From 44a759f3a81a503553d02c3391a19126dcf9fa19 Mon Sep 17 00:00:00 2001 From: Benjamin Bengfort Date: Fri, 6 Dec 2019 22:19:14 -0500 Subject: [PATCH 1/6] initial contours prototype --- app.py | 10 +++++- static/js/dataspace.js | 70 ++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 79 insertions(+), 1 deletion(-) diff --git a/app.py b/app.py index f70666d..0a9cee5 100644 --- a/app.py +++ b/app.py @@ -94,6 +94,7 @@ def fit(): data = request.get_json() params = data.get("model", {}) dataset = data.get("dataset", []) + grid = data.get("grid", []) model = { 'gaussiannb': GaussianNB(), 'multinomialnb': MultinomialNB(), @@ -132,8 +133,15 @@ def fit(): yhat = model.predict(X) metrics = prfs(y, yhat, average="macro") + # Make predictions on the grid + Xp = asarray([ + [point["x"], point["y"]] for point in grid + ]) + preds = model.predict(Xp) + return jsonify({ - "metrics": dict(zip(["precision", "recall", "f1", "support"], metrics)) + "metrics": dict(zip(["precision", "recall", "f1", "support"], metrics)), + "grid": [int(v) for v in preds], }) ########################################################################## diff --git a/static/js/dataspace.js b/static/js/dataspace.js index 532e9ec..7c18d4e 100644 --- a/static/js/dataspace.js +++ b/static/js/dataspace.js @@ -96,9 +96,13 @@ class Dataspace { return } + // The contours grid determines what to make predictions on. + // TODO: don't pass this to the server but allow the server to compute it. + var grid = this.contoursGrid() var data = { model: model, dataset: this.dataset, + grid: grid } d3.json("/fit", { @@ -110,6 +114,31 @@ class Dataspace { }).then(json => { $("#f1score").text(json.metrics.f1); $("#metrics").removeClass("invisible").addClass("visible"); + + // Update the gird with the predictions values. + $.each(json.grid, function(i, val) { + grid[i] = val; + }) + + // Add the contours from the predictions for each class + var contours = d3.contours() + .size([grid.n, grid.m]) + .thresholds(this.classes()) + (grid) + .map(grid.transform) + + // Draw the contours on the SVG + this.svg.insert("g", ":first-child") + .attr("fill", "none") + .attr("stroke", "#fff") + .attr("stroke-opacity", 0.65) + .selectAll("path") + .data(contours) // Here is where the contours gets added + .join("path") + .attr("fill", d => this.color(d.value)) // Here is the color value! + .style("opacity", 0.45) + .attr("d", d3.geoPath()); + }).catch(error => { alertMessage("Could not fit model, check JSON hyperparams and try again!"); }); @@ -119,6 +148,7 @@ class Dataspace { reset() { this.dataset = []; this.svg.selectAll("circle").remove(); + this.svg.selectAll("g").remove(); $("#metrics").removeClass("visible").addClass("invisible"); } @@ -132,6 +162,46 @@ class Dataspace { }, []); } + // Create the contours grid to pass to the predict function. + contoursGrid() { + var self = this; + const q = 4; + const x0 = -q / 2, x1 = this.width + margin.right + q; + const y0 = -q / 2, y1 = this.height + q; + const n = Math.ceil((x1-x0) / q); + const m = Math.ceil((y1-y0) / q); + const grid = new Array(n*m); + grid.x = -q; + grid.y = -q; + grid.k = q; + grid.n = n; + grid.m = m; + + // Converts from grid coordinates (indexes) to screen coordinates (pixels). + grid.transform = ({ type, value, coordinates }) => { + return { + type, value, coordinates: coordinates.map(rings => { + return rings.map(points => { + return points.map(([x, y]) => ([ + grid.x + grid.k * x, + grid.y + grid.k * y + ])); + }); + }) + }; + } + + // We just have to pass the x and y values to the