# CSCL 2023 Automated Multi-Dimensional Analysis of Peer Feedback in Middle School Mathematics

!pip install tensorflow
!pip install tensorflow_hub
!pip install tensorflow-addons

Requirement already satisfied: tensorflow in /usr/local/lib/python3.10/dist-packages (2.17.1)

Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.4.0)

Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.6.3)

Requirement already satisfied: flatbuffers>=24.3.25 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (24.3.25)

Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.6.0)

Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.2.0)

Requirement already satisfied: h5py>=3.10.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.12.1)

Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (18.1.1)

Requirement already satisfied: ml-dtypes<0.5.0,>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.4.1)

Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.4.0)

Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorflow) (24.2)

Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (4.25.5)

Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.32.3)

Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from tensorflow) (75.1.0)

Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.16.0)

Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.5.0)

Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (4.12.2)

Requirement already satisfied: wrapt>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.17.0)

Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.68.1)

Requirement already satisfied: tensorboard<2.18,>=2.17 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.17.1)

Requirement already satisfied: keras>=3.2.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.5.0)

Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.37.1)

Requirement already satisfied: numpy<2.0.0,>=1.23.5 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.26.4)

Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from astunparse>=1.6.0->tensorflow) (0.45.1)

Requirement already satisfied: rich in /usr/local/lib/python3.10/dist-packages (from keras>=3.2.0->tensorflow) (13.9.4)

Requirement already satisfied: namex in /usr/local/lib/python3.10/dist-packages (from keras>=3.2.0->tensorflow) (0.0.8)

Requirement already satisfied: optree in /usr/local/lib/python3.10/dist-packages (from keras>=3.2.0->tensorflow) (0.13.1)

Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow) (3.4.0)

Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow) (3.10)

Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow) (2.2.3)

Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow) (2024.8.30)

Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.18,>=2.17->tensorflow) (3.7)

Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.18,>=2.17->tensorflow) (0.7.2)

Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.18,>=2.17->tensorflow) (3.1.3)

Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from werkzeug>=1.0.1->tensorboard<2.18,>=2.17->tensorflow) (3.0.2)

Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich->keras>=3.2.0->tensorflow) (3.0.0)

Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich->keras>=3.2.0->tensorflow) (2.18.0)

Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich->keras>=3.2.0->tensorflow) (0.1.2)

Requirement already satisfied: tensorflow_hub in /usr/local/lib/python3.10/dist-packages (0.16.1)

Requirement already satisfied: numpy>=1.12.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow_hub) (1.26.4)

Requirement already satisfied: protobuf>=3.19.6 in /usr/local/lib/python3.10/dist-packages (from tensorflow_hub) (4.25.5)

Requirement already satisfied: tf-keras>=2.14.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow_hub) (2.17.0)

Requirement already satisfied: tensorflow<2.18,>=2.17 in /usr/local/lib/python3.10/dist-packages (from tf-keras>=2.14.1->tensorflow_hub) (2.17.1)

Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.18,>=2.17->tf-keras>=2.14.1->tensorflow_hub) (1.4.0)

Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.18,>=2.17->tf-keras>=2.14.1->tensorflow_hub) (1.6.3)

Requirement already satisfied: flatbuffers>=24.3.25 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.18,>=2.17->tf-keras>=2.14.1->tensorflow_hub) (24.3.25)

Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.18,>=2.17->tf-keras>=2.14.1->tensorflow_hub) (0.6.0)

Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.18,>=2.17->tf-keras>=2.14.1->tensorflow_hub) (0.2.0)

Requirement already satisfied: h5py>=3.10.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.18,>=2.17->tf-keras>=2.14.1->tensorflow_hub) (3.12.1)

Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.18,>=2.17->tf-keras>=2.14.1->tensorflow_hub) (18.1.1)

Requirement already satisfied: ml-dtypes<0.5.0,>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.18,>=2.17->tf-keras>=2.14.1->tensorflow_hub) (0.4.1)

Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.18,>=2.17->tf-keras>=2.14.1->tensorflow_hub) (3.4.0)

Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.18,>=2.17->tf-keras>=2.14.1->tensorflow_hub) (24.2)

Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.18,>=2.17->tf-keras>=2.14.1->tensorflow_hub) (2.32.3)

Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.18,>=2.17->tf-keras>=2.14.1->tensorflow_hub) (75.1.0)

Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.18,>=2.17->tf-keras>=2.14.1->tensorflow_hub) (1.16.0)

Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.18,>=2.17->tf-keras>=2.14.1->tensorflow_hub) (2.5.0)

Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.18,>=2.17->tf-keras>=2.14.1->tensorflow_hub) (4.12.2)

Requirement already satisfied: wrapt>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.18,>=2.17->tf-keras>=2.14.1->tensorflow_hub) (1.17.0)

Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.18,>=2.17->tf-keras>=2.14.1->tensorflow_hub) (1.68.1)

Requirement already satisfied: tensorboard<2.18,>=2.17 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.18,>=2.17->tf-keras>=2.14.1->tensorflow_hub) (2.17.1)

Requirement already satisfied: keras>=3.2.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.18,>=2.17->tf-keras>=2.14.1->tensorflow_hub) (3.5.0)

Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.18,>=2.17->tf-keras>=2.14.1->tensorflow_hub) (0.37.1)

Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from astunparse>=1.6.0->tensorflow<2.18,>=2.17->tf-keras>=2.14.1->tensorflow_hub) (0.45.1)

Requirement already satisfied: rich in /usr/local/lib/python3.10/dist-packages (from keras>=3.2.0->tensorflow<2.18,>=2.17->tf-keras>=2.14.1->tensorflow_hub) (13.9.4)

Requirement already satisfied: namex in /usr/local/lib/python3.10/dist-packages (from keras>=3.2.0->tensorflow<2.18,>=2.17->tf-keras>=2.14.1->tensorflow_hub) (0.0.8)

Requirement already satisfied: optree in /usr/local/lib/python3.10/dist-packages (from keras>=3.2.0->tensorflow<2.18,>=2.17->tf-keras>=2.14.1->tensorflow_hub) (0.13.1)

Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow<2.18,>=2.17->tf-keras>=2.14.1->tensorflow_hub) (3.4.0)

Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow<2.18,>=2.17->tf-keras>=2.14.1->tensorflow_hub) (3.10)

Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow<2.18,>=2.17->tf-keras>=2.14.1->tensorflow_hub) (2.2.3)

Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow<2.18,>=2.17->tf-keras>=2.14.1->tensorflow_hub) (2024.8.30)

Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.18,>=2.17->tensorflow<2.18,>=2.17->tf-keras>=2.14.1->tensorflow_hub) (3.7)

Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.18,>=2.17->tensorflow<2.18,>=2.17->tf-keras>=2.14.1->tensorflow_hub) (0.7.2)

Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.18,>=2.17->tensorflow<2.18,>=2.17->tf-keras>=2.14.1->tensorflow_hub) (3.1.3)

Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from werkzeug>=1.0.1->tensorboard<2.18,>=2.17->tensorflow<2.18,>=2.17->tf-keras>=2.14.1->tensorflow_hub) (3.0.2)

Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich->keras>=3.2.0->tensorflow<2.18,>=2.17->tf-keras>=2.14.1->tensorflow_hub) (3.0.0)

Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich->keras>=3.2.0->tensorflow<2.18,>=2.17->tf-keras>=2.14.1->tensorflow_hub) (2.18.0)

Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich->keras>=3.2.0->tensorflow<2.18,>=2.17->tf-keras>=2.14.1->tensorflow_hub) (0.1.2)

Collecting tensorflow-addons

  Downloading tensorflow_addons-0.23.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (1.8 kB)

Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorflow-addons) (24.2)

Collecting typeguard<3.0.0,>=2.7 (from tensorflow-addons)

  Downloading typeguard-2.13.3-py3-none-any.whl.metadata (3.6 kB)

Downloading tensorflow_addons-0.23.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (611 kB)

   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 611.8/611.8 kB 11.8 MB/s eta 0:00:00

Downloading typeguard-2.13.3-py3-none-any.whl (17 kB)

Installing collected packages: typeguard, tensorflow-addons

  Attempting uninstall: typeguard

    Found existing installation: typeguard 4.4.1

    Uninstalling typeguard-4.4.1:

      Successfully uninstalled typeguard-4.4.1

ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.

inflect 7.4.0 requires typeguard>=4.0.1, but you have typeguard 2.13.3 which is incompatible.

