File size: 6,151 Bytes
6f70897 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 |
# IMPORTS
import pandas as pd
import glob
from nltk import tokenize
from transformers import BertTokenizer, TFBertModel, BertConfig
from transformers.utils.dummy_tf_objects import TFBertMainLayer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow import convert_to_tensor
from tensorflow.keras.layers import Input, Dense
from tensorflow.keras.initializers import TruncatedNormal
from tensorflow.keras.models import load_model, Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.metrics import BinaryAccuracy, Precision, Recall
# SET PARAMETERS
DATA="..." # DATA need to be a list of texts
MODELS=".../"
SAVE_PREDICTIONS_TO="..."
# PREPROCESS TEXTS
def tokenize_abstracts(abstracts):
"""For given texts, adds '[CLS]' and '[SEP]' tokens
at the beginning and the end of each sentence, respectively.
"""
t_abstracts=[]
for abstract in abstracts:
t_abstract="[CLS] "
for sentence in tokenize.sent_tokenize(abstract):
t_abstract=t_abstract + sentence + " [SEP] "
t_abstracts.append(t_abstract)
return t_abstracts
tokenizer=BertTokenizer.from_pretrained('bert-base-multilingual-uncased')
def b_tokenize_abstracts(t_abstracts, max_len=512):
"""Tokenizes sentences with the help
of a 'bert-base-multilingual-uncased' tokenizer.
"""
b_t_abstracts=[tokenizer.tokenize(_)[:max_len] for _ in t_abstracts]
return b_t_abstracts
def convert_to_ids(b_t_abstracts):
"""Converts tokens to its specific
IDs in a bert vocabulary.
"""
input_ids=[tokenizer.convert_tokens_to_ids(_) for _ in b_t_abstracts]
return input_ids
def abstracts_to_ids(abstracts):
"""Tokenizes abstracts and converts
tokens to their specific IDs
in a bert vocabulary.
"""
tokenized_abstracts=tokenize_abstracts(abstracts)
b_tokenized_abstracts=b_tokenize_abstracts(tokenized_abstracts)
ids=convert_to_ids(b_tokenized_abstracts)
return ids
def pad_ids(input_ids, max_len=512):
"""Padds sequences of a given IDs.
"""
p_input_ids=pad_sequences(input_ids,
maxlen=max_len,
dtype="long",
truncating="post",
padding="post")
return p_input_ids
def create_attention_masks(inputs):
"""Creates attention masks
for a given seuquences.
"""
masks=[]
for sequence in inputs:
sequence_mask=[float(_>0) for _ in sequence]
masks.append(sequence_mask)
return masks
# PREDICT
def float_to_percent(float, decimal=3):
"""Takes a float from range 0. to 0.9... as an input
and converts it to a percentage with specified decimal places.
"""
return str(float*100)[:(decimal+3)]+"%"
def models_predict(directory, inputs, attention_masks, float_to_percent=False):
"""Loads separate .h5 models from a given directory.
For predictions, inputs are expected to be:
tensors of token's ids (bert vocab) and tensors of attention masks.
Output is of format:
{'model/target N': [the probability of a text N dealing with the target N , ...], ...}
"""
models=glob.glob(f"{directory}*.h5")
predictions_dict={}
for _ in models:
model=load_model(_)
print(f"Model {_} is loaded.")
predictions=model.predict_step([inputs, attention_masks])
print(f"Predictions from the model {_} are finished.")
predictions=[float(_) for _ in predictions]
if float_to_percent==True:
predictions=[float_to_percent(_) for _ in predictions]
predictions_dict[model.name]=predictions
print(f"Predictions from the model {_} are saved.")
del predictions, model
return predictions_dict
def predictions_dict_to_df(predictions_dictionary):
"""Converts model's predictions of format:
{'model/target N': [the probability of a text N dealing with the target N , ...], ...}
to a dataframe of format:
| text N | the probability of the text N dealing with the target N | ... |
"""
predictions_df=pd.DataFrame(predictions_dictionary)
predictions_df.columns=[_.replace("model_", "").replace("_", ".") for _ in predictions_df.columns]
predictions_df.insert(0, column="text", value=[_ for _ in range(len(predictions_df))])
return predictions_df
def predictions_above_treshold(predictions_dataframe, treshold=0.95):
"""Filters predictions above specified treshold.
Input is expected to be a dataframe of format:
| text N | the probability of the text N dealing with the target N | ... |
Output is of format:
{text N: [target N dealing with probability > trheshold with text N, ...], ...}
"""
above_treshold_dict={}
above_treshold=predictions_dataframe.iloc[:,1:].apply(lambda row: row[row > treshold].index, axis=1)
for _ in range(len(above_treshold)):
above_treshold_dict[_]=list(above_treshold[_])
return above_treshold_dict
# RUN
marks=[_ for _ in range(int(len(DATA)/100))]
output=pd.DataFrame()
for _ in marks:
if _ == 0:
abstracts=DATA[_: (_+1)*100]
else:
abstracts=DATA[_*100: (_+1)*100]
ids=abstracts_to_ids(abstracts)
padded_ids=pad_ids(ids)
masks=create_attention_masks(padded_ids)
masks=convert_to_tensor(masks)
inputs=convert_to_tensor(padded_ids)
predictions=models_predict(MODELS, inputs, masks)
predictions_df=predictions_dict_to_df(predictions)
output=output.append(predictions_df)
del abstracts, predictions, predictions_df
if len(DATA)!=((marks[-1]+1)*100):
rest_idx=((marks[-1]+1)*100)
abstracts=DATA[rest_idx:]
ids=abstracts_to_ids(abstracts)
padded_ids=pad_ids(ids)
masks=create_attention_masks(padded_ids)
masks=convert_to_tensor(masks)
inputs=convert_to_tensor(padded_ids)
predictions=models_predict(MODELS, inputs, masks)
predictions_df=predictions_dict_to_df(predictions)
output=output.append(predictions_df)
del abstracts, predictions, predictions_df
output.to_excel("SAVE_PREDICTIONS_TO/predictions.xlsx", index=False)
|