Create tag vectors and video vectors

@@ -7,12 +7,12 @@ nltk.download('stopwords')
 vocab = pd.read_csv('../vocabulary.csv')
 vocab = pd.read_csv('../vocabulary.csv')
 
 
 # Lower corpus and Remove () from name.
 # Lower corpus and Remove () from name.
-vocab['WikiDescription'] = vocab['WikiDescription'].str.lower().str.replace('[^a-zA-Z]', ' ')
-vocab['Name'] = vocab['Name'].str.lower()
+vocab['WikiDescription'] = vocab['WikiDescription'].str.lower().str.replace('[^a-zA-Z0-9]', ' ')
 for i in range(vocab['Name'].__len__()):
 for i in range(vocab['Name'].__len__()):
     name = vocab['Name'][i]
     name = vocab['Name'][i]
     if isinstance(name, str) and name.find(" (") != -1:
     if isinstance(name, str) and name.find(" (") != -1:
         vocab['Name'][i] = name[:name.find(" (")]
         vocab['Name'][i] = name[:name.find(" (")]
+vocab['Name'] = vocab['Name'].str.lower()
 
 
 # Combine separated names.(mobile phone -> mobile-phone)
 # Combine separated names.(mobile phone -> mobile-phone)
 for name in vocab['Name']:
 for name in vocab['Name']:
@@ -35,8 +35,8 @@ phraser = gensim.models.phrases.Phraser(phrases)
 vocab_phrased = phraser[tokenlist]
 vocab_phrased = phraser[tokenlist]
 
 
 # Vectorize tags.
 # Vectorize tags.
-w2v = gensim.models.word2vec.Word2Vec(sentences=tokenlist, workers=2, min_count=1)
-w2v.save('tags_word2vec.model')
+w2v = gensim.models.word2vec.Word2Vec(sentences=tokenlist, min_count=1)
+w2v.save('tag_vectors.model')
 
 
 # word_vectors = w2v.wv
 # word_vectors = w2v.wv
 # vocabs = word_vectors.vocab.keys()
 # vocabs = word_vectors.vocab.keys()

-vid_id,seg1,seg2,seg3,seg4,seg5
-Ndaa,Sports car:0.202,Shower:0.200,Racing:0.200,Greeting card:0.200,Car:0.199
-Dvaa,Tractor:0.363,Motorsport:0.323,Dance:0.145,Flour:0.092,Cappuccino:0.076
-gEaa,Cooking:0.246,Food:0.243,Dish (food):0.224,Vegetable:0.167,:0.120
-Pwaa,Dance:0.633,Wing Chun:0.095,Pencil:0.095,Eye shadow:0.095,Rubber band:0.083
-jgaa,Concert:0.332,Motorsport:0.209,Motorcycling:0.194,Motorcycle:0.159,Bicycle:0.106
-1Yaa,Concert:0.249,Dance:0.191,Tuna:0.188,Airplane:0.187,Association football:0.185
-yVaa,Weight training:0.372,Sport utility vehicle:0.241,Barbell:0.147,Luxury yacht:0.123,Icing (food):0.117
-BCaa,Mobile phone:0.397,Smartphone:0.395,Dance:0.090,Samsung Galaxy:0.073,Alpine skiing:0.046
-38aa,Food:0.269,Gold:0.211,Raven (comics):0.208,Car:0.171,Marching band:0.141
-AFaa,Car:0.386,Sports car:0.276,Motorsport:0.202,Volkswagen:0.078,Food:0.058
-Ajaa,Concert:0.355,Soldier:0.289,Cello:0.146,Drum kit:0.114,Arena:0.096
-2Faa,Orchestra:0.424,Disc jockey:0.288,Inflatable boat:0.115,Vegetarian cuisine:0.096,Concert:0.077
-ujaa,Mobile phone:0.273,Smartphone:0.215,IPhone 5S:0.199,Acoustic guitar:0.170,Door:0.143
-e2aa,Food:0.319,Cooking:0.313,Dish (food):0.285,Pikachu:0.048,Headset (audio):0.036
-UTaa,Pet:0.376,Wig:0.172,Mobile phone:0.170,Easter egg:0.156,Food:0.126
-12aa,Railroad car:0.342,Train:0.300,Muffler:0.142,Car:0.115,BMW 3 Series:0.101
-Duaa,Jaguar Cars:0.379,MacBook Air:0.189,Ferrari F430:0.168,Coupon:0.137,Hang gliding:0.126
-cpab,Car:0.408,Sports car:0.254,Motorsport:0.139,Sedan (automobile):0.139,Racing:0.060
-4rab,Food:0.310,Cooking:0.286,Dish (food):0.265,Meat:0.100,Bee:0.040
-Vtab,Choir:0.228,Handball:0.201,Hot air balloon:0.200,Fishing:0.199,Sedan (automobile):0.172
-gkab,Pet:0.374,Mercedes-Benz C-Class:0.285,Cat:0.162,Belle (Disney):0.111,Electric car:0.068
-RJab,Beer:0.317,Electric car:0.268,Acoustic guitar:0.169,Eye shadow:0.162,Vending machine:0.084
-utab,Concert:0.303,Booster pack:0.279,Fishing:0.159,Culinary art:0.138,Hair coloring:0.121
-Aeab,Samurai:0.278,Fishing:0.240,Association football:0.167,Chevrolet Corvette:0.167,Slam dunk:0.148
-t4ab,Association football:0.520,Barbell:0.166,Teacher:0.105,Biceps curl:0.105,Parachute:0.104
-53ab,Food:0.315,Cooking:0.269,Dish (food):0.257,Concealer:0.113,Bowling ball:0.046
-kaab,Necktie:0.257,Primary school:0.209,Turbine:0.187,Guitar amplifier:0.184,Dance:0.163
-Kdab,Cooking:0.306,Food:0.217,Train:0.175,Acoustic guitar:0.166,Tram:0.137
-Smab,Association football:0.292,Airbus A320 family:0.210,Racing:0.167,Vampire:0.165,Robot:0.165
-rAab,Association football:0.559,Pool (cue sports):0.170,Full moon:0.111,Fishing bait:0.091,Eye liner:0.070
-U3ab,Bride:0.414,Mobile phone:0.267,Smartphone:0.133,Mercedes-Benz C-Class:0.106,Loudspeaker:0.080
-mBab,Food:0.281,Cooking:0.261,Dish (food):0.260,:0.144,Vegetable:0.054
-18ab,Cooking:0.243,Dish (food):0.241,Food:0.239,Vegetable:0.166,:0.112
-NKab,Apartment:0.309,Piano:0.201,Association football:0.179,Table (furniture):0.176,Television set:0.134

