From 77ac659298b68b5a434703132019a2118f921f08 Mon Sep 17 00:00:00 2001 From: Mateusz Date: Tue, 22 Aug 2023 20:46:52 +0200 Subject: [PATCH 01/13] Update mix.installs --- notebooks/cv_gradient_boosting_tree.livemd | 2 +- notebooks/linear_regression.livemd | 137 +++++++++------------ 2 files changed, 59 insertions(+), 80 deletions(-) diff --git a/notebooks/cv_gradient_boosting_tree.livemd b/notebooks/cv_gradient_boosting_tree.livemd index 721f8641..6334479a 100644 --- a/notebooks/cv_gradient_boosting_tree.livemd +++ b/notebooks/cv_gradient_boosting_tree.livemd @@ -2,7 +2,7 @@ ```elixir Mix.install([ - {:scholar, github: "elixir-nx/scholar", override: true}, + {:scholar, "~> 0.2.0"}, {:explorer, "~> 0.6.1"}, {:exla, "~> 0.6.0"}, {:nx, "~> 0.6.0", override: true}, diff --git a/notebooks/linear_regression.livemd b/notebooks/linear_regression.livemd index 6249419e..62117367 100644 --- a/notebooks/linear_regression.livemd +++ b/notebooks/linear_regression.livemd @@ -4,10 +4,11 @@ ```elixir Mix.install([ - {:scholar, github: "elixir-nx/scholar", override: true}, + {:scholar, "~> 0.2.0"}, {:explorer, "~> 0.6.1"}, - {:exla, "~> 0.5.3"}, - {:nx, github: "elixir-nx/nx", sparse: "nx", override: true}, + {:exla, "~> 0.6.0"}, + {:nx, "~> 0.6.0", override: true}, + {:vega_lite, github: "livebook-dev/vega_lite", override: true}, {:req, "~> 0.3.9"}, {:kino_vega_lite, "~> 0.1.9"}, {:kino, "~> 0.10.0"}, @@ -27,6 +28,7 @@ In the livebook, we will cover the typical use cases of linear regression on pra ```elixir alias VegaLite, as: Vl +alias VegaLite.Data require Explorer.DataFrame, as: DF require Explorer.Series, as: S alias Scholar.Linear.LinearRegression, as: LR @@ -40,10 +42,18 @@ key = Nx.Random.key(42) +``` + +02:37:23.509 [info] TfrtCpuClient created. + +``` + + + ``` #Nx.Tensor< u32[2] - EXLA.Backend + EXLA.Backend [0, 42] > ``` @@ -100,7 +110,11 @@ Vl.new( scale: [domain: [-0.05, 2.05]], axis: [grid: false] ) -|> Vl.encode_field(:y, "y", type: :quantitative, scale: [domain: [2.5, 12]], axis: [grid: false]) +|> Vl.encode_field(:y, "y", + type: :quantitative, + scale: [domain: [2.5, 12]], + axis: [grid: false] +) ``` @@ -121,14 +135,14 @@ model = LR.fit(x, y) %Scholar.Linear.LinearRegression{ coefficients: #Nx.Tensor< f64[1][1] - EXLA.Backend + EXLA.Backend [ [2.9578183089017] ] >, intercept: #Nx.Tensor< f64[1] - EXLA.Backend + EXLA.Backend [4.023067929858136] > } @@ -175,18 +189,14 @@ Vl.new( scale: y_scale, axis: [grid: false] ), - Vl.new() - |> Vl.data_from_values(line) - |> Vl.mark(:line, color: :green) - |> Vl.encode_field(:x, "x", type: :quantitative, scale: x_scale) - |> Vl.encode_field(:y, "y", type: :quantitative, scale: y_scale) + Data.chart(line, [type: :line, color: :green], x: "x", y: "y") ]) ``` ```vega-lite 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of Generated Data"},"width":630} ``` Using `Scholar.Linear.LinearRegression.predict`, we can predict an expected value for a given input. However, we must remember that our prediction will be valid only if we consider linearly dependent data. Fortunately, our data set is perfect for this kind of prediction. @@ -239,23 +249,15 @@ Vl.new( scale: y_scale, axis: [grid: false] ), - Vl.new() - |> Vl.data_from_values(line) - |> Vl.mark(:line, color: :green) - |> Vl.encode_field(:x, "x", type: :quantitative, scale: x_scale) - |> Vl.encode_field(:y, "y", type: :quantitative, scale: y_scale), - Vl.new() - |> Vl.data_from_values(prediction) - |> Vl.mark(:circle, color: :red, size: 80) - |> Vl.encode_field(:x, "x", type: :quantitative) - |> Vl.encode_field(:y, "y", type: :quantitative) + Data.chart(line, [type: :line, color: :green], x: "x", y: "y"), + Data.chart(prediction, [type: :circle, color: :red, size: 80], x: "x", y: "y") ]) ``` ```vega-lite 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of Generated Data"},"width":630} +{"$schema":"https://vega.github.io/schema/vega-lite/v5.json","height":630,"layer":[{"data":{"values":[{"x":1.9004864711486005,"y":9.588568089587561},{"x":1.4863498556419192,"y":8.128091393416636},{"x":1.7991174211376775,"y":9.789160149027026},{"x":0.12072062082750934,"y":3.9726630516564603},{"x":1.857364920863604,"y":9.25767072810677},{"x":0.3501109776898361,"y":5.016196041913261},{"x":1.0511571984036023,"y":7.0072175252613835},{"x":0.2684341945193349,"y":4.529725798132315},{"x":1.2610012264029522,"y":7.981418930695978},{"x":0.4076748594070785,"y":4.789392547766224},{"x":0.6277976898420019,"y":5.473504548640071},{"x":1.1925149126570633,"y":7.979905959554976},{"x":1.9099838593057608,"y":9.933633616185963},{"x":1.11272036330339,"y":7.576762743580341},{"x":1.802092739386969,"y":9.325967122317106},{"x":1.196864286097009,"y":7.638266926193305},{"x":0.03815120245380177,"y":4.518300718070865},{"x":0.6918418995897215,"y":5.594422797388837},{"x":0.04790910633931178,"y":4.622640731795469},{"x":1.0082350389606831,"y":6.573314789785342},{"x":1.3268160391024382,"y":8.167074289870962},{"x":1.116235950693984,"y":7.205542036877259},{"x":1.5635870611482732,"y":9.11314041027242},{"x":1.5325239230646464,"y":8.46042736714775},{"x":0.22518938365611207,"y":4.359911858835845},{"x":1.8818046241717257,"y":9.422655165383151},{"x":0.866399894741126,"y":6.273503204133702},{"x":1.7074705527133416,"y":9.1952114549056},{"x":0.7228909843461526,"y":6.399740242846538},{"x":0.8645396760222455,"y":6.921156361139643},{"x":1.8376110894013347,"y":9.599848131970901},{"x":1.442060988129032,"y":8.259326839274358},{"x":1.1443219627898706,"y":7.398380325540283},{"x":1.9442669979686977,"y":9.916636301589834},{"x":1.7051984640589528,"y":8.810676014365272},{"x":0.9316907243298531,"y":6.706793253908984},{"x":0.32105137135146666,"y":5.33310397346077},{"x":1.4251172135313435,"y":8.204359875917337},{"x":0.21636814