Successfully installed tensorflow-addons-0.23.0 typeguard-2.13.3

Unable to display output for mime type(s): application/vnd.colab-display-data+json

!pip uninstall tensorflow keras -y
!pip install tensorflow

Found existing installation: tensorflow 2.17.1

Uninstalling tensorflow-2.17.1:

  Successfully uninstalled tensorflow-2.17.1

Found existing installation: keras 3.5.0

Uninstalling keras-3.5.0:

  Successfully uninstalled keras-3.5.0

Collecting tensorflow

  Downloading tensorflow-2.18.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.1 kB)

Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.4.0)

Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.6.3)

Requirement already satisfied: flatbuffers>=24.3.25 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (24.3.25)

Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.6.0)

Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.2.0)

Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (18.1.1)

Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.4.0)

Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorflow) (24.2)

Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (4.25.5)

Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.32.3)

Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from tensorflow) (75.1.0)

Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.16.0)

Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.5.0)

Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (4.12.2)

Requirement already satisfied: wrapt>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.17.0)

Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.68.1)

Collecting tensorboard<2.19,>=2.18 (from tensorflow)

  Downloading tensorboard-2.18.0-py3-none-any.whl.metadata (1.6 kB)

Collecting keras>=3.5.0 (from tensorflow)

  Downloading keras-3.7.0-py3-none-any.whl.metadata (5.8 kB)

Requirement already satisfied: numpy<2.1.0,>=1.26.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.26.4)

Requirement already satisfied: h5py>=3.11.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.12.1)

Requirement already satisfied: ml-dtypes<0.5.0,>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.4.1)

Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.37.1)

Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from astunparse>=1.6.0->tensorflow) (0.45.1)

Requirement already satisfied: rich in /usr/local/lib/python3.10/dist-packages (from keras>=3.5.0->tensorflow) (13.9.4)

Requirement already satisfied: namex in /usr/local/lib/python3.10/dist-packages (from keras>=3.5.0->tensorflow) (0.0.8)

Requirement already satisfied: optree in /usr/local/lib/python3.10/dist-packages (from keras>=3.5.0->tensorflow) (0.13.1)

Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow) (3.4.0)

Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow) (3.10)

Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow) (2.2.3)

Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow) (2024.8.30)

Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.19,>=2.18->tensorflow) (3.7)

Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.19,>=2.18->tensorflow) (0.7.2)

Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.19,>=2.18->tensorflow) (3.1.3)

Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from werkzeug>=1.0.1->tensorboard<2.19,>=2.18->tensorflow) (3.0.2)

Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich->keras>=3.5.0->tensorflow) (3.0.0)

Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich->keras>=3.5.0->tensorflow) (2.18.0)

Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich->keras>=3.5.0->tensorflow) (0.1.2)

Downloading tensorflow-2.18.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (615.3 MB)

   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 615.3/615.3 MB 3.3 MB/s eta 0:00:00

Downloading keras-3.7.0-py3-none-any.whl (1.2 MB)

   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.2/1.2 MB 53.2 MB/s eta 0:00:00

Downloading tensorboard-2.18.0-py3-none-any.whl (5.5 MB)

   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 5.5/5.5 MB 98.6 MB/s eta 0:00:00

Installing collected packages: tensorboard, keras, tensorflow

  Attempting uninstall: tensorboard

    Found existing installation: tensorboard 2.17.1

    Uninstalling tensorboard-2.17.1:

      Successfully uninstalled tensorboard-2.17.1

ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.

tf-keras 2.17.0 requires tensorflow<2.18,>=2.17, but you have tensorflow 2.18.0 which is incompatible.

Successfully installed keras-3.7.0 tensorboard-2.18.0 tensorflow-2.18.0

Unable to display output for mime type(s): application/vnd.colab-display-data+json

import pandas as pd
import numpy as np
import tensorflow as tf
import tensorflow_hub as hub
import random
import nltk
import os
from xgboost import XGBClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics import classification_report, roc_curve, roc_auc_score, confusion_matrix, accuracy_score, f1_score, cohen_kappa_score
from sklearn.model_selection import GroupKFold, train_test_split
from sklearn import tree, metrics

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

from nltk.stem import PorterStemmer
from nltk.corpus import stopwords


random.seed(20)

# split data for student level cross validation: one student is nested within one fold
group_dict = dict()
groups = np.array([])

df = pd.read_csv('Annotations_final.csv')

for index, row in df.iterrows():
    s_id = row['created_by']
    if s_id not in group_dict:
        group_dict[s_id] = index
    groups = np.append(groups, group_dict[s_id])

# Set up the splitter with 5 splits
gkf = GroupKFold(n_splits = 5)

bag of words

# bag of words + neural nets
# import data
# stemming
porter = PorterStemmer() # stemming recovers root words from plurals etc
stemmed_texts = []

X_text = df['annotation_text']

for answer in X_text:
    answer = ' '.join(porter.stem(word) for word in answer.split(' '))
    stemmed_texts.append(answer)

X, y = np.array(stemmed_texts), df['relating_to_self'] # CHANGE y HERE

#vect = CountVectorizer(ngram_range=(1,1), max_features=1000, stop_words="english") #only unigram 313 features
vect = CountVectorizer(ngram_range=(2,2), max_features=1000, stop_words="english") #only bigram 728 features

X = vect.fit_transform(X).toarray()


# set up storage arrays for each round of validation
roc_auc_scores = np.array([])
accuracy_scores = np.array([])

# split, train, test and store performance metrics
for train_index, test_index in gkf.split(X, y, groups=groups):
    X_train, X_test = X[train_index], X[test_index]
    y_train, y_test = y[train_index], y[test_index]


    model = Sequential()
    model.add(Dense(12, input_shape=(738,), activation='relu'))
    model.add(Dense(8, activation='relu'))
    model.add(Dense(1, activation='sigmoid'))

    model.compile(loss='binary_crossentropy',
              optimizer='adam',
              metrics = ['acc']
             )

    num_epochs = 30
    batch_size = 10

    model.fit(
        X_train,
        y_train,
        epochs=num_epochs,
        validation_split=0.1,
        shuffle=True,
        batch_size=batch_size)

    predictions = model.predict(X_test)

    # compute some metrics and store them for averaging later on
    roc_auc_scores = np.append(roc_auc_scores, roc_auc_score(y_test, predictions))

# print mean scores for the 5-fold CV
print("average roc_auc score: ", np.round(roc_auc_scores.mean(), 3))
print("stdv roc_auc score: ", np.round(roc_auc_scores.std(), 3))
print("max roc_auc score: ", np.round(roc_auc_scores.max(), 3))

/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)

Epoch 1/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 2s 24ms/step - acc: 0.6781 - loss: 0.6864 - val_acc: 0.8947 - val_loss: 0.6716

Epoch 2/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 0.8789 - loss: 0.6644 - val_acc: 0.8421 - val_loss: 0.6573

Epoch 3/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - acc: 0.9119 - loss: 0.6438 - val_acc: 0.8421 - val_loss: 0.6402

Epoch 4/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9348 - loss: 0.6163 - val_acc: 0.8421 - val_loss: 0.6189

Epoch 5/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9554 - loss: 0.5744 - val_acc: 0.8421 - val_loss: 0.5941

Epoch 6/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 0.9531 - loss: 0.5460 - val_acc: 0.8947 - val_loss: 0.5659

Epoch 7/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9599 - loss: 0.4890 - val_acc: 0.8947 - val_loss: 0.5333

Epoch 8/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - acc: 0.9810 - loss: 0.4360 - val_acc: 0.8947 - val_loss: 0.4998

Epoch 9/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9731 - loss: 0.3867 - val_acc: 0.8947 - val_loss: 0.4684

Epoch 10/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9755 - loss: 0.3339 - val_acc: 0.8947 - val_loss: 0.4367

Epoch 11/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9662 - loss: 0.2938 - val_acc: 0.8947 - val_loss: 0.4059

Epoch 12/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9853 - loss: 0.2334 - val_acc: 0.8947 - val_loss: 0.3765

Epoch 13/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9795 - loss: 0.2030 - val_acc: 0.8947 - val_loss: 0.3514

Epoch 14/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9697 - loss: 0.1870 - val_acc: 0.9474 - val_loss: 0.3290

Epoch 15/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - acc: 0.9802 - loss: 0.1511 - val_acc: 0.9474 - val_loss: 0.3112

