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invegat/bayesian-inference-uci-drugs.ipynb

Created February 14, 2020 16:57
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Bayesian Inference UCI Drugs.ipynb
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bayesian-inference-uci-drugs.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Bayesian Inference UCI Drugs.ipynb",
"provenance": [],
"toc_visible": true,
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/invegat/e97280d1fc928c2968f098c2c86ae077/bayesian-inference-uci-drugs.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "PKoynOgMeqwu",
"colab_type": "code",
"outputId": "4c73d905-f2e0-48bd-f42f-c0ec7947605d",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 200
}
},
"source": [
"# http://archive.ics.uci.edu/ml/datasets/Drug+Review+Dataset+%28Drugs.com%29#\n",
"!wget http://archive.ics.uci.edu/ml/machine-learning-databases/00462/drugsCom_raw.zip"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"--2020-02-14 16:23:47-- http://archive.ics.uci.edu/ml/machine-learning-databases/00462/drugsCom_raw.zip\n",
"Resolving archive.ics.uci.edu (archive.ics.uci.edu)... 128.195.10.252\n",
"Connecting to archive.ics.uci.edu (archive.ics.uci.edu)|128.195.10.252|:80... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 42989872 (41M) [application/x-httpd-php]\n",
"Saving to: ‘drugsCom_raw.zip’\n",
"\n",
"drugsCom_raw.zip 100%[===================>] 41.00M 26.3MB/s in 1.6s \n",
"\n",
"2020-02-14 16:23:48 (26.3 MB/s) - ‘drugsCom_raw.zip’ saved [42989872/42989872]\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "lVvazbzNkV96",
"colab_type": "code",
"outputId": "b9368177-058c-4656-f998-29f5759d7712",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 66
}
},
"source": [
"!unzip drugsCom_raw.zip"
],
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"text": [
"Archive: drugsCom_raw.zip\n",
" inflating: drugsComTest_raw.tsv \n",
" inflating: drugsComTrain_raw.tsv \n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "66ZTKBvnkbXp",
"colab_type": "code",
"outputId": "ba7976b7-81ed-47fb-b2f7-7554686f1dd2",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 197
}
},
"source": [
"import pandas as pd\n",
"from datetime import datetime\n",
"sample_size = 10000\n",
"r = 123\n",
"df = pd.read_csv('drugsComTrain_raw.tsv', sep='\\t')\n",
"#df = df[df.drugName.notnull() and df.condition.notnull() and df.rating.notnull()]\n",
"df = df.dropna()\n",
"df = df[~df.condition.str.contains('<')]\n",
"df = df.sample(n=sample_size, random_state=r).drop(['Unnamed: 0','review'], axis=1)\n",
"df.date = df.date.apply(lambda d: datetime.strptime(d,'%B %d, %Y'))\n",
"df.tail()"
],
"execution_count": 3,
"outputs": [
{
"output_type": "execute_result",
"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>drugName</th>\n",
" <th>condition</th>\n",
" <th>rating</th>\n",
" <th>date</th>\n",
" <th>usefulCount</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>135870</th>\n",
" <td>Etonogestrel</td>\n",
" <td>Birth Control</td>\n",
" <td>3.0</td>\n",
" <td>2012-05-15</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13001</th>\n",
" <td>Bisacodyl</td>\n",
" <td>Constipation</td>\n",
" <td>8.0</td>\n",
" <td>2016-07-13</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>91406</th>\n",
" <td>Budeprion SR</td>\n",
" <td>Depression</td>\n",
" <td>9.0</td>\n",
" <td>2010-11-18</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>120803</th>\n",
" <td>Sronyx</td>\n",
" <td>Birth Control</td>\n",
" <td>2.0</td>\n",
" <td>2016-04-30</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>127020</th>\n",
" <td>Atenolol</td>\n",
" <td>Ventricular Tachycardia</td>\n",
" <td>4.0</td>\n",
" <td>2011-07-29</td>\n",
" <td>19</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" drugName condition rating date usefulCount\n",
"135870 Etonogestrel Birth Control 3.0 2012-05-15 0\n",
"13001 Bisacodyl Constipation 8.0 2016-07-13 3\n",
