Scale Properties

Scales all property values into the range [0, 1] where the minimum value(s) get the scaled value 0 and the maximum value(s) get the scaled value 1, according to this formula:

The minimum and maximum values are reported as statistics when this scaler is used.

Scales all property values into the range [-1, 1] where the absolute maximum value(s) get the scaled value 1, according to this formula:

The maximum absolute value is reported as statistic when this scaler is used.

Scales all property values into the range [-1, 1] where the average value(s) get the scaled value 0.

The minimum, maximum and average values are reported as statistics when this scaler is used.

Transforms all property values using the natural logarithm. C denotes a configurable constant offset, which can be used to avoid negative values or zeros in the value space, as their logarithms are not finite values.

Scales all property values using the Standard Score (Wikipedia).

The average value and standard deviation are reported as statistics when this scaler is used.

Transforms all properties by subtracting the mean.

The average value is reported as statistic when this scaler is used.

The log scaler supports specific configuration, which we document here.

All other scalers do not support additional, custom configuration.

First off, we will estimate the cost of running the algorithm using the estimate procedure. This can be done with any execution mode. We will use the stream mode in this example. Estimating the algorithm is useful to understand the memory impact that running the algorithm on your graph will have. When you later actually run the algorithm in one of the execution modes the system will perform an estimation. If the estimation shows that there is a very high probability of the execution going over its memory limitations, the execution is prohibited. To read more about this, see Automatic estimation and execution blocking.

For more details on estimate in general, see Memory Estimation.

In the stream execution mode, the algorithm returns the scaled properties for each node. This allows us to inspect the results directly or post-process them in Cypher without any side effects. Note that the output is always a single list property, containing all scaled node properties in the input order.

For more details on the stream mode in general, see Stream.

In the results we can observe that the first element in the resulting scaledProperty we get the min-max-scaled values for buildYear, where the Plaza hotel has the minimum value and is scaled to zero, while the Polar hotel has the maximum value and is scaled to one. This can be verified with the example graph. The second value in the scaledProperty result are the scaled values of the avgReview property.

The mutate execution mode enables updating the named graph with a new node property containing the scaled properties for that node. The name of the new property is specified using the mandatory configuration parameter mutateProperty. The result is a single summary row containing metrics from the computation. The mutate mode is especially useful when multiple algorithms are used in conjunction.

For more details on the mutate mode in general, see Mutate.

In this example we will scale the two hotel properties of buildYear and avgReview using the Mean scaler. The output is a list property which we will call hotelFeatures, imagining that we will use this as input for a machine learning model later on.

The result shows that there are now eight new node properties in the in-memory graph. These contain the scaled values from the input properties, where the scaled buildYear values are in the first list position and scaled avgReview values are in the second position. To find out how to inspect the new schema of the in-memory graph, see Listing graphs in the catalog.

In the stats execution mode, the algorithm returns a single row containing a summary of the algorithm result. This execution mode does not have any side effects. It can be useful for evaluating algorithm performance by inspecting the computeMillis return item. In the examples below we will omit returning the timings. The full signature of the procedure can be found in the syntax section.

For more details on the stats mode in general, see Stats.

Different scalers will need to compute different statistics as part of their computation. This will be reflected in the scalerStatistics returned. Since the "Center" computes the average value of the various input properties, that is what we get as scaler statistics results in this case.

The write execution mode extends the stats mode with an important side effect: writing the scaled properties for each node as a property to the Neo4j database. The name of the new property is specified using the mandatory configuration parameter writeProperty. The result is a single summary row, similar to stats, but with some additional metrics. The write mode enables directly persisting the results to the database.

For more details on the write mode in general, see Write.

The result shows that there are now eight new node properties in the database graph on the nodes corresponding to those in the projection 'myGraph'. These node properties contain the scaled values from the input properties, where the scaled buildYear values are in the first list position and scaled avgReview values are in the second position.

The storyCapacity property models the amount of rooms on each story of the hotel. The property is normalized so that hotels with fewer stories have a zero value. This is because the Scale Properties algorithm requires that all values for the same property have the same length. In this example we will show how to scale the values in these lists using the Scale Properties algorithm. We imagine using the output as feature vector to input in a machine learning algorithm. Additionally, we will include the avgReview property in our feature vector.

The resulting feature vector contains the standard-score scaled value for the avgReview property in the first list position. We can see that some values are negative and that the maximum value sticks out for the Central hotel.

The other three list positions are the scaled values for the storyCapacity list property. Note that each list item is scaled only with respect to the corresponding item in the other lists. Thus, the Polar hotel has the greatest scaled value in all list positions.

The log scaler supports a configurable offset parameter. In this example we illustrate how to configure that offset.

We want to scale the avgReview property, but it contains negative numbers, for which the logarithm is not defined. First, we’ll determine what the minimum value is, by using Cypher’s min() aggregating function:

Learning this value, we can use a greater value, thus ensuring that the logarithm will be a finite value. We will use 5.12, as this will make the smallest scaled value zero.

As we can see, all scaled values are finite numbers. In particular, the smallest scaled value is zero. Try this example with an offset lower than 4.12 if you are curious about the results.