error in finding similarity using NFM and Tfidf for a Data set for tag “unknown”
$begingroup$
import pandas as pd
df = pd.read_csv('india-news-headlines.csv')
df.head()
nf = ' '.join(df['headline_text'].tolist())
Labels = df['headline_category'][:1000]
News = df['headline_text'][:1000]
hf = pd.DataFrame({'Category':Labels, 'Headlines': News})
from sklearn.feature_extraction.text import TfidfVectorizer
tfidf = TfidfVectorizer()
features = tfidf.fit_transform(hf['Category']).toarray()
features.shape
Perform the necessary imports
from sklearn.decomposition import NMF
from sklearn.preprocessing import MaxAbsScaler, Normalizer
from sklearn.pipeline import make_pipeline
Create a MaxAbsScaler: scaler
scaler = MaxAbsScaler()
Create an NMF model: nmf
nmf = NMF(n_components=10)
Create a Normalizer: normalizer
normalizer = Normalizer()
Create a pipeline: pipeline
pipeline = make_pipeline(scaler, nmf, normalizer)
Apply fit_transform to artists: norm_features
norm_features = pipeline.fit_transform(features)
Import pandas
import pandas as pd
Create a DataFrame: df
nf = pd.DataFrame(norm_features, index=Labels)
Select row of 'Bruce Springsteen': artist
artist = nf.loc['unknown']
Compute cosine similarities: similarities
similarities = nf.dot(artist.T)
Display those with highest cosine similarity
print(similarities.nlargest( ))
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$endgroup$
add a comment |
$begingroup$
import pandas as pd
df = pd.read_csv('india-news-headlines.csv')
df.head()
nf = ' '.join(df['headline_text'].tolist())
Labels = df['headline_category'][:1000]
News = df['headline_text'][:1000]
hf = pd.DataFrame({'Category':Labels, 'Headlines': News})
from sklearn.feature_extraction.text import TfidfVectorizer
tfidf = TfidfVectorizer()
features = tfidf.fit_transform(hf['Category']).toarray()
features.shape
Perform the necessary imports
from sklearn.decomposition import NMF
from sklearn.preprocessing import MaxAbsScaler, Normalizer
from sklearn.pipeline import make_pipeline
Create a MaxAbsScaler: scaler
scaler = MaxAbsScaler()
Create an NMF model: nmf
nmf = NMF(n_components=10)
Create a Normalizer: normalizer
normalizer = Normalizer()
Create a pipeline: pipeline
pipeline = make_pipeline(scaler, nmf, normalizer)
Apply fit_transform to artists: norm_features
norm_features = pipeline.fit_transform(features)
Import pandas
import pandas as pd
Create a DataFrame: df
nf = pd.DataFrame(norm_features, index=Labels)
Select row of 'Bruce Springsteen': artist
artist = nf.loc['unknown']
Compute cosine similarities: similarities
similarities = nf.dot(artist.T)
Display those with highest cosine similarity
print(similarities.nlargest( ))
recommender-system
asked 7 mins ago
manoj kumarmanoj kumar
1
New contributor
manoj kumar is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
import pandas as pd
df = pd.read_csv('india-news-headlines.csv')
df.head()
nf = ' '.join(df['headline_text'].tolist())
Labels = df['headline_category'][:1000]
News = df['headline_text'][:1000]
hf = pd.DataFrame({'Category':Labels, 'Headlines': News})
from sklearn.feature_extraction.text import TfidfVectorizer
tfidf = TfidfVectorizer()
features = tfidf.fit_transform(hf['Category']).toarray()
features.shape
Perform the necessary imports
from sklearn.decomposition import NMF
from sklearn.preprocessing import MaxAbsScaler, Normalizer
from sklearn.pipeline import make_pipeline
Create a MaxAbsScaler: scaler
scaler = MaxAbsScaler()
Create an NMF model: nmf
nmf = NMF(n_components=10)
Create a Normalizer: normalizer
normalizer = Normalizer()
Create a pipeline: pipeline
pipeline = make_pipeline(scaler, nmf, normalizer)
Apply fit_transform to artists: norm_features
norm_features = pipeline.fit_transform(features)
Import pandas
import pandas as pd
Create a DataFrame: df
nf = pd.DataFrame(norm_features, index=Labels)
Select row of 'Bruce Springsteen': artist
artist = nf.loc['unknown']
Compute cosine similarities: similarities
similarities = nf.dot(artist.T)
Display those with highest cosine similarity
print(similarities.nlargest( ))
recommender-system
asked 7 mins ago
manoj kumarmanoj kumar
1
New contributor
manoj kumar is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
import pandas as pd
df = pd.read_csv('india-news-headlines.csv')
df.head()
nf = ' '.join(df['headline_text'].tolist())
Labels = df['headline_category'][:1000]
News = df['headline_text'][:1000]
hf = pd.DataFrame({'Category':Labels, 'Headlines': News})
from sklearn.feature_extraction.text import TfidfVectorizer
tfidf = TfidfVectorizer()
features = tfidf.fit_transform(hf['Category']).toarray()
features.shape
Perform the necessary imports
from sklearn.decomposition import NMF
from sklearn.preprocessing import MaxAbsScaler, Normalizer
from sklearn.pipeline import make_pipeline
Create a MaxAbsScaler: scaler
scaler = MaxAbsScaler()
Create an NMF model: nmf
nmf = NMF(n_components=10)
Create a Normalizer: normalizer
normalizer = Normalizer()
Create a pipeline: pipeline
pipeline = make_pipeline(scaler, nmf, normalizer)
Apply fit_transform to artists: norm_features
norm_features = pipeline.fit_transform(features)
Import pandas
import pandas as pd
Create a DataFrame: df
nf = pd.DataFrame(norm_features, index=Labels)
Select row of 'Bruce Springsteen': artist
artist = nf.loc['unknown']
Compute cosine similarities: similarities
similarities = nf.dot(artist.T)
Display those with highest cosine similarity
print(similarities.nlargest( ))
recommender-system
recommender-system
asked 7 mins ago
manoj kumarmanoj kumar
1
New contributor
manoj kumar is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 7 mins ago
manoj kumarmanoj kumar
1
New contributor
manoj kumar is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 7 mins ago
manoj kumarmanoj kumar
1
New contributor
manoj kumar is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 7 mins ago
manoj kumarmanoj kumar
1
asked 7 mins ago
manoj kumarmanoj kumar
1
1
New contributor
manoj kumar is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
manoj kumar is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
manoj kumar is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
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