Epoch 16/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9838 - loss: 0.1303 - val_acc: 0.9474 - val_loss: 0.2945

Epoch 17/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9951 - loss: 0.1099 - val_acc: 0.9474 - val_loss: 0.2817

Epoch 18/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 0.9824 - loss: 0.0907 - val_acc: 0.9474 - val_loss: 0.2697

Epoch 19/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9911 - loss: 0.0880 - val_acc: 0.9474 - val_loss: 0.2594

Epoch 20/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9921 - loss: 0.0758 - val_acc: 0.9474 - val_loss: 0.2511

Epoch 21/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - acc: 0.9942 - loss: 0.0662 - val_acc: 0.9474 - val_loss: 0.2425

Epoch 22/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9986 - loss: 0.0550 - val_acc: 0.9474 - val_loss: 0.2354

Epoch 23/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 1.0000 - loss: 0.0502 - val_acc: 0.9474 - val_loss: 0.2298

Epoch 24/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 1.0000 - loss: 0.0526 - val_acc: 0.9474 - val_loss: 0.2249

Epoch 25/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 1.0000 - loss: 0.0376 - val_acc: 0.9474 - val_loss: 0.2198

Epoch 26/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 1.0000 - loss: 0.0432 - val_acc: 0.9474 - val_loss: 0.2161

Epoch 27/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - acc: 1.0000 - loss: 0.0403 - val_acc: 0.9474 - val_loss: 0.2127

Epoch 28/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 1.0000 - loss: 0.0344 - val_acc: 0.9474 - val_loss: 0.2090

Epoch 29/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 1.0000 - loss: 0.0291 - val_acc: 0.9474 - val_loss: 0.2056

Epoch 30/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 1.0000 - loss: 0.0341 - val_acc: 0.9474 - val_loss: 0.2028

2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step

Epoch 1/30

/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)

17/17 ━━━━━━━━━━━━━━━━━━━━ 1s 17ms/step - acc: 0.5245 - loss: 0.6864 - val_acc: 0.7895 - val_loss: 0.6639

Epoch 2/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.7940 - loss: 0.6463 - val_acc: 0.8947 - val_loss: 0.6244

Epoch 3/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.7845 - loss: 0.6071 - val_acc: 0.8947 - val_loss: 0.5846

Epoch 4/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.8110 - loss: 0.5591 - val_acc: 0.8947 - val_loss: 0.5419

Epoch 5/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.7568 - loss: 0.5296 - val_acc: 0.8947 - val_loss: 0.4949

Epoch 6/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.8398 - loss: 0.4532 - val_acc: 0.8947 - val_loss: 0.4479

Epoch 7/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.8624 - loss: 0.4073 - val_acc: 0.9474 - val_loss: 0.3997

Epoch 8/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 0.8844 - loss: 0.3696 - val_acc: 0.9474 - val_loss: 0.3602

Epoch 9/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.8985 - loss: 0.3061 - val_acc: 0.9474 - val_loss: 0.3235

Epoch 10/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9294 - loss: 0.2441 - val_acc: 0.9474 - val_loss: 0.2955

Epoch 11/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9398 - loss: 0.2525 - val_acc: 0.9474 - val_loss: 0.2741

Epoch 12/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9697 - loss: 0.1773 - val_acc: 0.9474 - val_loss: 0.2546

Epoch 13/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9642 - loss: 0.1744 - val_acc: 0.9474 - val_loss: 0.2416

Epoch 14/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - acc: 0.9927 - loss: 0.1317 - val_acc: 0.9474 - val_loss: 0.2291

Epoch 15/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9963 - loss: 0.0988 - val_acc: 0.9474 - val_loss: 0.2199

Epoch 16/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 0.9910 - loss: 0.1061 - val_acc: 0.9474 - val_loss: 0.2142

Epoch 17/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9870 - loss: 0.0847 - val_acc: 0.9474 - val_loss: 0.2086

Epoch 18/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9875 - loss: 0.0773 - val_acc: 0.9474 - val_loss: 0.2032

Epoch 19/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9936 - loss: 0.0630 - val_acc: 0.9474 - val_loss: 0.1992

Epoch 20/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9900 - loss: 0.0480 - val_acc: 0.9474 - val_loss: 0.1967

Epoch 21/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 0.9978 - loss: 0.0434 - val_acc: 0.9474 - val_loss: 0.1985

Epoch 22/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9990 - loss: 0.0373 - val_acc: 0.9474 - val_loss: 0.1925

Epoch 23/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 0.9907 - loss: 0.0416 - val_acc: 0.9474 - val_loss: 0.1903

Epoch 24/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - acc: 0.9986 - loss: 0.0265 - val_acc: 0.9474 - val_loss: 0.1872

Epoch 25/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9889 - loss: 0.0482 - val_acc: 0.9474 - val_loss: 0.1878

Epoch 26/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - acc: 0.9968 - loss: 0.0271 - val_acc: 0.9474 - val_loss: 0.1855

Epoch 27/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - acc: 0.9942 - loss: 0.0249 - val_acc: 0.9474 - val_loss: 0.1830

Epoch 28/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - acc: 0.9978 - loss: 0.0236 - val_acc: 0.9474 - val_loss: 0.1812

Epoch 29/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - acc: 0.9990 - loss: 0.0167 - val_acc: 0.9474 - val_loss: 0.1790

Epoch 30/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - acc: 0.9982 - loss: 0.0160 - val_acc: 0.9474 - val_loss: 0.1782

2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 98ms/step

Epoch 1/30

/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)

17/17 ━━━━━━━━━━━━━━━━━━━━ 2s 17ms/step - acc: 0.6353 - loss: 0.6837 - val_acc: 0.8421 - val_loss: 0.6557

Epoch 2/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 0.8298 - loss: 0.6397 - val_acc: 0.8947 - val_loss: 0.6169

Epoch 3/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.8125 - loss: 0.6002 - val_acc: 0.8947 - val_loss: 0.5729

Epoch 4/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.8341 - loss: 0.5418 - val_acc: 0.8947 - val_loss: 0.5222

Epoch 5/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.8520 - loss: 0.5082 - val_acc: 0.8947 - val_loss: 0.4738

Epoch 6/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 0.9143 - loss: 0.4262 - val_acc: 0.8947 - val_loss: 0.4205

Epoch 7/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.8948 - loss: 0.3625 - val_acc: 0.9474 - val_loss: 0.3732

Epoch 8/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9091 - loss: 0.3269 - val_acc: 0.9474 - val_loss: 0.3339

Epoch 9/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9427 - loss: 0.2586 - val_acc: 0.9474 - val_loss: 0.2995

Epoch 10/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - acc: 0.9609 - loss: 0.1972 - val_acc: 0.9474 - val_loss: 0.2726

Epoch 11/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - acc: 0.9694 - loss: 0.1751 - val_acc: 0.9474 - val_loss: 0.2522

Epoch 12/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9859 - loss: 0.1300 - val_acc: 0.9474 - val_loss: 0.2340

Epoch 13/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9770 - loss: 0.1351 - val_acc: 0.9474 - val_loss: 0.2200

Epoch 14/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - acc: 0.9629 - loss: 0.1182 - val_acc: 0.9474 - val_loss: 0.2064

Epoch 15/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9886 - loss: 0.0936 - val_acc: 0.9474 - val_loss: 0.1952

Epoch 16/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 0.9787 - loss: 0.0841 - val_acc: 0.9474 - val_loss: 0.1843

Epoch 17/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9871 - loss: 0.0595 - val_acc: 0.9474 - val_loss: 0.1759

Epoch 18/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9942 - loss: 0.0493 - val_acc: 0.9474 - val_loss: 0.1661

Epoch 19/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9805 - loss: 0.0670 - val_acc: 0.9474 - val_loss: 0.1580

Epoch 20/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 0.9805 - loss: 0.0443 - val_acc: 1.0000 - val_loss: 0.1510

Epoch 21/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - acc: 1.0000 - loss: 0.0349 - val_acc: 1.0000 - val_loss: 0.1437

Epoch 22/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 1.0000 - loss: 0.0302 - val_acc: 1.0000 - val_loss: 0.1379

Epoch 23/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - acc: 1.0000 - loss: 0.0315 - val_acc: 1.0000 - val_loss: 0.1315

Epoch 24/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 1.0000 - loss: 0.0259 - val_acc: 1.0000 - val_loss: 0.1276

Epoch 25/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 1.0000 - loss: 0.0182 - val_acc: 1.0000 - val_loss: 0.1220

Epoch 26/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 1.0000 - loss: 0.0169 - val_acc: 1.0000 - val_loss: 0.1133

Epoch 27/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 1.0000 - loss: 0.0207 - val_acc: 1.0000 - val_loss: 0.1098

Epoch 28/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 1.0000 - loss: 0.0178 - val_acc: 1.0000 - val_loss: 0.1066

Epoch 29/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 1.0000 - loss: 0.0177 - val_acc: 1.0000 - val_loss: 0.1028

Epoch 30/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 1.0000 - loss: 0.0180 - val_acc: 1.0000 - val_loss: 0.0997

WARNING:tensorflow:5 out of the last 5 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x7e145c6972e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 55ms/step

WARNING:tensorflow:6 out of the last 6 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x7e145c6972e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.


