def main(): # load features from file traindata = prodmaketrain.main() X = [x[1:] for x in traindata[0]] Y = [y[1:] for y in traindata[1]] clf=trainmodel(X,Y) joblib.dump(clf, MLLIB)
import prodmaketrain
import numpy as np
res = prodmaketrain.main()
X = [x[1:] for x in res[0]]
Y = [y[1:] for y in res[1]]
NUMFEATS = len(X[0])
print("Number of variabiables is: " + str(NUMFEATS))
# DOCS
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_classification
from sklearn.ensemble import ExtraTreesClassifier
# Build a forest and compute the feature importances
forest = ExtraTreesClassifier(n_estimators=250,
random_state=0)
forest.fit(X, Y)
importances = forest.feature_importances_
std = np.std([tree.feature_importances_ for tree in forest.estimators_],
axis=0)
indices = np.argsort(importances)[::-1]
# HEADERS
header = ['max','median', 'average', 'sumsqtot', 'sumsq25', 'sumsq75', 'std', 'sumdiff']
# Print the feature ranking