613954103,"y":5.060604277707321},{"x":0.8400572418693812,"y":6.803452904281498},{"x":0.9280411984247863,"y":6.879738120117415},{"x":0.6748888124318428,"y":5.668202282091746},{"x":0.9269337184466582,"y":6.309717290697355},{"x":1.614491258449418,"y":8.584383516652984},{"x":1.7556955423296916,"y":8.88204901427903},{"x":0.742569049365005,"y":6.3831292487291265},{"x":0.04689839244109928,"y":4.047375477112247},{"x":1.3063798501777497,"y":7.7838559727772125},{"x":0.7399148832784985,"y":5.7390385507242065},{"x":0.609370240470565,"y":5.496753043300161},{"x":0.5288005556403474,"y":5.710591843063962},{"x":1.8613281986490335,"y":9.617393755825786},{"x":1.4934621266846162,"y":8.775190727806926},{"x":1.3841503724034614,"y":8.097058620455151},{"x":1.4411812067428929,"y":8.714222294939118},{"x":0.07275682745349732,"y":3.8582708188217256},{"x":1.6964330896073725,"y":9.558937877400908},{"x":0.9736420283819163,"y":6.666882792923979},{"x":1.5039805870455312,"y":8.704474518801195},{"x":0.2945974391450479,"y":4.7723887278383},{"x":0.1868592779917515,"y":4.731889701559963},{"x":1.335963209381183,"y":8.49731857456425},{"x":1.12438463727741,"y":7.846542179584407},{"x":0.9611253246535423,"y":7.302305818866566},{"x":0.944121396326294,"y":6.59392561989239},{"x":0.9160306018359226,"y":6.537631541250617},{"x":1.8140749900473794,"y":8.956656518284662},{"x":1.2494284448899267,"y":7.462139448629185},{"x":1.5981009200282497,"y":8.488717108113233},{"x":1.5216871497808855,"y":8.239844283942187},{"x":1.0141522485693928,"y":7.119631726438168},{"x":0.24505073662469545,"y":4.76994960528683},{"x":0.23453054126675754,"y":4.740933976429053},{"x":0.8375073700191651,"y":6.203902724203168},{"x":0.9555904929091139,"y":6.539672305648001},{"x":1.5116208882261697,"y":8.251922682601373},{"x":0.5430644133381208,"y":5.94392000741476},{"x":1.5396799405255837,"y":8.562605860505936},{"x":1.3288332597446675,"y":8.394908971950938},{"x":0.8098737558236753,"y":6.119922380724123},{"x":0.7300870101089743,"y":5.820357872554606},{"x":0.6003486177307833,"y":6.046316972091446},{"x":1.6984776836543856,"y":8.777896137233139},{"x":0.7881943630297616,"y":6.2074823217226704},{"x":0.8022304689606252,"y":6.421563438226328},{"x":0.9461487553849501,"y":7.224992163390045},{"x":1.1062159638629834,"y":7.778505164261029},{"x":0.5336394605277772,"y":5.208679631530884},{"x":0.4584282301025495,"y":5.523717683548369},{"x":0.274737610635972,"y":5.32305240175822},{"x":1.2533091829664782,"y":7.266026618765184},{"x":0.10113017428721793,"y":4.752841138832883},{"x":0.14571861443282952,"y":4.726179278830611},{"x":1.15906054645597,"y":7.915833252969062},{"x":0.27790158114728714,"y":5.013847161673044},{"x":1.239949192481978,"y":7.517406034765404},{"x":0.6661499949625322,"y":5.568160896538142},{"x":1.2693312956846792,"y":7.889092741514168},{"x":1.2086907347723854,"y":7.6883993210876},{"x":1.7333203144056522,"y":8.889281033197426}]},"encoding":{"x":{"axis":{"grid":false},"field":"x","scale":{"domain":[-0.05,2.05]},"type":"quantitative"},"y":{"axis":{"grid":false},"field":"y","scale":{"domain":[2.5,12]},"type":"quantitative"}},"mark":"circl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of Generated Data"},"width":630} ``` As we expected, the red dot lies on the regression line. @@ -272,7 +274,7 @@ x_b |> Nx.LinAlg.pinv() |> Nx.dot(y) ``` #Nx.Tensor< f64[2][1] - EXLA.Backend + EXLA.Backend [ [4.023067929858136], [2.957818308901698] @@ -457,7 +459,7 @@ model.coefficients ``` #Nx.Tensor< f32[1][2] - EXLA.Backend + EXLA.Backend [ [3.023973226547241, 2.013108730316162] ] @@ -482,7 +484,7 @@ Nx.iota({n_samples, n_variables}) ``` #Nx.Tensor< s64[5][3] - EXLA.Backend + EXLA.Backend [ [0, 0, 0], [1, 1, 1], @@ -531,28 +533,19 @@ Vl.new( height: 630 ) |> Vl.layers([ - Vl.new() - |> Vl.data_from_values(df) - |> Vl.mark(:circle) - |> Vl.encode_field(:x, "x", type: :quantitative, scale: x_scale, axis: [grid: false]) - |> Vl.encode_field(:y, "y", type: :quantitative, scale: y_scale, axis: [grid: false]), - Vl.new() - |> Vl.data_from_values(df_results) - |> Vl.mark(:line, color: :green) - |> Vl.encode_field(:x, "x", type: :quantitative, scale: x_scale) - |> Vl.encode_field(:y, "y", type: :quantitative, scale: y_scale), - Vl.new() - |> Vl.data_from_values(df_prediction) - |> Vl.mark(:circle, color: :red, size: 80) - |> Vl.encode_field(:x, "x", type: :quantitative, scale: x_scale) - |> Vl.encode_field(:y, "y", type: :quantitative, scale: y_scale) + Data.chart(df, :circle, + x: [field: "x", type: :quantitative, scale: x_scale, axis: [grid: false]], + y: [field: "y", type: :quantitative, scale: y_scale, axis: [grid: false]] + ), + Data.chart(df_results, [type: :line, color: :green], x: "x", y: "y"), + Data.chart(df_prediction, [type: :circle, color: :red, size: 80], x: "x", y: "y") ]) ``` ```vega-lite 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of Generated Data and Predictions"},"width":630} ``` Now we are ready to go into a more complex example! @@ -763,7 +756,7 @@ x = ``` {#Nx.Tensor< f64[20640][12] - EXLA.Backend + EXLA.Backend [ [0.2707641196013289, 326.0, 15.0, 32.76, -117.02, 1.0278, 543.0, 1.665644171779141, 3.6932515337423313, 326.0, 1204.0, 1.0], [0.2598343685300207, 230.0, 27.0, 38.44, -122.71, 1.7, 462.0, 2.008695652173913, 4.2, 251.0, 966.0, 1.0], @@ -775,7 +768,7 @@ x = >, #Nx.Tensor< f64[20640] - EXLA.Backend + EXLA.Backend [1.542e5, 3.5e5, 2.667e5, 2.728e5, 1.163e5, 2.941e5, 4.178e5, 1.851e5, 5.68e4, 500001.0, 500001.0, 1.152e5, 2.343e5, 8.75e4, 8.37e4, 500001.0, 1.669e5, 3.003e5, 1.546e5, 6.69e4, 2.325e5, 1.367e5, 1.375e5, 1.27e5, 1.683e5, 1.441e5, 9.53e4, 1.834e5, 1.435e5, 6.85e4, 1.625e5, 1.661e5, 1.908e5, 2.431e5, 1.488e5, 3.036e5, 1.479e5, 1.5e5, 3.288e5, 7.08e4, 2.25e5, 1.375e5, 3.5e5, 3.742e5, 1.549e5, 4.5e5, 