle_length=1, shuffle_buffer_size=100, cache=True) + pipeline_kwargs = dict(cycle_length=1, shuffle_buffer_size=100, cache=True) - with get_ctxmgr( + with validate_args( history_size=history_size, prediction_size=prediction_size, history_columns=history_columns, meta_columns=meta_columns, prediction_columns=prediction_columns, ): - pipeline = WindowedTimeSeriesPipeline(**batch_kwds, **pipeline_kwds) + pipeline = WindowedTimeSeriesPipeline(**batch_kwargs, **pipeline_kwargs) ds = tf.data.Dataset.from_tensors(df[used_cols]) ds = pipeline(ds) validate_dataset( df, ds, - **batch_kwds, + **batch_kwargs, ) @@ -199,7 +199,7 @@ def test_windowed_time_series_pipeline_groupby( ids = df.id.unique() - batch_kwds = dict( + batch_kwargs = dict( history_size=history_size, prediction_size=prediction_size, shift=shift, @@ -209,19 +209,19 @@ def test_windowed_time_series_pipeline_groupby( batch_size=batch_size, drop_remainder=False, ) - pipeline_kwds = dict(cycle_length=len(ids), shuffle_buffer_size=1000, cache=True) + pipeline_kwargs = dict(cycle_length=len(ids), shuffle_buffer_size=1000, cache=True) - with get_ctxmgr( + with validate_args( history_size=history_size, prediction_size=prediction_size, history_columns=history_columns, meta_columns=meta_columns, prediction_columns=prediction_columns, ): - pipeline = WindowedTimeSeriesPipeline(**batch_kwds, **pipeline_kwds) + pipeline = WindowedTimeSeriesPipeline(**batch_kwargs, **pipeline_kwargs) ds = pipeline(ds) validate_dataset( df, ds, - **batch_kwds, + **batch_kwargs, ) diff --git a/tests/test_preprocessors.py b/tests/test_preprocessors.py index 34bde99..c80fbe9 100644 --- a/tests/test_preprocessors.py +++ b/tests/test_preprocessors.py @@ -37,8 +37,8 @@ def test_cyclical_data_encoder(cycl_df): cycl_getter=lambda df, k: df.index.hour * 60 + df.index.minute, ), } - for name, kwds in encs.items(): - enc = CyclicalFeatureEncoder(name, **kwds) + for name, kwargs in encs.items(): + enc = CyclicalFeatureEncoder(name, **kwargs) enc_dat = enc(cycl_df) cycl = enc.cycl_getter(cycl_df, name) assert np.isclose( @@ -57,8 +57,8 @@ def test_cyclical_data_encoder_except(cycl_df): cycl_getter=lambda df, k: df.index.hour * 60 + df.index.minute, ), } - for name, kwds in encs.items(): - enc = CyclicalFeatureEncoder(name, **kwds) + for name, kwargs in encs.items(): + enc = CyclicalFeatureEncoder(name, **kwargs) with pytest.raises(AssertionError): enc(cycl_df) From 9bc30dcc7ac3e8c1635458226a7223b93346bdc6 Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus <38564291+MArpogaus@users.noreply.github.com> Date: Mon, 19 Feb 2024 13:28:48 +0100 Subject: [PATCH 66/72] docs: update README to use images from githubusercontent --- README.md | 85 ++++++++++++---------- README.org | 206 ++++++++++++++++++++++++++++------------------------- 2 files changed, 153 insertions(+), 138 deletions(-) diff --git a/README.md b/README.md index 56870f9..364f9ab 100644 --- a/README.md +++ b/README.md @@ -12,27 +12,27 @@ # TensorFlow time-series Dataset -1. [About The Project](#org48e7e83) -2. [Installation](#org4124adc) -3. [Usage](#orgc2c9462) - 1. [Example Data](#org9a62649) - 2. [Single-Step Prediction](#org2b7f25f) - 3. [Multi-Step Prediction](#org96a3f28) - 4. [Preprocessing: Add Metadata features](#org869ce63) -4. [Contributing](#org8423a52) -5. [License](#orgaa9c5fe) -6. [Contact](#org5611eae) -7. [Acknowledgments](#orgce02d96) +1. [About The Project](#orgd7601fa) +2. [Installation](#org4baeaee) +3. [Usage](#org78d3afa) + 1. [Example Data](#org437e43f) + 2. [Single-Step Prediction](#org87ebbfe) + 3. [Multi-Step Prediction](#org11d5abf) + 4. [Preprocessing: Add Metadata features](#org3006a48) +4. [Contributing](#org5b01553) +5. [License](#org8585d54) +6. [Contact](#org66ff6ba) +7. [Acknowledgments](#org8768712) - + ## About The Project This python package should help you to create TensorFlow datasets for time-series data. - + ## Installation @@ -42,12 +42,12 @@ You install it and all of its dependencies using pip: pip install tensorflow_time_series_dataset - + ## Usage - + ### Example Data @@ -87,7 +87,7 @@ Suppose you have a dataset in the following form: 1992-01-01 02:00:00 0.432704 1.159077 2.005718 - + ### Single-Step Prediction @@ -96,7 +96,7 @@ We will use it now to create a dataset with `48` historic time-steps as the inpu from tensorflow_time_series_dataset.factory import WindowedTimeSeriesDatasetFactory as Factory - factory_kwds=dict( + factory_kwargs=dict( history_size=48, prediction_size=1, history_columns=['x1', 'x2', 'x3'], @@ -104,7 +104,7 @@ We will use it now to create a dataset with `48` historic time-steps as the inpu batch_size=4, drop_remainder=True, ) - factory=Factory(**factory_kwds) + factory=Factory(**factory_kwargs) ds1=factory(test_df) ds1 @@ -115,29 +115,33 @@ This returns the following TensorFlow Dataset: We can plot the result with the utility function `plot_path`: from tensorflow_time_series_dataset.utils.visualisation import plot_patch + + githubusercontent="https://raw.githubusercontent.com/MArpogaus/tensorflow_time_series_dataset/master/" + fig=plot_patch( ds1, figsize=(8,4), - **factory_kwds + **factory_kwargs ) fname='.images/example1.svg' fig.savefig(fname) - fname -![img](.images/example1.svg) + f"[[{githubusercontent}{fname}]]" + +![img](https://raw.githubusercontent.com/MArpogaus/tensorflow_time_series_dataset/master/.images/example1.svg) - + ### Multi-Step Prediction Lets now increase the prediction size to `6` half-hour time-steps. - factory_kwds.update(dict( + factory_kwargs.update(dict( prediction_size=6 )) - factory=Factory(**factory_kwds) + factory=Factory(**factory_kwargs) ds2=factory(test_df) ds2 @@ -150,17 +154,18 @@ Again, lets plot the results to see what changed: fig=plot_patch( ds2, figsize=(8,4), - **factory_kwds + **factory_kwargs ) fname='.images/example2.svg' fig.savefig(fname) - fname -![img](.images/example2.svg) + f"[[{githubusercontent}{fname}]]" +![img](https://raw.githubusercontent.com/MArpogaus/tensorflow_time_series_dataset/master/.images/example2.svg) - + + ### Preprocessing: Add Metadata features @@ -179,12 +184,13 @@ In this case we will be using the a class called `CyclicalFeatureEncoder` to enc cycl_getter=lambda df, k: df.index.hour * 60 + df.index.minute, ), } - factory_kwds.update(dict( + factory_kwargs.update(dict( meta_columns=list(itertools.chain(*[[c+'_sin', c+'_cos'] for c in encs.keys()])) )) - factory=Factory(**factory_kwds) - for name, kwds in encs.items(): - factory.add_preprocessor(CyclicalFeatureEncoder(name, **kwds)) + factory=Factory(**factory_kwargs) + for name, kwargs in encs.items(): + factory.add_preprocessor(CyclicalFeatureEncoder(name, **kwargs)) + ds3=factory(test_df) ds3 @@ -197,31 +203,32 @@ Again, lets plot the results to see what changed: fig=plot_patch( ds3, figsize=(8,4), - **factory_kwds + **factory_kwargs ) fname='.images/example3.svg' fig.savefig(fname) - fname -![img](.images/example3.svg) + f"[[{githubusercontent}{fname}]]" + +![img](https://raw.githubusercontent.com/MArpogaus/tensorflow_time_series_dataset/master/.images/example3.svg) - + ## Contributing Any Contributions are greatly appreciated! If you have a question, an issue or would like to contribute, please read our [contributing guidelines](CONTRIBUTING.md). - + ## License Distributed under the [Apache License 2.0](LICENSE) - + ## Contact @@ -231,7 +238,7 @@ Project Link: - + ## Acknowledgments diff --git a/README.org b/README.org index 6748e4f..e940d10 100644 --- a/README.org +++ b/README.org @@ -26,7 +26,7 @@ This package is available on [[https://pypi.org/project/tensorflow-time-series-d You install it and all of its dependencies using pip: #+begin_src bash :exports nil -pip install tensorflow_time_series_dataset + pip install tensorflow_time_series_dataset #+end_src ** Usage @@ -36,31 +36,31 @@ Suppose you have a dataset in the following form: #+NAME: df #+begin_src python :session :exports both -import numpy as np -import pandas as pd - -# make things determeinisteic -np.random.seed(1) - -columns=['x1', 'x2', 'x3'] -periods=48 * 14 -test_df=pd.DataFrame( - index=pd.date_range( - start='1/1/1992', - periods=periods, - freq='30min' - ), - data=np.stack( - [ - np.random.normal(0,0.5,periods), - np.random.normal(1,0.5,periods), - np.random.normal(2,0.5,periods) - ], - axis=1 - ), - columns=columns -) -test_df.head() + import numpy as np + import pandas as pd + + # make things determeinisteic + np.random.seed(1) + + columns=['x1', 'x2', 'x3'] + periods=48 * 14 + test_df=pd.DataFrame( + index=pd.date_range( + start='1/1/1992', + periods=periods, + freq='30min' + ), + data=np.stack( + [ + np.random.normal(0,0.5,periods), + np.random.normal(1,0.5,periods), + np.random.normal(2,0.5,periods) + ], + axis=1 + ), + columns=columns + ) + test_df.head() #+end_src #+RESULTS: df @@ -78,19 +78,19 @@ We will use it now to create a dataset with =48= historic time-steps as the inpu #+NAME: