diff --git a/examples/timeserie_prediction/narma10_esn-2.ipynb b/examples/timeserie_prediction/narma10_esn-2.ipynb deleted file mode 100644 index 42327c5..0000000 --- a/examples/timeserie_prediction/narma10_esn-2.ipynb +++ /dev/null @@ -1,390 +0,0 @@ -{ - "cells": [ - { - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-02T04:32:30.926846Z", - "start_time": "2024-11-02T04:32:30.813927Z" - } - }, - "cell_type": "code", - "source": "!which python", - "id": "15c8b18e679d0534", - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/raameshb/miniforge3/envs/echotorch3/bin/python\r\n" - ] - } - ], - "execution_count": 1 - }, - { - "cell_type": "code", - "id": "initial_id", - "metadata": { - "collapsed": true, - "ExecuteTime": { - "end_time": "2024-11-02T04:32:36.647893Z", - "start_time": "2024-11-02T04:32:33.721720Z" - } - }, - "source": [ - "# Imports\n", - "import torch\n", - "from echotorch.datasets.NARMADataset import NARMADataset\n", - "import echotorch.nn.reservoir as etrs\n", - "import echotorch.utils\n", - "import echotorch.utils.matrix_generation as mg\n", - "from torch.autograd import Variable\n", - "from torch.utils.data.dataloader import DataLoader\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ], - "outputs": [], - "execution_count": 2 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-02T04:32:38.503551Z", - "start_time": "2024-11-02T04:32:38.501248Z" - } - }, - "cell_type": "code", - "source": [ - "# Length of training samples\n", - "train_sample_length = 5000\n", - "\n", - "# Length of test samples\n", - "test_sample_length = 2000\n", - "\n", - "# How many training/test samples\n", - "n_train_samples = 1\n", - "n_test_samples = 1\n", - "\n", - "# Batch size (how many sample processed at the same time?)\n", - "batch_size = 1" - ], - "id": "b337d1a1fe052930", - "outputs": [], - "execution_count": 3 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-02T04:32:39.077007Z", - "start_time": "2024-11-02T04:32:39.074685Z" - } - }, - "cell_type": "code", - "source": [ - "# Reservoir hyper-parameters\n", - "spectral_radius = 1.07\n", - "leaky_rate = 0.9261\n", - "input_dim = 1\n", - "reservoir_size = 410\n", - "connectivity = 0.1954\n", - "ridge_param = 0.00000409\n", - "input_scaling = 0.9252\n", - "bias_scaling = 0.079079" - ], - "id": "55e2ff2c6b95d7b5", - "outputs": [], - "execution_count": 4 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-02T04:32:39.628773Z", - "start_time": "2024-11-02T04:32:39.626758Z" - } - }, - "cell_type": "code", - "source": [ - "# Predicted/target plot length\n", - "plot_length = 200" - ], - "id": "9945a538cebe8b20", - "outputs": [], - "execution_count": 5 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-02T04:32:40.185522Z", - "start_time": "2024-11-02T04:32:40.181467Z" - } - }, - "cell_type": "code", - "source": [ - "# Use CUDA?\n", - "use_cuda = False\n", - "use_cuda = torch.cuda.is_available() if use_cuda else False\n", - "\n", - "use_cuda" - ], - "id": "b691f8f2c2d7635e", - "outputs": [ - { - "data": { - "text/plain": [ - "False" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "execution_count": 6 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-02T04:32:40.830277Z", - "start_time": "2024-11-02T04:32:40.825494Z" - } - }, - "cell_type": "code", - "source": [ - "# Manual seed initialisation\n", - "np.random.seed(1)\n", - "torch.manual_seed(1)" - ], - "id": "43fabb647e327427", - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "execution_count": 7 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-02T04:32:41.664396Z", - "start_time": "2024-11-02T04:32:41.371300Z" - } - }, - "cell_type": "code", - "source": [ - "# NARMA30 dataset\n", - "narma10_train_dataset = NARMADataset(train_sample_length, n_train_samples, system_order=10)\n", - "narma10_test_dataset = NARMADataset(test_sample_length, n_test_samples, system_order=10)\n", - "\n", - "# Data loader\n", - "trainloader = DataLoader(narma10_train_dataset, batch_size=batch_size, shuffle=False, num_workers=2)\n", - "testloader = DataLoader(narma10_test_dataset, batch_size=batch_size, shuffle=False, num_workers=2)" - ], - "id": "b350406a405b1e9c", - "outputs": [], - "execution_count": 8 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-02T04:32:42.143696Z", - "start_time": "2024-11-02T04:32:42.141144Z" - } - }, - "cell_type": "code", - "source": [ - "# Internal matrix\n", - "w_generator = echotorch.utils.matrix_generation.NormalMatrixGenerator(\n", - " connectivity=connectivity,\n", - " spetral_radius=spectral_radius\n", - ")\n", - "\n", - "# Input weights\n", - "win_generator = echotorch.utils.matrix_generation.NormalMatrixGenerator(\n", - " connectivity=connectivity,\n", - " scale=input_scaling,\n", - " apply_spectral_radius=False\n", - ")\n", - "\n", - "# Bias vector\n", - "wbias_generator = echotorch.utils.matrix_generation.NormalMatrixGenerator(\n", - " connectivity=connectivity,\n", - " scale=bias_scaling,\n", - " apply_spectral_radius=False\n", - ")" - ], - "id": "b6f586e86ed0b9a2", - "outputs": [], - "execution_count": 9 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-02T04:32:42.821502Z", - "start_time": "2024-11-02T04:32:42.718046Z" - } - }, - "cell_type": "code", - "source": [ - "# Create a Leaky-integrated ESN,\n", - "# with least-square training algo.\n", - "# esn = etrs.ESN(\n", - "esn = etrs.LiESN(\n", - " input_dim=input_dim,\n", - " hidden_dim=reservoir_size,\n", - " output_dim=1,\n", - " leaky_rate=leaky_rate,\n", - " learning_algo='inv',\n", - " w_generator=w_generator,\n", - " win_generator=win_generator,\n", - " wbias_generator=wbias_generator,\n", - " ridge_param=ridge_param\n", - ")\n", - "\n", - "# Transfer in the GPU if possible\n", - "if use_cuda:\n", - " esn.cuda()\n", - "# end if" - ], - "id": "da979a5a1d1b2980", - "outputs": [], - "execution_count": 10 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-02T04:32:43.712638Z", - "start_time": "2024-11-02T04:32:43.357770Z" - } - }, - "cell_type": "code", - "source": [ - "# For each batch\n", - "for data in trainloader:\n", - " # Inputs and outputs\n", - " inputs, targets = data\n", - "\n", - " # Transform data to Variables\n", - " inputs, targets = Variable(inputs), Variable(targets)\n", - " if use_cuda: inputs, targets = inputs.cuda(), targets.cuda()\n", - "\n", - " # ESN need inputs and targets\n", - " esn(inputs, targets)\n", - "# end for\n", - "\n", - "# Now we finalize the training by\n", - "# computing the output matrix Wout.\n", - "esn.finalize()" - ], - "id": "3f61272c76bb314", - "outputs": [], - "execution_count": 11 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-02T04:32:44.810538Z", - "start_time": "2024-11-02T04:32:44.196115Z" - } - }, - "cell_type": "code", - "source": [ - "# Get the first sample in training set,\n", - "# and transform it to Variable.\n", - "dataiter = iter(trainloader)\n", - "train_u, train_y = next(dataiter)\n", - "train_u, train_y = Variable(train_u), Variable(train_y)\n", - "if use_cuda: train_u, train_y = train_u.cuda(), train_y.cuda()\n", - "\n", - "# Make a prediction with our trained ESN\n", - "y_predicted = esn(train_u)\n", - "\n", - "# Print training MSE and NRMSE\n", - "print((\"Train MSE: {}\".format(echotorch.utils.mse(y_predicted.data, train_y.data))))\n", - "print((\"Test NRMSE: {}\".format(echotorch.utils.nrmse(y_predicted.data, train_y.data))))\n", - "print(\"\")\n", - "\n", - "# Get the first sample in test set,\n", - "# and transform it to Variable.\n", - "dataiter = iter(testloader)\n", - "test_u, test_y = next(dataiter)\n", - "test_u, test_y = Variable(test_u), Variable(test_y)\n", - "if use_cuda: test_u, test_y = test_u.cuda(), test_y.cuda()\n", - "\n", - "# Make a prediction with our trained ESN\n", - "y_predicted = esn(test_u)\n", - "\n", - "# Print test MSE and NRMSE\n", - "print((\"Test MSE: {}\".format(echotorch.utils.mse(y_predicted.data, test_y.data))))\n", - "print((\"Test NRMSE: {}\".format(echotorch.utils.nrmse(y_predicted.data, test_y.data))))\n", - "print(\"\")\n", - "\n", - "# Show target and predicted\n", - "plt.plot(test_y[0, :plot_length, 0].data, 'r')\n", - "plt.plot(y_predicted[0, :plot_length, 0].data, 'b')\n", - "plt.show()" - ], - "id": "3304b235e86b8a1d", - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Train MSE: 0.0007712062797509134\n", - "Test NRMSE: 0.2590772212071233\n", - "\n", - "Test MSE: 0.0010526328114792705\n", - "Test NRMSE: 0.28439146193341414\n", - "\n" - ] - }, - { - "data": { - "text/plain": [ - "
