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Progress bar in Jupyter notebook for tracking the progress as your machine learning model is training
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‎python/model_is_training_progress_bar.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "zzD4-HxqXBmt"
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},
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"source": [
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"# **Progress Bar in Jupyter Notebook**\n",
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"\n",
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"Chanin Nantasenamat\n",
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"\n",
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"**Data Professor YouTube channel**, http://youtube.com/dataprofessor"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "An7XU557Y5ci"
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},
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"source": [
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"# **Progress Bar with the tqdm library**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "3yc04janmetd"
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},
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"outputs": [],
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"source": [
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"# ! pip install tqdm"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"id": "gxa8jup1DNjt"
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},
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"outputs": [],
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"source": [
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"from tqdm.notebook import tqdm\n",
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"from time import sleep"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"id": "009bdoXCE74q"
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},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "93cc2d7933af4faf96fda14e55f24e23",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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" 0%| | 0/100 [00:00<?, ?it/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"number_list = list(range(100))\n",
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"for x in tqdm(number_list):\n",
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" sleep(0.05)\n",
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"#print('Completed!')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "4tFGw2QFMz6N"
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},
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"source": [
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"# **Model Building**"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "zKKr9EoSVbOV"
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},
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"source": [
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"### Reading in the Delaney Solubility Dataset"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"id": "FHR0FBHEMyyL"
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},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"\n",
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"dataset = pd.read_csv('https://raw.githubusercontent.com/dataprofessor/data/master/delaney_solubility_with_descriptors.csv')\n",
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"\n",
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"X = dataset.drop(['logS'], axis=1)\n",
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"Y = dataset.iloc[:,-1]\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "BqqRRTtUVi7v"
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},
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"source": [
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"### Model Building with Progress Bar"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"id": "cpa2tS3kInAx",
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"scrolled": true
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},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "a1b762495ff545468e8b801795c6b708",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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" 0%| | 0/10 [00:00<?, ?it/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Tree: 100, R2: 0.9796508266364179, MSE: 0.08936295274735467\n",
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"Tree: 200, R2: 0.9805478792326812, MSE: 0.08542356575902461\n",
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"Tree: 300, R2: 0.9801470956638436, MSE: 0.08718359809468906\n",
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"Tree: 400, R2: 0.9803760482277171, MSE: 0.08617815788435489\n",
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"Tree: 500, R2: 0.9804686074892891, MSE: 0.08577168589797951\n",
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"Tree: 600, R2: 0.9804079256830844, MSE: 0.08603816873163578\n",
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"Tree: 700, R2: 0.9802975717717071, MSE: 0.0865227855360484\n",
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"Tree: 800, R2: 0.9803651322114956, MSE: 0.08622609533244484\n",
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"Tree: 900, R2: 0.98037907466393, MSE: 0.08616486735547396\n",
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"Tree: 1000, R2: 0.9804349669126423, MSE: 0.08591941775949379\n"
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]
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}
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],
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"source": [
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"from sklearn.ensemble import RandomForestRegressor\n",
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"from sklearn.metrics import mean_squared_error, r2_score\n",
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"\n",
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"parameter_n_estimators = [100,200,300,400,500,600,700,800,900,1000]\n",
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"\n",
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"for i in tqdm(parameter_n_estimators):\n",
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" model = RandomForestRegressor(n_estimators=i)\n",
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" model.fit(X,Y)\n",
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" Y_pred = model.predict(X)\n",
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" r2 = r2_score(Y, Y_pred)\n",
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" mse = mean_squared_error(Y, Y_pred)\n",
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" print('Tree: %s, R2: %s, MSE: %s' % (i, r2, mse))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"colab": {
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"collapsed_sections": [],
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"name": "Model-building-with-progress-bar.ipynb",
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}

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