479 lines
24 KiB
Plaintext
479 lines
24 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "25ba2626-2b93-48e7-b9cc-551fe03335f4",
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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"import yaml\n",
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"from sklearn import linear_model\n",
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"from sklearn.model_selection import LeaveOneGroupOut, cross_val_score\n",
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"import os\n",
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"import importlib\n",
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"import matplotlib.pyplot as plt\n",
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"import sys\n",
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"import numpy as np\n",
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"import seaborn as sns\n",
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"import pandas as pd\n",
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"\n",
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"nb_dir = os.path.split(os.getcwd())[0]\n",
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"if nb_dir not in sys.path:\n",
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" sys.path.append(nb_dir)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "b429e654-b065-4ea7-9dac-955584f7a016",
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"metadata": {},
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"outputs": [],
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"source": [
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"from machine_learning import pipeline, features_sensor, labels, model"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "5f2a92e0-d6ea-49a1-9f06-d808c1bd57e9",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<module 'machine_learning.labels' from 'C:\\\\Users\\\\junos\\\\Documents\\\\FWO-ARRS\\\\Analysis\\\\straw2analysis\\\\machine_learning\\\\labels.py'>"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"importlib.reload(labels)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "948cb320-f2c1-46a2-a42d-ab12894d321a",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"SensorFeatures initialized.\n",
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"Calculating features ...\n",
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"Read proximity features from the file.\n",
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"Read communication features from the file.\n"
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]
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}
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],
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"source": [
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"with open(\"./config/prox_comm_PANAS_features.yaml\", \"r\") as file:\n",
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" sensor_features_params = yaml.safe_load(file)\n",
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"sensor_features = features_sensor.SensorFeatures(**sensor_features_params)\n",
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"#sensor_features.set_sensor_data()\n",
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"sensor_features.calculate_features(cached=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "872679f6-e343-4d2a-bfc5-e4e3d224c766",
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"metadata": {},
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"outputs": [],
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"source": [
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"all_features = sensor_features.get_features(\"all\",\"all\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "52f0f3cb-733a-4345-ab36-e52dc3c5a76c",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Labels initialized.\n",
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"Aggregating labels ...\n",
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"Read labels from the file.\n"
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]
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}
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],
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"source": [
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"with open(\"./config/prox_comm_PANAS_labels.yaml\", \"r\") as file:\n",
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" labels_params = yaml.safe_load(file)\n",
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"labels_current = labels.Labels(**labels_params)\n",
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"#labels_current.set_labels()\n",
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"labels_current.aggregate_labels(cached=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "c366516a-6aa6-4101-a18d-0dc35f597d87",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"ModelValidation initialized.\n",
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"Validation method set.\n"
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]
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}
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],
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"source": [
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"model_validation = model.ModelValidation(\n",
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" sensor_features.get_features(\"all\", \"all\"),\n",
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" labels_current.get_aggregated_labels(),\n",
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" group_variable=\"participant_id\",\n",
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" cv_name=\"loso\",\n",
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")\n",
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"model_validation.model = linear_model.LinearRegression()\n",
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"model_validation.set_cv_method()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "0eab568d-ad7f-4243-be05-26bafb310c5c",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Running cross validation ...\n"
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]
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}
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],
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"source": [
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"model_loso_r2 = model_validation.cross_validate()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "fde0151b-c259-45e8-af2e-94f37edf0b01",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[-8.50176280e-02 -3.66239404e-02 -5.77416259e-01 -9.06942628e-01\n",
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" -3.13084482e+00 -5.25290051e-02 -2.32414699e+00 -7.65972845e+00\n",
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" -3.65181380e+00 -9.62417876e+00 -7.44270369e-02 6.78427260e-02\n",
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" -5.69919784e-01 -9.03242379e-01 -1.21151912e-01 -5.13453030e+00\n",
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" -1.60384696e+00 -3.19062741e+00 -6.63847516e-01 -9.90156817e-02\n",
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" -7.72057926e-01 -4.90843105e+01 3.69446095e-01 -2.08765985e+00\n",
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" -1.99641377e+00 -1.29034837e+03 -3.21364491e+00 -5.77331614e-01\n",
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" 0.00000000e+00 -6.84298747e-03 -1.63138097e+01 -1.66204067e+00\n",
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" -2.27751119e-01 -1.33661361e+00 -9.81485624e-01 -8.49005069e+00\n",
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" -1.54261232e+01 -1.07208976e+01 -5.94109632e-01 -1.46186838e-01\n",
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" -3.35992820e-01 -1.56058931e-01 -4.30691060e+00 -4.02218511e+00\n",
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" -1.76476411e+01 -4.87642855e-02 -5.30649694e+00 -2.17399142e-01\n",
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" -4.13520657e-01 -3.86148143e+00 -8.01412328e-01 -8.23760834e+00\n",
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" -2.06664107e+00 -1.18230651e+00 -2.06091099e-02 -9.97601126e-02]\n",
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"-26.655054402780422\n"
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]
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}
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],
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"source": [
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"print(model_loso_r2)\n",
