def main(): feature.main() ''' 利用网格搜索选择参数,时间较长,可以不运行 ''' # params.main() ''' 利用最后一星期的数据来测试模型的效果 ''' test.main() ''' 预测结果 ''' predict.main()
def which_mode(): while (1): wh=ad.stt() if wh is None: ad.tts( "try again") continue if ad.find(wh,"read"): ad.tts("ok, I am ready to assist you in reading.") a= feature.main(1) return a; elif ( ad.find(wh,"sketch") or ad.find(wh,"draw") ): ad.tts("ok, I am ready to assist you in sketching.") #sketch() return; elif ( ad.find(wh,"note") or ad.find(wh,"write") ): ad.tts("ok, I am ready to assist you in taking notes.") #note() return; else: ad.tts("Sorry, I didn't get you. Are you reading or sketching or taking notes?") which_mode()
from sklearn.neighbors import KNeighborsClassifier from sklearn.ensemble import AdaBoostClassifier from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import GradientBoostingClassifier from sklearn.ensemble import VotingClassifier from sklearn.grid_search import GridSearchCV from sklearn.metrics import classification_report from sklearn.preprocessing import normalize import feature as ft with open('pickles/train_df.pickle', 'rb') as f: train_df = pickle.load(f) test_df = pd.read_csv('test.csv') X = ft.main(train_df) y = train_df['Survived'] normalize(X, copy=False) X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.2, random_state=4) svm = SVC(probability=True, C=3) svm.fit(X_train, y_train) knn = KNeighborsClassifier(metric='minkowski', n_neighbors=70, p=1, weights='distance') knn.fit(X_train, y_train) grad = GradientBoostingClassifier(learning_rate=0.1, max_depth=2, max_features=2, n_estimators=400, subsample=1.0) grad.fit(X_train, y_train)
# -*- coding: utf-8 -*- import pandas as pd, numpy as np, plotting, pickle from sklearn import cross_validation from sklearn.svm import SVC from sklearn.metrics import accuracy_score from sklearn import preprocessing import feature as ft with open('pickles/eclf.pickle', 'rb') as f: clf = pickle.load(f) with open('pickles/test_df.pickle', 'rb') as f: test_df = pickle.load(f) test_df.drop([759], inplace=True) test_df.set_value(414, 'Title', 'Aristocratic') X = ft.main(test_df.copy()) pred = clf.predict(X) Titanic_predictions = pd.DataFrame(test_df['PassengerId'], dtype=int) Titanic_predictions['Survived'] = pred print Titanic_predictions.head() Titanic_predictions.to_csv('Titanic_predictions.csv', index_label=False, index=False) prd = pd.read_csv('Titanic_predictions.csv') print prd.head() print "Columns", X.columns.values