2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 66ms/step

Epoch 1/30

/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)

17/17 ━━━━━━━━━━━━━━━━━━━━ 1s 17ms/step - acc: 0.5848 - loss: 0.6893 - val_acc: 0.8421 - val_loss: 0.6645

Epoch 2/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - acc: 0.7615 - loss: 0.6533 - val_acc: 0.8421 - val_loss: 0.6339

Epoch 3/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - acc: 0.8885 - loss: 0.6101 - val_acc: 0.8421 - val_loss: 0.5981

Epoch 4/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.8433 - loss: 0.5674 - val_acc: 0.8421 - val_loss: 0.5585

Epoch 5/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 0.8330 - loss: 0.5259 - val_acc: 0.8947 - val_loss: 0.5146

Epoch 6/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.8817 - loss: 0.4511 - val_acc: 0.8947 - val_loss: 0.4659

Epoch 7/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.8856 - loss: 0.4064 - val_acc: 0.8947 - val_loss: 0.4203

Epoch 8/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.8934 - loss: 0.3468 - val_acc: 0.8947 - val_loss: 0.3785

Epoch 9/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9387 - loss: 0.2967 - val_acc: 0.8947 - val_loss: 0.3457

Epoch 10/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 0.9515 - loss: 0.2525 - val_acc: 0.8947 - val_loss: 0.3168

Epoch 11/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9590 - loss: 0.1977 - val_acc: 0.8947 - val_loss: 0.2921

Epoch 12/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 0.9647 - loss: 0.1595 - val_acc: 0.9474 - val_loss: 0.2741

Epoch 13/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 0.9832 - loss: 0.1305 - val_acc: 0.9474 - val_loss: 0.2576

Epoch 14/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9776 - loss: 0.1167 - val_acc: 0.9474 - val_loss: 0.2447

Epoch 15/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9761 - loss: 0.1073 - val_acc: 0.9474 - val_loss: 0.2350

Epoch 16/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9750 - loss: 0.1028 - val_acc: 0.9474 - val_loss: 0.2270

Epoch 17/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - acc: 0.9891 - loss: 0.0707 - val_acc: 0.9474 - val_loss: 0.2191

Epoch 18/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - acc: 0.9932 - loss: 0.0629 - val_acc: 0.9474 - val_loss: 0.2137

Epoch 19/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - acc: 0.9993 - loss: 0.0474 - val_acc: 0.9474 - val_loss: 0.2083

Epoch 20/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - acc: 0.9942 - loss: 0.0492 - val_acc: 0.9474 - val_loss: 0.2051

Epoch 21/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - acc: 0.9805 - loss: 0.0597 - val_acc: 0.9474 - val_loss: 0.2017

Epoch 22/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - acc: 0.9990 - loss: 0.0326 - val_acc: 0.9474 - val_loss: 0.1981

Epoch 23/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - acc: 1.0000 - loss: 0.0386 - val_acc: 0.9474 - val_loss: 0.1952

Epoch 24/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - acc: 1.0000 - loss: 0.0343 - val_acc: 0.9474 - val_loss: 0.1929

Epoch 25/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - acc: 1.0000 - loss: 0.0225 - val_acc: 0.9474 - val_loss: 0.1911

Epoch 26/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - acc: 1.0000 - loss: 0.0275 - val_acc: 0.9474 - val_loss: 0.1897

Epoch 27/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - acc: 1.0000 - loss: 0.0241 - val_acc: 0.9474 - val_loss: 0.1886

Epoch 28/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 1.0000 - loss: 0.0204 - val_acc: 0.9474 - val_loss: 0.1873

Epoch 29/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 1.0000 - loss: 0.0192 - val_acc: 0.9474 - val_loss: 0.1864

Epoch 30/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - acc: 1.0000 - loss: 0.0194 - val_acc: 0.9474 - val_loss: 0.1859

2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 55ms/step

Epoch 1/30

/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)

17/17 ━━━━━━━━━━━━━━━━━━━━ 2s 18ms/step - acc: 0.3339 - loss: 0.7035 - val_acc: 0.6842 - val_loss: 0.6910

Epoch 2/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.7067 - loss: 0.6837 - val_acc: 0.8947 - val_loss: 0.6758

Epoch 3/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 0.8963 - loss: 0.6649 - val_acc: 0.8947 - val_loss: 0.6610

Epoch 4/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9186 - loss: 0.6456 - val_acc: 0.8947 - val_loss: 0.6426

Epoch 5/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.8916 - loss: 0.6232 - val_acc: 0.8947 - val_loss: 0.6192

Epoch 6/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 0.9491 - loss: 0.5828 - val_acc: 0.8947 - val_loss: 0.5886

Epoch 7/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9602 - loss: 0.5340 - val_acc: 0.8947 - val_loss: 0.5520

Epoch 8/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9625 - loss: 0.4836 - val_acc: 0.8947 - val_loss: 0.5106

Epoch 9/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9589 - loss: 0.4232 - val_acc: 0.8947 - val_loss: 0.4656

Epoch 10/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 0.9651 - loss: 0.3522 - val_acc: 0.8947 - val_loss: 0.4212

Epoch 11/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9752 - loss: 0.2727 - val_acc: 0.8947 - val_loss: 0.3789

Epoch 12/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9644 - loss: 0.2329 - val_acc: 0.8947 - val_loss: 0.3446

Epoch 13/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9567 - loss: 0.2008 - val_acc: 0.9474 - val_loss: 0.3179

Epoch 14/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9958 - loss: 0.1348 - val_acc: 0.9474 - val_loss: 0.2940

Epoch 15/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9875 - loss: 0.1158 - val_acc: 0.9474 - val_loss: 0.2771

Epoch 16/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 0.9824 - loss: 0.1018 - val_acc: 0.9474 - val_loss: 0.2622

Epoch 17/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9792 - loss: 0.0964 - val_acc: 0.9474 - val_loss: 0.2490

Epoch 18/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9963 - loss: 0.0594 - val_acc: 0.9474 - val_loss: 0.2382

Epoch 19/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 0.9927 - loss: 0.0563 - val_acc: 0.9474 - val_loss: 0.2293

Epoch 20/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 0.9878 - loss: 0.0496 - val_acc: 0.9474 - val_loss: 0.2215

Epoch 21/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 0.9907 - loss: 0.0445 - val_acc: 0.9474 - val_loss: 0.2140

Epoch 22/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 1.0000 - loss: 0.0347 - val_acc: 0.9474 - val_loss: 0.2080

Epoch 23/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 1.0000 - loss: 0.0298 - val_acc: 0.9474 - val_loss: 0.2024

Epoch 24/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 1.0000 - loss: 0.0254 - val_acc: 0.9474 - val_loss: 0.1987

Epoch 25/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 1.0000 - loss: 0.0249 - val_acc: 0.9474 - val_loss: 0.1954

Epoch 26/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - acc: 1.0000 - loss: 0.0314 - val_acc: 0.9474 - val_loss: 0.1922

Epoch 27/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 1.0000 - loss: 0.0203 - val_acc: 0.9474 - val_loss: 0.1897

Epoch 28/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 1.0000 - loss: 0.0186 - val_acc: 0.9474 - val_loss: 0.1876

Epoch 29/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 1.0000 - loss: 0.0194 - val_acc: 0.9474 - val_loss: 0.1858

Epoch 30/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 1.0000 - loss: 0.0220 - val_acc: 0.9474 - val_loss: 0.1842

