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JoshBroomberg/preclass.ipynb

Created February 25, 2020 06:07
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preclass.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING: pip is being invoked by an old script wrapper. This will fail in a future version of pip.\n",
"Please see https://github.com/pypa/pip/issues/5599 for advice on fixing the underlying issue.\n",
"To avoid this problem you can invoke Python with '-m pip' instead of running pip directly.\n",
"Collecting pystan\n",
" Using cached pystan-2.19.1.1-cp37-cp37m-manylinux1_x86_64.whl (67.3 MB)\n",
"Requirement already satisfied: numpy>=1.7 in /opt/conda/lib/python3.7/site-packages (from pystan) (1.17.5)\n",
"Requirement already satisfied: Cython!=0.25.1,>=0.22 in /opt/conda/lib/python3.7/site-packages (from pystan) (0.29.14)\n",
"Installing collected packages: pystan\n",
"Successfully installed pystan-2.19.1.1\n"
]
}
],
"source": [
"!pip install pystan"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"from scipy import stats\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pystan\n",
"import seaborn as sns\n",
"import pandas as pd\n",
"from sklearn import preprocessing\n",
"import stan_utils"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Note:** The code below is a simplified version of a tutorial on this Kaggle challenge, [here](https://towardsdatascience.com/bayesian-strategy-for-modeling-retail-price-with-pystan-fd0571ed778)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"689"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plt.style.use('bmh')\n",
"df = pd.read_csv('train.tsv', sep = '\\t')\n",
"df = df.sample(frac=0.01, random_state=99)\n",
"df = df[pd.notnull(df['category_name'])]\n",
"df.category_name.nunique()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"run_control": {
"marked": true
}
},
"outputs": [
{
"data": {
"text/plain": [
"20"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"n_category = 20\n",
"selected_cats = df.category_name.unique()[:n_category]\n",
"df = df[df['category_name'].isin(selected_cats)]\n",
"df.category_name.nunique()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>train_id</th>\n",
" <th>name</th>\n",
" <th>item_condition_id</th>\n",
" <th>category_name</th>\n",
" <th>brand_name</th>\n",
" <th>price</th>\n",
" <th>shipping</th>\n",
" <th>item_description</th>\n",
" <th>category_code</th>\n",
" <th>log_price</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>800927</th>\n",
" <td>800927</td>\n",
" <td>Liz Lange maternity jeans FREE SHIP!!</td>\n",
" <td>3</td>\n",
" <td>Women/Maternity/Jeans</td>\n",
" <td>Liz Lange</td>\n",
" <td>14.0</td>\n",
" <td>1</td>\n",
" <td>Awesome maternity jeans. No bad, just elastic ...</td>\n",
" <td>15</td>\n",
" <td>2.646175</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1416912</th>\n",
" <td>1416912</td>\n",
" <td>Lipsense &amp; Gift Certificate</td>\n",
" <td>1</td>\n",
" <td>Beauty/Makeup/Lips</td>\n",
" <td>SeneGence</td>\n",
" <td>45.0</td>\n",
" <td>1</td>\n",
" <td>I have mulled wine with glossy gloss! I am als...</td>\n",
" <td>2</td>\n",
" <td>3.808882</td>\n",
" </tr>\n",
" <tr>\n",
" <th>656218</th>\n",
" <td>656218</td>\n",
" <td>False Lash Effect Mascara ❤Freeship</td>\n",
" <td>1</td>\n",
" <td>Beauty/Makeup/Eyes</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" <td>1</td>\n",
" <td>Color Black New, Never Opened. Shipping out to...</td>\n",
" <td>1</td>\n",
" <td>2.091864</td>\n",
" </tr>\n",
" <tr>\n",
" <th>809542</th>\n",
" <td>809542</td>\n",
" <td>FEKKAI TECHNICIAN COLOR CARE</td>\n",
" <td>1</td>\n",
" <td>Beauty/Hair Care/Styling Products</td>\n",
" <td>NaN</td>\n",
" <td>4.0</td>\n",
" <td>1</td>\n",
" <td>New in box</td>\n",
" <td>0</td>\n",
" <td>1.410987</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1455938</th>\n",
" <td>1455938</td>\n",
" <td>(20) 10x13 Yellow Flowers Poly Mailers</td>\n",
" <td>1</td>\n",
" <td>Other/Office supplies/Shipping Supplies</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" <td>1</td>\n",
" <td>Such a gorgeous color and design. Ship your sa...</td>\n",
" <td>11</td>\n",
" <td>2.091864</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" train_id name item_condition_id \\\n",
"800927 800927 Liz Lange maternity jeans FREE SHIP!! 3 \n",
"1416912 1416912 Lipsense & Gift Certificate 1 \n",
"656218 656218 False Lash Effect Mascara ❤Freeship 1 \n",
"809542 809542 FEKKAI TECHNICIAN COLOR CARE 1 \n",
"1455938 1455938 (20) 10x13 Yellow Flowers Poly Mailers 1 \n",
"\n",
" category_name brand_name price shipping \\\n",
"800927 Women/Maternity/Jeans Liz Lange 14.0 1 \n",
"1416912 Beauty/Makeup/Lips SeneGence 45.0 1 \n",
"656218 Beauty/Makeup/Eyes NaN 8.0 1 \n",
"809542 Beauty/Hair Care/Styling Products NaN 4.0 1 \n",
"1455938 Other/Office supplies/Shipping Supplies NaN 8.0 1 \n",
"\n",
" item_description category_code \\\n",
"800927 Awesome maternity jeans. No bad, just elastic ... 15 \n",
"1416912 I have mulled wine with glossy gloss! I am als... 2 \n",
"656218 Color Black New, Never Opened. Shipping out to... 1 \n",
"809542 New in box 0 \n",
"1455938 Such a gorgeous color and design. Ship your sa... 11 \n",
"\n",
" log_price \n",
"800927 2.646175 \n",
"1416912 3.808882 \n",
"656218 2.091864 \n",
"809542 1.410987 \n",
"1455938 2.091864 "
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"shipping_0 = df.loc[df['shipping'] == 0, 'price']\n",
"shipping_1 = df.loc[df['shipping'] == 1, 'price']\n",
"fig, ax = plt.subplots(figsize=(10,5))\n",
"ax.hist(shipping_0, color='#8CB4E1', alpha=1.0, bins=50, range = [0, 100],\n",
" label=0)\n",
"ax.hist(shipping_1, color='#007D00', alpha=0.7, bins=50, range = [0, 100],\n",
" label=1)\n",
"plt.xlabel('price', fontsize=12)\n",
"plt.ylabel('frequency', fontsize=12)\n",
"plt.title('Price Distribution by Shipping Type', fontsize=15)\n",
"plt.tick_params(labelsize=12)\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"le = preprocessing.LabelEncoder()\n",
"df['category_code'] = le.fit_transform(df['category_name'])\n",
"category_names = df.category_name.unique()\n",
"categories = len(category_names)\n",
"category = df['category_code'].values\n",
"\n",
"le = preprocessing.LabelEncoder()\n",
"df['condition_code'] = le.fit_transform(df['item_condition_id'])\n",
"condition_names = df.item_condition_id.unique()\n",
