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constructor-s/20230924_dimension_reduced_sors_revision.ipynb

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20230924_dimension_reduced_sors_revision.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/constructor-s/11f34dfe67c1fa097ec141d2f2dcd8f7/20230924_dimension_reduced_sors_revision.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "dOLIg1h9EAPM",
"outputId": "5247654e-429a-4ddb-af08-1d500848c7f9"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Requirement already satisfied: seaborn in /usr/local/lib/python3.10/dist-packages (0.12.2)\n",
"Requirement already satisfied: numpy!=1.24.0,>=1.17 in /usr/local/lib/python3.10/dist-packages (from seaborn) (1.23.5)\n",
"Requirement already satisfied: pandas>=0.25 in /usr/local/lib/python3.10/dist-packages (from seaborn) (1.5.3)\n",
"Requirement already satisfied: matplotlib!=3.6.1,>=3.1 in /usr/local/lib/python3.10/dist-packages (from seaborn) (3.7.1)\n",
"Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (1.1.0)\n",
"Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (0.11.0)\n",
"Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (4.42.1)\n",
"Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (1.4.5)\n",
"Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (23.1)\n",
"Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (9.4.0)\n",
"Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (3.1.1)\n",
"Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (2.8.2)\n",
"Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.25->seaborn) (2023.3.post1)\n",
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.1->seaborn) (1.16.0)\n"
]
}
],
"source": [
"#@title Setup {display-mode: \"form\"}\n",
"\n",
"try:\n",
" import pyvf\n",
"except ImportError:\n",
" # !pip install git+https://github.com/constructor-s/PyVF.git@822a4fbde6d91f8e94b62a6135a1145f3ea41d35\n",
" !pip install git+https://github.com/constructor-s/PyVF.git@9c70470d274cc5141f6b543629243571b91c284b\n",
"\n",
"from pathlib import Path\n",
"import site\n",
"\n",
"dist_packages = site.getsitepackages()[0]\n",
"\n",
"if dist_packages.startswith(\"/usr/local/lib/python\"): # Google colab environment\n",
" if not Path(f\"{dist_packages}/pyvf/resources/rotterdam2013/LongGlaucVF_20150216.zip\").exists():\n",
" !cp /content/drive/MyDrive/mobile_perimeter_documents/LongGlaucVF_20150216.zip {dist_packages}/pyvf/resources/rotterdam2013/LongGlaucVF_20150216.zip\n",
" if not Path(f\"{dist_packages}/pyvf/resources/uwhvf2022/VF_Data.csv\").exists():\n",
" !bzip2 -cdk /content/drive/MyDrive/mobile_perimeter_documents/VF_Data.csv.bz2 > {dist_packages}/pyvf/resources/uwhvf2022/VF_Data.csv\n",
" assert Path(f\"{dist_packages}/pyvf/resources/uwhvf2022/VF_Data.csv\").exists()\n",
"\n",
" !cp /content/drive/MyDrive/mobile_perimeter_documents/2022_TPP_Documents/2022_TORONTO/common.py common.py\n",
" !pip install seaborn --upgrade\n",
"\n",
"from common import *\n",
"def get_n_completed_points(state):\n",
" ret = 0\n",
" for i, batch in enumerate(state.batches):\n",
" terminated = [state.nodes[b].instance.terminated for b in batch]\n",
" ret += sum(terminated)\n",
" if not all(terminated):\n",
" return ret\n",
" return ret\n",
"\n",
"from pyvf.resources.rotterdam2013 import *\n",
"from pyvf.state_strategy import *\n",
"from pyvf.state_strategy.quadrant import *\n",
"from pyvf.state_strategy.fest import *\n",
"from pyvf.state_strategy.sors import *\n",
"from pyvf.state_strategy.staircase import *\n",
"from pyvf.plot import *\n",
"from sklearn.model_selection import *\n",
"from sklearn.neural_network import *\n",
"from sklearn.pipeline import *\n",
"from sklearn.linear_model import *\n",
"from sklearn.preprocessing import *\n",
"from sklearn.decomposition import *\n",
"from sklearn.cross_decomposition import *\n",
"from sklearn.compose import *\n",
"from sklearn.utils.validation import *\n",
"from sklearn.neighbors import *\n",
"from sklearn.metrics import *\n",
"from sklearn.dummy import *\n",
"from sklearn.ensemble import *\n",
"from sklearn.multioutput import *\n",
"import sklearn\n",
"from scipy.optimize import *\n",
"from collections import *\n",
"from functools import *\n",
"from itertools import *\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from tqdm.notebook import tqdm\n",
"import seaborn as sns\n",
"import time\n",
"import pickle\n",
"# import dill # To pickle lambda: https://stackoverflow.com/questions/25348532/can-python-pickle-lambda-functions\n",
"import bz2\n",
"import datetime\n",
"from dataclasses import dataclass\n",
"from hashlib import sha1\n",
"import torch\n",
"# import torch.nn as nn\n",
"# import torch.nn.functional as F\n",
"# import torch.optim as optim\n",
"from itertools import *\n",
"from functools import *\n",
"from typing import *\n",
"plt.rcParams[\"font.family\"] = \"serif\"\n",
"plt.rcParams[\"mathtext.fontset\"] = \"dejavuserif\"\n",
"plt.rcParams[\"font.size\"] = 8\n",
"plt.rcParams[\"legend.fontsize\"] = 6"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ya6JV00-gBcG"
},
"outputs": [],
"source": [
"plt.rcParams['svg.fonttype'] = 'none'\n",
"plt.rcParams[\"font.family\"] = \"serif\"\n",
"plt.rcParams[\"mathtext.fontset\"] = \"dejavuserif\"\n",
"plt.rcParams[\"font.size\"] = 8\n",
"plt.rcParams[\"legend.fontsize\"] = 6"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "g1YfxJoGELzI",
"outputId": "c843e1a5-d694-4c31-82e1-d529bd217ed3"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"('rotterdam2013', (278, 54))"
]
},
"metadata": {},
"execution_count": 8
}
],
"source": [
"#@title Load Dataset\n",
"\n",
"dataset_name = \"rotterdam2013\" #@param ['rotterdam2013', 'uwhvf2022']\n",
"if dataset_name == \"rotterdam2013\":\n",
" from pyvf.resources.rotterdam2013 import *\n",
"elif dataset_name == \"uwhvf2022\":\n",
" from pyvf.resources.uwhvf2022 import *\n",
"\n",
"MIN = 0\n",
"MAX = 35\n",
"\n",
"VF_THRESHOLD_BY_SITE = pd.DataFrame(VF_THRESHOLD).groupby(VF_THRESHOLD_SITES).median().values.clip(MIN, MAX)\n",
"VF_THRESHOLD = VF_THRESHOLD.clip(MIN, MAX)\n",
"\n",
"dataset_name, VF_THRESHOLD_BY_SITE.shape"
]
},
{
"cell_type": "code",
"source": [
"def model_factory(name, n_components, n_features, tt_transformer=None, random_state=0, **kwargs):\n",
" predict_unknown_only = True\n",
"\n",
" if name == \"LR\":\n",
" ret = make_pipeline(LinearRegression(fit_intercept=kwargs.get(\"fit_intercept\", True)))\n",
" elif name == \"PCR\":\n",
" ret = make_pipeline(StandardScaler(),\n",
" PCA(n_components=min(n_components, n_features), random_state=random_state),\n",
" LinearRegression())\n",
" elif name == \"ICR\":\n",
" ret = make_pipeline(StandardScaler(),\n",
