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daxiongshu/rank_gauss_inv.ipynb

Last active June 13, 2021 13:43
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rank_gauss_inv.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import warnings\n",
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"import cupy as cp\n",
"from cupyx.scipy.special import erfinv\n",
"import cudf as gd\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"from scipy.special import erfinv as sp_erfinv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### GaussRank transformation"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"x_gpu = cp.random.rand(20) # input array\n",
"x_cpu = cp.asnumpy(x_gpu)\n",
"\n",
"r_gpu = x_gpu.argsort().argsort() # compute the rank\n",
"r_cpu = x_cpu.argsort().argsort() \n",
"\n",
"r_gpu = (r_gpu/r_gpu.max()-0.5)*2 # scale to (-1,1)\n",
"epsilon = 1e-6\n",
"r_gpu = cp.clip(r_gpu,-1+epsilon,1-epsilon)\n",
"\n",
"r_cpu = (r_cpu/r_cpu.max()-0.5)*2 # scale to (-1,1)\n",
"r_cpu = cp.clip(r_cpu,-1+epsilon,1-epsilon)\n",
"\n",
"r_gpu = erfinv(r_gpu) # map to gaussian\n",
"r_cpu = sp_erfinv(r_cpu) # map to gaussian"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Inverse transformation step by step"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"df_cpu = pd.DataFrame({'src':x_cpu,'tgt':r_cpu}) \n",
"df_gpu = gd.DataFrame({'src':x_gpu,'tgt':r_gpu}) # pass cupy array to cudf dataframe\n",
"\n",
"df_cpu = df_cpu.sort_values('src') # sort\n",
"df_gpu = df_gpu.sort_values('src')\n",
"\n",
"pos_cpu = df_cpu['tgt'].searchsorted(r_cpu, side='left') # search\n",
"pos_gpu = df_gpu['tgt'].searchsorted(r_gpu, side='left')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def linear_inter_polate(df,x,pos):\n",
" N = df.shape[0]\n",
" pos[pos>=N] = N-1\n",
" pos[pos-1<=0] = 0\n",
" if isinstance(x,cp.ndarray):\n",
" pos = pos.values\n",
" \n",
" x1 = df['tgt'].values[pos]\n",
" x2 = df['tgt'].values[pos-1]\n",
" y1 = df['src'].values[pos]\n",
" y2 = df['src'].values[pos-1]\n",
"\n",
" relative = (x-x2) / (x1-x2)\n",
" return (1-relative)*y2 + relative*y1"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"x_inv_cpu = linear_inter_polate(df_cpu,r_cpu,pos_cpu) # linear inter polate\n",
"x_inv_gpu = linear_inter_polate(df_gpu,r_gpu,pos_gpu)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"GaussRank CPU\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 1080x360 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"n_bins = 5\n",
"fig, axs = plt.subplots(1, 3, sharey=True, tight_layout=True)\n",
"fig.set_figheight(5)\n",
"fig.set_figwidth(15)\n",
"axs[0].hist(x_cpu, bins=n_bins)\n",
"axs[0].set_title('input',fontsize=15)\n",
"axs[1].hist(r_cpu, bins=n_bins)\n",
"axs[1].set_title('transform',fontsize=15)\n",
"_ = axs[2].hist(x_inv_cpu, bins=n_bins)\n",
"axs[2].set_title('inverse transform',fontsize=15)\n",
"print('GaussRank CPU')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"GaussRank GPU\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x360 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"n_bins = 5\n",
"fig, axs = plt.subplots(1, 3, sharey=True, tight_layout=True)\n",
"fig.set_figheight(5)\n",
"fig.set_figwidth(15)\n",
"axs[0].hist(cp.asnumpy(x_gpu), bins=n_bins)\n",
"axs[0].set_title('input',fontsize=15)\n",
"axs[1].hist(cp.asnumpy(r_gpu), bins=n_bins)\n",
"axs[1].set_title('transform',fontsize=15)\n",
"_ = axs[2].hist(cp.asnumpy(x_inv_gpu), bins=n_bins)\n",
"axs[2].set_title('inverse transform',fontsize=15)\n",
"print('GaussRank GPU')"
]
}
],
"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": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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