PyCaret is an open-source, low-code machine learning library and end-to-end model management tool built in Python for automating ML worflows | Towards Data Science

PyCaret is an open-source, low-code machine learning library and end-to-end model management tool built-in Python for automating machine learning workflows. It is incredibly popular for its ease of use, simplicity, and ability to build and deploy end-to-end ML prototypes quickly and efficiently.
PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few lines only. This makes the experiment cycle exponentially fast and efficient.
PyCaret is simple and easy to use. All the operations performed in PyCaret are sequentially stored in a Pipeline that is fully automated for deployment. Whether it’s imputing missing values, one-hot-encoding, transforming categorical data, feature engineering, or even hyperparameter tuning, PyCaret automates all of it.
This tutorial assumes that you have some prior knowledge and experience with PyCaret. If you haven’t used it before, no problem — you can get a quick headstart through these tutorials:
PyCaret 2.2 is here — what’s new
Announcing PyCaret 2.0
Five things you don’t know about PyCaret
In my last tutorial, I have demonstrated how you can use PyCaret to forecast time-series data using Machine Learning through PyCaret Regression Module. If you haven’t read that yet, you can read Time Series Forecasting with PyCaret Regression Module tutorial before continuing with this one, as this tutorial builds upon some important concepts covered in the last tutorial.
Installing PyCaret is very easy and takes only a few minutes. We strongly recommend using a virtual environment to avoid potential conflicts with other libraries.
PyCaret’s default installation is a slim version of pycaret which only installs hard dependencies that are listed here.
# install slim version (default)pip install pycaret# install the full versionpip install pycaret[full]
When you install the full version of pycaret, all the optional dependencies as listed here are also installed.
PyCaret Regression Module is a supervised machine learning module used for estimating the relationships between a dependent variable (often called the ‘outcome variable’, or ‘target’) and one or more independent variables (often called ‘features’, or ‘predictors’).
The objective of regression is to predict continuous values such as sales amount, quantity, temperature, number of customers, etc. All modules in PyCaret provide many pre-processing features to prepare the data for modeling through the setup function. It has over 25 ready-to-use algorithms and several plots to analyze the performance of trained models.
For this tutorial, I will show the end-to-end implementation of multiple time-series data forecasting, including both the training as well as predicting future values.
I have used the Store Item Demand Forecasting Challenge dataset from Kaggle. This dataset has 10 different stores and each store has 50 items, i.e. total of 500 daily level time series data for five years (2013–2017).
# read the csv fileimport pandas as pddata = pd.read_csv('train.csv')data['date'] = pd.to_datetime(data['date'])# combine store and item column as time_seriesdata['store'] = ['store_' + str(i) for i in data['store']]data['item'] = ['item_' + str(i) for i in data['item']]data['time_series'] = data[['store', 'item']].apply(lambda x: '_'.join(x), axis=1)data.drop(['store', 'item'], axis=1, inplace=True)# extract features from datedata['month'] = [i.month for i in data['date']]data['year'] = [i.year for i in data['date']]data['day_of_week'] = [i.dayofweek for i in data['date']]data['day_of_year'] = [i.dayofyear for i in data['date']]data.head()
# check the unique time_seriesdata['time_series'].nunique()>>> 500
# plot multiple time series with moving avgs in a loopimport plotly.express as pxfor i in data['time_series'].unique():    subset = data[data['time_series'] == i]    subset['moving_average'] = subset['sales'].rolling(30).mean()    fig = px.line(subset, x="date", y=["sales","moving_average"], title = i, template = 'plotly_dark')    fig.show()
Now that we have the data ready, let’s start the training loop. Notice that verbose = False in all functions to avoid printing results on the console while training.
The code below is a loop around time_series column we created during the data preparatory step. There are a total of 150 time series (10 stores x 50 items).
Line 10 below is filtering the dataset for time_series variable. The first part inside the loop is initializing the setup function, followed by compare_models to find the best model. Line 24–26 captures the results and appends the performance metrics of the best model in a list called all_results . The last part of the code uses the finalize_model function to retrain the best model on the entire dataset including the 5% left in the test set and saves the entire pipeline including the model as a pickle file.
We can now create a data frame from all_results list. It will display the best model selected for each time series.
concat_results = pd.concat(all_results,axis=0)concat_results.head()
Now that we have trained models, let’s use them to generate predictions, but first, we need to create the dataset for scoring (X variables).
# create a date range from 2013 to 2019all_dates = pd.date_range(start='2013-01-01', end = '2019-12-31', freq = 'D')# create empty dataframescore_df = pd.DataFrame()# add columns to datasetscore_df['date'] = all_datesscore_df['month'] = [i.month for i in score_df['date']]score_df['year'] = [i.year for i in score_df['date']]score_df['day_of_week'] = [i.dayofweek for i in score_df['date']]score_df['day_of_year'] = [i.dayofyear for i in score_df['date']]score_df.head()
Now let’s create a loop to load the trained pipelines and use the predict_model function to generate prediction labels.
from pycaret.regression import load_model, predict_modelall_score_df = []for i in tqdm(data['time_series'].unique()):    l = load_model('trained_models/' + str(i), verbose=False)    p = predict_model(l, data=score_df)    p['time_series'] = i    all_score_df.append(p)concat_df = pd.concat(all_score_df, axis=0)concat_df.head()
We will now join the dataand concat_df .
final_df = pd.merge(concat_df, data, how = 'left', left_on=['date', 'time_series'], right_on = ['date', 'time_series'])final_df.head()
We can now create a loop to see all plots.
for i in final_df['time_series'].unique()[:5]:    sub_df = final_df[final_df['time_series'] == i]        import plotly.express as px    fig = px.line(sub_df, x="date", y=['sales', 'Label'], title=i, template = 'plotly_dark')    fig.show()
I hope that you will appreciate the ease of use and simplicity in PyCaret. In less than 50 lines of code and one hour of experimentation, I have trained over 10,000 models (25 estimators x 500 time series) and productionalized 500 best models to generate predictions.
Next week I will be writing a tutorial on unsupervised anomaly detection on time-series data using PyCaret Anomaly Detection Module. Please follow me on Medium, LinkedIn, and Twitter to get more updates.
There is no limit to what you can achieve using this lightweight workflow automation library in Python. If you find this useful, please do not forget to give us ⭐️ on our GitHub repository.
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Build your own AutoML in Power BI using PyCaret 2.0Deploy Machine Learning Pipeline on Azure using DockerDeploy Machine Learning Pipeline on Google Kubernetes EngineDeploy Machine Learning Pipeline on AWS FargateBuild and deploy your first machine learning web appDeploy PyCaret and Streamlit app using AWS Fargate serverlessBuild and deploy machine learning web app using PyCaret and StreamlitDeploy Machine Learning App built using Streamlit and PyCaret on GKE
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