plt.subplot(1, 2, 2)
plt.barh(pos, feature_importance[sorted_idx], align='center')
plt.yticks(pos, header[sorted_idx]) # boston.feature_names[sorted_idx])
plt.xlabel('Relative Importance')
plt.title('Variable Importance')
plt.show(block=False)
return header[sorted_idx], feature_importance[sorted_idx]
csvfile = 'rentList_All_final.csv'
# adding attributes did not make a difference
#csvfile = 'rentList_All_withAllAttr_trimmed.csv'
#csvfile = 'rentList_E_EC_N_final.csv'
data = readCsvIntoPandasDataframe(csvfile)
X, y, X_scaler, y_scaler, header = preprocessData(data)
#print "header type:"
#print type(header).__name__
#print header
#X, y = preprocessDataWithoutScale(data)
joblib.dump(X_scaler, 'pickle/X_scaler.pkl')
joblib.dump(y_scaler, 'pickle/y_scaler.pkl')
estimators = []
# K-Nearest Neighbors
estimators.append({
"name":
"KNN",
plt.subplot(1, 2, 2)
plt.barh(pos, feature_importance[sorted_idx], align='center')
plt.yticks(pos, header[sorted_idx]) # boston.feature_names[sorted_idx])
plt.xlabel('Relative Importance')
plt.title('Variable Importance')
plt.show(block=False)
return header[sorted_idx], feature_importance[sorted_idx]
csvfile = 'HousePriceList_All_Train.csv'
# adding attributes did not make a difference
#csvfile = 'rentList_All_withAllAttr_trimmed.csv'
#csvfile = 'rentList_E_EC_N_final.csv'
data = readCsvIntoPandasDataframe(csvfile)
X, y, X_scaler, y_scaler, header, imp, vec = preprocessData(data)
#print "header type:"
#print type(header).__name__
#print header
#X, y = preprocessDataWithoutScale(data)
joblib.dump(X_scaler, 'pickle-house/X_scaler.pkl')
joblib.dump(y_scaler, 'pickle-house/y_scaler.pkl')
joblib.dump(imp, 'pickle-house/Imputer.pkl')
joblib.dump(vec, 'pickle-house/Vector.pkl')
estimators = []
# K-Nearest Neighbors
plt.subplot(1, 2, 2)
plt.barh(pos, feature_importance[sorted_idx], align='center')
plt.yticks(pos, header[sorted_idx]) # boston.feature_names[sorted_idx])
plt.xlabel('Relative Importance')
plt.title('Variable Importance')
plt.show(block=False)
return header[sorted_idx], feature_importance[sorted_idx]
csvfile = 'rentList_All_final.csv'
# adding attributes did not make a difference
#csvfile = 'rentList_All_withAllAttr_trimmed.csv'
#csvfile = 'rentList_E_EC_N_final.csv'
data = readCsvIntoPandasDataframe(csvfile)
X, y, X_scaler, y_scaler, header, imp, vec = preprocessData(data)
#print "header type:"
#print type(header).__name__
#print header
#X, y = preprocessDataWithoutScale(data)
joblib.dump(X_scaler, 'pickle-final/X_scaler.pkl')
joblib.dump(y_scaler, 'pickle-final/y_scaler.pkl')
joblib.dump(imp, 'pickle-final/Imputer.pkl')
joblib.dump(vec, 'pickle-final/Vector.pkl')
estimators = []
# K-Nearest Neighbors
estimators.append({
plt.subplot(1, 2, 2)
plt.barh(pos, feature_importance[sorted_idx], align='center')
plt.yticks(pos, header[sorted_idx]) # boston.feature_names[sorted_idx])
plt.xlabel('Relative Importance')
plt.title('Variable Importance')
plt.show(block=False)
return header[sorted_idx], feature_importance[sorted_idx]
csvfile = 'rentList_All_final.csv'
# adding attributes did not make a difference
#csvfile = 'rentList_All_withAllAttr_trimmed.csv'
#csvfile = 'rentList_E_EC_N_final.csv'
data = readCsvIntoPandasDataframe(csvfile)
X, y, X_scaler, y_scaler, header = preprocessData(data)
#print "header type:"
#print type(header).__name__
#print header
#X, y = preprocessDataWithoutScale(data)
joblib.dump(X_scaler, 'pickle/X_scaler.pkl')
joblib.dump(y_scaler, 'pickle/y_scaler.pkl')
estimators = []
# K-Nearest Neighbors
estimators.append( {"name": "KNN",
"model": neighbors.KNeighborsRegressor(
