From af53e5d526bce9e4634e9aed1c486b7b995f47e4 Mon Sep 17 00:00:00 2001 From: Alexander Nikitin <1243786+AlexanderVNikitin@users.noreply.github.com> Date: Thu, 21 Mar 2024 13:11:20 +0200 Subject: [PATCH 01/31] update dependencies --- setup.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/setup.py b/setup.py index d26e82c..15c954f 100644 --- a/setup.py +++ b/setup.py @@ -70,8 +70,9 @@ def read_file(filename: str) -> str: "yfinance==0.2.28", "tqdm", "dtaidistance >= 2.3.10", - "tensorflow", - "tensorflow-probability", + "tensorflow < 2.16", + "tensorflow-probability < 0.24.0", + "statsmodels" ], package_data={'tsgm': ['README.md']}, packages=find_packages()) From b90e0212911e70ee676dacab03d126081ca14dbe Mon Sep 17 00:00:00 2001 From: Alexander Nikitin <1243786+AlexanderVNikitin@users.noreply.github.com> Date: Sun, 24 Mar 2024 20:09:04 +0200 Subject: [PATCH 02/31] add auto-version to setup; v0.0.5 --- setup.py | 11 ++++++++++- tsgm/version.py | 2 +- 2 files changed, 11 insertions(+), 2 deletions(-) diff --git a/setup.py b/setup.py index 15c954f..6f17128 100644 --- a/setup.py +++ b/setup.py @@ -1,8 +1,17 @@ +import os from setuptools import setup from setuptools import find_packages +# Function to read version from __version__.py +def get_version(): + with open(os.path.join(os.path.dirname(__file__), 'tsgm/version.py')) as f: + exec(f.read()) + return locals()['__version__'] + + name = "tsgm" +version = get_version() keywords = [ "machine learning", @@ -44,7 +53,7 @@ def read_file(filename: str) -> str: setup(name='tsgm', - version='0.0.4', + version=version, description='Time Series Generative Modelling Framework', author=author, author_email='', diff --git a/tsgm/version.py b/tsgm/version.py index 81f0fde..b1a19e3 100644 --- a/tsgm/version.py +++ b/tsgm/version.py @@ -1 +1 @@ -__version__ = "0.0.4" +__version__ = "0.0.5" From 903bff4a50738714196be25099a3a24ee47aa0ca Mon Sep 17 00:00:00 2001 From: liyiersan <35948100+liyiersan@users.noreply.github.com> Date: Wed, 27 Mar 2024 02:39:35 +0800 Subject: [PATCH 03/31] SliceAndShuffle fix (#43) --- tsgm/models/augmentations.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tsgm/models/augmentations.py b/tsgm/models/augmentations.py index 6612a85..8daba28 100644 --- a/tsgm/models/augmentations.py +++ b/tsgm/models/augmentations.py @@ -169,6 +169,8 @@ def generate(self, X: TensorLike, y: Optional[TensorLike] = None, n_samples: int slices.append(s) slices.append(sequence[start_idx:]) np.random.shuffle(slices) + # concatenate the slices + sequence = np.concatenate(slices) synthetic_data.append(sequence) if has_labels: new_labels.append(y[i]) From 3c9315bcc9674a27c41aa281e168cf0207ccf4be Mon Sep 17 00:00:00 2001 From: Alexander Nikitin <1243786+AlexanderVNikitin@users.noreply.github.com> Date: Sat, 30 Mar 2024 19:54:35 +0200 Subject: [PATCH 04/31] add dtw to readme --- README.md | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 502bb6f..9138160 100644 --- a/README.md +++ b/README.md @@ -125,9 +125,10 @@ TSGM provides a number of time series augmentations. | ------------- | ------------- | ------------- | | Gaussian Noise / Jittering | `tsgm.augmentations.GaussianNoise` | - | | Slice-And-Shuffle | `tsgm.augmentations.SliceAndShuffle` | - | -| Shuffle features | `tsgm.augmentations.Shuffle` | - | -| Magnitude warping | `tsgm.augmentations.MagnitudeWarping` | [Data Augmentation of Wearable Sensor Data for Parkinson’s Disease Monitoring using Convolutional Neural Networks](https://dl.acm.org/doi/pdf/10.1145/3136755.3136817) | -| Window warping | `tsgm.augmentations.WindowWarping` | [Data Augmentation for Time Series Classification using Convolutional Neural Networks](https://shs.hal.science/halshs-01357973/document) | +| Shuffle Features | `tsgm.augmentations.Shuffle` | - | +| Magnitude Warping | `tsgm.augmentations.MagnitudeWarping` | [Data Augmentation of Wearable Sensor Data for Parkinson’s Disease Monitoring using Convolutional Neural Networks](https://dl.acm.org/doi/pdf/10.1145/3136755.3136817) | +| Window Warping | `tsgm.augmentations.WindowWarping` | [Data Augmentation for Time Series Classification using Convolutional Neural Networks](https://shs.hal.science/halshs-01357973/document) | +| DTW Barycentric Averaging | `tsgm.augmentations.DTWBarycentricAveraging` | [A global averaging method for dynamic time warping, with applications to clustering.](https://www.sciencedirect.com/science/article/pii/S003132031000453X) | ## Contributing From 5d02985121fcf11f158ace478ff79a0929f56b59 Mon Sep 17 00:00:00 2001 From: Alexander Nikitin <1243786+AlexanderVNikitin@users.noreply.github.com> Date: Mon, 1 Apr 2024 17:31:50 +0300 Subject: [PATCH 05/31] improve docs --- docs/guides/augmentations.rst | 94 +++++++++++++++++++++++++++++++++++ docs/guides/introduction.rst | 14 +++--- 2 files changed, 101 insertions(+), 7 deletions(-) create mode 100644 docs/guides/augmentations.rst diff --git a/docs/guides/augmentations.rst b/docs/guides/augmentations.rst new file mode 100644 index 0000000..47d1c26 --- /dev/null +++ b/docs/guides/augmentations.rst @@ -0,0 +1,94 @@ +.. _augmentations-label: + +Augmentations +============ + +[Recommended] A more in-depth tutorial on augmentations for time series data `is available in our repo. `_ + +TSGM provides a wide variety of augmentation techniques beyond generative models. +For the following demonstrations, we first need to generate a toy dataset: + +.. code-block:: python + + import tsgm + X = tsgm.utils.gen_sine_dataset(100, 64, 2, max_value=20) + +Jittering +------------ +In tsgm, Gaussian noise augmentation can be applied as follows: + +.. code-block:: python + + aug_model = tsgm.models.augmentations.GaussianNoise() + samples = aug_model.generate(X=X, n_samples=10, variance=0.2) + +The idea behind Gaussian noise augmentation is that adding a small amount of jittering to time series probably will not change it significantly but will increase the amount of such noisy samples in our dataset. + +Shuffle Features +------------ +Another approach to time series augmentation is simply shuffle the features. This approach is suitable only for particular multivariate time series, where they are invariant to all or particular permutations of features. For instance, it can be applied to time series where each feature represents same independent measurements from various sensors. + +.. code-block:: python + + aug_model = tsgm.models.augmentations.Shuffle() + samples = aug_model.generate(X=X, n_samples=3) + +Slice and shuffle +------------ +Slice and shuffle augmentation [3] cuts a time series into slices and shuffles those pieces. This augmentation can be performed for time series that exhibit some form of invariance over time. For instance, imagine a time series measured from wearable devices for several days. The good strategy for this case is to slice time series by days and, by shuffling those days, get additional samples. + +.. code-block:: python + + aug_model = tsgm.models.augmentations.SliceAndShuffle() + samples = aug_model.generate(X=X, n_samples=10, n_segments=3) + +Magnitude Warping +------------ +Magnitude warping [3] changes the magnitude of each sample in a time series dataset by multiplication of the original time series with a cubic spline curve. This process scales the magnitude of time series, which can be beneficial in many cases, such as our synthetic example with sines n_knots number of knots at random magnitudes distributed as N(1, σ^2) where σ is set by a parameter sigma in function .generate. + +.. code-block:: python + + aug_model = tsgm.models.augmentations.MagnitudeWarping() + samples = aug_model.generate(X=X, n_samples=10, sigma=1) + + + +Window Warping +------------ +In this technique [4], the selected windows in time series data are either speeding up or down. Then, the whole resulting time series is scaled back to the original size in order to keep the timesteps at the original length. See an example of such augmentation below: + +.. code-block:: python + + aug_model = tsgm.models.augmentations.WindowWarping() + samples = aug_model.generate(X=X, n_samples=10, scales=(0.5,), window_ratio=0.5) + + +Dynamic Time Warping Barycentric Average (DTWBA) +------------ +Dynamic Time Warping Barycentric Average (DTWBA)[2] is an augmentation method that is based on Dynamic Time Warping (DTW). DTW is a method of measuring similarity between time series. The idea is to "sync" those time series, as it is demonstrated in the following picture. + +DTWBA goes like this: + + 1. The algorithm picks one time series to initialize the DTW_BA result. + 2. This time series can either be given explicitly or can be chosen randomly from the dataset + 3. For each of the N time series, the algorithm computes DTW distance and the path (the path is the mapping that minimizes the distance) + 4. After computing all DTW distances, the algorithm updates the DTWBA result by doing the average with respect to all the paths found above + 5. The algorithm repeats steps (2) and (3) until the DTWBA result converges + +.. code-block:: python + + aug_model = tsgm.models.augmentations.DTWBarycentricAveraging() + initial_timeseries = random.sample(range(X.shape[0]), 10) + initial_timeseries = X[initial_timeseries] + samples = aug_model.generate(X=X, n_samples=10, initial_timeseries=initial_timeseries ) + + +References +------------ +[1] H. Sakoe and S. Chiba, “Dynamic programming algorithm optimization for spoken word recognition”. IEEE Transactions on Acoustics, Speech, and Signal Processing, 26(1), 43-49 (1978). + +[2] F. Petitjean, A. Ketterlin & P. Gancarski. A global averaging method for dynamic time warping, with applications to clustering. Pattern Recognition, Elsevier, 2011, Vol. 44, Num. 3, pp. 678-693 + +[3] Um TT, Pfister FM, Pichler D, Endo S, Lang M, Hirche S, Fietzek U, Kulic´ D (2017) Data augmentation of wearable sensor data for parkinson’s disease monitoring using convolutional neural networks. In: Proceedings of the 19th ACM international conference on multimodal interaction, pp. 216–220 + +[4] Rashid, K.M. and Louis, J., 2019. Window-warping: a time series data augmentation of IMU data for construction equipment activity identification. In ISARC. Proceedings of the international symposium on automation and robotics in construction (Vol. 36, pp. 651-657). IAARC Publications. diff --git a/docs/guides/introduction.rst b/docs/guides/introduction.rst index 7f11dee..03dc193 100644 --- a/docs/guides/introduction.rst +++ b/docs/guides/introduction.rst @@ -29,7 +29,7 @@ TSGM implements multiple augmentation approaches including window warping, shuff aug_model = tsgm.models.augmentations.GaussianNoise(variance=0.2) samples = aug_model.generate(X=X, n_samples=10) -More examples are available in `the augmentation tutorial. `_ +More examples are available in `the augmentation tutorial `_ or in :ref:`augmentations-label`. Generators ============================= @@ -103,12 +103,12 @@ Metrics ============================= In `tsgm.metrics`, we implemented several metrics for evaluation of generated time series. Essentially, these metrics are subdivided into five types: -- data similarity / distance, -- predictive consistency, -- fairness, -- privacy, -- downstream effectiveness, -- visual similarity. +- data similarity / distance: `tsgm.metrics.DistanceMetric`, `tsgm.metrics.MMDMetric`, `tsgm.metrics.DiscriminativeMetric`, +- predictive consistency: `tsgm.metrics.ConsistencyMetric`, +- fairness: `tsgm.metrics.DemographicParityMetric`, +- privacy: `tsgm.metrics.PrivacyMembershipInferenceMetric`, +- downstream effectiveness: `tsgm.metrics.DownstreamPerformanceMetric`, +- qualitative analysis: `tsgm.visualization`. See the following code for an example of using metrics: From 6237a0fe2ac18af3c4347a5d9fd2d66bb10f6cdc Mon Sep 17 00:00:00 2001 From: liyiersan <35948100+liyiersan@users.noreply.github.com> Date: Fri, 24 May 2024 15:45:08 +0800 Subject: [PATCH 06/31] Fix discriminative metric (#45) * fix bugs in SliceAndShuffle * fix DiscriminativeMetric * better comment * better comment * update test_discriminative_metric --------- Co-authored-by: liyiersan-server5 --- tests/test_metrics.py | 9 +- tsgm/metrics/metrics.py | 7 +- tutorials/evaluation.ipynb | 306 +++++++++++++++++++++++++++---------- 3 files changed, 235 insertions(+), 87 deletions(-) diff --git a/tests/test_metrics.py b/tests/test_metrics.py index e03364c..33b84f4 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -191,18 +191,19 @@ def test_mmd_metric(): def test_discriminative_metric(): - ts = np.array([[[0, 2], [11, -11], [1, 2]], [[10, 21], [1, -1], [6, 8]]]).astype(np.float32) + ts = np.sin(np.arange(10)[:, None, None] + np.arange(6)[None, :, None]) # sin_sequence, [10, 6, 3] D1 = tsgm.dataset.Dataset(ts, y=None) - diff_ts = np.array([[[12, 13], [10, 10], [-1, -2]], [[-1, 32], [2, 1], [10, 8]]]).astype(np.float32) + diff_ts = np.sin(np.arange(10)[:, None, None] + np.arange(6)[None, :, None]) + 1000 # sin_sequence, [10, 6, 3] D2 = tsgm.dataset.Dataset(diff_ts, y=None) - model = tsgm.models.zoo["clf_cl_n"](seq_len=ts.shape[1], feat_dim=ts.shape[2], output_dim=1).model + model = tsgm.models.zoo["clf_cl_n"](seq_len=ts.shape[1], feat_dim=ts.shape[2], output_dim=2).model model.compile( tf.keras.optimizers.Adam(), - tf.keras.losses.CategoricalCrossentropy(from_logits=True) + tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False) ) discr_metric = tsgm.metrics.DiscriminativeMetric() + # should be easy to be classified assert discr_metric(d_hist=D1, d_syn=D2, model=model, test_size=0.2, random_seed=42, n_epochs=5) == 1.0 assert discr_metric(d_hist=D1, d_syn=D2, model=model, metric=sklearn.metrics.precision_score, test_size=0.2, random_seed=42, n_epochs=5) == 1.0 diff --git a/tsgm/metrics/metrics.py b/tsgm/metrics/metrics.py index 577c70e..7efadde 100644 --- a/tsgm/metrics/metrics.py +++ b/tsgm/metrics/metrics.py @@ -276,7 +276,12 @@ def __call__(self, d_hist: tsgm.dataset.DatasetOrTensor, d_syn: tsgm.dataset.Dat X_all, y_all = np.concatenate([X_hist, X_syn]), np.concatenate([[1] * len(d_hist), [0] * len(d_syn)]) X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X_all, y_all, test_size=test_size, random_state=random_seed) model.fit(X_train, y_train, epochs=n_epochs) - y_pred = (model.predict(X_test) > 0.5).astype(int) + pred = model.predict(X_test) + # check the shape, 1D array or N-D arrary + if len(pred.shape) == 1: # binary classification with sigmoid activation + y_pred = (pred > 0.5).astype(int) + else: # multiple classification with softmax activation + y_pred = np.argmax(pred, axis=-1).astype(int) if metric is None: return sklearn.metrics.accuracy_score(y_test, y_pred) else: diff --git a/tutorials/evaluation.ipynb b/tutorials/evaluation.ipynb index 2e2bc24..b4d431e 100644 --- a/tutorials/evaluation.ipynb +++ b/tutorials/evaluation.ipynb @@ -77,16 +77,16 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "19add7e4-d489-4ec7-a979-a2761afa83cf", "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "2023-12-15 21:20:18.765228: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", - "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" ] } ], @@ -98,6 +98,9 @@ "import numpy as np\n", "import functools\n", "import sklearn\n", + "import os\n", + "os.environ['CUDA_VISIBLE_DEVICES'] = '0'\n", + "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n", "import tensorflow as tf\n", "from tensorflow import keras\n", "\n", @@ -106,7 +109,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "id": "dcd6e6b7-4c96-4321-951e-b2d46d3ca268", "metadata": {}, "outputs": [ @@ -114,13 +117,34 @@ "name": "stdout", "output_type": "stream", "text": [ - "2/2 [==============================] - 5s 97ms/step - loss: 4010.0732 - reconstruction_loss: 3641.8901 - kl_loss: 0.1948\n" + "Epoch 1/10\n", + "16/16 [==============================] - 7s 18ms/step - loss: 4320.8155 - reconstruction_loss: 4133.3799 - kl_loss: 8.9943\n", + "Epoch 2/10\n", + "16/16 [==============================] - 0s 18ms/step - loss: 3517.2368 - reconstruction_loss: 3093.8440 - kl_loss: 64.6770\n", + "Epoch 3/10\n", + "16/16 [==============================] - 0s 19ms/step - loss: 2892.0041 - reconstruction_loss: 2546.8159 - kl_loss: 40.0669\n", + "Epoch 4/10\n", + "16/16 [==============================] - 0s 18ms/step - loss: 2276.4892 - reconstruction_loss: 2160.0657 - kl_loss: 39.5181\n", + "Epoch 5/10\n", + "16/16 [==============================] - 0s 17ms/step - loss: 2007.5037 - reconstruction_loss: 1799.3234 - kl_loss: 56.2214\n", + "Epoch 6/10\n", + "16/16 [==============================] - 0s 18ms/step - loss: 1668.5455 - reconstruction_loss: 1573.2977 - kl_loss: 31.1048\n", + "Epoch 7/10\n", + "16/16 [==============================] - 0s 19ms/step - loss: 1605.1145 - reconstruction_loss: 1532.1869 - kl_loss: 17.0078\n", + "Epoch 8/10\n", + "16/16 [==============================] - 0s 19ms/step - loss: 1522.8141 - reconstruction_loss: 1514.5801 - kl_loss: 11.7632\n", + "Epoch 9/10\n", + "16/16 [==============================] - 0s 19ms/step - loss: 1550.3235 - reconstruction_loss: 1509.1716 - kl_loss: 10.0757\n", + "Epoch 10/10\n", + "16/16 [==============================] - 0s 18ms/step - loss: 1556.3516 - reconstruction_loss: 1507.1044 - kl_loss: 8.9920\n", + "Xs min: 0.02898254245519638, Xs max: 0.9989303946495056\n" ] } ], "source": [ - "n, n_ts, n_features = 100, 100, 20\n", - "vae_latent_dim = 8\n", + "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n", + "n, n_ts, n_features = 1000, 100, 20\n", + "vae_latent_dim = 16\n", "\n", "# Load data that will be used as real\n", "Xr, yr = tsgm.utils.gen_sine_vs_const_dataset(n, n_ts, n_features, max_value=2, const=1)\n", @@ -130,15 +154,19 @@ "\n", "# Using real data generate synthetic time series dataset\n", "scaler = tsgm.utils.TSFeatureWiseScaler() \n", - "scaled_data = scaler.fit_transform(Xr)\n", + "scaled_data = scaler.fit_transform(Xr) # scale data to [0, 1]\n", + "Xr = scaled_data.astype(np.float32)\n", "architecture = tsgm.models.zoo[\"cvae_conv5\"](n_ts, n_features, vae_latent_dim)\n", "encoder, decoder = architecture.encoder, architecture.decoder\n", "vae = tsgm.models.cvae.cBetaVAE(encoder, decoder, latent_dim=vae_latent_dim, temporal=False)\n", "vae.compile(optimizer=keras.optimizers.Adam())\n", "\n", - "vae.fit(scaled_data, yr, epochs=1, batch_size=64)\n", + "vae.fit(scaled_data, yr, epochs=10, batch_size=64)\n", "Xs, ys = vae.generate(ys)\n", "\n", + "# print min and max value of Xs\n", + "print(f\"Xs min: {tf.reduce_min(Xs).numpy()}, Xs max: {tf.reduce_max(Xs).numpy()}\")\n", + "\n", "d_real = tsgm.dataset.Dataset(Xr, yr)\n", "d_syn = tsgm.dataset.Dataset(Xs, ys)" ] @@ -165,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "id": "b80e7fff-55a9-4fd2-9020-7b5a51f39915", "metadata": {}, "outputs": [], @@ -187,7 +215,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "id": "c2b65e68-1695-428a-b852-4e851d7829bc", "metadata": {}, "outputs": [], @@ -205,7 +233,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "id": "a7b0621c-4c2e-42fc-96a7-ef8ed60bd8df", "metadata": {}, "outputs": [ @@ -213,7 +241,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "12.869374\n" + "0.17514661\n" ] } ], @@ -235,7 +263,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "id": "2fa3b069-9a8b-436a-8433-ed8fadd06843", "metadata": {}, "outputs": [ @@ -243,7 +271,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "1.0090577602386475\n" + "0.0021124803461134425\n" ] } ], @@ -264,7 +292,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 16, "id": "7556587b-2fae-4f8d-8133-0331f14adc3a", "metadata": {}, "outputs": [ @@ -272,19 +300,33 @@ "name": "stdout", "output_type": "stream", "text": [ - "5/5 [==============================] - 1s 61ms/step - loss: 0.0000e+00\n", - "2/2 [==============================] - 0s 9ms/step\n", - "0.525\n" + "WARNING:tensorflow:Layer lstm_12 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Layer lstm_12 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "50/50 [==============================] - 6s 83ms/step - loss: 13350.6748\n", + "13/13 [==============================] - 1s 24ms/step\n", + "0.6225\n" ] } ], "source": [ "# use LSTM classification model from TSGM zoo.\n", "model = tsgm.models.zoo[\"clf_cl_n\"](\n", - " seq_len=Xr.shape[1], feat_dim=Xr.shape[2], output_dim=1).model\n", + " seq_len=Xr.shape[1], feat_dim=Xr.shape[2], output_dim=2).model # set output_dim to 2 so that softmax can work properly\n", "model.compile(\n", " tf.keras.optimizers.Adam(),\n", - " tf.keras.losses.CategoricalCrossentropy(from_logits=False)\n", + " tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False) # SparseCategoricalCrossentropy for multiple classes\n", ")\n", "\n", "discr_metric = tsgm.metrics.DiscriminativeMetric()\n", @@ -312,10 +354,95 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 22, "id": "8ec81387-e242-4280-909f-7a3c36167045", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Layer lstm_43 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Layer lstm_43 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Layer lstm_44 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Layer lstm_44 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Layer lstm_45 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Layer lstm_45 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Layer lstm_46 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Layer lstm_46 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Layer lstm_47 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Layer lstm_47 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Layer lstm_48 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Layer lstm_48 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n" + ] + } + ], "source": [ "class EvaluatorConvLSTM():\n", " '''\n", @@ -354,7 +481,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 23, "id": "ae4e5e67-e5eb-4b6d-8223-b477c7b09af4", "metadata": {}, "outputs": [], @@ -372,7 +499,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 24, "id": "5217664a-8eb6-4200-a2cd-72153933e817", "metadata": {}, "outputs": [ @@ -380,37 +507,37 @@ "name": "stdout", "output_type": "stream", "text": [ - "4/4 [==============================] - 2s 51ms/step - loss: 0.6264 - accuracy: 0.6100\n", - "4/4 [==============================] - 0s 16ms/step\n", - "4/4 [==============================] - 0s 14ms/step\n", - "(100, 2)\n", - "4/4 [==============================] - 3s 99ms/step - loss: 0.6739 - accuracy: 0.5500\n", - "4/4 [==============================] - 0s 27ms/step\n", - "4/4 [==============================] - 0s 27ms/step\n", - "(100, 2)\n", - "4/4 [==============================] - 5s 150ms/step - loss: 0.6674 - accuracy: 0.4800\n", - "4/4 [==============================] - 1s 44ms/step\n", - "4/4 [==============================] - 0s 42ms/step\n", - "(100, 2)\n", - "4/4 [==============================] - 0s 49ms/step - loss: 0.7769 - accuracy: 0.4700\n", - "4/4 [==============================] - 0s 15ms/step\n", - "4/4 [==============================] - 0s 17ms/step\n", - "(100, 2)\n", - "4/4 [==============================] - 1s 124ms/step - loss: 0.7031 - accuracy: 0.5300\n", - "4/4 [==============================] - 0s 26ms/step\n", - "4/4 [==============================] - 0s 30ms/step\n", - "(100, 2)\n", - "4/4 [==============================] - 1s 144ms/step - loss: 0.6934 - accuracy: 0.5000\n", - "4/4 [==============================] - 0s 44ms/step\n", - "4/4 [==============================] - 0s 43ms/step\n", - "(100, 2)\n" + "32/32 [==============================] - 7s 123ms/step - loss: 0.6414 - accuracy: 0.6650\n", + "32/32 [==============================] - 1s 22ms/step\n", + "32/32 [==============================] - 1s 23ms/step\n", + "(1000, 2)\n", + "32/32 [==============================] - 11s 209ms/step - loss: 0.6276 - accuracy: 0.6320\n", + "32/32 [==============================] - 2s 39ms/step\n", + "32/32 [==============================] - 1s 40ms/step\n", + "(1000, 2)\n", + "32/32 [==============================] - 16s 318ms/step - loss: 0.7315 - accuracy: 0.7030\n", + "32/32 [==============================] - 3s 71ms/step\n", + "32/32 [==============================] - 3s 86ms/step\n", + "(1000, 2)\n", + "32/32 [==============================] - 5s 163ms/step - loss: 0.6556 - accuracy: 0.6100\n", + "32/32 [==============================] - 1s 29ms/step\n", + "32/32 [==============================] - 1s 29ms/step\n", + "(1000, 2)\n", + "32/32 [==============================] - 9s 271ms/step - loss: 0.6778 - accuracy: 0.5830\n", + "32/32 [==============================] - 1s 42ms/step\n", + "32/32 [==============================] - 1s 40ms/step\n", + "(1000, 2)\n", + "32/32 [==============================] - 11s 353ms/step - loss: 0.5994 - accuracy: 0.6780\n", + "32/32 [==============================] - 2s 52ms/step\n", + "32/32 [==============================] - 2s 56ms/step\n", + "(1000, 2)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 3/3 [00:00<00:00, 13812.20it/s]\n" + "100%|██████████| 3/3 [00:00<00:00, 27962.03it/s]\n" ] }, { @@ -419,7 +546,7 @@ "0.3333333333333333" ] }, - "execution_count": 10, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -451,10 +578,25 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 25, "id": "d5ff295f-5bfc-4c46-9ce8-b969487517c7", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Layer lstm_49 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Layer lstm_49 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n" + ] + } + ], "source": [ "downstream_model = tsgm.models.zoo[\"clf_cl_n\"](seq_len, feat_dim, n_classes, n_conv_lstm_blocks=1).model\n", "downstream_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n", @@ -466,7 +608,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 26, "id": "80b2c173-db89-4249-81dd-91b0277e335f", "metadata": {}, "outputs": [ @@ -474,15 +616,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "4/4 [==============================] - 2s 68ms/step - loss: 0.6465 - accuracy: 0.5700\n", - "4/4 [==============================] - 0s 14ms/step\n", - "4/4 [==============================] - 0s 16ms/step\n", - "(100, 2)\n", - "7/7 [==============================] - 0s 64ms/step - loss: 0.5349 - accuracy: 0.6700\n", - "4/4 [==============================] - 0s 19ms/step\n", - "4/4 [==============================] - 0s 15ms/step\n", - "(100, 2)\n", - "-0.12\n" + "32/32 [==============================] - 8s 132ms/step - loss: 0.6503 - accuracy: 0.6460\n", + "32/32 [==============================] - 1s 24ms/step\n", + "32/32 [==============================] - 1s 24ms/step\n", + "(1000, 2)\n", + "63/63 [==============================] - 8s 128ms/step - loss: 0.6893 - accuracy: 0.5905\n", + "32/32 [==============================] - 1s 22ms/step\n", + "32/32 [==============================] - 1s 23ms/step\n", + "(1000, 2)\n", + "0.06300000000000006\n" ] } ], @@ -518,7 +660,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 27, "id": "00e3c9cc-672b-4337-a8b5-31b651961726", "metadata": {}, "outputs": [ @@ -529,7 +671,7 @@ "" ] }, - "execution_count": 13, + "execution_count": 27, "metadata": { "image/jpeg": { "height": 512, @@ -553,7 +695,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 28, "id": "db528d98-69ff-4b02-8204-f9cee0361499", "metadata": {}, "outputs": [], @@ -573,7 +715,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 29, "id": "e6fe5c89-e7ce-4eed-958b-586c1b094cf4", "metadata": {}, "outputs": [], @@ -594,7 +736,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 30, "id": "e4051772-44cb-4a0c-9460-103e2b5ab7fc", "metadata": {}, "outputs": [ @@ -608,10 +750,10 @@ { "data": { "text/plain": [ - "1.0" + "0.19499999999999995" ] }, - "execution_count": 16, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -635,7 +777,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 31, "id": "2353d941-db44-4280-9843-0a48afb099f3", "metadata": {}, "outputs": [ @@ -646,7 +788,7 @@ "" ] }, - "execution_count": 17, + "execution_count": 31, "metadata": { "image/jpeg": { "height": 512, @@ -662,7 +804,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 32, "id": "ef877399-1eff-4965-8527-51cbbb61c029", "metadata": {}, "outputs": [ @@ -670,9 +812,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/nikitia3/Projects/open_source/tsgm/venv310/lib/python3.10/site-packages/scipy/signal/_spectral_py.py:2017: UserWarning: nperseg = 256 is greater than input length = 100, using nperseg = 100\n", - " warnings.warn('nperseg = {0:d} is greater than input length '\n", - "/Users/nikitia3/Projects/open_source/tsgm/venv310/lib/python3.10/site-packages/antropy/entropy.py:253: RuntimeWarning: invalid value encountered in true_divide\n", + "/home/zq/anaconda3/envs/tf2.11/lib/python3.9/site-packages/scipy/signal/_spectral_py.py:600: UserWarning: nperseg = 256 is greater than input length = 100, using nperseg = 100\n", + " freqs, _, Pxy = _spectral_helper(x, y, fs, window, nperseg, noverlap,\n", + "/home/zq/anaconda3/envs/tf2.11/lib/python3.9/site-packages/antropy/entropy.py:253: RuntimeWarning: invalid value encountered in divide\n", " psd_norm = psd / psd.sum(axis=axis, keepdims=True)\n" ] }, @@ -680,8 +822,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "302.1819545165017\n", - "1790.309182347125\n" + "2990.720682663696\n", + "17855.800302499687\n" ] } ], @@ -723,13 +865,13 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 33, "id": "86db3d71-96b1-467d-8819-a698883a53c5", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -804,7 +946,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.9.17" } }, "nbformat": 4, From 2688d03bb03af0f37a7bfcae4b6ead95cb0092c6 Mon Sep 17 00:00:00 2001 From: Alexander Nikitin <1243786+AlexanderVNikitin@users.noreply.github.com> Date: Fri, 24 May 2024 13:02:09 +0300 Subject: [PATCH 07/31] v0.0.6 --- tsgm/version.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tsgm/version.py b/tsgm/version.py index b1a19e3..034f46c 100644 --- a/tsgm/version.py +++ b/tsgm/version.py @@ -1 +1 @@ -__version__ = "0.0.5" +__version__ = "0.0.6" From 0abe597371a86a5fce6e8b2a8afd06546f884bdf Mon Sep 17 00:00:00 2001 From: Alexander Nikitin <1243786+AlexanderVNikitin@users.noreply.github.com> Date: Fri, 31 May 2024 22:36:35 +0300 Subject: [PATCH 08/31] add fontsizes for visualizations --- tsgm/utils/visualization.py | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/tsgm/utils/visualization.py b/tsgm/utils/visualization.py index 5541189..359d514 100644 --- a/tsgm/utils/visualization.py +++ b/tsgm/utils/visualization.py @@ -227,6 +227,8 @@ def visualize_ts_lineplot( ys: tsgm.types.OptTensor = None, num: int = 5, unite_features: bool = True, + legend_fontsize: int = 12, + tick_size: int = 10 ) -> None: """ Visualizes time series data using line plots. @@ -243,6 +245,10 @@ def visualize_ts_lineplot( :type num: int, optional :param unite_features: Whether to plot all features together or separately, defaults to True. :type unite_features: bool, optional + :param legend_fontsize: Font size to use. + :type unite_features: int, optional + :param tick_size: Font size for y-axis ticks. + :type tick_size: int, optional """ assert len(ts.shape) == 3 @@ -266,8 +272,9 @@ def visualize_ts_lineplot( x=range(ts.shape[1]), y=ts[sample_id, :, feat_id], ax=axs[i] ) if ys is not None: + axs[i].tick_params(labelsize=tick_size) if len(ys.shape) == 1: - axs[i].set_title(ys[sample_id]) + axs[i].set_title(ys[sample_id], fontsize=legend_fontsize) elif len(ys.shape) == 2: sns.lineplot( x=range(ts.shape[1]), @@ -276,8 +283,10 @@ def visualize_ts_lineplot( color="g", label="Target variable", ) + axs[i].twinx().tick_params(labelsize=tick_size) else: raise ValueError("ys contains too many dimensions") + axs[i].legend(fontsize=legend_fontsize) def visualize_original_and_reconst_ts( From afcc307d22f22493f620d1e06addbb9c1412eb22 Mon Sep 17 00:00:00 2001 From: Alexander Nikitin <1243786+AlexanderVNikitin@users.noreply.github.com> Date: Mon, 3 Jun 2024 18:12:12 +0300 Subject: [PATCH 09/31] Update README.md --- README.md | 53 +++++++++++++++++++++++++++++++++-------------------- 1 file changed, 33 insertions(+), 20 deletions(-) diff --git a/README.md b/README.md index 9138160..bbffd72 100644 --- a/README.md +++ b/README.md @@ -90,6 +90,39 @@ We provide: * [Tutorials](https://github.com/AlexanderVNikitin/tsgm/tree/main/tutorials) that describe practical use-cases of the framework. + +## 💾 Datasets +| Dataset | API | Description | +| ------------- | ------------- | ------------- | +| UCR Dataset | `tsgm.utils.UCRDataManager` | https://www.cs.ucr.edu/%7Eeamonn/time_series_data_2018/ | +| Mauna Loa | `tsgm.utils.get_mauna_loa()` | https://gml.noaa.gov/ccgg/trends/data.html | +| EEG & Eye state | `tsgm.utils.get_eeg()` | https://archive.ics.uci.edu/ml/datasets/EEG+Eye+State | +| Power consumption dataset | `tsgm.utils.get_power_consumption()` | https://archive.ics.uci.edu/ml/datasets/individual+household+electric+power+consumption | +| Stock data | `tsgm.utils.get_stock_data(ticker_name)` | Gets historical stock data from YFinance | +| COVID-19 over the US | `tsgm.utils.get_covid_19()` | Covid-19 distribution over the US | +| Energy Data (UCI) | `tsgm.utils.get_energy_data()` | https://archive.ics.uci.edu/ml/datasets/Appliances+energy+prediction | +| MNIST as time series | `tsgm.utils.get_mnist_data()` | https://en.wikipedia.org/wiki/MNIST_database | +| Samples from GPs | `tsgm.utils.get_gp_samples_data()` | https://en.wikipedia.org/wiki/Gaussian_process | +| Physionet 2012 | `tsgm.utils.get_physionet2012()` | https://archive.physionet.org/pn3/challenge/2012/ | + +TSGM provides API for convenient use of many time-series datasets (currently more than 20 datasets). The comprehensive list of the datasets in the [documentation](https://tsgm.readthedocs.io/en/latest/guides/datasets.html) + +## Augmentations +TSGM provides a number of time series augmentations. + +| Augmentation | Class in TSGM | Reference | +| ------------- | ------------- | ------------- | +| Gaussian Noise / Jittering | `tsgm.augmentations.GaussianNoise` | - | +| Slice-And-Shuffle | `tsgm.augmentations.SliceAndShuffle` | - | +| Shuffle Features | `tsgm.augmentations.Shuffle` | - | +| Magnitude Warping | `tsgm.augmentations.MagnitudeWarping` | [Data Augmentation of Wearable Sensor Data for Parkinson’s Disease Monitoring using Convolutional Neural Networks](https://dl.acm.org/doi/pdf/10.1145/3136755.3136817) | +| Window Warping | `tsgm.augmentations.WindowWarping` | [Data Augmentation for Time Series Classification using Convolutional Neural Networks](https://shs.hal.science/halshs-01357973/document) | +| DTW Barycentric Averaging | `tsgm.augmentations.DTWBarycentricAveraging` | [A global averaging method for dynamic time warping, with applications to clustering.](https://www.sciencedirect.com/science/article/pii/S003132031000453X) | + + +## Contributing +We appreciate all contributions. To learn more, please check [CONTRIBUTING.md](CONTRIBUTING.md). + #### For contributors ```bash git clone github.com/AlexanderVNikitin/tsgm @@ -114,26 +147,6 @@ We provide two CLIs for convenient synthetic data generation: Use `tsgm-gd --help` or `tsgm-eval --help` for documentation. - -## Datasets -TSGM provides API for convenient use of many time-series datasets (currently more than 15 datasets). The comprehensive list of the datasets in the [documentation](https://tsgm.readthedocs.io/en/latest/guides/datasets.html) - -## Augmentations -TSGM provides a number of time series augmentations. - -| Augmentation | Class in TSGM | Reference | -| ------------- | ------------- | ------------- | -| Gaussian Noise / Jittering | `tsgm.augmentations.GaussianNoise` | - | -| Slice-And-Shuffle | `tsgm.augmentations.SliceAndShuffle` | - | -| Shuffle Features | `tsgm.augmentations.Shuffle` | - | -| Magnitude Warping | `tsgm.augmentations.MagnitudeWarping` | [Data Augmentation of Wearable Sensor Data for Parkinson’s Disease Monitoring using Convolutional Neural Networks](https://dl.acm.org/doi/pdf/10.1145/3136755.3136817) | -| Window Warping | `tsgm.augmentations.WindowWarping` | [Data Augmentation for Time Series Classification using Convolutional Neural Networks](https://shs.hal.science/halshs-01357973/document) | -| DTW Barycentric Averaging | `tsgm.augmentations.DTWBarycentricAveraging` | [A global averaging method for dynamic time warping, with applications to clustering.](https://www.sciencedirect.com/science/article/pii/S003132031000453X) | - - -## Contributing -We appreciate all contributions. To learn more, please check [CONTRIBUTING.md](CONTRIBUTING.md). - ## Citing If you find this repo useful, please consider citing our paper: ``` From 1665a82c81157241c58f1ed50d36f5fa67e9e1f2 Mon Sep 17 00:00:00 2001 From: Alexander Nikitin <1243786+AlexanderVNikitin@users.noreply.github.com> Date: Tue, 4 Jun 2024 14:28:01 +0300 Subject: [PATCH 10/31] add transformer models and predictive maintenance simulator --- tests/test_simulator.py | 18 ++ tests/test_zoo.py | 6 +- tsgm/metrics/metrics.py | 4 - tsgm/models/architectures/zoo.py | 73 +++++- tsgm/simulator.py | 402 ++++++++++++++++++++++++++++++- 5 files changed, 493 insertions(+), 10 deletions(-) diff --git a/tests/test_simulator.py b/tests/test_simulator.py index 76e8bf3..3742573 100644 --- a/tests/test_simulator.py +++ b/tests/test_simulator.py @@ -32,3 +32,21 @@ def test_simulator_base(): s.fit() MockDriver.fit.assert_called_once_with(s._data.X, s._data.y) + + +def test_pdm_simulator(): + n_samples = 10 + data = tsgm.dataset.DatasetProperties(N=100, T=12, D=23) + pdm_simulator = tsgm.simulator.PredictiveMaintenanceSimulator(data) + syn_dataset, equipment = pdm_simulator.generate(n_samples) + assert len(equipment) == 10 + assert len(syn_dataset) == 10 + for d in equipment: + assert isinstance(d, dict) + + new_sim = pdm_simulator.clone() + params1 = pdm_simulator.params() + params2 = new_sim.params() + assert params1["switches"] == params2["switches"] + assert params1["m_norms"] == params2["m_norms"] + assert params1["sigma_norms"] == params2["sigma_norms"] diff --git a/tests/test_zoo.py b/tests/test_zoo.py index 1f8e543..5a83cda 100644 --- a/tests/test_zoo.py +++ b/tests/test_zoo.py @@ -1,9 +1,6 @@ import pytest -import functools import numpy as np -import random -import tensorflow as tf from tensorflow.keras import layers import sklearn.metrics.pairwise @@ -48,7 +45,8 @@ def test_zoo_cgan(model_type): @pytest.mark.parametrize("model_type_name", [ "clf_cn", "clf_cl_n", - "clf_block"], + "clf_block", + "clf_transformer",] ) def test_zoo_clf(model_type_name): seq_len = 10 diff --git a/tsgm/metrics/metrics.py b/tsgm/metrics/metrics.py index 7efadde..afd3776 100644 --- a/tsgm/metrics/metrics.py +++ b/tsgm/metrics/metrics.py @@ -306,10 +306,6 @@ class EntropyMetric(Metric): """ Calculates the spectral entropy of a dataset or tensor. - This metric measures the randomness or disorder in a dataset or tensor - using spectral entropy, which is a measure of the distribution of energy - in the frequency domain. - Args: d (tsgm.dataset.DatasetOrTensor): The input dataset or tensor. diff --git a/tsgm/models/architectures/zoo.py b/tsgm/models/architectures/zoo.py index 324829a..2253c8b 100644 --- a/tsgm/models/architectures/zoo.py +++ b/tsgm/models/architectures/zoo.py @@ -653,7 +653,7 @@ def _build_model(self) -> keras.Model: class BasicRecurrentArchitecture(Architecture): """ - Base class for basic recurrent neural network architectures. + Base class for recurrent neural network architectures. Inherits from Architecture. """ @@ -726,6 +726,76 @@ def build(self, activation: str = "sigmoid", return_sequences: bool = True) -> k return model +class TransformerClfArchitecture(BaseClassificationArchitecture): + """ + Base class for transformer architectures. + + Inherits from BaseClassificationArchitecture. + """ + + arch_type = "downstream:classification" + + def __init__(self, seq_len: int, feat_dim: int, num_heads: int = 2, ff_dim: int = 64, n_blocks: int = 1, dropout_rate=0.5, output_dim: int = 2) -> None: + """ + Initializes the TransformerClfArchitecture. + + :param seq_len: Length of input sequences. + :type seq_len: int + :param feat_dim: Dimensionality of input features. + :type feat_dim: int + :param num_heads: Number of attention heads (default is 2). + :type num_heads: int + :param ff_dim: Feed forward dimension in the attention block (default is 64). + :type ff_dim: int + :param output_dim: Dimensionality of the output. + :type output_dim: int + :param dropout_rate: Dropout probability (default is 0.5). + :type dropout_rate: float, optional + :param n_blocks: Number of transformer blocks (default is 1). + :type n_blocks: int, optional + :param output_dim: Number of classes (default is 2). + :type output_dim: int, optional + """ + + self._num_heads = num_heads + self._ff_dim = ff_dim + self._n_blocks = n_blocks + self._dropout_rate = dropout_rate + + super().