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How to use test_append method in Slash
Best Python code snippet using slash
app.py
Source:app.py 
1from flask import Flask,render_template,request,redirect,url_for,session2from flask_bootstrap import Bootstrap3import MySQLdb4import os5from math import sqrt6from sklearn import neighbors7from os import listdir8from os.path import isdir, join, isfile, splitext9import shutil10import pickle11from PIL import Image, ImageFont, ImageDraw, ImageEnhance12import face_recognition13from face_recognition import face_locations14from face_recognition.face_recognition_cli import image_files_in_folder15from datetime import datetime,timedelta16from pytz import timezone17import xlsxwriter18import pandas as pd19from glob import glob20from flask_mail import Mail, Message21from io import BytesIO22import base6423import lable_image24app = Flask(__name__,static_folder="excel")25# mail settings26app.config.update(27 DEBUG = True,28 #Email settings29 MAIL_SERVER = 'smtp.gmail.com',30 MAIL_PORT = 465,31 MAIL_USE_SSL = True,32 MAIL_USERNAME = 'college1118@gmail.com',33 MAIL_PASSWORD = 'yog12345',34 MAIL_DEFAULT_SENDER = 'college1118@gmail.com'35 )36mail = Mail(app)37# declaring timezone then creating custom date format 38india = timezone('Asia/Kolkata')39date = str(datetime.now(india))[:10] + "@" + str(datetime.now())[11:13] + "hrs"40# getting the location of root directory of the webapp41APP_ROOT = os.path.dirname(os.path.abspath(__file__))42APP_ROOT1 = APP_ROOT.split('teachers_site')43# connection with mysql database using python package MySQLdb44conn = MySQLdb.connect(user='root', passwd='', port=2811, host='localhost',db="login_info")45@app.route('/')46def index():47 return render_template("index.html",title="Faculty Login")48@app.after_request49def set_response_headers(response):50 response.headers['Cache-Control'] = 'no-cache, no-store, must-revalidate'51 response.headers['Pragma'] = 'no-cache'52 response.headers['Expires'] = '0'53 return response54@app.route('/login',methods=['POST'])55def login():56 print(APP_ROOT)57 print(APP_ROOT1[0])58 user = str(request.form["user"])59 session['user'] = user60 paswd = str(request.form["password"])61 username = user.split(".",1)[0]62 username = str(username)63 print(username)64 print(type(username))65 cursor = conn.cursor()66 result = cursor.execute("SELECT * from teacher_login where binary username=%s and binary password=%s",[user,paswd])67 if(result is 1):68 return render_template("task.html",uname=username)69 else:70 return render_template("index.html",title="Faculty Login",msg="The username or password is incorrect")71@app.after_request72def set_response_headers(response):73 response.headers['Cache-Control'] = 'no-cache, no-store, must-revalidate'74 response.headers['Pragma'] = 'no-cache'75 response.headers['Expires'] = '0'76 return response77@app.route('/upload_redirect',methods=['POST'])78def upload_redirect():79 if(os.path.isfile(APP_ROOT+"/image.jpeg")):80 os.remove(APP_ROOT + "/image.jpeg")81 return render_template("upload.html")82@app.route("/upload", methods=['POST'])83def upload():84 if not os.path.isfile(APP_ROOT+"/image.jpeg"):85 return render_template("upload.html",msg="spoof detected")86 id_folder = str(request.form['id_folder'])87 session['id_folder']= id_folder88 target = os.path.join(APP_ROOT,"test/")89 if not os.path.isdir(target):90 os.mkdir(target)91 target1 = os.path.join(target,str(request.form["folder_name"])+"/")92 test_append = str(request.form["folder_name"])93 session['test_append']= test_append94 print(target1)95 if not os.path.isdir(target1):96 os.mkdir(target1)97 shutil.copyfile(APP_ROOT+"/"+"image.jpeg",target1+"image.jpeg")98 destination = APP_ROOT + "/" + "test/" + test_append + "/" + "image.jpeg"99 100 session['destination'] = destination101 teacher_name = str(session.get('user'))102 session['teacher_name'] = teacher_name103 #return render_template("upload.html",msg="uploaded successfully")104 return match()105@app.after_request106def set_response_headers(response):107 response.headers['Cache-Control'] = 'no-cache, no-store, must-revalidate'108 response.headers['Pragma'] = 'no-cache'109 response.headers['Expires'] = '0'110 return response111def match():112 destination = str(session.get('destination'))113 print(destination)114 if os.path.isfile(destination):115 test_append = str(session.get('test_append'))116 session['test_append'] = test_append117 id_folder = str(session.get('id_folder'))118 train_dir = APP_ROOT1[0]+"admin_site/train/"+ test_append119 try:120 model = APP_ROOT1[0]+"admin_site/model/"+test_append+"/" + id_folder + "/" +"model"121 print(model)122 return predict1(model)123 except FileNotFoundError:124 os.remove(APP_ROOT1[0]+"teachers_site/image.jpeg")125 return render_template("upload.html",msg="trained model not present for " + test_append + ": "+id_folder)126 127def predict(X_img_path, knn_clf = None, model_save_path ="", DIST_THRESH = .45):128 if knn_clf is None and model_save_path == "":129 raise Exception("must supply knn classifier either thourgh knn_clf or model_save_path")130 if knn_clf is None:131 with open(model_save_path, 'rb') as f:132 knn_clf = pickle.load(f)133 X_img = face_recognition.load_image_file(X_img_path)134 X_faces_loc = face_locations(X_img)135 if len(X_faces_loc) == 0:136 return []137 faces_encodings = face_recognition.face_encodings(X_img, known_face_locations=X_faces_loc)138 closest_distances = knn_clf.kneighbors(faces_encodings, n_neighbors=1)139 is_recognized = [closest_distances[0][i][0] <= DIST_THRESH for i in range(len(X_faces_loc))]140 141 return [(pred) if rec else ("unknown") for pred, rec in zip(knn_clf.predict(faces_encodings), is_recognized)]142def predict1(model):143 test_append = str(session.get('test_append'))144 test_dir = APP_ROOT1[0]+"teachers_site/test/" + test_append145 f_preds = []146 for img_path in listdir(test_dir):147 preds = predict(join(test_dir, img_path) ,model_save_path=model)148 f_preds.append(preds)149 print(f_preds)150 print(len(preds))151 print(len(f_preds))152 for i in range(len(f_preds)):153 if(f_preds[i]==[]):154 os.remove(APP_ROOT1[0]+"teachers_site/image.jpeg")155 return render_template("upload.html",msg="upload again, face not found")156 else:157 os.remove(APP_ROOT1[0]+"teachers_site/image.jpeg")158 excel = os.path.join(APP_ROOT,"excel/")159 if not os.path.isdir(excel):160 os.mkdir(excel)161 excel1 = os.path.join(excel,test_append)162 if not os.path.isdir(excel1):163 os.mkdir(excel1)164 teacher_name = str(session.get('teacher_name'))165 excel2 = os.path.join(excel1,teacher_name)166 if not os.path.isdir(excel2):167 os.mkdir(excel2)168 session['excel2'] = excel2169 excel3 = excel2+"/"+date+'.xlsx'170 if not os.path.isfile(excel3):171 workbook = xlsxwriter.Workbook(excel2+"/"+date+'.xlsx')172 worksheet = workbook.add_worksheet()173 worksheet.set_column(0,0,20)174 worksheet.write('A1','Roll Id')175 f_preds.sort()176 row = 1177 col = 0178 for i in range(len(f_preds)):179 for j in