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Python GradientBoostingRegressor.score方法代码示例

本文整理汇总了Python中sklearn.ensemble.GradientBoostingRegressor.score方法的典型用法代码示例。如果您正苦于以下问题:Python GradientBoostingRegressor.score方法的具体用法?Python GradientBoostingRegressor.score怎么用?Python GradientBoostingRegressor.score使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在sklearn.ensemble.GradientBoostingRegressor的用法示例。


在下文中一共展示了GradientBoostingRegressor.score方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: anm_fit

# 需要导入模块: from sklearn.ensemble import GradientBoostingRegressor [as 别名]
# 或者: from sklearn.ensemble.GradientBoostingRegressor import score [as 别名]
def anm_fit( (x, y) ):
  newX = np.array(x).reshape(len(x), 1)
  clf = GradientBoostingRegressor()
  clf.fit(newX, y)
  err = y - clf.predict(newX)
  ret =  [clf.score(newX, y)] + list(pearsonr(x, err))
  return ret
开发者ID:sibelius,项目名称:CauseEffect,代码行数:9,代码来源:anm.py

示例2: train_model_parallel

# 需要导入模块: from sklearn.ensemble import GradientBoostingRegressor [as 别名]
# 或者: from sklearn.ensemble.GradientBoostingRegressor import score [as 别名]
def train_model_parallel(xtrain, ytrain, index=0):
    xTrain, xTest, yTrain, yTest = train_test_split(xtrain, ytrain[:, index],
                                                    test_size=0.25)
#    model = RandomForestRegressor()
#    model = LogisticRegression()
    model = GradientBoostingRegressor(verbose=1)

    n_est = [10, 50]
    m_dep = [5, 3]

    model = GridSearchCV(estimator=model,
                                param_grid=dict(n_estimators=n_est,
                                                max_depth=m_dep),
                                scoring=scorer,
                                n_jobs=-1, verbose=1)

    model.fit(xTrain, yTrain)
    ypred = model.predict(xTest)
    if hasattr(model, 'best_params_'):
        print('best_params', model.best_params_)
    print('score %d %s' % (index, model.score(xTest, yTest)))
    print('RMSLE %d %s' % (index, np.sqrt(mean_squared_error(yTest, ypred))))
    with gzip.open('model_%d.pkl.gz' % index, 'wb') as pklfile:
        pickle.dump(model, pklfile, protocol=2)
    return
开发者ID:ddboline,项目名称:driven_data_predict_restraurant_inspections,代码行数:27,代码来源:my_model.py

示例3: GBRModel

# 需要导入模块: from sklearn.ensemble import GradientBoostingRegressor [as 别名]
# 或者: from sklearn.ensemble.GradientBoostingRegressor import score [as 别名]
def GBRModel(X_train,X_cv,y_train,y_cv):
	targets = get_target_array()
	#print len(train_features)
	#print train_features[0]

	#print len(test_features)
	n_estimators = [50, 100]#, 1500, 5000]
	max_depth = [3,8]
	

	best_GBR = None
	best_mse = float('inf')
	best_score = -float('inf')

	print "################# Performing Gradient Boosting Regression ####################### \n\n\n\n"
	for estm in n_estimators:
		for cur_depth in max_depth:
			#random_forest = RandomForestRegressor(n_estimators=estm)
			regr_GBR = GradientBoostingRegressor(n_estimators=estm, max_depth= cur_depth)
			predictor = regr_GBR.fit(X_train,y_train)
			score = regr_GBR.score(X_cv,y_cv)
			mse = np.mean((regr_GBR.predict(X_cv) - y_cv) **2)
			print "Number of estimators used: ",estm
			print "Tree depth used: ",cur_depth
			print "Residual sum of squares: %.2f "%mse
			print "Variance score: %.2f \n"%score
			if best_score <= score:
				if best_mse > mse:
					best_mse = mse
					best_score = score
					best_GBR = predictor	
	print "\nBest score: ",best_score
	print "Best mse: ",best_mse
	return best_GBR
开发者ID:SaarthakKhanna2104,项目名称:Home-Depot-Product-Search-Relevance,代码行数:36,代码来源:GBR.py