server to predict them using the model, then the rest of the code is the sames? + for (let j = 0; j < m; ++j) { + for (let i = 0; i < n; ++i) { + var obj = { x: this.xScale.invert(i * q + x0), y: this.yScale.invert(j * q + y0) }; + grid[j * grid.n + i] = obj; + } + } + + return grid; + } + } $(document).ready(function() { From 4ded7efa8c2b6e1f7a10bdc5d1f4806b93d4fc78 Mon Sep 17 00:00:00 2001 From: Benjamin Bengfort Date: Fri, 6 Dec 2019 22:35:20 -0500 Subject: [PATCH 2/6] grid on class --- static/js/dataspace.js | 112 ++++++++++++++++++++++------------------- 1 file changed, 59 insertions(+), 53 deletions(-) diff --git a/static/js/dataspace.js b/static/js/dataspace.js index 7c18d4e..3af1fb2 100644 --- a/static/js/dataspace.js +++ b/static/js/dataspace.js @@ -24,48 +24,49 @@ function alertMessage(message) { class Dataspace { constructor(selector) { - this.svg = d3.select(selector); - this.$svg = $(selector); - this.dataset = []; - - // drawing properties are hardcoded for now - this.width = this.$svg.width(); - this.height = this.$svg.height(); - this.color = d3.scaleOrdinal(d3.schemeCategory10); - - this.xScale = d3.scaleLinear() - .domain([0, 1]) - .range([margin.left, this.width - margin.right]); - - this.yScale = d3.scaleLinear() - .domain([0, 1]) - .range([margin.top, this.height - margin.bottom]) + this.svg = d3.select(selector); + this.$svg = $(selector); + this.dataset = []; + this.grid = null; + + // drawing properties are hardcoded for now + this.width = this.$svg.width(); + this.height = this.$svg.height(); + this.color = d3.scaleOrdinal(d3.schemeCategory10); + + this.xScale = d3.scaleLinear() + .domain([0, 1]) + .range([margin.left, this.width - margin.right]); + + this.yScale = d3.scaleLinear() + .domain([0, 1]) + .range([margin.top, this.height - margin.bottom]) } draw() { var self = this; self.svg.selectAll("circle") - .data(self.dataset) - .enter() - .append("circle") - .attr('cx', function (d) { return self.xScale(d.x); }) - .attr('cy', function (d) { return self.yScale(d.y); }) - .attr('fill', function (d) { return self.color(d.c); }) - .attr('r', radius); + .data(self.dataset) + .enter() + .append("circle") + .attr('cx', function (d) { return self.xScale(d.x); }) + .attr('cy', function (d) { return self.yScale(d.y); }) + .attr('fill', function (d) { return self.color(d.c); }) + .attr('r', radius); } // Add raw data point (e.g. where x and y are between 0 and 1) addPoint(point) { - this.dataset.push(point); - this.draw(); + this.dataset.push(point); + this.draw(); } // Add coordinates data point (e.g. where x and y are in the svg) addCoords(coords) { var point = { - x: this.xScale.invert(coords[0]), - y: this.yScale.invert(coords[1]), - c: currentClass + x: this.xScale.invert(coords[0]), + y: this.yScale.invert(coords[1]), + c: currentClass }; this.addPoint(point); } @@ -74,14 +75,14 @@ class Dataspace { fetch(data) { this.reset(); d3.json("/generate", { - method: "POST", - body: JSON.stringify(data), - headers: { - "Content-Type": "application/json; charset=UTF-8" - } + method: "POST", + body: JSON.stringify(data), + headers: { + "Content-Type": "application/json; charset=UTF-8" + } }).then(json => { - this.dataset = json; - this.draw(); + this.dataset = json; + this.draw(); }).catch(error => { console.log(error); alertMessage("Server could not generate dataset!"); @@ -92,17 +93,18 @@ class Dataspace { fit(model) { $("#metrics").removeClass("visible").addClass("invisible"); if (this.dataset.length == 0) { - console.log("cannot fit model to no data!"); - return + console.log("cannot fit model to no data!"); + return } // The contours grid determines what to make predictions on. // TODO: don't pass this to the server but allow the server to compute it. - var grid = this.contoursGrid() + var self = this; + self.grid = this.contoursGrid() var data = { - model: model, - dataset: this.dataset, - grid: grid + model: model, + dataset: self.dataset, + grid: self.grid, } d3.json("/fit", { @@ -112,30 +114,34 @@ class Dataspace { "Content-Type": "application/json; charset=UTF-8" } }).then(json => { + // Reset the old contours + this.svg.selectAll("g").remove(); + + // Update the metrics $("#f1score").text(json.metrics.f1); $("#metrics").removeClass("invisible").addClass("visible"); - // Update the gird with the predictions values. + // Update the grid with the predictions values. $.each(json.grid, function(i, val) { - grid[i] = val; + self.grid[i] = val; }) // Add the contours from the predictions for each class var contours = d3.contours() - .size([grid.n, grid.m]) - .thresholds(this.classes()) - (grid) - .map(grid.transform) + .size([self.grid.n, self.grid.m]) + .thresholds(self.classes()) + (self.grid) + .map(self.grid.transform) // Draw the contours on the SVG - this.svg.insert("g", ":first-child") + self.svg.insert("g", ":first-child") .attr("fill", "none") .attr("stroke", "#fff") .attr("stroke-opacity", 0.65) .selectAll("path") .data(contours) // Here is where the contours gets added .join("path") - .attr("fill", d => this.color(d.value)) // Here is the color value! + .attr("fill", d => self.color(d.value)) // Here is the color value! .style("opacity", 0.45) .attr("d", d3.geoPath()); @@ -146,10 +152,10 @@ class Dataspace { // Reset the plotting area reset() { - this.dataset = []; - this.svg.selectAll("circle").remove(); - this.svg.selectAll("g").remove(); - $("#metrics").removeClass("visible").addClass("invisible"); + this.dataset = []; + this.svg.selectAll("circle").remove(); + this.svg.selectAll("g").remove(); + $("#metrics").removeClass("visible").addClass("invisible"); } // Count the number of classes in the dataset From d1dc3702c628a7f08e06302d73437c97c817d541 Mon Sep 17 00:00:00 2001 From: Benjamin Bengfort Date: Sat, 7 Dec 2019 08:13:14 -0500 Subject: [PATCH 3/6] probability contours --- app.py | 10 +++++++--- static/js/dataspace.js | 18 ++++++++++++------ templates/index.html | 1 + 3 files changed, 20 insertions(+), 9 deletions(-) diff --git a/app.py b/app.py index 0a9cee5..8b7b61e 100644 --- a/app.py +++ b/app.py @@ -19,6 +19,7 @@ ########################################################################## import json +import numpy as np from flask import Flask from flask import render_template, jsonify, request @@ -133,15 +134,18 @@ def fit(): yhat = model.predict(X) metrics = prfs(y, yhat, average="macro") - # Make predictions on the grid + # Make probability predictions on the grid to implement contours Xp = asarray([ [point["x"], point["y"]] for point in grid ]) - preds = model.predict(Xp) + preds = [] + for proba in model.predict_proba(Xp): + c = np.argmax(proba) + preds.append(float(c+proba[c])) return jsonify({ "metrics": dict(zip(["precision", "recall", "f1", "support"], metrics)), - "grid": [int(v) for v in preds], + "grid": preds, }) ########################################################################## diff --git a/static/js/dataspace.js b/static/js/dataspace.js index 3af1fb2..e20b127 100644 --- a/static/js/dataspace.js +++ b/static/js/dataspace.js @@ -52,6 +52,7 @@ class Dataspace { .attr('cx', function (d) { return