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How to use fin method in pytest-django

Best Python code snippet using pytest-django_python

randomForest.py

Source:randomForest.py

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})...

accparse2.py

Source:accparse2.py

1# -*- mode: python; coding: utf-8 -*-2# Copyright (C) 2015, 2017  Laboratoire de Recherche et DÃ©veloppement3# 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)...

Chef and Hamming Distance.py

Source:Chef and Hamming Distance.py

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=' ')
...

readStark.py

Source:readStark.py

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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