"91406 Budeprion SR Depression 9.0 2010-11-18 30\n",
"120803 Sronyx Birth Control 2.0 2016-04-30 7\n",
"127020 Atenolol Ventricular Tachycardia 4.0 2011-07-29 19"
]
},
"metadata": {
"tags": []
},
"execution_count": 3
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Vuxe8uyukjSe",
"colab_type": "code",
"colab": {}
},
"source": [
"# Confidence intervals!\n",
"# Similar to hypothesis testing, but centered at sample mean\n",
"# Better than reporting the \"point estimate\" (sample mean)\n",
"# Why? Because point estimates aren't always perfect\n",
"\n",
"import numpy as np\n",
"from scipy import stats\n",
"\n",
"def confidence_interval(data, confidence=0.95):\n",
" \"\"\"\n",
" Calculate a confidence ifor nterval around a sample mean for given data.\n",
" Using t-distribution and two-tailed test, default 95% confidence. \n",
" \n",
" Arguments:\n",
" data - iterable (list or numpy array) of sample observations\n",
" confidence - level of confidence for the interval\n",
" \n",
" Returns:\n",
" tuple of (mean, lower bound, upper bound)\n",
" \"\"\"\n",
" mean, variance, std = stats.bayes_mvs(np.array(data), alpha=0.95)\n",
"\n",
" mean = np.mean(data)\n",
" n = len(data)\n",
" stderr = stats.sem(data)\n",
" interval = stderr * stats.t.ppf((1 + confidence) / 2., n - 1)\n",
" return (mean, mean - interval, mean + interval)\n",
"\n",
"def report_confidence_interval(confidence_interval):\n",
" \"\"\"\n",
" Return a string with a pretty report of a confidence interval.\n",
" \n",
" Arguments:\n",
" confidence_interval - tuple of (mean, lower bound, upper bound)\n",
" \n",
" Returns:\n",
" None, but prints to screen the report\n",
" \"\"\"\n",
" #print('Mean: {}'.format(confidence_interval[0]))\n",
" #print('Lower bound: {}'.format(confidence_interval[1]))\n",
" #print('Upper bound: {}'.format(confidence_interval[2]))\n",
" s = \"our mean lies in the interval ]{:.2}, {:.2}[\".format(\n",
" confidence_interval[1], confidence_interval[2])\n",
" s = f\"our mean {confidence_interval[0]:.2f} lies in the interval {confidence_interval[1]:.2f} - {confidence_interval[2]:.2f}\"\n",
" return s"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "c3fPwKFovRNA",
"colab_type": "code",
"outputId": "cb71b167-f4b1-477e-f885-84a9604969fc",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 150
}
},
"source": [
"!pip install -U matplotlib"
],
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"text": [
"Requirement already up-to-date: matplotlib in /usr/local/lib/python3.6/dist-packages (3.1.3)\n",
"Requirement already satisfied, skipping upgrade: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (0.10.0)\n",
"Requirement already satisfied, skipping upgrade: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (2.6.1)\n",
"Requirement already satisfied, skipping upgrade: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (1.1.0)\n",
"Requirement already satisfied, skipping upgrade: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (2.4.6)\n",
"Requirement already satisfied, skipping upgrade: numpy>=1.11 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (1.17.5)\n",
"Requirement already satisfied, skipping upgrade: six in /usr/local/lib/python3.6/dist-packages (from cycler>=0.10->matplotlib) (1.12.0)\n",
"Requirement already satisfied, skipping upgrade: setuptools in /usr/local/lib/python3.6/dist-packages (from kiwisolver>=1.0.1->matplotlib) (45.1.0)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "kmG18ek1uijl",
"colab_type": "code",
"outputId": "2d8ac9ed-19c8-47f9-8e11-5c7800b4d4ce",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 33
}
},
"source": [
"import matplotlib\n",
"matplotlib.__version__"
],
"execution_count": 6,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'3.1.3'"
]
},
"metadata": {
"tags": []
},
"execution_count": 6
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "-J_xCUKH9Yaj",
"colab_type": "code",
"colab": {}
},
"source": [
"\n",
"import matplotlib.pyplot as plt\n",
