How to use test_age method in green
Best Python code snippet using green
datas.py
Source:datas.py 
1import csv2import pandas as pd3import operator4import numpy as np5import json6def get_category():7 df1 = pd.read_csv("êµë¯¼ì°ë ¹ë³ì¶ì²ì´ëì ë³´.csv", encoding='utf-8')8 category1 = df1['BMI_IDEX_GRAD'] == 'ì ì'9 value1 = df1[category1]10 category2 = value1['SPORTS_STEP_NM'] == '본ì´ë'11 value2 = value1[category2]12 category3 = value2['SE_ACCTO_RECOMEND_SPORTS_RANK'] == 113 value3 = value2[category3]14 15 print(value3['RECOMEND_SPORTS_NM'].unique())16def get_each_average(data):17 ave12 = int(data['ITEM_F012'].mean())18 ave19 = int(data['ITEM_F019'].mean())19 ave20 = int(data['ITEM_F020'].mean())20 ave21 = int(data['ITEM_F021'].mean())21 ave22 = int(data['ITEM_F022'].mean())22 result = {"data" : [ave12, ave19, ave20, ave21, ave22]}23 return result24def get_all_average(*args):25 i = 026 section = ['M19to24', 'M25to29', 'M30to34', 'M35to39', 'M40to44', 'M45to49', 'M50to54', 'M55to59', 'M60to64', 'F19to24', 'F25to29', 'F30to34', 'F35to39', 'F40to44', 'F45to49', 'F50to54', 'F55to59', 'F60to64']27 average = []28 for arg in args:29 average.append(get_each_average(arg))30 31 for j in section:32 average[i][j] = average[i]['data']33 del average[i]['data']34 i += 135 return average36def get_average():37 df = pd.read_csv("ì²´ë ¥ì¸¡ì _í목ë³_측ì _ë°ì´í°.csv", encoding='utf-8')38 newdf=df[['AGE_GBN', 'TEST_AGE', 'CERT_GBN', 'TEST_SEX', 'ITEM_F001', 'ITEM_F002', 'ITEM_F012', 'ITEM_F019', 'ITEM_F020', 'ITEM_F021', 'ITEM_F022']]39 newdf=newdf.fillna(newdf.mean())40 is_adult=newdf['AGE_GBN']=='ì±ì¸'41 is_adult=newdf[is_adult]42 is_teen=newdf['AGE_GBN']=='ì ìë
'43 is_teen=newdf[is_teen]44 adult_Male=is_adult['TEST_SEX']=='M'45 adult_Male=is_adult[adult_Male]46 adult_Female=is_adult['TEST_SEX']=='F'47 adult_Female=is_adult[adult_Female]48 teen_Male=is_teen['TEST_SEX']=='M'49 teen_Male=is_teen[teen_Male]50 teen_Female=is_teen['TEST_SEX']=='F'51 teen_Female=is_teen[teen_Female]52 # Male53 M19to24=((adult_Male['TEST_AGE']>=19) & (adult_Male['TEST_AGE']<=24))54 M19to24=adult_Male[M19to24]55 M25to29=((adult_Male['TEST_AGE']>=25) & (adult_Male['TEST_AGE']<=29))56 M25to29=adult_Male[M25to29]57 M30to34=((adult_Male['TEST_AGE']>=30) & (adult_Male['TEST_AGE']<=34))58 M30to34=adult_Male[M30to34]59 M35to39=((adult_Male['TEST_AGE']>=35) & (adult_Male['TEST_AGE']<=39))60 M35to39=adult_Male[M35to39]61 M40to44=((adult_Male['TEST_AGE']>=40) & (adult_Male['TEST_AGE']<=44))62 M40to44=adult_Male[M40to44]63 M45to49=((adult_Male['TEST_AGE']>=45) & (adult_Male['TEST_AGE']<=49))64 M45to49=adult_Male[M45to49]65 M50to54=((adult_Male['TEST_AGE']>=50) & (adult_Male['TEST_AGE']<=54))66 