How to use test_all method in Behave
Best Python code snippet using behave
Text Mining Script.py
Source:Text Mining Script.py 
1# -*- coding: utf-8 -*-2"""3Created on Sat Mar 19 09:13:22 201645@author: Team A-066"""7import string8import numpy as np9import pandas as pd10import random11import matplotlib.pyplot as plt12random.seed(120)13import os14os.getcwd()1516Train = pd.read_csv('train.csv',encoding ="ISO-8859-1")17Test = pd.read_csv('test.csv',encoding ="ISO-8859-1")18len(Train)19Train_number = Train.shape[0]2021Prod_Desc = pd.read_csv('product_descriptions.csv',encoding ="ISO-8859-1")22Prod_Attr = pd.read_csv('attributes.csv',encoding ="ISO-8859-1")23Prod_Attr.head()2425Prod_attrBrandname = Prod_Attr[Prod_Attr.name== "MFG Brand Name"][["product_uid","value"]].rename(columns={"value":"Brand"})26len(Prod_attrBrandname)2728# Add features to the Train set from product description file and the attributes file29Train_all = pd.merge(Train,Prod_Desc,on='product_uid',how='left')30Train_all = pd.merge(Train_all,Prod_attrBrandname,on='product_uid',how='left')3132#Replacing missing values with Mode33Train_all['Brand'] = np.where(Train_all.Brand.isnull(),Train_all.Brand.mode(),Train_all.Brand)34Train_all.info()3536# Add features to the Test set37Test_all = pd.merge(Test,Prod_Desc,on='product_uid',how='left')38Test_all = pd.merge(Test_all,Prod_attrBrandname,on='product_uid',how='left')39#Replacing missing values with Mode40Test_all['Brand'] = np.where(Test_all.Brand.isnull(),Test_all.Brand.mode(),Test_all.Brand)4142Test_all.info()4344Y_train = Train_all['relevance']4546#Parsing Text Columns47from datetime import datetime48start = datetime.now()49# Preprocessing text data50#from nltk.corpus import stopwords51Num = {'zero':0,'one':1,'two':2,'three':3,'four':4,'five':5,'six':6,'seven':7,'eight':8,'nine':9}52from stemming.porter2 import stem53import re54#from textblob import TextBlob55def textprocess(term):56 if isinstance(term,str):57 term = re.sub(r"([0-9]+)( *)(inches|inch|in|')\.?", r"\inches ", term)58 term = re.sub(r"([0-9]+)( *)(foot|feet|ft|'')\.?", r"\feet ", term)59 term = re.sub(r"([0-9]+)( *)(pounds|pound|lbs|lb)\.?", r"\lb ", term)60 term = re.sub(r"([0-9]+)( *)(square|sq) ?\.?(feet|foot|ft)\.?", r"\1sq.ft. ", term)61 term = re.sub(r"([0-9]+)( *)(cubic|cu) ?\.?(feet|foot|ft)\.?", r"\1cu.ft. ", term)62 term = re.sub(r"([0-9]+)( *)(gallons|gallon|gal)\.?", r"\1gal. ", term)63 term = re.sub(r"([0-9]+)( *)(ounces|ounce|oz)\.?", r"\1oz. ", term)64 term = re.sub(r"([0-9]+)( *)(centimeters|cm)\.?", r"\1cm. ", term)65 term = re.sub(r"([0-9]+)( *)(milimeters|mm)\.?", r"\1mm. ", term)66 term = term.replace("°"," degrees ")67 term = re.sub(r"([0-9]+)( *)(degrees|degree)\.?", r"\1deg. ", term)68 term = term.replace(" v "," volts ")69 term = re.sub(r"([0-9]+)( *)(volts|volt)\.?", r"\1volt. ", term)70 term = re.sub(r"([0-9]+)( *)(watts|watt)\.?", r"\1watt. ", term)71 term = re.sub(r"([0-9]+)( *)(amperes|ampere|amps|amp)\.?", r"\1amp. ", term)72 term = term.replace(" "," ")73 term = term.replace(" . "," ")74 term = (" ").join([str(Num[z]) if z in Num else z for z in term.split(" ")])75 term = term.replace("toliet","toilet")76 term = term.replace("airconditioner","air conditioner")77 term = term.replace("vinal","vinyl")78 term = term.replace("vynal","vinyl")79 term = term.replace("skill","skil")80 term = term.replace("snowbl","snow bl")81 term = term.replace("plexigla","plexi gla")82 term = term.replace("rustoleum","rust-oleum")83 term = term.replace("whirpool","whirlpool")84 term = term.replace("whirlpoolga", "whirlpool ga")85 term = term.replace("whirlpoolstainless","whirlpool stainless")86 #Perform Stemming87 stemtxt = [stem(word) for word in term]88 lowertxt = [word.lower() for word in stemtxt]89 90 # Remove Punctuation91 nopunc = [word for word in lowertxt if word not in string.punctuation]92 nopunc = ''.join(nopunc)93 #Remove stopwords94# cleantxt = [word for word in nopunc.split() if word not in stopwords.words('english')]95# cleantxt = ''.join(cleantxt)96 return nopunc9798#Preprocessing of Train set99start = datetime.now() 100Train_all['product_title'] = Train_all['product_title'].map(lambda x:textprocess(x))101Train_all['search_term'] = Train_all['search_term'].map(lambda x:textprocess(x))102Train_all['product_description'] = Train_all['product_description'].map(lambda x:textprocess(x))103Train_all['Brand'] = Train_all['Brand'].map(lambda x:textprocess(x))104Train_all['prodtext'] = Train_all['search_term']+" "+Train_all['product_title']+" "+Train_all['product_description']+" "+Train_all['Brand']105Train_all['prodtext1'] = Train_all['search_term']+"\t"+Train_all['product_title']+"\t"+Train_all['Brand']+"\t"+Train_all['product_description']106print(datetime.now() - start)107108Train_all.prodtext.head()109110#Preprocessing for Test set111start = datetime.now() 112Test_all['product_title'] = Test_all['product_title'].map(lambda x:textprocess(x))113Test_all['search_term'] = Test_all['search_term'].map(lambda x:textprocess(x))114Test_all['product_description'] = Test_all['product_description'].map(lambda x:textprocess(x))115Test_all['Brand'] = Test_all['Brand'].map(lambda x:textprocess(x))116Test_all['prodtext'] = Test_all['search_term']+" "+Test_all['product_title']+" "+Test_all['Brand']+" "+Test_all['product_description']117Test_all['prodtext1'] = Test_all['search_term']+"\t"+Test_all['product_title']+"\t"+Test_all['Brand']+"\t"+Test_all['product_description']118print(datetime.now() - start)119Test_all.prodtext.head()120121Train_all.info()122Test_all.info()123Y_train = Train_all['relevance']124X_train = Train_all['prodtext']125X_test = Test_all['prodtext']126127128#Cross validation129start = datetime.now() 130131from sklearn.utils import shuffle132133X,y = shuffle(X_train,Y_train,random_state = 13)134offset = int(X.shape[0] *0.80)135X_crosstrain , y_crosstrain = X[:offset],y[:offset]136X_crosstest , y_crosstest = X[offset:],y[offset:]137138from sklearn.feature_extraction.text import TfidfVectorizer139tfidf = TfidfVectorizer(max_df=0.4, max_features=None,analyzer='char_wb',ngram_range=(1,2), 140 use_idf=True, smooth_idf=True, sublinear_tf=True, stop_words = 'english')141142143X_crosstrain = tfidf.fit_transform(X_crosstrain)144145X_crosstest = tfidf.transform(X_crosstest)146147from sklearn.decomposition