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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...

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