M50to54=adult_Male[M50to54]67 M55to59=((adult_Male['TEST_AGE']>=55) & (adult_Male['TEST_AGE']<=59))68 M55to59=adult_Male[M55to59]69 M60to64=((adult_Male['TEST_AGE']>=60) & (adult_Male['TEST_AGE']<=64))70 M60to64=adult_Male[M60to64]71 M65to69=((adult_Male['TEST_AGE']>=65) & (adult_Male['TEST_AGE']<=69))72 M65to69=adult_Male[M65to69]73 M70to74=((adult_Male['TEST_AGE']>=70) & (adult_Male['TEST_AGE']<=74))74 M70to74=adult_Male[M70to74]75 M75to79=((adult_Male['TEST_AGE']>=75) & (adult_Male['TEST_AGE']<=79))76 M75to79=adult_Male[M75to79]77 M80to84=((adult_Male['TEST_AGE']>=80) & (adult_Male['TEST_AGE']<=84))78 M80to84=adult_Male[M80to84]79 M85=adult_Male['TEST_AGE']>=8580 M85=adult_Male[M85]81 # Female82 F19to24=((adult_Female['TEST_AGE']>=19) & (adult_Female['TEST_AGE']<=24))83 F19to24=adult_Female[F19to24]84 F25to29=((adult_Female['TEST_AGE']>=25) & (adult_Female['TEST_AGE']<=29))85 F25to29=adult_Female[F25to29]86 F30to34=((adult_Female['TEST_AGE']>=30) & (adult_Female['TEST_AGE']<=34))87 F30to34=adult_Female[F30to34]88 F35to39=((adult_Female['TEST_AGE']>=35) & (adult_Female['TEST_AGE']<=39))89 F35to39=adult_Female[F35to39]90 F40to44=((adult_Female['TEST_AGE']>=40) & (adult_Female['TEST_AGE']<=44))91 F40to44=adult_Female[F40to44]92 F45to49=((adult_Female['TEST_AGE']>=45) & (adult_Female['TEST_AGE']<=49))93 F45to49=adult_Female[F45to49]94 F50to54=((adult_Female['TEST_AGE']>=50) & (adult_Female['TEST_AGE']<=54))95 F50to54=adult_Female[F50to54]96 F55to59=((adult_Female['TEST_AGE']>=55) & (adult_Female['TEST_AGE']<=59))97 F55to59=adult_Female[F55to59]98 F60to64=((adult_Female['TEST_AGE']>=60) & (adult_Female['TEST_AGE']<=64))99 F60to64=adult_Female[F60to64]100 F65to69=((adult_Female['TEST_AGE']>=65) & (adult_Female['TEST_AGE']<=69))101 F65to69=adult_Female[F65to69]102 F70to74=((adult_Female['TEST_AGE']>=70) & (adult_Female['TEST_AGE']<=74))103 F70to74=adult_Female[F70to74]104 F75to79=((adult_Female['TEST_AGE']>=75) & (adult_Female['TEST_AGE']<=79))105 F75to79=adult_Female[F75to79]106 F80to84=((adult_Female['TEST_AGE']>=80) & (adult_Female['TEST_AGE']<=84))107 F80to84=adult_Female[F80to84]108 F85=adult_Female['TEST_AGE']>=85109 F85=adult_Female[F85]110 result = get_all_average(M19to24, M25to29, M30to34, M35to39, M40to44, M45to49, M50to54, M55to59, M60to64, F19to24, F25to29, F30to34, F35to39, F40to44, F45to49, F50to54, F55to59, F60to64)111 return result112def get_each_item(item, data):113 gold=int(data.shape[0]*0.3)114 gold_boundary = int(data.shape[0]*0.34)115 silver=int(data.shape[0]*0.5)116 silver_boundary = int(data.shape[0]*0.55)117 bronze=int(data.shape[0]*0.7)118 bronze_boundary = int(data.shape[0]*0.335)119 attend_boundary = int(data.shape[0]*0.72)120 121 item_all = []122 #gold123 item_all.append(int(data[item].sort_values(ascending=False)[0:gold].max()))124 item_all.append(int(data[item].sort_values(ascending=False)[0:gold].mean()))125 item_all.append(int(data[item].sort_values(ascending=False)[0:gold].min()))126 #silver127 item_all.append(int(data[item].sort_values(ascending=False)[gold_boundary:silver].max()))128 item_all.append(int(data[item].sort_values(ascending=False)[gold_boundary:silver].mean()))129 item_all.append(int(data[item].sort_values(ascending=False)[gold_boundary:silver].min()))130 #bronze131 item_all.append(int(data[item].sort_values(ascending=False)[silver_boundary:bronze].max()))132 item_all.append(int(data[item].sort_values(ascending=False)[silver_boundary:bronze].mean()))133 item_all.append(int(data[item].sort_values(ascending=False)[silver_boundary:bronze].min()))134 #attend135 item_all.append(int(data[item].sort_values(ascending=False)[attend_boundary:].max()))136 item_all.append(int(data[item].sort_values(ascending=False)[attend_boundary:].mean()))137 item_all.append(int(data[item].sort_values(ascending=False)[attend_boundary:].min())) 138 if item_all[3] == item_all[2]:139 item_all[3] -= 1140 if item_all[6] == item_all[5]:141 item_all[6] -= 1142 if item_all[9] == item_all[8]:143 item_all[9] -= 1144 return item_all145# M19TO24, 0, 19, 24146def get_each_rank(data, gender, a1, a2):147 result = {"one_gold_top" : -1, "one_gold_avg" : -1, "one_gold_low" : -1, "one_silver_top" : -1, "one_silver_avg" : -1, "one_silver_low" : -1, "one_bronze_top" : -1, "one_bronze_avg" : -1, "one_bronze_low" : -1, "one_attend_top" : -1, "one_attend_avg" : -1, "one_attend_low" : -1,148 "two_gold_top" : -1, "two_gold_avg" : -1, "two_gold_low" : -1, "two_silver_top" : -1, "two_silver_avg" : -1, "two_silver_low" : -1, "two_bronze_top" : -1, "two_bronze_avg" : -1, "two_bronze_low" : -1, "two_attend_top" : -1, "two_attend_avg" : -1, "two_attend_low" : -1,149 "three_gold_top" : -1, "three_gold_avg" : -1, "three_gold_low" : -1, "three_silver_top" : -1, "three_silver_avg" : -1, "three_silver_low" : -1, "three_bronze_top" : -1, "three_bronze_avg" : -1, "three_bronze_low" : -1, "three_attend_top" : -1, "three_attend_avg" : -1, "three_attend_low" : -1,150 "four_gold_top" : -1, "four_gold_avg" : -1, "four_gold_low" : -1, "four_silver_top" : -1, "four_silver_avg" : -1, "four_silver_low" : -1, "four_bronze_top" : -1, "four_bronze_avg" : -1, "four_bronze_low" : -1, "four_attend_top" : -1, "four_attend_avg" : -1, "four_attend_low" : -1,151 "five_gold_top" : -1, "five_gold_avg" : -1, "five_gold_low" : -1, "five_silver_top" : -1, "five_silver_avg" : -1, "five_silver_low" : -1, "five_bronze_top" : -1, "five_bronze_avg" : -1, "five_bronze_low" : -1, "five_attend_top" : -1, "five_attend_avg" : -1, "five_attend_low" : -1,}152 each_rank = []153 each_rank.append(get_each_item("ITEM_F012", data))154 each_rank.append(get_each_item("ITEM_F019", data))155 each_rank.append(get_each_item("ITEM_F020", data))156 each_rank.append(get_each_item("ITEM_F021", data))157 each_rank.append(get_each_item("ITEM_F022", data))158 i = 0159 j = 0160 for key, value in result.items():161 result[key] = each_rank[i][j]162 j += 1163 if j == 12:164 i += 1165 if i == 5:166 break167 j = 0168 for key, value in result.items():169 if value < 0:170 result[key] = 0171 result['gender'] = gender172 result['a1'] = a1173 result['a2'] = a2174 return result175 176def get_rank():177 df = pd.read_csv("ì²´ë ¥ì¸¡ì _í목ë³_측ì _ë°ì´í°.csv", encoding='utf-8')178 newdf=df[['AGE_GBN', 'TEST_AGE', 'CERT_GBN', 'TEST_SEX', 'ITEM_F001', 'ITEM_F002', 'ITEM_F012', 'ITEM_F019', 'ITEM_F020', 'ITEM_F021', 'ITEM_F022']]179 newdf=newdf.fillna(newdf.mean())180 is_adult=newdf['AGE_GBN']=='ì±ì¸'181 is_adult=newdf[is_adult]182 is_teen=newdf['AGE_GBN']=='ì ìë
'183 is_teen=newdf[is_teen]184 adult_Male=is_adult['TEST_SEX']=='M'185 adult_Male=is_adult[adult_Male]186 adult_Female=is_adult['TEST_SEX']=='F'187 adult_Female=is_adult[adult_Female]188 teen_Male=is_teen['TEST_SEX']=='M'189 teen_Male=is_teen[teen_Male]190 teen_Female=is_teen['TEST_SEX']=='F'191 teen_Female=is_teen[teen_Female]192 # Male193 M19to24=((adult_Male['TEST_AGE']>=19) & (adult_Male['TEST_AGE']<=24))194 M19to24=adult_Male[M19to24]195 M25to29=((adult_Male['TEST_AGE']>=25) & (adult_Male['TEST_AGE']<=29))196 M25to29=adult_Male[M25to29]197 M30to34=((adult_Male['TEST_AGE']>=30) & (adult_Male['TEST_AGE']<=34))198 M30to34=adult_Male[M30to34]199 M35to39=((adult_Male['TEST_AGE']>=35) & (adult_Male['TEST_AGE']<=39))200 M35to39=adult_Male[M35to39]201 M40to44=((adult_Male['TEST_AGE']>=40) & (adult_Male['TEST_AGE']<=44))202 M40to44=adult_Male[M40to44]203 M45to49=((adult_Male['TEST_AGE']>=45) & (adult_Male['TEST_AGE']<=49))204 M45to49=adult_Male[M45to49]205 M50to54=((adult_Male['TEST_AGE']>=50) & (adult_Male['TEST_AGE']<=54))206 M50to54=adult_Male[M50to54]207 M55to59=((adult_Male['TEST_AGE']>=55) & (adult_Male['TEST_AGE']<=59))208 M55to59=adult_Male[M55to59]209 M60to64=((adult_Male['TEST_AGE']>=60) & (adult_Male['TEST_AGE']<=64))210 M60to64=adult_Male[M60to64]211 M65to69=((adult_Male['TEST_AGE']>=65) & (adult_Male['TEST_AGE']<=69))212 M65to69=adult_Male[M65to69]213 M70to74=((adult_Male['TEST_AGE']>=70) & (adult_Male['TEST_AGE']<=74))214 M70to74=adult_Male[M70to74]215 M75to79=((adult_Male['TEST_AGE']>=75) & (adult_Male['TEST_AGE']<=79))216 