require(enigma);
imports ["learning", "model", "activateFunction"] from "enigma";Some examples of solve a classification problem, and inspect the dataset at first for the one class classification problem:
data("bezdekIris");
print(dataset::description(bezdekIris));
# D1 D2 D3 D4 class
# --------------------------------------------------------------------
# <mode> <double> <double> <double> <double> <string>
# min 4.3 2 1 0.1 "Iris-setosa"
# max 7.9 4.4 6.9 2.5 "Iris-virginica"
# width 3.6 2.4 5.9 2.4 "3"
# mean 5.84333 3.05733 3.758 1.19933 "Iris-setosa"
# sd 0.0676113 0.0355883 0.144136 0.0622364 "NA"
# rsd 0.0115707 0.0116403 0.0383544 0.0518925 "NA"
rownames(bezdekIris) = unique.names(bezdekIris$class);
data = bezdekIris
|> toFeatureSet()
|> dataset::encoding(class = to_factors)
|> as.data.frame()
;
# D1 D2 D3 D4 class.Iris-setosa class.Iris-versicolor class.Iris-virginica
# ---------------------------------------------------------------------------------------------------------------
# <mode> <double> <double> <double> <double> <integer> <integer> <integer>
# Iris-setosa 5.1 3.5 1.4 0.2 1 0 0
# Iris-setosa_1 4.9 3 1.4 0.2 1 0 0
# Iris-setosa_2 4.7 3.2 1.3 0.2 1 0 0
# Iris-setosa_3 4.6 3.1 1.5 0.2 1 0 0
# Iris-setosa_4 5 3.6 1.4 0.2 1 0 0
# Iris-setosa_5 5.4 3.9 1.7 0.4 1 0 0
# [ reached 'max' / getOption("max.print") -- omitted 144 rows ]
i = data[, "class.Iris-setosa"] == 1;
v = rep("", length(i));
v[i] = "Iris-setosa";
v[!i] = "Other";
data[, "class.Iris-setosa"] = v;For use the svm classifier:
tensor(model = model::svm)
|> feed(data, features = ["D1","D2","D3","D4"])
|> output(labels = ["class.Iris-setosa"])
|> learn(
degree = 3,
gamma = 0.5,
coefficient0 = 0,
nu = 0.5,
cacheSize = 40,
c = 1,
EPS = 0.001,
P = 0.1,
shrinking = TRUE,
probability = FALSE
)
|> solve(data)
;For use the xgboost classifier:
tensor(model = model::xgboost)
|> feed(data, features = ["D1","D2","D3","D4"])
|> output(labels = ["class.Iris-setosa"])
|> learn(
loss = "logloss",
cost = "auc",
num_boost_round = 10,
early_stopping_rounds = 10,
maximize = TRUE,
eta = 0.3,
max_depth = 7,
scale_pos_weight = 1,
rowsample = 0.8,
colsample = 0.8,
min_child_weight = 1,
min_sample_split = 5,
lambda = 1,
gamma = 0,
num_thread = 8
)
|> solve(data)
;For use the ANN(Back Propagation Neural Network) classifier, then you could:
tensor(model = model::ANN)
|> feed(data, features = ["D1","D2","D3","D4"])
|> hidden_layer([50, 500, 5], activate = activateFunction::sigmoid(alpha = 2.0))
|> output(
labels = ["class.Iris-setosa","class.Iris-versicolor","class.Iris-virginica"],
activate = activateFunction::sigmoid(alpha = 2.0)
)
|> learn(
parallel = TRUE,
learn.rate = 0.01,
truncate = -1,
threshold = 0.01,
softmax = FALSE,
max.epochs = 10000
)
|> solve(data)By first of all, we prepares an example demo dataset for such regression modelling problem like:
# generate the demo test dataset
let x = 1:1000;
let y = x ^ 1.25 + runif(n = length(x));
let z = y / x;
data = data.frame(x, y, z, row.names = as.character(x));
# x y z
# -----------------------------------
# <mode> <integer> <double> <double>
# 1 1 1.99925 1.99925
# 2 2 3.34409 1.67205
# 3 3 4.40403 1.46801
# 4 4 5.8122 1.45305
# 5 5 7.9491 1.58982
# 6 6 10.1006 1.68343
# [ reached 'max' / getOption("max.print") -- omitted 994 rows ]The some of the example for solve the regression problem by use the enigma package:
test = tensor(model = model::xgboost)
|> feed(data, features = ["x", "y"])
|> output(labels = "z")
|> learn(
loss = "squareloss",
cost = "mse",
num_boost_round = 10,
early_stopping_rounds = 10,
maximize = TRUE,
eta = 0.3,
max_depth = 7,
scale_pos_weight = 1,
rowsample = 0.8,
colsample = 0.8,
min_child_weight = 1,
min_sample_split = 5,
lambda = 1,
gamma = 0,
num_thread = 8
)
|> solve(data)
;
test[, "errors"] = abs(test$z - test[, "z(predicts)"]);
i = order(test$errors);
test = test[i, ];
# x y z z(predicts) errors
# ------------------------------------------------------------
# <mode> <integer> <double> <double> <double> <double>
# 918 918 5053.48 5.50488 5.50487 3.06194E-06
# 696 696 3574.91 5.13637 5.13636 5.30553E-06
# 607 607 3012.96 4.9637 4.96369 7.10683E-06
# 826 826 4428.27 5.36111 5.36112 1.16761E-05
# 810 810 4322.19 5.33603 5.33604 1.23223E-05
# 879 879 4786.52 5.44542 5.44543 1.36714E-05
# [ reached 'max' / getOption("max.print") -- omitted 994 rows ]For solve a SVR regression problem, then we could just replace the tensor model to svr function:
test = tensor(model = model::svr)
|> feed(data, features = ["x", "y"])
|> output(labels = "z")
|> learn(
degree = 3,
gamma = 0.5,
coefficient0 = 0,
nu = 0.5,
cacheSize = 40,
c = 1,
EPS = 0.001,
P = 0.1,
shrinking = TRUE,
probability = FALSE
)
|> solve(data)
;
data[, "errors"] = abs(data$z.predicts. - data$z);
print(data, max.print =6);
bitmap(file = file, size = [2700, 1800]) {
ggplot(data, aes(x = "errors"), padding = "padding:250px 400px 200px 250px;")
+ geom_histogram(bins = 250, color = "steelblue", range = [0, 1])
+ ggtitle(title)
+ scale_x_continuous(labels = "F2")
+ scale_y_continuous(labels = "F0")
+ theme_default()
;
}