working on new python implementation with cleaner code

app.py

@@ -0,0 +1,27 @@
+from flask import Flask, request, jsonify
+
+import pandas as pd
+from nn.nn import NN
+from nn import train as train_nn
+
+app = Flask(__name__)
+
+
+@app.route("/neural-network", methods=["POST"])
+def neural_net():
+    args = request.json
+
+    try:
+        net = NN.from_dict(args)
+        df = pd.read_csv(args.pop("data"))
+    except Exception as e:
+        return jsonify({
+            "bad request": f"could not read csv data: {e}",
+        })
+
+    result = train_nn(nn=net)
+    return jsonify(result)
+
+
+if __name__ == "__main__":
+    app.run(debug=True)

example/main.py

@@ -7,10 +7,11 @@ ARGS = {
     "epochs": 100,
     "hidden_size": 12,
     "learning_rate": 0.01,
+    "test_size": 0.3,
     "activation": "tanh",
     "features": ["sepal width", "sepal length", "petal width", "petal length"],
     "target": "species",
-    "data": iris_data.decode('utf-8'),
+    "data": iris_data.decode("utf-8"),
 }
 
 r = requests.post(
@@ -19,4 +20,4 @@ r = requests.post(
 )
 
 if __name__ == "__main__":
-    print(r.json())
+    print(r.json())

go.mod

@@ -1,24 +0,0 @@
-module github.com/Jensen-holm/ml-from-scratch
-
-go 1.19
-
-require (
-	github.com/go-gota/gota v0.12.0
-	github.com/gofiber/fiber/v2 v2.49.2
-)
-
-require (
-	github.com/andybalholm/brotli v1.0.5 // indirect
-	github.com/google/uuid v1.3.1 // indirect
-	github.com/klauspost/compress v1.16.7 // indirect
-	github.com/mattn/go-colorable v0.1.13 // indirect
-	github.com/mattn/go-isatty v0.0.19 // indirect
-	github.com/mattn/go-runewidth v0.0.15 // indirect
-	github.com/rivo/uniseg v0.2.0 // indirect
-	github.com/valyala/bytebufferpool v1.0.0 // indirect
-	github.com/valyala/fasthttp v1.49.0 // indirect
-	github.com/valyala/tcplisten v1.0.0 // indirect
-	golang.org/x/net v0.17.0 // indirect
-	golang.org/x/sys v0.13.0 // indirect
-	gonum.org/v1/gonum v0.14.0 // indirect
-)