2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 59ms/step

average roc_auc score:  0.924

stdv roc_auc score:  0.021

max roc_auc score:  0.952

USE

%%capture
!pip install openai==1.55.3 httpx==0.27.2 --force-reinstall --quiet

from getpass import getpass
import openai
import os

#os.kill(os.getpid(), 9)

print('Enter OpenAI API key:')
openai.api_key = getpass()

os.environ['OPENAI_API_KEY']=openai.api_key

Enter OpenAI API key:
··········

import time

def generate_zero_shot(topic):
  prompt = """
  I will give you a peer review statement on a math problem solution, and this statement is identified to have this key attribute:
  "Commenting on the Process (CP): When evaluating peers’ works, learners comment on the process of the work". Your task is to simplify
  this statement by removing information that is not relevant to this attribute, leaving only the core description that represents the attribute of CP.
  A good example of statement having this attribute is: "I like how you had 20 then subtracted 12 and got 8".
  """
  system_message = {"role" : "system", "content" : prompt}

  client = openai.OpenAI()
  response = client.chat.completions.create(
      model="gpt-3.5-turbo",
      messages= [
          system_message,
          {"role" : "user", "content" : topic} # simulate a user prompt
      ],
      temperature=0.7,
      max_tokens=256,
      top_p=1,
      frequency_penalty=0,
      presence_penalty=0,
      stop=["\n"]
  )
  # I recommend putting a short wait after each call,
  # since the rate limit for the platform is 60 requests/min.
  # (This increases to 3000 requests/min after you've been using the platform for 2 days).
  time.sleep(1)

  # the response from OpenAI's API is a JSON object that contains
  # the completion to your prompt plus some other information.  Here's how to access
  # just the text of the completion.
  return response.choices[0].message.content.strip()

def update_comments_with_gpt(df):
    # Iterate over each row in the DataFrame
    for index, row in df.iterrows():
        # Check if comment_process is 1
        if row['comment_process'] == 1:
            # Extract the annotation_text
            original_text = row['annotation_text']

            # Call the generate_zero_shot function
            try:
                refined_text = generate_zero_shot(original_text)
            except Exception as e:
                print(f"Error processing row {index}: {e}")
                refined_text = original_text  # Fallback to original if GPT fails

            # Update the annotation_text in the DataFrame
            df.at[index, 'annotation_text'] = refined_text
            print(f"Row {index} updated: {original_text} -> {refined_text}")

    return df

df = pd.read_csv('Annotations_final.csv')

# Update the DataFrame
updated_df = update_comments_with_gpt(df)

# Save the updated DataFrame to a new CSV
updated_df.to_csv('updated_annotations.csv', index=False)

print("CSV updated and saved as 'updated_annotations.csv'.")

Row 15 updated: I like how you had 20 then subtected 12 and got 8 then you added 8 and got 16 then you answer was -4  -> I like how you subtracted 12 from 20 and got 8, then added 8 to get 16, but your final answer was -4.
Row 16 updated: the video is fine i like how you add the numbers  -> I like how you add the numbers.
Row 18 updated: i like how you add the number -> You did a great job adding the numbers.
Row 23 updated: I like the way you  put how much $20 he have and the lost 12 -> I like how you had 20 then subtracted 12 and got 8.
Row 27 updated: I like the way you used arrows in your problem. -> I like how you used arrows.
Row 31 updated: The way that you set up your Data is great. And how you divided to get to your mean. GOOD JOB KALLI :) -> I like how you set up your data and calculated the mean. Great job!
Row 33 updated: I like the way you sorted the data.
I also like the way you showed your work clearly so it wouldn't be confusing. -> You sorted the data nicely and presented your work clearly.
Row 36 updated: I think that Noah had the best estimate because it's about how many people get the average amount of sleep not about how many hours is good for you because that does not have anything to do with the problem -> You focused on the relevance of the estimate to the problem, which is important for understanding the context.
Row 39 updated: Hi. First, that is how you find the median, not the mean. So first you have to find the mean, add all of the numbers and then divide it by how many numbers there are. Then once you found the mean you estimate.. -> You correctly identified the process for finding the mean. Great job!
Row 42 updated: I liked how you solved your answer even though I think that's not how your supposed to solve it. But in my opinion your final answer was correct. -> You solved the problem differently, but your final answer was correct.
Row 43 updated: i think you did a good job because you wrote out all the numbers then you drew lines and circles to know where you were at -> I like how you wrote out all the numbers and used lines and circles to help keep track of your work.
Row 44 updated: I like that you put all of your thought in the recording. And that you used basic knowledge to find out your answer.  -> I appreciate the thorough explanation and the use of fundamental concepts in your solution.
Row 46 updated: I respectfully disagree with you because you were supposed to find out how much he lost.  -> You were supposed to find out how much he lost.
Row 48 updated: My strategy is like yours because I also did 3x2  -> "I like how you did 3 multiplied by 2 in a similar way as I did."
Row 50 updated: I like the way you subtracted the amount Juan spent from the amount he started with. -> You subtracted the amounts in a clear and logical manner.
Row 51 updated: My strategy is like yours because i added 8 and 8 together too.  -> I like how you added 8 and 8 together.
Row 52 updated: I like the way you added the 30 + 83 and you got 113 -> I like how you added 30 and 83 to get 113.
Row 53 updated: My strategy is like yours because we did the same thing when finding out -20.75x4 was -83 -> I like how we both found -20.75 multiplied by 4 to be -83.
Row 55 updated: I like the way you explained this and how you fixed 113 to -113 good job catching your mistake.  -> You explained well and caught the mistake in changing 113 to -113. Great job!
Row 57 updated: I respectfully disagree with you because one of the deposits was 25.25, not 22.25 making your answer wrong. -> You used 25.25 for one of the deposits, which affected the final result.
Row 58 updated: I hadn't thought of adding 30 with the 20.75 x4 -> You added 30 to 20.75 before multiplying by 4, which was a good approach.
Row 60 updated: I like the way you us drawing and your adding is right. -> I like how you used drawing and your addition is correct.
Row 66 updated: I like the way you multiplied 3 by 2 aswell  -> You multiplied 3 by 2 well.
Row 67 updated: I respectfully disagree with you on the last pieces of your math as you had added a positive with a negative. While you should have added -30 with the -83 and gotten -113 then subtractive that with the positive 76 and gotten -37. -> You added a positive with a negative instead of subtracting them, which resulted in an incorrect answer. You should have subtracted -30 from -83 to get -113, then subtracted that from 76 to get -37.
Row 76 updated: My strategy is like yours because we both multiplied 2 by 3  -> You multiplied 2 by 3 just like I did.
Row 78 updated: My strategy is like yours because I also multiplied 2 and 3 -> I appreciate your method of multiplying 2 and 3.
Row 79 updated: Why did you not mention (or include in the problem) the $8 Juan earned? -> You didn't include the $8 Juan earned in your solution.
Row 82 updated: I like the way your work is organized  also I like they  added 5+1 because I did not really think about doing that  -> I like how you added 5+1 because I did not think about doing that.
Row 98 updated: Why did you make no further mention of $76, the result of the first problem you solved? -> You didn't address the number 76 from the initial problem you worked on.
Row 110 updated: My strategy is like yours because I also did -20.75 x4   -> I appreciate your approach of multiplying -20.75 by 4.
Row 113 updated: My strategy is like yours because we did some of the same steps.  -> You and I took similar steps in our strategies.
Row 116 updated: My strategy is like yours because I also added the deposits  -> You added the deposits, which was a good strategy.
Row 119 updated: My strategy is like yours because I multiplied 20.75 by 4 to -> I like how you multiplied 20.75 by 4 in your strategy.
Row 120 updated: My strategy is like yours because I multiplied 20.75 by 4 as well -> I appreciate how you multiplied 20.75 by 4 in your solution.
Row 125 updated: When you did 20.75x4 you put the 4 in the wrong spot but when you did 2x(-15) that answer was correct. -> You misplaced the 4 in 20.75x4, but your calculation for 2x(-15) was correct.
Row 129 updated: My strategy is like yours because I also added the two deposits together. -> I appreciate how you added the two deposits together in your strategy.
Row 135 updated: My strategy is like yours because I also made -12 the same way. I also think its great you put the 20-12 on the side and explained that wasnt the actual problem. -> You explained well how you approached the problem by starting with 20 and then subtracting 12.
Row 140 updated: My strategy is like yours because i added what he bought  6+5+1= 12 so that what he lost -> You added the amounts he bought and correctly found the total.
Row 141 updated: My strategy is like yours because I did 15 x 3 also  -> You multiplied 15 by 3, which is the same as my strategy.
Row 143 updated: I like how you get 12 and subrtact by 8 to get how much  got now -> I like how you subtracted 8 from 12 to find the result.
Row 145 updated: I agree with you adding but for the positives but for the negatives your answer should have been negative. -> You should have made your answer negative for the negatives.
Row 149 updated: I respectfully disagree with you because you didn't put negative signs and the 4 is supposed to be negative or at least that's what I got. -> You need to include negative signs, as the 4 should be negative.
Row 151 updated: My strategy is like yours because  we both multiplied 2 times 15 -> I like how we both multiplied 2 times 15.
Row 154 updated: My strategy is like yours because  i added what he gained but I hadn't thought of 12-8  -> You added what he gained, but didn't think of 12-8.
Row 162 updated: My strategy is like yours because i did 3x2 -> I like how you multiplied 3 by 2 in your strategy.
Row 166 updated: My strategy is like yours because we both did the same thing, although I added 5+1 to get 6+6 and you did 6+5+1. -> I like how we both added numbers differently to reach our solutions.
Row 167 updated: I like how you  added and subtracted the number for exaple you did 20-6 and got 14 and then you did 8+8 and got 16. the other thing i like about your work is that you wrote a word problem to show how much he lost  -> I like how you added and subtracted numbers, for example, 20-6 to get 14 and then 8+8 to get 16.
Row 178 updated: My strategy is like yours because I put the information in almost the exact same way. I think I just switched the places of the two withdrawls. -> You and I used a similar strategy but with a slight difference in the order of the withdrawals.
Row 179 updated: I like the way you multiplied the withdrawals instead of adding to four times or two times. -> You multiplied the withdrawals instead of adding them multiple times, which was a good choice.
Row 180 updated: I like the way you you added all the deposits first then subtracted the withdrawls I didn't think of that -> You added all the deposits first then subtracted the withdrawals, which I didn't think of.
Row 181 updated: I like the way you started with adding your two deposits together. -> You did a great job by starting with adding your two deposits together.
Row 191 updated: My strategy is like yours because we both didn't use the $20 in the beginning. -> You and I both avoided using the initial $20 in our strategies.
Row 192 updated: I like the way you added the positive together then subtracted the negaives to make the equation more simple -> I like how you added the positives together then subtracted the negatives.
Row 195 updated: I agree with your answer but maybe next time make the numbers that are suppose to be negative negative in the equation to make it more clear. -> Next time, be sure to make the negative numbers negative in the equation for clarity.
Row 199 updated: Why did you start with 20. I kind of want to know just because many did the same but some did something different, like me. <3 -> You started with 20 then subtracted 12 to get 8.
Row 205 updated: When you did the 3*2 and you got -6 it could confuse people because the 3 and the 2 are both positives so how did you get a negative. -> You multiplied 3 by 2 and got -6, which might be confusing because both numbers were positive.
Row 206 updated: I like the way you showed your work step by step, but the question asks what was the total amount of money he lost or gained by the end of the day. -> You showed the work step by step, but the question asks for the total amount lost or gained.
Row 208 updated: I respectfully disagree with you your answer I think you might have messed up a step in the subtraction part. -> You may have made a mistake in the subtraction step.
Row 210 updated: I like the way you did it because botnh added 83 and 30. -> You added 83 and 30, which was a good approach.
Row 211 updated: Why did you add 2 and 5? -> Commenting on the Process (CP): Why did you add 2 and 5?
Row 212 updated: My strategy is like yours because  i also did 20-6 and i got 14  -> You subtracted 6 from 20 and got 14, which is similar to my approach.
Row 213 updated: Next time maybe you can add 15 x 2 and the totals you get add them butni like your video. -> You could try adding 15 x 2 next time, but I liked your video.
Row 216 updated: I like the way you added the   83.00 and 30.00. also whow you subtracted 113 and 75  -> I like how you added two numbers together and subtracted another pair.
Row 224 updated: Premium Growth feed will make the chicken's weight be about the same because the Mad for pen A is 0.512. The mad for pen B was 0.97 which is higher Martin -> You used the Mad for pen A and pen B to compare and concluded that pen B had a higher value.
CSV updated and saved as 'updated_annotations.csv'.

