keras1.py

	
import pandas as pd
import numpy as np
import pickle
from pathlib import Path



print("-------------start-------------")
# For reproducibility
np.random.seed(1237)
 
# Source file directory
path_data = "U:/data/"
fn_train = "train.csv" #"filewos_bib_random_nastates.csv"
 

trainingdata = pd.read_csv(path_data + fn_train, usecols=['CITY', 'INSTITUTION_FULL','ADDRESS_FULL','d_state']) #,'OBSERVATION'
print(trainingdata.head(20))

print("-------------1.0-------------")

print(trainingdata[0:20])

files_train = skds.load_files(path_train,load_content=False)
 




from keras.preprocessing.text import Tokenizer
from keras.models import Sequential
from keras.layers import Activation, Dense, Dropout
#from sklearn.preprocessing import LabelBinarizer
#import sklearn.datasets as skds



label_index = files_train.target
label_names = files_train.target_names
labelled_files = files_train.filenames
 
data_tags = ["filename","category","news"]
data_list = []
 
# Read and add data from file to a list
i=0
for f in labelled_files:
    data_list.append((f,label_names[label_index[i]],Path(f).read_text()))
    i += 1
 
# We have training data available as dictionary filename, category, data
data = pd.DataFrame.from_records(data_list, columns=data_tags)