Vendor dependencies for GCS

vendor/google.golang.org/api/examples/prediction.go

@@ -0,0 +1,137 @@
+// Copyright 2014 The Go Authors. All rights reserved.
+// Use of this source code is governed by a BSD-style
+// license that can be found in the LICENSE file.
+
+package main
+
+import (
+	"fmt"
+	"log"
+	"net/http"
+	"os"
+	"path/filepath"
+	"strings"
+
+	"google.golang.org/api/googleapi"
+	prediction "google.golang.org/api/prediction/v1.6"
+)
+
+func init() {
+	scopes := []string{
+		prediction.DevstorageFullControlScope,
+		prediction.DevstorageReadOnlyScope,
+		prediction.DevstorageReadWriteScope,
+		prediction.PredictionScope,
+	}
+	registerDemo("prediction", strings.Join(scopes, " "), predictionMain)
+}
+
+type predictionType struct {
+	api              *prediction.Service
+	projectNumber    string
+	bucketName       string
+	trainingFileName string
+	modelName        string
+}
+
+// This example demonstrates calling the Prediction API.
+// Training data is uploaded to a pre-created Google Cloud Storage Bucket and
+// then the Prediction API is called to train a model based on that data.
+// After a few minutes, the model should be completely trained and ready
+// for prediction. At that point, text is sent to the model and the Prediction
+// API attempts to classify the data, and the results are printed out.
+//
+// To get started, follow the instructions found in the "Hello Prediction!"
+// Getting Started Guide located here:
+// https://developers.google.com/prediction/docs/hello_world
+//
+// Example usage:
+//   go-api-demo -clientid="my-clientid" -secret="my-secret" prediction
+//       my-project-number my-bucket-name my-training-filename my-model-name
+//
+// Example output:
+//   Predict result: language=Spanish
+//   English Score: 0.000000
+//   French Score: 0.000000
+//   Spanish Score: 1.000000
+//   analyze: output feature text=&{157 English}
+//   analyze: output feature text=&{149 French}
+//   analyze: output feature text=&{100 Spanish}
+//   feature text count=406
+func predictionMain(client *http.Client, argv []string) {
+	if len(argv) != 4 {
+		fmt.Fprintln(os.Stderr,
+			"Usage: prediction project_number bucket training_data model_name")
+		return
+	}
+
+	api, err := prediction.New(client)
+	if err != nil {
+		log.Fatalf("unable to create prediction API client: %v", err)
+	}
+
+	t := &predictionType{
+		api:              api,
+		projectNumber:    argv[0],
+		bucketName:       argv[1],
+		trainingFileName: argv[2],
+		modelName:        argv[3],
+	}
+
+	t.trainModel()
+	t.predictModel()
+}
+
+func (t *predictionType) trainModel() {
+	// First, check to see if our trained model already exists.
+	res, err := t.api.Trainedmodels.Get(t.projectNumber, t.modelName).Do()
+	if err != nil {
+		if ae, ok := err.(*googleapi.Error); ok && ae.Code != http.StatusNotFound {
+			log.Fatalf("error getting trained model: %v", err)
+		}
+		log.Printf("Training model not found, creating new model.")
+		res, err = t.api.Trainedmodels.Insert(t.projectNumber, &prediction.Insert{
+			Id:                  t.modelName,
+			StorageDataLocation: filepath.Join(t.bucketName, t.trainingFileName),
+		}).Do()
+		if err != nil {
+			log.Fatalf("unable to create trained model: %v", err)
+		}
+	}
+	if res.TrainingStatus != "DONE" {
+		// Wait for the trained model to finish training.
+		fmt.Printf("Training model. Please wait and re-run program after a few minutes.")
+		os.Exit(0)
+	}
+}
+
+func (t *predictionType) predictModel() {
+	// Model has now been trained.  Predict with it.
+	input := &prediction.Input{
+		Input: &prediction.InputInput{
+			CsvInstance: []interface{}{
+				"Hola, con quien hablo",
+			},
+		},
+	}
+	res, err := t.api.Trainedmodels.Predict(t.projectNumber, t.modelName, input).Do()
+	if err != nil {
+		log.Fatalf("unable to get trained prediction: %v", err)
+	}
+	fmt.Printf("Predict result: language=%v\n", res.OutputLabel)
+	for _, m := range res.OutputMulti {
+		fmt.Printf("%v Score: %v\n", m.Label, m.Score)
+	}
+
+	// Now analyze the model.
+	an, err := t.api.Trainedmodels.Analyze(t.projectNumber, t.modelName).Do()
+	if err != nil {
+		log.Fatalf("unable to analyze trained model: %v", err)
+	}
+	for _, f := range an.DataDescription.OutputFeature.Text {
+		fmt.Printf("analyze: output feature text=%v\n", f)
+	}
+	for _, f := range an.DataDescription.Features {
+		fmt.Printf("feature text count=%v\n", f.Text.Count)
+	}
+}