"plt.rcParams['figure.constrained_layout.use'] = False\n",
"half_mean_height = 0.1\n",
"\n",
"def printChart(c,data):\n",
"\n",
" df = pd.DataFrame.from_dict(data)\n",
" # print(df.head())\n",
" n = len(data)\n",
" plt.figure(figsize=(max(n/5,5),5))\n",
" data_order = [\n",
" 'min_stars','below_ci', 'ci_half_l', 'mean', 'ci_half_h', 'max_stars'\n",
" ]\n",
" colors = [None, \"b\",'g', 'black', \"g\", \"gold\"]\n",
"# values = np.array([[d[name] for name in data_order] for d in data])\n",
" order = np.array(data_order)\n",
" ind = np.arange(n) # the x locations for the groups\n",
" bottom = np.array(df['min_stars'])\n",
" width = 0.4\n",
" name_dict = {'below_ci': 'Low Stars','ci_half_l': 'Confidence Interval','mean': 'Confidence Interval Mean', 'ci_half_h': None, 'max_stars': 'High Stars'}\n",
" for name,color in zip(data_order[1:],colors[1:]):\n",
" value = np.array(df[name])\n",
" # p1 = plt.bar(ind, value, width, color=color, bottom = bottom, label=name_dict[name], alpha=(1 if name == 'mean_low' else 0.5))\n",
" p1 = plt.bar(ind, value, width, color=color, bottom = bottom, label=name_dict[name]) \n",
" bottom += value\n",
" # print(f'name: {name} color: {color} value: {value}')\n",
" #plt.bar(ind,np.array(df.mean_high),width, color='black', bottom=np.array(df.mean_low), label = 'Mean')\n",
"\n",
"# plt.yticks(bottoms+0.4, [\"data %d\" % (t+1) for t in bottoms])\n",
" plt.legend(loc=\"best\", bbox_to_anchor=(1.0, 1.00))\n",
" plt.ylabel('Stars')\n",
" plt.title(f\"Drugs for {c}\")\n",
" plt.xticks(ind, np.array(df['drug']), rotation=('vertical' if n > 3 else 'horizontal'))\n",
"# start, end = plt.yaxis\n",
"# plt.yaxis.set_major_locator(ticker.MultipleLocator((end - start) / 10))\n",
" axes = plt.gca()\n",
" axes.set_ylim([0,10.5])\n",
" plt.yticks(np.arange(0, 11, 1))\n",
"# plt.subplots_adjust(right=0.85)\n",
"\n",
" plt.show();\n",
" "
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "KvCzq5Zu8_rC",
"colab_type": "code",
"outputId": "5fb657f5-e9da-41b3-94f3-a273bfed15c7",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 826
}
},
"source": [
"\n",
"c_unique = df.condition.unique()\n",
"minLen = 20\n",
"for c in c_unique:\n",
" # print(c)\n",
" d_unique = df[df.condition == c].drugName.unique()\n",
" cis = []\n",
" for d in d_unique:\n",
" cdf = df[(df.condition == c) & (df.drugName == d) ]['rating'] \n",
" ci = None\n",
" lcdf = len(cdf)\n",
" if lcdf >= minLen:\n",
" ci = confidence_interval(cdf)\n",
" \n",
" min_stars = cdf.min()\n",
" below_ci = max(ci[1] - min_stars,0)\n",
" ci_l = (ci[2] - ci[1])\n",
" ci_half_l = ci[0] - ci[1] - half_mean_height\n",
" ci_half_h = ci_l - ci_half_l\n",
" mean = 2 * half_mean_height\n",
" max_stars = max(cdf.max() - ci[2], 0) \n",
" cis.append({'ci': ci, 'drug': d, 'reviews_count': lcdf, 'min_stars': min_stars, 'below_ci': below_ci, 'ci_half_l': ci_half_l, 'mean': mean, 'ci_half_h': ci_half_h, 'max_stars': max_stars }) \n",
" # cis.append({'ci': ci, 'drug': d, 'reviews_count': lcdf, 'min_stars': cdf.min(), 'max_stars': cdf.max(), 'mean_low': ci[0] - half_mean_height, 'mean_high': ci[0] + half_mean_height, 'low': ci[1], 'high': ci[2] })\n",
" if len(cis) > 0:\n",
" cis = sorted(cis, key=lambda v: v['ci'][0], reverse=True) # sorting by ci mean\n",
" for _ci in cis:\n",
" print(f\"for condition {c} drug {_ci['drug']} 95% confidence interval {report_confidence_interval(_ci['ci'])}\")\n",
" printChart(c,cis)\n",
" break # remove break for all conditions"
],
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"text": [
"for condition Birth Control drug Ethinyl estradiol / norelgestromin 95% confidence interval our mean 7.70 lies in the interval 6.56 - 8.85\n",
"for condition Birth Control drug Skyla 95% confidence interval our mean 7.16 lies in the interval 6.23 - 8.09\n",
"for condition Birth Control drug NuvaRing 95% confidence interval our mean 6.83 lies in the interval 5.91 - 7.75\n",
"for condition Birth Control drug Mirena 95% confidence interval our mean 6.79 lies in the interval 5.94 - 7.64\n",