" FastICA(n_components=min(n_components, n_features), random_state=random_state),\n",
" LinearRegression())\n",
" elif name == \"TTICR\" or name == \"TTPCR\":\n",
" assert tt_transformer is not None\n",
" predict_unknown_only = False\n",
" ret = make_pipeline(StandardScaler(),\n",
" PretrainedTransformedTargetRegressor(\n",
" regressor=LinearRegression(),\n",
" transformer=tt_transformer,\n",
" check_inverse=False\n",
" ))\n",
" elif name == \"PLS\":\n",
" ret = make_pipeline(StandardScaler(), PLSRegression(n_components=min(n_components, n_features))) # PLS does scaling on its own?\n",
" elif name == \"KNR\":\n",
" ret = make_pipeline(StandardScaler(), GridSearchCV(KNeighborsRegressor(), param_grid={\n",
" \"n_neighbors\": (4, 16, 64)\n",
" }))\n",
" elif name == \"PCKNR\":\n",
" ret = make_pipeline(StandardScaler(),\n",
" PCA(n_components=min(n_components, n_features), random_state=random_state),\n",
" GridSearchCV(KNeighborsRegressor(), param_grid={\n",
" \"n_neighbors\": (4, 16, 64)\n",
" }))\n",
" elif name == \"NCR\":\n",
" ret = make_pipeline(\n",
" StandardScaler(),\n",
" NeighborhoodComponentsAnalysisRegression(n_components=min(n_components, n_features), random_state=random_state),\n",
" GridSearchCV(KNeighborsRegressor(), param_grid={\n",
" \"n_neighbors\": (4, 16, 64)\n",
" })\n",
" )\n",
" elif name == \"MLP\":\n",
" ret = make_pipeline(\n",
" StandardScaler(),\n",
" MLPRegressor(hidden_layer_sizes=(n_components, ),\n",
" random_state=random_state,\n",
" learning_rate_init=0.01))\n",
" elif name == \"Quadrant\":\n",
" predict_unknown_only = False\n",
" ret = QuadrantMapper()\n",
" elif name == \"Mean\":\n",
" predict_unknown_only = False\n",
" ret = make_pipeline(\n",
" DummyRegressor(strategy='mean')\n",
" )\n",
"\n",
"\n",
" elif name == \"RandomForest\":\n",
" ret = make_pipeline(RandomForestRegressor())\n",
" elif name == \"GradientBoosting\":\n",
" ret = make_pipeline(MultiOutputRegressor(GradientBoostingRegressor(learning_rate=0.1, n_estimators=30)))\n",
"\n",
"\n",
" else:\n",
" raise ValueError()\n",
"\n",
" return ReconstructionWrapper(ret, predict_unknown_only=predict_unknown_only)\n",
"\n"
],
"metadata": {
"id": "IV3yWW4kzz5C"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"```\n",
"print(\"Example usage:\")\n",
"known_indices = list(range(10))\n",
"model = model_factory(\"LR\", n_components=4, n_features=len(known_indices))\n",
"model = model.fit(VF_THRESHOLD_BY_SITE[:, known_indices], VF_THRESHOLD_BY_SITE, known_indices=known_indices)\n",
"print(model)\n",
"print(\"Output shape:\")\n",
"print(model.predict(VF_THRESHOLD_BY_SITE[:, known_indices], known_indices=known_indices).shape)\n",
"print(\"RMSE:\")\n",
"print(((model.predict(VF_THRESHOLD_BY_SITE[:, known_indices], known_indices=known_indices) - VF_THRESHOLD_BY_SITE)**2).mean()**0.5)\n",
"```"
],
"metadata": {
"id": "8nVnGk3qRMrf"
}
},
{
"cell_type": "code",
"source": [
"all_models"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "kd_x7fjDlsLR",
"outputId": "a970c101-a709-458d-9266-869faf1a711a"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'LR': {'folds': [{'train_index': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n",
" 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
" 34, 35, 36, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 49, 50, 51, 52,\n",
" 57, 58, 60, 61, 67]),\n",
" 'test_index': array([ 43, 48, 53, 54, 55, 56, 59, 62, 63, 64, 65, 66, 68,\n",
" 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81,\n",
" 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94,\n",
" 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107,\n",
" 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120,\n",
" 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133,\n",
" 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146,\n",
" 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,\n",
" 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172,\n",
" 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185,\n",
" 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198,\n",
" 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211,\n",
" 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224,\n",
" 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237,\n",
" 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250,\n",
" 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263,\n",
" 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276,\n",
" 277]),\n",
" 'sors': SORS(model_factory=None, verbose=True, tt_transformer=None)},\n",
" {'train_index': array([ 43, 48, 53, 54, 55, 56, 59, 62, 63, 64, 65, 66, 68,\n",
" 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81,\n",
" 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94,\n",
" 95, 96, 97, 98, 99, 100, 102, 103, 106, 108, 109, 112, 113,\n",
" 114, 115, 117, 121]),\n",
" 'test_index': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
" 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,\n",
" 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,\n",
" 39, 40, 41, 42, 44, 45, 46, 47, 49, 50, 51, 52, 57,\n",
" 58, 60, 61, 67, 101, 104, 105, 107, 110, 111, 116, 118, 119,\n",
" 120, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133,\n",
" 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146,\n",
" 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,\n",
" 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172,\n",
" 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185,\n",
" 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198,\n",
" 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211,\n",
" 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224,\n",
" 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237,\n",
" 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250,\n",
" 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263,\n",
" 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276,\n",
" 277]),\n",
" 'sors': SORS(model_factory=None, verbose=True, tt_transformer=None)},\n",
" {'train_index': array([101, 104, 105, 107, 110, 111, 116, 118, 119, 120, 122, 123, 124,\n",
" 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,\n",
" 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150,\n",
" 151, 152, 154, 155, 157, 158, 159, 160, 161, 162, 163, 164, 165,\n",
" 166, 167, 168, 169]),\n",
" 'test_index': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
" 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,\n",
" 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,\n",
" 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n",
" 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64,\n",
" 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77,\n",
