How to use test_rf method in pytest-django

Best Python code snippet using pytest-django_python

Libertas.py

Source:Libertas.py Github

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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')...

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rf_moving_pre_arrive_test.py

Source: rf_moving_pre_arrive_test.py Github

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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__":...

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rf_ping.py

Source: rf_ping.py Github

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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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