How to use test_pre method in avocado

Best Python code snippet using avocado_python

ocr.py

Source:ocr.py Github

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1from PIL import Image2import pytesseract3import argparse4import cv25import os6import numpy as np7import re8import nltk9nltk.download('stopwords')10from nltk.corpus import stopwords11from nltk.stem.porter import PorterStemmer12from sklearn.feature_extraction.text import CountVectorizer13# load the example image and convert it to grayscale14image = cv2.imread("example6.png")15#cv2.imshow("image",image)16#cv2.waitKey(0)17#print type(image)18gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)19#cv2.imshow("grayscale",gray)20#cv2.waitKey(0)21#print type(gray)22_ ,gray = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)23#cv2.imshow("grayscale_tesh",gray)24#cv2.waitKey(0)25 26# make a check to see if median blurring should be done to remove27# noise28gray = cv2.medianBlur(gray, 3)29#cv2.imshow("grayscale_blurr",gray)30#cv2.waitKey(0)31 32# write the grayscale image to disk as a temporary file so we can33# apply OCR to it34filename = "{}.png".format(os.getpid())35print (filename)36cv2.imwrite(filename, gray)37# load the image as a PIL/Pillow image, apply OCR, and then delete38# the temporary file39text = pytesseract.image_to_string(Image.open(filename))40os.remove(filename)41print(text)42text=text.split()43test=' '.join(text)44x_test=[]45from nltk import tokenize46#nltk.download('punkt')47x_test=tokenize.sent_tokenize(test)48x_size=len(x_test)49###########################################################################################################50######## or or orr or audio speech #####################51import speech_recognition as sr52 53# obtain audio from the microphone54r = sr.Recognizer()55with sr.Microphone() as source:56 57 58 print("Say something!")59 audio = r.listen(source)60 61# recognize speech using Google Speech Recognition62 try:63 64 # for testing purposes, we're just using the default API key65 # to use another API key, use `r.recognize_google(audio, key="GOOGLE_SPEECH_RECOGNITION_API_KEY")`66 # instead of `r.recognize_google(audio)`67 x_test = r.recognize_google(audio)68 print("Google Speech Recognition thinks you said " + x_test)69 except sr.UnknownValueError:70 71 print("Google Speech Recognition could not understand audio")72 except sr.RequestError as e:73 print("Could not request results from Google Speech Recognition service; {0}".format(e))74 75 76###########################################################################################################77###########################################################################################################78######## emotion detection79from PIL import Image80import pytesseract81import argparse82import cv283import os84import numpy as np85import re86import nltk87nltk.download('stopwords')88from nltk.corpus import stopwords89from nltk.stem.porter import PorterStemmer90from sklearn.feature_extraction.text import CountVectorizer91###importing data sets92import pandas as pd93dataset=pd.read_csv('texttsv.tsv',delimiter='\t',encoding = "ISO-8859-1")94#dataset=pd.read_csv('Restaurant_Reviews.tsv',delimiter='\t',quoting=3)95##categorizing input96x=dataset.iloc[:,0]97y=dataset.iloc[:,1]98from sklearn.preprocessing import LabelEncoder,OneHotEncoder99label_y=LabelEncoder()100y_cat1=label_y.fit_transform(y)101onehotencoder=OneHotEncoder(categorical_features=[0])102y_cat=onehotencoder.fit_transform(y_cat1.reshape(-1,1)).toarray()103y_cat=y_cat[:,1:13]104##################################################################################105############ ######## train neural networks ############106ps=PorterStemmer()107corpus=[]108for i in range (0,40000):109 110 test_train=re.sub('[^a-zA-z]',' ',dataset['content'][i])111 test_train=test_train.lower()112 test_train=test_train.split()113 test_train=[ps.stem(word) for word in test_train ]114 test_train=' '.join(test_train)115 