3.063e5, 2.051e5, ...] >} ``` @@ -794,12 +787,12 @@ train_ratio = 0.8 ``` {#Nx.Tensor< f64[16512] - EXLA.Backend + EXLA.Backend [1.542e5, 3.5e5, 2.667e5, 2.728e5, 1.163e5, 2.941e5, 4.178e5, 1.851e5, 5.68e4, 500001.0, 500001.0, 1.152e5, 2.343e5, 8.75e4, 8.37e4, 500001.0, 1.669e5, 3.003e5, 1.546e5, 6.69e4, 2.325e5, 1.367e5, 1.375e5, 1.27e5, 1.683e5, 1.441e5, 9.53e4, 1.834e5, 1.435e5, 6.85e4, 1.625e5, 1.661e5, 1.908e5, 2.431e5, 1.488e5, 3.036e5, 1.479e5, 1.5e5, 3.288e5, 7.08e4, 2.25e5, 1.375e5, 3.5e5, 3.742e5, 1.549e5, 4.5e5, 3.063e5, 2.051e5, 2.737e5, ...] >, #Nx.Tensor< f64[4128] - EXLA.Backend + EXLA.Backend [8.55e4, 1.743e5, 5.65e4, 1.308e5, 1.375e5, 2.472e5, 2.25e5, 1.164e5, 500001.0, 1.154e5, 1.269e5, 1.229e5, 7.04e4, 3.534e5, 7.14e4, 4.083e5, 3.0e5, 1.648e5, 1.125e5, 2.028e5, 1.139e5, 2.15e5, 500001.0, 1.48e5, 1.683e5, 2.819e5, 3.338e5, 1.078e5, 3.277e5, 3.412e5, 3.289e5, 2.879e5, 2.545e5, 2.667e5, 6.82e4, 1.406e5, 2.795e5, 2.25e5, 1.17e5, 1.375e5, 3.115e5, 2.423e5, 4.93e5, 3.458e5, 1.542e5, 3.728e5, 6.6e4, 1.281e5, ...] >} ``` @@ -817,12 +810,12 @@ x_nan_count = Nx.sum(Nx.is_nan(x)) ``` {#Nx.Tensor< u64 - EXLA.Backend + EXLA.Backend 414 >, #Nx.Tensor< u64 - EXLA.Backend + EXLA.Backend 0 >} ``` @@ -841,7 +834,7 @@ Nx.equal(x_nan_count, Nx.add(bedrooms_per_rooms_nan_count, total_bedrooms_nan_co ``` #Nx.Tensor< u8 - EXLA.Backend + EXLA.Backend 1 > ``` @@ -865,7 +858,7 @@ x_test = ``` #Nx.Tensor< f64[4128][12] - EXLA.Backend + EXLA.Backend [ [0.20019126554032515, 548.0, 23.0, 36.32, -119.33, 2.5, 1446.0, 2.6386861313868613, 5.724452554744525, 628.0, 3137.0, 4.0], [0.18461538461538463, 83.0, 34.0, 34.26, -118.44, 5.5124, 433.0, 5.216867469879518, 3.9156626506024095, 60.0, 325.0, 1.0], @@ -890,7 +883,7 @@ correlation = ``` #Nx.Tensor< f64[13][13] - EXLA.Backend + EXLA.Backend [ [1.0000000000000002, 0.06144282875654085, 0.13311283521287637, -0.12216156759996018, 0.09984860365269956, -0.612630933680883, 0.03517997111673592, 0.004697459810143742, -0.4078383723411937, 0.08095666109618091, -0.18612145325855575, -0.11835385450623682, -0.25385512001635363], [0.06144282875654085, 1.0, -0.305555948690877, -0.07566125267999342, 0.06170866872453172, 0.01398821983308101, 0.907460734474932, -0.02634672373043427, -0.07388109216765114, 0.9735036322842058, 0.9198029415934222, -0.04626792658479704, 0.0639675034251392], @@ -938,32 +931,18 @@ Vl.new( width: 630, height: 630 ) -|> Vl.data_from_values(corr_to_plot) -|> Vl.layers([ - Vl.new() - |> Vl.mark(:rect) - |> Vl.encode_field(:x, "x", type: :nominal, title: "", sort: false) - |> Vl.encode_field(:y, "y", type: :nominal, title: "", sort: false) - |> Vl.encode_field(:color, "corr_val", type: :quantitative, scale: [scheme: :viridis]), - Vl.new() - |> Vl.mark(:text) - |> Vl.encode_field(:x, "x", type: :nominal, title: "") - |> Vl.encode_field(:y, "y", type: :nominal, title: "") - |> Vl.encode_field(:text, "corr_val", type: :quantitative) - |> Vl.encode_field(:color, "corr_val", - type: :quantitative, - condition: [ - [test: "datum['corr_val'] < 0", value: :white], - [test: "datum['corr_val'] >= 0", value: :black] - ] - ) -]) +|> Data.heatmap(corr_to_plot, + x: "x", + y: "y", + color: [field: "corr_val", type: :quantitative, scale: [scheme: :viridis]], + text: "corr_val" +) ``` ```vega-lite -{"$schema":"https://vega.github.io/schema/vega-lite/v5.json","data":{"values":[{"corr_val":1.0,"x":"Bedrooms per rooms","y":"Bedrooms per rooms"},{"corr_val":0.06,"x":"Households","y":"Bedrooms per rooms"},{"corr_val":0.13,"x":"Housing median age","y":"Bedrooms per rooms"},{"corr_val":-0.12,"x":"Latitude","y":"Bedrooms per rooms"},{"corr_val":0.1,"x":"Longitude","y":"Bedrooms per rooms"},{"corr_val":-0.61,"x":"Median income","y":"Bedrooms per rooms"},{"corr_val":0.04,"x":"Population","y":"Bedrooms per rooms"},{"corr_val":0.0,"x":"Population per family","y":"Bedrooms per rooms"},{"corr_val":-0.41,"x":"Rooms per family","y":"Bedrooms per rooms"},{"corr_val":0.08,"x":"Total bedrooms","y":"Bedrooms per rooms"},{"corr_val":-0.19,"x":"Total rooms","y":"Bedrooms per 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bedrooms","y":"Median house value"},{"corr_val":0.13,"x":"Total rooms","y":"Median house value"},{"corr_val":-0.4,"x":"Ocean proximity","y":"Median house value"},{"corr_val":1.0,"x":"Median house value","y":"Median house value"}]},"height":630,"layer":[{"encoding":{"color":{"field":"corr_val","scale":{"scheme":"viridis"},"type":"quantitative"},"x":{"field":"x","type":"nominal"},"y":{"field":"y","type":"nominal"}},"mark":"rect"},{"encoding":{"text":{"field":"corr_val","type":"quantitative"},"x":{"field":"x","type":"nominal"},"y":{"field":"y","type":"nominal"}},"mark":"text"}],"title":{"offset":20,"text":"Correlation Matrix for California Housing"},"width":630} ``` We can spot that _median_house_value_ is strongly correlated with _median_income_. It's pretty straightforward, the more money you have, the more expensive house you can buy. Non-obvious is a negative correlation with _bedrooms_per_rooms_. But it also can be explained. Bedrooms are the most crucial rooms in the house. Firstly, you need to guarantee that you have a house with enough bedrooms. If this condition is satisfied, then you can focus on "additional rooms" like a chill room, cabinets and so on. So if you buy a house with more additional rooms, then you decrease the ratio. @@ -982,12 +961,12 @@ model = LR.fit(x_train, y_train) %Scholar.Linear.LinearRegression{ coefficients: #Nx.Tensor< f64[12] - EXLA.Backend + EXLA.Backend [292661.1879978386, 111.46237796967083, 1170.1666131366273, -33322.29721781791, -35394.90464853701, 41384.64324873476, -41.31598131072877, 50.507900596456054, 3040.877192253343, 7.253624678472778, 3.272614960520949, -8059.519389730254] >, intercept: #Nx.Tensor< f64 - EXLA.Backend + EXLA.Backend -3101555.563125743 > } @@ -1007,12 +986,12 @@ mae = Regression.mean_absolute_error(y_test, predictions) ``` {#Nx.Tensor< f64 - EXLA.Backend + EXLA.Backend 67648.9435367406 >, #Nx.Tensor< f64 - EXLA.Backend + EXLA.Backend 48942.11908533544 >} ``` @@ -1028,9 +1007,9 @@ Nx.mean(y) ``` #Nx.Tensor< f64 - EXLA.Backend + EXLA.Backend 206855.81690891474 > ``` -For such a simple model as linear regression, it seems to be a pretty good result. But there is space to improve this result. You can, for example, add some additional features to the data set. In the future, you will be able to try more complicated models, such as random forests. \ No newline at end of file +For such a simple model as linear regression, it seems to be a pretty good result. But there is space to improve this result. You can, for example, add some additional features to the data set. In the future, you will be able to try more complicated models, such as random forests. From 8ca6a320bf3edc5248e33af38d3c7e996a83ef5a Mon Sep 17 00:00:00 2001 From: 0urobor0s <0urobor0s@users.noreply.github.com> Date: Tue, 10 Oct 2023 15:56:59 +0100 Subject: [PATCH 02/13] F-beta score --- lib/scholar/metrics/classification.ex | 155 +++++++++++++++---- test/scholar/metrics/classification_test.exs | 20 +++ 2 files changed, 143 insertions(+), 32 deletions(-) diff --git a/lib/scholar/metrics/classification.ex b/lib/scholar/metrics/classification.ex index b2054757..7cda9b27 100644 --- a/lib/scholar/metrics/classification.ex +++ b/lib/scholar/metrics/classification.ex @@ -41,11 +41,13 @@ defmodule Scholar.Metrics.Classification do * `:micro` - Calculate metrics globally by counting the total true positives, false negatives and false positives. - * `:none` - The f1 scores for each class are returned. + * `:none` - The F-score values for each class are returned. """ ] ] + fbeta_score_schema = f1_score_schema + confusion_matrix_schema = general_schema ++ [ @@ -163,6 +165,7 @@ defmodule Scholar.Metrics.Classification do @confusion_matrix_schema NimbleOptions.new!(confusion_matrix_schema) @balanced_accuracy_schema NimbleOptions.new!(balanced_accuracy_schema) @cohen_kappa_schema NimbleOptions.new!(cohen_kappa_schema) + @fbeta_score_schema NimbleOptions.new!(fbeta_score_schema) @f1_score_schema NimbleOptions.new!(f1_score_schema) @brier_score_loss_schema NimbleOptions.new!(brier_score_loss_schema) @accuracy_schema NimbleOptions.new!(accuracy_schema) @@ -586,7 +589,7 @@ defmodule Scholar.Metrics.Classification do end @doc """ - Calculates F1 score given rank-1 tensors which represent + Calculates F-beta score given rank-1 tensors which represent the expected (`y_true`) and predicted (`y_pred`) classes. If all examples are true negatives, then the result is 0 to @@ -594,78 +597,166 @@ defmodule Scholar.Metrics.Classification do ## Options - #{NimbleOptions.docs(@f1_score_schema)} + #{NimbleOptions.docs(@fbeta_score_schema)} ## Examples iex> y_true = Nx.tensor([0, 1, 1, 1, 1, 0, 2, 1, 0, 1], type: :u32) iex> y_pred = Nx.tensor([0, 2, 1, 1, 2, 2, 2, 0, 0, 1], type: :u32) - iex> Scholar.Metrics.Classification.f1_score(y_true, y_pred, num_classes: 3) + iex> Scholar.Metrics.Classification.fbeta_score(y_true, y_pred, Nx.tensor(1), num_classes: 3) #Nx.Tensor< f32[3] [0.6666666865348816, 0.6666666865348816, 0.4000000059604645] > - iex> Scholar.Metrics.Classification.f1_score(y_true, y_pred, num_classes: 3, average: :macro) + iex> Scholar.Metrics.Classification.fbeta_score(y_true, y_pred, Nx.tensor(2), num_classes: 3) + #Nx.Tensor< + f32[3] + [0.6666666865348816, 0.5555555820465088, 0.625] + > + iex> Scholar.Metrics.Classification.fbeta_score(y_true, y_pred, Nx.tensor(0.5), num_classes: 3) + #Nx.Tensor< + f32[3] + [0.6666666865348816, 0.8333333134651184, 0.29411765933036804] + > + iex> Scholar.Metrics.Classification.fbeta_score(y_true, y_pred, Nx.tensor(2), num_classes: 3, average: :macro) #Nx.Tensor< f32 - 0.5777778029441833 + 0.6157407760620117 > - iex> Scholar.Metrics.Classification.f1_score(y_true, y_pred, num_classes: 3, average: :weighted) + iex> Scholar.Metrics.Classification.fbeta_score(y_true, y_pred, Nx.tensor(2), num_classes: 3, average: :weighted) #Nx.Tensor< f32 - 0.6399999856948853 + 0.5958333611488342 > - iex> Scholar.Metrics.Classification.f1_score(y_true, y_pred, num_classes: 3, average: :micro) + iex> Scholar.Metrics.Classification.fbeta_score(y_true, y_pred, Nx.tensor(0.5), num_classes: 3, average: :micro) #Nx.Tensor< f32 0.6000000238418579 > - iex> Scholar.Metrics.Classification.f1_score(Nx.tensor([1,0,1,0]), Nx.tensor([0, 1, 0, 1]), num_classes: 2, average: :none) + iex> Scholar.Metrics.Classification.fbeta_score(Nx.tensor([1, 0, 1, 0]), Nx.tensor([0, 1, 0, 1]), Nx.tensor(0.5), num_classes: 2, average: :none) #Nx.Tensor< f32[2] [0.0, 0.0] > """ - deftransform f1_score(y_true, y_pred, opts \\ []) do - f1_score_n(y_true, y_pred, NimbleOptions.validate!(opts, @f1_score_schema)) + deftransform fbeta_score(y_true, y_pred, beta, opts \\ []) do + fbeta_score_n(y_true, y_pred, beta, NimbleOptions.validate!