M75to79=adult_Male[M75to79]217 M80to84=((adult_Male['TEST_AGE']>=80) & (adult_Male['TEST_AGE']<=84))218 M80to84=adult_Male[M80to84]219 M85=adult_Male['TEST_AGE']>=85220 M85=adult_Male[M85]221 # Female222 F19to24=((adult_Female['TEST_AGE']>=19) & (adult_Female['TEST_AGE']<=24))223 F19to24=adult_Female[F19to24]224 F25to29=((adult_Female['TEST_AGE']>=25) & (adult_Female['TEST_AGE']<=29))225 F25to29=adult_Female[F25to29]226 F30to34=((adult_Female['TEST_AGE']>=30) & (adult_Female['TEST_AGE']<=34))227 F30to34=adult_Female[F30to34]228 F35to39=((adult_Female['TEST_AGE']>=35) & (adult_Female['TEST_AGE']<=39))229 F35to39=adult_Female[F35to39]230 F40to44=((adult_Female['TEST_AGE']>=40) & (adult_Female['TEST_AGE']<=44))231 F40to44=adult_Female[F40to44]232 F45to49=((adult_Female['TEST_AGE']>=45) & (adult_Female['TEST_AGE']<=49))233 F45to49=adult_Female[F45to49]234 F50to54=((adult_Female['TEST_AGE']>=50) & (adult_Female['TEST_AGE']<=54))235 F50to54=adult_Female[F50to54]236 F55to59=((adult_Female['TEST_AGE']>=55) & (adult_Female['TEST_AGE']<=59))237 F55to59=adult_Female[F55to59]238 F60to64=((adult_Female['TEST_AGE']>=60) & (adult_Female['TEST_AGE']<=64))239 F60to64=adult_Female[F60to64]240 F65to69=((adult_Female['TEST_AGE']>=65) & (adult_Female['TEST_AGE']<=69))241 F65to69=adult_Female[F65to69]242 F70to74=((adult_Female['TEST_AGE']>=70) & (adult_Female['TEST_AGE']<=74))243 F70to74=adult_Female[F70to74]244 F75to79=((adult_Female['TEST_AGE']>=75) & (adult_Female['TEST_AGE']<=79))245 F75to79=adult_Female[F75to79]246 F80to84=((adult_Female['TEST_AGE']>=80) & (adult_Female['TEST_AGE']<=84))247 F80to84=adult_Female[F80to84]248 F85=adult_Female['TEST_AGE']>=85249 F85=adult_Female[F85]250 result = []251 # Male252 result.append(get_each_rank(M19to24, 0, 19, 24))253 result.append(get_each_rank(M25to29, 0, 25, 29))254 result.append(get_each_rank(M30to34, 0, 30, 34))255 result.append(get_each_rank(M35to39, 0, 35, 39))256 result.append(get_each_rank(M40to44, 0, 40, 44))257 result.append(get_each_rank(M45to49, 0, 45, 49))258 result.append(get_each_rank(M50to54, 0, 50, 54))259 result.append(get_each_rank(M55to59, 0, 55, 59))260 result.append(get_each_rank(M60to64, 0, 60, 64))261 # Female262 result.append(get_each_rank(F19to24, 1, 19, 24))263 result.append(get_each_rank(F25to29, 1, 25, 29))264 result.append(get_each_rank(F30to34, 1, 30, 34))265 result.append(get_each_rank(F35to39, 1, 35, 39))266 result.append(get_each_rank(F40to44, 1, 40, 44))267 result.append(get_each_rank(F45to49, 1, 45, 49))268 result.append(get_each_rank(F50to54, 1, 50, 54))269 result.append(get_each_rank(F55to59, 1, 55, 59))270 result.append(get_each_rank(F60to64, 1, 60, 64))...