ds1 #+begin_src python :session :exports both -from tensorflow_time_series_dataset.factory import WindowedTimeSeriesDatasetFactory as Factory - -factory_kwds=dict( - history_size=48, - prediction_size=1, - history_columns=['x1', 'x2', 'x3'], - prediction_columns=['x3'], - batch_size=4, - drop_remainder=True, -) -factory=Factory(**factory_kwds) -ds1=factory(test_df) -ds1 + from tensorflow_time_series_dataset.factory import WindowedTimeSeriesDatasetFactory as Factory + + factory_kwargs=dict( + history_size=48, + prediction_size=1, + history_columns=['x1', 'x2', 'x3'], + prediction_columns=['x3'], + batch_size=4, + drop_remainder=True, + ) + factory=Factory(**factory_kwargs) + ds1=factory(test_df) + ds1 #+end_src This returns the following TensorFlow Dataset: @@ -101,32 +101,36 @@ This returns the following TensorFlow Dataset: We can plot the result with the utility function =plot_path=: #+NAME: ds1_plot -#+begin_src python :session :results file :exports both -from tensorflow_time_series_dataset.utils.visualisation import plot_patch -fig=plot_patch( - ds1, - figsize=(8,4), - ,**factory_kwds -) - -fname='.images/example1.svg' -fig.savefig(fname) -fname +#+begin_src python :session :results raw :exports both + from tensorflow_time_series_dataset.utils.visualisation import plot_patch + + githubusercontent="https://raw.githubusercontent.com/MArpogaus/tensorflow_time_series_dataset/master/" + + fig=plot_patch( + ds1, + figsize=(8,4), + ,**factory_kwargs + ) + + fname='.images/example1.svg' + fig.savefig(fname) + + f"[[{githubusercontent}{fname}]]" #+end_src #+RESULTS: ds1_plot -[[file:.images/example1.svg]] +[[https://raw.githubusercontent.com/MArpogaus/tensorflow_time_series_dataset/master/.images/example1.svg]] *** Multi-Step Prediction Lets now increase the prediction size to =6= half-hour time-steps. #+Name: ds2 #+begin_src python :session :exports both -factory_kwds.update(dict( - prediction_size=6 -)) -factory=Factory(**factory_kwds) -ds2=factory(test_df) -ds2 + factory_kwargs.update(dict( + prediction_size=6 + )) + factory=Factory(**factory_kwargs) + ds2=factory(test_df) + ds2 #+end_src @@ -136,20 +140,22 @@ This returns the following TensorFlow Dataset: Again, lets plot the results to see what changed: #+NAME: ds2_plot -#+begin_src python :session :results file :exports both -fig=plot_patch( - ds2, - figsize=(8,4), - **factory_kwds -) - -fname='.images/example2.svg' -fig.savefig(fname) -fname +#+begin_src python :session :results raw :exports both + fig=plot_patch( + ds2, + figsize=(8,4), + ,**factory_kwargs + ) + + fname='.images/example2.svg' + fig.savefig(fname) + + f"[[{githubusercontent}{fname}]]" #+end_src #+RESULTS: ds2_plot -[[file:.images/example2.svg]] +[[https://raw.githubusercontent.com/MArpogaus/tensorflow_time_series_dataset/master/.images/example2.svg]] + *** Preprocessing: Add Metadata features Preprocessors can be used to transform the data before it is fed into the model. @@ -157,47 +163,49 @@ A Preprocessor can be any python callable. In this case we will be using the a class called =CyclicalFeatureEncoder= to encode our one-dimensional cyclical features like the /time/ or /weekday/ to two-dimensional coordinates using a sine and cosine transformation as suggested in [this blogpost](https://www.kaggle.com/avanwyk/encoding-cyclical-features-for-deep-learning). #+NAME: ds3 #+begin_src python :session :exports both -import itertools -from tensorflow_time_series_dataset.preprocessors import CyclicalFeatureEncoder -encs = { - "weekday": dict(cycl_max=6), - "dayofyear": dict(cycl_max=366, cycl_min=1), - "month": dict(cycl_max=12, cycl_min=1), - "time": dict( - cycl_max=24 * 60 - 1, - cycl_getter=lambda df, k: df.index.hour * 60 + df.index.minute, - ), -} -factory_kwds.update(dict( - meta_columns=list(itertools.chain(*[[c+'_sin', c+'_cos'] for c in encs.keys()])) -)) -factory=Factory(**factory_kwds) -for name, kwds in encs.items(): - factory.add_preprocessor(CyclicalFeatureEncoder(name, **kwds)) -ds3=factory(test_df) -ds3 + import itertools + from tensorflow_time_series_dataset.preprocessors import CyclicalFeatureEncoder + encs = { + "weekday": dict(cycl_max=6), + "dayofyear": dict(cycl_max=366, cycl_min=1), + "month": dict(cycl_max=12, cycl_min=1), + "time": dict( + cycl_max=24 * 60 - 1, + cycl_getter=lambda df, k: df.index.hour * 60 + df.index.minute, + ), + } + factory_kwargs.update(dict( + meta_columns=list(itertools.chain(*[[c+'_sin', c+'_cos'] for