" 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90,\n",
" 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 102, 103, 106,\n",
" 108, 109, 112, 113, 114, 115, 117, 121, 153, 156, 170, 171, 172,\n",
" 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185,\n",
" 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198,\n",
" 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211,\n",
" 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224,\n",
" 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237,\n",
" 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250,\n",
" 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263,\n",
" 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276,\n",
" 277]),\n",
" 'sors': SORS(model_factory=None, verbose=True, tt_transformer=None)},\n",
" {'train_index': array([153, 156, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180,\n",
" 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193,\n",
" 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206,\n",
" 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219,\n",
" 221, 226, 227]),\n",
" 'test_index': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
" 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,\n",
" 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,\n",
" 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n",
" 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64,\n",
" 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77,\n",
" 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90,\n",
" 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103,\n",
" 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,\n",
" 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n",
" 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,\n",
" 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 157,\n",
" 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 220,\n",
" 222, 223, 224, 225, 228, 229, 230, 231, 232, 233, 234, 235, 236,\n",
" 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249,\n",
" 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262,\n",
" 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275,\n",
" 276, 277]),\n",
" 'sors': SORS(model_factory=None, verbose=True, tt_transformer=None)},\n",
" {'train_index': array([220, 222, 223, 224, 225, 228, 229, 230, 231, 232, 233, 234, 235,\n",
" 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248,\n",
" 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261,\n",
" 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274,\n",
" 275, 276, 277]),\n",
" 'test_index': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
" 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,\n",
" 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,\n",
" 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n",
" 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64,\n",
" 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77,\n",
" 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90,\n",
" 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103,\n",
" 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,\n",
" 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n",
" 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,\n",
" 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155,\n",
" 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168,\n",
" 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,\n",
" 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194,\n",
" 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207,\n",
" 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 221,\n",
" 226, 227]),\n",
" 'sors': SORS(model_factory=None, verbose=True, tt_transformer=None)}]},\n",
" 'TTPCR': {'folds': [{'train_index': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n",
" 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
" 34, 35, 36, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 49, 50, 51, 52,\n",
" 57, 58, 60, 61, 67]),\n",
" 'test_index': array([ 43, 48, 53, 54, 55, 56, 59, 62, 63, 64, 65, 66, 68,\n",
" 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81,\n",
" 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94,\n",
" 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107,\n",
" 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120,\n",
" 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133,\n",
" 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146,\n",
" 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,\n",
" 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172,\n",
" 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185,\n",
" 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198,\n",
" 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211,\n",
" 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224,\n",
" 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237,\n",
" 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250,\n",
" 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263,\n",
" 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276,\n",
" 277]),\n",
" 'sors': SORS(model_factory=None, verbose=True, tt_transformer=PCA(n_components=8))},\n",
" {'train_index': array([ 43, 48, 53, 54, 55, 56, 59, 62, 63, 64, 65, 66, 68,\n",
" 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81,\n",
" 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94,\n",
" 95, 96, 97, 98, 99, 100, 102, 103, 106, 108, 109, 112, 113,\n",
" 114, 115, 117, 121]),\n",
" 'test_index': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
" 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,\n",
" 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,\n",
" 39, 40, 41, 42, 44, 45, 46, 47, 49, 50, 51, 52, 57,\n",
" 58, 60, 61, 67, 101, 104, 105, 107, 110, 111, 116, 118, 119,\n",
" 120, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133,\n",
" 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146,\n",
" 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,\n",
" 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172,\n",
" 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185,\n",
" 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198,\n",
" 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211,\n",
" 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224,\n",
" 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237,\n",
" 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250,\n",
" 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263,\n",
" 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276,\n",
" 277]),\n",
" 'sors': SORS(model_factory=None, verbose=True, tt_transformer=PCA(n_components=8))},\n",
