How to use test_verify method in molecule

Best Python code snippet using molecule_python

oppty_2.py

Source:oppty_2.py

1#!/usr/bin/env python2# coding: utf-83# In[1]:4import os5import pandas as pd6import numpy as np7import matplotlib.pyplot as plt8import warnings9warnings.simplefilter("ignore")10# In[2]:11os.chdir('C:\oppty')12# In[4]:13df = pd.read_csv('oppty.csv', encoding='cp949')14# In[5]:15df.head(3)16# In[5]:17df['X_STATUS_CD'].value_counts()18# In[5]:19df.info()20# In[6]:21y_col = 'Result'22x_col = ['NAME', 'SUM_WIN_PROB','INVST_STG_CD', 'X_OPTY_TYPE','MARKET_CLASS_CD', 'CREATED', 'CLOSE_DT', 'BEF_1M_SLNG_AMT', 'CIRCUIT_NUM', 'X_CODE', 'X_TEXT','SLNG_AMT', 'PURE_PRFIT_AMT', 'MIN_CH_DT', 'MAX_CH_DT']23# In[7]:24df.describe(include='all')25# In[8]:26df.head(5)27# In[9]:28df['X_STATUS_CD'].value_counts()29# In[9]:30df['X_TEXT'].value_counts()31# In[10]:32odf = df.loc[df['X_STATUS_CD'].isin(['Win', 'Loss'])]33# In[10]:34odf['X_STATUS_CD'].value_counts()35# In[11]:36odf['Result'] = odf['X_STATUS_CD'].apply(lambda x:  1 if x=='Win' else 0)37# In[11]:38odf.head(5)39# In[12]:40odf.describe(include='all')41# In[13]:42odf['Result'].value_counts(), odf['X_STATUS_CD'].value_counts()43# In[14]:44odf.CREATED.min(), odf.CREATED.max()45# In[15]:46odf['CREATED_DATE'] = pd.to_datetime(odf.CREATED, format='%Y%m%d')47# In[16]:48odf.head(3)49# In[17]:50odf.CREATED_DATE.hist(xlabelsize=10)51# ### Train_Test Set ë¶ë¦¬52# In[18]:53train = odf.loc[odf.CREATED_DATE < '20200901' ]54test = odf.loc[odf.CREATED_DATE >= '20200901']55# In[19]:56odf.shape,  train.shape, test.shape57# In[20]:58train.CREATED_DATE.hist(xlabelsize=8,  figsize = (10, 5))59# In[21]:60test.CREATED_DATE.hist(xlabelsize = 8, figsize = (10, 5))61# In[22]:62simple_x_col = ['NAME', 'INVST_STG_CD', 'X_OPTY_TYPE','MARKET_CLASS_CD', 'BEF_1M_SLNG_AMT', 'CIRCUIT_NUM']63# In[23]:64odf[simple_x_col].describe(include='all')65# In[24]:66train_x = train[simple_x_col]67# In[25]:68train_x.set_index('NAME', inplace = True)69# In[26]:70train_x.head(3)71# In[27]:72train_x = pd.get_dummies(train_x)73# In[27]:74train_x.head()75# In[28]:76train_x.info()77# In[29]:78train_x.describe()79# In[30]:80train_y = train[y_col]81# In[31]:82train_y.shape, type(train_y)83# In[32]:84train_y.value_counts()85# In[33]:86##train_y[train_y.isin(['Drop', 'Proposal Reject'])] = 'Loss'87# In[34]:88from sklearn.ensemble import RandomForestClassifier89# In[35]:90classifier = RandomForestClassifier(n_estimators=500)91# In[36]:92classifier.fit(train_x, train_y)93# In[37]:94test_x = test[simple_x_col]95test_x.set_index('NAME', inplace = True)96# In[38]:97test_y = test[y_col]98#train_y[train_y.isin(['Drop', 'Proposal Reject'])] = 'Loss'99# In[39]:100test_x  = pd.get_dummies(test_x)101# In[40]:102score = classifier.score(test_x, test_y)103print(score)104# In[41]:105train_x.head(3)106# ### Scaling107# In[42]:108from sklearn import preprocessing109# In[43]:110scaler = preprocessing.StandardScaler().fit(train_x)111# In[44]:112train_x_scaled = scaler.transform(train_x)113# In[45]:114test_x_scaled = scaler.transform(test_x)115# In[46]:116classifier.fit(train_x_scaled, train_y)117# In[47]:118score = classifier.score(test_x_scaled, test_y)119print(score)120# ### Feature