def compute_distances(dataframe, period, gir_dict, mont_dict, gir_mont_diff):
period_vector = []
if period == 'month':
period_vector = dataframe['Year-Month'].tolist()
period_vector = pd.Series(period_vector)
tfidf_scores = dataframe['tfidf'].tolist()
elif period == 'date':
period_vector = dataframe['Date'].tolist()
period_vector = pd.Series(period_vector)
tfidf_scores = dataframe['tfidf'].tolist()
elif period == 'speaker':
period_vector = dataframe['Speaker'].tolist()
period_vector = pd.Series(period_vector)
tfidf_scores = dataframe['tfidf'].tolist()
else:
periods = ["Before convention", "Convention", "After convention"]
period_vector = pd.Series(periods)
# This assumes that tfidf_scores for the periods is a list not a pandas dataframe
tfidf_scores = dataframe
gir_dist = []
mont_dist = []
gir_mont_diff_dist = []
# This for loop is contingent on tfidf_scores being a list
for counter in tfidf_scores:
to_compare = convert_keys_to_string(counter)
# Checks if there tfidf_scores vector exists. If it doesn't, default values are assigned for the distance
# This was particularly relevant as there was a speaker with tfidf_scores of length 0
if len(to_compare) > 0:
gir_dist.append(1 - cosine_similarity(gir_dict, to_compare))
mont_dist.append(1 - cosine_similarity(mont_dict, to_compare))
gir_mont_diff_dist.append(
cosine_similarity(gir_mont_diff, to_compare))
else:
gir_dist.append(1)
mont_dist.append(1)
gir_mont_diff_dist.append(0)
# Merges the distance lists and creates a comprehensive dataframe to return
gir_dist = pd.Series(gir_dist)
mont_dist = pd.Series(mont_dist)
gir_mont_diff_dist = pd.Series(gir_mont_diff_dist)
comp_df = pd.DataFrame(
[period_vector, gir_dist, mont_dist, gir_mont_diff_dist])
comp_df = comp_df.transpose()
comp_df.columns = [
period, 'distance to gir', 'distance to mont', 'distance to diff'
]
return comp_df
def distance_analysis():
by_speaker = pickle.load(open("byspeaker.pickle", "rb"))
speakernumwords = pickle.load(open("speakernumwords.pickle", "rb"))
speaker_tfidf = {}
for speaker in by_speaker:
counter = by_speaker[speaker]
counter = convert_keys_to_string(counter)
# Tried doing v>=3 but there are some speakers who do not say any bigrams more than 3 times
freq = {k:v for k,v in counter.items() if (v >= 2)}
tfidf = compute_tfidf(freq, num_speeches, doc_freq)
speaker_tfidf[speaker] = tfidf
robespierre = speaker_tfidf["Maximilien-Francois-Marie-Isidore-Joseph de Robespierre"]
speaker_dist = {}
for speaker in speaker_tfidf:
if str(speaker) != "Maximilien-François-Marie-Isidore-Joseph de Robespierre":
print speaker_tfidf[speaker]
dist = 1-cosine_similarity(robespierre, speaker_tfidf[speaker])
speaker_dist[speaker] = dist
w = csv.writer(open("dist_to_robespierre_withlimit.csv", "w"))
for key, val in speaker_dist.items():
w.writerow([key,val])
def compute_distances(dataframe, period, gir_dict, mont_dict, gir_mont_diff):
period_vector = []
if (period == 'aggregation') or (period == 'speaker'):
period_vector = list(dataframe.keys())
period_vector = pd.Series(period_vector)
speaker_num_speeches = pickle.load(
open("speaker_num_speeches.pickle", "rb"))
"""period_vector = pd.Series(period_vector)
tfidf_scores = dataframe['tfidf'].tolist()"""
else:
periods = ["Before convention", "Convention", "After convention"]
period_vector = pd.Series(periods)
# This assumes that tfidf_scores for the periods is a list not a pandas dataframe
gir_dist = []
mont_dist = []
gir_mont_diff_dist = []
# This for loop is contingent on tfidf_scores being a list
for element in dataframe:
"""print type(element)
print type(dataframe[element])
to_compare = dataframe[element]"""
if period == 'speaker':
#gir = pickle.load(open("Girondins.pickle", "rb"))
#mont = pickle.load(open("Montagnards.pickle", "rb"))
# Consider dividing by number of speeches, to normalize
# Maintain num of speeches per group and number of chars per group
gir = pickle.load(open("Girondins.pickle", "rb"))
mont = pickle.load(open("Montagnards.pickle", "rb"))
file = open('gir_speeches_noplein.txt', 'r')
gir_num_speeches = int(file.read())
file = open('mont_speeches_noplein.txt', 'r')
mont_num_speeches = int(file.read())
speakers_to_analyze = load_list(
"Girondins and Montagnards New Mod.xlsx")
party = speakers_to_analyze.loc[element, "Party"]
print element
print party
print type(dataframe[element])
if party == 'Girondins':
gir = gir - dataframe[element]
if party == 'Montagnards':
mont = mont - dataframe[element]
# Normalizing by number of speeches
#gir_normalized = normalize_by_speeches(gir, gir_num_speeches)
#mont_normalized = normalize_by_speeches(mont, mont_num_speeches)
gir_dict = convert_keys_to_string(
compute_tfidf(gir, num_speeches, doc_freq))
mont_dict = convert_keys_to_string(
compute_tfidf(mont, num_speeches, doc_freq))