app.py
Source:app.py
1from flask import Flask, render_template, session, redirect, url_for, session2from flask_wtf import FlaskForm3from wtforms import TextField,SubmitField4from wtforms.validators import NumberRange5import numpy as np 6import pandas as pd7from tensorflow.keras.models import load_model8import joblib9app = Flask(__name__)10# Configure a secret SECRET_KEY 11# We will later learn much better ways to do this!!12app.config['SECRET_KEY'] = 'someRandomKey'13# Loading the model and scaler14youth_model = load_model("model/youth_model.h5")15adult_model = load_model("model/adult_model.h5")16elder_model = load_model("model/elder_model.h5")17youth_scaler = joblib.load("model/youth_scaler.pkl")18adult_scaler = joblib.load("model/adult_scaler.pkl")19elder_scaler = joblib.load("model/elder_scaler.pkl")20youth_group =pd.read_pickle("model/youth_group.pkl")21adult_group =pd.read_pickle("model/adult_group.pkl")22elder_group =pd.read_pickle("model/elder_group.pkl")23# Now create a WTForm Class24class input_youth(FlaskForm):25 test_age = TextField('ì°ë ¹')26 test_sex = TextField('ì±ë³')27 height = TextField('ì ì¥')28 weight = TextField('몸무ê²')29 core_cm = TextField('í리ëë ')30 run20 = TextField('20mìë³µì¤ëë¬ë¦¬ê¸°')31 situp = TextField('ì몸ë§ìì¬ë¦¬ê¸°')32 flex = TextField('ììì몸ìì¼ë¡êµ½í기')33 submit = SubmitField('ë¶ìí기')34class input_adult(FlaskForm):35 test_age = TextField('ì°ë ¹')36 test_sex = TextField('ì±ë³')37 height = TextField('ì ì¥')38 weight = TextField('몸무ê²')39 core_cm = TextField('í리ëë ')40 run20 = TextField('20mìë³µì¤ëë¬ë¦¬ê¸°')41 situp = TextField('êµì°¨ì몸ì¼ì¼í¤ê¸°')42 flex = TextField('ììì몸ìì¼ë¡êµ½í기')43 submit = SubmitField('ë¶ìí기')44class input_elder(FlaskForm):45 test_age = TextField('ì°ë ¹')46 test_sex = TextField('ì±ë³')47 height = TextField('ì ì¥')48 weight = TextField('몸무ê²')49 core_cm = TextField('í리ëë ')50 walk2m = TextField('2ë¶ì ì리걷기')51 chairup = TextField('ììììë¤ì¼ì´ì기')52 flex = TextField('ììì몸ìì¼ë¡êµ½í기')53 submit = SubmitField('ë¶ìí기')54class typeForm(FlaskForm):55 model_type = TextField('model_type')56 submit = SubmitField('ë¶ìí기')57# ë©ì¸ íì´ì§58@app.route('/', methods=['GET', 'POST'])59def mainp():60 # Create instance of the form.61 form = typeForm()62 63 # If the form is valid on submission64 if form.validate_on_submit():65 # Grab the data from the input on the form.66 session['model_type'] = form.model_type.data67 return redirect(url_for("index"))68 return render_template('home.html', form=form)69# ì측 íì´ì§70@app.route('/index', methods=['GET', 'POST'])71def index():72 # Create instance of the form.73 model_type = str(session['model_type'])74 if model_type == 'youth':75 form = input_youth()76 # If the form is valid on submission77 if form.validate_on_submit():78 session['test_age'] = form.test_age.data79 age = float(form.test_age.data)80 if age < 15:81 session['group'] = 'A'82 elif age < 17:83 session['group'] = 'B'84 else : session['group'] = 'C'85 86 session['test_sex'] = form.test_sex.data87 session['height'] = form.height.data88 session['weight'] = form.weight.data89 session['core_cm'] = form.core_cm.data90 session['run20'] = form.run20.data91 session['situp'] = form.situp.data92 session['flex'] = form.flex.data93 session['model_type'] = 'youth'94 return redirect(url_for("prediction"))95 return render_template('youth_main.html', form=form)96 97 elif model_type == 'elder':98 form = input_elder()99 if form.validate_on_submit():100 session['test_age'] = form.test_age.data101 age = float(form.test_age.data)102 if age < 70:103 session['group'] = 'A'104 elif age < 80:105 session['group'] = 'B'106 else : session['group'] = 'C'107 session['test_sex'] = form.test_sex.data108 session['height'] = form.height.data109 session['weight'] = form.weight.data110 session['core_cm'] = form.core_cm.data111 session['walk2m'] = form.walk2m.data112 session['chairup'] = form.chairup.data113 