"conditions = len(condition_names)\n",
"condition = df['item_condition_id'].values\n",
"\n",
"price = df.price\n",
"df['log_price'] = log_price = np.log(price + 0.1).values\n",
"shipping = df.shipping.values\n",
"category_lookup = dict(zip(category_names, range(len(category_names))))"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df.price.apply(lambda x: np.log(x+0.1)).hist(bins=25)\n",
"plt.title('Distribution of price (log scale)')\n",
"plt.xlabel('log (price)')\n",
"plt.ylabel('Frequency');"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_ac98307a0c8268fc0ddddb1a139d6835 NOW.\n"
]
}
],
"source": [
"model = stan_utils.compile_model('model.stan')"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
"To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 158 ms, sys: 200 ms, total: 357 ms\n",
"Wall time: 3.5 s\n"
]
}
],
"source": [
"%%time\n",
"\n",
"varying_intercept_data = {'N': len(log_price),\n",
" 'J': n_category,\n",
" 'category': category+1, # Stan counts starting at 1\n",
" 'x': shipping,\n",
" 'y': log_price}\n",
"varying_intercept_fit = model.sampling(\n",
" data=varying_intercept_data, iter=1000, chains=4)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'n_eff': True,\n",
" 'Rhat': True,\n",
" 'divergence': True,\n",
" 'treedepth': True,\n",
" 'energy': True}"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pystan.check_hmc_diagnostics(varying_intercept_fit)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Inference for Stan model: anon_model_ac98307a0c8268fc0ddddb1a139d6835.\n",
"4 chains, each with iter=1000; warmup=500; thin=1; \n",
"post-warmup draws per chain=500, total post-warmup draws=2000.\n",
"\n",
" mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat\n",
"a[1] 2.85 2.1e-3 0.13 2.61 2.76 2.86 2.95 3.1 3588 1.0\n",
"a[2] 2.75 1.1e-3 0.05 2.66 2.72 2.75 2.78 2.84 1707 1.0\n",
"a[3] 2.9 1.1e-3 0.04 2.81 2.87 2.9 2.92 2.98 1495 1.0\n",
"a[4] 2.55 1.0e-3 0.05 2.46 2.52 2.54 2.58 2.63 1928 1.0\n",
"a[5] 2.86 5.2e-3 0.23 2.39 2.71 2.86 3.01 3.3 1962 1.0\n",
"a[6] 2.91 1.1e-3 0.05 2.8 2.87 2.91 2.95 3.01 2497 1.0\n",
"a[7] 2.93 4.6e-3 0.25 2.45 2.76 2.93 3.08 3.41 2821 1.0\n",
"a[8] 3.04 3.0e-3 0.15 2.75 2.93 3.04 3.14 3.35 2652 1.0\n",
"a[9] 2.76 1.7e-3 0.09 2.59 2.7 2.76 2.82 2.92 2589 1.0\n",
"a[10] 2.75 3.9e-3 0.19 2.37 2.63 2.75 2.88 3.12 2408 1.0\n",
"a[11] 3.45 3.7e-3 0.16 3.13 3.33 3.44 3.56 3.78 1915 1.0\n",
"a[12] 2.6 2.0e-3 0.1 2.41 2.53 2.6 2.67 2.79 2478 1.0\n",
"a[13] 2.9 2.4e-3 0.12 2.66 2.82 2.9 2.99 3.14 2722 1.0\n",
"a[14] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"a[15] 2.87 1.1e-3 0.05 2.76 2.83 2.87 2.9 2.97 2333 1.0\n",
"a[16] 2.95 4.8e-3 0.23 2.51 2.79 2.95 3.1 3.4 2189 1.0\n",
"a[17] 2.88 1.1e-3 0.05 2.77 2.84 2.88 2.92 2.99 2270 1.0\n",
"a[18] 2.7 1.8e-3 0.1 2.51 2.64 2.7 2.77 2.89 3015 1.0\n",
"a[19] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"a[20] 3.1 1.2e-3 0.06 2.99 3.06 3.1 3.14 3.21 2420 1.0\n",
"b -0.26 8.5e-4 0.03 -0.31 -0.28 -0.26 -0.24 -0.21 991 1.0\n",
"mu_a 2.9 1.6e-3 0.07 2.77 2.85 2.9 2.95 3.04 1807 1.0\n",
"sigma_a 0.28 1.5e-3 0.06 0.18 0.23 0.27 0.31 0.41 1568 1.0\n",
"sigma_y 0.66 1.4e-4 8.8e-3 0.65 0.66 0.66 0.67 0.68 3807 1.0\n",
"y_hat[1] 2.69 4.8e-3 0.23 2.25 2.53 2.69 2.84 3.15 2291 1.0\n",
"y_hat[2] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[3] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
"y_hat[4] 2.59 1.9e-3 0.13 2.35 2.5 2.6 2.68 2.85 4369 1.0\n",
"y_hat[5] 2.34 1.6e-3 0.1 2.15 2.27 2.34 2.4 2.53 3594 1.0\n",
"y_hat[6] 3.1 1.2e-3 0.06 2.99 3.06 3.1 3.14 3.21 2420 1.0\n",
"y_hat[7] 2.87 1.1e-3 0.05 2.76 2.83 2.87 2.9 2.97 2333 1.0\n",
"y_hat[8] 2.76 1.7e-3 0.09 2.59 2.7 2.76 2.82 2.92 2589 1.0\n",
"y_hat[9] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
"y_hat[10] 2.55 1.0e-3 0.05 2.46 2.52 2.54 2.58 2.63 1928 1.0\n",
"y_hat[11] 2.75 3.9e-3 0.19 2.37 2.63 2.75 2.88 3.12 2408 1.0\n",
"y_hat[12] 2.86 5.2e-3 0.23 2.39 2.71 2.86 3.01 3.3 1962 1.0\n",
"y_hat[13] 2.64 2.4e-3 0.13 2.4 2.55 2.64 2.72 2.89 2671 1.0\n",
"y_hat[14] 2.9 1.1e-3 0.04 2.81 2.87 2.9 2.92 2.98 1495 1.0\n",
"y_hat[15] 2.93 4.6e-3 0.25 2.45 2.76 2.93 3.08 3.41 2821 1.0\n",
"y_hat[16] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[17] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[18] 2.88 1.1e-3 0.05 2.77 2.84 2.88 2.92 2.99 2270 1.0\n",
"y_hat[19] 2.64 6.7e-4 0.04 2.57 2.61 2.64 2.66 2.71 2803 1.0\n",
"y_hat[20] 2.7 1.8e-3 0.1 2.51 2.64 2.7 2.77 2.89 3015 1.0\n",
"y_hat[21] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[22] 3.45 3.7e-3 0.16 3.13 3.33 3.44 3.56 3.78 1915 1.0\n",
"y_hat[23] 2.91 1.1e-3 0.05 2.8 2.87 2.91 2.95 3.01 2497 1.0\n",
"y_hat[24] 3.04 3.0e-3 0.15 2.75 2.93 3.04 3.14 3.35 2652 1.0\n",
"y_hat[25] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[26] 2.64 6.7e-4 0.04 2.57 2.61 2.64 2.66 2.71 2803 1.0\n",
"y_hat[27] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[28] 2.7 1.8e-3 0.1 2.51 2.64 2.7 2.77 2.89 3015 1.0\n",
"y_hat[29] 2.61 7.9e-4 0.05 2.51 2.58 2.61 2.64 2.7 3814 1.0\n",
"y_hat[30] 2.91 1.1e-3 0.05 2.8 2.87 2.91 2.95 3.01 2497 1.0\n",
"y_hat[31] 2.88 1.1e-3 0.05 2.77 2.84 2.88 2.92 2.99 2270 1.0\n",
"y_hat[32] 2.9 1.1e-3 0.04 2.81 2.87 2.9 2.92 2.98 1495 1.0\n",
"y_hat[33] 3.1 1.2e-3 0.06 2.99 3.06 3.1 3.14 3.21 2420 1.0\n",
"y_hat[34] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[35] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
"y_hat[36] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[37] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[38] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[39] 2.87 1.1e-3 0.05 2.76 2.83 2.87 2.9 2.97 2333 1.0\n",
"y_hat[40] 2.88 1.1e-3 0.05 2.77 2.84 2.88 2.92 2.99 2270 1.0\n",
"y_hat[41] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[42] 2.28 7.2e-4 0.04 2.2 2.26 2.28 2.31 2.37 3357 1.0\n",
"y_hat[43] 2.28 7.2e-4 0.04 2.2 2.26 2.28 2.31 2.37 3357 1.0\n",
"y_hat[44] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[45] 2.28 7.2e-4 0.04 2.2 2.26 2.28 2.31 2.37 3357 1.0\n",
"y_hat[46] 2.88 1.1e-3 0.05 2.77 2.84 2.88 2.92 2.99 2270 1.0\n",
"y_hat[47] 2.5 1.6e-3 0.09 2.33 2.44 2.5 2.56 2.67 3007 1.0\n",
"y_hat[48] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[49] 2.75 1.1e-3 0.05 2.66 2.72 2.75 2.78 2.84 1707 1.0\n",
"y_hat[50] 2.28 7.2e-4 0.04 2.2 2.26 2.28 2.31 2.37 3357 1.0\n",
