How to use fin method in pytest-django

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randomForest.py

Source:randomForest.py Github

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1"""2__author__ : SRK3"""4import numpy as np5import pandas as pd6from sklearn import preprocessing, ensemble7# columns to be used as features #8feature_cols = ["ind_empleado","pais_residencia","sexo","age", "ind_nuevo", "antiguedad", "nomprov", "segmento"]9dtype_list = {'ind_cco_fin_ult1': 'float16', 'ind_deme_fin_ult1': 'float16', 'ind_aval_fin_ult1': 'float16', 'ind_valo_fin_ult1': 'float16', 'ind_reca_fin_ult1': 'float16', 'ind_ctju_fin_ult1': 'float16', 'ind_cder_fin_ult1': 'float16', 'ind_plan_fin_ult1': 'float16', 'ind_fond_fin_ult1': 'float16', 'ind_hip_fin_ult1': 'float16', 'ind_pres_fin_ult1': 'float16', 'ind_nomina_ult1': 'float16', 'ind_cno_fin_ult1': 'float16', 'ncodpers': 'int64', 'ind_ctpp_fin_ult1': 'float16', 'ind_ahor_fin_ult1': 'float16', 'ind_dela_fin_ult1': 'float16', 'ind_ecue_fin_ult1': 'float16', 'ind_nom_pens_ult1': 'float16', 'ind_recibo_ult1': 'float16', 'ind_deco_fin_ult1': 'float16', 'ind_tjcr_fin_ult1': 'float16', 'ind_ctop_fin_ult1': 'float16', 'ind_viv_fin_ult1': 'float16', 'ind_ctma_fin_ult1': 'float16'}10target_cols = ['ind_ahor_fin_ult1','ind_aval_fin_ult1','ind_cco_fin_ult1','ind_cder_fin_ult1','ind_cno_fin_ult1','ind_ctju_fin_ult1','ind_ctma_fin_ult1','ind_ctop_fin_ult1','ind_ctpp_fin_ult1','ind_deco_fin_ult1','ind_deme_fin_ult1','ind_dela_fin_ult1','ind_ecue_fin_ult1','ind_fond_fin_ult1','ind_hip_fin_ult1','ind_plan_fin_ult1','ind_pres_fin_ult1','ind_reca_fin_ult1','ind_tjcr_fin_ult1','ind_valo_fin_ult1','ind_viv_fin_ult1','ind_nomina_ult1','ind_nom_pens_ult1','ind_recibo_ult1'] 11if __name__ == "__main__":12 data_path = "../input/"13 train_file = data_path + "train_ver2.csv"14 test_file = data_path + "test_ver2.csv"15 train_file = data_path + "small_train.csv"16 test_file = data_path + "small_test.csv"17 train_size = 1364730918 nrows = 1000000 # change this value to read more rows from train19 start_index = train_size - nrows20 for ind, col in enumerate(feature_cols):21 print(col)22 train = pd.read_csv(train_file, usecols=[col])23 test = pd.read_csv(test_file, usecols=[col])24 train.fillna(-99, inplace=True)25 test.fillna(-99, inplace=True)26 if train[col].dtype == "object":27 le = preprocessing.LabelEncoder()28 le.fit(list(train[col].values) + list(test[col].values))29 temp_train_X = le.transform(list(train[col].values)).reshape(-1,1)[start_index:,:]30 temp_test_X = le.transform(list(test[col].values)).reshape(-1,1)31 else:32 temp_train_X = np.array(train[col]).reshape(-1,1)[start_index:,:]33 temp_test_X = np.array(test[col]).reshape(-1,1)34 if ind == 0:35 train_X = temp_train_X.copy()36 test_X = temp_test_X.copy()37 else:38 train_X = np.hstack([train_X, temp_train_X])39 test_X = np.hstack([test_X, temp_test_X])40 print(train_X.shape, test_X.shape)41 del train42 del test43 train_y = pd.read_csv(train_file, usecols=['ncodpers']+target_cols, dtype=dtype_list)44 last_instance_df = train_y.drop_duplicates('ncodpers', keep='last')45 train_y = np.array(train_y.fillna(0)).astype('int')[start_index:,1:]46 print(train_X.shape, train_y.shape)47 print(test_X.shape)48 print("Running Model..")49 model = ensemble.RandomForestClassifier(n_estimators=10, max_depth=10, min_samples_leaf=10, n_jobs=4, random_state=2016)50 model.fit(train_X, train_y)51 del train_X, train_y52 print("Predicting..")53 preds = np.array(model.predict_proba(test_X))[:,:,1].T54 del test_X55 print("Getting last instance dict..")56 last_instance_df = last_instance_df.fillna(0).astype('int')57 cust_dict = {}58 target_cols = np.array(target_cols)59 for ind, row in last_instance_df.iterrows():60 cust = row['ncodpers']61 used_products = set(target_cols[np.array(row[1:])==1])62 cust_dict[cust] = used_products63 del last_instance_df64 print("Creating submission..")65 preds = np.argsort(preds, axis=1)66 preds = np.fliplr(preds)67 test_id = np.array(pd.read_csv(test_file, usecols=['ncodpers'])['ncodpers'])68 final_preds = []69 for ind, pred in enumerate(preds):70 cust = test_id[ind]71 top_products = target_cols[pred]72 used_products = cust_dict.get(cust,[])73 new_top_products = []74 for product in top_products:75 if product not in used_products:76 new_top_products.append(product)77 if len(new_top_products) == 7:78 break79 final_preds.append(" ".join(new_top_products))80 out_df = pd.DataFrame({'ncodpers':test_id, 'added_products':final_preds})...