示例4: kfold_cv

# 需要导入模块: from sklearn.ensemble import GradientBoostingRegressor [as 别名]
# 或者: from sklearn.ensemble.GradientBoostingRegressor import score [as 别名]
    def kfold_cv(self, n_folds = 3):
        """
        Takes in: number of folds
        
        Prints out RMSE score and stores the results in self.results
        """

        cv = KFold(n = self.X_train.shape[0], n_folds = n_folds)
        gbr = GradientBoostingRegressor(**self.params)
        self.med_error = []
        self.rmse_cv = []
        self.pct_error=[]
        self.r2=[]
        self.results = {'pred': [],
                   'real': []}
        
        for train, test in cv:
            gbr.fit(self.X_train[train], self.y_train[train])
            pred = gbr.predict(self.X_train[test])
            print "Score", gbr.score(self.X_train[test], self.y_train[test])
            predExp=np.power(10, pred)
            testExp=np.power(10, self.y_train[test])
            medError=median_absolute_error(predExp, testExp)
            percentError=np.median([np.fabs(p-t)/t for p,t in zip(predExp, testExp)])
            error = mean_squared_error(np.power(10, pred), np.power(10, self.y_train[test]))**0.5
            self.results['pred'] += list(pred)
            self.results['real'] += list(self.y_train[test])
            self.rmse_cv += [error]
            self.med_error+=[medError]
            self.pct_error+=[percentError]
            self.r2+=[r2_score(self.y_train[test], pred)]
        print 'Abs Median Error:', np.mean(self.med_error)
        print 'Abs Percent Error:', np.mean(self.pct_error)
        print 'Mean RMSE:', np.mean(self.rmse_cv)
        print "R2",np.mean(self.r2)
开发者ID:jbrosamer,项目名称:PonyPricer,代码行数:37,代码来源:model.py

示例5: do_job_unit

# 需要导入模块: from sklearn.ensemble import GradientBoostingRegressor [as 别名]
# 或者: from sklearn.ensemble.GradientBoostingRegressor import score [as 别名]
    def do_job_unit(self, event, corpus, unit, **kwargs):
        assert unit == 0

        extractor = kwargs.get('extractor', "goose")
        thresh = kwargs.get('thresh', .8)
        delay = kwargs.get('delay', None)
        topk = kwargs.get('topk', 20)

        train_events = [e for e in cuttsum.events.get_events()
                        if e.query_num not in set([event.query_num, 7])]
        res = InputStreamResource()

        y = []
        X = []
        for train_event in train_events:

            y_e = []
            X_e = []

            istream = res.get_dataframes(
                train_event,
                cuttsum.corpora.get_raw_corpus(train_event), 
                extractor, thresh, delay, topk)
            for df in istream:

                selector = (df["n conf"] == 1) & (df["nugget probs"].apply(len) == 0)
                df.loc[selector, "nugget probs"] = \
                    df.loc[selector, "nuggets"].apply(lambda x: {n:1 for n in x})


                df["probs"] = df["nugget probs"].apply(lambda x: [val for key, val in x.items()] +[0])
                df["probs"] = df["probs"].apply(lambda x: np.max(x))
                df.loc[(df["n conf"] == 1) & (df["nuggets"].apply(len) == 0), "probs"] = 0
                y_t = df["probs"].values
                y_t = y_t[:, np.newaxis]
                y_e.append(y_t)
                X_t = df[self.cols].values
                X_e.append(X_t)

            y_e = np.vstack(y_e)
            y.append(y_e)
            X_e = np.vstack(X_e)
            X.append(X_e)

 #       print "WARNING NOT USING 2014 EVENTS"
        X = np.vstack(X)
        y = np.vstack(y)

        gbc = GradientBoostingRegressor(
            n_estimators=100, learning_rate=1.,
            max_depth=3, random_state=0)
        print "fitting", event
        gbc.fit(X, y.ravel())
        print event, "SCORE", gbc.score(X, y.ravel())
        
        model_dir = self.get_model_dir(event)
        if not os.path.exists(model_dir):
            os.makedirs(model_dir)
        joblib.dump(gbc, self.get_model_path(event), compress=9)
开发者ID:kedz,项目名称:cuttsum,代码行数:61,代码来源:_nugget_regressor.py