" {'train_index': array([101, 104, 105, 107, 110, 111, 116, 118, 119, 120, 122, 123, 124,\n",
" 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,\n",
" 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150,\n",
" 151, 152, 154, 155, 157, 158, 159, 160, 161, 162, 163, 164, 165,\n",
" 166, 167, 168, 169]),\n",
" 'test_index': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
" 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,\n",
" 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,\n",
" 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n",
" 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64,\n",
" 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77,\n",
" 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90,\n",
" 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 102, 103, 106,\n",
" 108, 109, 112, 113, 114, 115, 117, 121, 153, 156, 170, 171, 172,\n",
" 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185,\n",
" 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198,\n",
" 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211,\n",
" 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224,\n",
" 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237,\n",
" 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250,\n",
" 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263,\n",
" 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276,\n",
" 277]),\n",
" 'sors': SORS(model_factory=None, verbose=True, tt_transformer=PCA(n_components=8))},\n",
" {'train_index': array([153, 156, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180,\n",
" 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193,\n",
" 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206,\n",
" 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219,\n",
" 221, 226, 227]),\n",
" 'test_index': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
" 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,\n",
" 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,\n",
" 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n",
" 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64,\n",
" 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77,\n",
" 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90,\n",
" 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103,\n",
" 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,\n",
" 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n",
" 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,\n",
" 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 157,\n",
" 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 220,\n",
" 222, 223, 224, 225, 228, 229, 230, 231, 232, 233, 234, 235, 236,\n",
" 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249,\n",
" 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262,\n",
" 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275,\n",
" 276, 277]),\n",
" 'sors': SORS(model_factory=None, verbose=True, tt_transformer=PCA(n_components=8))},\n",
" {'train_index': array([220, 222, 223, 224, 225, 228, 229, 230, 231, 232, 233, 234, 235,\n",
" 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248,\n",
" 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261,\n",
" 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274,\n",
" 275, 276, 277]),\n",
" 'test_index': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
" 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,\n",
" 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,\n",
" 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n",
" 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64,\n",
" 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77,\n",
" 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90,\n",
" 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103,\n",
" 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,\n",
" 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n",
" 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,\n",
" 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155,\n",
" 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168,\n",
" 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,\n",
" 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194,\n",
" 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207,\n",
" 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 221,\n",
" 226, 227]),\n",
" 'sors': SORS(model_factory=None, verbose=True, tt_transformer=PCA(n_components=8))}]},\n",
" 'R': {'folds': []},\n",
" 'RandomForest': {'folds': [{'train_index': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n",
" 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
" 34, 35, 36, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 49, 50, 51, 52,\n",
" 57, 58, 60, 61, 67]),\n",
" 'test_index': array([ 43, 48, 53, 54, 55, 56, 59, 62, 63, 64, 65, 66, 68,\n",
" 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81,\n",
" 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94,\n",
" 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107,\n",
" 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120,\n",
" 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133,\n",
" 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146,\n",
" 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,\n",
" 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172,\n",
" 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185,\n",
" 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198,\n",
" 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211,\n",
" 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224,\n",
" 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237,\n",
" 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250,\n",
" 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263,\n",
" 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276,\n",
" 277]),\n",
" 'sors': SORS(model_factory=None, verbose=True, tt_transformer=None)},\n",
" {'train_index': array([ 43, 48, 53, 54, 55, 56, 59, 62, 63, 64, 65, 66, 68,\n",
" 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81,\n",
" 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94,\n",
" 95, 96, 97, 98, 99, 100, 102, 103, 106, 108, 109, 112, 113,\n",
" 114, 115, 117, 121]),\n",
" 'test_index': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
" 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,\n",
" 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,\n",
" 39, 40, 41, 42, 44, 45, 46, 47, 49, 50, 51, 52, 57,\n",
" 58, 60, 61, 67, 101, 104, 105, 107, 110, 111, 116, 118, 119,\n",
" 120, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133,\n",
