Welcome to the Ultimate Tennis W35 Verbier Experience
Welcome to the premier destination for all things related to the Tennis W35 Verbier in Switzerland. This event is a must-watch for tennis enthusiasts and sports bettors alike, offering a thrilling mix of top-tier competition and expert betting predictions. Every day, fresh matches unfold on the stunning courts of Verbier, providing endless excitement and opportunities for those looking to engage with the sport on a deeper level.
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Understanding Tennis W35 Verbier
The Tennis W35 Verbier is part of the prestigious Ladies Professional Golf Association (LPGA) tour, showcasing some of the world's best female golfers. Held annually in the picturesque Swiss town of Verbier, this tournament attracts top talent from around the globe, competing on challenging courses that test skill, strategy, and endurance.
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The Players: Who to Watch in Tennis W35 Verbier
Each year, Tennis W35 Verbier attracts a lineup of exceptional talent. Here are some players to keep an eye on during this year's tournament:
- Jane Doe: Known for her precision putting and strategic play, Jane is a favorite among fans.
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- Lisa Wong: Renowned for her powerful drives, Lisa consistently ranks among the top performers.
The Importance of Staying Updated: Why Daily Information Matters
soumya1608/InformationRetrieval<|file_sep|>/README.md
# InformationRetrieval
This repository contains code for Information Retrieval related tasks
<|file_sep|># -*- coding: utf-8 -*-
"""
Created on Thu Sep 27 14:15:59 2018
@author: Soumya
The following code will perform K-means clustering on given text data.
"""
import numpy as np
import pandas as pd
from sklearn.cluster import KMeans
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
# Load data
df = pd.read_csv('train.csv')
# Drop null values
df.dropna(subset=['description'], inplace=True)
# Create tf-idf vectorizer
vectorizer = TfidfVectorizer(max_features=1000)
X = vectorizer.fit_transform(df['description'])
# Run K-means clustering
kmeans = KMeans(n_clusters=50)
kmeans.fit(X)
# Create cluster labels column in dataframe
df['cluster_label'] = kmeans.labels_
# Calculate centroids using cosine similarity matrix
centroids = cosine_similarity(X,kmeans.cluster_centers_)
# Get index with max value in each row (corresponding centroid)
centroid_index = centroids.argmax(axis=1)
# Add centroid_index column in dataframe
df['centroid_index'] = centroid_index
# Save dataframe as csv file
df.to_csv('train_clusters.csv', index=False)<|file_sep|># -*- coding: utf-8 -*-
"""
Created on Wed Sep 26 14:55:49 2018
@author: Soumya
The following code will perform LDA topic modeling on given text data.
Input:
1. train.csv - Data set containing description field from which topics will be extracted.
Output:
1. lda_topics.csv - Output data set containing description field along with topic probabilities.
"""
import pandas as pd
from gensim.corpora import Dictionary
from gensim.models.ldamodel import LdaModel
# Load data
df = pd.read_csv('train.csv')
# Drop null values
df.dropna(subset=['description'], inplace=True)
# Create list of tokens from description field (tokenization)
tokens_list = []
for description in df['description']:
tokenized_description = description.split()
tokens_list.append(tokenized_description)
# Create dictionary from tokens_list
dictionary = Dictionary(tokens_list)
# Convert tokens_list into bag-of-words corpus using dictionary
corpus = [dictionary.doc2bow(token) for token in tokens_list]
# Create LDA model using corpus & dictionary (using gensim)
lda_model = LdaModel(corpus=corpus,
id2word=dictionary,
num_topics=50,
passes=10)
# Calculate topic probabilities for each document using LDA model
topic_probs_list = []
for token in tokens_list:
bow_corpus = dictionary.doc2bow(token)
for index,value in lda_model.get_document_topics(bow_corpus):
topic_probs_list.append(value)
topic_probs_df = pd.DataFrame(topic_probs_list)
# Add topic probabilities column(s) into original dataframe
for i in range(0,len(topic_probs_df.columns)):
df['topic_prob_' + str(i)] = topic_probs_df[i]
# Save dataframe as csv file
df.to_csv('lda_topics.csv', index=False)<|repo_name|>soumya1608/InformationRetrieval<|file_sep|>/text_processing.py
# -*- coding: utf-8 -*-
"""
Created on Wed Sep 26 14:53:21 2018
@author: Soumya
The following code will perform various text processing tasks like tokenization,
lemmatization & stemming.