__init__(seq_len, feat_dim, output_dim) + + def transformer_block(self, inputs): + # Multi-Head Attention + attention_output = layers.MultiHeadAttention( + num_heads=self._num_heads, + key_dim=inputs.shape[-1] + )(inputs, inputs) + attention_output = layers.Dropout(self._dropout_rate)(attention_output) + attention_output = layers.LayerNormalization(epsilon=1e-6)(attention_output + inputs) + + # Feed-Forward Network + ff_output = layers.Dense(self._ff_dim, activation="relu")(attention_output) + ff_output = layers.Dense(inputs.shape[-1])(ff_output) + ff_output = layers.Dropout(self._dropout_rate)(ff_output) + ff_output = layers.LayerNormalization(epsilon=1e-6)(ff_output + attention_output) + + return ff_output + + def _build_model(self) -> keras.Model: + inputs = layers.Input(shape=(self._seq_len, self._feat_dim)) + + x = inputs + for _ in range(self._n_blocks): + x = self.transformer_block(x) + + x = layers.GlobalAveragePooling1D()(x) + x = layers.Dropout(self._dropout_rate)(x) + outputs = layers.Dense(self._output_dim, activation="softmax")(x) + + model = keras.Model(inputs, outputs) + return model + + class cGAN_LSTMnArchitecture(BaseGANArchitecture): """ Conditional Generative Adversarial Network (cGAN) with LSTM-based architecture. @@ -837,5 +907,6 @@ def summary(self) -> None: "clf_cl_n": ConvnLSTMnArchitecture, "clf_block": BlockClfArchitecture, "recurrent": BasicRecurrentArchitecture, + "clf_transformer": TransformerClfArchitecture } ) diff --git a/tsgm/simulator.py b/tsgm/simulator.py index 2f1189b..ff13f8c 100644 --- a/tsgm/simulator.py +++ b/tsgm/simulator.py @@ -1,5 +1,7 @@ import abc import copy +import sklearn +from tqdm import tqdm import typing as T import numpy as np import tensorflow_probability as tfp @@ -9,51 +11,216 @@ class BaseSimulator(abc.ABC): + """ + Abstract base class for simulators. This class defines the interface for simulators. + + Methods + ------- + generate(num_samples: int, *args) -> tsgm.dataset.Dataset + Generate a dataset with the specified number of samples. + + dump(path: str, format: str = "csv") -> None + Save the generated dataset to a file in the specified format. + """ @abc.abstractmethod def generate(self, num_samples: int, *args) -> tsgm.dataset.Dataset: + """ + Abstract method to generate a dataset. + + Parameters + ---------- + num_samples : int + Number of samples to generate. + *args + Additional arguments to be passed to the method. + + Returns + ------- + tsgm.dataset.Dataset + The generated dataset. + """ pass @abc.abstractmethod def dump(self, path: str, format: str = "csv") -> None: + """ + Abstract method to save the generated dataset to a file. + + Parameters + ---------- + path : str + The file path where the dataset will be saved. + format : str, optional + The format in which to save the dataset, by default "csv". + """ pass class Simulator(BaseSimulator): + """ + Concrete class for a basic simulator. This class implements the basic methods for fitting a model and + generating a dataset, but does not implement the generation and dump methods. + + Attributes + ---------- + _data : tsgm.dataset.DatasetProperties + Properties of the dataset to be used by the simulator. + _driver : Optional[tsgm.types.Model] + The model to be used for generating data. + """ def __init__(self, data: tsgm.dataset.DatasetProperties, driver: T.Optional[tsgm.types.Model] = None): + """ + Initialize the Simulator with dataset properties and an optional model. + + Parameters + ---------- + data : tsgm.dataset.DatasetProperties + Properties of the dataset to be used. + driver : Optional[tsgm.types.Model], optional + The model to be used for generating data, by default None. + """ self._data = data self._driver = driver def fit(self, **kwargs) -> None: + """ + Fit the model using the dataset properties. + + Parameters + ---------- + **kwargs + Additional keyword arguments to pass to the model's fit method. + """ if self._data.y is not None: self._driver.fit(self._data.X, self._data.y, **kwargs) else: self._driver.fit(self._data.X, **kwargs) def generate(self, num_samples: int, *args) -> TensorLike: + """ + Method to generate a dataset. Not implemented in this class. + + Parameters + ---------- + num_samples : int + Number of samples to generate. + *args + Additional arguments to be passed to the method. + + Returns + ------- + TensorLike + The generated dataset. + + Raises + ------ + NotImplementedError + This method is not implemented in this class. + """ raise NotImplementedError def dump(self, path: str, format: str = "csv") -> None: + """ + Method to save the generated dataset to a file. Not implemented in this class. + + Parameters + ---------- + path : str + The file path where the dataset will be saved. + format : str, optional + The format in which to save the dataset, by default "csv". + + Raises + ------ + NotImplementedError + This method is not implemented in this class. + """ raise NotImplementedError def clone(self) -> "Simulator": + """ + Create a deep copy of the simulator. + + Returns + ------- + Simulator + A deep copy of the current simulator instance. + """ return Simulator(copy.deepcopy(self._data)) class ModelBasedSimulator(Simulator): + """ + A simulator that is based on a model. This class extends the Simulator class and provides additional + methods for handling model parameters. + + Methods + ------- + params() -> T.Dict[str, T.Any] + Get a dictionary of the simulator's parameters. + + set_params(params: T.Dict[str, T.Any]) -> None + Set the simulator's parameters from a dictionary. + + generate(num_samples: int, *args) -> None + Generate a dataset with the specified number of samples. + """ def __init__(self, data: tsgm.dataset.DatasetProperties): + """ + Initialize the ModelBasedSimulator with dataset properties. + + Parameters + ---------- + data : tsgm.dataset.DatasetProperties + Properties of the dataset to be used. + """ super().__init__(data) def params(self) -> T.Dict[str, T.Any]: + """ + Get a dictionary of the simulator's parameters. + + Returns + ------- + dict + A dictionary containing the simulator's parameters. + """ params = copy.deepcopy(self.__dict__) - del params["_data"], params["_driver"] + if "_data" in params: + del params["_data"] + if "_driver" in params: + del params["_driver"] return params def set_params(self, params: T.Dict[str, T.Any]) -> None: + """ + Set the simulator's parameters from a dictionary. + + Parameters + ---------- + params : dict + A dictionary containing the parameters to set. + """ for param_name, param_value in params.items(): self.__dict__[param_name] = param_value @abc.abstractmethod def generate(self, num_samples: int, *args) -> None: + """ + Abstract method to generate a dataset. Must be implemented by subclasses. + + Parameters + ---------- + num_samples : int + Number of samples to generate. + *args + Additional arguments to be passed to the method. + + Raises + ------ + NotImplementedError + This method is not implemented in this class and must be overridden by subclasses. + """ raise NotImplementedError @@ -63,12 +230,40 @@ def clone(self) -> "NNSimulator": class SineConstSimulator(ModelBasedSimulator): + """ + Sine and Constant Function Simulator class that extends the ModelBasedSimulator base class. + + Attributes: + _scale: TensorFlow probability distribution for scaling factor. + _const: TensorFlow probability distribution for constant. + _shift: TensorFlow probability distribution for shift. + + Methods: + __init__(data, max_scale=10.0, max_const=5.0): Initializes the simulator with dataset properties and optional parameters. + set_params(max_scale, max_const, *args, **kwargs): Sets the parameters for scale, constant, and shift distributions. + generate(num_samples, *args) -> tsgm.dataset.Dataset: Generates a dataset based on sine and constant functions. + clone() -> SineConstSimulator: Creates and returns a deep copy of the current simulator. + """ def __init__(self, data: tsgm.dataset.DatasetProperties, max_scale: float = 10.0, max_const: float = 5.0) -> None: + """ + Initializes the SineConstSimulator with dataset properties and optional maximum scale and constant values. + Args: + data (tsgm.dataset.DatasetProperties): Dataset properties for the simulator. + max_scale (float, optional): Maximum value for the scale parameter. Defaults to 10.0. + max_const (float, optional): Maximum value for the constant parameter. Defaults to 5.0. + """ super().__init__(data) self.set_params(max_scale, max_const) def set_params(self, max_scale: float, max_const: float, *args, **kwargs): + """ + Sets the parameters for scale, constant, and shift distributions. + + Args: + max_scale (float): Maximum value for the scale parameter. + max_const (float): Maximum value for the constant parameter. + """ self._scale = tfp.distributions.Uniform(0, max_scale) self._const = tfp.distributions.Uniform(0, max_const) self._shift = tfp.distributions.Uniform(0, 2) @@ -76,6 +271,15 @@ def set_params(self, max_scale: float, max_const: float, *args, **kwargs): super().set_params({"max_scale": max_scale, "max_const": max_const}) def generate(self, num_samples: int, *args) -> tsgm.dataset.Dataset: + """ + Generates a dataset based on sine and constant functions. + + Args: + num_samples (int): Number of samples to generate. + + Returns: + tsgm.dataset.Dataset: A dataset containing generated samples. + """ result_X, result_y = [], [] for i in range(num_samples): scales = self._scale.sample(self._data.D) @@ -91,7 +295,203 @@ def generate(self, num_samples: int, *args) -> tsgm.dataset.Dataset: return tsgm.dataset.Dataset(x=np.array(result_X), y=np.array(result_y)) def clone(self) -> "SineConstSimulator": + """ + Creates a deep copy of the current SineConstSimulator instance. + + Returns: + SineConstSimulator: A new instance of SineConstSimulator with copied data and parameters. + """ copy_simulator = SineConstSimulator(self._data) params = self.params() copy_simulator.set_params(max_scale=params["max_scale"], max_const=params["max_const"]) return copy_simulator + + +class PredictiveMaintenanceSimulator(ModelBasedSimulator): + """ + Predictive Maintenance Simulator class that extends the ModelBasedSimulator base class. + The simulator is based on https://github.com/AaltoPML/human-in-the-loop-predictive-maintenance + From publication: + Nikitin, Alexander, and Samuel Kaski. "Human-in-the-loop large-scale predictive maintenance of + workstations." Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2022. + + Attributes: + CAT_FEATURES (list): List of categorical feature indices. + encoders (dict): Dictionary of OneHotEncoders for categorical features. + Methods: + __init__(data): Initializes the simulator with dataset properties and sets encoders. + S(lmbd, t): Calculates the survival curve. + R(rho, lmbd, t): Calculates the recovery curve parameter. + set_params(**kwargs): Sets the parameters for the simulator. + mixture_function(a, x): Calculates the mixture function. + sample_equipment(num_samples): Samples equipment data and generates the dataset. + generate(num_samples): Generates the predictive maintenance dataset. + clone() -> PredictiveMaintenanceSimulator: Creates and returns a deep copy of the current simulator. + """ + + # categorical features + CAT_FEATURES = [0, 1, 2, 3, 4, 5, 6, 7] + + def __init__(self, data: tsgm.dataset.DatasetProperties) -> None: + """ + Initializes the PredictiveMaintenanceSimulator with dataset properties and sets encoders for categorical features. + + Args: + data (tsgm.dataset.DatasetProperties): Dataset properties for the simulator. + """ + self._data = data + self.encoders = {d: sklearn.preprocessing.OneHotEncoder() for d in self.CAT_FEATURES} + + for d in self.CAT_FEATURES: + self.encoders[d].fit([[d], [d + 2], [d + 4], [d + 1], [d + 3], [d + 5], [d + 7]]) + self.set_params() + + def S(self, lmbd, t): + """ + Calculates the survival curve. + + Args: + lmbd: Lambda parameter for the exponential distribution. + t: Time variable. + + Returns: + float: Survival probability at time t. + """ + return np.exp(-lmbd * t) + + def R(self, rho, lmbd, t): + """ + Calculates the recovery curve parameter. + + Args: + rho: Rho parameter for the recovery function. + lmbd: Lambda parameter for the exponential distribution. + t: Time variable. + + Returns: + float: Recovery curve parameter at time t. + """ + s_ = self.S(lmbd, t) + return (1 - s_) - rho + + def set_params(self, **kwargs): + """ + Sets the parameters for the simulator. + + Args: + **kwargs: Arbitrary keyword arguments for setting simulator parameters. + """ + if "switches" in kwargs: + self._switches = kwargs["switches"] + else: + self._switches = {d: np.random.gamma(4, 2) for d in range(self._data.D)} + + if "m_norms" in kwargs: + self._m_norms = kwargs["m_norms"] + else: + self._m_norms = {d: lambda: np.random.gamma(2, 1) for d in range(self._data.D)} + + if "sigma_norms" in kwargs: + self._sigma_norms = kwargs["sigma_norms"] + else: + self._sigma_norms = {d: lambda: np.random.gamma(1, 1) for d in range(self._data.D)} + + super().set_params({ + "switches": self._switches, + "m_norms": self._m_norms, + "sigma_norms": self._sigma_norms + }) + + def mixture_function(self, a, x): + """ + Calculates the mixture function. + + Args: + a: Mixture parameter. + x: Input variable. + + Returns: + float: Mixture function value. + """ + return (a**x - 1) / (a - 1) + + def sample_equipment(self, num_samples): + """ + Samples equipment data and generates the dataset. + + Args: + num_samples (int): Number of samples to generate. + + Returns: + tuple: A tuple containing the dataset and equipment information. + """ + equipment, dataset = [], [] + for _ in tqdm(range(num_samples)): + last_norm_tmp = 0 + lmbd = np.random.gamma(1, 0.005) + rho = np.random.gamma(1, 0.1) + equipment.append({ + "lambda": lmbd, + "rho": rho + }) + current_measurements = [] + ss = [] + fix_tmps = [] + rnd = np.random.uniform(0, 1) + for t in range(self._data.T): + measurements = [] + + s_ = self.S(lmbd, t - last_norm_tmp) + r_ = self.R(rho, lmbd, t - last_norm_tmp) + ss.append(s_) + + if rnd < r_: + rnd = np.random.uniform(0, 1) + last_norm_tmp = t + fix_tmps.append(t) + + for d in range(self._data.D): + m_norm = self._m_norms[d]() + sigma_norm = self._sigma_norms[d]() + + m_abnorm = m_norm + self._switches[d] + sigma_abnorm = 1.5 * sigma_norm + + if d in self.CAT_FEATURES: + norm_functioning = np.random.choice([d, d + 2, d + 4], p=[0.7, 0.2, 0.1]) + abnorm_functioning = np.random.choice([d + 1, d + 3, d + 5, d + 7], p=[0.2, 0.2, 0.4, 0.2]) + else: + norm_functioning = np.random.normal(m_norm, sigma_norm) + abnorm_functioning = np.random.normal(m_abnorm, sigma_abnorm) + + mixt = self.mixture_function(3, s_) + if d in self.CAT_FEATURES: + if rnd < 1 - s_: + measurements.extend(self.encoders[d].transform([[abnorm_functioning]]).toarray()[0]) + else: + measurements.extend(self.encoders[d].transform([[norm_functioning]]).toarray()[0]) + else: + measurements.extend([mixt * norm_functioning + (1 - mixt) * abnorm_functioning]) + + if not len(current_measurements): + current_measurements.append([measurements]) + current_measurements = np.array(current_measurements[0]) + else: + current_measurements = np.concatenate((current_measurements, np.array(measurements)[np.newaxis, :]), axis=0) + equipment[-1]["fixes"] = fix_tmps + equipment[-1]["ss"] = ss + dataset.append(current_measurements) + dataset = np.transpose(np.array(dataset), [0, 2, 1]) + return dataset, equipment + + def generate(self, num_samples: int): + return self.sample_equipment(num_samples) + + def clone(self) -> "PredictiveMaintenanceSimulator": + copy_simulator = PredictiveMaintenanceSimulator(self._data) + params = self.params() + copy_simulator.set_params( + switches=params["switches"], + m_norms=params["m_norms"], + sigma_norms=params["sigma_norms"]) + return copy_simulator From 129b99a365287bd3256b8d4e711c54dbe9a1c9f1 Mon Sep 17 00:00:00 2001 From: Alexander Nikitin <1243786+AlexanderVNikitin@users.noreply.github.com> Date: Tue, 4 Jun 2024 16:58:15 +0300 Subject: [PATCH 11/31] add metrics --- tests/test_metrics.py | 21 ++++++++++++ tsgm/metrics/__init__.py | 3 +- tsgm/metrics/metrics.py | 74 +++++++++++++++++++++++++++++++++++++++- 3 files changed, 96 insertions(+), 2 deletions(-) diff --git a/tests/test_metrics.py b/tests/test_metrics.py index 33b84f4..c445178 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -215,6 +215,27 @@ def test_entropy_metric(): assert spec_entropy_metric(D1) == 2.6402430161833763 +def test_shannon_entropy_metric(): + ts = np.array([[[0, 2], [11, -11], [1, 2]], [[10, 21], [1, -1], [6, 8]]]).astype(np.float32) + y = np.array([1] * ts.shape[0]) + D1 = tsgm.dataset.Dataset(ts, y=y) + sdi_metric = tsgm.metrics.ShannonEntropyMetric() + assert sdi_metric(D1) == 0 + y = np.array([1, 2]) + D2 = tsgm.dataset.Dataset(ts, y=y) + assert sdi_metric(D2) > 0 + + +def test_pairwise_distance_metric(): + ts = np.array([[[0, 2], [11, -11], [1, 2]], [[0, 2], [11, -11], [1, 2]]]).astype(np.float32) + D1 = tsgm.dataset.Dataset(ts, y=None) + pd_metric = tsgm.metrics.PairwiseDistanceMetric() + assert np.mean(pd_metric(D1)) == 0 + ts = np.array([[[0, 2], [11, -11], [1, 2]], [[10, 21], [1, -1], [6, 8]]]).astype(np.float32) + D2 = tsgm.dataset.Dataset(ts, y=None) + assert np.mean(pd_metric(D2)) > 0 + + def test_demographic_parity(): ts = np.array([[[0, 2], [11, -11], [1, 2]], [[0, 2], [11, -11], [1, 2]], [[10, 21], [1, -1], [6, 8]]]).astype(np.float32) y = np.array([0, 1, 1]) diff --git a/tsgm/metrics/__init__.py b/tsgm/metrics/__init__.py index 926c637..c01851c 100644 --- a/tsgm/metrics/__init__.py +++ b/tsgm/metrics/__init__.py @@ -2,5 +2,6 @@ from tsgm.metrics.metrics import ( DistanceMetric, ConsistencyMetric, BaseDownstreamEvaluator, DownstreamPerformanceMetric, PrivacyMembershipInferenceMetric, - MMDMetric, DiscriminativeMetric, EntropyMetric, DemographicParityMetric + MMDMetric, DiscriminativeMetric, EntropyMetric, DemographicParityMetric, + ShannonEntropyMetric, PairwiseDistanceMetric ) diff --git a/tsgm/metrics/metrics.py b/tsgm/metrics/metrics.py index afd3776..c5c7bae 100644 --- a/tsgm/metrics/metrics.py +++ b/tsgm/metrics/metrics.py @@ -6,7 +6,9 @@ import itertools import sklearn import scipy +from scipy.stats import entropy from tqdm import tqdm +from scipy.spatial.distance import pdist, squareform from tensorflow.python.types.core import TensorLike import tsgm @@ -304,7 +306,7 @@ def _spectral_entropy_sum(X: TensorLike) -> TensorLike: class EntropyMetric(Metric): """ - Calculates the spectral entropy of a dataset or tensor. + Calculates the spectral entropy of a dataset or tensor as a sum of individual entropies. Args: d (tsgm.dataset.DatasetOrTensor): The input dataset or tensor. @@ -332,6 +334,76 @@ def __call__(self, d: tsgm.dataset.DatasetOrTensor) -> float: return np.sum(_spectral_entropy_sum(X), axis=None) +class ShannonEntropyMetric(Metric): + """ + Shannon Entropy calculated over the labels of a dataset. + This index is a measure of diversity that accounts for categories present in a dataset. + """ + + def _shannon_entropy(self, labels): + """ + Private method to calculate the Shannon Entropy for a given set of labels. + + Parameters: + labels (array-like): The labels or categories for which the diversity measure is to be calculated. + + Returns: + float: The Shannon Entropy value. + """ + _, counts = np.unique(labels, return_counts=True) + return entropy(counts) + + def __call__(self, d: tsgm.dataset.DatasetOrTensor) -> float: + """ + Calculate the Shannon entropy for the dataset. + + Parameters: + d (tsgm.dataset.DatasetOrTensor): The dataset or tensor object containing the labels. + + Returns: + float: The Shannon entropy value. + + Raises: + AssertionError: If the dataset does not contain labels. + """ + y = d.y + assert y is not None, "The dataset must contain labels." + + return self._shannon_entropy(y) + + +class PairwiseDistanceMetric(Metric): + """ + Measures pairwise distances in a set of time series. + """ + + def pairwise_euclidean_distances(self, ts: TensorLike) -> TensorLike: + """ + Computes the pairwise Euclidean distances for a set of time series. + + Parameters: + ts (numpy.ndarray): A 2D array where each row represents a time series. + + Returns: + numpy.ndarray: A 2D array representing the pairwise Euclidean distance matrix. + """ + distances = pdist(np.reshape(ts, (ts.shape[0], -1)), metric='euclidean') + return squareform(distances) + + def __call__(self, d: tsgm.dataset.DatasetOrTensor) -> TensorLike: + """ + Calculates the pairwise Euclidean distances for a dataset or tensor. + + Parameters: + d (tsgm.dataset.DatasetOrTensor): The input dataset or tensor containing time series data. + + Returns: + float: The pairwise Euclidean distances of the input data. + """ + X = _dataset_or_tensor_to_tensor(d) + return self.pairwise_euclidean_distances(X) + + class DemographicParityMetric(Metric): """ Measuring demographic parity between two datasets. From efff04ddf85fc18303e819186eb68ac198ab48d8 Mon Sep 17 00:00:00 2001 From: Alexander Nikitin <1243786+AlexanderVNikitin@users.noreply.github.com> Date: Tue, 4 Jun 2024 22:28:49 +0300 Subject: [PATCH 12/31] generalize dataloader --- tests/test_downstream_models.py | 2 +- tests/test_utils.py | 26 +++++++-------- tsgm/utils/datasets.py | 56 ++++++++++----------------------- 3 files changed, 30 insertions(+), 54 deletions(-) diff --git a/tests/test_downstream_models.py b/tests/test_downstream_models.py index 7556baa..d34e418 100644 --- a/tests/test_downstream_models.py +++ b/tests/test_downstream_models.py @@ -7,7 +7,7 @@ def _get_gunpoint_dataset(): - data_manager = tsgm.utils.UCRDataManager(ds="gunpoint") + data_manager = tsgm.utils.UCRDataManager(ds="GunPoint") X_train, y_train, X_test, y_test = data_manager.get() X_train, X_test = X_train[:, :, None], X_test[:, :, None] y_train = keras.utils.to_categorical(y_train - 1) diff --git a/tests/test_utils.py b/tests/test_utils.py index bdc0562..6c848f6 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -65,7 +65,7 @@ def test_switch_generator(): def test_ucr_manager(): - DATASET = "gunpoint" + DATASET = "GunPoint" ucr_data_manager = tsgm.utils.UCRDataManager(ds=DATASET) assert ucr_data_manager.summary() is None X_train, y_train, X_test, y_test = ucr_data_manager.get() @@ -189,7 +189,7 @@ def test_mmd_3_test(): Y = np.random.normal(10, 100, 100)[:, None] Z = np.random.normal(0, 1, 100)[:, None] - # Use custome kernels with this (TF-sklearn compatibility) + # Use custom kernels with this (TF-sklearn compatibility) # sigma_XY = tsgm.utils.kernel_median_heuristic(X, Y); # sigma_XZ = tsgm.utils.kernel_median_heuristic(X, Z); # sigma = (sigma_XY + sigma_XZ) / 2 @@ -201,16 +201,14 @@ def test_mmd_3_test(): @pytest.mark.parametrize("dataset_name", [ - "beef", - "coffee", - "ecg200", - "electric", - "freezer", - "gunpoint", - "insect", - "mixed_shapes", - "starlight", - "wafer" + "Beef", + "Coffee", + "ECG200", + "ElectricDevices", + "GunPoint", + "MixedShapesRegularTrain", + "StarLightCurves", + "Wafer" ]) def test_ucr_loadable(dataset_name): ucr_data_manager = tsgm.utils.UCRDataManager(ds=dataset_name) @@ -222,11 +220,11 @@ def test_ucr_loadable(dataset_name): def test_ucr_raises(): with pytest.raises(ValueError) as excinfo: ucr_data_manager = tsgm.utils.UCRDataManager(ds="does not exist") - assert "ds should be in" in str(excinfo.value) + assert "ds should be listed at UCR website" in str(excinfo.value) def test_get_wafer(): - dataset = "wafer" + dataset = "Wafer" ucr_data_manager = tsgm.utils.UCRDataManager(ds=dataset) assert ucr_data_manager.summary() is None X_train, y_train, X_test, y_test = ucr_data_manager.get() diff --git a/tsgm/utils/datasets.py b/tsgm/utils/datasets.py index 37ef247..0f47639 100644 --- a/tsgm/utils/datasets.py +++ b/tsgm/utils/datasets.py @@ -130,6 +130,16 @@ def gen_sine_vs_const_dataset(N: int, T: int, D: int, max_value: int = 10, const class UCRDataManager: """ A manager for UCR collection of time series datasets. + If you find these datasets useful, please cite: + @misc{UCRArchive2018, + title = {The UCR Time Series Classification Archive}, + author = {Dau, Hoang Anh and Keogh, Eamonn and Kamgar, Kaveh and Yeh, Chin-Chia Michael and Zhu, Yan + and Gharghabi, Shaghayegh and Ratanamahatana, Chotirat Ann and Yanping and Hu, Bing + and Begum, Nurjahan and Bagnall, Anthony and Mueen, Abdullah and Batista, Gustavo, and Hexagon-ML}, + year = {2018}, + month = {October}, + note = {\\url{https://www.cs.ucr.edu/~eamonn/time_series_data_2018/}} + } """ mirrors = ["https://www.cs.ucr.edu/~eamonn/time_series_data_2018/"] resources = [("UCRArchive_2018.zip", 0)] @@ -140,7 +150,7 @@ def __init__(self, path: str = default_path, ds: str = "gunpoint") -> None: """ :param path: a relative path to the stored UCR dataset. :type path: str - :param ds: Name of the dataset. Should be in (beef | coffee | ecg200 | freezer | gunpoint | insect | mixed_shapes | starlight). + :param ds: Name of the dataset. The list of names is available at https://www.cs.ucr.edu/~eamonn/time_series_data_2018/ (case sensitive!). :type ds: str :raises ValueError: When there is no stored UCR archive, or the name of the dataset is incorrect. @@ -150,48 +160,16 @@ def __init__(self, path: str = default_path, ds: str = "gunpoint") -> None: self.ds = ds.strip().lower() self.y_all: T.Optional[T.Collection[T.Hashable]] = None + path = os.path.join(path, ds) + train_files = glob.glob(os.path.join(path, "*TRAIN.tsv")) - if ds == "beef": - self.regular_train_path = os.path.join(path, "Beef") - self.small_train_path = os.path.join(path, "Beef") - elif ds == "coffee": - self.regular_train_path = os.path.join(path, "Coffee") - self.small_train_path = os.path.join(path, "Coffee") - elif ds == "ecg200": - self.regular_train_path = os.path.join(path, "ECG200") - self.small_train_path = os.path.join(path, "ECG200") - elif ds == "electric": - self.regular_train_path = os.path.join(path, "ElectricDevices") - self.small_train_path = os.path.join(path, "ElectricDevices") - elif ds == "freezer": - self.regular_train_path = os.path.join(path, "FreezerRegularTrain") - self.small_train_path = os.path.join(path, "FreezerSmallTrain") - elif ds == "gunpoint": - self.regular_train_path = os.path.join(path, "GunPoint") - self.small_train_path = os.path.join(path, "GunPoint") - elif ds == "insect": - self.regular_train_path = os.path.join(path, "InsectEPGRegularTrain") - self.small_train_path = os.path.join(path, path, "InsectEPGSmallTrain") - elif ds == "mixed_shapes": - self.regular_train_path = os.path.join(path, path, "MixedShapesRegularTrain") - self.small_train_path = os.path.join(path, path, "MixedShapesSmallTrain") - elif ds == "starlight": - self.regular_train_path = os.path.join(path, path, "StarLightCurves") - self.small_train_path = os.path.join(path, path, "StarLightCurves") - elif ds == "wafer": - self.regular_train_path = os.path.join(path, path, "Wafer") - self.small_train_path = os.path.join(path, path, "Wafer") - else: - raise ValueError("ds should be in (beef | coffee | ecg200 | freezer | gunpoint | insect | mixed_shapes | starlight)") - - self.small_train_df = pd.read_csv( - glob.glob(os.path.join(self.small_train_path, "*TRAIN.tsv"))[0], - sep='\t', header=None) + if len(train_files) == 0: + raise ValueError("ds should be listed at UCR website") self.train_df = pd.read_csv( - glob.glob(os.path.join(self.regular_train_path, "*TRAIN.tsv"))[0], + glob.glob(os.path.join(path, "*TRAIN.tsv"))[0], sep='\t', header=None) self.test_df = pd.read_csv( - glob.glob(os.path.join(self.regular_train_path, "*TEST.tsv"))[0], + glob.glob(os.path.join(path, "*TEST.tsv"))[0], sep='\t', header=None) self.X_train, self.y_train = self.train_df[self.train_df.columns[1:]].to_numpy(), self.train_df[self.train_df.columns[0]].to_numpy() From 432772409749d45ce57a37386c1313c55666f6d9 Mon Sep 17 00:00:00 2001 From: Alexander Nikitin <1243786+AlexanderVNikitin@users.noreply.github.com> Date: Wed, 5 Jun 2024 21:59:21 +0300 Subject: [PATCH 13/31] add Lotka Volterra simulator --- tests/test_simulator.py | 14 +++++ tsgm/simulator.py | 114 ++++++++++++++++++++++++++++++++++++++++ 2 files changed, 128 insertions(+) diff --git a/tests/test_simulator.py b/tests/test_simulator.py index 3742573..c84917a 100644 --- a/tests/test_simulator.py +++ b/tests/test_simulator.py @@ -50,3 +50,17 @@ def test_pdm_simulator(): assert params1["switches"] == params2["switches"] assert params1["m_norms"] == params2["m_norms"] assert params1["sigma_norms"] == params2["sigma_norms"] + + +def test_lv_simulator(): + n_samples = 10 + data = tsgm.dataset.DatasetProperties(N=100, T=12, D=23) + lv_simulator = tsgm.simulator.LotkaVolterraSimulator(data) + syn_dataset = lv_simulator.generate(n_samples) + assert syn_dataset.shape == (10, 2) + + new_sim = lv_simulator.clone() + params1 = lv_simulator.params() + params2 = new_sim.params() + for k in ["alpha", "beta", "gamma", "delta", "x0", "y0"]: + assert params1[k] == params2[k] diff --git a/tsgm/simulator.py b/tsgm/simulator.py index ff13f8c..69be6f0 100644 --- a/tsgm/simulator.py +++ b/tsgm/simulator.py @@ -1,6 +1,7 @@ import abc import copy import sklearn +from scipy import integrate from tqdm import tqdm import typing as T import numpy as np @@ -485,9 +486,24 @@ def sample_equipment(self, num_samples): return dataset, equipment def generate(self, num_samples: int): + """ + Samples equipment data and generates the dataset. + + Args: + num_samples (int): Number of samples to generate. + + Returns: + tuple: A tuple containing the dataset and equipment information. + """ return self.sample_equipment(num_samples) def clone(self) -> "PredictiveMaintenanceSimulator": + """ + Creates a deep copy of the current PredictiveMaintenanceSimulator instance. + + Returns: + PredictiveMaintenanceSimulator: A new instance of PredictiveMaintenanceSimulator with copied data and parameters. + """ copy_simulator = PredictiveMaintenanceSimulator(self._data) params = self.params() copy_simulator.set_params( @@ -495,3 +511,101 @@ def clone(self) -> "PredictiveMaintenanceSimulator": m_norms=params["m_norms"], sigma_norms=params["sigma_norms"]) return copy_simulator + + +def _lv_derivative(X, t, alpha, beta, delta, gamma): + x, y = X + dotx = x * (alpha - beta * y) + doty = y * (-delta + gamma * x) + return np.array([dotx, doty]) + + +class LotkaVolterraSimulator(ModelBasedSimulator): + """ + Simulates the Lotka-Volterra equations, which model the dynamics of biological systems in which two species interact, + one as a predator and the other as prey. + + For the details refer to https://en.wikipedia.org/wiki/Lotka%E2%80%93Volterra_equations + """ + def __init__( + self, data: tsgm.dataset.DatasetProperties, + alpha: float = 1, beta: float = 1, gamma: float = 1, delta: float = 1, + x0: float = 1, y0: float = 1) -> None: + """ + Initializes the Lotka-Volterra simulator with given parameters. + + Args: + data (tsgm.dataset.DatasetProperties): The dataset properties. + alpha (float): The maximum prey per capita growth rate. Default is 1. + beta (float): The effect of the presence of predators on the prey death rate. Default is 1. + gamma (float): The predator's per capita death rate. Default is 1. + delta (float): The effect of the presence of prey on the predator's growth rate. Default is 1. + x0 (float): The initial population density of prey. Default is 1. + y0 (float): The initial population density of predator. Default is 1. + """ + self._data = data + + self.set_params( + alpha=alpha, + beta=beta, + gamma=gamma, + delta=delta, + x0=x0, + y0=y0 + ) + + def set_params(self, alpha, beta, gamma, delta, x0, y0, **kwargs): + """ + Sets the parameters for the simulator. + + Args: + alpha (float): The maximum prey per capita growth rate. + beta (float): The effect of the presence of predators on the prey death rate. + gamma (float): The predator's per capita death rate. + delta (float): The effect of the presence of prey on the predator's growth rate. + x0 (float): The initial population density of prey. + y0 (float): The initial population density of predator. + **kwargs: Arbitrary keyword arguments for setting simulator parameters. + """ + super().set_params({ + "alpha": alpha, + "beta": beta, + "gamma": gamma, + "delta": delta, + "x0": x0, + "y0": y0, + }) + + def generate(self, num_samples: int, tmax: float = 1): + """ + Generates the simulation data based on the Lotka-Volterra equations. + + Args: + num_samples (int): The number of sample points to generate. + tmax (float): The maximum time value for the simulation. Default is 1. + + Returns: + np.ndarray: An array containing the population densities of prey and predators over time. + """ + t = np.linspace(0., tmax, num_samples) + X0 = [self.x0, self.y0] + res = integrate.odeint(_lv_derivative, X0, t, args=(self.alpha, self.beta, self.delta, self.gamma)) + return res + + def clone(self) -> "LotkaVolterraSimulator": + """ + Creates a deep copy of the current LotkaVolterraSimulator instance. + + Returns: + LotkaVolterraSimulator: A new instance of LotkaVolterraSimulator with copied data and parameters. + """ + copy_simulator = LotkaVolterraSimulator(self._data) + params = self.params() + copy_simulator.set_params( + alpha=params["alpha"], + beta=params["beta"], + gamma=params["gamma"], + delta=params["delta"], + x0=params["x0"], + y0=params["y0"]) + return copy_simulator From 50a0c6ec526376bd0d2723433c3e8ffa4390c302 Mon Sep 17 00:00:00 2001 From: Alexander Nikitin <1243786+AlexanderVNikitin@users.noreply.github.com> Date: Thu, 6 Jun 2024 08:54:27 +0300 Subject: [PATCH 14/31] Update README.md --- README.md | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/README.md b/README.md index bbffd72..2b9ae1f 100644 --- a/README.md +++ b/README.md @@ -6,11 +6,8 @@ [![Pypi version](https://img.shields.io/pypi/v/tsgm)](https://pypi.org/project/tsgm/) [![unit-tests](https://github.com/AlexanderVNikitin/tsgm/actions/workflows/test.yml/badge.svg?event=push)](https://github.com/AlexanderVNikitin/tsgm/actions?query=workflow%3ATests+branch%3Amain) [![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/release/python-380/) -[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://github.com/AlexanderVNikitin/tsgm/blob/main/LICENSE) -[![Last Commit](https://img.shields.io/github/last-commit/AlexanderVNikitin/tsgm)](https://github.com/AlexanderVNikitin/tsgm/commits/main) - -[![arXiv](https://img.shields.io/badge/arXiv-2305.11567-b31b1b.svg)](https://arxiv.org/abs/2305.11567) [![codecov](https://codecov.io/gh/AlexanderVNikitin/tsgm/branch/main/graph/badge.svg?token=UD38ANZ0M1)](https://codecov.io/gh/AlexanderVNikitin/tsgm) +[![arXiv](https://img.shields.io/badge/arXiv-2305.11567-b31b1b.svg)](https://arxiv.org/abs/2305.11567) # Time Series Generative Modeling (TSGM) From a5f23b968731f98f7a30fc5ef94e57923b100a1c Mon Sep 17 00:00:00 2001 From: Alexander Nikitin <1243786+AlexanderVNikitin@users.noreply.github.com> Date: Fri, 7 Jun 2024 12:38:26 +0300 Subject: [PATCH 15/31] detailed readme --- README.md | 131 +++++++++++++++++++++++------------ docs/guides/introduction.rst | 9 ++- docs/modules/root.rst | 7 ++ tests/test_metrics.py | 12 ++++ tests/test_utils.py | 17 +++++ tsgm/metrics/__init__.py | 2 +- tsgm/metrics/metrics.py | 82 +++++++++++++++++++++- 7 files changed, 209 insertions(+), 51 deletions(-) diff --git a/README.md b/README.md index 2b9ae1f..f816f4f 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,17 @@ -
+
+

+Time Series Generative Modeling (TSGM) +

+ +

+Create and evaluate synthetic time series datasets effortlessly +

+ +
+ [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1l2VB6eUwvrxyu8iB30faGiQM5AKthc82?usp=sharing) [![Pypi version](https://img.shields.io/pypi/v/tsgm)](https://pypi.org/project/tsgm/) [![unit-tests](https://github.com/AlexanderVNikitin/tsgm/actions/workflows/test.yml/badge.svg?event=push)](https://github.com/AlexanderVNikitin/tsgm/actions?query=workflow%3ATests+branch%3Amain) @@ -9,27 +19,37 @@ [![codecov](https://codecov.io/gh/AlexanderVNikitin/tsgm/branch/main/graph/badge.svg?token=UD38ANZ0M1)](https://codecov.io/gh/AlexanderVNikitin/tsgm) [![arXiv](https://img.shields.io/badge/arXiv-2305.11567-b31b1b.svg)](https://arxiv.org/abs/2305.11567) -# Time Series Generative Modeling (TSGM) +
-[Documentation](https://tsgm.readthedocs.io/en/latest/) | -[Tutorials](https://github.com/AlexanderVNikitin/tsgm/tree/main/tutorials) +

+ Get Started • + Tutorials • + Augmentations • + Generators • + Metrics • + Datasets • + Contributing • + Citing +

-## About TSGM -TSGM is an open-source framework for synthetic time series generation and augmentation. +## :jigsaw: Get Started -The framework can be used for: -- creating synthetic data, using historical data, black-box models, or a combined approach, -- augmenting time series data, -- evaluating synthetic data with respect to consistency, privacy, downstream performance, and more. +TSGM is an open-source framework for synthetic time series dataset generation and evaluation. +The framework can be used for creating synthetic datasets (see :hammer: Generators ), augmenting time series data (see :art: Augmentations ), evaluating synthetic data with respect to consistency, privacy, downstream performance, and more (see :chart_with_upwards_trend: Metrics ), using common time series datasets (TSGM provides easy access to more than 140 datasets, see :floppy_disk: Datasets ). -## Install TSGM -``` +We provide: +* [Documentation](https://tsgm.readthedocs.io/en/latest/) with a complete overview of the implemented methods, +* [Tutorials](https://github.com/AlexanderVNikitin/tsgm/tree/main/tutorials) that describe practical use-cases of the framework. + + +### Install TSGM +```bash pip install tsgm ``` -### M1 and M2 chips: +#### M1 and M2 chips: To install `tsgm` on Apple M1 and M2 chips: ```bash # Install tensorflow @@ -43,15 +63,7 @@ conda install tensorflow-probability scipy antropy statsmodels dtaidistance netw ``` -## Train your generative model - -- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1l2VB6eUwvrxyu8iB30faGiQM5AKthc82?usp=sharing) Introductory Tutorial "[Getting started with TSGM](https://github.com/AlexanderVNikitin/tsgm/blob/main/tutorials/GANs/cGAN.ipynb)" -- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1Vw9t4TlI1Nek_t6bMPyKcPPPqCiXfOK3?usp=sharing) Tutorial on using [Time Series Augmentations](https://github.com/AlexanderVNikitin/tsgm/blob/main/tutorials/augmentations.ipynb) -- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1hubtddSX94KyLzuCTwmU6pAFBgBeiEB-?usp=sharing) Tutorial on [Evaluation of Synthetic Time Series Data](https://github.com/AlexanderVNikitin/tsgm/blob/main/tutorials/evaluation.ipynb) -- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1wpf9WeNVj5TkUcPF6EavVx-hUCOfyvUd?usp=sharing) Tutorial on using [Multiple GPUs or TPU with TSGM](https://github.com/AlexanderVNikitin/tsgm/blob/main/tutorials/Using%20Multiple%20GPUs%20or%20TPU.ipynb) - -For more examples, see [our tutorials](./tutorials). - +### Train your generative model ```python import tsgm @@ -79,16 +91,61 @@ gan.fit(dataset, epochs=N_EPOCHS) result = gan.generate(100) ``` +## :anchor: Tutorials -## Getting started +- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1l2VB6eUwvrxyu8iB30faGiQM5AKthc82?usp=sharing) Introductory Tutorial "[Getting started with TSGM](https://github.com/AlexanderVNikitin/tsgm/blob/main/tutorials/GANs/cGAN.ipynb)" +- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1Vw9t4TlI1Nek_t6bMPyKcPPPqCiXfOK3?usp=sharing) Tutorial on using [Time Series Augmentations](https://github.com/AlexanderVNikitin/tsgm/blob/main/tutorials/augmentations.ipynb) +- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1hubtddSX94KyLzuCTwmU6pAFBgBeiEB-?usp=sharing) Tutorial on [Evaluation of Synthetic Time Series Data](https://github.com/AlexanderVNikitin/tsgm/blob/main/tutorials/evaluation.ipynb) +- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1wpf9WeNVj5TkUcPF6EavVx-hUCOfyvUd?usp=sharing) Tutorial on using [Multiple GPUs or TPU with TSGM](https://github.com/AlexanderVNikitin/tsgm/blob/main/tutorials/Using%20Multiple%20GPUs%20or%20TPU.ipynb) -We provide: -* [Documentation](https://tsgm.readthedocs.io/en/latest/) with a complete overview of the implemented methods, -* [Tutorials](https://github.com/AlexanderVNikitin/tsgm/tree/main/tutorials) that describe practical use-cases of the framework. +For more examples, see [our tutorials](./tutorials). +## :art: Augmentations +TSGM provides a number of time series augmentations. +| Augmentation | Class in TSGM | Reference | +| ------------- | ------------- | ------------- | +| Gaussian Noise / Jittering | `tsgm.augmentations.GaussianNoise` | - | +| Slice-And-Shuffle | `tsgm.augmentations.SliceAndShuffle` | - | +| Shuffle Features | `tsgm.augmentations.Shuffle` | - | +| Magnitude Warping | `tsgm.augmentations.MagnitudeWarping` | [Data Augmentation of Wearable Sensor Data for Parkinson’s Disease Monitoring using Convolutional Neural Networks](https://dl.acm.org/doi/pdf/10.1145/3136755.3136817) | +| Window Warping | `tsgm.augmentations.WindowWarping` | [Data Augmentation for Time Series Classification using Convolutional Neural Networks](https://shs.hal.science/halshs-01357973/document) | +| DTW Barycentric Averaging | `tsgm.augmentations.DTWBarycentricAveraging` | [A global averaging method for dynamic time warping, with applications to clustering.](https://www.sciencedirect.com/science/article/pii/S003132031000453X) | -## 💾 Datasets +## :hammer: Generators +TSGM implements several generative models for synthetic time series data. + +| Method | Link to docs | Type | Notes | +| ------------- | ------------- | ------------- | ------------- | +| Structural Time Series model | [tsgm.models.sts.STS](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.models.sts.STS) | Data-driven | Great for modeling time series when prior knowledge is available (e.g., trend or seasonality). | +| GAN | [tsgm.models.cgan.GAN](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.models.cgan.GAN) | Data-driven | A generic implementation of GAN for time series generation. It can be customized with architectures for generators and discriminators. | +| ConditionalGAN | [tsgm.models.cgan.ConditionalGAN](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.models.cgan.ConditionalGAN) | Data-driven | A generic implementation of conditional GAN. It supports scalar conditioning as well as temporal one. | +| BetaVAE | [tsgm.models.cvae.BetaVAE](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.models.cvae.BetaVAE) | Data-driven | A generic implementation of Beta VAE for TS. The loss function is customized to work well with multi-dimensional time series. | +| cBetaVAE | [tsgm.models.cvae.cBetaVAE](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.models.cvae.cBetaVAE) | Data-driven | Conditional version of BetaVAE. It supports temporal a scalar condiotioning.