{ "cells": [ { "cell_type": "markdown", "id": "mediterranean-harbor", "metadata": {}, "source": [ "# Markov-Chain Monte Carlo in EasyVVUQ" ] }, { "cell_type": "markdown", "id": "efficient-nudist", "metadata": {}, "source": [ "**Author**: Vytautas Jancauskas, LRZ (jancauskas@lrz.de)" ] }, { "cell_type": "markdown", "id": "outside-circle", "metadata": {}, "source": [ "We have a model that returns an array of 50 integers where each is supposed to have come from a Poisson distribution with an unknown mean. The model has four input parameters ```a, b, c, d``` and the values of the means depend on the values of those parameters (albeit in some unknown to us way). \n", " This situation seems common in practice in cases where the author of a simulation wants to show that their code in some way is validated by real world data. If they have observed data and they have a simulation for the phenomenon that produced that data and they have measurements of the inputs for the simulation they could use the procedure here to see if the real world \"input\" parameters fall within a reasonable range of the simulation input parameters. For more information on Markov-Chain Monte Carlo you can start at the Wikipedia [page](https://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo)." ] }, { "cell_type": "code", "execution_count": 1, "id": "extreme-scout", "metadata": {}, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "id": "piano-lodge", "metadata": {}, "outputs": [], "source": [ "def model(params):\n", " import numpy as np\n", " a = int(params['a'])\n", " b = int(params['b'])\n", " c = int(params['c'])\n", " d = int(params['d'])\n", " x = np.linspace(0, 1, 50)\n", " return {'Values' : list(np.random.poisson(\n", " a * (0.5 * np.sin(2.0 * np.pi * x) + 1.0) +\\\n", " b * (0.5 * np.sin(4.0 * np.pi * x) + 1.0) +\\\n", " c * (0.5 * np.sin(6.0 * np.pi * x) + 1.0) +\\\n", " d * (0.5 * np.sin(8.0 * np.pi * x) + 1.0)))}" ] }, { "cell_type": "markdown", "id": "expired-accused", "metadata": {}, "source": [ "If we plot the values returned by the model with ```a = b = c = d = 50``` and repeat this 20 times we might get something like the figure below." ] }, { "cell_type": "code", "execution_count": 3, "id": "periodic-vulnerability", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "for _ in range(20):\n", " plt.plot(model({'a': 50, 'b': 80, 'c': 50, 'd': 80})['Values'], '.')" ] }, { "cell_type": "markdown", "id": "unable-department", "metadata": {}, "source": [ "Then we have an array of 50 values that came from the model. With ```a = 50, b = 80, c = 50 and d = 80```. We will treat them as our observed data. We want to know what input parameters of the model are most likely to result in the values in the ```observed``` array. Because suppose we forgot." ] }, { "cell_type": "code", "execution_count": 4, "id": "level-produce", "metadata": {}, "outputs": [], "source": [ "observed = np.array([248, 311, 344, 370, 375, 369, 302, 306, 285, 281, 258, 248, 254,\n", " 273, 292, 268, 312, 269, 230, 244, 227, 219, 247, 233, 225, 238,\n", " 271, 307, 294, 299, 248, 249, 271, 255, 231, 225, 243, 279, 271,\n", " 294, 237, 247, 197, 180, 161, 168, 178, 207, 204, 258])" ] }, { "cell_type": "markdown", "id": "shaped-report", "metadata": {}, "source": [ "We are interested in $\\mathbb{E}(X | y)$ with $X = [A, B, C, D]^T$ where $A, B, C, D$ are the random variables representing the input parameters. For this we need to know the probability density $f_{X|y}$ where $y$ are our observed values. From Bayes theorem it follows that $f_{X|y}$ is proportional to $f_{Y|X}(y|X)f(X)$. We usually can infer $f(X)$ (the prior probability) in advance, in this case we will leave them flat. We will use Markov-Chain Monte Carlo (MCMC) to approximate the probability density $f_{X|y}(x)$. We assume that $y$ (our observed data) is 50 numbers from 50 Poisson distributions with unknown means. We will estimate those means by running the model multiple times and calculating the sample mean because that is the MLE estimate for a Poisson distributed random variable. Suppose we have $\\hat{\\lambda}_i$ that is the estimate. Then we have $f_{X|y}(x) = \\prod_{i = 1}^{50} \\frac{\\hat\\lambda_i^{y_i} \\exp(-\\hat\\lambda_i)}{{y_i}!}$ where $\\lambda_i$ is the estimated Poisson parameter for th $i$-th output variable and $y_i$ is the $i$-th value in the observed vector. Since it is often more convenient to work with log-likelihoods we arrive at $\\sum_{i = 1}^{50} \\left(y_i \\log(\\hat\\lambda_i) - \\hat\\lambda_i - \\log({y_i}!)\\right)$." ] }, { "cell_type": "markdown", "id": "mechanical-fourth", "metadata": {}, "source": [ "## Sampling" ] }, { "cell_type": "markdown", "id": "tough-finish", "metadata": {}, "source": [ "First, mandatory imports." ] }, { "cell_type": "code", "execution_count": 5, "id": "dominant-newman", "metadata": {}, "outputs": [], "source": [ "import easyvvuq as uq\n", "from easyvvuq.actions import ExecutePython, Actions\n", "import scipy.special\n", "import chaospy as cp\n", "import numpy as np\n", "import pandas as pd\n", "import os\n", "import json" ] }, { "cell_type": "markdown", "id": "superior-vinyl", "metadata": {}, "source": [ "We define parameter specification, encoder, decoder and the campaign object. See the more detailed tutorials on these EasyVVUQ elements [here](basic_tutorial.ipynb) and [here](vector_qoi_tutorial.ipynb)." ] }, { "cell_type": "code", "execution_count": 6, "id": "varied-advocacy", "metadata": {}, "outputs": [], "source": [ "params = {\n", " \"a\": {\"type\": \"float\", \"min\": 0, \"max\": 100, \"default\": 50},\n", " \"b\": {\"type\": \"float\", \"min\": 0, \"max\": 100, \"default\": 50},\n", " \"c\": {\"type\": \"float\", \"min\": 0, \"max\": 100, \"default\": 50},\n", " \"d\": {\"type\": \"float\", \"min\": 0, \"max\": 100, \"default\": 50},\n", " \"outfile\": {\"type\": \"string\", \"default\": \"output.csv\"},\n", " \"ensemble_id\": {\"type\": \"integer\", \"default\": 0},\n", " \"chain_id\": {\"type\": \"integer\", \"default\": 0}\n", "}\n", "actions = Actions(ExecutePython(model))\n", "campaign = uq.Campaign(name=\"mcmc\", actions=actions, params=params, work_dir='.')" ] }, { "cell_type": "markdown", "id": "collected-fundamental", "metadata": {}, "source": [ "We need initial values for our MCMC chains. Here we simply set it to a uniformly distributed value from the valid range for that parameter. Note that each input parameter gets an array of 5 numbers because we will use 5 chains later on." ] }, { "cell_type": "code", "execution_count": 7, "id": "integral-demographic", "metadata": {}, "outputs": [], "source": [ "vary_init = {\n", " \"a\": np.random.uniform(0, 100, size=5),\n", " \"b\": np.random.uniform(0, 