import TruncatedSVD148from sklearn.preprocessing import StandardScaler149150svd = TruncatedSVD(n_components=300, algorithm='randomized', n_iter=5, random_state=None, tol=0.0)151X_crosstrain_svd = svd.fit_transform(X_crosstrain)152svd.explained_variance_ratio_.sum()153X_crosstest_svd = svd.transform(X_crosstest)154155scl = StandardScaler(copy=True, with_mean=True, with_std=True)156157X_crosstrain_scl = scl.fit_transform(X_crosstrain_svd)158X_crosstest_scl = scl.transform(X_crosstest_svd)159160from sklearn import ensemble161from sklearn.utils import shuffle162from sklearn.metrics import mean_squared_error163164start = datetime.now() 165from sklearn.svm import SVR166from sklearn.metrics import mean_squared_error167168Model_one = SVR(C=1.0, kernel='rbf', degree=3, gamma='auto', coef0=0.0, shrinking=True, 169 tol=0.001, cache_size=200,verbose=False, max_iter=30000)170Model_one.fit(X_crosstrain_scl,y_crosstrain)171SVMmse = Model_one.predict(X_crosstest_scl)172print("SVM_RMSE:",np.sqrt(mean_squared_error(y_crosstest,SVMmse)))173print(datetime.now() - start)174175start = datetime.now() 176from sklearn.neighbors import KNeighborsRegressor177Model_two = KNeighborsRegressor(n_neighbors=11)178Model_two.fit(X_crosstrain_scl,y_crosstrain)179KNNmse = Model_two.predict(X_crosstest_scl)180print("KNN_RMSE:",np.sqrt(mean_squared_error(y_crosstest,KNNmse)))181print(datetime.now() - start)182183start = datetime.now() 184from sklearn import ensemble185Model_three = ensemble.RandomForestRegressor(n_estimators = 500,verbose=1,n_jobs=-1,random_state = 120,max_depth=16)186Model_three.fit(X_crosstrain_svd,y_crosstrain)187RFmse = Model_three.predict(X_crosstest_svd)188print("RandomForest_RMSE:",np.sqrt(mean_squared_error(y_crosstest,RFmse)))189print(datetime.now() - start)190191start = datetime.now() 192from sklearn.linear_model import BayesianRidge193BR = BayesianRidge(n_iter=500,tol= 0.001,normalize=True).fit(X_crosstrain_scl,y_crosstrain)194pred_BR = BR.predict(X_crosstest_scl)195print("BayesinRidge_RMSE:",np.sqrt(mean_squared_error(y_crosstest,pred_BR)))196print(datetime.now() - start)197198start = datetime.now() 199from sklearn.linear_model import LinearRegression200LR = LinearRegression(fit_intercept = True,normalize = True,n_jobs=-1).fit(X_crosstrain_svd,y_crosstrain)201pred_LR = LR.predict(X_crosstest_svd)202print("LinearRegression_RMSE:",np.sqrt(mean_squared_error(y_crosstest,pred_LR)))203204print(datetime.now() - start)205206#decision tree along with Adaboost207start = datetime.now() 208from sklearn.tree import DecisionTreeRegressor209from sklearn.ensemble import AdaBoostRegressor210AR = AdaBoostRegressor(DecisionTreeRegressor(max_depth = 100),n_estimators = 100, random_state=120).fit(X_crosstrain_scl,y_crosstrain)211pred_AR = AR.predict(X_crosstest_scl)212print("AdaboostDecisionTreeRegression_RMSE:",np.sqrt(mean_squared_error(y_crosstest,pred_AR)))213print(datetime.now() - start)214215216#***********************************Regular Features**************************************************217218def findword(str1, str2):219 return sum(int(str2.find(word)>=0) for word in str1.split())220 221#Features in Train set222Train_all['length_pdt'] = Train_all['product_title'].apply(len)223Train_all['length_st'] = Train_all['search_term'].apply(len) 224Train_all['length_desc'] = Train_all['product_description'].apply(len)225Train_all['Length_Brand'] = Train_all['Brand'].apply(len)226Train_all['search_in_title'] = Train_all['prodtext1'].map(lambda x:findword(x.split('\t')[0],x.split('\t')[1]))227Train_all['search_in_description'] = Train_all['prodtext1'].map(lambda x:findword(x.split('\t')[0],x.split('\t')[2]))228Train_all['search_in_brand'] = Train_all['prodtext1'].map(lambda x:findword(x.split('\t')[0],x.split('\t')[3]))229Train_all['Ratio_title'] = Train_all['search_in_title']/Train_all['length_st']230Train_all['Ratio_desc'] = Train_all['search_in_description']/Train_all['length_st']231Train_all['Ratio_brand'] = Train_all['search_in_brand']/Train_all['length_st']232233Train_all.head()234 # Exploratory Data Analysis235Histgram_pdt = Train_all['length_pdt'].plot(bins=50,kind='hist') # Normal236Histgram_st = Train_all['length_st'].plot(bins=50,kind='hist',color='green') #Normal237Histgram_desc = Train_all['length_desc'].plot(bins=100,kind='hist',color='purple') # Right Skwed238Histgram_searchttitle = Train_all['search_in_title'].plot(kind='hist',color='blue')239Histgram_searchbrand = Train_all['search_in_brand'].plot(kind='hist',color='black')240Histgram_Ratiotitle = Train_all['Ratio_title'].plot(kind='hist',color='purple')241242# Summary statistics for engineered column - length243print(Train_all['length_pdt'].describe())244print(Train_all['length_st'].describe())245print(Train_all['length_desc'].describe())246247# Check the lenghtiest product title and search term individually248print(Train_all[Train_all['length_pdt'] == 147]['product_title'])249print(Train_all[Train_all['length_st'] == 60]['search_term'])250251252# Histogram of relevance vs lenght of product title and search term253print(Train_all.hist(column='length_pdt',by ='relevance',bins = 50, figsize=(15,6)))254print(Train_all.hist(column='length_st',by ='relevance',bins = 50, figsize=(15,6)))255print(Train_all.hist(column='length_desc',by ='relevance',bins = 100, figsize=(15,6)))256print(Train_all.hist(column='search_in_title',by ='relevance',bins = 10, figsize=(15,6)))257print(Train_all.hist(column='search_in_brand',by ='relevance',bins = 10, figsize=(15,6)))258print(Train_all.hist(column='Ratio_title',by ='relevance',bins = 10, figsize=(15,6)))259260#Features in Test set261Test_all['length_pdt'] = Test_all['product_title'].apply(len)262Test_all['length_st'] = Test_all['search_term'].apply(len)263Test_all['length_desc'] = Test_all['product_description'].apply(len)264Test_all['Length_Brand'] = Test_all['Brand'].apply(len)265Test_all['search_in_title'] = Test_all['prodtext1'].map(lambda x:findword(x.split('\t')[0],x.split('\t')[1]))266Test_all['search_in_description'] = Test_all['prodtext1'].map(lambda x:findword(x.split('\t')[0],x.split('\t')[2]))267Test_all['search_in_brand'] = Test_all['prodtext1'].map(lambda