"y_hat[51] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[52] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[53] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[54] 2.84 1.1e-3 0.06 2.73 2.8 2.84 2.88 2.96 2980 1.0\n",
"y_hat[55] 3.1 1.2e-3 0.06 2.99 3.06 3.1 3.14 3.21 2420 1.0\n",
"y_hat[56] 2.64 6.7e-4 0.04 2.57 2.61 2.64 2.66 2.71 2803 1.0\n",
"y_hat[57] 2.64 6.7e-4 0.04 2.57 2.61 2.64 2.66 2.71 2803 1.0\n",
"y_hat[58] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
"y_hat[59] 2.87 1.1e-3 0.05 2.76 2.83 2.87 2.9 2.97 2333 1.0\n",
"y_hat[60] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[61] 2.87 1.1e-3 0.05 2.76 2.83 2.87 2.9 2.97 2333 1.0\n",
"y_hat[62] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[63] 2.9 1.1e-3 0.04 2.81 2.87 2.9 2.92 2.98 1495 1.0\n",
"y_hat[64] 2.61 7.9e-4 0.05 2.51 2.58 2.61 2.64 2.7 3814 1.0\n",
"y_hat[65] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[66] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
"y_hat[67] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[68] 2.9 1.1e-3 0.04 2.81 2.87 2.9 2.92 2.98 1495 1.0\n",
"y_hat[69] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[70] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[71] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[72] 2.75 1.1e-3 0.05 2.66 2.72 2.75 2.78 2.84 1707 1.0\n",
"y_hat[73] 2.5 1.6e-3 0.09 2.33 2.44 2.5 2.56 2.67 3007 1.0\n",
"y_hat[74] 2.9 1.1e-3 0.04 2.81 2.87 2.9 2.92 2.98 1495 1.0\n",
"y_hat[75] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[76] 2.55 1.0e-3 0.05 2.46 2.52 2.54 2.58 2.63 1928 1.0\n",
"y_hat[77] 3.1 1.2e-3 0.06 2.99 3.06 3.1 3.14 3.21 2420 1.0\n",
"y_hat[78] 2.91 1.1e-3 0.05 2.8 2.87 2.91 2.95 3.01 2497 1.0\n",
"y_hat[79] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[80] 2.28 7.2e-4 0.04 2.2 2.26 2.28 2.31 2.37 3357 1.0\n",
"y_hat[81] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[82] 2.61 7.9e-4 0.05 2.51 2.58 2.61 2.64 2.7 3814 1.0\n",
"y_hat[83] 2.91 1.1e-3 0.05 2.8 2.87 2.91 2.95 3.01 2497 1.0\n",
"y_hat[84] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[85] 2.28 7.2e-4 0.04 2.2 2.26 2.28 2.31 2.37 3357 1.0\n",
"y_hat[86] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[87] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
"y_hat[88] 2.61 7.9e-4 0.05 2.51 2.58 2.61 2.64 2.7 3814 1.0\n",
"y_hat[89] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[90] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[91] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[92] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[93] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[94] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
"y_hat[95] 2.28 7.2e-4 0.04 2.2 2.26 2.28 2.31 2.37 3357 1.0\n",
"y_hat[96] 2.28 7.2e-4 0.04 2.2 2.26 2.28 2.31 2.37 3357 1.0\n",
"y_hat[97] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[98] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[99] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[100] 2.64 6.7e-4 0.04 2.57 2.61 2.64 2.66 2.71 2803 1.0\n",
"y_hat[101] 2.87 1.1e-3 0.05 2.76 2.83 2.87 2.9 2.97 2333 1.0\n",
"y_hat[102] 2.9 1.1e-3 0.04 2.81 2.87 2.9 2.92 2.98 1495 1.0\n",
"y_hat[103] 2.87 1.1e-3 0.05 2.76 2.83 2.87 2.9 2.97 2333 1.0\n",
"y_hat[104] 2.61 7.9e-4 0.05 2.51 2.58 2.61 2.64 2.7 3814 1.0\n",
"y_hat[105] 2.28 7.2e-4 0.04 2.2 2.26 2.28 2.31 2.37 3357 1.0\n",
"y_hat[106] 3.1 1.2e-3 0.06 2.99 3.06 3.1 3.14 3.21 2420 1.0\n",
"y_hat[107] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[108] 2.61 7.9e-4 0.05 2.51 2.58 2.61 2.64 2.7 3814 1.0\n",
"y_hat[109] 2.28 7.2e-4 0.04 2.2 2.26 2.28 2.31 2.37 3357 1.0\n",
"y_hat[110] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[111] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[112] 3.18 3.8e-3 0.16 2.87 3.07 3.18 3.29 3.53 1924 1.0\n",
"y_hat[113] 2.91 1.1e-3 0.05 2.8 2.87 2.91 2.95 3.01 2497 1.0\n",
"y_hat[114] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[115] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
"y_hat[116] 2.5 1.6e-3 0.09 2.33 2.44 2.5 2.56 2.67 3007 1.0\n",
"y_hat[117] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[118] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[119] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[120] 2.76 1.7e-3 0.09 2.59 2.7 2.76 2.82 2.92 2589 1.0\n",
"y_hat[121] 2.59 1.9e-3 0.13 2.35 2.5 2.6 2.68 2.85 4369 1.0\n",
"y_hat[122] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
"y_hat[123] 2.91 1.1e-3 0.05 2.8 2.87 2.91 2.95 3.01 2497 1.0\n",
"y_hat[124] 2.87 1.1e-3 0.05 2.76 2.83 2.87 2.9 2.97 2333 1.0\n",
"y_hat[125] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
"y_hat[126] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[127] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[128] 2.55 1.0e-3 0.05 2.46 2.52 2.54 2.58 2.63 1928 1.0\n",
"y_hat[129] 2.75 1.1e-3 0.05 2.66 2.72 2.75 2.78 2.84 1707 1.0\n",
"y_hat[130] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[131] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[132] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[133] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
"y_hat[134] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[135] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[136] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[137] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
"y_hat[138] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[139] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[140] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
"y_hat[141] 2.64 6.7e-4 0.04 2.57 2.61 2.64 2.66 2.71 2803 1.0\n",
"y_hat[142] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[143] 3.04 3.0e-3 0.15 2.75 2.93 3.04 3.14 3.35 2652 1.0\n",
"y_hat[144] 2.64 6.7e-4 0.04 2.57 2.61 2.64 2.66 2.71 2803 1.0\n",
"y_hat[145] 2.91 1.1e-3 0.05 2.8 2.87 2.91 2.95 3.01 2497 1.0\n",
"y_hat[146] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[147] 2.84 1.1e-3 0.06 2.73 2.8 2.84 2.88 2.96 2980 1.0\n",
"y_hat[148] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[149] 2.64 6.7e-4 0.04 2.57 2.61 2.64 2.66 2.71 2803 1.0\n",
"y_hat[150] 2.28 7.2e-4 0.04 2.2 2.26 2.28 2.31 2.37 3357 1.0\n",
"y_hat[151] 2.5 1.6e-3 0.09 2.33 2.44 2.5 2.56 2.67 3007 1.0\n",
"y_hat[152] 2.5 1.6e-3 0.09 2.33 2.44 2.5 2.56 2.67 3007 1.0\n",
