def train(self, model, save=False, make_chart=False):
"""
Trains an input model. Makes Calculations, Charts, and Saves
the model if necessary.
Parameters
----------
model: SKLearn Model The regression model to use
save: Boolean Whether or not the model should be saved
make_chart Boolean Whether or not to make/save a chart
Returns
-------
float, float, float: The Average CV Mean Squared Error, Mean Absolute Error, and Test MSE
"""
#get/split data
reader = DataReader()
df = reader.create_input_data()
df = self.preprocess(df)
self.X_train, self.X_test, self.y_train, self.y_test = self.split_data(
df)
parameters = {
'n_estimators': [1, 5, 10, 20, 30],
'max_depth': [1, 5, 10]
}
rf = RandomForestRegressor()
self.model = GridSearchCV(rf, parameters, cv=10)
#train model
self.model.fit(self.X_train, self.y_train)
#Feature importance
importances = self.model.best_estimator_.feature_importances_
cols = self.X_train.columns
for i in range(len(importances)):
print(cols[i], importances[i])
if save:
joblib.dump(self.model.best_estimator_,
"../models/" + self.name + "_2017.joblib")
print("------------------------")
MSEs = cross_val_score(estimator=self.model,
X=self.X_train,
y=self.y_train,
scoring='neg_mean_squared_error',
cv=8)
predicted = self.model.predict(self.X_test)
print("Average CV Mean Squared Error: ", abs(np.mean(MSEs)))
print(
"Testing Mean Absolute Error: ",
mean_absolute_error(self.y_test, self.model.predict(self.X_test)))
print("Testing MSE: ", mean_squared_error(self.y_test, predicted))
#print(self.model.feature_importances_)
if make_chart:
print("Generating Chart...")
plt.style.use('dark_background')
fig, ax = plt.subplots(nrows=1, ncols=1)
ax.set_ylabel('HDI')
ax.set_xlabel("Municipality Codmun ID")
ax.set_title(self.name + 'Real vs Predicted')
green, = ax.plot(np.arange(20),
self.y_test[0:100:5],
'g',
label='True')
red, = ax.plot(np.arange(20),
predicted[0:100:5],
'r',
label='Predicted')
ax.set_xticks(np.arange(20))
x_labels = self.X_test.iloc[0:100:5]['codmun'].tolist()
ax.set_xticklabels([str(int(y)) for y in x_labels],
rotation='vertical')
plt.legend(handles=[green, red], labels=["True", "Predicted"])
plt.tight_layout()
fig.savefig(self.name + "_real_v_predicted")
for x in range(0, 100, 5):
print(predicted[x], x_labels[int(x / 5)])
print(x_labels, predicted[0:100:5])
return np.mean(MSEs), mean_absolute_error(
self.y_test, self.model.predict(self.X_test)), mean_squared_error(
self.y_test, predicted)
print("Please add --train or --test after py Regressor.py")
options = None
if options == "--train":
r = Regressor("Random Forest", load_model=False)
mod = Regressor("Random Forest", load_model=False)
cv, ma, mse = r.train(mod, save=False, make_chart=False)
print(cv, ma, mse)
elif options == "--test":
model_name = sys.argv[2] + " " + sys.argv[
3] #Random Forest_2017 or Random Forest_2016
year = int(model_name.split("_")[-1])
r = Regressor(model_name, load_model=True)
reader = DataReader()
df = reader.create_input_data()
predictions = r.predict(df, year)
print("Actual || Predicted")
for i in range(len(predictions)):
print(df.iloc[i]['hdi'], "||", predictions[i])
######Training Code#########
#cv_error = []
#testing_ma_error = []
#testing_mse = []
#mod = RandomForestRegressor(bootstrap=True, criterion='mae', n_estimators=100)
#mod = RandomForestRegressor()
#r = Regressor("Random Forest_2017", load_model=True)
#importances = r.model.feature_importances_
#reader = DataReader()