x:findword(x.split('\t')[0],x.split('\t')[3]))268Test_all['Ratio_title'] = Test_all['search_in_title']/Test_all['length_st']269Test_all['Ratio_desc'] = Test_all['search_in_description']/Test_all['length_st']270Test_all['Ratio_brand'] = Test_all['search_in_brand']/Test_all['length_st']271272X1_train = Train_all.drop(['id','product_uid','relevance','product_title','prodtext','prodtext1','search_term','product_description','Brand'],axis=1)273X1_test = Test_all.drop(['id','product_uid','product_title','prodtext','search_term','prodtext1','product_description','Brand'],axis=1)274Y1_train = Train_all['relevance']275276#Cross validation277start = datetime.now() 278279X1,y1 = shuffle(X1_train,Y1_train,random_state = 13)280offset = int(X1.shape[0] *0.80)281X1_crosstrain , y1_crosstrain = X1[:offset],y1[:offset]282X1_crosstest , y1_crosstest = X1[offset:],y1[offset:]283284#Calculate RMSE for Random Forest285RF1cross = ensemble.RandomForestRegressor(n_estimators = 500,verbose=1,n_jobs=-1,random_state = 120,max_depth=16)286RF1cross_fit = RF1cross.fit(X1_crosstrain,y1_crosstrain)287RF1mse = RF1cross_fit.predict(X1_crosstest)288print("Random Forest:",np.sqrt(mean_squared_error(y1_crosstest,RF1mse)))289print(datetime.now() - start)290291start = datetime.now() 292from sklearn.svm import SVR293from sklearn.metrics import mean_squared_error294Model1_one = SVR(C=1.0, kernel='rbf', degree=3, gamma='auto', coef0=0.0, shrinking=True, 295 tol=0.001, cache_size=200,verbose=False, max_iter=30000)296Model1_one.fit(X1_crosstrain,y1_crosstrain)297SVM1mse = Model1_one.predict(X1_crosstest)298print("SVM_RMSE:",np.sqrt(mean_squared_error(y1_crosstest,SVM1mse)))299print(datetime.now() - start)300301start = datetime.now() 302from sklearn.neighbors import KNeighborsRegressor303Model2_two = KNeighborsRegressor(n_neighbors=31)304Model2_two.fit(X1_crosstrain,y1_crosstrain)305KNN1mse = Model2_two.predict(X1_crosstest)306print("KNN_RMSE:",np.sqrt(mean_squared_error(y1_crosstest,KNN1mse)))307print(datetime.now() - start)308 309## PCA Analysis for Regular Features310from sklearn.preprocessing import scale311from sklearn.decomposition import PCA312pca = PCA()313X1_reduced = pca.fit_transform(scale(X1_crosstrain))314315np.cumsum(np.round(pca.explained_variance_ratio_,decimals=4)*100) # 12 components explain 90% of the variation316plt.clf()317plt.plot(pca.explained_variance_,linewidth=2)318plt.xlabel('n_components')319plt.ylabel('explained_variance')320321RandomForest = ensemble.RandomForestRegressor(verbose=1,n_jobs=-1,random_state = 120)322323start = datetime.now()324from sklearn.pipeline import Pipeline325from sklearn.grid_search import GridSearchCV326327pipe = Pipeline(steps=[('pca', pca), ('RandomForest', RandomForest)])328estimator = GridSearchCV(pipe,dict(pca__n_components=[9,12,15],329 RandomForest__n_estimators=[250,500,750]))330331estimator.fit(X1_crosstrain,y1_crosstrain)332RF2mse = estimator.predict(X1_crosstest)333print("RF2_RMSE:",np.sqrt(mean_squared_error(y1_crosstest,RF2mse)))334print(datetime.now() - start)335336Train = tfidf.fit_transform(X_train)337338Test = tfidf.transform(X_test)339340from sklearn.decomposition import TruncatedSVD341from sklearn.preprocessing import StandardScaler342343svd = TruncatedSVD(n_components=300, algorithm='randomized', n_iter=5, random_state=None, tol=0.0)344X_crosstrain_svd = svd.fit_transform(Train)345svd.explained_variance_ratio_.sum()346X_crosstest_svd = svd.transform(Test)347348scl = StandardScaler(copy=True, with_mean=True, with_std=True)349350X_crosstrain_scl = scl.fit_transform(X_crosstrain_svd)351X_crosstest_scl = scl.transform(X_crosstest_svd)352353start = datetime.now()354start = datetime.now() 355from sklearn import ensemble356Model_three = ensemble.RandomForestRegressor(n_estimators = 500,verbose=1,n_jobs=-1,random_state = 120,max_depth=16)357Model_three.fit(X_crosstrain_scl,Y_train)358RFmse = Model_three.predict(X_crosstest_scl)359#print("RandomForest_RMSE:",np.sqrt(mean_squared_error(y_crosstest,RFmse)))360print(datetime.now() - start)361362start = datetime.now()363RF1cross = ensemble.RandomForestRegressor(n_estimators = 500,verbose=1,n_jobs=-1,random_state = 120,max_depth=16)364RF1cross_fit = RF1cross.fit(X1_train,Y1_train)365RF1mse = RF1cross_fit.predict(X1_test)366#print("Random Forest:",np.sqrt(mean_squared_error(y1_crosstest,RF1mse)))367WA = (RF1mse+RFmse)/2368pd.DataFrame({"id": Test_all.id, "relevance": WA}).to_csv('Relevance_file.csv',index=False)369print(datetime.now() - start)
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test_sorters.py
Source:test_sorters.py
1from spikeforestsorters import MountainSort4, SpykingCircus, KiloSort, KiloSort2, IronClust, HerdingSpikes2, JRClust, Tridesclous, Klusta, Waveclus, YASS, YASS12from mountaintools import client as mt3import spikeforest_analysis as sa4import numpy as np5import pytest6synth_magland_c4_recdir = 'sha1dir://fb52d510d2543634e247e0d2d1d4390be9ed9e20.synth_magland/datasets_noise10_K10_C4/001_synth'7synth_magland_c8_recdir = 'sha1dir://fb52d510d2543634e247e0d2d1d4390be9ed9e20.synth_magland/datasets_noise10_K10_C8/001_synth'8kampff1_recdir = 'sha1dir://c86202ca09f303b6c6d761b94975054c29c85d2b.paired_kampff/kampff1'9neuropix32c_recdir = 'sha1dir://d446c8e74fc4ca3a0dab491fca6c10189b527709.neuropix32c.c14'10boyden32c_recdir = 'sha1dir://b28dbf52748dcb401034d1c353807bcbff20e106.boyden32c.1103_1_1'11sqmea64c_recdir = 'sha1dir://e8de6ac2138bf775f29f8ab214d04aa92e20ca79'12paired_mea64c_recdir = 'sha1dir://7f12606802ade3c7c71eb306490b7840eb8b1fb4.paired_mea64c'13neurocube1c_recdir = 'sha1dir://e6cb8f3bb5228c73208a82d2854552af38ab6b40'14visapy30c_recdir = 'sha1dir://97253adc2581b1acbf9a9fffcbc00247d8088a1d.mea_c30.set1'15# synth_bionet_static1_recdir = 'sha1dir://abc900f5cd62436e7c89d914c9f36dcd7fcca0e7.synth_bionet/bionet_static/static_8x_C_4B'16# synth_bionet_static1_recdir = '/mnt/home/jjun/ceph/recordings/bionet_static_rec1'17# synth_bionet_static1_recdir = '/mnt/home/jjun/ceph/groundtruth/bionet/bionet_static/static_8x_A_4A'18synth_bionet_static1_recdir = '/mnt/home/jjun/ceph/groundtruth/bionet/bionet_static/static_8x_C_4B'19hybrid_janelia_static1_recdir = '/mnt/home/jjun/ceph/groundtruth/hybrid_synth/static_siprobe/rec_64c_1200s_11'20def main():21 mem_profile_test(IronClust, dict(fSave_spkwav=True), synth_magland_c8_recdir)22 mem_profile_test(IronClust, dict(fSave_spkwav=False), synth_magland_c8_recdir)23@pytest.mark.spikeforest24@pytest.mark.irc_hybrid_static25@pytest.mark.test_all26@pytest.mark.exclude27def