#22 compute probabilities of different amounts of ridership for a leg

DriverTracker/Controllers/AnalysisApiController.cs

@@ -69,7 +69,7 @@ public LogisticFarePredictionResult GetLogistic(int id)
             result.DriverID = id;
             result.FromDateTime = fromDateTime;
             result.ToDateTime = toDateTime;
-            result.RegressionResult = farePrediction.GetRegressionModel();
+            result.RegressionResult = farePrediction.GetRegressionModels();
 
             return result;
         }
@@ -82,8 +82,7 @@ public double[] GetMultiPickupProb(int id, double delay, double duration, double
             DateTime toDateTime = DateTime.Now;
             farePrediction.LearnFromDates(fromDateTime, toDateTime);
 
-            LogisticRegression regression = farePrediction.GetRegressionModel();
-            return regression?.Probabilities(new double[] { delay, duration, fare });
+            return farePrediction.RidershipClassProbabilities(delay, duration, fare);
         }
     }
 }

DriverTracker/Domain/FarePrediction.cs

@@ -12,18 +12,26 @@ namespace DriverTracker.Domain
 {
     public class FarePrediction
     {
-        private readonly LogisticRegressionAnalysis _logisticRegressionAnalysis;
-        private LogisticRegression _logisticRegression;
+        private readonly List<LogisticRegressionAnalysis> _logisticRegressionAnalyses;
+        private List<LogisticRegression> _logisticRegressions;
         private readonly MvcDriverContext _context;
         private readonly int _DriverID;
 
-        public FarePrediction(MvcDriverContext context, int DriverID) {
-            _logisticRegressionAnalysis = new LogisticRegressionAnalysis();
+        public FarePrediction(MvcDriverContext context, int DriverID, int maxPickups = 4) {
+            _logisticRegressionAnalyses = new List<LogisticRegressionAnalysis>();
+            for (int i = 0; i < maxPickups; i++) {
+                _logisticRegressionAnalyses.Add(new LogisticRegressionAnalysis());
+            }
             _context = context;
             _DriverID = DriverID;
 
-            _logisticRegressionAnalysis.Inputs = new string[] { "delay", "legDuration", "fare" };
-            _logisticRegressionAnalysis.Output = "multiplePickups";
+            _logisticRegressionAnalyses.ForEach(lra => lra.Inputs = new string[] { "delay", "duration", "fare" });
+            for (int i = 0; i < _logisticRegressionAnalyses.Count; i++) {
+                _logisticRegressionAnalyses[i].Output = "_" + (i + 1) + "PlusPickups";
+            }
+
+
+            _logisticRegressions = new List<LogisticRegression>();
         }
 
         /* Learn from legs with specified request times in a given date range */
@@ -43,29 +51,31 @@ public async void LearnFromDates(DateTime from, DateTime to) {
                     decimal.ToDouble(leg.Fare)
                 };
             }).ToArray();
-
-            /*{
-                // delays in minutes
-                legs.Select(leg => leg.StartTime.Subtract(leg.PickupRequestTime.Value).TotalMinutes)
-                    .ToArray(),
-
-                // leg duration in minutes
-                legs.Select(leg => leg.ArrivalTime.Subtract(leg.StartTime).TotalMinutes)
-                    .ToArray(),
 
-                // fare collected in local currency units
-                legs.Select(leg => decimal.ToDouble(leg.Fare)).ToArray()
-            };*/
 
-            double[] trainingOutputs =
-                legs.Select(leg => leg.NumOfPassengersPickedUp > 1 ? 1.0 : 0.0).ToArray();
 
-            _logisticRegression = _logisticRegressionAnalysis.Learn(trainingInputs, trainingOutputs);
+            _logisticRegressions.Clear();
+            _logisticRegressions.AddRange(_logisticRegressionAnalyses.Select((lra, i) => {
+                    double[] trainingOutputs =
+                    legs.Select(leg => leg.NumOfPassengersPickedUp > i + 1 ? 1.0 : 0.0).ToArray();
+                    return lra.Learn(trainingInputs, trainingOutputs);
+            }));
         }
 
         /* Get the regression result */
-        public LogisticRegression GetRegressionModel() {
-            return _logisticRegression;
+        public IEnumerable<LogisticRegression> GetRegressionModels() {
+            return _logisticRegressions;
+        }
+
+        /* Get a list of the probabilities of n or more pickups as an array of
+         * doubles with indices corresponding to n - 2 */
+        public double[] RidershipClassProbabilities(double delay, double duration, double fare) {
+            double[] result = new double[_logisticRegressions.Count];
+            for (int i = 0; i < _logisticRegressions.Count; i++) {
+                result[i] = _logisticRegressions[i].Probabilities(new double[] { delay, duration, fare })[1];
+                if (i > 0) result[i - 1] -= result[i];
+            }
+            return result;
         }
     }
 }

DriverTracker/Domain/LogisticFarePredictionResult.cs

@@ -1,4 +1,5 @@
 using System;
+using System.Collections.Generic;
 
 using Accord.Statistics.Models.Regression;
 
@@ -9,6 +10,6 @@ public class LogisticFarePredictionResult
         public int DriverID { get; set; }
         public DateTime FromDateTime { get; set; }
         public DateTime ToDateTime { get; set; }
-        public LogisticRegression RegressionResult { get; set; }
+        public IEnumerable<LogisticRegression> RegressionResult { get; set; }
     }
 }

DriverTracker/src/app/predictor/predictor.component.html

@@ -16,12 +16,13 @@ <h2>Ridership Prediction for Driver: {{driver.name}}</h2>
     <button type="submit" class="btn btn-success" [disabled]="!riderPredictionForm.form.valid">Predict</button>
 </form>
 
-
-<div class="row" *ngIf="ridershipProbabilities != null">
-    <div class="col">
-        <p class="p-5">Probability of multiple ridership:</p>
-    </div>
-    <div class="col">
-        <p class="p-5">{{ridershipProbabilities[1] * 100}}%</p>
-    </div>
-</div>
+<ng-template [ngIf]="ridershipProbabilities != null">
+    <div class="row" *ngFor="let i of ridershipProbabilityIndices">
+        <div class="col">
+            <p class="p-5">Probability of {{i+2}}<ng-template [ngIf]="i == 3">+</ng-template> riders:</p>
+        </div>
+        <div class="col">
+            <p class="p-5">{{ridershipProbabilities[i] * 100}}%</p>
+        </div>
+    </div>
+</ng-template>

DriverTracker/src/app/predictor/predictor.component.ts

@@ -18,6 +18,7 @@ export class PredictorComponent implements OnInit {
     duration: number;
     fare: number;
     ridershipProbabilities: number[];
+    ridershipProbabilityIndices: number[];
 
     getDriver(id: number): void {
         this.driverService.getDriver(id).subscribe(driver => this.driver = driver);
@@ -33,6 +34,7 @@ export class PredictorComponent implements OnInit {
         private driverService: DriverService,
         private aRoute: ActivatedRoute) { 
         this.aRoute.params.subscribe(p => this.getDriver(p['id']));
+        this.ridershipProbabilityIndices = (new Array(4)).fill(0).map((x,i) => i);
     }
 
     ngOnInit() {