def operation():
result = int(request.form['value'])
op = Operation()
op.v1 = int(request.form['v1'])
op.v2 = int(request.form['v2'])
op.timeStamp = float(request.form['timeStamp'])
if op.check(result):
regr = joblib.load('regr.pkl')
op.predict(regr)
op.ellapsed = time() - op.timeStamp
op.save()
return render_template("resultSimple.html", myOp = op)
else:
return render_template("form.html", myOp = op)
def train():
global nObservations
records = []
for v in collection.find({}):
op = Operation()
op.v1 = v["v1"]
op.v2 = v["v2"]
op.ellapsed = v["ellapsed"]
records.append(op.toTuple())
features = ["v1", "v2", "isEven", "isZero", "nDigits", "carryOn"]
target = "ellapsed"
labels = features + [target]
df = pd.DataFrame.from_records(records, columns=labels)
print("Before filtering ", df.shape)
df = df[df.ellapsed < 10]
print("After filtering ", df.shape)
if df.shape[0] <= nObservations:
return
nObservations = df.shape[0]
params = {
'n_estimators': 500,
'max_depth': 4,
'min_samples_split': 2,
'learning_rate': 0.01,
'loss': 'ls'
}
regr = ensemble.GradientBoostingRegressor(**params)
X = df[features]
y = df[target]
regr = regr.fit(X, y)
prediction = regr.predict(X)
print("Error", mean_squared_error(prediction, y))
joblib.dump(regr, 'regr.pkl', protocol=2)
print("DUMP")
return df.shape[0]