test_irc_hybrid_static():28 sorter = IronClust29 params = dict()30 do_sorting_test(sorter, params, hybrid_janelia_static1_recdir,31 assert_avg_accuracy=0.5, _keep_temp_files=True)32@pytest.mark.spikeforest33@pytest.mark.irc_bionet_static134@pytest.mark.test_all35@pytest.mark.exclude36def test_irc_bionet_static1():37 sorter = IronClust38 params = dict()39 do_sorting_test(sorter, params, synth_bionet_static1_recdir,40 assert_avg_accuracy=0.5, _keep_temp_files=True)41@pytest.mark.spikeforest42@pytest.mark.yass1_visapy30c43@pytest.mark.test_all44@pytest.mark.exclude45def test_yass1_visapy30c():46 sorter = YASS147 params = dict(48 detect_sign=-1,49 )50 do_sorting_test(sorter, params, visapy30c_recdir, assert_avg_accuracy=0.1, _keep_temp_files=True, container='default')51@pytest.mark.spikeforest52@pytest.mark.yass_visapy30c53@pytest.mark.test_all54@pytest.mark.exclude55def test_yass_visapy30c():56 sorter = YASS57 params = dict(58 detect_sign=-1,59 )60 do_sorting_test(sorter, params, visapy30c_recdir, assert_avg_accuracy=0.1, _keep_temp_files=True)61@pytest.mark.spikeforest62@pytest.mark.waveclus_neurocube1c63@pytest.mark.test_all64@pytest.mark.exclude65def test_waveclus_neurocube1c():66 sorter = Waveclus67 params = dict()68 do_sorting_test(sorter, params, neurocube1c_recdir, assert_avg_accuracy=0.1, _keep_temp_files=True)69@pytest.mark.spikeforest70@pytest.mark.ms4_neurocube1c71@pytest.mark.test_all72@pytest.mark.exclude73def test_ms4_neurocube1c():74 sorter = MountainSort475 sorter = MountainSort476 params = dict(77 detect_sign=-1,78 adjacency_radius=5079 )80 do_sorting_test(sorter, params, neurocube1c_recdir, assert_avg_accuracy=0.2)81@pytest.mark.spikeforest82@pytest.mark.irc_neurocube1c83@pytest.mark.test_all84@pytest.mark.exclude85def test_irc_neurocube1c():86 sorter = IronClust87 params = dict()88 do_sorting_test(sorter, params, neurocube1c_recdir, assert_avg_accuracy=0.1, _keep_temp_files=True)89@pytest.mark.spikeforest90@pytest.mark.ms491@pytest.mark.test_all92@pytest.mark.exclude93def test_ms4():94 sorter = MountainSort495 params = dict(96 detect_sign=-1,97 adjacency_radius=5098 )99 # do_sorting_test(sorter, params, synth_magland_c4_recdir, assert_avg_accuracy=0.8)100 do_sorting_test(sorter, params, synth_magland_c8_recdir, assert_avg_accuracy=0.8)101 # do_sorting_test(sorter, params, kampff1_recdir, assert_avg_accuracy=0.8) # jfm laptop: ~220 seconds102@pytest.mark.spikeforest103@pytest.mark.ms4_magland_c4104@pytest.mark.test_all105@pytest.mark.exclude106def test_ms4_magland_c4():107 sorter = MountainSort4108 params = dict(109 detect_sign=-1,110 adjacency_radius=75,111 )112 do_sorting_test(sorter, params, synth_magland_c4_recdir, assert_avg_accuracy=0.5)113@pytest.mark.spikeforest114@pytest.mark.ms4_magland_c8115@pytest.mark.test_all116@pytest.mark.exclude117def test_ms4_magland_c8():118 sorter = MountainSort4119 params = dict(120 detect_sign=-1,121 adjacency_radius=75,122 )123 do_sorting_test(sorter, params, synth_magland_c8_recdir, assert_avg_accuracy=0.5)124@pytest.mark.spikeforest125@pytest.mark.ms4_neuropix32c126@pytest.mark.test_all127@pytest.mark.exclude128def test_ms4_neuropix32c():129 sorter = MountainSort4130 params = dict(131 detect_sign=-1,132 adjacency_radius=75,133 )134 do_sorting_test(sorter, params, neuropix32c_recdir, assert_avg_accuracy=0.5)135@pytest.mark.spikeforest136@pytest.mark.sc_magland_c8137@pytest.mark.test_all138@pytest.mark.exclude139def test_sc():140 sorter = SpykingCircus141 params = dict(142 detect_sign=-1,143 )144 do_sorting_test(sorter, params, synth_magland_c8_recdir, assert_avg_accuracy=0.4)145@pytest.mark.spikeforest146@pytest.mark.ks2_hybrid_static147@pytest.mark.test_all148@pytest.mark.exclude149def test_ks2_hybrid_static():150 sorter = KiloSort2151 params = dict()152 do_sorting_test(sorter, params, hybrid_janelia_static1_recdir, assert_avg_accuracy=0.8, _keep_temp_files=True)153@pytest.mark.spikeforest154@pytest.mark.ks_hybrid_static155@pytest.mark.test_all156@pytest.mark.exclude157def test_ks_hybrid_static():158 sorter = KiloSort159 params = dict()160 do_sorting_test(sorter, params, hybrid_janelia_static1_recdir, assert_avg_accuracy=0.8, _keep_temp_files=True)161@pytest.mark.spikeforest162@pytest.mark.ks2_magland_c4163@pytest.mark.test_all164@pytest.mark.exclude165def test_ks2_magland_c4():166 sorter = KiloSort2167 params = dict()168 do_sorting_test(sorter, params, synth_magland_c4_recdir, assert_avg_accuracy=0.8, _keep_temp_files=True)169@pytest.mark.spikeforest170@pytest.mark.ks2_magland_c8171@pytest.mark.test_all172@pytest.mark.exclude173def test_ks2_magland_c8():174 sorter = KiloSort2175 params = dict()176 do_sorting_test(sorter, params, synth_magland_c8_recdir, assert_avg_accuracy=0.5, _keep_temp_files=True)177@pytest.mark.spikeforest178@pytest.mark.ks_magland_c8179@pytest.mark.test_all180@pytest.mark.exclude181def test_ks_magland_c8():182 sorter = KiloSort183 params = dict()184 do_sorting_test(sorter, params, synth_magland_c8_recdir, assert_avg_accuracy=0.5, _keep_temp_files=True)185@pytest.mark.spikeforest186@pytest.mark.ks_boyden32c187@pytest.mark.test_all188@pytest.mark.exclude189def test_ks_boyden32c():190 sorter = KiloSort191 params = dict()192 do_sorting_test(sorter, params, boyden32c_recdir, assert_avg_accuracy=0.5, _keep_temp_files=True)193@pytest.mark.spikeforest194@pytest.mark.klusta_magland_c4195@pytest.mark.test_all196@pytest.mark.exclude197def test_klusta_magland_c4():198 sorter = Klusta199 params = dict(200 detect_sign=-1,201 adjacency_radius=50202 )203 do_sorting_test(sorter, params, synth_magland_c4_recdir, assert_avg_accuracy=0.8)204@pytest.mark.spikeforest205@pytest.mark.klusta_magland_c8206@pytest.mark.test_all207@pytest.mark.exclude208def test_klusta_magland_c8():209 sorter = Klusta210 params = dict(211 detect_sign=-1,212 adjacency_radius=50213 )214 do_sorting_test(sorter, params, synth_magland_c8_recdir, assert_avg_accuracy=0.8)215@pytest.mark.spikeforest216@pytest.mark.tridesclous_magland_c4217@pytest.mark.test_all218@pytest.mark.exclude219def test_tridesclous_magland_c4():220 sorter = Tridesclous221 params = dict()222 do_sorting_test(sorter, params, synth_magland_c4_recdir, assert_avg_accuracy=0.5, container='default')223@pytest.mark.spikeforest224@pytest.mark.tridesclous_magland_c8225@pytest.mark.test_all226@pytest.mark.exclude227def