" 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146,\n",
" 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,\n",
" 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172,\n",
" 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185,\n",
" 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198,\n",
" 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211,\n",
" 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224,\n",
" 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237,\n",
" 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250,\n",
" 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263,\n",
" 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276,\n",
" 277]),\n",
" 'sors': SORS(model_factory=None, verbose=True, tt_transformer=None)},\n",
" {'train_index': array([101, 104, 105, 107, 110, 111, 116, 118, 119, 120, 122, 123, 124,\n",
" 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,\n",
" 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150,\n",
" 151, 152, 154, 155, 157, 158, 159, 160, 161, 162, 163, 164, 165,\n",
" 166, 167, 168, 169]),\n",
" 'test_index': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
" 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,\n",
" 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,\n",
" 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n",
" 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64,\n",
" 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77,\n",
" 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90,\n",
" 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 102, 103, 106,\n",
" 108, 109, 112, 113, 114, 115, 117, 121, 153, 156, 170, 171, 172,\n",
" 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185,\n",
" 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198,\n",
" 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211,\n",
" 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224,\n",
" 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237,\n",
" 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250,\n",
" 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263,\n",
" 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276,\n",
" 277]),\n",
" 'sors': SORS(model_factory=None, verbose=True, tt_transformer=None)},\n",
" {'train_index': array([153, 156, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180,\n",
" 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193,\n",
" 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206,\n",
" 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219,\n",
" 221, 226, 227]),\n",
" 'test_index': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
" 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,\n",
" 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,\n",
" 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n",
" 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64,\n",
" 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77,\n",
" 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90,\n",
" 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103,\n",
" 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,\n",
" 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n",
" 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,\n",
" 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 157,\n",
" 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 220,\n",
" 222, 223, 224, 225, 228, 229, 230, 231, 232, 233, 234, 235, 236,\n",
" 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249,\n",
" 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262,\n",
" 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275,\n",
" 276, 277]),\n",
" 'sors': SORS(model_factory=None, verbose=True, tt_transformer=None)},\n",
" {'train_index': array([220, 222, 223, 224, 225, 228, 229, 230, 231, 232, 233, 234, 235,\n",
" 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248,\n",
" 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261,\n",
" 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274,\n",
" 275, 276, 277]),\n",
" 'test_index': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
" 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,\n",
" 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,\n",
" 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n",
" 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64,\n",
" 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77,\n",
" 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90,\n",
" 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103,\n",
" 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,\n",
" 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n",
" 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,\n",
" 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155,\n",
" 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168,\n",
" 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,\n",
" 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194,\n",
" 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207,\n",
" 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 221,\n",
" 226, 227]),\n",
" 'sors': SORS(model_factory=None, verbose=True, tt_transformer=None)}]},\n",
" 'GradientBoostingRegressor': {'folds': []},\n",
" 'GradientBoosting': {'folds': [{'train_index': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n",
" 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
" 34, 35, 36, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 49, 50, 51, 52,\n",
" 57, 58, 60, 61, 67]),\n",
" 'test_index': array([ 43, 48, 53, 54, 55, 56, 59, 62, 63, 64, 65, 66, 68,\n",
" 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81,\n",
" 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94,\n",
" 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107,\n",
" 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120,\n",
" 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133,\n",
" 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146,\n",
" 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,\n",
" 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172,\n",
" 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185,\n",
" 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198,\n",
" 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211,\n",
" 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224,\n",
" 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237,\n",
" 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250,\n",
" 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263,\n",
" 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276,\n",
" 277]),\n",
