From d608da88f93e06e62d12cf652b6e84da23db7f16 Mon Sep 17 00:00:00 2001 From: beckynevin Date: Thu, 26 Sep 2024 14:26:09 -0600 Subject: [PATCH 1/4] adding notebooks relevant for paper --- notebooks/fig1.ipynb | 190 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 190 insertions(+) create mode 100644 notebooks/fig1.ipynb diff --git a/notebooks/fig1.ipynb b/notebooks/fig1.ipynb new file mode 100644 index 0000000..be5f6e0 --- /dev/null +++ b/notebooks/fig1.ipynb @@ -0,0 +1,190 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "09fcc173-1f36-4f32-8b22-d3f4c4307fc7", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from data.data import MyDataLoader, DataPreparation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ab352b73-3515-42b7-9d92-f49a5e8485c8", + "metadata": {}, + "outputs": [], + "source": [ + "# lookup dict\n", + "noise_to_sigma = {\n", + " 'low': 0.01,\n", + " 'medium': 0.05,\n", + " 'high': 0.10,\n", + " 'vhigh': 1.00\n", + "}\n", + "inject_x_image = {'low': 0.01/32, 'medium': 0.05/32, 'high': 0.10/32}\n", + "noise = 'high'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4760fcec-b43c-477d-abaf-b8407ba257b4", + "metadata": {}, + "outputs": [], + "source": [ + "data = DataPreparation()\n", + "size_df = 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "76baf2a1-fb9c-43be-8deb-51e5e71d48ac", + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(10,4))\n", + "\n", + "data.params = np.array([[ 0.2, 0]])\n", + "\n", + "\n", + "ax1 = fig.add_subplot(141)\n", + "data.simulate_data(data.params,\n", + " noise_to_sigma[noise], \n", + " simulation_name = 'linear_homoskedastic',\n", + " x=np.linspace(0, 10, 100),\n", + " inject_type = 'predictive'\n", + " )\n", + "ax1.plot(np.array(data.input), np.array(data.output).flatten(), color = 'black')\n", + "ax1.scatter(np.array(data.input), np.array(data.output), color = 'black', s=0.25, zorder=-100)\n", + "ax1.set_title(r'$\\sigma_y = $'+str(noise_to_sigma[noise]))\n", + "ax1.set_xlabel(r'$x$', size=15)\n", + "ax1.set_ylabel(r'$y$', size=15, rotation=90)\n", + "ax1.set_aspect(5, adjustable='box')\n", + "\n", + "\n", + "ax0 = fig.add_subplot(142)\n", + "data.simulate_data(data.params,\n", + " noise, \n", + " simulation_name = 'linear_homoskedastic',\n", + " x=np.linspace(0, 10, 100),\n", + " inject_type = 'feature',\n", + " vary_sigma = True\n", + " )\n", + "ax0.plot(np.array(data.input).flatten(), np.array(data.output).flatten(), color = 'black')\n", + "ax0.scatter(np.array(data.input), np.array(data.output), color = 'black', s=0.25, zorder=-100)\n", + "#ax0.set_title('Input injection', size=15)\n", + "ax0.set_title(r'$\\sigma_x = $'+str(round(noise_to_sigma[noise] / data.params[0][0],3))+\n", + " r'$\\rightarrow \\sigma_y = $'+str(round(noise_to_sigma[noise],3)))\n", + "ax0.set_xlabel(r'$x$', size=15)\n", + "\n", + "ax0.set_aspect(5, adjustable='box')\n", + "#ax.set_aspect('equal', adjustable='box')\n", + "\n", + "# now doing 2D\n", + "data.params = np.array([[ 0.005, 5, 0.69598183]])\n", + "image_size = 32\n", + "\n", + "\n", + "\n", + "ax3 = fig.add_subplot(143)\n", + "\n", + "image_p, y_p = data.simulate_data_2d(\n", + " size_df,\n", + " data.params,\n", + " inject_type=\"predictive\",\n", + " sigma=noise_to_sigma[noise]\n", + " )\n", + "ax3.imshow(image_p[0])\n", + "ax3.annotate('y = ' + str(round(y_p[0], 2)),\n", + " xy=(0.2, 0.05),\n", + " xycoords='axes fraction',\n", + " color='white',\n", + " size=15)\n", + "ax3.set_title(r'$\\sigma_y = $' + str(noise_to_sigma[noise]))\n", + "\n", + "#ax3.set_title('Output injection', size=15)\n", + "\n", + "\n", + "ax2 = fig.add_subplot(144)\n", + "\n", + "image_f, y_f = data.simulate_data_2d(\n", + " size_df,\n", + " data.params,\n", + " inject_type=\"feature\",\n", + " sigma=inject_x_image[noise]\n", + " )\n", + "ax2.imshow(image_f[0])\n", + "ax2.annotate('y = ' + str(round(y_f[0], 2)),\n", + " xy=(0.2, 0.05),\n", + " xycoords='axes fraction',\n", + " color='white',\n", + " size=15)\n", + "ax2.set_title(r'$\\sigma_x = $' + str(round(noise_to_sigma[noise]/32, 3)) \n", + " + r'$\\rightarrow \\sigma_y = $' + str(noise_to_sigma[noise]))\n", + "ax1.annotate('(a)', xy=(0.02, 0.88), xycoords='axes fraction', size=15, color='black')\n", + "ax0.annotate('(b)', xy=(0.02, 0.88), xycoords='axes fraction', size=15, color='black')\n", + "ax2.annotate('(d)', xy=(0.02, 