(opts, @fbeta_score_schema)) end - defnp f1_score_n(y_true, y_pred, opts) do + defnp fbeta_score_n(y_true, y_pred, beta, opts) do check_shape(y_pred, y_true) num_classes = check_num_classes(opts[:num_classes]) + average = opts[:average] + + {_precision, _recall, per_class_fscore} = precision_recall_fscore_n(y_true, y_pred, beta, num_classes, average) + + per_class_fscore + end + + defn fbeta_score_v(confusion_matrix, average) do + true_positive = Nx.take_diagonal(confusion_matrix) + false_positive = Nx.sum(confusion_matrix, axes: [0]) - true_positive + false_negative = Nx.sum(confusion_matrix, axes: [1]) - true_positive - case opts[:average] do + case average do :micro -> - accuracy(y_true, y_pred) + true_positive = Nx.sum(true_positive) + false_positive = Nx.sum(false_positive) + false_negative = Nx.sum(false_negative) + + {true_positive, false_positive, false_negative} _ -> - cm = confusion_matrix(y_true, y_pred, num_classes: num_classes) - true_positive = Nx.take_diagonal(cm) - false_positive = Nx.sum(cm, axes: [0]) - true_positive - false_negative = Nx.sum(cm, axes: [1]) - true_positive + {true_positive, false_positive, false_negative} + end + end - precision = safe_division(true_positive, true_positive + false_positive) + defn precision_recall_fscore_n(y_true, y_pred, beta, num_classes, average) do + confusion_matrix = confusion_matrix(y_true, y_pred, num_classes: num_classes) + {true_positive, false_positive, false_negative} = fbeta_score_v(confusion_matrix, average) + + precision = safe_division(true_positive, true_positive + false_positive) + recall = safe_division(true_positive, true_positive + false_negative) + + per_class_fscore = + cond do + # Should only be +Inf + Nx.is_infinity(beta) -> + recall + beta == 0 -> + precision + true -> + beta2 = Nx.pow(beta, 2) + safe_division((1 + beta2) * precision * recall, beta2 * precision + recall) + end - recall = safe_division(true_positive, true_positive + false_negative) + case average do + :none -> + {precision, recall, per_class_fscore} - per_class_f1 = safe_division(2 * precision * recall, precision + recall) + :micro -> + {precision, recall, per_class_fscore} - case opts[:average] do - :none -> - per_class_f1 + :macro -> + {precision, recall, Nx.mean(per_class_fscore)} - :macro -> - Nx.mean(per_class_f1) + :weighted -> + support = (y_true == Nx.iota({num_classes, 1})) |> Nx.sum(axes: [1]) - :weighted -> - support = (y_true == Nx.iota({num_classes, 1})) |> Nx.sum(axes: [1]) + per_class_fscore = + per_class_fscore * support + |> safe_division(Nx.sum(support)) + |> Nx.sum() - safe_division(per_class_f1 * support, Nx.sum(support)) - |> Nx.sum() - end + {precision, recall, per_class_fscore} end end + @doc """ + Calculates F1 score given rank-1 tensors which represent + the expected (`y_true`) and predicted (`y_pred`) classes. + + If all examples are true negatives, then the result is 0 to + avoid zero division. + + ## Options + + #{NimbleOptions.docs(@f1_score_schema)} + + ## Examples + + iex> y_true = Nx.tensor([0, 1, 1, 1, 1, 0, 2, 1, 0, 1], type: :u32) + iex> y_pred = Nx.tensor([0, 2, 1, 1, 2, 2, 2, 0, 0, 1], type: :u32) + iex> Scholar.Metrics.Classification.f1_score(y_true, y_pred, num_classes: 3) + #Nx.Tensor< + f32[3] + [0.6666666865348816, 0.6666666865348816, 0.4000000059604645] + > + iex> Scholar.Metrics.Classification.f1_score(y_true, y_pred, num_classes: 3, average: :macro) + #Nx.Tensor< + f32 + 0.5777778029441833 + > + iex> Scholar.Metrics.Classification.f1_score(y_true, y_pred, num_classes: 3, average: :weighted) + #Nx.Tensor< + f32 + 0.6399999856948853 + > + iex> Scholar.Metrics.Classification.f1_score(y_true, y_pred, num_classes: 3, average: :micro) + #Nx.Tensor< + f32 + 0.6000000238418579 + > + iex> Scholar.Metrics.Classification.f1_score(Nx.tensor([1, 0, 1, 0]), Nx.tensor([0, 1, 0, 1]), num_classes: 2, average: :none) + #Nx.Tensor< + f32[2] + [0.0, 0.0] + > + """ + deftransform f1_score(y_true, y_pred, opts \\ []) do + fbeta_score_n(y_true, y_pred, Nx.tensor(1), NimbleOptions.validate!(opts, @f1_score_schema)) + end + @doc """ Zero-one classification loss. diff --git a/test/scholar/metrics/classification_test.exs b/test/scholar/metrics/classification_test.exs index ed00802f..58d6fdb2 100644 --- a/test/scholar/metrics/classification_test.exs +++ b/test/scholar/metrics/classification_test.exs @@ -14,4 +14,24 @@ defmodule Scholar.Metrics.ClassificationTest do assert_all_close(tpr, Nx.tensor([0.0, 0.5, 1.0, 1.0])) assert_all_close(thresholds, Nx.tensor([1.3, 0.3, 0.2, 0.1])) end + + describe "fbeta_score" do + test "equals recall when beta is infinity" do + beta = Nx.tensor(:infinity) + y_true = Nx.tensor([0, 0, 0, 0, 0, 1, 1, 1, 1, 1], type: :u32) + y_pred = Nx.tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1], type: :u32) + fbeta_scores = Classification.fbeta_score(y_true, y_pred, beta, num_classes: 2) + + assert_all_close(fbeta_scores, Classification.recall(y_true, y_pred, num_classes: 2)) + end + + test "equals precision when beta is 0" do + beta = Nx.tensor(0) + y_true = Nx.tensor([0, 0, 0, 0, 0, 1, 1, 1, 1, 1], type: :u32) + y_pred = Nx.tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1], type: :u32) + fbeta_scores = Classification.fbeta_score(y_true, y_pred, beta, num_classes: 2) + + assert_all_close(fbeta_scores, Classification.precision(y_true, y_pred, num_classes: 2)) + end + end end From 8088028f65b22e3bdc1e3136c170c5b961bbfa76 Mon Sep 17 00:00:00 2001 From: 0urobor0s <0urobor0s@users.noreply.github.com> Date: Tue, 10 Oct 2023 16:25:14 +0100 Subject: [PATCH 03/13] Show possibility of non tensor values as beta --- lib/scholar/metrics/classification.ex | 7 ++++++- test/scholar/metrics/classification_test.exs | 2 +- 2 files changed, 7 insertions(+), 2 deletions(-) diff --git a/lib/scholar/metrics/classification.ex b/lib/scholar/metrics/classification.ex index 7cda9b27..0f06d86a 100644 --- a/lib/scholar/metrics/classification.ex +++ b/lib/scholar/metrics/classification.ex @@ -638,6 +638,11 @@ defmodule Scholar.Metrics.Classification do f32[2] [0.0, 0.0] > + iex> Scholar.Metrics.Classification.fbeta_score(Nx.tensor([1, 0, 1, 0]), Nx.tensor([0, 1, 0, 1]), 0.5, num_classes: 2, average: :none) + #Nx.Tensor< + f32[2] + [0.0, 0.0] + > """ deftransform fbeta_score(y_true, y_pred, beta, opts \\ []) do fbeta_score_n(y_true, y_pred, beta, NimbleOptions.validate!