Sunday, June 17, 2018

How to install turicreate on macOS 10.14 beta

Install turicreate on macOS 10.14 beta 1
shell script    Select all
# upgrade pip # curl https://bootstrap.pypa.io/get-pip.py | sudo python curl https://bootstrap.pypa.io/get-pip.py | python # install packages sudo pip install requests==2.18.4 turicreate==5.0b1


(1) Test turicreate example - Image Classifier
shell script    Select all
mkdir -p $HOME/MLClassifier cd $HOME/MLClassifier # download dataset and cleanup curl -L -o dataset.zip https://drive.google.com/uc?id=1ZLigrn7YcETalcj2qK6UqXceDdOV3244&export=download unzip dataset.zip rm -fr __MACOSX; rm dataset/.DS_Store dataset/*/.DS_Store # create python script cat > classifier.py << 'EOF' import turicreate as turi # load images from dataset folder url = "dataset/" data = turi.image_analysis.load_images(url) # define image categories data["foodType"] = data["path"].apply(lambda path: "Rice" if "rice" in path else "Soup") # create sframe data.save("rice_or_soup.sframe") # preview dataset data.explore() # load sframe dataBuffer = turi.SFrame("rice_or_soup.sframe") # create training data using 90% of dataset trainingBuffers, testingBuffers = dataBuffer.random_split(0.9) # create model model = turi.image_classifier.create(trainingBuffers, target="foodType", model="squeezenet_v1.1", max_iterations=100) # Alternate model use ResNet-50 # model = turi.image_classifier.create(trainingBuffers, target="foodType", model="resnet-50") # evaluate model evaluations = model.evaluate(testingBuffers) print evaluations["accuracy"] # save model model.save("rice_or_soup.model") model.export_coreml("RiceSoupClassifier.mlmodel") EOF #run script python classifier.py


(2) Test turicreate example - Logistic Regression
shell script    Select all
mkdir -p $HOME/LGClassifier cd $HOME/LGClassifier # create python script cat > classifier.py << 'EOF' import turicreate as turi data = turi.SFrame('http://static.turi.com/datasets/regression/yelp-data.csv') data['is_good'] = data['stars'] >= 3 # create sframe data.save("yelp.sframe") # preview dataset #data.show() # load sframe dataBuffer = turi.SFrame("yelp.sframe") # create training data using 80% of dataset train_data, test_data = dataBuffer.random_split(0.8) # create model model=turi.logistic_classifier.create(train_data, target='is_good', features = ['user_avg_stars', 'business_avg_stars', 'user_review_count', 'business_review_count', 'city', 'categories_dict'], max_iterations=200) print model # save predictions predictions = model.classify(test_data) print predictions # evaluate model evaluations = model.evaluate(test_data) print "Accuracy : %s" % evaluations["accuracy"] print "Confusion Matrix : \n%s" % evaluations["confusion_matrix"] EOF #run script python classifier.py


(3) Some data manipulation tips when preparing training data
shell script    Select all
# remove the quotes (replace the number with the quotes with the number without them) in csv file, typically "save as CSV" from excel file. # for example, "222,267.87","455,365.44",... convert to 222267.87,455365.44,... #In shell script cat exceldata.csv | perl -p -e 's/,(?=[\d,.]*\d")//g and s/"(\d[\d,.]*)"/\1/g' > dataset.csv # use map and lambda functions when convert and compute numeric data #In python script import math data['rate'] = map(lambda (x,y): 0 if x is None or y is None else (0 if math.isnan(x) or math.isnan(y) or math.isinf(y) or x==0 else (999999 if math.isinf(x) or y==0 else 999999 if x/y > 999999 else x/y)) , zip(data['OS'], data['Total Amount'])) # replace training data when values are inf(infinity) or nan(Not A Number) in 'amount' column #In python script import math train_data['amount'] = train_data['amount'].apply(lambda x: 0 if math.isnan(x) else x) train_data['amount'] = train_data['amount'].apply(lambda x: 999 if math.isinf(x) else x) # or use nested if else #In python script import math train_data['amount'] = train_data['amount'].apply(lambda x: 0 if math.isnan(x) else (999 if math.isinf(x) else x )) print train_data['amount'].summary() # remove rows in training data with inf(infinity) or nan(Not A Number) values in 'amount' column #In python script import math train_data = train_data[train_data['amount'].apply(lambda x: 0 if math.isinf(x) or math.isnan(x) else 1)] # SFrame methods but beware, some of the methods are not yet implemented https://apple.github.io/turicreate/docs/api/generated/turicreate.SFrame.html


