# Author : Debanjali Biswas

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

# http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

"""

Sequence Ordering of Alignments

"""

import os

import numpy as np

import pandas as pd

from constants import (

folder,

alignment_file,

recipe_folder_name,

)

from utils import (

fetch_parsed_recipe,

fetch_action_ids,

)

import warnings

warnings.simplefilter(action='ignore', category=FutureWarning)

class SequenceModel:

def test_sequence_model(self):

dish_list = os.listdir(folder)

dish_list = [dish for dish in dish_list if not dish.startswith(".")]

dish_list.sort()

correct_predictions = 0

num_actions = 0

for dish in dish_list:

data_folder = os.path.join(folder, dish) # dish folder

recipe_folder = os.path.join(data_folder, recipe_folder_name) # recipe folder

alignment_file_path = os.path.join(

data_folder, alignment_file

) # alignment file

# Gold Standard Alignments between all recipes for dish

alignments = pd.read_csv(

alignment_file_path, sep="\t", header=0, skiprows=0, encoding="utf-8"

)

# Group by Recipe pairs

group_alignments = alignments.groupby(["file1", "file2"])

dish_correct_predictions = 0

dish_num_actions = 0

for key in group_alignments.groups.keys():

#print('Recipe Pair: ')

#print(key)

recipe1_filename = os.path.join(recipe_folder, key[0] + ".conllu")

recipe2_filename = os.path.join(recipe_folder, key[1] + ".conllu")

parsed_recipe1 = fetch_parsed_recipe(recipe1_filename)

parsed_recipe2 = fetch_parsed_recipe(recipe2_filename)

action_ids1 = fetch_action_ids(parsed_recipe1)

#print('Actions in Recipe 1: ')

#print(action_ids1)

action_ids2 = fetch_action_ids(parsed_recipe2)

#print('Actions in Recipe 2: ')

#print(action_ids2)

if len(action_ids1) < len(action_ids2):

predictions = action_ids2[:len(action_ids1)]

else:

predictions = action_ids2

predictions.extend([0] * (len(action_ids1) - len(action_ids2)))

predictions = np.array(predictions)

#print('Predictions: ')

#print(predictions)

recipe_pair_alignment = group_alignments.get_group(key)

true_labels = list()

for i in action_ids1:

# True Action Id

action_line = recipe_pair_alignment.loc[

recipe_pair_alignment["token1"] == i

]

if not action_line.empty:

label = action_line["token2"].item()

true_labels.append(label)

else:

true_labels.append(0)

true_labels = np.array(true_labels)

#print('True Labels:')

#print(true_labels)

score = [predictions == true_labels]

dish_correct_predictions += np.sum(score)

dish_num_actions += len(action_ids1)

dish_accuracy = dish_correct_predictions * 100 / dish_num_actions

correct_predictions += dish_correct_predictions

num_actions += dish_num_actions

print("Accuracy of Dish {} : {:.2f}".format(dish, dish_accuracy))

model_accuracy = correct_predictions * 100 / num_actions

print("Model Accuracy: {:.2f}".format(model_accuracy))