test_tridesclous_magland_c8():228 sorter = Tridesclous229 params = dict()230 do_sorting_test(sorter, params, synth_magland_c8_recdir, assert_avg_accuracy=0.1, container='default')231@pytest.mark.spikeforest232@pytest.mark.hs2_magland_c8233@pytest.mark.test_all234@pytest.mark.exclude235def test_hs2_magland_c8():236 sorter = HerdingSpikes2237 params = dict()238 do_sorting_test(sorter, params, synth_magland_c8_recdir, assert_avg_accuracy=0.1, container='default')239@pytest.mark.spikeforest240@pytest.mark.hs2_paired_mea64c241@pytest.mark.test_all242@pytest.mark.exclude243def test_hs2_paired_mea64c():244 sorter = HerdingSpikes2245 params = dict()246 do_sorting_test(sorter, params, paired_mea64c_recdir, assert_avg_accuracy=0.1)247@pytest.mark.spikeforest248@pytest.mark.hs2_neuropix32c249@pytest.mark.test_all250@pytest.mark.exclude251def test_hs2_neuropix32c():252 sorter = HerdingSpikes2253 params = dict(254 adjacency_radius=50,255 )256 do_sorting_test(sorter, params, synth_magland_c8_recdir, assert_avg_accuracy=0.1)257@pytest.mark.spikeforest258@pytest.mark.hs2_visapy30c259@pytest.mark.test_all260@pytest.mark.exclude261def test_hs2_visapy30c():262 sorter = HerdingSpikes2263 params = dict()264 do_sorting_test(sorter, params, visapy30c_recdir, assert_avg_accuracy=0.1, container='default')265@pytest.mark.spikeforest266@pytest.mark.hs2_boyden32c267@pytest.mark.test_all268@pytest.mark.exclude269def test_hs2_boyden32c():270 sorter = HerdingSpikes2271 params = dict(272 adjacency_radius=50,273 )274 do_sorting_test(sorter, params, boyden32c_recdir, assert_avg_accuracy=0.1)275@pytest.mark.spikeforest276@pytest.mark.hs2_sqmea64c277@pytest.mark.test_all278@pytest.mark.exclude279def test_hs2_sqmea64c():280 sorter = HerdingSpikes2281 params = dict(282 adjacency_radius=50,283 )284 do_sorting_test(sorter, params, sqmea64c_recdir, assert_avg_accuracy=0.1)285@pytest.mark.spikeforest286@pytest.mark.ks2_neuropix32c287@pytest.mark.test_all288@pytest.mark.exclude289def test_ks2_neuropix32c():290 sorter = KiloSort2291 params = dict()292 do_sorting_test(sorter, params, neuropix32c_recdir, assert_avg_accuracy=0.2)293@pytest.mark.spikeforest294@pytest.mark.ks2_boyden32c295@pytest.mark.test_all296@pytest.mark.exclude297def test_ks2_boyden32c():298 sorter = KiloSort2299 params = dict()300 do_sorting_test(sorter, params, boyden32c_recdir, assert_avg_accuracy=0.2)301@pytest.mark.spikeforest302@pytest.mark.ks2_sqmea64c303@pytest.mark.test_all304@pytest.mark.exclude305def test_ks2_sqmea64c():306 sorter = KiloSort2307 params = dict()308 do_sorting_test(sorter, params, sqmea64c_recdir, assert_avg_accuracy=0.2)309@pytest.mark.spikeforest310@pytest.mark.irc_neuropix32c311@pytest.mark.test_all312@pytest.mark.exclude313def test_irc_neuropix32c():314 sorter = IronClust315 params = dict()316 do_sorting_test(sorter, params, neuropix32c_recdir, assert_avg_accuracy=0.2)317@pytest.mark.spikeforest318@pytest.mark.irc_magland_c8319@pytest.mark.test_all320@pytest.mark.exclude321def test_irc_magland_c8():322 sorter = IronClust323 params = dict()324 do_sorting_test(sorter, params, synth_magland_c8_recdir, assert_avg_accuracy=0.2)325@pytest.mark.spikeforest326@pytest.mark.irc_magland_c4327@pytest.mark.test_all328@pytest.mark.exclude329def test_irc_magland_c4():330 sorter = IronClust331 params = dict()332 do_sorting_test(sorter, params, synth_magland_c4_recdir, assert_avg_accuracy=0.2)333@pytest.mark.spikeforest334@pytest.mark.irc_sqmea64c335@pytest.mark.test_all336@pytest.mark.exclude337def test_irc_sqmea64c():338 sorter = IronClust339 params = dict()340 do_sorting_test(sorter, params, sqmea64c_recdir, assert_avg_accuracy=0.1)341@pytest.mark.spikeforest342@pytest.mark.jrc_magland_c8343@pytest.mark.test_all344@pytest.mark.exclude345def test_jrc_magland_c8():346 sorter = JRClust347 params = dict()348 do_sorting_test(sorter, params, synth_magland_c8_recdir, assert_avg_accuracy=0.1)349@pytest.mark.spikeforest350@pytest.mark.jrc_neuropix32c351@pytest.mark.test_all352@pytest.mark.exclude353def test_jrc_neuropix32c():354 sorter = JRClust355 params = dict(356 detect_sign=-1,357 adjacency_radius=75,358 )359 do_sorting_test(sorter, params, neuropix32c_recdir, assert_avg_accuracy=0.5)360@pytest.mark.spikeforest361@pytest.mark.jrc_visapy30c362@pytest.mark.test_all363@pytest.mark.exclude364def test_jrc_visapy30c():365 sorter = JRClust366 params = dict()367 do_sorting_test(sorter, params, visapy30c_recdir, assert_avg_accuracy=0.1, _keep_temp_files=True, container='default')368@pytest.mark.spikeforest369@pytest.mark.ks2_kampff370@pytest.mark.test_all371@pytest.mark.exclude372def test_ks2_kampff():373 sorter = KiloSort2374 params = dict()375 do_sorting_test(sorter, params, kampff1_recdir, assert_avg_accuracy=0.8)376def mem_profile_test(sorting_processor, params, recording_dir, container='default', _keep_temp_files=True):377 mt.configDownloadFrom('spikeforest.public')378 params['fMemProfile'] = True379 recdir = recording_dir380 mt.createSnapshot(path=recdir, download_recursive=True)381 sorting = sorting_processor.execute(382 recording_dir=recdir,383 firings_out={'ext': '.mda'},384 **params,385 _container=container,386 _force_run=True,387 _keep_temp_files=_keep_temp_files388 )389def do_sorting_test(sorting_processor, params, recording_dir, assert_avg_accuracy, container='default', _keep_temp_files=False):390 mt.configDownloadFrom('spikeforest.public')391 recdir = recording_dir392 mt.createSnapshot(path=recdir, download_recursive=True)393 sorting = sorting_processor.execute(394 recording_dir=recdir,395 firings_out={'ext': '.mda'},396 **params,397 _container=container,398 _force_run=True,399 _keep_temp_files=_keep_temp_files400 )401 comparison = sa.GenSortingComparisonTable.execute(402 firings=sorting.outputs['firings_out'],403 firings_true=recdir + '/firings_true.mda',404 units_true=[],405 json_out={'ext': '.json'},406 html_out={'ext': '.html'},407 _container='default',408 _force_run=True409 )410 X = mt.loadObject(path=comparison.outputs['json_out'])411 accuracies = [float(a['accuracy']) for a in X.values()]412 avg_accuracy = np.mean(accuracies)413 print('Average accuracy: {}'.format(avg_accuracy))414 assert(avg_accuracy >= assert_avg_accuracy)415if __name__ == "__main__":...