corpus.append(test_train)116cv=CountVectorizer(max_features=30000)117x=cv.fit_transform(corpus).toarray()118#y_cat=y_cat[0:2000]119##################################################################################120####### spiltting121from sklearn.model_selection import train_test_split122X_train, X_test, Y_train, Y_test = train_test_split(x, y_cat, test_size = 0.2, random_state = 0) 123import keras124from keras.models import Sequential125#from.keras.models import Dense126from keras.layers.core import Dense127from keras.models import load_model128from keras.layers import Dropout129clasy=Sequential()130clasy.add(Dense(output_dim = 8000, init = 'uniform', activation = 'relu', input_dim=4000 ))131clasy.add(Dropout(rate = 0.3))132#clasy.add(Dense(output_dim = 600, init = 'uniform', activation = 'relu'))133#clasy.add(Dropout(rate = 0.1))134#clasy.add(Dense(output_dim = 3000, init = 'uniform', activation = 'relu'))135#clasy.add(Dropout(rate = 0.3))136clasy.add(Dense(output_dim = 8000, init = 'uniform', activation = 'relu'))137#clasy.add(Dense(output_dim = 4800, init = 'uniform', activation = 'relu'))138clasy.add(Dropout(rate = 0.3))139#clasy.add(Dense(output_dim = 12000, init = 'uniform', activation = 'relu'))140clasy.add(Dense(output_dim = 12, init = 'uniform', activation = 'sigmoid'))141clasy.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])142#clasy.fit(x,y_cat,batch_size=10,nb_epoch=15)143clasy.fit(X_train,Y_train,batch_size=10,nb_epoch=1)144#clasy=load_model("2000_3000_3000_12.h5")145#clasy.save('5k-4k-5k-12.h5')146y_pred=clasy.predict(X_test)147from sklearn.metrics import accuracy_score148accuracy_score(Y_test, y_pred.round())149#accuracy_score(np.array(Y_test, y_pred), np.ones((2, 2)))150########################### predict ing ###############################################151##nltk processing152ps=PorterStemmer()153combined_all=[0,0,0,0,0,0,0,0,0,0,0,0]154for m in range (0,x_size):155 corpus=[]156 for i in range (0,40000):157 test_train=re.sub('[^a-zA-z]',' ',dataset['content'][i])158 test_train=test_train.lower()159 test_train=test_train.split()160 test_train=[ps.stem(word) for word in test_train if not word in set(stopwords.words('english'))]161 test_train=' '.join(test_train)162 corpus.append(test_train)163 164 165#testing_prepossing166 test_pre=re.sub('[^a-zA-z]',' ',x_test[m])167 test_pre=test_pre.lower()168 test_pre=test_pre.split()169 test_pre=[ps.stem(word) for word in test_pre if not word in set(stopwords.words('english'))]170 test_pre=' '.join(test_pre)171 corpus.append(test_pre) 172 173#tokenizing174 175 cv=CountVectorizer(max_features=2000)176 x=cv.fit_transform(corpus).toarray()177#y=dataset.iloc[:,1].values178 x_pred_val=[]179 x_pred_val=x[40000:40001,0:2000]180 x=x[0:40000,0:2000]181#co rpus=x[0:40000,0:30000]182 183 184##predict185 y_pred=clasy.predict(x_pred_val)186 187 188 prob_pred=[]189 prob_pred1=[]190 for k in range (0,12):191 prob_pred=y_pred[0][k]192 prob_pred=prob_pred * 100193 prob_pred=float("{0:.2f}".format(prob_pred))194 prob_pred1.append(prob_pred)195 pred_val=[]196 pred_val=["boredom","empty","enthusiam","fun","happiness","hate","love","netural","relief","sadness","suprise","worry"]197#### to print it to i console #####198 #combined = np.vstack((pred_val, prob_pred1)).T199 #print('yout text was = ',x_test[m])200 #for a in range (0,12):201 #print(pred_val[a],'=',prob_pred1[a],'%')202 203 for q in range (0,12):204 combined_all[q]=combined_all[q] + prob_pred1[q]205 206 207################# printing to file #############208######## delete outputfilr if existed #########209 with open('outputfile.txt', 'a') as f:210 print(x_test[m],'\n',file=f)211 for r in range (0,12): 212 print(pred_val[r],'=',prob_pred1[r],'%', file=f)213 print('\n',file=f)214 f.close()215with open('outputfile.txt', 'a') as f:216 print('over all emotion','\n',file=f) 217 for w in range (0,12): 218 combined_all[w]=combined_all[w]/x_size219 combined_all[w]=float("{0:.2f}".format( combined_all[w]))220 print(pred_val[w],'=',combined_all[w],'%', file=f)221f.close()...