示例6: _random_search

# 需要导入模块: from sklearn.ensemble import GradientBoostingRegressor [as 别名]
# 或者: from sklearn.ensemble.GradientBoostingRegressor import score [as 别名]
    def _random_search(self, random_iter, x, y):
        # Default values
        max_features = x.shape[1]
        learning_rate = 0.1   # [0.1, 0.05, 0.02, 0.01],
        max_depth = 3         # [4, 6],
        min_samples_leaf = 1  # [3, 5, 9, 17],
        n_estimators = 100    #
        best_score = -sys.maxint

        if random_iter > 0:
            sys.stdout.write("Do a random search %d times" % random_iter)

            param_dist = {"max_features": numpy.linspace(0.1, 1, num=10),
                          "learning_rate": 2**numpy.linspace(-1, -10, num=10),
                          "max_depth": range(1, 11),
                          "min_samples_leaf": range(2, 20, 2),
                          "n_estimators": range(10, 110, 10)}
            param_list = [{"max_features": max_features,
                           "learning_rate": learning_rate,
                           "max_depth": max_depth,
                           "min_samples_leaf": min_samples_leaf,
                           "n_estimators": n_estimators}]
            param_list.extend(list(ParameterSampler(param_dist, n_iter=random_iter-1, random_state=self._rng)))

        for idx, d in enumerate(param_list):
            gb = GradientBoostingRegressor(loss='ls',
                                           learning_rate=d["learning_rate"],
                                           n_estimators=d["n_estimators"],
                                           subsample=1.0,
                                           min_samples_split=2,
                                           min_samples_leaf=d["min_samples_leaf"],
                                           max_depth=d["max_depth"],
                                           init=None,
                                           random_state=self._rng,
                                           max_features=d["max_features"],
                                           alpha=0.9,
                                           verbose=0)
            train_x, test_x, train_y, test_y = train_test_split(x, y, test_size=0.5, random_state=self._rng)
            gb.fit(train_x, train_y)
            sc = gb.score(test_x, test_y)
            # Tiny output
            m = "."
            if idx % 10 == 0:
                m = "#"
            if sc > best_score:
                m = "<"
                best_score = sc
                max_features = d["max_features"]
                learning_rate = d["learning_rate"]
                max_depth = d["max_depth"]
                min_samples_leaf = d["min_samples_leaf"]
                n_estimators = d["n_estimators"]
            sys.stdout.write(m)
            sys.stdout.flush()
        sys.stdout.write("Using max_features %f, learning_rate: %f, max_depth: %d, min_samples_leaf: %d, "
                         "and n_estimators: %d\n" %
                         (max_features, learning_rate, max_depth, min_samples_leaf, n_estimators))

        return max_features, learning_rate, max_depth, min_samples_leaf, n_estimators
开发者ID:KEggensperger,项目名称:SurrogateBenchmarks,代码行数:61,代码来源:GradientBoosting.py

示例7: modelTheData

# 需要导入模块: from sklearn.ensemble import GradientBoostingRegressor [as 别名]
# 或者: from sklearn.ensemble.GradientBoostingRegressor import score [as 别名]
def modelTheData(dataSet,draw=False):

    dataSet = np.random.permutation(dataSet) 
    myData,myTarget=dataSet[:,1:-1],dataSet[:,-1]
    
    
    date = dataSet[:,0]
    rat = 0.7
    ratio = int(len(myData)*rat)
    
    
    
    
    myMachine =  GradientBoostingRegressor(n_estimators=100, learning_rate=1.0,
               max_depth=1, random_state=0, loss='ls')
    
    myMachine.fit(myData[:ratio], myTarget[:ratio])
    
    
    
    preDara=myMachine.predict(myData[ratio:])
    
    
    myDate = date[ratio:]
    