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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "execution_count": 12 - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": "", - "id": "3efb04d22b03c62" - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/timeserie_prediction/narma10_esn.ipynb-2 b/examples/timeserie_prediction/narma10_esn.ipynb-2 deleted file mode 100644 index 6ee9859..0000000 --- a/examples/timeserie_prediction/narma10_esn.ipynb-2 +++ /dev/null @@ -1,390 +0,0 @@ -{ - "cells": [ - { - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-02T04:31:55.615842Z", - "start_time": "2024-11-02T04:31:55.445013Z" - } - }, - "cell_type": "code", - "source": "!which python", - "id": "15c8b18e679d0534", - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/raameshb/miniforge3/envs/echotorch2/bin/python\r\n" - ] - } - ], - "execution_count": 2 - }, - { - "cell_type": "code", - "id": "initial_id", - "metadata": { - "collapsed": true, - "ExecuteTime": { - "end_time": "2024-11-02T04:31:52.330820Z", - "start_time": "2024-11-02T04:31:51.021026Z" - } - }, - "source": [ - "# Imports\n", - "import torch\n", - "from echotorch.datasets.NARMADataset import NARMADataset\n", - "import echotorch.nn.reservoir as etrs\n", - "import echotorch.utils\n", - "import echotorch.utils.matrix_generation as mg\n", - "from torch.autograd import Variable\n", - "from torch.utils.data.dataloader import DataLoader\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ], - "outputs": [], - "execution_count": 1 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-02T04:13:40.238835Z", - "start_time": "2024-11-02T04:13:40.236114Z" - } - }, - "cell_type": "code", - "source": [ - "# Length of training samples\n", - "train_sample_length = 5000\n", - "\n", - "# Length of test samples\n", - "test_sample_length = 2000\n", - "\n", - "# How many training/test samples\n", - "n_train_samples = 1\n", - "n_test_samples = 1\n", - "\n", - "# Batch size (how many sample processed at the same time?)\n", - "batch_size = 1" - ], - "id": "b337d1a1fe052930", - "outputs": [], - "execution_count": 4 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-02T04:13:41.431102Z", - "start_time": "2024-11-02T04:13:41.427987Z" - } - }, - "cell_type": "code", - "source": [ - "# Reservoir hyper-parameters\n", - "spectral_radius = 1.07\n", - "leaky_rate = 0.9261\n", - "input_dim = 1\n", - "reservoir_size = 410\n", - "connectivity = 0.1954\n", - "ridge_param = 0.00000409\n", - "input_scaling = 0.9252\n", - "bias_scaling = 0.079079" - ], - "id": "55e2ff2c6b95d7b5", - "outputs": [], - "execution_count": 5 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-02T04:13:41.963756Z", - "start_time": "2024-11-02T04:13:41.960780Z" - } - }, - "cell_type": "code", - "source": [ - "# Predicted/target plot length\n", - "plot_length = 200" - ], - "id": "9945a538cebe8b20", - "outputs": [], - "execution_count": 6 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-02T04:13:42.530207Z", - "start_time": "2024-11-02T04:13:42.524631Z" - } - }, - "cell_type": "code", - "source": [ - "# Use CUDA?\n", - "use_cuda = False\n", - "use_cuda = torch.cuda.is_available() if use_cuda else False\n", - "\n", - "use_cuda" - ], - "id": "b691f8f2c2d7635e", - "outputs": [ - { - "data": { - "text/plain": [ - "False" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "execution_count": 7 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-02T04:13:43.227074Z", - "start_time": "2024-11-02T04:13:43.223533Z" - } - }, - "cell_type": "code", - "source": [ - "# Manual seed initialisation\n", - "np.random.seed(1)\n", - "torch.manual_seed(1)" - ], - "id": "43fabb647e327427", - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "execution_count": 8 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-02T04:13:43.983614Z", - "start_time": "2024-11-02T04:13:43.772520Z" - } - }, - "cell_type": "code", - "source": [ - "# NARMA30 dataset\n", - "narma10_train_dataset = NARMADataset(train_sample_length, n_train_samples, system_order=10)\n", - "narma10_test_dataset = NARMADataset(test_sample_length, n_test_samples, system_order=10)\n", - "\n", - "# Data loader\n", - "trainloader = DataLoader(narma10_train_dataset, batch_size=batch_size, shuffle=False, num_workers=2)\n", - "testloader = DataLoader(narma10_test_dataset, batch_size=batch_size, shuffle=False, num_workers=2)" - ], - "id": "b350406a405b1e9c", - "outputs": [], - "execution_count": 9 