def initModels(_args, _type):
models = []
M = _args.models.split(",")
for m in M:
if m=="rf":
model = RandomForest()
models.append(model)
elif m=="m5":
model = M5()
models.append(model)
elif m=="ann":
model = ANN()
models.append(model)
elif m=="svm":
model = SVM()
model.modelType = _type
models.append(model)
else:
print("[ERROR] Model " + m + "not found", flush=True)
return models
def regressionRF(_training, _trees, _depth, _file):
e = Experiment(_training, verbose=False)
R = ResultMatrix()
for numTrees in range(1, _trees + 1):
for depth in range(1, _depth + 1):
rf = RandomForest()
rf.config.trees = numTrees
rf.config.depth = depth
header, result = e.regression([rf], 10)
mem = computeMemorySize(_training, rf, True)
header += ["arduino", "msp", "esp"]
result = np.hstack([result, mem])
print([
"#trees=" + str(numTrees) + "/" + str(_trees) + " depth=" +
str(depth) + "/" + str(_depth) + ' mem=', mem
],
flush=True)
R.add(header, result)
R.save(_file)
def regressionRF(_training, _trees, _depth, _file, _resultFolder,
_discretization):
csv = CSV(training)
attributes = csv.findAttributes(0)
R = ResultMatrix()
for numTrees in range(1, _trees + 1):
for depth in range(1, _depth + 1):
rf = RandomForest()
rf.config.trees = numTrees
rf.config.depth = depth
# perform a cross validation to generate the training/test files
e = Experiment(_training,
"example_rf_sweet_spot_disc",
verbose=False)
e.regression([rf], 10)
#
r, c = CodeEvaluator().crossValidation(rf, _training, attributes,
e.tmp(), _discretization)
result = np.hstack([r.data.mean(0), r.data.std(0)])
header = r.header + [x + "_std" for x in r.header]
mem = computeMemorySize(_training, rf, _resultFolder,
_discretization)
header += ["arduino", "msp", "esp"]
result = np.hstack([result, mem])
print([
"#trees=" + str(numTrees) + "/" + str(_trees) + " depth=" +
str(depth) + "/" + str(_depth) + ' mem=', mem
],
flush=True)
R.add(header, result)
R.save(_file)
from models.randomforest.RandomForest import RandomForest
from experiment.Experiment import Experiment
from code.CodeGenerator import CodeGenerator
from data.CSV import CSV
from data.ResultMatrix import ResultMatrix
import numpy as np
import matplotlib.pyplot as plt
from plot.PlotTool import PlotTool
from plot.ResultVisualizer import ResultVisualizer
# define the training data set and set up the model
training = "../examples/mnoA.csv"
model = RandomForest()
model.config.trees = 10
model.config.depth = 5
# perform a 10-fold cross validation
e = Experiment(training, "example_rf_mdi")
e.regression([model], 10)
#
M = CSV(e.path("features_0.csv")).toMatrix()
M.normalizeRows()
M.sortByMean()
M.save(e.path("rf_features.csv"))
#
ResultVisualizer().barchart(e.path("rf_features.csv"), xlabel="Feature", ylabel="Relative Feature Importance", savePNG=e.path(e.id+".png"))
from models.randomforest.RandomForest import RandomForest
from plot.ResultVisualizer import ResultVisualizer
from experiment.ConvergenceAnalysis import ConvergenceAnalysis
e = ConvergenceAnalysis("example_model_convergence")
e.run("../examples/mnoA.csv", RandomForest(), 100,
e.resultFolder + "convergence_rf.txt")
ResultVisualizer().errorbars([e.resultFolder + "convergence_rf.txt"],
"rmse",
xlabel='Number of Training Samples',
ylabel='RMSE',
savePNG=e.resultFolder +
'example_model_convergence.png')
from models.ann.ANN import ANN
from models.m5.M5 import M5
from models.randomforest.RandomForest import RandomForest
from models.svm.SVM import SVM
from experiment.Experiment import Experiment
from plot.ResultVisualizer import ResultVisualizer
import matplotlib.pyplot as plt
# define the training data set and set up the model
training = "../examples/mnoA.csv"
models = [ANN(), M5(), RandomForest(), SVM()]
# perform a 10-fold cross validation
e = Experiment(training, "example_experiment")
e.regression(models, 10)
# visualize
files = [e.path("cv_" + str(i) + ".csv") for i in range(len(models))]
fig, axs = plt.subplots(2, 2)
fig.set_size_inches(8, 5)
xticks = [model.modelName for model in models]
ResultVisualizer().boxplots(files,
"r2",
xticks,
ylabel='R2',
fig=fig,
ax=axs[0][0],
show=False)
ResultVisualizer().boxplots(files,
"mae",
from models.randomforest.RandomForest import RandomForest from weka.models.RandomForest import RandomForest as RandomForest_WEKA from experiment.Experiment import Experiment from data.CSV import CSV from code.CodeGenerator import CodeGenerator from data.FileHandler import FileHandler # define the training data set and set up the model training = "../examples/vehicleClassification.csv" model = RandomForest() model.config.depth = 7 # perform a 10-fold cross validation e = Experiment(training, "example_rf") e.classification([model], 10) # csv = CSV() csv.load(training) attributes = csv.findAttributes(0) data = "\n".join(FileHandler().read(e.tmp() + "raw0_0.txt")) RandomForest_WEKA(model).initModel(data, attributes) model.exportEps(model.depth+1, 10, 10, len(attributes)-1)