"y_hat[153] 2.84 1.1e-3 0.06 2.73 2.8 2.84 2.88 2.96 2980 1.0\n",
"y_hat[154] 2.64 6.7e-4 0.04 2.57 2.61 2.64 2.66 2.71 2803 1.0\n",
"y_hat[155] 2.9 1.1e-3 0.04 2.81 2.87 2.9 2.92 2.98 1495 1.0\n",
"y_hat[156] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[157] 2.55 1.0e-3 0.05 2.46 2.52 2.54 2.58 2.63 1928 1.0\n",
"y_hat[158] 2.76 1.7e-3 0.09 2.59 2.7 2.76 2.82 2.92 2589 1.0\n",
"y_hat[159] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[160] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[161] 2.91 1.1e-3 0.05 2.8 2.87 2.91 2.95 3.01 2497 1.0\n",
"y_hat[162] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[163] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[164] 2.28 7.2e-4 0.04 2.2 2.26 2.28 2.31 2.37 3357 1.0\n",
"y_hat[165] 2.91 1.1e-3 0.05 2.8 2.87 2.91 2.95 3.01 2497 1.0\n",
"y_hat[166] 2.44 1.8e-3 0.1 2.25 2.37 2.44 2.51 2.63 3028 1.0\n",
"y_hat[167] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
"y_hat[168] 2.5 1.6e-3 0.09 2.33 2.44 2.5 2.56 2.67 3007 1.0\n",
"y_hat[169] 2.91 1.1e-3 0.05 2.8 2.87 2.91 2.95 3.01 2497 1.0\n",
"y_hat[170] 2.9 2.4e-3 0.12 2.66 2.82 2.9 2.99 3.14 2722 1.0\n",
"y_hat[171] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[172] 2.88 1.1e-3 0.05 2.77 2.84 2.88 2.92 2.99 2270 1.0\n",
"y_hat[173] 2.62 1.2e-3 0.06 2.51 2.58 2.62 2.66 2.73 2299 1.0\n",
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"y_hat[1811] 2.91 1.1e-3 0.05 2.8 2.87 2.91 2.95 3.01 2497 1.0\n",
"y_hat[1812] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
"y_hat[1813] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[1814] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[1815] 2.61 7.9e-4 0.05 2.51 2.58 2.61 2.64 2.7 3814 1.0\n",
"y_hat[1816] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[1817] 2.86 5.2e-3 0.23 2.39 2.71 2.86 3.01 3.3 1962 1.0\n",
"y_hat[1818] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
"y_hat[1819] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[1820] 2.9 1.1e-3 0.04 2.81 2.87 2.9 2.92 2.98 1495 1.0\n",
"y_hat[1821] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
"y_hat[1822] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[1823] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[1824] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[1825] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
"y_hat[1826] 2.64 6.7e-4 0.04 2.57 2.61 2.64 2.66 2.71 2803 1.0\n",
"y_hat[1827] 2.28 7.2e-4 0.04 2.2 2.26 2.28 2.31 2.37 3357 1.0\n",
"y_hat[1828] 2.28 7.2e-4 0.04 2.2 2.26 2.28 2.31 2.37 3357 1.0\n",
"y_hat[1829] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[1830] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[1831] 2.91 1.1e-3 0.05 2.8 2.87 2.91 2.95 3.01 2497 1.0\n",
"y_hat[1832] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
"y_hat[1833] 3.1 1.2e-3 0.06 2.99 3.06 3.1 3.14 3.21 2420 1.0\n",
"y_hat[1834] 2.91 1.1e-3 0.05 2.8 2.87 2.91 2.95 3.01 2497 1.0\n",
"y_hat[1835] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[1836] 2.61 7.9e-4 0.05 2.51 2.58 2.61 2.64 2.7 3814 1.0\n",
"y_hat[1837] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[1838] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[1839] 2.61 7.9e-4 0.05 2.51 2.58 2.61 2.64 2.7 3814 1.0\n",
"y_hat[1840] 2.88 1.1e-3 0.05 2.77 2.84 2.88 2.92 2.99 2270 1.0\n",
"y_hat[1841] 2.5 1.6e-3 0.09 2.33 2.44 2.5 2.56 2.67 3007 1.0\n",
"y_hat[1842] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
"y_hat[1843] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[1844] 3.45 3.7e-3 0.16 3.13 3.33 3.44 3.56 3.78 1915 1.0\n",
"y_hat[1845] 2.61 7.9e-4 0.05 2.51 2.58 2.61 2.64 2.7 3814 1.0\n",
"y_hat[1846] 2.62 1.2e-3 0.06 2.51 2.58 2.62 2.66 2.73 2299 1.0\n",
"y_hat[1847] 2.28 7.2e-4 0.04 2.2 2.26 2.28 2.31 2.37 3357 1.0\n",
"y_hat[1848] 2.64 6.7e-4 0.04 2.57 2.61 2.64 2.66 2.71 2803 1.0\n",
"y_hat[1849] 2.28 7.2e-4 0.04 2.2 2.26 2.28 2.31 2.37 3357 1.0\n",
"y_hat[1850] 2.9 1.1e-3 0.04 2.81 2.87 2.9 2.92 2.98 1495 1.0\n",
"y_hat[1851] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[1852] 2.91 1.1e-3 0.05 2.8 2.87 2.91 2.95 3.01 2497 1.0\n",
"y_hat[1853] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[1854] 2.61 7.9e-4 0.05 2.51 2.58 2.61 2.64 2.7 3814 1.0\n",
"y_hat[1855] 2.84 1.1e-3 0.06 2.73 2.8 2.84 2.88 2.96 2980 1.0\n",
"y_hat[1856] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[1857] 2.64 6.7e-4 0.04 2.57 2.61 2.64 2.66 2.71 2803 1.0\n",
"y_hat[1858] 2.28 7.2e-4 0.04 2.2 2.26 2.28 2.31 2.37 3357 1.0\n",
"y_hat[1859] 2.88 1.1e-3 0.05 2.77 2.84 2.88 2.92 2.99 2270 1.0\n",
"y_hat[1860] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[1861] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
"y_hat[1862] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
"y_hat[1863] 2.64 6.7e-4 0.04 2.57 2.61 2.64 2.66 2.71 2803 1.0\n",
"y_hat[1864] 2.62 1.2e-3 0.06 2.51 2.58 2.62 2.66 2.73 2299 1.0\n",
"y_hat[1865] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[1866] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[1867] 2.62 1.2e-3 0.06 2.51 2.58 2.62 2.66 2.73 2299 1.0\n",
"y_hat[1868] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
"y_hat[1869] 2.86 5.2e-3 0.23 2.39 2.71 2.86 3.01 3.3 1962 1.0\n",
"y_hat[1870] 2.28 7.2e-4 0.04 2.2 2.26 2.28 2.31 2.37 3357 1.0\n",
"y_hat[1871] 2.55 1.0e-3 0.05 2.46 2.52 2.54 2.58 2.63 1928 1.0\n",
"y_hat[1872] 2.28 7.2e-4 0.04 2.2 2.26 2.28 2.31 2.37 3357 1.0\n",
"y_hat[1873] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
"y_hat[1874] 2.61 7.9e-4 0.05 2.51 2.58 2.61 2.64 2.7 3814 1.0\n",
"y_hat[1875] 2.62 1.2e-3 0.06 2.51 2.58 2.62 2.66 2.73 2299 1.0\n",
"y_hat[1876] 2.87 1.1e-3 0.05 2.76 2.83 2.87 2.9 2.97 2333 1.0\n",
"y_hat[1877] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[1878] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[1879] 2.64 6.7e-4 0.04 2.57 2.61 2.64 2.66 2.71 2803 1.0\n",
"y_hat[1880] 2.87 1.1e-3 0.05 2.76 2.83 2.87 2.9 2.97 2333 1.0\n",
"y_hat[1881] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[1882] 2.9 1.1e-3 0.04 2.81 2.87 2.9 2.92 2.98 1495 1.0\n",
"y_hat[1883] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[1884] 2.61 7.9e-4 0.05 2.51 2.58 2.61 2.64 2.7 3814 1.0\n",
"y_hat[1885] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[1886] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
"y_hat[1887] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