"for condition Birth Control drug Levonorgestrel 95% confidence interval our mean 6.76 lies in the interval 6.29 - 7.24\n",
"for condition Birth Control drug Copper 95% confidence interval our mean 6.65 lies in the interval 5.60 - 7.70\n",
"for condition Birth Control drug Ethinyl estradiol / etonogestrel 95% confidence interval our mean 6.48 lies in the interval 5.79 - 7.17\n",
"for condition Birth Control drug Implanon 95% confidence interval our mean 6.43 lies in the interval 5.58 - 7.28\n",
"for condition Birth Control drug Lo Loestrin Fe 95% confidence interval our mean 6.35 lies in the interval 5.42 - 7.29\n",
"for condition Birth Control drug Ethinyl estradiol / levonorgestrel 95% confidence interval our mean 6.29 lies in the interval 5.61 - 6.96\n",
"for condition Birth Control drug Desogestrel / ethinyl estradiol 95% confidence interval our mean 6.14 lies in the interval 4.87 - 7.41\n",
"for condition Birth Control drug Drospirenone / ethinyl estradiol 95% confidence interval our mean 6.00 lies in the interval 4.94 - 7.06\n",
"for condition Birth Control drug Depo-Provera 95% confidence interval our mean 5.87 lies in the interval 4.70 - 7.03\n",
"for condition Birth Control drug Etonogestrel 95% confidence interval our mean 5.82 lies in the interval 5.36 - 6.28\n",
"for condition Birth Control drug Ethinyl estradiol / norgestimate 95% confidence interval our mean 5.74 lies in the interval 5.12 - 6.36\n",
"for condition Birth Control drug Loestrin 24 Fe 95% confidence interval our mean 5.52 lies in the interval 3.96 - 7.08\n",
"for condition Birth Control drug Nexplanon 95% confidence interval our mean 5.52 lies in the interval 4.94 - 6.10\n",
"for condition Birth Control drug Ethinyl estradiol / norethindrone 95% confidence interval our mean 5.49 lies in the interval 4.94 - 6.04\n",
"for condition Birth Control drug Sprintec 95% confidence interval our mean 5.46 lies in the interval 4.16 - 6.76\n",
"for condition Birth Control drug Medroxyprogesterone 95% confidence interval our mean 5.43 lies in the interval 4.18 - 6.67\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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d6CcCWGxmP6S4Hk5xqisgIiISm1TPAz8fwOsproOIiIjvpCyAk6wNoB+Af4V5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\nAjvMutHgdqLLyXXWBQHYvPanE5rHw4C9WTXP3Gs7vKerWb9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JpJ0l3QTcnB8vkfTZAUMu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\nuXuLYoeFAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "GmPKLt4fgEHr",
"colab_type": "code",
"outputId": "362515d9-30e2-476e-961c-09b434d766e0",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 436
}
},
"source": [
"!pip install arviz"
],
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"text": [
"Collecting arviz\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/ec/8b/83472d660e004a69b8e7b3c1dd12a607167774097138445d0dda1a3590dc/arviz-0.6.1-py3-none-any.whl (1.4MB)\n",
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}
]
},
{
"cell_type": "code",
"metadata": {
"id": "9SzTyKBz7b7_",
"colab_type": "code",
"colab": {}
},
"source": [
"import matplotlib.pyplot as plt\n",
"import scipy.stats as stats\n",
"import numpy as np\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"import pymc3 as pm\n",
"import arviz as az\n",
"from sklearn import preprocessing\n",
"import pickle\n",
"import datetime as dt\n",
"\n",
"def plots(pname):\n",
" with open(pname, 'rb') as buff:\n",
" data = pickle.load(buff) \n",
" rows = len(data)\n",
" dist = stats.norm()\n",
" trace = data['trace'] \n",
" L = trace['μ'].shape[1]\n",
" az.style.use('arviz-white')\n",
" fig = plt.figure(figsize=(6,0.5))\n",
"\n",
" c_ax = fig.add_subplot(111)\n",
" # Turn off axis lines and ticks of the condition subplot\n",
" c_ax.spines['top'].set_color('none')\n",
" c_ax.spines['bottom'].set_color('none')\n",
" c_ax.spines['left'].set_color('none')\n",