range(len(f_preds[i])):180 worksheet.write_string(row,col,f_preds[i][j])181 row += 1182 workbook.close()183 return render_template("upload.html",msg= f_preds[0][0] + " present")184 else:185 df = pd.read_excel(excel2+"/"+date+'.xlsx')186 writer = pd.ExcelWriter(excel2 + "/" + date+'.xlsx')187 df.to_excel(writer,sheet_name="Sheet1",index=False)188 workbook = writer.book189 worksheet = writer.sheets['Sheet1']190 rows=df.shape[0]191 worksheet.write_string(rows+1,0,f_preds[0][0])192 writer.save()193 df = pd.read_excel(excel2+"/"+date+'.xlsx')194 df.drop_duplicates(['Roll Id'],keep='first',inplace=True)195 result = df.sort_values("Roll Id")196 writer = pd.ExcelWriter(excel2 + "/" + date+'.xlsx')197 result.to_excel(writer,'Sheet1',index=False)198 workbook = writer.book199 worksheet = writer.sheets['Sheet1']200 worksheet.set_column(0,0,20)201 writer.save()202 return render_template("upload.html",msg= f_preds[0][0] + " present")203@app.route('/view_report',methods=['POST'])204def view_report():205 return render_template("excel.html")206# view route to download excel files 207@app.route('/view',methods=['POST'])208def view():209 test_append = str(request.form['folder_name'])210 session['test_append']=test_append211 teacher_name = str(session.get('user'))212 excel_dir = APP_ROOT+"/excel/"+test_append+"/"+teacher_name+"/"213 excel_date = request.form['fname']214 time = request.form['ftime']215 time = time[:2]216 print(time)217 final_excel=glob(excel_dir + "/" + excel_date+ "@" + time +"*.xlsx")[0]218 print(final_excel)219 df = pd.read_excel(final_excel)220 df.index += 1221 return render_template("files.html",msg=final_excel,df=df,date=excel_date+"@"+time+"hrs")222@app.route('/excel/<path:filename>', methods=['POST'])223def download(filename): 224 return send_from_directory(directory='excel', filename=filename)225# route to send emails to parents and students226@app.route('/send_mail',methods=['POST'])227def send_mail():228 test_append = str(request.form['folder_name'])229 teacher_name = str(session.get('user'))230 excel_dir = APP_ROOT+"/excel/"+test_append+"/"+teacher_name+"/"231 excel_date = request.form['fname']232 time = request.form['ftime']233 time = time[:2]234 final_send = glob(excel_dir + "/" + excel_date+ "@" + time +"*.xlsx")[0]235 print(final_send)236 df = pd.read_excel(final_send)237 roll_id = list(df['Roll Id'])238 print(type(roll_id))239 print(roll_id)240 cursor = conn.cursor()241 for i in range(len(roll_id)):242 cursor.execute("SELECT student_email,parent_email from student_login where binary roll_id=%s",[roll_id[i]])243 email = list(cursor.fetchone())244 print(type(email[1]))245 print(email[0])246 print(email[1])247 msg = Message('Auto Generated',recipients= [email[0],email[1]])248 msg.body = "Hi.. " + roll_id[i] + " is present for the lecture of " + "Prof. " +str(teacher_name.split('.',1)[0]) + ", which is held on " + excel_date + "@" + time + "hrs"249 msg.html = "Hi.. " + roll_id[i] + " is present for the lecture of " + "Prof. " +str(teacher_name .split('.',1)[0])+ ", which is held on " + excel_date + "@" + time + "hrs"250 mail.send(msg)251 return "<h1>mail sent<h1>"252@app.route('/update',methods=['POST'])253def update():254 test_append = str(request.form['excel_folder'])255 print(test_append)256 teacher_name = str(session.get('user'))257 print(teacher_name)258 excel_dir = APP_ROOT + "/excel/" + test_append + "/" + teacher_name + "/"259 print(excel_dir)260 for file in request.files.getlist("updated_excel"):261 print(file)262 