"y_hat[1888] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[1889] 2.75 1.1e-3 0.05 2.66 2.72 2.75 2.78 2.84 1707 1.0\n",
"y_hat[1890] 2.55 1.0e-3 0.05 2.46 2.52 2.54 2.58 2.63 1928 1.0\n",
"y_hat[1891] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[1892] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[1893] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[1894] 2.61 7.9e-4 0.05 2.51 2.58 2.61 2.64 2.7 3814 1.0\n",
"y_hat[1895] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[1896] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[1897] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[1898] 2.9 1.1e-3 0.04 2.81 2.87 2.9 2.92 2.98 1495 1.0\n",
"y_hat[1899] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[1900] 2.61 7.9e-4 0.05 2.51 2.58 2.61 2.64 2.7 3814 1.0\n",
"y_hat[1901] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[1902] 3.18 3.8e-3 0.16 2.87 3.07 3.18 3.29 3.53 1924 1.0\n",
"y_hat[1903] 2.75 1.1e-3 0.05 2.66 2.72 2.75 2.78 2.84 1707 1.0\n",
"y_hat[1904] 2.7 1.8e-3 0.1 2.51 2.64 2.7 2.77 2.89 3015 1.0\n",
"y_hat[1905] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[1906] 2.88 1.1e-3 0.05 2.77 2.84 2.88 2.92 2.99 2270 1.0\n",
"y_hat[1907] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[1908] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[1909] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[1910] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[1911] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[1912] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
"y_hat[1913] 2.28 7.2e-4 0.04 2.2 2.26 2.28 2.31 2.37 3357 1.0\n",
"y_hat[1914] 2.85 2.1e-3 0.13 2.61 2.76 2.86 2.95 3.1 3588 1.0\n",
"y_hat[1915] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[1916] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[1917] 2.84 1.1e-3 0.06 2.73 2.8 2.84 2.88 2.96 2980 1.0\n",
"y_hat[1918] 2.28 7.2e-4 0.04 2.2 2.26 2.28 2.31 2.37 3357 1.0\n",
"y_hat[1919] 2.84 1.1e-3 0.06 2.73 2.8 2.84 2.88 2.96 2980 1.0\n",
"y_hat[1920] 2.61 7.9e-4 0.05 2.51 2.58 2.61 2.64 2.7 3814 1.0\n",
"y_hat[1921] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[1922] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[1923] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[1924] 2.64 6.7e-4 0.04 2.57 2.61 2.64 2.66 2.71 2803 1.0\n",
"y_hat[1925] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[1926] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[1927] 2.85 2.1e-3 0.13 2.61 2.76 2.86 2.95 3.1 3588 1.0\n",
"y_hat[1928] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[1929] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[1930] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[1931] 2.75 1.1e-3 0.05 2.66 2.72 2.75 2.78 2.84 1707 1.0\n",
"y_hat[1932] 2.64 6.7e-4 0.04 2.57 2.61 2.64 2.66 2.71 2803 1.0\n",
"y_hat[1933] 2.28 7.2e-4 0.04 2.2 2.26 2.28 2.31 2.37 3357 1.0\n",
"y_hat[1934] 2.6 2.0e-3 0.1 2.41 2.53 2.6 2.67 2.79 2478 1.0\n",
"y_hat[1935] 2.85 2.1e-3 0.13 2.61 2.76 2.86 2.95 3.1 3588 1.0\n",
"y_hat[1936] 2.88 1.1e-3 0.05 2.77 2.84 2.88 2.92 2.99 2270 1.0\n",
"y_hat[1937] 2.88 1.1e-3 0.05 2.77 2.84 2.88 2.92 2.99 2270 1.0\n",
"y_hat[1938] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[1939] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[1940] 2.61 7.9e-4 0.05 2.51 2.58 2.61 2.64 2.7 3814 1.0\n",
"y_hat[1941] 2.28 7.2e-4 0.04 2.2 2.26 2.28 2.31 2.37 3357 1.0\n",
"y_hat[1942] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[1943] 2.55 1.0e-3 0.05 2.46 2.52 2.54 2.58 2.63 1928 1.0\n",
"y_hat[1944] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[1945] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
"y_hat[1946] 2.28 7.2e-4 0.04 2.2 2.26 2.28 2.31 2.37 3357 1.0\n",
"y_hat[1947] 2.64 6.7e-4 0.04 2.57 2.61 2.64 2.66 2.71 2803 1.0\n",
"y_hat[1948] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[1949] 2.55 1.0e-3 0.05 2.46 2.52 2.54 2.58 2.63 1928 1.0\n",
"y_hat[1950] 2.64 2.4e-3 0.13 2.4 2.55 2.64 2.72 2.89 2671 1.0\n",
"y_hat[1951] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[1952] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
"y_hat[1953] 3.1 1.2e-3 0.06 2.99 3.06 3.1 3.14 3.21 2420 1.0\n",
"y_hat[1954] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[1955] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[1956] 2.64 6.7e-4 0.04 2.57 2.61 2.64 2.66 2.71 2803 1.0\n",
"y_hat[1957] 2.61 7.9e-4 0.05 2.51 2.58 2.61 2.64 2.7 3814 1.0\n",
"y_hat[1958] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[1959] 2.9 2.4e-3 0.12 2.66 2.82 2.9 2.99 3.14 2722 1.0\n",
"y_hat[1960] 2.28 7.2e-4 0.04 2.2 2.26 2.28 2.31 2.37 3357 1.0\n",
"y_hat[1961] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[1962] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[1963] 2.61 7.9e-4 0.05 2.51 2.58 2.61 2.64 2.7 3814 1.0\n",
"y_hat[1964] 2.28 7.2e-4 0.04 2.2 2.26 2.28 2.31 2.37 3357 1.0\n",
"y_hat[1965] 3.1 1.2e-3 0.06 2.99 3.06 3.1 3.14 3.21 2420 1.0\n",
"y_hat[1966] 2.61 7.9e-4 0.05 2.51 2.58 2.61 2.64 2.7 3814 1.0\n",
"y_hat[1967] 2.87 1.1e-3 0.05 2.76 2.83 2.87 2.9 2.97 2333 1.0\n",
"y_hat[1968] 2.64 6.7e-4 0.04 2.57 2.61 2.64 2.66 2.71 2803 1.0\n",
"y_hat[1969] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[1970] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[1971] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[1972] 2.84 1.1e-3 0.06 2.73 2.8 2.84 2.88 2.96 2980 1.0\n",
"y_hat[1973] 2.65 1.2e-3 0.06 2.53 2.61 2.65 2.69 2.76 2410 1.0\n",
"y_hat[1974] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[1975] 2.61 7.9e-4 0.05 2.51 2.58 2.61 2.64 2.7 3814 1.0\n",
"y_hat[1976] 2.91 1.1e-3 0.05 2.8 2.87 2.91 2.95 3.01 2497 1.0\n",
"y_hat[1977] 2.28 7.2e-4 0.04 2.2 2.26 2.28 2.31 2.37 3357 1.0\n",
"y_hat[1978] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[1979] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[1980] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[1981] 2.84 1.1e-3 0.06 2.73 2.8 2.84 2.88 2.96 2980 1.0\n",
"y_hat[1982] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[1983] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[1984] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[1985] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[1986] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
