end-to-end example of UCI Dataset

_layout/head.html

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       <li class="pure-menu-item {{ispage end-to-end/crabs-xgb/index.html}}pure-menu-selected{{end}}"><a href="/end-to-end/crabs-xgb/" class="pure-menu-link"><span style="padding-right:0.5rem;">•</span> Crabs (XGB)</a></li>
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+      <li class="pure-menu-item "><a href="/end-to-end/airfoil" class="pure-menu-link"><span style="padding-right:0.5rem;">•</span> Airfoil </a></li>
     </ul>
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_literate/EX-airfoil.jl

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+# ## Getting started
+# Here we use the [UCI "Airfoil Self-Noise" dataset](http://archive.ics.uci.edu/ml/datasets/Airfoil+Self-Noise)
+# ### Loading and  preparing the data
+
+using MLJ, PrettyPrinting, DataFrames, Statistics, CSV
+using PyPlot, HTTP
+
+MLJ.color_off() # hide
+
+req = HTTP.get("https://raw.githubusercontent.com/rupakc/UCI-Data-Analysis/master/Airfoil%20Dataset/airfoil_self_noise.dat");
+
+df = CSV.read(req.body, header=[
+                  "Frequency","Attack_Angle","Chord+Length","Free_Velocity","Suction_Side","Scaled_Sound"
+              ]
+       );
+df[1:5, :] |> pretty
+
+# 
+
+schema(df)
+
+#-unpack into the data and labels.
+
+y, X = unpack(df, ==(:Scaled_Sound), col -> true);
+
+# Now we Standardize the features using the transformer Standardizer()
+
+X = transform(fit!(machine(Standardizer(), X)), X);
+
+# Partition into train and test set
+
+train, test = partition(eachindex(y), 0.7, shuffle=true);
+
+# Let's first see which models are compatible with the scientific type and machine type of our data
+
+for model in models(matching(X, y))
+       print("Model Name: " , model.name , " , Package: " , model.package_name , "\n")
+end
+
+# ## DecisionTreeRegressor
+#
+# We will first try out DecisionTreeRegressor. We load the DecisionTreeRegressor from DecisionTree.jl
+
+dcr = @load DecisionTreeRegressor pkg=DecisionTree
+
+dcrm = machine(dcr, X, y)
+
+fit!(dcrm, rows=train, force=true)
+pred_dcrm = MLJ.predict(dcrm, rows=test);
+
+# Now you can call a loss function to asses the performace on test set.
+
+rms(pred_dcrm, y[test])
+
+# ## RandomForestRegressor
+#
+# Now let's try out RandomForestRegressor. We load the RandomForestRegressor from DecisionTree.jl
+
+rfr = @load RandomForestRegressor pkg=DecisionTree
+
+rfr_m = machine(rfr, X, y);
+
+# train on the rows corresponding to train
+
+fit!(rfr_m, rows=train);
+
+# predict values on the rows corresponding to test
+
+pred_rfr = MLJ.predict(rfr_m, rows=test);
+rms(pred_rfr, y[test])
+
+# Clearly, we have improved the performace by using RandomForestRegressor
+#
+# Can we do even better ?
+#
+# Yeah, we can!! We can make use of Model Tuning.
+#
+# ## Tuning
+# 
+# In case you are new to model tuning using MLJ, refer [lab5](https://alan-turing-institute.github.io/MLJTutorials/isl/lab-5/) and [model-tuning](https://alan-turing-institute.github.io/MLJTutorials/getting-started/model-tuning/)
+#
+# Range of values for parameters should be specified to do hyperparameter tuning
+
+r_maxD = range(rfr, :n_trees, lower=9, upper=15)
+r_samF = range(rfr, :sampling_fraction, lower=0.6, upper=0.8)
+r = [r_maxD, r_samF];
+
+# Now we specify how the tuning should be done. Let's just specify a coarse grid tuning with cross validation and instantiate a tuned model:
+tuning = Grid(resolution=7)
+resampling = CV(nfolds=6)
+
+tm = TunedModel(model=rfr, tuning=tuning, resampling=resampling, ranges=r, measure=rms)
+
+rfr_tm = machine(tm, X, y);
+
+# train on the rows corresponding to train
+
+fit!(rfr_tm, rows=train);
+
+# predict values on the rows corresponding to test
+
+pred_rfr_tm = MLJ.predict(rfr_tm, rows=test);
+rms(pred_rfr_tm, y[test])
+
+# That was great! We have further improved the accuracy
+#
+# Now to retrieve best model, You can use
+
+fitted_params(rfr_tm).best_model
+
+# Now we can investigate the tuning by using report.
+# Let's plot a heatmap of the measurements:
+
+r = report(rfr_tm)
+res = r.plotting
+
+md = res.parameter_values[:,1]
+mcw = res.parameter_values[:,2]
+
+figure(figsize=(8,6))
+tricontourf(md, mcw, res.measurements)
+
+xlabel("Number of trees", fontsize=14)
+ylabel("Sampling fraction", fontsize=14)
+xticks(9:1:15, fontsize=12)
+yticks(fontsize=12)
+plt.savefig(joinpath(@OUTPUT, "airfoil_heatmap.svg")) # hide
+
+# \figalt{Hyperparameter heatmap}{airfoil_heatmap.svg}

end-to-end/airfoil.md

@@ -0,0 +1,6 @@
+@def hascode = true
+@def showall = true
+
+# Airfoil
+
+\tutorial{EX-airfoil}