100, size=5),\n", " \"c\": np.random.uniform(0, 100, size=5),\n", " \"d\": np.random.uniform(0, 100, size=5)\n", "}" ] }, { "cell_type": "markdown", "id": "seven-superior", "metadata": {}, "source": [ "Then we need to define the proposal distribution. It can be arbitrary (for example assymetrical). This is conditional on current chain position in the search space. In this case we will use a normal distribution with $mu = a, b, c, d$ and $\\sigma = 5$." ] }, { "cell_type": "code", "execution_count": 8, "id": "chemical-student", "metadata": {}, "outputs": [], "source": [ "def proposal(x, b=2.5):\n", " return cp.J(cp.Normal(x['a'], b), \n", " cp.Normal(x['b'], b), \n", " cp.Normal(x['c'], b), \n", " cp.Normal(x['d'], b))" ] }, { "cell_type": "markdown", "id": "apparent-mounting", "metadata": {}, "source": [ "Next we want to construct the likelihood function. This function describes the probability that the observed data came from our model (assuming some fixed input parameters). In this case we will need the estimate of the Poisson mean for each of the 50 outputs of the simulation. These are the parameters $\\lambda$. Once we have those it is trivial to see what is the probability that each output came from a corresponding distribution. We then just multiply them together and take the logarithm (because of numerical issues this is preferable to straight up multiplying). This formula was derived above." ] }, { "cell_type": "code", "execution_count": 9, "id": "binding-salon", "metadata": {}, "outputs": [], "source": [ "def get_likelihood(observed):\n", " def likelihood(lmbda):\n", " return (observed * np.log(lmbda) - lmbda - \n", " scipy.special.gammaln(observed + 1.0)).sum()\n", " return likelihood" ] }, { "cell_type": "markdown", "id": "broad-increase", "metadata": {}, "source": [ "Finally we put everything together. Note the estimator parameter to the MCMCSampler class. It is used to produce the estimate for the mean of the Poisson distribution. In this case we use sample mean because that is the MLE for Poisson $\\lambda$ parameter." ] }, { "cell_type": "code", "execution_count": 10, "id": "perceived-cosmetic", "metadata": {}, "outputs": [], "source": [ "mcmc_sampler = uq.sampling.MCMCSampler(\n", " vary_init, proposal, 'Values', n_chains=5,\n", " likelihood=get_likelihood(observed), \n", " estimator=lambda x: np.mean(x))\n", "sampler = uq.sampling.ReplicaSampler(mcmc_sampler, replicas=20)\n", "campaign.set_sampler(sampler)" ] }, { "cell_type": "markdown", "id": "tender-ensemble", "metadata": {}, "source": [ "When we have workflows where each sampling stage relies on the results of the previous one (as is the case with MCMC) we need to call iterate on the campaign object and then use the Python keyword next to advance to the next sampling stage. This will return action statuses and we need to wait for them to finish in order to proceed." ] }, { "cell_type": "code", "execution_count": 17, "id": "finite-reward", "metadata": {}, "outputs": [], "source": [ "iterator = campaign.iterate(mark_invalid=True)" ] }, { "cell_type": "code", "execution_count": 18, "id": "identified-above", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/di73kuj2/Programming/EasyVVUQ/easyvvuq/sampling/mcmc.py:123: RuntimeWarning: invalid value encountered in double_scalars\n", " r = min(1.0, (f_y / self.f_x[chain_id]) * (q_xy / q_yx))\n", "/Users/di73kuj2/Programming/EasyVVUQ/easyvvuq/sampling/mcmc.py:123: RuntimeWarning: invalid value encountered in double_scalars\n", " r = min(1.0, (f_y / self.f_x[chain_id]) * (q_xy / q_yx))\n", "/Users/di73kuj2/Programming/EasyVVUQ/easyvvuq/sampling/mcmc.py:123: RuntimeWarning: invalid value encountered in double_scalars\n", " r = min(1.0, (f_y / self.f_x[chain_id]) * (q_xy / q_yx))\n", "/Users/di73kuj2/Programming/EasyVVUQ/easyvvuq/sampling/mcmc.py:123: RuntimeWarning: invalid value encountered in double_scalars\n", " r = min(1.0, (f_y / self.f_x[chain_id]) * (q_xy / q_yx))\n", "/Users/di73kuj2/Programming/EasyVVUQ/easyvvuq/sampling/mcmc.py:123: RuntimeWarning: invalid value encountered in double_scalars\n", " r = min(1.0, (f_y / self.f_x[chain_id]) * (q_xy / q_yx))\n", "/Users/di73kuj2/Programming/EasyVVUQ/easyvvuq/sampling/mcmc.py:123: RuntimeWarning: invalid value encountered in double_scalars\n", " r = min(1.0, (f_y / self.f_x[chain_id]) * (q_xy / q_yx))\n", "/Users/di73kuj2/Programming/EasyVVUQ/easyvvuq/sampling/mcmc.py:123: RuntimeWarning: invalid value encountered in double_scalars\n", " r = min(1.0, (f_y / self.f_x[chain_id]) * (q_xy / q_yx))\n", "/Users/di73kuj2/Programming/EasyVVUQ/easyvvuq/sampling/mcmc.py:123: RuntimeWarning: invalid value encountered in double_scalars\n", " r = min(1.0, (f_y / self.f_x[chain_id]) * (q_xy / q_yx))\n", "/Users/di73kuj2/Programming/EasyVVUQ/easyvvuq/sampling/mcmc.py:123: RuntimeWarning: invalid value encountered in double_scalars\n", " r = min(1.0, (f_y / self.f_x[chain_id]) * (q_xy / q_yx))\n", "/Users/di73kuj2/Programming/EasyVVUQ/easyvvuq/sampling/mcmc.py:123: RuntimeWarning: invalid value encountered in double_scalars\n", " r = min(1.0, (f_y / self.f_x[chain_id]) * (q_xy / q_yx))\n", "/Users/di73kuj2/Programming/EasyVVUQ/easyvvuq/sampling/mcmc.py:123: RuntimeWarning: invalid value encountered in double_scalars\n", " r = min(1.0, (f_y / self.f_x[chain_id]) * (q_xy / q_yx))\n", "/Users/di73kuj2/Programming/EasyVVUQ/easyvvuq/sampling/mcmc.py:123: RuntimeWarning: invalid value encountered in double_scalars\n", " r = min(1.0, (f_y / self.f_x[chain_id]) * (q_xy / q_yx))\n", "/Users/di73kuj2/Programming/EasyVVUQ/easyvvuq/sampling/mcmc.py:123: RuntimeWarning: invalid value encountered in double_scalars\n", " r = min(1.0, (f_y / self.f_x[chain_id]) * (q_xy / q_yx))\n", "/Users/di73kuj2/Programming/EasyVVUQ/easyvvuq/sampling/mcmc.py:123: RuntimeWarning: invalid value encountered in double_scalars\n", " r = min(1.0, (f_y / self.f_x[chain_id]) * (q_xy / q_yx))\n", "/Users/di73kuj2/Programming/EasyVVUQ/easyvvuq/sampling/mcmc.py:123: RuntimeWarning: invalid value encountered in double_scalars\n", " r = min(1.0, (f_y / self.f_x[chain_id]) * (q_xy / q_yx))\n", "/Users/di73kuj2/Programming/EasyVVUQ/easyvvuq/sampling/mcmc.py:123: RuntimeWarning: invalid value encountered in double_scalars\n", " r = min(1.0, (f_y / self.f_x[chain_id]) * (q_xy / q_yx))\n", "/Users/di73kuj2/Programming/EasyVVUQ/easyvvuq/sampling/mcmc.py:123: RuntimeWarning: invalid value encountered in double_scalars\n", " r = min(1.0, (f_y / self.f_x[chain_id]) * (q_xy / q_yx))\n", "/Users/di73kuj2/Programming/EasyVVUQ/easyvvuq/sampling/mcmc.py:123: RuntimeWarning: invalid value encountered in double_scalars\n", " r = min(1.0, (f_y / self.f_x[chain_id]) * (q_xy / q_yx))\n", "/Users/di73kuj2/Programming/EasyVVUQ/easyvvuq/sampling/mcmc.py:123: RuntimeWarning: invalid value encountered in double_scalars\n", " r = min(1.0, (f_y / self.f_x[chain_id]) * (q_xy / q_yx))\n", "/Users/di73kuj2/Programming/EasyVVUQ/easyvvuq/sampling/mcmc.py:123: RuntimeWarning: invalid value encountered in double_scalars\n", " r = min(1.0, (f_y / self.f_x[chain_id]) * (q_xy / q_yx))\n", "/Users/di73kuj2/Programming/EasyVVUQ/easyvvuq/sampling/mcmc.py:123: RuntimeWarning: invalid value encountered in double_scalars\n", " r = min(1.0, (f_y / self.f_x[chain_id]) * (q_xy / q_yx))\n", "/Users/di73kuj2/Programming/EasyVVUQ/easyvvuq/sampling/mcmc.py:123: RuntimeWarning: invalid value encountered in double_scalars\n", " r = min(1.0, (f_y / self.f_x[chain_id]) * (q_xy / q_yx))\n", "/Users/di73kuj2/Programming/EasyVVUQ/easyvvuq/sampling/mcmc.py:123: RuntimeWarning: invalid value encountered in double_scalars\n", " r = min(1.0, (f_y / self.f_x[chain_id]) * (q_xy / q_yx))\n", "/Users/di73kuj2/Programming/EasyVVUQ/easyvvuq/sampling/mcmc.py:123: RuntimeWarning: invalid value encountered in double_scalars\n", " r = min(1.0, (f_y / self.f_x[chain_id]) * (q_xy / q_yx))\n", "/Users/di73kuj2/Programming/EasyVVUQ/easyvvuq/sampling/mcmc.py:123: RuntimeWarning: invalid value encountered in double_scalars\n", " r = min(1.0, (f_y / self.f_x[chain_id]) * (q_xy / q_yx))\n", "/Users/di73kuj2/Programming/EasyVVUQ/easyvvuq/sampling/mcmc.py:123: RuntimeWarning: invalid value encountered in double_scalars\n", " r = min(1.0, (f_y / self.f_x[chain_id]) * (q_xy / q_yx))\n", "/Users/di73kuj2/Programming/EasyVVUQ/easyvvuq/sampling/mcmc.py:123: RuntimeWarning: invalid value encountered in double_scalars\n", " r = min(1.0, (f_y / self.f_x[chain_id]) * (q_xy / q_yx))\n", "/Users/di73kuj2/Programming/EasyVVUQ/easyvvuq/sampling/mcmc.py:123: RuntimeWarning: invalid value encountered in double_scalars\n", " r = min(1.0, (f_y / self.f_x[chain_id]) * (q_xy / q_yx))\n", "/Users/di73kuj2/Programming/EasyVVUQ/easyvvuq/sampling/mcmc.py:123: RuntimeWarning: invalid value encountered in double_scalars\n", " r = min(1.0, (f_y / self.f_x[chain_id]) * (q_xy / q_yx))\n", "/Users/di73kuj2/Programming/EasyVVUQ/easyvvuq/sampling/mcmc.py:123: RuntimeWarning: invalid value encountered in double_scalars\n", " r = min(1.0, (f_y / self.f_x[chain_id]) * (q_xy / q_yx))\n", "/Users/di73kuj2/Programming/EasyVVUQ/easyvvuq/sampling/mcmc.py:123: RuntimeWarning: invalid value encountered in double_scalars\n", " r = min(1.0, (f_y / self.f_x[chain_id]) * (q_xy / q_yx))\n", "/Users/di73kuj2/Programming/EasyVVUQ/easyvvuq/sampling/mcmc.py:123: RuntimeWarning: invalid value encountered in double_scalars\n", " r = min(1.0, (f_y / self.f_x[chain_id]) * (q_xy / q_yx))\n", "/Users/di73kuj2/Programming/EasyVVUQ/easyvvuq/sampling/mcmc.py:123: RuntimeWarning: invalid value encountered in double_scalars\n", " r = min(1.0, (f_y / self.f_x[chain_id]) * (q_xy / q_yx))\n", "/Users/di73kuj2/Programming/EasyVVUQ/easyvvuq/sampling/mcmc.py:123: RuntimeWarning: invalid value encountered in double_scalars\n", " r = min(1.0, (f_y / self.f_x[chain_id]) * (q_xy / q_yx))\n", "/Users/di73kuj2/Programming/EasyVVUQ/easyvvuq/sampling/mcmc.py:123: RuntimeWarning: invalid value encountered in double_scalars\n", " r = min(1.0, (f_y / self.f_x[chain_id]) * (q_xy / q_yx))\n", "/Users/di73kuj2/Programming/EasyVVUQ/easyvvuq/sampling/mcmc.py:123: RuntimeWarning: invalid value encountered in double_scalars\n", " r = min(1.0, (f_y / self.f_x[chain_id]) * (q_xy / q_yx))\n", "/Users/di73kuj2/Programming/EasyVVUQ/easyvvuq/sampling/mcmc.py:123: RuntimeWarning: invalid value encountered in double_scalars\n", " r = min(1.0, (f_y / self.f_x[chain_id]) * (q_xy / q_yx))\n", "/Users/di73kuj2/Programming/EasyVVUQ/easyvvuq/sampling/mcmc.py:123: RuntimeWarning: invalid value encountered in double_scalars\n", " r = min(1.0, (f_y / self.f_x[chain_id]) * (q_xy / q_yx))\n", "/Users/di73kuj2/Programming/EasyVVUQ/easyvvuq/sampling/mcmc.py:123: RuntimeWarning: invalid value encountered in double_scalars\n", " r = min(1.0, (f_y / self.f_x[chain_id]) * (q_xy / q_yx))\n", "/Users/di73kuj2/Programming/EasyVVUQ/easyvvuq/sampling/mcmc.py:123: RuntimeWarning: invalid value encountered in double_scalars\n", " r = min(1.0, (f_y / self.f_x[chain_id]) * (q_xy / q_yx))\n", "/Users/di73kuj2/Programming/EasyVVUQ/easyvvuq/sampling/mcmc.py:123: RuntimeWarning: invalid value encountered in double_scalars\n", " r = min(1.0, (f_y / self.f_x[chain_id]) * (q_xy / q_yx))\n" ] } ], "source": [ "for _ in range(10000):\n", " next(iterator).collate()" ] }, { "cell_type": "markdown", "id": "lightweight-biography", "metadata": {}, "source": [ "## Analysis" ] }, { "cell_type": "markdown", "id": "dominican-processor", "metadata": {}, "source": [ "Let us now save our progress so that we do not lose it. This way we can continue the tutorial just from this point onward and not redo the sampling stage." ] }, { "cell_type": "code", "execution_count": 19, "id": "democratic-offer", "metadata": {}, "outputs": [], "source": [ "campaign.save_state('mcmc_state.json')" ] }, { "cell_type": "code", "execution_count": 20, "id": "hearing-address", "metadata": {}, "outputs": [], "source": [ "campaign = uq.Campaign(state_file='mcmc_state.json')" ] }, { "cell_type": "markdown", "id": "interior-automation", "metadata": {}, "source": [ "After calling analyse we will get an instance of MCMCAnalysisResults that contains information about our chains. It also lets us plot and analyse that data." ] }, { "cell_type": "code", "execution_count": 21, "id": "noticed-freight", "metadata": {}, "outputs": [], "source": [ "result = campaign.analyse()" ] }, { "cell_type": "markdown", "id": "alike-alliance", "metadata": {}, "source": [ "We will now plot the chains for each parameter. We can see that they indeed seem to converge to the input parameters used to generate observed data. Which was $a = 50, b = 80, c = 50, d = 80$." ] }, { "cell_type": "code", "execution_count": 22, "id": "raising-chance", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "result.plot_chains('d')" ] }, { "cell_type": "markdown", "id": "liquid-teacher", "metadata": {}, "source": [ "We can also plot histograms of the parameters. This can let us determine the shape of the distribution of that parameter and determine confidence intervals, etc." ] }, { "cell_type": "code", "execution_count": 26, "id": "molecular-picnic", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "distributed.client - ERROR - Failed to reconnect to scheduler after 10.00 seconds, closing client\n" ] } ], "source": [ "result.plot_hist('d', skip=1000)" ] } ], "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": "3.8.6" } }, "nbformat": 4, "nbformat_minor": 5 }