finished the interface

estnltk_preprocessing.py

@@ -85,7 +85,9 @@ def construct_df_of_verbs(initial_df):
 	print(verbs_df)
 
 
-if __name__ == '__main__':
-	df = read_csv('cleaned_dataframe.csv', sep='~')
-	df.columns = ['distance', 'noun_like', 'noun_like_form', 'noun_like_pos', 'sentence', 'verb', 'verbs_form']
-	construct_df_of_verbs(df)
\ No newline at end of file
+# df = read_csv('cleaned_dataframe.csv', sep='~')
+# df.columns = ['distance', 'noun_like', 'noun_like_form', 'noun_like_pos', 'sentence', 'verb', 'verbs_form']
+# construct_df_of_verbs(df)
+
+df = read_csv('verbs.csv', sep='~', header=0)
+print(len(df.columns))
\ No newline at end of file

main.py

@@ -33,13 +33,21 @@ def plot_results(X, R, forms, df):
 		print('Cluster', k)
 		print(df[R[:,k] == 1]['verb'])
 
-def print_verb_info(verb, df, X, R, forms):
-	current_verb_index = df[df['verb'] == verb].index
-	print_cluster(current_verb_index, df, X, R)
-	pie_chart_verb(X[current_verb_index][0].tolist(), forms.tolist(), verb)
-	print_sample_usages(verb)
+def print_verb_info(df, X, R, forms):
+	while(True):
+		verb = input('\nPlease, enter the verb. Or \'e\' to exit: ')
+		if verb == 'e':
+			break
+		elif len(df[df['verb'] == verb]) != 0:
+			current_verb_index = df[df['verb'] == verb].index
+			print_cluster(current_verb_index, df, X, R)
+			pie_chart_verb(X[current_verb_index][0].tolist(), forms.tolist(), verb)
+			print_sample_usages(verb)
+		else:
+			print('Sorry. The verb was not found')
 
 def print_cluster(current_verb_index, df, X, R):
+	print('\nSimilar verbs are:')
 	R_verb = R[current_verb_index][0]
 	k = np.where(R_verb == 1)[0][0]
 	similar_verbs = df[R[:,k] == 1]['verb']
@@ -50,6 +58,7 @@ def print_cluster(current_verb_index, df, X, R):
 	print(list(similar_verbs_dict.keys())[1:])
 
 def print_sample_usages(verb):
+	print('\nSample usages of the verb:')
 	df = read_csv('cleaned_dataframe.csv', sep='~')
 	df.columns = ['distance', 'noun_like', 'noun_like_form', 'noun_like_pos', 'sentence', 'verb', 'verbs_form']
 	sentences = df[df['verb'] == verb]['sentence'].tolist()
@@ -69,4 +78,4 @@ def print_sample_usages(verb):
 R = np.genfromtxt('R.csv', delimiter='~')
 M = np.genfromtxt('M.csv', delimiter='~')
 X, verbs, forms, df = get_verbs_data()
-print_verb_info('armastama', df, X, R, forms)
\ No newline at end of file
+print_verb_info(df, X, R, forms)
\ No newline at end of file

plot_util.py

@@ -26,6 +26,8 @@ def plot_form_pdf(X, forms):
 			ax = pd.DataFrame(x).plot.density(bw_method=0.1)
 		labels.append('%s - mean: %.4f std: %.4f' % (forms[i], x.mean(), np.std(x)))
 	plt.xlim(-0.01, 0.015)
+	plt.xlabel('Usages per sample')
+	plt.yticks([])
 	plt.legend(labels=labels)
 	plt.show()