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accparse2.py

Source:accparse2.py Github

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1# -*- mode: python; coding: utf-8 -*-2# Copyright (C) 2015, 2017 Laboratoire de Recherche et Développement3# de l'Epita4#5# This file is part of Spot, a model checking library.6#7# Spot is free software; you can redistribute it and/or modify it8# under the terms of the GNU General Public License as published by9# the Free Software Foundation; either version 3 of the License, or10# (at your option) any later version.11#12# Spot is distributed in the hope that it will be useful, but WITHOUT13# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY14# or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public15# License for more details.16#17# You should have received a copy of the GNU General Public License18# along with this program. If not, see <http://www.gnu.org/licenses/>.19import spot20a = spot.acc_cond(5)21a.set_acceptance(spot.acc_code('parity min odd 5'))22assert(a.is_parity() == [True, False, True])23a.set_acceptance('parity max even 5')24assert(a.is_parity() == [True, True, False])25a.set_acceptance('generalized-Buchi 5')26assert(a.is_parity()[0] == False)27assert(a.is_parity(True)[0] == False)28a.set_acceptance('Inf(4) | (Fin(3)&Inf(2)) | (Fin(3)&Fin(1)&Inf(0))')29assert(a.is_parity()[0] == False)30assert(a.is_parity(True) == [True, True, False])31assert a.maybe_accepting([1, 2, 3], [0, 4]).is_true()32assert a.maybe_accepting([0], []).is_true()33assert a.maybe_accepting([0], [3]).is_false()34assert a.maybe_accepting([0, 3], []).is_maybe()35assert a.maybe_accepting([2, 3], [3]).is_false()36assert a.maybe_accepting([2, 3], []).is_maybe()37assert a.maybe_accepting([2], []).is_true()38assert a.maybe_accepting([0, 1], []).is_maybe()39assert a.maybe_accepting([0, 1], [1]).is_false()40a.set_acceptance('Fin(0)|Fin(1)')41assert a.maybe_accepting([0, 1], [1]).is_maybe()42assert a.maybe_accepting([0, 1], [0, 1]).is_false()43assert a.maybe_accepting([0], []).is_true()44assert a.maybe_accepting([], [0]).is_true()45a = spot.acc_cond(0)46a.set_acceptance('all')47assert(a.is_rabin() == -1)48assert(a.is_streett() == 0)49assert(a.is_parity() == [True, True, True])50a.set_acceptance('none')51assert(a.is_rabin() == 0)52assert(a.is_streett() == -1)53assert(a.is_parity() == [True, True, False])54a = spot.acc_cond('(Fin(0)&Inf(1))')55assert(a.is_rabin() == 1)56assert(a.is_streett() == -1)57a.set_acceptance('Inf(1)&Fin(0)')58assert(a.is_rabin() == 1)59assert(a.is_streett() == -1)60a.set_acceptance('(Fin(0)|Inf(1))')61assert(a.is_rabin() == -1)62assert(a.is_streett() == 1)63a.set_acceptance('Inf(1)|Fin(0)')64assert(a.is_rabin() == -1)65assert(a.is_streett() == 1)66a = spot.acc_cond('(Fin(0)&Inf(1))|(Fin(2)&Inf(3))')67assert(a.is_rabin() == 2)68assert(a.is_streett() == -1)69a.set_acceptance(spot.acc_code('(Inf(3)&Fin(2))|(Fin(0)&Inf(1))'))70assert(a.is_rabin() == 2)71assert(a.is_streett() == -1)72a.set_acceptance(spot.acc_code('(Inf(2)&Fin(3))|(Fin(0)&Inf(1))'))73assert(a.is_rabin() == -1)74assert(a.is_streett() == -1)75a.set_acceptance(spot.acc_code('(Inf(3)&Fin(2))|(Fin(2)&Inf(1))'))76assert(a.is_rabin() == -1)77assert(a.is_streett() == -1)78a.set_acceptance(spot.acc_code('(Inf(1)&Fin(0))|(Fin(0)&Inf(1))'))79assert(a.is_rabin() == -1)80assert(a.is_streett() == -1)81a.set_acceptance('(Fin(0)&Inf(1))|(Inf(1)&Fin(0))|(Inf(3)&Fin(2))')82assert(a.is_rabin() == 2)83assert(a.is_streett() == -1)84a.set_acceptance('(Fin(0)|Inf(1))&(Fin(2)|Inf(3))')85assert(a.is_rabin() == -1)86assert(a.is_streett() == 2)87a.set_acceptance('(Inf(3)|Fin(2))&(Fin(0)|Inf(1))')88assert(a.is_rabin() == -1)89assert(a.is_streett() == 2)90a.set_acceptance('(Inf(2)|Fin(3))&(Fin(0)|Inf(1))')91assert(a.is_rabin() == -1)92assert(a.is_streett() == -1)93a.set_acceptance('(Inf(3)|Fin(2))&(Fin(2)|Inf(1))')94assert(a.is_rabin() == -1)95assert(a.is_streett() == -1)96a.set_acceptance('(Inf(1)|Fin(0))&(Fin(0)|Inf(1))')97assert(a.is_rabin() == -1)98assert(a.is_streett() == -1)99a.set_acceptance('(Fin(0)|Inf(1))&(Inf(1)|Fin(0))&(Inf(3)|Fin(2))')100assert(a.is_rabin() == -1)...

