"Fossies" - the Fresh Open Source Software Archive  

Source code changes of the file "notebooks/uncertainty_intervals.ipynb" between
prophet-0.7.tar.gz and prophet-1.0.tar.gz

About: Prophet is a tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

uncertainty_intervals.ipynb  (prophet-0.7):uncertainty_intervals.ipynb  (prophet-1.0)
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"source": [ "source": [
"%load_ext rpy2.ipython\n", "%load_ext rpy2.ipython\n",
"%matplotlib inline\n", "%matplotlib inline\n",
"from fbprophet import Prophet\n", "from prophet import Prophet\n",
"import pandas as pd\n", "import pandas as pd\n",
"from matplotlib import pyplot as plt\n", "from matplotlib import pyplot as plt\n",
"import numpy as np\n", "import numpy as np\n",
"import logging\n", "import logging\n",
"logging.getLogger('fbprophet').setLevel(logging.ERROR)\n", "logging.getLogger('prophet').setLevel(logging.ERROR)\n",
"import warnings\n", "import warnings\n",
"warnings.filterwarnings(\"ignore\")\n", "warnings.filterwarnings(\"ignore\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"block_hidden": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n"
]
}
],
"source": [
"df = pd.read_csv('../examples/example_wp_log_peyton_manning.csv')\n", "df = pd.read_csv('../examples/example_wp_log_peyton_manning.csv')\n",
"df = df.loc[:180,] # Limit to first six months\n", "df = df.loc[:180,] # Limit to first six months\n",
"m = Prophet()\n", "m = Prophet()\n",
"m.fit(df)\n", "m.fit(df)\n",
"future = m.make_future_dataframe(periods=60)" "future = m.make_future_dataframe(periods=60)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": 2,
"metadata": { "metadata": {
"block_hidden": true "block_hidden": true
}, },
"outputs": [ "outputs": [
{ {
"data": { "name": "stderr",
"text/plain": [ "output_type": "stream",
"Initial log joint probability = -2.43365\n", "text": [
"Optimization terminated normally: \n", "R[write to console]: Loading required package: Rcpp\n",
" Convergence detected: absolute parameter change was below tolerance\n" "\n",
] "R[write to console]: Loading required package: rlang\n",
}, "\n",
"metadata": {}, "R[write to console]: Disabling yearly seasonality. Run prophet with yearl
"output_type": "display_data" y.seasonality=TRUE to override this.\n",
"\n",
"R[write to console]: Disabling daily seasonality. Run prophet with daily.
seasonality=TRUE to override this.\n",
"\n"
]
} }
], ],
"source": [ "source": [
"%%R\n", "%%R\n",
"library(prophet)\n", "library(prophet)\n",
"df <- read.csv('../examples/example_wp_log_peyton_manning.csv')\n", "df <- read.csv('../examples/example_wp_log_peyton_manning.csv')\n",
"df <- df[1:180,]\n", "df <- df[1:180,]\n",
"m <- prophet(df)\n", "m <- prophet(df)\n",
"future <- make_future_dataframe(m, periods=60)" "future <- make_future_dataframe(m, periods=60)"
] ]
skipping to change at line 81 skipping to change at line 101
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 3,
"metadata": { "metadata": {
"output_hidden": true "output_hidden": true
}, },
"outputs": [ "outputs": [
{ {
"data": { "name": "stderr",
"text/plain": [ "output_type": "stream",
"Initial log joint probability = -2.43365\n", "text": [
"Optimization terminated normally: \n", "R[write to console]: Disabling yearly seasonality. Run prophet with yearl
" Convergence detected: absolute parameter change was below tolerance\n" y.seasonality=TRUE to override this.\n",
] "\n",
}, "R[write to console]: Disabling daily seasonality. Run prophet with daily.