c in encs.keys()])) + )) + factory=Factory(**factory_kwargs) + for name, kwargs in encs.items(): + factory.add_preprocessor(CyclicalFeatureEncoder(name, **kwargs)) + + ds3=factory(test_df) + ds3 #+end_src This returns the following TensorFlow Dataset: #+RESULTS: ds3 -: +: <_PrefetchDataset element_spec=((TensorSpec(shape=(4, 48, 3), dtype=tf.float32, name=None), TensorSpec(shape=(4, 1, 8), dtype=tf.float32, name=None)), TensorSpec(shape=(4, 6, 1), dtype=tf.float32, name=None))> Again, lets plot the results to see what changed: #+NAME: ds3_plot -#+begin_src python :session :results file :exports both -fig=plot_patch( - ds3, - figsize=(8,4), - **factory_kwds -) - -fname='.images/example3.svg' -fig.savefig(fname) -fname +#+begin_src python :session :results raw :exports both + fig=plot_patch( + ds3, + figsize=(8,4), + ,**factory_kwargs + ) + + fname='.images/example3.svg' + fig.savefig(fname) + + f"[[{githubusercontent}{fname}]]" #+end_src #+RESULTS: ds3_plot -[[file:.images/example3.svg]] +[[https://raw.githubusercontent.com/MArpogaus/tensorflow_time_series_dataset/master/.images/example3.svg]] ** Contributing From 17f7eff4d653435c7d34e310bf97a8e8ec0070dd Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus <38564291+MArpogaus@users.noreply.github.com> Date: Mon, 19 Feb 2024 13:29:58 +0100 Subject: [PATCH 67/72] ci: add workflow to build and deploy docs as gh page --- .github/workflows/docs.yaml | 46 +++++++++++++++++++++++++++++++++++++ pyproject.toml | 6 ++++- 2 files changed, 51 insertions(+), 1 deletion(-) create mode 100644 .github/workflows/docs.yaml diff --git a/.github/workflows/docs.yaml b/.github/workflows/docs.yaml new file mode 100644 index 0000000..a031fdb --- /dev/null +++ b/.github/workflows/docs.yaml @@ -0,0 +1,46 @@ +# Adjusted workflow from: https://github.com/mitmproxy/pdoc/blob/25f325d06aaacba9711f957f48e770029f608f6a/.github/workflows/docs.yml + +name: Generate documentation using pdoc and deploy as gh page. + +# build the documentation whenever there are new commits on main +on: + push: + branches: + tags: + - '*' + +# security: restrict permissions for CI jobs. +permissions: + contents: read + +jobs: + # Build the documentation and upload the static HTML files as an artifact. + build: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: '3.11' + + # Install all dependencies (including pdoc) + - run: pip install -e .[docs] + - run: pdoc -d numpy -o docs tensorflow_time_series_dataset + - uses: actions/upload-pages-artifact@v3 + with: + path: docs/ + + # Deploy the artifact to GitHub pages. + # This is a separate job so that only actions/deploy-pages has the necessary permissions. + deploy: + needs: build + runs-on: ubuntu-latest + permissions: + pages: write + id-token: write + environment: + name: github-pages + url: ${{ steps.deployment.outputs.page_url }} + steps: + - id: deployment + uses: actions/deploy-pages@v4 diff --git a/pyproject.toml b/pyproject.toml index f632006..1aa0de7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -35,13 +35,17 @@ requires-python = ">=3.7" [project.optional-dependencies] develop = [ - 'python_project_skeleton[test]', + 'tensorflow_time_series_dataset[test]', 'ipykernel', 'pre-commit', 'python-lsp-ruff', 'python-lsp-server[all]', 'ruff' ] +docs = [ + 'tensorflow_time_series_dataset[test]', + 'pdoc' +] test = [ 'pytest', 'pytest-xdist' From 571e746fd7bc5e7ecbbf03319a65899053833291 Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus <38564291+MArpogaus@users.noreply.github.com> Date: Mon, 19 Feb 2024 13:53:00 +0100 Subject: [PATCH 68/72] ci: rename test workflow --- .github/workflows/test.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml index 718056b..ba6c48c 100644 --- a/.github/workflows/test.yaml +++ b/.github/workflows/test.yaml @@ -1,4 +1,4 @@ -name: Lint and test code +name: Run tests on: [push] From 85254330f392134298e3162daf9fcc34723d6cc2 Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus <38564291+MArpogaus@users.noreply.github.com> Date: Mon, 19 Feb 2024 13:55:53 +0100 Subject: [PATCH 69/72] build: uses setuptools_scm for dynamic versioning --- pyproject.toml | 8 +++++--- src/tensorflow_time_series_dataset/__init__.py | 3 +-- 2 files changed, 6 insertions(+), 5 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 1aa0de7..c17b846 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [build-system] build-backend = "setuptools.build_meta" -requires = ["setuptools >= 61.0.0"] +requires = ["setuptools >= 61", "setuptools_scm>=7"] [project] classifiers = [ @@ -62,5 +62,7 @@ target-version = "py37" [tool.ruff.lint] select = ["I", "E", "F"] -[tool.setuptools.dynamic] -version = {attr = "tensorflow_time_series_dataset.