" 'sors': SORS(model_factory=<function <lambda> at 0x7bd0849c2290>, verbose=True, tt_transformer=None)},\n",
" {'train_index': array([ 43, 48, 53, 54, 55, 56, 59, 62, 63, 64, 65, 66, 68,\n",
" 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81,\n",
" 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94,\n",
" 95, 96, 97, 98, 99, 100, 102, 103, 106, 108, 109, 112, 113,\n",
" 114, 115, 117, 121]),\n",
" 'test_index': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
" 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,\n",
" 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,\n",
" 39, 40, 41, 42, 44, 45, 46, 47, 49, 50, 51, 52, 57,\n",
" 58, 60, 61, 67, 101, 104, 105, 107, 110, 111, 116, 118, 119,\n",
" 120, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133,\n",
" 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146,\n",
" 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,\n",
" 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172,\n",
" 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185,\n",
" 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198,\n",
" 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211,\n",
" 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224,\n",
" 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237,\n",
" 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250,\n",
" 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263,\n",
" 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276,\n",
" 277]),\n",
" 'sors': SORS(model_factory=<function <lambda> at 0x7bd09059b9a0>, verbose=True, tt_transformer=None)},\n",
" {'train_index': array([101, 104, 105, 107, 110, 111, 116, 118, 119, 120, 122, 123, 124,\n",
" 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,\n",
" 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150,\n",
" 151, 152, 154, 155, 157, 158, 159, 160, 161, 162, 163, 164, 165,\n",
" 166, 167, 168, 169]),\n",
" 'test_index': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
" 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,\n",
" 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,\n",
" 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n",
" 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64,\n",
" 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77,\n",
" 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90,\n",
" 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 102, 103, 106,\n",
" 108, 109, 112, 113, 114, 115, 117, 121, 153, 156, 170, 171, 172,\n",
" 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185,\n",
" 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198,\n",
" 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211,\n",
" 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224,\n",
" 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237,\n",
" 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250,\n",
" 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263,\n",
" 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276,\n",
" 277]),\n",
" 'sors': SORS(model_factory=<function <lambda> at 0x7bd09059b880>, verbose=True, tt_transformer=None)},\n",
" {'train_index': array([153, 156, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180,\n",
" 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193,\n",
" 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206,\n",
" 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219,\n",
" 221, 226, 227]),\n",
" 'test_index': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
" 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,\n",
" 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,\n",
" 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n",
" 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64,\n",
" 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77,\n",
" 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90,\n",
" 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103,\n",
" 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,\n",
" 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n",
" 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,\n",
" 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 157,\n",
" 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 220,\n",
" 222, 223, 224, 225, 228, 229, 230, 231, 232, 233, 234, 235, 236,\n",
" 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249,\n",
" 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262,\n",
" 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275,\n",
" 276, 277]),\n",
" 'sors': SORS(model_factory=<function <lambda> at 0x7bd090295c60>, verbose=True, tt_transformer=None)},\n",
" {'train_index': array([220, 222, 223, 224, 225, 228, 229, 230, 231, 232, 233, 234, 235,\n",
" 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248,\n",
" 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261,\n",
" 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274,\n",
" 275, 276, 277]),\n",
" 'test_index': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
" 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,\n",
" 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,\n",
" 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n",
" 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64,\n",
" 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77,\n",
" 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90,\n",
" 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103,\n",
" 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,\n",
" 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n",
" 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,\n",
" 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155,\n",
" 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168,\n",
" 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,\n",
" 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194,\n",
" 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207,\n",
" 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 221,\n",
" 226, 227]),\n",
" 'sors': SORS(model_factory=<function <lambda> at 0x7bd0850bb2e0>, verbose=True, tt_transformer=None)}]}}"
]
},
"metadata": {},
"execution_count": 98
}
]
},
{
"cell_type": "code",
"source": [
"# all_models = {}"
],
"metadata": {
"id": "owcRQV-5VzX2"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"small_dataset = True\n",
"X = VF_THRESHOLD_BY_SITE\n",
"n_components = 8\n",
"\n",
"for model_name in (\"PLS\", ):\n",
" all_models[model_name] = (train_result := {\"folds\": []})\n",
" group_kfold = StratifiedKFold(n_splits=5 if small_dataset else 10).split(X, pd.cut(X.mean(axis=1), [-np.inf, +15, +25, +np.inf]).codes)\n",