" c_ax.spines['right'].set_color('none')\n",
" c_ax.tick_params(labelcolor='w', top=False, bottom=False, left=False, right=False)\n",
" c_ax.text(0.8,0,f'Condition {c}')\n",
"\n",
" az.style.use('arviz-darkgrid')\n",
" _, ax = plt.subplots(rows, 2, figsize=(12, 8*rows/3),constrained_layout=True)\n",
"\n",
" comparisons = [(i, j) for i in range(L) for j in range(i+1, L)]\n",
" pos = [(k, l) for k in range(rows) for l in (0, 1)]\n",
"\n",
" for (i, j), (k, l) in zip(comparisons, pos):\n",
" drug_i = d_unique[i]\n",
" means_diff = trace['μ'][:, i] - trace['μ'][:, j]\n",
" d_cohen = (means_diff / np.sqrt((trace['σ'][:, i]**2 + trace['σ'][:, j]**2) / 2)).mean()\n",
" ps = dist.cdf(d_cohen/(2**0.5))\n",
" az.plot_posterior(means_diff, ref_val=0, ax=ax[k, l])\n",
" ax[k, l].set_title(f'{d_unique[i]}-{d_unique[j]}')\n",
" ax[k, l].plot(\n",
" 0, label=f\"Cohen's d = {d_cohen:.2f}\\nProb sup = {ps:.2f}\", alpha=0)\n",
" ax[k, l].legend() "
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "DDVu2UQR9fB_",
"colab_type": "code",
"outputId": "b4618ab4-65bc-4a5f-9a00-fefac0c68c69",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 833
}
},
"source": [
"\n",
"\n",
"# from arviz.data.io_pymc3 import from_pymc3\n",
"\n",
"az.style.use('arviz-darkgrid')\n",
"\n",
"c_unique = df.condition.unique()\n",
"condition_length = len(c_unique)\n",
"for c in c_unique:\n",
" # print(c)\n",
" d_unique = df[df.condition == c].drugName.unique()\n",
" star = df[df.condition == c].rating.values\n",
" idx = pd.Categorical(df[df.condition == c].drugName,\n",
" categories=d_unique).codes\n",
" groups = len(np.unique(idx))\n",
" print(groups)\n",
" with pm.Model() as comparing_groups:\n",
" μ = pm.Normal('μ', mu=0, sd=10, shape=groups)\n",
" σ = pm.HalfNormal('σ', sd=10, shape=groups)\n",
"\n",
" y = pm.Normal('y', mu=μ[idx], sd=σ[idx], observed=star)\n",
"\n",
" trace = pm.sample(5000) \n",
" pname = f'r{r}.{c}.pkl'\n",
" with open(pname, 'wb') as buff:\n",
" pickle.dump({'model': comparing_groups, 'trace': trace}, buff) \n",
" plots(pname) \n",
" break # comment out for all conditions"
],
"execution_count": 20,
"outputs": [
{
"output_type": "stream",
"text": [
"5\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"Auto-assigning NUTS sampler...\n",
"Initializing NUTS using jitter+adapt_diag...\n",
"Sequential sampling (2 chains in 1 job)\n",
"NUTS: [σ, μ]\n",
"100%|██████████| 5500/5500 [00:10<00:00, 530.32it/s]\n",
"100%|██████████| 5500/5500 [00:09<00:00, 590.41it/s]\n",
"There were 520 divergences after tuning. Increase `target_accept` or reparameterize.\n",
"There were 1464 divergences after tuning. Increase `target_accept` or reparameterize.\n",
"The acceptance probability does not match the target. It is 0.7086012553201292, but should be close to 0.8. Try to increase the number of tuning steps.\n",
"The number of effective samples is smaller than 10% for some parameters.\n"
],
"name": "stderr"
},
{
"output_type": "display_data",
"data": {
"image/png": 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AAQMUGBjYrOu4u7tr9OjRWrp0qfbu3avi4mLNnDnzho9bu3Xrpr179+rEiRPWW9zXiouL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\nzAIAAMC2/g8/G/bISrq2/wAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 600x50 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"image/png": 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QQgghhBBCCCGEeGdIY5cQQgghhBBCCCGEeGcUyOsCCCHeTikpCtHREBUNMbeg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\nERERERGpMf4PZDeOPbP8cVUAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 1200x533.333 with 4 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Gl2JsF4UoDz1",
"colab_type": "code",
"colab": {}
},
"source": [
"plots('r123.Borderline Personality Disorde.pkl')"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "VQ2fFqY1ihGr",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": 0,
"outputs": []
}
]
}
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