"metadata": {}, seasonality=TRUE to override this.\n",
"output_type": "display_data" "\n"
]
} }
], ],
"source": [ "source": [
"%%R\n", "%%R\n",
"m <- prophet(df, interval.width = 0.95)\n", "m <- prophet(df, interval.width = 0.95)\n",
"forecast <- predict(m, future)" "forecast <- predict(m, future)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": 3,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"forecast = Prophet(interval_width=0.95).fit(df).predict(future)" "forecast = Prophet(interval_width=0.95).fit(df).predict(future)"
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"Again, these intervals assume that the future will see the same frequency a nd magnitude of rate changes as the past. This assumption is probably not true, so you should not expect to get accurate coverage on these uncertainty intervals .\n", "Again, these intervals assume that the future will see the same frequency a nd magnitude of rate changes as the past. This assumption is probably not true, so you should not expect to get accurate coverage on these uncertainty intervals .\n",
"\n", "\n",
"### Uncertainty in seasonality\n", "### Uncertainty in seasonality\n",
"By default Prophet will only return uncertainty in the trend and observatio n noise. To get uncertainty in seasonality, you must do full Bayesian sampling. This is done using the parameter `mcmc.samples` (which defaults to 0). We do thi s here for the first six months of the Peyton Manning data from the Quickstart:" "By default Prophet will only return uncertainty in the trend and observatio n noise. To get uncertainty in seasonality, you must do full Bayesian sampling. This is done using the parameter `mcmc.samples` (which defaults to 0). We do thi s here for the first six months of the Peyton Manning data from the Quickstart:"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 4,
"metadata": { "metadata": {
"output_hidden": true "output_hidden": true
}, },
"outputs": [ "outputs": [
{ {
"data": { "name": "stderr",
"text/plain": [ "output_type": "stream",
"\n", "text": [
"SAMPLING FOR MODEL 'prophet' NOW (CHAIN 1).\n", "R[write to console]: Disabling yearly seasonality. Run prophet with yearl
"\n", y.seasonality=TRUE to override this.\n",
"Gradient evaluation took 5.3e-05 seconds\n", "\n",
"1000 transitions using 10 leapfrog steps per transition would take 0.53 "R[write to console]: Disabling daily seasonality. Run prophet with daily.
seconds.\n", seasonality=TRUE to override this.\n",
"Adjust your expectations accordingly!\n", "\n"
"\n", ]
"\n", },
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"Iteration: 180 / 300 [ 60%] (Sampling)\n", "Chain 1: Gradient evaluation took 8.6e-05 seconds\n",
"Iteration: 210 / 300 [ 70%] (Sampling)\n", "Chain 1: 1000 transitions using 10 leapfrog steps per transition would ta
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"\n", "Chain 1: \n",
" Elapsed Time: 1.61713 seconds (Warm-up)\n", "Chain 1: Iteration: 1 / 300 [ 0%] (Warmup)\n",
" 1.46049 seconds (Sampling)\n", "Chain 1: Iteration: 30 / 300 [ 10%] (Warmup)\n",
" 3.07762 seconds (Total)\n", "Chain 1: Iteration: 60 / 300 [ 20%] (Warmup)\n",
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"\n", "Chain 1: Iteration: 120 / 300 [ 40%] (Warmup)\n",
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"\n", "Chain 1: Iteration: 151 / 300 [ 50%] (Sampling)\n",
"Gradient evaluation took 4.9e-05 seconds\n", "Chain 1: Iteration: 180 / 300 [ 60%] (Sampling)\n",
"1000 transitions using 10 leapfrog steps per transition would take 0.49 "Chain 1: Iteration: 210 / 300 [ 70%] (Sampling)\n",
seconds.\n", "Chain 1: Iteration: 240 / 300 [ 80%] (Sampling)\n",
"Adjust your expectations accordingly!\n", "Chain 1: Iteration: 270 / 300 [ 90%] (Sampling)\n",
"\n", "Chain 1: Iteration: 300 / 300 [100%] (Sampling)\n",
"\n", "Chain 1: \n",