filename = file.filename263 print(filename)264 destination = "/".join([excel_dir,filename])265 print(destination)266 file.save(destination)267 return render_template("excel.html",msg="updated successfully")268@app.route('/calculate',methods=['POST'])269def calculate():270 test_append = str(request.form['final_class'])271 print(test_append)272 teacher_name = str(session.get('user'))273 print(teacher_name)274 excel_root = APP_ROOT + "/excel/" + test_append + "/" + teacher_name + "/"275 print(excel_root)276 excel_names = os.listdir(excel_root)277 print(excel_names) 278 for i in range(len(excel_names)):279 if excel_names[i].startswith("."):280 os.remove(excel_root+excel_names[i])281 else:282 if os.path.isdir(excel_root+excel_names[i]):283 shutil.rmtree(excel_root+excel_names[i], ignore_errors=False, onerror=None)284 excel_names = os.listdir(excel_root)285 if(excel_names==[]):286 return render_template("excel.html",msg1="No excel files found")287 for i in range(len(excel_names)):288 excel_names[i] = excel_root + excel_names[i]289 print(type(excel_names))290 # read them in291 excels = [pd.ExcelFile(name) for name in excel_names]292 # turn them into dataframes293 frames = [x.parse(x.sheet_names[0], header=None,index_col=None) for x in excels]294 # delete the first row for all frames except the first295 # i.e. remove the header row -- assumes it's the first296 frames[1:] = [df[1:] for df in frames[1:]]297 # concatenate them..298 combined = pd.concat(frames)299 if not os.path.isdir(excel_root+"final/"):300 os.mkdir(excel_root + "final/")301 final = excel_root + "final/"302 print(final)303 # write it out304 combined.to_excel(final+"final.xlsx", header=False, index=False)305 # below code is to find actual repetative blocks306 workbook = pd.ExcelFile(final+"final.xlsx")307 df = workbook.parse('Sheet1')308 sample_data = df['Roll Id'].tolist()309 print (sample_data)310 #a dict that will store the poll results311 results = {}312 for response in sample_data:313 results[response] = results.setdefault(response, 0) + 1314 finaldf = (pd.DataFrame(list(results.items()), columns=['Roll Id', 'Total presenty']))315 finaldf = finaldf.sort_values("Roll Id")316 print (finaldf)317 writer = pd.ExcelWriter(final+"final.xlsx")318 finaldf.to_excel(writer,'Sheet1',index=False)319 workbook = writer.book320 worksheet = writer.sheets['Sheet1']321 worksheet.set_column(0,1,20)322 writer.save()323 final = final + "final.xlsx"324 session['final']=final325 final = final[91:]326 return viewfinal(final)327def viewfinal(final):328 test_append = str(session.get('test_append'))329 final_path = str(session.get('final'))330 df = pd.read_excel(final_path)331 df.index += 1332 return render_template("files.html",msg=final,course=test_append,df=df)333@app.route('/changetask',methods=['POST'])334def changetask():335 return render_template("task.html")336@app.route('/logout',methods=['POST'])337def logout():338 return render_template("index.html",title="Faculty Login",msg1="Logged out please login again")339@app.route('/hello',methods=['POST'])340def hello():341 data_url = request.values['imageBase64']342 data_url= data_url[22:] 343 im = Image.open(BytesIO(base64.b64decode(data_url)))344 print(type(im))345 im.save('image.jpeg')346 filepath = APP_ROOT + "/" + "image.jpeg"347 var = lable_image.function(filepath)348 print(var)349 for i in range(len(var)):350 if(var[i] > 0.8):351 os.remove(filepath)352 return ''353if(__name__ == '__main__'):354 app.secret_key = 'super secret key'...
