import spacy
import matplotlib.pyplot as plt
import seaborn as sns
from data import t0, t1, t2, t3, t4, t5, t6
from processing import tf_idf_scores
nlp = spacy.load('en')
docs = [nlp(text) for text in (t0, t1, t2, t3, t4, t5, t6)]
res = tf_idf_scores(docs)
sns.set()
fig, ax = plt.subplots(figsize=(15, 3))
sns.heatmap(res, ax = ax)
#plt.savefig("tf_idf_scores.png")
plt.show
import spacy
import seaborn as sns
from data import t0, t1, t2, t3, t4, t5, t6
from processing import tf_idf_scores
import matplotlib.pyplot as plt
nlp = spacy.load("en_core_web_sm")
doc0 = nlp(t0)
doc1 = nlp(t1)
doc2 = nlp(t2)
doc3 = nlp(t3)
doc4 = nlp(t4)
doc5 = nlp(t5)
doc6 = nlp(t6)
df = tf_idf_scores([doc0, doc1, doc2, doc3, doc4, doc5, doc6])
df_norm_col = (df - df.mean()) / df.std()
sns.heatmap(df_norm_col, cmap='BuPu', vmin=0, vmax=1.5)
plt.show()
plt.savefig('tf_idf_scores.png')
from spacy.lang.en.stop_words import STOP_WORDS
import spacy
import sys
import en_core_web_sm
nlp = en_core_web_sm.load()
import matplotlib.pyplot as plt
import seaborn as sns
from data import t0, t1, t2, t3, t4, t5, t6
from processing import tf_idf_scores
Docs = [nlp(x) for x in [t0, t1, t2, t3, t4, t5, t6]]
res = tf_idf_scores(Docs)
sns.set()
fig, ax = plt.subplots(figsize=(15, 3))
sns.heatmap(res, ax=ax)
plt.savefig('tf_idf_scores.png')
import spacy
import matplotlib.pyplot as plt
import seaborn as sns
from processing import tf_idf_scores
from data import t0, t1, t2, t3, t4, t5, t6
nlp = spacy.load("en_core_web_sm")
l = [t0, t1, t2, t3, t4, t5, t6]
df = tf_idf_scores(l)
sns.set()
fig, ax = plt.subplots(figsize=(15, 3))
sns.heatmap(df, ax=ax)
plt.show()