"Iteration: 1 / 300 [ 0%] (Warmup)\n", "Chain 1: Elapsed Time: 2.10541 seconds (Warm-up)\n",
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"Iteration: 210 / 300 [ 70%] (Sampling)\n", "Chain 2: 1000 transitions using 10 leapfrog steps per transition would ta
"Iteration: 240 / 300 [ 80%] (Sampling)\n", ke 0.74 seconds.\n",
"Iteration: 270 / 300 [ 90%] (Sampling)\n", "Chain 2: Adjust your expectations accordingly!\n",
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"\n", "Chain 2: \n",
" Elapsed Time: 1.56343 seconds (Warm-up)\n", "Chain 2: Iteration: 1 / 300 [ 0%] (Warmup)\n",
" 1.62792 seconds (Sampling)\n", "Chain 2: Iteration: 30 / 300 [ 10%] (Warmup)\n",
" 3.19134 seconds (Total)\n", "Chain 2: Iteration: 60 / 300 [ 20%] (Warmup)\n",
"\n", "Chain 2: Iteration: 90 / 300 [ 30%] (Warmup)\n",
"\n", "Chain 2: Iteration: 120 / 300 [ 40%] (Warmup)\n",
"SAMPLING FOR MODEL 'prophet' NOW (CHAIN 3).\n", "Chain 2: Iteration: 150 / 300 [ 50%] (Warmup)\n",
"\n", "Chain 2: Iteration: 151 / 300 [ 50%] (Sampling)\n",
"Gradient evaluation took 4.9e-05 seconds\n", "Chain 2: Iteration: 180 / 300 [ 60%] (Sampling)\n",
"1000 transitions using 10 leapfrog steps per transition would take 0.49 "Chain 2: Iteration: 210 / 300 [ 70%] (Sampling)\n",
seconds.\n", "Chain 2: Iteration: 240 / 300 [ 80%] (Sampling)\n",
"Adjust your expectations accordingly!\n", "Chain 2: Iteration: 270 / 300 [ 90%] (Sampling)\n",
"\n", "Chain 2: Iteration: 300 / 300 [100%] (Sampling)\n",
"\n", "Chain 2: \n",
"Iteration: 1 / 300 [ 0%] (Warmup)\n", "Chain 2: Elapsed Time: 2.04288 seconds (Warm-up)\n",
"Iteration: 30 / 300 [ 10%] (Warmup)\n", "Chain 2: 2.12795 seconds (Sampling)\n",
"Iteration: 60 / 300 [ 20%] (Warmup)\n", "Chain 2: 4.17083 seconds (Total)\n",
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"Iteration: 180 / 300 [ 60%] (Sampling)\n", "Chain 3: Gradient evaluation took 7.1e-05 seconds\n",
"Iteration: 210 / 300 [ 70%] (Sampling)\n", "Chain 3: 1000 transitions using 10 leapfrog steps per transition would ta
"Iteration: 240 / 300 [ 80%] (Sampling)\n", ke 0.71 seconds.\n",
"Iteration: 270 / 300 [ 90%] (Sampling)\n", "Chain 3: Adjust your expectations accordingly!\n",
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"\n", "Chain 3: \n",
" Elapsed Time: 1.67866 seconds (Warm-up)\n", "Chain 3: Iteration: 1 / 300 [ 0%] (Warmup)\n",
" 1.68797 seconds (Sampling)\n", "Chain 3: Iteration: 30 / 300 [ 10%] (Warmup)\n",
" 3.36663 seconds (Total)\n", "Chain 3: Iteration: 60 / 300 [ 20%] (Warmup)\n",
"\n", "Chain 3: Iteration: 90 / 300 [ 30%] (Warmup)\n",
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"\n", "Chain 3: Iteration: 151 / 300 [ 50%] (Sampling)\n",
"Gradient evaluation took 4.7e-05 seconds\n", "Chain 3: Iteration: 180 / 300 [ 60%] (Sampling)\n",
"1000 transitions using 10 leapfrog steps per transition would take 0.47 "Chain 3: Iteration: 210 / 300 [ 70%] (Sampling)\n",
seconds.\n", "Chain 3: Iteration: 240 / 300 [ 80%] (Sampling)\n",
"Adjust your expectations accordingly!\n", "Chain 3: Iteration: 270 / 300 [ 90%] (Sampling)\n",
"\n", "Chain 3: Iteration: 300 / 300 [100%] (Sampling)\n",
"\n", "Chain 3: \n",
"Iteration: 1 / 300 [ 0%] (Warmup)\n", "Chain 3: Elapsed Time: 2.24488 seconds (Warm-up)\n",
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"Iteration: 210 / 300 [ 70%] (Sampling)\n", "Chain 4: 1000 transitions using 10 leapfrog steps per transition would ta
"Iteration: 240 / 300 [ 80%] (Sampling)\n", ke 0.65 seconds.\n",
"Iteration: 270 / 300 [ 90%] (Sampling)\n", "Chain 4: Adjust your expectations accordingly!\n",
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"\n", "Chain 4: \n",
" Elapsed Time: 1.65952 seconds (Warm-up)\n", "Chain 4: Iteration: 1 / 300 [ 0%] (Warmup)\n",
" 1.51409 seconds (Sampling)\n", "Chain 4: Iteration: 30 / 300 [ 10%] (Warmup)\n",
" 3.17361 seconds (Total)\n", "Chain 4: Iteration: 60 / 300 [ 20%] (Warmup)\n",