    #  draw the wrong sssssampe

    error=preDara-myTarget[ratio:]
    
    if draw:
        print myMachine.score(myData[ratio:],myTarget[ratio:])
        plt.scatter(myDate,error)
        plt.show()
    
    careError=[]
    careMyDate=[]
    for i in range(len(error)):
        if abs(error[i])>=50:
            careError +=[error[i]]
            careMyDate += [myDate[i]]
#    print careMyDate
    
#    plt.scatter(careMyDate,careError)
#    plt.text()
#    plt.show()
    return careMyDate
开发者ID:wybert,项目名称:PMpredict,代码行数:48,代码来源:addDateFilter.py

示例8: boosting

# 需要导入模块: from sklearn.ensemble import GradientBoostingRegressor [as 别名]
# 或者: from sklearn.ensemble.GradientBoostingRegressor import score [as 别名]
def boosting(df1, features, pred_var, df2):
    #for x in [10, 100, 1000]:
	#for y in [3, 5, 7]:
    lr = GradientBoostingRegressor(n_estimators=100, max_depth=7)
    lr.fit(df1[features], df1[pred_var])
    print 'GradientBoostingRegressor Score: ',  lr.score(df2[features], df2[pred_var])
    #0.727261516253
    return lr.predict(df2[features])
开发者ID:blackaceatzworg,项目名称:NYC-Taxi-Fraud,代码行数:10,代码来源:superv_learn3.py

示例9: gbrt_regressor

# 需要导入模块: from sklearn.ensemble import GradientBoostingRegressor [as 别名]
# 或者: from sklearn.ensemble.GradientBoostingRegressor import score [as 别名]
def gbrt_regressor(X, y, weight):
    from sklearn.ensemble import GradientBoostingRegressor
    from sklearn import cross_validation
    
    X_train, X_test, y_train, y_test, weight_train, weight_test = cross_validation.train_test_split(
        X, y, weight, test_size=0.4, random_state=0)
    clf = GradientBoostingRegressor(n_estimators=100, max_features='sqrt')
    clf.fit(X_train, y_train, weight_train)
    print(clf.score(X_test, y_test, weight_test))
开发者ID:organization-lab,项目名称:weibo-predict,代码行数:11,代码来源:regressor.py

示例10: grid_search

# 需要导入模块: from sklearn.ensemble import GradientBoostingRegressor [as 别名]
# 或者: from sklearn.ensemble.GradientBoostingRegressor import score [as 别名]
def grid_search(X, y, split, learn=[.01], samples_leaf=[250, 350, 500],
                depth=[10, 15]):
    '''
    Runs a grid search for GBM on split data
    '''
    for l in learn:
        for s in samples_leaf:
            for d in depth:
                model = GradientBoostingRegressor(n_estimators=250,
                                                  learning_rate=l,
                                                  min_samples_leaf=s,
                                                  max_depth=d,
                                                  random_state=42)
                model.fit(X.values[:split], y.values[:split])
                in_score = model.score(X.values[:split], y.values[:split])
                out_score = model.score(X.values[split:], y.values[split:])
                print 'learning_rate: {}, min_samples_leaf: {}, max_depth: {}'.\
                    format(l, s, d)
                print 'in-sample score:', in_score
                print 'out-sample score:', out_score
                print ''
开发者ID:bamdadd,项目名称:bitpredict,代码行数:23,代码来源:model.py

示例11: main

# 需要导入模块: from sklearn.ensemble import GradientBoostingRegressor [as 别名]
# 或者: from sklearn.ensemble.GradientBoostingRegressor import score [as 别名]
def main():
    train, train_loss = load_data()
    train = impute_and_scale(train)

    # Fit the regressor
    train_regressor = []
    train_loss_regressor = []

    for i in range(len(train)):
        if(train_loss[i]) > 0:
            train_loss_regressor.append(train_loss[i])
            #train_regressor.append(train[i][0:769])
            train_regressor.append(train[i])

    for percent in [.99, .9, .66, .5, .33, .1, .01]:
        print percent

    #replaceNAStrategy = ['mean', 'median', 'most_frequent'][0]