Test.py
Source:Test.py
1'''2Place to test individual transforms.3Note: the newest tests tend to be near the top.4'''5from pathlib import Path6# Import all transform functions.7import Framework8from Plugins import *9this_dir = Path(__file__).resolve().parents[1]10Settings(11 extension_name = 'test_customizer',12 path_to_x4_folder = r'D:\Games\Steam\SteamApps\common\X4 Foundations',13 developer = 1,14 )15# For all tests to run, mostly to tease out exceptions after16# code changes. Note: some tests may be a bit outdated.17test_all = 018if 0:19 GUI.Start_GUI()20# Test sector resizing.21if 0 or test_all:22 Scale_Sector_Size(23 scaling_factor = 0.4, 24 scaling_factor_2 = 0.3, 25 transition_size_start = 200000,26 transition_size_end = 400000,27 recenter_sectors = False,28 precision_steps = 10,29 remove_ring_highways = True,30 remove_nonring_highways = False,31 extra_scaling_for_removed_highways = 0.7,32 _test = False33 )34 35# Test exe edits.36if 0:37 Remove_Sig_Errors()38if 0:39 Remove_Modified()40if 0:41 High_Precision_Systemtime()42 43if 0 or test_all:44 Increase_AI_Script_Waits(45 oos_multiplier = 2,46 oos_seta_multiplier = 4,47 oos_max_wait = 15,48 iv_multiplier = 1,49 iv_seta_multiplier = 2,50 iv_max_wait = 5,51 filter = '*',52 include_extensions = False,53 skip_combat_scripts = False,54 )55if 0 or test_all:56 Adjust_OOS_Damage(0.5)57# Diff generator test.58if 0 or test_all:59 Generate_Diff(60 original_file_path = this_dir / '../private/test' / 'test_original_node.xml',61 modified_file_path = this_dir / '../private/test' / 'test_modified_node.xml',62 output_file_path = this_dir / '../private/test' / 'test_patch_node.xml',63 )64 Generate_Diffs(65 original_dir_path = this_dir / '../private/test' / 'orig_files',66 modified_dir_path = this_dir / '../private/test' / 'mod_files',67 output_dir_path = this_dir / '../private/test' / 'diff_files',68 )69 70if 0 or test_all:71 # Try forcing an attribute.72 Settings(forced_xpath_attributes = 'id,method,tags')73 Generate_Diffs(74 original_dir_path = this_dir / '../private/test/deadair/orig',75 modified_dir_path = this_dir / '../private/test/deadair/mod',76 output_dir_path = this_dir / '../private/test/deadair/diff',77 )78 79# TODO: delete this, or fix god_edit error (bad xml, <object> closed by </stations>).80#if 0 or test_all:81# Generate_Diff(82# original_file_path = this_dir / '../private/test/god_orig.xml',83# modified_file_path = this_dir / '../private/test/god_edit.xml',84# output_file_path = this_dir / '../private/test/god_diff.xml',85# )86# Test the extension checker.87#if 0 or test_all:88# Check_Extension('test_mod')89if 0 or test_all:90 # Alternatively, check everything (may take longer).91 Check_All_Extensions()92if 0 or test_all:93 Color_Text((20005,3012,'C'))94if 0 or test_all:95 # Live editor tree builders.96 edit_tree = Framework.Live_Editor.Get_Tree_View('components')97 edit_tree = Framework.Live_Editor.Get_Tree_View('weapons')98if 0 or test_all:99 edit_tree = Framework.Live_Editor.Get_Tree_View('ships')100if 0 or test_all:101 Adjust_Mission_Reward_Mod_Chance(10)102# Ship transforms.103if 0 or test_all:104 Adjust_Ship_Speed(105 ('name ship_xen_xl_carrier_01_a*', 1.2),106 ('class ship_s' , 2.0),107 ('type corvette' , 1.5),108 ('purpose fight' , 1.2),109 ('*' , 1.1) )110 Adjust_Ship_Crew_Capacity(111 ('class ship_xl' , 2.0),112 ('*' , 1.5)113 )114 Adjust_Ship_Drone_Storage(115 ('class ship_xl' , 2.0),116 ('*' , 1.5)117 )118 Adjust_Ship_Missile_Storage(119 ('class ship_xl' , 2.0),120 ('*' , 1.5)121 )122 Adjust_Ship_Hull(123 ('class ship_xl' , 2.0),124 ('*' , 1.5)125 )126 Adjust_Ship_Turning(127 ('class ship_xl' , 2.0),128 ('*' , 1.5)129 )130 131 Print_Ship_Stats('ship_stats_postmod')132if 0:133 Adjust_Ship_Hull(134 ('class ship_l' , 1.5), 135 ('class ship_xl', 1.5))136 137if 0 or test_all:138 Set_Default_Radar_Ranges(139 ship_xl = 50,140 ship_l = 40,141 ship_m = 30,142 ship_s = 20,143 ship_xs = 20,144 spacesuit = 20,145 station = 40,146 satellite = 30,147 adv_satellite = 50,148 )149 Set_Ship_Radar_Ranges(150 ('type scout', 40),151 )152if 0 or test_all:153 Disable_AI_Travel_Drive()154 Remove_Engine_Travel_Bonus()155if 0 or test_all:156 Rebalance_Engines(purpose_speed_mults = None, adjust_cargo = True)157 Rebalance_Engines(race_speed_mults = None, adjust_cargo = True)158if 0 or test_all:159 Adjust_Engine_Boost_Duration(0.2)160 Adjust_Engine_Boost_Speed(0.25)161if 0 or test_all:162 # Adjust speeds per ship class.163 # Note: vanilla averages and ranges are: 164 # xs: 130 (58 to 152)165 # s : 328 (71 to 612)166 # m : 319 (75 to 998)167 # l : 146 (46 to 417)168 # xl: 102 (55 to 164)169 # Try clamping variation to within 0.5x (mostly affects medium).170 Rescale_Ship_Speeds(171 # Ignore the python (unfinished).172 {'match_any' : ['name ship_spl_xl_battleship_01_a_macro'], 'skip' : True, 'use_arg_engine' : True},173 {'match_all' : ['type scout' ], 'average' : 500, 'variation' : 0.2, 'use_arg_engine' : True},174 {'match_all' : ['class ship_s'], 'average' : 400, 'variation' : 0.5, 'use_arg_engine' : True},175 {'match_all' : ['class ship_m'], 'average' : 300, 'variation' : 0.5, 'use_arg_engine' : True},176 {'match_all' : ['class ship_l'], 'average' : 200, 'variation' : 0.5, 'use_arg_engine' : True},177 {'match_all' : ['class ship_xl'], 'average' : 150, 'variation' : 0.5, 'use_arg_engine' : True})178if 0 or test_all:179 Adjust_Ship_Cargo_Capacity(180 {'match_all' : ['purpose mine'], 'multiplier' : 2,},181 {'match_all' : ['purpose trade'], 'multiplier' : 1.5},182 )183 184# Test the gui live editor, doing a transform before and after185# the patch