second.py
Source:second.py
1import warnings2warnings.filterwarnings('ignore')3import pandas as pd4import numpy as np5import gc6import pickle7import time8import xgboost as xgb9from sklearn.preprocessing import LabelEncoder10from lightgbm import LGBMClassifier11from sklearn.linear_model import LinearRegression,Ridge,Lasso12from sklearn.metrics import mean_squared_error13from mlxtend.regressor import StackingRegressor,StackingCVRegressor14from sklearn.model_selection import cross_val_score,GridSearchCV15from sklearn import metrics16save_path = './data_final/'17def fun(x):18 if x >= 0.5:19 return 120 else:21 return 022with open(save_path + 'invite_info.pkl','rb') as file:23 invite_info = pickle.load(file)24member_feat = pd.read_hdf(save_path + 'member_feat.h5',key='data')25question_feat = pd.read_hdf(save_path + 'question_feat.h5',key='data')26member_question_feat = pd.read_hdf(save_path + 'member_question_feat.h5',key='data')27invite_info_evaluate = invite_info.ix[:1000]28invite_info_test = invite_info.ix[1000:2000]29invite_info = invite_info.ix[2000:]30tt = invite_info_evaluate['label']31ttt = invite_info_test['label']32del invite_info_evaluate['label'],invite_info_test['label']33invite_info['author_question_id'] = invite_info['author_id'] + invite_info['question_id']34invite_info_evaluate['author_question_id'] = invite_info_evaluate['author_id'] + invite_info_evaluate['question_id']35invite_info_test['author_question_id'] = invite_info_test['author_id'] + invite_info_test['question_id']36train = invite_info.merge(member_feat, 'left', 'author_id')37test = invite_info_evaluate.merge(member_feat, 'left', 'author_id')38pre = invite_info_test.merge(member_feat,'left','author_id')39train = train.merge(question_feat, 'left', 'question_id')40test = test.merge(question_feat, 'left', 'question_id')41pre = pre.merge(question_feat,'left','question_id')42train = train.merge(member_question_feat, 'left', 'author_question_id')43test = test.merge(member_question_feat, 'left', 'author_question_id')44pre = pre.merge(member_question_feat,'left','author_question_id')45del member_feat, question_feat, member_question_feat46gc.collect()47drop_feats = ['question_id', 'author_id', 'author_question_id', 'invite_time', 'label', 'invite_day']48used_feats = [f for f in train.columns if f not in drop_feats]49train_x = train[used_feats].reset_index(drop=True)50train_y = train['label'].reset_index(drop=True)51test_x = test[used_feats].reset_index(drop=True)52pre_x = pre[used_feats].reset_index(drop=True)53# LGBMClassifier54'''55model_lgb = LGBMClassifier(boostiong_type='gdbt',num_leaves=64,learning_rate=0.01,n_estimators=2500,max_bin=425,subsample_for_bin=50000,objective='binary',min_split_gain=0,min_child_weight=5,min_child_samples=10,subsample=0.8,subsample_freq=1,colsample_bytree=1,req_alpha=3,reg_lambda=5,seed=1000,n_jobs=-1,silent=True)56model_lgb.fit(train_x,train_y,eval_names=['train'],eval_metric=['logloss','auc'],eval_set=[(train_x,train_y)],early_stopping_rounds=10)57test_y = model_lgb.predict_proba(test_x)[:,1]58print("test auc: ",metrics.roc_auc_score(tt,test_y))59test_append = invite_info_evaluate[['question_id', 'author_id', 'invite_time']]60test_append['answer'] = test_y61test_append['answer'] = test_append['answer'].apply(lambda x: fun(x))62test_append['true'] = tt63test_append.to_csv('result_test_LGBM.txt',index=False,header=False,sep='\t')64pre_y = model_lgb.predict_proba(pre_x)[:,1]65print("pre auc: ",metrics.roc_auc_score(ttt,pre_y))66pre_append = invite_info_test[['question_id', 'author_id', 