Created a dataframe of verbs with forms and postags used as features

estnltk_preprocessing.py

@@ -2,6 +2,7 @@ from estnltk import Text
 from util import save_dict, load_dict, save_csv, read_csv
 from util import save_dict, load_dict, save_csv, read_csv
 import pandas as pd
 import pandas as pd
 import matplotlib.pyplot as plt
 import matplotlib.pyplot as plt
+import numpy as np
 
 
 def map_verbs_with_sentences():
 def map_verbs_with_sentences():
 	verbs = {}
 	verbs = {}
@@ -41,18 +42,38 @@ def verbs_dict_to_df():
 		i += 1
 		i += 1
 	return pd.DataFrame.from_dict(rows_list)
 	return pd.DataFrame.from_dict(rows_list)
 
 
-def clean_dataframe():
-	df = read_csv('verbs_with_noun_likes.csv', sep='~')
-	df.columns = ['distance', 'noun_like', 'noun_like_form', 'noun_like_pos', 'sentence', 'verb', 'verbs_form']
+def clean_dataframe(df):
 	df = df[pd.notnull(df['noun_like_form'])] # remove examples with null forms
 	df = df[pd.notnull(df['noun_like_form'])] # remove examples with null forms
 	df = df[~df['noun_like_form'].str.contains('\|')] # remove example with several forms
 	df = df[~df['noun_like_form'].str.contains('\|')] # remove example with several forms
 	df = df[~df['sentence'].str.contains('A post shared by')]
 	df = df[~df['sentence'].str.contains('A post shared by')]
 	df = df[df['noun_like_form'] != '?'] # remove examples with unknown forms
 	df = df[df['noun_like_form'] != '?'] # remove examples with unknown forms
 	save_csv(df, 'cleaned_dataframe.csv', sep='~')
 	save_csv(df, 'cleaned_dataframe.csv', sep='~')
 	print(df[df['distance'] > 100]['sentence'])
 	print(df[df['distance'] > 100]['sentence'])
-	plt.scatter(df['distance'], df['noun_like_pos'], alpha=0.05, c='green')
-	plt.show()
 
 
-# df = verbs_dict_to_df()
-# save_csv(df, 'verbs_with_noun_likes.csv', sep='~')
-clean_dataframe()
+def add_value_to_dict(value, dictionary, distance):
+	if not value in dictionary:
+		dictionary[value] = 0
\ No newline at end of file
+	dictionary[value] += 1 / distance
+
+def construct_df_of_verbs(initial_df):
+	verbs = load_dict('verbs_dict')
+	rows = []
+	total_verbs = len(verbs)
+	i = 0
+	for verb in verbs:
+		print('%i/%i %s' % (i, total_verbs, verb))
+		row = {'verb': verb, 'number_of_samples': len(initial_df[initial_df['verb'] == verb])}
+		for index, use_case in initial_df[initial_df['verb'] == verb].iterrows():
+			add_value_to_dict(use_case['noun_like_form'], row, use_case['distance'])
+			add_value_to_dict(use_case['noun_like_pos'], row, use_case['distance'])
+			add_value_to_dict(use_case['verbs_form'], row, use_case['distance'])
+		rows.append(row)
+		i += 1
+	verbs_df = pd.DataFrame.from_dict(rows)
+	verbs_df = verbs_df[verbs_df['number_of_samples'] != 0]
+	save_csv(verbs_df, 'verbs.csv', sep='~', header=True)
+	print(verbs_df)
+
+
+df = read_csv('verbs.csv', sep='~', header=0)
+print(df)
\ No newline at end of file

util.py

@@ -20,8 +20,8 @@ def write_string_to_file(string, path, mode):
 	with io.open(path, mode, encoding='utf-8') as file:
 	with io.open(path, mode, encoding='utf-8') as file:
 		file.write(string)
 		file.write(string)
 
 
-def save_csv(df, path, sep=','):
-	df.to_csv(path, index=False, header=False, sep=sep)
+def save_csv(df, path, sep=',', header=False):
+	df.to_csv(path, index=False, header=header, sep=sep)
 
 
 def read_csv(path, sep=',', header=None):
 def read_csv(path, sep=',', header=None):
 	df = pd.read_csv(path, sep=sep, encoding='utf8', header=header, engine='python')
 	df = pd.read_csv(path, sep=sep, encoding='utf8', header=header, engine='python')