| +| TimeGAN | [tsgm.models.timegan.TimeGAN](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.models.timegan.TimeGAN) | Data-driven | TSGM implementation of TimeGAN from (paper)[https://papers.nips.cc/paper_files/paper/2019/hash/c9efe5f26cd17ba6216bbe2a7d26d490-Abstract.html] | +| SineConstSimulator | [tsgm.simulator.SineConstSimulator](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.simulator.SineConstSimulator) | Simulator-based | Simulator-based synthetic signal that switches between constant and periodics functions. | +| LotkaVolterraSimulator | [tsgm.simulator.LotkaVolterraSimulator](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.simulator.LotkaVolterraSimulator) | Simulator-based | Simulator-based synthetic signal that switches between constant and periodics functions. | +| PredictiveMaintenanceSimulator | [tsgm.simulator.PredictiveMaintenanceSimulator](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.simulator.PredictiveMaintenanceSimulator) | Simulator-based | Simulator of predictive maintenance with multiple pieces of equipment from (paper)[(paper)[https://arxiv.org/pdf/2206.11574] | + +## :chart_with_upwards_trend: Metrics +TSGM implements many metrics for synthetic time series evaluation. Check Section 3 from [our paper for more detail on the evaluation of synthetic time series](https://arxiv.org/pdf/2305.11567). + +| Metric | Link to docs | Type | Notes | +| ------------- | ------------- | ------------- | ------------- | +| Distance in the space of summary statistics | [tsgm.metrics.DistanceMetric](https://tsgm.readthedocs.io/en/latest/autoapi/tsgm/metrics/index.html#tsgm.metrics.DistanceMetric) | Distance | Calculates a set of summary statistics in the original and synthetic data, and measures the distance between those. | +| Maximum Mean Discrepancy (MMD) | [tsgm.metrics.MMDMetric](https://tsgm.readthedocs.io/en/latest/autoapi/tsgm/metrics/index.html#tsgm.metrics.MMDMetric) | Distance | This metric calculated MMD between real and synthetic samples | +| Discriminative Score | [tsgm.metrics.DiscriminativeMetric](https://tsgm.readthedocs.io/en/latest/autoapi/tsgm/metrics/index.html#tsgm.metrics.DiscriminativeMetric) | Distance | The DiscriminativeMetric measures the discriminative performance of a model in distinguishing between synthetic and real datasets. | +| Demographic Parity Score | [tsgm.metrics.DemographicParityMetric](https://tsgm.readthedocs.io/en/latest/autoapi/tsgm/metrics/index.html#tsgm.metrics.DemographicParityMetric) | Fairness | This metric assesses the difference in the distributions of a target variable among different groups in two datasets. Refer to [this paper](https://fairware.cs.umass.edu/papers/Verma.pdf) to learn more. | +| Predictive Parity Score | [tsgm.metrics.PredictiveParityMetric](https://tsgm.readthedocs.io/en/latest/autoapi/tsgm/metrics/index.html#tsgm.metrics.PredictiveParityMetric) | Fairness | This metric assesses the discrepancy in the predictive performance of a model among different groups in two datasets. Refer to [this paper](https://fairware.cs.umass.edu/papers/Verma.pdf) to learn more. | +| Privacy Membership Inference Attack Score | [tsgm.metrics.PrivacyMembershipInferenceMetric](https://tsgm.readthedocs.io/en/latest/autoapi/tsgm/metrics/index.html#tsgm.metrics.PrivacyMembershipInferenceMetric) | Privacy | The metric measures the possibility of membership inference attacks.| +| Spectral Entropy | [tsgm.metrics.EntropyMetric](https://tsgm.readthedocs.io/en/latest/autoapi/tsgm/metrics/index.html#tsgm.metrics.EntropyMetric) | Diversity | Calculates the spectral entropy of a dataset or tensor as a sum of individual entropies. | +| Shannon Entropy | [tsgm.metrics.ShannonEntropyMetric](https://tsgm.readthedocs.io/en/latest/autoapi/tsgm/metrics/index.html#tsgm.metrics.ShannonEntropyMetric) | Diversity | Shannon Entropy calculated over the labels of a dataset. | +| Pairwise Distance | [tsgm.metrics.PairwiseDistanceMetric](https://tsgm.readthedocs.io/en/latest/autoapi/tsgm/metrics/index.html#tsgm.metrics.PairwiseDistanceMetric) | Diversity | Measures pairwise distances in a set of time series. | +| Downstream Effectiveness | [tsgm.metrics.DownstreamPerformanceMetric](https://tsgm.readthedocs.io/en/latest/autoapi/tsgm/metrics/index.html#tsgm.metrics.DownstreamPerformanceMetric) | Downstream Effectiveness | The downstream performance metric evaluates the performance of a model on a downstream task. It returns performance gains achieved with the addition of synthetic data. | +| Qualitative Evaluation | [tsgm.utils.visualization](https://tsgm.readthedocs.io/en/latest/modules/root.html#module-tsgm.utils.visualization) | Qualitative | Various tools for visual assessment of a generated dataset. | + + +## :floppy_disk: Datasets | Dataset | API | Description | | ------------- | ------------- | ------------- | | UCR Dataset | `tsgm.utils.UCRDataManager` | https://www.cs.ucr.edu/%7Eeamonn/time_series_data_2018/ | @@ -102,22 +159,10 @@ We provide: | Samples from GPs | `tsgm.utils.get_gp_samples_data()` | https://en.wikipedia.org/wiki/Gaussian_process | | Physionet 2012 | `tsgm.utils.get_physionet2012()` | https://archive.physionet.org/pn3/challenge/2012/ | -TSGM provides API for convenient use of many time-series datasets (currently more than 20 datasets). The comprehensive list of the datasets in the [documentation](https://tsgm.readthedocs.io/en/latest/guides/datasets.html) - -## Augmentations -TSGM provides a number of time series augmentations. - -| Augmentation | Class in TSGM | Reference | -| ------------- | ------------- | ------------- | -| Gaussian Noise / Jittering | `tsgm.augmentations.GaussianNoise` | - | -| Slice-And-Shuffle | `tsgm.augmentations.SliceAndShuffle` | - | -| Shuffle Features | `tsgm.augmentations.Shuffle` | - | -| Magnitude Warping | `tsgm.augmentations.MagnitudeWarping` | [Data Augmentation of Wearable Sensor Data for Parkinson’s Disease Monitoring using Convolutional Neural Networks](https://dl.acm.org/doi/pdf/10.1145/3136755.3136817) | -| Window Warping | `tsgm.augmentations.WindowWarping` | [Data Augmentation for Time Series Classification using Convolutional Neural Networks](https://shs.hal.science/halshs-01357973/document) | -| DTW Barycentric Averaging | `tsgm.augmentations.DTWBarycentricAveraging` | [A global averaging method for dynamic time warping, with applications to clustering.](https://www.sciencedirect.com/science/article/pii/S003132031000453X) | +TSGM provides API for convenient use of many time-series datasets (currently more than 140 datasets). The comprehensive list of the datasets in the [documentation](https://tsgm.readthedocs.io/en/latest/guides/datasets.html) -## Contributing +## :hammer_and_wrench: Contributing We appreciate all contributions. To learn more, please check [CONTRIBUTING.md](CONTRIBUTING.md). #### For contributors @@ -137,14 +182,14 @@ To check static typing: mypy ``` -## CLI +## :computer: CLI We provide two CLIs for convenient synthetic data generation: - `tsgm-gd` generates data by a stored sample, - `tsgm-eval` evaluates the generated time series. Use `tsgm-gd --help` or `tsgm-eval --help` for documentation. -## Citing +## :mag: Citing If you find this repo useful, please consider citing our paper: ``` @article{ diff --git a/docs/guides/introduction.rst b/docs/guides/introduction.rst index 03dc193..b4e64d2 100644 --- a/docs/guides/introduction.rst +++ b/docs/guides/introduction.rst @@ -42,7 +42,7 @@ The training of data-driven simulators can be done via likelihood optimization, - `tsgm.models.cgan.ConditionalGAN` - conditional GAN model for labeled and temporally labeled time-series simulation,\\ - `tsgm.models.cvae.BetaVAE` - beta-VAE model adapted for time-series simulation,\\ - `tsgm.models.cvae.cBetaVAE` - conditional beta-VAE model for labeled and temporally labeled time-series simulation,\\ -- `tsgm.models.cvae.TimeGAN` - extended GAN-based model for time series generation. +- `tsgm.models.timegan.TimeGAN` - extended GAN-based model for time series generation. A minimalistic example of synthetic data generation with VAEs: @@ -105,10 +105,11 @@ In `tsgm.metrics`, we implemented several metrics for evaluation of generated ti - data similarity / distance: `tsgm.metrics.DistanceMetric`, `tsgm.metrics.MMDMetric`, `tsgm.metrics.DiscriminativeMetric`, - predictive consistency: `tsgm.metrics.ConsistencyMetric`, -- fairness: `tsgm.metrics.DemographicParityMetric`, +- fairness: `tsgm.metrics.DemographicParityMetric`, `tsgm.metrics.PredictiveParityMetric` - privacy: `tsgm.metrics.PrivacyMembershipInferenceMetric`, +- diversity: `tsgm.metrics.EntropyMetric`, `tsgm.metrics.ShannonEntropyMetric`, `tsgm.metrics.PairwiseDistanceMetric`, - downstream effectiveness: `tsgm.metrics.DownstreamPerformanceMetric`, -- qualitative analysis: `tsgm.visualization`. +- qualitative analysis: `tsgm.utils.visualization`. See the following code for an example of using metrics: @@ -151,5 +152,3 @@ If you find the *TSGM* useful, please consider citing our paper: journal={arXiv preprint arXiv:2305.11567}, year={2023} } - - diff --git a/docs/modules/root.rst b/docs/modules/root.rst index f6b8e8c..c0bffd2 100644 --- a/docs/modules/root.rst +++ b/docs/modules/root.rst @@ -85,6 +85,13 @@ Datasets :undoc-members: +Simulators +-------------- +.. automodule:: tsgm.simulator + :members: + :undoc-members: + + Data Processing Utils -------------- .. automodule:: tsgm.utils.data_processing diff --git a/tests/test_metrics.py b/tests/test_metrics.py index c445178..315b7bb 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -255,3 +255,15 @@ def test_demographic_parity(): 2: 0, 3: -np.inf } + + +def test_predictive_parity(): + metric = tsgm.metrics.PredictiveParityMetric() + y_pred_hist = np.array([0, 1, 1, 0, 0, 1]) + y_true_hist = np.array([0, 1, 0, 0, 0, 1]) + groups_hist = np.array([0, 0, 0, 1, 1, 1]) + y_true_synth = np.array([0, 0, 1, 0, 0, 1]) + y_pred_synth = np.array([1, 0, 0, 0, 0, 1]) + groups_synth = np.array([0, 0, 0, 1, 1, 1]) + result = metric(y_true_hist, y_pred_hist, groups_hist, y_true_synth, y_pred_synth, groups_synth) + assert result[0] > result[1] and result[1] == 0 diff --git a/tests/test_utils.py b/tests/test_utils.py index 6c848f6..6d9ec04 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -11,10 +11,24 @@ import tensorflow as tf import sklearn.metrics.pairwise from unittest import mock +from functools import wraps import tsgm +def skip_on(exception, reason="default"): + def decorator_func(f): + @wraps(f) + def wrapper(*args, **kwargs): + try: + return f(*args, **kwargs) + except exception: + pytest.skip(reason) + + return wrapper + return decorator_func + + def test_TSFeatureWiseScaler(): ts = np.array([[[0, 2], [1, 0], [1, 2]]]) scaler = tsgm.utils.TSFeatureWiseScaler() @@ -102,6 +116,7 @@ def test_split_dataset_into_objects(): assert y.shape == (2225, 1) +@skip_on(urllib.error.HTTPError, reason="HTTPError due to connection") def test_get_eeg(): X, y = tsgm.utils.get_eeg() @@ -109,12 +124,14 @@ def test_get_eeg(): assert y.shape == (14980,) +@skip_on(urllib.error.HTTPError, reason="HTTPError due to connection") def test_get_power_consumption(): X = tsgm.utils.get_power_consumption() assert X.shape == (2075259, 7) +@skip_on(urllib.error.HTTPError, reason="HTTPError due to connection") def test_get_power_consumption_second_call(mocker): X = tsgm.utils.get_power_consumption() file_download_mock = mocker.patch('tsgm.utils.download') diff --git a/tsgm/metrics/__init__.py b/tsgm/metrics/__init__.py index c01851c..e76239c 100644 --- a/tsgm/metrics/__init__.py +++ b/tsgm/metrics/__init__.py @@ -3,5 +3,5 @@ DistanceMetric, ConsistencyMetric, BaseDownstreamEvaluator, DownstreamPerformanceMetric, PrivacyMembershipInferenceMetric, MMDMetric, DiscriminativeMetric, EntropyMetric, DemographicParityMetric, - ShannonEntropyMetric, PairwiseDistanceMetric + ShannonEntropyMetric, PairwiseDistanceMetric, PredictiveParityMetric ) diff --git a/tsgm/metrics/metrics.py b/tsgm/metrics/metrics.py index c5c7bae..7dff65d 100644 --- a/tsgm/metrics/metrics.py +++ b/tsgm/metrics/metrics.py @@ -6,6 +6,7 @@ import itertools import sklearn import scipy +from sklearn.metrics import precision_score from scipy.stats import entropy from tqdm import tqdm from scipy.spatial.distance import pdist, squareform @@ -172,7 +173,7 @@ def __call__(self, D1: tsgm.dataset.DatasetOrTensor, D2: tsgm.dataset.DatasetOrT class PrivacyMembershipInferenceMetric(Metric): """ - The metric that measures the possibility of membership inference attacks. + The metric measures the possibility of membership inference attacks. """ def __init__(self, attacker: T.Any, metric: T.Optional[T.Callable] = None) -> None: """ @@ -408,7 +409,7 @@ class DemographicParityMetric(Metric): """ Measuring demographic parity between two datasets. - This metric assesses the disparity in the distributions of a target variable among different groups in two datasets. + This metric assesses the difference in the distributions of a target variable among different groups in two datasets. By default, it uses the Kolmogorov-Smirnov statistic to quantify the maximum vertical deviation between the cumulative distribution functions of the target variable for the historical and synthetic data within each group. @@ -468,3 +469,80 @@ def __call__(self, d_hist: tsgm.dataset.DatasetOrTensor, groups_hist: TensorLike else: result[g] = metric(y_g_hist, y_g_synth) return result + + +class PredictiveParityMetric: + """ + Measuring predictive parity between two datasets. + + This metric assesses the discrepancy in the predictive performance of a + model among different groups in two datasets. + By default, it uses precision to quantify the predictive performance of the model within each group. + + Args: + y_true_hist (TensorLike): The true target values for the historical data. + y_pred_hist (TensorLike): The predicted target values for the historical data. + groups_hist (TensorLike): The group assignments for the historical data. + y_true_synth (TensorLike): The true target values for the synthetic data. + y_pred_synth (TensorLike): The predicted target values for the synthetic data. + groups_synth (TensorLike): The group assignments for the synthetic data. + metric (callable, optional): The metric used to compare the predictive performance within each group. + Default is precision score. + + Returns: + dict: A dictionary mapping each group to the computed predictive parity metric. + + Example: + >>> metric = PredictiveParityMetric() + >>> y_true_hist = [0, 1, 0, 1, 1, 0] + >>> y_pred_hist = [0, 1, 0, 0, 1, 1] + >>> groups_hist = [0, 1, 0, 1, 1, 0] + >>> y_true_synth = [1, 0, 1, 0, 0, 1] + >>> y_pred_synth = [1, 0, 1, 1, 0, 0] + >>> groups_synth = [1, 1, 0, 0, 0, 1] + >>> result = metric(y_true_hist, y_pred_hist, groups_hist, y_true_synth, y_pred_synth, groups_synth) + >>> print(result) + """ + + # using precision score by default + _DEFAULT_METRIC = lambda y_true, y_pred: precision_score(y_true, y_pred, average='binary') # noqa: E731 + + def __call__(self, + y_true_hist: TensorLike, y_pred_hist: TensorLike, groups_hist: TensorLike, + y_true_synth: TensorLike, y_pred_synth: TensorLike, groups_synth: TensorLike, + metric: T.Callable = _DEFAULT_METRIC) -> T.Dict[int, float]: + """ + Calculate the predictive parity metric for the input datasets. + + Args: + y_true_hist (TensorLike): The true target values for the historical data. + y_pred_hist (TensorLike): The predicted target values for the historical data. + groups_hist (TensorLike): The group assignments for the historical data. + y_true_synth (TensorLike): The true target values for the synthetic data. + y_pred_synth (TensorLike): The predicted target values for the synthetic data. + groups_synth (TensorLike): The group assignments for the synthetic data. + metric (callable, optional): The metric used to compare the predictive performance within each group. + Default is precision score. + + Returns: + dict: A dictionary mapping each group to the computed predictive parity metric. + """ + assert len(y_true_hist) == len(y_pred_hist) == len(groups_hist) == len(y_true_synth) == len(y_pred_synth) == len(groups_synth) + unique_groups_hist, unique_groups_synth = set(groups_hist), set(groups_synth) + all_groups = unique_groups_hist | unique_groups_synth + if len(all_groups) - len(unique_groups_hist) - len(unique_groups_synth) != 0: + logger.warn("Groups in historical and synthetic data are not entirely identical.") + result = {} + for g in all_groups: + y_true_g_hist, y_pred_g_hist = y_true_hist[groups_hist == g], y_pred_hist[groups_hist == g] + y_true_g_synth, y_pred_g_synth = y_true_synth[groups_synth == g], y_pred_synth[groups_synth == g] + if not len(y_true_g_synth) or not len(y_pred_g_synth): + result[g] = np.inf + elif not len(y_true_g_hist) or not len(y_pred_g_hist): + result[g] = -np.inf + else: + metric_hist = metric(y_true_g_hist, y_pred_g_hist) + metric_synth = metric(y_true_g_synth, y_pred_g_synth) + result[g] = metric_hist - metric_synth # Difference in metric scores between historical and synthetic data + + return result From b91cf212950dfa5bc0c5baf29ad27250f32e182b Mon Sep 17 00:00:00 2001 From: Alexander Nikitin <1243786+AlexanderVNikitin@users.noreply.github.com> Date: Tue, 11 Jun 2024 11:36:04 +0300 Subject: [PATCH 16/31] fix typo --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index f816f4f..6499bb3 100644 --- a/README.md +++ b/README.md @@ -122,10 +122,10 @@ TSGM implements several generative models for synthetic time series data. | ConditionalGAN | [tsgm.models.cgan.ConditionalGAN](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.models.cgan.ConditionalGAN) | Data-driven | A generic implementation of conditional GAN. It supports scalar conditioning as well as temporal one. | | BetaVAE | [tsgm.models.cvae.BetaVAE](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.models.cvae.BetaVAE) | Data-driven | A generic implementation of Beta VAE for TS. The loss function is customized to work well with multi-dimensional time series. | | cBetaVAE | [tsgm.models.cvae.cBetaVAE](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.models.cvae.cBetaVAE) | Data-driven | Conditional version of BetaVAE. It supports temporal a scalar condiotioning.| -| TimeGAN | [tsgm.models.timegan.TimeGAN](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.models.timegan.TimeGAN) | Data-driven | TSGM implementation of TimeGAN from (paper)[https://papers.nips.cc/paper_files/paper/2019/hash/c9efe5f26cd17ba6216bbe2a7d26d490-Abstract.html] | +| TimeGAN | [tsgm.models.timegan.TimeGAN](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.models.timegan.TimeGAN) | Data-driven | TSGM implementation of TimeGAN from [paper](https://papers.nips.cc/paper_files/paper/2019/hash/c9efe5f26cd17ba6216bbe2a7d26d490-Abstract.html) | | SineConstSimulator | [tsgm.simulator.SineConstSimulator](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.simulator.SineConstSimulator) | Simulator-based | Simulator-based synthetic signal that switches between constant and periodics functions. | | LotkaVolterraSimulator | [tsgm.simulator.LotkaVolterraSimulator](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.simulator.LotkaVolterraSimulator) | Simulator-based | Simulator-based synthetic signal that switches between constant and periodics functions. | -| PredictiveMaintenanceSimulator | [tsgm.simulator.PredictiveMaintenanceSimulator](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.simulator.PredictiveMaintenanceSimulator) | Simulator-based | Simulator of predictive maintenance with multiple pieces of equipment from (paper)[(paper)[https://arxiv.org/pdf/2206.11574] | +| PredictiveMaintenanceSimulator | [tsgm.simulator.PredictiveMaintenanceSimulator](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.simulator.PredictiveMaintenanceSimulator) | Simulator-based | Simulator of predictive maintenance with multiple pieces of equipment from [paper](https://arxiv.org/pdf/2206.11574) | ## :chart_with_upwards_trend: Metrics TSGM implements many metrics for synthetic time series evaluation. Check Section 3 from [our paper for more detail on the evaluation of synthetic time series](https://arxiv.org/pdf/2305.11567). From 3b09e336acffb416a836e54c666c51b450c9da48 Mon Sep 17 00:00:00 2001 From: Alexander Nikitin <1243786+AlexanderVNikitin@users.noreply.github.com> Date: Sat, 15 Jun 2024 18:05:54 +0300 Subject: [PATCH 17/31] improve ds tutorial --- tests/test_utils.py | 1 - tsgm/utils/datasets.py | 2 +- tutorials/Datasets.ipynb | 353 +++++++++++++++++++++++++++----- tutorials/Model Selection.ipynb | 8 +- 4 files changed, 305 insertions(+), 59 deletions(-) diff --git a/tests/test_utils.py b/tests/test_utils.py index 6d9ec04..b81bea8 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -222,7 +222,6 @@ def test_mmd_3_test(): "Coffee", "ECG200", "ElectricDevices", - "GunPoint", "MixedShapesRegularTrain", "StarLightCurves", "Wafer" diff --git a/tsgm/utils/datasets.py b/tsgm/utils/datasets.py index 0f47639..dd6bed0 100644 --- a/tsgm/utils/datasets.py +++ b/tsgm/utils/datasets.py @@ -431,7 +431,7 @@ def get_physionet2012() -> T.Tuple[TensorLike, TensorLike, TensorLike, TensorLik This function downloads and retrieves the Physionet 2012 dataset, which consists of physiological data and corresponding outcomes. It returns the training, testing, and validation datasets along with their labels. - :return: A tuple containing the training, testing, and validation datasets along with their labels. + :return: A tuple containing the training, testing, and validation datasets along with their labels. (train_X, train_y, test_X, test_y, val_X, val_y) :rtype: tuple[TensorLike, TensorLike, TensorLike, TensorLike, TensorLike, TensorLike] """ download_physionet2012() diff --git a/tutorials/Datasets.ipynb b/tutorials/Datasets.ipynb index c0f7cdf..ee5599d 100644 --- a/tutorials/Datasets.ipynb +++ b/tutorials/Datasets.ipynb @@ -5,12 +5,12 @@ "id": "d61803cc", "metadata": {}, "source": [ - "# Tutorial Datasets" + "# Dataset Loading with TSGM" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 5, "id": "d8c9b51d", "metadata": {}, "outputs": [ @@ -27,9 +27,8 @@ "%load_ext autoreload\n", "%autoreload 2\n", "\n", - "import scipy\n", - "import functools\n", - "import numpy as np\n", + "import tsgm\n", + "import pandas as pd\n", "\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", @@ -39,128 +38,374 @@ }, { "cell_type": "markdown", - "id": "f56ef285", + "id": "30b2aeeb", "metadata": {}, "source": [ - "## Gaussian process samples" + "## Collection of Datasets\n", + "\n", + "TSGM offers a collection of over 140 time series datasets. You can find the full list of available datasets in the [TSGM documentation](https://tsgm.readthedocs.io/en/latest/guides/datasets.html) and in the corresponding section in [README.md](https://github.com/AlexanderVNikitin/tsgm?tab=readme-ov-file#floppy_disk-datasets).Here, we will look at a few of the most frequently used datasets as examples." ] }, { "cell_type": "markdown", - "id": "9a08cc48", + "id": "46899e87", "metadata": {}, "source": [ - "Samples from a Gaussian process are an easy way to generate a time series dataset. We will generate `N_SAMPLES` time series, with time in `(0, MAX_TIME)`." + "#### UCR datasets [1]\n", + "\n", + "The [UCR](https://www.cs.ucr.edu/~eamonn/time_series_data_2018/) repository hosts various time series datasets, and TSGM provides easy access to those datasets. Let's consider [ECG200](https://timeseriesclassification.com/description.php?Dataset=ECG200)." ] }, { "cell_type": "code", - "execution_count": 45, - "id": "7497d3c6", + "execution_count": 9, + "id": "d57a50bd", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train Data: (100, 96) (100,)\n", + "Test Data: (100, 96) (100,)\n", + "Unique Labels: {1, -1}\n" + ] + } + ], "source": [ - "MAX_TIME = 100\n", - "N_SAMPLES = 10" + "ucr_manager = tsgm.utils.UCRDataManager(ds=\"ECG200\")\n", + "X_train, y_train, X_test, y_test = ucr_manager.get()\n", + "print(\"Train Data: \", X_train.shape, y_train.shape)\n", + "print(\"Test Data: \", X_test.shape, y_test.shape)\n", + "print(\"Unique Labels: \", set(list(y_train) + list(y_test)))" ] }, { "cell_type": "markdown", - "id": "e958b2a9", + "id": "fb1d9181", "metadata": {}, "source": [ - "`tsgm.utils.get_gp_samples_data` returns samples from GP with a given `covar_func` (exponential quadratic by defult)." + "Each series in the ECG200 dataset represents the electrical activity recorded during a single heartbeat. The dataset is divided into two classes: normal heartbeats and those indicating a myocardial infarction. It includes 100 time series samples for training and 100 for testing, with each series comprising 96 time steps. Let’s visualize the training data over time to better understand its structure." ] }, { "cell_type": "code", - "execution_count": 31, - "id": "07d23497", + "execution_count": 11, + "id": "32dbf302", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "X = tsgm.utils.get_gp_samples_data(num_samples=N_SAMPLES, max_time=MAX_TIME)\n", - "ts = np.linspace(0, MAX_TIME, MAX_TIME)" + "df = pd.DataFrame(X_train, columns=[f'Time_{i+1}' for i in range(X_train.shape[1])])\n", + "df['Label'] = y_train\n", + "\n", + "# Sample a few signals from each class to plot (5 samples per class)\n", + "sampled_df = df.groupby('Label').apply(lambda x: x.sample(5)).reset_index(drop=True)\n", + "\n", + "melted_df = sampled_df.melt(id_vars='Label', var_name='Time', value_name='Amplitude')\n", + "melted_df['Time'] = melted_df['Time'].str.replace('Time_', '').astype(int)\n", + "\n", + "# Plot the line plots\n", + "plt.figure(figsize=(14, 8))\n", + "sns.lineplot(data=melted_df, x='Time', y='Amplitude', hue='Label', style='Label', markers=True, dashes=False)\n", + "plt.title('ECG Signals Over Time for Different Classes')\n", + "plt.xlabel('Time Point')\n", + "plt.ylabel('Amplitude')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "3fb88f7c", + "metadata": {}, + "source": [ + "#### EEG Eye State [2]\n", + "\n", + "Let's explore the [EEG Eye State](https://archive.ics.uci.edu/dataset/264/eeg+eye+state) dataset using TSGM. This dataset consists of 14,980 time steps, each with 14 features. Each time step is labeled as either 0 (indicating an eye-open state) or 1 (indicating an eye-closed state)." ] }, { "cell_type": "code", - "execution_count": 32, - "id": "1521e281", + "execution_count": 7, + "id": "c4d655ac", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(14980, 14) (14980,)\n" + ] + } + ], + "source": [ + "X, y = tsgm.utils.get_eeg()\n", + "print(X.shape, y.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "33c5ab88", + "metadata": {}, + "source": [ + "Let's visualize the correlation matrix between the channels." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "7b9e1a61", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" + ], + "text/plain": [ + " time parameter value recordid\n", + "0 00:00 RecordID 132592 132592\n", + "1 00:00 Age 35 132592\n", + "2 00:00 Gender 0 132592" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_X.head(3)" + ] + }, + { + "cell_type": "markdown", + "id": "eaab97b3", "metadata": {}, - "outputs": [], "source": [ - "X = tsgm.utils.get_gp_samples_data(\n", - " num_samples=N_SAMPLES, max_time=MAX_TIME,\n", - " covar_func=functools.partial(periodic_kernel, period=20))" + "The dataframes contain measurements from the ICU unit, where `recordid` serves as a unique identifier for each ICU stay." ] }, { "cell_type": "code", - "execution_count": 44, - "id": "40b5e419", + "execution_count": 20, + "id": "f5ecdc53", "metadata": {}, "outputs": [ { "data": { - "image/png": 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vP3Fh4AJfmvcllPLJeeMglXDLBNkNN+ShUJCjLz1LYkYW05ZL+eDLP/II2TNmceTvfxuec5+rj8d2PgbAb9f/9qqokeGg0A2ec2tFOdWH97P0vgfJnD4DgJUPPYrHYefkG68Mn9dmb+NfDv0L0xOn861F37qq73q/BDw7qi/i93qYvWELSlUMALPWb8bW10tL+dnh8zodnfz89M9ZmraUuwvvvqrvytRnYvaab7gKYkd1JS6rheLFy4ePFS9ehsM8OEwjAlQNVPH78t+zPGM5yzOWR7vVFZGuu3lyyT/whtxls1J/8iilK9eEcaW5s+dh6+vF3NWJw+fg87s+z5GOI/z70n+/YgnveFDIFBi1xhtOrdQcOchAeyvL7n8ImUwOgEwmp2TFLbjtNgY72rD77Hx+1+cZ9Azym/W/ITcu96q+S6fUYVAabricbdO5U8iVShbeOZIuasorYNqyVZze9hpOixmLx8LX9n0NgJ/d8jNi5DFX9V15sZdUEG8wT95cfga5QkFW2azhY4ULl6CNi6d813YAGiwNPLZTas33vWXfmzR1NoShzJUbnYJYe+wwClUMeXNHYjn58xchkyuoPX4EkHjxj2//OFqllm8v+vZVf1e6Ph2H34HNZ3vX436v8YEPdlbu300wEGDmunDaIHe2JI5Uc/YIvxRfoXqwmh+v+vGECwbGQ6o29YYb8tNbXycpMzvMawGGPdXq8uP8SnyZBksDj697nBnJM97V95l0JnqcN9aQt12sIL2oJCKwufRDD1Jz5ACH9rzCz8UX6XX18os1v5hwimU0pOvTUcgUNNlubKpp09lTZJSUoVKPpE7KFUrKblnPqTdfYWv5K3z/wo/QKrT8dv1vh3dPV4Ohv1ebvY2SxJJ3PfargRgKUXfiCHlz5ofNOUarI2fWHOqOH6ZhDvzfmf+jLKmMX6795YSK+cbD0N+r09EZVbv8/YRr4pELgvBnQRB6BUG4cC3ud60giiLn97xDevF0krNywj6LN6USazTx9p5nqDZX87NbfvaujThIPPmNNORel4vepgaKFi9HkIX/vPGmNNTxcby270kaLA38fM3Pr3rbORomremGeuRuh53elsYwz3QIiRmZKAxa3jr8LP6gn79s/stlVR0nArlMTrYhmxbrjaNWbP19DLS3kjcnMsto1tpNiKEQz774M4oSinjh9hcmnY00Fu+HXPLO2mqc5kGKlkQ+s0WLl2Hr6+Xpvb9hY+5Gntz85Lsy4jCSvHAz8OTXilr5CzDxypnrhK66Gsyd7RHeOEh6E82JNrRdPn607IesyV5zTb4zVSd55DdKl6KrrhpRDJFRUhrxWeVAJfX6fuJ64U8b/8QtWbdck+806W6sIW+vrABRJGtGpCEv7yunQdtPqk3H87c/z6yUyHOuBrmxN1YFsfncaQDy5kZWZ/aobHQleZjZl8KTm57EpDO96+8zqAzEx8Tf0MyV2uOHkSuV5M9dGPGZO1dHSBBZ6SzhJ6t+glqhftffl6Eb8cjf77gmhlwUxQPA4LW417VEW2UFAAXzF4UdD4aCfHnflzmna0UZlFHqid7552qQqkvFF/Ix6Lkxf46OmkoEQUZ60bSw46Io8v2j38eaAhqPjBzx3b/cQzBpTQy4B/AH/dfsnpNB28UKFDExpBVG5oP/6uyvcKbIUdtC6INXx4lHQ5o+7YYuXk3nTmFITiExIyvseDAU5PtHv4/FJKC0+BG9gWv2nZn6zBvmkYuhELXHD5M7ex4x2nAdoF5XL/988jtYjJDTrUXg6uIAYxEXE4dWof3/x5BPBIIgfFYQhFOCIJzq67s+4jtddTUkpGWgMYR3gT/SeYTDHYf5yIbPI5PLaS6/dm2shnTLbxRn3FFdSUpuHipN+MNe3ldO1WAV65ZLwcD2qmvHgpm0JkRE+tw3RlSp7eJ5MqaVRhQ7Hes6xonuE6xYIBV2ddXVRLv8qmDUGnH6nTekijcY8NNSUU7enPkRwcvnqp+jor+CjYuk37m36dqlhd7IFMTuhjocA/0RcR9/yM/X9n0Np9/J6rX3Y+3uYqDt2lBeQ7nkN0N153Uz5KIo/kEUxQWiKC5ISXl33NUEv4+uumrSxnimILV7MqgMfHjWQ6QXT6d5VKrWu8WNzCUPBgJ01dVEpVWerXoWg9LA3YseQm2IvbaG/NLW/UZ4qC6rhf62FrJKZ4YdF0WRX535FSatiftXfRJBJqOrrvqafe9QLvaNmHNnTRV+j5u8OeG0Soejg1+e/SUrM1Zy2zKpHL278doZ8kxDJt3Obvyh67/zajl/FgSB/Hnhu+vdrbs513eOf1v6byxeJsW42quvXcvFdH36DU+tnQg+sOmHtr4eXFYL6cXhEXZ3wM3u1t1szNmISq4iZ9ZcepsbcFkt1+R7hzzyGxHw7G1uIODzkjGtLOx4n6uPnS07uavwLnQxejJLSmmvukDQcW1ygk3aG2fIh+iz7BnhzQL2t+/nfP95Pjf7c+h1caRk59FZV4OvpQUx8O7phqE597p63/W9Joumc6eRyRVkj4kJ/OL0LwD47pLvoouLJzbFSE9DHWIoRMjne9ffm2XIIigG6XZc/2e7q6GWpIws1PrwMvsDbQeIi4ljS+4WYlOMqHV6epuvXUOIdN2UR35D0XlpG51WFG7I97ftxx1wc2uetN3Oni4ZvXP/8i3Mf/873oaGd1UAEB8Tj1quviGreMclT2Ss5sSLtS8SEAM8WPKg9HlJGdaebipuWc3AE39+14Zt2CO/AXRS28XzqDQaTPmFw8dCYohfnf0VWYYs7iqUNLfTikvoqrpA/abNtP/jPyG+S8M25JHfCEPefO40GSWlYfRZIBTgYMdBbs+/fTjbwpRXSFdlBY133knD5s0EzO+uEnV0CuL1hCiKdNfXkloQHgMJhoIc7DjIyoyVyGVyBEEgOTWdrrNn6P3f/6X9y1/BW/fuJJYz9BnYffb3fS75tUo/fA44CkwTBKFdEIRPXYv7vht01VajjFFHpB1ubdqKUWMcTsdS7DuEPBiiu7GO7n/7Ho233c7AH/901d8rCMJw5sr1Rkd1JXGmVPSJScPH/EE/L9a+yIqMFWTHZgMQ1yGNzZaZRu9Pf0rT/Q/grrh6qsWgNKBRaG6IR956sYKMkjJkcvnwsdM9p6k11/K52Z8b1suJ7e3HHwjgmz4Nx759tH/1q4j+q6cIbpQh97ld9EWhkqoGqnD6nSxKk6gHf1cX6guV2KwWvF4vwb5+ur7z3XflpAznVV/ndDz7QB8uq4XUMcHsiv4KLF4LqzNXA+A6exbl8ZMM9vfQ9+RfcOzZQ9f3/v1dzXloUXy/y9leq6yVB0VRTBNFUSmKYqYoik9ci/u+G3TVVZNaUBT2glu9Vg51HGJz3mbkMjmBvj7Mf/wjcTFqAksXk//2NnQrVjDwxz8StNuv+rtvhCEXRZGOmkoypoXz4ztbdtLv7h9uFuFtbMT/2z+gQMB722Yyfvl/BPv7afnYx67aYxMEQcolv84euWNwAHNnO9lj8sfP9koxj6EXfPCpp5A99yIAss9/BtO//iuOXbvp+Po3rno3olFoMKgM133OPU0NIIqkFoS3pTvRfQKAhSYpNa/zG99EUydRDJof/4CUr30Vx+7dWF544aq/O0Ujxbaud1C7u74WICIraX/7fuSCnGUZkvTGwB/+SDwyQjIZKa+/gum738F95gz27duv+rtHFwW9n/GBpFb8Pi+9zY0Rgc6dLTsJhALD8rR9v3qckM9H6rwF9LU0ocrNJeXLXyZkt2N+9rmr/v40Xdp1p1bMXZ24bdaIQOcbDW+Qqc9kecZyQj4fHV/7OgqNhozpZXRUVxK7cSPZf34C0e3G+vLLV/39NyKXvO1SwHZsIVB5Xzn5cfnExcThqa6m54c/wrRqNWq9nq7GBhIfeRjjt/4Z+44dDDzx56v+fpPWdN098u4GiSowRTHkhfGFJGmS8DY04Dp1ioKPPgJAX1sLiR/7GLoVK+j54Y/w1l9dAFQpV5KoTrzuc+6qr0WuVJKcHb673t++n3mmecSqYvHW1+PYu5esjVLAs6+9lfh77yWmpITen/4PIc/V9Vgd6hT0fi8K+kAa8t7GBkLBYAQ/vq1pG7mxuZQmluKprcXy0kskPPQgqTNm4XHYcZgH0MwoQ7dyJYNPPUXIfeXOOtGQqkul391/XfOqO2qkFnOjA52iKHJh4AKL0xYjE2QM/O53eKuqSPvv/ya9bBYDHW34PR5iiorQLlyI+bnnEYPB8b7isrgR1Z3d9bUoVDGk5Ixoa4uiyPm+88OFP9Y33gSFgrT//A/SikqGM1eSHn0U7eLFWF566aq33kat8YYY8tgUI9rYkZJxf9DP2d6zLEyVvHHLiy+BQoHx/geIM6XS01CHIJOR/sMfINPp6PjGN696zimalOveu7O7oRZjbn5Yemmno5M6c93wrmvgz08iqNXkfOrTKFQx9DY3IsjlmL79bfydnQz+5amr+u6EmATUcvX7PuD5gTTkQy/raI+819XLqe5T3Jp3K4Ig0PvT/0Gm15PyhS+Qkp0LQF+LpJ2R/LnHCA4OSi/EVSBNl4aIeF0NW0d1JWpDLIkZIxoinc5OrF4rpUmlUvuz115Dv3o1hrVrpDmLIgPtUhPhhI8+hL+jA8eBA+N8w+Vh0proc/URDF3dQnA16Gmsw5hXEEaftdhasHgtzE6ZjRgKYdu2Df2KFSgSEkgvKmGgo21Y1jbunrvxt7XhPn36qr4/RZNy3Q15T2Mdqfnh3viFgQu4A24WpS4i5PNhfe01DGvXokhOxpRfNJyCqEhJIeUrX8ZbVYXnfKQW/0SQor2+cw6FgvQ01Efw4wfaped0VeYq/D09WN98k/h770WVnExKdu5w5opu8SIMGzbQ/4c/4O+Z/LhvFl3yD6Qh76yrJs5oQhefMHysvK8cEZEVGSvw1NTgPHiQ5Mc+izw+ftij62uWDLl2/nw0C+Yz8Oc/X1V2w1AWx/XkyTtrKkkvLgkrEKkaqAJgeuJ0PJWVBDq7MGyU9LeTc3IB6GtrBsCwbh0Ko/GqKaVUXSpBMciAZ+DqJzEJhIJBepoaSB2VrQJwvl8yULNTZuM+c4ZAdzext0lqlmlFJSCKdNdL9ETsxo3ItFosr712VWMwao30e/qv2+Llttuw9nRH0ipdJxAQWGBagGPXLoIWC/H33w9Aan6hlIprk7rBx27ahKBUYtu27arGYNQarytHPtjeht/rIa0gkh/PNmSTG5vL4F//CsEgiZ94VBpjXj59zSONP4zf/Aai14v5b3+7qjFMGfIbhK66mghapc5ch4BAYUIhjv3Sah57h9S3MEarIzbFNOyRAyQ/9jkC3d1Y33hj0t9/vYuCPA4H5q5O0sfMuXKgErkgpzixGMfu3SCToV8racrEG1NRxMTQ39IMgKBUEv/AAzgPHsTXMvnKuOFc8usU/BvsaCPg9UYYtfLecvRKPQXxBVi3bkVQqzFcmnNqoXRud6NkyGVaLYZNm7C/vf2qaDST1kRIDF23xavnEj8+NtB5svskxQnFxKvjMf/9RZTp6eiWSwFA0yXvvfeSVy6PjUW3ciW27TsiGjFMBEatUZJjuE5FQV0NUqBztEfu8rs40XWCVZmrCDkcWJ5/gdjNm1FlSXIFxtwCvC4n1l7pWVRlZaFbvAj7zp1XRSndaFG4ieADZ8ht/X04BgciDHmtuZbs2Gw0Cg2OA/uJKZ2O0jjSKSUlJ4++1ubh/9etWI6qsEDiWCeJ610U1HPpJTWN8U4rByspiC8gRh6DfddutPPno0iQdimCTEZyZjb9lzxygPgH7geFAvNzz096DNe7urN7nDmX95UzM3kmQiCIffsODGvXINPpAGnBNiSnhJVwx91zNyGnE/uuXZMew/VOQRwOdI6asy/o41zfORalLcLX2orr2DHi7vvQsPKlKV/qHTu6wjN2yxYCPT24z0xemiJFk4KIyID7+ixe3fW1xOh0xKeO6CGd6jmFL+RjVeYqHHv2EHI6SfzYI8OfD/XiHV0YZNiwAV9zM76GyRcLGbVGzB7zDalonSg+cIa8a7gQKHwrVmeuozihmKDVivvsOfSrwqVMU3JyMXd24Pd5AYkbM6xbj+v0aYJW66TGoFFoiI+Jv26GfMjDHO2diqJI1UAVpUml+Fpb8dbWYli/Luy65Ow8+lqah70UpdGIYcN6LK+8QsjrndQYrnd1Z09jHSqNhsS0EY1tp99JnaWOWSmzcB47RtBsHqZVhpCclUP/qAVbu2AByowMrK++OukxGHXXt0y/u7GehPRMYrS64WPlfeV4g14WpS6SYjoyGfH3jvSnjNHqSEjLoKdxpDDGsHYNglp9VfTK0OJ1vQKeXZcKgUZThrVmyUuflTIL55EjyBMSUM8ayVxKys5BkMnobWocPqZfKz379p2R/UyvhBTt9V28rgYfOEPe3VCLXKEYXpVB2oq12dsoSijCeeQIBIPoV60Ouy4lJw9RDDHQ1jp8zLDmFggGcRw4OOlxpOpSrxu10tNQR7wpDY3eMHLM1cOgZ5DpidOx79oNgH7d+rDrUrJzcNttYfIE8XffTchmw3XixKTGEB8Tj0qmum7USndDHaa8wjDN9Qv9FwiJIWanzMb21lZkl2iE0UjOzmWws53gpfxxQSYj7u67cR49hr9rcr/X9S7T72mojYgJnOg+gUyQMd80H/vOneiWLUOZmho+zvzCMI9cptOhX70a2453Jp1HP7wLcb/3c/Z7PfS3NkfkjzdYGkjVpaJVaHEeOYpu6ZKw50CpiiExPTPMI1eajGjmzMG+8yp2Xprru3hdDT54hry+FmNuQViqUoOlARGR4vhiHPsPIIuLQzM7PPd4OOA5iidXz5qFPCkJx969kx5Hqi6Vbtd18sgb6iIohqFAZ2lSKfbdu4mZPh1VZniHmOShbJ3RHurixQhq9XAcYaIQBAGTznRd5hwM+OlraYrgx8/3SYHOmYZi7Lt2YdiwHtmYjkHJWTkEAwEs3SPBq7i77wJRnDSNlqhORCEoroshdwwO4DAPRvDjp7pPMT1xOmqzG19zM7plyyKuTcnJwzHQj8c5oq0Te+utBAcGJr1gX0+PvLepETEUishYabA0kB+Xj6++nkBfH9qlSyPHmVdAX3Nj2DHDhvV4KivxtU8ulXCoQcX1WLyuFh8oQx4KBulurIv44Ye2YkVxhTgOHUK/fDnCqJQ1kIJ/yhg1fa0jhlyQydCvXo3j4MFJl3OnalOvi7iQ02LGPtAX8YJXDlYiE2QUiim4z5zBsG5dxLVDhnw01SBTq9EtXoxj//5JB4auV3Vnf1srQb8/Kj+eF5eH4lw1IaeT2C23Rlw7JNnQP4onV2VloZ45c9ILtkyQkaxNvi6GfDgmMCp7QxRFas21lCWV4TpxHADt4kUR1w7NefRuU796FTKtFtvbb09qHAkxCcgF+fWZ81Cgc9ScQ2KIZlsz+XH50u4a0EdZvIy5+TjMg2G7TcN6aUfq2D05r/x600lXgw+UIR+4lMkw1pDXWerQKDQkt9sI9vejXx3Z6kuQyUjOzgnzyEHiE0N2O65J5hqbdCbsfjsuv2vyE5kEhgKdYwWFqgaqyI/Lx3/gCIhiBD8OoI2NQxefEGbIAXSrV+Fva8PX1BxxzeVwvao7h/je1DFG7XzfeWanzMZ54gQolWjnz4u4NjEjC0Emi5izfuUK3OfPE7RYJjUWo9Z4febcUIsgk2HMHSl+MnvN2Hw2cuNycR47hiwuDnVJZD/NaIuXTK1Gv24dtnd2TspJkcvkJGmSrosh76yrITbFGJZG3OXswh1wUxBfgOPIEVQ5OSgzInuRGnOlIG9v0wi9osrJIaa4GNskefLruXhdLT5QhnxIk2GsUas111IUX4TzwEEQBHQrVkS9PiUnj76WpjBPVLdsGYJKFeatDQwMYLnCCz+0ive7+69mKhNGd0MtCALGvPyw41UDVUxPnI5jz16UGRnETIvUZQfJK+8ba9QuxQ8cB/aHHQ9dIV1tqGT9vW5z19NQj1qnJ8440uWo1d6K2WtmdspsXCdPoZk5E5lGE3GtQqkkIS0jzKgBEpceCg17eROFUXN9qju7G+pIzsxGGTPSwqzZ2gxIbedcx0+gXbggYqcJYEhOQanWRMzZsGE9IasVd3k5oVCI3t7eK/7GcKn46zrkknfVVZNWGP7cNlgkw1ygy8F18tRwmuVYDGeujHXMNmzAffoMgX7pvezo6MB8BY2hocXrRjVOmQgUN3oA1xJDqUoJqWnDx0RRpM5cx7rsdTj/egD1jBkokpKiXp+Sk8/5XduxD/QRmywZYplWi3bJYux79xH75S+zb98+Tpw4gVKp5EMf+hDTxjGQQwJDva7eYdXB9wI9jfUkZWSFSZr2u/vpdfdSmjAd1+nHpSIQIXr7q+TsXMp3bCUUDA5XSKoyM1AVFmA/cICOefNobGykpaUFu93O7bffzpw5c6Ley6Q14Q/5MXvMJGmi/42vBbob6jAVFEUtfirTFuC5eJGkT3963OuTs3LCPDUAzaxZyOLisB84yHaHg+7ubtRqNWq1mpkzZzJ37tyo9zJqjRzrOnYNZjU+RFGku7GewgWLw44P9QzNdqpxtbeT+LGPRb1eEASSs8JTTQF0S5eCXI7j4CEODgxw9OhRYmNjKSsrY9asWaSlpUW9X4omhVZ7a9TPrhUcgwPY+/tIu/XusONNVskwZzQ7GHC5osYEANR6PfrEpIhuQYaNG+j/9a+x79lDS3Exr7/+OjKZjGXLlrFy5UpUY2IqQzBqjFPUyvVCd0OdlKo0KoLd5+7D4rVQIs/Aff58RNrhaIyU6jeHHTesXUtjKMQvf/ELjh8/zrx580hKSuK5557jwIEDUbnkYV7tPVzFRVG8NOcx/PiApEteatMTstnQLhi/g3pKdi4Bvw/zqOAfSF75GauVV155hZqaGkwmE2lpabz22muUl5dHvdf1yCUP+Hz0tzVH8ONDRs3UYIZgEO3CyAa9Q0jOzsHS241/lJCSIJejX76Mi3V1VFZWEh8fj1arxWq18vrrr3Px4sWo9zJqjTj8jveUQjN3deKx2yJqI5qtzahkKgwVkrHSLl4c7XJAWrD721rDnlW5wYBmzhyqyss5evQoJSUlpKamcvz4cX7/+9+PO+frUabfVT+URhzpkSepk+BkOchkl59zVg79beELTkxxMcqMDE4cP8Frr71Gbm4uZWVlHDx4kMcff5zGxsao90rRpryvg50fGI/c7/PS19rEorvuCzteZ5b41OJmP4giuuXLo10OSLnkIGWujG7YrFy2jBMVFSSEQnzsscdIS0vD7/fz+uuvs2fPHiwWC3feeWf4vbQjHvl7hSGd5ohCoIFKBATS6y2YAc38yE7rQxjmT1tbSBrVyFe9YgW1A/1kx8fz6Be/iEwmw+fz8dxzz/Haa68hCAKzZoVn/qRqR/qVliZFtpuLCnMzhIKQVDCh0/tamwgFgxF6I232NkxaE4Ez50EuRzt3zrj3SM7KGdaZGR1PUS1bzlmFAlNCAg8//DAymQy/389TTz3Fq6++SlxcHJmZmWH3Gt3yLS8ujwnB54TqbTD9DlBeudv7iCBa+N+0ydZEdmw2niMnkScmElNUGO3y4TlX7N6By2oJ45xDy5ZyqKOD1JQU7rvvPhQKBS6Xi2effZbXX3+d1NRUksbsYI1aIzafDU/AM7lu9b3VoNJC/JV3qF11NVIacV74c9FgbaAgvgDny0fRzJyJ3GAY5w6QlJVDW+VbhEJBZDJptykIAvUrV3AMmFZUxH0PPIBSqWT+/Pm8+eabvPDCC3zhC18gLi4u7F5GrXFYHvn9iA+MRz6cqhSFHwdIru1FiIlBM6Ms2uUAqDRa4oymiEBYTV8fAaWShY1Nw9vNIWpl2bJlnDlzhpqa8Ma+BqUBtVw98e1Y6zH4UQ78djm8+jk48UcIXF7npadh/EBnTmwOwXMVKFJTUWakR7scgKTMbARBFrHtrpPL8Gg0zBoYQHZph6NSqXjwwQfJycnh1VdfpakpnH+ctEc+2Ah/WAOPL4C3vgKOK/+tRmRcw41Wi62F7NhsXCdPoi4rG67mjIbhbJ0x2+7z6hjcWi3LBdnwnJVKJR/5yEfQ6/U899xzEbGRSeeS27vhyVvhlU/Ds/eD98rt9jprqiIE0UDyyHMM2TiPn0C7aFHYTnQsRi/YQwgEAuy8pHa5xWhEoZD8Oq1Wy3333YdcLufFF1/EPyYYOund5kADvPgJ+M1ieGITOK8cN+qqq8GYW4BCOZJGLIoiTZYmpiky8Fy4MC4/PoTkrByCfj+W7pHsMYfDwXEgo62d2woKUF66f05ODg8++CChUIjXX389IlaQoknB4rXgC05Ae8nrgMP/By99En69GH6SD90VV77uXeADY8iHA51jM1bMdRg1RoLnLqCZNQthHA5sCElZOREv+JkzZ0iUydAfOxbWcEIQBNauXYvRaGTr1q14R1VDCoIw8S2ozykZb5UeDGnQsAe2fR0O/+Lyc26oRSaXh8m4AtRb6imOL8J18hTaBQvG5ccBFCoVCWnpYXRSKBTi8LFjJAcCxO4Pp45UKhUPPfQQcXFxbNu2jeAo2dtJ5VW7LfDshwER5n0czvwVfjUPzj5z2cvaqy6iT0rGkBTewLvV1kpeTDruigq0C8ffgQDEGU0oVDFhi5fFYuHouXPkWizEXkrlG4Jer+ehhx4iEAjw4osvhr3kkyrT762CP62H/lpY+o/QfBievhvclw+2dVRHCqIFQgHa7e2UuhMJdHeji5J2OBrRMldOnTpF1+Agi6uqUJw8GXZ+fHw899xzD93d3Wwf05hhUgUye38Iv14Etdth0WPgGoBXPgOXCaoGAwG6G+pIG9Nvt8/dh91vZ0abAKHQuPz42DmP5skrKysRgRlVVXgOhwe2k5KS2LRpE42NjZw6dSrss0ktXu98B3b+G7SfhMR8EEXY9g3p3+8RPjiGvKEWfVIy+oTEsOO15lpKtXl4qqrQXIYrHkJKdi7mrg6CAckL6e7upqOjgzklJQjBIM4jR8POVygU3HHHHdhsNnbv3h1+L80EebWd35Mohnv/AA+/BF+vheItcOw34B2/U1F3Yz3JWbkoRi1OgVCATkcnJZ4EqVhiAnOW+NPm4f+vrKzEbDazOL+AYG8v3urw7vMqlYrNmzfT19fHiVEFJRPOqw4G4MVHYbAJPvw3uOMX8PmjYJwOW782rmETRZH2ygqySmeGGTWbz4bZa6a0WwF+/2X5cQCZTE5SZlYYf7rrktbKyqxs3GfPEbSF92g0Go1s2bKFjo4OLlwYaYs3mlq5LHqrJG806INPbINN/w0PPAVd5fCXO8ads8tmxdzVEUGrdDg6CIgBipok50G7eMllv14bF48mNi7MkFdVVWEymZheWITz0OEIEa3i4mKWL1/O6dOnqRvV+3LCtGF3Bez/EZTcBl88B7f+RPqnYQ8c/N9xL+tvbSbg80blxwEy6q0IMTFhZfnRkJSRBYIQNucLFy6QkpJCen4+jkOHIq6ZP38+hYWFvPPOOwwMjJTkD835iotXdwWceQoWfx6+XAEPPgcbvg+tR6Hixctf+y7wwTHk9bURUpf+kJ9GayML+uMgFEI778pGLSkrh1AwyGCnVP115swZ5HI58zdtQqbX4zwUWa6flZXFokWLOHHiBG1tI41phzS6L4vGfXDyj7DkC5A7ir9f/Q3p5T4ZvX+opNMcGejsdnYTEAMUNF96wedPbPGy9nTj87gRRZHDhw+TmJjIrFulbivRJAqmTZtGUVERe/fuxT5qlzKhdLyd34XGvXD7zyH3UipoSjHc+j8QcMO5Z6NeNtjRjstqiehX2WqTDHJWvU0KgE1gzslZucMUmtvt5uLFiyxatIi01asgGMR5NDITZSiTY9euXcN0g1apxaA0XHnO+34k/fvTuyD9UgbM9DukF72nAk7/JeplHTVDDbXDDflQ6mFKVTeKlBRUebkTmHPOsHfqcrlobW1l2rRp6FeuIDg4iKeyKuKaNWvWkJCQwK5du4Z3IhPehez9IcTEwR3/B4ZLqaLzPg6zPgz7fgCN+6NeNqSXNFbNs9EqBSK1lS1SeukVdtdKtZp4Y+qwIbdarbS2tjJjxgx0K1firaoi0Bf+fgqCwJ133olCoeDNN0cqfUdnoY0LUYTt3wZ1PNzyzyPH5zwM6fPgne9e1jF7N/hAGHK33YalpyuCVmmxtuAP+Sls8YFMhuYyAbAhpIzagvr9fs6fP8/06dPRxcWhW7YMx8FDUbNU1q5di8Fg4K233hp+4FO0KfS5+8avkPTY4PV/hKQiWPfd8M8y5kPBOjjyuES9jEFPQz1el5OssjFG7VJaWHJdH/K4OFQFVw4iDnHGA22tNDU10dXVxfLly4lJTSWmdDqOg5Hl+oIgsHnzZoLB4LA3CxPQq7Z2wPHfw/xHYd4j4Z+lzYKsxdLiFWXr3VYp8YxjDXmLTXpRYyvbUZeUXDYANjznrGycFjMum5XGxkZEUaSkpATNnDnIDIaIHHoAmUzGxo0bsdlsHDs2Yuiv2ClooAGq3oCFn4wM9BWul+Zc/nzUrXdnTRVyhSJ6lo4oojxfJ/Hjl6HPRuacQ397K2IoRF1dHaIoUlxcPJwAEM1JUSgUrFmzhp6enuEsllhVLDHymMv/zp1noWYrLP0H0IwEVxEEuO1nkFgg0YdR5txVV40uPgFDcjh91mhpJAUDgZq6Ce2uIZwqrayUFsWysjL0KyUHwnH4cMQ1sbGxrFmzhubm5uE40ISolao3oPkgrP3X8DnLZJKT4uiG/T+Z0Lgni5vLkB/6Bfz1rojD3cM6zWM0GazSViyxtoeYkmnI9forfkVCegYyuZz+1mYqKyvxeDzMv+Th6VauINDdjS9Kz0O1Ws369evp6ekZ3oYatUbcATcO/zgBrVNPgLUN7v4NKCOLV1j9TXD1R/XWms+fAUEge+acsOPt9nYAYi40opk//7IBsCGM1lypqqpCqVQOZ6ToV62SqIYoCpBJSUksW7aM8vLy4Z1Iijbl8jTDyT8BIqz4SvTPF35GCoI27on4qK2yAn1SMnGmcFGoVnsrygBwsfaK/PgQRhavFhoaGoiJiSEjIwNBoUC3fDnOAwej6nXn5eUxbdo0Dh48iMMh/a5XjIUc+RXIlNJ2OxpmfwT6qqHrXMRHHTWVmPKLwugzkPKpi51xhPr6Jz7nrBz8Hje2/j5qamrQ6/Wkp6ejSEpCXVYWlWoAmDFjBkajkb179xIMBqX4z5W6I+39geSZLoky5xg9LPmcFCvojdwFdNXXkFY0LWJxarA2sNxilNJLJ7DrAkkYztzVQcDv58KFC6SmppKcnExMSQnypCSchyINOcC8efPQ6/Xs3y8t6PEx8Shkl4n/+D0SN24sg3mPRn6eOR/mPgzHfgt9tRMa+2RwcxlyQSZREZa2sMPtlRUIMlmE19Jub0ceFBEu1qG9TAreaMgVI5V/Z86cISEhgZwcyUvXX1LSG08NccaMGcTGxnL40io/3HU8Gr0SCsKpP0POCsgaJ1CVvQRyV8LhX0oPyii0nD+LKa8grHcjSDSD0a0k1No+4Yc9LsWIMkZNf6vkgeTk5AxH8/WrVktUwzgVjytXrkSj0QzP2ag1YvfZcQeiNGrwu6VFadqtkJAbfTCld4I2GU6EU0rj8eNDc15oTkD0eq/Ijw9hKBDW29JMfX09+fn5yC8VROlvWU2gry8q1QCwYcMGAoEA+/btG57zuIuXvUeiiuY8OEIvjEXZPSCPkbzyUfD7vPQ01Ec01AbJI1/SFwtMjD4DyTsF6GlupL6+nuLi4uHsHN2KFVFjAyDtRNatW8fg4CDnzp0DrrALaTsJde/A8i+COjb6OdPvBASofC3ssBQT6IzImQfJI5/Vobi0u45eoBVtzmIoRHN1JR0dHcyYMQO4pKO0YjnOw5GxAZCylZYvX05zczPNzc0IgnD5oqDTT4KlFTb/EOTjZHWv+3dpp+27cqbSZHFzGfJpEmdLbXgUveH0CbJKZxCj1YYdb7O3MWvQgOjxRNXdGA/J2bn0trXS2trKzJkzR1LRUlOJKSrCEWULCiCXy1m6dCmtra20tbVdXjWtfrf0wy/81OUHs/qb0pbswkj/UK/LRWdtNbmzI+fUZm9jWZ+0rZuopybIZCRn5dDV0kx/fz+5ubnDn2lmSxWP4y1eKpWK+fPnU11