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How to use test_rf method in pytest-django

Best Python code snippet using pytest-django_python

Libertas.py

Source:Libertas.py

1import pandas as pd2import numpy as np 3from sklearn import preprocessing4from sklearn.feature_extraction import DictVectorizer5from sklearn.preprocessing import LabelEncoder6from sklearn.ensemble import RandomForestRegressor7from sklearn.ensemble import BaggingRegressor8import xgboost as xgb9	10def xgboost_pred(train,labels,test):11	params = {}12	params["objective"] = "count:poisson"13	params["eta"] = 0.00514	params["min_child_weight"] = 615	params["subsample"] = 0.716	params["colsample_bytree"] = 0.717	params["scale_pos_weight"] = 118	params["silent"] = 119	params["max_depth"] = 920    21    22	plst = list(params.items())23	#Using 4000 rows for early stopping. 24	offset = 400025	num_rounds = 1000026	xgtest = xgb.DMatrix(test)27	#create a train and validation dmatrices 28	xgtrain = xgb.DMatrix(train[offset:,:], label=labels[offset:])29	xgval = xgb.DMatrix(train[:offset,:], label=labels[:offset])30	#train using early stopping and predict31	watchlist = [(xgtrain, 'train'),(xgval, 'val')]32	model = xgb.train(plst, xgtrain, num_rounds, watchlist, early_stopping_rounds=120)33	preds1 = model.predict(xgtest,ntree_limit=model.best_iteration)34	#reverse train and labels and use different 4k for early stopping. 35	train = train[::-1,:]36	labels = np.log(labels[::-1])37	xgtrain = xgb.DMatrix(train[offset:,:], label=labels[offset:])38	xgval = xgb.DMatrix(train[:offset,:], label=labels[:offset])39	watchlist = [(xgtrain, 'train'),(xgval, 'val')]40	model = xgb.train(plst, xgtrain, num_rounds, watchlist, early_stopping_rounds=120)41	preds2 = model.predict(xgtest,ntree_limit=model.best_iteration)42	#combine predictions43	#since the metric only cares about relative rank we don't need to average44	print "preds1 -> ", preds1 * 0.545	print "preds2 -> ", np.exp(preds2)*0.5, "\n"46	preds = (preds1)*0.5 + np.exp(preds2)*0.547	return preds48def xgboost_Label(train,test,labels):49	print("Train & Test xgboost_Label")50	train.drop('T2_V10', axis=1, inplace=True)51	train.drop('T2_V7', axis=1, inplace=True)52	train.drop('T1_V13', axis=1, inplace=True)53	train.drop('T1_V10', axis=1, inplace=True)54	test.drop('T2_V10', axis=1, inplace=True)55	test.drop('T2_V7', axis=1, inplace=True)56	test.drop('T1_V13', axis=1, inplace=True)57	test.drop('T1_V10', axis=1, inplace=True)58	train = np.array(train)59	test = np.array(test)60	for i in range(train.shape[1]):61	    lbl = preprocessing.LabelEncoder()62	    lbl.fit(list(train[:,i]) + list(test[:,i]))63	    train[:,i] = lbl.transform(train[:,i])64	    test[:,i] = lbl.transform(test[:,i])65	train = train.astype(float)66	test = test.astype(float)67	preds = xgboost_pred(train,labels,test)68	return preds69def xgboost_Vect(train,test,labels):70	print("Train & Test xgboost_Vect")71	test = test.T.to_dict().values()72	train = train.T.to_dict().values()73	vec = DictVectorizer()74	train = vec.fit_transform(train)75	test = vec.transform(test)76	preds = xgboost_pred(train,labels,test)77	return preds78def xgboost_Dummies(train,test,labels):79	print("Train & Test xgboost_Dummies")80	train = pd.get_dummies(train)81	test = pd.get_dummies(test)82	preds = xgboost_pred(train.values,labels,test.values)83	return preds84if __name__ == '__main__':85	#load train and test 86	print("Chargement des donnees")87	train  = pd.read_csv('train.csv', index_col=0)88	