__version__"} +[tool.setuptools_scm] +# https://stackoverflow.com/a/74404703 +# omits local version, useful because pypi does not support it +local_scheme = "no-local-version" diff --git a/src/tensorflow_time_series_dataset/__init__.py b/src/tensorflow_time_series_dataset/__init__.py index 794f943..0ae4d86 100644 --- a/src/tensorflow_time_series_dataset/__init__.py +++ b/src/tensorflow_time_series_dataset/__init__.py @@ -4,7 +4,7 @@ # author : Marcel Arpogaus # # created : 2022-01-07 09:02:38 (Marcel Arpogaus) -# changed : 2024-02-19 13:26:55 (Marcel Arpogaus) +# changed : 2024-02-19 13:55:19 (Marcel Arpogaus) # DESCRIPTION ################################################################# # ... # LICENSE ##################################################################### @@ -23,4 +23,3 @@ # limitations under the License. ############################################################################### """.. include:: ../../README.md""" -__version__ = "0.1.1" From 358d05491b01039c41bf93577f0ab0b2d08092d8 Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus <38564291+MArpogaus@users.noreply.github.com> Date: Mon, 19 Feb 2024 14:05:02 +0100 Subject: [PATCH 70/72] build(pyproject.toml): use commitizen for semenatic versioning and git msg linting --- .pre-commit-config.yaml | 7 +++++++ pyproject.toml | 9 +++++++++ 2 files changed, 16 insertions(+) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index fdfb27c..aec91b5 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -26,3 +26,10 @@ repos: args: [--autofix] - id: pretty-format-ini args: [--autofix] +- repo: https://github.com/commitizen-tools/commitizen + rev: v3.14.1 + hooks: + - id: commitizen + - id: commitizen-branch + stages: + - push diff --git a/pyproject.toml b/pyproject.toml index c17b846..0127e6c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -36,6 +36,7 @@ requires-python = ">=3.7" [project.optional-dependencies] develop = [ 'tensorflow_time_series_dataset[test]', + 'commitizen', 'ipykernel', 'pre-commit', 'python-lsp-ruff', @@ -54,6 +55,14 @@ test = [ [project.urls] Homepage = "https://github.com/MArpogaus/tensorflow_time_series_dataset" +[tool.commitizen] +major_version_zero = true +name = "cz_conventional_commits" +tag_format = "v$version" +update_changelog_on_bump = true +version_provider = "scm" +version_scheme = "pep440" + [tool.ruff] indent-width = 4 line-length = 88 From 1984884f71ba91aa7c4abc6549595320d6cc6cc7 Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus <38564291+MArpogaus@users.noreply.github.com> Date: Mon, 19 Feb 2024 14:59:00 +0100 Subject: [PATCH 71/72] build: update project urls --- pyproject.toml | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 0127e6c..31f4f4d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -53,7 +53,10 @@ test = [ ] [project.urls] -Homepage = "https://github.com/MArpogaus/tensorflow_time_series_dataset" +Changelog = "https://github.com/MArpogaus/tensorflow_time_series_dataset/blob/dev/CHANGELOG.md" +Documentation = "https://marpogaus.github.io/tensorflow_time_series_dataset" +Issues = "https://github.com/MArpogaus/tensorflow_time_series_dataset/issues" +Repository = "https://github.com/MArpogaus/tensorflow_time_series_dataset" [tool.commitizen] major_version_zero = true From 10aa9f09df433fb300f182ea853df401d39e8913 Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus <38564291+MArpogaus@users.noreply.github.com> Date: Mon, 19 Feb 2024 14:07:59 +0100 Subject: [PATCH 72/72] =?UTF-8?q?bump:=20version=200.1.1=20=E2=86=92=200.1?= =?UTF-8?q?.2?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- CHANGELOG.md | 13 +++++++++++++ 1 file changed, 13 insertions(+) create mode 100644 CHANGELOG.md diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 0000000..a8e9e4f --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,13 @@ +## v0.1.2 (2024-02-19) + +## v0.1.1 (2024-02-19) + +### Fix + +- use `min` instead of deprecated `T` for pandas freq +- reduce required matplotlib version for python 3.7 compatibility +- TimeSeriesSplit now works with newer pandas versions + + +- update project repository urls +- Update the readme with new project url