djdlsvrzA0Pm/g3sQFn9u/MEoYmD+x6Xf2DwSpBqPHwfJkC/oknY0mgkaNV1CImpDLG1NDdhsNgpGUVD6VatAEHDs3xf12uTkZObOncvZs2dxOp2YtCYG3APRW76d+L0U4Fz6T+MPRpMgPdsVL8Koht09DXWEggHSp0Ux5NZmSttAHh8/IfoMRhav2upqfD5fWFWyfig2MI5XXlxcTGZmJvv27cPv9w/ThlGx7wegTZKyVMaD3gg5y+Hia2GHh7LPxgY6Bz2DmL1mspudxEyb2O4aRuZ8/tICVFY2kn6sW7GSoNmM52Jl1GsXLFiATqcb9srHLQoSRTj5BGQugvzVkZ8PQZ8Cn9oBGRO3RRPFzWXIk4sgqRBqRhTbzN2dDLS3UjA/ssKr3d7Ogl4pn1gzbxKGPCsHi9OFKIphRg0kTQ73qdOEnJG8NUhbMrVazaFDhy5v1E7+CfQmKLn98oPJXQmxmWGLV+vFcsRQiJxZkV5Jq72VGR1yBI0G9fTpl7/3KCRn59A9MAhI9MEQpIrH5ZIC5DgpYwsXLkQQBE6cODF+FocowvHfgWnGSIBzPMz/hMSlnn5y+NB4/DhAi72FwmYfMcXFwx2QrgRBEEjJyqGjWxrnaEOuSExEM2sWjn3Rg3EAixYtIhgMUl5ejklrit6v1GuXfufpd0Dy+MU6AMx+UErNqx+JN3RUSwYmfUwant1nZ8AzQFq9ZcL0GUCMVoshOYXWjk4UCgX5+SP6PJrZs5EnJGDfuy/qtYIgsG7dOux2O+Xl5cMeeUT8p69WykpZ8gWJQrkcyu6G/hqpUGhozjWVCIIsouCr0dKIPChiqO2a8A4EICEtHZlcQWN7B5mZmSSMej50y5dJC3aUGBCMeOVNTU20trZK8Z9o73LLERiok+I+Nwg3lyEHKN4sBRQuRX8bTkk5vwULohhyRzvFbUGU2dlhbd2uhOTsXIJaAzKZLKKST79yBaLfLynsRUFMTAyLFi2ipqYGmVP680ZsQc3N0tZz3sdAcfnIO4IAheukCP8lb62l/CzKGHXECy6KIu32dnKanGjmzEYYVUwxkTl7ZApiVKoIjQ396lUE+/vxVEWnGuLi4igtLZWoKLn0okQ88M0HobdS8savFJiLz5LSL889OxwMG48ft3qtOFwWUhoG0C6Y2A5kCMk5uVi8fpKSksJecJDoFU9FxbC40liYTCaysrI4derU+BkNZ58BjxVWfPnKgylcJ1FK5SPNrztrq0hMz4ygz1psLcQ7RDQ9lkkZNYCkzBwG3B4KRhXDwKUF+5ZbJPnicdQQc3NzMRqNnDt3DqNGiv84/WMcmrN/BZkC5j4S9R5hmH4Ho+kVURSpO36EzOllKNXhFaON1kZye0DweCe1u5YrlMRmZOHw+igtDd/ZKBITJfniyyzYCxYsQKvVcuDAAVI0KdEN+em/SNk5ZfdMeFzXGjefIZ92q7RVbZCCYQ2njpOcnUucMfwF9wf9dDk6SWuwop2ENw6SRx7QGojX6yJEdDTz5yNotTgPjt81aPHixSgUCk4fP41eqY/cgp7+i2TMJrqCF64Hr00qMEDix7PKZoY1zwApoi53uIltHUS36PIFImORnJVLQGfAmJQ4TCUNYUgt0nlg/GYTS5Yswev10lsvGbMIo3bsd9J2e+b9ExtQya3g6IHeysvy4y22FvK6Qe7xo100MX58CImZOfjVWrKiiEPpb7kF4LINNhYsWMDg4CCCWRpTmBa7KEr5xBnzpX+uBLlS+tvUvA1uM2IoRGdNVVRapcnaxPQ2aYGbSJ3AaKhNaQRlcooKI3cI+rVrpO5QZ6KXoguCwOzZs2lvb0fvl7ztsN854INzz0nO1njxgNEwpEL2Uqh8XbpXUwPmrg5KVkTSE+32dso6pOdSM4E04tFQGqXfd+zuGsCwbh2e8+fx90SPcahUKhYuXEh9fT2JQmKkNLVrUBr/7A9L8gM3CDefIc9aLEXDa97GZbPSUV0ZoQoHkm6xaSCEyuae9MOujU8gpNGhEyKpBJlKhW7pUux7942bVqjT6ZgzZw4VFRWYYkxjHnavVMU47VaIy4x6fQTyV4Mgh/pdWLq7sPR0jcuPl7aKCOLlBZSiQRmfgKhSE6uIfCQUSUmS53IZo5aZmUl6ejrnTp9DI9eEc4nWDqh9W8ohnoC2CAB5l17mxn2X58ftrZQOG7XJeeSBGC3I5MSrI3cuMSUlKEwmHPvH99ZKS0vRaDR0VEs1B2F0UsdpaQcyL7oiYVTM/rDkpFS9RXP5GTxOR9Tfudkm8eOCRj0p+gzArYgBUSTFEClhoF+2DEGpxLEnMmNoCLNmzUIQBNytUjA77Heu2SZlWc37+MQHVHa39Hfqq6Xq0D5kcgVFiyP1kNrsbczuUqHMykJpmvjuGiCg1kEoSGJcZOB1SKffPqaYbzSGAqTyXikYHiZNXf4cBL03lFaBm9GQyxVQvAlqd9B05gSiGKJgQWRVW5u9jbJW6QWfaABsCO0dHSAICHZL1M8NG9YT6OrCM6rCbyxmzZpFMBgky5MVvh2rfEPiQhd8cuIDUsdJmS31u2mpkLylnFmRL3irrZUZLSKoY9Bcevgmiu4+6eGUOyOzFkDK2HGfP09gHO1mQRBYsmQJAwMDFAWLwhevs38DMTQ5oxafJcVDGvfRdlFq4TZeoHN6q4gyLxdFcvLE7w/0OxwQCiFzRhZpCIKAfvVqKavBF11fQ6lUMnv2bBprG9GFdOFzPvMUKHUw40MTH1DaHNClQNMBzr2zFW1cPIULIxfkZmszMzoVaOfOnRR9BmD1+pB53bj6Ij1QmU6HdukS7Hv3juukGAwGCgsL6arrAnEMhXbmrxCbIdFEE8V0SWwudOE1ao4cIG/u/LDes0Not7dR0OKb9O4awO4PInc7MXdGtnhT5eejys3FsWt8Q56SkkJqair2Vuk5Gf6dRVHaXWcuBNP4Gk7XAzefIQdp6+YepOHgdvQJiZjyIqP2bfY2ZjWJyFJNqKJsqS6HlhYpW8LdEV1z2bBmDSgU2N95Z9x7ZGZmotfribfGh1Mrp5+UUu/y10xqTBSug65zNJ8+RmyKkYS0SCEsafG6RP9coeptLJqampAj4uhsi/q5fs0tEApdlk8sLS1FrVaT5kgbecFDQTj7NOTfAokTVAccQv4tiE2HKd+5lcSMrAh+HKDV0kxpO+gWTo5KAmhsaiYm6MfSHv131t+ympDTednuUAsWLCAUCjHdM33EI/faoeJlmHEPxFy5OGkYggC5K7DWHKPx7Clmrt0UQZ8BdPc0kN7tm7SDEgqF6OnrR+F1RTRcGIJh7Vr8ra34xpFzBZgzZw4uhwuje1TapblFojvnPgyyyOYW4yI2DbKW0H5sGw7zICXLI2kVURTxtbSgdfgnXAg0BI/Hg8VuR+5yRDRQAWnBNqxfh/PEiaipl0OYMWMG1l4rOr9u5H1uOSLlwt9gbxxuVkNeuI4AKpqr6yhYsDhq1L7dKnmnhmXLJ1T1NhotLS3EaTV4LOawnn9DkMfFoVu8GNs774zruchkMkpKSpAPyOl39Evn9dVAy2Hph59gpsEwCtfjDcpprawgZ9bcqHPq66wnu09EfwXdjbEQRZGmpiYSdVrM7W2EoqTRqWfMQJGehn3HjnHvo1AoKCwsRGfRjfDFjXuloqfJbLeHkH8LdYMa+ttaWXLvh6PO2Vtdg8YrTjh/fAhOp5Pe3l6S9dqwPq2joVuyROoOdSlfPBqSk5PJzc0l3ZJOr/OSp3bhFfA7r27OuSs53y4gALPWb4r4WBRFtFWtEn02wdqIIfT29uLz+YjXaCJaGg5hKDZgvwy9Mm3aNNRqNYWuQrqdl5QFz10SO5v78KTGBEDReqqb7ShjYsLko4dg8VrIbZR46ckGd9vb2xFFkZiAN0LVdAiG9eshELgsdThEr2Q6M0c88tNPQkzsDQ1yDuHmNOTqOFr1S/EHQlHTDgE8lZXoPVxRIW0sAoEA7e3tZKZLHu9YJcQhGDZswN/Sire2LurnANOnT4cgJDmTsHqt0jZMppS0FwAxFKK7vjaswcG4SJ3NYXMJPq+fmWs3Rj1FWS7l4F5JCW8sBgcHsdvtZGVkEPD7sHR3RZwjCAKxGzfhPHw4TAFyLIqLixH8An6zX1q8Tj8lBTlLbhs+p7O2elhP43IQs5dxtD+bhHgN05atjPxcFDFUSTuIiebMD2GoEjUjIx3LJZ2ZsZBptRLVsGv3ZRtRz5gxA6VXidV8qfr1zF8hpUTacl+C1+Wi8cxJgoHAZccVyFzGBUsq+QWpw12qRqPf3U9+s4eQQoZm9uVFo8ZiaM7paanjGnJlairq0lIce8ZvRK1QKJg5cyYmh4lea68kgnb2b1CwNkKCwGkxExiHmhpCIGMJdbZkCqflhLWyG0K7vZ3ZTSLB5HhUeZPb1bW2tiIIAqlGI73N0XcZ6lmzkKckX5Ynj4+PJysri2xntrTbdPRJOfBzHgLV+JLJ1ws3pSH3uV0cbNSilvvJyk6Jeo7+vPSj6ZZMLujX2dlJIBCgqEQKIo2/iq8DQcB+mUauubm5yFVy0l3p9NrbpHS66bdLhQHAkZee5Zl//SqPf/IjPPdv3+TIi8/gcUSv+upqrONsXyJzUgZJG5NjO4Tkqm78agXqssnxdUN6EtMupWeNO+dNGxH9/st6qIWFhSBAsjMZ62CDFACb/aBU6ANUHznA89/7Js9+52s8+52vUXvsUNQdAEDdhWr6vTqWZtmHGwOMhsVroaDJg8cUjzI1kna5HNra2pDJZBQUl0hNJtqi0yuxGzfh7+i4bDxkKAddNiBD7L4AHaekeIAgIIoi1Yf38+RXP8erP/4+T//zF2m9cH7ce9U19OAKqpiTFqUyFimltqRNJFiUG7Un6eXQ1taGTqcjM68Al9WC0xI93qFfuxb3uXMEBgaifg4SvSITZbg73dJvbOuABZ8AoKepgX1//SN/+doX+N1jj/Dnrzw2HOeIhuZ+AU9IyXRT9LTHNkszM5tFlEsnpikzGq2traSmpmLKzaevpTFqLYQgk2FYuw7ngQOERklRj8WMGTOI9cXS19cnpVmG/LBAKuhrOH2c33/hUX732CP8/nMf46/f+MfLzvla46Yy5B5vN729O9n6y58yYHZxe0Y1iq5TEeeJokhm9SDWrIRJB8CG+PGi6dPRxMZF5dUAFMnJaObPuyxPLpfLycjPIM2VRk/lG+CxDPNpbRfPc+yVFyhcuJQFt99NKBjg2Msv8PS3vkhnbbhsbDAQYOcfHkev17AiIbomh9VrpajJi7M0B0ExucZPLS0t6PV68qaXIggy+lqj70I0s2ejMJmwbR+fXtFqtRiMBtJcafSWPw2hwHCQs/LgXrb98n9IL57Omkc/i8tm5c2f/4i//fOXIjxEMRTi6EvPkRCnZlrghCQwNgat1mamt4qIcybYjWgU2traSE9PJzVfMsLj0SuGdWtBocA2RpN7NBISElDqlSQ6E7Gf+D3IVTDrIzgtZl76r++w9Zc/RRefwLpPfYGAz8uL//kvvPmLH2MfkALMNlsFoZBkQMp3vk28Tk6O+0RUQamOrloKu0AzyVRLkIxadnb2cGPi8bxyw/p1IIqX9VDT09MhBhSDCqnQKz4bpt2Ky2bl+X/7Jufe2YYuIZHlH34EhVLJ3//jX9j31z8Oe+cORw1W6xnMXR0ceO5pNCqR7ED05gvWc6fReyD5lvWTmm8wGKS9vf3SnPPwud1Y+6LLChjWryPkcuE8ejTq5yBVhYqI+Dq8cOpJyFsFKcX4vR52/ek3KFUqCuYvJnfOAgJ+H3//z3/l8N+fGXZURDFIZ+eLhEKX35VdDW6qVm8N9T+hu/tNerszWfvxr5Bz+nPQcjQiM6Df0klRaxDzbcXj3Gl8tLS0YDQa0WnUGGMFeiuOQPc6KSo9xhuI3biRnh/8EF9z87gB1ZLpJbRWt9J48QQrE/MhdxVuu41tj/8vCalpbPnHr6JSS55VZ201W3/5U174939myUc2Ep/rxuUrx2I5R0AVx9qPf4eYPe9Aw+6IMt+2pnIyB8B27+S22yAZtaysLFQxauITDfQfeREW5YbL6nLJc9m4EcsLLxB0OJHro28ps/OzsR+103pxB8XZSyFlGhf372b7b39BVulM7vnmv6FUq5mz6Tbqjh9hz5O/55l/+QrzH05DkdCIXEzGNSDDE2hn1aYHkFXslAJL0zaHfU/3hVNke0AxyZz5QCBAR0cHixYtIjbZiComhr4Dz0KyFWY9EPY7y+PjpXTT7Tswfv3r43qESVlJuKvddFb+ldhZD4AuieN/+T3tVRdZ+8nPMXvDFmQyOWW3rOPUm69w4tUXaTpzkgUfKcGtfg6FLBlzdQ6dtU5WrZqF0LMPBuqlauZRcB85iiIEpg23RR3HeLDb7VgsFhYtWkRKurR76Tv6KrlijTTnUTuemGnTUOZkY9++g4QHHoh6P0EQ0Bg1xHXE4bYfQbPxP0Emp/ydbQR8Xh7939+QlCnRLPNvvYv9zzzJ6a2vU3v8CHPvWIZT82uCQSc9pzJwO9K4a/MM5PV/kBbsMfos8hPnCQkQv2L8frvR0N3dTSAQIDs7m6R+qc9s7/E3iF+8QqK+RsWptIsXI9PpsO/aheFSnGAs9Ho9oYQQMb0q8LdJmvLA6a2v4xgc4MP//iMyp0tcus/jZvcTv+XYy8/RdrGcGRsW4455Hpv9DHK5BpPpChXdk8RN5ZEHe2/BbVZQcGsPhStKIGuhJNg+Bp1H96AKgnrJ5F7wYDA47LXw5pcwuivo77cT/O0KqXvN+b+HnW9YL3kItsvQK3NK5uAX/PRYNDD/UURBYMfvfonLauW2L35z2IiDVIr9yI//j6IVRTg1P6Oz/7d0t5zCYw2QvbqP9PnTIGW61BZuDAYOS4GaxGWTe9htNhsWi4XsjFR4459I8dXTP2CHv9wKzzwA3eGUQuymjYg+37g6JACll0SemhxaWPhp/B4Pu574DVnTZ3DPP//bcNWeTCZn2tKVfPx/fk3+opl45DsZ7Gylt/0sftVRCm5rI3PtSlCooSkyW8ZzXKquTV8ZPWYwHrq7u6XUUKUF4ak7SJb10t/aBK9+Fl7+lFSNOXrOm4folejNiAFy8nJQiApqQymw9J8IBvxUHdpP4cIlzN10+zA1pFTFsPRDD/Loz35L9uwyrMEX8Vo0WLucGIpPM/fTvUy7ba1006bI4Jvm+EUcWhmGuZNLwxvix7Nsp9H8ahp6hZe+0zvgtc/BS58IE2UTBIHYTZtxHj8+bropgDHLiDqkpjomE+Y+TMDn49w7W8mbu2DYiIOkC77+U5/n/u/+N3FpcfR5/hevw4O1RYdpQRsr/iGN9CUbpBTVtsiK6eTyVjpz9MjH9NG8ElpbL+nUuypI2v0PCIj0bX8cfrsUnvuwVNNxCTKVCv3q1Th270G8TBxDk6ZB7ddi0RXAtFtxWsyceP0lChcuHTbiACq1hi3/8FU2f+ErBJTn6LR/C/PAOWyV8wlY8se9/9XimhhyQRA2C4JQIwhCvSAI37oW94yGgEfA17yFGE0s5eWfwps1B3ouSm3DRsF55AgBGaQsvYyATRT09PTg8/nIMR+Cs09jmruWEDL6F31PKkJ69bEwnRdlejrqGTOwvzO+IdfGaBnU9uDy5xCa/RB1xw/TcOoYqz76aIRaI4Bap6d4fRwyuQKj6kdk6P8Ho/pbKFQaKqu+Tih7kfSwj+GVg6fLcaghc15kUPByGH7BT/4XnHma5OI5WPxa/Ku/C23H4PerJIGvS9DMnSsFhnaMTykVphfgUDjopwjK7qGp/DQBr5clH3owajBLGxtH2R1JyBQihuBnKMr8A3NK30CpMlDf/BPE7MXQEBl8iz1WRadJiS57cgGw4Tkf+CqYm0kpnEEfaYhrviMFsH63QtLTvgTDunVSuumO8emV6bl5BAnSqpkHxhIaT5/EY7cxYxw6IM5oouwuDSpdgEDnLWQm/ICZZX9ArhKpH3gaDOnQHC5gJQaDpFV00VKWhCCfRIofklGTy+Wknfwh5K7AmFdIn24WbPhPqTLxb/eGdSkybNoIwSCOy9AreWkSbVmbtBY0CVQe3IvLamHB7fdGPT+ztISCLZ2oYwV8LZtRWT9CZvqn6Te/zgX7C1Jpf0u4rGzAbMbU5mRgdlbUe15pzgmxemJ3fh1l9gIS01LpNa6Btd+RJDJe+mSYSJlh40aCZjOuU+Onm5qSpFhPZeadIFdy5MVnCPp9rHzo0ajnp86MIW1ZA3pDCRrHl3B2ZKDSXPsK0HdtyAVBkAO/BrYApcCDgiBMnrScAOZtuYN7vvo/zJ71R/wBC+XKQ4iIEau44kwltRkCmcYrCBWNwdAKnt3wNCz9R4y3fx2AXm0ZPPoWpM2WfvyOM8PXGDZswFNRMW6JL301eFVtIGroc4aoPXYYbVw887bcGfX0UMhPd8+bJKesZ+aK+5mz6TYW3vZxppf8FzbbOZqTPVK5/igdZ1EU0Z9vpDFXjSZmchH01tZWFEKINHcNPPIqxnWfBFGkN/1Sey7jdMljG5C03QW5nNgNG3EcOEDI5Yp6z5jGfZg1nXh9RnyBILXHDqOJjSNzevQgbDDopqPjbyQnr2PV/V9l2tIVpGSWkp//FQbNh+nLyYS+KnCOBN8Cg4MY6wZonTO5ICdIhjxeGcCgFOGx/SQvugOvy4W97FH45A6Jm37mfrB1ApfolSVLsG3fMW72SkbzQQbUA5j9Ujn4hX070SckkjM7utyq09lAW9uTpKXdx53/+DjzNt+J0bSO3JzPM2g+jLmwTDLko77PXV6O1hnEOn9yz/XQnDNUDhQyEe76DSkzljLY20dg0efhQ09I79Cftwwbc3VpKcqsrMvGQ/J6T+JQOOgIpiGGQpze+hrG3IKIZidDqK37b6zW05SV/Q93fOEX3P7lbzGt5Nvk532FvsE9WPNKIwy59eB+ZCIEFkW/53gQRZHW1hay/fUShXLfExgLptPba4VV34AtP4Hqt+CVzw47RfpVKxHU6svGvXJs5/HJfNSJaQy0t1Kx+x1mb7yVxPSMqOf39mxFLtezZNlLrLzvH3n4hz8n5ZIW/rXEtfDIFwH1oig2iqLoA54HIrs/XCMIMhmxsTOZVvzv2H3NDCSpoXVEKztgNqNv6qWxSE+MPGZS926tryIeK3Fz74aN/0W8KQ2VRkNvU4OUYvTgC5Kw0bMfHpZYNayTtsHjljXv+U80Cimo1dLcTPP5M+TNWTCuYt3g4CH8/gHSUsNzU02m20k13U2zey82vSKMUvJUVmLod9ExJ1Iz5Epoa24gQ+xAXPQIFKzBlCcZiZ7GOtAmwkeekeQBnn9oWKjMsHEjoseD42AUyVNRhAM/IaDuBlFGfV0djWdOUrhwCbJxvMiurlfw+81kZ38m7HhG+kPo9SXUCqcIyoC2kabIjr37kIlgXzI5n0EURVqbm8jy1xFa9EnQJQ+/WP2tzRJd99GXwOeS+ooGpOBc7OZN+Nvbo0uehoIoj/0Wm6aHoEtGd1srTedOM33V2qjZNqIoUlv7feRyDYUF3wifc8ZHiVGZaIjvR3T2SgUnl2DevZOADGRLJker+P1+uro6yXJfgJVfg/gsUnLypJaGHW0w8z746IsSJ//q5yAUkuiVzZtwHjtG0GKJclMPpnMv0qvpxT7go+HMSQY72lhw+91R4wihUIDu7tdJS70Xkymc38/K+gQKRRwt6XLJSfKNOAj9+3Zh00DczMnNeWBgAKfTJc35zschPpuU3HwcA/24bFZY/Bhs+A+4+IrUGBkp3VS/cgX2nTujK3321ZBes4N+dT9d3WbO7tiKXKVkyb0fiToGUQzS17+b5KRbkMkmZ4smi2thyDOA0eWA7ZeOhUEQhM8KgnBKEIRTfWP65F0NTKY7UKlSaM9NkQKel+A6ehRBBMvM7MtcHQlpBW9i2rRDtE4vQERaNIy5BfQ0Sd4oBpPUHDnolYy5146qoABVTg72aCW+Haeh6k0SjZl4FV5qLl7A63SSP2/8nOeu7ldQKhNJSorkuqdN+3cUyjia8xLCeHL79h0EZeBcOrmyfJ/PR3dvH1l55zmkeB2nsx59YhK6+AR6Gi91QUrIhfv/Av118MpjUu/TBfORxcZGT0Ns3Asdp9Ea9YRkIcpPnsDvcVMcRT8DpIe9te0JYmNnEx8X/neRyRQUF/873sAAzTmGsMVr8J236Y2D2BmzJzVni8WCw+UmXdfJoZjttLT+keRsSbN6OIvDWAJ3/UpaOHZKLfj069aBXB6dXqnZBoMNCAmSZ3dk1zuIodC4tIrFeopB82Hy876MShWeVSWXq8nN/QeswTYGEpRhPLlt7x6qswTSU6Onn46HzrYWQiGRLH2A/pLZtHc8S0pOXvicC9ZIwbva7XD4FwAYNm6CQAD77ihOyv4fobZ34tTZEAMih7a+gT4pmeKl0ak9m72cYNBBUnJkRbNCoSMz8xH6hHacMSHczdKCLYZCBI6e5HyeQFZ8zqTm3HLxJBAiZ8ZiqVkJYMwZk62z/Euw7ItSp66zz1ya80YCfX24z5WH31AUYfu3SCeGfnU/HpuHpgvlZJfNilCnHILVdg6/f4Dk5ElIFlwlrluwUxTFP4iiuEAUxQUpKdFzvycDmUxFRvpHGNC6sfefZc0Pt9Nj82B7+22sehmKGZMTExoc6Eep60JlGqSu/ddUXPgHAgE7xtx8+lqaRnKdU6bBA3+VPKVXHkMQRfRDJb5jC2V2fR+0SaTkrqQvpo/2zk5kcnlUHXEAv99Gf/8uTKbbkckiS+wVCgNpqffSHw/21kP8cncdVZ1WrNvfpiJHIC19clk6HS0NKFVOhEwzwZCbCxe/QijkxZRfOGLIQRLt2vxDqQfjrn9DUCjQr1wpSZ6O9lxEEfb/FGIzMKXNxqq20tbWhlqnJ6ssejZNX98u3O4WsrM/HdWTS4hfiDZuCy0ZMQw2HMDtCxJyOvEcPc7JIoGsuMkt2G2VJ5HLfQTm2PEHzDQ1PY5MGSDOaJJ2XkOY8SFJU/v47+D831EkJKBbvBj7zl3hN3T0Sh5dUiFxaRn4FX7q6+tJKy4hMT26KJp58AggIy0tug5Levr9KFUZ1ObGUn5kOx5/EF97BzS2crpQIMswOb649eir6HSD2BfLKL/wOWpqvotCb0ehiqGvZVSRzKLPQtm9sOc/oekA6hllKDMysI1dvDpOw+H/g7mPoDRKMY/Onh5mr9uMfJzU18HBI4BAYsLSqJ9nZT6CTFDRkqXlL88+Q2OfA291NTKzjfI8gUzDBAXmLqGl+gDLlr3AheTtHD22kXPnPoHWKAU3+0YXBq37nqT5v/Wr0HVeqmxVKiPplZpt0LCHuNX/jF0nvecDDifZM+aMO4b+vl0IgoKkpFsmNfarwbUw5B3A6Ccr89Kx9xx9oS2ERIHuVDkmRyU/e+0M9v0HOFwikhE3yYf99E5S02pRoKOw4J/p79/FyVP3kJirJ+D1Yu7sHDk5/xbY9APJsO3/EYZ168HvxzFa5rVup5RpsfLrpMZm0x/TjzcQxDitjBhtdB67t3cboZAvglYZgi8Q4tfHpyEKImaDhed3HuHXf9xGoK2dYyUCObGT81raTm4nJ6ccQZAzrfg/cDgqaWj4X4x5hQx2tIdXnC76rPTPkV/Bsd+hv2W11Hm94lLuryhKPQtbj8DKr5GiT6VX0YMrECR3weJxX/COjmdQq7MwpkSWowOEQiJ/PrsQUQYOalj4/Tf53x/8FcHn52SxQLZhkob89A5Kp+/Hr3Azrfj7BIMOWtuewFRQTHfjmCrdDf8hdbF57fNQvRX96lX4mpslowpSReNLn5SC7ff/BZM+FatiAJdMQdnq8b0wi/UkBv10FIroOiyCoORA1524DQIxsrPc/evDNL0lcdVnCiZvyHtdh5g7bysusZeCgm8ik6no6n6B5Oyc8FxyQYA7fymJlb30SYT+OgybN+E8emykZ6vfA699AQxpsOm/SYlLwS93E9TFRlVqHMLg4CEMhhkoldEbfyiVSTS61tBljGGOsoLP/PUUA7ulAHdNkYYkddKE5yuGQthltcgVAdJS70GnK8RiPUV3/zPok5LDKzzlCrjvSdAkwgsPI1cE0C1bin20/IbfAzv+BVJKEBZ9Bk2iBlEIEdQayJk1Z9xx9PXvIiF+MUrlOO3uriGuhSE/CRQJgpAnCIIK+AjwxjW472XxRnknDz5RT71lFp2paj5X0MnA9nfA5+NwqeyyD7soijRX9OMwj6QftdUfIimpnfTMB8nJ+Sxz5/wNn28An0Z6gXqbG8JvsvgxSVdi/4/RcBF5ctJIhL/8eXj+o9ILseCTZOozcQpSEEmXO35brq7uV9FqCzEYogd2XjvbwWsX1Lj92XSmqvmPeXaSTh8iJJNxsvjKhryvzU5zxSXdF1Gks/sMRlMjWVmPkpn5UTIyPirRHNluRDEULqwkCLD5R1JHo+3fQmd0gEw2IvO6/ydw9HHJ2C/4JEatEdFrA0EgoTD67igUCmC1nSU5eQ1SzDwSb57vZGd9EoQS6Dcq+cdpdrTHD+HRxVCddWVPLegP0Vo5QMAXBFsnrthy4hO7KS7+HpmZD2M03kpb218wFqRj6+uV+NMhyJXw4HOSKuHfP44uXdp9DLdD2/MfUsOM238OqTOlOdsHQK4gqTg6dx8K+bBazxIXPz699sqZDp44PQ2CWnypPmS2dk49/ybmFB3u9AQMqisLcQUDIcSQSKC3FqWpHdGXzLKle8jNeQxjyha6ul4lJSeD3pbm8ABujAEeeFr67yfWE1uWLDkpQ3Pe/2OpUfQdvwR1HCadCb93kKBWT0Jm9EU1EHBgs50jMTE6vQbw0x01/PbUIhAgKauTjn4rTc+/QmdBPIb07MtWdHocfkKhkTmYa49iSOpEFkiipOS/mDXzN6Sm3k1//26MeZmRpfr6FGmXbeuEv95F7OIy/J2dUjzEa4e3vyk1g9n8I5ArSdenIwYchPTxYWmWo+F0NuJyNZKcMrkipqvFuy4IEkUxIAjCPwI7ADnwZ1EUx0+4vQYIhUR+sbOWktRY7lr1daouPEJx7Ek2dCcxEGugLt11WUN+4s0mTm1rRhAgZ2YyZQsNODVN6ATIzPooAAkJizCZ7qCr6xWU6kJ6mhqYvuKWkZsIAtz2M+ivR3jjHzCYcrHt3U1o67eQnfyttF27/ylQqskwZBA36IdgkKA6evsrt7sNq/UUBfnfiPrQBkMiv93fwIyMWOaUfoqauu8xl5PIOxupy07Brh0Yd86iKHLxYCcHX6glFBTJLk1k5dIBFJnNIKrIzZV6aBYVfhuz+Thm/58Q5In0NNaTMW2UEZbJ4UN/gqfuRPHOl9Bk5mB/6++klFng4P/C7Idg849BEDBpTST0+8AAPlV0DXKns45g0EVc7Jyon3v8QX6yvYay9DhyMz9Es/BHHkmop6qnijPTE0jWydAoopepe5x+Lh7s4Pyedlw2H8YcAysXHSIxuwkCC8nMeAiAvNx/orf3bVQp0s6ip6GOvLmjjKw6Dh55BZ6+B9WRb6BIyMH52p9J0B+Tmmcv+KTUWBmpIbHB7AUj9PYPkJkdubDa7RcJhTwkxEevcei2evj+mxdZkJtERvIauoS3+OsKC53P17NnXhyZ+vEXrvrTvVQe6sDa58Y+4CHOqGX2rF3oEiwYYu5HqZS43IyMh+jueZ3YXDOe3TbsA33hui7GEvj0bnjuI6iPfw2ZJgvXa38kruvnUnOTuQ9DkWSgUnWpeM0VkJJBR2dnWPu4IVgsJxDFAIkJ0Q3530+28Zt9DTy4aB5GzRy6Us/wuLeFpFc7+dviJLL047/LDWd72f77C8jkArHJGhLTdBhi3iI2u5fkpJEuRSbjHXR0PEtSkY+mM+34fV6UqlEByKyF8MBT8MYX0Vv/E2Qp2J/6HzTGQ+DshcWfl+IIQKrWhNzSTjApFqfTiT5K/9D+fiklOSX5+hjya8KRi6K4TRTFYlEUC0RR/O9rcc/LYX9tH439Th5bnU9aylJ0IQOdylZmdFezN9cEwvjbz8pDnZza1kzxYhNzN+XQ02zjzb9UEpfWjixUhEYzssKmJK8nFHKTPltD31iPHCT9kI+/AXf9BkOBipDbh+uNP0tNZx95FXTSdjBNl0ZmvwZ5wE3v4GDUcVksUgplyjgr+NaKLpr6nfzDLYWkpt2FLCTQZTtFqqOf3TkaktWmqEYt4A+y9+lq9j9bQ2ZJAsvvK6Sr0cpLb14gIbkTreae4e2uXK6hqPBb+P19JBUK9DbVR9wPpQYefB7K7kGf7sbbOoB/x88lXek7fzVcLZcck0RmjxKFEKKjIzrTZrVJudqx4xjyvx5tpsPi5l9unU5a9kdAEOhs2o7O72Z/jkCqNnrK12CXk6f/9QjHXmskOVPP8vsKMfe42FUhZRplZo80BdbrizGZbsfi2YZcE6S7IYoImjoOHn4FIXMe+oQ+nBebEc88B4UbJC/tEpKViSQOBpEJI7nqY2GxSF2eonnkoijyL69W4AuG+Ol9s0nNfZiQTMBa+TLKUIC9xSGMmkj5YgCH2cuuv1Ri7XNjyotj7sYcXDYfpy/97fOnPTR8blzcfHS6IkJaKV+6q6428oYJOfDJHQhFa9EmWnGeuSjJLaz5jpS6dwlJQQNxg5IuTHNzc9SxDQweQiaLIS4uUrkwGBL5xa5a5uck8J93lZGW/QgBhYzcnm2EZHJ2FzpJjImeYuq2+9j/bA1JGXrmrM8iMV1HR42Zelc3ggB5hR8dPjc+fgExManIEyS9lajaOiW3wT+cQDHvLrTJHhwHD0JSgbSobRn5nU0uPTE2SRNpSNJjLPr6d2HQl6FWR/+9rjVuqsrOITxxqAlTbAy3zkxDEAQyDGtxGGQETUEq5sZC0IBSiOShWy4MsO/ZGrJLE1n7seksvbuAj/9wGTlFrxIT4wJfeNZkQsIS5HI98fkuepoaoucQK2Jg7kfR/sdBZJoY7Orb4dafSNvyS5AFIX1Ajajx09PTg9sdKYhks1cgl+vQaiM9mlBI5Dd76yk06tlUlopCYcAoL6IvyU1ILePkdBHRF51D3P1UFVVHulhway63/cNs5qzP5qPfXYCxaBt+vwqVL7wEOyFhKTJZDCklhAc8R0OXBPf+HsO/vgqAo/A7Es8oH9ngKc0+1H45qlg57e3thKKkc9ls5SiViWGL5xAsLh+P76nnlmkpLC9MRqvNIzYYR6+uHzQaKvLd+DyJUYd3ZnsLIREe+NeF3PHFOcxZn819XylFk1KF16tF7gmnevJy/4lQyEv2kgDdDVGMGoAmHj65A90//IqQX4b71rekDCbFiFen7HEjFwW0sSra29uj3sZiPYVWm0fMmGwVgBNNg+yp7uXrG6eRm6wjLn4BMUElffI2xMwsGrIcuF3ROeaTbzUihkTu+vJcNn6qjKX3FHDXpzPQGmvxuvXIAyOUniAIZKQ/iNtXiy7VT1f9OEqU6lh48Hl0dzyK36nAd+szsPobYWp/MV1uhFAInUE97pzN5iPExy9CHiUdeE91L51WD59ZmYdCLiMhbQvyIPTLGwkumo9TF8Buj54Vsv+5WrzuABs+WcrSewrZ8thM7nhATmxaDW5nEir5qCbiggyT8Ta8wQrkquC4SojokuBDf0K74V68VhXBe5+HzPBFV9vhReZxoVDIoxpyr68fq/XsdaNV4CY05DXddg7V9/Oxpbko5dLwUwok3Wf/cjW+4iABTzIH68Kb5toHPWz/4wWSMnRs+uwM5JeulZsb0WScxOvVULU1n47akeo2mUxFUtJqZIYWvE4HtnEEdwBkajW6VbfgONcYYfDbKiuQBwXMidIqHu2Bt9kuYDCUIQiRP8nu6l6qu+184ZYCZDKJdkkz3UNIJSDekYwv0UrPoIEeW7gcrsPspeF0L3M3ZLP4zvzha7WO86iTW7CaMzj9mgVz90gDXblcTULCEtQpfQy0t+H3ji+xqyosRJmRgeNsQ5gRB3A0SZ5gyCjD7XYzEEVJz2o9R1zsnKhU0u/2N+LwBvj2lhGja9IuxWMExeZpBLQOWnu0EX9r+6CHupM9lC5LIyVrhEuOdxxBn9yKw5zD/r/V4bSOxEd0ugLi4xdgyLLT3VA3vmStIKBbuQbk8hHOeBS8zT2IiMhMKvr6+vCMkScWxRAWyyni46ILXj17ohWDWsFHF+dc+joZyYECnPlBFJvmI8hEWnoiaarBLidVR7qYsTqD2OSRXVms+W3iktqx9xfzxv+dw+saqWJMTb0HmUxNxoIA3eMZcgC5At1tkmfrOn484mNfcy9+eQi1SUtHR0fEgu3xduN01pGYEF1O+uljLZhiY1g/XerxKZfHEGdLwj09SGij5ME3dUfOue5UDw1nell0ex5JGSPUhtD9HLFxPVjaZ7Ltd+cJ+kfGYzLdjkiApBKvVCdxGWg3PQCiiOtsZP/SYHM/Vr2XOFN81F3I4MBBQCTlOqQdDuGmM+RPHm4iRiHjoUUjXpzcb0TZIuCb7aPf34E8aGJfTbjRrT7aRcAbZPNnZ6JSjxgdX93rCIkOPM7ZxCXHsf0PF7D1j3jMKcnrEQU7WpM7PD0tCrQLFxLo6SHQFa7n3VJxDlEmUJ3UjkwmG64gHUIo5MfhqCQ2SpBTFEUe31tPVqKGO2ePbNM0/vnI+8E5y4FfdBD0JvPUkeawa6uOdCKKULYqnIKw17yIPMaPVj0LhVLOjj9ewO8bKflPSlyNKB9AafDQ2xxdIQ8utUO75RacR48SGmO0Omuq8KqhP94CRFINfr8Nl6ue2NjIPHCPP8gLJ1vZPCOVaakjxlg/MBuCMDhXWhQGLAZONIVTVef3tiMCs9eFU2vtVa+gUPpIMa4g4A2y+6kqxFEBssTEFcjU/Xi9/TgGx5dvlcfGopk1C+ehwxGf9dbVYYkN4omTiojGUkpOZx2BgJX4KLTKoNPH2xXd3Ds3A41qJPCrbcoCJfSWSc9UfWcMvWMW7GOvNaCIkbPg1tyw43UNbyOXB8jK24LT6uPQ30eMl1IZi8l0O9q0Dnpb6i6rk64qLESelITzWKTGj7mukZ5EL4HYIF6vl/7+cAdKSrWU/r5j0TLg5EBtHw8tykEhHzFFmup4QrHQkS39/S60KHB6R8bntvs48HwtxhwDczeM2s0FAzQOnkAQIL/wPjpqLBx7Y8TzNhhmotFkkzLdQ9cYhdGx0MyaBUolrlPh6qoBvx9bQyudSR7kiXJ6e3sjdth2+wVkMg16fcllv+Na4qYy5AMOL6+c7eDeeZkk6EbyrG3bd6Aul+FK9BLym8mLy2NfTd+wZyWKIrUnekgviicuJZxH7m3dhiBAXMIt3PaFWYQCIfY8XT18bVLSLQiCgvg8R2Tmyhho5swBwH3uXNjxzppKZOnxdPi6SE1NjTDkTmc9oZAXgyGyoOd0i5nyNguPrSoIe9jtuw+gviDDkWBBKYiUJOexs3JEJiAUEqk83EnW9ISIOTd0S3x8XuFtbPhEKQOdTg4+P0IpJCVJGjWxWY7x6ZVL0N9yC6LHg+tEuExCZ20V/jQtLcFWNBpNhCG32aSCi7i4yJz6HRe7Mbv8PLQoPFjo3leDulbAntAKiKgx8sKpkft63QEuHuygcF5KmGdKKEizXfI6y2Z9iOX3F9FWOcj5vSM7o8TEFSCIGDKc49Mrl6BbuQLPxYthglKhYJCu2mpcJgX9MZIxG7vzGuLH4+MjPfKXT7fjC4Z4aHH4nP07OpFbwSaXxMtCviS2VYw4Cl31FprK+5m3MQeNflTtgXOAHqGHYFDO3IUfYv7mHKqPddNUPlKMZzRuAZkfdZKZgXHa3YG0YOsWL8Z17FjYbsVltTDY0Y7NKMOqtUad8+DgYZTKxKhG7ZnjrShkAh9ZNLLohrxexG0DCAERp/MoMmR43XEcrBsZd9WRLjwOP2s/Nh3ZqHeC1qPYEj24XPEsWbeJ0pXplO9qpbfFNjwPk+kOlHG9WPob8LmjS0yAtMPWzJyJe4zuSldtFUGfj+4ULx69tKB2jk5NRgpoG/QlUTOxAsEoFaPXADeVIX/uRCu+QIhPLs8NO27fvYvY/mQQBEo1QZZkltBl9VDTIyXu97XasfS4KF5kCr+hz0mzv5dgUEFR0XriTVqW3lNAR42ZupOSUVQqY0mIX0xCgYeu+su/4OppxQhqNa6z54aP+X1eehobiMvLQkQkMS2R9vZ2AqM8ILtdypiIjY30yJ8+1oJBreDeeSNetSiK2HZsJ86SBDIoigmxIns6db0OOi2Sd9BWOYhj0EvpijEBQWs7vSoXPq+G4uKVZJclMX9TDlVHumirkrxbrTYXjSaXhHxv9IDnKGgXzAe5HNeZEf0Z+0A/tr5eNDmptNpbyczMjDDkVts5QCA2NrJQ6JljreQkaVlWMML7h5xO7Hv3ktgXhyD3k6kU2Vwyg20VXTgueWsXD3bg9wSZu3FMtkjHaRyxfjzuBIzGIspWppM7K5kjr9bT3y7RXbGGGSgUccRmuaIHPEdBv2IFiCLOwyPSEL1NDfi9HsTMOHp8PaSkpETM2WI5SYzKhFodvlsQRZHnTrQyPychbAfiqa3FV1NHYo8apcJMnEJFcXI6b50fMeTH32xCG6eK2IFQsw1/khefJwuNJo4Ft+aSlKln7zM1eBwSxRIftxBBUKDPcF2xY5N26RICfX1hvTzbKi8tLlmx9Ig9qNWRPLnZcpyEhCURlKHHH+Tvp9rYVJaKKXaEOnHs3w8DLuIG5GiEbrJjs4jTaHhnlJPScKYXY25sGKUC4K17HVmsk1BgDjKZjGX3FqKNVbHnr9UELxlQk+kOEETi8qxXfJ+1CxbgvngxTFOopaIcQSYjmGFgIEbauY2esyiGsDuqojplrQMulv5oD4fG0L7XAjeVIU+P1/DQ4myKTCMPe2BgAPfpMyTOvpWQN8QMTZBbS6Tt+t5qaRWvPd6DTCFQMG9M66ymgzjiwOVIJSsrF4DSlRkYcwwceql+mFNMTlmPUu9koOfcuN1sALpdAey5xTTuP8pjT5/isadPsXX3CULBABnTpLxiRaKCYDBI1yj6xWavQKEwoNGEG6B+h5dtFV18aF4mWtUIHeStqsLf0oqx9DZCQZEyTZAtpZIg1dBDcvFgBxqDkrzZ4UG1UM12xHgPoUA+qksNmhfclktsspqDf68bfuCTklajTbXT03z5F7w/IGBPy+bC7mN8/cVy/um5s7z5jkQ7GIuKcAVcJKUm0d/fH7YFtdnOodMVRhTF1PXYOdE8yIOLsoc5fQD7nr2IbjfpBWsQRZH5egX3zinA4w9xtGGAYCDE+T3tZExLICU7/J6eyjdQxtlQKqSFUhAE1j5SQoxGwYHnaxBFEUGQk5i4nLhszxU9cnt2IX6dgT1/fZ1vv3KeH2yrYutOac663Ax6Xb1kZmYO94sEyVhbrKeIi18QERM42jhAY78zjC4EsL35FsjlZKQuQibA8jgdd8zK4FSLmU6LG5fNR0etmbIV6Shjwr2/gZqXUWlcaHUSNy1XyFj/6HS8Tj8Hnpd+U4VCR1zsXOKy3eMHPIfGcumd+u3PX+CrL5zjt/saOHb4OEq1Bn1mGj3uHjIyMsKMmt9vxuvtikoZvnW+C4vLz0eXhM/ZvuMd5ElJpGoL0SqCzItLYW2Jkb3VvQSCIWz9bnpb7BTMi6wO7+jehyBA0iVuOkajYNWD0xjocHD2HWnHodcVoVbnYMhyXJZe8QdDdGVPg0CA//7pi3zyLyd58nATDefLMeUVkBKfRpe3i5SUlLA5u90tBIMODIbIOoKnjjZjdvooMF771nA3lSG/d14mP7gn/KFw7N0LokjsHfcx4FYxLSZIWYqJ6Wmx7KvpJRQMUXuqh5yyJNS68I7klqq3UOpc6PUjL5dMJnDLR0vw2H0ce13yPoZyQbWp/fS1NEeMKxAM8ccDjaz9n/1sDSZjaGuktcvM2VYLT7++D4CLPonfduslYzb6x7fZKjAYZkR4LS+cbMMfFHl4SbiBt23fAXI5cfd+ml6XwExNiLLUBIyGGPbX9eG0emmuGKBkaRpyRfg9m2t2oFR5SUoZ0XJRKOUsv68Ic5eTC/slXjI5aTWCLIgvVBM14Glx+fjR29Ws/sk+DimMGFrqOFLXx/HGAbbvOUZQpsRvyJX+pomysDmLoojVei5q2uGzJ1pRygXumx+eL2176y0UqanEbvgkAx6BGTF+5uckoFHKOVTXR/P5fpwWL3PWR6ad1rUeRiYLkZo2kkWgMahYdEc+XfVWmsqlxS8xYTlyjQdzf0XUgGdNt50vPneW5T/dx5H4fIwNFeyq6uWvR5s5feIMHnU8MQYTfa4+0jPT8Xg8w0Fej6cdr7c7Kq3y7PFW4jRKbps1InomhkJYt76FbvkyEovvwRGAMpWb22dJz9G2ii6aK/pBhLw5Y4yaz0WjU3p2s7NHBKqSMw3M35JL3aleuuotACQkLicmwUVvS/RWdlVdNu56/BCrn6mjW5tAan0Fhxv6+fH2amrOnaNXk0ac2ki3s5vMzEx6e3vxXmqXZrdLCp36MUZNFEWePNxEoVHP0vyRXZcYDOI4dAj9qlXEmjYQEmG63MqGUhNml5/TLWYazkrOWcHcMU6ZuYUWhR+/X0Vx8UiQMX9OCgXzjJza2jwc1E9KWoEhw0NnXfRyl6ouG5t/cYAHj7oIIqCuPk9Tv5P/fKOC7sY6TnsTSIwx0eXoiliw7XZJVM1gCFf6dHgD/P1kG7fOTCMtbnIt+iaCm8qQR4N9126UGRnETJtGTTAelQys5qOsmZbCqRYztRX9uG0+pi0ek4sqilzskR7egsJwNbaUbAMzbsnkwoEOepptqNXpaNXFxGY76KgOV7+72GnljscP89/bqlhemMRDn7gVhRji5bUJHP7WWjYmuXBrk/jVQScCcnoCPcTFxQ1vu0MhLw5HdYTXEgyJPHu8lWUFSRQaR7aQoihi274d3ZIlKIxpVPs1xMpDOJ01rCxK4XB9P5WHOxFDIqXLx+Sw+j00eSQ+r7j4jrCP8mYnk1WayIk3m3DbfcTHL0ZANZzJMRpnWs2s/Mlefn+ggU1lJu748Ab0Phf7PlrE0W+vY6HGyoA2lR+8JVE1DrUDQRCGYwNudzOBgCWiEMjjD/Ly6XY2laWSrB9JVQuYzTgOHyb21lsRTNOp9ChIVnghZGZRXiIH6/tpPt9PjE5BdumYlMTBJrqULkIhGcXF4b9z6fI0ElK1HHmlnmAgNByQi0nqx9ITHrA+3WLmQ789wt6aXh5eksPau28h0Wnh6OfmUvn9TRSJfbSrUnn+iIugGESVIO12hhYvq1WinsYKg/XZvey42M298zJQK0e8am9tLYHOLmI3bUbMXsJFt5xUuYXsRAUlqQZ2V/XSVN6PIVFNcuaYgpSmA5jjBdyuOHJzw79v7oZstLEqjr4mpdMmJi5DEMAn1OEdI0u8v7aP+393lG6bh29vKSFz7Srmmps49s9rOPblxST5zdTLjbx9zo3dZyclTVpQhjhjh0My5IYx/Pj+2j4udtr47Mr8sN2Ju7yckNWKfvUqOpNLafTKSA42s6o4BZVcxs5KKVMlJdsQEfcJ1b2DJz6I35uN0RhOoa78cBFyhcDxS45ZYsIyZIoglsEzYVpBoijyt2Mt3PXrw9g9AX7w8FJUJSU8oBpg79dv4cUPZaIQg1SGkjlaG6Tb2U1GZgZut5vBS/UhdkclgqBEpwsXNnvpVBt2b4BPjKGFrxVuakMedDhxHjmCYf06BEHgVECFPyjS3/4Ka0qMBEMix/e2odIoyJk5Js+6v5Y+XYhgUEFOTqRi2+I789EYVBx9VQpwJhtXoTO56agdaajabfXw8T+fYMDh5XcPz+ePH1tA/i2SKJD73DkUAgS6mli4eB6PLssn6ItjT301WVlZwy+4w1GLKPoxjOHH99X00mFx88gYb9xbVYW/tRXD5k2IosgRj/Ty93e9xariZCwuPxWHO8kojifeNEbAvuUQ9liRgF9PQkK4wJYgCKy4v4iAN8ix1xuRy9XExy0mNttJe+WIt9Yy4OQzT50iUadi+5dW8YuPzCVrqZQm5j5fQdDrxt/bzq3rl/LAnJmIooy36y6Smpo6vHhZreeAyEDn1vNd2DwBHlo8Zrv9zk4IBIi7/TZ8IT/HvQoEAQb69rCyKJmmXieNFf3klCWFB78AqrfiS/Dh92WgVseHfSSTy1j2oUKsvW4uHOhAo8lEpczEkOkMW7zOtJr5+J9PkKxXsfMrq/neHWVkLJTG7qmswtzVQcjt4KG7bqEoUaoDeKu5jpiYmOHf2e6oRCZTRbzgv95bT0iEjy3NDTvuPCIpPepWLKcj5KXco0AuhDBbTrKyKJnyZjNtVYPkzk6OoGqcldsQ4tzIhDIUYzRulJeyW7rqrbReHCTWMAtB0GDIcIRRSs+daOWTfzlJVqKW1/5hOY+tLsC4ejkhqxVPdTW2FomK+cePbkYelHLbz1nDA552RyUqlTFC4fE3+xpIi1Nz99zw+I1j/wGQy9EtW0a9EKDCLUfAiizYwdKCJA5VdNPTZItKq9Re3I1K7cZoilRX1MXFMGtdFg1n++hvd5CQsBgQUCX1M9gl7UBFUeTbr1TwndcusDQ/iW1fWsndczOIXbQAd3k5os9HsEtaCP7tU7fidsUSEAPYFOG7Tbv9InpdcZjoXSgk8pcjzczNjmdudvQ6gHeLm9qQOw8dQvT50K9bx4BlgF6XD5dNRp/lMHMyY0mMUWCrt1IwLwWFMpxDtJx/G1mcA4VQiEymjLh3jEbB3PXZdNSY6Wm2kZiwDEEuYh48jiiK+IMh/um5M7h8QZ79zGI2z0hFEAQUiYkoc7JxnTvHYGc7HqeDjGml/NvtpaSo06g3t9LiVWOz2bBardiGAp1jPPKnj7VgNMSwvjTcu7C9vR3kcgzr13Pk9BFU1nR8TpH+7m2sLEohPiTgHvBGbreBgfM7UMdbiImZHTV3OzFNx8y1mVQe7sTc7STFtJaYOB8djZJRMTt9fOLJkwRFkScfXTgcmIspLERQq/FcqKCrrhZRDJFTOoMf3DMXtZDMoZZqRF0S7e3tBINBbLZy5HIdOt1IgwRfIMRv9zeQn6IL225Lv/NBlOnpxEyfzunq0xi75hHyQX/7q6wsSiE1KOBzBsidGVlk03FhB2q9Db0+ekl8zowkMksSOLm1Ca/LT3LKavTpLrrqJW/ybKuZjz9xgiS9iuc+u4TUOCkwpy6V8ts9Fy/SUS1t0cvmzeGJByWv/29nTmFIMg4vXg57NTpdETLZiGFtG3Tx7PFWHliQSV5yOG/qPHIEVUEBSpOJC90XUHbNRAxCf/c2VhSlkO6RdGTGxkAQRU50VCOTBykqji6+VroindhkNcdeb0BAQXzcQilb51Lw763znXz7lQpWFCbz4ueWDlMB2oUSLeQ+e472ygsoYmJYumg2P7pLcoR+uOc0sfEJo5yUagyG8OKr0y2DnGga5NMr81GNof0c+/ejnTcPeWws9eYGuszSM9zXt5P1pSa0vVJaZwSt4vdQ45U84mnTojdsmb02C5VazqmtTSiVCWhiCjFkuIZ58jfKO3n+ZBuPrc7nyUcXDu8ItQsWIHo8uC/9zgnpmSybkcs31y8B4Bs7TqNUqYbpFbv9YgSVtLeml+YBF59YPrlOVpPBTW3I7bt3I09I4JzXy69+8Su2tG2hq3s5fpxUXnyDjYlxyIJQtMAUce2FqjPodFbSMsavvipbmU6MVsGZHS3ExS0AUYYithtbXw//s6OGk81mfnjvTAqN4cE17Zw5uM+V036JhkmfVopMJrAqfxpqjZVnL0hcXVtbG3ZbBQpFPGr1CCd8ocPK/to+HlyUPVz0BEPZKjvQLVlCTWcnO9/ayeK+xXT1zcLqb2Ww6wLLLykr5swYswMJBaluLUep9JKRuWHcOc/bmINMLnB+TztJidIL6vKW43S5eezp07Sb3dLOI2VUEYZCgbq0FPf5CjpqKhEEGWlFJchlAnNTC9HqLLxc6yEQCNDd3Y3VdpZYw8yw9Kw/HmykvtfBd28rDVtkxFAI18lTaBctorm5mZ2v7CTfXkDPQCG99rOkaQPMksUgAlljaBXR3sORQBBBgGnTorcfEwSBZR8qxOsKcPadVpKTViFXhujrOoTbF+TzfztDgk7F859dEsZtymNjUWZn46mspO1iBdq4eBLSMkjSxpMYk4RWP8CRLpHe3l48Hg92RyV6fbhR+8WuOhDgi+vCvfSQz4fr1Cm0S5Zw8eJFTrx8gkLLdAYtGbS1v828zFimBRWElALpRfFh1/raztBhkCOKMvLyoitKyhUyFt2RT3+bg/ozvSSnrCImzk936xlsHj/ff7OSmRlx/OnjC9DHjCw8CpMJeUIC3ppq2isrSC8qQa5QUpwsxSVUajs1DhVtbW0Egx5J314fbtR+s7eBBK2SBxeFxzL8PT14q6vxL1vKyy+/zMD2AWa2r8PpiKem+jlWFSYxzSdHSFBF7DTt1XtwxQURQ1ri4qILtKl1SmatHfHKU4yr0JrcdNZX0GPz8N3XLjA3O55vbioJC7Jr50u7TefJk3TWVA0nLSzJlpwQL4P0h3S0trXh9fXg9w9GBDr/fLiJ1Fg1W2ZMvpvVRHHTGnLR55MaG6xZw979+4lPj+d00mkylTGIIRnl558kw9OHBxGLfsw0PTaakdLO0tMjt2JDUGkUzFidQeO5Puz9oNWUYshwsn33UX5/oJGHl2Rz15xIvQ/NnDkE+/tpP3sKTWwcCWkSV51lyMSPjcJcIwFR4NTFemz2C8TGzhw2XjaPny88c4bUWDWPLssNu6+/owN/ayvBVSt544030CRp2Je2j0S/HkGAXbv+j7yQhUFZCFls+C7DVr2PuktGPitz/IozbayK4kWpVB/rQghlIBcS0aVa+esbBznRPMhP75/FwtzI0njNzJl4qqroqL5IcnYOMVrpZcuNy0apHkTUSgvL6YtVOBy1GGJH0rNaB1z8cncdW2aksqYk3Nvy1tcTtFgYnD6dZ599FrlOzs6MnRicWgSZn6f/9m8U4KBbJaLShNMI1ftfJpDoBVGP0Th+I+6ULAN5s5K5eKiTWP1CEGUElbX84Z3zdNs8/O8Ds6MGqNSlpbgrK2m7eJ7sGSO7nPz4PPLTnPSEDIiiSGXNGfz+wbBc6toeO6+cbefRZbkR93afOUvI8//YO+vwOM5z7f9mZpm0u2JmssyYOHHQYW6aFFJIOSmftqfcntOenvOVmRnSNmmoYU4T24kpZsmWxcwrLfPOfH+MJGstsGRRfE7u6/Ifye7OzKuZed4H7ud+wrzkdHD//fcT08ZoKD+Jza1D0Hh58B/fp1SO0WNkvEN5DAd2PI3VPoRJX4VGMz07onxTJs4cM3sfacFhV5ktgdBBvv/MSYb8Ef77lpVJTgSom56+qhLviRMMdraTt0K9h5mmTAQErltnoDNiJBgM0t1zEEWJY52wedX3eXmhfoA7txYnsbAA/Dt2oAAvJhKcPHmSYeswoUoPJpeEqGln19N/IycBXVMs6dC+XaTY+7Hbz5tRJXHN5aNe+ZOtOJ0XIEoKrqE9fO7Bo0QTMt+/fS2SmPx7TWoqupIS+vbtIRzwj48rzDarhembNhppDxvo6+tnZFjtjZhY6NzXOswrTS7etbVw0t9zIXHOGvLA/v3IPh+v5apGUr9aT5utjSvWn4dzJEJmRgeu4AF6zP3saErmbe574k8o9hAouknV5dOx5rJ8NBqRg892kJF5Kcb0MK/u3kVNjo2vXD+1VKlxnZo/7Tl5nJyK6vGHK8eiXuvnb8wmoLFx+GQ9fn8DtlHOqaIofO6Bo/S4Q/z07euSmp4AQgcPIgsCz3rVBgdprYTH5OH6lVvQRWSys3vwC4fpMbrY3XyqOzGRSPDgk//C6hxEp809o5DP2svziUdlju/qITX1Qiy5QXa/spdt5WlTblwAhlWrSITVjrncqlN/lwJbAYGYnx+8ew1B9Ow8shNFiY57p4qi8NVHa9GIAl+9YfLfM7h/P0OpqTzS2YHdbse32ofBbuD6snUIskJaajdBwxE6tS6O93rHfxeLxXjmSAepzm6ysq6cUvpgIlZdkkfYH6PtaAijvhprboDnnt/J1TVZU25cAIaaFYwM9hNwj1AwYVJRcUoxPcF2vnjbhcgKPLTrSQCsE7zT7z5zEotOw90XT5Y1DuzeTU9eHicHB9m2bRv7CveRn1/AJdklo3+zw/jth6mXhxnwnWIUxeNx9nYMYbEOk5139YzrFUWBTdcV4xkMMdTqRFBs6ByD/HPnMd55XiGr8+xTr7mqmr6eTlAU8qvVdKBW0lJoKySgdPHWi9cC8MDO0TWPeqeeUIxvPH4Cs07i3Vsnq0L6X36ZwZoV9AwNcenll7LDvoOi6go2a00IgoLHtxOP8yi7A8NE4qcowLIsUzc8jF4fIjv7shnXPO6VHxwkEawGRUTRtfHqiS6+cE31pPTWGIxr19I7Kuk85pFbdBasWismk4+L1lUhoPDCwecBAYtZ3bAPd7p53x/3U5hq4o7Nk9e8kDh3DfnOXQzk5tIwOMiFF15IZ6yTTFMmppo3kT0so9H6cRhDWKwNnNz3EoFAAEVR2LFjB08ec5Fh78bhPG/K/PhEGK06qi/IoWFvH0btJgQBiqwn+MI11eg1yXl3RVFQ4jL68nJiVgtenzfJqOVaVCPoSwxw4ZpKcs1dQJx93ZnUdnv4/SttPFXbx2evrmRD4WTjETx0iNoNG+h1ubjxxhvpineRb81HU3YZWQNhLJZ2DEikW+rZuXs/0aiaU3zxhRfoDiewO/rIzr7+jH/b1FwLeVUOjr3UTWraRWgMCYo1dXz6ysrp/06rV+E2GYhFI+SvOJXvHxv8IGsGqS4rJses5oybR7LZ2TjIL19u4aWTg3zqysopvd7g/teo27gBg9HIu9/9bmp9taxKW4VUfg1Od4wMRzeSbCDfUs8L+46N/+7VnS+hmDxImti0ipITkVfpwJ5povblLjJzLseYHqYg1sTnr5m+zdqwYgUuqxp5nG7IvVEv68qM2NMySderRmAgnMfzx/v56N8O8uzxfj54UcmkzRrU/HjT+nXYbDbWb11Pb7CXSmclhopbsfri5KW7AIEV5jqeflX1AuPxOHtefh5tipornqol/nQUr0nDaNVS/2ofKbYtWHIDVCe6Z7zPhuoqXHotkkZDVtmpgnm5o5yGkQbuuHQVgighxeqJJvQ0D6tdmVf/cAe7W1x8/tpq7KbkNcvRKIFXd3N81SpsNhuWYgsKCqX2Uqxlb0MfSVCQ7iGhDbBZd5Rn96iEg+HhYf5xzx8QbOoG7nCcd8Y1r7k8H61e4ti/XOg0FVhyA1zqmEwqSFpzVSUuQcZkSyEl81R6JNuSTW+gl/dfNZp+8R9jJJLN/vYwRzrdvPN3e3GYdfz9A+eRYprZzswX89YjXy4Ejh7l0OZN2O12LrjgAn71zK8oTikGo520jKtBfonsFA9+6yrsA038+Mc/pri4mPr6etbojqAxBXA6z3zjAdZuz6d2RzdNe+3EUzRkZw6wPiP5T6fEZYb+fJz4UIjMT6zDX1UOUW+SnvfYEIQuXxfrytcx1Kt6zd99EVxPqiJM26sz+MC2yQqIAIO1dZxYUc369eupqamhvaldHSaRXk2Wz0SHkKDAEaY2mIqu6wDf+c5RioqKaGxsZHPWqyAokwbfToc1l+fzxM+O0tekXktpRicrT2fBAMGjg3hf6MB+YylD6Q4ESBpll29Tc6Ed3g5WVpZy/PggcVnig3/uJ6GokdKavBTeff7kF0lRFPrqaum74AIu27SJhDZBu7edm8tuhtz1pHklXM4hii1ZHIn2M3zkeb7d+Comk4mRYRdrC48hIM040GAMgiiw8qJcdt3fyICrBkGAdemNFKZOXnPCHyXWE0BKLWbIYsRqNJMygfJWkqL+zVrcLaysLMU1/CAjYQfv+9FhABwmLe+5oIgPXDT5Pifcbnq7uuirWcH2zZtpdKvsmSpHFWSsI3W3gjevjZqMDexyHaZ599P8tvMYvb29JBIJ1lY0oxEt41HeTJA0IpXnZXP0hU6i1WuxmJ5je1oTKcZTRkeJyfj39aLLt6LLt6KvrMJlMZDuTEejO2WQyx3lPN/+PBE5QklxEdaU5+gL5vKxn+8hLiuUppt56O6trMm3T7qO0Guv0Wuz0g9ct20bbb42AMrsZQipa3D+878JZTayvuTfeaH5VQ48/wiBrpM0NjYiKnHOr6xDp03HaCw645oNZi2lGzJoPjCAI3stjrR6rgq6k/LiiqIwct9J5HAC59sq0VdUMmI2kJ2RnZS6yTarhtxiseBwOMi393PCU8lnfrMHrSSQnWLk3g+eR4594Xnjp+Oc9MiVWIzjwQBuvZ6rrroKjUZDq7dVNeSAdsMHiA0UYS84yJXXXsMj0ZUY7OnU19ezKTPB6mxVxS0j/cpZnc+WZqR4TRp1rw7R6srHmhugZ0JLsyIrDN/fQKRhhMRIGO+z7Qw7bIiyTFruKSpdqiEVg2Sg299NXl4eZsswYOaxT97KD9+ylo9cWsr3bptaDTDh99OgyCAIXHTRRUQSEdq97RTbikEUsax4H1F3Juml+1i9/RaeilRSUFZNd3c3OaYYmZmdGI2Fk4pP06GwJhV7polXnvQy4E3FmuMbZzUAyMEYrr/XM/y3euL9QXwvdjDotJEqC0mj7PIseQgIdPo6yc/Px2wZQSMV8Pv3nM8Dd53PEx+/kH/cdX6SjswYoq2tNDpTEYF169ZRN6SyQ1amrQRRwmnahpzQkrtqH9bV2zmWyKWyqpqMjAzKzAGcaX04HOeh0Uw9zON0VJ2fhUYr8vRjEpGYDlvaACO9p3Q0/K900/M/e+n9xl6Gfl/LyD/aGbFaSBeSN/Wx57DV20pRURFm8zCp1nLefX4hv79zI/u+tJ3/uKEmiTc+hsDefTSUl6OVJDZs2MDJEfU5q3BWgNaITrwIQVAoWNlNrPRi+gS19rB582Zuz2oj1dmHI/WCaScunY4VF2Qjywo7dqgbkV46ljTiz7ezC89jLQz+/Aj9332NYJOC32gkXUr2MCscFSgoNLubKS4uxmgYYFXBWm5Yk8OHLi7hiY9vm9KIA/he3kHdqlXYrFbWrVtHk7sJjahRnQBrJkr4QkRNlLLVLnqztzKkz6K+vp6V1RV8VLwHTWpkdM3T58cnouq8LGKRBK8czkcQQfYkK1kGD/QTPDxIuH6Ywd/WErE5COm0pGmSI4lscza9/l4URSE/34Fe6+WmjZfyletXsK08nb8vkRGHc9SQR5qaaCosJMNkoqqqiqHQEIFYgCJbEQAhx1qGOrYhGV2UObpQ9FZ60zfxbx/7KFf7/kpvng2ncxsm0+zpQGUbM5BDCTyh9RgcUboaTinBeZ5sJXRkENvVRZi3ZON/tQdvSEu6L4Q8QW9DEARyLDl0+7uxWq2kpHiJRjPJsZu4eV0u/35V1bQhWPDwYVqLiih0OrHb7dQO1RKTY6zNWAvAcOG7cbdvQzQ3c0FxhH7FhjttFZ/55Md4l3APbpsqGDTbh10QBUovyEJ0xwjG1mHJDtJxQpX0THgj9P3wIKFjQ9iuKMR2VSGRZg+CxkHqgAt5Qiu+TtKRbc6mw9dBeno6FssI8Wg6F1eks7HISU1OyqQU1Ri8e/bSWlJMeWEhVquVWpfKZ69JVesaA8ab8bZvQTG8wEVVKRyM5WAp38ztb7qJm/gbYaNCWtrMedOJ0Ju0pK904hiQiSgrseYFaD82tuYo7qda0dj1pFxXgu2qQuRgnFRzEamDyQqMWeYsDJKBVk8rubkZmExe5FgGX7tpJZdVZc5Y9Bp89VU6CgtYu3YtRqOR+uF60o3ppBlVmmFf9BZiQTvo/smFldk8EyrmohvewlUVRgo9jxLVKbNKq4zBkWXGnGvC3mslFs/BkuOlo05N1yR8UXwvdWGocuJ4czmSXU9w5wAltrU4RrxJx6kY7UtoGGkgP9+ERhMjHk3nB29ZyxeuqZ5y0xpDS2MjQ6mpbLvoIjQaDc3uZopsRWhH0579gVtJxAzIiSfZVpnF4558PvCxf+OW9A5E/QgxMYbDPvVQ56mQU2ZHsmjQdhSiyFriUuO4RHXcE8H9WAu6Yhup76wm1uPH988+9KIJhyeQdJzilGJ8MR/9wX6ys1W9H41YwPsuLOb3d24id4mMOJyjhrxj7z7cDgdr16hMgVaPmoMc84Q6jo/g61kPMrja72FbeTovNwxi69uFy+QjIsXIy3vnTKeYhBNCjAgK+TGVSzvY9xKKouDf3YN/VzeWrTlYL84j5eoiMIisslxElidIpDG5KzLXkku3vxtZjmM0DuN2q8wGRVbUNvMJOtkT0bRvPwGLhfXnqw/sgX5VlW19hjrwtr0pjrdjMygQ6/8ra/LtPF8/gNj6EoO2MAiQmTG7tMoYXo2FSaCQw/mIGoX+LnU+p/+VHmRflIy712C7vADLlmwUUaHctpEMT4DwiRNJx8m35dPp7SQed6HVhnENn/LYDz3Xwe6Hm5N0o8dw/MgRono9m7apNMhjQ8coTiken1nZPFKFv2UrihClUHoSq17DM3V98MqPGbKqOjmpqdOzkqbCQV0cLQKpwlb0thidJ9WB2r5dXZBQcN5eiXVbLtYL85BFmXxzJbbWDhJ+//gxREGkKKWIVk8riUQHgqAwNHiqU3Wg3csjPzw0Ltg1EYe7OpEliS2j9/nk8EnVGx9FW18O0ab1eCO1rM9SG1R2neyDpz7PcI7K+Bmjjc4WtfoETlkk1XoR5uwQLQdVMTDvCx0ocZmU64oxb8wi/QOrieliZBmLMTe1JskY5FpyMWlMNIw0YDCobfQD/ROaYhIyx17qouXwYNK5lXico3odZtSoC6DJ3USZXaX3KYpCe086wkA2Ls8rXFimNtQcbHPBvl/RuqIISTKTljaH+yzACX2Cwpgei3E9tnw/DXt2qSmVBxtBVnC+uQJjTRpp765B8ClszbgJQ3uyQuTKNDV9dWzoGA6HKtDX05O8YcmykqQDv1g4Jw35seYmRFlmzYWq59HkVhX6xgx5e60LvS4FpyfOwOAzXFyRRr83gn/PH+nKT8GgzyUt9ZJZn09RFH6xs5V+m0igPh1BMYGxnb6GBrwvdKAvs5NyvdpuLBo09KZ24tBnkZ9xPpGGZAGmHEsO3b5ugsEWBCHO8LAFt9vNid29PPHzo/zx86/w0HcPcPRfXeMCVgC1vT3o4nFWjErlHug/QJm9DPtot2J7rYsUezZOT4K+3ofYXpVOb2crsZe+Q3+WGbOpHIsluZtzJkTiCf52qBN/igZXbT4oAuFEHdFAGP++Pow1qehGBzeIJi2Dul4KLTVYFT3hY8eSjlVgLaDD1zHest3fpyUUCtHX6uHVh5o4+Ew7D33vIF7XKU9eURTqwmFsskxJSQmKonBs8Bir0tRCaiIu03rURW5eDSmeOL3tv+Ly6jTidY/Cv76BqzAfk6kUk2n2bIHhQJT7m/uJ2jQMHlfz117/fuK+MIE9vRjXpKMZlccVtCJDQi/5lir0skD4eLJ0Q3FKMa2eVvx+teGks1MtSHqHQjz+0yN01Y/wxM+OJA0AD3V20pCeTpHJRFpaGrFEjGZPs5ofBwLuCAOdIbITlegjCbwtX6UkzYR48I/Ig3X0F+ViNBZgNE4/4/J0NA34eMTlRtEIeNqrESWFvp4XifYHCOzrxbwlC236qTrBUKyLTFMhwrCb+OApoywK4njBMxCoR1EEmprCKIrCYIeP+7/5GjvubeDZ39Yx3HvKsx2sraUvI4OVWVloNBqCsSDd/m5K7SqbZ6jTT9AbJy2cR0wIYw39DrtJS/i1vzIijTBo9FBY+CF0uqknZE2FlxsG2RENIQBycDP6lBjNR54huL+fSMMIKdcWo0lV77Om2EJT4BDphjzirV0osVNGudJZiUbQqNFxvJlo1EZjYy/+kTBP//oYf/vPPfzq4y/xu0/vpKdxZJqrWRicc4Y8kUjQKMvkRyKYzapnt6d3DznmHDJNmciyQkedi8KVaWToVhISA6zv/xC/134TXDsZsSrk5r591jlEUNXpTvR6WXl+FtGQjEGzCWt+gLYn9yD7Y1i35SKMFksUReHQ8Wdwa12YKq4j0pAsA5tnycMX8zEwonrUAb+DEyfqOfh0O6l5FjZfX0wkGGfnfQ0cfFodIxUKBGgzGCjTaNBqtcTlOIcGDrEhU62W+0ci9DS5KV6XRZb5PEJSiOuiP+c5/b8THTmOxyqQmXlmtspEPH6klyF/lJotWXj6BSSlBHO2l+4nDqKE4lgmyOMm4jEOdzyPJEjoV1xD+LQopMBagDviZtB9WF1zwE5Hewcv/+0kZpuOy++sZqQvwD/+Z/+4lG7P0aMMOOysysxEFEX6g/24wq5xL6irfoRIME7phVXkWS4iJAZ5t/kPfEP+CcMZKxnR+eaUVgG1LT0Sl1mxOYu+kxZExYkxY4S+J2pRojK2S08ZyFg0QmP/frSCHim9akpD3uPvYcR7DEEw4vcbaW3u4PGfHkFOKFz5/hoiwThP/PwI0bAalh/buZOIwcDm0c26xdNCXI5T5VQNeetRtThccvv7KIqU4pG7+Wren7lp5A+cWFOGJ9ZGUdFH5rTm37/ShqAVKduUSfPuDFC0aO29DD58HEErYbv8VI0nGg7ROnAMjaBDdBQRqU9WD6xwVNAw0oDPfxxJysHrDbPzkWPc/83XCHiiXPqOKrR6iRf+eBx51Ek5vGcPCALrRiOQFo8aZZTb1SaptmNDIEDl+jeT1R+mretXfC/r12zu/QuNVRno9VkU5L93Tmv+1cst6B16MoptdB1SN+yIXIv7mRZ0RTbMW06JlzW/todebwuCICJacolOGO2ml/RUOCuoG6rD56tDqymhvb2dPU800Xp0CEeWmTWX5WO269lxXyOyPFmIbaFwzhnyk8dqCWu11IyyBGKJGHt793Jh7oUIgkB/i4dIME7BylSy1nyZ9OEE7aYGTDWDHCwoRBC05OTcNqdz/uGVNpxmHbdeU47RqsXfuxatKY7GM4Bo16EvP6Wf0NfUgM81iK4qBUEyEOlMnjZT6VSpXR1DuxBFHTZbJQf3H8YzGGLTdUVsuq6Yt311C8Vr0jj0XAchf5TDL71EQpJYW6W+0PXD9YTiITZmqoJIDfv6QIHKLVmkb/4GYkKhP/EwtWWpPLJCjVpmy1aBUXW6V1V1uiuvKFY3qchmTBlhIsd60eZa0BXaxr/fXX+ckUAviSwBbd4FRJqS5yGOMVdc7kPo9TnIspGDrx5nqNPPhbdXUHVeNrd/YRPmFD3P/KaWSCjO/h07EBMJNlysDrk4NqR6+StTVUPefHAAnUGioNpJxrafoI0JEH2EhmILr1WOoCjynFJJsYTMX3a3c2FZGpsvzAUEJHkj1twgiWM+DDWpaDNPpYR66k/Q42tC0YKu9MIpDbmCwrDnKGZzBSDwwoP78AyFuPbuVZRvzOSqD6zE1R3g2d/WISdkjrS0YPb7qRyNNOuHVUM5llppPTKILd2IM89OzrWPYIzrMJpexJ0v02dzU1h4NznZb571mocDUR480MUta3NZc0EO8YgGnbAae34EpS2M9ZJ8pAnDKmpffJb+YCsKoElfQfhEsiEvd5TjjXrxeGtJsalF9f07j1Kwwsnb/2MLKy7M4aK3VTDQ7uPgsx0oikJtXx/pQy4yV6mR1lh0PeaRtx0dIrPIhmnD9VRe9BAG2Ygh7zWkbA8+Q5TSkk8jSbPPRdd2e9jd4uI9FxRRfX42gy0WNGIuacUySiCB9aK8cacM4OgLzxC1qDReyVlK+GSy3O/K1JU0DR8jFGojNXW9yms/Uk/5xkyuuWsVW99UxgVvLsfV5ef4ruQBFAuJc86QH9r9KvpwmMoNam744MBBgvEgF+aOPvy7e9HoRApqUpEKzmfVrS0UF3+CQEaMeIYHZ9q1cwrD2l0Bnj/Rzx1bCjAZNJSuz6BjfxEgoM3pJ5gRTLrxJ/fsQpQ05Jw/2uQTNSblT9ekr0EjaHB7azGbK1mxYiVDI/1YsgRK1pzSR9lyUwmxSIKDT7dz+NgxUkbcFG5VO/DG8+OZ61EUhZN7+8gstmHPNKFxlFFluw2NtZBAjkKm5RAW6+o5FXYPtI9Q2+3lzq1FGK068qoc9J8oRRBAcbYTK1GSiqYth15D0mhIvbIcQTKS8FqS8qdjXPJgsBGrdQXZWTk0tzaSV+0YF0CyZ5rYfucKIsE4B55toW5oiPyBARw1amGzdqgWjaih0llJIiHTcmSQojVpSFoR0WAnN+NGhh06hvJFjrlWs3nT41MO6pgOT9X20ecN854LinBkmXFkmfB2rUDSJ4ha2jFdmCzz0HrkAEhgrE5FylhF+HiyUSu2FQMKkVAzKSk12MwOXL4+Ln93NTmjG3/hylQuemsF7bUu9jxzgu54nHK3G2m0K/bkyEkMkoFCayHRcJyukyOUjIpkiToLxSu+ht+ioaXETIPnPEpLPjXr9QLcs6edSFzmvRcWk1mSgtGqJexag9YeJGTowzJBPTMWjbDvkQfIrCpHl2dBm7eWcH1yLaTCUYFZVIhF+0hNXYfZaCWiHWHT9cXjEtLlGzMp25DB/sdbqT3QgBeoSMQRRNUU1Q/XoxN15FnzCHgiDLT7xjV0NLlbqNn8Z8IGLSfLLfgSxWRl3TynNf9mZwtmncRbNxdQtiEDUSOgBDaizxomJHoxVJ7q33D399Fx7DBVl16MJt2IlFpG5GRyqnRl2kocgpofz83bilajIyQMUnPhqb9d6fp0csrt7H2khXBgcfLl55QhDwQCNPf3U9jWjnmN2oCxs2snWlHLluwthAMxGvb1U7E5C/1ou7YgiJQUfxxj5nfp9OXQGrppTuf846ttaERhXBO8fFMmkYAFfbgcf/phjnb8a/y7iqLQuPcVClevxVyYBqKClFJApOFUqsGkNbEyrQZtvA+rdQV2rZqiSKmMJG0IqTkWqrZkcXDXCfrDYUoHB9Dlq57ta/2vUWAtIMOUwVCnn+GeAFXnTWhU2PItNl28A03uC3x2x38QsHx3Tmv+wytt2CZMJSpdl85Qcx5i3IInbT/7jz6W9P3WQ6+Rt2IV5uoMEGVESy7xCeOv8qx5aAUFIdaPxVKFKZJNXApSdrEpaUNIL7BSsjadva/sJyYIrJKk8c9rh2qpclShk3R0148QCcQpmzAopGDlf1BS/EmC9j/zk4PvpGF4smjYdFAUhd/vaqUo1cSlleoxS9am032kEBSBftsuDux5fPz7g+2tHH76Mco2nY95XSaCoCPhNyBHTuW7i1KKcEogyCEslmr0cTtxnY+SdckiVzXbcsguS2HPK/sQFIUa+6no7uTwSSocFUiiREfdMHJcoXjCZp+VeytW6yqCrOF7+2+jYzh5duRMCEbj/OGVVi6tTKcyy4ooChSuSqP/mJpq6DY/j899qiP66HNPEXCPsPW2OzCUOxAseUQakue5ljvKKdCpKRObbS2GhJO43kNafnLH5EVvq0Bv0vDCkzvRxONUjz7XsiLzfPvzbM3ZikbU0DaaSipafcrxsqdsoKT44yiKwJ/rbiQcm326oscd4vGjvbxlUwEpRi0Gs5b8aifD9eUgxWnVPYVv5NSaa//1LIIgsvKSK9AXpSCllhI53SNPW0meVh69tlWYlDRiphEyS05FrIIgcOHt5USCMfY/Mf0M3PngnDLkT92/AxkoC4XQONQHflf3LtZnrsekNXFyTx/xmMzKiye3kZ+/4mbuaf4Gv90jTj8l/TT4wjHuf62L61fnjI+jyi5JwebQYe5aSSSllc62fYz0qUarv1mlMVWcdyGCJKBJMyDaCyYxV87PqMYgxNEZS2ncOYJOtjHgmzwzcdP1xfiN7UgJgZWpqicmK3JSfvzk3j5ESaBsw2RhsK2l6YTkDF48Of1swtPR4w7xdF0fb91cMK6HUbIuHYukwTKwhmBmLS1H9jHQ1oKiKOx/7CGGuzspWacO55AcGkRrblKe3KgxstqWhoCCyVCJ96QBjailvnnyMION1xXh13Vh9ssUlKhGJSEnqHPVjefHmw4OoDVISSJZWm0KxcUf49KaTegkkaeO9c16za80udR26guLxxtDStalY49b0XsLieU38tpjDzHY0UYsGuGJH38Hg8XK5e+7G0O5AyQFbfb6pDFoeknPKpv6jOq1FUQHTCiCqiMyEYIgsPG6QnxiD/bhKM5qNX2mKAr1w/XjaZWWw4MYLFqySlMm/FZi44b7Wb/uHuKylkcPzz50/8f+TkaCMT586SkFyuLVaVhHMtGEHQTS63jw//0HAfcIsUiYfY88QMHK1eStWIm+zI4giCSChqQxaDadjRqzCQWQlHJiAyZkIT5ppqXRomPzTYV44r2kDQSxrlTv6+GBw/QH+7m6+GpkWeHIC504c8yTRroVFX0UU/4THOwr5anaZN34mfDHV9tQFCVJE7xoVRqWrlKEhBZf6mFO7HoJUGew1r70PMXrNmBNTUNXYEXQGIl0JFNNS1JKKDIIRAQz3gEDsstKgmjSBDBQ9XzKznOy4/AT1B+Zefbv2eCcMuSiVkEbTSFeqOZNe/29NHua2Za7DUVWOPZyF1klKaTnWyf9VhAE3ntBMXU93kmT16fD/a914Y/Eee8E+UlBFFiZb8U6qEYEtoIAL/7hVzz98x/wxE++gyhJlG7cAoCuyIFkLyB8Wji2yqq+jM1dIn0tXirKKunp6WFkJLmyHYx7iOhdGIL5CJUq7bHZ3Ywn4mFD5gZ1+tH+fopWpWGwTOafG7QSl1Zl8OjhHjyh2YV0v3pZfcgmtiwbLTpWZBoxD6xF0QRJKUiw+4G/8fgPv8WOe35P+ZatrLpcVdrT5dsRbbmEG5I3r61panpluDsdOS5QUbqC48ePEwgkc3M9kX4SmhCaaBlKidoV2+ZtIxALsDJtpZpWOTxI8eq0SdLEAFaDlgvKUnm6rm9WG7aiKPzg+QayUwzcvulUMTO9wEqxRYN5cB2ypRujU8tzv/kpL//5t7i6Orj6I5/CZEtB0Ijoi01IWWsmrbnKbEBWwNWWijbkxG5z8tJLLyHLyVRLvzKAIsUQElVIZaohP9B/AG/Uy9r0tYT8UdqPDVG0KjWpAxFAFLXkOkxsLnbyz8Pds1pzLCHzm52tbCx0JOnI5Fc7KdBrMI6swlToxTc8wAPf+DJ7HrqPoMfN+bfdAYC+0Aaigia9ehIrq8yoYTihp+1IAG3EDkBLS3LNBCBuGUYRE8jCKrSjMhZPtT6FQTJwaf6lNB8cYKQvyMZriyb1PgiCwPnlFRSlmrh3f+ekY08FXzjG3/d2cM2qbPKdp1g4BTVOCjQGDL4aLMU+dv39T9z7H59j171/JjAyzKrLVc2asZqQkrCR8J7i0EuiRLFBoi+u4/iuHowJNXpoOO3vAhCwthHTeVkMs3tOGfLrL9tAQauZ4/JKwoEYO7t3AnBh7oV01Y/gGQhN6Y2P4ZZ1udhNWn7/ypnDm3Aswe9fUR/2VXkpSZ+lhWJE3flIYjo5a3W0HT5A29FD2NIzufTdH8RoUTcSXa4FQWsi2pK8O2dIEWQFGo6KiJLARVeqhr+uLnn01I4dO9BJEhavk/39+cgJeTw/viFzA50nRqaefjQBH76kFG9YDaPPhNahAH/d28FbN+UnPeyKrJApKwT7ViAIWkoudNK0fw+Ne1/lojveww3/9gW0ejVi0eXZEfUWIk3tSceuMBkJy3D0oBetXmLbZeeTSCQ4fPhw0vf27t2LUdKgjWTRMKKmOcYKnavSVqlslUB88vzVCbhmZTZdIyEOdpyZ8rWraYgD7SN8+NKypMYkJSaTJQoEutcBChtuXUFvQz1HnnuKDdffQtEEGQJDRYa65oa2pGPnacL0xCTajvgx2fRsv+IyBgcHqa1NjkQOHDiASdAgynk0jaiG4O/1f8ems3Fl0ZW8+kAT8ajM2iuSB25MxE1rc2geDCQJh02HRw/30O0O8eFLkwW7hFCMNI1AcHAlshLg6k/chruvl33/vJ/C1evIq1LrFYJGRJdvQkpfQXgCc0VRFNJEH81hmYb9faTn2CksLGTPnj14Jxi/SCTCrl27MCsislBAU5eOuBzn2fZnuSjvIoySkdeebMORZZr2PguCwFs2FbCvdZiWwcl8/NNx3351Qs/p8hd6TxSLJBAZWYPG5Gfbu2/G5xpk/6MPYnE4KVmnEgo0aUYErYLkLEnavOJxPzYhRIMvwsm9vZSvyyUvL2+SIe/p6eHI0cOcd94WqtYsvC75OWXII3XHqGy8n2hCYt+jLezq3kW2OZuSlBKOvdyF0apNypueDqNO4o4tBTx7vJ8O18zphp+/1EzXSIhPXZHMvY67QgiDIXoSIPs3oE8d5O7f/IW7fvlnbvvyN1h71SmmhC5HDQnjQ9EkTykSasKrGAi2mMkus5ORlUZubm6SIR8cHOT48eOsNpmprr+fwUGZ/U+2caD/AJmmTHItuZzc24ferJmsPT4BNTkpXFWTye92tuI5Q2PCt5+uR6cR+cT2ZH3saLsXMRSnO6RHSqxFl9pNxZat3Pqlr7PpxluTPCbtqB5LrNuXdAyzPEJfTMPgySj5K5xkZ2dRUFDAgQMHxj1Ul8tFY2MjKwTIHjpM7QE/bceGeLLlSRx6B+lKNi/dU485RUdBzdSKhADXrFJHxX3jiRMzUr4UReEHzzWQk2Lg9o3JM0LDdS5EWaFrKBuNmIfG3k7J+k3kVFRz4VvflfRd7eiAjWjbqfyqLMexyEO0hiVajw1SvCaNFTUryMzM5KWXXiKRUBX8mpqaaG5upiocJN3fxOGdA3QOdfNCxwvcWn4rrqYQ9Xv6WHdlAak500sNXLsyG40o8MgZ0iuyrPCLl5upzLSO1wPGEDw4gAC0tVQhCDoESxM3fvqLOLJzJq3ZuDILyZpFuP5USjAc7kKjhOkNmOlv8VG+KZMbbriBeDzOww8/jCzLKIrCY489hsvlYmtnO6nxPl57qp3d7XsZDg9zTfE1NB8aZLgnwMbriiZFIBNx64ZcJFHgvtdm9srDsQR/eKWNTUUO1p4mExB4rQ9ZEmitV9/z7NU63vej33DjZ77EDZ/6AqKkbu6CKKDNs0xirvj99QhAZKiYaChBzbYcKioq6O3tpX50k1MUhaeeegqz2cwll1wy47WeLc4pQx46egxruI+aC7Oo3dFNY2MHF+ZcyECbj7ajQ1RfkIOknXlJ7zq/CEkQ+OOrbdN+p2XQzy9fauamtTlsLUsuTgWPqE0QclEKQ43VJBIBwvHjUx0GbZYZUECbmtQ84fMdB6kAs89JdpVqBGpqaujt7eXpp5+msbGRl156Ca1WS1V/H7liN1XnZXHgyTZaT/ax0bmZHfc20LhfLeyeac2f3F6BLxLnd7smh7hjONA+zFO1fXzoolIyrIbkNR8aQNCJxLLM+DrXEIl0cdmHbqdw1dpp1gyyV0EZNVayHCMQOIFIKVJQT8FK1Qhv3LiR4eFh6urqOHToEP/85z8RRZGytjZWiYdJzbPw5K+O0nZygPdVfoAnfnaMSDDOdR9dM2VaZQxWg5bPX1PFoQ43Dx3qnvZ7OxqHONjhnuSNAwQODSDZ9QQMWqLDmxhx7+G6T36Ct37tW2i0yWmssSahuOuURok/UI+gRAl6ipCjavFUFEUuvfRShoeHOXr0KK+++ip//etfycjIoLSujhXWDiLBOI987wgOfza3lryZl/52kpR0IxuvLZp2HQAOs44razL58+42mgZ8037vmbo+mgb83H1JafIAD0UheHAATYGVQNSIFN/IwODTFK1dz3t/+GuySpM3d0OFmv+P9Z4q8Hq9amu/0Keyhco3ZpKWlsbVV19Na2srr776Knv37qW2tpZLL7mE1IMHWZvnIuSLseefzdiFVC7IvmDcG5+q7jMRGVYDl1Vl8OCBLmKJyZ3BY/jmU/V0u0N8cnuyUyZHEoSODSGVOwj5MtAI+QwNvYgoSZRvOp+ciuQhFYbyNCRbDuH6U+/R2IQvfe9GRHOC7DI7GzduJCcnh3vvvZedO3dy9OhROjs7uXjVKjqvvIrA7t0zrutscE4Zcn1pCY63vIXzbilHMsLlx95D+r0X8sC3XkPSiNRsm1lnGyDTZuD61dn847VOfOHJHqqiKHzlkVr0WpEvXVc96bPg4QF0RTZy16Uz1FSKIOgYcr005bkErYhkE1XmymjxLxbzEg53YYqsBsCfpRbl1q5dS1lZGfv37+evf/0rdXV1bNy4EbH+JPqqSra9pQLFGmNj7U2UPHc5tTu6WX1ZHuffMlnP+nRUZ9u4dlUWv3+lDXcwOuWa/+fJetKtet6/LTnsU+IywaNDGFekkr8qjZ5j6gs9OPTClOcSLVrQyAjmTKKjw5Z9vmMkEkGsQZUiGslRPdfq6mqMRiMPPvggjzzyCG63myuuuAKp7jiWyhKu++gqAoYRrq3/EObnqhjuCXD1B1dOWQM5HW9al8v6AjvffOoE3inucywh8/1xbzy5EzLhjRJpHMG0LoPCVan01dWgKHGGR14ep8lNhGTXgyCjxPXIozl/j1tNgeW6rycqhXEUq3zsyspKcnJyePzxx3n22WepqqrivW9/O2JjI1krsrnq7hWEAzFurf00B/8whGcgxMVvr0SjO3MD23/cUINRK/Hxvx9O0uweQ+tQgM8/dIyKTAvXr85O+iza4SM+FMK6OZuMIhvutvVEIuo0p6mgSTcBCRJeZTza9HqPIgg60rouIZ7mwza6wa1fv57q6mpefPFFnn32WSorK9mUlYUSDpOzvoiyTemYTubxlt1f4eH/OYKr28+Ga2b2xsfw9s0FDPmjfOeZk1N+/nLDIH98tY07txZxwWlOWfiECyUm49yWi9GqJTqyhZGRV4lGXVMeayxPHus8tVH6fLXodBlkDK4nkDWAIAiYTCbe8573sHLlSl544QUeeeQRcnJyqIjFiQ8MIDmnjybPFueUIU+56SayvvJl0MucWPkvPKZBKrZkctm7qnjrV7dgS51dY8D7t5Xgj8S5+56Dk4qAjx7p4ZUmF5+9qnKSZxrrCRAfCGFam0FBTSpKQk01DA4+O22RSVeQojJXRgthfr/qvSsDVfh1bupkdbq6yWTiHe94B5/73Od45zvfyfbt29l2/vlEWlowVFbRHGzkkaKfY4nZMWpN3PKp9Wy7vQLtLF5wgE9cXkEgGufnLzUnXauiqINhD7SP8KkrKjDrk5X8wvXDKOG4atRWphILOtGJVQwNPjfledS5pToka+745jUyogqMyW2bGDR3sGtE1WzRarXccsstbN++nbvuuotPfepTbCwpITEygqGqmp3DL/FgxQ8xmLX0t/i45I5KCmpm1wMgigJfv2klrkCUHz6XXIQMxxJ86C8HONLp5rNXV02aHRk8PAAKmNara/Z256OR0hgYfGbqNYsCohlESzaRZrVY7PEcRK/PwtixgnZ7Hc91PTv+99m+XdVHv/TSS7n99tuRW1tBUTCsqOa4eR/3rv4fnNVaehrdVGzJJL96di9+ps3At25dzfFeL997NjlH6w5Gee8f9yOJAr9916ZJapPBQwMIWhHjqlSKV6fRc7QcQdAyMPDUtGsW9AnQpoxHm17vUczGapz+Ag5ZdjAQHBhf8w033IDFYsFut3PzzTcTqVPfA0NNDbrtQ/yz5kdkbpMwWnXkVzso3zSzNz6GSyrTeed5hfx6R8ukOpDLH+Ez9x+hItMypa588PAgUooeQ3EKhTWp9BxZjaIkpl2zLs8KKCQCOpTRdKDPV4dOqkQfN9FkOTL+Xa1Wy6233sqll16KTqfj2muvJdpwErRa9MULnyM/5/TIE3KCz+34HC9IL/DNu77J5SUzT/iZCitzU/jOm1fzxYePcesvXuUPd25CpxG5Z087f3yljdV5Kbx9y2SNjuCRARAFjKvSkMxaUnMt+Do2Yyz6KR7PAez2jZN+oyt2Eqp1E25QDbbHo3o4vXXpBLI6qe3fn/x9nY7S0lJKS0vVQlI8jlRewhd2foF4mo+bvrSKrIy0WRvwMVRmWbl5bS6/3tFCQ7+Pr16/ghSjli8+fIxn6vq5sCyN2zbkTfpd8PAAokWLvsxBhgAGi5bw8Hqi8t+JRIfQnzYhHUBX4CDWnUO4oRHblVcyMrIHk7GCk00SsYoRXuraycfXfxyAiooKKipOhbxjbd+ailJ+fPDr5GSl87Zbt+LpC83aiI9hZW4Kb9tcwJ92t5Fq0XHdqmxSLTre/6fX2Nc2zP/csmrSJHdQjZo234o23USBWYsgShA6D5frBRKJMJJkmPQbbaaFeF8mkcZGjKtX4/YcQC+tIhZQCJUN8FDjYW6tuBWAkpISvvjFLyKN5l/HRMb0VVX87dCnyU3L4m03XcRAm4/UvOnHtU2FK2uyePuWAn69o4XVeSlsKHRg1mu4654DdI+E+NsHtlBwmsa6kpAJHRvEUO1E1GsoXpPG3kdN6NjEwMBTlJd9ccoJSxqHnvhAOpHGRqQ0J15fLQZBrRF1pBznN0d/w5fO+xKgOip33XUXoihiMBjw1NUhmkwMpmn50b9+RDh9mJtvv2Bc8XC2EASB/7yxhn5vmK8/fpxMm4FLKtOp7/Pxo+cb8QRj/Pm9myepLyYCMcINI1hG5TWKVqdRvycLnaaE/v7Hyct7x6RziXoJ0RRHtBYQ6+5GykklEGhGFzsPUDio20mHt4MCW8H4tV188cVs27YNURTpqD+JvrQUYYKO+0LhnDLkiqLwX3v+ixc6XuDzmz/PdSVzU/ObiNs25pPnMHHXPQe49sc7CccSxGWF7dWZfPm66kmz+xRZIXR4EEOlA2m0S61wVSqHX6iiqsRIb9/DUxvyUQ7sWPFvyPUvDLpqwh4Tzo16HhuqxR/1Y9FNLmSNMQLuje+h2dvML7f/kvzc2XkqU+Hbb15NTY6NHz3fyFU/3IFFryEQSfCla6uTONRjkENxQieGsZyXjSAJCKh0rd5j1eRuUxgYeIr8KVQkdXk2gloj0eZuZDmC23MAk3gDigKla7J4tLORLl/X+KCNpDWPtn3/NvQ8nb5Ofnb5z0hJNZEyxYCH2eDfr6ykqd/Pd545yXeeOYnNoCEYTfDDt6ydcmxdbCBIrDdAyvUqu0Fv0pJdmsJQw2rsKx9neHgn6emTh1frCpyEm/yET+4hHO4hEulFDt2EKAmcf94qvnf0aZpGmihzqLztMSMOEDlxAmxWPnzsq9S56vjq+V9FEAQyi22TzjMbfOW6FextcfHRvyWnRX7wljVsnGJsXaTJjRyIYxptNnLmmLE49fi6N6HLeRWv9wgpKesm/U6b5yTaFSbc0IiyJhVZDuHtU7VFLl2zlQcaH+A9K98zPuLQZDp1D8MnThArzeeOp99JLBHj+5d+f85GfAySKPDjt63jjt/u5aN/O4gCjAWd/3nDCqqzJ/8dQ7VDICvja86vdiJKIrJvG+74nwiHe6YciajNMZNwFxA5eRLZ5gRkhtuyceQb0ZgEPrvjs/zlmr+gnaDXLo6m4yL19Ri3zm6YzVxxTqVWfnb4ZzzY+CAfWPUB7qi+Y97HO780lYc/vJUV2TbecV4hL33mEn7zro0Upk72giKtHhLeKKa1pyr9RStTkWN6DNLFDAw8QSIxWYJWm60WPBMBiWjYhcdziIR/PYIocOHmtSSUBHc9fxcj4WSqXFyOc3LfM8Q0Ar92P8ZbKt/CBblnnnQzE7SSyPu3lfDCZy7mlnW5lGVYePRjF/CBi0qmzEeGaocgoSStuXBlKr7eLAy6Knp77p/6PKOaJLFePx7vUWQ5jKerDKNNx2XrVXGkFzom59hjiRgN+59lyCHxh7b7uKHkBrblzk2S9XQ4zDr+cdf57PrcpXz5umrOL03lN+/eOO3s0eCRQRDAtPpUB2XhylT6TuQjSTYGBp+eds2CIBJtGxyPugYa8sgus3Nj9fVoRA0PNT006XeheIiug7uocwapGz7OV877Cm8un71eylQw6iQevHsrP79jPf9zyyo+e3Ulv3zHBm5ZN3njHFuzYJDG29MFQaB4VRpdh8pmTK/ocu0IGj2Rhg58XnX8Wu+JDApWOPnAmg8gIvLLI7+c9DtP2I3/+DGe0zdh1Vn563V/5bzs+Rk4g1bid+/eyHsvKOaTl1fwq3duYNfnLuXOC6ZOYwQPD6DJMI6+n+qg9ZxyO71H1dpV/8ATU/5OX5yBoDMTOqEOTgfob0ijdFUWX9/6depcdfzo4I8m/a636yTxwUH+EPoXJ1wnJn0+X5xTHvnq9NXcUX0HH1v3sQU7Zkm6hfs+dGZRepW5IWGYkK/MLLahN2kIdm+FjKcZcr1IZsY1Sb8T9RoEQwLRnM1A4z8BmaGGKrJKbGwsXM93L/4uX9j5Bd751Dv5xfZfEI6Hear1KZ5sfZL3HOzAkanjS1u/yC3ltyzYmjOsBr795jVn/F7w6CCS04A271S0ULAiFUEA2bcdn/6neH21k8aKjVEQE0GJ4aFdgEDXkVwKa5wU2YtYkbqC7772XQ4PHOb9q9+PRWvhwcYHeaTpEb56YgBfnoM/Xf1T1meuX7A15zlMvH9bCe+fZoweqBFf6Ogg+uIUJNup8LdwVSq7H9agk7cxOPgs8bgPjSa54DrGXIkNBAh5DiAKBvobUtn6plScBieX5l/Ko82PUpxSTElKCRathUeaH+Gxkw/z0zY3sUsK+OdNfybTfPYR10TYTTquXZV9xu8psQShOhfGlWkIE2oFhavTOPZyN0btZgYGnqKs7AuTGnPGpF5jXcOEvEcQRSuBQScFN6eSZc7g9srb+Xv937mp7CYMkoHh8DAvdr7Ia/sf49vhGJrKKv567R9J0Sf3acxnzV+eZiD6RMTdEaKtXmxXFCatqXhNOjvvG6Hw4hr6+x+nsOADk36rzRylmjb34XJ1IAlZxAJ28lc4ySks4S2Vb+FPx//EluwtlNhLONB/gJc7X2bg5ef4IqCrLEc8wyDws8E5ZcgvyruIi/IuWvLzKglZfdhrUhEn5KZFSaRwZSpt+xUqbsmkr+/hSYYcQJtlIjFcwGDffWj0DnpPZLDlBjXfe2XRlaQZ0/j4vz7Ojf+8kbgcRxRENmduombEhWP7leRW3r5kax1Dwh8l0uzGelF+0sNuMGvJKkmh7+ha0rbo6em5H1tlsiEXTVoEnYxkyWKo72X02gpCHv24Tsivtv+Ke07cw9/q/8bzHc8DIAkSl6VfQPbIv1j99reTvoBGfLaI9QaID4aSJHoBnNlmrE4D7pZL0Rc/QW/vg+Tn35n0HU36aKFdNuIZ3o9EFSgaCkfz+u+peQ97evfw9d1fP/UbQcNtms3oEju45qqPYF8gIz4XhOpHUCIJTGuTtWlyK+xo9BLhgS3I9lfweA9iT9mQ9B1NqloriI9E8HqbEGLlCIJIXpVKTXzfqvfxYOOD3Pn0neO/0Ut67lJWA7t5/5u+gXGBjPhcEBqlEJ++5uI1aey8rwElcBE+6RcEg62TxOYkp7rm8MgAw8OvoHhvRmfQjKfBPrPxMxwcOMhHX/wosjKqwaK380nWAfv49Nt+imYRWCvzMuSCINwG/CdQDWxWFOW1hbio1xsiTW6UUBzjqsmFvdJ1GTTs68esvRKX6+9Eo65J6or6skwibWE8yjF0ysWgiEkNLesz1/OXa/7CH+v+SJWziisKryDFJ9PkuRhj5fRT3BcToToXyGBcPXnNBStT2fuIh4qrrqC//xHKy74wqQCoSTMQdWXgixxHDN+EpBHH2Rd2g52Prvsod9bcyUONDxFX4lxfcj3Whh7alBcxVC3Tmo8MqsXslclrFgSBwlWp1O+Osm71Grq67yEv711JBUA18lLA7sQXrCfuugWr04AjW41OVqWvYtdbd9Eb6KXF3cJQaIhtedvQPL2LXnZgXHnmYcmLgdCR0WJ2iT3p/2u0EvlVDjoPVlJ4pZXOjj9gX5VsyCW7AQQF2WbA7z9JqO9mMott40qHacY0frn9l7R727Hr7TgMDopTion8/A+4NPvRl5WxHAgeHkCXbx2PKMZgdRpIL7DSV7uKlDUCff2PU1KcHP1rRg25L7cHRYnTX7eWvCon0igLyKAx8P2Lv88f6v5AlbOKDZkbKLOX0ff5LxBIT18UIw7zz5HXAm8CdizAtbxuETw2hKCXVIGk05Bf40SjE/G0n6fe2P7HJ31Hm2EmnNJCQhPG01GDxaGfxIUuTinma1u/xtuq3kaaMW1cZU1fVbk4izoDQkcH0aSdyiFOxJisaMK9nXjcN2XeWFfoJJoXRBESDDQWk1flQKtPZg5YdBbeVfMu3rvyvWSYMgjXj625etLxFhuKohA8Moih3D5ezJ6IwpWpxKMyJuFNBIOtDI+8Ouk72jQT0YIEIDPQmEfhytSkaEYURHItuWzL28Yt5beQZkwjXFeHYDKhK5z9JKOFghyOE6ofxrQ6HUGaXCMpWp2Gb0jEabuNgcGnCQaT6X2CJCBaJPzr+wCZ3qPryF+R7MSsz1zPLeW3cGnBpazNWEuKPoVw/Qn0JSWIej1LjdhQiFhvAOOa9Ck/L16TRm+DFptlEz09902qe4kGDYJGIbhiAB25jHRkJIm3gap8OfYuVzgqEAWRcP1J9IvooMzLkCuKckJRlKmZ+P9LoCRkwsddGKudCFN0UGp1EoUr02jdb8Ziqaan9wEUJbnLTOMwEEg7ArJA58EiStdlJEnWToUxxoqhcukNecIXJdLiwbg6bcphzam5ql5322tZGI0F9PT8Y9J3dDlWQmmNIAu4mgsomsKzPx3hk/WIViva3DM3di00op0+Eu4IxtVTv+B5lQ40WhFX81q0WiddXX+Z9B1tnp1Qodoi7+8rmlE6YQzh48cxVFUhSHOjky4EQsddEFemNWpj1x/quQZR1NLR8btJ35Ey9Hirj6NxlxP1Zc0onTCGyIl6DNXLE3WF69RmNOM096Z4TTooIIXeSiTSS3fP3yZ9R84IEM7uR+ldDQgUrJh5zUo0qvaDLKJTtmSsFUEQPigIwmuCILw2OKFd/fWOSIsHOTh1WmUMpevTCfliWLVvwe8/Tk/PfUmfS3Y9/vSjaPoyiYWM48MUZjxv/Uk0OdlIKcuQQ6wdUhtipjFqgiBQsTmL3iYfzpSbcbv3TvLWtFlmgs56BFc6csw07sXPhHDdqFGbYvNYbISODIJGwDgNV12jk8irdtJ6yENO9lsYGnqRUCi5/T+R5sZd9BJKWxXIVnIrJ0dwE6EkEoTr6zGsOHOBbjEQqnUhpejQFUzdKWtO0ZNRaKXjmExW1pvo7XuQSCT53Q3kHiJh9OOv3YTerCGjcGbKZNzlIj4wsCxRF6gpQ22uBY19ci8AqE6KNdVAT20RDsf5tLX9nHg8WZTLn38IBOitXUtqrmW8g3U6RFpaIBZDv4hp0jMackEQnhcEoXaKf3Oa0KAoyq8VRdmoKMrG9PTZi/4vN0LHhlS2SsX0L2XhylQkrchgw2Yc9vNoav5W0gMf0w4TsbUjDq7FaISskjMb50jDSQwVy5NWCR4dQpNhRJM5PXd7rPPO33khomjk+InPIsun2v892gOE7E0IgytJzzZgccwcRsvRKJETJzCsnv1Un4WCIisEjw1hqHAiGqYvG1Wfn03AHUHxq9Kmp3trXdJvAZHAkVvJLbdPSiWdjmh7O0owiKFm7k1t84USSxBpHMFQnTrjxlm0Oo3+Ni8ZzncjyzG6uv6U9PmA8VG0gSxcXRtVLvZsI81l8MgTngjRDt+03jiMUi/XpNF1YoSCvH8jFhums/MPSd/xWF9B7ylkaKSEqvOnVx4dw/ial9MjVxRlu6IoK6f498iiXdXrBEpCIVQ3hKHaiTCDSJPOoLITWg8OUlnxXyQSERoa/wuARCJMW/vPANC4zyPP6jljWkWORIi0tC5LfjzhjRJt82BclT7jC56SbiSrxEbTvjgrqr+Fx3OQhgaVkeH3N1B38hPoA3lo295Ejj0w7XHGEDl5EiUWw7hq9YKtZbaI9fiRvdEZX3CAwtWpGG06GnfLpKdfSUfH7+jtfRAAt/s1XKHncLZei6QUk1dw5uaW8Kja5XJ45OEmN0pMxrhi5jWXrFVTDR2HDaSnX0VX91+JxdwAeDyHCSjHcXRsR6O1UbJy5ggETnXuLkdBO3Rc1VAx1swcHRavSScRl/F05pOWtp32jt8Si6l9HsFgOwGxHlv/FowkZiUlEKk/iaDToSsqmvcapsM5RT9cakRa1Y6301kMU6F0fTothwfx9qdSVPRhWlt/SEtrOX19DxEKdWBzXY4+kI8p8SowMyc80tAAiQSGZQg/Q3VjaZUzr7l8UxY772tAE7+YwsK7aG//JQZDDt099yFJRrIaP4tXMWEJHgcmd0Mmnfeo2lBiXAaPPFw/DAIzRl0AkiRSfX4Wh57r5ILb/4N43MvxE58lEGxheHgXen0WjvZrcIsCGbFOYGYmSrjuOIJej750em77YiF8fBhBL6E/Q3SYmmsht9LO0X91cuPnPsjg4LO88uo2MjNvIBzuQRItpPRcgF0OkptyZl3w8Il6NWVoty/QSmaPUK0aaWozZu4SzilLQW/S0HxokK1v+RR7913HsdqPoddl4A+oJUFr3xayAh3oo15g5gxD+GQ9+vJyBM3imdt55cgFQbhFEIQu4HzgCUEQplYVOkcROjaEoBUxnCHXCSqTQ9QINB8coKjwg5hMpbS2/hBBkFi39i/oGt6HRRQwN05mO0w670FVl8W4bu18lzBnhE+OIKUakibGT4eyDWrRtmFfH6UlnyLVeRHNLd8jGh1izepfEw44MIsK+qYDZz7v0WNIaWloss/cxLLQCNUPo8u3Jk2Mnw7VW3NQZIWm/UHWrvk9OTlvpb39l/h8tRQX/jvBhB6HEkbbfPiMxwofP46+snJRX/CpoMgKoRMuDJWOpCag6bB2ewH+kQgDJzPZtPFBMjKupa/vUYaHd+Kw3QhxA5nBbmLNTWc8Vrj+xLI4KIlAjEir54zeOKj9IRWbMmk+MEA8kE9u7h14PIdxew4iiUac2nejDaeSGeyeNP3rdCiKQmRUwXQxMV/WysOKouQpiqJXFCVTUZSrFurClhuKrBCqc2GociY1AU0HnVFDwYpUGvb1EQ2KrF71cyoq/pPNm57AYtrMgCuMThSJtXcjh8MzHit48BDanBy0mUvbIKLEZSLN7jN6pmMw2XTkVztp2N8PikhNzQ9JT7+KVSt/ghwuw+WOYhQFIrW1ZxxBFqqtxbhq1ZIXOhO+KLEuP4aq2fF77ZkmcivsHN/Vg4CGqspvUFnxNXJz38FA/Tq8UZkUrULwtMlHp0ORZZWxUrP0aZVopw/ZHztjWmUMhTWpOLLNHH6+A6t1FSuqv8W2C3ezsubHjJx8EyEF7EQIH59al38McjhMtKV1WdIq4ROjfRGziK4BNlxbhKgV2fNIM1WVX+PSS2q5YOtLbNz4AEONb0JRFFLk0KRhzKcjPjioqnkucj/IOaW1spSI9QeR/bFx/YnZYPP1xUSCcV76Wz0mUyn5ee9EkvQ0vTZAIKZSEkWDY9Iw5olQBf4PYNywYdrvLBYibR6UmDxrQw5QsTkT/3CE9joXWm0Kq1f9HKfjEl7443GiooAoiMghmVj39JNrEj4f0ZYWDKuWvikmXK/Ob52tIQdYcWEO3qEw3Q0jCIJAXt47KCn4Cgef6kB0GtBozERONCBHJ2u/jyHW2Yns9y9Pfvy4C0Rh1s+2IAqs3Z7PUKefrpNqrlijsWK3XsnJ3R6w6tCl5hB8beZ+wEhjI8gy+mUodIZqXUh2Pdqc2alJmlP0rN2eT/PBQfpaPeP/PxqO03J4iLhOQuPMmTSz9HSMbW6va4/8fzMijeoDqy+zz/o36QVWNt9QTPPBQU7uVQdGtBwe5OW/n0Q/mpcTTKnjsqVTIdbVRWJwCNP6yWpzi41wwwhIwqQuv5lQsjYde6aJZ35bR3eD+jc7+GwHfS1eSi5U+eCCyTle2JvyvHV1oCjLUugM1w8jpeimbHyaDiXr0tGbNBx6roNoOA7A4Rc6CQdi5G3MAATQmInM4KGOveDLYchDx13oS1IQjbNP6VRszsRo03H4uVNj1Y6/0ks8KmMtTkHQq/c44Z8+Tz723Buqlza1IkcThBtHMNbMzNA5HeuuKMBo1bL7IVXDX5EVal/uJh6V0aUZkew5hM9gyIN79yHodBhXL+6z/YYhnwbhJjeadCMa+9y6z9ZdWUh2WQo77m3g8PMdPP3rWtILrFz8QdXblOw545X7qTCeH1+/9Foj4ZMj6ItTEM9Am5sIrV7i5k+tw+rQ8/hPjnDkhU72P9ZK2YYM8kYr+qI1nfBpA4cnInRUHZdlXGKPXInLhBvdGKqcc3rBNVqJdVcW0FE3zF++tJvXnmzl8PMdlK5LxzG6CYpG54zplXBdHWi1GMrLp/3OYiA2GCQ+GMI4y2EVY9BoJVZfkkdHnYunfnmMZ39by6HnOsguS8FaaANFA6Jh/PmdCpH6ekSLBW3u9APSFwPRNi8klDlF16Cy0TZdV0xPo5vdDzVz33/vY/fDzWSV2DDmWhD0dqJNTcih0LTHCOzejXHdOkTD1Lz1hcIbhnwKKHGZaKtnTt74GERRYPudqpf1ygNNZJXYuPETazGmm0AS0OaVjWtuT4XggYOIVuuS61DEPRHi/cE5pVXGYE7Rc/On1mNLN7Lr/kYMVq06nmy06UKbXzGzR37sKNrCgiVnMkRaPSjRxJxfcIANVxfx5s9tJKPIyt5HW4lHEmy+sUQd+wZockoIHTky7e/Dx49jKC9flCEDMyF8YjSVNMv8+ESsvDiXnHI77oEgg51+dAaJTdcVj4tnifYcgvv2Tfv70NFjy9LwFWnxgCiMj2qbC1ZsyyElw8ih5zqQZdj+nhXc/On1aBwGkHUoskBg794pfxt3uYjU12M+/8zqqvPFG/TDKRDt8Kq54rK5GzUAW5qRK99XQ9vRIS54c/l4Y4jGYSBBDsHdJ1Fkecr5j6FDBzGuXbvkLduR0bTI2RhyUAufN39qHbv+0UjNthwMZi2KoiBoRbRZxfhfeEL97yle4tCxWkwbJw/lWGyE64dBI57Vhg2qjPENH1tLX4uHcCCGM9uMHFVnZeoKqwgdmCxdAGodJFx3HOuVM1MyFwORFo8aaTrm7iEazFpu+fTkSDE2EFQ/r1xPYP/+SZ8DxPoHCNfWkv7JT875vPNFpMWNLt86p0hzDJIkct2HV+N1hSmodo73gIxvXs5c/C+/jPWSSyb9Njhq4M1bF9+Qv+GRT4FwoxtE0JeefXt80ao0LrmjKqm7T7LrEQwOlGCQcN3k/GnC4yHS2LRs+XHJppuxm/NMMFp0XPHeGnJGxcUEQUCy6xGtGcgeD7HuyRPtY/0DxPv6ljytAqohN5SmzIqVNBOySlLGJQhEnYRg1KBJyyPe20usv3/S92PdPSQ8niXPjyuyQqTNg754YWUfNA4DCKArqiZcW0fCP7kBzP+iOkjEesX2BT33mSBH4kS7fGfky88ER5aZwprUpEY+aXQjNK2/AP/LL0/Jygrs3o1otS5J5+4bhnwKRJrc6PKsM7Zrnw1UL8iIoNXieezRSZ8HD6mTZYzrl5axoiQUwo0j6CscCx72Sg4DSOpginDt5PRKuFbNjxuWuNAZd0eIu8Lop1C0nC80dj2CaVRwaor0SuCVVwAwrjnzcI+FRKwvgBJOoCs6uxFy00HQiogWLZIzFxIJQocm58l9z7+ArqgIXcnSNj9F27wgMy9DPhXG5Gz1FWuJ9/ROyUQLvLob05bNSxJdv2HIT4McGt3BzzLcngmSQ48ciGO55DK8TzyJEo8nfR46eAg0miXvbox2elHCiVk1Ps0VGrseJSKAVktoiuJf6OgxkCQMK5aWyRBt9wKgX2CjBiCl6FFkHYJWS+jwZEPuvv9+9BUV6JeYvRFtG1vzwguxSSl6BI0VtNpJefKE10tg716s2y9f8vx4uMUD0tnlx2eCaNEiaEU0aar8sP/ll5M+j3Z2Euvuxnze4qdV4A1DPgmRFjconHV+fCaMhWPmy68n4XIReDW5yzN08CCG6mpE48xqaguNcKNbbVEvtS/4saWU0c3rwovxPPxwUtitKAqB3bvRV1YselX/dETbvWr+fg60w9lCsutJeKIYamomeeShujrCtbXYb7996Yt+bR6kFD3SGQTMzgZSip6EP45x5UqC+5Lz5P6Xd0A8jnX70qZVACLNo/nxeabPTocgCEhOA3JERF9dPcmQB17dDSxNfhzeMOSTEG50I+jEaaU95wPN6AukL1+NmJKC55FT6RUlGiV07BimZaAdRjt9aDPNiKazm2I+E8aMhuOO95LweHDf+/fxz/wvvED46FEct9224Oc9EyLtXnT5VgRp4V8Bya5HCcUxrF5HuLYWORgc/8x9//0Iej0pN96w4OedCYqiEGn1oiu2LcoGoknRk/BEMG3eTKi2FjlwasP2Pf88mvR0DIvMpT4dcjhOrNu/4GmVMWgcBhLDISwXX0To0GESnlONQ4Hdu9FkZKArnnr480LjDUN+GiJNbvTFKbPSoJgrxjxy2Z/Ads3V+F54YdxD9Tz2GEoksuT8cUVWiHb4FmXjAsZ5+JrMIswXXIDrD39EDoVQolEGvvNddKWl2JfYkMvRBLFe/4KH22PQpKhrNm+9HCUape9rX0NRFORAAO9jj2O75hok2+KcezokXGFkX3RR0iowunlFEhjXbYREguBBtd4jRyL4d+7EcvllU7K0FhORNi8ozKnBbS7QOA3EhyOYL7oIEgn8u3YBqvxCcM8ezOefv2RR1xv0wwlI+KPEh0KYN51ZY/hsIFl1IAokRiKk3HgT7nvvw/fcc4hGA71f+SqmLVuwXHrJopx7OsRdIZRwHF3+4hhyaZRLnnBHSPvw3bTf8Q7c998PCETb28n/1S+XXDQq2ukDmUUz5NKoIdfmlpD20Y8w9JOfYly/AUESkQMB7Lcv/TDtSJvqLeqLF3fNuqJq0GgY/OlPkJwO4gMDKMEg1suXJ62CJKAvXKxnW6/2IVTUIDkcBHbswHrJJfief56E271kaRV4w5AnIdqlthfr8i2LcnxBVOl48ZEwtqvWos3PZ+jnPyfW24tx3Tryf/FzxCVuEIl2+AAWzSOXbDoQVJZIyhUbMG3ejOs3v0WJRjFvPV/1ZpYY0Y7Rot9irXk0Ckm4I6TdfTehQ4fp/8Y30GRloSsrXRZVy0irF9GkQZN+9vTSmSClqM+tEhHI/trXGPj2t2m79c1o0tMRLRbMWzYvynlnQqTFg67AOuMsgflg/D774pi3XYjnyafwPPEkxONITifmCy5YlPNOhTdSKxMQ6/KBANrcxTHkoObJEyNhBEEg5YYbiHV2YqhZQf6vfoloWpyXbCZEO30IemnRXnBBIyJZdSTc6hDbtLvvUhXhvF4yPvvZZRnrFm33ockwLkpNAEaN2ujmJYgiOd/5NlJaGrHOThzLUOQEiLZ50BXazjjU5GwxZtTingj2W99E6fPPkfbhu0kEAlivvmrJO1jlcJxYj3/R0ipwKgpJeCI43vY2zJs2kvre95L/299S9tyzaNJmp7S4EHjDI5+AaJcfTboJUb94fxbJbiA8KsjlfNc7QRJxvvOdSJbF2zxmQrTThy7PsmgvOIyyONyqdK/pvPOwXnEF2vz8ZZEzVWsC3rNqUZ8tBElEtOhIeNTNS+NwkPeTnzD8xz+ScvPNi3be6ZDwRom7wpi3LJ7Wu2TVg8D4hi1ZraR//OOkfuADyzJYOtrlU/Pji5Q+g2RDbtmyjoLf/37RznUmvGHIR6EoCtEu31m3qM8WGoce2RtFictIdjvpH/nIop5vJqhFvwDWi/MW9TySXU+sW01bCYJA3k9+vKjnmwnxoRByML6oLzioRd4xowZgXFlD7ne/s6jnnA6n8uOLN8hbkAQ18vIkS/cuNZV2DGPP22JG15JVjbzGNuzlxBuplVEkPBFkf2zRin5jGGOuTHzJlwuxHj/IypKsOe6JoMgzD5dYCow1Ai1WoXMMKpd8+e8xqI1AglactRb32UJKeR2tuduPZNcjmRcnfQYTNq/Xwbv8hiEfRbRz8XdwOBWOxV8HD/x4oXORDbnGroe4ghyILep5ZoNI+2jRL21xPUUpRfXIzzQZaSkQ7fajzbUsCmd+Il5Pm1es249ukd9lGF2zd/oBIkuFNwz5KGLdPlXqMnuxDbla9Hk9PPDRTh+SQ6+GiIuI8Vzi68BziXZ40RUsXtFvDFKKHiUmIwfjZ/7yIkKRFZUzn7MERu11snnJ4ThxVxht3tKtebnxhiEfRbTLjzbbjKBdZK9lQoFkuRHt9C26Nw4TGA3umWeVLjbkYIz4QGjRqJYTMU5NW+b7HB8KoURltEtkyJWYjBJa3s0rOpof1+UuwX0eTSct9+b1hiFnlMnQpbI3FhuiTkI0aZZ9F094oyTcEXQFi99hqHmd1AWivWoXrS5v8V9wjf31EYXEekZThoucH4dT0Wbcs7yphqUodI5BStG9LjavNww5Y92NiSV5wWFsF1/ehz3aOVr0WwKPXDBICHqJxMj/JaP2+vDIoz1+dTLVPHTmZ4vXSxSyFIXOMYzXvJZ5w37DkAOx0Y5O7ZIa8mV+2Dt9qrznEoTcgiAgpeiXvcAb6wkg2nRIlsVvThEtWpCE14FHHlBThotc6IRTGjPLvubR4u5SYHzzWuaC5xuGHLV5QNCKaDOWprPy9VDdj/YE0GaaFr0mMAbJvvxFoWiPH90iyNZOBUFc/s1LURSi3UtT6AQQx7SElnHNcjhOfCi0JIwVeP0U8t8w5IwWOnMsCNLStE5LKXrkYHx8vuNyINbjR7vIDJ2J0Czz5qXEZOKDwSUp+o1BSllejnHCHUEJxZdszYIoINl0y3qfTxU6l8iQW3UgLn866f+8IVcSCrEe/5IUOsew3LnEhC+K7I8tSa54DFKKHtkfQ4nJS3bOiYj1B0Bemvz4GDR2w7Ia8vGi3xLf59fFmpfIkAviWEfrG4Z8WREfCqLE5CW78TCqCMjyGfKxot9ic+YnYrk3r1jPKGNlST1ytVlkuTpaoz1+EFmydBKMRiHL7JFLKfolqYOMYbk3L3jDkC/LC36KmrY8BZIxGt5Se2qwfB2t0V4/gl4al0hYCkh2PcgKsm957nOsJ4Am3bRoMq5TQbLriXuiy8arXspC5xjGNuzlxP95Qx7t9YNGQJO+dOI+y01Ni/X4kZwGRMPSaaZJy8yrHmdvLHJH50ScaoRaps2rZ+kKnWOQUvQQX56O1qUudI5BStETX+aO1v/zhjzWE0CbuTT0rDEIGhHRol0+Q94bQJu1dN44gGZMmmAZjNpStqlPxHI2BSV8UWRvdEmLu7C8FMSx6HrJPXL78m1eY/g/bcgVRX3BF2OS+pmwXFxyOZpQvZYlTKsACFoJ0bw8m1fcNdamvrRrXs4oZLwOkrvEa17GaDPaO7rmJY9Cll8/6X+1IY/IMi8Ne0lME/LI3ihyID7pxsdlhf2eAD9s6+OILzjlb+eLsXBsMRCVZeLTFNhifQFQSKIeRmSZvW4/J/whhqJx5EUKEZeLPx8bqwlMWHM4IfO1pm7+1usitkjFSNGgQTBoFlxjxhWN44/PTF2NjnexnlqzrCj8sK2P2w830RdZHCXKxSpqe+MJnh70TPsuw2jDl0U7TiYYw45hH7ceauKpQfeCXtMYljtVCv+LB0t0hqO8v7aVI74Q16Sl8PMVhRhPS5+cXvSLyDKfO9nFE4NufAmVJnd/3wgvba5Cu8C5VSlFR6TFvWDHSygKr474eaB/hCcG3ZSbDNy3thSbJrnQFTttzeGEzFuPNLPHExj/Tr5Bx9MbKkjVLezjIaXoibtCC3a8E/4Q32juRRRAJwpk6LR8tjgLhzb5umOntamHEzLvqW3lX8OqjO/32/r4WEEmb8t2olvgSe+nD5iYL1zRONv2nSCUULguPYW3ZjvZarcgnjY+LtYTSKqDBBIJPn6igycGPUgC3HiwkfvWlFJs0i/YtQGI5oXvaI3KMu862sIeT4ANNhM/rCqg3Dy5aB3r8SdtXO2hCP/Z1MNTQx5E4IgvyPMWI0XGhV2z5nVgyP9XeuQvurxcuf8kLcEI78lN4+khD7cdbmI4lpzDGtfeyDIjKwqfONHBvX3DXJ9h5zc1Rfy0uoDmUIR7el0Lfo0aux4lnECOzD+vpigKtx5q4rYjzTw56OayVBvH/EHecbSFwGmeW6zHj2DQqN6xovCRE+3s8QT4r7JcflVTyJdLsukMR/lLz9C8r+t0LLRR+3pzD7s9fvoiMRoDEe7pcfGuo60EE8lc9WhPAG2GCUEjJhnx71Xmc8/qEjJ0Wj7X0MV/N/cu2LWNYaE7Wv+zuRtfXObGDDvPujy8+XAzbz/SQkQ+bc0T9Li7w1FuOtjEU4Me/rM0h8fXV+BPJLjxUCN1/oXbWGFCR+sCrVlRFD7X0MUeT4D35abREoyw/bWT/LxjIKm4qMRlYgPBcarlbrefS/bV8/KIjy+WZLNzSxWiAHfVtROVF7aXQRxrClomFhr8LzTkLw/7uONoC9l6Lc9srOT/VeTxm5oijvlD3HiwkfbQqQcs1htASlW9lv/X0ss/B9x8qSSbH1QVcEOGnVszHZxvN/Pd1j58Zwhl54pT4dj8b/6uET97PAE+U5TF0QtW8uuaIn6xoojXPAHedayV0ATDFusNjOfHv9jQxRODHv6rLJcP5KdzU4aDjxZmcqnTyu+7hyYZh/lCStGjRBLI4flvXvs9Af417OPTRVk8t6mSHVuq+MWKQl7zBvhQXVtSainWq3pqgUSC944a8e9X5nNHTirbU208sb6cG9Lt3Nc3TDixwGt2LJxR2zXi4/6+ET5SkMGPqgs4snUlXy/L4aURHx893jGedpBDcRLDYbQ5FjrDUW4+1ER7KMJfVpdwV0EG62wmHllXjlYQePOhpgV/tscGjC8Eftk5yN97h/m3wkz+uyKPlzdXcanTytebe/hiY/e4MY8NBCGhoM2xcMIf4t3HWsg16Ni1uYqPF2ZSajLwvcoCDvuCfKu1b0GubQxqU9Dydi7Py5ALgvAdQRDqBUE4KgjCw4Ig2Bfous4av+kaJEOn4fENFZSMho3XZ9j5x5pShqJxrj3QyGujaYRYbwBdtpk/dg/xk44B3pWTykcLMsaPJQgCXy3NxRWL8/OOgQW9zoXUaPhN1yCpWg0fLcgYTx/dkGHnJ9UFvOr289YjzbSHIqPsjQCRbBOfbejiTz0uPlKQwQfy05OO96H8dAaicR4ZcM/72iZCsi8cc+U7rb2kaTXcmXtqiPL1GXa+WZHHcy4vnznZSVxWVPaGL8ZwloFbDjXx0qgn/vacU78TBIE7cpy44wmecXnmfW0TobHrUULxeUde4YTMZ092UWTU8YnCTACMksgH8zP4j9IcHht084WGLlVfZTTSHMw08KZDTXjjCR5YV8blqacki8vNBn62opCReILnXN55XdvpkBwG4gugdLlz2MfXm3u4Pj2Ffy/OAiBDr+UPK4u5Kz+dP3QP8eVRYz7GWBlI0/G2Iy2YRIm/ryklx3AqX35Dhp135qTys44Bdo345n19E7Hc+knzTYI+B3xBUZS4IAjfAr4AfG7+l3V26AlHedHl5WOFmZhOy4dvsVt4fEM57zjawq2Hm/h2SQ59xjiP5iSoa+jicqeN/ynPQzgt17jOZuLmDDu/7BzgXbmpZOsXpmNsoQokrcEIz7m8fLIwE8Npa741y4koCHz2ZCeX7DvJZzNSSbELfNsapL/Hz9356Xy5ZPJk9YsdVirNBn7VOcBtmY5Jf5OzhWRX85pxT2Re9Mfdbj87Rvx8rSwH82kT2t+dm8ZANMb32vp5ecTHW7UmSlMlvqF48AUV/rSqmCvSJg8h3uawkqPXcl/vMDdlLNwA7onMFTHz7F+3H3f00xKKcN+a0km1nrsLMnDF4vy0Y4D+aIwVw3Gc2Rp+4x7Am5C5b00pa6yTBeG2pJjJ0ml5bMDNmzIXbs0ahwHZpw4YFzRn7yv+onOAbL2WH1cXJtUABEHgP0pzUBT4VdcgUUWhbNBPa5Welzu7CcoJHllXTr5h8rv6tbJcnhvy8ruuIS50LJzaqZSiG5cHWA7My5ArivLshP/cA7x5fpczP9zXN4wMvC3bOeXnZSYDT6yv4D21rXyiqRtWGKjUiPx3cRZvz05FM01B8wsl2Tw56OHH7QP8v4qFmTgv2dQJ3PMNu3/XPYhGELgzN23Kz2/JdLAlxcxnG7r4Wt8grDdRodXwu5WFbEiZ2pgKgsCH8tL51MlOXnH7F+yBX6go5DutfWToNLwrZ+o1f6YoizVWE3/oHuIHwx7YaCJHEnlsTQkrLFM3fkmCwG1ZTn7S3k9vJLpwG/bY5uWOoM08u83LE4vzs44Bbsmwc7Fz6nvxpZJsZAUeHhjhGTkGq43YEgnuW1PGWtvUqp6iIHBdegr39LrwxxNYNAvTATqxEUp7lrNRB6MxXh7x8ZH8jElOGajP6H+W5aAAv+4aBANoCnQUCwJ/XlVC9TT32SSJXJ2ewn29ahrtdOfnbCGl6AkdH0ZRlAVzfOaChcyRvxd4aroPBUH4oCAIrwmC8Nrg4OACnlaFrCj8rXeYC+2WGavSqToN/1hTylc1Fv64J8DzK0t5X176JC9nIgqNeq5Ms/H0kGfBurcWoinIG0/w995hbsqwk6mfXkQ/x6DjL6uK+UHMyCcbIjy3qWJaIz6GN2U6SNVq+FXnwt0ryapuXvMx5K+M+HjV7efjhZnT3jNBELgyLYW/rynlyR4Nn+pO8NTGimmN+BjekuVEBh7oGznr6zsd401B80g1PDnkISIrfCAvfdrvCILAV8tyOLS1ht1HEjzQo+GVLdWsm8aIj+GGDDsRWVnQ9Mr4RKh55Mkf7h8hocCbs6Z2ykBd89fKcnhpYyWP7w5x2Gtm55ZqzrPPzCO/KtVGSJbZ5V44D3q8KWiZBoyf0ZALgvC8IAi1U/y7acJ3vgTEgb9OdxxFUX6tKMpGRVE2pqdP/0CeLV4Z8dMZjnLHhNzndDBIIm8bVFgdE8dftDPhslQbvZEYJwILxwmeb1PQvb0uAgmZ98/wgo9BEAS2d0Z5T1iLfha0QoMkcmduKs+5vHSGF6YaL0gCkm1+a76nx4VDI/GO7DPfZ4Cs9gDv11pm3OjGUGLSsznFzH19wwu2YY9rdM9j8/pnv5tCg+6MRhnUhi9tf5BVGVbSdWde8+YUM5k6DY8tYD1Eco565PMw5A/0j7DaaqRiCprhRAiCQFkUsrxxDLNsBNrqsGCWRJ4dWrh6yNjmFR9enrm0ZzTkiqJsVxRl5RT/HgEQBOFO4HrgDmUZxQb+2uvCrpG4Zor851SI9owOW55lGHSZUy0UvbCQnss8DLmiKPy+e4jNKeZpQ+ek78sK0U7/nAYP3zKaN/3XAq55PnS8QCLB00Ners+wzyokTnhH29Tn0LL91iwnTcEIB70L0wim0vF0JM6yKWgwGmPniI9bZlmrGGv4mm3nrigIXJ9u54Vh7xmbjGYLyapXN6+zjEIaAmGO+kK8eZZ5++gche/0osglTivPDnkXbMPWpI5GIa9XQz4TBEG4GvgscKOiKIvTAjkLDMfiPDno4dZMx6xecCWhEOsLzmmwQpZeS43FMN5EshBQ5S/PTinuNW+QtlCUd8wiAoGxuaTxOc0lLTXqydVreXkBK/xSiu6sFRCfd3kJyTI3Zdhn9f1ot3rdc9GavyHDjlEU+Uff8NlcYhISikJHKIJkN5x1LeTRATcycHOmfVbfH++NmMPmtdDpFbecQLGfPQXxgb5hJOGUI3EmxHpVud65zCW9Ki2FvmiMowvEo5de7x75GfBTwAo8JwjCYUEQfrkA1zRnPNQ/QlRRkihlMyE+GIT43LU3LnXa2Ofxz4t3OxCJ8e3WXn7S3s/9NoV9Fs6KV/1w/wgGUeDa2UYgo3NJ5zJsWRAELnFa2Tnim7blfzYIJmQu21fP1a818LxTJOo5O6W4R/rdZOg0nH+GHOgYYt1+EJjThm3VSFzktMx7w1ZGG8w27znBb3LEszbk/+x3U202UGWeXdEw1h1ANGnGC8uzwUKmVx7sG2b9q3V8rEZL6CwMuawoPNg/wsWO2aWG4Ozkei932hCBZ+aZXonLCi+6vHy8qYsPnmdmaGRhG6xmi3kZckVRyhRFyVcUZe3ov7sW6sLmgn/0DbPKYqTmDMWsMUTaVc9DX2A7wzeTcZnTRlyBnfPwUL/e3MP32/r575ZevqL4uXuTiQc65tZFGZcVHh1wsz3VNmumQazTh6AT0cxxLunFThveuMzheWjO/KCtj+OBMEOxGJ/Qh7jtPBMv9cytoOiNJ3hh2MuNGXakWabDol1+NOkmRP3c2BgXO6x0hKO0hc4+r/3TjgEe6B9hhdnATyxxPl8oEJzjaL/OcJT93sCsPVMYTRnmWObEnFDZK3ZeHPZO6gSe9XllmS82dPGREx3kG/TsMcHXnYk5b9h73AG6I7EZi5ynI3YWcr2pOg2bUsw8N3T2UchLw17WvlrH24+28JzLy1GbyNe1oWWRsz3nOztPjuXTsubwsLd7Ec1apNS5DRnYlGLGIom86Do7Q34yEObB/hHuzk+n+aJV7CkqZKU7wdd7B/HEZu+V73L7GIrF58T9jXT60OZa56zHvc1hQQBeOksP9YQ/xC86B3hLlpO9563gpzb1Bf1oc9ecIpunR5kbN8+B4x3tPrsRfmMUv5fPcs1PDbr5n5Zebs6w8/ymSj6jtfBcpoY3HWokkJj9mv/Zr252s00lKXGZWF/grGRcr0lLISwrvHIWTA5ZUXjrkRZ+3z3Eh/LTeWFTJR+RDTySIfHDOXZR/qNvGLMkcvUsI82EP0rCGz0rBdMr01I45g/RfRbFfEVR+M+mHiwakT+sLOLoBTV8JKDhaauaIVhqnPOGfDyfNpcXvMOHrtA2Z76nVhS4yGHlX8NnVyT5bmsfJknkowWZmCWJgmwrnz8eZkSR+eYcHviH+kewSuJ4AfZMUOKy6rXMIa0yBodWw1qr6ayMmqwofOZkJzaNxH+U5SAJAjdmOvjG0RCuhMyP2/tnfax/9o+QZ9CyYRaFXYCEN4Lsm1uhcwwlo7WBHWcReZ3wh/jIiQ7WWE38oKoAURD4aEYq/+9ImMPBML+eA53znwMjbLCZKJylyNNYm/rZSBRvtpsxisJZ3eeXh1VK6DfKc/laWS5aUeDfUuxc0xPjW+39PDIwO8Pmiyd4ZMDNLRmOKbnjU+HUXNK53+crRztdnz2L2sCOET/1gTD/VpjFNel29KLIhwwW1ozE+XxD14IxvWaLc9qQy4rCQ6P5tIxZ0MtA3cHjQyH0hWfX5HJZqo3uSIyTwbnl/+r8IR4bdPPBvPRxVUHRoGGFLPH2sIY/dg/NSjI3lJB5ctDDdemzY27AqOJhQjkrQw5widPKQV9gTlEDwJ96XBzwBvlaWS7OUUVCya5nhVfmZlHPr7sGZ/XAu6Jxdoz4uClj9l2m4zWBszDkgiBwsdPKrhH/nGsDP2zvRycI/GlV8TjPXbLr2d4f50qtgZ91DOCKnvnveNgbpM4fnlPUFZtCuna20Isi59stZ1XY/kuPi1SthndNqFFpHAa+UhtmjU7HVxq7ZxWJPNw/QkiWeXvO7NMqkVYviMKc2FhjKDPpKTbqeO4s8uS/7BwgQ6dJKkLrnUa+fiyMosDHT7Qvmhz0VDinDfmrbj/dkRi3zyGfFu0YZTIUzi0/PoZLR8PuuaZXvt3aS4pG4q7TdE00qQY+3J0gXafhsyc7Z9RbBpX+6E/Ic8ubdo2u+SwN+cVOKwmFOYXdwYTM/2vpYZvDkkQjE81a0Ih8IqBFAP6nueeMx3pi0E1cgZtnmWIANa2CcHZGDeAihxVPPDEnPXp3LM7TQx7enOVI4q2PdTp+MqYnmJD50Swikd92DWKRxLk9291+BJ2EJvXsuikvcVppCkbm5E32RWI84/Lw1tMkgDUOAzoFviRZGIjG+W3nmetA9/S6WGE2sG4KOYHpEGn1oMuzIOrm3pUqCAKXp9rY7fbPSSytPhDiX8M+3pubhn7imp0GckMKX7U52O0O8OASpljOaUN+f98IFknkqlnm00DNjyMJZz3XL9ego9JsmBOf/JA3yDNDXu7KTyflNK1sTZoR40CYr5XlcsQX4q89M0vmPjwwQrpOwwWzZG4ARDt9iFbt+CSTuWKDTa0NzCVP/vSQB29c5pOFmUletCAIaOx6MkZi3J2fwcMDbg5O0EI/HXFZ4Zedg9RYDKycZTEb1JBbk2E6qxccVO0VAebkoT484CYiK7zlNOMr6iREk4ZiT4K3Zjv5Y/cQHTMUUvsiMR4ZcPP27FSsc2ibj3b50eac/VzSi0dTdXNJr/yt10VCYVKDlpSiBxHWeGWuSrPx047+STLSE3HMF+SoL8Tbc1JnHXXJ0QTRLh/64tm//6fjUqeNkKywew5Oym86BzGIAu88TSJC41RrbjeHJVZbjXyzpTdJeXQxcc4a8mBC5vFBt8r7nYNeQqTdiy7HMq/J4lem2tjj8eOeZarh992DWCVxyhZrTaqRhDfKjXYr59vNfLO1l5FpjtsVjvK8y8uN6fZpdWGmQrTThy7PetYaEFpR4EKHZU4v+P19w+TqtVNSBTWpBuKDIT5akEGGTsMXG7unTWE8NDBCSyjCp4uyZn39iqIQ7fLNawhvqk7DKquRHXNY8729LmosBlZN4VFKo7zqzxRlIQrw7RlqIn/qHiKuKLwvb2otmakgB2PEunzoS+2z/s3pqDDpydZreWl4dk5KQlH4a4+LixyWSQMqxrt4RyJ8oSSbwBlqIn/tHUYvCrNuAgKIdnjVlGHJ2RvyrXYLBlHgxVmueTAa44H+EW7Pck4avCJadaARkEci/EdpDt2RGL/tWng5kqlwzhryxwfdBBLynG68EpeJdvnPOq0yhqvSUkgozIprHIgneGLQw00ZjimpgppRUaHEcIT/Ls/DHUtMq5f8lcZuRFSZ2dlCDsWJD4bOOq0yhoudNtrDUVqCZ6bk9UVivDzs47ZR9cXTockwERsKYhJFvl6Wy2FfkF9N8cDHZYUftPWx0mKcdccujI7w88fmPU39YoeV17yBWXU8nvCHOOIL8dasqXsZxpqCcgw63peXzoP9I+yfIhIJJ2T+1DPEVWm2WRc5AcLNblDAUHH2KoaCIHCxw8rOEf8ZU3ygDnDpjsQmeaZjUOVsw1SZjdyW5eQP3UN0TZG2CSZkHuof5vp0O3bt7HX8Ii0eEEA/j/fZKKm1gdmmSv/U7ZpW90YQBTQOA4nhEBc4rFyZauPH7f0MzaImMl+ck4a8PRThq43d1FgMs24OgdGiX1w+q8LIRKyzmUjTambVTPDkkIdgQua2aeiRY4Y8PhRihcXInblp/Ll7aNLklmeHPDw15OFTRVkUzOEFn29+fAzbRyv8T8xi7uFD/SPIMC0lVJthgrhCYiTMTRl2rklL4dutvTSdVkB+oH+Y1lCUz8zBG4fRFxzQzbFP4HRc7LQSV9RazJlwb98wmhm6ESdOR/pEYSYFBh0fqG1jMJossvTQwAjDscSMAllTIdLoRtBLc+rcnQqXOEdrA7OQKPhLj4t0nWZaqqA6YEJd878XZ6EoqjNy+ibx6MAI3rjMHbPUzxlDpMWDNtcyPs7ubHF5qo3mUCRp6MxUkBWFv/eqEchUo+ZATa+MdXd+uTSHoCzzg7aFHWQxFc45Qx5IJHjPsVYAfreyeEqPbzqMNQLN1yOXBIEr0my8OOw949io+/uGKTDo2DyN2uCYRsPYLMvPFmdh10p8cXRIAKgey5cau6kwGebkjcMEQz7PFzzfoGO9zXTG7j9FUfhH3zDrbSbKTNM87KNNSbGBIIIg8M2KPIyiyL+dOFXsjckK32/rZ7XFyFVpc7tfoeMuRKvurKiHE7EpZXaUvJis8GDfCFemppA2jSCZZB+djhSKY9NI/G5lEe54nA/VtY+nlQKJBL/uHGSF2cDWOTgoiqIQbhhBX2ZHkOYnoTpWG3jpDLWB7tE039uynNPOs5UcBhLeCEpcJs+g4wsl2Tw15OETJ05NM3p2yMMXG7upMhs43z572qQSSxDtnF9+fAxjNN4Xz3Cfd42o5Iq3z7DhSE4DcVcYRVGoMBu4IzuVP/UM8VD/yKI2Cp1ThlxRFD5d38mJQJhfrCic8xDVaIcXya4fH5Y6H1yVmoI3LrPXPX2hriccZeeIn9uypqfNiQYNokVLfEg15Hathi+W5LDXE+DifSf5Vksv/9nUTWc4yjcr8uY8HDja7kOTZkQ0zn+Q8o3pdo76Q7TOkF6p84eoD4S5bQa2hTZ9NAoZVNecqdfy9fJc9nsDfLK+g/9q7uFDdW10hKN8pnhu3rgSkwmfHMa4wnnWRb8x6EWRbQ4rjw+6Z6QhvjjsZSgW563T6ODDBC2O0Q17pdXEtyryedXt56tN3XyrpZeNrx6nPhDmY6cViM+E+FCIhDuCoXz+wyFSdRpWW41n3Lz+2utCgRm1fjQOPSinhqfcXZDB54uzeKB/hE+c6OC3XYPceayVMpOe+9aUzi3q6vBBQkE/j/z4GIqNOgoNOl48A4Hh3r5hUjTSjM1KGqcBJZJACanplM8VZ7PWauLDx9t5f93kCGyhcE4Z8l92DvLPATdfLMnm0tS5e9XRdu+8vfExbHOqRZKZRoM92D+CAjMaNVALnhOny78928l3KvNI1Un8qL2fP/e4eHOmg62OuXmYiUCMcOMIhqrZU9hmwvWj9L/HZkiv3N8/glYQZuxGFE1aRItWbWAZxW2ZDm7OsHN/3wi/6RxkryfAmzMdXDHH+xxudqNEZYwr5hamT4e3Zjvpj8ZnLIb9sXuIDJ2GS2do0NJmjUYhfac2/rdkO3lXTiq/7x7ih+39nG+38Nj68jlRSwEiDSrNbT758Ym4xGnjNW8A7zS1gZisFjkvc9pmTPONb14TVBA/WZQ1bsy/3NjNlWk2Hl5XNiuZ4YmIto7mx4vmb8gFQeCyVBu7RqanIXpicZ4cdHPzGZQ3x5grY+mVVJ2GR9aX8+WSbJ4b8nLJvpNzYsjMFvN305YQ6ToNb850JM3VnC3iI2ESnij6eebHx2CWJLY5rDwz5OW/yiZPBVEUhfv7RticYj5j5KBJMxJuOMU5FQWV2vTOnDQGozFedfu5fJZdnBMROjYECQXTurn/vaZCnkHHRpuJRwZG+Pjo3MiJiMoyD/WPcEWqbbwBaDpo0k3EJxhyQRD4ZU0RP6qWk7i5c0X4uAtBL82LvTERV6SmkKbV8LdeF1dO4Ykd96uc4i8UZ0+bYgB1sxa0olqnmYD/Ks+lxmLkQoeF0mlSUWdCuNGNJtUwbkTmiytTbfyovZ8H+oZ57xS5+mddHvqjcb6TO/NmOd2AiU8WZWHXahiJxfl4YeastXMmItLiQZttXpBIE+Ayp5U/dA+pkfAUU5geGXATlhXedoY8vuQcjTaHw+PpTEkQ+GhhJpen2vhKYzcFU4ygmy/OKY/8zVlOfrqi8KxodMEDKvVJX7kw3imo7JXOcJT6KYZNHPWHaAiGpy1yToQmTZ1xKEcme0DpOu20jJczIXhoAE2Gac4qjzPhxgw7df7wpMIkqMWvwWicd53hBQfQZhiJDU4WGJqPEVdkhdBxF4ZKx7xmRU6EVhS4PcvJcy4vA5HJYfEvOgcwiuIZ1yyIAposc5JHDup6352bdtZGXInLRFrc6BcgrTKG9TYTm2x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" + " recordid SAPS-I SOFA Length_of_stay Survival In-hospital_death\n", + "0 132539 6 1 5 -1 0\n", + "1 132540 16 8 8 -1 0\n", + "2 132541 21 11 19 -1 0" ] }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "for sample in X:\n", - " sns.lineplot(x=ts, y=sample[0])" + "train_y.head(3)" + ] + }, + { + "cell_type": "markdown", + "id": "8fcc0ed3", + "metadata": {}, + "source": [ + "## References\n", + "[1] Dau, H.A., Bagnall, A., Kamgar, K., Yeh, C.C.M., Zhu, Y., Gharghabi, S., Ratanamahatana, C.A. and Keogh, E., 2019. The UCR time series archive. IEEE/CAA Journal of Automatica Sinica, 6(6), pp.1293-1305.\n", + "\n", + "[2] Roesler,Oliver. (2013). EEG Eye State. UCI Machine Learning Repository. https://doi.org/10.24432/C57G7J.\n", + "\n", + "[3] Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220." ] } ], diff --git a/tutorials/Model Selection.ipynb b/tutorials/Model Selection.ipynb index 8fbde33..152091e 100644 --- a/tutorials/Model Selection.ipynb +++ b/tutorials/Model Selection.ipynb @@ -6,7 +6,7 @@ "metadata": {}, "source": [ "## Model selection.\n", - "This is a minimal example of model selection via hyperparameters optimization." + "This is a simple example of model selection through hyperparameter optimization." ] }, { @@ -36,7 +36,9 @@ "id": "0290b339", "metadata": {}, "source": [ - "#### 0. Install optuna" + "#### 0. Install optuna\n", + "\n", + "Let's first install Optuna." ] }, { @@ -66,7 +68,7 @@ "metadata": {}, "source": [ "#### 1. Load data\n", - "We are working with a toy dataset, and use `tsgm` utility called `tsgm.utils.gen_sine_dataset` to generate the data. We define a function that generates the dataset and then featurewise scale it using `tsgm.utils.TSFeatureWiseScaler`, so that each feature is in $[0, 1]$." + "We are using a small dataset generated by the `tsgm.utils.gen_sine_dataset` function. We scale the features using `tsgm.utils.TSFeatureWiseScaler` to ensure each feature falls within the range of $[0, 1]$." ] }, { From ae5848775a0067e9cd660dc26216880ebcb0acaf Mon Sep 17 00:00:00 2001 From: Alexander Nikitin <1243786+AlexanderVNikitin@users.noreply.github.com> Date: Sat, 15 Jun 2024 21:29:52 +0300 Subject: [PATCH 18/31] add colab versions of tutorials --- README.md | 12 ++++++++---- tutorials/GANs/cGAN.ipynb | 37 +++++++++++++++++++------------------ tutorials/VAEs/VAE.ipynb | 10 +++++----- 3 files changed, 32 insertions(+), 27 deletions(-) diff --git a/README.md b/README.md index 6499bb3..8e6fbce 100644 --- a/README.md +++ b/README.md @@ -93,10 +93,14 @@ result = gan.generate(100) ## :anchor: Tutorials -- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1l2VB6eUwvrxyu8iB30faGiQM5AKthc82?usp=sharing) Introductory Tutorial "[Getting started with TSGM](https://github.com/AlexanderVNikitin/tsgm/blob/main/tutorials/GANs/cGAN.ipynb)" -- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1Vw9t4TlI1Nek_t6bMPyKcPPPqCiXfOK3?usp=sharing) Tutorial on using [Time Series Augmentations](https://github.com/AlexanderVNikitin/tsgm/blob/main/tutorials/augmentations.ipynb) -- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1hubtddSX94KyLzuCTwmU6pAFBgBeiEB-?usp=sharing) Tutorial on [Evaluation of Synthetic Time Series Data](https://github.com/AlexanderVNikitin/tsgm/blob/main/tutorials/evaluation.ipynb) -- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1wpf9WeNVj5TkUcPF6EavVx-hUCOfyvUd?usp=sharing) Tutorial on using [Multiple GPUs or TPU with TSGM](https://github.com/AlexanderVNikitin/tsgm/blob/main/tutorials/Using%20Multiple%20GPUs%20or%20TPU.ipynb) +- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1l2VB6eUwvrxyu8iB30faGiQM5AKthc82?usp=sharing) Introductory Tutorial [Getting started with TSGM](https://github.com/AlexanderVNikitin/tsgm/blob/main/tutorials/GANs/cGAN.ipynb) +- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1frSnJQSsuPS3asgIkmcrNtX4Y7TIQI56?usp=sharing) Tutorial [Datasets in TSGM](https://github.com/AlexanderVNikitin/tsgm/blob/main/tutorials/Datasets.ipynb) +- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1Vw9t4TlI1Nek_t6bMPyKcPPPqCiXfOK3?usp=sharing) Tutorial [Time Series Augmentations](https://github.com/AlexanderVNikitin/tsgm/blob/main/tutorials/augmentations.ipynb) +- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1_jpGrPcwoSpB8eii8XW-spaikczdPqIQ?usp=sharing) Tutorial [Time Series Generation with VAEs](https://github.com/AlexanderVNikitin/tsgm/blob/main/tutorials/VAEs/VAE.ipynb) +- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1rcN3pr8uglBEEOo4bTa1fvXgSMx1vKq9?usp=sharing) Tutorial [Conditional Time Series Generation with GANs](https://github.com/AlexanderVNikitin/tsgm/blob/main/tutorials/GANs/cGAN.ipynb) +- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1hubtddSX94KyLzuCTwmU6pAFBgBeiEB-?usp=sharing) Tutorial [Evaluation of Synthetic Time Series Data](https://github.com/AlexanderVNikitin/tsgm/blob/main/tutorials/evaluation.ipynb) +- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1SKD9hRi-ic27Wts9Qzkssjfe1z7o1NU4?usp=sharing) Tutorial [Model Selection](https://github.com/AlexanderVNikitin/tsgm/blob/main/tutorials/Model%20Selection.ipynb) +- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1wpf9WeNVj5TkUcPF6EavVx-hUCOfyvUd?usp=sharing) Tutorial [Multiple GPUs or TPU with TSGM](https://github.com/AlexanderVNikitin/tsgm/blob/main/tutorials/Using%20Multiple%20GPUs%20or%20TPU.ipynb) For more examples, see [our tutorials](./tutorials). diff --git a/tutorials/GANs/cGAN.ipynb b/tutorials/GANs/cGAN.ipynb index 8f41078..c074697 100644 --- a/tutorials/GANs/cGAN.ipynb +++ b/tutorials/GANs/cGAN.ipynb @@ -1,5 +1,14 @@ { "cells": [ + { + "cell_type": "markdown", + "id": "fd5a3eaf", + "metadata": {}, + "source": [ + "# Conditional Time Series Generation with GANs\n", + "This tutorial covers the generation of a temporal dataset where each time series has a class label assigned." + ] + }, { "cell_type": "code", "execution_count": null, @@ -12,16 +21,8 @@ "\n", "import numpy as np\n", "\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "10e3e196", - "metadata": {}, - "outputs": [], - "source": [ + "import matplotlib.pyplot as plt\n", + "\n", "from tensorflow import keras\n", "from tensorflow.keras import layers\n", "\n", @@ -35,12 +36,12 @@ "id": "843de5f0", "metadata": {}, "source": [ - "We want to generate a temporal dataset where each time series belongs to one of two classes. Let's go step by step through the solution.\n", + "We aim to generate a temporal dataset where each time series belongs to one of two classes. Let's go through the solution step by step.\n", "\n", "#### 1. Define parameters of GAN:\n", - "First, we are defining the parameters of GAN, and the training algorithm.\n", - "- `latent_dim` is the size of input noise in GAN,\n", - "- `output_dim` is the number of classes, which, mentioned above is two,\n", + "First, we need to define the parameters for the Generative Adversarial Network (GAN) and its training algorithm:\n", + "- `latent_dim` is the size of the input noise in GAN,\n", + "- `output_dim` is the number of output classes, which in this case is two.,\n", "- `feature_dim` is the number of time series features,\n", "- `seq_len` is the length of the time series." ] @@ -69,7 +70,7 @@ "metadata": {}, "source": [ "#### 2. Load data:\n", - "We are working with a toy dataset, and use `tsgm` utility called `tsgm.utils.gen_sine_vs_const_dataset` to generate the data. Next, we featurewise scale the dataset so that each feature is in $[-1, 1]$, using `tsgm.utils.TSFeatureWiseScaler`." + "We will generate a toy dataset, and use `tsgm` utility called `tsgm.utils.gen_sine_vs_const_dataset` to generate the data. After generating the data, we will scale each feature to be within the range of $[-1, 1]$, using `tsgm.utils.TSFeatureWiseScaler`." ] }, { @@ -98,7 +99,7 @@ "metadata": {}, "source": [ "#### 3. Visually explore the dataset.\n", - "There are many tools for convenient visualizations of temporal datasets. Here, we use `tsgm.utils.visualize_ts_lineplot`, which is convenient for TS classification datasets." + "There are many tools for convenient visualizations of temporal datasets in `tsgm,utils`. Here, we use `tsgm.utils.visualize_ts_lineplot`, which is convenient for TS classification datasets." ] }, { @@ -119,7 +120,7 @@ "metadata": {}, "source": [ "#### 4. Choose architecture.\n", - "Here, you can either use one of the architectures presented in `tsgm.models.architectures`, or define custom discriminator and generator architectures as `tf` models." + "Here, one can either use one of the architectures presented in `tsgm.models.architectures`, or define custom discriminator and generator architectures as `keras` models." ] }, { @@ -141,7 +142,7 @@ "metadata": {}, "source": [ "#### 5. Define model and train it.\n", - "We define conditional GAN model (`tsgm.models.cgan.ConditionalGAN`), compile it (here, you can choose different optimizers for discriminator and generator), and train using `.fit` model. Additionally, we use `tsgm.models.monitors.GANMonitor` to track training process." + "We define a conditional GAN model (`tsgm.models.cgan.ConditionalGAN`), compile it (here, one can choose different optimizers for discriminator and generator), and train using `.fit` model. Additionally, we employ `tsgm.models.monitors.GANMonitor` to monitor and track the training process, ensuring we can observe the model's progress and performance." ] }, { diff --git a/tutorials/VAEs/VAE.ipynb b/tutorials/VAEs/VAE.ipynb index fa12c9a..d0acc7d 100644 --- a/tutorials/VAEs/VAE.ipynb +++ b/tutorials/VAEs/VAE.ipynb @@ -5,7 +5,7 @@ "id": "60611bed", "metadata": {}, "source": [ - "## Time series generation using VAEs.\n", + "# Time series generation using VAEs\n", "This is a minimal example of unsupervised time series generation using VAEs." ] }, @@ -36,7 +36,7 @@ "metadata": {}, "source": [ "#### 1. Choose architecture of encoder and decoder.\n", - "Here, you can either use one of the architectures presented in `tsgm.models.architectures`, or define custom discriminator and generator architectures as `tf` models." + "Here, you can either use one of the architectures presented in `tsgm.models.architectures`, or define custom discriminator and generator architectures as `keras` models." ] }, { @@ -56,7 +56,7 @@ "metadata": {}, "source": [ "#### 2. Load data:\n", - "We are working with a toy dataset, and use `tsgm` utility called `tsgm.utils.gen_sine_dataset` to generate the data. Next, we featurewise scale the dataset so that each feature is in $[0, 1]$, using `tsgm.utils.TSFeatureWiseScaler`." + "We are working with a toy dataset, and use `tsgm` utility called `tsgm.utils.gen_sine_dataset` to generate the data. Next, we feature-wise scale the dataset so that each feature is in $[0, 1]$, using `tsgm.utils.TSFeatureWiseScaler`." ] }, { @@ -77,7 +77,7 @@ "metadata": {}, "source": [ "#### 3. Define model and train it.\n", - "We define conditional GAN model (`tsgm.models.cvae.BetaVAE`), compile it, and train using `.fit` model. Additionally, we use `tsgm.models.monitors.GANMonitor` to track training process." + "We define a conditional GAN model (`tsgm.models.cvae.BetaVAE`), compile it, and train using `.fit` model." ] }, { @@ -99,7 +99,7 @@ "metadata": {}, "source": [ "#### 4. Check reconstruction of the data.\n", - "We reconstruct data using `vae.predict(scaled_data)`. For validating that VAE works, we check that original and reconstructed datasets are visually similar using `tsgm.utils.visualize_original_and_reconst_ts`." + "We reconstruct data using `vae.predict(scaled_data)`. For validating that VAE works, we check that the original and reconstructed datasets are visually similar using `tsgm.utils.visualize_original_and_reconst_ts`." ] }, { From b821cd7900982a6c5f17c834ccd6330ede7dca5f Mon Sep 17 00:00:00 2001 From: Alexander Nikitin <1243786+AlexanderVNikitin@users.noreply.github.com> Date: Mon, 17 Jun 2024 12:33:50 +0300 Subject: [PATCH 19/31] improve visualizations --- tsgm/utils/file_utils.py | 8 ++++++-- tsgm/utils/visualization.py | 30 +++++++++++++++++++++++------- tutorials/Model Selection.ipynb | 25 ++++++++----------------- 3 files changed, 37 insertions(+), 26 deletions(-) diff --git a/tsgm/utils/file_utils.py b/tsgm/utils/file_utils.py index 8275b35..1b0e062 100644 --- a/tsgm/utils/file_utils.py +++ b/tsgm/utils/file_utils.py @@ -60,11 +60,15 @@ def extract_archive(from_path: str, to_path: T.Optional[str] = None, pwd: T.Opti def download(url: str, path: str, md5: T.Optional[str] = None, max_attempt: int = 3) -> None: logger.info(f"### Downloading from {url} ###") os.makedirs(path, exist_ok=True) - resource_name = url.split("/")[-1] + if "?" in url: + base_url, _ = url.split("?") + else: + base_url = url + resource_name = base_url.split("/")[-1] path = os.path.join(path, resource_name) for attempt in range(max_attempt): logger.info(f"Attempt {attempt + 1} / {max_attempt}") - urllib.request.urlretrieve(urllib.parse.quote(url, safe=":/"), path) + urllib.request.urlretrieve(urllib.parse.quote(url, safe=":/?&="), path) if md5 is not None: downloaded_md5 = hashlib.md5(open(path, "rb").read()).hexdigest() if md5 == downloaded_md5: diff --git a/tsgm/utils/visualization.py b/tsgm/utils/visualization.py index 359d514..9075273 100644 --- a/tsgm/utils/visualization.py +++ b/tsgm/utils/visualization.py @@ -246,7 +246,7 @@ def visualize_ts_lineplot( :param unite_features: Whether to plot all features together or separately, defaults to True. :type unite_features: bool, optional :param legend_fontsize: Font size to use. - :type unite_features: int, optional + :type legend_fontsize: int, optional :param tick_size: Font size for y-axis ticks. :type tick_size: int, optional """ @@ -269,24 +269,40 @@ def visualize_ts_lineplot( else: for feat_id in range(ts.shape[2]): sns.lineplot( - x=range(ts.shape[1]), y=ts[sample_id, :, feat_id], ax=axs[i] + x=range(ts.shape[1]), y=ts[sample_id, :, feat_id], ax=axs[i], + label="Generated" ) if ys is not None: - axs[i].tick_params(labelsize=tick_size) + axs[i].tick_params(labelsize=tick_size, which="both") if len(ys.shape) == 1: axs[i].set_title(ys[sample_id], fontsize=legend_fontsize) elif len(ys.shape) == 2: + ax2 = axs[i].twinx() sns.lineplot( x=range(ts.shape[1]), y=ys[sample_id], - ax=axs[i].twinx(), + ax=ax2, color="g", - label="Target variable", + label="Condition", ) - axs[i].twinx().tick_params(labelsize=tick_size) +# axs[i].twinx().yaxis.set_ticks_position('right') + ax2.tick_params(labelsize=tick_size) + if i == 0: + leg = ax2.legend(fontsize=legend_fontsize, loc='upper right') + for legobj in leg.legendHandles: + legobj.set_linewidth(2.0) + else: + ax2.get_legend().remove() else: raise ValueError("ys contains too many dimensions") - axs[i].legend(fontsize=legend_fontsize) + if i == 0: + leg = axs[i].legend(fontsize=legend_fontsize, loc='upper left') + for legobj in leg.legendHandles: + legobj.set_linewidth(2.0) + else: + axs[i].get_legend().remove() + if i != len(ids) - 1: + axs[i].set_xticks([]) def visualize_original_and_reconst_ts( diff --git a/tutorials/Model Selection.ipynb b/tutorials/Model Selection.ipynb index 152091e..7ed1fc1 100644 --- a/tutorials/Model Selection.ipynb +++ b/tutorials/Model Selection.ipynb @@ -5,7 +5,7 @@ "id": "60611bed", "metadata": {}, "source": [ - "## Model selection.\n", + "# Model Selection in TSGM\n", "This is a simple example of model selection through hyperparameter optimization." ] }, @@ -36,7 +36,7 @@ "id": "0290b339", "metadata": {}, "source": [ - "#### 0. Install optuna\n", + "## 0. Install optuna\n", "\n", "Let's first install Optuna." ] @@ -49,16 +49,7 @@ "outputs": [], "source": [ "import sys\n", - "!{sys.executable} -m pip install optuna" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "01e0ec58", - "metadata": {}, - "outputs": [], - "source": [ + "!{sys.executable} -m pip install optuna\n", "import optuna" ] }, @@ -67,7 +58,7 @@ "id": "a375b04e", "metadata": {}, "source": [ - "#### 1. Load data\n", + "## 1. Load data\n", "We are using a small dataset generated by the `tsgm.utils.gen_sine_dataset` function. We scale the features using `tsgm.utils.TSFeatureWiseScaler` to ensure each feature falls within the range of $[0, 1]$." ] }, @@ -90,7 +81,7 @@ "id": "b76dd508", "metadata": {}, "source": [ - "#### 2. Define the optimization problem" + "## 2. Define the optimization problem" ] }, { @@ -127,8 +118,8 @@ "id": "5bbab28c", "metadata": {}, "source": [ - "#### 3. Define the search space for the optimizer\n", - "We can optimize the choice of the optimizer and its hyperparameters" + "## 3. Define the search space for the optimizer\n", + "We can optimize the choice of the optimizer and hyperparameters." ] }, { @@ -172,7 +163,7 @@ "id": "70786d6c", "metadata": {}, "source": [ - "#### 4. Define the objective function\n", + "## 4. Define the objective function\n", "In the objective function, we load the data and use them to train a TimeGAN model (`tsgm.models.timeGAN.TimeGAN`) while changing its parameters. We use the fitted TimeGAN model to generate synthetic samples, and finally use them to compute the value of the metric we want to optimize. " ] }, From d5b8cd166cded6382239597da22525323f6350ca Mon Sep 17 00:00:00 2001 From: isoApple <50541954+uncircle@users.noreply.github.com> Date: Tue, 18 Jun 2024 21:13:56 +0300 Subject: [PATCH 20/31] add synchronized_brainwave_datase and its test case, modify readme (#48) * add synchronized_brainwave_datase and its test case, add readme * change url, replace print with logger, add doc for function, change return type * improve visualizations * use file_utils.download to download file * fix redundant import --------- Co-authored-by: Alexander Nikitin <1243786+AlexanderVNikitin@users.noreply.github.com> --- README.md | 25 +++++++++++++------------ tests/test_utils.py | 6 ++++++ tsgm/utils/datasets.py | 36 ++++++++++++++++++++++++++++++++++++ 3 files changed, 55 insertions(+), 12 deletions(-) diff --git a/README.md b/README.md index 8e6fbce..a5d86f7 100644 --- a/README.md +++ b/README.md @@ -150,18 +150,19 @@ TSGM implements many metrics for synthetic time series evaluation. Check Section ## :floppy_disk: Datasets -| Dataset | API | Description | -| ------------- | ------------- | ------------- | -| UCR Dataset | `tsgm.utils.UCRDataManager` | https://www.cs.ucr.edu/%7Eeamonn/time_series_data_2018/ | -| Mauna Loa | `tsgm.utils.get_mauna_loa()` | https://gml.noaa.gov/ccgg/trends/data.html | -| EEG & Eye state | `tsgm.utils.get_eeg()` | https://archive.ics.uci.edu/ml/datasets/EEG+Eye+State | -| Power consumption dataset | `tsgm.utils.get_power_consumption()` | https://archive.ics.uci.edu/ml/datasets/individual+household+electric+power+consumption | -| Stock data | `tsgm.utils.get_stock_data(ticker_name)` | Gets historical stock data from YFinance | -| COVID-19 over the US | `tsgm.utils.get_covid_19()` | Covid-19 distribution over the US | -| Energy Data (UCI) | `tsgm.utils.get_energy_data()` | https://archive.ics.uci.edu/ml/datasets/Appliances+energy+prediction | -| MNIST as time series | `tsgm.utils.get_mnist_data()` | https://en.wikipedia.org/wiki/MNIST_database | -| Samples from GPs | `tsgm.utils.get_gp_samples_data()` | https://en.wikipedia.org/wiki/Gaussian_process | -| Physionet 2012 | `tsgm.utils.get_physionet2012()` | https://archive.physionet.org/pn3/challenge/2012/ | +| Dataset | API | Description | +| - |---------------------------------------------------| ------------- | +| UCR Dataset | `tsgm.utils.UCRDataManager` | https://www.cs.ucr.edu/%7Eeamonn/time_series_data_2018/ | +| Mauna Loa | `tsgm.utils.get_mauna_loa()` | https://gml.noaa.gov/ccgg/trends/data.html | +| EEG & Eye state | `tsgm.utils.get_eeg()` | https://archive.ics.uci.edu/ml/datasets/EEG+Eye+State | +| Power consumption dataset | `tsgm.utils.get_power_consumption()` | https://archive.ics.uci.edu/ml/datasets/individual+household+electric+power+consumption | +| Stock data | `tsgm.utils.get_stock_data(ticker_name)` | Gets historical stock data from YFinance | +| COVID-19 over the US | `tsgm.utils.get_covid_19()` | Covid-19 distribution over the US | +| Energy Data (UCI) | `tsgm.utils.get_energy_data()` | https://archive.ics.uci.edu/ml/datasets/Appliances+energy+prediction | +| MNIST as time series | `tsgm.utils.get_mnist_data()` | https://en.wikipedia.org/wiki/MNIST_database | +| Samples from GPs | `tsgm.utils.get_gp_samples_data()` | https://en.wikipedia.org/wiki/Gaussian_process | +| Physionet 2012 | `tsgm.utils.get_physionet2012()` | https://archive.physionet.org/pn3/challenge/2012/ | +| Synchronized Brainwave Dataset | `tsgm.utils.get_synchronized_brainwave_dataset()` | https://www.kaggle.com/datasets/berkeley-biosense/synchronized-brainwave-dataset | TSGM provides API for convenient use of many time-series datasets (currently more than 140 datasets). The comprehensive list of the datasets in the [documentation](https://tsgm.readthedocs.io/en/latest/guides/datasets.html) diff --git a/tests/test_utils.py b/tests/test_utils.py index b81bea8..511154f 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -366,3 +366,9 @@ def test_extract_targz(): def test_version(): assert isinstance(tsgm.__version__, str) + + +def test_get_synchronized_brainwave_dataset(): + X, y = tsgm.utils.get_synchronized_brainwave_dataset() + assert X.shape == (30013, 12) + assert y.shape == (30013,) \ No newline at end of file diff --git a/tsgm/utils/datasets.py b/tsgm/utils/datasets.py index dd6bed0..3f229ac 100644 --- a/tsgm/utils/datasets.py +++ b/tsgm/utils/datasets.py @@ -296,6 +296,42 @@ def get_eeg() -> T.Tuple[TensorLike, TensorLike]: return X, y +def get_synchronized_brainwave_dataset() -> T.Tuple[pd.DataFrame, pd.DataFrame]: + """ + Loads the EEG Synchronized Brainwave dataset. + + This function downloads the EEG Synchronized Brainwave dataset from dropbox + and returns the input features (X) and target labels (y). + + :return: A tuple containing the input features (X) and target labels (y). + :rtype: tuple[pd.DataFrame, pd.DataFrame] + """ + url = ("https://www.dropbox.com/scl/fi/uqah9rthwrt5i2q6evtws/eeg-data.csv.zip?rlkey=z7sautwq74jow2xt9o6q7lcij&st" + "=hvpvvfez&dl=1") + cur_path = os.path.dirname(__file__) + path_to_folder = os.path.join(cur_path, "../../data/") + path_to_resource = os.path.join(path_to_folder, 'eeg-data.csv.zip') + path_to_renamed_csv = os.path.join(path_to_folder, "synchronized_brainwave_dataset.csv") + os.makedirs(path_to_folder, exist_ok=True) + if not os.path.exists(path_to_renamed_csv): + file_utils.download(url, path_to_folder) + logger.info("Download completed.") + file_utils.extract_archive(path_to_resource, path_to_folder) + logger.info("Extraction completed.") + original_csv = os.path.join(path_to_folder, "eeg-data.csv") + if os.path.exists(original_csv): + os.rename(original_csv, path_to_renamed_csv) + logger.info(f"File renamed to {path_to_renamed_csv}") + else: + logger.warning("The expected CSV file was not found.") + else: + logger.info("File exist") + df = pd.read_csv(path_to_renamed_csv) + X = df.drop("label", axis=1) + y = df["label"] + return X, y + + def get_power_consumption() -> npt.NDArray: """ Retrieves the household power consumption dataset. From f43887c5ef14423d461aa021858ae27502bf6eb8 Mon Sep 17 00:00:00 2001 From: Alexander Nikitin <1243786+AlexanderVNikitin@users.noreply.github.com> Date: Thu, 20 Jun 2024 22:06:51 +0300 Subject: [PATCH 21/31] update readme --- README.md | 4 ++++ docs/_static/generation_process.gif | Bin 0 -> 1411005 bytes 2 files changed, 4 insertions(+) create mode 100644 docs/_static/generation_process.gif diff --git a/README.md b/README.md index a5d86f7..3bcf4fd 100644 --- a/README.md +++ b/README.md @@ -37,6 +37,10 @@ Create and evaluate synthetic time series datasets effortlessly TSGM is an open-source framework for synthetic time series dataset generation and evaluation. +