test  = pd.read_csv('test.csv', index_col=0)89	labels = train.Hazard90	train.drop('Hazard', axis=1, inplace=True)91	############ RF LABEL #################92	train_rf = train.copy()93	test_rf = test.copy()94	train_rf.drop('T2_V10', axis=1, inplace=True)95	train_rf.drop('T2_V7', axis=1, inplace=True)96	train_rf.drop('T1_V13', axis=1, inplace=True)97	train_rf.drop('T1_V10', axis=1, inplace=True)98	test_rf.drop('T2_V10', axis=1, inplace=True)99	test_rf.drop('T2_V7', axis=1, inplace=True)100	test_rf.drop('T1_V13', axis=1, inplace=True)101	test_rf.drop('T1_V10', axis=1, inplace=True)102	train_rf = np.array(train_rf)103	test_rf = np.array(test_rf)104	for i in range(train_rf.shape[1]):105	    lbl = LabelEncoder()106	    lbl.fit(list(train_rf[:,i]))107	    train_rf[:,i] = lbl.transform(train_rf[:,i])108	for i in range(test_rf.shape[1]):109	    lbl = LabelEncoder()110	    lbl.fit(list(test_rf[:,i]))111	    test_rf[:,i] = lbl.transform(test_rf[:,i])112	train_rf = train_rf.astype(float)113	test_rf = test_rf.astype(float)114	############# RF DUMMIES ##############115	train_du = train.copy()116	test_du = test.copy()117	train_du.drop('T2_V10', axis=1, inplace=True)118	train_du.drop('T2_V7', axis=1, inplace=True)119	train_du.drop('T1_V13', axis=1, inplace=True)120	train_du.drop('T1_V10', axis=1, inplace=True)121	test_du.drop('T2_V10', axis=1, inplace=True)122	test_du.drop('T2_V7', axis=1, inplace=True)123	test_du.drop('T1_V13', axis=1, inplace=True)124	test_du.drop('T1_V10', axis=1, inplace=True)125	train_du = pd.get_dummies(train_du)126	test_du = pd.get_dummies(test_du)127	algorithms = [128		[RandomForestRegressor(n_estimators=2048, max_depth=18, min_samples_split=4, min_samples_leaf=20), "labels"],129		[RandomForestRegressor(n_estimators=2048, max_depth=22, min_samples_split=4, min_samples_leaf=20), "dummies"],130		["xgboost_Dummies", ""],131		["xgboost_Label", ""],132		["xgboost_Vect", ""]133	]134	full_predictions = []135	for alg, predictors in algorithms:136		if(alg == "xgboost_Label"):137			full_predictions.append(xgboost_Label(train, test, labels))138		elif(alg == "xgboost_Vect"):139			full_predictions.append(xgboost_Vect(train, test, labels))140		elif(alg == "xgboost_Dummies"):141			full_predictions.append(xgboost_Dummies(train, test, labels))142		else :143			if(predictors == "dummies"):144				print("Train ", alg.__class__.__name__ , " dummies Model ")145				alg=BaggingRegressor(alg)146				alg.fit(train_du, labels)147				print 'Prediction :' , alg.__class__.__name__ , ' dummies Model '148				prediction = alg.predict(test_du)149				full_predictions.append(prediction)150			else :151				print("Train ", alg.__class__.__name__ , " Label Model ")152				alg=BaggingRegressor(alg)153				alg.fit(train_rf, labels)154				print 'Prediction :' , alg.__class__.__name__ , ' Label Model '155				prediction = alg.predict(test_rf)156				full_predictions.append(prediction)157	# Ensemble models158	RF_label_pred = full_predictions[0]159	RF_dummies_pred = full_predictions[1]160	pred_xgb_dummies = full_predictions[2]161	pred_xgb_Label = full_predictions[3]162	pred_xgb_Vect = full_predictions[4]163	pred_RF = RF_dummies_pred * 0.50 + RF_label_pred * 0.50 164	pred_xgb = pred_xgb_dummies * 0.45 + pred_xgb_Label * 0.28 + pred_xgb_Vect * 0.27 165	preds = pred_RF * 0.15 + pred_xgb * 0.85 166	#generate solution167	print("Construction csv")168	preds = pd.DataFrame({"Id": test.index, "Hazard": preds})169	preds = preds.set_index('Id')...