session['flex'] = form.flex.data114 session['model_type'] = 'elder'115 return redirect(url_for("prediction"))116 return render_template('elder_main.html', form=form)117 else :118 form = input_adult()119 if form.validate_on_submit():120 session['test_age'] = form.test_age.data121 age = float(form.test_age.data)122 if age < 30:123 session['group'] = 'A'124 elif age < 40:125 session['group'] = 'B'126 elif age < 50:127 session['group'] = 'C'128 else : session['group'] = 'D'129 session['test_sex'] = form.test_sex.data130 session['height'] = form.height.data131 session['weight'] = form.weight.data132 session['core_cm'] = form.core_cm.data133 session['run20'] = form.run20.data134 session['situp'] = form.situp.data135 session['flex'] = form.flex.data136 session['model_type'] = 'adult'137 return redirect(url_for("prediction"))138 return render_template('main.html', form=form)139@app.route('/prediction')140def prediction():141 model_type = session['model_type']142 if model_type == 'youth' :143 144 content = {}145 content['test_age'] = float(session['test_age'])146 content['test_sex'] = float(session['test_sex'])147 content['height'] = float(session['height'])148 content['weight'] = float(session['weight'])149 content['core_cm'] = float(session['core_cm'])150 content['run20'] = float(session['run20'])151 content['situp'] = float(session['situp'])152 content['flex'] = float(session['flex'])153 results = return_prediction(mode = 'youth', model=youth_model,scaler=youth_scaler,sample_json=content)154 155 # pivot data select156 group_label = str(session['group'])157 pivot_select = pd.DataFrame(youth_group.loc[group_label]).T158 if float(session['test_sex']) > 0.5:159 pivot_col = [pivot_select.columns[1][0],pivot_select.columns[3][0],pivot_select.columns[5][0]]160 pivot_data = [pivot_select.iloc[0,1],pivot_select.iloc[0,3],pivot_select.iloc[0,5]]161 else :162 pivot_col = [pivot_select.columns[0][0],pivot_select.columns[2][0],pivot_select.columns[4][0]]163 pivot_data = [pivot_select.iloc[0,0],pivot_select.iloc[0,2],pivot_select.iloc[0,4]]164 return render_template('youth_prediction.html',results=results, pivot_col = pivot_col, pivot_data = pivot_data)165 elif model_type == 'elder':166 content = {}167 content['test_age'] = float(session['test_age'])168 content['test_sex'] = float(session['test_sex'])169 content['height'] = float(session['height'])170 content['weight'] = float(session['weight'])171 content['core_cm'] = float(session['core_cm'])172 content['walk2m'] = float(session['walk2m'])173 content['chairup'] = float(session['chairup'])174 content['flex'] = float(session['flex'])175 results = return_prediction(mode = 'elder', model=elder_model,scaler=elder_scaler,sample_json=content)176 177 # pivot data select178 group_label = str(session['group'])179 pivot_select = pd.DataFrame(elder_group.loc[group_label]).T180 if float(session['test_sex']) > 0.5 :181 pivot_col = [pivot_select.columns[1][0],pivot_select.columns[3][0],pivot_select.columns[5][0]]182 pivot_data = [pivot_select.iloc[0,1],pivot_select.iloc[0,3],pivot_select.iloc[0,5]]183 else :184 pivot_col = [pivot_select.columns[0][0],pivot_select.columns[2][0],pivot_select.columns[4][0]]185 pivot_data = [pivot_select.iloc[0,0],pivot_select.iloc[0,2],pivot_select.iloc[0,4]]186 return render_template('elder_prediction.html',results=results, pivot_col = pivot_col, pivot_data = pivot_data)187 else:188 content = {}189 content['test_age'] = float(session['test_age'])190 content['test_sex'] = float(session['test_sex'])191 content['height'] = float(session['height'])192 content['weight'] = float(session['weight'])193 content['core_cm'] = float(session['core_cm'])194 content['run20'] = float(session['run20'])195 content['situp'] = float(session['situp'])196 content['flex'] = float(session['flex'])197 results = return_prediction(mode = 'adult', model=adult_model,scaler=adult_scaler,sample_json=content)198 