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How to use test_unique_list method in autotest
Best Python code snippet using autotest_python
BERT_CROSS_VALIDATION.py
Source:BERT_CROSS_VALIDATION.py 
1from simpletransformers.classification import ClassificationModel2from sklearn.model_selection import StratifiedKFold3from sklearn.metrics import accuracy_score4import pandas as pd5import re6import os 7import matplotlib.pyplot as plt8import seaborn as sns9import numpy as np10from transformers import AutoModel, AutoTokenizer,AutoModelForSequenceClassification,pipeline11from sklearn.metrics import accuracy_score, classification_report, confusion_matrix12def replace_with_index(x):13 index = unique_list.index(x)14 return index15def cleanText(input_sentence):16 17 tmp= [word.replace('A','a') for word in input_sentence.split(' ')]18 tmp= [word.lower() for word in tmp]19 tmp= [word.replace('iÌ','i') for word in tmp]20 tmp = [re.sub('[^A-Za-z0-9ÄüÅıçöiâî]+', ' ', word) for word in tmp]21 tmp = [word.strip(' ') for word in tmp]22 tmp1 =' '.join(tmp)23 return tmp124def get_model(path,X_test,y_test):25 result = {}26 plt.rcParams["figure.figsize"] = (50,30)27 for epoch in path:28 tokenizer= AutoTokenizer.from_pretrained(path)29 # build and load model, it take time depending on your internet connection30 model= AutoModelForSequenceClassification.from_pretrained(path)31 # make pipeline32 nlp=pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)33 34 test = pd.concat([X_test,y_test],axis=1)35 #print(test)36 test_unique_list = test['label'].unique().tolist()37 test_unique_list.sort()38 #print(test_unique_list)39 pred_label_list, true_label_list = [],[]40 cou = 041 for t,l in zip(test["metin"], test["label"]):42 cou+=143 true_label_list.append(l)44 pred_label_list.append(int(nlp(t)[0]["label"].lstrip("LABEL_")))45 if cou %1000 == 0:46 print(path.index(epoch),cou)47 df = pd.DataFrame()48 df["true"] = true_label_list49 df["pred"] = pred_label_list50 print(df)51 for item in test_unique_list:52 true_df = df[df.true == item]53 class_acc = accuracy_score(true_df["true"], true_df["pred"])54 print('ID = {} ACC = {}'.format(item, class_acc),file=open("output.txt", "a"))55 56 acc = accuracy_score(df["true"], df["pred"])57 result[f"{path.index(epoch)+1}. epoch Accuracy"] = acc58 print('acc = {}'.format(acc),file=open("output.txt", "a"))59 print(classification_report(df["true"], df["pred"]),file=open("output.txt", "a"))60 """sns.heatmap(confusion_matrix(df["true"], df["pred"]), annot = True, fmt = "g")61 plt.show()"""62 return df63HUGGINGFACE_MODEL_PATH = "loodos/bert-base-turkish-uncased"64tokenizer = AutoTokenizer.from_pretrained(HUGGINGFACE_MODEL_PATH)65print('Zero_Shotsız_ve_Zero_Shotlı_BERT_Result',file=open("output.txt", "a"))66list_url = [['Single_BERT_DATASET_PATH','No_ZEROSHOT'],['FÄ°LTERED_BY_ZEROSHOT_RESULT_DATASET_PATH','W_ZEROSHOT']]67 68for item in list_url:69 print(item[1],file=open("output.txt", "a"))70 data = pd.read_csv(item[0])71 unique_list = data['label'].unique().tolist()72 data['label'] = data['label'].apply(lambda x: replace_with_index(x))73 data = data.iloc[:,:2]74 for i in range(len(data['label'])):75 if isinstance(data['label'][i], np.generic):76 data['label'][i]= np.asscalar(data['label'][i])77 data.dropna(axis= 0, inplace=True)78 data = data.reset_index(drop = True)79 data["metin"] = data["metin"].apply(cleanText)80 print(len(data))81 data = data.drop_duplicates()82 print(len(data))83 data=data.sample(frac=1)84 data = data.reset_index(drop= True)85 # prepare cross validation86 n=587 kf = StratifiedKFold(n_splits=n, random_state=42, shuffle=True)88 no_labels = len(set(data["label"]))89 results = []90 for i,(train_index, val_index) in enumerate(kf.split(data["metin"],data["label"])):91 print('Kesit No: {}'.format(i),file=open("output.txt", "a"))92 # splitting Dataframe (dataset not included)93 X_train, X_test = data["metin"][train_index], data["metin"][val_index]94 y_train, y_test = data["label"][train_index], data["label"][val_index]95 96 train_df = pd.concat([X_train,y_train], axis = 1)97 val_df = pd.concat([X_test,y_test], axis = 1)98 # Defining Model99 MODEL_OUTPUT_DIR = 'BERT_{}_44_kategori_{}/'.format(item[1],i,)100 #model = AutoModel.from_pretrained(HUGGINGFACE_MODEL_PATH)101 model_args = {102 "use_early_stopping": True,103 "early_stopping_delta": 0.01,104 "early_stopping_metric": "mcc",105 "early_stopping_metric_minimize": False,106 "early_stopping_patience": 5,107 "evaluate_during_training_steps": 6000,108 "fp16": False,109 "num_train_epochs":3110 #"overwrite_output_dir": True111 }112 model = ClassificationModel(113 "bert", 114 HUGGINGFACE_MODEL_PATH,115 use_cuda=True, 116 args=model_args, 117 num_labels=no_labels118 )119 model.train_model(train_df, acc=accuracy_score, output_dir=MODEL_OUTPUT_DIR)120 EPOCH_PATH = [MODEL_OUTPUT_DIR +"/"+ t for t in os.listdir(MODEL_OUTPUT_DIR) if "epoch" in t and "-3"in t]121 EPOCH_PATH=EPOCH_PATH[0]122 data_test = pd.DataFrame()...