"y_hat[1987] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[1988] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
"y_hat[1989] 2.91 1.1e-3 0.05 2.8 2.87 2.91 2.95 3.01 2497 1.0\n",
"y_hat[1990] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[1991] 2.61 7.9e-4 0.05 2.51 2.58 2.61 2.64 2.7 3814 1.0\n",
"y_hat[1992] 2.91 1.1e-3 0.05 2.8 2.87 2.91 2.95 3.01 2497 1.0\n",
"y_hat[1993] 2.55 1.0e-3 0.05 2.46 2.52 2.54 2.58 2.63 1928 1.0\n",
"y_hat[1994] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[1995] 2.88 1.1e-3 0.05 2.77 2.84 2.88 2.92 2.99 2270 1.0\n",
"y_hat[1996] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[1997] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[1998] 2.64 6.7e-4 0.04 2.57 2.61 2.64 2.66 2.71 2803 1.0\n",
"y_hat[1999] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[2000] 3.45 3.7e-3 0.16 3.13 3.33 3.44 3.56 3.78 1915 1.0\n",
"y_hat[2001] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[2002] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[2003] 2.55 1.0e-3 0.05 2.46 2.52 2.54 2.58 2.63 1928 1.0\n",
"y_hat[2004] 2.64 6.7e-4 0.04 2.57 2.61 2.64 2.66 2.71 2803 1.0\n",
"y_hat[2005] 2.88 1.1e-3 0.05 2.77 2.84 2.88 2.92 2.99 2270 1.0\n",
"y_hat[2006] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[2007] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[2008] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[2009] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[2010] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
"y_hat[2011] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[2012] 2.62 1.2e-3 0.06 2.51 2.58 2.62 2.66 2.73 2299 1.0\n",
"y_hat[2013] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[2014] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[2015] 2.64 6.7e-4 0.04 2.57 2.61 2.64 2.66 2.71 2803 1.0\n",
"y_hat[2016] 2.88 1.1e-3 0.05 2.77 2.84 2.88 2.92 2.99 2270 1.0\n",
"y_hat[2017] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[2018] 2.28 7.2e-4 0.04 2.2 2.26 2.28 2.31 2.37 3357 1.0\n",
"y_hat[2019] 2.5 1.6e-3 0.09 2.33 2.44 2.5 2.56 2.67 3007 1.0\n",
"y_hat[2020] 2.62 1.2e-3 0.06 2.51 2.58 2.62 2.66 2.73 2299 1.0\n",
"y_hat[2021] 2.64 6.7e-4 0.04 2.57 2.61 2.64 2.66 2.71 2803 1.0\n",
"y_hat[2022] 2.91 1.1e-3 0.05 2.8 2.87 2.91 2.95 3.01 2497 1.0\n",
"y_hat[2023] 2.28 7.2e-4 0.04 2.2 2.26 2.28 2.31 2.37 3357 1.0\n",
"y_hat[2024] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[2025] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[2026] 2.75 1.1e-3 0.05 2.66 2.72 2.75 2.78 2.84 1707 1.0\n",
"y_hat[2027] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[2028] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
"y_hat[2029] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[2030] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[2031] 2.28 7.2e-4 0.04 2.2 2.26 2.28 2.31 2.37 3357 1.0\n",
"y_hat[2032] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[2033] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[2034] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[2035] 2.28 7.2e-4 0.04 2.2 2.26 2.28 2.31 2.37 3357 1.0\n",
"y_hat[2036] 2.9 2.4e-3 0.12 2.66 2.82 2.9 2.99 3.14 2722 1.0\n",
"y_hat[2037] 2.91 1.1e-3 0.05 2.8 2.87 2.91 2.95 3.01 2497 1.0\n",
"y_hat[2038] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[2039] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[2040] 2.55 1.0e-3 0.05 2.46 2.52 2.54 2.58 2.63 1928 1.0\n",
"y_hat[2041] 2.5 1.6e-3 0.09 2.33 2.44 2.5 2.56 2.67 3007 1.0\n",
"y_hat[2042] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[2043] 2.64 6.7e-4 0.04 2.57 2.61 2.64 2.66 2.71 2803 1.0\n",
"y_hat[2044] 2.64 6.7e-4 0.04 2.57 2.61 2.64 2.66 2.71 2803 1.0\n",
"y_hat[2045] 2.55 1.0e-3 0.05 2.46 2.52 2.54 2.58 2.63 1928 1.0\n",
"y_hat[2046] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[2047] 2.76 1.7e-3 0.09 2.59 2.7 2.76 2.82 2.92 2589 1.0\n",
"y_hat[2048] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
"y_hat[2049] 2.28 7.2e-4 0.04 2.2 2.26 2.28 2.31 2.37 3357 1.0\n",
"y_hat[2050] 2.88 1.1e-3 0.05 2.77 2.84 2.88 2.92 2.99 2270 1.0\n",
"y_hat[2051] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[2052] 2.75 1.1e-3 0.05 2.66 2.72 2.75 2.78 2.84 1707 1.0\n",
"y_hat[2053] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[2054] 2.9 1.1e-3 0.04 2.81 2.87 2.9 2.92 2.98 1495 1.0\n",
"y_hat[2055] 2.88 1.1e-3 0.05 2.77 2.84 2.88 2.92 2.99 2270 1.0\n",
"y_hat[2056] 2.28 7.2e-4 0.04 2.2 2.26 2.28 2.31 2.37 3357 1.0\n",
"y_hat[2057] 2.85 2.1e-3 0.13 2.61 2.76 2.86 2.95 3.1 3588 1.0\n",
"y_hat[2058] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[2059] 2.28 7.2e-4 0.04 2.2 2.26 2.28 2.31 2.37 3357 1.0\n",
"y_hat[2060] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[2061] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
"y_hat[2062] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[2063] 2.65 1.2e-3 0.06 2.53 2.61 2.65 2.69 2.76 2410 1.0\n",
"y_hat[2064] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[2065] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[2066] 2.61 7.9e-4 0.05 2.51 2.58 2.61 2.64 2.7 3814 1.0\n",
"y_hat[2067] 2.75 1.1e-3 0.05 2.66 2.72 2.75 2.78 2.84 1707 1.0\n",
"y_hat[2068] 3.1 1.2e-3 0.06 2.99 3.06 3.1 3.14 3.21 2420 1.0\n",
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"y_hat[2070] 2.55 1.0e-3 0.05 2.46 2.52 2.54 2.58 2.63 1928 1.0\n",
"y_hat[2071] 2.64 6.7e-4 0.04 2.57 2.61 2.64 2.66 2.71 2803 1.0\n",
"y_hat[2072] 2.28 7.2e-4 0.04 2.2 2.26 2.28 2.31 2.37 3357 1.0\n",
"y_hat[2073] 3.1 1.2e-3 0.06 2.99 3.06 3.1 3.14 3.21 2420 1.0\n",
"y_hat[2074] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[2075] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[2076] 2.64 6.7e-4 0.04 2.57 2.61 2.64 2.66 2.71 2803 1.0\n",
"y_hat[2077] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[2078] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[2079] 2.59 1.9e-3 0.13 2.35 2.5 2.6 2.68 2.85 4369 1.0\n",
"y_hat[2080] 2.64 2.4e-3 0.13 2.4 2.55 2.64 2.72 2.89 2671 1.0\n",
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"y_hat[2082] 2.88 1.1e-3 0.05 2.77 2.84 2.88 2.92 2.99 2270 1.0\n",
"y_hat[2083] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[2084] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
"y_hat[2085] 2.62 1.2e-3 0.06 2.51 2.58 2.62 2.66 2.73 2299 1.0\n",
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"y_hat[2088] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