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zEY9>&`&@k(Tr`UKMH5)W9e1y~3(3h*zEJwrtFl9bwgtgsFOSwfzX8mszN|Y`H7B)Ty9EB4Jf%c3Qc{1o7}>%R6F80l?sNb~J@@k` z*2^zDcP8Le+pmrCi5)FvghocTL54~|MMZ_2D1f$6zE4x0V?*Qt9@f91LGmJl1VNCD z9|q1vMlrEV5XcgLc3Sp_2#A*B&8P2SUyxlwMi#&%8*KRPHM`raA5IZKM<`z?%wN1D z>VbHC`3fjQ--2qrt6I4zA zc!vLygJV|Mz$x&f$MZ*#2K#2Yl53zdt%Bst$i}@qxP?2MYeXEw3t9AY +#+OPTIONS: toc:nil [[https://github.com/MArpogaus/tensorflow_timeseries_dataset/graphs/contributors][https://img.shields.io/github/contributors/MArpogaus/tensorflow_timeseries_dataset.svg?style=flat-square]] [[https://github.com/MArpogaus/tensorflow_timeseries_dataset/network/members][https://img.shields.io/github/forks/MArpogaus/tensorflow_timeseries_dataset.svg?style=flat-square]] @@ -7,6 +8,8 @@ [[https://github.com/MArpogaus/tensorflow_timeseries_dataset/blob/master/LICENSE][https://img.shields.io/github/license/MArpogaus/tensorflow_timeseries_dataset.svg?style=flat-square]] [[https://linkedin.com/in/MArpogaus][https://img.shields.io/badge/-LinkedIn-black.svg?style=flat-square&logo=linkedin&colorB=555]] +#+TOC: headlines 2 + ** About The Project :PROPERTIES: :CUSTOM_ID: about-the-project @@ -102,6 +105,8 @@ This returns the following TensorFlow Dataset: #+RESULTS: ds1 : +We can plot the result with the utility function =plot_path=: + #+NAME: ds1_plot #+begin_src python :session :results file :exports both from tensorflow_time_series_dataset.utils.visualisation import plot_patch @@ -118,7 +123,6 @@ fname #+RESULTS: ds1_plot [[file:.images/example1.png]] - *** Multi-Step Prediction Lets now change the prediction size to a whole day consisting of =6= half-hour time-steps. #+Name: ds2 @@ -136,6 +140,7 @@ This returns the following TensorFlow Dataset: #+RESULTS: ds2 : +Again, lets plot the results to see what changed: #+NAME: ds2_plot #+begin_src python :session :results file :exports both fig=plot_patch( @@ -181,6 +186,7 @@ This returns the following TensorFlow Dataset: #+RESULTS: ds3 : +Again, lets plot the results to see what changed: #+NAME: ds3_plot #+begin_src python :session :results file :exports both fig=plot_patch( @@ -211,9 +217,9 @@ the tag "enhancement". Don't forget to give the project a star! Thanks again! 1. Fork the Project -2. Create your Feature Branch (Symbol's value as variable is void: git) -3. Commit your Changes (Symbol's value as variable is void: git) -4. Push to the Branch (Symbol's value as variable is void: git) +2. Create your Feature Branch +3. Commit your Changes +4. Push to the Branch 5. Open a Pull Request Any contributions are *greatly appreciated*. From e94d55f7b54aa608773f37f13c25badcdc26da45 Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus Date: Fri, 7 Jan 2022 18:25:31 +0100 Subject: [PATCH 23/72] adds markdown README --- README.md | 231 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 231 insertions(+) create mode 100644 README.md diff --git a/README.md b/README.md new file mode 100644 index 0000000..d9d4abc --- /dev/null +++ b/README.md @@ -0,0 +1,231 @@ +[![img](https://img.shields.io/github/contributors/MArpogaus/tensorflow_timeseries_dataset.svg?style=flat-square)](https://github.com/MArpogaus/tensorflow_timeseries_dataset/graphs/contributors) +[![img](https://img.shields.io/github/forks/MArpogaus/tensorflow_timeseries_dataset.svg?style=flat-square)](https://github.com/MArpogaus/tensorflow_timeseries_dataset/network/members) +[![img](https://img.shields.io/github/stars/MArpogaus/tensorflow_timeseries_dataset.svg?style=flat-square)](https://github.com/MArpogaus/tensorflow_timeseries_dataset/stargazers) +[![img](https://img.shields.io/github/issues/MArpogaus/tensorflow_timeseries_dataset.svg?style=flat-square)](https://github.com/MArpogaus/tensorflow_timeseries_dataset/issues) +[![img](https://img.shields.io/github/license/MArpogaus/tensorflow_timeseries_dataset.svg?style=flat-square)](https://github.com/MArpogaus/tensorflow_timeseries_dataset/blob/master/LICENSE) +[![img](https://img.shields.io/badge/-LinkedIn-black.svg?style=flat-square&logo=linkedin&colorB=555)](https://linkedin.com/in/MArpogaus) + + +# Table of Contents + +1. [About The Project](#about-the-project) +2. [Usage](#usage) + 1. [Example Data](#org7c75667) + 2. [Single-Step Prediction](#orgd939b15) + 3. [Multi-Step Prediction](#org55070f1) + 4. [Add Meta data as features](#org876f56c) +3. [Contributing](#contributing) +4. [License](#license) +5. [Contact](#contact) +6. [Acknowledgments](#acknowledgments) + + + + +# About The Project + +This python packages