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/*
* Copyright (c) 2017-2019 AutoDeploy AI
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.pmml4s.common
import org.pmml4s.metadata.Field
import org.pmml4s.model.Model
/**
* Provides a basic framework for representing variable statistics.
*/
class ModelStats extends PmmlElement {
???
}
trait HasModelStats {
self: Model =>
def modelStats: Option[ModelStats]
}
/**
* A Partition contains statistics for a subset of records, for example it can describe the population in a cluster.
* The content of a Partition mirrors the definition of the general univariate statistics. That is, each Partition
* describes the distribution per field. For each field there can be information about frequencies, numeric moments,
* etc.
*
* The attribute name identifies the Partition. The attribute size is the number of records. All aggregates in
* PartitionFieldStats must have size = totalFrequency in Counts if specified.
*/
class Partition(
val name: String,
val partitionFieldStats: Array[PartitionFieldStats],
val size: Option[Double] = None) extends PmmlElement
/**
* field references to (the name of) a MiningField for background statistics. The sequence of NUM-ARRAYs is the same as
* for ContStats. For categorical fields there is only one array containing the frequencies; for numeric fields, the
* second and third array contain the sums of values and the sums of squared values, respectively. The number of values
* in each array must match the number of categories or intervals in UnivariateStats of the field.
*/
class PartitionFieldStats(
val field: Field,
val frequencies: Array[Double],
val weighted: Boolean = false,
val counts: Option[Counts] = None,
val numericInfo: Option[NumericInfo] = None,
val sumValues: Option[Array[Double]] = None,
val sumSquaredValues: Option[Array[Double]] = None) extends PmmlElement
/**
* The values for mean, minimum, maximum and standardDeviation are defined as usual. median is calculated as the 50%
* quantile; interQuartileRange is calculated as (75% quantile - 25% quantile).
*/
class NumericInfo(
val quantiles: Array[Quantile],
val minimum: Option[Double] = None,
val maximum: Option[Double] = None,
val mean: Option[Double] = None,
val standardDeviation: Option[Double] = None,
val median: Option[Double] = None,
val interQuartileRange: Option[Double] = None) extends PmmlElement
/**
* @param quantileLimit A percentage number between 0 and 100
* @param quantileValue The corresponding value in the domain of field values.
*/
class Quantile(val quantileLimit: Double, val quantileValue: Double) extends PmmlElement
/**
* Carries counters for frequency of values with respect to their state of being missing, invalid, or valid. The counts
* can be non-integer if they are weighted.
*
* @param totalFreq Counts all records, same as for statistics of all MiningFields.
* @param missingFreq Counts the number of records where value is missing.
* @param invalidFreq Counts the number of records with values other than valid. The total frequency includes the
* missing values and invalid values.
* @param cardinality The number of unique, or distinct, values that the variable has.
*/
class Counts(
val totalFreq: Double,
val missingFreq: Option[Double] = None,
val invalidFreq: Option[Double] = None,
val cardinality: Option[Double] = None) extends PmmlElement