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"y_hat[2090] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
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"y_hat[2092] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
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"y_hat[2531] 3.1 1.2e-3 0.06 2.99 3.06 3.1 3.14 3.21 2420 1.0\n",
"y_hat[2532] 2.88 1.1e-3 0.05 2.77 2.84 2.88 2.92 2.99 2270 1.0\n",
"y_hat[2533] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[2534] 2.61 7.9e-4 0.05 2.51 2.58 2.61 2.64 2.7 3814 1.0\n",
"y_hat[2535] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[2536] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
"y_hat[2537] 2.64 6.7e-4 0.04 2.57 2.61 2.64 2.66 2.71 2803 1.0\n",
"y_hat[2538] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[2539] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[2540] 2.62 1.2e-3 0.06 2.51 2.58 2.62 2.66 2.73 2299 1.0\n",
"y_hat[2541] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[2542] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[2543] 2.88 1.1e-3 0.05 2.77 2.84 2.88 2.92 2.99 2270 1.0\n",
"y_hat[2544] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[2545] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[2546] 2.87 1.1e-3 0.05 2.76 2.83 2.87 2.9 2.97 2333 1.0\n",
"y_hat[2547] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[2548] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[2549] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[2550] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[2551] 2.61 7.9e-4 0.05 2.51 2.58 2.61 2.64 2.7 3814 1.0\n",
"y_hat[2552] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
"y_hat[2553] 2.61 7.9e-4 0.05 2.51 2.58 2.61 2.64 2.7 3814 1.0\n",
"y_hat[2554] 2.87 1.1e-3 0.05 2.76 2.83 2.87 2.9 2.97 2333 1.0\n",
"y_hat[2555] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[2556] 2.7 1.8e-3 0.1 2.51 2.64 2.7 2.77 2.89 3015 1.0\n",
"y_hat[2557] 2.9 1.1e-3 0.04 2.81 2.87 2.9 2.92 2.98 1495 1.0\n",
"y_hat[2558] 2.61 7.9e-4 0.05 2.51 2.58 2.61 2.64 2.7 3814 1.0\n",
"y_hat[2559] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[2560] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[2561] 2.59 1.9e-3 0.13 2.35 2.5 2.6 2.68 2.85 4369 1.0\n",
"y_hat[2562] 2.85 2.1e-3 0.13 2.61 2.76 2.86 2.95 3.1 3588 1.0\n",
"y_hat[2563] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[2564] 2.28 7.2e-4 0.04 2.2 2.26 2.28 2.31 2.37 3357 1.0\n",
"y_hat[2565] 2.84 1.1e-3 0.06 2.73 2.8 2.84 2.88 2.96 2980 1.0\n",
"y_hat[2566] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[2567] 2.64 6.7e-4 0.04 2.57 2.61 2.64 2.66 2.71 2803 1.0\n",
"y_hat[2568] 2.28 7.2e-4 0.04 2.2 2.26 2.28 2.31 2.37 3357 1.0\n",
"y_hat[2569] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
"y_hat[2570] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[2571] 2.88 1.1e-3 0.05 2.77 2.84 2.88 2.92 2.99 2270 1.0\n",
"y_hat[2572] 2.61 7.9e-4 0.05 2.51 2.58 2.61 2.64 2.7 3814 1.0\n",
"y_hat[2573] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[2574] 2.55 1.0e-3 0.05 2.46 2.52 2.54 2.58 2.63 1928 1.0\n",
"y_hat[2575] 2.84 1.1e-3 0.06 2.73 2.8 2.84 2.88 2.96 2980 1.0\n",
"y_hat[2576] 2.64 6.7e-4 0.04 2.57 2.61 2.64 2.66 2.71 2803 1.0\n",
"y_hat[2577] 2.9 1.1e-3 0.04 2.81 2.87 2.9 2.92 2.98 1495 1.0\n",
"y_hat[2578] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[2579] 2.76 1.7e-3 0.09 2.59 2.7 2.76 2.82 2.92 2589 1.0\n",
"y_hat[2580] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[2581] 2.28 7.2e-4 0.04 2.2 2.26 2.28 2.31 2.37 3357 1.0\n",
"y_hat[2582] 2.62 1.2e-3 0.06 2.51 2.58 2.62 2.66 2.73 2299 1.0\n",
"y_hat[2583] 2.61 7.9e-4 0.05 2.51 2.58 2.61 2.64 2.7 3814 1.0\n",
"y_hat[2584] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[2585] 2.75 1.1e-3 0.05 2.66 2.72 2.75 2.78 2.84 1707 1.0\n",
"y_hat[2586] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
"y_hat[2587] 2.61 7.9e-4 0.05 2.51 2.58 2.61 2.64 2.7 3814 1.0\n",
"y_hat[2588] 2.87 1.1e-3 0.05 2.76 2.83 2.87 2.9 2.97 2333 1.0\n",
"y_hat[2589] 2.91 1.1e-3 0.05 2.8 2.87 2.91 2.95 3.01 2497 1.0\n",
"y_hat[2590] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[2591] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[2592] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[2593] 2.65 1.2e-3 0.06 2.53 2.61 2.65 2.69 2.76 2410 1.0\n",
"y_hat[2594] 2.91 1.1e-3 0.05 2.8 2.87 2.91 2.95 3.01 2497 1.0\n",
"y_hat[2595] 2.55 1.0e-3 0.05 2.46 2.52 2.54 2.58 2.63 1928 1.0\n",
"y_hat[2596] 2.76 1.7e-3 0.09 2.59 2.7 2.76 2.82 2.92 2589 1.0\n",
"y_hat[2597] 2.64 6.7e-4 0.04 2.57 2.61 2.64 2.66 2.71 2803 1.0\n",
"y_hat[2598] 2.64 6.7e-4 0.04 2.57 2.61 2.64 2.66 2.71 2803 1.0\n",
"y_hat[2599] 2.28 7.2e-4 0.04 2.2 2.26 2.28 2.31 2.37 3357 1.0\n",
"y_hat[2600] 3.1 1.2e-3 0.06 2.99 3.06 3.1 3.14 3.21 2420 1.0\n",
"y_hat[2601] 2.9 1.1e-3 0.04 2.81 2.87 2.9 2.92 2.98 1495 1.0\n",
"y_hat[2602] 2.7 1.8e-3 0.1 2.51 2.64 2.7 2.77 2.89 3015 1.0\n",
"y_hat[2603] 2.75 3.9e-3 0.19 2.37 2.63 2.75 2.88 3.12 2408 1.0\n",
"y_hat[2604] 3.1 1.2e-3 0.06 2.99 3.06 3.1 3.14 3.21 2420 1.0\n",
"y_hat[2605] 2.28 7.2e-4 0.04 2.2 2.26 2.28 2.31 2.37 3357 1.0\n",
"y_hat[2606] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
"y_hat[2607] 2.64 6.7e-4 0.04 2.57 2.61 2.64 2.66 2.71 2803 1.0\n",
"y_hat[2608] 2.9 1.1e-3 0.04 2.81 2.87 2.9 2.92 2.98 1495 1.0\n",
"y_hat[2609] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[2610] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[2611] 2.61 7.9e-4 0.05 2.51 2.58 2.61 2.64 2.7 3814 1.0\n",
"y_hat[2612] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[2613] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[2614] 2.62 1.2e-3 0.06 2.51 2.58 2.62 2.66 2.73 2299 1.0\n",
"y_hat[2615] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[2616] 2.64 6.7e-4 0.04 2.57 2.61 2.64 2.66 2.71 2803 1.0\n",
"y_hat[2617] 2.84 1.1e-3 0.06 2.73 2.8 2.84 2.88 2.96 2980 1.0\n",
"y_hat[2618] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[2619] 2.61 7.9e-4 0.05 2.51 2.58 2.61 2.64 2.7 3814 1.0\n",
"y_hat[2620] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[2621] 2.55 1.0e-3 0.05 2.46 2.52 2.54 2.58 2.63 1928 1.0\n",
"y_hat[2622] 2.5 1.6e-3 0.09 2.33 2.44 2.5 2.56 2.67 3007 1.0\n",
"y_hat[2623] 3.18 3.8e-3 0.16 2.87 3.07 3.18 3.29 3.53 1924 1.0\n",
"y_hat[2624] 2.7 1.8e-3 0.1 2.51 2.64 2.7 2.77 2.89 3015 1.0\n",