(opts, @fbeta_score_schema)) @@ -754,7 +759,7 @@ defmodule Scholar.Metrics.Classification do > """ deftransform f1_score(y_true, y_pred, opts \\ []) do - fbeta_score_n(y_true, y_pred, Nx.tensor(1), NimbleOptions.validate!(opts, @f1_score_schema)) + fbeta_score_n(y_true, y_pred, 1, NimbleOptions.validate!(opts, @f1_score_schema)) end @doc """ diff --git a/test/scholar/metrics/classification_test.exs b/test/scholar/metrics/classification_test.exs index 58d6fdb2..7667e2df 100644 --- a/test/scholar/metrics/classification_test.exs +++ b/test/scholar/metrics/classification_test.exs @@ -26,7 +26,7 @@ defmodule Scholar.Metrics.ClassificationTest do end test "equals precision when beta is 0" do - beta = Nx.tensor(0) + beta = 0 y_true = Nx.tensor([0, 0, 0, 0, 0, 1, 1, 1, 1, 1], type: :u32) y_pred = Nx.tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1], type: :u32) fbeta_scores = Classification.fbeta_score(y_true, y_pred, beta, num_classes: 2) From 0b334c85006363f9f8ab44ee777b2f429eb9eb77 Mon Sep 17 00:00:00 2001 From: 0urobor0s <0urobor0s@users.noreply.github.com> Date: Tue, 10 Oct 2023 16:29:36 +0100 Subject: [PATCH 04/13] Make auxiliary functions private --- lib/scholar/metrics/classification.ex | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/lib/scholar/metrics/classification.ex b/lib/scholar/metrics/classification.ex index 0f06d86a..32253b2a 100644 --- a/lib/scholar/metrics/classification.ex +++ b/lib/scholar/metrics/classification.ex @@ -658,7 +658,7 @@ defmodule Scholar.Metrics.Classification do per_class_fscore end - defn fbeta_score_v(confusion_matrix, average) do + defnp fbeta_score_v(confusion_matrix, average) do true_positive = Nx.take_diagonal(confusion_matrix) false_positive = Nx.sum(confusion_matrix, axes: [0]) - true_positive false_negative = Nx.sum(confusion_matrix, axes: [1]) - true_positive @@ -676,7 +676,7 @@ defmodule Scholar.Metrics.Classification do end end - defn precision_recall_fscore_n(y_true, y_pred, beta, num_classes, average) do + defnp precision_recall_fscore_n(y_true, y_pred, beta, num_classes, average) do confusion_matrix = confusion_matrix(y_true, y_pred, num_classes: num_classes) {true_positive, false_positive, false_negative} = fbeta_score_v(confusion_matrix, average) From f6a352aa1e5acab2a0dbfd4e5431ec4ac09d9633 Mon Sep 17 00:00:00 2001 From: Mateusz Date: Thu, 12 Oct 2023 14:35:47 +0200 Subject: [PATCH 05/13] Add improvements --- lib/scholar/interpolation/linear.ex | 134 +++++++++++++++++++++++++--- 1 file changed, 123 insertions(+), 11 deletions(-) diff --git a/lib/scholar/interpolation/linear.ex b/lib/scholar/interpolation/linear.ex index 9f65152a..46b99891 100644 --- a/lib/scholar/interpolation/linear.ex +++ b/lib/scholar/interpolation/linear.ex @@ -16,10 +16,26 @@ defmodule Scholar.Interpolation.Linear do $$ """ import Nx.Defn + import Scholar.Shared @derive {Nx.Container, containers: [:coefficients, :x]} defstruct [:coefficients, :x] + opts_schema = [ + left: [ + type: {:or, [:float, :integer]}, + doc: + "Value to return for values in `target_x` smaller that the smallest value in training set" + ], + right: [ + type: {:or, [:float, :integer]}, + doc: + "Value to return for values in `target_x` greater that the greatest value in training set" + ] + ] + + @opts_schema NimbleOptions.new!(opts_schema) + @doc """ Fits a linear interpolation of the given `(x, y)` points @@ -39,7 +55,7 @@ defmodule Scholar.Interpolation.Linear do ] ), x: Nx.tensor( - [0, 1] + [0, 1, 2] ) } """ @@ -77,7 +93,7 @@ defmodule Scholar.Interpolation.Linear do coefficients = Nx.stack([a, b], axis: 1) - %__MODULE__{coefficients: coefficients, x: x0} + %__MODULE__{coefficients: coefficients, x: x} end @doc """ @@ -95,24 +111,120 @@ defmodule Scholar.Interpolation.Linear do [2.0, 6.0] ] ) + + iex> x = Nx.iota({5}) + iex> y = Nx.tensor([2.0, 0.0, 1.0, 3.0, 4.0]) + iex> model = Scholar.Interpolation.Linear.fit(x, y) + iex> target_x = Nx.tensor([-2, -1, 1.25, 3, 3.25, 5.0]) + iex> Scholar.Interpolation.Linear.predict(model, target_x, left: 0.0, right: 10.0) + #Nx.Tensor< + f32[6] + [0.0, 0.0, 0.25, 3.0, 3.25, 10.0] + > """ - defn predict(%__MODULE__{x: x, coefficients: coefficients} = _model, target_x) do - original_shape = Nx.shape(target_x) + deftransform predict(model, target_x, opts \\ []) do + predict_n(model, target_x, NimbleOptions.validate!(opts, @opts_schema)) + end + + defnp predict_n(%__MODULE__{x: x, coefficients: coefficients} = _model, target_x, opts) do + shape = Nx.shape(target_x) target_x = Nx.flatten(target_x) - idx_selector = Nx.new_axis(target_x, 1) >= x + indices = Nx.argsort(target_x) + + left_bound = x[0] + right_bound = x[-1] + + target_x = Nx.sort(target_x) + res = Nx.broadcast(Nx.tensor(0, type: to_float_type(target_x)), {Nx.axis_size(target_x, 0)}) + + # while with smaller than left_bound + {{res, i}, _} = + while {{res, i = 0}, {x, coefficients, left_bound, target_x}}, + check_cond_left(target_x, i, left_bound) do + val = + case opts[:left] do + nil -> + coefficients[0][0] * Nx.take(target_x, i) + coefficients[0][1] + + _ -> + opts[:left] + end + + res = Nx.indexed_put(res, Nx.new_axis(i, -1), val) + {{res, i + 1}, {x, coefficients, left_bound, target_x}} + end + + {{res, i}, _} = + while {{res, i}, {x, right_bound, coefficients, target_x, j = 0}}, + check_cond_right(target_x, i, right_bound) do + {j, _} = + while {j, {i, x, target_x}}, + j < Nx.axis_size(x, 0) and Nx.take(x, j) < Nx.take(target_x, i) do + {j + 1, {i, x, target_x}} + end + + res = + Nx.indexed_put( + res, + Nx.new_axis(i, -1), + coefficients[Nx.max(j - 1, 0)][0] * Nx.take(target_x, i) + + coefficients[Nx.max(j - 1, 0)][1] + ) + + i = i + 1 + + {{res, i}, {x, right_bound, coefficients, target_x, j}} + end + + {res, i} + + # while with bigger than right_bound + + {res, _} = + while {res, {x, coefficients, target_x, i}}, + i < Nx.axis_size(target_x, 0) do + val = + case opts[:right] do + nil -> + coefficients[-1][0] * Nx.take(target_x, i) + coefficients[-1][1] + + _ -> + opts[:right] + end + + res = Nx.indexed_put(res, Nx.new_axis(i, -1), val) + {res, {x, coefficients, target_x, i + 1}} + end + + res = Nx.take(res, indices) + Nx.reshape(res, shape) + end - idx_poly = Nx.argmax(idx_selector, axis: 1, tie_break: :high) + defnp check_cond_left(target_x, i, left_bound) do + cond do + i >= Nx.axis_size(target_x, 0) -> + Nx.u8(0) - idx_poly = Nx.select(Nx.all(idx_selector == 0, axes: [1]), 0, idx_poly) + Nx.take(target_x, i) >= left_bound -> + Nx.u8(0) - coef_poly = Nx.take(coefficients, idx_poly) + true -> + Nx.u8(1) + end + end - x_poly = Nx.stack([target_x, Nx.broadcast(1, target_x)], axis: 1) + defnp check_cond_right(target_x, i, right_bound) do + cond do + i >= Nx.axis_size(target_x, 0) -> + Nx.u8(0) - result = Nx.dot(x_poly, [1], [0], coef_poly, [1], [0]) + Nx.take(target_x, i) > right_bound -> + Nx.u8(0) - Nx.reshape(result, original_shape) + true -> + Nx.u8(1) + end end end From ef7831b992ea0ca872633a6eff5983f297807543 Mon Sep 17 00:00:00 2001 From: Mateusz Date: Thu, 12 Oct 2023 14:39:51 +0200 Subject: [PATCH 06/13] Bring back type --- lib/scholar/interpolation/linear.ex | 2 ++ 1 file changed, 2 insertions(+) diff --git a/lib/scholar/interpolation/linear.ex b/lib/scholar/interpolation/linear.ex index 46b99891..9be2f79d 100644 --- a/lib/scholar/interpolation/linear.ex +++ b/lib/scholar/interpolation/linear.ex @@ -21,6 +21,8 @@ defmodule Scholar.Interpolation.Linear do @derive {Nx.Container, containers: [:coefficients, :x]} defstruct [:coefficients, :x] + @type t :: %Scholar.Interpolation.Linear{} + opts_schema = [ left: [ type: {:or, [:float, :integer]}, From cff206beba0d5f2b48a95578b74d52b3537c8036 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jos=C3=A9=20Valim?= Date: Fri, 13 Oct 2023 14:22:16 +0200 Subject: [PATCH 07/13] Update lib/scholar/interpolation/linear.ex --- lib/scholar/interpolation/linear.ex | 1 - 1 file changed, 1 deletion(-) diff --git a/lib/scholar/interpolation/linear.ex b/lib/scholar/interpolation/linear.ex index c2b07966..297627b9 100644 --- a/lib/scholar/interpolation/linear.ex +++ b/lib/scholar/interpolation/linear.ex @@ -37,7 +37,6 @@ defmodule Scholar.Interpolation.Linear do ] @opts_schema NimbleOptions.new!(opts_schema) - @doc """ Fits a linear interpolation of the given `(x, y)` points From 03f4707ea0ce0beccdd403c33948f84ac0a4d463 Mon Sep 17 00:00:00 2001 From: Mateusz Date: Tue, 17 Oct 2023 23:51:30 +0200 Subject: [PATCH 08/13] Correct tests --- lib/scholar/linear/isotonic_regression.ex | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/lib/scholar/linear/isotonic_regression.ex b/lib/scholar/linear/isotonic_regression.ex index 0360b995..46221c52 100644 --- a/lib/scholar/linear/isotonic_regression.ex +++ b/lib/scholar/linear/isotonic_regression.ex @@ -204,7 +204,7 @@ defmodule Scholar.Linear.IsotonicRegression do iex> Scholar.Linear.IsotonicRegression.predict(model, to_predict) #Nx.Tensor< f32[10] - [1.0, 1.6666667461395264, 2.3333334922790527, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0] + [1.0, 1.6666667461395264, 2.3333332538604736, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0] > """ defn predict(model, x) do @@ -259,7 +259,7 @@ defmodule Scholar.Linear.IsotonicRegression do ] ), x: Nx.tensor( - [1.0, 4.0, 7.0, 9.0, 10.0] + [1.0, 4.0, 7.0, 9.0, 10.0, 11.0] ) } } From bb41862da0fa0993d6fa7e6d771e9bfa2b4bd2cc Mon Sep 17 00:00:00 2001 From: Mateusz Date: Wed, 18 Oct 2023 00:04:59 +0200 Subject: [PATCH 09/13] Change resultant tensor shape --- test/scholar/linear/isotonic_regression_test.exs | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/scholar/linear/isotonic_regression_test.exs b/test/scholar/linear/isotonic_regression_test.exs index 4f21a7d3..a954a0bd 100644 --- a/test/scholar/linear/isotonic_regression_test.exs +++ b/test/scholar/linear/isotonic_regression_test.exs @@ -209,7 +209,7 @@ defmodule Scholar.Linear.IsotonicRegressionTest do [43.0, 44.0, 46.0, 47.0, 55.0, 56.0] ++ [60.0, 61.0, 66.0, 67.0, 71.0, 72.0] ++ [73.0, 76.0, 77.0, 80.0, 81.0, 87.0] ++ - [88.0, 92.0, 93.0, 97.0, 98.0] + [88.0, 92.0, 93.0, 97.0, 98.0, 99.0] ) ) end From c82d8a10ad8cad8b20cb0366af5819a53526b6ad Mon Sep 17 00:00:00 2001 From: Mateusz Date: Thu, 12 Oct 2023 14:35:47 +0200 Subject: [PATCH 10/13] Add improvements --- lib/scholar/interpolation/linear.ex | 135 +++++++++++++++++++++++++--- 1 file changed, 124 insertions(+), 11 deletions(-) diff --git a/lib/scholar/interpolation/linear.ex b/lib/scholar/interpolation/linear.ex index e6ae850f..0d25dba3 100644 --- a/lib/scholar/interpolation/linear.ex +++ b/lib/scholar/interpolation/linear.ex @@ -16,12 +16,29 @@ defmodule Scholar.Interpolation.Linear do $$ """ import Nx.Defn + import Scholar.Shared @derive {Nx.Container, containers: [:coefficients, :x]} defstruct [:coefficients, :x] + @type t :: %Scholar.Interpolation.Linear{} + opts_schema = [ + left: [ + type: {:or, [:float, :integer]}, + doc: + "Value to return for values in `target_x` smaller that the smallest value in training set" + ], + right: [ + type: {:or, [:float, :integer]}, + doc: + "Value to return for values in `target_x` greater that the greatest value in training set" + ] + ] + + @opts_schema NimbleOptions.new!