application. Transform before should show up in the186# gui edit tables; transform after should show up in the final187# game files (along with the hand edits from the gui).188if 0:189 # Pre-editor should have halved damage, post-editor 2x damage,190 # compared to vanilla or the input extensions.191 #Adjust_Weapon_Damage(0.5)192 Apply_Live_Editor_Patches()193 #Adjust_Weapon_Damage(4)194if 0 or test_all:195 Adjust_Mission_Rewards(0.5)196 #Write_To_Extension()197# Ware transforms and printout.198if 0 or test_all:199 #Print_Ware_Stats('ware_stats_premod')200 Adjust_Ware_Price_Spread(201 ('id energycells' , 2 ),202 ('group shiptech' , 0.8),203 ('container ship' , 1.5),204 ('tags crafting' , 0.2),205 ('*' , 0.1) )206 Adjust_Ware_Prices(207 ('container inventory' , 0.5) )208 #Print_Ware_Stats('ware_stats_postmod')209# Old style weapon edits.210if 0 or test_all:211 Adjust_Weapon_Damage(1.5)212 Adjust_Weapon_Damage(213 ('tags small standard weapon' , 2),214 ('*' , 1.2),215 )216 Adjust_Weapon_Range(217 ('tags small standard weapon' , 2),218 ('tags missile' , 2),219 )220 Adjust_Weapon_Shot_Speed(221 ('tags small standard weapon' , 2),222 ('tags missile' , 2),223 )224 Adjust_Weapon_Fire_Rate(225 ('tags small standard weapon' , 2),226 ('tags missile' , 2),227 )228# Weapon transforms and printout.229if 0 or test_all:230 #Print_Weapon_Stats('weapon_stats_premod')231 Adjust_Weapon_Damage(1.5)232 Adjust_Weapon_Damage(233 {'match_all' : ['tags small standard weapon'], 'multiplier' : 2},234 {'match_all' : ['*'], 'multiplier' : 1.2},235 )236 Adjust_Weapon_Range(237 {'match_all' : ['tags small standard weapon'], 'multiplier' : 2},238 {'match_all' : ['tags missile'], 'multiplier' : 2},239 )240 Adjust_Weapon_Shot_Speed(241 {'match_all' : ['tags small standard weapon'], 'multiplier' : 2},242 {'match_all' : ['tags missile'], 'multiplier' : 2},243 )244 Adjust_Weapon_Fire_Rate(245 {'match_all' : ['tags small standard weapon'], 'multiplier' : 2},246 {'match_all' : ['tags missile'], 'multiplier' : 2},247 )248 Adjust_Weapon_Fire_Rate(249 {'match_all' : ['tags small standard weapon'], 'multiplier' : 2, 'min' : 0.5},250 {'match_all' : ['tags missile'], 'multiplier' : 1.5},251 )252 #Print_Weapon_Stats('weapon_stats_postmod')253 254# Testing ways to call Jobs.255if 0 or test_all: 256 Adjust_Job_Count(257 ('id masstraffic*' , 0.5),258 ('tags military destroyer', 2 ),259 ('tags miner' , 1.5),260 ('size s' , 1.5),261 ('faction argon' , 1.2),262 ('*' , 1.1) )263 264# Simple cat unpack, allowing errors.265if 0 or test_all:266 Settings.allow_cat_md5_errors = True267 Cat_Unpack(268 source_cat_path = r'D:\X4\Pack',269 dest_dir_path = r'D:\X4\UnPack',270 )271# Slightly more complex cat unpack.272if 0 or test_all:273 # Pick where to grab cats from.274 # Could also call this script from that directory and use relative275 # paths for the cat file names.276 # Append the name of a cat file if wanting to unpack just one.277 cat_dir = Path(r'C:\Steam\SteamApps\common\X4 Foundations')278 # Pick the output folder. This script places it under the cat_dir.279 dest_dir_path = 'extracted'280 # Optional wildcard pattern to use for matching.281 # Just lua for quick test.282 include_pattern = ['*.lua']#['*.xml','*.xsd'] #,'*.xpl']283 exclude_pattern = None284 # Call the unpacker.285 Cat_Unpack(286 source_cat_path = cat_dir,287 #dest_dir_path = cat_dir / dest_dir_path,288 dest_dir_path = r'D:\X4_extracted',289 include_pattern = include_pattern,290 exclude_pattern = exclude_pattern291 )292# Cat pack test.293if 0 or test_all:294 # Pick where to grab files from.295 # Could also call this script from that directory and use relative296 # paths for the cat file names.297 dir_path = Path(r'C:\Steam\SteamApps\common\X4 Foundations\extensions\test_mod')298 # Optional wildcard pattern to use for matching.299 include_pattern = '*.xml'300 exclude_pattern = None301 # Name of the cat file.302 # For extensions, use prefix 'ext_' for patching game files, or303 # prefix 'subst_' for overwriting game files.304 cat_name = 'ext_01.cat'305 Cat_Pack(306 source_dir_path = dir_path,307 dest_cat_path = dir_path / cat_name,308 include_pattern = include_pattern,309 exclude_pattern = exclude_pattern,310 generate_sigs = True,311 separate_sigs = True,312 )313 314# Run diff patch test on whatever xml.315if 0 or test_all:316 jobs_game_file = Framework.Load_File('libraries/jobs.xml')317 Framework.File_Manager.XML_Diff.Unit_Test(318 test_node = jobs_game_file.Get_Root(), 319 # Shorten test count when in test_all mode.320 num_tests = 100 if not test_all else 5, 321 edits_per_test = 5,322 rand_seed = 1,323 )324 325# Manual testing of cat reading.326if 0 or test_all:327 Framework.File_Manager.File_System.Delayed_Init()328 # Test: open up a cat file, the one with text pages.329 cat09 = Framework.File_Manager.Cat_Reader.Cat_Reader(330 Settings.path_to_x4_folder / '09.cat')331 332 # Read the files from it.333 t44 = cat09.Read('t/0001-L044.xml')334 335 # Now try out the source reader.336 reader = Framework.File_Manager.Source_Reader.Source_Reader_class()337 reader.Init_From_Settings()338 t44_game_file = reader.Read('t/0001-L044.xml')339 jobs_game_file = reader.Read('libraries/jobs.xml')340 341 # Write to a new cat file.342 binary = t44_game_file.Get_Binary()343 cat_dir = Settings.path_to_x4_folder / 'extensions' / 'test_mod'344 if not cat_dir.exists():345 cat_dir.mkdir(parents = True)346 347 cat_writer = Framework.File_Manager.Cat_Writer.Cat_Writer(348 cat_path = cat_dir / 'test_01.cat')349 cat_writer.Add_File(t44_game_file)350 cat_writer.Write()351if 0 or test_all:352 Write_To_Extension()...
