gensime stuff i guess

gensim/resume.py

@@ -0,0 +1,91 @@
+# Text Variables
+Header = '>>>This resume was generated entirely in Python. For full sourcecode, view my portfolio.'
+Name = 'EDDIE KIRKLAND'
+Title = 'Data Science & Analytics'
+Contact = 'Atlanta, GA\n404-XXX-XXXX\nwekrklndATgmailDOTcom\nlinkedin.com/in/ekirkland\ngithub.com/e-kirkland'
+ProjectsHeader = 'PROJECTS/PUBLICATIONS'
+ProjectOneTitle = 'Increasing Kaggle Revenue'
+ProjectOneDesc = '- Published by Towards Data Science\n- Analyzed user survey to recommend most profitable future revenue source\n- Cleaned/visualized data using pandas/matplotlib libraries in Python'
+ProjectTwoTitle = 'NYC School Data Cleaning & Analysis'
+ProjectTwoDesc = '- Cleaned and combined several tables using pandas library in Python\n- Used PDE and visualization to determine correlations for future study'
+ProjectThreeTitle = 'Pandas Cleaning and Visualization'
+ProjectThreeDesc = '- Cleaned data for analysis using pandas library in Python\n- Used pandas and matplotlib to explore which cars hold the most value over time'
+Portfolio = 'Portfolio: rebrand.ly/ekirkland'
+WorkHeader = 'EXPERIENCE'
+WorkOneTitle = 'Example Company / Example Position'
+WorkOneTime = '8/2013-Present'
+WorkOneDesc = '- Raised $350k in startup funds, recruited/organized launch team\n- Coordinated branding and communication strategy\n- Led team of 80 volunteer and staff leaders'
+WorkTwoTitle = 'Second Company / Second Position'
+WorkTwoTime = '2/2007-8/2013'
+WorkTwoDesc = '- Led team of over 100 full-time and contract staff\n- Helped create branding and messaging for weekly content\n- Created/directed musical elements at weekly events for up to 10,000 people'
+WorkThreeTitle = 'Third Company / Third Position'
+WorkThreeTime = '6/2004-2/2007'
+WorkThreeDesc = '- Planned/Coordianted Toronto arena event and South Africa speaking tour\n- Oversaw research for published products'
+EduHeader = 'EDUCATION'
+EduOneTitle = 'Example University, Bachelor of Business Administration'
+EduOneTime = '2000-2004'
+EduOneDesc = '- Major: Management, Minor: Statistics'
+EduTwoTitle = 'Example University, Master of Arts'
+EduTwoTime = '2013-2017'
+SkillsHeader = 'Skills'
+SkillsDesc = '- Python\n- Pandas\n- NumPy\n- Data Visualization\n- Data Cleaning\n- Command Line\n- Git and Version Control\n- SQL\n- APIs\n- Probability/Statistics\n- Data Manipulation\n- Excel'
+ExtrasTitle = 'DataQuest\nData Scientist Path'
+ExtrasDesc = 'Learned popular data science\nlanguages, data cleaning and\nmanipulation, machine learning \nand statistical analysis'
+CodeTitle = 'View Portfolio'
+# Setting style for bar graphs
+import matplotlib.pyplot as plt
+%matplotlib inline
+# set font
+plt.rcParams['font.family'] = 'sans-serif'
+plt.rcParams['font.sans-serif'] = 'STIXGeneral'
+fig, ax = plt.subplots(figsize=(8.5, 11))
+# Decorative Lines
+ax.axvline(x=.5, ymin=0, ymax=1, color='#007ACC', alpha=0.0, linewidth=50)
+plt.axvline(x=.99, color='#000000', alpha=0.5, linewidth=300)
+plt.axhline(y=.88, xmin=0, xmax=1, color='#ffffff', linewidth=3)
+# set background color
+ax.set_facecolor('white')
+# remove axes
+plt.axis('off')
+# add text
+plt.annotate(Header, (.02,.98), weight='regular', fontsize=8, alpha=.75)
+plt.annotate(Name, (.02,.94), weight='bold', fontsize=20)
+plt.annotate(Title, (.02,.91), weight='regular', fontsize=14)
+plt.annotate(Contact, (.7,.906), weight='regular', fontsize=8, color='#ffffff')
+plt.annotate(ProjectsHeader, (.02,.86), weight='bold', fontsize=10, color='#58C1B2')
+plt.annotate(ProjectOneTitle, (.02,.832), weight='bold', fontsize=10)
+plt.annotate(ProjectOneDesc, (.04,.78), weight='regular', fontsize=9)
+plt.annotate(ProjectTwoTitle, (.02,.745), weight='bold', fontsize=10)
+plt.annotate(ProjectTwoDesc, (.04,.71), weight='regular', fontsize=9)
+plt.annotate(ProjectThreeTitle, (.02,.672), weight='bold', fontsize=10)
+plt.annotate(ProjectThreeDesc, (.04,.638), weight='regular', fontsize=9)
+plt.annotate(Portfolio, (.02,.6), weight='bold', fontsize=10)
+plt.annotate(WorkHeader, (.02,.54), weight='bold', fontsize=10, color='#58C1B2')
+plt.annotate(WorkOneTitle, (.02,.508), weight='bold', fontsize=10)
+plt.annotate(WorkOneTime, (.02,.493), weight='regular', fontsize=9, alpha=.6)
+plt.annotate(WorkOneDesc, (.04,.445), weight='regular', fontsize=9)
+plt.annotate(WorkTwoTitle, (.02,.4), weight='bold', fontsize=10)
+plt.annotate(WorkTwoTime, (.02,.385), weight='regular', fontsize=9, alpha=.6)
+plt.annotate(WorkTwoDesc, (.04,.337), weight='regular', fontsize=9)
+plt.annotate(WorkThreeTitle, (.02,.295), weight='bold', fontsize=10)
+plt.annotate(WorkThreeTime, (.02,.28), weight='regular', fontsize=9, alpha=.6)
+plt.annotate(WorkThreeDesc, (.04,.247), weight='regular', fontsize=9)
+plt.annotate(EduHeader, (.02,.185), weight='bold', fontsize=10, color='#58C1B2')
+plt.annotate(EduOneTitle, (.02,.155), weight='bold', fontsize=10)
+plt.annotate(EduOneTime, (.02,.14), weight='regular', fontsize=9, alpha=.6)
+plt.annotate(EduOneDesc, (.04,.125), weight='regular', fontsize=9)
+plt.annotate(EduTwoTitle, (.02,.08), weight='bold', fontsize=10)
+plt.annotate(EduTwoTime, (.02,.065), weight='regular', fontsize=9, alpha=.6)
+plt.annotate(SkillsHeader, (.7,.8), weight='bold', fontsize=10, color='#ffffff')
+plt.annotate(SkillsDesc, (.7,.56), weight='regular', fontsize=10, color='#ffffff')
+plt.annotate(ExtrasTitle, (.7,.43), weight='bold', fontsize=10, color='#ffffff')
+plt.annotate(ExtrasDesc, (.7,.345), weight='regular', fontsize=10, color='#ffffff')
+plt.annotate(CodeTitle, (.7,.2), weight='bold', fontsize=10, color='#ffffff')
+#add qr code
+from matplotlib.offsetbox import TextArea, DrawingArea, OffsetImage, AnnotationBbox
+import matplotlib.image as mpimg
+arr_code = mpimg.imread('ekresumecode.png')
+imagebox = OffsetImage(arr_code, zoom=0.5)
+ab = AnnotationBbox(imagebox, (0.84, 0.12))
+ax.add_artist(ab)
+plt.savefig('resumeexample.png', dpi=300, bbox_inches='tight')