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-02T04:13:44.646623Z", - "start_time": "2024-11-02T04:13:44.643830Z" - } - }, - "cell_type": "code", - "source": [ - "# Internal matrix\n", - "w_generator = echotorch.utils.matrix_generation.NormalMatrixGenerator(\n", - " connectivity=connectivity,\n", - " spetral_radius=spectral_radius\n", - ")\n", - "\n", - "# Input weights\n", - "win_generator = echotorch.utils.matrix_generation.NormalMatrixGenerator(\n", - " connectivity=connectivity,\n", - " scale=input_scaling,\n", - " apply_spectral_radius=False\n", - ")\n", - "\n", - "# Bias vector\n", - "wbias_generator = echotorch.utils.matrix_generation.NormalMatrixGenerator(\n", - " connectivity=connectivity,\n", - " scale=bias_scaling,\n", - " apply_spectral_radius=False\n", - ")" - ], - "id": "b6f586e86ed0b9a2", - "outputs": [], - "execution_count": 10 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-02T04:13:45.298389Z", - "start_time": "2024-11-02T04:13:45.157698Z" - } - }, - "cell_type": "code", - "source": [ - "# Create a Leaky-integrated ESN,\n", - "# with least-square training algo.\n", - "# esn = etrs.ESN(\n", - "esn = etrs.LiESN(\n", - " input_dim=input_dim,\n", - " hidden_dim=reservoir_size,\n", - " output_dim=1,\n", - " leaky_rate=leaky_rate,\n", - " learning_algo='inv',\n", - " w_generator=w_generator,\n", - " win_generator=win_generator,\n", - " wbias_generator=wbias_generator,\n", - " ridge_param=ridge_param\n", - ")\n", - "\n", - "# Transfer in the GPU if possible\n", - "if use_cuda:\n", - " esn.cuda()\n", - "# end if" - ], - "id": "da979a5a1d1b2980", - "outputs": [], - "execution_count": 11 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-02T04:13:46.133824Z", - "start_time": "2024-11-02T04:13:45.765398Z" - } - }, - "cell_type": "code", - "source": [ - "# For each batch\n", - "for data in trainloader:\n", - " # Inputs and outputs\n", - " inputs, targets = data\n", - "\n", - " # Transform data to Variables\n", - " inputs, targets = Variable(inputs), Variable(targets)\n", - " if use_cuda: inputs, targets = inputs.cuda(), targets.cuda()\n", - "\n", - " # ESN need inputs and targets\n", - " esn(inputs, targets)\n", - "# end for\n", - "\n", - "# Now we finalize the training by\n", - "# computing the output matrix Wout.\n", - "esn.finalize()" - ], - "id": "3f61272c76bb314", - "outputs": [], - "execution_count": 12 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-02T04:13:47.368047Z", - "start_time": "2024-11-02T04:13:46.585750Z" - } - }, - "cell_type": "code", - "source": [ - "# Get the first sample in training set,\n", - "# and transform it to Variable.\n", - "dataiter = iter(trainloader)\n", - "train_u, train_y = next(dataiter)\n", - "train_u, train_y = Variable(train_u), Variable(train_y)\n", - "if use_cuda: train_u, train_y = train_u.cuda(), train_y.cuda()\n", - "\n", - "# Make a prediction with our trained ESN\n", - "y_predicted = esn(train_u)\n", - "\n", - "# Print training MSE and NRMSE\n", - "print((\"Train MSE: {}\".format(echotorch.utils.mse(y_predicted.data, train_y.data))))\n", - "print((\"Test NRMSE: {}\".format(echotorch.utils.nrmse(y_predicted.data, train_y.data))))\n", - "print(\"\")\n", - "\n", - "# Get the first sample in test set,\n", - "# and transform it to Variable.\n", - "dataiter = iter(testloader)\n", - "test_u, test_y = next(dataiter)\n", - "test_u, test_y = Variable(test_u), Variable(test_y)\n", - "if use_cuda: test_u, test_y = test_u.cuda(), test_y.cuda()\n", - "\n", - "# Make a prediction with our trained ESN\n", - "y_predicted = esn(test_u)\n", - "\n", - "# Print test MSE and NRMSE\n", - "print((\"Test MSE: {}\".format(echotorch.utils.mse(y_predicted.data, test_y.data))))\n", - "print((\"Test NRMSE: {}\".format(echotorch.utils.nrmse(y_predicted.data, test_y.data))))\n", - "print(\"\")\n", - "\n", - "# Show target and predicted\n", - "plt.plot(test_y[0, :plot_length, 0].data, 'r')\n", - "plt.plot(y_predicted[0, :plot_length, 0].data, 'b')\n", - "plt.show()" - ], - "id": "3304b235e86b8a1d", - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Train MSE: 0.0007362717879004776\n", - "Test NRMSE: 0.253141322021768\n", - "\n", - "Test MSE: 0.0010275135282427073\n", - "Test NRMSE: 0.2809777133875123\n", - "\n" - ] - }, - { - "data": { - "text/plain": [ - "
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" 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