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Simple modifications to power function

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ASPP Student 2025-09-23 15:05:34 +03:00
parent 01e7a23ae2
commit a8b690821c
1 changed files with 81 additions and 50 deletions
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exercise.ipynb
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@ -2,15 +2,8 @@
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 1,
"metadata": { "metadata": {},
"execution": {
"iopub.execute_input": "2024-03-04T09:40:28.904Z",
"iopub.status.busy": "2024-03-04T09:40:28.896Z",
"iopub.status.idle": "2024-03-04T09:40:28.978Z",
"shell.execute_reply": "2024-03-04T09:40:28.967Z"
}
},
"outputs": [], "outputs": [],
"source": [ "source": [
"import numpy as np" "import numpy as np"
@ -18,15 +11,8 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 2,
"metadata": { "metadata": {},
"execution": {
"iopub.execute_input": "2024-03-04T10:02:39.062Z",
"iopub.status.busy": "2024-03-04T10:02:39.057Z",
"iopub.status.idle": "2024-03-04T10:02:39.068Z",
"shell.execute_reply": "2024-03-04T10:02:39.071Z"
}
},
"outputs": [], "outputs": [],
"source": [ "source": [
"n_series = 32\n", "n_series = 32\n",
@ -37,17 +23,20 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 3,
"metadata": { "metadata": {},
"execution": { "outputs": [
"iopub.execute_input": "2024-03-04T10:02:41.027Z", {
"iopub.status.busy": "2024-03-04T10:02:41.020Z", "name": "stdout",
"iopub.status.idle": "2024-03-04T10:02:41.036Z", "output_type": "stream",
"shell.execute_reply": "2024-03-04T10:02:41.040Z" "text": [
}, "Size of one time series: 40 M\n",
"scrolled": true "Size of collection: 1280 M\n",
}, "Gap size: 128 K\n",
"outputs": [], "Gapped series size: 2 K\n"
]
}
],
"source": [ "source": [
"print(f'Size of one time series: {int(time_series[0].nbytes/2**20)} M')\n", "print(f'Size of one time series: {int(time_series[0].nbytes/2**20)} M')\n",
"print(f'Size of collection: {int(time_series.nbytes/2**20)} M')\n", "print(f'Size of collection: {int(time_series.nbytes/2**20)} M')\n",
@ -80,15 +69,8 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 4,
"metadata": { "metadata": {},
"execution": {
"iopub.execute_input": "2024-03-04T10:06:08.461Z",
"iopub.status.busy": "2024-03-04T10:06:08.459Z",
"iopub.status.idle": "2024-03-04T10:06:08.466Z",
"shell.execute_reply": "2024-03-04T10:06:08.468Z"
}
},
"outputs": [], "outputs": [],
"source": [ "source": [
"# compute an approximation of a power series for a collection of gapped timeseries\n", "# compute an approximation of a power series for a collection of gapped timeseries\n",
@ -100,6 +82,32 @@
" " " "
] ]
}, },
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"P = np.zeros_like(time_series)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"5.53 s ± 1.3 s per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
]
}
],
"source": [
"%timeit power(time_series, P, gap)"
]
},
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
@ -132,7 +140,20 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"def power_improved(time_series, P, gap):\n",
" for pwr in range(30):\n",
" for row_idx, row_values in enumerate(time_series):\n",
" P[row_idx] += (row_values[::gap]**pwr).sum()\n",
" return P"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -163,21 +184,31 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 14,
"metadata": { "metadata": {},
"execution": { "outputs": [
"iopub.execute_input": "2024-03-04T10:06:20.056Z", {
"iopub.status.busy": "2024-03-04T10:06:20.053Z", "name": "stdout",
"iopub.status.idle": "2024-03-04T10:06:21.695Z", "output_type": "stream",
"shell.execute_reply": "2024-03-04T10:06:21.700Z" "text": [
"17.8 ms ± 1.56 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
]
} }
}, ],
"outputs": [],
"source": [ "source": [
"P = np.zeros(n_series, dtype='float64')\n", "P = np.zeros(n_series, dtype='float64')\n",
"%timeit power_improved(time_series, P, gap)" "%timeit power_improved(time_series, P, gap)"
] ]
}, },
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Learnings:\n",
"- just changing the order of the loops: 5.5 s -> 24.2 ms\n",
"- also enumerating and accessing the rows: 17.8 ms"
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
@ -202,12 +233,12 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.13.5" "version": "3.13.6"
}, },
"nteract": { "nteract": {
"version": "0.28.0" "version": "0.28.0"
} }
}, },
"nbformat": 4, "nbformat": 4,
"nbformat_minor": 2 "nbformat_minor": 4
} }