"y_hat[2625] 2.88 1.1e-3 0.05 2.77 2.84 2.88 2.92 2.99 2270 1.0\n",
"y_hat[2626] 2.61 7.9e-4 0.05 2.51 2.58 2.61 2.64 2.7 3814 1.0\n",
"y_hat[2627] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
"y_hat[2628] 2.28 7.2e-4 0.04 2.2 2.26 2.28 2.31 2.37 3357 1.0\n",
"y_hat[2629] 2.28 7.2e-4 0.04 2.2 2.26 2.28 2.31 2.37 3357 1.0\n",
"y_hat[2630] 2.76 1.7e-3 0.09 2.59 2.7 2.76 2.82 2.92 2589 1.0\n",
"y_hat[2631] 2.64 6.7e-4 0.04 2.57 2.61 2.64 2.66 2.71 2803 1.0\n",
"y_hat[2632] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[2633] 2.64 6.7e-4 0.04 2.57 2.61 2.64 2.66 2.71 2803 1.0\n",
"y_hat[2634] 2.55 1.0e-3 0.05 2.46 2.52 2.54 2.58 2.63 1928 1.0\n",
"y_hat[2635] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[2636] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[2637] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[2638] 2.5 1.6e-3 0.09 2.33 2.44 2.5 2.56 2.67 3007 1.0\n",
"y_hat[2639] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[2640] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[2641] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[2642] 2.64 6.7e-4 0.04 2.57 2.61 2.64 2.66 2.71 2803 1.0\n",
"y_hat[2643] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[2644] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
"y_hat[2645] 3.1 1.2e-3 0.06 2.99 3.06 3.1 3.14 3.21 2420 1.0\n",
"y_hat[2646] 2.61 7.9e-4 0.05 2.51 2.58 2.61 2.64 2.7 3814 1.0\n",
"y_hat[2647] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[2648] 2.64 6.7e-4 0.04 2.57 2.61 2.64 2.66 2.71 2803 1.0\n",
"y_hat[2649] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[2650] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[2651] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
"y_hat[2652] 2.28 7.2e-4 0.04 2.2 2.26 2.28 2.31 2.37 3357 1.0\n",
"y_hat[2653] 2.64 6.7e-4 0.04 2.57 2.61 2.64 2.66 2.71 2803 1.0\n",
"y_hat[2654] 2.55 1.0e-3 0.05 2.46 2.52 2.54 2.58 2.63 1928 1.0\n",
"y_hat[2655] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[2656] 2.34 1.6e-3 0.1 2.15 2.27 2.34 2.4 2.53 3594 1.0\n",
"y_hat[2657] 2.91 1.1e-3 0.05 2.8 2.87 2.91 2.95 3.01 2497 1.0\n",
"y_hat[2658] 2.7 1.8e-3 0.1 2.51 2.64 2.7 2.77 2.89 3015 1.0\n",
"y_hat[2659] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[2660] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[2661] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
"y_hat[2662] 2.9 1.1e-3 0.04 2.81 2.87 2.9 2.92 2.98 1495 1.0\n",
"y_hat[2663] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[2664] 2.91 1.1e-3 0.05 2.8 2.87 2.91 2.95 3.01 2497 1.0\n",
"y_hat[2665] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[2666] 2.28 7.2e-4 0.04 2.2 2.26 2.28 2.31 2.37 3357 1.0\n",
"y_hat[2667] 2.61 7.9e-4 0.05 2.51 2.58 2.61 2.64 2.7 3814 1.0\n",
"y_hat[2668] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[2669] 2.64 6.7e-4 0.04 2.57 2.61 2.64 2.66 2.71 2803 1.0\n",
"y_hat[2670] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[2671] 2.87 1.1e-3 0.05 2.76 2.83 2.87 2.9 2.97 2333 1.0\n",
"y_hat[2672] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[2673] 2.84 1.1e-3 0.06 2.73 2.8 2.84 2.88 2.96 2980 1.0\n",
"y_hat[2674] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[2675] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[2676] 2.55 1.0e-3 0.05 2.46 2.52 2.54 2.58 2.63 1928 1.0\n",
"y_hat[2677] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[2678] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[2679] 3.04 3.0e-3 0.15 2.75 2.93 3.04 3.14 3.35 2652 1.0\n",
"y_hat[2680] 2.84 1.1e-3 0.06 2.73 2.8 2.84 2.88 2.96 2980 1.0\n",
"y_hat[2681] 2.84 1.1e-3 0.06 2.73 2.8 2.84 2.88 2.96 2980 1.0\n",
"y_hat[2682] 2.64 6.7e-4 0.04 2.57 2.61 2.64 2.66 2.71 2803 1.0\n",
"y_hat[2683] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[2684] 2.28 7.2e-4 0.04 2.2 2.26 2.28 2.31 2.37 3357 1.0\n",
"y_hat[2685] 3.16 6.1e-4 0.03 3.1 3.14 3.16 3.18 3.22 2534 1.0\n",
"y_hat[2686] 2.9 1.1e-3 0.04 2.81 2.87 2.9 2.92 2.98 1495 1.0\n",
"y_hat[2687] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[2688] 2.9 6.1e-4 0.03 2.83 2.88 2.9 2.92 2.96 2777 1.0\n",
"y_hat[2689] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[2690] 2.87 1.1e-3 0.05 2.76 2.83 2.87 2.9 2.97 2333 1.0\n",
"y_hat[2691] 2.91 1.1e-3 0.05 2.8 2.87 2.91 2.95 3.01 2497 1.0\n",
"y_hat[2692] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[2693] 2.64 6.7e-4 0.04 2.57 2.61 2.64 2.66 2.71 2803 1.0\n",
"y_hat[2694] 2.88 1.1e-3 0.05 2.77 2.84 2.88 2.92 2.99 2270 1.0\n",
"y_hat[2695] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
"y_hat[2696] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"y_hat[2697] 2.49 6.9e-4 0.04 2.41 2.46 2.49 2.52 2.57 3741 1.0\n",
"y_hat[2698] 2.61 7.9e-4 0.05 2.51 2.58 2.61 2.64 2.7 3814 1.0\n",
"y_hat[2699] 3.42 6.9e-4 0.03 3.37 3.4 3.42 3.44 3.48 1806 1.0\n",
"y_hat[2700] 2.64 6.5e-4 0.04 2.56 2.61 2.64 2.66 2.71 3388 1.0\n",
"lp__ -223.9 0.13 3.57 -231.8 -226.0 -223.5 -221.4 -217.8 746 1.0\n",
"\n",
"Samples were drawn using NUTS at Tue Feb 25 05:46:49 2020.\n",
"For each parameter, n_eff is a crude measure of effective sample size,\n",
"and Rhat is the potential scale reduction factor on split chains (at \n",
"convergence, Rhat=1).\n"
]
}
],
"source": [
"print(varying_intercept_fit)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"a_sample = pd.DataFrame(varying_intercept_fit['a'])\n",
"plt.figure(figsize=(20, 5))\n",
"g = sns.boxplot(data=a_sample.iloc[:,0:20], whis=np.inf, color=\"g\")\n",
"g.set_title(\"Estimates of log(price), by category\")\n",
"g"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_0ea0c9da5768d74c1cae458dda8ef9b0 NOW.\n"
]
}
],
"source": [
"model2 = stan_utils.compile_model('model2.stan')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.7.6"
},
"varInspector": {
"cols": {
"lenName": 16,
"lenType": 16,
"lenVar": 40
},
"kernels_config": {
"python": {
"delete_cmd_postfix": "",
"delete_cmd_prefix": "del ",
"library": "var_list.py",
"varRefreshCmd": "print(var_dic_list())"
},
"r": {
"delete_cmd_postfix": ") ",
"delete_cmd_prefix": "rm(",
"library": "var_list.r",
"varRefreshCmd": "cat(var_dic_list()) "
}
},
"types_to_exclude": [
"module",
"function",
"builtin_function_or_method",
"instance",
"_Feature"
],
"window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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