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master
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2022-12-22
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@ -1,70 +0,0 @@
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import gptc
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amendments = [
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("1st", "First"),
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("2nd", "Second"),
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("3rd", "Third"),
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("4th", "Fourth"),
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("5th", "Fifth"),
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("6th", "Sixth"),
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("7th", "Seventh"),
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("8th", "Eighth"),
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("9th", "Ninth"),
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("10th", "Tenth"),
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("11th", "Eleventh"),
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("12th", "Twelfth"),
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("13th", "Thirteenth"),
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("14th", "Fourteenth"),
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("15th", "Fifteenth"),
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("16th", "Sixteenth"),
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("17th", "Seventeenth"),
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("18th", "Eighteenth"),
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("19th", "Nineteenth"),
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("20th", "Twentieth"),
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("21st", "Twenty-first"),
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("22nd", "Twenty-second"),
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("23rd", "Twenty-third"),
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("24th", "Twenty-fourth"),
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("25th", "Twenty-fifth"),
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("26th", "Twenty-sixth"),
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("27th", "Twenty-seventh"),
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]
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with open("model.gptc", "rb") as f:
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model = gptc.deserialize(f)
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data = {}
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for number, name in amendments:
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number_data = model.get(number + " Amendment")
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name_data = model.get(name + " Amendment")
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if number_data and not name_data:
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data[name] = number_data
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elif name_data and not number_data:
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data[name] = name_data
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elif number_data and name_data:
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data[name] = {
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key: (number_data[key] + name_data[key]) / 2
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for key in number_data.keys()
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}
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classified_amendments = sorted(data.items(), key=lambda x: x[1]["left"])
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print("# Constitutional Amendment Analysis")
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print()
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print("""This is an analysis of which amendments to the U.S. Constitution are mentioned
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more in right- or left-leaning American news sources. Data do not necessarily
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correlate with support or opposition for the amendment among right- or
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left-leaning Americans.""")
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print()
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print("| Amendment | Left | Right |")
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print("+----------------+-------+-------+")
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for amendment, data in classified_amendments:
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percent_right = f"{data['right']*100:>4.1f}%"
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percent_left = f"{data['left']*100:>4.1f}%"
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amendment_padding = " "*(14 - len(amendment))
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print(f"| {amendment}{amendment_padding} | {percent_left} | {percent_right} |")
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print("+----------------+-------+-------+")
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print("| Amendment | Left | Right |")
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@ -1,85 +0,0 @@
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import gptc
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states = [
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"Alabama",
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"Alaska",
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"Arizona",
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"Arkansas",
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"California",
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"Colorado",
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"Connecticut",
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"Delaware",
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"Florida",
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"Georgia",
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"Hawaii",
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"Idaho",
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"Illinois",
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"Indiana",
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"Iowa",
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"Kansas",
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"Kentucky",
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"Louisiana",
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"Maine",
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"Maryland",
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"Massachusetts",
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"Michigan",
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"Minnesota",
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"Mississippi",
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"Missouri",
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"Montana",
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"Nebraska",
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"Nevada",
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"New Hampshire",
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"New Jersey",
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"New Mexico",
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"New York",
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"North Carolina",
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"North Dakota",
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"Ohio",
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"Oklahoma",
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"Oregon",
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"Pennsylvania",
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"Rhode Island",
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"South Carolina",
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"South Dakota",
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"Tennessee",
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"Texas",
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"Utah",
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"Vermont",
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"Virginia",
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"Washington",
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"West Virginia",
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"Wisconsin",
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"Wyoming",
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]
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with open("model.gptc", "rb") as f:
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model = gptc.deserialize(f)
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classified_states = []
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for state in states:
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classified_states.append((state, model.get(state),))
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classified_states.sort(key=lambda x: x[1]["left"])
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longest = max([len(state) for state in states])
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print("# State Analysis")
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print()
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print("""This is an analysis of which states are mentioned more in right- or left-
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leaning American news sources. Results do not necessarily correlate with the
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political views of residents of the states; for example, the predominantly
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liberal state of Oregon is mentioned more in right-leaning sources than in
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left-leaning ones.""")
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print()
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print("| State | Left | Right |")
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print("+----------------+-------+-------+")
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for state, data in classified_states:
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percent_right = f"{round(data['right']*1000)/10}%"
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percent_left = f"{round(data['left']*1000)/10}%"
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state_padding = " "*(longest - len(state))
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print(f"| {state}{state_padding} | {percent_left} | {percent_right} |")
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print("+----------------+-------+-------+")
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print("| State | Left | Right |")
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12
compile.py
12
compile.py
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@ -32,11 +32,13 @@ raw_model = [
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]
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with open("model.gptc", "w+b") as f:
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gptc.compile(
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raw_model,
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max_ngram_length=config["max_ngram_length"],
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min_count=config["min_count"],
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).serialize(f)
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f.write(
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gptc.compile(
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raw_model,
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max_ngram_length=config["max_ngram_length"],
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min_count=config["min_count"],
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).serialize()
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)
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con.commit()
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con.close()
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@ -1,2 +1,2 @@
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max_ngram_length=5
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max_ngram_length=8
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min_count=5
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