    #train_classifier = train[['f527', 'f528', 'f271', 'f274']]
    #train_loss_classifier = train.loss.apply(lambda x: 0 if x==0 else 1)

    #train = impute_and_scale(train)
    #train = impute_random(train, 1)
    #train = train.values
    #train = impute_random(train, 1)
    #train = impute_to_zero(train)
    #train = filterNullsWithZero(train)
    #train_classifier = impute_and_scale(train_classifier)

    # Fit the classifier
    
    #clf = LogisticRegression(C=1e20,penalty='l2')
        #clf.fit(train_classifier,train_loss_classifier)
        #print "regressor.py - finished fitting classifier"

        #train = train[['f527', 'f528', 'f271', 'f274']]



        #print len(train[1])
        #print train[1]
        
        x_train, x_test, y_train, y_test = \
            cross_validation.train_test_split(train_regressor, train_loss_regressor, test_size=percent, random_state=0)
        print len(x_train)

        reg4 = GradientBoostingRegressor(n_estimators=100, verbose=1)
        reg4 = reg4.fit(x_train, y_train)

    #    print "zero: " + str(reg4.score(x_test, y_test))
        print str(percent) + ": " + str(reg4.score(x_test, y_test))
    print "regressor.py - finished fitting regressor"
开发者ID:mattrozak,项目名称:448-magic,代码行数:55,代码来源:regressor.py

示例12: Predictor

# 需要导入模块: from sklearn.ensemble import GradientBoostingRegressor [as 别名]
# 或者: from sklearn.ensemble.GradientBoostingRegressor import score [as 别名]
class Predictor(object):
    def __init__(self, n_estimators=50):
        """Constructor for the predictor object."""
        self.score = -1
        # use following code to test parameters for the regressor
        # regressor2 = GradientBoostingRegressor()
        # parameters = {'n_estimators': [50, 100], 'loss': ('ls', 'lad'),
        #     'max_depth': [3, 5, 7]}
        # self.regressor = GridSearchCV(regressor2, parameters, n_jobs=-1)
        self.regressor = GradientBoostingRegressor(n_estimators=n_estimators)

    def fit_algorithm(self, x, y):
        """Wrapper to the sklearn regressor fit function."""
        self.regressor.fit(x, y)
        print self.regressor.best_params_

    def predict_outputs(self, inputs):
        """Wrapper to the sklearn regressor predict function."""
        try:
            a = self.regressor.feature_importances_
        except sklearn.utils.validation.NotFittedError:
            print "Please fit the algorithm before calling this function."
            return
        prediction = self.regressor.predict(inputs)
        return prediction

    def predictor_metrics(self, outputs, prediction):
        """This function calculates metrics byt measuring MSE."""
        try:
            a = self.regressor.feature_importances_
        except sklearn.utils.validation.NotFittedError:
            print "Please fit the algorithm before calling this function."
            return
        return mean_squared_error(outputs, prediction)

    def score_predictor(self, x, y):
        """Wrapper to the score calculated with the predictor."""
        try:
            a = self.regressor.feature_importances_
        except sklearn.utils.validation.NotFittedError:
            print "Please fit the algorithm before calling this function."
            return
        return self.regressor.score(x, y)

    def get_feature_importances(self):
        """Returns a copy of features importances vector."""
        try:
            fi = self.regressor.feature_importances_
        except sklearn.utils.validation.NotFittedError:
            print "Please fit the algorithm before calling this function."
            return
        return fi
开发者ID:sanchezg,项目名称:pictor,代码行数:54,代码来源:predictor.py