'invite_time']]67pre_append['answer'] = pre_y68pre_append['answer'] = pre_append['answer'].apply(lambda x: fun(x))69pre_append['true'] = ttt70pre_append.to_csv('result_pre_LGBM.txt',index=False,header=False,sep='\t')71'''72'''73# Stacking One74print("Let's Begin Stacking Model One")75lr = LinearRegression()76ridge = Ridge(random_state = 2019,)77models = [lr,ridge]78for model in models:79 model.fit(train_x,train_y)80 pred = model.predict(test_x)81 print("loss is {}".format(mean_squared_error(tt,pred)))82sclf = StackingRegressor(regressors = models,meta_regressor = ridge)83sclf.fit(train_x,train_y)84test_y= sclf.predict(test_x)85print("test auc: ",metrics.roc_auc_score(tt,test_y))86test_append = invite_info_evaluate[['question_id', 'author_id', 'invite_time']]87test_append['answer'] = test_y88test_append['answer'] = test_append['answer'].apply(lambda x: fun(x))89test_append['true'] = tt90test_append.to_csv('result_test_S1.txt',index=False,header=False,sep='\t')91pre_y= sclf.predict(pre_x)92print("pre auc: ",metrics.roc_auc_score(ttt,pre_y))93pre_append = invite_info_test[['question_id', 'author_id', 'invite_time']]94pre_append['answer'] = pre_y95pre_append['answer'] = pre_append['answer'].apply(lambda x: fun(x))96pre_append['true'] = ttt97pre_append.to_csv('result_pre_S1.txt',index=False,header=False,sep='\t')98'''99# Xgboost100'''101params = {'n_estimators': 10, 'seed': 0, 'n_estimators': 600, 'max_depth': 6, 'min_child_weight' :1, 'gamma': 0.2, 'subsample': 0.8, 'colsample_bytree': 0.9, 'reg_alpha':0.1, 'reg_lambda':0.05, 'learning_rate':0.01}102model = xgb.XGBRegressor(params=params, booster='dart')103model.fit(train_x, train_y)104test_y = model.predict(test_x)105print("test auc: ",metrics.roc_auc_score(tt,test_y))106test_append = invite_info_evaluate[['question_id', 'author_id', 'invite_time']]107test_append['answer'] = test_y108test_append['answer'] = test_append['answer'].apply(lambda x: fun(x))109test_append['true'] = tt110test_append.to_csv('result_test_Xgboost.txt',index=False,header=False,sep='\t')111pre_y= model.predict(pre_x)112print("pre auc: ",metrics.roc_auc_score(ttt,pre_y))113pre_append = invite_info_test[['question_id', 'author_id', 'invite_time']]114pre_append['answer'] = pre_y115pre_append['answer'] = pre_append['answer'].apply(lambda x: fun(x))116pre_append['true'] = ttt117pre_append.to_csv('result_pre_Xgboost.txt',index=False,header=False,sep='\t')118'''119# Stacking_Two120print("Let's Begin Stacking Model Two!")121lr = LinearRegression()122ridge = Ridge(random_state=2019,)123lasso =Lasso()124models = [lr,ridge, lasso]125params = {'lasso__alpha': [0.1, 1.0, 10.0],'ridge__alpha': [0.1, 1.0, 10.0]}126sclf = StackingCVRegressor(regressors=models, meta_regressor=ridge)127sclf = StackingCVRegressor(regressors=models, meta_regressor=ridge)128grid = GridSearchCV(estimator=sclf, param_grid=params, cv=5, refit=True)129grid.fit(train_x, train_y)130test_y = grid.predict(test_x)131print("test auc: ",metrics.roc_auc_score(tt,test_y))132test_append = invite_info_evaluate[['question_id', 'author_id', 'invite_time']]133test_append['answer'] = test_y134test_append['answer'] = test_append['answer'].apply(lambda x: fun(x))135test_append['true'] = tt136test_append.to_csv('result_test_S2.txt',index=False,header=False,sep='\t')137pre_y= grid.predict(pre_x)138print("pre auc: ",metrics.roc_auc_score(ttt,pre_y))139pre_append = invite_info_test[['question_id', 'author_id', 'invite_time']]140pre_append['answer'] = pre_y141pre_append['answer'] = pre_append['answer'].apply(lambda x: fun(x))142pre_append['true'] = ttt...
