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Dev
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@bmeyers
bmeyers authored Sep 7, 2023
2 parents 45b7fec + bdd2229 commit 948467e
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9 changes: 9 additions & 0 deletions README.md
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# spcqe
Smooth periodic consistent quantile estimation


## Installation

Clone the repository to your local machine and install with pip by navigating to the project directory and running:

```
pip install .
```
15 changes: 4 additions & 11 deletions notebooks/basic_example.ipynb
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"name": "stderr",
"output_type": "stream",
"text": [
"100%|███████████████████████████████████████████| 11/11 [02:22<00:00, 12.92s/it]"
"100%|███████████████████████████████████████████| 11/11 [02:17<00:00, 12.45s/it]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"total time: 142.28 seconds\n"
"total time: 137.13 seconds\n"
]
},
{
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},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": 8,
"metadata": {
"tags": []
},
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},
{
"cell_type": "code",
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"execution_count": 9,
"metadata": {
"tags": []
},
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"plt.plot(xs, spq.fit_quantiles[slc, :], alpha=0.5)\n",
"plt.xticks(rotation=45);"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
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56 changes: 25 additions & 31 deletions notebooks/single_quantile-50-sig-decomp.ipynb
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},
{
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"execution_count": 12,
"metadata": {
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"outputs": [
{
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" quantiles=array([0.98]), solver=&#x27;osd&#x27;, verbose=True,\n",
" weight=0.1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SmoothPeriodicQuantiles</label><div class=\"sk-toggleable__content\"><pre>SmoothPeriodicQuantiles(num_harmonics=3, periods=[8760, 168, 24],\n",
" quantiles=array([0.98]), solver=&#x27;osd&#x27;, verbose=True,\n",
"<style>#sk-container-id-5 {color: black;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SmoothPeriodicQuantiles(num_harmonics=3, periods=[8760, 168, 24],\n",
" quantiles=array([0.6]), solver=&#x27;osd&#x27;, verbose=True,\n",
" weight=0.1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SmoothPeriodicQuantiles</label><div class=\"sk-toggleable__content\"><pre>SmoothPeriodicQuantiles(num_harmonics=3, periods=[8760, 168, 24],\n",
" quantiles=array([0.6]), solver=&#x27;osd&#x27;, verbose=True,\n",
" weight=0.1)</pre></div></div></div></div></div>"
],
"text/plain": [
"SmoothPeriodicQuantiles(num_harmonics=3, periods=[8760, 168, 24],\n",
" quantiles=array([0.98]), solver='osd', verbose=True,\n",
" quantiles=array([0.6]), solver='osd', verbose=True,\n",
" weight=0.1)"
]
},
"execution_count": 6,
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
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"P3 = int(24)\n",
"K = 3\n",
"l = 0.1\n",
"spq = SmoothPeriodicQuantiles(K, [P1, P2, P3], quantiles=.98, weight=l, verbose=True, solver='osd')\n",
"spq = SmoothPeriodicQuantiles(K, [P1, P2, P3], quantiles=.60, weight=l, verbose=True, solver='osd')\n",
"spq"
]
},
{
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"execution_count": 13,
"metadata": {
"tags": []
},
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" QSS: the Quadratic-Separable Solver \n",
" author: Luke Volpatti \n",
"---------------------------------------------------------------\n",
"initial factorization time: 1.04e+00s\n",
"problem size: 70256 variables\n",
"initial factorization time: 1.12e+00s\n",
"(standard algorithm)\n",
"\n",
" ADMM solve \n",
"---------------------------------------------------------------\n",
" iter | objective | primal res | dual res | rho | time (s) \n",
"---------------------------------------------------------------\n",
" 1 | 3.96e+02 1.58e+00 1.07e+00 [0.5 0.02] 3.52e-02\n",
" 25 | 6.71e+02 2.29e-01 6.55e-02 [0.5 0.02] 9.54e-01\n",
" 50 | 6.81e+02 9.21e-02 2.87e-02 [0.5 0.02] 1.93e+00\n",
" 75 | 6.84e+02 4.75e-02 1.62e-02 [0.5 0.02] 2.88e+00\n",
" 100 | 6.84e+02 5.23e-02 1.05e-02 [0.5 0.02] 3.85e+00\n",
" 125 | 6.84e+02 3.13e-02 6.34e-03 [0.5 0.02] 4.80e+00\n",
" 150 | 6.84e+02 2.34e-02 8.24e-03 [0.5 0.02] 5.78e+00\n",
" 175 | 6.84e+02 1.92e-02 3.82e-03 [0.5 0.02] 6.72e+00\n",
" 200 | 6.84e+02 1.46e-02 3.13e-03 [0.5 0.02] 7.71e+00\n",
" 225 | 6.84e+02 1.39e-02 2.78e-03 [0.5 0.02] 8.67e+00\n",
" 250 | 6.84e+02 1.26e-02 2.54e-03 [0.5 0.02] 9.64e+00\n",
" 275 | 6.84e+02 1.06e-02 2.10e-03 [0.5 0.02] 1.06e+01\n",
" 300 | 6.84e+02 9.96e-03 2.00e-03 [0.5 0.02] 1.16e+01\n",
" 325 | 6.84e+02 7.43e-03 1.50e-03 [0.5 0.02] 1.26e+01\n",
" 350 | 6.84e+02 9.06e-03 1.83e-03 [0.5 0.02] 1.35e+01\n",
" 375 | 6.84e+02 5.83e-03 1.19e-03 [0.5 0.02] 1.45e+01\n",
" 380 | 6.84e+02 5.62e-03 1.11e-03 [0.5 0.02] 1.47e+01\n",
" 1 | 8.77e+02 1.65e+00 8.26e-01 [0.5 0.02] 3.66e-02\n",
" 25 | 5.24e+03 7.81e-02 2.39e-02 [0.5 0.02] 9.95e-01\n",
" 50 | 5.26e+03 4.13e-02 1.26e-02 [0.5 0.02] 1.99e+00\n",
" 75 | 5.26e+03 2.85e-02 6.40e-03 [0.5 0.02] 2.96e+00\n",
" 100 | 5.26e+03 1.83e-02 5.21e-03 [0.5 0.02] 3.96e+00\n",
" 125 | 5.26e+03 1.38e-02 2.77e-03 [0.5 0.02] 4.93e+00\n",
" 150 | 5.26e+03 1.00e-02 2.39e-03 [0.5 0.02] 5.93e+00\n",
" 175 | 5.26e+03 6.80e-03 3.70e-03 [0.5 0.02] 6.90e+00\n",
" 200 | 5.26e+03 7.07e-03 1.91e-03 [0.5 0.02] 7.92e+00\n",
" 220 | 5.26e+03 5.52e-03 1.10e-03 [0.5 0.02] 8.72e+00\n",
"---------------------------------------------------------------\n",
"avg time per iter: 3.87e-02s\n",
"avg time per iter: 3.97e-02s\n",
"refactorizations: 0\n",
"total time spent refactorizing: 0.00e+00s\n",
"\n",
"objective value: 683.9867390209845\n",
"total solve time: 15.75s\n",
"total time: 15.92 seconds\n"
"objective value: 5264.373400376573\n",
"total solve time: 9.848s\n",
"total time: 10.02 seconds\n"
]
}
],
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