示例13: dummie_columns_gradient_boosting

# 需要导入模块: from sklearn.ensemble import GradientBoostingRegressor [as 别名]
# 或者: from sklearn.ensemble.GradientBoostingRegressor import score [as 别名]
def dummie_columns_gradient_boosting(train, test):
    from sklearn.ensemble import GradientBoostingRegressor
    print "-- {} --".format("Gradient Boosting Regression using all but remarks")
    predicting_columns = list(train._get_numeric_data().columns.values)
    predicting_columns.remove("LISTPRICE")
    predicting_columns.remove("SOLDPRICE")
    svr = GradientBoostingRegressor(n_estimators=300)
    svr.fit(train[predicting_columns], train["SOLDPRICE"])
    score = svr.score(test[predicting_columns], test["SOLDPRICE"])
    predictions = svr.predict(test[predicting_columns])
    sample_predictions(test, predictions)
    print "Accuracy: {}\n".format(score)
    return score, predictions
开发者ID:CurleySamuel,项目名称:Thesis,代码行数:15,代码来源:first_pass.py

示例14: train_model

# 需要导入模块: from sklearn.ensemble import GradientBoostingRegressor [as 别名]
# 或者: from sklearn.ensemble.GradientBoostingRegressor import score [as 别名]
def train_model():
	fil = get_training_data()
	X = []
	y = []
	f = get_features()
	for query in fil:
		for feat in f.get_X(query):
			X.append(feat[1])
			if feat[0] in fil[query]:
				y.append(1)
			else:
				y.append(0)
	print len(X),len(y),X,y
	clf = GradientBoostingRegressor()
	clf.fit(X, y) 
	print clf 
	print clf.score(X, y)
	filename = '/home/romil/Desktop/Model4/digits_classifier.joblib.pkl'
	_ = joblib.dump(clf, filename)
	with open('/home/romil/Desktop/Model4/X.pkl', 'wb') as fid:
		cPickle.dump(X, fid)
	with open('/home/romil/Desktop/Model4/y.pkl', 'wb') as fid:
		cPickle.dump(y, fid)
开发者ID:romilbansal,项目名称:EntityLinking,代码行数:25,代码来源:features_parallel.py

示例15: main

# 需要导入模块: from sklearn.ensemble import GradientBoostingRegressor [as 别名]
# 或者: from sklearn.ensemble.GradientBoostingRegressor import score [as 别名]
def main():
    xpath = '/Users/qiaotian/Downloads/dataset/sample1/feature.txt'
    ypath = '/Users/qiaotian/Downloads/dataset/sample1/label.txt'
    y = pd.read_csv(ypath, sep=',', header=None).iloc[:,1]
    X = pd.read_csv(xpath, sep=',', header=None).iloc[0:len(y),:]

    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1,
                                                    random_state=9)
    # 1. Linear Regressor
    lr_params = {}
    lr = LinearRegression()
    lr.fit(X_train, y_train)
    train_acc = lr.score(X_train, y_train)
    test_acc = lr.score(X_test, y_test)
    y_pred = lr.predict(X_test)
    print('-> Done Linear Regression: ', train_acc, test_acc, len([elem for elem in y_pred-y_test if abs(elem)<1.0])/len(y_test))

    # 2. Random Foreset Regressor
    rf_params = {'n_estimators':100}
    rf = RandomForestRegressor(**rf_params)
    rf.fit(X_train, y_train)
    train_acc = rf.score(X_train, y_train)
    test_acc = rf.score(X_test, y_test)
    y_pred = rf.predict(X_test)
    print('-> Done Random Forest Regression: ', train_acc, test_acc, len([elem for elem in y_pred-y_test if abs(elem)<1.0])/len(y_test))

    # 3. Gradient Booting Regressor
    gbdt_params = {'loss':'ls', 'n_estimators':100, 'max_depth':3,\
                   'subsample':0.9, 'learning_rate':0.1,\
                   'min_samples_leaf':1, 'random_state':1234}
    gbdt = GradientBoostingRegressor(**gbdt_params)
    gbdt.fit(X_train, y_train)
    train_acc = gbdt.score(X_train, y_train)
    test_acc = gbdt.score(X_test, y_test)
    y_pred = gbdt.predict(X_test)

    print('-> Done Gradient Boosting Regression: ', train_acc, test_acc, len([elem for elem in y_pred-y_test if abs(elem)<1.0])/len(y_test))
开发者ID:qiaotian,项目名称:VideoTracking,代码行数:39,代码来源:train.py


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