self.xScale(d.x); }) .attr('cy', function (d) { return self.yScale(d.y); }) .attr('fill', function (d) { return self.color(d.c); }) + .attr("stroke", "#FFFFFF") .attr('r', radius); } @@ -100,7 +101,7 @@ class Dataspace { // The contours grid determines what to make predictions on. // TODO: don't pass this to the server but allow the server to compute it. var self = this; - self.grid = this.contoursGrid() + self.grid = self.contoursGrid() var data = { model: model, dataset: self.dataset, @@ -115,7 +116,7 @@ class Dataspace { } }).then(json => { // Reset the old contours - this.svg.selectAll("g").remove(); + self.svg.selectAll("g").remove(); // Update the metrics $("#f1score").text(json.metrics.f1); @@ -126,26 +127,31 @@ class Dataspace { self.grid[i] = val; }) + var thresholds = self.classes().map(i => d3.range(i, i + 1, 0.25)).flat().sort(); + console.log(thresholds); + // Add the contours from the predictions for each class var contours = d3.contours() .size([self.grid.n, self.grid.m]) - .thresholds(self.classes()) + .thresholds(thresholds) + .smooth(true) (self.grid) .map(self.grid.transform) // Draw the contours on the SVG self.svg.insert("g", ":first-child") .attr("fill", "none") - .attr("stroke", "#fff") + .attr("stroke", "#FFFFFF") .attr("stroke-opacity", 0.65) .selectAll("path") .data(contours) // Here is where the contours gets added .join("path") - .attr("fill", d => self.color(d.value)) // Here is the color value! - .style("opacity", 0.45) + .attr("fill", d => self.color(Math.floor(d.value))) // Here is the color value! + .style("opacity", 0.85) .attr("d", d3.geoPath()); }).catch(error => { + console.log(error); alertMessage("Could not fit model, check JSON hyperparams and try again!"); }); } diff --git a/templates/index.html b/templates/index.html index ec1ab5c..8141de4 100644 --- a/templates/index.html +++ b/templates/index.html @@ -200,6 +200,7 @@
+
From 961e279cbb492b2ad1a320000d025c7e97383d36 Mon Sep 17 00:00:00 2001 From: Benjamin Bengfort Date: Sat, 7 Dec 2019 09:16:20 -0500 Subject: [PATCH 4/6] better thresholds and colors --- app.py | 6 +++++- static/js/dataspace.js | 17 ++++++++++++++--- 2 files changed, 19 insertions(+), 4 deletions(-) diff --git a/app.py b/app.py index 8b7b61e..5978736 100644 --- a/app.py +++ b/app.py @@ -135,13 +135,17 @@ def fit(): metrics = prfs(y, yhat, average="macro") # Make probability predictions on the grid to implement contours + # The returned value is the class index + the probability + # To get the selected class in JavaScript, use Math.floor(p) + # Where p is the probability returned by the grid. Note that this + # method guarantees that no P(c) == 1 to prevent class misidentification Xp = asarray([ [point["x"], point["y"]] for point in grid ]) preds = [] for proba in model.predict_proba(Xp): c = np.argmax(proba) - preds.append(float(c+proba[c])) + preds.append(float(c+proba[c])-0.000001) return jsonify({ "metrics": dict(zip(["precision", "recall", "f1", "support"], metrics)), diff --git a/static/js/dataspace.js b/static/js/dataspace.js index e20b127..85140c4 100644 --- a/static/js/dataspace.js +++ b/static/js/dataspace.js @@ -127,8 +127,19 @@ class Dataspace { self.grid[i] = val; }) - var thresholds = self.classes().map(i => d3.range(i, i + 1, 0.25)).flat().sort(); - console.log(thresholds); + // Compute the thresholds from the classes, then compute the colors + var thresholds = self.classes().map(i => d3.range(i, i + 1, 0.1)).flat().sort(); + var colorMap = {} + $.each(self.classes(), c => { + colorMap[c] = d3.scaleLinear().domain([c, c+1]) + .interpolate(d3.interpolateHcl) + .range(["#FFFFFF", self.color(c)]) + }); + + var getColor = d => { + console.log(d.value) + return colorMap[Math.floor(d.value)](d.value) + } // Add the contours from the predictions for each class var contours = d3.contours() @@ -146,7 +157,7 @@ class Dataspace { .selectAll("path") .data(contours) // Here is where the contours gets added .join("path") - .attr("fill", d => self.color(Math.floor(d.value))) // Here is the color value! + .attr("fill", getColor) // Here is the color value! .style("opacity", 0.85) .attr("d", d3.geoPath()); From c7f58af1714e050c336efe12f7d7ad11e49c2b87 Mon Sep 17 00:00:00 2001 From: Benjamin Bengfort Date: Sat, 7 Dec 2019 09:23:55 -0500 Subject: [PATCH 5/6] add screenshot to README --- README.md | 2 ++ static/img/screenshot.png | Bin 0 -> 1446302 bytes 2 files changed, 2 insertions(+) create mode 100644 static/img/screenshot.png diff --git a/README.md b/README.md index 79a8af3..1d2d9bb 100644 --- a/README.md +++ b/README.md @@ -4,6 +4,8 @@ In the tradition of Tkinter SVM GUI, the purpose of this app is to demonstrate how machine learning model forms are affected by the shape of the underlying dataset. By selecting a dataset or by creating one of your own, you can fit a model to the data and see how the model would make decisions based on the data it has been trained on. Although this is a toy example, hopefully it helps give you the intuition that the machine learning process is a model selection search for the best combination of features, algorithm, and hyperparameter that generalize well in a bounded feature space. +![Screenshot](static/img/screenshot.png) + ## Getting Started To run this app locally, first clone the repository and install the requirements: diff --git a/static/img/screenshot.png b/static/img/screenshot.png new file mode 100644 index 0000000000000000000000000000000000000000..c18f40cbba321c2db12e654077684e353165cfe7 GIT binary patch literal 1446302 zcmeFYc|26_|2Ix5?b=Z?rLwe`Pli*SW6O>$zM)E??5yykXA< z0Re%{`WMb#6%g3K5)fE7wr(waM9#=YNI+nd;T>Jw%lf*y`!Ccpyb_3Hvc6j!gRUw7+Vy5Z?-XSQDuJ0QJ!mrB})oBdK7=W%JS+)q>>;YQ-Mw~8;ai2tgjcOnAY zMbr%TkBjPP3kcXW#x{lu?9YEc;Nbt_T7!7hX1fzQ^;Zv%fB0h9U~xC+diSBj_x4lQ zKFDq!X}B-&zC=f>;0<1g_Y-r*J9=Hsb^*`&C?U2~qQLz_lq+l2Jh}MP`>VpV5=sr4 zk2^IJO>7c(@RC1rAUJgN7k1zWR3HIaZlLYb_2{ZjKmPKZw5-QduktuEu{1l^`pTEt zU8?mZ7r+cK?&=R 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zaq0>Pw?zXd!5%>>hgNwm<9!XFGbX8aXMr|1vsX~o#+$b_zJQ&bF4YCIu7ROF`q8Oy zEnyBcJq(sQMAzWEc1cw3@ z&spynqOtXO{8dHW-~`C4e;u8XnZ%+0XUcE5SNzyr@F3psV)_mD>Tja;{<}d#BH#ZS ze(!=50oBI@zp;k=iM9B!e#Q&qH;Zk5ckN$(7gSpO{x_rg=&$hO@!#@9m_SK<6pedF7gV?+-6<|An Date: Sun, 8 Dec 2019 18:25:49 -0500 Subject: [PATCH 6/6] update about --- templates/index.html | 1 + 1 file changed, 1 insertion(+) diff --git a/templates/index.html b/templates/index.html index 8141de4..310db2f 100644 --- a/templates/index.html +++ b/templates/index.html @@ -446,6 +446,7 @@ machine learning model forms are affected by the shape of the underlying dataset. By selecting a dataset or by creating one of your own, you can fit a model to the data and see how the model would make decisions based on the data it has been trained on. + The fitted contours display the highest likelihoods of the class the model would select. Although this is a toy example, hopefully it helps give you the intuition that the machine learning process is a model selection search for the best combination of features, algorithm, and hyperparameter that generalize well in a bounded feature space.