0.88), xycoords='axes fraction', size=15, color='white')\n", + "ax3.annotate('(c)', xy=(0.02, 0.88), xycoords='axes fraction', size=15, color='white')\n", + "\n", + "\n", + "# Add a shared title for the first two subplots\n", + "fig.text(0.31, 0.83, '0D Data', ha='center', fontsize=15)\n", + "\n", + "# Draw a bracket pointing to the first two subplots\n", + "x_bracket = [0.15, 0.15, 0.45, 0.45] # x-coordinates of the bracket\n", + "y_bracket = [0.77, 0.81, 0.81, 0.77] # y-coordinates of the bracket\n", + "plt.plot(x_bracket, y_bracket, color='black', lw=1.5, transform=fig.transFigure, clip_on=False)\n", + "\n", + "# Add a shared title for the first two subplots\n", + "fig.text(0.71, 0.83, '2D Data', ha='center', fontsize=15)\n", + "\n", + "# Draw a bracket pointing to the first two subplots\n", + "x_bracket = [0.56, 0.56, 0.86, 0.86] # x-coordinates of the bracket\n", + "y_bracket = [0.77, 0.81, 0.81, 0.77] # y-coordinates of the bracket\n", + "plt.plot(x_bracket, y_bracket, color='black', lw=1.5, transform=fig.transFigure, clip_on=False)\n", + "\n", + "\n", + "\n", + "\n", + "#plt.tight_layout()\n", + "plt.savefig('../../../Desktop/design_'+str(noise)+'.png', dpi=1000)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8df66b56-e524-405a-a7bd-c81c4a391594", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 133bc22033526e6fb27c3fb7b5b21c15d6e7eaeb Mon Sep 17 00:00:00 2001 From: beckynevin Date: Thu, 26 Sep 2024 14:30:01 -0600 Subject: [PATCH 2/4] notebook for fig2 pink plots --- notebooks/fig2.ipynb | 781 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 781 insertions(+) create mode 100644 notebooks/fig2.ipynb diff --git a/notebooks/fig2.ipynb b/notebooks/fig2.ipynb new file mode 100644 index 0000000..21cfe07 --- /dev/null +++ b/notebooks/fig2.ipynb @@ -0,0 +1,781 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "7258d113-3467-40d0-8e08-4a34f853d708", + "metadata": {}, + "outputs": [], + "source": [ + "from analyze.analyze import AggregateCheckpoints\n", + "from models.models import model_setup_DER, model_setup_DE\n", + "from data.data import DataPreparation\n", + "import torch\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from torch.utils.data import TensorDataset\n", + "import pandas as pd\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9e3a2cbb-05b3-4342-b332-61b0110f12d2", + "metadata": {}, + "outputs": [], + "source": [ + "new_title_lookup = {'predictive': 'output uncert', 'feature': 'input uncert'}\n", + "new_title_lookup = {'predictive': 'output', 'feature': 'input'}\n", + "DEVICE = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "checkpoints = AggregateCheckpoints()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d3696760-d5ab-4b8d-8dcb-acc151f2551a", + "metadata": {}, + "outputs": [], + "source": [ + "uniform = True\n", + "prescription = \"linear_homoskedastic\"\n", + "inject_type_list = [\"predictive\", \"feature\"]\n", + "data_dim_list = [\"0D\", \"2D\"]\n", + "model_type = [\"DE\", \"DER\"]\n", + "noise_list = [\"low\", \"medium\", \"high\"]\n", + "\n", + "'''\n", + "inject_type_list = [\"predictive\"]\n", + "data_dim_list = [\"2D\"]\n", + "model_type = [\"DE\"]\n", + "noise_list = [\"low\"]\n", + "'''\n", + "\n", + "\n", + "# these are the three colors for the three noise levels\n", + "color_list = [\"#DFA316\", \"#339989\", \"#ED6A5A\", \"#292F36\"]\n", + "size_df_linear = 10000 # 1000\n", + "size_df_image = 175000\n", + "epoch = 99\n", + "ensemble = True\n", + "n_models = 15\n", + "\n", + "mega_dict = {}\n", + "\n", + "# Nested loops to fill the dictionary\n", + "for noise in noise_list:\n", + " mega_dict[noise] = {} # Create a sub-dictionary for each noise level\n", + " for model in model_type:\n", + " mega_dict[noise][model] = {} # Create a sub-dictionary for each model type\n", + " for dim in data_dim_list:\n", + " mega_dict[noise][model][dim] = {} # Create a sub-dictionary for each data dimension\n", + " for inject_type in inject_type_list:\n", + " mega_dict[noise][model][dim][inject_type] = []\n", + "\n", + "sigma_y_lookup = {'low': 0.01, 'medium': 0.05, 'high': 0.10}\n", + "for n, noise in enumerate(noise_list):\n", + " for m, model in enumerate(model_type):\n", + " for j, dim in enumerate(data_dim_list):\n", + " for i, inject_type in enumerate(inject_type_list):\n", + "\n", + " chk = 0\n", + " \n", + " # make the test set\n", + " data = DataPreparation()\n", + " \n", + " if dim == \"0D\":\n", + " data.sample_params_from_prior(size_df_linear)\n", + " if inject_type == \"feature\":\n", + " data.simulate_data(\n", + " data.params,\n", + " noise,\n", + " \"linear_homoskedastic\",\n", + " inject_type=inject_type,\n", + " seed=41,\n", + " vary_sigma=True,\n", + " )\n", + " else:\n", + " sigma = DataPreparation.get_sigma(\n", + " noise, inject_type=inject_type, data_dimension=dim)\n", + " data.simulate_data(\n", + " data.params,\n", + " sigma,\n", + " \"linear_homoskedastic\",\n", + " inject_type=inject_type,\n", + " seed=41,\n", + " )\n", + " df_array = data.get_dict()\n", + " df = {key: torch.tensor(value) if not isinstance(value, TensorDataset) else value for key, value in df_array.items()}\n", + " len_df = len(df[\"params\"][:, 0].numpy())\n", + " len_x = np.shape(df[\"output\"])[1]\n", + " ms_array = np.repeat(df[\"params\"][:, 0].numpy(), len_x)\n", + " bs_array = np.repeat(df[\"params\"][:, 1].numpy(), len_x)\n", + " xs_array = np.reshape(df[\"inputs\"].numpy(), (len_df * len_x))\n", + " ys_array = np.reshape(df[\"output\"].numpy(), (len_df * len_x))\n", + " inputs = np.array([xs_array, ms_array, bs_array]).T\n", + " model_inputs = inputs\n", + " model_outputs = ys_array\n", + " elif dim == \"2D\":\n", + " sigma = DataPreparation.get_sigma(\n", + " noise, inject_type=inject_type, data_dimension=dim)\n", + " data.sample_params_from_prior(\n", + " size_df_image,\n", + " low=[0, 1, -1.5],\n", + " high=[0.01, 10, 1.5],\n", + " n_params=3,\n", + " seed=41)\n", + " model_inputs, model_outputs = data.simulate_data_2d(\n", + " size_df_image,\n", + " data.params,\n", + " image_size=32,\n", + " inject_type=inject_type,\n", + " sigma=sigma)\n", + " if uniform:\n", + " model_inputs, model_outputs = DataPreparation.select_uniform(\n", + " model_inputs, model_outputs, dim, verbose=False, rs=40\n", + " )\n", + " x_test = model_inputs\n", + " y_test = model_outputs\n", + " \n", + " path = \"../DeepUQResources/checkpoints/\"\n", + " if model == \"DER\":\n", + " setupmodel, lossFn = model_setup_DER(\n", + " model, DEVICE, n_hidden=64, data_type=dim)\n", + " COEFF = 0.01\n", + " file_name = (\n", + " str(path)\n", + " + f\"{model}_{prescription}_{inject_type}_{dim}\"\n", + " + f\"_noise_{noise}_loss_DER_COEFF_{COEFF}_epoch_{epoch}\"\n", + " )\n", + " if dim == \"0D\":\n", + " file_name += f\"_sizedf_{size_df_linear}\"\n", + " elif dim == \"2D\":\n", + " file_name += f\"_sizedf_{size_df_image}\"\n", + " file_name += \".pt\"\n", + " try:\n", + " #print('loading this file', file_name)\n", + " chk = torch.load(file_name, map_location=DEVICE)\n", + " except FileNotFoundError:\n", + " #print(\"cannot find this model\", file_name)\n", + " continue\n", + " setupmodel.load_state_dict(chk.get(\"model_state_dict\"))\n", + " setupmodel.eval()\n", + "\n", + " y_pred = setupmodel(torch.Tensor(x_test)).detach().numpy()\n", + " beta = y_pred[:, 3]\n", + " nu = y_pred[:, 1]\n", + " alpha = y_pred[:, 2]\n", + " u_al = np.sqrt(abs(beta * (1 + nu) / (alpha * nu)))\n", + " \n", + " \n", + " elif model == \"DE\":\n", + " models_used = 0\n", + " loss = \"bnll_loss\"\n", + " setupmodel, lossFn = model_setup_DE(\n", + " loss, DEVICE, n_hidden=64, data_type=dim)\n", + " BETA = 0.5\n", + " u_al = []\n", + " for m in range(n_models):\n", + " if models_used > 9:\n", + " break\n", + " file_name = (\n", + " str(path) +\n", + " f\"{model}_{prescription}_{inject_type}_{dim}\"\n", + " f\"_noise_{noise}_beta_{BETA}_nmodel_{m}_epoch_{epoch}\"\n", + " )\n", + " if dim == \"0D\":\n", + " file_name += f\"_sizedf_{size_df_linear}\"\n", + " elif dim == \"2D\":\n", + " file_name += f\"_sizedf_{size_df_image}\"\n", + " file_name += \".pt\"\n", + " try:\n", + " #print('loading this file', file_name)\n", + " chk = torch.load(file_name, map_location=DEVICE)\n", + " except FileNotFoundError:\n", + " # print(\"cannot find this model\", file_name)\n", + " continue\n", + " setupmodel.load_state_dict(chk.get(\"model_state_dict\"))\n", + " setupmodel.eval()\n", + " y_pred = setupmodel(torch.Tensor(x_test)).detach().numpy()\n", + " # for some reason if the model fails it predicts\n", + " # really large values for sigma\n", + " \n", + " if np.all(y_pred[:, 1] == y_pred[0, 1]) or