go.sum

@@ -1,109 +0,0 @@
-dmitri.shuralyov.com/gpu/mtl v0.0.0-20190408044501-666a987793e9/go.mod h1:H6x//7gZCb22OMCxBHrMx7a5I7Hp++hsVxbQ4BYO7hU=
-gioui.org v0.0.0-20210308172011-57750fc8a0a6/go.mod h1:RSH6KIUZ0p2xy5zHDxgAM4zumjgTw83q2ge/PI+yyw8=
-github.com/BurntSushi/xgb v0.0.0-20160522181843-27f122750802/go.mod h1:IVnqGOEym/WlBOVXweHU+Q+/VP0lqqI8lqeDx9IjBqo=
-github.com/ajstarks/svgo v0.0.0-20180226025133-644b8db467af/go.mod h1:K08gAheRH3/J6wwsYMMT4xOr94bZjxIelGM0+d/wbFw=
-github.com/andybalholm/brotli v1.0.5 h1:8uQZIdzKmjc/iuPu7O2ioW48L81FgatrcpfFmiq/cCs=
-github.com/andybalholm/brotli v1.0.5/go.mod h1:fO7iG3H7G2nSZ7m0zPUDn85XEX2GTukHGRSepvi9Eig=
-github.com/boombuler/barcode v1.0.0/go.mod h1:paBWMcWSl3LHKBqUq+rly7CNSldXjb2rDl3JlRe0mD8=
-github.com/davecgh/go-spew v1.1.0/go.mod h1:J7Y8YcW2NihsgmVo/mv3lAwl/skON4iLHjSsI+c5H38=
-github.com/fogleman/gg v1.2.1-0.20190220221249-0403632d5b90/go.mod h1:R/bRT+9gY/C5z7JzPU0zXsXHKM4/ayA+zqcVNZzPa1k=
-github.com/fogleman/gg v1.3.0/go.mod h1:R/bRT+9gY/C5z7JzPU0zXsXHKM4/ayA+zqcVNZzPa1k=
-github.com/go-fonts/dejavu v0.1.0/go.mod h1:4Wt4I4OU2Nq9asgDCteaAaWZOV24E+0/Pwo0gppep4g=
-github.com/go-fonts/latin-modern v0.2.0/go.mod h1:rQVLdDMK+mK1xscDwsqM5J8U2jrRa3T0ecnM9pNujks=
-github.com/go-fonts/liberation v0.1.1/go.mod h1:K6qoJYypsmfVjWg8KOVDQhLc8UDgIK2HYqyqAO9z7GY=
-github.com/go-fonts/stix v0.1.0/go.mod h1:w/c1f0ldAUlJmLBvlbkvVXLAD+tAMqobIIQpmnUIzUY=
-github.com/go-gl/glfw v0.0.0-20190409004039-e6da0acd62b1/go.mod h1:vR7hzQXu2zJy9AVAgeJqvqgH9Q5CA+iKCZ2gyEVpxRU=
-github.com/go-gota/gota v0.12.0 h1:T5BDg1hTf5fZ/CO+T/N0E+DDqUhvoKBl+UVckgcAAQg=
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-github.com/klauspost/compress v1.16.7/go.mod h1:ntbaceVETuRiXiv4DpjP66DpAtAGkEQskQzEyD//IeE=
-github.com/mattn/go-colorable v0.1.13 h1:fFA4WZxdEF4tXPZVKMLwD8oUnCTTo08duU7wxecdEvA=
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-golang.org/x/net v0.17.0/go.mod h1:NxSsAGuq816PNPmqtQdLE42eU2Fs7NoRIZrHJAlaCOE=
-golang.org/x/sync v0.0.0-20190423024810-112230192c58/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
-golang.org/x/sys v0.0.0-20190215142949-d0b11bdaac8a/go.mod h1:STP8DvDyc/dI5b8T5hshtkjS+E42TnysNCUPdjciGhY=
-golang.org/x/sys v0.0.0-20190312061237-fead79001313/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
-golang.org/x/sys v0.0.0-20190412213103-97732733099d/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
-golang.org/x/sys v0.0.0-20201119102817-f84b799fce68/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
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-golang.org/x/tools v0.0.0-20190927191325-030b2cf1153e/go.mod h1:b+2E5dAYhXwXZwtnZ6UAqBI28+e2cm9otk0dWdXHAEo=
-golang.org/x/xerrors v0.0.0-20190717185122-a985d3407aa7/go.mod h1:I/5z698sn9Ka8TeJc9MKroUUfqBBauWjQqLJ2OPfmY0=
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-gonum.org/v1/netlib v0.0.0-20190313105609-8cb42192e0e0/go.mod h1:wa6Ws7BG/ESfp6dHfk7C6KdzKA7wR7u/rKwOGE66zvw=
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-gonum.org/v1/plot v0.9.0/go.mod h1:3Pcqqmp6RHvJI72kgb8fThyUnav364FOsdDo2aGW5lY=
-rsc.io/pdf v0.1.1/go.mod h1:n8OzWcQ6Sp37PL01nO98y4iUCRdTGarVfzxY20ICaU4=