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Chef and Hamming Distance.py

Source:Chef and Hamming Distance.py Github

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1t=int(input())2for I in range(t):3 count=04 n=int(input())5 a=list(map(int,input().split(" ")))6 b=[]7 for i in a:8 b.append(i)9 #print(b)10 if(n==1):11 print(0)12 print(a[0])13 elif(n==2):14 if(a[0]==a[1]):15 print(0)16 print(a[0],a[1])17 else:18 print(2)19 print(a[1],a[0])20 else:21 if(n%2==0):22 ini=023 fin=n-124 if(b[0]==b[n-1]):25 b[0],b[1]=b[1],b[0]26 b[n-1],b[n-2]=b[n-2],b[n-1]27 ini=228 fin=n-329 while(ini+2<fin):30 #print('ini',ini)31 #print('fin',fin)32 #print(b)33 if(b[ini]==b[fin]):34 #print('hello')35 b[ini],b[ini+1]=b[ini+1],b[ini]36 b[fin],b[fin-1]=b[fin-1],b[fin]37 ini+=238 fin-=239 else:40 b[ini],b[fin]=b[fin],b[ini]41 ini+=142 fin-=143 a1=n//244 if(b[a1]==b[a1-1] and (a[a1]==b[a1] and a[a1-1]==b[a1-1])):45 b[a1-1],b[a1-2]=b[a1-2],b[a1-1]46 b[a1],b[a1+1]=b[a1+1],b[a1]47 else:48 b[a1],b[a1-1]=b[a1-1],b[a1]49 print(n)50 for i in b:51 print(i,end=' ')52 else:53 if(n==3):54 if(b[0]==b[2]):55 #print('hola')56 b[1],b[2]=b[2],b[1]57 elif(b[0]!=b[1] and b[0]!=b[2]):58 #print('hola')59 o1=b[0]60 o2=b[1]61 o3=b[2]62 b[0]=o263 b[1]=o364 b[2]=o165 elif(b[1]==b[2]):66 #print('hola')67 b[0],b[1]=b[1],b[0]68 elif(b[0]==b[1]):69 #print('hola')70 b[1],b[2]=b[2],b[1]71 else:72 ini=073 fin=n-174 if(b[0]==b[n-1]):75 b[0],b[1]=b[1],b[0]76 b[n-1],b[n-2]=b[n-2],b[n-1]77 ini=278 fin=n-379 while(ini+1<fin):80 if(b[ini]==b[fin] and ini!=(n//2-1)):81 #print('hello')82 b[ini],b[ini+1]=b[ini+1],b[ini]83 b[fin],b[fin-1]=b[fin-1],b[fin]84 ini+=285 fin-=286 elif(b[ini]==b[fin] and ini==(n//2-1)):87 b[ini],b[ini-1]=b[ini-1],b[ini]88 b[fin],b[fin+1]=b[fin+1],b[fin]89 ini+=290 fin-=291 else:92 b[ini],b[fin]=b[fin],b[ini]93 ini+=194 fin-=195 #b[(n//2)],b[(n//2)+1]=b[(n//2)+1],b[(n//2)]96 #print(b)97 num11=b[n//2]98 for i in range((n//2)+1,n):99 if(num11!=a[i] and num11!=b[i]):100 b[n//2],b[i]=b[i],b[n//2]101 break102 103 #print(a)104 #print(b)105 for i in range(len(b)):106 if(a[i]!=b[i]):107 count+=1108 print(count)109 for i in b:110 print(i,end=' ') ...