np.mean(np.sqrt(y_pred[:, 1])) > 0.2:\n", + " # All values are the same\n", + " print(\"All values in y_pred[:,1] are the same or they are really high\")\n", + " # then we need to skip this model\n", + " print('not using this model ', m)\n", + " continue\n", + " else:\n", + " '''\n", + " plt.clf()\n", + " plt.hist(np.sqrt(y_pred[:, 1]), bins=100)\n", + " plt.title(f'model m {m}')\n", + " plt.show()\n", + " '''\n", + " print('using this model', m)\n", + " models_used += 1\n", + " #print('using this model ', m) \n", + " u_al_one = np.sqrt(y_pred[:, 1])\n", + " u_al.append(u_al_one)\n", + "\n", + " \n", + " # populate the mega dict\n", + " mega_dict[noise][model][dim][inject_type] = u_al\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f2e49391-bf21-4c96-a2b6-8206458fe262", + "metadata": {}, + "outputs": [], + "source": [ + "print('mega dict', mega_dict)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f16be1c4-cd6d-4a95-b658-72d222722204", + "metadata": {}, + "outputs": [], + "source": [ + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import warnings\n", + "from scipy.stats import gaussian_kde\n", + "\n", + "# suppress seaborn/pandas specific warnings\n", + "warnings.filterwarnings(\"ignore\", category=FutureWarning, module=\"seaborn\")\n", + "\n", + "# Example setup, use your actual data/model outputs\n", + "noise_list = [\"low\", \"medium\", \"high\"]\n", + "model_type = [\"DE\", \"DER\"]\n", + "inject_type_list = [\"predictive\", \"feature\"]\n", + "data_dim_list = [\"0D\", \"2D\"]\n", + "\n", + "# Set up figure and axes\n", + "fig, axes = plt.subplots(1, len(model_type) * len(noise_list), figsize=(20, 3.5))\n", + "axes = axes.flatten()\n", + "index = 0\n", + "\n", + "sigma_y_lookup = {'low': 0.01, 'medium': 0.05, 'high': 0.10}\n", + "palette = sns.color_palette(\"Paired\", n_colors=len(data_dim_list) * len(inject_type_list))\n", + "\n", + "# Define x-axis limits for each noise level\n", + "x_limits = {\n", + " \"low\": (0, 0.05),\n", + " \"medium\": (0, 0.1),\n", + " \"high\": (0, 0.12)\n", + "}\n", + "\n", + "for n, noise in enumerate(noise_list):\n", + " for m, model in enumerate(model_type):\n", + " ax = axes[index]\n", + " \n", + " y_offset = 0 # Initialize a consistent y_offset\n", + " kde_max = 0 # Track the maximum y-value across all KDE plots\n", + " \n", + " # Collect KDE data to plot\n", + " kde_data = []\n", + " for j, dim in enumerate(data_dim_list):\n", + " for i, inject_type in enumerate(inject_type_list):\n", + " # Generate or load aleatoric uncertainty data (replace with your own logic)\n", + " u_al_data = mega_dict[noise][model][dim][inject_type]\n", + " \n", + " \n", + " # Stack the DEs\n", + " if model == \"DE\":\n", + " combined_array = np.concatenate(u_al_data)\n", + " elif model == \"DER\":\n", + " combined_array = u_al_data\n", + " \n", + " # Store KDE data\n", + " kde_data.append((combined_array, palette[j*len(inject_type_list)+i], f\"{new_title_lookup[inject_type]} - {dim}\"))\n", + " \n", + " # Plot KDEs in reverse order\n", + " for combined_array, color, label in reversed(kde_data):\n", + " '''\n", + " kde = sns.kdeplot(combined_array, fill=True, bw_adjust=0.5, alpha=0.75, ax=ax,\n", + " clip_on=False, color=color, label=label)\n", + "\n", + " # Retrieve the KDE data\n", + " x = kde.lines[0].get_data()[0]\n", + " y = kde.lines[0].get_data()[1]\n", + " \n", + " # Normalize the KDE data\n", + " max_height = 0.5 # Desired maximum height\n", + " y_normalized = (y / y.max()) * max_height\n", + " \n", + " # Clear the previous KDE plot\n", + " ax.clear()\n", + " \n", + " # Plot the normalized KDE\n", + " ax.fill_between(x, y_normalized, color='blue', alpha=0.75)\n", + " ax.plot(x, y_normalized, color='blue', label='Normalized KDE')\n", + " '''\n", + " # Compute KDE\n", + " kde = gaussian_kde(combined_array, bw_method=0.1) # Adjust bw_method as needed\n", + " \n", + " # Define range of x values\n", + " x = np.linspace(min(combined_array), max(combined_array), 1000)\n", + " \n", + " # Evaluate KDE over x values\n", + " y = kde(x)\n", + " \n", + " # Normalize the KDE data\n", + " max_height = 0.5 # Desired maximum height\n", + " y_normalized = (y / y.max()) * max_height\n", + " \n", + " # Plot the normalized KDE\n", + " ax.fill_between(x, y_offset, y_normalized + y_offset, color=color, alpha=0.75)\n", + " ax.plot(x, y_normalized + y_offset, color=color, label=label)\n", + " \n", + " '''\n", + " # Find the maximum y-value and adjust offsets\n", + " if kde.collections:\n", + " paths = kde.collections[-1].get_paths()\n", + " for path in paths:\n", + " vertices = path.vertices\n", + " vertices[:, 1] += y_offset # Offset the y-values of the vertices\n", + " kde_max = vertices[:, 1].max() / 1.5\n", + " '''\n", + " ax.scatter(\n", + " np.mean(combined_array), y_offset + 0.1,\n", + " color='black', edgecolor='black', zorder=100)\n", + " \n", + " ax.errorbar(\n", + " np.mean(combined_array), y_offset + 0.1,\n", + " xerr = 3*np.std(combined_array),\n", + " color='grey', capsize=5)\n", + " ax.errorbar(\n", + " np.mean(combined_array), y_offset + 0.1,\n", + " xerr = np.std(combined_array),\n", + " color='black', capsize=5)\n", + " y_offset += 0.25\n", + "\n", + " # Set title and limits\n", + " #ax.set_title(f'{model} - {noise} noise')\n", + " ax.set_yticks([]) # Hide y-ticks\n", + " ax.set_ylim(0, y_offset+0.25) # Adjust y-limits based on the max y_offset\n", + " \n", + " # Set x-axis limits after plotting\n", + " ax.set_xlim(x_limits[noise])\n", + "\n", + " # Add vertical line indicating sigma_y for reference\n", + " if noise == \"low\":\n", + " ax.axvline(x=0.01, color='grey', ls='--')\n", + " elif noise == \"medium\":\n", + " ax.axvline(x=0.05, color='grey', ls='--')\n", + " elif noise == \"high\":\n", + " ax.axvline(x=0.10, color='grey', ls='--')\n", + " \n", + " # Only add the legend on the first subplot\n", + " if index == 0:\n", + " handles, labels = ax.get_legend_handles_labels()\n", + " ax.legend(handles[::-1], labels[::-1], loc='upper right') # Reverse the order of the legend\n", + " else:\n", + " ax.legend().remove() # Remove the legend from all other subplots\n", + " ax.set_yticklabels([])\n", + " index += 1\n", + "\n", + "axes[0].set_xlim([0, 0.06])\n", + "axes[1].set_xlim([0, 0.03])\n", + "axes[2].set_xlim([0.01, 0.09])\n", + "axes[3].set_xlim([0.01, 0.07])\n", + "axes[4].set_xlim([0.01, 0.13])\n", + "axes[5].set_xlim([0.01, 0.13])\n", + "\n", + "axes[0].set_xlim([-0.01, 0.06])\n", + "axes[1].set_xlim([-0.01, 0.06])\n", + "axes[2].set_xlim([0.01, 0.08])\n", + "axes[3].set_xlim([0.01, 0.08])\n", + "axes[4].set_xlim([0.04, 0.11])\n", + "axes[5].set_xlim([0.04, 0.11])\n", + "\n", + "axes[0].tick_params(axis='both', which='major', labelsize=14)\n", + "axes[1].tick_params(axis='both', which='major', labelsize=14)\n", + "axes[2].tick_params(axis='both', which='major', labelsize=14)\n", + "axes[3].tick_params(axis='both', which='major', labelsize=14)\n", + "axes[4].tick_params(axis='both', which='major', labelsize=14)\n", + "axes[5].tick_params(axis='both', which='major', labelsize=14)\n", + "\n", + "#axes[0].set_xlabel(r'$\\sigma_{al}$')\n", + "plt.subplots_adjust(wspace=0)\n", + "\n", + "# Adjust layout and save the plot\n", + "plt.tight_layout()\n", + "plt.savefig(f'../../../Desktop/sigma_in_sigma_out_ridgeplot_ensemble_{n_models}_uniform.png', dpi=1000)" + ] + }, + { + "cell_type": "markdown", + "id": "acacdab8-4e2e-4828-b3bd-d51c7a9ff68b", + "metadata": {}, + "source": [ + "Create a version of this that has only two panels; one for DER and one for DE and puts all three noise levels on each plot." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1e98f53b-2969-4ef7-9c07-ee1ceb851ec5", + "metadata": {}, + "outputs": [], + "source": [ + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import warnings\n", + "from scipy.stats import gaussian_kde\n", + "\n", + "# suppress seaborn/pandas specific warnings\n", + "warnings.filterwarnings(\"ignore\", category=FutureWarning, module=\"seaborn\")\n", + "\n", + "# Example setup, use your actual data/model outputs\n", + "noise_list = [\"low\", \"medium\", \"high\"]\n", + "model_type = [\"DE\", \"DER\"]\n", + "inject_type_list = [\"predictive\", \"feature\"]\n", + "data_dim_list = [\"0D\", \"2D\"]\n", + "\n", + "# Set up figure and axes\n", + "fig, axes = plt.subplots(1, len(model_type), figsize=(20, 3.5))\n", + "axes = axes.flatten()\n", + "index = 0\n", + "\n", + "sigma_y_lookup = {'low': 0.01, 'medium': 0.05, 'high': 0.10}\n", + "palette = sns.color_palette(\"Paired\", n_colors=len(data_dim_list) * len(inject_type_list))\n", + "# these are colors for the low medium and high noise level\n", + "palette_list = ['#A2C5AC', '#9DB5B2', '#878E99', '#7F6A93']\n", + "palette_list = ['#86CB92', '#71B48D', '#404E7C', '#251F47']\n", + "# lighter version of the above\n", + "#palette_list = ['#C6E7CB', '#A1CEB4', '#A1ABCE', '#8A7FC7']\n", + "\n", + "# Define x-axis limits for each noise level\n", + "x_limits = {\n", + " \"low\": (0, 0.05),\n", + " \"medium\": (0, 0.1),\n", + " \"high\": (0, 0.12)\n", + "}\n", + "\n", + "\n", + "for m, model in enumerate(model_type):\n", + " ax = axes[index]\n", + " for n, noise in enumerate(noise_list):\n", + " \n", + " y_offset = 0 # Initialize a consistent y_offset\n", + " kde_max = 0 # Track the maximum y-value across all KDE plots\n", + " \n", + " # Collect KDE data to plot\n", + " kde_data = []\n", + " color_twerk = 0\n", + " for j, dim in enumerate(data_dim_list):\n", + " for i, inject_type in enumerate(inject_type_list):\n", + " palette = sns.dark_palette(palette_list[color_twerk], reverse=True)#, as_cmap=True)\n", + " color_twerk +=1\n", + " # Generate or load aleatoric uncertainty data (replace with your own logic)\n", + " u_al_data = mega_dict[noise][model][dim][inject_type]\n", + " \n", + " \n", + " # Stack the DEs\n", + " if model == \"DE\":\n", + " combined_array = np.concatenate(u_al_data)\n", + " elif model == \"DER\":\n", + " combined_array = u_al_data\n", + " \n", + " # Store KDE data\n", + " kde_data.append((combined_array, palette[int(n)], f\"{new_title_lookup[inject_type]}, {dim}\"))\n", + " \n", + " \n", + " # Plot KDEs in reverse order\n", + " for combined_array, color, label in reversed(kde_data):\n", + " '''\n", + " kde = sns.kdeplot(combined_array, fill=True, bw_adjust=0.5, alpha=0.75, ax=ax,\n", + " clip_on=False, color=color, label=label)\n", + "\n", + " # Retrieve the KDE data\n", + " x = kde.lines[0].get_data()[0]\n", + " y = kde.lines[0].get_data()[1]\n", + " \n", + " # Normalize the KDE data\n", + " max_height = 0.5 # Desired maximum height\n", + " y_normalized = (y / y.max()) * max_height\n", + " \n", + " # Clear the previous KDE plot\n", + " ax.clear()\n", + " \n", + " # Plot the normalized KDE\n", + " ax.fill_between(x, y_normalized, color='blue', alpha=0.75)\n", + " ax.plot(x, y_normalized, color='blue', label='Normalized KDE')\n", + " '''\n", + " # Compute KDE\n", + " kde = gaussian_kde(combined_array, bw_method=0.05) # Adjust bw_method as needed\n", + " \n", + " # Define range of x values\n", + " x = np.linspace(min(combined_array), max(combined_array), 1000)\n", + " \n", + " # Evaluate KDE over x values\n", + " y = kde(x)\n", + " \n", + " # Normalize the KDE data\n", + " max_height = 0.5 # Desired maximum height\n", + " y_normalized = (y / y.max()) * max_height\n", + " \n", + " # Plot the normalized KDE\n", + " ax.fill_between(x, y_offset, y_normalized + y_offset, color=color, alpha=0.75)\n", + " ax.plot(x, y_normalized + y_offset, color=color, label=label)\n", + " if n == 0:\n", + " ax.annotate(label, xy=(-0.007, y_offset+0.07), size=15)\n", + " ax.scatter(\n", + " np.mean(combined_array), y_offset + 0.1,\n", + " color='black', edgecolor='black', zorder=100)\n", + " ax.errorbar(\n", + " np.mean(combined_array), y_offset + 0.1,\n", + " xerr = np.std(combined_array),\n", + " color='black', capsize=5)\n", + " y_offset += 0.25\n", + "\n", + " # Set title and limits\n", + " #ax.set_title(f'{model} - {noise} noise')\n", + " ax.set_yticks([]) # Hide y-ticks\n", + " ax.set_ylim(0, y_offset+0.25) # Adjust y-limits based on the max y_offset\n", + " \n", + " # Set x-axis limits after plotting\n", + " ax.set_xlim(x_limits[noise])\n", + "\n", + " # Add vertical line indicating sigma_y for reference\n", + " if noise == \"low\":\n", + " ax.axvline(x=0.01, color='black', ls='--')\n", + " elif noise == \"medium\":\n", + " ax.axvline(x=0.05, color='black', ls='--')\n", + " elif noise == \"high\":\n", + " ax.axvline(x=0.10, color='black', ls='--')\n", + " '''\n", + " # Only add the legend on the first subplot\n", + " if index == 0:\n", + " handles, labels = ax.get_legend_handles_labels()\n", + " ax.legend(handles[::-1], labels[::-1], loc='upper right') # Reverse the order of the legend\n", + " else:\n", + " '''\n", + " ax.legend().remove() # Remove the legend from all other subplots\n", + " ax.set_yticklabels([])\n", + " index += 1\n", + "\n", + "axes[0].set_xlim([-0.008, 0.11])\n", + "axes[1].set_xlim([-0.008, 