055: Adding word level examples

1 files changed, 56 insertions, 47 deletions

diff --git a/examples/word_level/wmt_2018/de_en/microtransquest_zeroshot.py b/examples/word_level/wmt_2018/de_en/microtransquest_zeroshot.py
index 9db2d11..1b08bc5 100644
--- a/examples/word_level/wmt_2018/de_en/microtransquest_zeroshot.py
+++ b/examples/word_level/wmt_2018/de_en/microtransquest_zeroshot.py
@@ -1,6 +1,5 @@
 import os
 
-from examples.word_level.common.download import download_from_google_drive
 from examples.word_level.common.util import reader
 from examples.word_level.wmt_2018.de_en.microtransquest_config import microtransquest_config, TEST_PATH, \
     TEST_SOURCE_FILE, \
@@ -11,52 +10,62 @@ from transquest.algo.word_level.microtransquest.run_model import MicroTransQuest
 if not os.path.exists(TEMP_DIRECTORY):
     os.makedirs(TEMP_DIRECTORY)
 
+models = {
+    "en_cs": "/content/drive/MyDrive/TransQuestModels/MicroTransQuest/wmt2018/en_cs_smt_it/best_model",
+    "en_de_nmt": "/content/drive/MyDrive/TransQuestModels/MicroTransQuest/wmt2018/en_de_nmt_it/best_model",
+    "en_de_smt": "/content/drive/MyDrive/TransQuestModels/MicroTransQuest/wmt2018/en_de_smt_it/best_model",
+    "en_lv_nmt": "/content/drive/MyDrive/TransQuestModels/MicroTransQuest/wmt2018/en_lv_nmt_pharmaceutical/best_model",
+    "en_lv_smt": "/content/drive/MyDrive/TransQuestModels/MicroTransQuest/wmt2018/en_lv_smt_pharmaceutical/best_model"
+}
 
-model = MicroTransQuestModel(MODEL_TYPE, "/content/drive/MyDrive/TransQuestModels/MicroTransQuest/wmt2018/de_en_smt_pharmaceutical/best_model", labels=["OK", "BAD"],
-                                     args=microtransquest_config)
+for language, path in models.items():
 
-if not os.path.exists(TEMP_DIRECTORY):
-    os.makedirs(TEMP_DIRECTORY)
+    if not os.path.exists(TEMP_DIRECTORY, language):
+        os.makedirs(TEMP_DIRECTORY, language)
+
+    model = MicroTransQuestModel(MODEL_TYPE, path, labels=["OK", "BAD"], args=microtransquest_config)
+
+    if not os.path.exists(TEMP_DIRECTORY):
+        os.makedirs(TEMP_DIRECTORY)
+
+    raw_test_df = reader(TEST_PATH, microtransquest_config, TEST_SOURCE_FILE, TEST_TARGET_FILE)
+
+    test_sentences = prepare_testdata(raw_test_df, args=microtransquest_config)
+
+    fold_sources_tags = []
+    fold_targets_tags = []
+
+    predicted_labels, raw_predictions = model.predict(test_sentences, split_on_space=True)
+    sources_tags, targets_tags = post_process(predicted_labels, test_sentences, args=microtransquest_config)
+
+    test_source_sentences = raw_test_df[microtransquest_config["source_column"]].tolist()
+    test_target_sentences = raw_test_df[microtransquest_config["target_column"]].tolist()
+
+    with open(os.path.join(TEMP_DIRECTORY, language, TEST_SOURCE_TAGS_FILE), 'w') as f:
+        for sentence_id, (test_source_sentence, source_prediction) in enumerate(
+                zip(test_source_sentences, sources_tags)):
+            words = test_source_sentence.split()
+            for word_id, (word, word_prediction) in enumerate(zip(words, source_prediction)):
+                f.write("MicroTransQuest" + "\t" + "source" + "\t" +
+                        str(sentence_id) + "\t" + str(word_id) + "\t"
+                        + word + "\t" + word_prediction + '\n')
 
-raw_test_df = reader(TEST_PATH, microtransquest_config, TEST_SOURCE_FILE, TEST_TARGET_FILE)
-
-test_sentences = prepare_testdata(raw_test_df, args=microtransquest_config)
-
-fold_sources_tags = []
-fold_targets_tags = []
-
-
-predicted_labels, raw_predictions = model.predict(test_sentences, split_on_space=True)
-sources_tags, targets_tags = post_process(predicted_labels, test_sentences, args=microtransquest_config)
-
-test_source_sentences = raw_test_df[microtransquest_config["source_column"]].tolist()
-test_target_sentences = raw_test_df[microtransquest_config["target_column"]].tolist()
-
-with open(os.path.join(TEMP_DIRECTORY, TEST_SOURCE_TAGS_FILE), 'w') as f:
-    for sentence_id, (test_source_sentence, source_prediction) in enumerate(
-            zip(test_source_sentences, sources_tags)):
-        words = test_source_sentence.split()
-        for word_id, (word, word_prediction) in enumerate(zip(words, source_prediction)):
-            f.write("MicroTransQuest" + "\t" + "source" + "\t" +
-                    str(sentence_id) + "\t" + str(word_id) + "\t"
-                    + word + "\t" + word_prediction + '\n')
-
-with open(os.path.join(TEMP_DIRECTORY, TEST_TARGET_TAGS_FILE), 'w') as target_f, open(
-        os.path.join(TEMP_DIRECTORY, TEST_TARGET_GAPS_FILE), 'w') as gap_f:
-    for sentence_id, (test_sentence, target_prediction) in enumerate(zip(test_sentences, targets_tags)):
-        target_sentence = test_sentence.split("[SEP]")[1]
-        words = target_sentence.split()
-        # word_predictions = target_prediction.split()
-        gap_index = 0
-        word_index = 0
-        for word_id, (word, word_prediction) in enumerate(zip(words, target_prediction)):
-            if word_id % 2 == 0:
-                gap_f.write("MicroTransQuest" + "\t" + "gap" + "\t" +
-                            str(sentence_id) + "\t" + str(gap_index) + "\t"
-                            + "gap" + "\t" + word_prediction + '\n')
-                gap_index += 1
-            else:
-                target_f.write("MicroTransQuest" + "\t" + "mt" + "\t" +
-                               str(sentence_id) + "\t" + str(word_index) + "\t"
-                               + word + "\t" + word_prediction + '\n')
-                word_index += 1
+    with open(os.path.join(TEMP_DIRECTORY, language, TEST_TARGET_TAGS_FILE), 'w') as target_f, open(
+            os.path.join(TEMP_DIRECTORY, TEST_TARGET_GAPS_FILE), 'w') as gap_f:
+        for sentence_id, (test_sentence, target_prediction) in enumerate(zip(test_sentences, targets_tags)):
+            target_sentence = test_sentence.split("[SEP]")[1]
+            words = target_sentence.split()
+            # word_predictions = target_prediction.split()
+            gap_index = 0
+            word_index = 0
+            for word_id, (word, word_prediction) in enumerate(zip(words, target_prediction)):
+                if word_id % 2 == 0:
+                    gap_f.write("MicroTransQuest" + "\t" + "gap" + "\t" +
+                                str(sentence_id) + "\t" + str(gap_index) + "\t"
+                                + "gap" + "\t" + word_prediction + '\n')
+                    gap_index += 1
+                else:
+                    target_f.write("MicroTransQuest" + "\t" + "mt" + "\t" +
+                                   str(sentence_id) + "\t" + str(word_index) + "\t"
+                                   + word + "\t" + word_prediction + '\n')
+                    word_index += 1