Data cleaning
More often than not, you'll need to do some data cleaning before fitting your dataset to a model. Usually, this involves importing different libraries and writing many lines of code. Since ATOM is all about fast exploration and experimentation, it provides various data cleaning classes to apply the most common transformations fast and easy.
Note
All of atom's data cleaning methods automatically adopt the relevant
transformer attributes (n_jobs, verbose, logger, random_state)
from atom. A different choice can be added as parameter to the method
call, e.g. atom.scale(verbose=2).
Note
Like the add method, the data cleaning methods
accept the columns parameter to only transform a subset of the
dataset's features, e.g. atom.scale(columns=[0, 1]).
Balancing the data
One of the common issues found in datasets that are used for classification is imbalanced classes. Data imbalance usually reflects an unequal distribution of classes within a dataset. For example, in a credit card fraud detection dataset, most of the transactions are non-fraud, and a very few cases are fraud. This leaves us with a very unbalanced ratio of fraud vs non-fraud cases. The Balancer class can oversample the minority class or undersample the majority class using any of the transformers implemented in the imblearn package. It can be accessed from atom through the balance method.
Standard data cleaning
There are many data cleaning steps that are useful to perform on any dataset before modelling. These are general rules that apply almost on every use-case and every task. The Cleaner class is a convenient tool to apply such steps. It can be accessed from atom through the clean method. Use the class' parameters to choose which transformations to perform. The available steps are:
- Drop columns with specific data types.
- Strip categorical features from white spaces.
- Drop duplicate rows.
- Drop rows with missing values in the target column.
- Encode the target column.
Binning numerical features
Discretization (otherwise known as quantization or binning) provides a way to partition continuous features into discrete values. Certain datasets with continuous features may benefit from discretization, because discretization can transform the dataset of continuous attributes to one with only nominal attributes. Discretization is similar to constructing histograms for continuous data. However, histograms focus on counting features which fall into particular bins, whereas discretization focuses on assigning feature values to these bins. The Discretizer class can be used to bin continuous data into intervals. It can be accessed from atom through the discretize method.
Encoding categorical features
Many datasets contain categorical features. Their variables are typically stored as text values which represent various classes. Some examples include color (“Red”, “Yellow”, “Blue”), size (“Small”, “Medium”, “Large”) or geographic designations (city or country). Regardless of what the value is used for, the challenge is determining how to use this data in the analysis. The majority of sklearn's models don't support direct manipulation of this kind of data. Use the Encoder class to encode categorical features to numerical values. It can be accessed from atom through the encode method.
There are many strategies to encode categorical columns. The Encoder
class applies one strategy or another depending on the number of
classes in the column to be encoded. When there are only two, the values
are encoded with 0 or 1. When there are more than two, the columns can
be encoded using one-hot encoding or any other strategy of the
category-encoders
package, depending on the value of the max_onehot parameter.
One-hot
encodes the column making a dummy feature for every class. This
approach preserves all the information but increases the size of
the dataset considerably, making it often an undesirable strategy for
high cardinality features. Other strategies like Target
transform the column in place.
Imputing missing values
For various reasons, many real world datasets contain missing values, often encoded as blanks, NaNs or other placeholders. Such datasets however are incompatible with ATOM's models which assume that all values in an array are numerical, and that all have and hold meaning. The Imputer class handles missing values in the dataset by either dropping or imputing the value. It can be accessed from atom through the impute method.
Normalizing the feature set
Use the Normalizer class to transform the feature set to follow a Normal (Gaussian)-like distribution. In general, data must be transformed when using models that assume normality in the residuals. Examples of such models are LogisticRegression, LinearDiscriminantAnalysis and GaussianNB. The class can be accessed from atom through the normalize method.
Handling outliers
When modelling, it is important to clean the data sample to ensure that the observations best represent the problem. Sometimes a dataset can contain extreme values that are outside the range of what is expected and unlike the other data. These are called outliers. Often, machine learning modelling and model skill in general can be improved by understanding and even removing these outlier samples. The Pruner class offers 7 different strategies to detect outliers (described hereunder). It can be accessed from atom through the prune method.
z-score
The z-score of a value in the dataset is defined as the number of standard
deviations by which the value is above or below the mean of the column.
Values above or below a certain threshold (specified with the parameter
max_sigma) are considered outliers. Note that, contrary to the rest of
the strategies, this approach selects outlier values, not outlier samples!
Because of this, it is possible to replace the outlier value instead of
dropping the entire sample.
Isolation Forest
Uses a tree-based anomaly detection algorithm. It is based
on modeling the normal data in such a way as to isolate anomalies that are
both few and different in the feature space. Read more in sklearn's documentation.
Elliptic Envelope
If the input variables have a Gaussian distribution, then simple statistical
methods can be used to detect outliers. For example, if the dataset has two
input variables and both are Gaussian, the feature space forms a
multidimensional Gaussian, and knowledge of this distribution can be used to
identify values far from the distribution. This approach can be generalized by
defining a hypersphere (ellipsoid) that covers the normal data, and data that
falls outside this shape is considered an outlier. Read more in sklearn's documentation.
Local Outlier Factor
A simple approach to identifying outliers is to locate those examples that
are far from the other examples in the feature space. This can work well
for feature spaces with low dimensionality (few features) but becomes
less reliable as the number of features is increased. The local outlier
factor is a technique that attempts to harness the idea of nearest neighbors
for outlier detection. Each example is assigned a score of how isolated
or how likely it is to be outliers based on the size of its local
neighborhood. Those examples with the largest score are more likely to
be outliers. Read more in sklearn's documentation.
One-class SVM
The support vector machine algorithm, initially developed for binary
classification tasks, can also be used for one-class classification.
When modeling one class, the algorithm captures the density of the
majority class and classifies examples on the extremes of the density
function as outliers. This modification of SVM is referred to as
One-Class SVM. Read more in sklearn's documentation.
DBSCAN
The DBSCAN algorithm views clusters as areas of high density separated by
areas of low density. Due to this rather generic view, clusters found by
DBSCAN can be any shape, as opposed to k-means which assumes that clusters
are convex shaped. Samples that lie outside any cluster are considered outliers.
Read more in sklearn's documentation.
OPTICS
The OPTICS algorithm shares many similarities with the DBSCAN algorithm,
and can be considered a generalization of DBSCAN that relaxes the eps
requirement from a single value to a value range. The key difference
between DBSCAN and OPTICS is that the OPTICS algorithm builds a reachability
graph, and a spot within the cluster ordering. These two attributes are
assigned when the model is fitted, and are used to determine cluster
membership. Read more in sklearn's documentation.
Scaling the feature set
Standardization of a dataset is a common requirement for many machine learning estimators; they might behave badly if the individual features do not more or less look like standard normally distributed data (e.g. Gaussian with zero mean and unit variance). The Scaler class let you quickly scale atom's dataset using one of sklearn's scalers. It can be accessed from atom through the scale method.
Info
All strategies can utilize GPU speed-up. Click here for further information about GPU implementation.