The scikit-learn Python library for machine learning offers a suite of data transforms for changing the scale and distribution of input data, as well as removing input features (columns).
There are many simple data cleaning operations, such as removing outliers and removing columns with few observations, that are often performed manually to the data, requiring custom code.
The scikit-learn library provides a way to wrap these custom data transforms in a standard way so they can be used just like any other transform, either on data directly or as a part of a modeling pipeline.
In this tutorial, you will discover how to define and use custom data transforms for scikit-learn.
After completing this tutorial, you will know:
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![How to Create Custom Data Transforms for Scikit-Learn]
This tutorial is divided into four parts; they are:
Data preparation refers to changing the raw data in some way that makes it more appropriate for predictive modeling with machine learning algorithms.
The scikit-learn Python machine learning library offers many different data preparation techniques directly, such as techniques for scaling numerical input variables and changing the probability distribution of variables.
These transforms can be fit and then applied on a dataset or used as part of a predictive modeling pipeline, allowing a sequence of transforms to be applied correctly without data leakage when evaluating model performance with data sampling techniques, such as k-fold cross-validation.
Although the data preparation techniques available in scikit-learn are extensive, there may be additional data preparation steps that are required.
Typically, these additional steps are performed manually prior to modeling and require writing custom code. The risk is that these data preparation steps may be performed inconsistently.
The solution is to create a custom data transform in scikit-learn using the FunctionTransformer class.
This class allows you to specify a function that is called to transform the data. You can define the function and perform any valid change, such as changing values or removing columns of data (not removing rows).
The class can then be used just like any other data transform in scikit-learn, e.g. to transform data directly, or used in a modeling pipeline.
The catch is that the transform is stateless, meaning that no state can be kept.
This means that the transform cannot be used to calculate statistics on the training dataset that are then used to transform the train and test datasets.
In addition to custom scaling operations, this can be helpful for standard data cleaning operations, such as identifying and removing columns with few unique values and identifying and removing relative outliers.
We will explore both of these cases, but first, let’s define a dataset that we can use as the basis for exploration.
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The so-called “oil spill” dataset is a standard machine learning dataset.
The task involves predicting whether a patch contains an oil spill or not, e.g. from the illegal or accidental dumping of oil in the ocean, given a vector that describes the contents of a patch of a satellite image.
There are 937 cases. Each case is composed of 48 numerical computer vision derived features, a patch number, and a class label.
The normal case is no oil spill assigned the class label of 0, whereas an oil spill is indicated by a class label of 1. There are 896 cases for no oil spill and 41 cases of an oil spill.
You can access the entire dataset here:
Review the contents of the file.
#data preparation #data analysis
If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition
The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.
This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.
As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).
This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.
#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management
The typical overall machine learning workflow with scikit-learn looks something like this:
yto perform a train-test split, creating
X_trainusing the fitted preprocessors, and perform any other preprocessing steps (such as dropping columns)
X_trainas well as
X_testusing the fitted preprocessors, and perform any other preprocessing steps (such as dropping columns)
X_testas well as
Here is an example code snippet that follows these steps, using an antelope dataset (“antelope.csv”) from a statistics textbook. The goal is to predict the number of spring fawns based on the adult antelope population, annual precipitation, and winter severity. This is a very tiny dataset and should only be used for example purposes! This example skips any hyperparameter tuning, and simply fits a vanilla linear regression model on the preprocessed training data before evaluating it on the preprocessed testing data.
# Step 0: import relevant packages import pandas as pd from sklearn.model_selection import train_test_split from sklearn.preprocessing import OneHotEncoder from sklearn.linear_model import LinearRegression # Step 1: load all data into X and y antelope_df = pd.read_csv("antelope.csv") X = antelope_df.drop("spring_fawn_count", axis=1) y = antelope_df["spring_fawn_count"] # Step 2: train-test split X_train, X_test, y_train, y_test = train_test_split( X, y, random_state=42, test_size=3) # Step 3: fit preprocessor ohe = OneHotEncoder(sparse=False, handle_unknown="ignore") ohe.fit(X_train[["winter_severity_index"]]) # Step 4: transform X_train with fitted preprocessor(s), and perform # custom preprocessing step(s) train_winter_array = ohe.transform(X_train[["winter_severity_index"]]) train_winter_df = pd.DataFrame(train_winter_array, index=X_train.index) X_train = pd.concat([train_winter_df, X_train], axis=1) X_train.drop("winter_severity_index", axis=1, inplace=True) # for the sake of example, this "feature engineering" encodes a numeric column # as a binary column also ("low" meaning "less than 12" here) X_train["low_precipitation"] = [int(x < 12) for x in X_train["annual_precipitation"]] # Step 5: create a model (skipping cross-validation and hyperparameter tuning # for the moment) and fit on preprocessed training data model = LinearRegression() model.fit(X_train, y_train) # Step 6: transform X_test with fitted preprocessor(s), and perform # custom preprocessing step(s) test_winter_array = ohe.transform(X_test[["winter_severity_index"]]) test_winter_df = pd.DataFrame(test_winter_array, index=X_test.index) X_test = pd.concat([test_winter_df, X_test], axis=1) X_test.drop("winter_severity_index", axis=1, inplace=True) X_test["low_precipitation"] = [int(x < 12) for x in X_test["annual_precipitation"]] # Step 7: evaluate model on preprocessed testing data print("Final model score:", model.score(X_test, y_test)) view raw ml_example_without_pipelines.py hosted with ❤ by GitHub
An example without pipelines
The train-test split is one of the most important components of a machine learning workflow. It helps a data scientist understand model performance, particularly in terms of overfitting. A proper train-test split means that we have to perform the preprocessing steps on the training data and testing data separately, so there is no “leakage” of information from the testing set into the training set.
#scikit-learn #data-preparation #machine-learning #data-pipeline #data-science #data analysis
CVDC 2020, the Computer Vision conference of the year, is scheduled for 13th and 14th of August to bring together the leading experts on Computer Vision from around the world. Organised by the Association of Data Scientists (ADaSCi), the premier global professional body of data science and machine learning professionals, it is a first-of-its-kind virtual conference on Computer Vision.
The second day of the conference started with quite an informative talk on the current pandemic situation. Speaking of talks, the second session “Application of Data Science Algorithms on 3D Imagery Data” was presented by Ramana M, who is the Principal Data Scientist in Analytics at Cyient Ltd.
Ramana talked about one of the most important assets of organisations, data and how the digital world is moving from using 2D data to 3D data for highly accurate information along with realistic user experiences.
The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment, 3D data for object detection and two general case studies, which are-
This talk discussed the recent advances in 3D data processing, feature extraction methods, object type detection, object segmentation, and object measurements in different body cross-sections. It also covered the 3D imagery concepts, the various algorithms for faster data processing on the GPU environment, and the application of deep learning techniques for object detection and segmentation.
#developers corner #3d data #3d data alignment #applications of data science on 3d imagery data #computer vision #cvdc 2020 #deep learning techniques for 3d data #mesh data #point cloud data #uav data
Amilestone for open source projects — French President Emmanuel Macron has recently been introduced to Scikit-learn. In fact, in a recent tweet, Scikit-learn creator and Inria tenured research director, Gael Varoquaux announced the presentation of Scikit-Learn, with applications of machine learning in digital health, to the president of France.
He stated the advancement of this free software machine learning library — “started from the grassroots, built by a community, we are powering digital revolutions, adding transparency and independence.”
#news #application of scikit learn for machine learning #applications of scikit learn for digital health #scikit learn #scikit learn introduced to french president