test_storage.py
Source:test_storage.py
...8 def setUp(self):9 self.space = RNNSpace(20, 5)10 self.storage = Storage()11 self.arch = Architect(self.space)12 def test_append(self, na_points=0):13 c = 014 starting_len = len(self.storage)15 while c < 10 + na_points:16 description, logps, values, entropies = self.arch.sample()17 desc = self.space.preprocess(description, (-1, 128))18 if desc is not None:19 param_count = sum(self.space.parameter_count(desc)[:2]) / 1e620 self.storage.append(description, logps, values, entropies,21 uniform(.6, 1) if c < 10 else None,22 param_count)23 c += 124 self.assertEqual(len(self.storage), starting_len+10+na_points)25 def test_reward(self):26 chosen_description = None27 while chosen_description is None:28 description, logps, values, entropies = self.arch.sample()29 desc = self.space.preprocess(description, (-1, 128))30 if desc is not None:31 if uniform(0, 1) < .5:32 chosen_description = description33 param_count = sum(self.space.parameter_count(desc)[:2]) / 1e6 # Million parameters34 self.storage.append(description, logps, values, entropies, None, param_count)35 self.storage.reward(chosen_description, 1.)36 index = self.storage.find(chosen_description)37 self.assertIsNotNone(index)38 self.assertTrue(torch.eq(self.storage[index][-3], 1.).all())39 def test__update_advantages(self):40 chosen_description = None41 while chosen_description is None:42 description, logps, values, entropies = self.arch.sample()43 desc = self.space.preprocess(description, (-1, 128))44 if desc is not None:45 if uniform(0, 1) < .5:46 chosen_description = description47 param_count = sum(self.space.parameter_count(desc)[:2]) / 1e6 # Million parameters48 self.storage.append(description, logps, values, entropies, None, param_count)49 for adv in self.storage.advantages:50 self.assertEqual(torch.sum(adv != adv), adv.numel())51 self.storage.reward(chosen_description, 1.)52 index = self.storage.find(chosen_description)53 self.assertIsNotNone(index)54 self.assertFalse(np.isnan(self.storage[index][-3].mean().item()))55 def test_update(self):56 desc = None57 while desc is None:58 description, logps, values, entropies = self.arch.sample()59 desc = self.space.preprocess(description, (-1, 128))60 if desc is not None:61 param_count = sum(self.space.parameter_count(desc)[:2]) / 1e6 # Million parameters62 self.storage.append(description, logps, values, entropies, None, param_count)63 self.arch.reset()64 _, logps_, values_, entropies_ = self.arch.evaluate_description(description)65 self.storage.update(description, logps_, values_, entropies_)66 index = self.storage.find(description)67 self.assertIsNotNone(index)68 _, logps, values, entropies, _, _, _ = self.storage[index]69 self.assertTrue(torch.eq(logps, logps_).all())70 self.assertTrue(torch.eq(values, values_).all())71 self.assertTrue(torch.eq(entropies, entropies_).all())72 def test___get_item__(self):73 self.test_append()74 self.storage[5]75 self.storage[1:4]76 self.storage[1:4:-1]77 self.storage[np.arange(5)]78 def test___del_item__(self):79 self.test_append()80 item = self.storage[5]81 del self.storage[5]82 self.assertNotEqual(item, self.storage[5])83 def test___len__(self):84 self.test_append()85 self.assertIs(len(self.storage), 10)86 def test_filterna(self):87 self.test_append(5)88 self.assertEqual(len(self.storage), 15)89 self.storage.filter_na()90 self.assertEqual(len(self.storage), 10)91class TestCurriculumStorage(TestStorage):92 def setUp(self):93 super().setUp()94 self.storage = CurriculumStorage(20)95 def test_append(self):96 super().test_append()97 self.assertIs(len(self.storage), 10)98 self.storage.set_complexity(2)99 super().test_append()100 super().test_append()101 self.assertIs(len(self.storage), 20)102 self.storage.set_complexity(1)...