context.py
Source:context.py
...141 # '\tIs PN'142 '\n' )143 144 @staticmethod145 def test_all(tests):146 for t in tests:147 if not t:148 Context.test_fail()149 return150 Context.test_pass()151 @staticmethod152 def test_any(tests):153 for t in tests:154 if t:155 Context.test_pass()156 return157 Context.test_fail()158 159 @staticmethod160 def test_pass():161 stdout.write( '\t{}'.format(1) )162 @staticmethod163 def test_fail():164 stdout.write( '\t{}'.format(0) )165 166 @staticmethod167 def write(line, index, word, args):168 # Variables that we may use as part of other features.169 lemmata = line.get_lemmata(word)170 signs = word.split('-')171 (leftcx, leftlem) = \172 Context.get_left_context(line, index, word, args)173 (leftcx2, leftlem2) = \174 Context.get_left_context(line, index, word, args, offset = 2)175 (rightcx, rightlem) = \176 Context.get_right_context(line, index, word, args)177 # Print raw lemma tag. May include gloss, even when178 # --nogloss switch is provided. This is for the benefit179 # of human readers with some familiarity with Sumerian.180 181 stdout.write( '\t"{}/{}"'.format( word,182 Context.format_context(lemmata) ))183 # Index of word in line. 0-based.184 stdout.write( '\t{}'.format(index) )185 # Left context.186 stdout.write( '\t{}'.format(leftcx) )187 # Right context.188 stdout.write( '\t{}'.format(rightcx) )189 # Print line context.190 stdout.write( '\t"{}"'.format(line.line) )191 # Is word alone on line ?192 Context.test_all([ (leftcx, rightcx) == (None, None) ])193 194 # Left context is dumu.195 Context.test_all([ (leftcx == 'dumu') ])196 # Right context is dumu.197 Context.test_all([ (rightcx == 'dumu') ])198 # ^ ki <word> $199 Context.test_all([ (leftcx2, leftcx, rightcx) == \200 (None, 'ki', None) ])201 202 # ^ igi <word> $203 Context.test_all([ (leftcx2, leftcx, rightcx) == \204 (None, 'igi', None) ])205 # ^ igi <word>-sze $206 Context.test_all([ (leftcx2, leftcx, rightcx) == (None, 'igi', None),207 word.endswith('-sze3') ])208 # Personnenkeil: ^ 1(disz) <word> $209 Context.test_all([ (leftcx2, leftcx, rightcx) == \210 (None, '1(disz)', None) ])211 # ^ kiszib3 <word> $212 Context.test_all([ (leftcx2, leftcx, rightcx) == \213 (None, 'kiszib3', None) ])214 # ^ giri3 <word> $215 Context.test_all([ (leftcx2, leftcx, rightcx) == \216 (None, 'giri3', None) ])217 # First syllable repeated218 if len(signs) > 1:219 Context.test_all([ signs[0] == signs[1] ])220 else:221 Context.test_fail()222 # Last syllable repeated223 signs = word.split('-')224 if len(signs) > 1:225 Context.test_all([ signs[-2] == signs[-1] ])226 else:227 Context.test_fail()228 # Any syllable repeated229 if len(signs) > 1:230 Context.test_any([ a == b for (a, b)231 in zip(signs, signs[1:]) ])232 else:233 Context.test_fail()234 # Is profession235 Context.test_any([ pf == lem236 for pf in Context.professions237 for lem in lemmata ])238 # Contains profession239 Context.test_any([ pf in lem 240 for pf in Context.professions241 for lem in lemmata ])242 # Left context is profession243 if leftlem:244 Context.test_any([ pf == lem245 for pf in Context.professions246 for lem in line.get_lemmata(leftcx) ])247 else:248 Context.test_fail()249 # Left context contains profession250 if leftlem:251 Context.test_any([ pf in lem252 for pf in Context.professions253 for lem in line.get_lemmata(leftcx) ])254 else:255 Context.test_fail()256 # Right context is profession257 if rightlem:258 Context.test_any([ pf == lem259 for pf in Context.professions260 for lem in line.get_lemmata(rightcx) ])261 else:262 Context.test_fail()263 # Right context contains profession264 if rightlem:265 Context.test_any([ pf in lem266 for pf in Context.professions267 for lem in line.get_lemmata(rightcx) ])268 else:269 Context.test_fail()270 # Starts with ur-271 Context.test_all([ word.startswith('ur-') ])272 273 # Starts with lu2-274 Context.test_all([ word.startswith('lu2-') ])275 276 # Ends with -mu277 Context.test_all([ word.endswith('-mu') ])278 279 # Contains {d}280 Context.test_all([ '{d}' in word ])281 # Contains {ki}282 Context.test_all([ '{ki}' in word ])283 # Contains any determinative284 Context.test_all([ '{' in word ])285 # Contains q sound286 Context.test_all([ 'q' in word ])287 # Contains lugal288 Context.test_all([ 'lugal' in word ])289 # Contains numeric elements290 Context.test_any([ '(asz)' in word,291 '(disz)' in word,292 '(u)' in word ])293 # Followed by sag294 Context.test_all([ rightcx == 'sag' ])295 # Followed by zarin296 Context.test_all([ rightcx == 'zarin' ])297 # Preceded by numeric classifier298 Context.test_all([ leftcx in ( 'ba-an', 'ba-ri2-ga', 'bur3', 'da-na',299 'gin2-tur', 'gin2', 'gur-lugal', 300 'gur-sag-gal2', 'gur', 'iku', 'GAN2',301 'ku-li-mu', 'ku-li-kam', 'kusz3',302 'sar', 'sila3' ) ])303 # iti at head of sentence304 Context.test_all([ 'iti' == line.words[0][0] ])305 # mu at head of sentence306 Context.test_all([ 'mu' == line.words[0][0] ])307 """308 # Print boolean feature, 1 if word is PN, 0 if not.309 if 'PN' in lemmata:310 Context.test_pass()311 else:312 Context.test_fail()313 # Print most common tag for this word.314 # Actually, don't print the most common tag for this word.315 # This is not a good feature for CRF (or any feature-based316 # NLP algorithm) since it's # not immediately calculable317 # from context as a feature should be, but rather requires318 # reference to the index of the entire corpus.319 if word in INDEX:320 (bestlem, _) = INDEX[word].most_common(1)[0]...
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