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()