0.11])\n", + "\n", + "axes[0].tick_params(axis='both', which='major', labelsize=14)\n", + "axes[1].tick_params(axis='both', which='major', labelsize=14)\n", + "axes[0].set_title('Deep Ensemble (DE) experiments', size=15)\n", + "axes[1].set_title('Deep Evidential Regression (DER) experiments', size=15)\n", + "\n", + "#axes[0].set_xlabel(r'$\\sigma_{al}$')\n", + "plt.subplots_adjust(wspace=0)\n", + "fig.supxlabel(r'Predicted Aleatoric Uncertainty, $\\sigma_{al}$', size=15)\n", + "\n", + "# Adjust layout and save the plot\n", + "plt.tight_layout()\n", + "plt.savefig(f'../../../Desktop/sigma_in_sigma_out_ridgeplot_ensemble_two_panel_{n_models}_uniform.png', dpi=1000)" + ] + }, + { + "cell_type": "markdown", + "id": "5f2e227e-37b8-44b6-98f2-299f7c8b169a", + "metadata": {}, + "source": [ + "Making a version where the color is for the noise level." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9101d2b9-dc45-4235-9fd2-504a083d2204", + "metadata": {}, + "outputs": [], + "source": [ + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import warnings\n", + "from scipy.stats import gaussian_kde\n", + "\n", + "# suppress seaborn/pandas specific warnings\n", + "warnings.filterwarnings(\"ignore\", category=FutureWarning, module=\"seaborn\")\n", + "\n", + "# Example setup, use your actual data/model outputs\n", + "noise_list = [\"low\", \"medium\", \"high\"]\n", + "model_type = [\"DE\", \"DER\"]\n", + "inject_type_list = [\"predictive\", \"feature\"]\n", + "data_dim_list = [\"0D\", \"2D\"]\n", + "\n", + "# Set up figure and axes\n", + "fig, axes = plt.subplots(1, len(model_type), figsize=(20, 4))\n", + "axes = axes.flatten()\n", + "index = 0\n", + "\n", + "sigma_y_lookup = {'low': 0.01, 'medium': 0.05, 'high': 0.10}\n", + "color_noise = ['#FFC2E8', '#FF47B9', '#66003F']#'#B80071']\n", + "#color_noise = ['#C5C0EC', '#6F62D0', '#3C2F9D']\n", + "\n", + "# Define x-axis limits for each noise level\n", + "x_limits = {\n", + " \"low\": (0, 0.05),\n", + " \"medium\": (0, 0.1),\n", + " \"high\": (0, 0.12)\n", + "}\n", + "\n", + "\n", + "for m, model in enumerate(model_type):\n", + " ax = axes[index]\n", + " for n, noise in enumerate(noise_list):\n", + " \n", + " y_offset = 0 # Initialize a consistent y_offset\n", + " kde_max = 0 # Track the maximum y-value across all KDE plots\n", + " \n", + " # Collect KDE data to plot\n", + " kde_data = []\n", + " color_twerk = 0\n", + " for j, dim in enumerate(data_dim_list):\n", + " for i, inject_type in enumerate(inject_type_list):\n", + " # Generate or load aleatoric uncertainty data (replace with your own logic)\n", + " u_al_data = mega_dict[noise][model][dim][inject_type]\n", + " \n", + " \n", + " # Stack the DEs\n", + " if model == \"DE\":\n", + " combined_array = np.concatenate(u_al_data)\n", + " elif model == \"DER\":\n", + " combined_array = u_al_data\n", + " \n", + " # Store KDE data\n", + " kde_data.append((combined_array, f\"{dim}, {new_title_lookup[inject_type]}\"))\n", + " \n", + " \n", + " # Plot KDEs in reverse order\n", + " for combined_array, label in reversed(kde_data):\n", + " '''\n", + " kde = sns.kdeplot(combined_array, fill=True, bw_adjust=0.5, alpha=0.75, ax=ax,\n", + " clip_on=False, color=color, label=label)\n", + "\n", + " # Retrieve the KDE data\n", + " x = kde.lines[0].get_data()[0]\n", + " y = kde.lines[0].get_data()[1]\n", + " \n", + " # Normalize the KDE data\n", + " max_height = 0.5 # Desired maximum height\n", + " y_normalized = (y / y.max()) * max_height\n", + " \n", + " # Clear the previous KDE plot\n", + " ax.clear()\n", + " \n", + " # Plot the normalized KDE\n", + " ax.fill_between(x, y_normalized, color='blue', alpha=0.75)\n", + " ax.plot(x, y_normalized, color='blue', label='Normalized KDE')\n", + " '''\n", + " # Compute KDE\n", + " kde = gaussian_kde(combined_array, bw_method=0.05) # Adjust bw_method as needed\n", + " \n", + " # Define range of x values\n", + " x = np.linspace(min(combined_array), max(combined_array), 1000)\n", + " \n", + " # Evaluate KDE over x values\n", + " y = kde(x)\n", + " \n", + " # Normalize the KDE data\n", + " max_height = 0.5 # Desired maximum height\n", + " y_normalized = (y / y.max()) * max_height\n", + " \n", + " # Plot the normalized KDE\n", + " ax.fill_between(x, y_offset, y_normalized + y_offset, color=color_noise[n], alpha=0.25)\n", + " ax.plot(x, y_normalized + y_offset, color=color_noise[n], label=label)\n", + " if n == 0 and m == 0:\n", + " #transform = ax.get_xaxis_trasform()\n", + " #ann = ax.annotate(label, xy=(-0.07, y_offset+0.07), xycoords=transform, size=20)\n", + " ax.annotate(label, xy=(-0.013, y_offset+0.07), size=20, annotation_clip=False)\n", + " ax.scatter(\n", + " np.mean(combined_array), y_offset + 0.1,\n", + " color=color_noise[n], edgecolor='black', zorder=100)\n", + " ax.errorbar(\n", + " np.mean(combined_array), y_offset + 0.1,\n", + " xerr = np.std(combined_array),\n", + " color='black', capsize=5)\n", + " #y_offset += 0.25\n", + " y_offset += 0.27\n", + "\n", + " # Set title and limits\n", + " #ax.set_title(f'{model} - {noise} noise')\n", + " ax.set_yticks([]) # Hide y-ticks\n", + " #ax.set_ylim(0, y_offset+0.25) # Adjust y-limits based on the max y_offset\n", + " ax.set_ylim(0, y_offset+0.3)\n", + " \n", + " # Set x-axis limits after plotting\n", + " ax.set_xlim(x_limits[noise])\n", + "\n", + " # Add vertical line indicating sigma_y for reference\n", + " if noise == \"low\":\n", + " ax.axvline(x=0.01, color=color_noise[n], ls='--')\n", + " elif noise == \"medium\":\n", + " ax.axvline(x=0.05, color=color_noise[n], ls='--')\n", + " elif noise == \"high\":\n", + " ax.axvline(x=0.10, color=color_noise[n], ls='--')\n", + " '''\n", + " # Only add the legend on the first subplot\n", + " if index == 0:\n", + " handles, labels = ax.get_legend_handles_labels()\n", + " ax.legend(handles[::-1], labels[::-1], loc='upper right') # Reverse the order of the legend\n", + " else:\n", + " '''\n", + " ax.set_xticks([0.01, 0.05, 0.1])\n", + " ax.set_xticklabels([0.01, 0.05, 0.10])\n", + " ax.legend().remove() # Remove the legend from all other subplots\n", + " ax.set_yticklabels([])\n", + " index += 1\n", + "\n", + "axes[0].set_xlim([0.005, 0.11])\n", + "axes[1].set_xlim([0.005, 0.11])\n", + "\n", + "axes[0].tick_params(axis='both', which='major', labelsize=20)\n", + "axes[1].tick_params(axis='both', which='major', labelsize=20)\n", + "axes[0].set_title('Deep Ensemble (DE)', size=20)\n", + "axes[1].set_title('Deep Evidential Regression (DER)', size=20)\n", + "\n", + "#axes[0].set_xlabel(r'$\\sigma_{al}$')\n", + "#plt.subplots_adjust(wspace=0)\n", + "fig.supxlabel(r'Predicted Aleatoric Uncertainty, $\\sigma_{al}$', size=20, y=0.05)\n", + "\n", + "# Adjust layout and save the plot\n", + "plt.tight_layout()\n", + "plt.savefig(\n", + " f'../../../Desktop/sigma_in_sigma_out_ridgeplot_ensemble_two_panel_noise_color_{n_models}_uniform.png',\n", + " dpi=1000,\n", + " bbox_inches='tight')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 2976fe3e6d39fde7ca298b4566f9a037bfad344e Mon Sep 17 00:00:00 2001 From: beckynevin Date: Thu, 26 Sep 2024 14:30:33 -0600 Subject: [PATCH 3/4] pyproject updated to be the same as deepuq --- poetry.lock | 4308 ++++++++++++++++++++++++++++++++++++++---------- pyproject.toml | 25 +- 2 files changed, 3424 insertions(+), 909 deletions(-) diff --git a/poetry.lock b/poetry.lock index 8d18dd0..d4310bf 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,197 +1,541 @@ -# This file is automatically @generated by Poetry and should not be changed by hand. +# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. 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Mon Sep 17 00:00:00 2001 From: beckynevin Date: Fri, 25 Oct 2024 15:49:14 -0600 Subject: [PATCH 4/4] adding deepuq 0.1.1 to pyproject --- poetry.lock | 22 +++++++++++++++++++++- pyproject.toml | 1 + 2 files changed, 22 insertions(+), 1 deletion(-) diff --git a/poetry.lock b/poetry.lock index d4310bf..23dd15f 100644 --- a/poetry.lock +++ b/poetry.lock @@ -817,6 +817,26 @@ numpy = ">=1.25.0,<2.0.0" pyyaml = ">=6.0,<7.0" scikit-image = ">=0.23.0,<0.24.0" +[[package]] +name = "deepuq" +version = "0.1.1" +description = "a package for investigating and comparing the predictive uncertainties from deep learning models" +optional = false +python-versions = "<3.11,>=3.10" +files = [ + {file = "deepuq-0.1.1-py3-none-any.whl", hash = "sha256:d0556428fd5c22f5278f6546a7bdde6bdaeec51d9e02924cb5a5cd0d0ba71d09"}, + {file = "deepuq-0.1.1.tar.gz", hash = "sha256:3540e106dae64fdb5237cf73d6c7fb044c5a974ac6c91ee699af670847deafb2"}, +] + +[package.dependencies] +deepbench = ">=0.2.3,<0.3.0" +h5py = 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