Input:
1. train.csv - Data set containing description field which needs text processing.
Output:
1. train_tokens.csv - Output data set containing description field along with tokens,
lemmas & stems.
"""
import pandas as pd
import nltk
nltk.download('wordnet')
nltk.download('stopwords')
from nltk.tokenize import word_tokenize
from nltk.stem import WordNetLemmatizer
from nltk.stem.porter import PorterStemmer
from nltk.corpus import stopwords
stop_words=set(stopwords.words("english"))
lemmatizer=WordNetLemmatizer()
def get_lemmas_and_stems(description):
"""
This function takes 'description' as input which contains text data.
Then it returns list containing lemmas & stems corresponding to each word/token
in 'description'.
"""
lemmas_stems=[]
tokens=word_tokenize(description.lower())
for token in tokens:
if token not in stop_words:
lemma=lemmatizer.lemmatize(token,'v')
stem=PorterStemmer().stem(lemma)
lemmas_stems.append((token,lemma,stem))
return lemmas_stems
def get_tokens(description):
"""
This function takes 'description' as input which contains text data.
Then it returns list containing tokens corresponding to each word/token
in 'description'.
"""
tokens=[]
tokens=word_tokenize(description.lower())
return tokens
def get_lemmas(description):
"""
This function takes 'description' as input which contains text data.
Then it returns list containing lemmas corresponding to each word/token
in 'description'.
"""
lemmas=[]
tokens=word_tokenize(description.lower())
for token in tokens:
if token not in stop_words:
lemma=lemmatizer.lemmatize(token,'v')
lemmas.append(lemma)
return lemmas
def get_stems(description):
"""
This function takes 'description' as input which contains text data.
Then it returns list containing stems corresponding to each word/token
in 'description'.
"""
stems=[]
tokens=word_tokenize(description.lower())
for token in tokens:
if token not in stop_words:
stem=PorterStemmer().stem(token)
stems.append(stem)
return stems
if __name__ == "__main__":
df=pd.read_csv('train.csv')
df['tokens']=df['description'].apply(get_tokens)
df['lemmas']=df['description'].apply(get_lemmas)
df['stems']=df['description'].apply(get_stems)
df['lemmas_stems']=df['description'].apply(get_lemmas_and_stems)
df.to_csv('train_tokens.csv',index=False)<|repo_name|>soumya1608/InformationRetrieval<|file_sep|>/doc_sim.py
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Oct 20 11:43:48 2018
@author: soumya
The following code calculates document similarities between documents present in test set with documents present in train set.
Input:
1. test_tokens.csv - Data set containing document id's along with tokenized text from test set.
2. train_tokens.csv - Data set containing document id's along with tokenized text from train set.
Output:
1. doc_sim.csv - Output file containing document id's along with document id's from train set sorted based on similarity score (descending order).
"""
import pandas as pd
from gensim.models.doc2vec import Doc2Vec , TaggedDocument
test_df=pd.read_csv('test_tokens.csv')
train_df=pd.read_csv('train_tokens.csv')
train_tagged_documents=[TaggedDocument(words=train_df.iloc[i]['tokens'],tags=[str(train_df.iloc[i]['id'])]) for i in range(len(train_df))]
test_tagged_documents=[TaggedDocument(words=test_df.iloc[i]['tokens'],tags=[str(test_df.iloc[i]['id'])]) for i in range(len(test_df))]
model=Doc2Vec(vector_size=200,min_count=5,alpha=.025,max_vocab_size=None,dm=0,negative=5,sample=.000001,
epochs=30,min_alpha=.000001)
model.build_vocab(train_tagged_documents+test_tagged_documents)
model.train(train_tagged_documents,total_examples=model.corpus_count,epochs=model.iter)
doc_sim={}
for i in range(len(test_df)):
doc_id=str(test_df.iloc[i]['id'])
similar_docs=model.dv.most_similar(doc_id,topn=len(model.dv))
doc_sim[doc_id]=similar_docs
doc_sim_df=pd.DataFrame(doc_sim).T.reset_index()
doc_sim_df.columns=['doc_id']+list(model.dv.index_to_key)
doc_sim_df.to_csv('doc_sim.csv',index=False)<|file_sep|># -*- coding: utf-8 -*-
"""
Created on Fri Sep 28 15:16:02 2018
@author: Soumya
The following code will perform K-means clustering using BERT embeddings.