nn/activation.go

@@ -1,41 +0,0 @@
-package nn
-
-import "math"
-
-var ActivationMap = map[string]func(float64) float64{
-	"sigmoid": Sigmoid,
-	"tanh":    Tanh,
-	"relu":    Relu,
-}
-
-func Sigmoid(x float64) float64 {
-	return 1.0 / (1.0 + math.Exp(-x))
-}
-
-func SigmoidPrime(x float64) float64 {
-	s := Sigmoid(x)
-	return s / (1.0 - s)
-}
-
-func Tanh(x float64) float64 {
-	return math.Tanh(x)
-}
-
-func TanhPrime(x float64) float64 {
-	return math.Pow((1.0 / math.Cosh(x)), 2)
-}
-
-func Relu(x float64) float64 {
-	if x > 0 {
-		return x
-	}
-	return 0
-}
-
-func ReluPrime(x float64) float64 {
-	// maybe want to look into edge case if x == 0
-	if x > 0 {
-		return 1
-	}
-	return 0
-}

nn/args.go

@@ -1,17 +0,0 @@
-package nn
-
-type NNArgs struct {
-	epochs         int
-	hiddenSize     int
-	learningRate   float64
-	activationFunc func()
-}
-
-func NewArgs(argsMap map[string]interface{}) *NNArgs {
-	return &NNArgs{
-		epochs:         argsMap["epochs"].(int),
-		hiddenSize:     argsMap["hidden_size"].(int),
-		learningRate:   argsMap["learning_rate"].(float64),
-		activationFunc: argsMap["activation"].(func()),
-	}
-}

nn/backprop.go

@@ -1,99 +0,0 @@
-package nn
-
-import (
-	"fmt"
-
-	"gonum.org/v1/gonum/mat"
-)
-
-func (nn *NN) Backprop() {
-	var (
-		activation = *nn.ActivationFunc
-		// lossHist   []float64
-	)
-
-	for i := 0; i < nn.Epochs; i++ {
-		// compute output with current w + b
-		// then compute loss & backprop
-		hiddenOutput, err := computeOutput(
-			nn.XTrain,
-			nn.Wh,
-			nn.Bh,
-			activation,
-		)
-		if err != nil {
-			fmt.Printf("error computing hidden output: %v", err)
-		}
-
-		yHat, err := computeOutput(
-			hiddenOutput,
-			nn.Wo,
-			nn.Bo,
-			activation,
-		)
-		if err != nil {
-			fmt.Printf("error computing yHat: %v", err)
-		}
-
-		mse := meanSquaredError(nn.YTrain, yHat)
-		fmt.Println(mse)
-
-	}
-
-}
-
-func computeOutput(arr, w, b *mat.Dense, activationFunc func(float64) float64) (*mat.Dense, error) {
-	// Check if any of the input matrices is nil
-	if arr == nil || w == nil || b == nil {
-		return nil, fmt.Errorf("Input matrices cannot be nil")
-	}
-
-	// Check input dimensions
-	arrRows, arrCols := arr.Dims()
-	wRows, wCols := w.Dims()
-	bRows, bCols := b.Dims()
-
-	if arrCols != wRows || bCols != wCols {
-		return nil, fmt.Errorf("Matrix dimension mismatch: arr[%d, %d], w[%d, %d], b[%d, %d]", arrRows, arrCols, wRows, wCols, bRows, bCols)
-	}
-
-	// Compute the dot product between the input matrix 'arr' and the weight matrix 'w'
-	var product mat.Dense
-	product.Mul(arr, w)
-
-	// Check dimensions of product and bias
-	productRows, productCols := product.Dims()
-	if productCols != bCols {
-		return nil, fmt.Errorf("Matrix dimension mismatch: product[%d, %d], b[%d, %d]", productRows, productCols, bRows, bCols)
-	}
-
-	// Add the bias matrix 'b' to the product
-	var result mat.Dense
-	result.Add(&product, b)
-
-	// Apply the activation function to the result
-	applyActivation(&result, activationFunc)
-
-	return &result, nil
-}
-
-func applyActivation(m *mat.Dense, f func(float64) float64) {
-	r, c := m.Dims()
-	data := m.RawMatrix().Data
-	for i := 0; i < r*c; i++ {
-		data[i] = f(data[i])
-	}
-}
-
-func meanSquaredError(y, yHat *mat.Dense) float64 {
-	var sum float64
-	r, c := y.Dims()
-
-	for row := 0; row < r; row++ {
-		for col := 0; col < c; col++ {
-			diff := y.At(row, col) - yHat.At(row, col)
-			sum += (diff * diff)
-		}
-	}
-	return sum / float64((r * c))
-}