" for train_index, test_index in tqdm(group_kfold, total=5 if small_dataset else 10):\n",
" if small_dataset:\n",
" train_index, test_index = test_index, train_index\n",
" tic = time.perf_counter()\n",
" if model_name == \"TTPCR\":\n",
" tt_transformer = PCA(n_components=n_components).fit(X[train_index])\n",
" elif model_name == \"TTICR\":\n",
" tt_transformer = FastICA(n_components=n_components).fit(X[train_index])\n",
" else:\n",
" tt_transformer = None\n",
" pretraining_time = time.perf_counter() - tic\n",
" sors = SORS(model_factory=lambda **kwargs: model_factory(model_name, n_components=n_components, **kwargs), tt_transformer=tt_transformer)\n",
" sors.fit(X[train_index])\n",
" # sors.model_factory = None # Lambda appears to be non-pickle-able\n",
" sors.pretraining_time = pretraining_time\n",
" train_result[\"folds\"].append({\"train_index\": train_index, \"test_index\": test_index, \"sors\": sors})"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 209,
"referenced_widgets": [
"b56d4145d1cf4b038cbcb1ae17019876",
"d4312275c5ef4ba1ba25d0a65ba9dfec",
"071bb3de58f3454f93b4dafed6f816c9",
"0c7f93b59435444da0b94f7d9fd68de4",
"259fdfdf0cd44d2abcd029acec455382",
"fa7afdd2d7914070960a6d5581f9f247",
"1b6adb59bbeb4219aa88544d01699737",
"53b0cd02456c46da9a5845f8821a126d",
"3963c12f094f4eb98f6d77ccd4c489d0",
"3b9c8390f2684164a5f0c3515d5f25e3",
"fbb43e5d9799409a8a6642f558fae46a",
"3655f8d5059f407382632f05a713cd35",
"c889bc6b98fa4100b9b4d3c6b50e9277",
"781df672789d4077b4200c45ee19053d",
"69e1adf4678b42489633316864500b26",
"1313b02028d84349b1a24c7d5a404a43",
"4b3d4361e7684eaa8fc5ff4559f6a0cb",
"1b3397d3be774f8b878775543bf684db",
"5852b5e9a1d54128a0114a82de8aca29",
"f8382e79642a48049ed19eb8a8a3ab8b",
"23bed1c9f1324e549fcaee6fbba2d7b7",
"7a30a71ed3934c398c52b1066afaa1e6",
"42e53948f0734239960023180562c914",
"424d58c068a64b5fa5497c657a3c3b67",
"80f4412c5c7c40b0b75ed434175836ac",
"6220b28f89644c669056441a6331d53a",
"f761b9dbbb654588a4c7284679eb7e6d",
"98c62a86fd0c47948fe1e1f91ff25ea7",
"4ec7d4832c18494b90b0316744e343b6",
"b9f9e33caa5f4bc897de1b888be57624",
"7c6ea26394bd402aac638229b666c63d",
"b40c9eefe74943afbd9e77200f45b4d4",
"6d4c6b2e0c124c03b9db41cd5dff7140",
"efaff2aa75904ef1bee86dff249918dd",
"e2c925adfdd74265917f3e31bedf54bd",
"b19ef6fa59914bb3a6eca87d660f9f26",
"bd0cbc1edc624ec6bf8e6d34774186c2",
"99b5196975ff40d08cfc3f3f0ed7896d",
"f02336526ced404cb270e40e88a6e151",
"dcfe9574b30442a8aee6c953be2dbfbb",
"60c5c312035c47d7850bf0f70e54c519",
"53102c05cead47bdb7fd63c19e70ccb5",
"8d774795a59e4c26a25de236fa348f49",
"2e9a48aea3444f37a3ccc47934f17e30",
"4268a5e1e90e4fb5ade41f29645ffde1",
"c20a7ca21b1f4dd38c102e8852409f4e",
"463c7064f8b545cc9047b26f9b95c7ea",
"3d9f365d7a774fd8a16da87c3c9403e3",
"4667f86edcf3426f98cab5fecd9ced31",
"361e5aa992b646f0b73c2d0e82005e37",
"a3ced9a17a3248cfbf04aa266fc85274",
"fe68edb70e804dc88d3826b5808df28f",
"fa8c0db6a81e4275b0f89c4a85b96759",
"ffeceb43c7e34071b039e24e87035b32",
"1f534610ac1f4b02bdf6fdd6d196fb6a",
"865497ce4a7d413d8c11d311b97c7738",
"9807d5e4cca349ad9e726a2ce390cac0",
"b760cd6eda0645e793a3330d84267a61",
"e176eead65ba4506939016ea2e530f21",
"a94ecfce811242c18d7eb78763462e70",
"5726a22002024ee3b511ae26cee39cb5",
"424b9b7c41f34925a3e9dd224068a962",
"b5091e0ef3384115ad38647f60bb21df",
"d4ecf384cbd34d94942381618eee6ae4",
"aba3d323dac74b328ce34bca6644b31f",
"30ac1e2286cd49f698a53acc376310f4"
]
},
"id": "O0pQI_Y3z0T_",
"outputId": "b34e56b0-dc41-4b10-e5b3-96c010a783f8"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
" 0%| | 0/5 [00:00<?, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "b56d4145d1cf4b038cbcb1ae17019876"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
" 0%| | 0/1484.0 [00:00<?, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "3655f8d5059f407382632f05a713cd35"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
" 0%| | 0/1484.0 [00:00<?, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "42e53948f0734239960023180562c914"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
" 0%| | 0/1484.0 [00:00<?, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "efaff2aa75904ef1bee86dff249918dd"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
" 0%| | 0/1484.0 [00:00<?, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "4268a5e1e90e4fb5ade41f29645ffde1"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
" 0%| | 0/1484.0 [00:00<?, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "865497ce4a7d413d8c11d311b97c7738"
}
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"plot_results = []\n",
"\n",
"for model_name, train_result in all_models.items():\n",
" for fold in train_result[\"folds\"]:\n",
" for i, m in enumerate(fold[\"sors\"].models):\n",
" for train_test_type in (\"train\", \"test\"):\n",
" X_test = X[fold[f\"{train_test_type}_index\"]]\n",
" known_indices = fold[\"sors\"].test_sequence[:i+1]\n",
" X_pred = m.predict(X_test[:, known_indices], known_indices=known_indices)\n",
" rmse = ((X_pred - X_test) ** 2).mean() ** 0.5\n",
" plot_results.append({\n",
" \"model_name\": model_name,\n",
" \"n_known\": len(known_indices),\n",
" \"rmse\": rmse,\n",
" \"dataset\": train_test_type\n",
" })\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "7bRDxAnFTFxG",
"outputId": "a89b7d9f-f0d7-43e3-8818-e358caef10a5"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/content/common.py:267: UserWarning: Received regressor output of shape (56,). Converting it back to a 2D column vector.\n",
" warnings.warn(f\"Received regressor output of shape {pred.shape}. Converting it back to a 2D column vector.\")\n",
"/content/common.py:267: UserWarning: Received regressor output of shape (222,). Converting it back to a 2D column vector.\n",
" warnings.warn(f\"Received regressor output of shape {pred.shape}. Converting it back to a 2D column vector.\")\n",
"/content/common.py:267: UserWarning: Received regressor output of shape (56,). Converting it back to a 2D column vector.\n",
" warnings.warn(f\"Received regressor output of shape {pred.shape}. Converting it back to a 2D column vector.\")\n",
"/content/common.py:267: UserWarning: Received regressor output of shape (222,). Converting it back to a 2D column vector.\n",
" warnings.warn(f\"Received regressor output of shape {pred.shape}. Converting it back to a 2D column vector.\")\n",
"/content/common.py:267: UserWarning: Received regressor output of shape (56,). Converting it back to a 2D column vector.\n",
" warnings.warn(f\"Received regressor output of shape {pred.shape}. Converting it back to a 2D column vector.\")\n",
"/content/common.py:267: UserWarning: Received regressor output of shape (222,). Converting it back to a 2D column vector.\n",
" warnings.warn(f\"Received regressor output of shape {pred.shape}. Converting it back to a 2D column vector.\")\n",