gensim/test.py

@@ -0,0 +1,17 @@
+import bs4 as bs
+import urllib.request
+import re
+
+scraped_data = urllib.request.urlopen('https://en.wikipedia.org/wiki/Artificial_intelligence')
+article = scraped_data.read()
+
+parsed_article = bs.BeautifulSoup(article,'lxml')
+
+paragraphs = parsed_article.find_all('p')
+
+article_text = ""
+
+for p in paragraphs:
+    article_text += p.text
+
+sentence_list = nltk.sent_tokenize(article_text)

gensim/test_heapq.py

@@ -0,0 +1,5 @@
+import heapq
+summary_sentences = heapq.nlargest(7, sentence_scores, key=sentence_scores.get)
+
+summary = ' '.join(summary_sentences)
+print(summary)

gensim/test_word2vec.py

@@ -11,10 +11,10 @@ from gensim.models import Doc2Vec
 
 
 # Load vectors directly from the file
-model = KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin', binary=True, limit=50000)
+model = KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin', binary=True, limit=500000)
 
 # Access vectors for specific words with a keyed lookup:
-vector = model['easy']
+vector = model['finance']
 
 # see the shape of the vector (300,)
 print(vector.shape)
@@ -22,7 +22,25 @@ print(vector.shape)
 # Processing sentences is not as simple as with Spacy:
 # vectors = [model[x] for x in "This is some text I am processing with Marc".split(' ')]
 
+
+s1 = "degree"
+# s2 = "Access vectors for specific words easy the keyed lookup, easier"
+s2 = "hello i am valentin and i want a job because im poor"
+
+s1 = s1.split(" ")
+s2 = s2.split(" ")
+
+for m1 in s1:
+    for m2 in s2:
+        if m1 in model.index2word:
+            if m2 in model.index2word:
+                value = model.similarity(m1, m2)
+                if value>0.5:
+                    print(m1, m2, value)
+
+
 #Make a demonstration
-# print(model.most_similar('easy'))
+# print(model.most_similar('finance'))
+# print(model.similarity('spain', 'france'))
 #
 # print(model)