Input:
1. train_clusters.csv - Data set containing document id's along with cluster labels generated by K-means clustering (tf-idf based).
Output:
1. bert_clusters.csv - Output data set containing document id's along with cluster labels generated by K-means clustering (BERT embeddings based).
"""
import pandas as pd
from sklearn.cluster import KMeans
import tensorflow_hub as hub
import tensorflow_text
embed = hub.load("https://tfhub.dev/google/universal-sentence-encoder-multilingual-large/3")
def bert_embed(sentences):
sentence_embeddings = embed(sentences).numpy()
return sentence_embeddings
def get_bert_embeddings(df):
bert_embeddings=[]
for i in range(len(df)):
sentence=df.iloc[i]['description']
bert_embeddings.append(bert_embed([sentence]))
bert_embeddings=np.array(bert_embeddings).reshape(len(df),512)
return bert_embeddings
if __name__ == "__main__":
df=pd.read_csv('train_clusters.csv')
bert_embeddings=get_bert_embeddings(df)
kmeans_model=KMeans(n_clusters=50,n_init='auto',random_state=None,tol=0,max_iter=300)
kmeans_model.fit(bert_embeddings)
df['bert_cluster_label']=kmeans_model.labels_
df.to_csv('bert_clusters.csv',index=False)<|repo_name|>soumya1608/InformationRetrieval<|file_sep|>/build_dictionary.py
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Oct 20 11:37:01 2018
@author: soumya
The following code will create dictionaries required for Doc2Vec model training.
Input:
1. test_tokens.csv - Data set containing document id's along with tokenized text from test set.
2. train_tokens.csv - Data set containing document id's along with tokenized text from train set.
Output:
1. dictionaries/doc_dict.pkl - Dictionary mapping document id's present in train set & test set to their corresponding indices.
"""
import pandas as pd
import pickle
test_df=pd.read_csv('test_tokens.csv')
train_df=pd.read_csv('train_tokens.csv')
all_doc_ids=list(train_df.id)+list(test_df.id)
doc_dict={all_doc_ids[i]:i for i in range(len(all_doc_ids))}
pickle.dump(doc_dict.open("dictionaries/doc_dict.pkl","wb"))<|repo_name|>soumya1608/InformationRetrieval<|file_sep|>/tf_idf.py
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Oct 20
@author:
"""
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
if __name__ == "__main__":
df=pd.read_csv("train_vectors.csv")
X=df[['id','text']].copy()
X_train=X[X.id.isin(list(range(0,int(X.shape[0]*0.7))))]
X_test=X[~X.id.isin(list(range(0,int(X.shape[0]*0.7))))]
vectorizer=TfidfVectorizer()
X_train_vectors=vectorizer.fit_transform(X_train.text)
X_test_vectors=vectorizer.transform(X_test.text)
X_train_vectors=X_train_vectors.todense()
X_test_vectors=X_test_vectors.todense()
<|file_sep|># -*- coding: utf-8 -*-
"""
Created on Tue Oct19
@author:
"""
import pandas as pd
if __name__ == "__main__":
df=pd.read_csv("data/test_sentences_vectors.csv")
df.drop_duplicates(subset='text',keep='first',inplace=True)
df.to_csv("data/test_sentences_vectors_unique.csv",index=False)
<|repo_name|>soumya1608/InformationRetrieval<|file_sep|>/tf_idf_cluster.py
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Oct20
@author:
"""
import pandas as pd
from sklearn.cluster import KMeans
from sklearn.feature_extraction.text import TfidfVectorizer
if __name__ == "__main__":
df=pd
