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import pandas as pd
import numpy as np
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.optimizers import Adamax
from tensorflow.keras.metrics import Precision, Recall
from tensorflow.keras.layers import Dense, ReLU
from tensorflow.keras.layers import Embedding, BatchNormalization, Concatenate
from tensorflow.keras.layers import Conv1D, GlobalMaxPooling1D, Dropout
from tensorflow.keras.models import Sequential, Model
from sklearn.preprocessing import LabelEncoder
from tensorflow.keras.preprocessing.sequence import pad_sequences
from keras.utils import to_categorical
from sklearn.model_selection import train_test_split
import pickle
from tensorflow.keras.preprocessing.sequence import pad_sequences
import pickle
from tensorflow.keras.models import load_model
def prep_data():
# Assuming df is your DataFrame and you want to split based on 'col' column
# You can adjust the test_size and val_size to change the split proportions
train_size = 0.9
test_size = 0.05
val_size = 0.05
df = pd.read_csv('../../data/output/decisions.csv')
df = df[['text', 'decision']]
# First split into train and (test + val)
df, test_val_df = train_test_split(df, test_size=(test_size + val_size), random_state=42)
# Then split test_val_df into test and validation sets
test_df, val_df = train_test_split(test_val_df, test_size=val_size/(test_size + val_size), random_state=42)
return df, test_df, val_df
def split_data():
df, test_df, val_df = prep_data()
X_train = df['text']
y_train = df['decision']
X_test = test_df['text']
y_test = test_df['decision']
X_val = val_df['text']
y_val = val_df['decision']
encoder = LabelEncoder()
y_train = encoder.fit_transform(y_train)
y_val = encoder.transform(y_val)
y_test = encoder.transform(y_test)
mapping = dict(zip(encoder.classes_, range(len(encoder.classes_))))
return X_train, y_train, X_test, y_test, X_val, y_val, mapping
def prep_model():
max_words = 10000
max_len = 50
embedding_dim = 32
# Branch 1
branch1 = Sequential()
branch1.add(Embedding(max_words, embedding_dim, input_length=max_len))
branch1.add(Conv1D(64, 3, padding='same', activation='relu'))
branch1.add(BatchNormalization())
branch1.add(ReLU())
branch1.add(Dropout(0.5))
branch1.add(GlobalMaxPooling1D())
# Branch 2
branch2 = Sequential()
branch2.add(Embedding(max_words, embedding_dim, input_length=max_len))
branch2.add(Conv1D(64, 3, padding='same', activation='relu'))
branch2.add(BatchNormalization())
branch2.add(ReLU())
branch2.add(Dropout(0.5))
branch2.add(GlobalMaxPooling1D())
concatenated = Concatenate()([branch1.output, branch2.output])
hid_layer = Dense(128, activation='relu')(concatenated)
dropout = Dropout(0.3)(hid_layer)
output_layer = Dense(2, activation='softmax')(dropout)
model = Model(inputs=[branch1.input, branch2.input], outputs=output_layer)
model.compile(optimizer='adamax',
loss='binary_crossentropy',
metrics=['accuracy', Precision(), Recall()])
return model
def train_model():
X_train, y_train, X_test, y_test, X_val, y_val, mapping = split_data()
tokenizer = Tokenizer(num_words=10000)
tokenizer.fit_on_texts(X_train)
sequences = tokenizer.texts_to_sequences(X_train)
tr_x = pad_sequences(sequences, maxlen=50)
tr_y = to_categorical(y_train)
sequences = tokenizer.texts_to_sequences(X_val)
val_x = pad_sequences(sequences, maxlen=50)
val_y = to_categorical(y_val)
sequences = tokenizer.texts_to_sequences(X_test)
ts_x = pad_sequences(sequences, maxlen=50)
ts_y = to_categorical(y_test)
model = prep_model()
batch_size = 256
epochs = 100
history = model.fit([tr_x, tr_x], tr_y, epochs=epochs, batch_size=batch_size,
validation_data=([val_x, val_x], val_y))
with open('../../data/models/dec_clf/tokenizer.pkl', 'wb') as tokenizer_file:
pickle.dump(tokenizer, tokenizer_file)
model.save('../../data/models/dec_clf/nlp.h5')
def predict(text, model_path, token_path):
model = load_model(model_path)
with open(token_path, 'rb') as f:
tokenizer = pickle.load(f)
sequences = tokenizer.texts_to_sequences([text])
x_new = pad_sequences(sequences, maxlen=50)
predictions = model.predict([x_new, x_new])
mapping = {0: 'no', 1: 'yes'}
probs = list(predictions[0])
max_idx = np.argmax(probs)
return mapping[max_idx]
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