 import pandas as pd
 import pandas as pd
+import numpy as np
 from gensim.models import Word2Vec
 from gensim.models import Word2Vec
 
 
+BATCH_SIZE = 1000
+
 
 
 def vectorization_video():
 def vectorization_video():
     print('[0.1 0.2]')
     print('[0.1 0.2]')
 
 
 
 
 if __name__ == '__main__':
 if __name__ == '__main__':
-    tag_vectors = Word2Vec.load("esot3ria/tags_word2vec.model").wv
+    tag_vectors = Word2Vec.load("tag_vectors.model").wv
     video_vectors = Word2Vec().wv   # Empty model
     video_vectors = Word2Vec().wv   # Empty model
 
 
     # Load video recommendation tags.
     # Load video recommendation tags.
-    video_tags = pd.read_csv('esot3ria/video_recommendation_tags.csv')
+    video_tags = pd.read_csv('kaggle_solution_40k.csv')
+
+    # Define batch variables.
+    batch_video_ids = []
+    batch_video_vectors = []
+    error_tags = []
+
     for i, row in video_tags.iterrows():
     for i, row in video_tags.iterrows():
         video_id = row[0]
         video_id = row[0]
+        video_vector = np.zeros(100)
+        for segment_index in range(1, 6):
+            tag, weight = row[segment_index].split(":")
+            if tag in tag_vectors.vocab:
+                video_vector = video_vector + (tag_vectors[tag] * float(weight))
+            else:
+                # Pass if tag is unknown
+                if tag not in error_tags:
+                    error_tags.append(tag)
+
+        batch_video_ids.append(video_id)
+        batch_video_vectors.append(video_vector)
+        # Add video vectors.
+        if (i+1) % BATCH_SIZE == 0:
+            video_vectors.add(batch_video_ids, batch_video_vectors)
+            batch_video_ids = []
+            batch_video_vectors = []
+            print("Video vectors created: ", i+1)
+
+    # Add rest of video vectors.
+    video_vectors.add(batch_video_ids, batch_video_vectors)
+    print("error tags: ")
+    print(error_tags)
 
 
+    video_vectors.save("video_vectors.model")
 
 
+    # Usage
+    # video_vectors = Word2Vec().wv.load("video_vectors.model")
+    # video_vectors.most_similar("XwFj", topn=5)

@@ -377,7 +377,6 @@ def inference(reader, train_dir, data_pattern, out_file_location, batch_size,
           demoninator = float(temp[0][1] + temp[1][1] + temp[2][1] + temp[3][1] + temp[4][1])
           demoninator = float(temp[0][1] + temp[1][1] + temp[2][1] + temp[3][1] + temp[4][1])
           #for item in temp: 
           #for item in temp: 
           for itemIndex in range(0, top_k):
           for itemIndex in range(0, top_k):
-            # 20.05.31 Esot3riA
             # Normalize tag name
             # Normalize tag name
             segment_tag = str(voca_dict[str(temp[itemIndex][0])])
             segment_tag = str(voca_dict[str(temp[itemIndex][0])])
             normalized_tag = normalize_tag(segment_tag)
             normalized_tag = normalize_tag(segment_tag)