import numpy as np def score(y_predict, y_real): n = float(y_predict.shape[0]) n_ok = float(np.sum(y_predict == y_real)) return (n_ok/n) l = AllStateDataLoader() p = AllStatePredictor() # X_2 = l.get_X_train("2", "") y_2 = l.get_y("2", "ABCDEFG") y_2_predict = p.predict_cascade("2", "extratrees", "ABCDEFG", kind="train") # X_3 = l.get_X_train("3", "") y_3 = l.get_y("3", "ABCDEFG") y_3_predict = p.predict_cascade("3", "extratrees", "ABCDEFG", kind="train") # X_4 = l.get_X_train("4", "") y_4 = l.get_y("4", "ABCDEFG") y_4_predict = p.predict_cascade("4", "extratrees", "ABCDEFG", kind="train") # X_all = l.get_X_train("all", "") y_all = l.get_y("all", "ABCDEFG") y_all_predict = p.predict_cascade("all", "extratrees", "ABCDEFG", kind="train") print "score 2 extratrees cascade : %.4f" % (score(y_2, y_2_predict)) print "score 3 extratrees cascade : %.4f" % (score(y_3, y_3_predict))
import sys sys.path.append("lib") from AllStateDataLoader import AllStateDataLoader from AllStatePredictor import AllStatePredictor from sklearn import linear_model from sklearn import grid_search import numpy as np import pandas as pd p = AllStatePredictor() y_2_predict = p.predict_cascade("2", "extratrees", "ABCDEFG", kind="test") y_3_predict = p.predict_cascade("3", "extratrees", "ABCDEFG", kind="test") y_4_predict = p.predict_cascade("4", "extratrees", "ABCDEFG", kind="test") y_all_predict = p.predict_cascade("all", "extratrees", "ABCDEFG", kind="test") y_submission = y_2_predict.append([y_3_predict, y_4_predict, y_all_predict]) y_submission = y_submission.sort_index() df = pd.DataFrame(data={'plan': y_submission}, index=y_submission.index) df.to_csv("extratrees_cascade_sans_location.csv")
import numpy as np def score(y_predict, y_real): n = float(y_predict.shape[0]) n_ok = float(np.sum(y_predict == y_real)) return (n_ok/n) l = AllStateDataLoader() p = AllStatePredictor() # X_2 = l.get_X_train("2", "") y_2 = l.get_y("2", "ABCDEFG") y_2_predict = p.predict_cascade("2", "linearsvc", "ABCDEFG", kind="train") # X_3 = l.get_X_train("3", "") y_3 = l.get_y("3", "ABCDEFG") y_3_predict = p.predict_cascade("3", "linearsvc", "ABCDEFG", kind="train") # X_4 = l.get_X_train("4", "") y_4 = l.get_y("4", "ABCDEFG") y_4_predict = p.predict_cascade("4", "linearsvc", "ABCDEFG", kind="train") # X_all = l.get_X_train("all", "") y_all = l.get_y("all", "ABCDEFG") y_all_predict = p.predict_cascade("all", "linearsvc", "ABCDEFG", kind="train") print "score 2 linearsvc : %.4f" % (score(y_2, y_2_predict)) print "score 3 linearsvc : %.4f" % (score(y_3, y_3_predict))
def score(y_predict, y_real): n = float(y_predict.shape[0]) n_ok = float(np.sum(y_predict == y_real)) return (n_ok / n) l = AllStateDataLoader() p = AllStatePredictor() # X_2 = l.get_X_train("2", "") y_2 = l.get_y("2", "ABCDEFG") y_2_predict = p.predict_cascade("2", "linearsvc", "ABCDEFG", kind="train") # X_3 = l.get_X_train("3", "") y_3 = l.get_y("3", "ABCDEFG") y_3_predict = p.predict_cascade("3", "linearsvc", "ABCDEFG", kind="train") # X_4 = l.get_X_train("4", "") y_4 = l.get_y("4", "ABCDEFG") y_4_predict = p.predict_cascade("4", "linearsvc", "ABCDEFG", kind="train") # X_all = l.get_X_train("all", "") y_all = l.get_y("all", "ABCDEFG") y_all_predict = p.predict_cascade("all", "linearsvc", "ABCDEFG", kind="train") print "score 2 linearsvc : %.4f" % (score(y_2, y_2_predict)) print "score 3 linearsvc : %.4f" % (score(y_3, y_3_predict))