199 # pivot data select200 group_label = str(session['group'])201 pivot_select = pd.DataFrame(adult_group.loc[group_label]).T202 if float(session['test_sex']) >0.5 :203 pivot_col = [pivot_select.columns[1][0],pivot_select.columns[3][0],pivot_select.columns[5][0]]204 pivot_data = [pivot_select.iloc[0,1],pivot_select.iloc[0,3],pivot_select.iloc[0,5]]205 else :206 pivot_col = [pivot_select.columns[0][0],pivot_select.columns[2][0],pivot_select.columns[4][0]]207 pivot_data = [pivot_select.iloc[0,0],pivot_select.iloc[0,2],pivot_select.iloc[0,4]]208 return render_template('prediction.html',results=results, pivot_col = pivot_col, pivot_data = pivot_data)209 210def return_prediction(mode, model,scaler,sample_json):211 if mode == 'youth':212 213 test_age = sample_json['test_age']214 test_sex = sample_json['test_sex']215 height = sample_json['height']216 weight = sample_json['weight']217 core_cm = sample_json['core_cm']218 run20 = sample_json['run20']219 situp = sample_json['situp']220 flex = sample_json['flex']221 test_data = [[test_age,test_sex,height,weight,core_cm,run20,situp,flex]]222 elif mode == 'elder':223 224 test_age = sample_json['test_age']225 test_sex = sample_json['test_sex']226 height = sample_json['height']227 weight = sample_json['weight']228 core_cm = sample_json['core_cm']229 walk2m = sample_json['walk2m']230 chairup = sample_json['chairup']231 flex = sample_json['flex']232 test_data = [[test_age,test_sex,height,weight,core_cm,walk2m,chairup,flex]]233 else:234 235 test_age = sample_json['test_age']236 test_sex = sample_json['test_sex']237 height = sample_json['height']238 weight = sample_json['weight']239 core_cm = sample_json['core_cm']240 run20 = sample_json['run20']241 situp = sample_json['situp']242 flex = sample_json['flex']243 test_data = [[test_age,test_sex,height,weight,core_cm,run20,situp,flex]]244 test_data_scaled = scaler.transform(test_data)245 predict = model.predict(test_data_scaled)246 return str(round(predict[0][0],3))247if __name__ == '__main__':...
loops.py
Source:loops.py
1# Let's start with some variables to work with.2age = 123test_age = 14# Much like with an if statement, a while statement checks whether a value is true.5# Then, if it is, it does everything under it, in the indented block,6# then returns to the top and checks the condition again.7while test_age < age:8 print("Age is at least:")9 print(test_age)10 test_age = test_age + 111# The last line above is very important in a while loop. If you don't change the condition12# that's being checked, somewhere in the loop, it will evaluate the same way every time.13# This means that your loop will run forever!14# There are a couple ways to clean up the code above. First, we can replace the unwieldy15# increment line (adding one) with the shortcut +=. Next, we can shrink down the code16# to a single print statement by adding a formatted string. To do this, we do three things:17# First, insert the letter 'f' prior to the string itself.18# Next, add curly braces, {}, wherever you want a variable's value to be pasted.19# Finally, add the name of the variable to paste inside the curly braces.20while test_age <= age:21 print(f"Found the age! {test_age}")22 test_age += 123print("test with while loop completed")24# Next we can look at for loops. These loops behave very similarly to while loops,25# But they do some of the bookkeeping for us. Instead of having to manually change the 26# condition being tested each time, a for loop will do it for us.27# The easiest way to do this is with range(). This function will tell the for loop to28# iterate through a loop five times, each time changing the value of the variable in the 29# for _____ statement. In the case below, that's test_age.30for test_age in range(20):31 if test_age == age:32 print(f"Found the age! {test_age}")33 print(test_age)...
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