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"print(np.mean(model_loso_r2))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "0435685e-9998-4eff-a3ee-6edc781dde81",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([0.06784273, 0.36944609])"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"model_loso_r2[model_loso_r2 > 0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "c4560ac5-8c83-43d0-b6e0-b03dfd19c1c3",
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"metadata": {},
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"outputs": [],
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"source": [
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"logo = LeaveOneGroupOut()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "75dc95ca-ad77-4e36-bc8b-653d3b73037f",
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"metadata": {},
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"outputs": [],
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"source": [
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"try_X = model_validation.X.reset_index().drop([\"participant_id\",\"date_lj\"], axis=1)\n",
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"try_y = model_validation.y.reset_index().drop([\"participant_id\",\"date_lj\"], axis=1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "8a58963d-a10b-468a-ae82-6395e8b2e7b5",
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"metadata": {},
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"outputs": [],
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"source": [
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"model_loso_mean_absolute_error = -1 * cross_val_score(\n",
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"estimator=model_validation.model,\n",
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"X=try_X,\n",
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"y=try_y,\n",
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"groups=model_validation.groups,\n",
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"cv=logo.split(X=try_X, y=try_y, groups=model_validation.groups), \n",
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"scoring='neg_mean_absolute_error'\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"id": "c98e13d6-734f-4adc-909b-c4a400a01d3e",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([ 0.43618444, 0.39780929, 0.56970789, 0.38847095, 0.78244762,\n",
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" 0.29847855, 0.4348883 , 1.80633684, 0.29097794, 0.53106755,\n",
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" 0.32757327, 0.37845186, 0.30228743, 0.34129752, 0.2555845 ,\n",
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" 1.27981007, 0.40270591, 0.35411635, 0.2568122 , 0.5820276 ,\n",
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" 0.33293713, 0.47789249, 0.19690204, 0.68629304, 0.67457704,\n",
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" 13.0369228 , 0.41234072, 0.31384332, 0.45126702, 0.34806906,\n",
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" 0.52854722, 0.28707449, 0.28282637, 0.49286602, 0.26406791,\n",
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" 0.39567315, 0.33661383, 1.23764371, 0.43788937, 0.32592072,\n",
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" 0.47443271, 0.55999948, 0.50408039, 0.40523803, 0.50241167,\n",
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" 0.30617356, 0.31461521, 0.28494495, 0.32278505, 0.29084659,\n",
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" 0.47211231, 0.33807521, 0.34608592, 0.40624902, 0.22882316,\n",
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" 0.45563856])"
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]
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},
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"execution_count": 14,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"model_loso_mean_absolute_error"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"id": "cd821657-cc18-46f3-92d1-b331b863790f",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"0.39674122009711504"
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]
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},
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"execution_count": 15,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"np.median(model_loso_mean_absolute_error)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"id": "ead0d898-8a96-404d-a895-b213771dc7ea",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"LinearRegression()"
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]
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},
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"execution_count": 16,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"model_validation.model.fit(try_X, try_y)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"id": "406d82e7-488c-46a3-8426-ca49e01993f5",
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"metadata": {},
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"outputs": [],
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"source": [
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"Y_predicted = model_validation.model.predict(try_X)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"id": "310c6287-7d6e-4261-8c9c-3c592c822bd1",
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"metadata": {},
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"outputs": [],
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"source": [
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"try_y.rename(columns={\"NA\": \"NA_true\"}, inplace=True)\n",
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"try_y[\"NA_predicted\"] = Y_predicted\n",
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"NA_long = pd.wide_to_long(\n",
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" try_y.reset_index(),\n",
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" i=\"index\",\n",
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" j=\"value\",\n",
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" stubnames=\"NA\",\n",
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" sep=\"_\",\n",
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" suffix=\".+\",\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"id": "62f9312d-f9d7-403c-89c0-5c04d05e76bd",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<seaborn.axisgrid.FacetGrid at 0x26fc52a8d00>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 623.375x360 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"g1 = sns.displot(NA_long, x=\"NA\", hue=\"value\", binwidth=0.1, height=5, aspect=1.5)\n",
|
|
"sns.move_legend(g1, \"upper left\", bbox_to_anchor=(.55, .45))\n",
|
|
"g1.set_axis_labels(\"Daily mean\", \"Day count\")\n",
|
|
"\n",
|
|
"display(g1)\n",
|
|
"g1.savefig(\"prox_comm_PANAS_predictions.pdf\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 20,
|
|
"id": "7e84c79b-321a-4e8f-a795-515fafe169a4",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from sklearn.metrics import mean_absolute_error"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"id": "e1579333-b57b-4bce-9c86-f77c0cd0d3d4",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"0.42725018860641295"
|
|
]
|
|
},
|
|
"execution_count": 21,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"mean_absolute_error(try_y[\"NA_true\"], try_y[\"NA_predicted\"])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"id": "5f633f76-999a-436f-afc3-f3cc44061e5a",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"array([ 0.43618444, 0.39780929, 0.56970789, 0.38847095, 0.78244762,\n",
|
|
" 0.29847855, 0.4348883 , 1.80633684, 0.29097794, 0.53106755,\n",
|
|
" 0.32757327, 0.37845186, 0.30228743, 0.34129752, 0.2555845 ,\n",
|
|
" 1.27981007, 0.40270591, 0.35411635, 0.2568122 , 0.5820276 ,\n",
|
|
" 0.33293713, 0.47789249, 0.19690204, 0.68629304, 0.67457704,\n",
|
|
" 13.0369228 , 0.41234072, 0.31384332, 0.45126702, 0.34806906,\n",
|
|
" 0.52854722, 0.28707449, 0.28282637, 0.49286602, 0.26406791,\n",
|
|
" 0.39567315, 0.33661383, 1.23764371, 0.43788937, 0.32592072,\n",
|
|
" 0.47443271, 0.55999948, 0.50408039, 0.40523803, 0.50241167,\n",
|
|
" 0.30617356, 0.31461521, 0.28494495, 0.32278505, 0.29084659,\n",
|
|
" 0.47211231, 0.33807521, 0.34608592, 0.40624902, 0.22882316,\n",
|
|
" 0.45563856])"
|
|
]
|
|
},
|
|
"execution_count": 22,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"model_loso_mean_absolute_error"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "straw2analysis",
|
|
"language": "python",
|
|
"name": "straw2analysis"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.9.7"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|