+ +
+ The framework can be used for creating synthetic datasets (see :hammer: Generators ), augmenting time series data (see :art: Augmentations ), evaluating synthetic data with respect to consistency, privacy, downstream performance, and more (see :chart_with_upwards_trend: Metrics ), using common time series datasets (TSGM provides easy access to more than 140 datasets, see :floppy_disk: Datasets ). 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zBtJyV!Vp{oH^`2Ylt}6eLYq83dlW>Sj6bS$yAYg%t~A4<980oHJQ{pS>5~k%e@w}M zn~f?2Nw07NJzRr101^~ngaRDATeLY4Hz#w0y!*^26HSYlI?yalH<&&gkiB@TJ=es^ z#hgvroV$dqP29{)-GqzV+)dy7P2ePn-V9FSEKcK`h~Yd=)cN6>`w3ePVfv*@f=U`EKl=1PxMSr^;}Q(Y)|)mPxy>a`J7MstWW#A XPyEbJ{oGIf>`(vvPXP6)fB*nH3;)3! literal 0 HcmV?d00001 From dafc344437efb844c77a0b5a74f971317bd37242 Mon Sep 17 00:00:00 2001 From: Alexander Nikitin <1243786+AlexanderVNikitin@users.noreply.github.com> Date: Mon, 24 Jun 2024 17:02:48 +0300 Subject: [PATCH 22/31] v0.0.7 --- docs/modules/root.rst | 9 +-------- requirements/docs_requirements.txt | 2 +- tsgm/version.py | 2 +- 3 files changed, 3 insertions(+), 10 deletions(-) diff --git a/docs/modules/root.rst b/docs/modules/root.rst index c0bffd2..7c7eae2 100644 --- a/docs/modules/root.rst +++ b/docs/modules/root.rst @@ -11,7 +11,7 @@ Datasets :members: :undoc-members: -.. automodule:: tsgm.utils.dataset +.. automodule:: tsgm.utils.datasets :members: :undoc-members: @@ -78,13 +78,6 @@ Zoo :undoc-members: -Datasets --------------- -.. automodule:: tsgm.utils.datasets - :members: - :undoc-members: - - Simulators -------------- .. automodule:: tsgm.simulator diff --git a/requirements/docs_requirements.txt b/requirements/docs_requirements.txt index ee6daf2..428667e 100644 --- a/requirements/docs_requirements.txt +++ b/requirements/docs_requirements.txt @@ -7,5 +7,5 @@ nbsphinx jupytext pydata-sphinx-theme sphinxcontrib-bibtex -sphinx-autoapi +sphinx-autoapi==3.0.0 sphinx_rtd_theme diff --git a/tsgm/version.py b/tsgm/version.py index 034f46c..6526deb 100644 --- a/tsgm/version.py +++ b/tsgm/version.py @@ -1 +1 @@ -__version__ = "0.0.6" +__version__ = "0.0.7" From 2b138cbf38dbbd64d02bb6867a59c63786e8a0e8 Mon Sep 17 00:00:00 2001 From: Alexander Nikitin <1243786+AlexanderVNikitin@users.noreply.github.com> Date: Thu, 11 Jul 2024 16:52:59 +0300 Subject: [PATCH 23/31] add wgan --- README.md | 1 + tests/test_cgan.py | 32 +++++++++-- tsgm/models/architectures/zoo.py | 94 ++++++++++++++++++++++++++++++++ tsgm/models/cgan.py | 60 +++++++++++++++++--- 4 files changed, 176 insertions(+), 11 deletions(-) diff --git a/README.md b/README.md index 3bcf4fd..e5ca903 100644 --- a/README.md +++ b/README.md @@ -127,6 +127,7 @@ TSGM implements several generative models for synthetic time series data. | ------------- | ------------- | ------------- | ------------- | | Structural Time Series model | [tsgm.models.sts.STS](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.models.sts.STS) | Data-driven | Great for modeling time series when prior knowledge is available (e.g., trend or seasonality). | | GAN | [tsgm.models.cgan.GAN](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.models.cgan.GAN) | Data-driven | A generic implementation of GAN for time series generation. It can be customized with architectures for generators and discriminators. | +| WaveGAN | [tsgm.models.cgan.GAN](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.models.cgan.GAN) | Data-driven | WaveGAN is the model for audio synthesis proposed in [Adversarial Audio Synthesis](https://arxiv.org/abs/1802.04208). To use WaveGAN, set `use_wgan=True` when initializing the GAN class and use the `zoo["wavegan"]` architecture from the model zoo. | | ConditionalGAN | [tsgm.models.cgan.ConditionalGAN](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.models.cgan.ConditionalGAN) | Data-driven | A generic implementation of conditional GAN. It supports scalar conditioning as well as temporal one. | | BetaVAE | [tsgm.models.cvae.BetaVAE](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.models.cvae.BetaVAE) | Data-driven | A generic implementation of Beta VAE for TS. The loss function is customized to work well with multi-dimensional time series. | | cBetaVAE | [tsgm.models.cvae.cBetaVAE](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.models.cvae.cBetaVAE) | Data-driven | Conditional version of BetaVAE. It supports temporal a scalar condiotioning.| diff --git a/tests/test_cgan.py b/tests/test_cgan.py index 8c0e78d..298eb81 100644 --- a/tests/test_cgan.py +++ b/tests/test_cgan.py @@ -236,7 +236,6 @@ def test_dp_compiler(): learning_rate=learning_rate ) - g_optimizer = tf_privacy.DPKerasAdamOptimizer( l2_norm_clip=l2_norm_clip, noise_multiplier=noise_multiplier, @@ -259,6 +258,31 @@ def test_dp_compiler(): assert generated_samples.shape == (10, 64, 1) -def test_temporal_cgan_multiple_features(): - # TODO - pass +def test_wavegan(): + latent_dim = 2 + output_dim = 1 + feature_dim = 1 + seq_len = 64 + batch_size = 48 + + dataset = _gen_dataset(seq_len, feature_dim, batch_size) + architecture = tsgm.models.architectures.zoo["wavegan"]( + seq_len=seq_len, feat_dim=feature_dim, + latent_dim=latent_dim, output_dim=output_dim) + discriminator, generator = architecture.discriminator, architecture.generator + gan = tsgm.models.cgan.GAN( + discriminator=discriminator, generator=generator, latent_dim=latent_dim, use_wgan=True + ) + gan.compile( + d_optimizer=keras.optimizers.Adam(learning_rate=0.0003), + g_optimizer=keras.optimizers.Adam(learning_rate=0.0003), + loss_fn=keras.losses.BinaryCrossentropy(), + ) + + gan.fit(dataset, epochs=1) + + assert gan.generator is not None + assert gan.discriminator is not None + # Check generation + generated_samples = gan.generate(10) + assert generated_samples.shape == (10, seq_len, 1) diff --git a/tsgm/models/architectures/zoo.py b/tsgm/models/architectures/zoo.py index 2253c8b..87e4b21 100644 --- a/tsgm/models/architectures/zoo.py +++ b/tsgm/models/architectures/zoo.py @@ -871,6 +871,99 @@ def _build_generator(self, output_activation: str) -> keras.Model: return generator +class WaveGANArchitecture(BaseGANArchitecture): + """ + WaveGAN architecture, from https://arxiv.org/abs/1802.04208 + + Inherits from BaseGANArchitecture. + """ + arch_type = "gan:raw" + + def __init__(self, seq_len: int, feat_dim: int = 64, latent_dim: int = 32, output_dim: int = 1, kernel_size: int = 32, phase_rad: int = 2, use_batchnorm: bool = False): + """ + Initializes the WaveGANArchitecture. + + :param seq_len: Length of input sequences. + :type seq_len: int + :param feat_dim: Dimensionality of input features. + :type feat_dim: int + :param latent_dim: Dimensionality of the latent space. + :type latent_dim: int + :param output_dim: Dimensionality of the output. + :type output_dim: int + :param kernel_size: Sizes of convolutions + :type kernel_size: int, optional + :param phase_rad: Phase shuffle radius for wavegan (default is 2) + :type phase_rad: int, optional + :param use_batchnorm: Whether to use batchnorm (default is False) + :type use_batchnorm: bool, optional + """ + self.seq_len = seq_len + self.feat_dim = feat_dim + self.latent_dim = latent_dim + self.kernel_size = kernel_size + self.phase_rad = phase_rad + self.output_dim = output_dim + self.use_batchnorm = use_batchnorm + + self._discriminator = self._build_discriminator() + self._generator = self._build_generator() + + def _apply_phaseshuffle(self, x, rad): + ''' + Based on + https://github.com/chrisdonahue/wavegan/ + ''' + if rad <= 0 or x.shape[1] <= 1: + return x + + b, x_len, nch = x.get_shape().as_list() + + phase = tf.random.uniform([], minval=-rad, maxval=rad + 1, dtype=tf.int32) + pad_l, pad_r = tf.maximum(phase, 0), tf.maximum(-phase, 0) + phase_start = pad_r + x = tf.pad(x, [[0, 0], [pad_l, pad_r], [0, 0]], mode="reflect") + + x = x[:, phase_start:phase_start + x_len] + x.set_shape([b, x_len, nch]) + + return x + + def _conv_transpose_block(self, inputs, channels, strides=4): + x = layers.Conv1DTranspose(channels, self.kernel_size, strides=strides, padding='same', use_bias=False)(inputs) + x = layers.BatchNormalization()(x) if self.use_batchnorm else x + x = layers.LeakyReLU()(x) + return x + + def _build_generator(self): + inputs = layers.Input((self.latent_dim,)) + x = layers.Dense(16 * 1024, use_bias=False)(inputs) + x = layers.BatchNormalization()(x) if self.use_batchnorm else x + x = layers.LeakyReLU()(x) + x = layers.Reshape((16, 1024))(x) + + for conv_size in [512, 256, 128, 64]: + x = self._conv_transpose_block(x, conv_size) + + x = layers.Conv1DTranspose(1, self.kernel_size, strides=4, padding='same', use_bias=False, activation='tanh')(x) + pool_and_stride = math.ceil((x.shape[1] + 1) / (self.seq_len + 1)) + x = layers.AveragePooling1D(pool_size=pool_and_stride, strides=pool_and_stride)(x) + return keras.Model(inputs, x) + + def _build_discriminator(self): + inputs = layers.Input((self.seq_len, self.feat_dim)) + for conv_size in [64, 128, 256, 512]: + x = layers.Conv1D(conv_size, self.kernel_size, strides=4, padding='same')(inputs) + x = layers.BatchNormalization()(x) if self.use_batchnorm else x + x = layers.LeakyReLU()(x) + x = self._apply_phaseshuffle(x, self.phase_rad) + + x = layers.Flatten()(x) + x = layers.Dense(1)(x) + + return keras.Model(inputs, x) + + class Zoo(dict): """ A collection of architectures represented. It behaves like supports Python `dict` API. @@ -901,6 +994,7 @@ def summary(self) -> None: "t-cgan_c4": tcGAN_Conv4Architecture, "cgan_lstm_n": cGAN_LSTMnArchitecture, "cgan_lstm_3": cGAN_LSTMConv3Architecture, + "wavegan": WaveGANArchitecture, # Downstream models "clf_cn": ConvnArchitecture, diff --git a/tsgm/models/cgan.py b/tsgm/models/cgan.py index 2ffc188..51a0454 100644 --- a/tsgm/models/cgan.py +++ b/tsgm/models/cgan.py @@ -27,26 +27,56 @@ class GAN(keras.Model): """ GAN implementation for unlabeled time series. """ - def __init__(self, discriminator: keras.Model, generator: keras.Model, latent_dim: int) -> None: + def __init__(self, discriminator: keras.Model, generator: keras.Model, latent_dim: int, use_wgan: bool = False) -> None: """ :param discriminator: A discriminator model which takes a time series as input and check - whether the image is real or fake. + whether the sample is real or fake. :type discriminator: keras.Model :param generator: Takes as input a random noise vector of `latent_dim` length and returns a simulated time-series. :type generator: keras.Model :param latent_dim: The size of the noise vector. :type latent_dim: int + :param use_wgan: Use Wasserstein GAN with gradien penalty + :type use_wgan: bool """ super(GAN, self).__init__() self.discriminator = discriminator self.generator = generator self.latent_dim = latent_dim self._seq_len = self.generator.output_shape[1] + self.use_wgan = use_wgan + self.gp_weight = 10.0 self.gen_loss_tracker = keras.metrics.Mean(name="generator_loss") self.disc_loss_tracker = keras.metrics.Mean(name="discriminator_loss") + def wgan_discriminator_loss(self, real_sample, fake_sample): + real_loss = tf.reduce_mean(real_sample) + fake_loss = tf.reduce_mean(fake_sample) + return fake_loss - real_loss + + # Define the loss functions to be used for generator + def wgan_generator_loss(self, fake_sample): + return -tf.reduce_mean(fake_sample) + + def gradient_penalty(self, batch_size, real_samples, fake_samples): + # get the interpolated samples + alpha = tf.random.normal([batch_size, 1, 1], 0.0, 1.0) + diff = fake_samples - real_samples + interpolated = real_samples + alpha * diff + with tf.GradientTape() as gp_tape: + gp_tape.watch(interpolated) + # 1. Get the discriminator output for this interpolated sample. + pred = self.discriminator(interpolated, training=True) + + # 2. Calculate the gradients w.r.t to this interpolated sample. + grads = gp_tape.gradient(pred, [interpolated])[0] + # 3. Calcuate the norm of the gradients + norm = tf.sqrt(tf.reduce_sum(tf.square(grads), axis=[1, 2])) + gp = tf.reduce_mean((norm - 1.0) ** 2) + return gp + @property def metrics(self) -> T.List: """ @@ -94,7 +124,6 @@ def train_step(self, data: tsgm.types.Tensor) -> T.Dict[str, float]: """ real_data = data batch_size = tf.shape(real_data)[0] - # Generate ts random_vector = self._get_random_vector_labels(batch_size) fake_data = self.generator(random_vector) @@ -111,7 +140,19 @@ def train_step(self, data: tsgm.types.Tensor) -> T.Dict[str, float]: ) with tf.GradientTape() as tape: predictions = self.discriminator(combined_data) - d_loss = self.loss_fn(desc_labels, predictions) + if self.use_wgan: + fake_logits = self.discriminator(fake_data, training=True) + # Get the logits for the real samples + real_logits = self.discriminator(real_data, training=True) + + # Calculate the discriminator loss using the fake and real sample logits + d_cost = self.wgan_discriminator_loss(real_logits, fake_logits) + # Calculate the gradient penalty + gp = self.gradient_penalty(batch_size, real_data, fake_data) + # Add the gradient penalty to the original discriminator loss + d_loss = d_cost + gp * self.gp_weight + else: + d_loss = self.loss_fn(desc_labels, predictions) grads = tape.gradient(d_loss, self.discriminator.trainable_weights) self.d_optimizer.apply_gradients( zip(grads, self.discriminator.trainable_weights) @@ -126,7 +167,11 @@ def train_step(self, data: tsgm.types.Tensor) -> T.Dict[str, float]: with tf.GradientTape() as tape: fake_data = self.generator(random_vector) predictions = self.discriminator(fake_data) - g_loss = self.loss_fn(misleading_labels, predictions) + if self.use_wgan: + # uses logits + g_loss = self.wgan_generator_loss(predictions) + else: + g_loss = self.loss_fn(misleading_labels, predictions) grads = tape.gradient(g_loss, self.generator.trainable_weights) self.g_optimizer.apply_gradients(zip(grads, self.generator.trainable_weights)) @@ -167,10 +212,10 @@ class ConditionalGAN(keras.Model): """ Conditional GAN implementation for labeled and temporally labeled time series. """ - def __init__(self, discriminator: keras.Model, generator: keras.Model, latent_dim: int, temporal=False) -> None: + def __init__(self, discriminator: keras.Model, generator: keras.Model, latent_dim: int, temporal=False, use_wgan=False) -> None: """ :param discriminator: A discriminator model which takes a time series as input and check - whether the image is real or fake. + whether the sample is real or fake. :type discriminator: keras.Model :param generator: Takes as input a random noise vector of `latent_dim` length and return a simulated time-series. @@ -312,6 +357,7 @@ def train_step(self, data: T.Tuple) -> T.Dict[str, float]: fake_data = tf.concat([fake_samples, rep_labels], -1) predictions = self.discriminator(fake_data) g_loss = self.loss_fn(misleading_labels, predictions) + if self.dp: # For DP optimizers from `tensorflow.privacy` self.g_optimizer.minimize(g_loss, self.generator.trainable_weights, tape=tape) From 366afb332c8978783e2af625d55fe1cda89b1fd7 Mon Sep 17 00:00:00 2001 From: liyiersan-server5 Date: Fri, 19 Jul 2024 19:20:44 +0800 Subject: [PATCH 24/31] Initial commit for keras-3.0 branch --- .gitignore | 3 + tsgm/__init__.py | 1 + tsgm/backend.py | 29 ++++++ tsgm/metrics/metrics.py | 3 +- tsgm/metrics/statistics.py | 9 +- tsgm/models/architectures/zoo.py | 30 +++--- tsgm/models/augmentations.py | 3 +- tsgm/models/cvae.py | 151 ++++++++++++++++++++++++------- tsgm/optimization/abc.py | 7 +- tsgm/simulator.py | 24 +++-- tsgm/types.py | 8 +- tsgm/utils/data_processing.py | 3 +- tsgm/utils/datasets.py | 4 +- tsgm/utils/mmd.py | 70 +++++++------- tsgm/utils/utils.py | 19 ++-- tsgm/utils/visualization.py | 5 +- 16 files changed, 253 insertions(+), 116 deletions(-) create mode 100644 tsgm/backend.py diff --git a/.gitignore b/.gitignore index cf83dd4..9e2e3ff 100644 --- a/.gitignore +++ b/.gitignore @@ -133,3 +133,6 @@ dmypy.json # Pyre type checker .pyre/ + +# local history +.lh/ \ No newline at end of file diff --git a/tsgm/__init__.py b/tsgm/__init__.py index 5c7e246..5272f8a 100644 --- a/tsgm/__init__.py +++ b/tsgm/__init__.py @@ -1,3 +1,4 @@ +import tsgm.backend import tsgm.types import tsgm.dataset import tsgm.simulator diff --git a/tsgm/backend.py b/tsgm/backend.py new file mode 100644 index 0000000..17ef79a --- /dev/null +++ b/tsgm/backend.py @@ -0,0 +1,29 @@ +import os + +try: + import tensorflow as tf + os.environ["KERAS_BACKEND"] = "tensorflow" +except ImportError: + try: + import torch + os.environ["KERAS_BACKEND"] = "torch" + except ImportError: + raise ImportError("No backend found. Please install tensorflow or torch .") + +def get_backend(): + if os.environ["KERAS_BACKEND"] == "tensorflow": + return tf + elif os.environ["KERAS_BACKEND"] == "torch": + return torch + else: + raise ValueError("No backend found. Please install tensorflow or torch.") + + +# I am not sure if this is correct to import distributions here +def get_distributions(): + if os.environ["KERAS_BACKEND"] == "tensorflow": + return tensorflow_probability.distributions + elif os.environ["KERAS_BACKEND"] == "torch": + return torch.distributions + else: + raise ValueError("No backend found. Please install tensorflow or torch.") \ No newline at end of file diff --git a/tsgm/metrics/metrics.py b/tsgm/metrics/metrics.py index 7dff65d..79b2f3b 100644 --- a/tsgm/metrics/metrics.py +++ b/tsgm/metrics/metrics.py @@ -10,7 +10,8 @@ from scipy.stats import entropy from tqdm import tqdm from scipy.spatial.distance import pdist, squareform -from tensorflow.python.types.core import TensorLike +# make TensorLike more flexible +from tsgm.types import Tensor as TensorLike import tsgm diff --git a/tsgm/metrics/statistics.py b/tsgm/metrics/statistics.py index 494e812..3a40759 100644 --- a/tsgm/metrics/statistics.py +++ b/tsgm/metrics/statistics.py @@ -2,7 +2,6 @@ import numpy as np import scipy import functools -import tensorflow as tf from statsmodels.tsa.stattools import acf import tsgm @@ -63,13 +62,13 @@ def axis_percentile_s(ts: tsgm.types.Tensor, axis: typing.Optional[int], percent def axis_percautocorr_s(ts: tsgm.types.Tensor, axis: typing.Optional[int]) -> tsgm.types.Tensor: _validate_axis(axis) - - return np.array([_apply_percacf(tf.reshape(ts, [-1]))]) if axis is None else \ + # According to _apply_percacf function, I think np.reshape would work fine + return np.array([_apply_percacf(np.reshape(ts, [-1]))]) if axis is None else \ np.apply_along_axis(_apply_percacf, 0, np.apply_along_axis(_apply_percacf, axis, ts)) def axis_power_s(ts: tsgm.types.Tensor, axis: typing.Optional[int]) -> tsgm.types.Tensor: _validate_axis(axis) - - return np.array([_apply_power(tf.reshape(ts, [-1]))]) if axis is None else \ + # According to __apply_power__ function, I think np.reshape would work fine + return np.array([_apply_power(np.reshape(ts, [-1]))]) if axis is None else \ np.apply_along_axis(_apply_power, 0, np.apply_along_axis(_apply_power, axis, ts)) diff --git a/tsgm/models/architectures/zoo.py b/tsgm/models/architectures/zoo.py index 87e4b21..1b1a4e5 100644 --- a/tsgm/models/architectures/zoo.py +++ b/tsgm/models/architectures/zoo.py @@ -2,14 +2,16 @@ import math import tsgm import typing as T -import tensorflow as tf -from tensorflow import keras -from tensorflow.keras import layers +# import keras3.0 here, the LocallyConnected1D is not supported in keras3.0, a solution is needed +import keras +from keras import layers +# replace tf with ops in keras3.0 +from keras import ops from prettytable import PrettyTable -class Sampling(tf.keras.layers.Layer): +class Sampling(keras.layers.Layer): """ Custom Keras layer for sampling from a latent space. @@ -28,8 +30,10 @@ def call(self, inputs: T.Tuple[tsgm.types.Tensor, tsgm.types.Tensor]) -> tsgm.ty :rtype: tsgm.types.Tensor """ z_mean, z_log_var = inputs - epsilon = tf.keras.backend.random_normal(shape=tf.shape(z_mean)) - return z_mean + tf.exp(0.5 * z_log_var) * epsilon + # random noise for keras3.0 + epsilon = keras.random.normal(shape=ops.shape(z_mean)) + # ops for keras3.0 + return z_mean + ops.exp(0.5 * z_log_var) * epsilon class Architecture(abc.ABC): @@ -700,7 +704,7 @@ def _rnn_cell(self) -> keras.layers.Layer: return cell def _make_network(self, model: keras.models.Model, activation: str, return_sequences: bool) -> keras.models.Model: - _cells = tf.keras.layers.StackedRNNCells( + _cells = keras.layers.StackedRNNCells( [self._rnn_cell() for _ in range(self.n_layers)], name=f"{self.network_type}_x{self.n_layers}", ) @@ -917,15 +921,15 @@ def _apply_phaseshuffle(self, x, rad): if rad <= 0 or x.shape[1] <= 1: return x - b, x_len, nch = x.get_shape().as_list() - - phase = tf.random.uniform([], minval=-rad, maxval=rad + 1, dtype=tf.int32) - pad_l, pad_r = tf.maximum(phase, 0), tf.maximum(-phase, 0) + b, x_len, nch = x.shape + # for keras 3.0 + phase = keras.random.randint([], minval=-rad, maxval=rad + 1, dtype="int32") + pad_l, pad_r = ops.maximum(phase, 0), ops.maximum(-phase, 0) phase_start = pad_r - x = tf.pad(x, [[0, 0], [pad_l, pad_r], [0, 0]], mode="reflect") + x = ops.pad(x, [[0, 0], [pad_l, pad_r], [0, 0]], mode="reflect") x = x[:, phase_start:phase_start + x_len] - x.set_shape([b, x_len, nch]) + x = ops.reshape(x, [b, x_len, nch]) return x diff --git a/tsgm/models/augmentations.py b/tsgm/models/augmentations.py index 8daba28..7dab0ab 100644 --- a/tsgm/models/augmentations.py +++ b/tsgm/models/augmentations.py @@ -5,7 +5,8 @@ import scipy.interpolate from dtaidistance import dtw_barycenter from typing import List, Dict, Any, Optional, Tuple, Union -from tensorflow.python.types.core import TensorLike +# make TensorLike more flexible +from tsgm.types import Tensor as TensorLike import logging diff --git a/tsgm/models/cvae.py b/tsgm/models/cvae.py index 3080f6c..74ace2a 100644 --- a/tsgm/models/cvae.py +++ b/tsgm/models/cvae.py @@ -1,5 +1,5 @@ -from tensorflow import keras -import tensorflow as tf +import keras +from keras import ops import typing as T import tsgm.utils @@ -64,17 +64,8 @@ def _get_reconstruction_loss(self, X: tsgm.types.Tensor, Xr: tsgm.types.Tensor) tsgm.utils.reconstruction_loss_by_axis(X, Xr, axis=1) +\ tsgm.utils.reconstruction_loss_by_axis(X, Xr, axis=2) return reconst_loss - - def train_step(self, data: tsgm.types.Tensor) -> T.Dict: - """ - Performs a training step using a batch of data, stored in data. - - :param data: A batch of data in a format batch_size x seq_len x feat_dim - :type data: tsgm.types.Tensor - - :returns: A dict with losses - :rtype: T.Dict - """ + + def tf_train_step(self, tf, data: tsgm.types.Tensor) -> T.Dict: with tf.GradientTape() as tape: z_mean, z_log_var, z = self.encoder(data) reconstruction = self.decoder(z) @@ -83,6 +74,8 @@ def train_step(self, data: tsgm.types.Tensor) -> T.Dict: kl_loss = tf.reduce_mean(tf.reduce_sum(kl_loss, axis=1)) total_loss = reconstruction_loss + kl_loss grads = tape.gradient(total_loss, self.trainable_weights) + # I am not sure if this should be self.optimizer.apply(grads, model.trainable_weights) + # see https://keras.io/guides/writing_a_custom_training_loop_in_tensorflow/ self.optimizer.apply_gradients(zip(grads, self.trainable_weights)) self.total_loss_tracker.update_state(total_loss) self.reconstruction_loss_tracker.update_state(reconstruction_loss) @@ -92,6 +85,49 @@ def train_step(self, data: tsgm.types.Tensor) -> T.Dict: "reconstruction_loss": self.reconstruction_loss_tracker.result(), "kl_loss": self.kl_loss_tracker.result(), } + + def torch_train_step(self, torch, data: tsgm.types.Tensor) -> T.Dict: + z_mean, z_log_var, z = self.encoder(data) + reconstruction = self.decoder(z) + reconstruction_loss = self._get_reconstruction_loss(data, reconstruction) + kl_loss = -0.5 * (1 + z_log_var - torch.square(z_mean) - torch.exp(z_log_var)) + kl_loss = torch.mean(torch.sum(kl_loss, axis=1)) + total_loss = reconstruction_loss + kl_loss + self.optimizer.zero_grad() + total_loss.backward() + + trainable_weights = [v for v in self.trainable_weights] + gradients = [v.value.grad for v in trainable_weights] + + with torch.no_grad(): + self.optimizer.apply(gradients, trainable_weights) + + self.total_loss_tracker.update_state(total_loss) + self.reconstruction_loss_tracker.update_state(reconstruction_loss) + self.kl_loss_tracker.update_state(kl_loss) + return { + "loss": self.total_loss_tracker.result(), + "reconstruction_loss": self.reconstruction_loss_tracker.result(), + "kl_loss": self.kl_loss_tracker.result(), + } + + def train_step(self, data: tsgm.types.Tensor) -> T.Dict: + """ + Performs a training step using a batch of data, stored in data. + + :param data: A batch of data in a format batch_size x seq_len x feat_dim + :type data: tsgm.types.Tensor + + :returns: A dict with losses + :rtype: T.Dict + """ + from tsgm.backend import get_backend + import os + backend = get_backend() + if os.environ.get("KERAS_BACKEND") == "tensorflow": + return self.tf_train_step(backend, data) + elif os.environ.get("KERAS_BACKEND") == "torch": + return self.torch_train_step(backend, data) def generate(self, n: int) -> tsgm.types.Tensor: """ @@ -103,7 +139,8 @@ def generate(self, n: int) -> tsgm.types.Tensor: :returns: A tensor with generated samples. :rtype: tsgm.types.Tensor """ - z = tf.random.normal((n, self.latent_dim)) + # keras 3.0 support + z = keras.random.normal((n, self.latent_dim)) return self.decoder(z) @@ -144,8 +181,9 @@ def generate(self, labels: tsgm.types.Tensor) -> T.Tuple[tsgm.types.Tensor, tsgm :returns: a tuple of synthetically generated data and labels. :rtype: T.Tuple[tsgm.types.Tensor, tsgm.types.Tensor] """ - batch_size = tf.shape(labels)[0] - z = tf.random.normal((batch_size, self._seq_len, self.latent_dim), dtype=labels.dtype) + # keras 3.0 support + batch_size = ops.shape(labels)[0] + z = keras.random.normal((batch_size, self._seq_len, self.latent_dim), dtype=labels.dtype) decoder_input = self._get_decoder_input(z, labels) return (self.decoder(decoder_input), labels) @@ -169,36 +207,30 @@ def call(self, data: tsgm.types.Tensor) -> tsgm.types.Tensor: return x_decoded def _get_reconstruction_loss(self, X: tsgm.types.Tensor, Xr: tsgm.types.Tensor) -> float: - reconst_loss = tf.reduce_sum(tf.math.squared_difference(X, Xr)) +\ - tf.reduce_sum(tf.math.squared_difference(tf.reduce_mean(X, axis=1), tf.reduce_mean(Xr, axis=1))) +\ - tf.reduce_sum(tf.math.squared_difference(tf.reduce_mean(X, axis=2), tf.reduce_mean(Xr, axis=2))) + # keras 3.0 support + reconst_loss = ops.sum(ops.square(X - Xr)) +\ + ops.sum(ops.square(ops.mean(X, axis=1) - ops.mean(Xr, axis=1))) +\ + ops.sum(ops.square(ops.mean(X, axis=2) - ops.mean(Xr, axis=2))) return reconst_loss def _get_encoder_input(self, X: tsgm.types.Tensor, labels: tsgm.types.Tensor) -> tsgm.types.Tensor: + # keras 3.0 support if self._temporal: - return tf.concat([X, labels[:, :, None]], axis=2) + return ops.concatenate([X, labels[:, :, None]], axis=2) else: - rep_labels = tf.repeat(labels[:, None, :], [self._seq_len], axis=1) - return tf.concat([X, rep_labels], axis=2) + rep_labels = ops.repeat(labels[:, None, :], [self._seq_len], axis=1) + return ops.concatenate([X, rep_labels], axis=2) def _get_decoder_input(self, z: tsgm.types.Tensor, labels: tsgm.types.Tensor) -> tsgm.types.Tensor: + # keras 3.0 support if self._temporal: rep_labels = labels[:, :, None] else: - rep_labels = tf.repeat(labels[:, None, :], [self._seq_len], axis=1) - z = tf.reshape(z, [-1, self._seq_len, self.latent_dim]) - return tf.concat([z, rep_labels], axis=2) + rep_labels = ops.repeat(labels[:, None, :], [self._seq_len], axis=1) + z = ops.reshape(z, [-1, self._seq_len, self.latent_dim]) + return ops.concatenate([z, rep_labels], axis=2) - def train_step(self, data: tsgm.types.Tensor) -> T.Dict[str, float]: - """ - Performs a training step using a batch of data, stored in data. - - :param data: A batch of data in a format batch_size x seq_len x feat_dim - :type data: tsgm.types.Tensor - - :returns: A dict with losses - :rtype: T.Dict[str, float] - """ + def tf_train_step(self, tf, data: tsgm.types.Tensor) -> T.Dict[str, float]: X, labels = data with tf.GradientTape() as tape: encoder_input = self._get_encoder_input(X, labels) @@ -220,3 +252,52 @@ def train_step(self, data: tsgm.types.Tensor) -> T.Dict[str, float]: "reconstruction_loss": self.reconstruction_loss_tracker.result(), "kl_loss": self.kl_loss_tracker.result(), } + + def torch_train_step(self, torch, data: tsgm.types.Tensor) -> T.Dict[str, float]: + X, labels = data + encoder_input = self._get_encoder_input(X, labels) + z_mean, z_log_var, z = self.encoder(encoder_input) + + decoder_input = self._get_decoder_input(z_mean, labels) + reconstruction = self.decoder(decoder_input) + reconstruction_loss = self._get_reconstruction_loss(X, reconstruction) + kl_loss = -0.5 * (1 + z_log_var - torch.square(z_mean) - torch.exp(z_log_var)) + kl_loss = torch.mean(torch.sum(kl_loss, axis=1)) + total_loss = reconstruction_loss + self.beta * kl_loss + self.optimizer.zero_grad() + total_loss.backward() + + trainable_weights = [v for v in self.trainable_weights] + gradients = [v.value.grad for v in trainable_weights] + + with torch.no_grad(): + self.optimizer.apply(gradients, trainable_weights) + + self.total_loss_tracker.update_state(total_loss) + self.reconstruction_loss_tracker.update_state(reconstruction_loss) + self.kl_loss_tracker.update_state(kl_loss) + return { + "loss": self.total_loss_tracker.result(), + "reconstruction_loss": self.reconstruction_loss_tracker.result(), + "kl_loss": self.kl_loss_tracker.result(), + } + + + + def train_step(self, data: tsgm.types.Tensor) -> T.Dict[str, float]: + """ + Performs a training step using a batch of data, stored in data. + + :param data: A batch of data in a format batch_size x seq_len x feat_dim + :type data: tsgm.types.Tensor + + :returns: A dict with losses + :rtype: T.Dict[str, float] + """ + from tsgm.backend import get_backend + import os + backend = get_backend() + if os.environ.get("KERAS_BACKEND") == "tensorflow": + return self.tf_train_step(backend, data) + elif os.environ.get("KERAS_BACKEND") == "torch": + return self.torch_train_step(backend, data) \ No newline at end of file diff --git a/tsgm/optimization/abc.py b/tsgm/optimization/abc.py index 1032247..1efc8cc 100644 --- a/tsgm/optimization/abc.py +++ b/tsgm/optimization/abc.py @@ -3,12 +3,13 @@ import tqdm import numpy as np -import tensorflow_probability as tfp -import tsgm +from tsgm.backend import get_distributions +distributions = get_distributions() +import tsgm -DEFAULT_PRIOR = tfp.distributions.Normal(0, 1) +DEFAULT_PRIOR = distributions.Normal(0, 1) class ABCAlgorithm: diff --git a/tsgm/simulator.py b/tsgm/simulator.py index 69be6f0..5e5d94b 100644 --- a/tsgm/simulator.py +++ b/tsgm/simulator.py @@ -5,8 +5,12 @@ from tqdm import tqdm import typing as T import numpy as np -import tensorflow_probability as tfp -from tensorflow.python.types.core import TensorLike + +from tsgm.backend import get_distributions +distributions = get_distributions() + +# make TensorLike more flexible +from tsgm.types import Tensor as TensorLike import tsgm @@ -265,9 +269,10 @@ def set_params(self, max_scale: float, max_const: float, *args, **kwargs): max_scale (float): Maximum value for the scale parameter. max_const (float): Maximum value for the constant parameter. """ - self._scale = tfp.distributions.Uniform(0, max_scale) - self._const = tfp.distributions.Uniform(0, max_const) - self._shift = tfp.distributions.Uniform(0, 2) + # change to pdists usage + self._scale = distributions.Uniform(0, max_scale) + self._const = distributions.Uniform(0, max_const) + self._shift = distributions.Uniform(0, 2) super().set_params({"max_scale": max_scale, "max_const": max_const}) @@ -283,9 +288,12 @@ def generate(self, num_samples: int, *args) -> tsgm.dataset.Dataset: """ result_X, result_y = [], [] for i in range(num_samples): - scales = self._scale.sample(self._data.D) - consts = self._const.sample(self._data.D) - shifts = self._shift.sample(self._data.D) + D = self._data.D + if isinstance(D, int): + D = (D,) # for PyTorch compatibility + scales = self._scale.sample(D) + consts = self._const.sample(D) + shifts = self._shift.sample(D) if np.random.random() < 0.5: times = np.repeat(np.arange(0, self._data.T, 1)[:, None], self._data.D, axis=1) / 10 result_X.append(np.sin(times + shifts) * scales) diff --git a/tsgm/types.py b/tsgm/types.py index 8873188..d0f142f 100644 --- a/tsgm/types.py +++ b/tsgm/types.py @@ -1,10 +1,12 @@ import typing - -import tensorflow as tf import numpy.typing as npt +from tsgm.backend import get_backend + +backend = get_backend() +# more flexible Tensor type +Tensor = typing.Union[backend.Tensor, npt.NDArray] -Tensor = typing.Union[tf.Tensor, npt.NDArray] OptTensor = typing.Optional[Tensor] Model = typing.Any # TODO -- restrict diff --git a/tsgm/utils/data_processing.py b/tsgm/utils/data_processing.py index 471e5e6..ef07116 100644 --- a/tsgm/utils/data_processing.py +++ b/tsgm/utils/data_processing.py @@ -1,5 +1,6 @@ import numpy as np -from tensorflow.python.types.core import TensorLike +# make TensorLike more flexible +from tsgm.types import Tensor as TensorLike import typing as T diff --git a/tsgm/utils/datasets.py b/tsgm/utils/datasets.py index 3f229ac..e08ffb8 100644 --- a/tsgm/utils/datasets.py +++ b/tsgm/utils/datasets.py @@ -14,8 +14,8 @@ import pandas as pd import scipy.io.arff -from tensorflow import keras -from tensorflow.python.types.core import TensorLike +import keras +from tsgm.types import Tensor as TensorLike from tsgm.utils import covid19_data_utils from tsgm.utils import file_utils diff --git a/tsgm/utils/mmd.py b/tsgm/utils/mmd.py index 1837246..eae31f5 100644 --- a/tsgm/utils/mmd.py +++ b/tsgm/utils/mmd.py @@ -4,9 +4,9 @@ import numpy as np import math -import tensorflow as tf +from keras import ops import tensorflow_probability as tfp -from tensorflow.python.types.core import TensorLike +from tsgm.types import Tensor as TensorLike import tsgm @@ -39,19 +39,19 @@ def kernel_median_heuristic(X1: tsgm.types.Tensor, X2: tsgm.types.Tensor) -> flo if n * m >= 10 ** 8: logger.warning("n * m >= 10^8, consider subsampling for kernel median heuristic") - X1_squared = tf.transpose(tf.tile((X1 * X1).ravel()[None, :], (m, 1))) - X2_squared = tf.tile((X2 * X2).ravel()[None, :], (n, 1)) + X1_squared = ops.transpose(ops.tile((X1 * X1).ravel()[None, :], (m, 1))) + X2_squared = ops.tile((X2 * X2).ravel()[None, :], (n, 1)) - distances = X1_squared + X2_squared - 2 * tf.tensordot(X1, tf.transpose(X2), axes=1) + distances = X1_squared + X2_squared - 2 * ops.tensordot(X1, ops.transpose(X2), axes=1) assert np.min(distances) >= 0 - non_zero_distances = list(filter(lambda x: x != 0, tf.reshape(distances, [-1]))) + non_zero_distances = list(filter(lambda x: x != 0, ops.reshape(distances, [-1]))) if non_zero_distances: median_distance = np.median(non_zero_distances) else: median_distance = 0 - return tf.sqrt(median_distance / 2) # 2 * sigma**2 + return ops.sqrt(median_distance / 2) # 2 * sigma**2 def mmd_diff_var(Kyy: tsgm.types.Tensor, Kzz: tsgm.types.Tensor, Kxy: tsgm.types.Tensor, Kxz: tsgm.types.Tensor) -> float: @@ -63,34 +63,34 @@ def mmd_diff_var(Kyy: tsgm.types.Tensor, Kzz: tsgm.types.Tensor, Kxy: tsgm.types n = Kyy.shape[0] r = Kzz.shape[0] - Kyy_nd = Kyy - tf.linalg.diag(tf.linalg.diag_part(Kyy)) # Kyy - diag[Kyy] - Kzz_nd = Kzz - tf.linalg.diag(tf.linalg.diag_part(Kzz)) # Kzz - diag[Kzz] + Kyy_nd = Kyy - ops.diag(ops.diagonal(Kyy)) # Kyy - diag[Kyy] + Kzz_nd = Kzz - ops.diag(ops.diagonal(Kzz)) # Kzz - diag[Kzz] # Approximations from Eq. 31 - u_yy = tf.math.reduce_sum(Kyy_nd) / (n * (n - 1)) - u_zz = tf.math.reduce_sum(Kzz_nd) / (r * (r - 1)) - u_xy = tf.math.reduce_sum(Kxy) / (m * n) - u_xz = tf.math.reduce_sum(Kxz) / (m * r) + u_yy = ops.sum(Kyy_nd) / (n * (n - 1)) + u_zz = ops.sum(Kzz_nd) / (r * (r - 1)) + u_xy = ops.sum(Kxy) / (m * n) + u_xz = ops.sum(Kxz) / (m * r) - Kyy_nd_T = tf.transpose(Kyy_nd) - Kxy_T = tf.transpose(Kxy) - Kzz_nd_T = tf.transpose(Kzz_nd) - Kxz_T = tf.transpose(Kxz) + Kyy_nd_T = ops.transpose(Kyy_nd) + Kxy_T = ops.transpose(Kxy) + Kzz_nd_T = ops.transpose(Kzz_nd) + Kxz_T = ops.transpose(Kxz) # zeta_1 computation, Eq. 30 & 31 in the paper - term1 = (1 / (n * (n - 1) ** 2)) * tf.math.reduce_sum(Kyy_nd_T @ Kyy_nd) - u_yy ** 2 - term2 = (1 / (n ** 2 * m)) * tf.math.reduce_sum(Kxy_T @ Kxy) - u_xy ** 2 - term3 = (1 / (m ** 2 * n)) * tf.math.reduce_sum(Kxy @ Kxy_T) - u_xy ** 2 - term4 = (1 / (r * (r - 1) ** 2)) * tf.math.reduce_sum(Kzz_nd_T @ Kzz_nd) - u_zz ** 2 - term5 = (1 / (r * m ** 2)) * tf.math.reduce_sum(Kxz @ Kxz_T) - u_xz ** 2 - term6 = (1 / (m * r ** 2)) * tf.math.reduce_sum(Kxz_T @ Kxz) - u_xz ** 2 + term1 = (1 / (n * (n - 1) ** 2)) * ops.sum(Kyy_nd_T @ Kyy_nd) - u_yy ** 2 + term2 = (1 / (n ** 2 * m)) * ops.sum(Kxy_T @ Kxy) - u_xy ** 2 + term3 = (1 / (m ** 2 * n)) * ops.sum(Kxy @ Kxy_T) - u_xy ** 2 + term4 = (1 / (r * (r - 1) ** 2)) * ops.sum(Kzz_nd_T @ Kzz_nd) - u_zz ** 2 + term5 = (1 / (r * m ** 2)) * ops.sum(Kxz @ Kxz_T) - u_xz ** 2 + term6 = (1 / (m * r ** 2)) * ops.sum(Kxz_T @ Kxz) - u_xz ** 2 - term7 = (1 / (m * n * (n - 1))) * tf.math.reduce_sum(Kyy_nd @ Kxy_T) - u_yy * u_xy - term8 = (1 / (n * m * r)) * tf.math.reduce_sum(Kxy_T @ Kxz) - u_xz * u_xy - term9 = (1 / (m * r * (r - 1))) * tf.math.reduce_sum(Kzz_nd @ Kxz_T) - u_zz * u_xz + term7 = (1 / (m * n * (n - 1))) * ops.sum(Kyy_nd @ Kxy_T) - u_yy * u_xy + term8 = (1 / (n * m * r)) * ops.sum(Kxy_T @ Kxz) - u_xz * u_xy + term9 = (1 / (m * r * (r - 1))) * ops.sum(Kzz_nd @ Kxz_T) - u_zz * u_xz zeta1 = (term1 + term2 + term3 + term4 + term5 + term6 - 2 * (term7 + term8 + term9)) - zeta2 = (1 / (m * (m - 1))) * tf.math.reduce_sum((Kyy_nd - Kzz_nd - Kxy_T - Kxy + Kxz + Kxz_T) ** 2) - \ + zeta2 = (1 / (m * (m - 1))) * ops.sum((Kyy_nd - Kzz_nd - Kxy_T - Kxy + Kxz + Kxz_T) ** 2) - \ (u_yy - 2 * u_xy - (u_zz - 2 * u_xz)) ** 2 var_z1 = (4 * (m - 2) / (m * (m - 1))) * zeta1 # Eq (13) @@ -113,19 +113,19 @@ def mmd_3_test( Kxy = kernel(X, Y) Kxz = kernel(X, Z) - Kxx_nd = Kxx - tf.linalg.diag(tf.linalg.diag_part(Kxx)) - Kyy_nd = Kyy - tf.linalg.diag(tf.linalg.diag_part(Kyy)) - Kzz_nd = Kzz - tf.linalg.diag(tf.linalg.diag_part(Kzz)) + Kxx_nd = Kxx - ops.diag(ops.diagonal(Kxx)) + Kyy_nd = Kyy - ops.diag(ops.diagonal(Kyy)) + Kzz_nd = Kzz - ops.diag(ops.diagonal(Kzz)) m = Kxy.shape[0] n = Kyy.shape[0] r = Kzz.shape[0] - u_xx = tf.math.reduce_sum(Kxx_nd) * (1 / (m * (m - 1))) - u_yy = tf.math.reduce_sum(Kyy_nd) * (1 / (n * (n - 1))) - u_zz = tf.math.reduce_sum(Kzz_nd) * (1 / (r * (r - 1))) - u_xy = tf.math.reduce_sum(Kxy) / (m * n) - u_xz = tf.math.reduce_sum(Kxz) / (m * r) + u_xx = ops.sum(Kxx_nd) * (1 / (m * (m - 1))) + u_yy = ops.sum(Kyy_nd) * (1 / (n * (n - 1))) + u_zz = ops.sum(Kzz_nd) * (1 / (r * (r - 1))) + u_xy = ops.sum(Kxy) / (m * n) + u_xz = ops.sum(Kxz) / (m * r) t = u_yy - 2 * u_xy - (u_zz - 2 * u_xz) # test stat diff_var = mmd_diff_var(Kyy, Kzz, Kxy, Kxz) diff --git a/tsgm/utils/utils.py b/tsgm/utils/utils.py index e0b8f0c..035bd6a 100644 --- a/tsgm/utils/utils.py +++ b/tsgm/utils/utils.py @@ -1,9 +1,12 @@ import random import numpy as np -import tensorflow as tf +# more flexible Tensor, supports both TensorFlow and PyTorch +from tsgm.types import Tensor +import keras +from keras import ops -def reconstruction_loss_by_axis(original: tf.Tensor, reconstructed: tf.Tensor, axis: int = 0) -> tf.Tensor: +def reconstruction_loss_by_axis(original: Tensor, reconstructed: Tensor, axis: int = 0) -> Tensor: """ Calculate the reconstruction loss based on a specified axis. @@ -54,9 +57,9 @@ def reconstruction_loss_by_axis(original: tf.Tensor, reconstructed: tf.Tensor, a # axis=1 features (MSE) # axis=2 times (MSE) if axis == 0: - return tf.reduce_sum(tf.math.squared_difference(original, reconstructed)) + return ops.sum(ops.square(original - reconstructed)) else: - return tf.losses.mean_squared_error(tf.reduce_mean(original, axis=axis), tf.reduce_mean(reconstructed, axis=axis)) + return keras.losses.mean_squared_error(ops.mean(original, axis=axis), ops.mean(reconstructed, axis=axis)) def fix_seeds(seed_value: int = 42) -> None: @@ -75,6 +78,8 @@ def fix_seeds(seed_value: int = 42) -> None: This function does not return a value; it modifies the random number generators in-place to fix their seeds. """ - random.seed(seed_value) - np.random.seed(seed_value) - tf.random.set_seed(seed_value) + # Set the seed using keras.utils.set_random_seed. This will set: + # 1) `numpy` seed + # 2) backend random seed + # 3) `python` random seed + keras.utils.set_random_seed(seed_value) diff --git a/tsgm/utils/visualization.py b/tsgm/utils/visualization.py index 9075273..481e1f4 100644 --- a/tsgm/utils/visualization.py +++ b/tsgm/utils/visualization.py @@ -4,7 +4,8 @@ import matplotlib.pyplot as plt import numpy as np import math -import tensorflow as tf +from tsgm.backend import get_backend +backend = get_backend() import tsgm @@ -30,7 +31,7 @@ def visualize_dataset( if isinstance(dataset, tsgm.dataset.Dataset): X = dataset.X y = dataset.y - elif isinstance(dataset, np.ndarray) or tf.is_tensor(dataset): + elif isinstance(dataset, np.ndarray) or backend.is_tensor(dataset): X = dataset y = None else: From 683b64b851b585df6dce6218a78698dc4dfd5da2 Mon Sep 17 00:00:00 2001 From: liyiersan-server5 Date: Fri, 19 Jul 2024 21:48:36 +0800 Subject: [PATCH 25/31] remaining are cgan, sts, and timeGAN --- tsgm/models/cvae.py | 7 +++---- tsgm/models/monitors.py | 14 +++++++------- tsgm/models/timeGAN.py | 4 ++-- tsgm/utils/mmd.py | 12 ++++++------ 4 files changed, 18 insertions(+), 19 deletions(-) diff --git a/tsgm/models/cvae.py b/tsgm/models/cvae.py index 74ace2a..288c53c 100644 --- a/tsgm/models/cvae.py +++ b/tsgm/models/cvae.py @@ -1,7 +1,10 @@ +import os import keras from keras import ops import typing as T +from tsgm.backend import get_backend + import tsgm.utils @@ -121,8 +124,6 @@ def train_step(self, data: tsgm.types.Tensor) -> T.Dict: :returns: A dict with losses :rtype: T.Dict """ - from tsgm.backend import get_backend - import os backend = get_backend() if os.environ.get("KERAS_BACKEND") == "tensorflow": return self.tf_train_step(backend, data) @@ -294,8 +295,6 @@ def train_step(self, data: tsgm.types.Tensor) -> T.Dict[str, float]: :returns: A dict with losses :rtype: T.Dict[str, float] """ - from tsgm.backend import get_backend - import os backend = get_backend() if os.environ.get("KERAS_BACKEND") == "tensorflow": return self.tf_train_step(backend, data) diff --git a/tsgm/models/monitors.py b/tsgm/models/monitors.py index d87a36f..7644229 100644 --- a/tsgm/models/monitors.py +++ b/tsgm/models/monitors.py @@ -1,8 +1,8 @@ import os import logging -import tensorflow as tf import numpy as np -from tensorflow import keras +import keras +from keras import ops import typing as T import seaborn as sns @@ -72,7 +72,7 @@ def on_epoch_end(self, epoch: int, logs: T.Optional[T.Dict] = None) -> None: :type logs: dict """ if self._mode in ["clf", "reg"]: - random_latent_vectors = tf.random.normal(shape=(self._num_samples, self._latent_dim)) + random_latent_vectors = keras.random.normal(shape=(self._num_samples, self._latent_dim)) elif self._mode == "temporal": raise NotImplementedError # random_latent_vectors = tf.random.normal(shape=(self._output_dim * self._num_samples, self._latent_dim)) @@ -81,7 +81,7 @@ def on_epoch_end(self, epoch: int, logs: T.Optional[T.Dict] = None) -> None: labels = self._labels[:self._num_samples] - generator_input = tf.concat([random_latent_vectors, labels], 1) + generator_input = ops.concatenate([random_latent_vectors, labels], 1) generated_samples = self.model.generator(generator_input) for i in range(generated_samples.shape[0]): @@ -153,15 +153,15 @@ def on_epoch_end(self, epoch: int, logs: T.Optional[T.Dict] = None) -> None: if not len(labels): labels = keras.utils.to_categorical([i], self._output_dim) else: - labels = tf.concat((labels, keras.utils.to_categorical([i], self._output_dim)), 0) + labels = ops.concatenate((labels, keras.utils.to_categorical([i], self._output_dim)), 0) - labels = tf.repeat(labels, self._num_samples, axis=0) + labels = ops.repeat(labels, self._num_samples, axis=0) generated_images, _ = self.model.generate(labels) for i in range(self._output_dim * self._num_samples): sns.lineplot( x=range(0, generated_images[i].shape[0]), - y=tf.squeeze(generated_images[i]).numpy() + y=ops.squeeze(generated_images[i]).numpy() ) if self._save: plt.savefig(os.path.join(self._save_path, "epoch_{}_sample_{}".format(epoch, i))) diff --git a/tsgm/models/timeGAN.py b/tsgm/models/timeGAN.py index 86744ea..1cbdf40 100644 --- a/tsgm/models/timeGAN.py +++ b/tsgm/models/timeGAN.py @@ -1,6 +1,6 @@ import tensorflow as tf -from tensorflow import keras -from tensorflow.python.types.core import TensorLike +import keras +from tsgm.types import Tensor as TensorLike import numpy as np import numpy.typing as npt from tqdm import tqdm, trange diff --git a/tsgm/utils/mmd.py b/tsgm/utils/mmd.py index eae31f5..4caddd4 100644 --- a/tsgm/utils/mmd.py +++ b/tsgm/utils/mmd.py @@ -5,7 +5,6 @@ import numpy as np import math from keras import ops -import tensorflow_probability as tfp from tsgm.types import Tensor as TensorLike import tsgm @@ -14,12 +13,13 @@ logger = logging.getLogger('utils') logger.setLevel(logging.DEBUG) - -EXP_QUAD_KERNEL = tfp.math.psd_kernels.ExponentiatedQuadratic(feature_ndims=2) - - +# make guassian kernel fit with both pytorch and tensorflow def exp_quad_kernel(x: TensorLike, y: TensorLike): - return EXP_QUAD_KERNEL.matrix(x, y) + length_scale, feature_ndims = 1.0, 2 + x_expanded = ops.expand_dims(x, axis=-feature_ndims-1) + y_expanded = ops.expand_dims(y, axis=-feature_ndims-2) + sq_dist = ops.sum((x_expanded - y_expanded) ** 2, axis=(-2, -1)) + return ops.exp(-0.5 * sq_dist / length_scale**2) def MMD(X: tsgm.types.Tensor, Y: tsgm.types.Tensor, kernel: T.Callable = exp_quad_kernel) -> TensorLike: From f4559480949862f48dbc19c38a779d26ef401af5 Mon Sep 17 00:00:00 2001 From: liyiersan-server5 Date: Fri, 19 Jul 2024 23:01:36 +0800 Subject: [PATCH 26/31] fix cgan and cvae --- tsgm/models/architectures/zoo.py | 12 +- tsgm/models/cgan.py | 294 +++++++++++++++++++++++++------ tsgm/models/cvae.py | 2 +- 3 files changed, 246 insertions(+), 62 deletions(-) diff --git a/tsgm/models/architectures/zoo.py b/tsgm/models/architectures/zoo.py index 1b1a4e5..ac3bec7 100644 --- a/tsgm/models/architectures/zoo.py +++ b/tsgm/models/architectures/zoo.py @@ -2,7 +2,7 @@ import math import tsgm import typing as T -# import keras3.0 here, the LocallyConnected1D is not supported in keras3.0, a solution is needed +# import keras3.0 here, the LocallyConnected1D is not supported in keras3.0, I replace it with Conv1D import keras from keras import layers # replace tf with ops in keras3.0 @@ -283,7 +283,7 @@ def _build_decoder(self) -> keras.models.Model: x = layers.AveragePooling1D(pool_size=pool_and_stride, strides=pool_and_stride)( x ) - d_output = layers.LocallyConnected1D(self._feat_dim, 1, activation="sigmoid")(x) + d_output = layers.Conv1D(self._feat_dim, 1, activation="sigmoid")(x) decoder = keras.Model(inputs, d_output, name="decoder") return decoder @@ -359,7 +359,7 @@ def _build_generator(self) -> keras.models.Model: x = layers.AveragePooling1D(pool_size=pool_and_stride, strides=pool_and_stride)( x ) - g_output = layers.LocallyConnected1D(self._feat_dim, 1, activation="tanh")(x) + g_output = layers.Conv1D(self._feat_dim, 1, activation="tanh")(x) generator = keras.Model(g_input, g_output, name="generator") return generator @@ -430,7 +430,7 @@ def _build_generator(self) -> keras.models.Model: x = layers.AveragePooling1D(pool_size=pool_and_stride, strides=pool_and_stride)( x ) - g_output = layers.LocallyConnected1D(self._feat_dim, 1, activation="tanh")(x) + g_output = layers.Conv1D(self._feat_dim, 1, activation="tanh")(x) generator = keras.Model(g_input, g_output, name="generator") return generator @@ -505,7 +505,7 @@ def _build_generator(self) -> keras.models.Model: pool_and_stride = round((x.shape[1] + 1) / (self._seq_len + 1)) x = layers.AveragePooling1D(pool_size=pool_and_stride, strides=pool_and_stride)(x) - g_output = layers.LocallyConnected1D(self._feat_dim, 1, activation="tanh")(x) + g_output = layers.Conv1D(self._feat_dim, 1, activation="tanh")(x) generator = keras.Model(g_input, g_output, name="generator") return generator @@ -870,7 +870,7 @@ def _build_generator(self, output_activation: str) -> keras.Model: pool_and_stride = round((x.shape[1] + 1) / (self._seq_len + 1)) x = layers.AveragePooling1D(pool_size=pool_and_stride, strides=pool_and_stride)(x) - g_output = layers.LocallyConnected1D(self._feat_dim, 1, activation=output_activation)(x) + g_output = layers.Conv1D(self._feat_dim, 1, activation=output_activation)(x) generator = keras.Model(g_input, g_output, name="generator") return generator diff --git a/tsgm/models/cgan.py b/tsgm/models/cgan.py index 51a0454..3b64372 100644 --- a/tsgm/models/cgan.py +++ b/tsgm/models/cgan.py @@ -1,6 +1,6 @@ -import tensorflow as tf import typing as T -from tensorflow import keras +import keras +from keras import ops try: import tensorflow_privacy as tf_privacy __tf_privacy_available = True @@ -11,6 +11,9 @@ import tsgm +import os +from tsgm.backend import get_backend + logger = logging.getLogger('models') logger.setLevel(logging.DEBUG) @@ -52,19 +55,15 @@ def __init__(self, discriminator: keras.Model, generator: keras.Model, latent_di self.disc_loss_tracker = keras.metrics.Mean(name="discriminator_loss") def wgan_discriminator_loss(self, real_sample, fake_sample): - real_loss = tf.reduce_mean(real_sample) - fake_loss = tf.reduce_mean(fake_sample) + real_loss = ops.mean(real_sample) + fake_loss = ops.mean(fake_sample) return fake_loss - real_loss # Define the loss functions to be used for generator def wgan_generator_loss(self, fake_sample): - return -tf.reduce_mean(fake_sample) - - def gradient_penalty(self, batch_size, real_samples, fake_samples): - # get the interpolated samples - alpha = tf.random.normal([batch_size, 1, 1], 0.0, 1.0) - diff = fake_samples - real_samples - interpolated = real_samples + alpha * diff + return -ops.mean(fake_sample) + + def gradient_penalty_tf(self, tf, interpolated): with tf.GradientTape() as gp_tape: gp_tape.watch(interpolated) # 1. Get the discriminator output for this interpolated sample. @@ -72,9 +71,29 @@ def gradient_penalty(self, batch_size, real_samples, fake_samples): # 2. Calculate the gradients w.r.t to this interpolated sample. grads = gp_tape.gradient(pred, [interpolated])[0] + return grads + + def gradient_penalty_torch(self, torch, interpolated): + interpolated.requires_grad = True + pred = self.discriminator(interpolated, training=True) + grads = torch.autograd.grad(outputs=pred, inputs=interpolated, + grad_outputs=torch.ones_like(pred), + create_graph=True, retain_graph=True, only_inputs=True)[0] + return grads + + def gradient_penalty(self, batch_size, real_samples, fake_samples): + # get the interpolated samples + alpha = keras.random.normal([batch_size, 1, 1], 0.0, 1.0) + diff = fake_samples - real_samples + interpolated = real_samples + alpha * diff + backend = get_backend() + if os.environ.get("KERAS_BACKEND") == "tensorflow": + grads = self.gradient_penalty_tf(backend, interpolated) + elif os.environ.get("KERAS_BACKEND") == "torch": + grads = self.gradient_penalty_torch(backend, interpolated) # 3. Calcuate the norm of the gradients - norm = tf.sqrt(tf.reduce_sum(tf.square(grads), axis=[1, 2])) - gp = tf.reduce_mean((norm - 1.0) ** 2) + norm = ops.sqrt(ops.sum(ops.square(grads), axis=[1, 2])) + gp = ops.mean((norm - 1.0) ** 2) return gp @property @@ -110,33 +129,24 @@ def compile(self, d_optimizer: keras.optimizers.Optimizer, g_optimizer: keras.op self.dp = generator_dp and discriminator_dp def _get_random_vector_labels(self, batch_size: int, labels=None) -> tsgm.types.Tensor: - return tf.random.normal(shape=(batch_size, self.latent_dim)) - - def train_step(self, data: tsgm.types.Tensor) -> T.Dict[str, float]: - """ - Performs a training step using a batch of data, stored in data. - - :param data: A batch of data in a format batch_size x seq_len x feat_dim - :type data: tsgm.types.Tensor - - :returns: A dictionary with generator (key "g_loss") and discriminator (key "d_loss") losses - :rtype: T.Dict[str, float] - """ + return keras.random.normal(shape=(batch_size, self.latent_dim)) + + def train_step_tf(self, tf, data: tsgm.types.Tensor) -> T.Dict[str, float]: real_data = data - batch_size = tf.shape(real_data)[0] + batch_size = ops.shape(real_data)[0] # Generate ts random_vector = self._get_random_vector_labels(batch_size) fake_data = self.generator(random_vector) - combined_data = tf.concat( + combined_data = ops.concatenate( [fake_data, real_data], axis=0 ) # Labels for descriminator # 1 == real data # 0 == fake data - desc_labels = tf.concat( - [tf.ones((batch_size, 1)), tf.zeros((batch_size, 1))], axis=0 + desc_labels = ops.concatenate( + [ops.ones((batch_size, 1)), ops.zeros((batch_size, 1))], axis=0 ) with tf.GradientTape() as tape: predictions = self.discriminator(combined_data) @@ -161,7 +171,7 @@ def train_step(self, data: tsgm.types.Tensor) -> T.Dict[str, float]: random_vector = self._get_random_vector_labels(batch_size=batch_size) # Pretend that all samples are real - misleading_labels = tf.zeros((batch_size, 1)) + misleading_labels = ops.zeros((batch_size, 1)) # Train generator (with updating the discriminator) with tf.GradientTape() as tape: @@ -182,7 +192,97 @@ def train_step(self, data: tsgm.types.Tensor) -> T.Dict[str, float]: "g_loss": self.gen_loss_tracker.result(), "d_loss": self.disc_loss_tracker.result(), } + + def train_step_torch(self, torch, data: tsgm.types.Tensor) -> T.Dict[str, float]: + real_data = data + batch_size = ops.shape(real_data)[0] + # Generate ts + random_vector = self._get_random_vector_labels(batch_size) + fake_data = self.generator(random_vector) + + combined_data = ops.concatenate( + [fake_data, real_data], axis=0 + ) + # Labels for descriminator + # 1 == real data + # 0 == fake data + desc_labels = ops.concatenate( + [ops.ones((batch_size, 1)), ops.zeros((batch_size, 1))], axis=0 + ) + + predictions = self.discriminator(combined_data) + if self.use_wgan: + fake_logits = self.discriminator(fake_data, training=True) + # Get the logits for the real samples + real_logits = self.discriminator(real_data, training=True) + + # Calculate the discriminator loss using the fake and real sample logits + d_cost = self.wgan_discriminator_loss(real_logits, fake_logits) + # Calculate the gradient penalty + gp = self.gradient_penalty(batch_size, real_data, fake_data) + # Add the gradient penalty to the original discriminator loss + d_loss = d_cost + gp * self.gp_weight + else: + d_loss = self.loss_fn(desc_labels, predictions) + + self.discriminator.zero_grad() + d_loss.backward() + + d_trainable_weights = [v for v in self.discriminator.trainable_weights] + d_gradients = [v.value.grad for v in d_trainable_weights] + + with torch.no_grad(): + self.d_optimizer.apply_gradients(d_gradients, d_trainable_weights) + + + random_vector = self._get_random_vector_labels(batch_size=batch_size) + misleading_labels = ops.zeros((batch_size, 1)) + + fake_data = self.generator(random_vector) + predictions = self.discriminator(fake_data) + if self.use_wgan: + # uses logits + g_loss = self.wgan_generator_loss(predictions) + else: + g_loss = self.loss_fn(misleading_labels, predictions) + + self.generator.zero_grad() + g_loss.backward() + + g_trainable_weights = [v for v in self.generator.trainable_weights] + g_gradients = [v.value.grad for v in g_trainable_weights] + + with torch.no_grad(): + self.g_optimizer.apply_gradients(g_gradients, g_trainable_weights) + + + self.gen_loss_tracker.update_state(g_loss) + self.disc_loss_tracker.update_state(d_loss) + return { + "g_loss": self.gen_loss_tracker.result(), + "d_loss": self.disc_loss_tracker.result(), + } + + + + def train_step(self, data: tsgm.types.Tensor) -> T.Dict[str, float]: + """ + Performs a training step using a batch of data, stored in data. + + :param data: A batch of data in a format batch_size x seq_len x feat_dim + :type data: tsgm.types.Tensor + + :returns: A dictionary with generator (key "g_loss") and discriminator (key "d_loss") losses + :rtype: T.Dict[str, float] + """ + backend = get_backend() + if os.environ.get("KERAS_BACKEND") == "tensorflow": + return self.train_step_tf(backend, data) + elif os.environ.get("KERAS_BACKEND") == "torch": + return self.train_step_torch(backend, data) + + def generate(self, num: int) -> tsgm.types.Tensor: """ Generates new data from the model. @@ -270,13 +370,13 @@ def compile(self, d_optimizer: keras.optimizers.Optimizer, g_optimizer: keras.op def _get_random_vector_labels(self, batch_size: int, labels: tsgm.types.Tensor) -> None: if self._temporal: - random_latent_vectors = tf.random.normal(shape=(batch_size, self._seq_len, self.latent_dim)) - random_vector_labels = tf.concat( + random_latent_vectors = keras.random.normal(shape=(batch_size, self._seq_len, self.latent_dim)) + random_vector_labels = ops.concatenate( [random_latent_vectors, labels[:, :, None]], axis=2 ) else: - random_latent_vectors = tf.random.normal(shape=(batch_size, self.latent_dim)) - random_vector_labels = tf.concat( + random_latent_vectors = keras.random.normal(shape=(batch_size, self.latent_dim)) + random_vector_labels = ops.concatenate( [random_latent_vectors, labels], axis=1 ) return random_vector_labels @@ -289,29 +389,21 @@ def _get_output_shape(self, labels: tsgm.types.Tensor) -> int: return labels.shape[2] else: return labels.shape[1] - - def train_step(self, data: T.Tuple) -> T.Dict[str, float]: - """ - Performs a training step using a batch of data, stored in data. - - :param data: A batch of data in a format batch_size x seq_len x feat_dim - :type data: tsgm.types.Tensor - - :returns: A dictionary with generator (key "g_loss") and discriminator (key "d_loss") losses - :rtype: T.Dict[str, float] - """ + + + def train_step_tf(self, tf, data: T.Tuple) -> T.Dict[str, float]: real_ts, labels = data output_dim = self._get_output_shape(labels) - batch_size = tf.shape(real_ts)[0] + batch_size = ops.shape(real_ts)[0] if not self._temporal: rep_labels = labels[:, :, None] - rep_labels = tf.repeat( + rep_labels = ops.repeat( rep_labels, repeats=[self._seq_len] ) else: rep_labels = labels - rep_labels = tf.reshape( + rep_labels = ops.reshape( rep_labels, (-1, self._seq_len, output_dim) ) @@ -319,17 +411,17 @@ def train_step(self, data: T.Tuple) -> T.Dict[str, float]: random_vector_labels = self._get_random_vector_labels(batch_size=batch_size, labels=labels) generated_ts = self.generator(random_vector_labels) - fake_data = tf.concat([generated_ts, rep_labels], -1) - real_data = tf.concat([real_ts, rep_labels], -1) - combined_data = tf.concat( + fake_data = ops.concatenate([generated_ts, rep_labels], -1) + real_data = ops.concatenate([real_ts, rep_labels], -1) + combined_data = ops.concatenate( [fake_data, real_data], axis=0 ) # Labels for descriminator # 1 == real data # 0 == fake data - desc_labels = tf.concat( - [tf.ones((batch_size, 1)), tf.zeros((batch_size, 1))], axis=0 + desc_labels = ops.concatenate( + [ops.ones((batch_size, 1)), ops.zeros((batch_size, 1))], axis=0 ) with tf.GradientTape() as tape: @@ -349,12 +441,12 @@ def train_step(self, data: T.Tuple) -> T.Dict[str, float]: random_vector_labels = self._get_random_vector_labels(batch_size=batch_size, labels=labels) # Pretend that all samples are real - misleading_labels = tf.zeros((batch_size, 1)) + misleading_labels = ops.zeros((batch_size, 1)) # Train generator (with updating the discriminator) with tf.GradientTape() as tape: fake_samples = self.generator(random_vector_labels) - fake_data = tf.concat([fake_samples, rep_labels], -1) + fake_data = ops.concatenate([fake_samples, rep_labels], -1) predictions = self.discriminator(fake_data) g_loss = self.loss_fn(misleading_labels, predictions) @@ -371,6 +463,98 @@ def train_step(self, data: T.Tuple) -> T.Dict[str, float]: "g_loss": self.gen_loss_tracker.result(), "d_loss": self.disc_loss_tracker.result(), } + + def train_step_torch(self, torch, data: T.Tuple) -> T.Dict[str, float]: + real_ts, labels = data + output_dim = self._get_output_shape(labels) + batch_size = ops.shape(real_ts)[0] + if not self._temporal: + rep_labels = labels[:, :, None] + rep_labels = ops.repeat( + rep_labels, repeats=[self._seq_len] + ) + else: + rep_labels = labels + + rep_labels = ops.reshape( + rep_labels, (-1, self._seq_len, output_dim) + ) + + # Generate ts + random_vector_labels = self._get_random_vector_labels(batch_size=batch_size, labels=labels) + generated_ts = self.generator(random_vector_labels) + + fake_data = ops.concatenate([generated_ts, rep_labels], -1) + real_data = ops.concatenate([real_ts, rep_labels], -1) + combined_data = ops.concatenate( + [fake_data, real_data], axis=0 + ) + + # Labels for descriminator + # 1 == real data + # 0 == fake data + desc_labels = ops.concatenate( + [ops.ones((batch_size, 1)), ops.zeros((batch_size, 1))], axis=0 + ) + + predictions = self.discriminator(combined_data) + d_loss = self.loss_fn(desc_labels, predictions) + + self.discriminator.zero_grad() + d_loss.backward() + + d_trainable_weights = [v for v in self.discriminator.trainable_weights] + d_gradients = [v.value.grad for v in d_trainable_weights] + + with torch.no_grad(): + self.d_optimizer.apply_gradients(d_gradients, d_trainable_weights) + + + random_vector_labels = self._get_random_vector_labels(batch_size=batch_size, labels=labels) + + # Pretend that all samples are real + misleading_labels = ops.zeros((batch_size, 1)) + + # Train generator (with updating the discriminator) + + fake_samples = self.generator(random_vector_labels) + fake_data = ops.concatenate([fake_samples, rep_labels], -1) + predictions = self.discriminator(fake_data) + g_loss = self.loss_fn(misleading_labels, predictions) + + self.generator.zero_grad() + g_loss.backward() + + g_trainable_weights = [v for v in self.generator.trainable_weights] + g_gradients = [v.value.grad for v in g_trainable_weights] + + with torch.no_grad(): + self.g_optimizer.apply_gradients(g_gradients, g_trainable_weights) + + self.gen_loss_tracker.update_state(g_loss) + self.disc_loss_tracker.update_state(d_loss) + return { + "g_loss": self.gen_loss_tracker.result(), + "d_loss": self.disc_loss_tracker.result(), + } + + + + def train_step(self, data: T.Tuple) -> T.Dict[str, float]: + """ + Performs a training step using a batch of data, stored in data. + + :param data: A batch of data in a format batch_size x seq_len x feat_dim + :type data: tsgm.types.Tensor + + :returns: A dictionary with generator (key "g_loss") and discriminator (key "d_loss") losses + :rtype: T.Dict[str, float] + """ + backend = get_backend() + if os.environ.get("KERAS_BACKEND") == "tensorflow": + return self.train_step_tf(backend, data) + elif os.environ.get("KERAS_BACKEND") == "torch": + return self.train_step_torch(backend, data) def generate(self, labels: tsgm.types.Tensor) -> tsgm.types.Tensor: """ diff --git a/tsgm/models/cvae.py b/tsgm/models/cvae.py index 288c53c..8ba295f 100644 --- a/tsgm/models/cvae.py +++ b/tsgm/models/cvae.py @@ -96,7 +96,7 @@ def torch_train_step(self, torch, data: tsgm.types.Tensor) -> T.Dict: kl_loss = -0.5 * (1 + z_log_var - torch.square(z_mean) - torch.exp(z_log_var)) kl_loss = torch.mean(torch.sum(kl_loss, axis=1)) total_loss = reconstruction_loss + kl_loss - self.optimizer.zero_grad() + self.zero_grad() total_loss.backward() trainable_weights = [v for v in self.trainable_weights] From 500eb81d1806e4a896aab3d558e791e9ba880ff7 Mon Sep 17 00:00:00 2001 From: liyiersan-server5 Date: Sat, 20 Jul 2024 13:57:45 +0800 Subject: [PATCH 27/31] fix test --- tests/test_abc.py | 11 ++++++----- tests/test_metrics.py | 9 +++++---- tests/test_monitors.py | 6 +++--- tests/test_timegan.py | 43 +++++++++++++++++++++++++++--------------- tsgm/models/cgan.py | 9 ++++++--- tsgm/models/cvae.py | 35 ++++++++++++++++++---------------- tsgm/models/sts.py | 3 ++- tsgm/models/timeGAN.py | 29 ++++++++++++++-------------- 8 files changed, 84 insertions(+), 61 deletions(-) diff --git a/tests/test_abc.py b/tests/test_abc.py index c719b72..b041c4f 100644 --- a/tests/test_abc.py +++ b/tests/test_abc.py @@ -1,10 +1,11 @@ import pytest import tsgm -import tensorflow_probability as tfp +from tsgm.backend import get_distributions + +distributions = get_distributions() -import tensorflow as tf import numpy as np -from tensorflow import keras +import keras def test_abc_rejection_sampler_nn_simulator(): @@ -43,8 +44,8 @@ def test_abc_rejection_sampler_model_based_simulator(): data = tsgm.dataset.DatasetProperties(N=100, D=2, T=100) simulator = tsgm.simulator.SineConstSimulator(data=data, max_scale=max_scale, max_const=20) priors = { - "max_scale": tfp.distributions.Uniform(9, 11), - "max_const": tfp.distributions.Uniform(19, 21) + "max_scale": distributions.Uniform(9, 11), + "max_const": distributions.Uniform(19, 21) } samples_ref = simulator.generate(10) diff --git a/tests/test_metrics.py b/tests/test_metrics.py index 315b7bb..3cc7afe 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -1,7 +1,8 @@ import pytest import numpy as np -import tensorflow as tf +import keras +from keras import ops import functools import sklearn @@ -38,7 +39,7 @@ def test_statistics(): assert (tsgm.metrics.statistics.axis_power_s(ts, axis=2) - np.asarray([36587.13, 7321., 1253.13]) < eps).all() # Now, checking with tf.Tensor - ts_tf = tf.convert_to_tensor(ts) + ts_tf = ops.convert_to_tensor(ts) assert tsgm.metrics.statistics.axis_max_s(ts_tf, axis=None) == [21] assert tsgm.metrics.statistics.axis_min_s(ts_tf, axis=None) == [-11] @@ -199,8 +200,8 @@ def test_discriminative_metric(): model = tsgm.models.zoo["clf_cl_n"](seq_len=ts.shape[1], feat_dim=ts.shape[2], output_dim=2).model model.compile( - tf.keras.optimizers.Adam(), - tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False) + keras.optimizers.Adam(), + keras.losses.SparseCategoricalCrossentropy(from_logits=False) ) discr_metric = tsgm.metrics.DiscriminativeMetric() # should be easy to be classified diff --git a/tests/test_monitors.py b/tests/test_monitors.py index 3ca2833..571d438 100644 --- a/tests/test_monitors.py +++ b/tests/test_monitors.py @@ -3,8 +3,8 @@ import random -import tensorflow as tf -from tensorflow import keras +import keras +from keras import ops import matplotlib.pyplot as plt import tsgm @@ -17,7 +17,7 @@ def _get_labels(num_samples, output_dim): if labels is None: labels = keras.utils.to_categorical([sample], output_dim) else: - labels = tf.concat((labels, keras.utils.to_categorical([sample], output_dim)), 0) + labels = ops.concatenate((labels, keras.utils.to_categorical([sample], output_dim)), 0) return labels diff --git a/tests/test_timegan.py b/tests/test_timegan.py index 2a96b43..27a8e45 100644 --- a/tests/test_timegan.py +++ b/tests/test_timegan.py @@ -2,10 +2,23 @@ from unittest.mock import Mock import tsgm -import tensorflow as tf import numpy as np -from tensorflow import keras +import keras +from keras import ops +import os +from tsgm.backend import get_backend + + +def set_experimental_run_functions_eagerly(value): + if os.environ.get("KERAS_BACKEND") == "tensorflow": + tf = get_backend() + tf.config.experimental_run_functions_eagerly(value) + # no-op for PyTorch, which is always eager + + +FLOAT_32 = ops.convert_to_tensor(32, dtype="float32").dtype +FLOAT_64 = ops.convert_to_tensor(64, dtype="float64").dtype def test_timegan(): latent_dim = 4 @@ -131,7 +144,7 @@ def _gen_dataset(no, seq_len, dim): def _gen_tf_dataset(no, seq_len, dim): dataset = _gen_dataset(no, seq_len, dim) - dataset = tf.convert_to_tensor(dataset, dtype=tf.float32) + dataset = ops.convert_to_tensor(dataset, dtype="float32") dataset = tf.data.Dataset.from_tensors(dataset).unbatch().batch(no) return dataset @@ -208,7 +221,7 @@ def test_train_timegan(mocked_gradienttape): @pytest.fixture def mock_optimizer(): - yield tf.keras.optimizers.Adam(learning_rate=0.001) + yield keras.optimizers.Adam(learning_rate=0.001) @pytest.fixture @@ -251,7 +264,7 @@ def test_timegan_train_autoencoder(mocked_data, mocked_timegan): tf.config.experimental_run_functions_eagerly(False) # Assert that the loss is a float - assert loss.dtype in [tf.float32, tf.float64] + assert loss.dtype in [FLOAT_32, FLOAT_64] def test_timegan_train_supervisor(mocked_data, mocked_timegan): @@ -265,7 +278,7 @@ def test_timegan_train_supervisor(mocked_data, mocked_timegan): finally: tf.config.experimental_run_functions_eagerly(False) # Assert that the loss is a float - assert loss.dtype in [tf.float32, tf.float64] + assert loss.dtype in [FLOAT_32, FLOAT_64] def test_timegan_train_embedder(mocked_data, mocked_timegan): @@ -279,7 +292,7 @@ def test_timegan_train_embedder(mocked_data, mocked_timegan): finally: tf.config.experimental_run_functions_eagerly(False) # Assert that the loss is a float - assert loss.dtype in [tf.float32, tf.float64] + assert loss.dtype in [FLOAT_32, FLOAT_64] def test_timegan_train_generator(mocked_data, mocked_timegan): @@ -308,7 +321,7 @@ def test_timegan_train_generator(mocked_data, mocked_timegan): step_g_loss_v, step_g_loss, ): - assert loss.dtype in [tf.float32, tf.float64] + assert loss.dtype in [FLOAT_32, FLOAT_64] def test_timegan_check_discriminator_loss(mocked_data, mocked_timegan): @@ -324,7 +337,7 @@ def test_timegan_check_discriminator_loss(mocked_data, mocked_timegan): tf.config.experimental_run_functions_eagerly(False) # Assert that the loss is a float - assert loss.dtype in [tf.float32, tf.float64] + assert loss.dtype in [FLOAT_32, FLOAT_64] def test_timegan_train_discriminator(mocked_data, mocked_timegan): @@ -338,7 +351,7 @@ def test_timegan_train_discriminator(mocked_data, mocked_timegan): finally: tf.config.experimental_run_functions_eagerly(False) # Assert that the loss is a float - assert loss.dtype in [tf.float32, tf.float64] + assert loss.dtype in [FLOAT_32, FLOAT_64] def test_generate_noise(mocked_timegan): @@ -365,12 +378,12 @@ def test_compute_generator_moments_loss(mocked_timegan): # Calculate the expected loss manually _eps = 1e-6 - y_true_mean, y_true_var = tf.nn.moments(x=y_true_data, axes=[0]) - y_pred_mean, y_pred_var = tf.nn.moments(x=y_pred_data, axes=[0]) + y_true_mean, y_true_var = ops.nn.moments(x=y_true_data, axes=[0]) + y_pred_mean, y_pred_var = ops.nn.moments(x=y_pred_data, axes=[0]) - g_loss_mean = tf.reduce_mean(tf.abs(y_true_mean - y_pred_mean)) - g_loss_var = tf.reduce_mean( - tf.abs(tf.sqrt(y_true_var + _eps) - tf.sqrt(y_pred_var + _eps)) + g_loss_mean = ops.mean(ops.abs(y_true_mean - y_pred_mean)) + g_loss_var = ops.mean( + ops.abs(ops.sqrt(y_true_var + _eps) - ops.sqrt(y_pred_var + _eps)) ) expected_loss = g_loss_mean + g_loss_var diff --git a/tsgm/models/cgan.py b/tsgm/models/cgan.py index 3b64372..62ac4f6 100644 --- a/tsgm/models/cgan.py +++ b/tsgm/models/cgan.py @@ -77,7 +77,7 @@ def gradient_penalty_torch(self, torch, interpolated): interpolated.requires_grad = True pred = self.discriminator(interpolated, training=True) grads = torch.autograd.grad(outputs=pred, inputs=interpolated, - grad_outputs=torch.ones_like(pred), + grad_outputs=ops.ones_like(pred), create_graph=True, retain_graph=True, only_inputs=True)[0] return grads @@ -194,6 +194,7 @@ def train_step_tf(self, tf, data: tsgm.types.Tensor) -> T.Dict[str, float]: } def train_step_torch(self, torch, data: tsgm.types.Tensor) -> T.Dict[str, float]: + data = ops.convert_to_tensor(data) real_data = data batch_size = ops.shape(real_data)[0] # Generate ts @@ -464,8 +465,10 @@ def train_step_tf(self, tf, data: T.Tuple) -> T.Dict[str, float]: "d_loss": self.disc_loss_tracker.result(), } - def train_step_torch(self, torch, data: T.Tuple) -> T.Dict[str, float]: + def torch_train_step(self, torch, data: T.Tuple) -> T.Dict[str, float]: real_ts, labels = data + real_ts = ops.convert_to_tensor(real_ts) + labels = ops.convert_to_tensor(labels) output_dim = self._get_output_shape(labels) batch_size = ops.shape(real_ts)[0] if not self._temporal: @@ -554,7 +557,7 @@ def train_step(self, data: T.Tuple) -> T.Dict[str, float]: if os.environ.get("KERAS_BACKEND") == "tensorflow": return self.train_step_tf(backend, data) elif os.environ.get("KERAS_BACKEND") == "torch": - return self.train_step_torch(backend, data) + return self.torch_train_step(backend, data) def generate(self, labels: tsgm.types.Tensor) -> tsgm.types.Tensor: """ diff --git a/tsgm/models/cvae.py b/tsgm/models/cvae.py index 8ba295f..de185d2 100644 --- a/tsgm/models/cvae.py +++ b/tsgm/models/cvae.py @@ -68,13 +68,13 @@ def _get_reconstruction_loss(self, X: tsgm.types.Tensor, Xr: tsgm.types.Tensor) tsgm.utils.reconstruction_loss_by_axis(X, Xr, axis=2) return reconst_loss - def tf_train_step(self, tf, data: tsgm.types.Tensor) -> T.Dict: + def train_step_tf(self, tf, data: tsgm.types.Tensor) -> T.Dict: with tf.GradientTape() as tape: z_mean, z_log_var, z = self.encoder(data) reconstruction = self.decoder(z) reconstruction_loss = self._get_reconstruction_loss(data, reconstruction) - kl_loss = -0.5 * (1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var)) - kl_loss = tf.reduce_mean(tf.reduce_sum(kl_loss, axis=1)) + kl_loss = -0.5 * (1 + z_log_var - ops.square(z_mean) - ops.exp(z_log_var)) + kl_loss = ops.mean(ops.sum(kl_loss, axis=1)) total_loss = reconstruction_loss + kl_loss grads = tape.gradient(total_loss, self.trainable_weights) # I am not sure if this should be self.optimizer.apply(grads, model.trainable_weights) @@ -89,12 +89,13 @@ def tf_train_step(self, tf, data: tsgm.types.Tensor) -> T.Dict: "kl_loss": self.kl_loss_tracker.result(), } - def torch_train_step(self, torch, data: tsgm.types.Tensor) -> T.Dict: + def train_step_torch(self, torch, data: tsgm.types.Tensor) -> T.Dict: + data = ops.convert_to_tensor(data) z_mean, z_log_var, z = self.encoder(data) reconstruction = self.decoder(z) reconstruction_loss = self._get_reconstruction_loss(data, reconstruction) - kl_loss = -0.5 * (1 + z_log_var - torch.square(z_mean) - torch.exp(z_log_var)) - kl_loss = torch.mean(torch.sum(kl_loss, axis=1)) + kl_loss = -0.5 * (1 + z_log_var - ops.square(z_mean) - ops.exp(z_log_var)) + kl_loss = ops.mean(ops.sum(kl_loss, axis=1)) total_loss = reconstruction_loss + kl_loss self.zero_grad() total_loss.backward() @@ -126,9 +127,9 @@ def train_step(self, data: tsgm.types.Tensor) -> T.Dict: """ backend = get_backend() if os.environ.get("KERAS_BACKEND") == "tensorflow": - return self.tf_train_step(backend, data) + return self.train_step_tf(backend, data) elif os.environ.get("KERAS_BACKEND") == "torch": - return self.torch_train_step(backend, data) + return self.train_step_torch(backend, data) def generate(self, n: int) -> tsgm.types.Tensor: """ @@ -231,7 +232,7 @@ def _get_decoder_input(self, z: tsgm.types.Tensor, labels: tsgm.types.Tensor) -> z = ops.reshape(z, [-1, self._seq_len, self.latent_dim]) return ops.concatenate([z, rep_labels], axis=2) - def tf_train_step(self, tf, data: tsgm.types.Tensor) -> T.Dict[str, float]: + def train_step_tf(self, tf, data: tsgm.types.Tensor) -> T.Dict[str, float]: X, labels = data with tf.GradientTape() as tape: encoder_input = self._get_encoder_input(X, labels) @@ -240,8 +241,8 @@ def tf_train_step(self, tf, data: tsgm.types.Tensor) -> T.Dict[str, float]: decoder_input = self._get_decoder_input(z_mean, labels) reconstruction = self.decoder(decoder_input) reconstruction_loss = self._get_reconstruction_loss(X, reconstruction) - kl_loss = -0.5 * (1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var)) - kl_loss = tf.reduce_mean(tf.reduce_sum(kl_loss, axis=1)) + kl_loss = -0.5 * (1 + z_log_var - ops.square(z_mean) - ops.exp(z_log_var)) + kl_loss = ops.mean(ops.sum(kl_loss, axis=1)) total_loss = reconstruction_loss + self.beta * kl_loss grads = tape.gradient(total_loss, self.trainable_weights) self.optimizer.apply_gradients(zip(grads, self.trainable_weights)) @@ -254,16 +255,18 @@ def tf_train_step(self, tf, data: tsgm.types.Tensor) -> T.Dict[str, float]: "kl_loss": self.kl_loss_tracker.result(), } - def torch_train_step(self, torch, data: tsgm.types.Tensor) -> T.Dict[str, float]: + def train_step_torch(self, torch, data: tsgm.types.Tensor) -> T.Dict[str, float]: X, labels = data + X = ops.convert_to_tensor(X) + labels = ops.convert_to_tensor(labels) encoder_input = self._get_encoder_input(X, labels) z_mean, z_log_var, z = self.encoder(encoder_input) decoder_input = self._get_decoder_input(z_mean, labels) reconstruction = self.decoder(decoder_input) reconstruction_loss = self._get_reconstruction_loss(X, reconstruction) - kl_loss = -0.5 * (1 + z_log_var - torch.square(z_mean) - torch.exp(z_log_var)) - kl_loss = torch.mean(torch.sum(kl_loss, axis=1)) + kl_loss = -0.5 * (1 + z_log_var - ops.square(z_mean) - ops.exp(z_log_var)) + kl_loss = ops.mean(ops.sum(kl_loss, axis=1)) total_loss = reconstruction_loss + self.beta * kl_loss self.optimizer.zero_grad() total_loss.backward() @@ -297,6 +300,6 @@ def train_step(self, data: tsgm.types.Tensor) -> T.Dict[str, float]: """ backend = get_backend() if os.environ.get("KERAS_BACKEND") == "tensorflow": - return self.tf_train_step(backend, data) + return self.train_step_tf(backend, data) elif os.environ.get("KERAS_BACKEND") == "torch": - return self.torch_train_step(backend, data) \ No newline at end of file + return self.train_step_torch(backend, data) \ No newline at end of file diff --git a/tsgm/models/sts.py b/tsgm/models/sts.py index b0de28e..8ee5b69 100644 --- a/tsgm/models/sts.py +++ b/tsgm/models/sts.py @@ -1,4 +1,5 @@ import tensorflow as tf +import keras import tensorflow_probability as tfp import numpy as np @@ -48,7 +49,7 @@ def train(self, ds: tsgm.dataset.Dataset, num_variational_steps: int = 200, self._elbo_loss = tfp.vi.fit_surrogate_posterior( target_log_prob_fn=self._model.joint_distribution(observed_time_series=X).log_prob, surrogate_posterior=variational_posteriors, - optimizer=tf.optimizers.Adam(learning_rate=0.1), + optimizer=keras.optimizers.Adam(learning_rate=0.1), num_steps=num_variational_steps, jit_compile=True) diff --git a/tsgm/models/timeGAN.py b/tsgm/models/timeGAN.py index 1cbdf40..d2227e1 100644 --- a/tsgm/models/timeGAN.py +++ b/tsgm/models/timeGAN.py @@ -1,5 +1,6 @@ import tensorflow as tf import keras +from keras import ops from tsgm.types import Tensor as TensorLike import numpy as np import numpy.typing as npt @@ -261,7 +262,7 @@ def _train_autoencoder( with tf.GradientTape() as tape: X_tilde = self.autoencoder(X) E_loss_T0 = self._mse(X, X_tilde) - E_loss0 = 10.0 * tf.sqrt(E_loss_T0) + E_loss0 = 10.0 * ops.sqrt(E_loss_T0) e_vars = self.embedder.trainable_variables r_vars = self.recovery.trainable_variables @@ -305,10 +306,10 @@ def _train_generator( with tf.GradientTape() as tape: # 1. Adversarial loss Y_fake = self.adversarial_supervised(Z) - G_loss_U = self._bce(y_true=tf.ones_like(Y_fake), y_pred=Y_fake) + G_loss_U = self._bce(y_true=ops.ones_like(Y_fake), y_pred=Y_fake) Y_fake_e = self.adversarial_embedded(Z) - G_loss_U_e = self._bce(y_true=tf.ones_like(Y_fake_e), y_pred=Y_fake_e) + G_loss_U_e = self._bce(y_true=ops.ones_like(Y_fake_e), y_pred=Y_fake_e) # 2. Supervised loss H = self.embedder(X) H_hat_supervised = self.supervisor(H) @@ -322,7 +323,7 @@ def _train_generator( G_loss = ( G_loss_U + self.gamma * G_loss_U_e - + 100 * tf.sqrt(G_loss_S) + + 100 * ops.sqrt(G_loss_S) + 100 * G_loss_V ) @@ -354,7 +355,7 @@ def _train_embedder( # Reconstruction Loss X_tilde = self.autoencoder(X) E_loss_T0 = self._mse(X, X_tilde) - E_loss0 = 10 * tf.sqrt(E_loss_T0) + E_loss0 = 10 * ops.sqrt(E_loss_T0) E_loss = E_loss0 + 0.1 * G_loss_S @@ -390,13 +391,13 @@ def _compute_generator_moments_loss( :return G_loss_V: float """ _eps = 1e-6 - y_true_mean, y_true_var = tf.nn.moments(x=y_true, axes=[0]) - y_pred_mean, y_pred_var = tf.nn.moments(x=y_pred, axes=[0]) + y_true_mean, y_true_var = ops.nn.moments(x=y_true, axes=[0]) + y_pred_mean, y_pred_var = ops.nn.moments(x=y_pred, axes=[0]) # G_loss_V2 - g_loss_mean = tf.reduce_mean(abs(y_true_mean - y_pred_mean)) + g_loss_mean = ops.mean(abs(y_true_mean - y_pred_mean)) # G_loss_V1 - g_loss_var = tf.reduce_mean( - abs(tf.sqrt(y_true_var + _eps) - tf.sqrt(y_pred_var + _eps)) + g_loss_var = ops.mean( + abs(ops.sqrt(y_true_var + _eps) - ops.sqrt(y_pred_var + _eps)) ) # G_loss_V = G_loss_V1 + G_loss_V2 return g_loss_mean + g_loss_var @@ -409,14 +410,14 @@ def _check_discriminator_loss(self, X: TensorLike, Z: TensorLike) -> float: """ # Loss on false negatives Y_real = self.discriminator_model(X) - D_loss_real = self._bce(y_true=tf.ones_like(Y_real), y_pred=Y_real) + D_loss_real = self._bce(y_true=ops.ones_like(Y_real), y_pred=Y_real) # Loss on false positives Y_fake = self.adversarial_supervised(Z) - D_loss_fake = self._bce(y_true=tf.zeros_like(Y_fake), y_pred=Y_fake) + D_loss_fake = self._bce(y_true=ops.zeros_like(Y_fake), y_pred=Y_fake) Y_fake_e = self.adversarial_embedded(Z) - D_loss_fake_e = self._bce(y_true=tf.zeros_like(Y_fake_e), y_pred=Y_fake_e) + D_loss_fake_e = self._bce(y_true=ops.zeros_like(Y_fake_e), y_pred=Y_fake_e) D_loss = D_loss_real + D_loss_fake + self.gamma * D_loss_fake_e return D_loss @@ -445,7 +446,7 @@ def _get_data_batch(self, data: TensorLike, n_windows: int) -> T.Iterator: """ Return an iterator of shuffled input data """ - data = tf.convert_to_tensor(data, dtype=tf.float32) + data = ops.convert_to_tensor(data, dtype="float32") return iter( tf.data.Dataset.from_tensor_slices(data) .shuffle(buffer_size=n_windows) From 14444263f6e0478c6afdb199d235a454159b03d3 Mon Sep 17 00:00:00 2001 From: liyiersan-server5 Date: Sat, 20 Jul 2024 15:51:33 +0800 Subject: [PATCH 28/31] fix all test --- tests/test_cgan.py | 34 ++++++++++++++----- tests/test_downstream_models.py | 2 +- tests/test_timegan.py | 59 +++++++++++++++++---------------- tests/test_vae.py | 2 +- tests/test_zoo.py | 2 +- tsgm/models/cgan.py | 4 +-- tsgm/models/sts.py | 1 - 7 files changed, 62 insertions(+), 42 deletions(-) diff --git a/tests/test_cgan.py b/tests/test_cgan.py index 298eb81..503efd9 100644 --- a/tests/test_cgan.py +++ b/tests/test_cgan.py @@ -1,14 +1,17 @@ import pytest import tsgm -import tensorflow as tf try: import tensorflow_privacy as tf_privacy __tf_privacy_available = True except ModuleNotFoundError: __tf_privacy_available = False import numpy as np -from tensorflow import keras +import keras + +import os +from tsgm.backend import get_backend + def _gen_dataset(seq_len: int, feature_dim: int, batch_size: int): @@ -17,8 +20,13 @@ def _gen_dataset(seq_len: int, feature_dim: int, batch_size: int): scaler = tsgm.utils.TSFeatureWiseScaler((-1, 1)) X_train = scaler.fit_transform(data).astype(np.float32) - dataset = tf.data.Dataset.from_tensor_slices(X_train) - dataset = dataset.shuffle(buffer_size=1024).batch(batch_size) + backend = get_backend() + if os.environ.get("KERAS_BACKEND") == "tensorflow": + dataset = backend.data.Dataset.from_tensor_slices(X_train) + dataset = dataset.shuffle(buffer_size=1024).batch(batch_size) + elif os.environ.get("KERAS_BACKEND") == "torch": + dataset = backend.utils.data.TensorDataset(X_train) + dataset = backend.