"\n" "Chain 4: Iteration: 90 / 300 [ 30%] (Warmup)\n",
] "Chain 4: Iteration: 120 / 300 [ 40%] (Warmup)\n",
}, "Chain 4: Iteration: 150 / 300 [ 50%] (Warmup)\n",
"metadata": {}, "Chain 4: Iteration: 151 / 300 [ 50%] (Sampling)\n",
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"Chain 4: \n",
"Chain 4: Elapsed Time: 2.2803 seconds (Warm-up)\n",
"Chain 4: 2.73488 seconds (Sampling)\n",
"Chain 4: 5.01518 seconds (Total)\n",
"Chain 4: \n"
]
} }
], ],
"source": [ "source": [
"%%R\n", "%%R\n",
"m <- prophet(df, mcmc.samples = 300)\n", "m <- prophet(df, mcmc.samples = 300)\n",
"forecast <- predict(m, future)" "forecast <- predict(m, future)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": 4,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"m = Prophet(mcmc_samples=300)\n", "m = Prophet(mcmc_samples=300)\n",
"forecast = m.fit(df).predict(future)" "forecast = m.fit(df).predict(future)"
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"This replaces the typical MAP estimation with MCMC sampling, and can take m uch longer depending on how many observations there are - expect several minutes instead of several seconds. If you do full sampling, then you will see the unce rtainty in seasonal components when you plot them:" "This replaces the typical MAP estimation with MCMC sampling, and can take m uch longer depending on how many observations there are - expect several minutes instead of several seconds. If you do full sampling, then you will see the unce rtainty in seasonal components when you plot them:"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": 5,
"metadata": { "metadata": {
"output_hidden": true "output_hidden": true
}, },
"outputs": [ "outputs": [
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}, },
"metadata": {}, "metadata": {},
"output_type": "display_data" "output_type": "display_data"
} }
], ],
"source": [ "source": [
"%%R -w 9 -h 6 -u in\n", "%%R -w 9 -h 6 -u in\n",
"prophet_plot_components(m, forecast)" "prophet_plot_components(m, forecast)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 8, "execution_count": 5,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
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"text/plain": [ "text/plain": [
"<Figure size 648x432 with 2 Axes>" "<Figure size 648x432 with 2 Axes>"
] ]
}, },
"metadata": {}, "metadata": {},
"output_type": "display_data" "output_type": "display_data"
} }
], ],
"source": [ "source": [
"fig = m.plot_components(forecast)" "fig = m.plot_components(forecast)"
skipping to change at line 316 skipping to change at line 339
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"There are upstream issues in PyStan for Windows which make MCMC sampling ex tremely slow. The best choice for MCMC sampling in Windows is to use R, or Pytho n in a Linux VM." "There are upstream issues in PyStan for Windows which make MCMC sampling ex tremely slow. The best choice for MCMC sampling in Windows is to use R, or Pytho n in a Linux VM."
] ]
} }
], ],
"metadata": { "metadata": {
"celltoolbar": "Edit Metadata",
"kernelspec": { "kernelspec": {
"display_name": "Python 2", "display_name": "Python 3",
"language": "python", "language": "python",
"name": "python2" "name": "python3"
}, },
"language_info": { "language_info": {
"codemirror_mode": { "codemirror_mode": {
"name": "ipython", "name": "ipython",
"version": 2 "version": 3
}, },
"file_extension": ".py", "file_extension": ".py",
"mimetype": "text/x-python", "mimetype": "text/x-python",
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython2", "pygments_lexer": "ipython3",
"version": "2.7.14+" "version": "3.8.3"
} }
}, },
"nbformat": 4, "nbformat": 4,
"nbformat_minor": 1 "nbformat_minor": 1
} }
 End of changes. 19 change blocks. 
151 lines changed or deleted 181 lines changed or added

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