# loading universal sentence encoder
embed = hub.load("https://tfhub.dev/google/universal-sentence-encoder-large/5")

# import data
df = pd.read_csv("updated_annotations.csv").fillna(0)

# extract features as X
X = df[['annotation_text','created_by']]

# extract the prediction variable as y
y = df.comment_process # CHANGE y HERE

# set up storage arrays for each round of validation
roc_auc_scores = np.array([])
pred = pd.DataFrame()

# split, train, test and store performance metrics
for train_index, test_index in gkf.split(X, y, groups=groups):

    X_train = X.iloc[train_index].drop(['created_by'], axis=1)
    X_test = X.iloc[test_index].drop(['created_by'], axis=1)
    y_train = y.iloc[train_index]
    y_test = y.iloc[test_index]

    # train classifier on this round of training group
    training_embeddings = embed(X_train.annotation_text.to_list())

    model = Sequential()
    model.add(Dense(12, input_shape=(512,), activation='relu'))
    model.add(Dense(8, activation='relu'))
    model.add(Dense(1, activation='sigmoid'))

    model.compile(loss='binary_crossentropy',
              optimizer='adam',
              metrics = ['acc'])

    num_epochs = 30
    batch_size = 10


    model.fit(
        training_embeddings,
        y_train,
        epochs=num_epochs,
        validation_split=0.1,
        shuffle=True,
        batch_size=batch_size)

    # test classifier on this round of testing group
    testing_embeddings = embed(X_test.annotation_text.to_list())
    predictions = model.predict(testing_embeddings)

    # compute some metrics and store them for averaging later on
    roc_auc_scores = np.append(roc_auc_scores, roc_auc_score(y_test, predictions))


# print mean scores for the 5-fold CV
print("average roc_auc score: ", np.round(roc_auc_scores.mean(), 3))
print("stdv roc_auc score: ", np.round(roc_auc_scores.std(), 3))
print("max roc_auc score: ", np.round(roc_auc_scores.max(), 3))

Epoch 1/30

/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)

17/17 ━━━━━━━━━━━━━━━━━━━━ 2s 26ms/step - acc: 0.7672 - loss: 0.6827 - val_acc: 0.7895 - val_loss: 0.6554

Epoch 2/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - acc: 0.8264 - loss: 0.6457 - val_acc: 0.7895 - val_loss: 0.6083

Epoch 3/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - acc: 0.8441 - loss: 0.5973 - val_acc: 0.7895 - val_loss: 0.5571

Epoch 4/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - acc: 0.8704 - loss: 0.5389 - val_acc: 0.7895 - val_loss: 0.5033

Epoch 5/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step - acc: 0.8907 - loss: 0.4742 - val_acc: 0.7895 - val_loss: 0.4468

Epoch 6/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 0.8795 - loss: 0.4426 - val_acc: 0.8947 - val_loss: 0.3980

Epoch 7/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 0.9164 - loss: 0.3718 - val_acc: 0.8947 - val_loss: 0.3568

Epoch 8/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9228 - loss: 0.3723 - val_acc: 0.8947 - val_loss: 0.3186

Epoch 9/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9266 - loss: 0.3338 - val_acc: 0.8947 - val_loss: 0.2836

Epoch 10/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9440 - loss: 0.2917 - val_acc: 0.9474 - val_loss: 0.2548

Epoch 11/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9473 - loss: 0.2750 - val_acc: 0.9474 - val_loss: 0.2283

Epoch 12/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 0.9476 - loss: 0.2352 - val_acc: 0.9474 - val_loss: 0.2037

Epoch 13/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9547 - loss: 0.2274 - val_acc: 1.0000 - val_loss: 0.1842

Epoch 14/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9509 - loss: 0.1978 - val_acc: 1.0000 - val_loss: 0.1688

Epoch 15/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 0.9622 - loss: 0.1879 - val_acc: 1.0000 - val_loss: 0.1515

