const dfd = require("danfojs-node") const tf = require("@tensorflow/tfjs-node") let data = tf.tensor2d([[20,30,40], [23,90, 28]]) let df = new dfd.DataFrame(data) let tf_tensor = df.tensor console.log(tf_tensor); tf_tensor.print()
Tensor { kept: false, isDisposedInternal: false, shape: [ 2, 3 ], dtype: 'float32', size: 6, strides: [ 3 ], dataId: {}, id: 3, rankType: '2' } Tensor [[20, 30, 40], [23, 90, 28]]
const dfd = require("danfojs-node") json_data = [{ A: 0.4612, B: 4.28283, C: -1.509, D: -1.1352 }, { A: 0.5112, B: -0.22863, C: -3.39059, D: 1.1632 }, { A: 0.6911, B: -0.82863, C: -1.5059, D: 2.1352 }, { A: 0.4692, B: -1.28863, C: 4.5059, D: 4.1632 }] df = new dfd.DataFrame(json_data) df.print()
const dfd = require("danfojs-node") obj_data = {'A': [“A1”, “A2”, “A3”, “A4”], 'B': ["bval1", "bval2", "bval3", "bval4"], 'C': [10, 20, 30, 40], 'D': [1.2, 3.45, 60.1, 45], 'E': ["test", "train", "test", "train"] } df = new dfd.DataFrame(obj_data) df.print()
const dfd = require("danfojs-node") let data = {"Name":["Apples", "Mango", "Banana", undefined], "Count": [NaN, 5, NaN, 10], "Price": [200, 300, 40, 250]} let df = new dfd.DataFrame(data) let df_filled = df.fillna({columns: ["Name", "Count"], values: ["Apples", df["Count"].mean()]}) df_filled.print()
const dfd = require("danfojs-node") let data = { "Name": ["Apples", "Mango", "Banana", "Pear"] , "Count": [21, 5, 30, 10], "Price": [200, 300, 40, 250] } let df = new dfd.DataFrame(data) let sub_df = df.loc({ rows: ["0:2"], columns: ["Name", "Price"] }) sub_df.print()
const dfd = require("danfojs-node") //read the first 10000 rows dfd.read_csv("file:///home/Desktop/bigdata.csv", chunk=10000) .then(df => { df.tail().print() }).catch(err=>{ console.log(err); })
const dfd = require("danfojs-node") let data = ["dog","cat","man","dog","cat","man","man","cat"] let series = new dfd.Series(data) let encode = new dfd.LabelEncoder() encode.fit(series) let sf_enc = encode.transform(series) let new_sf = encode.transform(["dog","man"])
<!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <script src="https://cdn.jsdelivr.net/npm/danfojs@0.1.1/dist/index.min.js"></script> <title>Document</title> </head> <body> <div id="plot_div"></div> <script> dfd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv") .then(df => { var layout = { title: 'A financial charts', xaxis: {title: 'Date'}, yaxis: {title: 'Count'} } new_df = df.set_index({ key: "Date" }) new_df.plot("plot_div").line({ columns: ["AAPL.Open", "AAPL.High"], layout: layout }) }).catch(err => { console.log(err); }) </script> </body> </html>
const dfd = require("danfojs-node") const tf = require("@tensorflow/tfjs-node") async function load_process_data() { let df = await dfd.read_csv("https://web.stanford.edu/class/archive/cs/cs109/cs109.1166/stuff/titanic.csv") //A feature engineering: Extract all titles from names columns let title = df['Name'].apply((x) => { return x.split(".")[0] }).values //replace in df df.addColumn({ column: "Name", value: title }) //label Encode Name feature let encoder = new dfd.LabelEncoder() let cols = ["Sex", "Name"] cols.forEach(col => { encoder.fit(df[col]) enc_val = encoder.transform(df[col]) df.addColumn({ column: col, value: enc_val }) }) let Xtrain,ytrain; Xtrain = df.iloc({ columns: [`1:`] }) ytrain = df['Survived'] // Standardize the data with MinMaxScaler let scaler = new dfd.MinMaxScaler() scaler.fit(Xtrain) Xtrain = scaler.transform(Xtrain) return [Xtrain.tensor, ytrain.tensor] //return the data as tensors }
function get_model() { const model = tf.sequential(); model.add(tf.layers.dense({ inputShape: [7], units: 124, activation: 'relu', kernelInitializer: 'leCunNormal' })); model.add(tf.layers.dense({ units: 64, activation: 'relu' })); model.add(tf.layers.dense({ units: 32, activation: 'relu' })); model.add(tf.layers.dense({ units: 1, activation: "sigmoid" })) model.summary(); return model }
async function train() { const model = await get_model() const data = await load_process_data() const Xtrain = data[0] const ytrain = data[1] model.compile({ optimizer: "rmsprop", loss: 'binaryCrossentropy', metrics: ['accuracy'], }); console.log("Training started....") await model.fit(Xtrain, ytrain,{ batchSize: 32, epochs: 15, validationSplit: 0.2, callbacks:{ onEpochEnd: async(epoch, logs)=>{ console.log(`EPOCH (${epoch + 1}): Train Accuracy: ${(logs.acc * 100).toFixed(2)}, Val Accuracy: ${(logs.val_acc * 100).toFixed(2)}\n`); } } }); }; train()