gir_mont_diff = compute_difference_withplein(gir_dict, mont_dict)
# Resets the Gir and Mont vectors to their unnormalized version
#gir_dict_unnormalized = convert_keys_to_string(compute_tfidf(gir, num_speeches, doc_freq))
#mont_dict_unnormalized = convert_keys_to_string(compute_tfidf(mont, num_speeches, doc_freq))
w = csv.writer(open("gir_mont_diff.csv", "w"))
for key, val in gir_mont_diff.items():
w.writerow([key, val])
# Normalizing the speaker data as well
#speaker_speeches = speaker_num_speeches[element]
#speaker_dict = normalize_by_speeches(dataframe[element], speaker_speeches)
speaker_dict = dataframe[element]
tfidf_speaker = compute_tfidf(speaker_dict, num_speeches, doc_freq)
to_compare = convert_keys_to_string(tfidf_speaker)
elif period == 'aggregation':
to_compare = convert_keys_to_string(dataframe[element])
else:
to_compare = convert_keys_to_string(element)
# Checks if there tfidf_scores vector exists. If it doesn't, default values are assigned for the distance
# This was particularly relevant as there was a speaker with tfidf_scores of length 0
if len(to_compare) > 0:
#Normalized
gir_dist.append(1 - cosine_similarity(gir_dict, to_compare))
mont_dist.append(1 - cosine_similarity(mont_dict, to_compare))
#Unnormalized
#gir_dist.append(1 - cosine_similarity(gir_dict_unnormalized, to_compare))
#mont_dist.append(1 - cosine_similarity(mont_dict_unnormalized, to_compare))
gir_mont_diff_dist.append(
cosine_similarity(gir_mont_diff, to_compare))
else:
gir_dist.append(1)
mont_dist.append(1)
gir_mont_diff_dist.append(0)
# Merges the distance lists and creates a comprehensive dataframe to return
mont_dist = pd.Series(mont_dist)
gir_dist = pd.Series(gir_dist)
gir_mont_diff_dist = pd.Series(gir_mont_diff_dist)
comp_df = pd.DataFrame(
[period_vector, gir_dist, mont_dist, gir_mont_diff_dist])
comp_df = comp_df.transpose()
comp_df.columns = [
period, 'distance to gir', 'distance to mont', 'distance to diff'
]
return comp_df
def compute_distances(dataframe, period, gir_dict, mont_dict, plein_dict,
gir_mont_diff):
period_vector = []
if (period == 'aggregation') or (period == 'speaker'):
period_vector = list(dataframe.keys())
period_vector = pd.Series(period_vector)
"""period_vector = pd.Series(period_vector)
tfidf_scores = dataframe['tfidf'].tolist()"""
else:
periods = ["Before convention", "Convention", "After convention"]
period_vector = pd.Series(periods)
# This assumes that tfidf_scores for the periods is a list not a pandas dataframe
gir_dist = []
mont_dist = []
plein_dist = []
gir_mont_diff_dist = []
# This for loop is contingent on tfidf_scores being a list
for element in dataframe:
"""print type(element)
print type(dataframe[element])
to_compare = dataframe[element]"""
print element
if period == 'speaker':
#gir = pickle.load(open("Girondins.pickle", "rb"))
#mont = pickle.load(open("Montagnards.pickle", "rb"))
gir = pickle.load(open("Girondins_withplein.pickle", "rb"))
mont = pickle.load(open("Montagnards_withplein.pickle", "rb"))
speakers_to_analyze = load_list(
"Girondins and Montagnards New Mod.xlsx")
party = speakers_to_analyze.loc[element, "Party"]
if party == 'Girondins':
gir = gir - dataframe[element]
if party == 'Montagnards':
print "here"
mont = mont - dataframe[element]
gir_dict = convert_keys_to_string(
compute_tfidf(gir, num_speeches, doc_freq))
mont_dict = convert_keys_to_string(
compute_tfidf(mont, num_speeches, doc_freq))
gir_mont_diff = compute_difference_withplein(gir_dict, mont_dict)
tfidf_speaker = compute_tfidf(dataframe[element], num_speeches,
doc_freq)
to_compare = convert_keys_to_string(tfidf_speaker)
elif period == 'aggregation':
to_compare = convert_keys_to_string(dataframe[element])
else:
to_compare = convert_keys_to_string(element)
# Checks if there tfidf_scores vector exists. If it doesn't, default values are assigned for the distance
# This was particularly relevant as there was a speaker with tfidf_scores of length 0
if len(to_compare) > 0:
gir_dist.append(1 - cosine_similarity(gir_dict, to_compare))
mont_dist.append(1 - cosine_similarity(mont_dict, to_compare))
plein_dist.append(1 - cosine_similarity(plein_dict, to_compare))
gir_mont_diff_dist.append(
cosine_similarity(gir_mont_diff, to_compare))
else:
gir_dist.append(1)
mont_dist.append(1)
plein_dist.append(1)
gir_mont_diff_dist.append(0)
# Merges the distance lists and creates a comprehensive dataframe to return
gir_dist = pd.Series(gir_dist)
mont_dist = pd.Series(mont_dist)
plein_dist = pd.Series(plein_dist)
gir_mont_diff_dist = pd.Series(gir_mont_diff_dist)
comp_df = pd.DataFrame(
[period_vector, gir_dist, mont_dist, gir_mont_diff_dist, plein_dist])
comp_df = comp_df.transpose()
comp_df.columns = [
period, 'distance to gir', 'distance to mont', 'distance to diff',
'distance to plein'
]
return comp_df