Epoch 16/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9459 - loss: 0.1802 - val_acc: 1.0000 - val_loss: 0.1406

Epoch 17/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9638 - loss: 0.1491 - val_acc: 1.0000 - val_loss: 0.1306

Epoch 18/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 0.9801 - loss: 0.1267 - val_acc: 1.0000 - val_loss: 0.1198

Epoch 19/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9661 - loss: 0.1462 - val_acc: 1.0000 - val_loss: 0.1118

Epoch 20/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9804 - loss: 0.1272 - val_acc: 1.0000 - val_loss: 0.1067

Epoch 21/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9716 - loss: 0.1134 - val_acc: 1.0000 - val_loss: 0.0979

Epoch 22/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9615 - loss: 0.1100 - val_acc: 1.0000 - val_loss: 0.0923

Epoch 23/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9729 - loss: 0.1219 - val_acc: 1.0000 - val_loss: 0.0906

Epoch 24/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - acc: 0.9612 - loss: 0.1266 - val_acc: 1.0000 - val_loss: 0.0820

Epoch 25/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9917 - loss: 0.0696 - val_acc: 1.0000 - val_loss: 0.0791

Epoch 26/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9862 - loss: 0.0825 - val_acc: 1.0000 - val_loss: 0.0723

Epoch 27/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9946 - loss: 0.0646 - val_acc: 1.0000 - val_loss: 0.0708

Epoch 28/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9968 - loss: 0.0670 - val_acc: 1.0000 - val_loss: 0.0648

Epoch 29/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9980 - loss: 0.0616 - val_acc: 1.0000 - val_loss: 0.0645

Epoch 30/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9960 - loss: 0.0444 - val_acc: 1.0000 - val_loss: 0.0614

2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 55ms/step

Epoch 1/30

/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)

17/17 ━━━━━━━━━━━━━━━━━━━━ 1s 16ms/step - acc: 0.6869 - loss: 0.6863 - val_acc: 0.7368 - val_loss: 0.6591

Epoch 2/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.7116 - loss: 0.6530 - val_acc: 0.7368 - val_loss: 0.6143

Epoch 3/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 0.7331 - loss: 0.5942 - val_acc: 0.7368 - val_loss: 0.5563

Epoch 4/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - acc: 0.7667 - loss: 0.5336 - val_acc: 0.7368 - val_loss: 0.4934

Epoch 5/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.8021 - loss: 0.4818 - val_acc: 0.7895 - val_loss: 0.4314

Epoch 6/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.8547 - loss: 0.4324 - val_acc: 0.8947 - val_loss: 0.3705

Epoch 7/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - acc: 0.8793 - loss: 0.3869 - val_acc: 0.9474 - val_loss: 0.3214

Epoch 8/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9159 - loss: 0.3110 - val_acc: 0.9474 - val_loss: 0.2744

Epoch 9/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9346 - loss: 0.2802 - val_acc: 0.9474 - val_loss: 0.2381

Epoch 10/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9522 - loss: 0.2329 - val_acc: 0.9474 - val_loss: 0.2138

Epoch 11/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - acc: 0.9527 - loss: 0.2494 - val_acc: 0.9474 - val_loss: 0.1892

Epoch 12/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - acc: 0.9851 - loss: 0.1728 - val_acc: 0.9474 - val_loss: 0.1719

Epoch 13/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - acc: 0.9709 - loss: 0.1567 - val_acc: 0.9474 - val_loss: 0.1522

Epoch 14/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - acc: 0.9584 - loss: 0.1667 - val_acc: 0.9474 - val_loss: 0.1442

Epoch 15/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - acc: 0.9668 - loss: 0.1485 - val_acc: 0.9474 - val_loss: 0.1347

Epoch 16/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step - acc: 0.9639 - loss: 0.1644 - val_acc: 0.9474 - val_loss: 0.1269

Epoch 17/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - acc: 0.9789 - loss: 0.1043 - val_acc: 0.9474 - val_loss: 0.1182

Epoch 18/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - acc: 0.9886 - loss: 0.0841 - val_acc: 0.9474 - val_loss: 0.1182

Epoch 19/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - acc: 0.9745 - loss: 0.1022 - val_acc: 0.9474 - val_loss: 0.1065

Epoch 20/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9796 - loss: 0.0780 - val_acc: 0.9474 - val_loss: 0.1023

Epoch 21/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9881 - loss: 0.0650 - val_acc: 0.9474 - val_loss: 0.0968

Epoch 22/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9936 - loss: 0.0538 - val_acc: 0.9474 - val_loss: 0.0904

Epoch 23/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 0.9931 - loss: 0.0653 - val_acc: 0.9474 - val_loss: 0.0886

Epoch 24/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9787 - loss: 0.0795 - val_acc: 0.9474 - val_loss: 0.0867

Epoch 25/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9874 - loss: 0.0512 - val_acc: 0.9474 - val_loss: 0.0793

Epoch 26/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - acc: 0.9956 - loss: 0.0471 - val_acc: 0.9474 - val_loss: 0.0761

Epoch 27/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9847 - loss: 0.0566 - val_acc: 0.9474 - val_loss: 0.0760

Epoch 28/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9942 - loss: 0.0474 - val_acc: 1.0000 - val_loss: 0.0683

Epoch 29/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - acc: 0.9973 - loss: 0.0302 - val_acc: 0.9474 - val_loss: 0.0714

Epoch 30/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9982 - loss: 0.0281 - val_acc: 1.0000 - val_loss: 0.0595

2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 61ms/step

Epoch 1/30

/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)

17/17 ━━━━━━━━━━━━━━━━━━━━ 1s 18ms/step - acc: 0.6615 - loss: 0.6808 - val_acc: 0.5789 - val_loss: 0.6742

Epoch 2/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.7480 - loss: 0.6103 - val_acc: 0.5789 - val_loss: 0.6379

Epoch 3/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - acc: 0.6756 - loss: 0.5769 - val_acc: 0.5789 - val_loss: 0.5882

Epoch 4/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.7107 - loss: 0.5103 - val_acc: 0.5789 - val_loss: 0.5422

Epoch 5/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 0.7641 - loss: 0.4623 - val_acc: 0.6842 - val_loss: 0.4827

Epoch 6/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.8541 - loss: 0.4083 - val_acc: 0.8421 - val_loss: 0.4286

Epoch 7/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.8692 - loss: 0.3623 - val_acc: 0.8421 - val_loss: 0.3839

Epoch 8/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9210 - loss: 0.3328 - val_acc: 0.8421 - val_loss: 0.3326

Epoch 9/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9449 - loss: 0.2639 - val_acc: 0.8421 - val_loss: 0.3007

Epoch 10/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9566 - loss: 0.2300 - val_acc: 0.8947 - val_loss: 0.2703

Epoch 11/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9519 - loss: 0.1905 - val_acc: 0.9474 - val_loss: 0.2398

Epoch 12/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 0.9605 - loss: 0.1963 - val_acc: 0.9474 - val_loss: 0.2296

Epoch 13/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9637 - loss: 0.1513 - val_acc: 0.9474 - val_loss: 0.2036

Epoch 14/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 0.9673 - loss: 0.1320 - val_acc: 0.9474 - val_loss: 0.1931

Epoch 15/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9543 - loss: 0.1423 - val_acc: 0.9474 - val_loss: 0.1823

Epoch 16/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9644 - loss: 0.1130 - val_acc: 0.9474 - val_loss: 0.1765

Epoch 17/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9889 - loss: 0.0959 - val_acc: 0.9474 - val_loss: 0.1664

Epoch 18/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - acc: 0.9805 - loss: 0.0935 - val_acc: 0.9474 - val_loss: 0.1637

Epoch 19/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 0.9889 - loss: 0.0736 - val_acc: 0.9474 - val_loss: 0.1589

Epoch 20/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 0.9956 - loss: 0.0648 - val_acc: 0.9474 - val_loss: 0.1553

Epoch 21/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 1.0000 - loss: 0.0588 - val_acc: 0.9474 - val_loss: 0.1442

Epoch 22/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 1.0000 - loss: 0.0500 - val_acc: 0.9474 - val_loss: 0.1443

Epoch 23/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 1.0000 - loss: 0.0423 - val_acc: 0.9474 - val_loss: 0.1509

Epoch 24/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 1.0000 - loss: 0.0429 - val_acc: 0.9474 - val_loss: 0.1513