"/content/common.py:267: UserWarning: Received regressor output of shape (55,). Converting it back to a 2D column vector.\n",
" warnings.warn(f\"Received regressor output of shape {pred.shape}. Converting it back to a 2D column vector.\")\n",
"/content/common.py:267: UserWarning: Received regressor output of shape (223,). Converting it back to a 2D column vector.\n",
" warnings.warn(f\"Received regressor output of shape {pred.shape}. Converting it back to a 2D column vector.\")\n",
"/content/common.py:267: UserWarning: Received regressor output of shape (55,). Converting it back to a 2D column vector.\n",
" warnings.warn(f\"Received regressor output of shape {pred.shape}. Converting it back to a 2D column vector.\")\n",
"/content/common.py:267: UserWarning: Received regressor output of shape (223,). Converting it back to a 2D column vector.\n",
" warnings.warn(f\"Received regressor output of shape {pred.shape}. Converting it back to a 2D column vector.\")\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"fig, ax = plt.subplots(figsize=(4, 3), dpi=144)\n",
"ax = sns.lineplot(\n",
" ax=ax,\n",
" data=pd.DataFrame(plot_results).sort_values(\"model_name\"),\n",
" x=\"n_known\",\n",
" y=\"rmse\",\n",
" hue=\"model_name\",\n",
" style=\"dataset\",\n",
" errorbar=None\n",
")\n",
"ax.set(xlabel=\"Number of Input Locations\", ylabel=\"Point-wise RMSE (dB)\", xticks=list(range(0, 54, 4))+[54])\n",
"# ax.legend().set_title(\"\")\n",
"ax.grid()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 434
},
"id": "48X9NFaKTWmw",
"outputId": "777483cb-89df-4fd0-9fa1-61d70ff5bfdf"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"fig.savefig(\"/content/drive/MyDrive/mobile_perimeter_documents/2023_TPP_Documents/Reconstruction_Paper_PLoS_One_20230513/revision_202309/ensemble_reconstruction_train_test_20230924.pdf\", bbox_inches=\"tight\")\n",
"fig.savefig(\"/content/drive/MyDrive/mobile_perimeter_documents/2023_TPP_Documents/Reconstruction_Paper_PLoS_One_20230513/revision_202309/ensemble_reconstruction_train_test_20230924.svg\", bbox_inches=\"tight\")\n",
"fig.savefig(\"/content/drive/MyDrive/mobile_perimeter_documents/2023_TPP_Documents/Reconstruction_Paper_PLoS_One_20230513/revision_202309/ensemble_reconstruction_train_test_20230924.png\", bbox_inches=\"tight\", dpi=150, transparent=True)\n"
],
"metadata": {
"id": "pti4nZrcwTuH"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"fig, ax = plt.subplots(figsize=(4, 3), dpi=144)\n",
"ax = sns.lineplot(\n",
" ax=ax,\n",
" data=pd.DataFrame(plot_results).query(\"dataset=='test'\").sort_values(\"model_name\"),\n",
" x=\"n_known\",\n",
" y=\"rmse\",\n",
" hue=\"model_name\",\n",
" errorbar=None\n",
")\n",
"ax.set(xlabel=\"Number of Input Locations\", ylabel=\"Point-wise RMSE (dB)\", xticks=list(range(0, 54, 4))+[54])\n",
"ax.legend().set_title(\"\")\n",
"ax.grid()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 434
},
"id": "0MgXVxhrwjHr",
"outputId": "b2e54343-a9a0-46b4-b6d1-2cf83399d52d"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"fig.savefig(\"/content/drive/MyDrive/mobile_perimeter_documents/2023_TPP_Documents/Reconstruction_Paper_PLoS_One_20230513/revision_202309/ensemble_reconstruction_test_20230924.pdf\", bbox_inches=\"tight\")\n",
"fig.savefig(\"/content/drive/MyDrive/mobile_perimeter_documents/2023_TPP_Documents/Reconstruction_Paper_PLoS_One_20230513/revision_202309/ensemble_reconstruction_test_20230924.svg\", bbox_inches=\"tight\")\n",
"fig.savefig(\"/content/drive/MyDrive/mobile_perimeter_documents/2023_TPP_Documents/Reconstruction_Paper_PLoS_One_20230513/revision_202309/ensemble_reconstruction_test_20230924.png\", bbox_inches=\"tight\", dpi=150, transparent=True)\n"
],
"metadata": {
"id": "1t506XVovJ06"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"fig, ax = plt.subplots(figsize=(4, 3), dpi=144)\n",
"ax = sns.lineplot(\n",
" ax=ax,\n",
" data=pd.DataFrame(plot_results).query(\"dataset=='train'\").sort_values(\"model_name\"),\n",
" x=\"n_known\",\n",
" y=\"rmse\",\n",
" hue=\"model_name\",\n",
" errorbar=None\n",
")\n",
"ax.set(xlabel=\"Number of Input Locations\", ylabel=\"Point-wise RMSE (dB)\", xticks=list(range(0, 54, 4))+[54])\n",
"ax.legend().set_title(\"\")\n",
"ax.grid()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 434
},
"id": "ZvYwIPMZwxez",
"outputId": "e89de495-9ff0-463e-cc5b-6035f653a2e5"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"fig.savefig(\"/content/drive/MyDrive/mobile_perimeter_documents/2023_TPP_Documents/Reconstruction_Paper_PLoS_One_20230513/revision_202309/ensemble_reconstruction_train_20230924.pdf\", bbox_inches=\"tight\")\n",
"fig.savefig(\"/content/drive/MyDrive/mobile_perimeter_documents/2023_TPP_Documents/Reconstruction_Paper_PLoS_One_20230513/revision_202309/ensemble_reconstruction_train_20230924.svg\", bbox_inches=\"tight\")\n",
"fig.savefig(\"/content/drive/MyDrive/mobile_perimeter_documents/2023_TPP_Documents/Reconstruction_Paper_PLoS_One_20230513/revision_202309/ensemble_reconstruction_train_20230924.png\", bbox_inches=\"tight\", dpi=150, transparent=True)\n"
],
"metadata": {
"id": "bMIST2HiwyPk"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"for model_name, train_result in all_models.items():\n",
" for fold in train_result[\"folds\"]:\n",
" print(model_name, fold[\"sors\"].training_time + fold[\"sors\"].pretraining_time)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "po12zD-4L8Sz",
"outputId": "e65ec5a7-6055-4639-fb00-adfff8915923"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"LR 6.285632824999993\n",
"LR 4.581879592002224\n",
"LR 4.903881189000458\n",
"LR 4.7218283399997745\n",
"LR 4.112042418999408\n",
"TTPCR 7.384629128999222\n",
"TTPCR 6.632896215998699\n",
"TTPCR 7.405544225000995\n",
"TTPCR 6.068389542000659\n",
"TTPCR 6.307594009998866\n",
"RandomForest 323.37253220799903\n",
"RandomForest 319.699642384001\n",
"RandomForest 323.5411905590008\n",
"RandomForest 317.6006928099996\n",
"RandomForest 317.98711345800075\n",
"GradientBoosting 1109.4427285560014\n",
"GradientBoosting 1093.0771890230026\n",
"GradientBoosting 1091.0606778369984\n",
"GradientBoosting 1082.126539054003\n",
"GradientBoosting 1091.7598055749986\n",
"PLS 11.371629613997357\n",
"PLS 10.731885837001755\n",
"PLS 9.18308480399719\n",
"PLS 12.633002009999473\n",
"PLS 12.801171471004636\n"
]
}
]
}
],
"metadata": {
"colab": {
"provenance": [],
"mount_file_id": "1MHt42ix8nLmsqgE0SlUdEMq8i1mqjABE",
"authorship_tag": "ABX9TyNfkpXJ4qekIya4fgjVmF0L",
"include_colab_link": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.9.7"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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