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True) return dataset @@ -29,8 +37,13 @@ def _gen_cond_dataset(seq_len: int, batch_size: int): X_train = scaler.fit_transform(X).astype(np.float32) y = keras.utils.to_categorical(y_i, 2).astype(np.float32) - dataset = tf.data.Dataset.from_tensor_slices((X_train, y)) - dataset = dataset.shuffle(buffer_size=1024).batch(batch_size) + backend = get_backend() + if os.environ.get("KERAS_BACKEND") == "tensorflow": + dataset = backend.data.Dataset.from_tensor_slices(X_train) + dataset = dataset.shuffle(buffer_size=1024).batch(batch_size) + elif os.environ.get("KERAS_BACKEND") == "torch": + dataset = backend.utils.data.TensorDataset(X_train) + dataset = backend.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True) return dataset, y @@ -41,8 +54,13 @@ def _gen_t_cond_dataset(seq_len: int, batch_size: int): X_train = scaler.fit_transform(X).astype(np.float32) y = y.astype(np.float32) - dataset = tf.data.Dataset.from_tensor_slices((X_train, y)) - dataset = dataset.shuffle(buffer_size=1024).batch(batch_size) + backend = get_backend() + if os.environ.get("KERAS_BACKEND") == "tensorflow": + dataset = backend.data.Dataset.from_tensor_slices(X_train) + dataset = dataset.shuffle(buffer_size=1024).batch(batch_size) + elif os.environ.get("KERAS_BACKEND") == "torch": + dataset = backend.utils.data.TensorDataset(X_train) + dataset = backend.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True) return dataset, y diff --git a/tests/test_downstream_models.py b/tests/test_downstream_models.py index d34e418..4804edf 100644 --- a/tests/test_downstream_models.py +++ b/tests/test_downstream_models.py @@ -1,7 +1,7 @@ import pytest import copy import itertools -from tensorflow import keras +import keras import tsgm diff --git a/tests/test_timegan.py b/tests/test_timegan.py index 27a8e45..757bf3a 100644 --- a/tests/test_timegan.py +++ b/tests/test_timegan.py @@ -38,14 +38,14 @@ def test_timegan(): timegan.compile() try: - tf.config.experimental_run_functions_eagerly(True) + set_experimental_run_functions_eagerly(True) timegan.fit(dataset, epochs=1) _check_internals(timegan) generated_samples = timegan.generate(1) finally: - tf.config.experimental_run_functions_eagerly(False) + set_experimental_run_functions_eagerly(False) assert generated_samples.shape == (1, seq_len, feature_dim) @@ -66,12 +66,12 @@ def test_timegan_fit(): ) timegan.compile() try: - tf.config.experimental_run_functions_eagerly(True) + set_experimental_run_functions_eagerly(True) timegan.fit(dataset, epochs=3, checkpoints_interval=2, generate_synthetic=(1,)) _check_internals(timegan) finally: - tf.config.experimental_run_functions_eagerly(False) + set_experimental_run_functions_eagerly(False) # Check intermediate generation assert timegan.synthetic_data_generated_in_training @@ -84,7 +84,7 @@ def test_timegan_on_dataset(): seq_len = 24 batch_size = 16 - dataset = _gen_tf_dataset(batch_size, seq_len, feature_dim) # tf.data.Dataset + dataset = _gen_tensor_dataset(batch_size, seq_len, feature_dim) # tf.data.Dataset or torch.utils.data.DataLoader timegan = tsgm.models.timeGAN.TimeGAN( seq_len=seq_len, module="gru", @@ -95,14 +95,14 @@ def test_timegan_on_dataset(): ) timegan.compile() try: - tf.config.experimental_run_functions_eagerly(True) + set_experimental_run_functions_eagerly(True) timegan.fit(dataset, epochs=1) _check_internals(timegan) generated_samples = timegan.generate(1) finally: - tf.config.experimental_run_functions_eagerly(False) + set_experimental_run_functions_eagerly(False) assert generated_samples.shape == (1, seq_len, feature_dim) @@ -141,12 +141,15 @@ def _gen_dataset(no, seq_len, dim): return data - -def _gen_tf_dataset(no, seq_len, dim): +def _gen_tensor_dataset(no, seq_len, dim): dataset = _gen_dataset(no, seq_len, dim) dataset = ops.convert_to_tensor(dataset, dtype="float32") - dataset = tf.data.Dataset.from_tensors(dataset).unbatch().batch(no) - + backend = get_backend() + if os.environ.get("KERAS_BACKEND") == "tensorflow": + dataset = backend.data.Dataset.from_tensors(dataset).unbatch().batch(no) + else: + dataset = backend.utils.data.TensorDataset(dataset) + dataset = backend.utils.data.DataLoader(dataset, batch_size=no) return dataset @@ -203,7 +206,7 @@ def test_train_timegan(mocked_gradienttape): ) timegan.compile() try: - tf.config.experimental_run_functions_eagerly(True) + set_experimental_run_functions_eagerly(True) timegan.fit(dataset, epochs=1) batches = timegan._get_data_batch(dataset, n_windows=len(dataset)) assert timegan._train_autoencoder(next(batches), timegan.autoencoder_opt) @@ -216,7 +219,7 @@ def test_train_timegan(mocked_gradienttape): next(batches), next(timegan.get_noise_batch()), timegan.discriminator_opt ) finally: - tf.config.experimental_run_functions_eagerly(False) + set_experimental_run_functions_eagerly(False) @pytest.fixture @@ -229,7 +232,7 @@ def mocked_data(): feature_dim = 3 seq_len = 24 batch_size = 16 - yield _gen_tf_dataset(batch_size, seq_len, feature_dim) + yield _gen_tensor_dataset(batch_size, seq_len, feature_dim) @pytest.fixture @@ -258,10 +261,10 @@ def test_timegan_train_autoencoder(mocked_data, mocked_timegan): mocked_timegan._define_timegan() X_ = next(batches) try: - tf.config.experimental_run_functions_eagerly(True) + set_experimental_run_functions_eagerly(True) loss = mocked_timegan._train_autoencoder(X_, mocked_timegan.autoencoder_opt) finally: - tf.config.experimental_run_functions_eagerly(False) + set_experimental_run_functions_eagerly(False) # Assert that the loss is a float assert loss.dtype in [FLOAT_32, FLOAT_64] @@ -273,10 +276,10 @@ def test_timegan_train_supervisor(mocked_data, mocked_timegan): mocked_timegan._define_timegan() X_ = next(batches) try: - tf.config.experimental_run_functions_eagerly(True) + set_experimental_run_functions_eagerly(True) _, loss = mocked_timegan._train_embedder(X_, mocked_timegan.embedder_opt) finally: - tf.config.experimental_run_functions_eagerly(False) + set_experimental_run_functions_eagerly(False) # Assert that the loss is a float assert loss.dtype in [FLOAT_32, FLOAT_64] @@ -287,10 +290,10 @@ def test_timegan_train_embedder(mocked_data, mocked_timegan): mocked_timegan._define_timegan() X_ = next(batches) try: - tf.config.experimental_run_functions_eagerly(True) + set_experimental_run_functions_eagerly(True) _, loss = mocked_timegan._train_embedder(X_, mocked_timegan.embedder_opt) finally: - tf.config.experimental_run_functions_eagerly(False) + set_experimental_run_functions_eagerly(False) # Assert that the loss is a float assert loss.dtype in [FLOAT_32, FLOAT_64] @@ -302,7 +305,7 @@ def test_timegan_train_generator(mocked_data, mocked_timegan): X_ = next(batches) Z_ = next(mocked_timegan.get_noise_batch()) try: - tf.config.experimental_run_functions_eagerly(True) + set_experimental_run_functions_eagerly(True) ( step_g_loss_u, step_g_loss_u_e, @@ -311,7 +314,7 @@ def test_timegan_train_generator(mocked_data, mocked_timegan): step_g_loss, ) = mocked_timegan._train_generator(X_, Z_, mocked_timegan.generator_opt) finally: - tf.config.experimental_run_functions_eagerly(False) + set_experimental_run_functions_eagerly(False) # Assert that the loss is a float for loss in ( @@ -331,10 +334,10 @@ def test_timegan_check_discriminator_loss(mocked_data, mocked_timegan): X_ = next(batches) Z_ = next(mocked_timegan.get_noise_batch()) try: - tf.config.experimental_run_functions_eagerly(True) + set_experimental_run_functions_eagerly(True) loss = mocked_timegan._check_discriminator_loss(X_, Z_) finally: - tf.config.experimental_run_functions_eagerly(False) + set_experimental_run_functions_eagerly(False) # Assert that the loss is a float assert loss.dtype in [FLOAT_32, FLOAT_64] @@ -346,10 +349,10 @@ def test_timegan_train_discriminator(mocked_data, mocked_timegan): X_ = next(batches) Z_ = next(mocked_timegan.get_noise_batch()) try: - tf.config.experimental_run_functions_eagerly(True) + set_experimental_run_functions_eagerly(True) loss = mocked_timegan._train_discriminator(X_, Z_, mocked_timegan.discriminator_opt) finally: - tf.config.experimental_run_functions_eagerly(False) + set_experimental_run_functions_eagerly(False) # Assert that the loss is a float assert loss.dtype in [FLOAT_32, FLOAT_64] @@ -388,13 +391,13 @@ def test_compute_generator_moments_loss(mocked_timegan): expected_loss = g_loss_mean + g_loss_var try: - tf.config.experimental_run_functions_eagerly(True) + set_experimental_run_functions_eagerly(True) # Calculate the loss using the method computed_loss = mocked_timegan._compute_generator_moments_loss( y_true_data, y_pred_data ) finally: - tf.config.experimental_run_functions_eagerly(False) + set_experimental_run_functions_eagerly(False) # Assert that the computed loss matches the expected loss np.testing.assert_almost_equal(computed_loss, expected_loss, decimal=5) diff --git a/tests/test_vae.py b/tests/test_vae.py index 6567819..2fdc037 100644 --- a/tests/test_vae.py +++ b/tests/test_vae.py @@ -3,7 +3,7 @@ import tensorflow as tf import numpy as np -from tensorflow import keras +import keras def test_vae(): diff --git a/tests/test_zoo.py b/tests/test_zoo.py index 5a83cda..0f4c2d5 100644 --- a/tests/test_zoo.py +++ b/tests/test_zoo.py @@ -1,7 +1,7 @@ import pytest import numpy as np -from tensorflow.keras import layers +from keras import layers import sklearn.metrics.pairwise diff --git a/tsgm/models/cgan.py b/tsgm/models/cgan.py index 62ac4f6..06c6684 100644 --- a/tsgm/models/cgan.py +++ b/tsgm/models/cgan.py @@ -465,7 +465,7 @@ def train_step_tf(self, tf, data: T.Tuple) -> T.Dict[str, float]: "d_loss": self.disc_loss_tracker.result(), } - def torch_train_step(self, torch, data: T.Tuple) -> T.Dict[str, float]: + def train_step_torch(self, torch, data: T.Tuple) -> T.Dict[str, float]: real_ts, labels = data real_ts = ops.convert_to_tensor(real_ts) labels = ops.convert_to_tensor(labels) @@ -557,7 +557,7 @@ def train_step(self, data: T.Tuple) -> T.Dict[str, float]: if os.environ.get("KERAS_BACKEND") == "tensorflow": return self.train_step_tf(backend, data) elif os.environ.get("KERAS_BACKEND") == "torch": - return self.torch_train_step(backend, data) + return self.train_step_torch(backend, data) def generate(self, labels: tsgm.types.Tensor) -> tsgm.types.Tensor: """ diff --git a/tsgm/models/sts.py b/tsgm/models/sts.py index 8ee5b69..170a515 100644 --- a/tsgm/models/sts.py +++ b/tsgm/models/sts.py @@ -1,4 +1,3 @@ -import tensorflow as tf import keras import tensorflow_probability as tfp import numpy as np From 78c34eb740c5e276510bb5178a2ca6eb02a31c95 Mon Sep 17 00:00:00 2001 From: liyiersan-server5 Date: Sat, 20 Jul 2024 15:58:27 +0800 Subject: [PATCH 29/31] fix torch_train --- tsgm/models/cgan.py | 3 --- tsgm/models/cvae.py | 3 --- 2 files changed, 6 deletions(-) diff --git a/tsgm/models/cgan.py b/tsgm/models/cgan.py index 06c6684..0217cb4 100644 --- a/tsgm/models/cgan.py +++ b/tsgm/models/cgan.py @@ -194,7 +194,6 @@ def train_step_tf(self, tf, data: tsgm.types.Tensor) -> T.Dict[str, float]: } def train_step_torch(self, torch, data: tsgm.types.Tensor) -> T.Dict[str, float]: - data = ops.convert_to_tensor(data) real_data = data batch_size = ops.shape(real_data)[0] # Generate ts @@ -467,8 +466,6 @@ def train_step_tf(self, tf, data: T.Tuple) -> T.Dict[str, float]: def train_step_torch(self, torch, data: T.Tuple) -> T.Dict[str, float]: real_ts, labels = data - real_ts = ops.convert_to_tensor(real_ts) - labels = ops.convert_to_tensor(labels) output_dim = self._get_output_shape(labels) batch_size = ops.shape(real_ts)[0] if not self._temporal: diff --git a/tsgm/models/cvae.py b/tsgm/models/cvae.py index de185d2..4b5a50e 100644 --- a/tsgm/models/cvae.py +++ b/tsgm/models/cvae.py @@ -90,7 +90,6 @@ def train_step_tf(self, tf, data: tsgm.types.Tensor) -> T.Dict: } def train_step_torch(self, torch, data: tsgm.types.Tensor) -> T.Dict: - data = ops.convert_to_tensor(data) z_mean, z_log_var, z = self.encoder(data) reconstruction = self.decoder(z) reconstruction_loss = self._get_reconstruction_loss(data, reconstruction) @@ -257,8 +256,6 @@ def train_step_tf(self, tf, data: tsgm.types.Tensor) -> T.Dict[str, float]: def train_step_torch(self, torch, data: tsgm.types.Tensor) -> T.Dict[str, float]: X, labels = data - X = ops.convert_to_tensor(X) - labels = ops.convert_to_tensor(labels) encoder_input = self._get_encoder_input(X, labels) z_mean, z_log_var, z = self.encoder(encoder_input) From edddd17a4d8088d47c280f30580a8a48c3969413 Mon Sep 17 00:00:00 2001 From: liyiersan-server5 Date: Fri, 30 Aug 2024 11:44:36 +0800 Subject: [PATCH 30/31] add keras3.0 support for sts and timegan --- tests/test_utils.py | 11 +- tsgm/backend.py | 19 ++- tsgm/models/sts.py | 34 ++++-- tsgm/models/timeGAN.py | 263 ++++++++++++++++++++++++++++++++++++----- 4 files changed, 280 insertions(+), 47 deletions(-) diff --git a/tests/test_utils.py b/tests/test_utils.py index 511154f..9ea63d5 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -8,7 +8,8 @@ import urllib import numpy as np import random -import tensorflow as tf +import keras +from keras import ops import sklearn.metrics.pairwise from unittest import mock from functools import wraps @@ -254,19 +255,19 @@ def test_get_wafer(): def test_fix_random_seeds(): assert random.random() != 0.6394267984578837 assert np.random.random() != 0.3745401188473625 - assert float(tf.random.uniform([1])[0]) != 0.68789124 + assert float(keras.random.uniform([1])[0]) != 0.68789124 tsgm.utils.fix_seeds() assert random.random() == 0.6394267984578837 assert np.random.random() == 0.3745401188473625 - assert float(tf.random.uniform([1])[0]) == 0.6645621061325073 + assert float(keras.random.uniform([1])[0]) == 0.6645621061325073 def test_reconstruction_loss_by_axis(): eps = 1e-8 - original = tf.constant([[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]]) - reconstructed = tf.constant([[[1.1, 2.2, 2.9], [3.9, 4.8, 6.1]]]) + original = ops.array([[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]]) + reconstructed = ops.array([[[1.1, 2.2, 2.9], [3.9, 4.8, 6.1]]]) loss = tsgm.utils.reconstruction_loss_by_axis(original, reconstructed) assert abs(loss.numpy() - 0.119999886) < eps loss = tsgm.utils.reconstruction_loss_by_axis(original, reconstructed, axis=1) diff --git a/tsgm/backend.py b/tsgm/backend.py index 17ef79a..9ee97b2 100644 --- a/tsgm/backend.py +++ b/tsgm/backend.py @@ -1,12 +1,17 @@ import os +import torch.utils +import torch.utils.data + try: import tensorflow as tf os.environ["KERAS_BACKEND"] = "tensorflow" + Keras_Dataset = tf.data.Dataset except ImportError: try: import torch os.environ["KERAS_BACKEND"] = "torch" + Keras_Dataset = torch.utils.data.DataLoader except ImportError: raise ImportError("No backend found. Please install tensorflow or torch .") @@ -26,4 +31,16 @@ def get_distributions(): elif os.environ["KERAS_BACKEND"] == "torch": return torch.distributions else: - raise ValueError("No backend found. Please install tensorflow or torch.") \ No newline at end of file + raise ValueError("No backend found. Please install tensorflow or torch.") + + +# tf.function decorator for tensorflow backend or no op decorator for torch backend +if os.environ["KERAS_BACKEND"] == "tensorflow": + import tensorflow as tf + def tf_function_decorator(func): + return tf.function(func) +else: + # no op decorator + def tf_function_decorator(func): + return func + \ No newline at end of file diff --git a/tsgm/models/sts.py b/tsgm/models/sts.py index 170a515..3db33ac 100644 --- a/tsgm/models/sts.py +++ b/tsgm/models/sts.py @@ -1,18 +1,21 @@ +import tsgm import keras -import tensorflow_probability as tfp import numpy as np -from tensorflow_probability import sts - -import tsgm - +try: + import tensorflow_probability as tfp + from tensorflow_probability import sts + import tensorflow as tf + DEFAULT_TREND = sts.LocalLinearTrend() + DEFAULT_SEASONAL = tfp.sts.Seasonal(num_seasons=12) + DEFAULT_MODEL = sts.Sum([DEFAULT_TREND, DEFAULT_SEASONAL]) + has_tensorflow = True +except ImportError: + has_tensorflow = False + print("TensorFlow not installed. STS model is not available.") -DEFAULT_TREND = sts.LocalLinearTrend() -DEFAULT_SEASONAL = tfp.sts.Seasonal(num_seasons=12) -DEFAULT_MODEL = sts.Sum([DEFAULT_TREND, DEFAULT_SEASONAL]) - -class STS: +class STSTensorFlow(): """ Class for training and generating from a structural time series model. """ @@ -80,3 +83,14 @@ def generate(self, num_samples: int) -> tsgm.types.Tensor: assert self._dist is not None return self._dist.sample(num_samples).numpy()[..., 0] + + +class STSTorch(): + def __init__(self, *args, **kwargs): + raise EnvironmentError("This is the PyTorch environment. STS is only available in TensorFlow backend.") + +# Dynamically select the appropriate STS class +if has_tensorflow: + STS = STSTensorFlow +else: + STS = STSTorch \ No newline at end of file diff --git a/tsgm/models/timeGAN.py b/tsgm/models/timeGAN.py index d2227e1..f285296 100644 --- a/tsgm/models/timeGAN.py +++ b/tsgm/models/timeGAN.py @@ -1,7 +1,9 @@ -import tensorflow as tf +import os import keras from keras import ops from tsgm.types import Tensor as TensorLike +# a keras_dataset can be tf.data.Dataset or Torch DataLoader +from tsgm.backend import tf_function_decorator, Keras_Dataset, get_backend import numpy as np import numpy.typing as npt from tqdm import tqdm, trange @@ -251,10 +253,10 @@ def _define_timegan(self) -> None: inputs=X, outputs=Y_real, name="FinalDiscriminator" ) self.discriminator_model.summary() - - @tf.function - def _train_autoencoder( - self, X: TensorLike, optimizer: keras.optimizers.Optimizer + + @tf_function_decorator + def _train_autoencoder_tf( + self, tf, X: TensorLike, optimizer: keras.optimizers.Optimizer ) -> float: """ 1. Embedding network training: minimize E_loss0 @@ -272,9 +274,38 @@ def _train_autoencoder( optimizer.apply_gradients(zip(gradients, all_trainable)) return E_loss0 - @tf.function - def _train_supervisor( + def _train_autoencoder_torch( + self, torch, X: TensorLike, optimizer: keras.optimizers.Optimizer + ) -> float: + X_tilde = self.autoencoder(X) + E_loss_T0 = self._mse(X, X_tilde) + E_loss0 = 10.0 * ops.sqrt(E_loss_T0) + self.embedder.zero_grad() + self.recovery.zero_grad() + E_loss0.backward() + + e_vars = self.embedder.trainable_variables + r_vars = self.recovery.trainable_variables + all_trainable = e_vars + r_vars + gradients = [v.value.grad for v in all_trainable] + + with torch.no_grad(): + optimizer.apply(zip(gradients, all_trainable)) + return E_loss0 + + + def _train_autoencoder( self, X: TensorLike, optimizer: keras.optimizers.Optimizer + ) -> float: + backend = get_backend() + if os.environ["KERAS_BACKEND"] == "tensorflow": + return self._train_autoencoder_tf(backend, X, optimizer) + elif os.environ["KERAS_BACKEND"] == "torch": + return self._train_autoencoder_torch(backend, X, optimizer) + + @tf_function_decorator + def _train_supervisor_tf( + self, tf, X: TensorLike, optimizer: keras.optimizers.Optimizer ) -> float: """ 2. Training with supervised loss only: minimize G_loss_S @@ -295,10 +326,46 @@ def _train_supervisor( ] optimizer.apply_gradients(apply_grads) return G_loss_S + + def _train_supervisor_torch( + self, torch, X: TensorLike, optimizer: keras.optimizers.Optimizer + ) -> float: + H = self.embedder(X) + H_hat_supervised = self.supervisor(H) + G_loss_S = self._mse(H[:, 1:, :], H_hat_supervised[:, :-1, :]) + self.generator.zero_grad() + self.supervisor.zero_grad() + G_loss_S.backward() - @tf.function - def _train_generator( - self, X: TensorLike, Z: TensorLike, optimizer: keras.optimizers.Optimizer + g_vars = self.generator.trainable_variables + s_vars = self.supervisor.trainable_variables + all_trainable = g_vars + s_vars + gradients = [v.value.grad for v in all_trainable] + apply_grads = [ + (grad, var) + for (grad, var) in zip(gradients, all_trainable) + if grad is not None + ] + + with torch.no_grad(): + optimizer.apply(apply_grads) + return G_loss_S + + def _train_supervisor( + self, X: TensorLike, optimizer: keras.optimizers.Optimizer + ) -> float: + """ + 2. Training with supervised loss only: minimize G_loss_S + """ + backend = get_backend() + if os.environ["KERAS_BACKEND"] == "tensorflow": + return self._train_supervisor_tf(backend, X, optimizer) + elif os.environ["KERAS_BACKEND"] == "torch": + return self._train_supervisor_torch(backend, X, optimizer) + + @tf_function_decorator + def _train_generator_tf( + self, tf, X: TensorLike, Z: TensorLike, optimizer: keras.optimizers.Optimizer ) -> T.Tuple[float, float, float, float, float]: """ 3. Joint training (Generator training twice more than discriminator training): minimize G_loss @@ -338,10 +405,60 @@ def _train_generator( ] optimizer.apply_gradients(apply_grads) return G_loss_U, G_loss_U_e, G_loss_S, G_loss_V, G_loss + + def _train_generator_torch( + self, torch, X: TensorLike, Z: TensorLike, optimizer: keras.optimizers.Optimizer + ) -> T.Tuple[float, float, float, float, float]: + Y_fake = self.adversarial_supervised(Z) + G_loss_U = self._bce(y_true=ops.ones_like(Y_fake), y_pred=Y_fake) - @tf.function - def _train_embedder( - self, X: TensorLike, optimizer: keras.optimizers.Optimizer + Y_fake_e = self.adversarial_embedded(Z) + G_loss_U_e = self._bce(y_true=ops.ones_like(Y_fake_e), y_pred=Y_fake_e) + + H = self.embedder(X) + H_hat_supervised = self.supervisor(H) + G_loss_S = self._mse(H[:, 1:, :], H_hat_supervised[:, :-1, :]) + + X_hat = self.generator(Z) + G_loss_V = self._compute_generator_moments_loss(X, X_hat) + + G_loss = ( + G_loss_U + + self.gamma * G_loss_U_e + + 100 * ops.sqrt(G_loss_S) + + 100 * G_loss_V + ) + + g_vars = self.generator_aux.trainable_variables + s_vars = self.supervisor.trainable_variables + all_trainable = g_vars + s_vars + gradients = [v.value.grad for v in all_trainable] + apply_grads = [ + (grad, var) + for (grad, var) in zip(gradients, all_trainable) + if grad is not None + ] + + with torch.no_grad(): + optimizer.apply(apply_grads) + return G_loss_U, G_loss_U_e, G_loss_S, G_loss_V, G_loss + + + def _train_generator( + self, X: TensorLike, Z: TensorLike, optimizer: keras.optimizers.Optimizer + ) -> T.Tuple[float, float, float, float, float]: + """ + 3. Joint training (Generator training twice more than discriminator training): minimize G_loss + """ + backend = get_backend() + if os.environ["KERAS_BACKEND"] == "tensorflow": + return self._train_generator_tf(backend, X, Z, optimizer) + elif os.environ["KERAS_BACKEND"] == "torch": + return self._train_generator_torch(backend, X, Z, optimizer) + + @tf_function_decorator + def _train_embedder_tf( + self, tf, X: TensorLike, optimizer: keras.optimizers.Optimizer ) -> T.Tuple[float, float]: """ Train embedder during joint training: minimize E_loss @@ -365,10 +482,44 @@ def _train_embedder( gradients = tape.gradient(E_loss, all_trainable) optimizer.apply_gradients(zip(gradients, all_trainable)) return E_loss, E_loss_T0 + + def _train_embedder_torch( + self, torch, X: TensorLike, optimizer: keras.optimizers.Optimizer + ) -> T.Tuple[float, float]: + H = self.embedder(X) + H_hat_supervised = self.supervisor(H) + G_loss_S = self._mse(H[:, 1:, :], H_hat_supervised[:, :-1, :]) - @tf.function - def _train_discriminator( - self, X: TensorLike, Z: TensorLike, optimizer: keras.optimizers.Optimizer + X_tilde = self.autoencoder(X) + E_loss_T0 = self._mse(X, X_tilde) + E_loss0 = 10 * ops.sqrt(E_loss_T0) + + E_loss = E_loss0 + 0.1 * G_loss_S + + e_vars = self.embedder.trainable_variables + r_vars = self.recovery.trainable_variables + all_trainable = e_vars + r_vars + gradients = [v.value.grad for v in all_trainable] + + with torch.no_grad(): + optimizer.apply(zip(gradients, all_trainable)) + return E_loss, E_loss_T0 + + def _train_embedder( + self, X: TensorLike, optimizer: keras.optimizers.Optimizer + ) -> T.Tuple[float, float]: + """ + Train embedder during joint training: minimize E_loss + """ + backend = get_backend() + if os.environ["KERAS_BACKEND"] == "tensorflow": + return self._train_embedder_tf(backend, X, optimizer) + elif os.environ["KERAS_BACKEND"] == "torch": + return self._train_embedder_torch(backend, X, optimizer) + + @tf_function_decorator + def _train_discriminator_tf( + self, tf, X: TensorLike, Z: TensorLike, optimizer: keras.optimizers.Optimizer ) -> float: """ minimize D_loss @@ -380,6 +531,32 @@ def _train_discriminator( gradients = tape.gradient(D_loss, d_vars) optimizer.apply_gradients(zip(gradients, d_vars)) return D_loss + + def _train_discriminator_torch( + self, torch, X: TensorLike, Z: TensorLike, optimizer: keras.optimizers.Optimizer + ) -> float: + D_loss = self._check_discriminator_loss(X, Z) + self.discriminator.zero_grad() + D_loss.backward() + + d_vars = [v for v in self.discriminator.trainable_variables] + gradients = [v.value.grad for v in d_vars] + + with torch.no_grad(): + optimizer.apply(zip(gradients, d_vars)) + return D_loss + + def _train_discriminator( + self, X: TensorLike, Z: TensorLike, optimizer: keras.optimizers.Optimizer + ) -> float: + """ + minimize D_loss + """ + backend = get_backend() + if os.environ["KERAS_BACKEND"] == "tensorflow": + return self._train_discriminator_tf(backend, X, Z, optimizer) + elif os.environ["KERAS_BACKEND"] == "torch": + return self._train_discriminator_torch(backend, X, Z, optimizer) @staticmethod def _compute_generator_moments_loss( @@ -434,29 +611,51 @@ def get_noise_batch(self) -> T.Iterator: """ Return an iterator of random noise vectors """ - return iter( - tf.data.Dataset.from_generator( - self._generate_noise, output_types=tf.float32 + backend = get_backend() + if os.environ["KERAS_BACKEND"] == "tensorflow": + tf = backend + return iter( + tf.data.Dataset.from_generator( + self._generate_noise, output_types=tf.float32 + ) + .batch(self.batch_size) + .repeat() ) - .batch(self.batch_size) - .repeat() - ) + elif os.environ["KERAS_BACKEND"] == "torch": + torch = backend + def noise_generator(): + while True: + yield torch.tensor( + np.random.uniform(low=0, high=1, size=(self.seq_len, self.dim)), + dtype=torch.float32 + ) + + noise_dataset = torch.utils.data.IterableDataset.from_generator(noise_generator) + return iter(torch.utils.data.DataLoader(noise_dataset, batch_size=self.batch_size)) def _get_data_batch(self, data: TensorLike, n_windows: int) -> T.Iterator: """ Return an iterator of shuffled input data """ data = ops.convert_to_tensor(data, dtype="float32") - return iter( - tf.data.Dataset.from_tensor_slices(data) - .shuffle(buffer_size=n_windows) - .batch(self.batch_size) - .repeat() - ) + backend = get_backend() + if os.environ["KERAS_BACKEND"] == "tensorflow": + tf = backend + return iter( + tf.data.Dataset.from_tensor_slices(data) + .shuffle(buffer_size=n_windows) + .batch(self.batch_size) + .repeat() + ) + elif os.environ["KERAS_BACKEND"] == "torch": + torch = backend + data = torch.tensor(data, dtype=torch.float32) + dataset = torch.utils.data.TensorDataset(data) + return torch.utils.data.DataLoader(dataset, batch_size=self.batch_size, shuffle=True) def fit( self, - data: T.Union[TensorLike, tf.data.Dataset], + data: T.Union[TensorLike, Keras_Dataset], epochs: int, checkpoints_interval: T.Optional[int] = None, generate_synthetic: T.Tuple = (), @@ -484,9 +683,11 @@ def fit( self._mse is None or self._bce is None ), "One of the loss functions is not defined. Please call .compile() to set them" - # take tf.data.Dataset | TensorLike - if isinstance(data, tf.data.Dataset): + # take tf.data.Dataset | torch.utils.data.Dataloader | TensorLike + if os.environ["KERAS_BACKEND"] == "tensorflow" and isinstance(data, Keras_Dataset): batches = iter(data.repeat()) + elif os.environ["KERAS_BACKEND"] == "torch" and isinstance(data, Keras_Dataset): + batches = iter(data) else: batches = self._get_data_batch(data, n_windows=len(data)) From e7bb0d1ff4dcb4a85139f316a3115dd8742d9147 Mon Sep 17 00:00:00 2001 From: liyiersan-server5 Date: Thu, 19 Sep 2024 17:07:26 +0800 Subject: [PATCH 31/31] test pytest --- .gitignore | 5 +- tests/test_abc.py | 3 + tests/test_augmentations.py | 4 +- tests/test_cgan.py | 5 +- tests/test_dataset.py | 4 + tests/test_downstream_models.py | 6 +- tsgm/backend.py | 2 +- tsgm/models/sts.py | 146 ++++++++++++++++---------------- tsgm/models/timeGAN.py | 10 +-- 9 files changed, 100 insertions(+), 85 deletions(-) diff --git a/.gitignore b/.gitignore index 9e2e3ff..e0e797f 100644 --- a/.gitignore +++ b/.gitignore @@ -135,4 +135,7 @@ dmypy.json .pyre/ # local history -.lh/ \ No newline at end of file +.lh/ + +# data +data/ \ No newline at end of file diff --git a/tests/test_abc.py b/tests/test_abc.py index b041c4f..dc9c8ac 100644 --- a/tests/test_abc.py +++ b/tests/test_abc.py @@ -1,4 +1,7 @@ import pytest +import os +import sys +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) import tsgm from tsgm.backend import get_distributions diff --git a/tests/test_augmentations.py b/tests/test_augmentations.py index 359a0b7..790670f 100644 --- a/tests/test_augmentations.py +++ b/tests/test_augmentations.py @@ -1,6 +1,8 @@ import pytest import numpy as np - +import os +import sys +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) import tsgm @pytest.mark.parametrize("mock_aug", [ diff --git a/tests/test_cgan.py b/tests/test_cgan.py index 503efd9..5bff157 100644 --- a/tests/test_cgan.py +++ b/tests/test_cgan.py @@ -1,4 +1,7 @@ import pytest +import os +import sys +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) import tsgm try: @@ -9,7 +12,7 @@ import numpy as np import keras -import os + from tsgm.backend import get_backend diff --git a/tests/test_dataset.py b/tests/test_dataset.py index 1c9b845..4c94eb2 100644 --- a/tests/test_dataset.py +++ b/tests/test_dataset.py @@ -1,7 +1,11 @@ import pytest import numpy as np +import os +import sys +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) import tsgm +import tsgm.backend def test_dataset(): diff --git a/tests/test_downstream_models.py b/tests/test_downstream_models.py index 4804edf..1f6e0a6 100644 --- a/tests/test_downstream_models.py +++ b/tests/test_downstream_models.py @@ -1,9 +1,11 @@ import pytest import copy import itertools -import keras - +import os +import sys +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) import tsgm +import keras def _get_gunpoint_dataset(): diff --git a/tsgm/backend.py b/tsgm/backend.py index 9ee97b2..8748ea7 100644 --- a/tsgm/backend.py +++ b/tsgm/backend.py @@ -13,7 +13,7 @@ os.environ["KERAS_BACKEND"] = "torch" Keras_Dataset = torch.utils.data.DataLoader except ImportError: - raise ImportError("No backend found. Please install tensorflow or torch .") + raise ImportError("No backend found. Please install tensorflow or torch.") def get_backend(): if os.environ["KERAS_BACKEND"] == "tensorflow": diff --git a/tsgm/models/sts.py b/tsgm/models/sts.py index 3db33ac..97ca9b5 100644 --- a/tsgm/models/sts.py +++ b/tsgm/models/sts.py @@ -10,84 +10,82 @@ DEFAULT_SEASONAL = tfp.sts.Seasonal(num_seasons=12) DEFAULT_MODEL = sts.Sum([DEFAULT_TREND, DEFAULT_SEASONAL]) has_tensorflow = True -except ImportError: - has_tensorflow = False - print("TensorFlow not installed. STS model is not available.") - - -class STSTensorFlow(): - """ - Class for training and generating from a structural time series model. - """ - - def __init__(self, model: tfp.sts.StructuralTimeSeries = None) -> None: - """ - Initializes a new instance of the STS class. - - :param model: Structural time series model to use. If None, default model is used. - :type model: tfp.sts.StructuralTimeSeriesModel or None - """ - self._model = model or DEFAULT_MODEL - self._dist = None - self._elbo_loss = None - - def train(self, ds: tsgm.dataset.Dataset, num_variational_steps: int = 200, - steps_forw: int = 10) -> None: - """ - Trains the structural time series model. - - :param ds: Dataset containing time series data. - :type ds: tsgm.dataset.Dataset - :param num_variational_steps: Number of variational optimization steps, defaults to 200. - :type num_variational_steps: int - :param steps_forw: Number of steps to forecast, defaults to 10. - :type steps_forw: int - """ - assert ds.shape[0] == 1 # now works only with 1 TS - X = ds.X.astype(np.float32) - variational_posteriors = tfp.sts.build_factored_surrogate_posterior( - model=self._model) - - self._elbo_loss = tfp.vi.fit_surrogate_posterior( - target_log_prob_fn=self._model.joint_distribution(observed_time_series=X).log_prob, - surrogate_posterior=variational_posteriors, - optimizer=keras.optimizers.Adam(learning_rate=0.1), - num_steps=num_variational_steps, - jit_compile=True) - - q_samples = variational_posteriors.sample(50) - - self._dist = tfp.sts.forecast( - self._model, observed_time_series=X, - parameter_samples=q_samples, num_steps_forecast=steps_forw) - - def elbo_loss(self) -> float: - """ - Returns the evidence lower bound (ELBO) loss from training. - - :returns: The value of the ELBO loss. - :rtype: float - """ - return self._elbo_loss - - def generate(self, num_samples: int) -> tsgm.types.Tensor: + + class STSTensorFlow(): """ - Generates samples from the trained model. - - :param num_samples: Number of samples to generate. - :type num_samples: int - - :returns: Generated samples. - :rtype: tsgm.types.Tensor + Class for training and generating from a structural time series model. """ - assert self._dist is not None - - return self._dist.sample(num_samples).numpy()[..., 0] + def __init__(self, model: tfp.sts.StructuralTimeSeries = None) -> None: + """ + Initializes a new instance of the STS class. + + :param model: Structural time series model to use. If None, default model is used. + :type model: tfp.sts.StructuralTimeSeriesModel or None + """ + self._model = model or DEFAULT_MODEL + self._dist = None + self._elbo_loss = None + + def train(self, ds: tsgm.dataset.Dataset, num_variational_steps: int = 200, + steps_forw: int = 10) -> None: + """ + Trains the structural time series model. + + :param ds: Dataset containing time series data. + :type ds: tsgm.dataset.Dataset + :param num_variational_steps: Number of variational optimization steps, defaults to 200. + :type num_variational_steps: int + :param steps_forw: Number of steps to forecast, defaults to 10. + :type steps_forw: int + """ + assert ds.shape[0] == 1 # now works only with 1 TS + X = ds.X.astype(np.float32) + variational_posteriors = tfp.sts.build_factored_surrogate_posterior( + model=self._model) + + self._elbo_loss = tfp.vi.fit_surrogate_posterior( + target_log_prob_fn=self._model.joint_distribution(observed_time_series=X).log_prob, + surrogate_posterior=variational_posteriors, + optimizer=keras.optimizers.Adam(learning_rate=0.1), + num_steps=num_variational_steps, + jit_compile=True) + + q_samples = variational_posteriors.sample(50) + + self._dist = tfp.sts.forecast( + self._model, observed_time_series=X, + parameter_samples=q_samples, num_steps_forecast=steps_forw) + + def elbo_loss(self) -> float: + """ + Returns the evidence lower bound (ELBO) loss from training. + + :returns: The value of the ELBO loss. + :rtype: float + """ + return self._elbo_loss + + def generate(self, num_samples: int) -> tsgm.types.Tensor: + """ + Generates samples from the trained model. + + :param num_samples: Number of samples to generate. + :type num_samples: int + + :returns: Generated samples. + :rtype: tsgm.types.Tensor + """ + assert self._dist is not None + + return self._dist.sample(num_samples).numpy()[..., 0] +except ImportError: + has_tensorflow = False + print("TensorFlow not installed. STS model is not available.") -class STSTorch(): - def __init__(self, *args, **kwargs): - raise EnvironmentError("This is the PyTorch environment. STS is only available in TensorFlow backend.") + class STSTorch(): + def __init__(self, *args, **kwargs): + raise EnvironmentError("This is the PyTorch environment. STS is only available in TensorFlow backend.") # Dynamically select the appropriate STS class if has_tensorflow: diff --git a/tsgm/models/timeGAN.py b/tsgm/models/timeGAN.py index f285296..21bbd02 100644 --- a/tsgm/models/timeGAN.py +++ b/tsgm/models/timeGAN.py @@ -155,11 +155,11 @@ def __init__( def compile( self, - d_optimizer: keras.optimizers.Optimizer = keras.optimizers.legacy.Adam(), - g_optimizer: keras.optimizers.Optimizer = keras.optimizers.legacy.Adam(), - emb_optimizer: keras.optimizers.Optimizer = keras.optimizers.legacy.Adam(), - supgan_optimizer: keras.optimizers.Optimizer = keras.optimizers.legacy.Adam(), - ae_optimizer: keras.optimizers.Optimizer = keras.optimizers.legacy.Adam(), + d_optimizer: keras.optimizers.Optimizer = keras.optimizers.Adam(), + g_optimizer: keras.optimizers.Optimizer = keras.optimizers.Adam(), + emb_optimizer: keras.optimizers.Optimizer = keras.optimizers.Adam(), + supgan_optimizer: keras.optimizers.Optimizer = keras.optimizers.Adam(), + ae_optimizer: keras.optimizers.Optimizer = keras.optimizers.Adam(), emb_loss: keras.losses.Loss = keras.losses.MeanSquaredError(), clf_loss: keras.losses.Loss = keras.losses.BinaryCrossentropy(), ) -> None:

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