IUM_10

.gitignore

@@ -0,0 +1,4 @@
+/beer_reviews_train.csv
+/beer_reviews_test.csv
+/beer_review_sentiment_model.h5
+/beer_review_sentiment_predictions.csv

IUM_05-model.py

@@ -1,5 +1,6 @@
 import pandas as pd
 import tensorflow as tf
+import sys
 
 train_data = pd.read_csv('./beer_reviews_train.csv')
 X_train = train_data[['review_aroma', 'review_appearance', 'review_palate', 'review_taste']]
@@ -22,6 +23,6 @@ model.compile(optimizer='adam',
               loss='binary_crossentropy',
               metrics=['accuracy'])
 
-model.fit(X_train_pad, y_train, epochs=40, batch_size=32, validation_split=0.1)
+model.fit(X_train_pad, y_train, epochs=int(sys.argv[1]), batch_size=int(sys.argv[2]), validation_split=0.1)
 
 model.save('beer_review_sentiment_model.h5')

IUM_05-predict.py

@@ -1,18 +1,18 @@
 import pandas as pd
-import numpy as np
 import tensorflow as tf
 
 test_data = pd.read_csv('./beer_reviews_test.csv')
 X_test = test_data[['review_aroma', 'review_appearance', 'review_palate', 'review_taste']]
+y_test = test_data['review_overall']
 
 model = tf.keras.models.load_model('beer_review_sentiment_model.h5')
 
-tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words=10000)
+predictions = model.predict(X_test)
 
+print(f'Predictions shape: {predictions.shape}')
 
-X_test_seq = tokenizer.texts_to_sequences(X_test)
-X_test_pad = tf.keras.preprocessing.sequence.pad_sequences(X_test_seq, maxlen=100)
+if len(predictions.shape) > 1:
+    predictions = predictions[:, 0]
 
-predictions = model.predict(X_test_pad)
-
-np.savetxt('beer_review_sentiment_predictions.csv', predictions, delimiter=',', fmt='%.10f')
+results = pd.DataFrame({'Predictions': predictions, 'Actual': y_test})
+results.to_csv('beer_review_sentiment_predictions.csv', index=False)

IUM_05-split.py

@@ -1,7 +1,7 @@
 import pandas as pd
 from sklearn.model_selection import train_test_split
 
-data = pd.read_csv('./beer_reviews.csv')
+data = pd.read_csv('data/beer_reviews.csv')
 
 train_data, test_data = train_test_split(data, test_size=0.2, random_state=42)
 

dvc.lock

@@ -0,0 +1,46 @@
+schema: '2.0'
+stages:
+  split_data:
+    cmd: python IUM_05-split.py
+    deps:
+    - path: data/beer_reviews.csv
+      hash: md5
+      md5: 50f6eec0d0fe78bc0f10e35edd271998
+      size: 201644905
+    outs:
+    - path: beer_reviews_test.csv
+      hash: md5
+      md5: edbd0a7f05c59a0c0e936917f60e9b96
+      size: 40632354
+    - path: beer_reviews_train.csv
+      hash: md5
+      md5: 8c6877a26fef1542369bfae6b39d163c
+      size: 162599343
+  train_model:
+    cmd: python IUM_05-model.py 10 32
+    deps:
+    - path: beer_reviews_train.csv
+      hash: md5
+      md5: 8c6877a26fef1542369bfae6b39d163c
+      size: 162599343
+    outs:
+    - path: beer_review_sentiment_model.h5
+      hash: md5
+      md5: c126bd5d332a905262c66894585450e3
+      size: 1950856
+  predict:
+    cmd: python IUM_05-predict.py
+    deps:
+    - path: beer_review_sentiment_model.h5
+      hash: md5
+      md5: c126bd5d332a905262c66894585450e3
+      size: 1950856
+    - path: beer_reviews_test.csv
+      hash: md5
+      md5: edbd0a7f05c59a0c0e936917f60e9b96
+      size: 40632354
+    outs:
+    - path: beer_review_sentiment_predictions.csv
+      hash: md5
+      md5: 12a66fafb7f4d7d19eb0c4a90cc7d3ad
+      size: 4814242

dvc.yaml

@@ -0,0 +1,23 @@
+stages:
+  split_data:
+    cmd: python IUM_05-split.py
+    deps:
+      - data/beer_reviews.csv
+    outs:
+      - beer_reviews_train.csv
+      - beer_reviews_test.csv
+
+  train_model:
+    cmd: python IUM_05-model.py 10 32
+    deps:
+      - beer_reviews_train.csv
+    outs:
+      - beer_review_sentiment_model.h5
+
+  predict:
+    cmd: python IUM_05-predict.py
+    deps:
+      - beer_review_sentiment_model.h5
+      - beer_reviews_test.csv
+    outs:
+      - beer_review_sentiment_predictions.csv