nn/main.go

@@ -1,84 +0,0 @@
-package nn
-
-import (
-	"fmt"
-	"math/rand"
-	"strings"
-
-	"github.com/go-gota/gota/dataframe"
-	"github.com/gofiber/fiber/v2"
-	"gonum.org/v1/gonum/mat"
-)
-
-type NN struct {
-	// attributes set by request
-	CSVData      string   `json:"csv_data"`
-	Features     []string `json:"features"`
-	Target       string   `json:"target"`
-	Epochs       int      `json:"epochs"`
-	HiddenSize   int      `json:"hidden_size"`
-	LearningRate float64  `json:"learning_rate"`
-	Activation   string   `json:"activation"`
-	TestSize     float64  `json:"test_size"`
-
-	// attributes set after args above are parsed
-	ActivationFunc *func(float64) float64
-	Df             *dataframe.DataFrame
-	XTrain         *mat.Dense
-	YTrain         *mat.Dense
-	XTest          *mat.Dense
-	YTest          *mat.Dense
-	Wh             *mat.Dense
-	Bh             *mat.Dense
-	Wo             *mat.Dense
-	Bo             *mat.Dense
-}
-
-func NewNN(c *fiber.Ctx) (*NN, error) {
-	newNN := new(NN)
-	err := c.BodyParser(newNN)
-	if err != nil {
-		return nil, fmt.Errorf("invalid JSON data: %v", err)
-	}
-	df := dataframe.ReadCSV(strings.NewReader(newNN.CSVData))
-	activation := ActivationMap[newNN.Activation]
-
-	newNN.Df = &df
-	newNN.ActivationFunc = &activation
-	return newNN, nil
-}
-
-func (nn *NN) InitWnB() {
-	// randomly initialize weights and biases to start
-	inputSize := len(nn.Features)
-	hiddenSize := nn.HiddenSize
-	outputSize := 1 // only predicting one thing
-
-	// Initialize input hidden layer weights as a Gonum matrix
-	wh := mat.NewDense(inputSize, hiddenSize, nil)
-	wh.Apply(func(i, j int, v float64) float64 {
-		return rand.Float64() - 0.5
-	}, wh)
-
-	// Initialize hidden layer bias as a Gonum matrix
-	bh := mat.NewDense(1, hiddenSize, nil)
-	bh.Apply(func(i, j int, v float64) float64 {
-		return rand.Float64() - 0.5
-	}, bh)
-
-	// Initialize weights and biases for hidden -> output layer as Gonum matrices
-	wo := mat.NewDense(hiddenSize, outputSize, nil)
-	wo.Apply(func(i, j int, v float64) float64 {
-		return rand.Float64() - 0.5
-	}, wo)
-
-	bo := mat.NewDense(1, outputSize, nil)
-	bo.Apply(func(i, j int, v float64) float64 {
-		return rand.Float64() - 0.5
-	}, bo)
-
-	nn.Wh = wh
-	nn.Bh = bh
-	nn.Wo = wo
-	nn.Bo = bo
-}

nn/nn.py

@@ -0,0 +1,32 @@
+import pandas as pd
+
+
+class NN:
+    def __init__(
+        self,
+        epochs: int,
+        hidden_size: int,
+        learning_rate: float,
+        test_size: float,
+        activation: str,
+        features: list[str],
+        target: str,
+        data: str,
+    ):
+        self.epochs = epochs
+        self.hidden_size = hidden_size
+        self.learning_rate = learning_rate
+        self.test_size = test_size
+        self.activation = activation
+        self.features = features
+        self.target = target
+        self.data = data
+
+        self.df: pd.DataFrame = None
+
+    @classmethod
+    def from_dict(cls, dct):
+        """ Creates an instance of NN given a dictionary
+        we can use this to make sure that the arguments are right
+        """
+        return cls(**dct)