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readStark.py

Source:readStark.py Github

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1#!/usr/bin/env python2from __future__ import print_function3#from builtins import str4#from builtins import range5#from builtins import object6import numpy as np7import re8import ast9import matplotlib.pylab as plt10import os11def run():12 shotnr = 2986513 nevin = readDivData(getDivFname(shotnr,'nev','in'))14 nevout = readDivData(getDivFname(shotnr,'nev','out'))15 jsatin = readDivData(getDivFname(shotnr,'jsat','in'))16 jsatout = readDivData(getDivFname(shotnr,'jsat','out'))17def getDivFname(shotnr,what,pos):18 19 homedir = str(os.environ["HOME"])20 ##Expects data to be saved in "$HOME/Divertor"21 fin = homedir + '/Divertor/' + str(shotnr) + '/3D_'+ str(shotnr) + '_'22 if what == 'nev':23 fin = fin + 'nev'24 elif what == 'jsat':25 fin = fin + 'jsat'26 elif what == 'net':27 fin = fin + 'net'28 elif what == 'te':29 fin = fin + 'te'30 else:31 print("Item not recognized, defaulting to jsat")32 fin = fin + 'jsat'33 if pos == 'in':34 fin = fin+'_in.dat'35 else:36 fin = fin+'_out.dat'37 return fin38def readDivData(filename):39 """Reads contour-plot DIVERTOR data from an ascii file40 Parameters41 ----------42 filename:43 String with the filename outputted from DIVERTOR, ususally "3D_..."44 Returns45 ----------46 An object with three fields:47 obj.time: ndarray48 The times of the contour49 obj.deltas: ndarray50 The y axis of the contour, usually Delta S, but rho is also possible51 obj.data: matrix52 The matrix with the jsat, density, etc., data.53 54 """55 class objview(object):56 def __init__(self, d):57 self.__dict__=d58 59 try:60 fin = open(filename, 'r')61 except:62 print("No such file " + filename)63 print("Returning dummy data")64 data = np.array([[0.0, 0.0],[0.0,0.0]])65 #return dummy data66 #Return ranges that will always be covered by the times ELM analysis are performed67 return objview({'time': np.array([-21.0, 21.0]),68 'deltas': np.array([-17.0, 57.0]),69 'data': data})70 #Read dumb line71 fin.readline()72 #Time points73 tpts = int(fin.readline())74 #Read dumb lines75 fin.readline()76 fin.readline()77 #DS coordinate points78 spts = int(fin.readline())79 #Read dumb lines80 fin.readline()81 fin.readline()82 #Density scale83 nes = float(fin.readline())84 nesc = np.array(nes)85 #Read dumb lines86 fin.readline()87 fin.readline()88 #Read time vector89 time = []90 for i in range(0, tpts):91 time.append(float(fin.readline()))92 #Read dumb lines93 fin.readline()94 fin.readline()95 #Read Delta S vector96 deltas = []97 for i in range(0, spts):98 deltas.append(float(fin.readline()))99 #Read dumb lines100 fin.readline()101 fin.readline()102 #Read Density data103 data = []104 for i in range(0, tpts):105 string = fin.readline()106 string = re.sub(r"\s+",",",string)107 string = "[" + string[1:-1] + "]"108 vec = ast.literal_eval(string)109 data.append(vec)110 #Close shotfile111 fin.close()112 113 return objview({'time': np.array(time),114 'deltas': np.array(deltas),115 'data': np.array(data).T})116#a = readDivData(getDivFname(29865, "nev", "in"))...

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