rf_moving_pre_arrive_test.py

Source: rf_moving_pre_arrive_test.py

1#!/usr/bin/env python32import test_rf3import time4import argparse5import socket6def main():7    parser = argparse.ArgumentParser(description="rf arguments")8    parser.add_argument("gateway_ip", help="ip address of gateway", type=str)9    parser.add_argument("gateway_port", help="port of gateway", type=int)10    parser.add_argument("rf_address", help="rf address", type=str)11    parser.add_argument("test_times", help="how many times to test", type=int)12    parser.add_argument("target_pos", help="absolute target in counts in hex", type=str)13    parser.add_argument("velocity", help="velocity in counts/s", type=int)14    parser.add_argument("is_destination", help="0 - not | 1 - is dest", type=int)15    parser.add_argument("is_vertical", help="0 - horizontal | 1 - vertical", type=int)16    parser.add_argument("pre_arrive_count", help="pre arrive distance in counts", type=int)17    parser.add_argument("is_sensor_triggered", help="0 - no sensor | 1 - has sensor", type=int)18    gateway = parser.parse_args().gateway_ip19    port = parser.parse_args().gateway_port20    rf_addr = parser.parse_args().rf_address21    test = parser.parse_args().test_times22    pos = parser.parse_args().target_pos23    vel = parser.parse_args().velocity24    is_dest = parser.parse_args().is_destination25    is_vert = parser.parse_args().is_vertical26    pre_arrive = parser.parse_args().pre_arrive_count27    is_sensor = parser.parse_args().is_sensor_triggered28    recv_count = 029    timeout_count = 030    error_count = 031    test_rf.serverAddressPort = (gateway, port)32    test_rf.rf = rf_addr33    test_rf.moving_pre_arrive_count = pre_arrive34    for x in range(test):35        test_rf.error_count = 036        res = test_rf.moving(pos, vel, is_dest, is_vert, is_sensor)37        if res[0] == "received":38            recv_count += 139        if res[0] == "timeout":40            timeout_count += 141        error_count += res[1]42    print("{} {} {}".format(recv_count, timeout_count, error_count), end="")43if __name__ == "__main__":...

rf_ping.py

Source: rf_ping.py

1#!/usr/bin/env python32import test_rf3import time4import argparse5import socket6def main():7    parser = argparse.ArgumentParser(description="rf arguments")8    parser.add_argument("gateway_ip", help="ip address of gateway", type=str)9    parser.add_argument("gateway_port", help="port of gateway", type=str)10    parser.add_argument("rf_address", help="rf address", type=str)11    parser.add_argument("test_times", help="how many times to test", type=str)12    parser.add_argument("ping_operation", help="0 - read | 1 - online", type=str)13    gateway = parser.parse_args().gateway_ip14    port = int(parser.parse_args().gateway_port)15    rf_addr = parser.parse_args().rf_address16    sub = int(parser.parse_args().ping_operation)17    test = int(parser.parse_args().test_times)18    recv_count = 019    timeout_count = 020    error_count = 021    test_rf.serverAddressPort = (gateway, port)22    test_rf.rf = rf_addr23    for x in range(test):24        test_rf.error_count = 025        res = test_rf.ping(sub)26        if res[0] == "received":27            recv_count += 128        if res[0] == "timeout":29            timeout_count += 130        error_count += res[1]31    print("{} {} {}".format(recv_count, timeout_count, error_count), end="")32if __name__ == "__main__":...

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