nn/split.go

@@ -1,60 +0,0 @@
-package nn
-
-import (
-	"math"
-	"math/rand"
-
-	"github.com/go-gota/gota/dataframe"
-	"gonum.org/v1/gonum/mat"
-)
-
-func (nn *NN) TrainTestSplit() {
-	// now we split the data into training
-	// and testing based on user specified
-	// nn.TestSize.
-	nRows := nn.Df.Nrow()
-	testRows := int(math.Floor(float64(nRows) * nn.TestSize))
-
-	// subset the testing data
-	// randomly select trainRows number of rows
-	randStrt := rand.Intn(int(math.Floor(float64(nRows) * nn.TestSize)))
-	test := nn.Df.Subset([]int{randStrt, randStrt + testRows})
-
-	// use what is left for training
-	allIndices := make([]int, nRows)
-	for i := range allIndices {
-		allIndices[i] = i
-	}
-
-	// Remove the test indices using slice append and variadic parameter
-	trainIndices := append(allIndices[:randStrt], allIndices[randStrt+testRows:]...)
-
-	// Create the train DataFrame using the trainIndices
-	train := nn.Df.Subset(trainIndices)
-
-	XTrain := train.Select(nn.Features)
-	YTrain := train.Select(nn.Target)
-	XTest := test.Select(nn.Features)
-	YTest := test.Select(nn.Target)
-
-	// to make linear algebra easier & faster,
-	// we convert these dataframes that we are
-	// performing potentially expensive computations
-	// on into gonum matrices since we no longer need the
-	// column names.
-	nn.XTrain = df2mat(&XTrain)
-	nn.YTrain = df2mat(&YTrain)
-	nn.XTest = df2mat(&XTest)
-	nn.YTest = df2mat(&YTest)
-}
-
-// df2mat -> converts gota dataframe into gonum matrix
-func df2mat(df *dataframe.DataFrame) *mat.Dense {
-	m := mat.NewDense(df.Nrow(), df.Ncol(), nil)
-	for i := 0; i < df.Nrow(); i++ {
-		for j := 0; j < df.Ncol(); j++ {
-			m.Set(i, j, df.Elem(i, j).Float())
-		}
-	}
-	return m
-}

nn/subset.go

@@ -1,6 +0,0 @@
-package nn
-
-// subset the data frame into just the
-// features and target that the user specify
-
-func Subset() {}

nn/train.go

@@ -1,7 +0,0 @@
-package nn
-
-func (nn *NN) Train() {
-	nn.InitWnB()
-	nn.TrainTestSplit()
-	nn.Backprop()
-}

nn/train.py

@@ -0,0 +1,15 @@
+from sklearn.model_selection import train_test_split
+from nn.nn import NN
+import pandas as pd
+import numpy as np
+
+
+def train(nn: NN):
+    X_train, X_test, y_train, y_test = train_test_split(
+        nn.X,
+        nn.y,
+        test_size=nn.test_size,
+        random_state=88,
+    )
+
+

requirements.txt

@@ -0,0 +1,3 @@
+Flask==3.0.0
+pandas==2.1.1
+Requests==2.31.0

server.go

@@ -1,26 +0,0 @@
-package main
-
-import (
-	"github.com/Jensen-holm/ml-from-scratch/nn"
-	"github.com/gofiber/fiber/v2"
-)
-
-func main() {
-	app := fiber.New()
-
-	// eventually we might want to add a key to this endpoint
-	// that we will be able to validate.
-	app.Post("/neural-network", func(c *fiber.Ctx) error {
-		nn, err := nn.NewNN(c)
-		if err != nil {
-			return c.Status(fiber.StatusBadRequest).JSON(fiber.Map{
-				"error": err,
-			})
-		}
-
-		nn.Train()
-		return c.SendString("No error")
-	})
-
-	app.Listen(":3000")
-}