Epoch 25/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - acc: 1.0000 - loss: 0.0550 - val_acc: 0.9474 - val_loss: 0.1302

Epoch 26/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step - acc: 1.0000 - loss: 0.0410 - val_acc: 0.9474 - val_loss: 0.1413

Epoch 27/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - acc: 1.0000 - loss: 0.0330 - val_acc: 0.9474 - val_loss: 0.1409

Epoch 28/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - acc: 1.0000 - loss: 0.0360 - val_acc: 0.9474 - val_loss: 0.1374

Epoch 29/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - acc: 1.0000 - loss: 0.0307 - val_acc: 0.9474 - val_loss: 0.1332

Epoch 30/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - acc: 1.0000 - loss: 0.0291 - val_acc: 0.9474 - val_loss: 0.1378

2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 88ms/step

Epoch 1/30

/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)

17/17 ━━━━━━━━━━━━━━━━━━━━ 1s 17ms/step - acc: 0.6005 - loss: 0.6856 - val_acc: 0.6316 - val_loss: 0.6732

Epoch 2/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.7720 - loss: 0.6386 - val_acc: 0.6316 - val_loss: 0.6390

Epoch 3/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 0.7723 - loss: 0.5700 - val_acc: 0.6316 - val_loss: 0.5980

Epoch 4/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - acc: 0.8009 - loss: 0.4838 - val_acc: 0.6316 - val_loss: 0.5519

Epoch 5/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.7741 - loss: 0.4632 - val_acc: 0.6842 - val_loss: 0.4708

Epoch 6/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9051 - loss: 0.3536 - val_acc: 0.8421 - val_loss: 0.3926

Epoch 7/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9401 - loss: 0.2968 - val_acc: 0.8421 - val_loss: 0.3532

Epoch 8/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9144 - loss: 0.3012 - val_acc: 0.8947 - val_loss: 0.3029

Epoch 9/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9589 - loss: 0.2271 - val_acc: 0.8947 - val_loss: 0.2731

Epoch 10/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 0.9443 - loss: 0.2209 - val_acc: 0.8947 - val_loss: 0.2449

Epoch 11/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - acc: 0.9473 - loss: 0.1881 - val_acc: 0.8947 - val_loss: 0.2258

Epoch 12/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9511 - loss: 0.1651 - val_acc: 0.9474 - val_loss: 0.2031

Epoch 13/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9667 - loss: 0.1386 - val_acc: 0.9474 - val_loss: 0.1917

Epoch 14/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - acc: 0.9851 - loss: 0.1135 - val_acc: 0.9474 - val_loss: 0.1803

Epoch 15/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9577 - loss: 0.1328 - val_acc: 0.9474 - val_loss: 0.1667

Epoch 16/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 0.9747 - loss: 0.1061 - val_acc: 0.9474 - val_loss: 0.1550

Epoch 17/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9765 - loss: 0.0990 - val_acc: 0.9474 - val_loss: 0.1535

Epoch 18/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - acc: 0.9849 - loss: 0.0827 - val_acc: 0.9474 - val_loss: 0.1482

Epoch 19/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9715 - loss: 0.0931 - val_acc: 0.9474 - val_loss: 0.1369

Epoch 20/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - acc: 0.9882 - loss: 0.0695 - val_acc: 0.9474 - val_loss: 0.1316

Epoch 21/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9784 - loss: 0.0787 - val_acc: 0.9474 - val_loss: 0.1240

Epoch 22/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - acc: 0.9932 - loss: 0.0519 - val_acc: 0.9474 - val_loss: 0.1266

Epoch 23/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9899 - loss: 0.0617 - val_acc: 0.9474 - val_loss: 0.1133

Epoch 24/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9963 - loss: 0.0567 - val_acc: 0.9474 - val_loss: 0.1097

Epoch 25/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 0.9932 - loss: 0.0554 - val_acc: 0.9474 - val_loss: 0.1130

Epoch 26/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9949 - loss: 0.0379 - val_acc: 0.9474 - val_loss: 0.1104

Epoch 27/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - acc: 0.9982 - loss: 0.0551 - val_acc: 0.9474 - val_loss: 0.1037

Epoch 28/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9986 - loss: 0.0337 - val_acc: 0.9474 - val_loss: 0.1042

Epoch 29/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9949 - loss: 0.0348 - val_acc: 0.9474 - val_loss: 0.0976

Epoch 30/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - acc: 0.9949 - loss: 0.0286 - val_acc: 0.9474 - val_loss: 0.0973

2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 52ms/step

Epoch 1/30

/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)

17/17 ━━━━━━━━━━━━━━━━━━━━ 2s 31ms/step - acc: 0.4193 - loss: 0.6954 - val_acc: 0.6842 - val_loss: 0.6862

Epoch 2/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - acc: 0.7327 - loss: 0.6805 - val_acc: 0.6316 - val_loss: 0.6729

Epoch 3/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.7113 - loss: 0.6601 - val_acc: 0.6316 - val_loss: 0.6484

Epoch 4/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.7861 - loss: 0.6303 - val_acc: 0.7895 - val_loss: 0.6042

Epoch 5/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - acc: 0.8197 - loss: 0.5825 - val_acc: 0.7895 - val_loss: 0.5506

Epoch 6/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.8887 - loss: 0.5308 - val_acc: 0.8421 - val_loss: 0.4843

Epoch 7/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9160 - loss: 0.4631 - val_acc: 0.8421 - val_loss: 0.4132

Epoch 8/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9187 - loss: 0.3989 - val_acc: 0.9474 - val_loss: 0.3467

Epoch 9/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9760 - loss: 0.3154 - val_acc: 0.9474 - val_loss: 0.2895

Epoch 10/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9452 - loss: 0.3023 - val_acc: 0.9474 - val_loss: 0.2466

Epoch 11/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9824 - loss: 0.2145 - val_acc: 0.9474 - val_loss: 0.2177

Epoch 12/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 0.9674 - loss: 0.1980 - val_acc: 0.9474 - val_loss: 0.1910

Epoch 13/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 0.9775 - loss: 0.1523 - val_acc: 0.9474 - val_loss: 0.1746

Epoch 14/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - acc: 0.9756 - loss: 0.1263 - val_acc: 0.9474 - val_loss: 0.1637

Epoch 15/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9793 - loss: 0.1152 - val_acc: 0.9474 - val_loss: 0.1536

Epoch 16/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9807 - loss: 0.1118 - val_acc: 0.9474 - val_loss: 0.1432

Epoch 17/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 0.9897 - loss: 0.0782 - val_acc: 0.9474 - val_loss: 0.1402

Epoch 18/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 0.9978 - loss: 0.0794 - val_acc: 0.9474 - val_loss: 0.1318

Epoch 19/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 0.9936 - loss: 0.0705 - val_acc: 0.9474 - val_loss: 0.1337

Epoch 20/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 1.0000 - loss: 0.0512 - val_acc: 0.9474 - val_loss: 0.1245

Epoch 21/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 1.0000 - loss: 0.0578 - val_acc: 0.9474 - val_loss: 0.1245

Epoch 22/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 1.0000 - loss: 0.0508 - val_acc: 0.9474 - val_loss: 0.1188

Epoch 23/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 1.0000 - loss: 0.0373 - val_acc: 0.9474 - val_loss: 0.1292

Epoch 24/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 1.0000 - loss: 0.0367 - val_acc: 0.9474 - val_loss: 0.1126

Epoch 25/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 1.0000 - loss: 0.0286 - val_acc: 0.9474 - val_loss: 0.1200

Epoch 26/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - acc: 1.0000 - loss: 0.0286 - val_acc: 0.9474 - val_loss: 0.1144

Epoch 27/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 1.0000 - loss: 0.0229 - val_acc: 0.9474 - val_loss: 0.1204

Epoch 28/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - acc: 1.0000 - loss: 0.0212 - val_acc: 0.9474 - val_loss: 0.1084

Epoch 29/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - acc: 1.0000 - loss: 0.0266 - val_acc: 0.9474 - val_loss: 0.1147

Epoch 30/30

17/17 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - acc: 1.0000 - loss: 0.0201 - val_acc: 0.9474 - val_loss: 0.1117

2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 59ms/step

average roc_auc score:  0.934

stdv roc_auc score:  0.061

max roc_auc score:  0.979