(4) Some data examination tips
shell script    Select all
# summary print train_data['amount'].summary() # crosstab import pandas as pd pd.crosstab(data["Rating"], data["is_bad"],margins=True) # custom frequency count import pandas as pd pd.crosstab(train_data['amount'].apply(lambda x: " 0-10" if x <=10 else ("10-20" if x <=20 else ("20-30" if x <=30 else ("30-40" if x <=30 else ("40-50" if x <=50 else ">50"))))), "Count")


Saturday, June 9, 2018

Playground examples for XCode 10 Beta 1

Playground Support for iOS12 and Swift 4.2
iOS.playground    Select all
import UIKit import PlaygroundSupport //: **Markup** //: ### Define UIView class MyView : UIView { @objc public func changeTitle(_ sender:UIButton!) { sender.setTitle("Welcome to WWDC2018", for: []) } } let myView = MyView(frame: CGRect(x:0, y:0, width:500, height:500)) var button = UIButton(type: .system) button.frame = CGRect(x:100, y:100, width:300, height:200) button.setTitle("Hi Press me!", for: []) button.tintColor = .blue button.setTitleColor(.orange, for: []) button.addTarget(myView, action: #selector(MyView.changeTitle(_:)), for: .touchUpInside) myView.addSubview(button) PlaygroundPage.current.liveView = myView




Playground Icon Drawings in iOS and Swift 4.2
macOS.playground    Select all
import UIKit //: Define IconView class IconView: UIView { override func draw(_ rect: CGRect) { drawRawBackgroundWithBaseColor(strokeColor: UIColor.orange, backgroundRectangle: self.bounds) let textAttributes: [NSAttributedString.Key : Any] = [ NSAttributedString.Key.foregroundColor: UIColor.red, NSAttributedString.Key.font: UIFont.systemFont(ofSize: 32.0)] let FString: String = "Hello World" let distanceX: CGFloat = -12.0 let distanceY: CGFloat = 0.0 let centerX = self.bounds.midX let centerY = self.bounds.midY FString.draw(at: CGPoint(x:centerX+distanceX, y:centerY+distanceY), withAttributes: textAttributes) } } func drawRawBackgroundWithBaseColor(strokeColor: UIColor, backgroundRectangle:CGRect) { let lineWidth = backgroundRectangle.width/36.0 let cornerRadius = backgroundRectangle.width/16.0 let tileRectangle = backgroundRectangle.insetBy(dx: lineWidth/2.0, dy: lineWidth/2.0) // Stroke Drawing let strokePath = UIBezierPath(roundedRect:tileRectangle, cornerRadius:cornerRadius) strokeColor.setStroke() strokePath.lineWidth = lineWidth strokePath.stroke() // Draw an ellipse let ovalPath = UIBezierPath(ovalIn: backgroundRectangle.insetBy(dx: lineWidth*1.5, dy: lineWidth*1.5)) UIColor.blue.setStroke() ovalPath.lineWidth = lineWidth ovalPath.stroke() let context:CGContext = UIGraphicsGetCurrentContext()! context.setFillColor(UIColor.green.cgColor) context.addRect(CGRect(x: 100.0, y: 100.0, width: 60.0, height: 60.0)) context.fillPath() } //: Instantiate the UIView let rect = CGRect(x: 0.0, y: 0.0, width: 420.0, height: 320.0) let icon = IconView(frame: rect) icon.backgroundColor = UIColor.clear




CreateML for macOS 10.14 Beta 1 (requires macOS 10.14 Mojave)
macOS.playground    Select all
import Cocoa import CreateML //: Specify Data /* Input as CSV mycsv.csv: beds,baths,squareFt, price 2,2,2000,400000 4,3,2500,500000 3,2,1800,450000 3,2,1500,300000 let houseData = try MLDataTable(contentsOf: URL(fileURLWithPath: "mycsv.csv")) */ //: Input as dictionary let mydata : [String: MLDataValueConvertible] = [ "beds": [2,4,3,3], "baths": [2,3,2,2], "squareFt": [2000,2500,1800,1500], "price": [400000,500000,450000,300000] ] let houseData = try MLDataTable(dictionary:mydata) let (trainingData, testData) = houseData.randomSplit(by: 0.8, seed: 0) //: Create Model let pricer = try MLRegressor(trainingData: houseData, targetColumn: "price") //: Evaluate Model let evalator = pricer.evaluation(on: testData) print(pricer) //: Save Model try pricer.write(to:URL(fileURLWithPath: "HousePricer.mlmodel"))