should help you to create TensorFlow datasets for time-series data. + + + + +# Usage + + + + +## Example Data + +Suppose you have a dataset in the following form: + + import numpy as np + import pandas as pd + + columns=['x1', 'x2', 'x3'] + periods=48 * 14 + test_df=pd.DataFrame( + index=pd.date_range( + start='1/1/1992', + periods=periods, + freq='30T' + ), + data=np.stack( + [ + np.random.normal(0,0.5,periods), + np.random.normal(1,0.5,periods), + np.random.normal(2,0.5,periods) + ], + axis=1 + ), + columns=columns + ) + test_df.head() + + x1 x2 x3 + 1992-01-01 00:00:00 0.075585 1.494057 2.218411 + 1992-01-01 00:30:00 0.002626 0.894208 2.039533 + 1992-01-01 01:00:00 0.046454 0.529587 2.066533 + 1992-01-01 01:30:00 0.320631 1.071333 1.986123 + 1992-01-01 02:00:00 -0.540393 1.085246 2.805066 + + + + +## Single-Step Prediction + +this module contains the factory class `WindowedTimeSeriesDatasetFactory` to create a TensorFlow dataset from pandas dataframes, or other data sources as we will see later. +We will use it now to create a dataset with `48` historic time-steps as the input to predict a single time-step in the future. + + from tensorflow_time_series_dataset import WindowedTimeSeriesDatasetFactory as Factory + + factory_kwds=dict( + history_size=48, + prediction_size=1, + history_columns=['x1', 'x2', 'x3'], + prediction_columns=['x3'], + batch_size=4 + ) + factory=Factory(**factory_kwds) + ds1=factory(test_df) + ds1 + +This returns the following TensorFlow Dataset: + + + +We can plot the result with the utility function `plot_path`: + + from tensorflow_time_series_dataset.utils.visualisation import plot_patch + fig=plot_patch( + ds1, + figsize=(8,4), + **factory_kwds + ) + + fname='.images/example1.png' + fig.savefig(fname) + fname + +![img](.images/example1.png) + + + + +## Multi-Step Prediction + +Lets now change the prediction size to a whole day consisting of `6` half-hour time-steps. + + factory_kwds.update(dict( + prediction_size=6 + )) + factory=Factory(**factory_kwds) + ds2=factory(test_df) + ds2 + +This returns the following TensorFlow Dataset: + + + +Again, lets plot the results to see what changed: + + fig=plot_patch( + ds2, + figsize=(8,4), + **factory_kwds + ) + + fname='.images/example2.png' + fig.savefig(fname) + fname + +![img](.images/example2.png) + + + + +## Add Meta data as features + +this module contains the factory class `WindowedTimeSeriesDatasetFactory` to create a TensorFlow dataset from pandas dataframes, or other data sources as we will see later. + + import itertools + from tensorflow_time_series_dataset.preprocessors import CyclicalFeatureEncoder + encs = { + "weekday": dict(cycl_max=6), + "dayofyear": dict(cycl_max=366, cycl_min=1), + "month": dict(cycl_max=12, cycl_min=1), + "time": dict( + cycl_max=24 * 60 - 1, + cycl_getter=lambda df, k: df.index.hour * 60 + df.index.minute, + ), + } + factory_kwds.update(dict( + meta_columns=list(itertools.chain(*[[c+'_sin', c+'_cos'] for c in encs.keys()])) + )) + factory=Factory(**factory_kwds) + for name, kwds in encs.items(): + factory.add_preprocessor(CyclicalFeatureEncoder(name, **kwds)) + ds3=factory(test_df) + ds3 + +This returns the following TensorFlow Dataset: + +Again, lets plot the results to see what changed: + + fig=plot_patch( + ds3, + figsize=(8,4), + **factory_kwds + ) + + fname='.images/example3.png' + fig.savefig(fname) + fname + +![img](.images/example3.png) + + + + +# Contributing + +Contributions are what make the open source community such an amazing +place to learn, inspire, and create. Any contributions you make are +**greatly appreciated**. + +If you have a suggestion that would make this better, please fork the +repo and create a pull request. You can also simply open an issue with +the tag “enhancement”. Don’t forget to give the project a star! Thanks +again! + +1. Fork the Project +2. Create your Feature Branch +3. Commit your Changes +4. Push to the Branch +5. Open a Pull Request + +Any contributions are **greatly appreciated**. + + + + +# License + +Distributed under the [Apache License 2.0](LICENSE) + + + + +# Contact + +[Marcel Arpogaus](https://github.com/marpogaus) - [marcel.arpogaus@gmail.com](mailto:marcel.arpogaus@gmail.com) + +Project Link: + + + + + +# Acknowledgments + +Parts of this work have been funded by the Federal Ministry for the Environment, Nature Conservation and Nuclear Safety due to a decision of the German Federal Parliament (AI4Grids: 