Single_BERT.py
Source:Single_BERT.py
1from sklearn.model_selection import train_test_split2import sklearn3import os4from sklearn.metrics import accuracy_score, classification_report, confusion_matrix5import pandas as pd6import numpy as np7import matplotlib.pyplot as plt8import seaborn as sns9from transformers import AutoModel, AutoTokenizer,AutoModelForSequenceClassification,pipeline10import re11from simpletransformers.classification import ClassificationModel12data = pd.read_csv('data set path according to which method you will use ')13HUGGINGFACE_MODEL_PATH = "loodos/bert-base-turkish-uncased"14MODEL_OUTPUT_DIR = 'BERT4_no_zero_shot_44/'15#data = data[data.groupby('kategori')['kategori'].transform('size') > 200]16#data = data.reset_index(drop = True)17unique_list = data['label'].unique().tolist()18def replace_with_index(x):19 index = unique_list.index(x)20 return index21data['label'] = data['label'].apply(lambda x: replace_with_index(x))22data = data.iloc[:,:2]23for i in range(len(data['label'])):24 if isinstance(data['label'][i], np.generic):25 data['label'][i]= np.asscalar(data['label'][i])26data.dropna(axis= 0, inplace=True)27data = data.reset_index(drop = True)28def cleanText(input_sentence):29 30 tmp= [word.replace('A','a') for word in input_sentence.split(' ')]31 tmp= [word.lower() for word in tmp]32 tmp= [word.replace('iÌ','i') for word in tmp]33 tmp = [re.sub('[^A-Za-z0-9ÄüÅıçöiâî]+', ' ', word) for word in tmp]34 tmp = [word.strip(' ') for word in tmp]35 tmp1 =' '.join(tmp)36 return tmp137data["metin"] = data["metin"].apply(cleanText)38print(len(data))39data = data.drop_duplicates()40print(len(data))41data=data.sample(frac=1)42data = data.reset_index(drop= True)43#print(data['label'].unique())44X_train, X_test, y_train, y_test = train_test_split(data["metin"],data["label"], test_size= .2, stratify=data['label'], random_state = 42)45train = pd.concat([X_train,y_train], axis = 1)46tokenizer = AutoTokenizer.from_pretrained(HUGGINGFACE_MODEL_PATH)47model = AutoModel.from_pretrained(HUGGINGFACE_MODEL_PATH)48no_labels = len(set(data["label"]))49model_args = {50 "use_early_stopping": True,51 "early_stopping_delta": 0.01,52 "early_stopping_metric": "mcc",53 "early_stopping_metric_minimize": False,54 "early_stopping_patience": 5,55 "evaluate_during_training_steps": 6000,56 "fp16": False,57 "num_train_epochs":358}59model = ClassificationModel(60 "bert", 61 HUGGINGFACE_MODEL_PATH,62 use_cuda=True, 63 args=model_args, 64 num_labels=no_labels65)66model.train_model(train, acc=sklearn.metrics.accuracy_score, output_dir=MODEL_OUTPUT_DIR)67EPOCH_PATH = [MODEL_OUTPUT_DIR +"/"+ i for i in os.listdir(MODEL_OUTPUT_DIR) if "epoch" in i and "-3"in i]68EPOCH_PATH=EPOCH_PATH[0]69def get_model(path):70 result = {}71 plt.rcParams["figure.figsize"] = (50,30)72 for epoch in path:73 tokenizer= AutoTokenizer.from_pretrained(EPOCH_PATH)74 # build and load model, it take time depending on your internet connection75 model= AutoModelForSequenceClassification.from_pretrained(EPOCH_PATH)76 # make pipeline77 nlp=pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)78 79 test = pd.concat([X_test,y_test],axis=1)80 print(test)81 #test_unique_list = test['label'].unique().tolist()82 #test_unique_list.sort()83 #print(test_unique_list)84 pred_label_list, true_label_list = [],[]85 cou = 086 for t,l in zip(test["metin"], test["label"]):87 cou+=188 true_label_list.append(l)89 pred_label_list.append(int(nlp(t)[0]["label"].lstrip("LABEL_")))90 if cou %1000 == 0:91 print(EPOCH_PATH.index(epoch),cou)92 df = pd.DataFrame()93 df["true"] = true_label_list94 df["pred"] = pred_label_list95 print(df)96 """for item in test_unique_list:97 true_df = df[df.true == item]98 class_acc = accuracy_score(true_df["true"], true_df["pred"])99 print('ID = {} ACC = {}'.format(item, class_acc))"""100 101 acc = accuracy_score(df["true"], df["pred"])102 result[f"{EPOCH_PATH.index(epoch)+1}. epoch Accuracy"] = acc103 print(acc)104 print(classification_report(df["true"], df["pred"]))105 sns.heatmap(confusion_matrix(df["true"], df["pred"]), annot = True, fmt = "g")106 plt.show()107 return df108data_test = pd.DataFrame()...
utils.py
Source:utils.py
1import unittest2import numpy as np3from radbm.utils import unique_list, Ramp4class Test_unique_list(unittest.TestCase):5 def test_unique_list(self):6 it = [3,4,4,2,1,1,0,3,2]7 expected_list = [3,4,2,1,0]8 result = unique_list(it)9 self.assertEqual(result, expected_list)10 11class TestRamp(unittest.TestCase):12 def test_ramp(self):13 #from x=3..5 we ramp from y=-1..714 ramp = Ramp(3, 5, -1, 7)15 xs = np.array([2, 3, 4, 5, 6])16 ys = np.array([-1, -1, 3, 7, 7])...
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