test_linked_list.py
Source:test_linked_list.py
1from linked_list import __version__2from linked_list.linked_list import (LinkedList,Node)3def test_empty_linked_list():4 test_1=LinkedList()5 test_1.insert()6 actual=test_1.__str__()7 expected='(null) -> none'8 assert actual==expected9def test_insert_into_the_linked_list():10 test_2=LinkedList()11 test_2.insert(332)12 actual=test_2.head.value13 expected=33214 assert actual==expected15def test_first_node_in_the_linked_list():16 test_3=LinkedList()17 test_3.insert(332)18 test_3.insert(26)19 test_3.insert(778)20 test_3.insert(1000)21 actual=test_3.head.value22 expected=100023 assert actual==expected24def test_multiple_nodes_into_the_linked_list():25 test_4=LinkedList()26 test_4.insert(4)27 test_4.insert(5)28 actual=test_4.__str__()29 expected='(5) -> (4) -> none'30 assert actual==expected31def test_return_true_when_finding_a_value():32 test_5=LinkedList()33 test_5.insert('ahmad')34 test_5.insert('abudames')35 actual=test_5.includes('ahmad')36 expected=True37 assert actual==expected38def test_return_false_when_does_not_exist():39 test_5=LinkedList()40 test_5.insert('ahmad')41 test_5.insert('abudames')42 actual=test_5.includes('mais')43 expected=False44 assert actual==expected45def test_all_collection_values_in_in_the_linedlist():46 test_6=LinkedList()47 test_6.insert("abudames")48 test_6.insert("ameen")49 test_6.insert("shahar")50 test_6.insert("ahmad")51 actual=test_6.__str__()52 expected='(ahmad) -> (shahar) -> (ameen) -> (abudames) -> none'53 assert actual==expected54def test_end_of_the_linked_list():55 test_append=LinkedList()56 test_append.insert(22)57 test_append.insert(4)58 test_append.append(2)59 actual=test_append.__str__()60 expected='(4) -> (22) -> (2) -> none'61 assert actual==expected62def test_multiple_nodes_to_the_end_of_a_linked_list():63 test_append=LinkedList()64 test_append.append(2)65 test_append.append(1)66 actual=test_append.__str__()67 expected='(2) -> (1) -> none'68 assert actual==expected69def test_before_middle_of_the_linked_list():70 test_before=LinkedList()71 test_before.append("ahmad")72 test_before.append("shaher")73 test_before.append("abudames")74 test_before.insertBefore("abudames","ameen")75 actual=test_before.__str__()76 expected='(ahmad) -> (shaher) -> (ameen) -> (abudames) -> none'77 assert actual==expected78def test_before_the_first_node_of_a_linked_list():79 test_before_first=LinkedList()80 test_before_first.append(2)81 test_before_first.append(3)82 test_before_first.insertBefore(2,1)83 actual=test_before_first.__str__()84 expected='(1) -> (2) -> (3) -> none'85 assert actual==expected86def test_insert_after():87 test_after=LinkedList()88 test_after.append(1)89 test_after.append(3)90 test_after.insertAfter(1,2)91 actual=test_after.__str__()92 expected='(1) -> (2) -> (3) -> none'93 assert actual==expected94def test_insert_after_the_last_node():95 test_after_last_node=LinkedList()96 test_after_last_node.append(1)97 test_after_last_node.append(2)98 test_after_last_node.insertAfter(2,3)99 actual=test_after_last_node.__str__()100 expected='(1) -> (2) -> (3) -> none'101 assert actual==expected102def test_k_is_greater_than_the_length():103 test_k=LinkedList()104 test_k.insert(1)105 test_k.insert(3)106 test_k.insert(8)107 test_k.insert(2)108 test_k.NthFromLast(4)109 actual=test_k.__str__()110 expected="exception"111 assert actual==expected112def test_ths_same_of_length():113 test_k=LinkedList()114 test_k.insert(1)115 test_k.insert(3)116 test_k.insert(8)117 test_k.insert(2)118 test_k.NthFromLast(3)119 actual=test_k.__str__()120 expected='(2) -> (8) -> (3) -> (1) -> none'121 assert actual==expected122def test_not_a_positive_integer():123 test_k=LinkedList()124 test_k.insert(1)125 test_k.insert(3)126 test_k.insert(8)127 test_k.insert(2)128 test_k.NthFromLast(-1)129 actual=test_k.__str__()130 expected='(2) -> (8) -> (3) -> (1) -> none'131 assert actual==expected132def test_linked_list_is_of_a_size_1():133 test_k=LinkedList()134 test_k.insert(1)135 test_k.insert(3)136 test_k.insert(8)137 test_k.insert(2)138 test_k.NthFromLast(1)139 actual=test_k.__str__()140 expected='(2) -> (8) -> (3) -> (1) -> none'141 assert actual==expected142def test_k_in_the_middle():143 test_k=LinkedList()144 test_k.insert(1)145 test_k.insert(3)146 test_k.insert(8)147 test_k.insert(2)148 test_k.insert(5)149 test_k.NthFromLast(2)150 actual=test_k.__str__()151 expected='(5) -> (2) -> (8) -> (3) -> (1) -> none'...
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