67KI2012A). + From e10cf63154fda548dcbe4c5361f3d2388e9affad Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus Date: Fri, 7 Jan 2022 18:30:41 +0100 Subject: [PATCH 24/72] drops support for python 3.6 --- .github/workflows/pytest.yaml | 46 +++++++++++++++++------------------ setup.py | 3 +-- 2 files changed, 23 insertions(+), 26 deletions(-) diff --git a/.github/workflows/pytest.yaml b/.github/workflows/pytest.yaml index b6d940d..96a16d2 100644 --- a/.github/workflows/pytest.yaml +++ b/.github/workflows/pytest.yaml @@ -1,34 +1,32 @@ -name: Lint and test code +name: Lint and test code on: [push] jobs: build: - runs-on: ubuntu-latest strategy: matrix: - python-version: [3.6, 3.7, 3.8, 3.9] + python-version: [3.7, 3.8, 3.9] steps: - - uses: actions/checkout@v2 - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v2 - with: - python-version: ${{ matrix.python-version }} - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install flake8 pytest - if [ -f requirements.txt ]; then pip install -r requirements.txt; fi - pip install . - - name: Lint with flake8 - run: | - # stop the build if there are Python syntax errors or undefined names - flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics - # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide - flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics - - name: Test with pytest - run: | - pytest - + - uses: actions/checkout@v2 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v2 + with: + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install flake8 pytest + if [ -f requirements.txt ]; then pip install -r requirements.txt; fi + pip install . + - name: Lint with flake8 + run: | + # stop the build if there are Python syntax errors or undefined names + flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics + # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide + flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics + - name: Test with pytest + run: | + pytest diff --git a/setup.py b/setup.py index 2dbfcf5..963ec3d 100644 --- a/setup.py +++ b/setup.py @@ -24,7 +24,6 @@ "Intended Audience :: Science/Research", "License :: OSI Approved :: Apache Software License", "Programming Language :: Python :: 3 :: Only", - "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3.8", "Programming Language :: Python :: 3.9", @@ -35,5 +34,5 @@ "Topic :: Software Development :: Libraries", "Topic :: Software Development", ], - python_requires=">=3.6", + python_requires=">=3.7", ) From c9ea289d6ca116c6bd3a7771ce1524c1a501f7e4 Mon Sep 17 00:00:00 2001 From: Marcel Arpogaus Date: Fri, 7 Jan 2022 18:34:45 +0100 Subject: [PATCH 25/72] updates README --- .images/example1.png | Bin 75505 -> 75981 bytes .images/example2.png | Bin 69882 -> 68504 bytes .images/example3.png | Bin 72176 -> 71896 bytes README.md | 28 +++++++++++++++------------- README.org | 3 ++- 5 files changed, 17 insertions(+), 14 deletions(-) diff --git a/.images/example1.png b/.images/example1.png index e78fe359aae4c7e8163e553cec0999d4ab43073b..020c12246befd415570bf047d046287d45f43743 100644 GIT binary patch literal 75981 zcmce-Wmr_v+b=vrcS_gLqJ(sZG}52~Qqm$ZlynSAw}5m=2r3=YAq~N!Io_$Cj3(C?D@)(|KJq9ptLrF+_Lx|NH;_;^qzMgRA6 zBlcdB^C&x8WP!#2QVf16m=sow6oQEWLkXdA6sRG+Nh6Kjabo%`ujV86RA-~o>%{Tv z`0HCC5jXkrKk=ffB7<+_dBeP zq30y9|NRwKAj7-wNU{${{pS-D+*gSI^M7Bnun7L^6yQrjzL(JK|32>V$Dk)t|8qvU zAq~`!|NPX$NiZmXLNx#9B+q63zx^0{mXQ?GvWp1Y)2EDVY?yI9kG9wT=cG`XQNEOv zl+2vn>es_*&>EsXK(zWKDO=gM|nME3P>$xt!^J_LL%ozVfEnhebc36ib$$- zgGgYjB()lPiM{4^FcJ8j$U!sZVLPNAj@q;?k1D3&;fFZ zogYVKxE2n@YR8myj=I_5R=A#!Exl|1z_Fy*AR9crRbOsv;+ikm^UYE{JS2pne3_ZT zXDVT_VUX{3CEwbH3IABIwL%`|iHWRZTmeK+zg8gNDFvkvf4{94`%%{F;Xd6)wX$D$zcu#eaSa(PwhbF^qknXMHAn^yYf1UNj+|P%*%p2= zs^gda^VgHFJDQIpOxyfpW=KSgn3&Y@&+N;~)v>eP()zTuGo}Nx~?4+=rsVZ9N61(25NK(nO zhnJO=J%3XsJuDcR9y#`5Y>N*5b2|6$7*zgBGKdwo%d`sejLn&7BSI7%A^LjGzn^f5 z#zF2vbM&=LY9lV9e7zhYuC8rGtSVr_mq=zQ-V@QgT7_DDcoc19`F`T%-(5Sg(mm*a z`!!C}?7hYBi9C%98F2Xit|vMIo5kR(p9CvCx<-Z6BSIsD_hAQ_!H4lzg>Qa^F)Js$ zt(X?NyI8Tdw%$5-?CI%=PfXl_U!VQM9tk4}Yx+DF-B+W+W6E19YzJNNq1o<6Dg4aF zSnqQu$m=WPX)97s<$8RBKN$ShB1~hMGcca*mA}-V5-n4$km}BJuZ5oEIFF@VvYO)q z(Vnp0?A#v#1rqgPWPa}4Z+F5`-q>>7eCRONGi(wWQ`W3GJR7LCuBhf_`GDi>w6U2v z7^y=;5D3f0j)|7I3Q^{)4N!_>;kNtBm*I1!7Zx6_S6}7#uc61BK~PY~ueYZM^FNn7 zJ^2`GX-TOqIhmIj@!{2ubZzeQ=rkw~gNJPOV19e9LtB<0+j$r28uDymab%Gz3cdU`gW+t}OZeg7WP z+A6IkdFvto-}6hX&B^)H+1Y6i#|j+o##7XEBIK=*gYv}tz*6yT*8}N{FuqS1z9^N? 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