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data_splitter.py

Source:data_splitter.py Github

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1import numpy as np2class Data_splitter(object):3 """docstring for Data_splitter"""4 def __init__(self):5 return678 def auto_split_data(self, num_valid, num_test, X, y):9 l = len(set(y))10 standard = self.cnt_labels_rate(y, l)11 # 首先分割出验证集12 valid_pre = self.lowest_square_idx(num_valid, y, l, standard)13 # print(valid_pre)14 # 分割15 X_valid = X[valid_pre:valid_pre+num_valid]16 y_valid = y[valid_pre:valid_pre+num_valid]17 # 分割后剩下的y合并18 y_ = np.r_[y[:valid_pre], y[valid_pre+num_valid:]]19 # 测试集20 test_pre = self.lowest_square_idx(num_test, y_, l, standard)21 # print(test_pre)22 if (test_pre >= valid_pre):23 test_pre += num_valid24 test_bck = test_pre + num_test25 elif (test_pre + num_test > valid_pre):26 test_bck = num_valid + num_test + test_pre27 # print(test_pre)28 X_test = X[test_pre:test_bck]29 y_test = y[test_pre:test_bck]30 X_train, y_train = self.split_train(valid_pre, test_pre, num_valid, num_test, X, y)31 return X_train, X_valid, X_test, y_train, y_valid, y_test323334 # 分割训练集35 def split_train(self, valid_pre, test_pre, num_valid, num_test, X, y):36 if test_pre >= valid_pre:37 pre_l = test_pre38 pre_s = valid_pre39 num_l = num_test40 num_s = num_valid41 elif (test_pre + num_test > valid_pre):42 X_train = np.r_[X[:test_pre], X[num_valid + num_test + test_pre:]]43 y_train = np.r_[y[:test_pre], y[num_valid + num_test + test_pre:]]44 return X_train, y_train45 else:46 pre_s = test_pre47 pre_l = valid_pre48 num_l = num_valid49 num_s = num_test50 X_train = np.r_[np.r_[X[:pre_s], X[pre_s+num_s:pre_l]], X[pre_l+num_l:]]51 y_train = np.r_[np.r_[y[:pre_s], y[pre_s+num_s:pre_l]], y[pre_l+num_l:]]52 return X_train, y_train535455 # 均方值最小的下标56 def lowest_square_idx(self, num, y, l, standard):57 square = np.zeros(len(y) - num)58 for i in range(len(y) - num):59 # 计算与标准方差最小的一组数据60 w = (self.cnt_labels_rate(y[i:i + num], l) - standard)61 square[i] = w.dot(w.T)62 # 找到最小值的下标63 pre = np.argwhere(square == min(square))[0][0]64 return pre656667 # 计算当前数据的分布比例68 def cnt_labels_rate(self, y, l):69 cnt = np.zeros(l)70 for i in range(l):71 cnt[i] = (y == i).sum() / y.shape[0] ...

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stimuli_notebook.py

Source:stimuli_notebook.py Github

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1from ExperimentSpecificCode._2017_03_28_Neuroseeker_Auditory_Double.Stimulus import arnes_basic_analysis as ba2data_path = r'F:\Neuroseeker\Neuroseeker_2017_03_28_Anesthesia_Auditory_DoubleProbes'3overwrite = True4binwidth = 0.055stimulus = 'ToneSequence'6pre = 37post = 38plot_type = 'psth'9test_pre = 0.410test_post = 0.111min_rate = 0.512ba.tuning(data_path=data_path, overwrite=overwrite, binwidth=binwidth, stimulus=stimulus, frequencies_index=[0, 1, 2, 3],13 pre=pre, post=post, plot_type=plot_type, test_pre=test_pre, test_post=test_post, min_rate=min_rate)14overwrite = True15binwidth = 0.0516pre = 217post = 218test_pre = 0.519test_post = 0.220min_rate = 221ba.tones(data_path=data_path, overwrite=overwrite, binwidth=binwidth, pre=pre, post=post,22 test_pre=test_pre, test_post=test_post, min_rate=min_rate)23template = 12024df = pd.DataFrame(index=[template])25try:26 temp = df['a'].loc[template]27 temp[7, :] = np.ones(100) *528except:29 temp = np.empty((10, 100))30 temp[0, :] = np.ones(100) * 331finally:...

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