ì¤ìì± ë³´ê¸°121# In[48]:122classifier.feature_importances_123# In[49]:124plt.barh(test_x.columns, classifier.feature_importances_)125# In[50]:126test_predict = classifier.predict(test_x_scaled)127# In[51]:128test_predict_proba = classifier.predict_proba(test_x_scaled)129# In[56]:130test_predict[:20]131# In[57]:132test_y[:20]133# In[58]:134type(test_predict), type(test_y)135# In[59]:136test_predict = pd.Series(test_predict)137# In[94]:138test_y = test_y.reset_index()139# In[95]:140test_y_compare = pd.concat([test_y, test_predict], axis = 1, ignore_index = True)141# In[118]:142test_y_compare.columns = ['ID', 'REAL', 'PREDICT']143# In[119]:144test_y_compare.describe()145# In[122]:146test_y_compare['NAME'] = test_x.index147# In[129]:148test_y_compare.set_index('NAME', inplace = True)149# In[130]:150test_verify = pd.concat([test_y_compare, test_x], axis = 1)151# In[136]:152test_verify.loc[test_verify.REAL == test_verify.PREDICT , 'MATCH'] = 'MATCH'153test_verify.loc[test_verify.REAL != test_verify.PREDICT , 'MATCH'] = 'UNMATCH'154# In[138]:155test_verify.groupby('MATCH').count()156# In[155]:157old_customer  = test_verify.loc[test_verify['BEF_1M_SLNG_AMT']> 0,'MATCH'].value_counts()158new_customer  = test_verify.loc[test_verify['BEF_1M_SLNG_AMT'] == 0,'MATCH'].value_counts()159# In[168]:160new_old= pd.concat([old_customer, new_customer], axis = 1, keys =['OLD', 'NEW'])161# In[171]:162new_old = new_old.T163# In[173]:164new_old['match_rate'] = new_old['MATCH']/(new_old['MATCH']+new_old['UNMATCH'])165# In[187]:166new_old.index.name = 'customer_type'167# In[188]:168new_old169# In[198]:170new_old['match_rate'].plot(kind='bar')171# In[201]:172import pickle173# In[202]:174model_file =  'opty_randomforest.sav'175# In[204]:176pickle.dump(classifier, open(model_file, 'wb'))177# In[206]:178pwd179# In[207]:180scaler_file = 'opty_scaler.sav'181# In[208]:182pickle.dump(scaler,open(scaler_file, 'wb'))183# In[209]:184loaded_model = pickle.load(open(model_file, 'rb'))185# In[213]:186test_y.set_index('index', inplace = True)187loaded_model.score(test_x_scaled, test_y)188# In[249]:189type(test_x_scaled)190loaded_scaler = pickle.load(open(scaler_file, 'rb'))191sample_test = pd.DataFrame({'BEF_1M_SLNG_AMT': 816574, 'CIRCUIT_NUM': 40, 'INVST_STG_CD_A': 1,'INVST_STG_CD_B': 0,192               'X_OPTY_TYPE_A': 1, 'X_OPTY_TYPE_B':0, 'MARKET_CLASS_CD_201':0,'MARKET_CLASS_CD_402': 0, 'MARKET_CLASS_CD_701':1,193                           'MARKET_CLASS_CD_901':0, 'MARKET_CLASS_CD_201':0, 'MARKET_CLASS_CD_402':0, 'MARKET_CLASS_CD_404': 0,194                            'MARKET_CLASS_CD_701':1,'MARKET_CLASS_CD_901':0, 'MARKET_CLASS_CD_G01':0}, index=[0])195#sample_test = sample_test.reshape(-1, 1)196#sample_test[0:10]197#scaled_sample = loaded_scaler.transform(sample_test)198# In[250]:199sample_test.head(3)200# In[251]:201scaled_sample = loaded_scaler.transform(sample_test)202# In[242]:203test_x.head(3)...

test_verify2.py

Source:test_verify2.py

1from .test_verify import *2# This test file runs normally after test_verify.  We only clean up the .c3# sources, to check that it also works when we have only the .so.  The4# tests should run much faster than test_verify.5def setup_module():6    import cffi.verifier...

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.