(opts_schema) + @doc """ Fits a linear interpolation of the given `(x, y)` points @@ -41,7 +58,7 @@ defmodule Scholar.Interpolation.Linear do ] ), x: Nx.tensor( - [0, 1] + [0, 1, 2] ) } """ @@ -79,7 +96,7 @@ defmodule Scholar.Interpolation.Linear do coefficients = Nx.stack([a, b], axis: 1) - %__MODULE__{coefficients: coefficients, x: x0} + %__MODULE__{coefficients: coefficients, x: x} end @doc """ @@ -97,24 +114,120 @@ defmodule Scholar.Interpolation.Linear do [2.0, 6.0] ] ) + + iex> x = Nx.iota({5}) + iex> y = Nx.tensor([2.0, 0.0, 1.0, 3.0, 4.0]) + iex> model = Scholar.Interpolation.Linear.fit(x, y) + iex> target_x = Nx.tensor([-2, -1, 1.25, 3, 3.25, 5.0]) + iex> Scholar.Interpolation.Linear.predict(model, target_x, left: 0.0, right: 10.0) + #Nx.Tensor< + f32[6] + [0.0, 0.0, 0.25, 3.0, 3.25, 10.0] + > """ - defn predict(%__MODULE__{x: x, coefficients: coefficients} = _model, target_x) do - original_shape = Nx.shape(target_x) + deftransform predict(model, target_x, opts \\ []) do + predict_n(model, target_x, NimbleOptions.validate!(opts, @opts_schema)) + end + + defnp predict_n(%__MODULE__{x: x, coefficients: coefficients} = _model, target_x, opts) do + shape = Nx.shape(target_x) target_x = Nx.flatten(target_x) - idx_selector = Nx.new_axis(target_x, 1) >= x + indices = Nx.argsort(target_x) + + left_bound = x[0] + right_bound = x[-1] + + target_x = Nx.sort(target_x) + res = Nx.broadcast(Nx.tensor(0, type: to_float_type(target_x)), {Nx.axis_size(target_x, 0)}) + + # while with smaller than left_bound + {{res, i}, _} = + while {{res, i = 0}, {x, coefficients, left_bound, target_x}}, + check_cond_left(target_x, i, left_bound) do + val = + case opts[:left] do + nil -> + coefficients[0][0] * Nx.take(target_x, i) + coefficients[0][1] + + _ -> + opts[:left] + end + + res = Nx.indexed_put(res, Nx.new_axis(i, -1), val) + {{res, i + 1}, {x, coefficients, left_bound, target_x}} + end + + {{res, i}, _} = + while {{res, i}, {x, right_bound, coefficients, target_x, j = 0}}, + check_cond_right(target_x, i, right_bound) do + {j, _} = + while {j, {i, x, target_x}}, + j < Nx.axis_size(x, 0) and Nx.take(x, j) < Nx.take(target_x, i) do + {j + 1, {i, x, target_x}} + end + + res = + Nx.indexed_put( + res, + Nx.new_axis(i, -1), + coefficients[Nx.max(j - 1, 0)][0] * Nx.take(target_x, i) + + coefficients[Nx.max(j - 1, 0)][1] + ) + + i = i + 1 + + {{res, i}, {x, right_bound, coefficients, target_x, j}} + end + + {res, i} + + # while with bigger than right_bound + + {res, _} = + while {res, {x, coefficients, target_x, i}}, + i < Nx.axis_size(target_x, 0) do + val = + case opts[:right] do + nil -> + coefficients[-1][0] * Nx.take(target_x, i) + coefficients[-1][1] + + _ -> + opts[:right] + end + + res = Nx.indexed_put(res, Nx.new_axis(i, -1), val) + {res, {x, coefficients, target_x, i + 1}} + end + + res = Nx.take(res, indices) + Nx.reshape(res, shape) + end - idx_poly = Nx.argmax(idx_selector, axis: 1, tie_break: :high) + defnp check_cond_left(target_x, i, left_bound) do + cond do + i >= Nx.axis_size(target_x, 0) -> + Nx.u8(0) - idx_poly = Nx.select(Nx.all(idx_selector == 0, axes: [1]), 0, idx_poly) + Nx.take(target_x, i) >= left_bound -> + Nx.u8(0) - coef_poly = Nx.take(coefficients, idx_poly) + true -> + Nx.u8(1) + end + end - x_poly = Nx.stack([target_x, Nx.broadcast(1, target_x)], axis: 1) + defnp check_cond_right(target_x, i, right_bound) do + cond do + i >= Nx.axis_size(target_x, 0) -> + Nx.u8(0) - result = Nx.dot(x_poly, [1], [0], coef_poly, [1], [0]) + Nx.take(target_x, i) > right_bound -> + Nx.u8(0) - Nx.reshape(result, original_shape) + true -> + Nx.u8(1) + end end end From 2faba84da899f437c3e01a9057f39fd65f9166f1 Mon Sep 17 00:00:00 2001 From: Mateusz Date: Thu, 12 Oct 2023 14:39:51 +0200 Subject: [PATCH 11/13] Bring back type --- lib/scholar/interpolation/linear.ex | 2 -- 1 file changed, 2 deletions(-) diff --git a/lib/scholar/interpolation/linear.ex b/lib/scholar/interpolation/linear.ex index 0d25dba3..84b6ee78 100644 --- a/lib/scholar/interpolation/linear.ex +++ b/lib/scholar/interpolation/linear.ex @@ -20,8 +20,6 @@ defmodule Scholar.Interpolation.Linear do @derive {Nx.Container, containers: [:coefficients, :x]} defstruct [:coefficients, :x] - - @type t :: %Scholar.Interpolation.Linear{} opts_schema = [ From 24859a011c3f292f46f1145b603834167d9d115d Mon Sep 17 00:00:00 2001 From: Mateusz Date: Tue, 17 Oct 2023 23:51:30 +0200 Subject: [PATCH 12/13] Correct tests --- lib/scholar/linear/isotonic_regression.ex | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/lib/scholar/linear/isotonic_regression.ex b/lib/scholar/linear/isotonic_regression.ex index e4f641be..22996eb0 100644 --- a/lib/scholar/linear/isotonic_regression.ex +++ b/lib/scholar/linear/isotonic_regression.ex @@ -204,7 +204,7 @@ defmodule Scholar.Linear.IsotonicRegression do iex> Scholar.Linear.IsotonicRegression.predict(model, to_predict) #Nx.Tensor< f32[10] - [1.0, 1.6666667461395264, 2.3333334922790527, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0] + [1.0, 1.6666667461395264, 2.3333332538604736, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0] > """ defn predict(model, x) do @@ -259,7 +259,7 @@ defmodule Scholar.Linear.IsotonicRegression do ] ), x: Nx.tensor( - [1.0, 4.0, 7.0, 9.0, 10.0] + [1.0, 4.0, 7.0, 9.0, 10.0, 11.0] ) } } From f2bb1c609b57f125ad8485affd50ba782fbdfecb Mon Sep 17 00:00:00 2001 From: Mateusz Date: Wed, 18 Oct 2023 00:04:59 +0200 Subject: [PATCH 13/13] Change resultant tensor shape --- test/scholar/linear/isotonic_regression_test.exs | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/scholar/linear/isotonic_regression_test.exs b/test/scholar/linear/isotonic_regression_test.exs index 4f21a7d3..a954a0bd 100644 --- a/test/scholar/linear/isotonic_regression_test.exs +++ b/test/scholar/linear/isotonic_regression_test.exs @@ -209,7 +209,7 @@ defmodule Scholar.Linear.IsotonicRegressionTest do [43.0, 44.0, 46.0, 47.0, 55.0, 56.0] ++ [60.0, 61.0, 66.0, 67.0, 71.0, 72.0] ++ [73.0, 76.0, 77.0, 80.0, 81.0, 87.0] ++ - [88.0, 92.0, 93.0, 97.0, 98.0] + [88.0, 92.0, 93.0, 97.0, 98.0, 99.0] ) ) end